esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Description
### Mobile Data Acquisition System (MDAS)
#### 3.2.2 Software Components for the MDAS  
As for the software, the main interest for the team was making use of a tool that made data acquisition possible regarding signal strength for the location where the smartphone was placed, regardless of it being on land or in mid-air. Another aspect to consider was the chipset of the hardware component used in the MDAS. Two apps were tested which required root privileges and with chipsets from the Qualcomm family. Fortunately, the Xiaomi mi 10 lite met these requirements by having a Qualcomm Snapdragon 765G Model Processor, so it was capable of running the required software.  
While building a software application capable of measuring signal power received at a smartphone was considered (there are several different Java libraries used in the Android operating system that make that a possibility), software programs making use of those capabilities already exist and are fully functional. Among the programs available, there were two choices determined to be the most suitable: Network Cell Info [34] and Geo++ Rinex [35]. The former offers more direct usage and Comma Separated Values-formatted files as information output that are easy to port and transfer, so it was decided that it would be the program used. Network Cell Info can be described as a mobile and Wi-Fi network monitoring app with diagnostic and measurement tools compatible with 5G, LTE+, LTE, CDMA, WCDMA and GSM technologies. Among its main features are: (a) real-time monitoring of signals; (b) Internet speed tests, both in Wi-Fi and mobile networks; (c) Dual SIM support (a major feature for the smartphone used in the proposal); (d) a map with measurement information and connection status; (e) a location service (MLS, it provides an indicator of mobile locations on the Mozilla map); (f) capability to export measurements in Keyhole Markup Language 2.2, Mobile Location Service Geo-submit v.2, Cell Layout File v3, OpenCell Identifier Comma-Separated Values and CMWF data format; (g) device and SIM information. It offered a 1-year higher level subscription version at a cost of $3.29/year, so it was chosen due to its low price.  
The option of exporting the data to use it later for analysis and uploading it to another system, along with its low cost compared to other alternatives, made this the choice  
Figure 7: Xiaomi mounted with the UAV.  
for the proposed model. The CMWF Pro v.1 database exported with the pro version offers parameters such as the Received Signal Strength Indicator (RSSI), the carrier signal frequency, the device location accuracy in meters or channel quality indicators.
## 2 Analysis of Crack Initiation Pressure of Inclined Crossing Borehole Wall
### Analysis of Crack Initiation Pressure of Multi-Layered Coal
#### 2.3.2 Analysis of Cracking Pressure When Cracking Occurs along Axial Direction of Borehole  
The drilling inclination angle is \(\beta\), the angle between the borehole and the normal direction of the coal seam is \(\gamma=\beta-\varphi\), and, according to the axial stress \(\sigma_{z}\) of the borehole in Equation (1), the normal stress \(P_{f}\) on the coal layer can be obtained as follows:  
\[p_{f}=\left[\left(\sigma_{H}\cos^{2}\alpha+\sigma_{h}\sin^{2}\alpha\right) \sin^{2}\beta+\sigma_{v}\cos^{2}\beta\right]\cos(\beta-\phi) \tag{9}\]  
When \(\alpha=0\):  
\[p_{f}=\left(\sigma_{H}\sin^{2}\beta+\sigma_{v}\cos^{2}\beta\right)\cos(\beta-\phi) \tag{10}\]  
If the coal seam fracture occurs along the axial direction of the borehole, the water injection pressure \(P\) must overcome the normal stress \(P_{f}\) and the cohesive force \(C\) of the coal layer to cause damage to the coal layer. Let us consider \(C_{i}\) as the cohesive force of the \(i\)th coal layer, and \(\min\{C_{i}\}\) as the minimum cohesive force between the layers, where n is the number of coal layers. According to Equation (10), the water injection pressure when the borehole is cracked in the axial direction can be satisfied as follows:  
\[p_{2}=\left(\sigma_{H}\sin^{2}\beta+\sigma_{v}\cos^{2}\beta\right)\cos(\beta- \phi)+\min\{C_{i}\} \tag{11}\]  
To achieve multi-layered coal cracks initiated by axial or radial cracks, different water injection pressure conditions must be satisfied. The water injection pressure calculation model of the multi-layer coal seam can be established as follows:  
\[p=\min\{p_{1},p_{2}\}=\min\left\{\begin{array}{l}3\sigma_{h}-\left(\sigma_ {H}\cos^{2}\beta+\sigma_{v}\sin^{2}\beta\right)+\min\{\sigma_{ti}\},\\ 3\left(\sigma_{H}\cos^{2}\beta+\sigma_{v}\sin^{2}\beta\right)-\sigma_{h}+\min \{\sigma_{ti}\},\\ \left(\sigma_{H}\sin^{2}\beta+\sigma_{v}\cos^{2}\beta\right)\cos(\beta-\phi)+ \min\{C_{i}\}\end{array}\right\} \tag{12}\]  
where \(\sigma_{v}\) is the vertical principal stress, \(\sigma_{H}\) is the maximum horizontal principal stress, and \(\sigma_{h}\) is the minimum horizontal principal stress.  
From the above formula, it follows that the initiation pressure is affected by the coal seam geo-stress, dip angle, coal tensile strength, interface cohesive force, and borehole angle.
# Evaluation of Resource Exhaustion Attacks against Wireless Mobile Devices
## 4 Use Cases
Working without direct links to an external microcontroller, such as the Arduino Mega 2560, nevertheless an XBee module has possibilities for connecting some simple sensors, I/O user interfaces and actuators. As a result a network node is capable of operating from a local power supply, the key XBee configuration settings being stored in its nonvolatile memory. XBee is tuned for automatically taking readings of analog or digital input signals and their subsequent repeating passing to some node in the ZigBee network. The interval of sending data can be changed in a range of tenths of a second to several days. In addition, sending business data from an XBee can be initiated by changing the signal level on digital input pins. An XBee supports a power-saving mode (sleep mode), which is especially important in case when the module is powered from the battery. This mode is oriented primarily on the XBee end devices, which only pass and receive their own business data and do not realize coordination function and routing. So, correctly setting the end device parameters (information exchange, transition of the module into sleep mode and wake-up) allows an XBee to run smoothly and seamlessly for up to several years. An XBee allows setting a time interval \(t_{X}\), which the module is in an idle state in. After finishing \(t_{X}\), the module goes into the sleep mode for a time period \(t_{Y}\). So it leads to XBee subjection to denial-of-sleep attacks. Generally, in the sleep mode all main XBee units and processes are switched off, excepting a timer, thereby it allows decreasing the current energy expenses up to hundredths of a mA. During the sleep an XBee cannot receive and send radio packets and perform data communication through the UART (Universal Asynchronous Receiver/Transmitter) interface. However a message sent to the sleeping XBee wirelessly will not be lost--it is stored in a buffer at the parental device and waits for the end of the sleep period to be passed again. Apart from denial-of-sleep impacts the grows of wireless traffic generated by a compromised or false XBee node as well as quite universal attack on creation of electromagnetic interference are of great significance. Abnormal software usage attack has fairly limited application to a standalone XBee due to ability of an intruder to change some XBee configuration settings only. Figure 1: Self-organized wireless network. On the basis of the proposed (in Section 3) intruder model, representing the knowledge of typical types of ERE attacks, let us assess the possible types of these attacks for the two mentioned use cases and identify the most significant ones. The factors determined in the model allow us to obtain a qualitative assessment of the criticality of each ERE attack in its application to the use cases UC1 and UC2 (see Table 5).
# Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
## 4 Data Analysis and Preliminary Experiments
### Statistic Analysis  
To understand how the method works, we use AIS data of a specific maritime area. Figure 1 shows some routes, one incomplete from a specific target, which helps to deploy our method.  
The GetAIS data process consists of two stages: (a) statistic analysis; and (b) data adjustment. Figure 2 shows several data that exist for our specific target route.  
An analysis of each route's behavior was undertaken using this method to select the correct data. One drawback is the number of samples that a vessel can record on a whole journey. To understand what is happening in this process, we can describe the statistical analysis identifying central tendency and dispersion measures. Our method uses the Freedman-Diaconis rule to obtain a minimal difference between the area under the empirical probability distribution and the area under the theoretical probability distribution. It will help the method to know the behavior of data, so we can see a gap that can significantly differentiate the routes. The second step is to find visual differences among each route using a histogram and Kernel Density Estimation (KDE). If the KDE or the histogram follows a similar pattern for each route, the data behave similarly. However, that does not occur in real environments.  
Figure 1: Routes in the selected area with different historical ways. The actual vessel location is at the center.  
Figure 2: The actual route for a specific target-destination is marked with X.
## 7 Challenges and Limitations
### Scalability and Resource Constraints  
Implementing AI models in large-scale communication networks, especially in resource-constrained environments, poses significant challenges. In networks with low-power devices (e.g., IoT sensors, edge devices), implementing AI models such as deep neural networks (DNNs) may be impractical due to high computational and energy demands. These limitations become more pronounced when AI algorithms need to process real-time data, requiring both substantial processing power and memory. To address these challenges, model optimization techniques like model pruning, quantization, and edge-based computing are used. However, optimizing for scalability may sacrifice model accuracy or robustness. For example, using a compressed neural network might reduce memory requirements but could also lead to degraded performance in complex network environments [137].  
Table 11 summarizes the trade-offs between model complexity and computational resources in edge devices:
## 5 Attained Results and Discussion
### Thermal Behaviour Improvement: PCM Solution Combination (Step 4)  
Focusing on the overheating risk and the fact that the use of one type of PCM in some situations during a daily cycle cannot be totally discharged, an additional analysis was carried out, combining two PCMs with different melting temperature values.  
To achieve the optimised PCM combination with two different melting temperature values, the parameter related to the partial amount (in thickness) of the PCM employed was conditioned in the optimiser code. Thus, the different thickness of the PCM of each type was defined in the code as input according to the Equation (1):  
\[x_{1}=-\frac{x_{2}}{2}+\frac{x_{total}}{2} \tag{1}\]  
where:  
\(x_{1}/\text{m}=\text{thickness of the chosen PCM (M}_{91}\text{ or M}_{51}\text{ or M}_{27})\);  
\(x_{2}/\text{m}=\text{thickness of the chosen PCM (M}_{91}\text{ or M}_{51}\text{ or M}_{27})\);  
\(x_{total}/\text{m}=\text{total thickness of the PCM solution (see Figure 9).  
Figure 8: Pareto fronts and all simulated results: Step 3.  
The parameter \(x_{2}\) assumes the range of values: ]0.001 m; 0.037 m] for PCM type M\({}_{91}\); ]0.001 m; 0.021 m] for PCM type M\({}_{51}\); and ]0.001 m; 0.011 m] for PCM type M\({}_{27}\) for PCM type M\({}_{27}\). Thus, the final constructive solution combines layers of the different PCMs as shown in Figure 9.  
Using this formulation, the total thickness of incorporated PCM is always the same for all simulation steps.  
The obtained results reveal a better behaviour in terms of heating demand and overheating rate (reducing both) in the case of the use of PCM solutions with different melting temperatures. Thus, a reduction of 3% of the overheating rate was attained compared with the results from Step 3 (Figure 10).  
This solution (id12) combines 51% of BioPCM type M\({}_{91}\) with a melting temperature of 23 \({}^{\circ}\)C and 49% of BioPCM type M\({}_{91}\) with a melting temperature of 27 \({}^{\circ}\)C.  
Regarding the PCM position, in the case of the walls, the best position for the PCM layer is position 2 and for the roof, position 1.  
Figure 10: Pareto fronts and all simulated results: Step 4.  
Figure 9: Combination scheme of the PCMs.  
Thus, designers should specify a blend of PCM with melting points closest to the upper limit of the desirable range of temperature, as well as the positioning of the PCM in the inner surface of the habited space.  
Despite the small reduction attained using the solution with a combination of different PCMs, the strategy to combine PCM with different melting temperature values can potentiate a more effective impact of the use of PCM to compensate situations where the discharging process is not complete, as is the case on hotter days.
# DOA Estimation of GNSS Signals Based on Deconvolved Conventional Beamforming
## 4 Results  
The simulation parameters were as follows: a GPS L1 C/A signal was simulated, the code frequency was \(1.023\) Mbps, the signal sampling rate was \(6\) MHz, and the coherent integration time was \(1\) ms. The performance of the proposed method was verified using Monte Carlo simulation with \(1000\) iterations. The DOA estimation performance was evaluated using the Root Mean Square Error (RMSE), which is defined as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{PK}\sum_{p=1}^{P}\sum_{m=0}^{M-1}\left(\hat{\theta} _{m}^{(p)}-\theta_{m}\right)^{2}} \tag{14}\]  
where \(\hat{\theta}_{m}^{(p)}\) represents the estimated angle of the \(m\)th signal in the \(p\)th Monte Carlo simulation, while \(K\) denotes the number of Monte Carlo simulation iterations.  
The performance of the proposed method was compared with Capon, MUSIC, and OMP.
## 4 Simulation Results and Discussions  
In this section, the simulation parameters and system location setting are given, and then we present some necessary simulation results and give brief discussions. In Section 4.1, we give the user's power strategy probability and relay selection probability respectively. Moreover, the jammer's regret is also given. In Section 4.2, we analyze the influence of feedback error and eavesdropping error, and we also compare the user's and jammer's utility under the proposed hierarchical joint optimization algorithm (HJOA) with random selection algorithm to verify the algorithm's superiority.  
In the simulation, we assume the user's and jammer's discrete power sets are \(\mathcal{P}=\mathcal{J}=[0.5\,\mathrm{W},1\,\mathrm{W},1.5\,\mathrm{W},2\, \mathrm{W},2.5\,\mathrm{W}]\), and their transmission costs are \(C_{u}=C_{j}=0.1\). The relay set is \(\mathcal{R}=[R_{1},R_{2},R_{3},R_{4}]\) and different relay has the same power \(Q=2\,\mathrm{W}\). The number of time slots in one epoch \(T=100\). Path-loss factor \(\gamma=2\).  
As shown in Figure 3, there exist a BS, a user, a jammer and a relay group in the investigated scenario. The coordinates of BS and the user are (0 km, 10 km) and (10 km, 0 km). The jammer is located in (10 km, 10 km). The relay group consisted of four relays, which were located in (2.5 km, 2.5 km), (5 km, 2.5 km), (5 km, 5 km), (7.5 km, 5 km) respectively. The red arrow is the jamming signal and blue arrow is the communication signal.
## 4 Mobile Localization Algorithms
### Distributed Localization Algorithms
#### 4.2.6 Scalable Localization Scheme with Mobility Prediction (SLMP)  
In [55], by utilizing the predictable mobility patterns of underwater objects, a Scalable Localization scheme with Mobility Prediction (SLMP) for UWSNs is proposed. Localization in SLMP is performed in a hierarchical way. The whole localization process is divided into two parts: anchor node localization and unknown node localization. During the localization process, every node predicts its future mobility  
Figure 10: The Three-Dimensional Underwater Target Tracking.  
pattern according to its past known location information. Moreover, every node can estimate its future location based on its predicted mobility pattern.  
It is assumed that every sensor node needs to get its location periodically. During each localization period \(T_{p}\), anchor nodes can easily measure their locations since they can directly communicate with surface buoys. Based on the measured location \(Loc_{m}\) in the last localization period and the predicted mobility pattern, each anchor node can calculate its estimated location \(Loc_{e}\) in current localization period. The anchor node compares the estimated location \(Loc_{e}\) with its measured location \(Loc_{m}\). If the Euclidean distance between them is larger than the stipulated threshold, the anchor node will judge that its current mobility pattern is not accurate and needs to be updated. Then, it runs its mobility prediction algorithm to get a new mobility pattern. After that, it will broadcast a new localization message which contains its current location and new mobility pattern to the network.  
During unknown node localization process, all anchor nodes label themselves as reference nodes and set their confidence values to 1. When an unknown node receives localization information from anchor nodes, it runs its mobility prediction algorithm to estimate its own location and mobility pattern. If any unknown node has not received any localization message for a long period (larger than some predefined threshold), it will label itself as un-localized. With the advance of the localization process, more and more unknown nodes are localized and become reference nodes, as describe in [21; 22].  
Simulation results show that communication overhead and energy consumption of SLMP are relatively low. However, the performance of SLMP is easily influenced by the localization period \(T_{p}\). When \(T_{p}\) is short, a relatively high communication overhead is needed. Furthermore, The performance of SLMP heavily depends on the structure of the mobility pattern.
## 4 Workflow Overview and Details
### Colorize  
After the segmented image has been achieved by the previous Section 4.1, Segmentation, we use it as the concatenating data with the line art image for the next training step. As shown in Figure 5, the Seg2 pix network has an encoder-decoder network [36]. The rectangles in the figure imply convolution blocks and active layers that transform concatenated input data to feature maps. The convolution parameter grows by 64 per layer, and this encoder step progresses until the third layer of the encoder. Then, the features are progressively scaled up in dimension, and U-Net [29] follows the sub-networks to concatenate to the decoder part of the network. Here, the skip-connection is necessary because transporting information across the network to the generator to circumvent the bottleneck solves the problems of the encoder-decoder network's downsampling of the layers.  
For the discriminator network, we used the PatchGAN style based on the Markovian discriminator [37]. This discriminator is structured by the convolution layer, batch normalization, and leaky ReLU, similar to the encoder-decoder network. While past discriminators for GAN tried to classify fake or real by recognizing all the image data, the Markovian discriminator recognizes the particular size of the patch data pixels and the correlation factors of the neighboring patch. This change decreases the number of parameters for the discriminator network, which results in faster computing times. Furthermore, the Markovian discriminator retains the lightweight complexity of its network.
## 3 Results and Discussion
### Electrochemical Sensor  
Interdigitated electrodes have been used as electrochemical biosensors for the past two decades and are often fabricated by complex steps. However, they do not require detection markers for specific analytes and a reference electrode, considering the interdigitated electrode sensor monitors changes in the current caused by the direct interaction between the sensor surface and the analytes [48; 49; 50]. Therefore, we prepared the Au-SEBS interdigitated electrode by attaching a patterned mask on the SEBS film before thermal evaporation. We selected sodium chloride (NaCl) as our target analyte from numerous other bio-analytes in the body, considering they carry 0.5% of the total sweat [51; 52]. Hence, we prepared mixtures of pure DI water with different concentrations of NaCl solutions  
Figure 4: (**a**) Fracture strain and maximum gauge factor of the Au–SEBS strain sensor with their respective Au thicknesses. (**b**) Resistance trends and gauge factor as per the width of the strain sensor. (**c**) Gauge factor and linearity change as per the strain for determining the range of use of the sensor. (**d**) Relative resistance–strain curve depicting the sensitivity of the selected strain sensor. (**e**) Relative resistance–strain curve depicting the hysteresis of the selected strain sensor under a double sweep. (**f**) Reliable strain-sensing performance of the selected strain sensor under repeated stretching cycles.  
for NaCl sensing. An interdigitated sensor is primitively categorized as a capacitive or impedimetric sensor [53; 54; 55], but using only simple setup at Figure 5a, we measured the concentration of NaCl in the solution by the interdigitated electrodes connected with a source meter. As the NaCl concentration increased, the mobile ions increased in the solution. We confirmed the current was increased as the NaCl concentration increased in the solution that the electrode was immersed in. Figure 5b shows that sweat sensing is capable of diagnosing the health state of a subject even with other sensors attached.