esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Results
### Preprocessing  
Based on the analysis of patient data availability, some patients have less than 22 EEG channels. Pat13 and Pat17 have only 17 available channels; Pat4 and Pat9 have 20 and 21 available channels, respectively. In order to ensure the data can be merged, improve data effectiveness, and reduce computational complexity, this study adopts automatic channel selection to retain 16 effective channel data for each patient. The STFT is used to transform each 28-second EEG signal into a two-dimensional matrix consisting of a frequency axis and a time axis. Combining with the epileptic EEG signal annotated by clinical doctors second by second, this study first uses a cosine time window with a length of 1 s and 50% overlap, with a sampling rate of 256 Hz. The signal is filtered to remove 60 Hz power frequency interference based on the local power frequency of data acquisition. The data is trimmed to make the final dimension of each 28-second data (channel number \(\times\) X \(\times\) Y) = (16 \(\times\) 56 \(\times\) 112), where X and Y are the time and frequency dimensions, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Patients** & **Gender** & **Age (Years)** & **No. of Seizures** \\ \hline Patient 1 & Female & 11 & 7 \\ Patient 2 & Male & 11 & 3 \\ Patient 3 & Female & 14 & 7 \\ Patient 5 & Female & 7 & 5 \\ Patient 9 & Female & 10 & 3 \\ Patient 10 & Male & 3 & 7 \\ Patient 13 & Female & 3 & 8 \\ Patient 14 & Female & 9 & 7 \\ Patient 18 & Female & 18 & 6 \\ Patient 19 & Female & 19 & 3 \\ Patient 20 & Female & 6 & 6 \\ Patient 21 & Female & 13 & 4 \\ Patient 23 & Female & 6 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thirteen patients were used in this paper.
## 8 Numerical Results and Discussion
### The Effect of the Number of Epochs on Received Power in the VLC Scenario
The complexity is expressed as follows:  
\[O(N_{\text{epochs}}n_{l}\cdot n_{x}^{2}\cdot N_{\text{receivers}}), \tag{52}\]  
where \(n_{l}\) is the number of layers, and \(n_{x}\) is the input size. The BP network has lower complexity than RNN and CNN because it lacks iterative updates and convolutional operations, but it is less effective in dynamic optimization tasks. As we can see in Table 3, RNNs achieve the highest SNR values due to their ability to optimize IRS positions iteratively, considering feedback from previous states. CNNs, while capable of extracting spatial patterns, are less effective in sequential adjustments, resulting in lower SNR values. BP networks, being static, offer the least improvement due to their inability to adapt dynamically. The RNN framework shows faster convergence, achieving optimal IRS positions within 50 epochs in the study. CNNs require more epochs due to the complexity of extracting and learning spatial features, while BP networks, constrained by their static nature, converge the slowest. In the context of VLC systems, adaptability refers to the ability of a machine learning model to dynamically adjust its output (e.g., IRS placement or configuration) in response to changing environmental conditions. Adaptability is critical in real-world VLC systems because indoor environments are inherently dynamic. Factors such as moving obstacles, changes in user location, and varying signal conditions can significantly impact system performance. Models with higher adaptability, like RNNs, are better equipped to handle these challenges, making them more effective in optimizing IRS placement in practical scenarios. The RNN demonstrates superior adaptability in dynamic environments, optimizing IRS placement for changing conditions. CNNs exhibit moderate adaptability, while BP networks lack the capacity for iterative updates.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 3 System Model
### Data Transmission  
We let \(p_{n}(t)\) denote the transmission power of sensor \(n\) carried by patient in time slot \(t\). In the BAN, the sensor has a maximum transmission power \(p^{max}\). The following inequality must be satisfied:  
\[0\leq p_{n}(t)\leq p^{max},\forall n\in N. \tag{2}\]  
We use \(\beta_{n}(t)\) to denote the signal to interference plus noise ratio of sensor \(n\). According to the Shannon formula, the transmission rate \(r_{n}(t)\) is defined as \(r_{n}(t)=\log(1+K\beta_{n}(t))\). Here, \(K\) denotes the processing gain. Since \(\log(K\beta_{n}(t))\) is very close to \(\log(1+K\beta_{n}(t))\), so we let  
\[r_{n}(t)=\log(K\beta_{n}(t)),\forall n\in N. \tag{3}\]  
We use sleeping mode to achieve opportunistic transmission. During the sleeping mode, the sensor just can collect data, cannot transmit data. We use \(z_{n}(t)\) to denote the sensor's mode. If the data queue length is greater than \(L\), which is a constant, \(z_{n}(t)=1\), else \(z_{n}(t)=0\).
## 1 Introduction
The competitiveness of shortest remaining processing time (SRPT) for multiprocessor system is \(O\big{(}min\big{(}log\Big{(}\frac{m}{n}\Big{)},\ log\ \sigma\big{)}\big{)}\), where \(m\) is number of processors, \(n\) is total number of jobs and \(\sigma\) represents the ratio of minimum to maximum job size [13]. [PERSON] et al. [14] presented the idea of resource augmentation. If the resources are augmented and, \((2+\Delta)\)-speed \(p\) processors are used then the competitive ratio of Equi-partition lies between \(\frac{2}{3}(1+\Delta)\) and \(\Big{(}2+\frac{4}{\Delta}\Big{)}\)[15]. Multilevel feedback queue, a randomized algorithm with \(n\) jobs is \(O(log\ n)\)-competitive [16; 17]. The first algorithm with non trivial guarantee is \(O\big{(}\log^{2}\sigma\big{)}\)-competitive [18], where \(\sigma\) is the ratio of minimum to maximum job size. There are different algorithms proposed with different objectives over a span of time [19; 20; 21; 22; 23; 24; 25; 26; 27]. [PERSON] et al. [19] proposed algorithms with different approximation bounds for processors with/without constraints on the maximum processor speed. The concept of merging dual objective of energy used and total flow time into single objective of energy used plus total flow time is proposed by [PERSON] et al. [20]. [PERSON] et al. [21] proposed an algorithm, which uses highest density first (HDF) for the job selection with a traditional power function. [PERSON] et al. [22] proposed a multiprocessor algorithm for homogeneous processors in which job assignment policy is a variant of round robin, the job selection. Random dispatching can provide \((1+\Delta)\)-speed \(O\Big{(}\frac{1}{\Delta^{3}}\Big{)}\)-competitive non-migratory algorithm [23]. [PERSON] et al. [24] proposed an \(O(1)\)-competitive algorithm using sleep management for the objective of minimizing the flow time plus energy. [PERSON] et al. [25] studied an offline problem in polynomial time and proposed a fully combitorial algorithm that relies on repeated maximum flow computation. [PERSON] et al. [26] proved that highest density first, weighted shortest elapsed time first and weighted late arrival processor sharing are not \(O(1)\)-speed \(O(1)\)-competitive for the objective of minimizing the weighted flow time even in fixed variable speed processors for heterogeneous multiprocessor setting. [PERSON] et al. [27] studied an online clairvoyant sleep management algorithm scheduling with arrival-time-alignment (SATA) which is \((1+\Delta)\)-speed \(O\Big{(}\frac{1}{\Delta^{2}}\Big{)}\)-competitive for the objective of minimizing the flow time plus energy. For a detailed survey refer to [28; 29; 30; 31; 32; 33; 34]. In this paper, the problem of online non-clairvoyant (ON-C) DSS scheduling is studied and an algorithm multiprocessor with bounded speed (MBS) is proposed with an objective to minimize the IbFt+E. On the basis of potential function analysis MBS is O(1)- competitive. The notations used in this paper are mentioned in the Table 1.
## 2 Evolution of AI in GPR Interpretation
### Rise of Deep Learning in GPR Interpretation  
The introduction of DL models--particularly CNNs--marked a paradigm shift in GPR data interpretation. Unlike traditional ML methods, DL models can automatically learn spatial and temporal features from raw data, reducing the need for manual feature engineering. A heatmap of evaluation metric frequencies taken from studies of GPR with AI undertaken between 2021 and 2025 is presented in Figure 1.  
CNNs have been widely adopted for hyperbola detection, object localization, and subsurface image segmentation. Models such as YOLO (You Only Look Once), Faster R-CNN, and Mask R-CNN have been adapted to detect buried objects in GPR B-scans with remarkable precision. For instance, researchers have trained CNNs on annotated datasets to detect hyperbolic signatures with high accuracy, achieving real-time performance suitable for field deployment [21].  
More advanced architectures such as U-Net (originally designed for biomedical image segmentation) have been applied to GPR data with immense success. U-Net's encoder-decoder structure enables the model to capture both low-level and high-level  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **Rule-Based Systems** & **Classic ML** \\ \hline Interpretability & High & Moderate \\ Accuracy & Low & Moderate-High \\ Adaptability & Low & Moderate \\ Data requirements & Low & Moderate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of rule-based versus classic machine learning.  
Figure 1: Heatmap of evaluation metric frequencies from GPR + AI studies (2021–2025). The data were primarily sourced from the Web of Science database, using combinations of relevant keywords (e.g., “GPR,” “AI,” “deep learning,” “object detection,”).  
contextual information, making it ideal for segmenting complex subsurface images. Researchers have extended U-Net to 3D versions to work with volumetric GPR data, especially in applications such as archeological prospection and infrastructure inspection [22; 23; 24].  
Recurrent neural networks (RNNs) and long short-term memory (LSTM) networks have also been explored, particularly for analyzing sequential GPR data where temporal patterns are relevant. However, their adoption remains limited compared to CNN-based models [25].  
Figure 2 shows the data-to-decision pipeline using DL. Despite their impressive performance, DL models require large labeled datasets and substantial computational resources. Their \"black-box\" nature also raises concerns about interpretability, especially in safety-critical applications such as bridge inspections and utility mapping.
# Platelet-Rich Fibrin: A Viable Therapy for Endodontic-Periodantal Lesions? A Preliminary Assessment
## 3 Results
### Treatment of Endodontic-Periodontal Lesions  
In the studies included, the treatment was divided into two phases. In the first phase, an endodontic and a periodontal treatment were carried out. The periodontal treatment included scaling and root planning. An evaluation of the situation was conducted after a period of time. In two studies [19; 21], the period of time was of three months, which is the recommended period of time [1]. In two studies, the evaluation was performed earlier, after 2 weeks [22] or after 6 weeks [23] and in the rest of the studies the period of time was not mentioned [20; 24]. For the reason that periodontal parameters did not improve, regenerative periodontal therapy was planned for phase two. PRF was used alone for the regenerative periodontal procedures. More types of platelet concentrates were used: pure platelet-rich fibrin, leukocyte- and platelet-rich fibrin (L-PRF), and titanium-prepared platelet-rich fibrin (T-PRF). The patients were recalled after 10 days for the removal of the sutures, then they were scheduled for regular check-ups at 3, 6, 9, 12 months after the surgery. Encouraging results were obtained after the periodontal regenerative therapy. The efficiency of pure PRF for regenerative therapy was observed in two report cases [19; 21] which showed improvement in the clinical parameters. At the nine-month follow up, the probing depths of tooth 4.6 and tooth 4.4 were reduced from 10 mm to 4 mm, respectively from 8 mm to 3 mm [19]. In the other report case, at the 12-month follow up, the probing depth of tooth 3.1 was reduced from 8 mm to 4 mm [21]. Furthermore, in a clinical study [20] conducted on 15 patients diagnosed with primary endodontic lesions with secondary periodontal involvement, after endodontic microsurgery, the PRF was applied in the periodontal defects and the success rate of the treatment was of 83.33%. The performance of L-PRF was emphasized in the study conducted by [PERSON] et al. [22], where it showed great improvement in the periodontal parameters, with a decrease of the probing depth from 11 mm to 3 mm on meso-vestibular side and 14 mm to 5 mm on meso-palatal side of tooth 2.4. In two comparative clinical trials [23; 24] T-PRF was used for the periodontal regenerative therapy. In the first one [23] where the comparison was between T-PRF, guided tissue regeneration (GTR) and then open flap debridement, it was concluded that both T-PRF and GTR are more efficient than the open flap debridement. Moreover, regarding T-PRF and GTR, the decrease of the probing depth and the clinical attachment loss were similar, the only difference was noticed in the reduction of the infrabony defect, which was greater when GTR was used. Comparing PRF and T-PRF, [PERSON] et al. [24] discovered that the outcomes were similar and there was no statistically significant difference between them, both reducing the probing depths and being beneficial in the management of difficult endodontic-periodontal lesions.
# Vehicle Trajectory Reconstruction Using Lagrange-Interpolation-Based Framework
## 3 Methodology
### Outlier Detection  
The DWT is a time-frequency domain analysis method of the signal compared with the Fourier transform. It extracts time series features by frequency domain transformation to reduce the dimension of feature space and make features more concentrated [38]. The result of trajectory data is defined by Equations (1)-(3).  
\[f(x)=\frac{1}{\sqrt{M}}{\sum_{k}W_{\phi}(0,k)\phi_{0,k}(x)}+\frac{1}{\sqrt{M}}{ \sum_{j=0}^{\infty}\sum_{k}W_{\psi}(j,k)\psi_{j,k}(x)} \tag{1}\]  
\[\psi_{j,k}(x)=2^{\frac{j}{2}}\psi\left(2^{j}x-k\right) \tag{2}\]  
\[DWT(j,k)=\left\langle f(x),\psi_{j,k}(x)\right\rangle \tag{3}\]  
where \(f(x)\) is the original time domain signal; \(\phi_{0,k}(x)\) is the frequency domain function with position coefficient \(k\); and \(\psi_{j,k}(x)\) is the wavelet function with position coefficient \(k\) and frequency coefficient \(j\). \(W_{\phi}(0,k)\) is the approximation coefficient; \(W_{\psi}(j,k)\) is the detail coefficient.  
DWT does not need computers to provide enough RAM in the calculation process and directly discretize the data. According to the collected trajectory data, this paper uses DWT based on the Symlet8 wavelet framework to identify outliers from neighbor data in trajectory data.
## 2 Materials and Methods
### MODIS Reflectances and Related Vegetation Parameters
#### 2.1.1 Calculations of and Adjustments to Vegetation Indices  
The NDVI and NIR\({}_{V}\) (Normalized Difference Vegetation or Infrared Indices) are computed from MODIS Band 1 and 2 reflectances (\(\rho\)) using:  
\[\mathrm{NDVI}=\frac{\rho_{2}-\rho_{1}}{\rho_{2}+\rho_{1}}, \tag{4}\]  
and:  
\[\mathrm{NIR}_{V}=(\mathrm{NDVI}-C)\times\rho_{2}, \tag{5}\]  
where \(C\) is set to 0.08 as in [PERSON] et al. [23].  
As noted above, the NDVI offsets at low values (denoted here as \(N_{0}\)) are presumably the result of non-chlorophyll-containing substances that produce a positive NDVI when photosynthesis or GPP is at or near zero. The chlorophyll-containing vegetation fraction (\(f\)) occupying a given satellite pixel increases during the growing season along with the NDVI. Therefore, subtracting a constant at all values of NDVI to reduce the offset may produce a corresponding underestimate at high values. Instead, we compute an adjusted NDVI according to NDVI\({}^{\prime}\) = \(\mathrm{NDVI}-\mathrm{NDVI}_{0}\) where \(\mathrm{NDVI}_{0}\) decreases linearly with NDVI from \(N_{0}\) to zero as NDVI increases from \(N_{0}\) (at \(f=0\)) to \(>\)\(N_{1}\) (at \(f=1\)), i.e.,  
\[\mathrm{NDVI}_{0}=\left\{\begin{array}{ll}\mathrm{NDVI}&\mathrm{NDVI}<N_{0} \\ \frac{(\mathrm{NDVI}-N_{0})*-N_{0}}{(N_{1}-N_{0})}+N_{0}&N_{0}<\mathrm{NDVI}<N_{ 1}\\ 0&\mathrm{NDVI}>N_{1}\end{array}\right. \tag{6}\]  
An expectation is that \(N_{0}\) and perhaps \(N_{1}\) vary with location. Satellite SIF measurements are near or at zero when GPP is near zero (e.g., [33]) and therefore offer the potential to map \(N_{0}\) globally. We attempted to derive spatially-varying \(N_{0}\) using coincident observations of NDVI and satellite-based SIF. SIF data are only available with good temporal resolution at fairly low spatial resolution (\(\sim\)0.5\({}^{\circ}\)). We applied standard least-squares fitting for grid boxes with sufficient seasonal variability in \(f\). In this approach, we set \(N_{1}\) to an empirically-derived constant value of 0.75. In the end, we obtained improved results using a constant value of \(N_{0}=0.25\) rather than site-specific \(N_{0}\) derived with SIF data. Therefore, for the remainder of the paper, we use \(N_{0}=0.25\) and \(N_{1}=0.75\) to compute NDVI\({}^{\prime}\) according to Equation (6). It is hoped that improved results may be obtained in the future with higher spatial resolution and higher quality SIF data.
# Enhanced Photocatalytic Activity of Iron-Doped ZnO Nanowires for Water Purification
###### Abstract  
In order to improve the photocatalytic efficiency of ZnO nanowires, iron-doped ZnO nanowires (ZnO:Fe NWs) were successfully synthesized. The morphology, optical properties and photocatalytic performance of ZnO:Fe NWs were studied by scanning electron microscopy (SEM), UV-Visible spectrophotometry and photoluminescence spectroscopy (PL), respectively. The SEM observations showed that the morphology of the ZnO NWs was not modified by iron doping, but the band gap was reduced from 3.29 eV for ZnO NWs to 3.25 eV for ZnO:Fe NWs. This band gap reduction allows the semiconductor to harvest more photons to excite more electrons in the valence band; subsequently, resulting in an improvement of the degradability of the understudied organic dyes: methylene blue (MB), methyl orange (MO), and acid red 14 (AR14). The photocatalytic study showed that the photo-degradation rate of the MB, MO, and AR14 was improved 9%, 20%, and 5% respectively by 1% iron doping in the ZnO NWs.  
ZnO nanowires; iron doping; water purification; photocatalysis +
Footnote †: journal: Applied Physics Letters
## 2 Data and Methods  
### Study Area and Data  
This study focuses on Sichuan and Yunnan Provinces, located at the confluence of the Qinghai-Tibet Plateau and the Himalayan mountain range in the southwestern region of China. This region is well-known for its heightened seismic activity. Specifically, these provinces are situated within two seismic belts: the Dianxi Earthquake Belt in western Yunnan [25] and the Western Sichuan Earthquake Belt. Both of these regions have experienced significant seismic activity. In this study, the timeframe from 2013 to 2023 was selected and seismic events with magnitudes exceeding Ms. 4 were considered. Within this study area, the data on 639 seismic events were retrieved.  
The utilized data in this study are described in Table 1. We utilized NPP-VIIRS night light imagery for our analysis. The earthquake catalogue delineates seismic events within the research domain, while administrative divisions define its geographic scope. The road network, human residential data, and LandScan dataset collectively serve as the data sources for calculating the impact factor. Specifically, they provide information on the road network, points of interest, and population values, respectively. Furthermore, the statistical yearbook of urban areas supplies city area measurements, crucial for establishing the night light threshold, as discussed in Section 2.2 (4).
## 1 Introduction
At the same time, [PERSON] (2020) [15] argued that future policy schemes should be developed to incentivize DG projects with higher self-consumption ratios. The assessment of different supporting schemes for residential PV systems has also been conducted [16; 17]. However, studies on the impact of solar PV on electricity consumption and related cost-benefit analyses for agricultural customers are few [18; 19; 20; 21; 22], as are studies on the simultaneity of PV generation and load consumption [23]. Understanding the electricity demand of a poultry farm and how different capacities of designed PV arrays could impact the financial performance of the PV systems under various renewable energy pricing schemes are important as this information may aid future adoption by poultry producers. [PERSON] (2024) [24] studied regional distributed energy planning including photovoltaic and biogas systems by regulating electric load in greenhouses. In comparison, the aim of this paper is to investigate the economic viability of distributed PV systems on a model poultry farm. The continuous improvement in bird genetics and the vertical integration of broiler production in the US require strict adherence to prescribed thermal environment. This is achieved with high automation for feeding, heating and cooling, which in turn leads to little flexibility for managing the energy load demand of broiler farms. We did not address heating load in broiler buildings since heating fuel such as natural gas and propane are readily available in rural areas and are more cost-effective than electrical heat. Hence, our scope is confined to an analysis of electric load demand of a sample poultry broiler house complex, a focus that is narrower than that of [PERSON] (2024) [24]. Specifically, we perform the following:  
1. Quantify the hourly electric power demand/energy consumption of a broiler poultry operation;
2. Simulate the hourly electricity production of on-site PV systems at varying sizes to assess how hourly availability of PV interacts with hourly farm consumption and thereby energy export to the grid;
3. Investigate economic benefits of PV as a function of NEM and NEB at the avoided cost rate.