esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics of Deep Groundwater Flow and Temperature in the Tertiary Pohang Area, South Korea
## 4 Results  
### Lithological Profiles  
Figure 4 shows the stratigraphic columns of the four deep boreholes; it is evident that crystal tuff lies below the semiconsolidated mudstone at the following depths: 206-428 m for BH-3 and 217-443 m for BH-4. Crystal tuff was not observed for BH-1 and BH-2. The crystal tuff exhibits a moderately weathered grade and light-green color in the upper section (212-270 m in depth), with oxidized pyrite in the disseminated state and a relatively fresh grade in the lower section, which is light gray [9].  
Figure 4: Lithological profiles from BH-1 to BH-4. The sandstone and rhyolite layers are connected by dotted lines between the boreholes.  
Dacite can only be observed in BH-1 and is located between the semiconsolidated mudstone and crystalline/lapilli tuff at a depth of 359.1-401.4 m. It is dark gray and has a generally dense lithology. At a depth of 372-374 m, the dacite is moderately weathered; at 400.7-401.4 m, it is highly weathered.  
Numerous basic dykes and tufaceous sediments are distributed between the crystal tuff layers. The basic dykes intrude to a shallow depth, with sufficient horizontal elongation at the sandstone and lapilli tuff zones. The lapilli tuff, which is light green, exhibits a moderately weathered grade over the entire section. In BH-3, rhylolite is distributed at a depth of 630-900 m with a thin layer of sandstone/mudstone in the bottom section, and sandstone (525-630 m in depth) underlies the lapilli tuff in a different manner from that in BH-2, even though the two boreholes have a similar geological stratigraphy. The lapilli tuff in BH-2 lies between 352 and 425 m from the land surface with a thickness of approximately 357-427 m.  
Sandstone/mudstone mostly alternate in the lower part of the lapilli tuff with a few intercalations. The sandstone is greenish-gray, and the mudstone is dark gray and reddish-brown; similar colors can be seen in BH-1, BH-2, and BH-3. Slightly weathered coarse-grained light gray tufaceous sandstone can be observed in BH-4 at depths of 900-900.3 and 902.9-903 m. In BH-1, BH-2, BH-3, and BH-4, the sandstone/mudstone is dark gray in the upper parts and reddish-brown in the lower parts at a depth of 1000 m, suggesting an environmental change from oxidation to reduction. In BH-3, dark-gray mudstone is predominant, except for two reddish-brown zones in the lower part, at a depth of 428 m.  
Dark greenish-gray andesitic breccia can be observed at a zone lower than that of the sandstone/mudstone beds in BH-4; it cannot be observed in BH-1, BH-2, and BH-3. Andesitic breccia generally contains dark-red volcanic blocks and is fresher than the tuff. Medium-grained granodiorite contains a large amount of biotite and a minor amount of hornblende; it is light/dark gray and generally has a darker color than that of biotite granite.  
The detailed lithologies, fractures/faults, and casing installations of the four boreholes are presented in Appendix A.
## 5 Solketal Production
### Patents  
A meaningful indication of the industrial relevance of a process or new compound is the number and technological value of the patents developed by major market players. Patent research reveals the most recent advances in a specific field, the development of new technologies, and the relevant course of events concerning the subject searched. Additionally, it reveals the gaps in the literature that can be seen as an opportunity to develop and propose new processes. A good patent is comprehensive; therefore, the claims must be concise but not specific, which explains why most of the patents of solketal production generically mention the production of acetals and ketals [125]. Usually, when a technologically valuable innovative process or product is developed and patented, it is followed by several related patents with lower value when analyzed singly [126]. Concerning the use of solketal, it was initially deeply explored as solvent and plastifying agent in paints, films, and cleaning products; therefore, several products with different solketal contents were created and patented. Then, it was discovered to be useful as an antifreezing compound, which drove the research to its use as an additive for aviation fuel. Closely related, solketal use as fuel additive for surface transport was investigated due to the enhancement of fuel properties. Nowadays, the current trend of solketal application is its use as a biocompatible solvent in medicaments, since it is non-toxic for humans and it is soluble in body fluids. It is important to highlight that these research fields overlap each other; therefore, the described timeline is a draft of the trend of the new uses of this ketal based on the patents published. Figure 14: Flowsheet of the industrial solketal production plant with glycerol valorization in situ proposed by Al-Saadi et al. [123]. Concerning solketal production, the initial innovation was to carry the process in batch reactors with homogeneous catalyst, which was followed by the study of the performance of different homogeneous catalysts. Then, heterogeneous catalysts were proposed, still in batch, soon upgraded by the use of fixed-bed reactors, which nowadays have been extensively studied. Few patents were published proposing alternative production processes. A common conclusion to many of the authors that proposed and simulated industrial flowsheets for producing solketal is that the costliest step is the separation in distillation columns to remove the excess of acetone [25, 121, 122]. In view of that, the focus of several patents is intensifying the process to obtain purer products already in the outlet stream of the reactor or to use different separation technologies. The alternative technologies and the most promising patents (based on the possibility of scaling-up the process to the industrial level) proposed by some of the major players on the market are summarized in Table 5.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 5 Intrusion Detection in Fog, Edge, and Cloud Computing  
### Fog Computing  
By definition, fog computing is the decentralization of IT infrastructure--this can include the processing, storage, and intelligence control to the proximity of the data devices. When utilizing this technology, there is an opportunity to reduce the distances throughout the network environment and improve efficiency as well as the data volume required  
Figure 2: Cluster classification: intrusion and normal clusters.  
to transport to the cloud for processing, analysis, and storage. Thanks to the flexibility presented in fog computing, this can extend cloud computing services to the edge of the network. See Figure 3.  
Figure 3 presents three different levels: (1) The Cloud Layer sends the data to the Fog servers, (2) The Fog Layer only stores the recently used data, and in the (3) Edge Layer the user receives the data through the nearest Fog device which are connected through the internet. In the architecture of the topology there is a greater distance between the Cloud Layer (Level 1) and the Fog Layer (Level 2) than the Edge Layer (level 3) and the Fog Layer (Level 2).
## 3 Material and Methods  
AMT and gravity methods are commonly used in geothermal explorations where resistivity and density can be mapped, respectively. Resistivity and density play an important role in finding the permeability and porosity, which are directly associated with geothermal reservoirs [37; 38; 39].  
In this study, first, the AMT method was utilized to image the subsurface resistivity structure. A KMS-820 instrument was used to record the AMT data. The data-acquisition system was developed for EM and seismic applications to obtain the subsurface resistivity and velocity, respectively. This method is commonly used in oil and gas exploration as well as geothermal studies. By using KMS-820 units, we collected AMT data at 80 stations.  
Figure 1: Geological map of Midyan Terrane showing the distribution of massive granites and other different rock units (after [PERSON] et al. [30]).  
Second, a gravity survey was utilized to delineate the subsurface structure in terms of the density variation. A special emphasis was given to the subsurface imaging of the extension of the granitic rocks that were exposed at the surface, forming a massive regional belt. A total number of 246 gravity stations were collected using a CG5 gravimeter. The gravity method has been successfully used to describe regional faults, fracture zones, and the thickness of sedimentary layers [37]. However, the ambiguity of gravity data is still an issue in all gravity surveys. It can be reduced by high-level overlap-acquired datasets.  
After obtaining high-resolution data (e.g., relatively dense gravity stations), detailed density profiles could be utilized with the AMT data to perform a joint inversion. Figure 2 shows the locations of the AMT and gravity stations.
## 2 Related Work
### Adaptive Mechanisms to Overcome Static Limitations  
Given the drawbacks of purely static schemes, we want to focus on the experience we gained from our previous work and improve on that work by focusing on introducing adaptive mechanisms that respond proactively to changing network environments. The adaptive mechanisms can be utilized at different steps of the module to account for a variety of changes in the network.  
* Dynamic membership functions: Instead of utilizing fixed membership functions or adjusting the fuzzy set boundaries based on real-time estimates of given network parameters, in our case, we want to see how having multiple sets of such parameters in combination can perform. By shifting these boundaries dynamically, the fuzzy inference can stay relevant under varying conditions and avoid suboptimal broadcast decisions.
* Adaptive Parameter selection: A different strategy is to adapt the weight or importance of each input parameter (e.g., RSSI, Residual Energy, mobility, and Number of Neighbors) according to current network conditions. This dynamic weighting ensures that the most critical parameters for maintaining coverage and minimizing collisions are prioritized when making broadcast decisions.
* Hybrid Adaptation with RL: Reinforcement learning can be utilized to further optimize either the broadcasting ratio or the fuzzy logic rule base. For instance, RL agents can assign a reward for each successful broadcast that increases coverage while minimizing collisions, leading to fine-tuning of the fuzzy system over time. This integrated approach was shown to reduce overhead and collision rates compared to purely static or purely fuzzy-based methods in our previous work [1].  
Model A and Model B are designed to overcome the rigidity of conventional fuzzy systems and the complexity of RL-based methods by focusing on lightweight, profile-driven adaptability. Rather than relying on a fixed rule base or broad parameter sets, the models dynamically adjust behavior using targeted combinations of impactful parameters suited to different network conditions. This approach reduces unnecessary transmissions and computation while maintaining flexibility across scenarios like energy-constrained or delay-sensitive environments. To our knowledge, this specific use of selective parameter profiling in adaptive fuzzy systems remains underexplored in the recent MANET broadcasting literature.  
In this research, we want to implement a novel approach that uses adaptive parameter selection to understand the impact of each parameter and how they perform in tandem with each other, and compare this method with our previous work to understand the weaknesses and strengths of both of these approaches, understanding which one of them is the better candidate or how we can improve them further.
## 5 Cramer-Rao Lower Bound Analysis  
The Cramer-Rao lower bounds of the target localization in UWSN are derived in this section to compare the performance of the proposed methods. Let \(\mathbf{B}_{1}=(\mathbf{G}^{T}\mathbf{Q}^{-1}\mathbf{G})^{-1}\), and \(\mathbf{B}_{2}=(\mathbf{H}^{T}\mathbf{Q}^{-1}\mathbf{H})^{-1}\), where \(\mathbf{G}=[g_{1},g_{2},\cdots,g_{N}]^{T}\), and \(\mathbf{H}=[h_{1},h_{2},\cdots,h_{N}]^{T}\), with  
\[g_{i} =-\frac{10\gamma}{ln10}\frac{(\mathbf{x}-\mathbf{s}_{i})^{T}}{\|\mathbf{x}- \mathbf{s}_{i}\|^{2}}-\alpha_{f}\frac{(\mathbf{x}-\mathbf{s}_{i})^{T}}{\|\mathbf{x}-\mathbf{s}_{ i}\|},i=1,\ldots,N \tag{25}\] \[h_{i} =[-\frac{10\gamma}{ln10}\frac{(\mathbf{x}-\mathbf{s}_{i})^{T}}{\|\mathbf{x}- \mathbf{s}_{i}\|^{2}}-\alpha_{f}\frac{(\mathbf{x}-\mathbf{s}_{i})^{T}}{\|\mathbf{x}-\mathbf{s}_{i} \|},1]^{T},i=1,\ldots,N. \tag{26}\]  
Thus, the Cramer-Rao lower bounds (CRLBs) of the target node location \(\mathbf{x}\) in both known and unknown transmit power cases, i.e., CRLB-K (Cramer-Rao lower bound-known) and CRLB-U (Cramer-Rao lower bound-unknown) can respectively be written as  
\[\text{CRLB-K}(\mathbf{x}) =\text{trace}(\mathbf{B}_{1}) \tag{27}\] \[\text{CRLB-U}(\mathbf{x}) =\text{trace}(\mathbf{B}_{2}[1;2,1;2]). \tag{28}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Description** & **Complexity** \\ \hline SDP (semi–definite programming) & RSS-based SDP method in [12] & \(\mathcal{O}(N^{3.5})\) \\ WLS-K (weighted least square-known) & The proposed WLS-K Equation (13) & \(\mathcal{O}(K_{max}N)\) \\ WLS-U (weighted least square-unknown) & The proposed WLS-U Equation (22) & \(2\mathcal{O}(K_{max}N)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the considered methods.  
For detailed derivations, please refer to the Appendix.
## 1 Introduction
### Study Contributions  
This study makes the following contributions:  
1. This study develops a comprehensive classification of the types of DoS attacks in WSNs that can be addressed using trust models, including their key attack features and impact mechanisms.
2. By analyzing the key features of these DoS attacks, we identify the trust evidence that trust models are required to recognize DoS attacks effectively.
3. This study identifies, analyses, and compares the methods for extracting trust evidence.
4. This study provides a comprehensive summary of trust evaluation methods.  
This study identifies the key challenges in applying trust models for the defense of WSNs against DoS attacks.  
The remainder of this paper is organized as follows: The next section presents the methods used to identify the relevant research work used in this study. The Results Section classifies the types of DoS attacks in WSNs that can be addressed using trust models. It identifies the requirements for trust evidence, compares and analyzes various methods for extracting trust evidence, and summarizes trust evaluation methods. The Analysis and Discussion Section elaborates on the challenges in the deployment of trust models to counter DoS attacks in WSNs. The Conclusion Section summarizes this work, outlines its limitations, and suggests avenues for further research.
###### Abstract  
A neighborhood's walkability is associated with public health, economic and environmental benefits. The state of the walking surface on sidewalks is a key factor in assessing walkability, as it promotes pedestrian movement and exercise. Yet, conventional practices for assessing sidewalks are labor-intensive and rely on subject-matter experts, rendering them subjective, inefficient and ineffective. Wearable sensors can be utilized to address these limitations. This study proposes a novel classification method that employs a long short-term memory (LSTM) network to analyze gait data gathered from a single wearable accelerometer to automatically identify irregular walking surfaces. Three different input modalities--raw acceleration data, single-stride and multi-stride hand-crafted accelerometer-based gait features--were explored and their effects on the classification performance of the proposed method were compared and analyzed. To verify the effectiveness of the proposed approach, we compared the performance of the LSTM models to the traditional baseline support vector machine (SVM) machine learning method presented in our previous study. The results from the experiment demonstrated the effectiveness of the proposed framework, thereby validating its feasibility. Both LSTM networks trained with single-stride and multi-stride gait feature modalities outperformed the baseline SVM model. The LSTM network trained with multi-stride gait features achieved the highest average AUC of 83%. The classification performance of the LSTM model trained with single-stride gait features further improved to an AUC of 88% with post-processing, making it the most effective model. The proposed classification framework serves as an unbiased, user-oriented tool for conducting sidewalk surface condition assessments.  
deep learning; gait analysis; sidewalk surface assessment; walkability; wearable sensor Article
## 6 Discussion
[78] studied these uncertainties and suggested a raster resolution of 10 m for training/validation sample sizes 60/40% and 70/30%; and a resolution of 20 m for sample sizes of 80/20% and 90/10%. Another limitation of the current study is related to model selection. Each algorithm has a specific probability distribution function or rule, not all of which fit a given training dataset. Therefore, it is necessary to test the models and select the best one for a given study. This process is mainly done using a trial-and-error technique and is time-consuming. A limitation of landslide susceptibility mapping, in general, is that maps generated with machine learning techniques can accurately show where landslides are likely to occur based on geo-environmental factors, but the important physical, mechanical, and elastic properties of soil such as porosity, permeability, cohesion, and pore water pressure are not considered. These soil-related properties strongly control landslide occurrence at the site scale, yet preparing maps showing their distributions is costly and time-consuming. We recommend that researchers consider these factors and specially to use them in conjunction with slope stability models and deterministic numerical models that address the factor of safety (FOS). For example, Shallow Landsliding Stability (SHALSTAB) and SINMAP (Stability Index MAPping). These models couple a hydrologic model with an infinite slope form of the Mohr-Coulomb failure law to spatially predict slope failures. Therefore, one of the ways to enhance the accuracy of the susceptibility maps is to use the soil-related factors in future. Additionally, the application of high-resolution data such as airborne laser scanning of Light Detection and Ranging (LiDAR) not only could enhance the quality of the conditioning factors but also the prediction accuracy of the models. The ability of high-resolution data has been confirmed and evaluated by some landslide researchers [99; 126; 127; 128]. For example, [PERSON] et al. [128] by very high-resolution data, LiDAR, optimized the used landslide conditioning factors, and they concluded that a high-quality, informative database, is essential and classification of landslide types prior to landslide susceptibility assessment is necessary to help improve model performance.
## 4 Results and Analysis
### Analysis of Relationship between Pore water Pressure Response Time and Vehicle Speed  
As can be seen from Figure 6, there are two obvious cycles in the pore water pressure response curve at different vehicle speeds. The pore water pressure response time at different speeds are listed in Table 6.  
Table 6 shows that the pore water pressure response time is longer than vehicle loading time, indicating that even if the vehicle has passed through the pore, the scouring action and pumping action of the free water in the pore on the pore sidewall does not end immediately, but continues for a while. The higher the speed, the greater the ratio of the pore water pressure response time and vehicle loading time. For example, when the speed of the vehicle is 20 km/h, the pore water pressure response time is 1.34 times that of vehicle loading time, and the corresponding data at the speed of 80 km/h is 3.4 times. The time of two cycles at different speed is drawn in the Figure 8.  
At a certain speed, the first cycle response time is longer than the second cycle response time, and the first cycle response time is generally 1.5 to 2 times that of the second cycle response time. At different speeds, the first cycle response time and the second cycle response time gradually decreases as the vehicle speed increases. Therefore, as the vehicle speed increases, the scouring time for the pavement structure decreases.
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 4 Results and Discussion
### Simulation Results
#### 4.2.3 Time Delay Compensation Result  
The traditional noise cancellation algorithm can only be applied in some certain circumstances where the primary input lags behind the reference inputs. In this paper, the simulation is carried out in the circumstance where the reference input lags behind the primary input. Figure 15 shows the time delay control performance of proposed algorithm when mixed SNR in the primary input is \(-23\) dB. In the interval \([0,1500)\) and \([5000,6500)\), there is only noise in the primary input. In the interval \([1500,5000)\) and \([6500,10000)\), the communication signal is added to the primary input. In the interval \([1500,5000)\), the time delay is set to be \(3\)\(T_{s}\). Then, the time delay is set to be \(4\)\(T_{s}\). When there is only the noise in the primary input, the MCC-ETDE algorithm can estimate the time delay without error. When the communication signal is added to the primary input, the time delay estimation values will have bigger variance. When the MAE of time delay estimate values is bigger than the threshold, the time delay control module will select the previous value as the time delay. The threshold is set based on the actual situation. As shown in Figure 15, the time delay control (TDC) values can converge to the true time delay values.  
Figure 16 shows the adaptive noise cancellation performance of proposed algorithm when mixed SNR in the primary input is \(-23\) dB. As shown in Figure 16b, before the time delay estimation module obtains the time delay, the adaptive noise cancellation module cannot improve the SNR in the primary input. After obtaining the true time delay, the adaptive noise cancellation module achieves very good noise cancellation performance with the time delay compensated in the primary input. In addition, the proposed algorithm can track the changes of time delay with a fast convergence speed. When the time delay changes, the proposed algorithm still obtains good noise cancellation performance with a period  
Figure 14: MSE of filter output at different mixed SNR levels.  
Figure 13: Square error of signal when mixed SNR in the primary input is \(-23\) dB.  
of time to compensate the varied time delay. In Figure 16c, the noise cancellation performance without time delay compensation after 5000 samples is shown. Without time delay compensation, the adaptive noise cancellation module cannot remove the noise from the signal.
## 4 System Model
### Successful Transmission
#### 3.2.2 Void Hole Occurrence  
Void holes can be defined as nodes having no neighbors in their communication range. Void holes are generated due to many reasons; e.g., (1) A node can be dead due to again and again re-selection of the same node or (2) No next forwarder node exists in the transmission range of current forwarder node. The occurrence of void hole and blockage in transmission is summarized in Figure 2.