esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Statistical Output Responses (SORs) Derivation Based on Recursive Convolution
The initial conditions for the ISI-PDFs at the \(\mathrm{k}=\infty\) are defined by the Kreonecker delta functions summarized in (9). For an efficient recursive convolution, each PDF is scaled four times. Thus, at the final step, when deriving the statistical responses, \(\frac{1}{4}\) should be multiplied. \[\mathrm{ISI_{00}}^{-(\mathrm{k})}(t,v) =\sum\limits_{\mathrm{m}=00}^{01,10,11}\mathrm{Shift_{0}}\left( \frac{f_{\mathrm{m}}^{(\mathrm{k}+1)}}{4}\otimes\mathrm{ISI_{m}}^{-(\mathrm{ k}+1)}(t,v)\right) \tag{5}\] \[\mathrm{ISI_{01}}^{-(\mathrm{k})}(t,v) =\sum\limits_{\mathrm{m}=01}^{10,11,00}\mathrm{Shift_{1}}\left( \frac{f_{\mathrm{m}}^{(\mathrm{k}+1)}}{4}\otimes\mathrm{ISI_{m}}^{-(\mathrm{ k}+1)}(t,v)\right)\] (6) \[\mathrm{ISI_{10}}^{-(\mathrm{k})}(t,v) =\sum\limits_{\mathrm{m}=10}^{110,001}\mathrm{Shift_{0}}\left( \frac{f_{\mathrm{m}}^{(\mathrm{k}+1)}}{4}\otimes\mathrm{ISI_{m}}^{-(\mathrm{ k}+1)}(t,v)\right) \tag{7}\]  
Figure 6: Derived output responses are plotted. Steady-states (**a**) and pull-up/down responses (**b**) are shown, respectively. In the main-cursor PDFs, voltage uncertainty is clearly shown. In the ISI PDFs, various noises impacts are included. \[{\rm ISI_{11}}^{-({\rm k})}(t,v)=\sum\limits_{{\rm m}=11}^{00,01,10}{\rm Shift_{1}} \left(\frac{{\rm f_{11}}^{({\rm k+1})}}{4}\otimes{\rm ISI_{m}}^{-({\rm k+1})}(t, v)\right) \tag{8}\]  
\[{\rm ISI_{10,00}}^{-(\infty)}(t,v)=\delta(v-V_{\rm L})\ \&\ {\rm ISI_{01,11}}^{-( \infty)}(t,v)=\delta(v-V_{\rm H}) \tag{9}\]  
Statistical output responses (SORs) affected by the ISI-PDFs can be derived by taking the final convolution between main cursor-PDFs and ISI-PDFs affecting each other. The SOR can be derived by calculating (5)-(8) at \({\rm k}=0\). Finally, half (1 UI) part of a statistical eye-diagram can be obtained using Equation (10)  
\[Stateye_{1{\rm UI}}=\frac{1}{4}\ ({\rm ISI_{00}}^{-(0)}(t,v)+{\rm ISI_{11}}^{-(0 )}(t,v)+{\rm ISI_{01}}^{-(0)}(t,v)+{\rm ISI_{10}}^{-(0)}(t,v)) \tag{10}\]  
If (10) is expanded to 2 UIs, the final statistical eye-diagram, including various noises affecting the SI/PI, can be derived. In Figure 7, the proposed procedure is graphically depicted. From the derived statistical eye-diagram, BER can be derived by considering the random jitter and then calculating BER at every sample timing and voltage. The random jitter can be considered by taking a convolution between the derived statistical eye-diagram and random jitter distribution at every voltage level. Figure 7: Derivation of statistical output responses (SORs) are graphically depicted. (**a**) Final convolution to derive \({\rm ISI_{01}}^{-(0)}(t,v)\) is shown. It is a convolution between main cursor PDF \({\rm PDF}\ {\rm f_{01}}^{(1)}\) and total ISI affecting the logic zero state. (**b**) Derivation process of the final eye-diagram is shown. In the following sections, the proposed method is verified and applied for the SI/PI co-design and co-analysis for the high-speed interconnect.
## 5 Verification Test
### Stack Angle Verification Test  
The optimized parameters were used for simulation verification (X\({}_{1}\) = 0.455, X\({}_{2}\) = 0.427, X\({}_{3}\) = 0.158). The optimization solution of this group was subjected to five sets of repeated simulation experiments. The average value was obtained and used to obtain a stacking angle of 32.31\({}^{\circ}\) under this parameter combination. The stacking angle error obtained from the physical stacking test was 0.34%. The comparison between the simulation test and the physical test was illustrated in Figure 13. The results demonstrated that there was no significant difference between the accumulation angle simulation and accumulation angle physical test results under the optimized simulation parameters. Moreover, the shapes and angles of the stacking angles of the two are highly similar, indicating that the simulation parameters of this group are set accurately.  
Figure 12: Response surface of lily bulb stacking angle.
###### Abstract
In [17], the authors solve the generation start-up sequence and load pick up through a branch-and-bound and interior point method to provide an optimal skeleton-network restoration. In [18], the authors propose the integration of microgrids within the back-start optimization problem. In this case, the uncertainties of the microgrid black-start resources are modeled by discretizing the probability distribution of the forecast errors. A mixed integer linear programming model is implemented to solve the generator start-up sequence. In [19], the authors propose a distributed black-start optimization method for global transmission and distribution networks. In this case, the global black-start optimization problem is divided in several sub-problems in transmission and distribution networks taking advantage of distributed generation. Other methodologies to solve the optimal stat-up sequence for system restoration include bilevel optimization [9], dynamic programming [20], mixed-integer linear programming [21], Lagrangian relaxation [22], and Benders decomposition [23]. Depending on the structure and characteristics of the power system, its restoration process may be different. On the one hand, in power systems with a high number of BS units, the power system is restored quickly due to the availability of sufficient initial power resources. On the other hand, in power systems with a limited number of BS units, the system restoration results are more complicated and time consuming. In this research, the restoration of power systems with a limited number of BS resources and available renewable power plants is discussed. The main contributions of this paper are twofold: (i) it provides a novel mixed integer linear programming approach to solve the optimal generation start-up power system restoration problem, and (ii) it considers the effect of non-conventional renewable energy sources (NCRES) within the restoration process. Table 1 presents a brief account of several methodologies applied to the optimal generation start-up problem, where CG and MIQP stand for conventional generation and  
mixed integer quadratic programming, respectively. Note that the proposed approach is the only one that simultaneously considers CG, NCERS, and BESS. It is worth mentioning that BESS have already been considered in start-up methodologies such as in [24; 25]; nonetheless, in these works the authors integrate BESS in the black-start problem from the standpoint of the expansion planning aimed at improving the system resiliency, and not from an operative perspective, as carried out in this paper. This paper is organized as follows: Section 1 provides an introduction and literature review regarding the power system restoration problem. Section 2 describes the conventional and non-conventional generation start-up strategy. Section 3 displays the proposed mathematical formulation for the generation start-up problem. Section 4 offers the test and results performed on the IEEE-39 RTS test system evidencing the impact of considering NCRES and battery energy storage systems (BESS) in the restoration process; finally, Section 5 presents the conclusions.
## 4 Key Application Areas for Passive Wake-Up Radio  
Smart technologies propelled by wireless sensor networks (WSN) are employed in various applications to monitor and control different conditions ranging from natural, environmental and personal health features. In this section, we discuss the key evolving application areas where the passive wake-up radios can be utilized.
## Results  
### Seasonal Drivers of Solar Induced Fluorescence  
Two-dimensional MDS analysis of seasonal SIF drivers (plot stress = 0.16) showed a clustering of orange jasmine data points, with a low dispersion and a small location shift (opposite to the vectors for LUE, YPSII and PRI) relative to avocado data points (Figure 2). Avocado data points from each day showed clustering relative to each another, having a larger dispersal than orange jasmine data points. High significance was found for the vectors for all spectral and photosynthetic variables (\(p\leq 0.001\)), with the exception of \(\mathrm{YSIF}_{\mathrm{red}}\) (\(p\) = 0.256). Vectors for the environmental variables and leaf physical properties, particularly leaf thickness (\(p\leq 0.001\)), day length (\(p\leq 0.001\)), daily min air temperature (\(p\) = 0.005) and leaf PAR (\(p\leq 0.001\)), were all found to be significant, whereas vectors for all pigments were statistically insignificant. Regarding the spectral and photosynthetic vectors, \(\mathrm{YSIF}_{\mathrm{FR}}\) and \(\mathrm{SIF}_{\mathrm{FR}}\) were found to be tightly associated in the same direction as ETR. This \(\mathrm{YSIF}_{\mathrm{FR}}\), \(\mathrm{SIF}_{\mathrm{FR}}\) and ETR vector grouping was found to be opposite to the tight grouping of \(\mathrm{YSIF}_{\mathrm{red}}\), YNO and NDVI vectors. Independent to the SIFs, ETR, YNO and NDVI vectors, were the vectors for photosynthetic light use efficiency (LUE and YPSII) and stress (PRI and YNPQ), which formed two separate groups in opposing directions. The vectors for LUE and YPSII were associated with increasing PRI values (decreasing stress) and were in the opposite direction to the vector for YNPQ. Vectors for the environmental variables and leaf physical properties were found to radiate only toward negative values of the Y axis. Tight grouping was found for the pigment vectors (total Chl, Chl a/b and \(\mathrm{\sum VAZ}\)) and daily maximum temperature. Vectors for both global and leaf PARs were found to be tightly grouped in the same direction as \(\mathrm{\sum LLx}\). Unrelated to the vectors for global PAR, leaf PAR and \(\mathrm{\sum LLx}\) were the vectors for leaf thickness and \(\alpha/\beta\) car ratio, which were in an opposite direction to the vector for day length. The vector for daily minimum temperature was found in-between the vectors for leaf thickness, \(\alpha/\beta\) car ratio and other leaf pigment vectors. Comparing the two MDS plots, the significant SIF vectors were found in the same direction as the vectors for leaf and global PARs, leaf pigments (total Chl, Chl a/b and \(\mathrm{\sum VAZ}\)) and daily maximum temperature. Opposite these vectors were the vectors for NDVI, YNO and \(\mathrm{YSIF}_{\mathrm{red}}\). The vectors for LUE, PRI and YPSII, were in the same direction as the vectors for leaf thickness, \(\alpha/\beta\) car ratio and minimum daily temperature, which were opposite the vector for YNPQ. Figure 2: Two-dimensional multidimensional scaling (2D MDS) analysis of daily photosynthetic, physical, leaf pigment and remotely sensed measurements from leaves of avocado (red) and orange jasmine (black; stress = 0.16).
## 4 Results
### SLR Validation  
SLR is a powerful and independent technique that enables the independent validation of satellite orbits [21; 22]. The difference between the observed SLR values and the computed distances using BeiDou satellite orbits and SLR reference station coordinates can be used to identify whether there are systematic biases or unexpected errors in the estimated BeiDou satellite orbits.  
All BeiDou-3 satellites and some BeiDou-2 satellites are monitored by the International Laser Ranging Service (ILRS) SLR tracking stations. The SLR range observations can be accessed via the File Transfer Protocol (FTP) services provided by the ILRS. For this study, the latest SLR station coordinates and system eccentricities were obtained from the ILRS. The retro-reflector offsets of BeiDou-3 satellites were provided by the Operation Control Department, and those of the BeiDou-2 satellites were obtained from ILRS. There were 12, 29, 45, 10, 114, 36, 12,5 and 104 normal points (NP) available for satellites C31, C32, C33, C34, C01, C08, C10, and C11, respectively, during the study period. These NPs were from the Yarragadee, Monument Peak, Papeete, Changchun, Shanghai, Mount Stromlo, Herstmonceux, and Matera stations (cf. Figure 7). The SLR observations were corrected using the retro-reflector offsets. The mean and RMS of SLR residuals of the middle day for orbit solutions are summarized in Table 5 and illustrated in Figures 8-15. The result is generally consistent with the  
Figure 6: BeiDou C34 satellite overlap comparison in the radial, along, and cross-track components.  
Figure 7: BeiDou-2 satellite overlap comparison in the radial, along, and cross-track components.  
overlap comparison in the radial direction in the orbit determination. The overall RMS values for BeiDou-3 IGSO and MEO are approximately 11 cm and 40 cm, respectively. The overall RMS values for BeiDou-2 IGSO and MEO are approximately 10 cm and 6 cm, respectively. Table 5 illustrates that the mean offsets for BeiDou-3 IGSO and MEO are \(-4\) cm and 15 cm, respectively. Table 5 also shows that the mean offsets for BeiDou-2 IGSO and MEO are \(-4\) cm and 3 cm, respectively. Further, BeiDou-3 MEO satellites have slightly larger overall RMS values and mean offsets than the BeiDou-3 IGSO satellites, whereas BeiDou-2 MEO satellites have slightly smaller overall RMS values and mean offsets than the BeiDou-2 IGSO satellites. In addition, BeiDou-2 GEO satellites yield significantly larger RMS of SLR residuals compared to other BeiDou-2 satellites.  
Figure 11: SLR validation for BeiDou-3 C34 satellite.  
Figure 12: SLR validation for BeiDou-2 C01 satellite.  
Figure 10: SLR validation for BeiDou-3 C33 satellite.  
Figure 14: SLR validation for BeiDou-2 C10 satellite.  
Figure 13: SLR validation for BeiDou-2 C08 satellite.  
Figure 15: SLR validation for BeiDou-2 C11 satellite.
###### Abstract  
Due to the special meshing of convex and concave tooth profiles, an S-shaped tooth profile gear with a few teeth has the advantage of reducing the contact stress and improving the contact ratio. However, the theoretical inseparability of the center distance of an S-shaped tooth profile and shaft misalignments, including center distance errors and axis parallelism errors caused by manufacturing or installation errors, can easily cause tip contact and produce transmission errors. Accordingly, it is necessary to analyze the influence of shaft misalignments on the meshing performance. In this paper, a unified mathematical model of spur and helical gears with an S-shaped tooth profile is established and an analytic method for determination of the contact pattern, meshing path, and transmission error of a gear pair with shaft misalignments is proposed. Then, the validity of the analytic method is proved by practical examples and the influence of shaft misalignments on the contact pattern of an S-shaped tooth profile gear pair is obtained. Through comparison with an involute gear with the same parameters, it is found that shaft misalignments within a certain range result in an acceptable transmission error and contact area migration of the S-shaped tooth profile gear pair similar to that of the involute gear pair.  
gear with a few teeth; S-shaped tooth profile; tooth contact analysis; shaft misalignments; transmission error
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 2 Background
### Coloured Petri Net  
Petri nets (PN) [10] is a class of formalisms used to model an extensive variety of systems by a graphical notation. Petri nets enable us to check properties such as reachability, boundedness, liveness and reversibility [11]. Currently, PNs have several extensions, such as timed, stochastic, and coloured. Whatever the extension, however, it has four primary elements: places to express system variables, transitions to model actions (or events) occurring in the system, arcs to indicate relations between places and transitions and tokens that are places' values.  
Coloured Petri net (CPN) [5; 6; 7] is a PN extension that combines high-level programming language capabilities with core elements of Petri nets. CPN Tools [12] is the first tooling adopted to create coloured Petri nets. In CPN, every place has associated three basic information: a name to identify the place, a colour set to define the type of token stored in the place, and an initial marking whose objective is to start the CPN model. Figure 0(a) shows a place (\(p1\)) storing one token whose value is set to 0. Outside the place, it is possible to see an initial marking (set to 0) and a colour set (INT).  
Figure 1b depicts an arc annotated with a variable \(i\) whose type is the same as one of the input place's colour. Figure 1c represents a transition including: identification (\(t1\)), guard, time and code. Guard ([i = 0]) is a logical expression to enable/disable the transition. When the \(t1\) fires, time fragment (\(@+1\)) computes a specific amount of time and the code associated to the transition is executed. The code include an input, an output and an action. The action has the programming code to processes the input (input) and returns a value (output).  
Hierarchical CPN enables us to design CPN models possibly connected to each other and at different abstraction levels. This characteristic is helpful for building large models, such as ones to describe an application or a protocol stack. It is worth observing that this feature allows dividing the model into various modules where each one has a CPN.
# An Energy-Efficient Mobile Sink-Based Unequal Clustering Mechanism for WSNs
## 4 Proposed Algorithm
### Related Concepts
#### 4.1.2 Load Balancing  
In multi-hop clustering WSNs, the CHs located around BS have to relay not only the data packets belonging to own member nodes, but also they have to receive and transmit the data packets of outer clusters. Thus, the innermost CHs with maximum energy consumption ratio are critical, however, the CHs of outermost clusters have a minimum energy consumption ratio in comparison to others. Our main reason is to balance the energy consumption ratio of CHs located in different coronas in CCWSN. Energy consumption ratio is defined as the energy usage of CHs to the number of nodes located in the centroid region as CH candidates [44]. By equalizing the energy consumption ratio of CHs belonging to \(i\)th corona with innermost corona, we have:  
\[\frac{ECH_{1}}{NCH_{1}}=\frac{ECH_{i}}{NCH_{i}} \tag{12}\]By substituting the Equation (11) in the equation above, the following equation will be derived:  
\[ECH_{i}=(2i-1)ECH_{1} \tag{13}\]  
From Equation (13), it is concluded that to balance the energy consumption ratio among coronas, the CHs located in the innermost corona should consume \((2i-1)\) times less energy than the CHs belonging to the \(i\)th corona.
## 5 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{_Single_} & \multicolumn{3}{c}{**AdaBoost**} & \multicolumn{3}{c}{**Decision Tree**} & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**XGBoost**} \\ \cline{2-11}  & **GTV** & **CTV** & **PTV** & **GTV** & **CTV** & **PTV** & **GTV** & **CTV** & **PTV** & **GTV** & **CTV** & **PTV** \\ \hline Accuracy & **76.29** & 69.07 & 62.89 & 71.13 & 48.45 & 72.16 & 70.10 & 69.07 & 69.07 & 55.67 & 69.07 & 71.13 \\ \hline AUC & **75.99** & 68.22 & 62.75 & 70.50 & 47.81 & 72.02 & 68.01 & 66.87 & 68.42 & 51.71 & 68.22 & 69.14 \\ \hline Precision & **77.78** & 69.49 & 66.67 & 71.93 & 52.73 & 75.00 & 66.67 & 65.75 & 70.18 & 55.56 & 69.49 & 67.61 \\ \hline Recall & **79.25** & 77.36 & 64.15 & 77.36 & 54.72 & 73.58 & 90.57 & 90.57 & 75.47 & 94.34 & 77.36 & 90.57 \\ \hline \multicolumn{11}{c}{_Total_} & \multicolumn{3}{c}{**AdaBoost**} & \multicolumn{3}{c}{**Decision Tree**} & \multicolumn{3}{c}{**Random Forest**} & \multicolumn{3}{c}{**XGBoost**} \\ \cline{2-11} \multicolumn{1}{c}{_Ranking_} & **GTV** & **CTV** & **PTV** & **GTV** & **CTV** & **PTV** & **GTV** & **CTV** & **PTV** & **GTV** & **PTV** \\ \hline Accuracy & 74.23 & 73.20 & 52.58 & 61.86 & - & - & **75.26** & 72.16 & 71.13 & 56.70 & 32.99 & 67.01 \\ \hline AUC & 73.52 & 72.77 & 52.17 & 61.23 & - & - & **74.08** & 70.28 & 68.95 & 54.97 & 36.36 & 64.02 \\ \hline Precision & 74.14 & 74.55 & 56.60 & 64.29 & - & - & **73.02** & 68.57 & 67.12 & 58.21 & 0 & 62.96 \\ \hline Recall & 81.13 & 77.36 & 56.60 & 67.92 & - & - & **86.79** & 90.57 & 92.45 & 73.58 & 0 & 96.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: System performance excluding LBP descriptors from the final feature subset in all possible combinations of Table 2 for single ranking (upper panel) and total ranking (lower panel). The notation ‘-’ indicates that no feature was left in the final set after excluding LBP descriptors. The largest performance is highlighted in bold.
## 4 Analysis of the Existing Datasets
### Comparison of Surveillance Datasets  
The anomaly types in the datasets are listed in Table 9. It has been observed that humans mostly cause these anomalies, and the diversity of anomalies consisting of inanimate objects is low. In addition, when we examine Table 10, we see that the datasets are not recent and are generally low-resolution, and the ground truth data represent frame-level anomalies. When the structure and contents of the datasets are examined, we can categorize the datasets based on the recording environment and ground truth labeling.  
The CUHK Avenue, UCSD Ped2, and ShanghaiTech datasets are the most-suitable for evaluating anomaly detection performance in surveillance videos. Future studies should consider testing the proposed methods on these datasets to determine their effectiveness.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Datasets** & **Anomalies** \\ \hline Avenue & Throwing objects, loitering, and running \\ UCF-Crime/HR-Crime & Abuse, arson, arrest, assault, burglary, explosion, road accident, fighting, shooting, robbery, shoplifting, stealing, and vandalism \\ UMN & Unusual behavior of the crowd and running \\ ShangaiTech & Chasing, drawling, running, and non-pedestrian assets, such as skaters and bicycles, on the pedestrian path \\ Subway Entrance/Exit & Wrong direction, no payment, unusual interactions, and loitering \\ UCSD Ped1/Ped2 & Passage of non-pedestrian assets, such as vehicles and bicycles, from the pedestrian path \\ \hline \end{tabular}
\end{table}
Table 9: Anomalies of SVAD datasets.  
Figure 7: Examples of abnormal frames from the HR-Crime dataset are presented.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Datasets** & **Release Year** & **Ground Truth** & **Resolution** & **FFS** & **Environment** & **Normal Frames** & **Abnormal Frames** & **Training Frames** & **Test Frames** & **Total Frames** \\ \hline
**Avenue** & 2013 & frame/ & 640 \(\times\) 360 & 25 & outdoor & 26,832 & 3820 & 15,328 & 15,324 & 30,652 \\
**UCF-Cfime** & 2018 & frame/ & 320 \(\times\) 240 & 30 & indoor/ & N/A & N/A & 12,631,211 & 1,110,182 & 13,741,393 \\
**HR Crime** & 2021 & frame/ & 320 \(\times\) 240 & 30 & indoor/ & 485,227 & 335,378 & N/A & N/A & N/A \\
**UMN** & 2009 & frame & 320 \(\times\) 240 & 30 & indoor/ & 6165 & 1576 & N/A & N/A & 7740 \\
**ShanghaiTech** & 2016 & frame/ & 856 \(\times\) 480 & - & outdoor & 300,308 & 17,090 & 274,515 & 42,883 & 317,398 \\
**Subway** & & & & & & & & & \\
**Entrance** & 2008 & frame & 512 \(\times\) 384 & 25 & indoor & 132,138 & 12,112 & 76,453 & 67,797 & 144,250 \\
**Exit** & 2008 & frame & 512 \(\times\) 384 & 25 & indoor & 60,410 & 4491 & 22,500 & 42,401 & 64,901 \\
**UCSD** & & & & & & & & & \\
**Ped1** & 2010 & frame/ & 228 \(\times\) 158 & - & outdoor & 9995 & 4005 & 6800 & 7200 & 14,000 \\
**Fed2** & 2010 & pixel & 360 \(\times\) 240 & - & 2924 & 1636 & 250 & 2010 & 4560 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of existing SVAD datasets.