esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 System Model  
This research aims to improve energy efficiency and secure the SDWSN environment by performing resilient scheduling and routing techniques, respectively. This work also adopts edge computing technology to overcome the issue of latency in the SDWSN environment.  
The main entities involved in this work are WSN nodes, local gateway (LG), sink node, reconnaissance monitoring agent, edge-assisted SDN switches, and SDN controller. There are three layers involved in this work: the infrastructure layer, the edge-assisted switch layer, and the control layer. To ensure the network intelligence and accuracy, this work also utilizes Artificial Intelligence (AI) technology. Figure 1 represents the overall proposed system model.  
The brief working of entities is provided below:  
Figure 1: Overall architecture of the proposed model.  
* WSN Nodes--The responsibility of WSN nodes is to sense the environment in its vicinity. More clearly, the nodes are involved in cluster member (\(\text{WSN}^{\text{cm}}=\text{cm}^{\text{n}}\in\{\text{cm}^{\text{1}},\text{cm}^{ \text{2}},\dots,\text{cm}^{\text{n}}\}\)) and cluster leader (CL) \(\left(\text{WSN}^{\text{CL}}=\text{CL}^{\text{n}}\in\left\{\text{CL}^{\text{1}}, \text{CL}^{\text{2}},\dots,\text{CL}^{\text{n}}\right\}\right)\) nodes in which the cluster member continuously senses and provides the sensing results to the CLs.
* Local Base Station (LBS)--The LBS \(\left(\text{LBS}^{\text{n}}\in\left\{\text{LBS}^{\text{1}},\text{LBS}^{\text{2 }},\dots,\text{LBS}^{\text{n}}\right\}\right)\) is responsible for managing the one hexagonal grid with multiple WSN nodes. The sensing results from the CLs are provided to the LBS. Here, to ensure low latency and reliable transmission, a physical one-hop connection is needed.
* Sink Node (SN)--The SN is responsible for managing the complete 2D hexagonal grids. The aggregated sensing results from the LBS of every grid are provided to the SN for forwarding to the further layers.
* SDN Switches (SDN-SW)--The SDN-SW maintains the routing tables in which the sensed results follow the routing tables based on secure routing protocol. Further, the SDN-SWs can be active and idled for security purposes.
* Edge Server (ES)--The ES is responsible for handling the switches (i.e., active/idle). Further, it also triggers MTD to the SDN-SW.
* Reconnaissance Agent (RA)--The RA is responsible for monitoring the reconnaissance attacks in the proposed SDWSN environment. More clearly, the RA is placed in the infrastructure and edge-assisted switch layers for providing MTD commands.
* Controller--The controller acts as the heart of the SDWSN environment by providing the control messages to the underlying edge-assisted SDN switches. In our work, we utilize a centralized SDN controller.
## 5 Case Study and Results  
The applicability of the proposed framework is tested and validated in-vitro in a laboratory-based machine shop as well as in a real-life industrial scenario. The industrial scenario involves the machine tools used in an existing machine-shop. Taking into consideration the technological level of the machines, this use case is ideal since the machine shop consists of machines coming from different technological eras. Thus, it is of great importance to examine the actual impact of the proposed framework in varying machine tools. In Figure 6, the validation of the framework in the real-life  
Figure 5: Functionalities per Layer of Unity 3D UNet API.  
scenario are presented. More specifically, in Figure 6a, the malfunctioning machine is visible along with the shop-floor technician who is wearing the HoloLens HMD in order to perform the maintenance operations, as instructed by the expert engineer. Similarly, in Figure 6b, another engineer is testing the developed framework. It is stressed out that, in the background, the expert engineers using the laptop PC in order to communicate with the technician are visible.  
Similarly, in Figure 7, the practical implementation and validation of the framework in the lab-based scenario is presented. Concretely, in Figure 7a, the expert engineer, from his desktop, can view the user's FoV, which contains the malfunctioning machine tool. As can be seen, the spatial recognition algorithm of the Microsoft HoloLens device has recognized the technician's physical environment. Thus, the engineer based on that feature can place the 3D augmentations on the user's physical environment. On the other hand, in Figure 7b, the shop-floor technician is presented. What is worth noticing is that, in the top right corner of that figure, a snapshot from the HoloLens head up display is presented. The shop floor technician, after requesting assistance from the engineer, the connection has been established and a video teleconference has begun. Therefore, a communication window has opened in the technician's FoV, where the audio and video stream from the engineer are received and displayed.  
Figure 6: **(a)** Validation of the proposed framework by shop-floor technician; **(b)** Another technician tests the framework.  
Figure 7: **(a)** Expert engineer visualizes video stream from technician; **(b)** Technician is performing maintenance tasks based on expert’s instructions.
# Domain Model Based Design of Business Process Architectures
## 5 The dBPA Method
### Concepts
A data dictionary \(dd\) is represented by the tuple \((C,K,K_{s},comp)\) where:_  
* \(C\) _is a finite non-empty set of classes,_
* \(K\) _is a finite non-empty set of components,_
* \(K_{s}\subseteq K\) _is a finite set of state-related components such that for each class_ \(c\in C\) _it yields that_ \(K_{s}\cap S\
eq\emptyset\)_,_
* \(comp:K\to(C\cup K)\) _is a function that maps each component to the data class or component to which it belongs._  
**Definition 6** (Business process architecture model): _A business process architecture (BPA) model \(\Theta\) shows the business process architecture of an organization and is represented by the tuple \((\Phi,R)\), where:_  
* \(\Phi=\Phi_{H}\cup\Phi_{L}\) _is a finite non-empty set of business processes within the organization, such that_ \(\Phi_{H}\cap\Phi_{L}=\emptyset\) _and the business processes in_ \(\Phi_{H}\) _have a higher abstraction level than those in_ \(\Phi_{L}\)_,_
* \(R=R_{comp}\cup R_{spec}\cup R_{trig}\cup R_{flow}\) _is a finite non-empty set of business process relations such that_ \(R_{comp}\) _is the set of composition relations_ \(r_{comp}\)_,_ \(R_{spec}\) _the set of specialization relations_ \(r_{spec}\)_,_ \(R_{trig}\) _is the set of trigger relations_ \(r_{trig}\)_, and_ \(R_{flow}\) _is the set of resource flow relations_ \(r_{flow}\)_,_
* \(r_{comp}:\Phi_{L}\times\Phi_{H}\to\{true,false\}\) _is a function that returns true whenever_ \(\varphi_{l_{s}}\in\Phi_{L}\) _referred to as a sub-process is part of_ \(\varphi_{l_{t}}\in\Phi_{H}\) _referred to as a high-level process, such that one high-level process can have multiple sub-processes but one sub-process can only be part of one high-level process,_
* \(r_{spec}:\Phi_{L}\times\Phi_{L}\to\{true,false\}\) _is a function that returns true whenever_ \(\varphi_{l_{s}}\in\Phi_{L}\)_, referred to as a specialized process, is a variation of_ \(\varphi_{l_{t}}\in\Phi_{L}\)_, referred to as a general process,_
* \(r_{trig}:\Phi_{L}\times\Phi_{L}\to\{true,false\}\) _is a function that returns true whenever_ \(\varphi_{l_{s}}\in\Phi_{L}\)_, referred to as a trigger source, causes the start of_ \(\varphi_{l_{t}}\in\Phi_{L}\)_, referred to as a trigger target, and_
* \(r_{flow}:\Phi_{L}\times\Phi_{L}\to\{true,false\}\) _is a function that returns true whenever_ \(\varphi_{l_{s}}\in\Phi_{L}\)_, referred to as a resource source, enables resources used in the execution of_ \(\varphi_{l_{t}}\in\Phi_{L}\)_, referred to as a resource target._
## 4 Results and Discussion
### Biogas Production and COD Reduction  
COD is crucial for maximizing biogas yield and ensuring the efficient operation of the anaerobic digestion process. COD is a vital parameter in biogas production, as it quantifies the organic material available for conversion into biogas within an anaerobic digester [84]. The reduction in COD during anaerobic digestion is attributed to the breakdown of organic matter into gaseous products, such as methane and carbon dioxide, from the feedstock slurries [85]. In the present study, the CD:FW (2:1) ratio demonstrated the highest COD value of 8001 \(\pm\) 776.40 mg/L on day 3, as well as the highest biogas production, measuring 598.5 \(\pm\) 20.50 mL/g VS on day 6. This correlation indicates the significant contribution of COD to biogas production.  
Figure 4: Reduction in COD during anaerobic digestion. (**A**) WH:FW (1:1); (**B**) WH:FW (2:1); (**C**) WH:FW (1:2); (**D**) CD:WH (1:1); (**E**) CD:WH (2:1); (**F**) CD:WH (1:2); (**G**) CD:FW (1:1); (**H**) CD:FW (2:1); and (**I**) CD:FW (1:2).  
In the present study, the overall mean values of biogas production and overall COD reduction were observed by [PERSON]'s Least Significant Difference test, which is represented in Table 4. [PERSON]'s LSD method compares the means of each factor, and means that do not share a letter are significantly different. From the table, it can be observed that in co-digestion, the production of biogas from WH:FW (1:1) and WH:FW (2:1) showed a significant difference from other digestions, including WH:FW (2:1). It was also noted that during mono-digestion, the biogas production values from CD:WH (1:2), CD:WH (1:1), and CD:WH (2:1) are completely different from other digestions, and the differences were significant. On the other hand, the mean COD reduction values for days 3, 6, 9, 12, and 15 were significantly different except for on days 18 and 21.
## 3 Materials and Methodology
### Feedback Loop for Refinement  
The hybrid model (DKPS) proceeds with recommending the content using a GAN based on the predicted target module and then it collects learner's feedback after validating if the prediction of the module is valid and completed. Adjusting the feedback parameters is used to refine the model as mentioned in the Table 15. The alternative pathway or knowledge graph (KG) is used to differentiate the predicted target module based on the learner's insights, if the module is invalid, to guide the learner effectively.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Iteration** & **Feedback Collected** & **Action Taken** & **Result** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & Adjusted difficulty level of the content (explicit) & Increased learner (explicit) & \\ \cline{3-5}  & & & & \\ \hline \multirow{2}{*}{2} & Low time spent, high revisit counts (implicit) & Personalized module content (quizzes and hints). & Engagement trends improved by 12\%. \\ \hline \multirow{3}{*}{3} & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & Expanded knowledge graph edges by using GAN to create personalize (explicit) & Reduced invalid predictions by 20\%. \\ \cline{3-5}  & & & \\ \hline \multirow{2}{*}{4} & High drop-off rate in advanced modules (implicit) & Introduced intermediate modules dynamically by using GAN. & Learner retention increased by 15\%. \\ \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Feedback loop depiction with action taken.
## 1 Introduction
To the best of our knowledge, no GNSS 16-element systems have been documented in the available literature. Commercial solutions do exist, such as the Tualcom TUALAJ 16300--a 16-element Controlled Reception Pattern Antenna (CRPA). This system is capable of suppressing interference from up to 15 jamming sources, effectively generating 15 nulls across three distinct frequency bands. To improve upon the reported system's capabilities, in this work, we present a 16 antenna (4 \(\times\) 4 planar array) solution developed resorting to a powerful FPGA-based array and beamforming development platform (ABDP), designed to expedite the development of arrays and beamforming algorithms [19]. This approach leverages an innovative reconfigurable platform that supports the testing of any antenna array with up to 16 feeds and an unlimited number of algorithms without requiring major reconfiguration. It can also directly interface with conventional single-antenna receivers and RF test equipment. By modifying only the software layer, there is no need to develop new hardware between design iterations or across different systems. This significantly accelerates the development of complex antenna arrays and beamforming algorithms by eliminating the need to redesign the hardware layer, enabling faster progression toward dedicated solutions for industrial applications. At this stage of development, a commercial off-the-shelf (COTS) antenna element was used to operate in the L1/E1 band (1.57542 GHz). The array must be designed to be integrated into a car's rooftop without significantly changing its appearance; therefore, the system must have a small footprint and be as thin as possible. Additionally, the system must be compatible with standard GNSS receivers; therefore, its output signal must be compatible with what is expected from a single antenna that is fed to the receiver for correlation and tracking. The performance will be evaluated under interference constraints while directing the main beam to different directions and evaluating the system's capabilities to perform multibeam beamforming to track multiple satellites simultaneously. The precise effect of the rooftop's metal frame on the array's performance is not under the scope of this paper. Section 2 presents the design and simulation of the antenna array for GNSS applications. Section 3 describes the beamforming mechanism [19] employed in the ABDP and implemented in the ZCU 216 FPGA. Section 4 presents the integration of the array and the ABDP, along with the full system's performance evaluation in an anechoic chamber. Finally, Section 5 discusses the conclusions of this work.
## 3 Iterative Search Algorithms
### Local Search
#### 3.2.2 Simulated Annealing (SA)  
The HC algorithm suffers from several limitations, namely, it frequently becomes stuck at the local optima. To overcome this problem, the scientific community proposed several approaches, among them the SA algorithm that also uses the notion of the neighborhood [14]. One thing that distinguishes SA from HC is an explicit ability to escape from the local optima. This ability was conceived by simulating, in the computer, a well-known phenomenon in metallurgy called annealing (which is why the algorithm is called simulated annealing). Following what happens in metallurgy, in SA, the transition from the current state (the best-so-far candidate solution \(i\)), to a candidate new state (a neighbor of \(i\)), can happen for two reasons: either because the candidate state is better, or following the outcome of an acceptance probability function--a function that probabilistically accepts a transition toward the new candidate state, even if it is worse than the current state, depending on the the states' energy (fitness) and a global (time-decreasing) parameter called the temperature (\(t\)) [14]. From this perspective, SA can be seen as an attempt to improve upon HC by adding more \"intelligence\" in the search strategy. Within this library, the SA algorithm is materialized through the class _SimulatedAnnealing_, subclass of _HillClimbing,_ and it is characterized by the following instance attributes:  
* _pi_, _initializer_, _nl_function_, _nl_size_, _best_sol_, _seed_, and _device_ are instance attributes inherited from _HillClimbing;_
* _control_ is the control parameter (also known as temperature);
* _update_rate_: rate of control's decrease over the iterations. The Figure 4 provides the main steps of SA (for a maximization problem), that are mirrored in the **solve** method of _SimulatedAnnealing_ class.  
Figure 4: Procedural steps of simulated annealing.  
Figure 3: Procedural steps of hill climbing.  
Appendix B.2 demonstrates how to create an instance of _SimulatedAnnealing_ and apply it in different problem solving.
# A Wide-Angle Scanning Leaky-Wave Antenna Based on a Composite Right/Left-Handed Transmission Line
## 3 LWA Design and Experiment Results
### Fabrication and Measurement  
The LWA was fabricated and measured to verify the design concept. Since a substrate with a length of 4A (40 cm) and its etching process are not available in our laboratory, a prototype of a 10-unit cascaded LWA was fabricated to demonstrate the performance of the proposed LWA, which is shown in Figure 9a,b. The measured \(|S_{11}|\) and \(|S_{21}|\)in dB with three different bias voltages of the varactors are shown in Figure 10; those with other DC bias voltages are omitted in this figure for brevity. The measured results indicate that the LWA can be matched well at around 3.0 GHz, which is similar to the designed results of the 20-unitcascaded LWA (see Figure 6).  
Figure 9 cshows the measurement installation of the far field radiation pattern in the anechoic chamber. Figure 11 shows the measured normalized radiation patterns in co-polarization under different DC bias voltages of the loaded varactors. A scanning range from \(-60^{\circ}\) to \(60^{\circ}\)was observed when the applied voltage varied from 17 V to 2 V. Similarly, low SLL characteristics were still observed, as shown in the simulations in Figure 7. By comparing Figure 11 with Figure 7, it can be seen that  
Figure 10: Measured S-parameters of the overall antenna. (**a**) \(S_{11}\); (**b**) \(S_{21}\).  
Figure 9: Photograph of the fabricated 10-unit LWA prototype. (**a**) Top view; (**b**) Bottom view; (**c**) Measurement installation.  
the measured scanning range of the main beam matches that of the designed 20-unit cascaded LWA. On the whole, the simple radiation structure and DC feed network for the varactors result in only a small deviation between the experimental results and the proposed LWA's results. This slight deviation between simulation and measurement is due to the fabrication tolerances and the used varactor model.  
The measured beam-scanning range of the proposed LWA is listed in Table 3, which is compared with the three previous LWAs proposed in [19; 20; 21]. Just as with the simulated results, the proposed LWA possesses the widest beam-scanning range. This study demonstrates that the proposed LWA has broad application prospects in wide angle scanning applications.
# Aerial Coverage Analysis of Cellular Systems at LTE and mmWave Frequencies Using 3D City Models
## 2 System Model  
This section describes the system model that is used to simulate the viability and performance of UAVs in existing LTE cellular networks as well as UAVs in a simulated mmWave cellular network. For LTE, the simulations use the 1.8 GHz frequency band. Out of all 5G NR mmWave operating bands specified in [16], band n260 is chosen, specifying a frequency band of 37-40 GHz. Within this band, an operating frequency of 38 GHz was chosen, as for this frequency there is already extensive research on urban models and parameters [17; 18; 19]. The spectrum at 38 GHz is less congested and allows for a higher bandwidth \(B\), however a higher bandwidth also introduces more noise. In this article, we consider \(B=20\) MHz and \(B=400\) MHz; by doing this, the effect introduced by using a higher carrier frequency could be separated from the effect of using a higher bandwidth. The simulations focus on the downlink connection (i.e., from BS to UAV) of UAVs flying up to an altitude of 250 m above ground level (AGL). The research focuses on the interference experienced by the UAV.
## 1 Introduction
Accordingly, the present study performs an experimental investigation into the effects of optical diffraction on the machining capabilities of a 3D excimer laser micromachining system using a binary photo-mask. The blurring effect caused by optical diffraction is generally regarded as non-desirable in optical imaging systems. However, in this study, it is shown that, through an appropriate control of the optical diffraction effect, complex 3D micromachined patterns can be realized without the need for an expensive and small line-width photo-mask. The remainder of this paper is organized as follows. Section 2 quantitatively examines the effects of optical diffraction using a binary hole-array mask pattern. Section 3 demonstrates the practical feasibility of the proposed diffraction-based excimer laser micromachining system by fabricating conical, trihedral, and pyramidal 3D micro-structures on polycarbonate substrates. Finally, Section 4 provides brief concluding remarks.
## 4 Discussion
### Field Test and Strength  
The assessment of RP (W/kg) during the applied field testing for point 1, point 2, and the entire lap time provides important insights into athletes' court movement. This RP reflects the ability to generate force while maintaining movement or changing velocity, which is essential in a sport that has multiple directional changes. Therefore, SQ performance correlated significantly with RP (\(p=0.028\), \(r=-0.273\)) during the first part of the course (point 1 to point 2). This finding aligns with previous studies that emphasize the importance of lower body strength in enhancing explosive movements [44; 45] in various sports. Furthermore, the relationship between the BP force-to-body weight ratio and RP (\(p=0.030\), \(r=-0.261\)) suggests that upper body strength has an important role in court movement, consistent with research indicating that strong upper body musculature may be important for effective changes in direction during high-velocity movement [46]. Similarly, the strong correlations between the force-to-body weight ratio and RP reinforce the necessity of developing posterior chain strength to optimize efficiency. Previous literature reports have shown that posterior chain exercises significantly enhance athletic performance by improving stability [47]. In practical terms, integrating posterior chain exercises into training programs will help athletes maintain speed and agility during prolonged or intense volleyball matches. The observed correlations between strength performance and lap time further highlight the significance of RP in maintaining speed and agility. These findings are similar to earlier studies suggesting that athletes with higher strength levels can sustain faster speeds [48] and recover more quickly from various efforts [49]. Yet the relationship between strength training and agility is mediated by neuromuscular adaptations, such as improved motor unit synchronization and tendon stiffness. These adaptations enhance force transfer and reduce ground contact time during movement. Conversely, overloading isolated muscle groups without incorporating dynamic movements may impair intermuscular coordination.
## 2 Materials and Methods
### Proposed Model
#### 2.3.3 Frame Blocking and Windowing  
The speech signals were processed efficiently between short-time periods of 20 to 35 ms since distinctive features of sound signals were stable only within short periods of time. The speech data was divided into short periods of time intervals frame by frame.  
Windowing was applied to framed speech signals. The purpose of this step was to eliminate discontinuities at the beginning and end of each frame [43]. This operation can be accomplished using a function that is called the window function. The most commonly used windowing functions are the Hamming, Hanning, and rectangular windowing techniques [44; 45]. Windowing examples with these functions for 20 ms speech recorded from a female speaker are illustrated in Figure 4.  
A windowing operation is simply the multiplication of the speech data and windowing function for each data frame. For the purpose of performance analysis, three window functions were implemented and the corresponding outputs are indicated.  
Figure 4: _Cont._Figure 4: Examples of (**a**) Hamming, (**b**) Hanning, and (**c**) rectangular windows.