esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Structural Analysis  
Static analysis indicated that the maximum deformations experienced by each of the structures did not exceed the separation between them for the different load situations considered (Table 2). For the cultivation tank, the greatest deformation was 0.147 mm and the Von Mises equivalent stress was 12.982 MPa (Table 2, Figure 3). For the main structure, the sheet deformation for load combinations 2, 3 and 4 was more than half their thickness. For the main structure, the deformation and Von Mises equivalent stress were 2 mm and 113.85 MPa, respectively (Figure 4).  
The largest vertical displacement was 0.955 mm and the Von Mises equivalent stress is 32.429 MPa (Table 2). The settlements at the ends were uniform and no differential settlements compromised the proper performance of the lysimeter weighing system (Figure 5a). Otherwise, the levelling system included in the main structure could have absorbed the differential soil settlements. For the type of soil and the different loads considered in this study, the lysimeter base showed small total deformations. In any case, the Von Mises equivalent stress of the designed base did not exceed the elastic limit of the AISI 304 steel (Figure 5b).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Von Mises Equivalent** & **URES: Resulting** & **Security Factor** \\  & & **Stress (MPa)** & **Displacement (mm)** & **Security Factor** \\ \hline
**Cultivation tank** & Load combination 1 & 12.982 & 0.147 & 10.76 \\ \hline
**Main structure** & Load combination 1 & 36.254 & 0.746 & 5.633 \\  & Load combination 2 & 59.604 & 1.161 & 3.409 \\  & Load combination 3 & 84.979 & 1.576 & 2.402 \\  & Load combination 4 & 113.850 & 2.000 & 1.795 \\ \hline
**Base structure** & Load cases 1 and 2 & 10.803 & 0.364 & \\  & Load cases 1 and 3 & 21.616 & 0.659 & \\  & Load cases 1 and 4 & 32.429 & 0.955 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of the analysis for the walls of the cultivation tank, the main structure and the base structure.  
Figure 3: Three-dimensional view of the results obtained in load combination 1 for the walls of the cultivation tank of the removable weighing lysimeter. (**a**) Resulting displacement (mm) and (**b**) Von Mises equivalent stress (MPa).
## 2 Coded Aperture Compressive Temporal Imaging (CACTI)  
CACTI utilizes mechanical translation of a passive coding element to compress high-speed temporal information into low-frame rate video sequences via a process known as Code Division Multiple Access (CDMA). To explain the measurement process mathematically, as shown in Figure 1, the scene can be represented by a space-time data cube within one integration period \(T\) of the sensor. The high-speed spatiotemporal object voxels are patterned by a transmission function that shifts in time. Furthermore, distinct local coding structures are applied to each temporal channel prior to integration of the channels as limited-frame rate images on the detector. A high-speed estimation of scene may be reconstructed from each low-speed coded snapshot.  
\begin{table}
\begin{tabular}{c l} \hline \hline \multicolumn{1}{c}{**Symbols**} & \multicolumn{1}{c}{**Descriptions**} \\ \hline
**y** & Observation of each low-speed coded snapshot1111 \\
**x** & Space-time data cube of the scene corresponding to **y** \\
**\(\Phi\)** & Observation matrix for CACTI \\
**n** & Noises of the CACTI system \\ \(I\) & High-resolution image corresponding to one frame of **x** \\
**D** & Matrices correspond to down-sampling \\
**K** & Matrices correspond to filtering with blur kernel \(K\) \\
**M\({}_{i}\)** & Warping matrix corresponding to motion vector \\ \(Z_{l}(\eta),Z_{w}(\lambda),Z_{K}(\xi),Z_{f}(\xi)\) & Normalization constants respectively, depending on \(\eta,\xi,\lambda,\zeta\) only \\ \(N_{f}\) & Compressive ratio in the temporal domain \\
**F** & Matrices correspond to the Hadamard product \\
**w** & Scale-invariant feature transform (SIFT) Flow velocity field of image sequence, \(\mathbf{w}=[u,v]\) \\
**H** & Transformation matrix corresponding to Total Variation \\ \(\eta\) & Weight coefficients for Alternating Direction Method of Multipliers \\ \(\beta,\lambda\) & Weight coefficients for Accelerated Linearized Bregman algorithm \\ \(\eta_{1},\eta_{2}\) & Weight coefficients for total variation and motion consistency SIFT Flow \\ \(\alpha,d\) & Parameter for distance transform function used in SIFT Flow \\ \(\lambda_{1},\lambda_{2}\) & coefficients for multiple regularizations \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations.
## 5 Simulation Results
Figure 11: Network model. We investigated the packet collection ratio, that is, the number of packets received at the sink node divided by the total number of generated packets. We also investigated the energy consumption of the node with the heaviest load, which is determined by the maximum energy consumption, as well as the average energy consumption for all nodes when the packet generation rate (the number of data packets generated at each node per 1.0 s) is changed. Here, we use the term 'performance' to indicate the packet collection ratio, the maximum energy consumption, and the average energy consumption.
## 2 Methods
### Data Analysis
#### 2.3.2 Muscle Synergy Extraction  
Before extracting muscle synergies, the acquired sEMG data were preprocessed using MATLAB (R2024a) (MathWorks, Natick, MA, USA) (Figure 1d) [39]. First, the baseline of the raw EMG data was adjusted to zero. A fourth-order Butterworth bandpass filter was applied to the raw sEMG signal to remove noise, with a cutoff frequency set at 60 Hz, followed by full-wave rectification [40]. Subsequently, a fourth-order Butterworth low-pass filter was applied to the rectified signal, with a cutoff frequency of 50 Hz, to extract the linear envelope [41]. To eliminate variations in intrinsic muscle activation characteristics, the EMG signal of each muscle was then normalized [42]. NMF was employed to decompose the sEMG signals and extract muscle synergy modules [43], this algorithm was implemented in R (v4.3.2, R Foundation for Statistical Computing, Vienna, Austria). The algorithm decomposes muscle activity (\(A(t)\)) into a linear combination of time-invariant synergy vectors (\(W_{i}\)) and time-varying activation coefficients (\(B_{i}(t)\)), computed through an iterative multiplicative update rule. Thus, the sEMG signal can be reconstructed using the following equation:  
\[A(t)\approx\sum_{k=1}^{N_{syn}}B_{k}(t)W_{k}. \tag{1}\]  
To determine the number of muscle synergies (\(N_{syn}\)), we applied the NMF algorithm to each experimental dataset, sequentially extracting between 1 and 12 synergy modules. Since the data were collected from 12 muscles, the maximum number of synergies was set to 12. The optimal number of synergies was defined as the minimum number of synergy modules required to achieve approximately 90% of EMG reconstruction \(R^{2}\)[44]. Based on the standard definition of \(R^{2}\) in NMF [45], the calculation formula is as follows:  
\[R^{2}=1-\frac{SSE}{SST} \tag{2}\]  
\[SST=\sum_{i,j}\big{(}D_{ij}-\mu D_{i}\big{)}^{2} \tag{3}\]  
\[SSE=\sum_{i,j}\big{(}D_{ij}-\big{[}WC\big{]}_{ij}\big{)}^{2}. \tag{4}\]  
The total sum of squares (\(SST\)) represents the variance within the dataset, where \(D_{ij}\) denotes the sEMG data of the \(i\)th muscle at the \(j\)-th time point, and \(\mu D_{i}\) represents the average sEMG value of the \(i\)th muscle. The sum of squared errors (\(SSE\)) quantifies the reconstruction error. To prevent the extracted synergy vectors from converging to suboptimal local minima in an incorrect solution space, each synergy extraction was performed 20 times. The synergy vectors (\(W_{i}\)) and activation coefficients (\(B_{i}(t)\)) wereinitialized with random values uniformly distributed between zero and the maximum sEMG value. The extraction result with the highest \(R^{2}\) value was selected for further analysis. During each execution of NMF, the iterative update process was terminated when the change in EMG reconstruction \(R^{2}\) was less than 0.001% for 20 consecutive iterations [21]. The K-Means algorithm was then applied to cluster the muscle synergies of the elite and amateur groups, identifying synergy characteristics associated with different skill levels.
# Time Domain Spherical Harmonic Processing with Open Spherical Microphones Recording
## 4 A Filter Design Example
### Choice of Filter Length of \(\rho_{n}(\
u,R_{Q})\)  
Intuitively, a longer filter often brings us less error and better performance. Figure 3 supports this idea by showing the result of \(\rho_{n}(\
u,R_{Q})*p_{n}(\
u,R_{Q})-z_{n}(\
u)\) with different choices of \(L\), which refers to the error introduced into the system by the filtering processing. We observe that the filtering error decreases across all of the orders with a higher \(L\). This is due to the time truncation of \(z_{n}(\
u)\) (length of vector \(z_{n}\) in (25)), being related to \(L\). Thus, a higher \(L\) leads to less information loss in the time truncation of \(z_{n}(\
u)\), hence smaller error in \(\rho_{n}(\
u,R_{Q})\). However, Figure 4 shows the time domain filter \(\rho_{n}(\
u,R_{Q})\) with different lengths. We observe that a longer filter results in a higher group delay of filtering. This is not desirable because it leads to a higher system delay of our proposed method, while lowering the system delay is one of the most important motivations that we develop the proposed time domain method.  
Figure 2: Frequency response of up to 4-th order of the pre-designed order dependent FIR filter \(\rho_{n}(\
u,R_{Q})\) with \(z_{n}(\
u)\) frequency truncated at (**a**) \(f_{1}\), (**b**) \(f_{2}\) (**c**) \(f_{3}\).  
As a result, we need to balance the noise tolerance, group delay, and the filtering error when we choose \(L\). We suggest that filter length \(2L+1\) should be significantly larger than the main lobe width of \(z_{n}(\
u)\) and \(2L_{P}+1\), the length of \(p_{n}(\
u,R_{Q})\), but no more than 50 times of \(2L_{P}+1\). Additionally, \(L\) should be less than the maximum tolerance of the delay of the system. Based on these guidelines, for the current example, we choose \(2L+1=501\).
# Study on the Minimum Operation Width of Human-Powered Bicycles for Safe and Comfortable Cycling
## 5 Conclusions and Discussion
### Discussion of the Results  
The results appear to contradict the existing research based on lateral distance, which suggests a width of 1.25 m [54]. A key difference may stem from the cycling direction considered in the literature, which predominantly addresses two-way cycling, while our study focuses on a single-file condition. Additionally, [44] recommends a 1.6 m width for one-way bikeways, possibly due to the inclusion of wider bicycle dimensions in previous studies compared to our research. Also, their selected widths of 2.0 m and 3.0 m contribute to greater variability in the lateral distance, as evidenced by the 1.1 m width resulting in an effective measurement of approximately 1.29 m. Accounting for differences in bicycle width and cycling speeds, the actual results could be lower.  
Furthermore, our findings indicate a significant correlation between heart rate and cycling speed, aligning with the literature that suggests physiological indicators, such as heart rate, can effectively reflect the behaviors of road users [61]. This correlation can be leveraged to assess road infrastructure.  
Lastly, our research identifies trends linking cycling speed and operational width, consistent with the Dutch bikeway manual [49] and UK standards [48], as detailed in Table 1. These guidelines suggest that as cycling speeds decrease, wider pathways may be necessary. Also, the trends align with the Japanese standards discussed in Section 2, despite the differing threshold criteria. This phenomenon can be attributed to the fact that cyclists require a greater lateral deviation to maintain stability at low speeds. Conversely, at higher speeds, even minor steering adjustments can lead to significant deviations, necessitating careful handling to ensure balance. However, this finding contrasts with the research by [44]; according to their experiment results, the deviation at 10 km/h is the greatest and at 30 km/h is the lowest.
# A Low-Complexity Deep Learning Model for Predicting Targeted Sequencing Depth from Probe Sequence
## 2 Materials and Methods  
### Datasets  
The NGS experiment generated a large number of sequencing datasets, and the baseline dataset used in this paper was the same as the DLM, including three panels of SNP, long non-coding RNA (lncRNA) [20], and SynDNA. The SNP panel fastq file contained 21,857,262 reads, and the fasta file contained 39,145 probe sequences. By using the conventional read alignment tool BWA to match the fastq file and counting the matched values [21], a dataset of 38,040 probes was obtained after excluding 1105 probes with zero matched reads. The lncRNA panel consisted of 1966 probes with reads secured by using BWA to match the fastq file. The SynDNA panel included 7215 probes with reads gained using BWA to match the fastq file. Histograms of GC content and log sequencing depth for the three sets of probe sequences are shown in Figure 2, which shows that the SynDNA panel has a more concentrated GC content of around 0.5 and a tighter distribution in sequencing depth compared with the SNP and lncRNA panels. The figure also indicates that the distribution of GC content showed a correlation with the distribution of sequencing depth [22; 23].
## 5 Experimental Results and Discussion
### Experimental Results
Therefore, clearances in revolute joints should have much effect on influencing the positional uncertainties, but less effect on influencing the resultant stroke errors. From the stochastic results listed in Tables 6-8, it is found that when \(v\) = 5 and 15 mm/s, the mean values of \(F_{F(\text{max})}\) and \(F_{F(\text{rms})}\) in the FS and those of \(F_{B(\text{min})}\) and \(F_{B(\text{rms})}\) in the BS were with the smallest magnitudes as \(\Delta\)\(\pi_{8}\) = 0.25 mm, and were with the largest magnitudes as \(\Delta\)\(\pi_{8}\) = 0.75 mm. When \(v\) = 25 mm/s, the mean values of \(F_{F(\text{max})}\) and \(F_{B(\text{rms})}\) were with the smallest magnitudes as \(\Delta\)\(\pi_{8}\) = 0.25 mm, and those of \(F_{F(\text{rms})}\) and \(F_{B(\text{min})}\) were with the smallest magnitudes as \(\Delta\)\(\pi_{8}\) = 0.00 mm, while the four force terms were with the largest magnitudes as \(\Delta\)\(\pi_{8}\) = 0.75 mm. When \(\Delta\)\(\pi_{8}\) = 0.00 mm was set, the constructed mechanism could still not achieve an ideally symmetric configuration because of the influence of small mechanical errors of other link members. When \(\Delta\)\(\pi_{8}\) = 0.25 mm was set, the extent of asymmetry of the constructed mechanism might be reduced instead, which might further lead to the smallest magnitudes for most force terms. It is also noted that for all of the three input speeds, the mean values of \(F_{TP}\) had nonlinearly increasing trends as the mechanical error \(\Delta\)\(\pi_{8}\) increased; such situations can also be observed in Figures 14-16. Evidently, when \(\Delta\)\(\pi_{8}\) was not set to zero, the mean values of \(F_{TP}\) were not closed to zero. In other words, when the mechanical error \(\Delta\)\(\pi_{8}\) existed in the constructed mechanism, this mechanism might not achieve a very large MA in the toggle position. The MA of the constructed mechanism in the toggle position would be considerably reduced with the increased mechanical error \(\Delta\)\(\pi_{8}\). In addition, the averaged input force curves shown in Figures 14-16 reveal that the input speed \(v\) could influence their magnitudes to a certain extent, but had relatively less effects on influencing their overall varying trends as compared with the influence caused by the mechanical error \(\Delta\)\(\pi_{8}\). When \(\Delta\)\(\pi_{8}\) = 0.50 or 0.75 mm was set, the extent of asymmetry of the constructed mechanism would be considerably increased, and the induced force curves also drastically increased during the end portion of the FS (approximately when x \(\geq\) 35 mm). As a result, a larger magnitude of the mechanical error \(\Delta\)\(\pi_{8}\) would lead to a larger driving resistance and a worse toggle effect.
## 7 RF-EH Circuit
### Storage Element
#### 7.5.1 Capacitor and Supercapacitor  
Electromagnetic energy harvesting (EMEH) uses capacitors and supercapacitors as storage devices. For storage, the EMEH system includes a capacitor [161]. The capacitor has the advantages of being readily available and being inexpensive. Although the capacitor has some drawbacks, the EMEH system does not use it because of its low power density and extended charging and discharging times. The supercapacitor has been shown to have lower internal impedance and longer lifetimes (measured in terms of charging and discharging cycles) [162].  
Supercapacitors, on the other hand, can calculate the amount of residual energy with precision, taking into account charging and discharging capacity as well as energy density states (i.e., upper or lower). The disadvantage of the supercapacitor is the higher cost per kilowatt-hour. Using batteries to power autonomous sensors in remote areas has a number of drawbacks. Size, storage capacity, and lifecycle are just a few of the disadvantages [163].
## 4 Discussion  
This research determined the accuracy values of photogrammetric flights employing a rolling shutter camera for different flight scenarios, despite it being well-known that rolling shutter cameras are not recommended for UAV-DAP namely at high speed [33; 34]. The steps were as follows: (1) analysis of the combinations of forward strips and cross strips at different flight heights and perimeter with oblique images (Section 3.2); (2) defining the appropriate number of GCPs (Section 3.5); and (3) establishing the best location of GCPs in projects on corridors (Section 3.5). To perform an in-depth analysis of the results, a comparison between the results of this research and those found in similar research in flat terrain was generated in Table 1. By using nadir and oblique images, the four scenarios analysed in this study presented values of horizontal RMSE between 1.7 and 7.3x the GSD, and a vertical RMSE between 2.5 and 10.3x the GSD [28]. These results confirm that rolling shutters in DAP-UAV are not recommended to achieve high-accuracy results, which are close to 1.7x the GSD for horizontal RMSE and 3x for the vertical RMSE (Table 1). Only the UAV-DAP rolling shutter effect can be minimised in Scenario B, which uses oblique images and GCPs at the edges of the overlapping zones and the perimeter (Configuration 3).
## 2 Materials and Methods
### Design Choices
#### 2.5.5 Protecting Data in Transit  
To enforce isolation between teams (requirement #3) and to help control access to data (requirement #5), we need to protect data in transit to and from the Data Collaborative. In addition, we needed to make sure that the token that we decide to use for authentication would not traverse the Internet or Intranets in the clear. In our case, this means encrypting data in transit using Transport Layer Security (TLS) [36]. As this is a common practice with websites, the chief design decision came in of where to put the certificate and do the encryption/decryption: the TLS endpoint. We looked at the following options:  
1. On each notebook container
2. On a sidecar container
3. Proxy
4. Application gateway  
Option 1 is viable using services such as Let's Encrypt [37] but is potentially harder to manage and consumes more resources, as adding the certificate process to each container increases the tasks and complexity to each container instantiation and rollout. The sidecar container pattern [38] could be used--this pattern would instantiate a new container next to each notebook container, which would hold the certificate and be a TLS endpoint, communicating the notebook container in the clear. This is easier to manage than the first option, since all of those TLS sidecar containers could be identical, but it drives up the number of containers and costs (requirement #8) and results in twice as many containers to manage and monitor (affecting requirement #6). Putting the TLS endpoint on the proxies is a better option. This is a function that many proxies can perform, and there are far fewer proxies to manage for each competition. A better option is the last option--putting the endpoint on the Application Gateway. This leaves only one place to put the certificate--making container instantiation simpler, and it only needs to be done once. It lessens the amount of processing done on containers and the proxies, reducing cost (requirement #8). We selected this option, which also is a standard Cloud design pattern: gateway offloading [39].
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 3 Materials
### Semantic Segmentation Network
#### 3.3.3 U2 Net  
Over the years, we have seen auto-encoder models like Deeplab [50] used for semantic segmentation. Among all segmentation models, there is still a name that is at the top of the list: U-Net. U-Net was released in 2015. Since then, it has gained enormous popularity and is used for several different tasks related to segmentation. A variant of U-Net called U2-Net [49] was released in 2020. Basically, U2-Net is composed of two U-Nets.