esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Results Assessment Criteria  
A successfully trained ANN model should give an accurate output prediction, not only for input data used in the training process, but also for new testing data unfamiliar to the model within the range of the training database. In this study, six indicators were applied in order to evaluate the performance of the compressive strength ANN model and five indicators for the flexural strength ANN model, respectively. These three indicators are root mean square error (_RMS_), absolute fraction of variance (\(R^{2}\)), and integral absolute error (_IAE_), which are calculated by Equations (11)-(13), respectively [38,45]. Theoretically, When the _RMS_ and the _IAE_ tend to zero and the \(R^{2}\) tends to one, the proposed models predict the experimental data accurately.  
\[RMS=\sqrt{\frac{1}{n}\sum_{k=1}^{n}{(t_{k}-o_{k})^{2}}} \tag{11}\]  
Figure 2: Structure of ANN models, (**a**) Compressive strength ANN model, (**b**) Flexural strength ANN model; W/B (water to binder ratio); PCS (compressive strength of UHPFRC without fibers); VF (volume fractions); AR (aspect ratio); PFS (flexural strength of UHPFRC without fibers); CS (compressive strength of UHPFRC); FS (flexural strength of UHPFRC). In (**a**), the compressive strength ANN model developed in this study has three layers with six neurons in input layer, twenty neurons in hidden layer and one neural in output layer; in (**b**), the compressive strength ANN model that was developed in this study has three layers with five neurons in input layer, fifteen neurons in hidden layer, and one neural in output layer. In these two figures, \(\omega_{ij}\) is the weight between the \(i_{th}\) neuron in the input layer and the \(j_{th}\) neuron in the hidden layer, \(\omega_{ji}\) is the weight between the \(i_{th}\) neuron in the hidden layer and the \(j_{th}\) neuron in the output layer, and \(b_{j}\) is the bias of the \(j_{th}\) layer in the output layer.  
\[R^{2}=1-\left[\sum_{i=1}^{n}{\left(t_{i}-o_{i}\right)^{2}}\Big{/}\sum_{i=1}^{n}{o ^{2}}\right] \tag{12}\]  
\[IAE=\big{(}(\sum_{k=1}^{n}{\left(o_{k}-t_{k}\right)^{2}})^{1/2}\big{)}\Big{/}( \sum_{k=1}^{n}{o_{k}})\times 100\% \tag{13}\]  
where \(n\) is the total number of data; \(t_{k}\) is the target value of \(j_{th}\) data; \(o_{k}\) is the output value of \(j_{th}\) data; and, \(\overline{o}\) is the average value of output value.
# A Novel Hybrid Model for Cantonese Rumor Detection on Twitter
## 3 The Proposed Model
### Embedding Layer
#### 3.2.1 Text-based Embeddings  
Firstly, in order to capture the features of Cantonese, we construct a multi-domain Cantonese corpus, which includes new articles/blogs, the entities on Encyclopedia of Virtual Communities in Hong Kong (EVCHK) [25], restaurant reviews, forum threads, etc. Then, XLNet-Base is pre-trained on the corpus and fine-tuned using our constructed CR-Dataset. Specifically, the tokens of all the characters in the tweet are fed into the XLNet model and we get 768-dimensional vectors \(C_{t}=\{C_{t_{1}},C_{t_{2}},...,C_{t_{n}}\}\), which are the outputs of the last hidden layer. In order to obtain the words' external contexts and internal structures, we present an addition operation for semantic features between \(C_{t}=\{C_{t_{1}},C_{t_{2}},...,C_{t_{n}}\}\) (character embeddings) and Cantonese word embeddings \(W=\{W_{1},W_{2},...,W_{n}\}\) that provided by fastText [26]. The \(n\) denotes the number of characters in the tweet. Finally, we take the results of the addition operation as text-based embeddings \(E_{t}=\{E_{t_{1}},E_{t_{2}},...,E_{t_{n}}\}\) and then feed them into the BiGRU model. \(E_{t}\) is calculated by  
\[E_{t_{i}}=C_{t_{i}}+W_{i}, \tag{1}\]  
where \(E_{t_{i}}\) represents the text-based embedding of \(i\)th character in the tweet, \(C_{t_{i}}\) represents the character embedding of \(i\)th character in the tweet, and \(W_{i}\) represents the word embedding of \(i\)th character in the tweet.  
Figure 1: Structure of the XLNet-based BiGRU network with Attention mechanism (XGA) model.
# Research on Driving Scenario Knowledge Graphs
## 3 Methodology: DSKG Construction
### Knowledge Acquisition
#### 3.1.1 Domain Concepts  
A driving scenario [14] refers to the comprehensive and dynamic description of the interactive process between intelligent connected vehicles and elements in the driving environment (such as other vehicles, roads, traffic facilities, and weather conditions) within a specific time and space range. [PERSON]'s research [15; 16] introduces the concept of using a layered model to construct scene and environment descriptions, divided into four layers: base road network, situation-specific adaptations of the base road network, actors and their control, and environment conditions. In the PEGASUS project [17], the layered scene concept [16] is applied to highway scenarios. [PERSON] et al. [18] and [PERSON] et al. [19] subsequently introduced the fifth and sixth layers, forming the six-layer model for highway scenarios. The road layer defines road layout and topology, while the traffic infrastructure layer includes structural boundaries, traffic signs, and other elements. In subsequent work [20; 21], a new third layer was separated from the second layer to describe temporary changes, the fourth layer was named \"objects\", the fifth layer represented weather conditions, and the sixth layer represented digital information. Given the emphasis of the PEGASUS project [17] on highway applications, [PERSON] et al. [22] proposed a six-layer model for urban scenarios. Building upon the six-layer external scene construction performed by the CAICV-SOTIF Working Group, a seventh layer was added representing the vehicle state [23], to more accurately describe in-car information. Figure 3 illustrates the iterative process of the scene hierarchical concept, from the initial four layers to the latter seven layers, reflecting the ongoing deepening and refinement of driving scenario descriptions.  
Figure 2: Construction framework of the driving scenario knowledge graph.
# Analysis of Dynamic Behavior of ParReEx Robot Used in Upper Limb Rehabilitation
## 4 Structural Optimization of the Circular Guide of the ParReEx Robot  
The circular guide in the structure of the ParReEx robot is an important part that must maintain a certain torsional and bending rigidity and a corresponding resistance to fatigue. To achieve structural optimization of the circular guide, we start from its existing geometry, which we model parametrically with the help of the ANSYS Workbench-Design Modeler preprocessor, as shown in Figure 30.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter**Component** & **Def Tx(m)** & **Def Ty(m)** & **Def Dz(m)** & **Def D Mag(m)** \\ \hline Min & \(-6.638\times 10^{-8}\) & \(-4.132\times 10^{-8}\) & \(-6.603\times 10^{-8}\) & \(2.46\times 10^{-11}\) \\ Max & \(-1.948\times 10^{-11}\) & \(1.405\times 10^{-11}\) & \(-3.737\times 10^{-12}\) & \(9.960\times 10^{-8}\) \\ Avg & \(-6.256\times 10^{-8}\) & \(-2.094\times 10^{-8}\) & \(-5.735\times 10^{-8}\) & \(8.83\times 10^{-8}\) \\ RMS & \(6.268\times 10^{-8}\) & \(2.412\times 10^{-8}\) & \(5.739\times 10^{-8}\) & \(8.834\times 10^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 24: Deformation of the center of mass of the circular guide.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter**Component** & **Depl Tx (m)** & **Depl Ty(m)** & **Depl Tz(m)** & **Depl T Mag(m)** \\ \hline Min & \(-0.031\) & \(-0.0018\) & \(0.0865\) & \(0.0866\) \\ Max & \(-0.0042\) & \(7.620\times 10^{-6}\) & \(0.0865\) & \(0.0919\) \\ Avg & \(-0.0176\) & \(-9.243\times\) & \(0.0865\) & \(0.0887\) \\ RMS & \(0.0199\) & \(0.0011\) & \(0.0865\) & \(0.0887\) \\ \hline \hline \end{tabular}
\end{table}
Table 23: Displacement of the center of mass of the circular guide.  
Figure 29: Deformation map of the robot circular guide.  
Structural optimization involves two objectives:  
* Reducing the mass of the circular guide, which is made of aluminum alloy, by respecting the constraint of a maximum equivalent stress;
* Increased fatigue resistance by reducing peak tensions in areas with stress concentrators. These areas are the two spindles through which the guide is supported in the bearing housings.  
The optimization block diagram is shown in Figure 31.  
The steps of the structural optimization process of the circular guide in accordance with the block diagram in Figure 31 are described in the following.
## 2 Preliminaries and Basic Concepts  
### Fractional Derivative  
There are many definitions that describe the fractional derivative. Here, we use Caputo approach which is defined as follows:  
**Definition 1** (Caputo derivative).: _[_11_]_ _The fractional derivative of \(f(x)\) in the Caputo sense is defined by_  
\[D_{x}^{\alpha}f(x)=\frac{1}{\Gamma(n-\alpha)}\int_{0}^{x}f^{(n)}(t)(x-t)^{n- \alpha-1}dt\quad n\in\mathbf{N},\quad n-1<\alpha<n. \tag{2}\]  
It is worth mentioning that a Caputo derivative allows traditional initial and boundary conditions to be included in the formulation of the problem [12] as well as the derivative of the constant being zero. For more information and properties about Caputo derivative, we encourage the reader to see [11; 13; 14; 15].
# Low-Cost Solution for Kinematic Mapping Using Spherical Camera and GNSS
## 2 Materials and Methods
### Processing Methods
#### 2.6.4 Agisoft Metashape  
Agisoft Metashape 2.1.1 is a very well-known and used software in the field of photogrammetry. Among many other functions, it can process spherical images using the SfM-MVS method.  
The type of camera-recorded images was set as spherical images. The images were masked so that the GNSS equipment was not included in the calculation. The basic alignment of the images was performed with the position of all images. This process produced a thin point cloud that contained the key points. However, the alignment of the thin point cloud was subsequently performed only with images containing a 3D position quality of less than 8 cm and on a few GCPs spread over the measured area. In Agisoft Metashape, GCPs were manually marked in images where the target center was clearly visible. Considering the spherical image resolution (5760 \(\times\) 2880 px) and typical distances of 5-10 m, the ground resolution ranged from approximately 5 to 11 mm per pixel. Therefore, the target center was generally located with an estimated precision of 1-2 pixels, resulting in a real-world precision of about 0.5-2 cm. Only frames with a clear view were used for marking. Figure 7 shows the positions of the cameras, and, for the images for which their positions were used for alignment, the deviations are shown in the form of error ellipses.  
The final step was to calculate the dense point cloud. Important parameters of the calculation, such as the processing time and accuracy at the ground control points, are listed in Table 4. Properties of the computing station: RAM--32 GB; CPU--Intel i7-7700; and GPU--NVIDIA Quadro P1000S.
# Time-Dependent Alternative Route Planning: Theory and Practice +
## 3 The TDAG Algorithm
### Preprocessing Phase of Tdag
#### 3.1.2 Lookup Procedure for Timestamp Predecessors  
The lookup operation of preprocessed information, in order to obtain a time-dependent predecessor per landmark-to-vertex pair, is a procedure that is repeatedly used in the path-collection phase of the TDAG query algorithm (cf. PHASE-2 in Section 3.2). Briefly, the lookup operation takes, as input, a triple \((\ell,v,t_{\ell})\) of a landmark \(\ell\in L\), a current node \(v\in V\) and a departure time \(t_{\ell}\in[0,T)\) from \(\ell\). The lookup procedure starts by locating \(\Delta_{\ell,p}\), and then conducts a binary search in it, to locate the closest sampled departure times \(\mathit{dep}_{i}\leq t_{\ell}<\mathit{dep}_{i+1}\), in time \(\mathcal{O}(\log(|\Delta_{\ell,p}|))\). For a constant number of sampled departure times from \(\ell\) towards \(v\), this is also the \(\mathcal{O}(1)\) time.  
Then, the corresponding predecessors of \(v\) are located at positions \(i\) and \(i+1\) of \(\Pi_{\ell,v}\), and, thus, are retrieved also in \(\mathcal{O}(1)\) time. Since the number of sampled departure times  
Figure 2: Data structure for the succinct representation of preprocessed information of TDAG.  
only partitions the period \([0,T)\) in small time intervals, it is independent of the network size (e.g., for the EU instance, the maximum length of a sampled departure times sequence is 4407). Therefore, the entire lookup procedure takes \(\mathcal{O}(1)\) time (e.g., at most 13 comparisons, even for EU).  
Due to this novel methodology for the succinct representation of the preprocessed data, preprocessing a large number of landmarks is now possible, even for continental-size datasets. In terms of performance, this novel storage scheme provides a cache-friendly gain that beats the overhead of the bit-field and bit-mask extraction operations. This, in turn, leads to results of higher quality and significantly lowers the observed relative error of the travel-time approximations in the summaries of the landmarks.
## 2 Materials and Methods
### Reversed-Phase High Performance Liquid Chromatography (RP-HPLC)  
All the samples after the extraction in 80% methanol in water and enzyme-assisted extractions and all the samples after simulated digestion were analyzed using RP-HPLC (HPLC system 1260 Infinity II, a quaternary pump, a PDA detector, and a vial sampler) (Agilent Technology, Santa Clara, CA, USA). For the characterization of phenolic compounds, a Poroshell 120 EC C-18 column (4.6 mm \(\times\) 100 mm, 2.7 \(\upmu\)m) and a Poroshell 120 EC-C18 4.6 mm guard column were used. Phenolic compounds were separated using 0.1% H\({}_{3}\)PO\({}_{4}\) (mobile phase A) and 100% methanol (mobile phase B) with a gradient 0 min 5% B, 5 min 25% B, 14 min 34% B, 25 min 37% B, 30 min 40% B, 34 min 49% B, 35 min 50% B, 58 min 51% B, 60 min 55% B, 62 min 80% B, 65 min 80% B, 67 min 5% B, and 72 min 5% B [20]. Flow was 0.5 mL min\({}^{-1}\), and each sample was injected in 10 \(\upmu\)L. Phenolic compounds were identified by spiking samples with authentic standards and by comparing the UV/Vis spectra of standards with the spectra of peaks in apple samples (200 to 600 nm). Chlorogenic acid isomer, quercetin derivative, and quercetin-3-xyloside were tentatively identified with the help of literature [21; 22] and their UV/Vis spectra. Calibration curves of (+)-catechin, procyanidin B2, (\(-\))-epicatechin, phloretin-2-glucose, chlorogenic acid, quercetin, 3-galactosidase, quercetin-3-glucoside, quercetin-3-rhannoside, and cyanidin-3-galactosidase were prepared (r\({}^{2}\) from 0.9978 to 0.9997) and used for the quantification. Chlorogenic acid isomer, quercetin derivative, and quercetin-3-xyloside, which were tentatively identified, were quantified by using already constructed calibration curves of chlorogenic acid and quercetin, respectively. The coefficient of variation of phenolic compounds was 1.57-9.17% (peel) and 4-17.4% (flesh).
# A Survey of LoRaWAN for IoT: From Technology to Application
## 6 MAC Layer Performance Evaluation
### LoRaWAN Network Models  
Network wide performance is hard to be assessed by measurements in real networks as it is nearly impossible to have reproducible experiments. Moreover, there is still no large-scale LoRaWAN test-bed. Thus, models about self-interference, channel access techniques and joint-procedure can help to assess LoRaWAN performance. However, as near to reality the model is, the better performance assessment is. For this, different approaches are taken by researchers. There exist some mathematical models that consider only medium access characteristics while other researchers based their model on empirical studies of the physical layer performance. The following subsections review both of these approaches and models.
## 2 System Arrangement
### Description of Reconstructed Communication Channel Model  
Figure 3 shows the RCC model between two substations. In order to focus on the research object, the external network is replaced by equivalent sources. The length of transmission line between two substations ranges from 10 to 30 km. The distance between towers can be 0.3-0.5 km according to geographic circumstances. Therefore, the possible number of towers can be 30-125.  
Following the structure of the WSN-based monitoring system, the sensors placed around the towers send digital protection data from a substation to the remote one in hop-by-hop manners. The RCC model is built under the following three assumptions:  
1. Since the communication range of radio frequency (RF) modules with the state-of-the-art is around 1 km [24], the relay can skip some adjacent nodes, e.g., from tower 1 to tower 4.
2. To simplify installation, sensors are placed near the tower. Then, the distance between these sensors are less than 20 m, which is far less than span distance, so nodes at a tower can be regarded as an equivalent node.
3. During natural disasters, the OPGW is likely to be damaged, while the WSN nodes have redundancy and most of them can survive under natural disasters. In addition, taking the self-organized characteristic of WSNs into account, they can undertake the task of transmitting protection data.  
Figure 3: Description of reconstructed communication channel (RCC) model by hop-by-hop relaying.  
Figure 2: Practical structure of a wireless-sensor-networks (WSN)-based monitoring system.
# Earthquake Magnitude and Frequency Forecasting in Northeastern Algeria using Time Series Analysis
## 1 Introduction
In the current study, the consistency between the observation and the forecast is analyzed using the CSEP (Collaborative for the Study of Earthquake Predicability) test [42] in terms of the total number of events, also denoted as N-test. The results are presented as quantile scores, \(\delta_{1}\) and \(\delta_{2}\) indices. According to [PERSON] et al. [43], the forecast rate is too high (an overestimation) if \(\delta_{2}\) is very small, and too low (an underestimation) if \(\delta_{1}\) is very small for the N-test. Furthermore, according to the original CSEP testing framework, a model fails the test if its score is below a significance level of 2.5%, indicating an inconsistency between the forecast and the observation.
## 5 LPWAN Application Requirements
### Cost Effectiveness  
LPWAN applications are particularly sensitive to device and operational cost. In addition to the standard requirements of low deployment and operating costs for the network, the large number of devices involved puts major constraints on cost, operational expenses, and an imperative of low power consumption. Software upgradability without changing hardware is a key attribute that needs to be supported. Besides, it becomes imperative to support scalability, easy installation, and low cost maintenance.
## 4 Data Cleaning and Discretization
### Obtaining CAD Planimetry and Flat Images  
The planimetry of the final model was developed using AutoCAD 2024 software. To achieve this, the aligned point clouds were imported into 3D modeling software, from which the main outlines of the structure were traced. A selection was made of the relevant points required to generate a balanced representation that combined both detail and the regularization of planar areas or smooth curves, enabling the geometry of the heritage element to be reconstructed [23].  
Although the process was predominantly manual, planar sections of the point clouds or of the meshes generated from them were obtained for some of the views. These meshes, formed through triangulation of the original points, are produced by algorithms whose parameters must be carefully chosen. It is essential to find a balance that corrects minor errors inherent to the technique, while avoiding excessive loss of detail and, therefore, of valuable information.  
Figure 17: (**Left**): Textured exterior point cloud obtained from drone imagery. (**Center**): Photogrammetric model of the interior side. (**Right**): Point cloud generated through mobile laser scanning.  
Once the models were translated into planar and discretized geometry, a series of geometric analyses typical of this type of study were applied. These analyses aimed to identify the original layout patterns used in the construction of vaults, staircases, and other architectural features.  
In addition, for the analysis of masonry surfaces, this type of model allows the extraction of orthogonal, textured images. These images are created by projecting the textures from the photographs onto the previously generated meshes, thereby enabling the production of flat elevations of the walls. These images capture a high level of detail, which proved fundamental in our case study, enabling an in-depth reading of the construction phases visible on the wall surfaces [24; 25].  
Finally, by combining these outputs, we generated planimetric drawings--primarily sections with the projection of rear-facing walls. The resulting documents offer high informational value, allowing the simultaneous appreciation of many different aspects of the heritage asset (Figures 18 and 19).
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 5 Discussion
### Technical Challenges
#### 5.1.3 Privacy and Ethical Issues  
* Privacy Protection Physiological student data in remote teaching multimodal environments are employed extensively to determine learning status and adjust teaching practices. However, the sensitivity of this type of data is extremely high. Today, most remote learning systems have incomplete security measures to protect physiological data, making them vulnerable to unauthorized use, illegal access, and potential breaches. The application of data protection laws, such as GDPR, in education is unclear. This creates legal risks in data storage, processing, and use. A major challenge is protecting data privacy while making full use of multimodal data to improve education [75].
* Algorithmic Fairness Multimodal learning systems are prone to algorithmic biases, which can affect fairness and inclusivity. AI models are trained on historical datasets that may contain cultural, gender, or linguistic biases. These biases can lead to unfair feedback or unequal learning experiences for students from different backgrounds. Some speech recognition systems have difficulty processing non-native speakers' inputs, which can negatively impact their learning. Adaptive learning systems may also create inequalities. Personalized recommendation algorithms might assign different difficulty levels based on existing gaps, reinforcing disparities [76]. Future research should focus on improving fairness in multimodal AI algorithms to provide equal educational support for all students.
* Security ConcernsAn increase in remote education will mean that cloud-based systems used to store and process data are vulnerable to cyber attacks, data theft, or identity theft. The security and privacy threats of unauthorized access to student behavioral data, learning records, physiological information, etc., are serious. Distributed ledger technology (DLT). The existing encryption techniques and access control mechanisms should be further optimized to meet the needs and requirements of the increasing rate of attack on new remote education systems. Technologies such as federated learning, which allow for decentralized data usage, help reduce the risk of data falling, while advanced blockchain-based identity authentication provides stronger data security and integrity [77]. This foundation of sustainability and resilience depends on which multimodal curricula are designed, and this is where the data security framework must be laid.