esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Testing, Analysis, and Results
### Analysis  
The metrics we will utilize to analyze the scheduling algorithm are outlined in the bullet points below.  
Figure 7: Overview of job running times: short dataset vs. mixed dataset.  
* **Turnaround time:** the turnaround time of a process is determined by subtracting the arrival time of the process in the system from the time at which the process is completed [3]. \[T_{turnaround}=T_{completion}-T_{arrival}\] (5)
* **Response time:** the response time of a process is determined by subtracting the arrival time of the process in the system from the time at which the process is first scheduled [3]. \[T_{response}=T_{firstrun}-T_{arrival}\] (6)
* **Throughput:** traditionally, throughput in the realm of scheduling algorithms is defined as the number of processes completed within a specified time frame. In this analysis, we opt to examine throughput on a per-process basis, calculated by taking the minimum time a process should run and dividing it by the turnaround time. This yields a value within the \((0,1]\) range, where higher values indicate superior throughput for the respective process. \[P_{throughput}=\frac{T_{total}}{T_{turnaround}}\] (7)
* **Queue loads:** at predetermined intervals, the system measures the size of all queues. The queue sizes will illustrate the fluctuations over time throughout the simulation, providing a detailed view of load balancing.
* **[PERSON]'s fairness index:** [PERSON]'s fairness index is employed to evaluate how fairly CPU time is distributed among different processes. It provides a quantitative measure of fairness in terms of resource allocation in a multiprocess environment. It can be used to assess the effectiveness of scheduling algorithms in ensuring equitable access to resources for various processes [26]. Equation (8) is used for measuring the fairness, where \(n\) is the number of processes and \(x_{i}\) represents the individual share of the resource allocated to entity \(i\) (throughput for process \(i\)). The value ranges between 0 and 1, where a higher value corresponds to increased fairness. \[f(x)=\frac{(\sum_{i=1}^{n}x_{i})^{2}}{n\cdot\sum_{i=1}^{n}x_{i}^{2}}\] (8)  
Given that many of the metrics outlined above are on a per-process basis, calculating averages is also feasible. For instance, to determine the average turnaround time, we sum the turnaround times of all processes and divide by the total number of processes in the system. This principle also applies to response time and throughput.
## 3 Results
### Impact of a Static Compressive Stress  
In a second phase, a static compressive stress was applied to mimic the stack clamping pressure exerted on membranes during fuel cell operation. In that purpose, two aging tests similar to those previously presented were carried out by maintaining a constant pressure level of 5 MPa on membranes while circulating the Fenton solution during 8 or 20 h. Figure 3 illustrates the evolution of the FER after these two static aging tests for both XL and NR211 membranes in comparison with the results obtained in the case of cyclic aging tests. Similarly, an acceleration of the membrane decomposition was observed by extending the aging duration as a FER increase of about 30 % and 50 % for XL and NR211 membranes, respectively, after combined static compressive stress and Fenton solution exposure. In addition, and as previously observed for cyclic compression exposure, both membranes were degraded in a very similar proportion when the aging duration was increased from 8 to 20 h, which could indicate that maintaining a constant pressure level on the XL membrane also restricted its improved durability.  
Furthermore, the application of a static 5 MPa compression seemed slightly more aggressive than that of a cyclic 5 MPa compression for both membranes in the case of the short-term aging tests (8 h). However, this trend was not observed after long-term  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**FER (ug/gNaition/h)** & **Cyclic 5 MPa + Fenton** & **Cyclic 10 MPa + Fenton** & **Cyclic 5 MPa + Fenton** \\  & **Solution (8 h)** & **Solution (8 h)** & **Solution (20 h)** \\ \hline XL membrane & \(167\pm 44\) & 316 \(\pm\) 44 & 465 \\ NR211 membrane & \(264\pm 157\) & 324 \(\pm\) 79 & 492 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evolution of the fluoride emission rate as a function of the severity of conjoint mechanical and chemical stress exerted on NafionTM XL and NR211 membranes. Part of the data presented here has been already published in a previous work [2] but the values have been corrected here by taking into account the parasitic contribution of the TISAB II solution used for the measurements.  
Figure 3: Comparison of the FER evolution after conjoint mechanical and chemical stress tests with static compression or cyclic compression for XL and NR211 membranes.  
aging (20 h), which could indicate that, at an equal pressure level, applying a quite severe cyclic compressive stress was more detrimental for Nafion(tm) membranes than maintaining a constant compression.
# Artificial Intelligence-Based Controller for DC-DC Flyback Converter
## 6 Conclusions  
To keep the output voltage of an FC system regulated as necessary for modern electronic devices irrespective of the changes in input voltage or load current, an ANFIS-based controller was proposed in this research. A FIS structure containing 25 fuzzy rules (in our case) relating the inputs and the output originally generated by the fuzzy logic system was tuned by ANFIS. It was observed that the FIS tuned by ANFIS exhibited better output voltage response under static and transient loads. Better set-point tracking was also achieved. ANFIS may utilize past results or experiences to make immediate decisions about the response. Training of fuzzy rules by ANFIS made the ANFIS-based controller more adaptive. Simulation results depicted that both the static and dynamic performance of the ANFIS-based controller was better in all aspects compared to the FLC and PID controllers. Hardware implementation of the proposed compensated system can be carried out in future.  
**Author Contributions:** conceptualization, M.A.S. and G.A.; data curation, M.R.H. and A.B.A.; formal analysis, G.A. and V.E.B.; investigation, U.F.; methodology, M.A.S., G.A., and M.U.A.; resources, M.U.A., M.U. and M.R.H.; supervision, G.A.; validation, J.G. and V.E.B.; visualization, U.F. and T.Y.; writing--original draft, M.A.S. and G.A.; writing--review & editing, A.B.A., M.U., M.U.A., T.Y., J.G. and U.F.  
**Funding:** The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Research Group Project under grant number R.G.P. 1/166/40. This research work is also supported by the Deanship of Scientific Research, Majmaah University under the contract number RGP-2019-21.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effects of Biochar Production Methods and Biomass Types on Lead Removal from Aqueous Solution
## 3 Results and Discussion  
### Biochar Yield and Properties  
The biochar yield differed by production method and biomass type (Table 1). The yield from the tube furnace was 42-63%, while the yield from the BioCharlie was 36-51%. Previous studies reported that the biochar yield from slow pyrolysis at 300 \({}^{\circ}\)C ranged between 42 and 61% [21; 22; 23]. Overall, the BioCharlie-produced biochar resulted in lower yields than those from the tube furnace when compared to individual biomass. This difference may be attributed to the BioCharlie Log's uncontrolled temperatures and potential exposure to oxygen. The BioCharlie Log was not completely sealed off from the surrounding environment and thus may have fueled combustion processes (oxidation) or uneven carbonization, allowing for more biomass to be turned into ash or volatile matter components. This is supported by the greater volatile matter content in BioCharlie produced biochars in land-based biomasses (ASB, APB, and GPB). By biomass type, brown seaweed biochars showed the highest yield in both production methods (BST = 63%, BSB = 51%), while avocado seed biochars showed the lowest yield (AST = 42%, ASB = 37%). In general, increasing the pyrolysis temperature tends to decrease biochar yield% [24]. Note that the pyrolysis temperature in the tube furnace was maintained at 300 \({}^{\circ}\)C for 1 h, while the pyrolysis temperature in the BioCharlie reached >300 \({}^{\circ}\)C within 20 min in a separate trial equipped with a grill thermometer. A previous biochar study using a top-lit updraft (TLUD) stove also found that the temperature reached over 500 \({}^{\circ}\)C within 20 min for pinewood and bamboo biochars until the end of the charring period [25]. Biochar pH ranged from 6.88 to 9.65 depending on production method and biomass type (Table 1). BioCharlie-produced avocado seed and avocado peel biochars (pH 8.85 for ASB, pH 9.65 for APB) showed higher pH values than their tube-furnace-produced counterparts (pH 7.66 for AST, pH 9.40 for APT). However, a higher pH was observed with tube-furnace-produced grapreruit peel and brown seed biochars (pH 9.04 for GPT, pH 7.94 for BST) compared to those from BioCharlie (pH 8.30 for GPB, pH 6.88 for BSB), indicating an inconsistent effect of production methods on the biochar pH across biomass types. Typically, the pH of the biochars increases slightly with increasing pyrolysis temperature [26]. This general relationship was found for BioCharlie-produced avocado seed and avocado peel biochars (ASB and APB) assuming higher peak temperature in the biochar production by BioCharlie, but it was not the case for GPB and BSB. This discrepancy may be attributed to biomass-specific characteristics (e.g., lower C content and less carbonization in GPB and BSB) but the firm relationship between pH and production method was not clear in the given data. Other studies found that pH values for biochars pyroly
## 4 Results and Discussion
### Validation of Inversion GPP
#### 4.5.1 Comparison of GPP Spatial Patterns  
Figure 10 illustrates the agreement between the GPP spatial patterns and the detail features inversed by our model to the pattern inversed by the VPM model. The daily GPP significantly decreased from northeast to southwest across the rise in elevation. The spatial resolution of VPM was 500 m, and there were mixed pixels adjacent to the no vegetation regions, which may have influenced inversed GPP values in these pixels. The detail features were most consistent with our inversed results adjacent to the mask regions. Due to the coarse 1000 m spatial resolution of two other models, there were larger numbers of mixed pixels that increased uncertainty. The spatial details disappeared in the GPP patterns of BEPS and MODIS.
# Induction Motor Dynamics Regimes: A Comprehensive Study of Mathematical Models and Validation
## 3 Models of Induction Motors
### The Model of the Induction Motor in the d,q Coordinate System  
Using Equations (33) and (36), we can express the induction motor model in matrix form as follows:  
\[\left[\begin{array}{c}u_{1d}\\ u_{1q}\\ 0\\ 0\\ 0\end{array}\right]=\left[\begin{array}{cccc}R_{a}&0&0&0\\ 0&R_{a}&0&0\\ 0&0&R_{2}&0\\ 0&0&0&R_{2}\end{array}\right]\cdot\left[\begin{array}{c}i_{1d}\\ i_{1q}\\ i_{2d}\\ i_{2q}\end{array}\right]+\left[\begin{array}{c}-\omega_{0d}\psi_{1q}\\ \omega_{0d}\psi_{1d}\\ -(\omega_{x}-\omega)\psi_{2q}\\ (\omega_{x}-\omega)\psi_{2d}\end{array}\right]+\frac{d}{dt}\begin{bmatrix} \psi_{1d}\\ \psi_{1q}\\ \psi_{2d}\\ \psi_{2q}\end{bmatrix}, \tag{42}\]  
\[\frac{d\omega}{dt}=\big{[}X_{ad}\big{(}i_{2d}i_{1q}-i_{2d}i_{1d}\big{)}-T_{l} \big{]}. \tag{43}\]where flux linkages derivatives can be expressed as:  
\[\begin{array}{c}\left|\begin{array}{c}i_{1d}\\ i_{1d}\\ i_{2d}\\ i_{2q}\end{array}\right|=\left|\begin{array}{cccc}-C_{1}R_{1}&+C_{1}f_{1}&+C_{1 }X_{ad}&+C_{1}X_{ad}\omega\\ -C_{1}f_{1}&-C_{1}R_{1}&-C_{1}X_{ad}\omega&+C_{2}X_{ad}\\ C_{3}R_{1}&-X_{1}f_{2}&-C_{2}X_{1}&-f_{3}\\ X_{1}f_{2}&+C_{3}R_{1}&+f_{3}&-C_{2}X_{1}\end{array}\right|_{*}\left|\begin{array} []{c}i_{1d}\\ i_{1q}\\ i_{2d}\\ i_{2q}\end{array}\right|+\\ \left|\begin{array}{cccc}C_{1}&0&0&0\\ 0&C_{1}&0&0\\ 0&0&C_{1}&0\\ 0&0&0&C_{1}\end{array}\right|\ast\left|\begin{array}{c}U_{1d}\\ U_{1d}\\ U_{2d}\\ \end{array}\right|,\\ \left|\begin{array}{c}U_{1d}\\ U_{1d}\\ U_{2d}\\ \end{array}\right|,\\ \left|\begin{array}{c}U_{2d}\\ i_{2d}\\ \end{array}\right|,\\ \end{array} \tag{44}\]  
where \(u_{1d},u_{1d}\) stator applied voltages;  
\(i_{1d},i_{1q},i_{2d},i_{2q}\) stator and rotor currents components in the \(d,q\) coordinate system;  
\(\psi_{1d},\psi_{1q},\psi_{2d},\psi_{2q}\) stator and rotor flux linkages component in \(d,q\) coordinate system;  
The constants are defined as:  
\[\begin{array}{c}C_{1}=\frac{1}{X_{d}^{2}}=\frac{1}{X_{1}-\frac{X_{2d}^{2}}{X_ {2}}},\ C_{2}=\frac{R_{2}}{X_{2}}\cdot C_{1},\ C_{3}=\frac{X_{ad}}{X_{2}}\cdot C _{1},\ f_{1}=\left(X_{1}-\frac{X_{2d}^{2}}{X_{2}}\right)\omega_{x}+\frac{X_{ ad}^{2}}{X_{2}^{2}}\\ f_{2}=\omega\cdot C_{3},\ f_{3}=(C_{1}X_{1}(\omega-\omega_{x})).\end{array}\]  
Thus, this model provides a framework for solving the induction motor (IM) equations in terms of currents. The IM model in the \(d\),\(q\) coordinate system (referred to as model II) is expressed using current variables, enabling its integration into studies analyzing the motor's impact on power supply systems.
# The Static Frictional Behaviors of Rubber for Pipe-Laying Operation
###### Abstract  
Experimental research is carried out to reveal the static frictional behaviors of rubber pipe contact systems. This research is motivated by deep water pipe-laying operations where rubber blocks are used to clamp the pipe to supply sufficient static friction. Within this context, a friction testing instrument has been designed to mimic a situation of the beginning of the pipe-laying installation. Using this instrument, the maximum static friction forces (\(F\)) of a rubber pipe contact system are tested. The results show that the ultimate values of the static frictions fluctuate due to the increasing rate of the tangential load (\(F_{T}\)). The evolution of contact between rubber and polymethyl methacrylate (PMMA) pipe is observed to identify the formation and propagation of the folds within the apparent contact area. In addition, it is confirmed that the evolution of contact is influenced by the folds and creep of the rubber surface. The creep deformation takes primary effect in accelerating the separation of the interfaces of contact during relative high normal loads (20, 30, 40 N) and low increasing rate of \(F_{T}\); whereas for all of the testing normal loads (10-40 N), the propagation of the folds release the energy which is stored in the interface of rubber when the increasing rate of \(F_{T}\) is high. Therefore, the fluctuation of the maximum static friction of the contact system can be regarded as a consequence of interaction of the creep and folds. Furthermore, the instability of the coefficient of static friction in this test has been examined, and it indicated that the creep and folds could affect the static friction distinctly within a certain range of a normal load. This research is beneficial for arranging appropriate normal loads and laying speeds to avoid pipes slipping during a pipe-laying operation.  
pipe-laying; rubber contact; static friction; surface folds; viscoelasticity +
Footnote †: journal: Applied Physics Letters
## 4 Security and Performance Analyses
### Performance Comparison
Compared with other related schemes, the proposed scheme is much faster than in the registration phase; it increases computing performance by at least 63.0% in the PHI uploading phase, and increases computing performance by at least 54.9% in the emergency-exception-handling phase. The proposed scheme completely considers PHI uploading, access and emergency exceptions, and employs only extended chaotic maps, symmetric en/decryptions and hash operations, which have low computation burdens. Therefore, the proposed scheme not only provides more functionality, but also retains efficiency in computations. Table 5 compares the communication of the proposed scheme and other related schemes in term of required messages. The schemes of [PERSON] et al. [3], [PERSON] and [PERSON] [5] and [PERSON] and [PERSON] [7] require more messages than the proposed scheme in the PHI uploading and PHI access phases. Furthermore, the proposed scheme has similar numbers of communication messages with those of [PERSON] et al. [8], [PERSON] et al. [10], [PERSON] et al. [13] and [PERSON] et al. [15], and comprehensively considers PHI uploading, PHI access and emergency-exception phases. \begin{table}
\begin{tabular}{l} \hline \hline
**Hardware/Software Specification** \\ \hline Intel Xeon CPU E3-1231 v3 3.4 GHz \\
8 G Memory \\ Windows Server 2008 \\ Visual Studio 2012 and C++ programming language \\ Input string length- 256 bits \\ \hline Used Algorithms \\ \hline Hash function: SHA256 \\ Symmetric en/decryption algorithm: AES \\ Asymmetric en/decryption algorithm: RSA \\ Scalar multiplication: Elliptic curve \\ Extended Chebyshev chaotic maps \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation environment. Figure 7: The response time of other related schemes in terms of registration phase, PHI uploading phase, PHI access and emergency-exception-handling phase.
# Smart Grid Theft Detection Based on Hybrid Multi-Time Scale Neural Network
###### Abstract  
Despite the widespread use of artificial intelligence-based methods in detecting electricity theft by smart grid customers, current methods suffer from two main flaws: a limited amount of data on electricity theft customers compared to that on normal customers and an imbalanced dataset that can significantly affect the accuracy of the detection method. Additionally, most existing methods for detecting electricity theft rely solely on one-dimensional electricity consumption data, which fails to capture the periodicity of consumption and overlooks the temporal correlation of customers' electricity consumption based on their weekly, monthly, or other time scales. To address the mentioned issues, this paper proposes a novel approach that first employed a time series generative adversarial network to balance the dataset by generating synthetic data for electricity theft customers. Then, a hybrid multi-time-scale neural network-based model was utilized to extract customers' features and a CatBoost classifier was applied to achieve classification. Experiments were conducted on a real-world smart meter dataset obtained from the State Grid Corporation of China. The results demonstrated that the proposed method could detect electricity theft by customers with a precision rate of 96.64%, a recall rate of 96.87%, and a significantly reduced false detection rate of 3.77%.  
smart grid; electricity theft detection; time GAN; deep learning; imbalance data +
Footnote †: journal: Applied  
1
Footnote 1: [[https://doi.org/10.3390/app13095710](https://doi.org/10.3390/app13095710)]([https://doi.org/10.3390/app13095710](https://doi.org/10.3390/app13095710))
###### Abstract  
Wheelchair basketball is one of the most popular Paralypric sports, including players with different diagnoses. To date, there is scarce evidence on shoulder functionality in wheelchair basketball players, and there is no consensus on a shoulder injury prevention program in these athletes. Therefore, in the present pilot study, we aimed to evaluate the effects of a comprehensive rehabilitative approach on shoulder ROM, muscle activity, and functioning in wheelchair basketball athletes. We included adult wheelchair basketball athletes playing in the Italian Second League who completed an 8-week comprehensive rehabilitative program, based on education to avoid upper limb pain injuries, preventive exercises, and improvement of ergonomics through kinematic analysis. We administered the Wheelchair User's Shoulder Pain Index (WUSPI) and the Kerlan-Jobe Orthopaedic Clinic (KJOC) questionnaire to evaluate pain experienced during functional activities, and used kinematic analysis integrated with sEMG to evaluate shoulder function and propulsion pattern. A sample of 10 wheelchair athletes (33.75 \(\pm\) 6.42 years) were assessed. After the intervention there was a significant (\(p<0.05\)) difference in WUSPI score (27.0 \(\pm\) 18.5 vs. 25.0 \(\pm\) 21.5) and in KJOC score (89.3 \(\pm\) 10.4 to 95.4 \(\pm\) 9.1). Moreover, there was a significant improvement in scapular upward rotation, abduction, and extra-rotation of the glenohumeral joint. Propulsion techniques improved in pattern and acceleration. This approach played a key role in improving upper limb function, reducing the incidence of pain and cumulative trauma disorders. However, the small sample size could affect the generalizability of results. In conclusion, healthcare professionals should monitor wheelchair athletes, assessing the patient's function, ergonomics, equipment, and level of pain, and introducing specific exercises to prevent upper limb injuries.  
rehabilitation; biomechanics; shoulder; upper limb; wheelchair; basketball +
Footnote †: journal: Applied Sciences
###### Abstract
In addition, continuous contact conditions must be established, and the analysis required to solve complex related problems such as the dynamic behavior of the welding tool and deflection of the base material is extremely difficult. Specifically, the implementation of the welding process inside the base material post-softening using a general finite element method (FEM) is time intensive, and the probability of data divergence is high. To solve these problems, in this study, structural dynamic analysis was conducted using the FEM, and flow analysis to confirm the welding process inside the base material was performed by finite volume method (FVM) rather than FEM. Various studies have been conducted using FVM as a simulation method to implement and analyze friction stir welding [17; 18]. In addition, several studies on friction stir welding have been conducted through structural and flow analysis [19; 20; 21; 22]. Structural/dynamic analysis and flow analysis are performed using different specialized programs to obtain related data. The focus of this study was to analyze the FSW process for producing a high-quality power converter within a short period. Therefore, to minimize physical experiments, which involve prototype fabrication, the structural deflection of the base material and the dynamic characteristics of FSW were analyzed by conducting simulations.