esa-sceva/satcom-chunk-collection huggingface dataset:
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
###### Abstract  
Due to challenges posed by long propagation distances, high mobility, limited bandwidth, multipath propagation, and Doppler effects in underwater acoustic sensor networks (UASNs), the design of routing protocols faces numerous complexities. To enhance reliability in sparse node regions and reduce network energy consumption, this paper proposes a dependable and energy-efficient routing protocol termed WLQRP for weighting link-quality-based routing protocol. WLQRP leverages link quality information between nodes for data forwarding. When computing the link quality between nodes, it considers not only the historical transmission success ratio between nodes and their immediate relay but also incorporates the link quality between the relay and the relay's next-hop nodes. This approach effectively reduces the likelihood of encountering void regions. Moreover, the protocol judiciously selects the next-hop node to improve transmission success rates, thereby minimizing data packet retransmissions and reducing energy consumption. We conduct MATLAB simulations to evaluate the WLQRP protocol, comparing it against the VBF (vector-based forwarding) and the HH-VBF (hop-by-hop vector-based forwarding) underwater routing protocol. Experimental results demonstrate that WLQRP enhances network performance in terms of data transmission rate, energy consumption, and end-to-end delay. These findings validate the efficacy of the proposed protocol.  
underwater sensor network; routing protocol; channel-aware communications +
Footnote †: journal: remote sensing  
0023
## 6 Discussion
### Advantages and Limitations of the Presented Lava Flow Mapping Approach
subjective assessments may occur [21; 24; 66]. In Figure 12, the lava flow of the Karangetang I eruption phase was visually interpreted and manually digitized. This was compared with the semi-automatically generated results of the OBIA method. The agreement between the manually and semi-automatically derived areas is 85.17%, whereby the OBIA-derived lava area showed an overestimation by 14.83% compared to the manually derived reference. Another way to qualitatively validate the lava areas is to produce multispectral false color time series (Sentinel-2 and Landsat-8) as performed in Section 6.1 (Figures 9, A3 and A4). Due to the sensitivity of the SWIR bands to thermal anomalies, greater contrast can thus be produced [66] and lava flows can be tracked during an eruption period. Other validation opportunities include comparison with current geologic maps, which are scarce at remote volcanoes [21; 71], or comparison with numerical modelling results [50; 58]. The OBIA methodology can be applied to other tropical volcanoes. It is important that the study area around the volcano has vegetation, as this allows potential _dNDVI_ changes to be measured [23]. Application of the methodology in areas with little vegetation depends heavily on the availability of NHI pixels and is unlikely to provide satisfactory results (Figure 11). In addition, the OBIA change detection methodology can theoretically be applied to map other volcanic hazards that exhibit flow behavior, e.g., pyroclastic flows and lahars, with the latter requiring vegetated volcano flanks, as lahars cause no thermal anomaly that could be detected by the NHI algorithm.
###### Abstract  
The main objective of this study was (1) to analyze the patterns of acceleration (Ac) and deceleration (Dec) during football matches in elite youth football, both within and between different segments of the match; and (2) to investigate the impact of ball possession and various playing positions on these acceleration and deceleration patterns. To provide a broader explanatory context, the influence of tactical space management was assessed in terms of depth and width. A descriptive comparative design was used, and data were collected during two friendly matches. Player and ball tracking data were collected using a local positioning system. In the attack phase, differences were obtained in the average Ac (first half: 0.42 \(\pm\) 0.06 m\(\cdot\)s\({}^{-2}\), second half: 0.38 \(\pm\) 0.07 m\(\cdot\)s\({}^{-2}\); \(p\) = 0.021, \(d\) = 0.50) and average Dec (first half: \(-\)0.44 \(\pm\) 0.09 m\(\cdot\)s\({}^{-2}\), second half: \(-\)0.36 \(\pm\) 0.08 m\(\cdot\)s\({}^{-2}\); \(p\) = 0.001, \(d\) = 0.84). Wingers in the attack phase obtained higher values in maximum Ac (1.65 \(\pm\) 0.65 m\(\cdot\)s\({}^{-2}\); \(p\) = 0.007, \(\eta^{2}\) = 0.03), and in the total number of both Ac (68.7 \(\pm\) 45.22; \(p\) = 0.001, \(\eta^{2}\) = 0.10) and Dec (70.6 \(\pm\) 45.70; \(p\) = 0.001, \(\eta^{2}\) = 0.10). In the defense phase, full-backs obtained higher values in average Ac (0.53 \(\pm\) 0.17 m\(\cdot\)s\({}^{-2}\); \(p\) = 0.001, \(\eta^{2}\) = 0.07) and average Dec (\(-\)0.49 \(\pm\) 0.18 m\(\cdot\)s\({}^{-2}\); \(p\) = 0.001, \(\eta^{2}\) = 0.05) and wingers in the total number of Ac (43.9 \(\pm\) 27.30; \(p\) = 0.001, \(\eta^{2}\) = 0.11) and Dec (43.8 \(\pm\) 28.60; \(p\) = 0.001, \(\eta^{2}\) = 0.10). In young football players, Ac and Dec do not follow a decreasing end throughout the match, and their behavior is uneven depending on ball possession and the position assigned to the player, with the highest demands on Ac/Dec in winger and full-back positions.  
Keywords:local positioning system; ball tracking; live ball data; accelerations; young footballs +
Footnote †: journal: _Sci_
# AUV Adaptive Sampling Methods: A Review
## 4 Mission Objectives of Sampling  
Depending on the ultimate objective of each AUV mission, the principal approach in choosing a sampling method varies. For example, when the target is a dynamically dispersing plume, the primary objective is either searching for the source location [70; 71] or sensing the highest concentrated peak [4; 67] or mapping the two-dimensional boundary [72; 73] or three-dimensional structure [7].  
If there is a single source of a phenomenon, the survey strategies are often developed in a way to search for a maximum value: for example, the strongest concentration of chemicals or the highest flow velocity near the hydrothermal vent. The peak values indicate the origin of the phenomenon in many cases. One similar approach to this is a bio-inspired method, similar to that often observed in many insects' pheromone-modulated flight behaviour when looking for food or mating [70; 74].  
When the mission aims to define the spatial extent of a phenomenon such as a thermocline or upwelling, sampling methods often refer to the sudden rate of change or anomaly across the boundary [9; 11; 12]. In contrast to this, an isothermal line along the boundary could be sought using a desired threshold [7].  
This section describes various methods adopted for different mission objectives.
# Cubic \(q\)-Bezier Triangular Patch for Scattered Data Interpolation and Its Algorithm
###### Abstract  
This paper presents an approach to scattered data interpolation using \(q\)-Bezier triangular patches via an efficient algorithm. While existing studies have formed \(q\)-Bezier triangular patches through convex combination, their application to scattered data interpolation has not been previously explored. Therefore, this study aims to extend the use of \(q\)-Bezier triangular patches to scattered data interpolation by achieving \(C^{1}\) continuity throughout the data points. We test the proposed scheme using both established data points and real-life engineering problems. We compared the performance of the proposed interpolation scheme with a well-known existing scheme by varying the \(q\) parameter. The comparison was based on visualization and error analysis. Numerical and graphical results were generated using MATLAB. The findings indicate that the proposed scheme outperforms the existing scheme, demonstrating a higher coefficient of determination (\(R^{2}\)), smaller root mean square error (RMSE), and faster central processing unit (CPU) time. These results highlight the potential of the proposed \(q\)-Bezier triangular patches scheme for more accurate and reliable scattered data interpolation via the proposed algorithm.  
patches; scattered data interpolation; continuity; visualization; surface reconstruction Article
## 4 Simulation and Physical Experiments
### Reliability Simulation
#### 4.3.3 Whole Workspace Randomly Simulation  
In this section, 100,000 postures are generated randomly and evenly in workspace \(\mathbf{Q}_{work}\). Find the corresponding outriggers length space \(\mathbf{L}_{work}\) by \(IK(Q_{work})\), pass \(\mathbf{L}_{work}\) to the two algorithms, and find the solving results. Figure 9 illustrates the comparison between PSO and SA-PSO. The success rate of SA-PSO is 99.992% with only eight group points trapped into the local convergence. However, the PSO method only reaches 93.805% with 6195 groups error solving. The average iterations are 122.7256 versus 146.6126, and the SA-PSO algorithm shows its fast convergence speed than PSO. The success rate and iterations verify the robustness and feasibility of the proposed algorithm in the whole workspace.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **PSO** & **SA–PSO** \\ \hline Average iterations & 141.00 & 100.82 \\ Maximum iterations & 168 & 113 \\ Minimum iterations & 122 & 91 \\ Standard deviation & 5.98 & 3.10 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The comparison between PSO and SA–PSO in iterations.  
Figure 9: The fitness results and the iterations of the whole workspace postures. (**a**) Fitness of PSO solving results for workspace sampling. (**b**) Histogram of PSO iterations. (**c**) Fitness of SA–PSO solving results for workspace sampling. (**d**) Histogram of SA–PSO iterations.
# Finite Element Analysis on Block Shear Mechanism of Lean Duplex Stainless Steel Welded Connections
## 2 Available Design Equations for Block Shear Capacity of Fillet Welded Connection  
### AISI S100-2016  
Section 6.3 of the North American cold formed steel structures code [15] specifies the block shear capacity of the connections in the steel members to be as follows:  
\[R_{n}=F_{u}A_{nt}+0.6F_{y}A_{gp} \tag{1}\]  
\[R_{n}=F_{u}A_{nt}+0.6F_{u}A_{ntv} \tag{2}\]  
where \(F_{y}\) is the material yield stress, \(F_{u}\) is the material tensile strength, \(A_{nt}\) is the net tensile area, \(A_{nt}\) is the net shear area, and \(A_{gp}\) is the gross shear area.  
A well-established shear coefficient of 0.6, which is supported by [PERSON] and Schuster [16] through experimental tests, is used for determining the shear resistance component. The block shear provision of the AISI 2016 [15] is the same as the AASC specification for structural steel buildings [17], but subtly different from the Australasian cold-formed steel structures standard [6] that reflects the shear lag effect on the net section efficiency.  
The net area and gross area are always coincident with each other. Consequently, the nominal block shear strength is dominated by Equation (1), which assumes shear yielding without strain hardening and, thereby, the current design codes yield underestimated predictions for the block shear capacity of the welded connections by 10-34% [12; 18].
## Abbreviations  
The following abbreviations are used in this manuscript:  
AAL Ambient and Assisted Living  
UWB ultra-wideband  
ToA Time of Arrival  
TDoA Time Difference of Arrival  
BLE Bluetooth Low Energy  
EKF Extended Kalman Filter  
ToF Time of Flight  
FoV field of view  
SPAD Single-photon avalanche diode  
LOS Line of Sight  
OLOS Obstructed Line of Sight  
NLOS Non Line of Sight  
RSS Received Signal Strength
## 4 Results and Discussion  
### Experimental Datasets and Settings  
To evaluate the efficacy of our proposed image object detection algorithm, we utilized the DOTAv1.0 dataset of aerial remote sensing images, which was provided by Wuhan University. This dataset includes 15 categories, namely small vehicle (SV), baseball diamond (BD), plane (PL), basketball court (BC), ground track field (GTF), harbor (HA), swimming  
Figure 4: Rotating box regression via circular smoothing labels.  
pool (SP), bridge (BR), roundabout (RA), large vehicle (LV), tennis court (TC), helicopter (HC), ship (SH), soccer ball field (SBF), and storage tank (ST), and contains a total of 188,282 objects.  
The DOTA dataset is characterized by a significant number of small targets, with approximately 80,000 and 110,000 instances of small vehicles and ships, respectively. In general, the objects in this dataset are relatively small in size, accounting for less than 5% of the image area. The images in the DOTA dataset are high-resolution aerial photographs, with pixel dimensions ranging from 800 \(\times\) 800 to 4000 \(\times\) 4000. However, due to the large sizes of most images in the dataset, direct training with the model after uniform scaling would pose challenges in detecting numerous small objects. Therefore, we perform preprocessing on the training images.  
To preprocess the images, we first crop the original images to a uniform size of 1024 \(\times\) 1024. We ensure that a certain overlap is maintained between the cropped images to avoid losing object information at cut boundaries. After cropping, the resulting dataset comprises 21,046 images. We then divide the dataset into training, validation, and testing sets using a 3:1:1 ratio. We visualize the quantities of different target categories in the dataset by creating a bar graph, as shown in Figure 5. In the graph, the labels are plotted on a graph, with the names of the labels displayed on the horizontal axis and the number of labels on the vertical axis. The unit is (number).
## 6 Experiments Validation
### Motor Bearing Fault Detection  
Figure 10 shows the waveform and its frequency spectrum. It is not easy to identify the fault of rolling bearing from Figure 10. Thus, it is necessary to conduct further analysis.  
The proposed method is used to handle the measured signal of the gearbox bearing. In Figure 11, the changes in WPE distribution in the node [2,3] and the node [3,6] can be seen. Selecting the above two subspaces are applied as the eigenvalue of the outer race fault signals. To further prove the superiority of the proposed method, the individual FK method is applied for comparison. Figure 12 shows the analysis results of the proposed method and the individual FK method. Three obvious spectrum lines pointed at the characteristic frequencies of the bearing with outer race (i.e., \(f_{o}\), \(2f_{o}\), and \(3f_{o}\)) can be recognized in Figure 12a. In contrast, although three harmonics of fault frequency (i.e., \(f_{o}\), \(2f_{o}\), and \(3f_{o}\)) can be identified in Figure 12b, the noise is significantly more than the proposed method. The experimental results demonstrated that the proposed method can obtain more accurate recognized result than the individual FK method for bearing outer race fault detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bearing Designation** & **Pitch Diameter \(D_{m}\) (mm)** & **Ball Diameter \(d\) (mm)** & **Ball Numbers \(z\)** \\ \hline
6008 & 54 & 7.9 & 12 \\ \(f_{o}\) & \(f_{i}\) & \(f_{b}\) & \(f_{c}\) \\
49.25 & 65.17 & 33.60 & 4.10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of the supporting bearing.  
Figure 9: The motor bearing test rig.  
Figure 11: Energy distribution proportional histogram of the vibration signal.  
Figure 12: The results of the motor bearing outer race fault vibration signal: **(a)** the proposed method; **(b)** individual FK method.  
Figure 10: The motor vibration signal: **(a)** waveform; **(b)** spectrum.
# Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning
## 2 Literature Review of AAS  
In studies related to AAS and its subsets, such as Acute Aortic Dissection (AAD) criteria, the key measure of success is primarily assessed through the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), along with sensitivity and specificity. Analyzing these parameters allows for a comprehensive statistical evaluation of the results in the below-mentioned research studies and facilitates meaningful comparisons between them.  
In some research, the choice of methodology for predicting AAS is influenced by the type of dataset sources available. Some studies utilize Computed Tomography (CT) Scans and Magnetic Resonance Imaging (MRI), while others combine clinical variables and CTimages. As a result, researchers adopt a hybrid approach, incorporating both clinical and imaging data. Additionally, some studies concentrate specifically on clinical measurements and demographic data, employing machine learning techniques. Various researchers utilize a diverse array of machine learning strategies to analyze these datasets.
## Nomenclature  
The following abbreviations are used in this manuscript:  
\[x_{i} \quad x\] position of \[i\] -th UAV in the inertial frame \[y_{i} \quad y\] position of \[i\] -th UAV in the inertial frame \[x_{0} \quad x\] position of the target in the inertial frame \[y_{0} \quad y\] position of the target in the inertial frame \[\theta_{i} \quad\text{heading angle of $i$-th UAV in the inertial frame}\] \[v_{i} \quad v_{i} \quad\text{velocity of $i$-th UAV in the inertial frame}\] \[G \quad\text{undirected graph to describe the interactions among UAVs}\] GP \[\quad\text{Gaussian process}\] GPR \[\quad\text{Gaussian process regression}\] NNs \[\quad\text{neural networks}\] UAV \[\quad\text{unmanned aerial vehicle}\] DOF \[\quad\text{degree of freedom}\] MAS \[\quad\text{multi-agent systems}\] RMSE \[\quad\text{root mean squared error}\]
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## System and Threat Models  
### System Model  
Suppose that there are several event areas of interest. To monitor relevant events in these areas, a cluster of static sensor nodes is deployed in each event area, a static BS is deployed to control the sensors and collect data from the sensors, and a group of forwarding nodes are deployed between each event area and the BS. The nodes and the BS can be deployed via aerial scattering or physical installation. Assume that each node can obtain its location information through the Global Positioning System (GPS) or a localization scheme [24; 25], and all the nodes have the same sensing range \(R_{s}\) and same communication range \(R_{c}\). Typically, \(R_{c}\) is greater than \(2R_{s}\)[1]. Following deployment, the sensor nodes in each event area are organized into a cluster and each sensor node in the cluster is called a cluster node. Suppose that there are \(Nc\) cluster nodes in an event area, any of which can detect the events occurring in the event area and can participate in report generation. Besides, each cluster node can directly communicate with any other cluster node in the same event area. When an event occurs, \(t\) (\(t<Nc\)) cluster nodes in the event area are selected to jointly generate a signed event report, one of which is selected to be the CH. Finally, the CH transmits the signed event report to the BS through multi-hop routing.  
An example of a sensor network is shown in Figure 1. There are four event areas of interest to be monitored (the big black circles), denoted as A, B, C, and D respectively. A cluster of sensor nodes (the small circles filled with black color) are deployed in each event area. Suppose an event occurs in area A. The CH in area A (the small circle filled with red color) generates the event report, collects the partial signatures from \(t\) participating nodes in area A, and computes the final signature (called the cluster signature) for the report by using the \(t\) partial signatures. Then, the CH transmits the event report with its cluster signature to the BS through some forwarding nodes (the small blue circles).
# A Survey on Bias in Deep NLP
## 4 Discussion
### Deep Learning Versus Traditional Machine Learning Algorithms  
The bias problem is present throughout machine learning approaches. As long as model is trained on biased information, the model will adopt those deviations in its learned parameters. This is the expected behavior of a machine learning algorithm: to mimic reality by identifying patterns in data. Why differentiate bias in deep neural networks from previous algorithms? We consider that there are strong reasons that emphasize the problem when dealing with deep language models.  
1. Deep models are very greedy of data. Large datasets must be fed into the learning process. Gigabytes (and even terabytes) of texts are consumed during the training process. Most of the models are pre-trained with a language model approach (masking, sentence sequence) over corpora generated from available sources in the Internet, like movie subtitles, Wikipedia articles, news, tweets and so on. As these texts come from open communities but with specific cultural profiles from western world, bias expressions are naturally present in such a collection of texts.
2. As the demand of larger corpora for larger models grows, the bias digs deeper its footprint. Some of the most powerful architectures, like GPT-3, are examples of such a situation. Bigger models, bigger bias (and more stereotype patterns found).
3. Intensive research is being carried out in order to compress models, so the fit into environments with limited memory, latency and energy capabilities. Quantization, pruning or teacher-student approaches are enabling deep learning models to operate in more restricted infrastructures at a negligible cost in terms of performance [88]. It has been found that bias is, far from expected, emphasized by this reduction methods [89].  
The rise of deep learning algorithms has promoted the study of stereotypes in language models. Therefore, specific research on bias for this type of architectures is populating scientific production, becoming a major issue nowadays.
## 4 Acoustic Finite Element Analysis (AFEA) of Acoustic Wave Propagation in a Straight Pipe  
### Finite Element Modelling Description  
To perform the acoustic wave propagation using FEA in a pipe, three stages must be considered in the numerical modelling: (i) pre-processing, (ii) analysis, and (iii) post-processing. The analysis starts from the development of the geometry of the air cavity (domain) of an air-filled pipe. This is followed by the discretization of the geometry into smaller discrete elements. The elements are joined together by nodes to form a mesh. The amount of elements used for a particular model is referred to as the mesh density. The acoustic finite element is a unique element designed for the simulation of a system undergoing small variation in acoustic pressure. The simulation process of the AFEA of the acoustic wave reflectometry method involves the integration of the geometrical description of a model with the parameters of the fluid-medium, such as: pressure, momentum, and energy across the elements of the domain. The AFEA then evaluates the medium responses at each of the integration points of the elements, thereby obtaining the responses at the nodes that join the elements together.  
Thus, for any other point within the elements, results are obtained by interpolation from the nodal data. However, the geometrical order of interpolation is determined by the kind of element employed for the analysis. There are two principal interpolations, namely: linear interpolation used by element types ACAX4 and AC3D8, and quadratic interpolations used by element types ACAX8 and AC3D20 [19].