esa-sceva/satcom-chunk-collection huggingface dataset:
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 2 Materials and Methods
### Measurement-Level Hybridization Method
#### 2.2.1 Pseudoranges Between GNSS Satellites and the UE
The satellite elevation angle for each observation is computed based on the geometric relationship between the known position of the satellite and the UE position at the time of the measurement. This approach allows each GNSS observation to be weighted proportionally to its line-of-sight geometry, improving the robustness of the adjustment model.
## 2 Methodology and Data
### Data  
In this study, we analyse the unemployed jobseekers (i.e., officially registered jobseekers) in Slovakia and predict the duration of their unemployment, which we sort into categories according to the duration of the status \"unemployed\", as we mentioned above. To create the prediction models, we worked with the robust data coming from the database of jobseekers administered by the governmental public employment agency the Central Office of Labour, Social Affairs and Family of the Slovak Republic (COSAF). Registration in this database is mandatory for all unemployed jobseekers who are provided with related benefits such as unemployment allowance, participation in intervention programs, or state-paid health insurance, all in accordance with Act No. 5/2004 Coll. on employment services, as amended [59]. The database of jobseekers used in this study covers the period from April 2010 to June 2019. This restriction is due to the complexity of the data available from the COISAF. The dataset contains 1,048,551 registrations from 718,032 jobseekers registered in the database once or multiple times during the observed years. We worked with specific characteristics of jobseekers, i.e., qualitative and quantitative information requested from each jobseeker according to the law in force, namely [59], and that are usually detected during the process of registration or during the recording of jobseekers. All mentioned information is included in the register only on the basis of notification and the persons concerned who provide it. Taking into account these characteristics, which are detected when registering a jobseeker or already during his/her existing registration, the research question we set is whether the relevant variables (characteristics of an individual as a jobseeker) are suitable predictors of how long the respective individual will be \"about unemployed\". The variables selected by us as being suitable for our study, namely qualitative characteristics used as statistical variables with their short but exact description and with the distribution of their values in the dataset, are presented in Table 1, while the quantitative variables and their characteristics are provided in Table 2. \begin{table}
\begin{tabular}{l l l l} \hline
**Variable** & **Description** & **Values** & **Proportion** \\ \hline gender & gender of jobseeker & male & 52.26\% \\  & & female & 47.74\% \\ \hline nationality & nationality of jobseeker & Slovak & 89.31\% \\  & & Hungarian & 9.35\% \\  & & unknown or other & 0.80\% \\  & & Czech & 0.37\% \\  & & Roma & 0.16\% \\ \hline \end{tabular}
\end{table} 
Table 1: Qualitative input variables used in the study.
# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 1 Introduction
This work has considered \"Action Research Methodology\", where this methodology aims to build knowledge (stage 1) and practical action (stage 2), then engages in a cyclic process that interchanges between action and critical analysis (stage 3) with continuous adjustments and reflection (stage 4) to extend the understanding of considered action. Further, this methodology seeks transformative change throughout the simultaneous process of acting and doing research, which are associated together by critical reflection. Smart journalism or smart media terms have emerged and gained media attention over the last several years to offer unique perspectives that enrich news storytelling. These terminologies refer to the ability of using different forms of technologies such as portable and/or automated devices like camcorders, laptops, smartphones, tablets or even drones to perform media storytelling, reporting, and conveying multimedia content in more efficient and cost-effective manners. Indeed, with the advancement of drones, organizations, and journalists have employed this technology to capture and share breaking news, and to provide sights of natural disasters that would be hard and/or dangerous to be obtained when using traditional media gathering approaches. Figure 1 shows an overview of the smart journalism concept using a drone, where a drone can report news and/or collect multimedia contexts from some events (e.g., sport events) in a timely and geographically unrestricted news coverage. Then, the drone reports the obtained multimedia news to the control station using a radio frequency (RF) link for further analysis and storage. In addition, the drone can send the contexts to media reporters, who are either mobile and/or fixed ones. To the best of our knowledge, this is the first work that considers an optimized propagation model that integrates both drone and AI to empower smart journalism and make it more sustainable and an eco-efficient process. To obtain novelty and make a noticeable shift from existing work, there are key contributions of this work:  
* Testing and evaluating set propagation models, which are representatives of their types from an aerial perspective using NN-RBFN approach,
* The optimized propagation model would exhibit advantages of accuracy, strong connectivity, and wide media coverage,
* Validating obtained results against other well-known, optimized propagation models as proof-of-concept using 3D Remcom Wireless Insite tools. The rest of the paper is organized as follows: Section 2 presents related work and highlights this paper's motivation followed by Section 3 which describes the proposed drone-enabled AI framework. Both simulation, implementation, and discussion are presented in Section 4. Section 5 concludes.
## 4 Discussion
### Phenological Consistency between Remote Sensing and Ground  
Numerous scholars working in the phenology field have used double logistic function to fit the curve of GPP [32; 54], so we also used this method to fit the weekly mean GPP. We consider the phenology extracted of GPP under the five selected methods as real values to test the phenological results using image data extraction. To improve the consistency of phenological parameters between remote sensing and ground data, we should adopt suitable extraction methods at different latitudes. We found that Method 3 was most suitable for the extraction of the SOS above 30\({}^{\circ}\)N, Methods 2 and 4 were more suitable for the extraction of the SOS from 30\({}^{\circ}\)N to 45\({}^{\circ}\)N, and Methods 3 and 5 were more suitable for the extraction of the SOS from 45\({}^{\circ}\)N to 75\({}^{\circ}\)N. For EOS, Method 4 is most suitable for the extraction of the EOS above 30\({}^{\circ}\)N, and for 30\({}^{\circ}\)N to 45\({}^{\circ}\)N and 60\({}^{\circ}\)N to 75\({}^{\circ}\)N, Methods 3 and 4 are more suitable, respectively. The differences in the above results may be caused by changes in vegetation types and climate factors. However, we select fewer ground stations, which may also affect the accuracy of the verification results.
## 3 Part I: Localization of Real and Virtual Sources in the Absence of Visual Cues
### Results
Externalization rates for virtual sources generated with V11, V21 and HTK were comparable to externalization rates for real sources, whereas for virtual sources generated with V13 and V23, externalization rates were markedly lower. Figure 9 shows the externalization rates averaged over target positions. According to the Shapiro-Wilk test of normality, the average externalization rates could not be assumed to be normally distributed. Therefore, the Friedman test was applied, which revealed significant differences in the average externalization rates when listening to real or different virtual sources (\(p<10^{-4}\)). According to the multiple comparison after the Friedman test, virtual sources generated with V13 and V23 were significantly less externalized than other sources. There were no significant differences between the externalization rates of virtual sources generated with V11, V21 and HTK and the real sources. Figure 8: Externalization rate, defined as the percentage of responses classified as externalized, when localizing real sources in TestReal (Real Source) and virtual sources in TestVR (generated with V11, V13, V21, V23 and HTK). Figure 9: Externalization rate averaged over target positions, when listening to real sources in TestReal (Real Source) and to virtual sources in TestVR (generated with V11, V13, V21, V23 and HTK). Horizontal bars indicate significant differences (multiple comparison after Friedman test, \(p<0.05\)). Error bars indicate 95% confidence intervals. **Reversal rates:** Table 2 shows the reversal rates, defined as the percentages of responses classified as front-back reversals. With real sources, this rate was equal to 1.4%. For virtual sources generated with V11, V21 and HTK, reversal rates were smaller or comparable to those of real sources (between 0.4% and 1.4%). Reversal rates were slightly higher for V23 (1.9%) and much higher for V13 (4.2%).
## 5 Kinematic Assessment of Movement Performance
### Inertial Measurement Unit--IMU
#### 5.1.3 IMU Installation into Sports Equipment
Two-hundred-forty angular velocity signals were recorded from different body segments and hit types (forehand, backhand, and serve) from two competitive and two novice players. The angular velocity of the IMU gyroscopes was compared with the same parameter obtained by the system, and the study concluded that IMU and MoCap showed comparable values. Therefore, IMUs are a valid alternative for evaluating angular velocity during tennis shots [167]. In another study, a six DOF IMU was integrated inside a basketball ball to measure players' dynamics during the practice and game exercises [168]. This study aimed to investigate the backspin rotational velocity and ball rotation axis during a shot. These factors are directly associated with the type of shot and the player's dribbling ability, which can be assessed by analyzing his dribbling frequency measured through acceleration.
# Quality Assessment of Dry Soil Mixing Columns in Soft Soil Areas of Eastern China
## 4 Results of Quality Assessments
### Quality Assessment Results  
According to the quality assessment method in Section 3.5, the assessment results are presented in this section. Figure 6 shows the quality scores of the DSM columns in the different soft soil areas, including the upper and lower parts of the columns. The statistical magnitudes (numbers of the DSM columns) of the littoral soft soil area, lagoon soft soil area, alluvial soft soil (Ancient Yellow River) area, alluvial soft soil (Yangtze River) area, and lacustrine soft soil area were 2173, 869, 1212, 4075, and 298, respectively. In Figure 6, zone I represents that the quality of the upper parts of the DSM columns were qualified, but the quality of the lower parts of the DSM columns were unqualified. Zone II represents that the quality of the lower parts of the DSM columns were qualified, but the quality of the upper parts of the DSM columns were unqualified. Zone III represents the quality of the whole DSM column was unqualified.  
Figure 6: Score of DSM columns in: (**a**) littoral soft soil; (**b**) lagoon soft soil; (**c**) alluvial soft soil (Ancient Yellow River); (**d**) alluvial soft soil (Yangtze River); (**e**) lacustrine soft soil.  
It can be seen that there were three unqualified DSM columns in the littoral soft soil area; two columns were unqualified from the lower part and one column was unqualified from the upper part. In the lagoon soft soil area and the alluvial soft soil (Ancient Yellow River) area, all DSM columns were qualified. There were 17 unqualified DSM columns in the alluvial soft soil (Yangtze River) area, and nine unqualified DSM columns in lacustrine soft soil area. The quality assessment results showed that the number of unqualified columns in the upper part was larger than the number in the lower part.  
According to the quality assessment method in Section 3.5, the quality levels of the DSM columns were divided into great, good, general, and unqualified. The proportions of the levels of quality in the different soft soil areas are shown in Table 7. The data show that the proportion of great quality columns was the largest, and the proportion of unqualified columns was minimal. From great to unqualified, the proportions decreased. The proportion of DSM columns with great quality in the lagoon soft soil area was greater than in other areas. The proportion of unqualified columns in the lacustrine soft soil area was the largest.  
The quality assessment result from 8627 DSM columns is shown in Figure 7. The proportions of four quality levels varied widely. The proportion of great quality was 64.84%, which indicated that most DSM columns were of high quality. The proportion of unqualified DSM columns was 1.4%. The proportions of great quality, good quality, general quality, and unqualified soil decreased in that order.
## 3 Experimental Results
### Atmospheric Transmittance Calculation  
In the conventional method, the atmospheric transmittance is calculated with software. CART [20] software is used for atmosphere transmittance correction in this radiometry. During the experiment, the ground average temperature was about 7.5 \({}^{\circ}\)C, the pressure was about 856 hPa, the relative humidity was about 20%, the angle of pitch was 0\({}^{\circ}\), the altitude was 1400 m, and the visibility was about 23 km. The distance from the system to the target was 830 m. The average atmospheric transmittance in waveband 3-5 \(\upmu\)m was \(\tau_{atm1}~{}=~{}0.7399\), which was calculated by MODTRAN4.0 using the above-measured parameters, and the path radiance.  
\[L_{path1}~{}=~{}0.1942\mathrm{W}\cdot\mathrm{m}^{-2}\cdot\mathrm{sr}^{-1} \tag{10}\]  
In this paper, as the temperature of ambience was about 7.5 \({}^{\circ}\)C and the radiance was \(L(T_{e})\) = 0.6884, the path radiance can be expressed from Equation (7) as:  
\[\begin{array}{ll}L_{path}&=(1-\tau_{atm2})L(T_{e})\\ &=(1-\tau_{atm2})0.6884\end{array} \tag{11}\]  
The temperature of the reference was 36 \({}^{\circ}\)C and the radiance was 1.966 \(\mathrm{W}\cdot\mathrm{m}^{-2}\cdot\mathrm{sr}^{-1}\) near the target. To reduce the error, the image obtained at different integration times 2000, 3000, and 3500 \(\upmu\)s were employed, and the results are shown in Table 1.  
From Table 1, we can see that the radiance distribution of the reference is uniform and stable, so there was a steady transmittance. The average of the atmospheric transmittance was 0.7977. G/DN, Response gray value of infrared detector.
# Regional Seismic Damage Simulation of Corroded RC Frame Structures: A Case Study of Shenzhen City
###### Abstract  
Buildings in coastal cities are susceptible to chloride ion attack and the seismic performance of these buildings can be impaired due to corrosion of reinforcements. In this study, a regional seismic damage simulation method that considers the influence of corrosion-induced seismic performance degradation is proposed. Firstly, the framework of the method is introduced, and the simulation process is presented. Secondly, experimental data of corroded reinforced concrete (RC) components are collected to obtain the reduction rules of component level backbone curve parameters (i.e., initial stiffness, peak carrying capacity, peak displacement, and ultimate carrying capacity). Afterwards, pushover analyses of typical RC frames in different corrosion conditions (i.e., degree of corrosion of components and proportion of corroded components) are conducted to acquire the reduction rules of interstory backbone curve parameters of corroded RC frame structures. Finally, RC frame structures, in Shenzhen city, are simulated using different corrosion scenarios. Simulated results indicate that some buildings along the coastline are affected by airborne chloride-induced corrosion and severe seismic damage can be observed. Moreover, some buildings that are far from the coastline can also experience severe seismic damage due to irregular use of sea sand as constructional material. The proposed method can be used to simulate the seismic performance of corroded RC structures and the outcomes of this study are expected to provide a useful reference for the seismic risk management of coastal cities.  
keywords: corrosion of reinforcements; seismic damage simulation; backbone curve; reinforced concrete structures; time history analysis +
Footnote †: journal: Applied Sciences
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 5 Performance Evaluation
Rtimer, a real-time timer module in Contiki, provides the total number of timer ticks elapsed per second. In the Cooja simulator, individual nodes can periodically print their running tick count. In all the simulations, the target node was coded to print this value every 20 s, providing a snapshot of the ongoing component usage. The proposed solution was implemented on a target node within the sensor network. The evaluation process focused on monitoring the power consumption of three key node components within that node: CPU, radio transmission, and radio reception. Two additional key metrics were measured to assess the mitigation strategy's effectiveness alongside the main metrics: the number of messages dropped due to the defence mechanism, and the number of false positive messages that passed through the Bloom filter. \begin{table}
\begin{tabular}{l l} \hline Operating System & Contiki-OS \\ Simulation Environment & Cooja \\ Simulated Area & 100 m \(\times\) 100 m \\ Power Analysis & & Energest Time Function \\ \hline \multirow{6}{*}{Sky Mote} & RAM & 10 k, 48 k Flash \\  & Voltage & 3 V \\ \cline{1-1}  & TX Current & 17.4 mA \\ \cline{1-1}  & RX Current & 18.8 mA \\ \cline{1-1}  & LPM Current & 0.023 mA \\ \cline{1-1}  & CPU Current & 1.8 mA \\ \cline{1-1}  & Transceiver & CC2420 2.4 Ghz (802.15.4) \\ \hline RTimer & & 32,768 ticks per second \\ Simulation Time & & 240 s \\ Measurement Interval & & 20 s \\ Node Transmission range & & 50 m \\ Objective Function & MrHof \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 7: Scenario 2 DODAG visualisation: Two attackers.
## 2 Materials and Methods
### Testing Methods
#### 2.2.7 Electromagnetic Shielding Effectiveness (EMSE)  
This test followed ASTM 4935-10. The samples were evaluated for EMSE with a shielding effectiveness test fixture (E-Instrument Tech Ltd., Taoyuan, Taiwan) and a spectrum analyzer (Advantest R3132A, Burgeon Instrument Co., Ltd., Taoyuan, Taiwan). The incident frequency was between 300 kHz and 3 GHz and measured in decibels (dB).
## 2 Experimental Methods and Simulations
### Simulation Methods
#### 2.2.2 Constitutive Parameters of Single Phase  
The constitutive parameters of the titanium in this study were determined by synchrotron radiation carried out at the Advanced Photon Source 11-ID-C station in the Argonne National Laboratory in the US [30]. The X-ray energy (E) and its wavelength (\(\lambda\)) function are derived from De Broglie relation and the formula for the calculation is as follows:  
\[\text{E [keV]}=12.393/\lambda\ [\text{\AA}] \tag{5}\]  
The Fit2D software was used to analyze the data, mainly regarding the crystal plane spacing of each phase during the tensile process by Bragg's law (2d sin\(\theta\) = N\(\lambda\)). Subsequently, calculating the lattice strain \(\varepsilon\):  
\[\varepsilon_{hkl}=\frac{d_{hkl}-d_{0,hkl}}{d_{0,hkl}} \tag{6}\]  
and fitting to determine the constitutive parameters of each phase. Furthermore, \(d_{hkl}\) is the crystal plane spacing of (\(h\) \(k\) \(l\)) at different deformations and \(d_{0,hkl}\) is the initial crystal plane spacing before loading.  
In order to determine the constitutive parameters of single phase, the first step was to obtain the elastic constants of the single crystal [30]. Comparing the elastic stiffness \(E_{hkl}\) of experiment and self-consistent fitting by:  
\[E_{hkl}=\frac{\delta_{applied}}{\varepsilon_{hkl}} \tag{7}\]  
the fitted single-crystal elastic constants \(\text{C}_{\text{ij}}\) (GPa) of the \(\alpha\) and \(\beta\) phases was shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(\text{C}_{\text{11}}\)**(GPA)** & \(\text{C}_{\text{12}}\)**(GPA)** & \(\text{C}_{\text{13}}\)**(GPA)** & \(\text{C}_{\text{33}}\)**(GPA)** & \(\text{C}_{\text{44}}\)**(GPA)** \\ \hline \(\alpha\) phase & 160.0 & 86.0 & 55.0 & 183.0 & 54.0 \\ \hline \(\beta\) phase & 130.2 & 70.6 & 70.6 & 70.6 & 45.8 \\ \hline \end{tabular}
\end{table}
Table 1: Single-crystal elastic constants \(\text{C}_{\text{ij}}\) (GPa).  
Furthermore, the modified Voce equation [30] is used to calculate the strain hardening effect of the slip system, \(i\), in a single crystal:  
\[\tau^{i}(\Gamma)=\tau^{i}_{0}+(\tau^{i}_{1}+\theta^{i}_{1}\Gamma)\left[1-\exp( \frac{-\theta^{i}_{0}\Gamma}{\tau^{i}_{1}})\right] \tag{8}\]  
where \(\Gamma\) is the accumulating increment of shear strain of all slip systems during deformation, \(\tau\) is the transient flow stress. The deformation at early stage is characterized by CRSS (\(\tau_{0}\)) and hardening coefficient \(\theta_{0}\). The deformation at later stage is characterized by (\(\tau_{0}+\tau_{1}\)) and \(\theta_{1}\). The values of CRSS and hardening coefficient used in this study were shown in Table 2.
# Enabling Intelligent 6G Communications: A Scalable Deep Learning Framework for MIMO Detection
## 3 MIMO Detection: A System Model
### Conventional MIMO Detection  
Conventional MIMO detection methods have been proposed, which help to understand the MIMO detection system model and lead us toward the further development of more advanced MIMO detection methods.
## 1 Introduction
Although the pockmark field has a long history of marine geophysical investigations, the data acquired from specific parts of the field are sparse and fragmentary and do not allow a complete, detailed and in-depth knowledge of the field. The major findings of the past marine geophysical surveys were that the field consists of numerous craters (72 in number) of variable shapes and extents, limited to an area of about 2.5 km\({}^{2}\). Fifteen sediment cores collected within the PGFP showed that the surficial sediments consist of fine materials, mud and silt with low sand content [43]. Through years of repeated surveying of the area, in conjunction with the application of more precise and sophisticated marine remote sensing techniques, new aspects regarding the configuration of the area are constantly revealed. Within the long history of the investigation, the PGPF has been altered. From 1997 to 2015, within the area of the pockmark field, the new Patras harbour was built, and a significant part of the field was heavily modified. A number of pockmarks have been totally or partially covered by filling materials for the construction of the breakwater and the docks of the new harbour. In this paper, we present the new findings of the PGPF as revealed through detailed and systematic surveying, utilising modern marine sensing techniques, including multi-beam, chirp sub-bottom profiles and dual frequency side-scan sonar, operated following a dense grid of tracklines. The data cover gaps in information from previous surveys. Synthesising and comparing newly acquired data and similar data from previous surveying, we aim to present the spatial distribution of the pockmarks, define the seismostriagraphy of the field and the migration of the gas through the sediments strata, ascribe possible modifications in the configuration of the PGPF through time and investigate the link between the PGPF and the tectonic activity of the area. Furthermore, the origin of the seeping gas was assessed using isotopic analyses.