esa-sceva/satcom-chunk-collection huggingface dataset:
# Some Approaches for Light and Color on the Surface of Mars
## 3 Results
### Colorimetric Characteristics of Lights on Mars' Surface
#### 3.1.4. CIE \(x\),\(y\) Chromaticity Coordinates  
Figure 6 shows the \(x\),\(y\) chromaticity coordinates of the 3139 total spectral irradiances obtained from the COMIMART model, corresponding to lights on Mars' surface, together with parts of the Planckian and daylight (light on Earth) loci. It can be noted that daylights defined by CIE only have CCTs higher than 4000 K [3], which implies that \(x<0.380\). As mentioned before, we can see that most lights on Mars' surface are located below the Planckian locus, while daylights on Earth's surface are located above, in agreement with previous results [7]. For any value of opacity (Tau), when the solar zenith angle increases, both chromaticity coordinates \(x\) and \(y\) increase (i.e., more reddish lights).
# Autonomous Driving Decision-Making Method Based on Spatial-Temporal Fusion Trajectory Prediction
## 2 Method
### Decision Model Based on Reinforcement Learning
#### 2.3.2 Reward Function Design  
As known from Equation (11), one of the optimization objectives of the SAC reinforcement learning algorithm is to maximize the cumulative rewards of the intelligences. As a result, designing an appropriate reward function is crucial to realizing the optimal decision-making effect. In this paper, we focus on the design from three perspectives: security, comfort, and driving efficiency:  
_A. Security_  
Vehicle driving safety is guaranteed mainly from three aspects: avoiding collision, avoiding speeding and avoiding excessive yaw. A large negative reward is given to \(R_{c}=-20\) in case of collision, and a negative reward is set to \(R_{f}=-1\) in case of speeding. The speeding threshold depends on the specific driving scenario, and is judged by the average speed of the vehicles in the range of interest \(\overline{v}\). If the average speed is higher, it can be proved that the vehicle is traveling in a higher speed environment. In this case, the speeding threshold \(v_{\text{max}}\) should also be set higher:  
\[v_{\text{max}}=\left\{\begin{array}{cc}15&\overline{v}<=10\\ 20&\overline{v}>10\end{array}\right. \tag{17}\]  
The vehicles crossing the line excessively reward is set as:  
\[R_{o}=\left\{\begin{array}{cc}-1&\text{if }dis>\text{d}\\ 0&\text{else}\end{array}\right. \tag{18}\]  
where \(dis\) is denoted as the lateral distance between the autobahn and the centerline of the lane and d is the lane width, taken as d = 3.  
_B. Comfort_The main goal of the comfort factor is to avoid vehicle jerks due to frequent steering, which can lead to unstable driving, therefore, the frequent steering bonus is set as:  
\[R_{s}=-|\delta|^{2} \tag{19}\]  
where \(\delta\) is the steering wheel angle of the vehicle.  
**C. Driving Efficiency**  
In order to prevent the vehicle from stalling at the local optimum point, which affects the driving efficiency, the step reward and the low-speed reward are designed. The step reward is to encourage the vehicle to follow the specified road point trajectory:  
\[R_{m}=\left\{\begin{array}{rl}0.4&\text{if }|x_{\text{route}}-x|<0.2\\ 0&\text{else}\end{array}\right. \tag{20}\]  
where \(x_{\text{route}}\) is the vertical coordinate of the current distance from the nearest road point, and \(x\) is the vertical coordinate of the autobahn.  
The primary purpose of the low-speed reward is to avoid sustained vehicle stalling or low-speed driving in order to promote the learning of effective driving strategies.  
\[R_{l}=\left\{\begin{array}{rl}-0.5&\text{if }v<\ 2\\ 0&\text{else}\end{array}\right. \tag{21}\]  
Combining the above factors, the final reward function \(R\) is:  
\[R=w_{1}R_{c}+w_{2}R_{f}+w_{3}R_{o}+w_{4}R_{s}+w_{5}R_{m}+w_{6}R_{l} \tag{22}\]  
With safety as the first priority, followed by focusing on driving efficiency and finally comfort, the setting weights are set as \(w_{1}=w_{2}=w_{3}=1\), \(w_{4}=0.4\), \(w_{5}=w_{6}=0.8\).
# Advancing Digital Twin-Based Collision Avoidance:
## 3 Evaluation of Results
### Discussion  
During our experiments, the total traffic on the system was very low. In the highest load scenario, the network is likely to experience congestion, interference and fading, leading to a much higher latency and packet loss. In a high load scenario, a reserved slot would be recommended for any safety-related communication.  
The forklifts that we were using during the experiments had local sensing systems that relied on Lidar and would stop when a person stood in front of it. However, such Lidar systems are very costly and only work locally, which is a major motivation for us to switch to a much cheaper RTLS-based system which in addition works globally. Furthermore, a simple Lidar cannot differentiate between a person, a truck and infrastructure.
## 3 Classification of Optimization Objectives
### Multi-Objective Optimization Focussed on Design Related Problems in WSNs
#### 3.1.6 Reliability  
A reliable and complete knowledge of some event of interest is mandatory for taking the desired decision. For example up-to-date and accurate information of current plant state is essential for plant monitoring, control and real time optimization. The accuracy and provision of different estimates of various parameters largely depend on the sensor network deployed in the plant. In [65], optimal design of wireless sensor networks for chemical plants is discussed using stochastic optimization technique for selecting the type, number and location of the nodes to achieve the required accuracy. A multi-objective optimization technique has been used in [66] to design an optimal routing protocol for maximizing the reliability, performance and efficiency.  
The design of quality of service routing protocol is proposed in [67] which can accommodate different types of data traffic. The proposed routing protocol used multi-objective optimization to simultaneously optimize latency, reliability, residual energy in sensor nodes and transmission power between the nodes.
# Experimental Study on Cavity Formation and Ground Subsidence Behavior Based on Ground Conditions
## 1 Introduction
Therefore, in this study, to clarify the mechanism of ground subsidence, a soil box was fabricated to simulate the damage of underground facilities, and a laboratory model test was performed using Korean standard sand and fines. A soil box simulating damage to the upper part of an underground pipe was fabricated. The ground composed of standard sand was simulated in the produced soil box, and a laboratory model test was conducted to simulate ground subsidence under varying groundwater levels, relative densities, and fines content. After the experiment was performed, the time until cavity formation and ground surface collapse, as well as the amount of soil discharged until cavity formation, was measured to identify the ground subsidence mechanism under the varying conditions.
# A Thorough Evaluation of GaN HEMT Degradation under Realistic Power Amplifier Operation
## 5 Experimental Results
### Comparison of the Degradation Obtained at 100 \({}^{\circ}\)C  
Following the same approach adopted in Section 5.3, we will now compare the level of degradation obtained from the two operating conditions at 100 \({}^{\circ}\)C.  
Looking at the results presented in Figure 16, in this case the class-AB operation is the one that provides a higher degradation of the device performance. All the reported parameters, including the gate leakage current, show how the performance of the class-E condition remains more stable and produces a lower alteration of the device behavior also under DC operation. This kind of result suggests that the thermal state of the device is the dominant factor in the degradation mechanisms and therefore, in this context, the class-AB operation is unfavorable compared to class-E condition, because of the higher power dissipation.  
We report the complete I-V characteristics measured before and after the stress measurement in Figure 17. Again, these results confirm the previous observations, with the class-AB case showing the largest variation in the DC characteristics. In this case, the different levels of degradation achieved by the two conditions were clearly visible on the gate leakage current, which experienced the biggest variation (\(-62\%\) vs. \(-32\%\)) when the device operat  
Figure 16: Comparison between the degradation achieved from the class-AB (red continuous lines) and the class-E (blue dashed lines) stress conditions. Variations, along 60 h of stress of (**a**) output power, (**b**) average drain current, (**c**) drain efficiency, (**d**) threshold voltage at \(I_{D}=100\) mA/mm, (**e**) saturated drain current at \(V_{G}=2\) V and \(V_{D}=4\) V, and (**f**) gate leakage current at \(V_{G}=-4\) V and \(V_{D}=30\) V.
## 4 Experiment
### Synchronized Motion Experiments
#### 4.2.2 Synchronization Accuracy Verification
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Motion Planning**} & \multicolumn{2}{c}{**Amplitude Spectrum**} & \multicolumn{2}{c}{**Voltage Amplitude**} & \multicolumn{2}{c}{**Peak Voltage (V)**} & \multirow{2}{*}{\(\boldsymbol{\delta}\)} \\ \hline
**Acceleration (m/s\({}^{2}\))** & **Velocity (m/s)** & **Frequency (Hz)** & **Peak Value** & **Characteristic of the Motor Y\({}_{1}\) (dB)** & **Experiment** & **Calculation** & \\ \hline
20 & 0.8 & 2.5 & 0.0435 & 50.3265 & 14.8562 & 14.2828 & 3.86\% \\
30 & 1.2 & 2.5 & 0.0653 & 50.3265 & 22.2843 & 21.4406 & 3.79\% \\
40 & 1.6 & 2.5 & 0.0870 & 50.3265 & 29.7124 & 28.5655 & 3.86\% \\
50 & 2.0 & 2.5 & 0.1088 & 50.3265 & 37.1405 & 35.7233 & 3.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of calculated theoretical and experimental peak voltages.
## 3 Filter Design
Another interesting alternative, such as using a continuous modulation in the coupled section of the filter [27,28] and using Koch fractal geometry [29], have also been considered, but both are time consuming due to the need of many parameters optimization. In this contribution, defected ground structures (DGS) are used to act as a low-pass filter. The slots are placed in the ground plane, directly under the input/output feeding lines [30]. The dimensions of the slots were chosen as \(c=6.6\) mm, \(d=5\) mm, \(f=1.3\) mm, \(g=0.4\) mm, \(l=4.8\) mm, \(m=4.4\) mm, \(n=1.3\) mm, \(x=1.6\) mm, \(y=1.9\) mm (see Figure 5a). The conductor strip of the microstrip line on the top plane has a width of 1.8 mm, corresponding to 50 \(\Omega\) characteristic impedance. From simulation results in Figure 5b, the DGS structure under the microstrip transmission line exhibits a 3 dB cutoff frequency at 7.15 GHz and a center frequency of the stop band at 8.25 GHz with a maximum attenuation of 33 dB. The dimensions of the slots placed in the ground plane influence the 3 dB cutoff frequency and the attenuation. Figure 6 shows the parametric study of the most important parameters \"\(x\)\", \"\(l\)\", \"\(m\)\" and \"\(c\)\". Only one parameter is changed at a time during the analysis. By increasing the \"\(x\)\" parameter, the 3 dB cutoff frequency decreases whereas the attenuation is almost the same at 8.25 GHz. The \"\(l\)\" parameter controls both the attenuation at the center frequency of the stopband and the 3 dB cutoff frequency. On decreasing \"\(l\)\", the center frequency of the stopband, the attenuation and the 3 dB cutoff frequency increase. The parameters \"\(c\)\" and \"\(m\)\" influence only the 3 dB cutoff frequency. The 3 dB cutoff frequency decreases when the parameters \"\(c\)\" and \"\(m\)\" decrease. Figure 5: **(a)** Topology of defected ground structures (DGS) section (bottom view); and **(b)** transmission coefficient of DGS section.
## 2 Materials and Methods
### Weak-Labeling
First, we use the NLI model with the template _This expresses the emotion [emotion]_ to rank all examples obtained in Section 2.2 by the probability of entailment. Second, we load the top 150k examples by this score and re-rank them using the NLI model with the template _Speaker or someone [emotion definition]_. Table 2 shows our ranking and re-ranking hypothesis resulting from applying our templates to an emotion described in Table 1. From here, we take the top 6000 sentences by their re-ranked order and assign to them the respective emotion label. Next, we filter the examples to ensure semantic diversity. Using semantic embeddings provided by a MiniLM [49] model ([[https://huggingface.co/nreimers/MiniLM-L6-H384](https://huggingface.co/nreimers/MiniLM-L6-H384)]([https://huggingface.co/nreimers/MiniLM-L6-H384](https://huggingface.co/nreimers/MiniLM-L6-H384)) -uncased (accessed on 23 June 2023)) pretrained on multiple semantic similarity tasks [50]. Examples with a cosine similarity greater than 0.7 were discarded. Finally, we filter examples using simple token and stem [51] counting. The values were manually tweaked per emotion, based on the output. The max stem count was set between 600 while the maximum token count varied between 1000 and 2000. This variation is to account for the variation in the vocabulary for expressing different emotions. For example, \"boredom\" has a much more narrow vocabulary than \"disgust\". The top 6000 examples by score are selected as positives examples for the emotion. The same process is repeated for selecting negative examples except we pick the lowest scored examples and take the bottom 300,000 examples by ranking score, followed by the bottom 10,000 examples by the re-ranking score. A separate process handles neutral (no emotion) examples. First, the sentences are filtered by the NLI model using the hypothesis _This text expresses emotion_. Only sentences with a probability below 0.2 are selected. These are then filtered again by the NLI model using the hypothesis _The speaker or someone expresses or feels [emotion]_ for each emotion in our taxonomy. Only sentences with a probability below 0.5 for all emotions are selected as neutral examples. Approximately 5100 neutral examples were created in this way. At the end of this process described, we have a dataset with binary labels for each emotion, totaling 38 datasets, each with 26,000 examples, plus the neutral examples. \begin{table}
\begin{tabular}{l l} \hline \hline  & **Hypothesis** \\ \hline Short (Ranking) & This expresses the emotion fear. \\ \hline Long (Re-ranking) & Speaker or someone is afraid or scared due to a threat, danger, or harm. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Example of NLI hypothesis for the emotion “fear”. See also Table 1.  
Figure 1: Weak labeling process using an existing NLI model to identify positive and negative examples of sentences express emotions. The key steps, Ranking and Re-Ranking, in bold, use different templates described in this section. Inputs and outputs of each step underlined: from sentences, to candidates, to examples.
###### Abstract  
The proliferation of smart devices such as sensors and communication devices has necessitated the development of networks that can adopt device-to-device communication for delay-tolerant data transfer and energy efficiency. Therefore, there is a need to develop opportunistic networks to enhance energy efficiency through improved data routing. A sensor device equipped with computing, communication, and mobility capabilities can opportunistically transfer data to another device, either as a direct recipient or as an intermediary forwarding data to a third device. Routing algorithms designed for such opportunistic networks aim to increase the probability of successful message transmission by leveraging area information derived from historical data to forecast potential encounters. However, accurately determining the precise locations of mobile devices remains highly challenging and necessitates a robust prediction mechanism to provide reliable insights into mobility encounters. In this study, we propose incorporating a random forest regressor (RFR) to predict the future location of mobile users, thereby enhancing message routing efficiency. The RFR utilizes mobility traces from diverse users and is equipped with sensors for computing and communication purposes. These predictions improve message routing performance and reduce energy and bandwidth resource utilization during routine data transmissions. To evaluate the proposed approach, we compared the predictive performance of the RFR against existing benchmark schemes, including the Gaussian process, using real-world mobility data traces. The mobility traces from the University of Southern California (USC) were employed to underpin the simulations. Our findings demonstrate that the RFR significantly outperformed both the Gaussian process and existing methods in predicting mobility encounters. Furthermore, the integration of mobility predictions into device-to-device (D2D) communication and traditional internet networks showed potential energy consumption reductions of up to one-third, highlighting the practical benefits of the proposed approach. The contribution of this research is that it highlights the limitations of existing mobility prediction models and develops new resource optimization and energy-efficient opportunistic networks that overcome these limitations.  
sensor communications; mobility encounter; similarity analysis; mobility; device-to-device communications; random forest regressor; opportunistic connectivity +
Footnote †: journal:  
Article
## 4 Discussion
### Quantitative Accuracy  
The quantitative analysis showed that the analysed next-generation TMA(r) technique yields a high accuracy rate in rat soft tissue. A total of 90% of all analysed cores were found to be displaced less than 500 \(\upmu\)m from the targeted area. The accuracy decreases slightly with greater sectional depth, although, even in the deepest sectional plane S3, roughly 0.5 mm median depth, almost 90% of cores still had a deviation below 500 \(\upmu\)m.  
When analysing the accuracy using the measurement of the epithelial length in the aorta, the length in the S1 sectional plane was shorter than in the S2 and S3 planes (Figure 6). This can be explained by the fact that not all the TMA cores can be inserted on a precisely equal level in the TMA recipient block so the diameter of some of the tissue cores was not yet entirely represented in the S1 sectional plane. Overall, there was little distortion the endothelium in blood vessels during ngTMA creation.
## 3 Proposed Method
### Dynamic Scheduling
#### 3.2.2 Dynamic Selection by the Base Station  
At the start of each iteration, the base station evaluates the target's position, trajectory, and surrounding environmental factors, to determine which edge devices are most likely to capture the target's movement in the next iteration. It then generates a schedule specifying which edge devices should remain active and which should be temporarily deactivated. This schedule is communicated to the edge devices before the iteration begins. The base station dynamically selects and activates edge devices based on the target's expected trajectory. This activation-deactivation process continuously repeats throughout the tracking process, ensuring optimized resource allocation, reduced computational overhead, and efficient target tracking. The iterative scheduling process enables the system to adapt to dynamic scenarios, such as targets with unpredictable movement patterns or changing environmental conditions. By deactivating unnecessary edge devices, the TEDDY framework minimizes energy consumption while maintaining tracking accuracy. Furthermore, this iterative design allows the framework to scale efficiently, as additional edge devices can be integrated into the network without overwhelming the system.
## 5 Sensor Signal Feature Extraction  
Different signal processing methods were applied to extract diverse types of signal features from the segmented thrust force and torque signals:  
* Time domain signal features
* Frequency domain signal features
* Fractal analysis signal features  
The extracted signal features were then subjected to selection procedures aimed at identifying those with the highest relation with tool wear level. The selected features were used for the construction of feature pattern vectors (FPV) to be fed as input to ANN-based machine-learning paradigms in order to make decisions on the timely execution of drilling tool change.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Triangulation Method
#### 3.1.2 Triangular Positioning Method under Unknown or Unstable Wave Velocity  
For anisotropic structures, the wave speed is not constant. [PERSON] et al. [5] first performed sound source localization by the improved time difference method in steel-reinforced metal plates and proposed an easy-to-implement method for estimating the location of sound sources in orthogonal anisotropic plates, which was based on the energy of the recorded signal.  
Subsequently, [PERSON] et al. [6] proposed a sound source localization method based on the triangular time difference method applied to anisotropic materials with known wave velocities, which solved a system of nonlinear equations to obtain the location of the sound source, as shown in Figure 4d.  
[PERSON] et al. [7] presented a multi-step localization method (MLM) which does not require the wave velocity to be known in advance for heterogeneous media. But this method requires more sensors.  
Figure 4: Positioning methods based on time difference method: (**a**) rhombic AE localization method [3]; (**b**) normalized square localization method [4]; (**c**) planar square–triangle localization method [3]; (**d**) triangular time-difference localization method with known velocity.  
[PERSON] et al. [8] used a cluster of four closely positioned sensors forming the four vertices of a square and facing different directions. They proposed a formula for estimating the angle of incidence from the signals acquired by the square cluster. A method based on the shape of the wavefront can then be applied to estimate the sound source location.  
[PERSON] et al. [9] proposed a new method which used the Bayesian decomposition of single-source signals and required only a priori information about the surface displacements on the propagation path. The Bayesian decomposition method returned signals that contained only reflected waves and obtained parametric features to be used to assess damage.  
[PERSON] et al. [10] proposed a new method for localization of sound sources, which required only six sensors to quickly predict the damage location in both isotropic and anisotropic materials with the help of the L-shaped coupled sensor clusters. [PERSON] et al. [11] presented a new approach to damage assessment based on empirical wavelet transform and long- and short-term wavelet decomposition.  
[PERSON] et al. [11] used an AE source localization method based on empirical wavelet transform and long and short-term memory neural networks for crack monitoring in ortho-tropic anisotropic steel plates.  
Since the traditional time difference method does not consider the structural complexity that may change the wave propagation path and velocity, [PERSON] [12] proposed a new and improved fully automated delta T mapping technique, where a clustering algorithm was used to automatically identify and select the highly correlated events at each grid point, while a minimum-difference method was adopted to determine the source location.