esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Xylose Recovery in Oil Palm Empty Fruit Bunches for Xylitol Production
## 5 Discussion
Considering that melted lignin was not isolated before enzymatic hydrolysis was performed, the xylan recovery of 35% was adequate. A similar result was reported previously on hot water treatment using bamboo [35]. Other reports stated that enzyme can be absorbed by deposited lignin in the solid surface of biomass [49; 50; 51]. The low determination coefficient of xylose recovery for liquid pretreated OPEFB was due to the low xylose recovery, as can be seen in Figures 2b and 4b. Even though the model \(R^{2}\) was 61.99%, it fit significantly (\(p\)-value of 0.001). The ANOVA results confirmed that there was no interaction effect among factors, SL, temperature, and pretreatment time. However, there was obviously a need for optimization for these three factors to obtain maximum xylose recovery in moderate conditions as can be seen in Figure 5. The optimum temperature was \(165\pm 5\)\({}^{\circ}\)C, consistent with the results obtained on hydrothermal pretreatment of prairie cordgrass [24]. The optimum SL of 7% was in good agreement with the previous result in hydrothermal pretreatment of OPEFB [44]. The optimum pretreatment time of 60 min was consistent with the results obtained on hot-compressed hot water pretreatment of sugarcane bagasse [52].
# Clustering Moving Object Trajectories: Integration in CROSS-CPP Analytic Toolbox
## 2 Related Work
### Clustering of Trajectories  
Trajectory clustering is a common practice in trajectory data mining [9; 14]. It consists in grouping the trajectories based on a similarity measure. The nature of trajectories as multidimensional data enables different clustering approaches that may be domain- or application-specific.
# Long-Range Multispectral Thermal Imager for Simultaneous Measurement of Distance and Temperature
## 4 Conclusions  
Through theoretical derivation, this study systematically analyzed the imaging characteristics of a multispectral thermal imager with a multi-aperture spectral-splitting structure. Furthermore, the mathematical relationship between the offset of multi-aperture temperature fields and the distance of the target was established. The results of the calibration experiment demonstrated that the proposed method achieved a maximum error of 8.15%. Additionally, remote flame temperature measurement experiments validated the correctness of the mathematical relationship. The results showed that there is a certain correlation between the maximum flame temperature and the flame diameter, and the proposed method is capable of measuring distances ranging from 10 m to 280 m.  
The measurement model proposed in this paper is obtained by using the hypothesis of paraxial optics, which is suitable on the condition that the field of view is small, and the geometric alignment accuracy of the system optical axis is high. However, in a system with a large field of view or strong refraction, the deviation angle of the light may be large, and the paraxial assumption will introduce errors. Therefore, the measurement error will increase with the increase in distance. Since distance inversion is affected by matching accuracy, future research will focus on developing robust, high-precision sub-pixel temperature field-matching algorithms. Additionally, we plan to conduct theoretical studies and experimental validation for measurement scenarios at distances exceeding 300 m.  
Figure 5: Measurement results of the remote flame. (**a**) Temperature field images collected by the first group of multispectral thermal imagers. (**b**) The relationship between the first group of flame maximum temperature and distance. (**c**) The temperature field images collected by the second group of multispectral thermal imagers. (**d**) The relationship between the second group of flame maximum temperatures and distances.  
**Author Contributions:** J.L.: methodology, writing--original draft preparation; X.S.: supervision, funding acquisition; Y.W.: data validation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Basic Research Project (JCKY2019208B012) and in part by the National Natural Science Foundation of China (NSFC) (61875046).  
**Data Availability Statement:** Data will be made available on request.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Optimization of a Truss Structure Used to Design of the Manipulator Arm from a Set of Components
## 2 Method
### Formulation of Component Optimization Problem  
Let us assume a structure given by a set of connected components. A constrained component optimization problem is formulated as follows:  
\[\text{Minimize }weight(\textbf{X})=\sum_{i=1}^{N_{m}}m_{i}, \tag{1}\]  
subject to  
\[\begin{array}{c}\sigma_{i}^{EQV}\leq\sigma_{i}^{Y},i=1,2,...,N_{m},\\ abs(\delta_{k}^{x_{l}})\leq\delta^{Max},k=1,2,...,N_{k},l=1,2,3,\end{array} \tag{2}\]  
where \(weight(\textbf{X})\) is the weight of the structure, **X** is the vector of the design components, \(m_{i}\) is the component mass, \(\sigma_{i}^{EQV}\) is the maximum equivalent stress of the member, \(\sigma_{i}^{Y}\) is the yield stress or allowable stress of the member, \(N_{m}\) is the total number of structure members, \(\delta_{k}^{x_{l}}\) is the displacement in the \(x_{l}\) axis of the \(k\)-th node, \(\delta^{Max}\) is the allowable nodal displacement and \(N_{k}\) is the total number of controlled nodes. An index \(i\) will be used as a label of the structure member (\(X_{i}\)).  
A design component \(X_{i}\) contains one component from **C**:  
\[X_{i}\leftarrow\textbf{C}=[C_{j},j=1,2,...,N_{c}], \tag{3}\]  
where **C** is the vector of components and \(N_{c}\) is the total number of components, and the index \(j\) will be used as a component label.  
The constrained problem in Equations (1) and (2) can be transformed into the unconstrained form as:  
\[\text{Minimize }M(\textbf{X})=weight(\textbf{X})+\sum_{i=1}^{N_{m}}p_{i}^{ \sigma}(\textbf{X})+\sum_{k=1}^{N_{k}}p_{k}^{\delta}(\textbf{X}), \tag{4}\]  
where \(M(\textbf{X})\) is the final objective function, \(p_{i}^{\sigma}(\textbf{X})\) is a stress penalty function and \(p_{k}^{\delta}(\textbf{X})\) is a displacement penalty function. The penalty functions are defined as follows:  
\[p_{i}^{\sigma}(\textbf{X})=\begin{cases}c^{\sigma}(\sigma_{i}^{EQV}-\sigma_{j }^{Y}),\text{ for }\sigma_{i}^{EQV}\geq\sigma_{j}^{Y},\\ 0,\text{ otherwise },\end{cases}, \tag{5}\]  
\[p_{k}^{\delta}(\textbf{X})=\begin{cases}c^{\delta}(abs(\delta_{k})-\delta^{ Max}),\text{ for }abs(\delta_{k})\geq\delta^{Max},\\ 0,\text{ otherwise },\end{cases}, \tag{6}\]  
where \(c^{\sigma},c^{\delta}\) are the stress and displacement penalty constants, respectively.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 2 An Overview of The MPTCP Schedulers and Congestion Control Algorithms
### MPTCP Congestion Control Algorithms
#### 2.2.3 Balanced Linked Adaptation  
Through a fluid model analysis, balanced linked adaptation (BALIA) tries to find the existing problems in the previous MPTCP CCAs, such as LIA and OLIA, and attempts to resolve them by providing a better balance among responsiveness, fairness, and flappiness. Thus, BALIA is expected to perform better than the LIA and OLIA. However, according to [PERSON] et al. [37], the same fairness problem observed in LIA and OLIA also exists in BALIA.
# Optimal Design of Aperiodic Reconfigurable Antenna Array Suitable for Broadcasting Applications
## 5 Comparison with Other Optimization Methods  
PSOvm has been compared with all the optimization algorithms that can be found in CST: PSO, genetic algorithm (GA), trusted region framework (TRF), covariance matrix adaptation evolution strategy (CMAES), Nelder Mead simplex algorithm (NMSA), classic Powell (CP) and interpolated quasi Newton (IQN). All the optimization methods have been given a maximum of 2000 fitness evaluations in order to achieve the best result possible. Such benchmarks take a long time to be completed since, as already mentioned in earlier sections, each fitness evaluation is equal to one full wave analysis of the antenna performed by CST with an average computational time of one minute. In Table 6 we show the best result that they achieved within the fitness evaluations limit. In the last column we display the total time (in minutes) required by each method to achieve the best result. PSOvm manages to yield the best result in the least amount of time compared to the other optimization methods with TRF being close. Both PSOvm and TRF converged before hitting the limit of 2000 evaluations. The rest of the optimization methods fail to achieve multiple goals such as main lobe tilting, null level and SLL. The results of this comparison show that both PSOvm and TRF are very capable algorithms that can be used to solve complex multi-variable and multi-objective problems as is the one solved in this paper (26 variables and 13 objectives). Furthermore, it is evident that TRF is the best optimizer available found in CST.  
Figure 4: S-parameters of the array elements. All S-parameters are \(\leq-21\) dB at 698 MHz (DVB-T channel 49 center frequency) satisfying the optimization goals.  
Figure 3: Normalized radiation pattern versus elevation angle \(\theta\) at azimuth angle \(\phi=0\). The maximum value of the normalized radiation pattern (0 dB) corresponds to the actual maximum gain of the AMAA 13.9 dBi at \(\theta=93^{\circ}\) (main lobe tilting by \(3^{\circ}\)). The red dash-line shows the desired SLL outside the service area, and the green dash-line shows the desired null filling level inside the service area. The optimal radiation pattern should be below the red dash-line and above the green dash-line.
###### Abstract  
With the increasing application of CHP and an industry transition to distributed energy, it is necessary to make a comprehensive economic analysis and comparison of the entire lifetime of CHP from the net present value (NPV), payback period, and cost-saving ratio (CSR). Five systems, including micro-CHP, gas boiler (GB), air-source heat pump (ASHP), domestic gas-fired heater and domestic electric hot water-heater, are simulated. First, this paper takes annual heat use efficiency (AHUE) into account to compare the economy of each domestic hot water (DHW) system. The results show that a domestic gas-fired heater system is the most economical option in the AHUE of 31.28%. The economic influence of CHP and gas-fired heater under different AHUE are then analyzed. The results show that the DHW system based on CHP is the best when the AHUE is more than 55.35%. Finally, three different operation strategies of CHP are considered in this paper. From the perspective of annual energy cost and payback, the internal combustion-based CHP with thermal energy system (TES) is superior to the other two strategies being studied. Considering the optimal economic benefits, the CSR of the three different operation strategies is 41.3%, 69.69% and 69.77%, respectively.  
economical assessment; optimal control strategy; combine heat and power; domestic hot water systems; annual heat use efficiency
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 3 Concurrent Transmission Based on Distributed Scheduling
### Protocol Description
#### 3.2.3 Data Stage  
In the data stage, the source nodes that succeed in handshaking calculate their sending time and wait for transmission. Each source node independently executes a copy of the proposed scheduling algorithm. Because the proposed scheduling algorithm is deterministic and its input parameters are the node pairs that successfully shake hands in the current transmission cycle, all the source nodes can obtain the same sending time table for each source node through the proposed scheduling algorithm if they receive all the CTS packets correctly. When the sending time of a node arrives, it starts to transmit data to the corresponding destination node. Each node can independently calculate the ending time of the data stage using the proposed deterministic scheduling algorithm. Note that overlaps of data packets at their non-destination nodes are allowed in this stage, as shown in Figure 1, where the data packets from the source nodes S\({}_{1}\) and S\({}_{2}\) are allowed to overlap at the destination node D\({}_{3}\).  
Figure 1: Timing diagram of the proposed protocol.
# Evaluating Levels of Automation in Human-Robot Collaboration at Different Workload Levels
## 3 Results
### Usability  
The interaction of LOA and workload on usability was not significant (F (18.137) = 1.615, \(p\) = 0.064). However, the main effects of LOA (F (18, 135) = 7.768, \(p\) < 0.001) and level of workload (F (18, 137) = 11.905, \(p\) < 0.001) was significant. At high LOA, the usability was higher (M = 4.36, SD = 0.83) compared to the low LOA (M = 4.31, SD = 0.773), in agreement with H2. At LWL (M = 4.37, SD = 0.633), usability was higher compared to HWL (M = 4.25, SD = 0.742). Higher usability was obtained when task complexity increased (MWL1, M = 4.45, SD = 0.959) as compared to when there was a secondary task (MWL2, M = 4.29, SD = 0.835).  
There was no difference in the workload groups in terms of the perceived ease of use. However, workload level significantly influenced perceived workload as measured through the aggregated raw NASA-TLX scores (F (3, 152) = 11.767, \(p\) < 0.001), with the HWL (M = 14.6, SD = 4.337) resulting in higher perceived workload compared to the LWL (M = 12.58, 3.796) as expected. Between the medium workload groups, MWL2 (M = 15.33, SD = 3.318) resulted in higher perceived workload compared to MWL1 (M = 11.18, SD = 2.123).  
Workload also had significant effect (F (3, 152) = 3.646, \(p\) = 0.014) on perceived reliability as assessed through the questionnaire. The reliability was perceived as higher by the participants who experienced the LWL (M = 4.53, SD = 0.687) compared to the HWL (M = 4.5, SD = 0.555). Between the medium workload levels, MWL1 (4.63, SD = 0.628) resulted in higher perceived reliability compared to MWL2 (M = 4.19, SD = 0.634).
# A Machine Learning Framework for Student Retention Policy Development: A Case Study
## 4 Experimental Results and Discussion
### Feature Analysis of the Dataset
#### 4.1.10 Correlation Analysis of Features  
Feature correlation is a metric that shows the relationships between two features. Understanding the dependencies or strong correlations between two features is important for choosing the most optimal and minimum feature set when training ML classifiers. The correlation heatmap of the extracted features shown in Figure 10 illustrates how the features are interrelated such that correlation coefficients range from \(-1\) to \(1\), where \(1\) indicates a perfect positive correlation, \(-1\) indicates a perfect negative correlation, and \(0\) indicates no correlation. As shown in Figure 10, there is a very strong positive correlation between the features of faculty and department with a correlation coefficient of \(0.98\). This is followed by the high school graduation date and university registration date, which are also positively correlated with a coefficient of \(0.87\). Additionally, it can also be seen that, as expected, the number of courses a student repeats is positively correlated with both the number of courses failed in the first and second semesters with correlation coefficients \(0.51\) and \(0.69\), respectively. Since strongly correlated features may provide redundant information to the model, further work to eliminate the redundant attributes may contribute to performance.  
Figure 10: Correlation heatmap of the features employed.  
Negative correlations are indicative of a correlation, meaning that as one feature increases, the other decreases. As expected, registration age and birth year have a strong negative correlation of \(-0.93\). Registration semester has strong negative correlations with both the registration date and graduation date of high school. As noted previously, the registration date and high school graduation date are positively correlated. Features that have correlation coefficients close to zero are relatively independent and hence each of these features offers distinct information to the model.  
The correlation heatmap provides valuable insights into the relationships between pairs of features and understanding these relationships and choosing the most relevant features that have unique contributions will enable ML models to accurately identify at-risk students.
## 4 OFDM Transmission Tests
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No of Subcarriers**} & \multicolumn{2}{c}{**BER**} & \multicolumn{2}{c}{**BCH Code**} & \multicolumn{2}{c}{**BCH Code**} & \multicolumn{2}{c}{**RS Code**} \\  & **Less Than** & **Coding Rate** & **Data Rate [bps]** & **Coding Rate** & **Data Rate [bps]** \\ \hline
64 & \(10^{-2}\) & 0.3882 & 551.24 & 0.6392 & 907.66 \\
64 & \(10^{-3}\) & 0.1137 & 161.45 & 0.3858 & 547.84 \\
64 & \(10^{-4}\) & 0.0510 & 72.42 & - & - \\ \hline
128 & \(10^{-2}\) & 0.5451 & 774.04 & 0.8118 & 1152.76 \\
128 & \(10^{-3}\) & 0.2283 & 324.19 & 0.7020 & 996.84 \\
128 & \(10^{-4}\) & - & - & 0.3651 & 518.44 \\ \hline
256 & \(10^{-2}\) & 0.6392 & 907.66 & 0.8431 & 1197.20 \\
256 & \(10^{-3}\) & 0.2835 & 402.57 & 0.6063 & 860.95 \\
256 & \(10^{-4}\) & 0.2784 & 395.33 & 0.4603 & 653.63 \\ \hline
512 & \(10^{-2}\) & 0.1181 & 167.70 & 0.4510 & 640.42 \\
512 & \(10^{-3}\) & - & - & 0.4196 & 595.83 \\
512 & \(10^{-4}\) & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coding rates and data transmission rates during transmission with ECC. Figure 7: BER of OFDM data transmission as a function of ECC rate: BCH (*) and Reed–Solomon (o); black line indicates BER equal to \(10^{-4}\); green line indicates BER of transmission without ECC.
## 2 Methodology
### Convergence and Termination  
The algorithm iteratively updates the positions of wolves until a termination criterion is satisfied, such as achieving the maximum number of iterations \(T_{\max}\) or reaching a predefined fitness threshold.  
Application in WSNs  
In this study, GWO is applied to optimize QoS in WSNs for IoT applications. Key objectives include the following:  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Mean Fitness** & **Std Dev Fitness** & **Avg. Convergence Iterations** & **Execution Time (s)** \\ \hline Linear & \(1.2\times 10^{-3}\) & \(3.5\times 10^{-4}\) & 40 & 0.045 \\ Quadratic & \(1.6\times 10^{-3}\) & \(4.1\times 10^{-4}\) & 38 & 0.048 \\ Exponential & \(9.0\times 10^{-4}\) & \(2.8\times 10^{-4}\) & 35 & 0.042 \\ Cosine & \(2.5\times 10^{-3}\) & \(6.0\times 10^{-4}\) & 48 & 0.050 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of different decay strategies in GWO using the Sphere function.  
* Energy Efficiency: Minimizing energy consumption by optimizing cluster head selection and communication paths.
* Delay Reduction: Reducing end-to-end latency for time-sensitive IoT applications.
* Network Throughput: Enhancing packet delivery ratio and overall network performance.  
The fitness function for GWO in this context is defined as  
\[f=\alpha\cdot\frac{1}{E}+\beta\cdot\frac{1}{D}+\gamma\cdot T, \tag{23}\]  
where  
* \(E\): Total energy consumption;
* \(D\): Average delay in data transmission;
* \(T\): Total network throughput;
* \(\alpha,\beta,\gamma\): Weighting factors balancing the objectives.  
This approach enables dynamic resource allocation and efficient network management, addressing the unique challenges of WSNs in IoT applications [18].
## 6 Future Scope  
In line with the presented research work, other contributions can be made: (1) The RF energy exposure by the proposed antenna with SWs can be studied in real time by mimicking the phantom model. (2) In the current work, the effect of the SW screen was neglected. In future work, the effect of the SW screen, which is supported by PCB and has PEC properties, can be considered in the analysis. (3) Further, link margin analysis can be performed by considering the human body between SWs and mobile phones, since at the mmWave level, the human body creates obstruction, leading to NLOS communication.
## 1 Introduction
Therefore, in random fog weather, this paper considers the comprehensive impact of platform random jitter and non-zero boresight error on pointing error, and establishes a multi-hop AF FSOC link model based on Gamma-Gamma distributed atmospheric turbulence. In this work, we delve into a multi-hop AF relay FSOC system considering Gamma distribution random fog, Gamma-Gamma distribution atmospheric turbulence and Beckmann distribution generalized pointing error. By analyzing the link state, we establish a new closed expression for the probability density function (PDF) of the system signal-to-noise ratio (SNR), which is represented by the \(I\)-function [28]. Then, new closed expressions for OP, ABER, and average ergodic channel capacity of multi-hop FSOC systems are derived under fixed gain (FG) relay and channel state information (CSI) assisted relay. Finally, the simulation analyzes the influence of different parameters on the OP/ABER/average ergodic channel capacity of the system. This work contributes to the system design and performance optimization of multi-hop FSOC systems under composite channels.
###### Abstract  
This pilot study aimed to compare the fit accuracy of cobalt-chromium (Co-Cr) and polyether ether ketone (PEEK) removable partial denture frameworks, produced by digital technologies. Two study models of previously prepared [PERSON]'s Class I and Class III mandibular dental arches were scanned. For each model, two frameworks were digitally designed and manufactured using a Co-Cr alloy via the selective laser melting (SLM) technique, and using PEEK via the milling technique. A qualitative assessment of the framework's fit accuracy to the corresponding study models was carried out using calibrated endodontic instruments and image amplification. Best-fit superimpositions between the reference design and the scanned frameworks were performed using the Geomagic Control X version 2018, 3D Systems software, allowing the expression of trueness by calculating the root mean square (RMS) value. Higher fit accuracy was observed for the milled frameworks, with the Class I PEEK framework showing the best fit accuracy to the corresponding model. RMS values were Class I--148.3 um for Co-Cr and 69.2 um for PEEK; Class III--107.2 um for Co-Cr and 59.7 um for PEEK. In the experimental conditions used, the milled PEEK frameworks showed better fit accuracy and higher trueness than the SLM-printed Co-Cr ones in both Kennedy classes.  
removable partial denture; framework; CAD-CAM; digital; PEEK; Co-Cr; fit accuracy; trueness +
Footnote †: journal: Applied Sciences