esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Components of CRISPR/Cas Systems
### CRISPR Arrays: Repeats, Spacers and Leader Sequence  
In the CRISPR array, repeats alternate with spacer sequences [11, 30, 31, 32]. Within arrays, the repeats are typically identical in terms of length and sequence, but small differences do occur [16, 29]. In particular, the repeat at the end of an array is often truncated or deviates more from the consensus sequence [17, 33]. Repeats are usually between 23 and 47 bp in length and those of the same subtype share a consensus sequence in type I-C, I-E, I-F, and II [10, 34, 35]. Many repeat sequences show palindromes or short inverted repeats [16] and a predicted stable secondary structure in the form of a stem-loop [34]. However, there are repeats, which are not palindromic and are predicted to be unstructured. This difference in the ability to form secondary structures also has implications for the mechanism of pre-crRNA processing, which will be discussed later [10, 34].  
In contrast, the spacer sequences are mostly unique within a genome. Many spacers have been matched to sequences originating from extra-chromosomal sources such as phage or plasmids and other transferable elements [21, 22, 23, 36]. Indeed, it is the spacers that confer the sequence-specific immunity against those extra-chromosomal agents [26, 33, 37]. The sequences in the foreign genome from which spacers are derived are termed protospacers. It is likely that the majority of spacers are phage- or plasmid-derived, but the limited depth of sequence data on these abundant and diverse elements leads to an underestimation. Interestingly, some CRISPR/Cas systems also possess spacers that match sequences elsewhere within their own genome. Their function, if any, is not resolved, but it has been proposed that they have been accidentally incorporated [38]. Spacer length can vary slightly throughout an array (typically by 1-2 nt) and spacer lengths up to 72 bp have been reported, but usually the size is similar to that of the repeats in the same array [17].  
The third component of the CRISPR array is the leader sequence, which is located upstream of the first repeat [15]. This AT-rich sequence is about 200-500 bp long and includes the promoter necessary for transcription of the array (see below) [39, 40, 41, 42]. The leader region is also important for the acquisition of new spacers [43].  
A single genome can harbor more than one CRISPR array. Those can vary considerably in size with the largest identified to date, in _Haliangium ochraceum_ DSM 14365, containing 587 repeats [35]. The length of CRISPR arrays appears to correlate with the degree of activity in spacer acquisition, with longer arrays being more active than short arrays and those with degenerate repeat sequences [33].
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 5 Factor Analysis to Characterize Expense, Call, and SMS Attributes
### SMS of Telecom Customers Factor Analysis  
#### 5.3.1 Selection of Variables  
Factor analyses are performed to explore the factors that influence telecom customer experiences using certain variables, including customer SMS and MMS data [39]. Customer SMS data, for example relating to the SMS quantity in the telecom package, are suitable for use in factor analyses, which could help telecom companies to identify the factors that impact the customer satisfaction and loyalty [40]. The telecom sector has achieved impressive development in Bangladesh. Customer SMS data has been used in factor analyses, helping to understand the relationship between SMS data and customer loss [20]. Thus, the telecom customers' SMS and MMS data in the data source were selected to conduct the following factor analysis. All SMS-related factors, including (1) China Unicom's SMS quantity, (2) China Mobile's SMS quantity, (3) China Telecom's SMS quantity, (4) China Unicom's MMS quantity, and (5) CRBT, were used to conduct the factor analysis and analyze the characteristics of the cost factors. Later, KMO and Bartlett tests were applied to identify whether these factors could be used to conduct the factor analysis.
## 3 Results and Discussion
### Experiments on the Common Benchmark
#### 3.2.1 Evaluation on the OTB100 Dataset  
OTB100 dataset is a classical tracking benchmark with 100 sequences covering a diverse range of challenges such as scale variation, background clutter, and fast motion. We display the results on OTB100. Here we compare our tracker with 11 recent state-of-the-art methods: DaSiamRPN [31], TADT [32], GradNet [33], DeepSRDCF [34], CFNet [35], SiamFC [36], SESiamFC [37], SiamDWfc [38], SiamRPN [39], and SiamMask [40], as shown in Table 1. In order to show the curves in the graphs more clearly, we selected some representative algorithms to be plotted, as shown in Figure 4. Our model reaches 88.1% in the AUC score and 67% in the success score. When our equalized memory matching network is equipped to the baseline segmentation and tracker SiamMask, the improvement is 3.9 and 2.1 points gains.  
Figure 5 displays the performance of success rates and precision rates for our method and other advanced methods in challenging scenarios such as background clutters, fast motion, illumination variation, scale variation, etc. Our tracker outperforms most in terms of performance. Notably, our method performs best in the face of background clutter  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**OTB100**[28]} & \multicolumn{2}{c}{**TC128**[29]} & \multicolumn{2}{c}{**UAV123**[30]} \\ \cline{2-7}  & **SR** & **P** & **SR** & **P** & **SR** & **P** \\ \hline DaSiamRPN [31] & 0.658 & 0.880 & - & - & - & - \\ TADT [32] & 0.656 & 0.854 & - & - & - & - \\ GradNet [33] & 0.639 & 0.861 & - & - & - & - \\ DeepSRDCF [34] & 0.636 & 0.851 & - & - & - & - \\ CFNet [35] & 0.587 & 0.778 & - & - & - & - \\ SiamFC [36] & 0.587 & 0.772 & 0.489 & 0.672 & - & - \\ SESiamFC [37] & 0.650 & 0.864 & - & - & - & - \\ SiamDW [38] & - & - & - & - & 0.536 & 0.776 \\ SiamDWfc [38] & 0.627 & 0.828 & - & - & - & - \\ SiamRPN [39] & 0.629 & 0.847 & - & - & 0.581 & 0.772 \\ SiamMask [40] & 0.649 & 0.842 & 0.540 & 0.725 & 0.602 & 0.794 \\ SiamFC++ [41] & - & - & 0.566 & 0.763 & - & - \\ SiamRPN++ [42] & - & - & 0.577 & 0.775 & 0.611 & 0.804 \\ SiamGAT [43] & - & - & 0.559 & 0.753 & - & - \\ SiamCAR [44] & - & - & - & - & 0.615 & 0.804 \\ SiamBAN [45] & - & - & - & - & 0.604 & 0.795 \\ Ocean [46] & - & - & 0.557 & 0.752 & 0.621 & 0.823 \\ HCFT [47] & - & - & 0.495 & 0.692 & - & - \\ RMIT [48] & - & - & 0.551 & 0.761 & - & - \\ ADMT [49] & - & - & - & - & 0.535 & 0.754 \\ Ours & **0.670** & **0.881** & **0.580** & **0.779** & **0.625** & **0.827** \\ \hline \hline \end{tabular}
\end{table}
Table 1: State-of-the-art comparison on OTB100, TC128, and UAV123. Best in **bold**, second best underlined.  
and illumination variation. In our algorithm, we propose a pixel-level equalized memory matching network to constrain the background clutter and enhance the target. Furthermore, it is obvious that our method outperforms other approaches in the case of fast motion and scale variations. The reason is that we combine the historical mask information with the current frame features.
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
## 2 Related Works
Furthermore, the stochastic parameters of the degradation model may not follow the general assumption of the normal distribution in practice. DL is the strong candidate technique in constructing the relationship between raw observed data and high-level degradation process through multiple layers. Many researchers have applied DL techniques to the fault detection and diagnosis [35; 36; 37], while few DL-based studies are only conducted on the RUL prediction. [PERSON] et al. [38] proposed the RUL prediction approach by combining a convolutional neural network (CNN) and an outlier region correction. The proposed CNN consists of multiple convolution and pooling layers to extract features and a logistic regression to convert the output features into a health indicator. Furthermore, an outlier correction technique detects and removes outliers based on a confidence interval of the constructed health indicator. [PERSON] et al. [39] used time and frequency domain features as the input and then built the multi-bearing RUL prediction model using the deep neural network. This collaborative prediction relies on the RUL model of the same type of bearings under the identically controlled operating condition. While these works [38; 39] mainly evaluate the performance benefits in terms of the health indicator estimation, predicting the health indicator is not enough to estimate the RUL value. [PERSON] et al. [40] combined CNN to extract deep features from raw sequential data and a bi-directional long short-term memory network (LSTM) to encode the temporal information for the RUL prediction of a high-speed machine. The bi-directional LSTM captures long-term dependencies of time series data in forward and backward ways. The fully-connected layer and the regression layer are then added to predict the target RUL value. The data collection of whole-life bearings is expensive since the degradation process generally takes several months or years. Furthermore, the degradation data of bearings are not even available or cover only partial characteristics of data distribution in practice. Since many external disturbances considerably affect the degradation behaviors of bearings, the degradation processes of the same bearings have significantly different signal distributions, even under identically controlled operating conditions. The traditional Bayesian approaches may fail to provide accurate RUL estimation since it is not feasible to derive a universal analytical degradation model in practice. Furthermore, most existingDL works will face substantial performance losses if the data distributions of training and test sets are not equal [41]. Prior knowledge can help avoid such problems by combining analytical model-based and data-driven approaches. In this paper, we formulate the RUL estimation problem as the ridge regression to balance the training accuracy and generalization over the sliding window. The sliding window-based model optimization essentially reduces the performance sensitivity of the RUL estimation with the analytical degradation model.
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
###### Abstract  
Although deep learning (DL) methods are effective for detecting protocol attacks involving drones in sixth-generation (6G) nonterrestrial networks (NTNs), classifying novel attacks and identifying anomalous sequences remain challenging. The internal capture processes and matching results of DL models are useful for addressing these issues. The key challenges involve obtaining this internal information from DL-based anomaly detection methods, using this internal information to establish new classifications for uncovered protocol attacks and tracing the input back to the anomalous protocol sequences. Therefore, in this paper, we propose an interpretable anomaly classification and identification method for 6G NTN protocols. We design an interpretable anomaly detection framework for 6G NTN protocols. In particular, we introduce explainable artificial intelligence (XAI) techniques to obtain internal information, including the matching results and capture process, and design a collaborative approach involving different detection methods to utilize this internal information. We also design a self-evolving classification method for the proposed interpretable framework to classify uncovered protocol attacks. The rule and baseline detection approaches are made transparent and work synergistically to extract and learn from the fingerprint features of the uncovered protocol attacks. Furthermore, we propose an online method to identify anomalous protocol sequences; this intrinsic interpretable identification approach is based on a two-layer deep neural network (DNN) model. The simulation results show that the proposed classification and identification methods can be effectively used to classify uncovered protocol attacks and identify anomalous protocol sequences, with the precision increasing by a maximum of 32.8% and at least 26%, respectively, compared with that of existing methods.  
drones; 6G NTN; self-evolving protocol anomaly classification; online anomalous protocol identification; XAI +
Footnote †: journal:  
Article
## 3 Boundary Conditions and Modal Coefficients
\begin{array}{l}0=k\sum\limits_{n=-\infty}^{\infty}\Big{[}i^{n}\mathrm{e}^{- \mathrm{i}n\theta}J_{n-1}(k|h(\zeta)|)+A_{n}H_{n-1}^{(1)}(k|h(\zeta)|)\Big{]} \Big{\{}\frac{h(\zeta)}{|h(\zeta)|}\Big{\}}^{n-1}\hbar^{\prime}(\zeta)\mathrm{e }^{\mathrm{i}\theta}\\ -k\sum\limits_{n=-\infty}^{\infty}\Big{[}i^{n}\mathrm{e}^{-\mathrm{i}n\theta} J_{n+1}(k|h(\zeta)|)+A_{n}H_{n+1}^{(1)}(k|h(\zeta)|)\Big{]}\Big{\{}\frac{h(\zeta)}{|h( \zeta)|}\Big{\}}^{n+1}\overline{h^{\prime}(\zeta)}\mathrm{e}^{-\mathrm{i}\theta} \\ +\frac{2\lambda}{1+\lambda}\sum\limits_{n=1}^{\infty}n\big{(}B_{n}\mathrm{e}^{ -\mathrm{i}n\theta}+C_{n}\mathrm{e}^{\mathrm{i}n\theta}\big{)},\end{array}  \tag{24}\]  
where \(\lambda=h_{15}^{2}/c_{44}\mu_{11}\) is a dimensionless parameter that characterizes the fundamental properties of piezomagnetic materials. By multiplying both sides of Equations (21) and (24) by \(\mathrm{e}^{-\mathrm{i}n\theta}\) (\(\mathrm{m}\in\mathbf{Z}\)) and integrating over interval (0, 2\(\pi\)), we can obtain an infinite system of algebraic equations in the following form  
\[\sum\limits_{n=-\infty}^{\infty}F_{in}A_{n}+\sum\limits_{n=1}^{\infty}I_{in}B _{n}+\sum\limits_{n=1}^{\infty}P_{in}C_{n}+Q_{i}D_{0}=\varepsilon_{i}, \tag{25}\]  
where the elements \(F_{in}\), \(I_{in}\), \(P_{in}\), \(Q_{i}\), and \(\varepsilon_{i}\) in the equation are listed in Appendix A. As cylindrical functions exhibit convergence, we can set \(n\) as the maximum value of \(N\) in Equation (25), i.e., \(n=N\). This results in \(2N+1\) undetermined coefficients for \(A_{n}\), \(N\) undetermined coefficients for \(B_{n}\) and \(C_{n}\), and one undetermined coefficient for \(D_{0}\), making a total of \(4N+2\) undetermined coefficients. With two infinite algebraic equations, we can construct \(4N+2\) equations, providing sufficient conditions to solve these undetermined coefficients. To solve the equation \(\mathbf{\Omega}\mathbf{X}=\mathbf{\Psi}\), where \(\mathbf{X}\) is a \((4N+2)\times 1\) order matrix of undetermined coefficients, we employ linear algebra theory. \(\mathbf{\Omega}\) is a square matrix of order \((4N+2)\times(4N+4)\), constructed by \(F_{1n}\), \(F_{2n}\), \(I_{1n}\), \(I_{2n}\), \(P_{1n}\), \(P_{2n}\), \(Q_{1}\), and \(Q_{2}\), serving as the coefficient matrix. Meanwhile, \(\mathbf{\Psi}\) is a non-homogeneous coefficient matrix, which is a \((4N+2)\times 1\) matrix constructed by \(\varepsilon_{1}\) and \(\varepsilon_{2}\). We then use \(\mathbf{X}=\mathbf{\Omega}^{-1}\mathbf{\Psi}\) to obtain the solution for the undetermined coefficients.
## 2 Materials and Methods
### Methods
#### 2.3.3 Modifications to PRYM-Maize  
In PRYM-Maize, we simulated the growth of maize grain on a daily step as a function of development stage (DVS) and daily net primary productivity (\(\text{NPP}_{\text{daily}}\)), as presented in Supplementary Text S1.3. The DVS index is required to compute the proportions of photosynthesis-produced dry matters that were allocated to different organs, namely, root, stem, leaf, and grain. In the previous version of PRYM-Maize, the DVS is calculated as a function of GDDs, as also presented in other CGMs [41; 42; 43]. However, GDDs are vulnerable to uncertainties in input air temperature data. Alternatively, GPP was potentially useful to indicate the crop DVS because it is more directly related to crop growth than are GDDs [24]. In addition, RS information represented by GPP may partly eliminate the error in input air temperature data. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [24] proposed using normalized accumulated GPP (\(\text{GPP}_{\text{nor},\text{acc}}\)), estimated using an RS-based model, to indicate the DVS of crop and found that the two factors were tightly correlated. This method was successfully used in simulating rice grain-filling across three flux sites over the Korean Peninsula [24]. In this study, we do not use GDDs but adopt \(\text{GPP}_{\text{nor},\text{acc}}\) to compute the DVS. \(\text{GPP}_{\text{nor},\text{acc}}\) was designed to vary from 0 to 1 in Huang, Ryu, Jiang, Kimm, Kim, Kang and Shim [24] as crops grow accompanied by accumulating GPP, and \(\text{GPP}_{\text{nor}}=0\) and 1 correspond to emerging and maturing stages of crops. Such that  
\[\text{GPP}_{\text{nor},\text{acc}}^{(t_{0})}=\frac{\sum\limits_{t=\text{EM} }^{t_{0}}\text{GPP}^{(t)}}{\text{GPP}_{\text{acc}}} \tag{6}\]  
\[\text{GPP}_{\text{acc}}=\sum\limits_{t=\text{EM}}^{\text{HV}}\text{GPP}^{(t)} \tag{7}\]  
where \(\text{GPP}_{\text{acc}}\) denotes the accumulated GPP along the entire growing season; \(\text{GPP}_{\text{nor},\text{acc}}^{(t_{0})}\) denotes normalized accumulated GPP for day \(t_{0}\) and is equivalent to DVS; and EM and HV denote the emerging and harvest date, respectively. Pixel-level EM and HV were retrieved from the RS NDVI time series (Section 2.3.5).  
DVS varies from 0 to 2, indicating the development of crops from emerging to maturing stages; therefore, we estimated DVS by scaling \(\text{GPP}_{\text{nor},\text{acc}}\) using 2, as follows:  
\[\text{DVS}^{(t_{0})}=2\times\text{GPP}_{\text{nor},\text{acc}}^{(t_{0})} \tag{8}\]
## 2 Document Analysis and Translation to Structured Format
### Performance Evaluation of Document Translation Module  
We used precision and recall, which are widely used in the information retrieval field for a performance evaluation of the component extraction algorithm of a document. Precision and recall use the number of true positive (_TP_), true negative (_TN_), false positive (_FP_), and false negative (_FN_) values; _TP_ means that the extracted title sentence is correct, _TN_ means that the extracted content sentence is correctly recognized, and _FP_ means that the algorithm misrecognizes a sentence as a title. _FN_ means that the title sentence is not recognized as the title sentence. The equations used to measure the precision and recall are as follows.  
\[Precision=\frac{\textit{TP}}{\textit{TP}+\textit{FP}} \tag{5}\]  
\[Recall=\frac{\textit{TP}}{\textit{TP}+\textit{FN}} \tag{6}\]  
The hierarchies recognized by the proposed algorithm in this study are relative classification among heading symbols; the recognized results of the hierarchical classification of the preceding items affect the following items. Therefore, the module performance of this part was checked following these equations:  
\[GAH=\frac{C^{G}}{\textit{TP}} \tag{7}\]  
\[PAH=\frac{C^{P}}{\textit{TP}} \tag{8}\]  
where \(GAH\) means 'generalized accuracy for hierarchy labeler' and \(PAH\) means 'precise accuracy for hierarchy labeler'. \(C^{G}\) is the number of results from which relative hierarchical classification was performed correctly among TPs, and \(C^{P}\) is the number of results from which absolute hierarchical classification was performed correctly among TPs. Therefore, \(GAH\) and \(PAH\) represent ratio values corresponding to \(C^{G}\) and \(C^{P}\), respectively.  
The proposed algorithm performance, including the content extracting and hierarchical classification processes, was evaluated as the following equations.  
\[GAA=\frac{C^{G}}{\textit{TP}+\textit{FN}} \tag{9}\]  
\[PAA=\frac{C^{P}}{\textit{TP}+\textit{FN}} \tag{10}\]  
where \(GAA\) means 'generalized accuracy for application module' and \(PAA\) means 'precise accuracy for application module'.  
Figure 5 shows the results of applying Equations (5)-(10) to 20 bridge engineering documents with an average number of sentences of 4814 and the number of title sentences to be extracted as 433. The lowest and mean values of PAA are 97.36% and 99.47%, respectively, so it can be judged that the performance as a content item extraction algorithm for BIM-based integration is sufficient.
# Research on Dynamic Searchable Encryption Method Based on Bloom Filter
###### Abstract  
Data outsourcing has become more and more popular due to its low cost and flexibility. However, there is a problem that the cloud server used to store data is partially trusted. Searchable encryption is an efficient technology that is devoted to helping people conduct accurate searches without leaking information. Nonetheless, most existing schemes cannot support dynamic updates or meet the privacy requirements of all users. There have been some experiments to solve these issues by implementing a dynamically searchable asymmetric encryption scheme. This paper proposes an efficient searchable encryption scheme based on the Authenticator Bloom Filter (ABF). The solution can support dynamic updates and multiple users and meet forward and backward security. This paper uses an ABF to improve the efficiency of searches and updates while playing a significant role in dynamic updates. This paper designs a new token encryption scheme and file set encryption scheme, which not only helps users reduce time in searches and updates but also supports multi-user modes. Experiments show that the proposed scheme takes less time in searching and updating algorithms, especially when the keyword does not exist. The solution also takes into account the problem of history storage when updating, which reduces the unnecessary consumption of memory and avoids multiple storage states for the same file.  
forwardprivacy; backward privacy; asymmetric searchable encryption; multi-user; Bloom filter +
Footnote †: journal: Applied
## 1 Introduction and State of Art
In that research, the authors evaluate different LEM strategies based on the community storage and agent's revenues, cost, and local electricity consumption. The evaluation results suggest different opportunities to increase the energy transactions in the system. On the other hand, parallel structures are useful when problems depicts high complexity, which leaves the exhaustive enumeration as the only way to find the global optimum [11]. However, this search was previously dependent on human calculations, which led it to becoming error-prone, exhausting, and without coherent results that allowed concise conclusions. Nowadays, with the development of computers and more suitable structures for the parallelization of activities, brute force algorithms have become feasible possibilities to support the exhaustive search and the solutions of these problems. This computational development was initially shown with the use of Message Passing Interfaces--MPI architectures. Currently, cloud or fog architectures, along with FPGA and GPU architectures, are used in the parallel processing of operations. In the case of power systems, some parallelization applications have been shown in solving different problems. Some researchers have used parallel architectures mainly for the solution of power flow [26], transient stability [27; 28; 29], EMT analysis [30; 31], and the integration of renewable energy sources [32] problems. Reduced applications in power systems have been cited in the exhaustive search for solutions through brute force algorithms.