esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Few studies have used 3D-CNN to classify the clinical severity of prostate cancer. The main reason for this is that the cancerous tissue portion of a patient's whole prostate sequence often accounts for only a small part of the entire prostate image sequence. Due to the very small size of the cancerous tissue, although 3D-CNN can learn the features of the sequence images better than 2D-CNN, it is also difficult to learn the features of the cancerous tissue adequately with very small targets, and it is easy for the large number of useless features in the prostate cancer image sequence to affect the model's learning results. So, we proposed the method in this paper, which solves this problem perfectly, but it is difficult to determine an optimal sequence length when constructing a new image sequence; the original image sequence length is determined by the original sequence, but the newly constructed image sequence does not have a perfect graph column length. In this paper, we explored different alignment methods when constructing the sequence as much as possible, and in future experiments, the sequence length will be investigated in order to find an optimal sequence length. Although the method achieves great results in the direction of deep learning, it is not superior to the results of a simple semen test, so this method is one of the methods for assisting doctors in diagnosis. We carefully considered the issue of whether to use fully automatic segmentation of the prostate region when designing the method, and we also wish to automate the whole method as much as possible and to reduce the manual part as much as possible, so we will add this step to the CNN in future work to achieve full automation. However, in the current study, we also used some bounding boxes for manual animation, so the current method is still semi-automatic, and we will introduce automatic segmentation methods for medical images, such as U-net, into our method in future work.  
Figure 4: The 3D-CNN model was visualized for learning. The figure shows two cases; the first column of each case is the original image, the second column is the focus map obtained through calculation, and the third column is the heat map obtained through the 3D Grad-cam++ calculation.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 2 Application Scenario
### Drone Exploration  
Another example is the collaboration between rovers and drones. Due to the rough Martian surface, rovers travel extremely slowly. Perseverance holds the current record for the longest single-day drive: 319 m in one sol (i.e., Martian day). Drones can fly much faster. The Ingenuity mission demonstrated that a drone could travel a horizontal distance of 625 m in a single flight of 166 s at a maximum speed of 5 m/s on Mars [2]. Hence, drones can construct a detailed map of the area and quickly identify the most optimal route to usable resources, using onboard intelligence. Resource locations are transmitted to the  
Figure 2: Visualization of the envisioned scenario with architecture and functional description of the collaborative energy-aware sensors, rovers, drones, and satellites on Mars in this work.  
nearest rover(s) and spread over the swarm of autonomous vehicles through wireless mesh communication. In addition, the drones may also have the capability to fly to hard-to-reach areas and collect soil samples, in case no rovers are available or when they simply cannot reach the area of interest.
## 6 Simulation Result
### Analysis of Penalty and Failure Factors

Table 11: Results of analysis of penalty factors according to the DRL algorithm tested. (Bold text indicates the method that achieves the best performance for each metric). \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Town01** & **Town02** & **Town03** & **Town04** & **Town05** \\ \hline Vehicle stopped & 0.32 & 0.30 & 0.52 & 0.38 & 0.26 \\ Off-track & 0.03 & 0.02 & 0.06 & 0.00 & 0.02 \\ Too fast & 0.03 & 0.01 & 0.14 & 0.04 & 0.12 \\ Red light violation & 0.28 & 0.09 & 0.08 & 0.18 & 0.30 \\ Collision & 0.03 & 0.02 & 0.03 & 0.12 & 0.01 \\ \hline Total (mean) & 0.14 & 0.09 & **0.16** & 0.14 & 0.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results of analysis of penalty factors across different towns. (Bold text indicates the town with the highest penalty for each metric). In summary, the penalty analysis reveals that beyond overall Success Rate, significant variability exists in rule adherence and stability performance across both algorithmic designs and simulation environments. Notably, the presence of complex road structures and rule-based elements amplifies the differences among DRL models and underscores the critical impact of pretraining strategies in minimizing penalty risks.
# An Interference Cancellation Scheme for High Reliability Based on MIMO Systems
## 5 Conclusions  
In this article, a new interference cancellation using MIMO is proposed in two-path relay systems for higher error performance. Using the MIMO signal detection at a relay, the proposed scheme fully removes the IRI which is caused by simultaneous transmission of a source and one of the relay nodes. So, the IRI is not propagated to a destination and the BER performance is improved because less errors are propagated. After the removal of IRI, the proposed scheme can use the DF at the relays unlike the conventional FIC. Also, the BER performance is not degraded when the distance from one relay to another relay is decreased. Therefore, the proposed scheme is effective in the situation of wireless systems that the mobile users operating as relay nodes move and approach each other. Also, unlike the original two-path relay system, the proposed scheme can obtain spatial diversity gain by constructing the new symbol structures and can be used well such as area for cell edge or environments for severe multi-path fading in wireless communication systems.  
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government(MSIT) (No. 2017-0-00217, Development of Immersive Signage Based on Variable Transparency and Multiple Layers).  
[PERSON] proposed an algorithms for removal of IRI at the relays and low-complexity MIMO signal detection scheme; [PERSON] advised an algorithm for higher error performance; [PERSON]-[PERSON] gave feedbacks about a modified algorithm and all simulation results. Also, [PERSON]-[PERSON] provided the experimental materials for better computational simulations and revised critical errors of the manuscript.  
The authors declare no conflict of interest.
# Speech Communication System Based on Piezoelectric Electret Mechanical Antenna
###### Abstract  
Low-frequency electromagnetic waves have strong penetration and low attenuation performance, making them widely used in long-wave communication areas such as underwater communication and earthquake prediction. Mechanical antennas are seen as a promising alternative to traditional low-frequency resonant antennas, as they offer the potential for miniaturization and low power consumption. However, existing communication systems using mechanical antennas are usually rudimentary, incompatible, and inconvenient to use. To address these problems, a novel speech communication system based on a piezoelectric electrical mechanical antenna was proposed. This communication system includes a hardware system, a communication protocol for mechanical antennas, and a software system with a modular structure. This system includes hardware, a communication protocol, and a modular software system. The results of the experiments showed that this communication system provides real-time speech communication with a user-friendly design, high compatibility with different communication devices, and easy customization to meet specific requirements. This design holds great potential for applications in mineral detection, seismic research, submarine communication, industrial RF control, and other related mechanical antenna applications.  
low-frequency communication; mechanical antennas; communication system +
Footnote †: journal: Medical Instruments and Methods in Physics  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Speech Communication System Based on Piezoelectric Electret Mechanical Antenna. _Appl. Sci._**2023, 13**, 2332. [[https://doi.org/10.3390/app13042332](https://doi.org/10.3390/app13042332)]([https://doi.org/10.3390/app13042332](https://doi.org/10.3390/app13042332))
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 4 Information Data of Indirect Online Measurement on Tool Wear
### Current  
Tool wear causes the cutting torque to increase, and the equipment motor current increases accordingly. As a result, the current can be used for online tool wear monitoring [36; 87; 88]. Because the sensor installation hardly affects the processing operation, current monitoring is relatively simple and considered the most suitable method for workshop applications [89; 90; 91; 92]. [PERSON] et al. [88] collected, analyzed, and processed the motor current feature through the current sensor connected to the spindle motor and the X-axis drive motor. [PERSON] et al. [93] comparatively found that current and voltage sensors could replace the cutting force sensors for industrial tool wear monitoring. [PERSON] et al. [94] presented that the current feature has better anti-environmental noise performance and stronger correlation with tool wear, thereby better reflecting the tool wear condition than the vibration feature. [PERSON] et al. [95] studied the correlation between the tool wear condition with the input current of the servo motor inverter and monitored the tool wear condition online in real time based on the inverter input side current. [PERSON] et al. [96] collected the current feature of the motor stator through an instrument to monitor tool wear conditions. The method has the characteristics of convenient operation, high information integration, direct transmission path, suitable feature extraction, free from the influence of the processing environment, cheap, and easy to transplant. This should be a method worth exploring for machine tool applications where the transmission system is closed and available sensors are difficult to install.  
The disadvantage of the current feature is that it is greatly affected by noise. It is difficult to detect small features, and high-frequency features are lost due to filtering. The current feature is easily affected by the friction of the mechanical system and the viscous damping of the feed system. Experimental studies indicated that the current is not sensitive to the cutting force changes under high-frequency conditions. The motor current feature is not suitable for monitoring the tool wear condition at high spindle speeds [90; 96]. The current sensor should be installed away from the processing area, resulting in reduced sensitivity and reliability.  
The installation diagram of a current sensor is shown in Figure 2.
## 3 Results
This is assumed to be the maximum distance that the effect of seeding (rainfall or snowfall) can occur on the ground when precipitation enhancement experiments are conducted on seedable clouds [24]. Sc (stratocumulus), a low-level cloud, displayed the highest frequency for all seasons with an average of 63%, followed by Cu (cumulus) at 15%, As (altostratus), a mid-level cloud, at 13%, and Ac (altocumulus) at 6%. In particular, Sc clouds showed a very high frequency in the summer at approximately 70% of seedable clouds, while As displayed an approximately 4% higher frequency in winter and spring. High-level clouds, including Ci, Cs, and Cb, were not observed when classifying seedable clouds according to the conditions proposed in this study. Thus, seedable clouds occurring around the Korean Peninsula showed a characteristically higher frequency in stratiform than other cloud types. The average CTH of low-level clouds was 2.42 km, and the average CTT was 1.15 \({}^{\circ}\)C. Mid-level clouds exhibited top height and temperature averages of 3.27 km and \(-\)1.80 \({}^{\circ}\)C, respectively. St (stratus) and Ns (nimbostratus) clouds had a very low occurrence frequency in the ISCCP cloud classification due to the low frequency of cases in which the COT calculated from COMS exceeded 23. Hence, it is difficult to represent the cloud characteristics of cloud types when the seedable cloud occurrence frequency is less than 1%. Figures 4 and 5 show cloud frequency based on the water and ice cloud phases. Water phase seedable clouds displayed a frequency distribution of at least 20% on the southern coast during winter and at least 15% on the central western coast and sea distant from the eastern coast. Liquid phase seedable clouds showed higher frequencies on land than sea during nonwinter seasons, with a frequency of approximately 8% on the Korean Peninsula in the summer. Ice phase seedable clouds had frequencies of 10-25% in the northern East Sea and West Sea in the winter and an average frequency of 2% in other seasons. In the summer, the occurrence frequency of the ice cloud phase or mixed-phase clouds with a CTT below \(-\)5 \({}^{\circ}\)C within an altitude suitable for precipitation enhancement experiments was very low, at less than 1%. The average CTH of the low-level clouds was 3.33 km, and the average CTT was \(-\)5.64 \({}^{\circ}\)C. In all seasons, the average CTH for the ice cloud phase was 3.24 km, which was 0.81 km higher than water phase seedable clouds. The average temperature was 11.52 K lower at \(-\)8.41 \({}^{\circ}\)C. Winter clouds with an average CTH of 2.20 km that were located below 36\({}^{\circ}\) N exhibited a maximum frequency of approximately 31%, and clouds with an average CTH of 2.48 km occurring on land in the summer showed a maximum frequency of approximately 13%. Excluding clouds on land in the winter, the seasonal changes in ice phase seedable clouds had relatively similar characteristics on land (average of 5% in winter and 1% in summer).
## 5 Methodology, Experiment Design, and Data Analysis
### Experiment
#### 5.2.2 Subjects  
In a controlled environment, 24 female and male subjects aged between 20 and 47 years old participated in the experiment. All subjects were right-handed and had no fingerprint concerns, such as scars or not having a clear fingerprint, which might have impacted the reliability of their fingerprint acquisition. The subjects were informed that a fingerprint system was being evaluated, and the one who can trick the system will get a gift card. The rationale of the award was to motivate participants to try their best to fool the system and to create motivation when participating, which is important to compare willingness to provide a fingerprint vs. unwillingness.
## 3 Fuzzy Approach to Modeling Construction Duration Using Risk Matrix
### Theoretical Aspects of the Proposed Fuzzy Approach
#### 3.1.2 Basic Concepts of the Fuzzy Sets Theory  
Let \(X\) be a non-empty set considered to be the universe of discourse. The fuzzy set \(A\) is a pair (\(X\), \(\mu_{A}\)), where \(\mu_{A}\):\(X\to I\) and \(I=[0,\,1]\). The notion of the fuzzy set has been introduced by [PERSON] [54; 55].  
\[A=\{(x,\mu_{A}(x));x\in\text{X}\}, \tag{11}\]  
Figure 4: Example of input risk lag time into precedence diagram.  
\(\mu_{A}(x)\) is the membership function degree of \(x\) to \(A\). It may also be interpreted as the plausibility degree of the affirmation \"\(x\) belongs to \(A\)\". If \(\mu_{A}(x)=0\), \(x\) is definitely not in \(A\), and if \(\mu_{A}(x)=1\), \(x\) is definitely in \(A\). The intermediate cases are fuzzy.  
Operations on fuzzy sets \(A\) and \(B\), such as the standard intersection (\(\cap\)) and standard union (\(\cup\)), can be displayed in the following Equations (12) and (13), [54; 55]:  
\[\mu_{A\cap B}(x)=\min(\mu_{A}(x),\,\mu_{B}(x)),\,\forall x\in X, \tag{12}\]  
\[\mu_{A\cup B}(x)=\max(\mu_{A}(x),\,\mu_{B}(x)),\,\forall x\in X, \tag{13}\]  
Formulating the mapping from a given input to an output using fuzzy logic is a fuzzy inference system (FIS). The general scheme of the FIS, recorded in the form of fuzzy rules or control rules is as follows in Equation (14).  
\[\text{IF }u=A_{i}\text{ THEN }v=C_{i},\,i=1,\ldots,\,\text{n}, \tag{14}\]  
There are different methods of the FIS such as the Mamdani FIS and Takagi-Sugeno Fuzzy Model. In this study, the Mamdani FIS determines the consequent of rule by combining the rule strength and the output membership function. The Mamdani FIS block diagram is presented in Figure 5 [56].  
In cases in the Mamdani FIS approach, the fuzzy relation R is used, coded by (14), and additionally refers to the compositional rule of inference. Hence, having given as an input the value assignment \(u\):=\(A\) to the output variable \(v\) the value using Equation (15):  
\[v\text{: }=A\circ R, \tag{15}\]  
And it has a form of two-dimensional fuzzy set membership function:  
\[\mu_{A}\cdot_{R}(z)=\max_{x\in X}\{\min\{\mu_{A}(x),\mu_{R}(x,z)\}\},\text{ for all}z\in Z \tag{16}\]  
After getting the output distribution, combine all the consequents. The next step is the defuzzified output distribution. Several defuzzification methods for MamdANI composing rules are used in MATLAB (ver. R2023) software such as centroid, bisector, middle of maximum, smallest of maximum, and largest of maximum [57].  
* Centroid method. The crisp solution is obtained by taking the center point of the fuzzy area and can be written as Equation (17) and presented in Figure 6a:  
Figure 5: Schematic illustration of the Mamdani fuzzy inference system [56].
# Test Center Location Problem: A Bi-Objective Model and Algorithms
## 3 Test Center Location Problem  
The test center location problem (TCLP) seeks to determine the optimal arrangement of a set of \(k\) test centers in a manner that simultaneously minimizes the travel cost for individuals (who are typically tested by their closest center, reflecting real-world conditions) and maximizes the equitable distribution of workload among these centers. Given that we are examining identical test centers, we define a center's workload as the count of individuals it serves. Additionally, we take into account the average travel time between an individual and their closest center. We define the objective of workload balance as the minimization of the disparity between the most heavily populated center and the least heavily populated one. Since individuals are tested in the nearest center, there exists a trade-off between travel time and the balance of workload among the centers. Solving such a problem provides a set of _Pareto-optimal_ solutions, i.e., those that cannot be enhanced in one objective without compromising the other objective. Within this section, we formulate the TCLP formally, and subsequently, we articulate an integer linear programming model tailored to address the problem.
## 5 Performance Evaluation
### Experimental Results Discussion  
The experimental results of this study demonstrate that the LBP-FFT-CNNs method exhibits significant advantages in positioning accuracy, classification performance, and environmental adaptability. Compared to the traditional LRA method, the proposed approach reduces average error by 62.5%, while also achieving a 24% improvement over the LBP_CNNs method, indicating its superior capability in extracting image features and enhancing positioning accuracy. Furthermore, the method maintains a stable error range across different obstacle environments, with the lowest error observed in low-reflectivity materials (e.g., paper) at only 0.8557 cm, highlighting its strong environmental adaptability. Compared with the most representative depth estimation algorithms of the past decade, such as those proposed by [PERSON], [PERSON], and [PERSON], the LBP-FFT-CNNs approach improves accuracy by 51.1% to 66.7%, further validating the effectiveness of integrating LBP and FFT for feature extraction. Additionally, classification experiments reveal that the proposed method achieves an F1 score of 0.971 and an accuracy of 96.3%, demonstrating not only precise target positioning but also effective obstacle classification. Overall, by incorporating multi-scale local features and frequency domain information, the LBP-FFT-CNNs algorithm enhances model robustness and exhibits superior stability in complex environments, providing a novel technological approach for high-precision indoor positioning and obstacle recognition.
## 3 Results and Discussion
### Absorbed Energy  
The absorbed energy values of the V-notched specimens are presented in Table 4. The average value (54.6 ) \(\pm\) 8.01) is almost half that of AISI 316L, suggesting the brittleness of the metal specimens printed using the ME technique.  
Additionally, samples 2 and 5 underwent a complete fracture (Figure 6a), whereas the other specimens exhibited a partial fracture (Figure 6b).  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Absorbed Energy ()** \\ \hline Sample 1 & 62.5 \\ \hline Sample 2 & 58.5 \\ \hline Sample 3 & 42.0 \\ \hline Sample 4 & 48.5 \\ \hline Sample 5 & 61.5 \\ \hline Monolithic AISI316L & 103 [39] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Absorbed energy values of samples.  
Figure 5: Bending stress–stroke curves.  
Figure 6: Samples with complete fracture (**a**), samples with partial fracture (**b**).  
The brittleness of the metal ME samples can be attributed to two main causes. As reported in [29], 5% of porosity could reduce the ductility of the samples. Moreover, the X-ray diffraction (XRD) analysis (Figure 7) indicated the presence of austenite and \(\delta\)-ferrite phases. The portion of austenite represents a metallurgical characteristic of the material because AISI 316L is an austenitic stainless steel that is composed of a single stable austenitic phase at 20 \({}^{\circ}\)C, owing to the presence of nickel. In contrast, the existence of the \(\delta\)-ferrite phase is merely an effect of the thermal history of the samples produced by the ME process and subsequent sintering. The formation of the \(\delta\)-ferrite phase occurred immediately below 1400 \({}^{\circ}\)C. The temperature during the extrusion process reached close to 240 \({}^{\circ}\)C, whereas that during the sintering cycle reached close to the temperature window of stable delta ferrite and was compatible with its formation.  
During the cooling process, the primary \(\delta\)-ferrite solidifies within the metal matrix, and the transformation of \(\delta\) to \(\gamma\) ferrite may occur. Because this transformation is a diffusion-controlled process, the fast cooling rate in the sintering cycle (uncontrolled cooling from 1380 \({}^{\circ}\)C to room temperature) does not guarantee a sufficiently long time for the completion of the transformation [40]; thus, exceeding portions of \(\delta\)-ferrite may be retained in the austenitic matrix of specimens at room temperature. The non-equilibrium cooling phase results in the retention of \(\delta\)-ferrite at the subgrain boundaries [41]. This phenomenon may significantly decrease the strength and ductility and increase the stiffness of the metal ME samples.