esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Base Shear Load  
Figure 13 shows the results of the base shear load under the seven seismic ground records for the undamped and the three damped cases. For all damper systems, the ratio between the base shears of models with and without dampers for the low-rise building shows an important reduction (Figure 13a,b). Up to a 76.74% response reduction was achieved with viscous dampers in the longitudinal direction, and a maximum reduction attained by 36.61% in transversal direction. High values of reduction with friction dampers reach 39.93% and 59.14%, respectively, in both directions. With TMD, the percent of reductions are given as about a maximum of 15.74%. By evaluating the mean value of percentage reduction in both directions, which is equal to 2.54% and 5.32% with tuned mass damper, 79.96% and 31.06% with viscous dampers, and 27.38% and 41.66 % with friction dampers, both viscous and friction dampers appear to be effective for the low-rise building in base shear reduction.  
Figure 13: Base shear load for (**a,b**) the low-rise building, (**c,d**) the mid-rise building and (**e,f**) the high-rise building.  
Tuning of the three studied dampers resulted in a 14.79%, 70.09%, and 43.59% maximum reduction of the base shear values with the tuned mass damper, viscous dampers, and friction dampers systems, respectively, in the longitudinal direction, and in a 16.14%, 98.22%, and 40.70% maximum reduction, respectively, in the transversal direction. Considering all the results obtained, and by evaluating the mean value of percentage reduction in both directions, which is equal to 7.12% and 7.72% with tuned mass damper, 63.91% and 97.62% with viscous dampers, and 30.97% and 27.57% with friction dampers, the mid-rise building equipped with viscous dampers have the greatest impact on the reduction of the base shear response in earthquake excitations compared to the two other damping systems.  
Base shear results indicated a maximum reduction achieved with viscous dampers reaching 71.20% and 98.76% for ChiChi earthquake in longitudinal and transversal direction, respectively, and a maximum of 43.77% and 63.48% with friction dampers.
###### Abstract  
This review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The Sentinel-2 and Landsat 8/9 missions are highlighted as the primary core datasets due to their open-access policy, worldwide coverage, and demonstrated applicability in long-term coastal monitoring. Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. UAVs supply complementary high-resolution data for localized validation, and ground truthing based on GNSS increases the precision of the produced map results. The fusion of UAV imagery, satellite data, and machine learning aids a multi-resolution approach to real-time shoreline monitoring and early warnings. Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years.  
sentinel-2; sentinel-1; Landsat; coastal erosion; shoreline; remote sensing; shoreline detection; open access; satellite data; AI; UAV; machine learning +
Footnote †: journal: Applied sciences  
_Review_  
A Review of Open Remote Sensing Data with GIS, AI, and UAV Support for Shoreline Detection and Coastal Erosion Monitoring, _Appl. Sci._**2025**, _15_, _4771_. [[https://doi.org/10.3390/app15094771](https://doi.org/10.3390/app15094771)]([https://doi.org/10.3390/app15094771](https://doi.org/10.3390/app15094771))
## 5 Kinematic Assessment of Movement Performance
### Gym Physical Activity Tracking
Results showed good accuracy in the classification of the exercises considered, highlighting that sets with more repetitions seemed to be easier to classify if compared to groups with a low number of repetitions, maybe since subjects usually perform the movement worse on sets with few repetitions than on groups with a high number of repetitions. In [176], the authors presented SensX, a special architecture for analyzing measurements obtained by different families of sensors to evaluate human motion chains. In such a study, the system was employed to test twenty athletes performing eight body weight exercises, wearing a central processor unit on the chest and four small IMU sensors, each one provided with an accelerometer, gyroscope, barometer, and magnetometer (ten DoF) placed on limbs. These sensor devices have been wirelessly connected by exploiting a Bluetooth BLE radio link (Figure 7). Later, the method of classification was based on naive Bayes classification. Average training data and cross-validation for all eight performed exercises resulted in more than 94%. Only the pushup exercise data was notably worse than the others, maybe because of the similar position of the mountain climber exercise or since the high effort caused by this complex exercise, as the initial hypothesis. The paper [177] investigated a deep learning algorithm to recognize exercise and count the repetitions performed. In this experiment, the subjects were given two smartwatches worn on the wrist and ankle, respectively. They were asked to conduct ten complex full-body exercises (four bodyweight exercises, three with kettlebell, one with medicine ball, one with box, and one with pull-up bar), typical of CrossFit performances. The exploited deep learning algorithm was based on CNNs. The study employed a DAQ acquiring the output of an accelerometer, a gyroscope, and a magnetometer at a sampling rate of 100 Hz. The model proposed by the authors achieved excellent accuracy for exercise recognition and \(\pm 1\) error in repetition counting for 91% of the performed sets. Figure 7: Possible gym wearable device illustration. Data are recorded via smartwatch or IMU and processed through deep learning analysis. Research from [178] shows the production of a training tracking system called MiLift based on automatic segmentation. Through this system, which can be applied using several smartwatch models, it has been possible to recognize cardio, free-weight, and machine-based exercises, and count repetitions.
# Iterative Interferometric Denoising Filter for Traveltime Picking
## 6 Conclusions  
We present the interferometric denoising filter (IDF) theory to eliminate random or incoherent noises in first-arrival traveltime picking. Our proposed method could increase the S/N ratio and keep accurate traveltime information for picking. Using our methodology, the dynamic character is distorted and has higher frequency components than the original dataset. This methodology is based on auto-correlation operations to find regular patterns that random or incoherent data noises do not have. We applied it to synthetic data generated by the forward algorithm. It is obvious that the random or incoherent data noises were eliminated, and the slight difference between the picked traveltime from the original and the IDF-processed data proves that the kinematic character kept well. In addition, two sets of field data, gathered in the Gulf of Aqaba and King Abdullah Economic City (KAEC) near the Qademah fault, are used to demonstrate the proposed approach's feasibility. We compare picked traveltime and corresponding tomography results in these two field datasets. Both show great agreement with the picking information on original seismic data (although the picking procedure is complex due to the noise) and the corresponding traveltime tomography result. In the future, we could investigate the application of our proposed method in different fields, including geophysical case studies, environmental engineering, and pattern capture in other types of datasets. Furthermore, exploring the potential of the waveform, which is processed by our iterative IDF procedure and has a higher frequency component in waveform inversion application, is valuable.  
Methodology, H.Q.; Software, Y.Z.; Validation, H.Q., Y.Z. and S.M.H.; Formal analysis, Y.Z. and C.L.; Investigation, H.Q., S.M.H. and C.L.; Data curation, S.M.H.; Writing--original draft, C.L.; Writing--review & editing, S.M.H.; Project administration, H.Q. and Y.Z.; Funding acquisition, H.Q. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Traveltime tomography using first-arrival traveltime information picked from (**a**) original and (**b**) processed IDF datasets.  
**Funding:** This research was funded by the China Geological Survey Project (DD20230010); the China Geological Survey Project (DD20230324); and the National Natural Science Foundation of China (41904065).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to the pre-approvals required from KAUST and KFUPM.  
**Acknowledgments:** The author would like to thank King Abdullah University of Science and Technology (KAUST) and King Fahd University of Petroleum and Minerals (KFUPM) for their permission to use their land seismic data in this study.  
**Conflicts of Interest:** The authors declares no conflicts of interest.
# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
## 5 Real Scenarios Evaluation
### Test Framework
Other challenges like the existence of shaded zones not covered by any RSU or the interruption of the communication when the network cards of two vehicles encounter an obstacle between them, also produce network packet losses, principally in a bursty pattern. By using realistic scenarios all these situations are modeled and this fact allows a better evaluation of the proposed approaches.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 4 Experimental Setup
### Analysis and Validation  
In this work, we analyzed the quality of the solutions of each RE and the conventional LS method in three ways. First, we classified the solution regarding whether the outliers were detected or not. Second, we quantified detected, false positive, and unidentified outliers. Third, the numerical solution of the estimator was compared to the true and known co-ordinates of the points. The two first analyses were obtained using the residuals vector \(\mathbf{v}\), while the numerical comparison was extracted from the estimated co-ordinates vector \(\mathbf{\hat{x}}\).  
We divide the classification into six classes, represented by capital letters from \"A\" to \"F\":  
* A: All outliers detected, no false positive;
* B: All outliers detected, with at least one false positive;
* C: Some outliers detected, no false positive;
* D: Some outliers detected, with at least one false positive;
* E: No outlier detected, no false positive; and
* F: No outlier detected, with at least one false positive.  
We considered false positive to be a value bigger than 3\(\sigma\) in the residuals vector in a position where no outlier was inserted, as we truncated the random errors by up to three sigma.  
Comparison of the estimated solution with the true co-ordinates was carried out in order verify the impact of the errors in the final solution of the adjusted parameters (\(\mathbf{\hat{x}}\)). This allows us to consider, for example, whether an estimator was better for outlier detection or parameter estimation.
## 4 Discussion
### Key Results
The present findings compare to previous multicentric work in a group of 760 individuals with olfactory loss where measures of quality of life were better correlated to self-rated olfactory function than results from psychophysical tests of olfactory function [65]. This raises the question of what is rated when patients are asked to estimate the acuity of their own sense of smell. Using an alternative projection technique on more complex data than the present set, implemented as multidimensional scaling asan alternative classical data projection technique [67], the odor perception space was found to be complex, leaving room for different aspects captured either by self-assessments or clinical sensory testing [68]. For example, olfactory self-ratings were found to be more related to the affective impact of the odor, such as annoyance, but not to the results of the olfactory tests [6; 17]. Personality traits have also been associated with self-perception of the performance of one's sense of smell [69]. Importantly, nasal airflow has been shown to modify self-ratings of olfactory function [3]. Furthermore, the poor self-assessments of one's olfactory acuity could be due to the much more limited content of consciousness compared to the other main senses, which has been discussed as being a consequence of the mainly paleocortical processing of chemosensory information [70]. Finally, while other sensory perceptions such as seeing and hearing are subject to constant external feedback, this is less pronounced for olfactory function. Anosmia can go unnoticed despite the fact that, at least during eating, each day there are numerous olfactory encounters [23; 24].
## 2 Materials and Methods
### Constitution of PLA/Nettle Plates; Operating Mode
#### 2.3.3. Physico-Chemical Analysis of a PLA/Nettle Plate, Choice of Fiber Arrangement  
Performing DSC analysis is key to monitoring the nucleation effect of the lignocellulosic fibers on the PLA; several PLA/nettle plate samples were taken from the center of a plate to perform this DSC analysis, the same protocol as for the study of PLA alone was used. The numerous microfibrils that separate from the main beam should facilitate this nucleation [24; 36] resulting in a larger crystallization on the composites compared to PLA alone (Figure 8). The fiber nucleation effect results in a shift of the cold crystallization peak to lower temperatures due to greater ease of crystallization at low temperatures; these results were observed on a PLA/ramie composite [37]. Regarding our composite, a crystallinity of 14.1% is found for the 0\({}^{\circ}\) SF versus 9% for the RF. For the 45\({}^{\circ}\) oriented sorted fibers, the crystallinity reaches 26.1% versus 19.6% for RF. It thus seems that the SF participates much more significantly in the nucleation of PLA. This strong increase in the crystallinity is moreover directly related to the manufacturing process that we used: increased fiber distribution within the matrix leads to a much more significant nucleation effect.  
These results (for glass transition temperature Tg, crystallinity temperature Tc, fusion temperature Tf and crystallinity rate R) allow us to validate the chosen manufacturing concept based on the use of oriented fibers rather than non-oriented short fibers (Figure 7) and enable the realization of the tensile specimens used in the next step for mechanical test (Table 3).  
Figure 8: DSC analysis on the different fiber organizations in the composite (Tg: glass transition temperature, Tc: crystallinity temperature, Tf: fusion temperature).  
Figure 7: Examples of plates obtained with SF (oriented at 45\({}^{\circ}\)), RF (oriented at 45\({}^{\circ}\)), and CF (short ones).
## 5 Case Study
### Evaluation of the Importance of Urban Rail Network Nodes  
#### 5.2.1 Calculation of Evaluation Indicator Values  
Based on the Space L method, with the introduction of a virtual interchange edge, the urban rail transit network model has been constructed. Figure 5 presents the impact on the network after the station failure using the attack method in this paper in terms of the network efficiency, maximum connected sub-network, network efficiency accessibility, and level of unsatisfied passenger demand after the station failure. We found that there is a big difference in the impact of the different station failures on the network topology and operation performance. This is also in line with the actual operation of urban rail networks. For example, the 47 th station, Xinjiekou, is located in the core business area of Nanjing and is a large interchange station serving 192,218 passengers each day. When the Xinjiekou station (ECC station) of Line 2 fails, there will be a loss of network efficiency in terms of network topology of 0.0078 and a loss of maximum connectivity subgraph of 0.0240. In terms of passenger traveling service, 373,385 passengers will not be able to satisfy their traveling needs via urban rail, with a loss of network efficiency accessibility of 0.6823 and a loss of unmet passenger demand of 0.1667.  
Figure 5: Impact of station failure on the network.  
Table 1 lists 10 representative station indicators, with values calculated according to Formulas (1)-(6). These values represent the indicators of each station in the urban rail transit network.  
In Table 1, it can be seen that in the attack method based on a signaling system failure scenario, the values of the indicators for most of the non-centralized stations did not change. Most of the ECC and interlocking stations, such as China Pharmaceutical University Station and Xinjiekou Station, have values that are significantly larger than those of the original attack method, which indicates that the original attack method ignores the network failure mechanism during signaling system failure and weakens the negative impact of some station failures on network performance. However, there are also a few interchange stations, such as Gulou Station, Nanjing South Station, and Yuantong Station, where the values of the metrics are smaller than those of the original attack method. This is because under the original attack method, the characteristics of the urban rail network in which each line operates independently of each other are ignored, and when the interchange station fails, the station and its connected lines are all removed from the network, which overestimates the impact of its failure on the network.
## 4 Results
### Step 2: Interpretive Structure Modeling (ISM) and MICMAC Results
#### 4.2.2 Interpretive Structure Modeling (ISM)  
The structure model can be derived using the final reachability matrix (FRM). Subsequently, a digraph can be prepared by eliminating transitivity as discussed earlier. The LTM may be used to obtain a digraph that represents the relationship modeling. The digraph thus obtained provides a directed graph that helps in understanding the role of each lean barrier. The ISM model of LBs was prepared from the digraph presented in Figure 2. The figure shows that LB01, \"lack of resources\", affects LB05, \"resistance from top management\", which in turn affects LB08, \"resistance from workers\", through other LBs (LB12, \"insufficient information systems\", LB04, \"lack of formal training for workers,\" and LB03, \"lack of awareness\"). The digraph shown in Figure 2 can also be made with other contextual relationships.
## 1 Introduction  
The continuous growth in energy demand in the light of climate change and energy security concerns is driving countries to transition to alternative and renewable energy sources to reduce the reliance on traditional fossil fuels. While decarbonizing the energy sector is the key driver of the energy transformation, there are other factors for the transition. Falling renewable energy costs, jobs creation, energy access, human health and air quality, and energy security goals are additional factors driving energy transition [1]. Renewable energy accounts for approximately 10% of the global energy supply in 2018, of that share, bioenergy has the largest hold 70% of the total renewable energy supply [1, 2]. While the photovoltaics and wind energies are expected to experience the largest growth rates between 2019 and 2024 [3]. Bioenergy has a significant role in the energy transition process and decarbonization of the power/heating sectors if sourced from sustainable and affordable feedstocks. In addition, the versatility and flexibility of biofuel states (solid, liquid, gaseous) render bioenergy attractive for implementation within multiple sectors, including their utility as liquid fuels for transportation, as solid biomass for heat generation (through incineration), and as biogas for combined heat and power (CHP) production. Biogas is produced during the anaerobic-based biological processing of biodegradable organic compounds, known as anaerobic digestion (AD). Biogas is produced from a variety of feedstocks that are typically grouped into the following categories: crop residues and other agricultural feedstock, animal manure, organic waste within municipal solid waste, together with wastewater and wastewater sludge [4; 5; 6]. Raw biogas typically contains 60% methane (CH4), 40% carbon dioxide (CO2), and other impurities (mostly H2S, H2O, and H2).  
Bioenergy has the potential to play a role as a flexible resource within the renewable and variable power supply systems (wind and solar). In order to offset variability and increase the reliability of power supplies, dispatchable and flexible power plants--such as natural gas plants--are expected to play an important role in the energy transition process. Further decarbonization of the power sector is possible through expanded use of biomethane (a renewable natural gas), mainly upgraded from biogas [7; 8; 9; 10]. The utilization of biogas and biomethane can reduce GHG emissions by substituting fossil fuels and by avoiding CO2 and CH4 emissions from the decomposition of organic waste during storage and disposal. However, the production of biogas through AD also constitutes a source of both such gases. Consequently, the assessment for potential reduction of GHG emissions (mainly CO2) through biogas and biomethane is complex, since it largely depends on the type of feedstock, collection and transportation, production processes, CH4 leakage and end use. The CO2 emissions from biogas are affected by biomass production (carbon negative), biomass-to-biogas production (nearly carbon natural), and biogas end use (carbon positive) [11].
## 4 Experimental Results
### Implementation Details  
Following the options of most methods, we first use the pre-trained ResNet101 [33] to extract image features of dimension 2048. We use three fully connected layers with 2048 hidden units for the VAE encoder and decoder. LeakyReLU [34] is used as an activation function. We use the same hyperparameters of the visual feature generation module and disentanglement module as SDGZSL for fair comparisons. We use the 64 mini-batch sizes in all datasets, and the learning rate is set to 0.0001. For semantic decoder \(Dec\), we use two fully connected layers with twice the semantic embedding dimension. Three fully connected layers for the disentanglement module are used with 2048 hidden units. We optimize all networks with the Adam [35] optimizer for each module. We use a single fully connected layer for the classifier for GZSL. All models are implemented with PyTorch 1.10.0, CUDA 11.2.0, and cuDNN 8.2.0. We use a single RTX 3060 6 GB GPU for each training operation.