esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A previous proteomic study uncovered a relationship between nutritional stress and fluctuations in levels of diadenylate cyclases (DACs) and other proteins that regulate DAC activity, degrade, or interact with c-di-AMP, suggesting a possible role of this second messenger in _B. subtilis_ stress-associated mutagenesis (SAM). Here, we investigated a possible role of c-di-AMP in SAM and growth-associated mutagenesis (GAM). Our results showed that in growing cells of _B. subtilis_ YB955 (_hisC952, metB25_ and _levC427_), the DACs CdaA and DisA, which play crucial roles in cell wall homeostasis and chromosomal fidelity, respectively, counteracted spontaneous and Mitomycin-C-induced mutagenesis. However, experiments in which hydrogen peroxide was used to induce mutations showed that single deficiencies in DACs caused opposite effects compared to each other. In contrast, in the stationary-phase, DACs promoted mutations in conditions of nutritional stress. These results tracked with intracellular levels of c-di-AMP, which are significantly lower in _cdaA_- and _disA_-deficient strains. The restoration of DAC-deficient strains with single functional copies of the _cdaA_ and/or _disA_ returned SAM and GAM levels to those observed in the parental strain. Taken together, these results reveal a role for c-di-AMP in promoting genetic diversity in growth-limiting conditions in _B. subtilis_. Finally, we postulate that this novel function of c-di-AMP can be exerted through proteins that possess binding domains for this second messenger and play roles in DNA repair, ion transport, transcriptional regulation, as well as oxidative stress protection.  
Keywords:stress-associated mutagenesis; cyclic-di-AMP; _Bacillus subtilis_
## 5 Performance Evaluation
### Authentication between Entities
#### 5.2.1 Computational Cost
Once the client receives the ServerHello message in line 6, it verifies the server's certificate and generates a certificate message (line 7). Subsequently, after line 9, the server verifies the client's certificate, completing the mutual authentication process. In summary, TLS 1.3 facilitates secure communication between the client and server by exchanging cryptographic keys and verifying certificates to establish a trusted connection. The mutual authentication ensures that both parties can be confident in each other's identities during the communication session. The computational costs of several protocols are shown in Table 8. The 4G EPS-AKA only costs 0.856 ms in the edge tier. However, edge user IDs are not anonymized during the authentication process. Compared to other protocols, 3-Tier AKA has the highest efficiency.
# Densities and Viscosities of Ionic Liquid with Organic Solvents
## 3 Results and Discussion
### Experimental Density Data
Moreover, \(V^{\text{E}}\) is usually correlated using the Redlich-Kister type equation [49]:  
\[V^{E}=x(1-x)(A+BT+Cx) \tag{4}\]  
where \(T\) (K) is the temperature in Kelvin; the fit parameters of \(A\) (cm\({}^{3}\cdot\)mol\({}^{-1}\)), \(B\) (cm\({}^{3}\cdot\)mol\({}^{-1}\cdot\)K\({}^{-1}\)), and \(C\) (cm\({}^{3}\cdot\)mol\({}^{-1}\)) are correlated using the measurement data. Table S1 lists the parameters (in the Supplementary Material). Figure 2 and Figures S7-S9 (in the Supplementary Material) give \(V^{\text{E}}\) as a function of ionic liquid mole fraction. In the studied compositions and the temperatures, the values are negative, indicating that the molar volumes are smaller than those of the ideal ones. The minimum values of \(V^{\text{E}}\) occur at the IL mole fraction of 0.24 (0.50 in mass). Furthermore, based on the measurement density, the thermal expansion coefficient \(\alpha\) is calculated using the following equation:  
\[\alpha=-\frac{1}{\rho}\Bigg{(}\frac{\partial\rho}{\partial T}\Bigg{)}_{p}. \tag{5}\]  
Combined with Equation (3), Equation (5) is changed to:  
\[\alpha=V^{-1}\Bigg{[}\Bigg{(}\frac{\partial V^{E}}{\partial T}\Bigg{)}_{p,x} +\sum_{i}^{n}(x_{i}\alpha_{i}V_{i})\Bigg{]}. \tag{6}\]  
Figure 2: The excess molar volumes for [C\({}_{6}\)mim] [OAc] with solvents as a function of ionic liquid mole fraction. Dash curve calculated by Equation (4).
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 6 Joint Network-Channel Coding over Cooperative Link
### Joint Network-Channel Coding  
In order to transmit the data, it should be channel coded at the sending nodes. The channel coding is performed on both nodes as described,  
\[c_{S}=\zeta(n_{S}), \tag{7}\]  
\[c_{D}=\zeta(n_{D}), \tag{8}\]  
where \(c_{S}\) indicates the coded data at \(S\) and \(c_{D}\) at \(D\) while \(\zeta(.)\) indicates the channel coding function. The data to be shared among the nodes is then BPSK modulated to \(x_{S}\) and \(x_{D}\) and sent to the relay node in separate slots. The data received at the relay node from the respective nodes is \(y_{S}\) and \(y_{D}\), where  
\[y_{S}=h_{S}x_{S}+z, \tag{9}\]  
\[y_{D}=h_{D}x_{D}+z. \tag{10}\]  
Here, \(h_{S}\) and \(h_{D}\) are the channel coefficients of links \(S-R\) and \(D-R\), respectively, and \(z\) accounts for AWGN. The relay node \(R\) demodulates and decodes the data to get the estimates \(n_{S}\) and \(n_{D}^{\prime}\). At this point, the relay node has the data of both nodes. Its network encodes the estimates and then the channel encodes them in the following manner:  
\[c_{R}=\zeta(n_{S}\oplus n_{D}^{\prime}). \tag{11}\]  
If the source coding used is a linear code, then it does not make any difference whether channel coding is done before the network coding or vice versa. Likewise, if the estimated data is channel coded first to \(c_{S}\) and \(c_{D}^{\prime}\), and network coded later,  
\[c_{R}=c_{S}\oplus c_{D}=\zeta(n_{S})\oplus\zeta(n_{D}^{\prime})=\zeta(n_{S} \oplus n_{D}^{\prime}). \tag{12}\]This joint network-channel encoded data is then modulated as \(x_{R}\) and broadcasted to both nodes \(S\) and \(D\).
## 2 System Model  
### System Architecture  
First, we consider a system architecture of an MNO, as shown in Figure 1, which incorporates a set of picocells and small cells distributed over the coverage of a macrocell. Picocells serve outdoor users, particularly in hotspot areas, by offloading users from the macrocell. All outdoor macro user equipment (UE) is served either by the macrocell or by a picocell if offloaded. However, if a macro UE is found within a building, the corresponding indoor macro UE is served only by the macrocell. Small cells are deployed only within multistory buildings, such that in-building small cell UE is served only by small cells.  
Figure 1: (**a**) An illustration of the system architecture for reuse of millimeter-wave (mmWave) spectra for in-building small cells. (**b**) Co-channel interferers with both intra-floor and inter-floor levels for small-cell user equipment (UE).  
Assume that the MNO is allocated with the licensed and 28 GHz spectrum bands to operate 5G mobile networks. To increase the available spectrum bandwidth, assume that the MNO also considers operating at the 60 GHz unlicensed spectrum band. Due to the good channel quality and to ensure extensive coverage, we assume that macrocells operate only in the 2 GHz microwave spectrum, whereas to provide high data rate services within small coverage areas, small cells operate in both mmWave spectra, such that each small cell is enabled with dual-band operation in 28 and 60 GHz bands, as shown in Figure 1a.  
Similar to [9], we assume that each building consists of a set of square grid apartments measuring \(10\times 10\) m\({}^{2}\) per floor, and that there are multiple floors per building. For simplicity, we assume that each building has the same number of small cells, although in practice the number of apartments per building varies. Further, we assume that each small cell serves only one UE at any time and is located at the center of the ceiling of an apartment, as shown in Figure 1b. It is to be noted that since macrocells and small cells operate at different frequencies, and that all macro UE and small cell UE are served only by their respective cells, no co-channel interference management technique is needed between macrocell UE and small-cell UE. However, the reuse of the same mmWave spectra is considered within small cells in each building by forming a set of three-dimensional clusters of small cells that satisfy an optimal value of co-channel interference for any small-cell UE. Figure 1b shows the formation of a 3D cluster of small cells for any small-cell UE \(S\) within a building, satisfying an optimal co-channel interference value set by the MNO for both intra-floor and inter-floor levels, which we discuss in more detail in the following section.
# Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel
## 1 Introduction
However, this work differs from [1; 2] where the authors proposed an interference alignment scheme for the \(K\)-user multiple-input-single-output (MISO) interference channel with periodic and imperfect channel knowledge, respectively. The space-time relay transmission technique assists in increasing the recovered signal strength by incorporating two unknown transmitter and relay signals efficiently. The retrieval of the desired signal information is evaluated by the combination of two unknown co-interference signals from the transmitter and the relay facilitates the reconstruction of the interference signals from the unintended (additional) receiver by considering the cooperation between the receivers. If the distance between the intended and unintended receiver is too large, the cooperation between the receivers will be affected drastically, which, in turn, will degrade the sum rate and the bit error rate (BER) performance of the proposed system.
# Investigation on Eddy Current Sensor in Tension Measurement at a Resonant Frequency
## 2 Theoretical Model  
### LR Model  
An alternating current (AC) applied to the solenoid coil produces an alternating magnetic field, which is referred to as the initial magnetic field and induces an eddy current around test specimen. The eddy current simultaneously creates a secondary magnetic field, which circulates in the opposing direction to the initial magnetic field, thus resisting the variation of the initial magnetic field and changing the coil impedance [20; 21; 22]. The schematic diagram of the working principle is shown in Figure 1. Under low excitation frequencies, the capacitances from the coaxial cable and adjacent coil turns are negligible. Thus, the sensor system can be modeled by the inductor-resistor (LR) circuit shown in Figure 2.  
According to [PERSON]'s Voltage Law, the equations can be expressed as follows:  
\[R_{0}I_{0}+j\omega L_{0}I_{0}-j\omega kL_{1}I_{1}=U \tag{1}\]  
\[R_{1}I_{1}+j\omega L_{1}I_{1}-j\omega kL_{0}I_{0}=0 \tag{2}\]  
where \(R_{0}\) and \(L_{0}\) are the resistance and inductance of solenoid coil without the test specimen, \(I_{0}\) and \(I_{1}\) are the excitation current and induced eddy current, \(R_{1}\) and \(L_{1}\) are the resistance and inductance of induced eddy current, \(k\) is the coupling coefficient influenced by the skeleton thickness, \(\omega\) is the angular frequency of alternating current excitation signal, and \(U\) is the voltage of excitation signal, respectively.  
The LR model can be remodeled as the equivalent model displayed in Figure 2b, which has a single-equivalent circuit as functions of \(R_{0}\), \(L_{0}\), \(R_{1}\) and \(L_{1}\). The total impedance of the system is given by:  
\[Z_{LR}=R_{Z}+j\omega L_{Z} \tag{3}\]  
\[R_{z}=R_{0}+\frac{\omega^{2}k^{2}L_{0}L_{1}R_{1}}{{R_{1}}^{2}+\omega^{2}{L_{1 }}^{2}} \tag{4}\]  
\[L_{z}=L_{0}-\frac{\omega^{2}k^{2}L_{0}{L_{1}}^{2}}{{R_{1}}^{2}+\omega^{2}{L_{ 1}}^{2}} \tag{5}\]  
where \(Z_{LR}\) is the effective impedance, \(R_{z}\) and \(L_{z}\) are the equivalent resistance and inductance of solenoid coil, respectively.
## 4 Discussion
### Technical Features of the Method
Consistent with most of the previously proposed computer-based approaches for collagen estimation, our approach is not fully automatic. The user input is however reduced to the definition of a common threshold on a subset of samples and the supervision of the processing, enabling fast analysis on large batches of samples. The optimal threshold was determined by analyzing a reduced set of images from the database. The method is thus time-efficient when a large number of sections acquired with the same protocol must be analyzed, and it reduces the dependence on the operator to a minimum. The determination of a common threshold on a limited number of images and its extension to a whole dataset is similar to the approach in [15] for bright-field images, where the color thresholds were also assessed on a subset of images. Differently, in other approaches, which operated on picrositius red-stained bright-field images [17; 18; 19], the investigators had to set thresholds for every image that was analyzed, which made the approach laborious and sensitive to inter-observer variations. In general, it is worth noticing that the setting of a threshold needs to be adapted to the specific sample type, preparation, and acquisition protocol. In fact, the definition of the threshold depends on the contest and goal of the specific application, since the quantification from the birefringent signal provides only a relative assessment of collagen, suitable for comparative analysis, e.g., the comparison of different diagnostic profiles. As shown by our sensitivity analysis, differences in the setting of the threshold \(t_{col}\), e.g., arising from the analysis of different image subsets, determined changes in the absolute value of collagen detected. However, the observed changes were consistent throughout the database and the relative differences between patient groups were preserved in the considered threshold range.
# Emergency Repair Scheduling Model for Road Network Integrating Rescheduling Feature
## 3 Methodology
### Activity Precedence Relationship  
1. Relationship among repair activities. This study investigates the emergency repair issue from a scheduling perspective. Since the emergency repair work is quite urgent, this model assumes that all emergency repair works are completed at the disaster point and equipment resources are then immediately transported to the next disaster point through the connecting transit activity, and emergency repair work at the succeeding disaster point starts immediately after the arrival of the resources, which is the FS (finish-start) relationship in the scheduling precedence relationships, shown as Equation (8). \[S_{j}\,\geq\,Max\,\left\{F_{i}\,+\,LT_{ij}\right\}\,,\,\forall\,i\,\in\,SI\,, \,\forall\,j\,\in\,SI\] (8) 2. Relationship between repair activity and transit activity. Since resource transit can be further divided into two situations, such as transit from the resource point to the disaster point and transit from one disaster point to another disaster point, the relationship between transit activity and repair activity is showed as Equations (9) and (10), respectively. \[S_{j}\,\geq\,F_{nj}\,,\forall\,n\,\in\,SN\,,\forall j\,\in\,SJ\] (9) \[S_{j}\,\geq\,F_{ij}\,,\forall\,i\,\in\,SI\,,\forall j\,\in\,SJ\] (10)
## 4 Results and Discussion
### Spatial Autocorrelation of Elements
#### 4.1.2. Bivariate Spatial Cross Correlation between Two Elements  
The bivariate global Moran's I for 15 elements in the study area are shown in Table 4, and all the calculated results passed the statistical significance test. We quantified the  
Figure 9: Clustering map of local Moran’s I indicators for major elements.  
strength of spatial cross correlation between all element pairs, as shown in Table 4, the diagonal values of which are consistent with the univariate global Moran's I. The elements with strong positive correlations include Pb and Cd, Pb and Zn, Cu and Bi, and Zn and Cd, and those with negative correlations include La and Co, and La and Cu (Tables 5 and 6).  
The clustering map of bivariate local Moran's I divides the sampling points into five categories, i.e., insignificant, high-high (HH), low-low (LL), low-high (LH), and high-low (HL) clustered. However, their meanings are different from categories in a univariate clustering map. In the clustering map of bivariate local Moran's I, \(I_{ab}(i)\) indicates the spatial pattern of the related element, \(b\), around the main element, \(a\). From this, we plotted \(I_{\text{CuCu}}\), \(I_{\text{CoCu}}\), \(I_{\text{CuBi}}\), and \(I_{\text{AsSb}}\), as shown in Figure 10. In the \(I_{\text{CuCu}}\) and \(I_{\text{CoCu}}\), the sampling points with high-high (HH) and low-low (LL) clustering are consistent with the univariate \(I_{\text{Cu}}\) and \(I_{\text{Co}}\). Therefore, \(I_{\text{CuCu}}\) and \(I_{\text{CoCu}}\) show that the spatial distributions of Cu and Co in the study area are similar and positively cross-correlated. Due to the differences in the spatial distribution of Cu and Co, there are some differences in \(I_{\text{CuCu}}\) and \(I_{\text{CoCu}}\) after exchanging the main variable and related variable. The high-high (HH) clustering in \(I_{\text{CuBi}}\) and \(I_{\text{AsSb}}\) also has obvious regionality. Although these element pairs are globally positively cross-correlated, there are still some local negative cross-correlation (LH/HL) points beside mainly local positive cross-correlation points. The map for \(I_{\text{LaCu}}\) and \(I_{\text{LaCo}}\) is shown in Figure 11. There are apparent areas of low-high (LH) and high-low (HL) clustering in Figure 11, which indicate a negative cross correlation of the two elements. Although these two element pairs are globally negatively cross-correlated, there are still some local positive cross-correlation (HH/LL) points. Therefore, the bivariate local Moran's I not only effectively reveals whether two elements have a spatial cross correlation but also helps us to better understand the spatial distribution pattern of coexistence of elements.  
Figure 10: Clustering map of bivariate local Moran’s I with mainly positive cross correlation.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
###### Abstract  
Wireless communications are increasingly vulnerable to malicious jamming attacks due to the inherent openness and broadcast nature. In this regard, a novel anti-jamming scheme based on knowledge graph is proposed in this paper, where the knowledge graph which contains the prior knowledge of the commonly-used jamming patterns is utilized to make the fast anti-jamming decision with low complexity. Specifically, for the known jamming patterns, the anti-jamming strategy is directly extracted based on the search from the knowledge graph by using the sensing information. On the other hand, for the unknown jamming patterns, a time-frequency domain feature extraction method is first proposed to extract the jamming feature and match the jamming patterns in the knowledge graph. As such, multiple anti-jamming strategies can be obtained by the knowledge graph search. Finally, the optimal strategy can be achieved by the two communication decision criteria-based strategy fusion. The simulation results showed that the time-frequency domain feature extraction method can well extract the time-frequency domain feature of the jamming pattern, and the proposed algorithm has high decision accuracy and robustness.  
communication anti-jamming; knowledge graph; time-frequency domain feature extraction; anti-jamming decision-making 2022
## 4 Results
### Machine Learning Analysis
#### 4.2.1 Participant Groups Classification, PD/non-PD and PD/EL/S  
An initial and necessary classification experiment is that of distinguishing PD participants from non-PD. To do so, the EL and S groups of participants were clustered into one, namely non-PD, to test the ability of the machine learning model. The results show that the RF algorithm, when exploiting derived data from the WST Slow test, recognizes PD patients from non-PD with an accuracy reaching 88%, as shown in Table 8. Moreover, high accuracy rates are also found when exploiting the mTUG and WST High tests, with AB providing 85% and 83% accuracy, respectively. However, in the case of the WST Normal test the accuracy rates are lower, at 73%. These results, verify once more the need to employ different walking speeds when it comes to gait analysis for PD patients. Overall, the AB and RF algorithms appear to be the best-performing.  
Following the binary classification, we sought to perform a three-class classification, i.e., PD, EL, and S. As presented in Table 8, the classification accuracy reached 77%, when using the RF classifier in the WST-Slow. In the WST-Normal, High and the mTUG tests, the classification accuracy reached 64%, 70%, and 66%, respectively. The reduction in the classification accuracy in comparison to the binary classification is expected since recognizing PD from EL can be challenging due to possible pathology in gait caused by aging in the control group EL, and mild severity of disease in the PD group. Therefore, the results are encouraging, demonstrating the ability of the gait features to discriminate between the three classes PD, EL and S.
## 2 Literature Review
### AI-Based Educational Environments and the Evolving Role of Teachers
#### 2.1.1 The Impact of AI-Based Educational Environments on Teachers' Roles  
AI-based learning analytics, which analyze vast amounts of learning data in real time to provide personalized learning experiences, allow teachers to identify and tailor educational strategies to individual student patterns [40]. For instance, Singapore's implementation of the 'AI Learning Analytics' system enables customized education support by analyzing individual learning data [6]. Studies by Siemens and Baker [41] and [PERSON] [42] suggest that the use of AI learning analytics can enhance student achievement by 15-20% and increase learning efficiency by over 30% when teachers apply AI data critically in lesson planning. Consequently, teachers play a crucial role in interpreting AI analysis and adjusting educational strategies. The importance of integrating AI in teaching and assessment practices has been emphasized in the revised 2022 educational curriculum in South Korea [4]. AI-based assessment systems provide automated grading and adaptive feedback, reducing the workload for teachers even in large-scale assessments [43]. However, these systems face limitations in assessing creative problem-solving skills and may produce biased outcomes based on certain linguistic styles or grammatical structures [44; 45]. Efforts in China to apply and refine AI-based assessment systems in educational settings address these biases through research and policy development [7]. AI educational tools, such as AI chatbots and virtual tutors, offer instantaneous responses and support repetitive learning, but their effectiveness is not uniform across all learner groups, possibly leading to biased outcomes [46; 47]. Japan's 'GIGA School Program' supports customized learning through a one-device-per-student policy, guiding effective utilization of AI educational tools [5]. Teachers thus play a critical role in recognizing the limitations of AI tools and guiding students in their proper use. Ethical considerations in AI-based educational environments address significant issues such as algorithmic bias, data privacy, and transparency in AI decision-making [38]. These ethical concerns must be considered across all aspects of AI learning analytics, AI assessment systems, and AI educational tools. The EU's AI Act provides legal guidelines to address biases in AI educational tools, while initiatives in Saudi Arabia and the UAE focus on creating smart learning environments utilizing AI [9; 10]. The AI4K12 Framework includes AI ethics and fairness as core elements, underscoring the role of teachers in ensuring transparency in AI decision-making processes and educating students about the limitations and responsibilities associated with AI technologies.