esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
## 2 Balancing Circuit
### ZVS Analysis  
According to Figure 5, when the turn-off time of the MOSFETs' conduction equals the resonant capacitors' charging and discharging time, the voltage of the MOSFETs at the time of turn-on and turn-off is zero; then, ZVS can be achieved. During the turn-off time of the MOSFETs, the charging and discharging waveforms of the capacitor are symmetrical and the balancing parameters are the same. Thus, \(L_{m1}=L_{m2}\) and \(C_{r1}=C_{r2}\) are replaced by \(L_{m}\) and \(C_{r}\), respectively.  
In Figure 4a, Q1 and Q2 are controlled by the same clock; therefore, they have the same duration. The duration time of each stage can be obtained from (14) to (21):  
\[\left\{\begin{array}{l}t_{2}-t_{1}=arcot\frac{\sqrt{L_{m1}}\mu(t_{1})}{\sqrt {C_{r}}U_{B}}\sqrt{L_{m}C_{r}}\\ t_{3}-t_{2}=\frac{\pi}{2}\sqrt{L_{m}C_{r}}\\ t_{4}-t_{3}=\frac{\pi}{2}\sqrt{L_{m}C_{r}}\\ t_{5}-t_{4}=arcsin\frac{\sqrt{C_{r}}U_{B}}{\sqrt{L_{m}}i_{Lm}(t_{4})}\sqrt{L_ {m}C_{r}}\end{array}\right. \tag{22}\]  
where \(i_{Lm}(t)\) replaces \(i_{Lm1}(t)\) and \(i_{Lm2}(t)\), and \(U_{B}\) replaces \(U_{H}\) and \(U_{L}\).  
According to (22), the following relationship can be obtained:  
\[t_{3}-t_{2}=t_{4}-t_{3}=\frac{\pi}{2}\sqrt{L_{m}C_{r}} \tag{23}\]  
Therefore, \(T_{r}\) in Figure 5 can be calculated by:  
\[T_{r}=\pi\sqrt{L_{m}C_{r}} \tag{24}\]  
According to (23), when \(t_{2}-t_{1}\) and \(t_{5}-t_{4}\) satisfy (25), ZVS can be realized normally in \(T_{\text{off}}\):  
\[t_{2}-t_{1}=t_{5}-t_{4} \tag{25}\]  
We assume that:  
\[\left\{\begin{array}{l}\alpha_{1}=arcot\frac{\sqrt{L_{m1}}\mu(t_{1})}{ \sqrt{C_{r}}U_{B}}\\ \alpha_{2}=arcsin\frac{\sqrt{C_{r}}U_{B}}{\sqrt{L_{m}}i_{Lm}(t_{4})}\end{array}\right. \tag{26}\]  
If and only if \(\alpha_{1}\) and \(\alpha_{2}\) satisfy the following equation, (25) is true:  
\[\alpha_{1}=\alpha_{2}=\frac{13\pi}{45} \tag{27}\]  
According to (23) and (27), \(T_{c}\) can be calculated by:  
\[T_{c}=\frac{26\pi}{45}\sqrt{L_{m}C_{r}} \tag{28}\]  
From Figure 5, it can be seen that:  
\[T_{\text{off}}=T_{c}+T_{r}=\frac{71\pi}{45}\sqrt{L_{m}C_{r}} \tag{29}\]  
The duty cycle of the balancing circuit to realize ZVS can be expressed as:  
\[D=1-\frac{T_{off}}{T_{S}}=1-\frac{71\pi\sqrt{L_{m}C_{r}}}{45T_{S}} \tag{30}\]  
In summary, the duty cycle of the MOSFETs to realize ZVS at any switching cycle \(T_{S}\) can be calculated by (30).
# Concrete Crack Width Measurement Using a Laser Beam and Image Processing Algorithms
## 4 Conclusions and Recommendations  
This paper presented a novel method for measuring crack width in millimeters using image processing algorithms coupled with a unique laser beam technique. The main contribution of this method is the use of the laser beam projected onto the measuring surface to allow for the conversion of the crack width measured in pixels to millimeters. The following conclusions can be drawn from the study:  
* The relationship between the distance to the measurement plane and the diameter of the laser beam is well established.
* The conversion factor, \(\alpha_{c}\), is defined and can be obtained using the established relationship between the distance to the plane of the measurement and the laser beam diameter, in conjunction with the laser beam diameter in pixels resulting from image processing algorithms.
* A unique method for determining the width of concrete cracks in millimeters has been devised using a laser beam and image processing algorithms through the conversion factor.
* The developed method is innovative and produced highly accurate findings that were closely in agreement with the actual crack width.
* The outcomes obtained through this method are suitable for verifying compliance with the allowable limits established in international standards, which are commonly expressed in metric or SI units.  
It is expected further work could maximize the effective use of the developed method, such as expanding the image database for refined deep learning for the binarization and segmentation process and acquiring images by attaching the laser to a drone with cameras. Moreover, it is highly desirable to automate the process of performing calculations and crack segmentations on acquired images.  
Conceptualization, M.A.N., J.B. and I.D.W.; methodology, M.A.N., J.B. and I.D.W.; software, M.A.N.; validation, J.B. and I.D.W.; investigation, M.A.N.; resources, J.B.; data curation, M.A.N.; writing--original draft preparation, M.A.N.; writing--review and editing, J.B. and I.D.W.; visualization, M.A.N.; supervision, J.B. and I.D.W.; project administration, J.B. and I.D.W.; funding acquisition, J.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Volatile Component Analysis of CSB  
The volatile compounds present in CSB were analyzed utilizing a headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS) system, employing a 50/30 \(\upmu\)m PDMS/DVB/CAR fiber from Supelco (Bellefonte, PA, USA), as per the methodology previously described by [PERSON] et al. (2016). The GC-MS analysis was conducted using a Shimadzu QP2010 instrument (Shimadzu Corporation, Kyoto, Japan) equipped with a DB-17 MS chromatographic column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA). The SPME fiber was introduced into the GC injector, and the adsorbed volatiles were thermally desorbed at 250 \({}^{\circ}\)C for a duration of 3 min. A sample of approximately 5 g of CSB was placed in a 20 mL headspace vial for analysis. The initial oven temperature was set at 40 \({}^{\circ}\)C, increased to 120 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min, then further elevated to 240 \({}^{\circ}\)C at a rate of 6 \({}^{\circ}\)C/min, and maintained at this temperature for 9 min. The identification of volatile compounds was achieved through the use of retention indices and mass spectra, with only those compounds demonstrating a similarity of greater than 85%, with the NIST14 library being considered.
## 3 Results
### Regression Analysis of Rainfall Erosivity Parameters with Splash Erosion
#### 3.4.2 Splash Erosion and Mean Rainfall Intensity  
Splash erosion plotted against the \(I_{av}\) (calculated according to Equation (4)), resulted in linear relationships for the three soils (Figure 7). Compared to previous results with \(KE_{h}\), most of the data were grouped in the range of low intensities up to 10 mm h\({}^{-1}\), and the highest \(I_{av}\) corresponds to a \(KE_{h}\) of 650 J m\({}^{-2}\) h\({}^{-1}\). The three extreme measurements from the previous example with \(KE_{h}\) (E\({}_{1-3}\)) were more linearly distributed with increasing \(I_{av}\); however, they still deviate from the regression lines (Figure 7a).  
The regression analysis without extreme observations did not greatly affect the linear regression for the three soils; nevertheless, \(R^{2}\) increased for all soils, with the highest increase from 0.81 to 0.93 calculated for the ZW soil (Table 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Soil** & **Equation** & \(R^{2}\) & **RMSE** \\ \hline \multirow{3}{*}{\(I_{av}\) (including E\({}_{1}\), E\({}_{2}\), E\({}_{3}\))} & ZW & \(S_{h}=39.765\cdot I_{av}-47.492\) & 0.81 & 77.08 \\  & MI & \(S_{h}=38.379\cdot I_{av}-39.707\) & 0.86 & 62.50 \\  & BK & \(S_{h}=38.578\cdot I_{av}-34.877\) & 0.82 & 73.06 \\ \hline \multirow{3}{*}{\(I_{av}\) (excluding E\({}_{1}\), E\({}_{2}\), E\({}_{3}\))} & ZW & \(S_{h}=37.658\cdot I_{av}-42.101\) & 0.93 & 37.78 \\  & MI & \(S_{h}=38.691\cdot I_{av}-36.893\) & 0.93 & 37.68 \\ \cline{1-1}  & BK & \(S_{h}=36.960\cdot I_{av}-30.124\) & 0.93 & 37.66 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Main outputs of the regression analysis between mean splash erosion \(S_{h}\) (g m\({}^{-2}\) h\({}^{-1}\)) for Zwerbach (ZW), Mistelbach (MI), and Bilkovice (BK) soil, and mean rainfall intensity \(I_{av}\) (mm h\({}^{-1}\)). The \(R^{2}\) is the determination coefficient of the regression model, and RMSE (g m\({}^{-2}\) h\({}^{-1}\)) indicates the root mean squared error. E\({}_{1}\), E\({}_{2}\), and E\({}_{3}\) denote extreme observations.  
Figure 7: Mean splash erosion for Zwerbach (ZW), Mistelbach (MI), and Bilkovice (BK) soil versus mean rainfall intensity, including (**a**) all observations (\(n=80\)) and (**b**) without extreme observations (\(n=77\)). The dashed line represents the regression function for ZW, as does the solid line for MI and the dashed–dotted line for BK soil. The points inside the dashed boxes indicate extreme observations (E\({}_{1}\), E\({}_{2}\), and E\({}_{3}\)) per soil.
## 4 Applications of AI Techniques in Power Systems
### Voltage Stability  
Voltage must be kept within specific limits to preserve system stability and avoid voltage collapse. Continuous monitoring is critical for this. Recently, ANN-based systems have been presented [96] for ANN-based coordinated control of the under-load tap changing (ULTC) transformer and static synchronous compensator (STATCOM). Because of its complexity and ambiguity, FL can be employed efficiently in reactive power and voltage regulation. Recently, [PERSON] et al. developed a strategy for controlling regional voltage profiles using a knowledge base [88]. [PERSON] et al. [97] have proposed ES-based approaches for supervisory control and data acquisition (SCADA) systems. Heuristic search and predictive control have recently been applied [98] to achieve a coordination strategy to prevent voltage collapse. [PERSON] et al. [99] proposed neuro-fuzzy systems, which rank several contingencies for voltage stability, while [PERSON] et al. [99] proposed hybrid systems employing FL-ES. Because they are relatively slow for continuous monitoring, optimization techniques such as GA, SA, ACS, and TS have not been widely used for voltage stability concerns. FL and ES are projected to lead in the coming years, with hybridizations being possible. Voltage control may benefit from the hybridization of optimization methods and control approaches.
# Searchable Data Vault: Encrypted Queries in Secure Distributed Cloud Storage
## 5 Implementation
### Performance  
For performance, we measure the computation time for building the index table (Figure 11a) and for uploading the encrypted blocks (Figure 11b), as compared to directly uploading the original document unencrypted. This gives us the computation overhead due to the SDV system, and how practical it is. The uploading process is simulated by submitting blocks and documents through a FTP server, so that we do not have to take into account Internet connection delays. However, we note that the current deployed prototype does distribute encrypted blocks to both local storage and third-party cloud servers (i.e., Google Drive) and can be configured to add more servers. We divided a document  
Figure 10: Retrieving a document. (**a**) Downloaded file saved; (**b**) Download: Server log showing block identifiers and retrieval of blocks from Google Drive and local server.  
into 5, 10, 15, 20, or 25 blocks, and applied them to four different text documents with increasing file sizes: 1 MB, 5 MB, 10 MB and 100 MB. Table 2 shows the test data for 5 blocks and 25 blocks, while the detailed results are displayed in the graphs in Figure 11. The time measured is in milliseconds (ms).  
From Figure 11a, we may summarize that computation time of index generation grows linearly with the size of the document, which is an expected behavior. However, the number of blocks seems to have a marginal effect on computation to generating indexes, at least in the range of 5-25 blocks. In contrast, the number of blocks affects the efficiency of uploading a document to the storage server,  
Figure 11: Performance results. (**a**) Indexing; (**b**) Uploading Blocks.  
as can be observed in Figure 11b, where \"Full document\" denotes an original unencrypted document. Given the time required grows linearly with the number of blocks when uploading these blocks, a smaller number of blocks per document is desirable. However, there is a question as to whether such a configuration leaks more information as compared to having a large number of blocks.  
On the other hand, if we fix the block size, then the number of blocks grows linearly with the size of the document. Uploading blocks of a large document will thus incur larger overhead. For instance, we set the block size to 500 KB in our experiment. This means for a 100 MB document, 200 blocks were generated and it takes 2 s to build the index and 7.2 s to upload the document.
# Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
###### Abstract  
Traffic speed prediction is a vital part of the intelligent transportation system (ITS). Predicting accurate traffic speed is becoming an important and challenging task with the rapid development of deep learning and increasing traffic data size. In this study, we present a deep-learning-based architecture for network-wide traffic speed prediction. We propose a deep-learning-based model consisting of a fully convolutional neural network, bidirectional long short-term memory, and attention mechanism. Our design aims to consider both backward and forward dependencies of traffic data to predict multistep network-wide traffic speed. Thus, we propose a model named AttBDLTSM-FCN for multistep traffic speed prediction. We augmented the attention-based bidirectional long short-term memory recurrent neural network with the fully convolutional network to predict the network-wide traffic speed. In traffic speed prediction, this is the first time that augmentation of AttBDLSTM and FCN have been exploited to measure the backward dependency of traffic data, as a building block for a deep architecture model. We conducted comprehensive experiments, and the experimental evaluations illustrated that the proposed architecture achieved better performance compared to state-of-the-art models when considering the short and long traffic speed prediction, e.g., 15, 30, and 60 min, in multistep traffic speed prediction.  
attention mechanism; bidirectional long short-term memory; fully convolutional neural network; intelligent transportation system (ITS); speed prediction +
Footnote †: journal:  
0000-0001-999-723-3000
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Filters** & **East (m)** & **North (m)** & **Altitude (m)** \\ \hline EKF [29] & \(1.0496\times 10^{4}\) & \(2.8975\times 10^{3}\) & \(7.4510\times 10^{3}\) \\ MCCEKF-BC [31] & \(1.1562\times 10^{3}\) & \(752.3388\) & \(1.1221\times 10^{3}\) \\ MCCEKF-DS [34] & \(603.6376\) & \(469.2932\) & \(904.7247\) \\ MCCEKF-GL [32] & \(597.2612\) & \(493.8688\) & \(929.5508\) \\
**NRMCCEKF [Present]** & \(\mathbf{603.4894}\) & \(\mathbf{469.0979}\) & \(\mathbf{904.6238}\) \\
**Mixture-LRMCCEKF [Present]** & \(\mathbf{603.5379}\) & \(\mathbf{469.1823}\) & \(\mathbf{904.6829}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance comparison in terms of mean square errors (MSEs) for different filters with respect to different positions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Filters** & **East (m)** & **North (m)** & **Altitude (m)** \\ \hline EKF [29] & \(-4.4477\) & \(2.0030\) & \(-7.7829\) \\ MCCEKF-BC [31] & \(-3.5017\) & \(-0.4887\) & \(-8.6859\) \\ MCCEKF-DS [34] & \(-1.9928\) & \(0.3381\) & \(-7.2658\) \\ MCCEKF-GL [32] & \(-2.0206\) & \(0.3320\) & \(-7.3809\) \\
**MCCEKF-NR [Present]** & \(\mathbf{-1.8926}\) & \(\mathbf{0.2726}\) & \(\mathbf{-7.1856}\) \\
**Mixture-LRMCCEKF [Present]** & \(\mathbf{-2.0108}\) & \(\mathbf{0.2862}\) & \(\mathbf{-7.2701}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance comparison in terms of root mean square errors (RMSEs) for different filters with respect to different positions. Figure 7: _Cont._
## 3 Results and Discussion
### The Effects of Pressure Angle on Scuffing Load Capacity
#### 3.2.3. Tooth Flash Temperature and Scaffing Safety Assessment  
Table 9 illustrates the calculated flash temperature distribution on the tooth surfaces of five gear sets with varying pressure angles. The left column represents the highest speed operating conditions, while the right column represents the peak torque operating conditions. In the highest speed operating conditions, the maximum flash temperature on the tooth surface decreases with an increasing pressure angle, with a range of 54.3 \({}^{\circ}\)C to 36 \({}^{\circ}\)C, representing a 33.7% reduction. Similarly, under peak torque conditions, the maximum flash temperature decreases from 236 \({}^{\circ}\)C to 129 \({}^{\circ}\)C, representing a decrease of 107 \({}^{\circ}\)C, or a 45.3% reduction. It is evident that an increase in pressure angle represents an effective design strategy for the reduction of tooth surface friction flash temperature under extreme operating conditions.  
Figure 10 illustrates that both the flash temperature value and the scuffing safety factor are positively influenced by an increase in the pressure angle, thereby enhancing the gear's scuffing load-carrying capacity. At a pressure angle of 16\({}^{\circ}\), the scuffing safety factor reaches its minimum value of 1.0. An increase in the pressure angle to 23\({}^{\circ}\) results in an improvement in this factor to 1.569, representing a 56.9% increase. Therefore, provided that the minimum transverse contact ratio and bearing life are maintained, employing a larger pressure angle significantly enhances the gear's scuffing load-carrying capacity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gearset** & **Tooth Surface Load Distribution** & **Tooth Surface Load Distribution** \\  & **Peak Speed Load Case: 99 Nm, 25,000 rpm** & **Peak Torque Load Case: 450 Nm, 5000 rpm** \\ \hline A16 & 10.1 & 10.1 \\ A17.5 & 10.1 & 10.1 \\ A19.5 & 10.1 & 10.1 \\ A21.5 & 10.1 & 10.1 \\ A21.5 & 10.1 & 10.1 \\ A23 &
# Large-Scale Grid Integration of Renewable Energy Resources with a Double Synchronous Controller
## Nomenclatures  
### Abbreviation  
RERs Renewable Energy Resources  
VMP Virtual Mechanical Power  
DSC Double Synchronous Controller  
SG Synchronous Generator  
DESS Distributed Energy Storage System  
VSM Virtual Synchronous Machine  
PV Photovoltaic  
PCC Point of Common Coupling  
LPF Low Pass Filter
## 3 Omnidirectional and Directional Antennas Issues vs. Round-Robin MAC  
### Directional Antennas in MANET Common Issues  
As mentioned in the introduction section, using directional antennas in MANET communications in place of the classic omnidirectional systems implies several advantages. In order to understand the reasons for employing directional antennas, the following example scenario is considered:  
Figure 2 illustrates a wireless network scenario in which nodes use omnidirectional antennas to perform communications. In particular, the transmitter node \(T\) sends to the receiver \(R\) a communication signal by using an omnidirectional antenna; \(R\) attempts to capture the signal with the same antenna model. Because the transmitter signal is radiated in all directions with the same intensity, if there are such nodes in the neighboring of the transmitter/receiver (\(N_{T}\) and \(N_{R}\)), it is possible that the radiated signal is captured by these nodes that, in turn, may attempt a communication at the same time. In this case, interferences and collisions can occur [36; 37; 38]; these issues could enhance with increasing mobility of nodes. Nevertheless, in this case, because nodes radiate in the same way toward all directions, a huge waste of the battery life of nodes is certainly achieved [39; 40]. In any case, several issues have to be addressed in the case of directional communications among nodes in MANET; the most common include hidden terminal and deafness problems.  
Referring to directional MAC communications in which directional request-to-send (DRTS) and directional clear-to-send (DCTS) are used to perform a transmission/reception flow, it is possible that a special node (denoted by deaf node) that is engaged in a certain communication, but at the same time is solicited as a receiver by another source node, causes the deafness problem (Figure 3a). The node that experiences the deafness (the node \(A\) in Figure 3a) could try to retransmit many times MAC layer packets, resulting in a large amount of collisions and a considerable increase of the network overhead. In any case, due to recurring retransmissions attempted by the deaf node, a significant waste of energy could occur, and in turn this node tends to deplete its battery life quickly; as a consequence, the overall network throughput is subject to an important degradation. Another common issue while using directional antennas is represented by the handoff problem that is usually implied by the movements of nodes in the network. In Figure 3b, the handoff issue is depicted: the sender node \(T\) beamforms a communication toward a receiver \(R\); when the communication is still active, \(R\) shifts in the position \(R\); getting out of the transmitter beam; as a result, the ongoing communication fails and beams have to be re-pointed. If a proper mechanism of synchronization and node position refreshing is not provided, the directional beam remains tuned for a long time in an undesired direction due to node movement; for this reason, again, a great amount of energy consumption occurs.  
Figure 2: Interference caused by omnidirectional antennas.
## 4 Discussion
### The Design and Analysis of the SPAC Encoder  
In addition to the above high within-class variability, another important problem in complex scenes of remote sensing images is that low between-class variability and small objects are often ignored or mis-segmented. To solve this problem, this paper designs the SPAC Encoder based on spatial pyramid architecture and atrous convolution, which is a convolution variant used by most classical semantic segmentation networks. It directly extracts the shallow feature information of a remote sensing image and preserves the local information of each pixel in the image as much as possible. In this way, the proportion of small object feature information in the final segmentation result is increased.  
Compared with DeepLabV3+ and ResUNet-a, these networks all use atrous convolution as the main method to extract features. With the vigorous development of computer vision and related fields, models based on other convolutional variants or Transformer have shown better performance. In the face of advanced features in complex scenes, it seems that atrous convolution can no longer break through. However, the advantage that atrous convolution actively improves the convolutional receptive field too small should still not be ignored, and it still has an advantage in the extraction of low-order global information. Therefore, we use the spatial pyramid principle and atrous convolution to design a simple SPAC encoder to extract low-order feature information and fuse it with the high-order feature information extracted by the SFA encoder to obtain better performance. As shown in Tables 2 and 3, these ablation experiments prove that the accuracy of the segmentation of specific classes of objects is significantly improved by using the SPAC Encoder.