esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Testing Dataset and Experimental Setup  
The overall objective is to understand the impact of the hardware specifications on the performance attained by both the computational and rendering operations of ModAR. Moreover, the examination of the effectiveness of the developed techniques and strategies as well as the interpretation of the testing results lead to guidelines and suggestions. The testbed comprises one old low-end and one affordable mid-range Android mobile device. The minimum target version of ModAR is Android API level 19, a.k.a. Android 4.4 KitKat, released on 31 October 2013 (Table 2) and the OpenGL ES version is 2.0. The six different pattern images and four 3D models that come with the ModAR prototype (Figure 4) were used in the experiments. The pattern image hereinafter referred to as \"BOTERO\" is part of a book cover; the pattern image \"SCYTHE\" is the planar front surface of the box of a board game; the pattern images \"SURVEYING\" and \"JEFFERS\" are book covers; \"PAINTING\" is a wall painting inside a frame; and \"GRAFFITI\" is an outdoor wall mural. The size of the pattern images used in the experiments is within the range 0.07-0.25 MP (see Table 3 for the exact dimensions), while the size of the camera frames in the test devices is 0.92 MP for the low-end device (720 \(\times\) 1280 pixels) and 2.07 MP for the mid-range device (1080 \(\times\) 1920 pixels), both corresponding to a frame rate of 30 FPS. The textured Wavefront OBJ models are of multiple scales and heterogeneous sources, like image-based photogrammetry and terrestrial laser scanning. Each model differs in size and complexity, thus \"POTTERY\" and \"BUST\" are denoted as small models, \"STATUE\" as of regular size and \"CHURCH\" as large model. \"STATUE\" is the only one with vertex color and no texture.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specification** & **Low-End** & **Mid-Range** \\ \hline Model & Samsung Galaxy Ace 2 & ZTE Blade A5 \\ Year & 2012 & 2019 \\  & 800 MHz dual-core & 1.6 GHz octacore \\ CPU & ARM Cortex-A9 & Spreadrum SC9863A \\ GPU & ARM Mali 400 & IMG8322 \\ Memory & 4 GB & 16 GB \\  & 768 MB RAM & 2 GB RAM \\ Camera & 5 MP & 13 MP \\ Video & 720 \(\times\) 1280 pixels, 30 FPS & 1080 \(\times\) 1920 pixels, 30 FPS \\ Screen Resolution & 480 \(\times\) 800 pixels & 720 \(\times\) 1440 pixels \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions and resolution of the pattern images as well as size and number of faces and vertices of the 3D models used in the experiments.
# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 2 Environment Perception System  
An autonomous vehicle acquires knowledge of its surrounding in two stages. The first stage consists of scanning the road ahead to detect possible changes in driving conditions (traffic lights and signs, pedestrian crossing, and barriers, among others). The second stage relates to the perception of other vehicles.  
This section presents the most representative sensors that make up the perception systems of AVs: Ultrasonic, RADAR, LiDAR, cameras, IMU, GNSS, and RTK. There are numerous informative and scientific articles or books that show the types of sensors used in AVs, their applications, as well as their advantages and disadvantages. These works portray the sensors as black boxes, formed by a set of inputs and outputs, without delving into the physical foundations of their operation. As a novel aspect of this work, these sensors are presented from a point of view of the electromagnetic spectrum that they actively or passively use for their operation. This will allow researchers to acquire a deeper knowledge of the benefits and disadvantages of these sensors in degraded environments or adverse weather conditions. In Figure 2, the electromagnetic spectrum is divided into two scales: Wavelengths and frequencies. In addition, the spectral ranges used by the sensors analysed in this work are shown.
## 3 New Numerical Parametric Models
### Residual Cross-Section  
The numerical models developed within the scope to study the effect of different geometric parameters were evaluated considering the evolution of the char layer over time. To compare the influence of the geometric parameters, schematic images were obtained for the time instants 30, 60, 90, and 120 min, which correspond to time scales defined for non-bearing construction elements by standard EN 13501-2:2007 [27] and based on RJ-SCIE [28].  
In all models, the limit temperature for the formation of the char layer was imposed considering the reference of 300 \({}^{\circ}\)C imposed by ECS-1-2, EN 1995-1-2 (2003) [1]. Thus, the calculation of the residual section presented in Table 3 was obtained.  
Considering the results obtained in Table 4, it is concluded that the residual section in the models under study only differs at the time point of 60 min, since at 30 min there is no charred area and at 90 and 120 min amounts are charred.  
Also, in conclusion, with a 10 mm increment between models in width (W) of the beam, there is an increase of approximately 50% in the residual cross-section between the models [120 \(\times\) 30 + 12.5] \(\times\) 400 and [120 \(\times\) 40 + 12.5] \(\times\) 400, while from the model [120 \(\times\) 50 + 12.5] \(\times\) 400 the residual cross-section only increases around 10%. With this, width increments in the same dimension do not cause a linear increase in the residual cross-section. At 90 min, the smallest width model has no residual cross-section, while the largest width model has slightly better performance than the intermediate width model.  
Analyzing the remaining results, it is concluded that the model [120 \(\times\) 50 + 25] \(\times\) 400 presents the best performance among all models evaluated in terms of residual cross-section and thermal insulation criteria, assuming a fire resistance equal to 253.5 min.  
Additionally, it is concluded that the geometric parameter with the greatest influence on the protection of the constructive solution is the thickness of the gypsum board, given that double the board layer, in this case from 12.5 to 25 mm, causes an increase in fire resistance of approximately 175 min. Thus, increasing the Tg parameter causes a better overall performance of the constructive solution.
# A Strain Hardening and Softening Constitutive Model for Hard Brittle Rocks
## 3 Mechanical Behaviour of Brittle Hard Rock
### Plastic Parameter  
In FLAC\({}^{\text{3D}}\), the equivalent plastic strain increment \(\Delta\varepsilon^{ps}\) is used as the plastic parameter to describe the evolution process of the strength parameters in the softening regime and is defined as follows:  
\[\Delta\varepsilon^{ps}=\frac{1}{\sqrt{2}}\sqrt{\left(\Delta\varepsilon_{1}^{ps}- \Delta\varepsilon_{m}^{ps}\right)^{2}+\left(\Delta\varepsilon_{m}^{ps}\right)^ {2}+\left(\Delta\varepsilon_{3}^{ps}-\Delta\varepsilon_{m}^{ps}\right)^{2}} \tag{4}\]  
where \(\Delta\varepsilon_{1}^{ps}\) and \(\Delta\varepsilon_{3}^{ps}\) are the maximum and minimum principal plastic strain increments, and  
\[\Delta\varepsilon_{m}^{ps}=\frac{1}{3}\Big{(}\Delta\varepsilon_{1}^{ps}+ \Delta\varepsilon_{3}^{ps}\Big{)} \tag{5}\]  
The plastic potential function \(g^{s}\) corresponds to the noncorrelated flow rule, which can be expressed as follows:  
\[g^{s}=\sigma_{1}-\sigma_{3}N_{\psi} \tag{6}\]  
where  
\[N_{\psi}=\frac{1+\sin\psi}{1-\sin\psi} \tag{7}\]  
and \(\psi\) is the dilation angle of the rock. The principal plastic strain increment \(\Delta\varepsilon_{i}^{ps}\) can be calculated as follows:  
\[\Delta\varepsilon_{i}^{ps}=\lambda^{s}\frac{\partial g^{s}}{\partial\sigma_{ i}},i=1,2,3 \tag{8}\]  
where \(\lambda^{s}\) is the plastic multiplier, which cannot be calculated in this process. Substituting Equation (6) into Equation (26), the principal plastic strain increment \(\Delta\varepsilon_{i}^{ps}\) can be written as follows:  
\[\Delta\varepsilon_{1}^{ps}=\lambda^{s} \tag{9}\]  
\[\Delta\varepsilon_{3}^{ps}=-\lambda^{s}N_{\psi} \tag{10}\]  
\[\Delta\varepsilon_{3}^{ps}=-N_{\psi}\Delta\varepsilon_{1}^{ps} \tag{11}\]  
Using Equation (10) above, Equation (4) becomes the following:  
\[\Delta\varepsilon^{ps}=\frac{\sqrt{3}}{3}\sqrt{1+N_{\psi}+N_{\psi}^{2}}\Delta \varepsilon_{1}^{ps} \tag{12}\]  
If dilation angle \(\psi\) is constant, \(N_{\psi}\) is also constant, and relation between \(\varepsilon^{ps}\) and \(\varepsilon_{1}^{ps}\) can be established in the following form:  
\[\varepsilon^{ps}=\frac{\sqrt{3}}{3}\sqrt{1+N_{\psi}+N_{\psi}^{2}}\sum\Delta \varepsilon_{1}^{ps}=\frac{\sqrt{3}}{3}\sqrt{1+N_{\psi}+N_{\psi}^{2}} \varepsilon_{1}^{ps} \tag{13}\]  
According to Equation (13), the axial plastic strain \(\varepsilon_{1}^{ps}\) can be used as a plastic parameter in FIAC\({}^{\text{3D}}\) to control the strength parameter evolution process in the strain hardening and softening stages [31].  
\begin{table}
\begin{tabular}{c c c} \hline
**Rock Type** & \(E_{m}\)**/GPa** & \(\mu_{m}\) \\ \hline Granite & 65.47 & 0.26 \\ Diabase & 50.28 & 0.24 \\ \hline \end{tabular}
\end{table}
Table 1: Average values of Young’s modulus and Poisson’s ratio for Beishan granite [26] and Danjiangkou diabase [37].
# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 2 Clustering Algorithms
### Density-Based Spatial Clustering of Applications with Noise (DBSCAN)  
Density-based clustering algorithms are usual in the literature [43]. The best-known algorithm of this category is the Density-Based Spatial Clustering of Applications with Noise--DBSCAN [44]. The method can find clusters of an arbitrary shape, detecting them through high-density spheres and merging the nearby spheres forming clusters.  
The DBSCAN uses a simple estimate of the minimum density level, based on a threshold for the number of neighbors, _minPts_, within the radius \(\varepsilon\) (with an arbitrary distance). Objects with more neighboring _minPts_ within that radius (including the query point) are the central point.  
The DBSCAN intends to find areas that satisfy this minimum density separated by lower density areas. For efficiency reasons, DBSCAN does not estimate density between points. Instead, all neighbors within the radius \(\varepsilon\) by a central point are considered part of the same cluster (known as direct attainable density). If any of these neighbors is chosen again as a central point, their neighborhoods will be included transitively (reachable density). Non-essential points in this set are called boundary points, and all points in the same set are connected to density. Points that are not accessible by density are considered noise and do not belong to any group [45].  
Figure 1: The final solution of the hierarchical clustering method showing a 2D dataset and corresponding dendrogram.  
Figure 2 illustrates the working principle of the DBSCAN and the elements. The _miniPIs_ parameter is four, and the circles indicate the radius \(\varepsilon\). Note that \(N\) is a noise point, \(A\) is a central point and, \(B\) and \(C\) are border points.
# Implementing Federated Governance in Data Mesh Architecture +
## 5 Implementing Federated Data Mesh Governance
### The Data Product Quantum  
According to the data mesh paradigm, the data product quantum refers to the smallest, self-sufficient unit of data that can independently deliver value as a product. It encapsulates all necessary aspects--data, metadata, quality, security, and access mechanisms--required to be useful and meaningful to its consumers, adhering to the principles of domain orientation and self-service infrastructure.  
In Figure 6, we present the view of how the data assets deployed within the Fybrik platform constitute self-contained product quantums. Within our system, each data product includes four elements:  
* The product Metadata--schema, semantic tags, lineage, etc;
* Notebook code for the data manipulation which is deployed to workload computing nodes (created automatically by Fybrik);
* FybrikApplication specification defining the source and destination identifiers, and intended operations provided in yaml format;
* Policies define the product usage rules, e.g., what is allowed to which consumers.  
The product quantum may contain multiple policies. At the same time, each policy is applied to a single data output port that makes part of a data contract. This way the domain team can serve the same product to multiple consumers but on different conditions.  
In Section 3.2, we discussed the purpose of data contracts. It provides a formal way to establish provider-consumer relationships and serves as a source of trust during the governance enforcement conducted by the platform.  
For creating a new data product in our system, the domain team engineers have to follow the next procedure:  
Figure 4: Federated governance system architecture based on HLF.  
Figure 5: Pseudo code listing of the catalog smart contract.  
1. Request the data asset access, e.g., based on the protocol outlined in [16];
2. When the request is approved, the provider's metadata and policy are used to form a new data contract;
3. Submit the Notebook and FrybrikApplication documents for provisioning a new computing process;
4. Register a new data product in the catalog by providing the asset's metadata and policies.  
In this section, we described the architecture of our prototype system for building the federated data mesh governance. In the next section, we proceed by comparing our system and other related works based on the functional maturity requirements.
## 3 Feature Encoding Methods
### Mapping-Based Methods  
Mapping-based methods enable bidirectional conversion between sequences and feature vectors--not only transforming sequences into vectors but also reconstructing sequences from vectors. This capability ensures that output vectors produced by generative models can ultimately be translated back into functional sequences, bridging the gap between abstract representations and biologically interpretable data.  
**Token**  
Token is an encoding strategy that employs a dictionary to map each of the 20 natural amino acids and a termination symbol to distinct integer identifiers, with an additional integer reserved for padding. Specifically, sequences are padded to a uniform length corresponding to the maximum sequence length across the dataset plus one (to accommodate termination), ensuring dimensional consistency for ML/DL frameworks. As formalised in Equation (1), this method generates numerical representations directly compatible with model architectures.  
\[\begin{split} Token_{dict}&=\{A:1,R:2,N:3,D:4,C:5,E:6,Q:7,G:8, H:9,I:10,L:11,K:12,\\ M:13,F:14,P:15,S:16,T:17,W:18,Y:19,V:20,Padding:24,End:25\}.\end{split} \tag{1}\]  
One-Hot  
The one-hot encoding method represents each natural amino acid as a 20-dimensional binary vector, where all the elements are zero except for a single position uniquely assigned to the corresponding amino acid. Padding tokens, which are required to standardise sequence lengths, are encoded as all-zero vectors of the same dimensionality. For a given sequence, each amino acid (from the first position to the maximum predefined length) is replaced by its one-hot vector, and these vectors are concatenated sequentially to form a 20 \(\times\) Maximum\({}_{Length}\) dimensional feature matrix. This matrix serves as the standardised input for the ML or DL models. One-hot encoding, also referred to as binary encoding, was adopted in two seminal studies published in 2011 and 2013 [102; 103].
# Handover Management for Drones in Future Mobile Networks--A Survey
## 1 Introduction
This will enhance the performance of handover management, such as in [32; 33]. This survey paper contributes to the target of providing a comprehensive and deep-focus review of handover management for connected drones in future mobile networks. The work provides a more focused review on drone networks, mobility management for drones, and related works in the literature. To illustrate how conventional technology functions in cellular-connected drones, the LTE system, common mobile ad hoc networks (MANETs), vehicular ad hoc networks (VANETs), and IEEE 802.11 standards are employed. Since drone networks are susceptible to frequent handovers, a variety of different handover strategies are extensively addressed to further proceed in making drones a viable alternative to ground BSs or UEs. These strategies address underlying issues without jeopardizing the performance of communication systems. Due to their mobility and flexibility, drones are preferred in a wide variety of applications. This review addresses methods for developing such applications while maintaining seamless handover management. The main contributions of this article are as follows:  
1. Providing a brief introduction to drone networks and connectivity requirements for drones and, more specifically, handover management in drone networks. 2. Highlighting and discussing the main challenges facing the implementation of connected drones. The main focus is on the handover challenges that influence the mobility of connected drones in mobile networks, including the discussion of 6G and beyond in further detail. 3. Summarizing and discussing the previous conducted research that has mostly focused on mobility management for connected drone networks, including performance, network operation, and connectivity issues. 4. Discussing the key significant future research directions for connected drones. This includes mobility management, energy efficiency, machine learning, deep learning, IoT, MANETs applications, VANETs applications, new cellular technologies, security, and Mobile Edge Computing (MEC) with drones. The analysis and discussion of this survey study indicates that, by implementing intelligent handover schemes that are based on machine learning, deep learning, and automatic robust processes, the handover problems and related issues may be reduced significantly as compared to traditional techniques. The rest of this paper is organized as follows. Section 2 presents a brief description of the drone network. Section 3 discussing the connectivity requirements for drones. Section 4 reviews the handover management for efficient drone networks. Section 5 discusses the current handover challenges that must be addressed. Section 6 examines previous research regarding drone networks, mostly related to mobility management, performance, network operations, and connectivity issues. Section 7 highlights the most significant future directions. Section 8 presents the conclusion of this work. Table 1 lists and describes the abbreviations used in the text.
## 3 System Model and Problem Formation
### Assumptions and Calculations
#### 3.3.4 Delay and Packet Loss Calculations  
Each WMR computes its delay and PLR periodically with its all one-hop neighbors by sending the Hello packets, according to Equations (2) and (3), respectively. Every node stores these computed values locally and forward these values to source node during path finding process. Finally, source node calculates the end-to-end delay and PLR of each path according to Equations (4) and (5), respectively:  
\[D_{WMR_{j}}=D_{q,j}(t)+D_{l,j}+D_{p,j} \tag{2}\]  
where \(D_{WMR_{j}}\) is the total estimated delay at node \(j\) at any particular time \(t\), which is based on queuing delay \(D_{q,j}(t)\), link delay \(D_{t,j}\), and propagation delay \(D_{p,j}\):\[PLR_{WMR_{j}}=\frac{L_{q,j}(t)\times L_{CH,j}(t)}{L_{q,j}(t)\times L_{CH,j}(t)+ NPRS} \tag{3}\]  
where \(PLR_{WMR_{j}}\) is the total estimated PLR at node \(j\) at any particular time \(t\), which is based on packet loss value due to buffer overflow \(L_{q,j}(t)\) and packet loss value due to channel conditions \(L_{CH,j}(t)\). \(NPRS\) is the number of packet received successfully:  
\[\tau(t)_{p}=\sum_{j=0}^{n}D_{WMR_{j}} \tag{4}\]  
where \(\tau(t)_{p}\) is the total estimated end-to-end delay of path \(p\) at any particular time \(t\) and \(n\) is the total number of network nodes in path \(p\):  
\[l(t)_{p}=1-\prod_{j=0}^{n}(1-PLR_{WMR_{j}}) \tag{5}\]  
where \(l(t)_{p}\) is the total estimated end-to-end \(PLR\) of path \(p\) at any particular time \(t\) and \(n\) is the total number of network nodes in path \(p\).
# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 3 Material and Methods  
### Dataset  
The dataset was created by Figure Eight inc. (an Appen company) from Twitter data and used as a Kaggle competition hosted at [[https://www.kaggle.com/c/nlp-getting-started/data](https://www.kaggle.com/c/nlp-getting-started/data)]([https://www.kaggle.com/c/nlp-getting-started/data](https://www.kaggle.com/c/nlp-getting-started/data)) (accessed on 21 June 2021). There are 10,876 samples in the dataset, including 4692 positive samples (disaster) and 6184 negative samples (not a disaster). Table 1 shows four positive and four negative samples. It can be seen that the disaster and non-disaster Tweets could use similar keywords in different contexts, resulting in different interpretations. For example, \"pileup\" in sample 1, \"airplane's accident\" in sample 2, \"Horno blaze\" in sample 3, and the phrase \"a sign of the apocalypse\" in sample 4 are more indicative of a disaster. However, the words \"bleeding\", \"blaze\", \"ambulance\", and \"Apocalypse\" in samples 4 through 8 do not indicate a disaster, given their contexts. Figure 2 displays the histograms of three variables per Tweet: the number of characters, the number of words, and the average number of word lengths. Specifically, the means of the character number per Tweet for disaster and non-disaster Tweets are 108.11 and 95, respectively; the means of the word number per Tweet for disaster and non-disaster Tweets are 15.16 and 14.7, respectively; the means of the average word length for disaster and non-disaster Tweets are 5.92 and 5.14, respectively. The stats data show that the disaster Tweets are relatively longer than the non-disaster ones.
# Empirical Validation of a Streamlined Three-Repetition Sit-to-Stand Protocol Using MAI Motion
###### Abstract  
MAI Motion is a motion capture system designed to assess lower-limb biomechanics during functional movements like the sit-to-stand (STS) test. Determining how many repetitions are needed to obtain reliable measurements is critical for balancing data quality and participant burden. This study evaluates whether three repetitions (3x) of STS provide reliable data compared to five repetitions (5x). Three-dimensional videos of participants (\(n=20\)) performing 5x STS movements were captured using MAI Motion. Primary measurements were the mean values of each joint angle and the coefficient of variation (CV). Statistical comparisons (including one way ANOVA followed by paired \(t\)-tests or non-parametric equivalents) determined whether differences in mean (DiM) values or CV existed between 3x and 5x. The analysis revealed minimal DiM angles between 3x and 5x. Variability, assessed via CV, showed no clinically meaningful differences. Although ankle angle, knee and hip abduction had higher CV values than the other metrics, 3x and 5x performed similarly. Participants reported that 3x required less effort than 5x, underscoring its potential for clinical application. These findings demonstrate that the MAI Motion system captures comparable biomechanical data to the 5x STS protocol when using a 3x approach. This supports the adoption of 3x as a practical alternative, reducing participant burden.  
digital motion capture; biomechanics; kinematics; movement Article