esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results by Domain
Searching for terms related to the Delivery domain resulted in the following three clusters (Figure 9):  
1. The cluster colored in red in the figure focuses on the requirements, design, and development of the software and the control and assurance of its quality;  
Figure 8: Clusters identified in the Work domain. Figure 7: Clusters identified in the Planning domain. 2. The terms that appear in the second cluster (knowledge management, sustainable development, and construction industry, among others) are similar to those that define groups in other previous domains;
3. Decision support systems, human resources, and information management, together with product development, make up the last cluster, shown in blue in the figure. Analysis of the bibliographic results obtained related to the Measurement domain search gave rise to the following five clusters:  
1. Decision making in the construction industry seems to be the area of interest of the first cluster;
2. Different topics involved in the evaluation of the performance and development of projects such as knowledge management or risk evaluation give rise to the formation of the second cluster;
3. The cluster colored in blue in Figure 10 is configured around forecast, relating to topics such as information management, neural networks, and quality control, in the field of industrial research;
4. The fourth cluster is defined by different tools such as fuzzy logic or learning systems in the field of software design and engineering;
5. Sustainable product development and energy efficiency are the terms included in the fifth cluster, in purple in Figure 10.  
The last domain corresponds to \"Uncertainty\", and Figure 11 shows the following four clusters:  
1. The different aspects related to risks (perception, analysis, and management) in the field of computer software (design, development, and management) make up the first cluster of this domain. Techniques such as Bayesian networks also appear to be prominent;
2. Cluster 2 is made up of terms that are very similar to others that have already been repeated in other domains, related to information and knowledge management in the construction industry;
3. The terms related to decision making in a context of uncertainty give rise to the third cluster;
4. Cluster 4 is defined by a single term, \"sustainable development\", connected to others that appear in cluster 2. Figure 9: Clusters identified in the Delivery domain.
## 4 Environment Perception, Mapping, and Representation for Locomotion  
In order for robots to be able to operate autonomously and interact with the environment in any of the ways mentioned in Section 3 (obstacle avoidance, obstacle accommodation, or obstacle-aided locomotion), they need to acquire information about the environment that can be used to plan their actions accordingly. This task can be divided into three different challenges that need to be solved:  
1. _sensing_, using the adequate sensor or sensor combinations to capture information about the environment;
2. _mapping_, which combines and organises the sensing output in order to create a representation that can be exploited for the specific task to be performed by the robot;
3. _localisation_, which estimates the robot's pose in the environment representation according to the sensor inputs.  
These topics are well studied for different types of robots and environments, and are tackled by the Simultaneous Localisation And Mapping (SLAM) community which has been the foremost research area for the last years in robotics [51]. However, comparatively little work has been done in this field for snake robots, as research has been focused on understanding the fundamentals of snake locomotion, and on the development of the control techniques.  
Table 2 summarises the sensors most commonly found in the robotics literature for environment perception aimed at navigation. The table also contains some basic evaluation of the suitability of the specific sensor or sensing technology for the requirements and limitations of snake robots. For the sake of completeness, we have included references to representative robots different from snake robots for those technologies where we were not able to find examples of applications involving snake robots.
## 5 Conclusions  
In recent years, a number of studies on underwater communication with the LPI characteristic have been carried out for individual and for military purposes. Previous studies on covert underwater communication have focused only on improving the transmission rate at the time varying underwater channel or increasing the transmission and receiver distance, and few studies have been conducted on transmission performance in an environment in which covertness must be emphasized. Thus, this paper proposed a transmission and receive model by applying the direct sequence spread spectrum method for LPI covert underwater communication. The RAKE receive structure was applied to the iterative decoding-based turbo equalization model to propose an efficient decoding mode, and performance was improved by applying the threshold value and weighted coefficient. Through actual underwater experiments according to by the number of PN code bits, with regard to packets whose data were all decoded, we confirmed turbo equalized RAKE model based on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**PN Code Bits**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Packet Index**} \\ \cline{3-5}  & & **2** & **3** & **8** \\ \hline \multirow{6}{*}{\(N_{T_{c}}=16\)} & Only RAKE receiver & \multirow{2}{*}{\(4.88\times 10^{-1}\)} & \multirow{2}{*}{\(4.78\times 10^{-1}\)} & \multirow{2}{*}{\(4.76\times 10^{-1}\)} \\  & RAKE receiver & & & \\ \cline{1-1}  & with threshold & & & \\ \cline{1-1}  & RAKE receiver & & & \\ \cline{1-1}  & with threshold & & & \\ \cline{1-1}  & and weighted & & & \\ \cline{1-1}  & coefficient & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bit error rate for two users.  
Figure 6: Channel impulse response: (**a**) Ch-1; (**b**) Ch-2.  
threshold value and weighted coefficient proposed in this paper has the best performance compared to conventional algorithms.  
Furthermore, not only single sensor communication system but also multi-sensor transceiver structure was proposed based on the covert underwater communication study. As same as single user case, the results of the underwater experiments verified that performance of RAKE-based turbo equalization model proposed in this paper better than that of the conventional RAKE receive model.  
In conclusion, the proposed covert underwater communication model can be effectively utilized the underwater channel environment such as multiple paths by maintaining covertness, as verified in the simulation and underwater experiments.
## 4 Experiment and Analysis
### Ablation Experiments
#### 4.3.2 Ablation Experiment on Corrupted Datasets
These results prove that the MSE module's ability to extract critical features from multi-scale feature maps of forged images, effectively improves the detection ability of the model on corrupted images. While introducing the Aug and MSE modules simultaneously, the average Acc of the baseline on these corrupted datasets has increased by 24.35%, and the average AUC has increased by 0.265. The improved models that are pre-trained on enhanced data can learn the forgery traces that are not changed due to interference in the forged image. The two modules complement each other to improve the robustness of detection models. The quality of images in FaceShifer is better than the first two. There needs to be more than the mode and degree of corruption set in this experiment to affect the detection performance seriously. The above experimental results prove the effectiveness and robustness of the algorithm proposed in this paper.
# Fault Diagnosis in Solar Array I-V Curves Using Characteristic Simulation and Multi-Input Models
## 2 Current-Voltage Curves
### PID (Potential Induced Degradation) Faults  
In solar systems using transformerless inverters, there is no electrical isolation, and the negative pole of the solar module string does not need to be grounded. In grid-connected solar systems, solar modules are typically connected in series to form high-voltage outputs, and the module frames are grounded for safety reasons. This results in floating potentials; half of the modules are under positive bias and half under negative bias. The potential difference causes leakage currents to flow from the module frames to the solar cells. Under external voltage, sodium ions in the glass migrate and accumulate on or enter the surface of the cells, causing shunting and reducing their efficiency. PID faults occur after long-term operation of the module [17].  
When a solar module exhibits PID faults, characteristics such as decreased \(R_{sl}\), increased \(R_{s}\), and reduced open-circuit voltage (\(V_{oc}\)) appear. The current-voltage (I-V) curve will show characteristics as depicted in Figure 9. By paralleling a resistor and a diode with the solar cell module and adjusting the parallel resistance, internal \(R_{s}\), and \(V_{oc}\) parameters of the solar cell module as shown in Figure 10, the I-V curve of a PID fault is simulated as shown in Figure 11.  
Figure 8: Shading fault current–voltage curve.  
Figure 9: Current–voltage curve of normal and PID faulted solar modules [18].
# Wireless Mesh Networking: An IoT-Oriented Perspective Survey on Relevant Technologies
## 3 Mesh in IEEE 802.11 Networks
### Standard: IEEE 802.11s
#### 3.1.2 IEEE 802.11s Topology Formation  
The topology formation of an IEEE 802.11s-based network is based on the transmission of messages, called beacons, carrying information about the network itself. As in other routing protocols, the first phase of the network creation is the discovery phase, which follows two possible approaches: (i) passive scanning of the beacon frames or (ii) active scanning probe frames. During the discovery procedure, each mesh STA transmits beacon frames--which are used also for topology maintenance and synchronization--and responds with probe response frames when a probe request frame is received. In this way, the neighbors of the mesh STA become aware of the presence of a particular mesh node around them. One of the most important value that is carried in the beacons and in the probe response frames is the Mesh ID, which is used to identify the mesh BSS. Once a mesh station identify the correct node, the link is established and maintained through the Mesh Peer (Link) Management (MPM) protocol [8], until mesh stations are in visibility and share a common profile.  
Figure 1: Architecture of MBSS containing mesh STAs, APs and portals as designed in IEEE 802.11s standard [8].
# Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
## 2 Mathematical Models of the System Components
### PV System  
A \(PV\) system is composed of one or more solar panels integrated with inverter or other electrical and mechanical hardware, using energy from the Sun to generate electricity. The output power of the \(PV\) system is strongly affected by the amount of solar radiation and the ambient temperature. The expression for the \(PV\)-generated power is as follows [22]:  
\[P_{PV}=V_{pv}I_{pv}=I_{pv}\bigg{\{}\frac{q}{AKT}ln\bigg{(}\frac{I_{ph}-I_{pv}+ I_{pv0}}{I_{pv0}}\bigg{)}-I_{pv}R_{s}\bigg{\}} \tag{1}\]  
where \(k\) is the Boltzmann constant, \(A\) is the non-ideality factor, \(q\) is the electron charge, \(T\) is temperature, \(q\) is the light-generated current, \(I_{pv0}\) is the dark saturation current, and \(R_{s}\) is the series resistance.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 5 Conclusions, Open Problems, and Future Directions  
The use of UAVs is a promising approach for improving the QoS of 5G RANs due to their inherent ability to form LoS radio links in highly dynamic environments. However, design and deployment of U-RANs is a challenging problem, which cannot be easily solved by conventional model-based techniques. In this respect, ML-based solutions are expected to be a viable alternative, which is able to properly take into account all the valuable information and parameters of the problem. In this paper, the advantages and potentials of using ML algorithms in U-RANs have been discussed, in a number of different network design scenarios that cannot be easily solved by conventional model-based methods. A summary of the considered applications and results of the survey is reported in Table 2.  
To meet the stringent requirements for 5G and beyond systems, additional parameters must be used in ML algorithms for U-RANs design, such as latency and resiliency, as well as additional figures of merit based not only on QoS but also on the quality of experience (QoE) of the users. It is worth mentioning that the performance of ML algorithms may be degraded or might exhibit unexpected behaviors when the obtained solutions operate on data having different properties than those of the data used for training the model. Such issues deserve further investigations.  
Specific issues for future works include: (a) design of ML-based cross-layer algorithms; (b) integration of multiple antennas devices in ML solutions; (c) developing ML approaches explicitly accounting for security aspects; and (d) investigation of pros and cons of unsupervised learning procedures.  
Conceptualization, V.K., F.V., G.G., and J.A.; methodology, V.K. and F.V.; writing--original draft preparation, V.K. and J.A.; writing--review and editing, F.V. and G.G.; supervision, F.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Using ChatGPT in Software Requirements Engineering: A Comprehensive Review
## 6 Limitations and Risks of Using ChatGPT
### Addressing Bias in GenAI Systems  
GenAI systems are susceptible to incorporating biases present in their training data. This bias can lead to results that perpetuate discrimination or turn out to be misleading interpretations, which can lead to dissent and public disapproval at the political, social justice, and legal levels [30]. Manifestations of bias include the following:  
* **Training data bias:** Language models learn from large datasets of human language. If these datasets contain biases related to race, gender, or socioeconomic status, the model may internalize these biases and reproduce them in its responses. A classic example might be that if there is a gender bias in the training data, the model is more likely to favor a particular gender in its responses.
* **User interaction bias:** Responses generated by chatbots are influenced by user input. The model can learn and perpetuate these biases if users consistently ask biased or prejudiced questions. Consequently, if users frequently ask discriminatory questions that target a particular group, the model may generate responses that reinforce these biases.
* **Algorithmic bias:** Bias can arise from the algorithms used to train and run language models and chatbots. For example, suppose a model is optimized to achieve accuracy or engagement as its performance metric. In this case, it may favor responses that meet that metric, even though those responses may also be biased.
* **Contextual bias:** Contextual bias is possible when chatbots generate responses based on contextual information provided by the user. The model could generate biased responses if factors such as language or location are biased. For example, if a user asks about a particular culture or religion, and the model has not been trained in that particular context, it could potentially provide biased responses due to limited knowledge.  
Although ChatGPT provides good accuracy and efficiency in generating software requirements, there is still the possibility that the generated results contain biases [26]. Also, the use of specific data sets to train the model can lead to bias in the results [18]. To help mitigate these biases, increasing the model's training data is a viable solution. Furthermore, developing human supervision mechanisms to identify and correct incorrect requirements can also be an effective bias mitigation strategy [1; 3; 26].  
It is also important to consider bias in GenAI systems so that the requirements and specifications generated by tools such as ChatGPT are based on an ethical and professional context.
# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
## 2 Materials and Methods
### Feature Fusion Method Based on Attention Mechanisms  
For metal corrosion area extraction, in deep neural networks, shallow layers usually capture fine-grained features such as textures and edges, while deeper layers are better at capturing abstract high-level features. By using feature fusion, features from different layers can be combined, allowing the model to more comprehensively understand the input data and thus improve network performance.  
In the field of image processing, attention mechanisms have been widely used because they guide the model to focus on key parts of the input image, proving their effectiveness in various tasks. [PERSON] et al. proposed a lightweight Squeeze-and-Excitation (SE) module that enhances the weights of useful features in the feature map and models the interdependencies between feature channels, thereby improving the network's representation capability [20]. The SE module consists of three steps: squeeze, excitation, and activation. The structure of the module is shown in Figure 6.  
The operation process of the SE module is as follows:  
\[f_{c}=\sigma(FC(\Delta,\frac{1}{H\times W}{\sum_{h,w}{X_{h,w,c}}})) \tag{6}\]  
\[X^{\prime}c=f_{c}\cdot X_{c} \tag{7}\]  
In the formula, \(X_{c}\) represents the original input of the module, \(X_{h,w,c}\) represents the feature value of channel C, and \(X^{\prime}c\) represents the output of the module. \(\sigma\) denotes the  
Figure 6: SE  
sigmoid function, which is used to compress the attention values of the channels to a range between 0 and 1. \(\Delta\) represents the parameters during the fully connected operation.  
Based on the SE module, this paper integrates features from both the convolutional encoder and the decoder at different levels to improve the performance of metal corrosion area segmentation. The basic process is shown in Figure 7, where the features from the decoder are upsampled, and the transition connection features are input into the SE module. The processed features are then concatenated along the channel dimension, and a convolution block operation is performed to adjust the feature channels to the set output channels.  
By introducing this attention-based decoding module, which facilitates feature fusion, the model's understanding of multi-level features is enhanced, which is particularly important for image segmentation tasks such as metal corrosion detection. Additionally, deep networks often encounter issues like vanishing or exploding gradients. In the application of the SE module, residual connections are introduced, which help maintain the stability of the information flow, making deep network training more reliable and efficient.
## 2 VLC System Model
### Transmitter  
The objective of the proposed solution is to take advantage of the lighting infrastructure in hospitals. Therefore, the proposed solution uses three optical transmitters, each of which consists of a single LED. These light sources emit different wavelengths that are suitable for the hospital environment. These wavelengths must have enough separation between them so that the optical receiver can distinguish between the different LEDs. This difference between wavelengths provides an advantage for transmitter identification. Therefore, there is no need to transmit the identification of the LED as data. However, it is necessary to keep the LEDs continuously emitting light at a constant power. Using various wavelengths in the transmitters offers a significant benefit as it eliminates the need for data transmission to identify each LED. This means that the hardware required for light modulation is not necessary, simplifying the system's design and reducing its complexity.  
It is important to mention that, for the localization of the receivers, it is essential to know the positions of the transmitters in the scenario. Figure 1 illustrates the localization system, where each LED emits light that is received by a specific Photo-Diode (PD) in the optical reception stage. For practical purposes, it is assumed that the three LEDs are oriented in a vertical direction, pointing toward the floor of the room.  
In terms of the wavelength features to be used, the allocation mechanism adopted by the CSK modulation is implemented, which will be explained below.
# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 4 Results and Discussion
### Comparison with Related Works  
In recent years, several attempts have been made to develop computer-assisted diagnosis of ASD patients. In general, these studies have focused on rs-fMRI and related FC features with classification using classical ML techniques. The application of FC image-based deep learning algorithms for ASD diagnosis has recently shifted the focus of research, including in our work. To demonstrate the feasibility of our proposed framework, Table 5 compares the performance of existing models in ASD classification using the ABIDE dataset.  
Good classification accuracies for the diagnosis of ASD based on SFC images have been obtained in previous studies [17; 18] and are comparable to our results using SFC images with the proposed 3L-CNN model. However, these results are inferior to our results using dynamic FC images. Our proposed method using PWC images of ASD biological markers in CNN results in good accuracy of 95.2% based on 10-fold CV, e.g., 25.2%, 25% and 18.8% higher, respectively, than the methods proposed in [16; 17; 18]. On the other hand, our results outperform those of [PERSON] et al. [22] by 2%. Accordingly, the dynamic FC features using PWC images play an important role in developing ASD classification performance compared to static FC features, which have a relatively small impact on ASD detection. Furthermore, the ASD biomarkers identified in our study can be utilized individually for ASD diagnosis. In conclusion, the PWC-based approach proposed in this study can help physicians with automatic and accurate detection of ASD in patients.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **References** & **Sample Size** & **FC Patterns** & **AI Algorithms** & **Accuracy** \\ \hline
1 & [PERSON] (2016) [19] & 368 & Dynamic & SVM & 10-fold CV & 79.2\% \\
2 & [PERSON] (2018) [20] & 29 & Dynamic & SVM & 10-fold CV & 86.7\% \\
3 & [PERSON] (2018) [16] & 1035 & Static & DNN & 10-fold CV & 70.0\% \\
4 & [PERSON] (2019) [22] & 1054 & Static & DNN & 10-fold CV & 93.2\% \\
5 & [PERSON] (2020) [17] & 1035 & Static & CNN & 10-fold CV & 70.2\% \\
6 & [PERSON] (2020) [18] & 1035 & Static & DBN & 10-fold CV & 76.4\% \\
7 & [PERSON] (2021) [21] & 90 & Dynamic & SVM & 10-fold CV & 78.9\% \\
8 & [PERSON] (2021) [23] & 866 & Static & DNN & 5-fold CV & 87.9\% \\
9 & Our method & 1035 & Dynamic & CNN & 10-fold CV & 95.2\% \\ \hline \end{tabular}
\end{table}
Table 5: Benchmarking with existing ASD classification methods using ABIDE dataset.  
Figure 9: Confusion matrix for classification of PWC images of ASD and NC using the testing dataset.