esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
An accurate petrophysical model of the subsurface is essential for resource development and CO\({}_{2}\) sequestration. We present a new workflow that provides a high-resolution estimate of petrophysical reservoir properties using seismic data with rock physics modeling and machine-learning techniques (i.e., deep learning neural networks). First, we compare the sequential prediction of the following petrophysical attributes: mineralogy, porosity, and fluid saturation, with the simultaneous prediction of all of the properties using the Volve field in the Norwegian North Sea as an example. The workflow shows that the sequential prediction produces a more efficient and accurate classification of petrophysical properties (the RMS error between the predicted and the original seismic trace is 50% smaller for the sequential compared to the simultaneous procedure). Next, the seismic amplitude response of the reservoirs was studied using rock physics modeling and amplitude versus offset (AVO) analysis to distinguish the different lithologies and fluid types. To ascertain the optimal hydrocarbon production areas, we performed Bayesian seismic inversion and applied machine learning to estimate the petrophysical properties. We examined how porosity, V\({}_{\text{clay}}\), and fluid variations affect the elastic properties. In Poisson's ratio versus the P-wave impedance domain, a 10% porosity increase decreases the acoustic impedance (AI) by 30%, while a 20% V\({}_{\text{clay}}\) decrease increases the AI by 12%. The Utsira Formation in the Volve field (5 km north of the Slepner \(\mathcal{O}\)st field) was evaluated as a potential CO\({}_{2}\) geological storage unit using Gaussian fluid substitution and seismic modeling. We look to assess the elastic property variation caused by CO\({}_{2}\) saturation changes for monitoring purposes and simulate the effect. In the first 10% CO\({}_{2}\) substitution, the P-wave velocity decrease is 12%, a subtle effect is observed for higher CO\({}_{2}\) saturation values, and S-wave velocity (\(V_{s}\)) increases with CO\({}_{2}\) saturation. Our analysis aspires to assist future reservoir studies and CO\({}_{2}\) sequestration in similar fields.  
petrophysics; rock physics; interpretation; seismic; reservoir geophysics; parameter estimation 2024 14 1345 10.3390/app14041345
# Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System
## 5 Conclusions
\begin{tabular}{l l} \(J_{i}\) & Total moment of inertia of rotating parts. \\ \(F_{i}\) & Driving force of electric actuator. \\ \(M_{fi}\) & Friction moment. \\ \(M_{zi}\) & Tire self-aligning torque around the kingpin. \\ \(M_{ni}\) & Moment of uncertain force on the ground. \\ \(c_{i}\) & Equivalent damping coefficient. \\ \(k_{i}\) & Equivalent stiffness. \\ \(b_{r}\) & Length from the connection point to the kingpin. \\ \(\delta_{ri}\) & Rear wheel steering angle. \\ \(M_{gfi}\) & Initial value of friction moment. \\ \(F_{zri}\) & Rear wheel vertical load. \\ \(\mu_{ri}\) & Coulomb friction coefficient. \\ \(\gamma_{i}\) & Kingpin inclination. \\ \(s_{i}\) & Horizontal distance from the tire center to the kingpin. \\ \(m\) & Mass of the vehicle body. \\ \(I_{z}\) & Yaw moment of inertia of the vehicle. \\ \(k_{af}\) & Cornering stiffness of front wheels. \\ \(k_{ar}\) & Cornering stiffness of rear wheels. \\ \(a\) & Distance from the front axles to the vehicle mass center. \\ \(b\) & Distance from the rear axles to the vehicle mass center. \\ \(\delta_{f}\) & Front axle wheel angle. \\ \(\delta_{r}\) & Rear axle wheel angle. \\ \(v_{x}\) & Vehicle longitudinal speed. \\ \(a_{y}\). & Vehicle lateral acceleration. \\ \(\omega\) & Vehicle yaw rate. \\ \(\beta\) & Vehicle sideslip angle. \\ \(N\) & Reduction ratio. \\ \(f_{k}\) & Ratio between front axle steering angle and rear axle steering angle. \\ \(\theta_{sw}\) & Driver's steering wheel angle. \\ \(l\) & Wheel base. \\ \(w\) & Wheel track. \\ \(\rho\) & Turning radius. \\ \(\delta_{rl}\) & Left rear wheel steering angle. \\ \(\delta_{r2}\) & Right rear wheel steering angle. \\ \(m_{r}\) & Equivalent mass of the rack. \\ \(x_{r}\) & Translational displacement of the rack. \\ \(g_{r}\) & Radius of the virtual steering pinion gear. \\ \(k_{r}\) & Equivalent stiffness coefficient. \\ \(c_{r}\) & Equivalent damping coefficient. \\ \(F_{ri}\) & Forces fed back to the steering gear by the rear wheels on both sides. \\ \(F_{r}\) & Virtual synchronization driving force of the steering rack. \\ \(e_{s}\) & Synchronization error. \\ \(e_{t}\) & Steering wheel tracking error. \\ \(k_{rp}\) & Proportional coefficient. \\ \end{tabular} \begin{tabular}{l l} \(k_{td}\) & Differential coefficient. \\ \(\delta_{r}^{*}\) & Equivalent rear axle steering angle. \\ \(\delta_{r1}^{*}\delta_{r2}^{*}\) & Measured rear wheel steering angles. \\ \(\delta_{r1}\), \(\delta_{r2}\) & Given reference signal. \\ \(u_{01}\), \(u_{02}\) & Ideal control output. \\ \(k_{p}\), \(k_{d}\) & Gain coefficients of the feedback control law. \\ \(\omega_{c}\) & Controller bandwidth. \\ \(\omega_{o}\) & Observer bandwidth. \\ \(\lambda\) & Weight coefficient. \\ \end{tabular}
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 4 Discussion
In such cases, the availability of imagery is constrained by satellite acquisition schedules, making it necessary to work with images captured from different orbits and angles. This limitation can introduce geometric distortions and variations in backscatter, which may impact the accuracy of the terrain corrections and windthrow detection. Even though this is not the main focus of this paper, the authors have addressed the reality of suboptimal conditions in emergency applications in [18]. In [18], the authors thoroughly analysed the effects of using images with varying orbits and angles, comparing optimal pre- and post-event images with suboptimal ones. The findings demonstrate that while the results are generally more accurate with optimal imagery, the proposed methods remain effective even when working with less-than-ideal data. This highlights the robustness of the approach in real-world emergency situations, where rapid response often necessitates the use of available, rather than optimal, images. It is noteworthy that while there has been considerable attention devoted to developing algorithms for terrain correction, scarce research has delved into the influence of the DEM used for this purpose. A previous study [36] similarly noted that high-resolution DEMs contribute to higher-quality sigma0 images, underscoring the importance of DEM selection in SAR image processing. In [37] a detailed study on the preprocessing of Sentinel-1 images using the SNAP software has been done, comparing different freely available DEMs and resampling algorithms. The authors of [37] found that the DEMs affected the results, with SRTM performing better in the correction. It is worth noting that Sentinel-1 data spatial resolution is coarser compared to COSMO SkyMed.
###### Abstract  
We optimized and fabricated an ultra-bend-resistant 4-core simplex cable (SXC) employing 4-core multicore fiber (MCF) suitable for short-reach dense spatial division multiplexing (DSDM) optical transmission in the O-band. The characteristics of transmission loss, macro-bending and cross-talk (XT) between adjacent cores after cabling were firstly clarified. By introducing the trapezoid index and optimizing the cabling process, the maximum values of added XT of 1.17 dB/km due to 10 loops with a bending radius of 6 mm imposed over the 4-core SXC and a macro-bending loss of 0.37 dB/10 turns were, respectively, achieved.P Then, the optical transmission with low bit error rate (BER) was presented using a 100 GBASE-LR4 transceiver over the 1.2 km long 4-core SXC. The excellent bending resistance of the 4-core SXC may pave the way for a reduction in space pressure and increase in access density on short-reach optical interconnect (OI) based on DSDM.  
multicore fiber; optic cable; macro-bending loss; cross-talk; eye diagram Article
# Recent Trends and Progress in Corrosion Inhibitors and Electrochemical Evaluation
## 6 Recent Trends and Developments in Corrosion Inhibitors
Chitosan-based corrosion inhibitors are regarded as environmentally beneficial due to their natural origin [65; 181; 182]. In general, the corrosion prevention efficacy of chitosan that has been modified with different organic compounds is  
Figure 22: Optimized structures, total charge density, and frontier molecular orbital density distributions (HOMO and LUMO). Bond length, counter and Mulkin charge images obtained by DFT/B3 LYP/6-31 G+ (d,p) method for papaverine (1), thebaine (2), and narcotine (3) [173]. higher than that of pure chitosan. [PERSON] et al. [177] applied Ch-Cr-SB as a metal's anticorrosive agent AZ91E alloy in a corrosive medium of 3% NaCl. The obtained results showed that the corrosion rate lowers when the Ch-Cr-SB concentration rises. In another study, [PERSON] et al. [183] employed ChTSB as an anticorrosive layer on MS with the inhibitory potential demonstrated by WL, EIS, SEM, EDX, and AFM. The WL technique indicates that the inhibition performance is 86.94% at 1500 ppm concentration and 33 K. [PERSON] et al. [184] developed, for the first time, green CIs based on chitosan for the carbon dioxide corrosion of P1110 steel. Efficiency also rises when inhibitor concentration does. At 100 mg/L, both CHC and CAHC achieve their highest levels of corrosion inhibition effectiveness in a NaCl solution of dioxide, which, respectively, are 85.55% and 93.44%, demonstrating that both inhibitors have a sufficient inhibiting effect on carbon corrosion. An eco-friendly Schiff base (salicylaldehyde-chitosan Schiff base) was coated on a metal with a corrosive medium of 3.5% NaCl saturated with carbon dioxide at a 65 \({}^{\circ}\)C temperature. Weight loss, EIS, and potentiodynamic polarization (PDP) techniques were used to assess the inhibitory effect. According to the PDP findings, SCSB functions as a mixed-type inhibitor and successfully slows down corrosion at a concentration of 150 mg/L, with an IE of 95.2% and a corrosion rate of 0.444 mm/y. EIS measurements imply that the prevention of corrosion is a kinetic process [180; 185].
# A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications
## 3 Synthesis and Characterization of TWNTs
### Annealing of Confined Molecules inside DWNTs
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Catalyst/Precursor/Promoter** & **Carbon Source/Carrier/Atmosphere** & **Inner Tube \(d_{inner}\) (mm)** & **Middle Tube \(d_{middle}\)** & **Outer Tube \(d_{outer}\) (mm)** & **S Selectivity** & **Method** \\ \hline Fe/Mo/Quartz [25] & EtOH/Ar/H\({}_{2}\) & - & - & 3.80-4.80 & 62\% & Supporting (substrate) \\ C\({}_{60}\)dDWNT (pea pod) [26] & Ar & 0.60 & 1.20 & 2.00 & 45\% & Supporting (substrate) \\ Fe\({}_{0}\)O\({}_{\text{v}}\)/Si [147] & CH\({}_{4}\)/H\({}_{2}\) & 4.86 & - & - & 83\% & Supporting (substrate) \\ FeCl\({}_{\text{v}}\)/Si [145] & CH\({}_{4}\)/H\({}_{2}\) & 1.36-3.78 & 2.12-4.66 & 2.87-5.19 & 90\% & Supporting (substrate) \\ Fe/Al\({}_{6}\)O\({}_{\text{v}}\) [146] & HO/CH\({}_{4}\)/H\({}_{2}\) & - & - & 8.00 & 59\% & Supporting (substrate) \\ Co/Mo/Quartz [148] & EtOH/Ar/H\({}_{2}\) & - & - & 4.00 & 76\% & Supporting (substrate) \\ \hline \end{tabular}
\end{table} 
Table 2: Synthesis methods for the selective growth of triple-walled carbon nanotubes (TWNTs).
## 2 Related Work
### Continual Updating of Foundation Models  
FMs are pretrained on diverse datasets and capture extensive knowledge up to pretraining. However, real-world environments are dynamic and continuously evolving, leading to new tasks and subdomains that may render pretrained knowledge obsolete. Retraining or fully updating these models each time new data emerges is impractical because of the enormous computational cost.  
Recent research on continual and lifelong learning aims to incorporate newly acquired knowledge incrementally, addressing issues such as catastrophic forgetting [18], a phenomenon in which models lose previously learned information when integrating new information. Conventional continual learning approaches generally fall into two categories. The first category includes regularization-based methods. These methods add a penalty to the loss function based on the parameters' importance and suppress the variation in each parameter based on how important that parameter was in the execution of previous tasks. In elastic weight consolidation (EWC), a typical method, the importance is calculated using the Fisher information matrix [19]. The second category includes replay-based approaches, which store subsets of prior data or synthetic examples and use them alongside new data during training [20].  
Although conventional approaches are practical in some scenarios, they tend to be computationally expensive and less efficient when applied to large-scale models [21]. Thus, the concept of continual pretraining has gained traction. This approach begins with a pretrained FM and incrementally updates it with new data in a manner that scales to larger models and more complex tasks [22]. Despite its promise, continual pretraining remains underexplored, particularly regarding the integration of model merging techniques. Most previous work in this area has focused on adjusting training strategies through refined regularization objectives [23] or tailored learning rate schedules [24] without addressing how to effectively combine multiple fine-tuned models over successive update cycles.  
In addition, no existing work has investigated the application of FL frameworks for continual pretraining. This study explores how model merging can be embedded in a federated setting while maintaining a fixed training strategy and continuously updating the model. This research direction is crucial for reducing the overhead of full retraining and ensuring that the FM remains current in dynamic environments.
## 1 Introduction
For different types of crops, the textural, spectral, temporal, and spatial features can vary in remote sensing images. The effective use of these features is the theoretical basis for precision crop mapping [44]. Deep learning [45], random forest [46], support vector machine [47], and other machine learning methods have been applied to crop mapping. Through the feature learning of a large number of samples, crop classification models have gradually improved to meet higher accuracy requirements. There are also differences in internal structures among crops. Differences in the spectral responses of crops [48] are commonly used to distinguish crop types, allowing precise classification of crops. This method is often simpler and more general. In addition, remote sensing has specific advantages in terms of monitoring crop growth and evaluating cultivated land quality. Moderate resolution imaging spectroradiometer (MODIS) gross primary productivity (GPP) products reflect the total amount of organic carbon fixed by photosynthesis in green plants per unit area per unit time. GPP has been widely used in crop yield estimation [49] and cultivated land productivity evaluation [50]. [PERSON] et al. [51] used GPP to evaluate cultivated land quality and developed an assessment model based on MODIS-GPP through support vector regression and genetic algorithm-based back propagation neural network models; the findings indicated a linear relationship between cultivated land quality and GPP. In the estimation of NSP intensity, remote sensing can effectively solve the problems of crop type classification and crop growth monitoring in actual complex situations and during large fluctuations of cultivated land type and configuration. Thus, it provides a data source with high precision and timeliness for NSP intensity estimation. In this study, we examined the Yuecheng Reservoir catchment area in China as an example to estimate and map the NSP intensity in sensitive areas. We had two main objectives. First, we applied a phenology-GPP crop information extraction method to obtain the distribution map concerning crop types, multiple cropping, and difference in crop growth, thus providing reliable data sources for NSP intensity estimation. Second, we applied an improved farmland NSP intensity calculation model to estimate the NSP intensity in sensitive areas--the Yuecheng reservoir catchment. The results were compared using a traditional method to identify the effects of crop differences on pollution intensity estimation.
## 3 Classification
### Types of VRUs
#### 3.1.1 Pedestrians  
Pedestrians' typical walking speed is 1.4 m/s (5 km/h). Pedestrians may walk alone or in groups of various sizes. Walking speed of pedestrians may vary by age and physical ability. Based on pedestrians' physical characteristics, they may further be classified into following groups:  
1. Adults--This group adheres to the typical characteristics of pedestrians, such as speed and trajectory. 2. Children--This group may exhibit characteristics such as unpredictable trajectory, slow walking or running. 3. Senior and physically disadvantaged persons--This group may exhibit characteristics such as slow walking and may use some assistance (e.g., cane, wheelchair, or a guide dog). Multiple efforts have been made to research and design V2P crash prevention systems for aforementioned pedestrian groups [3; 4; 5; 9; 15; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31]. These efforts use various approaches to achieve their goal of V2P system. Table 1 lists various V2P systems with their key features. Here we discuss a few approaches. Figure 2: Example V2P System Architectures. [PERSON] et al. [3] have designed a DSRC-based V2P system for pedestrians. It uses a smartphone as a VRU device and leverages the smartphone sensors in order to optimize the transmission of safety messages. Both VRU device and vehicle, send safety messages that contain their location, speed, and direction information. [PERSON] et al. [4] propose V2 ProVu, a Wi-Fi-based system, that alerts pedestrians for potential crash. It uses a smartphone as a VRU device. This system requires a VRU device to listen for safety messages sent by vehicles and then predict the collision probability. [PERSON] et al. [5] propose a V2P system that enables the exchange of safety messages through a combination of cellular infrastructure and direct Wi-Fi communication. A central information processing server processes the safety messages that it receives from vehicles and pedestrians and calculates the collision risk. [PERSON] et al. [15] propose WiFiHonk, a Wi-Fi-based system, that enables vehicles and pedestrians to exchange safety messages without camping on Wi-Fi network. The system stuffs Wi-Fi beacons with safety messages in order to achieve this goal. [PERSON] et al. [21] propose an IEEE 802.15.4 based V2P system that involves a _Warning Unit_ as a vehicle equipment and a _Tag_ as a VRU device. The system is based on a paging process where the _Warning Unit_ sends \"Hello\" messages to which the _Tag_ responds by sending \"Here I am\" packet. The tag can be put into children's backpacks in order to make vehicles aware of the children's presence. The Ko-TAG project [27] proposes a co-operative pedestrian localization system. This system requires the pedestrian to carry a transponder
# Deep Analysis of Student Body Activities to Detect Engagement State in E-Learning Sessions
## 1 Introduction
### Contributions and Novelty  
In our previous work [9], through implementing a deep learning model for engagement detection based on analyzing student body activities, we analyzed, defined, and classified body activities in an e-learning environment into two main categories: (1) macro-body activities, e.g., eating, talking, laughing, etc., and (2) micro-body activities, e.g., head tilting, hand gestures, head scratching, etc. We were inspired by the proposed emotion-based affective model in [8] to map between expressed emotions and engagement levels. Based on our review of existing modalities in the literature (see Section 2), using spatiotemporal features from micro-/macro-body gestures has not been previously used and has shown improvements in the results. To summarize, our contributions to this work are as follows:  
* The new method supports future work on customizing interactive e-learning systems.
* This work presents a novel approach to the implementation of a solution for automatic engagement level detection by utilizing a deep 3D CNN model for learning the spatiotemporal attributes of micro-/macro-body actions from video inputs. The implemented 3D model learns the required gesture and appearance features from student micro-/macro-body gestures.
* In this study, we address the significance of learning the spatiotemporal features of micro- and macro-body activities. This work mainly contributes to academia by providing a deep 3D CNN model trained on realistic datasets; the proposed model outperforms previous works. Furthermore, this work contributes to emerging educational technology trends, and the proposed deep 3D CNN model can extend existing interactive e-learning systems by adding an additional indicator of learner performance based on the level of engagement.
* We collect and process two new versions of our original dataset, named dataset 1. We will call the new versions dataset 2 and dataset 3. The data were collected during real scenarios recorded by real students in an uncontrolled environment, which offers many challenges related to the recording settings (features from the dataset are available from the corresponding author on reasonable request).
* We implement two new prediction models to measure more precise engagement levels based on the new dataset versions.
* We empirically find the architecture of the models that give the highest performance.  
We assess the performance of the proposed models via a number of experiments.  
In the next section, we summarize related works. After that, in Section 3, we present the new video dataset and present the details of data collection and preparation in Section 4. In Section 5, we show the process of extracting the spatiotemporal features. After that, in Section 6, we present our proposed prediction models. In Section 7, we present and discuss the experimentation and evaluation of the proposed work in order to justify our choices. In Section 8, we discuss the results and findings of this study. Finally, in Section 9, we present the conclusion.