esa-sceva/satcom-chunk-collection huggingface dataset:
# Probabilistic Studies on the Shear Strength of Slender Steel Fiber Reinforced Concrete Structures
## 3 Shear Formulations and Experimental Database for SFRC Beams without Shear Reinforcements
### Shear Resistance Formulation
#### 3.1.1 German Guideline (DAfStB) [19]  
The design shear resistance of SFRC beam is obtained as a summation of shear resistance of the concrete contribution and fiber contribution expressed as Equation (1).  
\[V_{DA/StB}=\frac{0.15}{\gamma_{c}}kb_{w}d(100\rho_{l}.f_{ck})^{\frac{1}{3}}+\frac {\infty_{c}^{f}\ f_{ct,Rn}^{f}b_{w}h}{\gamma_{ct}^{f}} \tag{1}\]  
\[f_{ct,Ru}^{f}=k_{F}^{f}k_{G}^{f}0.37f_{cfI,L2}^{f} \tag{2}\]  
To obtain the values of \(f_{cfI,L2}^{f}\), the expressions in Equation (3) are used [32]. This is also adopted in the assessment conducted in [21]  
\[f_{spfc}=0.63\sqrt{f_{cuf}}+0.288F\sqrt{f_{cuf}}+0.052F \tag{3}\]  
where \(b_{w}\) and \(d\) is the width and effective depth of beam cross-section, respectively. \(k\) is the size effect factor. \(k=1+\left(\frac{200}{d}\right)^{0.5}\leq 2\ f_{ct,Ru}^{f}\) is the tensile strength of SFRC. \(\rho_{l}\) is the reinforcement ratio. \(k_{F}^{f}\) is fiber orientation factor taken as 0.5 for shear. \(f_{cfI,L2}^{f}\) is the post-cracking flexural strength for a deflection of 3.5 mm. \(A_{ct}^{f}\) is the effective area expressed as \(A_{ct}^{f}=b_{w}*\ min(d,\ 1.5m)\). \(k_{G}^{f}\) is the fiber size factor expressed as \(k_{G}^{f}=1.0+0.5A_{ct}^{f}\leq 1.7\). \(\infty_{c}^{f}\) is the factor accounting for the long-term effects. \(F\) is the fiber factor. \(\gamma_{c}\) is the partial factor for concrete taken as 1.5. \(\gamma_{ct}^{f}\) is the partial factor for tensile strength of fiber-reinforced concrete taken as 1.25. \(h\) is the height of cross-section.
# Machine Learning Techniques for Non-Terrestrial Networks
## 6 Machine Learning Techniques in NTN
### ML for NTN Service
\begin{table}
\begin{tabular}{l l l} \hline \hline
**NTN Service** & **ML Applications** & **Target NTE** \\ \hline Mobility cell connectivity & 3D Positioning, Trajectory planning, handover rate minimization & HAPS, LEO \\ \multirow{2}{*}{\begin{tabular}{l} Unserved/Under-served and isolated areas \\ Throughput increase \\ \end{tabular} } & 3D Positioning, Trajectory planning, Energy management, Multi-user MEC offloading & UAV, HAPS, LEO \\ \multirow{2}{*}{\begin{tabular}{l} Secondary link for backup \\ Disaster relief \\ \end{tabular} } & Content caching, selection of the Tx elements in Multi-connectivity & UAV, HAPS \\ \multirow{2}{*}{\begin{tabular}{l} Broadcasting/Multicasting \\ \end{tabular} } & Network failures classification, Radio link outage & HAPS, LEO \\ \multirow{2}{*}{\
\begin{tabular}{l} Broadcasting/Multicasting \\ \end{tabular} } & UAV position optimization, Object detection and tracking, MEC offloading & UAV, HAPS \\ \multirow{2}{*}{
\begin{tabular}{l} Broadcasting/Multicasting \\ \end{tabular} } & Content caching fetching/eviction strategy, MEC offloading & LEO, HAPS \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ML applications for NTN services.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
###### Abstract  
A head-related transfer function (HRTF) is a mathematical model that describes the acoustic path between a sound source and a listener's ear. Using binaural synthesis techniques, HRTFs play a crucial role in creating immersive audio experiences through headphones or loudspeakers, using binaural synthesis techniques. HRTF measurements can be conducted either with standardised mannequins or with in-ear microphones on real subjects. However, various challenges arise in, for example, individual differences in head shape, pinnae geometry, and torso dimensions, as well as in the extensive number of measurements required for optimal audio immersion. To address these issues, numerous methods have been developed to generate new HRTFs from existing data or through computer simulations. This review paper provides an overview of the current approaches and technologies for generating, adapting, and optimising HRTFs, with a focus on physical modelling, anthropometric techniques, machine learning methods, interpolation strategies, and their practical applications.  
spatial audio processing; head related transfer function (HRTF); HRTF measurement; HRTF numerical modelling; HRTF personalisation; HRTF anthropometric and morphological approaches; HRTF interpolation
## 2 State-of-the-Art: IAR Applications for Training and Assistance
### Analysis of the State-of-the-Art  
After analyzing the previously mentioned work of the state-of-the-art, it is possible to highlight some shortcomings that motivated the creation of this work:  
* First of all, there is a lack of solutions devoted specifically to solving the main issues related to AR training and assistance in shipbuilding.
* Second, there are not many solutions designed for Microsoft HoloLens, currently one of the more sophisticated HMD devices.
* Third, there is no significant recent literature that proposes a collaborative framework for IAR. There are only some early works that were systematically reviewed in [57].
* Finally, the fourth shortcoming is related to the fact that, although different experiments have been proposed mainly regarding visual fixtures and on the usability of IAR applications, there are no significant solution validations that include performance considerations for their deployment under production conditions in a real-world industrial scenario.  
This article considers the previously described issues and proposes a complete solution to tackle them.
# Gold Nanoparticles and Nanorods in Nuclear Medicine: A Mini Review
## 2 Gold Nanoparticle and Nanorods: Preparations and Features
### AuNPs
The post-synthesis functionalization allows modifying the surface of the already purified and monodispersed nanoparticles. In this way, new features or new molecules or drugs are introduced on the particle ligands. The post-synthesis modification of the nanoparticles surface can be obtained through four main approaches, as schematized in Figure 2: (i) covalent attachment; (ii) electrostatic interaction; (iii) direct thiol reaction; and (iv) secondary interaction [111]. All of these approaches can be used to link radionuclides or their derivatives on AuNPs, but in many systems, the secondary interaction was preferred. For example, many AuNPs-PEG systems were studied as contrast agents for CT imaging. These systems exhibited several advantages compared with the clinical iodine-based contrast agents, such as better imaging performance and biocompatibility, longer blood circulation time, and manageable behaviors. Moreover, several \({}^{99\text{m}}\)Tc-labelled PEG-AuNPs have been developed as radiopharmaceuticals for SPECT imaging applications in cancer, sentinel lymph node (SLN), and other biological systems. In fact, \({}^{99\text{m}}\)Tc  
Figure 2: The four major surface modification approaches for the functionalization of nanoparticles, using PEG and a peptide as an example of a biomolecule. (1) Covalent attachment: covalent reactions such as EDC/NHS coupling occur between the PEG layer and biomolecule end groups. (2) Electrostatic: use cationic/anionic PEG and biomolecules to allow charge–charge conjugation. (3) Direct thiol reaction: both PEG and biomolecules loaded through thiol binding to the surface. (4) Secondary interaction: ligands are loaded onto the surface followed by the biomolecules containing a specific receptor to allow a ligand-receptor specific conjugation. Reproduced with permission from [111]. is a perfect radioisotope for SPECT imaging due to its favorable low energy \(\gamma\)-ray (140 keV), suitable half-life (6.02 h), low cost, and commercial availability [80; 83; 85]. Recently, [PERSON] et al. has developed AuNPs-PEG functionalized with tumor-targeting moieties and chemotherapeutic agents, combining CT and chemotherapy for the treatment of prostate cancer. The surfaces of AuNPs were functionalized with the prostate-specific membrane antigen (PSMA) aptamer. Furthermore, PSMA aptamer-conjugated AuNPs were able to effectively bring DOX to LNCaP cells and produce important cytotoxicity in those in vitro cells. Multifunctional AuNPs are suitable platforms for combining CT and targeted chemotherapy [112].
# A Systematic Review and Future Development of Automotive Classis Control Technology
## 6 Discussion  
Chassis by wire technology, an advanced automotive technology that relies on an electronic control system to regulate the chassis of a vehicle, has the potential to enhance vehicle handling performance, comfort, and safety.  
Through an introduction to the chassis by a wire system, the key technologies and development status of steer-by-wire steering, and braking, drive and suspension systems can be comprehensively reviewed, shedding light on the current state of each system and areas for improvement. To further develop the chassis steer-by-wire system, advancements may be made in the following areas:The security and reliability of steer-by-wire technology has long been a fundamental obstacle to its implementation, which can be improved through hardware redundancy and fault-tolerant algorithms. Future steer-by-wire systems are expected to be highly intelligent platforms that integrate hardware redundancy and fault-tolerant algorithms. With the evolution of technology and the continuous upgradingof vehicle control systems, steer-by-wire technology will gradually become the mainstream trend in the automotive industry. The application of emerging technologies such as artificial intelligence, vehicle networking, and autonomous driving will further enhance the intelligence of steer-by-wire systems.  
The dynamic system of line control can be classified into EHB and EMB. It is expected that EHB will prevail as the mainstream scheme for a considerable period in the future, while EMB may encounter difficulties in commercialization in the short term due to issues such as redundancy and backup challenges.  
Another prominent feature is the hub motor system, which has the distinct advantage of simplifying the vehicle architecture, increasing available space, and enhancing transmission efficiency, all while integrating the steering, braking, and drive functions into a single system. Additionally, in terms of suspension, it is flexible in adjusting the body height and stiffness of the suspension system to enhance vehicle passability or handling performance.
# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
###### Abstract  
Accurately assessing image quality is a challenging task, especially without a reference image. Currently, most of the no-reference image quality assessment methods still require reference images in the training stage, but reference images are usually not available in real scenes. In this paper, we proposed a model named MSIQA inspired by biological vision and a convolution neural network (CNN), which does not require reference images in the training and testing phases. The model contains two modules, a multi-scale contour prediction network that simulates the contour response of the human optic nerve to images at different distances, and a central attention peripheral inhibition module inspired by the receptive field mechanism of retinal ganglion cells. There are two steps in the training stage. In the first step, the multi-scale contour prediction network learns to predict the contour features of images in different scales, and in the second step, the model combines the central attention peripheral inhibition module to learn to predict the quality score of the image. In the experiments, our method has achieved excellent performance. The Pearson linear correlation coefficient of the MSIQA model test on the LIVE database reached 0.988.  
no-reference image quality assessment; convolutional neural network; deep learning; image multi-scale; biological visual inspiration +
Footnote †: journal: Applied sciences  
1
## 3 Diversity and Reliability  
Diversity is the capability to exploit channel variations over time, frequency, and space for communication robustness. We study spatial diversity following the aforementioned transmission schemes to achieve URC. Reliability is the successful transmission of a certain amount of data from a source to a destination within a latency budget. Our working definition of reliability is the focus with 3 GPP definition [45] as \"capability of transmitting an amount of traffic within a predetermined time duration with high success probability. The minimum requirement for reliability is \(1-10^{-5}\) success probability of transmitting over layer two protocol data unit of 32 bytes within 1 ms\". In this sequel, using the SIR expressions defined in (1), we calculate the outage probability for each of the considered schemes. Furthermore, to elaborate more on the system performance and resource usage under the reliability constraint, we make further analysis on the expected value of SIR, rate control, energy efficiency and throughput-reliability with the considered system model.
# CNN-Based Model for Landslide Susceptibility Assessment from Multispectral Data
## 3 Proposed Landslide Susceptibility Identification Model
The selection of the input data dimensions is an important aspect since texture is a characteristic that can be related to landslide susceptibility, which, in turn, can be recognized by the CNN. In this sense, three sizes, namely \(3\times 3\), \(5\times 5\) and \(7\times 7\), were evaluated, taking into account the spatial resolution of the data (20 m), the depth of the proposed CNN and the downsampling (pooling) operations performed on the network. Although the best results were obtained with a \(5\times 5\) dimension, for data with different spatial resolution, or even for deeper networks, it is recommended to evaluate larger window sizes. One aspect to consider here has to do with the computational cost since the larger the window size, the higher the computational cost, due to the fact that for each pixel, a neighborhood with the established dimensions is extracted. Having said that, the first block allows communication between different spatial locations (a \(5\times 5\) neighborhood); it operates simultaneously on all channels and its num  
Figure 1: Proposed model for landslide susceptibility identification by means of remote sensing images and deep learning. ber of filters equals the number of channels of the input data. After the first convolution+_BN+GELUI_ the output size is \(b\times 5\times 5\times c\) (where \(b\) is the batch size). After the second convolution of this first block, the output size is \(b\times 1\times 1\times c\), i.e., the data that had been taken in a \(5\times 5\) neighborhood are now processed at the pixel level. For the second block, the computation occurs on the channel dimension, using \(1\times 1\) convolutions. In this case, it is important to note that a \(1\times 1\) convolutional layer is equivalent to an \(FC\) layer when applied on a per-pixel basis. After the second block is applied, the output size remains at \(b\times 1\times 1\times c\). Finally, a fully connected layer is applied at the end of the network to obtain the probability of each pixel belonging to one of the two classes. Considering the \(p\) input examples (i.e., \(p\) pixels of the input image), we will have output \(p\) values related to the level of susceptibility to landslides at each position or geographical point represented by each pixel.
###### Abstract
It has been widely applied in engineering surveying [7], intelligent driving [8; 9], urban planning [10], mobile robots [11], digital city construction [12], and various other fields. The emergence of MLS has facilitated their extensive and effective use in road-related applications, such as the 3D geometric reconstruction of roads [13], road surface damage identification [14; 15], road marking [16], and traffic sign extraction [17]. However, due to the complexity and richness of most road scenes, MLS data collection can result in datasets of up to 2 million points per second or approximately 62 GB/km [18]. Moreover, point cloud data often exhibit characteristics such as an uneven density distribution and strong non-structured patterns. Consequently, the accurate and efficient automated extraction of road surfaces from MLS data remains a significant challenge.
## 4 Conclusions  
Robocasting was successfully used for the production of dense, porous and bi-layer (FGM) hydroxyapatite structures. The bi-layer structure was composed of a dense core surrounded by a lattice part, fabricated in a single printing step. To the best of our knowledge, it was the first time that such a complex part, made by integrating monolithic and porous structures, was fabricated by the robocasting technique.  
While the dense structure showed some large flaws and consequently a moderate compressive strength, even if higher to structures made by binder-jetting, the produced lattice structure fulfilled all biological and mechanical criteria for the non-load-bearing biomedical applications. The osteeconductive properties of the bi-layer parts were verified by soaking the sintered samples in SBF, which showed good in vitro bioactivity, especially the porous parts: the precipitated layer was OCP which is believed to be a precursor of biological HA.  
As an innovation, a new PCL/CaP composite macroporous layer was also added to the already fabricated FGM structures to introduce more functionality e.g., antibacterial properties. This third hybrid layer combined the advantages of high biological properties of CaP component and effective drug entrapment in the polymeric part. The result was FGM tri-layer samples in which each layer had special functions: loadbearing for the dense core layer, osteoconductivity and osteoinductivity for the intermediate porous layer, and antibacterial and drug delivery for the outer composite layer. Antibacterial tests showed that the hybrid structure was effective in releasing antibacterial Ag\({}^{+}\) ions to the surrounding medium and showed its potential efficiency in limiting _Staphylococcus aureus_ proliferation during surgery. As a final note, this hybrid material can be considered as a model system to pave the way towards new multifunctional biomaterials for tissue engineering applications.  
Conceptualization, P.P., J.-M.T. and F.R.; methodology, M.M., P.P., P.-M., V.A. and G.B.; software, B.C.; validation, M.M., P.P., F.R., V.A. and G.B.; investigation, M.M., P.P., J.-M.T., and V.A.; discussion of results, M.M., P.P., J.-M.T., F.R., C.C. and A.M.C.; writing--original draft preparation, M.M., and V.A.; writing--review and editing, P.P., J.-M.T., B.C., F.R.; C.C. and A.M.C., visualization, M.M., P.P., and V.A.; supervision, P.P., and C.C.; project administration, P.P., F.R. and C.C. All authors have read and agreed to the published version of the manuscript.  
The research leading to some of these results has received funding from the European project AMITIE (Marie Sklodowska Curie Grant Agreement n\({}^{\circ}\)734342), and the Fondazione Cassa di Risparmio di Torino (RF = 2019.0588).  
The authors would like to thank [PERSON] from the University of Limoges for his help in adopting the CAD files for the purpose of the robocasting.  
The authors declare no conflict of interest.  
Figure 17: Micrographs of an Ag\({}^{+}\)-functionalized hybrid scaffold. (**A,C**) are high magnification of image (**B**): polymer (**A**) and ceramic (**C**) parts indicate clean bacterial free surfaces.
# A Novel CDMA-Based Secret Message Multiplexing Scheme by Image Data Hiding +
## 4 Experimental Results and Discussion
### Robustness against Noise Attacks
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Noise Density** & \(N=2\) & \(N=4\) & \(N=8\) & \(N=16\) & \(N=32\) \\ \hline
0.10 & 0.0605 & 0.0718 & 0.1680 & 0.1770 & 0.2894 \\
0.15 & 0.0887 & 0.1057 & 0.2206 & 0.2342 & 0.3365 \\
0.20 & 0.1174 & 0.1378 & 0.2567 & 0.2767 & 0.3648 \\ \hline \end{tabular}
\end{table} 
Table 7: BER of the extracted secret from the Lena image versus number of users \(N\) under salt-and-pepper noise. Figure 9: Marked Lena images with \(N\)=16 corrupted by salt-and-pepper noise. (a) density = 0 (noise-free); (b) density = 0.10; (c) density = 0.15; (d) density = 0.20.  
of both images decreases with the increase in \(N\), the Chinese characters in the second image are visible with smaller values of \(N\). The results coincide with the above BER analysis well. Since the characteristics of Gaussian noise and the salt-and-pepper noise are quite different, the preferable \(N\) values for noise reduction are also different. In resistance against noise attacks, the \(N\) value, which represents the multiplexity level, plays a major role.
###### Abstract  
Being often deployed in remote or hostile environments, wireless sensor networks are vulnerable to various types of security attacks. A possible solution to reduce the security risks is to use directional antennas instead of omnidirectional ones or in conjunction with them. Due to their increased complexity, higher costs and larger sizes, directional antennas are not traditionally used in wireless sensor networks, but recent technology trends may support this method. This paper surveys existing state of the art approaches in the field, offering a broad perspective of the future use of directional antennas in mitigating security risks, together with new challenges and open research issues.  
wireless sensor networks; directional antenna; security risks; malicious attacks +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Cutting Force of Winter Wheat Residues
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{1}{|c|}{} & \multicolumn{6}{c|}{**2016 y.**} \\ \hline
**Soil Properties** & **Control** & **Treatment 1** & **Treatment 2** & **Treatment 3** & **Treatment 4** & **Treatment 5** & **Treatment 6** & **Treatment 7** \\  & **(A)** & **(B)** & **(C)** & **(D)** & **(E)** & **(F)** & **(G)** & **(H)** \\ \hline Total porosity & \(48.60\pm 5.75\) & \(48.90\pm 3.68\) & \(53.00\pm 3.04\) & \(52.90\pm 7.19\) & \(52.90\pm 3.47\) & \(53.00\pm 3.07\) & \(52.00\pm 3.20\) & \(60.80\pm 6.94\) \\ (\%) & a & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) \\ \hline Soil density & \(1.42\pm 0.21\) & \(1.37\pm 0.09\) & \(1.26\pm 0.11\) & \(1.29\pm 0.21\) & \(1.38\pm 0.06\) & \(1.28\pm 0.04\) & \(1.41\pm 0.18\) & \(1.23\pm 0.13\) \\ (g cm\({}^{-3}\)) & & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) \\ \hline  & & & & & & & & a \\ \hline  & \multicolumn{6}{c|}{**2017 y.**} \\ \hline Total porosity & \(50.60\pm 2.85\) & \(54.80\pm 3.21\) & \(59.00\pm 6.08\) & \(56.40\pm 5.16\) & \(57.80\pm 2.83\) & \(56.60\pm 3.98\) & \(58.00\pm 2.32\) & \(57.40\pm 3.11\) \\ (\%) & a & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) \\ \hline Soil density & \(1.23\pm 0.14\) & \(1.09\pm 0.07\) & \(1.08\pm 0.08\) & \(1.19\pm 0.15\) & \(1.14\pm 0.12\) & \(1.15\pm 0.02\) & \(1.21\pm 0.14\) & \(1.22\pm 0.08\) \\ (g cm\({}^{-3}\)) & & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) & \(\blacklozenge\) \\ \hline Porosity HSD\({}_{05}\) & R(A) = 4.65 & R(B) = 3.87 & R(C) = 5.15 & R(D) = 5.02 & R(E) = 4.87 & R(F) = 4.79 & R(G) = 5.05 & R(H) = 5.84 \\ \hline Density HSD\({}_{05}\) & R(A) = 0.14 & R(B) = 0.18 & R(C) = 0.10 & R(D) = 0.09 & R(E) = 0.15 & R(F) = 0.12 & R(G) = 0.19 & R(H) = 0.12 \\ \hline \end{tabular}
\end{table} 
Table 2: Total soil porosity and soil density before the tillage. Looking to future research, it would be important to identify the frequency and the exact timing of biopreparations that would have the greatest impact on the physical properties of soil and the mechanical properties of plant residues, as well as on the traction force and diesel fuel consumption of tillage machines and the reduction of greenhouse gas emissions.