esa-sceva/satcom-chunk-collection huggingface dataset:
# A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
## 2 Related works  
### The 2D-to-3D Lifting Pipeline  
Compared to end-to-end 3D pose estimation, the 2D-to-3D pipeline approach divides the task into two independent parts: 2D pose estimation from the image and lifting the 2D pose to a 3D pose. Recent 2D-to-3D lifting methods [6; 19; 20; 21; 22; 23] have demonstrated superior performance compared to end-to-end approaches, owing to the reliable and effective 2D keypoint detection methods developed in previous works [24; 25; 26; 27]. To advance 2D-to-3D pose estimation, a Graph Stacked Hourglass Network [22] is introduced. This architecture comprises a repeated encoder-decoder and a graph-structured feature processing approach across multiple scales of skeletal representations, which enhances the ability of the model to learn both local and global feature representations. [PERSON] et al. [28] proposed breaking down the 3D pose estimation task into bone direction prediction and bone length prediction, aiding in addressing depth ambiguity. In addition, domain generalization [29] is applied in hand pose estimation to address the issue of images with characteristics different from the training data. These advances in 2D-to-3D lifting have significantly improved the accuracy of 3D pose estimation.
## 5 Discussions and Future Directions
### Challenges and Future Directions for ANN in CM
#### 5.2.1 Model Development and Application  
ANN models for specific problems such as cost prediction, performance evaluation, and safety assessment are relatively isolated even for similar application goals. This makes it difficult to replicate results from previous studies, resulting in inefficient output and discontinuous progress. To address this issue, a good starting point is to use techniques such as transfer learning [83], namely reusing known architectures or models with good classification or predictive performance to develop specific model guidelines. Future research can pay more attention to the solution system for a particular problem under different  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Progress** & **Research between 1989 and 2000 [17]** & **Research between 2000 and 2020** \\ \hline Type of ANN & BPNN, RBFNN & BPNN, MLPNN, CNN, RBFNN, GRNN, PNN, NNA, WNN, TDNN, QNN, BANN \\ \hline Fields of research & cost, schedule, resource allocation, dispute prediction & cost, performance, safety, quality, resource, risk, contract, schedule, procurement, environment, and sustainability \\ \hline Integratedalgorithm/method & GA, FL, Wavelets & GA, FL, Long Short-Term Memory, Wavelet, Clustering, K-Nearest Neighbors, Decision Tree, Case-based Reasoning, Support Vector Machine, Regression Analysis, Deep Learning method \\ \hline Emerging Technologies & / & computer vision, smart sensor, smart bracelet, smart phone, done, video camera, natural language processing \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of research progress.  
Figure 6: Challenges and future directions.  
scenarios, for example, cost estimation system under different information conditions at different stages. This research opportunity has been emphasized in some articles in the steel industry [83].  
Meanwhile, ANN in CM belongs to a multidisciplinary field involving computer science, data science, and traditional CM. Therefore, the application of ANN in CM requires cooperation between many fields, and research on organization management for ANN in CM deserves future attention. The government authority can actively implement cooperative pilot projects integrating the industry and academic communities to achieve standardized ANN applications. Packaging the developed ANN model into a user-friendly interface running on the Matlab platform may achieve this goal more quickly. Some attempts have been made in this research direction. [PERSON] and [PERSON] [84] developed an automated decision support system and designed a GUI for selecting the envelope and structure system of educational facilities. Program developers can also learn from mature software such as DesignBuilder in the energy simulation field to develop integrated ANN software to achieve practical applications [85]. It is expected to show users a series of dialog boxes with various menus and toolbars so that they can easily perform ANN for cost estimation, performance evaluation, etc., without programming.
# Characterizing Ionospheric Effects on GNSS Reflectometry at Grazing Angles from Space
## 6 Conclusions
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline  & & \multicolumn{3}{c}{**Very Low: 5\({}^{\circ}\)–10\({}^{\circ}\)**} & \multicolumn{3}{c}{**Low: 10\({}^{\circ}\)–20\({}^{\circ}\)**} & \multicolumn{3}{c}{**Mid-Low: 20\({}^{\circ}\)–30\({}^{\circ}\)**} \\ \cline{3-11}  & & \(\Delta_{p_{iano}}\) & 1\(f_{d}\)1 & \(Hm\) & \(\Delta_{p_{iano}}\) & 1\(f_{d}\)1 & \(Hm\) & \(\Delta_{p_{iano}}\) & 1\(f_{d}\)1 & \(Hm\) \\  & & **(m)** & **(Hz)** & **(km)** & **(m)** & **(Hz)** & **(km)** & **(m)** & **(Hz)** & **(km)** \\ \hline North & & & & & & & & & & & \\ DT & median & 2.77 & 0.516 & 306.8 & 9.18 & 0.075 & 307.6 & 8.09 & 0.049 & 307.5 \\  & std & 6.00 & 0.496 & 13.9 & 4.24 & 0.085 & 12.7 & 3.79 & 0.043 & 10.4 \\ \cline{2-11}  & median & 0.03 & 0.330 & 333.8 & 5.11 & 0.051 & 331.7 & 5.31 & 0.015 & 327.2 \\  & std & 3.90 & 0.148 & 15.9 & 1.64 & 0.066 & 14.4 & 1.16 & 0.011 & 12.0 \\ \hline Tropics & & & & & & & & & & \\ DT & median & \(-\)21.45 & 2.329 & 349.5 & 25.56 & 0.246 & 348.4 & 28.45 & 0.139 & 345.2 \\  & std & 24.43 & 0.698 & 26.7 & 9.30 & 0.377 & 27.3 & 5.96 & 0.068 & 28.9 \\ \cline{2-11}  & median & \(-\)5.21 & 0.817 & 368.1 & 8.82 & 0.144 & 371.9 & 11.55 & 0.022 & 371.9 \\  & std & 9.88 & 0.352 & 15.7 & 3.74 & 0.173 & 10.9 & 2.40 & 0.024 & 8.8 \\ \hline South & & & & & & & & & & \\ DT & median & 1.46 & 0.812 & 307.2 & 13.57 & 0.084 & 306.9 & 13.97 & 0.037 & 307.9 \\  & std & 7.58 & 0.333 & 6.5 & 3.39 & 0.109 & 6.4 & 2.62 & 0.043 & 6.0 \\ \cline{2-11}  & median & \(-\)1.98 & 0.472 & 351.7 & 6.26 & 0.081 & 351.2 & 7.17 & 0.015 & 351.8 \\ \cline{2-11}  & std & 3.99 & 0.146 & 14.1 & 1.84 & 0.092 & 12.3 & 0.92 & 0.015 & 11.4 \\ \hline \end{tabular}
\end{table} 
Table 6: Overview of ionospheric parameters from the NEDM2020 sTEC computations during high solar activity (F10.7 = 180). **Author Contributions:** Conceptualization: M.M., M.S. and G.S. ; methodology: M.M., M.S. and G.S. ; software: M.M. and M.S. ; data resources: M.M. and M.S. ; writing--original draft preparation: M.M. ; writing--review and editing: M.S., G.S., M.H. and J.W. ; visualization, M.M. ; supervision, M.S., J.W. and M.H. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the German Aerospace Center, Institute for Solar-Terrestrial Physics (DLR-SO). **Data Availability Statement:** Data are available under the authorization of Spire Global Inc.  
**Acknowledgments:** The authors would like to thank Spire Global for providing real orbit from Lemur-2 constellation LEO satellites. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Validation Experiments
### USRP Clock Problems  
The clock of the USRP device is a low-cost 64 MHz crystal oscillator with 20 ppm precision. This caused two problems to OpenBTS: First, the clock frequency was not well adjusted to the GSM symbol rate, since it was not its multiple. In this case, the transceiver should implement a resampler to adjust to the right frequency, with the added burden of computational cost. Second, the USRP clock is not sufficiently stable and oscillator drifts could take the signal frequency outside the search range of mobile phones. This was especially relevant in the GSM 1.8 GHz band.  
The solution to this problem was to install a specific 52 MHz clock with better precision. With it, the MSs were able to successfully camp on the cell without trouble and the computational load of the PC was greatly reduced. It was necessary to integrate the clock in the USRP motherboard and to change the OpenBTS software to disable frequency resampling.  
Figure 14: USRP board with auxiliary 52 MHz clock installed.  
Figure 13: Frequency response before (left), and after equalization and DC cancellation.  
Figure 14 shows the USRP with the 52 MHz clock installed. The clock is based on the Temperature Compensated Crystal Oscillator (TCXO) Connor Winfield D53G and has a frequency stability of 0.5 ppm and a RMS phase jitter of close to 2 picoseconds.
## 3 Results
The calculation results are shown on Figure 9. As seen from the plot, the increase of LER from 20 to 40 nm leads to growth of radiation optical losses: from 0.5 to 1.8 dB/cm for the TE-mode, and from 1.8 to 7.1 dB/cm for the TM-mode at 1.55 \(\upmu\)m. The longer the wavelength, the less sensitive it is to the sidewall roughness, resulting in lower optical radiation losses. Figure 8: The relative calculating error of the effective refractive indexes of the TE- and TM-modes by EIM and FDFD method versus etching depth for the set of sidewall slope angles: solid lines correspond to TE-modes, dashed lines—to TM-modes. Figure 7: The effective refractive indexes calculated by FDFD method and EIM for the set of single-mode waveguides versus the etching depth: (**a**) the results, obtained by FDFD: solid lines correspond to TE-modes, dashed lines–to TM-modes; (**b**) the results, obtained by EIM for the sidewall slope angle of 90\({}^{\circ}\). All simulations were run on an Intel core i7-6700K processor. The time and memory consumption were 15 s and about 10 MB for the FDFD simulation iteration and 1 s and about 15 KB for the EIM iteration, respectively.
## 7 Conclusions  
Many previous studies are focused on using multiple channels in wireless networks with omni-directional antennas, which have high interference. There are few studies considering multiple channels in wireless networks with directional antennas, which can lead to low interference. In this paper, we study the channel allocation problem in wireless networks with directional antennas. In particular, we derive the upper bounds on the number of channels to ensure the collision-free communication in multi-channel wireless networks using directional antennas. We found that the upper bounds heavily depend on the node density and are also related to the interference ratio. Our results can be used to estimate the number of channels in practical wireless networks.  
Figure 12: Two cases for Theorem 8. (**a**) Case 1; (**b**) Case 2.
# Design of a Piezoelectrically Actuated Ultrananocrystalline Diamond (UNCD) Microantilever Biosensor
## 3 Results and Discussion
### Eigenfrequency Matching of the Beam of Microcantilevers  
Eigenfrequency matching was initially explored for an actuator with a resonant frequency of 969 kHz, using a piezoelectric AlN layer with a thickness of 300 nm. The matching process involved estimating the required lengths of the UNCD and Si beams using Equation (2), which relates the beam length to its natural frequency. This approach provided a practical starting point for designing microcantilevers with resonant behavior aligned to that of the actuator.  
To match the resonant frequency, an eigenfrequency optimization procedure was employed. This optimization considered the inclusion of a 100 nm thick titanium (Ti) layer deposited on top of the UNCD or Si beams, serving as an adhesion layer for the platinum (Pt) film used as the bottom electrode in the integrated Pt/AlN/Pt piezoelectric actuator structure. The Ti layer was modeled with the same lateral dimensions as the actuator schematic depicted in Figure 2, ensuring consistency in the structural configuration across all simulations.  
The results of the simulations used to calculate the optimized lengths of the UNCD and Si beams--aimed at producing optimized microcantilevers for MEMS biosensor applications--are presented in Table 6. Length 1 refers to the initial estimate obtained directly from Equation (2), while Length 2 corresponds to the value derived from eigenfrequency optimization performed in COMSOL Multiphysics. Length 2 was adopted as the starting point for subsequent stages in the microcantilever design process.
## 5 Energy Optimization Approach Based on MAC/Routing Cross-Layer (EOAMRCL)
### Pre-Clustering Phase (Network Layer)  
In our proposed protocol, we assume that the system includes a certain percentage (\(m\%\)) of advanced nodes, which have an additional energy factor \(\theta\) compared to the normal nodes. Each sensor node initially has an energy level of \(E_{0}\). For advanced sensor nodes, their energy is increased by the factor \(\theta\), making their total energy \(E_{0}(1+\theta)\). Equation (1) is used to calculate the predicted network's total energy construction using \(n\) nodes.  
\[nE_{0}(1+m\theta)=nmE_{0}(1+\theta)+n(1-m)E_{0} \tag{1}\]  
Consequently, the total energy of the network is enhanced by this factor. We improved the election technique by incorporating the remaining energy of individual nodes, taking into account the different probabilities for advanced and ordinary nodes. The probability function for normal and advanced member nodes is defined by Equations (2) and (3), respectively:  
\[P_{normal} =\frac{m}{1+\theta m}.\frac{E_{residual}}{E_{0}} \tag{2}\] \[P_{advanced} =\frac{m(1+\theta)}{1+\theta m}.\frac{E_{residual}}{E_{0}} \tag{3}\]  
Figure 4: Integration of sleep mode in NAV.  
At the end of each clustering round, the BS calculates the threshold probability of clusterheads \(P_{threshold}\):  
\[P_{threshold}=\frac{m(1+\theta)}{1+\theta m}\cdot\frac{\text{ARE}}{E_{0}}, \tag{4}\]  
where ARE represents the average residual energy in the network.
## 4 Properties of Lignosulfonate-Improved Loess
### Static Strength Properties
#### 4.2.2 Shear Strength  
Shear strength is one of the important indicators that are used to characterize the strength characteristics of lignocellulosic-solidified soils. Through experiments, [PERSON] et al. [43] and [PERSON] [46] found that the cohesion of lignosulfonate-modified soil increases first and then decreases with the increase in the lignosulfonate dosage. The change of cohesion is obvious, which is the main reason for the change in shear strength performance; meanwhile, the change of internal friction angle with different dosages is small, the influence on shear performance is small, and the curing age is proportional to the shear strength of the improved soil.  
The shear strength parameters are related to the soil particle morphology, degree of cementation, pore distribution, water content, and clay mineral content [53; 54]. Calcium lignosulfonate can fill the pores at the initial stage, effectively cementing the soil particles to strengthen the skeleton structure [55], and the cohesion of the soil increases. The internal friction angle may be related to particle occlusion and the content of clay. Fine lignosulfonate particles are gradually embedded in the pores, and the interlocking effect with the particles enhances the occlusion force between particles, resulting in a small increase in the internal friction angle [46]. The shear strength index of the improved soil with the calcium lignosulfonate dosage of 1.0% is the highest, and the shear strength of the soil begins to decrease after the dosage is higher than 1% due to its characteristics. When the dosage is higher than 3%, the shear strength of the improved soil drops sharply. In conclusion, the recommended dosage range of lignosulfonate is approximately 1%.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Results and Discussion  
The simulation results are shown in Figures 7-15. The simulation tool is NS-2 AquaSim, which is the extension of NS-2 simulator and specially designed for aquatic environment. Table 4 provides the simulation parameters.The simulation results are explained in two phases: in the first phase, all of the reliable links are established between the nodes at different depth level and these links are evaluated under time, Packet Delivery Ratio (PDR), Energy Consumption (EC), and throughput except. Furthermore, its reliability model is compared in terms of PDR against 3H-RM and 2H-ACK. In the second phase, TBRL compares its performance against DBR and R-ER\(P^{2}\)R with respect to end to end delay, PDR, and EC of the network.
# Predicting Architectural Space Preferences Using EEG-Based Emotion Analysis: A CNN-LSTM Approach
## 4 Data Analysis and Model Development
### EEG Data Preprocessing
This procedure is consistent with recommendations from Makoto's pre-processing pipeline and the EEGLAB tutorial and is crucial for preventing rank deficiency issues such as the generation of ghost independent components [156; 163; 164]. Following preprocessing, artifacts such as motion-induced noise and excessive eye blinks were removed, resulting in 3200 usable trials (80% retention rate). We then segmented the EEG signals into 5-s epochs corresponding to each stimulus and labeled them either \"preference\" (preferred), \"neutral,\" or \"non-preference\" (disliked) before saving the data in the MATLAB (.mat) format. Given that our primary focus was on differentiating clear preference responses, trials labeled \"neutral\" were excluded from the final dataset, ensuring a balanced classification between preference and non-preference categories. Table 11 summarizes the final dataset configuration. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **No. Channels** & **Time Interval** & **No. Stimuli** \\ \hline Raw electroencephalograph trials & 10 & 5 s & 4000 \\ \hline Post artifact removal & 10 & 5 s & 3200 \\ \hline Preference data & 10 & 5 s & 1121 \\ \hline Non-preference data & 10 & 5 s & 814 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The final dataset. These preprocessing steps formed the foundation for exploring differences in neural signals between preference and non-preference stimuli, enabling a robust analysis of how users' emotional responses varied with architectural images. The final dataset was central to the subsequent deep learning model development.
# On the Design of a Network Digital Twin for the Radio Access Network in 5G and Beyond
## 2 NDT Architecture for the RAN
### NDT Management  
The NDT management module in Figure 1 fulfils the management function of the NDT. This includes the NDT's life-cycle management, i.e., the deployment, operation, optimization, maintenance and termination of the different models in the NDT. This also embraces the feed of data from the data repository to the different models and the validation of the performance of the models as well as the interactions between them. In the case of ML-based models, the NDT management module can provide support along the AI/ML life cycle of the ML-based models in the NDT (i.e., training based on new data, testing and operation). Moreover, the NDT management module is responsible for the activation of the models specified when creating a specific RAN NDT instance. Another functionality is the control of emulation processes on the NDT when it is used for assessing and exploring the network performance in different situations (i.e., what-if situations such as different traffic demand levels, diverse configurations of nodes, etc.). In addition, the NDT management module is in charge of the monitoring of the execution of the NDT modules, encompassing resource consumption, problem detection, connectivity between the RAN NDT and the _Apps_ and the experienced traffic.
## 2 Materials and Methods
### Adaptive Equalization  
Systems that are more complex employ training sequences for transmitter's compensation settings for tuning to the best performance. After a given period, the adjustment is remade. The transmitter and receiver are set to automatically adapt to the changing conditions of the interconnect. For this purpose, the scheme requires that the transmitter and receiver communicate with each other in a known and predictable way [5]. At intermediate data rates, the EQ coefficients are often set based on the average characteristic of the line. The design of the link introduces dependencies on technology corners that need to be compensated by the EQ via calibration and adaptive techniques of the EQ parameters for the actual channel. Usually variants of least mean square (LMS) algorithm are implemented. For rates in orders of Gb/s, Sign-Sign LMS is often the only option due to very short time needed to determine the EQ parameters. Based on probabilistic considerations applied to the sampled pulse response, the LMS convergence methodologies estimate the evolution in EQ structure [12]. This approach is based on permutation matrix to take into account the probability of the transmitted sequences and find adjustments  
Figure 10: Time domain analysis: (**a**) eye diagram typical representation with mask representation (in black), and corners marked (in red), (**b**) eye diagram for the specific arbitrary random sequence applied and determined at the output of the trace, and (**c**) eye diagram and metrics at initial point of transmission.  
to the LMS parameters. Evaluation in terms of cost, constraints, implementation, power, performance of CTLE (Continuous Time Linear Equalizer), FFE (Feed-Forward Equalizer), DFE (Decision Feedback Equalizer), or FEC (Forward Error Correction) for high data rates in [12] and [13] conclude that while DFE can reduce channel ISI with short tap length, FFE is needed to compensate pre-cursor ISI and compensate the power-voltage variations of transceivers, and also fits in DSP (Digital Signal Processing) implementation.  
The benefit of adaptive equalization is the ability to adjust lengths and data rates. The main disadvantages are an increased complexity and power consumption.