esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing IoT Web Fuzzing by Firmware Infomation Mining
## 6 Discussion  
From the experimental results, we can see that IoTParser's vulnerability discovery capability and vulnerability discovery speed are significantly better than state-of-the-art FirmHunter, with an average improvement of 44% and 48.2%, respectively. However, there were still some ways for future improvements.  
Maintaining authentication status: IoT devices are different from traditional web applications. Many operations need to interact with the underlying layer, which can easily lead to state loss during the fuzz process. If the authentication status is lost, the fuzz will not be able to access the function entries that require authentication, resulting in undiscovered vulnerabilities. Therefore, we will introduce monitoring of the authentication status in future work to ensure the effective operation of fuzz.  
Decompression of encrypted firmware: Many IoT vendors have recently packaged their firmwares with encryption to prevent information leakage. These firmwares cannot be decompressed by conventional tools [10], so IoTParser cannot perform vulnerability discovery on such devices. We will work on encrypted firmware in the future to enhance the scope of IoTParser.  
Detecting more types of vulnerabilities: With the development of IoT, web portals are becoming more powerful, and the proportion of traditional web vulnerabilities in IoT devices is increasing year on year. Detection for these vulnerabilities must continue to be refined in the following research.  
Support for more protocol types: In IoT devices, there are many customized protocol types, such as UPnP protocols, which send data in a different format to traditional data request methods, and for which future work could be further refined to enhance the discovery of vulnerabilities.  
Inspired by research into existing static and dynamic analysis techniques, we extracted interface and page paths through firmware information mining to extend the coverage of the crawler, and also extracted hidden shared keyword information from the firmware to speed up vulnerability discovery and reduce overhead, resulting in good experiments. In addition to the three types of information we extracted, there are many other hidden correlations in the firmware waiting to be discovered, and depending on the use case, different information can be analyzed and extracted to aid vulnerability discovery, all of which can be further investigated in the future.
# An Overview of GEO Satellite Communication Simulation Systems
## 2 Coverage Analysis
### Geometry-Based Visibility Analysis  
Geometry-based visibility analysis determines line-of-sight visibility and elevation constraints based on the geometric relationship between satellites and ground terminals. In [4], a novel approach using stochastic geometry was presented to model and analyze GEO satellite networks. The distribution of GEO satellites in the geostationary orbit was modeled using a binomial point process and satellite visibility was analyzed based on the terminal's latitude. A characteristic area-based method for continuous coverage analysis was proposed in [11], which modeled satellite visibility and coverage continuity through deterministic formulations derived from orbital trajectories and ground partitioning schemes. [PERSON] et al. proposed a geometry-based coverage evaluation in [12], where capacity comparisons between adjacent and overlapping multi-beam patterns were derived under line-of-sight channel assumptions. Closed-form expressions for SNR and capacity were obtained, enabling theoretical insight into the beam arrangement's impact on system performance. Focusing on improving the classical grid-point approach (GPA) for constellation-to-ground visibility evaluation, the authors in [13] introduced strategies to estimate upper and lower bounds of coverage results and to refine high-precision results from low-precision computations by analyzing the spatial and temporal characteristics of GPA. These enhancements improved both the accuracy and efficiency of coverage analysis in satellite applications. In [14], an analytical expression of the propagation delay within GEO satellite spot beams was derived based on the space geometry model. And differential propagation delays in several typical beams were calculated, according to a possible multi-beam coverage pattern of China.
## 2 Data and Methods
### Datasets  
We use monthly precipitation data from 60 rain-gauges spatially distributed across the study area (Figure 1 and Table 11). Data from Belize and Cuba were supplied by the respective Meteorological Services from each country, while data from the Dominican Republic are homogenized series from [36]. Station data for the rest of the study domain were those used by [34]. Figure 1: Study area and the location of the rain-gauge network. (**a**) Caribbean region, (**b**) the western Caribbean sub-region, and (**c**) the eastern Caribbean sub-region. We use fifteen global and one regional monthly GPP (Table 1). These datasets vary in their spatial and temporal resolutions, as well as in the methods used to develop them. The 16 GPP includes 6 products based on station data only, 2 that combine ground station information and satellite, 2 merging station and reanalysis, 4 based on reanalysis, and 2 using multisource (i.e., a combination of data sources) information. The GPP were selected based on their prior use in the region as well as on their availability and accessibility for the period 1983-2010 (28 years) which overlapped with the available station data. It is difficult to consider a longer time-interval due to, for example, limitations such as surface station data availability, completeness and inhomogeneities. However, as suggested by previous studies, this is a common constraint for most countries in the Caribbean [34; 37].
## 4 Example  
In Section 3, we introduced the construction and implementation process of the spacecraft knowledge graph and anomaly detection. In the following section, we will demonstrate the effectiveness and feasibility of knowledge graph construction and anomaly detection through a specific spacecraft anomaly detection example, following the process described in the previous section. This section's example will be divided into three parts: knowledge graph construction, feature data extraction, and telemetry data anomaly detection experiment.
## 2 Literature Survey  
### Speckle Noise Model  
In ultrasound imaging, the scatterer density, spatial distribution, and the characteristics of the ultrasound imaging system affect the speckle noise model. When the scatterer density is high (more than 10 scatterers per resolution), as is common in blood flow cells, for example, the amplitude of the backscattered signal usually follows a Rayleigh distribution. Conversely, if the scatterer density is low, the amplitude of the backscattered signal follows a K distribution. The Rayleigh distribution can be viewed as a particular case of the K distribution [17]. However, in clinical ultrasound imaging systems, with ultrasound display equipment, nonlinear signal processing techniques (such as logarithmic compression and low-pass filtering) are introduced to address the echo envelope signal problem. This nonlinear compression has a great influence on the statistical characteristics of the echo envelope signal. For example, if the echo envelope signal originally follows a Rayleigh distribution, then after logarithmic compression, it will exhibit an F-T (Fisher-Tippet) distribution. The noise of the F-T distribution can be viewed as white Gaussian noise contaminated by outliers, and this assumption degrades the performance of the filter [18; 19]. Usually, the echo envelope signal follows the K distribution, but unfortunately, the density function of the signal following the K distribution becomes complex after logarithmic compression [17]. [PERSON] et al. [20; 21] pointed out in their study that speckle noise in ultrasound images may be related to the signal after logarithmic compression, and proposed an explicit simulation model for log-compressed ultrasound images:  
\[\mu(x)=v(x)+v^{\gamma}(x)\theta(x) \tag{1}\]  
In this model, \(v(x)\) is the raw image without noise contamination, \(\mu(x)\) is the actual observed ultrasound image, \(\theta(x)\) is Gaussian noise with mean 0 and standard deviation \(\delta^{2}\), and \(\gamma\) is a constant depending on the ultrasound equipment and imaging process. In general, \(\gamma=0.5\) is taken. In this paper, Equation (1) is used as the speckle noise model.
# Digital Evaluation of the Accuracy of Computer-Guided Dental Implant Placement: An In Vitro Study
###### Abstract  
Compared to traditional implant surgical guides, computer-assisted implant surgical guides can be considered for positioning implants in the final prosthesis. These computer-assisted implant surgical guides can be easily fabricated with personal 3D printers after being designed with implant planning CAD software. Although the accuracy of computer-assisted implant surgical guides fabricated using personal 3D printers is an important factor in their clinical use, there is still a lack of research examining their accuracy. Therefore, this study evaluated the accuracy of computer-assisted implant surgical guides, which were designed using two implant planning CAD software programs (Deltanine and R2 gate software) and fabricated with personal 3D printers using a non-radiographic method. Amongst the patients who visited Kyungpook National University Dental Hospital, one patient scheduled to undergo surgery of the left mandibular second premolar was randomly selected. Twenty partially edentulous resin study models were produced using a 3D printer. Using the Deltanine and R2 gate implant planning CAD software, 10 implant surgical guides per software were designed and produced using a personal 3D printer. The implants (SIII SA (\(\mathcal{O}\) 4.0, L = 10 mm), Osstem, Busan, Korea) were placed by one skilled investigator using the computer-assisted implant surgical guides. To confirm the position of the actual implant fixture, the study models with the implant fixtures were scanned with a connected scan body to extract the STL files, and then overlapped with the scanned file by connecting the scan body-implant fixture complex. As a result, the mean apical deviation of the Deltanine and R2 gate software was \(0.603\pm 0.19\) mm and \(0.609\pm 0.18\) mm, while the mean angular deviation was \(1.97\pm 0.84^{\circ}\) and \(1.92\pm 0.52^{\circ}\), respectively. There was no significant difference between the two software programs (\(p>0.05\)). Thus, the accuracy of the personal 3D printing implant surgical guides is in the average range allowed by the dental clinician.  
Keywords:dental implant; computer-aided design; implant surgical guide; additive manufacturing +
Footnote †: journal: Applied Soft. 2019, 9, 3373; doi:10.3390/APP9163373
# Cooperative Power-Domain NOMA Systems: An Overview
## 1 Introduction
### Motivation and Contributions
Most of the literature on the spectrum efficiency and system capacity is addressed in the indicated efforts on cooperative communication. There have not been many studies published on the system's user-fairness, which suggests that an important aspects of B5G networks is to assure increased access of network resources for both far and near users. Besides, the opportunities and challenges associated with cooperative power-domain NOMA and integrating RISs with NOMA in the envisioned next-generation mobile networks are thoroughly covered in this paper. The list of surveys [23; 26; 40; 41; 42; 43], that have appeared on PD-NOMA schemes is compared with our work in Table 1. Specifically, we will focus on the following features. 1. The current literature on cooperative PD-NOMA is extensively reviewed to provide a better picture of the research that has been conducted in this field. In this paper, we discuss a thorough, up-to-date review of the integration of PD-NOMA with antenna selection schemes, MIMO systems, mmWave, CoMP, cognitive radio, THz bands, cooperative communications, SWIPT, transmit antenna selection, beamforming, clustered-based NOMA, and the other modern communications techniques, in order to improve the systems' overall rates and, consequently, the spectral efficiency in upcoming wireless networks. 2. An overview of the existing NOMA systems is presented. Furthermore, cooperative PD-NOMA, the concept, benefits, problems, and applications are outlined, and parallels are drawn with the well-established cooperative networks and the rationale for integrating cooperative approaches with PD-NOMA is provided. A qualitative analysis is conducted to establish the performance gain of PD-NOMA over OMA-based cooperative networks in particular. 3. The fundamentals of communication strategies under consideration (RIS-assisted NOMA) are reviewed. The main concept of RISs, namely the reconfiguration of the users' propagation environment, is demonstrated, and a description of NOMA's capacity to promote the sharing of spectra between mobile users to maximise spectral efficiency is illustrated. This survey also covers RIS-NOMA security provisioning. The influence of using a RIS with security is highlighted by studying the RIS-NOMA applications to improve physical layer security with regard to passive eavesdropping. The use of RIS-NOMA to cover cooperative PD-NOMA communications is then investigated. 4. The impact of machine learning on NOMA-based schemes is thoroughly investigated, and the directions for further research on NOMA with ML support are described. 5. In the end, we identify the open research challenges and prospective research directions which may enable the researchers to contribute some effective results in the aforementioned domains.
## 1 Aquaculture Production
### Aquaculture Production in the European Union  
In 2018, the aquaculture sector of the European Union (EU) achieved a production of EUR 5.1 billion, with EUR 1.7 MT of traded fish. In the EU, fish production is mostly concentrated in five countries: Spain (21%), France (15%), Italy (14%), the United Kingdom (14%), and Greece (10%) [4]. Table 4 shows the aquaculture production and fishery sectors at the global level, in the EU, and in Italy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **World** & **EU** & **Italy** & **\% World** & **\%EU** \\ \hline Fisheries & 93,204 & 5253 & 192 & 21 & 4 \\ Aquaculture & 111,966 & 1372 & 156 & 14 & 11 \\ Total & 205,170 & 6625 & 348 & 17 & 5 \\ \hline \end{tabular} Source: FAO, 2020 [1].  
\end{table}
Table 4: Production of farmed fish in the world, in the EU and Italy (\(\times\)1000 t).  
Figure 1: Major producers of rainbow trout worldwide (\(\times\)1000 t). Source: FAO, 2022 [1].  
Table 5 shows the main salmonid species farmed globally. In the EU, the recent increase in aquaculture production is due to the increase in marine fish products, while freshwater fish production has remained constant. In Europe, between 2002 and 2015, marine aquaculture production increased by 60% (mainly driven by Atlantic salmon production in Norway) [5], while in the EU, it increased by only 4%.  
In 2020, there were 12,500 active enterprises with 75,300 employees in the aquaculture sector in the EU. Note that approximately 90% of these firms are micro-enterprises with constant turnover over the last ten years. The primary farmed species are Atlantic salmon (mainly produced in Norway and the UK) and rainbow trout. From a production standpoint, the most important freshwater species is rainbow trout, which accounts for 69% of total production volume and 64% of total production value. In 2020, the EU-28 was the world's second-largest producer of trout (183,000 tons or 20% of global production). The main producers are Italy (19%), Denmark (17%), and France (14%) (Table 6).  
During the period 2010-2030, the overall production of salmonids is expected to increase by 2-10% and the price to decrease by 2%. Table 7 shows the main importing nations in the EU.
## 2 Materials and Methods
### GC \(\times\) GC-ToFMS Analysis  
After the extraction/concentration step, the SPME fiber containing the headspace volatile compounds was manually introduced into the LECO Pegasus 4D (LECO, St. Joseph, MI, USA) GC \(\times\) GC-ToFMS injection port at 250 \({}^{\circ}\)C and kept for 30 s for desorption. An Equity-5 column (30 m \(\times\) 0.32 mm I.D., 0.25 \(\upmu\)m film thickness, Supelco, Inc., Bellefonte, PA, USA) was used as 1D column and a DB-FFAP (0.79 m \(\times\) 0.25 mm I.D., 0.25 \(\upmu\)m film thickness, J&W Scientific Inc., Folsom, CA, USA) was used as a 2D column. The carrier gas used was helium at a constant flow rate of 2.50 mL/min and the primary oven temperature program was: initial temperature 35 \({}^{\circ}\)C (hold 1 min), raised to 170 \({}^{\circ}\)C (5 \({}^{\circ}\)C min\({}^{-1}\)), and then to 230 \({}^{\circ}\)C (20 \({}^{\circ}\)C min\({}^{-1}\)) (hold 2 min). The secondary oven temperature program was 20 \({}^{\circ}\)C offset above the primary oven. The MS transfer line and the MS source temperatures were 250 \({}^{\circ}\)C. The ToFMS was operated at a spectrum storage rate of 100 spectra/s. The mass spectrometer was operated in the EI mode at 70 eV using a range of \(m\)/z 35-350 and the voltage was \(-\)1468 V. The modulator temperature was kept at 20 \({}^{\circ}\)C offset (above primary oven). The modulation time was 6 s and total ion chromatograms were processed using the automated data processing software ChromaTOF at signal-to-noise threshold of 200. Contour plots were used to evaluate the separation general quality and for manual peak identification (Figure S1). For identification purposes, the mass spectrum of each detected compound was compared to those in mass spectral libraries, which included an in-house library of standards and two commercial databases (Wiley 275 and US National Institute of Science and Technology (NIST) V. 2.0--Mainlib and Replib). Mass spectral match factor (similarity >800, but >900 in 85% of the compounds) was also used to decide whether a peak was correctly identified or not. The identification was also supported by experimentally determined linear retention index (RI) values that were compared with those reported in the bibliography for chromatographic columns like the one used in the present work as the 1D column (Table S1 in Supplementary Material). RI values were determined using a C\({}_{8}\)-C\({}_{20}\)_n_-alkanes series (the solvent _n_-hexane was used as C\({}_{6}\) standard) and calculated according to the van [PERSON] and Kratz equation [31]. The DTIC (deconvoluted total ion current) GC \(\times\) GC area data were used as an approach to estimate the relative content of each volatile component in single-dose espresso coffee capsules powders and respective brews and were expressed as arbitrary units (a.u.). Each sample was analyzed in triplicate corresponding to three independent extractions.
## 4 Application Example
### Case for Impact Loading
#### 4.2.1 Laboratory Drop Weight Impact
For the second impact, due to the penetration of the AC layer in the first impact, the distance between the laser diode system and the face of the pavement slab is increased, while in the numerical model, the impact head is just placed at the position right before reaching the surface. Hence, the initial velocity in numerical model for the second impact is determined as the sum of the experimental recorded velocity and velocity caused by gravity acceleration. Thus, the velocity is calculated as 5.06 m/s for the second impact with the gravity acceleration of 9.8 m/s\({}^{2}\). After the first impact, the fully restarted method in LSDYNA is used to conduct the second impact simulation. At the beginning of the second impact, the stress and residual velocity within the pavement slab and the velocity from the first impact are set to be zero. The damage factor and plastic strain is retained in order to check the accumulated damaged behaviour after the second impact. The simulation of the second impact is carried out when the downward velocity of impactor reached zero, as it is very time consuming to continue to simulate the vibration of pavement slab after impact. It should be noticed that the pavement slab stopped rebounding, which might bring numerical errors (i.e., energy unbalance).
# Vertebral Center Points Locating and Cobb Angle Measurement Based on Deep Learning
## 3 Materials and Methods
### Loss Calculation  
#### 3.2.1 Heat Map Loss  
Since the detected object is the center point of vertebrae, the annotation data should be preprocessed to avoid an all-zero matrix while calculating the loss. Heat map regression [22; 23] is usually utilized to regress key points with high accuracy and convergence speed. The heat map consists of a series of two-dimensional Gaussian disks, with different weights are given to the regions near the actual value, where the closer it is to the actual value, the greater the weight. The pixel values in the Gaussian disk are all positive, and the point with the pixel value of 1 is the actual value of the marked point. The values in the Gaussian disk can be derived from \(e^{-\frac{\sigma^{2}+\sigma^{2}}{2\sigma^{2}}}\), where x and y determine the range of the Gaussian circle, and the size of the detected object determines \(\sigma\). Since the scales of the input images are the same, \(\sigma=1\) is taken for vertebral center points and the two locating points at the clavicle and \(\sigma=1.5\) for the two points at the hip bone in this experiment.  
Figure 2: The network structure for vertebral key points detection.  
The heatmap loss is calculated using the following Focal Loss function [24]:  
\[L_{Heatmap}=\frac{-1}{N}\sum_{i=1}^{h}\sum_{j=1}^{w}\begin{cases}(1-p_{ij})^{\alpha }log(p_{ij})&y_{ij}=1\\ (1-y_{ij})^{\beta}(p_{ij})^{\alpha}log(1-p_{ij})&others\end{cases} \tag{1}\]  
where \(N\) is the total number of all pixels on the heat map, \(h\) and \(w\) are the height and width of the heat map, respectively, \(p_{ij}\) is the predicted score of a pixel, \(y_{ij}\) represents the predicted score of pixels on the real heat map augmented by a non-normalized Gaussian function, and \(\alpha\) and \(\beta\) are hyper-parameters that affect the contribution of pixels. In this experiment, we choose \(\alpha=2\) and \(\beta=4\), which are consistent with the literature [21].