esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 3 Analysis and Discussion
### The Performance of OISL-MPLS  
To demonstrate OISL-MPLS switching, the IP capture tool Wireshark and the integrated logic analyzer (ILA) XILINX VIVADO (version 2022.1) are utilized to probe the IP data, in/out-labels, and in/out-ports of OISL-MPLS frames, as illustrated in Figure 7. Figure 7 depicts the IPv6 data transmitted from the source end and received at the destination end, as captured by Wireshark. Figure 7 illustrates the encapsulation of IPv6 data within the _Transfer frame data field_ of the OISL-MPLS frame in SCID #101. This frame is pushed with an out-label (i.e., 0x00D2) and forwarding equivalence class corresponding to the destination IPv6 address FE80:0:0:30::, and it is sent out of port #3 (ingress LER) of node  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **SCID Location** & **Software** & **Hardware** \\ \hline \multirow{2}{*}{Data Plane} & Virtual Node & INTEL WINDRIVER vxWorks 6.9 & INTEL CORE I7 \\  & Real Node & AMD VIVADO 2022.1 & XILINX 7V690T \\ \hline \multirow{2}{*}{Control Plane} & Virtual Node & INTEL WINDRIVER vxWorks 6.9 & INTEL CORE I7 \\  & Real Node & INTEL WINDRIVER vxWorks 6.9 & LOONGSON 1E300 \\ \hline \multicolumn{3}{c}{Management Plane} & ORACLE JAVA jre1.8.0 & INTEL CORE I7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation software and hardware setups.  
Figure 5: Satellite optical network topology.  
SCID #101 according to the forwarding information base (FIB). Upon receiving the OISL-MPLS frame with an in-label (i.e., 0x00D2), node SCID #1 immediately swaps the in-label (i.e., 0x00D2) to the out-label (i.e., 0 xFFFF, since node SCID #1 is the penultimate node in the forward label switching path (LSP)) and forwards the swapped OISL-MPLS frame to port #1 based on the label forwarding information base (LFIB), as shown in Figure 6(c). Finally, when node SCID #2 (egress LER) receives the OISL-MPLS frame with an in-label (i.e., 0 xFFFF), it pops the in-label, de-encapsulates the IPv6 data from the _Transfer frame data field_, and delivers the IPv6 data to the terminal User #3, as depicted in Figure 6(d). The process for the backward LSP follows a similar mechanism. Table 7 summarizes the in/out-label and in/out-port configurations for the bi-directional OISL-MPLS frame-switching process.  
Figure 6: Semi-physical simulation.  
Figure 7: OISL-MPLS end-to-end testing.
# Nanomaterials-Based Ion-Imprinted Electrochemical Sensors for Heavy Metal Ions Detection: A Review
## 4 Conclusions and Future Outlook
21906140, 21676003 and 22076161), the National Key R&D Program of China (2017 YFB0308701), the Natural Science Foundation of Jiangsu Province (BK20191480), and Jiangsu provincial Key R & D Program: Social Development Project (BE2020773). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
HMIs  
Heavy metal ions  
MAA  
Methacrylic acid  
IIECSs  
Ion-imprinted electrochemical sensors  
OPD  
O-phenylenediamine  
IIPs  
Ion-imprinted polymers  
AA  
Acrylic acid  
WHO  
World Health Organization  
AM  
Acrylamide  
IQ  
Intelligence quotient  
4-VVP  
4-Vinylpyridine  
SERS  
Surface-enhanced Raman spectroscopy  
Py
Pyyrrole  
AAS  
Atomic absorption spectrometry  
ECSs  
Electrochemical sensors  
CE  
Capillary electrophoresis  
CS  
Chitosan  
Inductively coupled plasma-atomic emission  
NNMBA  
N,N\({}^{\prime}\)-methylene double acrylamide  
IC-UV-vis  
Ion chromatography-ultraviolet vis  
spectrometry  
ICP-MS  
Inductively coupled plasma mass spectrometry  
MMWCNTs  
Magnetic multi-walled carbon nanotubes  
NPs  
Nanoparticles  
XFS  
X-ray fluorescence spectroscopy  
WE  
Working electrodes  
MIPs  
Molecularly imprinted polymers  
MEECSs  
Molecularly imprinted electrochemical sensors  
CE  
Counter electrodes  
MIT  
Molecular imprinting technique  
ISEs  
Ion selective electrodes  
Heavy metal ions imprinted electrochemical  
LOD  
Low limit of detection  
CPE  
Carbon paste electrode  
IIMs  
Ion-imprinted membranes  
Alpha  
Allyl phenoxyacetate  
Th  
Thiophene  
EGDMA  
Ethylene glycol dimethacrylate  
AlBN  
Azobisisobutyronitrile  
CV  
Cyclic voltammetry  
CNP  
Carbon nanofibers-grafted  
DPV  
P-phenylenediamine  
SAM  
Acryl amide  
THPP  
BAAP  
Bis-(2-acryloylamino-ethyl)-phosphinic acid  
IC  
Ionic liquid  
SCE  
Screen-printed carbon electrode  
GR  
Graphene  
PCE  
Pencil graphite electrode  
PC  
Poly catechol  
GP  
Graphic powder  
EP  
EABP  
Ethyl 6-(allyoxy)-2-amino-4-phenyl
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
## 3 Analysis and Verification
### Influencing Factors of Balancing Performance
#### 3.3.2 Effect of Switching Cycles  
According to analysis of (31)-(33), balancing performances are influenced by the switching period and duty cycle. As can be seen from Table 2, duty cycles have a positive relationship with switching periods. Therefore, the effect of the switching periods on the balancing performances is only discussed in this section. In addition to setting the associated voltages of cells and balancing circuit parameters, all relevant balancing modes are simulated and analyzed in this section.  
The relationships between the switching period and balancing performances are shown in Figure 8. In Figure 8a,b, for any balancing mode, when the switching period is increased, \(\rm P_{in}\) and \(\rm P_{out}\) are increased. Figure 8c shows that for any balancing model, balancing efficiency is less influenced by the change in the switching period. From Figure 8d, balancing time and switching period are inversely related in any balancing mode. In summary, increasing the switching period has less effect on the balancing efficiency, but \(\rm P_{in}\) and \(\rm P_{out}\) are enlarged by this increase. Then, the balancing time is shortened, helping to improve the balancing speed.
# An Evaluation Framework and Algorithms for Train Rescheduling
## 7 Description of the Experiment
### Delay Propagation  
According to the average delay propagation values in Table 17, ALG2 outperforms ALG1 in obtaining solutions with less delay propagation. We could confirm the difference in the performance of the two algorithms as statistically significant only for secondary delays \(\leq\) 3 min (\(p=0.03\)). The differences in the obtained secondary delays \(>\) 3 min are not statistically significant (\(p=0.09\)). The latter shows that the performance difference is likely to have occurred by chance.  
In the solutions of ALG1, many trains often incur secondary delays (see Table 14). In comparison, the solutions obtained using ALG2 typically have fewer trains with secondary delays. In the solutions obtained by ALG1 for disturbances 21-30, the delay caused by the disturbance is almost always propagated to other trains. In comparison, in the solutions obtained by ALG2, there is less propagation of delays caused by the disturbance (see Table 14). When using ALG1, secondary delays \(>\) 3 min also appear more frequently, compared to ALG2.
## 2 Related Work
### Progressive Structure for Super Resolution  
Among the many image super-resolution methods, some researchers have also proposed the idea of progressive super resolution. Nevertheless, there were still some shortcomings in these methods. In [37], a progressive cascading residual network was proposed in response to the instability of network training. First, a super-resolution module with a magnification of two was trained, and then a new two-times super-resolution network was connected to it for training. A total of three such modules were trained and finally connected together to complete the eight-times super resolution. The results showed that this property yielded a large performance gain compared to the non-progressive learning methods. This method used the idea of progressive training, but did not use multiple levelsof supervision. During the training of each module, only one supervision was used to evaluate the performance of the whole network. It still suffered from the problem of insufficient constraints. A leveraging multi-scale backbone with multi-level supervision [38] was proposed for thermal image super resolution. A total of five times supervision was performed for feature maps, feature fusion maps, and super-resolution results at different stages. Although the number of supervisions in this method was higher, most of the supervisions were applied to the feature map. The feature maps were visually quite different from a good super-resolution output image. In [35], a transformer-based multi-stage enhancement method was proposed for remote sensing image super resolution. An enhancement module was added after the super-resolution network to enrich the details. The super-resolution network was still a one-step approach, and the idea of a multi-stage approach was mainly reflected in the enhancement module.
# Fertilization and Soil Microbial Community: A Review
## 3 Results and Discussion
### Types of Fertilizers Used and Their Influence on Soil Microbial Community  
The sustainable development of agroecosystems is based on a better understanding of the complex responses of microbial communities to the various organic and inorganic fertilization regimes, as highlighted by [PERSON] et al. [111], demonstrating that a better understanding of the complex responses of microbial communities to various organic and inorganic fertilizations is critical for a sustainable development of agroecosystems. They found that using chemical fertilizer together with manure clearly increased soil fertility and were recommended for further optimization of fertilization patterns. In addition, the results of their research suggested that organic and inorganic fertilizers dominated in shaping bacterial and fungal community distributions in fluvo-aquic soils.  
[PERSON] and [PERSON] [81] compared the use of organic fertilizers with inorganic ones, observing that organically treated soils had the largest number of microorganisms (fungi and bacteria) and microbial biomass carbon. Chemical fertilizers, on the other hand, have been shown to have a smaller effect than other soil treatments on bacterial composition and diversity [112].  
Comparing the effects of chemical fertilizers with manures (farmyard manure, slurry and green manure), [PERSON] [113] concluded that there is no significant difference in the long-term effects on crop production between these two types of fertilization. However, manured soils had higher organic matter contents and higher numbers of microfauna than soils added with chemical fertilizers. The manured soils were also more enriched in P, K, Ca and Mg in the topsoil and nitrate, N, Ca and Mg in the subsoil.  
When an organic fertilizer is applied to soil, its decomposition is due to bacteria and fungi, which in turn support the soil fauna chain. The ratio of fungal to bacterial biomass can be considered an indicator of the activity of two pathways of the soil food web, formed by fungivores or bacteriores and their predators, respectively [114]. In general, bacteria are prevalent under conventional tillage, whereas fungi dominate under no-tillage. The use of nitrogen fertilizers by applying organic amendments decreases the fungi/bacteria ratio and can decrease the soil pH [114]. For this reason, several countries encourage farmers in economically developed areas to reduce their N fertilization rate [115]. [PERSON] et al. [116] concluded that mineral fertilization, which modifies available N and P and hence changes in soil fertility, can be a selective force causing structural and functional shifts in the soil microbial community. Furthermore, a mixed application of N, P and K has been shown to increase soil microbial biomass and diversify bacterial communities [117]. In several studies nitrogen addition is a key factor in bacterial and fungal community composition shifts [87].
# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 3 Proposed System Model
### Power Consumption Model  
The study referenced in [42] illustrates that power consumption in physical machines can be represented by a linear relationship with CPU usage. It also highlights that a fully idle physical machine typically consumes around 70% of its total power capacity. Using Equation (4), define power consumption as a function of CPU utilization.  
\[P(v)=n\times P_{max}+(1-n).P_{max}\times v \tag{4}\]  
where v is the CPU usage, n is the proportion of power used by an idle physical server, and \(P_{max}\) is the maximum power of a server while it is operating. Due to workload fluctuation, CPU usage varies over time, and v(t) is a function of time. Equation (5) can be used to define the overall energy usage. This model states that CPU use determines energy consumption.  
\[E=\int_{t}P(v(t))dt \tag{5}\]
# A Review of Experimental and Numerical Studies of Lithium Ion Battery Fires
## 5 Heat Release Rate
### HRR and TTI Measured Data for 18650 Batteries
The batteries were exposed to varying heat fluxes of 25, 30, 50, and 75 kW m\({}^{-2}\), which were deemed to correspond to a low thermal aggression situation, a mild fire, a flashover situation, and strong and sudden thermal aggression situation. The data are presented in Table 11. When exposed to the same heat flux, the TTI was observed to decrease noticeably with higher SOC. [PERSON]'s principle and [PERSON]'s relation formed the basis for determining the HRR using the oxygen depletion method. The time to peak HRR and the duration of the burning was reported to decrease with increasing SOC, whereas the peak HRR increased with increasing SOC (except for SOC at 0%). Experimental tests were conducted by [PERSON] [87] using a fire calorimeter to investigate the combustion performance of two separate commercial 18650 LIBs (2.6 Ah Samsung LCO and 1.3 Ah Sony LFP) at various SOC. Five tests were carried out on the Samsung LIB at 0, 25, 50, 75, and 100% SOC while three tests were conducted on the Sony LIBs at 0, 50, and 100% SOC. The maximum heat flux incident on the batteries was 20 \(\pm\) 0.3 kW m\({}^{-2}\). The HRR measurement method is based on the oxygen consumption principles in accordance with ISO 9705 and ISO 5660. The data presented in Table 12 show that LCO with 100% SOC gives 4.1 kW compared to 6.2 kW measured for LCO by [PERSON] [6] at 30 kW m\({}^{-2}\). The results from testing of the LFP show the magnitude of the maximum HRR peak increases with the increase of the SOC, as observed by [PERSON] [6] and [PERSON] [86]. However, the result from LCO testing produced was contrary to this, as the LIB at 75% SOC had a greater HRR peak value than the battery at 100% SOC. \begin{table}
\begin{tabular}{c c|c c|c} \hline \hline \multirow{2}{*}{**Condition**} & \multicolumn{2}{c}{**Total Energy**} & \multicolumn{2}{c}{**HRR\({}_{\text{max}}\)**} & \multicolumn{2}{c}{**Normalized HRR\({}_{\text{max}}\)**} \\  & \multicolumn{2}{c}{**Released (kJ)**} & \multicolumn{2}{c}{**(kW)**} & \multicolumn{2}{c}{**(kW m\({}^{-2}\))**} \\ \hline  & 0\%\(-\)50 kW m\({}^{-2}\) & 109 & 1.1 & 262.9 & \\  & 50\%\(-\)50 kW m\({}^{-2}\) & 120 & 1.5 & 358.5 & \\ SOC & 65\%\(-\)50 kW m\({}^{-2}\) & 104 & 5.8 & 1386.2 & \\  & 70\%\(-\)50 kW m\({}^{-2}\) & 101 & 6.5 & 1553.5 & \\  & 100\%\(-\)50 kW m\({}^{-2}\) & 100 & 6.8 & 1625.2 & \\ \hline Radiative heat source & 100\%\(-\)30 kW m\({}^{-2}\) & NP & 6.2 & 1441.8 & \\  & 100\%\(-\)60 kW m\({}^{-2}\) & NP & 9.1 & 2174.9 & \\ \hline \hline \end{tabular}  
Normalized HRR is the HRR divided by the battery surface area. NP indicates that the data were not provided in the results.  
\end{table} 
Table 10: HRR versus SOC for 18650 LCO LIBs [6]. It is observed that sometime higher SOC results in lower HRR, which could be due to blowing off the fire by gas injection during thermal runaway [140].
## 4 Descriptive Analysis of Data
### SPSS Analysis
#### 4.3.7 AI in Handover, Commissioning, and Warranty Period  
Handover, commissioning, and warranty period are considered at the last phase/stage of the project's life cycle. In these areas, the use of AI was studied. A total of 65.3% disagreed that using AI will streamline the handover process in aviation construction projects, 13.9% were neutral, and 20.8% agreed. In addition, 22.8% of participants agreed that AI systemsfacilitate better coordination during the handover period, 22.8% were neutral, and 54.5% disagreed entirely. However, 29.8% agreed that AI significantly reduces the time required for resolving warranty claims, 54.5% were neutral and 15.9% disagreed. The total score of this variable was neutral (Mean = 3.2116, S.D. = 1.2746).  
In view of the above scores, while the use of AI in the handover, commissioning, and warranty phases of aviation construction projects shows potential, the findings indicate mixed sentiments among participants. A significant portion (65.3%) disagreed that AI would streamline the handover process, and over half (54.5%) expressed concerns about AI's ability to facilitate better coordination during handover. However, a notable 29.8% acknowledged AI's ability to reduce the time for resolving warranty claims.
# Analysis and Visualization of Production Bottlenecks as Part of a Digital Twin in Industrial IoT
## 2 Literature Review
### Evaluation of Bottleneck Detection Methods  
In this section the advantages and disadvantages of each bottleneck detection method are discussed. An important aspect is the metric offered by the bottleneck detection algorithms. Whereas [15; 16] just identify a set of machines that are bottlenecks, the other methods [12; 14] offer a metric that measures how likely a machine is a bottleneck. The metric of the Average Active Period is the average duration of the active periods for each machine. The Active Period Method measures the duration of a machine being a bottleneck. This value is further split into the sole and shifting fraction and then returned as a percentage of the total operation time. The Turning Point Method measures the percentage of starvation and blockage times of each machine. The Average Active Period Method is easy to implement, but does not offer detailed information on the identified bottlenecks. In contrast, the Active Period Method as well as the Turning Point Method offer more accurate metrics with more information on identified bottlenecks.  
It is important to note that over the last two decades many approaches and algorithms were proposed for the sensible application of bottleneck detection methods. A conclusive approach for bottleneck detection was proposed in 2008 by [PERSON] et al. [17]. [PERSON] et al. [18] present a data driven algorithm to predict throughput bottlenecks; [PERSON] et al. [19] enhance an algorithm for including the detection of shifting bottlenecks; both are based on the Active Period Method. The application in a simulation software is explored by [PERSON] and [PERSON] [20]; they also investigated the Active Period Method and identified several advantages.  
Another aspect that should be highlighted is the availability of a quality metric, e.g., the accuracy. For the methods from [12; 14], no statement on the accuracy is given, while the Minerva method provides an accuracy, e.g., an accuracy of over 90% is reached by [18].  
Figure 6: Example of Turning Point Method.  
The bottleneck detection method provided in [15] was tested in 20 different production lines and a lot of bottlenecks not considered by experts before were identified.  
Although some of the above methods cannot deliver a statement on their accuracy, a big advantage is that they are real-time capable and are easily implemented. The following quality criteria were decisive when choosing the method to be applied:  
* Independent of production layout;
* Requires time series data only;
* Implementable in a small prototype;
* Delivers an interpretable metric.  
The Active Period Method fulfils all these requirements and is therefore chosen for the proposed bottleneck detection method.
# Research Advances in Large Deformation Analysis and Applications of the Material Point Method
###### Abstract  
Large deformation analysis is a crucial foundation for studying the nonlinear behavior and progressive damage of materials and structures. Traditional mesh methods often struggle with large-scale mesh distortion when dealing with such issues, which can compromise solution efficiency and accuracy, and in severe cases, even cause computational interruptions. In contrast, the material point method (MPM) employs a dual framework of Lagrangian particles and Eulerian background grids, effectively integrating the advantages of both Lagrangian and Eulerian approaches, thus avoiding mesh distortion and challenges in handling convective terms. Consequently, many researchers are dedicated to developing an MPM for addressing high-speed impact and fluid-structure interaction problems that involve material failure and large deformations. This paper begins by introducing the fundamental theory and contact algorithms of the MPM. It then systematically summarizes the latest advancements and applications of the MPM, including its hybridization and coupling with other algorithms, in simulating various large deformation scenarios such as high-speed impacts, explosions, dynamic cracking, penetration, and fluid-structure interactions. This paper concludes with a summary and a prospective view on future trends. This review highlights the robustness and accuracy of the MPM in tackling large deformation problems, offering valuable insights for the analysis of large deformations and damage evolution in various materials.  
material point method; large deformation; high-speed impact; explosion; fluid-structure interaction +
Footnote †: journal: Applied sciences
# Estimation of Stride Length, Foot Clearance, and Foot Progression Angle Using UWB Sensors
## 2 Materials and Methods
### Calculation of Gait Parameters Using UWB
#### 2.3.1 UWB Sensor Offset Compensation
The corresponding equations are as follows: \[\mathrm{Maximize}\ \mathrm{W}(\alpha)=\sum_{i=1}^{l}\alpha_{l}-\frac{1}{2} \sum_{i=1}^{l}\sum_{j=1}^{l}\alpha_{i}\alpha_{j}y_{i}y_{j}z_{i}\cdot z_{j}\] (4) subject to \(\sum_{i=1}^{l}y_{i}\alpha_{i}=0,\ 0\leq\alpha_{i}\leq C,\ i=1,\ 2,\ \cdots,\ l\)
6. Finally, the kernel function was used to map the data space to the shape space. In addition, a linear kernel is used to compensate for the offset of the data acquired from the UWB sensor. Previous research results [42] can be referred to for further details on the SVR.