esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Driving Test
### Multiple UGV/UAV Operation Test  
In order to operate two or more UGVs at the same time, autonomy must be secured. Since the operator has to simultaneously control several unmanned vehicles, it is impossible to always give a new waypoint to the UGV. Therefore, the operator gave the area to the UGV and implemented the function to repeatedly search the given area for the UGV to conduct the experiment [25].  
According to a predefined air pollution mapping scenario [26], shown in Figure 14, one person simultaneously controlled two UGVs and three UAVs, and several unmanned vehicles searched a space at the same time. Figure 15 shows the two UGVs and three UAVs operating at the same time, while Figure 16 shows the GPS log and obstacle point cloud graphs of the two UGVs and the speed and steering graphs of each UGV. When a region was given to the UGV, vertices were selected as waypoints and followed sequentially and repeatedly in a clockwise direction. If there was an obstacle, it was avoided according to the driving algorithm. In the simulation of Figure 7, it can be seen that a path avoiding the obstacle was created about 5 m in front of the obstacle. In the GPS log of UGV #1 in Figure 16a, it can be confirmed that the vehicle changed direction about 5 m in front of the obstacle. Furthermore, since the UGV was traveling clockwise, it continued to steer in one direction. As shown in the graph of Figure 16b, it steered in the negative direction after steering in the positive direction to avoid an obstacle. Images of the UGV avoiding obstacles are shown in Figure 17.  
Figure 13: UGV and obstacles: (**a**) obstacles and calculated local path; (**b**) potential field and calculated local path.  
\begin{table}
\begin{tabular}{c c} \hline Path\_length & 15 m \\ Path\_interval & 0.5 m \\ k & 10 \\ L & 10 m \\ Q & 10 m \\ D\_min & 1.5 m \\ Potential\_Value\_Max & 5 \\ Potential\_number & 100 \\ Potential\_dist & 5 m \\ \hline \end{tabular}
\end{table}
Table 3: Driving test environment.  
Figure 16: Log data of UGVs during operation of multiple UGVs/UAVs: (**a**) GPS log data of two UGVs and obstacle point cloud; (**b**) speed and steering log data.  
Figure 14: Air pollution mapping scenario using multiple unmanned vehicles [26]: a mission to quickly create an air pollution map with a number of unmanned vehicles in order to identify the state of air pollution occurring in a specific area. UAVs #1, #2, and #3 make a map while flying in a zigzag area divided into certain regions. UGVs #1 and #2 draw a map while circling the assigned area divided into certain regions.  
Figure 15: Operation of multiple UGVs/UAVs.
# DWPIs: Dynamic-Weight Parallel Instance and Skeleton Network for Railway Centerline Detection
## 3 Dynamic-Weight Parallel Instance and Skeleton Network
### Architecture of DWPis  
Previous skeleton-detection methods commonly use datasets in which the target occupies most of the image and there are no other objects in the simple background. However, our target object, a railway, is located in a complex environment in most of images to be evaluated, including other objects of a similar shape. This increases the difficulty of detection and the error rate. Additionally, the height of a UAV is not fixed due to the different inspection-mission requirements. As the UAV flies higher, the railways in the image become smaller and the background becomes more complex.  
To address the aforementioned problems, this paper proposes the novel dynamic-weight parallel instance and skeleton network. This network is divided into two parallel branches, one for instance segmentation and another for skeleton detection separately. They use the same backbone and attention module to extract features from input images first. Then, before a fused loss function merges the two branches, the loss of each respective branch is obtained. The detailed network, including training, inference and post-process, is shown in Figure 4.  
At the beginning of training, the network primarily relies on the instance-segmentation branch to extract useful target features, which guides the network to quickly determine the features of the correct location. At the same time, the skeleton-detection branch is optimized mainly through the former. As training progresses, the instance-segmentation branch should achieve good-enough results after only a few epochs and the focus of the network should gradually shift to the skeleton-detection branch. Since the training epochs required for skeleton detection are much more than that for instance segmentation, the skeleton-detection branch maintains dominance for a long time after the result of the instance-segmentation branch is stabilized.  
In order to achieve the above training strategy, this paper reformulates a fused loss function with dynamic weight, which is defined as:  
\[\omega=\frac{1}{1+e^{-\frac{\epsilon-\kappa}{\beta}}} \tag{4a}\] \[L=(1-\omega)L_{ins}+\omega L_{sklt} \tag{4b}\]  
where \(\omega\) is the weight for the loss of the skeleton-detection branch, \(\epsilon\) is the current training epoch, parameter \(\alpha\) represents the epoch where the weights of the two branches are both 0.5, parameter \(\beta\) determines the rate of weight changes, and \(L\) is the total loss for our network. As the function demonstrates, the sum of the weights for the two branches always remains at one. As the training epoch increases, the weight of the instance-segmentation branch decreases gradually while the weight of the skeleton-detection branch increases.  
Figure 4: Detailed network including training, inference and post-process.
## 3 The F-Channel Platform
### Coordination Module
#### 3.2.2 Auction Sub Module  
Auction Flow  
Once the products that are supported by the platform have been defined, the next step would be the design of the flow of the auctions, based on which the potential service providers would submit the bids and be selected, if they satisfy the required conditions. The basic flowchart of the process, as envisaged considering both the present situation in Greece and the expected changes in the upcoming period, is depicted in Figure 8.  
As can be seen, the entire auctioning process can roughly be divided into five sequential steps, out of which the first two take place way before the need for the product. The first step is a precondition for the remainder of the procedure since the provision of the services by the energy entities can only be expected if those entities can provide those services at all. To ensure that, the entities need to go through a prequalification process, after which the entities are added to the unified FSP register. Only the providers verified through the register can take part in the auctions regarding the products for which they went through  
Figure 8: F-channel platform: Auction flow diagram.  
the prequalification process. Once the register is formed, it can be accessed both by the SOs and by the MO. Based on the information presented in the register, the MO sets up an auction, defining all the necessary parameters, such as, for instance, the date and time of the auction and the products that will be auctioned. After the auction is all set, the bids can be submitted, with the SOs submitting the so-called \"buy bids\", defining the service that the system requires, along with all the remaining parameters, such as the necessary amount and the price that they would be willing to pay for that service. On the other hand, the FSPs define the \"sell bids\", specifying, in the process, the amount of the service that they would be able to provide in the specified time and the price that they would request for that service. Once all the bids are collected, the auction is launched at the scheduled time, with the outcomes depending on the results of the predefined optimization process. After the completion of the auction, the results are sent to all interested parties and published on the F-channel platform in a publicly available area.
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 2 Mission Overview
### iGNSS-R Sea Surface Height Retrieval
#### 2.4.2 DM Waveform Matching  
We employed the waveform matching method to determine the time delay of the specular point in the iGNSS-R DM waveform. The theoretical waveform library used in this process was generated through simulation using the GREEPS software [46]. Figure 8 presents a set of results from the matched BDS iGNSS-R DM waveforms. Given that waveform matching is a computationally intensive process, we implemented multithreading technology to significantly enhance the matching efficiency.
# A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
## 6 Conclusions  
Unlicensed accessed is one of the key enablers for 5G technologies as service requirements on licensed bands are becoming stringent and licensed bands alone ae unable to meet these demands. Co-existence studies for unlicensed and licensed bands are part of 3 GPP Release 15 and other future releases that are trying to exploit unlicensed bands whenever infrastructure allows it. In this paper, we have presented a comparison between two rate enhancement strategies, i.e., SBS cooperation and Wi-Fi Offloading based on network parameters, such as user density and rate threshold. For this, we consider maximum rate joint optimization problem for user association, resource block allocation and power allocation for cellular UEs in cooperative communication scenario. However, for Wi-Fi offloading scenario, resource block and power allocation is considered along with the Wi-Fi leftover capacity. The results indicate when UEs are unable to satisfy their rate constraint, they can enhance their data rate using either of the two heuristics depending on the network condition. SBS cooperation (SCRE) is more beneficial in a less dense network where several UEs are likely to be present at the cell edge and when the average user data rate requirement is higher. Wi-Fi offloading (SORE) is more advantageous when the density of cellular UEs is higher and significant number of UEs are present in the Wi-Fi coverage. Moreover, an SCO algorithm is also proposed that optimally switches between SCRE and SORE depending on network conditions. Our results quantify the set of conditions when Wi-Fi unlicensed band and (licensed) intercell cooperation could be used to meet tough rate requirements from the data hungry services, such as video streaming.  
Conceptualization, S.J. and F.A.B.; Formal analysis, A.A., S.J. and M.M.B.; Funding acquisition, F.A.B.; Investigation, S.J.; Methodology, A.A.; Project administration, F.A.B.; Writing--original draft, A.A., S.J. and F.A.B.; Writing--review and editing, M.M.B. and A.R.M. All authors have read and agreed to the published version of the manuscript.  
This work has emanated from the research funded by Higher Education Commission (HEC), Pakistan, under grant No. HEC NRPU 20-4339/_R&D/HEC_/14/235.  
The authors express deep gratitude to [PERSON] (Assist. Mathematics Dept, IST) for his valuable guidance and feedback in this research work.  
The authors declare no conflict of interest.  
Figure 7: Averaged Achievable data rates per user by changing No. of Total RBs using \(10,20,30\) UEs in Cooperative Communication and \(20\) UEs with \(30\) leftover capacity in Wi-Fi offloading and overall \(3\) SBSs.
## 3 Results
### Spatiotemporal Gait Parameters
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Spatiotemporal**} & \multicolumn{5}{c}{**Walking Conditions (Slope/Speed)**} \\ \cline{2-6}  & **A** & **B** & **C** & **D** & **E** \\ \hline  & \(-20\%\) & \(-10\%\) & \(0\%\) & \(+10\%\) & \(+20\%\) \\  & 3.5 km h\({}^{-1}\) & 5.0 km h\({}^{-1}\) & 5.0 km h\({}^{-1}\) & 3.5 km h\({}^{-1}\) & 2.5 km h\({}^{-1}\) \\ \hline Step length (cm) & 46.9 \(\pm\) 3.2 & 65.3 \(\pm\) 3.6 1  & 57.8 \(\pm\) 3.9 1  & 46.0 \(\pm\) 4.1 1  \\ Step width (cm) & 12.4 \(\pm\) 2.3 & 11.7 \(\pm\) 2.0 1  & 9.3 \(\pm\) 1.8 2  & 9.3 \(\pm\) 2.4 1  & 10.0 \(\pm\) 2.4 1  \\ Foot rotation (1) & 5.5 \(\pm\) 3.5 & 5.0 \(\pm\) 3.3 & 6.0 \(\pm\) 4.0 1  & 8.7 \(\pm\) 4.6 1  \\ Step time (s) & 0.48 \(\pm\) 0.03 & 0.47 \(\pm\) 0.03 1  & 0.49 \(\pm\) 0.02  & 0.59 \(\pm\) 0.04 1  & 0.67 \(\pm\) 0.06 1  \\ Cadence (step/min) & 125.2 \(\pm\) 9.2 & 128.4 \(\pm\) 7.3 1  & 121.9 \(\pm\) 6.0 1  & 91.1 \(\pm\) 8.3 2  \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Means \(\pm\) standard deviations of spatiotemporal gait parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Gait Phases**} & \multicolumn{5}{c}{**Walking Conditions (Slope/Speed)**} \\ \cline{2-5}  & **A** & **B** & **C** & **D** & **E** \\ \hline  & \(-20\%\) & \(-10\%\) & \(0\%\) & \(+10\%\) & \(+20\%\) \\  & 3.5 km h\({}^{-1}\) & 5.0 km h\({}^{-1}\) & 5.0 km h\({}^{-1}\) & 3.5 km h\({}^{-1}\) & 2.5 km h\({}^{-1}\) \\ \hline Stance phase (s) & 0.60 \(\pm\) 0.05 & 0.57 \(\pm\) 0.04 1  & 0.61 \(\pm\) 0.04 2  & 0.76 \(\pm\) 0.06 1  \\ Swing phase (s) & 0.36 \(\pm\) 0.02 & 0.37 \(\pm\) 0.02 & 0.38 \(\pm\) 0.02 1  & 0.43 \(\pm\) 0.03 1  \\ Double-stance phase (s) & 0.24 \(\pm\) 0.03 & 0.20 \(\pm\) 0.03 1  & 0.23 \(\pm\) 0.03 1  & 0.34 \(\pm\) 0.04 1  \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Means \(\pm\) standard deviations of the duration of gait phases.
## 2 Basic Theory
### IGTO Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Function** & **Parameters** & **GWO** & **PSO** & **GTO** & **CDO** & **IGTO** \\ \hline \multirow{3}{*}{F1} & Avg & \(2.49\times 10^{-27}\) & \(1.32\times 10^{-27}\) & \(3.68\times 10^{-275}\) & 0 & 0 \\  & Std & \(6.42\times 10^{-27}\) & \(5.4\times 10^{-73}\) & 0 & 0 & 0 \\  & Avg & \(1.35\times 10^{-5}\) & \(46.00\) & \(2.61\times 10^{-318}\) & 0 & 0 \\  & Std & \(2.66\times 10^{-5}\) & \(12.700\) & 0 & 0 & 0 \\  & Avg & \(8.38\times 10^{-7}\) & \(44.3\) & \(2.19\times 10^{-146}\) & \(9.71\times 10^{-193}\) & 0 \\  & Std & \(1.10\times 10^{-6}\) & \(29.2\) & \(1.20\times 10^{-145}\) & 0 & 0 \\  & Avg & \(27.5\) & \(27.8\) & \(11.7\) & \(4.95\) & \(7.46\times 10^{-5}\) \\  & Std & \(4.47\times 10^{-1}\) & \(6.10\times 10^{-1}\) & \(14.4\) & \(11.1\) & \(8.57\times 10^{-5}\) \\  & Avg & \(-6189.550\) & \(-103.34385\) & \(-125.690.055\) & \(-125.5687.78\) & \(-125.569.487\) \\  & Std & \(797\) & \(1770\) & \(3.51\times 10^{-1}\) & \(1.23\times 10^{-4}\) & \(3.02\times 10^{-5}\) \\  & Avg & \(1.02\times 10^{-13}\) & \(5.03\times 10^{-15}\) & \(8.88\times 10^{-16}\) & \(8.88\times 10^{-16}\) & \(8.88\times 10^{-16}\) \\  & Std & \(1.64\times 10^{-14}\) & \(2.30\times 10^{-15}\) & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Avg and Std values for each algorithm. Figure 3: (**a**) Convergence curves of F1; (**b**) convergence curves of F3; (**c**) convergence curves of F4; (**d**) convergence curves of F5; (**e**) convergence curves of F8; (**f**) convergence curves of F10. optimization process and delivers consistent and reliable performance across multiple runs, demonstrating strong potential when addressing complex optimization problems. For instance, in functions F5 and F10, IGTO not only maintains efficient optimization but also achieves optimal numerical precision, ensuring high accuracy and consistency in the results. Therefore, in practical applications--especially those requiring high precision and stability--IGTO proves to be an ideal algorithm, owing to its accuracy, robustness, and sustained optimization capability.
# Equivalent Axial Stiffness of Horizontal Stays
## 1 Introduction
These results suggest resorting to a more operational definition of the equivalent modulus, in some way accounting for the weight effects described before. In fact, in the usual mechanical model the cable is replaced by a weightless straight rod, and the weight of the cable is concentrated at the ends of the equivalent rod. From now on, an improved equivalence can be established subtracting from the work done by the cable weight for the end displacement \(dh\), the work amount associated with rigid body motion, \(W_{pr}\), (......)\". For a more exhaustive illustration of these relevant aspects, the interested reader can refer to [16]. More recently, several research works tackled the question [17; 18; 19; 20], suggesting improvements of classical formulae, mainly aiming to define appropriate cable elements to be implemented in finite element models. Anyhow, as it can be easily ascertained comparing the proposed solutions with those reported in [16], all these works arrive to approximate formulae, because, in solving the problem, some parameters, especially relevant in the low stress region, are disregarded. In addition, all these studies do not consider that the solution is also depending on the nature of the relative displacement of cable ends. In view of the implementation of suitable elements in the finite element method, another actual research topic focuses on the influence of bending and shear stiffness on the cable behavior [21; 22]. Nevertheless, as they are relevant only in peculiar situations, bending stiffness and shear stiffness can be often interpreted as local perturbations of the general solution, obtained by neglecting them. Horizontal stays can be seen as a particular case of inclined cables, so that their equivalent axial stiffness can be derived from the general expressions given in [16], considering the cable ends at the same level, so setting to zero the difference \(h\) between their elevations. Unfortunately, these general expressions are extremely intricate and composed by many terms, most of which assume an indeterminate form for \(h\) tending to zero. Consequently, although representing in principle the simplest practical situation, application of the general formulae to horizontal stays require cumbersome calculations; moreover, the complexity of the expression contributes to confuse the influence of different parameters. The aim of the study is to illustrate how the equations of the equivalent stiffness of horizontal stays can be deduced implementing the procedure based on the virtual work theorem, already successfully applied in the general case for inclined cables [16]. The outcomes of the procedure are new expressions of the equivalent tangent modulus of the cable, depending on the nature of the boundary conditions. As they accurately consider all influencing parameters, these rather compact innovative formulae can be again applied without restrictions. Referring to some relevant case studies, the results obtained adopting the proposed approach are critically discussed, also in comparison with the outcomes of classical simplified methods.
## 2 Method
### Data Selection and Frequency Analysis  
The data used for this study comes from Energiekoplopers, a Dutch Pilot project in Heerhugowaard aimed at assessing the potential of flexibility in residential energy systems [26]. The total dataset that was available for this study consisted of 33 households containing a heat pump. The heat pumps used were air-sourced heat pumps from the brand Inventum Evolution Combi 50. Their minimum electrical capacity was 5W and their maximum was 500W. From these heat pumps, data presenting the power demand for the heat pump per 5 min was used for a period of 30 weeks, between 01-09-2016 and 01-05-2017. No data was available from the end of December until the beginning of February. As a result of this missing data, an 8-week gap occurs in this period, leaving 22 weeks of useful data (see Appendix B--Number of households per week).  
Due to measurement errors during the pilot demonstration project, several data gaps occur in the dataset and the start date and end date between when data is available varies strongly per household. Since the bidding period as defined by TenneT is a week, the dataset was split into datafiles of one week. Data points containing extreme outliers were excluded. In addition, when more than 60 consecutive min. of heat pump data were missing, the data of that week were deleted for that heat pump. Theseresearch choices resulted in a relatively small, but reliable dataset. In order to obtain a viable portfolio size of 10 MW, the number of households was scaled up to 20,000 for every week.
## 5 Evaluation of the Deepfake Detection Models
### Experiment 3: Evaluation of the Specific Models Developed for Individual Speakers of Interest  
In this experiment, we trained the FastAudio classifier using 80% of the RTVE-UGR dataset and tested it on the remaining 20%.  
We repeated this experiment with two voices of interest, those of the King of Spain [PERSON] and the vice-president of Spain, [PERSON]. The results obtained are presented in Tables 4 and 5, respectively.  
The model exhibited improved performance compared to Experiment 2. The scores assigned to real audios were significantly higher than those for fake audios, allowing for better differentiation.  
The accuracy increased to 99%, the precision was 98.7%, indicating a very low rate of false positives. The recall remained at 100%.  
The model achieved an accuracy of 100%, precision and recall were also 100%, indicating no misclassifications. These outcomes are very positive but may be the result of overfitting due to the model being trained and tested on similar data. Although the fake samples for each target speaker were generated using two different techniques (deep speech synthesis and voice conversation), the model may have learned artifacts introduced by the fake audio generation methods used for this dataset. We have informally tested the models with a reduced number of external fakes that arrived to the RTVE verification team and the prediction of the model corresponded was accurate (it matched the opinion of the expert human verifiers). Nevertheless, the ability of the model to generalize to other fake audio types must be further tested. This is an important challenge when using fake detection models created for specific speakers, as it is very complicated to gather sufficient fake samples to train and test the models.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Fake Audios** & **Real Audios** \\ \hline True Positives (TP) & 220 & — \\ False Negatives (FN) & 0 & — \\ False Positives (FP) & — & 3 \\ True Negatives (TN) & — & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of Experiment 3 (King of Spain, [PERSON]).