esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Mechanical Beam Steering with Polarization Gratings: A Review
## 3 Applications of Non-Mechanical Beam Steering with PGs
### Broadband Polarization-Insensitive Passive Imaging
Figure 14: Geometry of the LCPG-based polarization independent shutter. (**a**) The optic axes of the two FLC quarter-wave switches are parallel and they add to form a half-wave which changes the handedness of the light resulting in continued beam divergence; (**b**) If the optic axes of the quarter-wave switches are crossed, the polarization does not change handedness and the light returns to the zero order and remains in the field of view of the sensor. Figure 15: Details of the seven-shutter array. (**a**) The PG shutter assemblies were placed in a Hex-7 pattern in a baffled housing; (**b**) The shutter array housing was cooled to liquid nitrogen temperatures to mitigate thermal noise. The imaging system was tested at a range of \(\sim\)50 m using different targets, including a space heater and nichrome wire that could be temperature controlled by varying the electrical current. Figure 17 shows one of the demonstration experiments. A space heater and a resolution target constructed of nichrome wire were placed in adjacent fields of view and the shutter array switched between the two targets. This particular arrangement provided a good example of the shutter-based imaging system's ability to block stray light between adjacent zones.
# Malware Detection on Byte Streams of Hangul Word Processor Files
###### Abstract  
While the exchange of data files or programs on the Internet grows exponentially, most users are vulnerable to infected files, especially to malicious non-executables. Due to the circumstances between South and North Korea, many malicious actions have recently been found in Hangul Word Processor (HWP) non-executable files because the HWP is widely used in schools, military facilities, and government institutions of South Korea. The HWP file usually has one or more byte streams that are often used for the malicious actions. Based on an assumption that infected byte streams have particular patterns, we design a convolutional neural network (CNN) to grasp such patterns. We conduct experiments on our prepared 534 HWP files, and demonstrate that the proposed CNN achieves the best performance compared to other machine learning models. As new malicious attacks keep emerging, we will keep collecting such HWP files and investigate better model structures.  
Keywords:malware detection; HWP file; byte stream; convolutional neural network +
Footnote †: journal: Applied Sciences
###### Abstract  
Temerate forests and grasslands carry key ecosystem functions and provide essential services. Remote-sensing derived greenness has been widely used to assess the response of ecosystem function to climate and land-cover changes. Although reforestation and grassland restoration have been proposed to enhance the regional greenness in Northern China, the independent contribution of climate without the interference of land-cover change at meso and large scales has rarely been explored. To separate the impacts of climate change on vegetation greenness from those of land-cover/use change, we identified large patches of forests and grasslands in Northern China without land-cover/use changes in 2001-2015 and derived their greenness using MODIS enhanced vegetation index (EVI). We found that most deciduous-broadleaved forest patches showed greening, and the significant slope of the annual mean and maximum EVI are \(3.97\pm 0.062\times 10^{-3}\) and \(4.8\pm 0.116\times 10^{-3}\) yr\({}^{-1}\), respectively. On the contrary, grassland patches showed great spatial heterogeneity and only those in the east showed greening. The partial correlation analysis between EVI and climate showed that the greening of grassland patches is primarily supported by the increased growing-season precipitation with mean significant coefficient of \(0.72\pm 0.01\). While wet-year (\(0.57\pm 0.01\)) and nongrowing-season precipitation (\(0.68\pm 0.01\)) significantly benefit greening of deciduous-broadleaved forests, the altered temperature seasonality modulates their greening spatial-heterogeneously. The increased growing-season minimum temperature might lengthen the growing season and contribute to the greening for the temperature-limited north as shown by positive partial correlation coefficient of \(0.66\pm 0.01\), but might elevate respiration and reduce greening of the forests in the south as shown by negative coefficient of \(-0.70\pm 0.01\). Daytime warming in growing season is found to favor the drought-tolerant oak dominated forest in the south due to enhanced photosynthesis, but may not favor the forests dominated by less-drought-tolerant birch in the north due to potential water stress. Therefore, grassland greening was essentially promoted by the growing-season precipitation, however, in addition to being driven by precipitation, greening of deciduous forests was regulated spatial-heterogeneously by asymmetrical diurnal and seasonal warming which could be attributed to species composition.  
Keywords: temperate grasslands; deciduous broadleaved forests; vegetation greening; asymmetrical warming +
Footnote †: journal: Remote Sensing, 2020, 12, 2569; doi:10.3390/rs12162569
## 3 Modeling of Various Stakeholders in the Distribution Network (DN)  
### Utility Model of Customers  
The electricity usage habits and preferences of customers are generally independent of each other. It is possible to capture the electricity habits of each user with the aid of IoT, smart meters and big data technologies. The electricity behaviors of customers can be analyzed by collecting data such as temperature, electricity price, electricity time, power, etc. This paper uses the functional concept of microeconomics to build a utility model of residential customers. The user's power line is modeled by selecting different utility functions \(U(x_{i}^{t},\omega_{i}^{t})\). For each customer, the function represents the user's utility satisfaction. In this paper, we reference [31] to define the \(U(x_{i}^{t},\omega_{i}^{t})\) as a quadratic function with decreasing marginal utility:  
\[U(x_{i}^{t},\omega_{i}^{t})=\left\{\begin{array}{l l}\omega_{i}^{t}x_{i}^{t}- \frac{\alpha_{0}}{2}(x_{i}^{t})^{2}&if\ \ 0\leq x_{i}^{t}\leq\frac{\omega_{i}^{t}}{\alpha_{0}}\\ \frac{(\omega_{i}^{t})^{2}}{2\alpha_{0}}&if\ \ \ \ x_{i}^{t}\geq\frac{\omega_{i}^{t}}{ \alpha_{0}}\end{array}\right. \tag{1}\]  
where \(x_{i}^{t}\) represents the customer's energy consumption quantity at time slot \(t\), and parameter \(\omega_{i}^{t}\) characterizes the user's electricity consumption behavior at time slot \(t\), the parameter \(\alpha_{0}\) which was set in advance, represents the uniform conditions.  
Figure 5 shows the utility changes for various customers as energy consumption changes, and it shows that the marginal utilities of all satisfaction levels are decreasing at the different degrees. The marginal utility \(\omega=0.5\) is taken as an example to illustrate the diminishing effect.  
To sum up, the total revenue of the customer contains two parts; the utility that the customer obtains and the cost of purchasing electricity. Therefore, the comprehensive utility of customer \(i\) in time interval \(t\) can be written as:  
\[R_{cumstomers,i}=\sum_{t}^{T}[U(x_{i}^{t},\omega_{i})-P_{\textit{retailer}}^{t }x_{i}^{t}] \tag{2}\]  
where \(P_{\textit{retailer}}^{t}\) is the real-time retail price for purchasing electricity of customers, and \(x_{i}^{t}\) is the quantity of energy consumption of customer \(i\) at time slot t.
## 3 Methodology and Data Preparation
### Wire Break Data  
The measurement data from the laboratory experiments was prepared using the same procedure as the operational data to ensure a high degree of comparability. To establish a comparable basis, the transient signals were first normalized to a preamplification level of 0 dB and resampled from 10 MHz to 100 kHz. A uniform threshold of 90 dB was introduced to determine the beginning of the signal, with the first crossing of this threshold being defined as the signal's onset. Subsequently, 10 ms long time segments were extracted from the data and the aforementioned features were calculated in both the time and frequency domain.
## 3 Data analysis and BP Neural Network Model Building
### Hidden Layer Node Number Determination  
Generally, the model accuracy will be improved by increasing the training sample size. However, when the sample size is large enough, even more data cannot improve the model performance. For the purpose of studying the relationship between original sample size and training model performance, different training sample sizes were tested and compared. The mean square error (MSE) and coefficient of determination (R\({}^{2}\)) between prediction value and virtual test value were calculated to evaluate the accuracy and stability of final prediction model. Seven models for Gradation-13 were built in this article with sample sizes of 25, 50, 75, 100, 125, 150 and 175 respectively. For all seven models, 25 samples were applied as the testing group to check the model performance by calculating R\({}^{2}\).  
For the BP model, except for training sample size, the hidden layer node number also plays a significant role in model training. According to empirical Equation (7), the range of node number is set as 3 to 13.  
\[\mathrm{h}=\sqrt{m+n}+a \tag{7}\]  
where, \(m\) and \(n\) refer to the number of input and output layer nodes respectively and a refers to a regulating constant in the range of 1 to 10. For each group, 10 parallel tests were carried out to determine the optimum node number. The detailed procedure is omitted in this article since it was introduced in another publication [32]. The following Figure 5 shows that coefficient of determination (R\({}^{2}\)) changes with node number.  
It can be observed that: (1) the optimum node number can be different for different models; (2) with training sample size increasing, model performance is less sensitive to node number. The optimum hidden layer node number for all seven groups are shown in the following Table 9.  
Figure 5: R\({}^{2}\) in different node numbers and training group sizes.  
Meanwhile, the following Figure 6 shows the R\({}^{2}\) for different models based on their optimum node number. The error bars here represent standard deviation.  
From this figure, it is clear that with the increase of training group sizes, R\({}^{2}\) rises sharply in the initial stage. After the number of training groups goes up to more than 75, the growth rate of R\({}^{2}\) becomes very small. Meanwhile, for model accuracy, when the amount of training data is too small, the model error is very large. With the increase of training data size, the model tends to be more stable. In this state, a more accurate prediction model can be obtained.  
Based on the above analysis, the number of training sets was recommended as 75 or 100, considering training accuracy and efficiency. Meanwhile, the best number of testing sets is about 1/2-1/3 of the training set.
# A High-Gain CNTFET-Based LNA Developed Using a Compact Design-Oriented Device Model
## 4 LNA Design
### Amplifier Noise Evaluation  
Considering all the CNTFET noise contributions, we have the following equations:  
\[(g_{\mathrm{o2}}+g_{\mathrm{o3}})\cdot v_{\mathrm{y}}=-g_{\mathrm{m2}}\cdot v _{\mathrm{x}}-i_{\mathrm{n2}}-i_{\mathrm{n3}} \tag{16}\]  
\[g_{\mathrm{o4}}\cdot v_{\mathrm{out}}+i_{\mathrm{n4}}=-g_{\mathrm{m1}}\cdot(v _{\mathrm{y}}-v_{\mathrm{x}})-g_{\mathrm{o1}}\cdot(v_{\mathrm{out}}-v_{ \mathrm{x}})-i_{\mathrm{n1}} \tag{17}\]  
\[g_{\mathrm{o5}}\cdot v_{\mathrm{x}}+i_{\mathrm{n5}}=-g_{\mathrm{o4}}\cdot v _{\mathrm{out}}-i_{\mathrm{n4}} \tag{18}\]  
where \(v_{\mathrm{x}}\) and \(v_{\mathrm{y}}\) are the voltages at the nodes X and Y of the equivalent circuit Figure 6, respectively.  
By solving (16)-(18) and considering the output conductances \(g_{\mathrm{o1}}\), \(g_{\mathrm{o2}}\), \(g_{\mathrm{o3}}\), \(g_{\mathrm{o5}}\) vanishing with respect to the CNTFET trans conductances \(g_{\mathrm{m}}\), the five CNTFET noise contributions to the output voltage \(v_{\mathrm{out}}\) result in:  
\[v_{\mathrm{out}}^{(n)}\simeq-\frac{(g_{\mathrm{o2}}+g_{\mathrm{o3}})\cdot g_{ \mathrm{o5}}}{g_{\mathrm{m1}}\cdot g_{\mathrm{m2}}\cdot g_{\mathrm{o4}}}\cdot i _{\mathrm{n1}}+\frac{g_{\mathrm{o5}}}{g_{\mathrm{m2}}\cdot g_{\mathrm{o4}}} \cdot i_{\mathrm{n2}}+\frac{g_{\mathrm{o5}}}{g_{\mathrm{m2}}\cdot g_{\mathrm{o 4}}}\cdot i_{\mathrm{n3}}-\frac{1}{g_{\mathrm{o4}}}\cdot i_{\mathrm{n4}}- \frac{1}{g_{\mathrm{o4}}}\cdot i_{\mathrm{n5}} \tag{19}\]
# An Anchor-Free Location Algorithm Based on Transition Coordinates
## 6 Conclusions  
In this paper, we propose an anchor-free location algorithm based on transition coordinates. The algorithm does not use the traditional methods based on cost function minimization or matrix inversion, but simply gives the difference of position vector according to the difference between the temporary distance and the real distance, and iterates gradually to approach the real coordinates. The computational complexity of this algorithm is much less than that of the traditional algorithm, and it can guarantee its convergence and achieve high precision for most topologies, and can meet the needs of most positioning scenarios, especially in the case of weak GPS signals or short-time position changes multiple times.  
In the simulation experiment, we investigated some factors that may affect the performance of the algorithm, including the initial topology of the transition coordinates, the positioning accuracy required by the algorithm, and the total number of nodes required by the algorithm. We also list the places that attention should be paid to in the application  
Figure 6: GER error of four different positioning algorithms with different numbers of nodes.  
of the algorithm, such as where the real coordinate of the node cannot be in the same position or a straight line, the distance between each node cannot be too low, or the effect of the algorithm may be poor. We also compare the computational complexity between the proposed algorithm and the existing centralized algorithm, semi-centralized algorithm, and distributed algorithm in the experiment. It can be seen that the computational complexity of the proposed algorithm is small, and the relative position graph between nodes can be obtained at a faster speed. At the same time, the global energy ratio (GER) shows great advantages over the classical AFL, R-AFL, and MDS-MAP algorithms. In the future, the goal of our work is to consider how to apply it to the field of moving coordinates or three-dimensional spatial positioning, how to combine the proposed algorithm with the anchored location algorithm, and how to optimize the convergence rate of the algorithm so as to further reduce the computation amount or improve the accuracy of the algorithm.  
Conceptualization, S.L.; methodology, J.F. and S.L.; validation, J.F.; resources, S.L.; data curation, J.F.; writing--original draft preparation, J.F.; writing--review and editing, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Professor [PERSON]'s Hubei Provincial Natural Science Foundation (2024 AFB872).  
Not applicable.  
We choose not to disclose data due to privacy reasons.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
GPSGlobal positioning system AFLAnchor-free localization CBD Centralized boundary discovery ABCAssumption-based coordinates MDSMultidimensional scaling R-AFL Refined anchor-free localization MDS-MAP Multidimensional scaling-map EPError proportion GERGlobal energy ratio
# Machine Learning and Deep Learning Based Model for the Detection of Rootkits Using Memory Analysis
## 4 Methodology
### Data Acquisition
#### 4.1.2 Creation of Memory Dump  
Each rootkit sample was executed in a virtual machine running Windows 11 (guest OS) and a memory image was captured using the Dumpit.exe tool. Precautions were taken to ensure a secure environment, including Windows activation, separate network segmentation, and disabling Windows Defender. Similarly, a benign image was also captured, i.e., one without any rootkit running.
# Improving Energy Efficiency on SDN Control-Plane Using Multi-Core Controllers
## 3 Materials and Methods  
Our proposed solution aims to reduce energy consumption by splitting controller's load among many cores on a processor, while lowering the frequency of operation, compared to the single-core high-frequency scenario. In this section, we discuss a few fundamental key concepts for our proposal and define the new method for energy efficiency. First, a model for energy consumption on SDN is presented showing the impact of processing packets on the controller. Then, the rationale behind multicore processors on energy savings is discussed. We also describe the hardware and software used to real-time control and measure: frequency, availability and energy consumption of processor's cores.
# Numerical Study on Aerodynamic Noise Reduction of Pantograph
## 4 Results and Discussion  
### Flowfield and Acoustic Source of the Original Model  
Turbulent eddies are an important flow structure for the noise generation. They evolve gradually along the flow direction. Figure 6 provides the instantaneous iso-surface of the \(Q\)-criterion, which is colored by the velocity magnitude. As can be seen, eddies generated by the panhead are small and they are elongated in the \(x\) direction when flowing downstream. The air flowing through the bottom area of the pantograph produces the large-scale eddies, of which the position increases as they flow downstream. The large-scale eddies merge with eddies in the wake of the middle area and the panhead area.  
Figure 5: Instantaneous contour of spanwise vorticity \(\omega_{2}\).  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Present** & **[PERSON] et al. [19]** & **[PERSON] et al. [23]** \\ \hline SPL\({}_{max}\) & 99.61 & 100.67 & 98.76 \\ \(St_{p}\) & 0.194 & 0.201 & 0.179 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison between the present and previous results.  
Figure 7 demonstrates the instantaneous contour of the pressure fluctuation, which is non-dimensionalized by the freestream pressure. The noise radiated from the panhead area of the pantograph is transparent, and the wave front is approximately circular and the directivity is nearly uniform. The middle and bottom areas also radiate out noise, but the noise amplitude is too low to be distinguishable. In the downstream of the pantograph, eddies in the wake lead to the hydrodynamic pressure fluctuations propagating downstream rather than the sound wave.  
Based on the time history of the surface pressure, the dimensional power spectral density (PSD) values of the pressure time derivative can be calculated. Then, the surface pressure level (SPL) for the 1/3 octaves \(f_{c}\) is provided as follows:  
\[\text{SPL}(f_{c})=10\cdot\text{log}_{10}\frac{\text{PSD}(f_{c})}{p_{\text{ ref}}^{2}},p_{\text{ref}}=2\times 10^{-5}\text{Pa} \tag{10}\]  
The contour of SPL on the pantograph surface at different frequencies is displayed in Figure 8. It indicates that the acoustic source of high-frequency noise is mainly concentrated in the panhead area, while the low-frequency noise is radiated from the middle and bottom areas of the pantograph. This acoustic source distribution is consistent with the eddy scales illustrated in Figure 6.  
Figure 6: Instantaneous snapshot of eddies extracted by the \(Q\)-criterion (\(Q=10^{4}s^{-2}\)) and colored by the velocity magnitude.  
Figure 7: Instantaneous contour of the non-dimensionalized pressure fluctuation \(p\prime/p_{\infty}\times 100\) on the symmetric surface (\(y=0\)).
# A Dynamic Analysis Method of Liquid-Filled Containers Considering the Fluid-Structure Interaction
## 3 Analysis of Liquid Sloshing Modes
### Theoretical Solutions to 2D Liquid Sloshing Modes
#### 3.1.1 Two-Dimensional Rectangular Container  
For a regular rectangular container (Figure 1a), the \(j\)th sloshing frequency of the liquid is [2,3]:  
\[\omega_{j}=\sqrt{g\Big{(}\frac{j\pi}{2a}\Big{)}\tanh\Big{(}\frac{j\pi h}{2a} \Big{)}}\quad j=1,2,3,\dots \tag{4}\]where g is the gravitational acceleration; \(a\) represents the half width of the rectangular container; and \(h\) is the liquid level. According to Equation (4), the sloshing of the free liquid surface is attributed to gravity. The \(j\)th sloshing mode of the free liquid surface is  
\[\phi_{j}(x,\ z)=\left\{\begin{matrix}C_{j}\sin\frac{j\pi}{2a}x&j=1,\ 3,\ 5,\ \dots\ \ (\text{antisymmetric mode})\\ D_{j}\cos\frac{j\pi}{2a}x&j=2,\ 4,\ 6,\ \dots\ \ (\text{symmetric mode})\end{matrix}\right. \tag{5}\]  
where \(C_{j}\) and \(D_{j}\) are constants. Figure 2 illustrates the first six sloshing modes of the free liquid surface. Therein, odd-order frequencies \(\omega_{1},\ \omega_{3},\ \omega_{5}\) correspond to the first three antisymmetric modes, while even-order frequencies \(\omega_{2},\ \omega_{4},\omega_{6}\) correspond to the first three symmetric modes, respectively.  
Figure 1: Two-dimensional containers. (**a**) Rectangular container. (**b**) Arbitrary section container.  
Figure 2: The first six sloshing modes of the free liquid surface in the rectangular container. (**a**) Antisymmetric (odd-order) modes (\(j=1,3,5\)). (**b**) Symmetric (even-order) modes (\(j=2,4,6\)).
# Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
## 2 Music Retrieval Application
### Datasets  
We use two databases of different sizes in our experiments (see Table 1). Our first set \(\mathbb{D}_{\text{sm}}\), which was already used as an evaluation set in the previous study [9] (there denoted by \(\mathbb{D}_{2}\)), consists of 330 audio files and comprises about 16 h of music (corresponding to more than 50,000 shingles). These recordings contain interpretations of orchestral and piano pieces by [PERSON], [PERSON], and [PERSON]. In our cross-version retrieval evaluation, we consider a recording relevant for a query if it represents a version of the piece underlying the query (e.g., [PERSON]'s _Verleh und Frieden_ or the first movement of [PERSON]'s Third Symphony). Since the dataset \(\mathbb{D}_{\text{sm}}\) contains twelve \"cliques\" (works or movements), having a different number of versions each (4 to 67), a query may correspond to 4 to 67 relevant documents in our cross-version retrieval scenario. This dataset is well annotated and corresponds to a controlled retrieval scenario. We make the annotations and shingles of the dataset available on our accompanying website for reproducibility.  
As a second dataset \(\mathbb{D}_{\text{lg}}\), we use the entire audio catalog of recordings offered by the Carus label. The dataset \(\mathbb{D}_{\text{lg}}\) contains 7115 audio files and about 390 h of professionally produced music (corresponding to more than 1.25 million shingles), mainly of vocal Western classical music. In contrast to \(\mathbb{D}_{\text{sm}}\), this dataset is less well annotated and corresponds to a rather uncontrolled scenario \"in the wild,\" having real practical relevance.