esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
## 4 Numerical Results
### Experimental Results
When comparing the BER performance of SC-CNN-BiLSTM in NOF (Figure 9) and KAU1 (Figure 12), the BER performance at NOF was better than that at KAU1. The multi-path structure and Doppler shift of KAU1 were more severe than those at NOF, which would degrade the BER performance. In the two experimental channels, SC-CNN-BiLSTM-based OTFS detection outperformed all the other signal detection methods.
## 2 Proposed Single Source Three-Phase Topology  
### Explanation of the SCMLI Topology  
The PT five-level 3-\(\phi\) SCMLI is displayed in Figure 1. Each phase leg of this inverter possesses four power semiconductor switches, one capacitor, and one power diode. To prevent the supply voltage from being accidentally shorted out, the SC cell's switches operate in a complimentary manner. The supply voltage (\(V_{DC}\)) is common to all phases \(X\) (\(X\in\) phase R, phase Y, phase B). The proposed topology synthesizes five levels of the line-to-line voltage (0, \(\pm 1V_{DC}\pm 2V_{DC}\)). All switches' voltage stresses are well below the supply voltage, except for \(S_{X4}\), where it is twice as high. The simple switching pattern for various modes of activity for phase \(R\) is introduced in Table 1. Switches' on/off states are represented by the numbers 0 and 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Switching Mode**} & \multicolumn{5}{c}{**Active Switch**} & \multirow{2}{*}{**Pole Voltage per Phase Leg \(\
u_{Rn}(t)\)**} \\ \cline{2-2} \cline{4-6}  & \(S_{R1}\) & \(S_{R2}\) & \(S_{R3}\) & \(S_{R4}\) \\ \hline \(\delta_{1}\) & 0 & 1 & 0 & 1 & 0 \\ \hline \(\delta_{2}\) & 0 & 1 & 1 & 0 & +1\(V_{DC}\) \\ \hline \(\delta_{3}\) & 1 & 0 & 1 & 0 & +2\(V_{DC}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different switching modes and their corresponding positive pole voltages for phase leg “R”.  
Figure 1: Proposed five level SCMLI topology.
# Evaluation of Mixed Traffic Flow Efficiency and Safety on Hard-Shoulder-Running Freeways
###### Abstract  
Most of the existing studies on the freeway hard-shoulder-running (HSR) strategy only focus on the traffic flow with human-driven vehicles (HDVs) but neglect the potential impacts of connected automated vehicles (CAVs) with various penetration rates on the macroscopic traffic flow. The purpose of this paper is to ascertain whether the effects of mixed traffic flow for the freeway HSR strategy differ from those of a traditional traffic flow or not. In this research, a sophisticated methodology is employed, integrating microscopic simulation with factorial experimental design. By meticulously examining and statistically analyzing the simulation results, significant and novel insights are obtained. We find a complex interplay between the penetration rate of CAVs and HSR strategy on traffic flow efficiency and safety metrics. Without HSR, the traffic flow efficiency does not exhibit an immediate enhancement as the penetration rate increases. Instead, a notable improvement in overall traffic flow efficiency is observed only when the penetration rate attains a specific threshold. After implementing the HSR, the penetration rate of CAVs has a pronounced positive influence both on the traffic efficiency and safety, regardless of the prevailing traffic flow conditions. However, different speed limits for the HSR can lead to varying degrees of efficiency improvement under different traffic demands. Within a penetration rate range of 0.4 to 0.7 during the congested traffic flow, the traffic efficiency achieved at a speed limit of 120 km/h does not necessarily exceed that achieved at a speed limit of 80 km/h. Finally, we observe that the opening of hard shoulders would result in a decrease in traffic safety under congested traffic conditions. This finding provides important guidance for determining the appropriate timing of hard shoulder utilization in mixed traffic flow scenarios in the future.  
mixed traffic flow; freeway hard shoulder running; safety and efficiency; simulated method +
Footnote †: journal: Applied sciences  
0000-000-0002-4143-3227][PERSON]  
1 Traffic Safety Development Research Department, Research Institute for Road Safety of the Ministry of Public Security, Beijing 100741, China; [EMAIL_ADDRESS] (CZ.j); [EMAIL_ADDRESS] (WZ.)1
## 3 Proposed Distributed Secondary Control Technique
### Controller Stability Analysis
If we denote \(U=[U_{1},\dots,U_{N}]^{T}\), \(\mathbf{C}=diag(\mathbb{C}_{1},\dots,\mathbb{C}_{N})\), \(\mathcal{H}=[\mathcal{H}_{1},\dots,\mathcal{H}_{N}]^{T}\), \(I=[I_{o_{1}},\dots,I_{N}]^{T}\), and \(Q=diag(\mathcal{P}_{1},\dots,\mathcal{P}_{N})\), then (10) and (11) can be expressed as:  
\[\Delta U_{dc}(t)=\dot{U}_{dc}(t)=\frac{\mathbf{1}_{\mathbf{1}\times\mathbf{N} }\mathbf{Q}}{\mathcal{F}}\dot{\mathcal{H}}(t) \tag{21}\]  
\[\Delta I_{o}(t)=\mathcal{L}\mathbf{C}^{-1}\mathbf{Q}\mathbf{\Gamma}\dot{ \mathcal{H}}(t) \tag{22}\]  
where \(\mathbf{\Gamma}=\mathbf{I}_{\mathbf{N}\times\mathbf{N}}-\mathbf{1}_{\mathbf{ 1}\times\mathbf{N}}\mathbf{1}_{\mathbf{N}\times\mathbf{Q}}\) and \(\mathcal{L}\) is the communication graph Laplacian matrix. **Lemma 1**. : _Assume a symmetric matrix \(\mathbf{D}\) that possesses the following characteristics:_  
\[\zeta_{min}\mathbf{I}\leq\mathbf{D}\leq\zeta_{max}\mathbf{I}\]  
_where \(\mathbf{I}\) represents the identity matrix of suitable dimension and \(\zeta_{min}\) and \(\zeta_{max}\) are positive values which denote the eigenvalues of matrix \(\mathbf{D}\); minimum and maximum, respectively. The Lyapunov equation provides a means to solve for the matrix \(D\):_  
\[\delta^{T}\mathbf{D}+\mathbf{D}\delta=-\mathbf{V} \tag{23}\]  
_where \(\delta=\frac{\alpha}{\tau}\mathbf{1}_{\mathbf{1}\times\mathbf{N}}\mathbf{1}_ {\mathbf{N}\times\mathbf{1}}\mathbf{Q}+\beta\mathcal{L}\mathbf{C}^{-1}\mathbf{I}\) and \(\mathbf{V}\) is a positive definite matrix._  
**Lemma 2**. : _Assume an undirected network \(\mathcal{L}(\mathcal{G})\) with a balanced current sharing objective, provided that \(\Delta I_{o}=\mathcal{L}\mathbf{C}^{-1}\mathbf{I}\), where \(\mathbf{C}^{-1}\mathbf{I}=\mathbf{1}_{N\times 1}\) is the result of solving \(\Delta I_{o}=0\)._  
**Theorem 1**. : _The voltage regulation and the current distribution objectives in (10) and (11) are actualized through the proposed FDSC in (14), which means that asymptotically, the cumulative deviation, \(u(t)\), approaches zero provided that the ensuing condition is satisfied: \(\frac{1}{\tau}\zeta_{min}(\mathbf{V})<0\), where \(\zeta_{min}\) denotes the minimum eigenvalue of \(\mathbf{V}\)._  
Proof. : Define a quadratic Lyapunov function [35]:  
\[\mathcal{B}(t)=u^{T}(t)\mathbf{D}u(t) \tag{24}\]  
the resilience and adaptability of our proposed FDSC. In the final case study, we assess the performance of FDSC under communication delays. We analyze how the proposed FDSC responds to communication latency within the MG. The results and performance of these case studies are compared to existing literature studies as a benchmark for evaluation.
# Full-Field Strain Determination for Additively Manufactured Parts Using Radial Basis Functions
## 3 Evaluation of Strain Determination Using RBFs  
In the following subsections, the behavior of the full-field strain measurement using radial basis functions is investigated. For this purpose, the basic properties of the approach is studied first using verification examples for which analytical solutions are known. Next, the concept is applied to real experimental data obtained from WAAM-produced specimens.
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**This Work**} & \multicolumn{3}{c}{[17]} & \multicolumn{3}{c}{[16]} & \multicolumn{3}{c}{[13]} & \multicolumn{3}{c}{[22]} \\ \hline \multirow{3}{*}{Support Curve} & 25519 & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\bullet\) & \(\bullet\) \\  & P-256 & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\circ\) & \(\circ\) & \(\circ\) \\  & P-384 & \(\circ\) & \(\circ\) & \(\bullet\) & \(\bullet\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) \\  & P-521 & \(\circ\) & \(\circ\) & \(\bullet\) & \(\bullet\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) \\ \hline FPGA & Virtex-6 & Virtex-7 & Virtex-6 & Virtex-7 & Virtex-6 & Virtex-7 & Virtex-7 & Virtex-7 & Kintex-7 & Kintex-7 \\ \hline Slices & 3025 & 2932 & 5957 & 7172 & 9246 & 8873 & 24,200 & 3362 & 8639 \\ \hline DSP & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 182 & 260 \\ \hline Slices equivalent & 3025 & 2932 & 5957 & 7172 & 9246 & 8873 & 24,200 & 21,562 & 34,639 \\ \hline Frequency (MHz) & 116.72 & 149.23 & 88.82 & 138.58 & 161.1 & 177.7 & 72.9 & 87 & 115 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Throughput- \\ (Kbps) \\ \end{tabular} } & 25519 & 91 & 117 & 69 & 108 & - & - & - & 5773 & 2159 \\  & P-256 & 114 & 146 & 87 & 135 & 157 & 173 & 1816 & - & - \\  & P-384 & - & - & 58 & 90 & - & - & - & - & - \\  & P-521 & - & - & 43 & 66 & - & - & - & - & - \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Slices2 \\ /Kbps \\ \(\times 10^{-3}\) \\ \end{tabular} } & 25519 & 100 & 74 & 511 & 475 & - & - & - & - & 81 & 556 \\  & P-256 & 80 & 59 & 409 & 380 & 545 & 455 & 322 & - & - \\ \cline{1-1}  & P-384 & - & - & 614 & 570 & - & - & - & - & - \\ \cline{1-1}  & P-521 & - & - & 833 & 774 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Elliptic curve point multiplication comparison, \(\bullet\)/\(\circ\) represents for supported/not-supported. and EdDSA/ECDSA process at the same time. Furthermore, the SHA2 functions and EdDSA/ECDSA functions can be used separately.
## 4 Design Consideration and Simulation
### CLC Using PI Control Method  
1. At a \(V_{in}\) of 9 V, the HPLDDC operated in the boost mode because (\(V_{in}\) = 9 V) \(<\) (\(V_{o}\) = 15 V), and a slight current spike was observed, with a peak value of 1.4 A, and the settling time was 60 ms (simulation was 40 ms), as shown in Figure 15a.
2. At a \(V_{in}\) of 15 V, the HPLD operated in the buck-boost mode because (\(V_{in}\) = 15 V) = (\(V_{o}\) = 15 V), and the current spike was observed, with a peak was 1.6 A, which overstepped the HPLD maximum operating current of 1.5 A (Table 3). The settling time was 260 ms, as shown in Figure 15b.
3. At a \(V_{in}\) of 16 V, the HPLD operated in the buck mode because (\(V_{in}\) = 16 V) \(>\) (\(V_{o}\) = 15 V), and the current spike was observed, with a peak was 1.65 A. This peak value was also over the HPLD maximum operating current of 1.5 A. Then, the settling time was 260 ms (simulation was 230 ms), as shown in Figure 15c.  
Thus, the start-up current spike must be mitigated, and it should be lower than the HPLD maximum operating current of 1.5 A.
## 3 Results
### The Temporal Characteristic of SIUS
#### 3.1.2 Specific Distribution of SIUS  
The buildings sector holds the largest share of SIUS, comprising both residential and non-residential buildings. On average, residential and non-residential building stocks make up 42% and 27% of the total SIUS, respectively. Since the 10 th Five-Year Plan, SIUS in urban residential buildings increased due to the migration of the rural population to urban areas. Consequently, urban residential buildings became the most significant accumulation sector for SIUS (Figure 3a), with a proportion ranging from 34% to 42%. This was followed by urban non-residential buildings stocks, which accounted for 21% of the total buildings sector in 2023.  
Figure 3b illustrates the evolution of SIUS in the domestic appliances sector, which grew significantly from 0.38 Mt in 2000 to 3.97 Mt in 2023. Driven by rising living standards since the reform and opening-up period, domestic appliances (primarily cleaning and thermoregulation devices) have become essential to daily life, comprising 85% of the sector's SIUS in 2023.  
In the infrastructure sector, SIUS increased significantly from 3.13 Mt to 36.09 Mt during 2000-2023. Among all components in the infrastructure domain, the bridge emerged as the most substantial element of SIUS (Figure 3c). During 2000-2023, SIUS in the bridge increased from 1.69 Mt to 27.05 Mt. This prominence is attributed to the high steel strength of bridges, which provides superior structural stability and durability. This is followed by pipelines and electricity infrastructures, accounting for 14% and 4% of the total sector in 2023.  
As shown in Figure 3d, the stocks of passenger cars and trucks represent a significant proportion of the transportation sector. Passenger car stocks have been growing faster, with an average annual growth rate of 16% from 2000 to 2023. The number of cars (especially passenger cars) increased since the late 2000s, accounting for 68% of the total sector. This is followed by truck stocks, which account for 28% of the total sector in 2023.  
Figure 3: The evolution of in-use product stocks in buildings (**a**), domestic appliances (**b**), infrastructures (**c**), transportation (**d**), and machinery (**e**) (see Text S1 for details on data sources and methodology).  
Between 2000 and 2023, the share of the machinery sector increased from 18% to 20%, making it the second-largest downstream sector for SIUS. It consists mainly of industrial and agricultural machinery. Industrial machinery holds the largest share, accounting for 81% of the total sector in 2023 (Figure 3e). Agricultural machinery includes nine primary types of equipment. Among them, small tractors' SIUS account for the largest proportion of the whole agricultural machinery sector, at 25%.
# Dispatchable Substation for Operation and Control of Renewable Energy Resources
## 3 Operation of Dispatchable Substation with Renewable Generator Mix
### ESS Configuration Considering RES Type and Forecast Accuracy
In this case, the ESS can operate under the discharge mode because it can provide a wider range of active power support. The joint distribution of the remaining cases is shown in Figure 8. The 80% zone was placed differently for each generation mix case, as shown in the graph. Therefore, the DSS control should be adjusted depending on the type and capacity of the interconnected generators. The capacity requirement of the ESS for each case is determined to reduce the forecast error to less than 0.01% of the total operation hours. The forecast and error data of solar irradiance and wind speed have a time step of 15 min. Thus, several assumptions are applied in the simulation. Assuming that the TSO performs dispatch in a period of 15 min, the battery capacity (MWh) can be calculated for a 15-min operation. Since the dispatch and forecast information is updated every 15 min, battery capacity for a 15-min operation is sufficient. This is a valid assumption, as the forecast error cannot be accumulated for more than 15 min period. One-year of wind speed and solar irradiance data are used for simulation, and the operation of ESS follows the scheme that depicted in Figure 4. The switch between charge and discharge mode of ESS in the substation is determined whether the forecast value lies in-between 80% chance zone or not. Using the given data, the required capacity(MW) of ESS is found via iterative simulation. Battery capacity(MWh) of ESS is calculated assuming 4-crate specification. The parameters for the DSS and ESS facilities are shown in Table 2.  
Figure 5:0 Joint distribution of measured and forecast generation output for (**a**) case 1, (**b**) case 3, (**c**) case 4, and (**d**) case 5. From Table 2, it can be noted that case 3 requires the smallest amount of ESS for forecast error reduction. Since the correlation between wind speed and solar irradiance is unnoticeable, uncertainty from forecast of each sources is mitigated when the capacity of each device became even. Furthermore, the lower range of 80% zone elevates as the portion of the PV generator increases. The PV forecast error tends to be focused at high irradiance level, while wind speed forecast error is distributed evenly.
## 2 Materials and Methods
### Features
#### 2.1.2 Tone  
In this study, the tone is quantified through the utilization of the gray histogram. This histogram calculates the quantity of pixels at each gray level present in the image, and subsequently, the pixels in the image are tallied based on their respective gray levels and presented in the form of a histogram. By quantifying the characteristics of the gray histogram, the tone of the image can be reflected from multiple facets.  
_Mean_: The average of the gray levels in the histogram, which represents the overall brightness level of the image.  
\[Mean=\sum_{i=0}^{N-1}i\cdot p_{i} \tag{1}\]  
_Variance_: Describes the degree of dispersion of the histogram, that is, the extent to which the gray levels deviate from the mean of the histogram.  
\[Variance=\sum_{i=0}^{N-1}{(i-Mean)^{2}\cdot p_{i}} \tag{2}\]  
_Skewness_: Describes the asymmetry of the histogram. A positive skewness indicates that the tone value in the image is higher; a negative skewness indicates that the tone value in the image is lower.  
\[Skewness=\frac{\sum_{i=0}^{N-1}{(i-Mean)^{3}\cdot p_{i}}}{Variance^{3/2}} \tag{3}\]  
_Kurtosis_: Describes the peakedness of the histogram. A positive kurtosis indicates that the gray values in the image are concentrated in certain specific gray levels. A kurtosis of 0 indicates that the peak of the data distribution is similar to that of the standard normal distribution.  
\[Kurtosis=\frac{\sum_{i=0}^{N-1}{(i-Mean)^{4}\cdot p_{i}}}{Variance^{2}}-3 \tag{4}\]  
_Energy_: The sum of the squares of the pixel counts at each gray level in the histogram, which reflects the uniformity of the gray level distribution in the image. A higher energy indicates that the gray level distribution in the image is more even.  
\[Energy=\frac{1}{S}\sum_{i=0}^{N-1}{p_{i}}^{2} \tag{5}\]  
\(N\) represents the number of gray levels, \(p_{i}\) denotes the frequency of the \(i\) gray level, and \(S\) indicates the size of the picture (total number of pixels).
# Cutting Performance and Wear/Damage Characteristics of PCBN Tool in Hard Milling
## 3 Experimental Analysis
### Tool Life  
The wear morphology and the wear rate of tool flank face at each cutting period were observed using a super-deep-scene 3D microscope. The wear curves of the PCBN tool and cemented carbide tool were obtained as shown in Figure 8.  
When the cutting speed was 350 m/min and the cutting time was 60 min, the cemented carbide tool appeared damaged (as shown in Figure 8a). The reason was that the Co binder cannot withstand high temperature at high speed [27]. When the cutting speed was 150 m/min and 250 m/min,  
Figure 8: Tool wear curve (Test 1): (**a**) carbide cutting tool; (**b**) PCBN tool.  
Figure 7: The machined surface roughness (Test 4): (**a**) PCBN tool; (**b**) carbide cutting tool.  
the cutting time before the cemented carbide tool reached the wear standard was significantly shorter than that of the PCBN tool. When the cutting speed was 50 m/min, the cutting performance of the cemented carbide tool was better than that of the PCBN tool. When the cutting speed was more than 150 m/min, the cutting performance of the PCBN tool was obviously higher than that of the cemented carbide tool. When the cutting speed was 50 m/min, the PCBN tool appeared damaged after 5 min of machining, as shown in Figure 8b.  
Therefore, it was found that 150 m/min was the critical point for the PCBN tool and cemented carbide tool for intermittent machining of hardened steel. When the cutting speed was greater than 150 m/min, the performance of the PCBN tool was better than that of the cemented carbide tool; however, when the cutting speed was less than 150 m/min, the conclusion was just the opposite.
# A Multilevel Road Alignment Model for Spatial-Query-by-Sketch
## 3 Materials and Methods
### Characteristics and Matching Approach for Individual Roads
#### 3.2.4 Circulation Direction  
The circulation direction [29] of a individual road (\(CD\)) identifies the direction of each road segment w.r.t. to its previous road segment. The proposed approach firstly computes the direction difference for two adjacent road segments with Formula (3) and then obtains the circulation direction of one road segment as depicted in Figure 6.  
\[v=(x_{p0}-x_{p1})*(y_{p1}-y_{p2})-(x_{p1}-x_{p2})*(y_{p0}-y_{p1}) \tag{3}\]  
where \(p0\) represents the start vertex of the first road segment; \(p1\) is the end vertex of the first segment and also the start vertex of next road segment; \(p2\) is the end point of the next road segment.  
The left and right directions for each road segment are represented as \"l\" and \"r\", respectively. Figure 7 shows the \(CD\) of two roads. The road in Figure 6(a) turns right, then left, then right, and finally right again. The road in Figure 6(b) turns left, then right, then left, and finally left again.  
If the \(CD\) of one road is the same as that of another road, the two roads are considered to be similar in terms of the \(CD\). If the \(CD\) of one road is part of that of another road, the two roads are considered to be partially similar. Formula (4) shows the computation of similarity in terms of the \(CD\).  
\[SimCD_{(Sketch,OSM)}\gets CD_{Sketch}\subseteq CD_{OSM}\?\ True\ :\ False \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**Road from a Sketch**} & \multicolumn{5}{c}{**Roods from OSM**} \\ \hline Road ID & R0 & R1 & R2 & R3 & R4 & R5 \\ \hline \(NCTP\) & 2 & 0 & 1 & 2 & 4 & 3 \\ \hline \(SimNCTP_{(Sketch,OSM)}\) & - & No & No & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(NCTP\) of R0 and R1–R5. (The threshold of the direction difference is 36\({}^{\circ}\)).  
Figure 6: Direction of one road segment. Note that, \(v\) is the difference value in direction of two adjacent road segments obtained in Formula (3).  
Figure 7: \(CD\) of two roads. Note that V0–V4 represent the vertices of the roads. V0 and V4 respectively represent the starting and end vertices of the road.  
where \(SimCD_{(Sketch,OSM)}\) represents the similarity between two roads in terms of the \(CD\); \(CD_{Sketch}\) and \(CD_{OSM}\) represent the \(CD\) of a road from a sketch and that of a road from OSM, respectively. Table 3 presents the \(CD\) of R0 and R1-R5. The \(CD\) of R1 is null, as it is a straight road. The \(CD\) of R0 is the same as that of R3 and R4. Moreover, the \(CD\) of R0 is part of that of R5, which means that R0 is partially similar to R5. These findings are consistent with those of a visual inspection.