esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
This proof-of-principle study investigated the safety and tolerability of a new self-adaptive IC device in reducing upper limb volume in women affected by BCRL. To the best of our knowledge, this is the first study investigating the use of IC in BCRL patients suggesting its potential implementation among the CDT interventions. CDT, consisting of MLD, therapeutic exercise, skincare to prevent infection, compression, and bandaging treatment [17; 18; 31] are considered cornerstones in the rehabilitative management of BCRL women. Moreover, it is well known that therapeutic exercise could be performed with adequate intensity by BC women with a significant reduction of BCRL volume and improvements in terms of upper limb range of motion and muscle strength [32]. In this context, our findings showed that IC could be considered as a safe and well-tolerated intervention by patients and therapists, as confirmed by the high treatment-related satisfaction at the end of the 2-week treatment and the 1-month follow-up. Furthermore, these results were confirmed by the absence of dropouts and side effects, excluding only a low self-limiting discomfort at the hand reported by 1 patient. To date, data about the safety and tolerability of IC in BCRL patients are lacking. However, it should be noted that the only study in literature was related to the use of velcro-band compression device (Circaid Juxta Lite(tm)) on lower limbs in 30 lymphedema patients without the arterial occlusive disease [33]. The Authors showed that a correct self-application of Velcro bands is effective in maintaining and adequate pressure on the lower limb. In this scenario, our results provide intriguing data about the safety and tolerability of IC as a new therapeutic strategy in the management of BCRL. Our results show that the self-application of an IC device has a fast-learning curve as confirmed by the significant reduction of the total application time from T0 to T2 (\(p\) = 0.0029). Furthermore, it should be noted that IC might improve a home-based rehabilitation approach in terms of self-treatment in BCRL patients, reducing hospital length of stay and improving self-treatment and active participation in the rehabilitative treatment. Regarding upper limb volume modifications, the statistical analysis showed a significant reduction in total upper limb volume after the rehabilitation treatment using IC (\(p\) = 0.0012). In this scenario, the IC might have a role in enhancing the effects of CDT in reducing BCRL volume in the intensive phase, with intriguing results in terms of HRQoL improvement. We assessed BCRL volume through the CM, considered as the most widely used method in the common clinical practice is still the CM. However, in the recent past, other promising techniques have been proposed for quick volume measurements, including the three-dimensional laser scanner (3 DLS).
## 4 Results and Discussion
### Integral Time and Length Scales  
Figure 11(a) presents the integral time scale in the streamwise direction, which was computed by the method described in the previous section. The integral time scale was normalized by the mean value at the freestream (\(z\) = 12-20 mm). The error bar range is the uncertainty \(q_{total\_1}\). The vertical axis representing the probe height was normalized by the boundary layer thickness: \(\delta_{smooth}\) = 10.2 mm for the smooth wall case and \(\delta_{riblet}\) = 10.2 mm for the riblet wall case. It is obvious from Figure 11(a) that the integral time scale for the riblet surface is larger than that for the smooth wall in the entire boundary layer region and that the riblet case is 30% larger than that for the smooth wall case on average.  
Figure 11(b) shows the derived integral length scale using Equation (12). The integral length scale was a product of the integral time scale and the mean \(U\) velocity. The uncertainty \(q_{total\_1}\) is also shown as error bars. The same trend as in Figure 11(a) is seen in Figure 11(b). Both the integral time and length scales for the riblet case are 30% larger compared to those for the smooth wall case. Since the integral time scale for the smooth wall case can be considered to be the life span of the turbulent structure and motion in the streamwise direction in the boundary layer, as a statistical average, the turbulent structure's life span and scale in the streamwise direction for the riblet case near the wall were elongated. A similar observation was obtained by computer simulation with a similar configuration of three-dimensional sinusoidal riblets [15]. It is also notable that the integral length scale reaches the maximum at a height of 1 to 2 mm (or \(H/\delta\) = 0.098-0.196) from the wall under the current flow conditions. It is emphasized that the previously reported insights gained by other techniques were observed by the proposed method in this study as well.
## 3 Results
### XRD Analysis  
The XRD patterns of as-prepared AC, Ca/AC, Na/AC, and CaNa/AC are shown in Figure 2a to clarify the crystallinity and crystal phase of the samples [39; 40]. The reflection within the diffraction peaks of AC corresponded to the typical phases of montmorillonite and quartz [41]. In comparison to AC diffraction peaks, two peaks at 27.9\({}^{\circ}\) and 29.5\({}^{\circ}\) of the Ca/AC pattern could correspond to anorthite and montmorillonite, respectively [42; 43]. The formation of these two peaks was attributed to the interaction between Ca(OH)\({}_{2}\) and AC acid sites, which indicated the successful introduction of calcium ions into the AC framework, the decrease in acid sites, and the production of weakly basic sites in the clay platelets. For Na/AC, the quartz characteristic peaks were mostly intact. On the contrary, the intensity of its montmorillonite peaks significantly decreased, especially at 2\(\theta\) = 5.8\({}^{\circ}\) and 19.7\({}^{\circ}\), which suggested that NaOH directly reacted with the montmorillonite phase due to the surface acidity of AC, destroying its crystallinity. This was consistent with the FT-IR result for Na/AC, which showed a dramatic drop in the band at 1040 cm\({}^{-1}\). In the XRD pattern of CaNa/AC, the intensity of the characteristic peaks belonging to quartz decreased, while the major peaks corresponding to montmorillonite remained relatively high in intensity, which indicated that CaNa/AC still retained the montmorillonite mineral structure. Therefore, it could be deduced that the addition of Ca contributed to reinforcing the stability of the catalyst, which is beneficial for reuse in biodiesel production. In addition, the 001 plane (the characteristic reflection) shifted to the larger angles in CaNa/AC and Na/AC catalysts, indicating a decrease in montmorillonite interlamellar distance caused by the introduction of sodium cations with reduced hydration energy. Furthermore, several new peaks of sodium compounds were generated in the XRD pattern of CaNa/AC. The characteristic peaks of sodium species with low intensity could be covered by the montmorillonite peaks. However, there were no new peaks in Na/AC, suggesting that the sodium ions were only adsorbed on the surface, which exerted a negative impact on the activity and stability of Na/AC.  
Figure 2: (**a**) The XRD patterns and (**b**) nitrogen adsorption–desorption isotherms of AC, CA/AC, Na/AC, and CaNa/AC.
## 1 Introduction
### Contributions
#### 1.1.4 Real-World Applicability  
* The proposed models demonstrate strong generalizability, scalability, and transparency, making them viable candidates for integration into clinical maternal health monitoring systems.
## 2 Materials and Methods
### Detection of the Reduction Factors for the Microbial Load  
After the PPA treatment and after selected storage days ranging from directly after the treatment up to 26 days, three apples from the treatment and control were taken from the batch for specific investigations. Therefore, the selected apples were transferred in a closed, sterile plastic box to a microbiological safety cabinet for guaranteeing aseptic conditions. By sterile cotton swaps soaked with TSB, each apple surface was wiped, and subsequently, the swap was transferred into a 15 mL tube with 1 mL of TSB. After a decimal, serial dilution up to 10-8, 10 \(\mu\)L of the diluted suspensions was dropped on each of the different agar plates and spread by tilting the plates. The detection limit was 2 log\({}_{10}\)-levels (equal to the CFU/mL resulting from a single colony in the lowest dilution). After one day of incubation at RT, the number of colonies (x) of the dilutions was counted manually and finally the colony forming units per milliliter calculated as follows:  
\[\frac{\text{CFU}}{\text{mL}}=\frac{\sum_{i}^{n}\frac{c_{i}+f_{i}}{V}}{n}\text { for }c_{i}:=\left\{\begin{array}{c}1\text{ \emph{if }}x_{i}\Lambda_{x_{i}+1}\
eq 0\\ x_{i}\text{ \emph{if }}x_{i}\
eq 0\\ 0\text{ \emph{if dilution not countable}}\end{array}\right. \tag{1}\]  
\(x_{i}\)--number of counted colonies;  
\(f_{i}\)--dilution factor of the i-th dilution;  
\(V\)--volume for the plating in mL;  
\(n\)--number of countable dilutions;  
A dilution is declared as \"not countable\" if the distinction between colonies is no longer possible (overgrown) or if the counted dilution and the next higher dilution are empty. In case of an empty plate (all dilutions have no colonies), the CFU/mL is set to the detection limit.  
The antimicrobial properties were described by the calculation of the reduction factor (RF) described in Equation (2).  
\[RF=\frac{\sum_{i=1}^{n}\left(\overline{x_{t}}-\left[\log_{10}\left(\frac{ \text{CFU}}{\text{mL}}\right)\right]_{t}\right)}{n} \tag{2}\]  
for the average of the log\({}_{10}\)(CFU/mL) of the reference at day \(t\) and the decadic logarithm of the CFU/mL of every sample.
## 2 Development of a Servo Valve Prediction Model
### Support Vector Machines (SVM)
#### 2.2.4 Copula Function Theory  
Copulas is a tool for modeling the dependencies of several random variables. The primary purpose of copulas is to describe the interrelationship of several random variables. This paper is dedicated to the use of the Copula function, the joint probability density distribution, to describe the relationship between the two variables, the predicted values and the test set data. The theory of Copula functions has been rapidly developed in statistics and has provided the theoretical basis for evaluating system reliability problems.  
All functions satisfying a particular condition are Copula functions [27]. The density function of the multivariate joint distribution is decomposed, and the value is obtained by multiplying the decomposed \(n\)-edge distributions with the Copula function.  
The multivariate Copula function \(C(u_{1},\,\ldots\,\ldots\,\mu_{n})\) is a function that satisfies the following conditions:  
1. The domain of definition of the function \(C(u_{1},\,\ldots\,\ldots\,u_{n})\) is \([0,\,1]^{n}\);
2. The function \(C(u_{1},\,\ldots,u_{n})\) is zero-based and \(n\)-dimensionally increasing;
3. Any \(u_{i}\)\(\in\)\([0,1]\), \(i=1,2,\,\ldots\,\ldots\,\,.n\), satisfies:  
\(C(1,\,\ldots,1,u_{i},1,\,\ldots,1)=u_{i}\);  
\(C(u_{1},\,\ldots\,\,u_{i-1},0,u_{i+1},\,\ldots,u_{n})=0\);
4. If \(u_{1},\,\ldots\,\,u_{n}\) are independent, then: \(C(u_{1},\,\ldots\,\,u_{n})=u_{1}\,\ldots\,\,u_{n}\).  
Construct the joint distribution from [PERSON]'s theorem for multivariate Copula functions, defined for [PERSON]'s theorem as assume that \(F_{1}(x_{1})F_{2}(x_{2}),\,\ldots\,\,F_{n}(x_{n})\) is a one-dimensional continuous distribution function and let \(u_{1}=F_{1}(x_{1})\), \(u_{2}=F_{2}(x_{2}),\,\ldots\,\,,\,u_{n}=F_{n}(x_{n})\), then \(u_{1}\),\(u_{2},\,\ldots\,u_{n}\) are uniformly distributed obeying \([0,\,1]\), i.e., \(C(u_{1},\,\ldots\,\ldots\,\ldots,u_{n})\) belongs to the marginal distribution and obeys the joint distribution function of \([0,\,1]\). Let \(F(x_{1}\),\(x_{2},\ldots\,x_{n})\) be a joint distribution function with an edge distribution function \(F_{1}(x_{1})\),\(F_{2}(x_{2}),\,\ldots\,\,F_{n}(x_{n})\), then there exists a multi-dimensional Copula function \(C(u_{1},\,\ldots\,\ldots\,\,\mu_{n})\) that can connect the starting edge distribution function \(F_{1}(x_{1})\),\(F_{2}(x_{2})\),...,\(F_{n}(x_{n})\) to the joint distribution function \(F(x_{1}\),\(x_{2}\),...,\(x_{n})\), as expressed by the following equation:  
\[F_{1}(x_{1},x_{2},\ldots,x_{n})=C[F_{1}(x_{1})F_{2}(x_{2}),\ldots,F_{n}(x_{n})]= C(u_{1},u_{2},\ldots,u_{n}) \tag{10}\]  
In summary, the Sklar theorem for multivariate Copula functions is a combination of multiple random variables, i.e., they can be associated by their respective marginal distributions. Copula functions complete the proof of the association between the variables, different Copula functions prove the corresponding association, and the relevant parameters of the Copula function prove the data on the degree of association.
# MPResiSDN: Multipath Resilient Routing Scheme for SDN-Enabled Smart Cities Networks
## 2 Background
### Smart Cities  
Smart cities is a general term that can be considered from various perspectives. Exploring the key layers of a smart city will clarify where this research is positioned. Figure 1 shows a general overview of smart city architecture [19].  
As can be seen, smart city architecture comprises four different layers. The first is the Information and Communication Technologies (ICT) infrastructure layer. This layer includes all essential network infrastructure, such as data centers, network devices, sensors, and actuators. This layer is considered the key driver of smart cities. The second layer is the E-governance layer. E-governance establishes policies related to the deployment of existing ICT infrastructure to provide services to citizens. The third layer is the Services layer that includes the various public services offered to citizens and other stakeholders. Finally, the Stakeholders' layer represents all beneficiaries of smart cities services, including citizens, government officials, and commercial operators. This research focuses on the ICT infrastructure layer.
## 4 Discussion
### Forecasting Electrical Characteristics of Flexible Sensing Materials by Regression Modelling
#### 4.3.1 Hysteresis
\begin{array}{c}y_{1}=3.01148-5.48927x_{1}+1.62977x_{2}-2.52130x_{3}+0.019542x_{4 }\ldots\\ \qquad\qquad+9.27250x_{1}x_{2}+9.17000x_{1}x_{3}-0.038762x_{1}x_{4}\ldots\\ \qquad\qquad-4.97750x_{2}x_{3}+0.008225x_{2}x_{4}+0.010900x_{3}x_{4}\ldots\\ \qquad\qquad-4.71108x_{1}^{2}-2.99858x_{2}^{2}-0.782333x_{3}^{2}-0.000046x_{4}^{2 }\end{array}  \tag{43}\]  
where \(y_{1}\) denotes the sensor hysteresis performance response value; \(x_{1}\), \(x_{2}\), and \(x_{3}\) denote the content of Fe\({}_{3}\)O\({}_{4}\), CNT, and MLG, respectively; and \(x_{4}\) is the moulding temperature. RF  
The model's hysteresis prediction outcomes are displayed in Table 11. Augmenting the quantity of decision trees does not enhance the model's performance on the test set. For instance, when comparing models with 100 and 200 decision trees using GridSearchCV, the former exhibits a marginally superior correlation coefficient R\({}^{2}\) (0.8277 compared to 0.7923). The RF model, which includes 42 decision trees and utilises the RandomizedSearchCV approach, exhibits a superior accuracy compared to that of the model with 102 decision  
Figure 12: Three-dimensional hysteresis response surface plots illustrating (**a**) the interaction between CNT and Fe\({}_{3}\)O\({}_{4}\) at varying concentrations; (**b**) the interaction between MLG and Fe\({}_{3}\)O\({}_{4}\) at different concentrations; (**c**) the interaction of MT with varying concentrations of Fe\({}_{3}\)O\({}_{4}\); (**d**) the interaction between MLG and CNT at differing concentrations; (**e**) the interaction of MT with varying concentrations of CNT; and (**f**) the interaction of MT with varying concentrations of MLG. trees (0.8153 vs. 0.7897). The model's performance on the test set generally diminishes as the maximum depth of the trees is reduced. For instance, employing GridSearchCV, the model with a maximum depth of 200 achieves an R\({}^{2}\) value of 0.8277 on the test set, whereas the model with a maximum depth of 20 attains an R\({}^{2}\) of 0.8135 with the same number of decision trees. The RandomisedSearchCV method indicates that the parameter combination with a maximum depth of 71 yields superior accuracy compared to a maximum depth of 94 (0.8153 vs. 0.7897). This combination also results in a higher R\({}^{2}\), implying that an increased maximum depth enables the model to comprehend more intricate relationships within the data, thereby enhancing the predictive performance. The RF model, utilising a hyperparameter configuration of 100 decision trees and a maximum depth of 200, attained the greatest R\({}^{2}\) values (1 and 0.8277, respectively) in both the training and test set through the GridSearchCV method.
# A Web Shell Detection Method Based on Multiview Feature Fusion
## 5 Conclusions  
Nowadays, web shell detection is important in the context of increasing levels of malicious attacks on web servers around the world. This paper proposes a web shell detection method based on multiview feature fusion.  
**The advantages of our work:** First, lexical features, syntactic features, and abstract features that can effectively represent PHP web shells are integrated and extracted from multiple levels. Second, in order to determine the features that are reasonable for classification, a Fisher scoring mechanism is used to rank the importance of the features, and then the sixteen best features for subsequent classification are determined through experiments. Finally, the optimized SVM (C = 0.8 and \(\gamma\) = 0.03) is used to distinguish web shells from benign web scripts. Our method has achieved satisfactory classification accuracy on large-scale data sets. The total accuracy (combining all the web shells and benign scripts) is 92.18%, and 95.26% of web shells can be detected successfully, which is better than the state-of-the-art web shell detection tools and methods.  
**The disadvantages and future work:** First, the coverage of our method needs to be improved. Our future work is to study the mechanism of web shells with other languages, such as JSP, ASP, and Python. Our goal is to find a general way to detect different types of web shells, e.g., using a system log [45] to detect web shells in real-time. Second, the feature engineering can be improved. The features used in this paper are mostly statistical-based. Our future work is to use deep neural networks to extract and select features automatically, e.g., using auto-encoding and decoding [46].  
**Author Contributions:** Each author contributed extensively to the preparation of this manuscript. [PERSON] and [PERSON] designed the experiment; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper is sponsored by Wenzhou scientific research projects for underdeveloped areas (WenRenSheFa [2020] 61 (No.5)) and is also supported by major scientific research projects of Wenzhou Polytechnic (No.WZY2020001).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Numerical Simulation Study on Mechanical Properties of Corroded Pipe
### Numerical Simulation Results Analysis
#### 3.2.3 Comparative Analysis of Experimental and Numerical Simulation Results  
As shown in Figure 10, it can be determined that the ultimate bearing capacity- corrosion depth curve obtained by the numerical simulation of the pipe section is straight, that is, the ultimate bearing capacity of the pipe decreases uniformly with the increase of the corrosion depth of the pipe. The numerical simulation results of the ultimate bearing capacity of pipes with different corrosion depths are compared with the experimental values. The simulation value of the ultimate bearing capacity of non-destructive pipes is 7421.94 N, which is smaller than the experimental value, and the error between the simulation value and the experimental value is only 4.47%. When the corrosion depth of the pipe is 2.5 mm, the simulated ultimate bearing capacity of the pipe is 6423.86 N, which is 12.10% lower than that of the non-destructive pipe joint, and the error with the experimental value is 1.31%. When the corrosion depth of the pipe is 5 mm, the simulated ultimate bearing capacity of the pipe is 5898.35 N, which is 20.52% lower than that of the non-destructive pipe, and the error with the experimental value is 5.05%. When the corrosion depth of the pipe is 7.5 mm, the corrosion depth is equal to the thickness of the inner protective layer of the pipe. The simulated ultimate bearing capacity of the pipe is 5073.23 N, which is 31.64% lower than that of the non-destructive pipe, and the error with the experimental value is 5.67%. In summary, the error between the simulated value  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Serial Number**} & \multicolumn{2}{c}{**The Ultimate Bearing Capacity**} & \multicolumn{2}{c}{**Ultimate Bearing Capacity**} \\  & **Corresponds to the Displacement (mm)** & **(N)** \\ \hline A1 & 4.51 & 7421.94 \\ B1 & 7.97 & 6523.86 \\ B2 & 10.50 & 5898.35 \\ B3 & 15.93 & 5073.23 \\ \hline \end{tabular}
\end{table}
Table 6: Numerical simulation of ultimate bearing capacity of pipes with different corrosion depths.  
Figure 9: Numerical simulation load-displacement curves of pipes with different corrosion depths.  
and the experimental value of the ultimate bearing capacity of the pipe under TEB test conditions is small (within 6%) and the accuracy of the simulated value is high.
# DriNet: Dynamic Backdoor Attack against Automatic Speech Recognition Models
## 4 Experiments  
### Experimental Settings  
Datasets and Target Models. To evaluate the effectiveness of our proposed method in this experiment, we take the raw audios from the AudioMNIST dataset [23], and the speech commands dataset [24], described in Table 2. The AudioMNIST dataset consists of 30,000 audio samples of spoken digits (0-9), in English, from 60 speakers of multiple age groups, different genders, and different accents. The speech commands dataset contains 105,829 audio files, constituted by 35 different kinds of spoken single-word commands, including ten digits, command words, and other common words. Among them, we choose 10 classes of digit words for evaluation, a total of 38,908 samples. These two datasets are split into the 80% training set and 20% test set.  
We use two target victim models with different architectures in this paper, namely 1D CNN [25] and X-vector [26]. The CNN models are well known for their excellent performance in the computer vision domain. Specifically, 1D CNN demonstrates its good performance in speaker recognition systems. The X-vector is the current baseline model framework in speech recognition. The network consists of a deep neural network that maps the speech feature sequences to a fixed dimensional embedding. We modify them to adapt to the Mel spectrogram features in this paper.  
Baseline and attack setup. We adopt the adapted BadNets method as the baseline for comparison. Similar to how it works in images, BadNets poisons the training set with a fixed static audio trigger to form a backdoor. The amplitude of the fixed static audio trigger used by BadNets is about the same as the average amplitude of the dynamic trigger we generate. Moreover, the frequency of the fixed static is permanently fixed at the lowest three frequencies on the Mel scale [27].  
All models are implemented in PyTorch [28] 1.11.0 and CUDA 11.4. We set \(epoch=100\) and \(\alpha=0.1\) for the 1D CNN model, while \(epoch=100\) and \(\alpha=0.07\) are set for the X-vector. We set the target label to zero for demonstration. In the training phase, we use an Adam [29] optimizer with a learning rate of 0.001. As for the size of the triggers, we discuss the performance impact of three kinds of shapes of triggers, (1 \(\times\) 5), (2 \(\times\) 5), and (3 \(\times\) 5).