esa-sceva/satcom-chunk-collection huggingface dataset:
# SGXDump: A Repeatable Code-Reuse Attack for Extracting SGX Enclave Memory
## 4 SGXDump Attack Overview
### Probe Array Monitoring  
The main task of the PAM is to detect a change in a PTE of the probe array and to recover the value by locating the accessed page number in the PTE. Then, PAM clears the access bit in the PTE and continues to wait for a change in the PTE. However, SGXDump is not able to detect the clearing event carried out by PAM, so it cannot decide when to read the next bytes. If the two components are not synchronized well, SGXDump could access the probe array multiple times before the PAM retrieves the information or vice versa, resulting in noisy or incorrect data leakage.  
In order to make sure that the SGXDump and PAM leak data correctly, we devised a simple binary semaphore using four-byte memory in the target application's stack area. We call this memory a _sync_. Unlike a normal binary semaphore, which uses zero or one, our _sync_ uses zero or an offset, which will vary depending on target enclave applications. Zero means that SGXDump can continue to read the next byte. After accessing the probe array, it writes an offset value at the _sync_. This offset is used to jump to the starting address of a waiting loop in the ROP payload of SGXDump, which will be explained in detail in the next section.  
Simultaneously, the PAM keeps checking the _sync_. If the value stored in the _sync_ is not zero, it means that SGXDump has finished accessing the probe array. Now, it is the PAM's turn to retrieve information from the PTE. After this, the PAM clears the access bit of the PTE and resets the _sync_ to zero, indirectly signaling SGXDump to read the subsequent byte.
# An Adaptive Active Learning Method for Multiclass Imbalanced Data Streams with Concept Drift
## 4 Experimental Evaluation and Analysis
### Algorithm Comparison Experiments
#### 4.3.1 Comparison and Analysis of Data Streams with Fixed Class Proportions  
This section presents the experimental results of the AdaAL-MID and comparison algorithms on the data streams with fixed class proportions. As shown in Tables 2-6, the AdaAL-MID achieves the best G-mean and kappa values across nine data streams. Specifically, for the Fixed_Mix_SIG data stream with mixed concept drift, AdaAL-MID's G-mean and kappa values were 1.57% and 0.54% higher, respectively, than those of the second-ranked CALMID algorithm. In terms of recall, accuracy, and F1-score, while AdaAL-MID did not achieve the best results for some data streams, its performance lagged only slightly behind some of the advanced supervised learning algorithms and remained the best among active learning algorithms. Specifically, for the Fixed_Mix_IGGI data stream, AdaAL-MID's accuracy was only 0.03% lower than that of the top-ranked ARFe. Additionally, AdaAL-MID's average rankings on the five metrics were 1.22, 1.11, 1.00, 1.56, and 1.00, outperforming other comparison algorithms.  
To more intuitively show the performance changes in the algorithm during the learning process for data streams with fixed class proportions, especially its ability to handle concept drift, this section uses recall change curves to show the real-time performance of each algorithm, with the recall calculated every 1000 instances. Figure 4 shows the recall curves of all algorithms on nine data streams. As shown in the figure, AdaAL-MID's overall performance is superior to other comparison algorithms. On Fixed_Stream_1 and Fixed_Stream_2 data streams without concept drift, all algorithms exhibit relatively good performance, with AdaAL-MID consistently maintaining a high level. In the presence of concept drift in the data streams, all algorithms showed a significant performance decline. As shown in Figure 4c-f, during sudden or gradual drift, the AdaAL-MID was less affected at the drift point compared with other algorithms and could recover and maintain high performance more quickly. The most affected algorithm was MOOB, which took a long time to recover due to its lack of a concept drift handling mechanism. For mixed drift data streams with incremental drift, as shown in Figure 4g-i, the AdaAL-MID's performance in the initial stages of incremental drift was slightly lower than that of the supervised learning algorithms CSARF and ROSE but better than other active learning algorithms. After learning a certain number of instances, AdaAL-MID can maintain high performance and become comparable to the supervised algorithms.  
It can be seen that the AdaAL-MID handles imbalances in data streams well and adapts to the occurrence of concept drift.
## 5 Conclusions  
To summarize, we have theoretically investigated bistability in aperiodic PT symmetry lattices incorporated with Fibonacci sequence dielectrics and graphene at terahertz frequencies. Two aperiodic Fibonacci sequence dielectrics are utilized to enhance the band-edge resonance in which the light field is restricted at the defect layer. The localization of light field and the Q-factor for the resonant cavity can be further enhanced through improving the gain-loss factor \(q\) of the PT symmetry system. The electric field localization can greatly strengthen the third-order nonlinearity of graphene and low-threshold OB is achieved. The threshold of OB could been decreased as low as the magnitude kW/cm\({}^{2}\). Meanwhile, the interval between the upper and lower thresholds of OB expands. The bistability thresholds and the interval of thresholds decrease with the increase of the momentum relaxation time of graphene. Furthermore, the chemical potential in graphene can also flexibly tune bistability. This research provides an option for development in photomemories and optical switches.  
Formal analysis, B.X., W.X. and S.K.; funding acquisition, D.Z. ([PERSON]), H.G., W.X. and D.Z. ([PERSON]); investigation, D.Z. ([PERSON]), B.X., H.G. and W.X.; software, D.Z. ([PERSON]) and S.K.; supervision, D.Z. ([PERSON]); writing--original draft, D.Z. ([PERSON]); writing--review & editing, D.Z. ([PERSON]), B.X., H.G., W.X., D.Z. ([PERSON]) and S.K.  
This research was funded by National Natural Science Foundation of China (51479155); the Colleges and Universities of National Innovation and Entrepreneurship the Training Plan (S201910927024); the Scientific Research Project of Hubei University of Science and Technology (2018-20 XB010); the Science and Technology Plan Research Project of Hubei Education Department (B2019162).  
The authors declare no conflict of interest.
## 4 Verification and Simulation
### Comparison of Fuzzy Model and Human Driver Data  
Although the real data of human drivers already showed a large variance in the subject study, the comparison of the behavior that was observed in the study indicates that the fuzzy model can well reproduce the characteristics of the human drivers' behavior. Each participant followed 86 steps to provide 85 pedal opening responses based on the given target speed, and 34 effective subjects gave a total of 2890 effective segments. Figure 5 compares four exemplary diagrams of the experimental results of participants and fuzzy model simulation in the same driving situation characterized by the same initial speed and target speed. The black lines represent the desired speed/target speed, the red lines represent the actual speed from the driver (participant) and the blue lines correspond to the velocity of the vehicle model (with the fuzzy control model). The first figure shows the continuous acceleration situation, and the second and last figures are deceleration situations. The third figure shows a low speed situation.  
Figure 4: Driver model with different degree of automation.  
In terms of total time, the fuzzy model always takes a shorter time than human drivers because machines do not have problems of faulty manipulation. In terms of driving behavior, there are hardly any significant differences in deceleration characteristics between the human driver and the model. However, the fuzzy control model has a smoother acceleration curve during acceleration, whereby the acceleration decreases as the actual speed approaches the target speed; the human drivers are less sensitive to the difference between current speed and desired speed. Overall, the fuzzy control model can adequately reflect the driving behavior of human drivers.
# Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
## 2 Algorithm Description
### Interface Definition
#### 2.2.1 Calibration  
The calibration procedure allows the partial computation of the equalization coefficients, with the help of an external processor to perform the costlier steps. By default, _dataEq_ is set to 1, thus applying no equalization. In order to obtain the proper values to use in the _dataEq_ field, the calibration procedure is necessary. The procedure involves setting _MCalSet_ to a number of time slots that want to be integrated. Once _startCal_ is set to true, the block will start integrating the Power Spectral Density (PSD) of the data in X and Y. When _MCalSet_ time slots have passed (_calDone_ is true), the accumulator will be output through the _dataCal_ port when _validCal_ is true. It is important to note that the real part of this port corresponds to the values for X-pol, and the imaginary part to Y-pol. It is possible to specify an address for this value to be output to, by using the _addrCal_ port, together with the _vaddrCal_ flag. With this value, the Equalization coefficients can be calculated by dividing them by _MCalSet_, performing the square root and inverting the value. The result of this operation will be set to the _dataEq_ port to complete the calibration.
# Phlogopite-Forming Reactions as Indicators of Metasomatism in the Lithospheric Mantle
## 2 Methods and Procedures
### Perple_X Calculation Strategy and Procedure  
Experimental data on phlogopite-forming reactions in peridotite systems with fluid containing variable concentrations of alkali salt components (see below) show that the products of these reactions involving chloride and carbonate fluid components are generally similar. This conclusion allows qualitative comparison of these reactions and corresponding phase assemblages by constructing \(\log(a_{\rm H_{2}O})-\log(a_{\rm K_{2}O})\) diagrams. Activity of K\({}_{2}\)O in this case represents a relative scale for predicting mutual effects of the potassium activity. The standard properties of alkali components can be assumed to be the properties of K\({}_{2}\)O and Na\({}_{2}\)O solid oxides or melts. The calculated K\({}_{2}\)O activities can be transformed into activities of the corresponding salt components at known activities of Cl and CO\({}_{2}\). The diagrams were calculated using the Gibbs free energy minimization procedure for a system of known bulk composition using the PERPLE_X software (version 6.7.7., [PERSON]. [PERSON], Institut fur Mineralogie und Petrographie, ETH-Zentrum, Zurich, Switzerland [29]). The standard properties of the alkali components were assumed to be those of solid K\({}_{2}\)O and Na\({}_{2}\)O oxides, which were compiled from the NIST thermodynamic database. Standard thermodynamic properties of endmembers from the database [30]. In contrast to previous calculations [28], which did not consider the Cr-content of minerals, in the present study we used the following models for Cr-bearing mineral solid solutions from [PERSON] [31]: O(JH) for olivine, Opx(JH) for orthopyroxene, Cpx(JH) for clinopyroxene, Grt(JH) for garnet and Sp(JH) for spinel. The model cAmph(G) [32], which includes K-pargasite component, was used for monoclinic amphibole. The model Bio(TCC) [33] was chosen for Ti-bearing biotite. In simulations of eclogite assemblages, the models Cpx(HP) for non-ideal disordered clinopyroxene, Gt(W) for Ca-Mg-Fe-Mn garnet [34], Pheng(HP) for dioctahedral mica, and the model \"feldspar\", based on the solution model of [35], for ternary feldspar were used. The fugacity of H\({}_{2}\)O was described by the CORK EoS [36]. Melting reactions were not taken into account because the compositions of alkaline melts in certain parts of the systems are beyond the scope of the melt models recommended in PERPLE_X under pressures above 1 GPa. For simplicity, Fe\({}_{2}\)O\({}_{3}\) was not included in the calculations.
_Int. J. Mol. Sci._**2013**, _14_, 2832-2845; doi:10.3390/ijms14022832  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Article_  
**Measurement of the Interaction Between Recombinant I-domain from Integrin alpha 2 beta 1 and a Triple Helical Collagen Peptide with the GFOGER Binding Motif Using Molecular Force Spectroscopy**  
**[PERSON] \({}^{1}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\), [PERSON] \({}^{2}\), [PERSON] \({}^{3}\), [PERSON] \({}^{2,\ast}\) and [PERSON] \({}^{1,\ast}\)**  
\({}^{1}\) Nanoscience Centre, Department of Engineering, Cambridge University, Cambridge, CB3 0 FF, UK;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Biochemistry, Cambridge University, Cambridge, CB2 1 QW, UK;  
E-Mails: [EMAIL_ADDRESS] (A.M.C.S.); [EMAIL_ADDRESS] (S.W.H.);  
[EMAIL_ADDRESS] (D.B.)  
\({}^{3}\) National Physical Laboratory, Teddington, TW11 0 LW, UK; E-Mail: [EMAIL_ADDRESS]  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (R.W.F.);  
[EMAIL_ADDRESS] (M.E.W.); Tel.: +44-1223-766111 (R.W.F.); +44-1223-760305 (M.E.W.);  
Fax: +44 -1223-760309 (M.E.W.).  
_Received: 4 December 2012; in revised form: 8 January 2013 / Accepted: 21 January 2013 / Published: 29 January 2013_  
**Abstract:** The role of the collagen-platelet interaction is of crucial importance to the haemostatic response during both injury and pathogenesis of the blood vessel wall. Of particular interest is the high affinity interaction of the platelet transmembrane receptor, alpha 2 beta 1, responsible for firm attachment of platelets to collagen at and around injury sites. We employ single molecule force spectroscopy (SMFS) using the atomic force microscope (AFM) to study the interaction of the I-domain from integrin alpha 2 beta 1 with a synthetic collagen related triple-helical peptide containing the high-affinity integrin-binding GFOGER motif, and a control peptide lacking this sequence, referred to as GPP. By utilising synthetic peptides in this manner we are able to study at the molecular level subtleties that would otherwise be lost when considering cell-to-collagen matrix interactions using ensemble techniques. We demonstrate for the first time the complexity of this interaction as illustrated by the complex multi-peaked force spectra and confirm specificity using control blocking experiments. In addition we observe specific interaction of the GPP peptide sequence with the I-domain. We propose a model to explain these observations.  
**Keywords:** integrin; alpha 2 beta 1; collagen; atomic force microscopy; single molecule force spectroscopy
## 4 Current Studies Focused on PPO Activity
### PPO Evolution in Fresh-Cut Produce  
In addition tothe mechanism and studies related to PPO, much research about PPO evolution in fresh-cut fruits and vegetables have been carried out to check PPO relevance, especially during storage. As commented elsewhere, PPO activity tends to increase during storage, achieving a summit. Afterwards, PPO activity might decrease. In fact, there are studies comparing both minimally processed and intact lettuce, escarole and rocket salad and their enzyme content evolution (including PPO). As expected, different behavior was found depending on the produce and their ascorbic acid content. Namely, escarole showed intense browning. Thus, cutting increased PAL activity, but did not affect PPO nor POD for lettuce and escarole [21]. On the other hand, another study pointed out the vital importance of using sharp blades in order to avoid an increase of PPO activity for fresh-cut eggplant, trying to reduce the impact on tissue destruction [29]. Some authors have found an increasing PPO activity in fresh-cut produce during storage, especially up to the eighth or ninth day, showing occasionally a decrease from then on, possibly due to the decrease in polyphenol content [10; 15; 22; 65].
# Seareed Exhibits Therapeutic Properties against Chronic Diseases: An Overview
## 6 Pharmacological Properties of Seaweed to Overcome Chronic Disease
### Osteoporosis
** \\ \hline \multicolumn{5}{c}{Brown Seaweed (Phaeophyceae)} \\ \hline _Dictyota metrensii_ & Fucoidan & In vitro & Protected bone tissue against oxidative stress & [217] \\ _Laminaria digitata_ & Fucoxanthin & In vitro & Low pro-osteogenic effects & [11] \\ _Ascophylum nodosum_ & Fucoxanthin & In vitro & Low pro-osteogenic effects & [11] \\ _Padina pavonica_ & Acetonic extract & In vitro & Promoted osteoblast differentiation & [218] \\ _Sargassum hemiphyllum_ & Fucoidan & In vitro & Protected osteoblast-specific markers & [219] \\ _Ishige okamurae_ & Diphlorethohydroxpermalol & In vitro & Promoted osteoblast differentiation from oxidative stress & [220] \\ \hline \multicolumn{5}{c}{Red Seaweed (Rhodophyta)} \\ \hline \multicolumn{5}{c}{Promoted osteogenic differentiation and} \\ _Plocamium lyngbyanum_ & Methanolic extract & In vitro and in vivo & \begin{tabular}{c} Promoted osteogenic differentiation and \\ mineralization \\ Increased opercular bone area \\ \end{tabular}  & [221] \\ _Ceranium secundatum_ & Methanolic extract & In vitro and in vivo & \begin{tabular}{c} Promoted osteogenic differentiation and \\ mineralization \\ \end{tabular}  & [221] \\ _Dichotomaria obtusata_ & Methanolic extract & In vitro & Increased opercular bone area & [221] \\ _Gracilaria verrucosa_ & Crude extract & In vitro and in vivo & \ \begin{tabular}{c} Urregualated osteogenic activity \\ Downregulated RANKL-induced \\ osteoclast differentiation \\ Inhibited bone loss \\ \end{tabular}  & [215] \\ _Ceranium pallidum_ & Dichloromethane and
\begin{tabular}{c} Urrello and Urrello and Urrello  
phlorotannin isolated from _Ishige okamurae_ showed anti-osteoporosis effects by upregulating osteoblast differentiation against H\({}_{2}\)O\({}_{2}\)-induced oxidative damage and promoting bone resorption, by decreasing the expression level of the receptor activator of NF-kB ligand [220]. The mechanism of osteoporosis is shown in Figure 4h. Osteoclasts are large multinucleated myeloid cells that can break down the bone matrix by proteolytic degradation and decalcification [224]. The receptor activator of nuclear factor-kB (RANKL) has been identified as an important transcription factor for osteoclast differentiation from the monocyte/macrophage lineage. _Gracilaria verrucose_ downregulated RANKL-induced osteoclast differentiation by inhibiting the c-Fos-NFATc1 signaling pathway in vitro, and inhibited bone loss in an OVX mouse model when tested in vivo [33]. Meanwhile, fucoidan extract from _Sargassum hemiphyllum_ prevents the differentiation of osteoclasts and inflammatory bone loss by regulating the Akt/GSK3\(\beta\)/PTEN/NFATc1 signaling pathway and calcineurin activity [219].
# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 3 The Proposed Method
### Data Normalization  
Data normalization was one of the essential preprocessing steps for many machine learning algorithms. Raw data and extracted features often have different scales. In other words, the variation between features can be inconsistent, which may result in many problems during data training, including lowering accuracy and increasing the training time. The normalization process for data contributed positively to preparing data that were suitable for training [31]. Moreover, data normalization can scale the data in the same range of values for each input feature to minimize bias in the input data. Furthermore, data normalization can also accelerate the training time by starting the training operation for each feature within the same scale. Various types of data normalization methods can be employed to reduce the bias of the training data, such as the Min-Max Normalization, Z-Score Normalization, Sigmoid Normalization, and others.  
In our case, after feature extraction using statistical analysis described in Section 3.2, 17 input features were obtained, including raw data from the sensor device. The variations between the input features were large and diverse, which may cause problems in the training phase. Thus, we needed to normalize the data. We used Min-Max Normalization that rescaled the features from one range of values to a new range of values. Typically, the features were rescaled within a range of 0 to 1 or from -1 to 1. Table 4 shows the application Min-Max Normalization on a sample training dataset, which is calculated using the following formula:  
\[x^{\prime}=(x_{max}-x_{min})\times\frac{(x_{i}-x_{min})}{x_{max}-x_{min}}+x_{ min} \tag{8}\]  
In Equation (8), \(i\) is \(i\)th element of the dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Equation** & **Data Type** \\ \hline \hline \multicolumn{3}{c}{Frequency-Domain Analysis} \\ \hline Root Mean Square Frequency & \(X_{mref}=\sqrt{\frac{\sum_{i=1}^{N}X^{2}(t_{i})}{4\pi^{2}\sum_{i=1}^{N}X^{2}(t_ {i})}}\) & Float \\ Root Variance Frequency & Float \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
###### Abstract
Analysis of variance-simultaneous component analysis (ASCA) was revealed to be particularly helpful for the analysis of the effects in complex data sets, where large number of variables have been measured. ASCA has been already applied to evaluate the effects of pressure (250-650 MPa) and pressure holding time (15-120 min) on the phenolic content of Nero D'avola Syrah red wine [16] to evaluate the effects of age and variety on the electronic tongue response and wine composition with respect to the organic acids, phenolics, and furanic derivatives, using HPLC data [17] to evaluate how storage temperature and time (as well as their interaction) influence cheddar cheese ripening, combining Raman and Mid-InfraRed (MIR) spectroscopy data [18], and to investigate the effects of CO\({}_{2}\) and soil-water levels on \({}^{1}\)H NMR-based metabolic fingerprints of Arabica coffee beans [19]. ASCA is a merging of analysis of variance (ANOVA) and principal component analysis (PCA) that allows the overcoming of the drawbacks of using both methods individually. ANOVA fails in cases when the number of the variables exceeds the number of samples or when dependent variables are correlated. On the other hand, PCA does not consider the experimental design, which means that the different contributions to the variation caused by the experimental design are confounded in the model [20; 21]. The ASCA method consists of partitioning the original data matrix into a set of matrices corresponding to the different factors of the experimental design and, subsequently, subjecting each one of these matrices to a PCA. The ANOVA-like model is constructed from the PCA models of all effects and interactions. Since loadings are calculated for each sub-model independently, the contribution of the variables to every source of variation can be identified. The significance of each factor can be assessed using a permutation test that consists of randomly changing the order of the rows in the data set [22; 23; 24]. The present research study aims to evaluate the reliability of a multivariate statistical tool (ASCA) to combine grape physicochemical attributes (berry weight, pH, titratable acidity, total sugars, total phenolics, antiradical activity, and free and glycosylated linked volatile composition) to evaluate the impact of harvest year conditions and vineyard parcel characteristics on grapes' oenological potential. For this, mature grapes belonging to five _Vitis vinifera_ autochthonous varieties (Arinto, Bical, Baga, Castelao, and Touriga Nacional)and a worldwide cultivated one (Sauvignon Blanc) from Bairrada Appellation (Portugal) were selected as a case study. Each variety was collected in three consecutive harvests and in three vineyard parcels with different topographical characteristics.