esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Future Work
Lastly, continuous SoC monitoring combined with real-time analysis of critical usage patterns would facilitate adaptive operational adjustments, ensuring optimal system performance and longevity. Integrating AI into the proposed framework would mark a substantial advancement in predictive control and energy forecasting. Achieving this requires the development of machine learning-based models capable of leveraging historical and real-time data to predict fluctuations in PV generation and energy demand accurately. To support these models, specialized hardware such as GPUs or TPUs must be incorporated, enabling efficient real-time processing of complex AI algorithms. Additionally, existing control platforms, such as MATLAB or LabVIEW, need to be enhanced with AI libraries like TensorFlow or PyTorch to implement advanced machine learning techniques seamlessly. Efficient data management systems are essential to handle large volumes of operational data, ensuring precise model training and validation. Finally, thorough experimental validation in real-world environments--particularly in smart grids with high renewable energy penetration--is a critical step to confirm the robustness and accuracy of the AI-enhanced framework.
# Agent-Based Virtual Machine Migration for Load Balancing and Co-Resident Attack in Cloud Computing
## 6 Evaluation and Empirical Results
### Agent-Based VM Migration (ALBA) versus Centralized VM Migration  
#### 6.1.1 Objective  
The objective of this experiment was to evaluate and compare the VM migration and security performance of ALBA with a centralized VM migration approach, as well as to analyze the advantages and disadvantages of distributed VM migration compared to the centralized approach in different aspects.
## 8 Cramer-Rao Bound on Covariance Matrix of Localization Errors
### Discussion on Success Probability of Direct Localization
#### 9.2.1 The First Set of Experiments  
Both the localization scenario and the array error model for the first set of experiments are the same as those in Section 9.1.1. Moreover, the parameters \(\Delta_{1}\) and \(\Delta_{2}\), which are used to specify the first SP, are set to the same value of 40, and the parameter \(\Delta\), which is related to the second SP, is also selected as 40. Because the localization scenario is on a 2-D plane, the theoretical value of the first SP can be obtained with (63). In Figures 13-15, we plot the two kinds of SP of the DPD method against the SNR of the emitter signal, standard deviation of the array model error \(\sigma_{\overline{\varphi}}\), and number of snapshots \(K\).  
Figure 12: RMSE of DPD as a function of number of snapshots.  
Figure 13: \(Cont\).  
Figure 14: SP of localization versus standard deviation of array model error. (**a**) The first SP of localization versus standard deviation of array model error. (**b**) The second SP of localization versus standard deviation of array model error.  
Figure 13: Success probability (SP) of localization versus SNR of the emitter signal. (**a**) The first SP of localization versus SNR of the emitter signal. (**b**) The second SP of localization versus SNR of the emitter signal.  
Figures 13-15 reveal that there is a close match between the analytical results and the simulation results and hence the validity of (63) and (68) can be supported. Furthermore, the simulated values in the absence of array model errors approach the lower bound calculated with the CRB in (93), which further indicates that the DPD estimator can achieve the CRB accuracy as long as the array is perfectly calibrated. However, when array model errors exist, the empirical values deviate significantly from the lower bound. Moreover, the difference increases with the standard deviation of array model error (see Figure 14). We thus need to develop a new DPD estimator with improved robustness against array model errors. Furthermore, it is seen that the first SP is always smaller than the second SP, which is consistent with the analysis in Remark 11.
# A Digital Bang-Bang Clock and Data Recovery Circuit Combined with ADC-Based Wireline Receiver
## 3 The Proposed DBB-CDR Circuit with an ADC-Based Wireline Receiver
### The Clock Scheme of the Proposed DBB-CDR Circuit in an ADC-Based Wireline Receiver  
In our 32 Gb/s ADC-based receiver, we adopted 4 \(\times\) 8 hierarchical sampling for the 4 \(\times\) 8 time-interleaved successive comparison register ADC structure shown in Figure 6. The first stage used a four-phase 8 GHz clock to perform four-phase 8 GS/s sampling and  
Figure 6: The block diagram of our DBB-CDR circuit combined with a 32 Gb/s ADC-based receiver.  
Figure 7: The DBB-CDR circuit in the ADC-based receiver.  
the second stage used an eight-phase 1 GHz clock to perform eight-phase 1 GS/s sampling for each phase of the first stage. Using this method, we could obtain a sampling time of 125 ps for each sub-ADC, with the four sub-ADC samples signaling simultaneously at each instant.  
The clock requirements for the two-stage hierarchical sampling were a four-phase 8 GHz clock and an eight-phase clock that divided each four-phase 8 GHz clock by eight. First, the four-phase 8 GHz clock in the design was generated from an 8 GHz differential clock using the injection locking technique. The generated clock also went through duty cycle detection (DCD)/quadrature error detection (QED) circuits and duty cycle calibration (DCC)/quadrature error calibration (QEC) circuits to generate a high-quality four-phase clock: _CKI_8G_, _CKQ_8G_, _CKIB_8G and CKQB_8G_. At the same time, before the four-phase clocks reached the four track and hold circuits, the time skew deviation between the sampling clocks was adjusted again through the delay cell.  
After obtaining the four-phase clock _CKI_8G_, _CKQ_8G_, _CKIB_8G and CKQB_8G_, we needed to divide the four-phase 8 GHz clock by eight to generate eight-phase clocks and then send them to the eight sub-ADCs for each lane. We used a ring shift register circuit to complete this task, as shown in Figure 8.  
Considering that the partial oversampling system that we added required an edge sampling clock, we needed to generate an edge sampling clock from the existing eight-phase clock. From the perspective of low power consumption, we added a small interpolator circuit in the ADC T/H clock buffer, as shown in Figure 9. The interpolator adopted a CMOS structure to interpolate the cki<1> and ckq<1> clocks and the intermediate phase clock CK<1.5>. The CKI<1>, CK<1.5> and CKQ<1> clocks were used for sub-ADC 1, sub-ADC 1.5 and sub-ADC 2, respectively. The DSP generated Data<1>, Data<1.5> and Data<2> and sent them to the DBB-CDR circuit to adjust the optimal sampling phase. A timing diagram of the overall ADC sampling clock, including the edge sampling clock CK<1.5>, is shown in Figure 10.  
Figure 8: A ring shift register circuit for generating eight-phase clocks.
## 3 Distortions Induced by Low Cost
### Nonlinear Impairments
#### 3.2.2 Power Fading Effect  
Due to the interaction between chromatic CD and direct detection, the induced power-fading effect will produce spectral zeros in the spectrum of the signal, which is the key points to determine transmission distance. Figure 6a-c plots the simulated magnitude response of a 28 Gbaud PAM4 system for various fiber lengths, namely 15 km, 50 km, and 100 km, respectively [65]. It can be clearly seen that the number of spectral zeros changes from none to one and then to three by increasing the transmission distance. Meanwhile, the probability of frequency notches in the high frequency is greater, which proves that the power-fading effect is more severe for high-speed transmission. For experimental demonstrate, the frequency response of the received 25 Gbaud PAM4 signal over 50 km SSMF transmission is depicted in Figure 6d [66]. The first frequency notch can be obviously found around 9 GHz, which indicates that the power fading effect is an inescapable problem in IM/DD system.  
Figure 5: The eye diagrams of (**a**) ideal PAM4 signal; (**b**) simulated PAM4 at 50 Gb/s using spatial-temporal modeling, (**c**) measured 56 Gb/s PAM4 signal when bias current is 5 mA; (**d**) measured 64 Gb/s PAM4 signal when bias current is 5 mA; (**e**) measured 56 Gb/s PAM4 signal when bias current is 7 mA; (**f**) measured 64 Gb/s PAM4 signal when bias current is 7 mA (reprinted from [64] with permission from authors).  
Figure 6: Magnitude responses of a 28 Gbaud PAM4 system after (**a**) 15 km; (**b**) 50 km; (**c**) 100 km in simulation (reprinted from [65] with permission from IEEE). (**d**) Frequency spectrum of the detected 25 Gbaud PAM4 signal over 50 km SSMF transmission (reprinted from [66] with permission from authors).
# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
## 2 Relevant Work
Furthermore, it just requires a small number of hyperparameters, which is ideal for unsupervised clustering with unclear data. HDBSCAN was employed in another study to identify trends in user behavior in a mobile app [16]. HDBSCAN was used to locate star, galaxy, and quasi-stellar object (QSO) clusters in a multidimensional color space in one study by [PERSON] and [PERSON] on the application of HDBSCAN for unsupervised star, galaxy, and QSO categorization. For star, galaxy, and QSO selection, they achieved F1 ratings of 98.9, 98.9, and 93.13, respectively. According to the findings, accurate categorization with HDBSCAN necessitates meticulous attribute selection. Because of the scalability of the trained model's applicability, they strongly proposed adopting their method for existing and upcoming data-rich surveys. The data were clustered using two clustering algorithms, including HDBSCAN, after the user interactions in the apps were retrieved. The HDBSCAN algorithm discovered three separate kinds of user behavior, with feature selection methods linked to user behavior picking up 73 percent of the features [17]. The advantages of HDBSCAN can be broken down into a couple main segments. HDBSCAN inherits all the advantages of the DBSCAN algorithm. Firstly, it requires very few hyperparameters, which is very intuitive. The rest of the association is learned solely from the data. The minimum cluster size is a very intuitive parameter. The HDBSCAN does not require the minimum sample parameter that is provided in DBSCAN. Aside from that, the HDBSCAN works significantly better than the partitioning method for specific use situations, such as the one we are looking at in this study, because it does not need to be preempted for the number of clusters. It also does not necessitate that the data be distributed globally. Because it is a challenging clustering technique, the Agglomerative clustering algorithm and the k-means algorithm are both unable to remove noise from data [15]. The HDBSCAN, on the other hand, is capable of filtering out noise, which is important in our study. In conclusion, the viability of the workflow is proven. The YOLO algorithm detects objects in the camera and then outputs the data as coordinates after some processing. The coordinates are then passed into the clustering algorithm and clusters are produced. In this study, we use the HDBSCAN algorithm, which requires only a small cluster size parameter. The minimum cluster size is determined by the area of detection, which is based on an estimate of the total area. The approach for creating the dataset and clustering the points is discussed in the following section.
# Hardware-Based Architecture for DNN Wireless Communication Models
## 3 Background
### Conventional Detector
#### 3.2.3 MLD Detector  
Maximum likelihood detection is also a more efficient signal detection algorithm than the ZF and MMSE methods presented above. The MLD algorithm calculates the Euclidean distance between the received signal vector and the product of all possible transmitted signal vectors with the given channel \(H\) to get an approximate value of the symbol. Therefore, the transmitted signals \(S(n)\) can be recovered from received signals \(Y(n)\) with very low BER. The detail of the MLD algorithm is given as follows  
\[Y_{MLD}(n)=\underset{x\in\mathbb{C}^{M}}{\arg\min}||Y(n)-HS(n)||^{2} \tag{8}\]  
where, \(||...||^{2}\) corresponds to the Frobenius norm. Let \(C\) and \(M\) denote a set of signal constellation symbol points and the number of transmit antennas, respectively. Although the MLD algorithm eliminates almost noise to achieve good BER performance, it still has some disadvantages, such as computational complexity and time consumption, which is because the MLD algorithm has to check all possible candidates.
## 2 Overview of the Basic Concepts of Wind Farm Design and Optimization
### The Wind Farm Design Variables and Constraints  
The WFDO problem aims at defining the best value of the following set of variables: (1) WTs emplacement locations; (2) WTs type (e.g., the technology including the control/operation strategy); (3) WTs size (e.g., capacity); (4) number of WTs; (5) WTs hub heights; (6) type of tower, sub-structure and foundation; and (7) type, size and capacity of auxiliary infrastructure (e.g., auxiliary roads, power collection systems (_i.e._, transformer number/type/size/capacity and other electric devices), electrical interconnection layout and capacity (_i.e._, power lines type and length), repeater stations, _etc._). The impact of these variables on the expected economic profit is difficult to assess either due to the uncertainty in the estimation of the involved costs or due to the random nature of some of the adverse factors influencing the variables. Any assumptions and/or constraints imposed on the decision variables will naturally facilitate the optimization process but will limit the design space for the optimal solution, thus possibly resulting in important opportunity costs. Therefore, during the micro-sitting optimization, those variables should not be limited or constrained without a reasonable justification. All the factors described in Section 2.2 will affect the design variables.  
All variables are naturally bounded and constrained by physical factors: (1) the WTs emplacement locations are bounded by the considered wind farm site area and constrained by local terrain aspects and environmental and climatological characteristics (e.g., land use, restricted areas, geotechnical capacity, setback constraints, obstacles, _etc._); (2) the WTs type is constrained by the available technology; (3) the size and (4) the number of WTs are constrained by the actual power system capacity and the expected demand from the energy consumers. In addition, the theoretical maximum number of WTs can be coarsely calculated as the ratio of the wind farm site area to the individual WT area \(\pi R^{2}\), assuming that two or more WTs cannot be located within an area of \(\pi R^{2}\), otherwise blade collisions would occur. In practice, the maximum number of WTs is much lower due to geometrical and geographical constraints; (5) The minimum and maximum WT heights are constrained by the rotor radius (\(R\)), the national aviation regulations and the available technology, respectively; (6) The type of foundation is a function of the water depth (in the case of offshore wind farms), the bearing capacity of the terrain, the expected loading and the available technology; and (7) the auxiliary infrastructure design, often considered an embedded optimization problem, is constrained by all described variables and the current electric, civil engineering and communication system capacity.
## 3 Unit Cell Analysis
### 2 \(\times\) 2 Array  
The 2 \(\times\) 2 array configuration of proposed MTM unit cell is shown is Figure 5a. In the array configuration the unit cells are not connected with each other. The gap between each unit cell remains 0.5 mm on each side. By using the same methodology, the effective parameters are retrieved and shown in Figure 5b-d. From Figure 5b, the negative permeability (\(\upmu\)) of this configuration exhibited from 6.65 GHz to 12.70 GHz, covering a bandwidth of more than 6 GHz, can be seen. In Figure 5c the negative magnitude of permittivity (\(\varepsilon\)) is from 2.61 GHz to 5.93 GHz, 7.67 GHz to 8.07 GHz and 8.49 GHz to 10.10 GHz. As mentioned earlier, the refractive index will be negative if the permeability and permittivity appear negative concurrently. The refractive index (\(\upeta\)) exhibits negative peaks from 6.68 GHz to 8.09 GHz and 8.50 GHz to 14.54 GHz. Moreover, the 2 \(\times\) 2 array structure shows a negative index metamaterial.  
Figure 4: 1 \(\times\) 2 array characteristics (**a**) Simulation setup (**b**) Permeability (**c**) Permittivity (**d**) Refractive index.  
Figure 5: _Cont._
## 9 Conclusions  
This work investigated the technical survey of Li-ion and LA batteries and their ability to withstand variable DC load demands in the MG. The operation of both electrochemical energy storage technologies was investigated in the proposed MG system using a variable DC load profile. A comprehensive review of a grid-connected MG system was conducted with comprehensive software, and the critical conclusions of this work are presented as follows:  
* It was found that applied electrochemical energy storage can aid the variable energy sources (PV systems) to meet the overall energy demand in the presented MG system.
* Based on the literature review, it was found that both batteries impressively reserved surplus power throughout low energy demand and supplied the reserved energy through high demand times.
* The applied electrochemical energy storage usage in a utility grid-tied MG system is less than when implemented in an isolated MG.
* An electrical system comprising a great magnitude of renewable energy needs a relative number of efficient batteries to store and compensate for their variability.
* An energy storage system can alleviate the main grid's utilized hours, minimizing utility costs and Green House Gas emissions (GHG). As a result, this prompts the use of green energy by end-users.
* An MG scheme with Li-ion batteries as a buffering method is expected to be more conducive than an MG consisting of LA batteries. This is due to their high efficiency and extended life cycle. Consequently, making Li-ion is considered a better long-term investment.
* LA batteries are still extensively utilized in distinct systems due to their economic advantage; however, they have low efficiency and energy density. Based on the results of this work, it was discovered that Li-ion batteries have better storage attributes and are more conducive to substitute lead-acid, and, correspondingly, are better employed in a microgeneration system.  
Conceptualization, C.S.M. and P.F.L.R.; methodology, C.S.M., P.F.L.R. and J.A.J.; formal analysis, C.S.M. and P.F.L.R.; investigation, C.S.M. and P.F.L.R.; resources, C.S.M. and P.F.L.R.; data curation, C.S.M. and P.F.L.R.; writing--original draft preparation, C.S.M. and P.F.L.R.; writing--review and editing, P.F.L.R. and J.A.J.; supervision, P.F.L.R. and J.A.J.; project administration, P.F.L.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation (NRF) and Tshwane University of Technology.  
Not applicable.  
I appreciate the support from my supervisors, [PERSON] and [PERSON]. I would not have accomplished this outcome without the guidance from my supervisors. I am highly appreciative. This work is based on research supported wholly by the National Research Foundation of South Africa (NRF) and Tshwane University of Technology.  
The authors declare no conflict of interest.
# A Study on Pine Larva Detection System Using Swin Transformer and Cascade R-CNN Hybrid Model
## 3 Designing Models for Pine Pest Detection
### Design a Structure That Mixes the Swin Transformer Model and the Cascade R-CNN Model  
In the research field of object detection, IoU thresholds are used to define positive and negative values. In the research process for the detection of pine pests based on the Faster R-CNN model, a low IoU threshold is applied. The resultant value of the bounding box position appears inaccurate in the detection process, and when a high IoU threshold is  
Figure 5: Design of pine pest Detection using the Cascade R-CNN model.  
applied, the accuracy of the detector is reduced. This can be very bad. These cases can be mainly classified into two causes. First, when the IoU threshold increases, the number of positive numbers decreases exponentially, resulting in overfitting. The second cause is that the IoU threshold shown in the model reasoning process may be an erroneous matching.  
In order to solve this problem, this study mixed a multi-stage Cascade R-CNN model to detect pests in pine trees. By changing the backbone in the Cascade R-CNN model and using the Swin Transformer model as the backbone, we wanted to increase the accuracy in detecting pine pests.  
Figure 6 shows the design of a model that combines the Swin Transformer model and the Cascade R-CNN model to increase the accuracy of the detection of pine pests in this study.  
Figure 6 shows the structure of the Tiny model of the Swin Transformer used in this study. The composition of the patch of five is a model of the converted patch partition layer and four stages. Herein, each stage is composed of a patch merging layer (PML) and two Swin Transformer Blocks (STBs), except for the linear embedding layer (LEL) of the first stage.  
The size of the Tiny model structure of the Swin Transformer is H/4 * W/4 * 48, found by dividing the input image of H * W * 3 into patches of 4 * 4 * 3 so that they do not overlap with each other through the patch partition, making it a feature after stage two, which is the patch merging stage, and the number of patches inside the window is kept constant by merging adjacent 2 * 2 matches.  
Figure 6 shows the internal structure of the Swin Transformer Block in four stages. Window multi-head self-attention (W-MSA) and shifted window were used as methods to calculate window-based self-attention in the existing Multi-Headed Self-Attention (MSA).  
Figure 6: Design of a mixed model composed of the Swin Transformer model and the Cascade R-CNN model.  
Herein, it has the same structure as the existing standard transformer except for the Shifted Window Multi-Head Self Attention (SW-MSA) model that calculates self-attention.  
The reason for choosing the mixed model for this study is that the structure of the Swin Transformer model is more effective than CNN for learning a large amount of data, and the accuracy is also higher. Because it has the advantage of minimizing the inference time discrepancy of the IoU, a design that mixes the two models was designed.