esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Study and Results
### In-Silico Modeling and Evaluation  
The in-silico experiments performed in CoppeliaSim Edu (Coppelia Robotics Ltd., Zurich, Switzerland) involve the conversion of the slave robot's 3D CAD model to a unified robot description format (URDF), which is then assembled within the simulation scene, as shown in Figure 12. We parameterized the static and dynamic properties of the robot to mimic the translation mode of the slave robot. To facilitate the position control of the device, a prismatic joint and the global positioning system (GPS) provided feedback about the device's position and orientation. To evaluate the RNN model's performance, a uniform displacement command (5 mm) is transmitted to the slave robot, while a random value (rand_val) represented by Equation (5) generates the uncertainties resulting in stepwise error. For each step, the RNN model predicts the slave robot's response based on input motion variables prior to the robot's translation. Afterwards, the difference between the model's prediction and the actual robot displacement is added to the initial slave robot's movement with the final error computed per step. This ensures that the slave robot reproduces exact position commands received from the master platform, thereby eliminating accruing errors within the slave robot along its traveling distance.  
\[\text{rand\_val}=\ \sigma\ \times\text{n}_{\text{p}}.\ \text{random\_normal}(0,\ 1) \tag{5}\]
## 2 Related Work
### Traffic Models in the City of Barcelona  
There has been several recent works on traffic models analyzing the specific case of Barcelona. For instance, ref. [17] compared the performance of different traffic assignment models through simulation. Such methodologies evaluate traffic levels within a track or road network based on its physical attributes, functional dynamics, and anticipated traffic volume. The case study focused on the province of Barcelona, and experiments compared five static traffic assignment approaches (all or nothing, stochastic assignment with a simulation-based method, the method of successive averages, incremental assignment, and user equilibrium using the Frank and Wolfe algorithm). Regression analysis was used to study the differences between estimated and observed flow. The best model was user equilibrium with an \(R^{2}\) index of 0.93 for light vehicles and 0.89 for heavy vehicles.  
Moreover, ref. [18] introduced two models, one for taxi stand services and the other for one-way carsharing, in the context of Barcelona's taxi demand. Each model determines optimal factors such as the number of cars and depots, depot capacity, system unitary costs, and level of service. Results indicate comparable operation between the two systems, although taxi services prove up to three times costlier due to driver hiring expenses. Ref. [19] developed a coupled macroscopic traffic and emission modeling system tailored to the Barcelona metropolitan area to estimate hourly road transport emissions at the road link level. Their analysis included an emission sensitivity assessment and investigation into typically high-uncertainty emission factors. The study found significant sensitivity to inputs such as vehicle fleet composition and meteorological impacts on diesel engines, with non-exhaust sources contributing substantially to total PM emissions. Discrepancies between the macroscopic and microscopic systems increased with congestion levels, particularly in \(NO_{x}\) emissions, reaching up to 65%. Ref. [20] examined methods for evaluating the impacts of pluvial flooding events on traffic flows under current and future climate change scenarios in Barcelona and Bristol. Using both meso-scale and micro-scale traffic models, they found that increased flood intensity led to greater disruptions in traffic flows, with climate change exacerbating these effects. Ref. [21] conducted a quantitative assessment of shared mobility service usage among residents in the Barcelona metropolitan region. Through 600 questionnaires responses from commuting travelers, they identified preferences based on factors such as age, regular commutes, and personal incomes. Findings revealed varied trends for intra-city and inter-city commutes, with younger demographics showing higher predicted use of ridesharing, carsharing, and ride-hailing services. Additionally, passengers tended to opt for the services that best suited their needs on each occasion rather than relying solely on one mode of transportation.
## 4 Conclusions  
We proposed the modulation format based spectral power allocation technique to mitigate the IMD noise and improve channel linearity in multi-IFoF transmission. For the 24-IFoF 10 km transmission system, the proposed technique obtained 7% of EVM improvement when 256 QAM was used, and also, the MBAPA technique suppressed IMD power around 10 dB. In addition, the system linearity was improved for a 48-IFoF 10 km transmission system and a 24-IFoF 10 km transmission system. The proposed technique in the reduction of IMD power effectively will be the key solution for the 5G MFN supporting multi-user.  
**Author Contributions:** Conceptualization, I.H.; Data curation, I.H.; Formal analysis, I.H. and H.-J.P.; Project administration, S.-K.H.; Writing--original draft, I.H.; Writing--review & editing, S.-M.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT; Ministry of Science and ICT) (No. NRF-2019R1A2C3007934).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Constellation of the received signal after 20 km transmission with 24-IFs (**a**) QPSK without the technique, (**b**) QPSK with the technique, (**c**) 256 QAM without the technique and (**d**) 256 QAM with the technique.  
Figure 12: Constellation of the received signal after 10 km transmission with 48-IFs (**a**) QPSK without the technique, (**b**) QPSK with the technique, (**c**) 256 QAM without the technique and (**d**) 256 QAM with the technique.
# AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding
## 4 AWMC Description
### User Interface  
The user interface of the AWMC system is shown in Figure 3. The example of the user interface is from 3 March to 8 May 2021 in Wonju.The figure shows that the weather anomaly score on 4 April was the highest in this time interval. Then, among changes in the relevant weather data below, vapor pressure and dew-point temperature had relatively high values on 4 April. At the same time, the size of the circles on the map also shows that Andong had the highest weather anomalies among all cities on 4 April.
# Scheduling Non-Preemptible Jobs to Minimize Peak Demand
## 1 Introduction  
The problem of scheduling non-preemptible jobs occurs naturally in smart power grid systems, in which communication can occur between energy consumers and the energy provider. Some household appliances require instant functionality (e.g., light bulbs), but many others have more flexibility in exactly when they operate (e.g., dishwasher or water heater). Peaks in power demand are proportionally more expensive to generate and provision for (since more infrastructure is required), so it is advantageous to schedule power-consuming jobs in such a way as to minimize peak demand. This problem has been previously formalized as the Peak demand minimization (PDM) problem, and has been studied extensively [1; 2; 3; 4; 5; 6; 7; 8]. The basic formulation of the problem is as follows: Each job \(j\) is non-preemptible, meaning once it begins execution, it must run to completion without any interruptions. Each job \(j\) is characterized by four parameters: an arrival time (\(a_{j}\)), deadline (\(d_{j}\)), and length (\(l_{j}\)) that are real-valued for continuous timescales and integer-valued for discrete timescales, and a real-valued instantaneous demand (\(h_{j}\)) which is conceptually the height of the job. The arrival time and deadline are within the fixed time interval \([0,T]\) and form the execution window of the job. Job \(j\) is scheduled by assigning it a start time, \(s_{j}\), which allows it to run in the closed interval \([s_{j},s_{j}+l_{j}]\) such that \([s_{j},s_{j}+l_{j}]\subseteq[a_{j},d_{j}]\). The demand at time \(t\) is the sum of the job heights that are scheduled to run during \(t\):  
\[H(t)=\sum_{j\in[s_{j},s_{j}+l_{j}]}h_{j}\]  
Then, the peak demand, \(H^{\text{max}}\), of the schedule is the maximum demand for any time \(t\) in \([0,T]\),  
\[H^{\text{max}}=\max_{t\in[0,T]}H(t)\]The PDM problem is to determine a job schedule, \(\mathcal{S}=\langle s_{j}\rangle\), that minimizes \(H^{\text{max}}\). In this work, we propose three algorithms to solve PDM:  
1. An optimal dynamic programming algorithm for discrete-timescale instances that utilizes branch-and-bound techniques that is fixed-parameter tractable.
2. A polynomial-time randomized algorithm based on linear programming that provides an \(O\big{(}\frac{\log n}{\log\log n}\big{)}\)-approximation, where \(n\) is the number of jobs, and is the first known approximation for PDM.
3. An effective and simple heuristic algorithm that can be used in either an online or offline fashion.  
The rest of this paper is organized as follows. We discuss related work in Section 2. Algorithms are presented in Section 3. Simulation results are presented in Section 4, and the paper is concluded in Section 5.
## 3 Collaborative Monitoring Algorithms for Situational Assessment
### Collaborative Traffic Trend Prediction Approach  
#### 3.2.1 Monitoring Data Pre-Processing  
Assuming that the road traffic situation is assessed for \(M\) indicators in the road network under an emergency event, the monitoring data of different indicators of each road need to be dimensioned and normalized to objectively reflect the correspondence between the actual value of the indicators and the evaluation value of the indicators. Since the measurement standards of each indicator are different, it is necessary to De-dimensionalize and normalize the data of each indicator to meet the consistency and comparability among different attributes of indicators. The \(d_{ij}\) in the original dataset \(D\) means the \(j\)th indicator value of road node \(v_{i}\). The positive indicator refers to the following: the larger the value of \(d_{ij}\), the better the evaluation. Additionally, the positive indicator is also called thebenefit-oriented indicator or the extremely large indicator. It is calculated as shown in Equation (16).  
\[d_{ij}^{*+}=\frac{d_{ij}-\min d_{ij}}{\max d_{ij}-\min_{ij}} \tag{16}\]  
The smaller the value of \(d_{ij}\), the better the evaluation of the class of indicators is a negative indicator. It is calculated as shown in Equation (17).  
\[d_{ij}^{*-}=\frac{\max_{ij}-d_{ij}}{\max_{ij}-\min_{ij}} \tag{17}\]  
Finally, all indicators after processing are in the range of 0-1. The normalized dataset \(D^{*}\) after collation is shown in Equation (18).  
\[D^{*}=\left[\begin{array}{ccc}d_{11}^{*}&\ldots&d_{1M}^{*}\\ d_{21}^{*}&\ldots&d_{2M}^{*}\\ \vdots&\ddots&\vdots\\ d_{N1}^{*}&\ldots&d_{NM}^{*}\end{array}\right]_{N\times M} \tag{18}\]
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 3 Materials and Methods
### Machine Learning and Deep Learning-Based Techniques
#### 3.7.1 Training and Testing Samples  
More than 200 training samples were selected by carefully reviewing the geological formation map (Figure 2) and field data. The 14 bands of ASTER and 7 bands of OLI were used to classify lithological units. The study area is 641 km\({}^{2}\) in size. The training dataset was meticulously chosen manually by taking into account the distributions and textural characteristics of the nine geological units shown in Figure 2. The MLC and SVM classifiers directly used the training datasets. Additionally, 110 randomly chosen ground truth locations from the research region were chosen as a testing dataset based on the geological map (Figure 2) and field data.
## 4 Conclusions and Prospects  
Both wetlands (CWs and FTWs) showed the potential for the remediation of phenanthrene-contaminated water. Between these two types of wetlands, the CWs were found to be more effective for the removal of phenanthrene from the water when compared with the FTWs. In addition, greater plant biomass and growth were observed in the CWs than in the FTWs. The augmented bacteria showed a persistence in the different components of the FTWs and CWs, which was relatively better in the CWs. However, further studies should be conducted focusing on the metabolic activities of the inoculated bacteria and the expression of their genes in different compartments of FTWs and CWs to completely understand and harness the phenanthrene degradation potential of these bacterial strains to further improve the treatment efficiency of wetlands. Moreover, different plant species are needed to develop wetlands to assess their phytoremediation potential and, most importantly, pilot-scale investigations are required. Nevertheless, the present study is a step forward to compare the efficacy of CWs and FTWs and to combine the use of plants and bacteria in wetlands for the maximum remediation of contaminated water.  
Conceptualization, S.I., M.A. ([PERSON]) and M.A.M.; methodology, S.Y. and E.I.; software, M.A. ([PERSON]); validation, E.J.J., M.A. ([PERSON]) and S.I.; formal analysis, S.Y.; investigation, I.A.; resources, M.A. ([PERSON]) and R.B.; data curation, S.Y. and E.I.; writing--original draft preparation, I.A.; writing--review and editing, M.A.M., M.A. ([PERSON]), R.B. and E.I.; visualization, E.J.J. and R.B.; supervision, M.A. ([PERSON]); project administration, M.A. ([PERSON]) and S.I.; funding acquisition, M.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Technology Transfer Support Fund of Higher Education Commission of Pakistan [TTSF-77] and The APC were waived off by the Publisher.  
Not applicable.  
The authors are highly obliged to Diamond Foam, Pakistan for the technical support.  
The authors declare no conflict of interest.  
Figure 9: Bioassay to compare the treated water of CWs and FTWs: (**A**) visual comparison of turbidity level of contaminated water treated in CTWs and FTWs; (**B**) _Bnssia napus_ seed germination in treated water of CWs and FTWs; (**C**) _Triticum aestivum_ seed germination in treated water of CWs and FTWs.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 4 Experiments
### Ablation Study
#### 4.6.4 Attention Modules in RHPNet  
The attention mechanism in deep learning is a methodology inspired by the human visual and cognitive systems. It allows neural networks to focus on relevant parts of the input data during processing. Via the attention mechanism, neural networks can autonomously learn and selectively attend to important information in the input, which improves model performance and generalization capabilities. After considering the advantages of the attention mechanism, we tried to add several excellent attention modules to different places in  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Combination** & **AP** & **AP\({}^{\textbf{M}}\)** & **AP\({}^{\textbf{L}}\)** \\ \hline \multirow{3}{*}{Running Human} & RFB + RFB & 94.6 & 88.7 & 94.9 \\ \cline{2-5}  & RFB-S + RFB-S & 94.3 & 90.2 & 94.5 \\ \cline{2-5}  & RFB-S + RFB & 95.7 & 92.5 & 95.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study with different combination of RFB modules applied to the Running Human val dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Data Augmentation** & **AP** & **AP\({}^{\textbf{M}}\)** & **AP\({}^{\textbf{L}}\)** \\ \hline \multirow{3}{*}{HRNet} & & 93.0 & 87.7 & 93.3 \\  & ✓ & 93.3 & 81.1 & 94.0 \\ \hline \multirow{3}{*}{RHPNet} & & 94.5 & 90.1 & 94.7 \\  & ✓ & 95.7 & 92.5 & 95.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study of HRNet and RHPNet showing results from before and after augmentation of the Running Human dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Sequence** & **AP** & **AP\({}^{\textbf{M}}\)** & **AP\({}^{\textbf{L}}\)** \\ \hline \multirow{3}{*}{Running Human} & RFB + RFB-S & 95.5 & 91.7 & 95.6 \\ \cline{2-5}  & RFB-S + RFB & 95.7 & 92.5 & 95.9 \\ \hline \multirow{3}{*}{COCO 2017} & RFB + RFB-S & 78.1 & 74.9 & 82.8 \\ \cline{2-5}  & RFB-S + RFB & 78.3 & 75.6 & 83.0 \\ \hline \multirow{2}{*}{**Dataset**} & **Sequence** & **Mean** & **[EMAIL_ADDRESS]** \\ \hline \multirow{3}{*}{MPII} & RFB + RFB-S & 91.9 & 43.8 \\ \cline{2-5}  & RFB-S + RFB & 92.0 & 44.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of the sequence of RFB modules in RHPNet applied to the Running Human val dataset, the COCO val2017 dataset and the MPII val dataset.  
the network, as illustrated in Figure 9. Additionally, we conducted ablation experiments on two datasets. However, the experimental results were unsatisfactory, as shown in Table 8.  
Our ablation experiments indicate that the attention mechanism may not effectively capture edge information when the edge is indistinct or when there is more noise in the input image. For instance, in the running posture images, many edge details are not clearly visible, and these details are also often occluded. This issue can lead to the attention module failing to properly attend to and emphasize these edge features, thus impacting overall performance.
# Activated Carbons as Effective Adsorbents of Non-Steroidal Anti-Inflammatory Drugs
## 3 Results
### Thermal Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{3}{c}{**TG**} & **DTG** & **DSC** \\ \cline{2-6}  & \(\Delta\)T [\({}^{\circ}\)C] & **Mass Loss [\%]** & **Total Mass Loss [\%]** & **T\({}_{\text{min}}\)** [\({}^{\circ}\)C]** & **endo/exo** \\ \hline \multirow{2}{*}{GAC} & 30–480 & 1.34 & 86.50 & - & - \\  & 480–930 & 85.16 & & 653 & exo \\ \hline \multirow{3}{*}{IBP/GAC} & 30–200 & 1.64 & 81.46 & 79.7 & endo \\  & 200–600 & 79.24 & & 274.1 & exo \\  & 200–600 & & & 454.7 & exo \\  & & & & 550.2 & exo \\  & 600–930 & 0.58 & & - & - \\ \hline \multirow{3}{*}{D/GAC} & 30–200 & 2.62 & 84.85 & 78.7 & endo \\  & 200–600 & 78.24 & & 252.7 & exo \\  & 600–930 & 3.99 & & 83.6 & exo \\ \hline \multirow{2}{*}{NPX/GAC} & 30–200 & 3.49 & 81.79 & 81.6 & endo \\  & 200–600 & 77.44 & & 266.6 & exo \\  & 600–930 & 0.86 & & - & - \\ \hline \end{tabular}
\end{table} 
Table 7: The thermal decomposition data of pure GAC activated carbon and after ibuprofen sodium, diclofenac sodium, and naproxen sodium adsorption. 30-200 \({}^{\circ}\)C temperature range (weight loss of 3.49%) and was endothermic, with a peak at 81.6 \({}^{\circ}\)C, which corresponded to the desorption of physically adsorbed water and the initial degradation of NPX. In the next stage, at the 200-600 \({}^{\circ}\)C temperature range, with weight loss of 77.44%, the exothermic processes of further decomposition of naproxen sodium, dehydration and degradation of GAC occurred. The peak maximum was observed at the temperatures of 266.6 and 516.6 \({}^{\circ}\)C, of which more intense was recorded at the highest temperature given. The last stage took place in the 600-930 \({}^{\circ}\)C temperature range, with weight losses of 0.86%, which can also be correlated with carbon combustion. In summary, a larger total mass loss was observed for pure GAC activated carbon compared to samples after pharmaceutical adsorption. At the same time, it should be noted that intensive decomposition of pure carbon material occurred at 653 \({}^{\circ}\)C. For the postadsorption samples, it occurred at 454.7, 516.6, and 583.6 \({}^{\circ}\)C for the system with ibuprofen sodium, naproxen sodium, and diclofenac sodium, respectively. This means that pure GAC activated carbon is more resistant to high temperatures than after the sorption of pharmaceuticals.
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 4 FPGA Implementation
### Phase Calculation and Approximation of the Inverse Tangent  
This part of the FPGA implements the algorithm for phase calculation through the inverse tangent approximation described in Section 2.4 like reported in Figure 11. The I and Q data present in the accumulators are the starting point that feed the \"Octant\" and \"Norm\" blocks. The previous block, like the name suggests, compares I and Q to find the octant like described in Table 4. The \"Norm\" block prepares the numerator, A, and denominator, B, for the 24-bit fixed-point divisor that follows. I and Q are left-shifted of \(j\) positions so that the maximum of them in absolute value fills the 24-bit dynamic. Then, the lower value is cut to the 12 MSB, further scaled by 212, and placed in the numerator A. The higher value is cut to 12 MSB, filled with 12 zeros on the left, and placed in the denominator B. This way leads to the calculation of the inverse tangent for even quadrants and inverse cotangent for odd quadrants, which always results in an angle less than 45\({}^{\circ}\). The shift has no effect on A/B but allows the exploitation of the full dynamics of the divisor; the 212 scaling applied to the numerator A is necessary for achieving an integer division. Table 6 summarizes the procedure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sign (I)** & **Sign (Q)** & \(|\mathbf{I}|_{\
u}\,|\mathbf{Q}|\) & **Numerator (A)** &
\begin{tabular}{c} **Denominator** \\ **(B)** \\ \end{tabular} \\ \hline + & + & \(|\mathbf{I}|\leq|\mathbf{Q}|\) & \(2^{|}\cdot|\mathbf{I}|\cdot 2^{12}\) & \(2^{|}\cdot|\mathbf{Q}|\) \\ - & + & \(|\mathbf{I}|\leq|\mathbf{Q}|\) & \(2^{|}\cdot|\mathbf{I}|\cdot 2^{12  
The output of the 24-bit divisor is processed by the \"Ph\" block that applies the simple operations reported in the last column of Table 4. Finally, the phase is ready to be read by the \(\upmu\)C.