esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of IMUs and Optical Systems in Layup Shot Biomechanics
## 3 Results  
### Discrete Measures  
A paired-sample \(t\)-test found no significance difference in jump height and take-off angle between the two systems ((jump height (Vicon: 0.34 m (0.039), Xsens: 0.34 m (0.042), \(t(18)=-0.09\), \(p\geq 0.925\), 95%CI [\(-\)0.10, 0.09]), take-off angle (Vicon: 43.47\({}^{\circ}\) (5.01), Xsens: 43.05\({}^{\circ}\) (5.17)), \(t(18)=0.53\), \(p\geq 0.727\), 95%CI [\(-\)4.52, 6.35]). Effect size analysis further confirmed the strong agreement between systems, with [PERSON]'s d indicating no effect for jump height (d = 0) and a negligible effect for take-off angle (d = 0.08). This was confirmed by the strong agreement in the Bland-Altman test plots (Figure 3), indicating most data points fell within the region of acceptable difference (highest agreement: 100%, lowest agreement: 90%), with the majority of participants displaying no data points falling outside the limits of acceptable disagreement range. A mean offset of 0.025 m and 1.5\({}^{\circ}\) was found between the two systems for jump height and take-off angle. Additionally, a paired sample \(t\)-test found no significant differences between the Vicon and Xsens systems in detecting changes between conditions (jump height: Vicon difference: 0.028 m (0.23), Xsens difference: 0.022 m (0.016), \(t=1.26\), \(p\geq 0.238\); take-off angle: Vicon difference: 2.03\({}^{\circ}\) (3.18), Xsens difference: 2.91\({}^{\circ}\) (4.48), \(t=-1.303\), \(p\geq 0.225\)). This result further supports the reliability of both systems in detecting these changes across conditions. Excellent reliability was found for jump height (ICC = 0.97 (95%CI: 0.89-0.99), \(p<0.001\)) and take-off angle (ICC = 0.96 (95%CI: 0.86-0.99) \(p<0.001\)).
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 2 Background and Related Work
### 5G Roaming  
Roaming refers to agreements between operators that allow for the expansion of a home PLMN (HPLMN) service coverage into areas where it is not present. This enables subscribers to use the services of another operator, whether in another country (international roaming) or within their own country (national roaming). Two types of roaming architecture supported in 5G are identified [9]:  
* Local breakout (LBO): In this model, the user's authentication and authorization on the visited network are performed by the home network, while the traffic is directly routed through the visited PLMN (VPLMN).
* Home routed (HR): In this scenario, the data traffic from the VPLMN is routed to the data network (DN) via the HPLMN.  
For communication between the HPLMN and the VPLMN, the Third-Generation Partnership Project (3 GPP) defined a new network function: the security edge protection proxy (SEPP), which acts as a proxy between PLMNs to provide security and privacy to the involved entities. All control plane messages pass through the SEPP of both the home and visited networks, ensuring the protection of messages before forwarding them to network functions or the service communication proxy (SCP).  
The SCP was introduced by the 3 GPP in the 5G system to enable indirect communication between NFs. In addition to performing load balancing, the SCP also handles overload. The SEPP can forward messages directly to the NFs or opt to use the SCP. When used, the SCP supports inter-PLMN routing, providing the necessary means for forwarding messages relevant to the SEPP [9].
# Herbal Support for the Nervous System: The Impact of Adaptogens in Humans and Dogs
## 5 Maral Root (_Leuzea carthamoides_ (Wild.) [PERSON])  
### Aspects and Composition  
_Leuzea carthamoides_, also known as maral root or Russian leuzea, is a perennial plant from the Asteraceae family, valued for its adaptogenic properties and rich bioactive compounds.  
_Leuzea carthamoides_ is an herbaceous plant reaching a height of 50 to 120 cm. It has a straight, upright stem, large pinnate leaves, and purple flower heads. It naturally occurs in mountainous regions of Siberia, particularly in the Altai and Sayan Mountains, at altitudes ranging from 1200 to 2000 m above sea level. It prefers sunny locations with well-drained soils [102].  
The plant is rich in bioactive compounds, including ecdysteroids flavonoids (e.g., hispidulin, eriodictyl), lignans, triterpenoids, and phenolic acids (e.g., chlorogenic acid) (Figure 5). Ecdysteroids are particularly valued for their anabolic and adaptogenic properties [103]. Maral root is particularly abundant in various phytoecdysteroids, with 20-hydroxyone (20E) being the most prominent. Concentrations of 20E in the plant can range from 0.1% to 1% of the dry root matter. Other notable ecdysteroids identified include turkesterone, integristerone A and B, makisterone C, taxisterone, and leuzea-terone. In total, up to 50 different ecdysteroid compounds have been isolated from _L. carthamoides_, underscoring its status as one of the richest plant sources of these bioactive molecules [104; 105]. Flavonoids exhibit strong antioxidant activity, helping neutralize free radicals in the body [106].  
Maral root extract demonstrates significant antioxidant properties. The study of [PERSON] et al. [105] assessed the effects of maral root extract on oxidative stress markers in Caenorhabditis elegans. The results indicated that treatment with the extract led to a reduction in reactive oxygen species levels and an increase in the expression of antioxidant enzymes such as superoxide dismutase and catalase. These findings suggest that maral root extract possesses potent antioxidant activity, supporting its potential use in functional foods or supplements aimed at mitigating oxidative stress.
# Influence of the Composition of the Activator on Mechanical Characteristics of a Geopolymer
## 2 Materials and Testing Procedures
### Materials  
The metakaolin used in this work is identified as HRM MetaMAx(r)[31] and was supplied by Engelhard, an American company from Iselin, New Jersey. This is a high-reactivity metakaolin and the grain particles are of smaller size than the usual metakaolin. In order to evaluate the chemical composition of this material, an elemental analysis was made (see Table 1) and this analysis indicated that the main components were silica (SiO\({}_{2}\)) and alumina (Al\({}_{2}\)O\({}_{3}\)).  
The sand was collected in Coimbra, Centre Region, Portugal. From the particle size analysis, the sand can be classified as a poorly graded sand according to the Unified Soil Classification System, ASTM D 2487-06 [32]. The density of the sand particles is G = 2.64 g/cm\({}^{3}\).  
During this work an activator has been used resulting from the combination in suitable proportions of sodium hydroxide (NaOH) (10 M) with sodium silicate (Na\({}_{2}\)SiO\({}_{3}\)). Table 2 shows the composition of the Base Mix of the studied mixtures. Six specimens (\(4\times 4\times 16\) cm) were made for each batch.  
Table 3 presents the 8 compositions used with the correspondent relative concentration of the activator (sodium hydroxide and sodium silicate). The results of these mixtures are, thereafter, compared to the ordinary Portland cement (OPC) mortar with no additives (see Table 4).  
The analysis of the influence of the activator composition is based on experimental results, namely the modulus of elasticity E, tensile strain \(\varepsilon_{t}\), tensile strength \(\sigma_{t}\) and compressive strength \(\sigma_{c}\). EN 196-1 [33] was followed. This European standard presents the procedure for the determination of both the tensile bending tests and compression tests for cement mortars and establishes the use of prismatic \(4\times 4\times 16\) cm specimens. EN 12390-parts 1, 3 and 5 [34; 35; 36] was also followed for the strength tests on mortar specimens.  
Mixing was carried out mechanically, using a mixer for this purpose with the dimensions and characteristics specified in EN 196-1 [33].
## 4 Seismic Fragility Assessment
### Analytical Models  
Analytical models of the RC structure were implemented and analyzed using OpenSees. As illustrated in Figure 14, two analytical models were considered: one incorporating shear springs and one without. The RC frame members were modeled using displacement-based beam-column elements, employing the material constitutive laws of Concrete02 [37] for concrete and Steel02 [38] for rebars, respectively. For the confined concrete, the confinement effect on the stress and strain was considered by utilizing the formulae developed by [PERSON] and [PERSON] (1994) [30]. In the first analytical model, to account for the shear deformation in RC columns, shear springs were added to both ends of each column. These springs were modeled using zero-length elements with the Pinching4 material model. To isolate the effect of shear springs in columns, the beam elements were assumed to be rigid in both models.  
The Pinching4 model parameters for the shear springs were estimated using the equations proposed in the previous section. Table 3 summarizes the predicted parameters for the three columns, and Figure 15 compares the resulting backbone curves to the pushover analysis of the flexure model. Although the backbone curves generally resemble the pushover results, column C1 exhibits a significantly lower predicted shear strength, indicating a dominance of shear behavior. In contrast, the shear strengths of columns C2 and C3 closely match the maximum lateral force of the flexural model, suggesting that these columns exhibit flexural or flexural-shear behavior.  
Figure 14: Analytical models with and without shear springs.  
To investigate the effects of shear deformation, cyclic analyses of RC columns with and without the shear spring were conducted, as shown in Figure 15. The loading pattern consisted of interstory drift ratios (IDRs) of 0.33%, 0.66%, 1%, 1.5%, 2%, 2.5%, and 3%, with three cycles applied at each level. As expected from Figure 15, column C1 exhibits shear-dominant behavior, as shown in Figure 16a. The column's strength rapidly deteriorates after an IDR of 1%, accompanied by a clear degradation in reloading and unloading stiffness due to the cyclic loading. As shown in Figure 16b, column C2 demonstrates that strength degradation begins at an IDR of 1.5% and accelerates after an IDR of 2%.
# Statistical Analysis of Cutting Force and Vibration in Turning X5 CrNi18-10 Steel
## 3 Results
### Statistical Analysis
#### 3.4.3 ANOVA Results Between Feed Rate and \(\rm AV_{b}\) RMS  
The first-order regression model for active vibration RMS as a function of feed rate, as shown in Figure 10, is given by Equation (5).  
\[y=8.17+44.93x \tag{5}\]  
With \(\rm R^{2}=47.4\), the statistical significance of the model is studied by ANOVA, as shown in Table 7. The probability of significance is less than 0.05, and the exact \(p\)-value is 0.001, which is statistically significant or of high significance. \({}^{\rm a}\) Dependent variable--active vibration RMS, \({}^{\rm b}\) predictors--(constant), feed rate. The value of \(\rm R^{2}\) is acceptable in both cases. Figure 9 represents the linear regression line graph between the feed and active force, where the circles represent observed values.  
Figure 11 represents the main effect plots, which are also called estimated marginal means plots, which are used to analyze the effect of three machining parameters (feed rate, depth of cut, and cutting speed) with active vibration (\(\rm AV_{b}\)). Figure 11 represents the plot between the feed and active vibration RMS. It highlights that the higher feed rate increases the vibration from 0.08 mm/rev to 0.16 mm/rev, with a moderate increase by increasing  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & \multicolumn{2}{c}{**ANOVA \({}^{\rm a}\)**} & & \\ \hline
**Model** & & **Sum of Squares** & **df** & **Mean Square** & **F** & **Sig.** \\ \hline
1 & Regression & 155,888.17 & 1 & 155,888.167 & 14.41 & 0.002 \({}^{\rm b}\) \\  & Residual & 173,046.81 & 16 & 10,815.426 & & \\  & Total & 328,934.98 & 17 & & & \\ \hline \end{tabular} \({}^{\rm a}\) Dependent variable: active force RMS (AF RMS), \({}^{\rm b}\) predictors: (constant), feed rate (\(f\)) mm/rev.  
\end{table}
Table 6: ANOVA of feed and active force RMS.  
Figure 10: Linear regression line graph between feed rate and active vibration RMS.
## 4 Analysis of the Results
### Analysis of the Experimental Results
#### 4.2.1 Qualitative Assessment  
The CT sequence contained a large number of images. In this section, the segmentation of the same sequence of juxtapleural nodules by four kinds of algorithms was performed, as shown in Figure 5. The images were classified in the following groups: (a) original CT image; (b) manual segmentation results obtained by a doctor; (c) improved segmentation algorithm; (d) segmentation results of the improved region growth (RG) algorithm; (e) regional growth and rolling-ball method repair algorithm segmentation results; (f) algorithm segmentation results.  
Figure 5: Segmentation of the same sequence of juxtapleural nodules by four kinds of algorithms.  
For the CT images with intact lung parenchyma, as shown in the first row and the third row of Figure 5, the segmentation by all the algorithms could ensure the integrity of the segmentation. However, on the basis of the automated threshold segmentation CT sequence images, seed points could be selected manually, and the threshold could be passed, greatly improving the efficiency of segmentation. For the sequence images of lung parenchyma having juxtapleural nodules, as shown in the second row of Figure 5, the RG and Watershed algorithm lost the juxtapleural nodules, and the repair results of ball-rolling method lost some parts of the juxtapleural nodules. Promisingly, our method could ensure the integrity of lung parenchyma segmentation (f). For the not connective area of lung parenchyma, the regional growth algorithm lost some parts of the lung parenchyma, while the other algorithms' segmentation results were more complete.  
The sample, could contain multiple nodules in the same CT slice at the same time. We chose the same methods as in Figure 5 for an experimental comparison, and the results are shown in Figure 6.  
The results showed that the regional growth algorithm still lost some parts of the lung parenchyma for the not connected areas. The comparison between column (e) and column (f) showed that the rolling-ball method provided better repair results for a single large juxtapleural nodule. The method described in this paper could repair multiple juxtapleural nodules at the same time and provided better results.  
Figure 6: Segmentation of images containing two juxtapleural nodules using the four algorithms.  
In order to verify the repair ability of our method for juxtapleural concave areas, we compared the proposed algorithm and the \"ball-and-roll\" method for the same sequence of slices, as shown Figure 7. In the ball-rolling method, the radius of repair had an important effect on the result.  
For the concave area caused by the juxtapleural nodules, if the radius of the ball-rolling method was too small, it caused under-segmentation. Because of the uncertainty of the size of a juxtapleural nodule, setting the threshold manually was useless and the segmentation results were unsatisfactory. In contrast, Figure 7 shows the effectiveness of the algorithm proposed in this paper.
# Image Inpainting Based on Multi-Patch Match with Adaptive Size
## 3 Proposed Approach
### Subregion Search  
In [PERSON]'s algorithm, the global search for the best sample patch is required for every target patch, which is computationally expensive and unnecessary. In fact, it is more appropriate to let the target patch selectively match those sample patches that have similar content to the target patch, and skip those sample patches that are far different from the target patch. By doing so, firstly the search area will be narrowed from the entire source region to its subregion with the similar content to the target patch, so that the match time and the computation could be reduced. Moreover, a large number of unsatisfactory sample patches can be filtered out to reduce the mismatch, thereby improving the inpainting accuracy to a certain extent. Our \"subregion search\" strategy comes as follows.  
Considering if the target patch center \(\hat{p}\) is in a rich-textured area or strong edge, it is nearly impossible for those sample patches \(\Psi_{q}\) from poor-textured or flat area to serve as the ideal sample patches, instead, desired sample patches are supposed to have the similar content to \(\Psi_{\hat{p}}\) and similar content means the similar nonuniformity level. Based on the above consideration, we narrow down the search area of \(\Psi_{\hat{p}}\) from the entire source region to its subregion according to the nonuniformity map \(T(\Phi)\) obtained by Equation (8). Specifically, the set of pixel \(q\) that satisfies the following equation is defined as the restricted subregion \(\phi\), which serves as the search area of the target patch \(\Psi_{\hat{p}}\):  
\[\phi=\Big{\{}q\ \big{|}\ \big{|}T(\hat{p})-T(q)\big{|}\leq\alpha,\ q\in\Phi \Big{\}}, \tag{10}\]where \(\alpha\) is a parameter adjusts the strictness of limiting the search area. Smaller \(\alpha\) means: (1) The subregion for search shrinks, the algorithm will be very careful with choosing the possible ideal candidates (see Figure 4a); (2) Lower fault-tolerance during the patch match (related to item 1), the method is more likely to miss the optimal candidate if search area is too small; (3) There will be more patch matches in an iteration because the space saved for each subregion makes room for more subregions (target patches) to get involved (see Section 3.4). By default, \(\alpha\) = 0.1. Figure 4b shows how subregion search works, the subregion \(\phi\) for search is colored with respect to its target patch \(\Psi_{\hat{p}}\) (blue patch, the size is magnified for clearer visualization). Only those sample patches whose center falls into subregion \(\phi\) are considered potentially ideal (green patches), since they have the similar content to \(\Psi_{\hat{p}}\), while those patches whose center outside the subregion \(\phi\) are considered undesirable (red patches) and will be ignored during match process for they are far different from \(\Psi_{\hat{p}}\). Intuitively, this strategy only allows the match between \(\Psi_{\hat{p}}\) and a small portion of similar sample patches, which greatly saves the match time while maintaining the match accuracy.
## 5 Experimental Results and Discussion
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Optimizer & Adam \\ Activation Function & Relu \\ Dropout & 0.5 \\ Epochs & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: 1D-CNN Parameters. Figure 2: Accuracy Graph over 50 Epochs. The artificial neural networks were used for the detection of anomaly-based intrusions by [PERSON] et al. [33] where they used unsupervised outlier detection along with ANN using the UNSW-NB-15 dataset. The authors achieved an accuracy of 84%. Furthermore, we compare some of the existing approaches for network intrusion detection in various domains using the CICIDS 2017, NSL-KDD, UNSW-NB15 and RedIRIS datasets. We focus on deep learning-based techniques employed to detect network intrusions in different settings [37; 43; 44; 45], presented the use of CNN, and RNN-based methodologies whereas the detailed comparison are presented in Table 10. Most of the researchers detect DDoS attacks while multi class classification is conducted by [PERSON] et al. [42] using RNN-LSTM algorithm which was tested on the CICIDS 2017 dataset, whereas [PERSON] et al. [41] used Bi-directional LSTM on the CICIDS 2017 dataset for intrusion detection. Federated learning and CNN-LSTM based methodologies were also adopted for the detection of network intrusions [34; 45].
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 4 Discussion
### Research Gaps and Future Directions
#### 4.2.4 Human Behavior in Flexibility Quantification Studies  
A key missing feature in previous work is the inclusion of complex human behavior and how it affects flexibility potential estimations. Although automated load flexibility is the goal, occupant preferences play a major role in the availability of this resource. Although acknowledged throughout the literature as a driving factor in quantifying demand flexibility potential, previous studies do not account for residents' variable interactions with the energy systems around them. The authors tend to account for variability among customers by simplifying user preferences to a range of indoor temperatures [3; 17; 29; 33] or by simply attributing household variability to differences in load profiles. These methods do not account for the immense diversity of needs among electricity customers and oversimplify their actions based on minimal datasets. Although there are a few authors who have incorporated data such as demographic [45], census [32], or survey data [12; 32; 40; 54; 55; 56], this may not be comprehensive of attributes that affect individual flexibility potential.  
There is a need for the development of human behavior models that reflect the general ways in which individuals interact with energy systems without becoming too narrowly focused on random patterns. This is of course a challenging task, mainly due to the lack of granular and comprehensive user data. Advanced machine learning models can be used to extract consumption patterns from raw data and convert them into meaningful and generalizable information to be used in flexibility models. Not only can this be used to improve the forecast accuracy of demand flexibility, but it can also be used to develop more customized DR programs.
# System Design and Reliability Improvement of Wireless Sensor Network in Plant Factory Scenario
## 2 Materials and Methods
### Network Coverage Optimization Model Development
#### 2.4.1 PSO Algorithm
Table 2 lists the parameter settings of the PSO algorithm for network coverage optimization.
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 3 Logic of Events
### Basic Definition
#### 3.2.2 Event Classes  
Event classes categorize events during the protocol interaction process to characterize the actions of the protocol. The event classes define seven types of events: send, receive, challenge, signature, verification, encryption, and decryption. Each event class encompasses associated information, and the types of this information are determined by the corresponding event class. The list of types includes the following:  
\[\begin{array}{l}New:EClass(Atom)\\ Send,Rcv:EClass(Data)\\ Encrypt,Decrypt:EClass(Data\times Key\times Atom)\\ Sign,Verify:EClass(Data\times Id\times Atom)\end{array}\]