esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 5 Simulation Results
### Use Case Network Comparison  
This subsection compares the three use cases. User coverage in all cases can be satisfied if lamp posts are used, as shown in Figure 13. Here, more than 90% coverage for the rural and more than 97% for urban environments is achieved only for scenarios where lamp posts are used. This is due to the lamp post's flexibility, providing better LoS and closer proximity to the CPE nodes. In contrast, the facade can only connect less than 40% of homes in urban areas, whereas it can connect more than 90% of homes in rural areas. This is because households are more dispersed, and more useful links can be established in rural settings.  
In urban environments, the usage of \(ENs/\text{km}^{2}\) is four times higher than in rural cases. This is mostly caused by the three times higher population density and greater difficulty locating suitable links due to the street canyon shape, as opposed to rural locations. Additionally, this decreases the ratio of CPE served by active ENs, which is 0.8 for urban areas and 1.0 for rural areas.  
The backhaul connectivity distances are similar for all scenarios, independent of the EN location. This is because the optimisation function maximises the user coverage, thus the cover area size, while maintaining the users' required bit rate and reducing the path loss. As a result, the average connected distance is driven by the minimum supported bit rate of 2.5 Gbps maintained in all cases and is set to 100 m. However, environmental factors affect the backhaul connectivity distances more in rural environments. Rain, for example, reduces the backhaul connectivity distance by 18% in rural and 8% in urban. For the vegetation, the reduction is 10% in rural and 3% in urban environments as there is less vegetation present in urban environments.  
The service capacity per EN in an urban environment (300-500 Mbps) is two-thirds compared to the rural environment, mainly due to the increase in the ENs. Oppositely, the supported capacity is higher in the urban scenario (4 Gbps), primarily due to the reduced serving CPE distance that supports higher bit rates.  
Figure 12: Use Case 3 solution example. Snapshot of the urban Ghent area.
## 2 Materials and Methods
### Concentration of Styrene Monomer in Yoghurt (Migration Kinetics)  
Migration kinetics (from both sheets and pots) were measured at 8 \({}^{\circ}\)C by total immersion in yoghurt using glass vials. Five time points were measured (i.e., 10, 20, 30, 40, and 50 days of storage). Natural yoghurt (3.5% fat content) that had not been in previous contact with styrene-based materials (500 g packaged in PP) was used and was preserved by mixing with 0.1% sodium azide in order to avoid mold growth during the migration experiment. Test specimens with dimensions of 3.8 cm \(\times\) 1.6 cm (0.12 dm\({}^{2}\) total surface, considering both sides and excluding the edges) were cut from the pots and the sheet samples and placed in glass vials containing 20 g of yoghurt. The applied surface/volume ratio in the migration experiments corresponded to the conventional EU cube model of 6 dm\({}^{2}\)/kg. Each migration test series was carried out in triplicate. At each contact time point, the test specimens were removed from the vials, and the remaining yoghurt clinging to the samples was scraped off into the vials using a metal spatula. The vials containing yoghurt were stored at 5 \({}^{\circ}\)C until analysis.  
The concentration of styrene in yoghurt was measured by dynamic headspace (purge and trap) gas chromatography coupled with mass spectrometry (Thermo GC Trace Ultra MS-System Thermo Scientific ISQ 7000, with VSP 4000 Autosampler, IMT, Moosbach, Germany), using a previous published method [20]. After mixing the yoghurt samples with water (HPLC grade) and spiking them with styrene d-8 (CAS 19361-62-7) (Sigma-Aldrich, Saint Louis, MO, USA), the analyses were performed with a VSP Autosampler (40 \({}^{\circ}\)C), a Peltier water trap, and a cooled Tenax(r) trap. The GC column was a Restek KXI-624 Sil MS. Quantification was obtained by monitoring the ions at \(m/z\) 104 for styrene and \(m/z\) 112 for styrene d-8. The calibration in yoghurt (using the same yoghurt as for the migration experiment) was prepared for external quantification at six levels, diluted from a stock solution prepared in dimethylacetamide. Additionally, independently prepared control standards (of a known spiked amount) in yoghurt were analyzed in parallel to the samples to check the recovery. Triplicate migration experiments and duplicate measurements were performed. The detection limit of the P&T-GC-MS in yoghurt was 0.4 ug/kg.
## 3 Quasi-Standardized 2-D Dynamic Time Warping
### Dynamic Time Warping  
Dynamic Time Warping (DTW) is a popular temporal sequence alignment method [15], with applications ranging in speech recognition [16; 17], human motion animation [18], human activity recognition [19], and time series classification [20]. One key ability of DTW which distinguished it from other alignment method lies in its ability to nonlinearly align two sequences, such that two similar signals will have small distance even though one of the signals is distorted or 'warped' in time, for example, by shifting it a few steps ahead [21]. A comparison of alignment result between Euclidean distance, which measures distance between \(i\)-th point in one signal to the \(i\)-th point in another signal, and DTW can be seen in Figure 1.  
It can be clearly seen from the figure that alignment using DTW provides a better measure of similarity since it aligns similar features together (peak vs. peak, trough vs. trough). Similarity measures using DTW is suitable for shape similarity approach in our detection method. This is because the reading of each sensor is not maintained between two consecutive time steps, however, the general shape of the point cloud, i.e., the'shape' of the environment is more or less maintained provided that the robot moves along a sufficiently small distance between any two consecutive time steps [14].  
Given \(\mathcal{P}\) and \(\mathcal{Q}\), two sequences of real-valued univariate time series of length \(\mathcal{L}_{\mathcal{P}}\) and \(\mathcal{L}_{\mathcal{Q}}\), that is \(\mathcal{P}=\left(p_{1},\ p_{2},\ \ldots,\ p_{\mathcal{L}_{\mathcal{P}}}\right)\) and \(\mathcal{Q}=\left(q_{1},\ q_{2},\ \ldots,\ q_{\mathcal{L}_{\mathcal{Q}}}\right)\). Let \(\mathcal{D}(\mathcal{P},\mathcal{Q})\in\mathcal{R}^{\mathcal{L}_{\mathcal{P}} \times\mathcal{L}_{\mathcal{Q}}}\) represent the pairwise distance matrix between \(\mathcal{P}\) and \(\mathcal{Q}\) such that \(\mathcal{D}(\mathcal{P},\mathcal{Q})_{ij}=\left|p_{i}-q_{j}\right|\). The alignment problem is to find the sequences of indices \(\mathcal{I}_{\mathcal{P}}\) and \(\mathcal{I}_{\mathcal{Q}}\) of the same length \(\ell(\ell\geq\max(\mathcal{L}_{\mathcal{P}},\mathcal{L}_{\mathcal{Q}}))\) which match index \(\mathcal{I}_{\mathcal{P}}(i)\) in \(\mathcal{P}\) and \(\mathcal{I}_{\mathcal{Q}}(i)\) in \(\mathcal{Q}\) that minimizes the cost \(\mathcal{C}=\sum_{i=1}^{l}\mathcal{D}(\mathcal{P},\mathcal{Q})_{\mathcal{I}_{ \mathcal{P}}(i),\mathcal{I}_{\mathcal{Q}}(i)}\).  
REMARK 1: The choice of path to minimize \(\mathcal{C}\) should fulfill three constraints. The first one is _Monotonicity_ constraint, which states that the path will never return on itself. Both matching indices will either stay the same or increase; they never decrease. The second one is _Continuity_ constraint, which means the path should only advance one step at a time, that is, both indices increase at most by one every step. The last constraint is _Boundary constraint_ which restricts the path to start from bottom left corner (initial time index of the sequence) to the top right corner (final time index of the sequence) [22].
# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
## 3 Methodology
### Modified RouteNet with Stacked RNN and Sequential Link Update  
Figure 9 shows the improved RouteNet architecture, which has undergone key enhancements to boost its ability to predict network performance. This architecture, implemented by Salzburg Research, achieved second position in the Graph Neural Networking Challenge 2020, as discussed in [26]. The path update replaces the one GRU with a stacked GRU cell, providing a deeper understanding of paths. Additionally, the link update process has been enhanced with the inclusion of three dense layers and a GRU cell, enabling RouteNet to capture network dynamics more effectively for improved delay predictions.  
The message-passing process involves the gathering and summing of sequences of hidden states of the paths that contain a link, and updating the link state using the link update network. The path update process replaces a single GRU cell with a stack of two GRU cells, both with 64 units, and is used to update the path state during the message-passing process. These GRU cells receive a message from the links and update the path state accordingly. The link update process consists of three dense layers followed by a GRU cell. A dense layer with 128 units, using the SELU activation function, and L2 regularization with a weight of 0.1, dropout with a weight of 0.1, a second dense layer with 128 units, using the ReLU activation function, and L2 regularization with a weight of 0.1, dropout with a weight of 0.1, and a final dense layer with 64 units and L2 regularization with a weight of 0.1.  
The readout network processes the path states after the message-passing process and predicts the delay for each path. It consists of three layers: A dense layer with 64 units, using the SELU activation function, and L2 regularization with a weight of 0.1, a dense layer with 64 units, using the ReLU activation function, and L2 regularization with a weight of 0.1, and a dense layer with a single output unit and L2 regularization with a weight of 0.1. The input to the readout network is the concatenation of two path states, and the output of the readout network is fed into a final dense layer with a single output unit.
## 4 Classification of Routing Protocols
### Cross-Layered Routing Protocols
#### 4.5.4 Biocomm and Biocomm-D  
In [75] a cross layer medium access control (MAC) and routing protocol, named Biocomm, is proposed. This protocol aims to optimize the overall performance of the network in terms of avoiding hotspot formation, saving the energy of implanted bio-medical sensor nodes, and at the same time reducing the network traffic.  
The interaction of the MAC and network layers to optimize the overall performance of network is supported by Cross-layer Messaging Interface (CMI), which helps them to exchange their status information with each other. Both MAC and network layers keep a record of the status of the neighbor nodes in a Neighbor Status Table. MAC logic sets the Free (F) or Blocked (B) status of the neighbor nodes and the MAC layer informs the network layer about any change in the status of neighbor nodes by sending FREE or BLOCKED message through CMI to update the Neighbor Status Table of the network layer. Similarly, the network layer keeps the MAC layer informed about any free spaces in its packet buffer through CMI and allows the MAC layer for higher frame transmission. Each node tries to use the shortest path in order to send the data packets to the destination node, unless and until it finds a hotspot and/or sleeping node in the path. To prevent infinite routing loops, a hop-count is associated with each packet and if the hop-count for any packet exceeds a predefined threshold, then that packet is dropped. The modified version of Biocomm is Biocomm-D, which intends to handle delay-sensitive data packets.
## 2 Chemical Synthesis of Nucleoside Di- and Triphosphates
### Synthesis of Nucleoside Di- and Triphosphates Using Carbodimides  
The limitations of the benzyl chlorophosphate strategy in the synthesis of diphosphates led [PERSON] et al. to the study of carbodimides as coupling agents in the condensation of mononucleotides with inorganic phosphate (Scheme 4).  
By using the most common carbodiimide, \(N\),\(N^{\prime}\)-dicyclohexylcarbodiimide (DCC), it was possible to synthesize a number of di- and triphosphates of naturally occurring nucleosides (adenosine, uridine, guanosine, thymidine, and deoxycytidine) in isolated yields of 25-35% [46; 47]. During the initial studies on the reaction mechanism using pyridine (Py) as a solvent, the formation of symmetrical dinucleoside pyrophosphates was observed. It is noteworthy that the desired products, nucleoside diphosphates, were not formed when tri-\(n\)-butylamine was present in the reaction mixture. This suggested a crucial role of the protonation of DCC in triggering this reaction. Consistent with this, the presence of strongly basic amines in the reaction mixture prevented the condensation [48]. An interesting observation was that using a 10-fold molar excess of phosphoric acid and a 50-molar excess of DCC over the nucleoside monophosphate favored the formation of nucleoside triphosphates over the corresponding diphosphates. For this, a credible mechanistic explanation was provided [49]. Upon optimization of the method, various NTPs could be obtained in 40-80% yields, depending on the type of the mononucleotide used as a substrate [49].  
Unfortunately, the method was not free from some drawbacks. The main one was the formation of symmetrical diphosphates (inorganic and dinucleoside ones) as a result of the competing dimerization of mononucleotides and inorganic phosphate, and other di- and triphosphates, which complicated the separation process. Currently, this method is not used very often.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Spatiotemporal Feature Enhancement Based on Spatiotemporal Convolution
#### 3.2.2 Two-Dimensional Spatiotemporal Convolution Enhancement  
Three-dimensional CNNs have demonstrated effectiveness in extracting temporal sequence information [69]; however, their substantial hardware requirements and computational demands limit the practical application of lip-reading models. As such, there is a rising focus on the integration of temporal feature extraction modules within 2D CNN architectures for video action recognition.  
In 2019, [PERSON] et al. [48] unveiled the Temporal Shift Module (TSM), a novel mechanism that displaces specific channels along the temporal axis to amplify the interaction between contiguous frames. Integrating the Temporal Shift Module (TSM) into the 2D CNN framework facilitates effectively performing temporal modeling without increasing the computational complexity associated with the original 2D CNN architecture [91]. In-evitably, the absence of explicit temporal action modeling in TSM constrains its capacity to thoroughly capture detailed lip motion information.  
In 2021, [PERSON] et al. [92] introduced a novel and efficient spatiotemporal feature extractor that amalgamates temporal convolution with 2D CNN architecture. This innovative approach not only emulates the temporal feature extraction capabilities of 3D models, adeptly capturing the details of lip motion, but also preserves a parameter volume and computational expense akin to that of 2D models. In the same year, [PERSON] et al. [93] further refined the Temporal Shift Module by incorporating multiple channel attention mechanisms, thereby empowering 2D convolution to glean intricate spatiotemporal features. This enhancement also alleviates the potential ramifications of channel dependencies inherent in time-shift modules.  
Drawing inspiration from the SlowFast action recognition network, [PERSON] [94] unveiled the SpotFast network, which employs a temporal window as the spot path while utilizing all frames as the fast path. Notably, spatiotemporal features can be enriched through the synthesis of dual temporal convolutions in SpotFast derived from both the time window trajectory and the comprehensive frame pathway.  
In essence, spatiotemporal convolution enhancement techniques extract temporal features via meticulously crafted convolutional architectures, effectively capturing both temporal and spatial information. Nonetheless, these methods encounter challenges in discerning the importance of diversity features and frequently fall short in their ability to emphasize the most discriminative elements.
# Non-Vertical Well Trajectory Design Based on Multi-Objective Optimization
## 6 Conclusions  
This study proposes a DR-NSGA-III-MGA algorithm for multi-objective wellbore trajectory optimization, integrating multi-granularity reference vectors and information entropy guidance. Key findings are as follows: (1) The algorithm outperforms NSGA-III, MOPSO, and MOEA/D in HV, IGD, and GD, with stable performance across 30 runs. (2) The optimized three-stage trajectory balances length, friction, torque, complexity, and accuracy, validated by field data from Daqing Oilfield. Limitations include the following: (1) There is a lack of validation in ultra-deep (>5000 m) wells. (2) There is a sensitivity to extreme geological uncertainties (e.g., faults). (3) The simplified friction/torque models do not account for dynamic drilling conditions. This study has several limitations: (1) The dynamic adjustment of multi-granularity vectors depends on clustering results, which may cause bias in ultra-high-dimensional objective spaces (>10 dimensions). (2) Information entropy calculation is sensitive to sample distribution; outliers in training data may distort high-entropy region identification. (3) Current simulations do not consider geological uncertainties (e.g., formation pressure mutations), requiring further validation of robustness in complex field conditions.  
Methodology and validation, H.G., Y.L., and Y.W.; survey and resources, X.L.; data monitoring, X.L.; writing--first draft preparation, Y.W.; writing--review and editing, H.G.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52274005) and the Guiding Innovation Fund of Northeast Petroleum University: Intelligent Optimization Design of Horizontal Well Trajectory (Grant No. 2022 YDL-16).  
Not applicable.  
The data presented in this study are not publicly available due to confidentiality and security restrictions. For further inquiries, please contact the corresponding author  
Figure 12: Normalized multi-objective parallel comparison chart.  
**Acknowledgments:** The authors would like to thank Northeast Petroleum University for providing technical and administrative support during this research. The authors also appreciate the constructive feedback provided by colleagues, which greatly improved the quality of this study.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Slip Estimation Model for Planetary Rover Using Gaussian Process Regression
## 3 Data Collection  
The ground tests were performed in an indoor test site at the Chinese Academy of Space Technology. The dataset \(D=\{(x_{i},s_{i})|_{i=1}^{n}\}\) for input feature vector \(x\) with the corresponding output, slip observation \(s\), was collected to establish slip estimation models. In this section, we first introduce the ground testing site, and then detail the input features and output observations used in our data collection.  
Some descriptions have been simplified in this paper; more details are available in our previous work [22].
###### Abstract  
Among several mechanisms involved in the plant stress response, synthesis of guanosine tetra and pentaphosphates (alarmones), homologous to the bacterial stringent response, is of crucial importance. Plant alarmones affect, among others, photosynthetic activity, metabolite accumulation, and nutrient remobilization, and thus regulate plant growth and development. The plant \(RSH\) (\(ReIa/SpoT\) homolog) genes, that encode synthetases and/or hydrolaseses of alarmones, have been characterized in a limited number of plant species, e.g., _Anhidopsis thaliana_, _Oryza sativa_, and _Ipmon nil_. Here, we used dry-to-wet laboratory research approaches to characterize \(RSH\) family genes in the polyploid plant \(Brassica\)_napus_. There are 12 \(RSH\) genes in the genome of rapeseed that belong to four types of \(RSH\) genes: 6 \(RSH\), 2 \(RSH\), 2 \(RSH\)3, and 1 \(CRSH\). \(BnRSH\) genes contain 13-24 introns in \(RSH\), 2-6 introns in \(RSH\)2, 1-6 introns in \(RSH\)3, and 2-3 introns in the \(CRSH\) genes. In the promoter regions of the \(RSH\) genes, we showed the presence of regulatory elements of the response to light, plant hormones, plant development, and abiotic and biotic stresses. The wet-lab analysis showed that expression of \(BnRSH\) genes is generally not significantly affected by salt stress, but that the presence of PGPR bacteria, mostly of _Serratia_ sp., increased the expression of \(BnRSH\) significantly. The obtained results show that \(BnRSH\) genes are differently affected by biotic and abiotic factors, which indicates their different functions in plants.  
rapeseed; ReIa/SpoT homolog; RSH; alarmones; salinity; stringent response; PGPR
# Research on Performance Metrics and Augmentation Methods in Lung Nodule Classification
## 2 Related Work
### Augmentation Methods to Reduce Overfitting  
The efficacy of deep learning models is heavily reliant on extensive datasets for training. However, in medical image processing, annotating large datasets is particularly costly and time-consuming, often resulting in a limited number of available samples for training. To address this challenge, data augmentation has become a pivotal strategy in training deep learning models to ensure they generalize effectively to new, unseen data. [PERSON] et al. [22] have categorized data augmentation techniques for deep learning into three primary groups: basic, deformable, and advanced deep learning augmentation techniques. [PERSON] et al. [23] implemented various augmentation strategies, such as the addition of salt-pepper noise, Gaussian noise, random rotation, and the use of a median filter. These techniques were applied to enhance lung CT scans, enlarging the training dataset by a factor of one hundred through median intensity projections. Similarly, [PERSON] et al. [24] utilized automated low-level segmented PET data to train sophisticated deep learning algorithms for analyzing corresponding CT data. To expand their dataset, they applied rotation and scaling to both CT images and the masks derived from the PET scans, and they also introduced zero-mean Gaussian noise to the CT images. These examples underscore the crucial role of data augmentation in overcoming the inherent limitations posed by small datasets in medical imaging, thus enhancing the performance and applicability of deep learning models in clinical settings.