esa-sceva/satcom-chunk-collection huggingface dataset:
## Related Work  
### Cache System  
The research of ICN cache systems has mostly focused on the design and implementation of caching strategy, such as leave copy down (LCD) [14], leave copy everywhere (LCE), leave copy with probability (LCP) [15], cache less for more (CL4M) [16], which are lightweight, uncooperative strategies with low computational complexity. There are also some strategies that are implemented with a high memory overhead. For instance, [PERSON] proposed hash caching [17], which makes cache decisions according to the caching state of content itself and the follow-up cache nodes. For popularity-based caching strategies, [PERSON] proposed a time-based mathematical function to select the caching content, which is the most frequently requested content in the given time interval [18]. For centrality-based caching strategies, [PERSON] proposed an efficient hybrid strategy to reduce multiple replications of the same content at the data dissemination path according to the judgement of an improved betweenness centrality of router [19].  
The modeling technology of traditional cache systems and related theoretical research has been widely studied. [PERSON] et al. [20] proposed a modeling method to deduce the cache performance of different level cache nodes based on individual document requests following arbitrary access frequency distribution, and they found out the fundamental design principles for hierarchical web caching. [PERSON] et al. [21] presented an optimized model of a hierarchical cache system for unbalanced traffic in a content delivery network (CDN). The aim of his model is to deduce how much memory must be allocated for cache node to achieve maximum cost-effectiveness.  
Compared with traditional caching systems (such as Web, CDN, Peer to Peer (P2P)) that only support a single traffic type, an ICN caching system has new features. Specifically, ICN supports a transparent and universal caching service for multiple upper-layer applications. The basic unit of ICN caching is no longer a file or a variable segment of a file; instead, it is a smaller data chunk with a global unique content identifier. As a counterexample, although the web cache system adopts the open HTTP protocol, the same object cannot be consistently identified between different domains.  
Due to the change of traffic characteristics from the object-level to the chunk level, it is difficult to directly apply the modeling and analysis method of a traditional cache system to ICN. Only a few studies have extended the modeling method of an ICN cache system. [PERSON] et al. [22] proved that the Che approximate approach we introduced above is suitable for estimating the performance of a single cache node in ICN through mathematical demonstration. Taking the effects of temporal locality into account, [PERSON] et al. [23] extended the Che approximate to model any cache node in a network with general (mesh) topology, but they only proposed a unified methodology under the LCE strategy. Additionally, [PERSON] et al. [24] developed a mathematical model for a single router based on continuous time Markov-chains that provided a new idea for modeling ICN.
## 3 Proposed Service Restoration Scheme
### Optimization-Based Intentional Islanding Scheme
When a fault occurs in a distribution system, the faulty section is identified by utilizing FI information transmitted from the field devices (e.g., FRTUs) through DNP 3.0 communication protocol, and the isolation of the fault section is conducted by opening upstream and downstream switches of the fault location. To restore outage loads and derive an optimal SR plan, the SR application executes the GA-based optimization function. The application generates a network topology and its tree structure by using the status of switching devices transmitted from FRTUs through the front-end processor (FEP). Then, the island partitioning function in the SR application is executed to make multiple islands through the tree search, and the load flow analysis is performed to check the network constraints. In the DAS environment, online load flow or state estimation applications are available to provide the SR application with network analysis results. To facilitate communication between relevant applications, middleware (e.g., memory database, message queue) can be used. Otherwise, network analysis functions such as load flow should be implemented in the SR application as illustrated in Figure 7.
## 4 Conclusions  
In this paper, DLC and nitrogen-doped N-DLC films were prepared by the RF-PECVD method. Based on different deposition conditions with nitrogen flow ratios of 0%, 10%, 20%, 30%, and 40%, the changes in the structure, morphology, and optical properties of the films with different gas flow ratios were investigated. The introduction of nitrogen can effectively increase the deposition rate of the films, and the maximum thickness of the films can reach 696 nm in the same amount of time. According to the AFM and SEM results, the morphology of the films becomes smoother and denser with an increase in nitrogen flow rate, and the surface cracks are reduced. According to Raman spectroscopy and XPS results, carbon and nitrogen form C-N and C=N bonds. With an increase in the nitrogen flow rate ratio, the content of nitrogen in the film gradually increased, and sp\({}^{3}\) first increased and then decreased, which indicated that a small amount of nitrogen could promote the generation of sp\({}^{3}\) phase, and further increases in nitrogen flow rate increased the content of sp\({}^{2}\) phase. These changed the optical properties of the samples. According to Figure 8, in the UV-visible range, the transmittance of several groups of samples decreased as the absorption intensity of the samples increased. In the IR range, these samples are highly transparent. The photonic band gap of the samples was minimized at a methane-to-nitrogen flux ratio of 12:8 and reached 3.46 eV. The extinction coefficients and absorption coefficients of the samples in the UV-visible range exhibited similar trends. By adjusting the gas flow passed into the sample during preparation, the relative percent contents of C and N in the films were regulated, causing changes in the structural and optical properties, and this paper provides a real and reliable data reference for related studies.  
Conceptualization, H.M. and L.W.; methodology, H.M.; software, L.W.; validation, H.M., L.W. and L.Z.; formal analysis, N.L. and J.L.; investigation, L.Z.; resources, L.Z.; data curation, N.L. and J.L.; writing--original draft preparation, H.M. and L.W.; writing--review and editing, L.Z.; visualization, H.M.; supervision, L.Z.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Zhongyuan Academician Fund] grant number [ZYQR2019120188].  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
Figure 11: Variation in refractive index of samples with different flow ratios.
## 3 Efficient Approximation of a Double Series  
For certain boundary conditions, in [PERSON]'s model infinite double series appear. We give approximations for these double series that can be evaluated efficiently. We also provide bounds for the approximation error. Let the parameter \(\lambda\) greater than or equal to 1 be given.  
For a complex number z, let D denote the double series  
\[\text{D(z)}=\Sigma_{\text{nm}}\text{d(n,m)}=\text{d(1,1)}+\text{d(1,2)}+\text {d(2,1)}+\text{d(3,1)}+\text{d(2,2)}+\text{d(1,3)}+... \tag{7}\]  
with  
\[\text{d(n,m)}=1/[(2\text{n}-1)^{2}(2\text{m}-1)^{2}(\lambda^{2}(2\text{n}-1) ^{2}+(2\text{m}-1)^{2}+z^{2})] \tag{8}\]  
Using the definition of H, we obtain the equation  
\[\text{D(z)}=\text{H(}(\lambda^{2}+z^{2})^{1/2})+1/3^{2}\text{H(}(\lambda^{2} 3^{2}+z^{2})^{1/2})+1/5^{2}\text{H(}(\lambda^{2}5^{2}+z^{2})^{1/2})+... \tag{9}\]  
Define the auxiliary function \(\text{G(z)}=\tanh(0.5\pi\text{z})/\text{z}\). Then \(\text{G(-z)}=\text{G(z)}\), that is, G is even. Using the representation (6) for H, we obtain  
\[\begin{split}\text{D(z)}=\pi^{2}/(8\lambda^{2})\text{H(z/\lambda )}&-\pi/4[\text{G(}(\lambda^{2}+z^{2})^{1/2})/(\lambda^{2}+z^{2}) +\text{G(}(\lambda^{2}3^{2}+z^{2})^{1/2})/(3^{2}(\lambda^{2}3^{2}+z^{2}))\\ &+\text{G(}(\lambda^{2}5^{2}+z^{2})^{1/2})/(5^{2}(\lambda^{2}5^{2 }+z^{2}))+...\end{split} \tag{10}\]Thus we have reduced the double series to a standard infinite series. As an approximation for D, we propose to use the function A defined as  
\[\begin{array}{l}\mbox{A(z)}=\pi^{2}/(8\lambda^{2})\mbox{H(z/\lambda)}-\pi/4[ \mbox{G(($\lambda^{2}+z^{2}$)}^{1/2})/(\lambda^{2}+z^{2})+\mbox{G(($\lambda^{2} 3^{2}+z^{2}$)}^{1/2})/(3^{2}(\lambda^{2}3^{2}+z^{2}))\\ +\mbox{G(($\lambda^{2}5^{2}+z^{2}$)}^{1/2})/(5^{2}(\lambda^{2}5^{2}+z^{2}))+ \ldots+\mbox{G(($\lambda^{2}(2k-1)^{2}+z^{2}$)}^{1/2})/((2k-1)^{2}(\lambda^{2}( 2k-1)^{2}+z^{2}))]\end{array} \tag{11}\]  
with a natural number k. Then we have D(z) = A(z) - R(z,k) with the remainder term R(z,k).  
If \(z^{2}\) is purely imaginary, we have \(|\lambda^{2}(2n+1)^{2}+z^{2}|\geq\lambda^{2}(2n+1)^{2}>1\).  
Moreover, \(|1+\exp(-\pi(\lambda^{2}(2n+1)^{2}+z^{2})^{1/2}|>0.5\). Since \(\tanh(0.5\pi z)\)= \(-1+2/(1+\exp(-\pi z))\) this implies  
\[|\mbox{G(($\lambda^{2}(2n+1)^{2}+z^{2}$)}^{1/2})|=|[-1+2/(1+\exp(-\pi(\lambda^ {2}(2n+1)^{2}+z^{2})^{1/2}))]/(\lambda^{2}(2n+1)^{2}+z^{2})^{1/2}|<5/(\lambda(2 n+1))\]  
and hence  
\[|\mbox{R(z,k)}|<5\pi/(4\lambda^{3})[1/(2k+1)^{5}+1/(2k+3)^{5}+1/(2k+5)^{5}+1/( 2k+7)^{5}+\ldots]\]  
For k = 7, we obtain \(|\mbox{R(z,7)}|<2\times 10^{-5}/\lambda^{3}\). Thus for k = 7 and purely imaginary z\({}^{2}\), A approximates D with a uniform bound that is less than \(2\times 10^{-5}\).
# Railway Vehicle Wheel Flat Detection with Multiple Records Using Spectral Kurtosis Analysis
## 3 Methodology for Flat Detection
### Track Response Obtained by the Accelerometers  
The previous section has shown that the envelope spectrum approach is able to detect a defective wheel from a healthy one for 12 virtual strain gauges located in the rail (layout scheme 1). However, as mentioned above, the use of strain gauges for long-term monitoring of wheel flat is not suitable due to their vulnerability to water and electromagnetic interferences. To compare the track response of strain gauges and accelerometers, both were placed in the same location. This is shown in layout scheme 2, in Figure 5.  
Figure 11 shows the envelope spectrum corresponding to the 12 acceleration responses obtained on the rail (position 1 to 12 shown in Figure 5) for the passage of the Alfa Pendular and the freight wagon, considering the track irregularities presented before. As for the SGs, it is possible to observe that, in the presence of a wheel flat, the responses of each accelerometer in terms of envelope spectrum present significant lag, while for the scenarios with a healthy wheel, this lag is practically insignificant. Moreover, the amplitude of the envelope spectral signal is also considerably different in the two situations, being much higher when the wheel is defective.  
Figure 10: Kurtosis values for healthy and defective wheels corresponding to 12 positions of the strain gauges for the passage of the Alfa Pendular.
# Shared Quantum Key Distribution Based on Asymmetric Double Quantum Teleportation
## 3 Double Teleportation as Superposition and Parallel Post-Processing
### Concrete Post-Processing and Information Transference via Post-Measurement  
We will consider the asymmetric post-processing performed by [PERSON]\({}_{0}\) and [PERSON]\({}_{1}\) as:  
\[\mathcal{U}_{i_{2i+2}}=\cos\omega_{i}\mathbf{1}+i\sin\omega_{i}\mathbf{Y}, \quad i=0,1 \tag{12}\]  
characterized by the parameter \(\omega_{i}\). Thus, asymmetry is introduced by the differentiated parameter \(\omega_{i}\) in each post-processing, together with the different value for the strength of the teleportation, \(p_{i}\) and the control measurement. By expressing \(|\psi_{0}\rangle=\cos\frac{\theta}{2}|0\rangle+e^{i\Phi}\sin\frac{\theta}{2}|1\rangle\) in its Bloch representation, in such scenario, the probabilities to get the measurement outcome of \(|b_{0}\rangle\) or \(|b_{1}\rangle\) becomes [30]:  
\[P_{0} = \beta_{0}^{2}p_{0}+\beta_{1}^{2}p_{1}+2\beta_{0}\beta_{1}\sqrt{p _{0}p_{1}}\cos\phi_{m}(\cos\Delta^{-}\omega-\tan\phi_{m}\sin\Delta^{-}\omega \sin\theta\sin\phi) \tag{13}\] \[P_{1} = \beta_{1}^{2}p_{0}+\beta_{0}^{2}p_{1}-2\beta_{0}\beta_{1}\sqrt{p _{0}p_{1}}\cos\phi_{m}(\cos\Delta^{-}\omega-\tan\phi_{m}\sin\Delta^{-}\omega \sin\theta\sin\phi) \tag{14}\]  
where \(\Delta^{\pm}\omega\equiv\omega_{0}\pm\omega_{1}\). Because \(P_{0}+P_{1}=1\), for such reason if \(\omega_{0}=\omega_{1}\), then the probability to get \(|\psi_{0}^{0}\rangle=|\psi_{0}^{1}\rangle\) is one in any case. As we will see, we can use the previous process to generate and distribute quantum keys, if [PERSON] works together with [PERSON]\({}_{0}\) as a central computer performing part of the processing, while [PERSON]\({}_{1}\) is an associated user.
# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
## 6 Experiment and Discussion
### Effectiveness of the Operation Assist Strategy
#### 6.3.2 Task and User Study  
One robot collisionless movement task was designed to evaluate the teleoperation assistance method. The task is described as follows:  
The operator manipulated the virtual robot through the wearable localization devices, and the operation assistance strategies were generated in real time. As is shown in Figure 20, the start area was labeled as point A, and five different obstacles were labeled as O1-O5. The distances between obstacles were 20 cm, 16 cm, 13 cm, and 11 cm, respectively. The operator was asked to move the  
As observed from Figure 21a, during the operation, it was difficult to intuitively determine whether the robot arm had collided with surrounding obstacles due to visual errors and other factors; therefore, the operator had to spend a lot of time to switch perspectives to carefully observe whether a collision had occurred. In the same case, with the assistance strategies, as is shown in Figure 21b, by the collision risk warning through the haptic (strategy 1) and by highlighting the local danger area (strategy 3), the operator could clearly identify which obstacle was closer to the robot arm and required our attention. Some risks that were ignored due to visual errors were warned.  
The task was tested 10 times. The number of collisions during each test was recorded as well as the time spent on reaching the target. The results of 10 parallel experiments verified the effectiveness of the proposed method, as is shown in Figure 22. The average number of collisions with the assistant strategy was significantly lower than the without strategies by 90%. Moreover, as is shown in Figure 23, the average time required to reach point B in Environment I and Environment II was 45.2 s and 33.6 s, respectively, which was reduced significantly by 36%. Therefore, it is obvious that the designed operation-assisted strategies can significantly reduce the possibility of collisions and enhance efficiency.  
Figure 21: Views in the same scenario: (**a**) Without assist strategy; (**b**) with assist strategy.  
Figure 20: The setup of the task.  
Due to the fact that the designed three assistant strategies are focused on identifying operation collision risk and improving the observation of risk points (strategy \(1\) and \(3\)), the ability to avoid collisions was insufficient. Therefore, the performance of the teleoperation was still influenced by the operator, and collisions could still occur due to human physiological limitations.
## 3 Performance Predictions of Cement-Eggshell Powder Binary System
### Prediction of Compressive Strength and Surface Electrical Resistivity  
We used the hydration model to calculate the cumulative hydration heat results for 3, 7, and 28 days. In our previous study, the compressive strength results for 3, 7, and 28 days were experimentally measured [24]. Using the power exponential function, the compressive strength Fc(t) and hydration heat Q(t) for 3 days, 7 days, and 28 days were regressed, and we found that the relationship between the two is:  
\[\text{Fc(t)}=0.0011\times\text{Q(t)}^{1.7687} \tag{6}\]  
For compressive strength, as shown in Figure 6, the correlation coefficient between the experimental results and the predicted results is 0.8474.  
Figure 4: Combined water and calcium hydroxide contents—7.5% eggshell powder. (**a**) Combine water content—7.5% eggshell powder. (**b**) Calcium hydroxide content—7.5% eggshell powder.  
Figure 5: Combined water and calcium hydroxide contents—15% eggshell powder. (**a**) Combine water content—15% eggshell powder. (**b**) Calcium hydroxide content—15% eggshell powder.  
Similarly, in our previous study, the surface electrical resistivity results for 3, 7, and 28 days were experimentally measured [24]. Using the power exponential function to regress the hydration heat Q(t) and surface electrical resistivity R(t) at 3 days, 7 days, and 28 days, we found that the relationship between the two is:  
\[\text{R(t)}=0.0001\times\text{Q(t)}^{2.0938} \tag{7}\]  
For surface electrical resistivity, as shown in Figure 6b, the correlation coefficient between the experimental results and the predicted results is 0.9714.  
By comparing the compressive strength and surface electrical resistivity, we can find that the correlation coefficient of the compressive strength prediction (0.8474) is lower than that of the electrical resistivity prediction (0.9714). We think this is not a mathematical coincidence. First, this is because the compressive strength is closely related to the solid composition of the products. Compared with other solid components, eggshell powder is relatively soft, which is equivalent to the weak point of the strength experiment. This weak point effect has little effect in the hydration heat experiment, but has a great effect in the strength experiment. Secondly, electrical resistivity mainly depends on the capillary water content of the material. For the hydration heat experiment and the surface electrical resistivity experiment, both are closely related to the physical effects of eggshell powder, so the correlation coefficient of electrical resistivity prediction is higher than that of compressive strength prediction.
## 2 Materials and Methods
### Sample Extraction
#### 2.4.2. HPLC and LC-MS/MS (Mass Spectrometry) Analysis of Flavonoids  
The flavonoids were analyzed as described by [PERSON] et al. [8] with some modifications. The sample, 100 \(\mu\)L of the supernatant obtained in 2.3, was diluted with 900 \(\mu\)L of 50% aqueous methanol. After filtration through a 0.45-\(\mu\)m membrane filter, the flavonoids were separated by gradient elution on a C18 column (Kinetex 100 A, 5 \(\mu\)m (150 \(\times\) 4.6 mm), Phenomenex, Torrance, CA, USA) with a Waters 2695 Alliance HPLC (Waters Inc., Milford, MA, USA). Solvent A was 5% acetic acid in water (\(v/v\)) and solvent B was acetonitrile. The followinggradient was used for solvent B: 5-14% (0-3 min), 14-16% (3-30 min), 16% (30-40 min), 16-90% (40-42 min), 90% (42-44 min), 90-5% (44-46 min), and 5% (46-49 min). An injection volume of 5 \(\upmu\)L and a flow rate of 0.5 mL/min were used. The peaks of the flavonoids were detected at 370 nm by a Waters 996 photodiode array detector (Waters Inc.). The quantitative analysis of flavonoids was calculated by the standard compound kaempfer-3-glucoside.  
The compounds were identified on an LC-MS/MS instrument (Thermo-Finnigan LTQ-Orbitrap, Thermo Scientific, Waltham, MA, USA). The HPLC conditions were the same as mentioned above. Xcalibur software was used for data acquisition. The MS/MS analytical conditions were as follows: the negative ion mass spectra of the column elute were recorded in the range \(m\)/z 150-2000; the fragmentation and capillary voltages were 0.2 kV and 4.5 kV, respectively; the capillary temperature was kept at 300 \({}^{\circ}\)C at 4.5 kV; the collision energy was set at 35 eV; and the sheath gas (nitrogen) flow rate was maintained at 10 L/min.  
Determination of Total Phenolics Content (TPC), Total Flavonoids Content (TFC), and Antioxidant Activities  
The sample consisted of 50 \(\upmu\)L of the supernatant obtained in 2.3 diluted with 950 \(\upmu\)L of 50% aqueous methanol. Colorimetric assays were used to determine TPC and TFC as described by [PERSON] and [PERSON] [20]. The TPC of the faba leaves was expressed as mg of gallic acid equivalent (GAE)/g dry weight (d.w.) and the TFC as mg of catechin equivalent (CE)/g d.w. To determine the antioxidant activity, the DPPH and ABTS free radical scavenging abilities were measured as described by [PERSON] et al. [21] The radical scavenging abilities were expressed as mg of vitamin C equivalent (VCE)/g d.w.  
Headspace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) Analysis of VOCs
# Communication-Traffic-Assisted Mining and Exploitation of Buffer Overflow Vulnerabilities in ADASs
## 4 Practical Results and Physical World Threats
### Return-Oriented Programming and Remote Command Execution  
Return-oriented programming is a technique by which an attacker can induce arbitrary behavior in a program whose control flow they have diverted, without injecting any code [17]. Its core idea is to control the stack call by exploiting stack BOF, thereby hijacking the program control flow to execute specific machine language instructions, known as \"gadgets\". These gadgets can modify the return address of a function and redirect it to any desired location, allowing attackers to execute unauthorized instructions and gain system privileges for arbitrary code/command execution and various illegal operations.  
In the previous subsection, we constructed a specific PoC to cause a denial-of-service attack on the ADAS device's business program. However, there is a better form of attack than a denial-of-service attack. In this subsection, we attempt to write a specific shellcode into a newly allocated buffer and use ROP techniques to control the EIP pointer, thereby executing the shellcode and gaining console access to the device.  
By analyzing the assembly code, we determined that the address where the program writes data (see Figure 8) is 0x1490C bytes away from the return address on the stack (see Figure 9), with enough length to accommodate a shellcode.