esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Validation of Quadrotors
### Experiment Results and Analysis  
According to the above model description, an experiment with three quadrotors tracking one ground robot on the basis of the UT-CLAT algorithm was carried out at 10 Hz. The experimental setup is shown in Table 2.  
One experimental snapshot of the three quadrotors with the corresponding captured images is shown in Figure 10 when the ground robot is within the FOV of all three quadrotor cameras.  
According to the CLAT algorithm, the trajectories of the three quadrotors' self-localization results and target tracking results are plotted in Figure 11. As observed in Figure 11, the three quadrotors are able to localize themselves while stably tracking the target. It is obvious that the self-localization result from quad1 is better than that of the other two quadrotors as it can obtain the navigation signal from the onboard navigation system. The errors of self-localization and target tracking are plotted in Figures 12 and 13, respectively. The local estimation errors (solid line in colors) and the corresponding approximated \(\pm 3\sigma\) envelopes (dashed lines in the same color) of the aforementioned three quadrotors are plotted. As observed in Figures 12 and 13, the estimation errors by each quadrotor are bounded by the \(\pm 3\sigma\) envelopes in the steady state.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Item** & **Quantity** \\ \hline \multirow{7}{*}{robot setup} & circular center (in meters): \\  & \(c_{1}=[-5,-5,15]^{\top}\), \(c_{2}=[5,-5,16.5]^{\top}\) \\  & \(c_{3}=[-5,5,18]^{\top}\) \\  & control input: \\  & \(v_{i}=0.35\) m/s, \(\omega_{i}=0.035\) rad/s \\  & \(Q_{i}^{v}=0.05^{2}\), \(Q_{i}^{w}=(\pi/180)^{2}\), \(Q_{i}^{z}=0.1^{2}\) \\  & initial state: \\  & \(\mathbf{P}_{i,0}=\text{diag}([1^{2},1^{2},0.5^{2},(10\pi/180)^{2}])\) \\  & \(\mathbf{x}_{1,0}=[-8.5,11.5,15,2.35]^{\top}\), \\  & \(\mathbf{x}_{2,0}=[-10,-11,16.5,-0.53]^{\top}\), \\  & \(\mathbf{x}_{3,0}=[-1,1,18,0.78]^{\top}\) \\ \hline \multirow{7}{*}{target setup} & control input: \\  & \(v_{i}=0.2\) m/s, \(\omega_{i}=0\) rad/s \\ \cline{1-1}  & \(Q_{t}^{v}=0.02^{2}\), \(Q_{t}^{w}=(0.5\pi/180)^{2}\), \(Q_{t}^{z}=0.05^{2}\) \\ \cline{1-1}  & initial state: \\ \cline{1-1}  & \(\mathbf{P}_{t_{i},0}=\text{diag}([0.1^{2},0.1^{2},0.5^{2},(\pi/180)^{2}])\), \\ \cline{1-1}  & \(\mathbf{x}_{t_{i},0}=[-15,-15,0,0.785]^{\top}\) \\ \hline \multirow{2}{*}{measurement Setup} & \(\mathbf{R}^{a}=\text{diag}([0.2^{2},0.2^{2},0.1^{2}])\), \\ \cline{1-1}  & \(\mathbf{R}^{c}=0.1^{2}\), \(\mathbf{R}^{t}=\text{diag}([5^{2},5^{2}])\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiment setup.  
Figure 11: Trajectories of three quadrotors’ self-localization results and target tracking results with different colors. Ground truth is indicated by black lines.  
Figure 10: Snapshot of the CLAT experiment setup (three quadrotors and corresponding captured image).  
Figure 12: The self-localization errors and covariance of three quadrotors.  
Figure 13: Object tracking errors and covariance of three quadrotors.
## 4 Experimental Setup
### Performance Metrics  
To assess the performance of our method, the following metrics were measured:  
* **Detection accuracy**: the percentage of correctly identified malicious nodes.
* **False positive rate**: the rate at which legitimate nodes were incorrectly identified as malicious.
* **Packet delivery ratio (PDR)**: the ratio of packets successfully delivered to the destination versus the total number of packets sent.
* **Network latency**: the average time taken for packets to reach their destination.
* **System overhead**: the additional computational and network resources consumed via the detection method.
## 4 Biodegradable Solvents for Protein Extraction
### Deep Eutectic Solvents (DESs)
carried out three sets of experiments applying different pretreatments to study the extraction of proteins, lipids, carbohydrates, carotenoids and chlorophylls from _Scenedesmus dimorphus_ (UTEX 1237) [118]. The NADES was prepared by combining 1,2-propanediol, choline chloride, and water in a 1:1:1 molar ratio. The experimental set-up consisted of putting in contact the solvent with the biomass and simultaneously applying different cell disruption methods (ball mill/ultrasonication/none). The highest protein extraction yield was achieved with the ultrasound-assisted experiments, attaining a 27%. No further purification was performed to isolate the extracted proteins. Subsequently, [PERSON] et al. conducted a study with the same microalgae, but using a new NADES (based on a mixture of octanoic and dodecanoic fatty acids) and a sequential two-stage extraction, consisting of a first extraction step using the solvent in its hydrophilic form (recovering the proteins), and then a second phase with the solvent in its hydrophobic mode, providing the solvent and other hydrophobic compounds. The protein extraction efficiency achieved was 100% [119]. Hence, solvent extraction with DESs or ATPS-DES is a promising sustainable technique for protein recovery. However, there is still much scope to further investigate these methodologies for microalgae protein extraction and also for the purification of the proteins once they are extracted. Table 2 compiles the protein recovery studies reviewed in this article, summarizing the IL or DES employed, the salt or polymer used (in case of following ATPS approach), the specific process conditions, and finally the protein yield addressed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Microalgae** \\ **Strain** \\ \end{tabular} & \begin{tabular}{c} **DES/IL Used** \\ **(Molar Ratio)** \\ \end{tabular} & \begin{tabular}{c} **Salt/Polymer** \\ **Used (in Case** \\ **of ATPS)** \\ \end{tabular} & \begin{tabular}{c} **Extraction Method** \\ **and Conditions** \\ \end{tabular} & **Yield** & **Reference** \\ \hline \multirow{6}{*}{_Spirulina platensis_} & 2-hydroxy & \multirow{6}{*}{None} & Single IL extraction & \multirow{6}{*}{\begin{tabular}{c} \\ \end{tabular} } \\  & ethylammonium & & US bath: 25 \({}^{\circ}\)C and 25 kHz & & \\ \cline{1-1}  & acetate and & & & 6.34 mg g\({}^{-1}\) & \\ \cline{1-1}  & 2-hydroxy & \multirow{6}{*}{None} & Solvent:Biomass concentration: & \multirow{6}{*}{\
\begin{tabular}{c} \\ 7.93 mL g\({}^{-1}\) \\ \end{tabular} } & \multirow{6}{*}{
\begin{tabular}{c} \\ \end{tabular} } \\  & ethylammonium & & & 5.95 mg g\({}^{-1}\) & \\ \cline{1-1}  & formate & & & pH: 6.50 & \\ \cline{1-1}  & & & & 2.62 mg g\({}^{-1}\) & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Protein recovery from different microalgae with ILs, DESs, and ATPS shown in this review.
## 7 Salmonella Enhances Chemosensitivity of Tumor  
An alarming increase of chemoresistance fueled by tumor heterogeneity among genetically-distinct sub-populations of tumor cells [3] demanded the exploration of alternative efficient therapeutic strategies that could combat drug resistance or a way to sensitize an already resistant phenotype. Mechanism of multi-drug resistance among various types of cancer primarily includes alteration of membrane permeability via transporter proteins, i.e. P-glycoprotein (P-gp), involved in drug efflux [75; 76]. Many other resistance mechanisms causing poor responses to various drugs include alteration of target enzymes, alteration of drug metabolism, suppression of apoptosis, and enhancement of DNA repair mechanisms, among others [76; 77]. Surprisingly, as shown in Figure 2 and Table 1, _Salmonella_ can sensitize drug resistant-tumor cells (K1735 melanoma cells) to cisplatin--a cytotoxic drug--by enhancing gap intercellular communication (GJIC) mediated by the upregulation of connexin 43 (Cx43) [41]. Furthermore, the study showed that Cx43 was mediated by p38 signaling cascade as determined by using inhibitor of p38 that blocked the _Salmonella_-mediated Cx43 expression. Furthermore, overexpression Cx43 increased the expression of p38, while Cx43-silenced cells showed no significant changes on p38 expressions [41]. A recent study revealed that the same _Salmonella_ mediated Cx43 upregulation and also contributed to the downregulation of P-gp, further confirming the ability of _Salmonella_ to sensitize multidrug-resistant tumor cells [51]. Previous findings have also demonstrated the potential of _S. enterica_ serovar Typhimurium in modulating the expression of P-gp in the epithelial lining of the intestine [78]. Poor prognosis in many patients with solid tumors and blood malignancies often have unfavorably enhanced P-gp efflux potential that drives cancer drug out of tumor cells [76]. The expression of P-gp, which is normally controlled by AKT/mTOR signaling via p70s6K phosphorylation [79; 80], was significantly reduced in B16F10 and 4T1 cells after _Salmonella_ treatment in a dose-dependent manner [51]. The downregulation of P-gp mediated by _Salmonella_ showed a significant increase in Rho-123 intracellular accumulation suggesting suppressed P-gp transport activity, and an increased susceptibility of tumor cells to 5-FU [51]. Previously, [PERSON] et al. [81], reported that \(\mathrm{SipA}\), a type III secretion effector in _S. enterica_, controls P-gp levels involving caspase-3-mediated protein degradation rather than transcriptional control.  
Figure 2: _Salmonella_ enhances gap intercellular communication (GJIC) via gap junctions and alters membrane permeability to re-sensitize drug-resistant tumor cells.
# NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services
## 2 Background
### Sidelink in 5G-V2X Mode 2  
For 5G-V2X, two sidelink modes, Mode 1 and Mode 2, that represent controlled and autonomous operation methods, are taken into consideration. The autonomous mode is, without a doubt, the more difficult of the two because the interference level and efficiency of the sidelink resource sharing among the vehicles depend heavily on the channel sensing procedures and distributed selection of the resources.  
In Mode 2, the UE detects and decodes SCIs transmitted by other UEs over the sidelink channel (associated to each message). The measured values of the Reference Signal Received Power (RSRP) and the decoded SCIs are saved together until a new resource selection needs to be made. These data are utilized to determine which resources must be rejected. Figure 4 shows the Uu-based 5G-D2D communication mode, where vehicles use a BS to communicate over conventional Uplink (UL) and Downlink (DL) channels. Vehicles using PC5-based 5G-D2D (Figure 4 right) communicate with one another through sidelinks, regardless of whether they receive help from BSs that schedule sidelink resources using UL and DL. Resources for transmission are selected at random from the pool of still-accessible resources following the exclusion process [15].  
Figure 3: Resource pool and sidelink bandwidth section for NR V2X sidelink [18].  
Depending on the configuration chosen for each resource pool, the 5G-D2D Mode 2's sensing window can last either 1100 ms or 100 ms. The UE detects and decodes the SCIs transmitted on the sidelink channel by other UEs during this time. The decoded SCIs and the RSRP measurement are stored together and used to identify which resources need to be overlooked when a new resource selection is required. Resources in 5G-D2D Mode 2 can only be excluded from consideration as potential candidate resources in two situations: either they have already been reserved by a previous SCI with an associated RSRP exceeding a predefined threshold, or the associated information is unavailable (e.g., measurements could not be taken because the station was transmitting). A resource from the pool of remaining accessible resources is chosen at random following the exclusion process [16].
## 2 Materials and Methods  
### Rock Sample Preparation  
In this test, granite samples were obtained from a depth of one kilometer at a gold mine in Shandong, China. The authors' team has analyzed the mineral composition of the granite used in this experiment in previous studies, and the results show that its main components are hornblende, feldspar, quartz, kaolinite, etc., with hornblende accounting for about 47.3% of the total composition, feldspar accounting for 40% of the total composition, quartz accounting for about 6.9% of the total composition, and kaolinite accounting for about 5.8% of the total composition [25]. The samples were prepared as cylindrical rock specimens with a diameter of 50 mm and a height of 25 mm, and they were uniform in texture, with no visible cracks, end face flatness \(\leq\) 0.02 mm, and axial perpendicularity \(\leq\) 0.25\({}^{\circ}\).  
The propagation speed of the sound wave as it passes through the rock will vary depending on the internal structural features. Therefore, the measurement results of wave velocity can be used as a reference material to judge the internal damage of rock samples. In this paper, the ZT801 geotechnical acoustic tester is used to measure the longitudinal wave velocity of the rock samples. Based on the basic theory of damage mechanics, the longitudinal wave velocity is defined as the damage variable, and the expression of this damage variable is shown in Equation (1) [26]:  
\[S_{n}=1-(\frac{V_{n}}{V_{0}})^{2}, \tag{1}\]  
In the equation, \(S_{n}\) is the degree of damage; \(V_{0}\) is the longitudinal wave velocity of the rock sample before impact, m/s; and \(V_{n}\) is the longitudinal wave velocity of the rock sample after impact, m/s.  
To meet the test requirements, the rock samples were pre-treated as follows:  
1. Ultrasonic Testing: The longitudinal wave velocities of the rock samples were measured using ultrasonic testing to exclude those with significant dispersion. The longitudinal wave velocity of the final stayed rock samples was mostly stabilized at 5680 m/s.
2. Initial Damage Creation: In preliminary tests, it was found that the critical impact air pressure for reaching a critical failure state under impact loading was 0.5 MPa. Two initial damage states were created with impact air pressures of 0.2 MPa and 0.3 MPa. The longitudinal wave velocity of the rock samples after 0.2 MPa and 0.3 MPa impacts was stabilized at 4810 m/s and 5260 m/s, respectively. Additionally, one set of rock samples was left undamaged. The damage levels of the three groups of rock samples were calculated as 0%, 14%, and 28%, respectively, according to Equation (1). Thus, there were three initial damage states: no damage, low damage, and moderate damage. Some rock samples are shown in Figure 1.
## 9 Estimation of Mechanical Parameters Based on RQD Value
### Estimation of Deformation Modulus Using RQD Value
#### 9.1.3 Analysis of the Suggested Equations  
By gathering more information from the published literature, [PERSON] and [PERSON] [48] enlarged their database (see Figure 22, utilizing the publication [43]). This extended database displayed a non-linear change in the E\({}_{\rm m}\)/E\({}_{\rm r}\) with RQD and spans the whole range (0 \(\leq\) RQD \(\leq\) 100%).  
Figure 21: Recommended relationships between RQD (%) and E\({}_{\rm m}\)/E\({}_{\rm r}\), according to [48].  
The difference in % between the linear and non-linear equations is shown in Figure 23 for the upper approximation and the average approximation. One can see that Equations (34)-(36) can be transformed to the following form:  
* Lower bound: \[\mathrm{E_{m}}/\mathrm{E_{r}}=0.0025\mathrm{e}^{0.0428\mathrm{RQD}}\] (37)
* Upper bound: \[\mathrm{E_{m}}/\mathrm{E_{r}}=0.0221\mathrm{e}^{0.0428\mathrm{RQD}}\] (38)
* Mean: \[\mathrm{E_{m}}/\mathrm{E_{r}}=0.0123\mathrm{e}^{0.0428\mathrm{RQD}}\] (39)  
The advantage of this equation is that it can be related to the damage model [49]. A number of empirical formulas based on RMR/GSI values can be used to determine the rock mass's deformation modulus from the intact rock's deformation modulus [50; 51; 52; 53; 54; 55; 56].  
Figure 23: The differences between the linear and non-linear approaches.  
Figure 22: Data expansion and distinct relationships between Em/Er and RQD [43].
# Laser Beam Positioning by Using a Broken-Down Optical Vortex Marker
## 7 Discussion  
The presented positioning methods use optical whirls as markers indicating the location of the beam center. Knowing the location of these tags enables effective positioning. Optical vortices are used as information carriers in optical communication. For this purpose, topological charges of OVs are sometimes recognized. The proposed methods can be used both for positioning and recognizing signs and topological charges. In measurements where the beam has passed through the air, the intensity distribution can be significantly distorted. In such cases, searching for vortex points based on the intensity minima is troublesome. The pseudo-phasic or phase field analysis method gives correct results under standard weather conditions. In addition, neural network algorithms applied to phase or pseudo-phase maps significantly improve positioning accuracy. The methods shown only analyze the center of the laser beam as the sensor aperture may be smaller than the beam aperture. An important aspect of the presented methods is the possibility to use only part of the optical beam for positioning.  
Conceptualization, E.F.; methodology, E.F. and A.P.-M.; software, W.F and E.F; validation, E.F., W.F. and A.P.-M.; formal analysis, E.F; investigation, E.F. and W.F; resources, A.P.-M.; data curation, E.F and W.F.; writing--original draft preparation, E.F. and W.F.; writing--review and editing, E.F.; visualization, E.F.; supervision, E.F.; All authors have read and agreed to the published version of the manuscript.  
Figure 13: Positions of the centroid of the three vortex point constellations (**a**) using the pseudo-phase method and (**b**) the bifurcated interference fringes method.  
**Funding:** We acknowledge the support of this work by the Wroclaw University of Science and Technology statutory fund no. 8211104160/K34W04D03.  
**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 Related Works  
For a training dataset containing a small number of samples and unbalanced condition, it can be solved by label-preserving transformations [23]. Common data augmentation approaches include rotating, shifting, zooming, and flipping images within the original dataset to increase the number of synthesized samples for training procedures [24]. These methods not only improve the accuracy of deep learning methods but also improve generalization abilities. Usually, image classification tasks applied the traditional data augmentation method to improve the performance of a trained model. In addition, adopting synthetic data examples created by a GAN can achieve more variability and further improve training effectiveness for deep learning classification. Since GAN was first published in 2014, various derivative GAN models have been proposed and widely used in various fields. [PERSON] et al. proposed a deep convolution GAN (DCGAN) for image synthesis in 2015 [25]. In order to solve the problem of mode collapse and vanishing or exploding gradients in GAN, [PERSON] et al. proposed Wasserstein GAN (WGAN) and used Wasserstein distance instead of Jensen-Shannon (JS) divergence to solve instability problems in GAN training [26]. [PERSON] et al. proposed an auxiliary classifier GAN (ACGAN) model to generate high-resolution images with labels based on a set of random vectors and labels [27]. [PERSON] et al. proposed an improved Wasserstein GAN (WGAN-GP) which applied a gradient penalty to limit the maximum norm of a gradient to 1, and which can ensure the discriminator as a Lipschitz function [28]. [PERSON] et al. proposed an auxiliary classifier Wasserstein GAN (ACWGAN-GP) with gradient penalty, and which can be applied to imbalanced datasets to generate new samples for classes with fewer samples [21].  
Recently, much medical imaging research has begun applying GAN models. [PERSON] et al. trained a GAN model to synthesis medical images to improve the performance of CNN in liver lesion classification [29]. [PERSON] et al. proposed that the GAN-CIRCLE framework generates images with better texture and sharper edges around the lesion [30]. [PERSON] et al. proposed a GAN with a 3D FCN structure to solve the tasks of generating _CT_ from MRI and generating 7 Tesla (7T) MRI from 3 Tesla (3T) MRI images [31]. [PERSON] et al. proposed MedGAN for medical image translation without any application-specific modifications [32]. [PERSON] et al. used a GAN augmentation method to introduce GAN synthesized data into the training set in two brain segmentation tasks, thereby increasing the Dice Similarity Coefficient (DSC) by 1 to 5 percentage points [33]. [PERSON] et al. proposed TOP-GAN, which combined transfer learning and GAN to classify cancer cells without staining. After the initial training of TOP-GAN, the last layer of the discriminator is replaced with three new untrained fully connected layers to transfer it into a new classifier network [34]. All these papers utilized GAN models to increase the amount of training datasets and improve the classification performance in medical-related applications.