esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of Bagging on the Accumulation of Chlorophyll and Carotenoids  
To determine the effect of bagging on color change in kiwifruit, the chlorophyll and carotenoid contents were determined. The contents of chlorophyll a, chlorophyll b, and total chlorophyll showed a decreasing trend during the fruit development period, whereas carotenoid content decreased during early development and remained stable in the late-development stage. Bagging significantly reduced the accumulation of chlorophyll and carotenoids during development, whereas after debugging, the chlorophyll and carotenoid contents increased significantly (Figure 3). These results indicated that bagging has a significant effect on chlorophyll and carotenoid metabolism in kiwifruit.  
Effects of Bagging on the Expression of Genes Associated with Chlorophyll and Carotenoids Metabolism-Related Genes in Kiwifruit  
To explore the reasons for the difference in chlorophyll and carotenoid contents after bagging, the expression levels of genes involved in chlorophyll and carotenoid metabolism during fruit development were investigated by qRT-PCR. The expression of chlorophyll metabolism-related genes showed an upward trend during fruit development. During most of the development period, the chlorophyll biosynthesis genes AcRCBS, AcGLUTR, and AcCHLG, and degradation genes AcPAG, AcPPAO, AcPH, AcCLH, and AcSGR had significantly lower expression levels in bagged fruit, while the expression of these genes increased after debugging (Figure 4). The results indicate that the reason for chlorosis during the fruit development period of yellow-fleshed kiwifruit is not due to the reduction of chlorophyll synthesis, but due to the degradation of chlorophyll. Regarding carotenoid metabolism, nine biosynthesis genes and one degradation gene were investigated during fruit development. The expression of AcPSY, AcPDS, AcCRTISO, and AcZEP were stable during the fruit development period, but the expression of AcPSY and AcZEP increased significantly at the fruit ripening stage. The expression of the biosynthesis genes AcLCYE, AcCYP97A, AcCHYB2, and AcVDE were decreased during fruit development, while the expression of AcPSY and AcZEP increased significantly at the fruit ripening stage. Bagging inhibited the expression of carotenoid metabolism genes, whereas debugging promoted the  
Figure 3: Effects of bagging on chlorophyll a (**A**), chlorophyll b (**B**), total chlorophyll (**C**), and carotenoid (**D**) content during kiwifruit development. Each assay was performed in triplicate to ensure the validity of the experimental data and to reduce the variance. All results are presented as mean \(\pm\) standard error (SE). Bars with the same letter are not significantly different at \(p\) > 0.05 among three treatments, according to paired\(-\)samples \(t-\)test, ‘ns’ means not significant.  
expression to a certain extent (Figure 5). Notably, AcLCYB was highly expressed during late fruit development and was significantly affected by shade bagging (Figure 5A), indicating that it may play an important role in carotenoid accumulation during fruit development.
# Enhanced Flow Control for Low Latency in QUIC
## 1 Introduction
The maximum receive window size is doubled if the receive window update  
Figure 1: Median web page size. Figure 2: Loading time and size of web pages. period is shorter than a threshold. Moreover, the receive window is no longer increased after reaching the upper limit or the update cycle has stabilized. In contrast, autotuning requires time to find a suitable receive window size. Therefore, reducing the maximum receive window search time is important for low-latency transmission. In this paper, we propose a fast autotuning scheme for low-latency transmission. The increase factor in fast autotuning is determined to be inversely proportional to the receive buffer occupancy. If the buffer occupancy is low, the increase factor is increased. Otherwise, the increase factor has been set to a low value. By rapidly widening the receive window while avoiding buffer overflow, the suggested approach can decrease transmission latency. We used the ns-3 simulator for performance evaluation [18]. Fast autotuning reduced transmission delays by 29% and increased throughput by 12% compared to autotuning in simulations employing a large buffer. The rest of this paper is organized as follows: We present related works along with the motivation of this work in Section 2. The proposed fast autotuning method is detailed in Section 3. In Section 4, we evaluate fast autotuning allowance based on extensive simulations. Finally, the conclusions are presented in Section 5.
# Robot Joint Vibration Suppression Method Based on Improved ADRC
## 3 ADRC Control of Robot Joint Module
### ESO Design
#### 3.2.1 Mathematical Model of PMSM  
When factors such as magnetic saturation, hysteresis damage, and eddy current losses are ignored, the stator voltage equation of the PMSM in a \(dq\) coordinate system can be described as [21]  
\[\left\{\begin{array}{l}u_{d}=R_{s}i_{d}-\omega_{e}L_{q}i_{q}+L_{d}\frac{{\rm d }i_{d}}{{\rm d}t}\\ u_{q}=R_{s}i_{q}+\omega_{e}L_{d}i_{d}+\omega_{e}\psi_{f}+L_{q}\frac{{\rm d}i_{q }}{{\rm d}t}\end{array}\right. \tag{22}\]  
The electromagnetic torque equation is  
\[T_{e}^{*}=\frac{3}{2}n_{p}\psi_{f}i_{q} \tag{23}\]  
The equation of motion is  
\[J\frac{{\rm d}\omega_{0}}{{\rm d}t}=T_{e}^{*}-T_{load} \tag{24}\]  
where \(u_{d}\) and \(u_{q}\) are the \(d\) axis and \(q\) axis components of the stator voltage, \(i_{\rm d}\) and \(i_{\rm q}\) are the \(d\) axis and \(q\) axis components of the stator current, \(n_{p}\) is the number of pole pairs, \(\varphi_{f}\) is the rotor magnetic flux, \(\omega_{e}\) is the rotor electrical angular velocity, and \(\omega_{e}=n_{p}\omega_{0}\). \(\omega_{0}\) are the motor angular velocity, \(J\) is the moment of inertia, \(T_{e}^{*}\) is the electromagnetic torque, and \(T_{load}\) is the load torque [22,23].  
When considering the disturbance term and damping term, (24) can be rewritten as  
\[J\frac{{\rm d}\omega_{0}}{{\rm d}t}=T_{e}^{*}-T_{load}-\widehat{d}\left(t\right) \tag{25}\]  
where \(\widehat{d}\left(t\right)\) is the estimated disturbances that include the damping disturbances, vibrational impact disturbances, and sudden load variations, among others.  
These disturbances encompass nonlinear factors and exhibit time-varying characteristics, making precise modeling challenging. Consequently, the ADRC theory treats both internal and external nonlinearities and all kinds of disturbances including the frictional forces and other force disturbances you mentioned as a unified \"total disturbance,\" which is estimated and compensated in real time, thereby reducing reliance on model accuracy.  
The core procedure primarily involves the design of an ESO: it treats unmodeled dynamics, nonlinear friction, external disturbances, etc., as total disturbance and provides real-time estimation of the total disturbance through the state observer.
# On the Local and String Stability Analysis of Traffic Collision Risk
## 3 Introduction of Traffic Collision Risk Stability
### Definition of Collision Risk Local Stability  
Consider vehicle \(n\) to be in equilibrium until a disturbance affects the vehicle's velocity. Vehicle \(n\) is risk-local-stable if the risk disturbance is suppressed over time and the collision risk \(r_{n}(t)\) can eventually return to the equilibrium state; otherwise, the vehicle is risk-local-unstable. The mathematical expression of risk local stability is formulated as  
\[\lim_{t\to\infty}\Delta r_{n}(t)\to 0 \tag{11}\]  
In the frequency domain, the difference between traffic local stability and the proposed risk local stability can be viewed from the block diagram in Figure 2. The green blocks and orange blocks represent the input and output of the system, respectively. The input is the Laplace transform of the disturbance from vehicle \(n-1\), denoted by \(Y_{n-1}(s)\). \(\Delta X_{n}(s)=X_{n}(s)-X_{n}^{e}(s)\) and \(\Delta R_{n}(s)=R_{n}(s)-R_{n}^{eq}(s)\) are the outputs, which denote the space variation and risk variation from the equilibrium state in the frequency domain, respectively. The yellow block denotes the initial state, which is also the equilibrium state. The blue block is used to indicate the frequency-domain operators. \(Q_{n}(s)\) is the transfer function of vehicle \(n\), which is derived from the car-following model (see Equation (3) for an example). the \(R(s)\) calculator is a manifestation of \(f(\cdot)\) in Equation (9) in the frequency domain. \(s\) is the frequency domain operation that corresponds to the differential operator in the time domain. \(X_{n}(s)\), \(V_{n}(s)\), and acceleration \(A_{n}(s)\) represent the position, velocity, and acceleration state of vehicle \(n\), respectively.  
In traffic local stability analysis, in light of the input disturbance \(Y_{n-1}(s)\) and the equilibrium state of vehicle \(n\), \(x_{n}^{eq}(t)\), vehicle \(n\) generates the response as \(X_{n}(s)\). Then, the deviation from the state \(X_{n}(s)\) to the equilibrium state \(X_{n}^{eq}(s)\) is obtained as \(\Delta X_{n}(s)\). Traffic local stability investigates if the output disturbance \(\Delta X_{n}(s)\) will be bounded with a bounded input disturbance \(Y_{n-1}(s)\). Similarly, the deviation of \(R_{n}(s)\) to the equilibrium risk state \(R_{n}^{eq}(s)\) is obtained as \(\Delta R_{n}(s)\). Unlike traffic local stability, risk local stability includes an extra calculation (i.e., \(R(s)\)) for the collision risk (i.e., Equation (9)) and investigates if \(\Delta R_{n}(s)\) is bounded with a bounded input disturbance \(Y_{n-1}(s)\).  
Figure 2: Block diagram of risk local stability.
# ZnO@MoS2 Core-Shell Heterostructures Enabling Improved Photocatalytic Performance
###### Abstract  
This work reports the fabrication of ZnO@MoS2 core-shell micro/nanomaterials and their photocatalytic performances. First, the ZnO@MoS2 core-shell micro/nanorods heterostructures were grown by a two-step, hydrothermal method. Second, X-ray diffraction, scanning-electron microscopy, Raman spectra, and UV-visible spectra were applied to confirm and characterize the ZnO@MoS2 core-shell micro/nanorods. Third, methylene blue was employed to investigate the photocatalytic performance of the ZnO@MoS2 core-shell micro/nanorods heterostructures. It was found that the shape of the MoS2 shell layer depended on the growth time. The shell layer was composed of MoS2 nanoparticles before the growth time of 6 h and then turned into MoS2 nanosheets. It was also found that the photocatalytic performance was significantly affected by the growth time of the MoS2 nanosheets. When the growth time of the MoS2 nanosheets was between 6 and 10 h, ZnO@MoS2 core-shell heterostructures grown for 6 h exhibited a best photocatalytic efficiency value of 69.24% after 3 h catalysis.  
Keywords:growth time; photocatalytic performances; ZnO@MoS2 core-shell heterostructures +
Footnote †: journal: Applied Sciences
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 4 Methodology
### Graph Construction  
Before training, convert the data into table format using the raw Netflow format. NetFlow format is an IP flow collection tool that utilizes network elements (such as routers and switches) to collect encountered IP flows and export them to external devices. These IP flows can be defined as a unidirectional sequence of packets encountered on a network device and contain a variety of important network information, including IP addresses, port numbers, number of packets and bytes, and other useful packet statistics. Pre-process the tabular data, including operations such as data standardization. Use IP as a node, and all attributes except IP as edge attributes to construct a graph, and divide it into training graphs and testing graphs. While building the training graph and testing graph, retain the coarse-grained labels and fine-grained labels so that it is possible to detect whether it is an attack flow and what kind of attack flow it is during the training process.  
Here is the process of Netflow format from each dataset into training and testing graphs. First, we split the tabular data into train and test samples. Then, source and destination port information is removed from each flow record. After that, target encoding is performed on categorical features in both the training and testing set. The training set is used to train a target encoder on categorical data. Encoding is then performed again using the fully trained encoder on both the training and testing sets. Meanwhile, any empty or infinite values arising from this process are replaced with a value of 0. Before finally generating graphs, training and testing sets are normalized using an L2 normalization approach. Similarly to the approach taken in target encoding, the normalizer is trained using the training set to guarantee a learning process. Finally, each graph's node features are set to a constant vector containing ones with the same dimensions as those of the edge features, for example, if 10 edge features are used, node features will be vectors of 10 ones.
# Enhancement of Conventional Beat Tracking System Using Teager-Kaiser Energy Operator
## 1 Introduction
The rest of the paper is organised as follows: Section 2 describes the onset detection function, the Teager-Kaiser energy operator, the proposed enhancement of the conventional beat tracking system and the beat detection method. It shows, how is the TKEO changing the spectra and therefore the output onset detection. Then, it introduces the reference and the string quartet database used for the GT and ABD estimation. Furthermore, a possible application is shown and the system evaluation is defined. Results are reported in Section 3 and discussed in Section 4. Finally, conclusions are given in Section 5.
# Securing the Future of Web-Enabled IoT: A Critical Analysis of Web of Things Security
## 4 Threat Modeling
### Threats and Related Concepts  
Many publications on the WoT do not clearly differentiate between threat concepts, leading to confusion and inadequate defenses. An asset is an entity (hardware or software) that is valuable to stakeholders. These assets are part of a system that includes applications and computational resources executing those applications. Applications perform functions for users; some are business (external) applications, while others perform internal functions, such as establishing a new connection between two endpoints. Vulnerabilities are weak points in a system that can be exploited to perform attacks. They can stem from code, design, or network management errors. Threats are possible ways to exploit vulnerabilities to conduct attacks. Several threats can use the same vulnerability. Attacks are the realization of threats, leading to the misuse of information. A misuse of information is the result of an attack and is a goal of the attacker, such as the denial of service or data leakage. To clarify these concepts, some studies use a structured approach to list threats: a table of vulnerabilities, a table of threats, and a table correlating vulnerabilities, threats, and defenses [34].  
An actual attack instance is called an incident. The execution of an attack may involve several steps, each achieving a subgoal. For example, capturing credentials can allow attackers to access a system component and further advance their goals. Attackers may be motivated by financial gain or the desire to cause damage or disruption. A subject is an active entity in a system, capable of requesting resources and performing computational activities. An attacker or adversary is a subject with the knowledge and resources to perform an attack. Catalogs of possible threats at different system levels, such as the CVE (Common Vulnerabilities and Exposures) list, provide references for publicly known vulnerabilities and exposures. Starting from higher-level threats based on attacker goals, we develop a catalog to propagate these goals to lower levels of the system, leading to misuse [24].
## 3 CB and Power Control  
The channel hardening phenomenon tends to determine the gain of the random channel vector. Cell-free massive MIMO with a single-antenna AP diminishes the channel hardening phenomenon. Therefore, in this section, we propose to use instantaneous CSI for precoding and power control instead of the statistical information. In a slowly varying channel environment, there are sufficient resources to feed the estimated downlink CSI to the AP and CPU.
## 4 Results
### Sensitivity to Training Sample Size  
We use \(R^{2}\), RMSE and MAE between the predicted LAI values and measured LAI values to assess the sensitivity of five ML methods to the training sample size. Eight datasets with different sample sizes (17-87) were generated by randomly sampling from the total training datasets (87 samples) at intervals of 10. Table 9 shows the number of training and testing samples of the eight datasets, while the testing datasets keep as the same datasets with 30 samples.  
To evaluate the robustness of five ML models for the training samples, we conducted 10 random repetitions of each model and provided the corresponding standard error. The standard error is given as follows:  
\[\sigma_{\overline{\tau}}=\frac{\sigma}{\sqrt{n}} \tag{17}\]  
where \(\sigma\) denotes the standard deviation, \(n\) represents the number of repetitions.  
Figure 7 shows the changes in \(R^{2}\), RMSE and MAE as the training sample sizes varied, the standard error is given at each training sample size (filling area of each model), lower values of the filling area corresponds to smaller standard error, and consequently, more robust model performance. According to Figure 7, different sensitivity performances of the sample size variation are found among the ML models. GPR and GBRT show robust performances for the training samples according to the standard error of different models. GBRT produces a remarkable performance for the training samples that is more robust than the other models, GPR behaves suboptimally. ANN and SVR are very sensitive to the training samples. Moreover, all models behave more robust with the growth of training sample size according to the variation of standard error. In summary, the model accuracy improves overall with an increasing training sample size, while GBRT provides the most robust performance for the training samples, and the best model accuracy among the variations in training sample size on average (\(\overline{R^{2}}=0.884\), \(\overline{RMSE}=0.615\) and \(\overline{MAE}=0.452\), calculated by all the 8 groups of training sample size).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample Name** & **Set 1** & **Set 2** & **Set 3** & **Set 4** & **Set 5** & **Set 6** & **Set 7** & **Set 8** \\ \hline Training Datasets & 17 & 27 & 37 & 47 & 57 & 67 & 77 & 87 \\ Testing Datasets & & & & & 30 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Number of training and testing samples of the eight datasets.  
Figure 6: The average processing speed for five ML models.
## 3 Results and Discussion
Although we cannot deduce the exact crystal orientation due to the discussed limitations, we can conclude from the pump-probe measurements themselves that the investigated LiNbO\({}_{3}\) layer does not exhibit a Z-cut orientation (001) where the optical axis is perpendicular to the sample surface, because no polarization dependence is expected in this case. Additionally, it is unlikely that the sample shows an X- (100) or Y-cut (010) orientation [25]: comparing the modulation depth in Figure 3 to the results obtained in Reference [23] as well as the observed sound velocities hint to an orientation not identical but close to the Z-axis, which was indeed corroborated by the X-ray measurements. Furthermore, we want to mention the possibility of obtaining the acoustic reflection coefficient \(r\) between the SiO\({}_{2}\) and the LiNbO\({}_{3}\) layer by comparison of the Brillouin amplitudes in Figure 2b before and after the first discontinuity. In this way, we obtain an experimental value of \(r=0.35\). With the earlier determined sound velocities of SiO\({}_{2}\) and LiNbO\({}_{3}\), the reflection coefficient for an assumed perfect interface can be calculated to be \(r=(Z_{LiNbO}-Z_{SiO_{2}})/(Z_{LiNbO}+Z_{SiO_{2}})\). For our crystal orientation of (\(\overline{1}\)14) with an acoustic impedance \(Z=\rho v\), we can calculate \(r=0.37\). This finding indicates a very good interface adhesion between the respective layers. The small deviation from the reflection coefficient obtained experimentally from the amplitudes can be explained by the measurement inaccuracy. Additionally, it is possible that an interdiffusion at the SiO\({}_{2}\)/LiNbO\({}_{3}\) interface appears, which can enhance the acoustic transmission. Indeed, such an interface has been observed in similar layer systems after annealing due to diffusion [26].
## 2 Background
### An Overview of Federated Learning
#### 2.2.3 Typical Federated Training Process  
The FL process begins with each device developing a localized model using its own dataset. After completing local training, the device transmits model changes--specifically weights and gradients--to a central server, rather than sending raw data [16]. This approach ensures that confidential information remains in its original location, effectively mitigating various privacy risks commonly associated with traditional data centralization [25]. The model updates from all participating devices are consolidated on the central server to create an enhanced global model that incorporates insights derived from all the decentralized data sources. This aggregated model is then distributed to all devices, allowing them to leverage the collective intelligence of the entire network. The iterative process involves local training, model update transmission, aggregation, and global model dissemination, with each iteration progressively improving the accuracy and resilience of the global model [21]. By employing this innovative methodology, FL addresses the challenges related to data privacy and ML efficiency, effectively utilizing a wide range of authentic data sources from the real world while safeguarding the security of individual data [26].