esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Algorithm Performance Analysis  
Thirty executions are carried out to evaluate the metaheuristic algorithm performance (DE/RAND/1/BIN, GA, PSO, MUMSA) for each constraint-handling technique (FR, SR, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Four-Bar** & **Cam–Linkage** & **Parameter** & **Four-Bar** & **Cam–Linkage** \\ \hline \(w_{1,1}^{1}\) & \(-5.1507\) & \(-9.7041\) & \(w_{3,2}^{3}\) & \(-4.5326\) & \(-1.7223\) \\ \(w_{2,1}^{1}\) & \(5.7785\) & \(4.6228\) & \(w_{1,1}^{4}\) & \(-6.4775\) & \(-9.5093\) \\ \(w_{3,1}^{1}\) & \(7.5795\) & \(-9.6850\) & \(w_{2,1}^{4}\) & \(0.7554\) & \(-3.7442\) \\ \(w_{4,1}^{1}\) & \(5.2518\) & \(-3.5547\) & \(w_{3,1}^{4}\) & \(0.8913\) & \(0.0632\) \\ \(w_{1,2}^{1}\) & \(8.4485\) & \(1.6000\) & \(w_{1,2}^{1}\) & \(-5.6703\) & \(-6.2279\) \\ \(w_{2,2}^{2}\) & \(-4.8087\) & \(5.5538\) & \(w_{2,2}^{2}\) & \(5.7473\) & \(-5.4144\) \\ \(w_{3,2}^{3}\) & \(6.4018\) & \(-4.7216\) & \(w_{3,2}^{3}\) & \(-2.1010\) & \(-1.4408\) \\ \(w_{4,2}^{4}\) & \(-0.0997\) & \(4.5662\) & \(w_{1,1}^{4}\) & \(5.0501\) & \(-5.8695\) \\ \(w_{1,3}^{1}\) & \(-6.4759\) & \(9.3248\) & \(w_{2,1}^{2}\) & \(2.7255\) & \(-4.0260\) \\ \(w_{2,3}^{2}\) & \(-6.9908\) & \(0.6948\) & \(w_{3,1}^{5}\) & \(-5.471\) & \(-0.5826\) \\ \(w_{3,3}^{1}\) & \(6.9994\) & \(-9.1374\) & \(u_{1}^{1}\) & \(5.0333\) & \(-5.8695\) \\ \(w_{4,3}^{1}\) & \(0.9477\) & \(-5.2051\) & \(u_{2}^{1}\) & \(2.7255\) & \(-4.0260\) \\ \(w_{1,1}^{2}\) & \(6.4335\) & \(-7.9333\) & \(u_{3}^{1}\) & \(-5.471\) & \(-0.5826\) \\ \(w_{2,1}^{2}\) & \(6.7337\) & \(-0.4223\) & \(u_{1}^{2}\) & \(5.0333\) & \(7.6293\) \\ \(w_{3,1}^{2}\) & \(9.1879\) & \(3.7733\) & \(u_{2}^{2}\) & \(-3.2977\) & \(-7.3464\) \\ \(w_{2,2}^{1}\) & \(2.2342\) & \(7.7584\) & \(u_{2}^{2}\) & \(-0.5276\) & \(-0.2524\) \\ \(w_{2,2}^{2}\) & \(-6.4004\) & \(-8.9817\) & \(u_{1}^{1}\) & \(4.4916\) & \(-5.0776\) \\ \(w_{3,2}^{2}\) & \(0.2861\) & \(-6.4264\) & \(u_{2}^{3}\) & \(6.1729\) & \(2.9905\) \\ \(w_{1,1}^{3}\) & \(7.0605\) & \(-7.6129\) & \(u_{3}^{3}\) & \(-9.4461\) & \(-9.8085\) \\ \(w_{2,1}^{2}\) & \(-3.3614\) & \(8.5456\) & \(u_{1}^{4}\) & \(-3.4358f\) & \(-6.9378\) \\ \(w_{3,1}^{3}\) & \(3.2812\) & \(-3.3421\) & \(u_{2}^{4}\) & \(0.8191\) & \(-4.6794\) \\ \(w_{1,2}^{3}\) & \(-4.3912\) & \(2.1478\) & \(u_{3}^{4}\) & \(-1.1605\) & \(4.4777\) \\ \(w_{2,2}^{3}\) & \(8.4263\) & \(7.2631\) & \(u_{1}^{5}\) & \(-1.7526\) & \(-2.6718\) \\ \(F_{max}\) & \(0.95\) & \(0.53\) & \(F_{min}\) & \(0.13\) & \(0.51\) \\ \(CR\) & \(0.95\) & \(0.87\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Algorithm parameters of the NCH obtained by the irace package.
# NextGen ADS-B Software-Defined Reception with Enhanced Techniques
## 3 Methodology and Solutions
### Enhanced Bit Detection and Confidence Declaration  
One of the prevalent methods for the detection of the bit and calculation of confidence values is the center sample technique. The actual amplitude of the center samples for two consecutive chips is measured and based on each chip position, the chip with greater amplitude is designated as a bit value. However, the center sample method is simple for implementation, it will fall short of the standards set for Class A2 and A3 in terms of performance. Only the multi-sample techniques can perform and accomplish the required performance for these classes of equipment [1,11].  
The bit and confidence values are calculated using all samples of each Mode S bit position in the multi-sample technique [1]. Figure 4 illustrates the required steps for this purpose.  
As an initial step, it is essential to create an amplitude window and to classify detected chips to A in which samples fall into \(\pm\)3 dB of the preamble reference level. In addition, classify detected chips to B if the sample amplitude value is below a minimum threshold. Nonetheless, a minimum amplitude threshold is set to 6 dB lower than the reference level.  
Figure 3: Enhance the preamble detection procedure.  
The second step is to determine and compute the weighting factor of each category for every chip according to the sample's position.  
The preamble is thought to be matched by samples that fall within the amplitude window and this is indicated by ChipTypeA and referred to as Match Preamble in the flowchart. While the lack of transmitted energy is thought to be indicated by samples that fall below the minimum threshold and it is indicated by ChipTypeB. Therefore, in the third step, counters of ChipTypeA and ChipTypeB for a bit \"0\" and \"1\" need to be computed.  
In step four, the results of two equations using the aforementioned counters are used to generate two scores that represent how much the sample pattern has a correlation either with a transmitted bit 0 or a transmitted bit 1. The score equations are presented in Figure 4.  
In step five, the bit value is determined by the highest score. When there is a tie, the bit is set to zero by default (0). The bit has high confidence if the difference is three or more, otherwise, it has low confidence.
## 3 Results
### SIF-EVI Relationships at Different Spatial Resolutions  
We examined the correlation between SIF and EVI at spatial resolutions of 5 km, 10 km, 20 km, 30 km, 40 km, and 50 km using the growing-season data from 2015 and 2016. The EVI was resampled to the corresponding spatial resolution, and average SIF values were obtained within each EVI pixel. From the result (Figure 6), we found that the R\({}^{2}\) values were different for different spatial resolutions, with a clear trend of lower spatial resolution with higher R\({}^{2}\) value. Between Julian days 177 and 241, R\({}^{2}\) values for the 20 km and 30 km spatial resolutions were higher than that for 10 km but lower than that of the 40 km spatial resolution. On Julian days 193 and 225, R\({}^{2}\) values of 20 km were higher than those of 30 km and on other days, R\({}^{2}\) values of 30 km were higher than those of 20 km.  
Figure 5: Scatter plots between SIF and EVI of each 16-day interval in 2015 and 2016.
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 2 Materials and Methods
### Mathematical Analysis
The TCP Westwood sets the slow start threshold according to the value of the estimated bandwidth at the end of the previous cycle using the following expression [5]:  
\[W_{0(\omega)}=\max\Bigg{\{}2,\left\lfloor\frac{W_{n(\omega-1)}RTT}{2}\right\rfloor \Bigg{\}} \tag{10}\]where  
\[W_{n+1}=\alpha W_{n}+(1-\alpha)\frac{b_{n+1}+b_{n}}{2}, \tag{11}\]  
where \(\alpha\) is related to the cut-off frequency of the low pass filter and \(b_{n}\) is the bandwidth information carried by the n-th ACK, expressed as  
\[b_{n}=\left\{ \begin{array}{cc}&\frac{R_{p}}{W_{C}-W_{m+1}},&x_{n}<x_{C}\\ &R_{p}&\text{otherwise}\end{array} \right. \tag{12}\]  
The evolution of the initial congestion window size, in each cycle, can be modelled as a Markov process. \(W_{0(\omega)}\) depends only on \(W_{0(\omega-I)}\) and \(n_{I(\omega-1)}\). The transition probability from \(W_{0(\omega-I)}=i\) for \(W_{0(\omega)}=j\) is given by  
\[ \begin{array}{cc}p_{i,j}=\Pr_{t}\Big{\{}W_{0(\omega)}=j\Big{\}}W_{0(\omega-1 )}=i\Big{\}};&i,j\in\{2,3,\ldots\ldots,W_{C}\}\\ &=\sum\limits_{n\in m_{I}}\Pr[n|W_{0}=i]\end{array}  \tag{13}\]  
where the probability that the packet \(n\) is dropped, given that the initial window \(W_{0}=i\), and is expressed as  
\[\Pr[n|W_{0}=i]=\left\{ \begin{array}{cc}P_{packet,TCP}\Big{(}1-P_{packet,TCP }\Big{)}^{n-1},&n<n_{of}(i)\\ \Big{(}1-P_{packet,TCP}\Big{)}^{n-1},&n=n_{of}(i)\end{array} \right. \tag{14}\]  
where \(P_{packet,TCP}\) is the TCP packet error probability. Finally, using (8) and (14) the throughput realized by the system is given by [5]  
\[\overline{\theta}=\sum\limits_{i=2}^{C}\pi_{(W_{0}=i)}\sum\limits_{n=2}^{n_{of }(W_{0}=i)}\frac{n-1}{\Delta_{t}(W_{0}=i,n)}\Pr[n\;|W_{0}=i]N_{TCP},\;[\text {bits}/\text{s}] \tag{15}\]  
where \(\pi_{(W_{0}=i)}\) is the asymptotic probability of the initial window size \(W_{0}\). Then, we added in (16) the packet loss probability effect due to wireless issues in order to fit the model to the TCP-NACK protocol behavior proposed. Therefore, if the packets are corrupted due to the wireless channel, these errors are recognized by NACKs, and the CWND increases normally (i.e., until the depletion of the receptor buffer). Finally, the TCP-NACK \(\overline{\theta}\) is defined as  
\[\overline{\theta}_{NACK}=\sum\limits_{i=2}^{C}\pi_{(W_{0}=i)}\sum\limits_{n=2 }^{n_{of}(W_{0}=i)}\frac{(n-1)\Big{(}1-P_{packet,TCP}\Big{)}}{\Delta_{t}(W_ {0}=i,n)}\Pr[n\;|W_{0}=i]N_{TCP},\;[\text{bits}/\text{s}] \tag{16}\]
## 3 Results  
### Analytical Method  
EEG Data Analysis: The alpha band (8-13 Hz) of the EEG signal is commonly observed in states of relaxation, tranquility, and non-stressful wakefulness, which is the required frequency range for this study. During the experiment, EEG signals were collected using SAGA software, and Python techniques were employed to extract and process the alpha wave amplitudes from each electrode in seconds. The relevant data were presented in the form of Excel spreadsheets. Subsequently, SPSS was used to analyze the differences in alpha values among the electrodes for conditions D1 and D2. Mean values and standard deviations were determined and other statistical analyses were performed to compare the impact of the two conditions on participant comfort.  
Analysis of subjective evaluation results: The questionnaire results reflect the participants' subjective preferences and differences between conditions D1 and D2. Initially, the average scores and standard deviations of the factors in D1 and D2 were calculated using SPSS to gain preliminary insight into the participants' subjective inclinations. Subsequently, normality tests and analyses of variance were conducted to assess the statistical differences in factor scores between the two conditions. Finally, the corresponding results were analyzed to determine the subjective experiences and preferences of the participants.  
Based on the analysis of EEG data and subjective ratings, a correlation analysis was performed between the significantly different alpha values of specific EEG electrodes and the subjective questionnaire scores, aiming to explore the relationship between landscape features and participant comfort.
# A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
## 3 Results
### Numerical Phantom Experiments
Figure 4: Comparison of the 128 th slice of 3D reconstructed images for slight motion: (**a**) Image reconstructed by OSEM; (**b**) Image reconstructed by OSEM reconstruction algorithm with motion correction by BD-iteration (OSEM-BD); (**c**) Image reconstructed by the combination of Richardson–Lucy deconvolution and OSEM reconstruction (RL-OSEM); (**d**) Image reconstructed by the proposed method. Figure 3: The phantom motion: (**a**) Slight motion; (**b**) large motion. To further evaluate the proposed method, the projection data were obtained by applying a large motion. Figure 5 shows the 128 th slice of images reconstructed from projection with large motion. The metric values for four different methods are shown in Table 2. Three metrics of the proposed method are better than the other three methods. Nevertheless, the motion-corrected image obtained by the proposed method still has residual artifacts. Because the reconstructed image is badly damaged and motion artifacts are very serious, it cannot be corrected by the proposed method. The simulation results of large motion show that the proposed method has a poor motion artifact reduction effect, but it is still better than OSEM-BD and RL-OSEM methods.
## 3 Analysis Process
### Data Analysis
#### 3.1.4 Search 4: Carbon Footprint and Industry 4.0 and Sustainable Manufacturing  
The analysis conducted using the keywords \"Carbon Footprint and Industry 4.0 and Sustainable Manufacturing\" can be summarized as follows:  
* **Number of Published Articles:** A growth rate of 30.77% was identified in 1069 documents between 2020 and 2024.
* **Key Findings:** Industry 4.0 technologies (IoT, big data analytics) have significant potential to reduce carbon emissions in manufacturing processes.
* Challenges: Data security, system integration, and the need for workforce skill development.
* - Opportunities: Opportunities for waste reduction and resource optimization.  
Table 4 summarizes the research on carbon footprint, Industry 4.0, and sustainable manufacturing. Over the past 10 years, the number of articles published on these topics has increased by 30.77%. Industry 4.0 technologies (Internet of Things, big data analytics) have the potential to reduce the carbon footprint by improving resource optimization, waste reduction, and production efficiency.  
Initially, research focused on conceptual frameworks but has shifted towards practical applications and case studies over time. The common challenges of the system include data security, privacy, and workforce skills development, while the opportunities focus on synergies between resource optimization and sustainable development goals. These findings demonstrate the significant contributions of Industry 4.0 technologies to sustainable manufacturing processes.  
In summary, research on \"Carbon footprint and Industry 4.0 and sustainable manufacturing\" has significantly increased, focusing on both the potential benefits and challenges of integrating these technologies for sustainable outcomes. Figure 4 summarizes the trends in research in the manufacturing sector, particularly within the framework of Industry 4.0.  
Figure 4 summarizes the trends in research conducted within the manufacturing sector, particularly within the framework of Industry 4.0. The research trends categorize the innovations emerging in modern manufacturing technologies, showing which topics have gained importance and how they are interrelated.  
In Figure 4, the main headings of Industry 4.0 and Sustainable Manufacturing are prominently highlighted, emphasizing how technology and environmental sustainability are intertwined. Industry 4.0 covers modern manufacturing processes based on advanced technologies such as smart factories, digital manufacturing, and automation, while the subtopics under sustainable manufacturing include SMEs and technological developments.  
This graphic illustrates how research trends in the manufacturing world prioritize technological innovation and environmental sustainability, offering a guiding framework for future studies.
## 3 Results
### The Performance of the Debgr
#### 3.2.1 The Effect of pH, Temperature and VFA/Alkalinity on the DEGBR Performance  
Organic matter degradation happens in the DEGBR and it involves the decomposition of a large organic compounds into a simpler constituent for assimilation. The rate at which complex organic matter is degraded depends on the pH, temperature of the bioreactor, and molecular size of the pollutants including biomass activity [25]. However, there is no overall ideal operational pH, because all processes use consortia of micro-organisms, whose growth rate is differentiated at different pH values [26]. However, obviously, extreme pH can kill micro-organisms, hence there is a need to monitor and control it. This means that the operational pH is a median value which can be tolerated by most enzymes and micro-organisms. Additionally, Figure 14 depicts a variation of the pH and the temperature during the experiment reported herein, and these two parameters were also maintained within a mesophilic range (37 to 38 \({}^{\circ}\)C) for the temperature and a suitable pH range of 6.5 to 8.  
However, despite the highly appraised pre-treatment step, other parameters are essential to a conducive anaerobic digestion in the DEGBR. These included the VFA/Alkalinity ratio that reflected a good acidic and alkaline balance within the bioreactor. Ideally, this ratio should be kept under 0.3 for a stable anaerobic operation [7]. A VFA/Alkalinity ratio test was performed in order to establish the progression in the digestion of the PSW and the stability of the reactor. Figure 15 shows average values of alkalinity, which ranged between 1048 to 702 mg/L, whereby the average VFA of 157.97 mg/L, and the alkalinity of 888.12 mg/L, culminated in the VFA/Alkalinity ratio of 0.2. Such a VFA/Alkalinity  
Figure 14: Variation of the pH and the temperature in the DEGBR during the processing of PSW.  
Figure 13: Performance of the DEGBR with respect to the TSS removal under varying OLRs.  
ratio was satisfactory, albeit slightly low for a high-performance reactor as compared to other similar processes used for anaerobic digestion. This was the case for the DEGBR, as illustrated in Figure 15, that provides a variation of the VFA/Alkalinity ratio, including the variation of the alkalinity and VFA concentration, throughout the experiment.
# Haptic-Enabled Hand Rehabilitation in Stroke Patients: A Scoping Review
## 3 Results
### Outcome Classification  
We classified the outcomes according to the International Classification of Functioning, Disability and Health (ICF) to portray health aspects evaluated in the retrieved articles. Outcomes mainly matched two ICF components, \"body function\" and \"Activity and Participation\". Figure 6 illustrates the representations of ICF components and domains according to each combination of intervention modalities. \"Body function\" is the most represented ICF component with almost the same repartition over the three combinations of intervention modalities (in percentage).
## 2 Materials and Methods
### Waterbody Extraction Method  
This is the most commonly used method to identify water by water index. It is efficient, and easy to apply; however, it cannot effectively restrain interference factors around the water, such as trees, shadows, and asphalt roads. Support Vector Machine (SVM), Decision Tree (DT), and other classification methods are required to manually draw high-precision samples with rich water types in advance; however, the sample selection is very complex and time-consuming, and the sample precision will also affect the water extraction results.  
To accurately identify water, we selected a global surface coverage product released by Gong Peng [25] in 2019 as the reference area. Because a waterbody is affected by external factors, the range of the product was expanded by 2 pixels and then used as the buffer for water, while the gray image of the Multi-Band Water Index (MBWI) [26] was used as enter data (MBWI gray image). K-Means was used to identify the waterbody within the buffer. The flowchart is Figure 3.  
\[\text{MBWI}=2\ R_{rs}(\text{Green})-R_{rs}(\text{Red})-R_{rs}(\text{NIR})-R_{ rs}(\text{SWIR1})-R_{rs}(\text{SWIR2}) \tag{2}\]  
The central wavelengths of \(R_{rs}\)(Green), \(R_{rs}\)(Red), \(R_{rs}\)(NIR), \(R_{rs}\)(SWIR1), and \(R_{rs}\)(SWIR2) in Equation (2) are 560 nm, 665 nm, 842 nm, 1610 nm, and 2190 nm.  
Determining the optimal threshold using the conventional threshold method is repetitive and tedious, and human error may be introduced into the classification results. The unsupervised classification method has the advantage of low human involvement and efficiency. K-means clustering is an effective unsupervised classification. The water and non-water classification results were obtained by K-means clustering of water body index  
Figure 3: Flowchart of the water extraction procedure.  
results. For the K-means clustering parameters, the classification number, change threshold, and maximum iteration numbers were 10, 0.01, and 10,000, respectively. After clustering, we obtained water and non-water classification results [26]. The result of water extraction is Figure 4.
## 2 Background
### Vehicle Modelling  
The vehicle planar dynamics can be approximated by the so-called single-track vehicle model, Figure 2b. This model is often employed in vehicle dynamics virtual sensing due to its reduced  
Figure 1: Overall picture of the effect-based road grip recognition approaches treated in this work.  
complexity [3; 52]. If force balance and moment equilibrium equations are taken, Equations (1)-(3) can be obtained.  
\[m(\dot{v}_{x}-v_{y}\dot{\psi})= F_{x,f}\cos(\delta)+F_{x,r}-F_{y,f}\sin(\delta) \tag{1}\] \[m(\dot{v}_{y}+v_{x}\dot{\psi})= F_{y,f}\cos(\delta)+F_{x,f}\sin(\delta)+F_{y,r}\] (2) \[I_{\dot{\psi}}\ddot{\psi}= (F_{y,f}\cos(\delta)+F_{x,f}\sin(\delta))I_{f}-F_{y,r}I_{r} \tag{3}\]  
The vehicle longitudinal and lateral velocities are denoted by \(v_{x}\) and \(v_{y}\) respectively, and the yaw rate by \(\dot{\psi}\). The vehicle mass is \(m\), the yaw inertia \(I_{\dot{\psi}}\), the distances from the front and rear axles to the centre of gravity \(I_{f}\) and \(I_{r}\) respectively, and the angle steered by the front wheels \(\delta\). Finally, the longitudinal forces are denoted by \(F_{x,i}\) and the lateral forces by \(F_{y,i}\), with \(i\in\{front,rear\}\). Once the vehicle planar motion states are determined, the tyre lateral slips \((\alpha_{f},\alpha_{r})\) can be computed from expressions (4) and (5).  
\[\alpha_{f}=\delta-\arctan\left(\frac{l_{f}\dot{\psi}+v_{y}}{v_{x}}\right) \tag{4}\] \[\alpha_{r}=-\arctan\left(\frac{-l_{r}\dot{\psi}+v_{y}}{v_{x}}\right) \tag{5}\]  
If the ISO convention is adopted, the tyre longitudinal slip (\(\lambda\)) is obtained from the expression (6),  
\[\lambda_{i}=\frac{\omega_{i}r_{e}-v_{x}}{v_{x}} \tag{6}\]  
with \(i\in\{front-left,front-right,rear-left,rear-right\}\), \(\omega\) being the wheel rotational speed, and \(r_{e}\) the effective radius [14]. Several slip conventions (e.g., ISO, PRAXIS) have been described in the literature. For simplicity, only the ISO convention is used here. For a more detailed discussion [53; 54] can be consulted.  
Finally, the wheel speed evolves according to the wheel rotating dynamics, expression (7),  
\[I_{tot}\dot{w}_{i}=T_{drv,i}-T_{b,i}-f_{res}F_{z,i}r_{e}-F_{x,i}r_{l} \tag{7}\]  
In this case, \(I_{tot}\) is the total rotating inertia of the wheel and driveline assembly, \(T_{drv}\) is the driving torque, \(T_{b}\) is the braking torque, \(f_{res}\) is the rolling resistance, \(F_{z}\) the tyre vertical force, and \(r_{l}\) the loaded radius.  
Figure 2: (a) Wheel rotating dynamics; (b) Single-track vehicle model.
## 4 Detection Performance and Discussion  
In this section, three well-known methods, namely constant false alarm ratio (CFAR) [33], advanced method (AM) [36], and, multiple higher order cumulant (MHOC) [37] are considered to evaluate the performance of the proposed technique.