esa-sceva/satcom-chunk-collection huggingface dataset:
# Cryogenic System for FTIR Analysis of Hydrocarbon Fuels at Low Temperature and Atmospheric Pressure
## 2 Materials and Methods
### Methods of Preliminary Results Processing  
In addition, spectral acquisition in practical conditions is often subject to environmental and instrumental errors that introduce noise and interference [30; 31]. Traditional chemometric methods require pre-processing steps, such as noise removal, to improve the signal-to-noise ratio. In this study, to demonstrate the capabilities of the developed cryogenic measurement system, we evaluated its performance by recording and analyzing the IR spectra of hydrocarbon samples (kerosene, methanol, ethanol) at temperatures ranging from 80 K to 100 K. OriginPro 9.9 software was used for pre-processing of the spectral data, which included intensity normalization of the spectra (baseline correction and area normalization), smoothing (Savitzky-Golay filter), and analysis of absorption band range. Thus, the recorded IR spectra of hydrocarbon compounds were processed using OriginPro to ensure accuracy and comparability under all temperature conditions. The following pre-processing steps were performed:  
Baseline correction: A polynomial fitting algorithm (order 3-5) was used to remove background drift and scattering effects caused by the instrument optics and sample surface roughness.  
Normalization: The spectra were normalized to the unit area under the curve (area normalization) to allow comparison of relative band intensities and to minimize the influence of sample amount or surface morphology.  
Smoothing: A Savitzky-Golay filter with a window size of 11 points and a second-order polynomial was applied to reduce high-frequency noise without distorting the peak shapes.
## 3 Trajectory Optimization Method Based on Improved Twin Delayed Deep Deterministic Policy Gradient
### Improved Twin Delayed Deep Deterministic Policy Gradient Algorithm
\tag{12}\]  
```
1:Initial estimated critic network parameters \(\theta^{\text{Q1}}\),\(\theta^{\text{Q2}}\), and estimated actor network parameter \(\theta^{\mu}\)
2:Initial target networks parameters \(\theta^{\text{Q1}^{\prime}}\leftarrow\theta^{\text{Q1}}\)\(\theta^{\text{Q2}^{\prime}}\leftarrow\theta^{\text{Q2}}\)
3:Set initial values of hyper-parameters according to the task requirements: experience playback buffer pool \(B\), mini-batch size \(n\), actor network learning rate \(l_{a}\), critic network learning rate \(l_{c}\), maximum episode \(E\), soft update rate \(\tau\)
4:for\(t=1\) to \(T\)do
5: Select action \(a_{t}=\mu(S_{t}|\theta^{\mu})\)
6: According to pre-evaluated reward value of action \(a_{t}\), select the size of noise variance \(\sigma\), and add noise \(N\) to new action \(a_{t}\sim clip(N(\mu(S_{t}|\theta^{\mu}),\sigma^{2}),a_{low},a_{high})\) with noise, and observe reward \(r_{t}\) in current state and new state \(S_{t+1}\)
7: Store transition tuple \((S_{t},a_{t},r_{t},S_{t+1})\) of this step in \(B\)
8: Sample mini-batch of \(n\) transactions \((S_{i},a_{i},r_{i},S_{i+1})\) from \(B\)
9: Compute target actions \(a_{t}^{\prime}\)=\(\mu^{\prime}(S_{i+1}|\theta^{\mu^{\prime}})+clip(N(0,\sigma),-c,c)\)
10: Compute Q-targets \(y_{i}=r_{i}+\gamma\min\limits_{j=1,2}Q^{\prime}(S_{i+1},a_{i}^{\prime}|\theta^ {\mathcal{Q}j})\)
11: Update estimated network parameters of the critic by minimizing loss: \(L=\frac{1}{n}\sum\limits_{i}^{n}{(y_{i}-Q(S_{i},a_{i}|\theta^{\text{Q}}))^{2}}\)
12:if\(t\) mod \(d\)then
13: Update the actor policy using sampled policy gradient:
14:\(\
abla_{\theta^{\mu}}J=\frac{1}{n}\sum\limits_{i}^{n}{\
abla_{a}Q(S,a|\theta^ {\text{Q}})|_{S=S_{i},a=\mu(S_{i})}\
abla_{\theta^{\mu}}\mu(S|\theta^{\mu})|_ {S_{i}}}\)
15: Update parameters of target network of the critic and the actor:
16:\(\theta^{\text{Q}j^{\prime}}\leftarrow\tau\theta^{\text{Q}j}+(1-\tau)\theta^{ \text{Q}j^{\prime}}\) for \(j\) = 1,2
17:\(\theta^{\mu^{\prime}}\leftarrow\tau\theta^{\mu}+(1-\tau)\theta^{\mu^{\prime}}\)
18:endif
19:endfor
```  
**Algorithm 1** Improved TD3
## 5 Discussion
### Comparison Results with State-of-the-Art Models
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{6}{c}{**F1-Score(\%)**} \\ \cline{2-9}  & **Chest** & **Forearm** & **Head** & **Shin** & **Thigh** & **Upper Arm** & **Waist** \\ \hline Two-stream CNN [54] & 76\% & 79\% & 78\% & 88\% & 52\% & 75\% & 86\% \\ RF [55] & 84\% & 80\% & 79\% & 87\% & 86\% & 83\% & 86\% \\ \hline Att-ResBiGRU & 98.12\% & 94.75\% & 96.70\% & 97.90\% & 98.52\% & 96.93\% & 97.93\% \\ \hline \hline \end{tabular}  \end{88% and 90%. Notably, a multi-stream CNN architecture achieved an F1-score of 90%, a mark we surpassed through our comprehensive management of positional independence.
# A Novel Robust Control System Design and Its Application to Servo Motor Drive
###### Abstract  
This paper proposes a new control system by integrating integral state feedback control and sliding mode control to eliminate the influences from the reference input change, external load, and parameter variations. For most control systems, integral action is used to overcome the reference input change and external load. However, its control performance cannot be guaranteed. State feedback control is used to dominate the pole location of the closed-loop control system. However, the system parameters determine their pole locations and may change due to uncertainties. Thus, the characteristics of the closed-loop control system are changed. Sliding mode control is used to compensate for the effect of the parameter variations and make the system invariant. The resulting system combines linear state feedback and sliding mode control to guarantee the desired performance. This shows that the proposed system can be easily applied and designed. A servo control system is used to demonstrate the performance, and simulations and experiments are carried out to evaluate the newly defined structure. They show that the strategies and control design can reach robust performance even with uncertainties or external load, and the chattering of the sliding mode control can be minimized.  
integral control; state feedback control; sliding mode control; servo drive system; position control +
Footnote †: journal: Applied Sciences
## 4 Simulation Results and Discussion
### PV Power Variation  
This scenario aims to investigate the performance of VSG control combining with the proposed power allocation Method#2 to suppress the frequency deviation which results from stochastic power output of PV. A worth case PV profile which is recorded on 24 January 2015 from laboratory Rooftop-PV (\(P\approx 2.1\) kW) is used as illustrated in Figure 13. Five minutes data in the real-time is scaled to 1 s in the simulation environment via Rate Transition Simulink Block.  
Figure 14 presents the frequency response of the VSG control and the current \(dq-decoupling\) control based on Method#2. This result is obtained when it takes into account the strong variation power output of PV. According to this waveform, it is obvious that the VSG (\(blue-graph\)) could suppress the oscillation frequency more effectively than the normal \(dq-decoupling\) control (\(red-graph\)) by using the same amount of power from HESS. The power and \(SoC\) of the HESS are provided in Figure 15. In Figure 15a, the \(blue-graph\) represents the power of supercapacitor and \(red-graph\) is the power of battery. Since the supercapacitor is responsible for high power variation, its power and \(SoC\) (Figure 15b) are strongly fluctuated. Meanwhile, the battery's power and \(SoC\) (Figure 15c) are smoothly changed. As a result, the battery lifespan is expected to be improved due to low stress.  
Figure 14: Frequency respond: VSG Vs DQ decoupling control.  
Figure 13: Worst PV power fluctuation profile.
# Applying Simheuristics to Minimize Overall Costs of an MRP Planned Production System
## 6 Results of the Simulation Study
### Sbm Application  
Our first research question (Q1) investigates the performance when applying SBM. The idea of SBM is to stop an iteration if the computed average costs of the current iteration's run is higher than a specified threshold based in the prior available results.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \multicolumn{2}{c}{**SBM NO**} & \multicolumn{4}{c}{**SBM NO**} \\ \hline
**Iterations** & **Simheuristic** & **SA NO** & **Iterations** & **Simheuristic** & **SA YES** \\ \hline
100 & STR & 3482 & 100 & STR & 3159 \\ \hline  & ERR & 2265 & & ERR & 2409 \\ \cline{2-6}  & BSBR & 3201 & & BSBR & 3184 \\ \hline
200 & STR & 3155 & 200 & STR & 3228 \\ \hline  & ERR & 2173 & & ERR & 2073 \\ \cline{2-6}  & BSBR & 3066 & & BSBR & 2909 \\ \hline
300 & STR & 3286 & 300 & STR & 3026 \\ \hline  & ERR & 2308 & & ERR & 2253 \\ \cline{2-6}  & BSBR & 3257 & & BSBR & 2828 \\ \hline  & **SBM YES** & & & **SBM YES** & \\ \hline
**Iterations** & **Simheuristic** & **SA NO** & **Iterations** & **Simheuristic** & **SA YES** \\ \hline
100 & STR & 3100 & 100 & STR & 3022 \\ \hline  & ERR & 2125 & & ERR & 2081 \\ \cline{2-6}  & BSBR & 3458 & & BSBR & 2767 \\ \hline
200 & STR & 2798 & 200 & STR & 2970 \\ \hline  & ERR & 2225 & & ERR & 2180 \\ \cline{2-6}  & BSBR & 3284 & & BSBR & 2549 \\ \hline
300 & STR & 2830 & 300 & STR & 2720 \\ \hline  & ERR & 2118 & & ERR & 1947 \\ \cline{2-6}  & BSBR & 3222 & & BSBR & 2525 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Scenario comparison using minimum overall costs per simulation experiment.  
Figure 2: Minimum overall cost per simheuristic version for all MRP planning parameters.  
The wasting of simulation budget, represented by additional non-promising runs, should be avoided. The leftover runs are shifted to the next iteration and, consequently, allow more iterations per optimization experiments with different parameter settings. To answer Q1, the results without SBM [SBM = NO and SA = NO] and applying SBM [SBM = YES and SA = NO] in Table 4 are compared. For the STR procedure, the application of SBM and an increasing simulation budget indicates a lowering overall cost. At 100 iterations, the overall cost decreased from 3482 CU to 3100 CU, which is a performance increase of 10.95%. Likewise, at 200 and 300 iterations, a cost reduction of 11.31% and 13.89% can be observed, respectively. For ERR, at 100 iterations a better performance of 6.18% is possible. At 300 iterations, a performance increase of 8.21% (from 2173 CU down to 2118 CU) is observed. For simheuristic BSBR, the application of SBM with SA = NO does not show a consistent behavior, although costs for 300 iterations slightly decrease. Note that the BSBR version is outperformed by the ERR one in all of the aforementioned scenarios. For Q1, it can be concluded that the SBM application leads to a performance increase, when the search space is focused on a single best solution and a constantly decreasing parameter space leads to an additional performance increase.
## 4 Results and Discussion
### Results and Discussion of Geo-Environment Suitability for Urban Construction  
According to the judgment matrix and weights of geo-environment indicators (Tables 3 and 4), the comprehensive evaluation indexes were calculated. The evaluation results showed that geo-environment suitability for urban construction in most areas of Rongcheng were in the high and very high grades, of which the very high zone covered an area of about 98 km\({}^{2}\), and the high zone was nearly 182 km\({}^{2}\) (Figure 11). The acreage of the moderate grade was approximately 5.5 km\({}^{2}\), and the low grade was close to 28.5 km\({}^{2}\). Meanwhile, the main affecting factors were dissimilar (Table 5); the impacts of geological faults, land subsidence rate, and potential liquefied sands should be noted preferentially for urban construction.  
Figure 10: Distributions of geothermal gradient.  
Figure 9: Zonal distributions of enrichment of deep groundwater.  
Generally, it is a gradual process for the planning and construction of Rongcheng as a Start Construction Region in Xiong'an New Area; however, the geo-environment suitability evaluation for urban construction should be regarded as preliminary work. This paper selected as many geological indicators as possible to analyze the geo-environment suitability for urban construction. Nevertheless, it focused on geo-environment characteristics, and other socioeconomic features were not included. In future studies, population quantity and industrial structure should be considered, in order to improve decision-making for precise urban land planning.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline  & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** & **C11** & **C12** & **C13** & **C14** & **C15** & **Weights** \\ \hline C1 & 1 & 1 & 1 & 5/4 & 5/4 & 5/8 & 5/9 & 5/4 & 5/7 & 5/3 & 5/1 & 5/4 & 5/4 & 6/5 & 5/4 & 0.07 \\ C2 & & 1 & 1 & 5/4 & 5/4 & 5/8 & 5/9 & 5/4 & 5/7 & 5/3 & 5/1 & 5/4 & 5/4 & 6/5 & 5/4 & 0.07 \\ C3 & & & 1 & 5/4 & 5/4 & 5/8 & 5/9 & 5/4 & 5/7 & 5/3 & 5/1 & 5/4 & 5/4 & 6/5 & 5/4 & 0.07 \\ C4 & & & & 1 & 1 & 1/2 & 4/9 & 6/5 & 2/3 & 3/2 & 4/1 & 6/5 & 6/5 & 7/6 & 6/5 & 0.06 \\ C5 & & & & & 1 & 1/2 & 4/9 & 6/5 & 2/3 & 3/2 & 4/1 & 6/5 & 6/5 & 7/6 & 6/5 & 0.06 \\ C6 & & & & & & 1 & 7/9 & 2/1 & 8/7 & 4/1 & 5/1 & 8/3 & 8/3 & 8/3 & 4/1 & 0.12 \\ C7 & & & & & & & 1 & 9/4 & 9/7 & 9/4 & 6/1 & 3/1 & 8/3 & 9/4 & 3/1 & 0.13 \\ C8 & & & & & & & & 1 & 4/7 & 2/1 & 2/1 & 4/5 & 4/5 & 5/2 & 5/3 & 0.06 \\ C9 & & & & & & & & 1 & 7/3 & 7/3 & 7/4 & 7/4 & 2/1 & 7/3 & 0.09 \\ C10 & & & & & & & & & & 1 & 8/7 & 1/2 & 1/2 & 7/9 & 7/8 & 0.04 \\ C11 & & & & & & & & & & 1 & 1/4 & 1/4 & 1/3 & 1/2 & 0.02 \\ C12 & & & & & & & & & & & 1 & 1 & 5/4 & 5/3 & 0.06 \\ C13 & & & & & & & & & & & & 1 & 4/3 & 0.05 \\ C14 & & & & & & & & & & & & & 1 & 0.04 \\ C15 & & & & & & & & & & & & & & & 1 & 0.04 \\ \hline \end{tabular} Annotation: the consistency ratio is 0.02.  
\end{table}
Table 4: Index judgment matrix and weights of geo-environment indicators.  
Figure 11: Zonal distributions of geo-environment suitability for urban construction.
# Review of Radiation-Induced Effects in Polyimide
## 3 Results
### Changes in Physical Properties  
Radiation-induced microscopic changes in the chemical state of the PI material manifest themselves as macroscopic alterations of physical properties, including color, optical transmission characteristics, and morphological changes.
# Impact of Audio Delay and Quality in Network Music Performance +
## 2 Related Work
On the other hand, when actual musical performances are tested, the results are far more diverse, indicating that musicians may be able to adapt to growing delays, possibly by reducing their tempo; the results may also depend on the music genre and the instruments used. To date, there appears to be no study specifically investigating how audio quality influences NMP. The majority of research in this area emphasizes latency, often selecting an acceptable audio quality level based on network conditions. Since compression is generally avoided in NMP to limit latency, we explored the impact of audio quality by varying the sampling rate. Notably, this method directly influences the transmission bitrate without adding delay; however, it does not uniformly degrade quality across the spectrum. Instead, it eliminates all frequencies above the Nyquist frequency (i.e., half the sampling rate). While prior work has linked spectral properties of NMP recordings to musicians' perceived Quality of Experience (QoE) [12], such studies focus on the perception of delay rather than audio quality itself. A range of objective methodologies for assessing audio quality is reviewed in [17]. Widely adopted metrics include Basic Audio Quality (BAQ), Perceptual Evaluation of Audio Quality (PEAQ), and several others [18; 19]. These typically involve subjective evaluations, where participants compare a reference (ideal) audio sample with a test sample and rate the latter on a 5-point Likert scale. In domains such as voice telephony, objective models exist that estimate these quality scores without human listeners [20; 21]. Another relevant question is whether musicians rely more heavily on auditory or visual cues for synchronization. A number of studies suggest that the human brain is more effective at processing auditory stimuli than visual ones in timing-related tasks. When exposed to unimodal stimuli, either auditory or visual, individuals generally exhibit more accurate synchronization with auditory cues such as beeps, compared to visual cues like light flashes [22; 23]. Furthermore, research indicates that synchronization performance improves when both auditory and visual stimuli are presented together (i.e., bimodal stimuli), as this tends to reduce temporal variability compared to either modality presented alone. A final question is whether the latency tolerances of NMP can be met by existing networks. The emergence of 5G networks, with their promises of ultra-low latencies and high bandwidth availability, make them a promising candidate for NMP [24]. A recent measurement study showed that a commercial 5G Non-Standalone network can provide median one-way delays for audio transmission only slightly over 40 ms within a cell [4]. The expectation is that 5G Standalone networks will exhibit latencies below 40 ms, which may be acceptable for NMP.
## 3 Master-Slave Optimization Framework
### Master Stage: Multi-Vorse Optimizer (MVO)
#### 3.1.5 Exploitation: Wormhole Mechanism  
The wormhole mechanism guides the search toward the current best solution. It is governed by two dynamic parameters:  
* Wormhole Existence Probability (WEP), which increases linearly with iterations: \[WEP=WEP_{Min}+iter\cdot\left(\frac{WEP_{Max}-WEP_{Min}}{iter_{Max}}\right),\] (25)
* Travel Distance Rate (TDR), which decreases nonlinearly to refine the local search: \[TDR=1-\left(\frac{iter^{1/p}}{iter^{1/p}_{Max}}\right),\] (26) where \(p\) is a user-defined constant (typically, \(1\leq p\leq 6\)), and \(iter\) is the current iteration number. The wormhole-based update is applied as \[U_{i,j}=\begin{cases}\text{Best}_{U,j}+TDR\cdot((U_{Max}(j)-U_{Min}(j))\cdot r _{4}+U_{Min}(j)),&\text{if }r_{3}<0.5,\\ \text{Best}_{U,j}-TDR\cdot((U_{Max}(j)-U_{Min}(j))\cdot r_{4}+U_{Min}(j)),& \text{otherwise},\end{cases}\] (27) where \(r_{3},r_{4}\in[0,1]\) are random values. This operator is applied probabilistically only when a randomly drawn value \(r_{2}\) satisfies \(r_{2}<WEP\).
## 3 Calculation of Cable Saddle Position
### Basic Formula of Cable Element Expressed by Cable End Force  
Based on the basic assumption, the main cable can be simulated by an elastic catenary with uniformly distributed dead load along its unstressed length, as shown in Figure 6. The self-weight uniform load of the cable is \(q\), and the none-stressed length is \(5_{0}\), the tensile stiffness is \(EA\), the span between two fulcrum is l, the height difference is \(h\), and the horizontal tension is \(H\). The vertical reaction force at the left end of the strand is \(V_{1}\). The angle between cable force and horizontal direction is \(\beta_{1}\). The vertical reaction force at  
Figure 5: Calculation model of cable saddle.  
the right end of the strand is \(V_{2}=qS_{0}-V_{1}\). The angle between cable force and horizontal direction is \(\beta_{2}\). Then, the curve expression satisfying the boundary conditions is as follows:  
\[l=\frac{HS_{0}}{EA}+\frac{H}{q}[\ln\Bigl{(}V_{1}+\sqrt{H^{2}+V_{1} ^{2}}\Bigr{)}-\ln\biggl{(}V_{1}-qS_{0}+\sqrt{\bigl{(}V_{1}-qS_{0}\bigr{)}^{2}+H ^{2}}\Bigr{)}] \tag{13}\]  
\[h=\frac{2V_{1}S_{0}-qS_{0}^{2}}{2 EA}+\frac{1}{q}[\sqrt{H^{2}+{V_{1}}^{2}}-\sqrt {\bigl{(}V_{1}-qS_{0}\bigr{)}^{2}+H^{2}}] \tag{14}\]
# Enhanced Atrous Extractor and Self-Dynamic Gate Network for Superpixel Segmentation
###### Abstract  
A superpixel is a group of pixels with similar low-level and mid-level properties, which can be seen as a basic unit in the pre-processing of remote sensing images. Therefore, superpixel segmentation can reduce the computation cost largely. However, all the deep-learning-based methods still suffer from the under-segmentation and low compactness problem of remote sensing images. To fix the problem, we propose EAGNet, an enhanced atrous extractor and self-dynamic gate network. The enhanced atrous extractor is used to extract the multi-scale superpixel feature with contextual information. The multi-scale superpixel feature with contextual information can solve the low compactness effectively. The self-dynamic gate network introduces the gating and dynamic mechanisms to inject detailed information, which solves the under-segmentation effectively. Massive experiments have shown that our EAGNet can achieve the state-of-the-art performance between k-means and deep-learning-based methods. Our methods achieved 97.61 in ASA and 18.85 in CO on the BSDS500. Furthermore, we also conduct the experiment on the remote sensing dataset to show the generalization of our EAGNet in remote sensing fields.  
superpixel segmentation; gating mechanism; multi-scale superpixel feature Article
## 3 Health Monitoring Data of the HZMB Immersed Tunnel  
### Background of the HZMB Immersed Tunnel  
The Hong Kong-Zhuhai-Macao Bridge (HZMB) spanning Lingdingyang Bay is a 55 km long mega-project, one of the largest in the world. It includes three components: the main project of bridge, island and tunnel in the sea; the ports of Hong Kong, Zhuhai and Macau; and the connecting line between the three cities. In the main project, the most challenging task was the island and tunnel project, which has a 6.7 km long undersea tunnel. The immersed tunnel section has a length of 5.67 km and consists of 33 elements [24], as shown in Figure 3. Among all the elements, E28-E33 are located on the flat curve with a radius (R) of 5500 m, and the rest are located on the straight section [25]. The closure joint is between E29 and E30, and the water depth at the bottom of the closure joint is 27.9 m.  
The standard tunnel element is 180 m long, consisting of eight segments, each of which stretches 22.5 m. The immersed tunnel provides a dual three-lane carriageway with a width of 2 \(\times\) 14.55 m and a vertical clearance of 8.4 m, as shown in Figure 4.  
As a complex sea-crossing project, the HZMB immersed tunnel has a transition section at the head of the artificial islands where different foundation solutions are adopted, additionally the thickness of the placed backfill is uneven [26]. Meanwhile, the deposition of sediments should also be taken into consideration during the operational life of the structure, which increases the overlying load on the tunnel structure. Load changes in the tunnel longitudinal direction will also result in larger internal forces in the tunnel. All these factors make threats to the operational safety of immersed tunnel structure a great challenge.