esa-sceva/satcom-chunk-collection huggingface dataset:
# SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer
## 2 System Model
### User Equipment Generates Routing Node Query Request  
The user equipment \(U\!E_{k}\) first generates a routing node query request and represents the required routing node requirements as an \(m\)-dimensional vector as \(\mathbf{q}_{i}^{k}=(q_{1}^{k},q_{2}^{k},\ldots,q_{m}^{k})\), where \(i=1,\,2,\,\ldots,\,m,\,k=1,\,2,\,\ldots,\,l\), and the number of user equipment in the network is \(l\). \(q_{1}^{k}\) represents the first constraint on the routing nodes of the user equipment \(U\!E_{k}\), for example, the network bandwidth of the requesting routing node \(BW_{j}=10\) Mbps, \(j=1,\,2,\,\ldots,\,n\). \(q_{2}^{k}\) represents the second constraint of the routing nodes, for example, the online time of the requesting routing node is \(T_{j}\geq 12\) h. \(q_{3}^{k}\) represents the third constraint of the routing nodes, for example, the number of requested routing nodes \(N_{k}=3,\,k=1,\,2,\,\ldots,\,l\). The user equipment \(U\!E_{k}\) blinds \(\mathbf{q}_{i}^{k}\) with \(w\times m\)-dimensional matrix \(\mathbf{B}_{k},\mathbf{B}_{k}=\begin{bmatrix}b_{11}&\ldots&b_{1m}\\ \vdots&\ddots&\vdots\\ b_{w1}&\ldots&b_{wm}\end{bmatrix},\) where \(\sum\limits_{a=1}^{w}s_{a}\cdot b_{ai}=1\), and \(b_{ai}\) is the selection bit, \(b_{ai}\in\{0,1\}\), \(i=1,\,2,\,\ldots,\,m\). The user equipment \(U\!E_{k}\) generates the \(w\times m\)-dimensional matrix:  
\[\mathbf{M}_{k}=\mathbf{B}_{k}\cdot\mathbf{q}_{i}^{k}\!=\!\begin{bmatrix}b_{11}\cdot q_{1} ^{k}&\ldots&b_{1m}\cdot q_{m}^{k}\\ \vdots&\ddots&\vdots\\ b_{w1}\cdot q_{1}^{k}&\ldots&b_{wm}\cdot q_{m}^{k}\end{bmatrix} \tag{1}\]  
The public and private keys of the user equipment \(U\!E_{k}\) are (\(sk,pk\)), and \(N_{k}+\Delta\) random public keys \(pk_{t}\), \(t=1,\,\ldots,\,\) and \(N_{k}+\Delta\) are sampled from the public key space, where \(\Delta\) is the increasing redundancy due to the \(N_{k}\) routing nodes required by the user equipment \(U\!E_{k}\)  
Figure 2: The system model of secure query of routing table in anonymous networks.  
The security of the SQRT scheme assumes that there is a public key encryption scheme [14], where \(N_{k}+\Delta\) random public keys are sampled without obtaining the corresponding private keys, and the semi-honest attack model is used to secure the scheme [15]. The DS can only see the \(N_{k}+\Delta\) public keys sent by the user equipment \(UE_{k}\) and cannot predict the corresponding private key, which public key \(UE_{k}\) has.  
The user equipment \(UE_{k}\) sends a routing node query request to the DS, including the blinded \(M_{k}\) and \(pk_{i}\), where \(t=1,\ldots,N_{k}+\Delta\).
## 4 Materials and Methods
### WBC Model
#### 4.2.2 BiLSTM Layer  
The second layer is BiLSTM, which learns contextual semantic relations in the texts through the word vector matrix. The long-range information is captured by the threshold unit of the LSTM, and a larger range of contextual semantic features can be obtained through the long-range information. This layer of the model can learn the contextual relationships of the sentences before and after the target sentence. The model takes a literary text as an input sequence and outputs a vector \(h=(h_{1},h_{2},h_{3},\ldots,h_{n})\) that is a sequence of each step of the input sequence. LSTM solves the long-term dependency problem by combining storage units, controlling the ratio of input, and forgetting the current storage unit, and forgetting gates are used to forget the previous control; the state of the unit is forgotten.  
There are \(n\) vectors, and each vector \(et\) represents the vector where the character vector \(v_{c_{t}}\) and the corresponding feature \(v_{d_{t}}\) are combined in the vector matrix. The parameter \(h_{t}\) is represented as the current hidden state by calculating \(x_{t}\) (input vector) and \(h_{t-1}\) (last state vector) for each position \(t\), as shown in the following formula [35].  
\[\mathrm{i_{t}}=\sigma(\mathrm{W_{i}}x_{t}+\mathrm{U_{i}}h_{t-1}+\mathrm{b_{i}}) \tag{1}\]  
Figure 2: WBC flowchart.  
\[\mathrm{f_{t}} =\sigma(W_{t}x_{t}+U_{d}h_{t-1}+b_{t}) \tag{2}\] \[\mathrm{c_{t}} =\mathrm{f_{t}}\odot\mathrm{c_{t-1}}+\mathrm{i_{t}}\odot\mathrm{ tanh}(W_{c}x_{t}+U_{c}h_{t-1}+b_{c})\] (3) \[\mathrm{o_{t}} =\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o})\] (4) \[\mathrm{h_{t}} =\mathrm{o_{t}}\odot\mathrm{tanh}(\mathrm{c_{t}}) \tag{5}\]  
However, one-way LSTM will have problems. In the process of state calculation, subsequent state information will be ignored; we use BiLSTM to solve the one-way LSTM limitation, as it will calculate a forward representation \(\overset{\rightarrow}{h_{t}}\), and a backward representation \(\overset{\leftarrow}{h_{t}}\), from which the contextual state of each input sequence is obtained, and hidden state \(h_{t}\) can be calculated as follows [35]:  
\[\mathrm{h_{t}}=\overset{\rightarrow}{h_{t}}\oplus\overset{\leftarrow}{h_{t}} \tag{6}\]
## 2 Materials and Methods  
### Preparation of C. sativa Burrs (CSB) Extract  
C. sativa burrs extract was prepared as previously described [22]. The spiny burrs of Castanea _sativa_ (Mill.), certified under the PGI (Protected Geographical Indication) Castagna del Monte Amiata (Reg. CEE n. 2081/92), were obtained from Tuscany, a notable chestnut-producing region in Italy. The burrs were meticulously cleaned and air-dried at room temperature until they reached a stable weight. Subsequently, they were ground into a fine powder and stored in sealed, dark plastic bags at \(-80\)\({}^{\circ}\)C to be preserved until extraction. The extraction process involved immersing 330 g of the powdered burrs in 1 L of water and treating the mixture with 20 kHz ultrasonic waves for three hours at room temperature. The resulting aqueous extract, referred to as CSB, was then freeze-dried using a lyophilizer (Lyovapor L-200, Bhuchi, India) and kept at \(-32\)\({}^{\circ}\)C for further experiments.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 2 Preliminaries
### Blockchain Technology in Agriculture and Animal Husbandry: An Overview
#### 2.2.1 Block and Agricultural and Animal Husbandry Data Storage  
Blocks in a blockchain are essential for securely storing agricultural and livestock-related data in an immutable manner. For instance, in a crop supply chain, each block can record critical information such as harvest dates, storage conditions, and transportation logs. Similarly, in animal husbandry, blocks can document the vaccination history, health status, and feeding records of livestock, ensuring that these data are both traceable and tamper-proof. This capability fosters transparency and builds consumer trust in agricultural and livestock products.  
The integration of blockchain and IoT technologies in animal husbandry significantly enhances data reliability, traceability, and transparency across the livestock management ecosystem. Blockchain systems ensure the authenticity of vaccination histories, health records, and feeding patterns, creating tamper-proof, immutable ledgers that streamline livestock monitoring. For instance, a livestock shed regulatory platform proposed by [PERSON] et al. uses blockchain and IoT to automate livestock insurance processes while ensuring real-time environmental data monitoring, reducing operational costs, and improving efficiency [59]. Similarly, a blockchain-supported framework demonstrates high efficiency in livestock behavior monitoring and supply chain transparency, bringing significant benefits to producers, regulators, and consumers [60].  
Moreover, blockchain applications extend to unmanned aerial vehicle data collection for livestock, ensuring secure monitoring and effective integration of decentralized systems, as proposed by [PERSON] et al. [61]. Such advancements not only foster consumer trust but also streamline compliance with animal welfare and food safety regulations, as demonstrated by prototypes like the Hyperledger-based system for animal surveillance [62].  
A blockchain is composed of blocks, each divided into a header and a body. The body contains verified transaction data, such as digital currency transfers or smart contract executions, while the header includes specific details such as the hash of the previous block, a nonce, difficulty level, and the Merkle root value. These blocks are cryptographically linked to form an ever-growing chain, ensuring data security and immutability. This distributed and decentralized structure provides unique advantages, including decentralization, transparency, and security. Figure 3 illustrates the structure of a single block, and Figure 4 depicts the chain-like formation of a blockchain.  
Figure 3: Structure diagram of the block.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 3 Methods
### KNN Model
#### 3.2.1 Preparing the Data  
The KNN classifier is sensitive to noise and large complex datasets. Therefore, the data preparation process plays a crucial role in ensuring the optimal performance of the model. The process we have implemented consists of three primary steps: pre-processing, segmentation, and feature extraction. These steps are designed to transform the raw image data into a structured and standardized format, which can be efficiently utilized to train and evaluate the KNN model while mitigating the impact of noise and complexity. Figure 6 illustrates the different steps described below.
# Energy Efficient and Reliable Routing Algorithm for Wireless Sensors Networks
## 4 Ds-Eera  
In this paper, the DS evidence theory is applied to WSN routing decision-making. For each sensor node, the factors such as residual energy, the shortest path to the sink node, node traffic, energy density of neighboring nodes and the forward distance are taken into account when selecting the next hop. Thus, the above factors are abstracted into three indexes: \"transmission energy efficiency ratio\", \"idleness degree\" and \"energy density factor\", which can be regarded as three evidences. Based on the above three indexes and triangular membership function, we establish the basic probability assignment (BPA) function, and the reliability of the node belonging to each membership function can be obtained. Then, the entropy weight method is used to objectively weigh the relative importance of each index in routing decision-making. Finally, the neighbor node with the best fusion result will be selected as the next hop. This section introduces the DS-EERA in detail and focuses on the establishment of attribute indexes, the weight distribution of each index and the routing selection.
## 3 Compact Model Validation
### Bandwidth Validation  
Once the validation of the compact model of the UTC-PD and electrical model parameter extraction have been performed on the DC and RF characteristics under non-illuminated operating conditions, the dynamic photo-response of the compact model has then been verified for both types of UTC PD technologies, for all available geometries under different applied bias conditions. For the InGaAs/InGaAsP UTC-PDs, the optical frequency response of the photodiodes was measured with a heterodyne measurement bench. A high responsivity of 0.79 A/W, at \(\lambda\) = 1550 nm, has been reported for these devices [3]. The optical frequency responses of the UTC-PDs are measured at -2 V bias under an optical power of 3 dBm. \(-\)3 dB extrinsic bandwidths ranging from 40 GHz to 47 GHz for all geometries studied can be observed in Figure 14a. The intrinsic bandwidth can be determined using the calculated/extracted transit times using the compact model. In addition, intrinsic bandwidths ranging from 45 GHz to 56 GHz are determined, indicating the detrimental impact of the pads and access lines on the bandwidth.  
For the optical characterization of the GaInAsSb/InP UTC-PDs, an optical signal (\(\lambda\) = 1550 nm), of 3 dBm power, modulated by a Mach Zehnder modulator (MZM) nm was used. Measurements were performed with a bias ranging from 0 to \(-\)2.5 V. Figure 14b shows an excellent agreement between the measurements and the model predictions over the entire frequency and voltage ranges. It should also be noted that the measured bandwidths of the UTC-PDs do not change significantly compared to their intrinsic bandwidth of the component since the values of the junction capacitances are much higher than those of the pads and the access lines. In other words, the pad parasitic elements are less significant for these types of UTC-PDs.  
Figure 12: The extracted total series resistance at various reverse biases and for several lengths of the InGaAs/InGaAsP UTC-PDs.
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 4 Satellite Architecture and Subsystems
### Thermal Control  
The thermal control design of RMCSat seeks to keep the satellite equipment within temperature limits with a 5 \({}^{\circ}\)C buffer. The temperature ranges of the satellite components are given in Table 12. The thermal operating range of the RMCSat is \(-15\)\({}^{\circ}\)C to 55 \({}^{\circ}\)C, limited by the ADCS and UHF communication systems. The integrated battery heater within the electrical power system allows a greater temperature range for the batteries than is typically found.  
A first-order analysis of the thermal environment and temperature indicates that thermal control does not pose an issue for the mission. The sources of external heat considered include the direct solar energy of the Sun, solar energy that is reflected off the Earth, thermal energy radiated from Earth, and atmospheric drag heating [28]. The internal heat sources are derived from heating caused by spacecraft components, which are taken as the lowest and highest power levels of the satellite operations. The extreme temperature range derived from the analysis is \(-6.8\)\({}^{\circ}\)C to \(64.8\)\({}^{\circ}\)C, which sits above the maximum temperature of some components. However, if a quarter millimeter layer of aluminized  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operating** & **Time in One** & **Percent of Orbit** & **Mode Power** & **Power Used (W)** \\
**Mode** & **Orbit (Minutes)** & **(\%)** & **Demand (W)** & **Power Used (W)** \\ \hline Communication & 7.17 & 0.08 & 13.7 & 1.0 \\ Solar Cruise & 35.33 & 0.37 & 11.2 & 4.1 \\ Eclipse Cruise & 35.60 & 0.37 & 7.6 & 2.8 \\ Solar & 17.50 & 0.18 & 7.9 & 1.4 \\ Observations & & & & \\ \hline TOTAL & 95.60 & 1.00 & average power & 9.4 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Average orbital power requirements for an orbit that include Communication.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Components** & **Temperature Range with 5 \({}^{\circ}\)C Buffer (\({}^{\circ}\)C)** \\ \hline Primary payload antenna \({}^{\star}\) & \(-15\) to \(55\) \\ Secondary payload antenna & \(-35\) to \(120\) \\ Payload receiver & \(-25\) to \(65\) \\ Electrical power system & \(-35\) to \(75\) \\ S-band communication patch antenna & \(-35\) to \(120\) \\ S-band transmitter & \(-25\) to \(65\) \\ UHF antenna & \(-15\) to \(55\) \\ UHF transceiver & \(-15\) to \(55\) \\ Onboard Computer & \(-20\) to \(55\) \\ ADCS & \(-15\) to \(55\) \\ GNSS antenna & \(-25\) to \(65\) \\ GNSS receiver & \(-35\) to \(80\) \\ Solar Panels & \(-45\) to \(145\) \\ \hline \hline \end{tabular}
\end{table}
Table 12: Temperature ranges for proposed RMCSat components. \({}^{\star}\) Temperature range of the primary antenna is estimated pending thermal testing.  
kapton is used, it lowers the temperatures to \(-7.8\) to \(50.7\)\({}^{\circ}\)C, within the thermal operating range of RMCSat.
## 4 Energy Management System Based on Fuzzy Logic Controller (FLC)
### Ultra Power Transfer Algorithm  
The designed EMS is intended to be used for the control of the power flow towards the battery pack from the regenerative braking system and to control the speed of the BLDC motor. The EMS power flow algorithm measures the SOC for the battery. If the SOC of the battery is below 85%, then it checks the SC's SOC. If the SC's SOC is greater than 45% and if both conditions are met, then the power flows from the SC towards the battery pack, as given by the truth table in Table 6. In this way, charging of the battery and SC is done by using AND gate. The power flow algorithm is shown in the flow chart given in Figure 5. According to this figure, firstly the battery's SOC will be measured through the FLC technique; if the battery's SOC is greater than 85%, then the motor will be run and the motor's speed will also be continuously measured through FLC, and during this process, if the current motor's speed (C) is greater than the previous motor's speed (P), then the FLC will calculate error currently. Now, if the error current is greater than zero, then the FLC will increase the duty cycle and start the motor, and if the duty cycle is less than zero, then the FLC will decrease the duty cycle and then start the motor corresponding to the duty cycle. On the other hand, if the battery's SOC is not greater than 85%, then at this point, the SC's SOC will be checked. If the SC's SOC is greater than 45%, then the SC will start charging the battery; if the SC's SOC is not greater than 45%, when the brakes are applied, the SC will start charging through the regenerative process. If the brakes are not applied, which means the motor is running, then at this point, the charging of SC will not take place because, during the running condition, the SC provides the power to the system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \
\begin{tabular}{c} **State of Charge of UC** \\ **(SOC\({}_{\text{U}}\))** \\ \end{tabular} &
\begin{tabular}{c} **State of Charge of Battery** \\ **(SOC\({}_{\text{B}}\))** \\ \end{tabular} & **Output** \\ \hline
1 & 0 & 0 \\
1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Truth Table for EMS Algo (AND-Gate).  
\begin{table}
\begin{tabular}{c c} \hline \hline Input Voltage (V\({}_{\text{i}}\)) & 0–180 V \\ Load Current (I\({}_{\text{O}}\)) & 15–25 A \\ Output Voltage Ripple & 0.1 V \\ Inductor (L\({}_{1}\)) & 78.1 \(\mu\)H \\ Inductor (L\({}_{2}\)) & 11.7 \(\mu\)H \\ Capacitor (C\({}_{1}\)) & 9.4 mF \\ Capacitor (C\({}_{2}\)) & 93.8 mF \\ Load Resistance (R\({}_{\text{L}}\)) & 7.5 \(\Omega\) \\ Output Voltage (V\({}_{\text{o}}\)) & 150 V \\ Output Current (I\({}_{\text{O}}\)) & 15–25 A \\ Output Power (P\({}_{\text{O}}\)) & 2–4 KW \\ Frequency (F\({}_{\text{S}}\)) & 20 kHz \\ Time Period (T) & 50 \(\upmu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters for SEPIC Converter.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 3 The Importance of Quantum Key Distribution (QKD)
### The Challenges of QKD in 5G Implementation
#### 3.1.5 Number of Nodes  
The fifth challenge is based on the number of end nodes in the network. Typically, the smart grid consists of different types of nodes with a massive number of nodes that have different capabilities. To control all nodes in the distributed network, the smart grid provides many sensors, energy and data storage, actuator, processors, transformer substations and renewable energy generators. Moreover, any network consumer is installed with a smart meter. Consequently, QKD is designed to function only in point-to-point pair communication nodes. The main challenge for QKD relies on allocating the secure key to many nodes where the nodes have different capabilities, and they are not all connected through fiber optic directly. The solution is to have a separate quantum channel for each control device. However, this solution is considerably expensive.
## 4 Experimental Set-Up
### Enhanced CAM-Based Spatial Interpretability  
We compute layer-wise class activation maps (Layer-CAM), a technique designed to enhance the interpretability of neural network decisions [29]. This method generates heatmaps that highlight the most relevant regions of the input, providing insights into the model's focus during classification. Unlike traditional CAM approaches, Layer-CAM identifies class-discriminative regions at intermediate layers, offering a multilevel perspective on feature importance. Additionally, it facilitates model refinement by localizing misinterpreted regions, thereby supporting targeted performance improvements.  
According to Equation (1), The MCD-enhanced model (e.g., MCD-EEGNet) apply dropout after convolutional layers, using rates \(p\in\{0.05,0.11,0.23,0.30\}\) optimized via cross-validation to ensure stable uncertainty estimates while maintaining computational efficiency [23]. CAMs are computed with \(T=100\) samples to stabilize weight estimates \(w_{k}^{c}\), providing interpretable spatial attention maps for MI tasks. This approach leverages recent advancements in uncertainty estimation and feature visualization, offering insights into brain activity patterns and supporting reliable classification, especially for subjects with variable performance. For each model, the activation maps are extracted from the first convolutional layer (i.e., Conv2D). This choice is motivated by the architectural design in which spatial convolutions are subsequently applied in the second convolutional stage, reducing the multi-channel EEG representation to a single channel. Extracting CAMs beyond this point would primarily emphasize temporal dynamics, thereby limiting spatial interpretability. By targeting the first convolutional stage, the resulting Layer-CAMs preserve relevant spatiotemporal information, enabling more interpretable analyses of neural representations.
# A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances
## 2 Tube Linear MPC Methodology
### Set Invariance Theory  
In this section, the invariant set theory and robust MPC formulation is introduced for the autonomous system.  
In this paper, we assume that \(f_{a}\) expresses the discrete time linear autonomous system.  
\[\xi_{k+1}=f_{a}(\xi_{k},u_{k})=A\xi_{k}+Bu_{k}, \tag{1}\]  
where \(\xi_{k}\) and \(u_{k}\) denote the state and the input vectors, respectively. \(A\) and \(B\) are the control matrix pair, and it is assumed the matrix pair (\(A\),\(B\)) will be controllable.  
When the autonomous system \(f_{a}\) is perturbed by a bounded additive disturbance, it can be presented as follows:  
\[\begin{split}&\xi_{k+1}=f_{a}(\xi_{k},u_{k})+w_{k},\\ &\xi_{k}\in\Xi\subseteq R^{n},\ \omega_{k}\in W\subseteq R^{d}, \end{split} \tag{2}\]  
where \(w_{k}\) is the disturbance vectors, \(\Xi\) and \(W\) are the polyhedras that contain the origin.  
**Definition 1**.: _(A reachable set with disturbance.) When the autonomous system \(f_{a}\) is perturbed by a bounded, additive disturbance [25], the reachable set of states \(S\) is_  
\[\xi=f_{\text{reach}}(\xi_{0},u,w)\ \exists\xi_{0}\in S,\exists u\in U,\exists w\in W \tag{3}\]  
_where \(f_{\text{reach}}=f_{a}(\xi_{0},u)+w\), and all the states in \(S\) are mapped into the reachable set under the map \(f_{\text{reach}}\) for all inputs \(u\) in \(U\), as well as the disturbances \(w\) in \(W\)._  
**Definition 2**.: _(The robust positively invariant set.) If the set \(\varepsilon\) meets the requirement detailed below, the set \(\varepsilon\subseteq\Xi\) is a robust positively invariant set of states \(S\)[25]._  
\[\xi_{0}\in\varepsilon\Rightarrow\xi_{k}\in\varepsilon,\forall w_{k}\in W, \forall k\in N. \tag{4}\]  
**Definition 3**.: _(The maximal robust positively invariant set.) If the set \(\varepsilon\) is a robust positively invariant set and contains all robust positively invariant sets, the set \(\varepsilon\subseteq\Xi\) is the maximal robust invariant set [25]._  
The Pontryagin difference and Minkowski sum is employed in this paper, and the definition of them can be presented as follows.  
Figure 2: Control architecture of tube MPC.  
The Pontryagin difference of the two polytopes \(P\) and \(Q\) is expressed in Equation (5):  
\[P\;\emptyset\;Q:=\{x\in R^{n}:x+q\in p,\forall q\in Q\}. \tag{5}\]  
The Minkowski sum of the two polytopes \(P\) and \(Q\) is expressed in Equation (6):  
\[P\oplus Q:=\{x+q\in R^{n}:x\in p,q\in Q\}. \tag{6}\]