esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
## 2 Background and Literature Review
**Hybrid architecture**--This architecture has been proposed to overcome the limitations of both distributed control and centralized control architectures [36]. In this architecture, the centralized controller can vary the control over the nodes be tween full to zero based on the requirement such that it can behave as a mixture of centralized control and distributed control [37]. Another promising SDVN architecture is the Intelligent Digital Twin (IDT) based SDVN architecture having real-world SDVN and its intelligent virtual digital twin, which is a self-evolving virtual network using intelligent algorithms and decisions of the virtual network, is implemented in the physical network [38]. However, for this IDT-based SDVN architecture, road traffic prediction, the requirement of multi-source data fusion, energy consumption in constructing IDT, and running safety applications requiring low latency have been found to be challenging.
## 3 Experimental Results and Analysis
### Qualitative Analysis  
For the purpose of a more intuitive presentation of the comparison results of the three models, part of the test results on the test set are presented and compared qualitatively, as  
Figure 11: LabelImg of automotive engine parts data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Faster R-CNN** & **Mask R-CNN** & **IA-Mask R-CNN** \\ \hline Backbone & VGG-16 & ResNet101 & ResNet101 \\ Pretrained & Yes/COCO & Yes/COCO & Yes/COCO \\ Iterations & 80,000 & 10,000 & 10,000 \\ Training Time (h) & 25 & 21 & 21 \\ Bump injury AP (\%) & 69.7 & 77.9 & 81.5 \\ Car mark AP (\%) & 82.4 & 91.2 & 93.9 \\ mAP (\%) & 76.1 & 84.6 & 87.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Surface defect detection results of automotive engine parts of the three detection models.  
shown in Figure 12. Since no mask branch was added to the Faster R-CNN, the detected defect area could give only an approximate range, and the detection results were not detailed. Although the Mask R-CNN could achieve detailed defect area detection, it did not consider small targets and also ignored the effect of anchor scales, resulting in missed detections. In contrast, the proposed IA-Mask R-CNN, with the improved anchor design, could detect all defects, even small ones, such as those in the red circle in the bottom row of Figure 12. In addition, even for relatively large defect areas, the IA-Mask R-CNN had a better detection performance than the Mask R-CNN, as shown in the blue circle in the bottom row of Figure 12, which further validated the effectiveness of the proposed detection model.  
Figure 12: _Cont._
# Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model
## 4 Results
### Dataset
#### 4.1.2 Dataset Structure  
The dataset contains a list of instances for training the text-to-Cypher task. Each instance includes the following fields:  
question: a question asking for specific information within the database.  
cypher: the Cypher query corresponding to the question.  
schema: this field provides information about all the relations and properties present in the graph database.  
\"key_data\": this field provides the key values extracted from the question. These key values were identified during dataset generation.  
\"relation_properties\": this field provides the relations and properties relevant to the question, also extracted during dataset generation.  
The example of dataset instance is shown in Figure 9.
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 3 Proposed Method
### Fuzzy Weighting  
For a test image \(I_{in}\), the final output of the illuminant estimation is synthesized by fuzzy weighting the outputs of all models, which is given as:  
\[\hat{\ell}=\sum\limits_{i=1}^{K}(\mathcal{F}_{I_{in}}M_{i})w_{i}, \tag{22}\]  
where \(w_{i}\) is the weight of the \(i\)-th model \(M_{i}\), and \(\mathcal{F}_{I_{in}}\) denotes the extracted features of the test image \(I_{in}\). For an image sample in the training set, \(w_{i}\) is known (it is the membership of degrees for this sample); while for a new test image not included in the training dataset, \(w_{i}\) is needed to be computed by determining what degree the test sample belongs to each cluster obtained via KFCM. That is to say, all possibilities that an image falls into each cluster should be acquired by calculation. To this end, we define a variable \(d_{f,i}=\|f-v_{i}\|^{2}\) as the squared Euclidean distance between the test image's feature vector \(f\) and the cluster centroid \(v_{i}\), \((i=1,2,\cdots,K)\). Then a radial basis function is constructed to represent the probability for the test image belonging to the cluster \(i\):  
\[w_{i}=\frac{e^{-d_{f,i}/2\sigma_{f}^{2}}}{\sum\limits_{l=1}^{K}e^{-d_{f,l}/2 \sigma_{f}^{2}}},i=1,2,\cdots,K, \tag{23}\]  
where \(\sigma_{f}\) denotes the radial fall-off factor. In this way the weights are obtained, and the illuminant color of the test image can be predicted by Equation (22).
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 2 Positioning Model of BDS Undifferenced NRTK
### BDS UD Observation Equation  
The UD observation equation uses the carrier phase observation \(L\), the unit is meters, and the pseudo-range observation \(P\) at the epoch \(i\) is [21]:  
\[L_{r,j}^{s}(i)=\rho_{r}^{s}(i)+T_{r}^{s}(i)+dt_{r}(i)+\lambda_{j}\beta_{r,j}(i) -dt^{s}(i)-\lambda_{j}\beta_{j}^{s}(i)-\mu_{j}\cdot t_{r}^{s}(i)+\lambda_{j}N_{ r,j}^{s}+\epsilon(L_{r,j}^{s}) \tag{1}\]  
\[P_{r,j}^{s}(i)=\rho_{r}^{s}(i)+T_{r}^{s}(i)+dt_{r}(i)+d_{r,j}(i)-dt^{s}(i)-d_{ j}^{s}(i)+\mu_{j}\cdot t_{r}^{s}(i)+\epsilon(P_{r,j}^{s}) \tag{2}\]  
where \(r\), \(j\), \(s\) represent the index of the receiver, frequency, and satellite; \(\rho_{r}^{s}\) is the geometric distance between the satellite and receiver; \(T_{r}^{s}\) is the tropospheric delay; \(dt_{r}\), \(\delta_{r,j}\), and \(d_{r,j}\) are the receiver clock error, carrier phase, and pseudo-range receiver hardware delays; \(dt^{s}\), \(\delta_{j}^{s}\), and \(d_{j}^{s}\) are the satellite clock error, carrier phase, and pseudo-range satellite hardware delays; \(\mu_{j}\) is the ionospheric delay coefficient for frequency \(j\), \(\mu_{j}=f_{1}^{2}/f_{j}^{2}\); \(t_{r}^{s}\) is the ionospheric delay of the first frequency; \(\lambda_{j}\) is the wavelength of carrier phase, \(\lambda_{j}=c/f_{j}\), \(c\) is the speed of light; \(N_{r,j}^{s}\) is the carrier phase ambiguity, it retains the integer feature; and \(\epsilon(L_{r,j}^{s})\), \(\epsilon(P_{r,j}^{s})\) are the observation noise of carrier phase and pseudo-range, respectively.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 4 Studies
### XPCS at Free-Electron Laser Sources
#### 4.2.3 Femtosecond Dynamics by Pulse Length Variations  
Access to dynamics in the range below 100 fs is realized by changing the pulse length of the FEL pulses. This was applied in [30] studying the dynamics of liquid water at LCLS.  
Figure 10: (**a**) Scattering pattern measured with AGIPD at SPB/SFX averaged over 500 trains (Top) and single-shot speckle pattern (Bottom). White parts are masked pixels and blind regions between the AGIPD modules. The intensity scale is given in analog-to-digital unit (ADU) values, one photon corresponds to 65 ADU. (**b**) Intermediate scattering functions measured from diffusing silica nanoparticles in water from as single train of 120 X-ray pulses. Figures adapted from [34].  
The X-ray pulse length was modified between 13 fs and 126 fs. The pulses impinged on a water jet of 100 \(\upmu\)m thickness giving rise to a speckle pattern measured in the vicinity of the structure factor peak of water around 2 A\({}^{-1}\). These patterns were analyzed using the XSVS scheme. With increasing pulse length, the contrast drops due to the dynamics of the sample. This decreasing contrast was compared to a ballistic dynamics model as well as two molecular dynamics (MD) models, see Figure 11a. While the ballistic model underestimates the speckle contrast and thus the relaxation time, the MD model match the data well. Here, the semi-classic MB-pol model [166] provided a slightly better match than the classic TIP4P/2005 [167], however, both models did not cover the temperature dependence of the contrast well. The results indicate that the water dynamics are influenced by cage effects, i.e., the molecules are slowed down due to the hydrogen bonding to their neighbours, in contrast to the common assumption of ballistic-like motion in the sub-150 fs regime. Hence, this real-time study of molecular dynamics represents a benchmark for MD simulations. To extend the time scales covering the full dynamics up to nanoseconds, double-pulse schemes can be applied as presented in the subsequent section.
# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Single-Layer Satellite Networks  
A single-layer satellite network includes satellites with the same orbital altitude and one or more orbital planes. A single-layer satellite network includes satellites with the same orbital altitude and one or more orbital planes. The single-layer dynamic network routings are mainly for the LEO satellite network, whose satellite network structure is shown in Figure 5. Owing to the dynamic LEO satellite topology, this class of routing algorithms is designed to shield it. The three primary methods can be separated into virtual topology, virtual nodes, and topology planning. These are described in the following section.
## 2 System Model  
The nodes are classified into two types: hub node and normal node. A hub node is a node that has low mobility:  
\[E\left[\left|v_{i}\right|\right]\leq\delta, \tag{1}\]  
where \(v_{i}\) denotes the mobility vector of a node \(n_{i}\) and \(\delta\) denotes a threshold. We assume that each node identifies its mobility vector with the aid of a global positioning system (GPS). Therefore, each node can determine by itself whether it is a hub node or not. A normal node is a node that does not belong to the hub. In this paper, we denote node \(i\) as \(n_{i}\). When it is a hub node or a normal node, it is denoted as \(H_{i}\) and \(N_{i}\), respectively.  
Let us assume that source node \(n_{s}\) sends its information to destination node \(n_{d}\), as shown in Figure 1. The proposed transmission control consists of macroscopic transmission and local transmission. First, the macroscopic transmission control finds the important hub nodes from \(n_{s}\) to \(n_{d}\)--i.e., \(n_{s}\)-\(H_{1}\)-\(H_{2}\)-\(H_{3}\)-\(n_{d}\). Then, the local transmission control finds the links between each hub link--i.e., \(n_{s}\)-\(N_{1}\)-\(H_{1}\), \(H_{1}\)-\(N_{2}\)-\(N_{3}\)-\(H2\), \(H_{2}\)-\(N_{4}\)-\(H_{3}\) and \(H_{3}\)-\(N_{5}\)-\(n_{d}\). Each hub node periodically broadcasts its identifier using a HelloHub message, and each broadcasting HelloHub message is propagated until it meets a hub node. Using the periodic hello packet, each hub node calculates and maintains its neighboring hub-link information:  
\[\text{hub-info}:=\{Hub-ID,d-avg,d-var,Topo\}\, \tag{2}\]  
where \(Hub-ID\) denotes the identifier of the hub node, \(d-avg\) and \(d-var\) denote the delay mean and variance, and \(Topo\) denotes the local hub topology that includes the 1-hop neighboring hub nodes. For example, if a hub node \(H_{1}\) maintains  
\[\text{hub-info}:=\{H_{3},5,1,\{H_{2},H_{6}\}\}, \tag{3}\]this means that hub \(H_{1}\) has a neighbor hub \(H_{3}\); the delay average and variance between \(H_{1}\) and \(H_{3}\) is 5 (ms) and 1 (ms), respectively; and \(H_{3}\) has neighbor hubs \(H_{2}\) and \(H_{6}\). Additionally, when a hub violates the hub condition in Equation (1), it sends a ByeHub message to its neighboring hub nodes.
## 3 System Model and Problem Statement
### Radio Model  
In order to evaluate the energy dissipation between node \(i\) and node \(j\), we use the radio model used in [11, 27]. The energy costs of transmitting and receiving a \(k\) bit data packet between node \(i\) and node \(j\) with distance \(d\) are denoted by \(E_{T}\)(\(i\),\(j\)) and \(E_{R}\)(\(i\),\(j\)) which may be computed by:  
\[E_{T}(i,j)=k(E_{i_{\mathit{dec}}}+E_{\mathit{amp}}\times d^{\mathit{\tau}}) \tag{1}\]  
\[E_{R}(i,j)=kE_{\mathit{elec}} \tag{2}\]  
where \(E_{\mathit{elec}}\) and \(E_{\mathit{amp}}\) are the energy dissipation of per bit for transmitter or receiver, and the transmit amplifier, respectively; \(\gamma\in\{2,4\}\) can be seen as the path loss exponent.
## 3 Research Design
### Relevant Technological Concepts
#### 3.3.5 Web Crawling  
Web crawling and web scraping are important tools to process large amounts of digital text files. Both tools can be integrated into programming environments such as Python or R [22], p. 77. The automatic search of the Internet for specific information and data by a computer program is called web crawling. This is done by web crawlers, also known as bots or spiders. In this process of web crawling, algorithms are used to narrow down the search results to the specific information [23].  
According to [PERSON] and [PERSON] [22], pp. 77-78, the web crawling process can be summarized in the following steps:  
1. The web crawler is annotated with a manually selected list of URLs (seeds). This list of URLs then grows iteratively.
2. The crawler selects an URL from this list. The selected URL is marked as \"crawled\". Then the crawler extracts further links and content from the selected website.
3. Content that has already been viewed is discarded. The remaining content will be added to the collection of webpages to be indexed and/or classified.
4. To ensure that the URL has not yet been seen and that the page exists and can be crawled, a check is performed for each new URL in the group. Only after all these checks have been passed is the new URL added to the URL list in step 1 and the crawler continues with step 2.  
[PERSON] [24] explains that all search engines have web crawlers. Some well-known examples are:  
* Googlebot for Google;
* Amazonbot is an Amazon web crawler for web content identification and backlink discovery;
* Bingbot for Bing search engine by Microsoft;
* DuckDuckBot for DuckDuckGo;  
The challenge in web crawling is that a web page overload must be avoided and at the same time large amounts of data must be processed. To reduce the amount of data to be processed, the order in which the URLs are scanned must be carefully decided [25].
# Road Scanner: A Road State Scanning Approach Based on Machine Learning Techniques
## 4 Application Architecture and Usage  
### Architecture Overview  
In this section, we give an overview of the general architecture of the proposed application. The application is called \"Road Scanner\". The overall solution architecture is described in Figure 2. Our application is compatible with both iOS and Android smartphones since it is based on cross-platform technology, which is React Native. React Native is a JavaScript-based framework developed by Facebook for cross-platform app development.  
To interact correctly with our proposed solution, two interfaces are needed. First the smartphone application and second a dedicated website to visualize and export data. The process of data storage is as follows: once the data are recorded from the mobile sensors (i.e., accelerometer, gyroscope, and GPS), it is initially stored locally on the smartphone. This offline process is useful, especially in the case of a loss of connectivity with the application's server. Afterward, the application will be continuously synchronizing records with the server to keep the consistency of the data and to ignore any data replication.  
Indeed, once the connection is established, the stored data are sent to a remote cloud server called \"Road Scanner Server\" via specified API calls. All the received data are then saved to a PostgreSQL database. At this stage of the development, to visualize the labeled segment in a map viewer and to export the data, the user needs to access these functionalities via the application's dedicated website.
# Novel Microfluidic Septum to Optimize Energy Recovery in Single-Chamber Microbial Fuel Cells
###### Abstract  
This study proposes a redesign of asymmetric single-chamber microbial fuel cells (a-SCMFCs) with the goal of optimizing energy production. In the present work, the new approach is based on the introduction of a novel intermediate microfluidic septum (IMS) inside the electrolyte chamber. This IMS was designed as a relatively simple and inexpensive method to optimize both electrolyte flow and species transfer inside the devices. a-SCMFCs, featuring the IMS, are compared to control cells (IMS-less), when operated with sodium acetate as the carbon energy source. Performances of cells are evaluated in terms both of maximum output potential achieved, and energy recovery (\(E_{rec}\)) as the ratio between the energy yield and the inner electrolyte volume. The a-SCMFCs with the novel IMS are demonstrated to enhance the energy recovery compared to control cells exhibiting \(E_{rec}\) values of \((37\pm 1)\) J/m\({}^{3}\), which is one order of magnitude higher than that achieved by control cells (3.0 \(\pm\) 0.3) J/m\({}^{3}\). Concerning the maximum output potential, IMS cells achieve \((2.8\pm 0.2)\) mV, compared to control cells at \((0.68\pm 0.07)\) mV. Furthermore, by varying the sodium acetate concentration, the \(E_{rec}\) and maximum potential output values change accordingly. By monitoring the activity of a-SCMFCs for over one year, the beneficial impact of the IMS on both the initial inoculation phase and the long-term stability of electrical performance are observed. These improvements support the effectiveness of IMS to allow the development of efficient biofilms, likely due to the reduction in oxygen cross-over towards the anode. Electrochemical characterizations confirm that the presence of the IMS impacts the diffusion processes inside the electrolytic chamber, supporting the hypothesis of a beneficial effect on oxygen cross-over.  
microphial fuel cell; microfluidics; fluidic separator; single-chamber microbial fuel cell; energy recovery optimization +
Footnote †: journal: Applied Science and Technology, Politecnico di Torino, 10129 Turin, Italy;  
[EMAIL_ADDRESS] (G.M.); [EMAIL_ADDRESS] (V.B.); [EMAIL_ADDRESS] (F.C.P.); [EMAIL_ADDRESS] (S.B)
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 6 Discussion and Recommendations
### Grounding Considerations in High-Altitude Mining Environments  
Power supply security in high-altitude mining operations presents unique engineering challenges that directly affect grounding system performance. At high altitudes, lower atmospheric pressure, reduced air ionization, and dry or frozen soil conditions significantly decrease soil conductivity, resulting in higher ground resistance and elevated ground potential rise (GPR). These factors increase the risk of unsafe step and touch voltages, especially during fault or lightning events. Moreover, extreme temperature variations can accelerate corrosion in grounding conductors and compromise the long-term reliability of the system.  
In such environments, conventional shallow-grounding techniques are often inadequate. Instead, deep-well electrodes, counterpoise systems, and soil conditioning compounds must be deployed to ensure adequate fault current dissipation. As highlighted in recent studies, the implementation of automated monitoring systems and remote control operations in high-altitude mines increases the reliance on stable and secure power systems, where grounding design becomes a foundational safety and reliability element [31]. The adaptation of grounding infrastructure to meet the demands of electrified and digitized mining systems is also emphasized in regional case studies [32].  
Furthermore, grounding systems should be considered in tandem with insulation studies to offer a more comprehensive electrical safety strategy. Insulation degradation at high altitudes, combined with elevated GPR, can exacerbate insulation stress and increase the likelihood of equipment failure or personnel hazard. Therefore, integrating grounding system optimization with insulation coordination and surge protection schemes is essential for ensuring safe and reliable operations in high-altitude mining environments.
## 4 Retrieval of Sea Surface Height
### Code Delay Sea Surface Altimetry
The use of spaceborne GNSS-R raw IF signals in marine surveying still faces challenges such as accurate estimations of signal delay, the correction of atmospheric delay, and orbit errors. In the future, we can focus on using optimized tracking algorithms, applying global ionospheric maps, improving orbit determination accuracy, upgrading hardware and software, and adopting broadband signal and dual-frequency observation technologies to solve these problems. Figure 6: Process flow for phase height retrieval of code ([PERSON] et al., 2018).
# An AC/DC Distribution Network DG Planning Problem: A Genetic-Ant Colony Hybrid Algorithm Approach
## 3 DG and Load Timing Characteristics  
### DG Timing Characteristics  
Distributed generator usually contains micro-turbine generators (MT), wind turbine generators (WGs), photovoltaics (PV), and so on. Different types of DG apply to different regions. For example, photovoltaics generate more electricity in areas with sufficient light; micro-turbine generators are more suitable for areas with high heat demand [19]. Different DGs contribute differently to the environment, PV and WG are cleaner distributed generators. However, the timing fluctuation characteristics of these two generators, increase the uncertainty and affect the stability of the system while improving environmental protection. We have fully considered the timing characteristics of PV and WG. If we do not, there is no essential difference between WG and PV, and we cannot reflect on the advantages of MT. Additionally, we do not need to install energy storage equipment in the distribution network as this is contrary to the reality. Therefore, for the DG planning problem, the timing characteristics must be taken into consideration.  
We took the three types of DG mentioned above as the research objects because these three types of DG are highly representative: the MT has rated capacity; its output is controllable and does not change with time; and the PV and WG represent a type of DG whose capacity changes with time. We also selected these two types of DG to study the impact of timing characteristics on the distribution network planning in depth.  
Wind speed has a great influence on the WG. Generally, wind speed is high in the evening and especially in the winter. Therefore, the output is highest in winter and the least in summer. Light intensity and temperature have a great impact on PV. During the day, solar energy is rich, and the PV output is strongest at noon. At night, PV does not generate electricity. In general, PV has the largest output in summer and the least in winter. PV and WG have a natural complementarity, which is the main reason for selecting these two DGs which have different timing characteristics. According to  
Figure 1: Voltage source converter (VSC) model.  
meteorological data [20], wind speed curves and light intensity were obtained in different seasons, and the PV and WG timing characteristics curves were obtained as shown in Figures 2 and 3.