esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Optimization Problem Solving  
This section discusses methods for solving the UAV flight path optimization problem of this paper. The objective function of this problem is difficult to optimize using a gradient method since the objective function's shape varies greatly depending on the environment's terrain and scattering objects. In addition, the huge computational complexity involved in simulating radio propagation makes the use of heuristics methods suitable for obtaining a good approximation of the solution, even if there is no guarantee of optimality. Meta-heuristics is a strategy to further improve the approximate solution obtained by heuristics by modifying the solution to get closer to the optimal one. In meta-heuristics approaches, the solution space is explored by iteratively (1) generating new solutions using the previous search history and (2) evaluating the generated solutions and feeding back the necessary information for the next solution search. In this paper, we focus on particle swarm optimization (PSO) and the genetic algorithm (GA) among these meta-heuristics. Since PSO was explained in Section 3.4, the following paragraph briefly explains about GAs.  
Genetic algorithms (GAs) are algorithms that search for solutions in a manner that mimics the mechanism of biological evolution. A population of chromosomes with genetic parameters is generated as candidate solutions, and genetic manipulations such as selection, crossover, and mutation are performed to search for solutions [24]. First, an initial population of genes is generated. Next, the degree of adaptation is evaluated according to the evaluation function. If the function meets the termination condition, the process is terminated. Otherwise, genetic manipulations, such as selection, crossover, and mutation, are performed, and the next generation is used to repeat the above process until the termination condition is satisfied and the optimal solution is obtained. The Parameters of GA in this paper is summarized in Table 11.  
We compare the accuracy of the aforementioned optimization methods, assuming eight fixed hovering UAV sensors are employed (It is equivalent to a single UAV flying with a free-path trajectory and visiting these locations consequently). Figures 12 and 13 show the placement of the receiving sensors and the distribution of the position estimation error, for three scenarios of random deployment and optimized deploy
# Detectron2 for Lesion Detection in Diabetic Retinopathy
## 4 Results  
### Evaluation Metrics  
The performance of the detector is evaluated using sensitivity, specificity, and accuracy [32]. These performance measures are commonly used in medical diagnosis and are defined as follows:  
Sensitivity is the ratio of true positive (TP) cases to the sum of true positive and false negative (FN) cases. Specificity is the ratio of true negative (TN) cases to the sum of true negative and false positive (FP) cases [33]. Accuracy is the ratio of the sum of true positive and true negative cases to the overall sample.  
\[Sensitivity = TP/(TP+FN) \tag{1}\] \[Specificity = TN/(TN+FP)\] (2) \[Accuracy = TP+TN/(TN+TP+FN+FP) \tag{3}\]  
In the evaluation of object detection and instance segmentation models in Section 4.3, the key metrics utilized to assess the performance are Intersection over Union (IoU) and the standard metrics provided by the Common Objects in Context (COCO) dataset [17]. IoU measures the overlap between the predicted and ground-truth bounding boxes and is calculated as the ratio of their intersection to their union. A high IoU score indicates a good match between the two bounding boxes. An example of IoU scores is shown pictorially in a Figure 5.  
In addition to IoU, COCO provides standard metrics, including Average Precision (AP) and Average Recall (AR) [34]. These metrics are listed in Table 2. AP measures the accuracy of the model's object detection, while AR measures the model's ability to recall objects. AP is calculated across different scales and at different IoU thresholds (IoU = 0.50, 0.75) and is divided into categories based on object size (small, medium, large). These categories are represented as APsmall (for small objects with an area < 322), APmedium (for medium objects with an area between 322 and 962), and APlarge (for large objects with an area > 962) [35].  
Similarly, average recall will be calculated at different levels of maximum detections per image (ARmax = 1, 10, 100) and divided into categories based on object size (ARSmall, ARmedium, ARlarge). These metrics provide a comprehensive evaluation of the performance of object detection models and help assess the strengths and weaknesses of the approach, specifically with respect to object size and detection count.
# Steel Surface Defect Classification Based on Small Sample Learning
## 1 Introduction
Therefore, it is particularly suitable for classification. Some applications are the Prototypical Networks proposed by [PERSON] [7], the Relation Network proposed by [PERSON] [8] and Siamese neural networks proposed by [PERSON] [9]. These studies adopt a four convolutional layer network as a feature extractor [7; 10]; the network structure is relatively simple and the training speed is fast, but it is very dependent on a good feature space. As to specific applications of small sample learning, the main focus is on hyperspectral images [11; 12; 13] and biological signals [14], while steel surface defects are relatively few. [PERSON] [1] used a twin neural network based on L1 distance to classify steel surface defect samples, but the performance of the model was not good under small datasets. [PERSON] [15] used image enhancement to expand the dataset, and pre-trained SqueezeNet to realize the classification of steel surface defect, but the sample number is big. In this paper, the model of feature extraction + feature transformation + nearest neighbor was used to classify the steel surface defects in a small dataset. A neural network was used to extract the image features of steel surface defects, after which the extracted image features were transformed to a new feature space and, finally, the nearest neighbor algorithm was used for classification. Section 2 introduces the core idea and advantages of three feature extraction networks, two effective feature transformation methods and the rules of nearest neighbor classification. Section 3 gives the settings and results of the experiment. The conclusions are given in Section 4.
## Materials and Methods
### Coulombic Efficiency  
Coulombic efficiency (\(Y_{Q}\)) is defined as the ratio between the number of coulombs transferred to the anode from the substrate and the theoretical maximum number of coulombs that could be produced if the entire substrate were available for current generation:  
\[Y_{Q}=\frac{\mathit{coulombs\ produced}}{\mathit{total\ theoretical\ coulombs}} \times\ 100 \tag{2}\]  
The number of total coulombs obtained is determined by integrating current versus time. The above equation can be rewritten in order to obtain the Coulombic efficiency in an MFC in batch mode as follows [24]:  
\[Y_{Q}=\frac{M_{m}\int_{0}^{t}i(t)dt}{F\cdot\Delta\mathit{COD}\cdot b\cdot V} \times\ 100 \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Water Parameter**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Initial**} & \multicolumn{3}{c}{**Final Values**} \\ \cline{3-5}  & & & **MPC** & **Baseline** & **Control** \\ \hline pH & [-] & 8.19 & 6.78 & 6.99 & 6.99 \\ Alkalinity (mg/L) & RODIER [22] & 67.60 & 19.60 & 23.60 & 31.60 \\ TSS (mg/L) & AFNOR 90-105 [23] & 0.18 & 1.40 & 0.98 & 0.58 \\ COD (mg/L) & AFNOR 90-101 [23] & 1760.00 & 334.00 & 1200.00 & 1092.00 \\ Total Phosphate (mg/L) & AFNOR 90-022 [23] & 0.51 & 16.44 & 19.27 & 1.58 \\ Kheldahl nitrogen (mg/L) & AFNOR 90-110 [23] & 11.20 & 5.60 & 8.40 & 7.00 \\ Nittre (mg/L) & AFNOR 90-013 [23] & 0.48 & 0.38 & 0.42 & 0.39 \\ Ammonium (mg/L) & AFNOR 90-015 [23] & 2.67 & 1.40 & 1.48 & 2.19 \\ Sulphate (mg/L) & RODIER [22] & 263.50 & 128.50 & 148.50 & 142.67 \\ Chloride (mg/L) & AFNOR 90-014 & 553.80 & 837.80 & 552.80 & 569.60 \\ Hardness (mg/L) & RODIER [22] & 876.00 & 1076.00 & 836.00 & 816.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of physicochemical parameters before and after water treatment in microbial fuel cells (MFCs), baseline and control.  
where \(M_{m}\) is the molecular mass of oxygen (32 g/mol), \(i(t)\) is the rated current (A = C/s), \(F\) is the Faraday constant (96,485 C/mol of e\({}^{-}\)), \(\Delta COD\) is the variation of COD during the time \(t\) (\([COD]_{0}-[COD]\)), \(b\) is the moles of electrons produced per mol of oxygen (\(b\) = 4) and \(V\) is the volume of liquid in the anode chamber (0.25 L).
# Equivalence between LC-CRF and HMM, and Discriminative Computing of HMM-Based MPM and MAP
## 2 Related Works  
Concerning the first contribution, several authors noticed similarities between LC-CRFs and HMMs in different previous works. Our first remark is to notice that trying to compare the two families directly is somewhat incorrect, as they are defined on different spaces. In particular, they cannot be equal. It is well-known that the posterior distribution \(p(x_{1:N}|y_{1:N})\) of an HMM \(p(x_{1:N},y_{1:N})\) is a LC-CRF. Conversely, showing that for a given CRF \(p(x_{1:N}|y_{1:N})\) it is possible to find an HMM \(q(x_{1:N},y_{1:N})\) such that \(q(x_{1:N}|y_{1:N})=p(x_{1:N}|y_{1:N})\) is more difficult and at our knowledge, there is no general results, except [14], published. However, let us mention [19] where authors show a similar result to ours assuming an additional constraint on the LC-CRF considered. In [7], authors comment similarities and differences between LC-CRFs and HMMs considered here; however, the problem of searching an HMM equivalent to a given LC-CRF is not addressed. In this paper we show how to compute, from the LC-CRF \(p(x_{1:N}|y_{1:N})\) given with Equation (3), an HMM \(q(x_{1:N},y_{1:N})\) verifying \(q(x_{1:N}|y_{1:N})=p(x_{1:N}|y_{1:N})\), without any additional constraints. Concerning the second contribution, the authors generally distinguish between \"discriminative\" classifiers, linked to discriminative models, and \"generative\" classifiers, linked to generative models. As mentioned above, our contribution is to show that the MPMs and MAPs based on generative HMMs can also be considered as discriminative classifiers. To our knowledge, there is no work on such conversions, except [20].
## 3 Proposed Reliable Routing Mechanism
### Reliable Inter-Cluster Routing  
Inter-cluster routing is achieved by extracting the relationships between clusters from the local routing table, which includes the next hop nodes in clusters adjacent to the cluster edge. The inter-cluster routing is obtained by exchanging the relationships between clusters. Discovering inter-cluster routes when the route is demanded can reduce the overhead of constructing and maintaining Inter-cluster routing. On-demand routing protocols such as DSR adopt flooding as route discovery method; no previous routes are available to guide the packets to the destination. Nevertheless, these methods increase not only the route discovery latency but also the overhead, depending on the flooding range. In this study, the cluster-based border gateway routing protocol is slightly modified to overcome this problem and provide the reliability information of the routing path. When the demand for an inter-cluster route occurs, the source node sends the inter-cluster Route REQUest packet (RREQ packet) in unicast mode to the border nodes to obtain the adjacent cluster's intra-cluster routing information, from which the inter-cluster route can be created. Figures 10 and 11 show the routing algorithm.  
For instance (as shown in Figure 12), node N53 in C5 wishes to communicate with node N12 in C1, but the route to N12 or C1 cannot be found in N53's intra-cluster routing table. The inter-cluster RREQ packet is sent to the border nodes N46 and N38 to obtain the adjacent cluster's intra-cluster routing information. The inter-cluster RREQ is cached in the node that received it, and is not removed until the expiry time is due.  
Figure 9: Relationship between local cluster and adjacent clusters.  
The RREQ packet is dropped when the node receives the inter-cluster RREQ with the same source and sequence number. If the route cannot be found in the border node's intra-cluster routing table, then the border node sends the inter-cluster RREQ again to other border nodes. This procedure is repeated as needed until the edge of the sensor network is reached. In this case, the path to C1 can be identified from the intra-cluster routing table of the border nodes in C3, and the border node sends an inter-cluster route reply RREP packet back to the source node; the route to C1 is added into the intra-cluster routing table of the nodes located on the reverse path of the inter-cluster RREP traveled. That is, C5 obtains the path to C1 by passing C3 or C4, but C3 has the best reliable path; moreover, if the N\({}_{38}\) nodes failure or other unavoidable reasons, then the route from N\({}_{53}\) to N\({}_{12}\) is reconstructed along the path through N\({}_{46}\), N\({}_{37}\) and N\({}_{15}\).  
Figure 11: Inter-cluster routing algorithms.  
Figure 10: Inter-cluster search for routing algorithm.
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
## 7 Conclusions  
The space industry is experiencing a significant transformation, leading to a highly competitive and innovative environment driving progress in the sector. A leading innovation in this transformation is using satellite constellations for IoT communications in low earth orbits, recognized for their potential to achieve global connectivity with low energy consumption results for devices. In this context, LPWAN networks have emerged as a practical solution for communication with satellites in LEOs. Specifically, LoRa technology shows promise in enhancing the scalability and transmission rate of connected devices in these communications.  
However, several challenges and limitations that need addressing exist. These include improving the energy efficiency, enhancing the device processing capacity, and developing more efficient and secure communication protocols. Additionally, the scalability of connected IoT nodes and the optimization of communications are critical for improving power consumption, bandwidth, and overall performance.  
Therefore, establishing a robust infrastructure and efficiently managing the increasing number of IoT devices is imperative. Optimizing communications is critical to leveraging the full benefits of global connectivity offered by satellites in IoT communications. Addressing these challenges is essential to fully capitalize on the advantages provided by global connectivity satellites in IoT communications.  
Conceptualization, P.L.; investigation, O.L.; writing, P.L., O.L. and J.A.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universidad Internacional de La Rioja, grant number JT-2022-04 (Project: \"Arquitectura y sistema de comunicaciones para la conectividad con nanosatellites de baja orbita\"). Also, it was funded by the Spanish Ministry of Science, Innovation and Universities under Project MIG-20231006 (\"Diseno de chips innovadores para la primera placa integrada multifunctional de alto rendimiento y bajo consumo energetico en globos estratosfericos\"). Part of this work was supported by EU's H2020 R&D program under the [PERSON] grant agreement No 101008233 (MISSION project) and the French National Research Agency (ANR) project ANR-22-CE25-0014-01.  
Data Availability Statement:Data sharing is not applicable.  
The authors declare no conflicts of interest.
# SPD-Safe: Secure Administration of Railway Intelligent Transportation Systems
## 2 Materials and Methods--Related Work
### Management Platforms and Reasoning Systems  
Agent technologies constitute the typical option for modeling ambient intelligent systems that exchange information with the environment and user [38,39]. Intelligent agents inspect the surrounding setting and react to upcoming events at normal operation. Their AI modules process context-aware data, as collected from the surrounding environment by the attached devices.  
Regarding the various agent technologies, 24 frameworks were analyzed in Kravari and Bassiliades [40 normal operation, which is stored in the server. Then, the new pieces of knowledge that are sensed by the devices are compared with the nominal values. The detected variations are further analyzed by the system, revealing safety-related incidents (e.g., rail cracks) that are presented to the user through a dedicated application. However, neither of the two systems considers security issues at the network link, nor do they integrate and manage the heterogeneous underlying embedded systems.  
SENSORAIL [52] is an early warning system for the railway monitoring infrastructure. WSNs collect and integrate data to enable the detection of structural failures and security threats. Sensor clusters communicate information towards distant controlling centers through GSM-R/GPRS mobile equipment. The integration of heterogeneous sensors is managed by a component referred to as \"scalable software architecture for the integration of heterogeneous sensor systems\" (SeNsIM) [53], while the detection of events is made by a model-based data correlation component, called \"novel framework for the detection of attacks to critical infrastructure\" (DETECT) [54]. SENSORAIL specifies the examined threats in the Event Description Language (EDL) [55] and maintains them within a scenario repository. Upcoming events are stored in a history database and model-checking is performed at runtime. Regarding the middleware and agent platform, SeNsIM does not utilize any semantic technologies and does not support related standards, thus lacking in terms of interoperability and ease of integration with existing setups. Moreover, SENSORAIL does not include any protection mechanisms, solely focusing on the detection of threats.
## 4 Results and Discussion
### Results of Analytical Thermal Modeling
From Figure 5a,b, it is obvious that the normalized Nusselt number increases with an increase in the velocity ratio, i.e., with an increase in the vibration amplitude at \(\mathrm{f_{V}}=120\) Hz. For the same cross-sections, the normalized Nusselt numbers are significantly higher for bus bars installed with a horizontal major axis (Figure 5b) than for the same bus bars installed with a vertical major axis (Figure 5a). This is caused by a significant increase in the velocity ratio in the case of bus bars installed with a horizontal major axis. For velocity ratios ranging from 0 to 20, the results from Figure 5a, related to bus bars with cross-sections of 0.0004 m \(\times\) 0.02 m, 0.0004 m \(\times\) 0.025 m, 0.0005 m \(\times\) 0.02 m, and 0.0005 m \(\times\) 0.025 m, match the corresponding experimental data from [21]. Specifically, the discrepancies between the simulated and existing experimental data are almost the same. This can be considered a good result. The dependencies shown in Figure 5a,b coincide perfectly with Equation (10). Heat transfer coefficients due to convection between the surfaces of different rectangular bus bars and the ambient air as functions of vibration amplitude are presented in Figures 6-8. These coefficients are generated for a vibration frequency of 120 Hz. Figure 6  
Figure 5: Normalized Nusselt number as a function of velocity ratio for different rectangular bus bars and different vibration amplitudes at a vibration frequency of 120 Hz: (**a**) Bus bars installed horizontally with a vertical major axis; (**b**) Bus bars installed horizontally with a horizontal major axis. Based on Figures 6 and 8, it is evident that the coefficients due to vibration-assisted convection from the side and bottom surfaces increase with an increase in the vibration amplitude at \(\mathbf{f}_{\mathrm{V}}=120\) Hz, and also with a decrease in the corresponding characteristic length. According to Figure 7, most of the coefficients due to vibration-assisted convection from the top surfaces are constant. The only exceptions are the coefficients that, according to Figure 7b, correspond to bus bars with cross-sectional areas of 0.0508 m \(\times\) 0.00635 m, 0.1524 m \(\times\) 0.00635 m, 0.1016 m \(\times\) 0.009525 m, and 0.1016 m \(\times\) 0.0127 m. The appearance of nonlinearities in Figures 7b and 8a,b is once again a consequence of modeling the effect of horizontal transverse vibrations on free convection by means of empirical correlations for forced convection. It is found that this effect is most pronounced in the case of coefficients due to convection from the bottom surfaces of the considered bus bars. In general, it is found that the vibration amplitude can enhance the convection heat transfer from the bus bar surfaces, which agrees with the conclusions drawn in [32]. The ampacity as a function of the vibration amplitude at a vibration frequency of 120 Hz for different rectangular bus bars is given in Table 4.
## 1 Introduction
### Problems of the Existing Methods  
Through the research and analysis of common noise reduction algorithms at home and abroad, some technical difficulties in existing noise reduction methods can be summarized:  
(1) When the traditional SVD is used to suppress the narrowband interference, it is difficult to determine the number of singular values of the narrowband interference. If it is not determined properly, it is easy to cause the PD singular value to be eliminated, thus affecting the narrowband interference suppression effect. In addition, when the narrowband interference amplitude is small, the singular value of the narrowband interference may intersect with the PD singular value, and the narrowband interference suppression effect becomes worse.  
(2) In the process of iterative decomposition, EMD and EEMD algorithms are prone to modal aliasing and endpoint distortion, which leads to low timeliness of the algorithm and inaccurate feature extraction. If the problem of modal aliasing cannot be eliminated, there will be a large amount of residual noise in the denoised PD signal.