esa-sceva/satcom-chunk-collection huggingface dataset:
# Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
## 5 Analysis and Discussion of Experimental Results
### Analysis of Imperceptibility and Robustness  
The imperceptibility check is done through evaluation parameters PSNR, MSE, CC, and SSIM. By applying different attacks such as gamma correction and Gaussian attacks, the robustness of the proposed work is tested. In this section, attacks are applied on the stego image to expose the hidden secret information. Then, after the attacks, the PSNR, MSE, SSIM, and CC values are calculated for stego and original cover file which help to find the imperceptibility of the technique.  
Gamma Correction Attack  
It works on the luminance value of the input image. As luminance value of the input does affect the excellence of the image. This attack includes compression as well as expansion of input image to expose the hidden secret data. Less than 1 value of \(\gamma\) represents a compression of the input image. Greater than 1 value of \(\gamma\) represents the expansion of the input image as shown in Equation (2) [28]:  
\[V_{out}=AV_{in}^{\gamma} \tag{2}\]  
where \(V_{out}\) is the output image, \(V_{in}\) is the input image, \(A\) is a constant, and it is generally taken as 1.  
Table 3 shows the effect of gamma correction attack on stego frame, retrieved secret image, and retrieved cover image. There is a slight variation in the quality of the cover file and stego file. Large is the difference between cover and stego file quality more are the chances of detection of secret information.  
Table 4 expresses the value of performance metrics of the stego file for gamma correction attack. The average value of PSNR is calculated as 55.2 dB, the average value of MSE is 0.228 dB\({}^{2}\), the average value of SSIM is 0.77, and the average value of CC is 0.95. The lower value of MSE means less is the error present between original and stego file. Higher value of PSNR, CC and SSIM represent good quality of stego file. High value of these three parameters means a lower chance of exposure of secret message.  
Table 5 shows the effect of Gaussian noise attack on stego frame, retrieved secret image, and retrieved carrier image. Greater the alteration between the carrier and the stego image, the higher the chances of detection of secret information.  
Table 6 represents the value of performance metrics of the stego files Gaussian noise attack. The values of performance metrics represent the quality of research work. The average value of PSNR is calculated as 55.3 dB, the average value of MSE is 0.224 dB\({}^{2}\) the average value of SSIM is 0.88 and the average value of CC is 0.88. We tried Gaussian attack to expose the secret message in this part. Value of the performance metrics represent the robustness of proposed work. It has been concluded from the result that even after applying Gamma correction and Gaussian attacks, we are not able to extract the hidden secret message.
## 4 Experiment and Results
### Comparison Experiment  
(1) A comparison was conducted using D3 QN, DQN, actual signal timing, Webster signal timing, actuated signal timing, and the Rainbow DQN algorithm-based signal timing. Among them, Webster signal timing and actuated signal timing are both traditional methods for signal optimization. For comparison, we selected two mainstream reinforcement learning models: DQN and D3 QN. The DQN model serves as the foundational model for Rainbow DQN, while D3 QN has garnered significant attention and is considered representative [50; 51]. The DQN model configuration is based on the approach in [47], with the addition of a CNN module. The model setup and modules used in the D3 QN model follow the configuration described in [35].  
The actual signal timing is shown in Figure 6, and the key parameter settings for Webster signal timing and actuated signal timing are provided in Table 4. The Webster signal timing parameters are calculated based on the actual intersection's road network structure, traffic volume, and phase distribution to determine the optimal fixed signal timing. The actuated signal timing is based on the Webster signal timing parameters, with the maximum and minimum durations set according to [19]. The hyperparameters required for the DQN and D3 QN algorithms are consistent with those used in Rainbow DQN.  
To further validate the effectiveness of the reward function, a comparative reward function, Reward-Wait Time, was established. The calculation formula is as follows:  
\[R_{T}=k_{3}D_{t-1}-D_{t} \tag{22}\]  
where \(D_{t}\) and \(D_{t-1}\) represent the total vehicle waiting time at the intersection's entry lane at time \(t\) and \(t-1\), respectively; \(k_{3}\) is set to 0.9.  
In summary, the experimental groups and control groups are set up as shown in Table 5.  
(2) Analyze the control impact of the model under different scenarios, including varying CO\({}_{2}\) emission factors for electric vehicles, different proportions of electric vehicles, varying traffic volumes, and considering the penetration rate of autonomous vehicles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Control Method** & **Parameters/Phases** & **Parameter Value (s)** \\ \hline \multirow{6}{*}{Webster signal timing} & East–West Straight & 7 \\  & East–West Left Turn & 7 \\  & North–South Straight & 8 \\  & North–South Left Turn & 4 \\  & Yellow Light Duration & 4 \\ \hline \multirow{2}{*}{actuated signal timing} & Minimum Duration & 5 \\  & Maximum Duration & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Signal timing parameter settings for the control group.
## 4 Conclusions  
A high-efficiency dual-broadband linear polarization converter based on a reflective metasurface is studied by simulation and experiments. The designed metasurface almost perfectly transform \(x\)-polarized or \(y\)-polarized incident waves into their mutually perpendicular counterparts in two different bands. Four resonances, which are attributed to the electric and magnetic resonances, emerge in the frequency ranges of 9.38-13.36 GHz and 14.84-20.36 GHz, indicating that the proposed polarization converter has multiple resonance characteristics. In these two bands, the PCR values are greater than 98.21% and 99.32%, respectively. The polarization conversion principle can be explained using the amplitude and phase differences between the incoming and reflected waves and the surface current distributions. Additionally, the effects of the dielectric layer thickness on both the bandwidth and PCR are analyzed. The results show that the bandwidth and PCR of the two frequency bands can be adjusted over a broad range, implying that the manipulation of EM waves is both flexible and selective. The designed dual-broadband and high-efficiency metasurface has great potential in the application of electromagnetic polarization control.  
[PERSON] put forward the idea, designed the structure, performed the numerical simulations and wrote the article; [PERSON] analyzed the data; [PERSON] was in charge of the experiment; [PERSON], [PERSON] and [PERSON] checked the spelling, grammar of this article and put forward some comments.  
This research was funded by the Natural Science Foundation of China (NSFC) (grant No. 51774092 and No. 51474069), Youth Science Foundation of Northeast Petroleum University of China (grant No. 2018 QNL-39), Postdoctoral Science Foundation of China (grant No. 2016M601400), and City University of Hong Kong Strategic Research Grant (SRG) (grant No. 7005105).  
The authors declare no conflict of interest.
###### Abstract  
Reasonably formulating the strawberry harvesting sequence can improve the quality of harvested strawberries and reduce strawberry decay. Growth information based on drone image processing can assist the strawberry harvesting, however, it is still a challenge to develop a reliable method for object identification in drone images. This study proposed a deep learning method, including an improved YOLOv8 model and a new image-processing framework, which could accurately and comprehensively identify mature strawberries, immature strawberries, and strawberry flowers in drone images. The improved YOLOv8 model used the shuffle attention block and the VoV-GSCSP block to enhance identification accuracy and detection speed. The environmental stability-based region segmentation was used to extract the strawberry plant area (including fruits, stems, and leaves). Edge extraction and peak detection were used to estimate the number of strawberry plants. Based on the number of strawberry plants and the distribution of mature strawberries, we draw a growth chart of strawberries (reflecting the urgency of picking in different regions). The experiment showed that the improved YOLOv8 model demonstrated an average accuracy of 82.50% in identifying immature strawberries, 87.40% for mature ones, and 82.90% for strawberry flowers in drone images. The model exhibited an average detection speed of 6.2 ms and a model size of 20.1 MB. The proposed new image-processing technique estimated the number of strawberry plants in a total of 100 images. The bias of the error for images captured at a height of 2 m is 1.1200, and the rmse is 1.3565: The bias of the error for the images captured at a height of 3 m is 2.8400, and the rmse is 3.0199. The assessment of picking priorities for various regions of the strawberry field in this study yielded an average accuracy of 80.53%, based on those provided by 10 experts. By capturing images throughout the entire growth cycle, we can calculate the harvest index for different regions. This means farmers can not only obtain overall ripeness information of strawberries in different regions but also adjust agricultural strategies based on the harvest index to improve both the quantity and quality of fruit set on strawberry plants, as well as plan the harvesting sequence for high-quality strawberry yields.  
Keywords:strawberry harvesting; YOLOv8 model; drone image analysis; growth information +
Footnote †: journal: _Agriculture_
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 2 Impedance Modeling of the DSOGI-PLL-Based Inverter and Verification
### Impedance Modeling under the Three-Phase Balanced Condition
#### 2.2.1 DSOGI-PLL  
The phase of voltage at the PCC is obtained by the PLL, which is used as the phase reference of the control system. The modeling of the PLL is vital to analyzing the interaction stability between the inverter and the grid, so the small-signal model of the PLL should be first derived.  
When the perturbation is applied to the system, there is a deviation in the output angle of the PLL. The output phase of the PLL can be expressed as  
\[\theta=\omega_{1}t+\Delta\theta \tag{4}\]  
where \(\theta\) is the output angle of the PLL, \(\omega_{1}\) is the fundamental angular frequency, and \(\Delta\theta\) is the phase deviation.  
The control block diagram of the SOGI is shown in Figure 3. The \(dq\)-axis positive-sequence signals (high-order small-signal components have been ignored) are expressed as  
\[\mathbf{V}_{\text{p}d+}=G_{\alpha 1}(s_{1})\mathbf{V}_{1}+G_{\text{ap}}(s_{\text{p}}-s_ {1})\mathbf{V}_{\text{p}}+G_{\text{ac}}(s_{1}-s_{\text{c}})\mathbf{V}_{\text{c}} \tag{5}\]  
\[\mathbf{V}_{\text{p}q+}=-\Delta\theta\big{[}G_{\alpha 1}(s_{1})\mathbf{V}_{1}+G_{ \text{ap}}(s_{\text{p}}-s_{1})\mathbf{V}_{\text{p}}+G_{\text{ac}}(s_{1}-s_{\text{ c}})\mathbf{V}_{\text{c}}\big{]} \tag{6}\]  
where \(G_{\alpha k}=G(s_{k})+\mathrm{j}G_{q}(s_{k})\), \(G_{\beta k}=G_{q}(s_{k})-\mathrm{j}G(s_{k})\), \(s_{k}=\mathrm{j}2\pi f_{k}\), and \(k=1,\mathrm{p},\mathrm{c}\). And the transfer functions between two orthogonal output signals and the input signal are \(G(s)=\frac{k\omega_{1}s}{s^{2}+k\omega_{1}s+\omega_{1}^{2}}\) and \(G_{q}(s)=\frac{k\omega_{1}s^{2}}{s^{2}+k\omega_{1}s+\omega_{1}^{2}}\).  
According to the control structure of the PLL and Equations (6)-(11), the output angle of the DSOGI-PLL can be obtained  
\[\Delta\theta=T_{\text{p}}(s)\mathbf{V}_{\text{p}}+T_{\text{c}}^{*}(s)\mathbf{V}_{\text{ c}}^{*} \tag{7}\]where \(T_{\rm p}(s)=\frac{-iH_{\rm ph}(s-1)\left[G(s)+iS\zeta_{\rm q}(s)\right]}{1+\left[G _{\rm q}(s)+iS\zeta_{\rm q}(s)\right]V_{\rm H}t_{\rm ph}(s-s_{1})}\), \(H_{\rm pll}(s-s_{1})=\frac{k_{\rm p}+k_{\rm f}/(s-s_{1})}{s-s_{1}}\), \(T_{\rm c}^{*}(s)=\frac{iH_{\rm ph}(s-1)\left[G(s)+iS\zeta_{\rm q}(s)\right]}{1+ \left[G_{\rm q}(s)+iS\zeta_{\rm q}(s)\right]V_{\rm H}t_{\rm ph}(s-s)}\), \(H_{\rm pll}(s_{1}-s)=\frac{k_{\rm p}+k_{\rm f}/(s-s_{1})}{s_{1}-s}\).
## 2 Materials and Methods  
In this section, the non-Euclidean metrics used in the paper are described, and the adaptation of K-means for use with covariance matrices from [17] is recalled. Clustering, grouping a set of objects, can be hard or soft (fuzzy). In hard clustering, each object belongs to only one cluster whereas objects can belong to more than one cluster in soft clustering. The most common used methods for hard and soft clustering are K-means and FCM, respectively.  
Recall that a covariance matrix is a square, symmetric, positive semi-definite matrix. Let \(D(\mathbf{A}_{j},\tilde{\mathbf{A}}_{i})\) represent the distance between the covariance matrices \(\mathbf{A}_{j}\) and \(\tilde{\mathbf{A}}_{i}\) where \(\tilde{\mathbf{A}}_{i}\) is the weighted mean of the covariance matrices in the cluster \(i\). The distance \(D(\mathbf{A}_{j},\tilde{\mathbf{A}}_{i})\) and weighted mean \(\tilde{\mathbf{A}}\) of the set \(A\) using Euclidean, log Euclidean, and root Euclidean metrics [6] are presented in Tables 1 and 2, with details on how to compute the power, log, and exp relegated to Appendix A.  
These metrics are the ones considered in this paper since the computation of their mean is faster (since there is no need to use numerical solutions) in comparison to non-Euclidean metrics (Procrustes and Riemannian) that do need numerical solutions. The computation of numerical solutions can be very time consuming, especially when processing large datasets, which violates a key requirement for most medical applications (i.e., efficiency).  
Furthermore, the Log Euclidean metric provides similar results to Riemannian [18] and the root Euclidean metric provides similar results to Procrustes [7]. The Cholesky metric is another standard non-Euclidean metric; however, its use is omitted because, whilst the computation of the Cholesky mean does not need numerical solutions, the Cholesky metric is not as reliable as the others due to not being invariant under orthogonal transformations [7].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Distance** \\ \hline Euclidean & \(\left\|\mathbf{A}_{j}-\tilde{\mathbf{A}}_{i}\right\|\) \\ Log Euclidean & \(\left\|\log(\mathbf{A}_{j})-\log(\tilde{\mathbf{A}}_{i})\right\|\) \\ Root Euclidean & \(\left\|\mathbf{A}_{j}^{1/2}-\tilde{\mathbf{A}}_{i}^{1/2}\right\|\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distance \(D(\mathbf{A}_{j},\tilde{\mathbf{A}}_{i})\) using the Euclidean, log, and root Euclidean.
## 2 Background and Review of Related Works
### Securing the ICV with XAI: Background Information
#### 2.4.2 Explainable-AI (XAI) Frameworks and Result Evaluation  
The ICV network is confronted with security issues that necessitate more adaptive, automated, and integrated IDS. Explainable AI (XAI) is necessary for mitigating vulnerability and intrusion detection, with explanation and interpretability of classification model decisions [64; 65; 66; 67], as well as managing evolving threats and attack mechanisms efficiently. XAI aims to develop ML algorithms that generate more explainable techniques while preserving efficient learning performance, as shown in Figure 3. It also allows human comprehension of the models' actions and decisions. As a rapidly expanding area, it aids in extracting information and the visualization of the results generated with maximum transparency [68].  
Figure 2: Various approaches to mitigating ICVs’ vulnerability to attacks highlighting the role of IDSs, among others [61].  
The national institute of standards (NIST), part of the U.S. Department of Commerce, enumerated five types of \"explanations\" for AI. These were enumerated in [69] as follows:  
1. Inform the subject of an algorithm: In the case of the IDS for ITS, an explanation of how the AI model guarantees the security of the system from intruders is critical.
2. Comply with compliance or regulatory requirements: As AI algorithms gain importance in regulated industries, they must be able to show that they follow rules. For instance, self-driving AI algorithms should detail how they adhere to any applicable traffic laws.
3. Build social trust in AI systems by using explanations that support the model and approach rather than focusing on specific outputs. This could involve detailing the algorithm's goals, development process, data used, and sources, as well as its advantages and disadvantages [70].
4. Help with future system development: In order to improve an AI system, technical employees must comprehend where and why a system produces incorrect results.
5. Benefit the owner of the algorithm: Businesses are implementing AI across all sectors in the hope of reaping considerable rewards. For instance, a streaming service benefits from recommendations that are easy to understand and keep people subscribed.
# Voxel-Based Digital Twin Framework for Earthwork Construction
## 3 Methodology  
### Proposed Voxel-Based Digital Twin Framework  
The proposed voxel-based digital twin framework for earthwork construction is presented in Figure 2. The proposed framework digitally replicates and manages earthwork construction through the following five components: physical parts, digital parts, data, connections, and services. At its core, the physical parts represent real-world entities on construction sites, including terrain, excavation and filling zones, geotechnical properties, and heavy equipment such as excavators and dump trucks. These physical parts are actively engaged in earthwork activities. For instance, the terrain changes volumetrically, which is captured through sensing technologies such as photogrammetry or laser scanning. Equipment operations at the construction site continuously generate data that reflects the evolution and serves as a data source for the updated digital twin.  
The digital part of the framework involves the representation of each physical part in a 3D environment, such as through BIM models. The terrain, geotechnical, design, and construction data are modeled and embedded with semantic information. Additionally, this includes a simulation of the earthwork operation, where equipment and models interact based on the as-built data. The digital counterpart facilitates both real-time model updating and simulation, enabling stakeholders to analyze the current state, simulate future scenarios, and visualize previous activity sequences.  
The data, connectivity, and service components of the framework contribute to developing the operational layer of the system. The data component integrates earthwork construction-related information such as geotechnical parameters, terrain information, equipment specification, design models, and as-built scans. The connection layer provides seamless communication between the physical and digital parts, ensuring volumetric updates and feedback. The service layer delivers application functions, including model-equipment interaction simulation, material tracking, and volumetric analysis. These components are linked together, creating a highly integrated and context-aware digital twin system tailored for earthwork construction.  
Figure 2: Proposed voxel-based digital twin framework.
# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 3 Human Mobility Research in Evolution Mobile Network
### 5G Mobile Network to Explore Human Mobility
#### 3.2.2 Open Challenges and Human Mobility Research Directions  
5G technology has already been deployed. Currently, we can estimate the revenue opportunities and its value associated with use cases in human mobility research. We rely on a number of key sources supported by a set of properties.  
In the signaling performed within the core network (CN), a property of SDN, should be optimized for handover and re-routing purposes enabling more better support, scalability and performance to human mobility. Moreover, a faster and efficient CN signaling is only possible using AI techniques like machine learning [88]. Here, by efficiency, we imply that CN signalling presents an optimized and reduced cost in transmission, processing, and other related metrics.  
5G serves different human mobility profiles. Users can effectively access different properties like services or networks with differing loads. For example, to help cater to the different 5G network slices according to their human mobility demands, and avoid the sub-optimal one size fits all approach can be helpful to adopt independent strategies for the various network slices [89]. Moreover, this slice-based strategies enable better network mobility management and quality of service (QoS) provisioning for 5G networks.  
Device-to-device (D2D) works by identifying the human mobility models, individual traces, and features of different human mobility research directions, concerning D2D communication applications in real-life scenarios. For example, 3 GPP Release-13 supports communication with devices in clusters, formalizing D2D cluster. Thus, through relaying strategies for CP/DP information can enhance the performance of the devices within the network in such clusters. Beyond this extension of CP-DP capabilities of the current D2D framework, policy-based methods also prevail in 5G and B5G networks taking into account the presence of D2D communications and other scenarios between user and device.  
User mobility trends or network load are parameters that enable the learning network to plan effective human mobility strategies for 5G and B5G networks. Furthermore, these dynamic network environments distribute human mobility functionality throughout the network, consequently alleviating congestion issues. Note that network functions virtualization (NFV) and software-defined networking (SDN) facilitate fast switching, while the user-device transits through the network, facilitating network programmability. Furthermore, these service migration strategies of 5G will greatly enhance the QoS at the same time that users crossover to another public land mobile network (PLMN) and lead to a change in the edge cloud that serves them during human mobility [90].
## 2 Materials and Methods
### Differential Expression Analysis of miRNA and Functional Annotation of Target Genes  
Read counts of each unique miRNA were normalized by DESeq for differential analysis. miRNAs with averaged reads of less than 10 in all 3 periods were excluded for differential expression analysis. miRNAs with a fold change of \(|\) log2 (ratio) \(|\)\(\geq\) 1 and a \(p\)-value of \(<\)0.05 were regarded as differentially expressed.