esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Comparison of Solutions  
In this paper, a robotic tool feeding assistant is proposed using an ABB Yumi two-arm collaborative robot, an RGBD camera, and a web camera. The workplace was programmed in ROS 2, while the robot receives commands from the operator using static hand gestures captured via a web camera. The tools on the table were captured using an RGBD camera.  
A similar solution was used in [56] with the help of a robotic workplace with a single-arm collaborative robot KUKA iiwa. The purpose of the robotic workplace was to feed tools and parts and also to hold the workpiece during assembly. The robot is controlled using static and dynamic gestures, while these gestures are recorded using five IMU sensors (Tech-MCS V3) mounted on the hands and torso of the operator's body. The operator is also equipped with a UWB (ultra wideband) broadband tag. It ensures the sensing of the operator's distance from the robot. The proposed solutions vary in the choice of collaborative robot, gesture sensing method, and tool detection capabilities. Results indicate that proposed implementation of gesture control, particularly in well-lit environments, offers a more intuitive and efficient interaction with the robot.  
A similar solution was proposed in [57]. The authors proposed a robot control method using the same IMU sensors (Tech-MCS V3) mounted on the operator's arms and torso as the authors in [56]. To capture finger and wrist movements, the operator wore a Myo armband sensor strip equipped with eight EMG sensors. The control scheme employed ten static and one dynamic gesture. The workspace served as both a platform for tool and part placement and a holding area for workpieces. Similarly, the findings suggest that the proposed robot control implementation is more efficient and intuitive in well-lit work environments.  
Finally, a CyberGlove 2 and a Polhemus Liberty magnetic tracker to control a robot in a workspace similar to [56] was employed in [58]. The UC2017 dataset, presented in [58], contains static and dynamic hand gestures captured using the sensor glove. While glove-based control offers a wider range of gestures, suitable for complex tasks like food preparation, it limits the operator to one free hand, potentially hindering manual work.
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 4 Simulation and Performance
### Performance Metrics
#### 4.1.2 Computational Complexity  
The initial step of forming the Toeplitz matrix requires a complexity of \(O(N^{2})\) operations, and the economy SVD rank reduction step requires \(O(N^{2}k)\) operations. The multiplication of the left singular vectors with the transmitted signal is of size \((r-N)\times N\). The convex relaxation requires a complexity of \(O(N^{3})\) operations. In the worst case the complexity of the whole algorithm is \(O(N^{3})\), and this is still within an acceptable range.
## Materials and Methods  
### Equivalent Radial Stiffness Theory  
[PERSON] and [PERSON] introduced the plane strain analysis of thick-walled cylinder theory to the study of rockbolting mechanics [34]. [PERSON] et al. further applied this method to investigate the stress distribution of the bolt subjected to axial loading and established a framework of rockbolting in field conditions, a theoretical model, and corresponding laboratory configuration (Figure 1) [35]. For laboratory testing to reflect the real situation, a confining tube with the same radial stiffness to the surrounding rock should be selected to prepare the testing sample.  
Under plane stress analysis, according to [PERSON]'s equation, the radial stiffness of a tube and the infinite surrounding rock can be expressed as [36]:  
\[S_{t}=\frac{E\left(b^{2}-a^{2}\right)}{a^{3}(1-v)+ab^{2}(1+v)} \tag{1}\]  
\[S_{r}=\frac{E_{r}}{a(1+v_{r})} \tag{2}\]  
where  
\(E\) is the elastic modulus of tube material, MPa;  
Figure 1: Rockbolting models, modified [34].  
\(v\) is Poisson's ratio of the tube material;  
\(a\) is the inner tube diameter (borehole radius), mm;  
\(b\) is the outer tube diameter, mm;  
\(E_{r}\) is the elastic modulus of the surrounding rock, MPa; and  
\(v_{r}\) is [PERSON]'s ratio of the surrounding rock.  
For a specific mining site or roadway, the equivalent radial stiffness principle should be implemented in laboratory SEPT design, i.e.,  
\[S_{t}=S_{r} \tag{3}\]
# Advances, Progress, and Future Directions of Renewable Wind Energy in Brazil (2000-2025-2050)
###### Abstract
The expansion of renewable energy must address critical issues, such as the need to modernize transmission and distribution infrastructure, ensure the fair share of benefits for local communities from these projects, and minimize the environmental impacts associated with installing wind and solar farms. Furthermore, this transition must be carried out inclusively, ensuring that the economic and social benefits reach all sectors of the population, especially those historically marginalized. The introduction of renewable energy in Brazil not only represents a technical response to the climate crisis, but also an opportunity to redefine the country's development model [13]. By harnessing its clean energy potential, Brazil can reduce its dependence on fossil fuels, strengthen its energy security, and significantly contribute to mitigating climate change. At the same time, this process can boost the creation of green jobs, foster technological innovation, and promote social inclusion, aligning with the United Nations Sustainable Development Goals (SDGs) [14]. In this sense, the energy transition in Brazil should not be understood solely as a technological change, but as a comprehensive process involving all sectors of society [15]. From public policies to private initiatives, including the active participation of citizens, building a sustainable energy system requires a collective effort and a long-term vision. Brazil has the opportunity to establish itself as a global leader in the transition to a low-carbon economy, demonstrating that it is possible to reconcile economic development with environmental protection and social justice [16]. This path will benefit the country and serve as an inspiring model for other nations in their pursuit of a more sustainable and equitable future. Brazil is prominent in the ranking of global installed onshore wind power capacity. According to 2023 data (Figure 1), the country is among the top ten in the world, alongside powerhouses such as China, the United States, Germany, and India. China has an installed capacity exceeding 400 GW, followed by the United States with over 150 GW. Brazil, with over 30 GW, is in sixth place, depending on annual fluctuations in other countries [17]. In the Latin American context, Brazil is by far the undisputed leader in wind energy. It represents more than 70% of the region's installed capacity, far surpassing countries such as Mexico, Chile, and Argentina, which have also advanced in this sector, albeit at a slower pace [17]. This regional leadership is due to its installed capacity, its experience in managing large-scale wind projects, and its ability to efficiently integrate this energy into its electricity grid.
# Influence of Hyperparameters in Deep Learning Models for Coffee Rust Detection
## 3 Methodology
### Hyperparameter Selection  
This is the most important phase of a model-centered methodology. First, when selecting the hyperparameters for the study, both architecture and training hyperparameters should be considered. According to state-of-the-art publications, the most commonly used architectural hyperparameters are the type of network, the depth of the network, and the number of neurons in the FC layers. On the other hand, at the level of training hyperparameters, the most used are the optimizer and its learning rate. Second, for each hyperparameter, the number of options and their corresponding values must be defined. At this stage, it is recommended to evaluate at least three options per hyperparameter. In the case of network type, it is common to select pre-trained models of different architecture types, for example, sequential, parallel, residual, and sparse convolutions. For the depth of the net, it is possible to select a shallow layer, a medium depth layer or a deep layer. Regarding the number of neurons in the fully connected layer prior to the classification layer, it typically ranges from 256 to 1024 neurons. In the case of the Optimizer, options such as Adam (or one of its variations), SGD, and RMSprop are usually chosen. Finally, in the case of the Learning Rate, values close to the default values are usually selected in each optimizer, varying them in powers of 10 (positive and negative).  
For the search and selection of hyperparameters, a first alternative is to use an algorithm to automatically search the entire space of possible configurations, which is not practical because the space of possible configurations is extremely large. The second alternative is to start with a base configuration and add features based on solid testing, constantly updating a more limited search space. Each round of experiments should have a clear objective and be narrow in order to achieve the goal [42].  
Accordingly, the objectives of each round of experiments conducted in this research were assigned as follows: evaluation by pre-trained model used as backbone (for eight pre-trained models), evaluation by the optimizer (between three optimizers), evaluation by the learning rate (LR, using three values), evaluation by the number of neurons in the top FC layer (three values) and evaluation by the network depth (four network depths) (see Table 4). Although a greater number of options per hyperparameter allows a better selection of its value, it must be taken into account that the number of models to be trained is equal to the multiplication of the options of each hyperparameter. For this study, 864 models were trained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classes** & **Training** & **Validation** & **Testing** \\ \hline Healthy & 1378 & 146 & 162 \\ Other & 1378 & 176 & 202 \\ Rust & 1566 & 159 & 170 \\ \hline
**Total** & 4322 & 481 & 534 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the distribution of images in the proposed dataset.
## 6 Solutions for Security and Trust in IoV
### Machine Learning-Based Solutions for Security  
The massive production and use of digital gadgets has led to tremendous growth in IoT usage. The IoT is currently being used in industries, transport systems, power systems, and agriculture, among other domains; this growth in usage, however, has also brought about myriad security problems. The IoT uses a variety of advanced technologies such as Bluetooth, RFIDs, wireless sensor networks, cloud computing, and Zigbee, among many other systems. As such, it is challenging to protect all these networks against malware attacks, eavesdropping, DoS attacks, and virus injection [76; 130]. ML has developed as a security-enhancing technology in recent years. A growing number of studies has established that there has been an increase in ML-based contributions to network security over the past few years. The five most frequently explored security fields are software protection, malware detection, reaction policy, game protection, and biometric authentication. Of all these fields, malware/intrusion detection is the most heavily researched. Moreover, autonomous vehicles and wireless networks are among the most frequently addressed targets of attacks. The IoT connects millions of devices. These devices are dynamic and result in a complex network. It is for this reason that ML techniques are used for detecting intrusions, CPS attack detection, malware detection, privacy maintenance, and trust. These elaborate techniques are ineffective, however, in performing dynamic responses as far as the IoT is concerned [131; 132; 133; 134]. In this survey, ML-based trust management schemes and security solutions are classified. We present trust and security solutions for IoV environments, and we classify them based on the three types of ML models: supervised learning, unsupervised learning, and RL.
## 5 Results and Discussion
### Dynamic Storm Analysis  
Due to the height of the structure, as well as its stiffness and mass distribution, it can be classified as an \"inverted pendulum\" system, meaning that a significant portion of its mass is concentrated at the top. Consequently, its dynamic behavior exhibits a pronounced tendency to \"pitch\" when subjected to wave loads. When waves impact the structure, it deforms in the same direction as the wave propagation.  
The Utilization Coefficient (U.C.) stress interaction ratios were assessed across different structural components. The maximum U.C. in the substructure (jacket) was found to be 0.987, while in the superstructure, it reached 0.638. In both cases, the U.C. values  
Figure 6: Summary of static storm and operation analysis results.  
remain below unity, thereby demonstrating compliance with the applicable regulatory requirements (Figure 7).  
Regarding tubular joint penetration, evaluations were performed for both penetration under loading conditions and structural resistance (STRN). The corresponding U.C. values were 0.777 and 0.969, respectively. Since these values remain below unity, the tubular joints conform to the current design standards.  
For piles and load safety factors in the foundation, structural analyses were conducted to verify compliance. The maximum U.C. for piles above the seabed (mudline) was 0.81, while for piles below the seabed, it was 0.79. Both values are below unity, indicating that the pile system operates within safe stress limits. Additionally, the safety factor for axial load during storm conditions was determined to be 1.52, which exceeds the minimum required value of 1.50, further ensuring that the foundation meets the criteria established by the current design codes. The results are summarized in Figure 7.  
The results indicate that the dynamic analysis yields more critical conditions compared to static operational and storm analyses. This discrepancy arises from the explicit consideration of the platform's dynamic response, including its vibration modes and natural periods.
## 6 Experimental Results and Evaluations
### Syntax Error Assessment and Detection  
A syntax error is an error where the program violates a structural rule of a certain programming language. To compile, source code must follow the structural rules of a programming language, if it does not, the compiler will output syntax errors. Common examples of syntax error include misspelled keywords, missing single or double quotes, missing matching brackets, and a missing semicolon at the end of a statement. To assess and detect syntax errors in source code, the proposed LSTM-AttM language model calculates the error probability of each error candidate word. The error probability determines the possibility of syntax errors in source code. The proposed model assesses the source code thoroughly and detects syntax error candidates, as shown in Figure 7.  
In the figure, the proposed LSTM-AttM model assesses the source code and estimates the error probability for each error candidate word. The corresponding location (line number) of each detected word is listed in Table 3. The error probability determines the syntax error possibility for a particular candidate word and location in the source code. Although the model detected all the potential locations of syntax error, the detected error candidates might not have all been accurately identified. Words with an error probability of more than 0.98 are outlined in blue in Figure 7. We considered these errors to be confirmed syntax errors.  
To compare our model with baseline models, in addition to the above-mentioned example (Figure 7) a large number of erroneous source code samples were used for the evaluation process.  
Figure 7: Syntax error assessment and detection for source code evaluated using LSTM-AttM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Line Number** &
\begin{tabular}{c} **Error Candidate** \\ **(Probability \textless{} 0.1)** \\ \end{tabular} & **Suggested Word** & **Estimated Error Probability** \\ \hline
8 & n & \& **0.999918** \\
13 & 1 & 0 & 0.60096426 \\
17 & n & key & 0.7747942 \\
19 & i & n & 0.4699676 \\
20 & ( & \& **0.98158526** \\
22 & 0 & 1 & **0.9885606** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimated error probability for source code in Figure 7.  
The obtained results are listed in Table 4. The syntax error assessment and detection accuracy results for the proposed model for all problem sets are better than those for the state-of-the-art models.
## 4 Experiment Results and Comparison
### The Experimental Setup
#### 4.4.2 The candidate Classifiers  
The labeling process is based on ensemble supervised learning algorithms; therefore, the type and the number of the supervised algorithms that are involved in this labeling process are left for the implementer. In this paper, a thorough investigation was conducted of a number of supervised algorithms. We concluded that the most three efficient supervised algorithms are the random forest classifier (RFC) [35], Ada Boost [36], and decision tree [37].
## 4 Determination of the Chemical Composition of Theabrownins
### Detection of tea Polyphenols in Theabrownins  
1. Preparation of gallic acid standard curve: Gallic acid standard solutions were prepared with concentrations of 10, 20, 30, 40, and 50 \(\mu\)g/mL. In total, 10% Folin phenol reagent was added to 1.0 mL of the standard solutions and shaken. After reacting for 5 min, 4 mL of 7.5% Na\({}_{2}\)CO\({}_{3}\) solution was added and shaken for 1 h at room temperature. The absorbance at 765 nm was measured with a glass cuvette, and the standard curve was drawn.
2. Detection of total phenolic content in theabrownins: 1 mL of the prepared 0.2 mg/mL theabrownins solution was transferred to a test tube. The subsequent steps were performed according to the above detection steps. This was calculated as follows: \[\text{Total phenolic content}\left(\%\right)=\frac{A\times V\times d}{SLOPE_{std }\times m\times 10^{6}\times w}\times 100\] where \(A\) is the absorbance of the sample test solution (\(\mu\)g/mL), \(V\) is the sample volume (mL), \(d\) is dilution factor (1 mL was typically diluted to 100 mL--a dilution factor of 100), _SLOPEstd_ is the gallic acid standard curve slope, \(w\) is the sample dry matter content (%), and \(m\) is the sample mass (g).
# Secure Automatic Identification System (SecAIS): Proof-of-Concept Implementation
## 5 How the SecAIS Works
### SecAIS Source Authentication
#### 5.1.2 Encapsulating the Signature in Conventional ID8 AIS Messages  
Next, the SecAIS-App of the Transmitter encapsulates the signature and the original AIS message into two conventional ID8 AIS messages. This is carried out in three steps, as follows:  
1. Encapsulate the PVT part of the signature inside the first conventional ID8 AIS message, as shown in Figure 5;
2. Encapsulate the original signed message, the timestamp, and the parameters R and S of the signature inside the second conventional ID8 AIS message, as shown in Figure 6;
3. Perform a validation check of the correctness of the signature before broadcasting the two ID8 AIS messages, as shown in Figure 7.  
The time taken to complete the above process is negligible: in our experiments, it took less than a second to complete on a PC with the following characteristics: processor--Intel Xeon(r); CPU--E5-1620 Vv2 @ 3.70 GHz; RAM--16 GB; OS--64-bit Windows 10 Pro. Specifically, the process started at 2019-06-12T10:00:47.865 (Figure 4 (part B)) and ended at 10:00:48.188 (Figure 7).  
Figure 5: ID8 AIS message that encapsulates the PVT part of the signature.  
Figure 6: ID8 AIS message that encapsulates the original signed message, the added timestamp, and the variable cryptographic parameters of the signature.  
Figure 7: Timestamp (**A**) and signature validity check (**B**).
# Real-Time Guitar Amplifier Emulation with Deep Learning +
## 4 Validation
### Visual Analysis  
Figure 15 shows a short segment of waveform output by the HT5M amplifier, plotted with the output predicted by the WaveNet3 and RNN-96 models. Additionally, Figure 16 plots the target spectrum produced by the HT5M amplifier, and the spectrum of the WaveNet3 and RNN-96 predictions. The plots in Figure 15a and in Figure 16(middle) clearly show that in this case the WaveNet3 produces a very close match to the target. For the RNN the prediction is not as accurate, as is apparent in Figure 15b and Figure 16(bottom).  
Figure 14: Mean results of MUSHRA test with 95% confidence interval, for models of the HT5M.  
Figure 15: Waveforms of amplifier and neural network model outputs. (**a**) Waveform of a guitar sound processed through the HT5M amplifier, and the WaveNet3.; (**b**) Waveform of a guitar sound processed through the HT5M amplifier, and the RNN-96.  
In order to estimate the aliasing introduced by the models, Figure 17 shows the spectrum of a 1245-Hz sinusoid fed through the WaveNet3 and RNN-96 models of the HT5M amplifier. It appears that even though the models were trained with non-aliased data, they suffer from aliasing, as was also observed in [25]. At low and middle frequencies, the aliased components are 40 dB softer than the first harmonic component, but at frequencies above about 15 kHz, some aliased components are louder than their nearest non-aliased components. However, while the aliasing is evident with a high-frequency sinusoidal input, no clear aliasing could be heard in the guitar and bass sounds processed through the models. If this becomes a problem, it is possible that aliasing suppression techniques, such as oversampling [40], could be applied during the neural network training, however, this is left for future work.  
Figure 16: Spectrum of a guitar sound processed (**top**) through the HT5M guitar amplifier, (**middle**) through the WaveNet3 model, and (**bottom**) through the RNN-96 model. The circles indicate the level of the first 15 harmonics in the target spectrum.  
Figure 17: Spectra of a 1245-Hz sinusoid fed through the WaveNet3 and RNN-96 models, the black circles indicate the non-aliased components.
# Robust Wireless Local Area Networks Based on Compressed Sensing
## 3 Proposed Algorithms  
The CS theory says many natural signals in a WLAN are sparse, or near sparse, in the sense that they have concise representations when expressed in the convenient basis. The CS scenario enables continuous waveform sampling data acquisition and compression that are suited for a variety of wireless networking applications [30]. The CS procedure that is presented in this paper addresses both the energy and telemetry bandwidth constraints of WLANs. The CS theory states sparse or compressible signals in a WLAN can be well recovered when minimizing \(\ell_{1}\) norm optimization, while satisfying some important conditions for the random measurement matrix \(\Phi\) and orthogonal basis \(\Psi\)[31].