esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Experiments and Discussions
### Performance Index
#### 3.3.3 Time Cost  
TTA is another important performance index, as shown in Table 1. The VA-RAIM takes an average of 10 ms in the simulation, since it only contains some fundamental matrix operations except image processing. In real applications, the image processing will be a major part of the time cost. Thus, some efficient feature detection methods would be applied for VA-RAIM to meet the TTA requirement, e.g., scale invariant feature transform (SIFT), which has been proved to be very effective and low time cost  
Figure 8: (**a**) The result of HPL with 1 pixel DE; (**b**) The result of HPL with 1 pixel DE.  
for object tracking. For example, the time cost of SIFT for an image with size (pixel \(\times\)pixel) of 256 \(\times\)256 and 441 \(\times\)552 are 1.7 ms and 4.4 ms, respectively [34].
## 6 Experimental Results
### SMT-Solvers  
Given a quantifier-free first-order formula with variables defined in Section 5, the objective is to find an assignment for the variables that makes the formula satisfiable. Solving the SMT problem becomes particularly useful when restricted to specific logical theories where existing tools, known as SMT solvers, are employed to find solutions. The most notable solvers include Z3 [50], Yices2 [51], and CVC4 [52].  
For a given input, which is a conjunction of two quantifier-free first-order formulae, it is denoted as \(\varphi\), the SMT solver has three possible outcomes: sat: when the input \(\varphi\) is satisfiable; unsat: when the input \(\varphi\) is not satisfiable; unknown: when it is indeterminate whether \(\varphi\) is sat or unsat, as this determination exceeds the capabilities of the decision procedures.  
Our choice for the Z3 SMT solver is motivated by several reasons. Z3 is used on an industrial scale; many experimental results for different systems, including MAS and timed automata, show that for reachability checking, Z3 gives the best results [34]. Z3 can also be trusted: when it answers sat, then it produces a witness (an assignment of variables) that makes the formula satisfiable; when it answers unsat, it produces proof that the formula is not satisfiable.
## 1 Introduction
However, dynamic models of Rs that couple the combined effects of plant functional traits and soil abiotic factors (SWC, ST, and soil properties) are rarely reported [22; 36]. Rs exhibits scale dependence in space and has different characteristics at different scales, thus showing a spatial scale effect [40; 41; 42; 43]. Spatial scale is one of the basic problems in ecological research and is usually expressed by spatial extent and spatial grain [44]. Spatial extent describes the overall coverage area of the research object. Spatial grain refers to the sampling length and area represented by the smallest identifiable unit, such as quadrat or pixel [45; 46; 47]. The determination of spatial scale is closely related to the nature and requirements of the research problem, which determine the sampling points, the design density and workload of the grid, and the investment of funds. Therefore, the nested sampling design method can be used to explore the spatial scale effect of Rs by setting plots with different grain sizes in the same region [48; 49], which can effectively reveal the distribution pattern and potential mechanism of Rs. Tugai forest, a natural plant community composed of desert plants, is widely distributed along the lakeside and supply rivers of the Ebiurr Lake Basin in northwest China [50]. As the only forest community that forms naturally in arid deserts, Tugai forestscan not only endure drought but can also adapt to saline soil. Tugai forest plays an important role as a windbreak and in sand fixation, water conservation, and biodiversity maintenance [51; 52]. This community is a valley forest dominated by arbor species and associated shrubs and herbs distributed along rivers in arid deserts. With the increase of distance from rivers, this type of community presents an obvious water gradient and community structure change [53], which provides an opportunity to identify the driving factors of Rs variability at different scales. Ecosystems such as the desert Tugai forest in the Ebinur Lake Basin are often highly sensitive to global climate change and play an important role in the carbon cycle in arid areas [54]. Thus, a study was designed to understand the variation pattern of Rs and its biotic and abiotic regulatory factors in the desert Tugai forest at different water gradients in the Ebinur Lake Basin. We assume that biotic and abiotic factors are different along the water gradient zone, and their regulatory mechanisms on Rs also differ. The objectives of this study were: (1) to quantify and compare the variability of Rs at different spatial scales, and (2) to evaluate the effects of biotic and abiotic factors on Rs at different spatial scales.
###### Abstract  
Eutectic solvents are currently being proposed as useful chemicals for enhanced oil recovery (EOR). In this work, for the first time, the preparation of eutectics based on surfactants and polymers was proposed for this application. These chemicals can be tailored to offer the most desired properties for oil recovery: water/oil interfacial tension reduction and increase of the aqueous phase viscosity, while concomitantly facilitating their handling due to their liquid character at ambient conditions. Sodium bis(2-ethylhexyl)sulfosuccinate (AOT) and polyethylene glycol (PEG) of three different molecular weights (namely 600, 1000, and 2000 g/mol) were paired in a search for eutectic behaviors. Melting temperatures for all the systems were determined by differential scanning calorimetry. The most promising combination was AOT + PEG-600, which exhibited a melting point of 275 K and thermal stability up to 473 K at a 40:60 molar ratio. A promising value of 5.1 \(\times\) 10\({}^{-2}\) mN/m was obtained for the interfacial tension between the optimized formulation and crude oil. The formulation was tested in core-holder experiments to extract oil from a sandstone rock at room temperature, achieving an encouraging 34% of additional oil recovery after the secondary extraction.  
eutectic; AOT; PEG; EOR Article  
AOT + Polyethylene Glycol Eutectics for Enhanced Oil Recovery  
[PERSON], [PERSON], [PERSON] and [PERSON]  
CRETUS (Cross-Research in Environmental Technologies), Department of Chemical Engineering, Universidade de Santiago de Compostela, E-15782 Santiago de Compostela, Spain; [PERSON]MAIL_ADDRESS] (A.S.); [EMAIL_ADDRESS] (H.R.)  
*Correspondence: [EMAIL_ADDRESS]
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
## 5 Experimentation
### Results and Discussion
#### 5.2.1 Throughput and Jitter  
The average throughput and jitter recorded on the mobile device over the time during the experiment plotted by UDP/TCP packets is depicted in Figure 7. These results are collected using local Linux logging tools, runnable also on Android (i.e., iptraf and trafshow). It can be seen that the UDP throughput is higher than the TCP throughput. Indeed, the TCP protocol uses connected mode, and it is highly optimized to make reliable use of the link. Therefore, this decreases the throughput and increases the jitter compared to UDP because of the handshake mechanism for the pre-/post-connection process. However, UDP is used for real-time data (e.g., voice and video over IP) and recommended for high-latency links.  
Now, with regards to the hop counts, UDP and TCP throughput averages within one hop are 38.8 and 32.71 Mb/s, respectively. Throughput decreases when the hop count increases to reach 18.47 and 9.87 Mb/s for UDP and TCP, respectively, within three hops. According to the authors of [33], a minimum data rate of 10 Mb/s is required for audio, medical imaging and video and hundreds of kbps for other WBAN applications. It is perceived that CROW\({}^{2}\) achieved a real throughput higher than the data rate requirements. It is also important to note that the throughput is expected to decrease significantly starting from four hops based on the behavior shown in Figure 7. The average throughput reduction is accompanied by jitter increase. Recorded jitter values increase also following the same pattern as the throughput. It is important to note here that the maximum accepted jitter for the video streaming application must be less than 40 ms according to [34] and under 30 ms according to Cisco for interactive video (video-conferencing) [35]. Indeed, jitter reaches 9.227 ms with TCP mode within three hops, which stays under the limits of the use of video-streaming. According to the results of throughput and jitter, we conclude that the recommended hop count that guarantees throughput for audio/video streaming and files (i.e., photos, reports, etc.) might be less than or equal to three hops, according to the standard definition video (3 Mb/s). The CROW\({}^{2}\) system assures an acceptable throughput and jitter for routes less than or equal to three hops with regards to the required thresholds cited above.
# Device Reliability Analysis of NNBI Beam Source System Based on Fault Tree
## 5 Fault Tree Analysis
### Quantitative Calculation
#### 5.6.1 Probability of Top Event Occurrence  
The probability of top event occurrence is one of the critical metrics for evaluating system reliability, directly reflecting the likelihood of system failure under specified operating conditions. In this study, the AutoFTA 3.9 software was employed to model and calculate the failure rates for each subsystem. Based on the collected data, statistical analysis shows that it follows a Poisson distribution (Formula (2)).  
\[P(X=k)=\frac{e^{-\lambda}\lambda^{k}}{k!} \tag{2}\]  
where \({}^{\prime}P(X=k)^{\prime}\) is the probability that the event occurs exactly \(k\) times within a specified interval, \({}^{\prime}\lambda^{\prime}\) is the average number of occurrences of an event within a unit interval. \({}^{\prime}e^{\prime}\) is the natural constant. \(k!\) is the factorial of \(k\).  
For the number of faults \(n\) that follows a Poisson distribution, the normal approximation method can be used for analysis, and its 95% confidence interval is \([n-1.96\sqrt{n},n+1.96\sqrt{n}]\) (1.96 is the critical value for the 95% confidence level of the normal distribution, and \(\sqrt{n}\) is the standard deviation of the Poisson distribution).  
In summary, the 95% confidence intervals of each subsystem are as follows: the confidence interval for faults in the negative ion accelerator is \([4.84\times 10^{-3}\),\(1.02\times 10^{-2}]1/\)h, the confidence interval for faults in the plasma expansion chamber is \([4.22\times 10^{-3}\),\(9.33\times 10^{-3}]1/\)h, and the confidence interval for faults in the radio frequency system is \([2.63\times 10^{-3}\),\(6.91\times 10^{-3}]1/\)h. After calculation, the confidence interval for faults in the NNBI beam source system is 1/h.  
The formula for deriving the mean time between failures (_MTBF_) [25; 26] from the failure rate is given as Formula (3) [19; 26]:  
\[MTBF=\int_{0}^{\infty}R(t)dt \tag{3}\]  
where \(R(t)=e^{-\lambda t}\), so: \(MTBF=\int_{0}^{\infty}R(t)dt=\frac{1}{\lambda}\).  
In summary, the 95% confidence interval for the mean time between failures (_MTBF_) of the beam source system is [42.6; 68] h [27].
## 2 Method  
### Method of Acquiring the Data with the GPS Technique  
In order to effectively acquire the monitoring data of dam deformation, the reference points, working basis points and deformation observation points are selected according to the \"GPS standard\", and this three-level monitoring network is set up. The layout requirements are exhibited in Table 1. During automated monitoring, the observation points receive GPS signals in real time, and send the data to the control center through the communication network, as exhibited in Figure 1. The control center is able to utilize the corresponding software to calculate the three-dimensional coordinates of each monitoring point in real time, so as to obtain the deformation of the dam.  
Figure 1: Schematic diagram of the GPS positioning.
# Privacy-Preserved Image Protection Supporting Different Access Rights
## 3 Proposed Scheme
### Image Management Method  
The cloud server can provide privacy-preserved image retrieval without knowing the data privacy. The user \(U_{i}\) (\(U_{i}\in C_{w}\)) sends a request to the cloud server to ask to see an encrypted image \(EI\). The cloud server can determine whether the user has rights to access this image or not according to the access policy \(T\). The user \(U_{i}\) has the access rights if \(C_{j}^{\prime}<C_{w}\) in \(T\) and then the cloud server will send \(EI\), \(IM\), \(AI\) and \(\delta\) to \(U_{i}\). Since the image manager sends \(PT_{j}\)\(=\)\(\{C_{r}\mid C_{r}\leq C_{j}\}\), \(DK_{j}\), and \(\{e_{r}\mid C_{r}\leq C_{j}\text{ and }C_{r}\in\{C_{1},C_{2},\ldots,C_{\alpha}\}\}\) to user \(U_{i}\in C_{j}\) through a secure channel in the initialization phase of the image protection method, \(U_{i}\) can execute the following steps to recover the original content of accessible blocks.  
Step 1. For each accessible \(C_{j}^{\prime}\) in \(EI\), \(U_{i}\) computes \(SK_{j}^{\prime}=DK_{w}^{C_{j}^{\prime}\in\prod\limits_{T_{w}=\{C_{j}^{\prime}\}} c_{j}^{\prime}}\mod\!m\) according to \(PT_{w}\); Step 2. For each accessible \(C_{j}^{\prime}\) in \(EI\), \(U_{i}\) uses \(Code_{j}\) and \(IM\) to find the region of the authorized pixels in \(EI\); Step 3. For the region of the authorized pixels of \(C_{j}^{\prime}\), \(U_{i}\) uses \(SK_{j}^{\prime}\) to decrypt the encrypted pixels to recover the original content.
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 5 Experiments and Analysis
### Analysis of Experimental Results
#### 5.5.2 Clustering Performance Analysis  
In this subsection, the \(k\)-means method was used for the clustering task. To reduce the randomness of initialization in \(k\)-means, we report the mean of 10 experiments. First, the effect of different parameters on the performance of the proposed method was tested. The experimental results of each balance parameter (i.e., \(\alpha\) and \(\beta\)) on the clustering of the RMFRASL method are shown in Figure 7. The results show that the influences of the balance parameters on the performance of our method were similar to those for the classification experiments. Then, after setting the optimal parameter settings for RMFRASL in Table 8, the clustering results on ACC and NMI were as shown in Tables 9 and 10, consistent with the classification experiments.  
Figure 6: Accuracy rate of each database in different dimensions: (**a**) AR; (**b**) CMU PIE; (**c**) Extended YaleB; (**d**) ORL; (**e**) COIL20.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Databases** & **ACC\%** & **NMI\%** & **Parameter \{\(\alpha\), \(\beta\)**\}** & **Dimension \(r\)** \\ \hline AR & 34.33 \(\pm\) 1.33 & 65.56 \(\pm\) 1.04 & \{0.01, 1\} & 380 \\ CMU PIE & 33.03 \(\pm\) 0.90 & 56.66 \(\pm\) 0.97 & \{0.01, 1\} & 480 \\ Extended YaleB & 34.42 \(\pm\) 1.16 & 57.26 \(\pm\) 1.87 & \{0.1, 0.01\} & 350 \\ ORL & 61.75 \(\pm\) 2.42 & 78.78 \(\pm\) 1.24 & \{0.01, 0.1\} & 270 \\ COIL20 & 59.90 \(\pm\) 1.56 & 72.56 \(\pm\) 1.07 & \{0.01, 1\} & 240 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The optimal combination of parameters and dimension on the clustering task.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AR** & **CMU PIE** & **Extended YaleB** & **ORL** & **COIL20** \\ \hline LSFS & 29.89 \(\pm\) 1.37 & 26.88 \(\pm\) 0.94 & 32.82 \(\pm\) 0.93 & 55.57 \(\pm\) 2.65 & 53.42 \(\pm\) 2.77 \\ RNEFS & 30.37 \(\pm\) 1.34 & 28.13 \(\pm\) 0.59 & 33.99 \(\pm\) 0.60 & 59.79 \(\pm\) 2.22 & 55.67 \(\pm\) 1.99 \\ USFS & 31.00 \(\pm\) 1.17 & 28.50 \(\pm\) 1.59 & 33.39 \(\pm\) 0.90 & 57.79 \(\pm\) 2.15 & 58.73 \(\pm\) 3.66 \\ CNAFS & 31.10 \(\pm\) 1.67 & 30.38 \(\pm\) 1.38 & 34.33 \(\pm\) 0.77 & 57.53 \(\pm\) 0.00 & 58.27 \(\pm\) 3.67 \\ NNSAFS & 31.10 \(\pm\) 1.70 & 31.32 \(\pm\) 1.43 & 33.76 \(\pm\) 0.62 & 57.79 \(\pm\) 5.01 & 58.55 \(\pm\) 3.25 \\ RSR & 32.91 \(\pm\) 1.22 & 32.17 \(\pm\) 1.42 & 33.52 \(\pm\) 0.80 & 58.79 \(\pm\) 3.52 & 55.77 \(\pm\) 1.65 \\ SPNFSR & 33.13 \(\pm\) 0.80 & 32.07 \(\pm\) 1.01 & 33.57 \(\pm\) 0.91 & 59.57 \(\pm\) 2.85 & 57.10 \(\pm\) 3.46 \\ RMFRASL & 34.33 \(\pm\) 1.33 & 33.03 \(\pm\) 0.90 & 34.42 \(\pm\) 1.16 & 61.75 \(\pm\) 2.42 & 59.90 \(\pm\) 1.56 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The best ACC% and STD of different algorithms on the five databases.  
Figure 7: The clustering accuracy (ACC) of different parameter values: (**a**) parameter \(\alpha\); (**b**) parameter \(\beta\).
## 4 Algorithms
### Local Training and Encryption at IoT Device  
Each IoT device trains a local model using its personal dataset, \(D_{i}\), through multiple epochs of gradient descent. The local model, \(w_{i}\), is initialized with a global model, \(w\), and iteratively updated using the computed gradients, \(g_{i}\). Once training is complete, the updated model is encrypted with the Salsa20 encryption algorithm using an encryption key, \(k_{i}\), to ensure data privacy before transmission. The encrypted model, \(c_{i}\), is then sent to the corresponding edge node for further aggregation.  
Subsequently, we systematically introduce the computational complexity of Algorithm 1 regarding the local training procedure and the subsequent encryption. During each local epoch, the device iterates over its local dataset \(D_{i}\), computing gradients \(\
abla f(w_{i};D_{i})\) and updating the model parameters \(w_{i}\). The time complexity of these operations depends on thenumber of training samples, input dimensionality, and gradient computation complexity. In practice, gradient evaluation often scales approximately linearly with the dataset size, and updating the model through gradient descent typically results in a time complexity on the order of \(O(E\cdot N\cdot d)\), where \(E\) denotes the number of epochs, \(N\) the number of local samples, and \(d\) the feature dimension. For the space complexity, storing the model parameters and intermediate variables (e.g., gradients) typically incurs memory requirements that scale at least linearly with the number of parameters. As the complexity of the model increases (e.g., with deeper neural networks or higher-dimensional feature spaces), its memory footprint also grows. Subsequently, the encryption step using Salsa20, a well-established stream cipher, introduces only linear time complexity for the size of the data being encrypted. Since Salsa20 operates by applying a series of simple arithmetic operations (e.g., addition, XOR, and rotation) over fixed-size blocks, the complexity of encrypting the model parameters \(w_{i}\) is effective \(O(L)\), where \(L\) is the length of the parameter vector.  
```
1:Input: Local data \(D_{i}\) at device \(i\), initial model \(w\), encryption key \(k_{i}\)
2:Output: Encrypted model update \(c_{i}\)
3:\(w_{i}\gets w\) [Initialize local model]
4:for each local epoch \(e=1,\ldots,E\)do
5:\(g_{i}\leftarrow\
abla f(w_{i};D_{i})\) (Compute gradient using local data)
6:\(w_{i}\gets w_{i}-\eta g_{i}\) [Update local model using gradient descent]
7:endfor
8:\(c_{i}\leftarrow\text{Salsa20}(w_{i},k_{i})\) [Encrypt updated model using Salsa20]
9: Send encrypted update \(c_{i}\) to edge node
```  
**Algorithm 1** Local Model Training and Encryption at IoT Device
## 2 Collation and Analysis of Measured Data of Temperature and Wind
### Probability Model Fitting of Wind Speed and Direction
#### 2.3.1 Fitting Method  
According to previous studies, it can be considered that there are three probability models for wind speed distribution [18]: Gumbel distribution probability, Frechet distribution probability and Weibull distribution probability, and the expression of probability density function is as follows:  
\[\text{Gumbel}:\ y=\frac{1}{A}e^{\frac{B-x}{A}}e^{[-\frac{B-x}{A}]} \tag{3}\]  
\[\text{Frechet}:\ y=\frac{C}{A}e^{[-(\frac{x}{A})^{-C}]}(\frac{x}{A})^{-1-C} \tag{4}\]  
\[\text{[PERSON]}:\ y=\frac{C}{A}e^{[-(\frac{x}{A})^{C}]}(\frac{x}{A})^{C-1} \tag{5}\]  
In the formula, \(A\), \(B\) and \(C\) are scale parameter, position parameter and shape parameter, respectively, which can be fitted according to the principle of least square method.  
In order to determine the wind speed distribution under all wind directions, the wind speed distribution law under discrete wind direction can be determined first, and then the distribution parameters under discrete wind directions can be fitted to obtain the wind speed distribution probability under continuous wind direction. Reference [19] assumes that the variation of wind direction density function \(f\) and wind speed fitting parameters \(A\), \(B\) and \(C\) on the circumference of wind direction satisfies harmonic function.  
The expressions are as follows:  
\[\begin{split} f(\theta)&=c^{f}+\sum\limits_{m=1}^{n_ {f}}d_{m}{}^{f}\cos(m\theta-e_{m}{}^{f})\\ A(\theta)&=c^{A}+\sum\limits_{m=1}^{n_{A}}d_{m}{}^ {A}\cos(m\theta-e_{m}{}^{A})\\ B(\theta)&=c^{B}+\sum\limits_{m=1}^{n_{B}}d_{m}{}^ {f}\cos(m\theta-e_{m}{}^{B})\\ C(\theta)&=c^{C}+\sum\limits_{m=1}^{n_{f}}d_{m}{}^ {f}\cos(m\theta-e_{m}{}^{C})\end{split} \tag{6}\]  
where \(f(\theta)\) is the frequency of occurrence of any wind direction interval obtained by harmonic fitting; \(A(\theta)\), \(B(\theta)\), \(C(\theta)\) are the distribution function; \(c\), \(d_{m}\), \(e_{m}\) are the harmonic function parameters; and \(n_{f}\), \(n_{A}\), \(n_{B}\), \(n_{C}\) are the harmonic function order.  
After obtaining the probability density function of the wind speed and direction distribution, the distribution function of the joint probability distribution of the wind speed and direction at the bridge site can be obtained by integration:  
\[P(\theta,x)=\iint f(\theta)\cdot y_{\theta}(x)dxd\theta,\theta\in(0,2\pi) \tag{7}\]  
In the formula, \(f(\theta)\) is the probability density function of wind direction distribution fitted by Formula (6), and \(y_{\theta}(x)\) is the probability density function of wind speed under wind direction angle \(\theta\); The parameters \(A(\theta)\), \(B(\theta)\), \(C(\theta)\) in \(y_{\theta}(x)\) are fitted by Formula (6), which are functions of wind direction angle \(\theta\).