esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study addresses the challenges in the non-destructive detection of diseased apples, specifically the high complexity and poor real-time performance of the classification model for detecting diseased fruits in apple grading. Research is conducted on a lightweight model for apple defect recognition, and an improved VEW-YOLOv8n method is proposed. The backbone network incorporates a lightweight, re-parameterization VanillaC2f module, reducing both complexity and the number of parameters, and it employs an extended activation function to enhance the model's nonlinear expression capability. In the neck network, an Efficient-Neck lightweight structure, developed using the lightweight modules and augmented with a channel shuffling strategy, decreases the computational load while ensuring comprehensive feature information fusion. The model's robustness and generalization ability are further enhanced by employing the WIoU bounding box loss function, evaluating the quality of anchor frames using outlier metrics, and incorporating a dynamically updated gradient gain assignment strategy. Experimental results indicate that the improved model surpasses the YOLOv8n model, achieving a 2.7% increase in average accuracy, a 24.3% reduction in parameters, a 28.0% decrease in computational volume, and an 8.5% improvement in inference speed. This technology offers a novel, effective method for the non-destructive detection of diseased fruits in apple grading working procedures.  
artificial intelligence; non-destructive detection; diseased apples; lightweight; YOLOv8n
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 3 Results
### Repast HPC  
Repast HPC proficiently models dynamic community interactions, simulating urban dynamics by representing individuals as agents engaged in movement, social interactions, and resource utilisation [75]. It supports learning and adaptation processes, allowing agents to dynamically adjust behaviours for realistic simulations [76]. This tool integrates socio-economic variables influencing adaptation strategies, with specifics depending on the model's design and implementation within the Repast HPC framework [93]. Repast HPC considers demographic factors, allowing the inclusion of agents with different characteristics for a more realistic representation of community dynamics. While not emphasising participatory modelling, Repast HPC excels in scalability and performance, especially for large-scale simulations. It lacks explicit support for collaborative decision-making but is well-suited for larger-scale and complex simulations due to its focus on high-performance computing [94]. Repast HPC efficiently handles diverse data types, including climate, geographical, and socio-economic data, incorporating spatial, temporal, network, and external data sources like CSV and Excel, depending on the model framework. It efficiently scales models for different community sizes, and is optimised for parallel computing to address scalability challenges in both larger and smaller community contexts. The tool includes mechanisms to assess and model uncertainties and risks associated with adaptation strategies. Although details on sensitivity analysis and scenario testing are limited, Repast HPC supports feedback loops to represent the impact of adaptation measures over time. Monitoring and evaluating implemented strategies are feasible, and it has the flexibility to integrate mechanisms as part of the simulation design [95].
## 3 Proposed Method
### Top-K Key Frame Extraction  
To represent summery of video key-frame extraction process plays a significant role. By extracting the frame we can reduce the computational complexity as well as required memory while processing the videos. In this paper, we extract the top-k key frame (e.g., k = 10 or 15) from the database videos and the query video by employing frame comparison which significantly increases the efficiency. Algorithm 1 explains the procedure for Top-K key frame extraction. Here, we first take the consecutive frames and find the histogram difference between them. Based on these histogram difference we return the top-k frames with the highest difference. From these Top-K key frames we extract the shape feature using our proposed feature descriptor.  
Figure 2: A detailed scenario for video retrieval on MAR.  
```
0: Video File Output: Top-K Key-Frames Key-Frame(video)
``` fori \(\leftarrow\) to Number-of-Frames do  A \(\leftarrow\) ReadFrame(video, i);  B \(\leftarrow\) ReadFrame(video, i + 1);  Threshold \(\leftarrow\) HistDifference(A, B);  X[i] \(\leftarrow\) Threshold [sortedX, sortingIndices] \(\leftarrow\) sort(X, 'descend'); ```  
**Algorithm 1** Top-K Key Frame Extraction
## 3 Optical Wireless Satellite Channel Model  
The optical signal that is used to transport the data symbols from the satellite gateway to the GEO satellite must go through the different layers of the Earth's atmosphere. Unfortunately, the power loss that the optical feeder link experiences in the uplink direction of communication is larger than the one observed in downlink. This is because, in the ground-to-satellite communication, the optical signal starts to spread and accumulate distortion as soon as it leaves the satellite gateway transmitter.
# Sparse Parabolic Radon Transform with Nonconvex Mixed Regularization for Multiple Attenuation
## 4 Real Data Application  
### CMP Gather of Real Data Application  
A marine CMP gather after normal movement correction was applied to further examine the effectiveness of the SPRTL\({}_{q1}-L_{q2}\) method. We chose the time window of 3.2 s-4.8 s in the field data to process because there exists a large number of multiple reflections, as shown in Figure 8a. The reason is that this part of the data has more developed multiple waves, and the effect of suppression can be clearly seen in the final result. There are 400 sampling points in the time direction and 92 offset traces. The sampling interval is 4 ms. In addition, those multiple reflections cover the primary data and affect the imaging effect of the primary reflections.  
Figure 8 is the multiple attenuation results. Figure 8b,c show the multiple attenuation results of the LSPRT and SPRTL\({}_{1}\), and there are still some residual multiple reflections, as indicated by the rectangle. The information of the primaries is masked, and it makes the seismic events discontinuous. Shown in Figure 8d is the multiple attenuation result obtained by SPRTL\({}_{q1}-L_{q2}\), and there are no obvious residual multiples. Because of the sparsity of SPRTL\({}_{q1}-L_{q2}\), the multiples are suppressed effectively at both near and far offsets. The primaries are highlighted, and the black arrows indicate that the continuity of the seismic events are improved. In addition, we can obviously see that the CMP gather is clear and clean.  
In order to further compare the advantages of the proposed algorithm with respect to the LSPRT and SPRTL\({}_{1}\), we extracted three single trace amplitudes after multiple attenuation, and they were compared with single-channel amplitudes before suppression. As shown in Figure 9, there is almost no disturbance in the original location of multiples after multiple attenuation by SPRTL\({}_{q1}-L_{q2}\), and it is obvious at 4.4 s-4.6 s, and the red rectangle marks the difference in this location. The amplitude of the primary reflection is intact and not attenuated, as at 3.4 s and 3.6 s. The proposed method is effective in suppressing multiples and achieves a better performance.  
Figure 7: Multiple attenuation results of missing trace data by SPRTL\({}_{q1}-L_{q2}\): (**a**) 30% missing traces; (**b**) 50% missing traces; (**c**) 70% missing traces.
## 2 Review of Phase-Locked Loops
### Discrete Model
#### 2.2.3 Numerical Integration  
To obtain an equivalent discrete model of a PLL that retains the frequency response of the original continuous-time PLL, designers typically apply a numerical integration method (also known as the \(z\)-transform method). The fundamental concept of this method is to represent the given continuous-time transfer function as a differential equation and to derive a difference equation by replacing \(s\) in the continuous transfer function with a function of \(z\).  
Table 2 summarizes the three types of numerical integration methods that are of interest in this paper, including their \(z\)-domain and discrete-time domain expressions, in addition to some differently used terminology in different fields. These methods are step-invariant (SI), impulse-invariant (II), and bilinear (BL) rules. A detailed digital implementation of these methods for first-/second-/third-order PLLs is presented in the Appendix A.  
Given a continuous-time transfer function \(H\)(s), a discrete equivalent \(H\)(z) can easily be found in these methods by substituting \(s\) with its counterpart equation for \(z\), which can itself be derived from the equations in Table 2. Therefore, each of the approximations in Table 2 can be viewed as a mapping function from the \(s\)-plane to the \(z\)-plane. To make the system stable in graphical interpretation, the region of the stable poles in the \(s\)-plane (i.e., the left half of the \(s\)-plane) should be mapped onto the inside of the unit circle in the \(z\)-plane [13].  
Table 3 lists the resulting \(\alpha_{i}\) and \(\beta_{j}\) in (5) for the first-/second-/third-order DPLLs for various combinations of digital representations of the integrators in the loop filter \(F(z)\) and NCO \(N(z)\). Note that all the systems are a function of \(\omega_{0}T\), such that \(BT\) determines the overall filter response.
## 4 Simulation Analysis  
### Simulation Parameters  
At present, the representative large-scale global LEO constellations are SpaceX, OneWeb and Telesat. There are also several characteristic LEO constellations under construction, such as Hongyan, Hongyun and Hiber. Without loss of generality, we adopt the SpaceX constellation for simulation analysis. The main parameters of the SpaceX core constellation are shown in Table 3.  
Table 4 lists the relevant parameters in the inertial measurement unit (IMU) model. Since the UKF algorithm is an excellent nonlinear algorithm, it has a wide range of applications in the navigation field; therefore, our filtering algorithm adopts the UKF algorithm based on UT transformation [45; 46].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Type** & **Value** \\ \hline Orbit radius (km) & 7521 \\ Height (km) & 1150 \\ Orbit surface number & 32 \\ Inclination (\({}^{\circ}\)) & 53 \\ Number of satellites per orbit & 50 \\ Total number of satellites & 1600 \\ Signal ionospheric delay (m) & 0.02 \\ Signal tropospheric delay(m) & 0.001 \\ Signal spatial error (m) & 0.01 \\ Pseudorange measurement noise (m) & 0.75 \\ Pseudorange rate measurement noise(m/s) & 0.045 \\ Code tracking error (m) & 0.1 \\ Range rate tracking error (m/s) & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SpaceX core constellation parameters [44].  
Table 5 shows the initial values of the UKF and aircraft parameters. In addition, the aircraft completes two 45\({}^{\circ}\) turns in opposite directions and climbs 500 m simultaneously during the flight; the simulation time is 418 s.
# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
###### Abstract  
With the extensive adoption of generative models across various domains, the protection of copyright for these models has become increasingly vital. Some researchers suggest embedding watermarks in the images generated by these models as a means of preserving IP rights. In this paper, we find that existing generative model watermarking introduces high-frequency artifacts in the high-frequency spectrum of the marked images, thereby compromising the imperceptibility and security of the generative model watermarking system. Given this revelation, we propose an innovative image watermarking technology that takes into account frequency-domain imperceptibility. Our approach abandons the conventional convolutional neural network (CNN) structure typically used as the watermarking embedding network in popular watermarking techniques. This helps the image watermarking system to avoid the inherent high-frequency artifacts commonly produced by CNNs. In addition, we design a frequency perturbation generation network to generate low-frequency perturbations. These perturbations are subsequently added as watermarks to the low-frequency components of the carrier image, thus minimizing the impact of the watermark embedding process on the high-frequency properties of the image. The results show that our proposed watermarking framework can effectively embed low-frequency perturbation watermarks into images and effectively suppress high-frequency artifacts in images, thus significantly improving the frequency-domain imperceptibility and security of the image watermarking system. The introduced approach enhances the average invisibility performance in the frequency domain by up to 24.9% when contrasted with prior methods. Moreover, the method attains superior image quality (>50 dB) in the spatial domain, accompanied by a 100% success rate in watermark extraction in the absence of attacks. This underscores its capability to uphold the efficacy of the protected network and preserve the integrity of the watermarking process. It always maintains excellent imperceptibility and robustness. Thus, the framework shows great potential as a state-of-the-art solution for protecting intellectual property.  
Keywords:model watermarking; security; high-frequency artifacts; imperceptibility; deep neural networks; image watermarking +
Footnote †: journal: Applied sciences
# A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging
## 3 Explanation Methods for Deep Learning Cancer Detection Models
### Post-Hoc
#### 3.1.2 Liver Cancer  
A CNN was developed to classify liver lesions by [101]. To interpret the network, the authors visualized the activations of the last convolution layer. The authors calculated a feature score for each feature by hindering the network to utilize that feature and observing the difference in class score. They tested if the network was able to discover explainable radiological features. The model obtained a PPV of \(76.5\pm 2.2\) in identifying the 1-4 correct radiological features for the 60 manually labeled test lesions.
# A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving
## Materials and Methods
### Learning Quantitative Features  
The continuous features of each type of behavior were learned by HMMs. In this section, the foundations of HMM are presented and the method of learning the features is provided. HMMs have been used extensively for sequential data processing in a lot of fields, such as speech recognition, speech tagging [36], and so on. For the isolated word recognizer, each word was modeled by a distinct HMM, based firstly on the characteristics of this word. Once each word in the vocabulary is learned, for any given unknown word that needed to be recognized, the likelihoods for all the possible models can be calculated. The corresponding word for the model that gains the maximum likelihood will be considered as the result for the unknown word. As with word recognition, HMMs are pretty suitable for modeling driving behavior. As a two-layer stochastic process, the HMM consists of a set of hidden states (\(Q\)), observations (\(O\)), transition probabilities (\(A\)), emission probabilities (\(E\)), and initial state probabilities (\(\Pi\)). They can be represented by the following equations:  
\[Q=\{\mathrm{q}_{i}\},\mathrm{i}=1,2,..,\mathrm{N} \tag{10}\]  
\[O=\{\mathrm{o}_{k}\},\mathrm{k}=1,2,..,O_{w} \tag{11}\]  
\[A=\{a_{ij}=\mathrm{P}(q_{j}^{t+1}|q_{i}^{t})\} \tag{12}\]  
\[E=\{e_{ik}=\mathrm{P}(\mathrm{o}_{k}|q_{i})\} \tag{13}\]  
\[\Pi=\Big{\{}\pi_{i}=\mathrm{P}(\mathrm{q}_{i}^{1})\Big{\}}. \tag{14}\]  
For a given time \(t\), the system being modeled is in one of the hidden states \(q_{i}^{t}\), so is not observable. However, this state produces a set of observations with a certain probability (\(E\)). The transition probability from the current state to other states at the following time step is represented by \(A\). The initial probability for each state is described by \(\Pi\), where \(\pi_{i}=\mathrm{P}(\mathrm{q}_{1}=s_{i})\). Then, an HMM can be determined by the triple \(\lambda(A,E,\Pi)\). Because the selected features of driving behavior are continuous values, the Gaussian Mixture Model (GMM) was applied to model the continuous probability density function (pdf) by
## 2 Proposed System Model  
Figure 1 shows the block diagram of the entire system. The system is modeled in Simulink/MATLAB environment with the help of mathematical equations. SC aids the charging of the battery pack during the driving mode. There is a different way of using energy storage devices in HEVs. Some researchers use series combination according to their design. A parallel combination of a fuel cell with a battery/SC was also proposed, but it increased the cost of vehicles. The parallel combination of a battery with SC is most abundant and preferred. Series combination of using these storage devices gives better  
efficiency but energy losses and the weight of the vehicle are increased [36]. SC is used as a battery backup and protects the battery from inrush current. SC has a high power density; low energy density can supply and absorb peak current, and can provide constant power. It can increase the vehicle's overall efficiency and the lifetime of batteries. It can handle the power output from a regenerative braking system. Due to these many advantages, an SC/battery HES for EVs is proposed and analyzed in this study. The overall block diagram of the proposed model is shown in Figure 1.  
Designing of an EMS comprises of FLC and ultra power transfer algorithm. The fuzzy rule-based method was employed in this system due to its robustness and adaptation application. A rule-based FLC is used to supervise and measure the motor speed and boost converter current. Then, the control output is delivered in the form of a duty cycle, which is fed into a boost converter, and then it is provided to the BLDC motor to meet the load requirement. The EMS controls the speed of the BLDC motor and power flow from SC to the battery pack. UPTA checks the SOC of SC and battery, then different logic gates can be used to decide for transferring energy from SC into the battery. The overall Simulink model that explains the whole process can be seen in Figure 2. In this figure, the battery and SC are the main ESSs that fulfill the energy demand. The block named \"load\" is the BLDC motor that is receiving the power from the battery via a boost converter. FLC is designed to generate pulse width modulation (PWM) pulses for the efficient control of the boost converter. The secondary ESS (SC) is being used to charge the battery and also provides power during acceleration mode when the power requirement is high. The main purpose of the SEPIC converter is to provide constant power to charge the battery, utilizing the energy of SC, while, for charging the SC, a regenerative braking scheme is being used. UPTA manages the power flow between the ESSs and the load.  
Figure 1: Block diagram of overall proposed system.
## 1 Introduction
For example, MuRF-1 overexpression increases ubiquitination of myofibrillar proteins and induces muscle atrophy [15]. Similarly, DOX-mediated MuRF-1 upregulation promoted muscle atrophy; in contrast, when MuRF-1 was suppressed by an in vitro electrical stimulation in the cells treated with DOX, cellular atrophy was mitigated [16]. This previous study has provided an interesting question of whether EXE-mediated myocyte protection against DOX-induced muscle atrophy in an _in-vivo_ model is associated with MuRF-1 modulation. In addition to the UPS, autophagy, a lysosome-dependent catabolic process, is involved in protein degradation. Autophagy is critical to sustaining normal cell function by removing damaged proteins, lipids, and small organelles, such as mitochondria, but dysregulated autophagy can contribute to cell death. Recent studies have shown that DOX-inducedautophagy dysregulation leads to muscular dysfunction and oxidative stress, whereas DOX-induced autophagy restriction reverses those impairments [17]. Similarly, a recent study has shown that acute DOX administration increases autophagy and contributes to oxidative stress and reinforced proteolysis; this study also shows that acute exercise preconditioning (DOX administration immediately after five days of 60 min treadmill running/day) prevents DOX-induced autophagy and results in skeletal muscle protection [7], suggesting that EXE-induced autophagy inhibition may be a potential protective mechanism against DOX. However, it should be noted that cellular outcomes from autophagy responses to acute exercise and chronic exercise may differ because some studies have shown that chronic EXE-induced autophagy is necessary for myocyte protection, especially in the heart [18; 19]. Thus, it is important to investigate how chronic EXE provided as an adjuvant therapy during the multiple chemotherapy cycles modulates autophagy in skeletal muscle. Thus, the present study hypothesized that the adjuvant chronic EXE intervention during the multiple DOX treatment would be a potent therapeutic strategy to attenuate DOX-induced myotoxicity.
## 3 Future Work
### Prosumer Communities
#### 3.1.2 Prosumer Roles  
This section outlines the need for further research for a deeper understanding of the role of prosumers in achieving sustainable PCGs.  
Studies like [37; 40] mentioned several propositions for prosumer roles. However, these need quantitative validation and empirical testing to develop hypotheses. For testing various factors such as the amount of prosumers' price reduction resulting from policy actions, the percentage of current vs. potential adopters, the percentage of prosumers involved in testing, and the percentage of early prosumers engaged in co-creation [40].  
The literature also indicates prosumers as actors engaging in innovation and value creation processes, but research is limited in this area. The innovative aspects of their role as prosumers as well as their motivations for co-creational activities require further attention in the future [38]. Co-creation is the building block for sustainable operation of prosumer communities, and hence understanding co-creation from different perspectives such as motivation, finance, and relationships will be critical.  
The differences between early market and late market prosumers can be further examined to understand prosumer behavior their requirements and best management approaches for a sustainable prosumer grid [37]. The literature also discusses prosumers' role in providing flexibility. However, the extent to which they are willing to provide flexibility and the different factors affecting their preference of flexibility option are areas for further investigation [39].  
There are several independent technical and social studies published in the literature. However, holistic studies that cover socio-technical aspects are possible areas to be explored in the future. Such interdisciplinary research would result in highly practical and useful solutions for the society.  
Furthermore, new roles or entities have emerged in the energy system. One of these is the \"prosumager\" which is defined in literature as a prosumer who engages in storing excess energy for later use [111]. In the future, these new entities could be explored further to examine the transformations of prosumer roles and investigate how these new roles may potentially drive advancements of energy management within the smart grids.