esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Baseline Comparison with End-to-End DDPG  
This subsection will present and discuss the results of the comparison experiment between end-to-end DDPG and baseline DDPG.  
In the experiment, baseline DDPG [39] was trained on trajectories 1, 2, and 3, described in Section 4, and named BS1, BS2, and BS3, respectively. These three segments were sequentially concatenated to form BS0, with its experimental results determined solely using the data from BS1, BS2, and BS3, without independent experiments. Subsequently, the end-to-end DDPG, described in Section 2, was used to train on trajectories 1, 2, and 3, named ETE1, ETE2, and ETE3, respectively. Finally, end-to-end DDPG was used for comprehensive training on trajectories 1, 2, and 3, named ETE0. The parameters for the aforementioned DDPG are provided in Table 5.  
In our study, we conducted a quantitative analysis of success rates and collision rates. Specifically, two algorithm models, each trained through 400 k iterations, were applied to different trajectories within the same scenario. During the experimental process, we conducted 2000 experiments in a simulated environment using the reward function defined using Equation (2). The success rate was calculated based on whether the robotic arm reached the target point within each episode, i.e., an error distance of less than 10 mm, and the percentage of all experimental cycles in which the target was achieved. The collision rate was determined using the percentage of experimental cycles in which the robotic arm collided with the surrounding environment.  
As shown in Figure 9, within the same algorithm, trajectory 2 required less time, and exhibited better success and collision rates compared to the other two trajectories, indicating its relative simplicity. This finding was corroborated by the description in Section 4. Notably, the time consumption for BS0 in the table is the sum of the time taken for the three baseline segments, with the success rate and collision rate being the average of these three baseline performances. In test scenarios, our end-to-end DRL model achieved a success rate of 90.1% and a collision rate of only 5.7%. Despite a slight increase in training time, the end-to-end DDPG showed significant advantages in terms of task success rate and collision reduction compared to the baseline model. This can be attributed to the deep network's precise mapping of state-action relationships and efficient execution of more complex tasks. Next, we will comprehensively assess the adaptability of the algorithm model to environmental changes in complex environments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Motion space actions & 6 \\ Training rounds episodes & 1000 \\ Maximum steps & 2000 \\ Learning rate & 0.003 \\ Discount factor \(\gamma\) & 0.99 \\ Exploring factor \(\epsilon\) & 0.9 \\ Soft update factor \(\tau\) & 0.005 \\ Batch size & 64 \\ Explore noise & OU noise (\(\mu^{\omega u}\) is 0, and \(\theta^{\omega u}\) 0.2) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter settings of DDPG in the experiment.
###### Abstract  
In the field of the automotive area as well as industrial control, real-time communication requires deterministic delivery with low delay and bounded jitter. Real-time communication in these networks requires transmission schedule and routing, which is an NP-hard problem. In this paper, we present an offline routing and scheduling method based on integer linear programming (ILP), with a flow preprocessing step to explore the period correlation of time-triggered (TT) traffic in time-sensitive networking (TSN). First, a multiperiod flow routing and scheduling algorithm based on flow classification is proposed to improve the scheduling success rate and reduce execution time. The flow classification technique obtained a more fine-grained TT traffic classification, which can be superimposed on any routing and scheduling algorithms. Second, an adaptive period compensation scheduling algorithm based on flow classification is proposed in simple network architecture conditions. The evaluations demonstrate that the proposed algorithms improve scheduling success rate and reduce execution time compared with baseline methods in all test cases. In addition, we can adapt our different proposed algorithms in different network architecture conditions to schedule various flows with different periods and sizes.  
time-sensitive network (TSN); integer linear programming (ILP); routing and scheduling algorithm; flow classification +
Footnote †: journal: Applied Sciences
## Materials and Methods
### VPM GPP Product  
The VPM GPP product (V20) is based on an improved light-use efficiency model driven by remote sensing data and climate data from National Centers for Environmental Prediction (NCEP) Reanalysis II [25]. Two main input data sets, including the enhanced vegetation index (EVI) [51] and land surface water index (LSWI) [52], in the VPM model are derived from the MOD09A1 (C6) land surface reflectance data with a resolution of 500 m on an 8-day time scale. Additionally, poor quality data were identified, gaps were filled, and the data were smoothed using the best index slope extraction algorithm, linear interpolation, and the Savitzky-Golay filter. Furthermore, it considered the C\({}_{3}\)/C\({}_{4}\) difference in plant photosynthesis pathways [28], which solved several critical problems in the previous GPP products. In this study, the VPM GPP products between 2001 and 2015 were used as auxiliary data with a temporal resolution of 8-day and spatial resolution of 0.05\({}^{\circ}\times 0.05^{\circ}\). These data were downloaded from the website [[https://doi.org/10.6084/m9.figshare.c.3789814](https://doi.org/10.6084/m9.figshare.c.3789814)]([https://doi.org/10.6084/m9.figshare.c.3789814](https://doi.org/10.6084/m9.figshare.c.3789814)). The algorithm can be simplified as follows [53]:  
\[\text{GPP}=\varepsilon\times\text{FPAR}_{chl}\times\text{PAR} \tag{4}\]  
\[\epsilon=\varepsilon_{max}\times\text{T}_{scalar}\times\text{W}_{scalar} \tag{5}\]  
\[\text{FPAR}_{chl}=(\text{EVI}-0.1)\times 1.25 \tag{6}\]  
\[\text{W}_{scalar}{=\frac{1+LSWI}{1+LSWI_{max}}} \tag{7}\]  
\[\text{T}_{scalar}=\frac{(T-\text{T}_{max})\times(T-\text{T}_{min})}{(T-\text{ T}_{max})\times(T-\text{T}_{min})-\left(T-\text{T}_{opt}\right)} \tag{8}\]  
where PAR is photosynthetically active radiation; \(\text{FPAR}_{chl}\) is the fraction of PAR absorbed by chlorophyll, which is approximated by a linear function of EVI [23]; and \(\text{T}_{scalar}\) and \(\text{W}_{scalar}\) represent the effects of temperature and water on light use efficiency of vegetation, respectively [25]. The adjustment coefficient, LSWI is the land surface water index; LSWI\({}_{max}\) is the maximum land surface water index during the growth period; \(T\), \(\text{T}_{max}\), \(\text{T}_{min}\), and \(\text{T}_{opt}\) are the average, the maximum, the minimum, and the optimum temperature for specific plant types.
## 4 Methodology
### Framework of Analysis  
The framework in Figure 2 incorporates users' subjective perceptions to overcome the shortcomings of an entirely objective assessment. The proposed approach effectively includes the subjective impressions and perceptions of PWD in addition to objective parameters like length, slope, absence of a footpath, and number of junctions. This approach entailed gathering data via survey to obtain users' subjective preferences for the chosen criteria. This technique seeks to offer a more thorough and precise evaluation of the accessibility of urban areas by integrating both objective and subjective factors.  
Consequently, the obtained information was used to make informed choices regarding urban design and infrastructure to enhance the accessibility of impaired individuals. The suggested methodology compared different routes depending on multiple criteria; for instance, a footpath may be considered the best option according to one criterion but the worst option according to another criterion. The method evaluates various factors rather than just one, helping to find the best routes that are not only the shortest or fastest but also the safest and most accessible. MCDM estimated the importance weights of the criteria and determined the most appropriate routes for PWD on each route by calculating the Accessibility Index (ACI) based on PWD preferences asked about in a survey. The fuzzy-based MCDM approach effectively dealt with the vagueness of users' opinions and the overall subjective nature of the issue. In addition, some parameters for the footpath's accessibility for wheelchair users were also investigated.  
The rationale for selecting this approach is as follows. The method accounts for important factors that significantly impact the mobility of PWD, such as the slope, the absence of footpaths, and the number of junctions, making it more suitable for identifying  
Figure 2: Flowchart of the development of a framework for evaluating routes.  
routes that provide easy access and navigation for them. Secondly, the method integrates objective data and user feedback, which helps create a complete and accurate understanding of the challenges they encounter. Thirdly, MCDM evaluates multiple factors, helping to find the best routes that are not only the shortest or fastest but also the safest and most accessible for PWD. The application of Geographic Information Systems (GIS) analyzes data more precisely, leading to reliable results that can improve urban planning and infrastructure design.
## 4 Performance Analysis of the Generated Models in Virtual Environments
### Performance Analysis of Models
#### 4.2.4 Application GPU Time (App T)  
This metric helps determine if an application is potentially GPU-bound, as it collects the amount of time the application takes to render a single frame. If the time measured in the metric is longer than the length of a frame (13.88 ms for 72 frames per second), the application can be considered GPU-bound. Meta's documentation provides a simple rule to indicate when an application is limited by the CPU or GPU. If App T > 13.88 ms and FPS < 72, then there is a 100% bottleneck caused by the GPU (it could also be happening in the CPU but is masked by the GPU), if App T < 13.88 and FPS < 72 then there is a bottleneckcaused by the CPU. This rule was used to develop the charts shown in Figure 10, showing the frames where bottlenecks occur or not, and of what type (CPU or GPU) if they occur, for the data obtained in the Meta Quest 3.  
As can be seen in Table 5, for scenes with LP models, in the Quest 2 and Pro, they begin to need more time than necessary to render a frame smoothly, starting from scenes with 48 objects, albeit with low percentages. However, in the scene with 96 objects, they obtained percentages of 48 and 71% in the Quest 2 and Pro, respectively, while in the Quest 3, it was 31%. These are moderately high percentages, since, taking the best performance obtained in the Quest 3 as a case, nearly a third of the total execution time, the application could have been limited by the GPU.  
Considering scenes with MP objects, the polygonal load begins to affect more severely, reducing the GPU's working capacity. In any of the three devices, at least one of the scenes reached a percentage close to 60%, representing a high percentage of the time in which the GPU was a bottleneck due to the work needed to render. Finally, we can see how, in scenes with HP objects, the Quest 3 presents an extremely high percentage, while the other two headsets present much lower percentages, even lower than in the scenes with MP models. This could be due to the main cause of poor performance in these headsets being a bottleneck in the CPU due to the low percentages of GPU usage time by the application.
## 3 Theoretical Analysis
### Algorithm Flow
Even for the 30 dB signal with weak noise intensity, the detection results are not reliable due to large errors in the amplitude and phase angle. For the harmonic detection results of the four groups of noisy signals, the proposed algorithm is able to fit closely to the parameters of the simulation signals. By computing the dominant factor of each frequency component in the detection results, it can be noted that the dominant factors of the frequency components close to the simulation signal parameters are far greater than zero, indicating their dominant roles in the signal, whereas those of other frequency components are roughly zero. Combined with dominant factors and Equation (20), the detection results of the proposed method are screened, and the final harmonic detection results and errors of the method are obtained in Figure 4.0

It can be seen from Table 2 that the amplitude, frequency and phase of the data results screened by dominant factors are close to respective parameters in the simulation signal, with very small errors. The final harmonic detection results of the four groups of noisy signals are largely consistent with the actual values, where the detection errors in amplitude and phase are mostly within 0.5%. The detected frequencies are particularly accurate, with errors mostly within 0.01%. These results show that the proposed method achieves effective and stable denoising for the four groups of noisy signals with different noise intensities, overcoming the noise sensitivity problem of the Prony algorithm. The proposed method is able to select the non-false components of fundamental, harmonic and interharmonic signals accurately through the dominant factor algorithm. ## 5 Conclusions

In the context of harmonic and interharmonic detection in power systems, this paper proposes a harmonic and interharmonic detection method based on enhanced SVD denoising and the Prony algorithm. It addresses the Prony algorithm shortcomings, namely its fitting accuracy being highly susceptible to noise and fitting results being difficult to be screened. The proposed adaptive order determination algorithm identifies the optimal order of SVD denoising, thereby mitigating the noise sensitivity problem of the Prony algorithm and achieving optimal removal of noise signal with boosted SVD denoising capability. Moreover, the dominant factor algorithm is proposed to solve the problem of the mixing of false components representing noise and non-false components representing useful signals in the fitting result of the Prony algorithm. Through the proposed dominant factor algorithm, the non-false components can be quickly screened according to the dominant factor. Simulation results show that four groups of noisy signals with SNRs of 5 dB, 10 dB, 20 dB and 30 dB, respectively, are improved by about 20 dB on average upon denoising. These improvements suggest that the proposed method is able to effectively remove the noise components and successfully overcome the Prony algorithm's poor accu
# Super-Resolution Multicomponent Joint-Interferometric Fabry-Perot-Based Technique
## 4 Results and Discussion
### Comparison and Analysis
Therefore, the MJI-HI is suitable for low-magnification interferometric spectroscopy super-resolution and has the advantages of high data utilization, high stability, and low cost.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System  
Several factors, including the feed characteristics, membrane and module properties (e.g., fiber length, diameter, and looseness), and hydrodynamic properties (e.g., flowrates), collectively affect the performance of submerged HF systems.
# Optimization of DC Energy Storage in Tokamak Poloidal Coils
## 6 Complementarity in Power Demands of PF Coils
### Equivalent Parameters for Shared DC Links  
An observation of the peaks in Figures 7 and 8 allows the introduction of a new global equivalent time, \(T_{E/P}\), based on these peaks:  
\[T_{E/P}\stackrel{{\text{def}}}{{=}}\frac{\max_{t}\Bigl{\{}\sum_{n= 1}^{6}e_{sn}(t)\Bigr{\}}}{\max_{t}\Bigl{\{}\sum_{n=1}^{6}p_{n}(t)\Bigr{\}}} \cong 5\text{ s} \tag{15}\]  
This time can be compared with the durations in the Ragone plot, as shown in Figure 4, showing again, in a different way, that SCs are the perfect solution to optimize the DTT PF system.  
Figure 9: Example of complementarity in the powers delivered to the PF coils in the JT-60 SA reference scenario [55] (names of coils are modified to coincide with the DTT ones).  
Formulas (12) and (13) can be extended to a DC link with a capacitance \(C\) shared among coil PS circuits, each having its own \(v_{n}(t)\) and \(e_{n}(t)\) but the same \(V_{\text{max}}\):  
\[C>\max_{t}\left\{\frac{2\sum_{n}e_{n}(t)}{V_{\text{max}}{}^{2}-\max \left\{v_{n}(t)^{2}\right\}}\right\} \tag{16}\]  
\[E_{\text{DC}}>\max_{t}\left\{\frac{\sum_{n}e_{n}(t)}{1-\max_{n} \left\{\left[\frac{v_{n}(t)}{V_{\text{max}}}\right]^{2}\right\}}\right\} \tag{17}\]  
These formulas seem complex but can be easily implemented in software simulations.  
The performances achieved by a shared DC link having an energy \(E_{\text{DC}}\) can be quantified by introducing new definitions of efficiency. The first one extends (7) to the global efficiency of the shared DC link by comparing it with the total energy of all the shared circuits:  
\[\eta_{\text{DC}}\stackrel{{\text{def}}}{{=}}\frac{\sum_{n}E_{ \text{SR}}}{E_{\text{DC}}} \tag{18}\]  
Alternatively, the sharing process between the PSs of different coils, each with bank energy \(E_{\text{DC}n}\), could be assessed by the following ratio:  
\[\eta_{\text{sharing}}\stackrel{{\text{def}}}{{=}}1-\frac{E_{ \text{DC}}}{\sum_{n}E_{\text{DC}n}} \tag{19}\]  
In both these definitions, the efficiencies may exceed 100%.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 3 Background
### Private Charging Infrastructure  
Private charging infrastructure is used to charge EVs either at home or at workplaces using slow alternating current (AC) chargers. For example, an individual can arrange the setup for chargers, or multi-residential charging can be put in place, which can be considered under the public charging [45]. EVs are equipped with on-board chargers for charging vehicles with AC (Level 1 and Level 2) slow charging by converting the power generated into the direct current (DC) that can be transferred to the battery of the EVs. AC slow charging is mostly considered when users want to travel a shorter distance, and vehicles can be charged at home overnight or at a workplace [46]. However, the charging time using AC charging tends to be higher than DC fast charging due to the limited power exchange between the vehicle and electricity grid. AC charging is further described in the next section in greater detail. If a user can set up private charging infrastructure at home, it may reduce the time spent traveling in searching for a public CS. However, it requires the installation of an electricity grid and charging connectors at home, which incur lower costs than public charging stations [47].  
Moreover, slow charging can sometimes be a disadvantage in an emergency situation or when fast charging is required. Therefore, to address and resolve the issues of residential or private EV charging infrastructure, [PERSON] et al. [48] implemented a simulation model for two countries, i.e., Switzerland and Finland, to analyze the benefit of home charging which provides freedom for users to charge their vehicles fully before traveling to their destination. [PERSON] et al. [35] analyzed the variation in residential or home parking availability in china by considering the essential aspects such as the resources required for charging, rules, or regulations that need to be followed for efficient charging and the required cost of the infrastructure.
## 3 Results and Discussion
### Pressure Affective Analysis  
The printing pressure was assigned as 1 bar, 1.5 bar, 2.5 bar, 3, bar, 3.5 bar, and 4 bar, respectively. The total mass of the composite solution was measured within 1 min, and then divided by the density and time of the solution. The relationship between flow rate and pressure was obtained, as shown in Figure 7. The flow rate increases exponentially with the increase of air pressure, indicating that the flow coefficient n is not 1, and the composite material is proved to be a non-Newtonian pseudoplastic fluid. The experimental data fit the theoretical value curve well, which proves the necessity of considering the slip effect, and also shows that the flow forecasting model is correct. The experimental results under the conditions of 1.5 bar, 1.8 bar, 2.1 bar and 2.4 bar show that the extrusion diameter at 1.8 bar pressure is less affected by the extrusion swell and the continuous forming ability is stronger, as shown in Figure 8.  
Figure 7: The relationship between the flow and pressure.
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 3 Proposed Scheme
### Authentication Phase  
Figure 2 shows the authentication phase of the proposed scheme. The steps are detailed as follows.  
**V1.** MU\(\rightarrow\)FA: mV1[A, U]  
The U inserts the smart card into the card reader and enters IDMU and PWMU. Then, the smart card chooses a random number x and calculates X = xP, X1 = xPHA, ZP = [zi\(\oplus\)H2(PWMU\(\oplus\)rn)\(\oplus\)H3(Wi)]P, IND = IDMU\(\oplus\)H1(X1 1 1 TMU), c1 = H(X1 1 1 Z), and A = aP, where TMU is the MU's timestamp. Subsequently, the MU calculates a shared key EC = aY = ayP, the message U = (Wi, X, IND, c1, IDHA, TMU)EC, and sends an authentication request message {A, U} to the FA.  
**V2.** FA\(\rightarrow\)HA: mV2[Wi, X, IND, c1, Y, TMU, TFA, MACFA]  
The FA calculates a shared key yA = ayP = EC and decrypts U by using EC to obtain Wi, X, IND, c1, Y, and TMU. Then, the FA checks the validity of the timestamp TMU. If it holds, the FA chooses a random number y and calculates Y = yP and MACFA = H1(Wi 1 1 1 IND 1 1 1 1 1 TMU 1 1 TMFA 1 1 SKHF), where TFA is the FA's current timestamp and SKHF is the session key between the HA and the FA. This key is mainly used for signature verification. The FA then sends the message {Wi, X, IND, c1, Y, TMU, TFA, MACFA} to the HA.  
**V3.** HA\(\rightarrow\)FA: mV3[MACHA, MAC'HA=MACHA\(\oplus\)H1(THA 1 1 SKHF), THA]  
The HA checks the validity of the timestamp TFA. If it is valid, the HA checks if the calculated value MAC'FA = H1(Wi 1 1 1 IND 1 1 1 IND 1 1 1 TMU 1 1 TMFA 1 1 SKHF) is the same as the received MACFA. If it is not valid, the HA terminates the execution. Otherwise, the HA calculates 1 = XN, IDMU\(\oplus\)H1(X1 1 1 TMU), and c1 = H1(X'1 1 1 H1(IDMU 1 IN 1 1 Wj)) and checks whether the equation c1 = c1 holds. If it is not valid, the HA terminates the execution. Otherwise, the HA calculates MACHA = H1(Wi 1 1 1 1 1 TDFA 1 1 1 DHA 1 1 TMFA 1 1 SKHF), where THA is the HA's current timestamp. The HA then sends the message {MACHA, MAC'HA = MACHA\(\oplus\)H1(THA 1 1 SKHF), THA} to the FA.  
**V4.** FA\(\rightarrow\)MU: mV4[c2 = (TCertMU)sk]  
The HA checks the validity of the timestamp THA. If it is valid, the FA calculates MACHA = H1(Wi 1 1 1 1 1 1 TDFA 1 1 TDHA 1 1 SKHF) and verifies whether this value is the same as the received MACHA. If it is not valid, the FA terminates the execution. Otherwise, the FA believes that the MU is an authenticated user. The FA then calculates MACHA = MAC'HA\(\oplus\)H1(THA 1 1 SKHF), Y = yP, and sk = yX = xyP and sends the message {c2 = (TCertMU)sk} to the MU, where TCertMU is a temporary certificate from the FA to the MU.  
**V5.** MU: Y, c2 = (TCertMU)sk  
The MU calculates a session key ssk' = xY = xyP = sk and decrypts c2. Finally, the MU obtains TCertMU from the FA.  
Figure 1: Registration phase.