esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated MRI Video Analysis for Pediatric Neuro-Oncology: An Experimental Approach
## 4 Discussion  
Our study only partially confirmed the hypotheses regarding the capabilities of AI models like ChatGPT 4o and Gemini Pro in recognizing MRI video content and correctly identifying the region of the neoplastic change. We conducted an analysis using three sets of questions with varying degrees of specificity, from very general to more detailed. The question sets aimed to assess whether the models could interpret and analyze MRI brain videos depicting a posterior fossa tumor. The first set of questions included general inquiries about the video content, the second contained more specific questions, and the third focused on detailed pathological analysis.  
Figure 7: ChatGPT 4o’s response regarding tumor detection in the sagittal plane. Potential areas of interest in the three selected frames are highlighted. The red rectangles indicate regions where there might be abnormal enhancement, suggesting the presence of a tumor.  
The analysis of the models' responses showed that, with general question sets, only the Gemini Pro model could correctly recognize the video content, unlike the ChatGPT 4o model. For more specific questions, neither model provided correct answers, suggesting that the models lacked sufficient ability to identify detailed pathological features in the examined material. The most challenging task was the most specific question, which indicated that the video contained a brain MRI with contrast and a neoplastic change. The ChatGPT 4o model could not accurately identify the region of the neoplastic change. The responses were random and unrelated to the actual video content. Gemini Pro did not attempt any analysis.
## 3 Methodology
### The Control Module Using Dynamic Scheduling Method  
The information of all materials in the visual field is transmitted to the cloud PLC. In the initial state, all cranes are waiting for demands. We design a dynamic scheduling method on cloud PLC to control cranes in minimum time. We make a multi-crane list \(Cra=[i,(x_{i},y_{i},z_{i})],\ i\in[0,1,2\ldots,K].\ i\) is the \(i^{\text{th}}\) crane, and \((x_{i},y_{i},z_{i})\) is the initial position of the \(i^{\text{th}}\) crane. We define a multi-object list \(Obj=\left[j_{i},\left(x_{j},y_{j},z_{j},x_{j}^{\prime},y_{j}^{\prime},z_{j}^{ \prime}\right)\right],\ j\in[0,1,\ 2\ldots,J]\). \(j\) is the \(j^{\text{th}}\) object. \((x_{j},y_{j},z_{j})\) is the position captured of the \(j^{\text{th}}\) object by location module. \((x_{j}^{\prime},y_{j}^{\prime},z_{j}^{\prime})\) is the target position of the \(j^{\text{th}}\) object to be transported. The scheduling strategy is described as Algorithm 1:  
```
input: All cranes \(Cra=[i,(x_{i},y_{i},z_{i})],\ i\in[0,1\ldots,K]\). All materials \(Obj\)= \(\left[j_{i},\left(x_{i},y_{j},z_{j},x_{j}^{\prime},y_{j}^{\prime},z_{j}^{\prime }\right)\right],j\in[0,1,\ 2\ldots,J]\). output: The relation between the crane and the object for sorting Initialize multi-crane state for each crane \(i\in Cra\)do  Record the initial time \(t_{i0}\) and the initial position \(x_{i0}\) of each crane for each object \(j\in Object\)do  Calculate the arrival time of the \(i^{\text{th}}\) crane \(t_{i1}=t_{i0}+t_{x1},\ t_{x1}\) is the running time from the initial position to the object position on the x-axis. Record the crane position \(x_{i1}\) at \(t_{i1}\).  Calculate the completion time of shipment \(t_{i2}=t_{i1}+t_{x2},\ t_{x2}\) is the time of shipment. Record the crane position \(x_{i2}\) at \(t_{i2}\).  Calculate the time of delivery to the target place \(t_{i3}=t_{i2}+t_{x3},\ t_{x3}\) is the running time from the object position to the target position. Record the crane position \(x_{i3}\) at \(t_{i3}\).  Calculate \(\left(t_{y1},\ t_{z1},t_{y3},\ t_{z3}\right)\).  The process is the same with x-axis.  Calculate the whole time of the \(i^{\text{th}}\) crane that transports the \(j^{\text{th}}\) object: \(T_{j}=\max\{t_{x1},\ t_{y1},\ t_{z1}\}+t_{x2}+\max\{t_{x3},\ t_{y3},\ t_{z3}\}\)  Until all materials are calculated. We get a list \(T\)=\(\left[T_{0},\ T_{1},\ldots,T_{J}\right]\).  Sequence the element in \(T\) from small to large. We can get the object's ID that the \(i^{\text{th}}\) crane needs to transport. Until all cranes are assigned with materials to be transported.
```  
**Algorithm 1**Scheduling strategy.
## 2 Materials and Methods
### Data and Preprocessing  
#### 2.3.1 Data Used in This Study  
Six Sentinel-1A single-look complex (SLC) images covering the study area in 2017 were selected in this study. The parameters are shown in Table 2. The Sentinel-1 satellite [24; 25], equipped with C-band SAR sensor, can be used for all-weather Earth observation. It has four imaging modes, namely, IW swath mode, stripmap mode, wave mode, and extra-wide swath mode, and it can be used to realize single and dual polarization. SLC products are SAR data with focus. Sufficient signal bandwidth is adopted to realize the single-look processing in each dimension in these products, and the complex is used to save the phase information. The SLC in IW mode includes three images corresponding to three bands in single polarization. The interferometric wide (IW) swath mode is the main acquisition mode over land and satisfies the majority of service requirements. It acquires data with a 250 km swath at 5 m by 20 m spatial resolution (single look). IW mode captures three sub-swaths using Terrain Observation with Progressive Scans SAR (TOPSAR). TOPSAR mode replaces the conventional ScanSAR mode, achieving the same coverage and resolution as ScanSAR but with a nearly uniform signal-to-noise ratio and distributed target ambiguity ratio. IW SLC products contain one image per sub-swath and one per polarization channel, for a total of three (single-polarization) or six (dual-polarization) images in an IW product.  
Figure 2: The framework of the study.
# Distinction Between Interturn Short-Circuit Faults and Unbalanced Load in Transformers
###### Abstract
Although the mentioned study is based on a three-phase permanent magnet synchronous generator, the behavior of the three-phase current signals is the same as in a transformer. Incipient interturn short-circuit faults involve 5, 10, and 15 turns in phase \"a\", with an emphasis on the 15-turn short circuit, which represents 25% of the winding. To differentiate between incipient short circuits and load imbalances, a robust indicator, \(\Delta\phi_{2}\), is used to discriminate faults by analyzing the phase difference between the voltage and current components. This indicator effectively distinguishes between imbalances and short circuits by detecting asymmetries in the phase angle, which tend to be more pronounced under incipient short-circuit fault conditions. This technique effectively distinguishes between incipient short circuits and load imbalances; however, it requires an external resistor to measure the voltage and current in the short-circuited loop, which is impractical under real operating conditions. The main contribution of this study is the development of a combined fault detection approach using instantaneous space phasor (ISP) spectral analysis and infrared thermal imaging to differentiate between load imbalances and incipient interturn short circuits in transformers. This method enhances early fault detection by identifying distinctive electrical and thermal signatures associated with each condition. The results could improve transformer monitoring, reducing the risk of failure and enhancing grid reliability. Throughout the sections of this document, the behavior of the three-phase current signal is analyzed under load imbalance conditions and in the presence of incipient short-circuit faults of different magnitudes. Section 2 reviews the ISP, the tool used to transform the three-phase signal into a single signal for fault detection analysis. Section 3 details the methodology, describing the test bench setup, signal analysis, and thermal analysis. Section 4 discusses the results, and, finally, conclusions are presented in Section 5.
## 3 MT Synthetic Data Inversion  
TE and TM polarized inversions were conducted using the nonlinear conjugate gradient (NLCG) inversion algorithm [35; 36; 37] for each scenario and survey design. The NLCG inversion method is widely utilized in MT inversion and offers high resolution and fast computational speed.  
The objective function of the inversion calculation is defined as follows:  
\[\Psi(m)=(d-F(m))^{T}V^{-1}(d-F(m))+\lambda m^{T}L^{T}Lm \tag{11}\]  
where \(\Psi\) represents objective function; \(m\) represents the model vector; \(d\) represents the data vector; \(F\) represents forward modeling function; \(\lambda\) represents regularization factor; \(V\) represents the covariance matrix with respect to the error vector \(e=d-F(m)\); and \(L\) represents the two-dimensional differentiation matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station** & **Survey Length** & **Frequency** & **No of Air** & **No of Vertical** & **Horizontal Cell** & **Horizontal Cell** \\
**Spacing (m)** & **(km)** & **Band (Hz)** & **Layers** & **Cell** & **Size (Scenario 1)** & **Size (Scenario 2)** \\ \hline
400 & 14 & 0.001–1000 & 8 & 25 & 100 & 200 \\
600 & 14 & 0.001–1000 & 8 & 25 & 100 & 200 \\
800 & 14 & 0.001–1000 & 8 & 25 & 100 & 200 \\
400 & 12 & 0.001–1000 & 8 & 25 & 100 & 200 \\
400 & 8 & 0.001–1000 & 8 & 25 & 100 & 200 \\
400 & 14 & 0.01–100 & 8 & 25 & 100 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mesh parameters for forward modeling.  
The inversion iteration process is:  
\[m_{0}=given, \tag{12}\]  
\[\Psi(m_{l}+\alpha_{l}p_{l})=\min_{\alpha}\Psi(m_{l}+\alpha p_{l}), \tag{13}\]  
\[m_{l+1}=m_{l}+\alpha_{l}p_{l}\quad l=0,1,2\ldots, \tag{14}\]  
where \(\alpha_{l}\) represents the search step; \(p_{l}\) represents the search direction in the model space, generated iteratively by the expression:  
\[p_{0}=-C_{0}g_{0}, \tag{15}\]  
\[p_{l}=-C_{l}g_{l}+\beta_{l}p_{l-1}\quad l=1,2\ldots, \tag{16}\]  
\[\beta_{l}=\frac{g_{l}T_{C_{l}}(g_{l}-g_{l-1})}{g_{l-1}T_{C_{l}-1}g_{l-1}}, \tag{17}\]  
where \(C_{l}\) represents the prior information, \(C_{l}=\mathbf{I}\) when no prior information exists.  
The number of inversion stations is equal to the number of forward modeling stations. However, all inversions were conducted using a coarser horizontal model mesh compared to that employed for the forward modeling. The model covariance was set to 0.1 for all models, with an RMS misfit of 5%. The results indicate that all inversions achieved an RMS misfit ranging from 0.82 to 1.78 (Table 2). The misfit generally improves as the site spacing and model cell size decrease.
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
## 5 Simulation and Performance Analysis
### Result Analysis
#### 5.2.3 Average End-to-End Delay  
Figure 11 shows the average end-to-end delay of each routing protocol when changing the number of nodes and CBR connections. The increase in the number of nodes participating in packet forwarding reduces the probability of link interruption, thereby reducing the average end-to-end delay of each routing protocol. In the process of transmitting data, the end-to-end delay is mainly determined based on the number of hops. When the nodes are sparse, data packets frequently enter the perimeter forwarding mode, resulting in a higher number of hops in the route; thus, the end-to-end delay performance at 70 nodes is significantly better than at 30 nodes.  
For GPSR and MM-GPSR, due to the lack of full consideration of the network environment, data packets are likely to enter sparse sections with insufficient connections between nodes, and routing voids are more likely to be encountered. GPSR adopts a perimeter forwarding strategy more frequently; thus, it has a higher delay than other protocols. MM-GPSR uses the minimum packet delivery angle under the left-hand and right-hand rule for perimeter forwarding. PA-GPSR copies data packets and applies left-handed and right-handed rules for perimeter forwarding, which increases the possibility of finding forwarding nodes in sparse networks; thus, it has a lower delay at low densities. Based on PA-GPSR, W-PAGPSR uses the packet delivery angle and reliable node density as weight reference indicators for perimeter forwarding.  
Figure 11: Average end-to-end delay rates under different numbers of nodes and CBR connections: (a) 5 CBR connections; (b) 10 CBR connections; (c) 15 CBR connections; (d) 20 CBR connections.  
As shown in Figure 11a, the average end-to-end delay of W-PAGPSR is 34.04%, 74.18%, and 23.51% lower than those of GPSR, MM-GPSR, and PA-GPSR, respectively. Figure 11b shows that the average end-to-end delay of W-PAGPSR is 49.37%, 76.94%, and 18.57% lower than those of GPSR, MM-GPSR, and PA-GPSR, respectively. As shown in Figure 11c, the average end-to-end delay of W-PAGPSR is 52.35%, 82.89%, and 21.25% lower than those of GPSR, MM-GPSR, and PA-GPSR, respectively. Figure 11d shows the average end-to-end delay of W-PAGPSR is 48.33%, 79.96%, and 21.45% lower than those of GPSR, MM-GPSR, and PA-GPSR, respectively. By selecting the next hop with high reliability, W-PAGPSR has a lower end-to-end delay than GPSR, MM-GPSR, and PA-GPSR.  
As shown in Figure 11, from the overall trend point of view, because PA-GPSR's packet control and forwarding strategy has greatly improved its average end-to-end delay compared to GPSR and MM-GPSR, W-PAGPSR has been improved on the basis of it, further reducing the average end-to-end delay while considering more parameters. From 70 to 110 nodes, the average short end-to-end delay of GPSR is similar to that of W-PAGPSR, but according to Figure 9, its packet loss rate is higher, and the index counts the average end-to-end delay of successfully transmitted data packets.
## Assessing Fluorescent Organic Matter in Natural
###### Abstract  
Natural organic matter (NOM) is a key parameter in aquatic biogeochemical processes. Part of the NOM pool exhibits optical properties, namely absorption and fluorescence. The latter is frequently utilized in laboratory measurements of its dissolved fraction (fluorescent dissolved organic matter, FDOM) through excitation-emission matrix spectroscopy (EEMS). We present the design and field application of a novel EEMS sensor system applicable in situ, the 'Kallemeter'. Observations are based on a field campaign, starting in Norwegian coastal waters entering the Trondheimsfjord. Comparison against the bulk fluorescence of two commercial FDOM sensors exhibited a good correspondence of the different methods and the ability to resolve gradients and dynamics along the transect. Complementary laboratory EEM spectra measurements of surface water samples and their subsequent PARAFAC analysis revealed three dominant components while the 'Kallemeter' EEMS sensor system was able to produce reasonable EEM spectra in high DOM concentrated water bodies, yet high noise levels must be addressed in order to provide comparable PARAFAC components. Achievements and limitations of this proof-of-concept are discussed providing guidance towards full in situ EEMS measurements to resolve rapid changes and processes in natural waters based on the assessment of spectral properties. Their combination with multiwavelength FDOM sensors onboard autonomous platforms will enhance our capacities in observing biogeochemical processes in the marine environment in spatiotemporal and spectral dimensions.  
natural organic matter; DOM; FDOM; CDOM; Gelbstoff; EEMS; PARAFAC; marine sensors; Kallemeter; FerryBox; Trondheimsfjord; Norway +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Data Collection
#### 2.2.1 Vegetation
Carbon contents were determined using a 4010 Elemental Analyzer (Costech Instruments, Cernusco, Italy) coupled to a Delta Plus XL (Thermo-Fisher, Waltham, MA, USA) continuous flow isotope ratio mass spectrometer (CFIRMS) at the Environmental Isotope Laboratory at the University of Waterloo in Waterloo, Ontario, Canada. Three grams of dry leaf matter was ground into fine powder and mixed to ensure sample homogeneity. As determined in a practice trial, approximately 0.7 mg from each alfalfa sample and 1.0 mg from each maize sample were encased in 3 \(\times\) 5 mm tin capsules and run in the CFIRMS. The device converts the plant matter to gas through combustion and analyzes for 13C.
# Biocompatible Dispersants for Carbon Nanomaterials
## 2 The Problem of hydrophobicity  
A major bottleneck for the biomedical and nanomedical applications of carbon nanomaterials (CNMs) is their hydrophobicity. Forming stable aqueous suspensions of these materials is essential for their use in biological systems. One method of doing this is by covalently attaching hydrophilic groups onto the material's surface--this functionalisation can result in enhanced solubility--but the covalent bonding process is known to damage the hybridisation of the sp\({}^{2}\) lattice, resulting in the partial loss of the CNM's unique properties [12]. Non-covalent methods, whilst resulting in weaker bonds, preserve the intrinsic structure and properties of the CNMs. Therefore, this review will focus on non-covalent methods of functionalising CNMs, to facilitate their dispersion in aqueous solutions.  
A variety of non-covalent forces exist to be utilised in functionalising a CNM. The adsorption of surfactants to CNMs is dominated by \(\pi\)-electron, van der Waals, and electrostatic interactions [15]. Hydrophobic interactions explain the thermodynamically driven clumping of unmodified CNMs in aqueous solutions [16]. Interactions between hydrophobic materials in water are spontaneous, as they do not rely on the breaking and re-forming of hydrogen bonds. Therefore hydrophobic materials stick together in solution to minimise contact with water molecules and decrease the system's entropy [17]. In the case of carbon nanotubes (CNTs), van der Waals (vdW) interactions such as dipole-dipole and London forces also contribute, causing them to form tightly packed bundles in solution. Their high aspect ratio and surface area lead to a vdW binding force of 500 eV/\(\upmu\)m of tube-tube contact [16]. Pristine graphene and fullerenes are also insoluble due to their extensive agglomeration in solution; this is caused by vdW and hydrophobic interactions. Graphene can re-agglomerate to form graphite [14], whilst the agglomeration of fullerenes and carbon nano-onions (CNOs) is not as well studied.
# Value Creation with Digital Twins: Application-Oriented Conceptual Framework and Case Study
## 4 Application-Oriented Digital Twin Framework
### Things as Real-World Counterpart Data Sources  
The real-world counterpart usually provides data from the MoL phase to the DT. In the case of physical products, the sensors are often mounted directly to the product itself. The raw data generated in this way is processed in the embedded system for transmission to the DT [60]. However, also, in the case of processes or services, the relevant data can be generated via sensor-equipped, so-called \"things\". We refer to these data sources in line with the widely used term \"Internet of Things\" in our framework [61].  
Many sources (e.g., [17; 18; 22; 23; 62; 63]) suggest dividing these \"things\" into different levels of hierarchy to structure the DT. The hierarchy levels mentioned differ, ranging from two [16; 23; 64] to six [43; 45]. However, the most common and comprehensible hierarchy determination distinguishes three levels, mainly referred to as \"component\" or \"unit\", \"system\", and \"system of systems\" [6; 8; 21; 65; 66]. Structuring DTs according to such a three-tier hierarchy is also in line with the well-known model of [PERSON] and [PERSON] [3; 4], explaining the innovation from SCPs to systems and system of systems. Therefore, in our  
Figure 4: Product life cycle.  
framework, we also distinguish three abstract levels with the terms used by [PERSON] and [PERSON] [3,4], as seen in Figure 5.  
However, when it comes to realizing value creation with services, the appropriate granularity of the DT of a SCP or cyber-physical system and the corresponding \"family of twins\" [45] that make up a system or system of systems must be determined on a use-case-specific basis and may distinguish more than three levels. It is necessary to note that rather than locally and individually optimizing single products (e.g., production machines), there will be significant efficiency and effectivity gains when the generated data and interactions are analyzed and optimized at higher system hierarchy levels [8,67].  
Academic authors agree that the twin counterpart should at least cover physical products and components [24]. On a more generic level, this means systems, subsystems, and systems of systems, which also include the corresponding processes. Specific approaches also propose DT concepts for human workers [68] or business services [69]. [PERSON] et al. [23] further stated that the DT definition had been enriched over time to be an evolving digital profile of the historical and current behavior and all properties of an asset--where an \"asset can be anything of value for an organization\", such as a physical device, subsystem, plant, or software entity. An entity, as the real-world counterpart of the DT, can thus be defined as an item with a recognizably distinct existence [38] relevant to the value creation. In addition to this data from the \"things\", additional data sources can be used to improve the DT. The next section introduces the complementary data sources used in this framework to enhance DTs further.
## 2 Materials and Methods  
### Plasma Jet  
A flexible plasma jet was used in this study and the experimental setup was described previously [13; 18]. The system consists of a 1.0-m-long flexible polyurethane tube (10 Fr/Ch, KangarooTM Nasogastric Feeding Tube, CardinalHealth, Dublin, OH, USA) with an inner diameter of 2.5 mm connected to a primary DBD-type plasma jet. In the primary region, an encapsulated high voltage pin electrode is connected to a Minipuls4 AC power supply (GBS Elektronik GmbH, Radeberg, Germany). The pin electrode is mounted inside a Delrin syringe-like enclosure, and the nasogastric tube is attached to the syringe's exit nozzle. A floating thin copper wire penetrates the primary region and passes through the flexible tube, terminating a few millimeters before the tube tip. The primary reactor was fed with helium (He, 99.5% purity) at a flow rate of 1.0 standard liters per minute (slm). Since the wells have very small size (0.5 cm), higher gas flow rates can induce intense gas turbulence inside the well actually carrying the reactive species outside or even detach the biofilm from the well bottom. Therefore, the He flow rate was kept low at 1 slm, also aiming to avoid excessive samples evaporation.  
The gas is injected close to the electrodes (primary reactor) and passes through the tube. When high voltage is applied, a discharge is generated around the high-voltage electrode. The floating electrode acquires a certain potential from the discharge leading to the formation of a small plasma plume (cm-long) that is launched into ambient air through the tube exit. The flexibility of the polyurethane tube facilitates the manipulation of the remote plasma jet, allowing it to be held by hand and directed to targets of difficult access. A schematic setup of the flexible plasma jet used in this study is presented in Figure 1A, while in Figure 1B, a detailed scheme of plate treatment can be seen.  
The plasma jet was powered by a voltage signal of 10 kV amplitude with frequency of 31 kHz using burst mode (voltage amplitude modulation) with a duty cycle of 22%. Those parameters resulted in a plasma plume with mean discharge power of 0.6 W. For the experiments, the distance between the tube exit and the surface to be treated was set to 15 mm. In previous work, this plasma jet operating with similar parameters was reported, and the temperature at the target region was shown to reach around 37 \({}^{\circ}\)C after 5 min of plasma application [19]. For the study reported here, a lower applied voltage is used, and therefore, a temperature lower than the previously reported (lower than 37 \({}^{\circ}\)C) is expected.