esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Elisa  
Sera from all mice were collected at different time points after immunization and evaluated for SARS-CoV-2-S-specific IgG antibodies using ELISA. Sera collected at week 4 after vaccination were also tested for SARS-CoV-2-S-specific IgG1 and IgG2a antibodies using ELISA. Briefly, ELISA plates (BRANDplates(r), immunoGrade, BRAND GMBH + CO KG) were coated with 100 ng of the recombinant SARS-CoV-2 Spike protein (S1 + S2 ECD, His-tag, Sino Biological) per well overnight at 4 \({}^{\circ}\)C in 50 \(\mu\)L PBS and then blocked with PBS-T/3% BSA (blocking buffer) for 1 h. The plates were subsequently incubated for 1 h at room temperature with 3-fold dilutions of the mouse sera in a blocking buffer. The plates were washed and bound specific IgG was detected with an HRP-conjugated goat anti-mouse IgG H&L antibody (ab6789, Abcam) diluted at 1: 10,000 in a blocking buffer. Color development was performed by the addition of 50 \(\mu\)L of TMB Single Solution (Thermo Fisher Scientific, Carlsbad, CA, USA). After 8 min, the enzyme reaction was stopped with 50 \(\mu\)L of 1 M sulfuric acid per well, and the absorbance was measured in a Bio-Rad Model 550 microplate reader (Bio-Rad Laboratories, Hercules, CA, USA). The sera were assayed in duplicates, and the antibody titer represents the last reciprocal serum dilution above blank.
## 5 Results and Discussion
### Discussion
These considerations provide actionable insights for optimizing LoRa installations and aligning system performance with the requirements of IIoT applications, where resistance interference is paramount. The investigation into LoRa in a software-defined radio-enhanced mobile receiver configuration for IIoT applications provides valuable insights into system performance across distance, SINR, BLER, and data rates. The study's findings serve as a foundation for enhancing LoRa system performance in industrial contexts. By optimizing communication parameters, such as spreading factor and bandwidth, tailored to specific environmental conditions, LoRa proves more reliable. Its ability to resist interference through adaptive mechanisms ensures consistent performance in dynamic industrial settings. The customization of LoRa protocols for different industrial landscapes, coupled with energy-efficient strategies, underscores its suitability for applications like smart agriculture or manufacturing. Additionally, the study affirms LoRa's resilience in NLoS environments, making it ideal for scenarios where traditional communication networks may face limitations, such as large warehouses with complex layouts. Overall, these findings provide actionable insights for refining LoRa systems, improving their relevance, and addressing the unique challenges presented by diverse industrial contexts. Moreover, the integration of SDR technology holds the potential for fine-tuning transmission parameters, modulation schemes, and coding rates, thereby increasing data rates and bolstering overall system reliability. The strategic configuration of spreading factor and bandwidth proves crucial for optimizing LoRa against interference, offering actionable insights for real-world implementations. As the landscape of IoT and Industrial IoT evolves, continual exploration and refinement of LoRa technology will play a pivotal role in shaping its future capabilities. To conclude, exploring LoRa in a mobile RX environment, along with SDR, provides valuable information on the system's performance in terms of distance, SINR, BLER, and data rates. This knowledge can be used as a basis for optimizing the system and improving its relevance to IIoT applications in various industrial contexts. In envisioning the future trajectory of LoRa technology, our study opens avenues for potential advancements and optimizations. One promising avenue is the integration of the SDR technology to enhance the system performance. By leveraging the flexibility of SDR, future implementations could explore fine-tuning transmission parameters, modulation schemes, and coding rates. This approach holds the potential for increasing data rates and bolstering overall system reliability. Additionally, exploring adaptive mechanisms based on real-time environmental conditions could be a promising avenue for further research. As the landscape of IoT and Industrial IoT continues to evolve, continual exploration and refinement of LoRa technology will play a pivotal role in shaping its future capabilities.
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 3 Results and Discussion
### Influence of Feed Volume on Flow Field
#### 3.1.2 Influence of Feeding Amount on Gas Volume Fraction Distribution
The dimensionless variance (DV) formula is:  
\[\text{{Dimensionless} Variance}=\frac{Variance}{\text{Re}^{2}} \tag{6}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Point** & **(X,Y,Z)** \\ \hline
1 & (0,\(-\)0.2,0.1) \\
2 & (0,\(-\)0.1,0.1) \\
3 & (0,0.1,0.1) \\
4 & (0,0.2,0.1) \\
5 & (0,\(-\)0.2,0.2) \\
6 & (0,\(-\)0.1,0.2) \\
7 & (0,0.1,0.2) \\
8 & (0,0.2,0.2) \\
9 & (0,\(-\)0.2,0.3) \\
10 & (0,\(-\)0.1,0.3) \\
11 & (0,0.1,0.3) \\
12 & (0,0.2,0.3) \\
13 & (0,\(-\)0.2,0.4) \\
14 & (0,\(-\)0.1,0.4) \\
15 & (0,0.1,0.4) \\
16 & (0,0.2,0.4) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Coordinate of a point. Figure 10: Selection points for variance calculation, with color transitioning from blue (low gas volume fraction) to red (high gas volume fraction). The points labeled 1–16 represent the sampling locations for variance calculation. Table 7 presents the mean, variance, and dimensionless variance of the gas volume fraction. A transient simulation was conducted, and the mean gas volume fraction on the ZY plane at the moment when the flow reached a steady state was used. From the data in the table, it can be observed that, as the velocity increases, the average gas volume fraction also increases. Notably, at a velocity of 5 m/s, the mean gas volume fraction reaches a relatively high level, indicating that the generation and distribution of bubbles are optimal at this velocity. Furthermore, the dimensionless variance is at its lowest at 5 m/s, indicating that the distribution of the gas volume fraction has reasonable stability. Considering the mean, variance, and dimensionless variance, we selected 5 m/s as the parameter for subsequent experiments. This choice is significant because operating at 5 m/s can improve the uniformity of gas-liquid mixing, thereby enhancing flotation efficiency. At this velocity, the improved mixing uniformity allows for more effective mineral separation and increased recovery rates, thus optimizing the overall performance of the flotation process.
## Results
### Peak-Intensity Matrices and Calculation of the Unsaturation Index  
Based on the FTIR spectra, from the initial matrix M\({}_{0}\) the individual wavenumbers and their corresponding peak intensities were extracted. There were 19 regions identified having specific wavenumber values for each individual oil, creating a second matrix (M). The statistics data applied on the M matrix using Unscrambler software and by comparison, the Metaboanalyst software are presented in Table 3. For each of the 19 regions, the mean peak intensity, root mean square (RMS), ANOVA \(p\) values and log 10(\(p\)), and FDR and Random forest MDA (mean discriminatory accuracy) respectively, are presented.  
These data reflect in a semi-quantitative way which can be the most significant relevant regions for a good discrimination between the oil groups. The \(p\)-value and log 10(\(p\)) showed that regions 2, 4, 7, 9, and 13 (bolded) had significant higher values comparatively to regions 16-19, and the less significant ones were regions 5, 8, 10-12, 14, and 15. These data were in good agreement with FDR values and Random forest MDA values.  
Meanwhile, the matrix M was used to calculate the unsaturation index (UI), based on the ratio of peak intensities between 2853/3005 cm\({}^{-1}\) for each oil group (I to VIII) and for each individual oil. The unsaturation index mean values and standard deviations expressed per oil groups and for individual oils are presented in Figure 4 (upside and downside, respectively).  
Figure 4: Mean (upside) and individual (downside) values of the unsaturation index, reflecting the features of the 8 groups of oils, as well as the similarities among oils. SFO—Sunflower oil; PO—Pumpkin oil; LO—Linesed oil; HO—Hempseed oil; SO—Soybean oil; WO—Walnut oil; SBO—Sea buckthorn oil; EO—Olive oil.  
Significant higher values of UI (between 6 and 7) were obtained for group VII and VIII (buckthorn and olive oils), group I and II (sunflower and pumpkin oils) showed values of 4-4.8, and groups III-VI had lowest values of 2.8 to 3.4. Looking to individual UI values, the main differences were confirmed between groups VII-VIII against groups I-II and groups III-VI. Inside some groups there were samples identified which showed deviations from the group values, e.g., sample SBO2 and EOO1 in groups VII-VIII. To conclude, the UI values can be considered good indicators of the general oil quality and may be used as a preliminary evaluation of group authenticity, of course if correlated with other criteria.
## 4 Motion Planning and Control in ADAS for City Buses
### Bus Model for Low-Speed Manoeuvring  
Urban buses, as shown in Figure 8, frequently perform complex low-speed manoeuvres to ensure safety and accuracy. These manoeuvres generally entail minor accelerations, resulting in negligible influence from the vehicle's dynamics. Consequently, a kinematic model is considered suitable for adequately capturing the key properties of the vehicle's motion throughout these manoeuvres.  
The kinematics of a rear wheel-driven car-like robot [34; 51] is illustrated in Figure 9.  
The kinematic configuration of the vehicle will be represented by the vector  
\[\boldsymbol{q}\triangleq\left[\begin{array}{c}\beta\\ \overline{\boldsymbol{q}}\end{array}\right]\in\mathcal{Q},\quad\overline{ \boldsymbol{q}}\triangleq\left[\begin{array}{c}\theta\\ \boldsymbol{p}\end{array}\right],\quad\boldsymbol{p}\triangleq\left[\begin{array} []{c}x\\ y\end{array}\right]\in\mathcal{P},\]  
where \(\mathcal{Q}\triangleq[-\beta_{m},\beta_{m}]\times\overline{\mathcal{Q}}, \quad\beta_{m}<\pi/2,\quad\overline{\mathcal{Q}}=\mathbb{R}\times\mathcal{P} \subset\mathbb{R}^{3}\) is a configuration space of the vehicle kinematics.  
\[\left[\begin{array}{c}\beta\\ \dot{\theta}\\ \dot{x}\\ \dot{y}\end{array}\right]=\left[\begin{array}{cc}1&0\\ 0&\frac{1}{L}\tan\beta\\ 0&\cos\theta\\ 0&\sin\theta\end{array}\right]\left[\begin{array}{c}\zeta\\ v\end{array}\right]\quad\Leftarrow\dot{\boldsymbol{q}}=S(\boldsymbol{q}) \boldsymbol{u}. \tag{1}\]  
Vector \(\boldsymbol{u}=[\zeta\,v]^{\top}\in\mathcal{U}\subset\mathbb{R}^{2}\) is a kinematic control input comprising the steering rate \(\zeta\) and longitudinal velocity \(v\) of the mid-point of the rear wheel's axle.  
Figure 7: Position of chargers at the Garbary PKM depot (**A**). The green marker indicates the charger for which the developed system was set up. The GNSS localisation mode around the bus depot (**B**), including the approach path for which the most accurate RTK FIXED mode was available. The arrows indicate the driving direction.  
The vehicle motion curvature \(x\triangleq\dot{\theta}/v\stackrel{{(1)}}{{=}}\tan\beta/L\) is only dependent on the steering angle and its rate. The curvature and curvature rate limitation of the vehicle motion is imposed by \(\beta_{m}<\pi/2\), hence,  
\[\sup_{\beta}|\kappa(\beta)|=\frac{\tan\beta_{m}}{L},\quad\sup_{\beta,\xi}|\dot{ \kappa}(\beta,\zeta)|=\frac{\zeta_{m}}{L\cos^{2}\beta_{m}}. \tag{2}\]  
Equation (2) provides the planning module with the kinematic constraints for the task of path planning.  
Figure 8: The electric urban bus, Solaris Urbino 12 Electric, used for the experiment in this work.  
Figure 9: Car-like robot kinematics used to describe the electric bus.
## 3 Application of Generic Patterns for an SOA IDS
### Use Cases  
#### 3.1.1 Medical Use Case  
Figure 4 shows a use case for interoperability in the OR where multiple medical devices form a network. Sample services that represent the patterns defined in Section 2.4 are also included.  
1. **Angiography System:** The angiography system is able to move or restrict/lock the movements of an OR table by RPC to avoid collisions or to prohibit movements during an imaging process.
2. **OR Table:** The OR table is the central component in the OR on which the patient lies. It provides its position and possibly restricted movement ranges to other participants via publish/subscribe services.
3. **Ventilator:** The ventilator ventilates the patient and can be paused and restarted via an RPC to improve the imaging process by suppressing breathing movements (based on interoperability example from [57]). The information about ongoing respiration (paused or not) is streamed as a respiration state.
4. **Remote Control/Footswitch:** A connected remote control or footswitch which is either connected with the OR table via SDC or directly via a proprietary protocol. Both are capable of moving different joints via an RPC of the OR table.
5. **OR Light with Integrated Camera:** The integrated camera can be used to detect a table movement or to determine the position of the OR table (lift, longitudinal shift, etc.). Thus, the state of the OR table can be determined externally to provide more context to the IDS.  
Figure 4: SDC network of different medical devices in an OR supervised by a distributed IDS.  
6. **External Accessory/Device:** Since OR tables have a variety of different accessories, it is conceivable in a future scenario that a device might be plugged into an OR table and use it as a gateway to the SDC-Network.  
In our approach, each medical device has its own IDS sensor which is capable of determining the current state of the hosting device and detecting certain events. This additional information is then communicated to a central supervisor NIDS, which is able to provide a context of the entire OR. By using sensors in hosts, it is possible to also consider the payload of the services and the device context.
## 5 Conclusions  
In this work, blind RIS-aided ONOMA system is presented for SR and AP configurations. The closed-form probability of outage expressions is derived for \(SU\) and \(W\!U\) for SR and AP scenarios. Additionally, the optimal power fractions to assign in order to maximize the DL sum capacity are determined. For various values of \(N\), the probability of outage, sum capacity, throughput and probability of error performances are investigated. It has been noted that performance for the probability of outage, sum capacity, throughput and probability of error has improved for higher values of \(N\). At 20 dB of SNR and for 32 passive reflector elements, the sum capacity of RIS-ONOMA system is increased by \(\approx\)33% compared to the ONOMA system. Over the traditional FNOMA and ONOMA systems, RIS-aided ONOMA has a lower outage probability. In future, the same system model could be implemented for UL scenario. For asymmetric DL channel, this approach  
Figure 15: Comparison of probability of error of \(SU\) for the blind RIS-AP-ONOMA and ONOMA systems.  
is tested. This could be studied further for DL symmetric channels, where each user has a similar channel gain. Deep learning or machine learning techniques may be employed for user selection. As a result of the improved performance, we are seeking to generalize the proposed system for multiple users in our future work. The sum capacity can be further enhanced by incorporating user grouping. We also intend to expand the hardware experimentation once the testbeds are commercially available.  
Methodology, H.K.J.; Project administration, V.B.K.; Resources, V.B.K.; Software, H.K.J.; Supervision, V.B.K.; Validation, H.K.J. and V.B.K.; Visualization, H.K.J.; Writing--original draft, H.K.J.; Writing--review & editing, V.B.K. All authors have read and agreed to the published version of the manuscript.  
This work is supported by School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.  
The data that support the findings of this paper are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Deep Learning Realizes Photoacoustic Imaging Artifact Removal
## 3 Results
### Brain Tumor Dataset  
The Br35H dataset is a medical imaging dataset for the detection of brain tumors. The dataset contains brain MRI tumor images and non-tumor brain MRI images. The Br35H dataset was used to define an initial photoacoustic stressor in k-waves to create simulated PAT images. In total, 801 of these MRI images with annotated brain tumors were selected. The medium was assumed to be homogeneous, with a sound velocity of 1500 m/s and an attenuation coefficient of 0.75 dB/(MHz\(\cdot\)cm), similar to human soft tissues. The sensor array has 64, 128, and 256 equally spaced detectors on a circle with a radius of 100 pixels to receive the photoacoustic waves. The reconstructed acoustic field images were post-processed with filtering, denoising, and interpolation operations to create a simulated PAT image dataset for deep learning reconstruction of photoacoustic images.  
During the experiment, the dataset is divided into a training set of 500 images, a validation set of 201 images, and a test set of 100 images. All the results are tested on the test set. The YOLOv8 network is trained to localize and classify the sources and artifacts of photoacoustic images and remove the artifacts outside the sources. Then, the artifacts overlapping the signal sources are removed using the Pix2 Pix algorithm. Finally, the artifact-removed image is used to segment the brain tumor using the YOLOv8 network to obtain an optoacoustic image that is artifact-free and accurately segments the brain tumor.
# A Survey on Swarm Robotics for Area Coverage Problem
## 3 Algorithms and Methods for Swarm Robotics
### Classical Algorithms
#### 3.2.7 Frontier-Led Swarming Algorithm  
A Frontier-Led Swarming algorithm was proposed for the exploration and coverage of the area of unknown environments while controlling a formation that allows for short-range communication. The algorithm includes two components: swarm rules to save a closeknit appearance and frontier search for maintaining exploration and coverage. Three experiments were conducted in various environments, using heterogeneous and homogeneous groups of mobile robots to test the algorithm. The first experiment used real heterogeneous swarm robots, the TurtleBot Burger and Pioneer UGVs, and the second used real robots in an environment containing unknown static obstacles. The third experiment used a simulation in a 2D large-scale urban-like environment with obstacles through a virtual Gazebo. The results demonstrate that the proposed algorithm performs better than the current map coverage approaches [66].
## 3 Observations Focused on Oil and Gas  
### Results: Gravity Aspects and Magnetic Anomalies for Egypt  
In general, there are two thirds of oil and gas accumulation located in large anastomosing rivers and their deltas. This is because such large river systems deposit and bury large amounts of organic materials that eventually become oil and gas resources [41; 42]. For example, the Nile is such a large river that contributes with its sediment to the accumulation of oil and gas deposits. The Nile Valley is between eastern (slightly higher elevation and more structurally active due to normal faulting structures) and western Eocene limestone plateaus, both with more or less horizontal strata, with a small regional tilt towards NNW [43]. This geology suggests that pre-existing rivers that existed in this area were likely to flow in the NNW direction, transporting their sediment into the Mediterranean Sea, reorganizing its bottom and contributing to its diverse tectonic history [44].  
We present the gravity aspects for Northern Egypt, Sinai and surrounding countries (Israel, Jordan, Lebanon, Cyprus, Turkey, incl. the whole Eastern Mediterranean). The _ETOPO 1_ topography (Figure 8) shows a deep ocean near Zohar; Figures 9 and 10 show \(T_{zz}\) and \(vd\). The most interesting are highly combed \(\theta\) for \(I<0.3\) (Figures 11 and 12) and \(I<0.9\) (this includes also deeper layers; see Figure 13). The magnetic anomalies are shown in Figure 14.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size \(a\times b\times c\) (km)** & **Depth (km)** & \(\Delta g\) (mGal) & \(T_{zz}\) [E]** \\ \hline \(100\times 70\times 5\) & 2 & \(-275\) & \(-125\) \\ \hline  & 3 & \(-264\) & \(-110\) \\ \hline \(50\times 30\times 5\) & 2 & \(-252\) & \(-188\) \\ \hline  & 3 & \(-235\) & \(-162\) \\ \hline \(100\times 70\times 2\) & 2 & \(-117\) & \(-59\) \\ \hline  & 3 & \(-112\) & \(-46\) \\ \hline \(50\times 30\times 2\) & 2 & \(-111\) & \(-90\) \\ \hline  & 3 & \(-103\) & \(-69\) \\ \hline \(10\times 10\times 1\) & 2 & \(-28\) & \(-96\) \\ \hline \end{tabular}
\end{table}
Table 2: The values of \(\Delta g\) and \(T_{zz}\) modeled for various sizes, shapes and depths of a hypothetical oil basin.  
Figure 8: The surface topography in our area of interest (from _ETOPO 1_ model in (m)). The large black dot is for Zohar.  
Figure 9: \(T_{zz}\) [E]. The gravity model EIGEN 6C4 used everywhere in this study.  
Figure 11: The strike angles \(\theta\) [deg] for \(I<0.3\).  
Figure 10: The virtual deformations (\(vd\)) [–] are shown in blue, where compression takes place, and in red where dilatation occurs.  
Figure 12: Highly combed areas of \(\theta\) (\(l<0.3\)) in context: a part of Egypt, Israel, Jordan, a part of Saudi Arabia, Lebanon, Cyprus, a part of Turkey, a part of Saudi Arabia, and the Eastern Mediterranean.  
Figure 13: The strike angles \(\theta\) [deg] for \(l<0.9\).  
Figure 14 shows that the regions east of the Nile delta have a clear absence of significant magnetic anomalies; see the next section for discussion of its interpretation.
## 2 Materials and Methods
### In Situ Measurements  
To evaluate the SCOPE model performance at both diurnal and seasonal time scales, ten diurnal in situ experiments were conducted on winter wheat (_Triticum aestivum_ L.) during the 2015-2016 vegetation growing season to measure the vegetation parameters, the diurnal flux and meteorological variables, and the canopy spectra (details listed in Table 2). Our selected field site was located in an open and flat area at the National Precision Agriculture Demonstration Base in the town of Xiaotangshan, Beijing, China (40.17\({}^{\circ}\)N, 116.39\({}^{\circ}\)E). Conventional fertilizer and irrigation management were used on the winter wheat in the sample plot, which had a uniform growth status.
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 3 Experiments Results and Security Analysis
### Security Analysis of the Proposed Chaos-Based Cryptosystem  
In this section, we first give the performance of the proposed S-box, then we evaluate the security of the proposed chaotic encryption system regarding statistical attacks and a cryptanalytic analysis. Eventually, we estimate the computing performance.