esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 4 Simulation Result  
### Simulation Setup  
The simulation is set in a 1000 \(\times\) 1000 m grid, within which the potential nodes are systematically positioned every 10 m, echoing the real-world spacing of street lights and, thus, yielding 100 \(\times\) 100 potential node sites. The donors, being the cornerstone of this simulation, are not predetermined but rather are introduced as variable elements. Their quantities and positions are randomly generated inputs, due to the in-the-real-world constraint of donors being tethered to the core network via wired connections. This introduces a level of unpredictability and variability into our simulations, ensuring that our model is not just theoretical but also works under various predefined donor cases. Based on the donor locations and number characterized by randomness, we proceed to incorporate statistical methods to analyse and comprehend the resultant effects on the algorithm's performance and efficacy. Each area within the 1000 \(\times\) 1000 grid is mandated to be under the coverage of either a node or a donor. The nodes, in turn, are linked to the donors ensuring a seamless backhaul communication, underpinned by our assumption of line of sight (LoS) communication. This dual conditionality of coverage and backhaul ensures that the deployment of nodes is not just about coverage but is the requirement for effective communication back to the core network via the donors. In our simulation, to encapsulate the variation in data rates for different nodes or donors, each donor and node is assigned a random access data rate ranging from 0.1 Gbps to 1.5 Gbps (100 Mbps to 1500 Mbps). This diverse data rate ensures that our simulation delivers fiber-like access speed with random access. Also, each donor's capacity is at 15 Gbps, a specification that ensures the robustness and efficacy of the wireless backhaul communication. Also, the link budget analysis of Figure 4 ensures that such data rate assumptions are reasonable. An overhead of 1.2 is configured to account for various communication inefficiencies and to ensure a more realistic simulation scenario.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 5 Application of Silica Mesopores in Drug Delivery
### Redox-Responsive Drug Delivery  
Many studies have been conducted about redox-responsive delivery systems with disulphide bonds, which extensively can be applied to attach to different gatekeeper such as peptides [187], antibodies [188], and proteins [189]. During the reduction of glutathione into sulfhydryl groups, the disulphide bonds break readily, which destroys the carriers and facilitates the release of the cargo.  
Figure 16: Forming reversible addition-fragmentation chain transfer (RAFT) agent-coated mesoporous silica nanoparticles (MSNs) as the novel, smart nanocontainers incorporating an MSN for pH sensitivity.  
[PERSON] et al. performed an experiment on attachment of collagen to MSNs by a disulphide linker followed by introduction of lactobionic acid, which resulted in redox-responsive system for cell-specific intracellular drug delivery and efficient endocytosis [189]. Other studies have investigated diselenide bonds, which have reduction sensitivities and redox-responsive abilities that are similar to those for disulphide bonds. In tumor therapy, more sensitive redox-responsive delivery systems can be designed with diselenide bonds due to the lower energy of Se-Se bonds compared to S-S bonds and C-Se bonds (Se-Se 172, C-Se 244, and S-S 268 kJ/mol). In addition to disulphide and diselenide bonds, there are some studies that have examined the succinimide-thioether linkage, delivery systems with trimethyl-locked benzoquinone (TMBQ), and some other delivery systems [190]. In another study, [PERSON] et al. investigated the role of the S-S bond in drug release [187]. First, they synthesized a mercapto acetic acid covered with CdS nanocrystals. CdS, due to its small size, can penetrate meso-channels conventionally, thus blocking them and preventing the drug's exit. Therefore, CdS is dissociated by reducing agents and released from inside the cavity; subsequently, the drug is released from the nanopores (Figure 17).
## 4 Multi-Objective Deep Reinforcement Learning Based on the PPO Algorithm  
In this section, we introduce the application of the proximal policy optimization (PPO) algorithm, a robust deep reinforcement learning approach, under the MEC environment. The PPO model was chosen for its notable benefits in multi-objective optimization, characterized by stability, adaptability, and efficient sample utilization, making it highly suitable for the dynamic and uncertain environments of MEC. The problem of computational offloading and resource allocation was first constructed as a Markov decision process (MDP) model, and the PPO algorithm was used to obtain the optimal solution.
# Ultrasound Navigation for Transcatheter Aortic Stent Deployment Using Global and Local Information
###### Abstract  
An ultrasound (US) navigation system using global and local information is presented for transcatheter aortic stent deployment. The system avoids the use of contrast agents and radiation required in traditional fluoroscopically-guided procedures and helps surgeons precisely visualize the surgical site. To obtain a global 3D (three-dimensional) navigation map, we use magnetic resonance (MR) to provide a 3D context to enhance 2D (two-dimensional) US images through image registration. The US images are further processed to obtain the trajectory of interventional catheter. A high-resolution aortic model is constructed by using trajectory and segmented intravascular ultrasound (IVUS) images. The constructed model reflects morphological characteristics of the aorta to provide local navigation information. Our navigation system was validated using in vitro phantom of heart and aorta. The mean target registration error is 2.70 mm and the average tracking error of the multi-feature particle filter is 0.87 mm. These results confirm that key parts of our navigation system are effective. In the catheter intervention experiment, the vessel reconstruction error of local navigation is reduced by 80% compared to global navigation. Moreover, the targeting error of the navigation combining global and local information is reduced compared to global navigation alone (1.72 mm versus 2.87 mm). Thus, the US navigation system which integrates the large view of global navigation and high accuracy of local navigation can facilitate transcatheter stent deployment.  
Keywords:ultrasound navigation; global and local information; registration; segmentation; catheter intervention +
Footnote †: journal: Medical Imaging
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 2 Related Work
### RAN Optimization Use Cases  
Mobile networks currently support various optimization methods. Self-organizing network (SON) and minimization of drive test (MDT) were initiated over a decade ago by  
Figure 5: RAN signaling for ML-based operations.  
the next generation mobile networks (NGMN) alliance and later standardized by the 3 GPP. These are continuously evolving features on the enhancements' roadmap for 5G.  
The mechanisms aim at automating the huge efforts the operators spent on collecting performance measurements, adjusting network configuration parameters, network planning, and monitoring tools configurations, including multivendor scenarios. Depending on a scenario, SON ensures autonomous adaptation of the network by an optimization algorithm tailored to a specific need, detailed solution, and requirement (e.g., for interference control, the network may autonomously change the settings of frequency/power restrictions and preferences for the resource usage in the different cells based on interference level). MDT specifically facilitates the automated collection of performance measurements, especially from the users. Both functions support the following fundamental use cases:  
* Coverage optimization, which consists of detecting and optimizing poor cell coverage and over coverage collectively, coverage maps planning, cell coverage, and capacity monitoring according to the operator-specific deployment strategy and requirements on traffic models and coverage holes detection;
* Mobility Optimization, which consists of mobility events observation (handovers), including the detection of mobility events resulting in errors, causing radio link failure (RLF) due to unsuccessful handovers, detection, and correction of errors in the mobility configuration, etc.;
* _QoS_ Optimization, which consists of the user's quality of experience monitoring, verification of whether the quality of service experienced by the end user is in line with the performance target defined in the planning strategy, data rate, and cell throughput observations, etc.  
These optimization processes reveal synergy with inferring optimal behavior from ML-based operations. For that reason, it is envisioned that the computational abilities of appropriate ML techniques can empower SON and MDT with intelligence and shape new applicability of the SON and MDT features in continuously developed 5G networks.
# Optimization of Pico-eNB Tx Power and the Effects of Picocell Range Expansion in Multiband HetNet
## 3 HetNet Model and Simulation Conditions  
### Multiband HetNet Model and Simulation Setup  
We investigate the average and 5-percentile user throughput of a multiband HetNet using system-level computer simulations. The system-level simulations are sufficient for evaluating user throughput for mobile networks, and contribute significantly to the requirements at early stages of development and hardware implementation.  
The parameters of the HetNet model used in the simulations are listed in Table 1. The carrier frequencies used in the macro- and pico-eNB are 2.0 and 4.5 GHz, respectively. The signal bandwidth for each macro-sector is 10 MHz, and the bandwidth for each picocell is 100 MHz. The number of picocells distributed in each macro-sector is fixed to 4, and the number of UE installations in each macro-sector is assumed to be 30, which is usually used in 3 GPP simulation assumptions [41; 42].  
Figure 2: Cell selection procedure based on RSRP in HetNet.  
The UE layout is important for executing system-level computer simulations because large amounts of UE should be distributed in the HetNet to evaluate the user throughput. Figure 3 shows the definition of cluster size for the UE layout, which comprises the radius of a circle centered at each pico-eNB. When the UE layout is based on the form of the cluster distribution near the pico-eNBs, the ratio of the UE cluster distribution is defined as the ratio of the number of UE within a circle of a specific radius (cluster size) to the number of UE outside a circle [41,43].  
Figure 3 shows an example of a two-thirds (2/3) UE cluster distribution. Assuming that the number of UE in the macro-sector is 30, 20 pieces of UE are distributed within the circle centered at pico-eNBs; the remaining 10 pieces of UE are distributed outside the  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}  & **Macro-eNB** & **Pico-eNB** \\ \hline \multirow{2}{*}{Cell layout} & Hexagonal grid, 19 cell sites & \multirow{2}{*}{4 picos per sector} \\  & 3 sectors per site & & \\ \hline Carrier frequency & 2.0 GHz & 4.5 GHz \\ \hline System bandwidth & 10 MHz & 100 MHz \\ \hline Cell radius (ISD) & 289 m (500 m) & – \\ \hline Tx antenna height & 32 m & 10 m \\ \hline Tx power & 46 dBm & from 28 to 45 dBm \\ \hline Tx antenna gain & 14 dBi & 5 dBi \\ \hline Tx antenna downtilt & 15 deg. & 10 deg. \\ \hline UE distribution & 30 UEs per sector, 2/3 clustered distribution \\ \hline Scheduling algorithm & Proportional fairness \\ \hline Link adaptation & QPSK to 1024-QAM (25 MCS indexes) \\ \hline Traffic model & Full buffer \\ \hline MIMO & \(2\times 2\) SU-MIMO \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multiband HetNet model used in the simulation.  
Figure 3: Definition of cluster size for UE layout around pico-eNBs.  
circle. The Tx power of pico-eNBs varies from 28 to 46 dBm, whereas that of macro-eNBs is fixed at 46 dBm. The path-loss model and other assumptions are obtained from [44].
## 2 The Proposed Pulse-OTFS Signal
### Signal Model
#### 2.1.2 Transmission and Reception Process  
Based on the previous introduction, we can summarize the transmission and reception process of Pulse-OTFS signals for LEO PNT as follows:  
Step 1: The navigation data are spread by the PRN code sequences and are arranged in the delay-Doppler grid by (5).  
Step 2: The delay-Doppler matrix is converted to a time-frequency domain signal by ISSFT via (1). The transformed time-frequency matrix is expressed as (6).  
Step 3: The time-frequency matrix is converted to a time-domain continuous baseband signal by the OFDM modulator via (3).  
Step 4: The time-domain continuous baseband signal is modulated to RF by the carrier and transmitted over the antenna.  
Now, we have completed the transmission process of the signal. The transmitted signal travels across the LEO channel and is received by the following steps:Step 1: The RF signal received by the antenna is down-converted to a time-domain baseband signal, and the continuous signal is converted to a discrete signal by an analog-to-digital converter (ADC).  
Step 2: The receiver uses the locally stored replica of the time domain signal to acquire the received signal to estimate the code phase and Doppler frequency.  
Step 3: The acquired signal is sent into the tracking loop to perform fine delay and Doppler measurements and obtain the demodulated navigation data.  
Step 4: The receiver uses the measurements and navigation data output from the tracking loop to complete the positioning and timing process.  
It can be seen that when Pulse-OTFS signals are employed for navigation, the reception process is the same as that of traditional navigation signals. This process is different from the communication signal in Figure 1. Since the orthogonality of the PRN code is not changed either in the time domain or the delay-Doppler domain, the delay measurement of Pulse-OTFS can also be completed in the time domain.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
###### Abstract  
Multi-object tracking stands as one of the most prominent domains in Computer Vision and has significant research value and practical importance. However, due to the complexity of scenarios in the real world, especially in crowded environments with frequent target occlusion, existing MOT frameworks often struggle to achieve precise tracking results. To enhance the trajectory association accuracy of MOT frameworks in occluded scenarios, this paper proposes a mask-enhanced occlusion-robust multi-target tracking and segmentation framework. Our method first introduces a mask-conditional feature fusion network and an occlusion-aware mask propagation network. The former network integrates a mask-guided attention mechanism with a spatial-temporal feature aggregation sub-network to improve tracking robustness in crowded scenes, and the latter network prevents the contamination of online tracking templates from noise inputs by perceiving a target occlusion state. The framework merges the mask-based methods above into a mask-integrated multi-hypothesis tracking algorithm, achieves superior adaptability in occluded scenarios, and enhances the robustness of MOTS tasks. Our framework achieves the best performance on the MOTSA (84.4%), MT, and FN metrics, with a 6.1% reduction in FN compared to the state-of-the-art method. Our method achieves significant improvements in both accuracy and precision and is validated on public datasets.  
multi-object tracking; mask; instance segment; target occlusion; trajectory association 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., 2020, pp. 20200-20200, 2020 IEEE
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 6 Predictive Maintenance Models
### Prognostics and Health Management  
The concept of Prognostics and Health Management (PHM) appeared in the 1990s with the launch of the US Army's Joint Strike Fighter (JSF) project [63]. The initial application of PHM is therefore in the field of military aviation. Given the accelerated development of sensor technology and predictive algorithms, practitioners have more opportunities to monitor and predict system states. This allows system actors to take proactive measures to avoid serious accidents. For this reason, PHM is currently popular in a growing number of fields [64].  
The objective behind the implementation of a PHM architecture includes the dynamic monitoring of the system state for the purpose of a predictable type of maintenance [65; 66]. In this respect, the prognostic activity is considered a key process; unlike diagnostics, which concerns the ability to detect and isolate the cause of an existing error, PHM has the ability to assess the development of future degradation or errors of a system: it guarantees the operation of the system and is an important step before being able to describe the different maintenance scenarios used for the prediction and prevention of malfunctions [67; 68]. Figure 10[69] shows the variation in the number of publications over the last 20 years on the topic of prognosis and health management of industrial equipment. The total number is 373, calculated on the basis of a Web of Science search result. The number of publications has increased rapidly since 2006. The total number of publications from 1999 to 2010 was 100, while the number of publications in the last five years was 198, which is much larger than the total number in the first ten years.  
The PHM life cycle can be illustrated in Figure 11, wherein the PHM cycle is composed of eight steps [50]. PHM methods can be classified into three categories as shown in Figure 12[63]. These categories are model-based approaches, data-based approaches and hybrid approaches that combine the advantages of the two previous approaches. The main objective of these approaches is to develop a model suitable for RUL evaluation under a wide range of operating conditions and at any time during the life cycle of a component or subsystem. The guidelines for PHM are selected based on the scope of its use, accuracy of the results, the cost, and the complexity of the operation.  
Figure 11: Steps in PHM [50].  
Figure 10: Publications and Citations related to PHM theme (August 2020) [69].
# An Evasion Attack against Stacked Capsule Autoencoder
## 4 Proposed Evasion Attack
### Computing the Perturbation
#### 4.2.1. Computing the Perturbation with Gradient Direction Update  
In this algorithm, we compute the sign of the gradient of Formula (2) to the image \(x\), and obtain the direction to update the value of each pixel, which decreases \(\sum_{i\in S}E(x)_{i}\). The adversarial sample is generated in this direction:  
\[x_{0}^{adv}=x,\;\;x_{N+1}^{adv}=\text{clip}_{0,1}\left(x_{N}^{adv}-\alpha\cdot \text{sign}\Big{(}\
abla_{x_{N}^{adv}}f\Big{(}x_{N}^{adv}\Big{)}\Big{)}\right) \tag{3}\]  
where \(x_{N}^{adv}\) is the generated adversarial sample based on image \(x\), \(\alpha>0\) is a hyperparameter that represents the step of change in each iteration, and \(\
abla_{x}f(x)\) represents the gradient of \(x\) to \(f(x)\). The pixel values of the generated sample are clipped between 0 and 1. The whole procedure is shown in Algorithm 1:  
```
1:Input: Image \(x\), SCAE model \(E\), classifier \(C\), hyperparameter \(\alpha\), the number of iterations \(n_{intr}\).
2:Output: Perturbation \(p\).
3:
4: Initialize \(x^{adv}\gets x\), \(x_{0}^{adv}\gets x\), \(\mathcal{L}_{p}\leftarrow+\infty\).
5:\(S\leftarrow\left\{i\middle|E(x)_{i}>\frac{1}{k}\sum_{j=1}^{k}E(x)_{j}\right\}\)
6:for\(i\) in \(n_{intr}\)do
7:\(I^{\bullet}\) Compute next \(x_{i}^{adv}\) /
8:\(x_{i+1}^{adv}\leftarrow\text{clip}_{0,1}\left(x_{i}^{adv}-\alpha\cdot\text{ sign}\Big{(}\
abla_{x_{i}^{adv}}\sum_{j\in S}E(x_{i}^{adv})_{j}\Big{)}\right)\)
9:\(I^{\bullet}\) Judge whether the current result is the best one \(\
icefrac{{\gamma}}{{\gamma}}\)
10:if\(C\left(E(x_{i+1}^{adv})\right)\
eq C(E(x))\) and \(\left\|\underline{x}_{i+1}^{adv}-x\right\|_{2}<\mathcal{L}_{p}\)do
11:\(x^{adv}\gets x_{i+1}^{adv}\)
12:\(\mathcal{L}_{p}\leftarrow\left\|x_{i+1}^{adv}-x\right\|_{2}\)
13:endif
14:endfor
15:\(p\gets x^{adv}-x\)
16:return\(p\)
```  
**Algorithm 1**Generating the perturbation with gradient direction update.
## 4 Analysis of the Inversion Results
### The Inversion Calculation Results of the Dynamic Modulus for Pavement Structures  
To further illustrate the accuracy of the proposed algorithm for the inversion of the dynamic modulus of the pavement structure, the 950 training samples used in Section 3.2 were used for exploration. The resulting changes in the RMSE and training loss curve of the training samples predicted by the CNN model are shown in Figure 9. During the first 300 iterations of the CNN training, the loss curve showed a downward trend with the improvement of prediction accuracy. After 3000 iterations at a certain stage of training, the loss value reached the minimum, the loss curve tended to be stable, and the CNN model converged. The changing trend of the RMSE curve of the training samples decreased greatly at first and then tended to be stable.  
\begin{table}
\begin{tabular}{c c c} \hline
**Sampling Method** & **Peak Values of 10 Deflection Curves** & **All Values of 10 Deflection Curves** \\ \hline RMSE of validation samples & 0.1564 & 0.0417 \\ \hline \end{tabular}
\end{table}
Table 6: The comparison of the training effects of CNN models with two kinds of input data.  
Figure 8: The RMSE change of the validation samples.  
To reflect the prediction effect of the proposed inversion model more intuitively for the dynamic moduli of the pavement structure layers, the inversion results of the validation samples were compared with the exact values, as exhibited in Figure 10. Figure 10a-c represent the comparison results of the dynamic elastic moduli \(E_{1}\), \(E_{2}\), and \(E_{3}\) of the pavement structure layers, respectively. The predicted and exact values of the dynamic elastic modulus of the first layer of the pavement were found to be basically completely matched. Furthermore, the dynamic elastic moduli of the second and third layers were also well-matched, with only a few samples having certain errors. As the FWD load is applied on the road surface, the deflection curves obtained contain more information on the dynamic characteristics of the surface layer than that of the deep pavement layers. That is, with the increase in the depth, the correlation of the pavement structure properties to the displacement of the road surface decreases; thus, the relative error of its dynamic modulus gradually increases. The relative errors of the inversion results of the dynamic elastic moduli \(E_{1}\), \(E_{2}\), and \(E_{3}\) of the three-layer pavement structure are shown in Figure 11. The MAPE of the first layer was 4.11%, and the maximum relative error was 9.53%. The MAPE of the second and third layers were basically within 3.34%, and their maximum relative errors were 11.33% and 11.91%, respectively. It is worth noting that only 950 samples were selected for training in this example. More accurate inversion results can be achieved if the number of training samples is increased.