esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Inspired by the special characteristics of the UWA channel and the existing DCS algorithms for time-varying sparse recovery, in this paper, we introduce the block sparse reconstruction into DCS algorithm and propose a block sparse DCS (BS-CS) algorithm. The proposed BS-CS algorithm can make use of the inherent block sparse and time-varying property of the UWA channel and improve the estimation performance for the block sparse time-varying UWA channel. Specifically, we firstly adopt a block orthogonal matching pursuit (BOMP) to compute the initial channel estimate. Then, the CS approach is applied on the BOMP error to compute the possible support additions, which are introduced by the time-varying components of UWA channel. Finally, we use the selected support to perform the BOMP estimate, and receive the estimated channel response. A channel impulse response (CIR) generated by the parabolic Equation (PE) propagation model [25] and the recorded signals obtained from a shallow sea experiment are applied to demonstrate the behavior of the proposed BS-CS algorithm, respectively. The numerical simulation and sea experiment results show that for the block sparse time-varying UWA channel, the proposed BS-CS performs better than the benchmark sparse and non-sparse channel estimation algorithms, namely, the least squares with the QR-factorization (LSQR) [26], the OMP [13], the BOMP [23], the BS-RLS [24], the KF-CS [15], and the SDD-CS [21]. The rest of the paper is structured as follows. In Section 2, we describe the problem formulation of UWA channel estimation and block sparse representation of UWA channel. Section 3 describes our proposed BS-CS algorithm. Sections 4 and 5 present the numerical simulation and sea experiment results, respectively. Finally, Section 6 draws the conclusions. **Notation 1**. : _Capital bold letters denote matrices, and small bold letters denote vectors. \(\cup\) denotes the set union operation. \(T\) and \(H\) denote the transpose, and conjugate transpose operator, respectively. \(||\mathbf{x}||\) and \(||\mathbf{x}||_{1}\) denote \(l_{2}\) norm and \(l_{1}\) norm of the vector \(\mathbf{x}\), respectively. \(\mathbf{X}^{t}\) denotes the pseudo inverse of the matrix \(\mathbf{X}\). \(|\text{supp}(\mathbf{x})|\) denotes the number of non-zero elements of the vector \(\mathbf{x}\)._
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 2 Additional Attenuation of Vegetation
### Statistical Models
#### 2.3.2 Maximum Exponential Decay Model (MED)  
Unlike maximum attenuation (MA), the maximum exponential decay model (MED) takes into account the frequency of the signal \(f\) given in MHz. The \(d_{\text{in}}\) measurements in the previous model represent the distance that the wave travels through the foliage, given in meters. The above two coefficients are weighted with \(a,b,c\) parameters, which are found with measurements:  
\[L_{F}=a\cdot f^{b}\cdot d_{in}^{c}. \tag{24}\]  
The example of parameter values found for the COST-235 model [25] is as follows: \(a=13.77,b=-0.009,c=0.26\).
# Soft Segmentation of Terrestrial Laser Scanning Point Cloud of Forests
## 1 Introduction
[PERSON] [27] presented an attempt at combining the mobile mapping mode and a multi-echo-recording laser scanner, as well as a new methodology based on the resulting single-scan point clouds, for enhancing the integrity of individual tree crown reconstruction. To address the challenge from leaf/branch occlusions, mirroring the half-crowns facing the Mobile Laser Scanning (MLS) system to the other sides can be assumed as a solution strategy. [PERSON] et al. [28] employed an implicit surface to reconstruct the exact shape of an irregular tree crown of various tree species based on the LiDAR point cloud and visualize their actual crown formation in three-dimensional space. The existing published literature indicates that the accuracy of tree counting and DBH (diameter at breast height) measuring from forest point cloud data is relatively high, which can meet the practical needs of the current forest inventory. However, the research progress of tree crown reconstruction is relatively small in recent years. Most of the existing tree crown segmentation methods are hard segmentation, and the division between overlapping trees is directly cut by a vertical plane, resulting in a plane shape of the segmentation area, which does not like the natural shape of the tree crown. In addition, the accuracy of crown point cloud segmentation affects the reconstruction effect of the tree crown shape. Therefore, for obtaining the realistic canopy silhouette shape, a soft segmentation algorithm of the forest point cloud is proposed in this study. The technical details are described in the next section.
## 5 Results and Discussion
### Walls of T2
#### 5.1.2 Vehicle Lane Board (Wall_2)  
Figure 10 shows the displacement and velocity of seven measurement points (A\({}_{2}\), B\({}_{2}\), C\({}_{2}\), D\({}_{2}\), E\({}_{2}\), F\({}_{2}\) and G\({}_{2}\)) of Wall_2 in T2. The variation law of displacement and velocity of Wall_2 is basically the same as that of Wall_1. The \(D_{\rm H}\) of Wall_2 is the same, which means that the tension and compression deformation of Wall_2 is small for the larger tension and compression stiffness (EA\({}_{\rm W2}\) = 3.65 \(\times\) 10\({}^{6}\) N/m), which is similar to the law of Wall_1. The more extensive \(D_{\rm V}\) at the left side of Wall_2 is caused by the near distance to the train vibration position.  
\(D_{\rm H}\) of Wall_1 is much larger than that of Wall_2 (Figures 8a and 10a). Wall_1 is located in the upper part of the cross-section in T2, and Wall_2 is located in the middle part of the cross-section in T2. Soil stress in the horizontal direction (stress-xx) around the two sides of Wall_1 is 1.90 \(\times\) 10\({}^{5}\) and \(-\)1.28 \(\times\) 10\({}^{6}\), and soil stress in the horizontal direction (stress-xx) around the two sides of Wall_2 are 4.09 \(\times\) 10\({}^{6}\) and \(-\)3.57 \(\times\) 10\({}^{6}\). Soil stress in the vertical direction (stress-yy) around the two sides of Wall_1 is \(-\)1.16 \(\times\) 10\({}^{6}\) and \(-\)7.72 \(\times\) 10\({}^{5}\), and soil stress in the vertical direction (stress-yy) around the two sides of Wall_2 is 1.76 \(\times\) 10\({}^{6}\) and \(-\)4.46 \(\times\) 10\({}^{6}\). The stress around Wall_1 is smaller than Wall_2, so Wall_1 has larger \(D_{\rm H}\) for smaller limitations. The stress state of surroundings significantly affects the \(D_{\rm H}\) of Wall_1 and Wall_2.  
The \(D_{\rm V}\) of Wall_2 is greater than that of Wall_1 at the midpoint (Figures 8b and 10b) as Wall_2 is close to the train vibration in T1. Therefore, the \(D_{\rm V}\) of the walls is much affected by distance while less affected by surroundings extrusion.  
Maximum and minimum values of the \(D_{\rm V}\) and \(V_{\rm V}\) of the seven measurement points of the Wall_2 are shown in Figure 11. The \(D_{\rm V}\) and \(V_{\rm V}\) decrease from point A\({}_{2}\) to point G\({}_{2}\). The variation law of \(D_{\rm V}\) and \(V_{\rm V}\) in Wall_2 conform to the law of \"near is large and far is small\".
# Effects of Monochromatic and Composite Light Withering on Black Tea Aroma
## 3 Results
### Aroma Profile of Black Tea Withered with Different Composite Lights  
In the RB_vs_CK group, 11 differential volatiles were identified (Table S2). Compared to CK, the level of a few alcohols, aldehydes, heterocyclic compounds, hydrocarbons, and terpenoids was decreased in the RB group (Figure 4A,B). Of these components, (E)-hex-2-enal, 2-methylbutanal, and 3-methylbutanal comprised an rOAV > 1, which exhibited almond and green odors (Table 5). In contrast, a total of 31 differential volatile compounds were screened in the RY_vs_CK group (Table S2), which included hydrocarbons, heterocyclic compounds, esters, aldehydes, terpenoids, etc. (Figure 4A). Among these substances, the content of nine hydrocarbons like 2,6,11-trimethyldodecane, dodecane, and 4,7-dimethylundecane was increased in the RY group. Additionally, a few alcohols (5-methyl-2-propan-2-ylheptan-1-ol), an aldehyde (benzene-1,3-dicarbaldehyde) and an ester ([(E)-hex-3-enyl] butanoate) were elevated by red-yellow light (Figure 4A,C). In the RY_vs_CK group, substances with an rOAV > 1 mainly presented fruity and floral flavors (Table 6).  
The results obtained above indicated that both single LED light and composite light withering improved black tea aroma. To explore the volatile components influenced only by composite light, we performed a Venn analysis of the combined light treatment group and the single LED light group. The results showed that the number of compounds that were only affected by the red-blue light withering was 0 (Figure 4D), whereas 17 volatiles were affected only by red-yellow light withering (Figure 4E). These 17 substances comprised hydrocarbons, acids, esters, alcohols, and aldehydes (Figure 4F). Except for 2-phenylethyl acetate and naphthalene, the content of these volatiles was higher in the RY group than in the RL and YL groups (Figure 4F), and only 2-phenylethyl acetate possessed an rOAV > 1. In the RB_vs_RY group, 19 differential metabolites were screened and it was dominated by hydrocarbons, along with a few alcohols, aldehydes, and terpenoids. Among these compounds, most exhibited higher concentrations in the RY group compared to the RB group (Figure S1D). This indicates that red-yellow light withering is more effective in altering the aroma of black tea.
# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 2 Fundamentals of the Classification Algorithms Applied
### Support Vector Machine  
Support vector machine (SVM) is a statistical learning algorithm, robust to data noise and adapted to classification and nonlinear regression problems [10]. SVM transforms a nonlinear regression model into a linear model using kernel functions to map the original input space into a new high-dimensional features space [7]. In this high-dimensional space (hyperplane), SVM finds unique solutions to classification and regression problems [18]. The advantage of SVM over traditional classifiers is that it solves learning problems better when only a small number of training samples are available [27]. SVM has shown good classification results for satellite images, presenting low omission errors [19].
## 4 Representing Data Provenance in DBMS
### A Compact Representation of TupPGraph  
As a subgraph of schema provenance graph SPGraph, each tuple provenance graph corresponds to one kind of schema transformation relations. To clarify the relationship between TupPGraph and SPGraph and reflect TupPGraph of each tuple in SPGraph (or every possibility in schema transformation relations), we put forward the concept of tuple provenance route (TupPRoute) to represent the provenance of tuples.  
**Definition 9**.: _Tuple Provenance Route(TupPRoute), \(TupPRoute(Q_{i},I,t)=<SPGraph(I,T),BP>\), where \(BP=\{||b_{rid},rid=1,\dots,MAX_{RID}\}\), \(b_{rid}=0\) if \(V_{rid}\
otin TupleGraph\) and \(b_{rid}=1\)\(V_{rid}\in TupleGraph\)._  
From the description of the provenance graph, TupPGraph is an expression tree with several branches. Each node in SPGraph has a semantic parent and multiple child nodes in the tree structure. In Definition 9, BP is a bitmap array for the provenance route, and \(V_{rid}\) represents a vertex identified as \(rid\). All nodes in a SPGraph (including relational table nodes and transformation nodes) have an identifier rid that the maximum identifier value is MAX_RID. The rid bit in the BP is set to 1 if a node belongs to a TupPGraph, and otherwise 0. Though rid is theoretically unique, the bitmap may be too large if each node is assigned an identifier bit. Therefore, this paper uses a more compact way to ascertain a route by preorder traversing the schema provenance graph and allocating the same rid  
Figure 6: SPGraph of the query \(Q_{2}\).  
for nodes on the same branch, meaning that the value of MAX_RID is small (Algorithm 1).  
\begin{tabular}{l} \hline \hline
**Algorithm 1** \\ \hline Alg. generateRids(SPGraph): \\
01 begin \\
02 root := SPGraph.rootNode \\
03 generateRidRecursive(root, 0) \\
04 end \\ Alg. generateRidRecursive(eachNode, currRid): \\
05 begin \\
06 n := eachNode \\
07 if n.rid has not been assigned then \\
08 n.rid := currRid \\
09 end if \\
10 foreach cn in n.childNodes \\
11 if count(n.childNodes) = 1 then \\
12 generateRidRecursive(cn, currRid) \\
13 else \\
14 generateRidRecursive(cn, currRid + 1) \\
15 end if \\
16 end foreach \\
17 end \\ \hline \hline \end{tabular}  
Figure 7 shows the bitmaps corresponding to the three provenance routes in the query \(Q_{2}\) (base query, recursive query with base query as input, recursive query with the last recursion as input) are \"101001\", \"101111\", and \"110000\". Combining the bitmap array and the schema provenance graph, we can deduce the transformation path of the tuple \(k_{3}\) in the provenance graph (the blue path in the figure).  
Figure 7: SPGraph and TupPRoute of the query \(Q_{2}\).
# Integrating BIM and GIS for an Existing Infrastructure
## 4 Interoperability Opportunities
### Applications of BIM-GIS  
Some applications where BIM-GIS integration has proved to be efficient in improving asset FM are presented below. For example, for the management of urban subway pipelines [6], a management platform has been developed with real-time monitoring that improves efficiency compared to traditional management, providing more accurate information on the condition and location of the pipelines.  
In the case of infrastructure management, studies have been carried out to monitor in real-time the quality of pavement construction on a highway [64] or the design of airport infrastructure [65]. In both cases, no real integration of BIM data and GIS data is detailed. Of note is the research by [PERSON] et al. [66] on the application of BIM  
Figure 1: Information flow for a BIM-GIS integration for infrastructure FM.  
methodology in the design and construction of a road infrastructure in China. A BIM+GIS based cloud management platform was used, offering a substantial improvement in project communication and management. However, the study recognizes some limitations such as integration with other information technologies and standardization.  
BIM-GIS integration has been mostly explored in the literature related to the concept of smart cities [12; 14]. [PERSON]. [7] explored an ontological framework for a coherent semantic and geometric data structure, but focused on building concepts, leaving aside urban infrastructures such as roads or pipelines. On the other hand, [PERSON]. [17] approached the implementation of a BIM-GIS asset management system through a platform based on a flexible and centralized database that improved the user experience of a university campus.  
In all these cases, one of the main challenges is the conversion of data between the two technologies. [PERSON] et al. [59] discussed the current status of this conversion using the IFC-CityGML standards, [PERSON] et al. [62] addressed the simplification of IFC models for ease of use in the GIS data environment using the SLPK standard, reducing the file size by up to 97.3% in the case of the door and window model. [PERSON] et al. [60] proposed a methodology for the transformation of IFC geometry to SHP.
## 4 Experimentation and Results  
### Experimental Setup  
#### 4.1.1 Dataset  
In order to evaluate the compression and indexing performance of our proposed method for spatial data, we utilized the reprocessed dataset of global ocean gridded L4 sea surface height and derived variables provided by the Copernicus Marine Environment Monitoring Service (CMEMS) for training and testing. This dataset is in NetCDF-4 format and includes data variables such as sea surface height above geoid (SSH), sea surface height above sea level (SSH), surface geostrophic eastward sea water velocity (UV), and surface geostrophic northward sea water velocity (UV). The main features of the dataset are detailed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Description** & **Dimension** & **Unit** \\ \hline crs & coordinate system description & (0) & - \\ longitude & longitude & (1440, 1) & - \\ latitude & latitude & (720, 1) & - \\ adt & sea surface height above geoid & (1, 720, 1440) & m \\ sla & sea surface height above sea level & (1, 720, 1440) & m \\ ugos & surface geostrophic eastward sea water velocity & (1,720,1440) & m/s \\ vgos & surface geostrophic northward sea water velocity & (1,720,1440) & m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of data for experiment.  
Figure 3: Architecture of restoration enhancement module.  
This dataset covers global ocean data with a time range from 1 January 1993 to 4 August 2022, with a temporal resolution of daily or monthly and a spatial resolution of 0.25 degrees. The entire dataset can reach up to 30 GB in size. Considering the training time for the model, we selected daily data from 2016 to 2020, within the geographical range of 0-25\({}^{\circ}\) N and 100-125\({}^{\circ}\) E. The selected variables included sea surface height (sla), eastward sea water velocity (ugos), and northward sea water velocity (vgos). The dataset was then divided into 48 \(\times\) 48-pixel segments. In total, we obtained 50,000 data samples, which were split into training, validation, and testing sets in an 8:1:1 ratio.
## 3 Results and Discussion
### Unit Cell Metamaterial Structure Analysis
Permittivity refers to a material's ability to store electrical energy in the presence of an  
Figure 6: (**a**) Transmission (S\({}_{21}\)) and reflection coefficient (S\({}_{11}\)) of simulation and measured result, (**b**) simulation result for permittivity, permeability, and refractive index, and (**c**) impedance. electric field. When the value of \(\varepsilon\) is low, it may be due to the lower tendency of electric polarization. The result of relative \(\mu\) shows that the frequency exhibited a negative value from 0 GHz, with three maximum values of \(-513.23\), \(-550.36\) and \(-92.57\) at frequencies of 5.53, 6.05 and 8.92 GHz, respectively. Furthermore, the refractive index's real part was negative. Thus, from the simulated result, the proposed oval-square-shaped SRR displayed left-handed metamaterial properties. Additionally, the real part of the impedance value is above zero, as shown in Figure 6c. At frequencies of 5.21, 5.99, and 8.92 GHz, there were three impedance peaks with values of 54.36, 12.41 and 1.82 \(\Omega\), respectively. The suggested structure is a passive medium if the impedance value is greater than zero.
## 4 Proposed Metrics
### Valid Routes  
A valid path is one that is effective at transporting packets. For AODV, a valid route is a route that has not expired and has not gotten an error packet called the route error (RERR), which reports that this route has been dropped. The same happens for DSR, except that it has no route expiration timers; in this case, a node knows that a route has become invalid, because it has received a route error packet. For MPH, the route validity is given by the persistence mechanism [13], which prevents unreliable changes in the neighbor tables caused by node faults. This scheme employs a slow erasure of a neighbor node based on the number of times that a node does not respond to a neighbor discovery packet.  
In order to analyze the network behavior for the four mentioned protocols, we turned off at random different percentages of nodes, and 10 s later, we turned on these nodes. When we turn on the nodes again, we analyze the recovery time of the topology. As soon as the nodes are on, we start measuring the time it takes for each protocol to reestablish the valid routes to the destination, which is the coordinator node. This test was performed by comparing the tables of the nodes when the network showed normal behavior (with no node disconnections) with the tables of the nodes when the topology is restored.  
Simulation results are shown in Figure 14 through bar plots of the recovery time as a function of the percentage of nodes off for all protocols. As can be appreciated, MPH quickly reestablishes the routes based on the restored topology. AODV and DSR, on the other hand, reestablish routes when needed, and this process also depends on the packet error report, the invalid routes and the expiration timers. In ZTR, with the connections and disconnections of nodes, the network reorganization is set off when nodes start receiving error messages. When a node wants to re-connect to the network, it must re-acquire an address, and this occurs when a parent node accepts it as a child node. The exchange of messages required for this task results in somewhat more time consumption than for MPH reorganization. MPH uses the reactivity ND packet mechanism to quickly recover connections with nodes that have been reconnected  
Figure 14: Recovery time depending on the percent of nodes off for the four protocols.  
to the network and the persistent nodes mechanism for maintaining reliable links to the neighbors [13]. Thus, for MPH, the recovery time for valid routes is lower. AODV and DSR use the route discovery in broadcast mode when the path to a new destination is needed and then have to wait until a node knows the path to the desired destination. We can determine in the worst case of 50% of nodes turned off that the recovery time of MPH is 58.8% that of AODV, 60.6% that of DSR and 64.5% that of ZTR
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
###### Abstract
In this paper, we test how reinforcement learning (RL) strategies allow for optimization of the RA procedure when the network model is fully represented as a Markov decision process (MDP). The main reason for experimenting with reinforcement learning is that transition probabilities are not known in advance in the MDP model described in this paper, due to the lack of a labeled data-set. The partial model of the random access environment with no predefined labeled data-set is such that our agents (users) observe the status of the network, take actions by selecting random access points, and collect rewards and punishments in the form of the environment's key performance indicators. For value approximation over finitely many states (i.e., many-dimensional input), powerful neural network approximations, as well as deep RL, have recently been proposed to solve the convergence problem [13; 14; 15]. In light of this, to further reduce the RA delay of users and to overcome the current inefficiency of TRxP selection, we propose a novel deep RL technique where users learn over time, through training, to select an efficient TRxP among available covering access points (TRxPs), then satisfy their RA performance expectations. In particular, the main contributions of this paper are outlined as follows:  
* Formulate the random access selection task as a mathematical optimization formulation. * Define the random access selection task in terms of MDP. * Propose a novel deep reinforcement learning algorithm that solves the random access selection problem formulated as MDP. This is performed by designing the system states, defining actions that are taken by agents, and defining a reward function. * Test and compare the performance of the proposed deep reinforcement learning algorithm against another learning algorithm and baseline approaches. The remainder of this paper is organized as follows. In Section 2, we assess relevant and up-to-date enhancements on RA performance proposed for delay-sensitive use cases of 5G and Beyond 5G (B5G) networks. We discuss the system model and problem formulation in Section 3. Section 4 presents an analysis of our proposed approach and model in terms of feasibility. In Section 5, we present experimental results by simulation. Finally, Section 6 concludes the paper.