esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Layout
### PV System  
In this section, the design of the PV system for the selected plant is to be discussed. The desalination unit load, with two high-pressure pumps (HPPP), a booster pump and a control panel, consumes a total power of 0.90 kWp. The estimated hours of operation are 24 h per day. Therefore, the Total RO load energy required is equal to 21.6 kWh/day (900 Watts * 24 h). The theoretical daily energy requirement for the system is about 21.6 kWh/day (\(E_{L}\)). The efficiency of the batteries (\(\eta_{lb}\)), inverters (\(\eta_{inv}\)), and the PV thermal losses (\(\eta_{TH}\)) are 80%, 95%, and 85% respectively. To be on the safe side, an average peak sunshine hours of 6 h was used instead of 7 h, as seen in Figure 10. The peak power of the PV-system (P\({}_{p-PV}\)) was calculated using Equation (2), and it equals 6.1579 kWp, where \(SF\) denotes the safety factor, which is estimated to be 1.3 [21,22].  
\[P_{p-PV}=\frac{E_{L}}{PSSH*\eta_{lb}*\eta_{inv}*\eta_{TH}}*SF \tag{2}\]  
The size of the PV modules must be such as to produce 6.1579 kWp. The selected bifacial module (B60) has a short-circuit current of 10.56 A, an open circuit voltage of 39.10 [V], a maximum rated current of 10.07 A, a maximum rated voltage of 31.80 V, and a maximum rated power of 320 Wp at STC. Therefore, the system needs approximately 20 (6157.9 Wp/320 Wp) PV modules to cover the daily energy requirements.  
Since a Tristar 45 PWM solar charge controller was selected to be used, the designed PV system has to be divided into five subarrays; each one has four PV modules connected in parallel. Thus, the maximum rated current for each subarray equals 40.28 A (10.07 Amps*4).
## 6 Strengths, Limitations, and Future Research Directions
### Future Research Directions
#### 6.3.1 Dataset Considerations  
The datasets used in most studies may not be representative of real-world scenarios. Future research should consider using more diverse datasets that better reflect the complexity and variability of real-world images. As an example, it would be interesting to study the impact that image resolution might have on the performance of deep learning architectures. That is, it would be important to find out in which of the architectures (i.e., ViT, CNN, and MLP-Mixer) the resolution of the images will influence their performance, as well as what impact it will have on the processing time of the deep learning architectures.
## Abbreviations  
\begin{tabular}{l l} ESS & Energy storage system \\ WF & Wind farm \\ KE & Kinetic energy \\ MA & Moving average \\ FLF & First-order low-pass filter \\ WG & Wind generator \\ MPPT & maximum power point tracking \\ SOC & State-of-charge \\ PCS & Power converter system \\ JPY & Japanese yen \\ DOD & Depth of discharge \\ SD & Standard deviation \\
**Nomenclature** & \\ \(n\) & Number of WGs \\ \(V\) & Wind speed \\ \(\omega\) & Mechanical rotor angular frequency \\ \(P\) & WG output \\ \(P_{\rm WF}\) & WF output \\ \(P_{\rm b}\) & ESS output \\ \(P_{\rm g}(=P_{\rm WF}-P_{\rm b})\) & Combined output power \\ \(\rho\) & Air-density \\ \(R\) & Radius of rotor blade \\ \(\lambda\) & Tip speed ratio \\ \(\beta\) & Blade pitch angle \\ \(C_{\rm p}\) & Power coefficient \\ \(H\) & Inertia constant of WG \\ \(T_{\rm m}\) & Mechanical torque \\ \(T_{\rm e}\) & Electrical torque \\ \(p_{\rm mprt}\) & MPPT output reference \\ \(P_{\rm loss}\) & Charge/discharge loss of the ESS \\ \(A\) & SOC-FB gain \\ \(K_{\rm p}\) & Gain of ESS controller \\ \(a_{0},a_{1}\) & Parameter of ESS controller \\ \(\Delta F_{\rm g}\) & Fluctuation ratio of combined output \\ \(E\) & Remaining energy of ESS \\ \(\Delta P_{\rm WF}\) & Short-period components of the WF output \\ \(P_{\rm WF}^{\rm MA}\) & MA of \(P_{\rm WF}\) \\ \(A\)\(P_{\rm pu}\) & Fluctuation component of WG \\ \(q\) & Nonlinear weight \\ \(K_{\rm q}\) & Weight \\ \(\alpha\) & Exponent weight \\ \end{tabular}
## 2 Related Work
### Multi-Person Pose Estimation and Tracking in Videos
#### 2.2.1 Multi-Person Pose Estimation  
Video-based multi-person pose estimation can be implemented through image-based approaches on a frame-by-frame basis, naturally falling into two categories:  
* Bottom-Up Approaches: These approaches estimate poses by performing keypoint detection and grouping them on a per-frame basis [14; 15; 24; 25]. However, most approaches struggle with body part association in crowded scenes with severe occlusion or complex movements. For instance, while DeciWatch [15] employs DenoiseNet to diminish the noise attributed to motion blur, its efficacy may be compromised in cases of non-periodic and intricate human motion.
* Top-Down Approaches: These approaches begin by detecting individuals in all frames and then conduct single-person pose estimation on each frame based on image-based techniques. Numerous CNN-based approaches [16; 26; 27] have been introduced to address occlusion challenges in videos. Recently, transformer-based approaches have demonstrated superior performance on well-established keypoint estimation benchmarks [17; 28].
# Intelligent Healthcare Platform for Diagnosis of Scalp and Hair Disorders
## 3 Results  
### Severity Classification of Six Hairy Scalp Disorders  
Three trained models based on ResNet-152, EfficientNet-B6, and ViT-B/16 predicted the severity level (0, 1, 2, and 3) of six individual scalp and hair disorders (dryness, oiliness, erythema, folliculitis, dandruff, and hair loss). We compared the overall performance of these trained models using the test dataset (Figure 3). Four metrics commonly used for performance examination of deep learning classification were evaluated: accuracy, precision, recall, and F1-score [29].  
\[\text{accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{recall}=\frac{\text{TP}}{\text{TP}+\text{TN}} \tag{3}\]  
\[\text{F1 score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{4}\]  
TP, TN, FP, and FN indicate the number of true positive, true negative, false positive, and false negative of each class, respectively.  
As a result, the ViT-B/16 model outperformed the other two deep learning models (ResNet-152, EfficientNet-B6) for all metrics (Figure 3). The trained ViT-B/16 model exhibited the highest classification performance with the overall accuracy of 78.31%, precision of 78.81%, recall of 79.02%, and F1-score of 78.91%. Therefore, the ViT-B/16 model was selected as the best classifier for diagnosing scalp and hair disorders among the tested models.  
Figure 4 presents confusion matrices showing the detailed classification results of the ViT-B/16 models trained by each scalp and hair disorder. The trained ViT model predicted the severity level of each disorder from 0 (none) to 3 (severe). Based on these confusion matrices, we calculated the accuracy, precision, recall, and F1-score for the severity classifi  
Figure 2: Flow chart illustrating execution of Scalp checker software. (**a**) User launches GUI-level Scalp checker using PC. (**b**) API-level Scalp checker checks GPU compatibility and predicts severities of each hair scalp disorder when user inspects hair scalp disorders. (**c**) After finishing the inspection, API-level Scalp checker requests the prediction results be stored in the local database. The results are displayed on the inspection page. (**d**) GUI-level Scalp checker shows the results stored in the local database when dashboard or history page at GUI-level Scalp checker is navigated by the user.
# A Seismic Phase Recognition Algorithm Based on Time Convolution Networks
## 1 Introduction
(2020) designed a joint loss function for classification and regression and demonstrated the excellent performance of deep neural networks in seismic phase detection by building a multitask convolutional neural network model [7]. [PERSON] et al. (2020) introduced a cascaded classification and regression framework for seismic phase detection, named the classification and regression phase net (CRPN), which contained two convolutional neural network models with different complexities to meet the accuracy and efficiency requirements [8]. [PERSON] et al. (2022) revealed the spatial and temporal distribution characteristics of earthquake sequences and the geometry of seismogenic faults by building a high-resolution earthquake catalog [9]. [PERSON] et al. (2021) processed continuous seismic waveform data and dense seismic sequences in real time and offline by designing a real-time seismic processing system based on artificial intelligence. Scientific research provided important technical support [10]. Recent findings have shown that temporal convolution networks (TCNs) [11; 12] outperform baseline recurrent architectures in a wide range of sequence modeling tasks, including action segmentation [13], speech analysis and synthesis tasks [11; 14]. The research results that were published by [PERSON] et al. (2020) [15] on the use of TCNs for weather forecasting tasks showed that TCNs performed well in prediction tasks using time series data. [PERSON] et al. (2020) [16] proposed an SELD-TCN architecture based on the sound event localization and detection (SELD) and TCN methods. By applying a short-time Fourier transform, the SELD-TCN method could extract the phases and magnitudes of the spectrum and stack them as separate input features, then connect the convolutional and recurrent blocks (bidirectional GRUs) and finally connect the fully connected blocks. The outputs of SELD-Net were sound event detection (SED) and the direction of arrival (DOA). Since the dilated convolutions enabled the network to handle a variety of inputs, deeper networks (which suffer from unstable gradients during backpropagation) could be required. The authors overcame this challenge by adopting a WaveNet [17]. They showed that SELD tasks did not require a recurrent layer and they successfully detected the start and end times of active sound events. Since earthquake phases are sets of time-correlated time series data, TCNs not only achieve a better performance but also reduce the computational costs of training compared to RNNs and their variants, without exploiting repetitive architectures. In view of the above analysis, this paper proposes an S-TCN model based on temporal convolutional neural networks, which could improve the model by using dilated convolutions and dilation frameworks. We also added a convolution module to extract the deep features of seismic waves to obtain accurate seismic phase detection and provide new ideas for earthquake early warning systems.
## 1 Introduction
The values of chloride diffusion coefficient usually vary from \(10^{-13}\) m\({}^{2}\)/s to \(10^{-10}\) m\({}^{2}\)/s [31] in relation to the concrete properties such as w/cm ratio, the type and proportion of mineral admixtures and cement, the material's compaction and curing state, chloride exposure conditions, as well as the presence of cracks [32]. Currently available experimental methods for the determination of concrete's resistance to chloride ion penetration include the rapid chloride permeability test (RCPT) as shown by [PERSON] [33], the rapid chloride test (RCT) method, and the rapid chloride migration test (RMT) [34; 35; 36; 37; 38; 39]. Probably the most straightforward way of measuring chloride penetration is to immerse the specimens under investigation in salty solutions with different concentrations for specific periods of time depending on the needs of the experiment [40; 41; 42]. [PERSON] et al. suggested the measurement of electrical resistivity as a non-destructive method for monitoring chloride ion penetration in concrete structures by means of the electrical properties of graphene-modified cement composites [6]. While considerable research efforts have dealt with concrete behavior with respect to chloride penetration [10; 16; 38; 39; 43] and the effect of sodium chloride in mortars [42; 44], there is currently a clear lack of knowledge on the effect of carbon nanotube (CNT) presence on chloride penetration in state-of-the-art nano-modified mortars intended for future exploitation in real constructions. The main purpose of the present study is to address the effect of chloride penetration in carbon nanotube-modified cementitious materials subjected to marine environments. For that reason, two different methods, namely the rapid chloride test method (RCT) and the rapid chloride permeability test method (RCPT) were used for the degradation of the specimens, while the mechanical and electrical behavior of the corroded materials was investigated.
# Lower-Latency Screen Updates over QUIC with Forward Error Correction
## 4 Design and Implementation  
We used ngtcp2 [12] in our benchmarking results, as it is one of the active, well-known, open-source implementations of QUIC in C++. Ngtcp2 has an active GitHub project and a forum for answering questions quickly. In addition, several research articles used ngtcp2 to benchmark the QUIC protocol [26; 27; 28]. Our implementation has two parts: (1) partial ordering for screen updates over QUIC and (2) adding FEC to ngtcp2. We explain each part in the next two sections.
###### Abstract  
In this paper, we present the first constant-time implementations of four-dimensional Gallant-Lambert-Vanstone and Galbraith-Lin-Scott (GLV-GLS) scalar multiplication using curve Ted127-glv4 on 8-bit AVR, 16-bit MSP430, and 32-bit ARM processors. In Asiacrypt 2012, [PERSON] and [PERSON] introduced the four-dimensional GLV-GLS scalar multiplication, and they reported the implementation results on Intel processors. However, they did not consider efficient implementations on resource-constrained embedded devices. We have optimized the performance of scalar multiplication using curve Ted127-glv4 on 8-bit AVR, 16-bit MSP430, and 32-bit ARM processors. Our implementations compute a variable-base scalar multiplication in 6,856,026, 4,158,453, and 447,836 cycles on AVR, MSP430, and ARM Cortex-M4 processors, respectively. Recently, FourQ-based scalar multiplication has provided the fastest implementation results on AVR, MSP430, and ARM Cortex-M4 processors to date. Compared to FourQ-based scalar multiplication, the proposed implementations require 4.49% more computational cost on AVR, but save 2.85% and 4.61% cycles on MSP430 and ARM, respectively. Our 16-bit and 32-bit implementation results set new speed records for variable-base scalar multiplication.  
elliptic curves; scalar multiplication; constant-time implementation; twisted Edwards curves; AVR; MSP430; ARM +
Footnote †: journal: Applied Sciences
## 1 Introduction
On this basis, it is of great significance to study high-frequency underwater acoustic channels to overcome the challenges mentioned above. To this end, this paper analyzes the experimental data of a deep-sea communication experiment in the South China Sea. Adopting previously reported channel characteristic analysis methods, it investigates the spatiotemporal channel characteristics of the shadow zone and the direct-arrival zone in a shallow-depth transmitted high-frequency underwater acoustic channel. Theoretical explanations of the findings are also provided. The main contributions of this paper are summarized below. (1) The main arrival paths of the multipath and vertical structures of channels at different depths are analyzed in a deep-sea shallow-depth high-frequency transmitted scenario. The simulated channel impulse response (CIR) structure is consistent with the measured CIR structure, providing a basis for determining the arrival paths of the multipath in subsequent analysis. The path clusters of different channel arrival paths are distinguished to provide the amplitude distributions of different path clusters in the channels at different receiving depths. Meanwhile, the randomness degrees of the path clusters are evaluated in terms of the coefficient of variation. (2) The study analyzes the spatial distribution of RMS delay spread, which is closely related to the communication performance. The instantaneous changes in RMS delay spread are investigated in the shadow zone and the direct-arrival zone. By elucidating the depth dependence of the fluctuations in average RMS delay spread, we can provide a parameter basis for communication decision algorithms. (3) The study analyzes the channel temporal coherences at different depths in the shadow and direct-arrival zones, along with the temporal coherences of individual path clusters in different zones. This research component can guide the design of algorithms for channel tracking and channel equalization in deep-sea shallow-depth high-frequency transmitted communications. The remainder of this paper is organized as follows. Section 2 introduces some brief information on the channel measurement experiment, and Section 3 defines the channel statistics (multipath amplitude, RMS delay spread, coefficient of variation, and channel temporal coherence). Section 4 analyzes the experimentally derived channel statistics and obtains the temporal and spatial characteristics of the channel. Section 5 concludes the study. Section 6 provides future work plans. Abbreviations part explains the abbreviations used in the paper.
# Unsupervised Deep Learning-Based RGB-D Visual Odometry
## 4 Experiments
### Performance Evaluation
A detailed quantitative analysis is shown in Table 2. Root Mean Square Error (RMSE) of translation and rotation are computed and listed. The values are averaged over all available errors on length 100, 200,..., 800 m. The smallest \(t_{rmse}\) and \(r_{rmse}\times 100\) in each sequence are shown in bold. Among the unsupervised deep learning systems, our system produces the lowest translation and rotation errors. Trajectories of sequence 11-20 are shown in Figure 8. Since the ground truth of these sequences is not published, we have only conducted qualitative analysis and took VISO2-Stereo as a reference. The trajectory of VISO2-Stereo, our system, and UnMono are represented by black, red, and blue curves. From Figure 8d,h,i, we can tell our trajectories stay closer to VISO2-Stereo than UnMono, which again proves the importance of the additional depth information. \begin{table}
\begin{tabular}{c|c c|c c|c c|c c} \hline \hline  & \multicolumn{2}{c}{**SfMLearner**} & \multicolumn{2}{c}{**UnMono**} & \multicolumn{2}{c}{**VISO2-Stereo**} & \multicolumn{2}{c}{**Ours**} \\  & \multicolumn{2}{c}{**Monocular**} & \multicolumn{2}{c}{**Monocular**} & \multicolumn{2}{c}{**Stereo**} & \multicolumn{2}{c}{**RGB-D**} \\  & \multicolumn{2}{c}{**Unsupervised**} & \multicolumn{2}{c}{**Unsupervised**} & \multicolumn{2}{c}{**Unsupervised**} & \multicolumn{2}{c}{**Feature Based**} & \multicolumn{2}{c}{**Unsupervised**} \\ \cline{2-9}
**Seq.** & \(t_{rmse}\) & \(r_{rmse}\times 100\) & \(t_{rmse}\) & \(r_{rmse}\times 100\) & \(t_{rmse}\) & \(r_{rmse}\times 100\) & \(t_{rmse}\) & \(r_{rmse}\times 100\) \\ \hline
00 & 45.89 & 6.23 & 5.14 & 2.13 & **1.86** & **0.58** & 2.97 & 1.53 \\
02 & 57.59 & 4.09 & 4.88 & 2.26 & 2.01 & **0.40** & **1.83** & 1.71 \\
03 & 13.08 & 3.79 & 6.03 & 1.83 & **3.21** & **0.73** & **3.21** & 1.15 \\
04 & 10.86 & 5.13 & 2.15 & 0.89 & 2.12 & **0.24** & **0.94** & 0.57 \\
05 & 16.76 & 4.06 & 3.84 & 1.29 & **1.53** & **0.53** & 2.31 & 1.03 \\
06 & 23.53 & 4.80 & 4.64 & 1.21 & 1.48 & **0.30** & **1.25** & 0.80 \\
07 & 17.52 & 5.38 & 3.80 & 1.71 & 1.85 & **0.78** & **1.47** & 1.31 \\
08 & 24.02 & 3.06 & 2.95 & 1.58 & 1.92 & **0.55** & **1.91** & 1.20 \\
09 & 22.27 & 3.62 & 5.59 & 2.57 & 1.99 & 0.53 & **1.86** & **0.50** \\
10 & 14.36 & 3.98 & 4.76 & 2.95 & 1.17 & **0.43** & **1.15** & 1.17 \\ \hline mean & 24.59 & 4.41 & 4.38 & 1.84 & 1.91 & **0.51** & **1.89** & 1.10 \\ \hline \hline \end{tabular} \(t_{rmse}\) (%): average translational RMSE drift (%) on length of 100–800 m; \(r_{rmse}\) (\({}^{\circ}\)/m): average rotational RMSE drift (\({}^{\circ}\)/m) on length of 100–800 m.  
\end{table} 
Table 2: Translational and rotational errors of our system, SfMLearner, UnMono and VISO2-Stereo. Our system, SfMLearner, UnMono are unsupervised deep learning based. VISO2-Stereo is feature based. SfMLearner poses were post-processed with ground truth depth information for scale recovery. Sequence 09 and 10 were not used for training.
# Solar Irradiance Forecasting with Transformer Model
## 2 Dataset and Methods
### Region Pruning  
Since too many regions were selected, they had to be reduced. Many of the regions overlapped by a substantial area, and thus, following the results of the intersection over union algorithm [21], a number of these regions were excluded. If the regions overlapped by more than 25%, they had to go through the exclusion process. The removal was based on the comparison of the power and area of the solar collectors in the regions. If a region performed better, its \"opponent\" was excluded from the list of regions. If the power was not specified, the region with the smaller area of solar collectors was excluded. If neither power nor area was given, the decision was based on random choice, when each region had the same chance of exclusion. The exception was the regions marked as permanent, which excluded their non-permanent opponent automatically. If both regions were permanent, none were excluded. Figure 1 shows an example of such thinning of regions. It is evident that this thinning not only reduced the number of regions but also spread them over the surface of India to account for the largest possible global surface in global solar irradiance prediction. The total number of regions after thinning was 18.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 3 Parent and PHY Selection Heuristic
### RPL Parent Selection  
In 6 TiSCH networks, it is the RPL routing protocol that organizes the network paths along which data are transmitted from the originating node to the network sink [19]. RPL organizes the network nodes in a tree topology, which is rooted at the sink node (i.e., a border router). Each node in the RPL routing tree can have zero or more child(ren), and one or more parent(s). A node that has no children is a leaf node, and the node in the tree that does not have any parents is the root node of the tree. When data have to be transmitted to the sink (or root) of the network, they are forwarded on the link between every node and its parent along the path from the originating node towards the root. The location of the node in this routing tree is determined by its _Rank_ value. The node calculates this value itself and broadcasts this information in so-called Destination Oriented Directed Acyclic Graph Information Object (DIO) messages. To calculate this Rank value, but also to select the preferred routing parent, a node applies an Objective Function (OF) that uses one or more routing metrics to approximate the distance of the node to the root. The minimal 6 TiSCH profile mandates using the default Objective Function Zero (OF0) [18; 20]. More specifically, when a node applies OF0 to select a parent among its set of neighbors, it uses a so-called _step_of_rank_ value, which represents the link properties to the specific neighbor. That _step_of_rank_ becomes part of a normalized _rank_increase_ value that is added to the Rank value received via routing broadcast messages (i.e., DIO messages) from each neighbor. The neighbor with the lowest resulting Rank value is picked as the (new) preferred parent (only if the difference with the rank of the previous parent exceeds a parent switching threshold). The minimal 6 TiSCH profile defines this _step_of_rank_ as a function of the Expected Transmission Count (ETX) (i.e., the estimated number of needed transmission attempts for one successful transmission) towards that neighbor. An alternative to OF0 is the Minimum Rank with Hysteresis Objective Function (MRHOF) [21]. Using hysteresis to limit the number of topology changes caused by only small metric changes, this Objective Function (OF) selects routes that minimize an additive metric. Those metrics are usually advertised in the so-called Metric Containers of RPL DIO messages. When no metrics are being advertised in DIO messages, the OF also uses the ETX metric to make parent selections.