esa-sceva/satcom-chunk-collection huggingface dataset:
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
## 2 5G Reference Signals
### The PRS  
The 5G downlink positioning reference signal is used to measure the downlink distance of User Equipment (UE), and the generated formula is as follows:  
\[\begin{split} r(m)=\frac{1}{\sqrt{2}}(1-2c(2m))+j\frac{1}{\sqrt{2 }}(1-2c(2m+1))\\ c(n)=(x_{1}(n+N_{c})+x_{2}(n+N_{c}))\text{mod}2\\ x_{1}(n+31)=(x_{1}(n+3)+x_{1}(n))\text{mod}2\\ x_{2}(n+31)=(x_{2}(n+3)+x_{2}(n+2)+x_{2}(n+1)+x_{2}(n))\text{ mod}2\end{split} \tag{1}\]Among them, \(N_{c}=1600\), and the length of the output sequence \(c(n)\), denoted as \(M_{PN}\), \(n=0,1,\ldots,M_{PN}-1\). The pseudo-random sequences \(x_{1}(n)\) and \(x_{2}(n)\) are composed of \(31^{th}\) order gold codes. The initial value of the first m-sequence \(x_{1}(n)\) is \(x_{1}(0)=1\), \(x_{1}(n)=0,n=1,2,\ldots,30\). The initial value of the second m-sequence \(x_{2}(n)\) is:  
\[\begin{array}{l}c_{init}=(2^{22}[\frac{n_{1D,seq}^{PRS}}{1024}]+2^{10}(N_{ sum}^{slot}n_{s,f}^{u}+l+1)(2(n_{ID,seq}^{PRS}mod\ 1024)+1)\\ \hskip 142.26378 pt+(n_{ID,seq}^{PRS}mod\ 1024))mod231\end{array} \tag{2}\]  
Among them, \(n_{s,f}^{u}\) is the time slot number, the downlink PRS sequence ID: \(n_{ID,seq}^{PRS}\in\{0,1,\ldots,4095\}\) is given by high-level parameters, and \(l\) is the OFDM symbol mapped from the sequence to the time slot.  
For each configured downlink PRS resource, the UE assumes that the sequence \(r(m)\) is scaled according to the factor \(\beta_{PRS}\) and mapped to the resource element \((k,l)_{p,\mu}\), and performs resource mapping according to the following equation:  
\[\begin{array}{l}a_{k,l}^{(p,\mu)}=\beta_{PRS}r(m)\\ \hskip 142.26378 ptm=0,1,\ldots\\ k=m_{comb}^{PRS}+\left(\left(k_{offset}^{PRS}+k^{\prime}\right)mod\ K_{comb}^{ PRS}\right)\\ l=l_{start}^{PRS}!_{start}^{PRS}+1,\ldots,l_{start}^{PRS}+L_{PRS}-1\end{array} \tag{3}\]  
Among them, \(l_{start}^{PRS}\) is the first symbol of the downlink PRS in the time slot. In the time domain, the size of the downlink PRS resource \(L_{PRS}\in\{2,4,6,12\}\), the comb tooth size \(K_{comb}^{PRS}\in\{2,4,6,12\}\), and the combination of \(\{L_{PRS},K_{comb}^{PRS}\}\) includes \(\{2,2\}\), \(\{4,2\}\), \(\{6,2\}\), \(\{12,2\}\), \(\{4,4\}\), \(\{12,4\}\), \(\{6,6\}\), \(\{12,6\}\) and \(\{12,12\}\), resource element offset \(k_{offset}^{PRS}\in\{0,1,\ldots,K_{comb}^{PRS}-1\}\).
# Tire Bubble Defect Detection Using Incremental Learning
## 2 Proposed Method
### Framework: Modify the YOLOv3 SPP Architecture  
The YOLOv3 architecture [8] is mainly divided into four sections: the Input terminal, Backbone, Neck and Prediction, as shown in Figure 5. The functions of each section are given below,  
1. Input terminal: Image pre-processing and data augmentation;
2. Backbone: Feature extraction for lower-level, shallow features which are relatively similar, including edges, colors and textures;
3. Neck: Feature enhancement conducted by processing and enhancing the shallow features extracted by the backbone and allowing the model to learn the characteristics of tire bubbles.
4. Prediction: Outputting a final prediction containing the loss function and the Non-Max Suppression Algorithm of the bounding and anchor boxes.  
Figure 4: Visualization of different grid sizes: (**a**) Large scale (32 \(\times\) 32), (**b**) Medium scale (16 \(\times\) 16), and (**c**) Small scale (8 \(\times\) 8).  
Figure 5: Four main sections of the YOLOv3 architecture [8].  
The original YOLOv3 SPP [14] uses Darknet 53 as the backbone with a three-scale detection network. Since the bubble defects only occupy a small portion of the speckle image, therefore, we modify the YOLOv3 SPP architecture, adjust it to a two-scale output and modify the backbone network. As shown in Figure 6, we removed the large-scale network layers in the Darknet 53 architecture, adding the same number of layers back to the small-scale network layer to improve small-scale feature acquisition. As the large-scale sections consist of many channels, removing the layers from the backbone network reduces the number of parameters by nearly 1/3, significantly reducing the required computing resources. The FPN [11] structure is referenced for re-joining the backbone network and the prediction layer. Furthermore, we have found that the addition of the SPP method effectively increases the range of backbone features and significantly separates the most important context features. The addition of a layer of SPP structure after up-sampling achieves two-scale local and global feature fusion.  
Figure 6: The YOLOv3 SPP two-scale output network architecture.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 3 Networked Microgrids' Control
### Communication  
In the context of networked microgrids, effective communication infrastructure plays a crucial role in ensuring the smooth management of energy and coordination among various components. These communication tools facilitate the exchange of information not only between microgrids but also with the central energy management system and end users [98]. They enable the implementation of advanced functionalities, including load balancing, demand response, and fault detection, which rely on continuous and reliable communication [99]. Communication technologies, protocols, and their impacts on the control and management of NMGs are reviewed.
# Passivity-Based Control for a Micro Air Vehicle Using Unit Quaternions
## 3 Quad-Rotor Quaternion Dynamic Model
### Euler-Lagrange Methodology
#### 3.1.1 Kinetic Energy  
The total kinetic energy is obtained by the sum of the translational and rotational kinetic energies, which are obtained using the translational and rotational velocities respectively,  
\[K_{\text{UAV}}=\frac{1}{2}m\dot{p}^{T}\dot{p}+\frac{1}{2}\dot{\delta}^{T}J\dot{ \theta}, \tag{11}\]  
where \(m\) denotes the vehicle's mass, and \(J\) represents the inertia matrix.
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 2 Materials and Methods
### Methods
#### 2.2.2 Disjoint Tree Mergers Pipelines  
Given an input dataset (here, a multiple sequence alignment), a DTM pipeline has four steps:  
* Step 1: compute a starting tree
* Step 2: use the starting tree to decompose the set of sequences into disjoint sets
* Step 3: construct the trees on the different sets using a selected phylogeny estimation method, and
* Step 4: merge the trees using a DTM method along with some auxiliary information (e.g., guide tree or distance matrix)  
Thus, Step 4 is where the DTM method is used. We begin by describing how TreeMerge, Constrained-INC, and GTM operate at a very high level, and then describe how they are used in divide-and-conquer pipelines.  
The input to each DTM is a set of disjoint subset trees, which are treated as constraint trees, as well as some auxiliary information. For the purpose of this study, each DTM is able to use the starting tree for its auxiliary information, and so we describe these methods with that modification.
# A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
## 1 Introduction
The results of the segmentation derived from these refined satellite imagery models undergo rigorous visual interpretation evaluation to ensure result accuracy and reliability. Consequently, the advancements realized in this study contribute significantly to wetland remote sensing image segmentation, especially within Macau's wetlands. This provides a fresh framework for devising sound and reliable conservation strategies. The introduction and successful integration of these novel methodologies suggest a comprehensive and sustainable trajectory for wetland conservation.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 1 Introduction
Through a reasonable hardware acceleration strategy, we reduce the running time of our algorithm. Additionally, our method was validated on the HISEA-1 satellite. The organization of this paper is as follows. Section 2 relates to the details of our proposed ship detection algorithm, including the CFAR algorithm and YOLOv4-tiny. Section 3 introduces the satellite computing platform and the construction of the ground verification system. Section 4 reports on the experiments. Sections 5 and 6 present our discussion and conclusion.
# Monitoring of Liquid Viscosity for Viscous Dampers through a Wireless Impedance Measurement System
## 2 Monitoring Method of Damper Fluid Viscosity  
### The Principle of the EMI Method  
The piezoelectric effect is a unique property of PZT transducers and is divided into positive piezoelectric effect and inverse piezoelectric effect. The positive piezoelectric effect refers to the deformation of the PZT transducer by applying mechanical force to the transducer, resulting in internal electrodeposition and the generation of positive and negative charges of opposite polarity at both ends of the crystal material. The inverse piezoelectric effect refers to the application of an electric field to the PZT transducer, causing a change in the shape or form of the PZT transducer. In the 1990s, [PERSON] et al. proposed a one-dimensional electromechanical impedance theory model, and the schematic diagram of the model is shown in Figure 1. The principle of the damage monitoring technology of the piezoelectric impedance method is that the mechanical impedance characteristics of the measured structure are coupled to the electrical impedance characteristics of the transducer through the mutual coupling of the piezoelectric ceramic transducer and the measured structure, and the change of the electrical impedance characteristics of the transducer can reflect the change of the mechanical impedance of the structure caused by the damage. Therefore, the damage of the measured structure can be evaluated by analyzing the electrical impedance characteristics of the PZT sensor. Equation (1) is the impedance expression of PZT sensor.  
\[Z(\omega)=\left\{i\omega\frac{w_{p}l_{p}}{h_{p}}\left[\bar{z}_{33}^{T}-d_{31} ^{2}\bar{Y}_{11}^{\mathrm{E}}\frac{Z_{\mathrm{S}}}{Z_{\mathrm{S}}+Z_{\mathrm{A }}}\right]\right\}^{-1} \tag{1}\]
## 3 Performance Analysis
### Demodulation Error Probability \(P_{c}\) of Constellation Symbols  
According to Figure 2, the decision variable of the index in the system is also the decision variable of the constellation symbols, so the mean and variance of the decision variable of the constellation symbols are respectively given by  
\[\mathbb{E}\left[\hat{S_{j}}\right]=\mu_{I1}+\mu_{I2}=\mu, \tag{22}\]  
\[\text{Var}\left[\hat{S_{j}}\right]=2\sigma_{1}^{2}=\sigma^{2}. \tag{23}\]  
When index detection is correct, the demodulation error probability of MPSK constellation symbols \(P_{c}\) is approximately calculated as [20]  
\[P_{c}\approx\frac{2}{M_{c}}Q\left(\frac{\pi E_{s}}{2^{M_{c}}J\sigma}\right), \tag{24}\]  
where \(Q(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}\exp\left(-\frac{t^{2}}{2}\right) \text{dt}\) is Gaussian Q-function and \(E_{s}\) is the symbol energy of the system. Substituting (24) into (11), the error probability of modulated bits \(P_{mod}\) is obtained. Subsequently, the BER of the MSSS-I/Q-IM system over an AWGN channel can be obtained.
## 3 Results and Discussion
### Gold Adsorption from Chloride Media
#### 3.3.5 Influence of the Adsorbent/Aqueous Phase Relationship on the Adsorption of Gold  
The results concerning the adsorption of gold(III) from an aqueous phase containing 0.005 g L\({}^{-1}\) Au(III) in 0.1 M HCl, at different volume of solution/carbon nanotubes weight ratios, revealed a change in the percentage of gold adsorption (92% at 1 h of contact) at the lowest solution/adsorbent relationship (4000) against the value of 51%, also at 1 h, obtained for a relationship of 8000. The gold uptakes were of 18.4 mg g\({}^{-1}\) and 20.4 mg g\({}^{-1}\) for the low and high ratios, respectively.
## 2 Object Models and Algorithms
Of course, the object size on the images is determined by telescope configuration and parameter setting, but it does not affect the extraction of column data with Gaussian form, and five continuous values can reflect the change trend of Gaussian morphology. If more data are selected, the computational complexity increases and overfitting occurs easily. Therefore, we select five consecutive pixels in a column to fit a Gaussian model at a time in the regions of interest or in the entire star image and take the center of the Gaussian model as the zero point, so the coordinates are  
\[x_{0}^{2}=0,x_{1}^{2}=x_{-1}^{2}=1,x_{2}^{2}=x_{-2}^{2}=4 \tag{5}\]  
In addition, we set a parameter \(t=\exp\{-1/(2\sigma^{2})\}\), and Equation (4) is rewritten as  
\[8 At^{7}-4\left(y_{2}+y_{-2}-2 BG\right)t^{3}+2 At-\left(y_{1}+y_{-1}-2 BG\right)\to 0 \tag{6}\]  
The local background \(BG\) and the peak value \(A\) in each group of column data are defined as  
\[BG=\frac{1}{2}(\min\{y_{k}\}+Next\min\{y_{k}\}) \tag{7}\]  
\[A=\max\{y_{k}\}-BG \tag{8}\]  
Figure 1: Grayscale distribution of space objects. (**a**) Point object. (**b**) Object streak. where \(\textit{Next}\text{min}\{\cdot\}\) is defined as the second minimum value of the group of data. It is easy to solve for \(t_{0}\), so the variance is  
Figure 1.0  
Whether or not each group of column data is an object, an ideal object model that is closest to the data is generated. We conduct star image segmentation and identify the objects and stars based on correlation between objects and ideal object models. Of course, a few false alarms with Gaussian morphology can be removed by analyzing standard deviation and 2D characteristics of space objects.
# Corrosion Susceptibility and Microhardness of Al-Ni Alloys with Different Grain Structures
## 3 Results and Discussion
### Microstructure and Microhardness
This behavior is attributed to the fact that in zones further away from the base of the furnace, the V\({}_{\rm e}\) values are lower, as observed in Figure 3. This decreasing behavior of the V\({}_{\rm e}\) generated a refinement of the grain size, distribution and morphology of the intermetallic compound, thus improving the mechanical property (HV). The improvement in the HV values can be observed in Figure 12a, where the variation of HV with respect to the secondary dendritic spacing, \(\lambda_{2}\), is observed for Al-1 wt. %Ni and Al-3 wt. %Ni hypoeutectic alloys. Figure 12b shows the variation in HV with respect to the lamellar spacing, S\({}_{2}\), for Al-5.7 wt. %Ni and Al-8 wt. %Ni eutectic and hypereeutectic alloys, respectively. In both cases, it was observed that the decrease in the values of \(\lambda_{2}\) and S\({}_{2}\) leads to an increase in the HV values. Finally, it is important to note that in all cases, a small increase in the HV values was observed as a function of the Ni-content and also, for each concentration, it was found that as the cooling rates increased, the values of HV also increased linearly because the mechanical properties are linked to microstructural refinement (the values of \(\lambda_{2}\) and S\({}_{2}\) were decreasing) and higher V\({}_{\rm e}\). Figure 11: Variation of secondary dendritic spacing, \(\lambda_{2}\), and lamellar spacing, S\({}_{2}\), with (a,c) cooling rate, V\({}_{\rm e}\), and (b,d) position. Similarly, [PERSON] et al. [32] found that by increasing the Ni-content in Al-Ni as cast alloys, a small increase in the hardness of the alloys was evident from a hypoeutectic to a hypereutectic composition. It should be noted that the aforementioned authors did not carry out differentiated microhardness measurements for the different phases of the alloys. In the present study, for the alloy with a hypereutectic composition, the HV of the eutectic region in the columnar zone is lower than that for the eutectic alloy. This may be due to the decrease in the Ni-content corresponding to the eutectic region at the expense of the precipitation of the intermetallic compound Al\({}_{3}\)Ni. The HV values measured for the Al-8 wt. %Ni alloy in the phase corresponding to the Al\({}_{3}\)Ni intermetallic are markedly higher than those measured with respect to the other areas of the alloys (see Figure 9).
# Towards mmWave Altimetry for UAS: Exploring the Potential of 77 GHz Automotive Radars
## 2 Literature Review and Related Work
### Innovative and Emerging Applications of mmWave Automotive Radars  
Initially developed with a primary focus on automotive applications, mmWave radars have undergone a transformative journey over time, expanding their scope far beyond their original intent. The early stages of mmWave radar deployment predominantly centered around automotive functionalities. However, as the technology advanced, these radars found new frontiers in various domains, showcasing their versatility and adaptability. The evolution of mmWave radar technology has paved the way for a diverse range of applications, each with its unique set of challenges and opportunities.  
The shift from a singular automotive focus to a multitude of applications highlights the agility and resilience of mmWave radar systems. The spectrum of applications spans from innovative realms such as 9 mm bullet radar cross-section (RCS) measurement to industrial domains like fluid level sensing and vital sign sensing. This proliferation of the technology into diverse sectors, including robotics, healthcare, and surveillance, signifies a remarkable transition. The adaptability of mmWave radar systems has not only broadened their utility but has also contributed significantly to advancements in various technological spheres. This expansion underscores the pivotal role these radars play in shaping a multifaceted landscape of applications, offering solutions that extend beyond the automotive realm. Table 1 lists a summary of related works.
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 4 Discussion  
### Merits of ONELAB for Thermoacoustic Imaging  
Using an open source platform, ONELAB, allowed a single interface to simulate hybrid thermoacoustic imaging, with the choice of optical, radio-frequency, or other sources to induce a pressure wave response. The components of ONELAB Gmsh (mesh) and GetDP (FEM solver) were applied for this purpose. Since GetDP only solves the user defined finite element equations, fine control over every aspect of the thermoacoustic simulation was possible. This is especially important when performing reconstruction, since, for instance, time-reversal can have drastically different performance depending on the amount of time steps and the duration per time step. The benefits of Gmsh include its standard mesh creation, with the ability to automatically interface with other standard mesh formats. This allows the user to write a wrapper around any given finite element mesh. Besides simulating expected results, this platform is also ideal for post-processing the results. All of the data from the simulated PACT and RACT experiments was in the same format, used the same finite element mesh, without the need for interpolation or data transformation. Using the data in such a complementary fashion demonstrated the benefits of ONELAB over more established simulation tools used for a single imaging modality.
###### Abstract  
With the rapid development of intelligent transportation systems, voice assistants are increasingly integrated into driving environments, providing an effective means to mitigate the risks of fatigued driving. This study explored drivers' interaction preferences with voice assistants under different fatigue states and proposed a fatigue-state-based dialogue-awakening mechanism. Using Grounded Theory and the Stimulus-Organism-Response (SOR) framework, in-depth interviews were conducted with 25 drivers from diverse occupational backgrounds. To validate the qualitative findings, a driving simulation experiment was carried out to examine the effects of different voice interaction styles on driver fatigue arousal across various fatigue levels. Results indicated that heavily fatigued drivers preferred highly stimulating and interactive voice communication; mildly fatigued drivers tended toward gentle and socially supportive dialogue; while drivers in a non-fatigued state preferred minimal voice interference, activating voice assistance only when necessary. Significant occupational differences were also observed: long-haul truck drivers emphasized practicality and safety in voice assistants, taxi drivers favored voice interactions combining navigation and social content, and private car owners preferred personalized and emotional support. This study enriches the theoretical understanding of fatigue-sensitive voice interactions and provides practical guidance for the adaptive design of intelligent voice assistants, promoting their application in driving safety.  
Grounded Theory; SOR theory; fatigued driving; voice assistant; driving simulation experiment; dialogue wake-up mechanism Article
## 2 Experimental Setup and Procedure  
### Configuration of the Inclined-Tube Settling Tank  
The forebay, intake shiuc, settling tank, filter screen, clean water tank, and drainage hole are the main structures of an inclined-tube settling tank (the structural design of the inclined-tube settling tank was proposed by [PERSON]). More specifically, the settling tank consists of a water inlet, tank, flow adjustment board, baffle, inclined tubes, and overflow weir (see Figure 1a,b). The working process of this tank is as follows (see Figure 1c): first, by opening the intake shiuc, the water flows into the tank from the forebay. Then the water flows through the flow adjustment board (the hole of the board is designed to be gradually larger from the bottom to the top and from the middle to the sides, and the holes on two sides are symmetrical (see Figure 1d)) from the flow adjustment area (FAA, located between the water inlet and the flow adjustment board) to the stable settlement area (SSA, located between the flow adjustment board and the baffle), and large particles settle at the bottom of this area first. Next, through the gap between the baffle and the tank bottom, the water flows down into the settlement area with inclined tubes (ISA, located between the baffle and the back wall of the tank). Fine particles can settle down the tube wall and slide down to the tank bottom. Finally, clean water overflows from the overflow weir and flows into the clean water tank through the filter screen. The sediment on the tank bottom can be washed away by opening the drainage hole. In contrast to the inclined-tube settling tank in previous research [12; 13; 14], the inclined tubes are laid out in a rectangular settling tank in this experiment (see Figure 2a,b). In addition, the flow adjustment board is also laid out in the tank, making the water flow into the tank in a more uniform way.  
Figure 1: \(Cont.\)