esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Insertion Torque for Orthodontic Anchoring Screw Placement: A Comprehensive Review
## 4 Discussion
### Shapes and Dimensions of OASs  
#### 4.2.1 Tapered and Cylindrical OASs  
[PERSON] et al. [13] compared tapered and cylindrical self-drilling \(\varphi\)1.5 \(\times\) 7 mm OASs and reported significantly higher ITs in tapered OASs (8.3 \(\pm\) 3.7 Ncm) than in cylindrical OASs (6.3 \(\pm\) 2.8 Ncm) in the maxillary buccal alveolar area. They reported no significant differences in success rates [13] (Tables 2 and 3, Figures 2 and 3).  
[PERSON] and Suzuki [15] used tapered (self-drilling) and cylindrical (self-tapping) OASs of the same dimension (\(\varphi\)1.5 \(\times\) 8 mm in the buccal alveolar area, and \(\varphi\)1.5 \(\times\) 6 mm in the mid-palatal suture area), and found that tapered OASs had higher ITs (12.1 \(\pm\) 3.1 Ncm, 15.7 \(\pm\) 2.3 Ncm, and 21.1 \(\pm\) 2.2 Ncm, respectively) than cylindrical OASs (7.2 \(\pm\) 1.4 Ncm, 12.4 \(\pm\) 1.2 Ncm, and 14.5 \(\pm\) 1.6 Ncm) in all sites [15] (Tables 2 and 3, Figures 2 and 3)[PERSON]  
In an in vitro study, [PERSON] et al. [16] evaluated the fracture torques for several OAS designs (\(\varphi\)1.5-1.6 \(\times\) 6-7 mm) and reported that the characteristic that most influenced the results was the ratio between the internal and external diameters. [PERSON] et al. [17] compared ITs in tapered and cylindrical \(\varphi\)1.6 \(\times\) 8 mm OASs using low- and high-density bovine bone and reported that tapered OASs had higher ITs than cylindrical OASs in the high-density bone, but there were no significant differences in the low-density bone [17].  
[PERSON] et al. [18] used finite element analysis to measure the stresses exerted on the bone with a 2 mm thick cortex assuming the maxillary interproximal area by tapered versus cylindrical OASs (\(\varphi\)1.6 \(\times\) 8 mm) produced during insertion. They concluded that the greatest bone stress during insertion was caused by the self-drilling tapered OAS (10.18 MPa) and the lowest stress was exerted by the self-drilling cylindrical OAS (0.74 MPa) [18]. However, excessive insertion torques may cause bone stress and deformation, leading to congestion and necrosis or even resorption and remodeling around the screw, which leads to OAS failure [19; 20]. Therefore, special attention must be paid during the placement of tapered OASs to prevent excessive IT.  
Consequently, it was suggested that IT varies with the OAS shape and bone features and that the tapered form of OASs seemed to influence the IT. Therefore, tapered OASs were considered to have higher ITs than cylindrical OASs, although there was no difference in the success rates.
## 4 Results and Discussion
### Experiment on ECG Beat Detection and Segmentation  
Using ECG-ID and MIT-BIH databases, the ECG record and its \(R_{peak}\) positions were obtained, as shown in Figure 2. For the ECG-ID database, three samples were taken from 90 people. The first record was used for training, while the second record was used for validation. There were ten annotated \(R_{peak}\) as shown in Figure 2 for each person. In the MIT-BIH database, records 201 and 202 belonged to the same patient with different ECG beats, so they were classified as two classes. The red dots in Figure 2 are an example of the \(R_{peak}\) position detected manually by cardiologists. Using this \(R_{peak}\) position, the ECG beat was detected and extracted. An example of ECG beat detection and segmentation for both databases is shown in Figure 3.  
Figure 3: Sample of detected and segmented ECG beats for training and validation.  
Figure 2: Sample of ECG records of three people obtained from the ECG-ID (the 1 st, 74 th, and 90 th person) and MIT-BIH (the 25 th, 26 th, and 27 th person) databases. The red dots were the \(R_{peak}\) positions annotated manually by cardiologists.  
As shown in Figure 2(a), ECG records from the ECG-ID database have been preprocessed to remove three types of noises, i.e., baseline drift, powerline noise, and high-frequency noise. Meanwhile, Figure 2(b) shows that the effect of baseline drift and powerline noise is still visible in the ECG signal.
## 2 State of the Art
### Dynamic Reliability Modelling  
To account for the complexity of processing systems, more elaborate methods are required to evaluate the performance and reliability of these systems aiming at detecting their dynamic behavior properly. One of the main factors in reliability modelling of complex dynamic systems is to consider the dynamic influence of the operations, environment, or interactions between components, processes and system performance over the failure rates or degradation process.  
To this end, dynamic reliability has been proposed by combining the traditional reliability methods with process modelling, automation and dynamic systems theory. [PERSON] et al. [35] proposed a dynamic opportunistic maintenance model for wind power generation systems to optimize the total life cost cycle (LCC) and to improve the WF energy-based availability. [PERSON] et al. [36] presented a dynamic reliability-centered maintenance method for natural gas compressor stations. The decision-making process determined that equipment may not benefit from frequent maintenance in terms of both availability and cost. In [37] the authors developed a numerical method based on piecewise deterministic Markov process to determine the optimal maintenance time for a heated hold-up tank. In [38] the authors studied the reliability centered maintenance strategies for process plants. [PERSON] and [PERSON] [39] investigated the dynamic reliability assessment and failure prognostics of a system with dependent multistate/continuous degrading components. [PERSON] et al. [40] presented the optimization of the periodic PM of a system with deteriorated components by considering both 1P and 2P activities at different PM stages for a mechanical system.  
The authors in [41] proposed a new dynamic fault tree simulation based on an event-driven approach. An optimization of CBM strategies for a slowly degrading system depending on soft failure and condition monitoring at equidistant, discrete time epochs is provided by [42]. A model to integrate production and preventive maintenance planning model is proposed by [43] for a multistate system with independent and common cause failures.  
Finally, a comprehensive review is reported in [4] on decision making in PM for smart manufacturing applications.
# Seismic Response Study of L-Shaped Frame Structure with Magnetorheological Dampers
## 3 Modeling Theory of the L-Shaped Frame Structure
### Determination of the MRD Location Matrix  
Due to the fact that the damping force of the MRD is relatively small, two dampers are arranged on each chevron support, as seen in Figure 6. There are the following relationships between the damping force of MRD and the force on the corresponding nodes of the structure:  
\[\left\{\begin{array}{cc}F_{jx}=F_{kx}=F_{i}\\ F_{px}=F_{gx}=-F_{i}\\ F_{jz}=-F_{kz}=-2F_{i}h/d\end{array}\right. \tag{25}\]  
The location matrix \(\mathbf{H}\) is used to distribute the control force vector \(\mathbf{F}\) of the MRD to the corresponding nodes of the structure, whose dimension is \(\text{n}\times\text{m}\), in which \(\text{n}\) is the amount of the structural DOFs, and \(\text{m}\) is the number of MRDs [39; 40]. For the MRDs on the \(i\)th chevron support, as shown in Figure 6, the elements corresponding to nodes \(j\) and \(k\) in location matrix \(\mathbf{H}\) are: \(\mathbf{H}(6j-5,i)=\mathbf{H}(6k-5,i)=1,\mathbf{H}(6j-3,i)=-H(6k-3,i)=-2h/d\), and the elements corresponding to nodes \(p\) and \(q\) in location matrix \(\mathbf{H}\) are: \(\mathbf{H}(6p-5,i)=\mathbf{H}(6q-5,i)=-1\), while the others without damping force are all 0.
# Trends in Compliance Monitoring Devices (CMDs) in Ships' Ballast Water Treatment Systems
## 2 A Holistic Approach to Monitoring Threats to the Environment in Ballast Water
### Automation of Ballast Water Verification  
One of the important strengths of using technology in ballast water monitoring and forecasting is that it enables the ease of automation, through Electronic Data Interchange (EDI), and thus forecasting, which is highly encouraged at IMO level, so that the achievement of tasks is automated and made easier for everyone in the shipping industry [19]. Therefore, monitoring discharges into the marine environment from ships' ballast tanks needs to be an automated cross-data sharing effort as there are ongoing threats to the environment. For example, there are still concerns about the presence of microplastics in ballast water discharges [20] as well as sediment that can accumulate for up to a year that is then exposed during the deballasting processes that has viable organisms [21]. This process needs to be automated so that technology that may deal with managing that threat can be deployed to assist the ship once the Port State has obtained the automated data and responded by creating an anti-pollution plan. Keeping electronic records and correct monitoring results will assist in this process. With the IMO getting ready for the introduction of electronic record books for ballast water management and likewise service providers from industry providing both hardware and software to make this possible, it is suggested that the communication of record books can create a more efficient method of dealing with aquatic pollution threats that may still be present in ballast water management. See for example Figure 1[22] and Figure 2. Figure 1 shows the potential solutions for electronic ballast water recording and how such information may be automated for stakeholders to fight ballast water pollution threats.  
Figure 1: NAPA Software and data services for ship design and operation ([[https://www.napa.fi/mepc-82-ballast-water-record-keeping-updates/](https://www.napa.fi/mepc-82-ballast-water-record-keeping-updates/)]([https://www.napa.fi/mepc-82-ballast-water-record-keeping-updates/](https://www.napa.fi/mepc-82-ballast-water-record-keeping-updates/)), accessed on 1 January 2020) (This is a developed ship management record system on a ship that forms a logbook on ballast water management in electronic form; it can be enhanced, and through AI can continue to develop marine pollution measures and ease the burden of compliance while creating a more reliable record).
# 4 KSecure: A Universal Method for Active Manipulation Detection in Images of Any Resolution
###### Abstract  
This paper presents a method for actively safeguarding image integrity based on embedding a hidden signature generated by a neural network. The proposed solution utilizes two cooperating networks: the first one is responsible for generating a signature pattern that is difficult to detect, while the second one embeds this signature into the original image in a way that ensures high visual transparency. This automatically generated signature enables the detection of manipulations in the image and allows for localizing areas where integrity violations have occurred. The main innovation of the proposed approach lies in its ability to handle images of virtually any resolution, including widely used standards in modern communication and publications, such as HD (1280 \(\times\) 720), Full HD (1920 \(\times\) 1080) and even 4K (3840 \(\times\) 2160). This represents a significant improvement over most existing methods, which are typically limited to the small, square images (e.g., 256 \(\times\) 256 or 512 \(\times\) 512 pixels) commonly found in popular object classification datasets, such as ImageNet (approximately 224 \(\times\) 224 pixels). As a result, the proposed method opens new possibilities for actively securing the integrity of large and non-standard image formats while maintaining reasonable computational requirements. It surpasses previous limitations in terms of scalability and image proportions.  
image forensics; deep learning; active detection; passive detection; tampering detection; watermarking; neural networks +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Data
#### 2.1.4 Fire Weather Indices  
Previous studies [46] have demonstrated that the Forest Weather Index (FWI) [62; 63] appears to be the most suitable index in a semiarid region. Furthermore, the Angstrom Index (AI) [64] can identify fire-danger warnings in the driest and highest temperature areas. Thus, both indices have been taken into account in the present research.  
AI only considers temperature and relative humidity, whereas FWI [65] takes into account weather conditions and their relationships with soil moisture and fire activity. Regarding the period, AI considers a one day-period whereas FWI is accumulative and considers longer periods depending on the previous index values. Concerning the fire occurrence, the lower the value of AI, the higher the probability of fire occurrence, especially for AI \(<\) 2; on the contrary, the higher the value of FWI, the more likely a wildfire is to occur, especially when FWI \(>\) 16. As it can be seen in Figure 2, despite FWI being the most suitable index to predict a fire day in the study area, the index itself does not show a clear pattern related to burned area, though its minimum values usually match with the years of smallest burned area. However, the further southeast the area is (Zones 3 and 4), the higher the FWI is, on average, on a fire day (around 25). AI seems to be more stable and its range remains around 3.5 in the studied period, with relative maximums on non-fire days, but it is not able to detect the burned area extension by itself. Nevertheless, both indices were taken into account combined with weather, temporal, and geographical variables in the present study due to their suitability in fire day prediction.  
Package \"fireDanger\" [66; 67; 68] from the R statistical software [69] was used in the calculation of FWI and AI for both 2000-2014 and 2071-2100 periods.
## 4 Results
### Results for the 2D Problem
#### 4.2.2 Diffusion Coefficient  
In this section we evaluate the diffusion coefficient of the optimized structure. In order to highlight the role of the cavities in the structure we compare it with the diffusion coefficient of a system without cavities, also optimized for perfect absorption for the same range of frequencies. As a reference system we consider a flat rigid panel with the same dimensions as the other two structures. The diffusion coefficient and the \(GDP\) have been evaluated as explained in Section 2.4 for frequencies ranging from \(f_{1}=200\) Hz to \(f_{2}=3000\) Hz and for normal incidence.  
Figure 7 shows the diffusion coefficients of the three evaluated structures. The flat panel presents a monotonous diffusion coefficient decreasing in frequencies, meaning that as frequency increases the scattered wave is more directional. The array without cavities is very close to that of the flat panel, except for the range of frequencies where it was optimized for perfect absorption. For the optimized array with cavities we can see that the diffusion coefficient presents several peaks due to the fact that sound penetrates in the system and combined effects of resonances and multiple scattering take place producing diffusion of sound.  
Figure 6: Anechoic chamber results: 2D system. Theoretical and experimental (**a**) absorption coefficient and (**b**) ILR, for a system composed of 5 Helmholtz resonators having the same geometry as in the 1D case.  
In order to quantify the diffusion for the overall range of frequencies we have used the \(GDP\) (see Section 2.4). We can see that for the system without cavities, although it is optimized between 400 and 560 Hz, the \(GDP\) takes a value close to 0, meaning that the system in terms of diffusion behaves like a flat panel. However, for the system with cavities, the \(GDP\) increases up to 0.125, showing that the behavior is far from a flat panel, producing an efficient sound diffusion due to the combined effect of resonances and multiple scattering in the structure.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 4 Measurement Scenarios and Data Preparation
### Experimental Setup  
For the UWB data measurement process in the experimental evaluations, we used a DWM1000 module [34] manufactured by Decawave as the UWB hardware and the STM32 development board (NUCLEO-L476 RG) [40] manufactured by STMicroelectronics as the main microcontroller (MCU). Table 1 provides the hardware configurations used in the experimental evaluations.  
Our previous work [7; 8] pointed out that the alternative double-sided two-way ranging (AltDS-TWR) method outperformed other available TWR methods in the literature in different tested scenarios. Therefore, we applied AltDS-TWR as a wireless ranging method in our evaluations. Furthermore, AltDS-TWR operated well without the need to use high precision external oscillators in the MCU [8]. Hence, the built-in high-speed internal (HSI) clock source (16 MHz) from the MCU was applied to all of the evaluation results presented in this article. According to the data sheet [41], the HSI has an accuracy of \(\pm 1\%\) using the factory-trimmed RC oscillator.  
During measurement, one of the transceivers among the two was connected to a computer for logging the data via a serial USART port. Both transceivers were executed with a two-way ranging software provided by Decawave for production testing of their evaluation kit (EVK1000), which is available on Decawave's website ([[https://www.decawave.com/software/](https://www.decawave.com/software/)]([https://www.decawave.com/software/](https://www.decawave.com/software/))). We modified the library of this software to extract all required features provided in Section 4.3. Then, we logged and saved the extracted features into a file for each trial in our measurement campaign. To avoid the effect of Fresnel zones in our measurement results, the antenna height was always maintained at 1.06 m in one of our UWB devices, i.e., the static one that recorded the measurement data via PC.
## 2 Stress Field Inversion Method and Verification of Its Accuracy
### Inversion Process  
The inversion algorithm developed in this study combines the principles of boundary load method and SMARS algorithm. A finite element model of the tunnel surrounding rock is established using ABAQUS software, and the parameters in the substitute equilibrium  
Figure 1: borehole stress relief and strain gauge locations.  
Figure 2: Strain release values at a certain point varies with borehole depths.  
stress subroutine are adjusted to represent boundary conditions of the model. The measured elastic strains obtained from the limited measurement points on the tunnel surface are used as target values. The genetic algorithm and improved SMARS algorithm are combined in the MATLAB(tm) platform, enabling a cost-effective inversion of the stress field. The specific steps (Figure 3) in the process are described thus:  
1. The geometric dimensions of the tunnel and rock mechanical parameters can be determined through geological surveys and rock mechanics experiments. Based on this information, a finite element model containing the tunnel is established in ABAQUS. The user subroutine SIGINI is adopted to apply the initial stress field \(X\); \[X=\left\{\sigma_{x},\sigma_{y},\sigma_{z},\tau_{xy},\tau_{yz},\tau_{xz}\right\}\] (1)
2. Reasonable ranges \(S\) of parameter values for the substitute equilibrium stress subroutine in the algorithm's main program, implemented in MATLAB(tm), can be defined. Within these ranges, multiple discrete arrays are generated. These arrays \(X_{j}\) can serve as random inputs for the parameters of the stress field subroutine; \[X_{j}=\left\{\sigma_{jx},\sigma_{jy},\sigma_{jz},\tau_{zy},\tau_{yzz},\tau_{yzz }\right\}\ X_{j}\in S,\ S\in\left(x_{j\text{min}},x_{j\text{max}}\right)\] (2)
3. The stress field is calculated for each array using ABAQUS. During the calculation process, the overall equilibrium of the stress field is first established, followed by the excavation of the tunnel. Finally, the calculated strains \(f^{m}(Xj)\) corresponding to each array at the measurement points are obtained;
4. The strains at the limited measurement points are extracted for each array, and error analysis is conducted using an error analysis subroutine and the on-site measured strain value \(f^{m}(Xj)\). The objective function \(J_{i}\) for error analysis is as follows; \[J_{i}=\frac{1}{n}\sum_{i=1}^{n}\ \|f_{i}^{am}(X_{j})-f_{i}^{cm}(X_{j})\ \|\] (3)
5. Based on the results of the error analysis, whether or not the minimum required engineering accuracy is met can be determined. If the error meets the requirements, the program terminates. Otherwise, the parameters are mutated, new random arrays \(X_{j}\) are generated, and Steps 3 and 4 are repeated until the minimum error tolerance is satisfied. Finally, the optimal solution \(X_{opt}\) is derived: \[X_{opt}=\left\{\sigma_{x}^{opt},\sigma_{y}^{opt},\sigma_{z}^{opt},\tau_{xy}^{ opt},\tau_{yz}^{opt},\tau_{xz}^{opt}\right\}\] (4)  
Figure 3: The inversion calculation process.
# Numerical Investigation of the Dynamics of 'Hot Spots' as Models of Dissipative Rogue Waves
## 3 Gain Localized in Space
### Emergence of a Pinned Mode  
However, when the gain is large enough, a _pinned mode_ localized in space with a stationary waveform will become the dominant feature (Figure 3c). There are two possible scenarios for the generation of a pinned mode, namely, a sufficiently large total gain \(\gamma_{1}\) or a sufficiently small \(\varepsilon\). For a fixed \(\gamma_{1}\), a smaller \(\varepsilon\) gives a sharper peak with greater maximum gain in Equation (4). When the maximum gain is large enough, the rogue wave absorbs the energy and grows into a pinned mode (Figure 3c). From our numerical results, the pinned mode tends to be a very narrow pulse. Resolution may be an issue and adaptive grids should perhaps be employed in future studies.  
Figure 3: Effect of linear gain localized in space (\(\sigma=1\), \(\beta=-0.1\), \(\varepsilon=0.1\), \(\alpha\equiv 0\)): (**a**) simulated result of a rogue wave and (**b**) the net gain at \(\gamma_{1}=0.1\); (**c**) simulated result of a pinned mode and (**d**) the net gain at \(\gamma_{1}=0.2\).
# A Survey on Parameters Affecting MANET Performance
## 3 Simulation in MANETs  
MANET technology is rapidly changing, and new protocols and mechanisms are continuously proposed by researchers. Evaluating a network's performance under different attacks is important to be able to propose protection mechanisms. Therefore, a cost-effective method that empowers researchers to set up and test MANETs plays an important role in research.
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 4 Construction of UPF Model
### System Setup  
SysParm(1) Construction  
* Input: s: security parameter.
* Output: \(\lambda=\left\{\begin{array}{ll}f,\ G_{r},\ g,\ G,\ C,\ D,\ RE,\ i\text{: system parameters' set.}\end{array}\right.\)  
The basis of the unidirectional proxy re-encryption system is established. \(f:\left\{0,1\right\}^{k}\times\left\{0,1\right\}^{*}\rightarrow\left\{0,1 \right\}^{k}\) is a pseudorandom function and \(G_{r}\) is a pseudorandom generator. G is a group of order p which is a large prime and g is a generator of a group G. i is each participant of group G. C and D are encryption and decryption functions, and RE is a re-encryption function.