esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Structure Optimization under DoS Attacks
### Structure Optimization under Dos Attacks  
For a given DoS attack \(\delta\), we know that the optimal structure and control law can be figured out by the iteration of Equation (20): The iteration derives as follows.  
\[N_{c}^{*}|_{\delta}=\arg\max_{N_{c}}r(N_{c},\delta). \tag{24}\]  
If \(j\
otin N_{c}^{*}(i)\), the iteration of Equation (20) usually obtains a small \(\left\|vec(W_{ij})\right\|\) instead of \(\left\|vec(W_{ij})\right\|=\)0 because it is an numerical method. Therefore, a threshold method is used in our algorithm to obtain \(\left.N_{c}^{*}\right|_{\delta}\). We can obtain the estimated \(\left.N_{c}^{*}\right|_{\delta}\) by Equation (25):  
\[\left.\bar{N}_{c}^{*}\right|_{\delta}(i)=\left\{j\right\}\left\|vec(W_{ij}) \right\|\leq\rho_{i}\right\},i\in\mathbf{N}^{+}\cap[1,n], \tag{25}\]where \(\mathbf{N}^{+}\) is the integer set, and \(\rho_{i}\) is a given threshold that is a small positive number. The equilibrium of Stackelberg game can be reached by solving the optimization problem of Equation (23), or an optimization problem of minimization with constraints as follows:  
\[\begin{array}{l}\delta^{*}=\arg\min\limits_{\delta}r\left(N_{c}^{*}\big{|}_{ \delta},\delta\right),\\ s.t.\ \ \ \sum\limits_{i=1}^{n}\sum\limits_{j\in N_{c}(i)}\delta_{ji}\leq C,\delta_{ ji}\in[0,1]\ \ \ \ j,i\in\mathbf{N}^{+}\cap[1,n].\end{array} \tag{26}\]  
In summary, the algorithm of structure optimization under DoS attacks is described in Algorithm 1.  
```
0: : the radial basis dimension \(l\), area number \(n\), learning rate \(\beta_{i}\) and \(\gamma_{i}\).
0: : system structure \(N_{c}^{*}\) and optimal attack strategy \(\delta^{*}\).
```  
**Algorithm 1** Algorithm of Structure Optimization under DoS Attacks  
The obtained \(N_{c}^{*}\) by Algorithm 1 is the optimized control structure of power system. The worst case of system performance with our controller is also known by Algorithm 1. However, we do not further consider the unstable control system caused by a large \(C\)[5] or the attacker has a lot of cyber resources to launch DoS attacks. These issues will be investigated in future.
## 2 A Brief Mention of System Concepts  
In what follows, we will make reference to system theory. Rather than analyzing the behavior of individual entities, system theorists focus on the communication or otherinteractions among entities [5; 6]. The entities may be human, animal, or machine, and important subfields of system science address questions of human--machine communication and control [7; 8].  
A system is not just a set of entities and interactions (\"nodes and links\"). It is the set of generative rules governing the birth and death of nodes and links, and the changing intensity of connections [9]. That is to say, it is a matter of dynamics. This paper will focus on historical dynamics, with examples from tribal space, social space, environmental space, personal space, and cyberspace.  
Other system concepts playing roles in this story are the butterfly effect, periodic attractors, holism vs. reductionism, variety, feedback, path-dependence and lock-in, and networks. Path-dependence simply means that history matters. Attractors are the conditions toward which a complex system may trend: Point attractors indicate stasis; periodic attractors mean cyclic behavior; and chaotic attractors have no easily described pattern and are to be avoided in most social and engineering systems.
# Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
## 3 Results and Analysis
### Maneuver Classification  
#### 3.1.1 Regression  
Understanding the relationship between dependent and independent variables is of paramount importance for the objectives of this study. To this end, we initiated a regression experiment as our foundational analytical approach. In the preparatory phase, all features in the training dataset were normalized and subsequently fed into four distinct regression models for training. Upon the completion of this training phase, immediate evaluation was conducted using a separate testing dataset. We then furnished both training and testing accuracies and engaged in a comparative analysis to evaluate the relative performance of the four models.  
To narrow down our focus, we proceeded with a more targeted training process using a carefully selected set of pivotal features, specifically \([v,vx,theta,pos_{x},maneuver]\). Based on the performance metrics, the model demonstrating the highest efficacy was earmarked for more in-depth evaluation. It is worth noting that logistic regression and stochastic gradient descent (SGD) regression emerged as strong contenders, displaying comparable performance and thus becoming viable candidates for further scrutiny.  
Following the initial regression tests, we dived deeper to ascertain the results.  
Table 3 elucidates the training and testing accuracies associated with each model. A noteworthy observation is that logistic regression emerged as the most efficacious model, albeit with marginally higher performance than SGD regression. Furthermore, the performance of logistic regression remained consistently superior. Ridge and lasso regression, although displaying commendable accuracy (attaining 97.54% on the testing dataset), were not as competitive when juxtaposed with the logistic and SGD regression models.  
Recognizing the indispensability of comprehensive evaluation protocols, we took meticulous steps to further validate our findings. To bolster the robustness of our analysis, we employed K-fold cross-validation, a technique widely acknowledged for mitigating overfitting and enhancing model performance. Specifically, for logistic regression, the dataset was partitioned into eight subsets, and K-fold cross-validation was conducted. Table 4 presents the confusion matrices for both the training and validation sets corresponding to the top-performing regression model. The results revealed an encouraging trend: While the peak validation accuracy reached an impressive 99.75%, the testing accuracy of the most optimal model remained stable at 99.63%.  
Having assessed the capabilities of the regression models, we then pivoted our investigative focus toward another powerful computational technique--support vector machines--to broaden the analytical spectrum of our study.
# Influence of Sugar Beet Pulp Supplementation on Pigs' Health and Production Quality
## 3 Results and Discussion
### Meat Quality Parameters
#### 3.5.2 Biogenic Amine Contents and Fatty Acid Profiles of Pork Meat  
The BA contents in the meat of pigs from CG and TG-I groups are given in Table 6. No BAs, except spermine, were found in all tested samples from both groups, indicating that SBP inclusion in the diet had no significant impact on the spermine content in both groups.  
Endogenic BAs are excreted by different tissues in humans or animals and transferred locally or via the blood system, whereas exogenic BAs are formed by the action of decarboxylase-positive microorganisms on free amino acids [83]. Usually, the BA level is considered an index for food product stability and quality because high contents of these compounds negatively affect human health [84]. The presence of spermine in the tested meat of both groups is related with the higher amount of this BA in fresh meat (20-60 mg/kg) [85]. Similar tendencies have been observed in other studies [86,87]. Finally, the inclusion of 3% of the SBP to the pigs' diet was not significant on BA formation in meat.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Quality Parameters** & **CG** & **TG-I** \\ \hline Dry matter, \% & 28.05 \(\pm\) 0.21 \({}^{\text{a}}\) & 28.28 \(\pm\) 0.23 \({}^{\text{a}}\) \\ \hline pH & & 5.59 \(\pm\) 0.03 \({}^{\text{a}}\) & 5.54 \(\pm\) 0.02 \({}^{\text{a}}\) \\ \hline Colour coordinates & L* & 62.92 \(\pm\) 0.31 \({}^{\text{b}}\) & 60.28 \(\pm\) 0.27 \({}^{\text{a}}\) \\  & a* & 14.91 \(\pm\) 0.11 \({}^{\text{a}}\) & 17.58 \(\pm\) 0.10 \({}^{\text{b}}\) \\  & b* & 10.10 \(\pm\) 0.09 \({}^{\text{a}}\) & 12.65 \(\pm\) 0.08 \({}^{\text{b}}\) \\ \hline \multicolumn{3}{c}{Drip loss, \%} & 3.45 \(\pm\) 0.12 \({}^{\text{a}}\) & 3.77 \(\pm\) 0.17 \({}^{\text{b}}\) \\ Water holding capacity, \% & 57.67 \(\pm\) 0.91 \({}^{\text{a}}\) & 58.19 \(\pm\) 0.56 \({}^{\text{a}}\) \\ Cooking loss, \% & 26.33 \(\pm\) 0.25 \({}^{\text{b}}\) & 25.09 \(\pm\) 0.23 \({}^{\text{a}}\) \\ Intramuscular fat, \% & 5.56 \(\pm\) 0.04 \({}^{\text{b}}\) & 4.66 \(\pm\) 0.03 \({}^{\text{a}}\) \\ Ash, \% & 1.19 \(\pm\) 0.02 \({}^{\text{a}}\) & 1.21 \(\pm\) 0.01 \({}^{\text{a}}\) \\ Protein, \% & 21.33 \(\pm\) 0.18 \({}^{\text{a}}\) & 22.41 \(\pm\) 0.21 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Basal diet (CG), basal diet with dried sugar beet pulp (TG-I). L*, lightness; a*, redness or -a*, greenness; b*, yellowness or -b*, blueness. Data are presented as mean \(\pm\) standard error (\(n=10/\text{group}\)). \({}^{\text{a}}\)Different letters indicate significant differences between treatments (\(p\leq 0.05\)).  
\end{table}
Table 5: Main meat quality parameters of piglets in control (CG) and treated (TG-I) groups.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
## 5 Effect of Other Elements  
### Influence of Mn and S Content  
From the metallurgical processing of electrical steels, S and Mn are usually present in the product. In some cases, Mn is one of the major components in the NGO electrical steel. During the production process, S and Mn may form MnS inclusions that have significant influence on the product properties.  
The thermomechanical behavior of MnS precipitation has been thoroughly investigated [63; 64; 65]. It is widely accepted that MnS particles in NGO electrical steels have a higher likelihood of precipitating on the dislocations and grain boundaries [66; 67]. MnS can also form interdendritically during solidification. Here the solidification rate can influence the size distribution of the MnS inclusions through the effects of the cooling rate on secondary dendrite arm spacing [68].  
The precipitation and coarsening of MnS particles not only influences the core loss, but also affects magnetic aging in NGO electrical steels [69]. During heat treatment processes, when the precipitate size is close to the size of the magnetic domain, it will highly hinder the magnetic domain movement [70]. This may lead to an increase in coercivity and hysteresis losses, which is called the pinning effect. When the size of the precipitates is dimensionally similar to the thickness of the domain wall, the magnetic domain movement may be delayed [71; 72; 73]. Due to the thickness of the domain wall, only small inclusions have a significant effect on the magnetic properties. In practical production, a high Mn/S level chemistry is sometimes used to avoid this pinning effect. Driving the MnS level too low can easily form smaller MnS inclusions in a size range that is exceptionally detrimental. By comparison, large oxides and sulfide precipitates have very little influence on magnetics. In some cases, the fine inclusions that are nucleated in the solid state are the deleterious ones.  
[PERSON] et al. [74] reported the application of rare-earth elements (REMs) on the coarsening of sulfides in NGO electrical steels. The cast slabs (1.0 wt.% Si, 0.42 wt.% Al, 0.005 wt.% C, 0.004 wt.% S, and 0.004 wt.% REMs) were hot-rolled to 2.6 mm, normalized at approximately 900 \({}^{\circ}\)C for 3 min, cold-rolled to 0.3 mm, and finally continuous-annealed at 900 \({}^{\circ}\)C. A proper REMs addition can lead to an increase in the average grain size, decrease of micro-sized inclusions, fine MnS, optimization of recrystallization textures, and better magnetic properties.  
Ca additions have also been used to decrease the number of small MnS particles. A 3.2 wt.% Si + 0.4 wt.% Al steel was 1-stage cold-rolled to 0.5 mm, and finally annealed at 900 \({}^{\circ}\)C. The effects of Ca on the core loss are illustrated in Figure 7[75].  
Figure 7: Influence of mass ratio of Ca and S (in wt.% Ca/S) on final annealed strip. (**a**) Core loss, (**b**) magnetic induction [75].
# An Efficient Relayed Broadcasting Based on the Duplication Estimation Model for IoT Applications
## 2 Statement of the Problem
### Related Work
* Our mechanism is completely free from any feedback information or signaling between nodes, so it neither requires a dedicated control frame nor incurs a signaling overhead, which is a crucial feature in the broadcasting to many nodes.
# Effect of the Umbrella Arch Technique Modelled as a Homogenized Area above a Cross Passage
## 5 Conclusions  
In this study, a 3D finite element simulation using the CESAR LCPC V5 finite element software was performed to investigate the effect of implementing the Umbrella Arch Method (UAM) in the intersection area between a tunnel and a cross-passage. The umbrella arch method was modelled as an equivalent homogenized medium using the theoretical homogenization tools for periodic media. The results showed that the use of this type of reinforcement effectively reduced the extent of surface settlement by nearly 30%; in addition, the extrusion at the front face along the vertical axis of the cross passage was decreased by about 20%. Furthermore, the extent of the plastic zone was significantly reduced in the area where the umbrella arch was installed. However, the results showed that the length of the umbrella arch elements had no significant influence on the extent of the ground deformation.  
The present investigation confirmed that the 3D staged excavation proves to be a useful tool for assessing induced movements in tunnel junctions. The homogenization method, on the other hand, can be smoothly integrated into the modelling process, as it has been proven by literature and numerical results to have many practical advantages, including ease and accuracy of modelling, as well as the elimination of the convergence problems encountered when modelling with bar elements. This paper also provides a paradigm for the evaluation of tunnel-induced settlements and their mitigation based on a realistic design situation for metro tunnels. It thus also serves as a guide for future design exercises and is one of the few references in the international literature on finite element modelling of tunnel junctions with pre-supports.  
Conceptualization, M.A. and P.S.; methodology, M.A.; software, I.S.; validation, I.S., M.A. and P.S.; formal analysis, P.S.; investigation, M.A.; resources, I.S.; data curation, P.S.; writing--original draft preparation, I.S.; writing--review and editing, P.S.; visualization, I.S.; supervision, P.S.; project administration, M.A.; funding acquisition, M.A. All authors have
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 3 Results
### Feasibility of Wireless Power Transfer when Non-Polar Solvents Were Used as Medium 2  
The objective of this test was to test for the feasibility of wireless power transfer if a non-polar substance was used as medium 2. In this test, we tested the following materials for medium 2: an oil, an oil/detergent mixture, and a plastic coating of an electric cable. The measured results are summarized in Table 1.  
As Table 1 shows, no power was detected at the receiving end or anywhere very close to the excitation source when oil was used as medium 2. No matter how high the excitation voltage was, there was no sign of wireless power transfer along the rubber/oil interface. We brought the modified live-wire detector very close to the excitation source, but the measured results remained the same. This finding suffices to prove that there was no observable near-field magnetic coupling in the experiment.  
Table 1 suggests that power was detected at the receiving end if the oil was replaced with an oil/detergent mixture. Since the detergent solution was still a polar solvent under a high voltage stress, it was successfully detected by the live-wire detector.  
However, a wireless power was successfully detected on the surface of an electric cable coating. The phenomenon demonstrates the feasibility of the proposed methodology to detect if a substance was polar or not.  
The plastic coating of a current-carrying cable has a large loss tangent. Using the proposed methodology, this loss tangent was successfully detected along the contact interface under a high voltage stress.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **Detection of Power** & & **Transmission Range** \\  & **Polarity of the** & **When the Modified** & **Detection** & **between the** \\  & **Substance** & **Live-Wire Detector** & **Near-Field** & **Detection Point** \\  & **Was in Contact** & **Magnetic Coupling** & & **and the** \\  & & **with Medium 2** & & **Excitation Source** \\ \hline Oil & Non-polar & No & None & 5 cm \\ Oil Mixed with Detergent & Polar & Yes & None & 1 m \\ Coating of a Current-Carrying & & & & \\ Electric Cable & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Measured results when a non-water substance was used as medium 2.  
Figure 4: Measured transmission efficiency as a function of salinity.
## 3 Results
### Characteristics of the Studies  
Ten studies used low-frequency vibrations [26; 27; 28; 29; 32; 33; 34; 35; 36], and five studies analyzed the high-frequency vibrations [22; 25; 29; 30; 31]. The devices used to generate the vibrations were as follows: AcceleDent (OrthoAccel Technologies, Inc., Bellaire, TX, USA) device [26; 27; 28; 29; 32; 34; 35], VPro5 (Propel Orthodontics, Ossining, NY, USA) [30], five studies applied the mechanical vibration with toothpushes [25; 28; 29; 31; 33], one custom-made vibratory device [29], and one performed it with a dental massager whose brand was not specified [22].  
The dental movement rate was measured by a digital caliper and by clinical inspections [23; 28; 32], measurements on plaster models [22; 24; 25; 26; 29; 31], digitized plaster models [27; 33; 35], or intraoral scanning [30; 34; 36].  
The follow-up periods ranged from 5 days [30], 60 days [29], 10 weeks [22,26], to the total treatment [24,27,32,36]. Only one study evaluated the first month after stimulus application [28], one article evaluated only two months [25], three articles evaluated the effect for 3 months [31,33,34], and two articles evaluated the time until space closure [23,35]. None of the articles evaluated the post-orthodontic treatment stage. Eight studies measured the rate of tooth movement during canine retraction [23,25,28,29,31,33,34,35,33], while seven studies evaluated the dental alignment phase [22,24,26,27,30,32,36].
# A Fabric Defect Segmentation Model Based on Improved Swin-Unet with Gabor Filter
## 4 Experiments
### Quantitative Comparison
#### 4.3.2 Comparison of Models of Different Sizes  
We designed three model structures with different sizes, from small to large, in order: tiny, small, and big. Their respective performance results are shown in Table 2. The results reveals that the performance of the larger model falls short of the smaller one. It can be seen that merely augmenting the model parameters does not necessarily enhance the results on the verification set. This insight steered our approach towards retaining the original downsampling structure of Swin-Unet while crafting an exceptionally lightweight design for the upsampling segment. In this way, the performance of our model was improved even further.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Cropped AITEX**} & \multicolumn{4}{c}{**Colored Fabric**} & \multicolumn{4}{c}{**One Class Fabric**} \\ \hline  & **ACC** & **SE** & **PC** & **IoU** & **ACC** & **SE** & **PC** & **IoU** & **ACC** & **SE** & **PC** & **IoU** \\ \hline UNet & 0.9908 & 0.6313 & 0.6294 & 0.5219 & 0.9919 & 0.7635 & 0.7758 & 0.6149 & 0.8879 & 0.2579 & 0.6106 & 0.2244 \\ AttUNet & 0.9906 & 0.6791 & 0.6584 & 0.5429 & 0.9922 & 0.7736 & 0.7811 & 0.6290 & 0.8866 & 0.3307 & 0.6675 & 02102 \\ NestedUNet & **0.9914** & 0.5314 & **0.6840** & 0.4764 & 0.9912 & 0.6790 & **0.7960** & 0.5679 & 0.8866 & 0.3345 & 0.5786 & 0.2643 \\ [PERSON] et al. [3] & 0.9897 & 0.5469 & 0.6311 & **0.6143** & 0.9918 & 0.5899 & 0.7896 & 0.4976 & 0.8273 & **0.3930** & 0.5920 & 0.1003 \\ STUNet(ours) & 0.9900 & **0.7446** & 0.6236 & 0.5493 & **0.9923** & **0.7914** & 0.7691 & **0.6323** & **0.9003** & 0.3674 & **0.7608** & **0.3370** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of various state of the art methods compared with the results of the proposed method on three different datasets.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Cropped AITEX**} & \multicolumn{4}{c}{**Colored Fabric**} & \multicolumn{4}{c}{**One Class Fabric**} \\ \hline  & **ACC** & **SE** & **PC** & **IoU** & **ACC** & **SE** & **PC** & **IoU** & **ACC** & **SE** & **PC** & **IoU** \\ \hline Tiny & 0.9900 & **0.7446** & 0.6236 & **0.5493** & **0.9923** & **0.7914** & **0.7691** & **0.6323** & **0.9003** & **0.3674** & **0.7608** & **0.3370** \\ Small & 0.9891 & 0.6808 & 0.5890 & 0.5102 & 0.9923 & 0.7680 & 0.7651 & 0.6233 & 0.8988 & 0.2526 & 0.6825 & 0.2373 \\ Big & **0.9903** & 0.6563 & **0.6278** & 0.5104 & 0.9919 & 0.7705 & 0.7597 & 0.6217 & 0.8981 & 0.3031 & 0.6244 & 0.2624 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of models of different sizes.