esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Case Study from eHealth  
The work presented here is based on a previous study performed by the SPIDEP project part of the Joint Call ERANET LAC 2015--FP7 [67], whose objective was to build an intelligent system based on information and communication technologies (ICT) to support the early diagnosis of infectious respiratory and urinary diseases in the elderly [21; 24]. For this, an international consortium was formed between the clinical research teams and the Infectious Diseases Unit of the Hospital Universitario Principe de Asturias (Alcala de Henares, Spain), the Chronic Diseases and Cancer Area of the Ramon y Cajal Institute for Health Research (Madrid, Spain) and the School of Medicine Autonomous University of Santo Domingo (Dominican Republic) and the research teams of the Computer Sciences group of the University of Alcala (Universidad de Alcala) and the Technological University of Panama (Universidad Tecnologica de Panama).  
This study incorporates patients living in the Francisco de Vitoria and Cardenal Cisneros nursing home in Spain and residents of the Carls George and Nuestra Serbora del Carmen Institution for the Care of the Elderly in the Dominican Republic. The main purpose of the study is to provide health services and care to people at risk or who have difficulties in accessing health services through the use of new technologies to achieve greater efficiency in the organization and care of special population groups, with the added value of cost savings [21].  
Actually, health monitoring has become one of the biggest areas in the technology industry, developing many smart devices like watches or another more specific body sensors. This has allowed researchers to contribute to the development of eHealth platforms focused on regularly monitoring the vital signs of at-risk patients, with the aim of carrying out treatments or preventive interventions to minimize health problems and reduce emergency care [10; 16; 17; 18; 46; 48; 58].
# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 1 Introduction
The objective of this study is to test whether Noah-MP LSM coupled with MLDAS (Noah-MP-MLDAS) can better predict land surface variables such as LAI, SM, sensible heat flux (H) and latent heat flux (LE) compared to Noah-MP open-loop simulations (OL) (simulations of Noah-MP without assimilation). Assimulating for 15 days, 45 days, 75 days, and 105 days from May 1 st, four sets of data assimilation experiments (four scenarios) were designed and conducted at an AmeriFlux crop site, namely Mead (Mead, NE, USA) during the growing season (1 May to 1 October) in 2003, 2004 and 2005, respectively. The SM, H, and LE assimilation outputs for the growing season served as benchmarks for evaluating the predictability of MLDAS and open-loop run (OL), the duration of impact on prediction in each scenario was further evaluated. This study demonstrates how data assimilation can improve the prediction capability of LSM and is expected to be useful for scientific research on LSM prediction and agricultural monitoring.
## 5 Case Study Analysis
### Carbon Reduction Benefits Analysis of Green Hydrogen  
Figure 5 illustrates that in Scenario 5, the implementation of CCS results in a substantial reduction of the system's carbon emissions. Specifically, there is a noteworthy decrease of 56.09% compared to Scenario 4. Furthermore, as a result of the implementation of tiered carbon emissions, there is a marginal decrease in overall expenses. Nevertheless, as the carbon emissions linked to gray hydrogen are solely accounted for in carbon quota trading and do not really contribute to emissions inside the system, the carbon emissions from gray hydrogen remain unaltered.  
Scenario 6 only relies on P2G technology for green hydrogen production, resulting in very poor efficiency. This process demands a significant electrical supply. Due to the inadequacy of wind power generation alone, it is necessary to enhance CCHP electricity output and substantially boost electricity purchasing. This leads to a 12.6-fold increase in carbon emissions compared to Scenario 5 and a corresponding 21.33% increase in total expenses.  
Figure 4: Scenario 4 and Scenario 5 cost comparison.  
In Scenario 7, prioritizing the use of wind power for green hydrogen production and supplementing with gray hydrogen as needed, the system efficiently accommodates wind power, reducing wind curtailment costs and dramatically cutting the use of gray hydrogen. The system's carbon emissions are reduced by 40.98% and 93.30% in comparison to the use of gray hydrogen and green hydrogen individually. Consequently, total costs reduce by 17.93% and 32.35%. This highlights the capacity of the integrated energy system to strategically manage the cost differentials between gray hydrogen (which is cheaper but has higher carbon emissions) and green hydrogen (which is more expensive but has lower carbon emissions) in response to fluctuating carbon prices. By flexibly adjusting the proportion of gray hydrogen and green hydrogen, the system maximizes its economic benefits.
## 4 Experiment
### Experimental Results and Analysis
#### 4.2.3 Comparison of Models and Other Baseline Models  
Finally, the BiLSTM+Attn+GCN (BAGCN) model proposed in this paper that incorporates pruning operations is compared with other baseline models. The other baseline models are a relation extraction model based on an SVM classifier and syntactic semantic features [34], a location-aware neural network model PA-LSTM [35], a GCN model based on contextual information [36], and an attention-guided graph convolutional network model (AGGCN) with a fully dependent tree as input [31]. Table 4 shows the results. Our BAGCN model substantially outperforms the baselines and achieves 5.80%, 2.61%, and 1.94% improvements over the state-of-art baseline method (AGGCN) in precision, recall, and F1-score, respectively. The experimental results demonstrate the effectiveness of our BAGCN model.
## 3 Results
### Relative Sensitivity of the Ecosystem Variables to All 21 Climate Indices at the Site Scale  
Since not all these 21 climate indices were sensitive for the ecosystem variables, stepwise multiple regressions (see Section 2.4) were performed between the three ecosystem variables and all the 21 climate indices for the 30 stations within the study area in order to derive sensitive climate variables for vegetation in Hubei Province. The number of times that each climate index was chosen as a candidate variable in the stepwise multiple regressions for the three ecosystems and for all stations was regarded as the relative ecological importance of the corresponding climate index.  
As shown in Table 3, the LAI, NDVI, and GPP were the most sensitive to SU35, SU25, and SU25, respectively, at sites scales. For the sum among the three ecosystem variables, SU25 was chosen 45 times during the stepwise multiple regressions, which ranked first among all of the climate indices. This means that the ecosystem variables in Hubei Province were mostly correlated with the SU25 index, followed by FD0 and WSDI temperature indices. Among the nine precipitation indices, simple precipitation intensity index (SDII), R50 and CDD had the greatest sensitivity for the ecosystem variables in Hubei Province. Consequently, these six climate indices were chosen for further study (i.e., the three temperature indices and the three precipitation indices). The selected indices covered hot days, cold days, extreme precipitation, and dry days, which frequently occurred in the study area and supposedly have significant impacts on interannual variations in the local ecosystem.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indices** & **LAI** & **NDVI** & **GPP** & **Sum** \\ \hline SU25 * & 7 & 14 & 24 & 45 \\ ID0 & 3 & 3 & 1 & 7 \\ TR20 & 6 & 0 & 4 & 10 \\ FD0 * & 5 & 3 & 11 & 19 \\ SU35 & 8 & 1 & 5 & 14 \\ GSL & 4 & 3 & 4 & 11 \\ TN10p & 1 & 3 & 5 & 9 \\ TX10p & 2 & 1 & 3 & 6 \\ TN90p & 1 & 4 & 5 & 10 \\ TX90p & 3 & 0 & 4 & 7 \\ WSDI * & 2 & 2 & 11 & 15 \\ CSDI & 3 & 2 & 2 & 7 \\ SDI * & 4 & 5 & 13 & 22 \\ R10 & 2 & 3 & 3 & 8 \\ R20 & 4 & 0 & 6 & 10 \\ R50 * & 0 & 6 & 7 & 13 \\ CDD * & 5 & 2 & 4 & 11 \\ CVD & 1 & 5 & 2 & 8 \\ R95p & 3 & 1 & 2 & 6 \\ R99p & 4 & 4 & 2 & 10 \\ PRCPIOT & 2 & 1 & 4 & 7 \\ \hline \hline \end{tabular}  
* The three most sensitive temperature indices and the three most sensitive precipitation indices are marked.  
\end{table}
Table 3: The number of times that the corresponding climate index was chosen as the candidate variable when the stepwise linear regression was performed between the corresponding ecosystem variables and the 21 climate indices at 30 meteorological stations within the study area.
# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 3 Methodology
### Generalized Mathematical Model for Criticality Assessment
#### 3.3.5 Simplified Practical Example  
Let us assume an asset (i) with the following values:  
Data, attributes, and conditions:  
* Asset i, condition \(\text{FF}_{1}\): If the asset is located in location \(\text{A}_{\text{z}}\) (for example, Line C4, Kilometer 16, and is also type rail, then FF = 2
* Asset i, Condition \(\text{CC}_{1}\): If the asset is Attribute \(\text{A}_{1}\) [rail type], and if Attribute \(\text{A}_{2}\) [zone] = main track, then \(\text{C}_{\text{i}\text{-}\text{safety}}\) = 8.
* Asset i, Condition \(\text{CC}_{2}\): If the asset in Attribute \(\text{A}_{3}\) [area] = Protected, then \(\text{C}_{\text{i,environment}}\) = 3
* Asset i, Condition \(\text{CC}_{3}\): If the asset in Attribute \(\text{A}_{4}\) [time] = Peak, then \(\text{C}_{\text{i,service}}\) = 7
* Active i, Condition \(\text{CC}_{4}\) If active Attribute \(\text{A}_{5}\) belongs to [class] Rail and Attribute \(\text{A}_{6}\) [failure mode] is Breakage then \(\text{C}_{\text{i,cost}}\) = 5.
* Failure Frequency: \(\text{FF}_{1}\) = 2 failures per year.
* Consequences on Safety: \(\text{C}_{\text{i,safety}}\) = 8
* Consequences on Environment: \(\text{C}_{\text{i,environment}}\) = 3
* Consequences on Service: \(\text{C}_{\text{i,service}}\) = 7
* Consequences on Costs: \(\text{C}_{\text{i,cost}}\) = 5  
Simplifying, if the weights are (\(w_{\text{safety}}\) = 0.4), (\(w_{\text{environment}}\) = 0.2), (\(w_{\text{service}}\) = 0.3), and (\(w_{\text{costs}}\) = 0.1), then the criticality would be  
\[\text{CR}i=2\cdot(0.4\cdot 8+0.2\cdot 3+0.3\cdot 7+0.1\cdot 5)=2\cdot(3.2+0.6+2.1 +0.5)=2\cdot 6.4=12.8\]  
This criticality asset value could be compared with other asset values to decide maintenance priorities. For further understanding, Figure 3 shows a real example of this methodology applied in the development of the use case. Appendix A shows the Python script developed.
# Hydrochemical Characteristics and Suitability Assessment of Groundwater Quality for Irrigation
## 2 Materials and Methods
### Irrigation Water Quality Indices  
Irrigation water suitability was assessed through indices like sodium percentage (SSP), sodium absorption ratio (SAR), residual sodium carbonate (RSC), permeability index (PI), Kelly's ratio (KR), magnesium adsorption ratio (MAR), and total hardness (TH), each providing insights into different aspects of water quality. These indices collectively help in evaluating potential risks associated with sodium, magnesium, and overall water hardness. Table 2 shows the standard formulas for indices of the irrigation groundwater quality.
###### Abstract
With advancements in satellite-based precision positioning systems (e.g., BeiDou, GPS), utilizing the geographical location information of nodes to aid in routing has emerged as a strategy to enhance node self-organization, consequently improving routing efficiency [9; 10]. Among various geographically assisted routing protocols, Greedy Perimeter Stateless Routing (GPSR) stands out by utilizing only one-hop neighbor information through a greedy algorithm to obtain an optimal next-hop forwarding node [11], eliminating the need for the dynamic establishment of complete routes or the dynamic maintenance of flow tables, thereby achieving simplicity and low routing overhead. Mitigating the issue of excessive control overhead in SD-MANETs, this paper proposes a Geographical Information-Based Software-Defined Greedy Perimeter Stateless Routing method (SD-GPSR). SD-GPSR utilizes the controller to provide location services, employing a distance and angle-based greedy forwarding algorithm in the data plane for data transmission. To tackle issues within the data plane such as routing voids and link interruptions, SDN technology is integrated to present a solution, rendering it more suitable for SD-MANET environments. Specifically, we have made the following contributions:  
* An SD-GPSR routing method is proposed. SD-GPSR decentralizes some of the routing functions in SD-MANET to the data plane and utilizes the node's geographic location information to achieve routing. * An improved distance and angle-based greedy forwarding algorithm (GPSR_DA) is proposed. The controller plays a key role in providing location services and facilitating partial centralized decision-making, while the nodes in the data plane employ the GPSR_DA algorithm for data forwarding. This enhances node self-organization and overall routing efficiency and reduces control overhead. * Addressing the issue of routing voids during the forwarding process, this paper introduces a solution by employing the \(A^{*}\) algorithm to calculate an optimal route that avoids these voids. Subsequently, the source routing concept is utilized to deploy flow tables, effectively reducing control overhead. * We validate and analyze the SD-GPSR routing method. The rest of this paper is organized as follows. Section 2 presents the related work. Section 3 presents the proposed SD-GPSR including architecture, control layer design, and data layer design. Section 4 describes the specific implementation process. Section 5 describes the simulation results and performance comparison. Section 6 summarizes the whole paper.
# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 2 Moving Object Detection  
This section briefly describes the moving object detection with a moving drone studied in [8]. Moving object detection consists of frame registration and subtraction followed by thresholding, morphological operations, and removing false alarms. Two consecutive gray-scaled images \(I_{k}\) and \(I_{k-1}\) are registered using the SAD between them. The \(SAD\) at \(k\)-th frame is obtained as  
\[SAD_{k}\big{(}p_{x},p_{y}\big{)}=\sum_{n=1}^{N}\sum_{m=1}^{M}\big{|}I_{k}\big{(} m+p_{x},n+p_{y}\big{)}-I_{k-1}(m,n)\big{|},k=2,\ldots,K, \tag{1}\]  
where \(M\) and \(N\) are the image sizes in the \(x\) and \(y\) directions, respectively; \(K\) is the total number of frames. The displacement vectors \(p_{x}\) and \(p_{y}\) are obtained in the \(x\) and \(y\) directions, respectively, by minimizing the \(SAD\) as  
\[\big{[}\hat{p}_{x}(k)\ \hat{p}_{y}(k)\big{]}=min_{(p_{x},p_{y})}SAD_{k}\big{(}p_{ x},p_{y}\big{)}. \tag{2}\]  
Figure 2: Block diagram of moving object detection and multiple target tracking.  
The subtraction and thresholding generate a binary image after the coordinate compensation as  
\[B_{k}(m,n;k)=\left\{\begin{array}{c}1,\ if\big{|}I_{k}(m+\hat{p}_{x}(k),n+\hat{ p}_{y}(k))-I_{k-1}(m,n)\big{|}\ \vartheta_{T}\\ 0,\ otherwise\end{array}\right\}, \tag{3}\]  
where \(\theta_{T}\) is a threshold value set to 85 and 30 for Video 1 and 2, respectively, in the experiments. Two morphological operations, erosion and dilation, are sequentially applied to the binary image. The structure elements for erosion and dilation are set at \([1]_{2\times 2}\) and \([1]_{20\times 20}\), respectively. Finally, assuming the true size of the object is known, the false alarm is removed as  
\[O_{i}(m,n)=\left\{\begin{array}{c}1,\ \text{size}\{O_{i}\}>\theta_{s}\\ 0,\ otherwise\end{array}\right\}, \tag{4}\]  
where \(O_{i}\) is the \(i\)-th object area, and \(\theta_{s}\) is the minimum object size, set to 400 or 100 for Video 1 and 2, respectively, in the experiments.
# An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification
## 6 Simulation Results
### Single Round Identification
#### 6.1.1 Number of Slots  
Firstly, Figure 8 gives the numbers of collision and idle slots needed to identify tags. It should be noted that in DPPS, each slot consists of two consecutive tag responses, which is actually one frame with two slots, and there is no \(Qrep\) command in the second slot. For a fair comparison, we consider this as two slots, and the state of each slot is related to the number of simultaneous tag responses. In CwT, the go-on slot takes such a very short time that they are compared with idle ones.  
As can be observed from Figure 8, the proposed ACDQT algorithm takes the least number of collision slots to identify tags, while DPPS and MCT take more collision slots than ACDQT. CwT takes the most number of collision slots. This is because DPPS only splits colliding tags into two or four subsets. MCT splits tags into a fixed number of subsets, whereas the proposed ACDQT algorithm splits colliding tags into various numbers of subsets which is based on the number of consecutive colliding bits. Dynamically splitting colliding tags into smaller subsets, the number of collision slots is significantly reduced. Moreover, one can also observe that CwT takes much more collision slots than comparable algorithms. The main reason is that CwT always splits colliding tags into two subsets based on the first colliding bit position. The splitting process is slow, especially when the number of colliding tags is large. Therefore, CwT takes much more collision slots than others, and the gap becomes larger with the increase of tag number.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(L\) & 96 bits & Data rate \(D\) & 160 kbps \\ \hline \(t_{1}\), \(t_{2}\) & 25 us & \(t_{3}\) & 12.5 us \\ \hline \(t_{Q}\) & \(L_{\text{cmd}}/D\) & \(t_{R}\) & 4/D \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters used in simulation.  
Figure 8: Number of slots needed to identify tags: (**a**) collision slots; (**b**) idle (or go-on) slots.  
Figure 8b illustrates the number of idle slots needed to identify tags. As shown, ACDQT takes more idle slots than other protocols. In MCT, DPPS and ACDQT, colliding tags are divided into more subsets according to the number of consecutive colliding bits. Each subset corresponds to a slot in the new frame. One can not sense the number of tags in each subset. So, there are some idle slots, and the number of idle slots increases with larger frame length. In CwT, there are no idle slots, but the number of go-on slots is large. Although ACDQT takes more idle slots than comparable algorithms, it takes the least number of collision slots. As known, a collision slot takes much more time than an idle (or go-on) slots. Therefore, ACDQT will take a shorter time to identify tags than comparable protocols.
## 3 Results
### Unimodal Data Approaches
#### 3.2.2 Unimodal Speech Data Approaches  
In the case of unimodal speech emotion recognition articles, databases exhibit significant diversity due to the nature of the data, comprising databases in numerous languages. For this reason, no specific results are included concerning databases; however, these are the identified trends in learning models and applications.  
* Several articles mention the use of transfer learning for speech emotion recognition. This technique involves training models on one dataset and applying them to another. This can improve the efficiency of emotion recognition across different datasets.
* Some articles discuss multitask learning models, which are designed to simultaneously learn multiple related tasks. In the context of speech emotion recognition, this approach may help capture commonalities and differences across different datasets or emotions.
* Data augmentation techniques are mentioned in multiple articles, which involve generating additional training data from existing data, which can improve model performance and generalization.
* Attention mechanisms are a common trend for improving emotion recognition. Attention models allow the model to focus on specific features or segments of the input data that are most relevant for recognizing emotions, such as in multi-level attention-based approaches.
* Many articles discuss the use of deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and some variants like \"Two-Stage Fuzzy Fusion Based-Convolution Neural Network, \"Deep Convolutional LSTM\", and \"Attention-Oriented Parallel CNN Encoders\".
* While deep learning is prevalent, some articles explore novel feature engineering methods, such as modulation spectral features and wavelet packet information gain entropy, to enhance emotion recognition.
* From the list of articles on unimodal emotion recognition through speech, 7.14% address the challenge of recognizing emotions across different datasets or corpora. This is an important trend for making emotion recognition models more versatile.
* A few articles focus on making emotion recognition models more interpretable and explainable, which is crucial for real-world applications and understanding how the model makes its predictions.
* Ensemble methods, which combine multiple models to make predictions, are mentioned in several articles as a way to improve the performance of emotion recognition systems.
* Some articles discuss emotion recognition in specific contexts, such as call/contact centers, school violence detection, depression detection, analysis of podcast recordings, noisy environment analysis, in-the-wild sentiment analysis, and speech emotion segmentation of vowel-like and non-vowel-like regions. This indicates a trend toward applying emotion recognition in diverse applications.
## 5 Experimental Results  
The input to our software is a polygonal model which we assume approximates a PSC. A user specified threshold for dihedral angles is used to select edges of the input as sharp features (elements of \(\mathcal{D}_{1}\)) which we protect. Non-manifold and boundary edges are also included as elements of \(\mathcal{D}_{1}\). In Figure 5 we show how our protection algorithm works on four different sets of curves.  
In Table 1 we show both the time to protect curves as well as the time to generate the surface mesh for twelve different datasets. All experiments were run on a PC with a 2.8 GHz CPU and 2 GB RAM. We set the parameter \(\lambda\) to 5% of the minimum dimension of the bounding box for each model. Those datasets which took no time for protection had no sharp features in their input; the PSC they approximate was assumed to be a single smooth patch. The majority of the datasets were meshed in under one minute, only those with complicated topologies took longer.  
\begin{table}
\begin{tabular}{l|r|r|r} \hline Dataset & Protection Time & Meshing Time & \# of vertices \\ \hline
9 Holes & 0.0 s & 105.6 s & 8725 \\ Arm & 52.6 s & 339.7 s & 20692 \\ Cog & 1.4 s & 56.2 s & 7697 \\ Guide & 2.6 s & 22.9 s & 4414 \\ Horn & 0.0 s & 21.5 s & 3192 \\ Lock & 10.6 s & 26.6 s & 5314 \\ Octo & 0.0 s & 7.3 s & 1410 \\ Part & 0.2 s & 23.2 s & 4261 \\ Plate & 17.9 s & 83.9 s & 9773 \\ Pump & 19.1 s & 319.6 s & 20301 \\ Swirl & 0.0 s & 61.1 s & 6880 \\ Wedge & 0.2 s & 13.2 s & 3080 \\ \hline \end{tabular}
\end{table}
Table 1: Protection and Meshing times for our datasets.  
Figure 5: Protection: (left) A 2d example of curve protection. (middle) 2 different 3d examples of curve complexes where we have run Protect. (right) The final set of protecting balls returned by Mesh2 Complex on the Wedge model.  
In Figure 6 we show output meshes for various input shapes. These meshes include four smooth shapes (9 Holes, Horn, Octo, and Swirl), two non-manifolds (Horn and Wedge), and eight piecewise-smooth shapes (Part, Guide, Wedge, Cog, Arm, Lock, Pump, and Plate). DelPSC has been used to mesh dozens of other models; further experimentation was presented in a recent multimedia presentation [16].