esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G Positioning: An Analysis of Early Datasets
## 2 The GINT05G Experiment
### 5G Testbed  
The GINT05G experiment, a collaborative initiative of the ESA with several European companies and universities, represents an important step in validating the potential of 5G for positioning and navigation. A designated testing area was selected to host the specialized infrastructure needed for the experimental 5G signal transmission and reception. This setup is unique in that it is not a conventional operational setup. Instead, it is a self-made engineering construct of a compact 5G private network, tailored specifically for the scope of the GINT05G project, namely, positioning with 5G signals. The testbed is located at the Fraunhofer Institute in Germany, close to Nurnberg. The measurement setup consists of a transmitting block and a receiving block. The transmitting block is composed of eleven transmitters, paired with an IT ELITE Antenna SEC3710 DP, that emulate a mini 5G private network able to transmit 5G positioning signals. The receiving block consists of a USRP X300 with an omnidirectional antenna programmed to receive 5G signals from the transmitters. Moreover, a Leica MS50 total station is used to survey the ground truth of each trajectory to be estimated.
## 2 Research Methods
### Radiance Correction  
Digital numbers (DN) in each band of the ALOS/AVNIR-2 and Aster images were converted to physical measurements of sensor radiance (L\({}_{\mathrm{sat}}\)). Conversion of DN to absolute radiance values is a necessary procedure for comparative analysis of several images taken by different sensors [26]. Since each sensor has its own calibration parameters used in recording the DN values, the same DN values in two images taken by two different sensors may represent two different radiance values. For this purpose we used the following formula, which implicitly includes a transformation of the analog signal received at the sensor to DN stored in the resulting image pixels (Equation 1):  
\[\mathrm{L}_{\mathrm{sat}}=(\mathrm{DN}+\alpha)\times\mathrm{UCC} \tag{1}\]  
Here, \(a\) represents the absolute calibration coefficients contained in the header file (0 for ALOS/AVNIR-2 [27] and \(-1\) for Aster satellite data [28]), and UCC is the Unit Conversion Coefficient, which is different for each image band and also depends on the gain setting that was used to acquire the image. Values of UCC for ALOS/AVNIR-2 and Aster images are given in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{ALOS/AVNIR-2} & \multicolumn{2}{c}{Aster} \\ \hline Band No & UCC & Band No & UCC \\ \hline
1 & 0.588 & 1 & 0.676 \\
2 & 0.573 & 2 & 0.708 \\
3 & 0.502 & 3N & 0.862 \\
4 & 0.835 & & \\ \hline \end{tabular}
\end{table}
Table 1: Unit Conversion Coefficient (UCC) for each band of satellite imagery.  
Figure 1: The research location: Denpasar, Bali, Indonesia.
## 4 Radiation Pressure-Driven PhC Cavity Sensors
### Sensing Applications Based on Radiation Pressure
#### 4.2.6 Summary of Radiation Pressure-Driven PhC Cavity Sensors  
In the previous discussion, we have reviewed most of the typical PhC cavities for radiation pressure-driven sensing applications based on the types of target signals to measure. In Table 5, some significant parameters of representative works are summarized in the views of experimental condition and optomechanical characteristics. Several conclusions can be derived from this table: first, silicon-based materials are frequently used in the integrated optomechanical devices due to their mature fabrication technology and feasible optical/mechanical properties in cavity design. Compared with silicon, silicon nitride expands the applications reaching up to a wavelength of 3.5 \(\upmu\)m with a larger electronic bandgap. Second, a vacuum condition is required in most studies on optomechanics. To eliminate or minimize the impact of air-damping on the mechanical resonator, a low-pressure vacuum condition can greatly enhance the mechanical quality factor. Therefore, this vacuum condition requirement raises a relatively rigorous condition for experimental optomechanical sensing applications. Another challenging work in the optomechanical device is the light coupling and collection architecture. It can be seen that tapered fiber is widely used in most studies due to its high coupling efficiency. However, in the practical fabrication process of dimpled tapered fiber, the operations of packaging and dimpling on the tapered fiber are so challenging that they could limit the development of integration on optomechanical photonics. In addition, due to its extremely flexible structure, the tapered fiber is usually placed and partially contacted with photonic resonator. In this case, the coupling between tapered fiber and optical cavity is hard to adjust for a high-performance optomechanical coupling. From the perspective of sensing applications, the above two points are obvious drawbacks in radiation pressure-driven PhC cavities.  
Figure 27: Optomechanical orbital angular momentum (OAM) sensor using a pair of zipper cavities [148].  
Figure 26: Optomechanical spin angular momentum sensor using a suspended waveguide-nanobeam structure [146].
## 4 Experimental Setup  
### Research Questions  
We aim to investigate whether a multimodal model can improve the performance of issue classification through our experiments. Specifically, we focus on the data types in the issue reports, namely, text, image, and code, to determine if each data type contributes to the improvement. To guide our research, we formulated the following detailed research questions:  
1. RQ1. Does a multimodal model that combines text and image improve the performance of issue classification?
2. RQ2. Does a multimodal model that combines text and code data improve the performance of issue classification?
3. RQ3. Does a multimodal model that combines text, image, and code data improve the performance of issue classification?
# Virtual Streamline Traction: Formation Cooperative Obstacle Avoidance Based on Dynamical Systems
## 1 Introduction
While geometry-based methods offer simplicity and good integrative capabilities, they often fail to yield satisfactory results when faced with complex obstacles, particularly as they struggle to adapt quickly to environmental changes in dynamic settings. The primary drawback of MPC and stochastic planning methods is the exponential increase in computational complexity with the addition of control dimensions, rendering these methods computationally inefficient during obstacle avoidance. Machine learning-based methods provide significant advantages in handling nonlinear, non-stationary, and complex tasks but suffer from poor portability and low sample efficiency. Furthermore, these methods are sensitive to noise and lack practical applicability. APF is highly practical but suffers from numerous local minima issues and poor stability, as depicted in Table 1.  
Inspired by the natural avoidance of obstacles by flowing water, as depicted in Figure 1, the continuous flow of water prompts scholars to select the velocity vector of a particle in the flow field at different observation times as a correspondence to the agent's velocity vector, thereby achieving the nonlinear simulation of water flow and avoidance of obstacles. Various methods can simulate obstacle-avoidance behavior of water flow, including those based on stream functions. Notably, the interfered fluid dynamical system (IFDS) [24; 25; 26; 27; 28], grounded in dynamical systems (DSs) [29], offers significant advantages. The IFDS algorithm exhibits low computational complexity and yields smooth trajectory solutions [28]. This approach is particularly suitable for certain underactuated USVs that lack lateral thrust. Its practicality is further enhanced by the optimized velocity vectors it produces. Unlike the vector-field approach of APF, the IFDS encounters fewer problems with local minima and demonstrates superior obstacle-avoidance capabilities, as elaborated in [26] and subsequent studies. Despite these strengths, the IFDS still faces challenges in two-dimensional obstacle avoidance and in its application to USV formations. Addressing these limitations, this study developed a combined IFDS and DS methodology, termed the virtual streamline traction (VST) method. This novel approach enhances IFDS by overcoming its two-dimensional constraints, incorporating an innovative formation strategy based on an improved IFDS, and devising a novel collision-avoidance technique for formation members using DS. This culminates in a robust and practical formation strategy for USVs.
# Magnetic Solid-Phase Extraction of Organic Compounds Based on Graphene Oxide Nanocomposites
## 2 Preparation and Applications of GO for the MSPE of Organic Compounds
### Nanocomposites of GO with Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
The GO/Fe\({}_{3}\)O\({}_{4}\) magnetic nanocomposite has been employed for the extraction of \(2 AA^{\prime}\)-trichlorobiphenyl (PCB 28) [34] and atrazine [48] from water samples prior to their determination by gas chromatography-mass spectrometry (GC-MS). Furthermore, di-2-ethylhexyl phthalate (DEHP) [49], polycyclic aromatic hydrocarbons (PAHs) [32], 2,4,6-trinitrotoluene (TNT) [41], malachite green and crystal violet [50], the cytostatic drugs imatinib and doxorubicin [51] as well as sulfonamides [52] have been extracted from water samples prior to their separation with HPLC and determination using a UV or DAD detector. Figure 2 shows the transmission electron microscopy (TEM) image, (a), a Fourier-Transformation Infrared spectroscopy (FT-IR) image (b), an X-ray diffraction (XRD) image (c) and a vibrating sample magnetometer (VSM) image (d) of GO/Fe\({}_{3}\)O\({}_{4}\). Figure 2: TEM (**a**), FT-IR (**b**), XRD (**c**) and vibrating sample magnetometer (VSM) (**d**) of GO/Fe\({}_{3}\)O\({}_{4}\). Reproduced with permission from [57]. Copyright Elsevier, 2017. Sulfonamides have been extracted from milk samples by GO/Fe\({}_{3}\)O\({}_{4}\) sorbent prior to their determination by HPLC-MS/MS [53] or capillary electrophoresis-diode array detection (CE-DAD) [54]. Sulfadiazine has been extracted from milk, honey and water samples with GO/Fe\({}_{3}\)O\({}_{4}\) prior to its spectrophotometric and optimized angle mode-mismatched thermal lens spectrometric determination (OAMTLS) [55]. Other applications of GO/Fe\({}_{3}\)O\({}_{4}\) for the extraction of organic compounds from food samples include the extraction of patulin from apple juice [56], flavors and fragrances (including ethyl vanillin, trans-cinnamic acid, methyl cinnamate, ethyl cinnamate, and benzyl cinnamate) from orange juice, chocolate and fruit sugar [57], lignans from sesame oil [42], flavonoids from tea, wine and urine samples [33], azo dyes (tartrazine, amaranth, carmine, sunset yellow, allura red) from jellies and candies [40]. The organic solvents that were mainly used for the elution of the adsorbed analytes from the GO/Fe\({}_{3}\)O\({}_{4}\) nanomaterial include acetone, n-hexane, acetonitrile, methanol etc., however, only a small quantity of these solvents was required. In most cases, the GO/Fe\({}_{3}\)O\({}_{4}\) sorbent was found to be reusable after a regeneration and a washing step. The as-prepared nanomaterial has some significant benefits including high surface area, superparamagnetic properties and strong magnetism. The synthetic procedures that were reported were simple, mild, rapid an economic and the MSPE method provided the benefits of limited organic waste, no requirement for time-consuming centrifugation, column-passing or filtration steps.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 7 FIT-IoT Performance Evaluation
### Many to Many Experiment  
A final set of experiments involving a larger number of senders and receivers, comprising MQTT-7 and CoAP-7, has been carried out. The frequency of messages has also been increased.  
For this set of experiments (rf. to Table 7), the TTC obtained for MQTT is the highest (of all cases), while CoAP keeps an average of 3611 ms, quite similar to the other experiments involving high intensity of messages. Both protocols experienced a small level of packet loss (0.01% for MQTT; 0.13% for CoAP). This again seems to imply that MQTT experiences more problems with an increase in the number of receivers than CoAP, in particular assuming a higher frequency of sent messages.
## 2 Test Sample  
The test sample in this paper contains thirteen groups of TSV daisy chains. Each via is 100 \(\upmu\)m deep and 10 \(\upmu\)m in diameter, which is filled with copper and with a Ti barrier layer 50 nm thick and a SiO\({}_{2}\) dielectric layer 250 nm thick. The TSV connects two metal levels of damascene redistribution copper at the top with one metal level of damascene redistribution copper at the bottom. The vertical structure schematic of each TSV daisy chain unit is as shown in Figure 1. In application, the current is input from the pad of the upper surface metal layer (TOP_M2), travels through the Top_M1 layer, then passes the holes of TSV, down to the lower surface, through the lower surface BTM_M1 layer, then passes the holes of TSV back to the upper surface; it repeats many times, and is finally output from the upper surface.  
After running a thermal cycle experiment from \(-55\)\({}^{\circ}\)C to 125 \({}^{\circ}\)C on the sample for 1000 cycles, the DC resistance of three groups of TSV daisy chains was increasing significantly. The changes in DC resistance for thirteen groups of TSV daisy chains are shown in Table 1. Channel 6, with the largest resistance change, was selected for analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Channel** & **DC Resistance before TC (\(\Upmu\))** & **DC Resistance after TC (\(\Upmu\))** \\ \hline C1 & 6.179 & 7.925 \\ C2 & 6.584 & 2.41 \(\times\) 10\({}^{4}\) \\ C3 & 6.420 & 6.414 \\ C4 & 6.643 & 6.449 \\ C5 & 6.743 & 6.307 \\ C6 & 6.808 & 9.76 \(\times\) 10\({}^{7}\) \\ C7 & 5.994 & 5.182 \\ C8 & 5.959 & 6.397 \\ C9 & 6.437 & 6.239 \\ C10 & 6.637 & 6.455 \\ C11 & 6.492 & 6.551 \\ C12 & 6.672 & 6.615 \\ C13 & 6.634 & 1.23 \(\times\) 10\({}^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Changes in DC resistance.  
Figure 1: Vertical structure of TSV daisy chain unit: (**a**) schematic diagram; (**b**) SEM image.
## 2 Related Works
### Optimization of Base Placement Based on Inverse Reachability Map  
Mobile robotic arms require a suitable base placement for efficient operation and increased success rate while avoiding the need for repetitive parking location adjustments. Several researchers have studied the optimization of base placement for mobile manipulators, such as [PERSON] et al. [8], who determined the placement of the robotic arm by constructing an accessibility map; [PERSON] et al. [9], who presented an approach to select an optimal stance location for a humanoid robot's feet relative to a desired grasp pose; and [PERSON] et al. [10], who proposed a reinforcement learning algorithm based on actor-critic that uses the workspace data of the robotic arm to train accessibility, thereby improving computational efficiency and achieving effective coordination of robot mobile operations.  
These studies mainly focus on the use and generation of inverse reachability maps. The current research is centered on enhancing IRM usage and algorithm efficiency, but there are still problems with obstacle fusion in household environments.  
Recent studies have shown improvements in indoor navigation methods for WMRA, with increased navigation and human-machine interaction efficiency. These robots recognize and collect environmental information using sensors for improved navigation safety and efficiency. Additionally, they can select the optimal base placement to enhance the success rate for different tasks. However, issues persist in combining the two during execution, and a well-established solution is yet to be developed for optimal base placement and environmental obstacle interference.
## 3 Classification and Synthesis of Bimetallic Nanoparticles
### Simultaneous Methods  
#### 3.3.1 Co-Reduction  
Chemical co-reduction is a straightforward method that affords bimetallic nanoparticles from the reduction of two metal precursors to zerovalent atoms [120]. Major advantages of the co-reduction technique are its simplicity and versatility; several Ag@Au bimetallic core-shell nanoparticles having various shapes have been generated using this strategy. For example, [PERSON] and co-workers prepared bimetallic AgAu nanocages using a polyol reduction reaction [69]. In these specific studies, an Au shell forms over a silver nanocube template to afford particles having a hollow interior, pinhole-containing surface, and crystalline walls. Silver nanocubes with Pt and Pd shells were also reported by [PERSON] et al. [98]. These researchers demonstrated the formation of AgPt and AgPd nanoboxes through a galvanic replacement between Ag nanocubes and Na\({}_{2}\)PtCl\({}_{4}\) and Na\({}_{2}\)PdCl\({}_{4}\), respectively. Similarly, hollow bimetallic Ag@Pt core-shell nanoparticles with high Pt content were generated through the successive reductions of an aqueous solution of AgNO\({}_{3}\) and H\({}_{2}\)PtCl\({}_{6}\) with hydrazine by [PERSON] et al. [21]. Similarly, concave Au@Pt nanocubes were prepared by [PERSON] et al. via galvanic replacement using Au@Ag nanocubes as templates, as shown in Figure 7[92].  
Separately, there are several syntheses of nanoparticles that have been prepared in organic media [22; 70; 96; 121]. The use of organic solvents is an attractive synthetic approach because it typically circumvents the agglomeration of nanoparticles that occurs in the presence of hydrophobic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Synthetic Method** & **Size** & **Reference** \\ \hline \multirow{2}{*}{FeNi} & Chemical reduction & 12.2 nm & [114] \\ \cline{2-3}  & Pyrolysis chemical reduction & 9 nm & [115] \\ \hline NiCo & Microwave irradiation & 71 nm & [19] \\ \hline \multirow{4}{*}{PtCo} & Chemical reduction & \multirow{2}{*}{3–18 nm} & [116] \\  & Thermal decomposition & & \\ \cline{1-1} \cline{2-3}  & Thermal decomposition & 6 nm & [40] \\ \cline{1-1} \cline{2-3}  & Chemical reduction & 5.3 nm & [117] \\ \hline CoCu & Sonochemical & 50 nm & [118] \\ \hline CuNi & Mechanical milling & 10 nm & [119] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 7: Schematic illustrations of (**a**) the formation of the concave Au@Pt nanocubes and TEM images and colloidal solutions (inset) of (**b**) Au NPs, (**c**) Au@Ag nanocubes, and (**d**) concave Au@Pt nanocubes. Reproduced with permission from ref. [92]. Copyright 2013 American Chemical Society.  
stabilizing ligands typically used in aqueous media [122]. For example, [PERSON] et al. reported the synthesis of Ag@Au core-shell nanoparticles via a replacement reaction between hydrophobic Ag nanoparticles and [AuCL4]\({}^{-}\) in toluene [70]. Furthermore, [PERSON] et al. prepared Ag@Pt core-shell particles through a replacement reaction between hydrophobic Au nanoparticles and hydrophilic PtCl6\({}^{-}\) ions in chloroform [93].
# Leveraging Large Language Models for Departmental Classification of Medical Records
## 6 Discussion
### Limitations in Complex Cases  
While the classifier demonstrated impressive performance in handling common medical scenarios, its accuracy was notably lower when diagnosing complex or rare conditions. This limitation reflects the model's dependency on the training dataset, which may lack sufficient representation of less common diseases. Enhancing the model's capabilities in these  
Figure 5: Performance comparison of steps per second for various models.  
Figure 6: Performance comparison of samples per second for multiple models.  
areas will require further training using more specialized datasets, such as those focusing on rare diseases or complex comorbidities, and potentially incorporating expert systems for more nuanced decision making [29]. Research has shown that hybrid models, which combine LLMs with rule-based expert systems, can improve performance in handling intricate medical cases [30]. Future development should explore this integration to enhance diagnostic accuracy and reliability.  
Figure 7 presents the top 10 error output types of the classifier. Based on this figure, the project identifies four medical departments--Internal Medicine, Obstetrics and Gynecology, Pediatrics, and Dermatology and Venereology--where the classifier is more likely to produce incorrect outputs. This finding suggests potential future research directions aimed at improving classification accuracy among these four departments.  
Table 5 presents two examples of error cases from the classifier, in both Chinese and English.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Index** & **Input** & **Label** & **Output** \\ \hline  & \(\mathcal{R}\)\(\rightarrow  
The example at index 4528 shows a top-ranked error, where the classifier incorrectly assigns the Surgery department to Internal Medicine. This error suggests that the classifier may be affected by an unbalanced training data distribution in the database. Additionally, it may indicate the model's limited ability to extract relevant features. This error highlights promising future research directions, such as constructing a more balanced dataset and refining the model architecture.  
The example index at 75 shows an error output of the Gemma-2-9B model regarding the Department of Dermatology and Venereology. The correct output should be \"Department of Dermatology and Venereology\". This error reveals the model's limitation in distinguishing between departments with overlapping clinical functions, which may stem from relying solely on LLMs. While LLMs can capture linguistic patterns effectively, they may struggle to differentiate between departments with similar functional scopes.  
To address this issue, future work could incorporate hybrid models that combine LLMs with expert systems, enabling more precise reasoning in ambiguous cases. For example, an expert system could encode clinical logic such as \"widespread rashes, regardless of patient age, are typically treated by dermatologists\", thereby guiding the model toward more accurate predictions in such scenarios.