esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Communication System Model and Theory
### Conventional Reception Method for M-SS  
After performing synchronization, filtering, and other preprocessing on the received signal, Doppler estimation is also required. This paper conducts both coarse and fine Doppler estimation on the received signal [24]. When the estimated frequency shift value after coarse Doppler estimation significantly deviates from the actual frequency shift value, the correlation of the spread spectrum symbols will be markedly reduced. In this case, it is essential to perform a Doppler frequency shift search within a certain range based on the coarse estimation to restore correlation; this process is defined as fine Doppler estimation. As shown in Figure 2, the preprocessed signal is passed through the coarse Doppler estimator to obtain the initially estimated frequency shift value, which serves as the initial search value for subsequent fine Doppler estimation, and the signal is then input into the fine Doppler estimator. First, the signal is input into multiple locally generated correlators for frequency shift searching. Given that the modulation scheme employed in this paper is M-SS spreading, the signal must also be input into the spreading code index library for correlation analysis, ultimately resulting in a correlation matrix composed of the carrier frequency offset (CFO) and the spreading code index dimensions, from which the corresponding frequency shift and index decoding values are selected based on the matrix peak, thereby obtaining the Doppler coefficient for the symbol (the detailed theoretical derivation of the estimation process is presented in reference [16]). However, the computational load of the search process is substantial; thus, we prefer a more accurate coarse Doppler estimation to reduce the search range. Numerous studies have been conducted on this aspect (see reference [19; 25; 26; 27]), primarily focusing on velocity prediction and interpolation methods, which often exhibit limited adaptability to complex motion states, and many methods are only applicable to direct sequence spread spectrum communication, rendering them unsuitable for M-ary spread spectrum communication. To address the issues of reduced correlation of spread spectrum symbols resulting from inaccurate coarse Doppler estimation during communication under complex motion states, as well as the computational efficiency of subsequent fine Doppler estimation, we propose a reception method correlation cost factor-based updates to matched filters for M-SS (CCF-MSS), focusing on enhancing Doppler tolerance.  
Figure 1: Underwater acoustic communication system model.
# Simulation of Autonomous Underwater Vehicles (AUVs) Swarm Diffusion
## 3 Conclusions  
By means of simulations, we expose the feasibility of a swarm of low-cost, low-intelligence drones that can act as a sentinel for a stretch of sea. First of all, we introduce the vehicles with a low and uncoordinated intelligence that have the ability to be resilient to the loss of one or more members thanks to the raising of the overall reliability based on the reasonable elevation of the number of members.  
The paradigm we have investigated is based on a series of constraints that are very loose between the various members of the swarm: The autonomy of each individual is maximum while the interaction with neighbors is limited to proximity; from here, the particle model is developed.  
Then, we study the swarm behavior for UAVs, which is formalized with a microscopic diffusion model in order to justify the simple single-drone program.  
Figure 8: “Snapshots” over time, at intervals of 0 (a), 0.5 (b), 1 (c), 4 (d), 8 (e), and 12 h (f).  
In the final part, we show that the drones are not a path defined by a computer center that coordinates the movements and, therefore, proceeds in a completely random way with the only constraint to move away from each other. Once this process has started, they tend to become more and more uniformly distributed on the surface of the assigned sea until they reach a constant probability over the entire surface, that is, after a time of 12 h.  
This means that the swarm, despite moving in a completely random way, manages to \"fill\" the entire predetermined area in a short time. The simulation defines and shows the behavior of a swarm of underwater drones after their insertion in a closed section of the sea, from a single point and their course for the whole area assigned to them. This will help us to define the characteristics of the drones (and, above all, their guidance and control subsystems) that we are developing.  
Conceptualization, E.P. and F.L.; methodology, E.P.; validation, F.L.; formal analysis, E.P.; investigation, M.C.; resources, F.L.; data curation, E.P.; writing--original draft preparation, E.P.; writing--review and editing, F.L. and M.C.; supervision, F.L.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Wastewater Treatment Process: Case Study
#### 2.1.2 Sludge Treatment  
The sludge treatment line contemplates the sifting, thickening, stabilizing, and dewatering of the sludge from the sedimentation or primary clarification process. This stage is based on the biological treatment of sludge, through the anaerobic digestion process. After removing the waste through a sieving process, the sludge is pretreated considering a thickening process, in order to obtain thickened sludge with an appropriate concentration for entering the sludge digesters. The purpose of the thickening process is to reduce the volume of sludge by partially removing useful water. In the thickener, the sludge is agitated  
Figure 1: Wastewater treatment process diagram for the case study. Source: Own elaboration.  
and remains in the chamber for a prolonged period, where the heavier particles settle at the bottom of the thickener, separating the useful water from the sludge, the former being extracted by mechanic suction.  
The sludge is subsequently derived from the anaerobic digesters for its stabilization. This stabilization is achieved through a biological procedure that allows degradation of the remaining organic matter, utilizing a biological fermentation carried out in a vacuum chamber [13]. A variety of gases are obtained from the chemical reaction processes involved in fermentation, mainly methane and carbon dioxide. To improve the mesophilic stabilization process, the sludge is preheated at the inlet of the digester to maintain a temperature of 35 \({}^{\circ}\)C. For this purpose, the sludge building contains a system of hot water boilers, spiral water, sludge and heat exchangers, and horizontal centrifugal pumps to propel the hot water to the exchangers. Finally, the stabilized sludge is stored in a buffer tank and later dried using decenter centrifuges. The dewatered sludge is finally transported for its storage in silos.
## 2 Materials and Methods
### Materials and Sample Preparation
#### 2.1.2. Asphalt Mixture  
Cylindrical samples (diameter 101.6 mm and height 64 mm) of asphalt mixture were prepared using a Servopac gyratory compact following AASHTO specification T 312-07. The number of gyrations for an equivalent single axial load (ESAL) for the study ranged from 10 to 30 \(\times\) 10\({}^{6}\), corresponding with N\({}_{\text{design}}\) of 100 gyrations. Three samples for each mixture were prepared using five modified binder contents (4.5%, 5.0%, 5.5%, 6.0% and 6.5% of the total asphalt mixture weight) to determine the optimum binder content (OBC).  
The aggregate used in this research was granite with a maximum particle size of 19.0 mm. The aggregate gradation is illustrated in Figure 2. Air voidage of 4% was selected for all mixtures of the OBC. Mixing and compaction temperatures were determined based on the results of rotational viscosity. The mixing temperature was chosen as the range corresponding to 0.17 \(\pm\) 0.02 Pa.s, while the compaction temperature was chosen as the range corresponding to 0.28 \(\pm\) 0.03 Pa.s. The selected mixing temperatures were 155 \({}^{\circ}\)C, 160 \({}^{\circ}\)C and 163 \({}^{\circ}\)C for 0 NCMB, 2 NCMB and 4 NCMB, respectively, while the selected compaction temperatures were 146 \({}^{\circ}\)C, 150 \({}^{\circ}\)C and 152 \({}^{\circ}\)C for 0 NCMB, 2 NCMB and 4 NCMB, respectively. The OBC for 0 NCMB, 2 NCMB and 4 NCMB was 5.2%, 5.5% and 5.8%, respectively. In this study, after determining the OBC, the mixtures were prepared at air voids of 7 \(\pm\) 0.5%. It is well-known that one of the factors leading to increased rate of ageing is higher air voidage. More air voidage allows oxygen to enter the mixture and results in more chemical change during STA and LTA, influencing the strength of the asphalt mixture.  
To simulate ageing of the mixture, the AASHTO R 30 standard process was followed. The asphalt mixture was subjected to STA, which simulates the mixing and compaction phase of the construction. After mixing in the mechanical mixer, the mixture was placed in the oven at compaction temperature for four hours, stirring hourly to ensure that all coated aggregate particles were exposed to the air inside the oven, and compacted prior to testing. The LTA simulates the ageing that occurs over the service life. It was performed, after the application of STA simulation, by subjecting the compacted mixture to a temperature of 85 \({}^{\circ}\)C for five days.  
Figure 2: Aggregate gradation.
# A Review of Air Pollution Monitoring Technology for Ports
## 2 Detection Methods for Air Pollutants in Ports
### Detection Methods for Gaseous Pollutants
#### 2.2.2 \(\mathrm{NO_{X}}\) Detection Methods  
NO and \(\mathrm{NO_{2}}\) are the most common nitrogen oxides in atmospheric environmental monitoring; they are mainly derived from the combustion of fossil fuels such as coal [31] and are one of the main parameters for monitoring ambient air. Similarly, NO and \(\mathrm{NO_{2}}\) analysis methods can be divided into manual analysis methods and automatic analysis methods. The main manual analytical method commonly used for the detection of NO and \(\mathrm{NO_{2}}\) is the ethylene diamine dihydrochloride spectrophotometric method [32], while chemiluminescence [33] and DOAS [34] are commonly used in automated analytical methods.
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## 2 Aspects of Variability in Voice
### Types of Phonation  
The pair of human vocal folds is a non-linear dynamic oscillating system that can vibrate in several distinct ways, which are often called _phonation types_ or _registers_ or _mechanisms_. The two most commonly observed are modal/chest voice (M1) [30], which is normally used in speech and low-pitch singing; and head/falsetto voice (M2), which is typically used in high-pitch singing, there is usually a bistable pitch region within which a person can _choose_ to phonate in M1 or M2, with a little training. This choice will switch the voice into another regime entirely. The skilled control of such switching is considered desirable in singing, while frequent involuntary switching, such as in voice mutation during male puberty, is considered to be undesirable in speech. Several voice metrics will present different scalar fields depending on whether phonation was done in M1 or M2 [31; 32]. In practice, this means that we have a third important independent variable, namely the _mechanism_. This variable can assume the discrete values M1, M2 and in unusal cases also M0 (\"glottal fry\") or M3 (\"whistle\"). Since this third dimension represents a small number of discrete categories, it can be (and has been) implemented using a separate 2D map layer for each category. So as not to make the present overview unduly complicated, this third \"control parameter\", which can take any of the values {M0 | M1 | M2 | M3}, will be disregarded here, and we will assume that the voice stays in M1, unless otherwise noted.
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## Appendix A Taxonomy of Malware Analysis  
### Malware Types  
Malware is a piece of code, which on executing performs, illegal actions such as stealing users' personal information, faltering the working of a system, creating any backdoor without user's information, and encrypting the data to make it useless for the user. Malware can be dedicated with the sole purpose to hinder the working of any system like Stuxnet as described by [84] or of a kind, which can victimize several systems or applications. Malware mostly falls into the following malware families:  
* Virus: It can replicate itself by getting attached to any file/document. It has the potential to corrupt the system, destroy the data, and can pose a great threat to assets.
* Worm: It behaves just like a virus but can replicate itself over the network.
* Trojan horse: It masquerades itself as a useful program but contains malicious code.
* Backdoor: It gets itself installed on the system and gives access to the attacker without or with very little authentication.
* Botnet: It behaves just like a backdoor. The difference lies when it comes to the command and control server. All systems compromised by the botnet receive the same command from the same command and control server.
* Spyware: It behaves as a useful application but leaks users' data.
* Downloader: It is normally installed by the attacker on victims' machines. Its sole purpose is to download malicious code on the system.
* Rootkit: It gets paired with other malware and hides the existence of that malware. Another devastating effect of the rootkit is the root level access that it gives to the malware.
* Scareware: It frightens the users to buy their products to keep their data and system safe.
* Many malware fall into more than one category as they exhibit features of more than one malware family.  
Malware analysis is the process that has become extremely important, not only to mitigate network attacks but massive destruction can also be prevented. These attacks can pave the way through the execution of malware on a standalone, dedicated system or by controlling no. of systems on a network.
# Modeling and Design of a Novel 5-DOF AC-DC Hybrid Magnetic Bearing
## 1 Introduction
This research proposes a 5-DOF AC-DC HMB with permanent magnets to increase the critical speed of high-speed electric machines, especially in high-speed mechanical systems. The proposed HMB can realize 5-DOF in one unit. Three-phase power inverters are employed for the radial drive, while DC power amplifiers are used for the axial drive. The radial and axial control fluxes do not interfere with each other. The structure of the inclined axial magnetic poles reduces the axial length of the rotor and is beneficial to improving the critical speed of the rotor. It has a series of advantages such as low cost, low power consumption, and compact structure. The structure and working principle of the proposed 5-DOF AC-DC HMB are first introduced. Then, the equivalent magnetic circuit method is employed to deduce the mathematical models of suspension forces. Based on the finite element analysis (FEA), the electromagnetic properties of the 5-DOF AC-DC HMB are analyzed, including the radial suspension forces versus the radial control current, the axial suspension forces versus the axial control current, flux field distributions, and the relationship between the suspension force and the control current with the offset, all of which verify the correctness of the theoretical analysis.
## 2 Laboratory Testing Program  
### Tested Materials and Apparatus  
The original tested material of the specimens was crushed coal gangue, which was collected from a coal mine in Xiangtan city. The waste coal gangue in this mine is shown in Figure 1. Nearly 15 tons of coal gangue were transported to the laboratory for indoor tests and research. The original particle size of coal gangue not only contains fine particles less than 0.075 mm, but also includes stones of tens of centimeters. It should be noted that the maximum particle size allowed by the test equipment in this test does not exceed 60 mm. Considering that the specimens in this test were prepared manually, CGSF with particle sizes greater than 60 mm were removed. The color of coal gangue particles is black and black-gray, and the coal gangue has not experienced spontaneous combustion. Coal gangue particles are angular, sharp, and hard with a rough surface and an irregular shape. The CGSF was dried to constant weight in an oven at 105-110 \({}^{\circ}\)C (more than 24 h), and then the standard sieve tests with aperture sizes of 60, 40, 20, 10, 5, 2, 0.5, and 0.075 mm were carried out. Coal gangue particles of each group after sieving tests were displayed in Figure 2. According to the JTG 3430-2020 Chinese standard for soil test method [18], the natural moisture content of CGSF is 2.20-2.98%, the liquid limit, the plastic limit, and the plasticity index of fine particles are 31.46%, 20.57%, and 10.89%, respectively. The coal gangue of this coal mine mainly includes sandstone, limestone, shale, and mudstone.  
An LSDSTA manufactured by Chengdu Donghua Zhuoyue Technology Co., Ltd. (Chengdu, China), was used in this test, which is shown in Figure 3. The specimen sizes allowed by the LSDSTA are \(D\) = 300 mm and \(H\) = 600 mm. The ratio between the triaxial specimen diameter and the maximum particle size should not be smaller than 5 [19; 20; 21]; therefore, the maximum particle size allowed by LSDSTA in this test is no greater than 60 mm, and then the effect of specimen size can be neglected. The LSDSTA mainly includes the following parts: (1) a data acquisition system through a computer, (2) a testing machine control system, (3) a triaxial pressure cover, (4) an axial loading system, (5) a volumetric strain measurement system, and (6) a sample preparation mold, which are shown in Figure 3. The LSDSTA can automatically collect test parameters, such as axial load and displacement, confining pressure, pore pressure, and volumetric strain, as well as realize the static shear test and cyclic dynamic loading test. In addition, the large-scale triaxial tests and the same specimen size were widely used in the previous studies, e.g., [PERSON] et al. [20] and [PERSON] et al. [21], which guarantees the reliability of this apparatus.  
Figure 1: The waste coal gangue of a coal mine.  
Figure 2: Coal gangue particles after sieving tests.
# An Empirical Recommendation Framework to Support Location-Based Services +
## 4 Methodology
### Map Representation of POIs  
The representation of locations is an essential part of LBS. There are different types of map representation for spatial datasets. It is necessary to find a perfect map to present the POIs so that the system can easily identify the location of a specific point of interest. From various representation types, we use the Grid-based structure because it can provide the quantified number of cells with various numbers of POIs, which helps to find a fast processing comparison [43]. Before generating the recommendation for the user we extract the POIs of a user's current location using Google API and place them into the different grid according to the location of those points. Algorithm 2 represents the detailed process of Grid generation.  
```
procedureGrids(\(P,N\))\(\triangleright\)\(P\) defines places, \(N\) is the value of \(N*N\) grids for all\(poi\) in \(P\)do for all\(poi\) in \(P\)do \(mnx\leftarrow\) minimum Cartesian x of the \(poi\) \(mxx\leftarrow\) maximum Cartesian x of the \(poi\) \(mny\leftarrow\) minimum Cartesian y of the \(poi\) \(mxy\leftarrow\) maximum Cartesian y of the \(poi\) \(gridsx\leftarrow(mxx-mnx)\div N\)\(\triangleright\) size in \(X\) axis \(gridsy\leftarrow(mxy-mny)\div N\)\(\triangleright\) size in \(Y\) axis Function\(geo-car(geolocation)\) \(lat\leftarrow\) latitude of geolocation \(lon\leftarrow\) longitude of geolocation \(r\leftarrow\)6371 \(\triangleright\)\(r=\)radius of the earth \(\Theta\leftarrow\Pi\div 2-toRadian(lat)\) \(\Pi\leftarrow\)\(toRadian(lon)\) \(x\gets r*sin(\Theta)*cos(\Pi)\) \(y\gets r*sin(\Theta)*sin(\Pi)\) \(z\gets r*cos(\Theta)\) return(\(x,y\)) end Function
```  
**Algorithm 2** Grid Structure.  
We see that at first each POI's location (latitude, longitude) is converted into cartesian co-ordinates. Then we consider four border points to cover the region at a certain location. To form \(N*N\) grid structure each length of \(X\) and \(Y\) axis is divided by the number of cells \(N\). Figure 4 shows a grid representation of some POIs.  
Now there are 15 POIs with different categories marked with various colors. From 12 grids only 10 grids contain POI and thus it reduces our processing time since we do not have to consider the whole. Table 8 gives a listed grid with POI.
## 4 Performance Comparison of Machine Learning
### Model Validation and Performance Comparison  
Using the above optimized hyperparameters, each model can be trained and established. To obtain independent test data and reliable results, each data set was split randomly (70:30) into a training and a test data set. In other words, we use 70% of data for training as model fitting and 30% for evaluating, among the 1098 cases we collected. The training data were evaluated and validated through K-fold cross validation. To reduce the influence of a single sampling method on model results, the seven-fold cross-validation method was adopted to select training data and test data. Seven-fold cross-validation method divided the whole dataset into seven disjoint subsets randomly and averagely. Seven subsets was identified as an optimal value in this model, among the most popular and common value (e.g., k = 3, 5, 7, 10) used in applied machine learning [33]. To compare the classification models with judicial cases, we performed 100 simulations (i.e., experiments are repeated 100 times) of each model to generalize its accuracy and compute the mean of misclassification rates, as shown in Table 4 and Figure 2. The greatest accuracy of classifications was achieved by CART, showing the lowest misclassification rate to be as same as that analyzed simultaneously. As a result, CART has the best performance rate for predicting judicial decisions.