esa-sceva/satcom-chunk-collection huggingface dataset:
# Free Vibration of a Taut Cable with Two Discrete Inertial Mass Dampers
## 4 Two IMDs at the Same End
### Parametric Studies  
Figure 7 presents the supplemental modal damping ratio of a cable with two identical VDs at the same end or a single VD versus damping coefficients. It is observed that attaching two VDs at the same ends of the cable may help to reduce the damper size but cannot increase the maximum supplemental modal damping ratio. Moreover, its maximum modal damping ratio is slightly smaller than that provided by a single VD at the further distance. These observations are in agreement with previous findings [76; 77].  
Figure 8 presents the supplemental modal damping ratio of a cable with two identical IMDs at the same end or a single IMD versus damping coefficients of the IMD. If two IMDs with relatively small  
Figure 6: Comparison of asymptotic and numerical complex wavenumbers of a cable with two identical IMDs at the same end (\(x_{1}=1\%L,x_{2}=2\%L\)).  
Figure 7: The modal damping ratios curves of a cable equipped with a single VD or two identical VDs at the same end (\(x_{1}=1\%L,x_{2}=2\%L\)).  
or big inertial masses (\(\chi\leq 0.1/(n\pi x_{2}/L)\) or \(\chi=0.9/(n\pi x_{2}/L)\)) are installed at the same end of the cable, similarly to the case of two VDs at the same end of a cable, there is no advantage of increasing the maximum modal damping ratio over that of a single IMD. However, if moderate inertial mass (\(0.4/(n\pi x_{2}/L)\leq\chi\leq 0.7/(n\pi x_{2}/L)\)) of the IMD is used, it is interesting to observe that two IMDs at the same end can lead to smaller optimum damping coefficients and larger maximum supplemental modal damping ratios than that of a single IMD at a bigger distance.  
The maximum achievable supplemental modal damping ratios of a cable equipped with a single IMD and two IMDs at the same end are directly compared in Figure 9. It is worth noting that the maximum supplemental modal damping ratio provided by two IMDs is higher than the sum of contributions from each IMD when inertial mass \(\chi=0.7/(n\pi x_{2}/L)\) is used. Though the strategy of two opposite IMDs has demonstrated that it can provide superior control performance, installing a damper at cable-tower anchorage is difficult and inconvenient. Thus, attaching two IMDs with appropriate inertial mass installed at the same end of the cable seems to be more promising for practical application.  
Figure 8: The modal damping ratios curves of a cable equipped with a single IMD or two identical IMDs at the same end (\(x_{1}=1\%L,x_{2}=2\%L\)).
# Virtual Flux Control Methods for Grid-Forming Converters: A Four-Method Comparison
## 4 HIL Results and Comparison
### Frequency Variation  
During this test, the grid frequency was reduced by 2.5 Hz with a Rate of Change of Frequency (ROCOF) of 2 Hz/s at t = 0.5 s, as depicted in the upper first of Figure 9. As observed in the second plot, the converter starts injecting active power proportionally to the frequency deviation. The injection of active power leads to a change in the voltage magnitude. To compensate for this effect, given that the converter operates as a PV node, it begins to absorb reactive current as shown in the third plot of Figure 9, ensuring that the voltage magnitude remains at 1 p.u. Since the constant D has been set to 50, for a 2.5% variation in frequency the converter would have to supply 250% of its rated steady state power, which would cause severe damage to the equipment. Therefore, the active current limiter starts to act, not allowing the maximum current of the converter, set at 1.25 p.u., to be exceeded, as shown in the last plot of Figure 9. This confirms that the converter is capable of injecting active power to the system during frequency variations while ensuring the proper operation of the active current limiter across all four control schemes evaluated. Furthermore, in terms of dynamic response, the converter exhibits nearly identical performance for all four control strategies analyzed.  
On the other hand, Figure 10 shows the output current waveforms of the converter once the active current limiter is activated. As observed, the proposed models achieve a similar waveform quality, except for the VFOC, which exhibits slight distortions compared to the other control algorithms.  
Figure 9: Frequency, active and reactive power and current during a frequency variation.
## 2 Materials and Methods
### Antioxidant Activity Assays and Total Phenolic Content Analyses  
#### 2.3.1 Ferric Reducing Antioxidant Power (FRAP)  
The ferric ion reducing antioxidant power (FRAP) assay measures the capacity of antioxidants to reduce Fe\({}^{3+}\) to Fe\({}^{2+}\). The Fe\({}^{2+}\) concentration was quantified spectrophotometrically by assessing the formation of a colored complex with 2,4,6-Tris (2-pyridyl)-s-triazine (TPTZ), which exhibits high absorption at 595 nm. The microplate FRAP assay was performed following the method outlined by [PERSON] et al. [2017]. To prepare the FRAP working solution, 10 mL of 300 mM acetate buffer (pH 3.6, adjusted with acetic acid), 1 mL of 20 mM ferric chloride hexahydrate 20 mM dissolved in distilled water, and 1 mL of 2,4,6-Tris(2-pyridyl)-s-triazine (TPTZ) 10 mM dissolved in 40 mM HCl were mixed freshly.  
In 96-well microplates, 20 \(\upmu\)L of the appropriate concentration of samples and Trolox dilutions were mixed with 180 \(\upmu\)L of the FRAP solution. The absorbance at 595 nm was measured using a Tecan Sunrise(tm) UV-Vis spectrophotometer (Tecan Group Ltd., Mannedorf, Switzerland) after incubation for 40 min at 37 \({}^{\circ}\)C. Trolox solutions, ranging from 2 to 0.002 mM, were used for standard curve. Three different experiments were carried out.  
Results were reported as \(\upmu\)mol TE/g DW according to the formula of [2017]:  
\[\text{FRAP value ($\upmu$mol TE/g DW)}=c\times V\times t/m\]  
where \(c\) is the Trolox concentration (\(\upmu\)mol/mL) of the corresponding standard curve of the diluted sample, \(V\) is the sample volume (mL), \(t\) is the dilution factor, and \(m\) is the weight of the sample dry matter (g).
# A Review on Upper Limb Rehabilitation Robots
## 2 Rehabilitation Treatments
### Active Therapy  
This kind of treatment is prescribed for patients who are able to move their impaired limb to some limit. The active term refers to the ability of moving the impaired limb but not efficiently [11]. Active therapy can be classified as active-assistive therapy or active-resistive therapy. Active-assistive therapy involves applying an external force by a therapist or robot to help the patient fulfil the  
Figure 1: Shows an example of passive therapy, where the impaired limb is moved according to the planned movement (flexion/extension of the upper limb at the elbow joint) by the rehabilitation robot. Source: [10].  
appointed task [16]. It is also used to improve the range of motion [17]. Active-assistive therapy was applied to a patient with an impaired shoulder and elbow, where the patient was asked to reach a specific target, and the attached robot helped the patient complete the task (Figure 2) [18]. The attached robot intervened once the patient was not able to perform the full task correctly and efficiently [3]. Active-assistive therapy was performed on eight subjects to evaluate its efficiency; the evaluation involved eighteen treatment sessions for six weeks and one hour for each session. The impaired limb movement was significantly improved [9].  
Active-resistive treatment, however, involves applying an opposing force on the impaired limbs. The opposing force can be applied by a therapist or robot (Figure 2) [15]. Studies showed that the performances of patients become better gradually where the opposing force can be increased gradually [7; 19]. The opposing force is determined by an algorithm according to the ability of the patient [19].  
Eight subjects obtained active-resistive therapy in 18 1-h sessions for six weeks [19]. This treatment is dedicated to improving the long-term strength of the arm [3].
## 3 Results and Discussion
### Consumer Study and Penalty Analysis (PA)

\begin{tabular}{l c c c c c c c c} \hline \hline  & **Color** & **Size** & **Flavor** & **Sour** & **Sweet** & **Hardness** & **Juciness** & **Aftertaste** & **Peel/Pulp** & **Global** \\ \hline \hline \multicolumn{13}{l}{**ANOVA\({}^{\ddagger}\)**} \\ Pear & **\(\ast\ast\ast\)** & **\(\ast\ast\)** & **\(\ast\ast\)** & **\(\ast\ast\)** & **\(\ast\ast\)** & **\(\ast\ast\)** & **\(\ast\ast\)** & **\(\ast\ast\)** & **\(\ast\ast\)** & **\(\ast\ast\)** \\ Round & **\(\ast\ast\)** & NS & **In pear cherry cultivars, consumers evaluated 8 of the 10 attributes reporting similar values for Angelle and Seychellles cultivars. The two attributes which had significant  
Figure 3: Penalty analysis (PA) of pear and round cherry tomato cultivars. differences among Angelle and Seychelles were color and peel/pulp; in both attributes, Angelle scored higher than Seychelles (color = 7.2 and 6.6, respectively; peel/pulp = 3.6 and 2.9, respectively). In general, Santylpum and Dolcetiini obtained lower intensities than Angelle and Seychelles. In the aftertaste attribute, there were no significant differences between Angelle and Santylpum. One of the most important attributes is global, since it gives an idea of the set of all the attributes for the consumer. In the case of pear cherry type, Angelle and Seychelles obtained a similar scoring, Seychelles being even higher than the reference one, Angelle, with values of 5.8 and 6.0, respectively. On the other hand, in round cherry cultivars, Katalina and Sweet star did not show significant differences in 8 of the 10 attributes analyzed by consumers, while only two attributes showed significant ones, color and peel/pulp. Katalina obtained the best color evaluation (6.9), reporting no significant differences in this attribute compared to the Karelya cultivar (6.6). Regarding peel/pulp, Sweet star obtained the best core (3.1), followed by the Katalina cultivar (2.9). Regarding the size attribute, there were no significant differences between the cultivars. In global, Katalina, Sweet star, and C-95 did not show significant differences, although the cultivar which obtained the highest score was Katalina (6.2). Penalty analysis for pear cherry cultivars showed how consumers consider that any attribute should be improved due to its intensity when talking about Angelle, Seychelles, and Dolcetiini; however, according to consumers, Santylpum cultivar has a very low level of sourness intensity. In round cherry cultivars, the trend of PA is very similar to pear cherry types, where according to consumers Katalina, Sweet star and Karelia should not improve any attribute due to its intensity except in the case of the size of Sweet star, which must be improved due to the reported big size.
## 4 Distributed Multi-Hop Localization Based on Regularized Extreme Learning
### Data Collection  
Suppose that \(n\) nodes are distributed in a two-dimensional plane. The first \(m\) (\(m<<n\)) anchor nodes \(\{S_{i}\}_{i=1}^{m}\) are the known self-coordinate information, and the remaining \(n-m\) nodes \(\{S_{i}\}_{i=m+1}^{n}\) are the normal nodes waiting to acquire the location information. When the program begins, any anchor node in the area firstly uses the distance vector routing protocols to broadcast a \"HELLO\" packet to the remaining nodes. After a period of time, the shortest hop-counts vector \(\mathbf{h}_{i}=[h_{i1},h_{i2},\ldots\,h_{im}]^{\mathrm{T}}\) of the anchor node \(S_{i}\) to the remaining anchor nodes can be obtained, and the shortest hop-counts vector \(\mathbf{h}_{k}=[h_{k1},h_{k2},\ldots\,h_{km}]^{\mathrm{T}}\) from the unknown node \(\{S_{i}\}_{i=m+1}^{n}\) to the anchor node can be obtained. Thus, the shortest hop-counts matrix corresponding to the anchor node and the unknown node is \(\mathbf{H}=[\mathbf{h}_{1},\mathbf{h}_{2},\ldots\,\mathbf{h}_{m}]\) and \(\mathbf{H}_{t}=[\mathbf{h}_{m+1},\mathbf{h}_{m+2},\ldots\,\mathbf{h}_{m}]\), respectively. The straight distance of the anchor nodes \(S_{i}\) to \(S_{j}\) can be expressed by Equation (6):  
\[\begin{split} d\big{(}S_{i},S_{j}\big{)}=d_{ij}=\left\|cor(S_{i} )-cor\big{(}S_{j}\big{)}\right\|_{2}\\ =\sqrt{\big{(}x_{i}-x_{j}\big{)}^{2}+\big{(}y_{i}-y_{j}\big{)}^{2 }},\end{split} \tag{6}\]  
where \(cor(S_{i})=(x_{i},y_{i})\) is the coordinate of node \(S_{i}\), and \(d_{ij}\) is the physical distance of the \(i\)-th node to the \(j\)-th node.
## 6 ICN and Edge Computing Convergence Suitability Analysis for IIoV
### Converged IoV Infrastructure
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Domain** & **Key Area** & **Mutual Benefit for IoV** & **AI Role** \\ \hline \multirow{8}{*}{ICN} & \multirow{4}{*}{Naming} & Name-based routing, naming and & \multirow{2}{*}{AI helps analyze and aggregate content name for efficient forwarding} \\  & & latency, naming caching, naming mobility & \\ \cline{2-3}  & & & caching, naming mobility \\ \cline{2-3}  & \multirow{2}{*}{Routing and forwarding} & Edge node support by running routing & \multirow{2}{*}{Learning algorithm can be used to predict delay and dynamic selection of forwarding path} \\  & & and forwarding algorithm & \\ \cline{2-3}  & \multirow{2}{*}{Mobility} & Edge computing to support & \multirow{2}{*}{Mobility prediction} \\  & & producer mobility & \\ \cline{2-3}  & \multirow{2}{*}{In network caching} & Edge node support caching of content & \multirow{2}{*}{Efficient caching algorithm} \\  & & which is useful for high bandwidth and & \\  & & low-latency edge computing & \\  & & applications, such as IoV and & \\  & & multimedia streaming & \\ \hline \multirow{8}{*}{Edge} & \multirow{4}{*}{Local processing} & \multirow{4}{*}{Local processing} & \multirow{4}{*}{Locally processed data can be easily contextualized to different users} & \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & & \\ \cline{1-1} \cline{3-3}  & & & & \\ \cline{1-1} \cline{3-3}  & & & & \\ \cline{1-1} \cline{3-3}  & \multirow{2}{*}{Storage} & Enable ICN caching services at the edge & \multirow{2}{*}{Optimal cache management} \\  & & server to provide mobility and delay & \\ \cline{1-1}  & & aware requirement & \\ \cline{1-1} \cline{3-3}  & \multirow{2}{*}{Load balancing} & \multirow{2}{*}{ICN provides load balancing} & Intelligent learning algorithm helps & \\ \cline{1-1}  & & & in optimal task distribution \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Role of AI in mutual benefits of ICN and edge computing for IIoV. Figure 11: The convergence of ICN-Edge Computing-AI for IIoV. allows adaptive video delivery optimization. This enables traffic optimization on the edge, in addition to backhaul and core networks using in-network caching. Caching multimedia content, based on popularity, request pattern, and quality awareness (e.g., user device capability and type, available bandwidth, etc. ), is possible through ICN and edge computing. Moreover, with the prevalence of content caching in edge computing, it is possible to have multiple active links and load balancing on dynamics and mobile conditions. Figure 12 shows the proposed IIoV layered protocol stack. It consists of different layers, such as physical, access, edge, ICN, and application, as well as security, AI, and intelligence dimensions. The ICN layer is an intermediate layer that provides core NDN aspects (naming, forwarding/routing, mobility, caching). The physical layer encompasses the various wireless communication interfaces (Wi-Fi, 4G/5G) employed by the ICN node for better availability of content and its distribution.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 2 Related Works  
In this section, we briefly describe wet paper code (WPC) for RDH. WPC is similar to matrix embedding as both are based on syndrome codes. Let us assume that the sender has a cover binary vector \(\mathbf{x}=\{x_{i}\}_{i=1}^{n}\) and a set of indices \(Q\subset\{1,\cdots,n\}\) (\(|Q|=k\)). The \(k\) bits are called dry elementsthat can be modified to embed watermark, and the remaining \((n-k)\) bits are called wet elements, which remain the same during embedding procedure.  
To embed \(m\) bits \(\mathbf{m}\in\left\{0,1\right\}^{m}\), the sender modifies the changeable bits so that the stego binary vector \(\mathbf{y}\) satisfies  
\[\mathbf{D}{\times}\mathbf{y}=\mathbf{m} \tag{1}\]  
where \(\mathbf{D}\) is an \(m\times n\) parity check matrix produced by the pseudo-random number generator. The receiver can extracts the watermark from \(\mathbf{y}\) by calculating the syndrome \(\mathbf{D}\mathbf{y}\).  
To solve the system of linear equations of Equation (1), we rewrite it as  
\[\mathbf{D}{\times}\mathbf{v}=\mathbf{m}-\mathbf{D}{\times}\mathbf{x} \tag{2}\]  
where \(\mathbf{v}=\mathbf{y}-\mathbf{x}\). There are \(k\) unknowns \(\mathbf{v}_{j}\) which are nonzeros, whereas the remaining \((n-k)\) values \(\mathbf{v}_{i}\) are zeros. Thus, on the left-hand side, we remove from \(\mathbf{D}\) all \((n-k)\) columns \(i\) and also remove from \(\mathbf{v}\) all \((n-k)\) elements \(\mathbf{v}_{i}\). Keeping the same symbol for \(\mathbf{v}\), Equation (2) now becomes  
\[\mathbf{H}{\times}\mathbf{v}=\mathbf{m}-\mathbf{D}{\times}\mathbf{x} \tag{3}\]  
where \(\mathbf{H}\) is a binary \(m\times k\) matrix consisting of those columns of \(\mathbf{D}\), and \(\mathbf{v}\) is an unknown binary \(k\times 1\) vector holding the embedding changes. According to the solvability of linear equations, the value of \(\mathbf{v}\) is solved. Thus, we get the modified binary column vector \(\mathbf{y}\) based on \(\mathbf{y}=\mathbf{v}+\mathbf{x}\).
# Safety and Security Considerations for Online Laboratory Management Systems
## 3 Literature Review
### Legal Safety and Security Requirements and Regulations
#### 3.3.4 Privacy Regulations  
There are many data privacy regulations worldwide, but the most well-known one that inspired many of these regulations is the General Data Protection Regulation (GDPR) [51]. GDPR is a comprehensive data protection and privacy regulation in the EU. It was adopted on 25 May 2018, and it changed the previous Data Protection Directive, introducing adequate changes in the procedures for collecting, processing, storage, and exchange of personal data within the EU. In addition, the Digital Fairness Act was proposed and it is expected to be introduced in 2026, addressing deceptive online practices, contracts, influencer marketing, and personalization in digital services.  
The California Consumer Privacy Act (CCPA) [52] is exclusively for Californian individuals and businesses in the US. This is the primary data privacy regulation at the state level in the United States. Drawing from GDPR, it has served as a blueprint for equal data privacy rules applied by other states. Effective from July 2020, the CCPA will empower consumers to manage various institutions' use of their personal information.
## 3 Development of ANN Model
### Results of ANN Analysis  
To develop the regression models, one layer of the hidden layer and two different preprocessing filters were applied to the ANN models. A total number of 52 and 24 hidden nodes were selected for the ANN models with two preprocessing algorithms: PCA and autoencoder. The number of hidden nodes was chosen based on the trial-and-error method. The activation function for the models was a sigmoid function. The calculated weight values between the input-to-hidden layers and hidden-to-output layers are summarized in Tables 1 and 2 for PCA and autoencoder, respectively.  
The maximum torsional strength of 151 specimens of the training dataset was predicted using the ANN models developed in this study and compared with the predictions of the most widely used design codes. Figure 3 shows comparison plots between the experimental results and the calculated torsional strength of specimens. The \(x\)-axis represents the measured torsional strength of specimens and the \(y\)-axis presents the predicted torsional strength by the design codes and developed ANN models. As shown in the figure, among the four design codes, ACI 318-19 (Figure 2(a)) showed better predictions with mean value and coefficient of variation (CV) of 0.98 and 25.18%, respectively. CSA-14 (Figure 2(c)) and JSCE-07 (Figure 2(d)) overestimated the maximum torsional strength of RC beams. The mean value of the ratio of experimental torsional strength to predicted torsional strength using CSA-14 was 0.71 with a CV of 25.18%, and the mean and CV for JSCE-07 were 0.86 and 23.46%, respectively. EC2-04 (Figure 2(b)) mainly underestimated the maximum torsional strength of RC beams. The mean and CV obtained using EC2-04 were 1.14 and 29.36%, respectively. In contrast, the ANN models successfully predicted the maximum torsional strength of RC beams with the best mean and CV, compared to the four design codes. The ANN model with two preprocessors--PCA (Figure 2(e)) and autoencoder (Figure 2(f))--resulted in a mean value of 1.0 for the ratio of experimental to predicted torsional strength. The CV with PCA and autoencoder was 5.47% and 2.86%, respectively, which is significantly lower than the CV of design code predictions.  
The accuracy of the analysis results using current design codes and the developed ANN algorithms was further evaluated based on three different errors: Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Root Relative Square Error (RRSE). Table 2 summarizes the calculated correlation coefficients and error percentages. The correlation coefficient of the developed ANN algorithms was 0.9968 and 0.9993 for PCA and autoencoder, respectively. The results from the developed ANN models well reflected the training data set, and significantly lower error percentages were obtained by utilizing the developed ANN models. All the calculated errors of the ANN models were lower than 10%.
## 3 Results
### Fibroblasts  
Fibroblasts and chondrocytes share the same functions of deposition and maintenance, and they are found in different tissues, with chondrocytes found in cartilage and fibroblasts in skin. We decided to analyze the effects of NAG and nNAG on fibroblasts. These cells, cultured in spheroids, were left untreated (CTL) or treated with either 10 \(\upmu\)M nNAG or 10 \(\upmu\)M NAG for 72 h. After 72 h, the spheroids were analyzed by immunofluorescence to highlight the collagen type I production, which is typical of fibroblasts. The untreated cells (CTL) showed only a slight green fluorescence, which was comparable to the green fluorescence observed in the cells treated with NAG, whereas cells treated with nNAG showed a very intense green staining (Figure 3).  
Figure 3: Effects of NAG and nNAG on collagen I production. Fibroblasts were left untreated (CTL) or treated with either 10 \(\upmu\)M NAG or nNAG for 72 h and then analyzed by immunofluorescence using anti-Coll-I primary antibody and Alexa Fluor 488 (green) secondary antibody. Nuclei were stained with DAPI (original magnification: 40X). The bar graph represents the pixel intensities in the region of interest, obtained using ImageJ.  
Figure 2: Effects of NAG and nNAG on MMP production. HPCs, cultured in spheroids, were left untreated (CTL) or treated with either 10 \(\upmu\)M NAG or nNAG for 72 h. After the treatment, the cell media were collected and analyzed by ELISA to measure the levels of MMP-3, MMP-13, and ADAMTS-5 protein secretion. The results are reported as ng/mL or pg/mL and are expressed as the mean \(\pm\) SEM of data obtained from three independent experiments. * \(p\) < 0.05 vs. CTL and ** \(p\) < 0.01 vs. CTL.  
The maintenance of the extracellular matrix of the skin is the result of the production of MMPs; however, their production in high amounts can be detrimental. We measured the production of MMP-3 and MMP-9 in fibroblasts treated with NAG and nNAG. MMP-3 was downregulated by NAG and nNAG, with nNAG being more effective than NAG. Notably, MMP-9 was downregulated only by nNAG, with NAG not being effective (Figure 4).