esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### EPS Box Strength Study
#### Stacking Strength Test
In the contour plots of Sim22 and Sim23, there was a strained area visibly yellow here, nearly 45 degrees to the ground, while Sim23X had no such pattern. This was probably caused by rounding on the bottom edges and awaits further investigation. The equivalent stress was observed to be most evenly distributed in Sim23X, and the maximum stress on the short side wall and the long side wall were the smallest among all boxes. Compared to Sim23X, the maximum stress on the short side wall of Sim23 was 3.1% higher and 5.0% higher on the long side wall. Sim22 was the one with the highest equivalent stress; its maximum values were 0.176 MPa and 0.181 MPa on the short side wall and the long side wall, respectively; 7.3% higher and 7.7% higher than that of Sim23. Plastic strain values are illustrated at five points in the contour plots. From left to right, take three points in the middle of the inner wall--denoted herein as Mid 1, 2, and 3--and two points on the bottom edge--denoted herein as Botm 1 and 2. The values are summarized in Table 7. Sim22 had higher plastic strain across the body; in the middle area, it nearly tripled compared to Sim23, while it was doubled compared to Sim23 on the bottom edges. This indicates that the box with a density of 22 kg/m\({}^{3}\) was more susceptible to external force and strained more. Even though Sim23X was 4 mm thinner on the bottom than Sim23, it had less plastic strain across the body, roughly half of Sim23 in the middle and one-quarter to one-third of Sim23 on the bottom edges. This means that reducing the wall height of the box or using smaller rounding could somehow augment the capability of withstanding stacking load.
## 2 Theoretical Background
### Cross-Validation  
The performance of the proposed model can be confirmed through the estimation error of the model. If the MLR is established using the entire data, the performance for prediction may be relatively favorable, but predicting the new data may be less accurate. Therefore, the independent data validation must be conducted to establish a robust prediction model, and leave-one-out cross-validation is a suitable technique for evaluating the error of a given model by sequentially excluding samples in the dataset used to derive the model to produce greater reliability in the estimate [31]. Leave-one-out cross-validation is a technique for generating a prediction formula through all the data except for one sample, as shown in Figure 4, and a prediction is made for that sample.  
This process is repeated for all samples, and the performance of the model is evaluated by an average estimation error. By estimating the error through independent data, further reliable prediction error estimation is feasible, and the resulting model can be more robust for new conditions [32].  
Figure 3: Activation functions in neural networks; (**a**) sigmoid, (**b**) hyperbolic tangent, and (**c**) rectified linear unit (ReLU).
## 4 Recurrence-Analysis-Based Detector
### Analytical Expression of the Probability of False Alarm
#### 4.2.4 Probability of False Alarm and Detection Threshold  
Based on Equation (38), the \(P_{fa}\) is expressed as follows:  
\[P_{fa}=\int_{\lambda}^{\infty}f_{T/\mathcal{H}_{0}}(t)dt=erfc\left(\frac{ \lambda}{\sigma_{0}\sqrt{2}}\right) \tag{39}\]  
\(erfc(x)\) denotes the Complementary Error Function--the Complementary Error Function--\(erfc(x)\) is defined as follows:  
\[erfc(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{+\infty}e^{-\theta^{2}}d\theta\]  
--and the detection threshold \(\lambda\) is  
\[\lambda=\sigma_{0}\sqrt{2}\;erfc^{-1}\left(P_{fa}\right) \tag{40}\]
# Performance Analysis of UAV-IRS Relay Multi-Hop FSO/THz Link
## 3 Performance Analysis  
In this section, we derive closed-form expressions for the outage probability, average BER, and average channel capacity of the proposed UAV-IRS hybrid multi-hop FSO/THz link system to analyze its performance.
# A Review of Machine Learning Applications in Land Surface Modeling
###### Abstract
They tend to violate constraints on individual samples while optimizing the overall performance. LSM outputs can be used to pose constraints on the ML models to overcome this. Recently, interrogative studies have also been invoked to interpret ML models to ask questions like: (1) how trustworthy are ML models? or (2) how to interpret the processes that drive ML model systems? The objectives of this review paper are (1) to feature the areas of improvement in land modeling using ML and (2) discuss the most important ML techniques in detail, and (3) suggest future directions for further improvements. To achieve these goals, we searched literature in google scholar database using a combination of the relevant key words:'machine learning', 'land surface modeling', 'crop modeling', 'evapotranspiration estimation', 'parameters and uncertainty' etc. Moreover, the bibliographic lists of these articles were also considered to create an extensive list of articles. The most relevant 83 articles are cited and discussed in this paper. This study demonstrates the potential of ML to propel LSMs and vice versa. Critically, this review paper provides information about (1) the importance of LSMs in different applications (2) the difficulties and limitations of traditional LSMs. (3) application of ML in LSMs in past literature. (4) concise and simple technical overview of ML for LSMs. (5) potential directions where ML can contribute to solving challenges in modeling land surface processes. The results from this study have implications for creating inexpensive, improved and tractable land surface models with less and quantifiable uncertainty. This in turn will help to construct upgraded coupled climate models to provide more realistic and refined future projections.
# Energy-Aware Control of Error Correction Rate for Solar-Powered Wireless Sensor Networks
## 2 Related Work
### Reed-Solomon Block in the FEC Scheme  
Figure 2 shows an RS block used in an RS coding scheme, which is representative among the various FEC methods [5]. Data to be transmitted are included in the payload, and a code is inserted into the parity part, which the receiver will use in order to restore errors. If the RS block consists of \(n\) symbols in total, with \(k\) symbols in the payload and \((n-k)\) symbols in the parity, then this is referred to as RS\((n,k)\). It is well known that when an RS block of \(n\) symbols is transmitted, the maximum number of restorable symbols is \((n-k)/2\)[15; 16; 17].  
Therefore, the number of restorable symbols can be adjusted by controlling the parity length in the RS block. If the parity length increases, then the number of restorable symbols also increases, and vice versa. On the contrary, in terms of energy consumption, an increase in the parity length inevitably leads to an increase in the energy required for encoding, decoding, transmitting and receiving data [15; 16; 17]. Table 2, which shows the amount of energy consumed for RS encoding and decoding for different parity lengths [15], supports this fact. This means that the energy efficiency and number of restorable symbols have a trade-off relationship.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RS Code** & **Encoding Energy (mJ)** & **Decoding Energy (mJ)** \\ \hline \((15,13)\) & 0.1844 & 0.2352 \\ \((31,27)\) & 0.3499 & 0.4281 \\ \((63,55)\) & 0.5631 & 0.6872 \\ \((127,115)\) & 0.8798 & 1.0354 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy consumption of RS encoding and decoding (Data from [15]).  
Figure 1: Total amount of data in the network required for the sink node to gather 80 KB of data. ARQ, automatic repeat request; FEC, forward error correction.  
Figure 2: Reed–Solomon (RS) block.
# Verification of Industrial Worker Walking Efficiency with Wearable Hip Exoskeleton
## 2 Materials and Methods
### Experimental Procedure  
This experiment was conducted at the Rehabilitation Engineering Research Institute in Incheon, South Korea. Participants had electromyography and metabolic energy expenditure measured while walking on a treadmill for 6 min and climbing five flights of stairs (Figure 2).  
The experimental flow chart of this study is shown in Figure 3.  
Figure 3: Experimental flow chart of this study.  
Figure 2: Examples of walking up stairs (**left**) and walking on a treadmill (**right**).  
Before starting the clinical trial, the researchers adjusted the size of the exoskeleton such that participants could wear the WIM comfortably; then, they conducted adaptation training for 10 min each on a treadmill and on a six-step custom staircase in the laboratory while wearing the device.  
In the adaptation training phase, the individual's preferred and most comfortable assistance torque of the WIM was determined.  
After the adaptation training, each subject had surface electromyography electrodes attached to their lower extremities, a heart rate monitor attached to their chest, and a face mask placed over their nose and mouth. Participants were instructed not to speak during the experiment.  
In this experiment, treadmill walking was conducted first, followed by stair walking after sufficient rest time (approximately 10 min) to stabilize breathing.  
The treadmill experiment was conducted by walking for 6 min at a speed of 4 km/h; the 6 min walking test is a functional test method used in clinical practice to measure cardiopulmonary capacity.  
Treadmill walking was conducted under two conditions: wearing and not wearing the WIM, and the conditions were provided randomly. After each condition, participants rested for 10 min to restore the metabolic rate to baseline.  
The stair-climbing experiment, similar to the treadmill walking, was performed in a random order under two conditions: wearing and not wearing the WIM.  
Participants were asked to climb five flights of stairs without stopping, at their chosen walking speed, with a 10 min break between the two conditions.  
To evaluate the speed difference between the two conditions while climbing stairs, the performance time was recorded with a stopwatch.
# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 4 Simulation Experiment
### Multi-DAG Influence Parameter Experiment  
As shown in Figure 9, we conducted experiments focusing on the balance ratio parameter in Formula (6). These experiments were conducted by using the Cache-Composite Priority algorithm to execute multiple random DAGs (\(n=\) 15) on edge computing nodes (\(m=\) 7). Figure 8(a) demonstrates that as the value of \(\alpha\) increased, the DAG completion time gradually decreased when scheduling subtasks. This is because in Formula (6), an increase in the value of \(\alpha\) results in a higher weighting of delay factors, which ultimately leads to a reduction in the DAG completion time. Figure 8(b) shows that as the value of \(\beta\) increased, the DAG total energy consumption gradually decreased when subtasks were scheduled. This is because in Formula (6), an increase in the value of \(\beta\) results in a higher weighting of energy consumption factors, which ultimately leads to a reduction in the DAG total energy consumption.  
The relationship between the balance ratio parameter and task completion degree is illustrated in Figure 10. In order to explain the experimental results more clearly, this experiment was conducted by using the Cache-Composite Priority algorithm to execute multiple random DAGs (\(n=\) 10) on edge computing nodes (\(m=\) 3). Figure 9(a) demonstrates that the task completion degree exhibited a gradual increase with the increase in the \(\alpha\) value. The reason for this is that as the \(\alpha\) value increased, the task completion time was reduced, thereby improving the task completion degree. Figure 9(b) shows that the task completion degree exhibited a gradual decrease with the increase in the \(\beta\) value. The reason for this is that an increase in the \(\beta\) value reduced the energy consumption, thereby affecting task execution efficiency and resulting in a reduced task completion degree.  
Figure 9: Diagram of balance ratio parameter’s relationship with DAG completion time and DAG total energy consumption.
###### Abstract  
The temperature has a significant effect on cathepsin activity, but the effect of temperature on the ripening of marinades, and the formation of protein hydrolysis products, is less studied than other technological factors. The results of this study showed that herring marinated at 2 \({}^{\circ}\)C showed a higher mass yield, but lower non-protein nitrogen (NPN), peptides, and free amino acid fraction content, than after marinating at 7 and 12 \({}^{\circ}\)C. The higher temperature increased the free amino acid content the most, and decreased the hardness, as measured via sensory assessment, of the marinated meat. This was confirmed by the hardness measurement in the texture profile analysis. The highest activity of cathepsin D and B in the meat was found at 7 \({}^{\circ}\)C, while cathepsin L was found at 2 \({}^{\circ}\)C. Increasing the temperature by 10 \({}^{\circ}\)C increased the diffusion/loss of nitrogenous substances from the meat to the brine by 36%. The meat and brine showed high antioxidant activity, which depended on the marinating temperature, and originated mainly from the 5-10 or \(<\)5 kDa fraction. The meat had a higher ABTS (2,2\({}^{\prime}\)-azinobis-3-ethylbenzothazoline-6-sulfonate) activity than the brine, opposite to the DPPH (2,2-diphenyl-1-picrylhydrazyl radical) activity, while the FRAP (Ferric Reducing Antioxidant Power) capacity was similar for meat and brine. The fractionation of the meat and brine extracts increased the antioxidant potential of FRAP and ABTS only for the brine. The most hydrophobic peptides were released during marinating at 7 \({}^{\circ}\)C. The meat and brine were dominated by 2-4 kDa peptides, followed by 4-6 and 0.5-2 kDa. The higher temperature favored a higher proportion of \(<\)4 kDa than \(>\)4 kDa peptides in the brine.  
herring; marinating; temperature; cathepsin; antioxidant activity; peptides; RP-HPLC
# Effectiveness of Augmented Reality in Stroke Rehabilitation: A Meta-Analysis
## 2 Methods
### Quantitative Analysis  
The mean, SD, and sample sizes of each outcome measurement in the baseline, control, and AR intervention groups were utilized for comprehensive Meta-analysis (Version 3.0, Biostat Inc., Englewood, NJ, USA). Because the included investigations reported their outcome of different scales, the effect size estimates were computed using SMD and 95% confidence interval (CI) by a random effect model to depict the magnitude of the enhancement compared with baseline or control group. The interpretation of SMD was as follows: <0.2 negligible; 0.2-0.5 small; 0.5-0.8 moderate; and >0.8 large. For all tests, \(p<0.05\) was considered statistically significant. If each group had over one investigation, meta-analysis was implemented. The forest plot was used to describe the effect size of favoring the AR (allocating in a positive value) and baseline or control group (assigning in a negative value). We assessed heterogeneity through inspection of I-square statistic, where I2 value over 50% indicates significant heterogeneity. Bias risk across investigations was evaluated using funnel plot and Egger's regression test.
## 3 Software Cost Model Considering Operating Environment
### Results of Model Comparison
#### 4.5.2 Results of Optimal Release Time for Real Data (Dataset #3)  
Table 15 lists the ORTimes for the minimum ETCs for the proposed model in Case #1 using real data (Dataset #3). The results show that the ORTime T* of the proposed model is 11.4 weeks in Case #1. In Case #2, after increasing the discount rate of the testing cost, \(\theta\), the ORTime T* is comparable with that in Case #1. Table 15 shows that as T* decreases, the discount rate, \(\theta\), increases. Conversely, T* increases when the discount rate, \(\theta\), decreases. In Case #3, the cost coefficients are changed to C\({}_{1}=10\), C\({}_{2}=5\), and C\({}_{1}=40\), C\({}_{2}=20\), thereby allowing comparison of ORTime T* with that in Case #1. T* increases as the cost coefficients C\({}_{1}\) and C\({}_{2}\) decrease. By contrast, T* decreases when the cost coefficients C\({}_{1}\) and C\({}_{2}\) increase. In Case #4, the cost coefficients are changed to C\({}_{4}=100\), \(200\), \(300\), and \(500\), and the ORTime, T*, can be compared with that in Case #1. T* increases with increasing cost coefficient C\({}_{4}\). Finally, to demonstrate the effect of the operating time, x, x is changed from x = 5 to 300, and the ORTimes and minimum ETCs for the proposed model in Case #5 are compared. The proposed model is affected by the operating time, x. Table 15 shows that the ORTimes, T*, of the proposed model are 7.8, 11.4, 26.5, 38.5, and 72.5 weeks when x = 5, 10, 26.5, 38.5, 100, 72.5, and 300, respectively. Figures 7 and 8 show the variation in the ORTime with the minimum ETC for the proposed model in Cases #1-#5 on real data. Additionally, Figure 7 shows the software reliability.
## 5 Discussion  
The use of GaN HEMT power semiconductor devices at VHF is unusual, as the latest Si-LDMOS devices at these frequencies can provide very good results in terms of power efficiency at a lower cost. Therefore, there is a scarcity of published scientific works based on GaN technology at these frequency bands, and it is difficult to find published results to compare with those obtained in this work because most of the developments are currently focused on the higher-frequency bands (above UHF) and ET PA architectures [42; 43; 44]. However, there are new communication services, such as the one addressed in this work (satellite communications at the VHF band), that are currently under development and can benefit from using GaN technology in RF power circuits. Regardless of the carrier frequency used, GaN HEMTs (which exhibit lower parasitic elements compared to those of Si-LDMOS devices) can provide a better performance in wide fractional bandwidth PAs, while maintaining good linearity figures, resulting in an increase in power efficiency, as already demonstrated by some broadband PAs operating in this band [45]. These linearity and power efficiency features can be enhanced if a polar-mode architecture is used in such a way that the RF GaN PA is combined with a wideband EA whose power stages have also been developed with GaN technology, as described in this paper.  
In view of the polar-mode PA performance results, it seems reasonable to compare this type of amplification architecture (which seems to be more complex at the hardware level) with Doherty PAs [46; 47]. However, although the power efficiency of Doherty PAs (for which there are no previously published results or similar works for a direct comparison) may be at levels close to those obtained by the polar-mode system developed in this study, it must be taken into consideration that to reach the linearity levels of the polar-mode transmitter, the Doherty topology would need to use complementary linearization techniques, such as wideband digital predistortion [48; 49; 50; 51; 52; 53], which, in addition to requiring additional hardware to digitalize the amplified output signals, requires complex adaptive digital processing hardware capable of operating in real time. It is also important to mention other techniques, such as _Chireix_[54], that allow users to obtain high-power-efficiency figures, but given the high fractional bandwidth that would be necessary in the combiner, as well as the control techniques required, it seems complicated to use it for this application.  
Therefore, these initial results suggest that the proposed full GaN-based polar-mode PA demonstrator shown in this paper, which still has room for improvement and optimization, could be an attractive alternative to consider for the implementation of PAs for this type of application.
# A Transformer-Based Channel Estimation Method for OTFS Systems
## 2 System Model
### Minimum Mean Square Error Detection Algorithm  
The BER is also a measure of channel estimation performance. We assume that in the received signal, \(\hat{x}=Wy\), where \(W\) denotes the weighted matrix, the error of estimation is \(e=\hat{x}-x=Wy-x\), and the mean square error can be written as \(e_{MSE}=E\|Wy-x\|^{2}\). The minimum mean square error (MMSE) detection algorithm minimizes the mean square error between the actual signal and the estimated signal using the minimum mean square error as a criterion. When \(e_{MSE}\) is minimized, the weighed matrix can be written as  
\[W_{MMSE}=H^{H}(H^{H}H+\sigma^{2}I)^{-1} \tag{11}\]  
where \(H\) represents the effective channel matrix of size \(MN\times MN\), and \(\sigma^{2}\) represents the variance of noise. The recovered signal after using this algorithm can be represented as \(\hat{x}=W_{MMSE}y\).
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
## 4 Results
### Model Performance on the Training Set and Testing Set  
To evaluate the predictive performance of each model, we computed three widely used error metrics on the test dataset: mean squared error (MSE), mean absolute error (MAE), and R-squared (R\({}^{2}\)). Table 1 summarizes the performance of each model on the training dataset and testing set.  
Linear regression (LR) typically shows similar performance on training and test data unless it underfits the data. In contrast, decision tree (DT) and random forest (RF) models often achieve very low training error and high R\({}^{2}\) scores, indicating potential overfitting. Meanwhile, MLP (Neural Network) models tend to perform better on training data as well, though the extent of overfitting is usually less severe and depends on factors like regularization and the training setup.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Data** & **MSE** & **MAE** & **R\({}^{2}\)** \\ \hline LR & Testing Set & 6.356 & 1.906 & 0.208 \\  & Training Set & 6.363 & 1.901 & 0.211 \\ DT & Testing Set & 3.496 & 1.078 & 0.564 \\  & Training Set & 3.222 & 0.962 & 0.601 \\ RF & Testing Set & 3.400 & 1.072 & 0.576 \\  & Training Set & 3.247 & 0.994 & 0.597 \\ MLP & Testing Set & 5.545 & 1.723 & 0.309 \\  & Training Set & 5.431 & 1.695 & 0.327 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model performance on training set.  
The random forest (RF) model consistently outperformed the other models across all three evaluation metrics, with the lowest MSE and MAE and the highest R\({}^{2}\) value. These results are consistent with findings from previous studies that highlight the strength of ensemble learning methods in forecasting financial time series [6; 9].  
The decision tree (DT) model also showed solid performance but exhibited a higher variance compared to RF, suggesting susceptibility to overfitting. MLP achieved moderately good results but not better than RF, possibly due to limitations in training data size or insufficient hyperparameter optimization. As expected, LR performed the weakest due to its linear constraints, which limits its ability to capture the complex relationships between macroeconomic variables and bond yields [8].
## 4 Results
### Ionospheric Data Analysis
#### 4.3.3 NOAA Satellite Data Analysis: Electron Burst Data Analysis  
NOAA electron fluxes were analyzed two days before the EQ and on the event day. Looking at the geomagnetic indices, the geomagnetic activity of these days was quite calm. Then, some candidate EBs were observed over the expected regions. For example, a flux of about 600 electrons cm\({}^{-2}\) s\({}^{-1}\) str\({}^{-1}\) was observed in the NOAA-15 track on November 8 at a latitude of 50-55\({}^{\circ}\) around 20:40 UT, as shown by a red circle in Figure 15. Slight perturbations of around 400 electrons cm\({}^{-2}\) s\({}^{-1}\) str\({}^{-1}\) were also observed at the previous eastward satellite trajectory, all occurring at around 19:00 UT. Thus, these two perturbations anticipated the Central Italy EQ by 9.5 and 11.2 h, respectively.