esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Background
### Support Vector Machine Method  
[PERSON] et al. (2013) proposed an SVM based on the structural risk-minimization principle in statistical learning theory [19]. The SVM can maximize the generalization ability of the learning machine by obtaining small errors in the discriminant function obtained from limited datasets, even for independent test sets. Furthermore, SVM is a convex quadratic optimization problem that guarantees that the extreme solution found is the global optimal solution. Research on SVMs can be traced to the end of the 1970s, and SVMs have been successfully applied to classification and regression problems in nondestructive testing and structural health monitoring, including structural damage detection [20; 21; 22], dam safety prediction [23], eddy vibration response prediction [24], and impact detection and localization [25].  
As shown in Figure 4, assume two types of linearly separable training sample sets \(\{\left(\varphi_{i},y_{i}\right),i=1,\ 2,\ 3,\ldots,n\}\), where \(x_{i}\in R^{n}\), \(m\) is the \(i\)th training sample, and \(y_{i}\in\{-1,\ 1\}\) is the label of the sample category. The general form of the linear discriminant function is  
\[f(x)=w\cdot x+b, \tag{3}\]  
where \(w\) is the normal vector of the optimal classification hyperplane, \(b\) is a constant, and the corresponding classification surface equation is \(w\cdot x+b=0\). The discriminant function is normalized such that all samples of the two categories satisfy \(f(x)\geq 1\). At this time, the sample closest to the classification surface is \(f(x)=1\), which requires that all samples of the classification surface be classified correctly, that is, satisfy  
\[y_{i}=[(w\cdot x_{i})+b]-1\geq 0,\ i=1,\ \ldots,n. \tag{4}\]  
Thus, the classification interval is equal to \(2/\left|\left|w\right|\right|\), and the largest interval is equivalent to the smallest value of \(\left|\left|w^{2}\right|\right|\). A classification surface that satisfies Equation (3) and minimizes \(\left|\left|w^{2}\right|\right|\) is the optimal classification line \(B\) shown in Figure 4, where \(\left|\left|\cdot\right|\right|\) represents the Euclidean distance. Between the two types of data samples, the samples closest to the classification surface and the data samples on hyperplanes \(B_{1}\) and \(B_{2}\) parallel to the classification surface \(B\) are the data samples that cause the sign in Equation (3) to be equal; these samples are support vectors.  
When the two types of samples are linearly indistinguishable, the idea of the non-linear discrimination problem is to first map the input vector into a high-dimensional space by a non-linear transformation, and then perform classification operations in this high-dimensional feature space to obtain the optimal classification surface.
## 3 Integration of 5G NPN Deployment Options and Edge Computing Scenarios
### On-Premises Edge Integrated into PNI-NPN with Shared RAN and Core Control Plane  
The deployment of public network integrated non-public network (PNI-NPN) scenarios enables the MNO to provide new offerings for enterprises, such as industrial parties. In the shared RAN and core control plane option, the user plane traffic remains on-premises, while the control plane functions are hosted by the MNO public network (shared RAN and control plane).  
The edge computing-related services and features that can be offered for the enterprise customer are quite similar to the listed ones in Section 3.2, but in this case, instead of a third-party integrator, the MNO can manage the NPN and the edge computing domains in a tightly integrated way.  
Since the MNO is more involved in this scenario, this enables the move towards a hybrid scenario for higher availability. The UEs could be allowed to connect to the MNO's public network, as well as backup industry application instances can be deployed on the edge data centers hosted by the MNO's sites near the factory premises. In the case of any on-premises user-plane NPN failure, the UEs can roam to the MNO public network and connect to the edge application on the MNO's sites.
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 2 Background  
### Adversarial Samples  
Adversarial samples are a type of attack data targeting machine learning models, especially in convolutional neural networks (CNNs) [41] for multi-class image classification. This attack method has a high degree of threat, as the attacker adds visually imperceptible interference to the original image, which may lead to image classification failure. Consider the deep learning model \(F:\mathbb{R}_{n}\to Y\) as a function that maps \(n\)-dimensional inputs to all possible label sets \(Y=\{1,\cdots,k\}\). In practical applications, the test image \(x\) is used as the model input, and the classification label \(y\) of the image is obtained through the function \(F_{\omega}\). The \(\omega\) here is a set of parameters that deep learning models need to learn during the training process. If instance \(x\) is correctly classified, we can obtain \(y=F_{\omega}(x)\).  
However, the attacker's goal is to introduce a small amount of perturbation \(\delta\in\mathbb{R}^{n}\) to \(x\) toward \(x\), i.e., \(x^{\prime}=x+\delta\), resulting in classifier errors. Specifically, adversarial attacks are defined as Equation (1), as follows:  
\[\begin{split}\min_{\delta}F_{\omega}(x+\delta)& \
eq y\\ s.t.&\quad F_{\omega}(x)&=y\\ ||\delta||_{p}&\leq\epsilon\end{split} \tag{1}\]  
In this study, it is generally presumed that the attacker's actions are bounded by a \(p\)-norm, which guarantees that \(||\delta||_{p}\leq\epsilon\). Here, \(\epsilon\) acts as a threshold that restricts the attacker's ability to alter the input. All attack methodologies employed within this research are founded upon the \(\infty\)-norm, adhering to the fundamental principle that the variation in pixel values, represented by \(\delta\), must not surpass the predetermined \(\epsilon\) value limit.
###### Abstract  
In-tube solid-phase microextraction (IT-SPME) coupled on-line to miniaturized liquid chromatography (LC) has emerged as a powerful tool to address a variety of analytical problems. However, in order to expand its applicability, the development of new sorbents that enhance the efficiency and specificity of the extraction is highly desirable. In this respect, the employment of capillary columns coated with sorbents functionalized with nanoparticles (NPs) replacing the loop of the injection valve (in-valve IT-SPME) is one of the most attractive options. In this work, polymers of tetraethyl orthosilicate (TEOS) and trimethoxyethylsilane (MTEOS) modified with SiO\({}_{2}\) and TiO\({}_{2}\) NPs have been synthetized and used for the extraction of a variety of water pollutants, using both Capillary-LC and Nano-LC. Compounds with different chemical structures and polarities such as the artificial sweetener saccharine, the polycyclic aromatic hydrocarbons (PAHs) naphthalene and fluoranthene, and some phenylurea and organophosphorous herbicides have been used as target analytes. The extraction efficiencies found with the synthetized capillaries have been compared to those obtained with commercially available capillaries coated with polydiphenyl-polydimethylsiloxane (PDMS), nitroterephthalatic acid modified polyetilenglicl (FFAP), and polystyrene-divinylbenzene (PS-DVB) phases. The results obtained in this preliminary study showed that, although PS-DVB phase has the strongest affinity for compounds with two or more aromatic rings, the extraction with TEOS-MTEOS coatings modified with NPs is the best option for a majority of the tested compounds. Examples of application are given.  
in-tube solid phase microextraction (IT-SPME); SiO\({}_{2}\) nanoparticles; TiO\({}_{2}\) nanoparticles; capillary liquid chromatography; nano-liquid chromatography +
Footnote †: journal: Physics Letters B
# Experimental and Modeling of Residual Deformation of Soil-Rock Mixture under Freeze-Thaw Cycles
## 3 Experimental Results  
### Freeze-Thaw Deformation Analysis  
According to the available experimental data, the skeletal effect of the blocks will gradually increase when the rock content in the soil-rock mixture is greater than 40%. Generally speaking, the amount of soil freezing and swelling is positively correlated with the water content, so four sets of tests were conducted with specimens saturated and under open recharge conditions at 60%, 70%, 80%, and 90% rock contents to obtain the freeze-thaw changes of soil-rock mixture with different rock contents. The freeze-thaw cycle process of the soil-rock mixture is shown in Figure 5 (taking group S1 as an example).  
The temperature of the top plate of each group of specimens varied cyclically within \(\pm\)20 \({}^{\circ}\)C during the freeze-thaw cycle, and the data recorded by the temperature sensors at the top and bottom of the specimens were observed to vary regularly within the predetermined range (as shown in Figure 6). It was found that the soil-rock mixture specimens showed more obvious and different degrees of freezing and thawing changes in each group during the freeze-thaw cycle. In the S1 group specimens with a higher rock content, as the number of freeze-thawing cycles increases, it can be observed that the blocks become closer to the inner wall of the transparent container. That is, the specimens are subject to significant radial displacement by freeze-swelling during the freeze-thawing cycle.  
According to the change of frost heave of each group of samples, the soil rock mixture samples have different degrees of frost heave during the freeze-thaw cycles (as shown in Figure 7). With the increase in the freeze-thaw cycles, the frost heave of the sample gradually decreases and tends to be stable. However, with the increase of the content of block rock in the sample, the number of freeze-thaw cycles required for the frost heave of the sample to reach a stable state also increases correspondingly. With the increase of the rock content, the frost heave of the sample first decreases at 60-80% and increases at  
Figure 4: Freeze–thaw cycle device schematic diagram.  
Figure 5: Freeze–thaw cycle process of soil–rock mixture samples.  
80-90%. The S2 group sample with the rock content of 80% at the extreme value even has the phenomenon of frost shrinkage.
# Overview of Voice Conversion Methods Based on Deep Learning
## 2 Voice Conversion Process
### Evaluation Methods  
Two evaluation methods are used in voice conversion: objective and subjective. In general, objective evaluation involves calculating some measure of difference or correlation between the outcome and the target. The works used the WER [7; 8; 16] (word error rate) and CER [7; 8; 15; 16] (character error rate) to evaluate linguistic consistency. The authors of [8] also used mel-cepstral distortion (MCD) [8] to measure spectral changes during conversion. In [7; 16], the PCC (Pearson correlation coefficient) was used to calculate prosody consistency. In [13], the effectiveness of speaker ratings shows the influence of attention mechanisms on this factor. A classification accuracy (CLS) metric was also introduced in [15]. The authors of [14] used the EER (equal error rate) as a metric for the quality of data separation. In addition, [7; 8; 11; 14; 16] provided a mean opinion score (MOS) for naturalness and similarity. Some works [8; 13; 15] used MOSnet, which simulated MOS feedback. Table 2 shows the resulting MOS values of the described algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Paper**} & \multirow{2}{*}{**Evaluations Methods**} & **MOS** & **MOS** & **MOS** & **MOS** & & \\  & & **M-M** & **M-M** & **A-A** & **A-A** & **A-A** & **Dataset** & **Public** \\  & & **Quality** & **Similarity** & **Quality** & **Similarity** & **Dataset** & **Code/Demo** \\ \hline \multirow{2}{*}{[8]} & MCD/MOS/CER/ & \multirow{2}{*}{3.81} & MCD/MOS/CER/ & \multirow{2}{*}{3.81} & \multirow{2}{*}{3.87} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & VCC2020 [61] & \(\boldsymbol{\check{\check{}}}\)\({}^{1}\)/\(\boldsymbol{\check{\check{}}}\)\({}^{2}\) \\  & WER/MOSNet [64] & & & & & & \\ \hline
[7] & MOS/WER/CER/PCC & 4.08 & 4.08 & 4.03 & 3.02 & VCTK [55], & \(\boldsymbol{\check{}}\)\({}^{3}\) \\ \hline \multicolumn{1}{c}{} & \multirow{2}{*}{MOS} & \multirow{2}{*}{4.09} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & VCTK [55], & \\  & & & & & & & Hi-Fi TTS [56] & & \\ \multirow{2}{*}{[11]} & \multirow{2}{*}{MOS} & \multirow{2}{*}{4.09} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & LibriSpeech [57], & \(\boldsymbol{\check{}}\)\({}^{4}\) \\  & & & & & & & CommonVoice [58], & \\ \multicolumn{1}{c}{} & \multirow{2}{*}{AVSpeech [59]} & & & & & & AVSpeech [59] & \\ \hline \end{tabular}
\end{table}
Table 2: Information about the described VC works. Abbreviations: CER—character error rate, CLS—classification accuracy, EER—equal error rate, MCD—mel-cepstral distortion, MOS—mean opinion score, PCC—Pearson correlation coefficient, WER—word error rate, M-M—many-to-many, and A-A—any-to-any, \(\boldsymbol{\check{\check{}}}\)—exist, \(\boldsymbol{\check{\check{}}}\)—no exist.
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 2 Technology Reviews
### Federated Learning
#### 2.1.1 Federated Learning on Mobility Applications  
This work is interested in federated learning models in mobility applications. Federated learning fits latency-sensitive mobility applications well in the sense that it improves network efficiency by communicating only model parameters instead of large amounts of raw data and that a local model can quickly adapt to changes in the environment of a device. Most of the previous studies have tried to solve research problems related to the mobility of humans and moving objects such as vehicles. _Human activity recognition_ aims to detect the movements of a human, such as sitting, running, jumping, lying down, or going upstairs, using cameras and sensors [10; 11; 12; 13; 14; 15]. _Location prediction_ focuses on finding the next locations of users [16; 17; 18]. _Point-of-interest recommendation_ suggests a location to users who might be interested [19]. The authors in [20; 21] use federated learning for the indoor localization of humans, i.e., finding their positions accurately. Similar to this, [PERSON] et al. [22] propose a method that tracks an object in an indoor environment. _Autonomous control_ for autonomous vehicles aims to track vehicles so as to reduce traffic accidents [23]. In references [24; 25], the authors try to solve collision avoidance problems by sensing the movement of many vehicles. _Traffic sign recognition_ is another interesting topic that classifies traffic signs by using cameras and on-vehicle sensors [26; 27]. _Energy demand prediction_ aims to predict when an electrical vehicle needs to be charged by using travel information about the vehicle, such as velocity, distance, and the number of drives taken [28; 29].
# Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing Approach
## 3 Results
### Efficiency  
Having fast corrections is of the utmost importance in real-time operational scenarios. Therefore, one of the requirements of the pipeline is that it has a response time suitable for rescue scenarios. To evaluate the pipeline efficiency, we first calculate the time needed to arrive at the IF module (1 st stage) and then the time required for the fusion (2 nd stage). Calculating the time of the 1 st stage is a matter of computing the longest time between the parallel branches of the pipeline (see Figure 11). In Table 4, we report the running time (s) for images of different shapes (Width (W), Height (H), Channels (C)) by averaging 10,000 experiments conducted on an NVIDIA GeForce GTX 1650 using the CUDA toolkit version 11.7.  
Figure 11: Stages overview. The figure represents the stages of the total computing time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \((W,H,C)\) & **1 st Stage (s)** & **2 nd Stage (s)** & **Total (s)** \\ \hline (128, 128, 3) & 0.0011 & 0.0015 & 0.0026 \\ (256, 256, 3) & 0.0014 & 0.0041 & 0.0055 \\ (512, 512, 3) & 0.0027 & 0.0238 & 0.0265 \\ (1024, 1024, 3) & 0.0125 & 0.1036 & 0.1161 \\ (2048, 2048, 3) & 0.0424 & 0.3975 & 0.4399 \\ \hline \end{tabular}
\end{table}
Table 4: Computation time. The table represents the average running time for images of different shapes.  
Figure 10: People detector. The figure represents the predicted boxes on some of the original and output images.
# Proposed Fuzzy-NN Algorithm with LoRa Communication Protocol for Clustered Irrigation Systems
## 3 Irrigation Systems Algorithms
The n + 1 day on watering flow meter value \(Fv_{n+1}\) shall be set automatically as follows: \(Fv_{n+1}=\text{K}\cdot\text{Error}_{n}+(\text{K}/\text{n})\)\(Error_{total}+(\text{Error}_{n}-\text{Error}_{n-1})\), where K is a statically assigned gain coefficient and n are the n days of \(Fv\) flow meter measurements [7; 8; 16]. Irrigation algorithms, such as PIC, leave the \"on\" state decision to the farmer, using statically assigned watering intervals. These algorithms do not use external sensory feedback information and in terms of irrigation decisions are not intelligent at all. The PIC algorithm tries to amend by calibrating the watering interval. The LWA and MVA algorithms try to make decisions both for the watering timeas well as the interval. Nevertheless, their decisions are not smart and therefore smooth enough and are prone to errors when the number of sensors increases. These algorithms may easily overwater or underwater a field. The EWMWA algorithm is a better algorithm in terms of adaptive decision, since it takes into account averages of past watering data. However, the decision is based on statically assigned weights and if the decision is mainly taken from past knowledge, it may take a lot of time intervals to compensate for an overestimation that needs immediate compensation. Daily resets of average values are also an EWMWA drawback, since the average estimation always floats and fluctuates. Deep knowledge and immediate adaptation is a necessity in order to make the right decisions over time. Apart from PTC, the LWA, MVA, PIC and EWMWA algorithms turn the actuator to the \"on\" position at a daily interval such as: summer at high temperatures, midday, or at a winter night at freezing temperatures. Such picked-up watering times may dry out or freeze the watering plants. * Fuzzy algorithms: This category includes algorithms of fuzzy decisions based on a pre-defined set of rules and Degree of Membership calculations upon sensor values (metrics) [7; 8]. Fuzzy algorithms are fast and smart adaptive algorithms and may also include error control capabilities similar to the PIC algorithm. Proposed by the authors, the FITRA algorithm is partly a fuzzy algorithm and when the irrigation system is in a stable state it is transformed to a neural network algorithm (NN) [32]. * FITRA algorithm: the FITRA algorithm is a neuro-fuzzy algorithm proposed by the authors and presented in a following section. FITRA at first instantiates as a fuzzy algorithm. Analytical description of the algorithm follows.
## 5 Experimental Results and Discussion
### SDCHM Model Identification and Validation
The remaining figures in Figure 10 are manipulator outputs with \([p_{1},\ p_{2},\ p_{3}]=[p^{*},\,p^{*},\,p^{*}]\). The results intuitively indicate that the SDCHM models are able to predict the stiffness-dependent hysteresis coupling behavior of the soft manipulator with high accuracy, surpassing the precision of the TSFNN model. This can be attributed to the limited number of fuzzy sets in the TSFNN used for interpolation and its simple structure when not combined with the inherent hysteresis submodule and stiffness-dependent model, making it difficult to meet the demands of high-precision modeling. Increasing the number of fuzzy sets in the TSFNN would significantly prolong the training time, and it would require collecting more experimental datasets to prevent overfitting in the training process. To quantitatively calculate the model precision, \(Err\), and root mean square error (RMSE), the RMSE is defined as:  
\[RMSE_{i}=\sqrt{\sum_{j=1}^{num}(y_{ij}-\hat{y}_{ij})^{2}/num},\ (i=1,2,3) \tag{27}\]  
where \(num\) denotes the number of data involved in the calculation and \(\hat{y}_{ij}(i=1,\ 2,\ 3)\) and \(y_{ij}(i=1,\ 2,\ 3)\) represent SDCHM model outputs and real outputs of contractile PAMs. Table 4 depicts comparisons of prediction accuracy between different models based on the results of Figure 10. Figure 11 shows the RMSE of the model results with \(p_{0}=0.1,\ 1.5,\ 1.7\) bar, intuitively indicating that the SDCHM model has better prediction ability and demonstrates good generalization under various working conditions of the soft manipulator. As mentioned in Section 2, contractile PAMs with the same pressure show inconsistent output behavior because of manual fabrication errors. However, the SDCHM model is capable of capturing complex nonlinear effects. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**SDCHM Model**} & \multicolumn{2}{c}{**TSFNN Model**} \\ \hline
**Group** & **Err (\%)** & **RMSE (mm)** & **Err (\%)** & **RMSE (mm)** \\ \hline (a) & 1.74 & 1.17 & 16.45 & 11.27 \\ (b) & 1.03 & 0.45 & 10.59 & 5.49 \\ (c) & 1.02 & 0.40 & 13.21 & 7.20 \\ (d) & 1.02 & 0.43 & 10.35 & 4.80 \\ (e) & 1.70 & 1.13 & 17.37 & 12.98 \\ (f) & 1.02 & 0.40 & 7.83 & 2.60 \\ (g) & 0.98 & 0.37 & 6.32 & 2.24 \\ (h) & 0.96 & 0.32 & 6.53 & 2.69 \\ (i) & 1.71 & 1.15 & 10.26 & 4.84 \\ (j) & 2.88 & 2.14 & 10.10 & 4.01 \\ (k) & 1.53 & 1.03 & 16.12 & 10.99 \\ (l) & 1.55 & 1.04 & 18.11 & 13.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of prediction accuracy between different models.
# Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks
## 2 Hybrid TOA/RSS Localization: Formulation and Resolution Approaches  
We consider, as a reference scenario, a WSN deployed over a given area with \(N\geq 3\) static _anchor_ nodes located at known positions \(\mathbf{x}_{i}=[x_{i}\;y_{i}]^{T}\), \(i=1,\ldots,N\), with \({}^{T}\) denoting the transpose operator, and a static _blind node_ (also called target) with unknown position \(\mathbf{x}_{b}=[x_{b}\;y_{b}]^{T}\). In the following, we assume that the target is properly equipped to extract both RSS and TOA information from the signals transmitted by the anchors. Moreover, we address the case in which the involved links are in LOS. It should be recognized that multipath and NLOS biases are main sources of errors that severely penalize the achievable localization performance, especially when operating in harsh environments, such as urban canyons or indoor areas. However, as we have seen in Section 1, most of the papers addressing this topic assume that a partial or even perfect knowledge of the TOA/RSS models parameters is available, so that nodes can quantify the detrimental effects of NLOS biases and try to mitigate them. Conversely, in this work, we steered our focus on the more realistic case of self-calibrated network, that is, the unknown model parameters are estimated adaptively \"on the field\". We anticipate that, even under the \"more simplistic\" assumption of LOS propagation, the hybrid TOA/RSS localization problem is highly non-linear and does not admit a closed-form solution, due to the presence of a very large number of nuisance parameters. Despite the relevance of the considered setup, to the best of our knowledge no practical solutions to this problem are available in the literature. For this reason, in this paper, we address the LOS-only localization problem, leaving the extension to the more challenging case of NLOS propagation as a future work. Starting from the set of \(N\) received signals (one from each anchor), the main goal is to estimate the target unknown position \(x_{b}\) based on RSS and TOA measurements, whose models are discussed in the following.