esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Phase 5: Construction and Section In Situ Tests
### Trial Sections Survey
#### 6.2.1 Deflection Measurements by FWD  
The deflection measurement enables characterising the structural capacity of the formed subgrade, as well as its layers along the road layout. A Dynatest HDW 8081 falling weight deflectometer (FWD) was used.  
A pressure of 850 kPa was applied through a 300-mm-diameter plate. The surface deformation due to the application of this load was measured by seven geophones located at 0, 300, 450, 600, 900, 1200 and 1500 mm from the application of the load.  
Surface deformation measurements were taken every 10 m in sections AS-1 and AS-2 and every 5 m in section AS-3 in both lanes, obtaining the average per section.  
Figures 12-14 show the mean deflection values measured in the dry and wet seasons for each section.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Everstress Results** & **AS-1** & **AS-2** & **AS-3** \\ \hline Theoretical deflection (mm/100) & 78.02 & 73.51 & 75.63 \\ Theoretical modulus of compressibility, \(E_{v}\) (MPa) & 168.55 & 178.89 & 173.87 \\ \hline \end{tabular}
\end{table}
Table 7: Theoretical deflection and modulus of compressibility obtained in Everstress 5.0.  
Figure 11: Annual rainfall distribution in Villacarrillo, Spain.  
Analysing Figures 12-14, two patterns of behaviour were observed: firstly, the addition of nanomaterials, both sodium silicates and organosilanes, together with acrylic copolymers, improved the bearing capacity of the soil, reducing the deflections measured during the test.  
Figure 14: Deflection measurement in AS-3.  
Figure 12: Deflection measurement in AS-1.  
Figure 13: Deflection measurement in AS-2.  
Secondly, the sections with nanomaterials showed a drop in the bearing capacity due to rainfall, in contrast to the AS-1 section, which remained practically constant after a year's weather.  
However, the AS-1 section showed greater dispersion in the data, which could be attributed to problems during the execution of the work, presenting over-compacted areas that reduce the average deflection of the section.  
Table 8 shows the average annual deflection of each section, as well as the weighted deflection for a load of 500 kPa, which enables calculating the elastic modulus through Equation (4).  
As is shown in Table 8, the average values of the sections show a reduction in the average annual deflections and an increase in the equivalent modulus of the sections made with nanomaterials.  
Comparing the deflection results obtained for the new hybrid stabilised sections, a better behaviour was observed than the alternative section analysed by [PERSON] et al. (2019), and a slight drop was observed when compared to the 80-cm-thickness control section. However, a section reduction of approximately 40% and a similar mechanical behaviour confirmed the suitability of the proposed method of hybrid stabilised solutions for road layers.  
All the sections can be classified as in the medium category, although with lower values than those obtained theoretically, due to the irregularities of the terrain and construction peculiarities of the linear works.
## 3 Communication Delay Issues with Combined TSC and TCR Controller
### Adapted Predictor Method  
The signal in Figure 8 was adjusted for transmission delay using the suggested improved predictor approach before being sent to the controller. Figure 9 depicts the flow diagram for the updated predictor approach. Before sending the delayed signal into the controller, the prediction method's algorithm could alter it to the original curve. The prediction approach was versatile in that it could adapt to any controller. If the time delay was \(t_{d}\), the predicted \(\Delta V\) could be determined given the level in comparison, the previous recorded points, and the estimation [43].  
\[\Delta V_{p}=\Delta V_{previous}+t_{d}\Delta Vc \tag{7}\]  
\[\Delta V=\frac{\Delta V_{k}-\Delta V_{(k-1)}}{\Delta t} \tag{8}\]  
with \(p\) indicating the predicted position; \(k\) representing the observed location position; \(c\) representing a steady value; and \(\Delta V\) representing the rapidity at which the controller's signal changed. \(c\) was 0.25, as determined through trial and error.  
Figure 8: Predictor model.  
Figure 7: Membership functions of fuzzy controller linear output.
## 6 Simulation Setup and Results
### Downlink Use Case
#### 6.6.3 DSS2  
Due to similarity with SINR graphs presented for DSS1, and to avoid duplication, we did not present SINR graphs for DSS2. Similar to DSS1, DSS2 manages to resolve all interference events on GSO and NGSO links, with SINR measurements keeping above the 9 dB threshold for the most part. All links are stable and are reacting when SINR  
Figure 8: SINR graphs in the downlink use case (DSS1). (**a**) GSO-SAT 1. (**b**) NGSO-SAT 2. (**c**) NGSO-SAT 3.  
Figure 9: KPIs for the downlink use case.  
drops below 9 dB within a few seconds. FWA nodes 8 and 9 do receive some interference from the downlink signals of the satellite links, but they stay within their safe margin, never dropping below 15 dB SINR. The FWA links had a 0% packet loss, like DSS1 and the baseline, as downlink communications do not seem to have a major impact on the Earth FWA links. If this impact is not negligible, DSS2 would not be able to protect FWA links, unless FWA nodes are supporting spectrum sensing and operating under DSS2 framework, which would make them adaptive and protected against external interference. It was found that DSS2 provides marginally lower packet loss and marginally better throughput in the dynamic part of our scenario, compared to DSS1. A closer look at our detailed logs reveals that DSS2 lost 1900 packets in total, while DSS1 lost 2000 packets, providing a small advantage, for a total loss in both of about 0.005% packets.
# A Novel High Gain Monopole Antenna Array for 60 GHz Millimeter-Wave Communications
## 2 Antenna Design  
In this section, the design of a single patch monopole antenna operating at 60 GHz is optimized and examined to increase the efficiency of the proposed antenna. Besides, in this section, an array design with a feed network is introduced to enhance the gain and bandwidth of the single element antenna to suit 5G communication system requirements.
###### Abstract  
In this paper, a state of the art automated defect recognition (ADR) system is presented that was developed specifically for Non-Destructive Testing (NDT) of powder metallurgy (PM) parts using three dimensional X-ray Computed Tomography (CT) imaging, towards enabling online quality assurance and enhanced integrity confidence. PM parts exhibit typical defects such as microscopic cracks, porosity, and voids, internal to components that without an effective detection system, limit the growth of industrial applications. Compared to typical testing methods (e.g., destructive such as metallography that is based on sampling, cutting, and polishing of parts), CT provides full coverage of defect detection. This paper establishes the importance and advantages of an automated NDT system for the PM industry applications with particular emphasis on image processing procedures for defect recognition. Moreover, the article describes how to establish a reference library based on real 3D X-ray CT images of net-shape parts. The paper follows the development of the ADR system from processing 2D image slices of a measured 3D X-ray image to processing the complete 3D X-ray image as a whole. The introduced technique is successfully integrated into an automated in-line quality control system highly sought by major industry sectors in Oil and Gas, Automotive, and Aerospace.  
Keywords:Non-Destructive Testing; computed tomography; automated defect recognition; quality assurance; quality control; powder metallurgy; additive manufacturing; X-ray; net-shape manufacturing +
Footnote †: journal: Applied Sciences
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 4 Hydrogen Transportation and Distribution Technologies
### Transport and Distribution by Pipelines
Hydrogen corrosion: When blended with natural gas, hydrogen may contribute to the corrosion of the piping systems [88]. Impurities, such as H\({}_{2}\)S, CO\({}_{2}\), and water, cause electrochemical corrosion and the generation of products, such as FeS and FeCO\({}_{3}\), resulting in corrosion failure of the pipelines [88]. The corrosion rate depends on various factors, such as the hydrogen blending ratio, material strength, and partial pressure ratio of H\({}_{2}\)S and CO\({}_{2}\) impurities [88]. Hydrogen embrittlement and corrosion of steel pipelines have coupling and competing effects, further explored in [88]. Design of compressor devices: Due to the hydrogen's low molecular weight, to match the volumetric energy density of methane, compressors need to be able to deliver high flows at high pressures. Two main compressor typologies may be considered, namely reciprocating and centrifugal compressors. Reciprocating compressors can deliver high pressures but not high flows. In the case of centrifugal compressors, the impeller tip speed needs to be three times higher than that of natural gas to match the mass flow [85], increasing the complexity of designing this equipment. To prevent some of the previously mentioned challenges, especially hydrogen embrittlement, two main approaches are presented in the literature. The first approach consists of applying protective coatings on the pipeline surface that is in direct contact with hydrogen. An example of this coating material is GALVA-LUME, composed of aluminum, zinc, and silicon [76]. The second approach consists of adding minimal amounts of gases that are more electronegative than hydrogen, such as carbon monoxide or oxygen, that inhibit hydrogen adsorption and dissociation on the pipeline metal surface [90]. Depending on the end-use of the transported hydrogen, this approach may require an additional purification step [85].
# Characteristic Analysis and Decision Model of Lane-Changing Game for Intelligent Connected Vehicles
## 4 Game Model
### Game Components
#### 4.2.2 Payoffs for Vehicle LV  
To simplify the model and highlight the focus of the game process, this paper assumes that the task of the game decision is to play between vehicles and complete the lane-change process without considering the maximization of the full trip driving gain of the LV. Therefore, the expected gain of the LV can be regarded as changing lanes to the target lane as soon as possible. The efficiency gain of the LV also includes the trip gain and the space gain, with the efficiency gain expressed as:  
\[P_{eff}^{LV}=P_{s}\cdot\frac{t_{lc}^{LV}-t_{min}^{LV}}{t_{max}^{LV}-t_{min}^{ LV}} \tag{7}\]  
where \(t_{lc}^{LV}\) is the lane-change time corresponding to the different decisions made by the LV; \(t_{max}^{LV}\) is the maximum time to consider the decision process of the vehicle game; and \(t_{min}^{LV}\) is the shortest lane-change time considering vehicle dynamics when the lane-change process is not constrained.  
According to the vehicle safety interactions, the safety gain of the LV can be similarly expressed as:  
\[P_{safe}^{LV}=\frac{S_{voh}-S_{min}^{veh}}{S_{max}^{veh}-S_{min}^{veh}} \tag{8}\]  
The LV lane change needs to consider the impact of decision moment location: when the LV is closer to the intersection inlet lanes, the expectation of lane changes is a stronger column; when the LV is further away from the intersection, it usually does not show a willingness to change lanes. For the LV and RV game decision, the decision to not change lanes increases the time for the LV to drive into the target lane and reduces the efficiency gain of the LV. Equation (9) shows the LV's expectation of driving efficiency.  
\[\alpha=max\Bigg{\{}min\Bigg{\{}\frac{S_{max}^{LV}-S_{cur}^{LV}}{S_{max}^{LV}-S _{min}^{LV}},0.7\Bigg{\}},0.3\Bigg{\}} \tag{9}\]  
where \(S_{cur}^{LV}\) is the distance from the LV decision moment position to the lane-gradient section; \(S_{min}^{LV}\) is the LV minimum desired lane-change distance, which is used to ensure that vehicles can change lanes earlier; and \(S_{max}^{LV}\) is the farthest lane-change game position, with vehicles less affected by the intersection bottleneck when the lane-change position exceeds this value.  
By creating separate safety and efficiency functions for the vehicle above, the payoff matrix of the LV in different strategy profiles is listed in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **RV** & \\ \cline{2-3}
**LV** & **Give Way** & **Do Not Give Way** \\ \hline Change lanes & \(-\beta P_{eff}^{RV}+(1-\beta)P_{safe}^{RV}\) & \(-\beta P_{eff}^{RV}-(1-\beta)P_{safe}^{RV}\) \\ Do not change lanes & \(-\beta P_{eff}^{RV}+(1-\beta)P_{safe}^{RV}\) & \(\beta P_{eff}^{RV}+(1-\beta)P_{safe}^{RV}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Payoffs of RV in different strategy profiles.
# Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
## 5 Conclusions  
This study proposes a data expansion SSD algorithm for a small data set of Hurricane Sandy. Given the use of the hurricane scenario to train the VGG16 convolution autoencoder, the weight of the coding part replaces the weight of the VGG16 network in the SSD model. The experiment proves that the pre-training method can effectively increase various indicators of the model by approximately 10%. Through data expansion, the detection accuracy can be effectively increased and mF1 and mAP increased by approximately 20% and 72% percent, respectively. The rate of false detections is also reduced. The introduction of the Gaussian noise and Gaussian blur can effectively improve the adaptability of the model in complex scenes. In addition, we used the dataset of Hurricane Irma to verify the method proposed in this paper, and also achieved good performance. This makes our conclusions more objective.  
This study is based on Hurricane Sandy's post-disaster building damage detection. In the future, data from other post-disaster scenarios can be collected for training, while the generalization ability of the model can be increased. Through research, the algorithm can also be installed on the camera of UAVs for real-time detection, thereby effectively aiding the rescue staff in search and rescue to reduce casualties.  
[PERSON] raised this idea. [PERSON], [PERSON] and [PERSON] verified the idea through experiments and completed the first draft of the paper. After [PERSON] proofreads the paper and improved the experiment, the final paper was completed.  
This research was funded by the Beijing Natural Science Foundation (No. 4182020) and National Key RD Program of China (No. 2017 YFB1201104).  
The authors would like to thank [PERSON] of Drexel University to provide us with the aerial images of Hurricane Sandy.  
The authors declare no conflict of interest.
## 1 Introduction
The algorithm combines target motion and appearance features, assigning dynamic feature weights for accuracy, robustness, and adaptability. The key contributions are:  
1. Multi-dimensional feature fusion and dynamic weight allocation mechanism: By integrating multi-dimensional feature information, the algorithm can better differentiate between targets and background noise. The adaptive weight allocation mechanism adjusts the weight of each feature based on target characteristics and environmental conditions, enhancing the algorithm's universality and accuracy. 2. Enhanced fuzzy clustering processing mechanism: Traditional fuzzy clustering algorithms are prone to noise interference in complex environments, reducing clustering accuracy. This algorithm mitigates the impact of environmental noise and non-target factors by optimizing clustering center selection and updating strategies, thereby improving clustering accuracy and stability. The remainder of this paper is organized as follows: Section 2 introduces the trajectory initialization algorithm based on MDF-clustering. Section 3 describes the experimental setup and the evaluation metrics used to assess the performance of the algorithm. Section 4 presents and analyzes the experimental results. Finally, Section 5 concludes the paper and discusses potential directions for future research.
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 4 Challenges Faced in the Development of AM Metal Mirror
### Dimensional Stability  
Dimensional instability refers to the change in the dimensional accuracy of mirrors during the designing, manufacturing, using, and storing stages. It is the main factor that determines whether AM mirrors can be used in engineering. Therefore, the smaller the change, the more likely it is that mirrors can meet the requirements of the technical conditions. The dimensional stability that metal mirrors need to reach is the 10\({}^{-6}\) level, so the size changes relative to the meter level should be controlled at the micron level [89; 90].  
Dimensional changes can be divided into four types: (1) First, when the part is placed in a stable environment, the dimension of the mirror changes with time. This process is prolonged and irreversible. (2) The second type is the dimensional change caused by thermal or force cycles, which refers to when the mirror returns from a changing environment to a stable environment. This situation is also a permanent deformation. (3,4) The third and fourth types are dimensional deformations caused by temperature and hysteresis, respectively. There are subtle differences between the two. Both describe the deformation of a part that is moved from a stable environment to another stable environment. The temperature-induced deformation is independent of the change path and a reversible deformation; the hysteresis-induced deformation is related to the change path, resulting in deformation that may be reversible or permanent [89; 90; 91; 92].  
Then, these types of dimensional change can be summarized into four factors through a detailed analysis: externally applied stress, non-uniformity, anisotropy of material properties, microstructural variations, and internal stress variations. In practical engineering, the dimensional stability of metal mirrors can be improved, starting from these factors.
# Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
## 4 Experimental Setup and Evaluation
### Dataset of Smart Home Data
To pre-process the raw sensor data and generate the input datasets of the models, multiple and incremental fuzzy temporal windows are used. Fuzzy temporal windows have been successfully used to capture signal sensors of a long and short duration of human activities, such as sleep or snack from raw sensor data, which help to increase the performance of the temporal models [17; 18; 46]. Besides, temporal models i.e., LSTM and 1D CNN achieve better performance for activity recognition when the input datasets are generated by fuzzy temporal windows when compared to other methods such as Equally Sized Temporal Windows (ESTWs), Raw and Last Activation (RLA), and Raw and Last Next Activation (RLNA) [17; 18; 46].
## 6 Conclusions and Future Works  
In our study, a scheduling problem frequently encountered by a company manufacturing in the space sector is discussed. With a process and method that we developed, we tried to solve the need for rescheduling caused by the difference between expected and actual values in the production period and the return of finished work to the system due to production errors. There are two strategies that company management can choose. These are minimization of the total weighted tardiness time and minimization of the number of delayed jobs. For these strategies, visible improvements have been achieved compared to the current system. Through rigorous analysis and comparison with traditional methods, MIU emerges as a powerful tool for optimizing scheduling strategies and enhancing overall efficiency.  
A key strength of MIU lies in its dynamic approach to scheduling, offering tailored solutions for each strategy while seamlessly adapting to evolving production scenarios. This flexibility empowers decision-makers to navigate uncertainties and optimize scheduling outcomes in real time, a stark departure from the rigid solutions of the past.  
Our process gives management the opportunity to assess strategy in periodic meetings and allows rework operations to be completely rescheduled along with all remaining work. Since the problems were NP-hard, there was a need to develop a heuristic procedure. The MIU Approach we developed is structured to give good results very quickly and is designed to use the advantages and avoid the disadvantages of dispatching rules for each situation by using 13 different rules.  
In our study, the stability, robustness, and nervousness value changes between the results obtained by the current application of the workplace and the results given by the developed algorithm are also shown. The trade-off between the improvement in objective function values and the worsening of the evaluation criteria was mentioned. In addition,expressing numerically the inverse proportion of the nervousness value to stability in the literature is another important contribution of our study.  
As we chart the course forward, we may also analyze the impact of varying period durations across diverse scenarios to further validate the robustness of the methodology. Additionally, investigations into scenarios involving variable technician speeds could offer deeper insights into real-world production dynamics. In future work, it is planned to conduct additional analyses to further validate the improvements introduced by the MIU approach. This will include statistical tests and significance analysis to quantify enhancements compared to existing methods, providing a more robust evaluation of the results.  
Writing--original draft, M.I.U.; Supervision, H.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.