esa-sceva/satcom-chunk-collection huggingface dataset:
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 2 Materials and Methods
### Theoretical Basis of Maximum Entropy Reinforcement Learning  
In standard reinforcement learning, the objective function is to maximize the cumulative reward:  
\[J(\pi)=\mathbb{E}_{\tau\sim\pi}\left[\sum_{t=0}^{\infty}\gamma^{t}r(s_{t},a_{t })\right] \tag{1}\]  
where \(\tau\) is the state-action sequence; \(t\) is the time step index, \(\gamma\) is the discount factor, and \(r(s_{t},a_{t})\) is the instant reward function.  
The maximum entropy reinforcement learning adds the entropy term of the strategy [23]:  
\[J(\pi)=\mathbb{E}_{\tau\sim\pi}\left[\sum_{t=0}^{T}\gamma^{t}(r(s_{t},a_{t})+ \alpha\mathcal{H}(\pi(\cdot|s)))\right] \tag{2}\]  
where \(\mathcal{H}(\pi(\cdot|s))=-\int\pi(a|s)\log\pi(a|s)da\) is the entropy term and \(\alpha\) is the entropy coefficient, which indicates that the intensity of control exploration is controlled. A larger \(\alpha\)encourages more exploration, meaning the strategy is more diverse. A smaller \(\alpha\) encourages strategic certainty.
## 3 Method
### Parallel Attention  
#### 3.2.1 General Attention  
The main responsibility of the attention mechanism is to calculate the importance weights of each element in the input sequence, thereby performing a weighted summation of the input information to highlight key parts and suppress irrelevant parts, given an input sequence \(X=[x_{1},x_{2},\ldots,x_{n}]\), where each \(x_{i}\) is a vector. The goal of attention mechanism is to generate a context vector \(c\) that captures the key information in the input sequence. First, we need to compute the attention score \(a_{i}\) for each input element \(x_{i}\), which is typically calculated using a function \(f\), such as dot product, additive models, or scaled dot product. Taking the scaled dot product as an example, the formula is \(a_{i}=\frac{\exp(q+k_{i}/\sqrt{d_{k}})}{\sum_{j=1}^{n}\exp(q+k_{j}/\sqrt{d_{k}})}\), where \(q\) is the query vector, \(k_{i}\) is the key vector, and \(d_{k}\) is the dimensionality of the key vector. This score represents the similarity between the current query and each key, and after softmax normalization, it yields the attention weight \(a_{i}\).  
Next, these weights are used to perform a weighted summation of the value vectors \(v_{i}\) in the input sequence to generate the context vector \(c\), i.e., \(c=\sum_{i=1}^{n}a_{i}v_{i}\). This process can be understood as filtering the input information, where elements with larger weights contribute more to the final result. In practical applications, the query vector \(q\), key vector \(k_{i}\), and value vector \(v_{i}\) are typically obtained by applying linear transformations to the input sequence, such as \(q=W_{q}x\), \(k_{i}=W_{k}x_{i}\), and \(v_{i}=W_{v}x_{i}\), where \(W_{q}\), \(W_{k}\), and \(W_{v}\) are learnable parameter matrices. In this way, the attention mechanism can dynamically adjust the weights of each input element, thereby demonstrating greater flexibility and adaptability when processing sequential data.  
It is worth noting that the attention mechanism can be applied not only to integrate information within a single sequence (i.e., self-attention mechanism) but also to handle relationships between two sequences (i.e., cross-attention mechanism). In the self-attention mechanism, the query, key, and value all come from the same sequence; in the cross-attention mechanism, the query comes from one sequence, and the key and value come from another sequence. This mechanism has been widely used in tasks such as machine translation and text generation, significantly improving model performance by capturing dependencies between sequences.
# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 2 Related Work
Two deep convolutional neural networks for healthy and diseased plants, ResNet18 and ResNet50, were trained on RGB images. For model training and testing, the authors used 532 images collected in the field. In the training process, they used potato images that were isolated and did not overlap with each other or other plants. For this reason, to obtain the isolated images, they only collected the images during the first weeks after plant emergence. The computation times for training and testing their model were four hours and 6 min for ResNet18 and seven hours and 5 min for ResNet50. ResNet18 achieved higher performance, with a precision of 95% and recall of 91% for the disease class. However, the proposed model was not able to deal with overlapping plants. Also, the time it took for training and testing was extensively high. In addition, validation of the model, which allows for the optimization of performance, was not implemented. Many researchers have developed machine learning models for detecting and classifying numerous crop diseases using different techniques. However, the previous works have some limitations, such as a lack of creative response, no common sense used in decision making, program unable to processing high-resolution images, using a small number of images with similar backgrounds, class imbalance in the manipulation of data, high computational time, and not being able to deal with overlapping plant images. To overcome the limitations described above as described in above, the proposed approach in this study utilized a range of techniques. Utilizing deep convolutional neural networks allowed the model to learn relevant features and patterns from the input images while including image preprocessing steps, such as median filtering and contrast enhancement, which helped to standardize low-quality samples. With SegNet semantic segmentation (specifically trained with our domain-agnostic examples of leaf images due to the existing classes), we are able to achieve the separation between object-of-interest and background, which helped in overcoming the overlapping plant image problem, as the model focuses only on the region where the target/leaf is present and on its attributes that differentiate it from the non-background area behind. This increases the diversity of training data distinguishably, mitigating the class imbalance problem and helping to better capture alternate/random images. In addition, carefully designing the CNN architecture with optimal layers and parameters resulted in a model that contains rather few parameters as compared to some other typical architectures. It also has high performance due to this additional resource consumption aspect, which overcomes one key limitation of previous attempts, i.e., very high computational time.
## 5 Paper Review
The authors averaged the outputs of the softmax layer of the three models to decrease the variance of the model output. The identification model achieved a sensitivity of 97.5% and a specificity of 97.7%, while the grading model reached 98.1% for sensitivity and 98.9% for specificity. [PERSON] et al. [69] explored three CNN architectures, namely VGG, ResNet, and an ensemble of both architectures. The authors experimented with using a private dataset with three classes of DR and with 148,266 images divided into 51.5% to train and 48.5% to validate the model. Three measures were used to assess the quality of the model, namely AUC, sensitivity, and specificity. The authors reported that transfer learning increased model accuracy. [PERSON] et al. [70] used a private dataset with 4476 images with four classes. The authors cut the original images into four \(300*300\) partitions that were the input of four InceptionV3 networks, and then they concatenated the results to a single layer. The original fully connected layers were removed, and only a softmax layer was used. The Adam optimizer was employed to fine-tune the InceptionV3 networks. The authors compared their method against ResNet18, ResNet101, VGG19, and InceptionV3. The reported results showed that their model achieved a higher accuracy than the other models. Table 3 shows the list of the reviewed papers that applied transfer learning to classify DR. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Study** & **Architecture** & **Number of Classes** & **Dataset** & **Dataset Size** & **Performance Measure** & **Results** \\ \hline [PERSON] et al. [53] & InceptionV3 & 2 classes & Private & 128,175 & Sensitivity & 97.5\% \\ \hline [PERSON] et al. [54] & InceptionV3 & 5 classes & Kaggle & 4000 & Accuracy & 48.8\% \\ \hline \multirow{3}{*}{[PERSON] et al. [55]} & AlexNet & 2 classes & Messidor & 1200 & AUC & 77.2\% * \\  & VGG-S & - & DRI & 1014 & - & **98.34\% * \\  & VGG16 & - & - & - & - & 74.3\% * \\  & VGG19 & - & - & - & - & 68.69\% * \\ \hline \multirow{3}{*}{[PERSON] et al. [56]} & InceptionV3 & 2 classes & Kaggle & 35,126 & Accuracy & **87.12\%** \\  & Xception & 4 classes & Private & 9443 & Accuracy & 81\% \\ \hline \multirow{3}{*}{[PERSON] et al. [58]} & VGG19 & \multirow{3}{*}{10 classes} & \multirow{3}{*}{STARE} & \multirow{3}{*}{10,000} & \multirow{3}{*}{AUC} & **90.3\% * \\  & AlexNet & & & & 81.6\% \\ \cline{1-1} \cline{6-6}  & VGG16 & 5 classes & Kaggle & 166 & Accuracy & 50.03\% \\ \cline{1-1} \cline{6-6}  & InceptionV3 & & & & **63.23\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Studies applying transfer learning.
## 3 Experiments
Afterward, to estimate the performance of sound localization, we structured the source distinction vector analogously to the directional vector. Likewise, we employ the mean-squared-error (MSE) loss to evaluate the efficacy of the sound localization. The corresponding equation is presented below. \[D_{\text{class}}=\sum_{j=0}^{2}(\hat{c_{j}}-c_{j})^{2} \tag{5}\]  
Here, the sound sources are categorized into three types: male, female, and child. Thus, the classification range is from 0 to 2. Ultimately, the performance metrics for sound localization and classification are aggregated with weights of 0.8 and 0.2, respectively, to derive the final performance indicator. These weights have been internally determined during the course of the project, such that  
\[D_{\text{total}}=0.8\cdot D_{\text{direction}}+0.2\cdot D_{\text{class}} \tag{6}\]  
Based on the values in Equation (6), a model can be considered to perform better as the number decreases. Table 1 presents the internal team rankings predicated on Equation (6). It has been substantiated that the sound event localization and detection technique, predicated on the proposed bipartite matching loss, exhibits a superior performance relative to that of the other contending teams. Regrettably, an in-depth exploration into the methodologies employed by the competing teams for solving this problem lies beyond the scope of this paper. Nevertheless, an objective assessment based on quantifiable metrics has corroborated the commendable efficacy of the proposed approach. It is pertinent to note that the evaluation dataset was meticulously constructed by teams not affiliated with this internal competition. Figure 4 shows the results of performing SED using the proposed method in this paper. Figure 4a illustrates the ground truth, and Figure 4b shows the predicted results (only left channel), respectively. It is confirmed that the segments where speech is present are detected with high accuracy. In terms of classification, the performance is moderately accurate for males and females, but there were instances of confusion between males or females for children. This is because the voice of a child can exhibit characteristics very similar to those of males or females. Table 2 implies the confusion matrix for classification performance. (a) represents the proposed method, while (b) refers to the results obtained \begin{table}
\begin{tabular}{c c} \hline \hline
**Teams** & \(\mathbf{D_{total}}\) \\ \hline Internal Team \#1 & 3.75385 \\ Internal Team \#2 & 3.74085 \\ Internal Team \#3 & 3.69416 \\ Internal Team \#4 & 3.40980 \\ Internal Team \#5 (Ours) & 3.15351 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The internal team rankings predicated on Equation (6). from GoogLeNet [25]. As shown in the table, there are instances where children are misclassified as male or female. It is observable that the proposed method demonstrates relatively better performance compared to GoogLeNet. Figure 4: The results of performing SED using the proposed method. (**a**) illustrates the ground truth, and (**b**) shows the predicted results (only left channel).
## 4 Model and Calculation Principle  
### Ball-Berry Model  
In practice, foliar temperature and relative humidity are not always constant. Considering the influence of these two factors on stomatal conductance, a correction factor should be added. It is assumed that the correction factor is a function of the relative humidity and temperature of leaves, and it is denoted by \(\rm f(h_{s},T_{1})\):  
\[g_{\rm S}=m\frac{A_{n}}{C_{s}}f(h_{s},T_{1})+g_{0} \tag{1}\]  
where \(h_{s}\) and \(T_{1}\) are the relative humidity (%) and temperature of the leaf surface (\({}^{\circ}\)C), respectively.  
If the leaf surface temperature remains unchanged, only the relative humidity of the leaf surface is changed, and the correction factor is assumed to be:  
\[f(h_{s},T_{1})=h_{s} \tag{2}\]  
By substituting Equation (2) into Equation (1), the mathematical expression of the Ball-Berry stomatal conductance model can be obtained:  
\[g_{\rm S}=m\frac{h_{s}A_{n}}{C_{s}}+g_{0} \tag{3}\]  
[PERSON] [41] replaced \(C_{s}\) in Equation (3) with \(C_{s}-\Gamma\), where \(C_{s}\) is the CO\({}_{2}\) concentration on the leaf surface and \(\Gamma\) is the CO\({}_{2}\) compensation point, which fits better. So, Equation (3) can be rewritten as follows:  
\[g_{s}=m\frac{A_{n}}{C_{s}-\Gamma}\cdot h_{s}+g_{0} \tag{4}\]Recent experiments have revealed that stomata respond to evaporation requirements more directly than relative air humidity [42]. Combined with the improved Leuning [25]-modified Ball-Berry model, the following equation is proposed:  
\[\mathrm{g_{s}}=\mathrm{m}\frac{\mathrm{A_{n}}}{(\mathrm{C_{i}-\Gamma})(1+\mathrm{ VPD_{s}}/\mathrm{VPD_{0}})}\cdot\mathrm{h_{s}}+\mathrm{g_{0}} \tag{5}\]  
where \(\mathrm{VPD_{0}}\) = 1500 Pa, \(\mathrm{m}\) = 20, and the residual stomatal conductance can simply be measured as the stomatal conductance of non-arid plants under low light during the day or the conductance of photosynthesis close to zero [43].  
Among these:  
\[\mathrm{VPD_{s}}=\mathrm{VPD_{a}}(1-\mathrm{g_{tw}}/\mathrm{g_{bw}}) \tag{6}\]  
where \(\mathrm{VPD_{a}}\) is the water vapor pressure difference (Pa) between cells and the atmosphere; \(\mathrm{g_{tw}}\) and \(\mathrm{g_{bw}}\) are the total stomatal conductance and the boundary layer conductance to water vapor (mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)), respectively, and \(\mathrm{VPD_{s}}\) is the driving force of transpiration.
# Strategies for Reducing Automobile Fuel Consumption
## 3 Vehicle Design and Technology
### Control Signals Transmission  
In contemporary vehicular power architectures, the integration of modern communication interfaces, which compute the measured operational variables to control powertrain modules, can contribute significantly to fuel efficiency. A synergistic interplay between the engine and the transmission allows for the crankshaft rotational velocity optimization. Specifically, by transferring data points to a computer for the calculation of torque and power requirements in a real-time context, vehicle dynamics, ride quality, and fuel efficiency can be improved. The central role of the _Controller Area Network_ (CAN) protocol is to promote better communication between engine subsystems and the _Electronic Engine Control Module_ (EECM). Modern controllers and algorithms are continuously improved, enhancing communication between powertrain modules and other vehicular systems.  
These improvements allow for adjustments in engine configuration and calibration and are reinforced by automated shift strategies and agile software, which can recalibrate in response to fluctuations in load demands, terrains, velocities, and engine torque outputs. Moreover, the integration of communication interfaces with navigation infrastructures and satellite-derived data further enhances fuel economy. Such integrations empower vehicles to judiciously modulate speeds during curvilinear trajectories or to preemptively accelerate before uphill gradients, tailoring operations to improve fuel efficiency.  
_Thermocasting_ is an innovative energy-conservation mechanism, which is electronically activated during regenerative processes in ICEs and employs engine braking without reducing (significantly) the vehicle's momentum. This mechanism is initiated when the accelerator is released, allowing the vehicle to roll. This strategy not only facilitates energy preservation during downhill trajectories but also mitigates drastic temperature variations, often observed in engines without energy recovery systems.  
While the necessary transition to electric powertrains progresses, a strategic approach to mitigate the negative impacts of the ICE (an obsolescent technology that has been the basis of our mobility paradigm for over a century) is paramount. Hence, there persists a necessity for the research and development of the ICE, aimed at improving both its spark-ignition and compression-ignition variations.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 3 System Design and Algorithm
### Concept of GLAN Protocol  
We constructed the GLAN protocol, overcoming traditional routing protocol and ad hoc network limitations, considering UAV characteristics. Figure 2 shows the concept of our proposed system. Our system advances from the flooding method, which was based on UAV routing. GLAN finds a forwarding region (FR) by computing a forwarding angle to form optimal routes from source nodes to destination nodes by utilizing geographic location information from each UAV. We optimize the calculated angles using RL and apply them to UAV systems. In the environment setting before learning, the mobility error for each episode is changed significantly so that the mobility characteristics of the UAV can be considered. The data requested for forwarding are delivered only when the UAV receiving the data is within the output angle area of the learning model. To check whether it is in the FR, the system obeys the end-to-end rule of data transmission by comparing only the IP addresses, rather than using the entirety of the received data.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 2 Implemented System
### Receiver  
The antenna is equipped with a corrugated feed horn, followed by a polarizer and by an orthomode transducer (OMT) to carry the left and right circular polarizations (LHCP for Tx and RHCP for Rx). A waveguide to coaxial transition performs the connection to the receiver. The receiver has a limiter and switch right at the input that handles an excess peak power of 20 W for about 10 ms and blocks the signal during transmission with an attenuation of 30 dB.  
The receiver, depicted in Figure 1 in purple, includes an analog chain that is responsible for amplifying the signals (target echoes) to the required digital entry levels of the acquisition board (yellow). The receiver starts with a low noise amplifier (LNA), internally developed, using GaAs technology, presenting a noise factor of 0.7 dB at 5.56 GHz. Next, there is a local oscillator at 5.160 GHz to convert it down to a 400 MHz intermediate frequency (IF). The local oscillator is provided by the same reference signal from the transmitter, guaranteeing a coherent conversion. The IF then passes through an IF filter and a digital variable gain amplifier (DVGA), which is connected to the controller board.  
Finally, the I/Q detector converts the signal to base-band (or 400 kHz) and outputs the I and Q components of the echo signal. The conversion to 400 kHz is used for accommodating Doppler shifts, providing velocity measurements up to 10.79 km/s. Table 4 summarizes all relevant parameters of the receiver chain.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**IF** & 400 MHz \\
**LNA Noise figure (<15 °C) (N/r)** & 0.7 dB \\
**Receiver Temperature (T)** & 15 °C \\
**IF Filter BW (B)** & 80 MHz @ – 3 dB \\
**IQ Detector output BW** & 50 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Receiver operating parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Peak power** & 5 kW \\
**Transmitter Frequency (1/\(\lambda\))** & 5.56 GHz \\
**Waveform** & Arbitrary amplitude modulation \\
**Max. Pulse length (\(\tau\))** & 10 s \\
**Phase Noise** & \(-\)91.3 dB [Hz] @ 100 kHz \\
**Modulator** & Modified D195 [29] \\
**PA Transistors** & CGHV59070 [30], CGHV59350 [27] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Transmitter operating parameters.
## 4 Experimental Results and Discussions
### Leakage Detection Results  
To investigate the contribution of intensity features and geometric features on leakage detection accuracy, ablation experiments were conducted. Figure 6 demonstrates that the proposed method accurately detects and effectively segments leakages in 3D space, with the segmented leakage shapes closely matching their corresponding ground truth representations. Table 4 further presents the recall, precision, and F1-score for different  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{\begin{tabular}{c} **Mean Values of** \\ **Cross-Sectional** \\ **Deformation** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Point Cloud** \\ **Density** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Leakage** \\ **Type** \\ \end{tabular} } & \multicolumn{2}{c}{**Training Samples**} & \multicolumn{3}{c}{**Testing Samples**} \\ \cline{5-10}  & & & & **Length** & **Total** \\ \hline Dataset 1 & 2 mm & 5865 pts/m\({}^{2}\) & Joint leakage & 800 m & 83,020,439 & 1:61 & 200 m & 19,412,661 & 1:22 \\ Dataset 2 & 4 mm & 1652 pts/m\({}^{2}\) & Joint leakage & 294 m & 4,846,982 & 1:24 & 22 m & 478,201 & 1:109 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selection of training and testing samples. Length represents total mileage of tunnel. Ratio denotes the number proportion of leakage points to tunnel lining points.  
Figure 5: Example results of point cloud data preprocessing. (**a**) Raw point cloud. (**b**) Denoised point cloud.  
feature sets across both datasets. For the two datasets, classification using only intensity features resulted in a precision of 87.17% and 95.56% for leakage detection, respectively. This indicates that intensity features are effective in distinguishing leakages from non-leakages. In Dataset 2, the recall for leakage reached 98.27%, demonstrating the efficacy of intensity features in extracting leakages. However, the lower recall for leakage in Dataset 1 may be attributed to a significant overlap in intensity distributions between leakage and tunnel lining, which diminishes the effectiveness of intensity features for leakage, as illustrated in Figure 7.