esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
We do not treat BDM and BNDM separately as, in terms of text character accesses, they are indistinguishable (see Section 2.2). This paper is organized as follows. In the next section, we give a brief review of the Horspool, B(N)DM and BOM algorithms. In Section 3, we define _deterministic arithmetic automata_ (DAAs). In Section 4, we present a simple general DAA construction for the analysis of window-based pattern matching algorithms. We also show that the state space of the DAA can be considerably reduced by adapting DFA minimization to DAAs. In Section 5, we summarize the PAA framework with its generic algorithms, define finite-memory text models and connect DAAs to PAAs. Given a pattern \(p\), an algorithm, and a random text model, this framework allows constructing a PAA that mimics the algorithms' behavior. By applying dynamic programming to this PAA we obtain an algorithm to compute the distribution of \(X_{n}^{p}\) for any finite text length \(n\). Section 6 introduces _difference DAAs_ by a product construction that allows to compare two algorithms on a given pattern. Results on the comparison of several algorithms for example patterns can be found in Section 7. There, we also provide statistics on automata sizes for different algorithms and pattern lengths. Section 8 contains a concluding discussion. An extended abstract of this work has been presented at LATA'10 [18] with two alternative DAA constructions. In contrast to that version, the DAA construction in the present paper can be seen as a combination of both of those, and is much simpler. Additionally, the DAA minimization introduced in the present paper allows the analysis of much longer patterns in practice. While [18] was focused on [PERSON]'s and [PERSON]'s algorithms, here, we give a general construction scheme applicable to any window-based pattern matching algorithm and discuss the most relevant algorithms, namely Horspool, BOM, and B(N)DM, as examples. NotationThroughout this paper, \(\Sigma\) denotes a finite alphabet, \(p\in\Sigma^{*}\) is an arbitrary but fixed pattern, and \(s\in\Sigma^{*}\) is the text to be searched for \(p\). Furthermore, \(m:=|p|\) and \(n:=|s|\). Indexing generally starts at zero, that is, \(s=s[0]\ldots s[|s|-1]\) for all \(s\in\Sigma^{*}\). The prefix, suffix, and substring of a string \(s\) are written \(s[..i]:=s[0]\ldots s[i]\), \(s[i..]:=s[i]\ldots s[|s|-1]\), and \(s[i\ldots j]:=s[i]\ldots s[j]\), respectively. By \(\overset{\leftarrow}{p}\), we denote the reverse pattern \(p[m-1]\ldots p[0]\). For a random variable \(X\), its distribution (law) is denoted by \(\mathcal{L}(X)\). Iverson brackets are written \(\llbracket\cdot\rrbracket\), _i.e._, \(\llbracket A\rrbracket=1\) if the statement \(A\) is true and \(\llbracket A\rrbracket=0\) otherwise.
## 3 Results and Discussion
### In Vitro Release  
Controlled Fe\({}^{2+}\) release is essential to evaluate the continuity and efficacy of treatments. The release was assayed by measuring the UV absorbance of Fe\({}^{2+}\) at \(\lambda\) = 520.0 nm as a function of the concentration of the solution. A calibration curve was drawn using standard solutions with concentrations from 0 to 4.99 mg/L (Figure S2).  
Figures 4 and 5 show the Fe\({}^{2+}\) release rates of the compound delivered.  
Regarding Fe(II)C 20 wt% with various amounts of PEG, the trend is quite similar for all of the systems.  
In particular, the amount of Fe(II)C released was very fast in the first 5 h, and then it slowed down, ending after 8 h. Furthermore, the 12 wt% PEG system released a higher concentration of Fe(II)C, likely because this is incorporated into the porous matrix of the system and is more easily released than when the PEG concentration is greater, and forms stronger interactions within the system.  
Instead, in the hybrid systems with a constant amount of PEG (50 wt%) and varying Fe(II)C content (5, 10, 15, or 20 wt%), the release of Fe(II)C was fast up to 4 h, and then it slowed down, ending after 8 h.
# Time-Domain Blind ICI Compensation in Coherent Optical FBMC/OQAM System
The numerical results show that the proposed algorithm achieves a significantly better performance compared to the M-BPS scheme while having a limited increase in CC. The remainder of this paper is organized as follows. A theoretical model of CO-FBMC/OQAM system with laser phase noise is derived in Section 2. Section 3 introduces the principle of the proposed PNC method. In Section 4, the overlapped symbol structure in the CO-FBMC/OQAM system is implemented to improve the effectiveness of the algorithm. We numerically investigate the performance of our method and propose the complexity analysis in Section 5. Finally, Section 6 summarizes the paper.
# Numerical Solution for the Single-Impulse Flyby Co-Orbital Spacecraft Problem
## 4 Numerical Method without Derivation for the Two Equations
### Solution of Single-Impulse Flyby Two Co-Orbital Spacecraft Problem without Derivation  
Equation (17) gives the functional relationship between the time \(t_{0}\) and the time \(t_{1}\); therefore, for any given \(t_{0}\), \(t_{1}\) can be found using Equation (17). Since Equation (17) is a transcendental equation and cannot be solved directly, it can generally be solved using an iterative method. Because Equation (17) is difficult to derivate, this study adopted a multi-solution secant method to solve it. The multi-solution secant method was to find all the initial points for which a solution may exist and iteratively solve them using the secant method. The flowchart of the method is shown in Figure 5:  
Figure 5: Flowchart the problem’s solution.  
The iterative formula is  
\[\left\{\begin{array}{l}t_{1,k+1}=t_{1,k}-\frac{f\big{(}t_{0}t_{1,k}\big{)}\big{(}t _{1,k}-t_{1,k-1}\big{)}}{f\big{(}t_{0}t_{1,k}-t_{1}\big{)}-f\big{(}t_{0}t_{1,k-1 }\big{)}}\\ f(t_{0},t_{1})=\frac{M_{2\rho_{1},\rho_{2}}-M_{2\rho_{1},\rho_{0}}}{n_{1}}- \frac{M_{1\rho_{2},\rho_{2}}-M_{1\rho_{2},\rho_{1}}}{n_{2}}+t_{0}-t_{1}\end{array}\right. \tag{28}\]
## 4 Results and Discussion
### Rock-Damaging Effects of Different Interval Medium
From Figure 6, it can be seen that the stresses applied to the rocks in the PT partition all exceed the dynamic tensile strength \(\sigma_{td}\). \(D_{max}\) is below 0.75, which is because the HJC model considers the tensile limit with the maximum tensile stress, which simplifies the expression of the tensile damage behavior of brittle materials under dynamic loading. Therefore, PT partition has tensile damage as the main form of damage, so the damaging effect of the rock cannot be judged only based on \(D_{max}\) values. In the range of 0.5 m\(\sim\)1 m, \(\sigma_{max}\) and \(D_{max}\) values under water interval conditions both show a double-peak trend of increasing and then decreasing, and there is an obvious correlation, indicating that the region is dominated by tensile damage while other forms of damage exist. There is no correlation in the case of air interval charges, which is due to the compressibility and fluidity of the air, which exacerbates the complexity of stress propagation and the irregularity of rock failure. The incompressibility of water and the high flow viscosity enhances the destructive effect of explosive energy on rocks. At the interface of the sub-division, the damage variable jumps from 0.5 to more than 0.75 at the hole depth of 1 m \(\pm\) 5 cm, indicating that other forms of damage other than tensile damage have occurred at this location, and the rock damage is no longer dominated by tensile damage. The change of damage forms shows that this place is reasonable as a sub-interface. Figure 6b shows that the \(C_{D}\) values of PD partition is about 30% and 42% under air interval and water medium interval conditions, while the average increase of \(D_{\text{max}}\) is about 12.6% under the water medium interval conditions. Water medium has some advantages in improving the energy utilization of explosives and distributing the energy of explosives uniformly.
# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 3 Measurement Implementation and Verification
### Measurement of Box Components
#### 3.2.2 Results Analysis  
Using the measurement method designed in this study, images were captured and the length of each specimen was measured. Visual measurement values were obtained based on the method proposed in this study. The values obtained were compared with those from a coordinate measuring machine with micrometer precision, serving as the standard. The comparison includes the results with the Canny edge detection algorithm, the sub-pixel edge detection algorithm, and the Otsu-Canny edge detection algorithm. The experimental results are shown in Table 2.  
The experimental results comparing this study with other algorithms are shown in Table 2. From the table, it can be seen that the measurement accuracy of the algorithm in this study is closest to that of the coordinate measuring machine, with a measurement of 30.501 mm and a relative error of 0.005 mm. By comparing the accuracies in Table 2, it can be observed that the highest accuracy of the sub-pixel edge detection algorithm is 43.961 mm, with a relative error of 0.02 mm, which shows a larger measurement error compared to the method proposed in this study. There is a noticeable measurement fluctuation from the center point of the central hole to the outer contour. This is due to the rough machining of the part's outer surface and low machining accuracy, leading to significant fluctuations in the distance between the hole and the outer contour in the image.  
Figure 11: The detection position of the part.  
Compared to the sub-pixel edge detection algorithm and the Otsu-Canny edge detection algorithm, the algorithm proposed in this study exhibits satisfactory measurement results. The use of ROI edge detection technology enables the extraction of target information from the ROI edges, thereby avoiding interference from irrelevant areas and improving measurement accuracy. The edge model can better describe the edge characteristics of the part, and when combined with weighted median filtering, it can effectively remove noise, thereby comprehensively improving measurement accuracy. Noise sources such as machining marks produced during the part's manufacturing process are considered to prevent deviations in visual measurements in actual production processes due to surface marks. This study investigated and analyzed these factors, demonstrating their comprehensive application in real scenarios. The algorithm exhibits excellent performance in terms of measurement accuracy and adaptability, highlighting the superiority of the algorithm design compared to similar applications.
## Materials
### GPP Datasets
#### 2.2.5 Gosif  
The GOSIF GPP product is generated from solar-induced chlorophyll fluorescence (SIF) data based on the relationship between SIF and GPP across various vegetation types, utilizing data from the Orbiting Carbon Observatory-2 (OCO-2), MODIS, and meteorological data from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2). Developed by Li and Xiao [67], this product has a spatial resolution of 0.05\({}^{\circ}\) and offers temporal resolutions of 8 days, monthly, and annually. The GOSIF GPP dataset has been validated through comparisons with independent flux tower data, demonstrating high precision and the ability to effectively represent spatiotemporal variations in large-scale GPP [68]. This validation, which considered each biome separately, confirmed the dataset's accuracy in representing photosynthetic activity, particularly in grassland ecosystems. The monthly GOSIF GPP values were provided by Li and Xiao [67].
# Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector
## 5 Experimental Analysis
### Dynamic Feature Selection Using a Meta-Learning Approach  
Algorithm 1 explains how features were selected for meta-learning. We begin by storing the output from each of the feature selection processes in six initial lists labelled \(L1\) to \(L6\). For lists \(L3\) and \(L5\) we begin by selecting all features that are common among Information Gain and Information Gain Ratio. In the next phase, we begin by removing all features from \(L1,L2,L3\) and \(L6\) that had no importance as reported by list \(L4\) from XGBoost. This step allows us to eliminate any features that do not contribute towards enhancing the performance of the classification model prior to feeding them to the meta-learner.  
In the final stage, the results from these four lists are evaluated to find the common features. These common features will serve as input for our meta-learner. While common features are being selected, we have also analyzed this common feature list \(L\) and removed features that appear to have a high correlation with other features and appear multiple times as reported by the output of correlated feature elimination processed and stored in \(D[\;]\). Finally the list \(L[\;]\) is sorted based on the XGBoost importance values to ensure that the most relevant features are selected earlier in the training process.  
Figure 2: Gradient Boosting importance values. (**a**) NSL-KDD dataset. (**b**) UNSW dataset.  
```
Step 1: Initialize the arrays by running the following methods. \(L1[\ ]\leftarrow\) ANOVA F-test \(L2[\ ]\leftarrow\) XG-Boost Most Important Features \(L2[\ feature:score]\leftarrow\) Dictionary with XG-Boost Importance values and scores \(L3[\ ]\leftarrow\) Information Gain \(L4[\ ]\leftarrow\) XG-Boost Zero Importance \(L5[\ ]\leftarrow\) Information Gain Ratio \(L6[\ ]\leftarrow\) Wrapper Method Output # Step 2: Remove Zero Importance Features from L1, L2, L3, L6 if\(L3==L5\)then  Return \(L3\) else \(L3\gets L3\cup L5\) end \(ZeroF[]\leftarrow[L1,L2,L3,L6]\) \(j\gets 0\) while\(j<len(ZeroF)\)do \(TemDist\gets ZeroF[]\) for(\(i=0\), \(i<len(L4)\), \(i++\))do \(k\gets 0\) while\(k<len(TemDist)\)do if(\(L4[i]==TemDist[k]\)then  Remove \(TemDist[k]\);  end if  k++;  end for  end for \(j++\)  end for # Step 3: Combine output and remove correlated features \(D[\ ]\leftarrow\) Correlated Features \(L\gets L1\bigcap L2\bigcap L3\bigcap L6\) for(\(i=0\), \(i<len(D)\), \(i++\))do \(k\gets 0\) while\(k<len(L)\)do if(\(L[k]==D[i]\)then  Remove \(L[k]\);  end if  k++;  end for  Sort List \(L[\ ]\) by XGBoost importance values from \(L2[feature:score]\)
```  
**Algorithm 1** Prepare Datasets for Meta-Learners.
## 2 Materials and Methods
### Specimen Preparation  
Access cavity preparations were made with a dental operating microscope (Leica M320, Wetzlar, Germany) under 6.4\(\times\) magnification. Following the principles for traditional access cavities [19], the specimens in Group 1 and Group 2 were accessed from the central fossa with a number 14 diamond round bur. The cavity walls were then paralleled with a number 12 diamond fissure bar to allow direct access of files to the coronal third of the canal.  
Conservative access cavity preparation was performed following the principles previously described in the studies of [PERSON] and [PERSON] [13]. Cavity preparation of the specimens in Group 3 and Group 4 was started slightly mesial to the central fossa using the number 12 diamond round bur. The cavity walls were made slightly parallel with the diamond fissure burr numbered 11. Organic tissue residues and calcifications in the pulp chamber were removed with the help of ultrasonic tips.  
Figure 3: Micro-CT images illustrate the differences between traditional and conservative access cavities. (**a**) Axial section of a traditional access cavity. (**b**) Axial section of a conservative access cavity. (**c**) Sagittal section of the conservative approach. (**d**) Sagittal section of the traditional approach.  
After the cavity preparations of all specimens, to determine the working length in the meso-buccal and meso-lingual canals, a #10 K-file (Dentsply Maillefer, Ballagues, Switzerland) was inserted and advanced until it could be visible through the apical foramen. By measuring this length and subtracting 1 mm from that, the working length was determined for each canal and recorded. Then, the canal preparation process was carried out using an endodontic motor (Changzhou Eighteenth Medical Technology Co., Ltd., Changzhou, China). According to the determined working lengths of the samples belonging to Group 1 and Group 3, a glide path was created with a One G file (MicroMega, Besancon, France) using 300 rpm speed and 1.2 Ncm torque value. Each canal was then rinsed using 2 mL of 2.5% NaOCl. After this step, the preparation was made according to the manufacturer's instructions using the OR 25/06 file up to the working length with 170\({}^{\circ}\) counterclockwise and 60\({}^{\circ}\) clockwise motion.  
In Group 2 and Group 4, the samples were prepared using PU according to the manufacturer's instructions with S1 and S2 files and F1 and F2 files, respectively. During the preparation procedures, the root canals were rinsed with 2 mL of 2.5% NaOCl at each file. Final irrigation was carried out with 5 mL of distilled water. All these procedures were performed by a single operator.
## 3 Results
### Transmit Subsampling
#### 3.2.4 Reconstruction of the Simulated Data  
The reconstructions for 4, 8, and 16 transmit elements chosen according to the proposed selection scheme as well as of the full FMC data are given in Figure 9. For all defect locations, transmit element counts, as well as both algorithms, the defects are clearly visible. The TFM reconstruction shows more background noise, predominantly for the reconstructions of the defect at \(z\ =\ 13\) mm. The difference of the perceived noise level of the TFM reconstruction for both defects is due to the colormap being adjusted to the maximum amplitude in the region of the defect of the according image. As the defect at \(z\ =\ 13\) mm has a smaller amplitude, the noise is more predominant here.  
The FISTA reconstructions show a better separation of defect and frontwall/backwall reflection as well as less noise than the TFM reconstructions, which is due to the algorithm favoring sparser images. Consequently, the algorithm shows a better behavior with regards to the noise in the measurement data. Still, this requires a suitably chosen \(\beta\) as well as number of iterations.  
Despite the strong subsampling, imaging the according defect is still possible for all settings. Using fewer transmit cycles increases the noise level, as there is less data to be averaged over. For (a) TFM using four transmit cycles the noise floor becomes  
Figure 9: B-scan views of the reconstructions of data subject to additive white Gaussian noise normalized such that the maximum of the colormap of each image is the maximum value of the defect, where each column shows a different number of transmit elements. The rows indexed by (**a**), (**b**) have been calculated for a defect located at \(z\ =\ 13\) mm and \(z\ =\ 15\) mm, respectively.  
more pronounced, for FISTA, the defect echo is a bit more faint than in the other images. Consequently, when using four elements, imaging the defects is still possible for the given setup. Using 8 or 16 transmit cycles improves the result so far that no apparent difference to the reconstruction of the FMC data is apparent in the region of interest.
## 4 Sensing-Based SPS Scheduling with Lookahead
### SPS/LA Algorithm  
Algorithm 2 shows the SPS/LA logic. There are three more variables, all related to the lookahead: \(LATxSubCh\), \(LATxSubframe\), \(LARC\) (line 7). These are selected when \(RC=RC_{LA}\) (lines 14, 16, 17, 19). In case we stay in the current resource location, the subchannel is maintained (line 17), but the first subframe index is set to the starting subframe for the first packet in the next streak (line 16). In case we need to move, the two values are selected using the lookaheads received from other UEs (line 19). When the current streak ends, the three values are used to set the RC counter (line 25) and the time-frequency coordinate (lines 27, 28, 30). However, before we commit, we double-check if the lookahead location is still available using both the lookaheads from other nodes and the sensed resource use map (line 26). In case the lookahead location is likely to be occupied, we revert to the SPS algorithm (line 30). Regarding this last case, Figure 3 shows an example. Suppose \(A\) and \(B\) are two  
Figure 2: Use of the lookahead. (**a**) SPS cannot resolve the collision if two close UEs choose the same resource. (**b**) SPS/LA can resolve most potential collisions; \(A\) knows \(B\)’s planned movement through the received lookahead in \(B\)’s packet  
vehicular UEs that happen to be transmitting their BSMs in the same subframes for their current streak. The current RC value of \(A\) is one smaller than \(B\)'s. When \(A\)'s RC value reaches \(RC_{LA}\), it plans for the next streak whose first packet will be transmitted at subframe \(n^{\prime}\). In one RRI, \(B\) also gets to choose its planned resource. Since these two are transmitting in the same subframes, however, they cannot hear each other and are not aware of each other's plans. If they happen to choose the same resource located at \(n^{\prime}+RRI\), they will collide without knowing it, that is, without the double-check in line 30. The double-check performed at \(n+RRI\times(RC_{LA}+1)\) by \(B\), however, reveals that an actual BSM transmission took place at \(n^{\prime}\). This is exactly one RRI before the \(B\)'s resource planned for its next streak. Thus, when \(B\) executes the SPS select, it will be able to avoid the collision at subframe \(n^{\prime}+RRI\).
# Sales Forecasting with LSTM, Custom Loss Function, and Hyperparameter Optimization: A Case Study
## 2 Materials and Methods
### LSTM Network Structure  
Figure 3 shows the structure of the proposed LSTM network and considers the following characteristics:  
* Input. Datasets generated for the product are to be forecasted by the LSTM network.
* LSTM layers. A set of three LSTM layers, each of them with a dropout rate, to handle data sequences, capture long-term dependencies, and avoid overfitting or underfitting.
* Dense layer. Layer with a linear activation function used to generate the forecasts by the LSTM network.
* Output. Network's forecast for sales of a product for the next period.  
Figure 3: LSTM network structure.  
Figure 2: Datasets generation.  
The LSTM network structure included nine hyperparameters that modified its behavior, which we explored through an evolutionary algorithm to find the best configuration of hyperparameters. Based on the preliminary experiment results, the value ranges shown in Table 3 were established for the hyperparameters.  
As for the rest of the LSTM network configurations, we considered the following:  
* We used the Adam optimizer because, in the literature, this optimizer has been demonstrated to be computationally efficient, uses little memory, is invariant to the diagonal rescaling of gradients, and is suitable for significant problems in terms of data or parameters [18].
* For the loss function for the LSTM network, we used the Root Mean Square Error (RMSE). The RMSE has been used as a standard to measure the performance of diverse models in multiple studies. This metric expresses the error in the same magnitude or unit as the variable measured for a model, facilitating a more straightforward interpretation of the errors [19].
* We initialized with zeros on the biases of the neurons in all layers of the LSTM network [20; 21], and we proved the generation of the initial values of the neuron weights with the following kernels: uniform random, Glorot, and Orthogonal [22; 23; 24; 25].