esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Use-Cases
#### 2.3.2 Dam under Construction: GERD, Ethiopia  
The second dam case study, the GERD, is still under completion along the northernmost Ethiopian stretch of the Blue Nile river, about 15 km from the border with Sudan (Figure 4). The construction of this monumental infrastructure started in 2011 and it was completed in July 2020 [77; 78; 79]. The impoundment is ongoing and it may take up to 15 years, eventually generating a major artificial lake with an area of 1763.3 km\({}^{2}\), a capacity of more than 70 billion cubic meters of water and flooding a 190 km long stretch of the Blue Nile [79; 80].  
GERD consists of two structures: the Main Dam and the Saddle Dam. The former is characterized by three sections: left bank, central section and right bank. The central section is at a lower level compared to the left and right banks in order to allow good maintenance of the body of the dam, as well as natural flooding during the rainy season. The Saddle Dam of GERD, which is 60 m in height and approximately 5 km in length, was constructed in order to increase storage capacity, permeability and erosion control. It is a concrete-faced rock fill and its embankment volume is 15 million cubic meters [80]. Sentinel-1 SAR and Sentinel-2 optical imagery have been recently used to document and monitor the filling process and its implications on the downstream river hydrology [79].  
Figure 4: Location in western Ethiopia (right inset) and satellite view (taken from Bing Satellite) of the region of the Grand Ethiopian Renaissance Dam (GERD), alongside the spatial distribution of the sites mapped during this research based on optical and SAR satellite data. The area reservoir shows the planned extent according to Madison and Sheng [78].  
According to the selection criteria illustrated before, the GERD can be considered as a perfect case study, to test the PCARA and the earliest part of the PFRAP methodology. It is a large-scale dam, characterized by extended forests and limited farmland. The construction of the dam infrastructures has been recently completed, although the impoundment is still ongoing. Moreover, the region is barely known, with previous archaeological and ethnoarchaeological investigations conducted only in a small part of the prospective reservoir area [81; 82]. Besides the substantial archaeological potential which has emerged from the handful of excavations, the entire region housed a large number of villages characterized by traditional architectural techniques and material culture as well as societal structures, traditions and rituals which closely resembled the old cultural traditions [83]. These cultural features represent an important part of the history, together with the tangible and intangible heritage of the region, which deserve to be documented.
# Numerical Study of Mode Conversion between Fundamental Lamb and Rayleigh Waves
## 5 Summary  
The primary objective of this work was to understand mode conversion between Lamb and Rayleigh waves. Finite element models were used to study the mode conversions in a plate attached to a quarter space. The results suggest that the incident \(A_{0}\) mode has a higher mode conversion efficiency compared to the incident \(S_{0}\) mode. Two hypotheses were developed to explain this difference in mode conversion efficiency and tested using numerical models. The first hypothesis of the grazing incidence of bulk modes was studied numerically, and the results suggest that the incident \(A_{0}\) mode has a higher SV component at grazing incidents on the stress-free boundary compared to the \(S_{0}\) mode, which results in P waves at the grazing incidence. The grazing incidence of SV waves forms the basis for exponentially decaying waves, which confirms the hypothesis of a higher generation efficiency of the \(A_{0}\) mode. The second hypothesis of generation length was also tested by carrying out numerical line scans. The results show that \(A_{0}\to R\) has a finite generation length, and \(R\to A_{0}\) and \(S_{0}\) results in a beat-like phenomenon.  
There are several applications of this work, including nondestructive evaluation and structural health monitoring, where a plate attached to a quarter or half space can be used as a waveguide to generate L waves, which will mode convert and generate bulk and R modes. Mode conversions at steps, wedges and delaminations are also of interest to the NDE community and understanding the fundamental mode conversion is important for efficient NDE. The guided wave aspect of Lamb waves allows us to reach components that are not accessible using conventional methods. Therefore, understanding the mode conversion between these wave modes can help us to develop advanced inspection capabilities.  
A.V.: formal analysis, data curation, investigation, writing; L.J.B.: writing-review and editing, formal analysis; S.K.C.: formal analysis, investigation, writing-review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data will be made available on request by contact [PERSON].  
The authors would like to acknowledge [PERSON] for many helpful discussions during the course of this research.  
The authors declare no conflict of interest.
# Optimization on Linkage System for Vehicle Wipers by the Method of Differential Evolution
## 2 Materials and Methods
### Optimization on the Angular acceleration of Output Links of CDLS  
Now we will use the method of Differential Evolution, discussed in Section 2.2, to solve an optimization problem on the maximal magnitude of _angular acceleration_ of the output-links of a commercially available center driven linkage system (CDLS) for vehicle wippers. We consider the objective function \(f\) defined as follows:  
\[f(r_{3},r_{4},r_{5},r_{6})=\max_{0\leq\theta_{2}\leq 2\pi}|\alpha_{4}(r_{3},r_{4 },\theta_{2})|+\max_{0\leq\theta_{2}\leq 2\pi}|\alpha_{6}(r_{5},r_{6}, \theta_{2})|\]  
See Section 2.1 for explanations of our model.  
We assume that the input link AB of this CDLS rotates at a _constant_ angular velocity \(\omega_{2}=1\ (\text{rad}/\text{s})\). See Figure 5. We record/calculate the relevant items per \(\pi/180\) second. The width of wiping region for the driver side of this CDLS is \(85\pi/180\ (\text{rad})\). The width of wiping region for the passenger side of this CDLS is \(80\pi/180\ (\text{rad})\). An error of \(1\pi/1800\ (\text{rad})\) is allowed for the width of the wiping region. Patterns of the wiping region are regulated in [27].  
We will use the method of Section 2.2 to consider the minimum problem of \(f\) subject to \(10\) specific constraints. For this CDLS, \(r_{1}=210.5\ (\text{mm})\), \(r_{11}=206.8\ (\text{mm})\) and \(r_{2}=45\ (\text{mm})\) are fixed constants. The constraints for the objective function \(f\) are listed as follows.  
(1). \(150\leq r_{3}\leq 250\ (\text{mm})\).  
(2). \(50\leq r_{4}\leq 75\ (\text{mm})\).  
(3). \(150\leq r_{5}\leq 250\ (\text{mm})\).  
(4). \(50\leq r_{6}\leq 75\ (\text{mm})\).  
(5). \(\left|\max_{0\leq\theta_{2}\leq 2\pi}\theta_{4}-\min_{0\leq\theta_{2}\leq 2 \pi}\theta_{4}-\frac{85\pi}{180}\right|\leq\frac{\pi}{1800}\ (\text{rad})\).  
(6). \(\frac{42\pi}{180}\leq\mu_{4}\leq\frac{138\pi}{180}\ (\text{rad})\).  
(7). \(|\omega_{4}|\leq 0.3\pi\ (\text{rad}/\text{s})\).  
(8). \(\left|\max_{0\leq\theta_{2}\leq 2\pi}\theta_{6}-\min_{0\leq\theta_{2}\leq 2 \pi}\theta_{6}-\frac{80\pi}{1800}\right|\leq\frac{\pi}{1800}\ (\text{rad})\).  
(9). \(\frac{42\pi}{180}\leq\mu_{6}\leq\frac{138\pi}{180}\ (\text{rad})\).  
(10). \(|\omega_{6}|\leq 0.3\pi\ (\text{rad}/\text{s})\).  
The values of the parameters for our DE method are chosen as follows.  
(1). Crossover ratio \(CR=0.6\).  
(2). Scale factor \(F=0.6\).  
(3). Population size \(M=150\).  
(4) Maximum number of iterations \(G_{\text{max}}=100\).
## 4 Conclusions  
As this is a recent topic, we suggest that a systematic literature review similar to the present one is done in some years' time, in order to check if the reported UX goals for the designing of cobots are still being used, if new ones have arisen, and their relative importance. Future research could also include more and different databases in its search, since this review only comprised publications of two databases, which could pose as a limitation of our results. These two limitations may hinder the results to be generalized, given the low number of studies involved, and the lack of countries' representativeness. In addition, it is necessary to consider the new generations of digital technology that can model UX and contribute to the design of cobots, such as digital twin technology [5]. [PERSON] and colleagues [5] studied this type of technology that could be used to support the design of the human-centric engineering system.  
Another limitation relates to the fact that not all the authors referenced in this review addressed the UX goals as such. Therefore, some of them were inferred as UX goals, given their description, even though this is a procedure that has the inevitable risk of biases.  
However, this said limitation can also be seen as a theoretical implication of our work, despite its inherent subjectivity. To our best knowledge, no attempt to systematically review UX goals has been made, which means this is a pioneering study in that sense. This is the first categorization of the different UX goals that can be utilized for the designing of cobots in a manufacturing setting, and the determined categories can be of use for further research on this topic. One example of how these categories can be applied is by the development of a single questionnaire for the evaluation of these seven UX goals specifically.  
Regarding the practical implications of this first study, the most evident one is that it is possible to empirically test if actual cobots match the seven UX goals categorized. That can be done, for instance, through the application of questionnaires and conducting interviews. Another important contribution is that these goals can and must be used at the designing stage of collaborative robotic devices, to ensure that they will comply with the desired UX from the start.  
Conceptualization, A.P. and C.C.; methodology, I.M.D.; software, I.M.D.; validation, A.P., C.C. and J.S.; formal analysis, I.M.D.; investigation, I.M.D.; resources, A.P., C.C. and J.S.; data curation, I.M.D.; writing--original draft preparation, I.M.D.; writing--review and editing, I.M.D., A.P., C.C., A.Z. and J.S.; visualization, I.M.D.; supervision, A.P., C.C. and A.Z.; project administration, A.P. and C.C.; funding acquisition, A.P. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by national funds through FCT--Fundacao para a Ciencia e a Tecnologia, I.P., grant number UIDB/05037/2020.  
The authors declare no conflict of interest.
# Hard SyDR: A Benchmarking Environment for Global Navigation Satellite System Algorithms +
## 2 Background and Motivation
### GNSS Processing Stages and Computational Complexity
Nevertheless, we minimize the complexity of the PCPS algorithm by performing no coherent/non-coherent integration and using only 1 ms of RF data. While this signal was chosen to have a high Signal to Noise Ratio (SNR), signals with lower SNRs would require increasing the integration time, which would lead to a significant increase in total processing time (right plot). * We employ a complete scenario time of 15 s (i.e., 15,000 ms). This time was chosen empirically, as a bare minimum of 12 s is needed to confirm the first navigation message page in GPS L1 C/A signals [5] and, thus, the first Time of Week (TOW). * Extending the scenario time to 60 s (or beyond) would drastically increase the total processing time spent in the correlators, rendering the acquisition stage relatively much less significant (right plot). From this discussion, it is clear that SyDR can help assess the cost of each stage in the GNSS processing chain. However, given the high abstraction level and associated overheads of Python, benchmarking in SyDR is restricted to the software level. To propel our benchmarks to assess energy consumption better, we propose moving the processing to a more constrained environment, namely, a dedicated hardware platform. We denote this version of our tool \"_Hard SyDR_\".
## 4 Case Studies
### Wind Power, Photovoltaic Power and Load Power Forecasting
#### 4.1.2 Evaluation Index for Forecasting  
Mean absolute error (MAE) [42], root mean square error (RMSE) [43] and mean absolute percentage error (MAPE) [44] are employed for evaluating the forecasting results; the formulas are as follows:  
\[\text{MAE}=\frac{1}{N}\sum_{t=1}^{N}\left|\hat{y}(t)-y(t)\right| \tag{46}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{t=1}^{N}\left[\hat{y}(t)-y(t)\right]^{2}} \tag{47}\]  
\[\text{MAPE}=\frac{1}{N}\sum_{t=1}^{N}\left|\frac{\hat{y}(t)-y(t)}{y(t)}\right| \tag{48}\]  
where \(N\) denotes the number of samples; \(\hat{y}(t)\) is the forecasting value; and \(y(t)\) is the real value.
## 3 Results
### Diffusion-Theory Based Analysis of Experiments  
#### 3.2.1 Flow Models for Fitting Experimental Data  
As described above, experimental phantoms included a flow channel with the fluid in the channel being either actively pumped (flow phantoms \(F_{1}\)-\(F_{3}\)) or with pump turned off (flow phantom \(F_{0}\)) and  
Figure 8: Estimated Brownian flow coefficients vs. simulated values. (**a**) shows the coefficients obtained directly by fitting simulated data, where each line shows data for different SDS. Symbols were average values obtained from 35 simulated media having fixed \(D_{B}\) (but changing optical properties); errorbars show standard deviations. (**b**) shows the same data in (**a**) after appropriate scaling by correction factors (see text). The errorbars in (**b**) are smaller than the size of symbols shown and different lines (for different SDS) are shown staggered only for clarity. The dashed line in Figure 8a,b shows \(y=x\).  
Figure 7: Scaling (correction) factors determined using simulations at two SDS (top: 0.25 cm; bottom: 1.05 cm) shown for two different media with two different flow coefficients (left: \(D_{B}=1\times 10^{-8}\) cm\({}^{2}\)/s; right: \(D_{B}=3\times 10^{-8}\) cm\({}^{2}\)/s). Each panel shows data for 35 MC simulations spanning seven \(\mu_{a}\) and five \(\mu_{s}^{\prime}\) values (see text). Note that the color-bar scale changes for data in top and bottom rows.  
were designed to examine the impact of flow dynamics on DCS measurements. However, theoretical fits to these data could be obtained by using the Brownian flow model (\((\Delta r^{2}(\tau))=6D_{B}\tau\)), the random flow model (\((\Delta r^{2}(\tau))=V^{2}\tau^{2}\)) or the shear-flow model (\((\Delta r^{2}(\tau))=D_{B}\tau+V^{2}\tau^{2}\)).  
Figure 9a,b show theoretical fits (lines) to an experimental acquisition of \(g_{2}(\tau)\) (symbols) when the pump was switched off (phantom \(D_{0}F_{0}\) Figure 9a) and when pump flow rate was \(F_{3}=5.76\) mL/hr (phantom \(D_{0}F_{3}\), Figure 9b). Theoretical fits for the three different flow models used are shown by lines (dash-dotted: Brownian flow; solid: random flow; dashed: shear flow). As seen visually (and by \(\chi^{2}\) residuals in figure legends), the fits to data are poor for the random flow model in Figure 9a and for the Brownian flow model in Figure 9b, while they are excellent for the random flow model in Figure 9b and for the Brownian flow model in Figure 9a. Given that experimentally measured \(g_{2}(\tau)\) had markedly different decay profiles (by design) in these two phantoms, the results are as anticipated. The shear-flow fit was however able to fit both experimental flow profiles with (\(\chi^{2}<\)0.01) and all experimental data presented below were analyzed using the shear-flow model. This fitting process yielded flow coefficients \(D_{B}\) and \(V\), corresponding to the diffusive and the bulk flow components, respectively, for each phantom.
## 5 Simulation Results
### Radar-System Performance Evaluation
By choosing these three configurations, we implemented both worst- and best-case scenarios, and a third case to back up the overall results. In order to illustrate the difference between the three codes, Figure 7 depicts the periodogram of UFMC-CDMA. The nonscrambled sequence yielded severe deterioration to the received signal compared to the other sequences, while the high-scrambled sequence presented optimal results. Interferences induced to the transmitted signal by its own echos in this multipath environment decreased the probability of detection due to constant energy spreading. Consequently, in order to minimize interference resulting from the presence of multiple copies of the spread signal, the use of a scrambled signature sequence divides the signal energy differently over all subcarriers, optimizing radar-system performance. Figure 6: Walsh-Hadamard spreading. codes representation. Figure 7: UFMC-code division multiple access (CDMA) radar performance with different spreading codes: (**a**) nonscrambled, (**b**) low-scrambled, and (**c**) high-scrambled codes.
# Addressing the Gaps of IoU Loss in 3D Object Detection with IIoU
## 2 Literature Review
### Bounding Box Regression  
The success of IoU-based losses on 2D object detection networks [26; 29] have widely adapted in 3D detection networks as well [43; 60; 61]. The shortcomings of IoU have been addressed with a variety of new variations. For instance, [62] proposed using projection area to estimate IoU directly. In this study [63], three new loss variants of IoU, such as 3D distance IoU, 3D Complete IoU, and Efficient IOU, were proposed. 3D Complete IoU estimates the distance between the box centers, and during the optimization stage of loss estimation, the partial derivative of length (l), width (w), and height (h) was considered. Efficient IOU loss calculates IoU loss, distance loss, and aspect ratio loss. However, the orientation of the boxes was not considered. Ref. [64] proposed ODIoU loss to supervise 3D detector using hard targets. Ref. [65] proposed the use of Manhattan distance instead of the Euclidean distance for all loss equations. Ref. [66] addresses the IoU drawbacks by considering interior overlapping pixels. Ref. [17] addresses the decoupling effect by considering the orientation of the bounding boxes as an additional 4 th-dimension parameter in loss estimation. To resolve this issue, ref. [17] proposed the usage of a decoupling rotation parameter to act as a 4 th-dimensional parameter with a fixed side length of k. This approach strengthens the IoU loss estimation but fails to address the edge cases of inclusion, where two boxes with centers with the same aspect ratio can still have a different overlap area. Another study, ref. [67], suggested sorting the overlapping vertices by rotation angle and followed by calculating intersection area by dividing them into smaller individual triangles. However, this approach is computationally expensive since there would be numerous bounding box predictions for each object, and it affects the model convergence. Authors of this research [68] proposed the usage of IoU as a constant factor added to L1 loss to address the orientation problem.
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 4 Experimental Results
### Edge Detection Results  
We considered \(F_{1}\) score as an evaluation measurement to quantitatively compare the results of the different techniques: \(F_{1}=2\times\frac{Precision+Recall}{Precision+Recall}\) where precision is defined as the proportion of correctly detected points by EDC-Net model and recall is defined as the proportion of labeled edge points in the ground-truth. Precision and recall are computed as: \(Precision=\frac{TP}{TP+FP}\), \(Recall=\frac{TP}{TP+FN}\) where \(TP\) stands for true positives representing the number of correctly detected points; \(FP\) stands for false positives representing the number of wrongly detected points; \(FN\) stands for false negatives, representing the number of false rejections, i.e., edge points in the ground-truth that are not detected as edges by EDC-Net model. The terms precision and recall in some references are defined as correctness and completeness [54].  
We have done extensive experiments to evaluate the performance of our proposed EDC-Net. The quantitative results are summarized in Table 1. The models that are denoted by WSL in Table 1 were trained on ShapeNet dataset in a weakly-supervised learning fashion as explained in Section 3.3.1; the rest of the models were merely trained on ABC dataset samples. We have trained EC-Net [21] according to the publicly available code [55] on the datasets described in Section 4.1 and not the dataset used by the authors in the original paper [21].  
We have provide some qualitative results of EDC-Net model on ABC [35] datasets and ShapeNet [34], the results are illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c|c c c|c c c|c c c|c c} \hline \hline  & & \multicolumn{3}{c|}{**EC-Net [21]**} & \multicolumn{3}{c|}{**EDC-Net (Ours)**} & \multicolumn{3}{c|}{**EC-Net [21] + WSL**} & \multicolumn{3}{c}{**EDC-Net + WSL (Ours)**} \\ \cline{3-13}  & & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline \multirow{2}{*}{ABC} & N = 1024 & 0.6780 & 0.7427 & 0.7089 & 0.8475 & 0.9310 & **0.8873** & 0.6652 & 0.7278 & 0.6951 & 0.8371 & 0.9051 & 0.8698 \\  & N = 2048 & 0.6782 & 0.7451 & 0.7101 & 0.8485 & 0.9561 & **0.8991** & 0.6727 & 0.7322 & 0.7012 & 0.8407 & 0.9322 & 0.8841 \\ \hline \multirow{2}{*}{ShapeNet} & N = 1024 & 0.5718 & 0.6845 & 0.6231 & 0.5879 & 0.6991 & 0.6387 & 0.5906 & 0.7034 & 0.6421 & 0.6357 & 0.7532 & **0.6895** \\  & N = 2048 & 0.6069 & 0.6823 & 0.6424 & 0.6658 & 0.7532 & 0.7068 & 0.6469 & 0.7021 & 0.6734 & 0.7537 & 0.7843 & **0.7687** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results on ABC [35] and ShapeNet [34] samples. WSL stands for weakly-supervised learning. \(P\) and \(R\) are represented for precision and recall respectively. The highest \(F1\) score at each line is shown in bold.