esa-sceva/satcom-chunk-collection huggingface dataset:
# Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing
## 4 Proposed Methodology
### Proposed Policy-Based Routing
#### 4.1.1 SDN-Based PBR Module  
Software-Defined Networking (SDN) decouples the control plane from the data plane. The data plane consists of specialized forwarding hardware, such as switches, arranged in a specific topology and connected to the control plane via TCP. The control plane, managed by a logically centralized SDN controller with a global view of the network, makes all decisions regarding packet handling--whether to forward, drop, or log packets for future use.  
As depicted in Figure 1, when a packet arrives at a switch, if it does not belong to an administrator-defined QoS flow and a corresponding flow table entry exists, the switch executes the action specified in the flow entry. This action may involve forwarding the packet to a specific port, sending it to the controller, modifying its fields, pushing/popping VLAN or MPLS headers, or dropping the packet. If no flow table entry exists, the packet is forwarded to the controller for further inspection. The controller checks the packet's validity; if the packet is invalid, it is discarded or logged. If valid, a flow table entry is created and sent to the switch via the OpenFlow protocol, and the action is applied to the entire flow.  
For packets from a QoS flow, the PBR module is invoked. The PBR module first checks the current queuing delay at the switch where the packets from the QoS flow are received. If the queuing delay is below the predefined threshold, the flow follows the standard OSPF path. However, if the queuing delay exceeds or equals the threshold, the PBR module calculates an alternate minimum delay path, as described in Section 4.1.2, and diverts  
Figure 1: PBR module integration within SDN.  
the traffic to this new path. In this study, the threshold is set to 100 ms. The value of 100 ms is arbitrary, as this threshold can be adjusted based on the specific requirements of real-time applications. To examine the effect of different threshold values on network performance, we have included results in the Results section for various threshold values. The PBR module then creates a flow entry to reroute the traffic along the new minimum delay path and pushes the flow entry to the switch's flow table. The flow entry creation process involves the following steps:  
* The module identifies links in the Shortest Path First (SPF) graph that have the lowest OSPF cost to the destination.
* The module checks the bandwidth and available buffer capacity of these links to select the optimal minimum delay path, as detailed in the next subsection.  
Based on these parameters, the optimal route is selected, and the flow is forwarded along the best low-delay path. If congestion is detected on the OSPF route, the process is repeated to reroute the flow. The results in Section 6 demonstrate the effectiveness of our QoS assurance and congestion control approach.
## 1 Introduction  
Economic development has led to increased research on electrochemical energy storage techniques for new energy sources, such as lithium-ion [1; 2; 3], lithium-sulfur [4], and sodium-ion batteries [5], of which lithium-ion batteries are the most promising and widely investigated [6; 7; 8; 9; 10]. The development of lithium-ion batteries has been restricted because the capacities of anode materials are much better than those of cathode materials. LiNi\({}_{1/3}\)Co\({}_{1/3}\)Mn\({}_{1/3}\)O\({}_{2}\) (NCM111) which has been widely investigated is a better cathode material choice than LiCoO\({}_{2}\) in terms of reversible capacity, thermal and structural stability, and cost. However, the rapid capacity fading and inferior rate capability of NCM limit its sustainability and prevent its wide use in high-power applications.  
Many different methods have been used to solve the above problems, e.g., formation of special morphologies, bulk doping, surface coating, and pretreatment. There are already relevant experiments on the above that constructing electrode materials with different microstructures, coating the surfaces of lithium-ion battery cathodes with materials, such as carbon and CoAl\({}_{2}\)O\({}_{4}\), and doping positive electrode materials with metal ions, such as Fe and Zn and so on. Spinel-phase LiM\({}_{2}\)O\({}_{4}\) with oxygen vacancies can be formed on the surfaces of cathode materials through pretreatment. The formed spinel phase improves the diffusion of lithium ions and the escape of O\({}_{2}\) gas from the cathode surface during the cycle, which helps improve electrochemical performance [11]. The most common electrolyte component, namely LiPF\({}_{6}\), produces a small amount of HF via reaction with water and corrodes the oxide electrode material during charging and discharging [12]. Electrode corrosion by a highly active organic electrolyte can be resolved by the addition of fluorides, such as CeF [13], Mg\({}_{2}\)F [14], ZrF\({}_{x}\)[15; 16], AlF\({}_{3}\)[17], FeF\({}_{3}\)[18], CaF\({}_{2}\)[19], and LiF [20; 21]. The LiFe\({}_{x}\)Ni\({}_{1/3\cdot x}\)Co\({}_{1/3}\)Mn\({}_{1/3}\)O\({}_{2}\) hollow micro-spheres as cathodes for LIBs have been shown to have the best electrochemical performance at \(x\) = 0.133 [22], although hollow microspherical structure enhances performance, the iron substitution increases microspheres aggregation, and also reduces electrochemical properties as decreasing lithium ion diffusion.  
In this study, we adopt a simple and reliable method for electrochemical performance improvement. The method involves NH\({}_{4}\)F pretreatment of Li\({}_{1.25}\)Ni\({}_{0.2}\)Fe\({}_{0.13}\)Co\({}_{0.33}\)Mn\({}_{0.33}\)O\({}_{2}\) cathodes, which were obtained by using MnO\({}_{2}\) microspheres as self-templates. X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and electrochemical tests showed that the structure, morphology, and electrochemical properties of Li\({}_{1.25}\)Ni\({}_{0.2}\)Fe\({}_{0.13}\)Co\({}_{0.33}\)Mn\({}_{0.33}\)O\({}_{2}\) after NH\({}_{4}\)F pretreatment need to be considered.
## 3 Proposed Model
### Complex Question Decomposition
#### 3.2.1 Semantic Encoder  
The first part of sequence tagging is the semantic encoder, which uses the pre-trained model Bert for semantic extraction. First, add \(CLS\) and \(ESP\) flag to \(S\) and generate position embedding \(\mathbf{e}_{i}^{p}\), segment embedding \(\mathbf{e}_{i}^{s}\), and token embedding \(\mathbf{e}_{i}^{t}\) for the \(i\)th word, then submit these three embeddings to [PERSON]. Finally, we obtain the context representation matrix of the sentence \(\mathbf{E}_{bert}=[\mathbf{e}_{CLS},\mathbf{e}_{1},\cdots,\mathbf{e}_{n}, \mathbf{e}_{ESP}]\), as shown in Equation (1):  
\[\mathbf{e}_{i}=Bert\Big{(}\mathbf{e}_{i}^{p}+\mathbf{e}_{i}^{s}+\mathbf{e}_{i} ^{t}\Big{)} \tag{1}\]  
Figure 2: Complex question decomposition model.  
Although \(\mathbf{E}_{bert}\) contains \(\mathbf{e}_{CLS}\) and \(\mathbf{e}_{ESP}\), they are not used in the subsequent step. \(\mathbf{E}_{bert}\) is then sent to the fusion layer for information modification. The input to the fusion layer consists of two components: the topic entity vector \(\mathbf{q}\) and the context representation matrix \(\mathbf{E}_{bert}\). The fusion layer is designed to follow the attention mechanism and is in charge of information filtering of \(\mathbf{E}_{bert}\) according to \(\mathbf{q}\) to improve information aggregation and attenuate external noise. The above process is shown in Equations (2)-(4):  
\[s(\mathbf{e}_{i},\mathbf{q})=\mathbf{v}^{T}\tanh\bigl{(}\mathbf{W}_{e} \mathbf{e}_{i}+\mathbf{U}_{q}\mathbf{q}\bigr{)} \tag{2}\]  
\[\mathbf{ff}_{i}=softmax(s(\mathbf{E}_{bert},\mathbf{q}))=\frac{\exp(s( \mathbf{e}_{i},\mathbf{q}))}{\sum_{j=1}^{n}\exp\bigl{(}s(\mathbf{e}_{j}, \mathbf{q})\bigr{)}} \tag{3}\]  
\[\mathbf{e}_{i}^{\prime}=\mathbf{ff}_{i}\mathbf{e}_{i} \tag{4}\]  
where \(\mathbf{v}\), \(\mathbf{W}_{e}\) and \(\mathbf{U}_{q}\) are trainable weights. \(\mathbf{e}_{i}^{\prime}\) is the \(i\)th word vector obtained after fusion layer processing. The feed-forward network (FFN) layer receives the word vector matrix \(\mathbf{E}_{att}=\left[\mathbf{e}_{1}^{\prime},\mathbf{e}_{2}^{\prime},\cdots,\mathbf{e}_{n}^{\prime}\right]\) for further processing. FFN is designed to be used here for two purposes: first, to improve the semantic encoder's network memory capacity; second, to decrease the dimensionality of the vector in order to fuse it with the dependency encoder's output. The process is represented by Equation (5):  
\[\mathbf{E}_{FFN}=\max(0,\mathbf{E}_{att}\mathbf{W}_{att}+\mathbf{b}_{att}) \mathbf{W}_{att}^{\prime}+\mathbf{b}_{att}^{\prime} \tag{5}\]  
where \(\mathbf{E}_{FFN}=\left[\mathbf{e}_{1}^{FFN},\mathbf{e}_{2}^{FFN},\cdots, \mathbf{e}_{n}^{FFN}\right]\) is the word vector matrix obtained after the FFN layer. \(\mathbf{W}_{att}\), \(\mathbf{W}_{att}^{\prime}\), \(\mathbf{b}_{att}\) and \(\mathbf{b}_{att}^{\prime}\) are trainable weights.
# Improving the Robustness of Model Compression by On-Manifold Adversarial Training
## 2 Related Works
### Robust Model Compression  
The robustness issue related to the model compression has been alerted by [PERSON] et al. [16]. They argue that the pruned networks with high sparsity rates are more vulnerable to adversarial attacks. Several studies have been conducted to accomplish adversarially robust model compression. [PERSON] et al. [17] and [PERSON] et al. [18] proposed a unified framework for concurrent adversarial training and model pruning using the alternating direction method for multipliers (ADMM).  
More recently, [PERSON] et al. [19] proposed a different method that prunes weights with a criterion based on the adversarial training objective function. [PERSON] et al. [45] suggested that the vulnerability against adversary is caused by the distortion in the latent feature space, and they developed an adversarial neural pruning method that removes vulnerable latent-features. In the work of [PERSON] and [PERSON] [20], they used the proximal gradient method (PGM) and knowledge distillation for robust model compression.  
Our focus is to improve the robustness of highly compressed models against natural noise, where we use the on-manifold adversarial examples as the samples with the largest noise. To the best of our knowledge, no studies have addressed this setting. Table 1 compares the existing robust DNN training methods and ours in the perspectives of model compression and on/off-manifold adversarial examples.
## 4 Experiments and Analysis
### Pruning Performance Analysis on Taylor-Expansion-Based Filter Sorting Criterion  
To illustrate the pruning performance of the proposed FPRL method by adopting the Taylor-expansion-based filtering sorting criterion, a comparison study against the minimum-weight-based filter sorting criterion is conducted and the results are provided in Table 2. The metrics on network accuracy and complexity including the top-1 accuracy (Acc), the change in accuracy before and after pruning (\(\Delta\)Acc), the compression rate of network parameters (Params. (\(\times\))), and the compression rate of FLOPs (FLOPs. (\(\times\))) are considered. From Table 2, it is observable that (1) generally, the FPRL achieves over \(10\times\) the parameter compression and over \(3\times\) the Flops reduction with small decreases in accuracy with both filter-sorting criteria on both datasets for all the networks. (2) Compared to CIFAR10, the classification task on CIFAR100 is more difficult and the decrease in accuracy is higher with both sorting criteria. (3) VGG-19 and ResNet-34 illustrate higher pruning rates than VGG-16 and ResNet-18, respectively. This demonstrates that the structures of VGG-19 and ResNet-34 are more redundant than that of VGG-16 and ResNet-18, respectively. (4) The Taylor-expansion-based filter sorting criterion improves the pruning performance in both network compression and accuracy. For the considered DCNNS, the Taylor-expansion-based pruning method generally achieves a higher compression rate in both Params. (\(\times\)) and FLOPs. (\(\times\)), but a smaller decrease in accuracy on both datasets. To further investigate how the proposed FPRL method works, the pruning rates for each layer are considered. Figure 3 illustrates an example of the optimal layer-wise filter pruning rates for VGG-16 offered by the DDPG algorithm on the CIFAR10 dataset. It is observable that the third, fourth, and fifth convolution layers are more important, and the pruning rates are low. Pruning these layers will greatly affect the performance of the model. If filters are pruned too much in these layers, the accuracy of the model cannot be recovered even with fine-tuning or retraining. The redundancy of the 10 th, 11 th, 12 th, and 13 th convolution layers \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network** & **Params** & **FLOPs** & **Acc** \\ \hline  & CIFAR10/CIFAR100 & CIFAR10/CIFAR100 & CIFAR10/CIFAR100 \\ \hline VGG-16 & 14.05M/14.09M & 299.48M/299.53M & 93.67\%/71.10\% \\ VGG-19 & 19.11M/19.16M & 380.56M/380.60M & 92.58\%/69.71\% \\ ResNet-18 & 10.66M/10.70M & 530.86M530.91M & 95.28\%/74.71\% \\ ResNet-34 & 20.30M/20.34M & 1107.64M/1107.69M & 94.46\%/74.51\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network initial performance. is serious as their pruning rates are more than 80%. This shows that the network does not learn much in deeper layers and these layers do not store much useful information. These layers can be greatly pruned without a significant impact on the model performance.
# Electromagnetic Characteristics Analysis of a Tubular Moving Magnet Linear Generator System
## 2 Linear Generator Design  
### Linear Generator Structure Design  
The linear generator used in FPEs must have a stable structure, low cost, good controllability, and high generating efficiency. This paper proposes a tubular single-phase moving magnet linear generator  
Figure 1: A typical composition of the free-piston engine system.  
(MMLG) to meet the requirements of FPEs. A simplified three-dimensional structure of the proposed linear generator is shown in Figure 2. As shown in the figure, the linear generator can be divided into three main parts: the stator, the mover, and the shell.  
The mover of the linear generator consists of two permanent magnet (PM) rings, four core rings, and a spacing ring. The PM rings are axial magnetized with opposite directions. The core rings are laminated by a silicon steel sheet to reduce the core loss. The non-magnetic spacing ring is used to space the core rings. The structure is designed to be stable and reliable to ensure safety so that the linear generator can work at a high-speed reciprocating motion of up to 50 Hz.  
The stator of the linear generator consists of the stator cores and the coils. The stator cores are laminated by a silicon steel sheet and divided into 12 identical parts for reducing the manufacturing difficulty. The coils are connected in series to form a single-phase winding. The shell of the linear generator has 12 toothed grooves, so the stator cores are fixed in the grooves. Two graphited linear bearings are installed to support and guide the reciprocating motion of the mover.  
Table 1 lists the main dimensions of the designed linear generator. Additionally, the neodymium iron boron \"N42 SH\" is used for the PM material for higher performance. The silicon steel \"47F240\" is selected for the core material. The aluminum alloy \"7075-T6\" is selected for the shell material for its low density and low magnetic permeability. A kind of high-strength plastic \"POM\" is used for the spacing ring material. The stainless steel \"304\" is used for the shaft material.
## 4 Application
### Visualization of Traffic Behaviors in Simulation Area \"B\"  
Simulation area \"B\" represents the entrance of Mokpo Port, the most congested area of the port. Figure 12 shows the result of visualizing the maritime traffic for the target area. The results of visualizing the maritime traffic data at the entrance of Mokpo Port are shown in Figure 12. Figure 12a shows the results of preprocessing and plotting the AIS data of the target area. Figure 12b presents the results of plotting the traffic density at six grid levels, while Figure 12c presents the results of clustering the representative azimuths of the ships in the target area at five grid levels. Figure 12d shows the results of clustering the navigation data into five major traffic flows identified by curve fitting and extracted into five representative routes. Finally, Figure 12h shows an integrated visualization of the results displayed in the electronic nautical charts in Figure 12e-g. In simulation area \"B\", five major routes were extracted for entering and exiting Mokpo Port. Notably, the traffic density distribution in the Mokpo Port narrow channel and the entrance and exit areas was high. Therefore, ships sailing in the target area face significant risks in navigation around the narrow channel.  
Figure 11: A layered projection of maritime traffic data visualization information: (**a**) Traffic density overlaid on an electronic nautical chart. (**b**) Traffic direction overlaid on an electronic nautical chart. (**c**) Traffic stream overlaid on an electronic nautical chart. (**d**) Result of integrated visualization for traffic behaviors in area \"A\".  
Figure 12: Integrated visualization of Area “B” traffic behaviors: (**a**) Preprocessed ship AIS and trajectories data. (**b**) Result of traffic density heatmap on grid level 6. (**c**) Result of traffic direction arrow on grid level 5. (**d**) Result of traffic stream clustering. (**e**) Traffic direction overlaid on an electronic nautical chart. (**f**) Traffic stream overlaid on an electronic nautical chart. (**g**) Result of integrated visualization for traffic behaviors. (**h**) Result of integrated visualization for traffic behaviors in area “B”.
# An Overview of FIR Filter Design in Future Multicarrier Communication Systems
## 3 FIR Prototype Filter Design
### Windowing Based Methods
#### 3.2.3. Mottaghi-Kashtiban's Method  
In [127], a special case of raised cosine windows is presented. The proposed window function is  
\[w[n]=a_{0}-a_{1}\cos(\frac{2\pi n}{N-1})-a_{3}\cos(\frac{6\pi n}{N-1}),\;0\leq n \leq N-1. \tag{44}\]  
For normalization, that is, \(w\left[\frac{N-1}{2}\right]=1\), then  
\[a_{0}+a_{1}+a_{3}=1. \tag{45}\]  
This new window is symmetric about \((N-1)/2\), and it has linear phase, thus the window function is obtained by  
\[w[n]+w[n-(N-1)/2]=2a_{0},\;\frac{N-1}{2}\leq n\leq N-1. \tag{46}\]  
Figure 3: Frequency responses of Hamming window, Gaussian window and the new window proposed in [126].  
Figure 2: Frequency responses of Hamming window, Blackman window and the new window proposed in [125].  
The expression in Equation (44) is a 4 th order raised cosine window [128] and its third term is zero  
\[w[n]=\sum_{i=0}^{3}a_{i}\cos\left(\frac{2i\pi n}{N-1}\right),\ \ 0\leq n\leq M,\ a_{2}=0. \tag{47}\]  
The optimal values are found by optimization and approximation. The relationship between the parameters in (47) and the length of window function \(N\) can be expressed as  
\[\begin{cases}a_{0}=0.537-\frac{0.3}{N+14},\\ a_{1}=0.46+\frac{0.25}{N+14},\\ a_{3}=1-a_{0}-a_{1}.\end{cases} \tag{48}\]  
Figure 4 shows the frequency responses of Hamming window, Gaussian window and the new window proposed in [127] for a typical filter length of \(N=41\). All of the windows have approximately equal mainlobe width. Compared with Hamming window, the new window has better peak level of maximum side-lobes, meanwhile it has better performance of side-lobe attenuation than Gaussian window.
## 3 Mobile RAN Entity Integrated into 5G and Beyond
### Integration of Low-Altitude UAVs into 5G and Beyond
In this framework, the UAS network function (UAS NF/NEF) engages with network functions such as the gateway mobile location center and the mobility management functions (e.g., access and mobility management function (AMF) in the case of 5G systems, mobility management entity (MME) in the case of LTE systems). Various UAV tracking modes have been established to cater for diverse operational scenarios: (1) UAV location reporting--The USS subscribes to the UAS NF, specifying preferences for accuracy and timing, to receive continuous UAV location updates; (2) UAV presence monitoring--The USS subscribes to event reports triggered when a UAV enters or exits a defined geographical area; (3) List of aerial UEs--The USS can request the UAS NF to provide a comprehensive list of UAVs within a geographical area served by the PLMN. This multifaceted tracking system enhances the precision and versatility of UAV monitoring within the network. These efforts are aimed at strengthening network support for UAVs, encompassing aspects such as predictive monitoring, flight-path planning, reliable communication, and enhancing UAV operational control and safety. This includes the ability to detect connected UAVs, manage flight paths, and identify non-3 GPP flying objects. Notably, the incorporation of UAVs into the 5G and beyond systems to extend coverage is currently outside the scope of consideration within 3 GPP. However, ongoing research endeavors are actively exploring the integration of UAVs as a mobile RAN entity into the forthcoming 6G system, with the goal of extending communication coverage beyond regional boundaries [(60; 48; 61; 62)].