esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Novel Laser Crosslink Systems Using Nanosatellites in Formation Flying: The VISION
## 5 Formation Flying Nanosatellite Bus  
### Bus Architecture  
Figure 14 shows the configurations and body reference coordinates of the nanosatellites. Although both satellites have the same architecture, their star tracker aperture and GNSS antenna are located opposite to each other to obtain visibility over the mission operations. The integrated attitude determination and control subsystem (ADCS) is a single box containing attitude actuators and sensors with its own processor for algorithm execution. Three sun sensors were attached to acquire the sun vector from each state. The designed bus structure provides space for safely mounting the laser crosslink payload, which includes a deployable space telescope. The two deployable solar panels generate electrical power to maintain the battery state of charge (SOC) over 50%, even at the end of the lifetime (EOL). Furthermore, the panels act as baffles by preventing direct sunlight on the payload optics during mission operations.  
Figure 14: Configurations and body reference frame coordinates of nanosatellites: (**a**) stowed and deployed exterior configurations of the Altair and Vega; (**b**) interior configurations of the Vega. The body  
Figure 13: Preliminary Matlab/Simulink analysis results for the PSD in frequency domain and errors in time-series according to the FSM tip-tilt control to reject jitters in the fine PAT stage: (**a**) x-axis (roll axis of the bus); (**b**) y-axis (pitch axis of the bus); (**c**) residual jitters in time-series with FSM controls.  
reference coordinates of both satellites are assigned, considering the optical axis (+Y) and antenna boresight (+Z). The GNSS antenna and star tracker aperture heads are opposite each other.  
The electrical interfaces for the power supply and data communication are shown in Figure 15. Two deployable solar panels and one body-mounted panel are connected with buck-boost converters on the power conditioning and distribution unit (PCDU) for battery charging. The PCDU manages the power supply for each component with latch-up protection to avoid damage from current or voltage during the mission lifetime. Before the on-orbit operation, dual kill switch mechanisms deactivate the PCDU and battery to prevent battery discharge. By applying two-wire bus interfaces, such as CAN and I\({}^{2}\)C, the wiring is significantly reduced compared to serial interfaces. To mitigate the susceptibility to bus faults of I\({}^{2}\)C interfaces, they are applied only to internal or back-up communication interfaces, including a redundancy system [15]. The X-band radio also provides a high-speed communication interface for future applications.  
For the LCT, an unregulated battery bus voltage is provided as the main power and a 3.3 V channel is used to control the device by the primary OBC. Through the SPI interface, the LCT provides an AOA for PAT implementation. Moreover, a GNSS receiver is connected so that the interfaces can synchronize with each other using the GPS clock.
## 3 Results and Discussion
### Dyne Pen and Ink Adhesion Tests  
Dyne pen tests on the untreated BOPP indicated a surface energy in the region of 29 mNm\({}^{-1}\), which is close to that calculated from the contact angle measurements (see Figure 7). Similarly, Dyne pen tests of the treated samples indicated values in the range 42 to 46 mNm\({}^{-1}\).  
Examples of the results for the ink adhesion tests are shown in Figures 16 and 17. As can be seen, the samples had poor ink adhesion with the Sericol UV ink, with approximately 90% ink pull off (Figure 16), whereas the samples printed with the Tornado and Sunprop solvent-based inks had excellent ink adhesion; Figure 17 gives a typical example for the Sunprop ink. Outside the treated region the ink is completely removed, but inside the treated region the ink is well-adhered and no removal can be observed.  
highest current (1.4 A). Further experiments using these optimum conditions were carried out in nitrogen and nitrogen containing admixtures in the 125 to 500 ppm concentration range. Treatment of BOPP film under these conditions was found to increase the polar part of the surface energy of the film by 12-14 mNm\({}^{-1}\). However, no differences were observed between the effect of a nitrogen discharge and discharges in nitrogen with admixtures of CO\({}_{2}\), N\({}_{2}\)O or C\({}_{2}\)H\({}_{2}\) over the range of concentrations tested. Furthermore, over 50% of this increase was lost through hydrophobic recovery over a 30-day period. XPS analysis indicated the presence of oxygen incorporated in the film surface after treatment, which appears to have reacted preferentially with the film surface, rather than the other gas additives present. Finally, Dyne pen tests corresponded to the values of surface energy calculated for the treated films and ink adhesion pull-off tests showed poor adhesion with UV-curable ink, but excellent adhesion with two types of solvent-based inks.  
The XPS measurements for this project were performed by [PERSON] from the University of Surrey. The authors acknowledge the support of Innovia Films Ltd. of Wigton, Cumbria for this project. Thanks, in particular, to [PERSON] of Innovia for conducting the ink adhesion trials.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and analysed the data; [PERSON] and [PERSON] provided technical support, materials and access to facilities at Innovia Films Ltd.; [PERSON] produced and tested the samples for ink adhesion trials; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Visual-Saliency-Based Abnormality Detection for MRI Brain Images--Alzheimer's Disease Analysis
## 2 Proposed Methodology
### Elliptical Local Binary Pattern  
An elliptical local binary pattern descriptor (\(E_{LBPD}\)) [29] was used to analyze the textural features of the MRI images. Ellipse-like topologies help obtain feature information from different orientations. To distinguish potential objects, a circular neighborhood was added to the texture descriptor. In elliptical local binary patterns, each center pixel (\(X_{cp}\),\(Y_{cp}\)) and neighboring pixels (N) are located on an ellipse with radius distances \(r_{1}\) and \(r_{2}\). It is given by,  
\[E_{LBPD}\textsuperscript{N}\textsuperscript{r}_{1}\textsuperscript{r}_{2}\big{(}X_{ cp},Y_{cp}\big{)}=\sum_{i=1}^{N}s\big{(}p_{i}\textsuperscript{N} \textsuperscript{r}_{1}\textsuperscript{r}_{2}-p_{cp}\big{)}\textsuperscript{2} \textsuperscript{i}^{-1} \tag{2}\]  
where the \(i\)th neighboring pixel is (\(X_{cp}\),\(Y_{cp}\)) is calculated as follows:  
\[A_{step}=2\cdot\frac{\pi}{N} \tag{3}\]  
\[x_{i}=x_{cp}+r_{1}\textsuperscript{-}cos\big{(}(i-1)\cdot A_{step}\big{)} \tag{4}\]  
\[y_{i}=y_{cp}-r_{2}\cdot sin\big{(}(i-1)\cdot A_{step}\big{)} \tag{5}\]  
\(E_{LBPD}\) descriptors were used to extract more specific features from MRI images. They add additional directional features at different orientations that cover the micro patterns [29] where \(p_{cp}\) is the gray level of the input image. This ensured that no pixels were omitted in accordance with the brain tissues. Gabor filters were utilized to highlight orientation features. These are linear filters. The orientation and frequency representations of Gabor filters are similar to those of humans. The real and imaginary components of the Gabor filter are given as:  
\[G(x,y:\delta,\theta,\vartheta,\tau,\gamma)=e^{-(\frac{2^{2}+2^{2}\tau^{2}}{2 \tau^{2}})}e^{i(\frac{2\pi\lambda}{3}+\theta)} \tag{6}\]  
where \(\delta\) denotes the wavelength of the sinusoidal factor, \(\theta\) represents the orientation of Gabor functions, \(\vartheta\) represents phase offset, \(\tau\) is the standard deviation of Gaussian envelope, and \(\gamma\) represents the spatial aspect ratio.  
The real and imaginary parts of the Gabor filter traveled in the orthogonal directions. Gabor filters with \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\) orientations were considered to represent directional features. These features were obtained by convolving Gabor filters with different orientation angles with brain volume. Figure 4 shows the Gabor filter for different orientations in the MRI images. The edge, texture, and orientation features were obtained from multiple-scale MRI images and kept in separate feature maps. Finally, all feature maps were combined to obtain the final saliency map.  
Bottom-up saliency maps are obtained by taking the geometric mean of the feature maps.  
\[S_{B}=\frac{1}{2}(Ma_{ed}+Ma_{LBPD}+M_{GOYB}) \tag{7}\]  
where \(Ma_{ed}\)--edge feature map, \(Ma_{LBPD}\)--texture feature map, \(M_{GOYB}\) is the orientation feature map.
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 2 Background  
A consumer drone communication system comprises four RF links: Navigation, Radio Control (RC), Telemetry, and Video. The Navigation link provides position and velocity data using a GNSS receiver. The RC link transmits the pilot's commands in small, high-speed data packets for low-latency control. The Telemetry link relays the drone's status (e.g., battery, speed) to the controller, while the Video link streams the camera feed to either the controller or a connected smartphone [5].
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 2 Background and Related Work
### Previous Studies on Alert Fatigue
#### 2.2.1 Filtering- and Correlation-Based Approaches  
Incident handling performance is strongly affected by the alert fatigue issue caused by low-quality IDS alerts. A common solution to false and redundant alerts in IDS outputs is to make them less sensitive. However, this approach conflicts with the need to not overlook incidents and to possess detailed information in post-detection analyses. It also results in experts wasting time tuning IDSs during setup and daily operations. A more effective approach is to allow the IDSs to be overly sensitive and address the filtering and correlation problems in a preprocessing step. Here, the filtering problem refers to the overwhelming number of false alerts in the IDS output that are caused by imperfections in the detection mechanism. The correlation problem refers to the extra number of reported alerts, which are caused by the presence of replicated or correlated alerts in the IDS output due to imperfections in the reporting mechanism. Numerous related works have investigated this approach and have shown it to achieve good results. The proposed solutions have been adopted by commercial products [5; 34; 35].  
One of the approaches is the correlation-based method introduced by [PERSON] et al. [36], which can reduce the number of alerts by up to 99.2% for honeypot datasets. However, its effectiveness drops to 53.0% on the MIT/LL 2000 dataset, and its dependence on correlation components makes it impractical in scenarios where access rights to the hostmay not be available. To tackle the issue of alert fatigue, [PERSON] et al. [37] proposed the NoDoze method, which incorporates an automated provenance triage process. This method adjusts the suspiciousness level of each event in the provenance graph based on its neighboring events, performs behavioral execution partitioning, and generates a dependency graph of accurate alerts to avoid dependency explosion resulting from prior data provenance. Another effective method is the Isolation Forest (IF) technique, as employed by [PERSON] et al. [38], which detects deviations from normal employee behavior by considering the temporal aspect. This technique involves collecting data for a specific period and conducting anomaly detection on the entire dataset.
## 3 Path Feature Recognition  
The recognition process of path features includes threshold segmentation of path pixels, feature extraction of the path pixel set, and path model classification, as shown in Figure 5. The workflow of path feature recognition can be divided into two stages: offline training and online recognition. At the offline stage, the sample images of guide paths with various interferences of illumination, coverings, and damages are captured by the vision guidance system. The path pixels are extracted from the background with under- or over-segmentation error points in the YCbCr colour space by threshold segmentation, generating a piece of binary path image. A compound scanning technique of region projection and contour scanning is used to extract the path features from the binary image with noises. Both region projections in the directions of row and column, and four boundaries of the path contours, constitute a high-dimension feature vector for a path image. The feature vectors from a set of sample images comprise a feature matrix for the offline training of a classifier. In order to simplify the training process, principal component analysis was used to reduce the dimensionality of the feature vectors in the high-dimension kernel space (KPCA). Then, the dimension-reduced feature matrix was provided for the training of the classifier based on back-propagation neural network (BPNN). This classifier will have the generalization ability for new unknown path images, thereby being able to discriminate between straight paths and curvilinear ones at the online stage.
# A Low-Complexity Ordered Statistics Decoding Algorithm for Short Polar Codes
## 2 Preliminary  
In this section, we briefly describe polar coding, the concept of codeword likelihood over binary input additive Gaussian white noise (BIAWGN) channels, and the concepts of the OSD algorithm.
# Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
## 4 Discussion
By designing the aggregation pooling layer, the point cloud's feature expression was enriched, enabling the model to better capture the spatial structure of road scene point clouds. In the intersection point cloud dataset used in this study, low vegetation and sidewalks had lower segmentation accuracy across all three networks due to their indistinct geometric structures and scattered distribution. Moreover, the improved network's performance in recognizing pole-like objects did not significantly increase (with a decrease in the recall rate for this category in Test Set 1), likely because the input sample size for this category was relatively small, and the improved model's ability to learn local feature differences and similarities was limited. Different model architectures were designed for ablation experiments to validate the enhancement effects of the proposed improved feature aggregation module and loss function on the model's segmentation performance, as  
The results indicate that after introducing the spatial information encoding module based on feature similarities, the model's overall accuracy and average F1 score on the two test sets improved by 1.77%, 0.89%, 0.75%, and 0.64%, respectively. This module enhanced the network's ability to learn neighborhood features from the input point cloud, improving the accuracy and completeness of segmentation for most categories. The use of max and average pooling functions to enhance significant local and global features of the input point cloud enabled the model to extract more spatial-structural information, thereby improving segmentation effects across all categories. Additionally, setting the weighted cross-entropy loss function improved the model's prediction F1 scores for pole-like objects, high vegetation, and low vegetation, reflecting an improvement of the imbalanced input point cloud sample sizes.
## 6 Pricing Problems
### Continuous Power Control  
Contrary to the discrete power control case, in the continuous power control the transmitting power that is applied by the nodes can change continuously. To express this, PP is modified in the following way:  
\[\max\ \ P=-f+\sum_{\varepsilon\in\mathcal{S}}\sum_{w\in\mathcal{V}} \sum_{\varepsilon\in\mathcal{S}^{+}(w)}\lambda^{*}_{s(w,\varepsilon)}U_{swv} \tag{9a}\] \[X_{w}\geq Y_{uu},\quad w\in\mathcal{V},u\in\delta^{+}(w)\] (9b) \[X_{w}\leq\sum_{u\in\delta^{+}(w)}Y_{uu},\quad w\in\mathcal{V}\] (9c) \[X_{w}+\sum_{u\in\delta^{-}(w)}Y_{uw}\leq 1,\quad w\in\mathcal{V}\] (9d) \[\sum_{m\in\mathcal{M}}z^{m}_{w}=X_{w},\,w\in\mathcal{V}\] (9e) \[p_{w}G(w,u)+M(w,u,m)(1-Y_{wu})\geq\] \[\gamma(m)z^{m}_{w}\eta+\gamma(m)\sum_{v\in\mathcal{V}\setminus\{ w,u\}}G(v,u)P^{m}_{uv},\quad(w,u)\in\mathcal{A},m\in\mathcal{M}\] (9f) \[P_{min}X_{w}\leq p_{w}\leq P_{max}X_{w},\quad w\in\mathcal{V}\] (9g) \[P^{m}_{uv}\leq P_{max}z^{m}_{w},\quad m\in\mathcal{M},w\in \mathcal{V},v\in V\setminus\{w\}\] (9h) \[p^{m}_{uv}\leq p_{v},\quad m\in\mathcal{M},w\in\mathcal{V},v\in V \setminus\{w\}\] (9i) \[P^{m}_{uv}\geq p_{v}-(1-z^{m}_{w})P_{max},\quad m\in\mathcal{M}, w\in\mathcal{V},v\in V\setminus\{w\}\] (9j) \[U_{swv}\leq g_{sw},U_{swv}\leq Y_{vw},\quad s\in\mathcal{S},w\in \mathcal{V},v\in\delta^{+}(v)\] (9k) \[\sum_{\varepsilon\in\mathcal{S}}g_{sw}\leq\sum_{m\in\mathcal{M}} B(m)F^{m}_{w},\,w\in\mathcal{V};\,\,g_{sw}\leq 1,\,s\in\mathcal{S},w\in \mathcal{V}\] (9l) \[F^{m}_{w}\leq|\mathcal{S}|z^{m}_{w},\,F^{m}_{w}\leq f,F^{m}_{w} \geq f-|\mathcal{S}|(1-z^{m}_{w}),\quad m\in\mathcal{M},w\in\mathcal{V}\] (9m) \[Y_{wv}\in\mathbb{B},\,w\in\mathcal{V},v\in\delta^{+}(w);\,X_{w} \in\mathbb{B},\,w\in\mathcal{V}\] (9m) \[z^{m}_{w}\in\mathbb{B},\,m\in\mathcal{M},w\in\mathcal{V};\,\,p_{ w}\in\mathbb{R}_{+},\,w\in\mathcal{V};\,\,P^{m}_{vw}\in\mathbb{R}_{+},\,m\in \mathcal{M},w\in\mathcal{V},v\in V\setminus\{w\}\] (9o) \[f\in\mathbb{R};\,g_{sw}\in\mathbb{R}_{+},\,s\in\mathcal{S},w\in \mathcal{V};\,F^{m}_{w}\in\mathbb{R},\,m\in\mathcal{M},w\in\mathcal{V}\] (9p) \[U_{swv}\in\mathbb{R}_{+},\,s\in\mathcal{S},w\in\mathcal{V},v\in \delta^{+}(w). \tag{9q}\]  
Instead of binary variables \(h^{p}_{w}\) used in the previous formulation, the continuous variables \(p_{w},w\in\mathcal{V}\) are now introduced to express the actual power that is applied at node \(w\). These variables are used in the SINR constraint modified analogously to the discrete case. It is assumed that the value of \(p_{w}\) cannot be smaller than the minimum power \(P_{min}\) (a parameter), and it cannot exceed the maximum power \(P_{max}\)--this is assured by the constraints (9g). Variables \(p^{m}_{wv}\), together with constraints (9h)-(9j), are the auxiliary constraints used to get rid of bi-linearities that otherwise appear in the SINR constraint because of the multiplication \(z^{m}_{w}p_{v}\).
# Signal to Noise Ratio of a Coded Slit Hyperspectral Sensor
## 4 Summary and Conclusions  
The contribution of the this paper is firstly the derivation of a detailed calculations for the noise performance of a coded aperture (CA) multiplexing imaging spectrometers, based on the characteristics and capabilities of real imaging components, and including factors that may affect the performance of these systems. Secondly, direct comparisons of the performance of the coded-aperture multiplexed systems with similar estimates for conventional dispersive sensors under 3 configurations of imaging system hardware have been made. It is shown that the CA multiplexed systems provide an advantage in SNR when it is operated in low light conditions, as well as when there are constraints that require  
Figure 10: Shows the SNR behavior of the conventional spectrograph sensing vs. the CA multiplexed sensing for the VNIR sensing system, at an illumination of 60\({}^{\circ}\) zenith.  
high frame rates for imaging the scene (e.g., for spectral video applications). In terms of the remote sensing context, this means that CA multiplexed system may be more suitable to be deployed for the surveillance of a small area within a specified limited period of time.  
However, when the input light level is high, the performance of the CA multiplexed systems in general is poorer than that of the conventional spectrograph forms. Even in circumstances where they are better, the SNR that they could achieve is poor in comparison with that of the conventional sensors when they are deployed under optimal conditions. This drawback may limit the usefulness of CA multiplexed systems for sub-pixel target detection applications, although they may still be useful for the detection of full-pixel targets. The present results are found consistent with expectations that the \"multiplex advantage\" may be a real advantage only if the dominant noise source is independent of the input signal level. This means that the CA multiplexed systems may be potentially useful for the cases of low light level or when fast frame rates are required, i.e., when the input signal is low and the shot noise is correspondingly low.  
Conceptualization, J.P. and P.W.T.Y.; methodology, J.P.; formal analysis, J.P. and P.W.T.Y.; resources, J.P.; writing--original draft preparation, J.P.; writing--review and editing, J.P. and P.W.T.Y.; visualization, J.P. and P.W.T.Y.; supervision, P.W.T.Y. and D.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the DSTL UK.  
Authors declare no conflict of interest.
# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 3 Experimental Results
### Order Selection of Derivatives of IHOG Features Used in Textual Regions  
As mentioned above, the IHOG features of the multi-order derivatives are adopted as structural features of textual regions of an SCI. Here, we investigate which combination method of HOG features of the different-order derivative is optimal for structural features of textual regions. Table 5 listed the experimental results of the order selection of derivatives used in the IHOG features on SIQAD. In Table 5, \"Com-0\" denotes that the HOG features of only the zero-order derivative are used, \"Com-1\" denotes that the HOG features of zero- and first-order derivatives are adopted, \"Com-2\" denotes that the HOG features of zero-, first- and second-order derivatives are used, \"Com-3\" denotes that the HOG features of zero-, first-, second- and third-order derivatives are used, \"Com-4\" denotes that the HOG features of zero-, first-, second-, third- and fourth-order derivatives are used and \"Com-5\" denotes that the HOG features of zero-, first-, second-, third-, fourth- and fifth-order derivatives are used. Figure 10 shows the curve of PLCC values for different combinations of HOG features. From Table 5 and Figure 10, we can observe that the PLCC value gradually increases from \"Com-0\" to \"Com-2\" while the PLCC value gradually decreases from \"Com-2\" to \"Com-5\". Among the five combination methods, \"Com-2\" achieves the maximal PLCC value. According to the experimental results, we select \"Com-2\" as the final combination method. Namely, in this paper, the HOG features of zero-, first- and second-order derivatives are adopted as structural features of textual regions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metrics** & **BRISQUE** & **GWH-GLBP** & **IL-NIQE** & **BQMS** & **SIQE** & **NRLT** & **BIQUP-SCI** & **BSRSF** \\ \hline BRISQUE & 0 & 1 & 1 & \(-\)1 & \(-\)1 & \(-\)1 & \(-\)1 & \(-\)1 \\ GWH-GLBP & \(-\)1 & 0 & 1 & \(-\)1 & \(-\)1 & \(-\)1 & \(-\)1 & \(-\)1 \\ IL-NIQE & \(-\)1 & \(-\)1 & 0 & \(-\)1 & \(-\)1 & \(-\)1 & \(-\)1 & \(-\)1 \\ BQMS & 1 & 1 & 1 & 0 & 0 & \(-\)1 & \(-\)1 & \(-\)1 \\ SIQE & 1 & 1 & 1 & 0 & 0 & \(-\)1 & 1 & \(-\)1 \\ NRLT & 1 & 1 & 1 & 1 & 1 & 0 & 0 & \(-\)1 \\ BLIQUP-SCI & 1 & 1 & 1 & 1 & \(-\)1 & 0 & 0 & \(-\)1 \\ BSRSF & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results of F-tests on SIQAD.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criteria** & **Com-0** & **Com-1** & **Com-2** & **Com-3** & **Com-4** & **Com-5** \\ \hline PLCC & 0.8654 & 0.8801 & 0.8905 & 0.8865 & 0.8805 & 0.8795 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results of the order selection of derivatives for IHOG features on SIQAD.