esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on the Performance of Concrete Containing Non-Potable Water
## 5 Hardened Properties  
### Compressive Strength  
#### 5.1.1 Effect of Untreated WW  
The effect of untreated WW on compressive strength was reported in 8 studies [10; 16; 18; 21; 24; 26; 29; 30], as shown in Figure 6. In general, an approximate 0-20% decline in compressive strength was observed when potable water was replaced with untreated WW (non-potable). [PERSON] et al. [18] studied the compressive strength of concrete with untreated WW, and they reported a decrease in early-age strength because of the presence of high amounts of organic matter. [PERSON] et al. [10] reported that compressive strength decreased by 30% at full replacement of potable water with untreated WW. They blamed this reduction in compressive strength on the presence of high amounts of organic matter. Similarly, [PERSON] [30] reported a decline in early-age compressive strength due to the presence of high amounts of organic matter in untreated WW. Moreover, the strength gap between potable water and untreated WW concrete decreased as the age of curing increased beyond 28-days. It was also reported that treated WW concrete had higher strength than untreated WW concrete due to fewer impurities [30]. Some studies [9; 24] reported an increase of 9-44% in the early-age strength with the use of untreated WW due to the presence of salts.  
Figure 5: Effect of the total solid concentration on the setting time of concrete.
## 4 Analysis of the Measured Fluctuating Wind Speed Data  
### Mean Wind Speed and Wind Direction  
Relevant 2D data on wind speed and direction are collected using R.M. Young 05103V-type propeller anemometers (R. M. Young Company, Michigan, US). The original data of high-rises A, B, and C are segmented into 146, 109, and 146 sub-samples based on the mean time interval of 10 min. Mean wind speed \(U\) and wind direction \(\theta\) are given in Figure 5.  
The mean wind speed and direction measured by the three anemometers were close, and the wind direction was discrete when the wind speed was low. The mean wind speed has a lower value 18 h before the typhoon landed, and it slowly fluctuates and increases. The wind direction was stable 6 h before the typhoon landed and the landing point of Typhoon Meranti (2016) was Xiamen. Northern hemisphere tropical cyclones rotate counter clockwise, and the wind direction slowly changed from northeast to north due to the landing location of the typhoon. The variation in the measured wind direction was consistent with this condition. As shown in the diagram, the maximum mean wind speeds measured on high-rises A, B, and C were 27.2, 15.6, and 28.9 m/s, respectively, with the mean wind directions ranging from 20\({}^{\circ}\) to 70\({}^{\circ}\).
# A Framework to Quantify the Quality of Source Code Obfuscation
## 4 Approach
### Cost  
To measure Cost, we assess the \"effect on program performance\". The greater the complexity of source code obfuscation, the more it can impair the program's performance. Specifically, obfuscation techniques result in runtime overhead, memory overhead, and increased file size. Consequently, we conduct performance measurements by compiling the obfuscated source code into binary files. To quantify cost, we employ measurement indicators such as program execution time, process memory usage, and program size.  
To assess the quality of obfuscation, we have established 12 measurement indicators for measuring each category: Potency, Resilience, and Cost. We employ 12 indicators to illustrate the differences between the obfuscated and original codes. Furthermore, we have developed an obfuscation quality quantification framework that accepts source code as input, applies 12 indicators during compilation, and produces quantified results as output.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Categories** & **Measurement Indicators** & **Description** \\ \hline \multirow{4}{*}{Potency} & McCabe Cyclomatic Complexity & [Number of Edges] – [Number of Nodes] + 2 \\ \cline{2-3}  & Control Flow Graph Size & Number of Nodes \\ \cline{2-3}  & Control Flow Depth & The maximum number of Edges it takes to get from one Node to another Node \\ \cline{2-3}  & Program Length & Lines of Code (LoC) in source code \\ \cline{2-3}  & Instruction Count & Number of Instructions \\ \hline \multirow{4}{*}{Resilience} & Symbolic Execution Time & Time taken by the Symbolic Execution tool to complete the analysis \\ \cline{2-3}  & Code Coverage & Percentage of instructions for which the Symbolic Execution tool \\ \cline{2-3}  & Code Coverage & performed analysis among all instructions \\ \cline{2-3}  & Static Analysis Time & Analysis time of Static Analysis tools \\ \cline{2-3}  & Code Optimization & Percentage of instructions optimized by Code Optimization tools \\ \hline \multirow{3}{*}{Cost} & Time Overhead & Run time of compiled binary \\ \cline{2-3}  & Space Overhead & Process memory usage, including \_data sections,.text sections, etc. \\ \cline{2-3}  & File Size & Size of binary file \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of measurement indicators for quantifying obfuscation quality.
# Anomaly Detection and Early Warning Model for Latency in Private 5G Networks
## 3 Proposed Methods
### Anomaly Detection
```
0: Dataset \(x\), label \(y\)
0: Encoder \(f_{\theta}\), decoder \(g_{\sigma}\), classification \(u_{\phi}\)
1:\(\theta,\sigma,\phi\leftarrow\) Initialization parameters;
2:repeat
3:\(z=f_{\theta}(\mathbf{x})\);
4:\(\hat{\mathbf{x}}=g_{\sigma}(\mathbf{z})\);
5:\(\hat{\mathbf{{E}}}_{\mathbf{x}}=\sum_{i=1}^{n}||x_{i}-\hat{\mathbf{x}}_{i}||^ {2}\);
6:\(L=\frac{1}{N}\sum\limits_{i}L_{i}=\frac{1}{N}\sum\limits_{i}-[y_{i}\log p_{i} +(1-y_{i})\log(1-p_{i})]\);
7:\(\theta,\sigma,\phi\leftarrow\) Update parameters according to combination of \(\mathbf{E}_{\mathbf{x}}\) and \(L\);
8:until Convergence of parameters \(\theta,\sigma,\phi\)
```  
**Algorithm 1** The ConvAE-Latency model training algorithm  
As shown in Algorithm 2 and Figure 3, the ConvAE-Latency model is implemented as follows:  
Figure 2: The network architecture of the ConvAE-Latency model. ```
0: Dataset \(\mathbf{x}\), threshold k
0: The results of anomaly detection
1: Pre-training ConvAE-Latency model obtained from Algorithm 1;
2:\(\mathbf{\hat{x}}=g_{\sigma}(f_{\theta}(\mathbf{x}))\) Calculate reconstruct result;
3:\(\mathbf{E_{x}}=\sum_{i=1}^{n}||\mathbf{x}_{i}-\hat{\mathbf{x}}_{i}||^{2}\) Calculate reconstruction error;
4:if\(\mathbf{E_{x}}>\mathbf{k}\)then
5:\(\mathbf{x}\) is an anomaly;
6:else
7:\(\mathbf{x}\) is not an anomaly;
8:endif
```  
**Algorithm 2** The ConvAE-Latency model based anomaly detection algorithm
## 5 Support for Firmware, Software and Middleware  
As it was explained above, the SensoTube architecture can ensure the development of WSAN firmware applications either in a distributed single-master MCU, or in a multi-master collaborative mode. The proposed particular expansion mechanisms facilitate the use of all the popular development tools, such as programmers and debuggers, for any possible MCU. Moreover, the firmware can be remotely maintained and managed with the use of programming and upgrade over-the-air (OTA) techniques, which can be implemented without intervention on the WSAN system. Regarding the development of the MCUs' firmware, the designers and the developers are free to use the software tool chains of their choice.  
Except from the firmware, a WSAN system may include the software development of particular PC software applications for either the in-house testing of the system, or for the rapid control prototyping (RCP) (e.g., by the use of Matlab, or LabVIEW software development suites [63]), or for the implementation of the final application for the operation and administration of the system. A SensoTube-based WSAN system completely supports these three tasks by the use of its wireless and wired data interconnection. For the RCP, in particular, any ARM-based SensoTube shield, and through the use of a PDL shield, can enable the hardware-in-the-loop technique of Matlab/Simulink. In other words, the developer can build, execute, and test ARM-based MCUs' firmware using the Matlab platform.  
In cases of medium up to very large-scale WSAN deployments, the software application development invokes middleware [91]. Towards this direction, several promising methodologies have been reported by the research community. Domain-specific modeling languages based on the Model-driven Engineering (MDE) approach [151] to describe the application, the middleware of systems' virtualization [152], are only an indication of the current research trends in this WSAN software aspect. In particular, as it is pointed out in [153], it is required for the application and services to be decoupled from the WSAN, i.e., the wireless networking technical operations. In addition, as it is enunciated in [154], the implementation of a substantial middleware would require a layered architecture, through which the overall system could be decomposed into specific modules (layers). Therefore, the abstraction of SensoTube architecture, with the foundation of the seven functional layers appears to be particularly convenient for the development of the middleware. Specifically, the existence of local intelligence in the MCU-based functional shields, together with the inherent support for multi-processor distributed logic, can support the WSAN sub-system's modeling [155], and allows for the development of comprehensive and substantial libraries of APIs.
## 5 Evaluation
### Deployment Modes of Sink Nodes  
Many research works have focused on how to deploy sink nodes at optimal locations, so that the resulting network could cover a relatively larger distance. In our opinion, it is more practical to improve the network's topology after the sink nodes are deployed using a routing protocol. Transmission power control for MSCWSNs has a significant effect on the percentage of network coverage. In this section, we will illustrate the coverage rate performance of MSCWSNs-PC by using geometric figures. Without loss of generality, we generally not only consider the fixed deployment of sink nodes, but also randomly deployed sinks.  
Figure 11: The radio range of sink nodes with power allocation. (a) sink fixed deployment. (b) sink deployment randomly.  
Figure 11 shows that whether sinks are fixed or randomly deployed, both conditions can obtain a satisfactory percentage of network coverage. The network is more regular in fixed mode [seen in Figure 11(a)], but more easy to deploy in random mode [as seen in Figure 11(b)].
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 4 Experiments
### Datasets
#### 4.1.2 VisA  
VisA [3] consists of 10,821 images. There are 9621 normal and 1200 abnormal images. VisA contains 12 subsets, each corresponding to one class of objects. We assigned 90% of the normal images to the training set, whereas 10% of the normal images and all anomalous samples were grouped as the test set.
## 4 Results and Discussion
### Container Volume Calculation
#### 4.4.1 Required Number of Containers  
The container volume calculation for the design in this study was conducted to examine whether the dimensions were sufficient. The calculation of the container volume used in the design and the ratio of the weekly waste amount used to the container volume are given in Equation (14). This formula was obtained by multiplying the dimensions of the container since it is a rectangular prism.  
\[V_{c}=l\cdot w\cdot h \tag{14}\]  
With container dimensions of \(l\) = 0.7 m, \(w\) = 1 m, \(h\) = 1 m, the container volume \(V_{c}\) was found to be 0.7 m\({}^{3}\).  
The number of containers required for the uninterrupted operation of the system (\(n\)) was obtained by multiplying the weekly waste volume \(V_{w}\) (Table 2[20; 42]) by the number of floors \(f\) in the building and then dividing by the container volume \(V_{c}\). The required number of containers is given in Equation (15).  
\[n=\frac{v_{w}}{v_{c}}\ f \tag{15}\]  
Here, with \(V_{w}\) = 0.3 m\({}^{3}\),\(f\) = 3, and \(V_{c}\) = 0.7 m\({}^{3}\), \(n\) = 1.28 was found. Accordingly, two containers were designed for this system: one will be used as the main, and the other as a backup.
# Directed Clustering of Multivariate Data Based on Linear or Quadratic Latent Variable Models
## 3 Methodology
### EM Algorithm  
Since the marginal likelihood resulting from specification (4) is not tractable, an EM algorithm is used, which is the most common way of estimating a mixture model [29]. In order to set up the EM algorithm, with component membership serving as the'missing information', let us define an indicator variable \(G_{ik}\), where it equals 1 if the observation \(i\) belongs to mixture component \(k\); otherwise, it equals 0. Defining further \(f_{ik}=f(x_{i}|z_{k},\theta)\), it is clear that \(P(y_{i},G_{ik}=1)=f_{ik}\pi_{k}\), and so the probability of the 'complete data' \((x_{i},G_{i1},\ldots,G_{iK})\) is as follows:  
\[\mathbb{P}(x_{i},G_{i1},\ldots,G_{K})=\prod_{k=1}^{K}(f_{ik}\pi_{k})^{G_{ik}}.\]  
Hence, the complete log-likelihood is as follows: \(\prod_{i=1}^{n}\prod_{k=1}^{K}(f_{ik}\pi_{k})^{G_{ik}}\), and the expected complete log-likelihood has the following form:  
\[l_{c}=\sum_{i=1}^{n}\sum_{k=1}^{K}\mathbb{E}[G_{ik}|x_{i}]\log(\pi_{k}f_{ik})= \sum_{i=1}^{n}\sum_{k=1}^{K}w_{ik}\log\pi_{k}+\sum_{i=1}^{n}\sum_{k=1}^{K}w_{ ik}\log f(x_{i}|z_{k},\theta), \tag{5}\]  
where \(w_{ik}=\mathbb{E}[G_{ik}|x_{i}]=\mathbb{P}(G_{ik}=1|x_{i})=\frac{\pi_{k}f_{ik }}{\sum_{i}\pi_{k}f_{ik}}\) is the 'posterior' probability of observation \(i\) belonging to component \(k\). The expected log-likelihood (5) with the specified expression for \(f(x_{i}|z_{k},\theta)\) can be rewritten as follows:  
\[\begin{split} l_{c}=\sum_{i=1}^{n}\sum_{k=1}^{K}w_{ik}\log(\pi_{ k})&+\sum_{i=1}^{n}\sum_{k=1}^{K}-\frac{1}{2}w_{ik}\log(|\Sigma|)+ \sum_{i=1}^{n}\sum_{k=1}^{K}-\frac{m}{2}\log(2\pi)w_{ik}\\ &+\sum_{i=1}^{n}\sum_{k=1}^{K}-\frac{1}{2}w_{ik}(x_{i}-g(z_{k}, \theta))^{T}\Sigma^{-1}(x_{i}-g(z_{k},\theta)).\end{split} \tag{6}\]  
The E-step of the expectation-maximization algorithm is then given by the following:  
\[w_{ik}=\frac{\pi_{k}f_{ik}}{\sum_{l}\pi_{l}f_{il}}\]  
and the M-step is derived by taking partial derivatives of \(l_{c}\) with respect to each parameter, setting these score equations to 0, and solving them. For the linear latent variable model (1), the resulting estimates, \(\hat{\theta}=(\hat{\pi}^{T},\hat{\beta}^{T})^{T}\), \(\hat{\Sigma}\), and \(\hat{z}_{k},\hat{\pi}_{k},k=1,\ldots,K\), are provided in [16], with publicly accessible implementation in the R package [30], and are not repeated here. For the quadratic model (2), these derivations are more involved, and detailed in the next subsection.
## 3 Numerical Methods
### Mesh and Grid Independence  
Figure 7 shows the grid schematic diagram of the impeller-diffuser fluid domain. In this paper, NUMCA--AutoGrid5 was used to mesh. The H type mesh was used in the inlet and outlet extension sections. For higher mesh quality, the O type mesh was used for blade meshing. The grids of the tip clearance and near the wall were refined to obtain accurate flow parameters. The value of Y+ near the wall was about 1, which met the calculation requirements of the SST turbulence model.  
In order to effectively utilize computing resources, the grid independence verification was carried out. The optimal design of the compressor was taken as an example. The grid independence is shown in Figure 8. With the increase of the grid number, the accuracy of the calculation model also increased. More details of flow loss could be captured in the numerical calculation, so the efficiency was gradually reduced. When the grid number increased from 800,000 to 1,600,000, the relative change of total static efficiency was less than 0.2%. So, when the grid number was more than 800,000, the calculation model could meet the demand of the calculation accuracy.  
The total static efficiency was determined by the output power, mass flow rate, isentropic enthalpy rise and inlet velocity. The isentropic enthalpy drop and the total static efficiency are shown in the Formulas (12) and (13), respectively.  
Figure 8: The grid independence.  
Figure 7: The grid schematic diagram of the impeller–diffuser fluid domain.  
\[\Delta h_{s}=c_{p}T_{inlet}\left[1-(\frac{p_{out}}{p_{inlet}})^{\frac{\kappa-1}{ \kappa}}\right] \tag{12}\]  
\[\eta_{ls}=\frac{p}{\dot{m}(\Delta h_{s}+c_{inlet}^{2}/2)} \tag{13}\]  
where \(\kappa\) is the adiabatic exponent and \(P\) (W) is the power. The subscript inlet represents the compressor inlet and the subscript outlet represents the compressor outlet.
## 4 Results
### Comparison Results of Different Segmentation Architectures  
This section compares the segmentation performance while using different segmentation network structures, such as FPENet and Swim Transformer. The network architecture retains its original configuration without modifications. We initialized the model with pre-trained weights, combined them with the same decoder for segmentation outputs, and conducted the same weakly supervised learning process. Across all evaluated network architectures, the loss function, input image dimensions, and segmentation head remain consistent. The results are shown in Table 6, where we can see our customized UNet++ has much better performance than the other two networks. The worse results of Swim Transformer may be due to its complex architecture, which limits the ability to recognize faces in robust scenarios. And the poor results of FPENet could be due to its limited visual perception fields.
## 3 Proposed Approach
### Possibilistic Fuzzy Multi-Class Novelty Detector
It should be noted that the highest typicality of the first instance processed after the offline phase is compared to the initial threshold \(\theta_{init}\). If the instance \(x_{j}\) is labelled, then its typicality value is used to update \(m_{ct}\), while the PFCM approach is used to update the clusters of the class \(y_{c}\). Otherwise, the highest typicality of the new instance \(x_{j}\) is compared to the difference between \(m_{ct}\) and the adaptation threshold \(\theta_{class}\). If the highest typicality is greater than the mentioned difference, then instance \(x_{j}\) belongs to the class \(y_{c}\), but a new cluster with \(x_{j}\) as its centroid should be created [5]. If instances belonging to the class \(y_{c}\) are already divided into \(c_{max}\) clusters, then the oldest cluster is removed and the new cluster with \(x_{j}\) as its centre is generated instead of it. If neither conditions are met, then the instance \(x_{j}\) will be marked as unknown and stored in the short memory until the novelty detection step will not be executed. The latter happens if the number of instances marked as unknown reaches \(N_{sm}\). In this case, firstly, the PFCM approach is applied to all the instances in the short memory and the pre-determined number \(k_{short}\) of clusters is designed. After that, for each generated cluster its fuzzy silhouette [75] is calculated and if the obtained value is greater than 0 and the considered cluster is not empty, then this cluster is evaluated as the valid one. All validated clusters of short memory represent new patterns or novelties. The next step of the novelty detection procedure consists of calculating the similarity between these validated clusters to the ones already existing (clusters belonging to the known classes), which is achieved by using the fuzzy clustering similarity metric introduced in [9]. Thus, the known cluster, that is the most similar to the examined cluster from the short memory, is determined. If the value of the mentioned metric for these two clusters is greater than \(\phi\) (which is another parameter of the PFuzzND approach), then all instances from the examined cluster are labelled the same way as instances of the considered known cluster. Consequently, clusters of the corresponding class are updated by the PFCM algorithm. In this case, the known class has evolved, and we are referring to the concept drift. Otherwise, a new class is created, therefore, we should increment \(L_{lst}\) by one, and instances from the examined cluster are labelled as belonging to the new class. If one of the short memory clusters is not validated, then it is discarded and its instances remain in the short memory until the model executes the novelty detection procedure again or decides to remove them all, which can happen if these instances are in the short memory for _ts_ iterations.
## 4 Principles and Solving Steps of the Multiplier Path Following Method
### Principles
#### 4.1.4 Determination of the Optimal Step Size  
After obtaining the search direction \((\Delta\mathbf{T},\Delta\mathbf{y},\Delta\mathbf{w})\), it is necessary to determine the step size parameter \(\lambda\) for movement along this direction to compute the initial value \((\mathbf{T}+\lambda\Delta\mathbf{T},\mathbf{y}+\lambda\Delta\mathbf{y},\mathbf{w}+\lambda\Delta\bm {w})\) for the subsequent iteration. However, since \(\mathbf{y}>0\) and \(\mathbf{w}>0\), the conditions for the value of \(\lambda\) should satisfy  
\[\left\{\begin{array}{l}\mathbf{y}+\lambda\Delta\mathbf{y}>0\\ \mathbf{w}+\lambda\Delta\mathbf{w}>0\end{array}\right. \tag{25}\]  
i.e.,  
\[\left\{\begin{array}{l}y_{\rm i}+\lambda\Delta y_{\rm i}>0\\ w_{\rm i}+\lambda\Delta w_{\rm i}>0\end{array}\right.(i=1,2,\cdots,m) \tag{26}\]Because \(y_{i}>0\), \(w_{i}>0\), \(\lambda>0\), it follows that  
\[\left\{\begin{array}{l}\frac{1}{\lambda}>-\frac{\Delta y_{i}}{y_{i}}\\ \frac{1}{\lambda}>-\frac{\Delta y_{i}}{y_{i}}\end{array}\right.(i=1,2,\cdots,m) \tag{27}\]  
i.e.,  
\[\frac{1}{\lambda}>\max_{i}\biggl{(}-\frac{\Delta y_{i}}{y_{i}},-\frac{\Delta w _{i}}{w_{i}}\biggr{)} \tag{28}\]  
In order to guarantee the strict satisfaction of Formula (25), we introduce a positive number \(p\), which is less than 1 and near 1. Consequently, the step size parameter \(\lambda\) is  
\[\lambda=\min\Biggl{\{}p\biggl{[}\max_{i}\biggl{(}-\frac{\Delta y_{i}}{y_{i}},- \frac{\Delta w_{i}}{w_{i}}\biggr{)}\biggr{]}^{-1},1\Biggr{\}} \tag{29}\]