esa-sceva/satcom-chunk-collection huggingface dataset:
# 5G Physical Layer-Based Procedure to Support Time-Sensitive Networking
## 2 Literature Review
### Configuration Methods in 3 GPP Standards
This functionality was initially introduced in Release 16; however, in Release 17, the TSN AF was replaced by the Time-Sensitive Communication Time-Synchronization Function (TSCTSF). The TSCTSF supports the control and configuration of the DS-TT and NW-TT and provides an additional Time-Sensitive Communication (TSC) toolbox for enhanced functionality such as Quality of Service (QoS). 3 GPP has proved to be of special interest for method (c), which focuses on the direct monitoring and reporting of timing synchronization status over the air interface. A dedicated key issue has been included in Technical Report (TR) 23.700-25 [10] to address this. The study's conclusion proposes an approach that involves the gNB sending a status report ID in the SIB to notify UEs of new RAN timing synchronization status reports. UEs can actively retrieve the information by transitioning to the RRC_Connected state or using the report ID as an index to automatically determine the synchronization status. Additional time-synchronization characteristics can be provided to UEs or AFs through Service Level Agreements (SLAs) or dedicated signaling. Thus, the report ID allows UEs to be aware of the availability of new information and prompts them to take action to retrieve the relevant timing synchronization status report from the network. However, the main limitation of this proposal is that the accuracy might be limited to 5-10 \(\upmu\)s [11]. Besides this, method (c) does not support external time domains such as TSN. The authors of [12] explore the integration of 5G and TSN in smart manufacturing, focusing on the synchronization requirements for industrial automation. They discuss the current state of 5G's support for time synchronization and integration with TSN and highlight Release 16 and Release 17 work refining the time-synchronization protocol in 5G. This integration is evaluated in [13], where the authors develop an analytic framework to assess the accuracy of over-the-air time synchronization; the study concludes that this accuracy may be affected by factors such as clock drift, air-interface timing errors, reference time granularity, and synchronization frequency. Moreover, the authors of [14] add that the key challenge in achieving accurate time synchronization in integrated 5G and TSN networks is the disparity between a traditional TSN transparent clock and the 5G System emulating a transparent clock. This can result in synchronization errors, even with small frequency offsets between the ingress and egress of the 5G System. In order to address this challenge, these authors proposed that future research can focus on minimizing the impact of syntonization errors between 5G devices on time synchronization in integrated 5G-TSN networks.
## 4 Study Results
### Confirmatory Factor Analysis Results
#### 4.5.1 Convergent Validity Evaluation Results  
The guideline proposed by [PERSON] and [PERSON] (1981) [34] was used to evaluate the convergent validity of the measurement. Their guideline suggested that all the standardized factor loadings of the calculated observed variables should be greater than 0.6 at a statistical significance level of \(p<0.05\), and the average variance extracted (AVE) and composite reliability values should be greater than 0.5 and 0.7, respectively. The calculated standardized factor loadings were observed. B8 (=0.599) and B9 (=0.500), which were used to measure the conation sub-construct, did not satisfy the minimum acceptable score. Furthermore, the root mean square error of approximation (RMSEA) value, one of the goodness of fit indices calculated to evaluate the model fit, was 0.086, which did not satisfy the acceptable score of 0.08 or less [35]. Thus, this study improved the model fit of the measurement by observing the modification indices of the model fit obtained through the confirmatory factor analysis after removing the two observed variables of B8 and B9.  
The covariance modification index of the observed variables C4 and C5, C6 and C7, and C6 and C10 were all 20.0 or greater. Thus, covariance was additionally set for each of them [35]. Table 7 displays the confirmatory factor analysis results for the modified  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{3}{c}{**Component**} \\ \cline{2-4}  & **1** & **2** & **3** \\ \hline C1 * & 0.407 & 0.219 & 0.29 \\ C2 & 0.671 & 0.236 & \(-\)0.038 \\ C3 & 0.668 & \(-\)0.108 & 0.283 \\ C4 & 0.821 & \(-\)0.061 & 0.005 \\ C5 & 0.89 & \(-\)0.068 & \(-\)0.01 \\ C6 & 0.822 & \(-\)0.035 & 0.109 \\ C7 & 0.835 & 0.04 & 0.054 \\ C8 & 0.693 & 0.245 & \(-\)0.071 \\ C9 * & 0.346 & 0.075 & 0.423 \\ C10 & 0.741 & 0.074 & 0.07 \\ C11 & 0.599 & 0.341 & 0.009 \\ C12 * & 0.441 & 0.437 & 0.03 \\ C13 * & 0.449 & 0.37 & \(-\)0.028 \\ \hline E1 & 0.157 & \(-\)0.095 & 0.828 \\ E2 & 0.253 & 0.031 & 0.717 \\ E3 & \(-\)0.199 & 0.356 & 0.714 \\ E4 & 0.116 & 0.099 & 0.676 \\ \hline B1 & 0.149 & 0.589 & 0.103 \\ B2 & 0.026 & 0.546 & 0.357 \\ B3 & 0.025 & 0.742 & 0.084 \\ B4 & 0.107 & 0.753 & 0.04 \\ B5 & 0.036 & 0.687 & 0.201 \\ B6 & 0.132 & 0.678 & 0.119 \\ B7 & \(-\)0.159 & 0.801 & 0.116 \\ B8 & 0.013 & 0.732 & \(-\)0.077 \\ B9 & 0.17 & 0.567 & \(-\)0.174 \\ \hline eigenvalue & 12.927 & 1.977 & 1.55 \\ \hline \(\%\)variance & 49.72 & 7.605 & 5.96 \\ Cronbach’s alpha & 0.939 & 0.858 & 0.905 \\ \hline \hline \end{tabular} Note. * * Items discarded in the final analysis.  
\end{table}
Table 6: The results of the exploratory factor analysis.  
measurement. The standardized factor loading of each observed variable was 0.6 or greater, and the AVE and composite reliability values were greater than 0.5 and 0.7, respectively. Thus, the measurement of this study satisfied the appropriate convergent validity.
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 4 Results
### Trust  
Researchers have discussed the possibility that the use of automation is likely to increase if humans' trust in systems increases. The perceived reliability, or trust, can be affected by operators' prior knowledge about the level of reliability, knowledge of the automation capabilities, and automation reliability [55]. Research suggests that if interactions between humans and machines become more similar to interactions between humans, teamwork between automated systems and humans will be more effective. [PERSON] et al. [33] suggest that operators' trust in automation is the main factor affecting human-machine teamwork.  
In the field of remote ship operation, operators have to trust the sensor information, the autonomous system, and the remote controlled systems. [PERSON] et al. [22] discuss the need to ensure these systems are completely reliable so that operators will trust them completely. This raises the question as to how systems can engender such trust and how much trust is appropriate. [PERSON] et al. [50] found that drivers who use adaptive cruise control over-rely on the system because they put excessive trust in it. A similar situation is possible with remote ship operation.  
[PERSON] et al. [29] considered the factors that affect the operators' trust in systems. They found that training programs affect the trust between operators and the automated system, suggesting that SCC operators should be trained well. On the other hand, high reliance on autonomous systems and sensor information over a long period of time leads to skilldegradation. This issue requires attention because humans will act as a backup system even for ships with the highest level of autonomy.
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 3 Results Discussion
### ADC/DAC Characterization  
The FMC126 and FMC230 boards with their design modifications were characterized and validated with the usual figures of merit such as SINAD, ENOB and SFDR metrics.  
With that in mind, an oscilloscope and a Spectrum Analyzer (SA) were used to measure the time and frequency responses of the DAC at multiple tones. With regards to the ADC, a signal generator was used to generate sine-wave signals with tuneable amplitude and frequency, which allowed both time and frequency responses assessment.  
The DAC performance assessment was evaluated with the DAC's SFDR, which is measured as the difference between RMS power level of the transmitted tone and the most significant spur signal, presented in the DAC's frequency response. The SFDR measurement result, for the FMC230, can be seen in Figure 14 considering the D1 channel and a frequency tone of 134.3 MHz.  
Additionally, the FMC230 was also characterized with SINAD, SFDR, and ENOB figures of merit for various frequency tones. Such results are summarized in Table 2 and plotted in Figure 15a,b, considering a sampling rate of 2456 MHz and DAC's D1 output channel. The average values for SFDR, SINAD, and ENOB measurements are 56.93 dB, 69.61 dB, and 11.34 bits, respectively, which according to 4 DSP datasheet is within DAC performance boundaries.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Freq.** & **Carrier** & **SFDR** & **SINAD** & **ENOB** \\
**[MHz]** & **Power (*)** [dBm]** & **[dB]** & **[dB]** & **[bits]** \\ \hline
18 & \(-\)3.5 & 54.2 & 75.48 & 12.25 \\
134.3 & \(-\)2.68 & 58.35 & 64.77 & 10.47 \\
282.8 & \(-\)4.37 & 57.36 & 68.75 & 11.13 \\
589.3 & \(-\)3.16 & 57.84 & 71.03 & 11.51 \\
893.9 & \(-\)4 & 53 & 68.05 & 11.01 \\ \hline \hline \end{tabular} (*) Average value, considering a SA resolution bandwidth of 3 kHz.  
\end{table}
Table 2: FMC230 performance boundaries versus frequency tone, for the D1 channel.  
Figure 14: DAC SFDR for a tone signal of 134.3 MHz, considering the DAC’s D1 channel.  
With FMC126 in mind, a similar experimental setup was used to assert this ADC board performance. Consequently, the reference signal connected into both ADC's channel A and B, was characterized with 72.5 dB of SFDR, which is limited by the SNR of the considered signal generator equipment (Figure 16a), for a signal power level of \(-2\) dBm (ADC full-scale input value).  
Moreover, comparing Figure 16a,b, it is verified that ADC sampling and quantization processes induce a performance degradation of 12.7 dB in terms of SFDR, leading to a maximum dynamic range of 59.8 dB.
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 1 Introduction
* **Inertial based systems:** these systems use self-contained sensors that measure the motion of the user and estimate its position relative to the starting point without the need of any physical infrastructure deployed in the building. * **Hybrid systems:** these systems jointly combine two or more different methods in order to enhance the estimation of position. A complete classification of IPS is shown in Figure 1 including references to remarkable works. Note that hybrid systems are not subclassified. There are lots of possible combinations of IPS that can form an hybrid system and a general classification of these systems is not feasible. In this work we only focus on those hybrid systems that can be scaled to mass market applications. The rest of the paper is organized as follows: Section 2 reviews the network based IPS. Section 3 is focused on the review of inertial based systems whereas Section 4 reviews the state of the art of hybrid systems for mass market applications. Finally, in Section 5 we present the conclusions and some possible future lines of work.
## 5 Evaluation
### Experimental Settings  
The implementations of the experimental evaluation, including model training, prediction, performance measurements, are all done using Python programming language along with open-source libraries including Scikit-Learn [43], TensorFlow [44], and dmlc/xgboost [45]. In the experiments, we first split a dataset of 5037 records into training and testing sets following an 80-20% ratio, and subsequently feed the training set into the XGBoost algorithm for model training. Grid search and cross validation are both used to tune the parameters of the XGBoost algorithm. More specifically, within the pre-defined parameter ranges and with a properly chosen step size, an iterative process was executed to explore all possible combinations of hyperparameters. Based on the results of cross-validation evaluations between each combination, the algorithm sequentially adjusted parameters to seek the optimal hyperparameter configuration. Due to the incorporation of regularization terms and control over tree complexity, the XGBoost algorithm demonstrates a degree of robustness against noise and outliers within the training data.
## 3 Results
### Flux Data Modeling, Uncertainty Analysis, and Benchmarking
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target** & \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Training/Testing Partition Setting**} & \multirow{2}{*}{**Average**} \\
**Variable** & & & & \\ \hline \multicolumn{5}{c}{**Training/Testing Partition Setting**} \\  & & **10-Fold CV** & **7-Day FW** & **14-Day FW** & \\ \hline \multirow{5}{*}{LE} & \multirow{2}{*}{LSLM} & 0.61 & 0.55 & 0.57 & 0.57 \\  & & (76.4) & (132.8) & (127.6) & (112.2) \\  & & 0.77 & 0.85 & 0.86 & 0.83 \\  & & (105.5) & (91.7) & (87.0) & (94.7) \\  & & 0.86 & 0.90 & 0.90 & 0.89 \\  & & (86.8) & (74.1) & (73.8) & (78.2) \\ \cline{2-6}  & & \multicolumn{3}{c}{**Feature Setting**} \\  & & **None** & **Rg** & **Rg + Tair + VPD** & \\ \cline{2-6}  & \multirow{2}{*}{REddyProc} & 0.79 & 0.85 & 0.87 & 0.83 \\  & & (114.4) & (95.6) & (93.1) & (101.0) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 8: Benchmarking results of filling artificial gaps of the net ecosystem exchange (NEE) data based on the optimal prediction models and cross-validation settings. Regressions of predicted NEE by the REddyProc software using just global radiation alone (**top-left**), support vector machine (SVM, **top-right**) and artificial neural network (ANN, **bottom-left**) using 7-day window training data flanking each missing/testing value, and the least-square linear model (LSLM) based on a 10-fold cross validation (10-fold CV, **bottom-right**); against the eddy-covariance (EC) measured NEE values are presented. The red line indicated 1:1 perfect prediction line.
# Hybrid Particle Swarm Optimization-Jaya Algorithm for Team Formation
###### Abstract  
Collaboration in a network is crucial for effective team formation. This paper addresses challenges in collaboration networks by identifying the skills required for effective team formation. The communication cost is low when agents with the same skills are connected. Our main objective is to minimize team communication costs by selecting agents with the required skills. However, finding an optimal team is a computationally hard problem. This study introduces a novel hybrid approach called I-PSO-Jaya (improved PSO-Jaya, which combines PSO (Particle Swarm Optimization) and the Jaya algorithm with the Modified Swap Operator to form efficient teams. A potential application scenario of the algorithm is to build a team of engineers for an IT project. The implementation results show that our approach gives an improvement of 73% in the Academia dataset and 92% in the ACM dataset compared to existing methods.  
team formation; I-JMSO; I-GWO; I-PSONSO; I-PSO-Jaya +
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performance and more optimized outcomes using best-fit heuristic algorithms with appropriate methodologies. Furthermore, there needs to be more strategies that utilize hybrid metaheuristic algorithms to enhance results in the team formation problem.  
We employ Particle Swarm Optimization (PSO) with the Modified Swap Operator (MSO) to identify teams with the lowest communication costs [9; 11]. To further enhance the performance of our selected team, we use the Jaya algorithm [12] to optimize the outcomes by eliminating less desirable solutions and choosing the most favorable ones. Integrating a hybrid model combining PSO and the Jaya algorithm with the Modified Swap Operator provides a significant improvement in our results, forming an acceptable team while minimizing communication costs.  
The main contributions of this work are given below.  
* We introduce a hybrid approach that combines the exploration abilities of the PSO algorithm with the refinement strengths of the Jaya algorithm. This integration allows for a more thorough search of the solution space while effectively eliminating less desirable solutions and selecting the most favorable ones.
* We implement our approach on real-world datasets. We compare the performance of our approach with some existing methods in the literature.  
The rest of the paper is structured as follows. Section 2 presents a comprehensive review of related work on team formation challenges. Section 3 provides an overview of the preliminary background to introduce the proposed methodology. Some metaheuristic-based approaches for team formation is discussed in Section 4. The proposed algorithm is given in Section 5. The experimental results are given in Section 6. A brief discussion about the proposed algorithm is given in Section 7. Section 8 concludes the paper and suggests areas for future research.
## Materials and Methods
### Mathematical Models and Methods
#### 2.2.4 Detecting Change between Urban Road Environments and Locating the Change Point  
Assume now a switch from the first marked Poisson process to the second, i.e., from \(\text{mPois}(\lambda^{0}T)\) to \(\text{mPois}(\mu^{0}T)\), and accordingly a switch from respective counting measures \(N^{k}(\cdot)\) to \(M^{k}(\cdot)\) at time \(\tau\). Then for \(T\leq\tau\)  
\[g_{T}\triangleq g_{T}(\lambda,\mu)\triangleq L_{T}(\lambda)-L_{T}(\mu) \tag{8}\]  
tends to decrease with \(T\). Let us now introduce the notation \(T^{*}=T-\tau\). For \(T>\tau\), i.e., for \(T^{*}>0\), \(g_{T}\) is defined as follows  
\[g_{T}\triangleq g_{T}(\lambda,\mu)\triangleq g_{\tau}+J_{T*}^{\tau}(\lambda)- J_{T*}^{\tau}(\mu), \tag{9}\]  
where \(J_{T*}^{\tau}(\lambda)-J_{T*}^{\tau}(\mu)\) is to be computed according to a modified version of Equation (7), as shown below  
\[J_{T*}^{\tau}(\lambda)-J_{T*}^{\tau}(\mu)=T^{*}\cdot\sum_{k}(\lambda_{k}-\mu_ {k})-\sum_{k}M_{\tau}^{k}(T^{*}+\tau)\cdot\log\Bigl{(}\frac{\lambda_{k}}{\mu_ {k}}\Bigr{)} \tag{10}\]  
here the counting measures \(M_{\tau}^{k}(\cdot)\) count events in the same way as \(M^{k}(\cdot)\) with the only difference that they now count events only from \(\tau\) onwards.  
For \(T>\tau,g_{T}\) tends to increase with \(T\). To estimate the location \(\tau\), one should monitor function \(g_{T}\). Then, in order to determine \(\tau\), we need to wait for an increasing trend in \(g_{T}\) to appear.  
In order to find out whether a change in the stochastic model has occurred, or not, and if it has, when/where, one should compute the minimum of \(g_{T}\) on-the-fly with a Page-Hinkley change detector (PHCD), see [42; 43] for the detailed derivation of the change detector.  
Using \(g_{T}\), the PHCD, \(h_{T}\) is defined as  
\[h_{T}\triangleq g_{T}-\inf_{s\leq T}g_{s}. \tag{11}\]  
A change is thought to have occurred if \(h_{T}\) exceeds a threshold \(\delta>0\). As it will be clear from the examples presented in Sections 3.1 and 3.2, the choice of \(\delta\) is crucial for the proper working of the detector.  
It can be shown--following the line of thoughts presented in [44]--that under the hypothesis of no-change, \(h_{T}\) is \(L\)-mixing, and the false alarm probability is exponentially decaying in \(\delta\): \(P(h_{T}\geq\delta)\leq Ce^{-a\delta}\) with some \(a>0\). Hence the false alarm rate itself is exponentially decaying in \(\delta\). As a consequence, the false alarm rate can be effectively reduced by choosing larger \(\delta\). On the other hand, if \(\delta\) is chosen to be too large, then the detection lag can be too long or even transitions can be missed.
## 4 Results and Discussion
The same power plant presents a considerably lower TCI (5979.69 $/kW), when operated as a coal plant. However, it is important to consider the fact that the hybrid power plant would present an important reduction in the cost of fuel, when compared to a conventional coal-fired plant. The TRR needed to maintain the electricity generation at 582 MW is presented in the Appendix A of the paper. The revenue required to cover the expenditures of the plant decreases over the years due to plant depreciation, since taxes and interest rates are reduced as the plant loses its value. However, fuel and operation and maintenance costs increase the required revenue over time due to their increasing trend.  
The LCOE of solar power tower plants ranges from 0.15 and 0.21 $/kWh in 2016 [58]. In this work, the value obtained from the detailed economic analysis of the hybrid plant was 0.19 $/kWh. The LCOE of the plant during night operation (when it did not use solar energy) was found to be 0.12 $/kWh. Published values for coal power plants are between 0.07 and 0.14 $/kWh [58]. The generation is considered to be constant during day and night. The difference of the LCOE of the hybrid and coal plants is due to the relatively higher purchased equipment cost of the first. The thermal storage is another critical factor that increases the investment cost of the solar plant by more than 60% when it offers reliable operation for 15 h instead of 6 h [2]. The complexity of the hybrid system increases the cost of the plant further, as more control instruments and specialized people in charge of the operation and maintenance are needed. Lastly, the legal, financial, and technological frameworks must be considered when studying the viability of hybrid power plants. Currently, renewable energy is encouraged by governments with \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\epsilon_{k}\) & **COAL USE (kg/s)** & **CO\({}_{2}\) EMISSIONS (kg/s)** \\ \hline
**DAY** & 35.83\% & 59.07 & 214.58 \\ \hline
**NIGHT** & 34.19\% & 61.91 & 224.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison between the operation of the hybrid power plant during night and day. subsidies. The European Union launched the European Union Emissions Trading System (EU ETS) that sets a maximum limit to the greenhouse gases that installations are allowed emit. The surplus can be sold in an emission trading system [59]. The aim of this program is to promote new ways of reducing pollution. In the technological framework, higher efficiencies and more developed processes are expected.
## 3 Analysis of Numerical Simulation Result
### Analysis of TBM Cutterhead Model  
#### 3.3.1 Compressive Force Acting at the Cutterhead's Face  
Here, the TBM compressive force was calculated from the sum of normal forces between the particle and TBM cutterhead, and it was, therefore, parallel to the advancement axis. Figure 13a plots the compressive force acting on the TBM cutterhead as the TBM advanced. The plot of the average compressive force in Figure 13b clearly shows that as the cutterhead RPM increased, the overall compressive force also increased. The DEM simulation showed that the calculated compressive force on the cutterhead and disc cutter could be considered to be a component of the required thrust force. Based on this consideration, the average compressive force on the cutterhead was estimated to be 31-37% of the actual TBM's maximum thrust.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Operating Condition** & **Average Chamber** & **Standard Deviation** \\  & **(Cutterhead RPM/Screw RPM)** & **Pressure** & **of Chamber Pressure** \\ \hline
1 & 1.0/12.0 & 27.51 & 4.98 \\
2 & 1.0/9.0 & 35.61 & 1.64 \\
3 & 1.0/6.0 & 38.45 & 0.86 \\
4 & 1.5/12.0 & 38.43 & 0.72 \\
5 & 1.5/9.0 & 38.82 & 0.97 \\
6 & 1.5/6.0 & 40.75 & 0.97 \\
7 & 2.0/12.0 & 41.08 & 0.90 \\
8 & 2.0/9.0 & 41.01 & 1.00 \\
9 & 2.