esa-sceva/satcom-chunk-collection huggingface dataset:
# A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization
## 4 Experiments and Discussion
### Comparison with Nine State-of-the-Art Intelligent Algorithms on 10 and 30 Dimension

+++  
All in all, MCPDE performs better than the compared algorithms on the unimodal, multimodal and composition problems with \(D=10\) and \(D=30\). Overall, Table 7 shows that MCPDE has a good performance on CEC2013 test problems. When D is set to 10, the overall ranking sequences on CEC2013 test problems are MCPDE, JADE, CPDE, jDE, DE, CoDE, TLBO, PSO, PSOcf and CMA-ES in descending direction. The overall ranking sequences on CEC2013 test functions with \(D=30\) are MCPDE, JADE, jDE, CPDE, DE, PSO, CoDE, CMA-ES, TLBO and PSOcf in descending direction. The convergence graphs of PSO, PSOcf, TLBO, DE, JADE, CoDE, jDE, CMA-ES, CPDE and MCPDE on different benchmark functions in terms of the mean errors (in logarithmic scale) in 30 runs are plotted in Figure 2 (\(D=10\)) and Figure 3 (\(D=30\)). Sixteen benchmark functions are selected to compare the performance of different algorithms in Figures 2 and 3. From Figure 2, it can be seen that MCPDE performs better than other compared algorithms on 9 out of 16 test problems. Figure 3 shows that MCPDE beats other compared algorithms on 8 out of 16 test problems. The comparison experiments indicate that MCPDE is a challenging method for these functions. Moreover, MCPDE has a higher convergence rate because of good exploration ability. The experimental results reveal that MCPDE works well for most benchmark problems. This is due to the effective parameter adaptation approach and the inertia factors which are used in MCPDE. Better control parameters are preserved to produce new control parameters for the next generation. Therefore, the probability of finding better solutions is greater and this is helpful for improving the performance of the proposed algorithm. The inertia factors are changed during the evolution process to favor, balance, and combine the exploration with exploitation. At the beginning of the search, the inertia factor \(\omega_{1}\) is less than \(\omega_{2}\), so it favors exploration. Then, \(\omega_{1}\) tends to increase continually while \(\omega_{2}\) tends to decrease. Accordingly, it balances the search direction. Later, the inertia factor \(\omega_{1}\) is greater than \(\omega_{2}\), so the exploitation ability of the algorithm is dynamically adjusted. In addition, the opposition mechanism and the orthogonal crossover are helpful for increasing the search ability during the evolutionary process. Therefore, both the exploration and exploitation aspects are done in parallel during the optimization process. Accordingly, MCPDE not only can improve the convergence rate of algorithm but also can decrease the risk of premature convergence as much as possible.
# Injury Patterns and Frequency in Swimming: A Systematic Review
## 4 Discussion
### Factors Contributing to Injury Risk  
The increased injury risk in female swimmers is influenced by multiple factors, including biomechanics, training load, and competition level. Shorter limb lengths in femaleswimmers result in a higher stroke count per distance, increasing joint stress and the likelihood of overuse injuries [17; 20]. Additionally, lower muscular strength and altered movement patterns place greater strain on the shoulder, which endures continuous cyclical loading during swimming [17; 20].  
The level of competition significantly influences the injury prevalence and anatomical distribution. Elite swimmers exhibit the highest injury rates, particularly shoulder tendinitis, impingement, and knee injuries, primarily due to higher training volumes and repetitive overhead motions [2; 9; 10; 25; 26; 27]. Trained (non-elite) swimmers also experience frequent shoulder and knee injuries; however, their injuries are more often related to joint inflammation and cumulative overuse rather than acute damage [16; 24]. In contrast, recreational swimmers present a lower overall injury prevalence, though shoulder impingement and tendinitis remain common [23]. Additionally, elite swimmers show a higher prevalence of degenerative changes in the lumbar spine compared to recreational athletes, reinforcing the long-term impact of high training loads [24].  
Improper training load management--both excessive and insufficient--can increase the injury risk by disrupting the balance between stress and recovery [17; 20]. High-intensity periods, particularly during competitions, may expose female swimmers to greater risks due to cumulative fatigue and hormonal fluctuations, which can compromise joint stability and neuromuscular control [17; 20]. Additionally, repetitive movements, including shoulder rotations, turns, and push-offs, contribute to progressive tissue strain.
## 3 Experiments and Results
### Motion Pattern Recognition Based on LSTM Neural Network  
Long short-term memory (LSTM) is a new type of recurrent neural network to prevent gradient burst and gradient vanishing. Based on the recurrent neural network (RNN) model, the gating cycle feature is introduced to improve the gradient vanishing problem. The LSTM neural network adds state control to the hidden nodes, enabling it to remember the input from a long time ago [18]. Since the action patterns of the human body occur in a time series, not at time nodes, in theory, the LSTM neural network is suitable for EMG signal processing.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 4 Hamming Error-Correcting Code, IIR and FIR Filters
### Filtering Systems  
In an industrial environment, the LoRa signal is affected by noise and interference from other devices or radio signals, which can degrade the quality of the received signal. To remedy this, digital filters can be used to eliminate unwanted frequencies and interfering signals. Digital filters can be categorized into two main types: finite impulse response (FIR) filters and infinite impulse response (IIR) filters. This classification is based on the way they react to a unit pulse, i.e., an input consisting of a single pulse [42].
## 4 Experiment
### Evaluation of Feature Matching Accuracy
#### 4.2.1 Datasets  
The HPatches [40] dataset was used for evaluation. It comprises 108 image sequences, including 52 sequences with viewpoint variations and 56 sequences with illumination changes. Each sequence contains six images captured under different viewpoints or lighting conditions, along with the corresponding essential matrices. An example of a sequence exhibiting viewpoint variation is shown in the Figure 8.  
Figure 8: Average Matching Accuracy (MMA) Achieved by Various Methods on the HPatches Dataset.
## 3 Results
### Composition of Bacterial and Fungal Communities at the Phylum Level  
At 0-20 cm depth, straw return treatments increased the relative abundance of _Proteobacteria_, _Acidobacteria_, _Chloroffexi_, _Bacteroidetes_, _Gemmatimor_, and _Verrumicrobia_ compared to CK (Figure 5A). The RTS treatment showed the greatest enhancement, with increases of 19.14%, 47.00%, 32.76%, and 23.26%, respectively. In contrast, straw return reduced the relative abundance of Actinobacteria and Firmicutes. At 20-40 cm depth, straw return increased the relative abundance of Proteobacteria, _Acidobacteria_, and _Gemmatimondetes_. The PTS treatment exhibited the highest increase in Proteobacteria (21.89%). On the contrary, straw return significantly decreased the relative abundance of _Actinobacteria_, with PTS showing the largest reduction (48.20%).  
For fungal communities at 0-20 cm depth, straw return significantly increased the relative abundance of _Ascomycota_ compared to CK, with the RTS treatment showing the largest increase of 34.95% (Figure 5B). Straw return elevated the abundance of _Cluytridiomycota_ and _Zygomycota_ while reducing that of _Basidiomycota_. In the deeper soil layer (20-40 cm), straw return treatments increased _Ascomycota_, _Cluytridiomycota_, and _Zygomycota_ abundance, and among which the increase was the greatest under PTS, while decreasing the abundance of _Basidiomycota_.  
Figure 5: Relative abundance of bacteria (**A**) and fungal (**B**) communities in different integrated straw return and tillage practices. Treatments as follows: CK (no straw + conventional tillage), NTS (straw mulch + no-tillage), RTS (straw incorporation + rotary tillage), PTS (straw incorporation + deep tillage).  
Correlation Analysis of Soil Microbial Community Composition, Organic Carbon Fractions and Crop Yield  
The correlation analysis (Figure 6) revealed that, among SOC and its labile fractions, SOC, ROC, and DOC showed significant positive correlations with corn yield (\(p<0.05\)). In contrast, the bacterial Simpson diversity index exhibited a significant negative correlation with yield (\(p<0.05\)). Furthermore, soil carbon components (SOC and its labile fractions) were positively correlated with both bacterial and fungal Chao1 diversity indices, while showing negative correlations with Simpson indices (\(p<0.05\)).
## 4 Experimental Results  
This section presents the experimental results based on the experimental procedure (See Figure 1). The performances of each prediction models were analyzed based on accuracy and the results were compared on two cases (with and without application of FS methods). The parameter settings for each classifier and search method are as shown in Tables 3 and 4. All prediction models were built based on the WEKA machine learning tool [41]. For replication of this study, all models and datasets used in this study are readily available in [42]. As mentioned previously, four FFR and fourteen FSS methods were applied one after the other to five software defect datasets. Four classifiers based on the selected and full features of the datasets were used to develop the SDP models. Tables 6-9 present the accuracy values of the four classifiers on the software defect datasets. These accuracy values were from two scenarios (With FS methods and Without FS methods). Specifically, in each of the tables, the accuracy performance value of each classifier on the individual datasets (full and reduced) is shown. The search methods for the FSS (CFS and CNS) were further grouped into two (i.e. Heuristic method and Exhaustive method). This is to show the distinction of how the respective search methods behaved when used as subset evaluation methods in FSS. The average performance of these classifiers (NB, DT, LR, and KNN) across all datasets were computed and the variation of the average performance of each prediction models with FS (FFR and FSS) methods to the average performance of each prediction model without FS methods were also computed. This is to show how significant the effectiveness of the application of FS methods in SDP. As shown in Tables 6-9, it is observed that the accuracy performance of the prediction models based on FS methods, in this case, FFR and FSS were better than when no FS methods are applied. This further strengthens the evidence that FS methods can improve the performance of prediction models in SDP.
## 3 Results and Discussion
### Dosimetric Characterization
#### 3.2.7 TL Sensitivity  
Sensitivity is one of the most significant dosimetric aspects of a TLD material that remarkably influences the dosimeter's efficiency. Absolute sensitivity is significantly influenced by random errors introduced by the TLD reader using optical filters and thermal gradient due to the heating planchet as well as dosimeter-dependent errors, i.e., inhomogeneous defect concentration, dirt contamination and an irregular surface that introduces thermal gradient due to improper surface contact. Therefore, relative sensitivity was introduced to overcome the uncertainties associated with absolute sensitivity. The absolute sensitivity was observed to be 1.87 uC-Gy-1.g-1, whereas the relative sensitivity was observed to be 0.0176. The low relative sensitivity could be attributed to the defect population.  
Figure 11: Reproducibility of MgB\({}_{4}\)O\({}_{7}\):Zn glass S35654.
## 5 Conclusions  
In this paper, an OFC detection and recognition system based on VLC using CNN is proposed. Different from the traditional encoding and decoding methods, this paper transforms the problem into an OFC recognition problem, which greatly simplifies the complexity of the VLC system. Frequency, phase, encoding sequence, and distance between receiver and transmitter are considered to produce various OFCs. A CNN structure with FCN style is applied. Once the OFC is recognized correctly, the related information can be obtained.  
The results show that the maximum recognizable distance and recognition accuracy were both improved compared with the traditional 2D barcode recognition method. In particular, the proposed scheme can identify approximately 1200 (50 \(\times\) 2375 = 118,750) different OFCs with high accuracy (more  
\begin{table}
\begin{tabular}{c c} \hline LED-OFC ID & Encoding Sequence \\ \hline
1 & 000001 \\
2 & 000011 \\
3 & 000101 \\
4 & 001001 \\
5 & 000111 \\
6 & 001011 \\
7 & 010101 \\
8 & 001111 \\
9 & 010111 \\
10 & 011011 \\
11 & 011111 \\ \hline \end{tabular}
\end{table}
Table 2: Encoding sequences.  
Figure 13: Recognition accuracy of different encoding sequences.  
than 95%), which is about 120 times that of Reference [3]. The effective recognition of distance is 3.5 m, which is suitable for most scenes. Therefore, the proposed scheme can complement the traditional two-dimensional bar code, and can be applied to various scenes.  
However, there still exist some inevitable limitations to our proposed method. Firstly, the performance of the scheme particularly relies on the restrictive condition that the camera and RGB-LEDs must be parallel to each other. Secondly, the recognition results will be imprecise when detecting high speed objects. Thirdly, the cost of modifying RGB-LEDs is too high for production. Although all the above disadvantages exist, they do not outweigh the innovation and improvement of this paper upon previous methods. These drawbacks will also become our focus and direction in future work.  
Conceptualization, W.G.; Data curation, X.Z., Y.Y., J.Z., J.J.; Investigation, J.L.; Methodology, W.G.; Project administration, J.L. and S.W.; Resources, X.Z.; Software, X.Z. and J.Z.; Supervision, S.W.; Visualization, J.Z. and X.Z.; Writing--original draft, W.G. and J.L.; Writing--review & editing, W.G. and J.L.  
This research was funded by National Undergraduate Innovative and Entrepreneurial Training Program grant number 201510561003, 201610561065, 201610561068, 201710561006, 201710561054, 201710561057, 201710561058, 201710561199, 201710561202, 201810561217, 201810561195, 201810561218, 201810561219, Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation (\"Climbing Program\" Special Funds) grant number pdjh2017b0040, pdjha0028, pdjh2019b0037 and Guangdong science and technology project grant number 2017B010114001.  
The authors declare no conflict of interest.
# A Review on Upper Limb Rehabilitation Robots
## 4 Examples of Rehabilitation Robots
### Reharob  
The Rehabilitation Robot (REHAROB) therapeutic system is a passive rehabilitation robot with three degrees of freedom dedicated for the shoulder and the elbow [50]. The movement trajectory is preprogrammed by the therapist and followed by repetitive movement for the impaired limb. The exercises performed by REHAROB are different than those performed by MIT-MANUS and MIME by providing slow repetitive movements with a constant velocity [51]. These kinds of exercises are efficient in reducing spasticity and increasing the range of mobility for the shoulder and elbow joints [51]. The patient's upper arm and forearm are attached to the robot's arm [52]. The REHAROB is equipped by sensors to control and monitor the generated forces [51].
# Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging
## 5 Design of the Fast Charging Robust Controller  
### Closed-Loop Control  
The objective was to design a two-degrees of freedom fast charging controller, as illustrated by Figure 5. In such a control scheme, the feedforward must ensure the initialization of the plant input, and the robust feedback must permit the desired accurate output tracking. The plant model output \(J_{sr,traj}\) and input \(I_{Ch,traj}\) reference signals (optimal trajectories) are known and provided by the optimal MCC trajectory planning.  
As shown in Figure 6, a low pass filter was implemented for the reference current profile smoothing. With the control scheme of Figure 6, if the battery behaviour fits the one used for the charging profile optimisation, the charging current \(I_{Charge}\) that makes it possible to track the optimal \(J_{sr,traj}\) is equal to the feedforward current \(I_{Ch,FF}\). The feedback controller thus has no effect. Conversely, if the battery behaviour differs from the behaviour of the model used for the optimisation of the charging profiles, the feedback loop adapts the charging current. It imposes the computed optimal side reaction current on the cell. As only cell temperature \(T\), charging current \(I_{Charge}\) and voltage \(U_{cell}\), are measureable signals, an aging observer using the cell model was designed to estimate the side reaction current.  
Figure 5: Transfer function \(J_{sr}(s)/I_{ch}(s)\) frequency responses for the considered operating points.
## 4 Discussion
revealed that simplified inlet velocity profiles impact mean WSS and oscillatory shear index (OSI), albeit to a lesser extent than using a non-patient-specific flow waveform and also to a lesser extent than differences in patient anatomy [60]. As geometry and flow waveform selection are greater modulators of these hemodynamic metrics (WSS, OSI), they firstly recommended a careful reconstruction of the vessel anatomy and, secondly, acquisition of the subject's flow waveform before becoming concerned with the velocity waveform selection. The PC-MRIor 4D-flow MRI is currently considered the gold standard for HDF calculation, and the hemodynamic uncertainty related to PC-MRI shall be taken into account. Limitations. This study is subject to several limitations that merit discussion. Firstly, the cohort size was relatively small, comprising only 41 cases. Additionally, while LV segmentation was automated, the manual placement of force axis directions (septal-lateral, inferior-anterior, and base-apex) may introduce potential errors in precision. To address this, conducting reliability tests for HDF quantification is recommended for future studies. Furthermore, cases were scarce, particularly in the severe MR category, necessitating validation in larger-scale studies before clinical implementation. We acknowledge that the MR patients were selected based on only existing MR with preserved EF of varying severities, ranging from trivial to severe. The study did not specifically focus on different types of MR (primary, secondary, or Carpentier types). Further research is recommended to draw precise conclusions about specific MR types. Although pressure calculation was performed for visualization purposes, utilizing the Pressure Poisson equation to derive numerical quantification is advisable for future investigations. Added Value. The present study reports LV functional alterations associated with HDF in MR patients with preserved EF. The ability of HDF to amplify mechanical abnormalities enables the detection of even small kinetic dysfunctions that cannot be recognized by direct observation. HDF can identify conditions that may lead to adverse LV remodeling or predict reverse remodeling even after a therapeutic intervention takes place.
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
###### Abstract  
Landslide susceptibility is crucial for assessing the probability and severity of landslide disasters in a region. Previous studies have focused on static landslide susceptibility, using landslide assessment factor data from varying years, making it difficult to estimate spatio-temporal consistency and resulting in low prediction accuracy. Taking Hong Kong, China, as the study region, this study proposes a framework to estimate spatio-temporally consistent landslide susceptibility. The landslide assessment factors are divided into static and dynamic factors, with a temporal resolution of 5 years. Specifically, the dynamic assessment of landslide susceptibility is conducted for the periods 2000-2004, 2005-2009, 2010-2014, and 2015-2019, covering a total span from 2000 to 2019. Results show that the accuracy of the proposed model, defined as the proportion of correctly classified samples relative to the total number of samples, exceeds 0.7 across these four time periods. Both the F1-Score and the receiver operating characteristic (ROC) curve indicate that the proposed research framework exhibits good accuracy and practicality in susceptibility assessment. The proposed framework could capture temporal variations in landslide occurrence, allowing for a more accurate prediction of landslide susceptibility. The findings provide valuable insights for landslide disaster prevention and mitigation in Hong Kong and would also be applicable in other countries or regions.  
Keywords:landslide; susceptibility; machine learning; spatio-temporal data _Article_
## 2 Design Basis  
### Design Criteria  
The dry storage system for SNF must be designed to maintain the safe storage conditions of SNF, prevent damage to SNF, assure the retrievability of SNF, and withstand the impacts of design-based conditions such as normal, off-normal, and accident conditions. The main design requirements for the dry storage of SNF are in accordance with the relevant regulations of the Korea Nuclear Safety Act, IAEA SSS No.SSG-15 [7], and US 10 CFR72. These regulations are declarative, and straightforward technical design requirements are described in USNRC NUREG-2215 [8]/1536, [9]/1567 [10], and ASME Sec. III [11]. The most important design criteria for SDSSs are to ensure that public health and safety are protected from the SDSS, and to maintain the integrity of the SNF throughout its design life under normal storage conditions. Therefore, it is necessary to develop a design that meets the regulatory requirements, establish the design requirements necessary to secure the safety of the SDSS's components, and then check whether they are satisfy the regulations. According to 10 CFR72, an SDSS must be able to safely store SNF under normal, off-normal, and accident conditions throughout the storage period, and the retrievability of the SNF must be secured. Normal conditions include SDSS handling, fuel loading and unloading, and ambient temperature changes occurring during normal operation. Off-normal conditions are events that can occur frequently in situations beyond normal operating conditions, such as damage to the confinement boundary, blockage of some cooling channels, human error, and equipment-handling error. Accident conditions are accidents beyond the off-normal conditions, and are events during operations such as falling, overturning, fire, loss of confinement boundary, and complete blockage of the cooling channel, as well as events caused by natural phenomena such as flooding, typhoons, tornadoes, earthquakes, and tsunamis.  
The design requirements of the SDSS are presented by the dry storage facility operator, reflecting the operating conditions and regulatory requirements of the facility. The design requirements of the spent fuel dry storage module proposed in this study are to ensure subcritical maintenance, radiation shielding, heat, and structural safety for a design life of 50 years under normal, off-normal, and accident conditions. The detailed design requirements are presented in Table 1.