esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of a Mesh-Topology LoRa Network
## 5 Proposed Approach: LoRaMesh
### Multi-Hop Parameter Selection  
Quality of Service (QoS) remains the highest priority in the LoRaMesh network. To achieve this goal, in a multi-hop network the priority of a Multi-Hop Parameter Selection (MHPS) mechanism should be to limit packet collisions, while maximising throughput and minimising power consumption. In the proposed multi-hop network, this mechanism optimises the following transmission parameters: the spreading factor, bandwidth, code rate, and carrier frequency. The MHPS does not qualify as an ADR mechanism, as it does not adapt dynamically based on network conditions.  
As all nodes in the network need to adhere to the duty cycle limitations of the 868 MHz ISM band, we limit the SF selection in the network to 7, 8, and 9, as this will keep the max transmission time slot to 500 ms and allow transmissions with a max packet size of 76 bytes.  
The centre frequency choice for the LoRaMesh model in this study is limited to only 868.1 MHz, 868.3 MHz, and 868.5 MHz, as these are the centre frequencies which are required by all LoRaWAN devices, thereby ensuring compatibility with existing hardware.  
The MHPS mechanism suggested in this paper is a static rule-based approach and is only implemented to improve the performance of the network as a proof of concept.  
During the network setup phase, the information related to all scheduled transmissions of a single up-link transmission window is logged. The MHPS mechanism iterates through all the transmissions and investigates possible packet collisions. The MHPS flags a possible packet as a collision whenever the following occur:  
* The timing of the two transmissions overlaps;
* The same centre frequency is used by both transmissions;
* The same SF is used by both transmissions;
* The two receiver locations are within \(D_{collision}\) km proximity to another.  
Whenever a packet collision is detected, the MHPS mechanism will attempt to alter the centre frequency and restart the packet collision detection until all listed centre frequencies have been tested. If a packet collision is unavoidable by a centre frequency change, the MHPS mechanism will postpone the transmission by one time slot length and restart the packet collision detection. If a packet collision is still present, the MHPS mechanism will postpone the transmission until no transmission collision is detected or the transmission has been postponed by a max of five time slots. If a collision is unavoidable, the MHPS mechanism will randomly assign a time slot delay and centre frequency.  
LoRa CAD is a solution, described in Section 3.2, that could possibly allow devices to automatically adjust their respective transmission slots (after updating their respective parent Rx time slot). This solution is recommended for a setup where the TDMA scheduler does not have an overview of the network (which is assumed in our simulation model) and reinforcement learning is used to optimise the network performance.
# Adaptive-Size Dictionary Learning Using Information Theoretic Criteria
## 4 Numerical Results
### Experiments with Known Sparsity Level
\begin{table}
\begin{tabular}{c c c c c c c|c c c c c} \hline
**SNR ( dB)** & \(\boldsymbol{s=4}\) & \(\boldsymbol{6}\) & \(\boldsymbol{8}\) & \(\boldsymbol{10}\) & \(\boldsymbol{12}\) & **SNR ( dB)** & \(\boldsymbol{s=4}\) & \(\boldsymbol{6}\) & \(\boldsymbol{8}\) & \(\boldsymbol{10}\) & \(\boldsymbol{12}\) \\ \hline \multirow{3}{*}{10} & 128 & 128 & 128 & 128 & 128 & & & 128 & 128 & 128 & 128 & 124 \\  & 129.02 & 128.22 & 128.04 & 128.12 & 129.74 & 10 & 128.82 & 128.24 & 128.00 & 128.06 & 127.86 \\  & 143 & 132 & 130 & 130 & 143 & & & 143 & 135 & 128 & 129 & 130 \\ \hline \multirow{3}{*}{20} & 128 & 128 & 128 & 128 & 128 & & & 128 & 128 & 128 & 128 \\  & 128.20 & 128.06 & 128.06 & 128.18 & 128.34 & 20 & 128.22 & 128.14 & 128.12 & 128.06 & 128.30 \\  & 130 & 129 & 129 & 131 & 137 & & & 130 & 131 & 132 & 129 & 131 \\ \hline \multirow{3}{*}{30} & 128 & 128 & 128 & 128 & 128 & & & 128 & 128 & 128 & 128 \\  & 129 & 133 & 130 & 131 & 138 & & & 130 & 129 & 132 & 133 & 135 \\ \hline \multirow{3}{*}{40} & 128 & 128 & 128 & 128 & 128 & & & & 128 & 128 & 128 & 128 \\  & 128.18 & 128.08 & 128.70 & 128.78 & 130.00 & 40 & 128.12 & 128.36 & 128.88 & 129.04 & 129.08 \\  & 129 & 129 & 143 & 132 & 140 & & & 129 & 132 & 133 & 137 & 132 \\ \hline \end{tabular}
\end{table} 
Table 1: Minimum (top), average (middle) and maximum (bottom) sizes computed when \(n_{\text{true}}=128\). Left: ERNML\({}_{1}\). Left: ERNML\({}_{1}\). Right: EBIC. \begin{table}
\begin{tabular}{c c c c c c c|c c c c c} \hline
**SNR ( dB)** & \(\boldsymbol{s=4}\) & \(\boldsymbol{6}\) & \(\boldsymbol{8}\) & \(\boldsymbol{10}\) & \(\boldsymbol{12}\) & **SNR ( dB)** & \(\boldsymbol{s=4}\) & \(\boldsymbol{6}\) & \(\boldsymbol{8}\) & \(\boldsymbol{10}\) & \(\boldsymbol{12}\) \\ \hline \multirow{3}{*}{10} & 1.0194 & 1.0224 & 1.0258 & 1.0313 & 1.0402 & & & 1.0194 & 1.0224 & 1.0257 & 1.0327 & 1.0553 \\  & 0.9482 & 0.9741 & 0.9809 & 0.9883 & 0.9820 & & & 0.9482 & 0.9741 & 0.9808 & 0.9896 & 0.9964 \\ \hline \multirow{3}{*}{20} & 1.0181 & 1.0203 & 1.0235 & 1.0256 & 1.0319 & & & & 1.0190 & 1.0192 & 1.0227 & 1.0296 & 1.0417 \\  & 0.7326 & 0.8360 & 0.8792 & 0.9153 & 0.9231 & & & 0.7332 & 0.8352 & 0.8786 & 0.9187 & 0.9318 \\ \hline \multirow{3}{*}{30} & 1.0178 & 1.0310 & 1.0432 & 1.0572 & 1.1006 & & & & 1.0380 & 1.0228 & 1.0445 & 1.0776 & 1.1427 \\  & 0.3257 & 0.4835 & 0.6283 & 0.7133 & 0.7467 & & & 0.3323 & 0.4809 & 0.6310 & 0.7242 & 0.7722 \\ \hline \multirow{3}{*}{40} & 1.0177 & 1.0181 & 1.2080 & 1.1955 & 1.3554 & & & & 1.0177 & 1.0536 & 1.1560 & 1.3266 & 1.5319 \\  & 0.1516 & 0.2402 & 0.5606 & 0.5114 & 0.5721 & & & 0.1516 & 0.2459 & 0.5399 & 0.5857 & 0.6495 \\ \hline \end{tabular}
\end{table} 
Table 2: Ratios RMSE/RMSEt and RMSE/RMSEn on test data, when \(n_{\text{true}}=128\). Left: ERNML\({}_{1}\). Right: EBIC.
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
## 3 Results Analysis
### Metamaterial Property  
The MA's Imaginary part of permittivity and permeability for both TE and TM modes and the refractive index is given in Figure 10. The effective refractive index (\(\eta_{r}\)) is calculated by Equation (7). Where k = 2\(\pi\lambda\)/c, c is the velocity of light, \(S_{11}\) is the reflection coefficient, \(S_{21}\) is the transmission coefficient, n is the integer number and d is the substrate thickness. Equations (8) and (9) are also used to retrieve the relative permittivity (\(\varepsilon_{r}\)) and relative permeability (\(\mu_{r}\)), where \(Z_{r}\) is the relative impedance.  
\[\eta_{r}=\pm\frac{1}{kd}\cos^{-1}[\frac{1}{2S_{11}}(1-S_{11}^{2}+S_{21}^{2})+2 n\pi] \tag{7}\]  
\[\varepsilon_{r}=\frac{\eta_{r}}{Z_{r}} \tag{8}\]  
\[\mu_{r}=\eta_{r}Z_{r} \tag{9}\]  
Figure 10: The MA’s (**a**) Permittivity and (**b**) Permeability (**c**) Refractive Index.  
Figure 9: PCR value.  
The imaginary part of permittivity is positive in the figure from 400 nm to 2400 nm wavelength. Also, the imaginary part of permeability is negative between 800 and 2400 nanometers. On the other hand, the real refractive index is positive in the resonating wavelengths. The effective EM parameters also cause the magnetic and electric resonance because it can be seen that permittivity and permeability were changed close to resonance peaks, improving the absorbing strength and boosting broadband absorption.
## 3 Node Clustering for WMSNs
### Cluster Formation and Cluster Membership
\begin{table}
\begin{tabular}{l} \hline \hline \multicolumn{1}{c}{**The Algorithm**} \\ \hline
1: k = 1, i = 1 // k is index of clusters, i is index of sensors // \\
2: C\_S = {0,...0}, F\_S = {0,...,0} // Mask vectors of size N indicating \\ whether node S¡ is clustered and whether node S¡ is a first-member // \\
3: **Repeat:** \\
4: Create an empty cluster (C\({}_{k}\) ): C\({}_{k}\) = \(\emptyset\) \\
5: Set C\_S¡ as a clustered multimedia node, member of C\({}_{k}\) \\
6: Set F\_S¡ as the first member of the cluster C\({}_{k}\) \\
7: **For all C\_S¡// un-clustered multimedia nodes //** \\
8: **If** (C\_S¡= 0) \\
9: Find intersection polygon of FoVs of S¡, S¡  
10: Compute Dij //overlapped area between S¡  
The algorithm is executed by the sink once upon deployment and thus all nodes will become clustered. If a node joins to the network hereinafter, it has to send its position and orientation to the sink for announcing itself as a new node. The sink computes the FoV of the new node and finds the first cluster that can accept it as a new member. For this purpose, the sink computes the overlapping regions between FoV of the new node and the first-member of each cluster and checks whether he is satisfying the cluster membership test. Then, the sink sends a message to the first-member in order that this node re-organizes the cluster with the new member. Depending on the application, this notification may suppose a new reconfiguration in the monitoring task (_i.e._, a new duty-cycle period). On the other hand, each node periodically sends a Hello message to the first-member notifying that he is alive. When a node dies, the first-member will notify the rest of the members about the new cluster set and will reconfigure any parameter related to the cluster. The first-member also periodically notifies to his cluster members about his availability. If a first-member dies, the cluster members will notify to the sink their availability to belong to another cluster or to create a new cluster. Note that the beaconing among cluster members implies low overhead since cluster sizes have few nodes and hello periods can be on the order of duty-cycle sensing periods.
# Multi-Layer QoE Learning System Implemented by Fiberhost
## 3 Multi-Layer QoE Metric Nature
### Internet Service Layer  
The Internet service layer is the most complex because many services can be offered there, including over-the-top (OTT) services. Therefore, the simple and most cited measure of bandwidth, commonly referred to as the Internet speed, is not a good representation of QoE levels. Latency or jitter may seem much more important, as their high values can be much more annoying to users. In fact, the Internet service itself consists of streams of data coming from many geographically and logically dispersed parts of the Internet. Therefore, in our multi-layered QoE assessment, we not only used throughput, latency and jitter  
Figure 4: Correlation between GPON signal levels and BER.  
Figure 3: Distribution of GPON signal levels.  
measurements, but also probed end-user observable KPIs, such as those shown in Table 1. These parameters also reflect the complex nature of the telecommunications services as defined by ETSI [27]. Thus, polling also performs a multi-level test scenario, where the overall test score is calculated based on the weighted scores from all the test types.  
The approach we took here was based on predefined weights set for all the parameters involved. These weights are currently set as fixed values based on the knowledge of network administrators, so these calculations were more like an expert system. However, in future work, we plan to use the weights obtained from machine learning models, while these weights will be trained offline. Offline training typically provides greater accuracy as compared to online training and, additionally, offline training has the advantage of using multiple data sources over a sufficiently long period of time in order to produce an accurate model.  
However, since the network environment is constantly changing, following the changes in the network topology, ever-increasing network bandwidth (which operators have to deal with every year), software and firmware updates, etc., weights need to be regularly trained to update the model to the current environment and to improve its accuracy.
###### Abstract  
Copper nanoparticles were synthesized via a bioreduction using sulfated polysaccharides (SPs) extracted from _Avicennia marina_ mangrove leaves and their phytotoxicity effect on wheat (_Trticulum aestivum_ L.) seedlings was assessed. As analyzed by high-performance liquid chromatography (HPLC), SPs extract constituted mainly 49.3% glucose, 24.6% galactose and 20.5% glucouronic acid by mol. A nanoformulation of ultrafine Cu\({}_{2}\)O/Cu(OH)\({}_{2}\) nanoparticles coated with SPs (Cu NPs) was prepared with an average particle size of \(2.11\pm 0.64\) nm and a slightly negatively-charged zeta potential of \(11\pm 0.46\) mV. Applying high concentrations of Cu NPs on wheat seeds inhibited the respective shoot and root relative growth percentages, yielding \(13.22\pm 9.91\) and \(36.72\pm 18.51\%\) at 0.06 mg/mL of the nanoformulation. Comparable values were obtained when the seeds were subjected to 0.06 mg/L of SPs extract applied in the free form. In a foliar application, 0.06 mg/mL of Cu NPs reduced the respective total chlorophyll and carotenoids contents by 17.4% and 24.3% relative to the control while the treatment with SPs reduced them by slightly higher values of 27.9% and 32.6%, respectively, after 14 days of application. Therefore, Cu NPs show a comparable inhibition effect to that of the free SPs extract but offer the additional advantages of nanoformulations. In soil applications, however, Cu NPs stimulated the growth of wheat seedlings in contrary to SPs and increased the total chlorophyll and carotenoids by 49.76% and 70%, respectively. This gives an additional merit to the nanoformulation that can potentially be used as an effective biostimulant in soil.  
Keywords: mangrove leaves; sulfated polysaccharides; copper nanoparticles; seed germination; phytotoxicity; wheat seedlings +
Footnote †: journal: Applied Physics Letters
## 3 Proposed Vehicle Detection and Tracking Scheme
### Overview of Proposed Work  
In this section, the YOLOv5 object detection model and Deep SORT object tracking method have been explored. These methods were taken into consideration to further explore vehicle detection and vehicle tracking in our study. These methods are more effective and efficient as compared to the existing methods discussed above. Deep SORT was deployed with YOLOv5 to track the vehicle for counting with more accuracy. Considering the benefits of these methods in vehicle detection and tracking, these algorithms are more appropriate for obtaining better performance results. These methods are explored in the below section. Data association in the context of YOLOv5 and Deep SORT for vehicle detection and tracking involves associating the bounding boxes generated by YOLOv5 with unique track identities maintained by Deep SORT. An overview of how this process is typically performed is given as follows:  
1. Object detection with YOLOv5: * YOLOv5 is employed for real-time object detection, including vehicles, in each frame of a video or a sequence of images. * The output of YOLOv5 includes bounding boxes around detected objects along with class labels and confidence scores.
2. Feature extraction and data association with Deep SORT: * Deep Simple Online and Realtime Tracking (SORT) is utilized for object tracking and maintaining unique track identities across frames. * Features, such as appearance and motion information, are extracted from the bounding boxes generated by YOLOv5.  
Figure 1: Proposed scheme for detecting and tracking vehicles in real-time using YOLOv5 and the Deep SORT algorithm.  
The Kalman filter is employed to predict the next location of each track based on its historical motion.
* The Hungarian algorithm is often used for data association, associating the predicted tracks with the newly detected bounding boxes.
* Updating Track Information:
* The association step helps link the detected bounding boxes with existing tracks, updating the track information with the latest detection.
* Tracks that are not associated with any new detection for a certain period may be considered as finished tracks, while new detections that are not associated with any existing track may result in the creation of new tracks.
* Handling occlusions and ambiguities:
* Deep SORT is designed to handle challenges such as occlusions, where a vehicle may be temporarily hidden from view by another object.
* The combination of YOLOv5's real-time detection and Deep SORT's tracking helps maintain track identities even when vehicles are temporarily obscured.
# Development of a Generic Numerical Transtibial Model for Limb-Prosthesis System Evaluation
## 4 Discussion  
The aim of the present study was to develop a generic 3D transtibial model that could be used to evaluate different socket-liner systems numerically. Such a model provides a virtual environment in which the biomechanical response of the residual limb is simulated and, hence, could help to design and assess lower limb prostheses before a physical prototype is manufactured. Various mechanical properties, such as stresses, strains, contact pressures, deformations, etc., can be analysed to improve the interaction between the residual limb and the prosthesis in the virtual environment using the developed transtibial model. The generic numerical model developed in this study provides a valid biomechanical response, as shown by comparison with the literature discussed in the following subsection and can therefore be used for a relative comparison of different prosthesis designs.  
Figure 12: Comparison of peak contact pressure results from the FEA to those reported in the literature for the donning, stance, and gait boundary conditions at the patellar tendon (PT) and posterior area (PA) [8; 14; 28; 30; 32; 33; 34; 35; 36].
# Personal Protective Equipment as a Potential Source of
###### Abstract
Mostly, PPE is manufactured from plastic materials, so various additives--including plasticizers, flame retardants, antioxidants, and stabilizers--are incorporated into PPE during manufacturing [18; 20]. Phthalates are a group of common plasticizers widely used in the plastic industry. The content of phthalates in some plastic products can reach up to 50% by weight. Phthalate plasticizers are mixed with plastic materials during formulation, but these compounds do not form chemical bonds with the other materials. Therefore, they can easily be released to the adjacent environment and contribute to elevating the level of exposure to humans [18; 21]. Phthalate plasticizers are known for their disruption effects to the endocrine system. A number of phthalate compounds have officially been classified by various authorities, e.g., the European Union, as endocrine disruptors [22]. Chronic exposure to these compounds is associated with adverse effects on the endocrine system, in addition to negative long-term impacts on the success of pregnancy, child growth and development, and reproductive systems in both young children and adolescents [23]. Some studies have also linked exposure to phthalates with obesity problems (metabolic syndrome), cancer, and increased tumor activity [24]. For example, di(2-ethylhexyl) phthalate (DEHP) has been classified as \"possibly carcinogenic to humans\" [25]. Currently, many countries are restricting or regulating the use of phthalates because of their adverse health and environmental effects [23]. Phthalates are ubiquitous compounds and are considered among those classified as everywhere chemicals. Exposure to phthalates can occur mainly via ingestion, inhalation, dermal contact, and parenteral administration [26]. COVID-19 pandemic prevention strategies are thought to increase individuals' exposures to toxic chemicals such as phthalates. Individuals spent more time indoors, which could have led to an increased exposure to phthalates. In addition, the prolonged use of PPE such as face masks, face shields, and gloves may increase the levels of exposure to the leachable, semivolatile compounds such as phthalates found in these products. Therefore, the aim of this study is to investigate the occurrence and quantify the concentrations of six commonly used phthalates (DBP, BBP, DEHP, DnOP, DINP, and DIDP) in samples from different types of PPE (face masks, face shields, and disposable gloves) collected during the early stage of the COVID-19 pandemic. In addition, the samples were qualitatively evaluated to identify the presence of any other harmful organic chemicals that may have been used during the manufacturing process.
## 2 Methodology
### Field Data
#### 2.2.4 Bathymetry, Surface Averaging, and Spatial and Spectral Binning  
Records with no reported bottom depth (-85% of the database) were matched to the nearest pixel in the UNESCO GEBCO 08 0.05 degree bathymetry grid ([[http://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_08.pdf](http://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_08.pdf)]([http://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_08.pdf](http://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_08.pdf))). The purpose of GOCAD is the development of satellite algorithms for surface retrievals, so data collected at depth were discarded as follows: on the continental shelf (defined here as bottom depth 1000 m or less) samples from deeper than 5 m were discarded, as were data from deeper than 10 m off the shelf (\(\sim\)34% of the database combined). 57,127 surface records remained. Samples collected in profile within the surface layer (top 5 m on-shelf, top 10 m off-shelf) were averaged. Samples collected in transect were additionally binned to a 0.5 km grid and averaged.  
All absorption related IOPs were matched to the following wavebands with a 2.5 nm tolerance: 245 nm, 1 nm resolution between 250 and 555 nm, 560, 620, 630, 645, 650, 665, 670, 676, 680, 705, and 715 nm. Backscattering data were similarly matched to 1 nm bands from 400 to 700 nm. Hyperspectral in situ \(R_{rs}(\lambda)\) were matched to both SeaWiFS bands (412, 443, 490, 510, 555, and 670 nm) and MODIS bands (412, 443 488, 531, 547, 667) by weighting the data to the instrument-specific spectral response functions for SeaWiFS, Aqua, and Terra ([[https://oceancolor.gsfc.nasa.gov/docs/rsr/rsr_tables/](https://oceancolor.gsfc.nasa.gov/docs/rsr/rsr_tables/)]([https://oceancolor.gsfc.nasa.gov/docs/rsr/rsr_tables/](https://oceancolor.gsfc.nasa.gov/docs/rsr/rsr_tables/))). Multispectral in situ \(R_{rs}(\lambda)\) were matched to satellite bands to within a 2.5 nm tolerance.