esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Adsorption Studies
#### 3.2.9. Desorption Experiments and Reusability  
Desorption studies have been performed to evaluate the regeneration potential of the selected biosorbents. Since acids are reported in the literature as the bestluents for desorption compared to other alternatives, a non-destructive method using nitric acid (0.1 M HNO\({}_{3}\) and 1 M HNO\({}_{3}\)) and EDTA (0.1 M) was employed to conduct the desorption experiments for the four selected sorbents. The results of this study are presented in Figure 15. It was found that all three desorbing agents can successfully regenerate the biosorbents M, LA, and L. However, for the plant material MO, 0.1 M HNO\({}_{3}\) is not an effective eluent, achieving only 34% desorption efficiency. This indicates that copper ions are more strongly bound to the functional groups of MO compared to the other sorbents, necessitating a higher acid concentration (1 M HNO\({}_{3}\)) to achieve efficient desorption (100%).  
The desorption behavior confirms that the adsorption of copper ions involves reversible interactions, including ion exchange and coordination, with the functional groups of the four biosorbents. This reversibility allows plant-based biosorbents to be efficiently regenerated and reused, enhancing their practicality and cost-effectiveness. From a practical and ecological perspective, EDTA is the preferred eluent. It is effective across all biosorbents while reducing the need for highly acidic conditions, making it a more environmentally friendly and less corrosive option for sorbent regeneration.  
In this regard, retaining the adsorption capacity of the sorbents after multiple cycles is particularly important. The following Figure 16 presents the results of four adsorption-desorption cycles using 0.1 M EDTA for the MO biosorbent as representative. The adsorption capacity of MO decreases only slightly, from 82% in the first cycle to 77% in the fourth cycle. Meanwhile, the desorption capacity remains high, with 100% desorption efficiency for the first three cycles and 96% for the fourth cycle. This demonstrates that when 0.1 M EDTA is used as the elution agent, the MO biosorbent can be effectively reused at least four times for the adsorption of copper(II) ions.  
Figure 15. Desorption (in %) of adsorbed copper ions onto four plant materials using eluents 0.1 M HNO\({}_{3}\), 1 M HNO\({}_{3}\), and 0.1 M EDTA.  
These findings demonstrate that the studied plant-based materials are promising biosorbents due to their reversible adsorption mechanism, which is highly advantageous for applications in water purification and heavy metal recovery.
## 6 Federated Learning for Early Health Prediction
### Federated Learning for Disease and Mortality Prediction
#### 6.2.3 Chronic Kidney Diseases  
The authors in [88] introduced an innovative approach to predicting chronic kidney diseases using federated learning. Their investigation leverages the collaborative power of federated learning to train predictive models on data from various healthcare providers while preserving the privacy of patient information. By aggregating insights from multiple sources, their study aimed to enhance the accuracy of chronic kidney disease prediction, offering valuable early diagnosis capabilities. They underscored the significance of federated learning in healthcare, particularly in chronic disease management, where privacy  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Year** & **Application** & **Methods** & **Results** & **Limitations** \\ \hline
[90] & 2023 & Dynamic & Dynamic federated & The proposed model & - Lack of information on the specific rare diseases studied and the datasets used for evaluation. \\  & & federated & meta-learning & (DFML) and & \
\begin{tabular}{c} the proposed model \\ improves the \\ prediction accuracy \\ by 13.28\% and \\ outperforms the \\ original federated \\ meta-learning \\ algorithm in accuracy \\ and speed. \\ \end{tabular} &
\begin{tabular}{c} - Lack of information on the specific rare diseases studied and the datasets used for evaluation. \\ - Does not provide details on the computational resources required for implementing the DFML approach. \\ - Does not compare the \\ with other state-of-the-art methods for rare disease prediction. \\ \end{tabular} \\ \hline
[91; 92] & 2021 & Federated & Logistic regression & The LASSO federated & - The study was limited to model outperformed & - The study was limited to data collected from hospitals within the Mount Sinai Health System in NYC. \\  & & predicting with & and selection & - The study only included \\  & & COVID-19 within & operator (LASSO) & \
\begin{tabular}{c} performed better \\ than the MLP local \\ model at all five \\ hospitals. \\ \end{tabular} &
\begin{tabular}{c} - The study only included \\ clinical data in the models. \\ - The study only implemented two widely used \\ classifiers within the framework. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._concerns are paramount. This approach holds promise for improving patient outcomes and resource allocation while maintaining the highest standards of data privacy and security.
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 5 Results Analysis
### Results SARS-CoV-2 Operation of the Best Model  
ML involves the parallel calculation of all processes and the selection of the best one; however, the processes may be conducted sequentially depending on the computer available. Images may be presented to Galois Fields mapping or switched directly to CSK/QAM modulation, depending on how the operation is configured, by entering the cursor \(k\) from Equation (A2).  
In this instance, the most important parameter to investigate is _recall_, which indicates how frequently the model generates type 2 errors. In terms of both this metric and precision, the XGBoost classifier model has the best performance. The associated parameters are  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Training Accuracy** & \multicolumn{3}{c}{100.0\%} \\ \hline
**Model Accuracy Score** & \multicolumn{3}{c}{96.03\%} \\ \hline
**Classification Report** & \multicolumn{3}{c}{} \\ \hline  & **precision** & **recall** & **f1-score** & **support** \\ \hline Ladder & 1.00 & 1.00 & 1.00 & 17 \\ \hline Positive (+) & 0.95 & 0.99 & 0.97 & 85 \\ \hline Negative (**—**) & 0.95 & 0.83 & 0.89 & 24 \\ \hline Accuracy & \multicolumn{3}{c}{} & 0.96 & 126 \\ \hline Macro avg & 0.97 & 0.94 & 0.95 & 126 \\ \hline Weighted avg & 0.96 & 0.96 & 0.96 & 126 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters obtained from the training of the XGBoost classifier.  
Figure 19: Application of [PERSON]’s correlation on the different columns of the electrophoresis sample.  
Figure 20: Comparison of the accuracy levels of the different trained classification models.  
displayed in Table 4. It can be seen that the model classifies the ladder correctly in all instances. It is important to note that _recall_ is computed using Equation (39). Figure 14 depicts the results of the application of this method to the image shown in Figure 21. This corresponds to the first three boxes in the transmitter and the last three boxes in the receiver in Figure 1, which were previously completed manually but are now performed by an ML subsystem.  
The accuracy of trained models will always be determined by the criteria used by the medical professional who prepared the data set. Due to uncertainty, the data can be propagated to the VLC channel. At the time of operation, the developed system is merely a tool; the results must be confirmed and interpreted by another health professional to determine the presence or absence of SARS-CoV-2. The created system reduces sample recognition times, allowing professionals to make more accurate diagnoses, and expands the data set size.
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
###### Abstract  
This paper presents a 1-GS/s12-bit pipelined analog-to-digital converter (ADC) fabricated in 40-nm CMOS technology that optimizes the settling time, bit error rate, and robustness. This ADC uses an improved timing called pre-quantization timing (PQT), which implements quantization in half the time of the sampling phase to maximize the output-settling time of the operational amplifier (op-amp). A complete clocking scheme along with a delay lock loop (DLL) is proposed to generate an accurate timing no matter how process, voltage, and temperature (PVT) change. Based on PQT, a high-speed comparator circuit is adopted to obtain a bit error rate (BER) below \(10^{-15}\). Sample and hold amplifier (SHA) is used to guarantee robustness over the wide input frequency. Furthermore, a low-cost automatic calibration is implemented to correct residual curves, and inter-stage gain errors are also corrected. This ADC achieves a signal-to-noise-and-distortion ratio (SNDR) of 57.3 dB and a spurious-free dynamic range (SFDR) of 78.5 dB at a 227 MHz input frequency. The measured differential nonlinearities (DNL) and integral nonlinearities (INL) after calibration are \(\pm\)0.7 LSB and \(\pm\)1.50 LSB, respectively. The power consumption of the ADC core is 97.6-mW, and the Walden figure of merit (FoM) is 172.9-f/conversion-step.  
analog-to-digital converter; pipelined; timing strategy; delay lock loop; bit error rate; residual curves; sample and hold amplifier +
Footnote †: journal: Electronics Letters
## 4 Challenges and Future Research Directions
### UAV-Enabled Smart Transportation  
As the growing number of on-ground smart vehicles is increasing, the integration of autonomous UAVs with smart vehicles could primarily facilitate the transportation system. Both autonomous UAVs and smart vehicles could be operated without any human intervention. Uninterrupted communication between UAVs and smart vehicles would help enable automated traffic monitoring, tracking, and traffic data acquisition. Continuous monitoring and real-time feedback would help regulate the traffic flow and reduce congestion. However, the collaborative navigation of both autonomous UAVs and smart vehicles is not fully mature enough to be deployed. Several factors could make the implementation more complex, including large infrastructure, automation of field support systems, traffic control units, and emergency rescue teams. In future studies, the automation of the total transportation systems should be considered.
# Behaviour of PPCP Substances in a Fluvial Aquifer after Infiltration of Treated Wastewater
## 4 Results
### Characterization of the Transport Process  
The transport of PPCPs from the VSK infiltration well to the PZ1, CP1, or PZ5 boreholes can be demonstrated based on increases in the content of PPCP substances. Temporal changes in content enable the approximate assessment of transport rates. The most obvious markers are potentially those substances which did not occur in monitoring wells at the beginning of the experiment, but appeared later. The focus was on substances that, could be used as tracers due to their physical and chemical properties. Such non-degrading and non-sorbing substances migrate in the rock environment at the same speed as groundwater flows--i.e., without retardation. Oxypurniol and Benzotriazole were supposed to be in this category.  
A comparison of both substances reveals that their content are significantly higher in treated wastewater than in the Morava river (Tables 3 and 4). In well PZ5, the content of both pharmaceuticals were already present before the start of the infiltration experiment and remained more or less stable during the entire test, or there was no fundamental fluctuation. Neither Benzotriazole nor Oxypurinol was present in the CP1 and PZ1 boreholes before the start of the infiltration test. Both substances appeared in these objects with a delay of 8-12 days from the start of the experiment and had an upward trend. The values given correspond to velocities of 6.6-4.4 m/day. The distance between VSK and CP1 wells is 52.5 m. In the context of objectivity, it is necessary to emphasize that the calculated mentioned transport rates are not natural, but influenced by pumping in well CP1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**CONCENTRATION OF BENZOTRIAZOL IN ng/L**} \\ \hline \multirow{2}{*}{**Date of Sampling**} & \multirow{2}{*}{**WWTP**} & **MORAVA** & **CP1** & **WELL** & **PZ1** & **WELL** & **PZ5** & **WELL** \\ \hline
7 November 2022 & 4390 & 657 & \textless{}20 & \textless{}20 & 159 \\ \hline
20 November 2022 & 6640 & 459 & \textless{}20 & \textless{}20 & 155 \\ \hline
24 November 2022 & 6610 & 606 & 93 & \textless{}20 & 173 \\ \hline
30 November 2022 & 6290 & 471 & 196 & 62 & 124 \\ \hline
8 December 2022 & 5620 & 838 & analysis error & 167 & 168 \\ \hline
12 December 2022 & 2140 & 498 & 420 & 187 & 155 \\ \hline AVERAGE & 5282 & 588 & 236 & 69 & 156 \\ \hline MEDIAN & 5955 & 552 & 93 & 31 & 157 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time development of Benzotriazole content at monitored objects.  
Figure 3: Results of cluster analysis documenting the relationships between monitored parameters.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 3 Background
### Study Area: New York City  
New York City (NYC), the largest city in the United States with a population of 8,097,282 as of 2024 [33], is an ideal study area for travel time prediction due to its high traffic volume, diverse transportation modes, and complex geography. The city's dense urban environment presents unique challenges, such as significant vehicular and pedestrian traffic, a varied terrain, and an intricate transportation network.  
These factors contribute to the complexity of travel in NYC, making it a valuable testbed for investigating the factors influencing travel time and developing effective prediction methods. Addressing these challenges offers opportunities to enhance travel efficiency and reliability, benefiting both researchers and transportation planners.
# Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks
## Appendix A  
The total rate of the NOMA can be expressed as:  
\[R=BW_{Total}\sum_{n=1}^{N_{Sc}}log_{2}(1+\delta_{n}p_{n}G_{1n})+log_{2}(1+p_{n }G_{2n})-log_{2}(1+\delta_{n}p_{n}G_{2n})\]  
We will define the Throughput per Bandwidth \(R_{BW}=R/BW_{Total}\)  
\[R_{BW}=\sum_{n=1}^{N_{Sc}}log_{2}(1+\delta_{n}p_{n}G_{1,n})+log_{2}(1+p_{n}G_{ 2,n})-log_{2}(1+\delta_{n}p_{n}G_{2,n})\]  
Define the power allocation factor \(a_{n}=\frac{p_{n}}{P_{Total}}\), (with \(\sum_{1}^{N_{Sc}}a_{n}=1\) and \(0<a_{n}<1\)) where \(P_{Total}\) is the total transmit BS power and \(P_{n}=a_{n}P_{Total}\) we get:  
\[R_{BW}=\sum_{n=1}^{N_{Sc}}log_{2}(1+\delta_{n}p_{n}G_{1,n})+log_{2}(1+p_{n}G_ {2,n})-log_{2}(1+\delta_{n}p_{n}G_{2,n})\]  
\[R_{BW}=\sum_{n=1}^{N_{Sc}}log_{2}(1+\delta_{n}a_{n}P_{1}G_{1,n})+log_{2}( \frac{1+a_{n}P_{1}G_{2,n})}{(1+\delta_{n}n_{n}P_{1}G_{2,n})}\]  
After we simplify the equation, we get the expression \(2^{R_{BW}}\):  
\[2^{R_{BW}}=\prod_{n=1}^{N_{Sc}}\left(\frac{(1+\delta_{n}a_{n}P_{1}G_{1n})(1+a _{n}P_{1}G_{2n})}{(1+\delta_{n}a_{n}P_{1}G_{2n})}\right)\]Under the assumption of \(G_{1,n}>G_{2,n}\) and \(\delta_{n}a_{n}P_{t}G_{1,n}\gg 1\), \(\delta_{n}a_{n}P_{t}G_{2,n}\gg 1\), which are in general valid assumptions, we get:  
\[2^{R_{\text{BW}}}\sim\prod_{n=1}^{N_{\text{sc}}}\left(\frac{(\delta_{n}a_{n}P_{ t}G_{1n})(a_{n}P_{t}G_{2n})}{(\delta_{n}a_{n}P_{t}G_{2n})}\right)\ \sim\prod_{n=1}^{N_{\text{sc}}}(a_{n}P_{t}G_{1n})=\prod_{n=1}^{N_{\text{sc}}}a _{n}\prod_{n=1}^{N_{\text{sc}}}P_{t}G_{1n}\]  
We know that the maximum of \(\prod_{n=1}^{N_{\text{sc}}}a_{n}\) with \(\sum_{n=1}^{N_{\text{sc}}}a_{n}=1\) and \(0<a_{n}<1\) is the equal power allocation \(a_{n}=\frac{1}{N_{\text{sc}}}\) where \(a_{n}=\frac{P_{n}}{P_{total}}\), Hence \(\frac{P_{n}}{P_{total}}=\frac{1}{N_{\text{sc}}}\); then \(P_{n}=\frac{P_{total}}{N_{\text{sc}}}\) which the exact expression of the equal power allocation.
# Service Process Modeling in Practice: A Case Study in an Automotive Repair Service Provider
## 4 Service Process Analysis and Modeling
### Identifying and Analyzing the Main Service Processes  
The service process within an automotive repair service provider organization can be understood as a series of five key stages, as illustrated in Figure 1. While Figure 1 presents these as distinct stages, it is important to recognize that, in practice, there are often interdependencies and feedback loops between them, rather than a strictly linear progression.  
Vehicle Scheduling: This initial stage involves the judicious allocation of resources (technicians, spare parts, equipment) to meet customer requests and ensure optimal utilization of workshop capacity. Effective scheduling sets the stage for a smooth vehicle reception process.  
Vehicle Reception: At this stage, the service advisor takes the vehicle from the customer, conducts an initial check, and collaborates with the customer to define the scope of the required service. This stage builds upon the scheduling and transitions into the diagnosis phase.  
Diagnosis: Specialized technicians perform an assessment to identify and analyze the vehicle's technical issues, employing specific equipment and procedures.  
Repair: This stage encompasses the execution of the necessary repair work and the replacement of any defective components, adhering to the diagnosis and quality standards  
Vehicle Handover: The concluding stage involves the service advisor presenting the completed work and associated invoice to the customer and ultimately returning the  
Figure 1: Key stages in the service process.  
vehicle. The handover stage also provides an opportunity to gather customer feedback, which can inform and improve future iterations of the service process.  
Figure 1 is an original contribution of this study, visually representing the five key stages of the automotive service process. It emphasizes the interconnectedness and flow between stages, rather than a strictly sequential progression.
###### Abstract  
Roughly 65% of the African continent is classified as savanna. Such regions are of critical importance given their high levels of biological productivity, role in the carbon cycle, structural differences, and support of large human populations. Across southern Africa there are 79 national parks within savanna landscapes. Understanding trends and factors of vegetation health in these parks is critical for proper management and sustainability. This research strives to understand factors and trends in vegetation health from 2000 to 2016 in and around the 79 national parks across southern Africa. A backward stepwise regression was used to understand the factors (e.g., precipitation, population density, and presence of transfrontier conservation areas) affecting the normalized difference vegetation index (NDVI) during the 21 st century. There was a statistically significant positive (\(p<0.05\)) relationship between mean annual precipitation and NDVI, and a significant negative relationship between population density and NDVI. To monitor vegetation trends in and around the parks, directional persistence, a seasonal NDVI time series-based trend analysis, was used. Directional persistence is the net accumulation of directional change in NDVI over time in a given period relative to a fixed benchmarked period. Parks and buffer zones across size classes were compared to examine differences in vegetation health. There was an overwhelmingly positive trend throughout. Additionally, national parks, overall, had higher amounts of positive persistence and lower amounts of negative persistence than the surrounding buffer zones. Having higher positive persistence inside of parks indicates that they are functioning favorably relative to the buffer zones in terms of vegetation resilience. This is an important finding for park managers and conservation overall in Southern Africa.  
remote sensing; vegetation dynamics; savanna landscapes; national parks; conservation +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Material Compatibility  
Material compatibility is a critical aspect when using DED for repairing and optimising existing components. Ensuring that the material used for repair matches the base material is crucial. Differences in material composition, thermal expansion coefficients, or mechanical properties between the base material and the additive material can lead to poor adhesion, cracking, or compromised structural integrity. Selecting the appropriate alloy or material for the DED process becomes a challenge, especially when dealing with a wide range of materials used in forging tools and moulds. The repair material must possess similar characteristics (mechanical, thermal, and chemical) as the original material to maintain the tool's performance and durability. Material incompatibility can induce thermal stresses during the DED process, causing cracking or delamination between layers or at the interface with the base material. Mismatched thermal properties may result in residual stresses, leading to material failure.  
Achieving uniform microstructural properties across the repaired area is necessary, as variations in cooling rates or heat-affected zones during the deposition process might lead to differences in grain structure, which can affect the mechanical properties of the repaired component. DED involves controlling various parameters like laser power, deposition speed, and material feed rate. Material selection influences these parameters, and using materials with significantly different characteristics may require adjustments that affect the overall deposition process and quality.  
Considering that material selection and compatibility profoundly impact the repair process, here are most important phases needed for a successful DED repair:  
1. Pre-repair assessment, where a comprehensive material analysis of the base material is made. This is crucial to understand its composition, structure, and mechanical properties. This information guides the selection of compatible repair materials and helps in defining optimal DED processing parameters.
2. Choosing the optimal process parameters is carried out based on the chosen material for repair; the DED process parameters need fine-tuning to ensure proper fusion and adhesion and minimal thermal stress. Parameter adjustments may include laser power, deposition strategy, and deposition speed, among others.
3. Continuous monitoring and quality checks during the deposition process and post-repair inspections are critical. Non-destructive testing techniques can help identify any material inconsistencies or structural flaws that might compromise the repaired tool or mould.  
The manufacturing environment (e.g., ambiance temperature and humidity) is an important factor for a successful DED repair process. Thus, despite meticulous planning, some degree of trial and error might be necessary, especially when dealing with novel materials or complex geometries. Iterative testing and adjustments may be required to achieve the desired material compatibility and quality.  
Overall, material selection and compatibility heavily influence the success of the repair process using DED.
# Ontology with Deep Learning for Forest Image Classification
## 6 Proposed Model
### Feature Extraction  
The feature selection preprocessing step plays a significant role in tasks relating to image classification. The ensemble stacked model of VGG16, Xception, and ResNet50 (top of Figure 5) was used to obtain features from the training dataset. The composite set of features obtained by the three deep learning techniques was used as a feature vector for the model. The ensemble technique helps to increase the scope of the feature vector. A single feature selection method only selects an optimal subset of features from the training dataset; hence, the final feature vector may not be a true reflective set to serve as a basis for the subsequent image classification process. Ensemble feature selection may produce a more accurate outcome by combining the different outputs of various techniques.