esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Impact of the Data Division into Training, Validation, and Test Sets  
The effect of the level at which the data are divided between the training, validation, and test sets was also examined. All the previous training was conducted on data divided into subsets at the level of individual samples. This was an assumption consistent with the experiments described in the baseline and ConvLSTM model information publication [14]. Splitting the data at the episode level means that recordings belonging to specific programs  
Figure 14: Result of the F1 measure for the general class depending on the amount of data in the training set.  
Figure 13: Average values of the F1 measure achieved during the experiment related to the number of dense layers at the end of the model.  
are not repeated in the training and test sets. This limits the model's ability to learn a particular person's voice or way of speaking. Such a restriction should force the model to better generalize the knowledge it acquires. The last option is to split the data at the level of the entire algorithm. In this variant, the absence of recordings belonging to the presenter in the training and test collections is guaranteed. As described in the publication on the SEP28-k collection, in some series, not only did the recorded individuals have speech disorders, but the presenter was also affected. In addition, such a division makes it possible to test the model on recordings created under entirely different conditions. It is likely that each series was recorded in different rooms, and different microphones were used. All of these factors may be present in the recorded signals, which means that deep learning models can learn the features associated with them. Partitioning was performed to replicate the previous sizes of specific subsets as much as possible. In the end, there were 18,449 samples in the training set, 4643 samples in the validation set, and 5063 samples in the test set. The series chosen as the validation set were \"My Stuttering Life\" and \"Strong Voices.\" The test collection included a program called \"StutterTalk.\" Table 3 shows the distribution of classes in every split.  
Figure 15 shows the results of the described study. After analyzing them, we concluded that the way the data are divided has a significant impact on the results of the models. Unfortunately, in the experiment, each of the test sets has different samples. Nonetheless, it can be seen that the models perform best when split at the sample level. This may be due to the fact that in such a situation, the training set has the greatest diversity in samples. Another interpretation could be that the models are learning how specific individuals speak, which would have a negative impact on the generalization of such a system.
# A Game Theoretic Approach for Energy Sharing in the Italian Renewable Energy Communities
## 3 Game Theoretic Approach for Value Redistribution
### Energy Community as a Coalitional Game
#### 3.3.1 The Game  
The game is defined as follows:  
* \(N\) is the set of players \(\{Gen_{1},Gen_{2},...,Gen_{N_{G}},User_{1},User_{2},...,User_{N_{U}}\}\), where \(N_{G}\) and \(N_{U}\) are respectively the number of generators and passive users participating to the energy community.
* The possible actions for each player are to take part to the community or not (In this planning phase we consider the worst case in which users are not changing their loads to increase the amount of energy shared. In real operation, the same game could become dynamic and users could also apply some demand response schema to maximise their payoff.).
* \(v\ :\ 2^{N}\rightarrow\mathbb{R}\) associates with each coalition \(S\subseteq N\) a real-valued payoff \(v(S)\) that the coalition's members can distribute among themselves. The value generated by coalition \(S\) at hour \(t\) is defined as: \[v_{t}(S)=E_{ec}^{\rightarrow market}(t)\cdot PE^{\rightarrow market}(t)+E_{ec }^{shared}(t)\cdot(SEsi+PE^{\leftarrow market}(t)+Inc)\] (40) where the energy sold to the market \(E_{ec}^{\rightarrow market}(t)\) and the energy shared \(E_{ec}^{shared}(t)\) depends on the members of the coalitions \(S\) and are obtained from Equations (21) and (23). The presence of each player in the community affects the quantity of energy produced and shared, and consequently the economical value produced. The value should be redistributed among the players depending on their contribution. For convenience, the value function could be evaluated yearly, keeping the results immediately comparable with the cash flow analysis. The yearly value \(v(S)\) is simply the summation over time of \(v_{t}(S)\). \[v(S)=\sum_{t\in y}v_{t}(S)\] (41)
# Fake Base Station Detection and Link Routing Defense +
## 8 Future Directions Discussion  
### More Advanced Threats  
Similar to many other detection-based schemes, there can be a fake base station that can avoid detection, i.e., it is not detected as a faulty base station, but that would result in the attacker reducing its availability threat impact and releasing the connectivity setup. This effectively reduces fake base station behavior to faulty base station behavior, i.e., the attacker behaves like a faulty base station. We consider our detection to be successful because, in such a case, the attacker behavior reduces to a legitimate but faulty base station, and it explicitly releases the connection. While our scheme does not raise the flag for detection/blacklisting, it effectively reduces the threat impact on availability.  
If the fake base station takes an identity that is listed as a known base station in the user equipment reputation list, then the user equipment can connect to the fake base station. In that case, the detection scheme blacklists the base station, and as a consequence, the fake base station defames a legitimate base station. It can include randomized base station selection in the link routing in addition to the reputation list. This randomized base station selection can also be useful in a situation where the user equipment enters a location where there is no base station within its reputation list. In such a scenario, if the user equipment detects a fake base station, then it cannot switch its connectivity to a legitimate base station because the legitimate base stations around it are not in its reputation list. In such a situation, the user equipment can use a randomized algorithm to select a base station rather than selecting a base station around it that has the highest signal strength. The fake base station cannot exploit the randomized selection to make the user equipment connect to it with \(100\%\) probability.  
An attacker can attempt to launch a threat beyond just availability and disrupting the connectivity, such as attempting to make the victim user equipment connect to a fake server. However, due to the core network's public key in the user equipment's physical USIM, as described in Section 3.3, such a threat requires breaking public-key cryptography (public-key key exchange) and is thus infeasible.
# Aspects of Cyber Security in Autonomous and Connected Vehicles
## 6 Defenses or Countermeasures for Autonomous and Connected Vehicles
### Defenses for V2X
#### 6.1.4 In-Vehicle Connectivity Defenses  
In [68], a security protocol was proposed to secure the CAN bus using advanced encryption standard (AES) encryption with a 32-bit message authentication code (MAC). The first 16 bits are extended IDs, and the remaining 16 bits are cyclic redundancy check (CRC) bits to make the CAN frame more robust. In [94], a secure specification was proposed in ECU with a preloaded pattern to detect false frames of CAN and avoid false frames. In [95], the proposed gateway firewall is the best solution to secure LIN, CAN, and FlexRay. For secure communication, gateways need to develop firewalls. If the ECU is capable of generating digital signatures or MAC, then the firewall's purpose is to follow the authorization given in the certificates of the ECU. Only authorized ECU can communicate with the firewall. In [69], the proposed convolutional neural network (CNN) IDS is used to  
Figure 7: (**a**) Signature-based detection (**b**) Anomaly-based detection.  
avoid attacks on the Ethernet. This model is tested for audio and video packets carried by the Ethernet.
# Dark-Center Based Insulator Detection Method in Foggy Environment
## 3 Dark-Center Fog Insulator Detection Algorithm
### Defogging Algorithm
#### 3.1.3 Adaptive Histogram Equalization  
To further improve the robustness of the algorithm, the restoration module uses the defogging feature of image enhancement to repair the defogged images in a targeted manner. Since the side effects of the defogged images are mainly reflected in the dark image brightness, the restoration module needs to enhance the image brightness and contrast to eliminate the side effects of the defogging module as much as possible, while the image enhancement also has a certain defogging effect, which can compensate for the degradation of the defogging effect brought by the compressed multiscale convolutional network [31].  
The commonly used image enhancement methods for improving image contrast and brightness are Retinex and HE. Both of them are complex and difficult to adjust parameters, which will seriously affect the defogging speed. The CLAHE algorithm has a faster computing speed compared with Retinex and HE. Therefore, in this paper, the restoration module uses histogram equalization for image processing.  
The CLAHE algorithm clips the part of the histogram that exceeds the limit by limiting the local contrast intensity and distributes the clipped part evenly to other regions of the histogram to avoid noise amplification. The algorithm flow is outlined as follows:  
1. The image region is divided into M \(\times\) N non-overlapping sub-regions.
2. The grayscale histogram is calculated for all sub-regions.
3. Given a limit value of T, \(H(i)\) is calculated as follows.  
\[H(i)=\left\{\begin{matrix}H(i)+L,&H(i)<T\\ H_{\text{max}}&,&H(i)\geq T\end{matrix}\right. \tag{10}\]  
where \(H_{\text{max}}\), \(T\), and \(L\) are related to \(H_{\text{max}}=L+T\). The value of \(L\) is calculated as follows:  
\[L=\frac{N_{\sum shadow}}{N_{g}} \tag{11}\]  
where \(N_{g}\) is the number of gray levels in the subregion, and \(N_{\sum shadow}\) is the total number of pixels in the shaded part.
4. Histogram equalization is performed for all subregions.
# A Subcell Finite-Difference Time-Domain Implementation for Narrow Slots on Conductive Panels
## 4 Results
### Slot on Indefinite Plate  
We have used a setup where:  
* The effects of non-normal propagation are relevant: we illuminate one side of the slot with a dipole source and observe the field on the other side at a point where there is  
Figure 4: Cross-section of a subgridded region with a refinement ratio of 1:2 and 3 subgrid divisions, \(N_{\text{sg}}=3\). Each color corresponds to cells at the same subgrid level. The aperture of the slot is located in the finest grid.  
no straight line of vision to the source (Figure 5 shows the positions of the dipole and probe with respect to the slot);
* The slot width is not thin with respect to the FDTD space step, specifically \(w=\Delta\). The insertion loss (IL) function of the electric field \(E_{p}\) is found by \[\mathrm{IL}(f)=20\log\frac{E_{\text{free-space,w}}(f)}{E_{w}(f)}\Big{|}_{\text{at p}}\] Current Dipole \[I_{\text{dipole}}\] Lossy panel \[\hat{\mu}\] \[\hat{\mu}\] \[\hat{\mu}\] \[\hat{\mu}\] \[\hat{\mu}\] \[\hat{\mu}\] \[\hat{\mu}\] \[\hat{\mu}\] \[\hat{\mu}\]  
We use a Hertzian dipole along \(\hat{w}\), excited with a current excitation having a Gaussian time profile with a \(-3\,\mathrm{dB}\) decay in amplitude at a frequency of \(5\,\mathrm{GHz}\). The slot has a width of \(5\,\mathrm{mm}\) and is placed on a plane aligned with the grid. We compare the results obtained using CA and DMMA with a spatial resolution of \(\Delta=5\,\mathrm{mm}\) to FDTD with a higher spatial resolution of \(\Delta=0.25\,\mathrm{mm}\).  
Figure 6 shows the results of the IL function for both a PEC and a conductive plane. The conductive plane is modeled using the SGBC method, with a conductivity of \(\sigma=200\,\mathrm{S/m}\) and a thickness of \(1\,\mathrm{mm}\). Note that the tendency of the conductive panel at low frequency (LF) (when the skin depth is greater than the thickness of the panel) is similar with and without the slot. However, above the first resonance, the slot response dominates the results. The convergence of the CA model and brute force at LF is similar to the conductive panel case. In our view, the observed differences with the DMMA model may be attributed to the fact that the DMMA model does not modify the surfaces affected by the slot, whereas the CA method naturally handles them in a conformal integral manner. This difference becomes particularly relevant when the slot dimensions are similar to the cell size. Notably, it is worth noting that this drawback does not appear for PEC panels, as the tangential electric fields are directly zero.  
Though DMMA agrees with full-wave methods at LF, we observe that CA outperforms DMMA over the first resonance when the DMMA method loses precision because the assumption that the fields are uniform on it begins to be false.
## 3 Results and Discussion
### Colour Parameters of Extracts in the CIEL\"a*b* System
The yellow-green colour of leaf extracts is directly influenced by chlorophylls (blue-green chlorophylll a and green-yellow chlorophylll b); carotenoids, specifically their oxygen derivatives; xanthophylls, which are highly soluble in alcohol; and the polyphenols themselves [54]. Chlorophylls could have the greatest impact on the colour of water and methanol extracts, which were significantly the greenest among apple leaf extracts. Indeed, the highest intensity of green colour in these extracts as well as in extracts from the leaves of the Ligol cultivar and extracts obtained using the ASE method indicates that they contain the most chlorophylls. Both xanthophylls and chlorophylls are strong antioxidant compounds [55,56]. Based on the values of colour parameters for the tested extracts, it can be concluded that the content of chlorophylls and xanthophylls could significantly influence the antioxidant activity of apple leaf extracts, although not to the same extent as the content of polyphenols. The values of parameters a* and b* were significantly and negatively correlated with the antiox  
Figure 6: Values of colour parameters in extracts made from different cultivars of apple leaves. idant capacity of the extracts, which means that the more green or blue (less yellow) the colour of the extract was, the greater its antioxidant activity. A three-factor analysis of variance for the L* parameter allowed for the identification of as many as 25 homogeneous groups, which indicates a huge diversity of extracts in terms of colour brightness. The brightest was the water extract from the apple leaves of the Ligol cultivar, obtained using ASE (97.16) and the water-ethanol extract from the leaves of the Gala cultivar and obtained using the SSE method, and it had the significantly lowest brightness (87.37). After performing the same analysis for colour parameter a*, 21 homogeneous groups were distinguished. All water-ethanol extracts, except those obtained using ASE, tended to turn red (the a* parameter assumed positive values). In fact, the reddest of all was the water-ethanol extract from the leaves of the Gala cultivar, obtained using the SSE method (6.37). The lowest value of the a* parameter was recorded for the water and methanol extract from the leaves of the Ligol cultivar, obtained using the classical method (\(-\)12.46). Also, in all water-methanol extracts and those made using ASE, the a* parameter had negative values, so their colour tended to green. A three-factor analysis of variance for the colour parameter b* in the extracts identified 25 homogeneous groups. In fact, the yellowish was the water-ethanol extract from the leaves of the Gala cultivar, obtained using the SSE method (95.98). However, the lowest values of the b* parameter were observed in water and water-ethanol extracts obtained using ASE. The least yellow was the water extract from apple leaves of the Ligol cultivar, obtained with accelerated extraction with a solvent (10.29).
# Dynamic Identification on an Irregular Structure
## 5 Discussion  
The ARTeMIS and the second FE models report concordant results for the identified first three modes which occur at almost coincident frequencies and have comparable modal shapes. Therefore, it can be assumed that the 3D FE model has reached a good approximation after the second updating phase and the importance of the insertion of the infill and the relative increase in the stiffness of the model.  
Due to the irregularity of the structure in plan and elevation, the participating masses and the variation of the center of masses at each level is identified for each FE model in Tables 5 and 6, in order to evaluate how these geometric irregularities affect the building dynamic behavior, as indicated in [33; 34].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**FE Model without**} & \multicolumn{2}{c}{**FE Model with Infill**} & \multicolumn{2}{c}{**ARTeMIS**} \\  & **Infill Walls Frequency** & **Walls Frequency** & **Mean Frequency** \\  & **[Hz]** & **[Hz]** & **[Hz]** \\ \hline
1 & 4.94 & 5.26 & 5.20 \\
2 & 7.67 & 7.72 & 7.66 \\
3 & 8.44 & 8.83 & 8.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between the identified frequency values for different set-up configurations.  
Figure 19: 3D view of the FE model of the “Municipal Headquarters” of Palagianello with infill walls defined by means of SAP2000.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Mode**} & \multicolumn{2}{c}{**FE Model without Infill Walls**} & \multicolumn{4}{c}{**FE Model with Infill Walls**} \\  & **Period** & **Ux** & **Uy** & **Rz** & **Period** & **Ux** & **Uy** & **Rz** \\  & **[s]** & **[-]** & **[-]** & **[-]** & **[s]** & **[-]** & **[-]** & **[-]** \\ \hline
1 & 0.202 & 0.260 & 0.050 & 0.331 & 0.189 & 0.297 & 0.068 & 0.304 \\
2 & 0.130 & 0.365 & 0.067 & 0.163 & 0.129 & 0.394 & 0.080 & 0.207 \\
3 & 0.118 & 0.000 & 0.490 & 0.096 & 0.113 & 0.003 & 0.485 & 0.163 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Modal Participating Mass Ratios.  
In Table 5, the constant presence of a torsional component is highlighted for all the first three natural frequencies of the structure; this component is almost certainly due to the structural irregularity.  
Table 6 demonstrates that, as previously indicated, the mass between the two FE models does not vary having almost coincident centers of mass, as can be seen from the comparison of the coordinates.
## 5 Simulation and Performance Analysis
### Experiment Setting
#### 5.1.2 Model Parameters  
In this paper, the convolutional layer of the GNN is set to \(L=2\), and the embedding dimension of the satellite network node is set to \(d=6\). For the MLP, the hidden dimension is set to \(d_{h}=64\) and \(d_{\pi}=d_{\phi}=4\). For training, the number of training iterations and maximum number of trial sizes were set to \(max\_episode=10,000\) and \(max\_step=50\), respectively. For the PPO loss function, the coefficients of policy loss (with a clipping ratio of 0.2), value loss, and entropy were set to 1, 0.6, and 0.02, respectively. The PPO optimization period was set to \(R=3\), and the discount factor was set to \(\gamma=1\). The actor and critic networks used the Adam optimizer to update the parameters, and the  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Number of satellites & 6048 \\ Simulation time & 5400 s \\ Simulation step & 60 s \\ Satellite orbit altitude & 550 km \\ Number of orbit planes & 84 \\ Number of satellites per plane & 72 \\ Inclination of orbit & 53\({}^{\circ}\) \\ Eccentricity of orbit & 0 \\ Phase factor & 2 \\ Number of ground stations & 117 \\ Number of function nodes & [3, 4, 5] \\ Number of user terminals & [2000, 4000, 6000, 8000] \\ Minimum elevation angle of ground station & 40\({}^{\circ}\) \\ Minimum elevation angle of user terminal & 20\({}^{\circ}\) \\ Central frequency & 20 GHz(UL), 30 GHz(DL) \\ Satellite Tx gain & 30 dBi \\ Satellite EIRP density & 4 dBW/MHz \\ Bandwidth & 250 MHz(UL), 62.5 (DL) \\ Satellite G/T & 13 dB K\({}^{-1}\) \\ Satellite Rx gain & 38.5 dBi \\ User terminal Rx antenna gain & 39.7 dBi \\ User terminal noise figure & 1.2 dB \\ User terminal antenna temperature & 150 K \\ Ambient temperature & 290 K \\ User terminal Tx antenna gain & 43.2 dBi \\ User terminal Tx transmit power & 2W (33 dBm) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of simulation parameters.  
learning rate was \(lr=10^{-4}\). To solve the problem of long model training time, we used OpenRL [54] to accelerate the training process. All experiments were performed using a machine equipped with an Intel i7-11700K CPU (Intel Corporation, Santa Clara, CA, USA), 32 GB of memory, an Nvidia GeForce RTX 3080 GPU (NVIDIA Corporation, Santa Clara, CA, USA), and an Ubuntu 22.04 64-bit OS.
# An Autonomous Wireless Device for Real-Time Monitoring of Water Needs
## 3 Materials and Methods
### Software Description  
Since the Moteino driver is fully compatible with Arduino, it was programmed using the Arduino IDE software with the C++ programming language.  
The software operation of the node is shown in the flow diagram (Figure 3). When the device is activated, the different outputs and inputs of the processor are configured. Next, the RN2483 chip is initialized, through which the information is later transmitted. Then, the necessary data are collected from the different sensors that are connected to the node at that moment. Since the sensors deliver a specific voltage depending on the physical parameter we wish to measure, it is necessary to transform this reading into specific physical units. To this end, data processing is carried out following the guidelines set by the manufacturers. To save battery, only the necessary data are processed (fog computing). The rest is processed in the cloud. Once the desired data are obtained, these are sent through the RN2483 module to the nearest gateway.  
After having sent the information, the LoRa module enters a \"sleep\" mode, through which it is possible to lower its consumption drastically. Then, the same is done with the Moteino microcontroller. In this way, the device has minimum consumption during the time between measurements, preserving the battery life well.  
The time between measurements must be programmed to extend the battery life, but also, in turn, to have sufficient precision to quickly detect changes in soil moisture or weather conditions, and it is not necessary to monitor the parameters in real-time [4]. In our experience, for the cultivation of berries in hydroponics, taking measurements every 30 min is sufficient. In irrigated systems with trees, they tend to be taken with less frequency, as well as in outdoor irrigated crops too. Thus, we used the most extreme case.  
Figure 2: Node sensor (end-device for the lab test).