esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective and Efficient Adaptive Probability Data Dissemination Protocol in VANET
## 4 Performance Assessment
### Adaptation of Proposed Protocol in the Realistic Environment  
With the help of probability formula for broadcasting data (refer to Equation (3)), each node can decide when to rebroadcast the accident message depend on the current state of the network. The performance of the EEAPD protocol to the networking environment is adopted on the safety application requirement by considering two parameters namely \"road segments\" and \"vehicle density\". For better proof of the correctness of EEAP adaptability in a realistic network environment, we demonstrate the proposed protocol under both parameters in which the higher data reachability obtained around 98% and 97% on both road segments and vehicle density respectively while saving network capacity and limited bandwidth consumption.
# Study of Subsidence and Earthquake Swarms in the Western Pakistan
## 1 Introduction
### A aquifers of the Study Area  
The city of Quetta has two primary aquifers that are bounded by alluvial sediments and carbonates. The bowl-shaped Quetta Valley is a landlocked watershed. The valley is mostly dry, and groundwater is the major source for domestic and agricultural utilization. The valley is syncline in nature and filled with thick alluvial deposits that include silt, clay, sand, and gravel. The dominant alluvial aquifers, which have an average thickness of \(\sim\)300 m and a porosity of \(\sim\)10%, are adjacent to the major mountain ranges: the Chiltan and Murdar Ranges [2]. The consolidated deposits of limestone, sandstone, and conglomerates are highly fractured, making aquifers, which are largely found in the Chiltan Formation and Shrinab Formation [20, 21]. The Shrinab Formation is a deep aquifer composed of thick-bedded limestone with occasional shale and marl layers. The primary porosity of this zone is low, but due to tectonic activity and fracturing, secondary porosity has developed in this area. The majority of wells are used to extract water from the alluvium in the central part of the valley, while less water is extracted from the carbonate. These two aquifers are physically and hydraulically interconnected [1].  
Up until 2010, there were 1619 wells for water withdrawal in Quetta [21]. The drilling depth of these wells was in the range of 100-200 m. Regular monitoring of the water table at various locations started in 1985 and has continued to the present in one form or another. Drilling has included many observation wells in regions where new production has been ongoing since 2000. From 1987-2006, the alluvial aquifer experienced a water level drop of \(\sim\)1 m/year for a total decrease of \(\sim\)18 m. Meanwhile, the carbonate aquifer experienced a total water level decline of \(\sim\)20 m from 2003-2006, an average decline rate of \(\sim\)5 m/year (Figure 2) [21].  
Figure 1: Map of major structures and earthquake hypocenters for the Quetta Valley and surrounding areas. The focal mechanisms of the October–December 2008 earthquakes are shown. Thrust faults in the Quetta Valley from [PERSON] et al. [17] are also shown. Two blind thrust faults identified using 2D seismic data are shown in indigo. The beach ball symbol indicates the centroid-moment-tensor (CMT) solution [18, 19].
## 2 Background
This was then used to train an artificial neural network (ANN) for the prediction of the SBP and DBP [7]. The trained model had mean average errors (MAEs) of 3.8 mmHg and 2.2 mmHg for SBP and DBP, respectively. However, this model was trained on a relatively small amount of data, approximately 4 h of waveforms. [7]. The exact number of patients was not stated, but since this study used the original MIMIC database, there could not be more than 90 patients. As such, it is unknown how well this study's results would generalise to the wider population. [PERSON] et al. [8] extracted 107 features from the PPG waveform. These include demographic, time-domain, frequency-domain, and statistical features of the waveform for 219 patients. A Gaussian process regression model was then trained to yield MAEs of 3.02 mmHg and 1.74 mmHg for SBP and DBP, respectively [8]. This study was carried out on data from 219 patients. [PERSON] et al. [14] took a different approach and used deep learning, rather than feature extraction with classical machine learning. An entire 5s PPG waveform, alongside its first and second derivatives, were fed into a custom ResNet architecture [14]. The results are worse than the feature extraction approach (MAEs of 9.4 mmHg and 6.9 mmHg for SBP and DBP, respectively) [14]. However, the work is described in meticulous detail and analysed one of the largest datasets of 510 patients and 700 h of data, making it a valuable guide to PPG-based blood pressure predictions. A more recent study by [PERSON] et al. [11] continued the trend of using the PPG waveform together with deep learning. Both the PPG and electrocardiogram (ECG) signals were fed into a range of architectures. SBP and DBP were predicted to MAEs of 4.1 mmHg and 2.2 mmHg, respectively [11]. Additionally, another model in the study predicts the entire ABP waveform to an MAE within 4 mmHg. However, the work only makes use of waveforms from 121 patients [11]. Additionally, it is not clear how well MAE can be used for classifying the accuracy of the waveform shape. A study by [PERSON] and [PERSON] [12] used PPG waveforms from 100 subjects with a U-Net architecture to achieve MAEs of 2.68 mmHg and 1.97 mmHg for SBP and DBP, respectively. This study presents a method for determining blood pressure prediction using only the PPG signal, with the aim of it being used for a cuffless and non-invasive continuous blood pressure measurement system, which is also low cost. Other bio-signals (such as ECG and non-invasive blood pressure measurements) are deliberately excluded to determine the feasibility of a PPG-based mechanism. The study also tackles the relatively novel waveform prediction. Any models developed will be classified as adequate, provided they meet the universal standard agreed upon between the Association for the Advancement of Medical Instrumentation (AAMI), European Society of Hypertension (ESH), and International Organisation for Standardization (ISO) [15]. The error should be less than 10 mmHg with a probability of at least 85% [15]. The minimum sample size for evaluation is 85 patients [15].
###### Abstract  
Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.  
crop yield prediction; independent variables; ANN; remote sensing Article  
Vol. 28 April 2021  
7 June 2021
## 5 Discussion  
During shield tunnel construction, muck accumulation and disposal inevitably lead to land occupation and environmental pollution. On the other hand, bentonite, a vital raw material for synchronous grouting slurry, entails significant costs in preparation and transportation. By replacing bentonite with the muck from shield construction, resource recycling is achieved, minimizing waste, and substantially lowering the cost of synchronous grouting. This approach fosters environmental protection and sustainable development. The present study investigates the influence of the soil content, particle size, and various other factors on the slurry properties of the new grouting material, used in lieu of bentonite, in silty clay strata shield tunnels. An optimal grout ratio that meets practical engineering demands has been identified, demonstrating its clear practical applicability.  
In this study, XRD analysis revealed the physical attributes and microstructural composition of the muck. The impact of multiple variables on the slurry performance was systematically evaluated through standardized tests and regression models, and three-dimensional response surface methodology, achieving an optimal slurry mix ratio via the SQP optimization approach. However, the derived slurry optimal ratio is project-specific and not directly transferable to other similar endeavors without first conducting field tests adhering to the methodology herein. Moreover, this method is tailored for synchronous grouting in the construction of general silty clay tunnels to control shield tunnel segment floating. For distinct deformation control demands in actual projects, the optimal slurry proportion necessitates re-evaluation. For instance, proximity to existing pipelines necessitates tighter control over segment floating and subsequent formation deformation. Additionally, the applicability of this method may be limited in certain strata or under special engineering constraints. For example, in strata where synchronous grouting material fails to readily harden around the tunnel lining, a shorter setting time and early-stage strength slurry may be required [42]. Thus, in scenarios involving intricate geological conditions or precise formation deformation control demands, conducting tests adhering to the present methodology becomes imperative to ascertain the optimal slurry ratio.
## 4 FEM Simulation Verification  
In order to verify the effectiveness of the proposed method, FEM simulation is carried out by using COMSOL Multiphysics (COMSOL Inc, Burlington, MA, USA) to obtain the surface charge distribution \(\sigma\) over the spherical surface of the SSEP probing ball. The parameters used for the simulation are those commonly used in real situations. The diameter of the probing ball is \(\phi\)1 mm, and the probing gap \(\delta\) between the probing ball and the probed plane is 0.3 \(\upmu\)m. The potential of the probing ball is set to be 1 V while the plane is grounded. The results for the surface charge distribution on the probing ball obtained with both methods are shown in Figure 5, in which the sphere stands for the probing ball of \(\phi\)1 mm in diameter. An expanded view of surface charge distribution, in which the spherical surface is unfolded by polar angle \(\theta\) and azimuthal angle \(\phi\), is shown in Figure 6 for clarity. The maximum relative differences in surface charge density on the probing point (\(\theta=90^{\circ}\), \(\phi=180^{\circ}\)) of different probing balls between the modeling analysis and the simulation results are shown in Table 1, while the curves of the surface charge ratio as a function of the surface area ratio for different probing balls are shown in Figure 7. These curves are proposed to present the surface charge concentrating around the probing point, which is further discussed in Section 5.1. The surface charge ratio in this paper is referred to as the ratio between the surface charge on the area centering on the probing point and the total surface charge, while the surface area ratio is the ratio between the area centering on the probing point and the total surface area. It can be concluded that the results for the surface charge concentration obtained with the two methods perfectly match with each other, and the effectiveness of the method proposed can be verified through this comparison.  
Figure 5: Modeling analysis and simulation results of surface charge distribution on the \(\phi\)1 mm probing ball.  
Figure 6: Modeling analysis and simulation results of surface charge distribution with the spherical surface unfolded.
# UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health
## 3 Results  
### Data Summary  
In total, 25 separate datasets were collected, processed, and available for inclusion in this analysis. In most cases, the field data and the UAV multispectral imagery were recorded on the same date. All UAV imagery from the 27 November was abandoned as unfavourable weather conditions made the data unusable. After March 2016, the study trees were harvested and the study was concluded as no further data could be collected. For a summary of the tree health data assessed from the ground, readers should refer to [PERSON] et al. [12].
## 3 Neural Machine Translation and ASPEC-JC Corpus  
### Neural Machine Translation  
For this research, we follow the NMT architecture by [PERSON] et al. [12], which implements as a global attentional encoder-decoder neural network with Long Short-Term Memory (LSTM). However, it is noteworthy that our proposed method is not specific to this architecture.  
The encoder is a bi-directional recurrent neural network with LSTM units that reads an input sequence \(x=(x_{1},\dots,x_{m})\) and calculates a forward sequence of hidden states \((\overrightarrow{h}_{1},\dots,\overrightarrow{h}_{m})\) and a backward sequence \((\overleftarrow{h}_{1},\dots,\overleftarrow{h}_{m})\). The hidden states \(\overrightarrow{h}_{j}\) and \(\overleftarrow{h}_{j}\) are concatenated to obtain annotation vector \(h_{j}\).  
The decoder is a recurrent neural network with LSTM units. It predicts a target sequence \(y=(y_{1},\dots,y_{n})\). Every word (or character in the case of character-level NMT) \(y_{i}\) is predicted based on a recurrent hidden state \(s_{i}\), the previously predicted word (or character) \(y_{i-1}\), and a context vector \(c_{i}\). Here, \(c_{i}\) is computed as the weighted sum of the annotations \(h_{j}\). Finally, the weight of each annotation \(h_{j}\) is computed through an alignment (or attention) model \(\alpha_{ij}\), which models the probability that \(y_{i}\) is aligned to \(x_{j}\).  
The NMT architecture was used at the character level in our experiments. The Chinese language was written using (simplified or traditional) Chinese characters, which are basically logograms. The Japanese writing system also uses kanji (adopted Chinese characters) along with syllabic kana. Several thousand Chinese characters are in regular use in both languages. Therefore, training models for those languages at the character level could be done more effectively than with a few million kinds of words at the word level. In addition, because the parameters to be trained are far fewer than at the word level, the training and translation time of experiment can probably be kept shorter.
# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
## 2 Materials and Methods
### Remote Sensing Data  
#### 2.2.1 Pre-Processing and Extracting Features from Sentinel-2 Satellite Images  
The study site is located in southeastern China, where rainfall is frequent. We chose images with as little cloud cover as possible. Therefore, a Sentinel-2 L1C MSI satellite image with tile number T5 IRTN was downloaded from the European Space Agency and collected on 24 May 2022 ([[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home))). The image comprises 100 km\({}^{2}\) of tiles using the UTM/WGS84 projection. Atmospheric correction was applied to the Sentinel-2 L1C scene using the Sen2 Cor plug-in of Sentinel Application Platform software. In this process, Top-Of-Atmosphere (TOA) reflectance images were converted to Bottom-of-Atmosphere (BOA) reflectance images.  
We selected only the 10 m and 20 m spectral bands of the Sentinel-2 satellite, with the other 60 m spatial resolution spectral bands (1, 9, and 10) dedicated to atmospheric correction and cloud screening. At 10 m, four spectral bands are available: blue (B2), green (B3), red (B4), and near-infrared (B8). At 20 m, six spectral bands are available: red-edge (B5, B6, and B7), near-infrared (B8A), and short-wave infrared (B11 and B12). All 20 m spectral bands were resampled to 10 m using the nearest neighbor strategy. The precise characteristics of the Sentinel-2 spectral bands are summarized in Table 1.  
Figure 1: Maps of the study area. (**a**) Location of Pan’an County, Jinhua City, Zhejiang Province; (**b**) location of the study area; (**c**) location of the sample plots; and (**d**) topography of the study area.
# Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size
## 2 Materials and Methods
### Evaluation of Calibration and Validation  
Evaluation of the performance of each calibration run was done by using goodness-of-fit indices (GOF) as presented by [PERSON] et al. [60]. GOF indices are based on pixel-by-pixel comparison between the observed rockfall area map (OR) and predicted rockfall area map (PR). Comparison of these two maps results in binary maps with positive values corresponding to \"actual rockfall area\" and negative values corresponding to \"not a rockfall area\". Correspondingly, four types of pixel outcomes are possible for each raster cell: (i) true positive (TP) is a pixel mapped as an \"actual rockfall area\" on both the OR and PR (correct prediction), (ii) true negative (TN) is a pixel mapped as \"not a rockfall area\" on both the OR and PR (correct detection of areas where rockfalls do not occur), (iii) false positive (FP) is a pixel that is actually \"not a rockfall area\" on the OR but is mapped as an \"actual rockfall area\" on the PR (false alarm), and (iv) false negative (FN) is a pixel that is an \"actual rockfall area\" on the OR but is mapped as \"not a rockfall area\" on the PR (missed alarm) (summarized based on [PERSON] et al. [60]). These indices are the basis of the concept of receiver operator characteristics (ROC) [61] that is used for assessing the model performance using the relation between benefits (TP) and costs (FP). [PERSON] et al. [60] incorporated eight GOF indices in the ROC system for quantification of model performance; however, four indices have been shown to be the most suitable for the evaluation of calibration runs and were used in this study (Table 2): the success index (SI), distance to perfect classification (D2 PC), the average index (AI), and true skill statistics (TSS). More comprehensive and detailed descriptions of the indices are available in [PERSON] et al. [60].  
The calibration run that achieved the most optimal value with those indices was selected as the most successful, and the rg values of that calibration run were used for modelling rockfall runout area at other DTMs considering two modelling scenarios: with and without forest. The actual rockfall area was mapped on the field by using a mobile application for collecting past rockfall deposits [62]. For the modelled rockfall area, the output propagation probability was used both in the calibration and validation step, since this raster layer represents the most realistic spatial distribution of the current rockfall event and can be used for calculating spatial occurrence probability, which is used in rockfall hazard analyses [21]. The same GOF indices as in calibration were also used for validating the performance of the modelling at DTMs with different grid cell sizes and for evaluating the effect of forest, following the same approach as for calibration.
## 3 Benefits of Employing Blockchain Technology for Healthcare  
Blockchain technology supports a decentralized network where no one is the sole owner of the system [39]. The layered architecture of blockchain-technology-supported applications is shown in Figure 2. At the application-layer level, the front end developed in HTML, React, Javascript, etc. is used to access the system [40]. All the logic is written in the form of smart contracts at the access layer. Then, consensus is achieved via different algorithms such as proof of work (PoW), proof of stake (PoS), etc., at the consensus layer [41; 42; 43; 44; 45]. It supports a peer-to-peer system at the networking level. Data are present in the form of transactions and blocks at the data layer.  
Multiple benefits can be achieved by different stakeholders from distributed ledger technology, as shown in Figure 3. A healthcare application can spur the construction of a new type of \"smart\" healthcare worker which can produce personalized treatment plans [46; 47; 48; 49]. All the stakeholders can access the information with proper access controls in a decentralized peer-to-peer network. The following are the benefits of employing this technology in the healthcare industry:  
1. **Towards complete and interoperable health records** It can help address the interoperability issue in a way better than current solutions because of its enhanced safety and capacity to develop belief between entities [50; 51; 52; 53; 54].
2. **Smart contracts for better coordination** This technology can gather the data from all the entities automatically with proper permissions from the owners of the data. Then every entity can collaborate with other entities for more constructive outcomes by using smart contacts [55; 56; 57; 58; 59].
3. **More successfully detecting fraud** If any patient applies for a fake insurance claim, then these claims can be detected with the help of smart contracts [60].  
**Improving the correctness of the provider directory**  
Every entity has some unique address that can be used by decentralized consensus mechanisms to make it easier for insurance companies and insurers to modify entries. Confidentiality is maintained by using cryptography in blockchain systems. Every entity has an associated public and private key pair. This pair is cryptographically connected, and it is not technically feasible to produce one key from another key of the pair [61; 62].  
**More client-centric to simplify it**  
Using a blockchain to provide an easier-to-access, more complete collection of medical information might provide relief and satisfaction from what has become an invasive and sometimes disappointing application process [63].  
**Assisting in the development of a dynamic insurer-client relationship**  
A dynamic relationship can be built between insurer and client. All the interaction between the entities can be managed with the help of smart contracts [64; 65].  
Figure 2: The layered architecture of blockchain-supported applications.