esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The prediction of settlement during tunneling presents multiple challenges, as such settlement is governed by not only the local geology but also construction methods and practices, such as tunnel boring machine (TBM). To avoid undesirable settlement, engineers must predict the settlement under given conditions. The widely used methods are analytical solutions, empirical solutions, and numerical solutions. Analytical or empirical solutions, however, have limitations, which cannot incorporate the major causes of subsidence, such as unexpected geological conditions and TBM operational issues, among which cutterhead pressure and thrust force-related factors are the most influential. In settlement prediction, to utilize the machine data of TBM, two phases of long short-term memory (LSTM) models are devised. The first LSTM model is designed to capture the features affecting surface settlement. The second model is for the prediction of subsidence against the extracted features. One thing to note is that predicted subsidence is the evolution of settlement along TBM drive rather than its maximum value. The proposed deep-learning models are capable of predicting the subsidence of training and test sets with excellent accuracy, anticipating that it could be an effective tool for real-world tunneling and other underground construction projects.  
t 15 November 2021 12 December 2021 20 December 2021 20 December 2021 20 December 2  
advance rate, much volume, torque, and thrust force. Cutterhead face pressure and thrust force related factors are most influential among these issues [4].  
The project, in this study, involves a mixed-face condition of alluvial and weathered soil, weathered rock, and soft rock from the top to the bottom of the tunnel face throughout the length of a tunnel. A previous study found that machine learning with geological information without TBM data generated poor settlement estimates [5]. We hypothesized that the correlation of key parameters, such as thrust force and advance rate, with respect to the amount of settlement would lead to an improved theoretical framework, as we analyzed more TBM data.  
In this paper, a series of LSTM models are adopted, where the first LSTM model extracted hidden features that could affect ground movement/subsidence, and the second LSTM model was trained for the prediction of the subsidence against the hidden features obtained from the earlier model. 10,660,000 TBM measurements and settlement data from 48 settlement markers were collected and fed into a long short-term memory (LSTM) model. After several weeks of training and testing, the LSTM models demonstrated excellent performance of subsidence prediction for both the training and test cases, indicating significant advances in the reliable prediction of subsidence against pure machine data.
## 1 Introduction
### Research Questions and Contributions  
* What threats do deepfake-driven social engineering attacks pose to corporate environments?
* Amplification of social engineering risks: We explore how deepfakes increase the risks associated with social engineering (Sections 5 and 5.1);
* Real-world examples: We discuss real-world cases demonstrating threats to trust and stability (Section 3.5);
* Exploitation of communication channels: We analyze how deepfakes can be used to exploit corporate communication channels (Section 5.2).
* What are the current detection techniques?
* Review of AI-driven tools: We evaluate AI-driven detection tools and their limitations, such as scalability and false positives (Sections 6.1 and 6.2);
* Gaps in security solutions: We highlight the existing gaps due to reliance on generalized security solutions (Section 6.3).
* What defensive strategies are used by or recommended for organizations?
* Employee training and multi-factor authentication: We emphasize the importance of employee training and multi-factor authentication (Sections 7.1 and 7.2);
* Integration of policies and legal frameworks: We discuss the integration of policies, legal frameworks, and incident response plans (Section 7.3);
* The Zero Trust deepfake framework: We propose the \"Zero Trust Deepfake Framework\" for robust verification processes (Section 7.4.3).
* How can organizations assess their readiness and improve their policy frameworks?
* The PREDICT model: We have developed the PREDICT model, addressing Policies, Readiness, Education, Detection, Incident Response, Continuous Improvement, and Testing (Sections 8 and 9.2);
* Proactive measures: We suggest proactive measures for continuous adaptation and corporate preparedness (Sections 9.2 and 11).
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 3 System Model
### Adversary Model  
In the disaster area, malicious adversaries can threaten the integrity and availability of the monitoring information collected by drones and then negatively impact the efficiency of the rescue and evacuation operations in the area. The \(CS\) is a trusted party; it is located in a protected place. It will not attempt to falsify the received information. Drones are non-trusted parties because of their location in the air; they are prone to be compromised by adversaries or malfunctioned because of the hostile surrounding environment. The attacker \(\mathcal{A}\) can compromise and impersonate \(D_{s}\), and also intercept and block their messages. In addition, \(\mathcal{A}\) can begin a replay attack, or attempt to forge the transmitted messages. However, \(\mathcal{A}\) has limited resources; he/she cannot compromise all drones in the subarea; only a limited number of them.
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 2 Optical Camera Communication
### Demodulation  
The main difficulty of the demodulation lies in the blooming effect affecting the extracted signal amplitude. As the demodulation intends to differentiate between the signal logic states to recover the binary signal, a sudden and significant change in the extinction ratio (ER) between the bright and dark strips therefore compromises the transmitted data integrity, considering a constant-value threshold demodulation. Hence, two different approaches were considered to overcome the blooming effect issue. The first consists of using adaptive thresholds whose values follow the signal amplitude variations, third-order polynomial curve fitting (3 rd-OPF) [11]. The second, more robust against the blooming effect, is based on the first-order derivative calculation to identify the rising and falling edges of the extracted signal. Indeed, different methods have been proposed, such as the Boundary Pixel-Based Piecewise Linear Function (BPBPLFT) scheme [12], which uses combined linear functions as thresholds based on the position of signal edges, the HyperSight (HS) method that directly performs the demodulation from the normalized first-order derivative without properly using a threshold [13] or by directly identifying the strips transitions based on the derivative slopes [14]. Hence, this paper introduces a new demodulation method based on a first-order derivative approach, described in Section 3, whose performance is compared to other proposed methods in the literature.  
Figure 2: Comparison of the image representation impact on chromatic aberration.
# A Thermodynamic Model for Cryogenic Liquid Hydrogen Fuel Tanks
## 4 Analysis of Cryogenic Fuel Tanks
### Analysis Results
This was because the heat that expanded to vaporize the liquid hydrogen also raised the gaseous temperature, and the mass of gaseous hydrogen inside the tank was reduced due to fuel consumption. For this reason, the temperature elevated rapidly during this period. The tank pressure behaviors as fuel consumption began were similar to the temperature behaviors, as displayed in Figure 7c. The pressure dropped initially, but the pressure in cases 3a and 3b rose gradually because the mass and temperature of the gaseous hydrogen increased. In cases 3b and 3c, the maximum pressure occurred just before fuel consumption began. In contrast, the maximum pressure point of case 3a occurred at a similar time as in case 2, reaching a maximum of 3.03 MPa at about 39 min. Comparing these cases, case 2, in which fuel consumption occurred immediately after filling the tank with liquid hydrogen, had smaller pressure values compared to the delayed cases. However, in reality, cryogenic fuel tanks are operated in a way that reflects the delayed cases, as it takes time to begin fuel consumption after fuel charging. In case 3c, the pressure rapidly decreased from the maximum after fuel consumption started, but in the other delayed cases, the pressure increased again. In all of the delayed cases, the maximum pressure was higher than in case 2. For this reason, the delay time before fuel consumption after fuel filling, along with the fuel consumption rate, should be considered as important factors in predicting the maximum pressure of a cryogenic fuel tank. Additionally, this indicates that there could be a large difference in the maximum pressure inside different tanks depending on various operating factors, and a model that quickly and accurately predicts this is needed. Therefore, using the present thermodynamic model developed in this study, it is possible to rapidly predict the maximum pressure and the pressure behavior of a cryogenic liquid hydrogen fuel tank under these conditions.
## 2 Materials and Methods  
### Chemicals  
All chemicals used in the present study were of analytical and pharmaceutical grade. Folin-Ciocalteu's phenol reagent was purchased from Merck (Darmstadt, Germany). Gallic acid, 2,2-azobis-(2-amidinopropane) dihydrochloride (AAPH), 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), fluorescein disodium salt, chlorogenic acid, neochlorogenic acid, benzoic acid, \(p\)-coumaric acid, caffe acid, ferulic acid, rosmarinic acid, cinnamic acid, vanilic acid, quercetin, quercetin-3-glucoside, quercetin-3-routinsoside, myrecetin, kaemperhol, and apigenin were delivered from Sigma-Aldrich (Steinheim, Germany). Cyanidin-3-glucoside, cyanidin-3-sambubioside, cyanidin-3-5-diglucoside, cyanidin-3-galactoside, cyanidin-3-routinside, cyanidin-3-arbinoside, pelargonidin-3-glucoside, cyanidin, delphinidin, pelargonidin, malvidin, peonidin were purchased from Extrasynthese S.A. (Genay Cedex, France). All other reagents and solvents used purchased from local distributors.
# Design and Implementation of a Tether-Powered Hexacopter for Long Endurance Missions
## 2 Design and Analysis  
The system diagram and photos of the proposed tether-powered hexacopter are shown in Figure 1. This system can be divided into two subsystems, the ground station and the aerial vehicle with a mission payload. Both subsystems are connected physically by a tether which transmits electrical power form the ground station to the hexacopter. Navigation signals and telemetry data are transmitted wirelessly through the off-the-shelf radio modules. Flight data are recorded in the flight controller and will be read out and analyzed after every experiment.
## 5 Discussions and Future Perspectives
### Current Challenges and Limitations  
Despite notable advances, multiple challenges remain:  
* Data Availability and Quality: The scarcity of extensive and varied datasets, especially those capturing dynamic scenes with complex motion and diverse lighting, hinders the training of robust models capable of generalizing to out-of-distribution (OOD) data [65; 78].  
* Computational Complexity and Real-Time HDR: Advanced architectures, particularly Transformer- and DM-based models, demand significant computational power and memory, potentially hindering their use in real-time scenarios or on resource-constrained devices like smartphones, drones, and AR/VR [10]. Achieving real-time or near real-time performance is a critical requirement for many practical applications, demanding efficient model designs and evaluation strategies that consider not only quality metrics but also inference speed, computational cost (e.g., FLOPS), and memory consumption. In this regard, some works have advanced toward low-cost efficient reconstruction like [32; 178].
* Artifact Handling: Artifacts, including blur, ghosting, halos, noise, and misalignment, remain formidable obstacles. Although alignment and fusion techniques have improved, extreme movements and saturation can still produce visual imperfections in HDR images [12; 63].
* Real-World Generalization: Models trained on synthetic or rigidly controlled datasets may fail to generalize to real-world scenarios where lighting and motion conditions are highly variable [84].
* Metric Adaptation and Correlation: Metrics originally designed for LDR must be revised or redesigned for HDR images, complicating the interpretation of results and the standardization of evaluations. Furthermore, ensuring that objective metrics accurately correlate with subjective human perception of HDR quality remains an ongoing challenge.
## 6 Conclusions  
D2D communication can be introduced into multicast services in cellular network to improve the transmission performance. In this paper, we investigated the problem of selecting an optimal number of D2D relays within a cluster to efficiently improve system sum data rate. We formulated the optimization problem as a combinatorial auction, and proposed an auction-based relay selection algorithm to allocate D2D relays for NACK-UEs. The proposed algorithm can converge in finite rounds and has low complexity. Simulation results show that the proposed algorithm achieves better performances than the greedy heuristic and the fixed single relay algorithm in terms of sum data rate and mean data rate. In addition, the results show that with different numbers of NACK-UEs and different numbers of ACK-UEs, the proposed algorithm provides high system efficiency.  
Figure 5: System efficiency: \(\mathcal{E}\) with different numbers of NACK-UEs and different numbers of ACK-UEs.
## 2 Problem Formulation
### Constraints of the Problem
#### 2.3.1 Power Flow Constraint  
The power flow constraints in EGHNs are presented below [25; 26; 27].  
* The active and reactive power balance in different buses \[DS_{e,t}^{p}-S_{e,t}^{p}+\sum_{m\in_{Hub}}AH_{e,m}^{e}H_{m,t}^{p}=\sum_{j\in \Omega_{e}}A_{e,j}^{e}F_{e,j,t}^{p}\qquad\forall e,t\] (2) \[DS_{e,t}^{q}-S_{e,t}^{q}+\sum_{m\in_{Hub}}AH_{e,m}^{e}H_{m,t}^{q}=\sum_{j\in \Omega_{e}}A_{e,j}^{e}F_{e,j,t}^{q}\qquad\forall e,t\] (3)
* Active and reactive power flow of lines \[F_{e,j,t}^{p}=G_{e,j}(V_{e,t})^{2}-V_{e,t}V_{j,t}\big{\{}G_{e,j}\cos(\delta_{e,t}-\delta_{j,t})+B_{e,j}\sin(\delta_{e,t}-\delta_{j,t})\big{\}}\qquad\forall e,t\] (4) \[F_{e,j,t}^{q}=-B_{e,j}(V_{e,t})^{2}+V_{e,t}V_{j,t}\big{\{}B_{e,j} \cos(\delta_{e,t}-\delta_{j,t})-G_{e,j}\sin(\delta_{e,t}-\delta_{j,t})\big{\}} \qquad\forall e,t\] (5)
* Voltage angle in the base bus The voltage angle in the base bus should follow the \(\delta_{e,t}=0\qquad\forall e=Stack\ bus,t\).
* Balance of gas power and flow The balance of gas power in different buses and gas flow through the pipeline at hour \(t\) is as follows: \[GS_{g,t}-L_{g,t}^{g}+\sum_{m\in_{Hub}}AH_{g,m}^{g}H_{m,t}^{g}=\sum_{j\in\Omega_ {g}}A_{g,j}^{g}F_{g,t}^{g}\qquad\forall g,t\] (6) \[F_{g,j,t}^{g}=\kappa_{g,j}\text{sign}(\pi_{g,t},\pi_{j,t})\sqrt{ sign(\pi_{g,t},\pi_{j,t})(\pi_{g,t}^{2},\pi_{j,t}^{2})}\qquad\forall g,j,t\] (7)
* The heating power balance in buses\[HS_{h,t}-L_{h,t}^{h}+\sum_{m\in_{Hish}}AH_{h,m}^{h}H_{m,t}^{h}=\sum_{j\in\Omega_{g}}A _{h,j}^{h}F_{h,j,t}^{h}\hskip 20.0 pt\forall h,t \tag{8}\]
* Heat power flow through a pipeline at time \(t\) is as follows: \[F_{h,j,t}^{h}=c_{h,j}\dot{m}_{h,j}(T_{h,t}-T_{j,t})\hskip 20.0 pt\forall h,j,t\] (9)
## 4 Enhanced Collaborative Localization Based on Belief Propagation
### Belief Propagation for Heterogeneous Information  
In this section, a belief-propagation algorithm is used to realize air-ground member position estimation and relative heterogeneous measurement information fusion to obtain more accurate positions of air-ground swarms.  
Specifically, each vehicle first collects position and navigation-related data using its own onboard sensors. For each sensor's measurement data, its marginal probability distribution, i.e., the probability distribution of the position or navigation information obtained from that sensor's measurement alone, is calculated. Belief probabilities are then computed by considering measurements from other vehicles, which indicate the probability of updating the local sensor measurements after considering other relevant information. For the \(i\)th member, according to the \(n(i)\) hybrid observation factors \(\mu_{p\to i}\) it receives, its optimized position estimates \(\overline{\mathbf{X}}_{i}\) and \(\mathbf{P}_{i}\) are obtained by the following formula:  
\[\begin{split} p(\mathbf{X}_{i})&=N(\overline{\mathbf{X}}_{ i},\mathbf{P}_{i})\propto\prod\limits_{p\in n(i)}\mu_{p\to i}(\mathbf{X}_{i})\times N( \mathbf{X}_{i},\mathbf{P}_{i})\\ &\propto\prod\limits_{p\in n(i)}N(\mathbf{\xi}_{p\to i},\mathbf{\Gamma}_ {p\to i})\times N(\mathbf{X}_{i},\mathbf{P}_{i})\end{split} \tag{16}\]  
Letting \(\overline{\mathbf{X}}_{i}=\mathbf{X}_{i}\) and \(\overline{\mathbf{P}}_{i}=\mathbf{P}_{i}\), the above formula can be obtained by updating \(N(\overline{\mathbf{X}}_{i},\overline{\mathbf{P}}_{i})\) with the Kalman filter \(n(i)\) times. Its observation formula is:  
\[\overline{\mathbf{z}}=\mathbf{H}_{p\to i}\overline{\mathbf{X}}_{i} \tag{17}\]  
Thus, more accurate position estimates \(\mathbf{X}_{i}\) and \(\mathbf{P}_{i}\) of the \(i\)th member are updated.
## 3 ML Architectures and Results
### RFs with Different Sets of Input Attributes  
Our first approach was based on the random forest algorithm. RF classifiers have the advantage of the decision being easier to track than with an ANN and the training is computationally less expensive. However, we verified that the accuracy obtained is lower than when using an ANN. An RF is a collection of decision trees, generated from a subset of the training set. In the decision process, all the trees are consulted and the classification is made via a democratic count of votes. The Matlab function that makes it possible to build a random forest is called **TreeBagger**. In our simulations, we considered an RF with 200 trees and the in-bag fraction considered was 50%. This is the fraction of input data to sample with replacement from the input data for growing each new tree.  
Below, we present our results for different sets of attributes.  
* Attributes \(M_{01}\), \(M_{10}\), and \(M_{11}\). The results obtained with these attributes are shown in Figure 3. Considering only MMEs \(M_{01}\), \(M_{10}\) and \(M_{11}\), the overall classification accuracy obtained with the optimized ANN is 72.4%. It is easily seen that some classes are poorly identified, which means that the number of attributes is insufficient.
* Attributes \(M_{01}\), \(M_{02}\), \(M_{10}\)\(M_{11}\), \(M_{12}\), \(M_{20}\), \(M_{21}\), and \(M_{22}\). Considering these eight MMEs as attributes, the overall classification accuracy obtained with the optimized ANN is 88.1% (we do not present the confusion table for brevity). The results are much better than with the minimal set, but the classes \"leaves\" and \"rocks\" are still not well distinguished.
* All MMEs as attributes. The results obtained with the complete set of attributes are shown in Figure 3 (right panel). Considering \(M_{01}\), \(M_{02}\), \(M_{10}\)\(M_{11}\), \(M_{12}\), \(M_{20}\), \(M_{21}\), and \(M_{22}\) as attributes, the overall classification accuracy obtained with the optimized ANN is 93.85%. The accuracy obtained is now very good.
## 3 Test Results and Discussion
### Effect of Variable Diameter Ratio on Vertical Bearing Characteristics of Piles  
In Figure 6a, the top load-settlement curves of the three variable-section piles in the figure also belong to the steep-drop type, and it can be seen from the figure that the vertical ultimate bearing capacity of the single pile for equal-section piles is 130 N, and the vertical ultimate bearing capacity of the single pile for variable-section piles with pile variable cross-section diameters of 17 mm, 15 mm, and 13 mm are 180 N, 150 N, and 110 N, respectively. When the length of the variable section is certain, and the ratio of the variable-section pile is 20/17, the pile's bearing capacity can be increased by up to 14%.  
In addition, observing the settlement at the top of the pile, the slope of the load-settlement curve becomes larger when the radial dimension of the variable section becomes smaller for a certain length of the variable section. However, the slopes of the load-settlement curves for variable-section diameters of 17 mm and 15 mm are smaller than those for equal-section piles; i.e., their corresponding settlements are smaller than those for variable-section straight piles. This indicates that, when the pile body has a variable cross-section, the soil body under the variable cross-section of the pile body is compacted to a certain extent during the load bearing process, and the lateral friction resistance of the pile body is improved.  
Figure 6: Load–displacement of pile with different inclinations: (**a**) 0%, (**b**) 2%, (**c**) 4%, and (**d**) 8%.  
Analyzing the phenomenon, the soil at the lower part of the variable section of the pile body shares part of the pressure transferred from the top of the pile, thus reducing the pressure of the soil at the end of the pile, thus reducing the settlement of the end of the pile. The slope of the curve of the 15 mm variable-section pile is smaller than that of the equal-section pile in the figure, mainly because the resistance produced by the pile body due to the variable section is, to a certain extent, slightly larger than the lateral frictional resistance lost, resulting in the ultimate bearing capacity of the curve remaining similar to that of the complete pile. When the ratio of the variable diameter is appropriate, the vertical bearing characteristics of the monopile are basically unaffected.