esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## 4 Comparative Experiment on Deep Learning Model Performance
### Discussion  
In summation, the evaluation of vehicle movement within the original image (OI) reveals a notable discrepancy between the AV and the actual velocity, coupled with a rapid diminution in object size as it recedes from the tunnel CCTV. This occurrence is predominantly attributed to a perspective effect, significantly influencing the training and inference phases of deep learning OD applications.  
The reason for this can be seen in the change in the object area and shape in distance, as shown in Figures 7b and A3. In Figure 7b, the object area for a single vehicle is larger than 100 pixels2 within \(-100\) m, which can be trained and inferred by the deep learning OD model, but it shrinks rapidly beyond \(-100\) m and becomes almost as small as a dot, as shown in Figure A3c,d. This size effect could be a major reason for poor OD performance in the \(-100\) to \(-200\) m range. Nevertheless, application of OI exhibits reasonable OD performance within a relatively proximate range of around 100 m.  
However, OD performance experiences a marked decline beyond this 100 m threshold. Consequently, it can be deduced that OD efficacy, relying on the original CCTV image, is confined to a distance limit of 100 m within the tunnel environment. This limitation is attributed to the common installation practice of positioning CCTVs at lower heights due to spatial constraints.  
In contrast, the utilization of TI through the IPT introduced in this study indicates that AV tends to closely match the actual velocity, regardless of the distance between the vehicle and the CCTV. This holds true even beyond distances of 100 m, extending at least up to 200 m. Furthermore, since object size remains relatively constant with distance in TI, the deep learning model trained on TI consistently demonstrates improved and stable OD performance across an ROI up to 200 m.  
The reason for consistent OD performance up to 200 m for the TI image could be found in Figures 7b and A3. As shown in Figure 7b, the area of the object in the case TRANS tends to increase, and the height of the object also increases slightly as the position of the vehicle increases from 0 m to \(-200\) m in Figure A3. Considering that deep learning OD models are affected by the size of the object, it can be seen that the experimental results for the case TRANS could be good evidence for overcoming the difficulties of OD in distance in tunnel environments at least up to 200 m.
(2017) carried out an observational analysis of an extremely severe hailstorm, developed over the Mediterranean Sea, and they showed the great capabilities of both GMI and DPR to depict the microphysical structure of the storm and to evidence its unique and rare features compared to global GPM observations. More recently, a preliminary study of GPM capabilities in monitoring the development of Medicare Numa and associated surface precipitation has been carried out by [PERSON] et al. (2018). The goal of the present paper is to illustrate the potential of the GPM-CO and of the GPM constellation to provide unprecedented insight of Medicare Numa, especially during its offshore development, where no ground-based observations are available. In this respect, since Numa is the first Medicare observed by the DPR, to the best of our knowledge, both passive and active MW measurements are used to infer the structure of the precipitation and to identify the main dynamical and microphysical processes responsible for its changes during the transition from the development to the mature stage. Moreover, the present study illustrates the potential of using GPM precipitation products as a tool to verify and improve Numerical Weather Prediction (NWP) model forecast, particularly useful over the sea. In the first part of this study we describe the evolution of Numa, from its formation to its dissipation, by using a \"traditional\" approach suitable for Near Real Time (NRT) applications, i.e., based on visible (VIS) observations, environmental conditions provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), and lightning activity associated with the storm, as measured by the LIghnting NETwork (LINET) ([PERSON] et al., 2017). In the second part of the paper we analyze in detail the added value of the GPM in characterizing the precipitation structure associated with the storm, also thanks to an overpass of the GPM-CO precipitation radar available during its development phase. MW observations and LINET strokes are used to identify specific features during the different stages of the storm evolution. We also compare the satellite-based precipitation rates and pattern to the ones provided by a ground-based radar and NWP simulations (specifically RAMS-ISAC ([PERSON], 2016)) for this case. The NWP is also used to support what derived from observations and to highlight the TLC characteristics of Numa. Finally, a sensitivity analysis of the RAMS-ISAC short-term forecast of Numa on the assimilation of DPR observations is also shown. The paper is organized as follows: Section 2 describes the sensors and products used in this study, while Section 3 briefly reviews the RAMS-ISAC model setup. In Section 4, ECMWF forecast and lightning observations are used to analyze the evolution of Numa. Section 5 is dedicated to the analysis of GPM observations, while Sections 6 and 7 investigate the performances of RAMS-ISACmodel and the impact of DPR assimilation. Section 8 discusses the main results of the paper. Finally some conclusions are drawn in the last Section.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 6 Trusted Execution Environment (TEE) Support and Feasibility  
Trusted execution environments (TEEs) provide hardware-isolated contexts for secure code execution. In this section, we evaluate the feasibility of implementing our covert channel in various TEE platforms--Intel SGX, ARM TrustZone (with OP-TEE), and AMD SEV/SEV-SNP--with a focus on their support for huge pages and cache coherence behavior.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Scheme** & **Bits/Round** & **Accuracy (All Bits)** & **Bandwidth (KB/s)** \\ \hline
10 bits/1024 lines & 10 & 64.31\% & 2654 \\ \hline
9+9 bits/512 lines each & 9 & 58.20\% & 4687 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of 10-bit and 18 (9+9)-bit transmission schemes.
###### Abstract  
The growing adoption of Building Information Modeling (BIM) within the architectural, engineering, and construction (AEC) sector raises questions about the quality of BIM data deliverables for project owners. Therefore, assessment and evaluation of such BIM data against relevant documents such as the BIM Execution Plan (BEP), the Level of Definition (LOD)/Level of Information (LOI) matrix, and quality control customized checklists become critical, especially in large construction projects. This study primarily aims to create an automated system for assessing the quality of 3D BIM model data, utilizing a proposed project quality control checklist. The automated system consists of four key elements: a BIM-based model, a Data Extraction and Analysis Module, a Data Storage Module, and a Data Visualization Module. The Data Extraction and Analysis Module extracts relevant information and parameters from BIM models to evaluate their quality against predefined checklists. Then, it transfers the information and stores the results in a database. The database is connected to an engineering project collaboration tool, ProjectWise, to automatically update and store the data in the cloud. The database is then connected to an interactive data visualization platform, Power BI, to enable automatic visualization of the generated quality assessment results of the BIM models' data. This system was applied to a Canadian infrastructure construction project by its BIM department during the preliminary and detailed design phases. It demonstrated an average quality score (AQS) of 87.6% for the BIM models and significantly reduced failing items by around 30%. This study concludes that the system offers a robust, practical solution for enhancing the quality control process in BIM model data management, thereby aiding engineers in timely model adjustments to meet project requirements.  
BIM; quality control; visualization; BEP; LOD; power BI +
Footnote †: journal: _Applied_  
_aciences_
# Characterization of Congolese Woody Biomass and Its Potential as a Bioenergy Source
###### Abstract  
This study assesses and characterizes six woody biomass (WB) species commonly harvested in the Republic of Congo: _Millettia luurenti_ (WB1), _Millettia eetveldeana_ (WB2), _Hymenocardiula umoides_ (WB3), _Markhumia tomentosa_ (WB4), _Pentaclethra eetveldeana_ (WB5), and _Hymenocardiia acida_ (WB6). Characterization was performed using proximate analysis with a Thermo Gravimetric Analyser (TGA), ultimate analysis with a CHNS Analyser, higher heating value (HHV) determination, metal content analysis by X-ray fluorescence (XRF), and aboveground biomass (AGB) estimation. The proximate analysis results showed that volatile matter varied between 74.6% and 77.3%, while the ultimate analysis indicated that carbon content ranged from 43% to 46%, with low nitrogen content. XRF analysis revealed low levels of heavy metals in all samples. The HHV results, using three models ([PERSON]'s equation, Friedl, and proximate analysis), showed higher values with [PERSON]'s method (17.3-18.2 MJ/kg) and proximate analysis (15.26-19.23 MJ/kg) compared to [PERSON]'s equation (13.9-14.9 MJ/kg). Savannah biomass (WB6) exhibited high AGB (7.28 t), 14.55 t/ha, and carbon stock (7.28 t). Compared to forest biomass, savannah biomass presents a higher potential for bioenergy production. Minimal statistical analysis of wood biomass showed that parameters such as volatile matter (VM), carbon (C), hydrogen (H), and calculated HHV have low variability, suggesting the biomass is relatively homogeneous. However, moisture and nitrogen showed significant standard deviations, indicating variability in storage conditions or sample nature. Statistical analysis of forest biomass estimation revealed different mean values for diameter, AGB (t and t/ha), and carbon stock, with high standard deviations, indicating a heterogeneous forest with both young and mature trees. These analyses and estimates indicate that these WB species are suitable for biofuel and bioenergy production using gasification, pyrolysis, and combustion processes. Among these thermochemical processes, gasification is the most efficient compared to combustion and pyrolysis.  
bioenergy; proximate and ultimate analysis; lignocellulosic biomass; aboveground biomass; higher heating value +
Footnote †: journal: applied sciences
###### Abstract  
This paper presents an analytical study of the dynamic responses in the vehicle-pavement-foundation system, where the vehicle is simplified to a two-degree-of-freedom system, the pavement is modeled using both Euler-Bernoulli (E-B) beam and Timoshenko beam with consideration of pavement roughness, and the subgrade is simulated with a Winkler foundation model featuring cubic nonlinear stiffness. The focus is on using approximate analytical solutions of pavement response to discuss the impact of nonlinear stiffness under various parameter conditions. In previous analytical studies of vehicle-pavement-foundation systems, vehicles were typically simplified to a constant moving force, leading to the conclusion that when the applied force is small, the impact of nonlinear stiffness on the pavement's dynamic response is minimal; whereas when the force is large, the pavement response increases with the increase in nonlinear stiffness. In this study, the force exerted by the vehicle on the pavement is harmonic, and the impact of nonlinear stiffness on the pavement response is different and much more complex. The research finds that there is a critical value for nonlinear stiffness under the given vehicle parameter conditions: when the nonlinear stiffness is less than the critical value, it has almost no effect on the pavement response; when it exceeds the critical value, the pavement's response first decreases and then increases with the increase in nonlinear stiffness. The critical value of nonlinear stiffness is not fixed and increases as the vehicle velocity and foundation damping. Moreover, an increase in nonlinear stiffness also causes an increase in the offset between the wheel position and the position of maximum pavement deformation. Under the same parameter conditions, the offset in the E-B beam is significantly greater than that in the Timoshenko beam. Our study's results enhance the understanding of the nonlinear dynamics within the vehicle-pavement interaction.  
vehicle-pavement-foundation system; nonlinear foundation; analytical solution; Euler-Bernoulli beam; Timoshenko beam 2024  
[PERSON], [PERSON], [PERSON], and [PERSON]
## 4 Conclusions  
The ESR method enables monitoring of spin-probe penetration into cells and differentiation of ESR spectra of spin probes inside and outside cells. The measurements carried out at 240 K result in different spectra of spin probes inside and outside cells. The rate/efficiency of the process observed depends on the ratio of spin-probe molecule amount/yeast cell number and the type of cells (healthy/cancerous). Additionally, external factors (oxygen and vitamin C) can affect the decay of spin-probe ESR signals. The results indicate that swift (within minutes) reduction in spin probe concentration inside cells makes them promising candidates for potential application as scavengers of free radicals in cells.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12167999/sl](https://www.mdpi.com/article/10.3390/app12167999/sl)]([https://www.mdpi.com/article/10.3390/app12167999/sl](https://www.mdpi.com/article/10.3390/app12167999/sl)).  
Conceptualization, B.D. and R.K.; methodology, B.D., R.K., M.K. and M.M.; software, R.K.; validation, B.D. and M.K.; formal analysis, B.D., R.K., G.S. and J.K.; investigation, B.D., R.K. and M.K.; resources, R.K. and M.M.; data curation, R.K. and M.K.; writing--original draft preparation, B.D., R.K., M.K., M.M., G.S. and J.K.; writing--review and editing, B.D., R.K., G.S. and J.K.; visualization, B.D. and R.K.; supervision, R.K.; project administration, R.K.; funding acquisition, B.D., M.M. and J.K. All authors have read and agreed to the published version of the manuscript.  
[PERSON] and [PERSON] are thankful to Adam Mickiewicz University in Poznan (AMU) for the financial support with the interfaculty project in AMU School of Exact Sciences. The cell line experiments were supported by the project from Polish National Science Center UMO2014/15/B/NZ5/01488 granted for [PERSON].  
Not applicable.  
Figure 15: Decrease of TEMPO concentration introduced into HL-60 leukemia cells over time.  
Data Availability Statement: Data supporting the result of our study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Vibration Reduction on Circular Disks with Vibroacoustic Metamaterials
### Vibrations of Rotating Disks
#### 1.1.1 Rotating Observer  
When measuring structural vibrations in simple disk structures using a rotating measurement system at high rotational speeds, gyroscopic effects, centrifugal forces, and shear forces influence the vibration behavior [22; 23]. Specifically, centrifugal or centripetal forces result in a stiffening of the structure, leading to an increase in natural frequencies.  
[PERSON] [24] demonstrated that the natural frequencies of a rotating plate can be derived from the natural frequencies of a non-rotating plate. According to [PERSON], the frequency associated with a specific mode \(f_{\mathrm{e}\omega}\), which is dependent on the rotation speed, can be approximated for modes with exclusively nodal diameters and without nodal circles using the stationary mode frequency \(f_{\mathrm{e}}\) and the rotation frequency \(f_{\mathrm{n}}\) as follows:  
\[f_{\mathrm{e}\omega}^{2}=f_{\mathrm{e}}^{2}+\lambda f_{\mathrm{n}}^{2} \tag{1}\]  
The factor \(\lambda\) takes into account the mode shape and can be calculated with the Poisson's ratio \(\
u\) and the number of nodal diameters \(k\): [25]  
\[\lambda=\frac{1-\
u}{4}\cdot k^{2}+\frac{3+\
u}{4}\cdot k \tag{2}\]
## 4 System Development
### Backend of the System
#### 4.2.1. Update of the Distance Information and Location Information Using a GPS  
In the system, the users' present locations are determined using the GPSs installed into their mobile information terminals. When the users' present locations are updated, the distances to all sightseeing spots and tourism-related facilities are displayed as images on the AR screens, which are are also updated. As the updated distance information is reflected on a real-time basis, a user can know the distance to each sightseeing spot by simply tapping on the image displayed on the AR screen. Figure 9 shows the real-time updates of distance information. Users can grasp the distances to their destinations, as the distance information that is updated is reflected in the system in real-time.  
Figure 8. Screen of the display function of sightseeing spots and tourism-related facilities using AR.  
2.2 Height Modification of the Images of Sightseeing Spots and Tourism-Related Facilities Based on the Updated Distance Information  
When the distance information is updated, images of sightseeing spots and tourism-related facilities that are further from the users' present locations are positioned higher, while those closer are positioned lower on the screen by means of AR. This reduces the possibility of images overlapping and helps users intuitively grasp the sightseeing spots and tourism-related facilities that are close to their present locations. Additionally, the size of the images of sightseeing spots and tourism-related facilities are modified according to the distance from the users' present locations. Figure 10 indicates that the height of the images of the sightseeing spots and tourism-related facilities are modified based on the updated distance information.
## 2 Equipment and Methodology
### Description of the Experimental Data  
The data were collected at the weather station located on the flat roof of the Higher Polytechnic School of the University of Burgos (42\({}^{\circ}\)21\({}^{\prime}\)04\({}^{\prime\prime}\) N, 3\({}^{\circ}\)41\({}^{\prime}\)20\({}^{\prime\prime}\) W, 956 m above mean sea level), shown in Figure 1. The experimental campaign ran from March 2020 to May 2022. Data were collected every 30 s and averages were recorded every 5 min. The experimental data I\({}_{\text{GH}}\), I\({}_{\text{DH}}\), I\({}_{\text{BH}}\) were subjected to the quality control (QC) procedure proposed in the MESoR project [36]. Regarding I\({}_{\text{UV}}\) data, it has been established that it could not be higher than extraterrestrial UV on the horizontal plane, UVH\({}_{0}\) corresponding to the same time frame. UVH\({}_{0}\) was calculated using Equation (1):  
\[\text{UVH}_{0}=\varepsilon_{0}\text{UV}_{\text{sc}}\text{cos}\,\text{Z} \tag{1}\]  
where UV\({}_{\text{sc}}\) is the UV solar constant, and f\({}_{\text{c}}\) is the correction factor based on estimated orbital eccentricity (\(\varepsilon_{0}=1+0.033\cdot\text{cos}\left(\frac{2\cdot\text{Tr}\cdot\text{d} _{\text{c}}}{305\cdot\text{a}}\right)\)) where d\({}_{\text{n}}\) is the day of the year multiplied by the cosine of the solar zenith angle (\(\text{cos}\,\text{Z}\)). UV\({}_{\text{sc}}\) was obtained from the integration of the solar spectrum, revised by [PERSON] [37] at between 280 and 400 nm, yielding a value of 102.15 W\(\cdot\)m\({}^{-2}\). Data corresponding to solar elevation angles lower than 5\({}^{\circ}\) were discarded in order to avoid the cosine response problems of the irradiance measurement instruments. After the filtering process, a total of 68,509 experimental measurement data were used in the study.  
I\({}_{\rm{CH}}\), I\({}_{\rm{DH}}\), and I\({}_{\rm{BH}}\) were measured with Hulseflux pyranometers (model SR11: sensitivity of 12.13 uV/(W/m\({}^{2}\)) and uncertainty of \(\pm\)1.8%) and a pyheliometer (model DR01: sensitivity of 10 \(\times\) 10\({}^{-6}\) V/(W/m\({}^{2}\)) and uncertainty of \(\pm\)1.2%), respectively. For direct and diffuse radiation measurements, I\({}_{\rm{DH}}\) and I\({}_{\rm{BH}}\), a GEONICA-SEMS-3000 sun tracker equipped with a shading disc has been used. A Kipp & Zonnen pyranometer (model CUV5: sensitivity of 300-500 uV/(W/m\({}^{2}\)) and uncertainty of \(\pm\)5%) was used to determine the I\({}_{\rm{UV}}\) values. The cloud cover was calculated with a commercial SONA201D all-sky camera (Sieltec Canary Islands, Spain). The trigger frequency of the camera was 1 s and its image resolution was 1158 \(\times\) 1172 pixels, recorded with the RGB color model (8-bit pixels with integer values between 0 and 255 were used). A complete description of the experimental facility and instruments can be found in previous works [14; 38; 39].