esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Principal Component Analysis of Steering Parameters  
In solving practical problems and research, it is often possible to collect more information about the research object in order to have a comprehensive understanding of the problem. However, due to the theoretical development and application of technical constraints, having too many variables to be processed and too much information have become analysis obstacles. To solve this problem, Principal Component Analysis (PCA) should be used to analyze data. PCA is a statistical analysis method that simplifies multiple indicators into a small number of comprehensive indicators, with as few as possible to reflect the original variable information [31], to ensure that the original loss of information and the number of variables is as small as possible. Let \(X=\left(X_{1},X_{2},\cdots,X_{p}\right)^{\prime}\) be a \(p\)-dimensional random vector, and its linear variation is as follows:  
\[\begin{array}{l}PC_{1}={a_{1}}^{\prime}X={a_{11}}X_{1}+{a_{21}}X_{2}+\ldots +{a_{p1}}X_{p}\\ PC_{2}={a_{2}}^{\prime}X={a_{12}}X_{1}+{a_{22}}X_{2}+\ldots+{a_{p2}}X_{p}\\ \cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\cdots\\ PC_{p}={a_{p}}^{\prime}X={a_{1p}}X_{1}+{a_{2p}}X_{2}+\ldots+{a_{pp}}X_{p}\\ \end{array} \tag{2}\]  
Using the new variable \(PC_{1}\) to replace the original \(p\) variables \(X_{1}\), \(X_{2}\),..., \(X_{p}\), \(PC_{1}\) should reflect as much as possible the original variable information. If the first principal component is not enough to represent the vast majority of the original variables' information, two main components \(PC_{2}\), and so on, will be used. The main purpose of principal component analysis is to simplify the data, so in practical applications, we will not take \(p\) principal components and usually use \(m\) (\(m<p\)) principal components. The number of principal components \(m\) should be based on the cumulative contribution of the variance of each principal component to the final decision.  
\[p_{r}=\lambda_{k}/\sum\lambda_{i} \tag{3}\]  
where \(\lambda\) is the eigenvalue corresponding to each principal component, \(k\) is the number of selected main components and \(I\) is the total number of components.  
The principal component analysis of 133 sets of experimental data is carried out by MATLAB software, and nine principal components (\(Y_{1}\), \(Y_{2}\),..., \(Y_{9}\)) were obtained. The eigenvalue, contribution rate and cumulative contribution rate of each principal component are shown in Table 4. According to the principal component analysis principle, the first four principal components are selected, and the correlation between the characteristic parameters and the principal components is analyzed. The representative average angular velocity, average steering angle, maximum yaw rate and maximum lateral acceleration, the four parameters of acceleration, are used for cluster analysis. The method of principal component analysis is shown in Figure 10.  
Figure 9: Yaw rate under different steering conditions: (**a**) turning right; (**b**) U-turn; (**c**) lane keeping; (**d**) lane changing.
## 3 Model Formulation
### Covariate Weight Estimation
#### 3.3.2 Genetic Algorithm  
Due to the inherent complexity of optimizing the log-likelihood function of Equation (3), particularly when confronted with an increasing number of covariates, the quest for optimization tools capable of addressing nonlinear problems becomes imperative.  
An appealing alternative is to use GAs for optimization. GAs are well suited for solving complex optimization problems and offer significant advantages over traditional techniques. They can explore multiple domains of feasible solutions and have a flexible optimization structure [10; 19; 22]. However, GAs typically require longer convergencetimes and a careful tuning of hyper-parameters to enhance solution quality [21; 23]. Despite these challenges, the suitability of GAs in this research, particularly in the development of predictive maintenance policies, diminished the significance of time constraints. Hence, a GA was chosen as the preferred optimization tool.  
A GA simulates chromosome reproduction to generate robust \"genes\" through fitness evaluation and genetic operations like selection, crossover, and mutation. It requires a fitness function, the one selected for this research mirrored Equation (3), and solutions are represented with a matrix of dimensions \((2+\text{m})\times 1\). Here, \(m\) is the number of covariates in the PHM, with the initial two columns representing variables \(\lambda\) and \(\alpha\), and the rest signifying weights for each covariate. For instance, with two covariates, the chromosome array appears as [\(\lambda\), \(\alpha\), \(\gamma_{1}\), \(\gamma_{2}\)].
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 2 Related Work
### SECN Architecture Design  
Taking into account the design of a multi-tier satellite edge computing framework and the synchronized scheduling of diverse edge computing resources, virtual network functions (VNFs) are deployed in the SECN to achieve low-latency network services by jointly optimizing the placement of VNFs and the routing of traffic under the capacity constraints of LEO satellites and the budget constraints of each NFV-enabled request [1]. Not only is the case of individual user requests considered, but also the uncertainty and dynamics of user requests in the online environment are taken into account. A SECN architecture with three layers, which includes GEO, LEO, and ground layers, is designed in [25]. The high-speed movement of LEO satellites is considered and an ADMM-based distributed optimization algorithm is proposed to solve the mobility-aware computation offloading problem in the SECN. An IoT supportable SECN architecture is proposed in [26], and a heuristic algorithm with low time complexity is proposed to reduce the weighted sum of latency, computational power, and transmission power attenuation. To address limited computing resources in remote satellite-terrestrial networks, a double-edge computing offloading algorithm that effectively reduces task processing delay and energy consumption by optimizing offloading to satellite and terrestrial edge nodes is proposed in [27].
# Move Over Law Compliance Analysis Utilizing a Deep Learning Computer Vision Approach
## 4 Results
### Traffic Volume  
Another traffic flow characteristic considered in this study is the approaching traffic volume. To check the possible dependencies, we computed the traffic flow per lane (number of vehicles/lane/hour) for each scene and then calculated correlation coefficients between the flow per lane and Move Over law compliance. The Pearson and Spearman correlation coefficients are \(-0.273\) and \(-0.320\). They are both negative, suggesting a decrease in the Move Over law compliance with traffic volume increase. However, the values of the correlations are relatively small, so the data did not show any strong patterns.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Radio Resource Management Techniques
#### 3.1.4 Evaluation Techniques for Radio Resource Management Methods
The performance of the joint scheme was compared with the individual energy minimization schemes in terms of the total energy consumption for varying QoS requirements and CPU cycles of the task. The proposed method outperformed the separate solutions in all cases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy** & **Ref.** & **Application** & **Goal** & **Problem Formulation** & **Technique Used** \\ \hline \multirow{6}{*}{Power Control} & \multirow{2}{*}{[39]} & Power-efficient resource allocation & Minimize total power consumption by determining an optimal beamforming solution & Second order cone optimization problem & DRL \\ \cline{2-5}  & & & Reduce total power consumption & Mixed integer programming problem (MITP) & Efficient local search algorithm (ELSA) \\ \cline{2-5}  & & & Load balancing among RRHs and controlling the signaling overhead & MIPP & ELSA and adaptive trigger mechanism \\ \cline{2-5}  & & & Reduce number of active BBUs and power consumption & Linear integer programming & Combined BPM and MFBD \\ \cline{2-5}  & & & Threshold-based RRH & Minimize power consumption in both BBU and RRH & Bin packing problem & Bisection method to determine the optimal threshold \\ \hline \multirow{6}{*}{Joint Optimization} & \multirow{2}{*}{[45]} & Downlink physical resource block allocation and BBU-RRH assignment & Minimize number of BBUs required to handle traffic load & Mixed linear integer problem and multiple knapsack problem & BCA \\ \cline{2-5}  & & Downlink resource allocation and admission control & PRE allocation for maximizing the total user throughput & MILP & Fixed time BCA \\ \cline{2-5}  & & & Minimize energy cost in mobile cloud and network considering QoS & Convex optimization problem & WMMSE-based iterative model \\ \hline \multirow{2}{*}{Sum-rate Optimization} & \multirow{2}{*}{[47]} & User-RRH association for uplink transmission & Maximize network sum-rate under limited computing resources & Non-linear programming problem & Iterative sub-optimal algorithm \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Radio resource management techniques in C-RAN. In [47], simulations were conducted by varying the computing resource constraints. The effect of the computing resources in both user-RRH association schemes and achievable system sum-rate was depicted. The users could not obtain high data rates even if they had high SINR when the computing resource was scarce. However, the optimal user association scheme can be achieved with sufficient computing resources. Moreover, a knee point was presented. It indicated the computing resources required by a BBU pool for achieving a satisfactory transmission rate in a C-RAN network.
###### Abstract
Furthermore, [PERSON] and [PERSON] [34] explored the outcomes of implementing asynchronous automated telehealth-based hearing screening and automated audiometric testing to detect or confirm hearing impairment in school-age children in rural South African schools. Their combined model showed a 100% specificity rate and a moderate sensitivity rate of around 65%, which they claim to be affected by randomization error and the susceptibility of children being easily distracted. Note that hearing screenings, diagnostic examinations, and rehabilitation interventions in speech audiometry (speech-in-noise or word-in-noise) must always be implemented in a language that is native to the listeners [16; 35]. Screening an individual's hearing with a language different than their native language often leads to poorer performance when compared to native listeners [22; 35]. Hence, to our knowledge, there are currently no versatile Arabic versions of word-in-noise tests that can facilitate and sup-port the deployment of hearing screening programs that can be utilized in hard-to-reach environments. Therefore, this study aims to develop an Arabic version of a word-in-noise hearing test that is cost-effective and has the potential to support its deployment in a low-resource setting and during community-based outreach programs. Furthermore, having such a hearing screening tool can reduce the burden on millions of people that have hidden hearing problems or do not have easy access to quality healthcare. The current study presents the development of the proposed Arabic word-in-noise using an easy-to-use custom-made MATLAB (MathWorks, Natick, MA, USA) program. The main features that were considered during the design of the proposed Arabic word-in-noise screening tool are its ease of use with minimal training for generalist healthcare personnel, usability for Arabic-speaking communities, short testing time with reliable screening results, and the use of simple and common monosyllabic Arabic words that can be used to screen school-age children, young adults, and elderly persons. The words in the proposed Arabic hearing screening test in this study are implanted in various types of background noise to incorporate everyday life scenarios. In addition, the words were presented at different SNR levels to enhance the accuracy of identifying possible hearing impairments.
## 5 Expertise Retrieval
### Findings and Discussion  
By first evaluating the performance of the models Doc2 Vec and SBERT, the empirical findings provide encouraging and compelling evidence that SBERT achieved results close to human judgments when comparing the similarity and relatedness between mobile apps' descriptions. From these findings, the superior model was utilized in the expertise retrieval component in our crowd selection approach.  
In addition, the designed retrieval algorithm which utilized SBERT for measuring semantic similarity has been shown to perform well in terms of retrieving a suitable subset of the crowd. The retrieved crowd including those who have obtained high similarity scores were seen to be optimal candidates to be selected for participating in crowdsourcing mobile app RE tasks. This is because the mobile apps they have used or developed were highly similar to the query mobile app description (one of the six queries). The designed retrieval algorithm has empirically been shown to assist in finding the most suitable members of the crowd based on the similarity and relatedness of the mobile apps they have used or developed to a query mobile app.  
By analyzing the results of the retrieval algorithm, it was noticed that the pre-trained model was not only effective in capturing similar mobile apps in terms of their specific domains but even the described features of an app can be captured since the model can accurately understand the context.  
In some cases, a mobile app from a slightly different subdomain than the query mobile app can obtain a considerably high similarity score since it has features similar to the query mobile app. For instance, when experimenting with \(q_{1}\) which contains a mobile app description about fitness and workouts, crowd members who have used or developed apps for tracking diet were included in the list of crowd members retrieved. This occurred since these mobile apps can have features in common. This is indeed acceptable and desirable since such types of mobile apps are closely related. Moreover, this could enhance the crowd selection by involving crowd members who have used similar apps which are not strictly the same as the query app but highly related.  
Due to that, it is evident that contextualized embeddings generated by the pre-trained SBERT, specifically the all-mpnet-base-v2 model, are the best fit for measuring similarity between app descriptions. These contextualized embeddings have not only helped in capturing the similarity between domains but also the shared features of a mobile app. Indeed, the uncont contextualized embeddings generated by Doc2 Vec have shown that they were able to measure similarity to an acceptable extent, however, they were not effective enough for our case.  
Generally, the retrieval algorithm effectively performed the retrieval process within our proposed crowd selection approach. To assess the suitability and effectiveness of the selected crowd, a real experiment will be performed utilizing the overall approach in the following section.
## 3 Results and Discussion
### Biofilm Assay on Polystyrene Microplates
#### 3.2.2 Biofilm Formation at Different Doses of BAC  
Only two concentrations of BAC (1.25, and 2.5 mg/L) affected the biofilm formation capacity of the two strains of \(L\). _monocytogenes_ differently. Table 2 shows the metabolic activity of the biofilm formed in the presence of BAC in relation to biofilms formed without BAC, by the two strains. In the presence of 1.25 mg/L of BAC, strain S2\({}^{\text{BAC}}\) showed significantly reduced biofilm levels in relation to the control, without BAC (\(p<0.05\), Student's \(t\)-test) (Table 2). In the presence of 2.5 mg/L of BAC, i.e., the MIC of BAC-S strain S2-1 (Table 2), S2\({}^{\text{BAC}}\) significantly increased biofilm metabolic activity, compared with the control, without BAC (\(p<0.05\), Student's \(t\)-test). Therefore, the resistance of S2\({}^{\text{BAC}}\) resulted in the stimulation of biofilm formation at a concentration of BAC that caused a reduction of the biofilm level in the parent strain S2-1 (Table 2). At 5 mg/L of BAC, the complete reduction of metabolic activity of the biofilm was observed in both strains (Table 2).  
RSZ-based viability staining is often used to quantify metabolically active biofilm cells grown in microtiter plates [22]. This approach is less expensive and less laborious than conventional plating techniques. The oxidation-reduction indicator, RSZ, undergoes a colorimetric change in response to cellular metabolic reduction. Thus, the effect of disinfectants on biofilm formation, by resistant and susceptible strains, can be quantified using RSZ, and RSZ-staining might improve the results obtained with crystal violet staining. For example, with low biofilm-forming _prfA_ mutants [19] and viable but non-culturable (VBNC) cells present in biofilms [29]. [PERSON] et al. [19] reported that biofilm stimulation by BAC was not observed with BAC-R _prfA_ mutant strains due to the low biofilm formed by _prfA_ mutants, while our unpublished results reveal a better inter-strain reproducibility and sensitivity for RSZ-staining than for crystal violet staining with _prfA_ mutant strains. These results support the association between reduced susceptibility to disinfectants in food-production environments and the widely recognized problem of the persistence of \(L\). _monocytogenes_, including long-term persistent _prfA_ mutant strains ([14], unpublished results).  
The most widely used technique to estimate biofilm cell viability is the determination of colony-forming units (CFU) on agar media. However, a subpopulation of biofilm cells can be VBNC and would not be detected by the CFU approach. It is known that \(L\). _monocytogenes_ and other pathogens are able to survive environmental stresses such as disinfectants by entering into a VBNC state [29]. As there is a good correlation between results obtained with RSZ-based quantification and CFU counts [22], and the lower limit of quantification with the RSZ method can reach \(10^{3}\) CFU/biofilm [22], RSZ can detect metabolic activity in VBNC bacteria present in biofilms if the number of cells is above that figure.
## 3 Communication Performance Analysis of Hybrid FSO/RF System  
### Average Bit Error Rate  
For the hybrid FSO/RF system, a binary modulation scheme is used for data transmission in the FSO or RF link. According to [42], the average BER of the hybrid system which is based on the binary modulation techniques can be expressed as:  
\[P_{b}=\frac{\delta}{2\Gamma(p)}\sum_{t=1}^{n}q_{t}^{p}\int_{0}^{\infty}\gamma^ {p-1}e^{-q\gamma}F_{\gamma^{MRC}}(\gamma)d\gamma \tag{8}\]  
where \(n\), \(\delta\), \(p\), and \(p\) represent various modulation schemes and detection types according to Table 2[38; 42; 43]. It is worth noting that some conditions have not been taken into account in the analysis of the BER, such as the thermal noise variance, shot noise, amplified spontaneous emission (ASE), weather, and climate conditions.  
Substituting (7) in (8) and applying \(e^{-x}=G_{0,1}^{1,0}\bigg{(}x\begin{array}{c}-\\ 0\end{array}\bigg{)}\) and Equation (07.34.21.0088.01) in [44], the average BER of the hybrid system can be obtained:  
\[P_{b}^{hy}=\frac{\delta A_{1}}{2\Gamma(p)}\sum_{t=1}^{n}\sum_{k=1}^{\beta}\sum _{i=0}^{\infty}c_{k}C_{1}q_{t}^{-D_{1}}G_{r+3,3\gamma+2}^{3\gamma,3}\bigg{(} \begin{array}{c}E_{1}\\ q_{t}\end{array}\bigg{|}\begin{array}{c}1-D_{1}-p,1-D_{1},1,\kappa_{1}\\ \kappa_{2},1-D_{1},-D_{1}\end{array}\bigg{)}, \tag{9}\]  
By substituting Equation (2) into Equation (8), the average BER of the only FSO link can be obtained:  
\[P_{b}^{FSO}=\frac{D\delta}{2\Gamma(p)}\sum_{t=1}^{n}\sum_{k=1}^{\beta}c_{k}G_{ r+2,3\gamma+1}^{3\gamma,2}\bigg{(}\begin{array}{c}E\\ \mu^{FSO\prime}q_{t}\end{array}\bigg{|}\begin{array}{c}1-p,1,\kappa_{1}\\ \kappa_{2},0\end{array}\bigg{)} \tag{10}\]
## 3 Control Objectives and Optimal Control
### \(H_{\infty}\) Control  
\(H_{\infty}\) control is revised in this section. It is a multivariable control algorithm consisting of generalized plant, P(s) as shown in Equation (18), and minimization of optimization function according to \(H_{\infty}\) norm. \(H_{\infty}\) control is formulated based on the system considered by the following:  
\[\begin{bmatrix}z\\ v\end{bmatrix}=P(jw)\begin{bmatrix}w\\ u\end{bmatrix}=\begin{bmatrix}P_{11}(jw)&P_{12}(jw)\\ P_{21}(jw)&P_{22}(jw)\end{bmatrix}\begin{bmatrix}w\\ u\end{bmatrix} \tag{18}\]  
\[u=K(jw)y \tag{19}\]  
with a state-space form of the generalized plant \(P(jw)\) given by the following.  
\[\dot{\mathbf{x}}(t) =Ax(t)+B_{1}w_{e}+B_{2}u\] \[\mathbf{z}(t) =C_{1}x(t)+D_{11}w_{e}+D_{12}u\] \[y(t) =C_{2}x(t)+D_{21}w_{e}+D_{22}u \tag{20}\]  
The plant has two input signals: the manipulated variables, \(u\), and \(w_{e}\) denotes exogenous signal that comprises disturbance, reference signals, etc. In addition, there are two output signals, the error signals \(\mathbf{z}\), which include reference tracking error and control effort which are to be minimized, and the measured variable \(y\). Therefore, the closed-loop transfer function from \(\mathrm{w}\) to \(\mathbf{z}\) is given by the following linear fractional transformation:  
\[z=F_{l}(P,K)w \tag{21}\]  
where the following is the case.  
\[F_{l}(P,K)=P_{11}+P_{12}K(I-P_{22}K)^{-1}P_{21} \tag{22}\]  
Hence, the main goal of the \(H_{\infty}\) control problem is to design a controller, \(K\), that minimizes the \(\left\|F_{l}(P,K)\right\|_{\infty}\) according to the \(H_{\infty}\) norm. The infinity norm of matrix \(F_{l}(P,K)\) transfer function is defined as follows:  
\[\left\|F_{l}(P,K)\right\|_{\infty}=\max_{w}\bar{\sigma}(F_{l}(P,K)(jw)) \tag{23}\]  
where \(\bar{\sigma}\) is the maximum singular value of the matrix \(F_{l}(P,K)(jw)\).
# Design, Fabrication and Testing of a Multifrequency Microstrip RFID Tag Antenna on Si
###### Abstract  
A configurable design of a microstrip square spiral RFID tag antenna, for a wide range of microwave frequencies in the S- and C-band, is presented. The design is parameterized in dimensions, and hence changing the design frequency (or frequencies) is easy, by changing only an initial value for the spiral geometry. A tag specimen was fabricated using a Cu electroplating technique according to the design for frequencies of interest in the areas of 2.4 and 5.8 GHz. The substrate material is 320 \(\upmu\)m high-resistivity Si and the bridge dielectric is 15 \(\upmu\)m polyimide PI2525. The steps of the microfabrication process involve metallic structure pattern transfer techniques with optical UV lithography procedures. The reflection coefficient and antenna gain of the specimen were measured inside an anechoic enclosure using a vector network analyzer (VNA) and a TEM horn test antenna over a frequency range of up to 6 GHz. Simulated and measured results, exhibiting reasonable agreement, are presented and discussed.  
RFID; antenna; silicon Article