esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Lightweight Deep Learning Approach
### Temporal Efficient Residual Network
#### 3.2.1 Overview of the Proposed TEResNet  
Conventional methods for processing STFT features typically use small convolutional kernels (e.g., 3 \(\times\) 3) to extract high-level characteristics by stacking multiple convolutional layers. It is effective for images, but for time-varying frequency signals, it is difficult for shallower networks to capture both low- and high-frequency information features. Time convolution [37] provides a new insight into the analysis of time-varying signals, transforming the time-frequency map into a frame-by-frame time series rather than a traditional visualization image. This sort of processing is more in line with the natural properties of time series data, highlights the dominance of the temporal dimension of the signal, and has been shown to be effective in speech signal processing [38; 39]. In addition, [PERSON] et al. [40] have shown that dilated convolutions are helpful in the processing of time-varying frequency signals; we employ dilated convolution [41] to increase the receptive field of the convolution kernel.  
The overall architecture of TEResNet consists of STFT feature inputs, feature reshaping, an initial convolutional layer, three basic ResNet-style [42] blocks, the pooling layer, the fully connected layer, and the sigmoid layer, as shown in Figure 10; we make full use of the important information of the frequency features, which are treated as the core of each time frame. The initial convolutional layer and the basic blocks have convolutional kernel sizes of 3 \(\times\) 1 and 9 \(\times\) 1, respectively, and both employ temporal convolution to extract high-level features of the signal. The convolutional layers of the last two basic blocks utilize dilated  
Figure 9: Time and frequency domain plots of intrusion signals in a construction environment, where inside the red box are signals that could threaten the metro, and outside the red box is noise. (**a**) Time domain map of the intrusion signal; (**b**) frequency domain map of the intrusion signal with a window size of 128 and a sliding step of 20.  
convolution. The dilated factors are two and four, respectively, as a way to increase the receptive field. Batch normalization (BN) accelerates the training process by normalizing the activations, while the rectified linear unit (ReLU) activation introduces non-linearity, enhancing the model's capacity to capture complex patterns. Max pooling and average pooling are applied to down-sample the input, reducing its spatial dimensions and the parameters of the model, which helps in extracting essential features from the data.
_Appl. Sci._**2015**, \(5\), 998-1015; doi:10.3390/app5040998  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
New Min-Max Approach to Optimal Choice of the Weights in Multi-Criteria Group Decision-Making Problems  
**[PERSON], [PERSON] \({}^{*}\) and [PERSON]**  
School of Mathematics and Statistics, Central South University, Changsha 410083, China;  
E-Mails: [EMAIL_ADDRESS] (M.C.); csu\({}_{-}\)[EMAIL_ADDRESS] (X.C.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel./Fax: +86-731-8866-0152.  
Academic Editor: [PERSON]  
_Received: 26 August 2015 / Accepted: 27 October 2015 / Published: 3 November 2015_  
**Abstract:** In multi-criteria group decision-making (MCGDM), one of the most important problems is to determine the weights of criteria and experts. This paper intends to present two Min-Max models to optimize the point estimates of the weights. Since each expert generally possesses a uniform viewpoint on the importance (weighted value) of each criterion when he/she needs to rank the alternatives, the objective function in the first model is to minimize the maximum variation between the actual score vector and the ideal one for all the alternatives such that the optimal weights of criteria are consistent in ranking all the alternatives for the same expert. The second model is designed to optimize the weights of experts such that the obtained overall evaluation for each alternative can collect the perspectives of the experts as many as possible. Thus, the objective function in the second model is to minimize the maximum variation between the actual vector of evaluations and the ideal one for all the experts, such that the optimal weights can reduce the difference among the experts in evaluating the same alternative. For the constructed Min-Max models, another focus in this paper is on the development of an efficient algorithm for the optimal weights. Some applications are employed to show the significance of the models and algorithm. From the numerical results, it is clear that the developed Min-Max models more effectively solve the MCGDM problems including the ones with incomplete score matrices, compared with the methods available in the literature. Specifically, by the proposed method, (1) the evaluation uniformity of each expert on the same criteria is guaranteed; (2) The overall evaluation for each alternative can collect the judgements of the experts as many as possible; (3) The highest discrimination degree of the alternatives is obtained.  
keywords:group decision-making; Min-Max model; algorithm; optimal weights; nonlinear program +
Footnote †: journal: Journal of the American Statistical Association
## 2 Initial Detection of Nodule Candidates
### Moment-of-Inertia Filter  
Nodules are often observed as globular regions, whereas blood vessels are as cylindrical regions. These two-type regions are distinguished based on the moment-of-inertia where voxel intensities are regarded as mass after applying the radial suppression filter. First, a gamma correction is applied to \(g(x,\ y,\ z)\) as follows:  
Figure 4: Results obtained by applying the radial suppression filter to the VOIs in Figure 1. (**a**) nodule; (**b**) blood vessel.  
Figure 3: Effect of a radial suppression. (**a**) example of an intensity profile; (**b**) suppression result.  
Figure 2: Radial suppression of voxel intensities.  
\[g^{\gamma}(x,\ y,\ z)=\{g(x,\ y,\ z)\}^{\gamma}, \tag{3}\]  
where \(g^{\gamma}(x,\ y,\ z)\) is the corrected image, and \(\gamma\) is the parameter of the gamma correction.  
A moment-of-inertia tensor \(\mathbf{I}\) is defined by  
\[\mathbf{I}=\left(\begin{array}{ccc}I_{xx}&I_{xy}&I_{xz}\\ I_{yx}&I_{yy}&I_{yz}\\ I_{zx}&I_{zy}&I_{zz}\end{array}\right), \tag{4}\]  
where  
\[I_{xx}=\sum_{(x,\,y,\,z)\ \in K_{sp}(r_{fix})}g^{\gamma}(x,\,y,\,z)( y^{2}+z^{2}), \tag{5}\] \[I_{yz}=I_{zy}=-\sum_{(x,\,y,\,z)\ \in\ K_{sp}(r_{fix})}g^{\gamma}(x,\,y,\,z)yz. \tag{6}\]  
\(K_{sp}(r_{fix})\) is a domain of the spherical VOI whose radius is \(r_{fix}\).  
\(I_{yy}\), \(I_{zz}\), \(I_{xy}\), \(I_{yx}\), \(I_{xz}\) and \(I_{zx}\) are defined in the same manner. From \(\mathbf{I}\), eigenvalues \(\sigma_{1}(\mathbf{I})\), \(\sigma_{2}(\mathbf{I})\) and \(\sigma_{3}(\mathbf{I})\ \left(\sigma_{1}(\mathbf{I})\leq\sigma_{2}(\mathbf{I}) \leq\sigma_{3}(\mathbf{I})\right)\) and eigenvectors \(\mathbf{u}_{1}(\mathbf{I})\), \(\mathbf{u}_{2}(\mathbf{I})\) and \(\mathbf{u}_{3}(\mathbf{I})\) are calculated, where \(\sigma_{i}(\mathbf{I})\) corresponds to \(\mathbf{u}_{i}(\mathbf{I})\). If a candidate region is spherical, the three eigenvalues are the same as shown in Figure 5(a). If it is cylindrical, the eigenvalue corresponding to the longest direction of the region is smaller than the others as shown in Figure 5(b). A ratio between the eigenvalues \(\frac{\sigma_{1}(\mathbf{I})}{\sigma_{3}(\mathbf{I})}\) is set into each voxel as the output of the filter as follows:  
\[h(x,\ y,\ z)=\frac{\sigma_{1}(\mathbf{I})}{\sigma_{3}(\mathbf{I})}. \tag{7}\]  
The ratio represents an indicator to distinguish a nodule from the others. The larger the ratio is, the higher the confidence of a nodule is. Figure 6 shows the results obtained by applying the moment-of-inertia filter to the VOIs in Figure 4. In Figure 6(a), the nodule is successfully enhanced. In Figure 6(b), on the other hand, the blood vessel has excessively high values in its integument regions. They are compensated by use of the _center displacement filter_ as described below.  
Figure 5: Relationship of three eigenvalues of the moment-of-inertia filter. (**a**) globular; (**b**) cylindrical.
# Single-Handed Gesture Recognition with RGB Camera for Drone Motion Control
## 3 Methodology
### Hand Gesture Vocabulary for Drone Control
Meanwhile, when presenting the neutral gesture, the drone movement is controlled by a combination of hand gestures. motion-based and posture-based gestures. For motion-based gestures, the translation of a user's hand forward and backward along the camera's focal axis can be intuitively aligned with the pitch direction. Consequently, moving the hand forward results in the drone pitching up and moving forward, while moving the hand backward causes the drone to pitch down and move backward. Similarly, the control of yaw can be achieved by rotating the hand to the right or left. From the neutral gesture, when the hand rotates from the baseline, we can measure the angle and direction of the rotation. This measurement can effectively correspond to the yaw rotation of the drone. The roll and throttle correspond to posture-based gestures. The roll represents a movement to the left or right. Since it is often used to maneuver the drone around obstacles or to follow a moving target, a low-fatigue and intuitive hand gesture is required. To address this, we use the thumb, which can be easily extended along with the index finger [23]. In other words, when the thumb is extended to the left with the index finger extended, it indicates a leftward movement. Conversely, it indicates a rightward movement when the thumb is extended to the right. Similarly, the throttle is defined by the extension of the middle and pinky fingers. When the middle finger is extended along with the index finger, the drone will increase its throttle and ascend. Conversely, if the pinky finger is extended, the drone will decrease its throttle and descend. The extension of the pinky finger to decrease the throttle is comparatively less intuitive than other gestures. This discrepancy arises because the direction of finger extension does not align well with the corresponding throttle movement. Conversely, extending the middle finger downward for throttle control is more intuitive. Nevertheless, the configuration of our RGB camera viewpoint leads to the occlusion of other fingers when employing this gesture. As a mitigation strategy, we opt to maintain the less intuitive throttle-down gesture, prioritizing the minimization of occlusion while ensuring the intuitiveness of the remaining gestures. Additionally, both of these finger gestures can be combined with the thumb gesture. Our posture-based gestures are shown in Figure 3.  
Figure 3: Hand gesture examples for manipulation of roll and throttle. From the neutral position, the commands for both throttle and roll movements consist of the thumb, middle, and pinky fingers. The stop sign is excluded from this illustration.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 3 Data Acquisition Methods
### Sensor Calibration
#### 3.2.3 DS18B20 Temperature Sensor Calibration  
The DS18B20 temperature sensor does not require frequent calibration since it is factory-calibrated with high precision. However, to ensure reliability in high-precision applications, we periodically verify its accuracy by comparing its readings to those of a reliable reference thermometer. If discrepancies are detected, the sensor's measurements are adjusted via software to maintain high accuracy in real-world applications. Routine maintenance of the temperature sensor also involves keeping its surface clean and avoiding exposure to extreme temperatures or environmental conditions, which could introduce potential measurement errors.
## 4 Establishment of a New Prediction Model for Blasting Vibration Attenuation
### Prediction Model for Blast Vibration Attenuation, Taking Elevation Effect into Account
As a result, the blasting vibration attenuation models for each of the three blasting tests' directions are shown in \[\frac{v}{c}=\varphi(\frac{\mu}{R},\frac{\rho}{QR^{-3}},\theta,\frac{a}{R^{-1}c^{2 }},\frac{f}{R^{-1}c},\frac{t}{Rc^{-1}},\frac{H}{R}) \tag{6}\]0  
Table 6 shows that the layer inclination angle-accounting blasting vibration attenuation prediction model has a high fitting degree in three directions, and fitting determination coefficients that are all above 0.93. Additionally, there is a gradual increase in the fitting degree in the X, Y and Z directions. Specifically, the Z direction has a substantially higher fitting degree than the X and Y directions. This indicates that Equation (14) applies better to the Z direction, indicating that the layer inclination angle may have a significant impact on the vibration speed in the Z direction. To further analyze the influence of bedding plane inclination on the peak particle velocity (PPV) and to evaluate the accuracy and advantages of the blasting vibration attenuation prediction model that incorporates bedding plane inclination, the traditional attenuation model and the elevation-effect-only model were used to fit the PPV in each direction for the three blasting tests. The summarized results are presented in \[\frac{v}{c}=\varphi(\frac{\mu}{R},\frac{\rho}{QR^{-3}},\theta,\frac{a}{R^{-1}c^{2 }},\frac{f}{R^{-1}c},\frac{t}{Rc^{-1}},\frac{H}{R}) \tag{6}\]1  
Table 7 shows that compared to the conventional blasting vibration attenuation prediction model, the model that takes the elevation effect into account has a higher fitting accuracy. The Z direction exhibits a significant variation in fitting degree, confirming the impact of slope elevation on blasting vibration. The vertical direction has the greatest influence among these directions. On the other hand, Tables 3, 4 and 7 show that the common fitting accuracy of multiple blasting test data is lower than the fitting accuracy of single \begin{table}
\begin{tabular}{c c c c c} \hline
**Test** & **Direction** & \multicolumn{2}{c}{**Fitting Results Considering Elevation Effect**} & \multicolumn{2}{c}{**Coefficient of**} \\
**Sequence** & **Direction** & \multicolumn{2}{c}{**Correction**} & \multicolumn{2}{c}{**Determination (\(r^{2}\))**} \\ \hline \multirow{4}{*}{I/II/III} & X-direction & \(v=28.1278\big{(}\frac{\sqrt{Q}}{R}\big{)}^{1.3251}\big{(}\frac{H}{R}\big{)}^{-0.0650}\big{(}\frac{\theta}{\pi}\big{)}^{-0.5353}\) & 0.9928 \\  & Y-direction & \(v=21.8309\big{(}\frac{\sqrt{Q}}{R}\big{)}^{1.1159}\big{(}\frac{H}{R}\big{)}^{-0.1881}\big{(}\frac{\theta}{\pi}\big{)}^{-0.0065}\) & 0.9862 \\  & Z-direction & \(v=6.0685\big{(}\frac{\sqrt{Q}}{R}\big{)}^{0.5862}\big{(}\frac{H}{R}\big{)}^{-0.5061}\big{(}\frac{\theta}{\pi}\big{)}^{-0.1699}\) & 0.9946 \\ \hline \end{tabular}
\end{table} 
Table 6: Fitting results considering layer inclination angle.
# A Novel Magnetic Respiratory Sensor for Human Healthcare
## 3 Results and Discussion  
### Real-Time Monitoring of Human Respiratory Motion at Different Ages  
Research has shown that respiratory rate varies considerably with age [21; 22; 23; 24; 25; 26; 27]. For instance, an infant (1-12 moths) breathes between 30 and 60 times per minute, toddlers (1-2 years) breathe anywhere from 24 to 40 times per minute, and preschoolers (3-5 years) breathe between 22 and 34 times per minute. School-aged children (6-18 years) breathe anywhere from 18 to 30 times per minute, while an adult (18-35 years) breathes between 12 and 18 times per minute. Elderly people (>50 years) have respiratory rates between 16 and 25 breaths per minute. A respiratory rate of more than 25 breaths per minute may indicate a respiratory issue, such as tachypnea [26; 27].  
Therefore, it is essential to examine if the magnetic respiratory device can precisely monitor and distinguish breathing patterns of people at different ages. The tests were performed on three volunteered subjects (a, b, c) of ages 30, 36, and 42 years old (YO). The results shown in Figure 2a-c indicate that the 30 YO breathed the strongest and at a rate of 13 times per minute. The 36 YO breathed 15 times per minute and the 42 YO breathed 17 times per minute. The tests were repeated several times and showed reproducible and consistent results (see Figure 2d-f). These results demonstrate that each person has a characteristic breathing pattern. When one gets older, they breathe weaker, and their number of breaths per minute (the respiratory rate) increases. This finding is consistent with those reported in previous works [21; 22; 23; 24; 25; 26; 27]. It is also worth noting in Figure 2a-c that the breath amplitude, which is proportional to the change in impedance due to breathing (defined as \(\Delta Z\) = \(Z_{\text{h}}\) - \(Z_{\text{l}}\)), of subject 2 is significantly lower than those of the other two (subjects 1 and 3). This could reveal a respiratory issue associated with this person, which also appears to be in line with the other test results presented below.  
Shortness of breath or abnormal breathing is noticed near \(t\) = 20 s for the 30 YO, between 20 and 40 s for the 36 YO, and at 35 and 45 s for the 42 YO, in Figure 2a-c. To gain more information from these features, we analyzed the peak-to-peak time and plotted it as a function of time. As one can see in Figure 3a-c, there are some abnormal changes in the breathing pattern at the previously mentioned times. Such features of abnormal breathing or shortness of breath are not detectable by an Apple Watch tracker or a commercially available respiratory monitor. This demonstrates the increased capacity of our ultrasensitive magnetic respiratory device for human healthcare monitoring.  
Figure 2: Normal breathing patterns of three subjects of ages (**a**) 30, (**b**) 36, and (**c**) 42 years old. (**d–f**) Tests were repeated five times and showed consistent and reproducible results in number of breaths per minute for each subject.
## 1 Introduction
The major contributions of this work are that the system was developed and tested in real-world scenarios, including mountainous forest and watercourse environments. We introduced the multi-hop network using IEEE 802.15.4 XBee3 micro-modules with an autonomous communication protocol. Additionally, the wireless node deployment solution being able to maintain reliable communications for practical testing was presented. The system's performances were evaluated through the RSSI level, PDR, and ETED, and the experimental results showed the success of the multi-hop WSN deployment and real-time communications. The structure of this paper is as follows: Section 2 describes the multi-hop WSN, including the wireless network and sensor node deployment solution. The experiments are explained in Section 3, while the performance metrics are in Section 4. Section 5 provides the results and a discussion. Finally, we conclude the paper in Section 6.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 3 Proposed ACS Architecture
### Access Network
#### 3.2.1 WiFi  
This network is fully SDN-based, having several OF-APs that are connected to an OF switch that concentrates all the traffic coming from the OF-APs. The use of additional aggregation OF switches depends on the number of deployed OF-APs.
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 3 Future Perspectives  
A discussion on the available data products and image classification algorithms is made in the attempt to solve some of the problems identified (Section 2.2). The special requirements of Li exploration are taken into account. New types of approaches and applications to other Li deposits are addressed.
## 3 Results and Discussion
### Microhardness  
Figure 4 shows the microhardness results of the surface-treated samples. The loading and unloading curves show the plastic elastic behavior of the surface layers. The measured microhardness and the determined elastic modulus of the samples are shown in Table 3. The hardness test loading and unloading curves show different gradients and, therefore, a different elastic modulus. The results are in line with the literature: (1) the hardness of TiAlN based on [37] is 22.7 \(\pm\) 0.9 GPa, whereas the hardness of N-H-PVD is 2938 HV0,01, corresponding to approximately 28.81 GPa; (2) the elastic modulus of TiAlN layer based on [37] is E = 304 \(\pm\) 11 GPa, correlating well with our measurement of sample N-H-PVD of E = 202 GPa.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Treatment** & **Sample Name** \\ \hline Hardened & N-H \\ Hardened + Plasma Nitrided & N-H-PN \\ Hardened + TiN/AlTiN (PVD coating) & N-H-PVD \\ Hardened + Plasma Nitrided +TiN/AlTiN & N-H-PN-PVD \\ (PVD coating) &  \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample treatments and designated sample names.  
Figure 4: The loading-unloading test results carried out on (**a**) N-H-PN (**b**) N-H-PVD and (**c**) N-H-PN-PVD.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 3 Geospatial Data Processing
### GPS Time Series Tendency and Seasonality Analysis
#### 3.3.1 Robust Time-Series Data: Outlier Removal and Interpolation  
Before calculating the IMFs, it is essential to prepare the signals by eliminating outliers and interpolating gaps. The interpolation for missing data is an essential step before processing GNSS time series with other advanced methods [47], as missing data may occur for various reasons [48], e.g., due to weather contingencies among other causes, and could affect the accuracy of the trend analysis. To achieve a robust data series, we decided to combine the signals from the GRCS and GBCS stations. As both stations are less than 50 km apart and above the same icefield, we can assume that the sites have a similar crustal uplift response. The study period considers 2154 days in total, with the GBCS series covering 42% and the GRCS series covering 17% of this period of time. In a first step, the time series of both stations were taken to the same altitudinal scale through a least-squares adjustment method since they had an offset of approximately 200 m. From now on, we will call the new signal GBGR-CS.  
For the analysis of the trend and seasonality of the GBGR-CS station, we annexed two continuous stations belonging to the IGS network, CHLT and VOGH. We removed outliers with the three-sigma criterion. Subsequently, an interpolator was used to fill in missing data in each series. Next, the average year without a trend was calculated. This was achieved by comparing and calculating the daily average for each site. The new incorporated data do not come from a simple linear interpolation of the previous and subsequent points. However, they are calculated from the average year and the trend of the site for that time.