esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Effect of Upstream Boundary Conditions on Lateral Migration  
Conceptually, the hydraulic geometry of alluvial rivers is closely related to stream characteristics that vary with time [21]. The previous n-year average water discharge and sediment concentration increasingly appear to be useful parameters that can be used to explain the delayed response of channel adjustments of rivers [5; 42; 37]. To investigate the effect of upstream boundary conditions on lateral migration in the Xiaobeiganliu reach, the relationships were established between the lateral migration index and the parameters of \(\overline{Q}_{n}\) and \(\overline{S}_{n}\). The correlation degree between \(M\) and \(\overline{Q}_{n}\) reached the maximum value at \(n=3\), and the correlation degree between the lateral migration index and \(\overline{S}_{n}\) reached the maximum value at \(n=4\). Figure 7a shows the relationship between the lateral migration index and \(\overline{Q}_{3}\), and it indicates that \(M\) increased with an increase in \(\overline{Q}_{3}\), with a correlation coefficient  
Figure 6: Spatial variations in section-scale thalweg migration distance along the Xiaobeiganliu reach.  
(R2) of 0.14 using a power function. The lateral migration index can also be expressed by a power function of \(\overline{S}_{4}\), and the correlation coefficient was approximately 0.23 (Figure 7b).  
Taking into account the factors of the previous n-year water discharge and sediment concentration, an empirical relationship between \(M\) and the two parameters was proposed, which can be expressed by:  
\[M=k\Big{(}\overline{Q}_{3}/100\Big{)}^{\alpha}\Big{(}\overline{S}_{4}\Big{)}^{\beta} \tag{4}\]  
The data of previous 3-year average water discharge, previous 4-year sediment concentration and lateral migration index in 1986-2001 were used to conduct a multiple nonlinear regression based on Equation (4). This regression analysis was conducted using the Statistical Product and Service Solution software (SPSS), which is widely used to perform statistical analysis in the areas of natural sciences and technical sciences. The results show that the calibrated parameters of \(k\), \(\alpha\), and \(\beta\) were equal to 0.003, 1.056, and 0.903, respectively. Figure 7c shows the relationship between the lateral migration index and the comprehensive factor of \(\big{(}\overline{Q}_{3}/100\big{)}^{1.056}\big{(}\overline{S}_{4}\big{)}^{0.903}\), with a higher correlation coefficient of 0.38. This result indicates that the upstream boundary conditions can affect the lateral migration in the Xiaolangdi reach, and the previous 3-year average water discharge and 4-year average sediment concentration were two key factors during this period.
## 2 Materials and Methods
### SIF Data Set  
The space-borne SIF observation offers the possibility to accurately quantify terrestrial ecosystem productivity. Recent research by [PERSON] et al. [26] and [PERSON] et al. 2018 [45] proved that the satellite-derived SIF retrievals from the NASA's Orbiting Carbon Observatory-2 (OCO-2) have become an alternative proxy for vegetation GPP across multiple spatial and temporal scales. Nevertheless, the tremendous applicable values were severely constrained by the spatially-sparse nature of OCO-2 data, particularly for regional and global scales. Thus, this study used a newly released global contiguous SIF product with the spatial resolution of 0.05 degree and temporal resolution of 8 days (GOSIF V2, [[http://globalecology.unh.edu](http://globalecology.unh.edu)]([http://globalecology.unh.edu](http://globalecology.unh.edu)), accessed on 20 October 2021). These SIF retrievals were derived from OCO-2, MERRA-2, and MODIS data sets and agreed well with the site-level GPP observations from more than 90 flux sites spanning various vegetation types. The GOSIF product exhibits amounts of advantages such as better spatial resolution, longer observation periods, and spatially continuous coverage in comparison to the coarse-resolution SIF product directly from discrete SIF signals. More descriptions of the features on the GOSIF data can be found in [PERSON] & [PERSON] [28]. Here, we used the GOSIF time series between 2003 and 2010 to extract the SIF retrievals at the two subtropical forest flux sites and explore its potential to capture the variability in site-level GPP.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 3 Results and Discussion
### Discussion  
This subsection summarizes the advantages of the proposed algorithm in this study compared to the existing works. In addition, the limitations of the proposed algorithm are presented.  
The receiver architecture proposed in this study is computationally efficient and can accommodate a wide instantaneous bandwidth, just like the receiver architecture proposed in Refs. [17, 32]. In addition, the receiver architecture proposed in this paper has three advantages over the Refs. [17, 32]. First, the proposed receiver architecture uses an adaptive threshold detection algorithm that does not require a priori information for signal detection. Second, the channelization sample rate can precisely match the prototype filter bandwidth due to adopting the WOLA-based channelization structure. Third, the estimation accuracy for the chirp signal is higher when the SNR is lower than 8 dB.  
Figure 11: The absolute frequency root-mean-square error (RMSE) of bandwidth and center frequency with different SNR: (**a**) the absolute frequency RMSE of bandwidth; (**b**) the absolute frequency RMSE of center frequency. (The blue line indicates the results of the Refs. [17, 32]; The red line indicates the results of the algorithm proposed in this paper).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Channelized Structures** & **Number of Multipliers** \\ \hline DDC structure [9] & \(NK(L+1)\) \\ polyphase filter structure [13] & \(N(2+L/K+\log_{2}K)\) \\ ours & \(N(1+L/K+\log_{2}K)\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The computational complexity of the three channelized structures.  
However, there is a limitation to the proposed algorithm. As shown in Figure 11, when the input SNR is below 0 dB, the performance of the proposed algorithm decreases. The principle of the algorithm determines this. This paper calculates the instantaneous frequency through the instantaneous phase, and it is computationally efficient and easy to implement. However, the instantaneous phase is heavily affected by noise. Although the algorithm adopted in this study can suppress the influence of noise to a certain extent, the estimation accuracy of the algorithm is limited when the noise is large enough. The algorithm proposed in this paper cannot meet the requirements for high-precision detection of ultra-low SNR signals.
# Personalized PPG Normalization Based on Subject Heartbeat in Resting State Condition
## 4 Signal Preprocessing: Denoising and Amplitude Normalization
### Amplitude Normalization  
In order to normalize the signals with respect to the amplitude, a Z-score operation, defined by the formula \(Z=(x-\mu)/\sigma\), was applied to the PPG recordings after the denoising procedure.  
In CLAWDAS, the amplitude normalization, (AmpN), as well as the denoising preprocessing were applied to the signal of each subject, before splitting it into the different experimental trials.  
A similar procedure was also applied to the CLAS dataset signals. However, in this case, the authors already split the data into single trials, according to their experimental protocol (see Section 3), with no preliminary amplitude normalization. Thus, in order to apply a similar procedure to both datasets, the segmented trials of each subject were concatenated to re-build the original acquired signal. Then, the Z-score amplitude normalization was applied to each subject signal. Finally, the amplitude normalized signals were  
Figure 2: Example of signal acquisition in the CLAWDAS dataset: (**left**) math calculation; (**right**) relaxing audio listening.  
split back into the trials, related to single tasks, using the markers properly defined during the previous phase of concatenation.
## 2 Related Work  
### Traditional Communication Method in CBTC System  
In the traditional CBTC system, WLAN is widely used in the communication between trains and the wayside base station. [PERSON] et al. [20] proposed a WLAN-based redundant connect scheme for train-ground communication. The train connects the backup link and active link simultaneously to deal with the interruption at the coverage boundaries of the two access nodes. However, many WLAN standards based on IEEE802.11 are not suitable for high-speed mobile environments [5]. Meanwhile, WLAN is in the open frequency band, which can easily be interfered with by other devices [21]. LTE has strong anti-interference ability and is more stable in switching between access nodes; thus, LTE-based approaches have been proposed in the CBTC system. In [6], a sensing-based semi-persistent scheduling method for LTE-based T2T communication was proposed, which greatly improved the transmission delay of system safety information. However, both LTE and WLAN have the problem of packet loss and delay due to the switch between the access nodes, in addition to the high cost of the base stations and ground core network. Since wireless ad hoc networks do not require a fixed infrastructure, their deployment is more flexible and cheaper. Therefore, ad hoc networks are also a better choice for T2T communication.
## 3 Preliminary Work
### Power Mileage  
To determine the power quality of a power grid, many different performance indexes have been proposed [20; 21; 22; 23]. In the adding lines problem, we needed a metric to indicate the voltage profile. Because our algorithm will iterate many times, a linear index with input is a reasonable choice. We defined the performance index and power mileage from the voltage drop equation.  
The definition of power mileage was deduced from the following:  
\[\Delta U_{i}=\frac{P_{i}\times R_{i}+Q_{i}\times X_{i}}{U_{0}} \tag{2}\]  
where \(\Delta U_{i}\) is the voltage drop in a branch, \(U_{o}\) is the normal voltage, \(P_{i}\) and \(Q_{i}\) are the active and reactive power flow through the branch, and \(R_{i}\) and \(X_{i}\) are the resistance and reactance of the line.  
For a distribution network in a microgrid, the material and per kilometer's parameters for the main line typically keep constant due to its relatively small size. To simplify the equation further, the resistance and reactance can be replaced by the resistance and reactance per kilometer (\(\rho_{p}\) and \(\rho_{Q}\)) multiplied by the length of a branch (\(l_{i}\)) so that the voltage drop between node \(n\) and node 0 can be written as  
\[\Delta U=\sum_{i=1}^{n}\Delta U_{i}=\frac{\rho_{p}}{U_{0}}\sum_{i=1}^{n}P_{i} \times l_{i}+\frac{\rho_{Q}}{U_{0}}\sum_{i=1}^{n}Q_{i}\times l_{i} \tag{3}\]  
where branch \(i\) is in the path from node 0 to node \(n\).  
Even if the resistance and reactance per kilometer are not constant in same networks, we can choose one as the base value and then modify the length of other branches to achieve the simplification in Equation (3).  
In a particular transmission line, the voltage drop in node \(n\) is a linear combination of \(\sum_{i=1}^{n}P_{i}\cdot l_{i}\) and \(\sum_{i=1}^{n}Q_{i}\cdot l_{i}\) so we called them the active power mileage and reactive power mileage. We calculated  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Explanation** \\ \hline \(G\) & it denotes a graph \\ \(PM_{p}\) & active power mileage \\ \(PM_{Q}\) & reactive power mileage \\ \(PM\) & total power mileage \\ \(P\) & power consumption matrix for the whole power grid \\ \(M\) & mileage matrix which denotes structure and the length of lines in a power grid \\ \(W\) & The edge weight matrix of a graph \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nomenclatures of the paper.  
the active and reactive power mileage of the whole grid (\(PM_{P}\) and \(PM_{Q}\)) with the method proposed in Section 3.3. Then, the performance index power mileage \(PM\) was defined:  
\[PM=\tau\times PM_{P}+(1-\tau)\times PM_{Q} \tag{4}\]  
where \(\tau\) is the weight denotes the relationship between resistance and reactance.  
As shown in the formulas, the power mileage has a linear relationship with the power condition so that it can be calculated with matrix. This formula indicates the voltage profile of a power grid and it can be used to optimize.
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 3 Methodology
### Framework Selection  
The selection of an appropriate framework is crucial for developing an efficient and scalable multi-agent system. The proposed system integrates JADE (Java Agent Development Framework) as the primary environment for agent creation and management. JADE facilitates distributed communication through the agent communication language (ACL), ensuring compliance with FIPA standards. This structured communication enables agents to collaborate seamlessly, making JADE an essential tool for developing complex agent-based systems. Additionally, its flexibility supports the creation of scalable and interoperable multi-agent frameworks, allowing for seamless integration across various governmental departments [18].  
Complementing JADE, Python integration for deep reinforcement learning (DRL) enhances the system's adaptability and intelligence. Python-based DRL implementations empower agents to continuously learn, optimize strategies, and adapt to changing environments. With access to extensive machine learning libraries such as TensorFlow 2.18.0 and PyTorch 2.6.0, agents can develop advanced learning capabilities, improving decision-making and operational efficiency. This integration bridges structured agent-based communication with intelligent learning models, creating a robust and adaptive framework for enhancing governmental interoperability [19].
## 4 Results and Discussion  
In this section, an analysis of the application potential of the DPCT in temperate climate conditions using the example of Poland is presented. The analysis is conducted for two examples: a gas power plant (Section 4.2) and an air conditioning system (Section 4.3).
## 5 Literature Review Summary
### Robot-Aided Rehabilitation of Upper Limb Motor Function
#### 5.2.2 Adaptability of Treatments  
The systems included in this review make available to the therapist different methods for adapting the treatment to the patient necessities. A wide variety of options for therapy customization are available for therapists. According to this adaptability, two categories of systems were identified. These systems are manually adapted and self-adaptive. The first category denotes for those systems that offer the clinician options to customize the therapy. For personalizing, the options are the selection among operation modes, exercises, or games (InMotion ARM(tm), MIME, AMADEO(r), BI-MANU TRACK, HWARD, ReoGo(tm), ADLER, L-EXOS, HEXORR, RUTGERS MASTER, WOTAS, ARMEO(r)POWER, GENTLE/G, MYOPRO, Mentor ProTM), tuning of robot parameters to modify the workspace (ARM-GUIDE), or the training intensity (ACT-3D, MEMOS, NEREBOT, RUPERT, WREX), and even creating new exercises (REHAROB). The second category denotes for those systems that automatically set the therapy parameters or the tasks, based on the user's performance. This one requires more advanced control and data analysis capabilities. This approach is covered by systems such as BRACCIO DI FERRO to fit the loading force, GENTLE/A to modify the task duration, DIEGO(r) and ARMEO(r)SPRING to adapt the support level, and iPAM to vary the movement range.  
A well-known feature of robotic systems, in general, is the capability to perform repetitive and controlled movements, keeping a high level of accuracy. Such a feature is also beneficial in the particular case of robot-aided systems for rehabilitation. Nevertheless, control strategies regarding the automatic modeling of the patient-robot interaction or the autonomy of therapy have not been fully explored. Note that in the manually-adapted systems, the customization depends on the clinician criteria, and thereby, an autonomous and unsupervised therapy would be not possible. In this regard, cutting-edge systems are considering new strategies for allowing cooperative treatments (DIEGO(R)) or even self-directed therapies (ARMEO(SPRING). Noteworthy as well is the RECUPERA system [131], a lightweight dual-arm exoskeleton with a high level of modularity (the system can be used as a wheel chair-mounted system or as a full-body system) and multi-therapy options. This system is promising for enabling mobile support, as well as for self-training during a hospital stay or at home. Overall, it can be seen that a trend towards more autonomous robot-based recovery procedures is arising.
## 5 Numerical Examples
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\(N\)R} & **0** & **5** & **10** & **15** & **20** & **25** & **30** & **35** & **40** & **45** \\ \hline Best weight (lb) & 74.434 & 73.019 & 72.963 & **72.348** & 72.641 & 73.275 & 73.842 & 74.881 & 77.250 & 75.932 \\ Mean (lb) & 77.031 & 77.735 & 76.835 & **76.024** & 76.088 & 77.136 & 77.410 & 77.654 & 78.890 & 80.458 \\ Std (lb) & 2.049 & 2.578 & 2.359 & **1.805** & 2.183 & 2.700 & 1.812 & 1.575 & 1.091 & 2.822 \\ Mean + Std (lb) & 79.080 & 80.313 & 79.194 & **77.829** & 78.271 & 79.836 & 79.222 & 79.229 & 79.982 & 83.280 \\ \hline \hline \end{tabular}  
\end{table} 
Table 3: Sensitivity analyses to find the optimal NR value in CSBO for a 15-bar truss. Figure 1: Statistical findings from the sensitivity analysis conducted for different Npop values: (**a**) CSBO and (**b**) NCSBO. Figure 2: Statistical findings from the sensitivity analysis conducted for different \(N\)\(R\) values. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\mathit{MP}\) & **0** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** & **1** \\ \hline Best weight (lb) & 73.013 & 73.544 & 73.996 & **72.959** & 72.681 & 72.768 & 73.053 & 73.880 & 73.220 & 73.558 & 72.933 \\ Mean (lb) & 77.063 & 76.808 & 77.484 & **75.975** & 76.456 & 76.578 & 76.389 & 77.562 & 76.179 & 75.622 & 76.489 \\ Std (lb) & 2.331 & 2.281 & 1.731 & **2.294** & 2.850 & 2.055 & 2.073 & 1.956 & 2.031 & 2.065 & 2.403 \\ Mean + Std (lb) & 79.394 & 79.089 & 79.215 & **78.270** & 79.306 & 78.633 & 78.463 & 79.519 & 78.210 & 77.687 & 78.892 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Sensitivity analyses to find the optimal \(\mathit{MP}\) value in NCSBO for a 15-bar truss.
## 3 Results and Discussion
### Contour Plots (2D) and Response Surface Plots (3D)
#### 3.4.3 UV 254 Removal  
The interaction between the dose of PSE and pH is shown in Figure 7a,b. It can be observed that when the minimal dose is maintained at neutral and basic pH levels, the removal of UV 254 reaches its maximum. However, at these pH levels, using high doses of PSE in water treatment has the drawback of increasing the organic load in the treated water and removing fewer compounds absorbing at 254 nm compared to other parameters such as COD, turbidity, and phosphate. In combination, pH and dose were statistically significant, as shown in Equation (4).  
Figure 7c,d shows that UV 254 removal is maximized at the high pH of 10 (basic) and minimized at a FeCl\({}_{3}\) dosage of 1 mL/L. This could be due to the fact that at low pH levels (4 to 7), lower doses of the chemical coagulant are sufficient to initiate charge neutralization and precipitation [46]. Additionally, at the highest FeCl\({}_{3}\) dosage (10 mL/L) and the lowest pH (4, acidic), the residual UV 254 is significantly reduced. This may be attributed to charge reversal and restabilization occurring at higher coagulant dosages [46]. Furthermore, the dose of PSE exhibits a positive synergistic effect when combined with pH and the chemical coagulant FeCl\({}_{3}\). This is confirmed by the positive terms in the model equation, as seen in Equation (4).  
By analyzing the residual values in Figure 7e,f, it can be seen that using a lower dose of PSE in combination with the chemical coagulant FeCl\({}_{3}\) (across all studied regions) resulted in a maximum reduction in UV 254 at a neutral pH (7). This behavior is mainly attributed to the fact that the combination of FeCl\({}_{3}\) and PSE at a lower dose may create a synergistic effect, enhancing the coagulation process and minimizing the presence of residual organic compounds that absorb UV at 254 nm, thereby leading to a higher UV 254 reduction.  
Figure 7: Cont.
## 5 Simulations Results and Discussions
### Case Study 2: Home Is Equipped with DGs
#### 5.2.4 User Comfort  
Appliances' scheduling by our proposed system model consisting of a GWDO-based ECS is evaluated and compared with the existing algorithms' ECSs, which are GA, PSO, BFA, ACO, WDO, GPSO, GBFA, and GACO. The user comfort is the delay appliances face while users operate with DGs, as shown in Figure 12. The overall delays in the operation of each appliance are shown in Figure 12, where the water heater faced the highest delay by using ACO, which is 1.4 h, and zero delay time for the cases of the GA, BFA, and GPSO. However, GWDO gave a delay time of 0.5 h for the water heater. In the case of the dishwasher, the WDO and BFA algorithms gave a delay of zero, and the highest delay was recorded using the GA algorithm. In the case of the clothes dryer, our proposed GWDO-based ECS gave the lowest delay, which is 1.1 h after the GBFA algorithm, and the highest delay faced by the clothes dryer is in the case of using GA-based ECS, which is 1.6. In the case of the iron, the lowest delay faced is by using the GWDO-based ECS, and the highest delay is recorded by using GA, PSO, and GBFA. In the case of EV, the overall delay faced by EV in the case of all techniques is 1 h, where GA gave the highest, and our proposed GWDO gave a delay of 1 h. Furthermore, our proposed framework based on the GWDO algorithm notably minimized the average delay of all the user's appliances compared to the parent algorithms, which are the GA and WDO algorithms. Thus, the GWDO-based ECS effectively increased user comfort compared to all the other benchmark algorithms for all appliances combined.  
The developed model is compared with existing models in aspects such as convergence speed, computational complexity, cost, carbon emission, and PADR. The obtained results are listed in Table 4. From the above discussion and evaluation, it is concluded that the developed model outperforms existing models and is suitable for solving EM problems from different perspectives.  
Figure 11: Case study 2: Carbon emission evaluation.