esa-sceva/satcom-chunk-collection huggingface dataset:
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 3 Calibration of GNSS-R Land Reflections
### Level 1 Normalized Bistatic Radar Cross Section (NBRCS) Land Calibration  
The Level 1a conversion from instrument counts to received power (\(P\) in Watts) is performed using the same formulation as for the CYGNSS ocean L1a calibration [9]:  
\[P=\frac{(C-C_{N})(P_{B}+P_{r})}{C_{B}} \tag{22}\]  
where \(C\) and \(C_{N}\) are the total Level 0 counts and noise counts, \(C_{B}\) and \(P_{B}\) are the black body calibration load counts and external noise power, respectively, and \(P_{r}\) is the instrument noise power. All of these parameters are calculated exactly as in the ocean L1a calibration with the exception of the DDM noise counts \(C_{N}\). The value of \(C_{N}\) is obtained from portions of the DDM that occur at times prior to the specular reflection point, so that only thermal noise is present. Because the delay and Doppler location of ocean DDM returns can be reliably predicted using the WGS84 ellipsoid, a fixed set of time offsets from the WGS84 delay is used for computing \(C_{N}\) for ocean observations. Over land, high surface elevations above the WGS84 ellipsoid can cause surface scattering to impinge upon the standard noise time offsets. Therefore, the delays used for computing \(C_{N}\) over land surfaces exclude any points that approach the land surface delay, calculated as  
\[\tau_{L}=\tau_{O}-2\cos(\theta)\Delta H \tag{23}\]  
where \(\Delta H\) is the DEM height above the WGS84 ellipsoid specular point, \(\theta\) is the WGS84 specular point incidence angle, and \(\tau_{O}\) is the delay pixel at the ocean surface, or roughly the WGS84 ellipsoid. Whereas \(C_{N}\) for ocean observations is calculated using noise pixels at delays prior to \(\tau_{O}\), \(C_{N}\) for land observations is computed using only delays less than \(\tau_{L}\). Due to the repeating nature of the GNSS code delay sequences, code phase roll-overs should be corrected accordingly.  
The Level 1b conversion from power in Watts (\(P\)) to an estimate of the BRCS can be calculated using the same algorithm as for an ocean reflected signal, using the estimated transmitter power and antenna gain, receiver antenna gain and propagation path losses associated with the reflection geometry determined by the land geolocation point [9]):  
\[\sigma=\frac{P(4\pi)^{3}R_{R}^{2}R_{T}^{2}}{\text{Y}\lambda^{2}G^{R}} \tag{24}\]  
where Y is the effective isotropic radiated power (EIRP), including the transmitter power and antenna gain combined, \(G^{R}\) is the receive antenna gain, \(R_{R}\) and \(R_{T}\) are the ranges from the surface to the receiver and transmitter, respectively. The constant \(\frac{4\pi}{\lambda^{2}}\) accounts for the diffuse scattering spreading terms. The Level 1 BRCS is calculated over a range of delay ( \(\tau\) ) and Doppler frequency (\(f\)) bins. The delay and frequency bins containing the maximum received BRCS are used to evaluate the estimated surface geolocation point.
## 3 Interference Suppression through Beamforming Techniques
### Adaptive Beamforming (ABF)
The decrease in background level allows a greater number of targets' trajectories to be observed. It is interesting to highlight that now, a trajectory appears at ranges further than 2000 bins and around 300 Hz Doppler frequency shift, which is probably due to an aerial target, becausethat Doppler shift corresponds to velocities too high for maritime vehicles. This trajectory is even clear in the case of the second proposal, i.e., estimation employing all Doppler cells for some specific range gates corresponding to interference ones. Figure 14 shows the SNR of two targets: the maritime one also shown for DN case in Figure 10(a), and the aerial one that appeared after ABF implementation. It is possible to check what was observed in Figure 13: SNR is improved significantly, employing ABF for interference suppression. In the case of the maritime target, the improvement is similar for both proposals, but in the case of the aerial one, the second proposal works better. Now, the SNR values achieved for both maritime and aerial targets in most of the trajectory will assure detection requirements stated if a CFAR detector is employed. Figure 12: RD map of CPI 13 with adaptive weigths through MVDR algorithm and employing the two proposals for interference covariance matrix estimation. Figure 13: Results of processing the 158 CPI with adaptive weights through MVDR algorithm generating first beam and employing the two proposals for interference covariance matrix estimation.
# Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
## 3 Efficient Soft Iterative Decoding Algorithms for HARQ with Rateless Codes in Satellite Systems  
### HARQ for Satellite Systems  
Even though the HARQ scheme has been considered to be an efficient means for terrestrial wireless communication systems, direct application of HARQ to a satellite system may cause serious issues due to a long round trip delay, which reaches up to 0.25 seconds for geostationary earth orbit (GEO) satellite systems. For this reason, a number of research studies have developed satellite-specific HARQ schemes [11]. However, most of these previous proposals were focused on modifying conventional HARQ schemes for delay sensitive satellite systems, instead of the specific incremental redundancy schemes and decoding methods.  
The performance of type-I hybrid ARQ relies heavily on the error correction capability of the FEC code used. This scheme is not adequate for mobile satellite channels with high time or frequency selectivities because the fixed FEC code cannot efficiently manage the limited channel resources as the satellite channel conditions change. Recent satellite systems designed to provide high data rate services such as high-definition digital television (HDTV) services usually use GEO and high frequency bands over 10 GHz, and these high frequency bands have severe time selectivities due to rain fading. In this situation, type-II and type-III HARQ can be an effective solution when combined with ACM.  
A type-II scheme transmits only the added parity blocks at every retransmission, so it is suitable for the system with prevailed line of sight (LOS) conditions or with a mild rain fading condition. A type-III scheme transmits a complementary code by combining the added parity with the previous transmission, so every transmission has self-decodability and it is advantageous for heavy fading conditions.
## 2 Construction of the Contact Group Contact Model
### Morphological Feature Extraction of Contact Groups
#### 2.2.2 Entropy Rate Superpixel Segmentation  
Use \(G=\left(V,E\right)\) to represent an undirected graph, where \(V\) represents the set of vertices, and \(E\) represents the set of edges. Vertices and edges are denoted by \(v_{i}\) and \(e_{i}\) respectively. The similarity between vertices is represented by a weight function \(\omega:\ E\rightarrow\ R+U\left\{0\right\},\) where the weights of edges are symmetric, i.e., \(\omega_{i,j}=\omega_{j,i},\ X=\left\{Xt|t\in T,Xt\in V\right\}\) represents a random walk process on the graph \(G=\left(V,E\right)\) with non-negative similarity weights \(\omega\)[23]. A random walk model proposed in the literature is adopted, the transition probability is defined as Equation (5).  
\[P_{i,j}=P_{r}\big{(}X_{t+1}=v_{j}\big{|}X_{t}=v_{i}\big{)}=\omega_{i,j}/\omega_ {i} \tag{5}\]  
Figure 3: Contact group gradient direction degree image: (**a**) moving contact; (**b**) static contact.  
Figure 2: Normalized intensity contact group morphology mirror image: (**a**) moving contact; (**b**) static contact.  
In which, \(\omega_{i}=\sum_{k:e_{ij}\in E}\omega_{i,k}\) represents the sum of weights associated with the node.  
The normalized result is as shown in Equation (6).  
\[\mu=\left(\mu_{1},\mu_{2},\ldots,\mu_{|V|}\right)^{T}=\left(\frac{\omega_{1}}{ \omega_{T}},\frac{\omega_{2}}{\omega_{T}},\ldots,\frac{\omega_{|V|}}{\omega_{T }}\right)^{T} \tag{6}\]  
In which, \(\omega_{T}=\sum_{i=1}^{|V|}\omega_{i}\) is the normalization constant.  
The goal is to select a subset of edges \(A\subseteq E\), such that the resulting graph \(G=(V,E)\) contains exactly \(k\) adjacent nodes.  
Entropy rate: To obtain compact clusters consistent with the region, the entropy rate of random walks on the connection graph is used as a criterion. The mapping function for the transition probabilities \(p_{i,j}\ :\ 2^{E}->R\) is defined by Equation (7).  
\[p_{i,j}(A)=\begin{cases}\frac{\omega_{i,j}}{\omega_{i}}&if\ i\
eq j\quad e_{ ij}\epsilon A\\ 0&if\ i\
eq j\quad e_{ij}\epsilon A\\ 1-\frac{\sum_{i,j_{i}\in A}\omega_{i,j}}{\omega_{i}}&i=j\end{cases} \tag{7}\]  
Therefore, the entropy rate of a random walk on a graph \(G=(V,E)\) is defined as a set mapping, as shown in Equation (8).  
\[h(A)=-\sum_{i}\mu_{i}\sum_{j}p_{ij}(A)log(p_{i,j}(A)) \tag{8}\]  
Balance function: to ensure that the formed clusters have similar sizes, a balancing function is used as the criterion for judgment. Let \(N_{a}\) be the number of connecting subgraphs in the graph, and \(Z_{a}\) be the distribution of cluster members. The partition boundary of the graph is \(S_{A}=\left\{S_{1},S_{2},\ldots,S_{N_{A}}\right\}\).  
\[P_{Z_{A}}=\frac{|S_{i}|}{V},\ i=\{1,\ldots,N_{a}\} \tag{9}\]  
The balance function is defined by Equation (10).  
\[B(A)\equiv h(Z_{a})-N_{a}=-\sum_{i}P_{Z_{a}}(i)logP_{Z_{a}}(i)-N_{a} \tag{10}\]  
Equations (5)-(10) are from references [21; 22; 23].
## 2 Materials and Methods
### Real-Time Recovery of Precise Orbit and Clock Correction Products  
The precise orbit can be constituted by the combination of broadcast ephemeris and Galileo HAS corrections [25; 31], which is articulated as:  
\[\partial COO(t)=\begin{bmatrix}\partial COO_{r}\\ \partial COO_{a}\\ \partial COO_{c}\end{bmatrix}+\begin{bmatrix}\delta_{r}\\ \delta_{a}\\ \delta_{c}\end{bmatrix}(t-t_{0}) \tag{7}\]  
Here, \(\partial COO(t)\) is the orbit correction parameters \(t\), where \(\partial COO_{r}\), \(\partial COO_{a}\), \(\partial COO_{c}\) denotes the corrections in the RAC (Radio, Along, Cross) directions. \(\delta_{r}\), \(\delta_{a}\), \(\delta_{c}\) represent the rates of change in the RAC directions. Subsequently, the calculation of the rotation matrix components for each direction is required.  
\[\begin{array}{l}e_{a}=\frac{r}{|r|}e_{c}=\frac{r_{a}(r)}{|r_{a}(r)|}e_{r}=e_{ a}*e_{c}\\ \partial x(t)=[e_{r}e_{a}e_{c}]\partial COO(t)\end{array} \tag{8}\]  
Here, \(\partial x(t)\) is the HAS correction parameters. \(e_{r},e_{a},e_{c}\) are the coefficients in the RAC directions, respectively. \(\dot{r}\), \(r\) denote the velocity and position vectors of the satellite.  
\[\partial pcoo(t)=\partial bcoo(t)+\partial x(t) \tag{9}\]  
Here, \(\partial bcoo(t)\) denotes the broadcast ephemeris coordinate. \(\partial pcoo(t)\) is the precise coordinates.  
The precise clock correction values are needed.  
\[\partial pclk(t)=\partial bclk(t)+\partialCLK(t)/c \tag{10}\]  
Here, \(\partial pclk(t)\) is the precise clock, \(\partial bclk(t)\) is the broadcast ephemeris clock, and \(c\) stands for the speed of light.
# Load-Balanced Dynamic SFC Migration Based on Resource Demand Prediction
## 2 Related Work
In summary, we use high-precision predictions to anticipate SFC resource demands in advance and determine the appropriate timing for migration. We introduce a load-balancing model to ensure efficient use of resources, reduce the frequency of VNF migrations in the network, and avoid potential issues caused by uneven resource usage. Additionally, we design a deep reinforcement learning algorithm to proactively make migration decisions for SFCs on overloaded nodes, preventing the degradation or even interruption of network service quality.
# Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework
## 3 Materials and Methods
### Training and Test Data Split  
The data column is Xlist, the label is Ylist, and training and test split process involves dividing the training and test data with a 7:3 ratio for use in the learning and verification processes of the model, respectively. The value of test_size in the training and test split function is set to 0.3 to achieve this ratio, and the results are listed in Table 4. The red border in Table 4 shows the training set, and the green border shows the test set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Hash** & **Legitimate** & **File Size** & **Magic** \\ \hline memtest.exe & 631 ea355665f28d4707448e442 fbfb5b8 & 1 & 474.38 KB & PE32 exe Intel80386 32 bit \\ ose.exe & 9d10f99a6712e28f8 acd5641e3a7 ea6b & 1 & 145.85 KB & PE32 exe for GUI 32 bit \\ DW20.EXE & a41e524f8d45f0074 fd07805 ff0c9b12 & 1 & 818.88 KB & PE32 exe for GUI 32 bit \\ \hline ZNsAsuO.docx & 8e292b418568d6e7b87f2a32 aee7074b & 0 & 4.68 MB & PE32 exe for GUI 32 bit \\ Lesbian-pool-playexe & 260d9e2258 aed4c8a3 bbd703 ce895822 & 1 & 2.18 MB & PE32 exe for GUI 32 bit \\ \hline Dwtrig20.exe & c87e561258f2f8650 cef999 bf643a731 & 1 & 507.41 KB & PE32 exe for GUI 32 bit \\ Setup.exe & 4d92f518527353 cddb88a70 fddcfd390 & 1 & 1.05 MB & PE32 exe for GUI 32 bit \\ \hline \hline \end{tabular}
\end{table}
Table 2: Malicious code analysis and feature extraction results (partial).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Hash** & **Machine** & **Char** & **Legitimate** \\ \hline memtest.exe & 631 ea355665f28d4707448e442 fb5b8 & 332 & 258 & 1 \\ ose.exe & 9d10f99a6712e28f8 acd5641e3a7 ea6b & 332 & 3330 & 1 \\ setup.exe & 4d92f51852735c0 db88a70 fdddcf390 & 332 & 3330 & 1 \\ \hline DW20.EXE & a41e524f8d45f0074 fd07805 ff0c9b12 & 332 & 258 & 1 \\ Dwtrig20.exe & c87e561258f28650 cef999 bf643a731 & 332 & 258 & 1 \\ \hline ZNsAsuO.docx & 8e292b418568d6e7b87f2a32 aee7074b & 332 & 258 & 0 \\ Lesbian-pool-playexe & 260d9e2258 aed4c8a3 bbd703 ce895822 & 332 & 33167 & 1 \\
7 xdm.tar.bz2 & 8d088a51b7d22559f5d11d239791 ec3f & 332 & 258 & 0 \\ Paper2 dxf\_exe & 4286 dccf67 ca220 fe67635388229a9f3 & 332 & 33166 & 0 \\ \hline Wnb0 cLVS.drv & d7648 eae45f09b3 adb75127f43 bee6d11 & 332 & 258 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Part of contradictory data (before preprocessing).
## 3 Results
### Develop Surplus Value Model  
The surplus value model adopted in this case study is shown in Equation (1):  
\[\begin{array}{l}\mathit{Surplus\ Value}\ =\ \mathit{Disc}_{p}\ \times\ \mathit{MarketSize}\ \times\{\mathit{Disc}_{c}\ \times\ (\mathit{Revenue\ per\ year\ -\ SK\ cost}\ -\ \mathit{OR\ costs}\ -\ \mathit{ propellant\ costs}\ -\ \mathit{launch\ cost}\ -\ \mathit{satellite\ costs\ without\ EP}\ -\ \mathit{EPS\ product\ cost}\ -\ \mathit{interest\ cost}\ -\ \mathit{insurance\ cost}\}\times\ -\mathit{test\ and\ qualification\ costs,}\end{array} \tag{1}\]  
where:  
1. \(\mathit{Disc}_{p}\) and \(\mathit{Disc}_{c}\) are multipliers on a single year's revenue and costs based on the discount rate and mission life for the producers/manufacturers and customer (operator) respectively.
2. _Market size_ is the number of satellites sold every year.
3. _Revenue per year_ generated from satellite operations.
4. _SK costs_ refer to the ground operations necessary to manoeuvre the satellite during station keeping (SK).
5. _OR costs_ refer to the ground operations necessary to manoeuvre the satellite during orbit raising (OR).
6. _Propellant costs_ refer to cost of propellant used to propel the satellite during OR and SK.
7. _Launch costs_ refers to the costs incurred during launch.
8. _Satellite costs without \(\mathit{EP}\)_ refers to the cost sustained during the production and integration of the satellite.
9. _The total capital investment_ is the sum of _Launch costs_, _Satellite costs without \(\mathit{EP}\)_ and \(\mathit{EP}\)_ product costs_.
10. _Interest costs_ refers to the costs incurred by the interest on capital.
11. _Insurance costs_ refers to the costs incurred by the insurance paid for the satellite in orbit.
12. _Test and qualification costs_ refers to the development costs for satellite and \(\mathit{EP}\). These costs derive from the test and qualification model that will be described in the next section. Often, these costs are also defined in the space sector as non-recurring costs.  
The surplus value model described in Equation (1) has been programmed using a discrete event simulation (DES) technique. DES models the operation of a system as a (discrete) sequence of eventsin time [54]. This means that the whole lifecycle of a megaconstellation business has been divided into discrete events. One of these critical events is the test and qualification activity, which has been modelled using the method described in the next section.
# Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
## 2 Plant Disease Diagnosis an Overview--Why Early Detection Matters--The Problem
An answer could be that early detection allows for prompt action to prevent the spread of the disease to other plants, reducing the risk of epidemic outbreaks (reduces crop losses and minimizes economic impact). To address the challenges mentioned above and focus on early detection, it becomes essential to join the modern methods of detection with computer-aided automated studies such as machine learning and deep understanding detection models or field-based disease detection and monitoring [28; 29]. But these techniques require specialized expertise and equipment, which can be costly and may not be feasible for researchers and agronomists. Overall, to overcome these above-mentioned challenges, researchers are employing innovative approaches, such as the following:  
* Advanced genomics and transcriptomics: Analyzing gene expression and genomic data to better understand pathogen-host interactions and disease development [30]. * Machine learning algorithms: Using machine learning algorithms to analyze large datasets and identify patterns in plant disease latency [31; 32; 33; 34]. * Experimental design and simulation modeling: Designing experiments to study disease latency under controlled conditions and using simulation models to predict plant disease spread and management [35; 36]. We believe that these innovative approaches have limited access to researchers and can be expensive. Based on these problems, we propose in this novel review a system (Raman spectroscopy/microscopy) which (i) can detect disease-related biomarkers on the surface of plant leaves without causing physical damage, (ii) can analyze the chemical composition of plant samples, providing information on disease-related changes, (iii) can be combined with other techniques, such as hyperspectral imaging or machine learning, to enhance the detection accuracy and (iv) can detect very low concentrations of biomarkers, making it potentially suitable for early detection of disease.
## 3 Nonlinear Creep Model of AS
### Determination of Creep Parameters  
#### 3.3.1 Cracking Stress \(\sigma_{s_{1}}\) and Creep Dilatancy Stress \(\sigma_{s_{2}}\)  
According to the creep test results, when the loading stress reached approximately 80% of the deviatoric stress before failure, the volume of AS samples began to change from compressive to expansive. Therefore, 80% of the loading stress was taken as the value of \(\sigma_{s_{1}}\). When the loading stress reached approximately 85% of the final loading stress in the expansion stage, the sample began to expand. Therefore, 85% of the final loading stress in the expansion stage was taken as \(\sigma_{s_{2}}\) value.  
The values of creep parameters \(\sigma_{s_{1}}\) and \(\sigma_{s_{2}}\) under different confining pressure conditions are shown in Table 3.
## 5 Flight Results
### Synchronization and Timing  
When establishing an ISL session, both the caller and responder unit must synchronize before exchanging data frames. The sync procedure is the dominant source of latency, and contains the Automatic Gain Control (AGC) (and optionally the Antenna Search Algorithm (ANS)). During AGC, the responder performs a gain sweep (800 ms for one sweep) and tries to detect preambles sent by the caller. Once a preamble is detected, it starts transmitting a reply frame to the caller unit. Simultaneously, the caller performs a RX gain sweep (320 ms for one sweep) to identify the preambles send by the responder. Sync is achieved when both units have detected preambles from the counterpart (see, also, Section 3.5 for the frame structure). Additionally, ANS is performed if the proper antenna pair is unknown prior to the ISL session. In this case, an antenna switch is done after each AGC sweep.  
Figure 17 shows the cumulative distribution function for the sync timing of an ISL session in orbit. In 90% of cases, a P2P connection could be established within 1500 ms using AGC only. When using AGC and the optional ANS together, 90% of synchronization was established within 3500 ms. For dedicated IoT/M2M missions with near-real-time latency requirements (\(\sim\)s), the sync time can be further shortened by reducing the sweep steps of AGC. The ANS can be alleviated by determining the proper antenna pairs prior to each session, which requires knowledge in attitude and orbit position of each pair. By this means, overall P2P latency of less than a second is achievable. For IoT applications with moderate latency requirements (i.e., greater than a few minutes), the sync time carries no weight. Thus, from a timing perspective, all scenarios presented in Table 4, except for the real-time applications (\(\ll\)1 s), such as voice communications or co-operative driving, can be supported by this technology.  
Figure 16: Measured SNR for various distances and pointings within an ISL session [25].  
Figure 17: Cumulative distribution function of sync time during ISL sessions [25].