esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Biometrics Mapping Network Based on DNN Architecture
#### 3.2.3 Training Network  
As mentioned above, our proposed DNN model includes two components: feature extraction and binary code mapping. So as to effectively learn the mapping between biometric image and random binary code, we combine three objective functions to implement an end-to-end training network.  
First, for the feature extraction component, we use ArcFace loss [55] as classification loss to train this component, which is used to generate a discriminative feature vector for the user's face image. Hence, the first objective function \(J_{1}\) is expressed by the ArcFace loss. Second, for the binary code mapping component, the output of this network is an \(l\)-dimensional binary code; this is actually a regression task. To reduce the quantization loss between mapping real-value \(Y\) and binary codes \(K\), the second loss is defined as:  
\[J_{2}=\frac{1}{N}\sum_{i=0}^{N}\|Y_{i}-K_{i}\|^{2} \tag{4}\]  
where \(N\) denotes the batch size, and \(i\) represents \(i\)-th sample. Moreover, the binary code is high entropy, that is, the distributions of 0 and 1 are equal probability. To maximize the entropy of the binary code, the third objective function is selected as:  
\[J_{3}=\frac{1}{N}\sum_{i=0}^{N}\|\text{mean}(Y_{i})-0.5\|^{2} \tag{5}\]  
where mean \((Y_{i})\) donates average of \(Y_{i}\). Therefore, the final loss \(L\) can be defined as:  
\[L=\alpha\ J_{1}+\ \beta J_{2}+\gamma J_{3} \tag{6}\]  
where \(\alpha\), \(\beta\), and \(\gamma\) are the scale factor of each term, respectively.  
We give the implementation process in Algorithm 1. In our training network process, binary codes \(K\) are firstly assigned by the random binary code generator module according to different users. Then, we can set up the mapping relationship between biometric images \(X\) and binary codes \(K\). Next, we initialize the weight parameter \(W\) and bias parameters \(b\), and the full objective function as the mentioned Equation (6) is adopted to train our network. Subsequently, multiple pairs of \(X\) and \(K\) are fed into the DNN model to update parameters \(W\) and \(b\) by using a stochastic gradient descent (SGD) method. Finally, the parameters are computed to obtain the trained model parameters. All steps are presented in Equation (1). To improve the security for preventing information leakage, a random binary code is assigned to each user and used as the label data to train the biometrics mapping model based on the DNN framework. Therefore, during every new enrolment, we should assign a new random binary code to a new subject, and then retrain the network to learn the mapping between the new biometric image and binary code, which can provide better accuracy and security.
## 4 Analysis
### Security Analysis
#### 4.2.3 Relay Nodes' Deferred Forwarding  
In this case, the adversary may attempt to make compromised relay nodes hold the packet for a period of time before forwarding it. The adversary may also launch the aforementioned attacks, such as reporting the false time differences at the same time. When the relay nodes hold the packet deliberately before forwarding, the next relay node will receive packets a bit later. Nonetheless, compromised nodes cannot manipulate the time difference \(\Delta T_{i}\) reported by other normal relay nodes.  
Therefore, the transmission delay is still correct, except the delays \(D_{i}\) and \(D_{i+1}\), which are related to the compromised node \(i\). Then, there are only \(D_{i}\) and \(D_{i+1}\) to deviate from the sample mean, which can be easily detected according to the method described above.
## 5 WST Applications
### Environmental Monitoring  
WSN become an important issue in environmental monitoring. The relatively low cost of the devices allow the installation of a dense population of nodes that can adequately represent the variability present in the environment. They can provide risk assessment information, like for example alerting farmers at the onset of frost damage and providing better microclimate awareness.
### Tar Concentration  
As described previously, tar sampling was conducted wet chemically and two sample analysis methods were used: GC analysis and gravimetric analysis. By GC analysis 18 tar species (named in [14]) were quantified. In the following, the GC tars are shown as ECN tar classes [40]. The majority of the GC-detectable tars were found to be benzene, toluene, xylene (BTX) included in the here used classes \"benzene\" and \"ECN3\". In previous work [36] it was found, that also unneglectable amounts of heterocyclic species, mainly pyridine, are present in tar from sewage sludge gasification. However, pyridine was not quantified in the GC analysis presented in this work since the used GC was not calibrated for that.  
The gravimetric analysis detects mostly heavy tars, but also inorganic components e.g., salts from ammonia, sulfur and chlorine can be present (see previous work [14; 36]). The carbon mass fraction in the gravimetric tars was found to be around 0.5 kg kg\({}^{-1}\), which has to be considered when using the presented tar concentrations for calculating a carbon mass balance.  
Figure 5: Permanent gas concentrations for different \(n_{\text{WHSV}}\) in dry N\({}_{2}\)-free basis, for H\({}_{2}\)O wet N\({}_{2}\)-free basis and dry N\({}_{2}\)-free gas yield per dry ash free fuel.  
All concentrations are shown as tar mass per volume of dry and N\({}_{2}\)-free syngas in STP conditions. The tar yields can be calculated by multiplying the tar concentration with the gas yields from Section 3.1.  
In the following, the influence of different operation conditions on the tar concentrations are shown. In this study the influence of the operation parameters \(n_{\text{O2}}\), \(\vartheta\), \(n_{\text{SC}}\), \(n_{\text{WHSV}}\) is shown. The tar concentration is also significantly dependent on the catalytic activity of the bed material e.g. in case of limestone or dolomite addition which was not subject of this work but was investigated in literature [51].
## Materials and Methods
### System Suitability and Quality Control  
A set of QC samples was produced by pooling 1 mL from 50 urine samples (including urine samples used in this paper) and aliquoting this into 1 mL samples. These acted as technical replicates run with each batch of study samples to provide a reference to indicate temporal stability measure HS-SPME-GC-MS. Parts of the SPME fibre or stationary phase of the GC column contain polysiloxanes that can degrade to produce volatile siloxanes [6]. Therefore, before every batch of sample processing, a \"laboratory air\" (uncapped, empty vial) was run, such that the SPME fibre sampled the air for 20 min at room temperature. We ran \"blank\" samples (capped, empty vials) after every 8 study samples. Compounds appearing in either laboratory air or blank samples revealed impurities and were considered contaminants if they were present in >50% of controls and blanks. These were removed from statistical analysis to ensure that analysed VOCs originated from individual urine samples being tested and prevented carry-over of VOCs on the SPME fibre between samples [5]. Collectively, QC, blanks, and laboratory air samples made up a set of system suitability samples, that were processed under the same experimental conditions as'real' samples, and assured analytic metrics were \"fit for purpose\" [52].
## 2 Materials and Methods
### Experimental Set-Up for a Nonreactive Radiotracer (HTO) and a Reactive Radiotracer (Se and Cs)  
Firstly, tritium (\({}^{3}\)H, \(\beta\)-decay, and a half-life of approximately 12.3 years) served as the nonreactive radiotracer, and tritiated water (HTO) was used as a conservative or nonreactive tracer due to the fact that it is representative of all chemical elements that do not exhibit retention in the solid phase. The three sets of granite columns were prepared at the same time to observe the HTO, Se(IV), and Cs transport characteristics. Generally, the effluent was collected and measured for the purpose of plotting BTCs of different lengths from a series of up-flooding (sorption) processes [15; 20; 25]. The HTO samples were measured using a liquid scintillation analyzer (LSA, Perkin Elmer Tri-Carb 3170 TR/SL) with a counting efficiency of 51%, and 10 mL of the sample was combined with 10 mL of a scintillation cocktail (Packard LLT USA) in a 20 mL polyethylene counting vial. All chemicals used in the experiments were of analytical purity, and deionized water (DIW) was used throughout the experiments. After finishing the HTO experiments, both reactive radiotracers, Se(IV) and Cs, were spiked with selenium dioxide (SeO\({}_{2}\), Sigma Aldrich, Germany) and cesium chloride (CsCl, Merck, Germany) as stable isotope tracers, respectively. Both Se(IV) and Cs followed the same experimental process of up-flooding (sorption) processes in HTO. Three mL of the solution was reserved to perform the concentration analysis for Se(IV), using ICP-OES, and for Cs by the flame atomic absorption spectrometer (FAAS, iCE3000, Thermo, Germany).
## 4 Higher-Order Cubature Kalman Filter Design  
### Vehicle State Estimation Algorithm  
According to the nonlinear vehicle dynamics model, the vehicle state is estimated. For the general nonlinear system, the state estimation model can be expressed as  
\[\left\{\begin{array}{l}\dot{x}(t)=f(x(t),u(t))+w(t)\\ \\ \dot{z}(t)=h(x(t),u(t))+v(t)\end{array}\right. \tag{19}\]  
In the formula, \(w(t)\) and \(v(t)\) are, respectively, the process noise and measurement noise of the system, which conform to Gaussian distribution and are independent of each other.  
The state vector of the vehicle state estimator is defined as \(\mathbf{x_{c}(t)=[v_{x}v_{y},\omega_{r}]^{T}}\), and the estimated state variable \(\mathbf{x_{c}(t)}\) is fed back to the vehicle calculation module in Figure 1. The measurement vector of the vehicle state estimator is defined as \(z_{\mathbf{c}}(t)=[a_{x}a_{y}\omega_{r}]^{T}\); the input vector of the vehicle state estimator is defined as \(\mathbf{u_{c}(t)=[\delta,F_{xij},F_{yij}]^{T}}\), where \(F_{xij}\) and \(F_{yij}\) are calculated by the nonlinear vehicle dynamics model and the Magic Formula tire model. According to Formulas (1)-(6), the state equation and measurement equation for vehicle state estimation are established.  
The state equation of the vehicle estimator is established:  
\[\dot{\mathbf{x_{c}}(t)}=\left[\begin{array}{ccc}0&\omega_{r}&0\\ -\omega_{r}&0&0\\ 0&0&0\end{array}\right]\left[\begin{array}{c}v_{x}\\ v_{y}\\ \omega_{r}\end{array}\right]+\left[\begin{array}{c}\Delta_{1}\\ \Delta_{2}\\ \Delta_{3}\end{array}\right]-\left[\begin{array}{c}\frac{1}{m}\\ 0\\ 0\end{array}\right]\mathbf{F_{w}(t)+w(t)} \tag{20}\]  
In the formula,  
\[\begin{array}{rcl}\Delta_{1}&=&\frac{1}{m}(F_{xfl}\cos\delta_{fl}+F_{xfr} \cos\delta_{fr}-F_{yfl}\sin\delta_{fl}-F_{yfr}\sin\delta_{fr}+F_{xrl}+F_{xrr} )\\ \Delta_{2}&=&\frac{1}{m}(F_{xfl}\sin\delta_{fl}+F_{xfr}\sin\delta_{fr}+F_{yfl} \cos\delta_{fl}+F_{yfr}\cos\delta_{fr}+F_{yrl}+F_{yrr})\\ \\ \Delta_{3}&=&\frac{1}{l_{z}}[a(F_{xfl}\sin\delta_{fl}+F_{xfr}\sin\delta_{fr}+F_ {yfl}\cos\delta_{fl}+F_{yrf}\cos\delta_{fl}+F_{yrf}\cos\delta_{fr})+\\ \\ &\frac{B_{f}}{2}(F_{xrf}\cos\delta_{fr}-F_{xfl}\cos\delta_{fl})+\frac{B_{f}}{2}(F _{yfl}\sin\delta_{fl}-F_{yfr}\sin\delta_{fr})\\ \\ &+(F_{xrr}-F_{xrl})\frac{B_{r}}{2}-(F_{yrl}+F_{yrr})b]\end{array} \tag{21}\]The measurement equation of the vehicle estimator is established:  
\[\mathbf{z_{c}(t)}=\left[\begin{array}{ccc}0&0&0\\ 0&0&0\\ 0&0&1\end{array}\right]\left[\begin{array}{c}v_{x}\\ v_{y}\\ \omega_{r}\end{array}\right]+\left[\begin{array}{c}\Delta_{1}\\ \Delta_{2}\\ 0\end{array}\right]-\left[\begin{array}{c}\frac{1}{m}\\ 0\\ 0\end{array}\right]\mathbf{F_{w}(t)+v(t)} \tag{22}\]
## 2 Materials and Methods
### Correlation Function  
In this paper, a helix deviation network of the gear is generated from the measured helix deviation data according to the method proposed and devised in the previous studies [14; 15]. This section describes the derivation method of the shape deviation network. Firstly, nodes and links are required for constructing networks. To obtain a precise under standing of the relationships that exist among nodes within a network, it is important to examine not only the existence of the links within the nodes but also the strength or intensity of their connection. This study has achieved this by computing correlation coefficients between helix deviations. In the methodology adopted based on previous studies, the teeth of gears are regarded as nodes, and the computed correlation coefficients serve as links within the network. The correlation coefficient is determined by deriving the inner product between two helix deviations as illustrated in the following Equation (9).  
\[\langle f_{\beta j}(x),f_{jk}(x)\rangle=\frac{1}{L}\int_{0}^{L}f_{\beta j}(x)f_ {\beta k}(x)dx \tag{9}\]  
where \(f_{\beta j}(x)\) represents the helix deviation of \(j^{th}\) tooth. \(j\) and \(k\) are positive integers from tooth number \(1\) to the number of teeth \(z\); however, \(k\
eq j\). \(x\) is defined as the position of the evaluation length and \(L\) is defined as the integral range. The orthogonality of signals can be expressed by defining the inner product as illustrated in Equation (9). When \(\langle f_{\beta j}(x),f_{\beta k}(x)\rangle=0\), \(f_{\beta j}(x)\) and \(f_{\beta k}(x)\) are orthogonal. By calculating the inner product as illustrated in Equation (9) above, the phase difference between the two tooth helix deviations can be obtained. The quotient of the inner product and the norm of each deviation is calculated as the correlation coefficient. Additionally, the norm of the helix deviation is defined in the following Equation (10).  
\[\|f_{\beta j}(x)\|=\sqrt{\frac{1}{L}\int_{0}^{L}\{f_{\beta j}(x)\}^{2}dx} \tag{10}\]  
By applying both Equations (9) and (10), the correlation coefficient of a network is defined as the following Equation (11).  
\[r_{j,k}=\frac{\langle f_{\beta j}(x),f_{\beta k}(x)\rangle}{\|f_{\beta j}(x)\| \|f_{\beta k}(x)\|} \tag{11}\]  
It is important to note that the correlation coefficient measures the degree of linear relationship between two variables, and it ranges from the value \(-1.0\) to \(1.0\), where the value \(-1.0\) denotes a perfect negative correlation, the value \(0\) denotes no correlation, and the value \(1.0\) indicates a perfect positive correlation. When the two tooth helix deviations exhibit a small phase difference, the correlation coefficient tends to approach the value of \(1.0\). Conversely, when the two deviations display a large phase difference, the correlation coefficient tends to approach the value of \(-1.0\). Therefore, it is crucial to consider the phase difference between the two tooth helix deviations when interpreting the value of the correlation coefficient.
## 5 Conclusions  
Leakage in WDNs is a major concern for water utilities. Hence, a timely inspection of the network is required for the early detection of fluctuations in the system that are indicative of leaks. This raises the necessity of exploring possible approaches to remedy the issue. In this paper, we have described an automatic GIS-based method for leak detection using machine learning, specifically GWR (supervised ML) and LOF (unsupervised ML) models. The method combines sensor readings of flow, pressure, and chlorine using GWR and LOF to detect irregularities that occur in the WDN in real-time. In collaboration with SEWA, the method was tested on a part of Sharjah's WDN successfully. The seasonal variations in water parameters have been predicted to be consistent with the temperature variations. This study can be very useful for water utilities for prompt leak localization. The results indicated the effectiveness of our method in detecting the presence of leaks within the network. For large WDNs, carrying out leakage studies is often time-consuming and resource-intensive. This model can be used with the existing sensor network to identify potential leakage locations.  
The proposed approach has limitations even though the results were successful in this particular WDN. The ability of the model is dependent on the availability, size, and accuracy of the dataset. Even though the use of different hydraulic and water quality parameters can reduce inaccuracies, a large number of monitoring points are needed for a large WDN for the model to be accurate. Future studies should investigate the applicability of this model with more datasets and in other large WDNs.  
Conceptualization, T.A. and M.M.M.; methodology, T.A. and D.E.; software, R.G., D.E., and L.K.; validation, T.A., M.M.M., S.A., and L.K.; formal analysis, D.E. and T.A.; investigation, T.A. and M.M.M.; resources, M.M.M. and T.A.; data curation, T.A. and M.M.M.M.; writing--original draft preparation, D.E. and T.A.; writing--review and editing, T.A., M.M.M., and S.A.; visualization, D.E. and R.G.; supervision, T.A. and M.M.M.; project administration, M.M.M. and T.A.; funding acquisition, M.M.M. and T.A. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support of the American University of Sharjah through grants FRG-22-C-15 and OAP24-CEN-088. This paper represents the opinions of the author(s) and does not mean to represent the position or opinions of the American University of Sharjah.
## 3 Experimental Results
### Hysteresis and Backbone Curves
#### 3.5.2 Backbone Curves  
As Figure 18a shows, with the increase of reinforcement ratio, buried pile depth and prestressing level, the peak displacement of specimens also increased and generally reached 80 mm for all concrete specimens. The ultimate displacement of specimens was also increased as the reinforcement ratio, buried pile depth and prestressing level increased and generally reached to 120 mm for all concrete specimens, as shown in Figure 18b. It worth mentioning that the reinforcement ratio and prestressing level had similar effects on increasing the peak and ultimate displacements of the piles.  
Figure 19 shows the typical force-displacement curve of the pile with respect to its failure mechanism based on the backbone curves of all specimens. The curve is divided into seven stages: elastic behavior, imperceptible crack but linear behavior, minor damage, moderate damage but repairable, significant damage but no collapse, unacceptable damage. For the elastic behavior stage, the horizontal deformation was limited to 5 mm, which was in the elastic range. For the second stage, the pile had a linear behavior as observed from the backbone curves and imperceptible cracks had occurred as observed from the tension strains of the concrete pile. The horizontal deformation was limited to 10 mm for this stage. For the third stage, the obvious cracks with minor damage had occurred under the horizontal deformation of 20 mm. In the fourth stage, the horizontal deformation reached 40 mm and the concrete was crushed with moderate damage. In the last stage, the deformations reached to 80 mm and 120 mm with significant damage to the pile.  
Figure 18: (**a**) Peak and (**b**) ultimate displacements of the piles obtained from the backbone curves.
# TCNformer Model for Photovoltaic Power Prediction
## 4 Experiment
### Prediction Results of Different Prediction Steps  
In order to explore the prediction performance of each model under different prediction steps, this study selected LSTM, SkipGRU, Transformer, and Informer to compare with TCNformer.  
The results are shown in Table 4. It can be seen from the results that, when the number of prediction steps is 1, the MSE errors of the five models have little difference. With the increase in the number of the prediction steps, the LSTM model demonstrated the largest error growth rate, and the error accumulation is obvious. Informer and TCNformer use the generative prediction method, so the error was relatively stable, and the error accumulation was low. The TCNformer model proposed in this paper not only had a low level of error accumulation, but it also had the lowest MSE error. In order to more intuitively observe the error accumulation in the models, the prediction results were visualized, as shown in Figure 9.
## 4 Evaluation
### Test Case Coverage  
A test case represents a single issue of conformance against one or more standard requirements. The ISO/IEC 15118 Part 4 (Committee Draft, CD version) presents 14 test cases for high-level communication and three test cases for basic signaling (IEC 61851), while Part 5 (CD version) presents five test cases for SLAC testing. Among the 17 test cases in Part 4, our test system covers 15 test cases, missing one for AC charging and one for renegotiation. In addition, we added four new test cases for high-level communication (including one negative test case) and seven new test cases for IEC 61850-90-8. For Part 5, we support only three test cases related to SLAC. In total, we support 29 test cases, three for basic signaling, 16 for high-level communication, seven for grid communication and three for the SLAC protocol. Table 1 summarizes the test case coverage of our test system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Our Test System** & **ISO/IEC 15118 (Parts 4 and 5)** \\ \hline ISO/IEC 15118-2 DC & 16 & 13 \\ ISO/IEC 15118-2 AC & 0 & 1 \\ IEC 61851 (Basic signaling) & 3 & 3 \\ IEC 61850-90-8 (EVSE-Grid) & 7 & 0 \\ HPCP SLAC & 3 & 5 \\ Total & 29 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test case coverage. EVSE, EV Supply Equipment; HPCP, Home-Plug Green PHY; SLAC, Signal Level Attenuation Characterization.  
Figure 8: Hardware adaptor of the test system.
###### Abstract  
One of the methods in structural control is the application of combinational control systems in order to reduce the response of structures during earthquakes. The aim of the present paper is to verify the effectiveness of a hybrid control strategy, combining base isolation and non-traditional tuned mass dampers (TMDs) (i.e., TMDs with dashpots directly connected to the ground) in suppressing structural vibrations of high-rise buildings. The study was conducted for structures with a different number of stories exposed to various far-field and near-field earthquake records. Multi degree-of-freedom models of buildings as well as non-linear models of the base isolation system were employed in the analysis. The results of the study clearly confirmed that the response of high-rise buildings during earthquakes could be significantly reduced using base isolation devices and non-traditional TMDs. They showed also that the effectiveness in suppressing structural vibrations substantially depends on the type of the control system used. The influence of the base isolation in the reduction of structural response under different earthquake records was much larger than the influence of non-traditional TMDs. In the case of buildings analyzed in the study, the application of TMDs alone resulted in a reduction not larger than 20%, as compared to the response without any system. On the other hand, the response of buildings equipped with only base isolation devices was reduced by more than 70% under different ground motions. However, the largest reductions (larger than 80%) were obtained for the cases when both control systems (base isolation and non-traditional TMDs) were used simultaneously.  
high-rise buildings; hybrid control system; base isolation; non-traditional tuned mass damper; seismic response +
Footnote †: journal: Applied Sciences