esa-sceva/satcom-chunk-collection huggingface dataset:
# Global Power Dynamics in the Contemporary Space System
## 5 System Dynamics--Global Power Dynamics  
As previously mentioned, the concept of power in the international system is inherently relational, implying that the power of a state is determined in relation to other states. Consequently, global power dynamics--defined as the interactions and relationships between nations as they exercise influence over one another in political, economic, and military domains--become a critical factor in understanding the distribution of power in the system. The dynamics of international power politics are shaped by a range of factors, including competition over resources, capabilities, strategic alliances, and partnerships. These dynamics have the potential to shift power balances and produce changes in the structure of the space system.  
In recent decades, states have demonstrated a marked emphasis on the development of space power. This emphasis has given rise to a variety of space strategies that are related to resources, relationships, and space development.
# Ensemble Learning for Skeleton-Based Body Mass Index Classification
## 4 Ensemble for BMI Classification
```
0: Training data \(D_{\text{train}}\) consisting of \(N_{\text{train}}\) human motion sequences
0: Trained ensemble model \(E\)
1: Set the frame division parameter \(T\);
2: Create a 3D matrix of zeros \(M_{3d}\);
3:for seq_idx = 1:1:\(N_{\text{train}}\)do
4: seq = \(D_{\text{train}}\)[seq_idx];
5: len = size(seq);
6: Initialize the index of \(k\)-NN \(q=0\);
7:fordiv_idx = 1:1:7 do
8:for seg_idx = 1:1:div_idx do
9:\(q=q+1\);
10: start_idx = round(len*(seg_idx-1)/div_idx)+1;
11: end_idx = round(len*(seg_idx)/div_idx);
12: seg_range = start_idx:1:end_idx;
13: segemetended_data = \(D_{\text{train}}\)[seg_range];
14: Extract \(AF\) from segmented_data using (6);
15:\(M_{3d}\)[seq_idx, \(1:20,q\)] = \(AF\);
16:\(M_{3d}\)[seq_idx, \(21,q\)] = corresponding_BMI_class;
17:endfor
18:endfor
19:endfor
20:model = build_model(\(T\));

# In build_model, an ensemble model consisting of \(T\cdot(T+1)/2\)\(k\)-NNs are constructed. 21:\(E\) = ensemble_learning(model, \(M_{3d}\));
22:return\(E\);
```  
**Algorithm 1** Pseudo-code for the proposed ensemble learning algorithm (training phase). ```
0:\(C_{Q}\) in (7)
0: majority class \(C_{M}\)
1: Set the class set \(S=[1,2,\cdots,N_{\mathbb{C}}]\);
# Here, elements in \(S\) indicate BMI classes. 2: Create a 1-by-\(N_{\mathbb{C}}\) vector of zeros \(V=[0,0,\cdots,0]\);
3: Initialize the majority class \(C_{M}\) to 0;
# Count the frequency of each class in \(C_{Q}\). 4:for\(q\) = 1:1:size(\(C_{Q}\))do
5:\(V[C_{Q}[q]]=V[C_{Q}[q]]+1\);
6:endfor
7: Set \(C_{M}\) to the index that corresponds to the maximum value in \(V\);
8:return\(C_{M}\);
```  
**Algorithm 2** Pseudo-code for the majority voting algorithm.
## 5 Results
### Topology 2
#### 5.2.3 Scenario 9  
In this scenario, both paths were alternatively configured with low delay, rate limiting, and packet loss on one of the links and higher delay on the other link, as depicted in Table 2. The static method was configured to select the h26->h30->h27 path, whereas the SNN algorithms made dynamic selections at h26, h29, and h30 to choose an outbound link.  
Figure 16(a) shows that snnr was 9-30% better than snndp, but 153% worse at 10 bundles/s. snnr was 10-20% better than snnd, except at 10 bundles/s, where it was 78% worse. snnr was 3-126% worse than snnp. The average response time with snnr was 2-79% better than static, whereas snndp was 47-80% better at 8-10 bundles/s. snndp showed 1-29% better performance than snnd and 14-59% worse performance than snnp.  
Figure 16(b) shows the bundle loss percentage with snnr being 33-80% and 13-81% better than snndp and snnd. snnp achieved the best performance by reducing 210% and 491% packet losses than snnr and snndp since it avoided the links with packet losses at h26 and h29. The snnr method showed 40-85% better performance than static, whereas snndp showed 8-59% better packet loss performance than static. snndp performed 4-94% worse than snnd.  
Figure 16(c) shows the throughput with snnr being up to 17-149% and 2-105% better than snndp and snnd. snnp showed 1-25% and 15-61% better throughput than snnr and snndp. snnr showed 38-127% better throughput than static, whereas snndp showed 10-93% better throughput. snndp showed 7-18% inferior throughput to snnd.  
Figure 17(a) shows that the path length with the SNN methods increased due to selecting the better-quality link h29->h30 through learning in scenario 7. Hence, the packet loss percentage was less with them with an increased average path length. Figure 17(b) shows the path lengths were higher with the SNN methods because of using the h29->h30 link in scenario 8.  
We can verify that snnp showed the maximum path lengths in Figure 17(c) by choosing h26->h29->h30->h27 or h26->h30->h29->h27 in scenario 9.  
Figure 17: Routing performance: Scenario 9. (**a**) Average response time; (**b**) Packet loss ratio (%); (**c**) Throughput.  
Figure 18: Path lengths traveled by bundles in Topology 2. (**a**) Scenario 7; (**b**) Scenario 8; (**c**) Scenario 9.
# A Comprehensive Study on EDFA Characteristics: Temperature Impact
## 2 Analytical Model
### Absorption and Emission Cross-Sections  
The absorption or emission cross-section is a material characteristic that determines the rate of a transition such as absorption or stimulated emission transition. An accurate determination of absorption and emission cross-sections is necessary in order to get an exact solution for both rate and propagation equations. Usually, the absorption cross-section is measured experimentally using commercial spectrophotometers [8; 25]. Many techniques have been raised in order to get a complete representation of both cross-sections, such as: Ladenburg-Fuchtbauer relation [26], and McCumber relation [8; 27]. The McCumber relation is usually used to relate the absorption and emission cross-sections due to its great validity and simplicity [2; 28]. The McCumber relation takes the form [27]:  
\[\sigma_{\mathrm{e}}(\lambda)=\sigma_{\mathrm{a}}(\lambda)\mathrm{e}^{\frac{ \epsilon-\mathrm{h}\
u}{\mathrm{K}\mathrm{B}^{2}}} \tag{1}\]  
where \(\sigma_{\mathrm{e}}(\lambda)\) is the emission cross-section, \(\sigma_{\mathrm{a}}(\lambda)\) is the absorption cross-section, \(\mathrm{k_{B}}\) is [PERSON]'s constant, T is the absolute temperature, h is [PERSON]'s constant, \(\
u\) represents the operating frequency, and \(\varepsilon\) is the net free energy required to excite one \(\mathrm{E_{r}^{+3}}\) ion from the \({}^{4}\)I\({}_{15/2}\) to the \({}^{4}\)I\({}_{13/2}\) state at temperature T.  
It can be noted that at only one frequency (\(\
u=\varepsilon/\mathrm{h}\)), the two spectra of the cross-sections are the same. At higher frequencies (shorter wavelengths), the absorption cross-section is larger, while at lower frequencies (longer wavelengths), the emission cross-section is larger [8].
## 3 Results and Discussion
The upper rows show the fatigue as a function of the Weibull scale factor \(v_{s}\), whereas the lower row has the corresponding Weibull probability density functions for selected \(v_{s}\) values (3 m/s, 5 m/s, and 7 m/s). The columns are organized according to their Weibull shape factor (\(k=2,1.5,3\)). In the standard case of \(k=2\) (also referred to as a Rayleigh distribution), the normal ordering was largely conserved, with the stall-regulated turbine showing the lowest average fatigue life up to Weibull scale factors of about 7 m/s. Given that most small wind turbines operated at sites within this wind speed range, it is safe to say that normally stall regulation will lead to lower fatigue life. The differentiation was somewhat exacerbated in the case of \(k=3\), where the average fatigue life of stall turbines remained consistently about an order of magnitude below that of their pitch-regulated counterparts. This stronger differentiation can be attributed to the narrower distributions obtained in the case of \(k=3\). Conversely, sites with a low shape factor, as illustrated by the \(k=1.5\) case, showed wider distributions, leading to a cross-over between the stall and pitch fatigue life curves, at Weibull shape  
Figure 11: Fatigue life as a function of the wind speed class for all three power regulation schemes. (a) Low turbulence intensity. (b) High turbulence intensity. factors as low as about 4.5 m/s. The situation at high TI levels was analogous to the one at low TI; therefore, the corresponding graphs were omitted for brevity.
## 5 Conclusions  
In this study, a model which is known as the WT-ANFIS-HFPSO, was developed which aims to achieve supreme model performance for solar power forecasting. The proposed model has utilized the WT to remove noisy data and has hybridized FF and PSO algorithms to optimize the premise parameters of the ANFIS model. The results of the proposed work are divided into three conditions of days, which are sunny days, partially cloudy days and cloudy days. It appears from the results that the usage of the WT has improved the forecasting model, particularly the ANFIS model, for all conditions of the days. These can be proven according to the small values of the MAE and RMSE, as well as the high values of the LCE and WIA that are shown by the WT-ANFIS. This work applies a hybrid algorithm which combines the FF and PSO algorithms. The outcome has shown the supreme performance of the WT-ANFIS-HFPSO when it is compared with the WT-ANFIS-FF and WT-ANFIS-PSO. This work also compares the performance of the WT-ANFIS-HFPSO for every condition of the day with other forecasting strategies such as the WT-ANFIS-PSO, WT-ANFIS-FF, WT-ANFIS, WT-RBFNN, WT-MLP, ANFIS, RBFNN and MLP. Compared with the selected forecasting strategies, the WT-ANFIS-HFPSO yields better results. The proposed model provides the lowest RMSE, MSE and NRMSE values that outperform other eight forecasting strategies. These demonstrated results have affirmed the effectiveness of the WT-ANFIS-HFPSO in providing the best predictive capability of solar power. However, the usage of all above-mentioned forecasting models is limited to a dataset from the UTeM,Malacca. This means that all of the forecasting models cannot be generalized to different locations. Hence, the future direction of this work should include datasets from various locations to estimate the efficiency of the proposed model, the WT-ANFIS-HFPSO, to forecast hourly solar power data.  
Conceptualization, N.A.A.; Formal analysis, N.N.A.; Methodology, N.A.A.; Resources, C.K.G.; Supervision, N.A.R.; Validation, N.A.R.; Writing-original draft, N.A.A.; Writing-review & editing, N.A.R.  
This research and the APC were funded by the Higher Institution Centre of Excellence (HICoE) Program Research Grant and Centre for Research Grant Management (PPGP) at the University of Malaya, grant number [UMPEDAC-2018 (MOHE HICoE-UMPEDAC) and RU005-2015].  
The authors would like to acknowledge the financial support of the Centre for Research Grant Management (PPGP) at the University of Malaya [RU005-2015, MATCHING GRANT (UMPEDAC AND HLAF); Higher Institution Centre of Excellence (HICoE); and Ministry of Education Malaysia (MOE HICoE-UMPEDAC).  
The authors declare no conflict of interest.
# An Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization
## 2 Improved Wavelet Modulus Algorithm Based on Fusion of Light Intensity and Degree of Polarization  
The principle of the improved wavelet modulus maxima algorithm for the fusion of light intensity and degree of polarization is shown in Figure 1. The specific process of the algorithm is as follows: the total light intensity image and the degree of polarization image are obtained by using the Stokes parameter calculation method for the polarization images at different angles, and the light intensity image and the degree of polarization image are respectively brought into the improved wavelet modulus maximum edge-detection algorithm to obtain the light intensity edge and the degree of polarization edge. Finally, the light intensity edge and the degree of polarization edge are refined and fused to obtain the final edge with rich details. Among them, the improved wavelet modulus maxima algorithm introduces NLM (non-local mean) filtering to improve the denoising ability of traditional wavelet modulus maxima, uses an iterative method for threshold selection to improve the adaptive ability of traditional wavelet modulus maxima, and uses double threshold selection and connection edge to improve the edge continuity of traditional wavelet modulus maxima.  
Improved Wavelet Modulus Maxima Algorithm to Extract the Light Intensity Edge and the Degree of Polarization Edge  
The improved wavelet modulus maxima detection algorithm is mainly composed of the following steps: denoising the image by the NLM algorithm, calculating the magnitude and direction of the gradient, suppressing the non-maximum value of the gradient, using the iterative method to find the threshold, and using the double threshold algorithm to select and connect edges.
# Deformation Study of Strongly Structured Clays Considering Damage Effects
## 3 Test Results and Analysis
### The Compressive Characteristics of Structurally Strong Clay at Different Disturbance Levels
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Disturbance Degree D/\%** & **Fitting Curve Equation** & **R\({}^{2}\)** \\ \hline
10.4 & \(a_{v}=0.025\times\exp\left[0.764/\left\langle W+0.181\right\rangle\right]\) & 0.99 \\
38.4 & \(a_{v}=0.035\times\exp\left[0.716/\left\langle W+0.104\right\rangle\right]\) & 0.99 \\
45.3 & \(a_{v}=0.023\times\exp\left[1.082/\left\langle W+0.155\right\rangle\right]\) & 0.99 \\
62.3 & \(a_{v}=0.013\times\exp\left[1.720/\left\langle W+0.241\right\rangle\right]\) & 0.99 \\
100 & \(a_{v}=2.027\times 10^{-5}\times\exp\left[14.732/\left\langle W+1.104\right\rangle\right]\) & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fitting results of the relationship between compression coefficient \(a_{v}\) and damage degree \(E\) of samples with different disturbance degrees. Establishment of an Empirical Formula for the Deformation Modulus Considering Soil Damage and Disturbance Factors  
The deformation modulus \(E_{0}\) of soil is a compressibility parameter obtained through in situ load tests, which accurately reflects the deformation characteristics of natural soil layers. However, this method has several drawbacks, including cumbersome equipment, time consumption, and significant financial investment. Furthermore, conducting load tests on deep soil layers presents technical challenges. As a result, it is often necessary to estimate the deformation modulus based on available data for the compressive modulus. The relationship for converting the compressive modulus to the deformation modulus is as follows:  
\[E_{0}=(1-\frac{2\mu^{2}}{1-\mu})\times\mathrm{E}_{S}, \tag{3}\]  
In this equation, \(\mu\) represents the Poisson's ratio of the clay, which can be estimated based on the state of the clay, with empirical values ranging from 0.25 to 0.42. By incorporating empirical formulas for the compressive modulus that account for damage under various disturbance conditions, the following can be obtained:  
\[E_{0}=(1-\frac{2\mu^{2}}{1-\mu})\times(A_{1}W^{2}+A_{2}W+A_{3}), \tag{4}\]  
In the equation, \(A_{1}\), \(A_{2}\), and \(A_{3}\) represent structural damage coefficients that are related to the magnitude of disturbance. To clarify the specific values of \(A_{1}\), \(A_{2}\), and \(A_{3}\) under each disturbance level, the structural damage coefficients \(A_{1}\), \(A_{2}\), and \(A_{3}\) were fitted with the disturbance degree \(D\). The fitting results are shown in Figure 5.  
The fitting results of the soil structure damage coefficients \(A_{1}\), \(A_{2}\), and \(A_{3}\) with the disturbance degree D were substituted into Equation (4) to derive the empirical formula for calculating the deformation modulus considering damage:  
\[E_{0}=(1-\frac{2\mu^{2}}{1-\mu})\times\{(0.013D^{2}-0.806D+114.86)W^{2}+(-0.005 D^{2}-0.228D+24.73)W+(0.0013D^{2}-0.125D+3.03)\}, \tag{5}\]
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 4 Proposed Mechanism
### Markov Decision Policy
According to Formulas (1) and (2), we can obtain the power-density function:  
\[H_{M_{j}}(\theta)=E_{M_{j}}h_{M}[(D_{M_{j}}cos\theta-rcos\theta)^{2}+(D_{M_{j} }sin\theta-r_{j}sin\theta)^{2}]^{-2\beta}10^{\frac{\xi}{10}} \tag{5}\]  
\[H_{F_{j}}(\theta)=E_{F_{j}}h_{F_{n}}[(D_{F_{j}}cos\theta-rcos\theta)^{2}+(D_{ F_{j}}sin\theta-r_{j}sin\theta)^{2}]^{-2\beta}10^{\frac{\xi}{10}} \tag{6}\]  
Thus, the transition-reward function can also be expressed as:  
\[RSS_{M_{j}}=\frac{1}{2\pi}\int_{0}^{2\pi}H_{M_{j}}(\theta)d\theta \tag{7}\]  
\[RSS_{F_{j}}=\frac{1}{2\pi}\int_{0}^{2\pi}H_{F_{j}}(\theta)d\theta \tag{8}\]  
Figure 6: Vehicle-mobility model.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.11. China Forest Biomass Map for 2004-2008  
[PERSON] et al. [73] used the seventh national forest inventory data collected from 2004 to 2008 and the conversion factor continuous function method to estimate forest biomass statistics. Also, they calibrated forest area from MODIS data products with field inventory statistics, then downscaled the estimated biomass statistics using the calibrated MODIS land cover data, and finally generated the spatially explicit forest biomass map at 0.05-degree resolution. Leave-one-out validation results showed the R\({}^{2}\) between the estimated forest biomass and forest inventory data was 0.76; however, there was no evaluation of the accuracy of estimated biomass at the pixel level.
## 2 Related Works
### Deep-Learning Approaches  
For modeling an artificial intelligence (AI)-based diagnostic system for kidney disease, the authors of [29] independently generated 12,446 CT images of the whole abdomen via CT urogram. Resnet50, VGG16, and Inception v3 were used to achieve accuracy rates of 73.40%, 98.20%, and 99.30%, respectively. The results from the models were further explained with the GradCAM XAI approach. [PERSON] et al. [33] endeavored to identify kidney cysts and stones using YOLO architectures, which were augmented with XAI features. The performance analysis included CT images from three categories, including 72 kidney cysts, 394 kidney stones, and 192 healthy kidneys. The results indicated that their YOLOv7 architecture design outperformed the YOLOv7 tiny architecture design, achieving a mAP50 of 0.85, a precision of 0.882, a sensitivity of 0.829, and an F1-score of 0.854. A study by [PERSON] et al. [30] focused on identifying the three types of kidney abnormalities (stones, cysts, and tumors) using AI techniques on a dataset of over 12,000 CT images. A hybrid approach of pre-trained models and machine-learning algorithms was used, and the Densenet-201 model and random-forest classification yielded an accuracy rate of 99.44%. [PERSON] et al. [31] proposed a framework for classifying renal CT images as healthy or malignant using a pre-trained deep-learning approach. To improve its accuracy, they employed a threshold-filter-based pre-processing scheme to eliminate artifacts. The process included four stages: image collection, deep feature extraction, feature reduction and fusion, and binary classification through a fivefold cross-validation. Experimentation on CT images with and without artifacts showed the KNN classifier achieved 100% accuracy by using pre-processed images.Using a XResNet-50 model, [PERSON] et al. [32] automated the diagnosis of kidney stones (the presence of stones vs. no stones present) using coronal CT images. Each subject had distinct cross-sectional CT scans taken, yielding a total of 1799 images, and the results had a 96.82% accuracy rate for diagnosing kidney stones. Using the features extracted from ResNet-101, ShuffleNet, and MobileNet-v2, kidney ultrasound images were categorized by the authors of [47] using an SVM. The final predictions were generated using the majority-vote technique to achieve a maximum accuracy of 95.58%. While considering stones, cysts, hyper-echogenicity, space-occupying lesions, and hydrocephalus as abnormalities, [PERSON] et al. [48] collected 330 normal and 1269 abnormal pediatric renal images from a U.S. database.After performing the pre-processing tasks, the final linking layer of ResNet50 was redefined, and an accuracy of 92.9% was achieved. Though several studies have employed different DL approaches for better accuracy, they have primarily used DL models that consisted of more parameters, and they have not focused on improving the clarity and transparency of the result from DL models. The proposed work clearly showed the superiority of the customized model in terms of performance, clarity, and training parameters.
# Early Infection for Mass Production of Artificial Single-Stranded DNA with _Escherichia coli_
###### Abstract  
Large quantities of artificial single-stranded DNA (ssDNA) with user-defined sequences are increasingly required to exploit the potential of DNA nanotechnology. Cross-contamination-free ssDNA production can be achieved using _Escherichia coli_ with an optimized helper plasmid in high-cell-density cultivation via the secretion of phagemid particles containing ssDNA with user-defined sequences. In our study, we aimed to reduce the number of phagemid particles for the initiation of ssDNA production. We tested different infection densities, ranging from a multiplicity of infection (MOI) of 10\({}^{-6}\)-10\({}^{-2}\) ftu cfu\({}^{-1}\) at the start of the initial batch phase in a 2.5 L stirred tank bioreactor. A MOI of 10\({}^{-3}\) ftu cfu\({}^{-1}\) was the best compromise between process time and ssDNA concentration. Early initiation of ssDNA production with low MOI reduced the number of phagemid particles by a factor of 250,000. The early infection strategy was successfully scaled up to the 25 L scale, resulting in ssDNA concentrations of >100 mg L\({}^{-1}\) within a process time of one day. Transferring the infection strategy to a 1000 L scale gained 65 mg L\({}^{-1}\) ssDNA because of incomplete initial infection. The versatility of the early infection strategy was further proven with a second prolonged, user-defined ssDNA sequence.  
phagemid particle; ssDNA; _Escherichia coli_; helper plasmid; high-cell-density fermentation; scale-up