esa-sceva/satcom-chunk-collection huggingface dataset:
Entropy_**2013**, _15_, 1705-1725; doi:10.3390/e15051705  
_Article_  
Information-Dispersion-Entropy-Based Blind Recognition of Binary BCH Codes in Soft Decision Situations  
[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]  
School of Mechatronics Engineering and Automation, National University of Defense Technology,  
Deya Road, Changsha 410073, Hunan Province, China; E-Mails: [EMAIL_ADDRESS] (Z.H.);  
[EMAIL_ADDRESS] (C.L.); [PERSON]MAIL_ADDRESS](S.S.); [EMAIL_ADDRESS]cn(Y.Z.)  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-0731-84576387; Fax: +86-0731-84576387.  
_Received: 18 March 2013; in revised form: 24 April 2013 / Accepted: 1 May 2013 / Published: 8 May 2013_  
A method of blind recognition of the coding parameters for binary Bose-Chaudhuri-Hocquenghem (BCH) codes is proposed in this paper. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary BCH codes, while the coding parameters are unknown. The problem can be addressed on the context of the non-cooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. The recognition processing includes two major procedures: code length estimation and generator polynomial reconstruction. A hard decision method has been proposed in a previous literature. In this paper we propose the recognition approach in soft decision situations with Binary-Phase-Shift-Key modulations and Additive-White-Gaussian-Noise (AWGN) channels. The code length is estimated by maximizing the root information dispersion entropy function. And then we search for the code roots to reconstruct the primitive and generator polynomials. By utilizing the soft output of the channel, the recognition performance is improved and the simulations show the efficiency of the proposed algorithm.  
information dispersion entropy; blind recognition; channel coding; BCH codes; adaptive coding and modulation (ACM)  
89.70.+c
# Design of a Ku-Band Monopulse Antenna with a Truncated Reflector and an Open-Ended Waveguide Feed
## 2 Feed Design
The sidelobe level is less than \(-\)10.3 dB at 14-17 GHz in both \(E\) and \(H\) planes. The \(E\)- and \(H\)-plane 3-dB beamwidths are 52.2\({}^{\circ}\), 29.2 \({}^{\circ}\) at 14 GHz; 48.2\({}^{\circ}\), 27.3\({}^{\circ}\) at 15 GHz; 41.6\({}^{\circ}\), 25.1\({}^{\circ}\) at 16 GHz; and 36.3\({}^{\circ}\), and 23.3\({}^{\circ}\) at 17 GHz. The \(E\)- and \(H\)-plane 10-dB beamwidths are 86.24\({}^{\circ}\), 48.27 \({}^{\circ}\) at 14 GHz; 78.65\({}^{\circ}\), 45.39\({}^{\circ}\) at 15 GHz; 73.6\({}^{\circ}\), 42.13\({}^{\circ}\) at 16 GHz; and 70.33\({}^{\circ}\), and 39.26\({}^{\circ}\) at 17 GHz. \begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & \(d\) (mm) & \(a\) (mm) & \(b\) (mm) & \(g\) (mm) \\ \hline
**Value** & 65,0 & 16.0 & 8.50 & 3.50 \\ \hline
**Parameter** & \(K\) (mm) & \(L\) (mm) & \(C\) (mm) & \(E\) (mm) \\ \hline
**Value** & 36,5 & 4.00 & 8.00 & 1.00 \\ \hline
**Parameter** & \(\varepsilon_{r}\) & \(F\) (mm) & & \\ \hline
**Value** & 2.10 & 3.33 & & \\ \hline \end{tabular}
\end{table} 
Table 1: Dimensions of the feed and the truncated parabolic reflector. Figure 2: Four-quadrant aperture. Figure 5a shows the directivity patterns of the elevation difference channel. The result shows a directivity of 11.3 dB at 14 GHz; 11.6 dB at 15 GHz; 11.7 dB at 16 GHz; and 11.5 dB at 17 GHz. The sidelobe level is less than \(-\)19.6 dB at 14-17 GHz. The directivity of the azimuth difference channel is shown in Figure 5b. The result shows a directivity of 10.6 dB at 14 GHz; 11.4 dB at 15 GHz; 12.2 dB at 16 GHz, and 12.8 dB at 17 GHz. The sidelobe level is less than \(-\)24.8 dB at 14-17 GHz. One can observe a deep null at 0 degrees in the elevation- and azimuth-difference patterns. Figure 4: (**a**) \(E\)-plane directivity patterns of the sum channel. (**b**) \(H\)-plane directivity patterns of the sum channel. Figure 5: (**a**) Elevation difference channel pattern. (**b**) Azimuth difference channel pattern. Figure 3: (**a**) Reflection and transmission coefficients of the TE\({}_{10}\)-mode-excited rectangular feed. (**b**) Reflection and transmission coefficients of the probe-excited rectangular feed. The initial phase center of the feed is obtained using the phase center functionality of the CST Studio Suite for the sum pattern. With the coordinate origin for patterns calculation at the initial phase center, the phase patterns of the sum and two different channels are calculated. An optimum phase center is obtained by adjusting the coordinate origin so that the phase variations in the 10-dB beamwidth of the sum pattern are minimum for the sum and difference channels. The optimum phase center was found to be 1.0 mm into the aperture. The \(E\)- and \(H\)-plane phase patterns of the sum channel are shown in Figure 6a,b, respectively. Figure 7a,b show the phase pattern of the elevation and azimuth difference channels, respectively. Good phase uniformity can be observed in the sum- and difference-channel phase patterns.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 4 Routing Protocols
### Topology Aware Routing Protocols
#### 4.1.2 Topology Construction Method (TCM)  
In [38], a problem for FANET topology is investigated, in which we know the location of ground control base stations (GCS) and the tasks to be performed, but do not give any pre-configured topology. Suppose there are two types of UAVs: mission UAVs (mUAVs) and relay UAVs (rUAVs). The goal of this paper is to develop an algorithm to construct a good topology by locating the UAV in the appropriate location. With this goal in mind, rUAVs should be positioned to ensure that each UAV communicates effectively with the GCS. In addition, as this problem is easily proved to be NP-hard problem, particle swarm optimization algorithm (PSO) is used to solve this problem. In the iteration  
Figure 4: Routing classification for Flying Ad Hoc Network (FANET).  
phase, the particle moving speed is increased and updated, and the particle with the lowest utility value can be selected according to the following formula,  
\[V_{V_{k}^{i}}\leftarrow\omega V_{V_{k}^{i}}+c1u1(X_{V_{k}^{i*}}-X_{V_{k}^{i}})+c2 u2(X_{V_{k}^{*}}-X_{V_{k}^{i}}),V_{V_{k}^{i}}\gets X_{V_{k}^{i}}+V_{V_{k}^{i}}. \tag{1}\]  
where \(\omega\) represents inertia weight; \(c1,c2\) are, respectively, cognitive and social factors; and \(u1,u2\) represent the dimension. This iterative process will end when the termination condition is satisfied.  
**Advantages:** In this paper, the most suitable particle is selected to construct a stable topology by optimizing the fitness function of the particles. Compared with the random topological scheme, this algorithm has higher performance gain and good topological structure in end-to-end communication, communication quality, and collision safety as long as more than seven rUAVs are used.  
**Disadvantages:** (1) This paper assumes that all mobile mUAVs flying in the preset position, without considering that the UAVs will change their position due to the influence of environmental factors such as wind. (2) In order to achieve the trade-off between complexity and diversity in topology construct process, the number of iterations will increases as the number of particles increases.
## 2 Materials and Methods
### Instrumentation  
The VICON three-dimensional motion analysis system along with 6 cameras operating at a sampling rate of 120 Hz (Vicon Motion Systems, Ltd., Oxford, UK) measured the kinematic variables (lower limb joint angles) during walking. Kinetic variables were collected using two force plates (Kistler 5223A) sampling the ground reaction forces in three axes, acting on each foot during the support phase at 960 Hz. Joint moments were calculated with Nexus software ((1.7.1) Vicon, Oxford, UK). A three-dimensional biomechanical model (lower-body model Plug-in Gait) by Vicon(r) was used. Retro-reflective markers were placed bilaterally to identify the posterior superior iliac spine, anterior superior iliac spine, mid-lateral thigh, mid-lateral shin, medial and lateral malleoli and a point bisecting the heel and base of the second metatar'sus (Figure 1). The Vicon software and Plug-in Gait model directly calculate the kinematic model's joint centers from the measured XYZ marker positions on a frame-by-frame basis and specific subject anthropometric measurements. The model uses three or more points to define each segment [24,25]. The testing commenced with a static calibration phase; a knee alignment device (KAD) was used to evaluate the knee axis. During the dynamic trial, the KAD was removed, and a marker was placed on the lateral femoral condyle.  
The force plates sampled the ground reaction force in Newton units. Parameters were normalized in relation to each soldier's weight (M/BW). The Vicon analysis system moment results were expressed in Newton meters divided by body mass (weight) (Nm/BW).  
The following variables were tested during stance phase (defined from heel-strike to toe-off): (1) maximum angle (in degrees) during the stance phase for the following joint movements: hip adduction, hip internal rotation, knee flexion, knee valgus, knee internal rotation, foot pronation (Figure 2); (2) range of motion (in degrees) during the stance phase of the gait cycle for the following joint movements: hip adduction-abduction, hip internal-external rotation, knee flexion--extension, knee valgus-varus, knee internal-external rotation, foot pronation--supination; (3) mean peak moments (Nm/BW) in the stance phase (reference to movement is in terms of external torque): hip abduction/adduction, hip internal/external rotation, knee flexion/extension, knee vars/valgus, knee internal/external rotation, foot supination/pronation; (4) spatiotemporal variables: walking cadence (steps/minute), step time (second), one-leg support (seconds), two-leg support (seconds), stride length (meters), walking speed (meters/second).  
Figure 2: Diagrammatic representation of the joint angle measurements.
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 3 Proposed Method
### SIFT Feature Point Detection  
We use the SIFT feature point detector [10] to detect feature points from VS-IR images of the train and test sets. The SIFT detector is based on scale space images which are computed by convolving the input image with variable-scale Gaussian kernel.
# Zero-Day Threat Mitigation via Deep Learning in Cloud Environments
## 1 Introduction  
Cybersecurity threats have become increasingly sophisticated, posing significant risks to data integrity and system availability. Among the most critical are zero-day attacks, which exploit unknown vulnerabilities and evade traditional signature-based detection mechanisms [1, 2, 3]. The increasing reliance on cloud computing infrastructures has expanded the attack surface, demanding advanced and adaptive threat detection strategies [4, 5].  
Cloud environments present unique challenges due to their dynamic, distributed, and scalable nature. Features such as elastic resource provisioning, shared responsibility models, and multitenant architectures introduce variability and operational complexity. These factors complicate the deployment of static defense mechanisms, making them less effective against fast-evolving malware strains [5, 6, 7].  
Traditional detection systems often fall short when facing modern threats like polymorphic or fileless malware [8, 9]. To address these shortcomings, the cybersecurity community has shifted towards data-driven approaches using machine learning (ML) and deep learning (DL) [10, 11].  
Deep learning, particularly transformer-based models, has shown great promise in malware detection tasks [12, 13]. These models can process complex features extractedfrom binary files or image-based representations of malware [14], enabling improved classification accuracy and generalization. Moreover, explainable AI is gaining traction, providing insights into model decisions and making security tools more trustworthy [13, 15]. While these approaches demonstrate promise, they still encounter key challenges: the presence of severe class imbalance in malware datasets, the oversimplification introduced by binary classification, and the limited interpretability of deep models.  
In this context, we propose a novel deep learning-based architecture named mixed vision transformer (MVT), tailored for detecting zero-day malware in cloud environments. The MVT model transforms binary executable files into grayscale images (byteplots) and processes them using vision transformer layers. This design enables the extraction of both local and global patterns relevant to malware behavior. Additionally, the model is evaluated under realistic conditions using a containerized cloud-based framework and the MaLeX dataset [16], which provides a balanced and scalable testbed for malware classification.  
The remainder of this paper is structured as follows: Section 2 reviews related work. Section 3 provides background on zero-day threats and visual malware detection. Section 4 presents the proposed methodology and experimental setup. Section 5 discusses results and insights. Section 6 outlines implications, limitations, and future research. Finally, Section 7 concludes the paper.
## 2 Methods  
### PKIKP and pPKIKP Phases  
Figure 1a demonstrates the seismic ray path of PKIKP and pPKIKP phases. Inner core phase PKIKP is a compressional refraction wave across downward the inner-core boundary (ICB), and pPKIKP is the phase which goes up firstly from the source and then is reflected at the earth's surface downward to the same like PKIKP. To illustrate the sensitivity of the pPKIKP-PKIKP time interval and focal depth, it is assumed that the great circle from station to event is 150\({}^{\circ}\) (Figure 1a), and the hypocenter depth ranges from 50 to 600 km. The time difference pPKIKP-PKIKP is calculated using ak135 Earth model [16] and plotted as function of focal depth (Figure 1b). The slope of the function is about 0.22 s/km, thus the pPKIKP-PKIKP phase can offer a good constraint on focal depth. Compared to the theoretical pP-P time (Figure 1a in [10]), pPKIKP-PKIKP time differences are more sensitive to earthquake depth. In addition, the ray path in Figure 1 indicates that the pPKIKP-PKIKP time interval data is mainly affected by the velocity structure above the focal point.
## 2 Related Work
Phase Transfer Entropy (PTE): In a given frequency band, we can define the PTE as lag \(\delta\), following [13]:  
\[PTE_{x\to y}=H\big{(}\theta_{y}(t),\theta_{y}\big{(}t^{\prime}\big{)}\big{)}+H \big{(}\theta_{y}\big{(}t^{\prime}\big{)},\theta_{x}\big{(}t^{\prime}\big{)} \big{)}-H\big{(}\theta_{y}\big{(}t^{\prime}\big{)}\big{)}-H\big{(}\theta_{y} \big{(}t\big{)},\theta_{y}\big{(}t^{\prime}\big{)},\theta_{x}\big{(}t^{\prime} \big{)}\big{)} \tag{9}\]  
where \(H\) is the Hilbert transform and \(\theta_{x}(t^{\prime})\) and \(\theta_{y}(t^{\prime})\) are states of previous time point. Connectivity Matrix Construction:  
The connectivity metrics are assessed for all pairs of EEG channels, leading to an adjacency matrix of dimension N*N, where N represents the number of channels. Then, we apply statistical thresholding by calculating the median of each correlation and binarizing the adjacency matrix. The final matrix is the connectivity matrix--a weighted undirected graph with EEG channels as nodes and edges as connectivity values. Finally, we construct the connectivity matrices over the time-series data (EEG epochs) length in all frequency bands. We use the leakage-corrected functional-connectivity metrics for our analysis. Least Absolute Shrinkage and Selection Operator (LASSO):  
LASSO is used in statistical modeling in the context of regression and graphical modeling. LASSO is primarily used in linear regression for variable selection and regularization. Graphical LASSO is a regularization technique used to learn the structure of an undirected graphical model and is particularly useful for estimating the precision matrix (or inverse covariance matrix) when dealing with high-dimensional data. The goal is to infer the connectivity between variables (in this case, EEG channels or regions) by promoting sparsity in the precision matrix, where a zero in the matrix implies conditional independence between the corresponding variables, given all other variables. This modeling can be used in compressive sensing that promotes sparse representation-based classifications [14]. The graphical LASSO approach can be adapted from the sparse representation of EEG signals by focusing on the precision matrix (\(\Theta\)) estimation for EEG channels, which reflects the functional connectivity. The graphical LASSO optimization problem can be formulated as:  
\[min_{\Theta>0}\big{\{}-\log\det(\Theta)+tr(S\Theta)+\lambda||\Theta||_{1}\big{\}} \tag{10}\]  
where \(\Theta\) is the precision matrix (inverse covariance matrix), \(\log\det(\Theta)\) ensures that \(\Theta\) is a positive definite, and \(tr(S\Theta)\) is the trace of the product of the sample covariance matrix \(S\) and \(\Theta\), encouraging the model to fit the data. \(\lambda\) is the regularization parameter controlling the level of sparsity and \(||\Theta||_{1}\) is the L1 norm of \(\Theta\), promoting sparsity in the precision matrix.
## 5 Discussion  
The approach presented is beneficial to avoid collision between the Delta manipulator and the surroundings during PPO, so as to enhance the robot system's safety. The results of simulations and experiments indicate both the proposed method and comparison method have good motion performance in PPO. Moreover, the proposed method also possesses the following benefits.  
1. The proposed method can be used to generate PPO trajectory of Delta parallel robot to meet a prescribed geometrical constraint, and can quickly and easily solve the relationship between PH curve and the prescribed geometrical constraint. The maximum deviation of the comparison method in reference [35] can be calculated by \[d=\sqrt{\left[x_{w}-x(H)\right]^{2}+\left[y_{w}-y(H)\right]^{2}+\left[z_{h}-z( H)\right]^{2}},\] (31) where \[x_{w}=w_{a}\cos\theta,\hskip 28.452756 pty_{w}=w_{a}\sin\theta,\hskip 28.452756 ptz_{h}= 6{ht_{h}}^{5}-15{ht_{h}}^{4}+10{ht_{h}}^{3},\hskip 28.452756 ptt_{h}=\frac{t}{t_{h}},\] \[w_{a}=6{wt_{w}}^{5}-15{wt_{w}}^{4}+10{wt_{w}}^{3},\hskip 28.452756 ptt_{w} =\frac{t-0.5T_{h}}{T_{w}},\hskip 28.452756 ptT_{h}=\frac{1.875{5}h}{v_{ \max}h},\hskip 28.452756 ptT_{w}=\frac{1.875{5}w}{v_{\max}},\hskip 28.452756 pt\theta\text { is the angular between horizontal motion direction and }X\text{ axis of robot base coordinate system, }v_{\max h}\text{ is the maximum speed in the vertical direction of PPO, }v_{\max w}\text{ is the maximum speed in the horizontal direction of the PPO, }H\text{, }h\text{ and }w\text{ are illustrated in Figure 2, }t\text{ represents the current time. By bringing the root of the equation \(\left(d^{2}\right)^{\prime}=0\) into (31), the maximum deviation can be obtained. Thus the maximum deviation in the comparison method is fixed and cannot be changed. It can be seen from the above process that the calculation of the deviation between the transition segment path and prescribed geometrical constraint in comparison method is more complicated. While the maximum deviation between the PH curve and right angle proposed in this paper is easier to be solved as shown in subsection--'path planning' of Section 3.
2. In the subsection--'trajectory planning' of Section 3, there are some techniques to improve the computational efficiency, which helps to achieve real-time interpolation. 1. If there are two PPO trajectories that need to satisfy the same prescribed geometrical constraint, their PH curves in the _XOY_ coordinate system shown in Figure 3 are the same. Hence the PH curves do not have to be solved again, which saves time and is beneficial for improving system's real-time performance. 2. Since both PPO trajectory and PH curve are symmetric, symmetry can be utilized to simplify the calculation to save time.
## Results and Discussion
### Composition of the Volatiles Obtained by Hydrodistillation  
In the hydrodistillate of FrDV (HD-FrDV) and of DrDV (HD-DrDV), 86.36% and 90.88% of TIC (total ion chromatogram) areas were identified altogether, respectively. Aliphatic compounds were dominant in both fresh (43.00%) and air-dried (61.42%) HD samples. In HD-FrDV, the majority of aliphatic compounds were hydrocarbons (both saturated and unsaturated), with the long-chain hydrocarbon docosane (7.69%) as the most abundant (Table 2). It decreased 23.6 times after the drying. The portion of unsaturated hydrocarbons increased 2.0 times after the drying with (_E_)-nonadec-9-ene (12.79%) as the most dominant. This could probably be the result of fatty acids' decarboxylation, which follows its 4.4 times abundance decrement in HD-DrDV. The increment in saturated (2.4 times) and unsaturated alcohols (7.5 times) in HD-DrDV could be an indicator of the hydrocarbons' oxidation during the drying. Among them, hexadecan-1-ol (10.37%) and (Z)-octadec-9-en-1-ol (8.13%) were the most abundant. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**RI**} & \multicolumn{2}{c}{**Area\% \(\pm\) SD**} \\ \cline{3-5}  & & & **V** & **VI** \\ \hline
1 & Nonane & 900 & 0.36 \(\pm\) 0.15 & 0.06 \(\pm\) 0.01 \\ \hline
2 & Heptanal & 904 & - & 0.12 \(\pm\) 0.03 \\ \hline
3 & Benzaldehyde & 968 & 0.08 \(\pm\) 0.00 & 0.33 \(\pm\) 0.12 \\ \hline
4 & Oct-1-en-3-ol & 984 & 0.24 \(\pm\) 0.05 & 0.22 \(\pm\) 0.02 \\ \hline
5 & 2-Pentyfuran & 994 & 0.10 \(\pm\) 0.01 & 0.22 \(\pm\) 0.06 \\ \hline
6 & Octanal & 1006 & - & 0.09 \(\pm\) 0.03 \\ \hline
7 & (_E,E_)-Hepta-2,4-dienal & 1015 & - & 0.06 \(\pm\) 0.00 \\ \hline
8 & Benzyl alcohol & 1040 & - & 0.15 \(\pm\) 0.02 \\ \hline
9 & phenylacetaldehyde & 1051 & 0.15 \(\pm\) 0.02 & 0.08 \(\pm\) 0.00 \\ \hline
10 & (_E_)-Oct-2-enal & 1064 & - & 0.08 \(\pm\) 0.03 \\ \hline
11 & (_E_)-Oct-2-en-1-ol & 1073 & 0.22 \(\pm\) 0.07 & 0.24 \(\pm\) 0.05 \\ \hline
12 & (_E,E_)-Octa-3,5-dien-2-one & 1076 & 0.48 \(\pm\) 0.23 & 0.16 \(\pm\) 0.02 \\ \hline
13 & Nonan-2-one & 1096 & 0.77 \(\pm\) 0.17 & 0.03 \(\pm\) 0.00 \\ \hline
14 & Linalool & 1103 & 0.11 \(\pm\) 0.00 & - \\ \hline
15 & Nonanal & 1107 & - & 0.08 \(\pm\) 0.01 \\ \hline
16 & 2,6-Dimethylcyclohexan-1-ol & 1113 & - & 0.40 \(\pm\) 0.13 \\ \hline
17 & 6-[(1Z)-Butenyl]-cyclolemeta- & & & \\  & 1,4-diene] & & & \\  & (Dictypotlerene D') & & & \\ \hline
18 & (_Z_)-Non-2-enal & 1165 & - & 0.07 \(\pm\) 0.02 \\ \hline
19 & [6-Butylcyclolemeta-1,4-diene] & 1174 & 0.13 \(\pm\) 0.03 & - \\  & (Dictypoterene C') & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The VOCs from _D. vermicularis_ isolated by hydrodistillation (HD) and analysed by gas chromatography–mass spectrometry (GC–MS): (V—hydrodistillate of fresh _D. vermicularis_, VI—hydrodistillate of air-dried _D. vermicularis_).