esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating and Ranking SCPMS Enablers Using ISM and SWARA
## 4 Results
### Phase 1  
Phase 1 was useful in identifying SCPMS enablers. The various enablers of SCPMs were collected using various keywords such as'supply chain performance system enablers','supply chain performance system criteria','supply chain performance', and'supply chain 4.0 drivers' from various databases of ScienceDirect, Web of Science, and Scopus. To meet the requirements of supply chain 4.0, additional filters were applied to the identified research papers of SCPMS enablers. Thus, the 14 most important SCPMS enablers were found, and, based on their significance, degree of application to the case issue, and impact on other SCPMSs, the SCPMS enablers were chosen. A decision-making group with 30 experts, involving three SC executives each from the food industry, tin can manufacturer, paper industry, electrical switchgear manufacturing industry, and automobile industry, along with 15 faculty members from marketing, business management, human resources management, financial management, operations management, and information technology, was invited. In their respective fields, each expert had more than 5 years of professional experience. Working professionals from the management and engineering fields had at minimum a graduate degree, while academic experts had PhDs in their areas of expertise. A brief group discussion was called to discuss the present study's objectives. After a quick group meeting, all the experts freely agreed to take part. All participants agreed to take part in the study. The formation of such an expert group was in line with earlier research having problem similarity and a solution approach [57; 58]. The expert group was given access to the relevant literature on SCPMS enablers. The study's goals and potential research approaches were also covered in the briefing. For discovering SCPMS enablers, many techniques such as 'identifying by literature study', 'assessment by experts', or 'brainstorming' were suggested. It was further decided to involve the identified expert group in applying the value-focused brainstorming technique to narrow down the SCPMS enablers [59].  
When selecting the SCPMS enablers, the brainstorming method plays a crucial role [57]. Therefore, value-focused brainstorming was utilized in the current study to decide how to apply SCPMS enablers. Over 15 days, two value-focused brainstorming sessions were held. The first session served as an introduction to the mock round, which was performed following the 'dos' and 'do nots' for fostering creativity and avoiding criticism. After the second session, the 16 SCPMS enablers were reduced to 14, depending on their applicability.  
The 14 SCPMS enablers are discussed in the current study to determine their relevance. Three SCPMS enablers-- 'overcoming mistrust', 'funds for PMS implementation', and 'proactive management'--were found to be redundant and, hence, dropped. As a result, 14 SCPMS enablers were identified by the current study after consultation with the expert group. Table 2 outlines the 14 SCPMS enablers found in the SCPMS-based literature.
# Resource- and Neighbor-Aware Observation Transmission Scheme in Satellite Networks
## 4 Constrained Stochastic Game
### Optimization Formulation
\[\pi_{i}^{*}(S_{i},A_{i})=\frac{\phi_{i;\pi_{-i}}^{*}(S_{i},A_{i})}{\sum\limits \limits_{A_{i}^{\prime}\in A_{i}}\phi_{i;\pi_{-i}}^{*}(S_{i},A_{i}^{\prime})}. \tag{24}\]  
To obtain the optimal policies (i.e., best-response policies) of observation satellites, we develop a best-response-dynamics-based algorithm as in Algorithm 1. Each observation satellite first initializes its policy (line 1 in Algorithm 1). Then, each observation satellite transmits its current policy to other observation satellites for the interaction (line 4 in Algorithm 1). Based on the policies of other observation satellites, each observation satellite can calculate the expected queue length \(E[Q]\) when the observation arrives at the relay satellite (line 5 in Algorithm 1). Specifically, \(E[Q]\) can be calculated as \(\frac{1}{2}\sum\limits_{i}\sum\limits_{S_{i}}\left[\phi_{i;\pi_{-i}}(S_{i},A_{ i}=1)+\phi_{i;\pi_{-i}}(S_{i},A_{i}=3)\right]\). After that, each observation satellite solves the LP problem to obtain the optimal policy \(\pi_{i}^{*}\) (line 6 in Algorithm 1). The algorithm is finished when the policies of all observation satellites converge (line 7 in Algorithm 1). ```
1:Initialize the policies \(\pi_{i}\) for \(\forall i\). 2:repeat
3:for All observation satellites \(i\)do
4: Transmit the policy \(\pi_{i}\) to other observation satellites
5: Calculate the expected queue length \(E[Q]\) when the observation is arrived at the relay satellite
6: Solve the LP problem to obtain the optimal policy \(\pi_{i}^{*}\)
7:endfor
8:until Stationary policies of all observation satellites converge
```  
**Algorithm 1** Best-response-dynamics-based algorithm. The complexity of solving the LP problem is relatively low [26]. For example, the complexity of [PERSON]'s algorithm, which is a representative LP-solving algorithm, is a polynomial time (i.e., \(\mathcal{O}(|\mathbf{S}_{i}|\cdot|\mathbf{A}_{i}|^{3})\) for our problem). In addition, the stationary policies can be obtained within few iterations (see Section 5.1). Therefore, the proposed algorithm can be implemented without significant overhead.
# Remote Servo Tuning System for Multi-Axis CNC Machine Tools Using a Virtual Machine Tool Approach
## 4 Intelligent Servo Tuning Cloud System: Machine Dr.  
### Servo Tuning Method by PSO  
In this study, an intelligent servo tuning method was proposed to adjust the servo parameters of each axis for synchronization. This approach was a two-step adjustment method; the velocity loop control parameters (\(K_{vp}\), \(K_{vi}\)) were first designed, and then the position loop \(K_{pp}\) was chosen to enhance  
Figure 8: FRF comparison results of experiment and simulation. (**a**) \(X\)-axis, (**b**) \(Y\)-axis, (**c**) \(Z\)-axis, (**d**) \(A\)-axis, (**e**) \(C\)-axis.  
the bandwidth at several constraints. Figure 8 shows the flow chart of the proposed method, it contains data acquisition, system identification, virtual servo system setup, parameters optimization of velocity loop, and parameters optimization of the position loop. After data analysis and system identification, we established the virtual system of the feed drive. By using the virtual model, the PSO was adopted to optimize velocity controllers of five axes in the same bandwidth for synchronization. In this procedure, velocity loop servo parameters (\(K_{vp}\), \(K_{vi}\)) of each axis were obtained by PSO with constraints.  
Maximize BW\({}_{vel.}\)  
Subject to Max\(|G_{v}(iw)|\leq 1\)  
Gain margin (GM) \(>\) 10 dB  
Phase margin (PM) \(>\) 45\({}^{\circ}\).  
where BW\({}_{vel.}\) denotes the bandwidth of velocity loop. According our experience, excessive gain of FRF may result unstable and vibrations. We used this Constraint 1 to ensure that the system was stable and vibration was low. Constraints 2 and 3 related to gain margin (GM) and phase margin (PM). GM and PM are widely used to confirm system stability and robustness. Herein, for the initialization of PSO, the default values were assigned to be for initial particles; thus, the tuning method would ensure that bandwidth would not decrease. By this method, we obtained velocity controller parameters with better bandwidth. After updating the velocity servo parameters, we then optimized the position controller (\(K_{pp}\)). Similar to velocity loop, we have:  
Maximize BW\({}_{pos.}\)  
Subject to Max\(|G_{p}(iw)|\leq 1\)  
Gain margin (GM) \(>\) 15 dB  
Phase margin (PM) \(>\) 45\({}^{\circ}\)  
BW\({}_{pos.}\)\(<\)\(\frac{\text{BW}_{vel.}}{3}\)  
where BW\({}_{pos.}\) denotes the bandwidth of the position loop.  
Note that Constraint 4, BW\({}_{pos.}\)\(<\)\(\frac{\text{BW}_{vel.}}{3}\), ensured system stability and only a small overshoot of the time response [33].
# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 3 System Model
(5) Cyclic Prefix Insertion  
\[\mathbf{x}_{\text{CP}}=[x[N-L_{\text{CP}}],\ldots,x[N-1],x[0],\ldots,x[N-1]]^{ \top}. \tag{6}\]  
To eliminate inter-block interference and enable circular convolution at the receiver side, a cyclic prefix (CP) of length \(L_{CP}\) was appended to the front of the signal. The CP duplicates the last \(L_{CP}\) samples of the GFDM block, ensuring that the effects of channel delay spread do not corrupt the orthogonality of the modulation basis. This step is essential in systems with multipath propagation, especially the RF part of the hybrid channel.
## 4 Proposed Method Flow Differentiation Detector
### Step 1: Locate the Target Links  
For the first step, to locate the targeted links, the FDD calculates the link weight of each link according to Formula (1) and selects the top 20% links as the target links based on the Pareto principle, where roughly 80% of consequences come from 20% of causes [21]. Link weight (\(W\)) represents the importance of the links enclosed in an area, \(\sum\limits_{m=1}^{M}f_{m.dst}\) is the number of flows in the destination IP equal to the target area, \(\sum\limits_{m=1}^{M}f_{m.src}\) is and the number of flows in the source IP equal to the target area. We multiplied the two values to obtain the link weight and sorted the link from high to low by link weight.  
\[\text{Link Weight}(W)=\sum\limits_{m=1}^{M}f_{m.dst}\times\sum\limits_{m=1}^{M }f_{m.src} \tag{1}\]  
After finding the target link, we selected the switch that was farthest from the target area at the end to the ends of the target link. It was necessary to use the end of the target link to measure the service performance considered attacked by an SEA and LFA. We merged the flow tables from these switches and divided these flows in the target link into input flows and output flows. The input flows with a destination IP equal to the target area represent an external request to access the target area, and the output flows with a source IP equal to the target area represent a service response from the target area. Regardless of the type of attack, the number of input flows will increase, and the number of output flows will maintain a value or increase slowly. Therefore, we calculated the frequency of the differentiation between the number of input flows and output flows.  
The OpenFlow protocol supports IP Flow Information Export (IPFIX) [25]. We used common features called flow keys, such as five tuples, source and destination IP addresses, source and destination port number, and communication protocols (ip_src, ip_dst, port_src, port_dst, proto). We monitored the behavior of the network by flow traffic transmission and defined that flows with the same source IP addresses, destination IP addresses, and protocols will be considered the same flow.  
In order to detect hybrid attacks, it was necessary to calculate the flow's traffic attacking the target area. For this purpose, we used an SDN controller to complete the process and calculated the flow differentiation by the number of flows to be the service performance in the network [10; 12; 14; 15] and the topology to find target links and set the observation points. Figure 2 shows the FDD process schematic diagram.  
Figure 2: FDD process schematic diagram.
# TalkRoBots: A Middleware for Robotic Systems in Industry 4.0 +
###### Abstract  
This paper proposes a middleware called TalkRoBots that handles interoperability issues, which could be encountered in Industry 4.0. The latter proposes a unified communication approach facilitating the collaboration between heterogeneous equipment without needing to change neither the already used software nor the existing hardware. It allows heterogeneous robots, using both open and proprietary robotic frameworks (i.e., ROS, ABB, Universal Robots, etc.), to communicate and to share information in a transparent manner. It allows robots and Industrial Internet of Things (IIoT) devices to communicate together. Furthermore, a resilience mechanism based on an Artificial Intelligence (AI) approach was designed in order to allow automatically replacing a defective robot with an optimal alternatively available robot. Finally, a remote interface, which could be run through the Cloud, allows users to manipulate fleets of robots from anywhere and to obtain access to sensors' data. A practical scenario using five different robots has been realized to demonstrate the different possibilities. This demonstrates the cost effectiveness of our middleware in terms of its impacts on the communication network. Finally, a simulation study that evaluates the scalability of our middleware clearly shows that TalkRoBots can be used efficiently in industrial scenarios involving a huge number of heterogeneous robots and IIoT devices.  
middleware; communications; IIoT; AI; robots; resilience; ROS +
Footnote †: This paper is an extended version of \"A new middleware for managing heterogeneous robot in ubiquitous environments\" published in the Proceedings of 2020 8 th International Conference on Wireless Networks and Mobile Communications (WINCOM), Reims, France, 27-29 October 2020.
## 4 Numerical Simulations and Analysis
### Obstacle Avoidance in Three-Dimensional Space
Figure 10: The translational velocity \(\mathbf{u}\), \(\mathbf{v}\), rotational velocity \(\mathbf{r}\), Euler angle \(\mathbf{\psi}\), and input \(\mathbf{u}=[T,\delta]^{\mathrm{T}}\) for each dynamic obstacle avoidance result. The efficacy of the proposed method in achieving three-dimensional obstacle avoidance for the AUV is substantiated by the results illustrated in Figures 11 and 12. In Figure 11, the AUV obstacle avoidance paths are depicted for four distinct scenarios. The black solid line represents the path without any obstacles, while the magenta dash-dotted curve, blue dashed curve, red dotted curve, and greed curve represent the AUV's obstacle avoidance trajectories in their respective scenarios. Correspondingly, Figure 12 provides insight into the AUV's state and input under these four scenarios, maintaining the same representation as in Figure 11. Analyzing the simulation results, it is evident that the proposed method remarkably enables the AUV to adeptly navigate three-dimensional space while avoiding obstacles. Additionally, the AUV's state variables and input parameters consistently adhere to the specified constraint range throughout the obstacle avoidance maneuvers. It is essential to note that, owing to the disparity between the center of gravity and center of buoyancy in the \(z_{b}\) direction, maintaining the AUV's stability during movement necessitates significant adjustments in the fin angle during the control process. Figure 12: The state and input for each obstacle avoidance path. The results from these simulations validate the efficacy of the proposed method in enabling three-dimensional obstacle avoidance for the AUVs. The AUV adeptly maneuvers around obstacles while strictly adhering to the constraints governing its state and input. This research serves as a valuable contribution to the field of autonomous underwater systems, shedding light on effective three-dimensional obstacle avoidance strategies and elevating the overall competency and safety of AUVs operating in intricate underwater environments.
# When Deep Learning Meets Data Alignment:
### Developments Relevant to Registration  
The number of works addressing registration with learning techniques has increased in recent years. To identify them, strategic searches were performed in Scopus, Web of Science (WoS), and ArXiv. The results obtained from Scopus and WoS come from indexed journals, which means that they have passed a peer-review process. However, most of the recent works in this review were reached through ArXiv, which is a preprint repository without peer review. This is a double-edged sword. ArXiv allows disseminating the work immediately while it is being reviewed for publication in a journal. However, as some works here studied were not yet reviewed in another place, the authors had to perform a more in-depth evaluation.  
Figure 2 shows the research papers published in the intersection of deep learning and registration involving 3D data over the last years in each repository. It is noticeable how the number of publications has increased over the last three years. Several search strings were employed to identify the methods surveyed in this paper. The keywords grouped by the concept are:  
* To include learning-based methods: _deep learning; machine learning_.
* To indicate the data type employed: _3D; point cloud; mesh_.
* To gather registration proposals: _registration; alignment; transformation; reconstruction_.  
The search strings were designed by combining the words from the previous groups by choosing one from each.  
In the following sections, an analysis of learning-based approaches for registration is performed using a workflow extracted from traditional solutions. We gather in Table 1 the reviewed methods showing the individual properties for each one. These approaches allow more complex inputs such as conceptual models as well as 3D datasets. However, since each proposal uses different datasets, a fair quantitative comparison cannot be done.
# Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants
## 5 Challenges and Perspectives for Water and Energy Nexus
### Perspectives  
With the rapid development of energy-saving technology in wastewater treatment, many new technologies have attracted the attention of researchers.  
Internet of Things (IoT) is an extension and expansion of the Internet, combining various information sensing devices with the Internet to form a huge network. It can realize the interconnection of people, machines and things. Low-cost sensors and low-power wireless telecommunication are the core of IoT for information acquisition and interaction. Compared with traditional sensors, sensors of IoT have lower costs and can be installed at higher densities, making it gather more data [102]. Intelligent sensors are installed in different locations of WWTPs to collect data on water quality, temperature, pressure, water, and chemical leakage, and transmit these data to data center through the Internet. By using these data, WWTPs can track wastewater flow, monitor wastewater leakage, monitor bacterial and microbial distribution, and achieve other functions [58]. A Spain WWTP using IoT has achieved a great performance. The production of sludge dropped by 17% and the use of chemicals for P removal dropped 14%, while the electricity consumption dropped by 13.5% [185].  
In terms of energy recovery of sludge, pretreatment technology has also attracted the attention of some researchers [57]. [PERSON] et al. compared the main pretreatment technologies, such as mechanical, thermal, chemical and biological methods from the perspectives of efficiency, energy balance, cost and process sustainability [186]. Pretreatment methods can recover more energy in AD, reduce the cost of sterilization, and accelerate hydrolysis [187; 188]. Through pretreatments, the concentration and amount of CH\({}_{4}\) produced by AD have been greatly increased [186; 189]. Heat treatment is the most commonly used pretreatment and has been applied in the industry [190; 191]. However, pretreatments also require energy consumption and treatment costs, which should be taken into consideration. Only by utilizing the produced biogas and other products can the technology be feasible in practice [189].  
Compared with a single energy-saving technology, energy self-sufficiency technology has become a research hotspot and future development direction [45; 46]. Energy self-sufficiency is a more systematic energy-saving framework, including green energy, system optimization, industrial design, and other aspects. [PERSON] et al. investigated two WWTPs in Austria and found that nutrient removal and aerobic sludge digestion can achieve energy self-sufficiency [192]. This is mainly achieved through the optimization of the control system and the application of green energy. In fact, the two points described in Sections 2 and 3 are applicable to all WWTPs that want to reduce energy consumption and achieve energy self-sufficiency [18]. [PERSON] et al. maintained that most WWTPs can achieve or close to self-sufficiency by upgrading energy recovery technologies such as AD [46].
## 4 Data Preparation
### Experimental Environment  
Data processing, code debugging, and model testing for this experiment were conducted on a computer with the Windows 11 operating system, an Intel Core i9-9900 CPU (Intel, Santa Clara, CA, USA), and an RTX 3060 GPU (NVIDIA, Santa Clara, CA, USA). The software was written using the Python 3.8 programming language. Data augmentation was performed using dependencies such as OpenCV 4.6.0 and NumPy 1.23.0, and the network model was built using the PyTorch 1.11 deep learning framework. During the model training phase, experiments were conducted by connecting to a remote server. The operating system of the remote server was Ubuntu 20.04, with an NVIDIA V100 GPU and 32 GB of memory. The relevant GPU dependencies were CUDA 11.3 and cuDNN 8.9.5. The other software dependencies used were consistent with those used on the original experimental computer.
## 3 All-Optical Ultrasound Transducers and Imaging
### Development of Transducers and Imaging
#### 3.1.3 Performance Enhancement
Furthermore, a nonlinear chirp excitation, where the nonlinearity should be determined based on the spectral sensitivity of the system, can be used to increase the bandwidth and consequently the axial resolution, at the expense of a lower SNR.
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 5 Simulation
### Specific Example Analysis
Therefore, RAIM availability is conservatively assessed when using \(\mathrm{VPL}_{e}\) and successfully assessed when using \(Slope_{max}\), \(\mathrm{VPL}_{c}\) or \(\mathrm{VPL}_{d}\). As shown in Figure 10, the real \(\mu_{s}-lgP(\mathrm{HMI}|1F)\) curve for \(Slope_{max}\) is separated from the \(lgP_{r}(\mathrm{HMI}|1F)\) line, which means that RAIM is actually available in the third example. Both the \(\mu_{s}-lgP(\mathrm{HMI}|1F)\) curve for \(T_{Slope}\) and the \(\mu_{s}-lg_{a}P(\mathrm{HMI}|1F)\) curve for \(\mathrm{VPL}_{d}\) are higher than the real \(\mu_{s}-lgP(\mathrm{HMI}|1F)\) curve, i.e., \(Slope<Slope_{max}\) and \(\mathrm{VPL}_{d}<\mathrm{VAL}\), which means that RAIM is assessed to be available when using \(Slope_{max}\) or \(\mathrm{VPL}_{d}\). Additionally, both the \(\mu_{s}-lg_{a}P(\mathrm{HMI}|1F)\) curves for \(\mathrm{VPL}_{c}\) and \(\mathrm{VPL}_{e}\) are lower than the real \(\mu_{s}-lgP(\mathrm{HMI}|1F)\) curve, i.e., \(\mathrm{VPL}_{c}\geq\mathrm{VAL}\) and \(\mathrm{VPL}_{c}\geq\mathrm{VAL}\), which means that RAIM is assessed to be unavailable when using \(\mathrm{VPL}_{c}\) or \(\mathrm{VPL}_{e}\). Therefore, RAIM availability is conservatively assessed when using \(\mathrm{VPL}_{c}\) or \(\mathrm{VPL}_{e}\), and it is successfully assessed when using \(\mathrm{VPL}_{c}\) or \(\mathrm{VPL}_{e}\), and it is successfully assessed when using \(Slope_{max}\) or \(\mathrm{VPL}_{d}\) in this example. These three specific examples intuitively illustrate that both optimistic and conservative RAIM availability assessments might happen when using the classic-PL-based method; only conservative assessment might happen when using the enhanced-PL method; and both optimistic and conservative assessments can be prevented when using the ideal-PL-based or the slope-base methods.