esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Approach Details
### The Process of Creating a Digital Image
Otherwise, if the downstream read noise is strong, increased exposure in post-processing will also increase the downstream read noise and ruin the image. This concept is further illustrated in Figure 13. This property of some cameras that have low downstream read noise is called 'ISO Invariance'. The Sony Alpha camera series is popularly known for having this feature, and hence they are an ideal choice for low-light imaging. Then, if the exposure values (EV) are balanced in post-production, it is observed that each image has the same amount of noise. Thus, boosting the exposure in post is the same as increasing the ISO in the camera due to the lack of downstream read noise. Therefore, this justifies our selection of the camera body for this work. There are primarily two types of sensors, CMOS and CCD (charge-coupled device) sensors in camera bodies [11]. These have different methods for producing a digital image, and hence these two have different properties. In CCD sensors, each row of charge is individually converted to voltage first. After the conversion, the signal is amplified altogether. Timers and ADC converts are only integrated outside the chip, after the amplifier, as shown in Figure 14a. Since the charge in each pixel is converted individually, processing speed is low. The same amplifier is used for each pixel, as evident from the diagram. Therefore, the amplification is identical, and the noise pattern remains the same throughout the frame. Figure 12: Sony CMOS dark current comparison with counterparts. CMOS cameras can have higher frame rates than their CCD counterparts. This is because the reading of the pixels can be performed faster than having to wait for a CCD's charge transfer. This feature is essential for machine vision systems that often rely on real-time image processing for automation or image data analysis. In the CMOS sensor, timers and ADC converters are integrated with each pixel, as shown in Figure 14b. The charge in each pixel is converted simultaneously using a pixel select switch; hence, the processing speed is high. Since a different amplifier is used for each pixel, the amplification and the noise pattern vary throughout the frame.
## 4 Intraoral Molar Distalization Methods
### Miniscrew-Assisted Frog Appliance
#### 4.2.3 Infrazzygomatic Miniscrew-Assisted Distalization Applications  
The infrazzygomatic crest region (IZC) is a suitable anatomical site for the placement of miniscrews and mini-plates in the maxilla. It features the thickest cortical bone in the maxilla, second only to the mandible. Clinically, the IZC is palpable between the alveolar bone of the maxilla and the zygomatic process. In young individuals, it is typically located at the level of the second premolars and first molars, while in adults, it is generally found at the level of the first molars [59].  
Maxillary molar distalization using the zygomatic plate as an anchorage unit provides a stable and effective method for the simultaneous distalization of both the first and second molars. The literature suggests that this approach minimizes undesirable dental side effects and achieves distalization without anchorage loss [60]. However, due to the anatomical location of the zygomatic region, the surgical procedure for mini-plate placement is more complex than that for other skeletal anchorage devices and should be performed by an experienced surgeon.  
The IZC screw was first introduced in 2003 and was applied using a self-drilling method to the attached gingiva and the mobile mucosa border between the first and second maxillary molars. Since then, numerous researchers have proposed modifications to this technique [61].  
[PERSON] et al. [59] placed stainless steel IZC screws (Bioray, Taiwan) with a diameter of 2 mm and a length of 10 mm in the infrazzygomatic region of 20 patients (16 female and 4 male) with a mean age of 12 \(\pm\) 5 years. They used 0.019 \(\times\) 0.025-inch stainless steel archwires between the lateral incisors and canines, with 4 mm high retraction hooks positioned in the same region. A force of 300 g was applied using Ni-Ti springs anchored to the IZC screws. This method enabled the distalization of the entire maxillary dentition [58,59].  
[PERSON] et al. [62] assessed the 6-month success rate of infrazzygomatic crest bone screws relative to patient age, insertion angle, sinus penetration, and terminal insertion torque. In conclusion, both sinus penetration and IZC bone quality increased with age. sinus penetration did not significantly affect the 6-month survival rate of the IZC TADs because the loss of bone quantity at the interface was offset by the age-related increase in bone quality at the IZC site.  
An infrazzygomatic mini-screw-assisted distalization applications are illustrated in Figure 11.
## 4 SHINE Additional Building Blocks
### Test Applications  
In addition to the core building blocks used to build the network infrastructure, SHINE provides out-of-the-box two flexible Test Applications, one for SpaceFibre and one for SpaceWire, that can be used to study the network performances under different load conditions.  
As shown in Figure 5, both the SpaceFibre and the SpaceWire Test Applications can be directly connected to the correspondent Endpoint. They can be configured by the user to simulate a generic device, in particular it is possible to set:  
* Data generation rate and packet size. The test application will try to send packets of the predefined length in order to achieve the average transmission rate (Bits/sec) defined by the user;
* Data consumption rate. The test application will read the data coming from its Endpoint at a rate limited to the one specified by the user. This can be used to simulate slow destination devices that are not able to consume data at maximum speed;
* Destination nodes. A test application will try to send packets only to the nodes specified in this list. In case the path addressing is used, the chain of output ports along the path from the source to the destination is automatically derived by SHINE.  
Figure 4: Routing Switch with a SpaceWire Adapter Port.  
Note that in case of SpaceFibre test application, the parameters described above can be specified individually for each virtual channel. Another possible usage of these example applications is to validate the correctness of the packets content: the generated packets contain incremental integers that are checked for integrity on reception. In case of errors in the transmission (e.g., bugs in the code or error injection on links), the receiving application raises an error.
## 3 Results
### Assessing Accuracy of Orthomosaic
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multicolumn{1}{c}{**Units in m.**} & \multicolumn{4}{c}{**KKENC**} & \multicolumn{2}{c}{**MANGART**} \\ \cline{2-7}  & **Orthomosaic, Traditional** & **Orthomosaic, PPK** & **Orthomosaic, Traditional** & **Orthomosaic, PPK** & **Orthomosaic, Traditional** & **Orthomosaic, PPK** \\ \hline diff\({}_{\text{x}}\)-MIN & \(-0.007\) & \(0.008\) & \(-0.104\) & \(-0.079\) & \(-0.011\) & \(-0.139\) \\ diff\({}_{\text{x}}\)-MAX & \(0.045\) & \(0.061\) & \(0.046\) & \(0.052\) & \(0.037\) & \(-0.026\) \\ diff\({}_{\text{x}}\)-MEAN & \(0.018\) & \(0.002\) & \(0.000\) & \(0.014\) & \(0.011\) & \(-0.089\) \\ diff\({}_{\text{x}}\)-MEDIAN & \(0.018\) & \(0.031\) & \(0.003\) & \(0.017\) & \(0.012\) & \(-0.088\) \\ diff\({}_{\text{x}}\)-SD & \(0.012\) & \(0.012\) & \(0.027\) & \(0.028\) & \(0.012\) & \(0.026\) \\ RMSE & \(0.021\) & \(0.034\) & \(0.025\) & \(0.031\) & \(0.016\) & \(0.093\) \\ \hline diff\({}_{\text{x}}\)-MIN & \(-0.036\) & \(-0.013\) & \(-0.056\) & \(-0.041\) & \(-0.044\) & \(-0.181\) \\ diff\({}_{\text{x}}\)-MAX & \(0.011\) & \(0.039\) & \(0.078\) & \(0.106\) & \(0.016\) & \(-0.016\) \\ diff\({}_{\text{x}}\)-MEAN & \(-0.017\) & \(0.013\) & \(-0.012\) & \(0.006\) & \(-0.005\) & \(-0.119\) \\ diff\({}_{\text{x}}\)-MEDIAN & \(-0.016\) & \(0.011\) & \(-0.013\) & \(0.002\) & \(-0.002\) & \(-0.133\) \\ diff\({}_{\text{x}}\)-SD & \(0.011\) & \(0.012\) & \(0.022\) & \(0.029\) & \(0.012\) & \(0.045\) \\ RMSE & \(0.020\) & \(0.017\) & \(0.025\) & \(0.030\) & \(0.013\) & \(0.127\) \\ \hline \multicolumn{1}{c}{RMSE} & \multicolumn{2}{c}{0.029} & \multicolumn{2}{c}{0.039} & \multicolumn{2}{c}{0.036} & \multicolumn{2}{c}{0.043} & \multicolumn{2}{c}{0.020} & \multicolumn{2}{c}{0.157} \\ \hline \multicolumn{1}{c}{} & \multicolumn{2}{c}{**LEGEND**} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ diff\({}_{\text{x}}\)(x)-MIN & & & & & & \\ diff\({}_{\text{x}}\)(x)-MAX & & & & & & \\ diff\({}_{\text{x}}\)(x)-MEDIAN & & & & & & \\ diff\({}_{\text{x}}\)-SD & & & & & & \\ RMSE(\({}_{\text{x}}\)) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Basic statistics for differences in X and Y coordinates measured in the field by the total station and obtained by the orthomosaic,traditional or orthomosaic,PPK methods. A paired Student's \(t\)-test, for testing if there was a statistically significance difference in the mean value of differences between the BBA_traditional and BBA_PPK, was used in the following cases: diff\({}_{\rm X}\) and diff\({}_{\rm Y}\) in Kekec, and diff\({}_{\rm X}\) in Mangart. At all locations and coordinates, there were significance differences between the mean values (\(p\leq 0.05\)). With other pairs, a non-parametric Wilcoxon signed-rank test was used, which also showed that there were statistically significant differences between the median values between the georeferencing methods in all of the cases (\(p\leq 0.05\)).
## 3 The Conventional and Proposed UWB Localization Approaches  
Conventional UWB localization approaches tend to use different localization algorithms in the system controller to estimate the current user positions. The positional accuracy of the conventional methods depends on the type of the localization algorithm and the configuration of the system controller. To improve the indoor position accuracy for UWB localization, we used deep learning instead of localization algorithms and this proposed UWB indoor localization system leverages the advantages of LSTM networks in predicting user positions. As compared to the conventional UWB system, the proposed system utilizes a deep learning architecture in the system controller to predict the user position. The deep learning model is trained on user distance from anchors and gives accurate position results. Figure 1 shows the conventional and proposed UWB localization systems.  
The proposed model consists of anchor-tag communication using UWB pulses, the TOA-distance model and deep learning system for user position prediction. First, we place the anchor nodes (ANs) with known coordinates in the experiment area. The UWB tag sends UWB pulses to the ANs. All ANs receive UWB pulses and estimate the distances from each AN. Since the anchors receive UWB pulses at different intervals from the UWB tag, the signals arrive at each anchor at a different time. The signal arrival of time from UWB tag is multiplied by the constant speed of light in the air resulting in the ANs and UWB tag distances. To estimate the UWB tag position, the concept of trilateration is used. To use trilateration, it is necessary to use at least three anchors in the experiment area. In our simulation, we created an experiment area with three anchors and the anchors were placed in known coordinates to cover the entire experiment area. To generate UWB pulses, we used Gaussian pulses with the IEEE 802.15.4a UWB system standard. The generated UWB pulses transmitted from the UWB tag to all anchors. The UWB tag moved in the predefined paths and sends pulses to the ANs. The ANs use UWB TOA information and estimate the tag distances from ANs. The distance information matrix from TOA-distance model is used as the input to the deep learning system. The deep learning system uses two LSTM layers to predict the tag's \(x\) and \(y\) positions. The LSTM layer takes three inputs, the three distance values from three anchors, and predicts the current UWB tag position. The proposed LSTM model predicts accurate tag position results with minimal localization errors. The experiment results show that the proposed deep learning approach solved the LOS problems associated with UWB localization systems and reduces the computational complexity of localization algorithms. As compared to conventional localization approaches, the proposed method can be easily used for complex trajectories and estimate the user position with minimum position errors.  
Figure 1: Ultra-wideband (UWB) localization systems. (**a**) Conventional UWB localization system. (**b**) Proposed UWB localization system using deep learning.
# The Value of Fractal Analysis in Ultrasound Imaging: Exploring Intricate Patterns
## 3 Results
### Pancreatic Cancer Delineation  
Enhanced endoscopic ultrasound (EUS) imaging is a vital tool in the diagnosis and characterization of pancreatic lesions. This advanced imaging technique combines traditional EUS with fractal analysis and other advanced methods to improve lesion delineation, aiding in more accurate diagnosis and effective clinical decision making [25]. Fractal analysis has been integrated into EUS to quantify the complexity of pancreatic lesion structures. Malignant lesions tend to exhibit higher fractal dimensions due to their irregular and chaotic growth patterns compared to the more orderly structures of benign lesions. [PERSON] et al. [26] showed that fractal geometry can effectively quantify surface roughness, which helps distinguish different types of pancreatic tissue [26; 27]. The integration of fractal analysis, contrast enhancement and elastography into endoscopic ultrasound imaging significantly enhances the diagnostic capabilities for pancreatic lesions.
## 3 General Concept and Passive Detection
### Defining Normal Case and Significant Deviation
As defined in NMEA-0183V20 standards [35], enacted by National U.S. Marine Electronics Association, a typical GPS (Global Positioning System) positioning information is 88 bytes long. However, this paper can treat most of its constituent parts (such as velocity and magnetic declination) as redundant, and only the coordinate's information is reserved.
## 2 Materials and Methods  
### Study Site  
The study was conducted at the Shangqiu Farmland Ecosystem National Field Scientific Observation and Research Station (34.52\({}^{\circ}\)N, 115.59\({}^{\circ}\)E, elevation 55 m), located in Shangqiu, Henan Province, from June to September in 2018 (Figure 1). The site has a warm temperate climate with a mean annual air temperature of 13.9 \({}^{\circ}\)C, mean annual precipitation of 708 mm, and mean annual solar radiation of 4823 MJ-m\({}^{-2}\). The soil is fluvo-aquic, which is the main soil type in the North China Plain and annually produces almost 60-80% of wheat and 35-40% of maize in China. The study site is a traditionally managed cropland with a typical crop rotation system, which consists of one season of winter wheat and another season of summer maize. During the 2018 study period, summer maize (_Zea mays L_) was planted in the agricultural field. The entire growing season lasted for four months, from sowing in early June (Day of Year (DOY), 155) to harvest in late September (DOY, 267). The phenology of maize is usually divided into two main growth stages: the vegetative (V) stages (emergence to DOY 207) and the ripening (R) stages (DOY 208 to harvest). The V stages included emergence on DOY 160, 5 th leaf stage on DOY 170, elongation stage on DOY 183, and tasseling stage on DOY 206. The R stages were characterized by the silking stage on DOY 208, and the physiological maturity stage on DOY 256, until harvest on DOY 267. The crop was fertilized (40.5 kg-N-ha\({}^{-1}\) and 103.5 kg-P-ha\({}^{-1}\)) at the time of sowing and was topdressed (225 kg-N-ha\({}^{-1}\), 37.5 kg-P-ha\({}^{-1}\), and 37.5 kg-K-ha\({}^{-1}\)) prior to the elongation stage. The soil was irrigated (45 mm) before the emergence of maize plants (DOY 158). The field measurements began on June 16 (DOY 167) when the maize was emerging and ended on September 24 (DOY 267) when the maize was at the mature grain stage prior to harvest.  
Figure 1: Location of the study site at Shangqiu station, Henan province, China. Panel (**a**) was a satellite image of the maize field in our study area from Google Earth. (**b**) and (**c**) represented the location of the study site in Henan province and China, respectively.
# Spiking Neural P Systems for Basic Arithmetic Operations
## 3 Arithmetic Operation in Spiking Neural P Systems
### Binary Addition in SNP Systems
Starting from time t = k + 3, \(\sigma_{Add}\) receives \(sp\)-\(x_{i}\) and \(sp\)-\(y_{i}\) (\(0\leq i\leq k-1\)) at the same time. Readers can refer to [29] for details of the addition operation in \(\sigma_{Add}\). 5. From time t = k + 4, the environment starts to receive calculation results in sequence. 6. From t = 2k + 1 time slice, \(\sigma_{Input}\) no longer accepts the input of the environment, and only sends the spikes it contains to \(\sigma_{aux5}\) and \(\sigma_{aux6}\) by using the rules. 7. At t = 2k + 2, the highest bit \(x_{k-1}\) of X and \(y_{k-1}\) of Y reach the neuron \(\sigma_{Add}\), if X + Y \(<\) 2\({}^{k+1}\), the system will reach the termination pattern at t = 2k + 3; If X + Y \(\geq\) 2\({}^{k+1}\), the system will eventually reach the termination pattern at t = 2k + 4. Based on the above description, readers can verify that for k \(\geq\) 2, the SNPS \(\mathbf{\Pi_{BASNP}}\) for addition constructed above can correctly solve the sum of two natural numbers with a binary length of k, and the proof is complete. \(\square\)  
Figure 3 shows a \(\mathbf{\Pi_{BASNP}}\) structure for three-bit binary addition. Based on this \(\mathbf{\Pi_{BASNP}}\), the addition process of the natural numbers 7 and 5 is listed in Table 1, which shows the number of spikes contained in each neuron in \(\mathbf{\Pi_{BASNP(7,5)}}\). The two natural numbers expressed in binary form are 1112, 1012, and 1112 + 1012 = 11002. In Figure 3, the augend and the addend are input through the neuron _Input_, the auxiliary neuron group \((aux_{1},aux_{2})\) continuously sends a spike to the neuron \(aux_{3}\), \(aux_{4}\) respectively, the auxiliary neuron group \((aux_{4}\), \(aux_{5})\) controls the input of the augend 7 (111) to the augend cache neuron group \((num_{1}\), \(num_{2}\), \(num_{3})\), and the auxiliary neuron group \((aux_{3}\), \(aux_{6})\) controls the input of the addend 5 (111) to the neuron _Add_. Finally, the addition operation is performed in the neuron _Add_. In Table 1, the first column represents the system moment, and the second to the last column represent each neuron in the system. Each row of Table 1 represents the number of spikes in each neuron in the system at the corresponding moment. For example, the number in the first column of the sixth row (step t = 5) is 0, indicating that the binary bit currently input to the neuron _Input_ is 0. The number from the eighth column to the tenth  
Figure 3: \(\Pi_{BASNP}\) for adding two three-digit numbers. column is 1, which successively indicates that the binary bit of the augend buffer neuron group (\(num_{1}\), \(num_{2}\), \(num_{3}\)) is 1 and the input of the augend 7 has been completed. The \(\Pi_{BASNP}\) designed in this section can complete the addition of two k-bit binary numbers within 2k + 4 time slices, and the number of neurons used is k + 8. The neurons in \(\Pi_{BASNP}\) use three types of non-delay spiking rules and three types of forgetting rules.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 3 Methodology
### Road Network Search
#### 3.5.2 Range Search in Road Network Distance  
In the road network distance method, the objective of range queries was to retrieve results within specific distance intervals utilizing actual road distance. The distance thresholds ranged from 1 km to 150 km, with increments of 1 km, 5 km, and subsequent 5 km intervals (1, 5, 10, 15, 20, 25, 30,..., 150). The process involved extracting distances from the Open Source Routing Machine (OSRM) and executing SQL queries to limit the distance for each interval. An illustrative example of a SQL statement for retrieving results within a 5 km interval is shown in Listing 5.  
```
1 SELECT*
2 FROMhospital_q1
3 WHEREosrm_dist<5
4 ORDERBYosm_dist;
```  
Listing 5: SQL statement for retrieving range query results within a 5 km for road network distance.  
This SQL statement demonstrates the retrieval process for the 5 km interval. Similar queries were executed for each distance threshold, thereby encompassing the 1 km to 150 km range. The osrm_dist column represents distances measured along the road network between the query point and POIs.
# Droplets Transmission Mechanism in a Commercial Wide-Body Aircraft Cabin
## 3 Results and Discussion  
### Flow Fields of Different Ventilation Systems  
The flow fields of different ventilation systems were analyzed and presented as shown in Figure 4.  
Figure 3: Droplets dispersion profile of 20 \(\upmu\)m droplets in a steady turbulent cough jet.  
Figure 4a shows the cross-sectional velocity field of CMVS. There are two streams of strong airflow along the sidewalls from air supply diffusers in air distribution of CMVS. The two streams are influenced by the thermal plumes of the middle passenger on the way, converging in the middle of the cabin and rising. Thus, the two large scale symmetrical vortex formed on either side of the cabin. However, the two vortices generated in SMVS are opposite to those generated in CMVS. As shown in Figure 4b, the cold air comes out from the air supply diffusers and forms an attached jet flowing along the ceiling. Due to the obstruction of the luggage rack, the supply air loses a lot of kinetic energy and drops down. Then, because of the negative pressure in the exhaust, the airflow flows to the bottom of the cabin. In the process, one part of the airflow directly flows to the side exhaust diffusers and the other part of the airflow moves upward with the human thermal plume. Two clockwise vortices are generated in SMVS. Figure 4c shows the cross-sectional velocity field of DVS. It can be seen that the cold air supplied by the exhaust diffuser firstly rises to the height of the handrail, and then gathers in the passengers' leg area due to its relatively large density. Driven by the thermal plume of passengers, the air rises again and finally exhausts by the outlets at the ceiling. Unlike CMVS and SMVS, there is no large-scale air vortex, and the air distribution mainly presents a straight flow. The air velocity in the whole aircraft cabin is extremely slow. In Figure 4d, since PVS is based on CMVS, the air distribution is almost similar to that of CMVS. The influence area of seat exhaust diffusers is concentrated in the microenvironment of the passengers. The negative pressure formed at the exhaust diffusers on the seat is conducive to more fresh air entering the microenvironment around the passengers and promoting the air ventilation.