esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
It should be noticed that \(V_{max}\) is the higher voltage in the string, and it belongs to the module with less degradation. This criterion is simple to incorporate into other fault detection proposals that use differential voltages as input signals such as [44; 45]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fault Type** & **Slope \(m\)** & **Intersection \(b\)** & \(r\) & \(R^{2}\)**(\%)** \\ \hline SRD & 1.0311 & \(-\)1.1082 & 0.9999 & 99.99 \\ OD & 1.3257 & \(-\)1.5509 & 0.9946 & 98.94 \\ OHD & 1.3539 & \(-\)1.9737 & 0.9942 & 98.85 \\ PID\({}^{1}\) & 1.2502 & \(-\)1.7063 & 0.9987 & 99.75 \\ PID\({}^{2}\) & 1.1499 & \(-\)1.2365 & 0.9997 & 99.94 \\ MC & 1.3309 & \(-\)1.9012 & 0.9959 & 99.18 \\ LID & 1.0191 & \(-\)1.0461 & 0.9999 & 99.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Parameters of the best fit line equation for all fault types. \[Detect(V_{max},V(i),\delta)=\left\{ \begin{array}{lll}True&\text{ if }&\dfrac{V_{max}-V(i)}{V_{max}}>\delta\\ &&\\ False&\text{ if }&\dfrac{V_{max}-V(i)}{V_{max}}\leq\delta\end{array} \right.,\forall i=\{1,2,...,m\}. \tag{10}\]  
To define the fault threshold, it is crucial to know the environment in which the solar array is placed. For instance, soiling may not be a problem in tropical regions benefiting from rainfall cleaning. On the contrary deserts or dry places, the PV array could be affected by dust storms or air pollution that could reduce the general performance [46,47]. In [48] it has been reported that power losses in outdoor conditions could be reduced between \([5,6]\%\) and for laboratory conditions, it is possible to reduce it up to 40%. For instance, in tropical weather like Phitsanulok Tayland, it has been reported in [49] a decrease in solar radiation of \([3.71,11.15]\%\) when the dust deposition rate (DDR) is 425 mg/m\({}^{2}\)d in 60 days. On the contrary dry cities like Mexico City for also 60 days, the DDR reported is 102 g/m\({}^{2}\)d to reduce the performance ratio up to 15%. Detecting permanent faults such as PID, hotspots, or micro-cracks with online real-time methods is always a challenge. For instance, if a hotspot is considered mild, its temperature is just 10 \({}^{\circ}\)C higher than the other parts of the cells; however, if the hot spot is considered severe, it presents a temperature higher by approximately 18 \({}^{\circ}\)C. These facts mean that power production could be reduced between 4% and 10% [50]. A similar analysis can be done with micro-crack; for instance, in a PV module formed with 60 cells, if just one cell has an inactive area of 25%, the power loss in the whole module is about 10% [30]. Therefore, a rule to avoid false faults due to normal soiling or other temporal issues should use a \(\delta\) value between 5 and 10 % as a threshold.
## 3 Multiband Ambient RF Energy Harvester Using the Proposed Antenna
### Rectifiers Design and Full Testing System
which consumes several milliwatts in full active mode. For our experiment, the distance from the rectenna to the power source was quite far away; therefore, the harvested power was not enough to supply any sensor. However, it was enough for low-power wireless sensors in sleep modes, which only need several microwatts [33; 34]. Table 3 shows the maximum efficiencies over the power levels of this work and previous works. The proposed rectenna can harvest energy from the four bands of 3G/4G/5G/Wi-Fi with the conversion efficiency from 25.5% to 27.5% at \(-6\) dBm. The conversion efficiency at \(-10\) dBm of this work is higher than the triple-band rectenna in [35] and the quad-band rectenna in [11], thanks to the CP antenna. Although the efficiency can reach 52% at 3.5 dBm in [35], the conversion efficiency drops to 20% at \(-10\) dBm, and requires a load of up to 14 k\(\Omega\). Although the linearly polarized antennas in [11; 35] are easy to realize, they can only harvest in one polarization. The RF-DC conversion efficiency in [8] is higher than in our work because the rectifier was designed on a high-cost substrate RT/Duroid 5880. It also requires a very high load of 5 k\(\Omega\), leading to a very small output current. The RF-DC efficiency in [9] is the highest with an input power of 20 dBm, and it is more suitable for wireless power transfer as the incident RF power in the environment is usually low (less than \(-5\) dBm). At \(-10\) dBm, the efficiency is less than 5%. Although the dual-polarized rectennas in [8; 9] harvest from many polarizations, one multiband rectifier is required for each polarization, leading to bulky systems. Our work already has the advantages of being suitable for energy harvesting systems for self-powered wireless sensors with low load and higher conversion efficiency at low power, less than \(-10\) dBm. Figure 14: Experimentation of the proposed multiband rectenna. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref. ** & **Input Power** & **Freq. Band (GHz)** & \begin{tabular}{c} **Antenna** \\ **polarization** \\ \end{tabular}  & **Max.Eff** & \ \begin{tabular}{c} **Eff.** \\ **(\(-10\) dBm)** \\ \end{tabular}  & \begin{tabular}{c} **Min.** \\ **Load** \\ \end{tabular}  \\ \hline
[8] & \(-35\)—\(-10\) dBm & \(0.91\), \(1.85\), \(2.1\) & Dual-polarized & \(40\)\%, \(33\)\%, \(25\)\% & \(40\)\% & \(5\) K\(\Omega\) \\
[9] & \(-10\)–\(30\) dBm & \(4.75\), \(54.2\), \(57.
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 4 Experiments and Analysis
### Ablation Studies  
To verify the effectiveness of each block, we reduced each block separately and trained these structures on the GoPro dataset [7]. The baseline was U-Net with a single input and a single output. It does not include CAFB, SAB, and LGFCB, and only adopts eight residual blocks in each encoder and decoder stage to extract features. We adopted the same loss function as mentioned in Section 3.5, and used the same training strategy. The Table 2 shows the results of our ablation experiment.  
Figure 7: Some visual comparisons on the GoPro dataset. From top left to bottom right: blurry images, the ground truth images, the restoration of SRN [12], BANet [17], MPRNet [2], MIMO-Unet+ [8], Restomer [18], and our model.  
As shown in Table 2, we evaluated the PSNR and SSIM results of each structure. The evaluation results show that each proposed block is effective to some extent. The first and second rows in Table 2 indicate the contributions of SAB and CAFB blocks, which result in 0.31 dB and 0.26 dB improvement, respectively. Notably, we observe that MRB can improve the results by 0.57 dB and the transformer block can increase results by 0.61 dB. This demonstrates that both the local feature of the CNN structure and the global feature of the transformer block play an important role in image deblurring. With the use of LGFCB, our model can focus on the global and local feature extraction simultaneously, drastically influencing performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & \multirow{2}{*}{**CAFB**} & \multirow{2}{*}{**SAB**} & \multicolumn{2}{c}{**LGFCB**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM**} \\ \cline{5-6}  & & & & **MRB** & & & 31.44 & 0.943 \\ \(\surd\) & \(\surd\) & & & \(\surd\) & \(\surd\) & 32.37 & 0.957 \\ \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) & 32.42 & 0.959 \\ \(\surd\) & \(\surd\) & \(\surd\) & & \(\surd\) & 32.11 & 0.952 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & 32.07 & 0.951 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 32.68 & 0.962 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiment on GoPro dataset [7]. TB indicates the transformer block. The PSNR and SSIM are average results per image. The ’\(\surd\)’ means that the block is selected for training.  
Figure 8: Some visual comparisons on the RealBlur dataset, our models only trained on the GoPro dataset. From top left to bottom right: blurry images, the ground-truth images, the restoration of [PERSON] et al. [7], SRN [12], DeblurGAN-v2 [10], MIMO-Unet+ [8], Restomer [18], and our model.
## 2 Results and Discussion
### Potential Bioactive Properties of Volatile Metabolites Present in Studied Vegetables and Fruits
Diallyi disulfide from garlic and its metabolite allyI mercaptan have been shown to increase histone H3  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Volatile Metabolites** & **Biological Effects** & **Sample** & **Reference** \\ \hline \(\beta\)-Myrcene & Analgesic, anti-inflammatory, antibiotic, anticancer, antioxidant & Beetroot, orange carrot, spinach, & [73; 74] \\ Camphene & Antimicrobial, antioxidant & Beetroot, red onion, orange & [75; 76] \\ Camphene & Antimicrobial, antioxidant & and white carrot, spinach & [77; 78] \\ \(\alpha\)-Terpinene & Antimicrobial & Beetroot, orange and white & [77; 78] \\ Terpinolene & Antioxidant & carrot, tomato, tamafilo & [79] \\ Terpinolene & Antioxidant & Beetroot, red onion, orange & [79] \\ \(\gamma\)-terpinene & Anti-inflammatory, antioxidant & onion, orange and white & [39; 80] \\  & & carrot, tomato, tamafilo & \\ \(\beta\)-Phellandrene & Antibacterial, anticancer & Beetroot, orange and white & [81; 82] \\ \(\beta\)-Pinene & Antitumor, anti-inflammatory, antimicrobial, antioxidant, antineoplastic, chemotherapeutic & Beetroot, orange and white & [80; 83; 84] \\ D-Limonene & Antimutagenic, antitumor, antioxidant, antimicrobial, antimicrobial, chemoprotective & carrot, yellow onion, brocoli, & [80; 84; 85] \\ Eucalyptus & Anti-inflammatory & Broccoli, tomato & [86] \\ Geraniol & Chemopreventive activity, antimutagenic, anti-inflammatory & Tomato & [86; 87] \\ Eugenol & Anticancer, antimicrobial, antioxidant & Tomato, tamafilo & [79] \\ \(\beta\)-ionone & Chemopreventive activity & Orange carrot, broccoli, watermecress, spinach, tomato, tamafilo & [87] \\ \(\alpha\)-Pinene & Antimicrobial, anticancer, anti-inflammatory, antiallergic & Beetroot, red onion, orange & [88; 89] \\  
acetylation in HT-29 and Caco2 cell lines, playing a role in its protective properties on colon carcinogenesis [106]. As demonstrated by [PERSON] et al. [106], in HT-29 cells and Caco-2, diallyl disulfide could inhibit cell proliferation through the inhibition of histone deacetylase (HDAC) activity, histone hyperacetylation and an increase in p21(waf1/cip1) expression.
# Two-Stage Dimensionality Reduction for Social Media Engagement Classification
## 4 Experimental Results
### Benchmark Datasets
The results presented in Table 4 show a significant improvement in the silhouette coefficient when comparing the original data to the subset utilized by the TSDR approach for classification, with all values more closely approaching 1, indicating that the clusters are more clearly separable and distinguishable from one another. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Original Dataset**} & \multicolumn{4}{c}{**TSDR Subset**} \\ \cline{2-7}  & _k_**-nn** & **MLP** & **LDA** & _k_**-nn** & **MLP** & **LDA** & **TSDR _f_** \\ \hline Cancer & 95.63\% & 95.91\% & 95.84\% & 96.13\% & **96.27\%** & 95.77\% & **96.27\%** \\ Crab & 67.50\% & **96.75\%** & 96.00\% & 92.25\% & 92.00\% & 93.50\% & 93.50\% \\ Glass & 90.12\% & 91.76\% & **93.88\%** & 88.71\% & 91.29\% & 89.65\% & 91.76\% \\ Ovarian & 89.77\% & 86.51\% & 71.40\% & 91.63\% & **92.56\%** & 91.63\% & **92.56\%** \\ \hline Overall & 85.75\% & 92.74\% & 89.28\% & 92.18\% & 93.03\% & 92.64\% & **93.52\%** \\ \hline \hline \end{tabular} Note: The highest accuracy result for each dataset is highlighted in bold.  
\end{table} 
Table 2: Average accuracies for selected benchmark datasets. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**Original Dataset**} & \multicolumn{4}{c}{**TSDR Subset**} \\ \cline{2-7}  & _k_**-nn** & **MLP** & **LDA** & _k_**-nn** & **MLP** & **LDA** & **TSDR _f_** \\ \hline Cancer & 95.29\% & 95.61\% & 95.52\% & 95.85\% & **96.06\%** & 95.45\% & 95.98\% \\ Crab & 69.94\% & **96.80\%** & 95.99\% & 92.33\% & 91.96\% & 93.75\% & 93.74\% \\ Glass & 86.24\% & 88.98\% & **91.35\%** & 84.39\% & 88.20\% & 85.58\% & 88.93\% \\ Ovarian & 90.00\% & 86.95\% & 70.95\% & 91.54\% & 92.38\% & 91.81\% & **92.82\%** \\ \hline Overall & 87.36\% & 92.55\% & 88.45\% & 91.22\% & 92.07\% & 91.65\% & **92.71\%** \\ \hline \hline \end{tabular} Note: The highest accuracy result for each dataset is highlighted in bold.  
\end{table} 
Table 3: Average F1 scores for selected benchmark datasets.
# Adaptive Modulation Tracking for High-Precision Time-Delay Estimation in Multipath HF Channels
## 1 Introduction
Addressing the aforementioned challenges, this paper introduces a novel HF time-delay estimation algorithm: the Modulation signal-based Adaptive Time-delay Estimation (MATE) algorithm, which leverages adaptive phase tracking. Specifically, MATE achieves carrier frequency deviation compensation and mitigates multipath interference through the decoupling of carrier and modulation signals, as well as the integration of phase-locked loop (PLL) techniques within the time-delay estimation framework. Compared to existing approaches, MATE eliminates the reliance on pseudo-random sequences and is directly applicable to non-spreading modulation schemes, exemplified by 8 PSK. It demonstrates notable resilience against frequency-shift perturbations, robust performance under multipath conditions, and superior algorithmic generalizability. The proposed MATE algorithm thus provides a promising solution for high-precision time-delay sensing in practical HF communication and sensing scenarios, contributing to the development of more accurate and robust sensor systems reliant on HF signal propagation.
# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
## 1 Introduction
Section 2 describes the three stages of the proposed method for module detection, their 3D reconstruction, and tilt angle optimization. Section 3 provides results, where the detection of individual modules is demonstrated and validated with measurements from real PV systems. Section 4 provides discussion, while Section 5 concludes this paper.
## 4 Discussion
* To guarantee the safety and system acceptance to the user, an operator (such as a medical expert) should be present to validate each phase of the process
* There are several parameters (such as contractility, tile size for CLAHE, etc.) that have used their typical settings to derive the remarks from the proposed vein extraction module. An optimization process will be planned to find the suitable range for these modules' parameters. * As there is no ground truth for the collected dataset, conducting an objective evaluation would be doubtful. Therefore, a subjective evaluation is planned to evaluate the performance of the vein extraction module. Table 1 shows the results clustered in five groups, each group's weight compared to the total number of cases and definition. In Table 2, the data about each case in this study are presented. In Table 3, the results are grouped according to genders. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Test Case** & \begin{tabular}{l} **Gender** \\ **(M/F)** \\ \end{tabular} & \begin{tabular}{l} **Model A** \\ **No. of Veins** \\ **Detected** \\ \end{tabular} & \begin{tabular}{l} **Model B** \\ **No. of Veins** \\ **Detected** \\ \end{tabular} & \
\begin{tabular}{l} **Result Group** \\ **(A, B, C, D, E, or F)** \\ \end{tabular} &
\begin{tabular}{l} **Preferred** \\ **Model** \\ \end{tabular} \\ \hline  
& None \\ \hline
19 & M & 1 & 1 & A & Models A and B \\ \hline
20 & M & 1 & 2 & C & Model A \\ \hline
21 & M & 1 & 2 & D & Model A \\ \hline
22 & M & 1 & 3 & B & Models A and B \\ \hline
23 & M & 1 & 2 & F & None \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test cases used in this study. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Result** & **Percentage** & **No. of Test Cases** & **Meaning** \\ \hline Group A & 21.74\% & 5 & \begin{tabular}{l} Both Models A and B have identical vein selection \\ (Same correct solution) \\ \end{tabular} \\ \hline Group B & 26.10\% & 6 & \begin{tabular}{l} Both Models A and B have similar vein selection \\ (Similar correct solution) \\ \end{tabular} \\ \hline Group C & 8.70\% & 2 & \begin{tabular}{l} Models A and B have different vein selections \\ (Multiple correct solutions) \\ \end{tabular} \\ \hline Group D & 30.43\% & 7 & \begin{tabular}{l} Model A has correct vein selection on \\ contrary to Model B (Partial selection is correct) \\ \end{tabular} \\ \hline Group E & 4.30\% & 1 & \
\begin{tabular}{l} Model B has correct vein selection on \\ contrary to Model B (Partial selection is correct) \\ \end{tabular} \\ \hline Group F & 8.70\% & 2 &
\begin{tabular}{l} False vein selection selections from both \\ Models A and B \\ \end{tabular} \\ \hline Total & 100\% & 23 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Groups’ results and definitions.
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 3 Results
### Statistics and Analysis
#### 3.1.4 Optimal Temporal Information Combination  
To select the optimal combination of multi-temporal information, the four groups shown in Table 12 were considered and trained separately with the LightGBM algorithm. The models were validated in a real context using the reference image. Unlike all previous results, OA and Kappa do not rise and fall simultaneously, making it insufficient to rely on only one of them. Since the main objective of these models was to detect organ trees, the Agram Precision metric was selected as the deciding factor in choosing which group to use for the remainder of the study. With this in mind, Group C (Table 12) was selected as it showed the highest performance compared to the other groups. Therefore, we will employ two models: the first, trained with 2017/2018 multi-temporal data, will generate an organ tree map for 2017/2018, and the second, trained with 2022/2023 multi-temporal data, will generate a map for the 2022/2023 period.
## 3 Methodology
### Unsupervised ML
#### 3.3.2 t_SNE and Clustering  
t-SNE is a popular manifold learning method designed to embed high-dimensional data into a low-dimensional space, typically 2D, while preserving the local structure of the data [21]. Given a dataset with high-dimensional features, such as keyword frequency and first occurrence positions, t-SNE minimizes the divergence between two probability distributions: one that measures pairwise similarities in the high-dimensional space and another in the reduced space. The objective function of t-SNE minimizes the Kullback-Leibler divergence  
\[KL(P||Q)=\sum_{i\
eq j}p_{ij}log\frac{p_{ij}}{q_{ij}} \tag{7}\]  
where \(p_{ij}\) represents the joint probability of similarity between points \(i\) and \(j\) in the high-dimensional space and \(q_{ij}\) represents the similarity in the low-dimensional space, modeled using Student's t-distribution. By projecting the feature set into two dimensions, t-SNE enables intuitive visualization and downstream clustering.  
After reducing the features to two dimensions with t-SNE, the workflow applied k-means clustering, a type of UML model, to group the documents into two clusters, C0 and C1. The algorithm aimed to minimize intra-cluster variance by iteratively updating cluster centroids and assigning data points based on Euclidean distance. The analysis evaluated the performance of this clustering method using a confusion index (\(CI\)), defined as  
\[CI=\frac{\min(n_{\text{C0}},n_{\text{C1}})}{\max(n_{\text{C0}},n_{\text{C1}})} \tag{8}\]  
where \(n_{\text{C0}}\) and \(n_{\text{C1}}\) are the number of relevant documents in C0 and C1, respectively. Hence, a lower \(CI\) indicates less confusion, meaning one cluster mostly contains relevant documents while the other mostly contains irrelevant documents. A perfect bifurcation of documents would yield \(CI=0\), indicating complete separation of relevant and irrelevant documents. This method provides an interpretable way to assess the relevance filtering performance of feature-based clustering, with the visualization from t-SNE aiding in identifying separable clusters and their alignment with the labeled relevance.
## 4 Experiments and Results
### Experimental Setting and Extracted Data  
We recorded the videos using the normal mobile back-facing cameras of a Xiaomi note 8 pro and an iPhone 6. The phones were placed on a platform which was directly aligned with the guitarist's left hand. We used two different cameras in order to test whether different resolutions affected how the data were extracted, and whether this would affect the guitarist's playing in real-time in the future. However, there was no difference as the finger positions are normalized in proportion to the resolution of each frame. Our dataset was recorded in two settings, using two different guitars, guitarists, and cameras. Tables 1 and 2 summarize the results of the proposed data collection methods described in Sections 3.3 and 3.2. Each row in the obtained.csv file included the \(x\), \(y\), \(z\) coordinates of the landmarks depicted in Figure 3. The videos did not require any pre-processing, as MediaPipe was able to detect and normalize the finger positions in multiple settings. The output.csv file consisted of a time-series of each finger's position across numerous frames for each video. The sections below detail the evaluation of each machine learning algorithm when trained on the provided dataset, including the signal detection metrics.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Size** & **2.5 GB** \\ \hline Number of rows or frames & 25,544 rows \\ Number of features & 68 \\ Duration & From 7 to 30 s \\ Number of videos & 67 \\ Number of classes & 2 \\ Number of incorrect classes & 33 \\ Number of correct classes & 32 \\ Guitar type & Cort cr50 Electric Guitar \\ Camera for recording & iPhone 6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electric guitar recordings.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Control Co-Design  
Two reciprocal standards are used to acknowledge control the application over the wireless system, i.e., network-aware control design and control-aware network design. Previously, the objective was to improvise network layer protocols to upgrade real-time execution. This modification holds as a piece of evidence for advancing work towards the TI. Subsequently, the design and configuration of control feedback codes are matched to the shortcomings of the communication medium. For the teleoperation framework, the structure of adaptive controllers [71] is a classic case of this modification. Thus, this modification needs to be closely associated with networks to mutually improve control-aware network design and network-aware control design.  
To consolidate the dynamic nature of B5G networks, the RTT minimisation will always be a concern while having communication between master and slave sections. The rising timing requests are put on the control loop, thus causing a new challenge to  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Attributes/Generic Services** & **eMBB** & **mMTC** & **URLLC** \\ \hline RTT & Moderately Low & Moderately Low & Extremely Low \\ Throughput & Moderately & Moderately Low & Moderately \\  & High/High & & Low/Moderate/ \\  & & & \\ Availability & Moderate & Moderate & Extremely High \\ Frequency of Data Transfers & Moderately High & Moderately Low & Moderately High \\ \hline \hline \end{tabular}
\end{table}
Table 4: The three generic services under 5G framework and their attributes.  
Figure 8: The three generic services offered by 5G.  
reduce the overall RTT of the system. Hence, new approaches are needed to solve these RTT issues. However, the decoupling of the control loops can be done to make the two sections independent of each other by employing parameter weights on each side. Hence, incorporating the machine-learning or prediction models would be a potential solution to resolve the RTT issues in B5G networks [72].