esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comprehensive Simulation Analysis
#### 4.2.2 Analysis of Comprehensive Simulation Results  
Simulations were conducted on the six scenarios in Table 4 to study the promotion effect of policy combination schemes on the entire system, as shown in Figure 9. By comparing the increase of three critical indicators caused by six different scenarios, the newly added area of PBs and the supply of PB scale are similar, and the promotion effect of various scenarios is ranked as Scenario 6 > Scenario 4 > Scenario 5 > Scenario 2 > Scenario 3 > Scenario 1 > initial value. The promotion effect of a policy combination is much higher than that of a single policy. The policy control schemes in scenario 6 can bring the maximum promotion effect, which is much more significant than other scenarios. Scenario 4 also causes a greater promotion effect, while the promotion effects of Scenario 5, scenario 2, and Scenario 3 are similar, and the promotion effect of Scenario 1 is the least. For carbon emission reduction, the ranking of the increasing effect caused by different scenarios is Scenario 6 > Scenario 5 > Scenario 4 > Scenario 3 > Scenario 2 > Scenario 1 > initial value. Scenario 6 and Scenario 5 can significantly improve the effect of reducing carbon emissions of PBs in Shenyang. Scenario 4 and Scenario 3 have the same promotion effect, while Scenario 1 and Scenario 2 have the same effect. It can be seen that the reduction of carbon emissions is greatly affected by the R&DSP.  
Figure 9: Comprehensive simulation results of PBIPs. (**a**) Simulation results of the newly added area of PBs, (**b**) Simulation results of the supply of PBs, (**c**) Simulation results of the carbon emission reduction.
## 4 Data Collection and Result Discussion  
### Experiment Data Collection  
In order to achieve 1 mm level absolute antenna phase center calibration it is necessary that all possible orientations should be covered with highly dense and homogeneous observations, so that station dependencies such as blank holes can be avoided. Meanwhile, observations down to zero elevation are recommended. However, it is simply not possible to satisfy this requirement in a static relative calibration procedure, even if tracking for as long as 24 h (see Figure 4).  
To avert such defects, a high-precision robot which can be flexibly rotated and tilted was introduced in our calibration procedure. It takes only several hours to collect data from several thousand precise positions (required for high precise derived PCO and PCV) and angle changes. This automatic observation procedure will stop when the full coverage of the test antenna pan is reached (see Figure 5).  
Preliminary calibration experiments were performed to verify some key algorithms discussed in the paper and the implemented calibrating procedure. For the antenna phase center value validation, three  
Figure 5: Observations coverage on test antenna hemisphere when the antenna was being rotated (4 h rotating and tilting mounted on the robot, DoY2014240 in Wuhan).  
types of GNSS geodetic antennas (TRM57971.00/NONE, TRM59800.00/NONE and LEIAR25/NONE) were chosen. These types of antenna have been calibrated by several institutes and published in the document igs05.atx and igs08.atx ([[ftp://igscb.jpl.nasa.gov/igscb/station/general/igs08.atx](ftp://igscb.jpl.nasa.gov/igscb/station/general/igs08.atx)]([ftp://igscb.jpl.nasa.gov/igscb/station/general/igs08.atx](ftp://igscb.jpl.nasa.gov/igscb/station/general/igs08.atx))). Although some GPS-only solutions show that PCCs in igs08.atx, may cause small position offsets [16], the PCC model differences are very small: \(<\)0.5 mm, compared to igs05.atx. Therefore, the above three types' PCCs in igs08.atx will be treated as true standards. In an absolute sense, the reference antenna can be arbitrarily chosen. However, a choke ring antenna Trimble TRM59800.00/NONE was used in the course of calibration for the sake of preserving observables from being affected by multipath errors (see Figure 2).  
Our experiments were conducted on different days with the above three antenna types to investigate the absolute calibration repeatability precision based on the developed robot automation observation system. Among the experiments, the TRM57971.00/NONE type was calibrated during up to five sessions on different days and the other two types were tested in three sessions. Each experiment's data collection was completed when the full coverage of the test antenna pan was reached (6-7 h on average with intervals of 1 s). Then the collected data sets were processed carefully (following Section 3.1 in this paper). Importantly, all the estimated ambiguities were fixed to integer values using LAMBDA and further investigated to make sure ambiguities were fixed with the highest possible ambiguity success rates [21,22] (lower bound \(>\)0.9999 for all the sessions).
## 4 Case Study
### Plant Description and Control Objective  
The eight-coupled tanks plant comprises eight interconnected tanks (Figure 3) with four upper tanks (3, 4, 7, and 8) that discharge into the lower ones (1, 2, 5, and 6), which in turn, discharge into sinking tanks. The plant is controlled by four pumps whose flows are divided through six three-way valves \(\gamma_{v}\) with \(v\in\{1,2,\ldots,6\}\) manually operated.  
The system is divided into \(s\) subsystems with \(s=\{1,2,3,4\}\): Tanks 1 and 3 are part of Subsystem 1; Tanks 2 and 4 form Subsystem 2; Tanks 5 and 7 belong to Subsystem 3; and the rest of the tanks form Subsystem 4. The level of Tank \(h_{n}\) with \(n\in\{1,2,\ldots,8\}\), being the controlled variables the levels \(h_{1},h_{2},h_{5}\), and \(h_{6}\) renamed \(l_{5}^{s}\) as, \(h_{1}^{c}=h_{1}\), \(h_{2}^{c}=h_{2}\), \(h_{3}^{c}=h_{5}\), and \(h_{4}^{c}=h_{6}\) for each agent. The manipulated variable \(q_{s}\) is the flow given by the pump of each Subsystem \(s\). The tank level operating point is established, \(h_{n}^{0}\) for \(n\in\{1,2,\ldots,8\}\) as \(h_{1}^{0}=0.10\), \(h_{2}^{0}=0.15\), \(h_{3}^{0}=0.07\), \(h_{4}^{0}=0.03\), \(h_{5}^{0}=0.10\), \(h_{6}^{0}=0.15\), \(h_{7}^{0}=0.025\), \(h_{8}^{0}=0.10\) (units: meters). In addition, the operating point of the flow of the pumps \(q_{s}^{0}\) as \(q_{1}^{0}=0.142\), \(q_{2}^{0}=0.421\), \(q_{3}^{0}=0.421\), \(q_{4}^{0}=0.140\) (units: m\({}^{3}\)/h).  
The aggregated state vector and input state vector are defined as  
\[\begin{array}{l}\tilde{x}_{\mathcal{N}}=\big{[}h_{1}^{c}(t)-h_{1}^{0},h_{2} ^{c}(t)-h_{2}^{0},h_{3}(t)-h_{3}^{0},h_{4}(t)-h_{4}^{0},\\ h_{3}^{c}(t)-h_{5}^{0},h_{4}^{c}(t)-h_{6}^{0},h_{7}(t)-h_{7}^{0},h_{8}(t)-h_{8} ^{0}\big{]}^{\intercal},\\ \\ \tilde{u}_{\mathcal{N}}=\big{[}q_{1}(t)-q_{1}^{0},\ldots,q_{4}(t)-q_{4}^{0} \big{]}^{\intercal},\\ \end{array}\]  
The disturbance state vector is defined as \(\widetilde{d}_{\mathcal{N}}=\big{[}d_{1}(t)-d_{1}^{0},\ldots,d_{4}(t)-d_{4}^{0 }\big{]}^{\intercal}\).  
The control objective is to solve a tracking problem to reach the reference \(\widetilde{\mathcal{T}}_{\mathcal{N}}\),  
\[\widetilde{\mathcal{T}}_{\mathcal{N}}=\big{[}h_{1}^{\mathcal{T}}(t)-h_{1}^{0},h_{2}^{\mathcal{T}}(t)-h_{2}^{0},0,0,h_{3}^{\mathcal{T}}(t)-h_{5}^{0},h_{4}^ {\mathcal{T}}(t)-h_{6}^{0},0,0\big{]},\]  
where \(h_{s}^{\mathcal{T}}\) is the target level of the controlled variable \(h_{s}^{c}\).  
For the upper tank levels, the objective is to keep the operating point despite the disturbance \(\widetilde{d}_{\mathcal{N}}\) affecting the system. Additionally, the state and input vectors are constrained by  
\[\begin{array}{l}-h_{n}^{0}<\tilde{x}_{n}(t)\leq 0.08,\,-q_{i}^{0}<\tilde{u}_{i} (t)\leq 0.04,\\ \forall n\in\{1,2,\ldots,8\},\,\forall i\in\{1,2,3,4\}.\end{array}\]  
The linear state space models of the global system and subsystems are detailed in [30].
# Energy Management Scheme for an EV Smart
## 2 Modern Distribution Grids  
As grid load continues to increase, the high cost of renewing the infrastructure such as distribution lines and transformers has become a serious constraint [26; 27]. These constraints have given rise to the introduction of generation in the form of DGs in proximity to the load [28], which reduces the stress on the distribution transformers and lines. Appropriately-placed and -sized DGs reduce losses and also tend to satisfy the power and voltage limitations [29]. Furthermore, solar or wind-based DGs are an eco-friendly way to generate power, which helps to reduce the greenhouse gases emission [30].  
Solar and wind power are well suited to be integrated as DGs owing to the ease of integration at the required location because of their small size and expandable nature. Environmental policy requirements have also encouraged the introduction of renewables. At the same time, EVs have been introduced in the transportation sector to shift the trend away from conventional vehicles. Figure 1 depicts a schematic of a renewable energy DG and EV integration at the PCC, adjacent to the load being fed from the AC grid and the DG. EVs are connected parallel to each other at the DC-bus of the charger.  
The grid impedance is mainly inductive [31; 32]; thus, \(X_{g}\gg R_{g}\) is assumed. The inductance \(X_{g}\) represents the connection between the grid bus and the PCC. The transfer of active power \(P_{g}\) and reactive power \(Q_{g}\) from the grid to the PCC is given as follows:  
\[P_{g}=\frac{V_{g}V_{pcc}}{X_{g}}\sin\delta \tag{1}\]  
\[Q_{g}=\frac{V_{g}^{2}}{X_{g}}-\left(\frac{V_{g}V_{pcc}}{X_{g}}\cos\delta\right) \tag{2}\]  
where \(V_{g}\) is the voltage on the grid bus, \(V_{pcc}\) is the voltage on the PCC, the power angle is \(\phi_{g}-\phi_{pcc}=\delta\), \(\phi_{g}~{}~{}=~{}0\) is the phase angle of the grid bus and \(\phi_{pcc}=-\delta\) is the phase angle of the PCC bus. This configuration shows that the active power transfer depends on the power angle, while the reactive power is related to the voltage.
## 5 Simulation Framework and Experimental Scheme
### Performance Metrics  
This study employs the MMtQHU model to estimate the probability of route changes for individual drivers by incorporating an extensive array of human, social, and environmental factors. These route deviations introduce variability into the network's traffic dynamics, which is quantified using two key performance metrics: call drop rate (CDR) and resource allocation efficiency.
# Technology Trends for Massive MIMO towards 6G
## 9 Conclusions  
This article undertakes an in-depth exploration of the technological evolutions underpinning the future of massive MIMO, a critical element as we transition into the 6G era. We begin with a detailed overview of the contemporary advancements in massive MIMO standardization and research, drawing attention to the intricate dynamics shaping this arena. Further, we delve into the potential of IRS/IOS technologies to revolutionize massive MIMO communications and the associated challenges, particularly in harnessing IRS/IOS to bolster localization and sensing capabilities.  
We continue our exploration with an inspection of ultra-massive MIMO at THz frequencies, uncovering the complex considerations influencing its design. The spotlight then turns to cell-free massive MIMO architecture, a promising innovation with the capacity to dramatically enhance the spectral and energy efficiencies of wireless systems and networks. In parallel, we outline the hurdles and emergent trends in the application of AI to massive MIMO, emphasizing its increasing relevance in future vertical applications.  
Additionally, we offer a practical analysis of massive MIMO-OFDM-enabled high-speed communications, featuring tangible design examples to illustrate their potential. The review concludes with a rigorous investigation into the evolving landscape of massive MIMO communications in non-terrestrial networks, where the emphasis lies on the frontier spaces of near-Earth and deep space.  
This conclusion highlights the importance of further exploring these technologies, given their potential to expand and transform our communication capabilities beyond terrestrial confines. We underscore the significance of envisioning and preparing for this transformative shift in communication paradigms, from Earth-bound networks to expansive non-terrestrial systems such as in other celestial bodies across the solar system. In the final analysis, these technologies promise to deliver unprecedented levels of connectivity, redefining our understanding of what is possible within the realm of communication technology.  
Conceptualization, all authors; methodology, all authors; software, all authors; validation, Y.H.; formal analysis, all authors; writing--original draft preparation, all authors; writing--review and editing, Y.H.; visualization, all authors; supervision, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We express sincere thanks to the IEEE Future Networks Massive MIMO Working Group, and the Organizing Committee of the IEEE Future Networks Second Massive MIMO Workshop.  
The author declares no conflict of interest.
### Motivation  
In IoV, huge challenges need to be addressed. The primary motivation behind this work is to resolve the existing research issues of IoV. The significant challenges are unreliable data delivery, poor scalability, dynamic topologies and dynamic spectrum availability. Each challenge requires a different solution to resolve it. However, the recent research growth in the IoV and IoT fields solves these challenges. Primarily, CR and mmWave are the two major solutions for resolving the IoV issues. On the other hand, integrated IoV architecture is vulnerable to security threats or attacks that affect the overall performance of the system. Motivated by this fact, this research work designs a novel integrated IoV architecture with CR and mmWave technologies along with security solutions. The major aim of the proposed IoV design is to enhance overall performance by incorporating optimal processes. In order to achieve this goal, the following contributions are made to this work.  
* A novel mmWave-assisted CR-IoV architecture is designed with the required level of security. In the proposed architecture, communication is optimized by using 5G technology. Further, the overall performance of the system is improved in three aspects such as security, beamforming and routing.
* Security is attained by detecting and mitigating two major attacks such as SSDF and PUE attacks. SSDF mitigation is enabled by a modified ML algorithm, namely the Hybrid Kernel Support Vector Machine (HK-SVM) algorithm. The proposed HK-SVM classifies the SSDF attackers accurately by analyzing the sensing reports. PUE attack is detected based on an accurate hybrid localization approach, namely Fang Algorithm enabled Time Different of Arrival (FA-TDoA) position location system. Isolation of SSDF and PUE attackers in the network assures the required level of security.
* A lightweight ID-based Certificate Validation (LID-CV) procedure is proposed to assure the legitimacy of all SUs. Thus, the available spectrum can only be utilized by legitimate SUs.
* For optimal beamforming, Beampattern Function (BPF) is computed in terms of the 3D position of the SUs, i.e., vehicles in the mmWave-assisted CR-IoV system. The BPF is computed as the function of the Array Factor (AF) of the massive MIMO antenna.
* To enable optimal routing, Forwarding Zone (FZ) enabled Multi-Objective Whale Optimization (MOWO) algorithm is proposed. The proposed FZ-MOWO algorithm uses a novel fitness function, namely Potential Score, to select an optimal route for data transmission.
## 3 Fault Diagnosis Method of Planet Gear Based on CVS and MED
### Minimum Entropy Deconvolution Filter  
Besides modulation effects caused by planetary movements, fault-related impulses detection under noises is a challenge for fault diagnosis of planet gear. Fault-related impulses are transient signal that decays quickly, and they only exist when the faulted gear tooth meshes with other gear. Moreover, they could be easily polluted by regular vibrations and noises as well. In this paper, MED is used for enhancement detection of fault-related impulses.  
TSMA signal \(x\) mainly consists of three parts. The most dominate one is regular vibrations \(u\), fault-induced impulses \(d\) will be introduced when gear tooth is damaged, and noises \(n\) is inevitable. Effects of signal transferring path can be modeled as \(h\). As shown in Figure 8, TSMA signal can be written as  
\[x=h\star(u+d+n), \tag{13}\]  
where \(\star\) is convolution operator, and  
\[x=\left[\begin{array}{c}x_{1}  
A practical approach to solve the optimized filter can be written as [48]  
\[\mathbf{f}=\frac{\sum\limits_{n=1}^{N}y^{2}[n]}{\sum\limits_{n=1}^{N}y^{4}[n]}\big{(} \mathbf{X}_{0}\mathbf{X}_{0}^{T}\big{)}^{-1}\mathbf{X}_{0}\big{[}\ \ y^{3}[1]\ \ \ y^{3}[2]\ \ \ \cdots\ \ \ y^{3}[N]\ \ \big{]}, \tag{17}\]  
where  
\[\mathbf{X}_{0}=\left[\begin{array}{ccccc}x_{1}&x_{2}&x_{3}&\cdots&x_{N}\\ 0&x_{1}&x_{2}&\cdots&x_{N-1}\\ 0&0&x_{1}&\cdots&x_{N-2}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\cdots&x_{N-E+1}\end{array}\right]_{E\times N}. \tag{18}\]
# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 4 Experimental Results and Analyses
### Experimental Environments  
In this study, trace-driven experiments, which consider vehicle movement traces and MS communication traces, are designed to evaluate the traffic information estimation and forecasting methods based on CFVD (shown in Figure 6). The inputs of the trace generator include the road conditions (e.g., the length of the road, the number of lanes, the locations of handover points and traffic flows), the vehicle movement behaviors (e.g., vehicles speeds, car following model and lane-changing model) and MS communication behaviors (e.g., call inter-arrival time and call holding time). The output is a trace file, which records the vehicle's ID, vehicle speed, its CAs, its HOs, its NLUs and its PLUs. For the generation of vehicle movement traces, the practical traffic information, which included traffic flows and vehicle speeds from VDs on National Highway No. 3 in Taiwan during October in 2010, was collected and expressed as the characteristics of road conditions (_i.e._, traffic flows) and vehicle movement behaviors (_i.e._, vehicle speeds) for traffic simulation. Furthermore, call holding time is exponentially distributed with the mean \(1/\lambda\), and the call inter-arrival time is exponentially distributed with the mean \(1/\lambda\) in accordance with the mobile communication records from Chunghwa Telecom for  
Figure 5: Vehicle speed estimation based on the back-propagation neural network algorithm.  
the generation of MS communication traces [13]. Then, vehicle movement and MS communication traces can be generated by a traffic simulation program, VISSIM. Figure 7 shows nine handover points and nine cells distributed in three location areas on the road from 0-9 km, and the coverage of a cell is 1 km. Moreover, this study assumes that there are nine VDs, which are built in the same locations with handover points for evaluating the traffic information of each road segment covered by a cell.
## 2 Theoretical Framework  
### Grounding Systems  
GSs are present in almost all points of the electrical grid, including power stations and transmission lines and cables, which distribute electrical energy.  
The primary function of a GS is to provide various routes to drive electrical currents to the ground or to establish contact with the ground without exceeding the limits of any operating equipment, thereby preventing service interruptions.  
Using a GS with various shapes and geometries ensures the safety of people and the reliable operation of electrical equipment. Researchers have proposed various ways to identify optimal GS designs, such as the approach proposed by [6]. These authors performed a comparative analysis of three grounding grid configurations in a two-layer soil model. After designing equally and unequally spaced grounding grids for each shape, they found that the unequally spaced rectangular grid outperformed the other designs. The study showed that the rectangular grid had the minimum GR value.
## 3 System Architecture
### Federated Learning Protocol  
To avoid transmitting raw data and ensure user privacy, the system utilizes federated averaging (FedAvg) as the core mechanism for decentralized learning. FedAvg is a communication-efficient algorithm that aggregates model updates from multiple clients by averaging their local weights, allowing for global model improvements without centralizing sensitive data.  
* The coordinator initializes a global model.
* Each device trains its local model on locally stored anomaly-labeled data.
* After a training round, devices transmit only their model weights to the coordinator.
* The coordinator aggregates updates (e.g., weighted average) and broadcasts the new global model. This process is repeated over multiple communication rounds. The framework assumes partial client participation and supports asynchronous updates to accommodate device availability.
## 3 Results  
### Isolation and Screening of Lacase-Producing Microbes  
Fifty-three microbial isolates were obtained from soil sample cultures. Among them, 30 were identified as _Actinomycetes_ based on their morphological studies. They were named as CDBT-A-1 to 30, respectively. Twenty isolates were _Streptomyces_ and were named as CDBT-S-1 to 20, respectively. Three were fungal isolates and named as CDBT-F-G1, G2, and G3, respectively. In the initial screening for laccase production, oxidation of guaiaicol, tannic acid, and 1-naphth were used and the plates were scored visually for laccase activity after five days of culture. The oxidative polymerization of guaiaicol led to the formation of a reddish-brown zone, oxidation of tannic acid formed a brown color zone and oxidation of 1-naphth formed a deep purple zone. Among the 53 isolates tested, 12 isolates tested positive for laccase activity in terms of guaiaicol test, tannic acid test, or 1-naphth. Among them, one tested positive, two were _Actinomycetes_, seven were _Streptomyces_, and three were fungi (Table 1). The _Streptomyces_ isolate, _viz._, CDBT-S-16, and the fungal isolate, _viz._, CDBT-F-G1, appearto secrete highest levels of laccase (Figure 1). CDBT-F-G1 fungal isolate was tested several times to confirm the fact that it produces the highest level of laccase activity among all of the 53 isolates tested herein. All further studies described in this paper were performed using the CDBT-F-G1 fungal isolate.
## 4 Experimental Results and Discussion
### Performance Assessment Metrics  
For the quantitative evaluation of invisibility, the peak-signal-to-noise-ratio (PSNR) of Equation (5) has been used primarily in the previous works, and it is thus used in this study, too. As previously mentioned, the pixel value of the WM embedder's output is ranged to [\(-\)1. 1] because of normalization. It is converted to an integer in the range of [0, 255] as a final watermarked image used for the invisibility evaluation.  
\[PSNR=10 log_{10}(\frac{255^{2}}{MSE}) \tag{5}\]  
Robustness is evaluated by the bit error ratio (BER) of the extracted WM information. Because the WM information comprises binary images, the original and extracted, WM information's pixel value is measured as one if they are the same, and 0 if they are different. The resulting values are averaged for the number of pixels. It is shown in Equation (6).  
\[BER(\%)=100\frac{1}{XY}\sum_{i,j}^{XY}\delta(WM_{o}(i,j),WM_{ext}(i,j)) \tag{6}\] \[\delta(A,B)=\begin{cases}0,&A\
eq B\\ 1,&A=B\end{cases}\]  
Besides, the WM capacity is shown in Equation (7), which is the ratio of the WM resolution to the host image resolution. In this equation, the resolution of the WM and the host image is \((X,Y)\) and \((M,N)\), respectively. In this study, the WM capacity was fixed at 0.0039, without the loss of generality and practicality.  
\[capacity=\frac{XY}{MN} \tag{7}\]