esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Information Fusion Indoor Localization Using Smartphones
## 5 Experimental Verification and Analysis
### Experimental Setup  
We exploited the localization system based on acoustic and inertial data, which contained a client terminal and a backend server terminal.  
Smartphones with IMU sensors were preinstalled in the client program. The pedestrian carried the smartphone in their hands to collect accelerometer, gyroscope, and magnetometer data as they walked along a designated route. While collecting data, time stamps and acoustic signals were collected from the beacons installed at the trial sites.  
A 64-bit computer with the Windows 10 operating system operated as the server terminal. The computer with GPU GT730 and 8 GB RAM has Intel i7-7700 CPU with the frequency of 3.6 GHz. The sever terminal stored the received data and ran the localization program. Localization estimation was performed using the proposed method based on acoustic and inertial sequences.  
Ten beacons in the first experimental scene and fourteen beacons in the second scene transmit ultrasonic signals periodically. The smartphone that was preinstalled with the client application was used as the target.  
We recruited two volunteers to capture acoustic and IMU data who came from the local university. One female volunteer (height 155 cm, number #1) and one male volunteer (height 180 cm, number #2) each held a VivoY85a and Honor60 device to move along the survey path at each scene. At the trial scenes, we asked the volunteers to collect the data several times. The mobile phone technical information of the experiment is shown in Table 1.  
Figure 6: Trial experimental site floor plans. (**a**) Scenario 1. (**b**) Scenario 2.
## 4 Results and Discussion
### Comparison with the Existing Approaches by Varying Map Sizes and Obstacle Counts
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Maps Group No.**} & \multicolumn{2}{c}{**3D Maps\({}^{\prime}\) Sizes (in m)**} & **Obstacles Strength** & **Proposed PP Method** & **ApVL Algorithm [60]** & **RRT\({}^{\prime}\)-AB Algorithm [70]** \\ \cline{2-6}  & \multicolumn{2}{c}{**(r \(\times\) y \(\times\) z)**} & **(upto 50)** & **Avg. Computing Time (in s)** & **Avg. Computing Time (in s)** & **Avg. Computing Time (in s)** \\ \hline
1. & 100 \(\times\) 100 \(\times\) 300 & 5 & 0.98 & 1.46 & 2.97 \\
2. & 200 \(\times\) 200 \(\times\) 300 & 10 & 5.61 & 8.31 & 16.81 \\
3. & 300 \(\times\) 300 \(\times\) 300 & 15 & 17.01 & 23.54 & 34.20 \\
4. & 400 \(\times\) 400 \(\times\) 300 & 20 & 39.28 & 49.52 & 65.21 \\
5. & 500 \(\times\) 500 \(\times\) 300 & 25 & 76.26 & 98.81 & 115.04 \\
6. & 600 \(\times\) 600 \(\times\) 400 & 30 & 100.65 & 111.19 & 141.81 \\
7. & 700 \(\times\) 700 \(\times\) 400 & 35 & 134.03 & 137.599 & 185.93 \\
8. & 800 \(\times\) 800 \(\times\) 400 & 40 & 155.01 & 186.29 & 211.11 \\
9. & 900 \(\times\) 900 \(\times\) 400 & 45 & 180.21 & 201.56 & 225.13 \\
10. & 1000 \(\times\) 1000 \(\times\) 400 & 50 & 191.25 & 209.25 & 249.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the 3D maps used in simulations and comparisons of task modeling results.
## 4 Vehicular Ad Hoc Networks
### Routing
#### 4.2.1 Highly Dynamic Topology  
In VANET, unlike other ad hoc networks or sensor networks, the network topology is very dynamic because of the speed of vehicles in the network. This means that they can directly communicate with each other for a short time interval. This shows that it is very difficult to make a communication link between them.  
Figure 1: Vehicular ad hoc networks (VANETs).
# Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles
## 2 Literature Overview
### Scenario Selection Methods
#### 2.4.3 Coverage-Based Methods  
Coverage-based methods generate a set of test scenarios that cover the scenario space well. They sample concrete scenarios [49] either from parameter ranges or parameter distributions. The latter has the advantage that the distributions provide the occurrence probability of a scenario and thus its relevance in the real world. A wide variety of sampling techniques was already applied to generate scenarios from parameter ranges, for example, Design of Experiments (DoE) [13], Satisfiability Modulo Theory (SMT) solvers [50], Rapidly Exploring Random Trees (RRTs) [12] or the In-Parameter-Order-Generalized (IPOG) algorithm [51]. Distributional sampling techniques are based on Monte Carlo methods. Most papers use accelerated techniques to cope with the fact that critical traffic scenarios are rare events. For instance, [PERSON] [52] uses Importance Sampling (IS) to speed-up the computation, and [PERSON] et al. [53] use Extreme Value Theory (EVT).
## 5 Discussion and Explanation Analysis  
The higher-level visual constructs [38] and global context information [39] play significant roles in road extraction from HR remote sensing imagery with complex background. In terms of HR remote sensing images interpretation, it is vital to explain the effectiveness of the extracted method. The class activation map (CAM) is a widely adopted and efficacious approach for interpretation in various domains. Thus, we utilize Grad-CAM [40] generated class activation maps with gradient information, as shown in Figure 11. It leverages the gradient information flowing into the last convolutional layer of both branches to assign importance value to each neuron for a particular decision of interest [40].  
To acquire the road-discriminating localization map Grad-CAM loss \(L_{Grad-CAM}^{road}\)  
\[L_{Grad-CAM}^{road}=ReLU(\sum_{k}\alpha_{k}^{road}A^{k}) \tag{14}\]  
we first compute the gradient of the score of road class \(y^{road}\), with respect to feature map \(A^{k}\) of activated convolution layers. These gradients flowing back are global-average-pooled over the width and height dimensions to obtain the neuron importance weights \(\alpha_{k}^{road}\):  
\[\alpha_{k}^{road}=\frac{1}{Z}\sum_{i}\sum_{j}\frac{\partial y^{road}}{\partial A _{ij}^{k}} \tag{15}\]  
To intuitively demonstrate the effectiveness of the DPBFN, we present Grad-CAMs for comparison between the CNN branch without DCPA (in Figure 11(b)), the CNN branch with DCPA (in Figure 11(c)), and the transformer branch (in Figure 11(d)). Compared with the second and the third column, the mixed pixels problem in the Grad-CAMs (of CNN without DCPA module) is severe, and the road pixels are indistinguishable from the roof of the surrounding buildings. As shown in Figure 11(c), the CNN branch with the DPCA module can alleviate the phenomenon of mixed pixels effectively, but there are still two obstacles as follows: (1) There is noise caused by the misrepresentation of similar feature pixels; (2) the stability of the convolution branch to the shadowed road recognition is poor.  
As shown in Figure 11(d), based on the Grad-CAMs of the transformer branch, it can be observed that the multi-head attention module has a positive impact on addressing  
Figure 12: Visualization of Grad-CAMs from the dual branches.  
local shadow occlusion and mixed pixels. Specifically, areas shown in Figure 12a that are significantly obscured by roadside trees can be effectively connected in trans-feature maps. The main reason is that embedding the patches sequence can focus on the context order; furthermore, as shown in Figure 12d, the transformer branch is not affected by mixing pixels. Even so, the transformer still has some problems; the trans module has the problem of boundary blurring due to insufficient edge refining in the road interpretation task of large-scale high-resolution remote sensing images. Therefore, the fusion of a transformer (focus on context information) branch and a CNN branch with a DPCA module (focus on edge and details) can effectively complement each other's features, resulting in improved robustness and accuracy.
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Technical Approaches
#### 3.3.22 JavaScript Obfuscation  
Obfuscation is the process of creating obfuscated code, which is source or machine code that is difficult for humans to understand and decipher. This is similar to encryption, but the difference is that machines can understand and execute the code.  
When code is obfuscated, the logic behind the code is hidden from outside users. This reduces the file size and, therefore, increases the speed of the transfer of data from the client and server. Obfuscation also makes reverse engineering of code extremely difficult.  
Obfuscation was first introduced at DEFCON 16 in 2008. Recent analysis of the code of compromised websites found that the code deployed by malicious hackers used the same techniques as those demonstrated at DEFCON 16. Using obfuscation, the hackers were able to hide the fact that they used placed malicious links on the web site, which allowed them to collect victims' personal information and sensitive data.
# Dynamic Simulation of Underground Cable Laying for Digital Three-Dimensional Transmission Lines
## 4 Engineering Examples and Analysis of Results
### Safety Survey and Hazard Analysis
#### 4.2.2 Analysis of the Axial Tension of the Cable over the Pulley Moment  
The axial tension at the moment the cable micro-segment moves over the pulley is shown in Figure 11. When the cable passes through the pulley, there will be a large fluctuation in the axial tension of the cable. This fluctuation is mainly due to the friction between the cable and the pulley and the cable's own inertia. The entire cable laying system will be affected by the impact of the cable on the pulley in the process, resulting in a short period of large tension fluctuations. The axial tension of the cable passing through the pulley is extracted, and the axial tension value of each cylindrical micro-segment of the cable passing through the pulley is compared with the calculated value of the theoretical formula. The maximum value of axial tension is calculated using theoretical methods (as shown in Figure 12), and the following axial tension amplification factor is proposed:  
\[\alpha=\frac{T_{\text{imax}}}{T_{i}} \tag{12}\]  
where \(T_{\text{imax}}\) is the maximum value of axial tension at position i, N; \(T_{i}\) is the theoretically calculated value of axial tension at position i, N.  
In the simulation study of cable laying, it can be seen that the tension fluctuation of the cable is significant during the initial traction period and when the cable passes through the pulley. This fluctuation shows that the actual tension value exceeds the theoretically calculated value, and the amplification of tension is in the range of 1.2\(\sim\)1.5, from which it can be concluded that the amplification coefficient of the overload safety value of the traction force \(\alpha\) is 1.5.  
Figure 10: Axial tension of cable microsegment.
## 1 Introduction
[28] tried to solve this problem by encapsulating pDNA into micro-particles of biodegradable polymers. In this way, they protect the plasmid from being digested by ribonucleases and also assist the transport and control release rate. Recent reports on pDNA encapsulation into polymeric NPs are focused on cancer gene therapy and vaccination applications [29; 30; 31; 32; 33] where the authors have addressed encapsulation of pDNA into polymeric NPs by using different approaches. In general, the process of pDNA encapsulation into polymeric NPs involves several steps and preparation parameters that should be modulated according to desired NPs characteristics. Experimental pDNA release from biodegradable matrices have been reported for different plasmids [34; 35; 36] and, to the best of our knowledge, only few efforts to mathematically describe the pDNA release kinetics have been reported, where the entire plasmid release process has been attributed to the diffusion mechanism [37]. Contrary to pDNA release kinetics, mathematical analysis of drug release from biodegradable matrices has been extensively reported in literature by considering one or more mechanisms of release [38; 39]. In this present research, a preparation process of biodegradable nanoparticles loaded with plasmid pVAX1-NH36 was developed. pVAX1-NH36 was replicated using _E. coli_ bacteria and purified to obtain a pure plasmid, followed by capsuling into polymeric NPs prepared with PLGA-PEG-FA by using different theoretical DNA loadings. Several techniques were used to characterize NPs such as dynamic light scattering, laser doppler electrophoresis, atomic force microscopy (AFM), high performance liquid chromatography-hydrophobic interaction chromatography (HPLC-HIC) and agarose electrophoresis. pVAX1-NH36 release was evaluated in vitro under physiological conditions and release kinetics were analyzed by using an effective mathematical release model that simultaneously considers the mechanism of initial burst as well as mechanism of degradation-relaxation of NPs [40]. Mechanism of initial burst was calculated using first-order rate burst release and the mechanism of release due to bulk degradation of polymer or NPs relaxation was evaluated in an analogous way to thermal decomposition of potassium permanganate crystals [41; 42]. These two mechanisms were linearly coupled to effectively describe the entire plasmid release process.
# Optimizing Flight Delay Predictions with Scorecard Systems
## 1 Introduction
It enables stakeholders to monitor key performance indicators, such as on-time performance and delay durations, while incorporating external variables such as weather conditions and air traffic constraints. This approach facilitates proactive strategies for delay mitigation, supporting continuous improvement, and benchmarking against industry standards. Summarizing, the objective of this article is to offer a practical solution for a pressing issue in air transportation, providing an effective, accessible, and actionable tool for predicting and managing flight delays. The study of scorecard results can deliver not only a forecasting tool, but also an intuitive and clear visualization of data to understand the process, which can point to the main factors and explain reasons for a change. This research directly contributes to the ongoing development of data-driven solutions in transportation systems by integrating interpretable modeling techniques with real-world-aviation data. In particular, the use of scorecards--a method well known in finance but rarely applied in the transport sector--offers a transparent and lightweight alternative to traditional machine learning models. This approach emphasizes practical applications of analytics to optimize operations and improve service performance in aviation and logistics. The main contributions of this paper are as follows:  
1. The introduction of a novel application of scorecards for flight delay prediction, demonstrating that a transparent and interpretable model can match or exceed the predictive power of more complex machine learning approaches. 2. The development and validation of a high-performing model using a large-scale dataset of over 30 million flights from the U.S. Bureau of Transportation Statistics. 3. Comparative analysis highlighting the advantages of the scorecard approach over traditional statistical and ML models in terms of interpretability, simplicity, and operational usability. 4. Operational insights derived from model outputs, supporting decision-making in airline resource allocation, delay mitigation, and service reliability improvement. 5. The discussion of sustainability implications, including how scorecard-based monitoring can contribute to lower fuel usage, reduced CO2 emissions, and improved passenger satisfaction. This article is structured as follows: the rationale for addressing the issue of flight delays is outlined first, followed by a review of conventional forecasting methods and their limitations. Subsequently, the proposed scorecard approach is introduced, along with its practical implementation and advantages. The paper concludes with a discussion of its implications for operational efficiency and sustainability in the aviation industry. By addressing a multifaceted challenge with a scalable and user-friendly tool, this research contributes to advancing the efficiency, reliability, and environmental sustainability of air transport systems.
# Semantic Communication on Digital Wireless Communication Systems
## 5 Conclusions  
To address the issue of how the semantic information can be compatible with existing wireless communication systems, we design a bit-conversion-based JSCC transmission framework and develop the specific physical layer procedures. After this, we develop a semantic communication simulation platform. Based on the bit-conversion-based JSCC transmission framework, we propose a new physical layer semantic metric, the IER. Simulations reveal that the IER is more suitable than the BER as a physical layer metric for evaluating the transmission quality of semantic communication, especially for semantic image transmission. Regarding the IER, we optimize the bit-conversion-based JSCC transmission framework. We propose a minimum Manhattan distance constellation mapping scheme for semantic communication. The simulation results indicate that this scheme could enhance the IER without compromising the BER, thereby improving the transmission quality of semantic communication. Furthermore, to address the issue regarding the coupling requirement between the quantization output range of semantic encoding and modulation order in the minimum Manhattan distance constellation mapping scheme, we propose a hybrid JSCC/SSCC transmission scheme. This scheme further decouples the semantic quantization output from the modulation method by segmenting the bits for transmission and providing a framework for the separate optimization of the semantic quantization output and transmission modulation order. Simulations demonstrate that this scheme could enhance the transmission quality of semantic communication in low SNR scenarios while saving much bandwidth and illustrating the feasibility of the bit-conversion-based JSCC transmission framework for evolution within existing wireless communication systems.  
For future research directions, whether the IER has a significant correlation with all semantic encoding algorithms is expected to be studied. Additionally, the performance enhancement of the bit-conversion-based JSCC transmission framework can be a subject of ongoing research efforts.  
**Author Contributions**: Formal analysis, C.G.; investigation, B.H.; methodology, B.H. and H.C.; Software, B.H.; supervision, N.M. and P.Z.; writing--original draft, B.H.; writing--review and editing, H.C., C.G. and X.X. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This work was supported by National Science and Technology Major Project-Mobile Information Networks under Grant No. 2024 ZD1300700.  
**Data Availability Statement**: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 2 Materials and Methods  
### Shear Bond Strength  
The samples, consisting of 72 bovine mandibular incisors, were divided into eight groups of nine specimens each. Bovine enamel was employed due to being an acceptable substrate for bonding testing [9]. The enamel was flattened and polished up to #2000-grit with silicon carbide paper. The pre-etching (Transbond XT Etching Gel; 3M Unitek, Monrovia, CA, USA [[https://www.3m.com/3M/en_US/p/c/dental-orthodontics/](https://www.3m.com/3M/en_US/p/c/dental-orthodontics/)]([https://www.3m.com/3M/en_US/p/c/dental-orthodontics/](https://www.3m.com/3M/en_US/p/c/dental-orthodontics/)) accessed on 16 August 2021; and GC Ortho Etching Gel; GC Ortholy, Tokyo, Japan, [[https://www.gcortholy.com/](https://www.gcortholy.com/)]([https://www.gcortholy.com/](https://www.gcortholy.com/)) accessed on 16 August 2021, application time; 30 s) was performed in circles of different areas (diameter: 1.0, 2.0 or 3.0 mm). The area was standardised with a hole in a 0.2 mm-thick polypropylene sheet (Figure 1). The pre-etched surface was rinsed and dried for 20 s. A paste (Transbond XT [TB]; 3M Unitek (functional monomer-free), or Universal Bond [UB]; GC Ortholy (containing functional monomer)) was smeared on the bracket and pressed against the pre-etched enamel surface. After removing the excess paste, the meso-distal portion of the bracket was irradiated with an LED curing unit (Valo; Ultradent Products, South Jordan, UT, USA, [[https://www.ultradent.com/](https://www.ultradent.com/)]([https://www.ultradent.com/](https://www.ultradent.com/)) accessed on 16 August 2021) for 20 s. After being stored in distilled water (37 \({}^{\circ}\)C for 24 h), each specimen was tested in shear mode using a universal testing machine (5567; Instron, Norwood, MA, USA, [[https://www.instron.com/](https://www.instron.com/)]([https://www.instron.com/](https://www.instron.com/)) accessed on 16 August 2021, experimental environment; 23 \(\pm\) 1 \({}^{\circ}\)C and 50 \(\pm\) 5% relative humidity) at a crosshead speed of 1 mm/min. Shear bond strength (SBS) was calculated as the peak load at failure divided by the bonded area (12.8 mm\({}^{2}\)) of the bracket base. For the baseline groups, the bracket was bonded to a pre-etched enamel surface that was the same size as the bracket base. The SBSs of the baseline groups were measured under the same conditions described above.