esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Small, cheap, highly mobile robots can be used to solve a wide variety of problems [1]. The availability of off-the-shelf components for such micro air vehicles (MAVs) has made them an active topic of current research and even commercial development (e.g., [2; 3; 4]). In particular, MAVs are well suited for exploration of unknown or difficult to access indoor environments, such as buildings and caves. They can be used to map out a space of interest, or search within for items of interest. MAVs can also be used to deploy a stationary wireless sensor network (WSN) for long-term persistent sensing of that environment.  
Though there are many details differentiating specific mission scenarios, at the most basic they all involve gathering data throughout an unknown environment and passing it to a remote base station. Only in a very narrow range of specifications can such a mission be accomplished without any communication at all (e.g., by storing data on board then physically returning to the base station); otherwise, multihop wireless network design becomes an integral consideration in such missions. Different mission parameters present different tradeoffs, and there may be no single network design that will satisfy all MAV requirements. However, there are a number common traits, which leads to a general framework that can be used as a starting point for developing a ubiquitous MAV network architecture.  
In this paper we present a design for a network infrastructure that can accommodate typical MAV missions. By building on existing solutions for the various subproblems, we can create a hybrid network system that can effectively operate in a mode appropriate to the task at hand. In Section 2 we will describe typical MAV missions, breaking it up into common subproblems. A description of previous research, along with hardware used for such missions will also be included. The communications requirements inherent to a MAV will be discussed in Section 3, followed by the state of the art protocol for communications in a wireless mesh network in Section 4. These two modes will be combined into a single hybrid protocol, as described in Section 5. Finally, we will identify areas where this implementation is still insufficient or unclear, and propose various solutions to complete the design in Section 6. We will close with a discussion of future research needs to properly design a network infrastructure to support MAV missions.
## 3 Nonlinear Robust Control Laws  
### Control Model of Water Surface Take-Off  
It is necessary to simplify the dynamic model before designing the control law. Firstly, let the moment of inertia matrix be  
\[J=\begin{bmatrix}I_{xx}&0&0\\ 0&I_{yy}&0\\ 0&0&I_{zz}\end{bmatrix},I_{\Delta}(t)=\begin{bmatrix}\Delta I_{xx}&\Delta I_{ xy}&\Delta I_{zz}\\ \Delta I_{yx}&\Delta I_{yy}&\Delta I_{yz}\\ \Delta I_{zx}&\Delta I_{zy}&\Delta I_{zz}\end{bmatrix} \tag{11}\]  
Consider the small angle of the HAUQ, and the dynamics can be rewritten as  
\[\begin{cases}(M+M_{\Delta}(t))\ddot{P}=R_{\wp}A_{\wp}(\dot{P})+R_{\wp}NF^{b} +(G+G_{\Delta}(t))g+R_{\wp}\Delta_{1}\\ (J+J_{\Delta}(t))R_{\Theta}^{-1}\dot{\Theta}=-S(\dot{\Theta})(J+J_{\Delta}(t ))\dot{\Theta}+(L+L_{\Delta}(t))F^{b}+B_{\omega}(\omega^{b})+\Delta_{2}\end{cases} \tag{12}\]  
where \(R_{\wp}A_{\wp}(\dot{P})\) and \(B_{\omega}(\omega^{b})\) are unknown terms. \(M_{\Delta}(t)\), \(J_{\Delta}(t)\), and \(L_{\Delta}(t)\) are time-varying variables. \(R_{\wp}\Delta_{1}\) and \(\Delta_{2}\) are bounded disturbances. Note that the small-angle assumption is reasonable, because the HAUQ needs to move horizontally with a slight incline to ensure its stability such that the water inside the body can not shake violently and the high-speed rotating propellers are not damaged by water. The HAUQ control system is divided into the position control subsystem and the attitude control subsystem, that is,  
\[\Pi_{1}:\ \ddot{P}= \frac{1}{M+M_{\Delta}(t)}A_{P}(t,\dot{P})+\frac{1}{M+M_{\Delta}( t)}R_{\wp}NF^{b}+\mathcal{G}g+\Delta_{P} \tag{13}\]  
\[\Pi_{2}:\ (J+J_{\Delta})R_{\Theta}^{-1}\dot{\Theta}= -S(\dot{\Theta})(J+J_{\Delta}(t))\dot{\Theta}+B_{\omega}( \omega^{b})+(L+L_{\Delta}(t))F^{b}+\Delta_{2} \tag{14}\]  
where  
\[A_{P}(t,\dot{P})=R_{\wp}A_{\wp}(\dot{P}),\ \mathcal{G}=\begin{bmatrix} 0&0&1\end{bmatrix}^{T},\Delta_{P}=\frac{1}{M+M_{\Delta}(t)}R_{\wp}\Delta_{1}\]  
As we use the small-angle assumption, the take-off process actually involves some actual physical constraints. Therefore, the following assumption is considered as well.  
Assumption 1: _The water's surface keeps flat as the HAUQ flies from water to air._  
Assumption 1 actually means that the modeling and impact of waves is not considered, and some tiny waves can be considered as the disturbance under the condition of tolerant performance of the proposed controller, and it is also an acceptable assumption about the flight environment.
## 3 Results
### Water Quantity Assessment  
The environmental quality standards for surface water trigger values in China were employed to evaluate whether the concentration of nutrients in the effluent of the PAP system was safe to the natural water bodies [33]. The results revealed that the PAP system with zeolite and limestone bedding layers was unable to reduce the nutrient concentrations below the trigger values for class IV standard for surface water.  
The variations of Nemerow index at various sampling times of three sets of experiments are shown in Figure 5. The nutrients' concentrations slowly increased until about 90 min while the concentrations in effluent remained constant were between 3.1-3.8 mg/L for NH\({}_{4}\)\({}^{+}\)-N, 0.7-0.8 mg/L for TP in limestone and zeolite bedding, respectively. By the end of the artificial rainfall event, from 90 to 120 min, the Nemerow indices in both PAP systems with limestone and zeolite bedding were approximately close to 1.8. As a result, they all exceeded the standard water quality of Class V for surface water in China. In comparison, the Nemerow index of the PAP system with zeolite followed by MZP-PM increased slowly within the first 60 min, which remained below 0.8 upon the whole rainfall event, demonstrating that the initial effluent was even capable of meeting water quality standards of class II for surface water. The effluent gradually dropped to the class IV of water quality standards in 90 min, even after the end of the rainfall event, but it could still reach the class V for surface water as the rainfall event continued, which was much more efficient than the PAP system with limestone and zeolite. The results showed that the discharge of stormwater from the tested PAP system with MZP-PM as a post-treatment was appropriate for draining into surface water.  
The PAP system with an MZP-PM column was designed to process stormwater runoff comparatively at low rainfall intensity, which could enhance the nutrients removal. This enables the PAP system as a perfect low impact developed selection to process small stormwater events. Although enhanced adsorbent is important for the removal of nutrients, MZP-PM (modified zeolite porous microspheres) did not significantly influence NO\({}_{3}\)\({}^{-}\)-N and NO\({}_{2}\)\({}^{-}\)-N removal in the presented research. Hence, more research is required in this field, including removal of heavy metals and total nitrogen, analysis period of adsorption, and minimum pore water contact time in the MZP-PM needed for a target N and P elimination objective.
## 3 Hierarchical State-of-the-Art Control
### Power Sharing for Primary Control  
Power sharing is allocated in the second phase within the primary control level. This control can be ordered depending on whether or not it utilizes the concept of the droop method. Meanwhile, a power sharing control employing a centralized controller belongs to the secondary control [71]. The advantages and drawbacks of each technique are listed in Table 2.
## 2 Materials and Methods
### Data Analysis  
The raw data files were preprocessed using Mascot Distiller software (version 2.4.2.0, Matrix Science Inc., Boston, MA, USA). The obtained peptide masses and their identified fragmentation pattern were compared with the protein sequence database (UniProt KB) [28] using the Mascot search engine (Mascot Daemon v. 2.4.0, Mascot Server v.2.4.1, Matrix Science, London, UK). The \"mammals\" option was chosen as the taxonomy constraint parameter. The search parameters applied were as follows: enzyme specificity, none; peptide mass tolerance, 5 ppm; and fragment mass tolerance, 0.01 Da. The protein mass was left unrestricted, and the mass values were assumed as monoisotopic with a maximum of two missed cleavages allowed. Methylthiolation, oxidation, and carbamidomethylylation were set as fixed and variable modifications. The peptide sequences from unknown original proteins were excluded. Peptide identification was performed using the Mascot search engine (Matrix Science), with a probability-based algorithm. The expected value threshold was set at 0.05 for the analysis (all peptide identification had <0.05% chance of being a random match). The principal component analysis (PCA) method was used to graphically present the relationship between the peptide sequences. PCA was performed using STATISTICA(r)13.1. software. The number of ingredients was set to the maximum and the minimum custom was set to 1.0. The standardization of variables was performed by default in the statistical program. The correct use of PCA was confirmed by the Kaiser-Meyer-Olkin test for sampling adequacy and the Bartlett sphericity test. The number of main components was determined as 4 based on the percentages of the explained variance (Kaiser criterion <80% was adopted). Normal distribution analysis was not performed when performing PCA.
## 4 IDPSO Algorithm Analysis and Design
### Improve the Flow of Hybrid Discrete Particle Swarm Optimization  
The IDPSO algorithm flow for solving multi-objective FJSPs is shown in Figure 7. The specific steps are as follows:  
Figure 6: \(\textit{op}_{3}\) operator MS sequence adjustment.  
Step 1: Determine parameters. Set the population size, \(P\), iteration times, \(t\), adaptive inertia weight, \(w\), and learning factors, \(c_{1}\) and \(c_{2}\).  
Step 2: Population initialization. When each particle is initialized, the process sequencing part adopts the random initialization method, and the machine selection part adopts the mixed initialization method. The position, \(\left\{X_{1},X_{2},X_{3},\cdots,X_{p}\right\}\), of the initialized particle is obtained according to the OScode of each particle and the corresponding MScode.  
Step 3: Calculate the fitness value of each particle. Calculate the fitness of the initial particle swarm according to the initial position of the initial particle swarm, and record the initial individual optimal value, \(Pb_{i}^{t}=X_{i}\).  
Step 4: The initial particle population is non-dominated, and the Pareto frontier set, \(S\), the global optimal particle _gbest_ and global optimal value, \(Gb^{t}\), are obtained.  
Step 5: Update particle positions in accordance with Formula (20).  
Step 6: Determine the boundary conditions.  
Step 7: Solve the fitness of the current particle swarm, and update the optimal location of each particle in accordance with the _pbest_ selection method.  
Step 8: Sort the non-dominating current particle swarm, update the optimal set, \(S\), according to the sorting result, and select and update the optimal particle _gbest_ and global optimal value, \(Gb^{t}\), from the optimal set, \(S\).  
Figure 7: Flow chart of IDPSO algorithm solving multi-objective FJSP.  
Step 9: Determine and correct the convergence condition. When the number of iterations meets the set value, the algorithm ends. Otherwise, continue iteration optimization in step 3.  
Step 10: At the end of the cycle, the optimal Pareto solution set \(\mathbf{S}\) is obtained, and the global optimal particle _gbest_ is output.
# 5G Support for Industrial IoT Applications--Challenges, Solutions, and Research Gaps
## 10 AI Support for 5G Operations and Services
### Identified Research Gaps  
While application-agnostic AI-based improvements are widely investigated in the 5G platform, there is room for advancements for smart solutions in the industrial IoT scene. AI-based optimization methods, specifically crafted for such applications running on 5G connectivity and compute capabilities, have the potential to ensure higher service reliability and more economical operation. For example, robot control on a factory floor can be assisted with intelligent forecast methods by learning reoccurring patterns, and making decisive actions before sensor-based reactive decisions would be made. Such time series analysis and recurrent neural network learning algorithms should be implemented in the 5G infrastructure's edge to keep latency to the least possible level, and crafted carefully to the needs of the IoT application. The research gaps are therefore found to lie within the IoT application-specific information services that the 5G platform could offer. AI-related platform APIs that provide management services as the ones summarized in Table 10, could enable application programmers to integrate their IoT systems fast and seamlessly to 5G providers' offerings.
## 1 Introduction  
Testing is an essential activity in software engineering. In the simplest terms, it amounts to observing the execution of a software system to validate whether it behaves as intended and to identify potential malfunctions [1]. Software testing constitutes about 40% to 60% of the development costs [2]. A major part of the testing effort is for specifying and running the tests, and these are usually performed manually. Model-based testing is a promising technology to reduce the testing effort and increase the test efficiency.  
Automation of software development and software testing on the basis of executable models and simulation promises significant reductions in fault-removal cost and development time. [PERSON] _et al._[3] define model-based testing as a variant of testing that relies on explicit behavior models that encode the intended behavior of a system, and possibly the behavior of its environment. According to [PERSON] [1], the major goal of model-based testing (MBT) is automatic generation of test artifacts from models. MBT offers considerable promise in reducing the cost of test generation, increasing the effectiveness of the tests, and shortening the testing effort [4].  
The software life cycle starts with requirements capturing by the business analyst; these requirements are used by the design analyst for designing the software architecture, and later both of these are used for developing test models. Requirements specification, design models and test models can be considered the integral parts of model-based testing. Defining relationships between models could provide effective verification and analysis of the consistencies and dependencies between models. Study has to be focused to bridge the gap by defining strong relationships between these models.  
In this paper, we present an approach for defining relationships between the major constituents of model-based testing. We discuss how these models can be defined and how to define the relationships between these models, thus filling the gap between different phases of the software life cycle. This paper is organized into an overview of the need for traceability between requirements model, design model, and test model, related work, and the new approach.
# Sustainable Optimization of the Injection Molding Process Using Particle Swarm Optimization (PSO)
## 2 Literature Review
Immediately after filling, the pressure-holding stage begins to counteract material shrinkage associated with cooling transitions. Precision control of holding pressure is vital; excessive pressure can induce residual stress, while insufficient pressure can lead to underpacking and result in dimensional inaccuracies. Following the packing process, the cooling phase solidifies the molded part. Designing an efficient cooling system--typically employing water or oil channels embedded within the mold--is crucial for ensuring uniform cooling rates. Uneven cooling can lead to thermal stresses and warpage, affecting the parts' dimensional stability and surface integrity. The duration of the cooling phase directly influences cycle time and, thus, production efficiency; optimizing cooling parameters is fundamental to meeting quality standards and controlling operational costs. The final step in the injection molding process involves mold opening and part ejection. A carefully designed ejection system prevents part deformation and surface scratches during demolding. Every stage of the injection molding process--ranging from raw materials to ejection--plays a vital role in determining the final product's quality. Variations or mismanagement at any point can propagate defects, magnifying issues such as warpages and voids. As modern practices evolve, there is an increasing reliance on integrated process monitoring systems, simulation technologies, and optimization methodologies to ensure the production of high-quality components within the injection molding domain. The injection molding process is a central pillar of plastics manufacturing and a complex interplay of variables that demands a thorough understanding and precise control. Ongoing research focusing on process optimization, particularly through machine learning and innovative methodologies, is essential for enhancing the efficiency and efficacy of injection molding practices across various sectors, ensuring the continued advancement and evolution of the industry.
## 3 Related Work and Literature Review
### Early Watermarking Techniques: Spatial and Transform Domains  
Early watermarking methods were based on the spatial domain, where the watermarks were embedded directly in the pixel values of the images. The Least Significant Bit (LSB) method is a prominent example of this approach. The approach embeds watermark bits in the Least Significant Bits of the pixel values of the image. Despite their straightforward implementation and computational efficiency, LSB-based methods demonstrate high susceptibility to common image processing operations, including compression, noise addition, and geometric transformations such as cropping [5; 7; 14]. This vulnerability renders them unsuitable for applications where images undergo manipulation.  
Research has shifted to transform domain watermarking, where watermarks are embedded in the frequency components of images instead of spatial pixel values. Common techniques include Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Contourlet transforms [19; 24; 25]. These methods offer improved robustness against image processing operations, particularly compression, which constitutes a common attack vector in digital forensics [46]. Hybrid techniques exemplify advantages by combining DWT's multi-resolution analysis with SVD's stability to improve both robustness and imperceptibility.
# Continuous-Service M/M/1 Queuing Systems
## 3 Derivation of \(L\) and \(L_{q}\)  
This section derives the average number of customers in the system and the average number of customers in the queue for our continuous-service M/M/1 queuing systems. These results differ somewhat from that of the standard M/M/1 system. We denote the average number of (real) customers in the system by \(L\), and the average number of (real) customers in the queue by \(L_{q}\). Recall that we have an infinite number of imaginary customers to attend to for our systems to be continuous. Therefore, we are focused on real customers for our results.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 3 Underwater Acoustic OTFS System Model
### UWA OTFS Channel Model  
In the DD domain, an UAW channel can be modeled as follows:  
\[h(\tau,v)=\sum\limits_{i=1}^{p}h_{i}\delta(\tau-\tau_{i})\delta(v-v_{i}), \tag{3}\]  
where \(h_{i}\), \(\tau_{i}\), and \(v_{i}\) are the path gain, the path delay, and the Doppler shift of the \(i\)th path, respectively.  
Then, we can obtain the delay shift and Doppler shift in the DD grid for the \(i\)th path:  
\[\tau_{i}=\frac{l_{i}}{M\Delta f}, \tag{4}\]  
\[v_{i}=\frac{q_{i}}{NT}=\frac{q_{v_{i}}+\tilde{q}_{v_{i}}}{NT}, \tag{5}\]  
where \(l_{i}\) is an integer delay tap when \(M\) is large enough and \(q_{v_{i}}\) and \(\tilde{q}_{v_{i}}\) are the integer part and the fractional part of Doppler shift, respectively, with \(\tilde{q}_{v_{i}}\in(-\frac{1}{2},\frac{1}{2}]\).  
Based on the resolution in the DD domain, we define \(\tau_{\max}<\frac{1}{\Delta f}\) and \(v_{\max}<\frac{1}{T}\) as the maximum delay and Doppler spread, respectively. Most OTFS system designs suppose that the channel is underspread [22; 23]:  
\[\tau_{\max}v_{\max}<<1. \tag{6}\]  
Based on the underspread assumption, \(l_{i}<M\) and \(-\frac{N}{2}<q_{i}<\frac{N}{2}\) must be met. The passband OTFS signal after passing through the UWA channel can be represented as follows:  
\[\begin{array}{c}\tilde{r}(t)=\int\int h(\tau,v)\tilde{s}(t-\tau)e^{i2\pi v(t -\tau)}d\tau dv+\tilde{\omega}(t)\\ =\sum\limits_{i}^{p}h_{i}s(t-\tau_{i})e^{i2\pi f_{c}(t-\tau_{i})}e^{i2\pi v_{i }(t-\tau_{i})}+\tilde{\omega}(t),\end{array} \tag{7}\]  
where \(\tilde{\omega}(t)\) denotes white Gaussian noise.  
After down-conversion to baseband \(r(t)=\tilde{r}(t)e^{-i2\pi f^{\prime}c}t\), we obtain the following:  
\[r(t)=\sum\limits_{i}^{p}h^{\prime}_{i}e^{i2\pi v\ell,t}s(t-\tau_{i})e^{i2\pi v _{i}(t-\tau_{i})}+\omega(t), \tag{8}\]  
where \(h^{\prime}_{i}=h_{i}e^{-i2\pi f_{c}\tau}\) is the complex path gain and the exponential term \(e^{i2\pi v\ell,t}\) is caused by residual carrier frequency offset (CFO). After Wigner transform and SFFT, we can obtain the received symbols \(y(q,l)\) in the DD domain.
## 4 Numerical Simulations  
In this section, we evaluate the performance of the proposed algorithm for both CIR and DD spread function estimations within the single-carrier transmission scheme. A two-component Gaussian mixture noise (GMN) model [2] is adopted to simulate environments contaminated by impulsive noise with the probability density function denoted as  
\[P(\mathbf{v}[i])=(1-q)\mathcal{N}(0,\sigma_{W}^{2})+q\mathcal{N}(0,\sigma_{ T}^{2}),\quad i=1,\cdots,N \tag{38}\]  
Here, \(\mathbf{v}=\mathbf{v}_{W}+\mathbf{v}_{I}\). \(\mathbf{v}_{W}\) and \(\mathbf{v}_{I}\) denote AWGN and impulsive noise with variances \(\sigma_{W}^{2}\) and \(\sigma_{I}^{2}\), respectively, \(0\leq q<1\) indicates the impulsive noise occurrence probability, and \(\mathcal{N}(\cdot)\) represents the complex Gaussian distribution function. We benchmarked our algorithm against three widely applied first-order methods: OMP, Fast ISTA (FISTA), and the original ADMM developed to solve the \(\ell_{1}\)-norm regularized least squares problem in (10). The second-order IPM with Preconditioned Conjugate Gradient (PCG) is also considered [21]. For clarity, the algorithms from our previous work [28] and this study are termed mADMM (monotone) and nADMM (non-monotone), respectively. All simulations were conducted on a desktop PC manufactured by Dell, located in China, utilizingan Intel Core i5 2.30 GHz CPU and 16 GB RAM (Intel, Santa Clara, CA, USA), running MATLAB 2023a and Windows 10 (Microsoft, Redmond, WA, USA).