esa-sceva/satcom-chunk-collection huggingface dataset:
# Force Analysis of Circular Diaphragm Wall Based on Circular Cylindrical Shell Theory
## 3 Force Analysis of Circular Diaphrag Wall
### Horizontal Supporting Force of the Lining  
As shown in Figure 4, the horizontal support effects of the linings are considered as the distributed forces \(q_{\rm Ci}\), which are exerted on the pit-facing surface of the circular diaphragm wall at the corresponding position in the normal direction. Along direction \(\alpha\), the normal distributed forces \(q_{\rm Ci}\) per unit depth can be expressed by Equation (2) below. In Equation (2), \(K_{\rm Ci}\) represents the support stiffness per unit depth of the lining at Layer \(i\); \(i\) represents the lining layers or the soil layers from the 1 st layer to the 15 th layer, and the values are 1, 2, 3, \(\ldots\) 15; \(w(\alpha)\) represents the horizontal displacement at a depth of \(\alpha\), caused by the excavation of the soil layer with \(\Delta h\) in thickness in every working condition; \(E_{\rm C}\) represents the [PERSON]'s modulus of the lining material; \(\delta_{\rm Ci}\) represents the thickness of the lining at Layer \(i\); and \(R_{\rm Ci}\) represents the central radius of the lining at Layer \(i\).  
\[q_{\rm Ci}(\alpha)=K_{\rm Ci}\cdot w(\alpha)=\frac{E_{\rm C}\delta_{\rm Ci}}{ R_{\rm Ci}^{2}}\cdot w(\alpha) \tag{2}\]
# Cyber Threat Actors for the Factory of the Future
## 4 Threat Actor Landscape for the Factory of the Future (FoF)
### Threat Actors for the FoF
#### 4.2.5 Assessment of Threat Actors for the FoF  
Prediction is very difficult, especially concerning the FoF. However, by combining the special characteristics that FoF environments have, we can make estimates on the most probable threat actors affecting FoF environments.  
FoF environments are likely to be a composed of shop floor industrial networks combined to the industrial internet and cloud resources. This poses challenges to information security management.  
Industrial networks have parts that have highly specialized equipment requiring highly specialized know-how. However, when access is achieved, the networks are often easy to disrupt due to high-availability requirements preventing the usage of security-enhancing techniques (e.g., security monitoring, encryption).  
The networking aspect of the FoF environment is typically operated under a more robust security management strategy. The environment, however, is typical of many CTAs, meaning that while attacks against any secure cloud provider require expertise, the more skilled attackers may already have tools available. New partners and de facto insiders who manage the infrastructure used by FoF uses will considerably increase the attack surface. The new tools and techniques require vigilant information security management for a secure application. This combined with the soft foundation of industrial networks, makes the systems very attractive to many CTAs.  
In Table 2 we present our assessment of how the observed threats listed in ENISA's 2019 report [5] relate to threat actors and the four aforementioned aspects of a FoF: network connectivity, collaboration, flexible automation and intelligence. Threat actors are given the weight of \"1\" if the CTA is a primary threat and \"0.5\" if they are considered a secondary threat by the report. This enables an estimation of how widely different CTAs take advantage of the most common threat types. The table shows an estimate of the most likely feature of the FoF to be affected by this kind of threat and is ordered so that the most often detected threat is on the top. Note, that partner and thrill seeker CTAs were not present in the report [5].  
The table shows threat actors in all categories being able to mount offense against FoF environments using existing techniques. Especially high scores are gained by cyber criminals, nation-state actors and competitors. In this analysis competitors threat technique capabilities match those of nation states and cyber criminals.
# Robust Wireless Local Area Networks Based on Compressed Sensing
## 3 Proposed Algorithms
### RTNTM Algorithm
Estimate the coefficients for CS with the least squares approach. 6. Estimate QoS degradation vector. 7. Identifies which attributes are causing the QoS degradation. 8. Calculate degradation indicator. 9. Calculate the correlation between latency and signal strength indicator (SSI). The new RTNTM method has the following advantages:  
* Detect, diagnose, and resolve network performance issues and errors either for each user or each networking device. * Track response time, transmission rate, power management, availability and uptime of routers, switches and other networking devices. * Analyze and monitor network bandwidth performance, modulation, protocol, network traffic, and traffic patterns. * Manage and control bandwidth of each wireless user and application. * Graphically display routing metrics, network performance metrics in real-time, based on dynamic routing protocol. Figure 1 shows the proposed flowchart for the RTNTM algorithm. The proposed RTNTM architecture allows reducing sampling rate, as well as network traffic. In addition, the proposed algorithm outperforms existing algorithms by improving QoS in WLANs. The proposed algorithm is established on the following steps:  
* The input data set of a WLAN partition into a random number of clusters
* Iterate over all data vectors to determine clusters based on nearest subspace
* Compute the data vector's contribution to the total residual by re-assignment of the input data vectors to their found clusters
* The algorithm is repeated by alternate applications of Steps 2 and 3 until convergence. In addition, the new RNTC method has the following advantages: \begin{table}
\begin{tabular}{c c} \hline \hline
**Quality of Service (QoS) of a WLAN** & **Value of MOS** \\ \hline Very Satisfied & 3.8–4 \\ Satisfied & 3.5–3.7 \\ Some segments are satisfied & 3.2–3.4 \\ Many segments are dissatisfied & 2.9–3.1 \\ Bad quality & Less than 2.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different quality levels of a wireless local area network (WLAN) to the different number of Mean Opinion Score (MOS). Analyze and monitor network bandwidth performance, modulation, protocol, network traffic, and traffic patterns. * Manage and control bandwidth of each user, class, wireless node, and application.
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 1 Introduction
### Contributions  
This paper focuses on developing a method for mounting sensors on a bridge wall using an aerial manipulator. The first contribution is augmenting the model-based motion planning with the adaptive impedance controller. The motion planning method accounts for the underactuated nature of the multirotor UAV and corrects the end-effector configuration for an appropriate approach. This method also relies on the dexterity analysis which keeps the manipulator configuration within its optimal region, ensuring that the manipulator is never fully extended or contracted while mounting a sensor. The second contribution is the visual blob detection which locates and tracks the appropriate sensor mounting point. The blob detection has been experimentally verified in an indoor environment, yielding the reliable and robust tracking of the mount location, as well as the blob plane orientation. Finally, the third contribution is the simulation analysis of the system's performance, conducted on a straight and inclined wall approach. The simulation concentrates on testing the motion planning together with the impedance controller, performing a repeatability analysis and ensuring that the desired contact force is achieved.  
Figure 1: Two aerial robots working together to attach sensors to different parts of a bridge and similar grade separation infrastructure. The one on the left is used to spray the resin onto the surface, while the aerial robot on the right maintains contact to the surface with the sensor attached to its end-effector.
# Parasitic Parameters Extraction of High-Speed Vertical-Cavity Surface-Emitting Lasers
## 1 Introduction  
VCSELs have important applications in short-range optical interconnects due to their low cost, low threshold, low power consumption, and high speed. In recent years, the development of big data, cloud services, and supercomputers has increased the demand for short-range optical interconnects [1] and higher speed and bandwidth are expected for 850 nm VCSEL technology. To realize higher bandwidth, it is essential to deeply understand the intrinsic dynamics and bandwidth limiting effects of VCSELs. High bandwidths of 28 GHz and 24 GHz were achieved by using four AlGaAs oxide layers with a low aluminium component and two primary oxide layers and four secondary oxide layers on the p-side of the active areas [2; 3], respectively. An 850 nm VCSEL with a bandwidth of 28.2 GHz was demonstrated using InGaAs/AlGaAs quantum wells and four oxide layers [4]. The recorded bandwidth of the VCSEL was 30 GHz [5]. A commonly optimized parasitic response method was used to analyse device parasitism by extracting parasitic parameters. Parasities can be isolated from the reflection coefficient by using numerical optimization techniques [6; 7; 8; 9; 10; 11; 12; 13; 14]. The simplest circuit model is a first-order low-pass filter containing only C\({}_{a}\), R\({}_{a}\), and R\({}_{m}\). As the authors of [14] mentioned, the approach of extracting data using a first-order low-pass filter transfer function is oversimplified, and there are large deviations between the obtained data and the actual measured data. Nevertheless, in most of the literature [15; 16; 17], this approach is still accepted as an approximation to theexact extrinsic transfer function. In addition, the equations of the plate capacitor presented in [14; 18] are inappropriate for extracting the pad capacities and resistance of the device.  
In this paper, we proposed a modified parasitic extraction method that extracts the parasitic data directly by using the least squares approach based on the equivalent circuit model. The method described the extrinsic behaviour of the high-speed oxide-confined VCSELs well and easily extracted the values of all parasitic parameters. In addition, we designed, fabricated, and characterized 850 nm VCSELs with six oxide-confined layers, and the parasitic values of the devices with different diameters of oxide apertures and mesa have been systematically compared and analysed. Finally, suggested improvements are discussed to improve the parasitic bandwidth and address the performance enhancement limitations.
# Current-Assisted Single Photon Avalanche Diode (CASPAD) Fabricated in 350 nm Conventional CMOS
## 2 Detector Design  
This first CASPAD is fabricated in a standard 350-nm CMOS process, making only use of the standard doping profiles for transistor fabrication like p\({}^{+}\) and n\({}^{+}\) zones for source and drain and P- and N-wells as local substrate. This limits quite much the number of options for the topological design, but it is good enough to show basic functionality of the CASPAD.  
The cross section of the proposed CASPAD detector is shown in Figure 1. When there is a voltage difference between two highly doped p\({}^{+}\) regions in a p\({}^{-}\) epilayer, there is a majority current which flows between the two p\({}^{+}\) regions, in this case a hole current. This current has an associated drift field and when there is an electron generated in this drift field region, it is quickly guided toward the p\({}^{+}\) region with the higher voltage. This \"current assistance\" detection principle has been used in photodetectors to improve device speed while having a larger detection volume [10]. A modulated hole current and drift field have been used in photonics demodulators for time-of-flight [11; 12], and fast gated detectors for fluorescence lifetime imaging [13]. More recently, using the same structure as in this letter, linear gain mode avalanche photodiode operation [14] has been characterized below the breakdown voltage.  
Figure 1: Schematic cross-section of the current-assisted single-photon avalanche diode (CASPAD) (not to scale) with the p-n junction in the middle and a current assistance “ring” to guide the photo-generated  
electrons towards the middle based on the additional electric field distribution. The lines with arrows represent the inverse of the drift field indicating the guiding direction for the absorbed photo-electrons. The dotted line represents the extent of the depletion region.  
The CASPAD device is embedded in a high resistive (\(\sim\)1000 \(\Omega\).cm) 14 \(\mu\)m thick p-type epilayer to limit the majority hole current level for the current assistance and its associated power dissipation. The cathode consists of an n-diffusion embedded in an N-well. The backside contact is formed of silver paste to the highly doped p-substrate which acts similarly to a metal contact. Minimum widths and spacings allowed by the CMOS process are chosen with a certain added tolerance. The dimensions of the CASPAD device are illustrated in Figure 2.
## 3 Data Collection
### Data Collected for Building the Performance Models  
Two types of data used to build the maintenance performance models were: the available recorded maintenance performance data and the expertise data.  
The available maintenance performance data for specific sections were collected from the GDOM of Riyadh Municipality. These sections were then categorized based on the initial PCI values (recorded before applying the maintenance). The criterion on which these sections were selected was the availability of recorded PCI values from at least ten years ago. The obtained information was only for the sections treated by basic routine maintenance and overlay.  
To fill the gap of unavailable data, the study attempted to capture the knowledge of the experts in the pavement maintenance field. To attain this goal, a questionnaire was designed for this purpose and distributed to the experts working with GDOM.
## 6 Simulation Results
### Validaton of the MASSIVE Test-Bed
#### 6.1.2 Validation of the ME-SSA Receiver and Implementation Loss Analysis
This meant that the scheduler accumulated chunks until a certain size was reached. When the vector was full it was delivered to the burst demodulator block,
* The size of the vectors was under a fine-tuning procedure which had the goal of finding a trade-off between providing a sufficient number of interfering bursts to the burst demodulator based on MSD while not significantly impacting the real-time functioning of the system.
## 1 Introduction
### Tall Buildings and Visibility
Today visibility analysis is performed in both binary and fuzzy forms. The binary form presents the possibility of seeing the building only, while the fuzzy method determines the percentage of visibility. Since binary visibility only examines whether a building is seen or not, it can only be useful in certain cases. The purpose of visibility in this research is fuzzy visibility in order to determine the amount of visibility of tall buildings from urban spaces, and based on this to be able to measure their impact.
## 5 Conclusions and Future Recommendations  
Multiple attempts have been made to harness Raman spectroscopy for hepatitis diagnosis. Most of these studies have targeted hepatitis B and hepatitis C in Raman-based diagnosis. Plasma, serum, PCR/HCR-amplified nucleic acids, and extracted viral DNA/RNA are used as the test samples. In the case of plasma and serum, studies have been carried out in the liquid state, whereas dried samples have predominantly been used in the biosensors and Raman reporters used for the detection of disease instead of the biochemical features of a sample. The most frequently studied test sample is blood serum. Different methodologies are being tested, including SERS, centrifugally filtered devices/membranes, immunoassay, biosensors, and microfluidic devices. Surface enhancement using nanoparticles is most frequently incorporated into all types of strategies, whether immunoassays or filter devices. The review revealed studies of relatively large cohorts of up to 1000, and a differentiation accuracy of 100% by integrating advanced multivariate data analysis tools. PCA is the most frequently used data analysis tool, reinforcing that multiple types of neural networks are enhancing the sensitivity, specificity, and accuracy of discrimination among disease and healthy samples. Quantification of the viral load and the prediction of blind samples have been achieved with very high R\({}^{2}\) values and minimal errors (RMSEPs). It can be concluded that Raman spectroscopy along with multivariate data analysis tools have strong diagnostic potential.  
In the clinical integration of RS as a hepatic diagnostic tool, the reproducibility of the results is the primary concern. Similar spectral features may also originate in the Raman spectra for different viral infections and make it difficult to assure the accuracy of the results as an independent diagnostic tool. If a patient has multiple infections or acute/chronic diseases, how the Raman spectroscopy will discriminate between them is an important research question. No research has yet reported on standard, reproducible reference spectra or spectral bands that could be linked to a distinct type of hepatitis and used as reference for the confirmation of disease. Most of the research has been based on discrimination but not the development of a reference or standard. For its use as a diagnostic tool, either a large database of diseased and healthy samples and multiple niches of samples is required, or distinct reference spectra or bands are. Future research may provide solutions to these questions, and RS-based hepatic diagnostic tools may become available for clinical interventions in the near future.  
Conceptualization, M.K. and H.J.B.; methodology, M.K. and H.J.B.; investigation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, M.K. and H.J.B.; supervision, H.J.B.; project administration, H.J.B.; funding acquisition, H.J.B. All authors have read and agreed to the published version of the manuscript.  
M.K. was funded by Research Ireland Frontiers for the Future Award 20/FFP-P/8517.  
The authors declare no conflict of interest.
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 4 Hybrid Sensor Fusion Algorithm Overview
### Testing Obstacle Detection and Tracking using Kalman Filtering  
In this section, we use the radar and LiDAR sensors to detect obstacles and measure their states by processing each sensor data i.e., the radar beat signal and LiDAR point cloud individually. We then perform a late data fusion or an object-level fusion to add the processed data from the radar and LiDAR. Then, using a non-linear Kalman Filter method, we take those noisy sensor measurements to estimate and track obstacle states with a higher accuracy than each sensor.
###### Abstract  
This work reports the preparation and characterization of poly(styrene-acrylic acid) (St/AA) submicrocapsules by using the miniemulsion copolymerization method. AA was introduced to miniemulsion polymerization of St to increase the zeta potential and the resulting electrostatic stability of St/AA submicrocapsules. Phytoncide oil was adopted as the core model material. Miniemulsion copolymerization of St and AA was conducted at a fixed monomer concentration (0.172 mol) with a varying monomer feed ratio [AA]/[St] (0.2, 0.25, 0.33, 0.5, and 1.0). Concentrations of initiator (azobisisobutytronitrile; 1.0 \(\times\) 10\({}^{-3}\), 2.0 \(\times\) 10\({}^{-3}\), 3.0 \(\times\) 10\({}^{-3}\), and 4.0 \(\times\) 10\({}^{-3}\) mol/mol of monomer) and surfactant (sodium dodecyl sulfate; 0.6 \(\times\) 10\({}^{-3}\), 1.0 \(\times\) 10\({}^{-3}\), and 1.4 \(\times\) 10\({}^{-3}\) mol) were also controlled to optimize the miniemulsion copolymerization of St and AA. Dynamic light scattering and microscopic analyses confirmed the optimum condition of miniemulsion copolymerization of St and AA. Long-term colloidal stability of aqueous St/AA submicrocapsule suspension was evaluated by using TurbiscanTM Lab. In this work, the optimum condition for miniemulsion copolymerization of St and AA was determined ([AA]/[St] = 0.33; [SDS] = 1.0 \(\times\) 10\({}^{-3}\) mol; [AIBN] = 2.0 \(\times\) 10\({}^{-3}\) mol/mol of monomer). St/AA submicrocapsules prepared at the optimum condition (392.6 nm and \(-\)55.2 mV of mean particle size and zeta potential, respectively) showed almost no variations in backscattering intensity (stable colloids without aggregation).  
Keywords:styrene; acrylic acid; submicrocapsule; miniemulsion copolymerization; zeta potential +
Footnote †: journal:
## 3 Integrated Navigation
### GNSS/INS Integration  
GNSS/INS integration is very common because the systems are complementary in many aspects. In particular, INS is more accurate in the short term, it can supply data with very high rate and it can also provide attitude information. On the other hand, GNSS is more accurate in the long term and the error is effectively time invariant. The following sections describe the two most common approaches to integrating GNSS and inertial data, namely loose and tight coupling.