esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Asymmetric Hyperchaotic Image Encryption Scheme  
In this scheme, we suppose that the sender wants to transmit a confidential image through the public channel to the receiver, and the whole procedure is summarized as follow. First, the sender encrypts the plaintext image using hash value derived from the original image, and the cipher image is obtained through the permutation and diffusion. Then, the received public key from the receiver is used to encrypt the hash value by ECIES, followed by the process of combining it with encrypted images into encrypted packet. After that, the encrypted packet is transferred to the receiver through the public channel. When the receiver gets the encrypted data packet, the decrypted hash value is obtained by the use of the private key and ECIES. Finally, the receiver can use the hash value and the decryption scheme to extract the corresponding plaintext image. The whole process of the scheme is shown in Figure 3.  
As for the encryption algorithm, it can be generally divided into two components: the permutation and the diffusion. For the permutation step, not only has the place of each pixel in the image changed, but with the separation and the combination of the bits in each pixel, the value of each pixel has also changed, which demonstrates the additional effects of diffusion. Moreover, with the consideration of the amount of information that the different bits in the same pixel contain, the corresponding divided bit-level planes have also been parted into the different sized block units for the following step of permutating, making both time efficiency and the strength of security strongly enhanced.  
In the process of diffusion, for the advanced utilization ratio of the chaotic sequences, the same sequences applied in permutation are reused. In a two-dimensional image, the method proposed in [31] is applied to augment the complexity of the correlation  
Figure 3: An example for the whole transmission process.  
between different pixels. It is started from the bottom to the top and from the right to the left, and after a round of diffusion, for each pixel, it is related with two proximal pixels in the downward and rightward direction.  
The specific procedure of the algorithm is displayed in Figure 4, and the part in the green frame is the novel different-sized blocks permutation proposed in this paper.
## 3 Results
### Reproducibility  
The experiment was conducted 40 times to validate the stability of the model in reproducing the results. A T-test was performed to investigate the statistical significance of the discrepancy (Table 10). The experiment showed that, at the 0.05 level, the population mean (0.9859) is not significantly different from the test mean (0.9861).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **ACC** & **SEN** & **SPE** & **PPV** & **NPV** & **F1** \\ \hline \hline \multicolumn{1}{c}{Batch Sizes} & & & & & & \\
4 & 0.9660 & 0.9711 & 0.9545 & 0.9798 & 0.9356 & 0.9754 \\
8 & 0.9506 & 0.9511 & 0.9495 & 0.9772 & 0.8952 & 0.9640 \\
16 & 0.9861 & 0.9889 & 0.9798 & 0.9911 & 0.9749 & 0.9900 \\
32 & Ab. & Ab. & Ab. & Ab. & Ab. & Ab. \\
64 & Ab. & Ab. & Ab. & Ab. & Ab. & Ab. \\ Early Stopping at & & & & & & \\ validation accuracy & & & & & & \\
0.99 & 0.9799 & 0.9867 & 0.9646 & 0.9845 & 0.9695 & 0.9856 \\
0.98 & 0.9861 & 0.9889 & 0.9798 & 0.9911 & 0.9749 & 0.9900 \\
0.97 & 0.9645 & 0.9711 & 0.9495 & 0.9776 & 0.9353 & 0.9744 \\
0.96 & 0.9830 & 0.9867 & 0.9747 & 0.9889 & 0.9698 & 0.9878 \\
0.95 & 0.9645 & 0.9644 & 0.9646 & 0.9841 & 0.9227 & 0.9742 \\ \hline \hline \end{tabular}  
* ACC: accuracy; SENS: sensitivity; SPE: specificity; PPV: positive predictive value; NPV: negative predictive value; F1: F1 score.  
\end{table}
Table 9: Classification results of ParaNet+ under different batch sizes and early stopping settings. The abbreviation for Ab. is “Aborted due to computational resources limit”.
# A Lightweight Fire Detection Algorithm Based on the Improved YOLOv8 Model
## 2 Model and Methods  
### Improved YOLOv8 Model  
YOLOv8 (You Only Look Once version 8, YOLOv8) emerges as a real-time target detection model founded upon a deep convolutional neural network, initiated by Ultralytics. Renowned for its outstanding performance, versatility, and efficiency, this model incorporates several key architectural innovations. Its backbone network segment employs the C2f structure, based on the CSPNet framework, facilitating the extraction and prioritization of crucial information by mapping channel features to focal features. In the neck segment, YOLOv8 integrates the PANet structure, enabling the seamless transfer and aggregation of information through multiple pathways, effectively integrating global and local feature insights. The Detection Head segment adopts the Anchor-Free methodology, enabling the direct prediction of target location and size from the feature map, without reliance on fixed anchor frame scales and aspect ratios. Furthermore, the loss function incorporates the Task Aligned Assigner positive sample allocation strategy, along with Distribution Focal Loss [16], which effectively alleviates issues related to sample imbalance and category distribution disparities.  
Currently, fire detection algorithms typically demand substantial computational resources to process complex image data and model structures, relying on high-performance devices such as GPUs with robust computing power. Therefore, this paper puts forward a lightweight fire detection network based on an improved YOLOv8 model, named GCM-YOLO after the modifications. The structure of the improved model is shown in Figure 1.
# Flexural Strength and Stiffness of Donut-Type Voided Slab
## 2 Optimization of Void Shape  
### Parameters of Void Shape  
To find out the parameters of void shape, the current void shapes were compared to each other. As a result, three parameters were found, such as base shapes, curvature radius of edge and hole diameter, as shown in Figure 2. According to previous studies [10; 11; 12], it was deduced that base shapes were related with the volume of the void. In case of the same height in the void, the volume of cuboid-shaped void was larger than that of sphere-shaped void. The volume of void was a key factor on voided slab, because it affected the self-weight of the slab directly. It was expected that the curvature radius of the edge influences a flexural strength and a ductility due to stress concentration at the edges, and because the curvature radius at the edge of the void became smaller, stress was concentrated more at the edge. It was also expected that the hole in voids was related with a flexural stiffness, because the center hole in the void shaper might improve the flexural stiffness by its good geometric shape, such as the reduction of the aspect ratio of the width and height of the void.  
Considering the parameters of the void shape, eight types of void shape were designed to find the optimal void shape by the nonlinear finite element analysis, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Ratio of Voided Slab**} \\ \cline{3-5}
**Researcher** & **Shape of Void** & \multicolumn{3}{c}{**to Solid Slab (\%)**} & **Displacement** \\ \cline{3-5}  & & **Flexural** & **Flexural** & **(\(\mu\))*** \\ \hline BubbleDeck [1] & Spherical & 91 & 87 & 2.7 \\ \hline [PERSON] [4] & Capsule & 87–92 & 91–107 & 2.07 \\ \hline \multirow{2}{*}{[PERSON] [5]} & Spherical & 93.4 & 93 & 5.72 \\ \cline{2-5}  & Oval & 72.3 & 81 & 4.17 \\ \hline \hline \end{tabular}  
* A measure of the ductility may be defined by the displacement ductility ratio.  
\end{table}
Table 1: Flexural capacities of the current voided slab system.  
Figure 2: Parameters of the void shape.  
The slab thickness was set to be 250 mm, having a heavy self-weight, and the width of the basic module of a voided slab was set to be 300 mm. The height of the void was assumed according to the depth of the compressive stress region with the maximum reinforcement ratio in the slab. The maximum depth of the compressive stress region was calculated by using the strain compatibility method. In order to prevent forming a void in the compressive stress region, the height of the void had to be less than 152 mm in a 250-mm-thick slab with 24 MPa of concrete and SD400 rebar. Taking into consideration the depth of the compressive stress region, slab thickness, workability such as rebar settlement and concrete casting, the height of the voids was set to be 140 mm, and the distance between them was 30 mm. Considering 300 mm in the basic module, the width or diameter of the voids was set to be 270 mm in all the voids except for the sphere-type void, whose width was decided by its height.
## 6 Therapeutic Approaches of Dentin Hypersensitivity
### At-Home Therapeutic Treatments of Dentin Hypersensitivity
#### 6.1.6 Bioactive Glasses  
Additionally, toothpastes containing bioactive glasses in combination with fluoride have been suggested to alleviate the symptoms of DH. Bioactive glasses can effectively occlude or block the dentin tubules. When applied to the tooth surface, bioactive glass particles can penetrate the tubules and form a physical barrier within them, as well as attract tooth structural ions (Ca\({}^{+2}\) and PO\({}_{4}\)\({}^{-3}\)) and promote the formation of hydroxyapatite crystals [76; 81; 89].
## 3 Results and Discussion
### Characteristics of OSB Boards
#### 3.1.1 OSB Board Density  
It is observed (Figures 8 and 9) that the density of the OSB boards was about 30% higher than that of the solid wood from which it came, this being determined by the adhesive in the board and the exerted pressure (about 3 MPa) which has compressed and compacted the strand mat. Also, the density of the two species of fast-growing hardwoods was about 12% higher than that of the softwood ones, a fact also observed by other authors [26].  
Figure 8: Density of OSB boards compared to solid wood.  
Figure 7: Modeling the specific surface of the strands according to species and thickness.  
Analysis of variance (ANOVA) with null hypothesis and significance level of 0.05 was used and was obtained a good significance F-value of 4.57%. In this way the range of values was determined for 95% confidence interval as was for spruce species (621.65-652.65). Also, by the same method it was determined Tukey Pairwise Comparisons with the obtained order: poplar, willow, spruce, and pine.
# PriSign, A Privacy-Preserving Single Sign-On System for Cloud Environments
## 3 Preliminaries
### Bilinear Pairing  
Let \(\mathbb{G}_{1}\), \(\mathbb{G}_{2}\), and \(\mathbb{G}_{T}\) be cyclic groups of prime order \(p\). Let \(g\) and \(\widetilde{g}\) be generators of \(\mathbb{G}_{1}\) and \(\mathbb{G}_{2}\), respectively. The mapping \(e:\mathbb{G}_{1}\times\mathbb{G}_{2}\rightarrow\mathbb{G}_{T}\) is a bilinear map if it has three properties: (1) _bilinearity_: \(\forall g\in\mathbb{G}_{1},\widetilde{g}\in\mathbb{G}_{2}\), and \(a,b\in\mathbb{Z}_{p}\), and we have \(e(g^{a},\widetilde{g}^{b})=e(g,\widetilde{g})^{ab}\); (2) _non-degeneracy_: \(e(g,\widetilde{g})\
eq\mathbb{1}_{\mathbb{G}_{T}}\); (3) _computability_: \(e\) can be efficiently computed. We denote a bilinear group \(\mathcal{BL}=(\mathbb{G}_{1},\mathbb{G}_{2},\mathbb{G}_{T},e,p,g,\widetilde{g})\). Our scheme is based on the Type-III pairing [32], which means that there is no efficiently computable homomorphism between \(\mathbb{G}_{1}\) and \(\mathbb{G}_{2}\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multirow{2}{*}{**Unlinkability**} & \multirow{2}{*}{**Designated**} & \multirow{2}{*}{**Proxy**} & \multirow{2}{*}{**Threshold Key**} & \multirow{2}{*}{**Token-Hiding**} & \multirow{2}{*}{**Traceability**} & \multirow{2}{*}{**Fine-Grained Access Control**} \\  & & & **Verifiers** & & & & & \\ \hline \([4]\) & \(\surd\) & \(\times\) & \(\times\) & \(-\) & \(\times\) & \(\surd\) & \(\times\) \\ \([5]\) & \(\times\) & \(\times\) & \(\times\) & \(-\) & \(\times\) & \(\surd\) & \(\times\) \\ \([6]\) & \(\surd\) & \(\times\) & \(\times\) & \(-\) & \(\times\
## 3 Results and Discussion
### Microstructure and Morphology  
Figure 1 shows the XRD spectra of GA (Figure 1A) and spray-dried cashew particles (Figure 1B-E). Diffuse, broad peaks indicate an amorphous state typical of randomly oriented structures, while sharp, well-defined peaks are typical of crystalline materials with highly ordered molecular structures [26]. According to the XRD spectra, GA and spray-dried cashew particles presented predominantly amorphous structures, although spray-dried cashew particles (Figure 1B-E) showed a noisy, relatively more defined peak around 2\(\theta\) = 20\({}^{\circ}\), which is characteristic of semi-crystalline materials. The higher crystallinity of spray-dried particles, when compared to GA, is desirable because crystalline properties are associated with lower water absorption and retention in powdered matrices, which can increase storage stability and shelf-life [20]. The similarities among the spectra of all spray-dried groups C1-C4 indicate that the different spray drying conditions assessed in this study (inlet temperature and GA concentration) did not significantly alter the microstructure of spray-dried cashew particles. Similar trends were reported by [PERSON] et al. [61] for spray-dried cashew particles obtained with whey protein isolate-chitosan blends, and by [PERSON] et al. [20] for spray-dried date fruit particles with MD-GA blends.  
The SEM micrographs in Figure 2 show that spray-dried cashew particles mostly consist of spherically shaped structures (Figure 2B-E), as expected for spray-dried fruit products [20; 45]. GA (Figure 2A) had irregularly shaped and larger structures when compared to spray-dried cashew particles. It was also observed that when a lower GA concentration was used (15% \(w/v\), groups C1 and C2), smaller particles with more adhesion were produced (Figure 2B,C), while larger, more spherical, and more dispersed particles were obtained at higher drying carrier addition rates (25% \(w/v\), groups C3 and C4; Figure 2D,E). These findings correlate with the particle size and hygroscopicity results of the group C3 and C4 samples (Table 1). Previous reports have demonstrated that particle morphology directly affects the stability of spray-dried phytochemicals [62]. Fissues, holes, and excessive surface roughness may weaken the protective role of the outer structure created by the drying carrier, either by promoting gas permeation or facilitating oxidation reactions [63; 64]. In our study, all groups yielded particles with a smooth surface and
## 3 Results and Discussion
### Duration of Nest Visits of the Laying Hens Based on Video Evaluation  
It is worth emphasizing that the time spent in the nest is mostly, but not always, the time of egg-laying. It is possible for the hen to simply be resting there, or to have entered the nest to seek shelter.  
A significant difference was found in the average duration of the use of the lower nest between the studied genotypes (Table 4). This means that the laying hens that belonged to the Patternal genotype spent more time on average in the lower nests (13.4 min) than the Commercial hybrid (7.9 min) and the Maternal genotypes (8.6 min). The measured difference is also statistically proven.  
The basic data reveal that the values for the individual nests show a rather large variation, which unfortunately significantly increased the margin of error of the statistical calculations; therefore, no further significant difference could be verified. At the same time, it is thought-provoking that some nests were quite exceptional in the duration of stays among the different egg nests. For example, the top level nest number five used by the Patternal line and the top level nest number one used by the Maternal line. The time spent in these areas were far more than what is necessary for oviposition.  
In the case of the Commercial hybrid, the laying hens did not spend an average of 1 min in the upper nests, while the individuals of the Patternal genotype spent roughly twenty times that, but here the three 'honored' nests (number 1, 5, and 7) played a special role for a reason. One thing is for sure: the duration of stay there sometimes showed huge differences in the case of some egg nests, but it is very difficult to clarify the hen's preference, as nest 1 and 7 are in extreme positions, while nest 5 is almost in the middle.  
[PERSON] et al. [9] also similarly used a video system in an aviary-type barn and investigated the nest use patterns and nest behavior of two genotypes of laying hens, the dual-purpose Lohmann Dual and egg-type Lohmann Brown Plus, and also examined the effect of the placement of the nests.  
The results showed that the patterns of nest use and behavior in the nest differed between the conventional, dual-purpose, and egg-laying hens. Dual-use hens, for example, used the same nests in greater numbers. Both genotypes used nest number one more than nest number six, but spent more time in the latter. Based on their experience, dual-purpose hens were more affected by the location of the nest than egg-laying hens.
###### Abstract  
In this study, reinforcement learning (RL) was used in factory simulation to optimize storage devices for use in Industry 4.0 and digital twins. Industry 4.0 is increasing productivity and efficiency in manufacturing through automation, data exchange, and the integration of new technologies. Innovative technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics are smartly automating manufacturing processes and integrating data with production systems to monitor and analyze production data in real time and optimize factory operations. A digital twin is a digital model of a physical product or process in the real world. It is built on data and real-time information collected through sensors and accurately simulates the behavior and performance of a real-world manufacturing floor. With a digital twin, one can leverage data at every stage of product design, development, manufacturing, and maintenance to predict, solve, and optimize problems. First, we defined an RL environment, modeled it, and validated its ability to simulate a real physical system. Subsequently, we introduced a method to calculate reward signals and apply them to the environment to ensure the alignment of the behavior of the RL agent with the task objective. Traditional approaches use simple reward functions to tune the behavior of reinforcement learning agents. These approaches issue rewards according to predefined rules and often use reward signals that are unrelated to the task goal. However, in this study, the reward signal calculation method was modified to consider the task goal and the characteristics of the physical system and calculate more realistic and meaningful rewards. This method reflects the complex interactions and constraints that occur during the optimization process of the storage device and generates more accurate episodes of reinforcement learning in agent behavior. Unlike the traditional simple reward function, this reflects the complexity and realism of the storage optimization task, making the reward more sophisticated and effective.The stocker simulation model was used to validate the effectiveness of RL. The model is a storage device that simulates logistics in a manufacturing production area. The results revealed that RL is a useful tool for automating and optimizing complex logistics systems, increasing the applicability of RL in logistics. We proposed a novel method for creating an agent through learning using the proximal policy optimization algorithm, and the agent was optimized by configuring various learning options. The application of reinforcement learning resulted in an effectiveness of 30-100%, and the methods can be expanded to other fields.  
onceptualization; methodology; job allocation; reinforcement learning; stocker; digital twin; simulation; Industry 4.0
## 3 Methodology
### Implementation of MFELCM  
#### 3.4.1 Implementation of Random Forest  
We use Random Forest to classify the static feature dataset \(SF\). It is an integrated learning algorithm that is based on decision trees, which has the advantages of low bias, low variance, and high generalization ability. The method to create Random Forest is as follows.  
1. Select \(n\) samples from the training set randomly, which are used to create a decision tree. The decision tree is trained by the CART (Classification and Regression Tree) algorithm. In each node of a decision tree, m features of samples are randomly selected as an alternative set (AS) for node splitting. The feature \(k\) in AS and its threshold \(t_{k}\) according to which the samples in this node are split into the left and the right nodes are then determined by minimizing the cost function, as shown in Equation (14). In Equation (14), \(G_{left/right}\) and \(m_{left/right}\) is the Gini Impurity and the number of samples of the _left/right_ node, respectively. \(G_{left/right}\) is calculated by Equation (15), in which \(p_{i,j}\) is the proportion of class \(j\) samples in the \(i\)th node; \[J(k,t_{k})=(m_{left}/m)*G_{left}+(m_{right}/m)*G_{right}\] (14)  
Figure 9: Split the datasets by MMSI.  
\[G_{i}=1-\sum_{j=1}^{q}p_{i,j}^{2} \tag{15}\]
2. When the decision tree reaches its maximum depth or the cost function cannot be reduced, stop node splitting and terminate the decision tree creation.
3. Repeat the first step to create a large number of decision trees. We then obtain the Random Forest. When classifying the ship type, the decision trees vote on the class of the ship.
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## Principles of Coil Guns
### Mechanical Model  
The mechanical model has been defined following the coil gun structure described in [11]. However, our kicking system is very similar in terms of dimensions and weight to that one which serves as a reference design for most RoboCup teams. Consequently, this model will be reusable for other teams, and can be adapted for other use cases by changing the content of some blocks according to the chosen mechanical design. However, changing parameters of the coil requires to simulate again the electromagnetic part, as force depends on coil gun geometry, current and plunger position.  
As for the electrical model, an analytical calculation of the force is not possible. The finite elements model using _FEMM 4.2_ shows that force varies in our case study from \(-1900\) N to \(19{,}000\) N for the same coil depending on the plunger position and on the coil current (Figure 16). Discrete values of the force are calculated for positions of the plunger (Figure 15) varying from \(x=-100\) mm to \(60\) mm by increment of \(5\) mm, and for coil currents varying from \(1\) A to \(200\) A by increments of \(50\) A.  
It is important to note that:  
* Force \(F\) is almost linear with coil current in any situation.
* Force \(F\) is highly dependent on the plunger position. For a current \(I=100\) A, \(F\) varies, depending on plunger position, from \(F=-954\) N to \(F=954\) N, with \(F=0\) N when the plunger is exactly aligned with the centre of the coil. This is because magnetic flux tends to be maximized in a magnetic circuit, leading to reduce the air gap. Consequently, as shown in Figure 16, the magnetic force on the plunger is symmetrical around the point where the centre of the plunger is aligned with the centre of the coil. Thus, it is important to stop powering the coil as soon as the plunger has crossed the coil.
* Force is very small if the plunger is outside the coil. This is normal considering the important length of the air gap replacing the plunger for looping back the magnetic circuit. We can also note that when the plunger is at the centre of the coil, force is null for any value of the current because magnetic flux can not be maximized.  
Figure 15: RoboCup reluctance coil gun kicking system.