esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Motivated by the need for enabling high-speed connectivity for aircraft, in this paper, we developed a ground-to-aircraft propagation model for the THz frequency band by accounting for atmospheric absorption and cloud impairments. To this aim, we first studied the geometric and molecular properties of clouds at different layers characterizing their attenuation impairments and composition properties depending on the type of weather conditions and Earth region. Then, we utilized the tools of integral geometry and probability theory to calculate cloud blockage probabilities and characterize the overall cloud-induced attenuation.  
In our numerical results, we illustrated the attenuation caused by different weather conditions in different parts of the Earth. Specifically, we have shown that the difference between sunny and rainy conditions may reach 30-50 dB that may require a considerable increase in the emitted power. Secondly, the overall path loss heavily depends on the region time and the difference may reach 10-30 dB. The worst conditions are logically provided by rain, where the additional attenuation on top of sunny conditions reaches 50 dB over the whole THz band. The Middle Earth zone is also the worst out of the considered regions with additional attenuation reaching 30 dB.  
The main contribution of the paper is a way to capture cloud-induced attenuation. The proposed model can be utilized as a propagation model when assessing the averaged received signal strength at the aircraft-to-ground links or as a building block for specifying more precise models that involve additional propagation effects such as fading.  
Conceptualization, V.B. and K.S.; methodology, V.D.; software, V.B. and V.D.; validation, V.B. and K.S.; formal analysis, V.B.; investigation, V.D.; resources, V.B. and V.D.; data curation, V.B.; writing--original draft preparation, V.B. and K.S.; writing--review and editing, V.D.; visualization, V.B.; supervision, K.S.; project administration, V.B. and K.S.; funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript.  
Figure 7: The received power in the sunny weather conditions. (**a**) Rx power as a function of frequency; (**b**) Rx power as a function of altitude; (**c**) Rx power as a function of Tx gain; (**d**) Rx power as a function of Rx gain; (**e**) Rx power as a function of Tx power.  
**Funding:** Sections 3-5 were written by [PERSON] under the support of the Russian Science Foundation, project no. 21-79-10139. This paper has been supported by the RUDN University Strategic Academic Leadership Program (recipient [PERSON], Sections 1, 2 and 6).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Insect Cell-Baculovirus Expression Vector System
### Development of BEVS Cell Lines  
To effectively produce eukaryotic proteins in BEVS, insect cells must possess the capability to recognize and process signal peptides, support oligomerization, and execute  
Figure 2: Construction map of baculovirus vector. (**A**) BacPAK6 vector undergoes a “triple-cut” process via Bsu36l digestion, leading to its linearization and the removal of one _lacZ_ gene and two essential gene fragments (open reading frame ORF603 and ORF1629), then linearized viral DNA and a transfer plasmid containing foreign genes are transfected into insect cells. (**B**) pFastBac vector contains a polyhedron promoter (pPh), so that the target gene can be inserted into pPh site, destroying _lacZ_ sequence, and then entering insect cells. (**C**) pFastBacDual vector segregates the pPh and the p10 late viral promoter (pP10) into two separate boxes with restriction sites, allowing for continuous subcloning of two foreign genes for co-expression. Among them, Tn7L and Tn7R are the smallest units needed for transposing Tn7 transposon. SV40 pA: Simian vacuolating virus 40 polyadenylation site; f1 ori: origin of replication of bacteriophage f1; pUC ori: origin of replication of pUC.  
post-translational modifications, including glycosylation, acylation, phosphorylation, and disulfide bond formation. Two widely used insect host cell lines are the _Trichoplusia ni_ cell line (Tn) (common High Five(tm) (H5) cells) and the _Spodoptera frugiperda_ cell line (SF) (common Sf21 and Sf9 cells). Sf21 cells originate from the pupal ovary, with Sf9 being a prevalent subclone [48]. Sf9 cells are more suitable for recombinant virus expansion and packaging, Sf21 is preferable for viral titer plaque experiment observation due to its larger cell diameter, and H5 cells are more suitable for secreted protein expression [49].  
New cell lines, such as express Sf+ by Protein Science Corporation, approved by the FDA, have enhanced the functionality of BEVS for influenza vaccine production [50]. [PERSON] et al. developed an Sf-RVN cell line with transversal negativity, exhibiting similar properties to Sf9 cells, including doubling time, diameter, and morphology, with no difference in the production of recombinant proteins [51]. To address limitations in glycosylation modification, Mimic(tm) Sf9 was developed, enhancing glycosylation complexity [52]. VE-CL02, a cell line derived from SP9, displayed inhibited apoptosis and extended cell survival through the expression of the _Campoletis sonorensis ichnovirus vankyrin_ (CsIV) gene P-vank-1 after baculovirus infection, UV irradiation, or treatment with apoptosis-inducing chemicals. VE01/02, another cell line, overcame the limitation of insect cell lines in BEVS by extending the cell growth cycle with the Vankyrin enhancement technique, providing a complex human-type glycosylation pattern [53].
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 4 Performance Evaluation of Overload Mitigation Strategies
### Grid RoCoF Disturbance Test
#### 4.2.2 Normal Operation Recovery  
When the grid frequency increases and inverters return to normal operation, the overload mitigation strategies exhibit different recovery behaviors, as detailed in Figure 13d.  
In the VP strategy, virtual power suppresses the primary frequency regulation when the frequency drops below \(\omega_{min}\) (1 pu). Above this threshold, the virtual power term is zero, recovering the primary frequency regulation capability. This transition is marked by an active power transient at t = 3.25 s.  
In the parallel PI strategy, the PI outputs a \(\omega_{v}\) term that disables the frequency regulation of the GFM inverter. When the grid frequency increases, its output moves towards zero until saturation region is reached. During this process, the dynamics of the system are still determined by the PI controller and the inertial power is limited for the time range [3; 3.25] s. Once the output of the PI saturates, control transitions back to PSL dynamics. The slow recovery process of the parallel PI leads to a reduced inertial power response compared to a GFM inverter without overload mitigation.  
AL and EFS also show a reduced inertial response during the first periods of the transient, but their recovery process is considerably faster than the parallel PI. The main reason for the fast recovery is that these strategies rely on a saturation block at the output of the controller, without including the additional dynamics of a parallel PI during the recovery. EFS shows the fastest recovery time, as the dynamics of the outer controller (saturated during overload) depend on a part of the overall inertia, for this case, 4.36 s over the overall 5 s inertia. However, as has already been discussed, EFS presents a more oscillatory behavior during normal operation. The lower damping can be identified in the higher overshoot and higher settling time of the power transient.
# Comparative Test on the Bond Damage of Steel and GFRP Bars Reinforcing Soft Rock Slopes
## 5 Conclusions  
In this paper, the bond damage characteristics of the steel and GFRP bar anchorage systems under three different loading conditions are studied. Through the comprehensive comparison of these characteristics, a more suitable application scenario of GFRP bolt is obtained. The specific conclusions are as follows:  
(1) Both steel and GFRP bar bodies in soft rock are affected by cyclic load, and cyclic load can aggravate the bond damage of the bar interface. The steel and GFRP bars in soft rock are both affected by the previous maximum load, which is the main factor that influences the bond damage of the anchorage bar, compared with the number of cyclic loadings.  
(2) The interface bond behaviour of the GFRP bar is better than the steel bar. Because of the small difference in elastic modulus of GFRP bar and the grouting body, the interface bond around the GFRP bar produces more efficient resistance of the grouting body which demonstrates a more effective anchorage performance than the steel bar under the same constant loading.  
(3) The anchorage structure of steel bar in soft rock can generate larger interfacial relative displacement with the load increasing than the GFRP bar in anchorage section, even though the elastic modulus of steel is much larger than GFRP. In the expansive soil, the anchorage structure deformations of steel and GFRP bar are almost the same because of the weaker bond at the interface of the grouting body and the surrounding soil than that of the bar interface.  
(4) The GFRP bar can bear a greater ultimate strength than the steel bar. Under the ultimate loading of the anchorage structure in soft rock, the steel bar with 450 MPa which is less than its ultimate strength shows the failure of the bar body pulling-out, and the GFRP bar with 508 MPa which is larger than its ultimate strength shows the failure of the bar body fracture. The steel bar anchorage structure in soft rock is destroyed at the interface around the grouting body. It can be seen from the results that the GFRP bar performs more efficiently when compared to the steel bar.  
Due to time constraints, numerical simulation could not be carried out to verify the conclusion of the article. Later work will use numerical simulation and indoor model testing to verify the content of the article.  
X.H. (formal analysis, methodology, validation, writing original draft preparation); G.L. (formal analysis, Funding acquisition, writing-review& editing); S.K.B. (writing-review & editing; methodology). J.W. (conceptualization, supervision,). W.Y. (methodology, validation). All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China] grant number [41472240, 51378008].  
Not applicable.  
The authors wish to express their gratitude and sincere appreciation for the financial support received from the National Natural Science Foundation of China (Grant No. 41472240, 51378008).
## 1 Introduction
Among the methods that do not require training the model for each degree of compression are the Flexible Blind Convolutional Neural Network (FBCNN) [20], that can predict an adjustable quality factor via decoupler module to control the balance between artifact removal and preservation of the details, and Quantization Guided JPEG Artifact Correction [21], that uses quantization data stored in the JPEG file. Due to the very large size of WSIs, simple and computationally efficient image processing methods might be more beneficial than deep learning methods, which operate mostly on smaller images. To eliminate blocking artifacts, in [22], a simple edge-preserving smoothing filter was introduced, that might be efficient in WSIs pre-processing. The pixels are first tagged as edge/non-edge and the pixels are identified that lie on the \(8\times 8\) tile boundary used in the JPEG compression. Then, a sigma filter is applied for each non-edge pixel on the tiling boundary to smooth out the blocking artifacts. In successive years, a modified version of this edge-preserving method was used to remove compression artifacts from histopathological images with success [23]. The authors improved the original algorithm to make it adequate for processing H&E stained images. To create the edge map, they only considered the pixels that belonged to the edges in all color channels. Then, they performed a weighted smoothing of the image using a Gaussian filter. The removal of artifacts resulted in an improvement in image quality for images compressed with \(QF=30\), but failed for higher \(QF\) values. This algorithm was also not tested on the labeled datasets used for segmentation. This work proposes two fully unsupervised edge-preserving algorithms for removing compression artifacts from H&E stained images, named Edge-Preserving Algorithm Increasing nuclei Clarity (EPIIC), and its modification, which uses the Sobel operator to extract the information about the edges, named EPIIC Sobel. We presented the results of the developed algorithms and other edge-preserving methods on a set of histopathological HE-stained images and other natural image datasets compressed with different \(QF\) values. Moreover, we compared the best results achieved by the developed methods with a state-of-the-art solution based on deep learning. Finally, we checked the effect of the compression artifacts removal on the nuclei segmentation. The proposed methods are adapted to process entire WSI slides at once.
## 2 Literature Review
propose a solution to create UAV-based network topology that is adjustable with the topological changes that occur due to the unpredictable mission-based movement of UAVs. For making topology adaptive, they use relay nodes named relay UAVs for relaying data between adjacent UAVs. This also supports reliable communication in-ground site wireless link and also maximizes the performance of a network. In the proposed particle swarm optimization (PSO), they considered dependency between metric and routing protocol. In [41], an iterative algorithm is proposed to obtain the suboptimal solutions used to efficiently solve the power allocation sub problem. Reconfigurable intelligent surface (RIS) has been identified as a promising solution for constructing a reconfigurable radio environment of the propagation channel and increasing received signal power by smartly coordinating the phase shifts of the passive elements at the RIS [42]. To properly group the multiple users into clusters, [43] proposed a novel user pairing scheme in which more than two users are paired in each cluster based on channel correlation and channel gain difference. We have examined the research carried out between 2012 and 2022. The literature review mentioned above came to the conclusion that a number of researchers have looked for and used UAVs in a disaster area. They prioritize UAV deployment and primarily expand the network's coverage. In some research publications, the tracking of user equipment and UAV communication were studied. They have not, however, concentrated on the UAV's limitations, such as battery life, communication range, and processing power. When deploying a UAV network, it is crucial to consider battery longevity; instead, they primarily concentrate on expanding UAV coverage. The majority of the researchers spend a lot of time on each drone or ground station performing computing work. To overcome the drawbacks of earlier work, a protocol for optimal UAV deployment is required. In this protocol, an effective UAV network employs the clustering technique to perform computation tasks, conserve battery life, and assist the ground team in searching for and locating missing people during rescue operations. Additionally, information gathered from the disaster region aids in pinpointing a stranded person's specific location. Additionally, in a disaster area, the enabled communication services promptly alert trusted authorities.
# Enhanced Photocatalytic Performance of Bi2O2 CO3 Loaded Activated Carbon for Toluene Removal in Air
## 2 Materials and Methods
### Photocatalytic Activity Test  
#### 2.5.1 Photocatalytic Activity Test of Bi\({}_{2}\)O\({}_{2}\)Co\({}_{3}\)  
To prepare the sample for photocatalytic toluene removal performance in air, 0.025 g BOC was dispersed into 150 mL de-ionized water under ultrasonication for 15 min. The suspension was filtered using a 0.1 \(\upmu\)m nylon membrane through a vacuum pump. The photocatalytic performances of the BOC samples were evaluated by toluene removal in air using a gas phase photocatalytic testing system developed by our group (China Patent, NO.202210103256.X). The reactor is a stainless steel canister and the BOC membrane was placed at the bottom of the reactor. The gas product in the reactor was analyzed at regular time intervals, using a gas chromatograph (GC), equipped with two flame-ionization detectors (FID) [28]. Toluene analysis was performed with one FID loaded with an Rt-Q-Bond Plot column (30 m \(\times\) 0.25 mm, film thickness 10 \(\upmu\)m), while CO\({}_{2}\) analysis was performed by the other FID loaded with a packed column (TDX-01, 3 m \(\times\) 3 mm) followed by a methanizer CO\({}_{2}\) concentration. The gas samples were fed to the GC online through an automatic gas sampling valve.
# Detection of Frozen Soil Using Sentinel-1 SAR Data
## 2 Radar Signal Modeling
### Semi-Empirical Dielectric Mixing Model for Frozen Soil-Water
The dielectric constant of frozen soil-water mixture \(\varepsilon_{mf}\) (=\(\varepsilon^{\prime}_{mf}-\varepsilon^{\prime}_{mf}\)) is given by:  
\[\varepsilon_{m}^{\prime}=\left[1+\frac{\rho_{b}}{\rho_{s}}(\varepsilon_{s}^{a }-1)+m_{p}^{g^{\prime}}\varepsilon_{fw}^{\prime a}-m_{v}\right]^{1/\alpha} \tag{2}\]1  
\[\varepsilon_{m}^{\prime}=\left[1+\frac{\rho_{b}}{\rho_{s}}(\varepsilon_{s}^{a }-1)+m_{p}^{g^{\prime}}\varepsilon_{fw}^{\prime a}-m_{v}\right]^{1/\alpha} \tag{2}\]2  
where \(m_{vu}\) is the unfrozen volumetric moisture content (vol%), \(m_{vi}\) is the volumetric ice content (vol%), and \(\varepsilon_{i}\) is the dielectric constant of ice (=3.15). \(m_{vu}\) and \(m_{vi}\) are given by [PERSON] et al. [14]:  
\[\varepsilon_{m}^{\prime}=\left[1+\frac{\rho_{b}}{\rho_{s}}(\varepsilon_{s}^{a }-1)+m_{p}^{g^{\prime}}\varepsilon_{fw}^{\prime a}-m_{v}\right]^{1/\alpha} \tag{2}\]3\[\varepsilon_{m}^{\prime}=\left[1+\frac{\rho_{b}}{\rho_{s}}(\varepsilon_{s}^{a }-1)+m_{p}^{g^{\prime}}\varepsilon_{fw}^{\prime a}-m_{v}\right]^{1/\alpha} \tag{2}\]4  
where \(T_{s}\) is the soil temperature steering the amount of liquid to frozen water in the soil (K), \(\rho_{w}\) is the density of water (=1 g/cm\({}^{3}\)), \(\rho_{i}\) is the specific density of ice (=0.9175 g/cm\({}^{3}\)) [5], and A and B are empirical coefficients related to soil texture. In [PERSON] et al. [14], A and B were calculated for three soil types (Silty-clay, Silt-loam and Sandy-loam, Table 1). For \(T_{s}\) above 273.2 \({}^{\circ}\)K, \(m_{vi}=0\) and \(m_{vu}=m_{v}\).
# Cyber Physical Systems Dependability Using CPS-IOT Monitoring
## 1 Introduction
### Contributions and Outline  
The epidemic modelling of the CPS-IoT can help solve the issue of oscillation of the CPS-IoT during routing updates by (i) ensuring that routing updates are performed at the right moment when the stability of the network has moved far from its optimal point and (ii) providing a combination of epidemic states that may guarantee the safety of the CPS-IoT. Dependability is a key issue of future CPS that may be solved by redesigning current Collection Tree Protocols with enhanced safety features or proposing new dependability-aware protocols for CPS-IoT subsystems. This paper addresses the issues of dependability of CPS by building on (i) mathematical epidemiology to model the CPS-IoT as a dynamic system and (ii) interference-aware CPS-IoT reconfiguration to improve CPS dependability.  
The contributions of this paper are threefold. Firstly, we propose a unified framework that builds upon a CPS orchestration model to efficiently manage and monitor the CPS. We then present a diffusion model for such a framework. While many epidemic models are based on a macro approach that consider only transitions between infectious groups, our work extends the state-of-the-art by including micro structures. These micro structures, called diffusion sets, reveal the propagation of information as defined by CTPs currently used in emerging IoT networking architectures. Secondly, a numerical analysis and validation of the efficacy of the proposed diffusion model with respect to network information flow and CPS dependability is presented. Finally, we propose a novel interference-aware clustering protocol that uses node migration between clusters as an efficient reconfiguration tool when the CPS-IoT has \"drifted\" from its safe operating state.  
In this paper, CPS dependability is supported by (i) identifying a combination of epidemic state transitions that are more suitable for sensitive and non-sensitive applications and (ii) associating transition rates to these transition states and using them to re-optimise the CPS-IoT. Mathematical epidemiology has been used to model wireless sensor networks, mostly for mitigation against worm attacks. However, to the best of our knowledge,  
Figure 1: Relevance of efficient Cyber-Physical Systems-Internet-of-Things (CPS-IoT) monitoring.  
none of these models have addressed the issue of information dissemination based on the structures of the collection trees and diffusion sets as presented in this paper.  
The rest of this paper is organised as follows. After this current section, related works are reviewed in Section 2. We then present the main components of the CPS orchestration model, and its underlying inner feedback loop and features in Section 3, thereafter, the epidemic dynamic model is proposed and its stability proven. The CPS monitoring model is described in Section 4, while in Section 5 the corresponding numerical results are presented. Section 6 then draws a conclusion and proposes some avenues for future research.
## 3 Non-Thermal Technologies in Winemaking Process
### Other Non-Thermal Technologies in Winemaking Process  
#### 3.4.1 Ultra-High Pressure Homogenization (UHPH) Effects on Microbial Populations and Wine Quality  
Ultra-high pressure homogenization has also been recognized as an emerging non-thermal technology that operates in continuous mode at pressures in the range of 200-600 MPa [86]. Principally, the working mechanism consists of pumping a liquid food containing particles lower than 500 \(\upmu\)m at high pressure, and later depressurizing when the fluid passes through a special valve [4]. The fluid medium inside the valve is exposed to extreme impact and shear forces, causing submicron nanofragmentation of microorganisms, colloids, and biopolymers (Figure 7) [15]. The application of UHPH in winemaking is limited to processing grape musts without peels, seeds, and other solids which can clog the homogenizer valves [85; 14]. In addition, UHPH mechanical action and nanofragmentation of colloidal particles are the main mechanisms affecting the extraction of anthocyanins and other phenolic compounds by enhancing their release from the cells and oxidative stability [87; 15]. Due to its effectiveness in the elimination of wild microorganisms, inactivation