esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion of the Results
### Effect of the Circumferential Joint
#### 4.2.1 Effect of the Circumferential Shear Stiffness
As shown in this case, the bending moment in the neighboring section related to segment F is almost twice the bending moment of segment F.  
It should be noted that the phenomenon of bending moment transmission in the staggered assembly will increase the maximum values of positive and negative bending moments in the whole ring, although the bending moments in longitudinal joint sections become smaller which is beneficial for the safety of the joints. Relevant specific measures should be taken for the neighboring segment design, like increasing the amount of reinforcement, in case of possible cracks at the segment bodies due to the bending moment transmission from longitudinal joints in the neighboring rings.
# Analysis on Response of a Single Pile Subjected to Tension Load Considering Excavation Effects
## 4 Difference Numerical Method to Solve the Load Transfer Model
### Iterative Algorithm for the Proposed Difference Scheme  
Considering the interaction between shaft resistance and shear-induced soil displacement, Equations (23) and (24) comprise a system of nonlinear equations. An iterative updating procedure is adopted to solve Equations (23) and (24). The iterative updating procedure consists of the following steps:  
1. Calculate the greenfield soil heave using Equation (9). Assume that the initial pile displacement and shear-induced soil displacement at each node are equal to zero.
2. Calculate the pile-soil relative displacement and pile shaft resistance of each node using Equations (1) and (11). As a consequence, the parameter \(\lambda_{1,i}\) of each node could be determined.
3. Calculate the node stiffness matrix \(\mathbf{K}_{1}\) and node force vector \(\mathbf{F}_{1}\). Update the pile displacement of each node by solving Equation (23).
4. Update the pile-soil relative displacement, the pile shaft resistance, and the shear-induced soil displacement of each node using the new pile node displacement. Then, update the parameter \(\lambda_{1,i}\) of each node.
5. Update the node stiffness matrix \(\mathbf{K}_{1}\) and node force vector \(\mathbf{F}_{1}\). The new pile displacement at each node can thus be obtained.
6. Repeatedly execute steps 4 and 5 until convergence is achieved.
7. Obtain the pile-soil relative displacement after excavation. Calculate the residual pile-soil relative displacement \(S_{p}\) at each node. Assume that the initial pile displacement \(\omega_{p2}\) at each node is equal to the pile head settlement \(\omega_{p0}\), and shear-induced soil displacement \(\omega_{s\tau 2}\) at each node is equal to zero.
8. Calculate the pile-soil relative displacement and pile shaft resistance of each node using Equations (2) and (18). Consequently, the parameters \(\lambda_{2,i}\) of each node could be determined.
9. Calculate the node stiffness matrix \(\mathbf{K}_{2}\) and node force vector \(\mathbf{F}_{2}\). Update the pile displacement of each node by solving Equation (29).
10. Update the pile-soil relative displacement, the pile shaft resistance, and the shear-induced soil displacement of each node using the new pile node displacement. Then, update the parameters \(\lambda_{1,i}\) of each node.  
(11) Update the node stiffness matrix \(\mathbf{K}_{2}\) and node force vector \(\mathbf{F}_{2}\). The new pile displacement at each node can thus be obtained. (12) Repeatedly execute steps 10 and 11 until convergence is achieved.  
Following the above steps, the iterative procedure was implemented in the commercial software MATLAB. Executing the proposed approach only requires a few seconds in MATLAB, which demonstrates the proposed method is a practical and economical tool to evaluate the uplift pile load-displacement behavior considering excavation effects.
###### Abstract
It also displaces essential metal cofactors in enzymes, such as superoxide dismutase, impairing bacterial defenses against oxidative stress [6; 7]. Furthermore, zinc interacts with membrane components such as phospholipids and membrane-bound proteins, leading to compromised membrane integrity, altered permeability, and ion imbalance, which together contribute to its bacteriostatic and bactericidal effects. The mechanism of action of zinc-based antimicrobials is dependent on the solubility, dissociation properties, and ionic bioavailability of zinc compounds. For instance, zinc chloride is highly soluble and readily dissociates into Zn\({}^{2+}\) ions, which induces increased antimicrobial activity. In contrast, zinc sulfate exhibits different solubility and reactivity that may alter its impact on bacterial biofilms [7]. Experiments showed that zinc nanoparticles can prevent bacteria adhesion and inhibit biofilm growth in _Pseudomonas aeruginosa_ and _Klebsiella pneumoniae_[8]. However, the mechanisms by which zinc salts regulate biofilm formation in fecal indicator bacteria such as _E. coli_ and _E. faecalis_ have not been well elucidated. Evidence also shows that exposure to zinc may influence bacterial antibiotic resistance. Long-term exposure to zinc has been linked to multidrug resistance (MDR) in _E. coli_, particularly against aminoglycos, cephalospor, and sulfonamides, necessitating research on its involvement in antibiotic susceptibility [9]. Additionally, zinc regulates the response of bacteria to stress, likely modulating virulence factors and quorum-sensing gene expression that are responsible for antibiotic resistance and biofilm formation [10]. While there is a growing interest in the antimicrobial and biofilm-modulating characteristics of zinc, significant knowledge gaps remain concerning its effects on _E. coli_ and _E. faecalis_ biofilms in water distribution systems. This study evaluates the antibacterial effectiveness of zinc salts using minimum inhibitory concentration assays, biofilm formation tests, and antibiotic sensitivity profiling to assess their impact on these organisms. It aims to inform the potential design of antimicrobial strategies or additives for pipeline materials, to reduce microbial colonization. Although this research does not fully elucidate molecular mechanisms, it provides essential phenotypic evidence of zinc's influence on bacterial growth, biofilm development, and antibiotic susceptibility. These findings contribute to a broader understanding that can inform the future design of antimicrobial pipeline materials and support the development of biofilm control strategies aimed at enhancing water quality and infrastructure resilience.
###### Abstract  
In the contemporary context, the significance of detecting harmful gases cannot be overstated, as it profoundly affects both environmental integrity and human welfare. In this study, theoretically, density functional theory was employed to explore the adsorption behavior of three prevalent hazardous gases, namely CO, NO\({}_{2}\), and SO\({}_{2}\), on silver-atom-modified tungsten disulfide (WS\({}_{2}\)) monolayer. The multifaceted analysis encompasses an array of critical aspects, including the adsorption structure, adsorption energy, electron transfer, and charge density difference to unravel the adsorption behavior. Further exploration of electronic properties encompassing band structure, density of states (DOS), and work function was conducted. The ambit of our exploration extends to the desorption properties based on adsorption-free energies. Among these gas molecules, NO\({}_{2}\) stands out with the highest adsorption energy and the most substantial electron transfer. Notably, each of these adsorption processes triggers a redistribution of electron density, with NO\({}_{2}\) exhibiting the most pronounced effect. Furthermore, the adsorptions of CO, NO\({}_{2}\), and SO\({}_{2}\) induce a noteworthy reduction in the band gap, prompting the reconfiguration of molecular orbitals. Additionally, the adsorption of these gases also leads to an increase in the work function of Ag-WS\({}_{2}\) to a different extent. Our investigation of desorption properties uncovers that Ag-WS\({}_{2}\) can adeply function at ambient temperatures to detect CO and SO\({}_{2}\). However, for NO\({}_{2}\) detection, higher temperatures become imperative due to the necessity for poison removal. The implications of our findings underscore the tremendous potential of Ag-WS\({}_{2}\) as a sensing material for detecting these hazardous gases. Our research extends to the broader realm of surface modification of transition metal dichalcogenides and their promising applications in the domain of gas sensing.  
Keywords:hazardous gases; Ag doped WS\({}_{2}\) monolayer; DFT; adsorption; gas sensor +
Footnote †: journal: Applied Sciences
## 3 Methodology
### The Modified Ratio Vegetation Index (MRVI) for Detecting Wheat Canopy \(C_{ab}\)  
It was reported that the ratio of NIR to green (RVI2) was linearly related to the total leaf N accumulation and that it was useful for monitoring the rice N status [43]. However, the higher deviation was found using the RVI2 for monitoring the winter wheat C\({}_{\text{ab}}\) in exp1 at Yucheng Station [44]. Thus, the study involved the addition of blue band information to modify the ratio index (RVI2), to improve the inversion accuracy using multispectral images, for example, MODIS. The new modified ratio vegetation index (MRVI) was defined as follows:  
\[\text{MRVI}=\frac{1}{\alpha}\Big{[}\sqrt{\rho_{\text{NIR}}}\times\rho_{\text {blue}}/\left(\rho_{\text{green}}-\rho_{\text{blue}}\right)^{2}\Big{]} \tag{2}\]  
where \(\rho_{\text{NIR}}\) is the reflectance at NIR, \(\rho_{\text{green}}\) is the reflectance at green band, \(\rho_{\text{blue}}\) is the reflectance at blue band, \(\alpha\) is the empirical coefficient, which controls the dispersion of MRVI values and lets the value of MRVI change between 0 and 1, was set to 35 in the study.
## 4 Methodology
### Feature Selection (FS)  
As the application of the FaaC method considerably increases the number of variables that compose one sample of the dataset compared to the original one, a feature selection step is required to reduce the number of variables keeping the most meaningful ones and removing irrelevant features. In this work, a Least Absolute Shrinkage and Selection Operator (LASSO) [35] model was used to perform feature selection. LASSO is a simple linear model which is typically used to address problems where the number of input variables \(P\) is high. Let us suppose a dataset defined as \(\mathcal{D}=\{\mathbf{x_{i}},y_{i}\}_{i=1}^{N}\) consisting of \(N\) samples, where \(\mathbf{x_{i}}\in\mathcal{R}^{P}\) is a \(P\)-dimensional vector representing the input variables and \(y_{i}\) being the response variable for the \(i\)-th sample. In a binary classification problem, where the response variable takes two values (e.g., 0/1, or negative/positive, etc.), LASSO includes an \(l_{1}\)-penalty term to the minimization problem solved in a linear regression to find the optimal \(\beta\) coefficients, as shown in Equation (1):  
\[\mathbf{\hat{\beta}}_{\lambda}=\operatorname*{arg\,min}_{\mathbf{\beta}}\ \ ||\mathbf{y}-f(\mathbf{\beta}X^{T})||_{2}^{2}+ \lambda||\mathbf{\beta}||_{1} \tag{1}\]  
where \(f(\mathbf{\beta}X^{T})\) is the logistic or sigmoid function and \(\lambda\) is the regularization rate which controls how many input features are selected. In this case, the problem addressed consists of a multi-label classification problem with \(K=5\) labels, i.e., \(y_{i}\in[0,1,...,K-1]\). In this sense, \(K\) LASSO models are trained in parallel as described before, but with an extra grouped-lasso penalty added on all the \(K\) coefficients for a particular input feature, which makes them all be zero or nonzero together, i.e., the retained variables will be those which are significant to predict all \(K\) labels. The final response of this multinomial LASSO is obtained through the _softmax_ function depicted in Equation (2):  
Figure 2: Example illustration on how the FaaC approach works as a feature engineering process.  
\[Pr(y_{i}=k|X)=\frac{Pr(y_{i}=k|X)}{\sum_{z=0}^{K-1}Pr(y_{i}=z|X)} \tag{2}\]
## 2 Related Works
Apnea events are accompanied by cyclic variations in RRIs, which consist of bradycardia during apnea, followed by tachycardia during cessation [42]. This pattern has been utilized in previous studies to develop new methods to distinguish apnea from normal events in the RRI data. For this purpose, RRIs obtained from ECG signals were divided into 1 min-long segments. Then, each segment was labeled as apnea or normal events by experts and used as input data. [PERSON] et al. used three 1 min RRIs with ResNet to classify apnea and normal events [35]. [PERSON] et al. used five 1 min RRIs as input data using a modified LeNet-5 CNN model [36]. [PERSON] et al. implemented a multiscale dilation attention CNN model using 1 min RRI segments [37]. These studies on apnea detection also achieved high accuracies of approximately 88-94%. However, most previous studies on the automated prediction of age were based on alterations in cardiac autonomic regulation and implemented feature-dependent classical machine learning methods using HRV features, as described in the Introduction section. To the best of our knowledge, only one previous study has demonstrated deep learning-based age prediction using RRI data [38]. That study used a deep learning architecture comprising a CNN and LSTM to classify seven age groups of 181 healthy subjects. Two prediction methods--classification and regression--were separately used for age prediction because classification does not consider orders of age classes. In the regression analysis, the closest age decade was selected as the predicted class. The highest accuracy achieved with the classification and regression methods was 10.81% and 32.43%, respectively. The overall performance was not adequate, even after considering the number of total classes, suggesting that RRI-based age prediction using deep learning is difficult. In this study, we developed a novel end-to-end learning method for classifying age groups in healthy subjects using RRIs. We combined the classification loss and MSEfunctions to enable the proposed model to learn the ordinal relationships of the age classes. A modified ResNet was implemented with 5 min-long RRI sequences as input data. In addition, we established an HRV feature-based prediction model using the same RRI data for performance comparisons. Finally, we demonstrated age prediction using both RRI and HRV data.
## 4 Results
### Constructs and Methodological Approaches
#### 4.3.3 General Attitude, Situation Awareness, and Stress
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Collection Method for General Attitude (n)** & **Parameter** & \(n_{p}\) \\ \hline Self-Defined Questionnaire (15) & _N/D_ & 15 \\ \hline \multirow{2}{*}{Interviews (5)} & Semi-Structured Interview & 4 \\ \cline{2-3}  & Unstructured Interview & 1 \\ \hline Standardized Questionnaire (2) & BIG 5 & 2 \\ \hline Observation (1) & _N/D_ & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: General Attitude collection methods and parameters. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Collection Method for Situation Awareness (n)** & **Parameter** & \(n_{p}\) \\ \hline \multirow{8}{*}{Eye Tracking/Gaze Behavior (7)} & Gaze Duration & 5 \\ \cline{2-3}  & Gaze Number & 4 \\ \cline{2-3}  & Gaze Percentages & 3 \\ \cline{2-3}  & Glancing Behavior & 2 \\ \cline{2-3}  & Blink Behavior & 1 \\ \cline{2-3}  & Reaction Time & 1 \\ \hline Self-Defined Questionnaire (6) & _N/D_ & 6 \\ \hline Standardized Questionnaire (3) & Situational Awareness Rating Technique (SART) & 3 \\ \hline Probing (2) & Situation Awareness Global Assessment Technique (SAGAT) & 3 \\ \hline \multirow{8}{*}{TOR Performance (2)} & Lateral Position & 2 \\ \cline{2-3}  & Reaction Time & 2 \\ \cline{2-3}  & Acceleration & 1 \\ \cline{2-3}  & Time to Collision & 1 \\ \hline Interviews (2) & Semi-structured Interview & 2 \\ \hline \multirow{2}{*}{Observation (1)} & Accuracy & 1 \\ \cline{2-3}  & Reaction Time & 1 \\ \hline Secondary Task Performance (1) & NDRT Performance & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Situation Awareness collection methods and parameters. _Stress_ is investigated by psycho-physiological (n\({}_{\text{p}}\) = 11) and self-rated data (n\({}_{\text{p}}\) = 8). Hence, standardized questionnaires like the Short Stress State Questionnaire (SSSQ) [103], Dundee Stress State Questionnaire (DSSQ) [104], or the Driver Stress Inventory (DSI) [105] are used, but also heart rate variability, GSR and EMG. Observation, interviews and driving performance measures were only used by single papers. See Table 8. In total, n\({}_{\text{p}}\) = 17 different parameters were collected across all papers investigating stress--80% of all studies regarding stress were performed in a driving simulator and 20% in a real vehicle--respectively, 7% used either static text or a Wizard-of-Oz setup.
## 5 Calibration of STMD Using Numerical Optimization
### Analysis of the Optimized STMD at Its Performance Limit  
#### 5.4.1 Frequency Responses  
Figure 9 presents the frequency responses for the optimized STMD with mass ratio \(\mu=1\%\) and the highest achievable efficiency at \(K=3\). The results for the optimized STMD are compared with those for the sub-optimal STMD and the ATMD (both with the same \(\mu\) and \(K\)). For additional context, this figure also includes results for the TMD with \(\mu=1\%\) and \(3\%\).  
Figure 9a confirms that the optimized STMD performs significantly better than the sub-optimal STMD and nearly as well as the ATMD with the same \(K\). More precisely, while the shapes of the responses for the STMD and ATMD are slightly different, the left peak of the STMD response is 2.4% higher and the right peak is slightly smaller than that of the ATMD.  
Significantly, the maximum amplitude of relative displacement for the optimized STMD is not greater than that for the TMD and ATMD with the same mass ratio. In fact, the relative displacement for the optimized STMD is lower than that for the ATMD across the entire frequency range, as shown in Figure 9b.
## 5 Unicast Communication
### Key Management for Unicast Communication  
Messages in AMI systems were analyzed, and the results revealed that the unicast transmission method comprises three distinct kinds of messages: the remote load control, meter data, and subscription to or cancellation of DR projects. The messages may proceed either way, from the MS to the SX or from the SX to the MS. It is essential that both the secrecy and the integrity of the communications are maintained throughout the process of communication. In order to accomplish this goal, the session key has to be reset at the beginning of each session.  
There are three distinct groups into which the key management steps for unicast communication can be divided. The phases for the unicasting communication method are shown in Figure 5.  
Figure 5: Steps for unicasting communication process.  
An initial session key generation is required. The session key ski is built in Steps 1.1 and 1.2 using the metering data Mi, the metering date CDATE, the value Counti, and the user key ki. In Step 1.3 and Step 1.4, upcoming information for sharing is encrypted and signed using this session key before it is sent. In step 2, the data that have been encrypted together with the signature value are sent to the recipient. In step 3, the receiver is able to obtain the components that are required to produce the session key. Step 3.1 and Step 3.2 are responsible for the generation of the session key ski. It is possible to decode the data and verify the signature using the session key provided. Step 3.3 and Step 3.4 are responsible for verifying the signature between the transmitter and receiver before extracting the data using the decryption function (DE). Figure 6 shows the flowchart for the unicasting communication process.
# Impact of Mesh Resolution and Temperature Effects in Jet Ejector CFD Calculations
## 2 Materials and Methods
### Simulation Campaign  
The simulation campaign was multistage. It consisted of first creating a line of the working map (\(\tau_{\text{sp}}\) = 0.26) of the considered jet ejector with constant values for primary pressure (p\({}_{\text{P}}\) = 35 bars), primary temperature (T\({}_{\text{P}}\) = 380 K), secondary temperature (T\({}_{\text{s}}\) = 313 K), and p\({}_{\text{out}}\) changing. The values of \(\tau_{\text{op}}\) were adjusted throughout the simulations in order to complete the line of the jet ejector working map. This line of the working map has been plotted by using two meshes--_carser_ and _refined_.  
The second stage was to then repeat the calculation of the \(\tau_{\text{sp}}\) = 0.26 line with the _refined_ mesh, while changing the inlet temperatures (T\({}_{\text{P}}\) and T\({}_{\text{s}}\)) proportionally in order to determine the sensibility of the jet ejector performance to these changes. A scaling coefficient \(\zeta\) is introduced to alter both inlet temperatures while maintaining a controlled variation systematically. The values used for this coefficient and temperatures are expressed in Table 4. The chosen \(\zeta\) values assured the working fluid to be in a supercritical phase and that the results make physical sense.  
The last stage of the work has as an objective to change only the primary temperature (T\({}_{\text{p}}\)). To do so, we conducted the third part of the analysis, multiplying the T\({}_{\text{P}}\) by a factor of 1.2 (so T\({}_{\text{P}}\) = 456 K), but keeping the T\({}_{\text{s}}\) constant (T\({}_{\text{s}}\) = 313 K).
# On Multi-Hop Decode-and-Forward Cooperative Relaying for Industrial Wireless Sensor Networks
## Appendix A Bit Error Rate Performance Analysis  
### Derivation for the Average BER \(\mathcal{P}_{\ell}\) in Proposition 1  
Let \(\mathcal{P}_{\ell}(e|\gamma)\) be the conditional BER of the \(\ell\)-th hop conditioned on SNR \(\gamma\). By interpreting the MCA channel as a Gaussian-mixture model, the BER for M-ary PSK signals as a function of the SNR can be expressed as [49]  
\[\mathcal{P}_{\ell}(e|\gamma)=\sum_{n=0}^{\infty}\alpha_{n}\cdot\frac{2}{ \zeta_{M}}\sum_{i=1}^{\max(\frac{M}{4},1)}Q\bigg{(}\sqrt{\frac{A\gamma(1+\rho )}{(n+A\rho)}}\cdot\eta_{i}\bigg{)}, \tag{10}\]  
where \(\zeta_{M}=\max(\log_{2}\mathrm{M},2)\), \(\eta_{i}=\sin\Bigl{(}\frac{(2-1)\cdot\pi}{M}\Bigr{)}\), \(\alpha_{n}\) is given in (8) and \(Q(\cdot)\) is the Gaussian Q-function.  
The average BER \(\mathcal{P}_{\ell}\) of the \(\ell\)-th hop over Nakagami-\(m\) fading in the presence of impulsive noise is given by  
\[\mathcal{P}_{\ell}=\int_{0}^{\infty}\mathcal{P}_{\ell}(e|\gamma) \cdot f_{\gamma_{1}}(\gamma)\,d\gamma=\sum_{n=0}^{\infty}\alpha_{n}\cdot\frac {(mN_{0})^{m}}{(\Omega E_{b}P_{Rx,\ell+1})^{m}\Gamma(m)}\cdot\frac{2}{\zeta_{ M}}\sum_{i=1}^{max(\frac{M}{4},1)}\cdot\int_{0}^{\infty}\gamma^{m-1}\] \[\cdot Q\bigg{(}\sqrt{\frac{A\gamma(1+\rho)}{(n+A\rho)}}\cdot\eta _{i}\bigg{)}\cdot\exp\Bigl{(}-\frac{m\gamma N_{0}}{\Omega E_{b}P_{Rx,\ell+1}} \Bigr{)}\,d\gamma. \tag{11}\]  
The indefinite integral in above expression is in the form of \(I(m,\mu_{1},\mu_{2})=\int_{0}^{\infty}\gamma^{m-1}\cdot Q(\mu_{1}\sqrt{\gamma })\cdot\exp(-\mu_{2}\gamma)\,d\gamma\), which has the following solution [50]:  
\[I(m,\mu_{1},\mu_{2})=\frac{u_{2}^{m}\cdot\Gamma(m)}{2}-\sqrt{\frac{u_{1}^{2}} {2\pi}}\cdot\frac{\Gamma(m+\frac{1}{2})}{\bigl{(}u_{2}+\frac{u_{1}^{2}}{2} \bigr{)}^{m+\frac{1}{2}}}\cdot 2F_{1}\Bigl{(}1,m+\frac{1}{2};\frac{3}{2};\frac{u_{1}^{2}} {u_{1}^{2}+2u_{2}}\Bigr{)}, \tag{12}\]  
where \({}_{2}F_{1}(a,b;c;x)\) is the Gauss hypergeometric function.  
Utilizing (12) in (11), we obtain the closed-form expression of the average BER \(\mathcal{P}_{\ell}\) of the \(\ell\)-th hop as shown in (18) and (19).