esa-sceva/satcom-chunk-collection huggingface dataset:
# Hide45: A Method for Optimal Payload Data Hiding in Base45 Encoded Strings
## 5 Proposed Algorithm
### Optimization Method
#### 5.1.4 Linear Model  
Taking into consideration the previous discussion the new variable definition and the added constraints, we can write down the complete _linear_ program.  
maximize \[\sum_{i=0}^{25,589-1}\sum_{k=0}^{14}e_{i}kx_{ik}\] (14) subject to \[\sum_{i=0}^{25,589-1}\sum_{k=0}^{14}\left(2^{k}-1\right)x_{ik}\leq 25,589\] (15) \[\sum_{k=0}^{14}x_{ik}=1\qquad\qquad\qquad\qquad\qquad\qquad \qquad\forall\ 0\leq i<25,589\] (16) where \[x_{ik}\in\{0,1\}\] (17)  
The model (14)-(17) can then be solved by means of standard and efficient well-known optimization methods and approximated with good quality heuristics. In the empirical analysis part, Section 6, we are going to obtain the optimum, hence the best possible assignment given the frequencies, by solving this model with CPLEX commercial solver [45]. A _greedy approximation algorithm_ that gives results indistinguishable from the real optima, given the particular structure [46] of the model, is under development.  
It is worth noting that the input dimension for the optimization model never changes: since for Base45 we are going to have always 25,589 unused configurations and the maximum possible required power of 2 is 14, the number of variables \(x_{ik}\) is always 25,589 \(\times\) 14 and the number of constraints (16) is always 25,589. Given this consideration, we remark that for these relatively _small_ dimensions it is easy for any solver to efficiently find the real optima in a small amount of time.  
Considering also the fact that, given the configuration frequencies, the entire optimization must be done only _once_ for each file type to build the table that will be later used for the embedding, the few seconds of execution time required by the solver to find the best possible assignment has no impact over the encoding/embedding procedure.
## 2 General Notations and Model Definition
### AUV Kinematic and Dynamic Model
#### 2.2.1 Kinematic Model  
The kinematic model links the derivative of the absolute position and the yaw-pitch-roll Euler derivatives to the relative velocity of the vehicle w.r.t. the water, the ocean current velocity and the angular velocity of the vehicle using the following relationship,  
\[\dot{\mathbf{\eta}}=\begin{bmatrix}\mathbf{R}(\mathbf{\eta}_{2}){}^{b}\mathbf{v}_{\mathbf{r}}\\ \mathbf{J}_{\mathbf{c}}(\mathbf{\eta}_{2}){}^{b}\mathbf{\omega}_{b/w}\end{bmatrix}+\begin{bmatrix} {}^{w}\mathbf{v}_{\mathbf{c}}\\ 0\end{bmatrix}, \tag{2}\]  
where \(\dot{\mathbf{\eta}}=[\dot{x},\dot{y},\dot{z},\dot{\phi},\dot{\theta},\dot{\psi}]^{ \top}\), \({}^{b}\mathbf{v}_{\mathbf{r}}\in\mathbb{R}^{3\times 1}\) is the relative linear velocity vector (w.r.t. the water), \({}^{w}\mathbf{v}_{\mathbf{c}}\in\mathbb{R}^{3\times 1}\) is the linear velocity of ocean current (assuming the current to be irrotational  
Figure 1: Autonomous Underwater Vehicle (AUV) coordinate frame assignments.  
and \(\hat{v}_{c}=0\)). \(\mathbf{I}_{e}(\mathbf{\eta}_{2})\) is the Jacobian connecting the body angular velocities \({}^{b}\mathbf{w}_{b/iw}\) to the y-p-r Euler rates \(\hat{\mathbf{\eta}}_{2}\), given as  
\[\mathbf{I}_{e}(\mathbf{\eta}_{2})=\begin{bmatrix}1&s\phi t\theta&c\phi t\theta\\ 0&c\phi&-s\phi\\ 0&s\phi/c\theta&c\phi/c\theta\end{bmatrix}, \tag{3}\]  
where \(c\), \(s\) and \(t\) denote the cosine, sine and tangent values of the respective Euler angles.
# A Comparative Study of Coverage Hole Detection Techniques in Wireless Sensor Networks +
###### Abstract  
In crucial applications, sensor node coverage of the objective zone must be stabilized in Wireless Sensor Networks (WSN). A network with holes in coverage is more susceptible to node failures or malicious attacks. According to the total number of hops used to transport data, nodes may calculate their distance from the sink node. A coverage hole may be present if a node notices a much higher hop count than its neighbors. The network becomes more robust and resilient to diverse problems by proactively recognizing and correcting coverage holes. Coverage hole identification aids in the efficient use of network resources. By identifying places with poor coverage, resources such as electricity and bandwidth may be efficiently deployed to increase coverage in specific areas or extend the network lifetime overall. However, some node sensors die while the network operates due to energy restrictions, which may disturb the inclusion of the objective zone, resulting in a coverage hole. Due to limited battery life, the existence of impediments and physical damage to sensor nodes, coverage holes may emerge in sensor networks. Early identification of coverage holes enables prompt maintenance and troubleshooting, which minimizes the need for future major and expensive replacements or reconfigurations. The loss on the region of interest may be calculated by locating the coverage holes and identifying the malfunctioning node that created it. This article discusses many coverage-hole-detecting methods, classification approaches, and different performance comparison assessments. Compared to conventional techniques for detecting coverage holes, the investigated methods contribute to the universal viewpoint on holes and compute the number of holes quite precisely.  
coverage hole detection; Wireless Sensor Networks; node failure mitigation; energy-aware wireless sensor networks; coverage hole analysis +
Footnote †: Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion
### Major Barriers and Policy Implications in India
#### 3.7.1 Economic Barriers or Financial Barriers in India  
Economic and financial barriers are one of the major drawbacks in developing CSP technology in India. This is due to the lack of investors and financial institutions. In addition to this, the government of India needs to encourage the local manufacturing industry to meet the demand for and supply of CSP technology in order to lower imports from international manufacturing units. This is in line with recent major national programs called \"Make in India\" and \"Atmanirbbar Bharat\" [80; 81]. The main aim of these programs is to support the local manufacturing units. Such government initiatives cannot be isolated from broader national issues such as security issues, debt, and state and central political issues, which can drive away potential entrepreneurs or investors. The central government of India thus needs to provide an additional incentive to state governments, such as \"special status\", which will be discussed in further Section 3.7.6, so that state governments can benefit from it and more investors can be attracted, which will consequently help to boost the country's economy. The government also needs to provide further financial benefits to investors, such as exemption from the good and service tax (GST), loans from the banks, and guarantees. Such benefits will encourage investors and entrepreneurs to invest in renewable energy-related technologies.
## 3 Case Study: Measurement Campaign Goal and Setup
### Introduction and Goal  
The DLR currently develops and manufactures a set of next-generation active radar calibrators (transponders) [18], see Figure 1. These Kalibri transponders will be used in the upcoming C-bandSentinel-1 SAR mission. The most critical transponder parameter is its radar reflectivity expressed in ERCS because the transponder will be used as a calibration standard for absolute radiometric calibration. Any error in the calibration of the calibration standards has a direct impact on the derived absolute calibration factor for Sentinel-1. First ERCS measurements of the Kalibri prototype were performed in DLR's compact antenna test range.  
The campaign was conducted for a second, independent derivation and verification of the Kalibri prototype ERCS. It consisted of eight repeated RADARSAT-2 acquisitions of a test site in which 15 trihedral reference corner reflectors of two different sizes (inner leg lengths of 1.5 m and 3.0 m) and the Kalibri transponder prototype were deployed and aligned for the respective acquisition. The data were acquired in April 2013.  
The goal of the campaign was to derive the estimated transponder ERCS _including its confidence interval_. The data analysis, see Section 4, uses hierarchical Bayesian modeling as described above. The research question answered in the case study, derivation of the transponder ERCS \(\varsigma_{t}\), is conceptually identical to the derivation of the absolute calibration factor \(K\) (linking SAR system indications \(P\) to the measurement quantity, (E)RCS) because only the roles of knowns and unknowns are reversed:  
\[\varsigma_{t}=\frac{P}{K}\qquad\Longleftrightarrow\qquad K=\frac{P}{\varsigma_{t}} \tag{2}\]  
Therefore, the approach used in the case study is equally applicable to the radiometric calibration of SAR systems.
## 2 Material and Methods
### Two Investigated Biomechanical Responses for Model Classification  
Two biomechanical responses are considered through the full factorial design. Response 1 is the first investigated response, defined by the average of Von Mises stress in the peri-implant cortical bone. The implant significantly affects the stress field in relation to a physiological state, and the most loaded bone area is around the neck of the implant [7]. Its deterioration is one of the main causes of dental implant failure [65]. An example of stress distribution in the vestibulo-lingual plane is shown in Figure 2a. Response 1 is calculated through a virtual area corresponding to a concentric cylinder to the implant (7-mm diameter). The average of Von Mises stress is calculated from all nodes of the mesh elements belonging to this peri-implanted area; see Figure 2b.  
Response 2 focuses on the load transfer between cortical bone and implant, related to stress-shielding problematic and micromovements at the interface [66]. This response reflects a discontinuity at the bone-implant interface and is assessed by calculating a stress jump along the bone-implant interface. This consists of subtracting average stress from all nodes of the implant mesh from the average stress of the nodes of the cortical bone along the surface.  
These two responses are complementary and give an overall view of the biomechanical effect of the 7 investigated parameters, highlighting problems encountered in surgery that impact the life duration of implants. Since these answers are scalar values, it becomes easy to analyse the results and to compare the models to each other through a full factorial design to assist in selecting the most appropriate choice of implant for a given physiological configuration.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Range in the Literature** & **Investigated in the Study** \\ \hline
**Diameter** & (3; 6 mm) & 3 mm and 4 mm \\ \hline
**Length** & (5; 20 mm) & 10 mm and 15 mm \\ \hline
**General shape and taper** & Various shapes and tapers & Cylindrical and conical \\ \hline
**Angle of inclination** & [0; 20\({}^{\circ}\)] & 0\({}^{\circ}\), 5\({}^{\circ}\), 10\({}^{\circ}\) and 15\({}^{\circ}\) \\ \hline
**Cortical thickness** & Various shapes and thicknesses & 1 mm, 1.5 mm, 2.0 mm and 2.5 mm \\ \hline
**Cancellous Young’s modulus** & (0.1; 9.5 GPa) & 0.75 GPa and 1.5 GPa \\ \hline
**Implant Young’s modulus** & Ti-6 Al-4V (110 GPa) & Ti-6 Al-4V (110 GPa) \\ **(E\({}_{\text{implant}}\))** & Zirconia (200 GPa) & Ti-Nb (60 GPa) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Range of the parameters in the literature and investigated values in the study.
# Semantic 3D Reconstruction with Learning MVS and 2D Segmentation of Aerial Images
## 5 Results and Discussion
### Discussion
#### 5.2.3. Down-Sample Rate  
Since the dense point cloud's scale of a specific outdoor scene collected by UAV is usually around 20M or bigger, global-wise algorithms cannot handle all points at once. For instance, PKU-M1 contains 27 million points. Table 3 shows a trend that the Pixel Accuracy generally reaches its peak at the down-sample rate of 1, equivalent to which means there are no down sampling process is taken at all. Increasing of down-sample rate makes the filtered point cloud denser, which intends the neighbors of a single point to become closer. The closer points are, the more likely they belong to the same semantic class. So it is sensible that the increasing of the down-sample rate avails the final Pixel Accuracy. If the performance of a method with lower sampling rate is higher than another, it is reasonable to believe that the former method is better.  
Figure 7: Ablation study on parameter selection for k-nearest neighbor and soft vs. hard decision strategy. For both Prob and Argmax methods, \(k=15\) is the best parameter. In most circumstances, the soft decision strategy Prob dominates hard decision strategy Argmax.
## 5 Applications Across Diverse Sectors
### Recent Innovations
#### 5.1.6 Hybrid Technologies  
Hybrid additive manufacturing started by mixing traditional manufacturing with AM to fix their separate issues. This new method is great for making complex parts of medium to large sizes very accurately and efficiently. Recent trends, shown in Figure 3, indicate a growing adoption of hybrid AM practices, with notable advancements in steel-based materials through powder bed fusion (PBF) and directed energy deposition (DED) techniques [70]. Moreover, combining directed energy deposition (DED) methods with standard CNC machining offers more versatility for uses in hybrid manufacturing, applying protective layers, and fixing parts.  
Laser-based PBF further facilitates hybrid AM by enabling the conventional manufacturing of simple-shaped substrate components, while directly printing complex-shaped parts onto them, such as integrating conformal cooling into existing bulk molds [46].  
Recent developments in hybrid additive manufacturing include merging various manufacturing processes to create 3D objects. A key strategy in hybrid AM involves blending additive practices with subtractive methods, like milling or machining. This method efficiently produces complex components by utilizing the strengths of both additive and subtractive technologies. Consequently, production speed is enhanced, and surface quality is refined [71].  
The combination of binder jetting printing and standard heat-based finishing represents a form of hybrid manufacturing. In this approach, additive manufacturing is used to create initial models, and then traditional powder metallurgy techniques like sintering and liquid metal infiltration are applied to solidify and refine these models, achieving the necessary density and characteristics [72; 73; 74; 75; 76]. Merging AM with follow-up processing techniques is beneficial for producing functional components and composites, making it possible to craft parts with specific properties and detailed designs. Integrating binder jetting with conventional heat treatments facilitates the development of new materials and effective components. Continuous research is being conducted to improve this hybrid technique for broader application in various industrial fields [46; 75; 76].
# Efficient Tree Policy with Attention-Based State Representation for Interactive Recommendation
## 4 Results and Discussion
### Performance Evaluation (RQ1)  
We investigate the recommendation performance of ATRec against the state-of-the-art baselines with respect to average reward@K, _HR@K_, and _MRR@K_. In this part, we fixed the length of each episode to 32. The experimental results are summarized in Table 2, where the best result in each row is highlighted in bold. The proposed method performs clearly better than the comparison baselines on the three Amazon datasets, especially in the instant video dataset. The average improvement of \(HR@30\) is 42.3% and the average improvement of \(MRR@30\) is 21.4%.  
The conventional methods such as popularity and matrix factorization obtain bad performance under the three datasets. DDPG-KNN method performs worse in \(K=1\), and improves at \(K=0.1N\) in both Instant Video and Baby datasets, where \(N\) denotes the total number of items. However, in the Musical Instruments dataset, which contains more items than the other two datasets, DDPG-KNN performs even worse at \(K=0.1N\). The DQN-R performs much better than the DDPG-KNN method. TPGR is better than the other baseline methods in the three datasets.  
Compared with TPGR, the proposed ATRec method outperforms TPGR in all three datasets, which can be explained for two reasons. First, the state representation model in ATRec can capture more context information in sequence than TPGR, which only uses the final states of RNNs. The attentive state representation model is more robust in dealing with the long and noisy dependencies lying in the data. Second, the state representation model in ATRec is fully learned with an online reinforcement learning method, while the state representation model in TPGR is trained using offline supervised learning. The online learning method of the state representation model can update the model with the growth of the data it encountered, making it able to adjust the dynamic change of user preference and also lead to better performance in both reward and hit rate.
## 3 The Proposed Protocol
### System Initialization
#### 3.2.2 Dynamic Key Generation
The security level of this cryptosystem relies on the fact that the discrete logarithm problem over this defined group was shown to be hard to solve. This meant that cryptosystems that are designed based on the defined additive groups could achieve a higher or equal level of security with a smaller size for the used secret keys, as compared to other cryptosystems based on different arbitrary groups. Let \(G\) be a finite cyclic group with a generator point \(g\). Given a point \(a\ \in\ G\), then \(a\ =\ gr\) for some secret \(r\). Find \(r\ =log_{g}(a)\). In cryptosystems based on the discrete logarithm, the problem that is required to break and solve the system is defined as the Computational Diffie-Hellman Problem (CDHP). In other words, Elliptic Curve Discrete Logarithm Problem (ECDLP) can be defined as follows. Given \(Q,\ P\ \in\ E\), find an integer \(a\in F_{p}\) such that \(P\ =\ a\), there is no efficient algorithm that can obtain \(a\) in a short time. Up to now, there is no polynomial algorithm that can solve the ECDLP problem. We briefly introduce the bilinear pairing function \(e\) properties using Equations (1)-(4). Each operation for computing \(e\ (P,\ Q)\) is a pairing operation where \(P\in G_{1}\), \(Q\in\ G_{2}\) and \(a,\ b\in F_{p}\) (finite field) [32]. In Equations (1)-(4), \(e\) is a pairing function that efficiently satisfies bilinearity, non-degeneracy, and computable properties. \[e:\ G_{2}\ \times\ G_{1}\ \rightarrow\ G_{T} \tag{1}\]  
\[e\ (aP,\ Q)=\ e\ (P\ +\ P\ +\ \ldots\ +\ P,\ Q)\]  
\[=e\ (P,\ Q).\ \ldots\.\ e(P,\ Q)\]  
\[=e\ (P,\ Q)^{a}\]  
\[e\ (P^{a},\ Q^{b})\ =\ e\ (P,\ Q)^{ab}\]  
\[e\ (P,\ Q)\ \
eq 1 \tag{4}\]
# Underwater Target Recognition Based on Multi-Decision
## 3 LOFAR Spectral Line Enhancement Based on Multi-Step Decision  
The line spectrum has been widely used in the field of passive sonar ship target recognition because of its significant sound source information and relatively high signal-to-noise ratio. The Low-Frequency Analysis Representation (LOFAR) spectrum transforms the signal received by the passive sonar from time domain to time-frequency domain using the short-time Fourier transform (STFT), which can reflect the signal in the two dimensions of time domain and frequency domain. Scientists observe the line spectrum in the LOFAR spectrum to determine the presence or absence of the target, and perform tracking and recognition [9]. Because there is more demand of the stealth technology of the ship and the radiated noise of the ship's target is greatly reduced, the signal-to-noise ratio of the ship radiated noise received by the hydrophone array is also decreasing. The line spectrum components become more difficult to identify. There are many research results on automatic detection and extraction of line spectrum under low signal-to-noise ratio.  
In this paper, we study from the multi-step decision algorithm based on the line spectrum feature cost function proposed by [PERSON] [32]. Then we propose a specific calculation method of double threshold, and retain the continuous spectrum information in the original LOFAR spectrum. At last, we combine the original LOFAR spectrum with the extracted line spectrum, and complete the recognition and detection of underwater target by making full use of the advantages of deep neural network feature extraction.
# Anisotropy and Directivity Effects on Uniaxial Compression of Carbonaceous Slate Form Jinman Mine
## 3 Results and Discussion  
### Effect of Rock Structure on Stress-Strain Curves  
The repeatability of experimental results allows the use of experimental outcomes for engineering applications. To evaluate the repeatability of experimental results of samples containing different rock structures, four identical samples of each formation were prepared and subjected to the same loading conditions. Figures 4 and 5 show the typical stress-strain curves including axial stress-strain curve (\(\sigma\)-\(\varepsilon_{xx}\)), hoop stress-strain curve (\(\sigma\)-\(\varepsilon_{xx}\), \(\sigma\)-\(\varepsilon_{yy}\)), and volumetric stress-strain curve (\(\sigma\)-\(\varepsilon_{v}\)) for vertical loading and parallel loading of gray and cinerous samples. The structural deformations of horizontal rock layers were similar to vertical loading conditions, and we only tested the hoop stress-strain curve (\(\sigma\)-\(\varepsilon_{yy}\)). It is observed that the stress-strain curves for different loading conditions can be divided into the initial compaction stage (stage I), elastic deformation stage (stage II), and damage accumulation stage (stage III), as shown in Figures 4 and 5. The initial compaction stage (stage I) shows that the axial deformations for both vertical loading and parallel loading are similar. When the vertical stress was loaded, the slate sample was compacted by a small load due to many micropores among the weak surface structures. The vertically superimposed slate rock had a rock pillar effect during parallel loading [17]. The strength of natural slate is noticeably greater than the strength of bedding cementation. The superimposed slate rock may only undergo axial compression and an adjustment of the alignment of mineral bundles due to a small axial loading, but not show a significant compaction process similar to that due to vertical loading. The slate samples enter the elastic deformation stage (stage II) after the completion of existing micro-fractures and cementation compaction with further loading of axial stress. Thereafter, the integral rigidity increases and the stress-strain curve increases approximately linearly. The gray and cinerous samples show linear deformation when the load is perpendicular to the weak surface structures; axial compression controls the overall deformation, and the axial deformation is about 1.50 to 2.50 times that of hoop deformation, as shown in Figure 4. The rock pillar formed by superimposition is sufficient to support the axial load when the load is parallel to weak surface structures; the relative axial strain of gray slate and cinerous slate is smaller than the vertical loading condition, as shown in Figure 5. Tables 1 and 2 show that the average elastic modulus of the cinerous samples is significantly greater than that of the gray samples for both vertical and parallel loading. This is because the weak surfaces of the slate rock layers and main stresses are related in series during vertical loading, and slate rock is dominated by compression deformation while hoop deformation is constrained.