esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
There is a call to apply SDP techniques in the early stages of software development lifecycle so that practitioners can prepare to test defect-prone modules with more rigor [12]. This is found to reduce software development and maintenance costs [13; 14]. Some researchers have also defined \"an ideal DeP model\" [15]. Yet, SDP has not yet become a part of the software development process. The level of understanding of SDP models is low among software practitioners and managers [7]. Reasons for such low awareness could be attributed to multiple unsolved problems with SDP identified by researchers including [PERSON] [7], [PERSON] [16], [PERSON] et al. [17], [PERSON] et al. [18] and [PERSON] et al. [15]. However, the study did not find any literature that directly attributes low usage of SDP to any specific subset of SDP problems. However, the research found that in most cases, SDP was described as a stand-alone technique. The research did not find any practical attempt to describe SDP to software practitioners in a pragmatic, industry-friendly manner or allow easy access to a testbed of ongoing SDP research. Lack of industry participation could also be a strong reason for the fragmented state of SDP research. To consolidate the ongoing fragmented SDP research, and to bring SDP closer to the industry practitioners, a cloud-based, multi-model SDP framework has been proposed. It is named as _DePaaS--Defect Prediction as a Service._ It is intended to serve as a global, unified platform that serves both researchers who build SDP models and software industry practitioners who consume defect prediction services provided by these SDP models. The proposed framework has the potential to help overcoming issues related to SDP's core objectives, input data quality, cross-project usage, SDP model performance and practical utilization by the software development community. The current research field-tested the idea of cloud-based SDP in many contexts and gathered deep insights into the way software practitioners perceive the defect prediction problem and the proposed DePaaS solution. The complex process of SDP was better understood by software practitioners when it was presented as a cloud-based service with a componentized architecture and a process flowchart. The current research has produced a strong, detailed vision for a global, unified, AI based SDP framework called DePaaS. It has also obtained an early feedback on both SDP and DePaaS from the software practitioners. This article explains both results in Sections 3 and 4, respectively.
# Angular Circle Array Multiple Input Multiple Output Underwater Optical Wireless Communications +
###### Abstract  
This paper constructs a simulation platform for underwater wireless optical single-input single-output (SISO) communication systems and quantitatively evaluates communication performance indicators. To improve channel capacity, we propose an angular circle array MIMO scheme. The path loss, CIR, and channel capacity of the angular circular array MIMO communication system are calculated by using the Monte Carlo method. Results show that the proposed angular circular array MIMO communication system has a higher channel capacity compared to planar circular array MIMO communication systems and SISO communication systems.  
Keywords:underwater wireless optical communication; MIMO communication; angular circle array +
Footnote †: This article is a revised and expanded version of a paper entitled Design and Simulation of Underwater Wireless Optical Angular Circle Array MIMO Communication Systems, which was presented at 2024 IEEE 10 th International Conference on Underwater System Technology: Theory and Applications, Xi’an, Shaarvi, China, 18-20 October 2024.
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 4 Conclusions  
Following our previous feasibility studies of GNSS as navigation system to reach the Moon, this research paper describes the proof of concept of the GPS L1 C/A \"_WeakHEO_\" receiver for lunar mission, wholly developed in our laboratory in the last two years. After highlighting the characteristics of the GPS L1 C/A signals for the considered MTO, which have been identified in our previous research work, the requirements and constraints in the receiver design are defined. Afterwards, the general receiver architecture is described, providing a more detailed description of the acquisition, tracking and navigation modules. These modules are specifically designed for use with the dynamic environment and signal conditions seen in high altitude space applications, and the architecture presented is capable of performing acquisition, tracking, data synchronization and demodulation down to a level of 15 dB-Hz. This is verified on the hardware with tests using representative RF signals produced by a GNSS simulator. The navigation module makes use of an adaptive orbital filter, which exploits an orbital forces model to filter the noisy GPS least square single-epoch solutions and is able to significantly increase the navigation accuracy to a few hundred meters at Moon altitude. The filtered navigation solution can then be used to aid the acquisition and tracking modules allowing higher sensitivities also in high dynamics.
## 3 Results
### Hydrological Simulations
#### 3.1.2 Runoff Simulations Calibration  
Short simulation periods were taken into account in order to consider the vegetation as static. For the calibration of the runoff simulations, distributed soil parameters were considered according to the different land uses. Green and Ampt model was selected for the simulation of the infiltration process. After several iterations, the automatic calibration allowed to minimize the error between the measured and simulated surface runoff. The use of such automatic calibration allowed to reduce the computation time.  
Although not shown here because beyond the scope of this paper, several trials with different swarm sizes, and goodness of fit functions with different number of iterations were tested including different numbers of iterations, swarm sizes and boundary conditions. Figure 4 presents the histogram of calibrated values. The vertical red line highlights the optimum value achieved by each parameter. These histograms show near normal distribution or skewed distribution near to the upper boundary.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Ranking** & **Parameters** \\ \hline
1 & Saturated water content: \(\uptheta\)s \\
2 & Particle size distribution index: psdi \\
3 & Saturated hydraulic conductivity: ksat \\
4 & Water content at field capacity: fc \\
5 & Water content at wilting point: wp \\
6 & Residual water content: \(\uptheta\)r \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sensitive parameter ranking of each of tested infiltration method.  
The calibration is based on the optimization of a goodness of fit function, in this case the Nash-Sutcliffe Efficiency was selected. Figure 5 reports the evolution of the GoF as a function of the iteration number. At the end of the calibration, the GoF reached a value equal to 0.88 which refers to a good match between the observations and calibration results.  
In order to better analyze these simulations, several short simulation periods were selected as reported in Table 3.  
Figure 4: Histograms of calibrated parameter values.  
Figure 5: Example of evolution of the goodness of fit function (Nash-Sutcliffe Efficiency) as a function of the iteration number for the calibration.  
In Figure 6 results of the automatic calibration were compared with measured runoff volumes. The performance of the calibration varies from one simulation period to another, but as an overall calibration outcome, satisfactory results were achieved.  
In addition, the validation of the calibration results was carried out by selecting some rainfall events. Some of the events selected are reported in Figure 7. As shown in the figure, the calibration allowed to improve the simulation results, reaching different levels of accuracy according to different selected events.
## 5 Gaofen-3 Time Series Results Analysis and Comparison
### The Spatial and Temporal Evolution of Land Subsidence during 2017-2021  
Characterizing the variation of Beijing's land subsidence is important not only to warn of and mitigate disasters but also to understand the temporal evolution of land subsidence in Beijing. The Gaofen-3 images could assist in evaluating the spatial and temporal variation of subsidence over Beijing in recent years while comparing them to the measurements derived from Sentinel-1 from 2017 to 2020. Figure 14 highlights the deformation from 2017 to 2020 and from 2020 to 2021 that was calculated with Sentinel-1 and Gaofen-3 SAR data, respectively. Five major subsidence bowls were identified from 2017 to 2021, including three large-scale subsidence bowls located east of the Chaoyang district and northwest of the Haidian district and two relatively small subsidence bowls in the Changping district. The main subsidence bowls show a strong spatial correlation between the period from 2017 to 2020 and from 2020 to 2021, and the subsidence patterns of every subsidence bowl decreased gradually, all of which have experienced significant decelerating in subsidence rates and areas in recent years. The decorrelation may be attributed to the uplift of the groundwater from the South-to-North Water Diversion Project (SNWDP) that has been operating in recent years [21].  
The magnitude of the land subsidence inside the three large subsidence bowls decreased gradually during the observation period. We observed that the highest subsidence velocity occurred northeast of the Chaoyang subsidence bowls during both periods, with a maximum value of \(-122\) mm/a from 2017 to 2018, which is much higher than the subsidence velocity from 2020 and 2021, of \(-80\) mm/a. Similar subsidence trends are observed at the Haidian subsidence bowls, and the two small subsidence bowls in Changping district have disappeared since 2019. Even though the extent and scale of all the subsidence decreased gradually, the subsidence bowls inside the district of Chaoyang and Haidian remained subsidence during the observation period, indicating that the land subsidence areas require more attention.  
Figure 13: The histograms for the difference in subsidence velocity of the SDFPs measured in the Gaofen-3 and Sentinel-1 SAR datasets.
## 2 Results and Discussion
### Development of the Microextraction Methodologies
#### 2.2.2 Optimization of the HS-BA\(\mu\)E Methodology
The influence of this parameter was evaluated by carrying out experiments within 0.5, 1, 2, 3 and 16 h, as illustrated in Figure 4c, where the recovery yields increase until 3 h, whereas longer microextraction periods resulted in lower yields. These data could be explained by the fact that the less polar the compounds  
Figure 4: Effect of sorbent selectivity (**a**), LD time (**b**), equilibrium time (**c**) and temperature (**d**) on the enrichment of the five BVOCs obtained by HS-BA\(\mu\)E/GC-MS. The error bars represent the standard deviation of three replicates. are, such as the target BVOCs (\(2.35\leq\log\,K_{\rm O/W}\leq 3.54\)), the greater the adsorption on the inner walls of the sampling flasks is after longer periods of time, thus minimizing the efficiency [11]. Therefore, 3 h was chosen as the optimum equilibrium time and was used in the subsequent assays. Finally, since this is the first time that BA\(\mu\)E has been applied in the HS sampling mode, the thermostatization can have a great influence on the efficiency, such as in the SPME technique, and, therefore, several assays were performed under different temperatures (20, 30 and 40 \({}^{\circ}\)C). According to Figure 4d, a temperature of 30 \({}^{\circ}\)C appears to be the optimum value in attaining the maximum microextraction recovery yields. Lower or higher temperatures seem to significantly decrease the efficiency performance due to the mass transfer equilibrium of the selected BVOCs towards the sorbent phase being strongly affected.
## 4 Experimental Results and Discussions
### Geometric Characteristics of Pseudo-Zernike Moments  
An image \(I\) with size \(512\times 512\) is selected to obtain the Zernike moment feature [31]. The two parameters \(n\) and \(m\) of pseudo-Zernike moments, are in the order of an orthogonal polynomial. The values of (\(n\), \(m\)) are (0, 0), (2, 2), (4, 4), (8, 8), (9, 9), and (11, 11). Three kinds of attacks are performed on the image \(I\), including translation, scaling, and rotation. Specifically, five pseudo-Zernike moment features of images are shown in Figure 12, including the original image \(I_{o}\), the image shift twenty pixels to the left \(I_{20}\), a two-times magnified image \(I_{2}\), rotating the image thirty degrees counter-clockwise \(I_{-30}\), and rotating the image thirty degrees clockwise \(I_{30}\).  
When the image is subjected to different geometric attacks, the differences in pseudo-Zernike moments are relatively obvious. Hence, pseudo-Zernike moments can remarkably represent the global geometric features of the image.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 3 Smart Grid Communication
### Communication Network Structure
The NAN connects premises networks within a neighborhood via smart meters at the end-user. The NAN enable services such as monitoring and controlling electricity delivery to each end-user, demand response and distribution automation. The area NAN/FAN covers can in some cases be large, one of the features of NAN/FAN is communication between intelligent electronic devices (IEDs). The data in a NAN/FAN is transmitted from a large number of sources to a data concentrator or substation. This requires a high data rate and large coverage distance. For the existing grid infrastructure in the NAN/FAN covered areas, it in most cases not possible to make extensive alterations to the infrastructure. Because of the varying nature of the physical environment of which the NAN/FAN operate, coverage requirements, etc., different technologies for communication are used. When the coverage requirements are lower, standards from NAN can be applied, if longer coverage is required, other technologies will be more suitable. The communication technologies used therefore have to be adapted to each specific situation. Both wired and wireless technologies are used in NAN/FAN, and the different communication technologies should be complementary. As distributed energy generation are deployed, these are connected to the NAN/FAN. Communication technologies such as ZigBee, Wi-Fi, Ethernet, or PLC are widely used in these networks [20; 57; 58]. **Premise Area Network**  
The Premise Area Network divides into three sections depending on the environment, HAN (Home Area Network), Building Area Network (BAN), and IAN (Industrial Area Network). These are wired or wireless networks within the end-user's premise. The purpose of the HAN is to provide communication between for example the smart meter and home automation, appliances, Home Energy Management Systems (HEMS), solar panels, or electric vehicles. BAN and IAN are commercial and industrial focused and communicate typically with building automation systems such as heating and ventilation or energy management systems. These applications do not require large coverage, high speed, or high data rate, and can be managed with low power, low-cost technologies such as Power Line Communication (PLC), Wi-Fi, or ZigBee [57]. The required bandwidth in HANs vary from 10 to 100 kbps for each device, depending on function. The premise networks should be expandable to allow for the number of connected devices to increase [59]. Other applications for the smart metering devices within the premise area are delivering information such as power and real-time price information to the end-user through HEMS. The end-user can then make decisions whether to use appliances during high price periodsor wait for lower price. This can in turn help with peak demand reduction and load shifting [60].
# Confirmation of Final Bolt Tightening via Deep Learning-Based Image Processing
## 4 Examination
### Evaluation of Processing Speed  
According to a preliminary survey of the construction company, the inspection time for each bolt was approximately 3 s. The time required to check the bolt tightening points for this experiment is approximately 1089 s.  
A personal computer with a GPU was used for this evaluation: Intel core i9-7920X as the CPU, RTX2080 Ti as the GPU, with 64 GB of memory. In this experiment, it took approximately 23 s to capture the video and approximately 708 s from the time the captured video was input until all processing was completed, giving a total processing time of 731 s. The processing speed per bolt was approximately 2 s. The proposed method reduced the time required for tightening confirmation by 33%. The more bolts that are tightened at a site, the larger the reduction in time required.
## 3 Test Results and Discussion
### Behavioral Analysis
#### 3.3.2 Behavioral Threshold Analysis  
A behavioral threshold can be defined as a threshold where a small change in mixing ratio resulted in a significant change in overall behavior or response of the soil. The behavioral thresholds exist at a critical fines content where a minimum void ratio occurs; in other words, it happens when the voids between coarse particles are fully filled by fine particles [33]. [PERSON] [34] and [PERSON] et al. [5] described critical fines content \(f_{crit}\) as a function of minimum void ratio and specific gravity (Equation (2)):  
\[f_{crit}=\frac{e_{min,c}G_{f}}{G_{s}(1+e_{min,f})+e_{min,c}G_{f}} \tag{2}\]  
where \(e_{min,c}\) is the minimum void ratio of the coarse particles, \(e_{minf}\) is the minimum void ratio of the fine particles, \(G_{s}\) is the specific gravity of the host material, and \(G_{f}\) is the specific gravity of fines.  
To identify the behavioral threshold of sand-kaolinite mixture, the value of \(e_{min,c}=0.49\), \(e_{minf}=0.9\), \(G_{s}=2.607\), and \(G_{f}=2.80\) were used. As a result, the critical fines content\(f_{crit}\) was equal to 21.67%. Therefore, this critical fines content is where the threshold was defined as the point changes in sand-kaolinite mixture ratio result in behavior changes. Moreover, this value was found within the possible range of transition fines content (21-26% KC) which was obtained from experimental observation. In other words, this value of \(f_{crit}\) could be related to the TFCs.
# Integrating Geometric Dimensioning and Tolerancing with Additive Manufacturing: A Perspective
## 7 GD&T Adaptation to Different Technologies  
GD&T needs also to be adapted to different processes commonly adopted to manufacture mechanical parts, such as Powder Bed Fusion (PBF) and Directed Energy Deposition (DED), while also considering the unique behaviors of materials like metals, polymers, and ceramics. In PBF processes, dimensional accuracy is influenced by powder particle size, laser scanning strategies, and thermal distortions, requiring tighter controls on form tolerances (flatness, cylindricity) and position tolerances (true position, concentricity) [35]. DED processes, on the other hand, produce larger, near-net-shape components with higher thermal input, often leading to higher residual stress and lower feature resolution, making profile tolerances more critical for accounting for surface variations [36]. Because of anisotropic shrinkage and microstructural alterations, metal AM parts require process-specific tolerance compensation. With an emphasis on profile and straightness tolerances, polymer AM, like FDM or SLS, necessitates GD&T modifications for warping and layer bonding irregularities. Because of its brittleness and sintering shrinkage, ceramic AM, which finds utility in biomedical and aerospace applications, poses difficulties that call for GD&T solutions that take post-processing dimensional changes into account. By tailoring GD&T to each AM process and material, functional accuracy is guaranteed, post-processing requirements are decreased, and reliability is improved across a range of applications.
# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 3 Dynamic Environment Target Identification and Localization (DETIL)
### Task Scenarios and Evaluation Metrics  
To validate the superiority of the algorithms proposed in this study, multiple rounds of simulation experiments were conducted on the Webots simulation platform using different lunar surface areas. The extraterrestrial rover (intelligent agent) has three-dimensional dimensions of approximately 400 \(\times\) 300 \(\times\) 200 mm, with four independently driven wheels, capable of forward and reverse motion without a steering mechanism, and capable of differential speed turning. It is equipped with two RGB-D cameras, front and rear, with a field-of-view angle of 1.57 radians. The RGB-D outputs images at 1280 \(\times\) 720 pixels, with a minimum depth perception distance of 0.4 m and a maximum depth perception distance of 6 m. The cameras are approximately 100 mm off the ground, with the front camera angled downwards and the rear camera angled upwards at a 20\({}^{\circ}\) angle to the horizontal. The agent needs to autonomously move from the starting point to the target area based on the given map and find and discover interest targets during its journey, with the entire map measuring 50 m \(\times\) 50 m and five interest targets set.  
This experiment aims to validate the performance of the adaptive heuristic function A* path planning algorithm and the DETIL algorithm, as well as to test the robustness of the multi-task mixed control scheme composed of them. For the path planning algorithm, this study evaluates the algorithm's performance by examining the path length, the computation time, the number of expanded nodes, the maximum elevation difference of the path, and the total elevation change of the path. The performance of the DETIL algorithm is evaluated by examining the probability of obstacle discovery, the obstacle avoidance judgment time, the number of interest targets found, and the accuracy of interest target localization.