esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### Computational Complexity Analysis  
The computational complexity of the proposed model is analyzed by examining neural network components. We define \(B\) as the batch size, \(C_{in1}\) and \(C_{in2}\) as the numbers of input channels for 1D and 2D branches, \(L\) the sequence length in the 1D branch, \(H\times W\) as the spatial dimensions in the 2D branch, \(K_{1},K_{2},K_{3}\) the sizes of kernels, \(C_{out1},C_{out2},C_{out3}\) as the number of output channels of different stages, \(N\) as the number of output classification categories, \(S_{1}\) and \(S_{2}\) as the sizes after 1D-CNN and 2D-CNN feature extraction, \(D_{attl}\) as the attention dimension, and \(D_{fc1}\) as the first fully connected layer dimension. Then, the complexity of the 1D-CNN branch, which processes time-domain signals with input shape \((B,C_{in1},L)\), is \(O_{1D}\approx\mathcal{O}(B\cdot L\cdot C_{in1}\cdot C_{out1}\cdot K_{1})\), while the complexity of the 2D-CNN, which processes delay-Doppler images with input shape \((B,C_{in2},H,W)\), is \(O_{2D}\approx\mathcal{O}(B\cdot H\cdot W\cdot C_{in2}\cdot C_{out1}\cdot K_{1} ^{2})\). The complexity of the attention mechanism is \(O_{Attention}\approx\mathcal{O}(B\cdot C_{out3}\cdot D_{attl})\), and the complexity of the classifier module is \(\mathcal{O}_{Classifier}\approx\mathcal{O}(B\cdot C_{out3}\cdot D_{fc1})\). Therefore, the total complexity is \(O_{model}=O_{1D}+O_{2D}+O_{Attention}+O_{Classifier}\), which can be approximately represented as \(O_{model}\approx\mathcal{O}(B\cdot L\cdot C_{in1}\cdot C_{out1}\cdot K_{1} )+\mathcal{O}(B\cdot H\cdot W\cdot C_{in2}\cdot C_{out1}\cdot K_{1}^{2})\), considering \(L\gg\max(H,W)\) in typical implementations.
###### Abstract  
One of the biggest challenges, both from a technical and economic point of view, of the Distribution Network Operators refers to identifying the locations (electric distribution substations) integrated into a supervisory, control, and acquisition (SCADA) system to perform on-site measurements used in the state estimation of the electric distribution networks (EDNs). In response to this challenge, a robust and resilient three-layer methodology has been proposed to solve the state estimate issue of the EDNs based on an optimal placement algorithm of the remote terminal units integrated into the SCADA system at the level of the EDSs. The first layer allows a clustering algorithm-based determination of the classes of the EDSs with similar features of the load profiles. The second layer identifies the \"candidate\" classes and decides the pilot EDSs with on-site SCADA measurements. The third layer allows the state estimation of the EDN based on the load values measured in the pilot EDEs. The framework was tested and validated using a medium voltage EDN of a Romanian DNO supplying an urban area. The results obtained highlighted that the accuracy had been ensured for on-site measurements in 12 of 39 EDSs (representing approximately 30% of EDSs integrated into the SCADA system), leading to a mean average percentage error of 2.6% for the load estimation and below 1% for the state variables determined by a power flow calculation at the level of the EDN. Consequently, the framework can significantly decrease the investments associated with integrating the SCADA system by the DNOs, with great benefits regarding the state estimation of the EDNs.  
C 2025, 15, 1942 +
Footnote †: journal: Applied Sciences  
Article
# Grid-Based Vessel Deviation from Route Identification with Unsupervised Learning
## 3 Module Design
### Inference and Online Learning  
The present section explains the inference and online learning capabilities of the module, depicted in Figure 11. As new AIS messages arrive in the system, they are preprocessed, in order to extract the necessary information for the inference model.  
For inference, the following information is required to be stored:  
1. The binary tree structure for each vessel, which includes node IDs and leap count matrices. These matrices include the count of navigations registered between the node and each adjacent node, and the total number of navigations registered from each node, stored in the central value of the matrix.
2. The current location of the vessel on the grid.  
For every received AIS message from the data stream, we utilize the vessel-specific binary search to infer its grid position. We then compare the current position of the vessel with its previously registered position. Two scenarios can happen.  
1. The previously registered position of the vessel is the same as its current position. If this is the case, no leap has been registered, the vessel is not in an anomalous trajectory, and nothing is changed.
2. The previously registered position is different to its current position. In this case, we must infer the rate associated with the leap between the two nodes and update the vessel information.  
The leap rate values are calculated by dividing the leap count matrix by the total number of navigations registered for the node, which results in the matrix represented in Figure 12. The centre of the matrix, representing vessels that have made a move from one point and another, both inside the same grid node, is always equal to 1.  
Figure 11: Inference pipeline.  
Figure 10: Cubic representation of the learnt patterns for all vessels V.  
If the calculated leap rate between the previously registered position and the current position is below a certain threshold, we define it to be an abnormal trajectory. In our tests, by observing the found matrices for different vessels and deciding what would make sense to be considered an anomalous trajectory, we have found the value of 0.125 to work well for this problem.  
Finally, for online learning, we must update the trajectory information, by increasing the count of the specific registered leap for the vessel and the total number of registered navigations for the node. The current node location of the vessel is also updated.  
The inference operation includes several sequential statements. First, a simple query fetches the appropriate binary search tree based on a hashed ID, with the complexity of \(\mathcal{O}(1)\), followed by a binary search for the current position of the vessel, with a \(\mathcal{O}(\log n)\) complexity. The online learning updates the leaf node information in the BST and updates the registry in the database, which has a \(\mathcal{O}(\log n)\) complexity. For this reason, the total complexity of the inference and online learning capabilities is \(\mathcal{O}(\log n)\).
# Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks
## 4 Analysis of Spectral Efficiency
### Spectral Efficiency of Conventional HARQ Scheme  
Since \(Q\) is equal to \(L\) in the conventional scheme, its spectral efficiency metric, \(\eta_{c}\), is expressed as  
\[\eta_{c}=\frac{(1-\gamma)\alpha\beta R_{d}}{(R_{d}+KR_{u})L_{c}}, \tag{4}\]  
where \(L_{c}\) is the average number of HARQ transmissions per packet in the conventional method. In (4), all parameters except \(L_{c}\) are determined by the scheduler and the physical specifications for packet transmission. Due to this, the transmitter stores all this information. Since \(L_{c}\) is closely related to link performance, we derive an expression to compute \(L_{c}\) using PER values in the HARQ link. If a random variable \(M\) is the number of transmissions required until a packet transfer is terminated, then, \(L_{c}\) is given by  
\[L_{c}=\sum_{m=1}^{M_{\max}}m\cdot P[M=m]=\sum_{m=1}^{M_{\max}}m\cdot p_{c,m}, \tag{5}\]  
where \(m\) is the index of the HARQ transmission trial for a multicast packet, \(p_{c,m}=P[M=m]\) denotes the probability that a packet transfer is terminated at the \(m\)-th transmission for the conventional HARQ. When \(M=1\), all users successfully decode the packet at the first transmission. Thus, \(p_{c,1}\) is computed as  
\[p_{c,1}=\prod_{k=1}^{K}{(1-q_{1,k})}, \tag{6}\]  
where \(q_{m,k}\) is the PER corresponding to the \(m\)-th HARQ transmission to the \(k\)-th user. In general, the receiver combines the retransmitted packet with previously transmitted erroneous packets to achieve the HARQ gain. Thus, \(q_{m,k}\) decreases as \(m\) is increased. As with \(P[M\leq 1]\) in (6), \(P[M\leq 2]\) is denoted as  
\[P[M\leq 2]=\prod_{k=1}^{K}{(1-q_{1,k}q_{2,k})}. \tag{7}\]  
From (6) and (7), we get  
\[\begin{array}{ll}p_{c,2}&=P[M\leq 2]-p_{c,1}\\ &=\prod_{k=1}^{K}{(1-q_{1,k}q_{2,k})}-p_{c,1}.\end{array} \tag{8}\]  
By generalizing the procedure shown in (6)-(8), \(p_{c,m}\) can be expressed as  
\[p_{c,m}=\prod_{k=1}^{K}{\left(1-\prod_{n=1}^{m}{q_{n,k}}\right)}-\sum_{n=1}^{m -1}{p_{c,n}}, \tag{9}\]  
where \(1\leq m\leq M_{\max}-1\). When the maximum number of transmissions is reached, packet transfer is terminated without considering whether the transmission was a success or failure. Therefore, \(P[M=M_{\max}]\) is given by  
\[p_{c,M_{\max}}=1-\sum_{m=1}^{M_{\max}-1}{p_{c,m}}. \tag{10}\]  
Note that Equation (10) is obtained from the fact that \(p_{c,1}+p_{c,2}+\cdots+p_{c,M_{\max}}=1\). From (9) and (10), \(p_{c,m}\) is expressed as \[p_{c,m}=\left\{\begin{array}{cc}\prod\limits_{k=1}^{K}\left(1-\prod\limits_{n=1}^ {m}q_{n,k}\right)-\sum\limits_{n=1}^{m-1}p_{c,n},&\text{for }1\leq m\leq M_{\max}-1,\\ 1-\sum\limits_{n=1}^{M_{\max}-1}p_{c,n},&\text{for }m=M_{\max}.\end{array}\right. \tag{11}\]  
When the value of \(q_{m,k}\) is known, \(p_{c,m}\) can be computed using (11), and \(\eta_{c}\) and \(L_{c}\) can, in turn, be evaluated by (4) and (5), respectively.
# Speaker Diarization: A Review of Objectives and Methods
## 5 Models to Apply for Speaker Diarization
### Neural (Machine Learning) Methods
#### 5.5.3 Convolutional Neural Networks (CNNs)  
For many classification problems, pertinent information is found mostly in small local ranges of data (e.g., for speech, in the center frequencies of high-energy formants). To exploit this, ANNs often have _convolutional_ layers of nodes that only process data locally within small regions. If the input has a two-dimensional representation (e.g., for speech, a display of time versus frequency), data values are multiplied by a small square weight matrix (e.g., \(3\times 3\)), summing (_pooling_) results spatially [47]. One type of CNN applies 1-D convolution (multiplication) in time and is called _time-delay NN_ (TDNN); these models have no recurrence (feedback) [48].  
So-called _conformers_ are composed of four stacked modules, including an initial _feed-forward network_ (FFN) module, a multi-head self-attention module (see below), a convolution module, and a second FFN module [49].
# Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
## Appendix A
\begin{table}
\begin{tabular}{c c c} \hline
**RT** & **Compound Name** & **Abundance Mean** \\ \hline
0.840 & Glycidol & 75,856.5 \\
0.950 & \_alpha-Chloroacrylic acid & 59,320 \\
1.530 & 2-Pentanol, 3-chloro-4-methyl-, (R\({}^{\circ}\),R\({}^{\circ}\))-(.+/\(-\).)- & 299,363 \\
1.560 & 1-Methoxy-3-methyl-3-butene & 289,385 \\
3.484 & \_beta-Myrcene & 64,340 \\
5.194 & \_beta-Myrcene & 1,191,105.5 \\
5.938 & \_D-Limonene & 4,419,800.5 \\
6.681 & 3-Hexenal, (Z-) & 472,247.5 \\
6.964 & N-(Trifluoroacetyl)-N,O,O',O',-tetrakis(trimethylsilyl)norepinephrinephrine & 122,798 \\
7.596 & Tricyclol[2.2.1.0(2,6)]heptane, 1,7,7-trimethyl- & 510,285 \\
7.607 & 4-Carene, (1S,3R,6R)-(-)- & 531,680 \\
8.187 & 1,3,7-Octatriene, 3,7-dimethyl- & 520,381.5 \\
8.550 & p-Cymene & 182,369 \\
9.023 & (+)-4-Carene & 47,706 \\
10.463 & Dodecane, 2-methyl- & 128,492 \\
10.481 & \_Nonane & 91,987 \\
10.740 & Undecane, 3,7-dimethyl- & 45,039 \\
11.600 & Sulfurrous acid, hexyl pentadecyl ester & 39,988 \\
11.911 & 5-Hepten-2-one, 6-methyl- & 99,068 \\
11.943 & 1-Hepten-6-one, 2-methyl- & 95,405 \\
13.370 & 1-Butanol, 3-methoxy- & 97,327.5 \\
14.450 & 3-Hexen-1-ol & 49,402 \\ \hline \end{tabular}
\end{table} 
Table 8: Results for the EBB sample fingerprint determination with indication of the retention time (RT) and abundance mean for every identified compound.
## 3 Challenges in Terahertz-Based FSO Communication
### Signal Distortion

Table 7: _Cont._* Adaptive equalization: machine learning-based adaptive equalizers dynamically compensate for distortions;
* Beam shaping: adjusting beam profiles to counteract turbulence effects improves signal stability;
* Error-correction codes: advanced error-correction techniques reduce the impact of distortion on the data accuracy [66]. * Real-time sensing: using real-time environmental monitoring to optimize transmission paths;
* Polarization techniques: exploiting polarization diversity reduces the sensitivity to turbulence-induced scattering. The key mathematical issues identified in Table 8 concern the power of THz signals, the relationship between turbulence and scattering, and potential solutions for reducing THz signal distortion. The problems are well solved by techniques like adaptive equalization, beam shaping, and real-time sensing, but at the cost of increased computational complexity, costs, and energy.
# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
## 2 Background
### Neural Networks
It employs a unique architecture to address the challenges of training deep neural networks, particularly the degradation problem where adding layers reduces performance. Each residual block typically comprises two or more convolutional layers. Depending on the specific version of the NN (such as ResNet18, ResNet34, ResNet50, ResNet101, or ResNet152), the model contains a certain number of residual blocks, enabling it to learn more complex input-output functions. SqueezeNet is know for its exceptionally small model size, achieving AlexNet-level accuracy with 50 times fewer parameters [38]. It is a compact and efficient neural network. Its architecture begins with a simple convolutional layer, followed by eight Fire modules, which consist of a sequence of squeeze and expand layers that operate along the depth dimension. Each module gradually increases the number of filters. The final layer employs max-pooling with a stride of 2 to further reduce dimensionality in the later stages, allowing the model to maintain accuracy despite its smaller size. A unique aspect of SqueezeNet is its lack of fully connected layers; instead, feature learning is integrated within the transformations between the Fire modules. This design enables SqueezeNet to achieve up to a 50-fold reduction in model size compared to AlexNet while maintaining comparable or even better performance. The design of each NN, whether through residual connections in ResNet or the Fire modules in SqueezeNet, ensures that gradients can flow efficiently through the layers during back-propagation [44]. Since NNs rely on differentiable operations, it is possible to compute the gradients of the loss function with respect to the network weights. Once the gradients are obtained, an optimizer, such as stochastic gradient descent (SGD) [45] or Adam [44], updates the weights to minimize the loss function. These forward and backward steps are repeated for each batch of training data over several epochs, where an epoch refers to a complete pass through the entire training dataset. During each epoch, the adjustments made in the multidimensional weight space are scaled by the learning rate (lr). The lr can also be progressively adjusted using a scaling factor. By appropriately configuring the hyperparameters, it is possible to ensure that the training algorithm converges to a sub-optimal state with a low loss value.
## 1 Introduction
On the other hand, if they charge during the night, an additional stationary battery could help to reduce grid impact by storing surplus PV production during the day to use it at night. The charging scenario must be adapted in order to maximize PV self-consumption ratio, i.e., to increase the percentage of the bus load covered by the PV production. However, charging during the daylight at a low power to maximize PV self-consumption ratio might induce additional charging time, which can cause delays in the service. All of these factors show that the location and the size of charging infrastructures, as well as the charging strategy, can have a significant impact on both the utility grid and the transport network, which need to be carefully analysed with a detailed model of the bus transportation network. The aim of this study is to focus on the influence of sizing and placement of charging infrastructures on both the utility grid and transportation network. This preliminary study simulates a bus fleet operation with several scenarios, which differ according to the location of chargers (depot, terminals, or stops) and their rated power. First, the bus network is modelled and the operation of buses is simulated. After that, charging stations are placed on the transportation network--at depot, terminals, or stops--and charging rules are applied. Performance indicators are defined to compare studied configurations. The main contributions of this work are listed as follows:  
1. A bus consumption model that considers the influence of the battery capacity on the bus gross weight and thus on the bus consumption;
2. Modelling of a bus network is presented based on General Transit Feed Specification (GTFS) data;3. A sequential simulation of the EB operation is performed in order to analyse the influence of the charging scenarios on the quality of transportation services (delay indicator);
4. A comparison between bus energy demand and PV production in order to assess the potential of PV energy to reduce impacts of EB charging on the utility grid. The rest of the paper is structured as follows. Section 2 analyses the scientific literature to underline the research gaps. Section 3 presents the modelling and simulation approach for the buses' operation and charging. The case study is introduced in Section 4. Section 5 displays the simulation results, and Section 6 analyses them. Finally, Section 7 contains a conclusion and perspectives for future works.
## 3 System Analysis for a Two-User System  
For the analytical insight of the proposed technique, two users are used to avoid system's complexity. For a two user system, the same situation (Figure 2) is considered with \([PERSON]_{1}\) and \([PERSON]_{2}\) only. The superposed signal by the [PERSON] for the broadcast can be given as: \(s_{1}[t]=\sqrt{g_{1}P_{T}}(\sqrt{\alpha_{1}}x_{1}[t]+\sqrt{\alpha_{2}}x_{2}[t ])+A_{1}\). Likewise, \(Bob_{1}\) add \(A_{2}\) before forwarding the signal of \([PERSON]_{2}\) as \(s_{2}[t]=x_{2}[t]j+A_{2}\).  
The signal received by \([PERSON]_{1}\) also includes the self-interference for its co-channel transmission as given in (2) of [37] :  
\[\begin{split} y_{1}[t]&=K_{1}d_{0}{}^{\gamma}d_{1} {}^{-\gamma}\Big{[}\sqrt{g_{1}P_{T}}\Big{(}\sqrt{\alpha_{1}}x_{1}[t]+\sqrt{ \alpha_{2}}x_{2}[t]\Big{)}\Big{]}\\ &+\sqrt{g_{11}[t]P_{c}}s[t]j+w_{1}[t],\end{split} \tag{1}\]  
where \(w_{n}\sim CN(0,\sigma^{2})\) is the additive white Gaussian noise (AWGN) and \(s[t]\) is the signal transmitted by \([PERSON]_{1}\) (received along with the previous transmission) to \([PERSON]_{2}\) in the IBFD mode, which causes self-interference, \(K_{n}\) is the path loss factor for node \(n\), \(d_{0}\) is reference distance, \(P_{c}\) is the power allocated by \([PERSON]_{1}\) and \(\gamma\) is the path loss exponent.  
The total received signal at \([PERSON]_{2}\) from \([PERSON]_{1}\) is given below according to (5) in [37] :  
\[y_{12}[t]=K_{12}d_{0}{}^{\gamma}d_{12}{}^{-\gamma}\sqrt{g_{12}P_{c}}x_{2}j[t- \tau]+w_{2}[t]. \tag{2}\]  
In the above equation, the signal received from \([PERSON]_{1}\) contains only the real part, as \(g_{12}A_{2}=0\). The IBFD cooperative communication is used for \([PERSON]_{2}\); therefore, without the loss of generality a delay \(\tau\) has been introduced.  
[PERSON]'s received signal can be determined by adding AN in (2). [PERSON] receives a signal with high interference due to a lack of information about AN and the modulation alteration used.  
\[y_{e}[t]=K_{1e}d_{0}{}^{\gamma}d_{1e}{}^{-\gamma}g_{1e}[t]\Big{(}\sqrt{P_{e}}x_{ 2}[t-\tau]\hat{j}+A_{2}\Big{)}+w_{e}[t]. \tag{3}\]
## 6 Limitations and Future Research  
A limitation of this study is that the methodology consisted entirely of a survey-based correlational design. While the researchers acknowledged that a sample of 86 respondents could make a statistical difference in terms of generalisability, the study has shown to be valid in deducing generalized perspectives since its findings can be applied to the population due to generalization also being a measure of how useful the result of a study is for a specific population. The results of the study apply to many different types of people susceptible to different cybercrime (sextortion and phishing) situations. Attention to each of the email design cues was not experimentally manipulated and outside extraneous variables that may have confounded the results were not experimentally controlled. It therefore cannot be entirely assumed that attention to the email design cues that were measured actually caused the changes in cyber sextortion susceptibility scores that were observed. Future research should attempt to use an experimental methodology to determine whether the relationship between attention to email design cues and susceptibility to cyber sextortion attacks is causal and whether the effect of each of the design cues continues to show the same results. Experimental methodologies have been used in past research on phishingsusceptibility to provide more stringent control measures. Another limitation is that the survey was a self-report questionnaire, and participants knew in advance that the study was investigating the topic of cyber sextortion. This would have primed participants to the knowledge that the emails were deceitful, increasing suspicion and causing participants to rate their level of susceptibility as less than it would otherwise have been [32]. Future research should attempt to remove participant expectations from measurements of cyber sextortion susceptibility.
# Monitoring Theophylline Concentrations in Saline Using Terahertz ATR Spectroscopy
## 3 Results and Discussion
From the measurements of theophylline solutions, changes in the concentration of theophylline could be observed. This indicates the possibility of monitoring drug concentrations using THz techniques. The measurement is broadband spectroscopy because the method is based on THz TDS, but information on the concentration can be obtained by measurement of a single wavelength. Further, TDS usually requires both amplitude changes and phase shifts of the THz pulse, but the measurements in this study are from amplitude changes only. It is not necessary to perform a complex calculation to obtain data, and the method can be simplified. It is possible to make measurements by simply setting a sample onto the Si prism, with no complex procedure. Moreover, the measurement uses the evanescent wave; thus, it may be possible to make non-invasive measurements. The timing of THz ATR measurements is about a few minutes, clearly short enough for TDM. We expect further improvements in the measurement ability by improving THz spectroscopy in future.