esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Constructions of Low-Hit Zone Frequency-Hopping Sequence Set Based on _m_-Sequence
## 2 Preliminaries
### \(q\)-Ary \(m\)-Sequences and Its Decimated Sequence
One can have  
\[H_{c}(\mathbf{S}_{\mathbf{c}})=\frac{q^{n-k}-1}{d} \tag{39}\]1  
The equal sign in Peng-Fan bound (4) is true, which indicates \(\mathbf{S_{c}}\) is an optimal LHZ FHS set. The parameter \(d\) determines the sampling width and the number of FHSs in \(\mathbf{S_{c}}\). When \(d\mid(q-1)\), it ensures that each element in the \(q\)-ary \(m\)-sequence with degree \(n\) appears exactly once in the \(d\)-decimated sequence. Furthermore, if \(\gcd(d,n)\
eq 1\), Lemma 4 does not hold and in such cases the Hamming correlation of \(\mathbf{S_{c}}\) cannot achieve optimality. Now, we use the following example to illustrate the construction C.  
**Example 2**. : _Let \(q=7\), \(k=1\), \(n=3\), \(d=2\). \(\alpha\) is a primitive element of \(\mathbb{F}_{75}\) which is generated by \(\alpha^{3}+\alpha^{2}+\alpha+2=0\). We first obtain a set of \(d\)-decimated sequence \(\mathbf{a}=\{a_{0},a_{1}\}\) derived from 7-ary \(m\)-sequence with degree 3 as follows._  
\(a_{0}=\) _(0, 1, 0, 3, 1, 1, 0, 0, 4, 3, 2, 2, 2, 5, 2, 0, 5, 3, 5, 3, 3, 5, 2, 4, 1, 5, 0, 5, 1, 0,...),_  
\(a_{1}=\) _(0, 6, 6, 5, 2, 2, 3, 4, 6, 4, 2, 6, 2, 3, 6, 4, 5, 3, 0, 1, 4, 6, 3, 3, 2, 5, 6, 3, 0, 5,...)._  
_Take \(L_{z}=28\). Then, according to step 2 and step 3 of construction \(C\), \(\mathbf{S_{c}}=\{s_{c_{0}},s_{c_{1}},...,s_{a_{9}}\}\) is obtained as follows._  
\(s_{c_{0}}=\) _(0, 1, 0, 3, 1, 1, 0, 0, 4, 3, 2, 2, 5, 2, 0, 5, 3, 5, 3, 3, 5, 2, 4, 1, 5, 0, 5, 1,...),_  
\(s_{c_{1}}=\) _(0, 2, 2, 4, 3, 3, 1, 6, 2, 6, 3, 2, 3, 1, 2, 6, 4, 1, 0, 5, 6, 2, 1, 1, 3, 4, 2, 1, 0,...),_  
\(s_{c_{2}}=\) _(4, 0, 5, 4, 4, 0, 0, 2, 5, 1, 1, 1, 6, 1, 0, 6, 5, 6, 5, 6, 1, 2, 4, 6, 0, 6, 4, 0,...),_  
\(...\)__  
\(s_{c_{8}}=\) _(0, 4, 6, 6, 0, 0, 3, 4, 5, 5, 5, 2, 5, 0, 2, 4, 2, 4, 4, 2, 5, 3, 6, 2, 0, 2, 6, 0, 5,...),_\(s_{\varsigma_{0}}\) = (5, 3, 4, 4, 6, 1, 5, 1, 4, 5, 4, 6, 5, 1, 3, 6, 0, 2, 1, 5, 6, 6, 4, 3, 5, 6, 0, 3, 0,...). Simulation experiments demonstrate that the MPHAC and the MPHCC of \(\mathbf{S_{c}}\) equal 24 for \(\tau\leq 28\), and the MPHCC and the MPHC of which have the same distribution for \(0\leq\tau\leq 170\). As illustrated in Figure 2, the MPHC of \(\mathbf{S_{c}}\) is equal to 24 for \(\tau\leq 28\). One can check that \(\mathbf{S_{c}}\) is an optimal \((171,10,7,28,24)\) LHZ FHS set according to Peng-Fan bound (4). Compared with the constructions of [PERSON] et al. [15] and [PERSON] et al. [14], different values are taken for \(L_{z}\), and we can obtain more LHZ FHS sets with optimal MPHC by Example 2, which are listed in \[H_{c}(\mathbf{S}_{\mathbf{c}})=\frac{q^{n-k}-1}{d} \tag{39}\]2
## 5 Results and Validation
### Evaluation with Existing Soil Wetness Iso-Lines  
According to the approach assumption, the soil temperatures for all of the pixels along the iso-line of equal water availability in the _T_-\(\alpha\) space are equivalent and are also similar for vegetation temperature. The basic theory of soil wetness iso-line is appropriate for the croplands in the oasis of Zhangye City, which are fully irrigated. The irrigated region mainly covers an area of 11 km \(\times\) 13 km and there are more than 30,000 thermal-IR pixels; however, the wetness iso-lines have a wide range in value, from near zero to on the order of hundreds. Thus, there were only a few pixels to define the water stress iso-line in the _T_-\(\alpha\) space, which may likely introduce additional uncertainties. For these reasons, defining a water stress iso-line may not be valid. In addition, the soil temperatures or vegetation temperatures at the water stress iso-line show fluctuations that are not physically realistic when compared to changes in values of surface albedo. Therefore, in this study, the water stress iso-line was defined using more pixels, and the resulting soil and vegetation temperatures along this iso-line are then illustrated in the two dimensional plot of surface temperature-albedo (Figure 7).  
Figure 7 illustrates the variation in soil and vegetation component temperatures along the water stress iso-line. The soil and vegetation temperatures for all pixels along the iso-line are nearly identical. Based on these results, the performance of component temperatures separated from the composite land surface temperatures derived from ASTER thermal infrared bands verifies a theory of water stress iso-line in the space. The results further indicate that the soil and vegetation component temperatures were successfully separated using the _T_-\(\alpha\) space method. Although there were slight temperature fluctuations, they ranged within 1 K, and changes in the soil temperatures on the iso-line were relatively stable, but fluctuations of vegetation temperatures are still relatively larger. The slight fluctuations of soil temperature according to the changes in the surface albedo are reasonable, and the slight fluctuations may be attributed to the uncertainty produced by the process of determining the maximum vegetation in the space. As in the early season, the crops grow extremely quickly, and it is difficult to acquire the withered vegetation pixels from the images with a spatial resolution of 90 m. The limited number of withered vegetation pixels, if they do exist, is likely to cause significant uncertainty in determining the dry edge in the _T_-\(\alpha\) space. The relatively large fluctuations of vegetation temperatures can be mainly attributed to the  
accuracy of separated soil temperatures and land surface composite temperatures derived from the ASTER images when using Equation (5).
# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
## 1 Introduction
### Industry 4.0 and Industry 5.0  
The term Industry 4.0 originated in 2011 by a German initiative of the federal government with academic associations and commercial enterprises [5] and was defined as a means to achieve flexible production of high-quality personalized products at mass efficiency through the integration of CPS (Cyber-Physical Systems) into production [6]. Globally, many countries have created similar local strategy initiatives, for instance, Industrial Internet Consortium (USA), Made in China 2025 (China), La Nouvelle France Industrielle (France), Society 5.0 (Japan), Towards Industry 4.0 (Brazil), to list a few [2,7]. Hence, Industry 4.0 has since become a globally adopted term and the focus of significant research efforts [2] with Smart Factories being among the key initiatives. Industry 4.0 is identified as four-dimensional front-end technology: Smart Manufacturing, Smart Products, Smart Supply Chain, and Smart Working, and relies on four elemental base technologies: IoT, cloud services, big data, and analytics [8]. When it comes to economic indicators, Industry 4.0 has decreased production costs by 10-30%, logistic costs by 10-30%, and quality management costs by 10-20% [9]. Industry 4.0 is technology-driven [2], providing technological pushes and solutions.  
In the meantime, Industry 5.0, regarded as the next industrial revolution, aims to leverage the creativity of human experts in collaboration with smart machines to obtain user-preferred manufacturing solutions [4]. Industry 5.0 recognizes the power of industry to achieve societal goals and to support long-term service to humanity [10]. It involves the combination of advanced technologies and human common life, with the return of the man to the \"Centre of the Universe\" [1], taking advantage of humans' decision-making capability to harmonize the working conditions and efficiency of humans and machines in a consistent manner [4].  
The aim of Industry 4.0 is to lessen the participation of human operators and to stress automation systems, whereas Industry 5.0 aims to achieve maximum benefits through the HMI [11]. Therefore, advanced HMI solutions between more intelligent machinery and the productive abilities of human beings are required [12]. Among those, digital twin [13,14] is considered one of the key technologies to enhance HMI through advanced modeling techniques. In parallel, the \"Age of Augmentation\" has arrived, in which humans and machines work symbiotically to leverage XR technologies, enhancing the operator's cognitive and interaction capabilities [10].
## Materials and Methods
### Storage Stability Test  
The storage stability is an integral parameter to consider while working with binders modified with nanoparticles. A Hot Storage Stability Test was carried out to check whether the suspension of the GNPs in the bitumen was stable for storage or not. This test was performed according to BS EN 13399 (2017). During this, 50 g of GNP-modified binder was poured into an aluminum tube. It was kept in the oven at 163 \({}^{\circ}\)C for 72 h. After the sample was taken out, it was cut into three sections. The top and bottom sections were heated and poured into rings for a softening point test. For a stable storage sample, the difference in the softening point of the top and bottom sections should be less than 2.2 \({}^{\circ}\)C. Although the softening point test is the most common method to determine the storage stability, it is not highly accurate [16]. For accuracy, the performance grades of all of the three sections were also determined by using a Dynamic Shear Rheometer (DSR) to ensure the homogeneous mixing of the GNPs in the binder.  
The softening point results for the top and bottom sections of the tube are shown in Table 4.  
Figure 5: A Scanning Electron Microscope (SEM) image of the Graphene Nano-Platelets (GNPs) in the asphalt binder (500 \(\upmu\)m).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of Mix** & **Optimum Binder Content** \\ \hline Base Binder & 4.38 \\
2\% GNPs & 4.49 \\
4\% GNPs & 4.67 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The Optimum Binder Content (OBC) of the asphalt mixtures.  
According to the results, the difference between the softening point of the top and bottom sections of the 2% and 4% GNP-modified binder was less than 2.2 \({}^{\circ}\)C. This indicates that the GNP-modified asphalt binder is a stable storage blend [17].  
The performance grades of the top and bottom sections of the samples for all of the blends were also determined using a DSR to ensure the homogeneous dispersion of the GNPs in the asphalt binder, as the rheological characterization of the binder is affected by its composition [16]. The performance grades of the binders did not vary between the sections.
# An Examination of Thermal Features' Relevance in the Task of Battery-Fault Detection
## 2 Experiment Design
### Methodology of Data Exploration  
Raw vectors of the thermal and voltage features were processed in several ways to examine their distinguishing ability and relevance towards the fault indicating annotation. The following statistical methods were applied in our testing work-flow:  
1. Statistical analysis * An estimation of correlation among temperature and voltage progress to test the similarity of their behavior. * ANOVA test to examine the significance of statistical difference among battery sets
2. Unsupervised clustering * Dimensionality reduction of multivariate inputs on their intrinsic dimension with further clustering and its evaluation
3. Supervised relevancy estimation * [PERSON]'s estimation of multivariate mutual information (MI) among reduced data and their annotations. * k-nearest neighbor classification on four different sets of data (voltage and temperature inputs for predicting six battery sets and fault/failure-free battery set states respectively).  
A brief description of the applied methods and algorithms follows.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 3 Framework Design
### MDP Problem Formulation  
The MDP problem for modeling DASH's ABR algorithm consists of five elements as shown in Equation (1).  
\[\mathcal{M}\ =\ <\mathcal{S},\mathcal{A},\mathcal{P},\mathcal{R},\gamma> \tag{1}\]  
\(\mathcal{S}\) is the set of states as the state space, \(\mathcal{A}\) is the set of actions as the action space, \(\mathcal{P}\) is the transition probability function, \(\mathcal{R}\) is the reward function, and \(\gamma\) is the discount factor for the future reward.
# Artificial Intelligence-Enhanced Colorimetric Assessment of Self-Cleaning Road Marking Points
## 2 Methodology
### Methods
#### 2.2.5 Photocatalytic Efficiency and Color Variation Assessment  
The photocatalytic efficiency (PE) and color variation (\(\Delta\)E) measurements manifest the self-cleaning capability of RM paint after functionalization. Higher values for both parameters indicate a greater pollutant degradation on the surface of the specimens. At the specified irradiation times, after obtaining the color coordinates using both methods (spectrophotometry and AI-enhanced processing), the chroma or color saturation (CS) was determined using the chromatic coordinates (a* and b*) as per Equation (4):  
\[\mathrm{CS}=\left(\mathrm{a*}^{2}+\mathrm{b*}^{2}\right)^{0.5} \tag{4}\]  
Subsequently, the PE was calculated using the CIELAB color space, based on the discoloration of the sample (pollutant degradation), as described in Equation (5) [20,35]:  
\[\mathrm{PE}(\%)=\frac{\mathrm{CS}(0)-\mathrm{CS}(\mathrm{t})}{\mathrm{CS}(0)- \mathrm{CS}(\mathrm{w})}\times 100 \tag{5}\]where CS(0) represents the color saturation of the polluted sample before starting irradiation, CS(t) is the color saturation at a specified light exposure time, and CS(w) is the color saturation of the sample before pollution.  
In addition to photocatalytic efficiency, another indicator that works as a perceptibility factor of the pollutant removal is \(\Delta\)E, calculated according to Equation (6) [20; 36]:  
\[\Delta\mathrm{E}=\left(\Delta\mathrm{L}^{\bullet 2}+\Delta\mathrm{a}^{\bullet 2}+ \Delta\mathrm{b}^{\bullet 2}\right)^{0.5} \tag{6}\]  
where \(\Delta\)L*, \(\Delta\)a*, and \(\Delta\)b* indicate the difference in the values of the coordinates L*, a*, and b* at a known irradiation period and the same polluted sample before starting irradiation.  
Additionally, a linearity study was also performed to verify the correlation between the parameters calculated via spectrophotometric (PEs and \(\Delta\)Es) and AI-enhanced (PEai and \(\Delta\)Eai) colorimetry. Linear trend lines were obtained with the respective coefficient of determination (R2) for PEs versus PEai and \(\Delta\)Es versus \(\Delta\)Eai.
## 3 Results and Discussion (Numerical Example)
Allowing for the hypothetical assumption that spatial distances \(d_{CL}\) are free of errors, one can note a positive impact of the additional classical measurements (Difference 3) on most distances measured with GNSS (Difference 2).
## 4 Predictive Current Control Strategy
### Cost Function Selection
#### 4.3.2 Cost Function of the Capacitor Voltage Balance  
One of the most interesting aspects of predictive control methods is the simplicity of voltage balance in the DC bus [24]. This feature disconnects the midpoint of the DC bus from the source and combines the predictive control method with the following cost function:  
\[g=\left|i_{a}^{*}-i_{a}^{p}\right|+\left|i_{\beta}^{*}-i_{\beta}^{p}\right|+ \lambda_{dc}\Big{|}v_{c1}^{p}-v_{c2}^{p}\Big{|} \tag{31}\]  
In this control method, it is necessary to adjust the parameters \(\lambda_{dc}\) and \(\lambda_{n}\). To this end, some design procedures of the system should be established. The different units and sizes of the variables involved in the cost function should be considered [25]. Some ideas will be given, which, from the order of magnitude of the weighting factors, are of equal importance. If the designer just wants to select the appropriate switch state from the redundancy state that produces the given voltage vector to maintain neutral point voltage balance in the DC link, a small value of \(\lambda_{dc}\) should be adopted. The minimum value allowed by the platform will work for this purpose. The same principle has been applied to \(\lambda_{n}\). When increasing \(\lambda_{n}\), this method can select the switching state in the reference tracking that does not belong to the optimal voltage vector range, but this means fewer commutations.  
On this basis, the method of predictive current control is realized, and the simulation results are verified. This strategy successfully maintains the voltage balance of the DC link and reduces the switching frequency. Compared with the carrier-based approach, this method achieves better reference tracking at the same switching frequency.
# Influence of the Radial Gap on the External Gear Pump Performance
## 4 CFD Results and Verification
### Experimental Verification of the CFD Results  
The CFD experimental verification results were obtained by an existing laboratory test bench described in detail in [41]. Figure 8 shows the test bench hydraulic circuit diagram and its realization. The hydraulic system consisted of a tank with a volume of 130 L to which a motor pump group was mounted, consisting of an external gear pump driven by a three-phase asynchronous electric motor, which had a power of 7.5 kW and a nominal rotation frequency of 1450 RPM. The electric motor was equipped with a frequency inverter allowing the rotation frequency change in the range of 0-1450 RPM. A gear flowmeter with an integrated pressure transducer was connected to the pump discharge pipeline. For safety reasons, the pressure in the pump discharge pipeline was also visually measured with a manometer. A pressure loading system was connected after the flow meter. It consisted of a direct operated pressure-relief valve and a parallel-connected precision adjustable throttle check valve. A three-way \"L\"-type valve was connected between the pressure-relief valve and the throttle valve in order to switch between the two main operation modes--loading and unloading. In the loading mode, the maximum pressure value was determined by the pressure-relief valve setting, while the pressure load adjustment between the minimum and maximum values was made with the throttle valve. The loading system output was connected to the tank through a return filter group.  
The flow meter output signals (flow rate and pressure) were both connected to a specially developed data acquisition (DAQ) system. It is based on an NI USB 6211 processing and recording device, connected to a laptop via USB. The user interface was developed in the LABView(tm) ver. 2010 software environment. Additionally, the system measured the motor and pump rotation frequency by a laser tachometer.  
Figure 7: **Simulated flow rate \(q_{i}\)** and \(q\) at radial gap sizes 0.02 and 0.04 mm: (a) 500 RPM; (b) 2500 RPM.  
The experimentally obtained flow rate values \(q_{exp}\) are presented in Table 1, where they are compared with the results of the CFD model and the experimental information from the manufacturer (marked as Tech Data). Since the function \(q_{exp}(n)\) is linear, the values of \(q_{exp}\) at 2500 and 3500 RPM were determined by the extrapolation of the experimentally obtained curve.  
As can be seen from Table 1, the percentage difference \(\Delta q_{exp}\) between the CFD simulation results and the experimentally obtained results at 1450 RPM did not exceed 2.5%, which confirms that the model provides reliable results in the verified range of rotation frequencies. It can be further used to investigate pump efficiency at different values of radial gaps, changing as a result of housing wear.
# Review on Quality Control Methods in Metal Additive Manufacturing
## 3 Numerical Quality Monitoring Methods in Metal AM and Welding
### Numerical Quality Monitoring Methods in Welding  
Numerical quality monitoring methods in welding have focused on reducing the residual stress in welded areas. It was found that a higher welding speed [12; 38; 39; 40; 41], shorter cooling time [42], and substrate preheating at higher temperatures [43] can retard the residual stress generation in the welded area. [PERSON] et al. [41] designed a polynomial model considering the laser beam power, scanning speed, and defocus parameters on dilution and residual stress using response surface methodology. It was found that the defocus parameter and scanning speed had a significant effect on dilution and residual stress. Additionally, the authors applied a multiobjective quantum-behaved particle swarm optimization algorithm to find parameters that minimize dilution and residual stress. [PERSON] et al. [43] found that the residual stress along the weld direction was much greater than that perpendicular to the weld direction. [PERSON] et al. [42] used a numerical model to examine the residual stress and distortion in thin welded plates and observed that concentrating the energy could reduce the residual stress-generating area in thin plates. [PERSON] et al. [44] developed a simulation model based on the thermal-metallurgical-mechanical relationship to simulate the microhardness and residual stress of cladded specimens. The effects of various process parameter sets were studied, and the proposed model was utilized to optimize the cladding process parameters to minimize the residual stress. [PERSON] et al. [45] proposed a numerical model considering the effect of phase transformation on the residual stress during single-and multipass laser cladding processes.
## 3 Experimental Program
### Description of the Test Specim
#### 3.1.5 Material Properties  
The two wall panels and the foundation beam of the specimen were cast with C40 concrete, and three 150 mm \(\times\) 150 mm \(\times\) 150 mm control cubes for the utilized concrete were produced and cured with the test specimens in the same surroundings. According to the standard coupon test recommended in GB/T50081-2019 [38], the cubic compressive strength of the used concrete was 42.2 MPa. HRB400 steel rebar were employed for the lateral and vertical reinforcing rebar of the wall panels. Based on GB/T228.1-2021 [39], three coupon tests were conducted for each kind of key dissipating component in the two kinds of energy dissipators to determine the basic mechanical indices. The material properties of all the steel materials are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Diameter/Thickness (mm)** & **Yield Stress (MPa)** & **Ultimate Stress (MPa)** & **Elongation (\%)** \\ \hline HRB400 & 8 & 455 & 641 & 19 \\ HRB400 & 10 & 447 & 625 & 18.5 \\ HRB400 & 16 & 441 & 615 & 17.3 \\ HRB400 & 18 & 438 & 611 & 17.1 \\ Steel strand & 15.2 & 1728 & 1953 & 4.81 \\ Steel bar & 24 & 265 & 408.7 & 39.2 \\ Steel plate & 8 & 266.9 & 422.5 & 30.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of steel materials.  
Figure 5: Details of MBSF. (**a**) Configurations of the MBSF (unit: mm); (**b**) Connection details in Test RW1-2.