esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Error Function Analysis  
The error functions are an important statistical parameter used to measure the deviation of theoretically predicted isotherm parameters for evaluating the suitability of the nonlinearized empirical model onto experimental results. Various error functions have been established either empirically or based on theoretical considerations. To validate the best fitting of the adsorption isotherm of two- and three-parameter models as mentioned above, seven different statistical error functions, namely Residual Sum of Squares Error (ERRSQ/SSE) [22], Chi-square (\(\chi^{2}\)) [23], Coefficient of determination (R\({}^{2}\)), Average Relative Error (ARE) [24], Hybrid Fractional Error Function (HYBRID) [25], Marquardt's Percent Standard Deviation (MPSD) [26] and Root Mean Square Error (RMSE, polynomial p: the number of terms) [27; 28; 29], were assessed to describe the adsorption characteristics of methylene blue adsorbed onto the magnetic nanosorbent. The error analysis equations are conveyed as follows:  
\[\text{ERRSQ/SSE}=\sum_{i=1}^{\text{n}}\left(\text{q}_{\text{e, exp}}-\text{q}_{\text{e, cal}}\right)^{2} \tag{8}\]  
\[\text{Chi-square}^{2}=\sum_{i=1}^{\text{n}}\frac{\left(\text{q}_{\text{e, cal}}-\text{q}_{\text{e, exp}}\right)^{2}}{\text{q}_{\text{e, exp}}} \tag{9}\]  
\[\text{R}^{2}{=}1-\frac{\sum_{\text{n=1}}^{\text{n}}\left(\text{q}_{\text{e, exp}}-\text{q}_{\text{e, cal}}\right)^{2}}{\sum_{\text{n=1}}^{\text{n}}\left(\text{q}_{\text{e, exp}}-\overline{\text{q}_{\text{e, exp}}}\right)^{2}} \tag{10}\]  
\[\text{ARE}=\frac{100}{\text{n}}{\cdot}\sum_{i=1}^{\text{n}}\left(\frac{\left( \text{q}_{\text{e, exp}}-\text{q}_{\text{e, cal}}\right)}{\text{q}_{\text{e, exp}}}\right) \tag{11}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{\text{n=1}}^{\text{n}}\left(\text{q}_{\text{e, exp}}-\text{q}_{\text{e, cal}}\right)^{2}}{\text{n}-\text{p}}} \tag{12}\]  
\[\text{HYBRID}=\frac{100}{\text{n}-\text{p}}{\cdot}\sum_{i=1}^{\text{n}}\left( \frac{\left(\text{q}_{\text{e, exp}}-\text{q}_{\text{e, cal}}\right)}{\text{q}_{\text{e, exp}}}\right) \tag{13}\]\[\text{MPSD}=100\cdot\sqrt{\frac{1}{n-p}\cdot\sum_{i=1}^{n}\left(\frac{(q_{e,\,\text{ exp}}-q_{e,\,\text{cal}})}{q_{e,\,\text{exp}}}\right)^{2}} \tag{14}\]  
where n is the number of experimental data points, p (polynomial model) is the number of parameters in each isotherm model, qe,cal (mg g-1) is the theoretically calculated adsorption capacity of methylene blue adsorbed at equilibrium and qe,exp (mg g-1) is the experimental capacity of methylene blue adsorbed at equilibrium.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 3 Discussion
### Case Studies in Multi-Omics Platforms
#### 3.1.2 Disease-Specific Insights  
iODA has become a cornerstone in cancer research. A recent study utilized iODA to analyze the molecular drivers of therapy resistance in lung cancer, revealing miRNA-protein interactions that contribute to tumor heterogeneity. This insight has guided the development of personalized therapeutic strategies targeting specific pathways.  
In aging research, Aging Bank has enabled the study of molecular mechanisms underlying age-related diseases. By integrating epigenomics and transcriptomics data, the platform has facilitated the discovery of biomarkers predictive of neurodegenerative diseases like Parkinson's, providing a foundation for early diagnostics.
## 3 Results
### Engineering Mechanism of the Intelligent Respiratory Monitoring System
After the sampling, filtering, amplification, and transmission of the respiratory signals and machine learning, the dynamic respiratory curves and statistical data can be displayed in multi-terminal consumer electronics for real-time respiratory monitoring, health assessment, online consultation, and early disease warnings.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 7 Combination of LNN and RLSTM Based on Pre-Labeled Data  
In this section, a neural network model with the combination of LNN and RLSTM will be elaborated, which has the ability to identify (1) anomalies, (2) anomalous cycles, (3) anomalous frequencies and other anomalous changes (based on operator experience) that occur in a range of business scenarios.  
Figure 11 shows the data-flow diagram for training the anomaly detection neural network in this paper. The network service data mainly come from the terminal layer, which consists of various devices and mainly completes the operation of collecting raw data and reporting them. Taking the mobile communication industry as an example, these data will be captured by the nearby base stations. The base station computing layer realizes the response of basic services through reasonable deployment and deployment of storage capacity. The reported data from the base stations will be permanently stored by the cloud computing center. In the cloud computing center, our model is enabled to be used. Through the pre-processing of the data, the data take on the characteristics of a time series, when the network signal data are not tagged. During the initial input period of the model, to train the LNN, the data are initially labeled by using the quadrature method combined with compressed windows for outlier and anomaly cycle detection, respectively, for the network data. At this point, the data labels contain numerically anomalous outliers and anomalous cycles, and then manual anomalous network data are input (\"individual examples\" thatthe salesperson considers anomalous). The purpose of the preceding step is to solve the problem of missing LNN training data in the case of missing data labels. The labeled data will be substituted into the LNN, a sliding window and BP-based neural network model, for training, which will have the ability to accurately label the data on each given cycle. After training reaches a certain number, the LNN will replace all previous data pre-processing operations and directly label the initial data. The LNN will identify anomalies from the data perspective and business perspective, but due to the existence of the sliding window, the LNN labeling step is \"fragmented\", so the labeled data are substituted into the RSTM. The RLSTM is a recurrent neural network model with continuous training, which can solve the \"fragmentation\" phenomenon of LNN labeling. Through training, RLSTM will inherit the abnormal data recognition ability of LNN and further recognize abnormalities from the perspective of time-series continuity, so a neural network model that can accurately recognize data abnormalities is constructed. The final anomalous data detection results are shown in Figure 12.  
Figure 11: Flow chart of anomaly detection.  
Figure 12: _Cont._
###### Abstract  
Emotion plays a nuclear part in human attention, decision-making, and communication. Electroencephalogram (EEG)-based emotion recognition has developed a lot due to the application of Brain-Computer Interface (BCI) and its effectiveness compared to body expressions and other physiological signals. Despite significant progress in affective computing, emotion recognition is still an unexplored problem. This paper introduced Logistic Regression (LR) with Gaussian kernel and Laplacian prior for EEG-based emotion recognition. The Gaussian kernel enhances the EEG data separability in the transformed space. The Laplacian prior promotes the sparsity of learned LR regressors to avoid over-specification. The LR regressors are optimized using the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm. For simplicity, the introduced method is noted as LORSAL. Experiments were conducted on the dataset for emotion analysis using EEG, physiological and video signals (DEAP). Various spectral features and features by combining electrodes (power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU)) were extracted from different frequency bands (Delta, Theta, Alpha, Beta, Gamma, and Total) with EEG signals. The Naive Bayes (NB), support vector machine (SVM), linear LR with L1-regularization (LR_L1), linear LR with L2-regularization (LR_L2) were used for comparison in the binary emotion classification for valence and arousal. LORSAL obtained the best classification accuracies (77.17% and 77.03% for valence and arousal, respectively) on the DE features extracted from total frequency bands. This paper also investigates the critical frequency bands in emotion recognition. The experimental results showed the superiority of Gamma and Beta bands in classifying emotions. It was presented that DE was the most informative and DASM and DCAU had lower computational complexity with relatively ideal accuracies. An analysis of LORSAL and the recently deep learning (DL) methods is included in the discussion. Conclusions and future work are presented in the final section.  
emotion recognition; electroencephalogram (EEG); logistic regression; Gaussian kernel; Laplacian prior; affective computing
## 4 Discussion
In contrast, the AZUL and CHOY stations, situated over the storm area and specifically within the region of higher electrical activity, displayed lower amplitudes. This finding contrasts with expectations, as one would anticipate greater variations in the storm region (Zone 1). These discrepancies are clearly visible in Figure 10, where it can be seen that, as we move away from the storm area, the variability in the waves increases. These variations could be attributed to gravity and medium-scale wave propagation processes, as detailed in the introduction. The alignment and direction of the medium-scale waves (NE-SW) could explain the differences observed in Figure 16 between the day without a storm and the day with the storm, where these differences are accentuated. Therefore, we infer that the storms could have generated gravity waves and that the remarkable differences found could be due to the interaction of these waves with another source of gravity waves. In this context, the Andes Mountains, the Antarctic Peninsula, and the Patagonian region have the potential to generate such waves. These areas are considered the epicenter of the most intense gravity wave activity worldwide, as highlighted in the studies in [51; 52]. Finally, five stations close to the region where the storms developed were analyzed for both days. From the wavelet analysis, variabilities with periods less than or equal to 100 min, spectral amplitudes up to 1.4, and peak-to-peak DVTEC amplitudes up to 0.93 TECU were obtained during the day with the storm. Conversely, during the reference day, wavelets with periods less than 75 min, spectral amplitudes up to 0.2, and peak-to-peak DVTEC amplitudes up to 0.29 TECU were observed.
# An Efficient BGV-type Encryption Scheme for IoT Systems
## 3 Introduction of BGV
In this condition, the ciphertext outputted by Mul is as follows: \(c_{0}\leftarrow\{p_{i}c_{0}^{(1)}c_{0}^{(2)}+b_{j}c_{1}^{(1)}c_{1}^{(2)}\}\bmod q _{i}\) and \(c_{1}\leftarrow\{p_{i}(c_{0}^{(1)}c_{1}^{(2)}+c_{0}^{(2)}c_{1}^{(1)})+a_{j}c_{ 1}^{(1)}c_{1}^{(2)}\}\bmod q_{i}\)._  
Given the secret key \(s\), we have  
\[\langle ct,s\rangle=c_{0}+s\cdot c_{1}=\{p_{i}c_{0}^{(1)}c_{0}^{( 2)}+p_{i}sc_{0}^{(1)}c_{1}^{(2)}+p_{i}sc_{0}^{(2)}c_{1}^{(1)}+p_{j}s^{2}c_{1}^ {(1)}c_{1}^{(2)}-pe_{j}c_{1}^{(1)}c_{1}^{(2)}\}\bmod q_{i}\] \[=p_{i}\bmod q_{i}\cdot c_{0}^{(1)}c_{0}^{(2)}\bmod q_{i}+p_{i} \bmod q_{i}\cdot sc_{0}^{(1)}c_{1}^{(2)}\bmod q_{i}+p_{i}\bmod q_{i}\cdot sc _{0}^{(2)}c_{1}^{(1)}\bmod q_{i}+p_{j}\bmod q_{i}\] \[\cdot s^{2}c_{1}^{(1)}c_{1}^{(2)}\bmod q_{i}-pe_{j}c_{1}^{(1)}c_{ 1}^{(2)}\bmod q_{i}\] \[\
eq p_{i}(c_{0}^{(1)}+sc_{1}^{(1)})(c_{0}^{(2)}+sc_{1}^{(2)}) \bmod q_{i}-pe_{i}c_{1}^{(1)}c_{1}^{(2)}\bmod q_{i}\]  
However, if \(p_{i}\)=1 mod \(p\) and \(q_{i}=\prod_{j=0}^{i}p_{j}\) for \(i=1,\cdots,L\), we have \(p_{i}\bmod q_{i}\bmod p=p_{j}\bmod q_{i}\mod p=1\). Thus, \(\langle ct,s\rangle\bmod p=(c_{0}^{(1)}+sc_{1}^{(1)})(c_{0}^{(2)}+sc_{1}^{(2) })\bmod q_{i}\bmod p-pe_{i}c_{1}^{(1)}c_{1}^{(2)}\bmod q_{i}\bmod p\). The correctness condition is the same as BGV. If the error terms are less than \(q_{i}\), we also have \(\langle ct,s\rangle\bmod p=m_{1}m_{2}\). As a result, we do not need to generate a switch key for each level after the modification. Only one \(l\)-dimension switch key is enough for multiplication. Thus, the total size of public keys can be reduced from linear to constant. **Observation 2**. : _Two ciphertexts and their product are at the same sublayer after the modifications. The reason that each \(b_{i}\) should modulo \(t_{i}\cdot q_{i}\), but not \(q_{i}\) in BGV is the SwitchKey operation in Mul will lift elements from \(R_{q_{i}}\) to \(R_{t_{i}\cdot q_{i}}\). After replacing \(t_{i}\) with \(p_{i+1}\), the elements will be lifted from \(R_{q_{i}}\) to \(R_{q_{i+1}}\). The reason we move the SwitchModulu operation in Mul from the last step to the first step is to satisfy this requirement. Because all of the input elements belong to \(R_{q_{i}}\) in Mul, the accumulative errors are far less than \(R_{q_{i+1}}\). Thus, the decryption can succeed and another SwitchModulu operation can be omitted._  
The advantage of this change is all of the multiplication operations can be repeatedly done between \(R_{q_{i}}\) and \(R_{q_{i+1}}\). Subsequently, the multiplication times will not be limited by \(L\) in an \(L\)-level circuit and, thus, naturally become unlimited without bootstrapping. That is to say, \(L\) can be very small. We may use small number of large primes to replace large number of small primes. As a matter of fact, the product of several large primes is far less than the product of many small primes. For example, we know \(100^{2}\ll 2^{100}\). As a result, the ciphertext evaluation time will decrease significantly.
## 5 Collision Avoidance Trajectory Tracking Control
### USV Model  
The USV model is described as follows [41; 42]:  
\[\left\{\begin{array}{l}\dot{x}=u\cos\psi-v\sin\psi\\ \dot{y}=u\sin\psi+v\cos\psi\\ \dot{\psi}=r\\ \dot{u}=\frac{m_{12}}{m_{11}}vr-\frac{d_{11}}{m_{11}}u+\frac{1}{m_{11}}\tau_{u }\\ \dot{v}=-\frac{m_{11}}{m_{11}}ur-\frac{d_{22}}{m_{22}}v\\ \dot{r}=\frac{m_{11}-m_{22}}{m_{33}}uv-\frac{d_{31}}{m_{33}}r+\frac{1}{m_{33}} \tau_{r}\end{array}\right., \tag{25}\]  
where \((x,y,\psi)\) denotes the surge displacement, sway displacement and yaw angle. \((u,v,r)\) denotes the velocities of surge, sway and yaw directions. The control inputs are surge force \(\tau_{u}\) and yaw moment \(\tau_{r}\). \(m_{ii}\) and \(d_{ii}\) are the vessel inertia coefficients and damping coefficients.
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 7 Manufacturing Processes for CMCs
### Electrophoretic Deposition of a Ceramic Powder  
During electrophoretic deposition (EPD), a stable deposit is created by charging particles in a liquid medium moving with an oppositely charged electrode and coagulating.  
Figure 15: Fabrication procedure steps for CMCs.  
This process has been mainly used for the fabrication of oxide CMC matrices and fibers, as it reduces the processing times and improves the control of green body microstructures [187; 188]. The base material matrices used for this manufacturing process are aqueous or non-aqueous suspensions of ceramic particles and non-conductive NextelTM or conductive fibers such as SiC-Nicalon and carbon reinforcements [189; 190]. For the manufacturing of non-conductive fibers, the fiber weave goes on the front of the deposition electrode, while the ceramic deposit is formed on the electrode and grows around and through the fiber [186; 191].  
Two alternative processes, based on EPD, have been developed and evaluated, where Al2O3 particles and NexteITM 720 fibers were used. The first processing method presented the infiltration of single fiber mats and subsequent lamination, which led to high-density composites but with a small amount of microstructural damage during sintering [192]. On the other hand, the second approach used simultaneous infiltration of a variety of fiber mats, exhibiting a homogeneous green microstructure with a high particle packing density, allowing for the fabrication of thick components (>10 mm thick) [193]. The EPD technique has been used in a variety of ceramic composite systems during the decades, such as laminated composites [194], composite coatings [195], whisker-reinforced composites [196], and functionally graded materials [197].
# A Numerical Model to Estimate the Soil Thermal Conductivity Using Field Experimental Data
## 4 Conclusions  
In this paper, we proposed a numerical model to estimate the thermal conductivity from experimental measurements of the soil heat flux and soil temperature at different depths. The proposal is based on the Fourier Law adding a constant empirical parameter (\(\varepsilon\)). The \(\varepsilon\) parameter minimizes the uncertainties contained in the data from field experiments, leaving the model able to fit the behavior observed in the experimental data.  
Different numerical techniques of solving Equation (2) were used and were compared to the method used in the proposed model (Equation (3)). This study shows that isolate \(\lambda\) in Equation (2) (\(M1\), \(M2\), \(M3\)) can affect the accuracy of the soil thermal conductivity estimates. The use of the LSM to estimate the thermal conductivity (\(M4\) and \(M5\)) presented better results than the other methods, which allows us to conclude that LSM is an adequate technique for this estimation. However, the \(M5\) presented the best results, thus confirming the hypothesis that the addition of an empirical parameter is necessary and influences the accuracy of the soil heat flux estimate.  
Currently, many micrometeorological experimental fields are measuring atmospheric and soil variables to improve the weather and climate models. For the soil, the data are obtained similarly to the experimental design used in this work. Therefore, we believe the results obtained here will contribute to this issue since the methodology proposed improved the estimation on soil thermal properties, consequently the soil heat flux, an essential component for studies on surface energy balance closure.  
**Author Contributions:** Conceptualization, L.C.R., D.R.R., L.B.; methodology, L.C.R., D.R.R., L.B.; software, L.C.R.; validation, L.C.R.; T.Z.; formal analysis, L.C.R., D.R.R., L.B.; investigation, L.C.R., D.R.R., L.B.; writing-original draft preparation, L.C.R., D.R.R., L.B., T.Z., G.A.D.; writing-review and editing, L.C.R., D.R.R., L.B.; supervision, D.R.R.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors acknowledge the National Council for Scientific and Technological Development (CNPq, Brazil), the Coordination for the Improvement of Higher Education Personnel (CAPES, Brazil), and the Foundation for Research of Rio Grande do Sul State (FAPERGS).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Original—Tong Model** & \(M4\) & \(M5\) \\ \hline \(a\) & 1.88 & 1.14 & 1.25 \\ \(b\) & 1.67 & 1.07 & 1.01 \\ \(c\) & 3.90 & 14.06 & 10.02 \\ R\({}^{2}\) & 0.65 & 0.66 & 0.88 \\ RMSE & 0.0953 & 0.0941 & 0.0545 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Original and Calibrated parameters of Tong Model [6], and statistical index (R\({}^{2}\) and RMSE).
## 2 Related Work  
In recent years, DoS attacks have attracted the noteworthy consideration of researchers, as they can affect the routing performance of MANETs exceptionally. Most of the existing mechanisms focus only on the route discovery phase or the data transmission phase to mitigate malicious nodes. Moreover, many mechanisms have not used any attack models to judge the performance of the network. We have presented various mechanisms which are also used to isolate the malicious nodes from the MANETs. Some of the authors have proposed solutions, however, these solutions still have drawbacks. Table 1 lists some of the other existing techniques along with their drawbacks.  
\begin{table}
\begin{tabular}{c c c c} \hline
**S.No** & **Author \& Year** & **Technique** & **Drawbacks** \\ \hline
01 & [PERSON]. [PERSON] (2018) [21] & Frame-checking sequence & The process is slow and has computational complexity \\ \hline
02 & [PERSON] (2017) [22] & Bair request & The delay is significant if the route is long \\ \hline
03 & [PERSON] (2017) [23] & Fuzzy logic & Fails to detect smart gray holes \\ \hline
04 & [PERSON]. [PERSON] (2018) [24] & Location base & Required extra hardware, i.e., antenna, GPS, etc. \\ \hline
05 & [PERSON] (2014) [25] & Light-weight intrusion detection system (IDS) & The packet delivery ratio is affected due to gray holes being undetected \\ \hline
06 & [PERSON] (2016) [26] & Destination Sequence & The method fails when smart gray holes send an average sequence number \\ \hline
07 & [PERSON] (2011) [27] & Cooperative bait detection scheme (GBDS) & The method is unclear and complex \\ \hline
08 & [PERSON] (2017) [28] & AODV, MBDP-ADDV & High routing overhead \\ \hline
09 & [PERSON] (2018) [29] & AODV, BAODV, IBSAODV, MBDP-ADDV & Fails to detect smart gray holes \\ \hline
10 & [PERSON] (2016) [30] & Crypto-key based on Diffie-Hellman & Fails to detect gray holes \\ \hline
11 & [PERSON] (2017) [31] & Dynamic threshold cumulative sum & Only detects black hole attacks \\ \hline
12 & [PERSON] (2017) [32] & Extended Data Routing Information & Fails to detect smart gray holes \\ \hline \end{tabular}
\end{table}
Table 1: Existing techniques along with their drawbacks.
# Preliminary Analysis of a Lightweight and Deployable Soft Robot for Space Applications
## 2 Robot Concept Design and Models
### Robot Kinematic Model  
The inflatable link model is adopted to improve the simple robot model introduced before, allowing to consider the deflections due to the soft structures. The link model is extended to evaluate the deflections along two orthogonal axes, neglecting the torsional deformations. Therefore, two torsional springs are considered for each link. The introduction of the pseudo rigid body model for the inflatable links adds additional uncontrolled degrees of freedom at the entire robotic arm. The robotic arm reaches 7 degrees of freedom: 3 for the actuated joints and 2 for each link introduced by the virtual springs. A 3D representation of the robot is shown in Figure 3.  
Figure 3: Robot 3D Model.  
Figure 2: Inflatable link pseudo-rigid body model, 2D scheme.  
The new kinematic model is shown in Figure 4, and the D-H parameters are explicated in Table 2. The joint variables \(\mathbf{q}=~{}[\theta_{1},\theta_{2},\theta_{3},~{}\theta_{4},~{}\theta_{5},\theta_{6},~{}\theta_{7}]^{T}\) are divided in motor joint variable \(\mathbf{q}_{m}=~{}[\theta_{1},~{}\theta_{2},~{}\theta_{5}]^{T}\) and virtual spring joint variables \(\mathbf{q}_{k}=~{}[\theta_{3},~{}\theta_{4},~{}\theta_{6},~{}\theta_{7}]^{T}\). In all this work the notation with the subscript \(m\) indicates the motor-associated components \(i=1,~{}2,~{}5\) and \(k\) indicates the spring-associated components \(i=3,~{}4,~{}6,~{}7\).