esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 3 Background  
### Tsch  
IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) is an amendment to the IEEE 802.15.4 standard outlining the physical (PHY) and medium access control (MAC) layers for low-rate wireless personal area networks (WPANs). Designed for industrial and automation applications, TSCH offers synchronized time-based communication, making it dependable and energy-efficient. It is ideal for applications that require precise timing and coordination, such as industrial control systems or environmental sensor networks [19]. While TSCH specifies functionality, its implementation depends on network architecture and application needs. Channels are managed by dividing time into fixed time slots and periodically changing frequency channels, with each node assigned specific times for data transmission or reception and synchronized with neighboring nodes, thereby minimizing channel access conflicts [20].  
TSCH is a MAC mode in the IEEE 802.15.4e standard that allocates time slots for data transmission or reception. These allocations can be static or dynamic based on network requirements. Time slots are organized into slot frames, which define cycles of time slots that repeat according to the slot frame size [21]. Nodes communicate within these time slots, where they are synchronized through a shared channel hopping schedule that periodically changes the frequency channels to reduce interference. This ensures precise communication and time synchronization between nodes, with the slot frame length being adjustable based on network and application needs. TSCH operation utilizes about 16 available channels, which allows for high coordination within the network and reduced channel access conflicts [22].
## 5 Simulation  
We have selected two railway stations for testing the developed approach and the simulation model. The first station is Sukhovskaya, a freight station located on East Siberian railway, Russia; the second is Bailey Yard, a marshalling yard located in North Platt, NE, USA. The systems have different purposes. Freight stations provide mass loading and unloading of cargo. As a rule, they have relatively small car traffic; five trains per day arrive on Sukhovskaya on average. Such stations may perform some of the functions of marshalling yards. Marshalling yards are intended for mass disbanding of trains into separate groups of cars, sorting these groups in accordance with the further direction of movement as well as forming and dispatching new trains.  
Let us consider the freight RS Sukhovskaya, which was selected for the following reasons. First, it is a standard station on the East Siberian Railway. Second, its structure includes a round route for cars moving, which complicates the study. As for Bailey Yard, it is one of the largest marshalling yards in the world and meets one of the modern directions of development, which is to maximize the automation of technical processes [45; 46]. Besides, for both stations, it managed to get statistics on arriving train traffic.
## 2 Materials and Methods
### Linearity and Sensitivity  
Calibration was performed to accurately determine the concentration of the analyte in a sample. The external calibration method was applied to determine the linearity of the developed method. Peak area values were plotted against five different solution concentrations prepared by diluting the stock solutions of the studied vitamins, organic acids, and sugars to be quantitatively determined. The limit of detection (LOD) and limit of quantitation (LOQ) were calculated. These parameters were determined according to signal/to noise ratios of 3.0:1 and 10:1. The described method was linear for five compounds. The results meet the acceptance criteria according to ICH guidelines Q2 (R1), which state that the correlation coefficient value (r) must be >0.999 [23]. The slope and intercept values of the obtained linear functions were used for quantitative calculations of vitamins, organic acids, and sugars in the samples.
# Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
## 1 Introduction
### Monitoring of the PV Arrays  
With the increasing demand for solar energy worldwide, there is an urgent need for advanced monitoring methods. Monitoring involves observing and recording parameters from solar PV power plants in real time. A reliable monitoring technology enhances performance efficiency by providing up-to-date information and implementing preventive measures when flaws are detected. Additionally, this monitoring approach alerts users to unexpected events in the solar PV system [13].  
One approach to real-time monitoring is using Internet of Things (IoT) technology. IoT represents a breakthrough in electronics, employing sensor technology connected to the internet either through wired or wireless means. This enables users to access data and control the system remotely from anywhere at any time via the Internet [14]. The IoT technology allows users to monitor the performance of the PV systems in terms of produced power, evaluating the voltage, current, produced energy, ambient and module temperatures, wind velocity, humidity, and irradiance value [15; 16].  
The primary objective of the monitoring system for the PV power plant is to transmit data reliably, securely, and efficiently. However, various challenges can significantly impact the performance of monitoring technologies in terms of efficiency, security, range, data processing capability, sampling rate, and signal interference. For instance, any disruptions in real-time communication can lead to interruptions in data reception, thereby affecting performance predictions.  
To address the above-mentioned challenges, the use of unmanned aerial vehicles (UAVs) and thermography is increasing as an alternative monitoring approach. This tool provides valuable information to maintenance workers, planners, and engineers, enabling  
Figure 1: 2022 Primary Global Energy Consumption [1].  
timely decision-making. Thermal imaging allows for non-intrusive assessment of the temperature distribution across PV panels, aiding in identifying hotspots and inefficient regions that may impact overall efficiency. Anomalies and defects in PV cells can result in localized heating. It also helps in identifying these irregularities, enabling timely maintenance and repair [17; 18]. During manufacturing and installation, thermal imaging can ensure uniform temperature distribution and detect potential defects, contributing to higher-quality PV panels. Continuous thermal monitoring provides insights into panel performance, facilitates predictive maintenance, and reduces downtime.
# Genus, Species, and Subspecies Classification of _Salmonella_ Isolates by Proteomics
## 1 Introduction
A pipeline that further computed accurate statistical significance was proposed to assign the microorganism(s) [28; 29]. The degree to which MS can distinguish strains within a given type of bacteria depends on the degree of divergence within a given genera, e.g., some bacteria are remarkably different genetically even beyond the species level while others are nearly indistinguishable [15]. _Salmonella_ is extremely complex and therefore is often overlooked when it comes to MS-based subtyping. The aim of this study is to address how well proteomics can subtype _Salmonella_ as well as differentiate this pathogen from its close relatives _E. coli_ and _Stigella_. Here, a collection of nineteen isolates encompassing a wide range of taxonomic ranks were analyzed by LC-MS/MS. This consists of three genera (_Salmonella_, _Escherichia_, and _Shigella_), two species (_Salmonella enterica_ and _Salmonella bongori_), two subspecies (_Salmonella enterica_ subsp. _enterica_ and _Salmonella enterica_ subsp. _arizonae_), six serovars (_S. typhimuriumium_, _S. saintpaul_, _S. heidelberg_, _S. paratyphi_ B, _S. muenchen_, and _S. enteritidis_), and multiple strains of _S. typhimurium_, _S. saintpaul_, _S. heidelberg_, and _S. enteritidis_. This range of phylogenetic diversity provides a realistic assessment of the discriminatory power of proteomics and mass spectrometry for _Salmonella_. The performance and related findings will be discussed.
# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 2 Related Work
### Deep Learning Approaches  
The advent of deep learning has revolutionized image dehazing research. Convolutional Neural Networks (CNNs) have demonstrated remarkable performance in learning complex nonlinear transformations for haze removal [22; 23]. Methods like DehazeNet [7] and MSCNN [4] effectively remove haze while preserving image details by training on large datasets of hazy and clear image pairs [24; 25]. However, these deep learning models often involve complex architectures with a large number of parameters, leading to high computational demands that limit their applicability in real-time or resource-constrained environments [3; 6].  
To address these challenges, lightweight CNN architectures have been proposed. [PERSON] et al. introduced Light-DehazeNet, a compact model designed for single image dehazing with reduced computational overhead, making it suitable for deployment on edge devices [22]. These models balance performance and efficiency, but further optimization is necessary for specific applications, particularly in fixed camera environments where the scene remains constant.  
Fine-tuning techniques have been employed to enhance the performance of deep learning-based dehazing models [28; 29]. By adjusting a pre-trained model to specific datasets or tasks, fine-tuning allows for significant performance improvements even with limited data [30; 34]. This approach is particularly beneficial for adapting models to the specific haze characteristics of certain regions or times of day, increasing their versatility and effectiveness in real-world environments.
# Curcumin Innovative Delivery Forms: Paving the Yellow Brick Road' of Antitumoral Phytotherapy
## 4 Innovative Curcumin Formulations and High-Performance Delivery Forms
### Oral Delivery Forms
#### 4.1.1 Marketed Products
Micronization of solid curcuminoids to particles with 10 \(\upmu\)m average size and blending them with a lipophilic matrix is another simple strategy to improve absorption and achieve sustained release up to 12 h. The product, commercially available as MicroActive(r) Curcumin, was tested on three healthy volunteers, affording a ten-fold absorption increase [167]. Combination therapy allies curcumin with piperine to address losses in activity caused by glucuronidation, one of the major metabolic pathways responsible for the low oral bioavailability of curcumin. The alkaloid piperine (1-piperylpiperidine) is one of the major active components of the pepper plants _Piper nigra_ and _Piper longa_, known to act as a bioavailability enhancer for a variety of drugs by inhibiting glucuronidation both in the liver and in the small intestine [180]. A comparative study with healthy volunteers taking 2 g/kg curcumin, either alone or in combination with 20 mg/kg of piperine, has shown a 20-fold curcumin bioavailability enhancement for the combined therapy, as well as an earlier plasma peak (T\({}_{\text{max}}\) of 40 min vs. T\({}_{\text{max}}\) of 60 min for curcumin alone) [181]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trade Name** & **Formulation Strategy** & **Dosage Form** & **Product Composition** & **Ref.** \\ \hline \multirow{3}{*}{CurcuminRich} & Colloidal NP & & & Thereacurmin: polysaccharide NPs loaded with \\  & w/curcunimoids & 30 mg capsules & & curcuminoids \\  & (theracurmin) & & Excipients: MCC, magnesium stearate, silica \\ \multirow{3}{*}{Biocurcumax} & Curcuminoids & & & Curcunimoids (titrated 95\% curcumin) \\  & w/turmeric essential oil & 350 mg capsules & Excipients: Maltoederkin, magnesium \\ \multirow{3}{*}{Biomor} & Curcuminoids & & & steater, HPMC \\  & w/turmeric essential oil & 500 mg capsules & Curcunimoids (titrated 95\% curcumin) in a & \\ \cline{1-1}  & & & vegetable capsule \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Curcumin oral dosage formulations currently available in the market.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion
### Energy Consumption  
Because WBANs primarily operate on limited battery power, energy consumption is essential when assessing routing and clustering protocol performance. Figure 13 illustrates how MT-MAC uses more energy than M-GWO, DHCO, and ALOC. Because of the two-hop cluster and the ideal CH number, this method's overall energy use curve is slow and steady. Using the cluster's two-hop neighbourhood data, two-hop nodes can use one-hop neighbouring nodes as relay nodes to send biological data to CH nodes. Using this strategy requires less energy to receive biological data from remote nodes.  
Comparing energy utilization to established methods, the suggested ABBSOA-MSSOA model shows considerable numerical advantages. At 42 mJ, the recommended procedure uses less energy than MT-MAC (55 mJ), DHCO (51 mJ), ALOC (48 mJ), and M-GWO (46 mJ): comparative). The hybrid ABBSOA-MSSOA architecture allows for clustering and routing without inefficiency by reducing energy-wasting transmissions and spreading them equally amongst nodes. For ABBSOA-MSSOA, the first node death (FND) occurs after 3650 cycles, extending network lifespan. MT-MAC, DHCO, ALOC, and M-GWO die after 2000, 2200, 2500, and 3000 rounds, respectively. More parallels exist. Numerical results show that the suggested solution improves energy efficiency and network lifespan over existing methods.  
Figure 12: Communication overhead analysis.  
Figure 13: Energy consumption analysis.
# Hyperspectral Imaging Techniques for the Study, Conservation and Management of Rock Art
## 2 Materials and Methods  
### Study Area  
The study area corresponds to a series of panels in the Cave of El Castillo in Puente Viesgo, Cantabria, Spain, which was declared a World Heritage Site by United Nations Educational, Scientific and Cultural Organization (UNESCO) in 2008.  
Today this cave has the world's oldest Paleolithic art, at least 40,800 years old. The cave contains animal figures such as bison, horses, deer, mammoth, aurochs, goats, etc. The panels also feature hands, dots and rectangular shapes. Also found is one of the oldest industrial and technical heritage sites, associated with bone and lithic industry.  
It is possible to find engravings with different characteristics and paintings that have red, black or yellow colors, applied with finger, airbrush, pencil or brush. The state of conservation of the panels is not very good. The panels have suffered graffiti, glazes, cleanings, etc., that make the motives unrecognizable with the naked eye. In order to reveal latent information, hyperspectral data from the panels have been captured and analyzed.  
The panel de las manos of the cave of El Castillo (Figure 1) is possibly one of the most complex among Paleolithic art in general. The use of different techniques and colorants allows us to establish differences between groups and help us understand the chronostylistic evolution based on the superpositions of figures. The chronological frame of the figures has been set between 41,400 and 10,000 years before the present (BP) thanks to uranium-thorium and radiocarbon dating, although we have focused on the overlays of the Gravettian-Aurignacian phases. The panels were first studied by [PERSON] (1911) in 1911, but during the last century many researchers have revisited the site and have applied different techniques to study the rock art panels of the cave such as hand-drawing, direct tracing, rubbing or _frontage_ tracing; making molds directly from the panels; analogue photography; digital images; and techniques to enhance visualization such as decorrelation of individual elements ([PERSON] et al., 2008; [PERSON] et al., 2010; [PERSON] and [PERSON], 2011; [PERSON] et al., 2012; [PERSON] et al., 2013; [PERSON] et al., 2014).  
As will be shown, hyperspectral remote sensing can help not only to better identify figures, but also isolate them by type of dye and analyze superimpositions.  
In the first study carried out on the panel de las manos Alcalde (1911), 9 animals painted in yellow, 33 negative hands, 13 painted signs and some groups of red punctuation, and 6 engraved figures, mainly deer, were identified. Using hyperspectral technology, 35 zoomorphs (23 in yellow, 2 in red, 19 in black and 1 in white), 56 hands (in 2 phases) and 22 signs have been documented today ([PERSON] et al., 2013).  
The study was carried out on the panel as a whole; the archaeological interpretation was made by [PERSON] (1998). Nevertheless, the present study shows three areas of special interest and the hand analysis of the whole panel.
# Simulation-Based Shading Loss Analysis of a Shingled String for High-Density Photovoltaic Modules
## 2 Materials and Methods
### Shingled PV Module Circuit Modeling  
Figure 3 shows a schematic of the model that was used to simulate a shingled string. To produce a shingled string, each divided solar cell was bonded using an ECA. However, in the bonding process, each divided cell overlaps and is bonded, as shown in Figure 2, and the lower divided cell is shaded [16]. Equation (2) was used to calculate the voltage overlap to represent the shading loss of the divided solar cell in the lower part as a voltage source [17]. The modeled divided cells were connected in series to simulate the shading of a shingled string. In addition, a shingled string circuit was formed by adding the resistance value of the ECA used for manufacturing the shingled string between each pair of interconnected cells. An ECA resistance value of 2.2 m\(\Omega\), obtained by varying the ECA resistance reported by [PERSON] et al. [18], was used.  
\[V_{overlap}=(\frac{A_{Divided\ cell}-A_{overlap}}{A_{Divided\ cell}-A_{ busbar}})\times 1000 \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Measurement Value** & **Simulation Value** \\ \hline Size (cm\({}^{2}\)) & 49.14 & 49.14 \\ I\({}_{\text{sc}}\) (A) & 1.937 & 1.937 \\ V\({}_{\text{oc}}\) (V) & 0.669 & 0.670 \\ FF (\%) & 78.44 & 78.40 \\ Eff (\%) & 20.72 & 20.72 \\ P\({}_{\text{m}}\) (W) & 1.018 & 1.018 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulated and measured values of a solar cell divided into five.  
Figure 3: Schematic illustration of shingled string modeling.  
Figure 2: Simulated and measured I–V curve of a divided solar cell.  
The modeled shingled string was connected in series and parallel, as shown in Figure 4. The shingled PV module used in this study consists of 6 parallel 2 series circuits and two bypass diodes. The wiring resistance was calculated and added based on the wiring length of the designed shingled PV module, and the resistance value of the 6 mm wide wiring ribbon was 9.5 m\(\Omega\)/m. In addition, photovoltaic modules generally exhibit light loss that occurs in the front cover glass and Ethyl Vinyl Acetate (EVA) sheet. The shingled PV module modeled in this study was simulated by multiplying the \(V_{module}\) calculated as in Equation (3) by a loss of \(-\)2.28%.  
\[V_{module}=V_{overlap}\times 0.9772 \tag{3}\]
# Exploring Distributed Deep Learning Inference Using Raspberry Pi Spark Cluster
## 5 Discussion
### Ineffectiveness of Distributed DL Inference Using Pi
This finding highlights the ineffectiveness of distributed DL inference when using the Spark TensorFlow Distributor even when the diminishing speedup factor was eliminated for the experiments. It is hoped that this finding will bring awareness towards implementing distributed DL inference in TensorFlow, as it is currently not the preferred option for ML/DL clustering solution for inference. The most likely hypothesis for this is the lack of capability for distributing a model across different nodes. In a distributed training scenario, the same neural network model with unmodified architecture would be copied over to worker nodes as model replicas. This process speeds up training by comparing different accuracy and loss values in an epoch across all nodes. The node with the best-performing values has its parameters passed to some parameter server and the training process enters a new epoch until several epochs have been reached. Finally, the parameter server holds the best set of parameters that allow it to maximise accuracy and minimise loss, as seen in [28]. A neural network model in TensorFlow could not be partially distributed across worker nodes for inference as there is no method of segregating and re-joining the model architecture, given that the neural network models run in a top-down manner where data is passed layer after layer. This input dependency between layers renders the distribution of a neural network model impossible. Distributed inference in Spark TensorFlow Distributor is limited to copying the replica models over worker nodes. Ultimately, this results in the lack of speedup in cluster systems when running model inference. Spark TensorFlow Distributor is less preferred than Spark MLlib for the time being, as it could not increase inference performance with \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experiment 1 Test**} & \multicolumn{3}{c}{**Average Execution Time (s)**} \\ \cline{2-4}  & **Pi 4B** & **2-Node Pi 4B** & **Speedup** \\  & **(Baseline)** & **Spark Cluster** & \\ \hline MobileNetV3 on CIFAR10 & 20.67 & 16.89 & 1.22 \\ MobileNetV3 on ImageNetV2 & 828.33 & 835.79 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of average execution time and speedup between a single Pi 4B and a 2-node Pi 4B cluster for Experiment 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Experiment 3 Test**} & \multicolumn{3}{c}{**Average FPS**} \\ \cline{2-4}  & **Pi 4B** (Baseline) & **2-Node Pi 4B Spark Cluster** & **Speedup** \\ \hline MTCNN & 2.64 & 2.82 & 1.22 \\ YOLOface & 0.21 & 0.19 & 0.90 \\ RetinaFace & 0.21 & 0.20 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of average FPS and speedup between a single Pi 4B and a 2-node Pi 4B cluster for Experiment 3.  
scale. At the time of writing, no other DL frameworks with adequate clustering support for inference tasks are available.
## 2 Discrete Element Parameter Model and Parameter Calibration of Soil-Soil Interaction
### Simulation Model
#### 2.4.2 Soil Particle Model and Particle Factory Establishment  
Before conducting simulations, it is necessary to model the soil particle system. Soil is a medium characterized by nonlinearity and discreteness, with a complex interweaving of composition and structure. The spatial configuration information of soil particles cannot be directly obtained. While ensuring the integrity of the soil discrete element model, this paper, for the convenience of simulation experiments, adopts the approach of approximating the geometric morphology of real soil particles using standard spherical particles and combinations of these spheres when modeling soil particles with the discrete element method. This significantly enhances simulation efficiency. To more accurately simulate the geometric morphology of real soil particles in Southern Chinese orchards, single-sphere particles, double-sphere particles, triple-sphere particles, and quadruple-sphere particles were used to model soil particles with different shapes. It is worth noting that a reduction in particle size in discrete element simulations leads to an exponential growth in simulation run time [28]. Considering both computer performance and experimental accuracy, and in line with real soil particles, this study selected a particle radius of 4 mm. Through experimentation and reference to relevant literature [23; 29; 30], the basic parameters for soil and steel plates were obtained, as presented in Table 2.