esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Torsional Shear Deformation Characteristics of Segment Joints
### Damage Characteristics of Concrete  
In this study, the tensile damage factor of the concrete is used as an indicator to evaluate crack development, while the compressive damage factor is employed to assess crushing damage. Figure 15 presents the distribution of tensile damage in the segment concrete under torsional shear deformation at the circumferential joints. The results indicate that tensile damage is concentrated around the bolt holes and mortise and tenon. The distribution of this tensile damage appears to be consistent across varying axial force conditions. As torsional shear deformation progresses, one side of each bolt hole experiences compression due to the bolt, which induces tensile cracks on the opposite side of the hole. Furthermore, as illustrated in Figure 15a-d, with increasing torsional deformation, these cracks propagate outward from the bolt hole toward both the outer and inner arc surfaces of the segment. During this process, both the mortise and tenon experience compression, with distinct behaviors at each joint type. In particular, tenons are subjected to shear forces at the surface of the segment, leading to significant cracking near the compressed regions. In contrast, cracking in the mortise predominantly occurs on the non-compressed side, as the compressed side is in contact with the tenon. The formation of tensile cracks on the non-compressed side is a direct result of this contact during torsional shear.  
Figure 16 illustrates the compressive damage distribution in segmental concrete structures under torsional shear deformation at circumferential joints. The compressive damage predominantly concentrates around the bolt holes and the mortise and tenon, where significant stress is generated. Under different axial force, a consistent damage pattern is observed, with the progression in damage intensifying as torsional deformation increases. Specifically, during high torsional shear, the bolts and mortise and tenon experience sub  
Figure 15: Distribution of tensile damage in segment concrete. (**a**) \(N=1000\) kN; (**b**) \(N=2000\) kN; (**c**) \(N=3000\) kN; (**d**) \(N=4000\) kN.  
stantial compressive forces, leading to localized crushing of the concrete around these critical areas. Figure 16a-d further reveals that, under large deformations, the tenons undergo complete crushing, while the mortise exhibit damage primarily on the compressed side. This suggests that the mortise and tenon geometry and bolt positioning significantly influence the failure modes and shear resistance of the joints.
## 3 Results and Discussion
### Adsorption Mechanism Study  
#### 3.5.1 FTIR and XPS Analyses  
To further study the mechanism of U(VI) adsorption by SiPMA-TEPA, FTIR and XPS analyses were performed. As shown in Figure 11a, the characteristic peak at 1566 cm\({}^{-1}\) for SiPMA-TEPA corresponded to the bending vibration of the N-H bond of the polyamine functional group of the resin [30]; moreover, the peak of the N-H bond of the resin SiPMA-TEPA-U shifted, and a new peak for nitrate appeared. This indicates that the polyamine functional groups in the resin were involved in coordination. A peak for nitrate [45] (1385 cm\({}^{-1}\)) was detected on the adsorbent after uranium adsorption; this peak corresponded to a small amount of nitric acid extracted by SiPMA-TEPA.  
Figure 11: (**a**) FTIR spectra of SiPMA-TEPA and SiPMA-TEPA loaded with U; (**b**) XPS spectra of SiPMA-TEPA and SiPMA-TEPA loaded with U, including fine XPS C 1s and O 1s spectra (**c,d**); (**e**) fine XPS U 4f spectrum; and (**f**) fine XPS N 1s spectrum.  
The full XPS spectra of the SiPMA-TEPA adsorbent before and after adsorption of U(VI) are shown in Figure 11b. The appearance of the characteristic U(VI) peak on the U-loaded SiPMA-TEPA adsorbent indicates that U(VI) was successfully adsorbed. Figure 11c shows the XPS fine scan C 1s spectrum. The peak area percentage of C=O (288.5 eV) [46; 47; 48] became larger after uranium adsorption by SiPMA-TEPA, indicating the contribution of carbonate. Figure 11d presents the high-resolution XPS O 1s spectrum. Changes in the peak areas of C=O (532 eV) and C-O (533 eV) [49; 50] after uranium adsorption further confirmed that CO\({}_{3}\)\({}^{2-}\) was bound to UO\({}_{2}\)\({}^{2+}\). Figure 11c illustrates the high-resolution U 4f spectrum, where the peaks at 381.5 eV and 392.5 eV correspond to the 7/2 and 5/2 U 4f levels, respectively. Figure 11f shows the high-resolution XPS N 1s spectrum. The binding energy of N-H shifted from 396.9 eV to 397.5 eV, and the binding energy of N-C shifted from 397.9 eV to 399.4 eV. After U adsorption, the resin showed a characteristic peak between nitrate and uranium at 404.6 eV [38]; this indicates that the primary amine groups of the resin extracted nitric acid, further corroborating the FTIR results. Additionally, the binding energy of N-C shifted from 397.9 eV to 399.4 eV, suggesting the involvement of N-containing functional groups on the polymer in the coordination [30; 51]. The predominant chemical form of uranium at pH 6-7 was UO\({}_{2}\)(CO\({}_{3}\))\({}_{3}\)\({}^{4-}\), and all the polyamine groups on the adsorbent were protonated to coordinate with UO\({}_{2}\)(CO\({}_{3}\))\({}^{4-}\). The mechanism is illustrated in Figure 12.
###### Abstract  
The integration of communication infrastructures into traditional power systems, transforming them into cyber-physical power systems (CPPS), accentuates the significance of communication in influencing system performance and sustainability. This paper presents a versatile, innovative cyber-physical co-simulation framework that integrates the physical power system and communication networks, uniting OPAL-RT, a network simulator (ns3), and Docker containers into a sophisticated platform, facilitating intensive studies into CPPS dynamics. The proposed experimental study provides an innovative way to assess the frequency control response of a cyber-physical inverter-based microgrid (MG), addressing the MG sustainability challenges. We consider diverse real-world scenarios, focusing on communication delays and distributed denial of service (DDoS) attacks within the communication channels. We propose a precise ns3-based communication model that bridges the MG's primary and secondary control layers, an aspect often overlooked in previous studies; this is a noteworthy contribution to elucidating the adverse impacts of communication latency on MG frequency performance. The experimental results demonstrate the effectiveness of the centralized secondary controller in eliminating the frequency deviations. Furthermore, the findings offer insights into stable and unstable regions, revealing how the communication delay value affects the frequency stability under different operating conditions. In addition, the developed real-time DDoS attacks model within the proposed communication surface unveils crucial insights into the MG's resilience to cyber threats. This work's revelations offer a foundational awareness of MG vulnerabilities, paving the way for designing robust and resilient communication networks and control strategies within the cyber-physical inverter-based microgrids.  
c 2024 14 997. 12 January 2024  
Energy Systems Research Laboratory, Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA; [EMAIL_ADDRESS] (T.-L.N.); [EMAIL_ADDRESS] (O.A.M.)  
* Correspondence: [EMAIL_ADDRESS]
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
###### Abstract  
In this study, chitosan beads modified with sodium dodecyl sulfate (SDS) were successfully synthesized and employed for the removal of chromium(VI) (Cr(VI)). The adsorption performance of the adsorbent (SDS-chitosan beads) was examined by batch experiments. The partition coefficient (PC) as well as the adsorption capacity were evaluated to assess the true performance of the adsorbent in this work. The adsorbent (SDS-chitosan beads) showed a maximum Cr(VI) adsorption capacity of 3.23 mg\(\cdot\)g\({}^{-1}\) and PC of 9.5 mg\(\cdot\)g\({}^{-1}\)-mM\({}^{-1}\) for Cr(VI). The prepared adsorbent was characterized by different techniques such as scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), X-ray photoelectron spectroscopy (XPS) and Fourier transform-infrared spectroscopy (FT-IR). We used inductively coupled plasma mass spectrometry (ICP-MS) for the determination of Cr(VI) in solution. The experimental data could be well-fitted by pseudo-second-order kinetic and Langmuir isotherm models. The thermodynamic studies indicated that the adsorption process was favorable under the higher temperature condition. The SDS-modified chitosan beads synthesized in this work represent a promising adsorbent for removing Cr(VI).  
Keywords:chitosan beads; sodium dodecyl sulfate (SDS); hexavalent chromium ions; adsorption capacity; partition coefficient +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Afm  
The purpose of performing AFM analysis of the membrane is to measure its roughness. The roughness parameters are surface kurtosis (Rku), average roughness (Ra), and root mean square roughness (Rq). Figure 6 represents the surface topography of the TNF/CNF membrane using AFM analysis. Roughness parameters of the membrane were determined from the topography image of a scanning area of 10 \(\times\) 10 \(\upmu\)m. The values of Rku, Rq, and Ra were 3.12, 5.3 nm, and 4.05 nm, respectively. The roughness parameters values were relatively low, because the relatively fine and long nanofibers were well bonded together, which results in a lower valley region in the membrane surface [23]. The stacking of fine and interpenetrated nanofibers renders the membrane with smooth surfaces. The link between surface roughness and membrane fouling during membrane filtration processes has been established [24]. The smoother the membrane surface is, the less pollutants block the valleys of the membranes, which results in less membrane fouling [25; 26]. The observation of the contact angle in Figure 7 also supports the low fouling tendency, as a thin hydration layer was assumed to form on the membrane surface owing to the high hydrophilicity of the membrane, which will repel the oil pollutants from the membrane surface, as illustrated in Figure 3.  
Figure 5: (a) XRD patterns of synthesized TNF material. (b) Energy Dispersive Spectroscopy (EDS) analysis of TNF/CNF membrane material, showing the chemical elements (Ti, C, and O) of the new membranes.
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 5 Feature Extraction
### Statistical Analysis  
Many feature extraction methods for BCIs are based on the statistical information of EEG information. The two more extensively used are:  
* Principal component analysis (PCA) is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets (such as in the case of EEG information), by transforming a large set of variables into a smaller one that still contains most of the information in the large set. Reducing the number of variables of a dataset naturally comes at the expense of accuracy [42].
* Independent component analysis [42] (ICA). This method is used when mixed signals are separated into individual component signals. Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples. In the model, the data variables are assumed to be linear mixtures of some unknown latent variables, and the mixing system is also unknown. The latent variables are assumed non-Gaussian and mutually independent, and they are called the independent components of the observed data. These independent components, also called sources or factors, can be found by ICA.  
Additionally, in the pre-processing step, we can find approaches to separate the source signal information of their components. Existing methods are classified as \"blind methods\". Blind methods are identified as a parallel of \"unsupervised\" methods because these types of techniques are based on output. Three types of algorithms are used based on the information that takes to do the analysis. These types are statistical independence of sources, positive, and sparsity [43]. Among these, there are algorithms such as ICA or PCA that allow obtaining the desired signal information, in the case of BCI the information related to the brain motor activity.
## 1 Introduction
### Second-Order plus Dead-Time (SOPDT) Model  
If the inertial plant has a dominant time constant, it can be sufficiently well approximated using the FOPTD model. When this is not the case, the properties of the plant can be better approximated using the SOPDT model, with a transfer function in the following general form:  
\[G_{m}(s)=\frac{Ke^{-\theta s}}{T^{2}s^{2}+2\xi T_{b}+1} \tag{25}\]  
where \(K\)--static gain, \(T\)--time constant, \(\xi\)--non-dimensional damping factor, \(\theta\)--time delay.  
If \(\xi\geq 1\), the SOPDT model can be represented as follows:  
\[G_{m}(s)=\frac{Ke^{-\theta s}}{(1+T_{1}s)(1+T_{2}s)} \tag{26}\]  
where \(T_{1}\), \(T_{2}\)--time constants.  
A PID controller can also be used to control a second-order process. Let us now determine the simplified transfer function of the open-loop system, assuming that the plant (26) is controlled by an ideal PID controller:  
\[G_{o}(s)=K_{p}\cdot\left(1+\frac{1}{T_{i}s}+T_{d}s\right)\cdot\frac{Ke^{- \theta s}}{(1+T_{1}s)(1+T_{2}s)} \tag{27}\]  
where \(K_{p}\) proportional gain, \(T_{i}\)--integral time constant, \(T_{d}\)--derivative time constant.  
[PERSON] and [PERSON] assumed that the desired closed-loop transfer function is to be the same as that of the first-order plant (13). To achieve this goal, after carrying out the derivation as in the previous section, they recommend the following settings:  
\[K_{p}=\frac{T_{1}+T_{2}}{K(\tau_{c}+\theta)}\ \ \ T_{i}=T_{1}+T_{2}\ \ \ T_{d}=\frac{T_{1}T_{2}}{T_{1}+T_{2}} \tag{28}\]  
Identical PI/PID settings were obtained by [PERSON], [PERSON], and [PERSON] using the IMC approach [5].  
The Chien method [8] belongs to the methods of tuning PID controllers based on direct synthesis dedicated to control the processes described by Equation (2). These settings should be equal to  
\[K_{p}=\frac{2\zeta T}{K(\tau_{c}+\theta)}\ \ \ T_{i}=2\zeta T\ \ \ T_{D}=\frac{T}{2\zeta} \tag{29}\]  
The tuning parameter \(\tau_{c}\) is almost equivalent to the closed-loop time constant \(\tau_{c}=\max(0.25\theta,0.2T)\).
## 4 Experimental Verification and Analysis
### Comparative Analysis
#### 4.5.3 Comparison with Other Fault Diagnosis Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**The Average Test Accuracy \(\pm\) Standard Deviation (\%)**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Parameters** \\ \end{tabular} } \\ \cline{2-2} \cline{5-6}  & **DataSet A** & **DataSet B** & **DataSet C** & **DataSet D** & **Training Time (min)** & **Number of** \\ \hline DCNN & 92.65 \(\pm\) 1.49 & 93.83 \(\pm\) 1.84 & 98.71 \(\pm\) 0.67 & 91.83 \(\pm\) 1.64 & 10.6 (CPU) & \(\approx\)258318 \\
2 DCNN & 84.35 \(\pm\) 3.81 & 77.92 \(\pm\) 3.35 & 87.11 \(\pm\) 3.91 & 83.92 \(\pm\) 3.24 & 8 (CPU) & \(\approx\)177615 \\ CNN\_EMD & 82.11 \(\pm\) 2.45 & 75.59 \(\pm\) 3.21 & 84.65 \(\pm\) 2.09 & 70.16 \(\pm\) 2.30 & 15 (CPU) & \(\approx\)654360 \\ IDSCNN & 91.91 \(\pm\) 1.30 & 92.37 \(\pm\) 2.69 & 96.41 \(\pm\) 1.25 & 90.45 \(\pm\) 2.63 & 15 (GPU) & \(\approx\)532650 \\ Proposed method & 99.95 \(\pm\) 0.06 & 99.87 \(\pm\) 0.12 & 99.92 \(\pm\) 0.07 & 98.99 \(\pm\) 0.05 & 6.72 (CPU) & \(\approx\)117705 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance analysis of comparative methods.
## 3 The Proposed Feature Selection Framework  
### Overview  
The overview of our proposed SCM framework, which contains a **Feature Clustering** step and a **Feature Ranking** step, is depicted in Figure 1. In the **Feature Clustering** step, SC groups highly correlated features into clusters by capturing both linear and non-linear relationships among features. This clustering not only reduces feature redundancy but also provides a structured foundation for further analysis. In the **Feature Ranking** step, the grouped features are first normalized to ensure consistency in scale. This preprocessing step prevents features with larger ranges from disproportionately influencing the MIC computation, enabling fair comparisons across features within each cluster. MIC is then used to evaluate the relevance of each feature to the target labels, and the most representative feature from each cluster is selected to form the final optimized feature subset.  
The interaction between these two components is crucial: **Feature Clustering** reduces the complexity of the data by grouping related features, while **Feature Ranking** refines these groups by isolating the most impactful features. This collaborative process produces an optimized feature subset, which is then input into the decision tree classifier to predict whether the residence of a crashing fault is inside or outside the stack trace.  
Detailed descriptions of the SC and MIC methods are provided in Sections 3.2 and 3.3, respectively, as both are integral to the proposed SCM framework.  
Figure 1: An overview of our SCM framework.
## 5 Discussion of Results
**Recommendations to Businesses:**
* Companies need to pay more attention to the proper processes' establishment. The systematic approach is the basement of any organization. A poorly set system deteriorates the overall effectiveness of the enterprise;
* A culture of self-learning from international experience should be supported. If it does not exist, it should be developed. It is an excellent opportunity for SMEs with low financial possibilities to study from international experience for an affordable price. Moreover, it is great not only to imply that experience to the company's processes but to train employees;
* Proper formation of the vertical integration within company is a need for the successful implementation of I4.0 projects. Lack of communication leads to misunderstandings, which is a reason for 70% of cases of projects fail, according to interviewee 4;
* A system of change management needs to be introduced. It might include systematic continuous training for employees, since the project is dependent on the capabilities of employees' success. The explanatory sessions, presenting the need of the organization to apply new tools and other benefits, especially for employees need to be carried. Develop the inter-organizational culture of life-long learning, allowing the workforce to optimize their approach to work by searching for new optimal ways;
* Leaders with a high knowledge of digital transformations are required, especially for large enterprises and governmental entities. This critically important, yet simple suggestion, is missed by governmental entities in most cases;
* The smooth transition to Industry 4.0 can be reached through a prior defined strategy, which will allow avoiding chaos and application of costly and inappropriate technologies. It was indicated that most of the companies in the country tend to learn or make major changes in the strategy in the process of transformation; this approach must be exterminated;
* Digital/Industry 4.0 roadmap should be created to clearly define needed steps, roles of the involved parties, employees, partners, etc. On top of that, a roadmap is required to focus on the critical aspects, and avoid wasting resources. It should be clear for stakeholders that not the strategy, but the roadmap can be adjusted, changed, and modified according to the needs of the enterprise. Recommendations indeed are not limited to only provided ones, and their further development can be considered.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 2 Related Work
### Basic CCN  
In-network caching is one significant feature of CCN, where content is scattered and cached among routers. Different from an IP-based network, CCN uses name-based routing that retrieves contents from nearby router caches. Multipath forwarding is also supported by CCN to reduce response time. Between the application layer and transportation layer, the CCN protocol alters IP-based protocol by adding three layers: a security layer, a named contents layer, and a strategy layer [19].  
_Interest packets_ and _data packets_ are two types of packets in basic CCN [20; 21; 22; 23]. When receiving an interest packet, the router first tries to retrieve _Name_ from its Content Store (CS) and return the data packet. In the router, CS is used to cache network content. If the CS retrieval fails, the router adds the interest packet to the PIT. The information stored in PIT includes _Name_, _incoming interface_, and _outgoing interface_. The following interest request for a duplicate _Name_ is aggregated to the same entry as if it was in PIT [7]. FIB contains entries with _Name_ and _outgoing interface_. There are multiple outgoing interfaces for each unique _Name_. Many forwarding strategies [3; 4; 5; 7; 24; 25; 26] have been proposed, such as broadcast, which forwards interest through all outgoing interfaces, and best route, which forwards interest through one outgoing interface at a time starting with the lowest cost.
## 2 Background  
### System Model  
Consider \(N\) antennas at the BS are serving simultaneously \(K\) user terminals where \(K\ll N\). The symbol vector \(\mathbf{x}=[x_{1},\,x_{2},\dots.,\,x_{K}]^{T}\) is transmitted by users and the symbol vector  
matrix inversion is expanded as a sum of infinite number of terms. The computational complexity increases when the number of terms and iterations (\(n\)) increases. In the NS method, (\(\mathbf{G}\)) is decomposed into a diagonal matrix (\(\mathbf{D}\)) and a non-diagonal matrix \(\mathbf{E}\). In the NS method, \(\mathbf{A}^{-1}\) can be iteratively approximated as:  
\[\mathbf{A}_{(n+1)}^{-1}=\sum_{n=0}^{\infty}\left(\mathbf{I}-\mathbf{A}_{n}^{-1 }\mathbf{A}\right)^{n}\mathbf{A}_{n}^{-1}. \tag{5}\]  
In the NI method, \(\mathbf{A}^{-1}\) can be presented as  
\[\mathbf{A}_{(n+1)}^{-1}=\mathbf{A}_{n}^{-1}(2\mathbf{I}-\mathbf{A}\mathbf{A}_ {n}^{-1}). \tag{6}\]  
Selection of the initial inversion \(\mathbf{A}_{(0)}^{-1}\) is crucial. In this paper, we investigate the selection of \(\mathbf{A}_{(0)}^{-1}\) in the NS and NI methods as:  
* \(\mathbf{A}_{(0)}^{-1}=\mathbf{D}^{-1}\), where \(\mathbf{D}\) is the diagonal matrix,
* \(\mathbf{A}_{(0)}^{-1}=\mathbf{S}^{-1}\), where \(\mathbf{S}\) is the stair matrix.
* \(\mathbf{A}_{(0)}^{-1}\) is the initial solution to iterations shown in Equations (5) and (6), which converge quadratically to \(\mathbf{A}^{-1}\) if \[\left\|\mathbf{I}-\mathbf{A}\mathbf{A}_{(0)}^{-1}\right\|<1.\] (7)  
\(\mathbf{A}_{(0)}^{-1}\) is refined iteratively. In order to investigate the condition in Equation (7), \(10^{4}\) channel matrices are generated where \(\mathbf{D}\) and \(\mathbf{S}\) are extracted from each \(\mathbf{H}\). Figure 1 shows that the condition in Equation (7) is always valid for both diagonal and stair matrices.  
The signal \(\mathbf{\hat{x}}\) can be estimated as  
\[\mathbf{\hat{x}}=\mathbf{A}^{-1}\mathbf{y}_{MF}. \tag{8}\]