esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
## 2 Data and Methodology  
### Data  
The details of the data used for the experiments are shown in Tables 1 and 2. This experiment used the Jilin-1 Gaofen 03-1 satellites with a spatial resolution of 1 m, and the image acquisition date is 11 October 2020. The study area is located in Yokosuka, Japan. Four representative images were intercepted from the original image for experimentation.  
The optical remote sensing images used in this experiment and the corresponding ground truth maps are shown in Figure 1.
## 5 Discussions and Conclusions  
### Discussions  
In the process of multi-hop quantum communication, the quantum communication delay is introduced mostly by quantum measurements, unitary operations, and transmission of classical information. Due to the limited decoherence time in the quantum memory and the demand for QOS (Quality of Service), a short delay is expected in quantum communication. Assume that each quantum measurement takes \(d_{m}\) milliseconds, unitary operation takes \(d_{u}\) milliseconds, and one-hop transmission of classic information through classical channel takes \(d_{t}\) milliseconds.  
In our scheme, as shown in Figure 7, quantum measurements (CNOT, Hadamard operations, and projective measurements are included) are performed simultaneously, and classical information is sent to the destination node independently. Taking \(H_{i}^{d}(i=1,\ldots,n)\) to denote the hop count of the classical information transmitted from the \(i\)-th node to the destination node, therefore the total end-to-end quantum communication delay in the \(n\)-hop case is given by  
\[d_{A,total}=\max\left\{H_{i}^{d}\right\}\times d_{t}+d_{u}+d_{m} \tag{13}\]  
For hop-by-hop qubit transmission [7], as shown in Figure 8, the total end-to-end quantum communication delay is given by  
\[d_{B,total}=n(d_{t}+d_{u}+d_{m}) \tag{14}\]  
Given that \(d_{t}=2d_{u}=2d_{m}=0.6 ms\), the end-to-end quantum communication delay in our scheme and hop-by-hop qubit transmission scheme is demonstrated by Figure 9.  
From Figure 9, it is obvious that our scheme has a shorter end-to-end quantum communication delay. In a wide-area quantum communication network where the hop count number \(n\) is large, our scheme shows obvious advantage in the end-to-end quantum communication delay.  
In addition to the shorter quantum communication delay, our scheme shows other notable advantages, such as: (i) Duplex quantum communication is available for two arbitrary nodes compared with other quantum network schemes [6; 7; 8; 11; 12]. (ii) The composite GHZ-GHZ states are used as the quantum channel, which is less prone to the decoherence effects caused by the surrounding  
Figure 8: Hop-by-hop qubit transmission.  
Figure 7: The proposed scheme in this paper.  
Figure 9: The comparison of two schemes in terms of communication delay.  
environment [22]. (iii) Only quantum projective measurements and unitary operations are needed to implement qubit transmission. Auxiliary qubits are not required, in contrast to the bidirectional quantum communication scheme based on GHZ-Bell states [20]. (iv) In our scheme, for an intermediate node, it is not necessary to recover the original qubit locally and then teleport it to its next-hop node. Therefore, information leakage can be avoided compared with the hop-by-hop qubit transmission scheme [7].
# Topical Use of _Camabis sativa_ L. Biochemical
## 6 Hemp Seed Oil  
### Generalities  
Hemp Seed Oil is extracted from the seeds by cold-pressed extraction or supercritical CO\({}_{2}\) for better stability [129]. It represents about 30% of the raw material [130]. The cultivars employed and the growth condition of the plant directly impact the composition of the oil. The oil contains linoleic acid (55.41-59.64%), \(\alpha\)-linoleic acid (16.51-20.40%), oleic acid (11.40-15.88%), palmitic acid (6.08-6.82%), and stearic acid (2.34-2.67%) [8]. Furthermore 25-35% of the oil weight are proteins, 10-15% are fibers, and 20-30% are carbohydrates [131]. Certain strains also contain up to 4% of \(\gamma\)-linoleic acid [129]. Sometimes, Hemp Seed Oil can also be classified by saturated, monounsaturated, and poly-unsaturated acids or by omega-3, omega-6, omega-9 acids composition:  
* 80.0% poly-unsaturated acids, 10.8% monounsaturated acids, and 9.2% saturated acids [132].
* 59.6% omega-6 acids, 29.7% of omega-3 acids, and 10.8% omega-9 acids [132].  
Hemp Seed Oil, as an oil rich in essential fatty acids (ELA), has an action on atopic dermatitis, psoriasis, and particularly acne. Many studies find conflicting results, which indicates that the actions of ELA are dose-dependant and length-dependant [133]. Both \(\alpha\)-linolenic acid and linoleic acids reduce UV damage and hyperpigmentation [134]. Clinical evidence highlights the positive hydrating and anti-aging effect of essentials fatty acids on the skin for oral use [135]. A favorable portion of fatty acids in Hemp Seed Oil improves the gliding of a skin care cream and the smoothness of the skin [4; 136]. Moreover, Hemp Seed Oil is a non-comedogenic [137] dry oil that does not leave a greasy and sticky layer on the skin [137]. As a result, formulations have been developed with Hemp Seed Oil as long-term moisturizing patches [138] and stable emulsions in sunscreen cosme [5; 136]. Hemp Seed Oil's global antioxidant activity can be measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,20-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) test [130]. DPPH leads to 60-65% scavenging activity and ABTS leads to 40-88% scavenging activity [130; 132].  
Hemp Seed Oil extraction leads to byproducts of seed paste which can be recycled as a dermo-cosmetic agent by use of eco-friendly processes: Ultrasound-Assisted Extraction or Supercritical Fluid Extraction. These pastes contain 14 bioactive metabolites: seven cannabinoid acid derivates, four lignamides, two amides, and a phenolic acid. The paste showed more than 80% of inhibition for the collagenase enzyme. The global antioxidant activity was measured by DPPH; it is up to 50% of radical scavenging [9].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
## 3 SDMob Architecture
### Basic SDMob Architecture
#### 3.1.2 RPL-Aware Leaf (RAL)  
In SDMob installing a new rule on the MNs is troublesome since their links are lossy. A recent IETF draft [43] defines RALs as a host that does not participate in further advertising the DODAG and relies on the RPL routers to forward its traffic. This can solve the problem of routing loops that could happen in the network, for example when MN is denoted as the parent node of another node. Another upside is the reduced memory footprint in the MN. Most importantly, it is beneficial as the controller is mandated to send routes to more reliable SNs rather than using MN's lossy links.
## 4 Materials and Methods
### Differential Scanning Fluorimetry (DSF)
#### 4.6.2 hNudt16-Ligand Thermal Stability Screen  
Protein thermal stability in the presence of various ligands was measured using two DSF methods: assays with SYPRO orange dye and dye-free assays. DSF assays with SYPRO orange dye were performed at specific ligand concentrations for each protein-ligand pair. This measurement was performed as described in Section 4.6.1, with a constant MgCl\({}_{2}\) concentration of 20 mM and a concentration of the tested ligand maintained at 0.5 mM. DSF dye-free measurements were performed using a Prometheus NT.48 nanoDSF device (NanoTemper Technologies, Munchen, Germany). The composition of the samples was identical to that in the DSF with SYPRO orange dye measurements, without the addition of any dye. The conditions during measurements were as follows: 5 uM protein, 20 mM MgCl\({}_{2}\), 10 mM HEPES pH 7.9, 150 mM NaCl, 5% glycerol, 1 mM TCEP, and 0.5 mM ligand. The measurements were performed at a single ligand concentration for all ligands. After 10 min of pre-incubation, the samples were loaded into standard capillaries (NanoTemper Technologies, Munchen, Germany). During the measurement, the samples were heated in a temperature range of 20-95 \({}^{\circ}\)C at a rate of 1 \({}^{\circ}\)C/min, and the measurements were performed with an excitation power of 30%. All of the collected data measured with using the above two DSF methods were analyzed according to the two-state transition numerical model described previously [51], using the Marquardt algorithm implemented in the Origin 2019 package (OriginLab, Northampton, MA, USA). We fitted the melting temperatures (T\({}_{\text{m}}\)) globally for at least three independent experiments (but separately for different DSF methods).
# From Bottom of Sea to Space: Quo Vadis IoT? So What About Security?
## 1 Introduction  
In recent years, extensions of IoT have been of great interest to researchers [1]. An extension of the IoT is formed by a set of geographically distributed, special-purpose cyber-physical, or cyber-enabled things, which are connected using special-purpose protocols on top of the Internet to achieve a specific goal. To mention a few of these extensions, one may refer to Internet of Vehicles [2] and Vehicular Things [3], Internet of Medical [4] and Healthcare [5] Things, Internet of Bodies [6], Internet of Vessels [7], or Internet of Energy [8].  
These extensions have been studied from different technological aspects [9, 10]. Different objectives have been considered by IoT extension designers and researchers. Among these objectives, some may refer to performance [11], Quality of Service (QoS) [12], Quality of Experience (QoE) [13], timeliness [14], reliability [15], scalability [16], fault tolerance [17], or energy efficiency [18]. However, security is probably the most challenging design objective for IoT extensions [19].  
Extensions of the IoT are spread from the bottom of the sea [20] to space [21]. This is shown in Figure 1. The application of IoT extensions under the ground, under the sea, on the ground, in the sky, and in space makes it pertinent to study these extensions from different perspectives. Related challenges, as well as current and future trends, need to be thoroughly investigated. A comprehensive survey in this area can serve to meet thesegoals. Although there are some surveys that are somewhat relevant, their shortcomings (discussed in Section 2.6 of Section 2 motivate our work in this paper).  
This paper provides a comprehensive review of the current state of research on IoT extensions, with a focus on security. We first classify extensions of the IoT based on the geographical locations where they are deployed, e.g., under the sea, under the ground, on the ground, in the sky, or in space. We study security challenges in the design of each extension. Moreover, we discuss security mechanisms used in each extension. Then, we present a taxonomy of existing IoT extensions. Lastly, we develop a future roadmap with a focus on the role of AI in the future of IoT extensions. This study is unique in its broad perspective in the sense that we cover all types of IoT extensions, all security challenges, and all security mechanisms.
## 2 System Overview
### Data Transfer Principle  
As can be seen from Figure 1, the data transfer circuit can be divided into the transmitting-side circuit and the receiving-side circuit. The transmitting-side circuit includes an amplitude shift keying (ASK) modulation module and a power amplifier. The receiving-side circuit includes a bandpass filter, an envelope detector, and a comparator. The generation of the ASK modulation can be expressed as:  
\[y(t)=\begin{cases}A\cos(2\pi f_{\mathrm{D}}t),&1\\ 0,&0\end{cases} \tag{5}\]  
where \(A\) is the amplitude of the data carrier. The principle of data modulation is shown in Figure 2.  
By using KVL in the backward data transfer channel as shown in Figure 1, one can obtain:  
\[\left\{\begin{array}{c}\dot{U}_{\mathrm{DS}}=\left(j\omega_{\mathrm{D}}L_{ \mathrm{S}}+\frac{1}{\mu_{\mathrm{w}}c_{\mathrm{C}\mathrm{D}\mathrm{S}}}+\frac {1}{\mu_{\mathrm{w}}c_{\mathrm{S}}}+R_{\mathrm{eq}}\right)\dot{I}_{\mathrm{DS }}-j\omega_{\mathrm{D}}M\dot{I}_{\mathrm{DP}}\\ 0=-j\omega_{\mathrm{D}}M\dot{I}_{\mathrm{DS}}+\left(j\omega_{\mathrm{D}}L_{ \mathrm{P}}+\frac{1}{\mu_{\mathrm{w}}c_{\mathrm{DP}}}+\frac{1}{\mu_{\mathrm{ w}}c_{\mathrm{P}}}+\frac{1}{\mu_{\mathrm{w}}c_{\mathrm{T}}}+R_{\mathrm{Peq}} \right)\dot{I}_{\mathrm{DP}}\\ \dot{U}_{\mathrm{DP}}=R_{\mathrm{Peq}}\dot{I}_{\mathrm{DP}}\end{array}\right. \tag{6}\]  
where \(\dot{U}_{\mathrm{DS}}\) and \(\dot{U}_{\mathrm{DP}}\) are the output voltage of the transmitting-side power amplifier and the input voltage of the receiving-side bandpass filter, respectively. \(R_{\mathrm{Peq}}\) is the equivalent input resistance of the receiving-side bandpass filter.  
By submitting (1) into (6), \(U_{\mathrm{DP}}\) can be calculated as:  
\[\dot{U}_{\mathrm{DP}}=\frac{jU_{\mathrm{DS}}R_{\mathrm{Peq}}MC_{\mathrm{P}} C_{\mathrm{S}}C_{\mathrm{T}}\omega_{\mathrm{D}}{}^{3}}{M^{2}C_{\mathrm{P}}C_{ \mathrm{S}}C_{\mathrm{T}}\omega_{\mathrm{D}}{}^{4}+C_{\mathrm{P}}C_{\mathrm{S }}C_{\mathrm{T}}\omega_{\mathrm{D}}{}^{2}R_{\mathrm{Peq}}R_{\mathrm{eq}}-jC_{ \mathrm{P}}C_{\mathrm{S}}\omega_{\mathrm{D}}R_{\mathrm{eq}}-jC_{\mathrm{P}}C _{\mathrm{T}}\omega_{\mathrm{D}}R_{\mathrm{Peq}}-jC_{\mathrm{S}}C_{\mathrm{T }}\omega_{\mathrm{D}}R_{\mathrm{eq}}-C_{\mathrm{T}}-C_{\mathrm{P}}} \tag{7}\]
## 4 Numerical Simulation
### Numerical Analysis of Charge Structure
#### 4.1.1 Numerical Model  
A quasi-three-dimensional finite element model for coal-rock mixed blasting under different stemming lengths of boreholes was developed using the dynamic analysis software Ansys/Ls-dyna (2022) to assess the rationality of the charge structure. Charge structure optimization analysis was performed. The blasting parameters of the numerical model are listed in Table 4. For the model, an axial uncoupled charge is applied for loosening blasting in the coal seam location, and a coupled charge is used for cast blasting in the overlying rock location. The numerical calculation model is shown in Figure 8.  
Non-reflective boundary conditions are applied to the bottom and lateral surfaces of the model to minimize the impact of stress wave reflection at the model boundaries on the calculation results. Gravitational acceleration is applied in the vertical direction, and a fixed-end constraint is imposed at the model's bottom. The ALE algorithm is employed between coal, rock, and explosives to avoid mesh distortion in the Lagrangian grid during large deformation analysis, which can lead to computational inaccuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{\
\begin{tabular}{c} **Type** \\ **Scheme** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **\(\boldsymbol{\phi}\) (mm)** \\ \end{tabular} } & \multicolumn{4}{c}{**Rock**} & \multicolumn{4}{c}{**Coal**} \\  & & \(\boldsymbol{H_{\text{R}}}\) **(m)** & \(\boldsymbol{l_{\text{S}}}\) **(m)** & \(\boldsymbol{l_{\text{E}}}\) **(m)** & \(\boldsymbol{l_{\text{S}^{\prime}}}\) **(m)** & \(\boldsymbol{H_{\text{C}}}\) **(m)** & \(\boldsymbol{l_{\text{Sc}}}\) **(m)** & \(\boldsymbol{l_{\text{Ec}}}\) **(m)** & \(\boldsymbol{l_{\text{Ic2}}}\) **(m)** & \(\boldsymbol{l_{\text{Ic1}}}\) **(m)** \\ \hline
1 & 170 & 14.9 & 4.5 & 9.0 & 1.0 & 6.4 & 2 & 0.715 & 0.8 & 2.17 \\
2 & 170 & 14.9 & 4.5 & 8.5 & 1.5 & 6.4 & 2 & 0.715 & 0.8 & 2.17 \\
3 & 170 & 14 & 4.5 & 8.0 & 2.0 & 6.4 & 2 & 0.715 & 0.8 & 2.17 \\
4 & 170 & 14 & 4.5 & 7.5 & 2.5 & 6.4 & 2 & 0.715 & 0.8 & 2.17 \\
5 & 170 & 14 & 4.5 & 7.0 & 3.0 & 6.4 & 2 & 0.715 & 0.8 & 2.17 \\
6 & 170 & 14 & 4.5 & 8.0 & 2.0 & 6.4 & 2.5 & 0.715 & 0.8 & 1.67 \\
7 & 170 & 14 & 4.5 & 8.0 & 2.0 & 6.4 & 3.0 & 0.715 & 0.8 & 1.17 \\
8 & 170 & 14 & 4.5 & 8.0 & 2.0 & 6.4 & 3.5 & 0.715 & 0.8 & 0.67 \\ \hline \end{tabular}
\end{table}
Table 4: Model parameters.
# Discrete Element Analysis of Grouting Reinforcement and Slurry Diffusion in Overburden Strata
## 4 Discrete Element Grouting Diffusion Results  
### Analysis of Grouting Reinforcement Form  
The grouting duration is set to 144 s (i.e., 2.4 min), corresponding to the time required for all slurry particles to be discharged from the grouting pipe under a maximum pressure of 3 MPa, as determined in preliminary experiments. The slurry inlet pressure is 2 MPa, the model boundary pressure is set to 0.5 MPa, and the numerical model and parameter selection follow Section 3.1 and Table 2. The discrete element simulation results of slurry diffusion are presented in Figure 6. As shown in the figure, (1) in the initial stage of grouting, the slurry enters the formation along the pipeline under grouting pressure, then diffuses radially outward from the grouting hole into the overburden, forming an approximately pear-shaped diffusion pattern. (2) The reinforcement effect of the grout is comprehensive. Initially, the slurry enters the formation through compaction and diffusion. As grouting continues, it gradually overcomes the bonding forces between overburden particles, leading  
to a splitting diffusion pattern. Subsequently, the slurry seeps through the created fissures, enveloping some overburden particles and forming a slurry-soil mixture.
# User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
## 2 Related Work
In summary, existing algorithms either do not provide fair load distribution, skew results towards RF bands due to higher intrinsic SINR/lower blockage probability, or have issues with high complexity, signaling overhead or data requirements as shown in Table 1. A low-complexity, efficient algorithm is needed to avoid these limitations.
## Materials and Methods
### Attenuation Coefficient Estimation  
Backscattered light, which forms the OCT image, is attenuated in arterial wall parts. Consequently, intensities of the same tissue type, e.g., endothelial, diverge. However, it is preferable that regions of a certain tissue type share a similar appearance. The latter is attempted by estimating attenuation coefficient values in every pixel of an OCT image. The outcome image is more realistic, i.e., a specific tissue type is represented by a smaller range of intensities while maintaining texture information. To test the effect of using the attenuation coefficient in the classification of A-lines, transformations of OCT-images were carried out according to the work presented by [PERSON] et al. [29]. The attenuation \(\mu\) in every pixel of an OCT image can be estimated with the OCT image intensities by using the formula [29]:  
\[\mu[i,j]=I[i,j]/(2*\Delta*\Sigma_{n=i+1}^{\infty}I[i,n]) \tag{2}\]  
where I[i,j] is the intensity in the original image; \(\Delta\) is the physical size of a pixel (mm/pixel); i indicates the A-line number; j indicates the depth. In the rest of this document, transformed images are often referred to as attenuation images for brevity.  
In Figure 3, an example of these transformations is presented. In the Section 4, we show that A-line patches extracted from images representing estimated attenuation coefficient values are classified with higher accuracy than A-line patches extracted from images with original intensity values.
## 2 System Model and Problem Formulation
### System Model  
The paper presents a system model, as depicted in Figure 1, which focuses on a multi-user OFDMA V2X communication network with a single BS, which is a technique that allows multiple users to access the same channel by dividing it into smaller subchannels or subcarriers. Each subcarrier can be assigned to a different user, depending on their channel conditions and data rate requirements [45]. This way, the spectrum resources can be utilized more efficiently, and the interference can be reduced. In contrast to conventional device-to-device users, individuals utilizing vehicles exhibit a higher movement velocity. Variations in vehicle speed and safety distance exist to prioritize safety, leading to distinct location modeling approaches for vehicle users compared to D2D users. On average, highway models [46; 47] show a spacing of 2.5 m between cars. Assuming that traffic is moving in a straight line at a constant speed, the distribution of car positions on the road can be described by a Poisson process. Y vehicles need V2I communication, denoted as CUEs, and X pairs of vehicles need V2V data transmission, denoted as VUEs (where X > Y). The quantity of subcarriers is denoted as \(V\). To meet the data rate demands of CUEs, the subcarriers are orthogonally allocated to the CUEs, so there is no interference between the CUEs. To enhance spectrum utilization and VUEs' data rate, each pair of VUEs can use multiple subcarriers, and to allow more VUEs to access, each subcarrier also allows multiple VUEs to use it, that is, a multiaccess resource match scheme. The allocation of uplink subcarriers has been regarded as a potential solution to the co-channel interference problem of uplink resources, which is more effectively addressed than the downlink.  
Figure 1: System model.
###### Abstract  
Particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), and heavy metals (HMs) present in polluted air are strongly associated with an increased risk of respiratory diseases. In our study, we grouped cities based on their pollution levels using a method called [PERSON]'s cluster analysis and looked at the increased cancer risk from PM\({}_{10}\)-bound harmful substances for adult men and women living in Polish cities. The analysis was based on data from 8 monitoring stations where concentrations of PM\({}_{10}\), PAHs, and HMs were measured simultaneously between 2018 and 2022. The cluster analysis made it possible to distinguish three separate agglomeration clusters: cluster I (Upper Silesia, Wroclaw) with the highest concentrations of heavy metals and PAHs, with mean levels of lead 14.97 \(\pm\) 7.27 ng\(\cdot\)m\({}^{-3}\), arsenic 1.73 \(\pm\) 0.60 ng\(\cdot\)m\({}^{-3}\), nickel 1.77 \(\pm\) 0.95 ng\(\cdot\)m\({}^{-3}\), cadmium 0.49 \(\pm\) 0.28 ng\(\cdot\)m\({}^{-3}\), and \(\sum\)PAHs 15.53 \(\pm\) 6.44 ng\(\cdot\)m\({}^{-3}\), cluster II (Warsaw, Lodz, Lublin, Cracow) with dominant road traffic emissions and low emissions, with average levels of lead 8.00 \(\pm\) 3.14 ng\(\cdot\)m\({}^{-3}\), arsenic 0.70 \(\pm\) 0.17 ng\(\cdot\)m\({}^{-3}\), nickel 1.64 \(\pm\) 0.96 ng\(\cdot\)m\({}^{-3}\), and cadmium 0.49 \(\pm\) 0.28 ng\(\cdot\)m\({}^{-3}\), and cluster III (Szczecin, Tricity) with the lowest concentration levels with favourable ventilation conditions. All calculated _ILCR_ values were in the range of 1.20 \(\times\) 10\({}^{-6}\) to 1.11 \(\times\) 10\({}^{-5}\), indicating a potential cancer risk associated with long-term exposure. The highest _ILCR_ values were reached in Upper Silesia and Wroclaw (cluster I), and the lowest in Tricity, which was classified in cluster III. Our findings suggest that there are continued preventive actions and stricter air quality control. The results confirm that PM\({}_{10}\) is a significant carrier of airborne carcinogens and should remain a priority in both environmental and public health policy.  
health risk assessment; carcinogenic; air pollution; urban area; cluster analysis; Poland +
Footnote †: journal: Applied sciences