esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Error Magnitude Evaluation in Reed-Solomon Codes
### Lu Method  
The method presented by [PERSON] et al. [25] performs error magnitude computation with less computational effort in respect to the Forney method, as shown in Table 1. It is composed by three main phases, each one performing different operations: preprocessing phase, syndrome refining phase, and error-magnitude extraction phase. In the preprocessing phase, partial results, \(P_{i,j}\) and \(Q_{i,j}\), are introduced for convenience, and are defined as follows:  
\[P_{i,0}=Q_{i,0}=\alpha^{l_{i}}\;,\;\text{for}\;i=1,\;2,\;\ldots,\
u\;, \tag{6}\]  
\[P_{i,j}=\alpha^{l_{i}}+\alpha^{l_{j}}\;,\;\text{for}\;1\leq j<i\leq\
u\;, \tag{7}\]  
\[Q_{i,j}=Q_{i,j-1}P_{i,j}\;,\;\text{for}\;1\leq j<i\leq\
u\;. \tag{8}\]  
In the subsequent syndrome refining phase, following the idea that, to evaluate \(\
u\) error magnitudes, knowing all error location numbers, only the first \(\
u\) syndromes are needed, the original syndromes \(S_{k}\) are re-elaborated to compute \(S_{w}^{(k)}\) as  
\[S_{w}^{(1)}=S_{w}\;,\;\text{for}\;w=1,\;2,\;\ldots,\
u\;, \tag{9}\]  
\[S_{w}^{(k)}=S_{w+1}^{(k-1)}+S_{w}^{(k-1)}P_{k-1,0}\;,\;\text{for}\;k=3,\;4, \;\ldots,\
u\;\text{and}\;w=3,\;4,\;\ldots,\
u-k+1\;. \tag{10}\]  
Figure 4: Error magnitude evaluation with \(\
u=3\)  
Finally, in the error magnitude extraction phase, \(\delta_{k}\) can be recursively computed as  
\[\delta_{\
u}=\frac{S_{1}^{(\
u)}}{Q_{\
u,\
u-1}}\;, \tag{11}\]  
\[\delta_{k}=\frac{S_{1}^{(k)}+\sum_{i=k+1}^{\
u}\delta_{i}Q_{i,k-1}}{Q_{k,k-1}}\;,\;\text{for}\;=\
u-1,\;\
u-2,\;\ldots,1\;. \tag{12}\]  
An implementation for \(\
u=3\) is reported in Figure 5.  
As for the Forney example in Figure 4, the number of used operations is found to follow the equations of Table 1: 2 inversions (plus 1 required to generate the inverse of the \(\alpha_{1}\) coefficient), 9 additions, and 12 multiplications. Compared with the Forney method, the new method requires less operations to obtain the result. Moreover, as can be seen in the diagram, the new method does not need the values of the error locator polynomial (\(\sigma_{k}\)) as input.
# Optical Hyperspectral Image Cryptosystem Based on Affine Transform and Fractional Fourier Transform
## 2 The Hyperspectral Encryption Scheme  
### Fractional Fourier Transform  
Firstly, the FrFT will be introduced briefly. In the signal processing field, the FrFT can be regarded as time-frequency joint representation and it has been widely utilized in signal and image processing [20; 21]. The mathematical definition of FrFT can be written as  
\[\begin{array}{ll}F(u,v)&=\varphi^{  
\[F^{-1}(\theta)=\left[\begin{array}{cc}\frac{\cos[\theta(x,y)]}{\psi(x,y)}&\psi(x,y)\sin[\theta(x,y)]\\ -\frac{\sin[\theta(x,y)]}{\psi(x,y)}&\psi(x,y)\cos[\theta(x,y)]\end{array}\right], \tag{3}\]  
where the function \(\theta(x,y)\) and \(\psi(x,y)\) are two random real number matrices having the same size as the image, and satisfying uniform distribution. The affine transform described in Equations (2) and (3) will be employed in the encryption and decryption approaches to complete the cryptosystem.
## 3 A Non-Real-Time DTN-Based Forwarding Algorithm
### Distributed Forwarding Algorithm  
Nodes have to make forwarding decisions with local and limited information. Thus, in our solution, nodes use a distributed forwarding algorithm based on historical and social information. Each node meets its neighbors and gives them an availability probability that will decay over time and will increase when they meet again. Also, when a node meets a neighbor, they share their delivery probabilities to the destination node. Sensor nodes will start with a delivery probability near zero, so that nodes will prefer to store their messages until they contact the Gateway or another node that had been in contact with the Gateway, updating its delivery probability. They will also update their delivery probabilities through time, diminishing them as time goes by without further contacts with the same node. This updating will follow Equation (9). MAPs and the Gateway will start with a delivery probability near 0.9, since they have the links that must be transited on the network. Sensor nodes can use other sensor nodes to deliver their messages to the Gateway if they have a better delivery probability and are available to receive the messages. The Gateway will also update its delivery probability according to its knowledge of the network, giving higher delivery probabilities to MAPs closer to a scheduled or predicted encounter and closer, in time, to deliver the message to the Internet Connected Node. \[d_{ij}(t+v)=d_{ij}(t)^{v} \tag{9}\]  
Figure 6: Availability table and delivery probabilities table for each node. Nodes have to save the last contact time, the number of contacts, and calculate the mean time between contacts for every neighboring node. Based on this mean time between contacts, nodes will update the availability probabilities, giving them the maximum value when the contact should be close in time and diminishing them exponentially using Equation (9), where \(v\) is the number of time values since the last update. If the node has multiple neighbors with paths to the destination, then it can make the decision to wait for a certain node to become available or to deliver its message through an already available node, depending on their delivery probabilities and mean time between contacts. We are also using a replication option, where a source node can create up to one copy of a message (there will exist up to two identical messages in the network at any given time) and give a copy to each of the two neighboring nodes with the highest delivery probabilities. Sensor nodes, then, can use other nodes to reach the Gateway. The Gateway can use one of several MAPs that will pass nearby to deliver their messages. The messages will eventually reach their destination, the Central Management and Monitoring Platform (MMS). The Distributed Forwarding Algorithm is shown in pseudo-code in Algorithm 1. Figure 6 shows the tables that the nodes will create to keep their availability and delivery probabilities to their neighbors and messages destinations. The number between parentheses in the availability table is the number of contacts with each node.
###### Abstract  
In electrode-supported solid oxide fuel cells (SOFCs) with a thin electrolyte, the electrolyte performance can be affected by its interaction with the electrode, therefore, it is particularly important to study the charge transport properties of thin electrode-supported electrolytes. The transport numbers of charged species in Ni-cermet supported Sr\({}_{0.98}\)Zr\({}_{0.95}\)Y\({}_{0.05}\)O\({}_{3-\delta}\) (SZY) membranes were studied and compared to those of the bulk membrane. SZY films of 2.5 um thickness were fabricated by the chemical solution deposition technique. It was shown that the surface layer of the films contained 1.5-2 at.% Ni due to Ni diffusion from the substrate. The Ni-cermet supported 2.5 um-thick membrane operating in the fuel cell mode was found to possess the effective transport number of oxygen ions of 0.97 at 550 \({}^{\circ}\)C, close to that for the bulk SZY membrane (0.99). The high ionic transport numbers indicate that diffusional interaction between SZY films and Ni-cermet supporting electrodes does not entail electrolyte degradation. The relationship between SZY conductivity and oxygen partial pressure was derived from the data on effective conductivity and ionic transport numbers for the membrane operating under two different oxygen partial pressure gradients--in air/argon and air/hydrogen concentration cells.  
Y-doped strontium zirconate; proton electrolyte; ionic transport number; proton transport number; thin-film electrolyte; conductivity +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
The results indicated that considering building geometry, CHTC models can accurately predict building energy demand, especially for high-rise buildings. [PERSON] et al. [23] carried out CFD and heat transfer simulations for high-rise buildings with and without the facade appurtenances, and concluded that local-CHTC variation and the effect of wind directions are nonnegligible for buildings with facade appurtenances. Additionally, attention should be paid to air infiltration caused by the airtightness of building envelopes during the improvement of thermal comfort and energy efficiency under different climates [24; 25]. For instance, the predicted annual energy-saving potential is 3-36% for office buildings in different climates zones of the U.S. [26], and 2.43-16.44 kWh/m\({}^{2}\) for dwellings in the Mediterranean climate area of Spain [27]. Since wind and buoyancy act simultaneously on air infiltration [28], wind pressure distribution on the exterior surface of the building should be considered in the energy efficiency of buildings in cold regions. Furthermore, room base temperature, which represents the hourly indoor air temperature without the air conditioning and heating system operating, is indispensable to assess the indoor thermal environment and the energy-saving potential of building envelopes [29; 30; 31]. The target building cluster faces weather characteristics distinctly different from those of the plains, which are located at the Haituuo Mountains of the Yanqing region in Beijing, China. Considering features like low temperature, high wind speed and uneven precipitation distribution, gabions may affect the thermal performance of the building by changing the wind speed on the exterior wall surface. Traditional studies mainly analyze the effect of gabions on building reinforcement and force [32; 33; 34], but there are few models for building thermal processes and performance analyses. Therefore, this paper establishes a building thermal process analysis model based on the gabion structure, focusing on the analysis of the heat transfer and airflow between a simulated gabion building and the external environment. The model was validated based on experiment results from the literature. Then, the influences on the CHTC, wind pressure, and air infiltration were analyzed. The room base temperature and the building heating load were simulated in DeST software by inputting CFD simulation results. This study could guide the thermal design of the new buildings and the energy-saving retrofits of existing buildings in cold regions with similar climates.
## 2 Problem Statement and Preliminaries
\right] \tag{4}\]  
where \(\mathbf{Z}\) represents the measurement vector; \(\mathbf{H}=\mathbf{I}_{3}\) represents the measurement transformation matrix; \(\mathbf{v}=\left[ \begin{array}{cc}\text{v}_{L}&\text{v}_{\lambda}&\text{v} _{h}\end{array} \right]^{T}\) is the measurement noise; \(\text{v}_{L}\), \(\text{v}_{\lambda}\), and \(\text{v}_{h}\) are the measurement noises on the three axes, respectively; \(\mathbf{R}\) is the measurement noise matrix; the measurement noise is assumed to follow the Gaussian distribution with zero mean and the covariance matrix \(\mathbf{R}\). **Remark 1:**_The tri-axis MEMS accelerometers, tri-axis MEMS gyroscopes, and tri-axis magnetometers are used to collect data._  
\[\Lambda=\left\{x_{1},x_{2},\ldots,x_{k}\right\} \tag{5}\]  
_where \(x_{k}\) is the sampled data at time \(k\). However, it is unavoidable that there will be outliers in the sampled data. In order to eliminate the outliers, linear smoothing is used to remove the outliers. First, the average absolute error \(\widetilde{Z}\) is derived by Equation (6):_  
\[\widetilde{Z}=\left.\left(\sum_{i=1}^{k-1}|z_{i}|\right)\right/k-1 \tag{6}\]  
_where the absolute error \(z_{i}\) is defined as follows:_  
\[z_{i}=x_{i+1}-x_{i},i=1,2,\ldots,k-1 \tag{7}\]  
**Criterion:**_The data \(x_{i+1}\) is retained if \(z_{i}<\widetilde{Z}\), or the outliers are replaced by a mean function \(z_{i}=\frac{\frac{2m}{2m}}{\left(\frac{2z_{i-m}+\ldots+z_{i-1}+z_{i+1}+\ldots+ z_{i+m}}{2m}\right)}\). Figure 1 shows the effect of data processing. The data comparisons (accelerometer, gyroscope, and magnetometer) between the pre-processing and processed data are shown in Figures 1-3._  
In order to quantitatively compare the processed data with the pre-processing data, the root mean square error (RMSE) is used here:  
\[\text{RMSE}=\sqrt{\sum_{i=1}^{k-1}\left(z_{i}-\widetilde{Z}\right)^{2}}\Bigg{/} (k-1) \tag{8}\]  
where Acce, Gyro, and Mag are the abbreviations for the acceleration sensors, gyroscope sensors, and magnetic sensors, respectively. Figure 1: Processing of sampled data (accelerometer). Figure 2: Processing of sampled data (gyroscope). The RMSE values obtained using data processing method are calculated in Table 1. It can be seen that the probability of the outliers decreased by 29.68% for the accelerometer, by 25.71% for the gyroscope, and by 32.89% for the magnetometer. The average absolute error shown in Equation (6) is as the criterion. The raw data is observed if the data meets this criterion, or else the raw data is thought of as the outlier and should be deleted. When the outlier is rejected, the points near the outliers are averaged to smooth the data. Therefore, it not only eliminates the outliers but also describes the accurate characteristics for the raw data. The proposed rejection method has better denoising performance.
## 2 Materials and Methods
### Genetic Programming (GP)  
Genetic Programming (GP) is an evolutionary algorithm that generates symbolic models to predict \(C_{c}\). It begins with a population of random equations and iteratively refines them using crossover, mutation, and selection. The fitness function is defined as follows:  
\[\text{Fitness}=\frac{1}{1+\text{MSE}} \tag{13}\]  
where MSE is the mean squared error between the predicted and actual \(C_{c}\). Operations such as crossover combine parts of two parent models:  
\[\text{f}_{\text{offspring}}=\alpha\text{f}_{\text{parent1}}+(1-\alpha)\text{f }_{\text{parent2}} \tag{14}\]  
Mutation introduces diversity by randomly altering parts of the equation. This process continues until convergence to an optimal model or a predefined termination criterion (e.g., maximum iterations).  
GP excels in capturing nonlinear relationships and produces interpretable equations for \(C_{c}\). However, it is computationally expensive compared to simpler methods.  
Figure 2 illustrates the basic workflow of GP, which is inspired by the principles of natural selection and evolution. The process begins with the generation of an initial random population of potential solutions, often represented as symbolic mathematical expressions or trees. Each individual in this population is evaluated, and a fitness score is assigned based on its ability to accurately model the target outcome (e.g., predicting the compression index, \(C_{c}\)). The selection function then identifies the most \"fit\" individuals, which are chosen to participate in the next generation. These selected individuals undergo genetic operations such as crossover (a recombination of parts from two parent solutions) and mutation (random alterations to introduce diversity), creating a new population with potentially improved solutions. The algorithm continues to iterate through these steps--selection, crossover, mutation, and fitness evaluation--until a specified termination condition is met, such as reaching a maximum number of generations or achieving an acceptable fitness level. Once the process concludes, the algorithm outputs the best-performing solution, representing the optimized predictive model.  
Figure 2: Workflow of GP for model optimization.
## 4 Model Application
### Results
#### 4.3.3 Impact of Maintenance and Repair Scenario  
Compared to the scenario discussed in Section 4.3.2, the assessment of the effects of failure and post-collision repair has been added to the replacement optimization model presented in Figures 7 and 8. The environmental profile of failure repairs is shown in Figures S3 and S4 of the Supplementary Materials, and various scenarios of post-collision repair are in Figure S5 (Supplementary Materials). The post-collision repair profile with the greatest environmental impact was used for the assessment. It was assumed that the post-collision repair forecast applies to an old car and one event. Optimization results of such a model indicate an acceleration of the decision to replace the car. The use of an old Ford Focus with a diesel engine should not be less than 47,500 vkm or 3.4 years and a petrol model should not be less than 48,900 vkm or 3.5 years.  
Figure 6: Replacement optimization model based on drive efficiency and maintenance of petrol Ford Focus vehicle.  
Figure 7: Replacement optimization model based on drive efficiency and Ford Focus diesel vehicle maintenance and repair.  
The results of the comparison show that in the short period of use, maintenance and repairs have little effect on changing the car replacement cycle.
## 3 Maintenance and Lifespan Management  
The maintenance of the future in the _SG_ suggests the use of devices with self-diagnosis, and the integration of new digital technologies, which will allow advanced diagnostics. Therefore, the lines of investigation placed in the area of Maintenance and Lifespan Management are:  
* Developing the Means that Enable Signals to be Digitally Acquired.
* Maintenance-based Asset Lifecycle Management.
* Advanced Decision-Making Systems.  
Next, the main contributions in each of the above mentioned research lines are presented.
## 5 Experiment and Results
### Evaluation of Level 1
#### 5.1.2 Average Waiting Time  
Figure 6 shows the results of our evaluation of the average waiting time when one street is congested. It is evident from the figure that the average waiting time for the vehicles changed according to changes in the inter-arrival time of vehicles. STL and ITMS yielded similar results for all the inter-arrival times. Compared to the traditional traffic light, these algorithms yielded better results when the inter-arrival time was greater than 2 s and less than 8 s. This implies that when the street is highly congested, STL and ITMS significantly reduce the average waiting times of vehicles.  
However, when two streets were congested, our experimental results demonstrate that STL performed better than ITMS when the inter-arrival time on both streets was less than 7 s, as shown in Figure 7. This implies that when two streets are highly congested, STL not only outperforms the traditional traffic light but also ITMS.  
Figure 5: Comparison of number of vehicles serviced by the traffic-light algorithms.  
The results shown in Figures 5-7 clearly indicate that STL performed better than the traditional method in terms of the number of vehicles serviced and the average waiting times for vehicles when the streets were highly congested. These results also indicate that STL performed very closely to ITMS in terms of the number of vehicles serviced and the average waiting times of vehicles when a single street was highly congested. However, STL outperformed ITMS in reducing the average waiting times for vehicles when two streets were highly congested. This is because ITMS keeps the street open until all the cars have passed, which affects the congestion of the other street. In contrast, our algorithm distributes the time equally between the two streets. In other words, a more fair distribution of timing between the two streets takes place in our algorithm, which is the reason for its better performance in reducing the average waiting time.
## 6 Epigenetic Regulation of CMT
### Studies Using Drosophila Models
#### 6.2.2 Histone Modifications  
The human SLC25A46 protein is involved in mitochondrial dynamics by regulating the association of mitochondria with the endoplasmic reticulum and/or by controlling the oligomerization of MFN1 and MFN2 [155; 156]. Mutations in the _SLC25A46_ gene are associated with the so-called mitochondrial diseases that are sometimes classified as CMT type 2, optic atrophy, and Leigh syndrome [157; 158]. The diverse symptoms of diseases caused by mutations in _SLC25A46_ may be related to the dysregulation of epigenetic mechanisms. There are two homologues of human _SLC25A46_ in _Drosophila_, designated as _dSLC25A46a_ (_CG8931_) [159] and _dSLC25A46b_ (_CG5755_) [160]. _Drosophila_ models targeting _dSLC25A46a_ or _dSLC25A46b_ exhibited similar phenotypes such as locomotive defects and aberrant morphology of synapses at the NMJ [159; 160]. Gene Expression Omnibus (GEO) analysis revealed that HDAC1 is associated with several _SLC25A46_ genomic regions in human cultured CD4-positive cells, and this association was further evaluated using the _Drosophila_ models [161]. Rpd3, a _Drosophila_ HDAC1/HDAC2 homologue, was found to regulate the histone H4K8 acetylation state in the _dSLC25A46b_ genomic regions [161]. Depletion of _Rpd3_ effectively rescued the locomotive defects and morphological deficits of synapses at the NMJ induced by _dSLC25A46b_ knockdown [161]. These studies using _Drosophila_ models suggest that HDAC1 is involved in the pathogenic process of CMT.
## 4 Experimental Results and Analysis
### Modbus AUDP Gateway Analysis
The calculated normalized results for item \(P_{24}\) are \[X_{i,P_{24}}^{\text{norm}}=\{0.4286,\ 1.0000,\ 0.8571,\ 0.3333,\ 0.3809,\ 0\}.\]From (1), obtain the information entropy \(e_{j}\) of item \(P_{24}\) and the proportion \(f_{ij}\) of \(P_{24}\) at different \(N_{\max}\); the results are \[f_{ij}=\{0.1429,\ 0.3333,\ 0.2857,\ 0.1111,\ 0.1270,\ 0.0001\},\] so \[e_{j}=-\frac{1}{\ln n}\sum f_{ij}\ln f_{ij}=0.8532.\] (3) According to Equation (21), \[d_{j}=1-e_{j}=0.1468,\quad w_{j}=\frac{d_{j}}{\sum_{j=1}^{n}(1-e_{j})}=20.55\%.\] The same applies for \(P_{45}\), \(\pi_{3}\), \(\pi_{4}\), \(\pi_{5}\), and \(\pi_{0}+\pi_{1}+\pi_{2}+\pi_{3}\), and the calculation results are given in Table 2.  
Finally, compute the comprehensive score for each \(N_{max}\) via weighted summation using the following formula:  
\[S_{i}=\sum w_{j}\times x_{ij}^{\rm nom} \tag{25}\]  
From Table 3, the highest score is obtained when A = 2. Therefore, the maximum retransmission count in the gateway application layer is set to \(N_{max}\) = 2.
# Detection of Induced GNSS Spoofing Using S-Curve-Bias
###### Abstract  
In Global Navigation Satellite System (GNSS), a spoofing attack consists of forged signals which possibly cause the attacked receivers to deduce a false position, a false clock, or both. In contrast to simplistic spoofing, the induced spoofing captures the victim tracking loops by gradually adjusting it's parameters, e.g., code phase and power. Then the victims smoothly deviates from the correct position or timing. Therefore, it is more difficult to detect the induced spoofing than the simplistic one. In this paper, by utilizing the dynamic nature of such gradual adjustment process, an induced spoofing detection method is proposed based on the S-curve-bias (SCB). Firstly, SCB in the inducing process is theoretically derived. Then, in order to detect the induced spoofing, a detection metric is defined. After that, a series of experiments using the Texas spoofing test battery (TEXBAT) are performed to demonstrate the effectiveness of the proposed algorithm.  
global navigation satellite system (GNSS); induced spoofing; S-curve-bias (SCB); Texas spoofing test battery (TEXBAT) +
Footnote †: journal: Sensors