esa-sceva/satcom-chunk-collection huggingface dataset:
# E-Waste Challenges in India: Environmental and Human Health Impacts
## 7 Solution and Recommendation
The government plays a significant part in regulating rules by maintaining and enforcing these standards [82]. Along with that, there is an urgent need to spread awareness and educate local communities, producers, consumers, stakeholders, and policymakers about the negative impacts of electronic waste on environmental conditions and individual health. The 3 Rs (reducing, reusing, and recycling) should be prioritized, following circular economy practices through collaboration between governments, NGOs, and businesses to create effective policies, infrastructure, and innovation, which could minimize waste, extend product lifecycle, and maximize resource efficiency. Table 4 provides a step-by-step outline of the essential steps crucial for effective e-waste management in India. \begin{table}
\begin{tabular}{c c c} \hline \hline
**S. No** & **Key Dimensions** & **Description** \\ \hline
1 & Collection and Transportation & Setting up of strategic collecting sites in a large area and forming partnerships with the logistics companies that will ensure the transportation of e-waste securely to the strategic locations [83]. \\ \hline
2 & Sorting and Separation & The hazardous e-waste and non-hazardous e-waste are separated in the collecting sites, so the waste can be recycled and managed in a safe way [48; 84]. \\ \hline
3 & Innovation and Technology & The advanced innovative technologies promote the development of durable and repairable electronic assets, which can prioritize circular economy practices such as the reprocessing and reuse of e-waste [85; 86; 87]. \\ \hline
4 & Recycling and Disposal & The establishment of efficient recycling centers in large areas and in partnership with specific companies should be followed for the responsible management of waste generated from electronics [88; 89]. \\ \hline
5 & Legal Policies and Regulations & Implementing norms and guidelines for managing e-waste involves regulatory framework, punishments and penalties to be paid for non-compliance, and rewards for promoting environmentally friendly practices [90]. \\ \hline
6 & Public Awareness and Education & Educating the public about managing e-waste in a sustainable manner, its impact on the environment and health, and ways to adopt sustainable practices. \\ \hline
7 & Corporate Responsibility & Companies should promote eco-friendly or green policies, recycling practices to be supported, and reduce the generation of e-waste. \\ \hline
8 & Ecological and Social Effects & Ecological and health risks related to e-wastes need to be addressed, promoting safe disposal methods, supporting and helping affected communities [20]. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Essential steps for effective e-waste management in India. Figure 8: An illustration outlining the key aspects in managing e-waste. These aspects are essential for ensuring a safer, healthier, eco-friendly, and more sustainable future, which also ensures accountability and a sustainable way of managing e-waste.
## 2 Related Works  
### Liver and Tumor Segmentation  
Recently, many researchers have proposed liver and tumor segmentation methods based on CNN. [PERSON] et al. [29] proposed a method for automatically segmenting livers and tumors in CT and MRI abdominal images using cascaded fully convolutional neural networks, which included two sub-networks for liver segmentation and tumors. The first FCN segments the liver, and the second FCN only segments the lesions within the liver predicted in step 1. In order to extract multiscale features, [PERSON] et al. [30] proposed a Scale-Axis-Attention (SAA) mechanism to model multiscale features and spatial information, respectively. Furthermore, they incorporated it into a U-shaped network (SAA-Net) to improve liver tumor segmentation performance. [PERSON] et al. [31] introduced CPAD-Net, a novel network for liver tumor segmentation, which integrates parallel contextual attention and dilated convolutions. This network substitutes max-pooling with a subsampling module to retain fine-grained features, integrates a contextual parallel attention module at the skip connections, and combines multiscale contextual features while concurrently extracting channel-space features. [PERSON] et al. [32] proposed a deeply supervised network based on channel attention and Res-U-Net++ for segmenting liver CT images. An efficient attention module is introduced to combine the deep feature map with spatial information to alleviate the influence of uneven sample distribution. [PERSON] et al. [33] proposed HFRU-Net to modify-skip connections using a feature fusion mechanism and local feature reconstruction. [PERSON] et al. [34] proposed a novel residual multiscale attention U-Net (RMAU-Net) for liver and tumor segmentation by introducing two modules, Res-SE-Block and Multiscale Attention Block (MAB). Res-SE-Block improves the quality of representations by explicitly modeling interdependencies between feature channels and feature recalibration. MAB can leverage rich multiscale feature information while simultaneously capturing inter-channel and spatial relationships of features. Currently, most of the algorithms are based on CT images, and there needs to be more research on CE-MRI images.
## 4 Results and Discussion
### Phase 1: Assess the Potential and Maturity for IoT Solution Implementation
#### 4.1.8 Are Advanced Control Systems Necessary?  
In Table 5, item 8 shows the need for advanced control systems for production activities for the companies surveyed. Only 43% of companies answered this question in the affirmative, indicating that these systems are intended for granular control of costs and production.
# The RHO Family GTPases: Mechanisms of Regulation and Signaling
## 3 Regulation of RHO Family GTPases
### GTPase-Activating Proteins (GAPs)
#### 3.4.1. RHOGAP Family Proteins  
The GTPase reaction is of great medical significance, since any disruption of this reaction such as that caused by inhibitory mutations in genes encoding GAP proteins results in persistent downstream signaling. The discovery that GAPs are required for GTPase downregulation was made on the basis that microinjection of recombinant GTP-bound RAS into living cells results in faster GTP hydrolysis than is realized in vitro [125]. The first discovered RHOGAP, p50 RHOGAP, was identified by biochemical analysis of human spleen cell extracts with recombinant RHOA [119], and this discovery led to the identification of other RHOGAP-containing proteins such as chimaerin and BCR, whose amino acid sequences are related to p50 RHOGAP [126]. Since then, more than 66 RHOGAP-containing proteins have been identified in humans [114; 127] The RHOGAP family is identified by the presence of a conserved catalytic GAP domain that is sufficient for RHOGAP interaction with RHO proteins and, in most cases, stimulation of the intrinsic GTP hydrolysis reaction of RHO GTPases [115]. In addition to their signature RHOGAP domain, most RHOGAP family members frequently harbor several other functional domains (Figure 4). The majority of these domains can be classified into the following three major groups: (i) lipid- and membrane-binding domains; (ii) peptide- and protein-interacting domains; and (iii) catalytic domains with enzymatic activities. The most widespread domains are PH, CC, P, SRC homology 3, and BAR/F-BAR (Figure 4). These domains are implicated in regulation, membrane targeting, localization, and potential phosphorylation sites and indicate the complexity of the regulation of GTPase activity. Thirteen GAPs lack any additional putative domains but contain highly variable regions in their N- and C-termini (Figure 4). It is possible that these regions consist of motifs that have not yet been identified, and these regions may contribute to their specific function in the cell.
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
## 3 The Proposed Scheme
### Region Construction and Region Head Election  
The geocasting scheme is used for disseminating tactical messages to nodes within a specific area. This study constructs target regions firstly and then elects a region head in  
Figure 1: Flowchart of Cell Head Election Algorithm.  
each target region. The sink defines an area of interest as a target region and includes one or more cell, which is calculated by using the location and the GID. As shown in Figure 2, in a target region, it also needs a region head \(H\) for carrying out the data collection and data transmission. The region head \(H\) of the target region is the node that has the minimum distance between the sink and the nodes in the target region. This study uses the MAODV algorithm [24] to construct a shortest path tree at the region head \(H\).
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 2 Literature Review
The system uses the traffic information around the carpool vehicle to optimize the travel time of passengers and generates a detailed route plan for the carpool vehicle. [PERSON] et al. [25] proposed a route planning mechanism for supermarket shuttle service based on large-scale taxi GPS trajectory information, which optimizes the shuttle routes by exploring pick-up and drop-off information in order to improve the quality of service. [PERSON] et al. [26] proposed an algorithm to optimize taxi route planning by combining multiple factors by constructing a grid-based road network graph and using deep learning techniques to predict taxi demand and destination distribution. By combing through the literature, it was found that existing studies usually conduct multi-objective route planning based on trajectory data in the dimensions of cost and time. However, taxi loading is not a mere passenger demand event, and if only the fixed point-to-point person-vehicle matching between taxis and passengers is studied and only a single path between a fixed starting point and a destination point is considered, the algorithms and modeling will inevitably suffer from the problem of incomplete influencing factors that lead to biased results. In summary, it is found that the existing research based on artificial intelligence, trajectory data-driven hotspot area mining for picking up and dropping off passengers, the construction of passenger-seeking recommendation index model, and the recommendation of taxi passenger-seeking strategy are developed in the direction of high accuracy, reasonableness, and practicability, which lays the foundation of the research in this paper. However, there are still the following shortcomings: the strong time-varying nature of the road passenger probability brings great challenges to the accuracy of the related research results, and the existing research focuses on the performance of clustering algorithms and optimization algorithms but ignores the pursuit of road passenger probability, which is the most essential part of the taxi driver's passenger search behavior. Therefore, this paper proposes a method for calculating and predicting the probability of passenger-seeking road loads, constructs a road load prediction model based on the BiLSTM neural network, and calculates the load probability of all roads by the road load probability calculation method. Then, a passenger-seeking route recommendation model for taxi drivers is proposed, and the Pareto solution set of the passenger-seeking scheme is solved based on the NSGA-II algorithm, which solves the problem of recommending passenger-seeking paths for taxis according to the conditions of real-time changes in the probability of carrying passengers. The technical route is shown in Figure 1.  
Figure 1: Technology roadmap.
## 2 Materials and Methods
### Parallel Algorithms
#### 2.4.3 classification_parallel Algorithm (Parallel Classification)
Selecting the best error type based on the minimum MSE: After the parallel optimization is complete, the algorithm finds the index of the minimum value in the list of best_losses using the argmin function. This index best_loss_idx indicates the error type that provided the lowest MSE under the optimal parameters. The name of the best error type best_error and the corresponding minimum MSE value best_loss are extracted from the lists errors and best_losses, respectively, using the index found. Result Generation: A result list is generated containing two elements: the name of the best error type best_error and the corresponding minimum MSE value best_loss. Output data: The algorithm returns a list of result, which contains the name of the error type classified as the most likely for the input signal \(y\) and the minimum MSE value achieved for that error type. In case the input signal was recognized as \"bad\", the algorithm returns [\"Bad signal\", \"Nan\"]. Remarks  
The classification_parallel algorithm is an efficient method for classifying error types, combining automatic parameter optimization using Optuna and parallel computation to speed up the process. Parallelization is particularly important because optimizing parameters for each error type can be computationally expensive. The selection of the error type with the lowest MSE as the most likely error type is based on the assumption that the error type that can be best \"fitted\" to the signal \(y\) (in the sense of MSE) and is the most likely error type present in the signal. The efficiency of classification depends on the adequacy of the set of error types errors and the quality of parameter optimization.
## 8 Discussion
### Element Type and Size Impact Analysis  
Since fatigue life assessment based on equivalent structural stress method is insensitive to meshing style, it is indispensable to verify the negligible effect of changing element type and size on the computing results near the weld. If the verification is failed, it means the results are incorrect.  
Take Location 3 as an example, for the alteration of meshing styles is relatively easy and convenient. Different element types, which contain linear element SOLID45 (8 nodes) and quadratic element SOLID95 (20 nodes), or different meshing sizes, which contain 1 mm and 2 mm in the vicinity of Location 3 (in the direction perpendicular to the weld seam) as shown in Figure 10 are adopted, respectively. Table 3 lists the description of these four meshing types. Time history analysis is performed as previously stated. The results of effective equivalent structural stress range \(\Delta S_{eq}\) and fatigue life are illustrated in Figure 11.  
According to the results obtained above, it can be found that with the decreasing element size dimension and the use of high order elements, the effective equivalent structural stress range and fatigue life assessment results based on equivalent structural stress method keep almost invariant, which verifies that the equivalent structural stress method is indeed mesh-insensitive.
## 2 Background and Related Work
### Overview of Related Studies
#### 2.2.5 Summary  
To summarize, several points stand out. First, numerous authors considered an IAB network with the number of hops restricted to one or two for simplicity. Second, only a few studies considered the end-to-end latency as the main metric of interest; UE throughput and spectral efficiency were often optimized instead. Third, limited results were available for in-band half-duplex transmission, which must be supported by IAB systems. Furthermore, to the best of our knowledge, there have been no studies proposing an efficient general TDM pattern for IAB-node operation in this regime. Finally, we note that, due to the complexity of the problem, many authors resort to machine learning techniques, namely DRL. However, the state space of the systems is often extremely large, resulting in high solution complexity and limited tractability.
# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
## 3 Experiments and Results
### Effects of Hyperparameter Setting on Prediction Performance
The optimal value was larger in predicting DPU side effects than in predicting non-DPU side effects. (0.7 and 0.1 in predicting DPU and non-DPU, respectively). That is, when the multidrug inputs included _new_ drugs for which no multidrug clinical information is known, considering the regularization based on connective strengths more than the side effect labels in training models led to better predictive performance. Additionally, we investigated performance under other three hyperparameters: unified embedding dimension (UED), hidden layer dimension (HLD), and learning rate (LR). The default value is 256, 384, and 0.01 for HLD, UED, and LR, respectively. We did not use a grid search to find the optimal hyperparameter combination because the search space is too large with many possible combinations. Figure 4 shows the experimental results. The appropriate UED and HLD for both DPU and non-DPU side effects predictions seem to be 256 and 384, respectively. In DPU side effects prediction, the best performance was clearly shown at the UED 256 and HLD 384. On the other hand, in non-DPU side effects prediction, the performance increased slightly when UED 256 or higher was used, but the performance change was very little. Similarly, even if the performance increased slightly when using HLD 384 or higher, the performance change was very little. Meanwhile, the appropriate LR seem to be 0.01 in both DPU and non-DPU side effects predictions. In predicting DPU side effects, the best performance was clearly shown at the LR 0.01. In predicting non-DPU side effects, the performance seems to stabilize around LR 0.01. Although performance for LR less than 0.01 (e.g., LR 0.002) is similar to that for LR 0.01, we used LR = 0.01 because of training time. In our experimental environment, the training time for 1 epoch was about 0.2 s using GPU of Quadro RTX 6000. Figure 3: Polypharmacy side effects prediction performance under different choices of the hyperparameter \(\alpha\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Type** & **AUC** & **AUC-PR** & \
\begin{tabular}{c} **Targeted Side** \\ **Effects (Ses)** \\ \end{tabular} &
\begin{tabular}{c} **Used Resources for** \\ **Graph Construction** \\ \end{tabular} & **Used Data** \\ \hline Proposed model & 0.912 & 0.901 & \multirow{3}{*}{200 Ses} & Drug-protein (DP), and & 14,247 labeled drug pairs \\  & & & & Protein-Protein (PP) & and 284 labeled single drugs \\ \hline Decagon [14] & 0.805 & 0.792 & \multirow{3}{*}{200 Ses} & PP, DP, and multi-typed & 14,247 labeled drug pairs \\  & & & Drug-Drug & and negative sampling \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Non-DPU side effects prediction performance comparison with existing models trained with our data.
## 4 Discussion
Meanwhile, the innovative point of our results is that our DSPPs are potentially commercial pills that could be utilized on a large scale. To evaluate the change that occurs during digestion, our current study examined the TPC and AOC during simulated in vitro digestion. Following digestion, the TPC and AOC content in DSPPs rose, showing that they were released from the food matrix during digestion. During digestion, macromolecules obtain a pH that is favorable for hydrolysis and the release of polyphenols attached to them [40]. As a result, DSPPs that were digested tend to have higher TPC, which supports antioxidant capabilities. This claim is plausible, since polyphenols are what provide the samples' antioxidant activity; therefore, an increase in the overall phenolic content will correlate to an increase in antioxidant activity. Both the DPPH radical scavenging experiment and ABTS assay findings showed that after digestion, the AOC of DSPPs considerably increased, indicating the release of TPC from the food matrix. The AOC was unaffected by the drop in TPC, as shown by the results of the DPPH and ABTS radical scavenging experiment. The antioxidant capacities measured by DPPH and ABTS methods are based on the occurrence of hydroxylated antioxidative components. Meanwhile, the ABTS radicals are soluble in water and organic solvents, enabling the determination of antioxidant capacity of both hydrophilic and lipophilic compounds/samples. Additionally, the DPPH radicals have higher stability and lower reactivity than ABTS ones. Thus, the results of the ABTS method are higher than that of DPPH in our current study, especially with DSPPs, since it had a group of components rather than only one. DSPPs also protected preadipocytes against H2O2-induced detrimental effects, and this property could be ascribed to their direct antioxidant effects versus free radicals in co-exposure conditions. It has been noted that the constitution of polyphenols in terms of hydrophilic and hydrophobic domains could also determine their binding with lipid bilayers. Given that a rudimentary combination of polyphenols is exposed to the digestive process, the results after intestinal digestion are not shocking. It is known that during intestinal digestion, polyphenols suffer irreversible structural changes due to auto-oxidation, isomerization, and conjugation in an alkaline pH, yet certain species are more stable than others, depending on the structure [41]. A similar finding was noted where [PERSON] et al. [42] showed that DSPPs such as procyanidin B, catechins, and epicatechin decreased with time when digestion moved from the stomach to the intestinal phases. Polyphenol losses between the phases of intestinal and stomach digestion have already been noted with a loss ratio of 44% in wild blueberries [43].
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 2 State-of-the-Art
### Unlicensed Spectrum LPWANs  
_Unlicensed spectrum LPWANs_ use any of the publicly available open spectrums in the industrial, scientific and medical (ISM) bands and can be in turn classified into three categories:  
1. Proprietary systems: SIGFOX\({}^{\text{TM}}\)[13], Ingenu\({}^{\text{TM}}\)[14], and Telensa [15].
2. Systems under an alliance: LoRa\({}^{\text{TM}}\)[16], Weightless\({}^{\text{TM}}\)[17], DASH7 [18], and Wi-SUN [19].
3. Standards: IEEE 802.11 ah (also known as Wi-Fi HaLow) [20], ETSI low throughput networks (ETSI-LTN) [21], and Wireless M-BUS (EN 13757-4) [22].