esa-sceva/satcom-chunk-collection huggingface dataset:
# CRISPR-Cas Systems: Prospects for Use in Medicine
## 2 Structure, Classification, and Function of CRISPR-Cas Systems
### Function  
The general scheme of the CRISPR-Cas system function includes the following stages: the adaptation phase, during which exogenous nucleic acids enter the organism, are digested and integrated as spacers in the CRISPR cassette; expression phase, when the CRISPR cassette is transcribed to crRNA precursor (pre-crRNA), which is cleaved by RNAses to generate mature crRNA guides; interference phase: mature crRNA is used to target effector complexes that capture and destroy exogenous nucleic acids [18]. There are additional mechanisms, which prevent CRISPR-Cas systems from targeting the cell's own chromosomal DNA. The inviolability of a bacterium's own chromosome is mediated by specific sequences that are present in the bacterial genome with a certain frequency and prevent the use of any parts of the genome as spacers. It has been shown on _Staphylococcus epidermidis_ that mismatches between the target and crRNA at certain positions outside the spacer sequence cause digestion of foreign DNA, while the coincidence between crRNA and CRISPR-repeats on the bacterial chromosome prevents its cleavage. This kind of differential complementarity beyond the spacer sequence is characteristic to all the CRISPR-Cas systems [31].  
To acquire new spacers for type I, II, and V CRISPR-Cas systems, exogenous DNA must be marked with a special DNA motif (protospacer adjacent motif, PAM), numbering from 3 to 8 nucleotides. Only the region adjacent to the PAM can become a protospacer (a section of exogenous DNA that will be included in the CRISPR cassette in the form of a spacer). PAMs are also important in the interference phase. PAMs are required to prevent the system from targeting the cell's own DNA, which would lead to its destruction [32]. Due to the presence of multiple PAMs in the genome of an exogenous element, the encounter of the same agent by different cells may lead to the acquisition of different spacers. This makes certain evolutionary sense, since a mutation in the viral genome at the protospacer site can make such a spacer useless.
## 3 Research Method
### Exploratory (EFA) and Confirmatory Factor Analyses (CFA)  
The scale's GPP variables, consisting of 4 sub-dimensions and 22 items in total, were analyzed using the IBM software package SPSS Amos version 24. The results are presented in Table 2.  
According to the EFA and CFA results in Table 2, the model satisfied the requirement of CMIN/DF < 5 for the goodness of fit index [73]. In addition, the CFI, GFI, and NFI indices were at acceptable levels with values of 0.90 and above. A GFI index exceeding 0.90 is considered to indicate a good model [74]. The AGFI index also conformed to the expectation of a value above 0.80 and close to 1 [75]. The RMSEA and RMR indices, considered key values to identify a lack of fit, also conformed to the requirement of being less than 0.05 and 0.08, respectively, which indicate optimal or sufficient levels [69]. The requirement of \(p<0.01\) was satisfied and deemed compatible with the data structure of this proposed model; therefore, all of the sub-parameters of the measurement model were accepted.  
Similarly, the EFA and CFA analysis results for the dependent variables of the scale are presented in Table 3. Accordingly, the model's goodness of fit index satisfied the requirement of CMIN/DF < 5. The CA, CR, and AVE values show that the reliability, convergent, and discriminant validity requirements were all satisfied. They show that the theoretical structures exhibit satisfactory psychometric properties [34;76].  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Factors**} & \multicolumn{2}{c}{**EFA**} & \multicolumn{2}{c}{**CFA**} \\  & **Item** & **Std. Loads** & **AV** & \(\mathbf{\alpha}\) & **CR** & **AVE** \\ \hline Green Purchasing Practices (GPP) & & & 0.93 & 0.91 & 0.54 \\ \hline  & GSS1 & 0.69 & & & & \\  & GSS3 & 0.66 & & & & \\ Green Supplier & GSS4 & 0.76 & & & & \\ Selection (GSS) & GSS9 & 0.67 & 20.28\% & 0.88 & 0.88 & 0.55 \\  & GSS12 & 0.60 & & & & \\  & GSS13 & 0.61 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Factor Analysis for GPP.
# DDS: A Delay-Based Differentiated Service Virtual Network Embedding Algorithm
## 5 Performance Evaluation
### Evaluation Results
#### 5.2.3 Resource Utilization  
Figures 7 and 8 describe the node computing resource utilization and storage resource utilization, while Figure 9 describes the link bandwidth resource utilization. With the increase of the virtual network request arrival ratio, the node resource utilization and link resource utilization of all algorithms increase accordingly. Compared with other algorithms, our algorithm has the highest resource utilization. Our node resource utilization is approximately 80%, while other algorithms are less than 70%, indicating an optimization of 10%. The link resource utilization is approximately 30%. The reason for the low link resource utilization in all algorithms is that the existence of the link propagation delay constraint and path splitting is not supported. The high resource utilization is because our algorithm can receive more virtual network requests than other algorithms and can use the substrate network resources for mapping more efficiently.  
Figure 6: Revenue–cost ratio under various VNR arrival ratios.  
Figure 7: Average node computing resource utilization under various VNR arrival ratios.
# Online Facility Location in Evolving Metrics
## 1 Introduction
The objective function is the sum of the hourly opening costs for each open facility plus the connection costs of each client plus the switching costs (\(g\) per change of facility per client). We note that any solution pays switching cost \(gn\) at round \(t=1\), since it switches to an initial assignment of the clients to facilities. We measure the performance of our online algorithm using the notion of the competitive ratio. Given a request sequence \(\sigma\), let \(\text{ALG}(\sigma)\) denote the cost paid by an online algorithm on \(\sigma\) and let \(\text{OPT}(\sigma)\) denote the cost paid on \(\sigma\) by an optimal offline algorithm, which knows \(\sigma\) in advance. The online algorithm is \(c\)-competitive if there exists a constant \(a\) such that:  
\[\text{ALG}(\sigma)\leq c\cdot\text{OPT}(\sigma)+a\]  
for all request sequences \(\sigma\). The factor \(c\) is called the competitive ratio. **Related work. ** The offline and online variants of Facility Location have been studied extensively in the literature. For the offline Facility Location problem, the approximability is \(\Theta(\log n)\)[4] for the non-metric case while, for the metric case, the best lower bound is 1.463 [5], and the best algorithm has an approximation ratio 1.488 [6]. Online Facility Location is known to have a competitive ratio of \(\Theta(\log n/\log\log n)\) in the adversarial case for both deterministic and randomized algorithms [7] and a constant competitive ratio if the clients are drawn from a known distribution [8]. The study of Dynamic Facility Location so far concerns the offline case where the changes between distances of clients and facilities are known in advance. [PERSON] et al. [3] showed an upper bound of \(O(\log nT)\) for the most interesting variant of Dynamic Facility Location with hourly facility costs, where facilities can be closed and are paid for all rounds in which they remain open. This result was later improved by [9], which gave an \(O(1)\)-approximation algorithm. In [10], they provided a framework for designing competitive online algorithms using regularization, which is a widely used technique in online learning. They designed a \(O(\log m)\)-competitive deterministic algorithm for generating a fractional solution that satisfies a time-varying set of constraints, where \(m\) is the number of variables. Then, they provided an \(O(\log m\log n)\)-competitive randomized algorithm for the online set cover problem with a service cost, where \(m\) is the number of sets and \(n\) is the number of elements. The first step of our online algorithm, which provides a \(O(\log m)\)-competitive fractional solution, where \(m\) is the number of facilities, is inspired by the approach of [10] and a large part of the analysis follows their proof. In the second step of our online algorithm, we show a rounding scheme that works favorably with the fractional solution to obtain a non-trivial additive competitive ratio of \(O(\log m+\log n)\), where \(n\) is the number clients. To the best of knowledge, this is the first upper bound for ODFL.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 5 Evaluation
### Simulation Results
#### 5.5.3 Effect of Mobility Speed  
This evaluation is performed to observe how well IACS reacted under different maximum velocity compared with MSL* and LCC. To simulate a highly dynamic topology network, the modified random  
Figure 7: **(a)** Total Average Estimation Error, **(b)** Total Average Closeness, **(c)** Total Average Computation Cost, **(d)** Total Average Communication Cost, and **(e)** Total Average Energy Consumption for Different Max Samples Scenario.  
waypoint mobility model [21] and zero pause time are implemented into each of the nodes. The nodes in the network are assumed to move during each time slot at velocity, \(v=1/maxv\), where \(maxv\) is the maximum velocity of the sensor nodes. \(maxv\) values tested are from 5-200 while the default speed used in MSL* and LCC is 10 (\(\times\)marked). According to the results, sensor node velocity is directly proportional to estimation error and computation cost, while it is inversely proportional to communication cost and overall energy consumption. As the velocity of the sensor node increases, the network topology changing rate will be faster. Therefore, each node will lose connection with its neighbor in a short time. Figure 8a,b shows that the error estimation and closeness value are not much different for the three methods.  
Figure 8: (**a**) Total Average Estimation Error, (**b**) Total Average Closeness, (**c**) Total Average Computation Cost, (**d**) Total Average Communication Cost, and (**e**) Total Average Energy Consumption for Different Maximum Velocity Scenario.  
From the simulation results shown in Figure 8c-e, IACS outperformed MSL* and LCC in terms of communication, computation, and energy costs while maintaining the similar error estimation and closeness value of normal nodes.
# On the Statistical Characterization of Lightning-Induced Voltages
## 2 Lightning-Induced Voltage: AESs
### Finite Conductivity Ground Effects and Countermeasures to Mitigate the Induced Overvoltages  
[PERSON] [19] presented an empirical formula for the calculation of the peak values of the induced voltages at \(x=0\), which was derived from experimental data, theoretical considerations, and values obtained from computational simulations. [PERSON]'s formula can be an extension of Equations (1) and (2), but in this case the actual height of the line is replaced by an \"effective\" value that accounts for the ground conductivity. This effective height is given by  
\[h_{eff}=h+0.15\,\sqrt{\rho}\,\,\,, \tag{3}\]  
where \(\rho\) is the ground resistivity.  
In Figure 4, as an example, the effects of the ground conductivity on the induced voltage are shown. The waveforms have been obtained by using (3) in (1) and (2).  
As for the main countermeasures to mitigate the induced overvoltage, they are as follows: (i) improvement of the critical flashover voltage (CFO) of the line; (ii) use of surge arresters; and (iii) use of ground wire(s). We will focus our attention on the last one. The presence of ground wire(s) allows us to reduce the induced voltage on the phase conductor(s) [11; 16; 20]. For the case of a line equipped with a ground wire, which is earthed at only one point, the general formulation for the induced voltage evaluated at the grounding connection is [17; 18]  
\[v^{\prime}_{a}=v_{a}-\frac{Z_{bb}}{Z_{bb}+2R_{b}}\cdot v_{b}, \tag{4}\]  
where \(b\) denotes the ground wire, \(v^{\prime}_{a}\) the voltage induced on the considered conductor, \(v_{a}\) the voltage that would be induced in the same conductor by removing the ground wire(s), \(v_{b}\) the voltage induced in the ground wire, \(R_{b}\) is the grounding resistance, \(Z_{ba}\) is the mutual surge impedance of ground wire \(b\) and the phase conductor \(a\), and \(Z_{bb}\) is the self-surge impedance of the overhead ground wire \(b\) (see [11] for more details).  
The extension to the case of multiple earthing (e.g., earthing at every pole) can be obtained by selecting a lower effective earth resistance and verifying that the front time of the induced voltages is much longer than the distances between the poles [11].  
Figure 4: Induced voltage evaluated at \(x=0\) in the presence of a lossy ground (\(h=10\) m, \(d=50\) m, \(I=10\) kA, \(\beta=0.4\)) due to: step-function current (**a**); linearly-rising current (**b**), with \(t_{f}=0.5\)\(\mu\)s.  
In Figure 5, the effect of a 16 mm\({}^{2}\) ground wire placed 1 m above the line conductor (i.e., at a height of 11 m) and earthed only at \(x=0\) is shown for different values of the grounding resistance.
## 5 Discussion
### Network Reliability  
Network reliability is a major concern in industrial applications, even more than latency and energy consumption, because wireless communications are replacing wired buses that instrument critical systems. In that sense, it has been demonstrated that TSCH networks can provide 99.999% reliability [17], but this imposes certain constraints in the network design. Network density is very important in multi-hop networks, as network bottlenecks become single points of failure that channel hopping cannot combat. Thus, to achieve high reliability, TSCH networks require some redundant paths to relay data. Despite this consideration, TSCH has been designed to be very robust against multi-path effects and external interferences thanks to the use of slow channel hopping, short packets, scheduled communications and network synchronization, which ensures a very low collision probability even between hidden terminals.  
The approach to provide network reliability in D7 AM is radically different from TSCH. Instead of relaying on multi-hop communications, which requires to have a dense network, D7 AM bases its operation on single-hop communications. To ensure that single-hop communications are reliable, the standard uses a robust modulation scheme combined with channel coding, which allows to achieve high packet delivery ratios, even when nodes are at the sensitivity edge. In addition, D7 AM can use acknowledgments and retransmission to ensure the delivery of certain packets. Finally, D7 AM can also provide reliability against internal and external interferences through slow channel hopping; each communication instance can take place at a different channel. However, slow channel hopping does not provide any advantage regarding multi-path propagation, because the channels used in D7 AM are narrowband [23].
## 2 Materials and Methods
### Collecting Data in Parallel  
To predict KAM, accurate and reliable baseline data are required. Baseline KAM data are data that accurately reflect the loads on the knee joint and their distribution, which is essential to ensure the accuracy and reliability of model training. Without accurate ground truth, the model may learn incorrect patterns, which can lead to inaccurate predictions. Reliable ground truth data are essential for machine learning models to accurately  
Figure 1: Overview of Multi-model Gait-based KAM Prediction System.  
reflect real-world situations. To obtain this data, baseline KAM data are collected through systematic measurements using motion and pressure sensors.  
Figure 2 shows a lab environment for collecting baseline KAM data. The lab is equipped with a camera (A) to track motion and a force plate (B) to measure Ground Reaction Forces (GRFs). The study involves participants wearing sensors to collect data. The participants attache 29 spherical markers to fixed locations on the body and perform a linear reciprocating motion at regular intervals in the lab. The pressure data after the participant steps on the force plate and the data obtained from the motion camera are synthesized to measure the reference standard value of the KAM acting on the knee joint. The data obtained through this process serve as an accurate and reliable reference for the machine learning model to predict the knee load moment.  
At the same time as measuring KAM, the participant wears the IMU sensors at the prescribed attachment points to collect data from the IMU sensors. A total of nine IMU sensors are used, all from Delsys' Trigno Avanti Sensor. Four of the sensors include acceleration and angular velocity for the X, Y, and Z axes, and the remaining five sensors include only acceleration for the X, Y, and Z axes.  
Figure 3 shows the IMU sensor attachment locations for the participant. Sensors 1, 2, 3, and 4 are 6-axis sensors, attached to the knee, collecting data from the knee. Sensors numbered 5, 6, 7, 8, and 9 are 3-axis sensors and collect ankle and thigh data.  
As shown in Figure 4, the experimenter steps on the two pressure plates with the right foot and then the left foot to collect sensor data, and then turn around and does the same thing to generate and collect KAM data.  
Figure 3: Detailed sensor attachment locations for the participants.  
Figure 2: Laboratory environment with motion camera (**A**) and force plate (**B**) installed.
## 3 Results
### FTIR Spectroscopy  
The spectra of BC-HiFlow (Figure 2A,B) and BC-EndoSequence (Figure 2C,D) revealed similar composition of both BC-sealers with variable band intensity. They detected bands of calcium hydroxide \(\approx\) 3640 cm\({}^{-1}\)[11; 23; 24; 25; 26], OH of absorbent water associated with hydration reaction of sealer \(\approx\) 3400 cm\({}^{-1}\)[24; 25; 27], C-H and OH- of organic filler (polyethylene glycol) at 2980-2800 and \(\approx\) 1635 cm\({}^{-1}\)[23; 25; 27], carbonate (CO\({}_{3}\)\({}^{2-}\)) at 1420 and 874 cm\({}^{-1}\)[23; 24; 28], phosphate bands of \(\
u_{3}\)PO\({}_{4}\)\({}^{3-}\)\(\approx\) 1066 [27; 29; 30] and at 974 cm\({}^{-1}\)[28], \(\
u_{4}\)PO\({}_{4}\)\({}^{3-}\) at 665 cm\({}^{-1}\)[27; 29; 30], SiO\({}_{4}\)\({}^{4-}\) of calcium silicate incorporated with \(\
u_{3}\)PO\({}_{4}\)\({}^{3-}\), calcite at 974 and 874 cm\({}^{-1}\), respectively [28; 31], \(\
u_{4}\)SiO\({}_{4}\)\(\approx\) 750 cm\({}^{-1}\), SiO\({}_{4}\)\({}^{4-}\) bending mode of tricalcium silicate (C\({}_{3}\)S) \(\approx\) 570 cm\({}^{-1}\) and dicalcium silicate (C\({}_{2}\)S) at 502 and 494 cm\({}^{-1}\)[25], Si-O- stretching mode of calcium silicate hydrate (CSH) at 450 cm\({}^{-1}\)[25; 28; 32].  
The spectra of Adseal showed different composition. It detected intense bands of C=O of organic filler \(\approx\) 1635 cm\({}^{-1}\)[23; 25] carbonate band (CO\({}_{3}\)\({}^{2-}\)) at 1458 cm\({}^{-1}\), silicate group (Si-O) at 1113 cm\({}^{-1}\)[25], phosphate bands (\(\
u_{3}\) and \(\
u_{4}\)PO) at 1035 and 680-560 cm\({}^{-1}\)[30], respectively, and polymerized silicon (SiO) at 420 cm\({}^{-1}\)[25], (Figure 2E,F).
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 3 Observation of Distribution for Per-User Throughput  
The following per-user throughput distribution in the user-centric cell-free massive MIMO system is observed. Figure 2 shows the distribution of per-user throughput for four random users in a situation where 100 APs and 40 users exist. The most gains from each AP to specific user \(k\) are concentrated at a low value, and it can be seen that only a few APs occupy most of the total sum rate for users. That is, it can be seen from Figure 2 that each user can obtain very different gains from all connected APs. It is also found that the average of users' throughput per user is significantly greater than the median, and as a result, a significant difference between these averages and the median more clearly shows the nonuniformity of the distribution. In Figure 3, the result of disconnecting the connection between the user and the AP based on the observation in Figure 2 is shown as a bar graph. It can be seen that the per-user throughput hardly changes even if only half of the number of APs serve a specific user \(k\). Therefore, through the above observation and per-user gain, this paper proposes a scheme for serving unserved users in Section 4.  
The proposed scheme is performed based on the above results, unlike the previous scheme, which is called MS assignment, in [20]. In the proposed scheme, since the number of users served by each AP is limited to a reasonable number (the number of installed antennas), for the feasible operation of the system, it is applicable even if the number of users in the network increases, and the amount of calculation does not increase in proportion to the number of users. In the following Section 4, an analysis of the previous scheme is conducted, and the new scheme is proposed.
## 3 Experiments and Results
### Selection of Disaster-Causing Factors  
This study meticulously examined 17 landslide-contributing factors across five key perspectives: regional geology, topography and geomorphology, hydrogeology, climatic environment, and human-induced activities. However, the inclusion of a multitude of causative factors can lead to data redundancy, which in turn diminishes the efficiency of model training and fitting. In some instances, this overabundance may even compromise the model's predictive capability. When numerous factors are considered, there is a tendency for highly correlated variables to be repeatedly selected, which can skew the susceptibility outcomes. Moreover, the selection of causative factors is often based on the empirical knowledge accumulated from past studies, which may not always be applicable to the unique geological and environmental conditions of Moxi Town. To enhance the reliability of the susceptibility analysis, it is crucial to identify and mitigate the impact of high correlation values among the selected factors. To this end, an analysis was conducted to determine the intercorrelations among the 17 causative factors using [PERSON]'s rank correlation method.  
Following the criteria established by [PERSON] et al., a correlation is considered significant if the absolute value of the correlation coefficient exceeds 0.8. After pinpointing the variables with correlation coefficients exceeding an absolute value threshold of 0.8, and taking into account the intrinsic importance of these factors, eight elements--flow, annual rainfall, land use category, NDVI, flow direction, relative elevation, surface roughness, and mean curvature--were eliminated from consideration. Nine influential factors were consequently preserved. The correlation coefficients for these factors are presented in Table 2 By synthesizing findings from prior studies and considering the geological context of the research area, this study has chosen nine evaluative parameters to input into the coupled model. These parameters are as follows: slope direction, slope gradient, proximity to road influences, distance to fault influences, water system-impact distance, planar curvature, profile curvature, elevation, and degree of terrain undulation.