esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Bending Strength under Environmental Condition  
Table 6 shows the bending strength of the double raschel GCCM under environmental conditions. The bending strength exhibited in the presence of seawater did not show a significant tendency.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test** & **Test No.** & **Thickness (mm)** & **Width (mm)** & **Impact Energy (kJ/m\({}^{2}\))** \\ \hline \multirow{6}{*}{Before} & 1 & 15.14 & 12.40 & 80.67 \\  & 2 & 15.03 & 12.11 & 82.55 \\  & 3 & 15.22 & 12.04 & 82.18 \\  & 4 & 15.78 & 11.42 & 83.27 \\  & 5 & 15.61 & 11.00 & 87.42 \\  & Average & 15.36 & 11.79 & 83.22 \\  & Standard & & & \\  & Deviaton & 0.2883 & 0.5095 & 2.2660 \\ \hline \multirow{6}{*}{After} & 1 & 10.31 & 13.54 & 127.91 \\  & 2 & 10.42 & 13.68 & 127.65 \\  & 3 & 10.15 & 13.74 & 129.17 \\ \cline{1-1}  & 4 & 10.17 & 13.65 & 129.38 \\ \cline{1-1}  & 5 & 10.30 & 13.94 & 124.79 \\ \cline{1-1}  & Average & 10.27 & 13.71 & 127.78 \\ \cline{1-1}  & Standard & & & \\  & Deviaton & 0.0994 & 0.1321 & 1.6411 \\ \hline \end{tabular}
\end{table}
Table 4: Impact test results of double raschel GCCM.  
\begin{table}
\begin{tabular}{c c} \hline
**Curing Period (Days)** & **Bending Strength (MPa)** \\ \hline
10 & 6.60 \\ \hline
30 & 9.29 \\ \hline \end{tabular}
\end{table}
Table 5: Bending strength of double raschel GCCM with curing period.  
The freezing and thawing phenomena is attributed to the fact that the moisture present within the concrete during the repeated thawing cycles did not undergo proper curing, consequently giving rise to the formation of pores within the concrete as the freezing-thawing cycle continued, even under the challenging condition of ice, where concrete typically shows its weakest performance, the double raschel GCCM exhibited a bending strength of 6.12 MPa. Also, for the double raschel GCCM before and after undergoing acid and alkali treatment, a marginal decrease was observed after alkali treatment, while an increase was actually observed after acidic treatment. From this, it is seen that the marginal reduction in bending strength resulted from the influence of the polyester fiber, which is susceptible to the effects of the alkaline condition. Conversely, the impact under the acidic condition was not substantial, and in fact, a slight increase in bending strength was noted as the curing period extended.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 6 Efficient Power and Data Transfer in BackCom  
The efficient energy and data transfer in BackCom is receiving attention from the research community to power up passive devices. This section highlights various power sources in different types of BackCom. One of the leading power sources that bring BackCom back to life is Amb-BackCom. In addition to efficient power transfer techniques, energy management is also considered for enhancement of lifetime of passive devices. Recent technology conversion and frequency shifting techniques allowing BackCom to use the frequency spectrum of multiple technologies are also discussed.
## 3 Methods
### Clinical Risk Factor Analysis  
We correlated BDS with clinical risk factors. Analysis of clinical risk factors included a subset of participants with critical CHD who required corrective or palliative cardiac surgery within the first month of life who were prospectively enrolled for pre- and post-operative brain MRI scans from 2009-2016. Two hundred ninety-one participants were prospectively enrolled from June 2009 to October 2016. Of these participants, 158 met the exclusion criteria including 57 with no MRI performed, 38 due to prematurity, 38 passed the age threshold, 11 expired preoperatively, 10 had no neonatal surgery, and 4 had a postnatal major genetic diagnosis. Of the 133 term CHD infants with brain MRI meeting the inclusion criteria, 90 participants had sufficient imaging quality for structural analysis and clinical risk factor data collected comprised the study group for this analysis. Participants that had a known major chromosomal abnormality, were premature (<=36 weeks of age), died prior to MRI, or did not require neonatal cardiac surgery were excluded in this analysis. Clinical data were collected from the electronic medical records and included 18 patient-specific and 9 preoperative variables associated with preoperative scan and 6 intra-operative (e.g., cardiopulmonary bypass, deep hypothermia circulatory arrest times) and 12 postoperative variables associated with postoperative scan, as described previously [39; 61]. CHD lesions were classified in several ways (not mutually exclusive), including postnatal cyanosis, presence of aortic arch obstruction, single vs. double ventricles, d-transposition of the great arteries, conotruncal defects, heterotaxy, whether the lesion alters fetal cerebral substrate delivery, and severity of this alteration (normal, altered, severely altered).
## 3 The Proposed Encoder-Decoder Network
### Decoding Search Space Reduction
#### 3.4.2 How to Train the Proposed Decoder  
The back-propagation through the CTC layer to obtain the closed form of \(\frac{\partial y_{t}(k*)}{\partial a_{t}(k)}\) is quite cumbersome, and may last for several pages, so we chose not to present the mathematical derivation in too much detail; for readers interested in the complete derivation of Equation (7) to obtain the gradient of \(P(\mathbf{\ell}|x)\), it can be easily found in the relevant literature or tutorials, such as [7; 42], based on dynamic programming under chained rule of derivation.  
Here we briefly give the closed form for forward loss function calculation \(\mathcal{J}=-\ln P(\mathbf{\ell}|\mathbf{x})\), together with its backward gradient w.r.t. the neural network output \(y_{t}(k)\) (the response of label \(k\) at time \(t\)):  
\[P(\mathbf{\ell}|\mathbf{x}) =\sum_{j=1}^{J=|\mathbf{\ell}|}\alpha_{t}(j)\cdot\beta_{t}(j) \tag{7}\] \[\frac{\partial\mathcal{J}}{\partial y_{t}(k)} =-\frac{\partial\ln P(\mathbf{\ell}|\mathbf{x})}{\partial y_{t}(k)}=- \frac{1}{P(\mathbf{\ell}|\mathbf{x})}\,\frac{\partial P(\mathbf{\ell}|\mathbf{x})}{\partial y _{t}(k)}\] \[=-\frac{1}{P(\mathbf{\ell}|\mathbf{x})\cdot y_{t}(k)}\sum_{j\in label(k)} \alpha_{t}(j)\cdot\beta_{t}(j)\] \[\frac{\partial\mathcal{J}}{\partial a_{t}(k)} =\sum_{k*}\frac{\partial\mathcal{J}}{\partial y_{t}(k*)}\cdot \frac{\partial y_{t}(k*)}{\partial a_{t}(k)}\] \[=y_{t}(k)-\frac{1}{P(\mathbf{\ell}|\mathbf{x})}\left(\sum_{j\in label(k)} \alpha_{t}(j)\cdot\beta_{t}(j)\right)\]  
where:  
* are the un-normalised outputs before the softmax activation function is applied: \(y_{t}(k)=\frac{\exp(a_{t}(k))}{\sum_{k*}\exp(a_{t}(k*))}\), \(k*\) ranges over all outputs; and
* \(\alpha_{t}(j)\), \(\beta_{t}(j)\) are _forward_ and _backward_ variables, respectively. \(\alpha_{t}(j)\) is defined as the summed probability of all paths satisfying \(\mathcal{B}(\mathbf{\pi}_{1:t})=I_{1:j}\), and \(\beta_{t}(j)\) appends to \(\alpha_{t}(j)\) from \(t+1\) that completes \(\mathbf{l}\), where \(\mathbf{l}_{1:j}\) is the first \(j\) actions of \(\mathbf{\ell}\); both \(\alpha_{t}(j)\) and \(\beta_{t}(j)\) can be calculated by recursive inductions: \[\alpha_{t}(j) =\alpha_{t-1}(j)y_{t-1}(b)+\alpha_{t-1}(j-1)y_{t-1}(j)\] (8) \[\beta_{t}(j) =\beta_{t+1}(j)y_{t}(b)+\beta_{t+1}(j+1)y_{t}(j+1)\] \[\alpha_{1}(j) =\begin{cases}\alpha_{t-1}(j=1)=1\\ \alpha_{t-1}(j\
eq 1)=0\end{cases}\] \[\beta_{T}(j) =\begin{cases}\beta_{t-2}(j=|\mathbf{\ell}|)=1\\ \beta_{t-1}(j\
eq|\mathbf{\ell}|)=0\end{cases}\] \[j =1,\cdots,|\mathbf{\ell}|\]  
\(\frac{\partial y_{t}(k*)}{\partial a_{t}(k)}\) in Equation (7) is the final 'error signal' back-propagated through the network during training.
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 8 Conclusions  
This work presents a Random Forest-based approach for event detection and classification in road event detection, using vibration and acceleration data. The results demonstrate the effectiveness of the proposed method in detecting events such as speed bumps, sudden braking, and potholes, while addressing challenges such as class imbalance and feature variability.  
Key findings of this research include the following.  
* The baseline model (Configuration A) provided a solid foundation, achieving an AUC of 0.98106. However, it exhibited limitations in optimizing precision and recall for minority classes.
* Configuration B demonstrated the importance of addressing the class imbalance by improving recall for the majority class, although it came at the cost of slightly reduced overall accuracy and AUC.
* Configuration C, which incorporated hyperparameter tuning, achieved the best balance between precision (0.80), recall (0.75), and AUC (0.94682), demonstrating its robustness for real-world applications.  
Analysis of algorithmic complexity revealed that the computational demands of feature extraction and model training remain feasible for offline implementations but may require optimization for real-time applications. Furthermore, the results highlighted the importance of feature engineering and parameter tuning in improving model performance.  
This research contributes to the field of road event detection by providing a scalable and interpretable framework for event detection using accessible sensor data. The proposed methodology is versatile and can be adapted to other domains that involve similar data types.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a18030127/s1](https://www.mdpi.com/article/10.3390/a18030127/s1)]([https://www.mdpi.com/article/10.3390/a18030127/s1](https://www.mdpi.com/article/10.3390/a18030127/s1)).
* [1]**Author Contributions:** Conceptualization, Investigation: A.A.-G. Validation and Writing--Original Draft: A.M.S. All authors have read and agreed to the published version of the manuscript.
* [2]**Funding:** This research received no external funding.
* [3]**Data Availability Statement:** The generated data are provided as supplementary material to the current manuscript.
* [4]**Acknowledgments:** Acknowledgements to INAOE for supporting the development of this postdoctoral research under the supervision of [PERSON] (Researcher for Mexico); this work will strengthen Project 882 of Conahcyt.
* [5]**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Influence of the Radiometric Resolution  
The radiometric resolution represents the range that is normalized when performing the proposed method, which is closely related to the complexity and time cost. Therefore, the performance evaluation according to the radiometric resolution is performed and the range is set from 8-bit to 14-bit, as mentioned earlier. The average of the NRMSE in the entire band is calculated and considered with the training time, as shown in Figure 11. In the case of site 1, the performance of 8-bit is the highest, and the training time is the lowest, while the performance of 14-bit is the lowest and the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Method** & **Band 1** & **Band 2** & **Band 3** & **Band 4** & **Average** \\ \hline \multirow{6}{*}{Site1} & Subject image & 0.3948 & 0.5077 & 0.5672 & 0.6888 & 0.5396 \\  & Proposed method & **0.1440** & **0.1987** & **0.3073** & **0.1545** & **0.2011** \\  & MS &  
training time is the highest. At site 2, 8-bit has the highest performance and the lowest training time, and 14-bit has the lowest performance (same as site 1), while the training time is the highest in 12-bit mode. The best performance at site 3 is also 8-bit, but the lowest training time is 9-bit, and the lowest performance and the highest training time are the same as at site 1. Overall, similar results are obtained at all sites. As the radiometric resolution increases, the performance decreases, and the training time and complexity increase. In other words, high radiometric resolution leads to overtraining in the radiometric normalization, which requires more complex computations and it interferes with the performance improvements. Thus, 8-bit is selected as the optimal radiometric resolution in this study.
# Optimal Placement of Multiple Feeder Terminal Units Using Intelligent Algorithms
## 6 Conclusions  
In this paper, a mixed configuration optimization model of multiple centralized feeder automation terminal units based on LCC is established, and the reliability of distribution network under the condition of centralized feeder automation is analyzed in detail. Under the constraints of meeting the system reliability requirements, the model effectively solves the optimal FTU placement problem and provides a more targeted and elaborate solution including three types of FTUs rather than configuring \"three remote\" terminal units excessively. The main conclusions can be drawn as follows:  
* Satisfying the reliability constraints of the distribution network means that the reliability requirement is allowed to be lower than the highest level. In this case, when the configuration of FTUs is the only variable, the mixed configuration of multiple FTUs can better coordinate the economic cost and reliability than the blind configuration of \"three remote\" terminal units.
* The FTU configuration scheme with the minimum initial investment cost or the minimum sum of initial investment cost, maintenance cost, and outage loss as the objective function may not be the scheme with the minimum LCC.
* The initial investment cost in the early stage has a certain impact on the cost generated in the operation stage of the distribution network project. The scheme with high initial investment of FTUs may reduce the future outage loss, thus reducing the LCC of the configuration scheme.
* Compared with GA and ACO, DPSO has better performance in solving the optimization problem of FTUs' configuration. The result solved by DPSO is closest to the global optimal solution, with a good stability and fast convergence speed. GA is a feasible option when solving planning problem which does not require high calculation speed, but multiple simulation runs are recommended due to its poor stability. In addition, ACO is not suitable here for the defects in converging to local optimum easily.  
Conceptualization and methodology, D.L. and Q.L.; programming, F.L.; Writing--Original Draft preparation, D.L.; Writing--Review and Editing, G.Z., Z.W., Y.C., and T.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China, grant number 51777078.  
The authors gratefully acknowledge Guangdong Power Grid Planning and Research Centre for providing some data needed in case study.  
The authors declare no conflict of interest.
## 5 Dynamic Bandwidth Allocation
### Initial VN to PN Allocation  
For one VN modeled with SCG, the VDC Allocation component of NXT-Freedom searches for suitable positions to accommodate the VMs and links of this VN. After the VN is mapped onto PN, the upper limit of the sending/receiving rate for each VM will be set according to the redundant bandwidth of every access link that the VN is mapped to, and the intermediate redundant link bandwidth will be calculated. As shown in Figure 6a, two VDCs have their specific VN demands. Suppose they are mapped to the PN in Figure 6b, and each link has the capacity of 100. Because the communication behavior within the VDC is not sufficiently known, it is difficult to determine where the narrowest link is. To prevent VMs from sending more traffic, the bandwidth of access link is used as the initial rate of each VM. Eventually, VM-1 gets 55, VM-3 gets 45, VM-2 and VM-5 get 50 each, and VM-4 gets 100.
## 1 Introduction
As a result, MIMO antennas for GHz applications must be designed to optimize the trade-off between size, gain, and efficiency [26]. Another important consideration in designing MIMO antennas for GHz applications is the radiation pattern [27]. In general, MIMO antennas should radiate in a way that maximizes signal strength while minimizing interference between the different antenna elements. This requires careful design of the antenna elements and their placement within the MIMO array [26; 28]. One common approach is to use directional antennas with a narrow beamwidth to maximize signal strength in the desired direction while minimizing interference with other antennas [29]. MIMO antennas for GHz applications can take a variety of forms, including patch antennas [30], dipole antennas [31], slot antennas [20], and others. Patch antennas are particularly well-suited for GHz applications due to their small size and ease of integration with other electronic components. In addition, patch antennas can be designed to have a wide impedance bandwidth, which allows them to operate over a broad range of frequencies. In this manuscript, we have numerically investigated and experimentally verified the design of a six-element split-ring resonator and circular patch-shaped multi-input, multi-output antenna in the 1-25 GHz frequency range. MIMO antennas are analyzed in terms of several physical parameters, such as reflectance, gain, directivity, VSWR, and electric field distribution. In order to determine whether or not this antenna is suitable for multichannel transmission, we have also provided additional MIMO antenna characteristics, including envelope correlation coefficient, total active reflection coefficient, channel capacity loss, mean effective gain and diversity gain. This manuscript is majorly divided into three subdivisions. The first segments include the design and specification of all five stages of the proposed antenna. The second segment consists of measurement and simulation results and discussions of the proposed antenna. Finally, the last segment demonstrates the influence of the various antenna characteristics in a situation where numerous ports are excited simultaneously.
# Stability Conditions in Lignite Open Pits from Romania, Case Study: Olteu Open Pit
## 3 Results and Discussion  
As shown in the introduction, there are a number of factors that influence the stability of slopes. For the case study analyzed in the paper, the main factors that can be considered are the hydrostatic pressure generated by the water from rock pores, seismic shocks (the analyzed area being characterized by a seismic intensity of 8/MSK scale, the average return period being about half a century [70]) and the vibrations generated by machines.  
Following the in situ documentation, it was found that the vibrations transmitted mainly by the E-02 excavator to the slope, under the conditions where the yellow-brown clay layer was at natural moisture (approximately 40%, after a period without of significant precipitations) favored its fragmentation and the appearance of \"dry flow\" type slides (rolling, falling of \"lumps\" of clay) characterized as rotational and successive slides. These types of slides involve small volumes of rocks, gradually unloading the massif, so the mining activity can continue under relatively safe and satisfactory conditions.  
It is difficult to appreciate the influence of the vibrations on slope stability in conditions of high moisture of the rocks (mainly clayy rocks) when such simulations cannot be carried out in the laboratory [65].  
The authors considered that the vibrations produced by the heavy tonnage machines (excavators) are much lower in intensity and amplitude compared to those originating from natural earthquakes such that they cannot trigger liquefaction-type slides or slides caused by the thixotropic behavior of the material.  
The maximum value for the seismic acceleration in the studied area is a = 0.2 g, the return period of the earthquake is over 2 centuries (about 225 years according to [70]), and  
Figure 5: Laboratory tests: (**a**) direct shearing tests on yellow-brown clay and sandy marl; (**b**) shearing tests on lignite (mandatory planes); (**c**) uniaxial compression and traction (Brazilian method) on lignite [65].  
the exceeding probability is low. Knowing these and the fact that the vibrations contribute to a small degree to triggering a major landslide, in the performed stability analyses, the seismic acceleration was considered to be a = 0.1 g.
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
## 3 Surface Correlation-based Fingerprinting Method
### Generation of Reference Trajectory
#### 3.4.2 Generation of Reference Trajectory after Initial Phase  
After the initial reference trajectory is fixed, a new reference trajectory is again generated by combining the current reference trajectory and the adjacent links. A reference trajectory is fixed for the moving direction and the route of the pedestrian by correlating the newly generated reference trajectories. Specifically, the walking direction is estimated by selecting a reference trajectory between the initial bidirectional links, not in the absolute direction from PDR. Let us assume that the pedestrian moves as shown in Figure 7a; measured _Surface_ during the movement is shown in Figure 7b. The proposed fingerprinting method generates two reference trajectories by combining connected links with the current reference trajectory as shown in Figure 8a and b. Figure 8c and d show the respective reference RSS patterns on the generated reference trajectory.  
In the initial phase, the proposed method fixes a reference trajectory based on the minimum SSD. After the initial phase, it fixes a reference trajectory based on the ratio of SSDs as shown in Equation (5) below:  
\[R=\frac{\rho_{1}}{\rho_{2}},\quad if\ \rho_{1}>\rho_{2} \tag{5}\]  
where \(\rho_{2}\) is the minimum SSD between the reference trajectories and \(\rho_{1}\) is the second smallest value of the SSD. The values of SSDs change after the pedestrian has passed a crossroad as shown in Figure 9a. The reason why the ratio \(R\) has a value 1 before the crossroad is that the reference trajectory 1 and the reference trajectory 2 have a common current reference trajectory. As shown in Figure 9b, after the crossroad, \(R\) increases because of the difference between the measured RSS pattern and the reference RSS patterns. Therefore, it is possible to fix a reference trajectory that matches the pedestrian's walking route using only a threshold for the ratio of SSDs. After fixing the reference trajectory, new reference trajectories are generated by combining links with the fixed reference trajectory again.  
Figure 7: Movement scenario of a pedestrian: (**a**) Route; (**b**) Measured RSS during movement.  
Figure 8: Generation of reference RSS pattern: (**a**) Regenerated reference trajectory 1; (**b**) Regenerated reference trajectory 2; (**c**) RSS pattern of reference trajectory 1; (**d**) RSS pattern of reference trajectory 2.  
Figure 9: Fix a reference trajectory using a ratio between values of SSD: (**a**) SSDs of reference trajectories; (**b**) Ratio between SSDs.