esa-sceva/satcom-chunk-collection huggingface dataset:
# Unravelling the Environmental Application of Biochar as Low-Cost Biosorbent: A Review
## 4 Biochar Adsorption of Emerging Contaminants
### Pharmaceuticals and Personal Care Products (PPCPs)  
Due to drug abuse, the concentration of this kind of pollutant in several water scenarios far exceeds the pollution limit, so there is an urgent need for a potentially efficient, sustainable and eco-friendly method such as biochar adsorption to avoid this problem. Thus, to obtain a stable and repeatable adsorbent with better pollutant removal efficiency, it is necessary to prepare engineered biochar by the application of modification techniques such as metal embedding, nanomaterial decoration or surface functionalisation.  
In this context, by hydrothermal treatment, hierarchical MoS\({}_{2}\) nanomaterials were embedded in rice straw biochar, obtaining a novel biochar-based nanomaterial \"g-MoS\({}_{2}\) nanosheet-decorated biochar\" that was tested in the removal of tetracycline hydrochloride [109]. The pore structures and surface properties of biochar enhanced the tetracycline hydrochloride adsorption by several binding mechanisms: electrostatic interaction, pore-filling, hydrogen bonding and \(\pi\)-\(\pi\) conjugate effect (Figure 3). In addition, this material showed excellent reusability, as NaOH treatment detected only a slight reduction after five adsorption/desorption cycles.  
In the literature, different metal-biochar composites have been synthesized, providing good results thanks to their multifunctionality (adsorption, reduction, and catalytic activity) that make them good materials for the removal of different contaminants [110]. [PERSON] et al. [111] prepared by pyrolysis a \"multifunctional mixed metal-biochar composites\" using a mixture of two abundant industrial wastes, red mud and lignin, and demonstrated its ability to perform the removal of p-nitropenol and p-chlorobenzoic acid and a great variety of contaminants.  
The removal of bisphenol A and sulfamethoxazole was successfully performed by adsorption on a \"biochar-supported magnetic CuZnFe\({}_{2}\)O\({}_{4}\) composite\" [112]. The involved mechanisms were charge-assisted H-bonding, hydrophobic, and \(\pi\)-\(\pi\) electron donor-acceptor interactions. The main advantages of this composite are its magnetic properties, which made the separation process easy and reduced the reusability to lower than 10% after four cycles of regeneration, as well as the removal efficiency of both pollutants.  
Another conventional process such as the physical and chemical activation of the biochar also allows for obtaining substantial improvements in the material properties that favor the removal of pharmaceuticals and personal care products [113]. Thus, steam and phosphoric acid can activate the surface of bagasse biochar at high temperature, creating many holes on the surface, thereby increasing the adsorption of ibuprofen [114]. In addition, this biochar presented reusability potential by ibuprofen desorbing with methanol, but after four adsorption/desorption cycles, the removal percentage decreased by around 30%.
# Transpiler-Based Architecture Design Model for Back-End Layers in Software Development
## 3 Transpiler-Based Design Model for Back-End Layers
### Detailed Design
#### 3.4.10 Transversal Components  
* Post-Build Utility The post-build utility plays a crucial role in compiling technology-specific artifacts for each execution environment. It is an essential element that runs across the entire back-end layer and follows the process established in the design proposal. The tool executes the preparation stage in sequence, deploys nest implementations, and generates back-end proxies.
* DB Gen If the database-first strategy is employed, wherein the database is designed prior to programming, the DB Gen component should be considered. It can automatically read the structure of tables and views in the database, then generate the programming of this layer in a comprehensive manner that is compatible with the generated ORM.  
A DB Gen version is necessary for each database engine brand supported in the generation process.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 2 Related Works and Contributions
* We use in our optimization problem a precise model of energy consumption that considers different modes of the class A LoRa transceiver over a period of time between two successive transmissions. * We prove the convexity of our problem, and we use the Dinkelbach iterative method, where convergence is rapidly attained. * Our algorithm, EE-LoRa, does not require signaling (beacons) or an additional synchronization process. It does not introduce any change to the LoRaWAN specification and can be applied to all LoRaWAN classes, particularly class A. Actually, the network server only sends downlink notifications to nodes that should change their configurable parameters (SF, TP) in one of their receiving windows. * We evaluate our algorithm in a realistic scenario and compare the results to legacy LoRaWAN and relevant state-of-the-art algorithms, namely, OPT-MAX and OPT-DELTA. Moreover, we assess the results in different contexts related to minimum TP and idle current. We demonstrate that EE-LoRa outperforms the energy efficiency of legacy LoRaWAN and relevant state-of-the-art algorithms.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 8 Performance Improvement of IoB-DTN Based on Data Aggregation Mechanism
### Discussion Results  
We recall that without aggregation the compromise between the delivery ratio and the delivery delay metrics is settled by the buffer size, as shown in Figures 44 and 45. In these figures, we observe that the buffer size impacts the delivery rate and the delivery delay of received packets more than the duty cycle. More precisely, using small buffer sizes improves the delivery delay, as shown in Figure 45. In such case, the buffer will be full very fast and oldest packets are always dropped thanks to the GPP buffer management policy that gives priority to the self-generated packets. Using large buffer sizes enhances the delivery ratio as depicted in Figure 44. This is because there is more space to store generated and received packets, resulting in a higher transmission rate.  
Based on the previous results cited in this section, we can argue that IoB-DTN protocol applying data aggregation approach improves the compromise between several metrics. It increases the delivery rate and the throughput as well as it saves the communication cost, and thus the network capacity. However, it does not impact much the delivery delay. It is interesting to point out that using IoB with aggregation provides less sensitivity to the buffer size regarding to all evaluated metrics.  
Figure 43: Average number of aggregated packets per packet for IoB-STA.  
In an urban application, the choice of which variant of IoB should be used depends on the sensed values. To understand the limitations of each variant of IoB, we depict in Figure 46 a radar schema summarizing their performances with respect to evaluated metrics. Here, we use a score between 1 and 5 illustrating that the higher value offers the best performance.  
As mentioned above, each application needs to achieve better performances for some metrics than others. For that, we present, as an example in Figure 47, another radar schema that indicates the performances required for five proposed applications with regard to temporal aggregation, spatial aggregation, delivery delay and throughput. For example, accident detection application require an important delay to be announced as well as an important temporal and spatial aggregation since the time and place of an accident are major parameters in such application while it requires a moderate throughput. Considering the road quality application, it requires a low delivery delay to be transmitted, low throughput and temporal aggregation. The paramount parameter, in this application, is the road location.  
Figure 44: Loss rate.  
Figure 45: Delivery delay.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 2 Materials and Methods
### Subject-Independent Classification  
BCI systems need large-scale MI data for training and classification. Many subject-specific classification models have been proposed in the literature for BCI systems [2; 10; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. However, individuals cannot imagine a motor task beyond a certain duration, and even across different sessions, the data collected from the same subject may present any external artifacts, leading to intrasubject variability [39]. Consequently, researchers have started collecting small samples from many subjects instead of collecting many samples from each subject in a single session [22; 39]. A BCI system should perform subject-independent classification (see Figure 8) as it helps adapt to many people [40]. We used 82 subjects (80%) EEG data for training and validation and the remaining 21 subjects (20%) EEG data for testing. The sampling frequency of the data was 160 Hz. We excluded the data from 6 subjects because of the presence of mistrials, which would lead to discrepancies in the training, validation, and testing of the model [41]. Hence, subject-independent classification is performed on the EEG data from 103 subjects recorded during 4 MI tasks (left fist, right fist, both fits, and both feet). The subjects were split randomly for k-fold crossvalidation, where (k-1) folds are used for training, and the remaining fold was used for validation. Finally, we saved the model weights and tested the model with the remaining 20% of the samples from the subjects not known to the model.  
Figure 7: ResNet Module architecture. The ResNet module consists of a combination of Convolution block and Identity blocks. (*) represents the number of blocks of each type.
## 1 Introduction
### Modeling Framework  
The approach presented in this work is illustrated in the block diagram of Figure 1. It depicts a connected EV (CEV) that acquires information \(s\) from the environment and performs longitudinal and lateral actions. The parallel closed-loop scheme implements a lateral pure pursuit controller [49] and one of two longitudinal control options: rule-based or fuzzy inference system (FIS) control systems. The presented approach aims to solve the EAD problem by implementing a coupled GLOSA-RBS system. The simulations and numerical analysis of this work demonstrate that power consumption during navigation is reduced. It also seeks to show that both longitudinal approaches have high motoring and regenerative efficiency during navigation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Navigation Algorithm** & **Vehicle Type** & **Metric** \\ \hline Consensus motion control algorithm [17] & ICEV & MOVES [44] 20\% time reduction, 23.7\% fuel consumption reduction \\ Longitudinal speed guidance [18] & ICEV & MOVES 3–5\% energy reduction \\ GlidePath [19] & ICEV & CMEM [45] 17\% fuel consumption reduction, up to 64\% time reduction \\ Rule-based [20] & ICEV & Physical platform 6\% energy savings \\ GLOSA [22] & ICEV & 21\% emission reduction, 121\% time increment \\ Mixed-integer linear programming [23] & Electric bus & Up to 40\% energy reduction \\ Cooperative eco-driving system [24] & ICEV & MOVES 7\% energy reduction, 59\% emissions reduction \\ Prediction-Based EAD [25] & ICEV & 4.0\% energy savings, 4.0–41.7\% emissions reduction \\ Dynamic programming with MPC [26] & Four-wheel-drive EV & 15–20\% energy reduction \\ EAD along signalized corridors [27] & ICEV & 12–28\% fuel savings \\ Sequential quadratic programming & EV & 10.33\% extended battery life \\ with MPC [28] & EV & 101.96\% power regenerated than rule-based approach \\ FIS-based EAD/RBS (this work) & EV & 101.96\% power regenerated than rule-based approach \\ \hline \hline \end{tabular}
\end{table}
Table 1: Longitudinal navigation performance comparison.  
Figure 1: AV navigation block diagram. Sensor and SPaT data is represented by a green arrow, processed perception information in pink, longitudinal control in red, lateral control in blue, navigation inputs in orange and actions in black.  
The proposed model uses a hierarchical approach (Figure 1). First, odometry data are retrieved by the internal sensors of the vehicle. It is assumed that the internal states of the vehicles are calculated through sensor fusion techniques and that proprioceptive data are always available and noise-free. Simultaneously, surrounding infrastructure provides SPaT data, which are assumed to be noise-free. Noisy measurements, data loss, and partial observations will be covered in future research. Then, perception module fuses the data to obtain pose information of the vehicle. Next, the pose is fed to the control module, which contains a rule-based or a FIS control systems for longitudinal actions and a pure pursuit controller for lateral actions. Finally, the control signals are sent to the three-degree-of-freedom (3-DOF) dynamics module of the CEV.
###### Abstract  
Climate change has made weather more erratic and unpredictable. As a result, a growing need to develop more reliable short-term weather prediction models paved the way for a new era in satellite instrumentation technology, where radar systems for meteorological applications became critically important. This paper presents a comprehensive review of the evolution of weather forecasting satellites. We trace the technological development from the early weather and climate monitoring systems of the 1960s. Since the use of stabilized TV camera platforms on satellites aimed at capturing cloud cover data and storing it on magnetic tape for later readout and transmission back to ground stations, satellite sensor instrument technologies took great strides in the following decades, incorporating advancements in image and signal processing into satellite imagery methodologies. As innovative as they were, these technologies still lacked the capabilities needed to allow for practical use cases other than scientific research. The paper further examines how the next phase of satellite platforms is aimed at addressing this technological gap by leveraging the advantages of low Earth orbit (LEO) based satellite constellation deployments for near-real-time tracking of atmospheric hydrometers and precipitation profiles through innovative methods. These methods involve combining the collected data into big-data lakes on internet cloud platforms and constructing innovative AI-based multi-layered weather prediction models specifically tailored to remote sensing. Finally, we discuss how these recent advancements form the basis for new applications in aviation, severe weather readiness, energy, agriculture, and beyond.  
Space-based remote sensing; radar; multi-sensor satellite constellations; neural networks; weather forecasting models; cloud technology; radiometers
# Off-Grid Power Plant Load Management System Applied in a Rural Area of Africa
## 5 Pattern Matching-Based Load Anomaly Detector
### Benign Data Training  
The pattern matching-based load anomaly detector is developed with a \"binary\" rule, i.e., the patterns that do not match the defined normal behaviors are detected [21]. If the test data do not correspond to a learned power consumption pattern, the detector identifies the data as an anomaly. To reduce the training cost, the autoclustering method is introduced. The autoclustering method proposed in this study includes the k-means method with silhouette coefficients [17]. The k-means clustering is a widely used clustering method that is fast and simple [22]. Under the premise of knowing the number of target groups \(k\), k-means can assign the data to \(k\) disjoint clusters. However, for new data when \(k\) is unknown, the application of k-means has limitations [5]. To overcome this limitation, the clustering evaluation method silhouette coefficient is employed. The silhouette coefficient can effectively reflect the similarity between objects in the same cluster and objects in other clusters.  
The silhouette coefficient ranges from \(-1\) to \(+1\), and the larger the value is, the higher the similarity. The silhouette coefficient is defined as [17]:  
\[SC(k)\ =\ \frac{b(k)-a(k)}{max\{a(k),b(k)\}} \tag{3}\]  
where \(a(k)\) is the mean intracluster distance and \(b(k)\) is the mean nearest-cluster distance for \(k\). In this study, to verify the data efficiency of the proposed method, we only use five-day data for training, as shown in Figure 2a. We assume that the maximum number of groups is \(m\) and label clustering scenarios from 2 to \(m\). For each scenario, k-means is used to cluster the data, and the silhouette coefficient is used to evaluate the result. After \(m\) clustering epochs, we obtain \(m\) silhouette coefficients, and the maximum value of the silhouette coefficient is selected to identify the best clustering result [22].  
According to the autoclustering results shown in Figure 3, there are 4 patterns in the training data, labeled from 0 to 3. Patterns 2 and 3 mainly occur during the non-variation period; however, during the daytime, the patterns are irregular. The results of autoclustering are used as the training set for our method, and any data detected that are not associated with one of these patterns are identified as an anomaly.
# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 4 Experiments
### Experimental Setup
#### 4.1.4 Dimension 4: XAI
For each event log resulting from the previous step, * Conduct data analysis using the same techniques as in step (1); * Train and build a separate predictive model using Logistic Regression and XGBoost; * Query each model for the important features it depends on (intrinsic explainability); \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Event Log** & **Preprocessing** & **XGboost** & **Logistic Regression** \\ \hline \multirow{3}{*}{Sepsis1} & single\_agg & 0.33427 & 0.57124 \\ \cline{2-4}  & prefix\_index (avg. AUC of len 1–16, gap = 5) & 0.4292 & 0.52599 \\ \hline \multirow{3}{*}{Sepsis2} & single\_agg & 0.91374 & 0.88567 \\ \cline{2-4}  & prefix\_index (avg. AUC of len 1–11, gap = 5) & 0.81379 & 0.46253 \\ \hline \multirow{3}{*}{Traffic\_fines} & single\_agg & 0.73918 & 0.7949 \\ \cline{2-4}  & prefix\_index (avg. AUC of len 1 \& 6, gap = 5) & 0.6515 & 0.67804 \\ \hline \multirow{3}{*}{BPIC2017\_Accepted} & single\_agg & 0.86429 & 0.8244 \\ \cline{2-4}  & single\_agg & 0.68328 & 0.70706 \\ \cline{1-1} \cline{2-4}  & prefix\_index (avg. AUC of len 1–16, gap = 5) & 0.7556 & 0.7677 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: AUC scores of predictive models categorized according to preprocessing configurations. * Compare the most important features of each model over two runs (stability check);
* Compare the most important features of the two models with each other;
* Explain the predictions of the two models based on two XAI methods, i.e., Permutation Importance and SHAP (global explainability). We generated explanations twice and checked the similarity of the results in both runs (stability check).
# An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services
## 2 Preliminaries
### System Model  
In reality, a large amount of IoT applications are based on LBS which are the key elements for implementing IoT location services (as shown in Figure 1). For instance, the location information of an object being transported can be accurately located through the LBS while in the process of transportation. The Enhanced-DLP scheme can be adapted to these scenarios in the IoT.  
There are two main parts in the system model: the LBS server and IoT user devices, as shown in Figure 2.  
* **The LBS server**: The LBS server receives the location service request from the user devices, and then searches the requested service in the LBS database and sends the search results back to the user. The LBS server contains various types of service databases and provides users with diverse services. Moreover, the server may also count the probability of each user's query at all locations when providing service information for each user according to the location.
* **The IoT user device**: The IoT user device is a typical part of the location service system, which usually obtains user location information through a GPS module embedded in the device (e.g., a smartphone). The IoT device obtains the corresponding high-quality location service by sending its high-precision location information to the LBS server. In order to prevent the user's location information from being used twice, the location information submitted to the LBS server should include the accurate location and dummy location, which ensures that the LBS server is not able to obtain the user's real location.  
Figure 1: Location services and applications in the Internet of Things (IoT).
## 7 Simulation Environment
### Packet drop calculation  
The packet drop calculation is done using the formula (number packet sent--number of packet received). Figure 12 compares the packet drop performance of the nodes for various protocols namely GA-LEACH, MW-LEACH, CSOGA, and MWCSGA. For this scenario, the simulation experiment is carried using 100 nodes. The packet drop performance of MWCSGA protocol is 153 packets. Whereas the earlier protocols such as GA-LEACH, MW-LEACH, and CSOGA protocol produces energy efficiency as 639, 536, and 340 packets, respectively. According to analyzes it proves that the performance of MWCSGA protocol produces less packet drop when compared with the others.
# Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
## 2 Related Work
### Embedding-Based Reasoning  
Recently, embedding-based methods have attracted much attention. These methods aim to learn distributed embeddings for entities and relations in KGs. Generally, the current KGE methods can be divided into three classes: (1) translation-based models that learn embeddings by translating one entity into another entity through a specific relation [22; 23]; (2) compositional models that use simple mathematical operations to model facts, including linear mapping [24], bilinear mapping [25; 26; 27], and circular correlation [28]; (3) neural-network-based models that utilize a multilayer neural structure to learn embeddings and estimate the plausibility of triples with nonlinear features: for example, R-GCN [29], ConvE [30] and so on [31; 32; 33]. The above methods learn representations based only on the triples existing in KGs, and the sparsity of data limits them. In order to address this issue and acquire semantic-rich representations, recent studies have made efforts to incorporate additional information apart from triples. These include contextual information [34], entity type information [35; 36], ontological information [37], taxonomic information [38], textual descriptions [39], commonsense knowledge [40], and hierarchical information [41]. In contrast, the proposed Iterlogic-E utilizes embeddings to eliminate erroneous conclusions derived from rules, which combines the advantages of soft rules and embeddings.