esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methodology
### Case Study Area  
We selected the CMC area in Sri Lanka (6\({}^{\circ}\)56\({}^{\prime}\)04\({}^{\prime\prime}\)N, 79\({}^{\circ}\)50\({}^{\prime}\)34\({}^{\prime\prime}\)E) as the case study due to its significance as a developing commercial capital in South Asia. The CMC area, encompassing Sri Lanka's commercial and administrative capital as well as its largest city, is part of the Colombo Metropolitan Area (CMA), which contributes over 50% of the national GDP. According to a JICA Study [47], the CMC area accounts for the highest trip generation in Sri Lanka. Therefore, a considerable number of trips are made within the CMC, as well as between the CMC and the surrounding Divisional Secretariat (DS) divisions, highlighting its commercial and transportation importance (see Figure 2b). Figure 2 depicts the spatial composition of the study area.  
Figure 2: The spatial composition of the study area. In (**a**), the geographical location is depicted; (**b**) shows the trip generation density in the CMC area under study, and (**c**) displays the CMC area along with major roads and their classifications.  
Despite the economic and social importance of the CMC area, it is highly prone to natural disasters and human-induced disruptions in its transportation network, significantly affecting commuters' day-to-day activities [48]. Colombo District, including the CMC area, is ranked as the most flood-vulnerable area in Sri Lanka for housing and urban development [49]. On 10 November 2010, torrential rainfall exceeding 440 mm caused widespread flooding, affecting over 213,000 individuals and disrupting housing, infrastructure, electricity, and transportation [50]. Such issues lead to sudden flash flood occurrences in urban road networks, eventually disrupting the functionality of the urban transportation network [13]. Additionally, human-induced disruptions, such as road construction, traffic barricades, and demonstrations, frequently disrupt the transportation network. Hence, the outcome of this study enhances transportation network redundancy and robustness by identifying critical components and mitigating disruption impacts on performance and traffic flow.
# Memory and Cache Contention Denial-of-Service Attack in Mobile Edge Devices
## 5 Evaluation
### Energy Comparison  
Figure 10 depicts the results of energy comparison for nine cases: _spec_, _spec+mal_tX_, and _spec+mal_tX_ft_, where \(X\) is 1-4. The memory and cache contention DoS attack not only causes significant performance degradation, but also significantly increases the energy consumption of the system. In the case of _spec+mal_t1_, the energy consumption of the system increases by 2.1\(\times\) on average. In the case of _xalancbmk_, system energy consumption increases by up to 4.8\(\times\). It means that the memory and cache contention DoS attack maliciously consumes system energy, which may eventually result in catastrophic impacts on availability and user experience in mobile edge devices. In the other cases, where we run different types of the malicious codes, it also shows similar energy increases (~2.1\(\times\)).  
When we employ our detection and mitigation techniques, we can also reduce energy consumption that is caused by the attack. When applying our detection and mitigation techniques (_spec+mal_tX_ft_), energy consumption is significantly reduced by 43.5% on average, as compared to _spec+mal_tX_. When comparing the energy consumptions between _spec+mal_tX_ft_ and _spec_, _spec+mal_tX_ft_ leads to more energy consumption only by 4.4% when compared to the case of _spec_.  
Table 4 summarizes the energy recovery rates (\(\mathbf{R}_{energy\_recovery}\)) when employing our detection and mitigation method across the 13 SPEC2006 workloads. The energy recovery rates are calculated, as in Equation (3):  
\[\mathbf{R}_{energy\_recovery}=1-\frac{\mathbf{E}_{SPEC+mal\_tX_ft}-\mathbf{E}_{SPEC}}{\mathbf{E }_{SPEC+mal\_tX}-\mathbf{E}_{SPEC}} \tag{3}\]  
where \(\mathbf{E}_{SPEC}\), \(\mathbf{E}_{SPEC+mal\_tX}\), and \(\mathbf{E}_{SPEC+mal\_tX_ft}\) represent energy consumptions of the CPU in the case of _spec_, _spec+mal_tX_, and _spec+mal_tX_ft_, respectively. When we employ the detection and mitigation techniques (_spec+mal_tX_ft_), we can recover energy consumption by 94.7% on average, leaving only a marginal gap between energy consumptions in the cases of _spec_ and _spec+mal_tX_ft_. In summary, our detection and mitigation techniques successfully recover malicious energy consumption from the memory and cache contention DoS attack, which eventually results in longer battery life as well as better availability and user experience in mobile edge devices.  
Figure 10: Energy comparison across three broad cases (total nine cases): _spec_, _spec+mal_tX_, and _spec+mal_tX_ft_. The results are normalized to _spec_ for each workload.
## 4 Proposed Path and Shape Planning Method
### Boundary Waypoints Detection
\[f_{w}(x_{n}^{bl}+r+i,y_{n}^{bl})=\sum_{1\leq q\leq m}M(x_{n}^{bl}+r+i(sig)q,y_ {n}^{bl})\Phi(1,q), \tag{3}\]  
where \(sig=1\) to find the boundary pixel on left \(sig=-1\) to find the boundary pixel on the right  
\[\hat{i}=i\Big{|}0\leq i\leq M_{w}-x_{n}^{bl}-r\;and\;f_{w}(x_{n}^{bl}+r+i,y_{n }^{bl})=100 th-1-m, \tag{4}\]  
```
1:functionGetLeftBoundaryWaypoints(map \(M\) the origin \((x_{o},y_{o})\), threshold \(th\), \(m\), hTetro width \(w_{m}\))
2:initial\(x_{n}^{bl}=x_{o}\), \(y_{n}^{bl}=y_{o}\), select random pixel \((x_{n}^{bl}+r,y_{n}^{bl})\), \(r\): random value
3:whilepixel \((x_{n}^{bl}+r,y_{n}^{bl})\) is the free space pixels \(M(x_{n}^{bl}+r,y_{n}^{bl})=0\)do
4:\(i\gets 0\)
5: filter the pixel \((x_{n}^{bl}+r+i,y_{n}^{bl})\) with the filter mask \(\Phi\) length \(m\), value = 1
6:while number of undefined pixels in range \([(x_{n}^{bl}+r+i,y_{n}^{bl}),(x_{n}^{bl}+r+m+i,y_{n}^{bl})]\leq m-1-th\)do
7:\(i\gets 1\)
8: the right boundary waypoint \(W_{n}(bl)\leftarrow(x_{n}^{bl}+r+i+w_{r}/2,y_{n}^{bl})\)
9:endwhile
10:functionGetRightBoundaryWaypoints(map \(M\), left boundary waypoint \((x_{n}^{bl},y_{n}^{bl})\), threshold \(th\), \(m\), hTetro width \(w_{m}\))
11:initial\(x_{n}^{br}\gets x_{n}^{bl}\), \(y_{n}^{br}\gets y_{n}^{bl}\), select random pixel \((x_{n}^{br}+r,y_{n}^{br})\), \(r\): random value
12:while pixel \((x_{n}^{br}+r,y_{n}^{br})\) is the free space pixels \(M(x_{n}^{br}+r,y_{n}^{br})=0\)do
13:\(i\gets 0\)
14: filter the pixel \((x_{n}^{br}+r+i,y_{n}^{br})\) with the filter mask \(\Phi\) length \(m\), value = 1
15:while number of undefined pixels in range \([(x_{n}^{br}+r+i,y_{n}^{br}),(x_{n}^{br}+r+m+i,y_{n}^{br})]\leq m-1-th\)do
16:\(i\gets i+1\)
17: the left boundary waypoint \(W_{n}(br)\leftarrow(x_{n}^{br}+r+i-w_{r}/2,y_{n}^{br})\)
18:endwhile
19:endwhile
20:endfunctionGetRightBoundaryWaypoints
21: increase the y coordinate \(y_{n}^{bl}\gets y_{n}^{bl}+w_{m}\)
22:endwhile
23:return left boundary waypoints, right boundary waypoint
24:endfunctionGetLeftBoundaryWaypoints
```  
**Algorithm 1** Determination of boundary waypointsOnce the right and left boundary waypoints on the \(y=y_{n}^{l}\) line have been determined, the Algorithm 1 will continue to identify the two similar boundary waypoints on the line \(y=y_{n}^{bl}+w_{m}\). The same mechanism described in the previous section is applied to find the left and right boundary waypoints on this line. After increasing the value \(y\) of the current boundary waypoint, if it is impossible to select any random point having the pixel value \(M(x_{n}^{bl}+r,y_{n}^{bl})=0\) on the line \(y=y_{n}\) of the map, the process of searching boundary waypoints will stop. If this condition happens, the previous boundary waypoint stays at the uppermost boundary of the map and it is identified as the last boundary waypoint to complete the zigzag trajectory as Figure 9.
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
## 4 Discussion
### Future UAV Applications for High-Resolution Phenology  
While spaceborne remote sensing drastically altered how we perceive and quantify the Earth system, UAVs are continuing this revolution by democratizing remote sensing. As both UAV and sensor technology decrease in price, and increase in capabilities, the adoption and application of UAV-based remote sensing will broaden, providing researchers of even modest means powerful remote sensing tools. We envision daily automated drone data collection that can provide high resolution imagery, mapping phenology, and structure at both high temporal and spatial resolution. For field sites with extensive infrastructure (e.g., Long-Term Ecological Research network, National Ecological Observatory Network, AmeriFlux), UAVs offer unique opportunities to expand the impact of existing research for a marginal investment of resources.
## 4 Conclusions  
This paper shows the comparison of relative increment loops of the lift coefficient caused by particles between an aircraft airfoil (NACA0012) and a wind turbine airfoil (S809) at different reduced frequencies. Then, the differences of relative increment loops of the lift coefficient between two airfoils at three mean angles of attack are also discussed. Three important conclusions can be reached as follows:  
1. The reduced frequency has little effect on the value of the maximum increment of the lift coefficient caused by particles, but a larger one can enhance the hysteresis effect and change the angle of attack, at which the maximum increment is obtained, from the up stroke to the down stroke. The increment of the lift coefficient and its difference between two airfoils at a larger reduced frequency are smaller and larger during up and down strokes, respectively.
2. Compared to that of NACA0012 airfoil, the relative increments of S809 airfoil are obviously greater at three mean angles of attack, especially at \(8^{\circ}\), which is the commonly used operating angle.
3. The angle of attack, at which the maximum lift coefficient is obtained, can be significantly changed by particles in two regions: one is under the effect of deep stall, the other is under the effect of light deep at a low reduced frequency.  
Conceptualization, [PERSON] and Z.L.; data curation, D.Z.; methodology, J.J., Z.L. and T.G.; project administration, Z.L.; software, J.J.; validation, J.J., Z.L., D.Z. and Q.L.; writing--original draft preparation, J.J.; writing--review and editing, D.Z. and Q.L.; visualization, [PERSON]; supervision, Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 11872212), the National Science Foundation of Jiangsu Province (Grant No. BK20190386), and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Advanced Instruments in Remote Sensing of EBC
### Hyperspectral
Besides the application of land cover classification, the relationships between LAI and spectral reflectance were studied by [34] using narrowband (EO-1 Hyperion) and broadband (Landsat ETM+ [Enhanced Thematic Mapper Plus]) remotely sensed data in Sulawesi (Indonesia). [PERSON] and [PERSON] preliminarily discussed the strengths and drawbacks of hyperspatial (_i.e._, high spatial resolution) and hyperspectral data [17]. Hyperspatial data was considered to be best suited for facilitating the accurate location of features such as tree canopies, but less suited to the identification of aspects such as species identity. However, conversely, hyperspectral data appear capable of identifying features with significantly increasing accuracy. Therefore, the integration of Hyperion and IKONOS imagery was proposed to differentiate the subtle spectral differences of land-use/land-cover types on household farms in the Northern Ecuadorian Amazon with an emphasis on secondary and successional forests, and the promising results supported the integrated use of hyperspectral and hyperspatial data [35]. \begin{table}
\begin{tabular}{l c} \hline
**Characteristics** & **Values** \\ \hline Sensor Type & Push-broom imager \\ Wavelength Range & 400–2,500 nm \\ Number of Spectral Bands & 220 \\ Spectral Resolution & 10 nm \\ Spatial Resolution & 30 m \\ Swath & 7.5 km \\ Digitization & 12 bits \\ Altitude & 705 km \\ Repeat & 16 day \\ \hline \end{tabular}
\end{table} 
Table 1: Hyperion Imaging Spectrometer Characteristics (adapted from [26]).
## 6 Simulation Results
Figure 16: The experimental result for \(X\)–\(Y\) plot of \(V_{o1}\) and \(V_{o2}\) outputs in Figure 15.  
Figure 14: The experimental evidence frequency ranges of AD844 when \(R_{1}=R_{2}=1\) k\(\Omega\) (yellow line; \(R_{1}=R_{2}=5\) k\(\Omega\) (cyan line); and \(R_{1}=R_{2}=10\) k\(\Omega\) (purple line). Figure 15: The experimental waveforms of the quadrature outputs \(V_{o1}\) (channel 1) and \(V_{o2}\) (channel 2). Figure 17: Measured result of operation as an AM signal generator (channel 2) when \(I_{B2}\) is the applied sinusoidal signal (channel 1). Figure 3.0 Measured result of operation as an AM signal generator (channel 2) when \(I_{B2}\) is the applied triangular signal (channel 1). Figure 3.1 Measured result of operation as an AM signal generator (channel 2) when \(I_{B2}\) is the applied pulse signal (channel 1). The frequency spectrum of the oscillator output voltage, \(V_{o1}\), is shown in Figure 3.2 The measured oscillation frequency was 296.68 kHz, which is close to the theoretical value of 303.15 kHz with a 2.13% error rate. The THD, including the first harmonic through to the ninth harmonic components, is approximately 2.35%. The experimental results are consistent with the theoretical values. For oscillators, noise is a major concern where even a small noise in an oscillator will cause dramatic changes in its frequency spectrum and timing properties. Figure 21 shows the phase noise using the Agilent phase noise measurement solution. The phase noise of the proposed oscillator is less than \(-\)88.2 dBc/Hz at 10 kHz offset. Figure 22 shows the root mean square (RMS) jitter of the proposed oscillator, which is 6
# A Parametric Study of Wind Pressure Distribution on Facades Using Computational Fluid Dynamics
## 4 Results
### Results for Geometry No. 5 (g = 160 mm d = 560 mm)  
In Figure 10a, the wind flow behavior is typical of that for turbulent flow around bluff-bodies. Regions of flow separation develop as wind is accelerated by over 78% (from a mean speed of 24 m/s to 44.4 m/s) where oncoming air first contacts the corners of structure. This generates regions of strong negative pressure at sides of the building and fins parallel to the direction of the flow. Recirculation is also apparent as wind at the rear of the building tends to reverse direction, against the trajectory of mean flow. Vortex shedding occurs in areas close to corner regions throughout. These rotational eddies of low pressure develop while attached to the fins, before being transported away from the structure when of sufficient size.  
The critical fin was established as the leading fin. Within this facade arrangement, the leading fin encounters the highest net pressures due to more significant windward pressure and leeward suction compared to the sheltered fins. As can be seen in Figure 10b, time-averaged pressures indicate the tendency of windward pressure to be at its maximum toward the top of the fin due to the high level of suction at the bottom edge. In turn, leeward suction is at its maximum magnitude at the base of the fin, decreasing from this point before increasing gradually at the top of the fin. From comparing the bottom and top ends in Figure 10b, it is apparent that constricting flow leads to a complex redistribution of pressure between the top end of the fin and the building that minimizes the magnitude of suction along this narrow width of the fin.  
The contour plot for peak pressures represented in Figure 10c describes the condition where, with 95% probability, pressure and suction will not be exceeded. The profile for windward and leeward pressure are like the time-average case. This means that the shape of the net pressure distributions in Figure 10d are also similar. Wind load between the time-averaged case and the peak pressure values on the critical fin is increased by, approximately, 2.1 times in terms of force per meter height of the fin and 2.14 times higher for moments acting at the bracket connection. The lever arm--for moments--is approximately at the midpoint of the fin's length with its position for the peak pressures case being fractionally closer to the bottom end of the fin than for time-averaged forces and pressures.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 3 Algorithm Framework and Modules
### Registration Module  
In engineering applications, registration tasks are divided into non-rigid and rigid categories. The non-rigid is often applied to image distortions, while the rigid category is used for global image deformations. Therefore, we combine both approaches to suit practical engineering environments.  
First, the mutual information [23] between homogeneous features is detected to determine whether image registration is required to save computational resources and improve processing efficiency. Then, non-rigid registration is performed to correct small-scale image distortions. Finally, rigid registration is applied to handle large-scale deformations. Specifically, non-rigid registration involves solving a deformation field S that matches the resolution of the original image. A non-singular \(3\times 3\) matrix represents the rigid registration transformation matrix H, as shown below:  
\[\begin{bmatrix}x_{1}^{\prime}\\ x_{2}^{\prime}\\ x_{3}^{\prime}\end{bmatrix}=\begin{bmatrix}h_{11}&h_{12}&h_{13}\\ h_{21}&h_{22}&h_{23}\\ h_{31}&h_{32}&h_{33}\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\\ x_{3}\end{bmatrix}. \tag{9}\]  
In homogeneous coordinates, it is common to set \(x_{3}^{\prime}=x_{3}=1\) and \(h_{33}=1\). Thus, the transformation matrix H has eight parameters that must be solved. Thanks to the feature extraction processing of the feature extraction network, the input to the registration module is changed from the traditional unregistered image pair \(I^{x}\), \(I^{y}\) to unregistered homogeneous feature pair \(f_{h}^{x}\), \(f_{h}^{y}\). This operation transforms the complex multimodal image registration task into a more straightforward single-modal image registration task, improving efficiency and accuracy. The non-rigid registration parameter prediction network is based on the U-Net architecture, while the rigid registration parameter prediction network is based on VGG-Net [24]. The network structures are shown in Figure 4. A bidirectional validation mechanism using fully supervised and self-supervised loss is introduced to enhance the matrix prediction network's accuracy. The mutual information (MI) is employedto detect whether cross-modal registration is required by analyzing information shared between modalities.
# Machine Vision System for Counting Small Metal Parts in Electro-Deposition Industry
## Materials and Methods
### Image Acquisition Requirements  
The brightness-based segmentation approach seems the most promising for the specific application but needs to be tailored to the peculiarities entailed by the small dimensions and high reflectivity of the items to be counted. Consequently, the definition of a proper input image has primary importance for the success of the method.  
Depending on the finishing and on the material of which the items are made, their aspect is rarely opaque but rather it is highly reflexive. Obviously, even color may change, varying from copper-like to silver and gold, thus resulting in different brightness. All these characteristics make it very difficult (or even impossible) to obtain satisfactory threshold values or ranges, on which set the segmentation.  
To make it more complicated, the silhouette of the same items knotted to the galvanic frame varies significantly, due to their almost-random orientation. Moreover, the placement is far from being equally spaced.  
Therefore, in order to make the segmentation algorithms effective, it is of critical importance to obtain a suitable image, where it is possible to separate the items from the background. To this purpose, three different lighting settings have been considered in order to evaluate their efficacy in favoring image segmentation operations:  
1. Frontal lighting with a black uniform background;
2. Lighted white background;
3. Light rear-projection.
## 4 Simulation Results
### Load Reliability  
Figure 7 illustrates the percentage of time for which the load demand is fully met for each MPPT method. The load reliability is expressed as a percentage and plotted against the months of the year.  
The RL-based MPPT method achieves 100% load reliability throughout the year, ensuring that the load demand is always met, even during periods of low solar irradiance. The ANN-based MPPT method also performs well, with load reliability exceeding 95% in all months. Minor reliability issues are observed during the winter months, likely due to reduced PV energy production. However, the conventional MPPT method exhibits the lowest load reliability, particularly during the winter months, when the battery frequently reaches its minimum SOC limit. The results demonstrate the critical role of advanced MPPT methods in ensuring reliable power delivery to the load, particularly under challenging environmental conditions.  
The obtained simulation results provide valuable insights into the performance of the designed PV system and the effectiveness of the three MPPT methods. The key findings can be summarized as follows:  
1. Energy production: The RL-based MPPT method consistently outperforms the ANN-based and traditional methods in terms of PV energy production, achieving the highest efficiency gains.
2. Battery management: The RL and ANN methods effectively manage the battery SOC, minimizing energy losses and prolonging battery lifespan. The conventional method exhibits significant limitations in this regard.
3. Load reliability: The RL-based MPPT method ensures 100% load reliability, highlighting its potential for real-world applications in which an uninterrupted power supply is critical.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Total Annual PV Production (DL-RL MPPT) & 16,069.46 kWh \\ Total Annual PV Production (ANN-MLP MPPT) & 15,448.76 kWh \\ Total Annual PV Production (Conventional MPPT) & 14,637.11 kWh \\ Total Annual Consumption & 17,520.00 kWh \\ DL–Conventional Average Efficiency Difference & 9.72\% \\ ANN–Conventional Average Efficiency Difference & 5.56\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of inputs and layers.  
Figure 7: Load reliability.  
* Adaptability: The advanced MPPT methods demonstrate superior adaptability to varying environmental conditions, with the RL method excelling in both summer and winter scenarios.