esa-sceva/satcom-chunk-collection huggingface dataset:
# Three Metaheuristic Approaches for Tumor Phylogeny Inference: An Experimental Comparison
## 3 Methods
### Genetic Programming
#### 3.2.4 Crossover  
The crossover operator, also known as sexual recombination, introduces some variation in the population by combining genetic material from the parent individuals [56]. In the context of GP [50] for our problem, the crossover operator selects a random node--the crossover point--in each parent tree \(T_{1}\) and \(T_{2}\). The crossover _fragment_ for a parent \(T_{i}\) is the subtree rooted at the crossover point. The offspring is created by replacing the crossover fragment of \(T_{1}\) with the crossover fragment of \(T_{2}\)--this operation can be seen as an extension to a pair of trees of the Subtree Prune and Regraft operation of the PSO. Additionally, a symmetric offspring is created by replacing the crossover fragment of \(T_{2}\) with the crossover fragment of \(T_{1}\).  
Designing a crossover operator for our problem is challenging because we need to guarantee that all offspring generated are feasible solutions. The crossover procedure involves the use of the function \(mate\_individual()\) to create two offspring from the parents. The mating procedure must maintain the validity of the individuals in the population. The crossover procedure (Algorithm 4) starts by randomly selecting a mutation and checking if it can identify a valid crossover point in each tree. In other words, the crossover operation should result in two valid tumor phylogenies. If the check is successful, the offspring are created, and the procedure terminates. If the check fails, another mutation is selected, and the procedure is iterated.  
It is important to note that the crossover operation is performed with a certain probability, known as the crossover probability, set to 0.9 in our approach.  
```
Data: A population \(pop\); a crossover probability \(c\) Result:\(pop^{*}\): the population after crossover
1\(pop^{*}\leftarrow\) a copy of \(pop\);
2\(n\leftarrow\mid pop\mid\);
3 for\(i\gets 1\) to \(n/2\)do
4 with probability \(c\)
5\(i_{1},i_{2}\leftarrow\) two random distinct individuals from \(pop\);
6 repeat
7\(x_{1},x_{2}\leftarrow\) a random crossover point for \(i_{1}\) and \(i_{2}\) respectively;
8\(j_{1},j_{2}\leftarrow\) the result of swapping the crossover fragments identified by \(x_{1}\) and \(x_{2}\);
9 until\(j_{1},j_{2}\) are both feasible solutions;
10 Replace \(i_{1}\) with \(j_{1}\) and \(i_{2}\) with \(j_{2}\) in \(pop\);
11 return\(pop^{*}\)
```  
**Algorithm 4**Crossover
## 3 Results and Discussion
### Impact of Internal Fitting Diameter on BGD Power Characteristics
However, in the range of \(d=6\) to 8 mm, the lifting force remains practically not variable with the increase in compressed air flow rate for \(r_{0}=15\) and 20 mm, and at \(r_{0}=10\) mm, the lifting force increases uniformly with increasing flow rate. Therefore, for \(r_{0}=10\) mm, it is advisable to use the maximum diameter of the fitting constriction. The C-factor (Figure 16) is used for a detailed analysis of the effect of fittings on the energy characteristics of the grip at different radii of the conical insert. Figure 14: Influence of supply pressure on a BGD’s flow characteristics at different diameters of fitting constriction. From Figure 16, it is obvious that the gripper with a radius of conical insert \(r_{0}\) = 20 mm is the most effective. It provides a lifting capacity of one kilogram at a supply pressure of 300 kPa, the diameter of the narrowing of the fitting is \(d\) = 5 to 7 mm, and it consumes only 105 W. This is 25% less than a gripper with a radius of its conical insert of \(r_{0}\) = 15 mm. In addition, it should be noted that less energy is spent for \(r_{0}\) = 15 and 20 mm at \(d\) = 5 to 7 mm, and for \(r_{0}\) = 10 mm, it is less at \(d\) = 8 mm. This is due to the fact that with a small radius of the conical insert, \(r_{0}\) = 10 mm, and a large grip radius, \(r_{1}\) = 30 mm, much more air flow energy is used to overcome the friction forces of the air flow arising at the radii from \(r_{0}\) to \(r_{1}\).
## 3 Results and Discussion
### The Thermal Environment Monitoring of Enclosed Vertical-Type Composting Facilities
It was also noted that the temperature in the T layer decreased due to the heat conducted from the lid of the reactor (Figure 5). During the composting period, the decomposition of organic matter generates approximately 16-19 MJ of energy per 1 kg of volatile solids throughout the period [31]. The temperature change associated with the composting period began at 35 \({}^{\circ}\)C on Day 0, reaching a peak of 53 \({}^{\circ}\)C on Day 1. Throughout the experiment, the temperature gradually decreased back to 35 \({}^{\circ}\)C, showing a consistent pattern of change at all heights, including the T, MT, MB, and B layers. Since the temperature within the composting reactor varies by height, there is a need to closely analyze the temperature changes at the same height [32]. The T layer, located at the top of the reactor, displayed a temperature range of 34 to 50 \({}^{\circ}\)C, the MT layer from 36 \({}^{\circ}\)C to 66 \({}^{\circ}\)C, the MB layer from 35 to 60 \({}^{\circ}\)C, and the B layer from 30 to 38 \({}^{\circ}\)C. On Day 0, the average temperature across the different height sections was relatively small, between 34 and 36 \({}^{\circ}\)C, but on Day 1, the temperature range expanded to 38 to 66 \({}^{\circ}\)C, showing a significant difference of about 28 \({}^{\circ}\)C between the B and MT layers. The increase in microbial activity was identified as the primary cause of temperature rise during the composting period. Based on temperatures indicative of high microbial activity, the most active composting sections were listed in order of MT, MB, T, and B layers (Figure 6).
## 3 Cellular Network Modeling
### The Cellular Networks  
From a high-level perspective, regardless of the technology, cellular networks can be described with the entities depicted in Figure 2. Each network operator (entity PLMN) is identified by attributes _mcc_ and _mmc_ and is characterized by the derived spatial attribute _coverage_. Each PLMN is divided upon the basis of different cellular technologies (entity SubPLMN). Specifically, a PLMN is composed by 1 to 3 SubPLMNs (i.e., in the last case, the PLMN supports all three considered cellular technologies). Furthermore, the relationship between these two entities is a spatial aggregation: the attribute _coverage_ of PLMN results from the combination of the coverages of the associated SubPLMNs. SubPLMN is a weak entity with respect to PLMN which has the attribute _type_net_ as its partial identifier.  
Considering the architectural structure of a network, each SubPLMN deals with one or more network controller, each managing, through some base stations, a set of cells. A network controller is a weak entity with respect to SubPLMN (as we shall see, the partial identifier, omitted here, depends on the specific network technology). Depending on the specific technology, the network controller can be incorporated into the base stations (as for LTE), or it can be organized into different components (as for GSM and UMTS).  
From the administrative point of view, cells are grouped in administrative areas. Note the chain of spatial aggregations involving the attribute _coverage_ of entities PLMN, SubPLMN, administrative area, and cell. It remains only to define how, practically, the calculation of such attributes is performed, an aspect that, together with the calculation of the network controller's derived attribute _position_, is postponed to the introduction of the overall entity relationship diagram (Section 3.5).
## 3 Experimental and Simulation Analysis of Normal Contact Stiffness in Bolted Flat Plate Structures
### Experimental Modal Testing of Different Contact States in Bolted Flat Plate Structures
As demonstrated in the results of experiments, when the fixture and the flat plate come into contact, the fundamental frequency of the plate increases to nearly 1000 Hz. In the high-frequency vibration modes, the displacement of the plate's vibration is very small--less than 1 \(\upmu\)m. Moreover, the experiments employed elastomers; although there is a minute deformation during vibration, most of the deformation occurs on the surface of the contact body.
## 4 Simulation Verification
### Trajectory Tracking  
In this test, the trajectory tracking of the OMR along a pulse trajectory is conducted to evaluate the performance of the proposed MPC strategy. The node parameters of the trajectory are shown in Table 2.  
There are six nodes, including the starting point and end point, in the pulse trajectory. The trajectory tracking is achieved by tracking each node according to the position and target direction given in Table 2. In order to simulate the real state of the OMR, the noise is added to the output state.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index \(i\)** & \(x_{c}\) **(m)** & \(y_{c}\) **(m)** & \(\theta_{c}\) **(rad)** \\ \hline
2 & 2 & 1 & 0 \\
3 & 2 & 3 & 1.57 \\
4 & 4 & 3 & 0 \\
5 & 4 & 1 & 1.57 \\
6 & 6 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The node parameters of the tracking trajectory.  
Figure 10: The tracking error.  
Figure 9: The trajectory coordinates of the OMR.  
The omni-directivity and flexibility of the kinematic structure of the OMR are verified through these two tests. The OMR can achieve movement of any direction without turning, which solves the problem that nonholonomic wheeled mobile robots are not flexible in real-time avoidance. In addition, the results of the first test show that the MPC algorithm can be applied in stabilization control of the robot and the target position and angle can be reached quickly and steadily.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## 3 Results and Discussion
### Current GNSS Constellations and the Results of Eclipsing Monitor
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Models** \\ \hline Satellite antenna phase center offset & \begin{tabular}{l} FCO and PCV values for GPS /GLONASS/GALILEO from figs.atx were \\ used; Corrections for BDS adopted the values provided by WHU ([PERSON], 2017); \\ \end{tabular} \\ \hline Receiver antenna phase center offset & \
\begin{tabular}{l} FCO and PCV values for GPS and GLONASS from figs.atx were used; \\ Corrections for GALILEO/BDS were assumed the same as that of GPS; \\ \end{tabular} \\ \hline Tidal effects & Solid tides, ocean tide loading and polar tides (IERS 2010 ([PERSON], 2017)) \\ \hline Wind-up effect & Corrected ([PERSON], 2017) \\ \hline Satellite DCB & Corrected by MGEX DCB products (DLR) ([PERSON], 2017) \\ \hline Station coordinate & Estimated as constant/white noise (\(10^{4}\) m\({}^{2}\)) in static/kinematic modes \\ \hline Zenith wet tropospheric delay & Estimated as random-walk model (\(5\times 10^{-8}\) m\({}^{2}\)/s) \\ \hline Receiver clock & Estimated as white noise (\(10^{4}\) m\({}^{2}\)) \\ \hline Receiver ISBs & Estimated as 1-day constant \\ \hline Phase ambiguity &
\begin{tabular}{l} Estimated as float constant for each arc; when cycle-slip happened, \\ estimated as white noise model (\(10^{4}\) m\({}^{2}\)) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Since the satellite PCO errors induced by the non-nominal yaw attitude are related to the magnitude of the PCO, the average values of GNSS satellite PCO from jgs.atx are listed in Table 4. The mean values are calculated by all the current operational satellites (July 2017) with the same generation. The satellite PCO errors induced by the non-nominal yaw attitude of GPS IIF, GLONASS-M and BDS satellites are the largest, followed by the GALILEO satellites. For the GLONASS-K1 and GPS IIR satellites, the satellite PCO errors caused by the non-nominal attitude can be neglected. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constellation** & **Type** & **x** & **y** & **z** \\ \hline \multirow{4}{*}{GPS} & IIR-A & +2.5 & \(-\)0.8 & +1270.0 \\  & IIR-B & +5.8 & +1.8 & +788.4 \\  & IIR-M & +3.4 & +0.5 & +782.3 \\  & IIF & +394.0 & 0.0 & +1505.5 \\ \hline \multirow{2}{*}{GLO} & M & \(-\)54.5 & 0.0 & +2396.2 \\  & K1 (R09/R26) & 0.0 & 0.0 & +2083.0/+2014.7 \\ \hline \multirow{2}{*}{GAL} & IOV & \(-\)170.0 & +30.0 & +950.0 \\  & FOC & +120.0/+160.0 1 & \(-\)10.0/\(-\)10.0 & +1100.0/+1050.0 \\ \hline \multirow{2}{*}{BDS} & GEO & +600.0 & 0.0 & +1100.0 \\  & IGSO & +588.7 & 0.0 & +2902.4 \\  & MEO & +575.0 & 0.0 & +2128.0 \\ \hline \end{tabular} \({}^{1}\) The PCO for E14/E18: (+160.0, \(-\)10.0, +1050.0).  
\end{table} 
Table 4: The average antenna phase offset for GNSS satellites (unit: mm). Figure 1: The monitoring results of the eclipsing satellites in July 2017.
# A Comparative Analysis of Arabic Text Steganography
## 6 Recommendations for Future Works  
This study provides recommendations and opens new paths, which are highlighted as follows:  
* Although some researchers have considered Arabic characters, most of them have not applied their suggested methods to social media. Meanwhile, such media are fertile environments for information hiding, as a large volume of texts is pumped on social networks every day. This volume of texts makes it difficult for the eavesdropper to specifically select any of them that may contain hidden information. Researchers can thus apply some of these methods to social media while facilitating the support for Arabic characters.
* The integration of text steganography methods improves the capacity and increases the difficulty experienced by the eavesdropper in an attempt to trace the embedding algorithm. However, these methods inherit the disadvantages of the methods that make them up. This is especially obvious in the kashida approach, which increases the stego file size which also raising the level of suspicion in specific cases. Therefore, the integration should be well studied to identify which methods achieve the desired objectives while minimizing the constituent methods' drawbacks.
* A compression method reduces the amount of hidden information, thereby increasing the capacity. It also increases the complexity of extracting the secret message from the cover text. Despite this, only a few researchers have used compression to improve the efficiency of their methods.
* A few of the proposed studies have provided solutions to enhance the protection of secret messages prior to the embedding process by combining both cryptography and steganography methods, especially for protecting the stego key. This combined method constitutes another layer of protection if the embedding algorithm is detected.
* During this survey, it was observed that the use of full diacritics text is lacking, which is the obstacle preventing the exploitation of such diacritics. It is worth noting that students of religious studies or linguistic sciences at all stages adopt this type of text. Similarly, the Quran and Hadiths scripts are omnipresent on the web and social media and widely used as references and an inference.
* Regardless of kashida's weaknesses, such as increasing the stego file size and thus increasing the suspicion to a reader, utilizing kashida in text watermarking, especially in Quranic scripts, is ideal than text steganography. In text watermarking, kashida is used to protect or copyright the text without affecting the meaning of the text, unlike the diacritics.
* Most Arabic text steganography methods suffer from low capacity because of the limited number of bits per location and usable characters. Integrated Arabic features with font attributes are used to enhance the capacity.
* The selection of the embedding positions sequentially tells the attacker the order of the secret bits. Therefore, it is imperative to propose embedding methods with non-sequence position as the additional security layer.
## The Proposed Method  
### Data Preprocessing  
As mentioned above, HSI has high dimensions and the data among adjacent spectral bands have strong correlations. If the raw data are trained directly, it may cause unnecessary calculation and even reduce the accuracy and speed of classification. Therefore, the PCA method [44; 45] is used to reduce the dimensions of HSI. During the experiment processing on extensively-used datasets of HSI, the first 25 principle components are selected, which remains at least 99% of the initial information. In HSI, the spectral information is connected with the reflectance properties of each pixel on each spectral band. Differently, the spatial information is derived by considering its neighborhood pixels [29]. Therefore, in this paper, spectral and spatial information are combined as samples. For the sake of brevity, we call the samples combined with spatial and spectral information, SS Images. The sample generation process is shown in Figure 1.  
The detailed sampling procedure is described as the following.  
1. After PCA is conducted, we assume that a labelled pixel \(p_{i,j}\) at location of \((i,j)\) is selected as a sample, and labeled as the class of \(l_{i,j}\).
2. Then, we center on pixel \(p_{i,j}\), increase the rows and columns from \((i-2,j-2)\) to \((i+2,j+2)\) respectively, and capture an area of \(5\times 5\) to form a three-dimensional cube of \(5\times 5\times c_{r}\).  
Figure 1: The procedure of sampling, where \(w\), \(h\), and \(c\) represent the width, height, and the number of bands in original image, respectively, and \(c_{r}\) represents the number of components retained after principle component analysis (PCA). One sample combined with spatial and spectral information (called an SS Image) belongs to a class.  
Finally, the three-dimensional cube is unfolded by extracting the spectral band values of each pixel to form a row vector from left to right and from top to bottom, thus a \(25\times c_{r}\) image is formed as shown in Figure 2, which combine spectral and spatial information as an input, denoted as \(x_{i,j}\). A sample of \(d_{i,j}\), an SS Image, is formed as \(d_{i,j}=(x_{i,j},l_{i,j})\).
4. Repeat steps (1-3), and we can form the dataset \(D=\{d_{i,j},i=1,2,...,w,j=1,2,...,h\}\).
## 5 Performance Evaluation
### Network Models  
We assume \(N\) sensor nodes to be uniformly distributed in an \(M\times M\) field, with the following properties:  
1. All the sensor nodes are stationary after the sensors are deployed.
2. All the sensor nodes are time synchronous.
3. All the sensor nodes are homogeneous and power limited, and they have exclusive identification.
4. All the sensor nodes are equipped with power control capabilities to vary their transmitted power.
5. The communication link is symmetrical. The nodes can estimate the distance between the transmitter and receiver by the received signal strength indication (RSSI).  
We assume a radio hardware energy dissipation model, similar to those used in [7]. To transmit an \(l\)-bit message a distance \(d\), the radio consumes  
\[E_{Tx}(l,d)=\left\{\begin{array}{ll}lE_{elec}+l\varepsilon_{fs}d^{2},&d<d_{0 }\\ lE_{elec}+l\varepsilon_{mp}d^{4},&d\geq d_{0}\end{array}\right. \tag{5}\]  
and to receive this message, the radio consumes:  
\[E_{Rx}(l)=E_{Rx-elec}(l)=lE_{elec} \tag{6}\]  
The electronics energy, \(E_{elec}\), depends on factors such as the digital coding, modulation, filtering, and spreading of the signal, whereas the amplifier energy, \(\varepsilon_{fs}d^{2}\) or \(\varepsilon_{mp}d^{4}\), depends on the distance to the receiver and the acceptable bit-error rate.  
In WSNs, the cluster heads need to aggregate and fuse the data, and the energy for data aggregation parameter is set as \(E_{DF}\).
## 3 Preliminaries
### DIRECT Algorithm
#### 3.1.3 The Flowchart of the DIRECT Algorithm  
The DIRECT algorithm starts by sampling at the center of the unit hypercube. Each iteration first identifies potentially optimal hyper-rectangles. Subsequently, these hyper-rectangles are sampled and subdivided. This iterative process continues until reaching the predetermined iteration limit. Algorithm 2 provides a formal statement of the DIRECT algorithm. According to the DIRECT algorithm, the value of \(\min_{\mathbf{x}\in[x^{\text{low}},x^{\text{low}}]}f_{i}(\mathbf{x})\) is denoted by \(f_{\min,i}\).  
Figure 4 illustrates the first three iterations of the DIRECT algorithm. The potentially optimal rectangles (in gray) are depicted in the first row, while the sampling (represented by red points) and subdivision of these rectangles are demonstrated in the second row.  
```
Step 0 Parameter setting: set the maximum number of iterations as \(K_{\max}\). Step 1 Standardize the search space to the unit hypercube. Assess \(f_{i}(A^{-1}\mathbf{c}_{1}+\mathbf{x}^{\text{low}})\), with \(\mathbf{c}_{1}\) being its center point. Define \(f_{\min,i}=f_{i}(A^{-1}\mathbf{c}_{1}+x^{\text{low}})\), \(n=1\), and \(k=1\). Step 2 Determine the set \(S\) of potentially optimal rectangles. Step 3 Choose any rectangle \(j\) from the set \(S\). Step 4 Use Algorithm 1 to determine the sampling locations within rectangle \(j\) and to divide it into sub-rectangles. Update \(f_{\min,i}\) and set \(n=n+\Delta n\), where \(\Delta n\) is the number of new sampled points. Step 5 Set \(S=S-\{j\}\); if \(S\
eq\varnothing\), go to Step 3. Step 6 Set \(k=k+1\). If \(k=K_{\max}\), stop and output the minimum value \(f_{\min,i}\). Otherwise, proceed to Step 2.
```  
**Algorithm 2** DIRECT algorithm
# Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring
## 3 Experiment
### Uncertainties of the Model  
In this section, model uncertainties are evaluated by repeated training. In the experiment, ten models are obtained through training. The MSE and RMSE of the obtained models on the test set are shown in Figure 13. RMSE is the arithmetic square root of MSE, which definition is shown in Equation (5).  
\[\text{RMSE(y,\$)}= \sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}, \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ResNet50** & **DenseNet121** & **Focus \& TSOM-CNN** \\ \hline MSE/nm\({}^{2}\) & 258.50 & 176.19 & 5.18 \\ MAE/nm & 5.26 & 4.61 & 1.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance summary of other TSOM methods and Focus & TSOM-CNN.  
Figure 12: Four measuring examples on the test set. (**a**) Linewidth of 247 nm. (**b**) linewidth of 357 nm. (**c**) linewidth of 625 nm and (**d**) linewidth of 768 nm.  
The standard deviation of the mean of RMSE (Equation (6)) is normally used to evaluate uncertainties of the model. The result of model uncertainties is shown in Table 2.  
\[\mu_{2}=\sqrt{\frac{\sum_{i=1}^{n}\left(\text{RMSE}_{i}-\overline{\text{RMSE}_{ i}}\right)^{2}}{n(n-1)}}, \tag{6}\]  
Many factors effect uncertainties in the deep-learning TSOM measurement, including the model, the parameters of the imaging system, the truth uncertainty, the imaging noise, etc. In practical experiments, some methods can be used to reduce the uncertainties of measurement, such as imaging system optimization, image denoising, etc. Reducing uncertainties would be one of the improvements of TSOM in the future.