esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Shape-Aware Network for Elongated Target Detection
## 7 Results and Discussion
### Comprehensive Discussion  
The detection results for scenes with feature bias are shown in Figure 7. These results demonstrate that our detection method performs effectively in addressing issues caused by occlusion, camouflage, and low-light conditions, which often lead to feature discrepancies. This is due to our network's ability to extract and focus on key features of the target, ensuring robust detection even under challenging conditions. The network's adaptive feature alignment capabilities, along with the integration of modules like Boundary-Guided Spatial Feature Perception and Shape-Sensing, allow for accurate target identification and localization, even when the object is partially occluded or surrounded by complex backgrounds. This highlights the robustness and effectiveness of our method in real-world remote sensing applications, where environmental factors can significantly impact detection performance.  
Our experimental results on the HRSC2016 and UCAS-AOD datasets highlight the effectiveness of SSAD-Net in detecting elongated and multi-directionally rotated targets. In object detection, the focus has often been on the independent aspects of predefined candidate anchor box settings and feature modeling within the ground truth boxes, while neglecting the influence of the intersection form and content between the predefined boxes and ground truth boxes on the detection performance. In SSAD-Net, we address this issue by considering three perspectives: feature extraction, shape sensing, and training feedback. By tackling these key challenges, we demonstrate that SSAD-Net outperforms existing methods and achieves superior performance across various scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Backbone** & **mAP (\%)** \\ \hline R2 CNN [38] & ResNet101 & 73.07 \\ RRPN [18] & ResNet101 & 79.08 \\ RRD [39] & VGG16 & 84.30 \\ RoI-Transformer [35] & ResNet101 & 86.20 \\ DAL [10] & ResNet101 & 88.95 \\ R3 Det [16] & ResNet101 & 89.26 \\ OPLD [40] & ResNet50 & 88.44 \\ Oriented R-CNN [41] & ResNet101 & 90.50 \\ RSDet [42] & ResNet50 & 86.50 \\ SLA [33] & ResNet101 & 89.51 \\ SSAD-Net & ResNet50 & 91.20 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of different methods on the HRSC2016 dataset.  
Figure 6: Partial detection results on the HRSC2016 dataset.  
Although SSAD-Net performs well in detecting complex targets, the computational complexity introduced by the dynamic modules may limit its real-time performance, especially when applied to larger datasets. Furthermore, further investigation is needed to improve the model's performance in cases of highly occluded or cluttered targets, and to reduce its dependence on high-quality, well-annotated data.
# A Statistical Approach to Detect Jamming Attacks in Wireless Sensor Networks
## 4 Detection Metrics for Jamming Attack
Adopting ECA as a metric to detect jamming attacks comes with its own issues, such as the difficulty in sampling the threshold energy consumption under different traffic load and the difficulty to detect jamming attack when the jammer uses a simple power attack. Signal-to-Noise Ratio (SNR)_: SNR can be obtained by finding the ratio of the received signal power to the received noise power at the node. It is a very effective metric to determine jamming attacks in the physical layer because for jamming to occur here, there must be a drop in the SNR value. Packet inter-arrival time (IAT):Packet IAT is the time that elapse between the receipt of a packet and subsequent packets in WSN [31]. The distribution of the packet IAT can be used to determine the probability of the occurrence of DoS jamming attacks during the transmission of signals. In this work, we have used packet IAT as the sole metric to distinguish between normal traffic pattern and jamming attack. We believe that the IAT between packets of normal flow exhibit a strong regularity, thus can be used to track the presence of any form of jamming attacks which causes a mean shift due to the intensity of the jamming attack. Furthermore, the choice of only IAT is well suited to resource constrained sensor nodes, therefore using packet IAT presents a metric that can be easily measured by the node without introducing complexity and additional overhead to the system.
## 4 Instrument Sensitivity, Accelerometer and Drag Compensation Assessment
### The Laser Tracking Instrument Preliminary Design
The error contributions are split into laser interferometer and spacecraft coupling noise sources as shown in the error tree in Figure 13.  
Figure 11: Functional scheme of the optical transponder interferometer concept, where LH = laser head, CAV = optical reference cavity for laser frequency stabilization, ICU = instrument control unit (including phasemeter), M1 = mirror 1, M2 = mirror 2, BS = beam splitter, P = photoreceiver, RR = retroreflector, and CoM = center of mass. Each error source has been allocated and estimated as a current best estimate (CBE). The budgets have been computed also for worst-case conditions (WC), when, e.g., the maximum allowed separation between the satellite pair is reached and the measurement is still possible with a minimum amount of received photons. More details on the budgeting for the two concepts can be found in [50]. Similar to the accelerometer, the errors were combined linearly if correlated and with RSS if uncorrelated, but the LTI overall performance is given by the combination of the error budgets originating from the individual satellites since the LTI instrument is split--in the two different designs--between the satellites of a pair. Figure 12: Functional scheme of the retroreflector interferometer concept (same nomenclature as in Figure 11). Figure 13: Intersatellite distance variation measurement error tree budget. The ultimate limiting factor of the performance of both interferometer concepts is the stability of the laser frequency \(\
u\), which is the first error source in the budget of Figure 13: a frequency variation \(\delta\
u\) induces a distance variation measurement error \(\delta\)d = d\(\cdot\)(\(\delta\
u\)/\(\
u\)), where d is the distance between the satellites (baseline 100 km). Consequently, to achieve the required accuracy of the intersatellite distance measurement error, the laser frequency stability spectral density of the master oscillator shall be better than 40 Hz/\(\sqrt{\text{Hz}}\) (threshold) and 20 Hz/\(\sqrt{\text{Hz}}\) (goal) values [51]. The required stability can be achieved by locking the frequency of the master oscillator to the resonance of an optical cavity made from low thermal expansion material thermally insulated. Such a frequency stabilization system is now flying on GRACE-FO, and an ad hoc design for NGGM is in progress in the ESA technology program.
## 1 Introduction
It enables instantaneous stable measurement of the infrared polarization map, allowing for the simultaneous acquisition of polarization information, interference information, and spatial information within the infrared band. Moreover, it provides these information with high spectral resolution, high throughput, and exceptional stability. According to the structural characteristics of the polarization spectrum data cube, a decoupled reconstruction method of the infrared polarization spectrum is proposed in this paper, where, through image segmentation, optical path difference matching, and image registration, the problem of interference order migration caused by device machining error and system assembly error is solved, and the high-precision reconstruction of the polarization spectrum information of the system is realized. At the same time, aiming to address the inherent disadvantages of infrared images such as the huge noise and low resolution, a fusion method of infrared polarization images based on the Haar wavelet transform is proposed, through which the Stokes vector image, polarization degree image, and polarization angle image are fused by multi-scale analysis and fusion, thus solving the problem of unclear images caused by a low signal-to-noise ratio and poor contrast of infrared polarization images, and eventually realizing high-quality imaging of the system.
## 4 Experiments and Analysis
### Datasets and Evaluation Metrics
These metrics capture the four potential scenarios that arise between the classification outcome and the actual ground truth object: (1) TPs: Detection boxes with an IoU greater than the threshold accurately represent detected objects. (2) FPs: Detection boxes lacking corresponding ground truth annotations are deemed false positives. (3) FNs: Ground truth annotations without corresponding detection boxes are classified as false negatives. (4) TNs: Instances where neither ground truth annotations nor detection boxes are present. Drawing from these scenarios, the evaluation of precision and recall becomes feasible. Precision, also referred to as the positive predictive value, assesses the proportion of correctly identified positive instances out of all instances predicted as positive. On the other hand, recall, also known as sensitivity, measures the proportion of correctly identified positive instances out of all actual positive instances. The formulas for precision and recall computation are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{16}\]  
\[Recall=\frac{TP}{TP+FN} \tag{17}\]  
Accuracy signifies the proportion of correct outcomes among all the targets predicted by the network, whereas recall indicates the proportion of correctly predicted detection boxes output by the network in comparison to all the true target annotations. In the COCO dataset, each annotated detection box is accompanied by a class confidence score. By varying the confidence score thresholds, different detection results can be generated, yielding varying accuracy and recall values, which are graphically represented in the precision-recall (P-R) curve. The preeminent metric, mean average precision (mAP), is calculated through a series of IoU thresholds spanning from 0.5 to 0.95, with increments of 0.05. The determination of mAP entails computing the precision of detection at each IoU threshold, culminating in the final mAP value. Within the ambit of COCO's evaluation metrics, there exists an element that computes precision based on target size. The COCO dataset classifies targets into three size categories: large, medium, and small, contingent on the area of their authentic annotation boxes. Targets featuring an annotation pixel area exceeding 96 \(\times\) 96 are designated as large, while those below 32 \(\times\) 32 are denoted as small, and those encompassed between 32 \(\times\) 32 and 96 \(\times\) 96 are categorized as medium. In the ultimate assessment, the COCO dataset computes precision separately for these three target sizes, denoted as \(AP_{small}\), \(AP_{medium}\), and \(AP_{large}\).
## 4 Experiments and Numerical Results
### Comparison with Existing Methods  
Table 4 presents a comparison between RiQ-KGC and the baseline models for the FB15K-237 and WN18 RR datasets. The results show that RiQ-KGC demonstrates notable advantages in terms of the MRR, H@1, and H@3 indicators for both datasets. Notably, RiQ-KGC exhibited a remarkable performance improvement of 1.5% in the MRR indicator for the WN18 RR dataset. This highlights RiQ-KGC's superior precision in identifying target entities. However, it should be noted that RiQ-KGC under-performed compared to StAR and KGLM in the H@10 indicator. While the BERT-based models excel in extracting extensive entity feature information from textual descriptions, thereby enhancing rough recall rates, the presence of noise stemming from these descriptions poses a challenge to achieving precise recall. The relation instantiation and quaternion transform mechanism employed in RiQ-KGC contribute to a sparser distribution between entities, facilitating the precise identification of relevant entities. This characteristic grants RiQ-KGC a notable advantage over non-BERT-based models in H@10.  
Table 5 presents a performance comparison between RiQ-KGC and the baseline model on CoDEx-M and CoDEx-L. As shown in Table 5, RiQ-KGC outperforms the baseline model in all evaluation metrics, particularly exhibiting a remarkable performance improvement of 0.8% and 1.8% in the H@10 indicator, for the CoDEx-M and CoDEx-L datasets, respectively, where it demonstrates the greatest advantage. This suggests that RiQ-KGC can maintain good recall performance even in challenging scenarios and, as a result, outperform other models.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Datasets** & **1-Hop** & **1-Hop Dropout** & **2-Hop** & **3-Hop** & **4-Hop** \\ \hline FB15K-237 & 40 & 0.3 & 50 & 150 & 500 \\ WN18 RR & 30 & 0.5 & 30 & 100 & 250 \\ CoDEx-M & 40 & 0.3 & 50 & 150 & 500 \\ CoDEx-L & 20 & 0.5 & 30 & 100 & 250 \\ \hline \end{tabular}
\end{table}
Table 3: Settings of the number of multi-hop neighbors.
## 2 Infrastructure, Instrumentation and Calibration Methods
### Calibration and Validation Methods  
Independent calibration and validation activities are required to assess the end-to-end performance characteristics of satellite altimeter measurements. Cal/Val activities monitor the measurement uncertainty, stability and behavior, providing independent knowledge of measurement quality. External techniques that regularly support post-launch calibration of satellite altimeters include: (1) sea-surface Cal/Val, (2) transponder Cal/Val, (3) backscatter coefficient Cal/Val, (4) crossover analysis, (5) tandem phase analysis, and (6) comparison to tide gauge networks. The latter is an indirect method, which compares the altimeter-derived sea surface heights with measurements obtained by a global network of tide gauges [13]. Details on PFAC-employed Cal/Val techniques can be found in [14]. A short description of the remaining four techniques and their implementation in the PFAC is given in the following sections.
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 5 Networking Protocol Based on Digital Twins
### Architectural Design of DT-Enabled LEO Satellite Network  
In the architectural design of the digital twin platform for low Earth orbit (LEO) satellite constellation networks, strict adherence to modular software design principles is ensured. Cloud platform [25] resource management technology is employed to dynamically allocate and accelerate the construction and teardown processes of computing and network resources in the simulation platform, facilitating the dynamic creation of network topologies. Host virtualization technology is utilized to improve the hardware resource utilization, enhance the scalability of the simulation system, and provide a runtime environment identical to the actual devices. This enables the direct testing of real network protocols and upper-layer applications without the need for porting, significantly improving the efficiency and accuracy of simulation testing. Network virtualization and software-defined networking technologies are leveraged for fine-grained network traffic control, enabling multi-granularity network simulation based on the precise physical-layer modeling of the channels. Therefore, the digital network environment offers numerous advantages, such as low construction costs, high scalability, ease of management, flexibility, and high maturity, making it suitable for the entire lifecycle of target network system design, development, delivery, and operation.  
The architecture of the digital twin [26; 27] platform for the LEO satellite constellation network, as shown in Figure 3, consists of the following layers: resource layer, virtualization layer, management layer, and human-machine interaction layer. The resource layer utilizes common hardware devices such as computing and networking equipment with horizontal scalability. The virtualization layer deploys tools for compute virtualization and network virtualization. The management layer facilitates network node management, as well as topology and link management. The human-machine interaction layer enables user operations on the platform. Based on this platform, various types of twin scenarios can be generated according to the requirements of the LEO satellite constellation twin scenarios.
## 5 Performance Analysis  
The SER, secrecy rate, and robustness are three primary metrics for assessing the performance of DM systems [44]. Next, the SER, secrecy rate, and robustness of the proposed DM schemes are analyzed. Finally, the comparisons among the multiple antenna arrays DM schemes and the traditional single antenna array DM schemes are also provided.
# Visualization of a Multidimensional Point Cloud as a 3D Swarm of Avatars
## 3 Methodology
### Implementation
#### 3.2.3 View Matrix  
The view matrix \(V\) is implemented as an affine \((k+1)\times(k+1)\) matrix, combining spatial rotation (the \(k\times k\) upper-left block) and translation (the final column) for interactive navigation in homogeneous coordinates. This approach enables efficient and flexible real-time manipulation of the data cloud in 3D or 4D space.  
In the current plugin implementation, all 4D rotations are carried out in coordinate planes such as XY, XZ, XT, YZ, YT, or ZT. These correspond to classic SO(2) rotations within two-dimensional subspaces. However, the implementation can be extended to support full SO(4) rotations, offering a more expressive and flexible navigation model.  
The remaining part of the view matrix stores a translation vector and a homogeneous row to enable affine transformations.  
In code, the matrix can be built as follows:  
// Rotation only (k = k) Eigen::MatrixXd R = generateRotationMatrix(k, axis1, axis2, angle);  
// Create full (k+1 = k+1) view matrix with translation Eigen::MatrixXd V = Eigen::MatrixXd::Identity(k + 1, k + 1); V.topLeftCorner(k, k) = R; V.topRightCorner(k, 1) = translation_vector; // optional  
To apply the transformation, the spatial data must be converted into homogeneous coordinates by adding a row of ones:  
Eigen::MatrixXd X_hom(k + 1, N); X_hom.topRows(k) = X_spatial; X_hom.row(k) = Eigen::RowVectorXd::Ones(N);  
Eigen::MatrixXd X_view = V * X_hom;  
This corresponds directly to the transformation defined in Equation (4), where the spatial coordinates are mapped into the observation space for visualization. This procedure enables smooth 4D navigation, including both rotation and panning of the data cloud in the observation space.
## 3 Results  
All the operators performing de-bonding procedures with fluorescent composite found the use of this novel composite easier and therefore faster in comparison to the conventional composite used in the DSC group. The mean duration of composite removal in the DFC group (visible under UV light) was 38 s, and for the DSC group (conventional composite) it was 77 s (Table 1). The differences in the duration was significant (Mann-Whitney test \(p\) = 0.015 57.7 \(\pm\) 19.9 95% C.I.) under the study conditions.  
Figure 4: After de-bonding tooth surface. After the de-bonding, all the teeth were grouped, and de-bonding was evaluated by a blinded single operator. Each tooth was classified using a modified ARI (Adhesive Remnant Index) scale from 0 to 2 with the use of 5.0\(\times\)/235 telescopic head-worn magnifier as follows.  
Figure 5: Illustration of Adhesive Remnant Index (ARI): ARI 0, no adhesive on the tooth; ARI 1, less than 1/2 adhesive on the tooth; ARI 2, more than 1/2 of the adhesive on the tooth; ARI 3, all of the adhesive on the tooth.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **DFC** & **DSC** \\ \hline Operator \#1 & 40 s & 234 s \\ \hline Operator \#2 & 60 s & 87 s \\ \hline Operator \#3 & 36 s & 54 s \\ \hline Operator \#4 & 23 s & 56 s \\ \hline Operator \#5 & 17 s & 31 s \\ \hline Operator \#6 & 37 s & 67 s \\ \hline Operator \#7 & 40 s & 80 s \\ \hline Operator \#8 & 53 s & 27 s \\ \hline Operator \#9 & 37 s & 69 s \\ \hline Operator \#10 & 41 s & 66 s \\ \hline
**SD** & 12.4 & 58.3 \\ \hline
**mean** & 38 s & 77 s \\ \hline
**maximum** & 60 s & 234 s \\ \hline
**minimum** & 17 s & 27 s \\ \hline
**Mann–Whitney** & \(p=0.015\) & \\ \hline
**95\% C.I.** & 19.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Duration of de-bonding among similarly experienced operators and similar anatomy dental elements.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Operator**} & \multicolumn{3}{c}{**A.R.I Grade**} & \multicolumn{1}{c}{**Grades Description**} \\ \cline{2-4}  & **DFC** & **DSC** & \\ \hline Operator \#1 & 2 & 1 & 0 grade: visible composite remnants OR significant damage to the enamel surface visible without loupes \(5\times\) from any projection of the observer OR any anatomical change to the tooth. \\ \hline Operator \#2 & 1 & 1 & 1 grade: minor enamel scratching or damage with no exposition of the dentine partially visible without loupes \(5\times\) from a limited number of visible projections of the observer or/and composite remnants visible with loupes \(5\times\) only. \\ \hline Operator \#3 & 2 & 1 & 2 grade: absence or presence of minor scratches to the enamel surface visible only with the use of head-worn loupe \(5\times\). \\ \hline Operator \#4 & 2 & 0 & \\ \hline Operator \#5 & 2 & 0 & \\ \hline Operator \#6 & 2 & 1 & \\ \hline Operator \#7 & 2 & 2 & \\ \hline Operator \#8 & 2 & 2 & \\ \hline Operator \#9 & 1 & 2 & \\ \hline Operator \#10 & 2 & 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modified ARI evaluation.
# Experimental Demonstration of 3 \(\times\) 3 MIMO LED-to-LED Communication Using RGB Colors
## 3 MIMO LED-to-LED Communication Using RGB Colors
Figure 10: Constellation diagram of SISO LED-to-LED system. (**a**) RtoR, (**b**) GtoG, and (**c**) BtoB when transfer data rate is 27.2, 15.31, and 15.31 kbps, respectively. Figure 9: BER of the SISO LED-to-LED systems and 3 \(\times\) 3 MIMO LED-to-LED system. In [23], performance analysis of a MIMO VLC system using different equalizers was studied. The ZF equalizer, zero-forcing with successive interference cancellation (ZF-SIC) equalizer, and minimum mean squared error with successive interference cancellation (MMSE-SIC) equalizer were tested in 2 \(\times\) 2 and 4 \(\times\) 4 MIMO conditions. The results were good in the order of MMSE-SIC, ZF-SIC, and ZF equalizers. Thereby, we think that the MIMO LED-to-LED system could be further improved with the ZF-SIC or MMSE-SIC equalizer. In this work, we have experimentally demonstrated the feasibility of the LED-to-LED MIMO OWC system using off-the-shelf LEDs. The results show that MIMO LED-to-LED communication could successfully recover the transmitted signal with low inter-channel interferences using the receiver LED's wavelength-selective characteristics. Obviously, the data rate of the LED-to-LED communication system is low compared to other OWC subcategories. However, the advantage of LED-to-LED communication is that the LED-to-LED communication system is able to realize low-data IoT at the lowest costs in comparison with other OWC schemes. We believe that LED-to-LED communication is an effective solution to resolve the communication burden arising from massive connectivity in B5G IoT. Additional research issues such as communication distance, modulation, equalization, etc., will be handled in our future work.