esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
It is essential to monitor the health status of the PV system in order to identify and locate premature faults that could severely affect the output power generation. IRT imaging is the gold standard to identify, classify and locate defect(s) or defective PV module(s). However, ground-based IRT imaging analysis performed by qualified operators is a time-intensive complex inspection process that increases the operation costs. Moreover, it is impractical for the inspection of large-scale PV systems.  
Therefore, the adoption of UAV-based approaches is justified. However, the implementation of a cost-effective method to scan and check huge PV plants represents major challenges, such as the cost and time of detecting PV module defects with their classification and exact localization within the solar plant. Moreover, many technological considerations should be taken into account for a proper inspection of photovoltaic plants. In this work, a fully automated approach for the detection, classification, and location of the thermal defects in the PV modules is carried out. In this work, a robust and comprehensive procedure is proposed, providing innovative characteristics not addressed before in the literature. The system has been tested in a real large PV plant owned by TSK company, showing that the proposed system can be used for a proper automated thermal inspection of PV plants.  
Figure 16: Error detecting and classifying the thermal defects.  
Figure 15: Not adequate segmentation of the panels surface.  
**Author Contributions:** [PERSON] took the leadership of this work. [PERSON] designed and wrote the image undistortion procedure, and reviewed the paper. [PERSON] worked on the UAV configuration and calibration and executed the UAV flights. [PERSON] reviewed the paper and participate in the analysis of O&M PV procedures. [PERSON] designed the data management, procedure workflow, and processing architecture. [PERSON] worked on the UAV image acquisition. [PERSON] participated in the implementation of the algorithms. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussions
### Plasmonic Lens Design
Line scans of the normalized magnetic field intensity in the transverse (\(z_{f}=0\)) (**a**) and in the longitudinal \(x=x_{0}\) (**b**) focal plane respectively, for four values of the couple \((N_{p},d)\): \((N_{p},d)\) \(\{(65,6.75\) μm\()\), \((85,8.75\) μm\()\), \((105,10.75\) μm\()\), \((125,1275\) μm\()\). Numerical parameters: \(\lambda=0.637\) μm, \(h=400\) nm, TM polarization. (**a**) \(|H_{y}(x,0)|^{2}/\int_{Z}|H_{y}^{inc}(x,z_{s})|^{2}dx\); (**b**) \(|H_{y}(x_{0},z)|^{2}/\int_{Z}|H_{y}^{inc}(x,z_{s})|^{2}dx\). Figure 9: Design of a plasmonic silver-flat-metalens of \(d=41\) μm-wide consisting of \(N_{p}=248\) silver-nanorridges and nanoslits focusing a normal incident TM-polarized plane wave at a wavelength \(\lambda=0.637\) μm at a distance of \(15\lambda\). (**a**,**b**) presents the cartography of the normalized magnetic field intensity in the plane \((X,O,Z)\). (**c**,**d**) shows the values of the (nanorridges, nanoslits)-widths sequence \([e_{k}]_{k}\) with respect to their locations labelled by \(k\). Numerical parameters: \(\lambda=0.637\) μm, \(N_{p}=205\), \(e_{z}=400\) nm, TM polarization. (**a**) \(d=20.55\) μm; (**b**) \(d=41\) μm; (**c**) \(n_{p}=205\); (**d**) \(N_{p}=408=205+(205-2)\).
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.14 A Global Forest AGB Map at 1 km Spatial Resolution  
[PERSON] et al. [100] presented a global forest AGB map at 1 km spatial resolution for 2005 by combining a referenced biomass dataset generated from field data and LiDAR-derived biomass product, satellite LAI, gross primary production data, forest cover data, land cover type data, and auxiliary datasets using the Gradient Boosting Regression Tree method. Validation results from a 20% independent sample showed the accuracy of the estimated global forest AGB map had an R\({}^{2}\) of 0.90 and RMSE of 35.87 Mg/ha.
# Detecting SPIT Attacks in VoIP Networks Using Convolutional Autoencoders: A Deep Learning Approach
## 1 Introduction
In this paper, a novel method for detecting SPIT attacks in VoIP networks is proposed. The approach involves an end-to-end detection system that utilizes a deep learning autoencoder-based model to analyze Session Initiation Protocol (SIP) messages. Unlike traditional methods, this model does not require feature engineering to identify potential attacks. To our knowledge, the proposed model is the first of its kind to use autoencoders for this purpose. The primary contributions of this study include (1) introducing the One-Dimensional Deep Convolutional Autoencoder (1D-DCAE) as a new model for SPIT  
Figure 1: Autoencoder with one-layer encoder and one-layer decoder. detection, (2) achieving high accuracy in detecting SPIT attacks across two different datasets, and (3) comparing the performance of the 1D-DCAE model with state-of-the-art classical machine learning methods. The structure of this paper is as follows: The next section provides a comprehensive review of related research in the field. Section 3 presents the proposed method for identifying SPIT attacks in detail. Section 4 provides an overview of the two datasets used to evaluate the proposed model. Section 5 outlines the experimental setup, performance metrics, and results. Finally, the findings of the study and directions for future research are concluded in Section 6.
###### Abstract  
Background: The aim of this study was to compare the impact of three different types of intraoperative antibiotic prophylaxis on the risk of postoperative surgical site infection (SSI). Material and Methods: Single-center retrospective cohort study. Patients who underwent surgery for osteosynthesis of a tibial plateau fracture (January 2009-November 2018) in Hospital Clinic i Provincial de Barcelona were included. Three types of prophylaxis during the study period were used: group A (cefruoxime single-dose treatment), group B (meropenem + teicoplain), and group C (ceftriaxone + teicoplain). Demographics, co-morbidity, type of fracture, need for external fixation, microbiology data, surgical time, and outcome were recorded. Failure was defined as the need for reintervention due to postoperative surgical site infection. Results: From a total of 148 patients included, 20 cases developed SSI, 8 from group A, 8 from group B, and 4 from group C. Higher ASA scores, Schatzker II classification, need for external fixation, and a prolonged surgical time were associated with a significantly (\(p<0.005\)) increased incidence of SSI. Group C showed the overall highest survival and lowest cumulative risk, but differences were not statistically significant. Conclusions: Group C showed the lowest incidence of infection in this sample. It is necessary to confirm these findings with larger studies.  
Keywords:tibial; plateau; fracture; infection; prophylaxis; perioperative; cefruoxime; ceftriaxone; meropenem; teicoplain +
Footnote †: journal: Applied
# Biosynthetic Pathways of Hormones in Plants
## 3 Ethylene  
The volatile phytohormone ethylene plays a key role in controlling various developmental and physiological activities, including seed dormancy and germination, growth of vegetation, flowering, maturation of climacteric fruit, and aging. Moreover, ethylene is seen as a critical component that protects plants from both biological and non-biological stressors. Being a gaseous plant hormone, ethylene has the ability to easily spread from its creation sites without needing any biochemical alterations or metabolic processes, allowing it to be sensed promptly [29].  
Ethylene production is intricately controlled by internal cues throughout its development. Additionally, ethylene production can be swayed by environmental triggers, including biological triggers, like an onslaught of pathogens, and non-biological triggers, like injury, low oxygen levels, exposure to ozone, cold temperatures, or freezing conditions [30].  
The biosynthesis of ethylene is a relatively straightforward process that involves two key enzymatic reactions (Figure 3).  
Methionine, which is the precursor of ethylene, is converted to S-adenosylmethionine (SAM) by the enzyme S-adenosylmethionine synthetase. Around 80% of cellular methionine is directed towards SAM synthesis, which is involved in various pathways, including polyamines (PAs) and ethylene biosynthesis [31; 32; 33]. SAM also participates in methylation reactions that modify lipids, proteins, and nucleic acids [30]. Next, SAM is transformed into 1-aminocytoprone-1-carboxylic acid (ACC) and 5\({}^{\prime}\)-methyl-thioadenosine (MTA) by the enzyme ACC synthase (ACS) [34; 35; 36]. This conversion represents the rate-limiting step in the ethylene biosynthetic pathway, as the expression of _ACS_ genes is tightly regulated by environmental factors. ACS is present at low levels under normal conditions, though its activity increases in response to biotic or abiotic stress, suggesting tight control over ethylene production [37; 38].  
In the second step, ACC is further converted into ethylene and other by-products, such as CO\({}_{2}\) and cyanide, by the enzyme ACC oxidase (ACO) (Figure 3). In _A. thaliana_, ACO proteins are encoded by five genes (_ACO1-5_) and belong to a superfamily of oxygen-nases/oxidases. The toxic cyanide by-product is rapidly detoxified through its conversion into \(\beta\)-cyanoalanine by \(\beta\)-cyanoalanine synthase [39]. Alternatively, ACC can be conjugated to malonyl-ACC, and this process regulates the availability of ACC for conversion to ethylene. By controlling the ratio of malonyl-ACC to ACC, plants can modulate ethylene biosynthesis [29; 40; 41].
## 3 Discussion
Although copper slag is an abundant and inexpensive material, its economic viability in H\({}_{2}\) production must consider the following:  
* Costs of collecting and transporting copper slag from smelters to H\({}_{2}\) production plants. * Pretreatment costs (impurity removal, structural modification). * Capital expenditures (CAPEXs) and operating costs (OPEXs) for high temperature thermolysis reactors and solar concentrators. * Costs of purifying water for the process and hydrogen for industrial applications. A levelized cost of hydrogen (LCOH) analysis should be conducted to assess whether H\({}_{2}\) production using copper slag can compete economically with conventional technologies such as water electrolysis, natural gas reforming (SMR), or biomass gasification. Integration with concentrated solar power (CSP) could significantly improve the process's profitability and sustainability. Future research is needed to consolidate and scale the use of copper slag as a thermocatalytic material for hydrogen production (and other electrochemical processes), considering approaches from materials science, process engineering, sustainability, and the circular economy. Key areas of focus include the following: (i) advanced thermochemical and kinetic studies to develop thermodynamic models capable of predicting the energy required for oxidation processes during the interaction between copper slag and water vapor at high temperatures, and to evaluate reaction rates, corrosion behavior, and the specific mechanism of H2 production during thermolysis; (ii) investigation into the functionalization and modification of copper slag to enhance its catalytic or electrocatalytic activity, for example, through doping with metals such as Ni, Co, or Cu to improve thermal activation under operational conditions; (iii) development and design of electrodes and solar thermolysis reactors to assess solar-thermochemical performance; (iv) environmental and circularity assessments, including life cycle analysis (LCA), with particular emphasis on evaluating the potential of copper slag to substitute exotic materials such as CeO2.
## 3 Path-Planning Design and Dynamic-Obstacle Avoidance Strategy
### Global Path-Planning Design Based on 3D JPS Algorithm  
The JPS algorithm is a global path-planning algorithm based on a discrete grid map model in a static environment. In general, static obstacles in the initial drone flight airspace are relatively easy to obtain or the static obstacles themselves are known. Therefore, this paper develops a 3D jump point search algorithm based on the original JPS algorithm for the generation of global paths for drones in a static initial environment.
# A Review of Deep Learning Applications for Railway Safety
## 5 Train Safety  
### Train Door  
Train door failures damage the train system and account for 40% of all train failure cases, leading to huge operation and maintenance expenditures. [PERSON] et al. [67] studied a data-based approach to address train door failures. Eight failures were considered in four different scenarios. For each scenario, the change in the amount of current in the electric motor operating the train entrance was measured. Then, two techniques were used to analyze the current change data. First, 13 features were extracted from the time-series signal data using traditional feature engineering techniques based on pass filters (high and low). A KNN (k-nearest neighborhood) model detected door failures based on the extracted features. Another method is a deep learning model based on 1 Dconvolution. Figure 6 shows components of a train door test rigs for the experiments. The evaluation results showed that both methods showed an accuracy of 98% or more, and CNN models showed slightly higher performance, even though they used row current signals without preprocessing.
## 4 Data Preprocessing
### Missing Data  
Missing data result when the measuring device or the database does not record the value of the variable of interest at a given instant of the time series. Missing data can be caused by measurement, transmission, or recording problems. [PERSON] et al. (2019) provide an example, replicated in Figure 8. The plot shows one missing data on 13 January and three successive days--16, 17, and 18--without recording information (producing missing data). In this step of data preprocessing, we count the total observations in the weekly time series. \(S_{md}\) represents the missing data subset in the time series \(\mathbb{S}\). If \(|S_{md}|>56\) observations are missing, the system does not record the time series. It indicates that there is insufficient data to pass this combination PQM-variable to the next pipeline component. If \(|S_{md}|\leq 56\) observations are missing, an imputation process is executed. A recovery operation uses the Incomplete Datasets archive of the data-ingestion component.  
Figure 8: Time series with missing data.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 3 Sensitivity Analysis of Filtering Probability
### Observation Parameter Variation  
In this subsection, the sensitivity of the filtering probability \(p(x_{v}^{t},\mathbf{y_{e}^{1:t}})\) to the observation parameter variation is derived based on SMF. Let us consider the sensitivity functions \(p(x_{v}^{t},\mathbf{y_{e}^{1:t}})(\theta_{o})\) for a filtering probability and variation in the observation parameter \(\theta_{o}=o_{r,s}\). From Equation (6), the recursive filtering probability sensitivity function, it follows that for \(t=1\) we have \(p(x_{v}^{1},\mathbf{y_{e}^{1}})(\theta_{0})=o_{v,\mathbf{e}^{1}}(\theta_{o}) \cdot\gamma_{v}\), and for \(t>1\),  
\[p(x_{v}^{t},\mathbf{y_{e}^{1:t}})(\theta_{o})=o_{v,\mathbf{e}^{1}}(\theta_{o} )\cdot\sum_{z=1}^{n}a_{z,v}\cdot p(x_{v}^{t-1},\mathbf{y_{e}^{1:t-1}})(\theta_ {o}) \tag{9}\]
###### Abstract  
This study presents a novel method for automatically extracting Doppler envelopes from Frequency-Modulated Continuous Wave (FMCW) radar signals for gait analysis. In contrast to conventional percentile-based approaches that require manual selection of Doppler envelopes for specific body parts (Spine, Leg, and Foot), the proposed contour-based method enables fully automated estimation of representative speed values from the Doppler map. Experiments were conducted on five participants with varying physical characteristics, and key gait parameters--such as walking speed, step length, and stride time--were estimated and compared against motion capture-based ground truth. The proposed method demonstrated relative errors typically below 10%, with key parameters such as Foot Speed and Leg Speed falling below the commonly cited 5% clinical threshold. Paired \(t\)-tests revealed no statistically significant differences between the proposed estimates and the ground truth across all gait parameters (\(p>0.05\)), supporting the method's accuracy and reliability.  
FMCW radar; gait analysis; Doppler envelope; automatic extraction; motion capture; non-contact sensing Article
## 2 Materials and Methods
### Fatty Acid Analysis  
Fats were extracted from the samples according to Folch method, and equivalent volumes of extracts from the same product type (i.e., kefir, ryzhenka, amasi and manewu) were pooled together. The extracted fatty acids were derivatized (i.e., fatty acid methyl esters were prepared) by acid-catalyzed transmethylation with 3 M methanolic HCl (Supelco, Bellefonte, USA), according to the manufacturer's protocol. Chromatographic separation on an MDN-5 column (30 m \(\times\) 0.25 mm; Supelco, Japan) was carried out using GC 2010 chromatograph (Shimadzu, Kyoto, Japan) equipped with a mass detector GCMS-QP 2010 in the regime of temperature gradient (initial hold at 70 \({}^{\circ}\)C for 1 min; heating up to 90 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C per min; heating up to 240 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C per min; hold at 240 \({}^{\circ}\)C for 4 min; heating up to 300 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C per min; final hold at 300 \({}^{\circ}\)C for 3 min) at the following temperatures: injector, 200 \({}^{\circ}\)C; interface, 210 \({}^{\circ}\)C; and detector, 200 \({}^{\circ}\)C. Helium with a flow of 1.0 mL-min\({}^{\text{-1}}\) and a flow pressure of 1:20 was used as a carrier gas. The total analysis time was 32 min. Mass detection was carried out under the TIC registration mode in the mass acquisition range (_m/z_) from 45 to 400 (Supplementary Materials Figures S1-S3). The PUFA-2 (Supelco, Bellefonte, PE, USA), fatty acids of animal origin, were used as a standard. The relative intensities (further relative abundances) of fatty acids were obtained by normalization on the total intensity of the assigned peaks. All experiments were performed in triplicate.  
FA were identified by comparing their experimental spectra with those from the National Institute of Standards and Technology (NIST/EPA/NIH Mass Spectral Database (NIST 11), Gaithersburg, MD, USA). Only the assignments of peaks with \(\geq\)90% confidence were considered reliable and reported here.  
FA nutritional indices were calculated according to [PERSON] et al. [24]  
\[PUFA/SFA=\frac{\sum PUFA}{\sum SFA} \tag{1}\]  
\[IA=\frac{C12:0+(4\times C14:0)+C16:0}{\sum MUFA+\sum PUFA} \tag{2}\]\[HPI=\frac{\sum MUFA+\sum PUFA}{\text{C12}:0+(4\times\text{C14}:0)+\text{C16}:0} \tag{3}\]  
\[IT=\frac{\text{C14}:0+\text{C16}:0+\text{C18}:0}{0.5\times\left[\sum MUFA+\sum PUFA (n-6)\right]} \tag{4}\]  
\[HH=\frac{\text{C18}:1+\sum PUFA}{\text{C12}:0+\text{C14}:0+\text{C16}:0} \tag{5}\]  
\[UI=1\times(\%\text{monoenocics})+2\times(\%\text{dienoics})+3\times(\% \text{trienoics})+4\times(\%\text{tetraenoics}) \tag{6}\]
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 4 Results
### Performance Comparison Against JPEG Image Transmission  
The performance of the proposed communication system is compared against a JPEG-based communication system under similar constraints. With the proposed autoencoder, the images are compressed by a factor of approximately 40 (0.2 bits per pixel), whereas JPEG manages to compress the images by only a factor of 20 (0.4 bits per pixel) while maintaining reasonable image quality. The JPEG encoder generates the JPEG-compressed bit stream, which is then transmitted over the same AWGN channels under the same channel and modulation types used with the proposed E2E system. Figures 13 and 14 present the performance comparison between the proposed and JPEG systems. A compression factor of 20 is the lowest achievable with the JPEG encoder. Below 4.0 dB, the JPEG system fails to maintain any image quality, while the proposed autoencoder-based system continues to perform well. Even at high SNRs (above 3.5 dB), JPEG produces poor image quality compared to the proposed autoencoder-based system due to the excessive quantization noise. As in conventional communication systems, the performance of our system declines under low SNR conditions due to the combined effects of technical noise and semantic noise. However, it is important to emphasize that the impact of technical noise in our system is considerably lower compared to conventional systems like JPEG. Despite the degradation at low SNRs, our approach remains more resilient. In Section 4, we demonstrate that the proposed approach achieves up to 17 dB coding gain at mid SNRs (3 dB) compared to the JPEG system. Finally, it should be noted that this is achieved at a lower compression ratio, meaning JPEG consumes much higher bandwidth yet results in inferior image quality at the receiver.  
The diagram in Figure 15 shows the performance of an image transmission system as a function of the compression ratio, specifically varying the dimension of the latent vector (LV size). Diagram (a) illustrates how the PSNR changes with varying LV sizes. As the LV size increases, the PSNR also increases, indicating higher image quality but plateaus around LV size 30-50, showing diminishing returns. Diagram (b) shows how the accuracy of a CNN image classification model varies with LV size. Classification accuracy rises sharply with an increase in LV size up to about 30, beyond which gains are marginal. This  
Figure 13: Performance comparison of the proposed system versus JPEG (SNR vs. PSNR).  
suggests that a latent vector size of around 30-40 is sufficient for achieving near-optimal classification accuracy.  
Extensive experiments determined the optimal LV size to minimize the bitrate while maintaining image quality. An LV size of around 30-40 provides a good trade-off between compression and quality (both in terms of PSNR and classification accuracy). This enables efficient image transmission in resource-constrained environments, ensuring image reconstruction and accurate image classification with minimal data rates.