esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Development of the Spectrogram  
Previous studies of the spectrogram can be divided into three situations:  
1. Improving the recognition network. Reference [36] inputs the standard spectrogram into a deep CNN with a rectangular kernel for recognition, reference [37] inputs the standard spectrogram into a CNN with an improved pooling strategy for recognition, and reference [38] inputs the Mel spectrogram into a visual attention CNN for recognition.
2. Processing the spectrogram to improve the recognition effect better. In references [39; 40], log-Mels, deltas, and delta-deltas were used as the inputs of the structure in the hybrid system. In reference [41], the K-means clusters after extracting 88 viterbit symbols from each frame signal were sent to a three-dimensional CNN by the standard spectrogram of the key frames. In reference [42], the multiscale standard spectrogram was processed by a Chirplet filter, and the local direction, time information, intensity, and frequency comparison features were sent to a support vector machine (SVM).
3. Combining acoustic features with the spectrogram. Reference [43] combined the IS09 feature in openSMILE [44] with the Mel spectrogram and sent it to the model of the attention-LSTM- attention model. Reference [45] proposed combining the acoustic features and spectrogram features to improve the recognition rate.  
To summarize, these studies mainly used the standard spectrogram or Mel spectrogram to construct SER models. To represent emotional information in a more comprehensive manner, we propose the IMel spectrogram, which pays more attention to high-frequency information.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 2 Satellite Networks Penetration Testing Method
### Penetration Stage Execution  
The penetration testing process for satellite networks involves a structured approach to identify, analyze, exploit, and mitigate vulnerabilities of the space, ground, and user infrastructure.  
The planning phase involves three key steps: (1) defining the scope (e.g., communication protocols, ground control systems, and user terminals), (2) setting objectives (e.g., evaluating cross-link encryption integrity or command chain robustness), and (3) gathering technical specifications. This phase ensures compliance with regulatory frameworks and minimizes operational disruptions during testing.  
Vulnerability discovery employs various techniques to identify weaknesses in satellite networks. Automated tools analyze telemetry streams for unencrypted data transfers, insecure ranging protocols, or misconfigured access controls in space-based payloads, while ground infrastructure audits detect unpatched vulnerabilities in command-and-control software or exposed application programming interface (API) endpoints. User segment testing focuses on Internet of Things (IoT) device firmware flaws, session hijacking risks in hybrid terrestrial-satellite networks, and insecure credential management in navigation applications.  
Vulnerability analysis in satellite networks demands a holistic approach to assess inherent technical weaknesses, potential exploitation mechanisms, and mission-critical consequences. Adversaries can exploit unencrypted telemetry feeds, insecure APIs, and legacy system vulnerabilities through interception during orbital handovers, malicious code injection via uplinks, or attacks on user terminals. Such breaches may trigger operational failures across interconnected space-terrestrial infrastructures, jeopardizing data integrity and service continuity.  
Vulnerability exploitation is conducted in simulated environments to validate risks without disrupting live operations. Techniques include fuzzing satellite firmware for logic errors, leveraging Metasploit modules for known ground system vulnerabilities, and simulating attacks on user terminals. Exploit validation procedures must be conducted under ethical compliance frameworks and legal obligations governing adversarial testing, while maintaining technical safeguards to prevent unintended service degradation.  
The reporting and remediation phase systematically documents the identified vulnerabilities and prescribes mitigation strategies for satellite networks. By formalizing closed-loop mechanisms for threat documentation and corrective implementation, this phase ensures the continuity of communication services while preserving data integrity under adversarial conditions.  
Structured stages ensure systematic vulnerability management, from scope definition in planning to actionable recommendations in reporting, minimizing operational disruption while maximizing threat detection.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 2 Materials and Methods
### The Bottoms-Up Correlated Error Model
#### 2.2.2 The Full Correlated Error Model  
Considering the terms in Table 1 and applying Equation (5), the Level 1 correlated error model is constructed:  
\[R_{mod}(i,j)=\frac{1}{\mathcal{N}}\left(\begin{array}{c}R_{P\xi}(i,j)+\\ R_{P\xi}(i,j)+\\ R_{\xi^{2}}(i,j)+\\ R_{\xi}(i,j)\end{array}\right), \tag{7}\]  
where \(R_{P\xi}\) is the correlated error component from the calibrated nadir receiver power and is discussed in Appendix B; \(R_{P\xi}\) is the correlated error component from the zenith receiver and is discussed in Appendix C; \(R_{\xi^{R}}\) and \(R_{\xi^{Z}}\) are the correlated error components from the nadir and zenith CYGNSS antenna patterns, respectively, and are discussed in Appendix D; and \(R_{\xi}\) is the correlated error component due to the zenith-specular ratio used for dynamic EIRP estimation and is discussed in Appendix E.
## 6 Data Transmission Using XOR Encoding and Decoding
### Decoding Process  
On receiving the encoded packet bundles, the receiving hub proceeds to comprehend an array of undeviating situations to acquire the initial packet bundles from the information in encoded form. If an encoded vector 'v' is received by the recipient node, the vector is fixed as \((\text{encv}_{1},\text{EPB}^{1}),\ldots,(\text{encv}_{p},\text{EPB}^{p})\) by the mid-point. The receiving midpoint resolves the undeviating conditions with 'p' conditions and 'k' queries, aiming to extricate the prevailing condition.  
\[\text{EPB}^{j}=\sum_{m=1}^{k}\text{encv}_{m}^{j}\times E_{m,j}\quad j=1,2, \ldots,p \tag{28}\]In any event with \(p\) un-deviated self-governing encoded data packet bundles, the primary objective is to extricate the packet bundles. The chief ambiguous \(G_{m}\) retains the initial data bundles, which are about to be forwarded in the system. \(k\) distinct bundles are deduced by enlightening the straightforward condition explained in Equation (28) to fetch \(k\) direct free packet bundles. The XOR encoding scheme is a well-organized coding method and is also a distinctive example of direct system coding followed in this study. The packet bundles that are encoded and used in the system contain a location as the constituent in \(\text{ENV}(2)=\{0,1\}\), and the bitwise XOR encoding in \(\text{ENV}(2)\) is employed in this study. The operation of the packet data processing, encoding, and decoding algorithms is explained below.  
Algorithm: packet data processing (Pkt_m) at a node within the network code layer.  
Pre-requisites: while the PacketData transmission/reception begins, the received packet PacketData will be included in the ReceiveDataQueue() To be monitored: check whether the encoded PacketData are forwarded or eliminated.  
Step 1: collect the PacketData Pkt_m from the ReceiveDataQueue().  
Step 2: if the PacketData Pkt_m \(\in\)EnNodeSet(), proceed.  
Step 3: if native (Pkt_m), then.  
Step 4: EP=XOREncode().  
Step 5: The k node forwards the encoded PacketData EP to the base station (sink node).  
Step 6: Enter the processed PacketData Pkt_m to the TxdPPacketDataSet();.  
Else  
Step 7: Eliminate (Pkt_m).  
Endif  
Else  
Step 8: the node k operates as a forwarding node and forwards the PacketData Pkt_m to the sink node.  
Endif  
Endif  
Step 9: if(ReceiveDataQueue()=blank)  
Step 10: go to the Step 1.  
Else  
Step 11: exit.  
endif  
Algorithm: encoding algorithm XOREncode()  
Pre-requisites: ReceiveDataQueue() and the SensedQueue() has to be maintained in the encoder node. To be monitored: encoded PacketData EP generation.  
Step 1: If the SensedQueue() is not blank, then proceed.  
Step 2: Collect a PacketData Pkt_m from the top of the ReceiveDataQueue().  
Step 3: Collect a PacketData Pkt_j from the top of the SensedQueue().  
Step 4: EP = Pkt_m \(\oplus\) Pkt_j.  
Else  
Step 5: Collect the next data packet Pkt_m+1 from the ReceiveDataQueue().  
Step 6: EP = Pkt_m \(\oplus\) Pkt_m+1.  
Endif  
Step 7: return EP.
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 3 Experiments
### Comparison and Analysis
#### 3.5.1 Comparative Experiments on Moving Occluders
When viewed from the side, BundleSDF's reconstruction result is not a flat plane  
Figure 6: Experiment with moving occluders: qualitative comparison with BundleSDF on the custom dataset. On the left are the visualized results of the estimated poses of the target (yellow box) by the two methods, while on the right, the final reconstructed meshes of the two methods are displayed. but rather, from the waist of the plane, there is an additional plane connected to the main body, forming a Y shape. Combined with the tracking process, the reason for this situation is that during the movement of the occluder, due to the inaccurate segmentation result, the key points used to calculate the relative pose change are sampled onto the moving occluder, causing the model to think that the target object is in motion. This incorrect calculation of the object's movement (which is actually stationary but misinterpreted as moving) leads to a bifurcation in the model. The method described in our research does not suffer from the aforementioned issues. From the tracking results, regardless of how the foreground occluder moves, there is no interference, and the pose of the target object is accurately predicted throughout the process. This is also confirmed by the reconstruction results, which show a complete rectangle without any holes on the front side and a smooth surface, consistent with the original shape of the target object. When viewed from the side, there are no layers or additional structures, completely matching the ideal reconstruction results. This indicates that the occlusion-aware pose-tracking method described in this paper has the ability to overcome interference from moving occluders and can continuously and stably track the pose of the target object even with limited segmentation accuracy.
# Robust Learning from Demonstration Based on GANs and Affine Transformation
## 3 Proposed Method
### Generalization Based on Affine Transformation
Point \(c^{\prime}\) corresponding to point \(c\) can be determined by Formula (21):  
\[ \begin{array}{c}dist(c^{\prime},\mathrm{L}_{new})=dist\big{(}c,L_{origin} \big{)},\\ dist(c^{\prime},\mathrm{PBnew})=dist(c,PB_{origin}\big{)},\\ \begin{vmatrix}1&a_{x}&a_{y}\\ 1&b_{x}&b_{y}\\ 1&c_{x}&c_{y}\end{vmatrix}\times\begin{vmatrix}1&a_{x}^{\prime}&a_{y}^{\prime} \\ 1&b_{x}^{\prime}&b_{y}^{\prime}\\ 1&c_{x}^{\prime}&c_{y}\end{vmatrix}>0.\end{array}  \tag{21}\]After the unique \(c^{\prime}\) has been determined, the affine matrix \(M\) can be solved by the following linear equations:  
\[ \begin{bmatrix}a_{x}&a_{y}&1&0&0&0\\ 0&0&0&a_{x}&a_{y}&1\\ b_{x}&b_{y}&1&0&0&0\\ 0&0&0&b_{x}&b_{y}&1\\ c_{x}&c_{y}&1&0&0&0\\ 0&0&0&c_{x}&c_{y}&1\end{bmatrix} \times \begin{bmatrix}M_{00}\\ M_{01}\\ M_{02}\\ M_{10}\\ M_{11}\\ M_{12}\end{bmatrix} = \begin{bmatrix}a_{x}^{\prime}\\ a_{y}^{\prime}\\ b_{x}^{\prime}\\ b_{y}^{\prime}\\ c_{x}^{\prime}\\ c_{y}^{\prime}\end{bmatrix} , \tag{22}\]  
where \((\ )_{ij}\) represents the element in the \(i\)-th row and \(j\)-th column of the affine matrix \(M\). Finally, the generalized trajectory can be obtained from Formula (18). The process of the algorithm is illustrated in Figure 4. The overall complexity of the algorithm is \(O(n)\), making it conducive to rapid trajectory generalization and parameterized trajectory shape transformation. In instances where specific intermediate points are designated, the affine transformation point pair relationship can be recalculated as needed. In comparison to KMP, which has a complexity of \(O\big{(}n^{3}\big{)}\), our method boasts faster processing speed.
## 4 Experimental Results and Analysis
### Dilation Rate Combination Experiment  
In the encoder, the LMRC convolution block splits the ordinary convolution into four branches with the same number of channels, three of which use different dilation rates to enhance the asymmetric convolution receptive field. The combination of different dilation rates directly affects the extraction of ice-covered transmission line image features. Three groups of different combinations of dilation rates were selected for experiments. The first group is (2, 3, 5), the second group is (2, 5, 8), and the third group is (5, 8, 11). The experimental results are shown in Figure 6.  
As shown in Figure 6, the combination (2, 3, 5) consists of convolutions with small dilation rates, and its receptive field is limited, resulting in a lack of attention to large-scale features. Although the combination (5, 8, 11) has a large dilation rate, it also loses attention to smaller features, resulting in the lowest segmentation accuracy among the three groups. When the dilation rate combination is (2, 5, 8), the mIoU and F1-Score are the highest. This combination takes into account both large and small receptive fields, so it can dynamically perceive different ice-covered transmission line characteristics.
# Measurements of Room Acoustic and Thermo-Hygrometric Parameters--A Case Study +
## 3 Data Analysis and Results
### Regression Analysis Using R
* T\({}_{30}\): The model's goodness of fit (R-squared values) ranged from 0.02 to 0.45 at different frequencies, implying that the model's ability to explain the variance of the dependent variable varied considerably across frequencies and was more explanatory at higher frequencies;
* EDT: With the exception of 0.08 at 2000 Hz, there was a slight variation in the models' goodness of fit;
* G: The model's capacity to account for the dependent variable's variation did not differ significantly among frequencies;
* C\({}_{50}\) and C\({}_{80}\): For low and mid frequencies, there was not much of a difference in the model's goodness of fit, and it decreased noticeably at high frequencies;
* D\({}_{50}\): The goodness of fit of the model ranged from 0.004 to 0.47, with the model's capacity to explain the variance of the dependent variable shifting dramatically across frequencies and being more explanatory at low and medium frequencies;
* IACC: It can be clearly concluded that the ability of the model to explain the variance of the dependent variable was stronger when the frequency was high;
* LE and LF: Variable across frequency bands with no discernible trend. The predict command can be used to make a prediction after the independent variable values have been determined. The lower the RMSE (root mean square error) and MAE (mean absolute error) values, the greater the model's predictive capability. RMSE is defined as:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\widehat{y_{i}})^{2}} \tag{1}\]  
where \(n\) is the number of samples, \(y_{i}\) is the true value of the \(i\) sample, and \(\widehat{y}_{i}\) is the predicted value of the \(i\) sample. MAE is denoted as:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\widehat{y}_{i}\right| \tag{2}\]  
The number of samples is \(n\), the actual value of the \(i\) sample is \(y_{i}\), and the predicted value of the \(i\) sample is \(\widehat{y}_{i}\).
# Research on Driving Style Recognition of Autonomous Vehicles Based on ACO-BP
## 3 Methodology
### Introduction to the ACO-BP Model
#### 3.1.2 Ant Colony Algorithm  
The ant colony algorithm is a heuristic algorithm developed by simulating ants' behavior in searching for food. It finds the optimal solution by searching and optimizing the problem space. In the ant colony algorithm, ants search the problem space by releasing pheromones and guide the choices of other ants based on the change in pheromone concentration. The commonly used formula is as follows:  
Calculate the probability \(p_{ij}^{k}(t)\) that ant \(k\) is at position \(i\) going to position \(j\) at time \(t\)[24] with the following equation:  
\[p_{ij}^{k}(t)=\begin{cases}\frac{\left[\tau_{ij}(t)\right]^{\alpha}\left[\eta _{ij}(t)\right]^{\beta}}{\sum_{\alpha\in allowed_{k}}\left[\tau_{ij}(t)\right] ^{\alpha}\left[\eta_{ij}(t)\right]^{\beta}},j\in allowed_{k}\\ 0,otherwise\end{cases} \tag{9}\]  
In Equation (3), \(\tau_{ij}\) is the pheromone; \(\eta_{ij}(t)=\
icefrac{{1}}{{d_{ij}}}\) is the heuristic function, which indicates the expected degree of transferring ants from position \(i\) to position \(j\); and \(allowed_{k}\) (\(k=1,2,\cdots,m\)) is the set of positions to be visited by the ant \(k\). \(\alpha\) represents the pheromone importance factor, and as its value increases, the significance of pheromone concentration in the transfer process also increases. \(\beta\) represents the significance coefficient of the heuristic function, whereby a higher value signifies a greater influence of the heuristic function on the transfer process. In other words, as \(\beta\) increases, the likelihood of ants transferring to locations with shorter distances also increases.  
Once every ant in the system finishes a cycle, it becomes necessary to dynamically update the pheromone concentration on the path connecting each location in real-time [25], and the expression of the update equation is shown below:  
\[\begin{cases}\tau_{ij}(t+1)=(1-\rho)\times\tau_{ij}(t)+\Delta\tau_{ij},0<\rho <1\\ \Delta\tau_{ij}=\sum_{k=1}^{n}\Delta\tau_{ij}^{k}\end{cases} \tag{10}\]  
In Equation (4), \(\Delta\tau_{ij}^{k}\) is the amount of information left by the ant \(k\) on the path between position \(i\) and position \(j\) in this cycle, and \(\Delta\tau_{ij}\) is the sum of pheromone concentration released by all ants on the path connecting position \(i\) and \(j\).
## 5 Fuels from Isoprenoid and Fatty Acid Pathways  
### Biofuels from Fatty Acids  
Fatty acids (FAs) are chains of hydrocarbons with a carboxylic group at one terminal and a methyl group at another terminal [65]. As the long chains of hydrocarbon are great sources of energy, they have become promising compounds for alternate fuel research [84]. Generally, two major steps take place in the biosynthesis of FAs: the first one is the ATP-dependent acetyl-CoA Carboxylase-driven conversion of acetyl-CoA to malonyl-CoA, and the second one is the integration of malonyl-CoA into the growing chain of FAs [85]. In bacteria, the same reaction is carried out through fatty acid synthase type II (FAS II) involving many enzymes like fabI, fabG, fabH, fabA/Z, and fabF/B (Figure 3). Whereas in yeast, it is carried out through fatty acid synthase type I (FAS I), a protein (2.6 MDa) which has two subunits, \(\alpha\)-subunit and \(\beta\)-subunit, encoded by the genes FAS2 and FAS1, respectively (Figure 4).  
In general, FAs produced in any organism are required in low quantities and hence their enhanced production needs metabolic engineering. Furthermore, many commercially viable spinoffs (fatty alcohols-FOH, fatty acid ethyl esters-FAEE, etc.) can be produced in microbes from free fatty acids (FFAs) by introducing the required genes. By esterifying the fatty acyl product/FFA with ethanol in the presence of wax ester synthase, FAEE can be synthesized [86], and by reducing the fatty acyl product/FFA in the presence of various  
Figure 3: Illustrative representation of \(\beta\)-oxidation pathways as well as fatty acid biosynthesis in _E. coli._ The native pathway is represented with blue arrows, whereas the heterologous pathway is represented with black arrows (adapted from [65]).  
reductases, it can produce fatty aldehyde (Figure 3). However, by reducing FFA to fatty aldehydes and the thereafter decarbonylation of fatty aldehydes through aldehyde decarboxylase, enzyme alkenes as well as alkanes are synthesized. By modifying the \(\beta\)-oxidation pathway, even methyl ketones can be synthesized (Figure 5). Alkanes/alkenes as well as methyl ketones have applications in biofuel, perfume, and artificial flavour industries, whereas FOH, FFA, and FAEE are majorly involved in the detergent and biofuel industries.
# _Thuja occidentalis_ L. (Cupressaceae): Ethnobotany, Phytochemistry and Biological Activity
## 4 Biological Activities
### Antibacterial and Antifungal Activity  
_T. occidentalis_ has been shown to have antibacterial properties against a significant number of species, such as _Salmonella_ sp., _Enterobacter cloacae_, _Staphylococcus aureus_, _Escherichia coli_, _Pseudomonas aeruginosa_, _Klebsiella pneumonia_, _Shigella flexenari_, _Candida albicans_, _Proteus vulgaris_, _Enterococcus faecalis_, and _Staphylococcus_[79; 80]. They observed that the two components, \(\alpha\)-thujone and \(\beta\)-thujone, showed protective effect against Gram-negative bacteria such as _Pseudomonas aeruginosa_ and _Klebsiella pneumonia_ and a mild protective effect against _Staphylococcus aureus_, _Escherichia coli_, and _Candida albicans_[80]. _Thuja occidentalis_ also showed significant antibacterial activity against bacteria and fungi [79]. The antimicrobial profile of essential oil of _Thuja occidentalis_ was also evidenced by [PERSON] et al. (2009) [26].  
The antifungal properties of _T. occidentalis_ have been highlighted against _Saccharomyces cerevisiae_, _Aspergillus parasitious_, _Aspergillus niger_, _Aspergillus flavus_, _Trichophyton rubrum_, _Macrophonina_, and _Fusarium solani_[81; 82]. In the recent study made by [PERSON] et al. (2018), essential oil extracted from leaves and cones of _Thuja occidentalis_ showed antimicrobial activity against Gram-negative bacteria (_Escherichia coli_, _Salmonella typhimurium_, _Aeromonas hydrophilia_, and _Pseudomonas aeruginosa_), Gram-positive bacteria (_Staphylococcus aureus_, _Listeria monocytogenes_, and _Bacillus cereus_), fungus (_Aspergillus flavus_ and _Aspergillus niger_), and yeast (_Candida albicans_) [33].