esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, the radiation levels and metals contamination in nine different samples of food salt available at the Italy markets were assessed. This is extremely important because salt is a source of many elements and minerals essential and necessary for various biological processes in the human body.  
The activity concentration of the main natural radionuclide present in food salt, \({}^{40}\)K, was measured using HpGe gamma spectrometry with the aim to estimate the health risk, for the age category higher than 17 years, by the effective dose due to their ingestion. The coefficient of the effective dose for ingestion was that reported by the Italian Legislative Decree 230/95, and then in successful modifications. The estimated annual effective dose was found to be in the range (0.018-0.27) mSv/year, about 0.7-11% of the total natural radioactivity value for humans (2.4 mSv/year) and under allowable levels (1 mSv/year), thus excluding the risk of ionizing radiation effects on humans.  
The anthropogenic radioactivity was evaluated through the \({}^{137}\)Cs specific activity measurement. It was lower than the minimum detectable activity value, thus excluding its presence as a pollutant.  
Regarding the metals concentration, it was lower than the contamination threshold value for Cu, As, Cd, Hg and Pb, thus excluding their presence as contaminants.  
Results of this study can be useful within a broader look at the issue of food contamination, and there is a need for further research in the future for the Regulatory Authorities in the country to control public exposure to radioisotopes and metals due to the intake of salt.  
[PERSON] collected data, designed the study and drafted the manuscript; [PERSON] performed gamma-spectrometry analysis; M.D. and A.B. contributed to perform gamma-spectrometry analysis; M.M. performed ICP-MS analysis; [PERSON] contributed to perform ICP-MS analysis; [PERSON] supervised the study.  
This research received no external funding  
The authors declare no conflict of interest.
## 3 SMTRPM Design
### Network Structures
#### 3.2.1 Ad Hoc  
The patient nodes and the nurse server are connected via a local _ad hoc_ remote wireless network. The dynamic nature of this type of network requires intelligence in each of the network nodes for the delivery of information packets and for routing analysis [23]. Although the patient nodes have little mobility, they must find routes to deliver information. The patient nodes must determine if a specific or general anomaly has occurred in the room by sharing individual diagnostic information with the last patient node. When a patient presents an anomaly, the node sends the information to the last patient node and sends an alarm to the neighbor. This alarm causes the neighbor patient node to begin its own monitor process, and both patient nodes send the information to the last patient node, which is the only sink that obtains information from all the patient nodes. A reactive AODV (_ad hoc_ on-demand vector) protocol is used to determine routing and anomaly conditions. The AODV algorithm determines a destination sequence number to evaluate a destination route [24]. The SMTRPM was tested with five patient nodes per room. Figures 7 and 8 show examples of specific and general anomaly detection, respectively.  
Figure 7: Specific anomaly detection via AODV (_ad hoc_ on-demand vector) routing.  
In Figure 7, only two patient nodes have detected a hyperthermia anomaly. This information is sent to the last patient node via AODV routing. In the example, the last patient node determines that a specific hyperthermia anomaly has occurred because the SMTRP is programmed to identify a general anomaly only when three or more patients present the same anomaly, as in Figure 8.  
The system adopts a hierarchical structure whereby there is a last patient node in each room. The last patient node is also a patient node with a program routine to create HR and BT package. Then, the last patient node sends the package to the nurse server. Table 4 shows an example of the structure of the HR package with specific anomalies at patients D and E.
## 5 Computational Experiments
### Experiment Results
#### 5.2.2 Effectiveness Validation under Large-Batch Instances  
In this experiment, the configuration sizes of the experimental instances vary from \(S=10,~{}12\) stacks and \(T=8,~{}\ldots,~{}10\) tiers. The batch size varies from \(B=5,~{}\ldots,~{}12\) and  
Figure 5: Comparison between \(SPFH\) and \(LL\) at fill rate \(50\%\) and \(67\%\) respectively.  
the batch number varies from \(W=8,10\). So, each instance configuration has a different file rate.  
Table 5 summarizes the validation of the algorithm \(SPFH\) and the heuristic \(LL\) for the large-size instances in the real-time operation scenarios. The column titled \"MT\" refers to the runtime of the most time-consuming instance out of 30 instances. The column \"AT\" refers to the average runtime of 30 instances. The meanings of other variables, namely \(I\), \(IB\), \(I_{cb}\), and \(Act\), are the same as those in the previous tables.  
The results in Table 5 imply that both algorithms \(SPFH\) and \(LL\) can solve most instances quickly. However, when the configuration of instances  
target containers (e.g., always retrieving the target containers with the lowest number of blocking containers) instead of complete iteration to improve the solution efficiency, and, second, dividing the current larger batch into two smaller batches before processing, e.g., the current batch of 10 containers is divided into two small batches of 5 containers.  
Under large-batch instances, it is more common for the value of \(Act\) to be greater than the theoretical upper bound, \((IB+I_{eb})\). The reason for this case has been given in the experiment mentioned above under small-batch instances. Moreover, it can be seen that the discrepancy between the \(Act\) and \((IB+I_{eb})\) decreases with increasing batch size.
# Dynamic Performance Analysis for an Absorption Chiller under Different Working Conditions
## 4 Dynamic Performance Analysis
### Different Cooling Water Inlet Temperature (\(T_{cin}\))  
Figure 9 displays the effect of different \(T_{cin}\) on the \(COP\) when \(T_{gin}\) = 100 \({}^{\circ}\)C and on the cooling capacities when \(T_{gin}\) = 90 \({}^{\circ}\)C or 100 \({}^{\circ}\)C, the evaporator inlet temperature \(T_{cin}\) is 18 \({}^{\circ}\)C. These values come from steady-state model. The rise of \(T_{cin}\) leads to the increase of the generation pressure and the absorption temperature, restraining the generation and absorption processes, thus the \(COP\) and cooling capacities decrease.  
When the generator inlet temperature always has a step change from 90 \({}^{\circ}\)C to 100 \({}^{\circ}\)C at time 0 and the evaporator inlet temperature \(T_{cin}\) is 12 \({}^{\circ}\)C, Figure 10 shows the relaxation time and \(\Delta Q_{e}\) under different \(T_{cin}\). And the relative cooling capacity difference \(\Delta Q_{e}\) is obtained with the data in Figure 9:  
\[\Delta Q_{e}=\frac{Q_{e,100}-Q_{e,90}}{Q_{e,90}}\cdot 100\% \tag{33}\]  
where \(Q_{e,100}\) and \(Q_{e,90}\) are the cooling capacities in steady-state when \(T_{gin}\) = 100 \({}^{\circ}\)C and \(T_{gin}\) = 90 \({}^{\circ}\)C, respectively, kW.  
Figure 8: The effect of different \(\Delta T_{gin}\) on the relaxation time and \(\Delta Q_{e,g}\).  
Figure 9: The effect of different \(T_{cin}\) on the \(COP\) and cooling capacity.  
Although the cooling capacity in steady-state decreases with the increase of the \(T_{cin}\), its relative variation \(\Delta Q_{e}\) grows at the same time in Figure 10. Thus, the absorption chiller needs more time to achieve the next steady-state, the relaxation time increases by 5.65% when \(T_{cin}\) rises from 25 \({}^{\circ}\)C to 35 \({}^{\circ}\)C.
## 3 CAD Systems of Brain Disorders Based on Multimodal Fusion
### Multimodal Fusion Techniques Proposed in Literature: Description, Advantages and Disadvantages
#### 3.2.1 Spatial Domain Techniques
Several spatial domain techniques represent a simple means to obtain a fusion image but, due to degrading performance and weak or ineffective results, they have not been sufficiently applied, especially in real-time applications. Some of these methods are mentioned below. * **Simple average:** The pixel value of each image is added. The sum is then divided by 2 to obtain the average. The average value is assigned to the corresponding pixel of the output image. The principle is repeated for all pixel values. This technique [217; 236] is a simple way to obtain a fusion image with focusing of all the regions of the original images. However, the quality of the outputimage is reduced by incorporating noise into the merged image, which results in undesirable effects, such as reduced contrast. In addition, the possibility of having clear objects from all of the images is not guaranteed. * **Weighted average:** It calculates the sum of the pixels affected by coefficients, divided by the number of pixels. The weighted average value is assigned to each pixel of the input image to obtain the value of the corresponding pixel in the output image. This technique used in some works [217, 231, 237, 238, 239] improves the reliability of detection. However, there is a risk of increasing noise. * **Simple block replacement:** In this technique [223, 240], for each pixel, its neighboring pixels are added and a block average is calculated. The pixel of the merged image is obtained by taking the pixel with a maximum block average among all the corresponding pixels in the input image. * **Max and Min pixel values:** These techniques are used in many works [217, 236, 240], they choose the focused regions of each input image by choosing the highest value (or the lowest in the case of the min pixel value technique) for each pixel. This value is assigned to the corresponding pixel in the merge image. * **Max-Min:** For this technique [240], in the merged image, the output pixels are obtained by averaging the smallest and largest values of the corresponding pixels in all of the input images. The last four techniques are easy to implement and provide several rules for merging images, most of which are very simple. However, they produce a fuzzy output that affects the contrast of the image, which limits their potential for real-time applications.
## 4 Measurement Results
### Baseline Model
However, due to certain similarities such as events of interest (e.g., GP and RL), it is still possible to use the results of the paper as a reference. The results of the paper are shown in Table 6.  
Figure 23: (a) FW-GP Scores of 4 performance metrics for intrasubject classification. (b) F1 score of the 6 events. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Participant** & **Accuracy (\%)** & **Precision (\%)** & **Sensitivity (\%)** & **Specificity (\%)** & **F1-Score** \\ \hline
1 & 93 & 92.16 & 94 & 92 & 0.9307 \\ \hline
2 & 100 & 100 & 100 & 100 & 1 \\ \hline
3 & 52 & 56.25 & 18 & 86 & 0.2727 \\ \hline
4 & 99 & 98.04 & 100 & 98 & 0.9901 \\ \hline
5 & 96 & 92.59 & 100 & 96 & 0.9615 \\ \hline
6 & 90 & 90 & 90 & 90 & 0.9 \\ \hline
8 & 82 & 89.09 & 68 & 96 & 0.7907 \\ \hline
9 & 93 & 89.09 & 98 & 88 & 0.9333 \\ \hline
10 & 100 & 100 & 100 & 100 & 1 \\ \hline
11 & 100 & 100 & 100 & 100 & 1 \\ \hline
12 & 100 & 100 & 100 & 100 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: FW-GP Intrasubject Classification Results. From Tables 6 and 7, it can be observed that the performances of the two systems are very similar. Of course, as mentioned before, these comparisons are made not in terms of direct measurements but in terms of measurements that can provide a general idea/pattern of the performance of the two systems.
# Experimental Study of LoRa Transmission over Seawater
## 3 Materials, Experimental Setup and Methods
### Brief Description of the Transceivers Used in the Experiments  
The ODT utilizes the \(SX1276\)[18; 19] LoRa modem from Semtech. The modem can operate in several frequency bands using two different RF ports: one for frequencies up to 525  MHz, and one for 525-1020 MHz. The operating frequency band is controlled by the register settings over the following values: \(169,434,470,490,868\) MHz and 915 MHz, but changing the frequency band requires also changing the corresponding impedance matching in the RF front end to prevent reflections that degrade the performance. The ODT was fitted with a matching network for two ISM bands: 868 MHz and 434 MHz, providing 14 dBm of output power on the high-frequency port and 20 dBm on the low-frequency one. The transceiver is capable of using seven different spreading factors, \(SF\in[6,12]\), within four different bandwidths: \(62.5,125,250\), and 500 kHz.  
Besides the LoRa modem, the ODT features the Energy Micro \(EFM32 GG990F1024\)[20] microcontroller and 32 MB of NAND flash memory from Micron, NAND256W3A2 BZA6E[21] for logging purposes. There is also one expansion port for connecting different types of sensors, two LEDs for signaling, and a push button. The ODT is depicted in Figure 3a, while the complete device used in the field experiments is shown in Figure 3b.  
Figure 2: Trade-off between symbol period and spreading factor.  
The Libelium device uses the Semtech \(SX1272\)[22] LoRa modem, and supports only one frequency band, 860-1020 MHz, offering output power up to 20 dBm. It can use spreading factors in the range of \(SF\in[6,12]\), within three different bandwidths: \(125,250\), and 500 kHz. This transceiver is mounted on a Seeeduino Stalker v2.3 carrier board [23].  
The Uptronics commercial transceiver uses the RFM95 LoRa module from HopeRF [24], which has identical characteristics to those of the \(SX1276\), as determined by comparing the corresponding datasheets. As with the Libelium transceiver, this also requires a host system for its operation and is therefore mounted as a shield on a Raspberry Pi3 [25].  
Omnidirectional antennas were used in all experiments: for the LOS condition, low-cost off-the-shelf rubber duck antennas were deployed in both frequency bands. The antennas have a gain of 4.5 dBi and 0.7 dBi for 868 MHz and 434 MHz, respectively. In the obstructed LOS path scenario, three element collinear dipoles were deployed as well, providing gains of 8.5 dBi and 6.5 dBi for 868 MHz and 434 MHz band, respectively.
## 2 Principle of OSC Systems and LG Modes
### Principle of OSC Systems  
For the long-distance data transmission between two satellites, high-power signals are transmitted (\(T_{P}\)) towards the receiver, and receiver power (\(R_{P}\)) is expressed as:  
\[R_{P}=T_{P}T_{E}R_{E}(\frac{\lambda}{4\pi L})^{2}T_{G}R_{G}T_{PL}R_{PL} \tag{1}\]  
It is depicted in Equation (1) that \(R_{P}\) depends on different satellite parameters such as the optical efficiency of the transmitter (\(T_{E}\)) and receiver \(R_{E}\), wavelength of operation \(\lambda\), transmitter and receiver telescope gain denoted by \(T_{G}\) and \(R_{G}\) respectively, and loss due to pointing errors in the transmitter \(T_{PL}\), and in the receiver \(R_{PL}\). \(T_{G}\) and \(R_{G}\) are calculated as \(T_{G}=\left(\frac{\pi D_{TTS}}{\lambda}\right)^{2}\), and \(R_{G}=\left(\frac{\pi D_{TTS}}{\lambda}\right)^{2}\), respectively, where \(TTS\) is used for the transmitter telescope, \(RTS\) for the receiver telescope and D is the telescope diameter. Narrow beam-based lasers are employed in OSC communication because they are coherent, directional, and have the potential to cover prolonged distances. However, PTE in OSC are two of the maximum performance deteriorating issues and in particular, a smaller field of view (FOV) of the receiver introduces maximum signal loss due to misalignment between transmitter and receiver satellite antennas. Therefore, \(T_{\theta}\) is azimuth pointing error angle in the transmitter and \(R_{\theta}\) in receiver, and \(T_{PL}\) and \(R_{PL}\) are expressed in Equations (2) and (3), respectively, as [55]:  
\[T_{PL}=e^{-T_{G}T_{\theta}^{2}} \tag{2}\]  
\[R_{PL}=e^{-R_{G}R_{\theta}^{2}} \tag{3}\]  
For the realization of an ideal OSC system, as observed from Equations (1)-(3), \(T_{PL}\) and \(R_{PL}\) should be minimum, and \(T_{G}\) and \(R_{G}\) along with \(T_{E}\) and \(R_{E}\) should be maximum.
## 3 Creation of the Prototype and Establishment of the Dynamic and Static Testing Systems
### Construction of the Static Testing System  
To verify the static performance of the compact pressure measurement catheter, this paper designed and constructed a high-precision static pressure automatic testing system. The system responds quickly and controls pressure with high precision, capable of reaching the desired pressure value within 30 s inside the cavity, with pressure control precision reaching \(\pm\)1 Pa. The overall system error is within 10 Pa, enabling the full automation of testing for the compact pressure measurement catheter.  
As shown in Figure 5, the schematic diagram of the high-precision static testing system comprises hardware and software components. The ability to achieve rapid, high-precision pressure control primarily depends on the system's hardware performance, which includes a pressure chamber, pressure regulation module  
and perform automatic testing of the pressure sensor. The physical system photo is shown in Figure 6.  
To validate the system's control accuracy within the human body's pressure range, an average of 11 desired pressure values was selected within a range of 100-120 kPa. Three pressure cycle controls were then conducted. Based on the actual pressure measured in the chamber by the reference pressure sensor, the experimental data are shown within the pressure adjustment range of 100-120 kPa, and the maximum deviation between the measured chamber pressure values and the desired pressure values is 1 Pa. The test results indicate that this system has high pressure control accuracy and good reliability. Furthermore, as shown in Figure 7, the time taken to adjust the chamber pressure from atmospheric pressure to any desired pressure point does not exceed 30 s. In conclusion, the results demonstrate that the performance of this system meets the requirements for static pressure performance testing in this study.  
Figure 5: Diagram of the static testing system components: 1. Pressure chamber; 2. Reference pressure sensor; 3. Pressure-reducing valve; 4. Proportional pressure valve (fast pressure adjustment); 5. Boost fine-tuning valve; 6. Pressure reduction fine-tuning valve; 7. Comparative pressure sensor; 8. Control module.  
Figure 6: Static testing system physical photo.
# A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals
## 2 Data and Methods
### Data  
Table 2 shows the classification of the MGEX stations that can process BDS-3 signals from 14 May to 20 May 2019 (DOY's 2019, 134-140). The earlier use of data here is because BDS-3 data are just available at this time, and BDS-2 data are also relatively complete, laying a solid foundation for the subsequent BDS performance analysis. In the experiment, as the stations equipped with the same type of receiver showed high consistency, the observations of the representative stations were used for experimental analysis to compare the characteristics of C/N0, code noise, and the multipath between BDS-3 and the other GNSS systems in different types of receivers. All the selected stations were installed in an open sky environment, and data can be found at the IGS official website ([[http://www.igs.org/mgex/data-products/#data](http://www.igs.org/mgex/data-products/#data)]([http://www.igs.org/mgex/data-products/#data](http://www.igs.org/mgex/data-products/#data)), accessed on 2 April 2024).
## 4 Results and Discussion
### Image and Semantic Classification Detection Results
This adversarial mechanism, combined with the symmetric projection, enables the proposed method to retain high-quality features for both classification and object detection tasks, which accounts for the higher precision, recall, and F1 score values. Furthermore, the F1 score provides a more balanced evaluation of the model's performance, particularly in scenarios where precision and recall are not equally weighted. Unlike traditional methods like DP, which add noise and degrade performance, the proposed method balances privacy and utility through its innovative framework, enhancing classification and detection tasks without compromising the model's integrity. The combination of these techniques allows the proposed method to achieve a better balance between privacy and performance, making it highly suitable for tasks requiring both security and accuracy.
## 4 The STO Strategy  
Using the thrust vector can effectively shorten the takeoff distance. When the aircraft takes off, the control of the thrust vector and the lift fan bear part of the lift, which can effectively reduce the takeoff distance. The short takeoff process comprises three stages: (1) from the starting point to the nose wheel lifting; (2) from the lift the nose wheel until the aircraft lifts off the ground; and (3) lifting off the ground to a safe height, as shown in Figure 12.  
1 Stage I: Taxing  
In this stage, the altitude and pitch attitude of the aircraft are almost unchanged, and the taxiing speed is parallel to the straight deck. Therefore, the following approximate conditions are met: \(\dot{h}=0\), \(\dot{\theta}=q=0\), and \(\gamma=0\). Since the engine is at full opening force in this process, a reasonable thrust deflection angle must be set in advance to ensure the short takeoff performance. According to the takeoff performance analysis, we chose a lift fan thrust of 50% and tilt angle of 40\({}^{\circ}\).  
Figure 11: Conversion corridor of V/STOL aircraft.  
Figure 12: STO strategy.  
Stage II: Liftoff  
The speed gradually increases to 0.7\(\sim\)0.9 times the ground clearance speed. The pilot controls the aircraft to lift the front wheels and then keeps the two main wheels on the ground to continue to accelerate the taxiing. At this time, the aircraft is still parallel to the runway, and \(\gamma=0\), but \(\theta\) starts to increase, and \(q\
eq 0\).  
Stage III: Takeoff  
When the speed reaches the ground clearance speed and the vehicle lift is equal to the aircraft gravity, the main wheel of the aircraft leaves the ground and turns to an accelerated ascent. Meanwhile, the ground effect disappears, but as the aircraft leaves the ground, the jet flow is affected by the opposite incoming flow, resulting in the jet-induced effect. This effect is essentially caused by the downward jet flow between the lift fan and the 3 BSD and the upward \"fountain flow\" between them. However, it is generally manifested as the loss of lift and the generation of head-up torque. In addition, considering the thrust vector control characteristics of the aircraft, the lift fan can be turned off and the tail nozzle can be transferred to the normal flight state.  
It can be seen in Table 3 that the shortest running distance is between 40-70% fan thrust. In order to obtain accurate optimal results, the batch gradient descent algorithm was used to iteratively optimize the fan value, and the calculation flow chart is as follows in Figures 13 and 14:  
Figure 13: Optimization algorithm of the shortest takeoff distance.  
The final short takeoff strategy is as follows: