esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Ankle Sagittal Moment-Test Machine & Biomechanical Results
#### 3.2.2 Ramp  
The incline-walking prosthetic foot ankle moment and angle for the machine and biomechanical tests are depicted in Figure 8. The decline-walking prosthetic foot ankle moment and angle results for the machine and biomechanical tests are shown in Figure 9.  
Figure 6: VSA prosthetic foot load versus displacement heel and keel stiffness test results for condition 1 (softest), condition 2 (mid), and condition 3 (stiffest).  
Figure 7: Ankle moment versus ankle angle during level-ground walking for three stiffness settings-condition 1 (softest), condition 2 (mid), and condition 3 (stiffest): (**a**) test machine results and (**b**) biomechanical test results–average of five users.  
The maximum ankle angles for plantarflexion and dorsiflexion were recorded for each stiffness condition, task, and test method. The results are presented in Table 2.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & \multicolumn{4}{c}{**Maximum Ankle Plantarflexion (\({}^{\circ}\))**} & \multicolumn{4}{c}{**Maximum Ankle Dorsiflexion (\({}^{\circ}\))**} \\ \cline{2-9}  & **Machine Test** & **Biomechanical Test** & **Correlation Coefficient** & \(p\) & **Machine Test** & **Biomechanical Test** & **Correlation Coefficient** & \(p\) \\ \hline COND 1: Level & \(-9.6\) & \(-5.9\) & & & 17.2 & 16.5 & & \\ COND 2: Level & \(-9.4\) & \(-5.7\) & 0.961 & 0.179 & 16.1 & 15.9 & 0.998 & 0.043 \\ COND 3: Level & \(-8.8\) & \(-5.5\) & & & 14.8 & 15.0 & & \\ \hline COND 1: Incline & \(-4.6\) & \(-0.2\) & & & 17.4 & 18.4 & & \\ COND 2: Incline & \(-4.2\) & \(-0.5\) & \(-0.564\) & 0.619 & 16.6 & 17.7 & 0.995 & 0.065 \\ COND 3: Incline & \(-3.6\) & \(-0.4\) & & & 15.8 & 16.7 & & \\ \hline COND 1: Decline & \(-14.8\) & \(-8.0\) & & & 13.5 & 15.7 & & \\ COND 2: Decline & \(-14.5\) & \(-7.8\) & 0.963 & 0.173 & 12.6 & 15.1 & 0.986 & 0.106 \\ COND 3: Decline & \(-14.1\) & \(-7.7\) & & & 12.2 & 14.6 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum ankle angles for plantarflexion and dorsiflexion in the test machine and biomechanical tests.  
Figure 8: Ankle moment versus ankle angle during \(7.5^{\circ}\) incline walking for three stiffness settings-condition 1 (softest), condition 2 (mid), and condition 3 (stiffest): (**a**) test machine results and (**b**) biomechanical test results-average of five users.  
Figure 9: Ankle moment versus ankle angle during \(7.5^{\circ}\) decline walking for three stiffness settings-condition 1 (softest), condition 2 (mid), and condition 3 (stiffest): (**a**) test machine results and (**b**) biomechanical test results-average of five users.
## 3 Matrix-Based Establishment of Mathematical Model
### Matrix-Based Description of Topological Relationships between Components  
#### 3.1.1 Adjacency Matrix  
As previously mentioned, significant progress has been made in the field of assembly and disassembly sequence planning for mechanics with well-established theories. This paper introduced the component-fastener graph [34] and adjacency matrix as auxiliary tools to describe the connections between components.  
Component-fastener graph \(G=(V,E)\) represents the components of a product and their relationships. The vertices \(V=\{v_{1},v_{2},v_{3},\ldots,v_{n}\}\) represent the components, where \(n\) is the number of components. Meanwhile, the edges \(E=\{e_{1},e_{2},e_{3},\ldots,e_{m}\}\), where \(m\) is the number of edges, represent the relationships between components. If two components \(v_{i}\) and \(v_{j}\) (\(i\
eq j\)) are assembled, then \((v_{i},\,v_{j})\in E\); otherwise, \((v_{i},\,v_{j})\
otin E\).  
Fasteners can be neglected for the assembly of prefabricated wall components, and the component-fastener graph can be simplified as the assembly linking graph [35] which is similar to the liaison graph [36]. Although assembly linking graph is intuitionistic and comprehensible, it is more convenient for the computer to process the information if the graph is converted into an adjacency matrix. Let \(M=[m_{ij}]\) be \(G\)'s adjacency matrix. The adjacency matrix \(M\) is defined as follows:  
\[M=\begin{bmatrix}m_{11}&m_{12}&\cdots&m_{1n}\\ m_{21}&m_{22}&\cdots&m_{2n}\\ \vdots&\vdots&\cdots&\vdots\\ m_{n1}&m_{n2}&\cdots&m_{nn}\end{bmatrix} \tag{2}\]  
where  
\[m_{ij}=\left\{\begin{array}{ll}1,\text{ if component }i\text{ is connected to component }j\\ 0,\text{ otherwise}\end{array}\right.\]  
Taking a group of three walls as an example, Figure 1 displays the corresponding linking graph and adjacency matrix of the three walls.
## 5 Discussion
### Performance of AGB Upscaling Model
Based on the use of field FVC measurements as the reference data, the validation results indicated that the performance of the GLASS-AVHRR FVC product (R\({}^{2}\) = 0.834, RMSE = 0.145) was slightly superior to that of the long-term GEO1 FVC product (R\({}^{2}\) = 0.799, RMSE = 0.174). In this study, we used the established relationship between ground AGB observations and independent variables to generate a MODIS-derived AGB dataset with a 1 km spatial resolution. To enhance the spatial representativeness of the training set for generating long-term AGB datasets with a 5 km spatial resolution, we aggregated the MODIS-derived reference AGB from a 1 km to 5 km spatial resolution with a simple average method as the training set of the GBRT model. Compared with a previous study for upscaling AGB from ground observations to an 8 km spatial resolution directly [10], our upscaling strategy has the ability to generate a training set with stronger spatial representativeness to reduce the uncertainties [80].
# Power Sources for Unmanned Aerial Vehicles: A State-of-the Art
## 2 Unmanned Aerial Vehicle (UAV)
### Battery-Based Supply Techniques
#### 2.2.1 Battery-Powered Unmanned Aerial Vehicles (UAVs)
They enhance reliability by providing redundancy--if one source fails, others can take over. Additionally, hybrid systems can be more environmentally friendly, using cleaner energy sources in combination with traditional ones [76; 77]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristics** & **Ni-Cd** & **Ni-Mh** & **LiPo** & **Li-S** \\ \hline Specific energy (Wh/kg) & 40 & 80 & 180 & 350 \\ \hline Energy density (Wh/L) & 100 & 300 & 300 & 350 \\ \hline Specific power (W/kg) & 300 & 900 & 2800 & 600 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different batteries with their characteristics [57; 58].
## 4 Experiment and Results
### Derived Crowd Dynamics from Detected Tourism Activity Chains  
In order to assess the accuracy of the rules for the tourist filtering strategy, the results of the number of tourist visits identified by the strategy were compared with official public data for the same period. The number of visitor visits for all attractions in Dalian and the Dalian Forest Zoo during the November holiday in 2020 was approximately 2,952,500 and 130,000, respectively. According to the contemporaneous results of the filtering strategy, the number of tourist visits to Dalian was 3,184,049, while Dalian Forest Zoo was 123,305, with relative errors of 7.84% and \(-\)5.15%, respectively. The results show that the accuracy of our filtering strategy is sufficiently guaranteed for macro tourism activity studies on an urban scale.  
Approximately 0.89 million tourists are identified from 1 October to 3 October. Figure 5(a) depicts an example of a tourist's two-day tourism activity chain. Figure 5(b) visualizes the crowd interaction network of attractions, where the nodes' size represents the nodes' degree of centrality; the thicker and denser the edges between nodes, the stronger the interaction between attractions. Notably, the significant tendency for the average degree and the average weighted degree suggests that the network interaction strength is generally weak in the morning and evening. Furthermore, according to its time differences, it can effectively confirm that it is necessary to take time factor into account when calculating attractions'  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**User ID** & **CI** & **LAC** & **Timestamp** \\ \hline
0000****c2b8** & 16782 & 223776194 & 2020/10/1 11:49:56 \\
0000****c2b8** & 16659 & 60862987 & 2020/10/1 11:52:16 \\
000b****497c** & 16810 & 68539659 & 2020/10/1 14:10:00 \\
000b****497c** & 16766 & 226422245 & 2020/10/1 14:12:58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of raw records of mobile tracking data.  
Figure 5: Distribution of major tourism attractions in Dalian.  
status. Figure 6c reflects the temporal variation of crowd flow indicators of the six example attractions in three days. Most attractions exhibit an upward-then-downward trend, with some experiencing a slight decline during midday. Figure 6d shows the crowd structure similarity \(S_{CSI}\) in five different time windows, with horizontal and vertical coordinates representing the attraction's IDs and the color in each pixel cell representing the \(S_{CSI}\) between two attractions.
# A Novel Method for Modeling the Electromagnetic Characteristics of Switched Reluctance Motors
## 1 Introduction
This method uses only five sampling data points of flux-current-position \(\psi(i,\theta)\) characteristics. Although these modeling methods require only a small amount of sampling data, these sampling data need be evenly distributed along the rotor position of SRM. Therefore, in the process of measuring these sampling data, the rotor clamping device is required, which increases the cost of measurement and complicates the measurement process. Fortunately, based on the symmetric structure of the SRM, the flux-linkage samples at some special rotor positions can be quickly measured without rotor clamping devices [21]. However, there is a little work on the accuracy modeling of SRM only based on the sampling data of these special rotor positions. In this paper, a novel method is proposed to build an accurate model of a three-phase 12/8 SRM based on the flux-linkage characteristics of these special positions. First, the flux-linkage curves of the SRM at four equilibrium rotor positions are quickly measured without any rotor clamping device. Further, the flux-linkage curve in the middle position of an electric period is estimated by the variation characteristics of the flux-linkage with the rotor position. Then, a five-order Fourier series is firstly developed to construct the entire electromagnetic characteristics of the three-phase 12/8 pole SRM. By combining the variation characteristics of the flux-linkage with the rotor position, the accuracy modeling of SRM is realized only based on four flux-linkage curves obtained through experimental measurements. Finally, the experiment and simulated result identify the accuracy of the proposed method. The remainder of this paper is organized as follows. Section 2 describes the measurement method of flux-linkage curves at four equilibrium positions of SRM. In Section 3, the nonlinear analytical model of SRM is developed. The simulation and experiment results are shown in Section 4. Section 5 contains conclusions.
## 5 Result and Discussion
### Impact of Temperature on Modal Parameters without Seeded Crack  
Figure 8 presents the result of the impact test to find the fundamental frequency at 200 \({}^{\circ}\)C. While Figure 9 represents the modal amplitude of the specimen running at its fundamental frequency at 200 \({}^{\circ}\)C. In order to remove the possibility of error in measurements, a total of three experiments were performed and averaged to get the natural frequency and modal amplitude at a given temperature. All the measurements of the modal response of the specimens were taken while the temperature was maintained at the required level. The temperature along the beam was heated at different temperature rates to investigate  
Figure 7: Temperature exposure duration for  
the dynamics response accordingly. The comparisons of experimental results of ramped temperature at 2, 5, and 8 \({}^{\circ}\)C/min with the numerical and analytical results are presented.  
Figure 10 presents the comparison of natural frequency for different heating rates for the specific temperature. The graph suggests that the beam natural frequency decreased with an increase in temperature. This implies that the temperature on the beam surface deteriorated the beam stiffness to a certain level. Moreover, ramping of temperature at different rates had some effect on the beam natural frequency. Ramping of temperature at the rate of 2 \({}^{\circ}\)C/min had more impact on natural frequency by diminishing its frequency. However, ramping at 5 and 8 \({}^{\circ}\)C/min had little influence compared to ramping at 2 \({}^{\circ}\)C/min. This effect of ramping temperature was more significant for higher temperatures as shown in Figure 10. This means that heating at 2 \({}^{\circ}\)C/min took more time to reach the desired temperature as shown in Figure 7, thereby accumulating the heat on the beam for a longer duration which might have softened the material properties. This could be explained as changes in interatomic bond length especially at higher temperatures as suggested in [10]. Nevertheless, experimental results of 2 \({}^{\circ}\)C/min cases are very close to the numerical and analytical results for all the temperature values.  
Figure 8: Determined natural frequency at 200 \({}^{\circ}\)C.  
Figure 9: The determined modal amplitude at 200 \({}^{\circ}\)C.  
The modal amplitude for different temperatures is shown in Figure 11. The graph illustrates that the amplitude decayed as the temperature increased. The drop in amplitude was associated with the beam's fundamental frequency. Therefore, the modal amplitude displayed a heating effect similar to the fundamental frequency. The heating rate at 2 \({}^{\circ}\)C/min showed lower vibration amplitude compared to the others. This indicates structures went through a more elastic behaviour at higher heating rates. This influence of heating rate was quite visible at high temperature values as shown in Figure 11.
# Blockage Effects in Wind Tunnel Tests for Tall Buildings with Surrounding Buildings
## 3 Blockage Effects of Isolated Model Wind Tunnel Test  
### Test Results of Isolated Model Test  
Table 3 shows the drag coefficient of the single pressure model with different blockage ratios. As seen in Table 3, the drag coefficient increases with an increase in the blockage ratio.
## 6 Discussion
### Limitations and Future Work  
One assumption in all experiments was that all attributes could be changed. In reality, this is not always true. For example, if we had a decision model for assessing soil quality, the type of the soil at a given location cannot be changed. In the future, this condition can be easily addressed by providing as input only the attributes that can be changed. Additionally, the method implementation is restricted to preferentially ordered ordinal attributes. However, there is a possibility of considering other types of distance measures that would work in nominal settings (e.g., Levenshtein distance).  
BAG-DSM is a \"black-box\" method; given a decision support model, BAG-DSM considers only its input and output attributes but is oblivious to its internal structure. This is both an advantage and a limitation. As an advantage, BAG-DSM is not limited to DEX models but can, in principle, work with any qualitative models that employ preferentially ordered ordinal attributes. As a limitation, BAG-DSM does not use information about internal attribute structure and decision rules; employing this information might considerably reduce the search space but also restrict the method to DEX models only.  
Regarding future work, BAG-DSM is stochastic, and the optimality of the final solution cannot be guaranteed. In order to do that, BAG-DSM needs to be additionally validated. Promising options include a comparison of BAG-DSM with deterministic methods and methods that utilize internal rules by which the decision models operate. Finally, BAG-DSM could also be validated on real decision models in order to examine the validity of the output solutions.
## 3 Methods and Materials
### Application Logic Unit Layer
#### 3.4.2 Kernelized Correlation Filter Algorithm  
_KCF_ is one of the faster tracking algorithm present in the literature [28]. Its power derives by the combination of the _motion model_ with the _appearance model_. This combination permits identifying the object among different frames without losing the high performance given by the \(YOLOv2\) neural network. Indeed, in this work, a combination of the two algorithms is used r to obtain a single tracking algorithm that exploits the information taken from the \(YOLOv2\) and _KCF_. The \(YOLOv2\) recognizes objects, as already explained in Section 3.4.1, and sends the position of the selected bounding box corner coordinates to _KCF_. This information is exploited to calculate the (X,Y) coordinates in the camera plane with respect to object center of gravity. After that, a \(10 px\times 10 px\) square is created around this point, and its area is tracked by the _KCF_.  
To this aim, it extracts data about the shape and color of the object, and it reuses them to track the object in the next frames.  
Nevertheless, _KCF_ suffers from the _occlusion_ problem: if a part of the tracked area is covered during the motion, _KCF_ loses it, and it is not able to recognize it even after it becomes visible again. This leads to a total loss of the interested area after several frames.  
Thus, the AI re-sends the position of the object after a predefined quantity of frames, in order to calibrate the _KCF_ algorithm. This technique permits to increase the frames rate and to improve the target tracking to avoid its loss during the movement.  
_KCF_ was implemented by using the same approach used in the version of _OpenCV_.
## 2 Wireless Communication for Maritime Applications
### Communication between Sensors and Relays
#### 2.1.4 SigFox  
SigFox operates in the 868 MHz band (ISM) and provides a simple way to connect low-energy isolated modules to customer applications at a low cost. Connected devices can transmit messages with payloads of 1 to 12 bytes and are limited to a maximum of 140 messages per day, with data rates of 100 bps [16].  
SigFox uses Ultra Narrow Band signals with duty cycles in Europe, and it has a maximum radiated power of 14 dBm per device [16].  
In order to save power and maximize battery life, the radio is only turned on when there is a message to transmit. Consequently, the radio operates for a few seconds every day, leading to battery lives of several years [16]. However, if a device needs to be reachedby the network, it can only be done after an uplink (the device waits for 20 to 30 s after an uplink) [16].  
SigFox achieves ranges of up to 10 km in urban environments and 40 km in rural environments [17].
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 4 Discussion
The model was also able to accurately detect shifts in seasonal patterns and declines in the concentration of dissolved oxygen that typically occur during heat waves or periods of low circulation, leading to hypoxic events. These events can be detrimental to fish, resulting in increased mortality and significant energetic costs associated with their stress response [57; 58]. Therefore, an accurate monitoring tool for critical environmental parameters that affect fish behavior andsurvival, such as sea surface temperature and dissolved oxygen, is crucial to detect these rare but life-threatening events for fish. The spatial component in aquaculture monitoring is a vital part in its effectiveness. Resolution especially must match the scale of the farms to provide meaningful and useful information. With a resolution of 10 m, Sentinel-2 data can provide information on sea surface conditions for each cage, respectively, whereas the 300 m resolution of Sentinel-3 can cover the whole farm with additional coverage of the surrounding environment. The combined use of differential spatial resolution datasets can be utilized to monitor the conditions within the farm but also to detect threats in a wider area which could potentially affect the aquaculture farm. As such, their incorporation enables the system to make both near-real-time estimations and early warning predictions. Given the limited coastal space available for aquaculture farms, which are often clustered together either as single operations or organized industrial parks, it is crucial for producers to monitor dissolved oxygen levels at a broader spatial scale. This need for monitoring also applies to administrative authorities who may wish to supervise zones of organized aquaculture activity at the regional level. Aquaculture farms have limited geographical footprints in a continuously varying environment. Contrary to traditional terrestrial agriculture activities, where the parameters that affect the health or the amount of the yield remain somewhat constant, in the marine environment these parameters are determined at a much smaller temporal scale. Even within a single day, the conditions can change from favorable to threatening, and as such a steady monitoring framework is paramount for successful precision fish farming. Daily interpolation of the dataset ensures a continuous stream of information, especially on days with unfavorable weather which in many cases are also the ones that are threatening to the aquaculture farms. Through the interpolation, a complete data time series is built that can be useful for research purposes, correlating yield to environmental parameters, and can even be used in updating and retraining the existing ones.
## 2 Basic Structure and Key Technologies
### The 3D Measuring Probe  
A contact probe [PERSON] _et al._ (2013) is adopted in this micro CMM. The physical and schematic structures of the probe are shown in Figure 3. The probe system mainly consists of an autocollimator (1), a mini Michelson interferometer (2), a suspension mechanism constructed by high sensitive elastic leaf springs (4), a reflection mirror (3) and a stylus with ruby ball (5). When the sample on the 3D stage contacts the probe ball and causes a deflection of the stylus, the reflection mirror will generate two angular displacements and a vertical displacement. Displacements of the mirror are functions of the linear deflection of the probe ball in \(X\), \(Y\) and \(Z\) directions. The two angular displacements of the mirror are detected by the home-made autocollimator, and the vertical displacement is detected by the home-made Michelson interferometer. Then, the displacement of the probe ball in \(X\), \(Y\) and \(Z\) directions can be calculated according to the output signals of the autocollimator and the interferometer. The measurement range of probe is up to 20 \(\upmu\)m and the resolution is 1 nm in all \(X\), \(Y\) and \(Z\) directions. The repeatability of the probe is better than 30 nm (2\(\sigma\)). By careful design and fabrication, the stiffness of the probe in three directions is nearly uniform. The maximum touch force is less than 12 mN when the probe ball is deviated from the original position by 20 \(\upmu\)m. In other words, the stiffness is about 0.6 mN/\(\upmu\)m.  
Figure 3: Physical and schematic structures of the 3D measuring probe. (**a**) physical picture of the probe; (**b**) schematic structure. 1: Autocollimator, 2: Michelson interferometer, 3: Mirror, 4: High sensitive elastic leaf spring, 5: Stylus with ruby ball.