esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 2 The Proposed Method
### Pole Central Axis Fitting and Angle Calculation
\[\theta=90^{\lx@math@degree}-\arctan\left(\frac{Y_{2}-Y_{1}}{\sqrt{\left(X_ {2}-X_{1}\right)^{2}+\left(Z_{2}-Z_{1}\right)^{2}}}\right)\times\frac{180}{\pi} \tag{20}\]  
where, \((X_{1},Y_{1},Z_{1})\), \((X_{2},Y_{2},Z_{2})\) are the coordinate values of the two points on the central axis, respectively.
# Positioning Information Privacy in Intelligent Transportation Systems:
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & 5 th generation cellular networks \\ AOA & Angle of Arrival \\ AP & Access Point \\ BDS & BeilDou Navigation Satellite System \\ BLE & Bluetooth Low Energy \\ BSID & Base station identificator \\ CP & Cooperative positioning \\ DSRC & Dedicated Short Range Communications \\ DOS & Denial of Service Attack \\ D2D & Device-to-Device communications \\ EN & European Union \\ ETSI & European Telecommunications Institute \\ FHE & Fully homomorphic encryption \\ GNSS & Global Navigation Satellite System \\ GDPR & General Data Protection Regulation \\ GPS & Global Positioning System \\ ITS & Intelligent Transportation Systems \\ ITS-G5 & Intelligent Transport Systems operating in the 5 GHz frequency band \\ LoS & Lines of sight \\ LPWA & Low-Power Wide-Area Wireless Technology \\ LSP & Location Solution Provider \\ LIDAR & Light Detection and Ranging \\ MANET & Mobile ad hoc network \\ MEO & Medium Earth orbit \\ MITM & Man-in-the-middle attack \\ MIMO & Multiple Input Multiple Output \\ NR & New radio \\ RSS & Received signal strength \\ RSSI & Received signal strength indicator \\ TDOA & Time difference of arrival \\ TPM & Trusted Platform Module \\ TOA & Time of arrival \\ VANET & Vehicular Ad-Hoc Network \\ V2I & Vehicle-to-infrastructure paradigm \\ V2V & Vehicle-to-vehicle paradigm \\ V2X & Vehicle-to-everything paradigm \\ XOR & Exclusive OR operation \\ \end{tabular}
###### Abstract  
This paper introduces a numerical methodology for the investigation of two-dimensional, incompressible and unsteady flows. The analyses involve Fluid-Structure Interaction (FSI) over solid boundaries of known shape with effects of mixed convection heat transfer. The main contribution is the implementation of a Large-Eddy Simulation (LES) model for the energy equation. LES is a mathematical model for simulating turbulent flows. The Boussinesq approximation links the vorticity transport equation with the energy equation to include buoyancy forces. The methodology consists of discretizing the vorticity field and heat by using particles (computational points), which characterizes a purely Lagrangian description. The vorticity field is discretized by using Lamb discrete vortices (vortex blobs) and the heat by using temperature particles. The velocity field is computed over each particle as the vortex cloud contribution requires high computational cost. The buoyancy forces computation is necessary over each vortex blob because of the temperature particles and also requires high computational cost. Thus, all those computations involving particles interactions demand the use of parallel computing in OpenMP-Fortran. The turbulence calculation makes use of the second-order velocity structure function model; that computation is necessary over each computational point during every time increment of a typical numerical simulation. As examples of application, two problems are chosen: nominally, the flow around a single circular cylinder and the interaction of airplane wake vortices with a ground plane. The numerical results are compared with experimental data, exhibiting very good agreement with the expected physics for each investigated problem.  
bluff body aerodynamics; airplane wingtip vortices; large-eddy simulation (LES); particles interactions; Lagrangian dynamics 2020
## 3 Materials and Methods
### SPME Extraction Conditions  
For the method optimization, two SPME fibers supplied by Merck Life Science (Darmstadt, Germany) were tested: carboxen/polydimethylsiloxane (CAR/PDMS), 75 \(\mathrm{\SIUnitSymbolMicro m}\) 1 cm long (57343-U), and divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS), 50/30 \(\mathrm{\SIUnitSymbolMicro m}\) 1 cm long (57329-U). The fibers were conditioned before the initial use according to manufacturer's instructions, and a cleaning step of 20 min at 10 \(\mathrm{\SIUnitSymbolMicro C}\) below fiber recommended maximum temperature was applied between consecutive analyses. GC analyses were carried out using for each test a 10 mL vial with 0.2, 0.3, and 0.4 g of ground sample, respectively, inserting the fiber 2 cm above the sample, and the best results were obtained for a 0.2 g sample weight.  
Four different fiber exposure times were tested: 30, 40, 50, and 60 min. The highest volatile extraction yield was achieved after an exposure time of 50 min, and most of the  
Figure 7: Aroma profile of three different species of pepper chosen as example from descriptive sensory analysis on line scale (_n_ = 10). Panel (**A**) shows sample 1 (Naga Morich, _Capsicum chinese_); panel (**B**) shows sample 14 (Calabrian pepper, _Capsicum annuum_); and panel (**C**) shows sample 17 (Aji, _Capsicum bacatum_).  
heavier molecular weight volatiles remained substantially stable thereafter. Furthermore, a sample conditioning time of 5 and 10 min was evaluated at the same temperature (30 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, or 60 \({}^{\circ}\)C) employed for the extraction stage, and the analytical repeatability was excellent in both conditions. Different stirring rates (200 and 300 rpm) for sample conditioning and extraction were also investigated.  
In this investigation, the (DVB/CAR/PDMS) 50/30 \(\upmu\)m fiber was found to be the most useful in covering the wide range of chili pepper volatile analytes; a conditioning time of 5 min and an extraction temperature of 50 \({}^{\circ}\)C were the best compromise between equilibration time and method sensitivity. Furthermore, a time of 50 min at the same temperature and stirring rate of 300 rpm were proven to be the best choices for an exhaustive extraction of the volatiles components (Figures S6-S10 from Supplementary Materials show method optimization).  
The same extraction condition was adopted for the flavored extra virgin olive oils, using 1 mL as sample volume.  
After the extraction, the analytes were manually injected in splitless mode and thermally desorbed for 1 min at 260 \({}^{\circ}\)C in the GC injector port.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 5 Reinforcement Learning
### Hyperparameter Tuning Using Random Search  
Hyperparameter tuning is required for the proposed DQL algorithm to achieve better performance and stability during the training process. In this work, we use the random search technique to effectively tune the hyperparameters. Random search is a simple and intuitive hyperparameter tuning technique widely used in machine learning and deep learning. Its primary goal is to find optimal hyperparameter configurations that maximise the performance of a model in a given task. Random search samples hyperparameter values randomly from predefined ranges. The process of random search involves defining a search space for each hyperparameter, typically specified by minimum and maximum values. During each iteration, the algorithm selects random values for each hyperparameter from their respective search spaces. These randomly sampled hyperparameter configurations are then used to train the model. The random search method is implemented similar to [31] as the environments used in their work are similar to ours. Random search entails the following steps:  
1. Define search space: This is a range of possible values to sample from for each hyperparameter, i.e., learning rate (\(\alpha\)), discount factor (\(\gamma\)), and exploration rate (\(\epsilon\)).
2. Initialise optimal hyperparameters: Before the random search begins, we initialise the hyperparameters to an initial value. In the proposed algorithm, a maximization problem is considered; therefore, the initial values are set to a very low value. After, this random search is performed.
3. Train the DQL agent with sampled hyperparameters: Using the sampled values obtained after the random search, the DQL agent is trained within the environment until convergence.
4. Evaluate the performance: Evaluate the agent's performance by running the agent in the environment for a fixed no. of episodes. Compute the average reward as given by expression in Table 2 as a performance metric.
5. Update the optimal hyperparameters: If the values of hyperparameters after random search are better than initial optimal values, then the optimal values are updated to current values.  
The optimal hyperparameter values obtained from this method are provided in Table 4.
# Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma
## 3 Results
### Mutational Profiles Associated with Response to NAC
#### 3.2.2 mRNA Expression Level Supports the Dysregulation of EAC Driver Genes in Non-Responders  
We reasoned that if these amplification/deletion peaks played a role in affecting the response to NAC, then we would observe corresponding signals in their related downstream pathways and patient survival would be affected. To do this we used matched RNA-seq data (\(n\) = 9 responders and \(n\) = 21 non-responders) and compared the FPKM values (fragments per kilobase of transcript per million mapped reads) of recurrently amplified and deleted EAC driver genes between groups. Consistent with copy number alterations, we observed the upregulation of CDK6 (Wilcoxon rank-sum test, \(p\) = 0.004), CCND1 (\(p\) = 0.004), GATA4 (\(p\) = 0.037) and MYC (\(p\) < 0.001) at the transcript level in non-responders (Figure 4A). Furthermore, patients with amplification at the corresponding chromosomal regions of cell cycle regulators had a worse prognosis with a shorter overall survival, including CCND1 (median survival 20.8 months in CCND1 amplified samples vs. 78.5 months in CCND1 neutral samples, \(p\) = 0.007), CDK6 (median survival 33.8 months in CDK6 amplified samples vs. 73.0 months in CDK6 neutral samples, \(p\) = 0.01) and deletion of regions harboring CDKN2A (median survival 33.8 months in CDKN2A deleted samples vs. 73.0 months in CDKN2A neutral samples, \(p\) = 0.01) (Figure 4B). Among pathways related to these genes, only MYC signalling was significantly enriched in the non-responders using Gene Set Enrichment Analysis (GSEA) (FDR = 0.04, Figure 4C and Tables S8 and S9). Although we observed a significantly elevated expression of MYC in non-responders, MYC amplification was not significantly associated with overall survival (median survival 30.7 months in MYC amplified samples vs. 73.0 months in MYC neutral samples, \(p\) = 0.068). Overall, we found no significant influence of these copy number changes on overall survival in responders or non-responders alone, as our study was underpowered for these comparisons (Figure S1). However, in responders CDK6 amplification was associated with shorter overall survival (median survival 35.4 months in CDK6 amplified samples vs. 78.5 months in CDK6 neutral samples, \(p\) = 0.0011).  
Figure 4: Amplified EAC driver genes are overexpressed in non-responders and copy number alterations associate with poor survival. **(A)** Violin plots comparing mRNA expression levels (FPKM) in matched RNA-Seq data (\(n=9\) responders, \(n=21\) non-responders) for copy number altered EAC driver genes in responders and non-responders. \(p\)-values were based on one-tailed Wilcoxon rank-sum test. **(B)** Kaplan–[PERSON] plot comparing overall survival of patients with _CDK6_, _CCND1_, _CDKN2A_, _GATA4_ and _MYC_ copy number changes (red) vs. neutral patients (blue). **(C)** Gene Set Enrichment Analysis (GSEA) of _MYC_ target genes in available RNA-Seq data (\(n=30\)).
## 2 Algorithms in the Classification System
### Optimization Algorithm of SMO  
Sequential minimal optimization (SMO) is a kind of optimization algorithm to increase the efficiency of solving quadratic programming problems [37; 43]. The principle of SMO is to decompose the large-scale quadratic programming problem into a series of subproblems with the smallest possible quadratic programming, and then solve them analytically in inner loops to obtain the solution of the original problem [44]. It can be divided into the following three steps for SMO to solve the quadratic programming problem:  
**First** Initialize variables including Lagrange multiplier \(\alpha\) and threshold \(b\), and set accuracy parameter \(\varepsilon\).  
**Second** According to heuristic rules, select a pair of optimization variables \((\alpha_{u},\ \alpha_{v})\) and solve the quadratic subproblems of \((\alpha_{u},\alpha_{v})\) to obtain the optimal solution and update variables. The heuristic rules are as follows:  
* The outer loop is the selection of the first optimization variable \(\alpha_{u}\). Search out and optimize all the samples that violate the KKT condition, and then optimize the others. Traverse and optimize all the unbound samples (the samples that meet the condition \(0<\alpha_{i}<C\)) until they all meet KKT conditions. To be noticed, the samples that violate KKT conditions are prioritized to take optimization. The outer loop does not stop until all the samples satisfy KKT conditions.
* The inner loop is the selection of the second optimization variable \(\alpha_{v}\). After selecting \((x_{u},y_{u})\) to take optimization, the sample \((x_{v},y_{v})\) is chosen according to a certain calculation method to ensure the optimized step is maximum, which determines \(\alpha_{v}\).  
**Third** Check whether the stopping condition of SMO is satisfied within the accuracy \(\varepsilon\). If the condition is met, the solution is completed and the optimal solution of the original quadratic programming problem (shown as \(\alpha^{*},b^{*}\)) is obtained. Otherwise, return to the second step.
# Extended Reality (XR) Engines for Developing Gamified Apps and Serious Games: A Scoping Review
## 3 Results  
We conducted a comprehensive review of the 40 included studies (see Table 3) with respect to our four research questions (see Section 2.1). As previously mentioned, the use of XR benefits the learning process [23]. Additionally, using XR in gamified apps and serious games is an opportunity to further study architectures and motivation models and to extend its use [24; 25]. According to our findings, gamification trends lean toward the use of gamified apps and the development of game engines for these apps [26; 27; 28; 29; 30].  
Trends in XR currently revolve around the implementation of XR models, the study of architectures to define the compatibility among development technologies for XR-based gamified apps, and the study of the benefits of these technologies on current XR models [31; 32; 33; 34; 35; 36; 37]. Research also focuses on exploring the benefits of intermixing XR, AR, and VR technologies [38], specially by incorporating 3D models in virtual environments in both gamified apps and serious games [39; 40; 41].  
Figure 3: Geographical Distribution of Primary Studies.  
Figure 2: Annual Distribution of Primary Studies.  
Game engines are the leading technology for XR implementation. Some XR engines are game engines that initially implemented either AR or VR and were then upgraded to provide support for the entire XR umbrella. Examples of these engines include Vuforia [63], Unity [64], and Three.js [65]. Game engines purposely developed to support XR, such as ReactXR [66] and Babilon.JS [67], mainly combine XR features such as 3D models or 3D scenes with real-world settings.
# Semantic Image Segmentation Using Scant Pixel Annotations
## 5 Experimental Results and Discussion
### Melanoma Dataset  
For this dataset, experiments were conducted using \(J=5\) (10% of the training data) in each iteration. Two test images were randomly chosen for evaluation from the dataset and rest of the data were used to generate three successive models using the SSPA approach. Models obtained using HE pixel label replacements outperformed others.  
**Pixel label replacements for HE images.** Figure 6 shows the results obtained using the HE pixel label replacements. As depicted in the figure, model \(M_{2}\) was obtained by replacing only 0.8% of the pixel labels from the 5 HE images in the output of model \(M_{1}\). Model \(M_{3}\) was obtained by replacing 4.05% of the pixel labels from the 5 HE images in the output of model \(M_{2}\). Since the mean entropy of \(M_{3}\) (1.689) was higher than that of \(M_{2}\) (1.432), the algorithm terminated. Model \(M_{2}\) with IoU value 0.824 and Dice Score 0.973 outperformed the benchmark model \(M_{0}\), having IoU value 0.764, and Dice score of 0.962. The mean entropy of \(M_{0}\), 2.232, was higher than that of \(M_{2}\).  
To visually track and assess the change in entropy induced by pixel label replacements, we constructed a heatmap for each image in the training set using the normalized prediction confidence values. The heatmap of a sample image from Figure 7A demonstrated that the confidence predictions of \(M_{2}\) were consistent with the \(GT\) labels shown in Figure 2. A more detailed view of the pixels (in red) to be annotated by experts can be seen in Figure 7B,C, at two different scales. The target regions in the range [0.45,0.55] (\(\delta=0.05\)) occurring mostly around the boundaries of the lesion in Figure 7B,C, showed the highest uncertainty.  
Figure 5: Entropy distribution across models.  
**Pixel label replacements for LE images.** On the other hand, \(M_{2}\) with LE pixel label replacements (also depicted in Figure 6) resulted in a mean entropy 2.064, Dice score 0.958, and IoU 0.718, comparable to those of \(M_{0}\). However, \(M_{2}\) was generated using 11.04% pixel label replacements. Recall that model \(M_{2}\) was generated using only 0.8% pixel label replacements and had a much lower mean entropy value of 1.432 in the HE case. Since the mean entropy of \(M_{3}\), 2.669, was higher than that of \(M_{2}\), the SSPA, approach terminated. The performance of \(M_{3}\) with LE pixel label replacements was comparable to that \(M_{3}\) with HE pixel label replacements but required 15.51% pixels to be replaced in comparison to 4.05%.
# Rotating Target Detection Method of Concrete Bridge Crack Based on YOLO v5
###### Abstract  
Crack detection is a critical and essential aspect of concrete bridge maintenance and management. Manual inspection often falls short in meeting the demands of large-scale crack detection in terms of cost, efficiency, accuracy, and data management. To address the challenges faced by existing generic object detection algorithms in achieving high accuracy or efficiency when detecting cracks with large aspect ratios, overlapping structures, and clear directional characteristics, this paper presents improvements to the YOLO v5 model. These enhancements include the introduction of angle regression variables, the definition of a new loss function, the integration of PSA-Neck and ECA-Layer attention mechanism modules into the network architecture, consideration of the contribution of each node's features to the network, and the addition of skip connections within the same feature scale. This results in a novel crack image rotation object detection algorithm named \"R-YOLO v5\". After training the R-YOLO v5 model for 300 iterations on a dataset comprising 1628 crack images, the model achieved an [EMAIL_ADDRESS] of 94.03% on the test set, which is significantly higher than other rotation object detection algorithms such as SASM, S2A Net, Re Det, as well as the horizontal-box YOLO v5 model. Furthermore, R-YOLO v5 demonstrates clear advantages in terms of model size (4.17 MB) and detection speed (0.01 s per image). These results demonstrate that the designed model effectively detects cracks in concrete bridges and exhibits robustness, minimal memory usage, making it suitable for real-time crack detection on small devices like smartphones or drones. Additionally, the rotation object detection improvement strategy discussed in this study holds potential applicability for enhancing other object detection algorithms.  
health monitoring; crack identification of concrete bridge; machine vision; yolo; rotating target detection +
Footnote †: journal: Applied sciences  
Article  
Rotating Target Detection Method of Concrete Bridge Crack Based on YOLO v5
## 5 Discussion  
### Proposed Parameters of Spatial Resolution  
Although GIS spatial analyst tool provides the fast result of a thematic map of weighted sum with proposed parameters, the result may be less useful due to the inconsistency of data sources and spatial resolution. However, the use of a finer grid for pointwise exploration is expensive and may still not be able to determine in areas where traffic cannot be reached. Under the issue of achieving the goal of a cost-effective method in remote areas by integrating RS and GIS techniques, the global Landsat imagery and ASTER GDEM can be obtained from the National Aeronautics and Space Administration (NASA) website with a uniform spatial resolution of \(30\) m \(\times\) 30 m. The proposed parameters can be  
Figure 10: Synthesized map of groundwater potential in mountainous regions of Taiwan.  
produced from the raw data. Thus, a raster-based empirical GIS model is recommended to calculate the thematic map of groundwater potential and validate it with investigated data.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 5 Evaluation
### Maximum Amount of Collected Data: Experimental Results  
Section 5.3 evaluated the maximum amount of collected data per contact interval, following a theoretical method. However, empirical evidence has shown that several factors limit the maximum amount of data that can be transferred via a BLE link in a given time interval [8]. For the sake of a realistic analysis, in this section we conduct experiments in order to determine the achievable amount of collected data per contact interval with the Bluegiga BLE121 LR platform. We study the influence of the _comInterval_ parameter, as well as the impact of distance between the two BLE devices, on the achievable amount of collected data per contact interval. For the experiments, _comSlaveLatency_ has been set to 0, in order to maximize throughput during data collection, while for _comSupervisionTimeout_, the default setting of the platform (i.e., 160 ms) has been used. We consider \(T_{contact}\) values of 45 s and 150 s.
# On Localized Countermeasure Against Reactive Jamming Attacks in Smart Grid Wireless Mesh Networks
## 3 Network and Jamming Models
### Problem Statement  
Given a Smart Grid wireless mesh network \(G=(V,E)\) and a group of jammers \(J\). Considering the above network and jamming models in the Figure 1, our goal is to efficiently detect all the devices activating the jammers in the network with low message and time complexities. We would be able to use the triggers information to construct an overlying routing protocol where trigger nodes are receivers only, so that the invoking of any jammer is avoided and the reactive jamming attack is completely nullified.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 3 Material and Methods
### Benchmark Algorithms  
We use Random Forest (RF) as the state-of-the-art algorithm for SITS classification, and Recurrent Neural Networks (RNNs) as the leading deep learning algorithm for sequential data. Both benchmark algorithms are briefly introduced.