esa-sceva/satcom-chunk-collection huggingface dataset:
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 3 Multi-Channel Recurrent Predictor
### Recurrent Neural Networks  
An RNN can encode a sequence of arbitrary length \(\{\mathbf{x}_{t}\}\) into a fixed-sized state vector \(\mathbf{s}_{t}\) by reading one data point at a time. In most cases, at each time step \(t\), the state vector \(\mathbf{s}_{t}\) will first be updated and then be used to calculate the model output:  
\[\mathbf{s}_{t} =\mathcal{T}(\mathbf{x}_{t},\mathbf{s}_{t-1}), \tag{4}\] \[\mathbf{y}_{t} =\mathcal{G}(\mathbf{s}_{t}), \tag{5}\]  
where \(\mathcal{T}\) is the _state transition function_, \(\mathcal{G}\) is the _output function_, and \(\mathbf{x}_{t}\) and \(\mathbf{y}_{t}\) represent the input and output vector of the RNN at time step \(t\), respectively. One of the most fundamental building blocks of RNNs is the _perceptron operator_[25], which is defined as:  
\[\mathcal{P}(\mathbf{u}_{1},\cdots,\mathbf{u}_{M};\phi)=\phi(\mathbf{b}+\sum_ {i=1}^{M}\mathbf{W}_{i}\mathbf{u}_{i}), \tag{6}\]  
where \(\mathbf{W}_{i}\) is called the weight matrix relating to input \(\mathbf{u}_{i}\), \(\mathbf{b}\) is the bias vector, and \(\phi\) represents the non-linear activation function. The most simple RNN architecture simply uses a perceptron operator as the state transition function.  
To alleviate the well-known vanishing and exploding gradient issue of RNNs, a _gate unit_ is introduced [16], and the state transition function is constructed as a highway operator [25]\(\mathcal{H}\), which can be generally defined as:  
\[\mathbf{s}_{t}=\mathcal{H}(\mathbf{x}_{t},\mathbf{s}_{t-1})=\mathbf{g}_{t}^{ \beta}\odot\mathbf{s}_{t-1}+\mathbf{g}_{t}^{\alpha}\odot\mathbf{s}_{t}, \tag{7}\]where \(\odot\) represents element-wise multiplication, \(\mathbf{\hat{s}}_{t}\) is a candidate state vector, and \(\mathbf{g}_{t}^{\alpha}\) and \(\mathbf{g}_{t}^{\beta}\) are gate vectors, in which each value is between 0 and 1. For example, candidate state and gate vectors in the popular gated recurrent unit (GRU, [26]) are calculated as:  
\[\mathbf{\hat{s}}_{t} =\mathcal{P}_{s}(\mathbf{x}_{t},\mathcal{P}_{r}(\mathbf{x}_{t}, \mathbf{s}_{t-1};\sigma)\odot\mathbf{s}_{t-1};\text{tanh}), \tag{8}\] \[\mathbf{g}_{t}^{\alpha} =\mathcal{P}_{\alpha}(\mathbf{x}_{t},\mathbf{s}_{t-1};\sigma),\] (9) \[\mathbf{g}_{t}^{\beta} =1-\mathbf{g}_{t}^{\alpha}, \tag{10}\]  
where \(\sigma\) and tanh represent the sigmoid and hyperbolic activation function, respectively. In this paper, we denote such a highway operator as \(\mathcal{H}_{\text{GRU}}\).
# Temperature-Dependent Dynamic on Resistance in Gamma-Irradiated AlGaN/GaN Power HEMTs
## 3 Results and Discussion
### Irradiation at a High Temperature (133 \({}^{\circ}\)C)
This injection of electrons could help to reduce the number of holes that were created by the gamma irradiation and, therefore, helped to reduce the increases in gate leakage current (Figure 7). The dynamic on-resistance is a parameter that is dependent on the trapping or detrapping rate. Thus, we needed to consider the influence of gamma radiation on the trapping processes to establish the relationships between the obtained results and the dynamic on-resistance. The effects of irradiation depend on the structures, temperatures, pre-existing trap densities, dose rates, total accumulated doses, and experimental bias conditions that are applied during irradiation. The improvement or degradation of the electrical characteristics (e.g., gm, I\({}_{\text{D}}\), R\({}_{\text{ON}}\), Vth) have been discussed in the literature [23; 24; 25]. When gamma rays are focused on matter, a process of matter ionisation occurs, primarily via indirect ionisation. Electrons interact and may become absorbed, scatter, or produce electron-positron pairs, depending on their energy. Overall, low doses of accumulated radiation result in improvements and when higher doses are applied, degradation begins. In our case, the rate that was achieved was not very high (374 krad) and we distinguished between improvement or degradation depending on the temperature that was applied during the irradiation tests. In any case, the effects of gamma radiation together with the higher temperature created an increase in the gate leakage current and a shift in the threshold voltage to the left, which caused the degradation of the HEMT and an increase in the dynamic on-resistance, as we explain later. Figure 7: The leakage currents versus drain voltages in the off state (VGS = 0 V) before (black lines) and after (magenta lines) gamma radiation with a total ionising dose of 374 krad(SiO2) at a base temperature of 133 \({}^{\circ}\)C. (a) Device G2 stressed during irradiation with VDS = 500 V and VGS = \(-5\) V. (b) Device G4 stressed during irradiation with VDS = 500 V and VGS = 0.
## 2 Materials and Methods
### Crop Variables Estimation Using Remote Sensing Data
#### 2.4.3 Harvest Components, Crop Yield, and Quality  
Estimates of crop yield, protein content, starch, and hectoliter weight obtained at harvest were analyzed by examining the relationships between these components and combinations of two spectral bands from the drone and satellite datasets. A linear model was applied to quantify these relationships at various phenological stages of the crop, incorporating accumulated growing degree days (\(GDD\)) from the start of the season (\(SOS\)) to the specific date corresponding to each stage. The model is expressed as  
\[\textit{Harvest component}=m_{1}\rho_{\lambda 1_{GDD}}+m_{2}\rho_{\lambda 2_{GDD}}+n \tag{5}\]  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Index** & **Formula** & **Reference** \\ \hline RVI & \(\frac{NIR}{Rad}\) & [64] \\ DVI & \(NIR-Red\) & [59; 60] \\ NDVI & \(\frac{NIR-Red}{NIR-Red}\) & [59; 60] \\ SAVI & \(\frac{(NIR-Red)_{1}S}{NIR+Red+0.5}\) & [65] \\ WDVI & \(NIR-0.58*Red\) & [66] \\ GNDVI & \(\frac{NIR-Green}{NIR+Green}\) & [67] \\ MCARI & \([(RE-Red)-0.2(RE-Green)]\Big{(}\frac{RE}{Rad}\Big{)}\) & [68] \\ EVI & \(2.5\frac{NIR-Red}{NIR+Red-7.5*Blue)+1}\) & [69] \\ TRBI & \(\frac{Green+Red}{NIR}\) & [70] \\ NDI45 & \(\frac{RE-Red}{NIR-Red}\) & [71] \\ SeLI & \(\frac{NIR-RE}{NIR+RE}\) & [72] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculated vegetation indices based on the blue, green, red, red edge (RE) and near infrared (NIR) spectral bands of the PlanetScope instrument or MicaSense multispectral camera.  
where \(\rho_{\lambda 1,GDD}\) and \(\rho_{\lambda 2,GDD}\) represent the reflectance values for bands \(\lambda_{1}\) and \(\lambda_{2}\) at a given \(GDD\), \(m_{1}\) and \(m_{2}\) are coefficients for each band, and \(n\) is the intercept. This approach allowed us to evaluate how well spectral data captured key crop traits at different growth stages.  
The study focused on identifying phenological stages, particularly those before the peak of the growing season, that exhibited the strongest correlations with spectral data. This approach aimed to evaluate the potential for predicting crop components, including yield, protein content, starch, and hectoliter weight, using satellite- and drone-derived data. Validation was performed by comparing remotely sensed data with ground-based measurements, using metrics such as the coefficient of determination (R\({}^{2}\)), mean residuals (_bias_), standard deviation of residuals (\(\sigma\)), and root mean square error (RMSE). These metrics were applied consistently with the validation framework described for vegetation cover predictions.
## 4 Proposed Localization Method
### Algorithm Development  
The proposed solution mainly includes two processing stages, referred to as calibration stage and localization stage, respectively. The calibration stage is devoted to refining the inaccurate transmitter and receiver positions, and then the localization stage is devoted to determining the target position on the basis of the refined transmitter and receiver positions.
## 3 Results
### Temporal and Geographical Distribution of Publications  
This section analyzes the evolution of learning analytics (LA) research using machine learning (ML) and generative AI (GenAI) in higher education between 2018 and 2025, focusing on temporal trends and geographical spread.  
The annual trends (Figure 2) show consistent growth in ML-based LA studies, with a peak in 2022. GenAI studies first appeared in 2022 and accelerated in 2024, coinciding with the widespread release and adoption of large language models (LLMs) such as GPT-4. This pattern reflects two coexisting trajectories: the consolidation of traditional ML approaches--classification, regression, and ensemble models applied to LMS data--and the experimental rise of GenAI applications for feedback generation, engagement modeling, and content personalization. While ML remains foundational, GenAI is rapidly gaining traction, though it is still in the early stages of methodological standardization and empirical validation.  
Geographical distribution (Figure 3) reveals notable disparities. The United States leads with 17 studies (10 ML, 7 GenAI), followed by Australia, China, and Germany, which show relatively balanced activity across both paradigms. However, GenAI research remains highly concentrated in high-income regions, with limited representation from Latin America, Sub-Saharan Africa, and parts of Asia. These gaps raise concerns about equity, contextual relevance, and disparities in access to GenAI infrastructure for research and implementation in education.  
Figure 2: Temporal trends in learning analytics studies using traditional machine learning (**left**) and generative AI (**right**) in higher education between 2018 and 2025.  
The coexistence of ML and GenAI suggests a transitional phase in LA research. ML offers robustness and interpretability, while GenAI brings adaptability and personalization. Hybrid approaches could combine their strengths to enhance flexibility and inclusiveness.  
Three key observations emerge: (1) GenAI adoption aligns with the release of tools like GPT-4, highlighting the role of accessibility; (2) its concentration in high-income regions may deepen epistemic inequalities; and (3) the parallel rise of both paradigms invites integrated, transparent, and context-aware frameworks.
# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
## 4 Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving Tag Identification  
In this section, a novel admission-control-based dynamic query tree (ACQQT) protocol is developed to effectively identify fast-moving tags while maintain a minimum tag lost  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{_Query_ command} & Head & \(L_{pre}\) & _pre_ & _cbit_ & CRC-16 \\ \cline{2-6}  & 37 bits & 8 bits & Var. & 2 bits & 16 bits \\ \hline \multirow{2}{*}{_Qrep_ command} & Command & \multirow{2}{*}{Tag’s response} & \multirow{2}{*}{Preamble} & \multirow{2}{*}{Data} \\ \cline{2-6}  & 4 bits & & & 9 bits & Var. \\ \hline \end{tabular}
\end{table}
Table 2: Command and data frame structure.  
Figure 2: Frame slot structure: (**a**) when \(fl=1\), the frame consists of two slots; (**b**) when \(fl=2\), the frame consists of four slots; (**c**) when \(fl=3\), the frame consists of eight slots.  
ratio. In conveyor belt systems, the tag set constantly changes during the identification process. For easy implementation, the reader divides the identification process into multiple reading rounds. In each round, there may be three types of tags in the reader's coverage area, that is, deactivated tags that were recognized in preceding rounds, active tags that are participating in the recognition of the current round and newly arrived tags that are not participating in the recognition of this round as illustrated in Figure 1.  
The main idea of \(\Lambda\)CDQT is to restrict the number of participating tags with admission control and recognize them with a high-efficiency dynamic query tree protocol in each reading round. In general, the reader first performs three rounds to estimate the tag flow rate. Next, it controls the number of admitted tags based on the estimation and system requirement. Each subsequent round only needs to control a similar number of incoming tags to ensure that the entire system meets the minimum tag lost ratio requirement. When there are enough tags entering the reader's reading range, a new round begins. In each round, a dynamic query tree protocol is designed to quickly identify tags. When collision occurs, the reader first checks the position of the first colliding bit \(k\) and the number of consecutive colliding bits after that position. Then, it divides these tags into smaller groups according to the number of consecutive collision bits. Compared with existing bit tracking protocols, the number of subsets of \(\Lambda\)CDQT is more flexible and appropriate. Thus, the proposed protocol can significantly reduce the number of collision slots and speed up the identification process. Before describing the proposed \(\Lambda\)CDQT protocol, Table 3 defines some symbols, functions and commands used in the paper.
## 2 Thermal Barrier Coating  
### Background of Thermal Barrier Coating  
Recently, various fields have been researched to improve the efficiency of mechanical systems operating in high-temperature harsh environments such as gas turbines. Improvements in efficiency in mechanical systems, in particular gas turbines, include prolonged operating life, high operating efficiency, and reduced emissions. To satisfy these conditions, increasing the thermodynamic efficiency by increasing the turbine entry temperature is the most effective, but as the operating temperature increases, a severe operating environment is created, which damages the core parts. Core components, such as TBCs, are applied to parts of gas turbines that are directly exposed to high-temperature environments. By applying low thermal conductivity coatings on components such as blades, the temperature of the components is reduced by approximately 100-300 \({}^{\circ}\)C. By using this technique, the gas turbine can be operated at a combustion gas temperature 250 \({}^{\circ}\)C higher than the melting temperature of the nickel-based superalloy. In addition, TBCs increase the service life of gas turbine components because they reduce the temperature of metal components [54; 55]. These TBCs consist of a top coating of ceramic material that directly blocks the flames and reduces heat transfer and a metal material that binds the top coating and the base material, as shown in Figure 10. Top coatings are most commonly used with a ZrO\({}_{2}\) component called yttria stabilized zirconia (YSZ). In addition, YSZ has low thermal conductivity and high oxygen permeability [56]. Bond coating is a layer of metal material composed of McrAIY (M: Co, Ni), which is used to prevent flame penetration into the superalloy base material and to relieve thermal stress due to the differences in thermal expansion coefficient between the superalloy and top coating. Because of the high oxygen permeability of the top coating, the Al component present in the bond coating reacts with the penetrated oxygen to form a thermally grown oxide (TGO). A TGO is formed on the bond coat layer with the \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) component to protect the inner surface from further oxidation. To improve the adhesive strength before spraying the ceramic top coating layer, 2-3 \(\upmu\)m of TGO are formed on the bond coating via proper heat treatment. However, the growth of 8-10 \(\upmu\)m thick TGO during the operation of high-temperature components generates interfacial stress with the ceramic layer, reduces the adhesion, and causes TBC delamination [57]. Therefore, in the design of TBCs, consideration should be given to the TGO as well as the heat resistance, high temperature properties, and the techniques used for the deposition of coatings [56].
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 2 Related Work  
When analyzing composite protocols, it is commonly assumed that protocol participants have successfully shared secrets to abstract sub-protocols. However, the combination of two independently proven secure encryption protocols may not necessarily maintain security. [PERSON] et al. introduced the chain space method [9], which has yielded theoretical results regarding the composition of protocols. The chain space method attempts to identify abstract properties that two protocols must satisfy for secure composition. In 2019, [PERSON] et al. [10] also formally verified a voting protocol using the chain space method. However, this method exhibits deficiencies in the efficiency of verifying composite protocols and considering sequential combinations. [PERSON] et al. proposed a generic framework [11] for proving the secure composition of protocols and revised this framework in 2020, addressing multiple ambiguities and modeling issues present in previous versions [12]. In 2022, [PERSON] et al. [13] used this secure composition framework to model and analyze the Signal messaging protocol.  
The Logic of Events Theory adopted in this paper shares similarities with this framework, but LoET demonstrates a stronger characterization capability for the security properties of protocols and is more conducive to the verification of protocol properties. [PERSON] et al. [14]introduced Protocol Composition Logic (PCL), whose authentication proof process is concise and supports compositional reasoning for complex security protocols. In 2021, [PERSON] et al. [15] extended the PCL theory to formally analyze a security protocol based on a public-key cryptosystem from the aspects of authentication, confidentiality, and data integrity. However, PCL can only describe a subset of cryptographic protocols and cannot handle protocols with data signatures, a limitation addressed by Logic of Events Theory. [PERSON] et al. [16] introduced the concept of independence in composite protocol sets, defined relevant security property classes, and proved many theorems within this class, but did not specifically discuss how to perform sequential protocol composition. In 2020, [PERSON] et al. [17] proposed an automated compositional analysis technique for authentication protocols, reducing verification time. However, formal methods are still required for compositional reasoning in protocol combinations. In 2021, [PERSON] et al. [18] constructed a general framework to express the vertical composition of application and channel protocols, contrasting sequential and parallel combinations with horizontal composition. They independently verified several layers of protocol stacks. In our work, we also consider this aspect, using the DTLS protocol as the transport layer handshake protocol and the SRTP protocol as the application protocol for sequential composition, proving its security properties.
# Security in Wireless Sensor Networks: A Cryptography Performance Analysis at MAC Layer
## 1 Introduction
In some circumstances, developers sacrifice the level of security by using cost-effective solutions without adequate mechanisms, for example, for key distribution. A WSN requires more flexible methods for distributing keys across the network. There are two types of approaches: classical, based on the use of symmetric or asymmetric cryptography, and advanced, based on elliptic curve cryptography (ECC), they are particularly known for a more efficient use of resources than any other public key technique. WSNs should send data in a safe way towards a central node in a multi-hop transmission guaranteeing data confidentiality and integrity. Figure 1: Wireless Sensor Network. There are numerous attacks and threats toward a WSN, for example, information loss, communication interruption, service slowdown causing delays, denial of service [8; 9]). Thus, we have taken into account three of the main security properties: confidentiality, integrity, and authentication. Researchers propose different countermeasures such as intrusion prevention, intrusion detection [10; 11], and cryptography approaches. In this paper, starting from a previous work [12] where we have shown a MAC protocols comparison for that concern received packets and energy consumption of the sensor nodes inside the WSN, we will show an analysis on the security aspects for WSNs based on cryptography mechanisms considering energy drain and impersonation attacks and their possible mitigation. This paper has the following structure: Section 2 presents related work on energy and security aspects in WSNs; Section 3 provides an analysis of the cryptographic methods used for experimentation; implemented attacks with relative countermeasures are provided in Section 4; Section 5 shows the results of the performed experiments; lastly, Section 6 summarizes the work.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols  
This section presents a detailed security analysis of the proposed PQC standalone and PQC-AES hybrid PKE protocols. We evaluate the resilience of these protocols against various attack vectors, considering both classical and post-quantum threats.
## 3 Results
### Speciation Analysis  
Ammonium oxalate 0.2 M extraction was used for the chemical speciation analysis of As, combining HPLC with ICP-MS techniques (Figure 4). Generally, As extracted was almost entirely As(V) species and only a very low percentage of As(III) was measured. The organic species MMAA and DMAA were completely absent. Here, we show the results concerning the soil sample deriving from the S53/1D core (depth 4-5 m), because it was the sample with the highest As percentage extracted by ammonium oxalate 0.2 M (8.6%). In this sample As(V) was 96.9% of As extracted, whereas As(III) was 3.1%.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & & \multicolumn{2}{c}{**As from Leaching Test**} \\ \hline
**Station ID** & **Depth (m)** & **ppb** & **\%** \\ \hline  & 0.0–1.0 & 10 & 0.05 \\  & 1.0–1.5 & 8 & 0.09 \\ S53/1C & 1.5–2.0 & 10 & 0.11 \\  & 2.0–3.0 & 9 & 0.03 \\  & 3.0–4.0 & 47 & 0.26 \\  & 4.0–5.0 & 56 & 0.26 \\ \hline  & 0.0–1.0 & 6 & 0.04 \\  & 1.0–1.5 & 6 & 0.02 \\ S53/1D & 1.5–2.0 & 9 & 0.03 \\  & 2.0–3.0 & 27 & 0.10 \\  & 3.0–4.0 & 46 & 0.27 \\  & 4.0–5.0 & 73 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 4: Chromatogram showing the peaks related to the chemical speciation analysis of As for the soil sample coming from the level 4.0–5.0 m of the S53/1D core.  
Moreover, we used phosphoric acid 0.5 M, an extraction agent more effective than ammonium oxalate 0.2 M, on the same soil sample (S53/1D core, depth 4-5 m) in order to determine As species: As extracted was 21% of total As and As(V) was the only species present (data not shown).