esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Time-Varying Pole Radius of the Filter
### Starting Pole Radius  
In this subsection, a method for selecting the value of \(r_{start}\) is proposed. In order to select the starting value of the filter pole radius the mean square error for the time-invariant IIR notch filter with various pole radii has been calculated using the following formula:  
\[MSE_{ti}(r)=\frac{1}{K}\sum_{n=1}^{K}\left|y(n)\right|^{2}, \tag{10}\]  
where \(K\) is the duration of the input signal and \(y(n)\) is the output of the filter. The useful signal \(s(n)\) used in the procedure for determining the value of the starting pole radius is equal to zero. Therefore, this signal has been omitted in Equation (10). In the considered procedure the 50 Hz sine wave signal is used as an input signal. The values of \(MSE_{ti}(r)\) have been determined based on filtering the notch frequency sinusoidal signal using filters with pole radii from 0.01 to 0.999. The relationship between \(MSE_{ti}(r)\) and the value of the pole radius is presented in Figure 2.  
As one can notice, \(MSE_{ti}(r)\) grows rapidly for \(r\in(0.9,1)\). In order to find a strategic value of the pole radius the second derivative of \(MSE_{ti}(r)\) has been used. For this purpose a function that approximates the obtained \(MSE_{ti}(r)\) has been derived. The proposed function is a sum of three exponential base functions, and has the following form:  
\[f(r)=\text{a}\cdot e^{-\left(\frac{r-\text{b}}{\text{c}}\right)^{2}}+\text{d }\cdot e^{-\left(\frac{r-\text{g}}{\text{f}}\right)^{2}}+\text{g}\cdot e^{- \left(\frac{r-\text{h}}{\text{h}}\right)^{2}}, \tag{11}\]  
where \(\text{a}=0.02678\), \(\text{b}=928.9\), \(\text{c}=25.43\), \(\text{d}=0.0005132\), \(\text{e}=886.1\), \(\text{f}=61.95\), \(\text{g}=0.0008081\), \(\text{h}=1230\), and \(\text{i}=402.6\). The second derivative of \(f(r)\) given by Equation (11) can be written as:  
\[\frac{d^{2}f(r)}{dr^{2}}=-\frac{2\text{a}\cdot e^{-\left(\frac{r-\text{b}}{ \text{c}}\right)^{2}}}{\text{c}^{2}}-\frac{2\text{d}\cdot e^{-\left(\frac{r- \text{g}}{\text{f}}\right)^{2}}}{\text{f}^{2}}-\frac{2\text{g}\cdot e^{- \left(\frac{r-\text{h}}{\text{h}}\right)^{2}}}{\text{i}^{2}}+\]  
\[\frac{\text{a}\cdot e^{-\left(\frac{r-\text{b}}{\text{c}}\right)^{2}}(2\text{ b}-2r)^{2}}{\text{c}^{4}}+\frac{\text{d}\cdot e^{-\left(\frac{r-\text{g}}{ \text{f}}\right)^{2}}(2\text{e}-2r)^{2}}{f^{4}}+\frac{\text{g}\cdot e^{-\left( \frac{r-\text{h}}{\text{h}}\right)^{2}}(2\text{h}-2r)^{2}}{\text{i}^{4}}. \tag{12}\]  
In Figure 3 the second derivative of \(f(r)\) on the interval between \(r=0.1\) and \(r=0.999\) is depicted. The value of \(r_{start}\) has been chosen as the pole radius for which the function given by  
Figure 2: Mean square error with various filter pole radius.  
Equation (12) approaches the local minimum. All important parameters of the proposed notch filter with time-varying pole radius are presented in Table 2.
## 1 Introduction
### Nanotechnology Offers Diagnostic and Antibacterial and Antibiofilm Properties
Diving more specifically into the theranostic nanomedicine, it encompasses the NPs tagged by labels or possessing intrinsic physicochemical properties that can diagnose the disease and induce treatment themselves or through their carried cargo (e.g., chemo-, radio- or gene therapeutics or combinations of them) [110; 111]. Tagging bacterial-derived antigens with NPs ameliorates the signals detected from the targeted specific binding of even low bacterial concentration and forms a thorough diagnostic probe [112]. The early diagnosis and treatment of diseases are priceless because at the earliest stage, the diseases are curable or at the least treatable [107]. Therefore, theranostics facilitate superior control of the diseases [111], increase therapeutic efficacy, and avoid the drawbacks [108; 111]. In contrast to extensive theranostic research on different cancer types, there is much less information about theranostic effects against bacterial infections and their biofilms, which also pose a growing public health concern worldwide. However, antibacterial nanotheranostics can play a pivotal role in tackling this bacterial antibiotic resistance quandary and their biofilms via detecting (sensing) the presence of bacterial infection and consequently facilitating treatment [111]. Nanotheranostics could provide prowess rapid and accurate bacterial identification, helping to cross the hurdle of a low number of pathogens at early infectious stages [112] and the long (i.e., two days) traditional antibacterial susceptibility tests. Consequently, nanotheranostics could achieve higher success treatment rates [108; 112]. Previous review articles [106; 107; 108; 110] focusing on understanding the powerful impact of developing nanotheranostics for chemotherapy and different biological applications do exist. However, the exact nanoparticles' antibacterial working mechanisms are still not known. This comprehensive review generates fresh insights into the detailed antibacterial and antibiofilm therapeutic mechanisms of nanotheranostics. We elucidate such mechanisms after providing a deeper understanding of _S. aureus_ resistance and biofilm (lifestyle, mysteries surrounding it, and its resistance and tolerance) in a grasping narrative context supported by our constructed informative, artistic figures. Therefore, this review makes several significant contributions to the more in-depth understanding of the emergent resistant bacterial infections, specifically MRSA and their tolerant biofilms associated with implants (orthopedic and breast) and wounds. The present review explores the critical roles of different antibacterial nanotheranostics in curbing bacterial resistance and biofilm dilemmas. We provide essential insights on the challenges dampening the progress of such antibacterial nanotheranostics that are usually overlooked in studies. Ultimately, we reflect on our critical perspectives, highlight research gaps, and answer the big question in this field: How can we solve _S. aureus_ resistance and its tolerant biofilms?
# Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
## 4 Experimental Results and Comparative Performances
### Temperature Inversion Classification  
As described in Section 3.3, CART and CNN were developed to perform the temperature inversion classification. In the machine learning literature, the commonly used measures for evaluating the performance of a two-class (positive or negative) classifier are defined by the confusion matrix as illustrated in Figure 8.  
Figure 8: Confusion matrix.  
Several useful classification performance metrics such as accuracy, precision, recall, and F1-Score can be evaluated from the confusion matrix as follows.  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}), \tag{3}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}), \tag{4}\]  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{FP}+\text{FN}+\text{TN }), \tag{5}\]  
\[\text{F1}-\text{Score}=2\times\text{Precision}\times\text{Recall}/(\text{ Precision}+\text{Recall}). \tag{6}\]  
Each metric gauges the classification performance from a different perspective. Precision focuses on the effectiveness of the samples which the classifier recognizes as positive, while recall emphasizes how many true positive samples are correctly recognized by the classifier. Precision and recall are contrasting measures. A perfect recall can be obtained at the cost of reduced precision by letting the classifier recognize more samples as positive. Accuracy reports the correct recognitions on both positive and negative classes from all test samples. Accuracy could be a biased measure if the data size of the two classes differs significantly. F1-Score is a balanced measure by taking into account both precision and recall simultaneously.  
As the time span of our data collected from Taiwan EPA ([[https://www.epa.gov.tw/](https://www.epa.gov.tw/)]([https://www.epa.gov.tw/](https://www.epa.gov.tw/)) accessed on 10 December 2021) is between 1 January 2016 and 31 December 2019, we used all days in 2016 as the training set for the classifiers and randomly selected 72 test days (two days in each month) from 2017 to 2019 to construct the test set. Table 1 shows the comparative performances between CART and CNN. It can be seen that CART outperformed CNN on almost all classification measures across the three test years. The only two exception cases are the recall in 2017 and the precision in 2018. More importantly, CART surpassed CNN in F1-Score for all cases, indicating CART is more suitable than CNN for temperature inversion classification on our dataset.
# Impact of Preheating Temperatures and RAP Characteristics on the Activation of RAP Binder
## 3 Materials and Methods
### Materials
#### 3.1.2 Artificial RAP  
Since RAP from different old roads has differences in gradation, asphalt content, asphalt type and aging, it is hard to evaluate the effect of aging on activation using different sources of RAP. Therefore, the accelerated aging method was used to produce artificial RAP (RAP-Art) with different degrees  
Figure 1: The asphalt content, gradations of reclaimed asphalt pavement (RAP) and extracted RAP aggregates of (**a**) RAP-A, (**b**) RAP-B and (**c**) RAP-C. Percentage passing (%) is the percentage by mass of material passing a sieve of a selected size (mm).  
of aging, but the same gradation, asphalt content and asphalt type. Three aging conditions (unaged, with short-term aging and with long-term aging) and two types of asphalt, namely penetration grade #70 from Korea (SK70) and styrene-butadiene-styrene (SBS) modified bitumen, were considered for RAP-Art.
# A Novel Hybrid Whale-Chimp Optimization Algorithm for Structural Damage Detection
## 3 Mathematical Model of Structural Damage Detection
### Definition of Objective Function  
Inspired by the model updating method, the measured responses of the structure and the finite element model information of the structure are used to establish the objective function. The frequency and mode shape can be calculated from the finite element model of the structure and also can be measured from the structure in the laboratory, respectively. Then, the modal information under the two conditions is compared by the modal guarantee criterion and the objective function. Finally, structural damage detection can be transformed into a function optimization problem with constraints. The objective function of this study can be expressed as:  
\[\text{min}f(\alpha)=\sum_{i=1}^{s}((1-MAC(\mathbf{\Phi}_{i}^{m},\mathbf{\Phi} _{i}^{c}))+ER(\omega_{i}^{m},\omega_{i}^{c})) \tag{16}\]  
where \(\mathbf{\Phi}_{i}^{m}\) and \(\omega_{i}^{m}\) represent the measured \(i\)-th mode shape vector and frequency of the structure, respectively. \(\mathbf{\[PERSON]}_{i}^{c}\) and \(\omega_{i}^{c}\) represent the calculated \(i\)-th mode shape vector and frequency of the finite element model of the structure, respectively. \(s\) represents the selected structural modal order. \(\alpha\) represents the damage factor of each element. \(MAC\bigl{(}\mathbf{\[PERSON]}_{i}^{m},\mathbf{\[PERSON]}_{i}^{c}\bigr{)}\) and \(ER\bigl{(}\omega_{i}^{m},\omega_{i}^{c}\bigr{)}\) represent change rate of the modal confidence and frequency of the structure, respectively. The functions can be defined as:  
\[MAC(\mathbf{\[PERSON]}_{i}^{m},\mathbf{\[PERSON]}_{i}^{c})=\frac{\left|\bigl{(}\mathbf{ \[PERSON]}_{i}^{m}\bigr{)}^{\text{T}}\mathbf{\[PERSON]}_{i}^{c}\right|^{2}}{\left| \bigl{(}\mathbf{\[PERSON]}_{i}^{m}\bigr{)}^{\text{T}}\mathbf{\[PERSON]}_{i}^{m}\right| \left|\bigl{(}\mathbf{\[PERSON]}_{i}^{c}\bigr{)}^{\text{T}}\mathbf{\[PERSON]}_{i}^{c} \right|} \tag{17}\]  
\[ER(\omega_{i}^{m},\omega_{i}^{c})=\left|\frac{\omega_{i}^{m}-\omega_{i}^{c}} {\omega_{i}^{m}}\right|\times 100\% \tag{18}\]  
where superscript T represents the transpose of a matrix.  
By defining the structural damage and the objective function, structural damage detection can be converted to solve the independent variable \(\alpha\) corresponding to the optimal value of the objective function. Then, the ChOA and the proposed W-ChoA can be used to identify the location and degree of structural damage.
## 4 Comparison with Volterra Method
### Generalized Memory Polynomial (GMP)  
GMP has been productively utilized for DPD linearization of Power Amplifiers. Here, we will now use it to aid linearization of RoF links using the Digital predistortion method. If we observe the equation below, we see that, the GMP model, in contrast with MP, holds both, memory of diagonal terms along with crossing terms as well, which is why it outperforms MP and in the coming sections, we will evaluate comparisons with GMP only having established it is superior to MP [1; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]:  
\[\begin{array}{l}y(n)=\sum\limits_{k=0}^{k_{-1}}\sum\limits_{q=0}^{Q_{a}-1}c _{kq}x(n-q)|x(n-q)|^{k}\\ +\sum\limits_{k=1}^{k_{0}}\sum\limits_{q=0}^{Q_{a}-1}\sum\limits_{r=1}^{R_{b}} d_{kqr}\ x(n-q)|x(n-q-r)|^{k}\\ +\sum\limits_{k=1}^{k_{c}}\sum\limits_{q=0}^{Q_{c}-1}\sum\limits_{r=1}^{R_{c} }e_{kqr}\ x(n-q)|x(n-q+r)|^{k}\end{array} \tag{11}\]  
where \(x(n)\) is the DPD input and \(y(n)\) is the DPD output.  
The complex coefficients \(c_{kq}\), \(d_{kqr}\) and \(e_{kqr}\) denote signal and the envelope; signal and lagging envelope and signal and leading envelope, respectively. \(K_{a}\), \(K_{b}\), \(K_{c}\) are the maximum nonlinearity orders, \(Q_{a}\), \(Q_{b}\), \(Q_{c}\) are the memory depths. \(q\), \(r\) represents the indices of the memory \(k\) is nonlinearity index and \(R_{c}\) and \(R_{b}\) exhibit the leading and lagging delay tap lengths, respectively. Since, in [62; 63; 64; 65; 66; 67; 68; 69; 70; 71], it was established that GMP is better as compared to MP, therefore for simplicity, evaluation with GMP is included in this paper.
## 2 Study Region and Methodology
### Study Region
#### 2.1.2 Earthquake-Induced Geohazards  
In history, fifteen strong earthquakes (M\({}_{\text{s}}\geq\) 6.0) occurred along the N-S-trending Minjiang faults [24], five of which exceeded M\({}_{\text{s}}\)7.0. Since Juzhaigou park is far from the epicenters, it was hardly influenced by the five strong earthquakes. However, the Juzhaigou earthquake induced landscape fragmentation and frequent geohazards. For example, one of the natural landscape heritage sites, Panda Lake, was seriously damaged by the earthquake-induced geohazards, with vegetation destruction (Figure 3a).  
In addition to the direct destruction of vegetation by landslides, debris flow poses an indirect threat to vegetation by inducingoseismic landslides. Three debris flow events occurred in Xiajijiehai Gully shortly after the Juzhaigou earthquake on 9, 14, and 25 September 2017, which severely damaged the road, vegetation, and water body (Figure 3b). By remote-sensing and field surveys after the earthquake, 21 earthquake-induced landslides, 84 collapses, and 23 potential debris-flow gullies were identified, respectively. [PERSON] et al. [6] indicated that approximately 30% of the ecosystem in the park, including forests, shrubs,  
Figure 3: Geohazards caused by the Juzhaigou earthquake and their impact on the vegetation. (**a**) Panda Lake andoseismic landslides; (**b**) Xiajijiehai Lake and debris-flow deposit.  
Figure 2: Geomorphological and geological settings of the study region. (**A**) Location of study region; (**B**) distribution of elevation and earthquake-induced geohazards in the park; the base map with a spatial resolution of 16 \(\times\) 16 m was downloaded from Google earth, and the spatial resolution of DEM is 10 m \(\times\) 10 m. “IX” and “VIII” refer to seismic intensity VIII and IX, respectively.  
and suitable land for forests, was seriously affected by the Juzhaigou earthquake. In some areas, after geohazards, the stability of dam reservoirs may be destroyed, and they may gradually evolve into new terrain, such as barrier dams, under certain conditions [25].
## 2 Literature Review
### Learning Process  
Another aspect of the learning process deals with the depth of processing new information and its retention. According to [PERSON]'s Dual Coding study, it was found that using two forms of representational information--verbal and visual--leads to long-term information retention [17]. In our study, we utilized visual and verbal representations of the learned words while integrating the background cognitive and emotional stimuli.  
Furthermore, according to the model of the Levels of Processing, the differences between the various types of memory (short-term, long-term, and working memory) became evident by the volume of information processed. Instead of separating the types of memory, this model suggests referring to them as different levels that are part of a continuing process of information retention [18].  
When discussing knowledge of vocabulary, it is customary to distinguish between the size of the vocabulary (number of known words) and the depth of vocabulary knowledge.  
[PERSON]. [19] described several aspects of 'word knowledge' and divided it into three categories: the form of the word, its meaning, and the ability to use it. This distinction differentiates between the ability to recall the word from memory (form) and the need for a clue to identify the meaning. [PERSON] and [PERSON] [18] added a distinction--the ability to retrieve a word from an internal association (recall), which depends on the meaning one wishes to express, constituting active knowledge, versus the ability to recognize the word (recognition), which is passive knowledge. Using this distinction, they tested the levels of knowledge and its strength and created a corresponding scale representing different types of associative knowledge. They differentiated between the ability to recall and recognize the word either actively or passively. We based our study on this set of principles. Thus, our third hypothesis (3) was that participants would achieve higher scores in recall questions than in recognition questions.  
In conclusion, our study aimed to evaluate if accelerated learning occurred within a short time frame. The accelerated learning method, which includes background cognitive and emotional stimuli, promotes accelerated foreign language learning in a relaxed, less stressful environment. We employed this accelerated learning method in both 2D computerized and VRH environments to examine their impact on the learning process.  
The research hypotheses are: (1) The implementation of background cognitive and emotional stimuli significantly increases the scope of knowledge in both the 2D computerized and the VRH environments; (2) learning achievements in the VRH environment would be better than those in a 2D computer environment; and (3) participants would achieve higher scores in recall questions than in recognition questions.
## 3 Engineering Context and Site Condition
### Slope Rock Mass Structure Testing Research
#### 3.1.1 Geological Radar Testing  
As depicted in Figure 3, the process flow diagram of the geological radar testing is presented. Considering the site conditions of the transfer station and the mining field slope, the radar method was employed to survey three measurement lines positioned below the mining field floor and three additional lines arranged around the mining field. These measurement lines were located on the left side, the wedge-shaped body, and the right side, respectively. A 900 MHz antenna was utilized, with the time window set between 600 and 700 ns, enabling detection at a depth of 30-35 m. Continuous data acquisition was conducted throughout the testing process.  
The depth \(z\) of the reflector is automatically calculated using Equation (21) based on the measured radar wave travel time. The range and characteristics of the reflector are then further analyzed and determined.  
\[t=\frac{\sqrt{4z^{2}+x^{2}}}{v} \tag{21}\]  
Figure 2: Diagram of the study area and hazard sources.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Preprocessing  
The formatting issues in images can have a negative impact on the accuracy of OCR models. Examples of these issues include problems related to image orientation or color correction. To improve the accuracy of these models during the training phase, image preprocessing techniques are commonly used. These techniques may involve resizing, grayscale conversion, skew correction, and/or enhancing the resolution of the image.
# Detecting Underground Geospatial Features for Protection of Rights in 3D Space: Korean Cases
## 3 Results
### Direct Survey Method
The data acquisition process was performed by dividing the entire shopping center into five blocks and 12 gates, as shown in Figure 2. This minimizes the accumulating errors in the mobile equipment as the distance increases. A transformation is a function that takes a point and maps the point onto another point. The coordinate transformation in the form of Equation (1) is called 3D affine transformation [19]. \[\left( \begin{array}{c}x^{\prime}\\ y^{\prime}\\ z^{\prime}\\ 1\end{array} \right)=\left  
Figure 2: Building point cloud data for each block. (**a**) Point cloud data for each block (inside the shopping center). (**b**) Point cloud data for each block (gates). Color is a relative height value, higher is red and lower is blue. After integrating the point cloud datasets, wire framing was performed to extract topographic information from the point cloud data, as shown in Figure 4. The external boundary exposed toward the passage can be easily verified owing to the nature of the underground shopping center; however, it is often difficult to obtain the internal boundary of the store owing to changes in the structure or display of goods. Therefore, the boundaries of individual stores were established by referring to the drawings and the internal end points of stores located at the starting point of the underground shopping center [20]. Figure 4: Construction of topographic information for each store using point cloud data. (**a**) Point cloud vectorization. (**b**) Determination of occupancy status by store. (**c**) Final determined vectorization. Figure 3: Absolute coordinate conversion and point cloud data integration. (**a**) Observed control point. (**b**) Matching the control point and point cloud data. (**c**) Control point and point cloud data. (**d**) Matched point cloud data. Color is a relative height value, higher is red and lower is blue. After generating the 2D topographic information, 3D topographic information was generated through modeling, as shown in Figure 5, and the attribute information namely, store name, store number, store owner, type of business, contract date, area, and rent was mapped to each store. This information was obtained from the infrastructure technology, corporation that manages the underground shopping center.