esa-sceva/satcom-chunk-collection huggingface dataset:
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 3 Methodology  
The anomaly detection process in our proposed ChaMTeC framework follows a systematic and modular pipeline that is designed to capture both temporal and feature-wise dependencies in multivariate time-series data. The process begins with input sequences \(\mathbf{x}\in\mathbb{R}^{B\times L\times F}\), where \(B\) is the batch size, \(L\) is the sequence length, and \(F\) is the number of features (16 in our WaterLog dataset). The input is normalized and passed through the embedding layer called \"DataEmbeddingInverted\", which not only transforms feature dimensions but also prepares the data for temporal processing by transposing the sequence layout for improved temporal correlation learning. The core of the architecture consists of a multi-stage encoder composed of the following: feature-wise transformations using fully connected layers; a temporal self-attention module (optional) to capture long-range dependencies; a temporal convolutional module to extract localized temporal patterns; residual connections and layer normalization to stabilize training and enhance feature reuse. Once the embedding is encoded, a reconstruction of the original input is produced. The reconstruction error is computed as the Mean Squared Error (MSE), which serves as the basis for anomaly scoring. This error is then fed back into a Recurrent Neural Network (RNN), specifically, a GRU-based feedback mechanism, which adjusts the reconstruction iteratively by modeling the evolution of error patterns over time. The final corrected reconstruction is obtained by learning a residual correction from the RNN's hidden states. To decide whether a sample is anomalous, we apply a sliding-window-based dynamic thresholding technique. This method calculates a moving average and standard deviation of error within a local window and computes a threshold. This allows the system to adapt to local variations and avoid false positives in high-variance but non-anomalous regions.  
We propose a novel time-series anomaly detection framework that combines an inverted embedding strategy, channel (feature) fusion, multi-layer temporal encoding, and an MSE-based feedback mechanism with dynamic thresholding, as illustrated in Figure 1. The framework consists of four main components: (1) data embedding and input transformation (Section 3.1), which invert temporal and feature dimensions for temporal processing; (2) an encoder layer for temporal and feature processing (Section 3.2), which leverages both feature-wise transformations and convolutional temporal dependencies; (3) an MSE-based feedback recurrent neural network (Section 3.3), which iteratively refines reconstructions using squared error feedback; (4) sliding-window-based dynamic thresholding (Section 3.4.2), which adapts anomaly detection sensitivity to local data distributions.  
Figure 1: General overview of the proposed architecture of ChaMTeC.
## 2 Calculation Methods
### Hardening and Softening Due to Hydrogen Microscopic Distribution  
According to the Voigt-Reuss-Hill polycrystalline approximation, [PERSON]'s modulus E, the bulk modulus B, and shear modulus G can be obtained from \(C_{ij}\)[46]  
\[\begin{array}{l}E=\frac{(C_{11}-C_{12}+3C_{44})(C_{11}+2C_{12})}{2C_{11}+3C _{12}+C_{44}}\\ B=\frac{1}{3}(C_{11}+2C_{12})\\ G=\frac{1}{5}(C_{11}+3C_{44}-C_{12})\end{array} \tag{12}\]  
They can reflect the strength, stiffness, and hardness of materials [27]. Denoting these moduli as \(M\), the following results can be obtained:  
\[\begin{array}{l}M(c_{H})=M(C(c_{H}))\\ \Delta M^{vol}(\Delta V)=M\Big{(}C(0)+\Delta C^{vol}(\Delta V(c_{H}))\Big{)}- M(C(0))\\ \Delta M^{coh}(c_{H})=\frac{1}{N}\sum_{p=1}^{N}\Big{(}M(\mathcal{C}^{p}(c_{H}) )-M\Big{(}C(0)+\Delta C^{vol}(\Delta V(c_{H}))\Big{)}\Big{)}\\ \Delta M^{dis,q}(c_{H})=M(\mathcal{C}^{q}(c_{H}))-M\Big{(}C(0)+\Delta C^{vol}( \Delta V(c_{H}))\Big{)}-\Delta M^{coh}(c_{H})\end{array} \tag{13}\]  
These represent the elastic moduli of different \(c_{H}\), as well as the influence of volume change, the cohesion of Fe-H, and the microscopic distribution of hydrogen on the elastic moduli, respectively.  
As the strength of metals are related to E and G, and the compressibility is related to B, we used \(\Delta E^{dis,q}\) and \(\Delta G^{dis,q}\) to discuss the hardening and softening caused by the microscopic hydrogen distribution, and \(\Delta B^{dis,q}\) to discuss the influences on compression. In the following, we write \(\Delta E^{dis,q}\), \(\Delta G^{dis,q}\), and \(\Delta B^{dis,q}\) as \(\Delta E^{dis}\), \(\Delta G^{dis}\), and \(\Delta B^{dis}\), respectively.  
We used the mean-shift algorithm [47] provided by the machine learning library scikit-learn [48] to perform clustering on \(\Delta E^{dis}\), \(\Delta G^{dis}\), and \(\Delta B^{dis}\) at \(c_{H}\) = 0.8%, using the distance between data points as the clustering criterion. Then, we calculated the partial pair-correlation function (PCF) between hydrogen atoms in each structure according to Equation (14) [49]:  
\[g(r)=\frac{1}{\rho_{H}N_{H}}\left\langle\sum_{i\
eq j}\delta\left(r-\left|R_{i} -R_{j}\right|\right)\right\rangle \tag{14}\]  
where \(\rho_{H}\) and \(N_{H}\) are the average atomic density and the number of hydrogen atoms respectively, and \(R_{i}\), \(R_{j}\) are the position vectors. The average result of the \(g(r)\) of different structures in each group according to the clustering results is obtained as \(\overline{g}(r)\), based on which the distributions of hydrogen atoms inducing hardening or softening in distances can be found, and then the related slip planes can be discussed. The crystal structures in the article were drawn with the visualization software VESTA [50].
## 8 Conclusions and Future Research  
### Conclusions  
By using the LDA subject model and DEMATEL-ISM from the perspective of holistic and systematic analysis, this paper explores the uncertainties of the application of a new generation of digital technology in China's construction industry and constructs a causal relationship diagram and a multi-layer hierarchical structure model between uncertainties based on the DEMATEL-ISM method. The main findings of the study are as follows:  
Firstly, this paper identifies a total of 13 indicators of uncertainty in the application of digital technology in the construction industry using the LDA topic model, which can be classified into five levels: the policy level, the industry level, the personnel level, the economic level, and the legal level, which together constitute a system of indicators of uncertainty in the application of digital technology.  
Secondly, based on the construction of the uncertainty index system for the application of digital technology in the construction industry, according to the characteristics of intersection and concurrence among the uncertainty indexes, the DEMATEL method is used to calculate the degree of influence and the impact of the uncertainty indexes, influence degree, affected degree, centre degree and cause degree of the uncertainty indexes and the causal relationship among the indexes is studied. Among them, perfection of the policy protection system, perfection of the regulatory system, perfection of the legal system of intellectual property protection and perfection of technical standards and guidelines are the main causes. The cycle of return on investment for digital technology applications, the willingness to share results and data, the number of professional and technical personnel and the acceptance of digital technology are the main result factors.  
Third, using the DEMATEL-ISM method to construct a multi-level hierarchical structure model of an uncertainty index system, the model includes surface factors, middle factors and deep factors. In the whole uncertainty index system, the perfection of the policy guarantee system is the key uncertainty index for the application of digital technology in the construction industry. The cycle of return on investment for digital technology applications is the direct uncertainty indicators of digital technology application in the construction industry.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 4 Thermal Hazards of Lithium-Ion Battery
### Thermal Hazards of Typical Commercial Lithium-Ion Batteries
#### 4.4.3 Lithium nickel manganese cobalt oxide  
NMC ternary material is developed on the basis of LCO positive electrode materials by adjusting the content of Ni, Mn and Co elements. Co element plays an important role in the electronic conductance of battery, while Ni element can improve the capacity. Mn element is responsible for stabilizing the structure of positive electrode material and preventing the capacity decline of battery [179; 180]. With the rapid development of EVs, ternary materials have attracted extensive attention due to their high capacity, while ternary materials with a high Ni content have gradually replaced low-Ni materials due to the lower price. However, with the increase of Ni content, the safety of batteries is greatly compromised [181]. The content of Ni in NMC materials increases gradually, from NMC111 to NMC532 and NMC622, but this still cannot meet the current demand for power battery capacity and low cost. Therefore, NMC811 materials have received extensive attention in recent years, but the extremely high reactivity between positive electrode and electrolyte also brings great safety risks [16]. [PERSON] et al. studied the effect of vinylene carbonate (VC) and fluoroethylene carbonate (FEC) additives on the reactivity of lithium intercalation carbon (Li\({}_{x}\)C) and electrolyte, and the results showed that the addition of FEC could reduce the self-heating rate between graphite and electrolyte [182]. Three component additives, such as VC211 and PES211, could improve the cycling performance and the chemical stability of electrode/electrolyte, and the effect was much better than that of single or binary additives [183]. According to the latest research results, the thermal runaway of NMC batteries is mainly caused by chemical crosstalk between the electrodes, the oxygen released by cathode diffuses to the anode, at which point, an oxidizing reaction occurs and emits a large amount of heat, causingthe thermal runaway of the NMC batteries. This is different from the traditional view that the main reason is the breakage of the diaphragm material, which leads to the internal short circuit then causes thermal runaway. Thus, it is possible to fundamentally solve the safety problem of high Ni ternary material [184].
## 5 Experimental Validation
### Case 1: Rotor Rub-Impact Fault Detection  
Figure 18a,b shows the waveform and FFT spectrum of the rotor early rub-impact signal, respectively. As seen in Figure 18a,b, the rotor slight rub-impact signal is expressed approximatively by a sine wave, the frequency component of 29.5 Hz (approximately equal to the rotating frequency \(f_{1}\)) is very clear in the FFT spectrum, but the amplitude at the harmonics is very small. That is to say, it is not easy to judge whether rub-impact fault occurs when directly using the FFT spectrum.  
Figure 17: Schematic diagram of the rotor test bench.  
Five approaches (i.e., IAVMD, OVMD, LMD, EMD, and WT) are applied to decompose the rub-impact fault signal, respectively. Figure 18c-g respectively depicts the TFR resulting from the five methods. The optimal inside parameters in IAVMD are selected as \(\hat{\alpha}=1177\) and \(K=4\) through using the GWO algorithm. The optimal parameters of OVMD are determined as \(\hat{\alpha}=536\) and \(K=4\) through using the PSO algorithm. As seen in Figure 18c, the fundamental frequency of 29.5 Hz and its various harmonics (59 Hz, 147.5 Hz, and 265.5 Hz) can be identified. In addition, the high frequency is relatively ambiguous due to a resolution problem, but the spectral line is very clear in the low frequency. This indicates that our presented IAVMD approach can detect early characteristics of rotor rub-impact faults.  
As shown in Figure 18d, we can easily find the frequency components of 29.5 Hz and its second-harmonic (59 Hz), but the results have overlap and there is a serious end effect (see the parts marked with a red line). In Figure 18e,f, the instantaneous frequencies of 29.5 Hz can be found, but their  
Figure 18: The analyzed results of the rotor rub-impact signal: (**a**) Temporal waveform, (**b**) FFT spectrum, time-frequency representations obtained by (**c**) IAVMD, (**d**) OVMD, (**e**) LMD, (**f**) EMD, and (**g**) WT.  
time-frequency trajectory is obscure. It is obvious from Figure 18g that the TFR obtained by WT can dimly extract the rotating frequency of 29.5 Hz, but the frequency points are very scattered and their time-frequency resolution is clearly worse. The contrastive analysis demonstrated that the efficacy of IAVMD in rotor rub-impact fault detection is superior to that of the other four approaches (i.e., OVMD, LMD, EMD, and WT).
# Efficient and HRA Secure Universal Conditional Proxy Re-Encryption for Cloud-Based Data Sharing
## 3 Definition of Universal Conditional Proxy Re-Encryption  
A UCPRE system includes three types of entities: the delegator, the proxy, and the delegatee. Our UCPRE allows the proxy to transfer the ciphertext of the public key (delegator) under a homomorphic encryption scheme \(\Pi_{f}\) into the ciphertext of the public key (delegatee) under a homomorphic encryption scheme \(\Pi_{t}\) that may be different from the scheme \(\Pi_{f}\). A UCPRE scheme does not need to set setup, key generation, and decryption algorithms. That is, it generates the public parameter, key pairs, and decrypt the ciphertext by executing the existing schemes \(\Pi_{f}\) and \(\Pi_{t}\). Therefore, UCPRE can be seen as a general construction of conditional proxy re-encryption. We give the definitions of syntax, correctness, and HAR security according to the works [2; 34] (note that the notation and presentation have minor changes).  
**Notations**. In our system, we denote separately delegator and delegatee key pairs by \((\text{pk}_{f},\text{sk}_{f})\) and \((\text{pk}_{t},\text{sk}_{t})\) (\(f\) and \(t\) mean \"from\" and \"to\", respectively) if we emphasize which user is a delegator or delegatee. When we write \((\text{pk}_{i},\text{sk}_{i})\leftarrow\Pi_{i}\cdot\text{KeyGen}\big{(}1^{ \lambda}\big{)}\), it mean that a key pair is generated by the key generation algorithm KeyGen of the scheme \(\Pi_{t}\), where \(i\in\{f,t\}\). When we write \(\text{ct}_{f}^{m}\leftarrow\Pi_{f}\cdot\text{Enc}(\text{pk}_{f},m)\), it mean that the ciphertext \(\text{ct}_{f}^{m}\) encrypting \(m\) is generated by the key encryption algorithm Enc of the scheme \(\Pi_{f}\). When algorithm of UCPRE take as an input \(\Pi_{f}\) (or \(\Pi_{f}\)), we interpret it as a description of algorithms (rather than circuits or Turing machines).
## 4 Results and Discussion
### Level 2: Inferential Statistics
#### 4.2.2 Anova  
To answer RQ2.2 (Is there a notable difference in the performance of one specific fuel type (or vehicle type) in comparison to the rest of the vehicle types in Canada?), a one-way ANOVA one-tailed test was implemented to compare the means of each vehicle class in terms of total fuel consumption, using the following assumptions.  
* The samples are not dependent;
* Each sample comes from a population that is normally distributed;
* The group population standard deviations are all equal (homoscedasticity).  
Firstly, the means of total fuel consumption for each class through the years is calculated based on the descriptive statistics method, as shown in Figure 9.  
The following configurations have been set out.  
* Null Hypothesis (H0): means of each vehicle class are the same;
* Alternative Hypothesis (Ha): At least one of the means for each class is not equal to the other;  
Figure 8: Total fuel consumption (L/100 km) of each fuel type over time.  
After the test, the result showed that:  
\[p\text{-value}=2.3552\times 10^{-27}<\alpha=0.01. \tag{2}\]  
Therefore, the null hypothesis can be rejected, meaning that at least one mean of total fuel consumption for each vehicle class is significantly different from the rest.  
Similarly, using the same assumptions, hypothesis, and confidence level, one-way ANOVA one-tailed tests have been conducted in CO\({}_{2}\) emissions and fuel consumption of each vehicle class and fuel type (Figures 10 and 11, respectively) of each fuel type, and each result is presented as the following.  
Figure 10: CO\({}_{2}\) emissions of each vehicle class over time.  
Figure 9: Total fuel consumption distribution of vehicle classes over time.  
\[p\text{-value}=6.81894\times 10^{-27}<\alpha=0.01. \tag{3}\]  
Consequently, the null hypothesis can be rejected, meaning that at least one mean of CO\({}_{2}\) emissions for each vehicle class is significantly different from the rest.  
Total fuel consumption of each fuel type over time:  
\[p\text{-value}=1.3362\times 10^{-13}<\alpha=0.01. \tag{4}\]  
Therefore, the null hypothesis can be rejected, meaning that at least one mean of total fuel consumption for each fuel type is significantly different from the rest.  
Emissions of each fuel type over time:  
\[p\text{-value}=5.5127\times 10^{-05}<\alpha=0.01. \tag{5}\]  
From that comparison, the null hypothesis can be rejected, meaning that at least one mean of CO\({}_{2}\) emissions for each fuel type is significantly different from the rest.
## 4 Data Analysis in Maritime IoT Applications
### Role of Data Analysis in Enhancing Maritime Operations
#### 4.2.2 Improve Safety  
Safety is paramount in the maritime industry. Data analysis plays a crucial role in identifying potential safety hazards and risks. By analyzing data from various sensors and systems aboard a vessel, such as navigation systems, engine control systems, and safety equipment, anomalies and potential hazards can be detected early.  
This early detection enables proactive measures, such as adjusting the vessel's course or performing emergency maintenance, preventing accidents and enhancing the safety of the crew, cargo, and vessel. In the long run, this could lead to the development of safer maritime practices and protocols, reducing the occurrence of maritime incidents [81].
## Materials and Methods
### Fabrication Process and Experimental Setup  
The four-probe bridges were fabricated using the Polysilicon Multi-User MEMS Processes (PolyMUMPsTM). PolyMUMPs process is a standard surface micromachining fabrication process with three polysilicon layers, two sacrificial layers and one metal layer. The process begins with (100) silicon wafers, which are first covered with a 600 nm thick silicon nitride layer as a dielectric isolation layer. A 500 nm low-stress low pressure chemical vapor deposition (LPCVD) polysilicon layer (Poly 0) is deposited on the surface of the wafer and patterned by photolithography and plasma etching techniques. After the first polysilicon layer is fabricated, a 2 um thick PSG layer (Oxide 1) is deposited and patterned, followed by the fabrication of a 2 um thick polysilicon layer (Poly 1). Then, another PSG layer (Oxide 2) is deposited at a thickness of 750 nm. After the PSG layer is etched, a 1.5 um thick polysilicon layer (Poly 2) is deposited on the surface of the PSG layer and patterned. Both Poly 1 layer and Poly 2 layer are used as the structural materials for four-probe bridges, while the single layer of Oxide 1 and the stacked layer of Oxide 1-Oxide 2 are served as the etched sacrificial layers so that the following deposited polysilicon layer can be patterned to form the step structures. The final deposited layer is a 0.5 um thick gold layer that used for probing [29].  
The SEM photographs of the test structure are displayed in Figure 4. Each unit contains 10 continuous step structures. The bridges are deposited horizontally and at an angle of 45\({}^{\circ}\) and 60\({}^{\circ}\), respectively.  
Figure 4: SEM photographs of the test structure: (**a**) Deposited horizontally; (**b**) Deposited at an angle of 45\({}^{\circ}\); (**c**) Deposited at an angle of 60\({}^{\circ}\).  
Schematic diagram of the experiment system is depicted in Figure 5. It contains a semiconductor characterization system (Keithley 4200-SCS) and a probe station. The experiments are performed using four-point probe technique. The measurement method is presented as follows. First, a Van der Pauw test is performed on the Greek cross structure. The sheet resistance of polysilicon thin film is then extracted by Equation (4). Then, a current \(I\) is applied between pads C1 and S1 while the potential different between pads A1 and S3 (\(V_{a}\)) is measured. The width of the bridges is obtained from Equation (5). Resistances between pads C1 and D1, C2 and D2, C3 and D3, B2 and E2, B3 and E3 are measured by using four-probe I-V measurement method. The thickness of the polysilicon layer and the PSG layer and the sidewall etch angle of the PSG layer are then calculated from Equations (9)-(11).
# Weather Radar in Complex Orography
## 4 Solutions and Results
### Data Processing and Product Generation
For a detailed description and discussion of RainForest and the underlying data set, see [108]. (**c**) shows the performance of RainForest, including dual-polarisation data, whereas (**d**) shows RainForest trained and tested on only single-polarisation data. The geographic domain corresponds to that of Figure 1.  
Several other radar products, that have not been described in detail here, such as echo tops, vertically integrated liquid water (VIL) and wind speed, direction and shear estimated in the presence of radar echoes from precipitation, insects and Bragg scatter, are also available, see, for instance, [112] for an overview. As for QPE, the data processing has to be adapted to the particular situation over complex orography.