esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Future Directions
### Kalman Filter  
The Kalman Filter (KF) is a recursive mathematical process based on a set of equations that is widely used in many engineering and scientific fields. It is known for its fast convergence speed, efficient tracking, and compensation ability. Using KF-based algorithms for HO and mobility management in 5G HetNets is an important research direction with significant potential benefits.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Challenge** & **Reason** & **Consequences** \\ \hline High Speed & It occurs due to limitations in equipment capabilities, insufficient network coverage, interference, signal attenuation, and insufficient backhaul capacity. & Increases the number of UHOs; reduces network capacity and network performance in terms of HOF and RLF rate. \\ \hline Load Balancing & Due to irregular user distribution, cell size, capacity differences, ineffective HO mechanisms, and limited coordination between network layers. & Heavily loaded cells due to load imbalance can cause high HOF, uneven utilization of resources, and degraded QoS. \\ \hline Signaling Overhead & A large number of small cells and frequent HOs due to their small coverage area is the main cause of increased signaling overhead. & The higher the HOR, the higher the signaling overhead will be, which consequently increases the HOIT and HO delay in WCS. \\ \hline ICI & It occurs due to high user density, overlapping cell coverage, and inadequate frequency reuse. & Increased call drop rate (CDR) and degraded user satisfaction due to overlapping cell coverage area. \\ \hline Security and Privacy & Network densification (huge number of cells and connected UE) and multiple technologies and interfaces. & Increased risk of cyber attacks, compromised user data, and reduced trust in network reliability and integrity. \\ \hline \hline \end{tabular}
\end{table}
Table 10: Description of focused challenges in 5G HetNet.  
Firstly, KF can provide an accurate and real-time estimation of user location and velocity, which is critical for efficient HO and network optimization. This means that the filter can predict the optimal time and location for the next HO and adjust network parameters accordingly to ensure that users stay connected to the network and experience minimal disruption.
* Secondly, the KF can track user movements and predict their future trajectory, allowing for more efficient use of network resources. The filter can reduce interference and improve overall network reliability by adjusting network parameters such as antenna beamforming and transmission power. This can result in better overall network performance and improved user experience.
* Finally, the KF is a flexible and adaptable approach that can be customized to different network scenarios and configurations. This means that it can be used in a wide range of 5G HetNets, including those with different BS volumes, user densities, and network topologies.
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 6 Conclusions  
This work has presented D-SWIM, a dynamic programmable line buffer microarchitecture for arbitrary sized streaming image processing on FPGAs. The D-SWIM architecture facilitates high-throughput realignment of multi-pixel blocks into line buffers suitable for further streaming image processing. In addition, through a rapid instruction code update, D-SWIM allows for the size of line buffers to adjust dynamically to accommodate varying size requirements of the application during run time. Compared to prior studies where SWIM can only work on a predetermined image size, D-SWIM achieves dynamic programmability for varying image sizes with a slight logic resource overhead. In our experiment, the D-SWIM buffer reached a maximum operating frequency of 329.5 MHz and saved BRAM resources up to 56% that contributed to a power consumption reduction of 45.7%. When compared to other state-of-the-art FPGA-based streaming architectures using two real-world image applications as benchmarks, D-SWIM contributes to a significant hardware efficiency improvement of 25\(\times\) in LUT and 30\(\times\) in REG. For the benchmark cases, the D-SWIM based design reaches a pixel throughput of 4.2 GPPS when 16 pixels are input every cycle.  
As more applications domain begin to take advantage of vision-based intelligence, the number of systems that demand high-performance image processing is going to increase. D-SWIM represents our first step in systematically generating flexible high-throughput low-latency streaming image processing hardware. In the future, we expect to further the capability of D-SWIM to facilitate generation of the complete intelligent image processing system automatically for FPGAs.  
Conceptualization, R.S. and H.K.-H.S.; Investigation and implementation, R.S. and J.S.J.W.; Validation, J.S.J.W.; Writing-original draft, R.S. and J.S.J.W.; Writing-review and editing J.S.J.W.; Funding acquisition, H.K.-H.S.  
This research was funded in part by the Croucher Foundation (Croucher Innovation Award 2013) and the Research Grants Council of Hong Kong grant number CRF C7047-16G, GRF 17245716.  
The authors declare no conflict of interest.
## 2 Framework of RUL Prediction Based on TCNMABG
### Multi-Domain Mixed Feature Extraction and Selection
#### 2.3.3 Identifying the First Prediction Time  
The first prediction time (FPT) is an important index to evaluate the rolling bearing degrading from the normal state to a degenerate state, which is important for RUL prediction. To better illustrate the degradation trend of rolling bearings, a segmented function is used to characterize the degradation process of bearings, which is shown in Equation (3).  
\[f(t_{i})=\begin{cases}1&t_{i}\leq t_{j}\\ \left(\frac{1}{t_{j}-t_{n}}\right)\cdot t_{i}+\left(\frac{t_{n}}{t_{n}-t_{j}} \right)&t_{i}\geq t_{j}\end{cases} \tag{3}\]  
where \(t_{j}\) is the initial degradation time, the health status of the device remains unchanged until the \(t_{j}\) moment, and then linearly declines to the \(t_{n}\) moment when the device completely fails.  
Traditional methods identify FPT by using artificially set thresholds, such as \(3\sigma\) criteria for constructed health indicators, which introduce subjective factors and affect the reliability of the results. Moreover, the method of using quadratic differentiation to determine FPT for health indicators has problems, such as difficulty in observing and determining the results. Therefore, this article adopts the Kernel Fuzzy C-means (KFCM) clustering method [36] to identify FTP. KFCM considers the fuzzy affiliation between the data points and the clustering centers and the kernel similarity between the data points, which can more accurately curve the complex structure and intrinsic correlation of the data. The detailed process of KFCM is summarized as follows.  
The KFCM clustering algorithm is an improved fuzzy C-means algorithm, which maps data to high-dimensional space by introducing a kernel function to deal better with nonlinear data. The process includes initializing the cluster center and membership matrix, iteratively updating the cluster center and membership matrix, using the kernel function to perform spatial transformation, and finally outputting cluster results with fuzzy membership.
## 3 System Model  
### Elastic Optical Path Networks with Multi-Core Fibers  
Figure 1 illustrates the system model of elastic optical path networks assumed in this paper. Let \(\mathcal{G}=(\mathcal{V},\mathcal{E})\) denote an elastic optical path network represented by a directed graph, where \(\mathcal{V}\) represents a set of nodes and \(\mathcal{E}\) represents a set of links. Each node has input links and output links, each of which consists of a multi-core fiber. All multi-core fibers have the same number of cores. Let \(\mathcal{C}=\{c_{1},c_{2},\ldots,c_{|\mathcal{C}|}\}\) denote a set of cores in each fiber. In each core, frequency-spectrum resources are divided into FSs, the unit of which is 12.5 [GHz]. Let \(\mathcal{F}=\{f_{1},f_{2},\ldots,f_{|\mathcal{F}|}\}\) represent a set of available FSs. In this paper, we assume that the FSs are numbered in ascending order. Multiple optical signals of different FSs can simultaneously pass through each core.  
As shown in Figure 1, lightpaths are established to transmit data requested by senders with the use of successive FSs. Note that there is a gap called a guard band between successive lightpaths on the FSs to avoid interference. In this paper, the guard band length is assumed to be 1 FS. The required number \(R_{p}\) of FSs for lightpath-setup request \(p\) is calculated based on the traffic demand and the modulation level, which is given by  
\[R_{p}=\lceil\frac{D}{TM_{p}}\rceil, \tag{1}\]  
where \(D\) represents the traffic demand [bps] and \(T\) represents the transmission capacity [bps] per FS for 1 bit/symbol transmission. \(M_{p}\) denotes the modulation level of lightpath \(p\), which is determined according to the transmission distance of the lightpath. Due to the restriction of the OSNR, simple modulation schemes such as Binary Phase-Shift Keying (BPSK) (\(M_{p}=1\)) and Quadrature FSK (QPSK) (\(M_{p}=2\)) are used for long-distance lightpaths. On the other hand, for short-distance lightpaths with high OSNRs, modulation schemes such as 8-QAM (\(M_{p}=3\)) and 16-QAM (\(M_{p}=4\)), which have high frequency utilization efficiency, can be used.
## 4 ME-SSA Receiver Design
### Burst Demodulator
Such composite signal is sent in parallel to \(K\) lines, one per burst, where the relevant burst time and Doppler rate and frequency offsets are corrected and then despreading is applied. The \(K\) symbol vectors obtained in this manner represent the input to the next stage, which performs the same processing, and so on until all the \(S\) stages have been executed. The symbols at the output of each stage are also weighted and summed together. When the last stage has ended, such weighted sum represents the \(K\) despread bursts as they were obtained by a linear MMSE detector.
# A Novel Dual-Permanent-Magnet Mechanical Antenna for Pipeline Robot Localization and Communication
## 3 Development of the Novel Mechanical Antenna
### Experiment in Air  
The operating frequency of the antenna was maintained at 30 Hz. By switching \(\alpha\) between 0\({}^{\circ}\) and 120\({}^{\circ}\), the antenna transmitted codes 0 and 1 at a rate of 1 bit/s. Figure 6a shows the waveforms of the magnetic flux densities \(B_{x}\), \(B_{y}\), and \(B_{z}\) measured at \(y=5\) m along the positive \(y\) axis using a three-dimensional symmetrical induction coil [28]. In addition to the spectral component at 30 Hz, the components at 50 Hz and its harmonic frequencies were also measured. The waveforms after filtering these components are shown in Figure 6b. The distribution of magnetic flux density \(B\) and the modulation depth \(T_{m}\) along the positive \(y\) axis are shown in Figure 7 when \(\alpha=0\) and \(\alpha=120^{\circ}\). It can be seen the magnetic field reached 2.35 nT at \(y=10\) m when \(\alpha=0\) and the values of \(T_{m}\) ranged from 50% to 60%.  
Figure 5: Dual-permanent-magnet mechanical antenna: (**a**) principle of the structure; (**b**) physical prototype.  
Figure 6: Three-dimensional magnetic flux density and its spectral characteristics: (**a**) before filtering; (**b**) after filtering.  
Figure 7: The distribution of magnetic flux density \(B\) and modulation depth \(T_{m}\).
# SINDVis: User-Centered Dynamic Interactive Visualization System for Space Information Networks
## 3 Theory and Methods  
The purpose of this section is to introduce the related theory and methods of SINDVis. As we know that framework is a guidance for system design and implementation, we constructed the architecture of SINDVis first. Based on this, we illustrate the functions of GeoView in the entity domain and TopolView in the topology domain, respectively. The GeoView and TopolView play key roles in SINDVis, and the implementation principles of them are different, so we separately explain theory and methods of the GeoView and TopolView. In addition, we focus on the implementation methods of the TopolView in the topology domain, including an improved FDA (Force-Directed Algorithm) layout, FAT (Fusion of Animation and Timeline) visualization and P&Z (Panning and Zooming) interaction.
# Analytical Model of Gear Thermal Stiffness Based on the Potential Energy Method
## 2. Gear Stiffness Based on Potential Energy Method  
Reference [25] presented an improved method for calculating gear stiffness based on the potential energy method of a nonuniform cantilever beam. The stiffness of a single tooth, \(K\), the mesh stiffness of a single tooth pair, \(K_{i}\), and the time-varying mesh stiffness, \(K\), are given by Formulas (1)-(3) [25], respectively.  
\[\frac{1}{K^{j}}=\frac{1}{K_{b}^{j}}+\frac{1}{K_{a}^{j}}+\frac{1}{K_{s}^{j}}+ \frac{1}{K_{f}^{j}} \tag{1}\]  
\[\frac{1}{K_{i}}=\frac{1}{K^{p}}+\frac{1}{Ks}+\frac{1}{K^{h}} \tag{2}\]  
\[K=\sum_{i=1}^{N}K_{i} \tag{3}\]  
where \(K_{b}^{j}\), \(K_{a}^{j}\), and \(K_{s}^{j}\) represent the bending stiffness, axial compression stiffness and shear stiffness of a single gear tooth, respectively; \(K_{f}^{j}\) is the fillet foundation stiffness, and the superscript \(j=(p\) or \(g)\) represents the pinion or gear, respectively; \(K^{h}\) is the local contact stiffness; and \(N\) is the number of gear tooth pairs participating in meshing at the same time. In this paper, the value of \(N\) is 1 or 2.  
The gear tooth profile model is shown in Figure 2. Based on the potential energy method, \(K_{b}^{j}\), \(K_{a}^{j}\), and \(K_{s}^{j}\) are determined by Equations (4)-(6) [26], respectively; \(K_{f}^{j}\) is determined by Equation (7) [24; 27], where \(L^{*}\), \(M^{*}\), \(P^{*}\), \(Q^{*}\) and other coefficient calculation methods are shown in Reference [28]; and \(K^{h}\) is determined by Formula (8) [26]. The definitions of the variables in the following formulas are shown in Figure 2 and the literature [26].  
\[\frac{1}{K_{b}^{j}}=\int_{-\alpha_{1}}^{\alpha_{2}}\frac{\left\{3\{1+\cos( \alpha_{1})[(\alpha_{2}-\alpha)\sin(\alpha)-\cos(\alpha)]\}^{2}(\alpha_{2}- \alpha)\cos(\alpha)\right\}}{2 EL[\sin(\alpha)+(\alpha_{2}-\alpha)\cos(\alpha )]^{3}}d\alpha \tag{4}\]  
\[\frac{1}{K_{a}^{j}}=\int_{-\alpha_{1}}^{\alpha_{2}}\frac{(\alpha_{2}-\alpha) \cos(\alpha)\sin^{2}(\alpha)}{2 EL[\sin(\alpha)+(\alpha_{2}-\alpha)\cos( \alpha)]}d\alpha \tag{5}\]  
Figure 1. The flow chart to summarize the proposed model.  
\[\frac{1}{K_{f}^{l}}=\int_{-\alpha_{1}}^{\alpha_{2}}\frac{1.2(1+\
u)(\alpha_{2}- \alpha)\cos(\alpha)\sin^{2}(\alpha)}{EL[\sin(\alpha)+(\alpha_{2}-\alpha)\cos( \alpha)]}d\alpha \tag{6}\]  
\[\frac{1}{K_{f}^{l}}=\frac{\cos^{2}(\alpha_{1})}{Eb}\Bigg{\{}L^{*}(\frac{u_{f}}{ S_{f}})^{2}+M^{*}(\frac{u_{f}}{S_{f}})+P^{*}\Big{(}1+Q^{*}\tan^{2}(\alpha_{1}) \Big{)}^{2}\Bigg{\}} \tag{7}\]  
\[K^{h}=\frac{\pi EL}{4(1-v^{2})} \tag{8}\]  
For the convenience of description, the gear stiffness that does not consider the effect of temperature will be referred to as elastic stiffness in subsequent sections.
## 3 The Proposed Scheme
### Data Embedding
#### 3.3.2. The Solution for the Overflow/Underflow Problems  
Unfortunately, in the embedding process, overflow/underflow problems may occur in the stego pixel values after applying MGEMD. That is to say, the stego pixel values may exceed the maximal value  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Formula** & **Example** \\ \hline \(s_{1}=5429\) & \(D=(s-f_{c})\bmod 2^{nk+1}\) & \(D=7810\) \\ \hline \(D>2^{nk}\) & \(D=2^{nk+1}-D\) & \(382=8192-7810\) \\ \hline \(D_{10}\to d_{2}\) & \(d=(d_{3}d_{2}d_{1}d_{0})_{8}\) & \((0576)_{8}\) \\ \hline \((x_{1},x_{2},x_{3},x_{4})\) & \((x_{1},x_{2},x_{3},x_{4})\) & \((10,19,5,9)\) \\ \hline \(d_{3}d_{2}d_{1}d_{0}\) & \((0-d_{3})(d_{3}-d_{2})\) & \((0-0)(0-5)(5-7)(7-6)\) \\ \((d_{2}-d_{1})(d_{1}-d_{0})\) & \(0,-5,-2,1\) \\ \hline \((x_{1}^{{}^{\prime}},x_{2}^{{}^{\prime}},x_{3}^{{}^{\prime}},x_{4}^{{}^{\prime}})\) & \((x_{1}+d_{0},x_{2}+d_{1},x_{3}+d_{2},x_{4}+d_{3})\) & \\  & \((10+1,19-2,5-5,9+0)\) & \((11,17,0,9)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Speeding up the embedding method when \(D>2^{nk}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Formula** & **Example** \\ \hline \(s_{2}=6643\) & \(D=(s-f_{c})\bmod 2^{nk+1}\) & \(D=832\) \\ \hline \(D<2^{nk}\) & \(D=D\) & \(832\) \\ \hline \(D_{10}\to d_{2}\) & \(d=(d_{3}d_{2}d_{1}d_{0})_{8}\) & \((1500)_{8}\) \\ \hline \((x_{1},x_{2},x_{3},x_or may be smaller than the minimal value zero for the gray-level image. Therefore, the proposed scheme provides a scheme to address this problem. In our scheme, \(2^{k}\) bits are embedded in each cover pixel, where the pixel value of the cover image can fall between zero and \(2^{k}-1\). In order to avoid problems, we can modify the pixel value to \([0,1,\ldots,2^{k-2}]\) and \([255-(2^{k}-2),\ldots,255]\), respectively. Therefore, the value of the cover pixel will be modified to \(2^{k}-1\) when its value is between zero and \(2^{k}-2\). Similarly, the value of the cover pixel will be modified to \(255-(2^{k}-1)\) when its value is between \(255\) and \(255-(2^{k}-2)\). Hence, this solves the overflow/underflow problems during the embedding phase.
range, used in the latest WiFi standard, is causing serious bottlenecks in WiFi capacity. The introduction of visible light communication (VLC) and optical wireless communication (OWC) was a notable solution for bandwidth scarcity in WLANs. The use of optical signals comes with virtually unlimited bandwidth, allowing these networks to easily scale towards multi-Gbps data rates. These kinds of data rates in indoor environments are still challenging for 5G networks. Thus, in order to meet the service requirements of 6G by 2030, it is vital to exploit the capabilities of WLANs.  
Despite the high data rates of WLANs, the latency and reliability of WLANs fall behind those of 5G/6G networks. Most of the development in WiFi in the past decade has been targeted towards increasing data rates and capacity. Naturally, the early works of VLC and OWC were also focused on improving data rates. As a result, WLANs were not considered for the 5G/6G networks in recent literature. However, the latest advancements in WLANs, such as WiFi 6/6E/7 and IEEE 802.11 bb, are focused on providing latency-guaranteed, reliable WLANs. Therefore, a comprehensive study on the capabilities of the latest WLANs and their suitability for the 6G ecosystem is a timely contribution.  
This paper therefore discusses how the latest advancements in WLANs place them in a viable position to support future 6G deployments. Our main contributions in the paper are as follows.  
* We identify the key user applications that need critical service requirements from the 6G network;
* We employ a systematic literature review approach to provide a comprehensive review and analyses of the latest development of major WLAN technologies, including WiFi 6/6E/7, 60 GHz WiFi, VLC, and OWC, in terms of the physical layer and upper layers, and to identify research gaps;
* We also provide a discussion on their key performance metrics and how each technology is suitable for supporting 6G and emerging applications;
* Further, we also identify the research challenges and opportunities we have in using WLAN technologies to support 6G-and-beyond wireless access.  
Figure 1: Components of the 6G ecosystem.  
To the best of our knowledge, this is the first paper that provides a comprehensive analysis of all three major WLAN technologies and discusses the integration of WLANs in the 6G ecosystem.  
The remainder of this paper is structured as follows. First, we discuss the upcoming indoor wireless applications and their requirements in Section 2. Next, the upcoming WLANs are introduced and briefly explained in Section 3. After the introduction, a detailed discussion of latest advancements in WiFi, VLC, and OWC is given in Sections 4-8. Finally, we discuss the opportunities and challenges in integrating these WLANs in the 6G ecosystem in Section 9, before concluding the paper in Section 10.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 5 Enhancing the EPR Effect
### Physical Strategies
#### 5.2.1 Radiation Therapy (RT)  
Radiation therapy (RT) (Figure 3a) is a well-established physical strategy that enhances the EPR effect by increasing vascular permeability and reducing interstitial fluid pressure within tumors. RT achieves this by inducing endothelial cell apoptosis and upregulating the expression of pro-angiogenic factors such as vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) [174]. These changes facilitate the penetration and distribution of nanoparticles (NPs) within the tumor, thereby improving drug delivery [175].  
RT is often combined with liposomal chemotherapy to maximize therapeutic outcomes. For instance, [PERSON] et al. demonstrated that combining RT with Caelyx(r) (liposomal doxorubicin) significantly improved drug distribution throughout osteosarcoma xenograft models. This combination not only delayed tumor growth but also extended the tumor doubling time compared to control conditions, highlighting the synergistic effects of RT and liposomal chemotherapy [176].  
Further studies by [PERSON] et al. explored the combination of carrier-based radiochemotherapy for solid tumors. They found that administering RT 24 h after the intravenous (IV) injection of 31 and 65 kDa hydroxypropyl methacrylate (HPMA) copolymer NPs significantly increased NP accumulation in various tumor types. This approach consistently enhanced the EPR effect across different tumor models, demonstrating its potential for improving nanoparticle-based cancer treatment [177].  
However, RT faces challenges related to non-specific tissue targeting, which can result in damage to healthy cells. To address this, researchers have explored the use of radiosensitizers, such as NPs loaded with radioenhancers or chemotherapy agents [178]. These radiosensitizers aim to intensify tumor treatment efficacy while sparing healthy tissue. For example, gold nanoparticles have been studied for their ability to enhance the EPR effect during RT. [PERSON] et al. investigated vascular-targeted AuNPs combined with RT and liposomal iodine delivery. They found that AuNPs significantly increased the accumulation of liposomal iodine in tumors, particularly at lower radiation doses, demonstrating their potential as effective radiosensitizers compared to non-functionalized nanomedicine combined with RT [179].