esa-sceva/satcom-chunk-collection huggingface dataset:
# Watermarking Protocols: A Short Guide for Beginners
## 5 Conclusions  
Buyer and seller watermarking protocols based on asymmetric fingerprinting have proven to be effective tools to protect digital copyright. They are mainly based on the primitives documented in Section 3, which can be assumed to be building blocks to design secure interaction schemes able to meet the requirements reported in Section 2. However, such primitives are often characterized by high computational costs, which make their use in the current web context rather difficult.  
On the other hand, attempts to develop watermarking protocols that do not use the primitives described above have often resulted in solutions that are not able to achieve sufficient levels of usability and security. As a consequence, a first key challenge in the next few years consists of designing new and more efficient homomorphic encryption schemes since such new schemes can increase the efficiency of buyer and seller watermarking protocols based on asymmetric fingerprinting, which remain the most promising solutions to the problem of digital copyright protection.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Methods
### Feature Extraction Techniques
#### 3.4.3 Fast Fourier Transform  
Fast Fourier Transform (FFT) analyses certain signals and samples for certain space and time. It splits them into smaller frequency components. It processes the discrete Fourier transform for few data samples. This data that should be transformed is partitioned into smaller frames [9]. Each individual frame is transformed and the obtained result is added to the matrix. Short-Time Fourier Transform (STFT) is a type of Fourier transform that can be represented by following equation:  
\[STFT=X(m,w)=\sum_{-\infty}^{\infty}x[n]\ w[n-m]e^{-jun} \tag{6}\]  
Here, \(w\) is continuous and \(m\) is discrete. This is performed using FFT; hence, both of these variables are quantized and discrete. The FFT is very consistent and robust in obtaining the most optimum features.
## 4 Simulation Results
### Performance Metrics  
We will consider the following performance metrics:  
* _Channel estimation:_ The accuracy of the channel estimate is measured through the normalized mean square error (NMSE) computed as \[\text{NMSE}=\mathbb{E}\Bigg{[}\frac{\|\mathbf{H}-\hat{\mathbf{H}}\|_{F}^{2}}{ \|\mathbf{H}\|_{F}^{2}}\Bigg{]}.\] (23)
* _Channel parameter estimation:_ The accuracy of the channel parameter estimates is also considered evaluating the normalized root mean square error (NRMSE) as \[\text{NRMSE}(\vartheta)=\sqrt{\frac{\mathbb{E}\Big{[}\sum_{i=1}^{P}|\theta_{i} -\hat{\theta}_{i}|^{2}\Big{]}}{\sum_{i=1}^{P}|\theta_{i}|^{2}}},\] (24) where \(\vartheta\) is a variable that represents delay, Doppler, or channel gain.
* _Model order performance:_ The estimation of \(P\) will be evaluated by the average number of estimated paths and the probability mass function (PMF) of the number of detected paths.
* _Communication:_ Communication performance of the proposed approach are evaluated by measuring the bit error rate (BER) as a function of the SNR/bit in order to investigate for the detection robustness when real channel estimation with the proposed variant is performed at the receiver. The SNR/bit is given by \(E_{b}/N_{0}\) where \(E_{b}\) is the average energy per bit. We consider a quadrature amplitude modulation (QAM) with \(Q=16\) constellation points and \(E_{b}=\frac{2(Q-1)}{30g_{2}Q}\). The input-output relation for data  
Figure 1: Received DD domain pilot signal.  
frames is the one in Equation (9). At the receiver, a linear minimum mean square error (LMMSE) equalizer provides the estimated symbols as  
\[\hat{\mathbf{x}}=\left(\mathbf{H}^{H}\mathbf{H}+\frac{1}{\text{SNR}}\mathbf{I} \right)^{-1}\mathbf{\hat{H}}^{H}\mathbf{y}, \tag{25}\]  
where \(\text{SNR}=\frac{MNE_{s}/NT}{M\text{/N}_{0}}=\frac{E_{s}}{N_{0}}\) is the communication signal-to-noise ratio and \(E_{s}=E_{b}log_{2}Q\) is the average energy per symbol. After that, a symbol-by-symbol maximum likelihood (ML) detector takes the decision.
# Parameter-Matching Multi-Objective Optimization for Diesel Engine Torsional Dampers
###### Abstract  
Torsional vibration dampers effectively mitigate torsional oscillations and additional stresses in diesel engine crankshaft systems, ensuring operational safety and reliability. Traditional damper selection principles, grounded in dual-pendulum dynamic models, focus on minimizing maximum torsional angles but fail to accurately characterize vibration behaviors in multi-cylinder engines. This study addresses this limitation by investigating dynamic modeling and numerical methods for an eight-cylinder diesel crankshaft system. A torsional vibration model was developed using Cholesky decomposition and the Jacobi sweep method for free vibration analysis, followed by dynamic response calculations through model decoupling and modal superposition. Parameter optimization of the damper was achieved via the NSGA-II multi-objective algorithm combined with a Bayesian-hyperparameter-optimized BP neural network. The results show that high-inertia-ratio dampers effectively suppress vibration and stress, while low-inertia-ratio configurations require approximately 20% elevated tuning ratios beyond theoretical parameters to achieve an additional 5% stress reduction, albeit with amplified torsional oscillations. Additionally, the study critically evaluates the numerical reliability of conventional dual-pendulum-based tuning ratio selection methods. This integrated approach enhances the precision of damper parameter matching for multi-cylinder engine applications.  
torsional vibration; crankshaft system; neural network; additional stress; bi-objective optimization Article  
[PERSON], [PERSON].
# Topological Photonic Media and the Possibility of Toroidal Electromagnetic Wavepackets
## 4 Topological Photonic Media
**(b)** A sample 2D photonic crystal in non-topological phase for relative permittivity of 1 and 10 in white and black regions, respectively. **(c)** Band diagram of TM modes of the photonic crystal in **(a)**; dotted lines show the case where the relative permittivities of the gray and black rods are set to 10. The vertical axis represents the dimensionless frequency \(\omega a/(2\pi c)\). (\(a\): lattice constant and \(c\): speed of light). Figure 5: **(a)** Unit cell of a 2D superlattice composed of quasi-topological and non-topological photonic crystals in Figure 4. **(b)** Projected band diagram of TM modes of **(a)**. Edge modes are emphasized in red. The red-dotted lines show the edge modes where the relative permittivities of the dark gray and black rods in the middle part are set to 10. The vertical axis represents dimensionless frequency \(\omega a/(2\pi c)\). (\(a\): lattice constant and, \(c\): speed of light) **(c)** Energy flux density of an edge mode (\(\omega a/(2\pi c)=0.455\)) belonging to the lowest branch (the lowest red curve) in **(b)**. range. The linear source was set at the left end of the left branch of the waveguide, and the vortex modes were excited by electric field oscillations along the rods, or specifically, in the vertical direction to the page. Figure 6b shows the \(z\)-component of electric field \(E_{z}\). Here, we can see an extremely anisotropic propagation through helical edge channels. Figure 6c shows the transmission spectra (blue and red solid lines) measured at the ends of the upper and lower right branches along with the spectra (blue and red broken lines) of the case where the Y-shaped region is replaced by vacuum, for comparison. The blue and red broken lines should overlap for the idealistic simulation treating each rod as a material with an exactly sharp boundary. However, smearing each boundary was introduced in the real simulation and the order of introducing the parts influenced the actually simulated structure. For the present case, the simulated structure of the block layer weakly broke the space inversion symmetry; hence, a small discrepancy appeared between the blue and red broken lines. By contrast, a large difference appeared between the blue and red solid lines: the transmission characteristics of the target system were quite asymmetric.
## 2 Materials and Methods
### Application of the Attended Encoder-Decoder to the Short-Term Load Forecasting
#### 2.4.2 Application of Encoder-Decoder Architecture  
The encoder accepts inputs for the last 24 h before the forecast day as shown in Figure 6. The decoder is fed with hourly chunks of the time-series data related to hourly ambient temperature, hourly wet bulb, the type of the day, etc. at the forecast day and the output of the decoder cell at the previous time instance (except the first decoded time step).  
Each encoder and decoder input consists of:  
* the hour \(h\),
* the load power from hour \(h-1\) (for decoder the previous predicted value),
* the ambient temperature and wet bulb from hour \(h\),
* the day of the week (0-6),
* the type of the day concerning the temperature (hot, cold, regular),
* the type of the day concerning holiday or working day,
* the length of the day  
The usage of the load power alone as encoder input as done in [24] increased the prediction error, therefore it was not applied here. The encoder and decoder input is processed with a 1D convolutional filter to account for changing relevance of the input data depending on temperature and day type.  
The encoder is used as a sequential model, the decoder as a stacked architecture as shown in Figure 6. During the test, it turned out that it is beneficial concerning the forecast error to apply a separate convolution and attention layer for each decoded instance. This might be related to the varying correlation between the encoder outputs and the decoder output at time \(t\).
## 3 Results and Discussion
### Glycemic Index of SPI Noodles  
Low-glycemic-index foods have been shown to elicit hypoglycemic and normal insulin responses, improving blood sugar control in individuals with type 2 diabetes. To test the GI of noodles fortified with 20% SPI versus control noodles, a blood sugar test of three subjects aged 20-24 years with BMI (body mass index) ranging from about 19 to about 25.7 was conducted after the subjects at noodles. Figure 1 displays glucose level changes in blood sugar with time. The control noodles resulted in a maximum blood sugar level of 126 mg/dl after 0.5 h of eating noodles, whereas the blood glucose peak of 20% SPI noodles was relatively flat, indicating a slower rise in blood glucose, with a maximum value of 106 mg/dL at 0.5 h, showing a significant difference. After calculating the GI value using Equation (1), the control noodles had a high GI value of 74.12, while for 20% SPI noodles, the GI value decreased significantly to 48.63. Typically, foods with a GI value of 70 or above are considered to have a high GI, while a medium GI ranges from 56 to 69, and a low GI has a value of 55 or less [49; 50]. Therefore, the GI value of 20% SPI noodles falls within the low-GI food category. Soybean has also been reported to lower GI and aids in maintaining blood glucose levels in patients suffering from diabetes mellitus. This could be due to the inhibitory effect of protein on carbohydrate digestibility as protein covers the surface of starch granules, absorbs water, prevents water from moving into the core of the granules, and limits the expansion of the granules after heating. Moreover, the protein covering might also lower the accessibility of carbohydrates to hydrolytic enzymes, thereby reducing their digestibility and ultimately lowering GI [26]. The most common system for ranking foods is the GI, which is a relative ranking of carbohydrates in foods in comparison to a reference food and which measures the ways in which it affects blood glucose response [51]. Low-GI foods are digested, absorbed, and metabolized slowly, resulting in a lower and more gradual increase in blood sugar levels, thereby reducing the need for insulin [52]. [PERSON] et al. reported an increase in protein content and a decrease in GI for multigrain noodles (refined wheat flour (62.2%), sorghum flour (24.6%), soy flour
# Kinematic Analysis and Verification of a New 5-DOF Parallel Mechanism
## 2 Design and Verification of the Scheme
### Scheme Improvement and Singularity Analysis
#### 2.4.2 Singularity Analysis of Improved Parallel Mechanism  
Singularity is an inherent property of parallel mechanism [38]. It is found that the improved parallel mechanism scheme still has singularity, which occurs at the position shown in Figure 6. At this time, the singularity of the line cluster rank belongs to the (b) spatial distribution of line cluster rank 4, which is parabolic congruence. The rank of the five force line vectors is 4, plus the rank of the constraining force couple; the final composite screw system has a rank of 5. The corresponding physical meaning is that after fixing five input sliders, the rotational freedom of the moving platform around the line connecting the spherical hinges \(S_{1}\) and \(S_{2}\) is not effectively constrained, and the singularity occurs. However, after carefully studying the position of the singularity occurring in the new scheme, it is found that due to the restriction of the spherical hinge angle in the parallel mechanism, the moving platform cannot move to this position. That means the position of the posture has exceeded the workspace of the moving platform. It can be seen from this that the improved parallel mechanism has no singularity in the workspace, and each position can be uniquely determined by the mechanism.
### Literature Review
#### 1.2.2. Spatial Interpolation Methods
These 3D interpolation methods provide modeling results that more concern the real world but have limits on reflecting the borehole data that have a vertically dense distribution. In 2D GIS, the search area is generalized to a circle, and the search distance to a radius. Yet, for vertically concentrated data, such as borehole data, searching neighbors by using an asymmetrical distance in a 3D space is more desirable than to search a neighbor by using the same distance for every direction. 3D search neighborhoods improvement is important in the spatial interpolation method since the outcome may greatly vary depending on the approach determining the neighbors though the interpolation function is the same. In terms of 3D search neighborhoods, [PERSON] et al. proposed 3D search neighborhoods based on a constrained Delaunay triangulation and the large scaled 3D geological modeling method based on borehole data using IDW interpolation (Figure 3a) [31]. And [PERSON] et al. proposed an improved IDW in which they created a bounding box in vertical and horizontal directions, which is indexed from the location with unknown values, and used it in the neighborhood search for interpolation (Figure 3b) [9]. an approach was used in separating and running the neighborhood search distance horizontally and vertically, and is advantageous in effectively reflecting the characteristics of borehole data that are densely populated vertically. However, neighborhood search by using Delaunay triangle has limitation on explicit search distance limits. In addition, horizontal/vertical bounding box-based approach that converts the Rook that searches neighbors perpendicular to the XY axis into the XY and XZ axes, and thus, it is limited in searching neighborhoods located diagonally. In order to alleviate the problems, this study proposed an improved IDW algorithm to which fine spheroid-based 3D search neighborhoods is applied, which can search all directional neighborhoods. This approach can maintain the accuracy of IDW interpolation, which is the most actively used in 3D geological modeling and can efficiently expand the traditional search neighborhood method to a 3D environment. Finally, proposed IDW results were examined by performing the seismic liquefaction assessment and mapping. Figure 3: 3D search neighborhoods: **(a)** Constrained Delaunay triangle-based approach [31]; **(b)** Two search-grid surround boxes-based approach [9].
## 4 Simulation Results and Discussions  
In this section, MaMPA was used to solve SOOPF, MOOPF, and MaOPF problems. Two test systems including the IEEE 30- and 118-bus systems were used, and four objectives which are cost, emission, transmission loss, and VSI were chosen to be the objective functions in each system as in the 20 case studies shown in Table 1. The IEEE 30-bus system consists of 6 generators, 4 transformers, and 41 transmission lines, and the system active and reactive power demands were constantly 283.4 MW and 126.6 MVAR, respectively. The IEEE 118-bus system has 54 generators, 9 transformers, and 186 transmission lines, and the active and reactive power demands were considered fixed at 4242 MW and 1439 MVAR, respectively. The detailed data and system single-line diagrams of the IEEE 30- and 118-bus systems can be found in [57; 58], respectively, and the emission coefficients for the IEEE 30- and 118-bus systems can be found in [57; 58].  
Figure 1: Flowchart of the many-objective marine predators algorithm (MaMPA).  
118-bus system are provided in [36]. The number of population and maximum iteration were both 100, for the IEEE 30-bus system. For the IEEE 118-bus system, the population and maximum iteration numbers were 100 and 500, respectively.
## 5 Simulation Analysis
### LoRa
BW8, BW9, and BW 10 are represented for 125 kHz, 250 kHz, and 500 kHz. The bit rate is represented in bits per second (bps). It can be observed that in every SF, the bit rate is eventually high at the BW 10 (500 kHz) and CR1. It can be observed that an increase in the SF7 is inversely affected by the bit rate, as the bit rate drops progressively from SF 7 to SF 12. In SF 7, the bit rate of the LoRa met 22,000 bps and in SF 12 the bit rate is limited to 2000 bps. An increase in SF will lead to communicating a low amount of data during transmission, so SF 7 is the optimal SF that needs to be considered for sending a large amount of the data. Figure 15: (a–f) A bit rate of SF7-12. (b)Link budget:  
Link budget or received power is the sum of the losses and gains. The concept of the link budget. To calculate the link budget, we considered Equation (7). In this study, the three BW, namely 125 kHz, 250 kHz, and 500 kHz, along with SF 7 & SF 12 are considered for calculating the link budget. The bandwidth is represented as BW1 (125 kHz), BW2 (250 kHz), and BW3 (500 kHz). 2 dBm, 5 dBm, 8 dBm, 11 dBm, and 14 dBm are the transmission power. The increase in the \(T_{x}\) is leading to an increase in the link budget, and it can be observed in Figure 16a. Every increase in the SF causes an increase in the link budget. The link budget attained maximum dBm for 125 kHz of SF 12 at 14 dBm. The link budget detected a low dBm for 500 kHz at SF7 of 2 dBm. It is observed that high \(T_{x}\) power and low BW achieve are feasible parameters for attaining the highest link budget. (c)Receiver sensitivity:  
The BW, SF, and noise figures are the input parameters used for calculating the receiver's LoRa sensitivity. Equation. 6 is employed for calculating and also BW of 125 kHz, 250 kHz, and 500 kHz are considered along. This BW is represented as BW1 (125 kHz), BW2 (250 kHz), and BW3 (500 KHz) respectively. The Signal-to-noise ratio (SNR) value is different for distinct SF. The following SNR are considered for SF 7 is \(-7.5\), SF 8 is  
Figure 16: Link budget and receiver sensitivity. \(-10\), SP9 is \(-12.5\), SF 10 is \(-15\), SF 11 is \(17.5\), and SF 12 is \(-20\). Generally, power sensitivity has a negative number i.e., \(-127\) dBm, here the value beyond that number implies that it is decreasing. Figure 16b presents the sensitivity from the SF 7 to SF 12; the sensitivity power is highest for the SF 7 at the BW3 (500 kHz), and the lowest sensitivity is observed for the SF12 at the BW 1 (125 kHz).
# Preventing Overturning of Mobile Cranes Using an Electrical Resistivity Measurement System
## 6 Discussion  
In this study, we propose an Electrical Resistivity Measurement System (ERMS) and an analytical approach to prevent overturning accidents in mobile cranes. The ERMS is more convenient than typical ground exploration methods and enables efficient and accurate  
Figure 16: Results of ground condition measurement of ERMS applied to AT crane: (**a**) apparent resistivity pseudo-depth in the right direction; (**b**) apparent resistivity pseudo-depth in the left direction.  
Figure 14: Installation of the ERMS and electrodes mounted on an all-terrain crane.  
Figure 15: 250-ton AT crane with ERMS (top view).  
analysis of ground conditions through the main measuring device and foldable electrode, including the water supply device.  
One of the key elements of the ERMS is ensuring the operational safety of mobile cranes, which is addressed using an underground safety evaluation algorithm for data analysis. This algorithm is designed to analyze the risk of ground subsidence in real time, without the typical complex software inversion processes. Typical methods take considerable time to interpret data and incur software costs; however, the proposed algorithm can address these issues. The proposed method enables rapid analysis of the ground's electrical resistivity, allowing operators to assess the potential for ground erosion or subsidence in advance.  
The developed foldable electrode was tested and verified as a replacement for existing pin-buried electrodes and allows operators to perform electrode installation more conveniently and quickly. This provides significant advantages not only in terms of time savings during installation and dismantling but also in rapidly assessing ground conditions before mobile crane operations. Additionally, the foldable electrode, combined with the water-supply device, reduces contact resistance and measurement errors. It maximizes user convenience while enhancing operational safety.  
An analysis of the test results indicates that the ERMS exhibits high reliability under various field conditions and is expected to play a crucial role in preventing overturning accidents caused by ground issues during mobile crane operations. This system can accurately assess ground conditions before the operation of mobile cranes, effectively preventing overturning accidents and ultimately ensuring operator safety and improving operational efficiency.  
However, the ERMS controller requires separate operation from the crane controller, which causes operational inconvenience. This highlights the need for controller integration to realize efficient use. Additionally, to enhance the reliability of the ground evaluation algorithm, repeated experiments and additional data collection in various ground environments are necessary. It is also necessary to enhance the accuracy of measurement data by using various electrode array methods, such as Schlumberger and dipole-dipole arrays, in conjunction with the Wenner method. These issues highlight the need for further research that enhances the convenience and reliability of ERMSs.
# Towards Understanding Driver Acceptance of C-ITS Services--A Multi-Use Case Field Study Approach
## 4 Discussion
### Usefulness
An extensive study with 3000 test drivers over 12 months found significant changes in drivers' behaviour towards safer trends, including reductions in average speed, hard braking rate, severe deceleration rate, speeding rate, and excessive speeding rate, after receiving C-ITS warning services. These findings underscore the importance of C-ITS in fostering public acceptance of cooperative autonomous driving strategies [34]. The usefulness is rated high in the field studies and literature if users perceive safety benefits. Additionally, information must be easy to understand. Another aspect covered in the literature is the way of warning drivers in their vehicles. It seems that the right combination of acoustic and visual warning further increases the perceived usefulness. Interesting to mention that the approval rates were equal among the different users regardless of age, gender, or driving experience.