esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The main object of this study is to introduce hybrid integration approaches that consist of state-of-the-art artificial intelligence algorithms (SysFor) and two bivariate models, namely the frequency ratio (FR) and index of entropy (IoE), to carry out landslide spatial prediction research. Hybrid integration approaches of these two bivariate models and logistic regression (LR) were used as benchmark models. Nanzheng County was considered as the study area. First, a landslide distribution map was produced using news reports, interpreting satellite images and a regional survey. A total of 202 landslides were identified and marked. According to the previous studies and local geological environment conditions, 16 landslide conditioning factors were chosen for landslide spatial prediction research: elevation, profile curvature, plan curvature, slope angle, slope aspect, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), distance to roads, distance to rivers, distance to faults, lithology, rainfall, soil, normalized different vegetation index (NDVI), and land use. Then, the 202 landslides were randomly segmented into two parts with a ratio of 70:30. Seventy percent of the landslides (141) were used as the training dataset and the remaining landslides (61) were used as the validating dataset. Next, the evaluation models were built using the training dataset and compared by the receiver operating characteristics (ROC) curve. The results showed that all models performed well; the FR_SysFor model exhibited the best prediction ability (0.831), followed by the IoE_SysFor model (0.819), IoE_LR model (0.702), FR_LR model (0.696), IoE model (0.691), and FR model (0.681). Overall, these six models are practical tools for landslide spatial prediction research and the results can provide a reference for landslide prevention and control in the study area.  
hybrid integration approaches; bivariate models; artificial intelligence algorithm; landslide 1
## 4 Results and Discussion
### Results Analysis
#### 4.2.1 Pre-Test Case  
When running the Pre-Test Case Dataset (Figure 9), the FIS correctly detected all the stall events. The orange vertical lines in Figure 9 show the datapoints where the PDMSstatus output had a value \(\geq\) 0.5, which is defined as VH (Very High) or EH (Extremely High) and is where the FIS inferred the stall event is being detected. The figure below shows the stall events happening around. 09:43, 09:54, and 09:57. Looking closer, a pressure spike can be seen around 09:46, which the system successfully did not flag as a stall, since there was no _WOB_ applied to define it as such.  
The FIS detected 3 out of 3 stalls present in this data.
## 3 Results and Analysis
### Characteristics of LTP Jet Array
The further increasing of the O\({}_{2}\) admixture ratio to 1% decreased NO concentration below the detection limit of the measurement device. Besides the decreasing discharge intensity, the transition of NO to NO\({}_{2}\) through the reaction of NO + O => NO\({}_{2}\)[9; 10] was also the main reason for the decreasing NO concentration with the increasing O\({}_{2}\) admixture ratio. As the main RONS generated by plasma, O, O\({}_{3}\), and NO can increase skin permeability through a complex series of reactions. The increase in the number of C-O or C=O bonds after the LTP treatment of the skin layer also confirmed the incorporation of oxygen to the stratum corneum lipids [32]. The reactions between O, O\({}_{3}\), and \(a\)-linolenic acid as a representative of fatty acid can create a double bond, incorporate alcohols or aldehyde groups, and increase the hydrophilic properties of fatty acids and change the lipid composition of skin, and thus eventually increases skin permeability [33]. Moreover, NO\({}_{x}\) species can acidify the lipids stripped from the stratum corneum, which also affects the skin permeability significantly [34; 35], although the pH of the skin can return to the initial value within 30 min after plasma treatment [36]. Therefore, the LTP jet array with the working gas of argon + 0.1% O\({}_{2}\) was used to treat IMQ-induced psoriasisform dermatitis with TwHF. LTP has an essential effect on the structural integrity of the stratum corneum (SC) layer. Figure 6 shows that the 5 min LTP treatment (the processing point remained fixed) caused damages to the SC layer of the isolated skin (blue dashed rectangle), and that the 10 min LTP treatment obliterated the SC layer. Similar changes to the SC structures induced by LTP were also observed in the treatments that used other LTP sources [31; 37; 38]. Besides the lipid peroxidation caused by the RONS of plasma, the strong electric field and high-speed plasma particles also contributed to the structural change of the SC layer [3; 4; 10; 11]. Figure 5: Concentration of O\({}_{3}\) and NO for the LTP jet array with the working gas of argon, argon + 0.1% O\({}_{2}\), argon + 1% O\({}_{2}\). The scanning process of the LTP jet array on the IMQ-induced psoriatic region on the back of live mice in the following experiment decreased the contact time between the plasma and SC. The highly active plasma can still affect the structural integrity of the SC [39, 40]. Even without noticeable structural change in the SC, the plasma-induced nitric oxides from keratinocytes can regulate the junctions between the cells and the extracellular matrix in the epidermal layer, which made the epidermal barrier permeable and facilitated the transdermal permeability of aniline blue [41].
# Modeling and NVH Analysis of a Full Engine Dynamic Model with Valve Train System
## 6 Conclusions  
A completed coupled engine dynamic model with the valve train system is proposed. The valve train system is integrated into the engine model, forming the full engine dynamic model. Based on the coupled multi-body dynamic model, the dynamic simulation is performed and the vibration and noise are predicted. The results of the coupled approach are compared with those of the uncoupled approach and the experimental values. The conclusions are summarized as follows:  
1. The coupled approach considers interaction ignored in the uncoupled approach, which can improve the fidelity of the model. The impact of the cylinder head deformation to the valve train and the support and constraints of the valve train on the cylinder head are taken into consideration in the coupled approach. The camshaft speed is obtained from the crankshaft speed in the simulation, instead of a constant or preset speed. Therefore, the influence of speed fluctuation on the valve train is taken into consideration as well.
2. Applying the coupled approach, more detailed dynamic results of the valve train are presented. The influence of the interaction between the valve train system and the cylinder head can be observed in the simulation results of the parts displacements, velocities, accelerations, and forces. The coupled approach is more instructive for analyzing the fatigue problems and abnormal noise problems of the valve train system.
3. Compared with the uncoupled approach, the results of the coupled approach have a better accuracy in vibration simulations. The vibration of the cylinder head measuring point at the second-order frequency (83.3 Hz) is severely overestimated in the uncoupled approach, resulting in the overestimation of the overall vibration and noise. In the coupled approach, the results of vibration at the second-order frequency are closer to the experimental values.
4. The coupled approach has a better accuracy in noise prediction as well. The calculated overall sound power level values in the uncoupled and coupled approaches are 95.4 dB(A) and 94.5 dB(A), respectively, while the experimental value is 93.6 dB(A). After using the coupled approach, the difference from the experimental value is reduced from 1.8 dB(A) to 0.9 dB(A).  
For this research, [PERSON] and [PERSON] put forward the approaches and established the simulation models; [PERSON] and [PERSON] provided the experiment support; [PERSON] and [PERSON] wrote the paper; while [PERSON] and [PERSON] further improved the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number 51876188 and 51705454 and the National Key Research and Development Program of China grant number 2016 YFB0101604.  
The authors declare no conflict of interest.
# Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era
## 4 Experimental Results
### Analysis of the Results
#### 4.3.2 Estimated Cost of the Computational Complexity  
In addition to the QoS assessment, Figure 8 presents the estimated cost of computational complexity during the execution of the nodes. Figure 8 details the average energy consumption of each node across the different scenarios, while Figure 8 depicts the average CPU usage of the MQTT Broker and The ONE Simulator during the evaluation tests. Both components were executed in a MacBook Pro M1 Pro. These results have been calculated based on the application logs and the consumption estimates from the reports.  
Figure 8 represents the average energy consumption of each mobile node in the network, identifying Node A as the gateway in partially online contexts. As can be seen, there are slight differences between the fixed and dynamic scenarios, with very similar consumption across all devices. Considering that opportunistic data traffic increases when nodes are permanently connected, consumption rises in contexts where interactions become more frequent. However, in dynamic scenarios, consumption decreases due to lower usage of local interfaces. Additionally, there is no evidence of extra consumption in the gateway node, even in offline contexts. In the case of Node D, consumption is higher than the rest, indicating a heavier workload as an intermediate entity.  
Figure 8 depicts the average CPU usage percentage for the MQTT Broker and The ONE Simulator, two of the main components of the DT. As can be seen, the CPU usage is quite similar across all cases, suggesting reduced sensitivity to incoming traffic. The ONE Simulator uses the CPU more intensively than MQTT, reaching a peak of 15.91%. These results show that, despite the input intensity, the simulator consumes the CPU to generate reports of the scenarios.
## 4 Discussion  
Previous research has thoroughly investigated the contextual significance, development trends, and practical applications of GBSAR studies for glacier monitoring. A comprehensive one-dimensional detection experiment was conducted to measure the movement speed of the glacier terminus using GB radar. Through comparative analysis with Sentinel-1 satellite radar data, the viability and superiority of GB radar in deformation monitoring applications were substantiated. It is important to note that the precision of glacier movement speed measurement is intricately linked to various factors including the characteristics of the glacier, the surrounding environment, the measurement properties of the radar system, and data processing methodologies. These findings underscore the complex interplay between these elements in achieving accurate and reliable results in glaciological research. The analysis is as follows:
## 5 Results and Discussions
### Optimization Results
Figure 13 illustrates the motor's performance based on PSSEE evaluation criteria through a Z-score plot. It is evident from the figure that the maximum value of the Z-score plot has decreased for both optimization algorithms. Specifically, the Z\({}_{\max}\) value for  
Figure 12: Motor speed results during sudden speed changes. the NSGA-II optimization algorithm, initially falling in the C7 interval, reduces to 5.75, while for the MOPSO optimization algorithm, also in the C7 interval, decreases to 5.36. These results demonstrate that both novel optimization algorithms, compared with STSM, effectively reduce torque aggregation. Moreover, MOPSO exhibits a lower Z-score and a more reasonable torque aggregation distribution, which is conducive to prolonging the motor's life and enhancing its performance. Utilizing the Z-score of PSSEE allows for a more reasonable and efficient evaluation of the motor's torque, ultimately leading to the judgment of MOPSO as a more effective optimization algorithm. This analysis underscores the advantages of employing the PSSEE methodology for evaluating motor performance and highlights MOPSO's superiority in optimizing torque aggregation and overall motor performance.
## Appendix A Supplementary Data of Total Chlorophyll and Carotenoid Determination
### Model Graphs  
The evaluation of the predicted model behavior is visualized through the model graphs, which subsequently contribute to MAE's optimization. The graphic display of each response is presented through the two-dimensional contour plots for the selected factors of extraction temperature and microwave power, including snapshots at the low, central and high levels of extraction duration and solvent-to-biomass ratio (Figures A1-A4). The value increase of each response is expressed by the color transition from dark blue to green, yellow and, finally, red.  
Figure A1 confirms the positive effect of the simultaneous temperature, microwave power, duration, and solvent-to-biomass ratio increase on the extraction yield. The most significant combined term of temperature, microwave power, and duration (T P t), according to Table A1, leads to a remarkable yield elevation with its increase, while the increase of a statistically significant factor of solvent-to-biomass ratio seems to affect the extraction yield up to 55 mL\({}_{\text{solv}}\)/g\({}_{\text{biom}}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{2}{c}{**Yield**} & \multicolumn{2}{c}{**Chlorophylls**} & \multicolumn{2}{c}{**Carotenoids**} & \multicolumn{2}{c}{**IC\({}_{50}\)**} \\
**Source** & _p-Value_ & & _p-Value_ & & _p-Value_ & & _p-Value_ & \\ \hline Model & \textless{}0.0001 & sign.\({}^{1}\) & \textless{}0.00  
Moreover, Figure A2 illustrates the significant effect of the combined term of temperature and microwave power (T P) on the total chlorophyll content. The improved results are observed diagonally, from moderate to high microwave power values under lower temperatures to higher microwave power under higher temperature values. The solvent-to-biomass ratio increase leads to severe deterioration from 20 to 55 mLsolv/gbiom and imperceptible improvement up to 90 mLsolv/gbiom, while the duration increases up to the central level is considered beneficial.  
Similar behavior is depicted in Figure A3 regarding the total carotenoid content. Likewise, carotenoids are increased following the same diagonal while maintaining a low solvent-to-biomass ratio and intermediate extraction duration.  
Finally, according to Figure 14, significant improvement of the antioxidant activity, i.e., a decrease of IC\({}_{50}\), is observed during the midpoint of the extraction duration range, while the solvent-to-biomass ratio reduction offers a positive, yet less strong, contribution. Therefore, while maintaining a medium and a low value of duration and ratio, respectively, stronger antioxidant activity is achieved either under high temperature and low microwave power or under below midpoint temperature and microwave power above 400 watts.
# English-Welsh Cross-Lingual Embeddings
## 2 Background
### Cross-Lingual Embeddings  
Earlier attempts to train cross-lingual word embeddings required access to parallel (or, at least, comparable) corpora [19; 20; 21; 22; 23; 24; 25]. Finding such corpus especially for a minoritized language can prove challenging. Therefore, the research in this space gravitated towards using bilingual dictionaries instead of aligning the words in respective languages [6; 26]. It was later shown that such cross-lingual supervision is not necessary to align word embedding [7]. Instead, adversarial training can be used to initialize a linear mapping between two vector spaces and produce a synthetic parallel dictionary. The success of this approach was based on the use of two metrices: one for unsupervised validation and the other one for similarity measure. Such combination reduces the hubness problem while improving the translation accuracy.  
Hubness is a phenomenon that occurs in high-dimensional spaces, where some objects tend to concentrate around a centroid while others have few nearest neighbors [27]. Specifically, hubness associated with cross-lingual embeddings was explored in [28], who proposed incorporating a nearest-neighbor reciprocity as a way of managing hubness. Different measures were used to down-weigh similarities associated with hub words, including cross-domain similarity local scaling (CSLS) [7] and inverted softmax [29]. In ad dition, adding an orthogonality constraint, which conveniently has a closed-form solution, can improve performance further [30].  
Alternatively, to align monolingual embedding spaces with no supervision, [PERSON] et al. [31] used adversarial training to exploit sudden drops in accuracy for model selection followed by minimizing the earth-mover distance [32]. Conversely, [PERSON] et al. [7] do not base model selection on its performance, which allows for hyper-parameters to be tuned specifically for a given language pair as they tend to vary significantly across languages. Similar approaches used to induce bilingual dictionaries from data [5; 10; 14; 33] yielded state-of-the-art performance in many language pairs, although the experimental setup followed in the literature has also been closely scrutinized, and there exist studies that argue for experiments that account for different genres in source and target corpora, studying (dis)similarities between languages, etc. [34; 35].  
Although the advent of language models in the current NLP landscape (BERT, GPT, or RoBERTa) [36; 37; 38] has transformed the field, it is also true that even for languages where the availability of raw data is small, having access to pre-trained static word embeddings can make the difference between developing a language technology or not at all. Recent work has, for example, focused on dialectal Arabic, by combining BERT-based encodings with Arabic word embeddings for underrepresented domains and dialects [39].
## 5 Results
### Prediction Model Establishment
#### 5.2.3 Prediction Comparison Between RSM and BPNN of 60 Sets Data  
Through the application of the aforementioned two methods, modeling of the input and output variables of the grinding process was conducted, coupled with regression analysis to evaluate the models. When fitting with 60 sets of real data, the comparison between the predicted values and the actual values on the entire dataset for the two methods is shown in Figure 8a. The prediction of surface roughness using three models and the comparison with the measured values are depicted in Figure 8b. For an experimental dataset of 60 sets, the \(R^{2}\) values for the test set and the entire dataset using the response surface method were 71.26% and 76.46%, respectively. Meanwhile, the \(R^{2}\) values for the test set and the entire dataset using the BPNN method were 83.49% and 93.30%, respectively. These final results indicate that, in this scenario, the BPNN model demonstrates a good fitting effect.
# Flexible Antennas: A Review
## 3 Fabrication Techniques for Flexible Antennas  
The performance of a flexible antenna is determined by the fabrication method (which is different for different substrates). The common fabrication techniques include wet-etching, inkjet printing, screen printing, and other special methods for fabricating flexible wearable antennas. A detailed overview of different fabrication techniques of flexible antennas can be found in earlier reports [15; 16].
# Optimal Energy Management within a Microgrid: A Comparative Study
## 2 Model for Microgrid Components
### Points of Common Coupling  
It is considered that the microgrid operates in grid-connected mode, such that it is connected to the main grid via \(N_{\text{pcc}}\) (\(N_{\text{pcc}}\geq 1\)) Points of Common Coupling (PCC). It is assumed that each PCC is electrically robust and is an interface for the unlimited exchange of both active and reactive power between the microgrid and the main grid. In this context, the PCC are modeled as generational sources that operate at voltage levels magnitude \(V_{j}^{t_{z}}\) and angle \(\theta_{j}^{t_{z}}\) within the limits given by Equation (1):  
\[\underline{V}_{j}\leq V_{j}^{t_{z}}\leq\overline{V}_{j};\underline{\theta}_{j} \leq\theta_{j}^{t_{z}}\leq\overline{\theta}_{j};\forall t_{z}\in T;\forall j \in\text{PCC} \tag{1}\]  
For time \(t_{z}\), the cost of active total power exchanged through the PCC is modeled by Equation (2), where \(a_{j}\), \(b_{j}\), and \(c_{j}\) are weight constant coefficients for the \(j\)th PCC. The variable \(P_{RP\;j}^{t_{z}}\) denotes the power exchanged through the \(j\)th PCC. The decision variables associated with the PCC are \(\left[V_{j}^{t_{z}},\theta_{j}^{t_{z}},P_{RP\;j}^{t_{z}}\right]\in y_{RD}\) (\(\forall t_{z}\in T,\;\forall j\in\text{PCC}\)) where \(y_{RD}\) are the distribution grid variables.  
\[f^{t_{z}}\big{(}y^{t_{z}}\big{)}=\sum_{j=1}^{N_{\text{pcc}}}a_{j}+b_{j}\Big{(} P_{RP\;j}^{t_{z}}\Big{)}+c_{j}\Big{(}P_{RP\;j}^{t_{z}}\Big{)}^{2};\forall t_{z}\in T \tag{2}\]
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 5 Description of the Considered Signaling Schemes  
Given the advantages of LACO-OFDM, as explained in the previous section, it is considered in this paper, as well as the popular DCO- and ACO-OFDM. A brief description of these schemes is provided in the following.
# Experimental Validation of a Guaranteed Nonlinear Model Predictive Control
## 2 Background of Dynamic Modeling of the Inverted Pendulum  
### Robotic Device  
The inverted pendulum is a simple system with two serial rotational joints (active and passive). We have designed and manufactured one in our laboratory (see Figure 1). Our experimental device is equipped with a brushless DC motor to actuate the first joint and two incremental encoders to measure the rotational angles of both the motor shaft and the passive joint. Its kinematics is sketched in Figure 1. This device must be identified and stabilized in a validated and guaranteed way, considering all the errors and uncertainties related to the system modeling and data measurements.  
Figure 1: (**Left**) Definition of links frame (configuration \(y_{1}=y_{3}=0^{\circ}\)); (**Right**) representation of the real rotary inverted pendulum manufactured in our laboratory (\(S_{0}\): pendulum housing, \(S_{1}\): horizontal arm, \(S_{2}\): pendulum arm, \(S_{3}\): balancing mass).