esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussions  
### The Potential of Moonlight Remote Sensing to Increase Temporal Resolution  
Undoubtedly, our study strongly demonstrates that moonlight remote sensing in the detection of non-self-emitting objects at night is feasible. For example, using either VIIRS/DNB or ISS moonlight data, moonlight remote sensing could be used to identify snow glaciers. In recent years, the continuous melting of glaciers has posed more serious problems for human habitats, as well as for climate change. Due to the limited night-time observation data, the melting rate of permanent glaciers at night has not been extensively studied [41; 56; 57]. Thus, there is a great potential for nightlight remote sensing in glacier studies.  
Our findings further support calls for a newer generation of nightlight sensors [58; 59], but also strongly suggest that attention should be paid to detecting reflected moonlight from land surface outside of urban areas. With advanced low light detecting capabilities, these sensors will help to fill the observation gaps on the night side of a full orbit cycle for optical satellites. Currently, Landsat and Sentinel-2 sensors apply a mechanism to shut down when the satellites enter the night side, leading to a huge waste of orbit resources.  
To efficiently utilize moonlight as a useful remote sensing illuminating source, we must rely on the advent of light-sensing hardware technologies. As shown above, thanks to its improved sensitivity, VIIRS/DNB allows the detection of moonlit features out of urban areas. However, its band settings are still limited to a broad band and its spatial resolution is still relatively coarse (750 m), to guarantee enough light energy to be captured during a relatively short period of a single scan. The ISS and UAV sensors can capture photos in three bands and with finer spatial resolutions, but require much longer exposure times. These technical limitations are expected to be overcome soon with advancements in light-sensing hardware technology.
# Advanced Bayesian Network for Task Effort Estimation in Agile Software Development
## 3 The BN Model
### The New Proposed BN Model
#### 3.2.2 Parameter Estimation  
Conditional and a priori probabilities are learned from the data using the WEKA (Waikato Environment for Knowledge Analysis (WEKA) 3.6.11, [[https://www.cs.waikato.ac.nz/ml/weka/](https://www.cs.waikato.ac.nz/ml/weka/)]([https://www.cs.waikato.ac.nz/ml/weka/](https://www.cs.waikato.ac.nz/ml/weka/)), accessed on 16 April 2023) machine learning suite.  
As already mentioned, the data used in this research originate from agile projects of a small software company. Tasks are grouped chronologically based on their creation time. Grouping is made neither by size, nor by complexity, nor according to the developer who performs the task. The dataset includes tasks of different duration and complexity, created by different developers.  
Empirical data are not available for all the nodes. The nodes Task Complexity and Working Hours Classification are added to simplify the possible outcomes, as well as to provide better model accuracy. The manual definition of the Node Probability Tables (NPTs) (each row in the NPT represents a conditional probability distribution and, therefore, its values sum up to 1) can be a lengthy and error-prone process. Consequently, the values of these nodes are evaluated based on the empirical values of their parents. The probabilities are automatically learned both for empirical and added nodes.  
An example of a table with complete data for parameter estimation in the BN model is shown in Table 4. It consists of empirical data, completed with the data estimated by the authors (grey background).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Node Name** & \multicolumn{6}{c}{**Description**} \\ \hline  & \multicolumn{6}{c}{Intervals of spent working hours:} \\ \cline{2-7}  & 0–1 h & very simple task & (33 instances) \\ \cline{2-7}  & 1.1–2 h & simple task & (23 instances) \\ \cline{2-7} Working Hours Classification & 2.1–5 h & complex simple task & (33 instances) \\ \cline{2-7}  & 5.1–10 h & simple moderate task & (37 instances) \\ \cline{2-7}  & 10.1–25 h & moderate task & (22 instances) \\ \cline{2-7}  & 25.1–40 h & complex task & (6 instances) \\ \cline{2-7}  & \textgreater{}40.1 h & very complex task & (6 instances) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Node Name** & \multicolumn{6}{c}{**Description**} \\ \hline  & \multicolumn{6}{c}{Intervals of spent working hours:} \\ \cline{2-7}  & 0–1 h & very simple task & (33 instances) \\ \cline{2-7}  & 1.1–2 h & simple task & (23 instances) \\ \cline{2-7} Working Hours Classification & 2.1–5 h & complex simple task & (33 instances) \\ \cline{2-7}  & 5.1–10 h & simple moderate task & (37 instances) \\ \cline{2-7}  & 10.1–25 h & moderate task & (22 instances) \\ \cline{2-7}  & 25.1–40 h & complex task & (6 instances) \\ \cline{2-7}  & \textgreater{}40.1 h & very complex task & (6 instances) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Complete data used for parameter learning (12-task example).
## 5 Discussion and Contribution  
### Discussion  
This paper evaluated the effectiveness of identifying images with carcinogenic lacerations with a statistical classifier and neural networks through the following different texture spectra: LBP; OC-LBP; CS-LTP; ICS-TS, and CCR, thus defining the techniques with the most efficiency in the classification of carcinogenic images.  
When comparing the obtained results of the statistical classifier from Figure 8 vs. the neural network when the three types are combined from Figure 10, the statistical classifier was more efficient than the neural network. To illustrate this, the mean value for each technique was calculated, and, afterward, Table 7 was generated.  
From the data in Table 3, the following can be inferred: the five texture-extraction techniques are efficient with the statistical classifier since the parameter \(E(\%)\) can be found within the 83.3333-100% threshold. This high efficiency can be attributed to three motives [45; 46; 48]: (1) combining the classifier and the textural data extracted with any of the five techniques renders the high efficiency of the classification of carcinogenic images possible; (2) the number of sub-images allows the correct characterization of each class; and (3) the sub-image size is appropriate for adequate characterization of the classes. On the other hand, with the neural network, the three efficient techniques comprised LBP [E(\(\%\)) = 98.3333%], OC_LBP [E(\(\%\)) = 81.6667%)], and CCR [E(\(\%\)) = 98.3333%], and the two least efficient ones were CS LTP [E(\(\%\)) = 36.6667%] and ICS_TS [E(\(\%\)) = 55%]. This difference in efficiency is due to the definition of the texture spectrum for each technique; subsequently, the histograms exhibit different behaviors (see Figure 13), and their content is distinct. This causes each texture-extraction technique to have its own efficiency in the task of the identification of carcinogenic images.  
In agreement with the results of the performed tests, to achieve a good classification efficiency of carcinogenic images, LBP, OC_LBP, and CCR are recommended; their efficiency was corroborated in both classifiers. Additionally, CS_LTP and ICS_TS could be employed if their histograms are deployed as a characteristic vector with a statistical classifier (see Figure 8). Henceforth, our future work is to develop an artificial vision system for identification of the type of cervical cancer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Texture Spectrum (Histogram)**} & **Statistical Classifier** & **Neural Network** \\ \cline{2-3}  & **E (\%)** & **E (\%)** \\ \hline \hline LBP & 100 & 98.3333 \\ \hline OC\_LBP & 98.3333 & 81.6667 \\ \hline CS\_LTP & 96.6667 & 36.6667 \\ \hline ICS\_TS & 98.3333 & 55 \\ \hline CCR & 83.3333 & 98.3333 \\ \hline \hline \end{tabular} Note: Green denotes the most efficient techniques, and blue highlights the least efficient ones.  
\end{table}
Table 7: Mean value of classification efficiency of carcinogenic images.
# DSA-SOLO: Double Split Attention SOLO for Side-Scan Sonar Target Segmentation
## 5 Conclusions  
Aiming at the problems of high noise interference, weak boundary information, and difficult target feature extraction of sonar images, we proposed a lightweight network which fuses channel attention and spatial attention effectively for side-scan sonar target segmentation. The experimental results show that our proposed DSA-SOLO demonstrates strong side-scan sonar image instance segmentation capabilities. To deal with the weak  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attention Module** & **mAP5:95** & **mAP5** & **mAP75** & **FPS** \\ \hline SENet & 37.8\% & 72.6\% & 31.8\% & 18.89 \\ STN & 36.9\% & 73.5\% & 32.8\% & 18.35 \\ CBAM & 39.2\% & 74.3\% & 39.8\% & **19.04** \\ DANet & 38.1\% & 73.8\% & 31.6\% & 19.02 \\ DSA & **42.7\%** & **78.4\%** & **43.2\%** & 18.14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative Results of Embedding Different Attention Modules. The bold numbers represent the highest one of each indicator.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **mAP5:95** & **mAP5** & **mAP75** & **FPS** \\ \hline SOLOv2 & 40.0\% & 73.3\% & 35.8\% & 18.64 \\ C-S Unit Only & 40.2\% & 75.2\% & 35.7\% & 19.02 \\ S-C Unit Only & 41.9\% & 75.9\% & 44.3\% & 18.39 \\ DSA & **42.7\%** & **78.4\%** & **43.2\%** & 18.14 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative Results of Ablation Experiments. The bold numbers represent the highest one of each indicator.  
Figure 7: The segmentation results of embedding STN, DANet, SENet, CBAM, and DSA modules in SOLOv2. The red box shows the instance category and the segmentation precision which is enlarged in the upper left corner of the images.  
boundary features of the targets in side-scan sonar images, the proposed network utilizes two attention units, C-S and S-C, to determine 'where' and 'what' is worthy of attention, thus improving the resistance of the network to noise. Though DSA-SOLO achieves better segmentation accuracy, the parameters introduced by the DSA module are a little larger, which has affected the segmentation speed. Further work will focus on reducing the parameters of the model and improving the segmentation speed while ensuring accuracy.  
Conceptualization, H.H. and Z.Z.; methodology, H.H.; software, H.H. and B.S.; validation, H.H., B.S. and J.Z.; formal analysis, H.H. and P.W.; data curation, H.H., J.Z. and P.W.; writing--original draft preparation, H.H.; writing--review and editing, Z.Z., P.W. and B.S.; project administration, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hunan Province of China, grant number 2020 JJ5672; National Natural Science Foundation of China, grant number 52101377; and Hunan Province Innovation Foundation for Postgraduate, grant number CX20210020.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available now due to deficient maintenance capacity.  
The authors declare no conflict of interest.
## 2 Background
### Frame Synchronization and Zadoff-Chu Sequences
This is an important feature for wireless communications, enabling misaligned ZC sequences to correlate between themselves. Hence, it is possible to generate multiple orthogonal sequences just by shifting the ZC sequence;
* If we guarantee that the Zadoff-Chu sequence length \(N_{ZC}\) is a prime number, then the cross-correlation of these two sequences is \(1/\sqrt{N_{ZC}}\). Sequences that combine the first two properties are designated as constant amplitude zero-autocorrelation (CAZAC) sequences [25]. A crucial application of ZC sequence is for time synchronization due to their CAZAC properties. Therefore, the receiver, by analysing the sequence correlation peak inside a timing slot, can easily identify the transmitted frame beginning [26]. However, ZC sequences are affected by channel fading delay effects, resulting in additional time-domain correlation peaks. Furthermore, the correct sequence correlation peak amplitude is attenuated at high Doppler frequencies, which can lead to false detection or missing the frame beginning from the several time-delay correlation peaks, causing miscalculations of the total channel delay, consequently worsening overall system performance [15].
## 9 Design and Manufacture of Robotic Prostheses with 3D Printing
### Electronics and Control Integration with 3D Printing  
One of the most distinctive features of robotic prostheses is the ability to interact with the environment through integrated actuators and sensors. At the final manufacturingstage, electronic components are assembled to enable precise prosthesis control. This includes installing microcontrollers, such as Arduino or Raspberry Pi, servo motors for limb movement, and environmental sensors, such as pressure or temperature sensors. While 3D printing primarily focuses on creating the structural components, it offers the advantage of customizable designs that simplify the housing and placement of electronics within the prosthetic device, making it easier to incorporate more sophisticated control systems at a lower cost.  
A major advancement in this field is the use of myoelectric signals, where sensors placed on the skin detect muscle electrical signals, which are then interpreted by a microcontroller to translate the user's intentions into movement. This type of control, enabled by precise electronic integration, is particularly beneficial for upper-limb prostheses that require nuanced grip and motion control for everyday tasks. With 3D-printed prosthetics, customized sensor placements and module housings can be incorporated into the design, allowing for quick iterations that enhance the prosthesis's comfort and functionality [13].  
In addition, the integration of artificial intelligence and machine learning has opened new possibilities for adaptive prostheses, where devices adjust to user needs by analyzing movement patterns. These technologies improve control accuracy and energy efficiency, which, in turn, extends battery life and device longevity. For instance, adaptive grip mechanisms can now be 3D-printed directly into the design to allow for responsive feedback adjustments in real-time--a feature that significantly improves the user experience in both comfort and functionality [14; 15]. This combination of 3D printing and intelligent control systems demonstrates how rapid prototyping can support continuous development and adaptability in prosthetic technology. Figure 11 shows a flow chart explaining the overall process required to create a 3D-printed prosthesis.
# Review on Computer Aided Weld Defect Detection from Radiography Images
###### Abstract
Furthermore, developments in computer hardware technology and representation learning has provided the perfect conditions for automation of weld defects inspection. Especially, with recent advances of deep learning theory, in optical image recognition domain, considerable effort has been made to design multistage architectures which learn the hierarchical features from images automatically. This paper aims to review the common practices for weld defects detection and classification based on the digitized radiographic images. The radiation involved in these studies is X-ray (or sometimes Gamma-ray). The two radiographic sources are used in different occasions which are not distinguished in this paper. The paper focuses on the summary of analysis methods for digitized radiographic images. It gives a detailed and comprehensive summary of literatures from image pre-processing, defect segmentation and defect classification. It elaborates from four aspects: (1) the quality improvement of weld images; (2) traditional techniques for defect detection and classification; (3) the application of novel models based on learning; and (4) the achievements and challenges of current methods.
###### Abstract  
IoT has begun to be employed pervasively in industrial environments and critical infrastructures thanks to its positive impact on performance and efficiency. Among these environments, the Smart Grid (SG) excels as the perfect host for this technology, mainly due to its potential to become the motor of the rest of electrically-dependent infrastructures. To make this SG-oriented IoT cost-effective, most deployments employ unlicensed ISM bands, specifically the 2400 MHz one, due to its extended communication bandwidth in comparison with lower bands. This band has been extensively used for years by Wireless Sensor Networks (WSN) and Mobile Ad-hoc Networks (MANET), from which the IoT technologically inherits. However, this work questions and evaluates the suitability of such a \"default\" communication band in SG environments, compared with the 915 MHz ISM band. A comprehensive quantitative comparison of these bands has been accomplished in terms of: power consumption, average network delay, and packet reception rate. To allow such a study, a dual-band propagation model specifically designed for the SG has been derived, tested, and incorporated into the well-known TOSSIM simulator. Simulation results reveal that only in the absence of other 2400 MHz interfering devices (such as WiFi or Bluetooth) or in small networks, is the 2400 MHz band the best option. In any other case, SG-oriented IoT quantitatively perform better if operating in the 915 MHz band.  
smart grid; internet of things; wireless sensor networks; evaluation; propagation +
Footnote †: journal: Sensors
## 7 Conclusions  
This paper was dedicated to solving the network resource allocation problem in static SCNs. First, the network resource allocation problem in SCNs was defined as the RMSSA problem. Flexible MFs for SCNs were studied and MFC is applied for the first time to solve the RMSSA problem. Then, the RMSSA problem in static SCNs was modeled and the heuristic LBMSA algorithm was proposed to solve the RMSSA problem. Finally, the LBMSA was evaluated in three different network topologies and both the KSP algorithm and the KSP-CNs algorithm were used to compare with the LBMSA algorithm to demonstrate the effectiveness of the algorithm. The simulation results show that when the total throughput of SCNs is less than 350 Tb/s, the LBMSA algorithm can traffic requests with the least amount of SBs. When the total network throughput is larger, it is not always possible to maximize network resource utilization. This is because the LBMSA algorithm prefers to carry all traffic requests rather than efficiently transmit them. Moreover, by reasonably setting CNs to execute MFC, the LBMSA algorithm will select the optimal MF option. This not only effectively solves the RMSSA problem, but also saves at least 20% of the network resources, further improving the network resource utilization. However, this paper still has some limitations. In this paper, the complexity and the economic costs of deploying transceivers, as well as the tradeoff between spectrum block, SLs, and transceiver utilization, have not been considered. In addition, this paper only studies the resource allocation problem in static SCNs, and only considers reducing the resource occupation of traffic requests and improving network resource utilization. The more complex problem of resource allocation in dynamic SCNs, which is dedicated to reducing traffic request blocking rate, will be investigated in our future work.  
Conceptualization, methodology, formal analysis, investigation, data curation, writing--original draft preparation, writing--review and editing, X.Y.; software, validation, X.Y. and Y.Z.; supervision, project administration, Q.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData available upon requestThe data presented in this study are available on request from the corresponding author. The data are not publicly available due to our further study on these data is needed.  
The authors declare no conflict of interest.  
Figure 8: Percentage of traffic requests performing MFC.
# Building Wireless Control Applications with XBee and LabVIEW
###### Abstract  
This study analyzed the application of the XBee technology for control applications embedded in big machinery. In particular, it presented the control layout, topology, design of frames, and error control mechanisms that are required for implementing wireless control networked applications. This study was devoted to presenting experimental use cases of wireless control systems that help the scientific community to design and implement new control systems. Experimental tests were carried out over a Single Input Single Output (SISO) control application to evaluate its use in close loop applications. The results of these tests have been discussed in detail. In order to clarify the use of XBee in control applications, a high precision positioning application has been presented. It involved a piezoelectric actuator that was remotely controlled by means of a Proportional Integral (PI) controller, which was implemented in LabVIEW over a NI myRIO platform. Both the sensor and the actuators were connected with the NI myRIO by means of the XBee antennae. The results showed that even though XBee technologies present some drawbacks, such as a lower performance when compared to wired connections and the influence of interferences, they have the potential to be used in some scenarios for non-critical control applications.  
XBee; LabVIEW; wireless control; piezoelectric actuator; precise positioning Article