esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Chromatic Dispersion Equalization FIR Digital Filter for Coherent Receiver
## 1 Introduction
The first aspect is the use of a more superior Nuttall second-order self-convolution window instead of a rectangular window, effectively reducing the impact of energy leakage on filter performance. In order to reduce the computational complexity during convolutional operations, to be suitable for the long-distance coherent optical communication system, the second aspect adopts a uniform quantization strategy to improve the multiplicity of the filter tap coefficients, and the digital signal is preprocessed by using the combination law of addition, the distribution rate of multiplication and the symmetry of the filter tap coefficient, which greatly reduces the computational complexity of convolution operations and is more suitable for commercial coherent receivers. The rest of this paper is organized as follows; Section 2 briefly introduces the existing FD-CDE and TD-CDE methods, analyzes the main drawbacks of TD-CDE, elaborates the optimized way in detail, and gives the theoretical derivation. In Section 3, numerical simulations are performed to verify that the proposed optimization algorithm can implement effective CDE with low computational complexity. The computational complexity of TD-CDE, FD-CDE, and the proposed TD-CDE optimization method are discussed in Section 4. Finally, Section 5 gives the conclusion.
# Collaborative Trajectories Optimization for Tram and Connected Vehicles at Signalized Intersection
## 2 Literature Review
Different from the usual research on control of a single mode of urban transportation [15; 17; 24], this paper puts forward a conception of digital integration of trans, connected vehicles and intersection signal machines in order to achieve intelligent traffic on tram lines. Figure 2: Schematic diagram of tram and CAV (same direction) trajectory under different TSP schemes. (**a**) No signal priority scheme. (**b**) Green extension scheme. (**c**) Phase insertion scheme. (**d**) Early green scheme. 2. In general, studies on intersection priority control of trans are rough to establish the social vehicle model, and the positive influence of the special TSP of trans on social vehicles has not been considered [6; 7; 9; 12]. In this study, the influence of tram body length on TSP is considered, and V2I and V2V communication technology can be used to realize full utilization of TSP in the network environment. 3. A new online collaborative optimization algorithm is proposed, and three objective functions combining rail and road are constructed. In this way, the integrated optimization problem of cross-transportation modes is realized. Additionally, the adaptive elitist genetic algorithm is used to solve the problem. These innovative and effective methods have not been proposed in previous tram control studies [6; 11; 12].
## 6 Simulation Results and Discussion
Figure 8: System output errors: (**a**) Altitude and Roll angle output errors; (**b**) Pitch and Yaw angle output errors. Figure 9: \(k_{p}\) and \(k_{d}\) adaptive values: (**a**) \(k_{p}\) adaptive value; (**b**) \(k_{d}\) adaptive value. Figures 11 and 12 show that the BP\(-\)ALADRC with the mass adaptive law can effectively reduce the error caused by the load mass change, and the BP\(-\)ALADRC without the mass adaptive law has significantly larger overshoots in the pitch angle and roll angle channels. At the same time, the response speed of the system is not as good as that of the BP\(-\)ALADRC controller with the quality adaptive law. Figure 11: System output: (a) Altitude and Roll angle output; (b) Pitch and Yaw angle output. Figure 10: Load mass variation diagram. In Case Study 1, the control scheme designed in this paper and the control methods of LADRC [30] and DSADRC are applied to the attitude control of the quadrotor system. It can be seen that the controller designed in this paper has less overshoot and faster response speed in the dynamic response process. In Case Study 2, we conducted interference experiments. By adding white noise interference to the system, we verified that the controller has a strong ability to suppress interference. Finally, Case Study 3 shows that the controller has strong adaptability to the change of load mass. All the above case studies show that the designed controller can track the given signal quickly and stably, effectively reducing the adverse effects of interference on the quadrotor system. It is proved that the controller designed in this paper has more significant signal tracking ability, interference suppression ability and better system adjustment ability. In addition, the introduction of adaptive control effectively compensates the observation error of LESO, making the controller more robust. Finally, when the load mass of the quadrotor changes, the designed mass adaptive law can effectively overcome the adverse effects of mass changes on the system and enhance the stability of the system.
## 3 Using LTE/5G-based Public Mobile Land Networks for Autarkic Microgrid Blackout Recovery
### Preferential, Differentiated MTC Using PLMN-Based Access Barring in LTE
As tested, the microgrid consisted of four photovoltaic generators and four battery storage systems feeding four dynamic loads. The generators and storages are connected to the grid via inverters which implement a distributed consensus-based secondary control algorithm [6]. The inverter control algorithms are implemented on Raspberry PI microcomputers which receive grid measurements from the grid simulation and send control signals back. Each inverter interfaces to an agent on the same Raspberry PI. The agents form a MAS, implemented using the Calvin framework [18]. The Raspberry PIs have dongles through which the agents communicate peer-to-peer with each other over a private LTE network to implement the autarkic microgrid black start use case. Two PLMNs are configured in a single network cell, the first PLMN serving the MAS MTC devices (instantiated in dongles inserted in the Raspberry PIs hosting the individual agents) and the second PLMN serving a number of normal smartphones. The MAS MTC devices are attached on PLMN1 and the smartphones on PLMN2. Then a simulated microgrid blackout was initiated. In the test, we assume that this blackout leads to emergency conditions in PLMN2, leading to blocking of normal subscribers by setting the Barring Factor to 0 for ACs 0 to 9. In PLMN1, no ACB restrictions were set for the MAS MTC devices during the blackout emergency. Figure 2: Test setup for PLMN-based Access Barring lab test. The observed result was that only PLMN1 UEs i.e. MAS MTC devices could access the cell, and that PLMN2 UEs were barred from accessing the network. The tests confirmed that, by having a shared LTE network environment with two separate PLMNs configured in the network cell, different Access Barring treatment can be provided to different UEs registered in different PLMNs by broadcasting different Access Barring factors to the different PLMNs' devices. The lab tests confirmed that the method enables the MAS MTC devices to receive temporary preferential treatment under particular emergency circumstances so as to be able to continue to communicate with each other during the blackout, enabling a black start to be performed in the islanded microgrid.
# Intelligent Trajectory Prediction Algorithm for Reentry Glide Target Based on Intention Inference
## 2 Dynamic Models and Parameter Prediction Models of Reentry Glide Targets  
### Dynamic Model of Reentry Glide Targets  
Without considering the Earth's curvature and rotation, the dynamic model of reentry glide targets is usually expressed as  
\[\left\{\begin{array}{l}\dot{h}=v\sin\theta\\ \dot{\phi}=\frac{v\cos\theta\sin\psi}{(R_{e}+h)\cos\varphi}\\ \dot{\varphi}=\frac{v\cos\theta\cos\psi}{(R_{e}+h)}\\ \dot{v}=-\frac{D}{m}-g\sin\theta\\ \dot{\theta}=\frac{\cos\theta}{mv}+\cos\theta(\frac{v}{R_{e}+h}-\frac{g}{v}) \\ \dot{\psi}=\frac{L\sin\beta}{mv\cos\theta}+\frac{v\cos\theta\sin\psi\tan\varphi} {R_{e}+h}\end{array}\right. \tag{1}\]  
where \(h\) represents the flight height, \(R_{e}\) represents the radius of earth, \(\phi\), \(\varphi\) represent the longitude and latitude, respectively, \(v\) represents the velocity, \(\theta\) is the flight path angle, \(\psi\) is the flight heading angle, \(m\) represents the mass of target, \(S\) is the stress area, and \(L\), \(D\) represent the lift and drag force, respectively. It can be expressed as  
\[\left\{\begin{array}{l}L=C_{L}\rho v^{2}S/2\\ D=C_{D}\rho v^{2}S/2\end{array}\right. \tag{2}\]  
where \(C_{L},C_{D}\) represent the lift and drag parameters of target, and \(\rho\) is the atmospheric density that is usually calculated as \(\rho=\rho_{0}e^{-h/H_{0}}\). Here, \(\rho_{0}\) represents atmospheric density constant and \(H_{0}\) represents atmospheric altitude constant.
# Deep Reinforcement Learning for Query-Conditioned Video Summarization
## 3 Our Approach
### Summarization Network
#### 3.2.3 Relatedness Reward Using MapNet  
We take the video embedding after the concatenating layer \(\{f_{t}^{c}\}_{t=1}^{T}\) as illustrated in Section 3.1, and feed them into the pretrained MapNet. MapNet maps each shot to the query embedding space, producing \(\{\tilde{f}_{t}^{q}\}_{t=1}^{T}\). These feature vectors are used to compute the relatedness reward in our proposed framework by considering their distance to the input query. We propose that with larger relatedness reward (smaller distance between the input query embedding and the predictions), the selected video shots are more relevant to the query making them stronger candidates for the predicted summary. We therefore compute the average squared difference between the mapping results produced by MapNet and the current user query for the selected shots as the relatedness reward and use the exponential function to ensure a positive reward. The relatedness reward is thus defined as:  
\[R_{rel}=exp(-\frac{1}{|\mathcal{M}|}\sum_{f_{t}^{q}\in\mathcal{M}}||\tilde{f}_ {t}^{q}-f^{q}||_{2}), \tag{3}\]  
where \(f^{q}\) denotes the query embedding for the current user query, and \(\mathcal{M}\) denotes the selected video shots in the video.  
Figure 3: The architecture of our proposed Summarization Network (SummNet). Given a user query, it takes both the visual embedding \(\{f_{t}^{c}\}_{t=1}^{T}\) and the query embedding \(f_{i}^{c_{i}}\) as the input, processes each using a fc layer and then concatenates both representations to generate the joint video-query embedding. A Bi-LSTM module is then used to model long temporal relations and the output is passed to two fc layers, with a dropout layer and a batch normalization layer in between. Afterwards, a sigmoid activation is used to predict the importance scores \(\{s_{t}\}_{t=1}^{T}\) for the video shots. Based on the predicted importance scores, the reinforcement learning-based loss is used to guide the agent to take good actions. A policy \(\pi\) is learned based on the following three rewards to perform the right actions \(\{a_{t}\}_{t=1}^{T}\) in order to select the summary shots: (1) Relatedness reward \(R_{rel}\) uses MapNet and measures the distances between the predicted and ground-truth query embedding. (2) Diversity reward \(R_{dip}\) reduces the redundancies in generated summaries. (3) Representativeness reward \(R_{rep}\) aims to provide the most representative video shots of the original video as the summary.
# Emergency Evacuation Simulation Study Based on Improved YOLOv5s and Anylogic
## 3 Analysis of Experimental Results
### YOLOv5s-SE Based Target Detection Model
[EMAIL_ADDRESS]:0.95 represents the average \(mAP\) over different IoU thresholds (0.5 to 0.95 in 0.05 steps, i.e., 0.5, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95). Larger values of \(mAP\) also demonstrate better network performance of the model. In this paper, the \(mAP\) curves based on the YOLOv5s-SE target detection model are shown in Figure 3:0  
By looking at Figure 7, we can see that the values of [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 increase with the number of training sessions. Ultimately, when the number of training sessions reaches 100, the value of [EMAIL_ADDRESS] can reach 95.83%, while the value of [EMAIL_ADDRESS]:0.95 can eventually reach 52.79%. Another important measure of network performance is the loss value, which measures the difference between actual network performance and perfect prediction when predicting the entire training image set. The loss function curves for this paper are shown in Figure 3:1  
It can be clearly seen from Figure 8 that the loss value gradually becomes smaller as the number of training times increases, and when the number of training times reaches about 30, the rate of decrease of the loss value gradually decreases, and the model starts to converge and level off. The experimental results show that the network model has relatively good network performance and can meet the needs of pedestrian head detection. Finally, we trained YOLOv4-Tiny, YOLOv5s, YOLOv7-Tiny, Faster-RCNN, and the improved YOLOv5s under the same dataset and compared them for their mAP and FPS. The comparison results are shown in Figure 3:2  
Figure 3:3 Precision–recall curve. Figure 3:4 Loss curve. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **[EMAIL_ADDRESS]** & **FPS** \\ \hline YOLOv4-Tiny & 83.97\% & 27.29 \\ YOLOv5s & 93.75\% & 79.32 \\ YOLOv7-Tiny & 92.84\% & 60.24 \\ Faster-RCNN & 96.77\% & 6.58 \\ Improved YOLOv5s & 95.83\% & 76.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of test results of YOLOv4-Tiny, YOLOv5s, YOLOv7-Tiny, Faster-RCNN, and the improved method. Figure 7: mAP curve. Analysis of Table 2 shows that the [EMAIL_ADDRESS] scores and FPS of YOLOv4-Tiny and YOLOv7-Tiny are both lower than the original YOLOv5s and are not suitable for application to data acquisition during emergency evacuation. Faster-RCNN has the highest [EMAIL_ADDRESS] score, but its FPS is only 6.58, which is nearly 13 times lower than YOLOv5s and does not effectively solve the problems arising from traditional methods. The improved network model based on YOLOv5s proposed in this paper, although the FPS is 2.83 lower than the original YOLOv5s, has a [EMAIL_ADDRESS] score that is 2.08% higher than the original YOLOv5s. Therefore, the improved YOLOv5s not only improves the accuracy of small target object detection, but also has excellent detection speed, which is very suitable for fast detection of pedestrians in emergency situations.
# Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments
## 2 Related Work
[32] proposes PayloadPark (based on P4 [33]), which parks payloads in programmable switch memory, sends header-only packets to network-function servers (that primarily make decisions based on headers), and resembles them as such packets come back. Ref. [34] uses P4-enabled switches to aggregate packet sharing common headers into one and then to disaggregate the aggregated packet before reaching the destination. Ref. [35] programs P4-enabled switches to generate an alarm if MQTT messages convey values exceeding a given threshold. Inspired by the work outlined above, we propose application-aware network traffic management in MEC-integrated industrial environments through the SDN-elNP interplay. Such an interplay makes the SDN side (how traffic flows traverse the industrial network, i.e., flow prioritization and steering) and the eINP one (how those flows are to be processedalong their path, i.e., data aggregation, filtering, and encryption) work synergistically. The rationale of eINP is to provide the network with additional capabilities for packet payload inspection and processing through ENs connected to NDs via high-speed links to minimize communication overhead. If compared to related work, the adoption of the proposed eINP fuels NDs with ENs, thus making available general-purpose computing resources to offload payload inspection and processing. This, in turn, enables forms of payload processing at a higher degree of expressiveness that neither traditional nor INP-enabled NDs support at their current maturity stage. Such ENs may be deployed either on-premises, i.e., within the industrial boundaries, or off-premises, i.e., on MEC-enabled microdatacenters.
## 2 Materials and Methods  
### Datasets  
The proposed data are evaluated on the Seoul National University Bundang Hospital Breast Ultrasound (SNUBH BUS) dataset for BUS images and further tested on the Stanford Dog dataset for general images. While the SNUBH BUS dataset has both \(D_{strong}\) and \(D_{weak}\) images, the Stanford Dog dataset only contains \(D_{strong}\) images. Thus, the Stanford Dog data are manually divided into \(D_{strong}\) and \(D_{weak}\), where only image labels are used in images selected as \(D_{weak}\).  
The SNUBH dataset collected from the Seoul National University Bundang Hospital is obtained from different ultrasound systems described in [10], including Philips (ATL HDI 5000, iU22), SuperSonic Imagine (Aixplorer), and Samsung Medison (RS80A). The dataset contains a total of 5624 images from 2578 patients. The \(D_{strong}\) subset is comprised of 1200 images, 600 of which are benign and the other 600 of which are malignant. We use 400 images from each class as a training set, and 200 as the test set. \(D_{weak}\) subset is comprised of 4224 images, 3291 of which are benign and the remaining 933 malignant. All of the image labels are proven with biopsy results, also meaning that the data are the cases where biopsy was needed to diagnose the patient, making classification with BUS images an even more difficult task.  
The Stanford Dog dataset is a collection of color images of 120 breeds of dogs with a total of 20,580 images, all including class labels and bounding box coordinates. In order to mimic the situation in BUS images, we select two similar looking middle size breeds to classify, the Bloodhoound and the English foxhound and then converted them to grayscale images. The number of images in each class is 187 and 157, respectively. Each dataset is subdivided into 20 \(D_{strong}\) training set, 60 test set, and the remaining 107 and 77 images from Blackhound and English Foxhound, respectively, to \(D_{weak}\) dataset. This setting enforces a situation where there are limited amount of strong annotations. The Stanford Dog dataset is tested to demonstrate the validness of the presented method on general images. Only a limited amount of strongly annotated images are available for training and the task is not straight forward, since the images are grayscale images, having room for improvement. The dataset is available online ([[http://vision.stanford.edu/aditya86/ImageNetDogs/](http://vision.stanford.edu/aditya86/ImageNetDogs/)]([http://vision.stanford.edu/aditya86/ImageNetDogs/](http://vision.stanford.edu/aditya86/ImageNetDogs/))). A summary of the number of images for both datasets is provided in Table 1.
###### Abstract  
The high altitude platform station (HAPS) system is an essential component of the air-based network. It can shorten transmission delay and make a better user experience compared with satellite networks, and it can also be easily deployed and cover a larger area compared with international mobile telecommunications (IMT). In order to meet the needs of users with asymmetric and random data flow, the spectrum sharing and dynamic time division duplexing (TDD) mode are used in HAPS-IMT heterogeneous network. However, the cross-link interference brought by TDD mode will lead to the degradation of system performance. In this paper, a resource allocation algorithm based on power control and dynamic transmission protocol configuration is proposed. Firstly, a specific timeslot, \"low power almost-bank subframe (LP-ABS)\" is introduced into the frame structure of the HAPS physical layer. The transmission protocol designing could mitigate inter-layer interference efficiently by power control in \"LP-ABS\". Secondly, the utilization function is adopted for assessing the system performance, which gives attention to both diversified requirements on the quality of services (QoS) and the throughput of the HAPS-IMT system. Simulation results show that power control and resource allocation technologies proposed in this paper can effectively improve system performance and user satisfaction.  
HAPS-resource allocation; transmission protocol; power control; integrated system +
Footnote †: journal: International Journal of Information and Communication Technology  
1
# GPU-Accelerated Algorithm for Polygon Reconstruction
## 2 Problem Definition  
Input: The input to the problem is a set of line segments, each represented by two endpoints in a two-dimensional plane:  
\[S_{i}=\{v_{i}^{1},v_{i}^{2}\} \tag{1}\]Here, \(v_{i}^{1}\) and \(v_{i}^{2}\) represent the starting and end points of the line segment, respectively; each point, \(v_{i}=(x_{i},y_{i})\), is a two-dimensional coordinate, where \(x_{i}\) and \(y_{i}\) denote the horizontal and vertical coordinates, respectively.  
Output: The desired output is a set of closed polygons represented by vertex coordinates, where the outer contour is represented counterclockwise and the inner contour is represented clockwise. Given each outer contour polygon, it is necessary to quickly find all the inner contours belonging to it. Similarly, given an inner contour, it is necessary to quickly locate the outer contour to which it belongs.  
In the dataset shown in Figure 1, the input to this question is as follows:  
\[B\to A\] \[D\to C\] \[E\to F\] \[A\to I\] \[G\to E\] \[F\to H\] \[C\to K\] \[R\to D\] \[I\to J\] \[H\to G\]  
The expected output of this question is as follows:  
\[1.0:D\to C\to K\] \[2.0:B\to A\to I\to J\] \[2.1:E\to F\to H\to G\]  
In this problem, there are no restrictions on the order of the segments. However, since the algorithm is capable of handling self-intersecting polygons, the segments must be oriented in the correct direction.  
In the sample output, there are two polygons labeled as '2': the first represents the outer contour and the second represents the inner contour.  
The core of the problem lies in efficiently indexing the next line segment based on the endpoint of the current segment. The challenge in parallelization arises from the inability to predict which segments belong to the same polygon. This results in different threads potentially processing segments that belong to the same polygon, leading to inefficient use of computational resources and inaccuracies in the number of resulting  
Figure 1: An example set of polygons. The letters in the figure represent different vertices. Algorithm input: line segments forming these polygons. Output: oriented point sets.  
polygons. Additionally, the uncertainty in the number of polygons contained within the set of segments makes it difficult to pre-allocate storage space for the threads in advance.