esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
### Results of Explanatory Factors Selection  
Table 2 illustrates the average merit (AM) values of the 14 spring explanatory factors based on the LSVM algorithm classifier using a 10-fold cross-validation method [53]. The results of the performed LSVM analysis revealed that lithology had the highest predictive power (14.0), followed by elevation (12.8), SPI (12.2), and soil cover (10.6), thus being the most significant factors that contribute to the predictive performance of a model. Since, all spring explanatory factors appear to have a positive predictive value, none of them were excluded from the analysis that followed.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Explanatory Factors** & **AM** & **Standard Deviation** \\ \hline
1 & Lithology & 14.0 & \(\pm\)0.000 \\
2 & Elevation & 12.8 & \(\pm\)0.400 \\
3 & SPI & 12.2 & \(\pm\)0.400 \\
4 & Soil cover & 10.6 & \(\pm\)0.490 \\
5 & Distance to roads & 9.9 & \(\pm\)0.831 \\
6 & Slope aspect & 9.3 & \(\pm\)0.900 \\
7 & TWI & 7.4 & \(\pm\)1.020 \\
8 & Slope angle & 6.2 & \(\pm\)1.327 \\
9 & STI & 5.6 & \(\pm\)1.685 \\
10 & Land use & 4.9 & \(\pm\)1.758 \\
11 & Distance to streams & 4.7 & \(\pm\)1.487 \\
12 & Profile curvature & 3.0 & \(\pm\)1.789 \\
13 & Plan curvature & 2.5 & \(\pm\)0.671 \\
14 & NDVI & 1.9 & \(\pm\)1.814 \\ \hline \end{tabular}
\end{table}
Table 2: Predictive capabilities of spring explanatory factors using the LSVM algorithm. AM: average merit.  
Figure 4: Thematic maps: (**a**) slope aspect; (**b**) slope angle; (**c**) plan curvature; (**d**) profile curvature; (**e**) elevation; (**f**) stream power index (SPI); (**g**) sediment transport index (STI); (**h**) topographic wetness index (TWI); (**i**) distance to streams; (**j**) distance to roads; (**k**) normalized difference vegetation index (NDVI); (**l**) lithology; (**m**) soil; (**n**) land use.
# New Hybrid Algorithms for Prediction of Daily Load of Power Network
## 4 Experiments and Results
### Experimental Results  
For verifying the results, SGWO and SGWOD are compared to GWO, SFLA and GWO-DE, which is an improved GWO with differential evolution [55]. They run 100 times on each benchmark function, and the testing parameters are listed in Table 5. Table 6 shows the statistical results of the algorithms, including average (AVG) and standard deviation (STD).
## 3 Materials and Methods
### Prequential Evaluation  
In the performance evaluation of machine learning models, the hold-out evaluation method is the commonly used approach, wherein the available data are split into two parts: a training set and a test set [3]. The model is trained on the training set, and its performance is evaluated on the test set to evaluate how well the model can perform on new unseen data.  
Prequential evaluation, on the other hand, is a more recent and alternative approach to model evaluation used for data generated continuously in time. In prequential evaluation (or test-then-train evaluation), the basic idea is to use new data instances to firstly test the model and then to train it [29]. The process of prequential evaluation involves the following steps:  
* Pretrain the model with initial data;
* Test and train: For each incoming data instance, the model is tested on the current instance and then trained using the same instance, using the partial_fit() method;
* Evaluate: The model's performance is tracked over time where metrics are updated over time.  
For the prequential evaluation, several parameters are tuned amongst test step, max_samples, and pretrain_size, which refer, respectively, to the number of samples between each model test, the maximum number of samples to process from the data stream, and the number of samples to use for pretraining the model before starting the prequential learning process [12].  
An example of pseudo code for prequential accuracy evaluation is outlined in the Algorithm 3 below:In this study, we use Accuracy, Precision, Recall, and F1-score metrics computed according to the following equations:  
\[\text{Accuracy}=(\text{tp}+\text{tn})/(\text{tp}+\text{fp}+\text{fn}+\text{tn}) \tag{3}\]  
\[\text{Precision}=\text{tp}/((\text{tp}+\text{fp})) \tag{4}\]  
\[\text{Recall}=\text{tp}/(\text{tp}+\text{fn}) \tag{5}\]  
\[\text{F1}-\text{score}=2*((\text{Precision}*\text{Recall})/(\text{Precision}+ \text{Recall})) \tag{6}\]  
where tp, fp, fn, and tn are, respectively, number of instances correctly classified as positive, number of instances incorrectly classified as negative, number of instances incorrectly classified as negative, and number of instances correctly classified as negative.  
Experiments are performed on Jupyter notebook, and Python 3.9.13 is used along with the Matplotlib 3.5.2, NumPy 1.20.0, and pandas 1.4.4 libraries. Implementation of incremental online algorithms is performed using scikit-multiflow 0.5.3, a Python library that provides a range of tools, algorithms, and evaluation metrics to handle data streams in real-time scenarios. Scikit-multiflow can be used along with other Python libraries like Numpy and SciPy, pandas and scikit-learn.  
In the subsequent section, we will exhibit and discuss the results of evaluating the overall performance of the Adaptive Random Forest (ARF) and K-Nearest Neighbors (KNN) classifiers.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 4 Experimental Results
### Evaluation on Embedded Systems  
To assess the practical deployability of MLKD-Net-S, we evaluated its performance on an NVIDIA Jetson Orin Nano board. The model, trained on the RESIDE-Outdoor dataset, was converted to TensorRT format and tested under three precision modes: FP32, FP16, and INT8. Inference was conducted on \(256\times 256\) resolution images from the SOTS-Outdoor test set (Figure 4). As shown in Table 3, MLKD-Net-S achieved inference times of 74.97 ms (13.34 FPS) in FP32 and 55.24 ms (18.2 FPS) in INT8 mode. These results indicate that the model can perform real-time inference on embedded platforms, underscoring its suitability for deployment in resource-constrained systems such as drones and autonomous vehicles.  
Notably, the accuracy degradation under INT8 quantization was minimal, with the PSNR and SSIM remaining nearly unchanged. We attribute this robustness to the model's quantization-friendly architecture: depth-wise convolutions are inherently less sensitive to quantization noise due to their channel-wise independence, and channel attention mechanisms rely on relative feature scaling, making them stable under reduced precision.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**SOTS-Indoor**} & \multicolumn{2}{c}{**SOTS-Outdoor**} & \multicolumn{2}{c}{**Overhead**} \\  & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **\#Params** & **FLOPs** \\ \hline DCP (TPAMI 10) & 16.62 & 0.818 & 19.13 & 0.815 & – & – \\ DehazeNet (TIP 16) & 19.82 & 0.821 & 22.46 & 0.852 & 0.01M & 0.6G \\ FFA-Net (AAAI 20) & 36.39 & 0.988 & 32.14 & 0.978 & 4.46M & 287.8G \\ Dehamer (CVPR’22) & 36.36 & 0.988 & 35.18 & 0.986 & 132.45M & 48.93G \\ LKD-Net (ICME 23) & 37.04 & 0.991 & 36.88 & 0.993 & 0.634M & 6.34G \\ DehazeFormer (TIP 23) & 38.46 & 0.994 & 34.29 & 0.983 & 4.634M & 48.64G \\ MB-TaylorFormer (ICCV 23) & 42.62 & 0.994 & 37.98 & 0.991 & 7.43M & 88.1G \\ DEA-Net (TIP 24) & 41.31 & 0.995 & 35.97 & 0.990 & 3.653M & 32.23G \\ OCNet (AAAI 24) & 40.79 & 0.996 & 37.68 & 0.995 & 2.4M & 17.86G \\ ConvIR (TPAMI 24) & 41.53 & 0.996 & 37.95 & 0.994 & 5.53M & 42.1G \\ \hline MLKD-Net–T (Ours) & 35.62 & 0.988 & 36.05 & 0.991 & 0.364M & 3.84G \\ MLKD-Net–S (Ours) & 37.58 & 0.993 & 37.26 & 0.993 & 0.676M & 7.19G \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison on SOTS datasets with inference overhead. All FLOPs were measured on \(256\times 256\) input images.  
Figure 4: Dehazing results of MLKD-Net-S with INT8 precision ((**left**): input; (**right**): output).
## 5 Conclusions and Suggestions  
ERT can provide the stratum 2D or even 3D resistivity profile to further understand the situation of stratigraphic change, but it is a big challenge to engineer the data interpretation of the ERT spatial resolution and testing result. One important phenomenon in the 3D effect that was observed whilst exploring the pipeline stratum model was the mapping phenomenon of the 3D effect of the pipeline stratum, which will produce an illusion of high and low resistivity in the 2D resistivity profile.  
The boundary effect will cause the resistivity value of the 2D profile boundary to abnormally increase, reduce resolution, and increase interpretation error. Through the model established in this study, it was found that there was roughly similar influence trend in the parameter analysis of the 3D and boundary effects. The following are suggestions for future testing.  
1. Under different resistivity ratios (n), when the n value increases (that is, the difference in resistivity between the formation and the medium becomes smaller), the influence ranges of the 3D and boundary effects both decrease.
2. The 3D and boundary effects have a similar influence range, with an influence distance of 6-8 m.
3. Different electrode spacings showed that it is not appropriate to use electrode spacing as a normalization parameter because the influence distance does not increase as the electrode spacing increases.
4. With different medium sizes, an increase in the pipeline size will increase the range of the 3D effect, but the boundary effect is not affected by the size of the medium.
5. At different embedding depths, the deeper the buried depth, the smaller the 3D effect of the pipeline, but the greater the boundary effect of the medium. This is because the deeper the boundary medium, the more the resistivity changes at the deeper boundary profile.  
Currently, the scope of this study is only to establish a numerical model. In the future, we will try to confirm the results of the numerical simulation through in situ testing.  
Conceptualization, C.-P.L.; Methodology, C.-P.L.; Modeling Y.-C.H.; Software, Y.-C.H. and C.-T.L.; Data analysis Y.-C.H.; Conclusions, C.-P.L. and Y.-C.H.; Electrology consultation, C.-T.L. and K.-W.W.  
This study has no funding.  
The authors declare no conflict of interest.
## 3 Experiment and Analyses
### Analyses of the Dynamic Experiment
#### 3.2.3 The Positioning Results of DAFV-LAMBDA in the Dynamic Test  
We install the receiver on the dolly and let it move back and forth on a fixed straight line track. After static initialization, the AFM search space is set up based on the DPL convergence value. When the real peak is obtained, its coordinate is used as the initial value to update the float solution. Then the LAMBDA method is used to fix the ambiguities and dynamic positioning can be achieved. The result is shown in Figure 11.  
After the ambiguities are fixed, the results conform to the actual movement and the maximum deviation of the positioning is not more than 5 cm. The picture on the right reflects the change of the GDOP value in the dynamic experiment, and the GDOP is widely used to reflect the location of the layout of the pseudolites. In the experiment, the GDOP value in the moving area of the dolly is less than 5, which indicates that the location of the pseudolites in the experiment is reasonable. It also shows the special DOP value caused by the fixed constellation of the pseudolite system.  
Combining all the experimental results, we can consider that the static positioning precision can reach the millimeter level and the dynamic positioning can reach the centimeter level. This shows that, using this method, high-precision indoor pseudolite positioning can be realized. We can also overcome the limitation of KPI and the high accuracy requirement of LAMBDA for initial value.
## 2 Materials and Methods
### Cell Types and In Vitro Culture Conditions  
Four human cell lines were used to assess the cytotoxic and antitumor properties of BMNPs and BMNP-MTX samples in vitro--lung adenocarcinoma A549 (ATCC CCL-185(tm)), HeLa derived from cervical adenocarcinoma (ATCC CCL-2 tm), HT-29 colorectal adenocarcinoma (ATCC HTB-38) and normal fetal foreskin fibroblast cells denoted as HFFC (CLS Cell Lines Service GmbH, Eppelheim, Germany). Adenocarcinoma cell lines were cultured in Dulbecco's modified Eagle's medium (DMEM), supplemented with 10% fetal bovine serum (FBS), 100 ug/mL streptomycin and 100 IU penicillin (all from Merck KGaA, Darmstadt, Germany) and designated as complete DMEM. HFFC cells were cultured in DMEM containing 15% FBS and the same amount of antibiotics. Prior to the experiments, all cell types were grown in 75 cm\({}^{2}\) culture dishes (TPP, Trasadingen, Switzerland) until reaching 80% confluency. The cultures were maintained under standard conditions (37 \({}^{\circ}\)C, high humidity and a mixture of 5% CO\({}_{2}\)/95% atmospheric air in the gas phase).
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 2 Materials And Methods
### Tests Procedure and Evaluation Criteria
#### 2.6.2 F-Score  
The F-score \(F\) is evaluated using Equations (14) and (15). The first Equation (14) is known as the PASCAL criterion and it helps to identify false positives and true positives. The criterion depends on how the rectangular window \(T^{i}\) is given as a result for a certain frame (also called truth) intersects with the ground truth \(GT^{i}\) and how this area compares to the union of both as illustrated in Figure 6. The ground truth data was also provided as part of the ALOV++ dataset to allow faster testing and measurement of new algorithms.  
\[\frac{|T^{i}\cap GT^{i}|}{|T^{i}\cup GT^{i}|}\geq 0.5 \tag{14}\]  
The number of false positives \(n_{fp}\) increases when the PASCAL criterion fails; the number of true positives \(n_{tp}\) increases on the opposite case, and the number of false negatives \(n_{fn}\) increases when the object is present on the frame according to the ground truth, but the tested algorithm determines the object was not detected.  
\[\begin{split} precision&=\frac{n_{tp}}{n_{tp}+n_{fp }}\\ recall&=\frac{n_{tp}}{n_{tp}+n_{fn}}\\ \text{F-score}&=2\cdot\frac{precision\cdot recall }{precision+recall}\end{split} \tag{15}\]  
Note \(F\) only has non-negative values lesser or equal to 1, and the PASCAL criterion measures not only that the result is in the correct place but also that it reflects the current, correct size of the tracked object.
# Phenotypic, Genetic, and Metabolite Variability among Genotypes of _Vicia sativa_ L.
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Molecular Analysis  
The genetic diversity of _V. sativa_ populations, the number of different alleles (_Na_), the number of effective alleles (_Ne_), the percentage of polymorphic loci (_P_%), the [PERSON]'s diversity index (_I_), the diversity (_H_), and the unbiased diversity (_uh_) were calculated using GenALEx v.6.5 lb2 [43]. The hierarchical distribution of genetic variation among and within populations was also characterized by an analysis of molecular variance (AMOVA) [2] using GenALEx v. 6.5 lb2 software [43], with variation being examined among and within populations. The tests were implemented using estimates of \(\Phi\)ST, based on distances calculated from allelic data. Tests of significance were performed using 999 permutations within the total dataset. A cluster analysis using an unweighted pair group method with arithmetic averaging (UPGMA) [44] was carried out using the software GenAlex v.6.51b2 [43]. Dendrograms were generated using Mega v.11 [45]. The genetic structure of _V. sativa_ populations was analyzed by performing a principal coordinate analysis (PCoA) using GenAlEx v.6.51b2 [43] based on the standardized covariance of genetic distance for dominant markers. The polymorphism information content (PIC) was calculated using the online program Online Marker Efficiency Calculator (iMEC) [46].  
STRUCTURE 2.3.4 [47; 48] was used employing the \"admixture\" and the \"independent allele frequencies\" models to investigate genetic structure patterns. Twenty independent runs, for K = 1 up to K = 7, were executed in parallel with the Structure_threader 1.3.0 software [49]. For each run, the burn-in period was set to 2 \(\times\) 10\({}^{5}\) followed by 5 \(\times\) 10\({}^{5}\) iterations. The CLUMPAK main pipeline was used to aggregate results [50].  
To estimate the association between the molecular markers and the 22 metabolites and the yield in seed and biomass, a stepwise multiple regression analysis (MRA) was performed [51; 52], where every metabolite and yield characteristic was treated as a dependent variable, and the DNA fragments produced by the molecular markers were treated as independent variables. The MRA analysis was performed using SPSS v.26.0.0.
#### Significance of IEEE 802.11 ax
This growth towards better design opportunities and the increased complexity in networks and network-based applications have expanded the demand for better automation of network in agile infrastructures. Researchers in communication networks are applying AI techniques to improve network design, management, and to enhance performance leading to even more automation in network functions. Machine learning (ML) applies AI by using different algorithms to analyze the data, learn from the data, and make accurate predictions and decisions about real-world applications. Deep learning (DL) is an ML execution technology that empowers ML to perform and extends the scope of AI. DL involves huge amounts of training data and considerable computational capability. In recent years, data volumes have grown while computing power costs have decreased significantly, allowing deep learning applications to explode. Given the uncertainties and dynamics of wireless networking environments, traditional approaches require complete and perfect systems knowledge and are inefficient. RL has proven to be a practical tool for solving real-time decision-making challenges. Reinforcement Learning (RL) is a field of ML that focuses on learning via interaction. Deep Reinforcement Learning (DRL) combines the architecture of DL(NN) and RL [8; 9]. In DRL, NNs act as function approximators and can be used in RL to estimate value function or policy. DRL has been intended to conquer these shortcomings, as it has the potential for managing dynamic systems on a large scale. Unlike traditional ML, RL does not provide instant results; instead, only a short-term reward is observed [10]. DRL on the other hand, is a potential solution [11; 12]. Predicting the optimal Contention Window (CW) value for better performance of Wi-Fi networks is important for avoiding collision and enhancing the performance of Wi-Fi. Current approaches to improving MAC layer channel access and collision control have been found to have some research gaps, mainly when used with real-time applications. This research focuses on the effective use of a Deep Reinforcement Learning (DRL) technique i.e., DDQN for enhancing the performance of WLAN. For this, we have proposed an algorithm named DRL-based Contention Window Optimization (DCWO) for setting an optimal contention window (CW) under different network conditions. The main research objectives are:  
* Address the open problems in the future deployable 802.11 networks (IEEE 802.11 ax) using DRL. * Enhance WLAN performance by predicting the values of CW correctly using DRL.
## 4 Experiments and Discussion  
To assess the efficiency and resilience of our proposed FGRL, we have implemented and compared it to a baseline, the TD3-based mapless navigation approach. In addition, we enhanced the baseline by integrating federated learning (FL), resulting in the FL-TD3 approach. This approach facilitates the simultaneous training of multiple agents and establishes a global model by averaging local models. Therefore, this section aims to evaluate the performance of the three approaches, i.e., TD3, FL-TD3, and our FGRL. The experiments are conducted in the Gazebo environment, utilizing UGVs equipped with a 2D LiDAR sensor with a 180-degree field-of-view and 24 laser beams, as depicted in Figure 3(a). The laser scanner updates at a rate of 30 Hz, providing situational awareness of the environment.  
Figure 4: Training and testing environments for mapless navigation tasks.  
Figure 3: Illustration of model aggregation of the proposed FGRL approach.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 3 Framework of the Interference Analysis
### Space-Time Distribution of Satellite EIRP  
Many NGSO satellite systems utilize advanced phased-array technology with a limited number of beams to provide high-speed internet access to terrestrial users through time-division multiplexing. Changes in the direction of the satellite beam can also result in changes to the interference experienced by RAS telescopes. In order to characterize the radiation of the satellite in different directions, this section proposes a model for the space-time distribution of the NGSO satellite radiation. The radiation of a satellite in a given direction is determined by the space random variable \(S\), which is a two-dimensional vector consisting of the elevation and azimuth angles of the satellite beam pointing, and the time random variable \(T\), which characterizes whether the beam is active or not. The probability of a beam activation is correlated with user demand and is equal to 1 if the beam is active for the entire duration of the satellite's flight, which is proportional to the duration of activation.
## 3 Data and Methods
### MODIS Products Used in This Study
#### 3.1.3 MOD15A2 LAI/FPAR  
Earth observations by the MODIS instruments on the Terra and Aqua satellites are being used to derive global vegetation properties such as the leaf area index (LAI) and absorbed fraction of photosynthetically active radiation (FPAR) at a 1-km pixel resolution and are updated once each 8-day period throughout each calendar year [24,47].  
LAI is a biophysical variable influencing transpiration and photosynthesis at the plant canopy scale and carbon, water and energy dynamics at the regional scale [54]. The LAI is useful in measuring carbon flux because of its strong influence on canopy energy balance and rates of gas exchange [55]. It has been suggested, based primarily on theoretical studies, that canopy reflectance data provide more reliable indicators of canopy photosynthetically active radiation (PAR) interception and potential photosynthesis rates than do purely structural parameters such as the LAI and biomass [56].  
The computation of the LAI from satellite data is based on a known relationship between the fractions of incident light transmitted through a canopy and the light extinction coefficient (k) (Equation 3):  
\[LAI=\frac{-\ln(1-FVC)}{k(\theta)} \tag{3}\]  
where the FVC (fractional vegetation cover) is defined as the vegetation-covered fraction of the ground viewed in the nadir direction and k(\(\theta\)) is the light extinction coefficient for a solar zenith angle 0. For a detailed description of LAI, please see the paper by [57].  
Converting LAI to FPAR uses a simple Beer's Law approach, which enables the computation of FPAR as a function of LAI and the canopy light extinction coefficient (Equation 4):  
\[FPAR=0.95(1-e^{-LAI}) \tag{4}\]  
The value of the extinction coefficient k is 0.5 [58].  
The accuracy of LAI is 0.66 LAI units RMSE when all biomes are taken in account. If broadleaf forests are excluded, the accuracy in all the other biomes is 0.5 LAI units RMSE. The accuracy is 0.12 FPAR units RMSE [53]. MODIS/Terra LAI/FPAR 8-Day L4 version 005 (MOD15A2) with a resolution of 1-km was used in this study.