esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System on Power Generation using Double-Stator Generator
### Conversion of Power Mapping to v-i (voltage-current), v-p (voltage-power) Characteristics  
The generator characteristics from power mapping are converted to the voltage current _v-i_ (voltage-current) characteristic and the voltage power _v-p_ (voltage-power) characteristic in order to estimate the output power in the simulation environment. Figure 4a shows the schematic of the generator and Figure 4b shows the generator with the rectifier model. The fitting process from the power mapping characteristics is done through MATLAB and the relation between the voltage, current and speed is expressed as in Equation (3).  
\[I\,=\,\,-4\times 10^{-4}\times V^{2}\,+\,(-0.137\,+\,(8.35\times 10^{-5}\times \mathrm{N}))\times V\,+\,4.66\,+\,3.84\times 10^{-4}\times\mathrm{N} \tag{3}\]  
where \(I\) is the current in (A), \(V\) is the voltage in (V) and N is the speed of the generator in (rpm).  
Figure 5 shows the characteristics of _v-i_ and _v-p_ from the power mapping. Figure 5a shows the current variations compared to that of the load. The _v-i_ characteristics in Figure 5b show the value for every speed condition. Initially, the power is increased with the increase in the voltage until the maximum value is reached, and then it starts reducing at a certain voltage. For instance, at the speed of 1500 rpm, the maximum output power is 180 W and the voltage is 55 V. At each instant the speed shows the difference between the maximum value and the optimal voltage value. The calculations  
Figure 4: Simplified double-stator generator and rectifier model. (**a**) Model of the double-stator generator with a rectifier; (**b**) Simulation mode. \(I_{\mathrm{rc}}\): load current; \(V_{\mathrm{rc}}\): load voltage; \(C_{\mathrm{rc}}\): load capacitance; \(V_{\mathrm{G}}\): generator voltage; \(I_{\mathrm{G}}\): generator current.  
Figure 3: Flowchart of the method used for the estimator. p-v: power-voltage; v-i: voltage-current; N: speed; MPPT: maximum power point tracker.  
of the current and voltage are also plotted in the same graph and the results agree with that of the measurement value. The maximum difference of 8% does not affect the simulation value for estimation.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 1 Introduction
### Overview of the Zeus Panda Banking Malware  
Zeus Panda, a variant of the original Zeus malware, is a sophisticated banking malware designed to steal sensitive financial data through techniques such as keylogging, web form data theft, and man-in-the-browser (MITB) attacks. It primarily propagates via phishing emails, malicious attachments, and compromised websites, often targeting financial institutions and systems in specific regions [19]. Panda communicates with remote Command and Control (C&C) servers to exfiltrate data and receive instructions, using encrypted communication and Domain Generation Algorithms (DGAs) for resilience. Its modular design allows it to adapt to various targets, and it employs advanced evasion techniques like anti-debugging and polymorphism to avoid detection, making it a significant threat in the cybersecurity landscape.  
The Zeus Panda banking malware portrays similar characteristics to the Zeus banking malware. Research shows that it infects devices using spam emails and exploit kits, and it has been known to spread like a virus [19]. Zeus Panda's communication architecture is similar to the Zeus banking malware architecture, and its communication is generally encrypted using RC4 or AES [19]. Zeus Panda's authors have also enhanced the code to allow it to detect and evade security protection tools such as anti-virus software and firewalls [23]. Zeus Panda is intelligent enough to detect that it is running in a virtual environment, and upon sensing such an environment, it can disable itself to ensure that researchers are unable to detect communication patterns [23]. The Zeus Panda malware is difficult to detect and can persist on a device for a long time, and researchers have concluded that Zeus Panda is a sophisticated variant of the Zeus malware [23].
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 2 Zero-Copy DMA Technology Overview
### Netmap  
Netmap [11] achieves zero-copy mainly by mapping the packet buffer of the NIC to the Shared Memory Region, where the Netmap ring in memory corresponds to the NIC ring in the NIC, and the application accesses the buffer of the Netmap ring by calling the Netmap API. Netmap's network driver is mainly a patching implementation of the Linux kernel's NIC driver, which turns to Netmap's custom logic when executing critical logic (such as OPEN/RX/Tx). The NIC maintaining the compatibility in the driver allows easy integration into standard operating systems.  
Netmap is integrated into the FreeBSD kernel [18], and several applications have shown performance improvements by employing Netmap, such as Click (a software router) [19], VALE (virtual switch) [20], and ipfw (FreeBSD firewall) [21]. However, Netmap ignores the NIC's hardware offload feature (e.g., VLAN offload, checksum offload, TSO, etc.). It does not define other libraries for packet processing, which does not facilitate the extension of applications such as GRO, GSO, IP slicing/reconfiguration, quality of service, encryption and decryption, etc.
## 7 Discussion
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Ref**} & \multicolumn{6}{c}{**(Cross-Entropy Loss)**} \\ \cline{3-10}  & & **B-1** & **B-2** & **B-3** & **B-4** & **M** & **R** & **C** & **S** \\ \hline
2015 & Hard-Attention [3] & 71.8 & 50.4 & 35.7 & 25.0 & 23.04 & - & - & - \\  & Soft-Attention [3] & 70.7 & 49.2 & 34.4 & 24.3 & 23.9 & - & - & - \\  & NIC [4] & - & - & - & 27.7 & 23.7 & - & 85.5 & - \\  & Mind’s Eye [52] & - & - & - & 18.8 & 19.6 & - & - & - \\  & glLSTM [54] & 67.0 & 49.1 & 35.8 & 26.4 & 22.74 & - & 81.25 & - \\ \hline
2016 & ATT-FCN [47] & 0.709 & 0.537 & 0.402 & 0.304 & 0.243 & - & - & - \\ \hline
2017 & SCA-CNN [5] & 71.9 & 54.8 & 41.1 & 31.1 & 25.0 & - & - & - \\  & GLA [64] & 72.5 & 55.6 & 41.7 & 31.2 & 24.9 & 53.3 & 96.4 & \\  & Adaptive Attention [76] & 74.2 & 58.0 & 43.9 & 33.2 & 26.6 & - & 108.5 & - \\ \hline
2018 & RFNet [49] & 76.4 & 60.4 & 46.6 & 35.8 & 27.4 & 56.5 & 112.5 & 20.5 \\  & Bottom-Up [50] & 76.2 & 60.4 & 46.8 & 36.3 & 27.9 & 56.7 & 114.6 & 20.9 \\  & Up-Down [56] & 77.2 & - & - & 36.2 & 27.0 & 56.4 & 113.5 & 20.3 \\ \hline
2019 & AoANet [79] & 77.4 & - & - & 37.2 & 28.4 & 57.5 & 119.8 & 21.3 \\  & Obj-R+Rel-A+CIDEr. [55] & 0.767 & 0.598 & 0.453 & 0.338 & 0.262 & 0.549 & 1.103 & 0.198 \\  & MT [107] & 77.3 & - & - & 37.4 & 28.7 & 57.4 & 119.6 & - \\  & LBPF [77] & 77.8 & - & - & 37.4 & 28.1 & 57.5 & 116.4 & - \\  & ETA [70] & 77.3 & - & - & 37.1 & 28.2 & 57.1 & 117.9 & 21.4 \\ \hline
2020 & MAD+SAP (F) [6] & - & - & - & 37.1 & 28.1 & 57.2 & 117.3 & 21.3 \\  & D-ada [48] & 73.9 & 57.0 & 42.2 & 32.6 & 27.0 & - & - & - \\  & X-LAN [60] & 78.0 & 62.3 & 48.9 & 38.2 & 28.8 & 58.0 & 122.0 & 21.9 \\  & X-Transformer [60] & 77.3 & 61.5 & 47.8 & 37.0 & 28.7 & 57.5 & 120.0 & 21.8 \\ \hline
2021 & Trans[D2 GPO+MLE]+KG [65] & 76.24 & - & - & 34.39 & 27.71 & - & 112.60 & - \\  & Dual-GCN+Transformer+CL [66] & 82.2 & 67.6 & 52.4 & 39.7 & 29.7 & 59.7 & 129.2 & - \\  & SG2 Caps [67] & - & - & - & 32.6 & 26.4 & 55.0 & 106.6 & 19.8 \\ \hline
2022 & [PERSON] et al. [81] & 76.6 & - & - & 36.3 & 28.2 & 56.9 & 116.1 & - \\  & ReFormer [68] & 82.3 & - & - & 39.8 & 29.7 & 59.8 & 131.9 & 23.0 \\ \hline
2023 & LATGeO [63] & 76.5 & - & - & 36.4 & 27.8 & 56.7 & 115.8 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The reported results from Microsoft COCO Karpathy test split [85], with cross-entropy loss function.
## 3 Results and Discussion
### Effect of the Clusters Separation Distance on the Accuracy of Peak Analysis Method  
By using the peak analysis method to analyze the regular lattice distributed clusters, [PERSON] et al. [16] concluded that the predicted average radius of clusters \(\mathrm{R}^{\mathrm{mes}}_{\mathrm{cluster}}\) monotonically increases from the actual radius \(\mathrm{R}^{\mathrm{set}}_{\mathrm{cluster}}\) to \(2\mathrm{R}^{\mathrm{set}}_{\mathrm{cluster}}\) as the cluster separation S increases to arbitrarily large values. In our case, to simulate naive-like conditions, we investigated the relationship between \(\mathrm{R}^{\mathrm{mes}}_{\mathrm{cluster}}\) and S by using randomly distributed clusters; the average distance of clusters separation was defined as \(\mathrm{S}^{\mathrm{average}}_{\mathrm{cluster}}\). The protein density inside/outside the cluster was given as above. The simulated SMLM images at different cluster separations \(\mathrm{S}^{\mathrm{average}}_{\mathrm{cluster}}\) and the peak analysis results are shown in Figure S3. As a higher aggregation state of clusters reduces the accumulative effects of peak analysis [16], the error of peak analysis significantly increases as the cluster separation increases from \(100~{}\mathrm{nm}\) to \(800~{}\mathrm{nm}\) (Figure 2a). It is indicated that by using peak analysis, a smaller area of analysis with a high density of clusters gives a better estimation of the cluster radius. Interestingly, our results present  
Figure 1: (**a**) Spatial point patterns with 10 clusters present, protein density for outside and inside clusters was \(\mathrm{D}_{\mathrm{out}}=~{}1000\) molecules/\(\upmu\)m\({}^{2}\) and \(\mathrm{D}_{\mathrm{in}}=10,000\) molecules/\(\upmu\)m\({}^{2}\), respectively. (**b**) Disk-shaped cluster domains in (**a**); the radius of the cluster is \(\mathrm{R}^{\mathrm{set}}_{\mathrm{cluster}}=~{}50\mathrm{nm}\). (**c**) Simulated image produced by Gaussian convolution of points in (**a**) using \(30~{}\mathrm{nm}\) resolution. (**d**) Peak analysis results, the peak value of H is r = \(63~{}\mathrm{nm}\).  
a more accurate prediction of cluster radius than previous studies. We repeated 100\(\times\) the estimation of cluster radius for each cluster separation and found that even with an average separation of clusters that reaches 20R\({}_{\text{cluster}}^{\text{set}}\), the cluster radius can be estimated within a factor of 1.3 of the actual radius; moreover, the mean and standard deviations of every 100 independent point distributions were not related to the cluster separation (Figure 2b). Notice that for simplicity, in this simulation, we increased the clusters separation distance using the identical clusters with an average diameter of 50 nm.
## 3 Attitude Controller Design  
This section presents the design of a model predictive controller (MPC) for the attitude control of the quadrotor UAV, based on the feedback-linearized model from the previous section. The structure of the attitude control system is illustrated in Figure 2, while the detailed design process of the attitude controller is provided in the Appendix A.
## 4 Results and Discussion  
### Microstructure of Sections A and B before Heat Treatment  
Figure 3 shows the non-heated A365 alloy samples from sections A and B. The microstructural characteristics of each section, such as the IMC distributions, were significantly different. The crosslines in the images are examples of measurements of the secondary dendrite arm spacing (SDAS) based on ASTM E112-96 (Reapproved 2004). The average SDAS values in sections A and B were 51.85 and 35.04 \(\upmu\)m, respectively. The microstructure of section A was coarser than that of section B. The solidification rate of each section was calculated using Equation (4), where V is the solidification rate [28]. The solidification rate in Section B was determined as 87 K/s, which was 3.45 times higher than that in Section A (25 K/s).  
\[\text{SDAS}=39.4\text{V}^{-0.317} \tag{4}\]  
When the microstructure is affected by rotational force, IMCs are observed opposite the center of rotation. In contrast, more IMCs were observed in section A than in section B. This indicates that the microstructure was less affected by rotation force than by the solidification rate. Thus, the Fe-rich IMCs were more concentrated in section A owing to its lower solidification rate. The Fe-rich IMCs were distributed adjacent to the dendrite owing to their low solubilities in the Al matrix. These compounds experienced difficulty in escaping from the dendrite, thereby causing micro-segregation. The IMCs were impurities on the A356 alloys, therewith degrading the mechanical properties [24].
## 2 Materials and Methods  
### NDVI-Based Classification Overview  
As a joint product of NASA/USGS Landsat 8 and ESA Sentinel-2A and Sentinel-2B satellites, NASA's HLS provides surface reflectance data at a 30 m resolution for up to two to three days. Despite the limited spectral (7 bands) and spatial resolution of HLS compared to very-high-resolution multi-spectral products, such as WorldView, a seasonal timeseries of HLS-NDVI proved very effective at classifying patch-types within wetlands. A marked advantage of HLS is the short revisit time, which even after discarding cloud-obscured images, still resolves the seasonal temporal timeseries of NDVI for pixels within the wetland [18]. We followed the approach developed by [PERSON] and [PERSON] [10]. The approach is explained briefly in the sections below and illustrated in Figure 1. While the somewhat coarse HLS resolution leads to a high number of mixed pixels (given that wetland spatial heterogeneity could occur at a resolution of a few meters), it supports a high rate of correct classifications of the dominant patch-type [10]. Consequently, we can classify the patch structure within the wetland at the same spatial resolution as that of HLS, and during every season and year.  
Figure 1: A schematic flowchart of the classification process of US-LA3.
## 2 Global Sensitivity Analysis Method
### Variance-Based Global Sensitivity Index  
For a structural system with \(n\)-dimensional mutually independent random input variables \(X=[X_{1},X_{2},\cdots,X_{n}]^{T}\), its input-output relationship can be expressed using the system response function \(Y=g(X)\). Based on the high-dimensional model representation (HDMR) theory [19] and the decomposition concept from the analysis of variance (ANOVA) [20], the system response function can be decomposed into a sum of orthogonal components [21], each with zero expected value, namely  
\[Y=g(X)=g_{0}+\sum_{i=1}^{n}g_{i}(X_{i})+\sum_{i<j}g_{ij}\big{(}X_{i},X_{j} \big{)}+\sum_{i<j<k}g_{ijk}\big{(}X_{i},X_{j},X_{k}\big{)}+\cdots+g_{1,2,\cdots,n}(X) \tag{1}\]In this equation, each decomposed term has a mean of zero and is mutually orthogonal. \(g_{0}\) represents the expected value of the output response \(Y\), where \(g_{0}=E(Y)\), \(g_{i}=E(Y|X_{i})-g_{0}\), and \(g_{ij}=E(Y|X_{i},X_{j})-g_{i}-g_{j}-g_{0}\).  
Taking the variance on both sides of Equation (1) results in  
\[V_{Y}=\sum_{i=1}^{n}V_{i}+\sum_{i<j}V_{ij}+\cdots+V_{1,2,\cdots,n} \tag{2}\]  
In this equation, \(V_{i}=V(g_{i})=V[E(Y|X_{i})]\) and \(V_{ij}=V\big{(}g_{ij}\big{)}=V\big{[}E\big{(}Y|X_{i},X_{j}\big{)}\big{]}-V_{i}- V_{j}\).  
The first-order partial variance \(V_{i}\) quantifies the independent contribution of input variable \(X_{i}\) to the output response variance \(V(Y)\). The second-order partial variance \(V_{ij}\) captures the contribution of the interaction between input variables \(X_{i}\) and \(X_{j}\) to the output response variance. By normalizing the first-order partial variance \(V_{i}\), the global sensitivity analysis main index \(S_{i}\) can be obtained:  
\[S_{i}=\frac{V_{i}}{V_{Y}}=\frac{V[E(Y|X_{i})]}{V_{Y}},\;0\leq S_{i}\leq 1 \tag{3}\]
## 2 Deburring Forces  
Limited information is available about the estimation of deburring forces. However, a simple model has been developed starting from the grinding process [6; 29; 30]. In the deburring configuration considered (Figure 1), the material is removed by the cylindrical surface of the deburring wheel, with the workpiece moving from the right to the left (along the \(y\) axis) and the deburring wheel rotating counterclockwise.  
The material removing rate (MRR) of a single chunk of material can be calculated from the burr dimensions and workpiece movement speed:  
\[MRR=b_{w}\cdot h\cdot v_{a}\quad\left[\frac{m^{3}}{s}\right] \tag{1}\]  
where \(b_{w}\) and \(h\) are the width and height of the material chunk, and \(v_{a}\) is the linear workpiece movement speed. If the material and its specific removal energy \(u\) are known  
Figure 1: Scheme of the workpiece position in relation to the mechanism axis of movement (**a**); deburring forces (**b**).  
and the rotational speed \(\omega\) of the grinding wheel is defined, it is possible to calculate the tangential cutting force needed to remove the slice of material as follows:  
\[F_{t}=\frac{b_{w}\cdot h\cdot v_{a}\cdot u}{\omega\cdot R}\quad\left[N\right] \tag{2}\]  
where \(R\) is the radius of the grinding wheel.  
In practice, (2) depends on the position of the mechanism since the burr height that the grinding wheel removes varies with its \(x\) position. As a result, \(F_{t}\) can be expressed as:  
\[F_{t}=\frac{b_{w}\cdot v_{a}\cdot u}{\omega}\cdot\frac{\left(h-x\right)}{R} \tag{3}\]  
It is worthy to point out that the other main force component involved in the cutting process is the force \(F_{n}\) normal to the burr surface. The ratio \(\left(\mu=F_{t}/F_{n}\right)\) between \(F_{t}\) and \(F_{n}\) depends on the material, but its value is usually between 0.2 (ceramics and high strength steel) and 0.8 (mild steel) [31]  
If the material is removed by a small portion of the grinding wheel surface (Figure 1):  
\[h-x=R-Rcos(\theta)\quad\rightarrow\quad\theta=\arccos\left(1-\frac{h-x}{R}\right) \tag{4}\]  
Since \(F_{t}\) and \(F_{n}\) are applied evenly on the surface of the material being removed, it is possible to consider them applied in the middle of the surface, so they are inclined of \(\theta/2\) with respect to the \(y\) and \(x\) axis, respectively. The resulting force along the mechanism \(x\) axis is:  
\[F_{tx}=F_{t}\sin\left(\frac{\theta}{2}\right),\quad F_{nx}=F_{n}\cos\left( \frac{\theta}{2}\right)=\frac{F_{t}}{\mu}\cos\left(\frac{\theta}{2}\right) \tag{5}\]  
It is worth to notice that in practice \(R\gg h\), so usually \(\theta\to 0\). Equation (5) is still valid for a generic deburring process, but in this case it is possible to simplify it to calculate the actual normal component of the deburring force applied to the mechanism \(F_{b}\):  
\[F_{b}\approx\frac{b_{w}\cdot v_{a}\cdot u}{\mu\ \omega}\cdot\frac{\left(h-x \right)}{R} \tag{6}\]
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
###### Abstract  
With the rapid development of cloud computing, it is playing an increasingly important role in data sharing. Meanwhile, attribute-based encryption (ABE) has been an effective way to share data securely in cloud computing. In real circumstances, there is often a mutual access sub-policy in different providers' access policies, and the significance of each attribute is usual diverse. In this paper, a secure and efficient data-sharing scheme in cloud computing, which is called unified ciphertext-policy weighted attribute-based encryption (UCP-WABE), is proposed. The weighted attribute authority assigns weights to attributes depending on their importance. The mutual information extractor extracts the mutual access sub-policy and generates the mutual information. Thus, UCP-WABE lowers the total encryption time cost of multiple providers. We prove that UCP-WABE is selectively secure on the basis of the security of ciphertext-policy weighted attribute-based encryption (CP-WABE). Additionally, the results of the implementation shows that UCP-WABE is efficient in terms of time.  
attribute-based encryption; cloud computing; data sharing +
Footnote †: journal: Applied Sciences
# Segmentation of River Scenes Based on Water Surface Reflection Mechanism
###### Abstract  
Segmentation of a river scene is a representative case of complex image segmentation. Different from road segmentation, river scenes often have unstructured boundaries and contain complex light and shadow on the water's surface. According to the imaging mechanism of water pixels, this paper designed a water description feature based on a multi-block local binary pattern (MB-LBP) and Hue variance in HSI color space to detect the water region in the image. The improved Local Binary Pattern (LBP) feature was used to recognize the water region and the local texture descriptor in HSI color space using Hue variance was used to detect the shadow area of the river surface. Tested on two data sets including simple and complex river scenes, the proposed method has better segmentation performance and consumes less time than those of two other widely used methods.  
Keywords: river scene segmentation; local binary pattern; hue variance; surface reflection; hand-designed image descriptors +
Footnote †: journal: Applied Sciences
## 3 Online Root Fault Identification Method  
This study proposes an online root fault identification method based on a similarity analysis. The idea of the method is based on the assumption that similar alarms tend to have the same root faults. The flowchart of the proposed method is shown in Figure 1. There are two processes: feature vector extraction and online root fault identification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Turbine Number** & **Time** & **Type** & **Code** & **Flag** & **Description** \\ \hline P01 & 2017/5/22 16:30:05 & information & I2 & start & The wind turbine is started \\ P01 & 2017/5/22 17:38:18 & warning & A264 & start & The first measuring point temperature of the \\  & & & & & generator stator is high \\ P01 & 2017/5/22 17:38:37 & warning & A264 & end & The first measuring point temperature of the \\  & & & & & generator stator is high \\ P01 & 2017/5/22 17:38:51 & fault & T21 & start & The communication of the pitch system is an error \\ P01 & 2017/5/22 17:38:52 & information & I2 & end & The wind turbine is started \\ P01 & 2017/5/23 00:15:20 & fault & T21 & end & The communication of the pitch system is an error \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of SCADA alarms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Turbine Number** & **Start Time** & **End Time** & **Subcomponents** & **Types of Faults** & **Solutions** & **Downtime/h** \\ \hline \multirow{2}{*}{\(1\)} & 2016/12/21 & 2016/12/25 & Pitch & Slip ring is & Replace the slip & \\  & 17:34:00 & 12:45:00 & system & damaged & ring & 91.18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: An example of a record in maintenance log.  
Briefly, in the process of feature vector extraction, alarms are firstly segmented into alarm lists, and the information and chattering alarms are removed. Subsequently, the alarm lists and their root faults are matched. Finally, the feature vectors of faults are extracted and the fault-template database is built. To establish the coupling correspondence between an alarm and a fault, the weight of an alarm in a fault is defined. In the process of online fault identification, firstly, the online alarms are preprocessed and represented by vectors. Afterward, the weighted distance between an online alarm vector and the feature vectors is calculated. The root fault of the online alarm vector is deduced by the value of similarity.