esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental and Numerical Study on Reduction of Generated Axial Force
## 2 GAF Model
### Generated Axial Force Model  
From the theoretical and experimental approaches previously described, all information that is required to build a complete GAF model can be obtained. As a spherical roller moves on the track, an axial force is produced, and it varies with the spherical roller's phase angle. As a result, the GAF can be modeled by Equation (23).  
\[GAF=\sum_{i=1}^{3}\mu_{s}N_{st}sin(\alpha_{i})sign|V_{t\_z}|+\mu_{rs}N_{st}cos( \alpha_{i})sign|V_{t\_x}| \tag{23}\]  
The first term represents the sliding friction force produced along the trunnion axis and the second term represents the rolling-sliding friction force produced along the radial direction of the spherical roller. These forces change directions as the spherical roller makes reciprocal motions on the track, so the equation has two sign terms whose values are determined by the direction. When the spherical roller moves in the positive Z-direction, the sign of the first term is positive. In the same manner, when moving in the positive X-direction, the sign of the second term is positive. In the second term, \(\mu_{rs}\) is the product of the rolling-sliding ratio and the friction coefficient of pure sliding. The rolling-sliding ratio is between 0 and 1, so \(\mu_{rs}\) is always smaller than \(\mu_{s}\).  
From Equation (23), it is known that the main parameters of GAF are normal force and friction coefficient. Among these parameters, friction coefficient is the only parameter that can be managed by design modification since normal force is determined by driving conditions. GAF comprises of the rolling-sliding and pure sliding terms, and the rolling-sliding term comprises of the friction coefficients of pure sliding and the rolling-sliding ratio. Accordingly, the main parameters that determine GAF are these friction coefficients.  
From the previous study, the friction coefficient of pure sliding and the rolling-sliding ratio are mainly determined by sliding velocity and normal pressure [10]. Figures 5 and 6 show the friction coefficient of pure sliding and the rolling-sliding ratio between the track and the spherical roller, respectively. As shown, both rolling sliding ratio and friction coefficient are proportional to normal pressure. On the other hand, friction coefficient is inversely proportional to sliding velocity whereas rolling sliding ratio is proportional.
## 5 Result and Analysis
### Performance Comparison  
#### 5.2.1 Performance Comparison on Training Dataset  
In Table 1, our proposed model's performance is compared to the other-state-of-the-art models (such as PredNet, UNet, ConvLSTM, and Autoencoder) on a training dataset. The comparison is made in terms of precision, recall, and accuracy at prediction horizons of 10, 30, and 60 min, respectively. Due to a class imbalance in background and congestion level classes and variations in the number of pixels for each road segment, evaluating the model's performance based on entire pixels or the road segments can lead to inaccurate results. Therefore, in this study, we opted to evaluate the model's performance based on a single value from each road segment. On the training dataset, for prediction horizons, beyond 1 h, the performance of the model decreases drastically; therefore, in this study, we choose to present our results up to prediction horizons of 1 h.  
Our proposed HRNet outperforms all other models for all three prediction horizons and all three performance metrics. Specifically, RHRNet showed an improvement of approximately 3% in precision for 10- and 30-min prediction horizons and around 1% for the 60-min prediction horizon, compared to the next-best model, PredNet. Moreover, the proposed model showed a gain of around 3% in recall for the 10-min prediction horizon and around a 1-2% gain for the 30- and 60-min prediction horizon, compared to PredNet. In term of accuracy, the proposed model demonstrated a 4% improvement for the 10-min prediction horizon and was approximately 2% better for the 30- and 60-min prediction horizons compared to the next-best model, PredNet. Based on performance metrics from Table 1, the models are ranked as follows, from high to low: proposed RHRNet, PredNet, UNet, ConvLSTM, Autoencoder.
## 4 Experimental Results and Dataset
### Experimental Results

Table 1: The model performance on test set, leucorrhea dataset, and blood dataset. Some formed elements in leucorrhea are similar to those in feces, such as white blood cells, fungal spores, and red blood cells, and the cell morphology is comparable. Similarly, blood samples also contain red blood cells and white blood cells. Although the models were trained on the feces dataset, some L0 could achieve acceptable results on the leucorrhea and blood datasets, with poor robustness of model performance. To show the availability of MA, we plotted the attention heat maps visualized by Grad-CAM++ [30]. Figure 8 presents the attentions in the output of Layer5 of L0 and L1. It can be seen that the attentions of model L0 are mainly distributed around the regions of fungal spores and impurities. After applying MA, the attentions in blurred regions or the background are suppressed. This indicates that the introduction of MA causes the network to pay more attention to sharp regions. The performance of GMANet based on backbone resnet50 is shown in the Supplementary Materials.
## 3 Application Instance
### Parameter Settings  
The three parameters required for the improved CELMD algorithm include the proportion coefficient of adding white noise, the number of times white noise is added, and the correlation coefficient threshold for filtering the main components.  
The proportion coefficient \(\alpha\) is the ratio of the standard deviation of the white noise signal to the standard deviation of the original signal. A larger proportion coefficient \(\alpha\) increases the likelihood of generating false PF components, while a smaller coefficient may affect the effectiveness of reducing mode mixing. According to the method proposed by the researchers, the value of the proportional coefficient is determined to be between 0.05  
Figure 5: Typical acoustic emission waveform signal: (**a**) BO; (**b**) SO; (**c**) RD; (**d**) MI; (**e**) OD; (**f**) EI; (**g**) RS.  
and 0.8 for various types of signals, as shown in the following formula. In this article, after calculating the signal data used, we used 0.2 [36].  
\[\frac{\sigma_{h}}{4\sigma_{0}}<\alpha<\frac{3\sigma_{h}}{4\sigma_{0}} \tag{8}\]  
In the formula, \(\sigma_{h}\) represents the standard deviation of high-frequency components in the signal under analysis, and \(\sigma_{0}\) represents the standard deviation of the signal under analysis.  
In theory, the more times white noise is added, the fewer white noise components are included in the final PF components. However, in simulations, it was found that when the number of noise additions is too high, the improvement in decomposition is minimal but the computational time significantly increases. Therefore, the number of times white noise is added is set to 100.  
After decomposition, some PF components may not contribute significantly to the overall characteristics. Therefore, it is common to filter the PF components first. Common filtering parameters include correlation coefficients, variance contribution rates, and spectral coefficients. Researchers have demonstrated that these parameters are positively correlated [37]. This paper adopts the correlation coefficient as the filtering parameter. Generally, a correlation coefficient less than 0.5 indicates weak correlation between the PF component and the signal. Therefore, the threshold is set to 0.5.  
The learning rate of the Swin Transformer neural network is set to 0.0001, and the epoch is set to 50. In addition, ViT neural network and convolutional neural network are added as control groups, with the same learning rate and epoch as Swin Transformer. The ratio of training set to validation set is set to 8.2. Through experiments, it has been verified that the parameter settings can make the neural network training achieve convergence and meet the requirements. Official pre-training weights are used to accelerate training, and parameters such as success rate, loss rate, time, and confusion matrix are recorded for each training round.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 3 Simulation Setup and Performance Metrics
### Performance Metrics  
This section describes the performance of the topology-based routing protocols on the basis of the following performance metrics:
# Aminated Covalent Organic Polymers for Anionic Dye Adsorption in Aqueous Systems
## 3 Results and Discussion
### Adsorption Experiments in Mixed-Dye System and Real Water Sample  
To investigate the selective anionic dye adsorption of ACOP in a mixed-dye system, MB was selected as a cationic dye and mixed with MO (MB + MO) and OG (MB + OG) (Figure 8). In the MB solution, the adsorption efficiency of ACOP was approximately 33.4% despite the negative charge of MB. This result might be ascribed to the ACOP structure comprising two aromatic rings and a central heterocyclic, which facilitates adsorption on  
Figure 7: Anionic dye adsorption efficiency of ACOP as a function of initial pH (initial dye concentration: 100 mg/L, initial adsorbent concentration: 2.0 g/L, initial pH: 4, 6, 8, and 10, and contact time: 1440 min).  
ACOP through \(\pi\)-\(\pi\) interactions. The prepared ACOP exhibited enhanced dye adsorption efficiency in the mixed-dye solution, not only for MO and OG but also for MB. When MO was mixed with MB, the adsorption efficiencies of the prepared ACOP were 2.8 times and 1.1 times higher than those in the MB and MO single-dye systems, respectively. Moreover, in the MB + OG system, the adsorption efficiency of the prepared ACOP was 2.9 and 1.4 times higher than it was for MB and OG, respectively. These results indicated that the adsorption of MO and OG on the prepared ACOP did not compete with that of the cationic dye MB; instead, a synergistic effect was observed. According to [PERSON] et al., the adsorption efficiency of bentonite for a mixed-dye solution (Basic Violet 16 (BV16) and Reactive Red 195 (RR19)) was higher than that for the individual single-dye systems because the positively charged quaternized nitrogen atoms of BV\({}_{16}\) induced a positive surface charge, allowing the RR\({}_{19}\) molecules to form ion pairs that influenced the coating or layer-formation process [71]. As mentioned above, the positively charged quaternized nitrogen in MB facilitates the adsorption of anionic dyes (MO and OG).  
To evaluate the effect of interference by coexisting ions in water, the adsorption experiment was further conducted in DI and tap water, as shown in Figure S3. According to the report about the tap water quality issued by the Seoul Metropolitan Waterworks in 2021, the tap water included various ions such as Na\({}^{+}\), Mg\({}^{2+}\), K\({}^{+}\), SO\({}_{4}\)\({}^{2-}\), and Cl\({}^{-}\) (TDS = 125 mg/L). The removal efficiencies of MO and OG decreased by only around 9% and 16%, respectively. The decrease in removal efficiency was due to the competitive adsorption between dye molecules and coexisting anions towards the active sites.
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Model Estimation
#### 2.2.4 Impedance Method [27]  
The principle of the impedance method for leak detection can be given as follows: the hydraulic impedance is defined as the ratio of the head fluctuation to the discharge and the characteristic impedance is defined according to the pipe parameters and propagation constant. When the pipe friction is neglected, the final formulation of the pipe impedance with a leak is a function of hydraulic impedance. Therefore, the leak point position is determined by calculating the value of hydraulic impedance for each point where a leak could be detected. The impedance method is mainly used for linear pipelines with less curves and bends, because complicated pipelines with many curves bring difficulties and major uncertainties in the calculation of characteristic impedance and in the statement of boundary conditions.
# Simulation of a Solar-Assisted Air-Conditioning System Applied to a Remote School
###### Abstract  
In this work, we present an absorption cooling system with 35 kW capacity driven by solar thermal energy, installed in the school of Puertecitos, Mexico, an off-grid community with a high level of social marginalization. The cooling system provides thermal comfort to the school's classrooms through four 8.75-kW cooling coils, while a 110-m\({}^{2}\) field of evacuated tube solar collectors delivers the thermal energy needed to activate the cooling machine. The characteristics of the equipment installed in the school were used for simulation and operative analysis of the system under the influence of typical factors of an isolated coastal community, such as the influence of climate, thermal load, and water consumption in the cooling tower, among others. The aim of this simulation study was to determine the best operating conditions prior to system start-up, to establish the requirements for external heating and cooling services, and to quantify the freshwater requirements for the proper functioning of the system. The results show that, with the simulated strategies implemented, with a maximum load operation, the system can maintain thermal comfort in the classrooms for five days of classes. This is feasible as long as weekends are dedicated to raising the water temperature in the thermal storage tank. As the total capacity of the system is distributed in the four cooling coils, it is possible to control the cooling demand in order to extend the operation periods. Utilizing 75% or less of the cooling capacity, the system can operate continuously, taking advantage of stored energy. The cooling tower requires about 750 kg of water per day, which becomes critical given the scarcity of this resource in the community.  
renewable energy; solar cooling; isolated community; absorption chiller; TRNSYS +
Footnote †: journal: Applied  
1
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 4 Conclusions  
Radiation-hardened SRAM is proposed, which is optimized for high reliability in radiation environments such as space. The WRTU-16T has a read-decoupled architecture  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**SRAMs**} & \multirow{2}{*}{**Qc (fC)**} & **RAT** & **RSNM** & **WAT** & **WWTV** & **HPWR** \\  & & **(ps)** & **(mV)** & **(ps)** & **(mV)** & **(nW)** \\ \hline We-Quatro [2] & 20.70 & 34.16 & 284 & 32.15 & 178.53 & 99.29 \\ RRHS-14T [3] & 7.19 & 186.39 & 300 & 20.87 & 291.32 & 75.52 \\ RHSCC-16T & 39.03 & 36.68 & 285 & 35.90 & 145.84 & 125.56 \\ SUR-16T [5] & 13.49 & 101.12 & 223 & 93.78 & 64.43 & 26.51 \\ SSPS81-16T [6] & \textgreater{}100 & 149.64 & 78 & 35.61 & 143.84 & 87.57 \\ WRTU-16T & \textgreater{}100 & 52.95 & 119 & 13.39 & 366.13 & 57.93 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison.  
Figure 14: Comparison of relative EQM with other SRAMs.  
and a charge-sharing suppression mechanism to enhance resilience against soft errors, including SEU. By isolating the storage nodes and applying a feedback loop structure, the WRTU-16T enables rapid recovery and maintains data integrity even under severe radiation exposure. Compared to existing radiation-hardened SRAMs, the proposed WRTU-16T demonstrates several advantages. First, it achieves great performance, such as a WAT of 13.39 ps and WWTV of 366.13 mV, and a low HPWR of 57.93 nW, thereby simultaneously realizing high-speed operations and low power consumption. Second, it exhibits better radiation tolerance and overall electrical robustness, with a Qc exceeding 100 fC and up to a 164 times improvement in the EQM compared to benchmarks. Third, the layout-level physical separation of sensitive nodes (3.11 \(\upmu\)m) combined with a dual-feedback structure maximizes recovery capabilities against SEU. While the RSNM of 119 mV is lower than that of several recent cells, this limitation can be mitigated by system-level techniques such as ECC, or it can be improved by changing the transistor sizing ratio, especially the access CR or pull-down ratio.  
Conceptualization, S.-H.L. and S.-H.J.; methodology, S.-H.L.; validation, S.-H.L.; data curation, S.-H.L.; writing--original draft preparation, S.-H.L.; writing--review and editing, S.-H.J.; visualization, S.-H.L.; supervision, S.-H.J.; project administration, S.-H.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The EDA Tool was supported by the IC Design Education Center, Korea.  
The authors declare no conflicts of interest.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 2 Definition of Algorithm
### Failure Maintenance Decision
#### 2.4.1 Power Problem  
The function of power supply for information systems is critical. There are two kinds of field equipment for information system connection, one is the information system equipment with independent power supply, the other is the information system equipment with bus power supply. For the information system equipment with independent power supply, if the equipment is a network node, the unstable power supply can make the communication of the node appear or even disappear completely, but it has little impact on the key communication equipment of other nodes, and the information system will automatically start the bypass function to avoid the impact on other nodes. For the key equipment [19], such as the repeater, photoelectric conversion module, transceiver, hub, and other equipment, the instability of the power system will inevitably lead to the paralysis of the whole communication system. Therefore, in the case of high reliability and long communication distance, ring topology, multi power supply, or centralized power supply should be considered to isolate the interference of information system. The other is bus power supply equipment, whose power supply is also very important. Power supply problems are often the primary factor of communication system failure. Therefore, in addition to confirming the voltage ripple of the power supply, it is necessary to further confirm whether the voltage of each node reaches the allowable range, and carefully check whether the capacity of the power supply equipment meets the requirements of power consumption of each node, and the power supply in the network communication position cannot be ignored.
## 5 Discussion  
Overall, it was found that the use of optical and radar vegetation indices, namely the NDVI and the RVI, did not provide comparable results (see Figure 8). Optical indices were more easily interpreted while the use of the RVI produced noisy data. However, some findings, such as the those of Figure 6, suggest that radar products can be used as an alternative to the optical data for detecting patterns (e.g., vegetation growth) in specific areas of interest.  
To investigate the potential use of both proportions of vegetation indices derived from Sentinel-1 and -2 sensors, we multiplied the RVI \(\times\) NDVI, as shown in Figure 12. This new product that integrates the RVI and NDVI outcomes can be used with the VV and VH polarizations of Sentinel-1 to create a new pseudo-colour composite. The unique characteristic of the radar sensors to depict urban areas (see also Figure 3c) can be used further to enhance the proportion of vegetation and urban areas. Such a new composite is shown in Figure 13. Using Sentinel-2 spectral bands 4 and 8, we can estimate the NDVI vegetation proportion while using the Sentinel-1 VV and VH polarizations; therefore, we can estimate the RVI vegetation proportion. The combination of the two new products generates the new RVI \(\times\) NDVI vegetation proportion index, which can then be integrated again with the VV and the VH polarization to highlight vegetated areas (red colour under the \"pseudo-colour composite of Figure 13) while buildings are also visible (with green colour in the same column).
###### Abstract  
This study analyzed the biomechanical behavior under simulated conditions between a novel modification of the Low Z plasticy (NM-Low Z) technique and the conventional Hunsuck-Epker (HE) technique on mandibular advancement surgery. The NM-Low Z technique was developed with the purpose of reducing post-operative complications by facilitating the operative method of lower jaw surgery. The models were investigated under physiological muscular and occlusal loads at 1, 2, 4, 6, and 8 weeks post-operation. Finite element analysis was used to analyze stress distribution, elastic strain, and model displacement. Maximum equivalent von Mises stresses were observed on the fixation system and bone. The elastic strain at the fracture site represented the optimal bone-healing capacity. The NM-Low Z model showed lower stress than the HE model at the 2-week post-operation stage and onwards. The elastic strains observed in both models were situated within the normal range for bone healing. The ranges of displacement for the NM-Low Z model were less than those in the HE model. Based on the data sets studied, the biomechanical study of the NM-Low Z technique can be favorably compared to the conventional HE technique. The NM-Low Z technique promotes post-operation skeletal stability by lowering stress on fixation and bone, with less displacement of the segment during bone-healing periods.  
bliateral sagittal split osteotomy; BSSO; Hunsuck/Epker; NM-Low Z plasticy; finite element analysis; orthognathic surgery; mandibular advancement