esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 FIAE-GAN Digital Watermarking Model
### Watermark Extraction Decoder  
The noise module \(Noise\) simulates various attacks during iterative training by incorporating differentiable noises to enhance watermarking robustness. This approach helps the network learn to embed watermarks in regions less prone to distortion and to develop a robust watermarking pattern that resists both trained and untrained image noises. The noised image \(F_{noise}\) is created by applying various types of noise  
\[F_{noise}=Noise(F_{en},NO_{train}) \tag{6}\]  
where \(NO_{train}\) is a trained noise. Subsequently, the decoder \(D_{\beta}\) is employed to extract \(W_{out}\) from \(F_{noise}\)  
\[W_{out}=D_{\beta}(F_{noise}) \tag{7}\]  
Decoder training enhances watermarking robustness by minimizing the difference between \(W_{out}\) and \(W_{in}\) through updating parameter \(\beta\). The decoding loss \(L_{D}\) is defined as follows:  
\[L_{D}=\frac{\sqrt{(W_{in}-W_{out})^{2}}}{L} \tag{8}\]  
Watermark extraction is the inverse of embedding, utilizing a similar structure as the FIM to recover the watermark from encoded image features. The decoder employs dense connections for deep feature extraction to obtain the final watermark. These techniques provide error tolerance, ensuring accurate watermark extraction despite noise-induced distortion. However, excessive distortion can suppress decoding ability. Feedback from the decoder helps the encoder adjust features during noise training to avoid fragile regions, ensuring effective watermark extraction without the original image.
# VENuS-Derived NDVI and REIP at Different View Azimuth Angles
## 2 Materials and Methods  
### VEN\(\mu\)S Data Collection  
Images, available on a designated website ([[https://venus.bgu.ac.il/venus/](https://venus.bgu.ac.il/venus/)]([https://venus.bgu.ac.il/venus/](https://venus.bgu.ac.il/venus/))), were taken from November 2017 to the end of October 2020. Three levels of VEN\(\mu\)S scientific products were available, free of charge, to the scientific community: Level 1 (L1), Level 2 (L2), and Level 3 (L3). The L1 product included the top of the atmosphere reflectance with a spatial resolution of 5 m, whereas L2 and L3 contained surface reflectance for a single day and for a 10-day time composite, respectively. To reduce storage space, the L2 and L3 data were generated at a 10-m resolution after atmospheric corrections performed by the MACCS-ATCOR Joint Algorithm (i.e., MAJA [10]). Note that recently, the entire archive has been reprocessed to provide surface reflectance images at a 5-m resolution. In this study, single-day L2 data were used from three selected dates: 18 April, 27 June, and 11 September 2018.  
Figure 1: (**a**) Spatial distribution of the three VEN\(\mu\)S strips over Israel; (**b**) the overlapping zone of the VEN\(\mu\)S tiles, located in the S01 (red) and W08 (green); (**c**) the three selected polygons of Urban (brown), CircleField-N (green), and CircleField-S (yellow).
# A Channel Allocation Mechanism for Cellular Networks
## 2 Related Work
### Channel Allocation Mechanisms
#### 2.2.2 Dynamic Channel Allocation  
The SCA mechanism can't achieve high efficiency of channels using the variation of traffic in the cellular system. In order to overcome this problem, the Dynamic Channel Allocation (DCA) mechanism in recent years has been studied widely. In contrast to the SCA mechanism, the DCA mechanism has no fixed relationship with each cell and channel [21]. All channels are in the central pool. When any cell has a new call arrival, it will allocate one free channel to that cell [10; 22; 23; 24; 25]. After the call is complete, the channel will be return to the central pool.  
In DCA, a channel can be used in any cell provided that the signal interference constraints are satisfied. Generally, more channels can be assigned to the cells' required channel by the central pool. The main idea of the DCA mechanism is that it minimizes the cost to select and use the candidate channels provided that the interference constraints are satisfied. The selections of cost functions are designed with the different schemes. The factors when we design the function include the usage frequency of the candidate channel, the reuse distance, channel occupancy distribution under current traffic conditions, and radio channel measurements of individual MSs of the average CBP of the system [12]. In this part, the DCA mechanism can dynamically allocate free channelsto the cell-required channel, depending on the variation of traffic information. Therefore, the DCA mechanism is suitable for dynamical environment. We design the mechanism for channel allocation based on the DCA scheme in PCS. Finally, depending on the type of control, the DCA mechanism can be divided into centralized and distributed mechanisms [10].
## 2 Architecture Description
### OCP and Input Switch  
The OCP circuit, as shown in Figure 6, is designed to protect the 4-to-1 TCA from being damaged by a very high input current [16]. The transistor \(M_{5}\) turns on when its source voltage is larger than 1.04 V. As shown in Figure 7, when the input current is approximately 400 \(\mu\)A, the source voltage of \(M_{5}\) reaches 1.043 V. When the input current is larger than 400 \(\mu\)A, the increase in the input voltage is suppressed and the sink current to the OCP circuit increases. Therefore, the effective range of the input voltage is from the DC bias voltage of the input current buffer, approximately 610 mV to 1.043 V, before the OCP circuit turns on.  
Figure 5: Block diagram of the proposed switched 4-to-1 TCA with a balun and an output buffer.  
Figure 6: Schematic diagram of the proposed switched 4-to-1 TCA.  
To expand the ROI with multiple partitioned photodetectors, a switch is added on each input current path. Several types of switches, such as n-channel metal-oxide-semiconductor field effect transistor (NMOSFET or NMOS), p-channel MOSFET (PMOSFET or PMOS), and CMOS transmission gates, are available. In this study, NMOS switches \(M_{6}\) are used on all input current paths, as shown in Figure 6. A high output will be degraded by the NMOS switch, since the NMOS switch turns off when the input becomes \(EN-V_{th}\), where \(EN\) is a control signal of the switch and \(V_{th}\) is the threshold voltage of the switch transistor \(M_{6}\). The maximum input value of the NMOS switch without signal degradation is approximately 1.175 V when the \(V_{th}\) of \(M_{6}\) is about 625 mV, and the available maximum input voltage dependent on the input photocurrent is 1.043 V. Therefore, an NMOS switch on the input path is capable of passing an input signal having a value from 610 mV to 1.043 V. A PMOS switch is not a viable solution, since the threshold voltage is larger than 610 mV and a low input signal cannot be passed through a PMOS switch.
## 3 Heat Transport Pipeline Damage Probability Analysis
### Period of Use  
Damage probability was analyzed according to the period of use. Among the damage history data, 1761 records included period-of-use information, while 504 did not. Excluding data without clear period-of-use information could underestimate the overall damage probability; therefore, a correction was applied. The correction allocated the period of use for damage cases without this information proportionally, as shown in Equation (1).  
\[\mathrm{D}^{\prime}_{\mathrm{T,p-i}}\,=\,\mathrm{D}_{\mathrm{T,p-i}}\,\frac{ \mathrm{D}_{\mathrm{i}}\,+\,\mathrm{D}_{\mathrm{e}}}{\mathrm{D}_{\mathrm{i}}} \tag{1}\]  
. \(\mathrm{D}^{\prime}_{\mathrm{T,p-i}}\): Number of damage cases for heat transport pipelines used for \(Y_{\mathrm{u}}\) years (#) (corrected value).  
Figure 1: Maximum and minimum length setting method.  
\(\rm D_{T,\rm p-i}\): Number of damage cases for heat transport pipelines used for \(Y_{\rm u}\) years (#) (actual value). \(\rm D_{i}\): Number of damage cases with period-of-use information (1761). \(\rm D_{e}\): Number of damage cases without period-of-use information (504).  
The damage probability for the period of use was calculated using Equation (2) after analyzing the equipment history and damage history of heat transport pipelines. In Equation (2), the damage probability by period of use was calculated by dividing the cumulative number of damage cases of the heat transport pipelines used for T years over the last N years by the cumulative pipe length of the heat transport pipelines used for T years over the last N years.  
\[\rm F_{T}=\frac{\sum_{i=1}^{N}D_{T,\rm p-i}}{\sum_{i=1}^{N}L_{T,\rm p-i}} \tag{2}\]  
\(\rm F_{T}\): Damage probability according to the period of use (#/km/year). \(\rm D_{T,\rm p-i}\): Number of heat transport pipeline damage cases by period of use (#). \(\rm L_{T,\rm p-i}\): Length of heat transport pipelines by period of use (km). \(\rm p\): Relevant year (year). \(\rm N\): Damage history collection period (year).  
Figure 2 shows the number of damage cases and damage probability according to the period of use. It was found that damage probability tended to increase as the period of use increased. In particular, damage probability sharply increased around 20 and 30 years. This indicates that the period of use has a significant impact on damage to heat transport pipelines.
## 1 Introduction
However, with the recent launch of new satellites, there is a need to validate these methods for the new sensors. In recent years, China has launched two moderate-resolution satellites, Huanjing satellite (HJ), and Gaofen satellite no. 1 (GF-1). [PERSON] _et al._[48] compared the data quality of the HJ charge coupled device (CCD) and the Landsat Thematic Mapper (TM) sensor data. They found that the radiation accuracy, clarity and signal-to-noise ratio (SNR) of the HJ CCD data were lower than for the Landsat TM data. This issue is particularly important for Chinese satellite data because of the poor data quality. To address this problem, the objectives of the present study are: (1) to validate the applicability of ESTARFM and STDFA to HJ and GF satellite data and (2) to analyse the influence of MODIS data of different spatial resolutions on the application of ESTARFM and STDFA. Figure 1: Locations of the study areas.
## 3 Datasets and Evaluation Metrics
### NIQE  
The Natural Image Quality Evaluator also operates without a reference, relying on NSSs. It calculates the distance between a multi-variate statistical model fitted to the NSSs features of the test image and a precomputed model from \"pristine\" images. The metric represents this statistical distance and is not described by a simple algebraic formula [98]. The lower the NIQE value, the better the image quality.
# A Finite State Machine-Based Improved Cryptographic Technique
### Motivation  
Error-control coding is a method used to improve a channel's resistance to interference. The proper unity between mathematical modelling and efficient algorithms, and more critically the realization of this principle in actual systems, is essential for data transfer that is both quick and secure. To code channels in a way that comes close to the Shannon limit, Turbo Code (TC) is a major contender. The bit error rate (BER) performance of currently available Turbo Codes is vastly enhanced, either in the waterfall zone or the error-floor region, or both. Yet, the flattening effect is an issue for most forward-correcting codes. Yet, the development of communication technology calls for an overly complex coding scheme with great potential to address this problem. As a result, the development of better TC designs has been a hot topic in the scientific community during the last several decades.  
However, from the early nineteenth century, a large amount of study has been conducted in the field of computing intelligence approaches. Scientists have successfully implemented these algorithms to improve the efficiency of modern communication networks. Existing linear programming methodologies, such as [PERSON]'s method, [PERSON]'s theory, etc., cannot simplify the complexities of real-world engineering challenges and cannot produce stable optimum values for multi-optima-based rough surfaces. Considering this, the development of strong meta-heuristic algorithms inspired by nature and their use in a wide range of engineering issues has arisen as a new and significant topic of study.  
The literature has sparked some fresh ideas for creating more effective TC. As adding another dimension to a Turbo Code (TC) significantly boosts the system's BER performance, this study tries to build both enhanced Three-Dimensional (3D) and unique Four-Dimensional (4D) TCs. A new modulation method called Superposition Modulation (SM) has been included into this setting. In addition, the unique Simple Augmented State Diagram (SASD) method has been used to assess the suggested 4D-performance TCs. Moreover, utilizing the finite length rule and the asymptotic spectral shape function, an in-depth investigation of the asymptotic behavior of the innovative hybrid interleave-based 4D-TC has been performed. By using the time-slicing and interleave-gain technique, we have also analyzed the worst-case upper limit of the suggested structure. Improved versions of previously developed algorithms inspired by nature have also been successfully utilized in the effective design of the TC. Lastly, the suggested TCs' BER performances based on existing communication standards such as WiMAX and WIFI are discussed.
# Chosen Plaintext Combined Attack against SM4 Algorithm
## 4 Experiments
### Comparison with other Attack Methods  
Compared with the previous chosen plaintext attack, the combined round reduction attack in this paper has obvious advantages on the number of rounds needed for attack, the selection of attack points and the number of times for collecting traces. The SM4 encryption attack is used as an example for comparison.  
As shown in Table 1, our combined attack reduces the number of attack rounds by half, and our attack only needs to collect traces twice, which is significantly less than the number of plaintext selections in previous attacks, thus improving the efficiency of attack. In addition, compared with the previous linear XOR or L transformation and round output, our attack chooses the output of S-box as the attack point, which effectively improves the SNR and success rate of the attack. Furthermore, the sum number of traces, i.e., \(4\times N\), (N is the number of traces for a single successful attack) required for recovering the round keys of the first four rounds in our attack is obviously less than those (\(16\times N\)) for the previous  
Figure 5: The second byte of \(V_{1}\) CPA attack result.  
Figure 6: The third byte of \(V_{1}\) CPA attack result.  
Figure 7: The fourth byte of \(V_{1}\) CPA attack result.  
chosen-plaintext attacks [13; 15; 16]. Although the sum number of traces in Reference [14] is \(4\times N\), needing to collect traces four times, our combined attack only needs to attack 2 times and collect traces twice, reducing collection time and attack time. Finally, key search space complexity is smaller than previous chosen-plaintext attacks [13; 15; 16].
## 2 Structures, Synthesis, and Magnetic Properties of Fe\({}_{3}\)O\({}_{4}\) Nanoparticles
### Fe\({}_{3}\)O\({}_{4}\) Spherical Nanoparticles (SNPs)  
In this section, Fe\({}_{3}\)O\({}_{4}\) spherical nanoparticles (SNPs) will be categorized into two size ranges based on their structures, crystallinity, and magnetic behaviors. The first type is Fe\({}_{3}\)O\({}_{4}\) SNPs with a size smaller than 25 nm and a single-crystalline structure that probably exhibits superparamagnetic behavior at room temperature. This type of SNP is widely applied in hyperthermia, as a contrast agent in magnetic resonance imaging, and in drug delivery. Table 1 summarizes the available synthetic methods and the strategy for controlling the size to obtain single-crystalline Fe\({}_{3}\)O\({}_{4}\) SNPs with a size less than 25 nm, as well as the magnetic properties of saturation magnetization at room temperature for comparisons of these SNPs. The second type of SNP is usually polycrystalline NPs with diameters generally larger than 25 nm to even hundreds of nanometers. The structure of these Fe\({}_{3}\)O\({}_{4}\) SNPs is often characterized by the stacking or agglomeration of small primary nanocrystals to form larger SNPs as the secondary structure. In this size range, the magnetic properties of SNPs at room temperature are either ferrimagnetic or superparamagnetic, depending on the crystallite size, the overall size of NPs, and the interaction of nanosized subunits. Together with summarizing the available synthetic methods of Fe\({}_{3}\)O\({}_{4}\) SNPs with sizes ranging from 25 nm to a few hundred nanometers, Table 2 also provides detailed information on the particle size, crystallite size, and magnetic properties, including M\({}_{\text{S}}\) and H\({}_{\text{C}}\), at room temperature. Thermal decomposition of iron complexes at high temperature in high-boiling point organic solvents has proven to be an effective method to generate Fe\({}_{3}\)O\({}_{4}\) nanospheres with sizes ranging from 4 to 30 nm [54; 55; 56; 57; 58]. [PERSON] et al. reported the thermal decomposition of Fe(acac)\({}_{3}\) in phenyl ether (boiling point: 260 \({}^{\circ}\)C) at 265 \({}^{\circ}\)C to synthesize monodisperse 4 nm Fe\({}_{3}\)O\({}_{4}\) SNPs [54]. 1,2-Hexadecanediol was used as the reducing agent, and a mixture of oleic acid (OA) and oleylamine (OAm) was utilized as the surfactant. The as-synthesized 4 nm NPs were then used as seeds for seed-mediated growth to obtain 8, 16, and 20 nm SNPs with the assistance of stearyl alcohol. Larger seeds with an average diameter of 6 nm were
# SGXDump: A Repeatable Code-Reuse Attack for Extracting SGX Enclave Memory
## 5 Attack Design
### Choosing the Probe Array  
The probe array is a trampoline memory array used to set the access bit in the page table entries (PTE). To minimize noise, we choose the memory area as a probe array that satisfies the following conditions.  
* **Mapped memory area.** If an unmapped memory area is chosen as the probe array, unnecessary exceptions will occur whenever the ROP accesses the probe array. To avoid this situation, we select a mapped memory area for the probe array.
* **No access by other threads.** Our main idea is to detect the access bit of the probe array by executing ROP gadgets. If other threads access the probe array, this access will cause unwanted noise. Therefore, we choose a memory area that is not accessed by other threads.
* **Memory area outside of the Enclave.** Since SGXDump will read through all the enclave memory, access bits of the memory will change as a consequence. To avoid undesirable noise on the access bits, it is necessary to pick a memory area outside of the enclave.  
Since we assume the attacker obtained the root privilege, the attacker can find a memory area that satisfies the above conditions as follows. The first step is to obtain the memory mapping information of the target process by analyzing the _/proc/pid/maps_ file as shown in the Figure 3. After checking the mapped memory area in lines 1\(\sim\)6 and the enclave memory area in lines 15\(\sim\)20, the attacker can find memory areas located outside of the enclave. The second step is to identify every thread that accesses the possible probe array memory area by using the debugger. Then, the attacker can select a memory area that there are no threads accessing the area. In this research, we often selected one from the stack area, since it normally satisfied the above conditions.
## 3 Algorithms Descriptions
### Greedy Randomized Adaptive Search Procedure (GRASP)  
GRASP is a multi-start metaheuristic algorithm that builds a solution by selecting one promising fragment of the solution at a time [33], see Algorithm 3. The inner loop of the GRASP, in line 5 builds a _Solution_ by adding random individual elements from a Restricted Candidate List (\(RCL\)) (see line 11). In order to build the \(RCL\), the algorithm evaluates the increase of the partial objective and stores its maximum and minimum values (see lines 7 and 8) to set a limit for the original candidate list (\(CL\)) (see line 9), thus creating the \(RCL\) (see line 10); this process occurs at every step of the construction.  
```
0: Problem to solve
0:\(s_{best}\)
1:\(s_{best}\leftarrow\) Random initial solution
2:while Stopping criterion not reached do
3:\(Solution\leftarrow\emptyset\)\(\triangleright\) An empty initial solution
4:\(i=0\)
5:while\(Solution\) is not complete do
6:\(CL\gets SelectFeasibleElements()\)
7:\(f_{max}\leftarrow\max\left(PartialObjectiveEvaluation(t_{i})\forall t_{i}\in CL\right)\)
8:\(f_{min}\leftarrow\min\left(PartialObjectiveEvaluation(t_{i})\forall t_{i}\in CL\right)\)
9:\(limit\gets f_{min}+\alpha(f_{max}-f_{min})\)
10:\(RCL\gets BuildRCL(CL,limit)\)
11:\(Solution_{i}=t_{r}|t_{r}\in RCL\)\(\triangleright\) Add to \(Solution\) a random element from the \(RCL\)
12:\(i=i+1\)
13:endwhile
14:\(Solution\leftarrow\)\(LocalSearch(Solution)\)\(\triangleright\) The Local Search procedure is optional
15:if\(f(\text{Solution})<f(s_{best})\)then
16:\(s_{best}\gets Solution\)
17:endif
18:endwhile
```  
**Algorithm 3** Greedy Randomized Adaptive Search Procedure.  
The \(RCL\) only includes candidate tasks whose incremental costs are bounded by \(f_{min}+\alpha(f_{max}-f_{min})\) in line 9. Where \(f_{max}\) and \(f_{min}\) are the maximum and minimum incremental cost of the objective function, for all the candidate elements \(t_{i}\in CL\), which is calculated with a modification of Algorithm 1 named \(PartialObjectiveEvaluation\) that evaluates up to the last element in the partial solution. Additionally, \(\alpha\in[0,1]\) defines the greedy level of the algorithm; where \(\alpha=0\) defines a completely greedy search, and \(\alpha=1\) defines a completely random search. Additionally, the candidate list (\(CL\)) must be created or updated every iteration of the inner loop (see line 6). Once the \(Solution\) is constructed, an optional \(LocalSearch\) procedure can be used to improve the current \(Solution\) (see line 14). Furthermore, \(s_{best}\) is updated every time a complete \(Solution\) outperforms the objective value of \(s_{best}\) (see line 16). Finally, the outer loop in line 2 restarts the \(Solution\) and iterates until the algorithm reaches the stopping criterion.
## 2 Theoretical Background and Mathematical Models
### Rusck Closed Formulation and Rusck Coupling Model  
The electromagnetic induction, coupling model, closed formulation, and field-ETL relationship evaluated by [PERSON] in the subtitles are depicted mathematically. The proposed closed-form solutions of the Rusck model are considered critical according to IEEE-1410 standards [24; 25].
## 5 Experimental Results
For a given inflow, we can generate multiple candidate signal timing plans, use the trained model in inference mode to evaluate each timing plan based on the distribution of wait times. Using these trained models for MOEs estimation is highly scalable compared to simulation-based approaches. Figure 5: Summary statistics such as 50 th/70 th percentile etc. can be computed from the predicted distribution. Actual vs. predicted scatter plot of 50 th, 70 th percentile of wait time computed from the distribution. The key advantage is that predicting distribution enables us to compute any statistic of interest. Figure 6: Actual vs. predicted distribution of wait time for different green time splits for a given input traffic. These plots show that the model is able to capture the interrelationship between input traffic and signal timing parameters. For a given input traffic flow, to simulate 3200 different signal timing parameter combinations took more than 13,400 s on a 32 core machine. While the trained neural network model when used in inference mode generates output in 0.08 s for the same number of combinations (batch size 3200). This suggests that neural network models are at least four to five orders of magnitude faster.