esa-sceva/satcom-chunk-collection huggingface dataset:
# Stressing State Analysis on a Single Tube CFST Arch Under Spatial Loads
## 4 Stressing State Analysis of the Arch Model
### Characteristic Investigation into Strains  
Strains can reflect the working state of the structure to some extent. Thus, by modelling the experimental strains at typical sections, it can reflect the internal working behavior characteristics of the arch model. Here, the GSED sum of each cross section are adopted to describe the stressing state of one section, that is  
\[E_{j}=E_{1}+E_{2}+E_{3}+E_{4}, \tag{8}\]  
where \(E_{j}\) is the GSED sum of the cross section under \(j\)-th load, \(E_{1}\)-\(E_{4}\) are GSEDs calculated by strains \(\varepsilon_{1}\)-\(\varepsilon_{4}\) under the \(j\)-th load, respectively.  
As shown in Figure 4a, the \(E_{j}\)-\(F\) curves of each cross section are plotted to reflect the change trend of the structural stressing state mode. It can be seen that before 66 kN, \(E_{j}\) at all these sections keeps a slight and stable increase with no sharp break between adjacent loads. From then on, with the increasing of loading, \(E_{j}\) at some sections embody greatly faster increasing trends than other sections, especially sections at the arch foot and mid-span (Sections 7 and 13). The larger the \(E_{j}\) is, the greater the stress the cross section has. Therefore, it indicates that these zones, around Sections 7 and 13, are the weak parts of the arch, which controls the arch's instability failure during the later part of the loading application. Besides, the asymmetry of the \(E_{j}\)-\(F\) curves at symmetrical sections also embodies the effect of many factors, such as uncertainties in the experiment, both geometrical and material nonlinearities. Even so, the developing trend of the curves would not be changed and have a good presentation of mutation characteristics of the stressing state mode.  
To better view the change trend of the \(E_{j}\)-\(F\) curves, the increment difference of \(E_{j}\) between adjacent loads are modelled as the characteristic parameter \(\Delta E_{j}\), that is  
\[\Delta E_{j}=E_{j}-E_{j-1}. \tag{9}\]  
As shown in Figure 4b, the \(\Delta E_{j}\)-\(F\) curves of some typical sections are plotted. It shows that curves of some sections, like Sections 1 and 3, have little increments during the whole load process, and the fluctuating ranges of these curves are not obvious enough after 85 kN. By comparison, the curves of typical sections, like Sections 7 and 13, show an obvious increasing trend from 66 kN to 85 kN and great increments after 85 kN, illustrating the important qualitative change of stressing the state mode of the arch model. Hence, from the perspective of experimental strains, it can evidently embody the leap change of the arch's stressing state mode after the failure load.  
Figure 4: The GSED-based stressing state mode of sectional strains: (**a**) \(E_{j}\)-\(F\) curves; (**b**) \(\Delta E_{j}\)-\(F\) curves.
## 5 Numerical Results
### Comparison with Other Grouping Schemes  
We start by comparing the average data rate (ADR) parameter for the proposed scheme and the one reported by [PERSON] et al. in [PERSON] et al. (2019), so-called IMG scheme. To this aim, in Figure 3 both schemes are compared using the same default setup from from Tables 2 and 3. Here, we first define optimal radius with our algorithm and then put it to the IMG scheme for subgrouping. Once we obtain this subgrouping, we calculate and compare ADR for multicast service only. Recall, that [PERSON] et al. [PERSON] et al. (2019) utilizes an increment of 10 degrees for HPBW at each iteration.  
By analyzing the presented data, one may observe that IMG scheme utilizes the widest beams with the longest coverage distance in approximately 50-70% of cases when UE density is \(\lambda\geq 0.3\). Similar numbers are observed for our algorithm. In fact, the two considered grouping schemes converge in dense conditions. Recall, that, at the same time, the proposed scheme is much simpler in terms of implementation as the number of groups can be directly computed. Thus, in what follows, we utilize the proposed scheme to present the performance evaluation results for multi-layer VR service.
## 1 Introduction
To improve the robustness of the HL-RF algorithm, [PERSON] and [PERSON] [29] put forward a finite step length (FSL) algorithm, although with a low computational efficiency as a consequence of inflexible step lengths. Then, efforts were made by [PERSON] et al. [30] and [PERSON] et al. [31] to improve the convergence rate of partial functions by finding a better choice of the step length of the FSL algorithm. Meanwhile, [PERSON] [32] proposed a conjugate HL-RF (CHL-RF) algorithm based on the conjugate gradient direction, and some relevant works were also given [33; 34]. The CHL-RF algorithm can generally improve the convergence rate, but its performance depends strongly on the choice of the conjugate gradient factor and step length. In addition, there are other algorithms similar to the FORM, such as the first-order saddlepoint approximation methods [35], HLRF-BFGS algorithm [36], and hybrid particle swarm optimization algorithm [37]. Robustness and efficiency of the iterative algorithms are the main indexes of the FORM. Benefitting from simplicity and high efficiency, the HL-RF algorithm is the most frequently selected for general reliability analysis. LSFs of structures, however, tend to be increasingly high-dimensional with highly nonlinear functions as a consequence of structure complexity and the scale incremental in an incredible speed, which proved to result in the HL-RF algorithm failing to converge any more. The improved algorithms, e.g., STM, HLRF-BFGS, and RHL-RF algorithms, are alternative options with low efficiency, indicating that the mentioned developed algorithms are still limited in aspects of low robustness and poor efficiency. Motivated by this, this paper puts forward a new algorithm, namely, adaptive finite step length (AFSL), for structural reliability analysis based on the FSL algorithm and the Armijo line search method. In terms of the developed algorithm, a step length together with an Armijo-line-search based optimization method are introduced to improve the robustness and guarantee the efficiency of the algorithm, respectively. Besides, the initial step length and adjusting coefficient are optimized to ensure applicable of the algorithm in nonlinear scenarios. The structure of the paper is organized as follows. Section 2 presents the proposed AFSL algorithm. Numerical analysis of the robustness and efficiency of the developed algorithms is illustrated in Section 3. Section 4 is the discussions, and the conclusions appear at the end.
# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 2 Literature Review Methodology and State-of-the-Art
### Article Selection and Research Category Definition  
The approach described below was used to identify articles from the Scopus database relating to specific types of image processing using ANNs.  
The selection of appropriate categories (domains) was preceded by an extensive search combining specific words in the title, abstract, and keywords of the publications according to the scheme presented in Figure 1.  
In the first step, a search in the Scopus database was applied with the following limitations, all of which needed to be fulfilled:  
* A combined field that searches abstracts by: neural network
* Years: between 2005 and 2024
* Limited by language: English
* Limited by publication stage: final
* Limited by subject areas: computer science, engineering
* Limited by main keywords: image sensors OR thermal imaging OR infrared detectors OR X-ray.  
As a result of this search, a list of 206 articles was obtained. However, because some of the articles were incorrectly classified, we increased the precision of the search by applying an additional filter to check whether at least one of the following keywords was present:After applying the above constraints, a list of 100 articles was obtained. However, after manual verification it turned out that two articles [10; 11] were incorrectly classified by the Scopus database; thus, these were removed from further analysis. On the basis of the remaining articles, the second step divided them into research categories. The first category consisted of a group based on basic keywords, with additional keywords assigned to subsequent categories to build the second group.  
Information about selected publications was exported to a CSV (Comma Separate Values) file containing data such as title, authors, year of publication, etc., which was then imported to a PostgreSQL 16.2 database. This enabled later analysis through SQL queries for data mining and aggregation. This process was automated. A programme written in Python 3.12.2 was used for this purpose and to prepare tables and graphs. These data summaries allowed researchers to conduct further analysis. The statistical information is presented in Section 3.
## 2 Results and Discussion
### The Production of the \(\alpha\)-Hemolysin Is Impaired in ppGpp-Deficient Strains
Bacterial cultures of the K-12 derivative AAG1 with plasmid ppSF4000 (pACYC184 carrying _hlyCABD\({}_{\Pi}\)_) and its ppGpp\({}^{0}\) derivative (JFV2) were grown in LB at 37 \({}^{\circ}\)C up to late exponential phase (OD\({}_{600\text{nm}}\) of 0.8). Data shown are mean and standard deviation of four independent determinations. Statistical significance levels are reported as **\(=\)**\(p<\) 0.005. (**B**) Hemolytic phenotype on blood agar plates of the AAG1 (Wt) and JFV2 (ppGpp\({}^{0}\)) carrying the plasmid pSF4000. (**C**) Amount of \(\alpha\)-hemolysin produced in bacterial cultures of the same strains as in panel (**B**), grown in LB at 37 \({}^{\circ}\)C up to an OD\({}_{600\text{nm}}\) of 0.8. Left panel, secreted \(\alpha\)-hemolysin (extracellular) was detected in cell-free supernatant from the cultures by Coomassie blue stained 10% SDS-PAGE. Lane M: molecular mass markers (size in kDa indicated along the left side). Right panel, intracellular \(\alpha\)-hemolysin was determined by immunoblot analysis. The band corresponding to the \(\alpha\)-hemolysin is indicated by an arrowhead. RNA polymerase, respectively [23]. The _rpoB3370_ allele carries a missense mutation causing the change in the amino acid 563 of the \(\beta\) subunit of the RNA polymerase from a threonine to a proline (T563P). The _rpoB3370_ allele restores ppGpp-deficiency phenotypes [11; 23; 24]. The effect of _rpoB3370_ on \(\alpha\)-hemolysin expression from the _hly_\(CABD_{II}\) operon was scored. The \(\alpha\)-hemolysin expression in the ppGpp-deficient derivative strain carrying the _rpoB3370_ allele was restored to the same level as in the Wt strain (Figure 2). Moreover, spontaneous prototrophic mutants from the ppGpp-deficient strain were selected in minimal media plates and the production of \(\alpha\)-hemolysin was monitored on blood agar plates for 38 independent mutants. Up to 76% of the mutants restore \(\alpha\)-hemolysin expression to levels detected in the ppGpp-proficient strain. The genetic link between the \(\alpha\)-hemolysin expression and suppressor mutations of the ppGpp-deficiency phenotype further confirm the involvement of ppGpp in the regulation of \(\alpha\)-hemolysin expression.
## 4 Numerical Examples
### Validation on Homogeneous Layered Velocity Model  
The horizontally layered velocity model consisted of four distinct layers with P-wave, S-wave, and density parameters, as illustrated in Figure 8b. Velocity and density inversion interfaces formed within a four-layer geological structure are crucial for studying phenomena such as the generation of reflected waves and inter-layer multiples. The synthetic model used in this research aims to provide a clear and controllable environment to validate and demonstrate the core ideas and methods of our study. Although the density values are on the lower side relative to their corresponding P-wave velocities according to [PERSON]'s empirical formula, this allows for the inclusion of atypical conditions under specific geological settings, such as high porosity or particular fluid fills, thereby effectively supporting the core argument of this method. The observation system is illustrated in Figure 8b, which features a source point positioned 50 m below the ground with a 12 m offset. Receivers were distributed at depths ranging from 250 to 4250 m, with 10 m intervals between each receiver. A Ricker wavelet with a dominant frequency of 50 Hz was used as the source signal. The VSP data were generated through elastic wave forward modeling; the modeling parameters are listed in Table 2. The resulting raw VSP records are presented in Figure 8a, with the corresponding f-k spectra shown in Figure 8c, where the energy concentration is apparent at approximately 50 Hz.
# Hybrid-Compliant System for Soft Capture of Uncooperative Space Debris
## 5 Simulation Study and Results  
A series of simulations were conducted with three objectives in mind: to study the relationship between the debris mass and the required compliance and to demonstrate the importance of the proposed hybrid compliant system (Section 5.2); to study the impact of the design parameter \(l_{m}\) (Section 5.3); and to test the impedance controller and analyze its role to achieve a soft capture of space debris (Section 5.4).
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 4 Cooperative Caching Decisions Based on Temporal Convolutional Networks and Hybrid Content Values
### Hybrid Content Value (HCV)  
The traditional popularity-based caching strategy is to improve cache performance by caching the most popular content in RSU. Most of them ignore the cooperation between RSUs, resulting in multiple RSU caches of the same content, which can easily cause cache redundancy. Moreover, if only popularity is considered without taking into account other factors that affect caching, overall performance will be affected.  
Facing this problem, we propose a new metric named Hybrid Content Value. From the perspective of cooperation, HCV combines factors such as content popularity, content size, and transmission delay. When a user requests content \(e_{i}\) from a cooperative RSU, the average delivery delay of the user-perceived request content \(e_{i}\) is given by the following formula:  
\[\sum_{n=1}^{M}d_{r_{m},r_{n},i}P_{n,i,t} \tag{17}\]  
HCV \(\mathbb{Z}_{t}(r_{m},e_{i})\) weights the value of content \(e_{i}\) on \(r_{m}\) for different situations.  
Case 1: If RSU \(r_{m}\) does not cache content \(e_{i}\), the average delay of requesting content \(e_{i}\) after \(r_{m}\) caching content \(e_{i}\) is as follows:  
\[\sum_{n=1}^{M}d_{r_{m},r_{n},i}p_{n,i,t} \tag{18}\]where \(x_{m,i}=0\), if \(r_{m}\) caches content \(e_{i}\), the benefit of HCV \(\mathbb{Z}_{t}(r_{m},e_{i})\):  
\[\begin{split}\mathbb{Z}_{t}(r_{m},e_{i})&=\sum_{n=1} ^{M}d_{r_{m},r_{n,i}}P_{n,i,t}-\sum_{n=1}^{M}  
```
0: Cache space \(u_{m}\), Cache remaining space \(j\), Total cache content \(k\), \(a_{i}=(e_{id_{i}},s_{id_{i}},r_{id_{i}})\). Output: Decision \(Z\).
1:for\(i=1\) to \(k\)do;
2:for\(j=1\) to \(u_{m}\)do;
3:if\(a_{i-1,1}>j\)then;
4:\(\mathbb{R}_{i,j}=\mathbb{R}_{i-1,j}\);
5:else
6:\(\mathbb{R}_{i,j}=\max\{\mathbb{R}_{i-1,j},\mathbb{R}_{i-1,j-a_{i-1,1}}+a_{i-1,2}\}\);
7:endif
8:endfor
9:endfor
10: Get cache parameters \((\mathbb{R},Z,k,u_{m})\)
11:update \(Z\)
```  
**Algorithm 1** HCV-based dynamic planning caching algorithm.
###### Abstract  
One-dimensional (1D) SnO2 nanostructures, as \(n\)-type semiconductors with a wide band gap, exhibit unique photoelectric properties that offer potential applications in electronic, photoelectric, gas sensing, and energy generation devices. A detailed study of template-free reaction systems is essential to regulate and efficiently synthesise 1 DSnO2 nanostructures. This study employed the hydrothermal method to prepare 1 DSnO2 nanostructures, with SnCl4 5H2O as the tin source. The impact of various experimental conditions on SnO2 morphology is analysed. Here, 1 DSnO2 nanostructures were characterised by analytical methods such as X-ray powder diffraction, transmission electron microscopy, scanning electron microscopy, and field emission double-beam electron microscopy. The results confirmed the formation of 1 DSnO2 nanostructures with a mixed morphology of nanorods and nanowires. The nanorods exhibited a length of 422.87 \(\pm\) 110.15 nm, a width of 81.4 \(\pm\) 16.75 nm, and an aspect ratio of 5:1, whereas the nanowires displayed a length of 200 \(\pm\) 45.24 nm, a width of 15 \(\pm\) 5.62 nm, and an aspect ratio of 13:1. With the addition of 50 mg of polyvinylpyrrolidone and seed crystal, the acquisition time of the 1 DSnO2 nanostructures decreased from 48 to 12 h. The 1 DSnO2 nanostructures were efficiently obtained without a template, laying the foundation for large-scale production and application.  
one-dimensional SnO2 nanostructures; hydrothermal method; morphology +
Footnote †: journal: Applied Sciences
## 4 Experimental Verification and Analysis
### Description of Experimental Equipment  
The experimental system of the planetary gearbox is shown in Figure 4a. The system was composed of an operating console, drive motor, elastic coupling, helical gearbox, planetary gearbox, magnetic powder brake, and isolation floor. The operating console changed the speed of the motor and the damping coefficient of the brake by adjusting the voltage and current values. The drive motor provided the power source, which was transmitted to the magnetic particle brake through the helical gearbox, elastic coupling and planetary gearbox. The fault simulation experiment was conducted in the planetary gearbox, which consisted of an 18-tooth sun gear, three 27-tooth planetary gears, a planet carrier and a 72-tooth ring gear; the internal structure is shown in Figure 4b. The sun gear connected with the input shaft was surrounded by three planetary gears, the planetary gears revolved around the sun gear while rotating, and the planetary gears meshed with the sun gear and the ring gear at the same time. In the experiment, five wear faults of different severity were simulated on a planetary gear, five different health states of the planet gears were simulated, namely, normal state, single tooth wear, two teeth wear, three teeth wear and all teeth wear. The planet gears in the five health states are shown in Figure 4d-h.  
Figure 4: (**a**) Structure of the planetary gearbox test rig, (**b**) The internal structure of planetary gearbox, (**c**) locations of five accelerometer sensors, (**d**) normal state, (**e**) single tooth wear, (**f**) two teeth wear, (**g**) three teeth wear, and (**h**) all teeth wear.  
Considering the special structure of the planetary gearbox and the influence of the change of the vibration transmission path, during the experiment, the acceleration sensors were respectively arranged at five different plane positions on the surface of the planetary gearbox according to the experimental scheme in [24]. The two three-direction sensors were mounted on both sides of the planetary gearbox body to measure acceleration in (x, y, z) directions; two one-direction sensors were installed above the inner bearing seat of the input and output shafts of the planetary gearbox to measure the acceleration in z direction. A one-direction sensor was placed above the center of the planetary gearbox top cover. The acceleration sensor information is listed in Table 1. The monitoring point positions of each sensor are shown in Figure 4c. In each failure mode, the motor was tested once at 1200 rpm, 1500 rpm and 600 rpm (corresponding loads are 0.3 HP, 0.5 HP and 1 HP, respectively). In each test, the sampling frequency of the acquisition system for all sensors is set to 20.4 kHz, and the sampling time was 30 s.
# Empowering a Broadband Communications Course with a Unified Module on 5G and Fixed 5G Networks +
## 5 Future Directions and Forward-Looking Topics  
In the final question, the students highlighted their preferences about the topics that need to be further exemplified or added in the 5G/F5G module for next year's course. The most common topic was the introduction of more forward-looking services, such as virtual reality, augmented reality, holographic communications, robotics, services associated with the Internet of Things, and smart infrastructure. Other topics of interest were the impact of radiation of 5G antennas on the human body and the environment, the satellite internet, security/privacy aspects, the role of AI in 6G networks, and finally, the technologies for the sixth generation of wireless networks.  
Overall, we can say that despite the encouraging results from Question 1, showing that more than 50% of the students obtained some new knowledge about the 5G/F5G, these results leave enough space for improvement. For this reason, we propose some future plans, which include:  
* The development of short notes (focusing on the various aspects of 5G/F5G) combined with \"knowledge-checking\" pre-course multiple-choice questions to be answered through the e-class platform. This will also help students enhance their theory comprehension about the topic.
* The development of more multiple-choice questions to be answered by the students during the asynchronous part of the course. The students could submit their answers via the e-class platform and receive their scores automatically. This method has two main benefits. First, it allows the instructors to perform statistical analysis, which can designate the exact student's difficulties and misunderstandings about the topic, as well as the extent. Secondly, the students can assess themselves, which can enhance their active participation and improve their overall knowledge.
* The assignment of small hands-on tasks/projects regarding 5G/F5G topics. For example, the students will be asked to work on a specific network service/app, which they will present to their classmates at the end of the course on a dedicated presentation day. Using this method, the students will enhance their presentation skills while attending a variety of more practical topics presented by their classmates.
* Encouraging students to participate in webinars and/or workshops related to 5G/F5G topics. In this way, they will become more familiar with these topics and also have the opportunity to interact actively with highly skilled researchers and engineers in the field.  
Figure 10: Grading distribution for the 22 students who submitted their answers in the 20-question multiple choice quiz. The average score is 6.86 and the standard deviation equals 0.86.
## 3 The Consecutive Iteration Based on Signal Cancellation (SCCI) Algorithm
### Adaptive Variable Convergence Factor  
The convergence factor used in the SCCME algorithm has a fixed value, which cannot simultaneously achieve fast convergence and a low steady-state error. The SCCI algorithm uses a hyperbolic tangent function to form an adaptive and variable convergence factor for the enhancing iterative performance effectively.
# Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics
## 1 Introduction
Based on the temporal method, we develop a robust and efficient algorithm to extract a wave-celerity vector field of swells from pixel intensity signals. The depth inversion capability is tested over four different cases with varying wave and bathymetry conditions. We describe the video acquisition and rectification processes in Section 2 and then introduce the depth inversion method in Section 3. Then, we show the test results in Section 4 and identify the factors affecting the accuracy and discuss the applicability of the proposed method in Section 5. Finally, we present the conclusions in Section 6.