esa-sceva/satcom-chunk-collection huggingface dataset:
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 3 System Architecture  
### AES Algorithm Parameters  
The AES algorithm has the following three parameters:  
1. The number of encrypted blocks (_Nb_): The input plaintext, in 32 bits, is 1 word, because the AES plaintext block is fixed at 128 bits, which is the state matrix composed of four words.
2. Number of key segments (_Nk_): The length of the key is 32 bits for one word, and is divided into four words, six words, and eight words according to the key length of 128 bits, 192 bits, and 256 bits.
3. Number of encryption rounds (_Nr_): This is the number of iterations required for encryption and decryption, which is related to the key length. The number of rounds from short to long key length is 10, 12 and 14 rounds, respectively. The relationship between the number of encryption rounds and the number of key segments can be calculated as follows: \[Nr=6+Nk\] (1)  
The above parameters, according to the different key lengths, can be organized as shown in Table 1:
## 3 Network, Assumptions and Adversary Model
### Adversary Model  
We have assumed that an adversary can mostly perform passive attacks (like eavesdropping [18], and traffic analysis), since such attacks helps to conceal the adversary's presence in the network. Nevertheless, the adversary is also capable of performing some active attacks like fabrication and packet drop attacks. We also assumed that the adversary is both device-rich and resource-rich [4]. These characteristics are defined below.  
* Device-rich: the adversary is equipped with devices like antenna and spectrum analyzers, so that the adversary can measure the angle of arrival of the packet and received signal strength. These devices will help the adversary to find out the immediate sender of the packet and move to that node. This kind of hop-by-hop trace back mechanism will be carried out by the adversary until the actual sender node is reached.  
Figure 1: Typical WSN scenario.  
* Resource-rich: the adversary has no resource constraint in computation power, memory or energy.  
It is also assumed that the adversary has some basic domain knowledge like the range of identities assigned to the sensor nodes, the public key of the base station and information about the cipher algorithms used in the network. However, adversary has no knowledge which identity is physically associated with which node.  
A detection and prevention strategy against non-privacy disclosure attacks at various layers such as jamming attacks is out of the scope of this paper. However, trust management methodology (Section 4.1) that we adopted in this paper is useful to detect and prevent some non-privacy disclosure threats such as, black hole attack, sink hole attack, and selective forwarding or gray hole attack.
## 4 Experimental Analysis
### Experimental Analyses
#### 4.3.4 Comparison with Existing Popular Models
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method/Dataset** & **D\({}_{\text{A}}\)/(\%)** & **D\({}_{\text{B}}\)/(\%)** & **D\({}_{\text{C}}\)/(\%)** & **D\({}_{\text{D}}\)/(\%)** \\ \hline ResNet50 [23; 24] & 98.5 & 96.8 & 99.1 & 97.3 \\ NCNN [25] & 94.5 & 92.5 & 92.7 & 91.4 \\ FcANet [17] & 98.5 & **99.5** & 98.6 & **98.6** \\ SE-ResNet [26] & 99 & 99.1 & 99.1 & 98.2 \\ MobileNet-v3 [27] & 87.7 & 89.5 & 88.6 & 83.6 \\ MARN & **99.5** & **99.5** & **99.5** & **98.6** \\ \hline \end{tabular}
\end{table} 
Table 5: Highest validation accuracy of different methods on four datasets (Bold are optimal data). As can be seen from Tables 6-9; \(P_{r}\) on D\({}_{\rm B}\); and D\({}_{\rm D}\), \(F1\) on D\({}_{\rm B}\), the proposed model is not as good as FcaNet, and the other metrics are better than the other models. It is also easy to see that the training time of the proposed method is longer and much higher than the shallow model, but this phenomenon will be improved with the updating of hardware equipment. Overall, the MARN is applicable to the field of fault diagnosis. To further explore the classification effects inherent in each model, Figures 16 and 17 show the confusion matrices and t-SNE dimensionality reduction in the fully connected layers for the different methods. From Figure 16(44), it can be seen that although FcaNet achieves the same accuracy as the proposed method on D\({}_{\rm D}\), FcaNet misclassifies two labels, and the proposed method misclassifies only one label; the other models have large-scale misclassification on most of the labels. As can be seen from Figure 17(e1-e4), MobileNet-v3 performs very poorly in the all-connected layer clustering visualisation, with large-scale fault features undergoing confounding. While other models can achieve clustering of different classes of fault features, there is still a small amount of conflation between individual labels. Compared with the proposed method clustering, it still needs further improvement. Therefore, MARN has clear advantages in the field of fault diagnosis. Figure 16: Confusion matrices comparing different methods. (From **(a–e)** for FcaNet/SE-ResNet/ResNet50/NCNN/MobileNet-v3, and from **(1–4)** for D\({}_{\rm A}\)–D\({}_{\rm D}\), respectively).
## 2 Theoretical Considerations
### Mass Transfer of Ammonia  
In the process of ammonia stripping, the mass transfer rate of ammonia (\(F_{NH_{3}}\)) from liquid to gas phase is given by Equation (11) [14; 21; 22].  
\[F_{NH_{3}}=V_{L}\frac{\left[NH_{3,L}\right]^{T}}{dt}, \tag{11}\]  
where \(t\) is time and \(V_{L}\) is the total volume of liquid.  
In a batch reactor, the mass transfer rate for liquid ammonia to gas is defined by Matter-Mueller, as shown in Equation (12) [14; 22]:  
\[F_{NH_{3}}=Q_{G}H_{NH_{3}}[NH_{3,L}]\left\{1-exp\left(-\frac{K_{L,\;NH_{3}}aV_{ L}}{Q_{G}H_{NH_{3}}}\right)\right\}, \tag{12}\]  
where \(Q_{G}\) is the gas flow rate and \(H_{NH_{3}}\) is the dimensionless [PERSON]'s law constant. \(K_{L,\;NH_{3}}\) is the mass transfer coefficient of liquid ammonia. The interfacial area per unit volume between liquid and gas is indicated by \(a\).  
Combining Equations (8), (10) and (12) and integrating the total ammonia concentration over time gives Equation (13):  
\[-\ln\frac{\left[NH_{3,L}\right]_{t}^{T}}{\left[NH_{3,L}\right]_{0}^{T}}=\frac {Q_{G}H_{NH_{3}}f_{NH_{3}}}{V_{L}}\left\{1-exp\left(-\frac{K_{L,\;NH_{3}}aV_{ L}}{Q_{G}H_{NH_{3}}}\right)\right\}t, \tag{13}\]  
The slope for the logarithm of ammonia concentration ratio over time is then given by Equation (14):  
\[\text{slope}=\frac{Q_{G}H_{NH_{3}}f_{NH_{3}}}{V_{L}}\left\{1-exp\left(-\frac {K_{L,\;NH_{3}}aV_{L}}{Q_{G}H_{NH_{3}}}\right)\right\}, \tag{14}\]  
From Equations (13) and (14), the overall mass transfer coefficient for liquid ammonia to gas is calculated as follows:  
\[K_{L,\;NH_{3}}a=-\frac{Q_{G}H_{NH_{3}}}{V_{L}}\ln\left\{1+\frac{V_{L}}{Q_{G}H_ {NH_{3}}f_{NH_{3}}t}\ln\left(\frac{\left[NH_{3,L}\right]_{t}^{T}}{\left[NH_{3, L}\right]_{0}^{T}}\right)\right\}. \tag{15}\]\[K_{L,\;NH_{3}}a=-\frac{Q_{\rm{G}}H_{NH_{3}}}{V_{L}}\ln\biggl{\{}1-\frac{V_{L}}{Q_{ \rm{G}}H_{NH_{3}}f_{NH_{3}}}\times(\rm{slope})\biggr{\}}, \tag{16}\]  
The main variables in ammonia stripping are the ammonia fraction, the rate of change in the ammonia concentration, the volume of gas supplied, and the overall mass transfer coefficient according to the type of reactor as shown in Equations (12) and (14). Hence, in this study, we analyzed the effect of the major stripping factors--ammonia concentration, fraction and gas flow rate--on the ammonia removal efficiency and mass transfer coefficient.
# New Product Short-Term Demands Forecasting with Boxplot-Based Fractional Grey Prediction Model
## 2 Literature Review
### Box-and-Whisker Plot  
A box-and-whisker plot (or simply called a boxplot) is a method of visualizing the range of data, displaying the main data tendencies and of distributing symmetry and integrating the above information into a histogram-like chart in order to illustrate how values vary and concentrate with respect to a given data distribution [15].  
When drawing a boxplot, five statistics are required: the minimum value, the 1 st quartile (lower, Q1), the 2 nd quartile (regarded as median, Q2), the 3 rd quartile (upper, Q3) and the maximum value, which divide all the data into four equal parts, and each section includes approximately 25% of the values. The 1 st quartile divides data into two groups, data with values lower than Q1 (25% of the data amount) and others (75% of the data amount). In the same way, the 2 nd quartile divides data into the subset with values lower than Q2 and the subset with values larger than Q2; each subset contains an even half of the data amount, and so on for Q3. In terms of the form of a \"boxplot\", the main feature is a rectangle that extends from the 1 st quartile through to the 3 rd quartile, i.e., the box's length equals the interquartile range, and the range between Q1 and Q3, also named IQR [16].  
The dash lines stretching from both the box's sides are named the \"whiskers\", used to indicate variability outside both quartiles. The corresponding lengths are obtained by \(\text{Q1}-1.5\times\text{IQR}\) (lower fence) and \(\text{Q3}+1.5\times\text{IQR}\) (upper fence). Typically, outliers are plotted as individual dots beyond the fences. Additionally, boxplots can be drawn either vertically or horizontally [16]. Figure 1 shows the horizontal form of a boxplot.
## 1 Introduction  
Mechanical biofilm control, including tooth brushing or interdental cleaning, are the cornerstone in supraggingival dental biofilm control [1], although a lack of compliance, poor motivation, inadequate skills and hard-to-reach areas can hinder effective mechanical biofilm control, whereas the adjunctive use of chemotherapeutic agents delivered as mouth rinses or denittifaces can significantly enhance dental biofilm management. These agents provide substantial benefits in maintaining oral and periodontal health [2].  
The advent of new dendrimice/mouth rinse formulations requires appropriate testing to evaluate their actual antiplaque/antgingivitis effect, since the inclusion of a well-known active agent in a formulation does not guarantee its clinical efficacy [3]. The final clinical recommendations should be based on 6-month, home-use, randomized clinical trials (RCTs), following accepted regulations [4], while recently, the American Dental Association (ADA) Council on Scientific Affairs also endorsed 3-month studies [5].  
A new mouth rinse formulation (_Lacer Oros Accion Integral_, Lacer SA, Barcelona, Spain) incorporating O-Cymen-5-ol, potassium nitrate, zinc chloride, dipotassium glycyrrhizate, sodium fluoride, panthenol and xylitol in its formulation has been recently proposed, which makes an evaluation of its safety and efficacy relevant.  
It was, therefore, the main objective of this RCT to evaluate the safety of this mouth rinse formulation, by assessing its tolerability and the advent of adverse events. As secondary objectives, outcomes related to its efficacy were also evaluated: (1) the impact on plaque and gingival inflammation, (2) the impact on dentin hypersensitivity, (3) the patient's evaluation of the product, by means of patient-reported outcomes (PROs), and (4) the microbiological impact.
## 3 Results  
### Results of the Sea Trials  
A total of 10 sea trials was performed using 4 types of tie-down gillnets, and the results of 7 trials were comparatively analyzed. Three trials were excluded because of gillnet breakage, lost gillnets, and so on. A total of 18 species were caught. The total number of catches was 2466 (526,363 g), of which blackfin flounder accounted for the highest proportion of individuals (1144; 122,302 g), followed by red halibut (306; 31,241 g) and black-edged sculpin (289; 93,478 g). The catches and catch rates for each experimental net are listed in Table 2. The statistical analysis, Kolmogorov-Smirnov test, and results for normality of the catches had a normal distribution (\(p\)-value \(>\) 0.05) for the seven successful trials.  
The catches of the experimental nets with each mesh size were as follows: 682 fish (104,411 g) were caught with the 84-mm mesh, 643 (113,843 g) with the 90-mm mesh, 673 (166,014 g) with the 105-mm mesh, and 475 (142,230 g) with the 120-mm mesh. The 84-mm mesh had the largest catch in terms of total number of catches, and the 105-mm and 120-mm meshes had the largest catch in terms of catch weight. This was because there was a relatively high proportion of cod in the 105-mm and 120-mm meshes (Table 2).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**System Name**} & \multicolumn{4}{c}{**Task Name**} & \multicolumn{4}{c}{**Task Name**} & \multicolumn{4}{c}{**Task Name**} & \multicolumn{4}{c}{**Task Name**} & \multicolumn{4}{c}{**Task Name**} \\ \cline{2-15}  & \multirow{2}{*}{**Methods  
The blackfin flounder was the main target fish in this study. Our sea trial results showed that with an increase in the mesh size, the size of the caught fish increased, and the catch size decreased considerably. Among the experimental nets with 4 different mesh sizes, the net with the 84-mm mesh had the highest number of blackfin flounder catches (373 fish), followed by the 90-mm net (363 fish) and the 105-mm net (307 fish). However, the number of catches decreased sharply for the net with a mesh size of 120 mm (101 fish). The number of catches and the ranges of the fish's total lengths for each mesh size are listed in Table 3, and the corresponding frequency distributions are depicted in Figure 4.  
The mode of the total length showed a pattern of a slight increase with an increase in the mesh size. We considered small individuals as those with a total length \(\leq\)200 mm. Four small individuals were caught with the 120-mm net, which was a considerable decrease compared with the 42 small individuals caught with the 84-mm net (Table 3 and Figure 4).
## 3 Materials and Methods
### Traffic Aggregation  
To better distinguish benign from malicious traffic, we adopt a hierarchical aggregation mechanism that transforms fine-grained packets into semantically meaningful channel units. Initially, packets are grouped into flows based on quintuple features, including source IP, destination IP, source port, destination port, and protocol. A fixed time window is applied to ensure that only packets within a valid interval are merged, preservingtemporal continuity and reducing fragmentation. Flows sharing the same source and destination IP addresses are then further aggregated into higher-level channels.  
This multi-stage process constructs coherent and consistent units that capture both detailed flow attributes and broader communication patterns. The quintuple features encapsulate the identity, port information, and protocol semantics of communicating parties. By encoding and fusing these channel-level features in parallel, the system enhances the precision of malicious traffic detection and improves the overall pattern recognition capability. Operating on channel-based representations provides the classifier with more coherent and semantically enriched input, which helps to mitigate the impact of isolated or noisy flows.  
Malicious traffic often exhibits behavioral similarity and aggregation within a single channel. For instance, single-node continuous attacks frequently reuse the same communication paths for port scanning or repeated access to targeted servers. A typical case is the Mirai botnet, which maintains regular connections with its command and control server, producing multiple flows within the same channel that share similar temporal and structural properties.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 1 Introduction  
Intelligent shipping has become the main direction of the development of the shipping industry, and the autonomous navigation technology of ships is the key to realizing intelligent shipping. As one of the core technologies, situational awareness is the basis for realizing the autonomous navigation of ships. Particularly, detecting and tracking the target ships with collision risks are essential to situational awareness.  
In the recent decade, the rapid development of artificial intelligence technology and high-speed processors have promoted ship object detection tracking methods based on computer vision. However, existing high-precision ship object detection and object tracking models rely on vast amounts of data with high-quality labelling [1]. However, samples for inland ships are relatively few, and labeling is time consuming. Moreover, a large amount of data brings a long training time. In addition, the trained models suffer from poor generalization capability. The model application scenario is affected by the annotation of the dataset [2].
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 8 Conclusions  
A rectangular microstrip antenna has been proposed for 5G applications in response to the growing demand for mobile data and mobile devices. This antenna has a resonance frequency of 28.00 GHz, for which the reflectance is equal to \(-22.50\) dB. The proposed antenna has a radiation efficiency of 80.18% and the antenna gain for the resonance frequency is 5.06 dB. The results also show that its bandwidth is 5.57 GHz (relative operational band 19.89%), which is a very good result, much greater than results achieved in other published works, such as [15,16], where the operating bandwidth of the proposed antennas is around 1 or 2 GHz [18, 19, 20]. The proposed antenna would be a good option for 5G mobile communication, as it offers the high throughput required. The antenna is very compact and lightweight, which makes it suitable for devices where space is a major limitation.  
Conceptualization, R.P.; Methodology, R.P. and M.B.; Resources, M.B. and L.N.; Visualization, R.P. and M.B.; Writing--original draft, R.P., M.B. and L.N.; Writing--review & editing, L.N. For other cases, all authors have participated. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Material and Methods
### Feature Selection  
In the present work, the features studied are the nonlinear indices of SE, DET, AFCL, and DF, computed in the PVs and in the anterior and posterior free wall of the left atrium. With the aim to remove data containing redundant or irrelevant variables without losing information, a feature selection procedure was applied to the data [46]. The adopted strategy combined two main techniques: the _filters_ method, which selected the features independently from the predictor choice in a preprocessing step, and the _wrapper_ method, which selected the features with machine learning techniques for classification, see Figure 1. For the _filters_ method, we applied a filter to the correlation matrix, removing the most redundant variables with respect to the others, with a cut-off value of 0.60.  
After correlation matrix filtering, the _wrapper_ method selected was the Recursive Feature Elimination, which has been implemented via Random Forest (RF) for variable importance, providing a ranking of the most relevant features for classification [47]. The RF assigned to each variable a ranking from 0 to 100 based on the mean decrease Gini score, a measure that defines the contribution of each variable to the homogeneity of nodes and leaves in the RF. The higher the value of the Gini coefficient, the more important the feature and its value. As a trade-off empirical decision, the variables having scores higher than 40 were selected.
# A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC
## 4 Experimental Results
### Qualitative Comparison  
Due to the outstanding performance, FCN-8s is selected to be combined with BSLIC in our work. Our proposed algorithm is compared with three other methods: FCN-8s, FCN-16s, and FCN-32s. All of the test dataset from PASCAL VOC 2012 are processed by four algorithms. Owing to the better performance observed in Figure 7, FCN-8s is selected as the reference for comparison. Figure 8 compares the FCN semantic segmentation results with our semantic segmentation results. From Figure 8c,d, it is observed that the proposed algorithm can reach the same semantic recognition accuracy as FCN-8s. At the same time, the improved algorithm has a better accuracy when dealing with the small edges of the target, such as the wheel of aeroplane, the bird's feet, the sheep's ear, and the rear view mirror of a car in Figure 8d. In FCN-8s, small objects are often ignored and misclassified as background. In conclusion, the combination of FCN and BSLIC outperforms FCN-8s, FCN-16s and FCN-32s, especially the ability to keep the detailed structure of an object.  
Figure 8: _Cont._Table 1 shows the time-consuming of four methods. The proposed method allows for FCN and BSLIC to run at the same time, so there is no time to waste. However, it requires a little extra time to combine FCN and BSLIC by using the four criteria. This is the reason why the four criteria are designed in a simple and efficient way.
# Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms
## 4 Definition of the Tuning Problem Based on Optimization Algorithms  
The problem to be solved in this work is a search for more optimal values for three PI controllers that regulate the rotor speed of the PMSM. To solve the optimization problem, it is necessary to use a target function to generate an adequate search space and find the best parameters of PI regulators. The objective function can be defined using different error criteria of the dynamic response of the system, such as: (a) Integrated absolute error (IAE), (b) Integrated Squared Error (ISE), (c) Integrated Time Squared Error (ITSE), (d) Integrated Time Absolute Error (ITAE). Although these definitions work properly, in this work we propose to use the following objective function [40], which allows us to obtain better results:  
\[\min J=\left(1-e^{-\beta}\right)\left(M_{p}+e_{ss}\right)+e^{-\beta}(t_{s}-t _{r}) \tag{10}\]  
where \(M_{p}\) is the maximum overshoot; \(e_{ss}\) is the error in steady state; \(t_{s}\) is the time of establishment; \(t_{r}\) is the rise time; and \(\beta\) is the weighting factor that can be modified depending on the dynamic characteristics that are required to be reached. In relation to the objective function, Equation (10) the optimization problem can be defined as follows: minimize \(J\) subject to:  
\[k_{p,j}^{min}\leq k_{p}\leq k_{p,j}^{max} \tag{11}\]  
\[k_{i,j}^{min}\leq k_{i}\leq k_{i,j}^{max} \tag{12}\]  
for \(j=[i_{q},i_{d},w]\). It should be mentioned that \(\beta\) in (10) determines the magnitude of the characteristic values of the transient response of the system. If \(\beta>0.7\) is reduced \(M_{P}\) and \(e_{ss}\); if \(\beta<0.7\) is minimized \(t_{r}\) and \(t_{s}\). The search space of the parameters is defined as:  
\[\begin{array}{ll}\mathbf{k_{p,i_{q}}}=(0.01,300)&\mathbf{k_{i,i_{q}}}=(0.01,50)\\ \mathbf{k_{p,i_{d}}}=(0.01,180)&\mathbf{k_{i,i_{d}}}=(0.01,150)\\ \mathbf{k_{p,\omega_{r}}}=(0.01,500)&\mathbf{k_{i,\omega_{r}}}=(0.01,350)\\ \end{array}\]  
The control scheme for PMSM is exposed in section three, however, a proper operation requires robust strategies or adaptive control that ensure safe and reliable performance also must respond quickly and appropriately to various scenarios that it may face.