esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Code reuse is widespread in software development as well as internet of things (IoT) devices. However, code reuse introduces many problems, e.g., software plagiarism and known vulnerabilities. Solving these problems requires extensive manual reverse analysis. Fortunately, binary clone detection can help analysts mitigate manual work by matching reusable code and known parts. However, many binary clone detection methods are not robust to various compiler optimization options and different architectures. While some clone detection methods can be applied across different architectures, they rely on manual features based on human prior knowledge to generate feature vectors for assembly functions and fail to consider the internal associations between features from a semantic perspective. To address this problem, we propose and implement a prototype GeneDiff, a semantic-based representation binary clone detection approach for cross-architectures. GeneDiff utilizes a representation model based on natural language processing (NLP) to generate high-dimensional numeric vectors for each function based on the Valgrind intermediate representation (VEX) representation. This is the first work that translates assembly instructions into an intermediate representation and uses a semantic representation model to implement clone detection for cross-architectures. GeneDiff is robust to various compiler optimization options and different architectures. Compared to approaches using symbolic execution, GeneDiff is significantly more efficient and accurate. The area under the curve (AUC) of the receiver operating characteristic (ROC) of GeneDiff reaches 92.35%, which is considerably higher than the approaches that use symbolic execution. Extensive experiments indicate that GeneDiff can detect similarity with high accuracy even when the code has been compiled with different optimization options and targeted to different architectures. We also use real-world IoT firmware across different architectures as targets, therein proving the practicality of GeneDiff in being able to detect known vulnerabilities.  
Keywords:binary clone detection; Semantic representation; cross-architectures; IoT devices; real-world vulnerabilities +
Footnote †: journal: Applied Sciences
## 1 Introduction
In many LMIC settings where logistical constraints result in less efficient documentation, these temporal measurements are frequently recorded with considerable noise. Therefore, using these features in machine learning models requires careful standardised, which can be tedious and time-consuming. In Ghana, very little effort has been made to exploit machine learning techniques for childhood vaccination defaulter risk analysis. Current methods are predominantly statistical [23; 24]. One of the few attempts is seen in the work of [PERSON] and colleagues [25], where the random forest classifier was evaluated with other statistical methods to analyse predictors of vaccination default among 600 children. In their study, the statistical techniques used provided the best prediction results, while the ensemble learning algorithm underperformed. Indeed, the malaria vaccine had been newly introduced, and so the difficulty in obtaining sufficiently large data for machine learning-based analysis would not be unusual, which could have also accounted for the relatively low depth of analysis. Meanwhile, the impact of limited data on the effectiveness of such advanced analytical techniques has been mentioned by [PERSON] and colleagues [26]. In effect, a more thorough study into how machine learning methods could be leveraged for analysing childhood vaccination defaulter risk predictors in Ghana is lacking. We take this opportunity to bridge this gap through a more rigorous comparison of statistical and machine learning methods on a relatively larger dataset created by merging data from multiple surveys. We also evaluate the relevance of new predictors from temporal data, specifically the timing of vaccination and birth, in predicting childhood vaccination default.
## 4 System Design
### Binocular Camera Ranging  
Because the binocular camera obtains information from two angles, it must be calibrated to accurately acquire the scene depth and three-dimensional information.  
This calibration process encompasses two primary stages: internal and external parameter calibrations. The former corresponds to the intrinsic parameters of each camera, such as the focal length, pixel size, and aberration parameters, whereas the latter refers to the designation of the relative position and direction between the cameras as well as the distance between the cameras and the scene. The schematic diagram of the binocular camera ranging principle is shown in Figure 7.
## 3 Roasting Process
### Infrared Roasting  
Technically, infrared radiation (IR) is a form of energy within the electromagnetic spectrum, with wavelengths ranging from approximately 0.75 to 1000 \(\upmu\)m, falling between microwaves and visible light. The infrared spectrum is divided into three bands: near-infrared (0.75 to 2 \(\upmu\)m), mid-infrared (2 to 4 \(\upmu\)m), and far-infrared (4 to 1000 \(\upmu\)m). IR roasts food in a different way; the IR energy interacts with the food, penetrates it, and transfers thermal energy in the form of electromagnetic waves, uniformly heating the food mass. An important advantage of using IR technologies for roasting nuts is the ability to better control parameters and select different IR emitters [26].  
[PERSON] et al. (2019) investigated the impact of infrared roasting on peanut kernels. They varied the IR power between 250 and 450 W and the roasting time between 10 and 30 min. Subsequently, they assessed various physical parameters such as moisture content, color, texture, taste, and sensory evaluation. The authors reported a decrease in moisture content with increasing IR power and roasting time. A similar linear relationship was observed for color, with an increase in brown pigments as time and power increased. Conversely, longer roasting times and higher power levels adversely affected the texture of peanuts. This is likely due to the degradation of biomolecules during the roasting process. Infrared roasting presents an effective alternative to conventional roasting methods, offering higher efficiency and reduced pollutant emissions [32].  
In hazelnuts, infrared (IR) heating offers the advantage of internal heating, minimizing aroma loss compared to conventional methods. This study investigated two roasting conditions (conventional hot air and infrared) using both low-temperature, long-time and high-temperature, short-time approaches. For conventional hot air roasting, the conditions of 135 \({}^{\circ}\)C for 45 min were used for low-intensity roasting, and 195 \({}^{\circ}\)C for 27 min for high-intensity roasting. For infrared roasting, the conditions were 135 \({}^{\circ}\)C for 40 min and 195 \({}^{\circ}\)C for 20 min, respectively. After roasting, pastes and oils were produced to assess the impact of different roasting methods on the final products. The antioxidant profile, electronic nose (E-nose) fingerprint, acidity, peroxide value, and fatty acid composition were analyzed. The results indicated that low-intensity infrared roasting was less destructive to antioxidant activity, likely due to the preservation of polyphenols. E-nose analysis revealed that infrared-roasted hazelnuts were perceived as having more intense flavors, likely due to reduced aroma loss [33].
## 6 Numerical Experiment
### Results and Discussion: Tuning GMD Components \(n\) for Model Performance
In contrast, the DNN achieves significantly lower accuracy than both the SVC and the RF do in all months. This may be because the sample size of training dataset is small for DNN. Therefore, the following discussion focuses primarily on the SVC and RF. Focusing on the transitions in accuracy with respect to the GMD components \(n\), RF tends to reach accuracy saturation earlier than SVC across all months for the test data. This trend is also observed in the transitions in accuracy for each model on the training data, as shown in Figure 10, although the training accuracy of SVC tends to increase more gradually than that of the test data. From this observation, it can be seen that although SVC tends to have lower accuracy than RF does, it appropriately handles the complexity associated with the increase in the dimensionality of feature vectors, effectively suppressing overfitting. This is particularly evident during the period from March to June. On the other hand, RF demonstrates high accuracy even with small values of \(n\), that is, low-dimensional feature vectors, but it also tends to overfit. From these results, it can be concluded that SVC exhibits higher generalization performance than RF does. Moreover, the RF achieves high accuracy even with low-dimensional feature vectors. In the computation of the GMD, an increase in the number of components \(n\) leads to an increase in the computational cost; thus, the RF is appropriate for scenarios where the computational cost is a priority. Conversely, when the generalization performance is emphasized, SVC is preferred. These findings are based on the accuracy obtained within the hyperparameter space described in Section 5.3. Hence, different results may emerge when a broader hyperparameter space is considered. Note that because the GMD components \(n\) obtained in this experiment are the results for only one area, i.e., Narashino, Chiba, Japan, the components are not necessarily practically applicable to any area and could depend on the scale and shape of the target area.
## 4 Results and Discussion
### Local Atomic Relaxations
Particularly the latter might not be negligible for alloy compositions different from the equiatomic composition, especially for those sublattices with small deviations from integer occupation, e.g., the A and D sublattices for \(x_{V}=0.60\) or the B sublattice for \(x_{V}=0.40\). These effects could be taken into account similiar as for the equiatomic composition (see Equation (6)), but such calculations are not presented in this work. \begin{table}
\begin{tabular}{l c c c c c c} \hline  & \(\mathbf{x_{V}}\) & **A** & **B** & **C** & **D** & **E** \\ \hline \(J_{\alpha}^{(1)}\) (mRy/cell) & 0.40 & 23.625 & \(-\)7.031 & \(-\)0.094 & 19.406 & 0.0 \\  & 0.50 & 24.750 & \(-\)7.594 & 0.656 & 20.344 & 0.0 \\  & 0.60 & 24.938 & \(-\)8.625 & 0.844 & 20.531 & 0.0 \\ \(\mu_{\alpha}^{Fe}(\mu_{B})\) & 0.40 & 1.158 & 1.908 & 1.659 & 1.337 & 1.265 \\  & 0.50 & 1.047 & 1.790 & 1.518 & 1.240 & 1.079 \\  & 0.60 & 0.947 & 1.644 & 1.355 & 1.148 & 0.908 \\ \(\mu_{\alpha}^{V}(\mu_{B})\) & 0.40 & 0.168 & 0.221 & 0.199 & 0.181 & 0.172 \\  & 0.50 & 0.170 & 0.217 & 0.200 & 0.183 & 0.171 \\  & 0.60 & 0.171 & 0.213 & 0.210 & 0.184 & 0.169 \\ \hline \end{tabular}
\end{table} 
Table 5: On-site parameters \(J_{\alpha}^{(1)}\) (mRy/cell) and local magnetic moments of Fe and V, \(\mu_{\alpha}^{Fe}\) and \(\mu_{\alpha}^{V}\), calculated for different alloy compositions \(x_{V}=\{0.40;0.50;0.60\}\) in the Fe-V system with the corresponding lattice parameters \(V=\{0.37747;0.38319;0.38892\}\) nm\({}^{3}\) and \(c/a=\{0.518;0.517;0.516\}\) within the DLM-LSF model at \(T\) = 1,000 K.
# Modal Properties of Photonic Crystal Cavities and Applications to Lasers
## 1 Introduction
### Key Mode Characteristics: The Q-Factor and the Mode Volume
In this case, Equation (1), with suitable integration boundaries enclosing the cavity, is often a good approximation. We emphasize, though, that one should also specify the integration volume, \(V\), when employing Equation (1) [19]. Unfortunately, this is not always the case within the literature. In the case of many emitters or nonlinear effects in bulk media, other mode volumes reflect the scaling of the light-matter interaction. Specifically, mode volumes relevant for nonlinear interactions are [20; 21]  
\[V_{\text{Kerr,TPA}}=\frac{\left(\int_{V}n^{2}(\mathbf{r})|\mathbf{E}(\mathbf{ r})|^{2}d^{3}\mathbf{r}\right)^{2}}{\int_{V_{\text{nl}}}n^{4}(\mathbf{r})| \mathbf{E}(\mathbf{r})|^{4}d^{3}\mathbf{r}},\quad V_{\text{FCA}}=\left[\frac{ \left(\int_{V}n^{2}(\mathbf{r})|\mathbf{E}(\mathbf{r})|^{2}d^{3}\mathbf{r} \right)^{3}}{\int_{V_{\text{nl}}}n^{6}(\mathbf{r})|\mathbf{E}(\mathbf{r})|^{6 }d^{3}\mathbf{r}}\right]^{\frac{1}{2}} \tag{3}\]  
where \(V_{\text{Kerr,TPA}}\) (\(V_{\text{FCA}}\)) is associated with the optical Kerr effect and two-photon absorption (free-carrier absorption). Here, \(V_{\text{nl}}\) is the volume of the nonlinear material. These mode volumes are usually larger than \(V_{\text{eff}}(\mathbf{r}_{\text{max}})\)[22]. Finally, it should be noted that in the case of semiconductor lasers, the mode volume is conventionally defined as \(V_{p}=V_{\text{act}}/\Gamma\). Here, \(V_{\text{act}}\) is the physical volume of the active region, and \(\Gamma\) is the optical confinement factor, which roughly corresponds to the fraction of electric field energy stored inside the active region volume [23; 24]  
\[\Gamma=\frac{\int_{V_{\text{act}}}|\mathbf{E}(\mathbf{r})|^{2}d^{3}\mathbf{r} }{\int_{V}|\mathbf{E}(\mathbf{r})|^{2}d^{3}\mathbf{r}} \tag{4}\]  
If the active medium consists of layers of quantum dots, the optical confinement factor per active layer is often computed as if the quantum dot medium were entirely filling the layer with thickness equal to the average thickness of the quantum dots [25]. This is correct as long as the filling factor of the quantum dot layer is accounted for in the expression of the material gain [25].
## 1 Introduction
demonstrated experimentally the use of computational ghost imaging (CGI) for imaging an object in a water tank, which might be useful for practical underwater imaging application [30]. Later, [PERSON] and co-workers theoretically studied the effects of oceanic turbulence on the quality of the image in CGI and computational ghost diffraction (CGD) [31]. Recently, the visibility and quality of the image in GI system in an underwater environment have been deeply investigated by some groups [32; 33], and the results showed that the GI can also reduce the effects of water turbulence compared to the conventional imaging system. However, in the aforementioned studies, the illumination light in a GI system is the conventional partially coherent beam, only the spatial coherence of the light is considered. In addition to spatial coherence, a partially coherent beam has another freedom, i.e., twist phase. The concept of the twist phase was first introduced by [PERSON] [34]. Unlike the conventional phase curvature, the twist phase is a two-point function and cannot be separated into a sum of simpler one-dimensional contributions. In addition, the twist phase displays intrinsic chiral or handedness property, which is responsible for the beam rotation with respect to the propagation axis during propagation. Such a twist phase only appears in partially coherent beams and vanishes in the coherent limit. The statistical properties of a partially coherent beam carrying twist phase have been investigated in [35; 36; 37; 38; 39; 40; 41]. The influences of the twist phase on GI in free space have been reported in [42]. It is found that the twist phase degrades the quality of the image whereas the visibility of image increases. However, the influences of the twist phase on GI in a turbulent ocean have not been investigated. One can expect to reduce the turbulence-induced degeneration using a partially coherent beam with twist phase, named TGSM beam. Our aim in this paper is to study the effects of twist phase on GI in the presence of simulated oceanic turbulence. In Section 2, based on the optical coherence theory, we formulate the FOCFfunction in lensless GI system with a TGSM beam in a turbulent ocean. In Section 3, we give numerical examples of the formed image of double slits, and analyze the effects of the twist phase and oceanic turbulence on visibility and quality of the image. In Section 4, we summarize the results.
## 3 Results
### Mass Spectrometry Imaging
#### 3.1.2 Inositol  
The localization of inositol (Figure 2e) was strongly affected by drought stress during the ripening period. In addition, almost no inositol was observed in the gel surrounding seeds throughout the maturation process.  
During the ripening period, inositol levels increased overall in the drought-stressed group, and this metabolite was detected in seeds, the middle pericarp, and the outer pericarp Figure 2(e2). In mature fruits, high levels of inositol were detected in the mesocarp in the control group (Figure 2(e3)). However, in the drought-stressed group, almost no inositol was detected in the mesocarp, but strong signals were detected in the seeds and the gel around the seeds (Figure 2(e4)). Based on the imaging results, under normal conditions, inositol accumulates in seeds during the early stages of development, and its localization changes to the mesocarp as maturation progresses. However, in the presence of drought stress, inositol began to accumulate in the mesocarp during the early stage of fruit development and was concentrated in seeds during the full maturity period (mature red).  
Figure 2: Mass spectrometry imaging of fatty acid and inositol distribution during ripening in tomato fruits under drought stress. (**a**) Bright-field images of control or drought-stressed fruits. (**b–d**) Mass spectrometry imaging of palmitic acid +H \(m/z\) 257 (**b**), sterile acid +H \(m/z\) 285 (**c**), oleic acid +H \(m/z\) 283 (**d**), and inositol +Na \(m/z\) 203 (**e**). Red pixels represent the highest signal intensity (100%) of the particular ion, and black pixels represent the lowest signal (0%). For interpretation of the references to color in this figure legend, the reader should refer to the web version of this article.
## 7 RegTech and Industrial Internet of Things
### Data Security and Access Control  
Information privacy is a significant point that needs to be addressed. Psychologically, it is human nature to be reluctant to make public disclosures [91]. From a business perspective, companies may be concerned about their trade secrets being leaked. Implementing IoT for data collection can provide a good database for maximum data sharing, and at the same time, the privacy of enterprises should be properly protected. In addition, the threat to cyber security is another serious problem in the existing network [92; 93; 94; 95]. Data sharing should be conducted within safe networks for both regulators and regulated entities. Blockchain, defined by [PERSON] and [PERSON] [96] as \"an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value\", is one of the potential solutions to this issue due to its significant advantages or features such as transparency, immutability, and trustworthiness with high reliability [97; 98; 99]. With the advantages of the technology, blockchain is identified as an effective tool in the bill operation of commercial banks through building a decentralized digital billing system to decrease bill-forgery risk and credit risks, as well as in the cross-border payment operation to save service charges and transfer time compared with the traditional payment method such as the SWIFT system [100]. Similarly, blockchain technology can be used in RegTech applications to ensure data security and smooth information communication between stakeholders.  
In addition to adopting blockchain technology to protect data privacy and security, another effective way is to set proper access control as agreed upon by all stakeholderswho have access to relevant data pools. For example, Attribute-Based Encryption (ABE) is the best way to apply access control [101; 102; 103]. It is also taken as a useful technique to solve the issue of \"data privacy and fine-grained access control\" in popular cloud storage systems [104]. By taking advantage of this technology, access to the relevant data can be restricted to desired groups of users only through data encryption and authorized decryption [105]. This will prevent data breaches in daily compliance activities and protect the producer's commercial confidentiality. The combination of ABE and blockchain (as shown in Figure 5) can make data sharing flexible and secure.
# Influence of Atmospheric Flow Structure on Optical Turbulence Characteristics
###### Abstract  
This article discusses the quality of astronomical images under conditions of moderate small-scale turbulence and varying meso-scale airflows above the Baikal Astrophysical Observatory (BAO). We applied a Weather Research and Forecasting (WRF) Model, as well as statistical estimations of the Fried parameter from the differential motion of the solar images. The simulations were performed with a fairly high horizontal resolution within a large area of 1600 \(\times\)1600 km. A high horizontal resolution provides representative estimations of atmospheric characteristics and correct accounting of large-scale air advection. We considered the influence of atmospheric motions over the cold water area of Lake Baikal, as well as meso-scale vortex structures over rough terrain on solar image quality. A better understanding of structured turbulent small-scale motions and optical turbulence over rough terrain may help to develop advanced methods for diagnostics and prediction of image quality. For the first time, we have shown that the BAO is located at the periphery of a meso-scale atmospheric vortex structure with an anticyclonic direction of airflows in the daytime. An increase in image quality was associated with weakening airflows over Lake Baikal and a decrease in the intensity of wind speed fluctuations. Calculated spectra of atmospheric turbulence in the daytime were close to the classical form. At night and in the morning, the spectra had a steeper slope on small scales. Deformations of the spectra were due to the suppression of turbulence under stable stratification of the atmosphere. The characteristic horizontal scales of the transition from \"\(-5/3\)\" to \(\sim\)\"-3\" spectral slope were 2-2.5 km. The results obtained using the WRF model and analysis of optical turbulence strength (namely, the Fried parameter) indicated that the parameterization schemes used in the WRF model were accurate.  
turbulence; meso-scales; WRF Article
## 2 Materials and Methods
### Selection of Appropriate HP Pre-Treatment Conditions  
For HP pre-treatment, whole tomatoes (approximately 200 g) were packaged in polyethylene bags filled with water, to ensure a homogeneous application of pressure. High Pressure treatments were performed using a pilot scale HP device capable of achieving pressures up to 1000 MPa and temperature up to 90 \({}^{\circ}\)C (Food Pressure Unit FPU 1.01, Resato International BV, Roden, Holland), using polyethylene glycol as a pressure transfer medium in a pressure vessel with a total volume of 1.5 L. The heating rate of the samples inside the HP vessel due to adiabatic heating during pressurization was measured via a CR1000 thermocoule data logger system (Campbell Scientific, Logan, UT, USA) connected to the vessel and was equal to 3 \({}^{\circ}\)C per 100 MPa. This temperature increase was quickly equilibrated through the temperature control system described above, with a cooling rate equal to about 5 \({}^{\circ}\)C/min for the large pressure vessel and 20 \({}^{\circ}\)C/min for the small pressure vessels. The pressurization rate achieved with the HP system used in the present study was 13 MPa/s. Depressurization of the chamber was achieved almost instantly (1-3 s). Samples were treated at 100, 200, 400 and 600 MPa for 5 min. These treatment conditions were selected based on the results of our previous study where tomato enzyme HP inactivation was explored in depth [16]. The following treatments, for enzyme activity (PME, PG) and firmness, were performed based on the protocols described in Section 2.4.