esa-sceva/satcom-chunk-collection huggingface dataset:
# Wind-Induced Dynamic Performance Evaluation of Tall Buildings Considering Future Wind Climate
## 2 Basic Settings for the Simulations
### Validation of Simulation Results with Wind Tunnel Test  
This study conducts a wind tunnel test for the structural wind vibration response of the case study building. Figure 9 presents the wind tunnel test setup. The wind tunnel is equipped with a collectible automatic three-dimensional transfer frame system, using a multi-functional sharp split grid combination device and three rough elements, which can quickly simulate the atmospheric boundary layer airflow that matches the scale model and adapt to different site terrain categories. The high-frequency force balance (HFFB) test was conducted using the rigid building model in the wind tunnel, in which the geometric scale ratio of the wind tunnel model is 1:300. This test calculates the acceleration response of the building under different return periods and provides the peak acceleration of the highest residential floor.  
The peak acceleration of the highest residential floor (at the height of 139.15 m) in the Y-direction (along-wind direction) of the building obtained from the wind tunnel test was 0.1302 m/s\({}^{2}\) for a basic wind speed of a 10-year return period of 21.9 m/s. Moreover, to validate the framework, a wind analysis of the building was performed for the same basic wind speed (21.9 m/s) using the proposed numerical method. Figure 10 presents the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Return** & \multirow{2}{*}{**Wind Cases**} & \multicolumn{5}{c}{**Accelerations (m/s\({}^{2}\))**} \\ \cline{3-6}
**Period** & & \(\mathbf{a_{max,y}}\) & \(\mathbf{a_{min,y}}\) & \(\mathbf{a_{max,x}}\) & \(\mathbf{a_{min,x}}\) \\ \hline \multirow{3}{*}{10-year} & Historical & 0.1550 & \(-\)0.1486 & 0.0072 & \(-\)0.0071 \\  & Mid-future & 0.1674 & \(-\)0.1690 & 0.0087 & \(-\)0.0086 \\  & Late-future & 0.1708 & \(-\)0.1695 & 0.0094 & \(-\)0.0093 \\ \hline \multirow{3}{*}{50-year} & Historical & 0.2319 & \(-\)0.2188 & 0.0118 & \(-\)0.0119 \\  & Mid-future & 0.2483 & \(-\)0.2466 & 0.0137 & \(-\)0.0137 \\  & Late-future & 0.2732 & \(-\)0.2705 & 0.0153 & \(-\)0.0151 \\ \hline \multirow{3}{*}{100-year} & Historical & 0.2682 & \(-\)0.2655 & 0.0150 & \(-\)0.0148 \\  & Mid-future & 0.2850 & \(-\)0.2792 & 0.0170 & \(-\)0.0171 \\ \cline{1-1}  & Late-future & 0.3218 & \(-\)0.3016 & 0.0186 & \(-\)0.0184 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Acceleration responses at 44 th floor of the building.  
Figure 9: Wind tunnel test of the case study building.  
simulated acceleration response for the same basic wind speed used. The peak acceleration value obtained numerically is 0.1267 m/s\({}^{2}\), which shows a difference of only 2.68% compared to the wind tunnel result. This close correlation between the numerical and experimental results supports the validity of the proposed method.
## 2 Materials and Methods
### Data Synthesis and Analysis  
Meta-analysis was conducted using R (Version 3.5.0; R Project for Statistical Computing). The standardized mean difference (SMD) and 95% confidence interval (CI) were used as summary statistics. A random-effect method was used for meta-analysis. The level of significance was set at 0.05. \(I^{2}\) and chi-square test were performed to quantify the heterogeneity across studies. This study utilized methods from the Cochrane Handbook to manage the within-patient correlation of treatment outcomes in clustered trials [17]. For studies that did not adjust for clustering, the standard error was inflated using a correlation coefficient of 0.07 ([PERSON] et al., 2018).
## 5 Discussion
### Communication Performance  
In Table 5, we analyze the communication costs at every phase. In a 4G environment, the maximum transmission speed is 100 Mbps [46]. In a 5G environment, the maximum transmission speed is 20 Gbps [47]. In our analysis, it was assumed that an access or event message required 304 bits, a hash operation required 160 bits, a signature operation required 1024 bits, and the other message required 80 bits. The 304 bits of an access or event message is assumed by the maximum length of the message (five IDs and one timestamp) that sends at the allocating task phase, which is 5 \(\times\) 80 bits + 1 \(\times\) 32 bits = 432 bits. The communication costs are calculated depending on the number of messages. For example, in the authentication phase, the AP sends one access message, two hash values, two signatures, and three other messages to the SCC. After that, the SCC sends two signatures and five other messages. In total, it requires 1 \(\times\) 432 bits + 2 \(\times\) 160 bits + 4 \(\times\) 1024 bits + 8 \(\times\) 80 bits = 5488 bits. In a 4G environment, the maximum transmission speed is 100 Mbps and it only takes 0.054 ms to transfer all the messages. In a 5G environment, the maximum transmission speed is 20 Gbps; the transmission time needed is only 0.27 us to complete the authentication phase. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Security Control Center** & **Other Party** \\ \hline
**Authentication phase** & \(2T_{asy}+1T_{v}+1T_{h}+3T_{add}+2T_{sub}\) & Accessing Party: \\  & \(+3T_{mid}+1T_{div}+5T_{cap}\) & \(2T_{asy}+1T_{v}+3T_{h}+3T_{add}+2T_{sub}+3T_{mid}+1T_{div}+4T_{cap}\) \\ \hline
**Event-trigger phase** & \(1T_{asy}+1T_{v}+3T_{h}+3T_{add}+2T_{sub}\) & Client House’s IoT Main Controller: \\  & \(+3T_{mid}+1T_{div}+5T_{cap}\) & \(1T_{asy}+2T_{h}+2T_{add}+1T_{sub}+2T_{mid}+1T_{div}+3T_{cap}\) \\ \hline
**Task allocating phase** & \(2T_{asy}+1T_{v}+5T_{h}+5T_{hid}+3T_{sub}\) & Security Guard: \\  & \(+5T_{mid}+2T_{div}+8T_{cap}\) & \(2T_{asy}+1T_{v}+3T_{hid}+2T_{sub}+3T_{mid}+1T_{div}+5T_{cap}\) \\ \hline
**Task accomplished phase** & \(3T_{asy}+2T_{v}+5T_{h}+5T_{hid}+4T_{sub}\) & \(1T_{asy}+2T_{h}+2T_{add}+1T_{sub}+2T_{mid}+1T_{div}+3T_{cap}\) \\  & \(+5T_{mid}+2T_{div}+8T_{cap}\) & Client: \\  & \(2T_{asy}+1T_{v}+3T_{h}+3T_{add}+2T_{sub}+3T_{mid}+1T_{div}+5T_{cap}\) \\ \hline \hline \end{tabular} Notes: \(T_{asy}\): the time required for an asymmetrical encryption/decryption. \(T_{v}\): the time required for verifying the hash value. \(T_{h}\): the time required for a one-way hash function. \(T_{add}\): the time required for an additional operation. \(T_{sub}\): the time required for a subtraction operation. \(T_{mul}\): the time required for a multiplication operation. \(T_{div}\): the time required for a division operation. \(T_{cap}\): the time required for an exponential operation.  
\end{table} 
Table 4: Computation costs of the proposed scheme.
## 2 System and Channel Model
### One-Hop Hybrid FSO/RF Link Based on Selective Combination Scheme
With weak turbulence, CBPSK modulation, heterodyne detection, the RF fading parameter \(m=2\), the pointing errors parameter \(\xi=1\), and the decision threshold \(\gamma_{\text{th}}=10\) dB, Figure 11 shows the relationship between the performance metrics and the SNR of the relay-aided hybrid PSO/RF system under different direct distances, the SNR of the RF sub-link, and relay-aided structures. From Figure 11, it can be clearly seen that the changes in the SNR of the RF sub-links have a significant impact on the systems of hybrid direct links and relay-aided hybrid systems. Further, compared with Figure 10, we can find that the performance of the hybrid direct link and the relay-aided hybrid system with different transmission distances under the same conditions is significantly different after the SNR of the RF sub-link changes. Figure 9: Relationship between the outage probability and the SNR of the relay-aided hybrid PSO/RF system with different structures and the hybrid direct link under different decision thresholds.
# The Effect of Particle Type and Size on CoCr Surface Properties by Fine-Particle Shot Peaning
## 2 Materials and Methods
### Experimental Methods  
Fine-Particle Shot Peening (FPSP)  
A peening machine (MY-30 AP, Sinto brand, Nagoya, Japan) was used for the fine-particle shot peening process. The pressure of shooting was set at 0.5 MPa. The distance between the nozzle and material surface was 20 cm, with the angle between the nozzle and material surface at 90 degrees. The environment was controlled, including room temperature and atmospheric pressure. The media type, particle size, and peening conditions are also shown in Table 3. The number of samples was three in each group, including an un-peened sample and peened sample (total 21 samples).  
The main objectives in this study were to compare the effect of the particle type and size on the surface properties of the materials. The process started by pumping media to the nozzle with a pressure of 0.5 MPa, and the media were shot from the nozzle onto the material surface from a distance of 20 cm and an angle of 90 degrees. The media were shot until 100% of the material surface was covered.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Code Number** & **Particle Type** & **Particle Size (\(\mu\)m)** & **Pressure (MPa)** & **Nozzle Distance (mm)** \\ \hline S63 & Silica & 63–106 & 0.5 & 20 \\ \hline S125 & Silica & 125–180 & 0.5 & 20 \\ \hline S250 & Silica & 250–355 & 0.5 & 20 \\ \hline C63 & Ceramic bead & 63–125 & 0.5 & 20 \\ \hline C125 & Ceramic bead & 125–250 & 0.5 & 20 \\ \hline C250 & Ceramic bead & 250–425 & 0.5 & 20 \\ \hline \end{tabular}
\end{table}
Table 2: Peening conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{4}{c}{**\% Cell Viability \(\pm\) Uncertainly Value**} \\ \cline{2-5}
**Sample** & \multicolumn{4}{c}{**Concentration (\%)**} & **Negative Control** & **Positive Control** \\ \cline{2-5}  & **0.02** & **0.2** & **2** & **20** & \\ \hline Un-peened & 95.13 \(\pm\) 4.18 & 92.79 \(\pm\) 1.39 & 91.81 \(\pm\) 2.34 & 90.84 \(\pm\) 1.70 & 97.66 & 9.16 \\ \hline Ceramic group & 98.68 \(\pm\) 6.11 & 94.72 \(\pm\) 3.70 & 93.40 \(\pm\) 3.36 & 90.57 \(\pm\) 1.74 & 98.30 & 9.81 \\ \hline Silica group & 101.95 \(\pm\) 4.68 & 98.05 \(\pm\) 1.96 & 97.83 \(\pm\) 4.30 & 94.79 \(\pm\) 3.98 & 98.70 & 9.11 \\ \hline \end{tabular}
\end{table}
Table 3: Cytotoxicity test results evaluated by mouse fibroblast (I.929).
## 4 Discussion
### Limitations
#### 4.2.2 Mathematical Model  
In contrast to a Joule-Thomson valve, this component was modeled as a translated current source, or power dissipation source, during the current implementation. This power dissipation source implemented a Peltier element, as shown below in Figure 20, since, between the two points of heat transfer, no heat exchange took place through this component.  
One main difference between the actual functionality and the implemented solution is that, by choosing the same thermal resistor value overall pressure lines, a temperature increase occurred for probes P1.1 to P1.4. In contrast, probes' values would indicate a temperature below the ambient value in a real Hampson-Linde topology. This suggests better results would be obtained by further dividing and grouping the model variables and improving the throttle valve model.  
Moreover, the Joule-Thomson coefficient gives the maximum temperature difference before and after the valve in a Hampson-Linde system. As an ideal decompression, power dissipation was described from Equation (9), and due to both variable grouping and functional dissimilarity, dissipated power caused a temperature deviation in the high  
Figure 19: Maintaining a constant optimizing density of 3 agents per axis versus total optimizing time. In this case, a significant increase in optimizing time occurs for a linear increase in the number of particles, limiting the number of variables that can be optimized. Simulation time is 0.1 s, while the total time is shown for one single iteration of PSO.  
Figure 20: Thermal analysis and further functionality description of the prototype. Gradient intensity shows the temperature difference in comparison with ambient temperature. The red color shows temperatures above ambient value, while blue shows temperatures below ambient. All heat exchanger discrete sections correspond to Figure 14.  
pressure line. However, as shown below in Figure 21, consistent results were obtained for the low-pressure line.  
Overall, consistency in results was maintained except for the high-pressure line, for which occurring deviations were mentioned and explained, occurring because of discrete variables grouped over all gas heat transfer contribution prototypes, and due to the throttle valve model.
# Free Surface Energy and Hansen Solubility Parameter Vector Field. Interface Thickness
## 3 Data and Discussion
### Free Surface Energy: General Equation  
Figure 9 plots the surface free energy against the square of the modulus of the interaction vector for liquid-gas interactions (large nonpolar and polar molecules, \(LG\), and small polar molecules, \(LG\)*), solid-gas interactions (\(SG\)), and solid-liquid (\(SL\)) interactions. The linear fit for all values is of high quality, since all types of molecules, large and small, polarized and non-polarized, solid, liquid, or gaseous, are correlated.  
All of this means that the following expression can be established as a general equation for the evaluation of free surface energy between two substances:  
\[\sigma_{12}=\tau\Big{|}\overset{\rightarrow}{S}_{12}\Big{|}^{2} \tag{53}\]  
Figure 8: Correlation between interfacial tension of solids with liquids, \(\sigma_{SL}\), and the square of the module of the interaction vector.  
where \(\tau\) is a general constant (\(\tau=0.025\) nm = 25\(\cdot\)10\({}^{-12}\) m) and \(\left|\overset{\rightarrow}{S}_{12}\right|^{2}\) is the square of the magnitude of the interaction vector between substance 1 and 2.  
For most liquid-gas interactions, except for those involving low-molecular-mass polarized ones, the interactions occur in the \(DP\) plane:  
\[\left|\overset{\rightarrow}{S}_{L}\right|^{2}=4\delta_{DL}^{2}+\delta_{PL}^{2} \tag{54}\]  
In the case of high-molecular-mass liquids, the interaction vector operates exclusively in the \(DP\) plane.  
For polarized low-molecular-mass liquids, the entire Hansen field (\(DPH\) space) is used:  
\[\left|\overset{\rightarrow}{S}_{L}\right|^{2}=4\delta_{DL}^{2}+\delta_{PL}^{2} +\delta_{HL}^{2} \tag{55}\]  
For solid-gas interactions, the \(DPH\) space is used, too:  
\[\left|\overset{\rightarrow}{S}_{S}\right|^{2}=4\delta_{DS}^{2}+\delta_{PS}^{2 }+\delta_{HS}^{2} \tag{56}\]  
For the interaction of any given substance 1 with any given substance 2:  
\[\left|\overset{\rightarrow}{S}_{12}\right|^{2}=4(\delta_{D2}-\delta_{D1})^{2 }+(\delta_{P2}-\delta_{P1})^{2}+(\delta_{H2}-\delta_{H1})^{2} \tag{57}\]  
If substance 1 or 2 is a gas, this general expression is then transformed into (55) and (56).
## 3 Energy Storage Siting Based on Sensitivity Analysis
\(x_{j}^{*}\) is the ideal point for the \(j\)th evaluation metric. Let the active power and the reactive power of the load grow in a certain proportion [16] in the following manner. \[\frac{\Delta Q_{i}}{\Delta P_{i}}=\tan\varphi_{i} \tag{7}\]  
In (7), \(\varphi_{i}\) is the power factor angle of load growth, used to describe the proportional relationship of load growth. The load increment of the system is assumed to be as follows. \[\left\{ \begin{aligned} \Delta P_{i}&=m_{1} \mathrm{d}P_{i}\\ \Delta Q_{i}&=m_{2}\mathrm{d}Q_{i}\end{aligned} \right. \tag{8}\]  
In (8), \(m_{1}\) is the active power growth coefficient. \(m_{2}\) is the reactive power growth coefficient. Substituting (8) into (7) yields (9). \[\frac{m_{2}\mathrm{d}Q_{i}}{m_{1}\mathrm{d}P_{i}}=\frac{\sin\varphi_{i}}{\cos \varphi_{i}} \tag{9}\]  
Substituting (9) into (6) yields (10). \[\left\{ \begin{aligned} \omega_{1}&=\frac{1}{m_{1}}\\ \omega_{2}&=\frac{1}{m_{2}}\end{aligned} \right. \tag{10}\]  
Taking into account the weighting coefficient constraints, (11) can be obtained. \[\left\{ \begin{aligned} \omega_{1}&=\frac{\cos\varphi_{i}}{ \sin\varphi_{i}+\cos\varphi_{i}}\\ \omega_{2}&=\frac{\sin\varphi_{i}}{\sin\varphi_{i}+ \cos\varphi_{i}}\end{aligned} \right. \tag{11}\]  
It can be observed that an increase in the sensitivity value will result in a proportional increase in the impact of power fluctuations on the system voltage. It can therefore be concluded that connecting energy storage to nodes with higher sensitivity values will result in an effective improvement in the voltage distribution of both the aforementioned nodes and the entire system. Accordingly, the selection of an energy storage site may be based on the calculated sensitivity values.
# Welding Bead Inspection Using Image and Multi-Sensor Fusion
## 4 Experimental Results  
In this section, experiments were conducted to determine the quality of welding beads using the proposed method, which utilized sensor data, such as average current, average voltage, and mixed gas data generated during welding, as well as image data. Additionally, comparative experiments with existing algorithms were conducted to evaluate the performance of the proposed approach.
## 3 The Proposed Methods
### Target Localization in the Known Transmit Power Case  
In the known transmit power case, \(P_{0}\) is assumed to be known in the centralized localization, so \(\eta\) is a constant that depends only on the path-loss exponent \(\gamma\). Given \(\gamma\), \(\alpha_{f}\), \(\{P_{i}\}_{i=1}^{N}\), and the coefficients \(\{\lambda_{i}\}_{i=1}^{N}\), the target node localization can be determined from Equation (9). Next, we can rewrite Equation (9) as  
\[\lambda_{i}^{2}\|\mathbf{x}-\mathbf{s}_{i}\|^{2}\approx\eta^{2}. \tag{10}\]  
Based on Equation (10), we propose the following WLS estimation problem:  
\[\min_{\mathbf{x}}\ \sum_{i=1}^{N}(\lambda_{i}^{2}\|\mathbf{x}-\mathbf{s}_{i}\|^{2}-\eta^{2 })^{T}\mathbf{Q}^{-1}(\lambda_{i}^{2}\|\mathbf{x}-\mathbf{s}_{i}\|^{2}-\eta^{2}). \tag{11}\]  
The above WLS estimator is still a non-convex optimization problem. In the following section, we can transform Equation (11) into an equivalent quadratic programming problem with a quadratic constraint whose global solution can be computed efficiently [19].  
Let \(\mathbf{y}=\left[\mathbf{x}^{T},\|\mathbf{x}\|^{2}\right]^{T}\), and Equation (11) can be written in an equivalent GTRS problem as  
\[\min_{\mathbf{y}}\ \|\mathbf{Q}^{-\frac{1}{2}}(\mathbf{A}\mathbf{y}-\mathbf{b})\|^{2} \tag{12}\] \[\text{s.t.}\ \mathbf{y}^{T}\mathbf{D}\mathbf{y}+2\mathbf{v}^{T}\mathbf{y}=0\]  
where \(\mathbf{D}=diag([1,1,0]),\mathbf{v}=[0,0,-\frac{1}{2}]^{T}\), and  
\[\mathbf{A}=\begin{bmatrix}-2\lambda_{1}^{2}s_{1}^{T}&\lambda_{1}^{2}\\ \vdots&\vdots\\ -2\lambda_{N}^{2}s_{N}^{T}&\lambda_{N}^{2}\end{bmatrix},\mathbf{b}=\begin{bmatrix} \eta^{2}-\lambda_{1}^{2}\|\mathbf{s}_{1}\|^{2}\\ \vdots\\ \eta^{2}-\lambda_{N}^{2}\|\mathbf{s}_{N}\|^{2}\end{bmatrix}. \tag{13}\]  
Although Equation (12) is still non-convex, both the objective function and the constraint in Equation (12) are quadratic. This is a typical quadratic programming problem with quadratic constraint and can be solved using the bisection method [19]. In the sequel, this method is noted as \"WLS-K\"(weighted least square-known).
## 3 Numerical Analysis
### Material Properties  
Buried flexible pipes are usually modeled using plate, beam-column, or shell elements (Kal
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 3 Background and Attack Model
### Threat Model
#### 3.2.2 Fuzzy Attack  
Fuzzy attack means that the attacker actively sends a large number of random or forged data frames to the CAN bus, causing network congestion or arbitration failure, affecting the transmission of normal messages. In the car-hacking dataset, the active attack mode is used, the fuzzy data packet is constructed randomly with the ID from 0x000 to 0x7 ff, and the fuzzy attack packet is injected into the CAN bus at an interval of 0.3 ms, with an interval of 10 s. Total data collection lasts 5 min.
## 4 Model Validation
### Comparisons of Solar Radiation and PAR Variation of Different Research Sites  
In this study, to evaluate the accuracy of the PAR, the estimated PAR was compared with the ground observation data obtained from meteorological radiation stations in 2008. Firstly, the solar radiation top of atmosphere, solar radiation in clear sky and daily solar radiation were estimated based on the model. As shown by the analysis on the solar radiation of six typical stations (Figure 3), it was evident that the distribution results of the stations basically conformed to the typical theoretical distribution model, and the simulated values of solar radiation in clear sky were consistent with the actual daily maximum values observed over many years. However, due to differences in atmospheric composition, cloud cover, atmospheric moisture content and atmospheric suspended matter content, the absorption and scattering of daily solar radiation top of the atmosphere were different, and this affected the total radiation reaching the ground. The correlation coefficient between the estimated PAR and the observed data surpassed 0.90 in Harbin and Yanji stations (Figure 4). The results indicated that the simulation accuracy of PAR generally met the requirements.  
Figure 4: Comparison of daily estimated photosynthetically active radiation (PAR) and observed PAR in Harbin and Yanji stations, in 2008.  
Figure 3: Variation of daily solar radiation, solar radiation top of atmosphere and solar radiation in clear sky, under different typical six stations, in 2008.
## 1 Introduction
### Power Ramp-Rate Control (PRRC)  
The aim of this strategy is to smooth the active power output oscillations of the PV system. In particular, the algorithm imposes a maximum rate of variation of the active power output when variations of irradiance occur. In this case, the criterion to curtail the power does not come from the absolute value of the active power output but from its change rate with time [12]. The PV power ramp-rate can be calculated as in Equation (2):  
\[R_{r}(t)=dP_{pv}/dt \tag{2}\]  
If \(R_{r}(t)\) is higher than the limit value required by the system operator, the voltage \(v_{PV}\star\) is perturbed in order to reduce the change rate of the PV power to that value. This kind of control can be performed both by controlling the voltage (as just explained) or the power.
## 3 Proposed Approach
### Evaluation of Model Performance
#### 3.10.2 Iou  
IOU can be important in assessing how accurately a predicted bounding box or segmentation region aligns with its ground-truth counterpart, which in turn directly impacts the precision of building dimension estimates. When the overlap between prediction and ground truth is high, it indicates that the boundaries of the detected or segmented building closely match reality--leading to more reliable height and width measurements. Conversely, a low IOU suggests that the predicted building area is misaligned or incorrectly sized, potentially causing significant errors in dimension calculations. Consequently, IOU serves as a clear and intuitive metric for quantifying how well the model identifies and outlines a building, making it indispensable in building dimension estimation tasks.