esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Validation of GNSS Interferometric Radio Occultation
## 4 Data Processing, Analysis and Discussion
### Reference Doppler  
With the aim of comparing different radio-occultation techniques, we have taken a reference Doppler predicted from the transmitter and receiver geometry. The transmitter position and velocity were obtained with the precision orbit from the GFZ Analysis Center of IGS [11] ([[ftp://ftp.gfz-potsdam.de/GNSS/products/final/](ftp://ftp.gfz-potsdam.de/GNSS/products/final/)]([ftp://ftp.gfz-potsdam.de/GNSS/products/final/](ftp://ftp.gfz-potsdam.de/GNSS/products/final/))), and the receiver position was obtained from the output of the JAVAD receiver.  
With the positions of the receiver (\(\vec{R}\)) and transmitter (\(\vec{T}\)) together with the velocity of the transmitter (\(\vec{V}_{T}\)) the predicted Doppler frequency is  
\[f_{\text{D,predict}}=-\frac{f_{\text{L1}}}{c}\left[\vec{V}_{T}\cdot\frac{\vec {T}-\vec{R}}{\left|\vec{T}-\vec{R}\right|}\right] \tag{13}\]  
with \(f_{\text{L1}}=1.57542\) GHz being the frequency of GPS L1 band signal and \(c\) as the speed of light in vacuum.  
In Figure 4 the elevation of PRN24 is shown together with predicted Doppler as the function of SoD. At higher elevations the predicted Doppler frequency is smaller and it increases when the satellite approaches the horizon. Once the elevation is negative (below the horizon) the predicted Doppler starts decreasing again. Note that we have only considered geometric aspects in predicting the Doppler frequency. Along the whole paper we have used the positive sign convention for the Doppler frequency, even if the distance between transmitter and receiver is increasing, as this is the usual one used in radio-occultations. It is noted here that this predicted Doppler is also used as the reference for the open loop tracking using both the clean replica processing and the zenith iRO processing introduced later.
# Grating-Assisted Fiber to Chip Coupling for SOI Photonic Circuits
## 5 Lowering Footprint
### Focusing Gratings
It is now clear that incorporation of focusing gratings has considerably reduced footprint without any significant impact on coupling performance in terms of insertion loss or bandwidth. In the next section, we discuss a newly established means of low footprint grating coupling by way of compact tapers. Figure 10: Simulated and characterization results for (**a**) TE gratings with 640 nm period. Experimental results are for 24 \(\upmu\)m focal length and (**b**) TM couplers with 980 nm period with optimum focal length of gratings used in experiments being 20 \(\upmu\)m.  
Figure 9: Top down SEM image fabricated of focusing gratings for (**a**) transverse electric (TE) (**b**) transverse magnetic (TM) fundamental mode coupling.
## 2 Pipe-Soil Collapse Analysis Model
### Engineering Calculation Model  
The ASCE-PD&C (2007) specification [9] provides a simple approximate calculation method to solve the problem of uneven settlement of pipe and soil. This method assumes that the bottom of the pipe is in constant contact with the soil and that the deformation curve of the soil is sinusoidal. This analysis method is elastic analysis, which has a certain guiding significance for nonlinear analysis. The uneven settlement deformation of pipe soil is shown in Figure 1.  
Figure 1: Uneven settlement.  
Bending calculation theory used in engineering:  
\[EI\frac{d^{2}y}{dx^{2}}=M \tag{1}\]  
\[\frac{d^{2}y}{dx^{2}}=\frac{M}{EI}=\frac{1}{R} \tag{2}\]  
where \(M\) is the bending moment, \(E\) is the elastic modulus, \(R\) is the radius of curvature, and \(I\) is the section moment of inertia.  
Sinusoidal curve:  
\[y=a\sin\frac{\pi x}{l} \tag{3}\]  
where \(l\) is the half-span length, \(a\) is the deflection amplitude, and \(\Delta\) is the total settlement displacement of the pipeline.  
Mid-span curvature:  
\[\frac{d^{2}y}{dx^{2}}=-a\frac{\pi^{2}}{l^{2}}=\frac{1}{R} \tag{4}\]  
\[\Delta=2a \tag{5}\]  
\[L=2l \tag{6}\]  
The total pipeline settlement displacement can be obtained from Formulas (4)-(6):  
\[\Delta=\frac{-L^{2}}{2\pi^{2}R} \tag{7}\]  
Substituting Formula (1) into Formula (7), we can obtain the relationship between bending moment and settlement displacement:  
\[\Delta=-\frac{L^{2}M}{2\pi^{2}EI} \tag{8}\]
# Two-Stage Efficient Parking Space Detection Method Based on Deep Learning and Computer Vision
## 2 Research Content
### Parking Space Inference
\(\zeta_{b}=\left(-\frac{\sqrt{\left(x_{m}-x_{n}\right)^{2}+\left(y_{m}-y_{n} \right)^{2}}}{2},\frac{L(i,j)\lambda_{1}}{)}\) represents the endpoint of the vector \(\zeta_{a}\zeta_{b}\), and vector \(\overset{\rightarrow}{\zeta_{d}\zeta_{b}}\) can be transformed into vector \(\zeta_{\lambda}\) in the global coordinate system through the coordinate transformation matrix \(T\), which is represented as \(\zeta=T\zeta_{a}\overset{\rightarrow}{\zeta_{b}}\). For any parking space entrance line, as long as the identification function \(L(i,j)\) of its rotation and the key points of the parking space, \(\mathcal{P}_{n}\) and \(\mathcal{P}_{m}\), can be represented by four vectors, \(\mathcal{P}_{n}\overset{\rightarrow}{\mathcal{P}_{m}},\lambda_{2}\overset{ \rightarrow}{c},\lambda_{2}\overset{\rightarrow}{d}\), and \(\zeta_{\lambda}\), the same representation applies to the vertical coordinate system, which can also be represented by four vectors: \(\mathcal{P}_{n}\overset{\rightarrow}{\mathcal{P}_{m}},\mu_{2}\overset{ \rightarrow}{c},\mu_{2}\overset{\rightarrow}{d},\zeta_{\mu}\). The coordinates of all points and the settings of hyperparameters are in pixels. The upper left corner of the image is the coordinate origin, with the rightward direction being the positive direction of the x-axis, and the downward direction being the positive direction of the y-axis. The point \((x_{n},y_{n})\) is located \(x_{n}\) pixels to the right of the coordinate origin and \(y_{n}\) pixels below. When there are only two parking space key points in the image, the parking space can be directly inferred using the method above. However, an image may contain multiple feasible parking spaces, as shown in Figure 4(b), where three feasible parking spaces are present. Therefore, when YOLOv11 detects multiple parking space key points, the first step is to determine the parking space rotation direction. Key points with the same rotation direction in the same image are grouped together. Then, all possible combinations of the parking space key points are formed. If there are N key points with the same rotation direction, there are \(C_{N}^{2}\) possible combinations. Afterward, the combinations that meet the conditions in Formula (1) and the length constraints are identified to form valid parking space entrance lines. Finally, the correct parking spaces are inferred in sequence.
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
## 3 Thermal Analysis and Optimization of Heat Sink Based on Sealed Cabin  
### Influences of Heat Sink Geometry on Heat Dissipation  
For most underwater vehicles, the propeller driver can often be sealed in a cylindrical sealing pressure cabin. Therefore, a new kind of cylindrical heat sink is designed for the propeller driver system and is analyzed in this section. The structure of the cylindrical heat sink is shown in Figure 12. The heat sink has a function of both heat dissipation and driver installation. The heat sink has a flat cylindrical body. It has a radius of 90 mm and a thickness of 35 mm. There is a rectangular through-hole inside. The driver base locates on one short side of the rectangular hole. Several rectangular fins are symmetrically distributed along the long side of the rectangular hole, as shown in Figure 12. The driver device is mounted on the base. The base thickness D1 is set as 40 mm. The base width is set as 72 mm. The fin thickness D2 is set as 2 mm. Initially, the fin length L1 is set to 16 mm, and the fin spacing L2 is set to 4 mm.  
The dimensions of the computational domain are set to be 575.6 mm in length, 631 mm in width, and 179 mm in height. The boundaries of the domain are all set as open boundaries, with an ambient temperature of 25 \({}^{\circ}\)C and a gauge pressure of 0 Pa. Gravity is set to act in the negative Z direction. The fluid used is air at 1 atm. At these conditions, the density of air is 1.1614 kg/m\({}^{3}\), the thermal conductivity is 0.0261 \(W/(\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature** & **1 st PCB** & **2 nd PCB** & **3 rd PCB** & **The Connection between** \\
**Measurement Position** & **Layer** & **Layer** & **Layer** & **the Base and Heat Sink** \\ \hline Simulation results/\({}^{\circ}\)C & 59.5 & 62.4 & 65.6 & 63.8 \\ Experimental & 59.6 & 64.4 & 66 & 63.3 \\ results/\({}^{\circ}\)C & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical simulation temperature values and experimental measurement temperature values.  
Figure 12: (**a**) Cylindrical heat sink model. (**b**) Cylindrical heat sink structure illustration: 1 base, 2 drivers, 3 fins.  
heat capacity is 1005 J/(Kg\(\cdot\)K). The Computational domain and discretized grid of the cylindrical radiator are shown in Figure 13.  
The typical temperature field of the cylindrical heat sink and driver is shown in Figure 14. It can be seen that the maximum and the minimum temperatures for the driver are 50.8 \({}^{\circ}\)C and 28.7 \({}^{\circ}\)C, respectively. The highest temperature is 14.6 \({}^{\circ}\)C lower than the result for the model in Figure 3. The minimum temperature is 13.3 \({}^{\circ}\)C lower, too. It shows that the cylindrical heat sink can improve thermal conduction efficiency significantly compared with the old rectangular heat sink.  
Figure 13: (**a**) Computational domain. (**b**) discretized grid. (**c**) discretized grid.
## 2 Materials and Methods
### Genetic Analysis  
#### 2.2.1 SSR Analysis  
Genome DNA of the 60 accessions (Table 1) was extracted from young leaves collected in greenhouse, from young wild hops. Samples, after immersion in liquid nitrogen were stored at \(-80\)\({}^{\circ}\)C until DNA extraction. Genomic DNA was extracted following the Cetyltrimethylammonium Bromide (CTAB) procedure [25]. For DNA amplification, eight couples of SSR primers were used, chosen for the high discriminating capacity [26] (Table 2). The Polimerize Chain Reaction (PCR) amplification was performed in a 25 \(\upmu\)L volume containing: 1\(\times\) Reaction Buffer (Biotools), B&M Labs, S.A., Madrid, Spain), 1.5 mM MgCl\({}_{2}\) (Biotools, B&M Labs, S.A., Madrid, Spain), 0.2 mM dNTPs (Amersham Biosciences, Piscataway, NJ, USA), 0.2 IM primer (MWG Biotech, Ebersberg, Germany), 20 ng genomic DNA and 0.6 U of Taq polymerase (Biotools, B&M Labs, S.A., Madrid, Spain). For primer HIACA3, HIGA31, HIGA23, MgCl\({}_{2}\) concentration was 2.5 mM, to obtain a better quality of amplification. The PCR amplification was optimized in thermal cycler MJ PCT 100 Research (Watertown, MA, USA), programming a first step at 95 \({}^{\circ}\)C for 5 min, followed by 30 cycles of 4500 at 94 \({}^{\circ}\)C, 3000 at the specific annealing temperature for each couple of primers and 9000 at 72 \({}^{\circ}\)C, for denaturation, annealing and primer extension, respectively; at the end of the cycles were allowed 8 min of incubation at 72 \({}^{\circ}\)C. The amplification products were separated with a CEQ 8000 Genetic Analysis System (Beckman Coulter, Inc., Brea, CA, USA) sequencer on acrylamide gel CEQ Separation Gel LPA-1 (Beckman Coulter, Inc.). A marker CEQ DNA Size Standard kit 400 (Beckman Coulter, Inc.) was used to estimate the
# Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection
## 4 Conclusions  
This study presented a novel approach to improve violent attack detection systems by integrating advanced artificial intelligence techniques to enhance the safety of smart cities. The proposed model integrates image-to-image stable diffusion, YOLO v7, MediaPipe, and an LSTM classifier with an edge computing device, demonstrating significant advancements in real-time violence detection. The innovative use of image-to-image stable diffusion addresses the challenge of handling small datasets by generating synthetic data, contributing to enhancing the model performance. Meanwhile, the YOLO v7 object detection approach helps to identify violent attack objects like baseball bats, knives, and pistols. Moreover, MediaPipe is utilized for violent action pose detection and a long short-term memory (LSTM) network classifies the action of attacks involving violent objects by using the recorded key points using MediaPipe as the input. Finally, the trained model has been deployed into the edge device for testing the proposed model using unseen test data in the smart city. The object detection accuracy values of the YOLO v7 model for three classes, baseball bat, knife, and pistol, are 84.5%, 94.7%, and 89.2%, respectively. The mean average precision (mAP) of YOLO v7 for all classes is 89.5% at a threshold of 0.5. Moreover, the accuracy of the LSTM model for classifying the baseball bat action is 96%; for the knife action, it is 82%, and for the gun action, it is 87%. Therefore, our proposed integration of YOLO v7, MediaPipe, and the LSTM model improves the violence detection performance and enhances the safety of smart cities.  
Conceptualization, P.K., G.-L.S., B.-L.G. and P.-C.P.; methodology, P.K., G.-L.S., B.-L.G., S.K.N. and P.-C.P.; data preparation, P.K., G.-L.S., B.-L.G., S.K.N., M.A.A. and P.-C.P.; software, P.K., G.-L.S., B.-L.G. and P.-C.P.; model validation, P.K., G.-L.S., B.-L.G. and P.-C.P.; formal analysis, P.K., G.-L.S., S.K.N., C.-K.Y., Y.C.M., M.A.A. and P.-C.P.; investigation, P.K., G.-L.S., B.-L.G., S.K.N., Y.C.M., C.-K.Y., M.A.A. and P.-C.P. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Science and Technology Council, Taiwan, under Grant NSTC 112-2221-E-027-076-MY2.  
The data presented in this study are available in this article.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Chemical Composition
#### 3.2.2 Carbonaceous Compounds
Samples representing scenario 1 (black squares) show variable Lev concentrations but lower OC/EC ratios, while time periods of scenario 2 (black triangles) show higher  
Figure 5: Scatterplot between organic carbon (OC) and elemental carbon (EC) concentrations in PM\({}_{2.5}\) samples at the urban sampling site in Graz DB, 2 January–31 March 2017. Black highlighted squares mark time periods with DB/BB PM\({}_{10}\) ratios \(>\) 2 (A: 2 January–3 January 2017; B: 11 January–12 January 2017; C: 21 February–23 February 2017). Black highlighted triangles mark time periods with DB/BB PM\({}_{10}\) ratios \(\leq\) 1.5 (D: 22 January–23 January 2017; E: 31 January–2 February 2017 and F: 9 February–17 February 2017). Remaining data points are shown by grey squares. OC/EC ratios despite Lev concentrations similar to scenario 1. This is mainly driven by the relative contribution of EC and the occurrence of other EC sources. During scenario 2 the average contribution of EC to PM\({}_{2.5}\) mass is 4%, while it averages at 7% during scenario 1. Figure 6: Correlation between OC/EC ratio and the contribution of secondary inorganic aerosols (SIA) to PM\({}_{2.5}\) at the urban sampling site in Graz DB, 2 January–31 March 2017. Black highlighted squares mark time periods with DB/BB PM\({}_{10}\) ratios > 2 (A: 2 January–3 January 2017; B: 11 January–12 January 2017; C: 21 February–23 February 2017). Black highlighted triangles mark time periods with DB/BB PM\({}_{10}\) ratios \(\leq\) 1.5 (D: 22 January–23 January 2017; E: 31 January–2 February 2017 and F: 9 February–17 February 2017). Remaining data points are shown by grey squares. Figure 7: (**a–c**). Correlation between levoglucosan (Lev), OC, and K\({}^{+}\) concentrations in PM\({}_{2.5}\) samples at the urban sampling site in Graz DB, 2 January–31 March 2017. Black highlighted squares mark time periods with DB/BB PM\({}_{10}\) ratios > 2 (A: 2 January–3 January 2017; B: 11 January–12 January 2017; C: 21 February–23 February 2017). Black highlighted triangles mark time periods with DB/BB PM\({}_{10}\) ratios \(\leq\) 1.5 (D: 22 January–23 January 2017; E: 31 January–2 February 2017 and F: 9 February–17 February 2017). Remaining data points are shown by grey squares. In addition, the correlation of OC with potassium (r = 0.76, Figure 7c) suggests biomass burning as a possible source of particulate matter [35; 36]. Two data points associated with scenario 2 do not follow the general trend. Period F shows rather high potassium concentrations and during that time period the influence of long-range transport of desert dust appears likely [28]. No explanation can be given for the rather low potassium concentrations determined for data point E.
## 5 Group Communication Using Unicasts
### Entity Modification and Behavior Manipulation  
It is possible that a client wants to modify an entity after its creation. For example, a client might want to add new members to the collection of members in the entity or remove a number of members. Alternatively, the client may want to customize the behavior of an existing entity. The latter can include aspects such as the default number or percentage of members that should respond before the entity manager replies to the client, the default content format of the response, the default operation (e.g., average, max, min, _etc._) that should be performed on the results before sending them to the client, _etc._ Modifications to the entity or to its behavior can be made by updating the entity's profile and posting the updated information (PUT or POST) to coap://[EM]/.well-known/profile?path=\"[ENTITY_URI]\", in which /.well-known/profile is a resource for accessing the profile of a resource as described in [25] and ENTITY_URI the URI of the entity, e.g., \"/1\" in our example. When a client wants to modify the profile of an entity, this information is passed to the EM, which will validate the request and change the profile if the validation was successful. Finally, removing an entity can be realized by sending a GET request to the entity management resource that includes action=\"delete\" URI query and specifying the entity to be deleted, e.g., coap://[EM]/e?path=\"ENTITY_URI\"action=\"delete\".  
Figure 10: A CoAP client requesting from an Entity Manager to obtain the values for the entity that was previously created in Figure 7.
# Application of Biochar for the Restoration of Metal(loid)s Contaminated Soils
## 3 Results
### Plant Response  
#### 3.3.1 Plant Germination  
The germination of _Trifolium pratense_ in the unamended contaminated soil (RA) was four times lower than that of the unamended soil (NC) used as a control (Figure 3). In RA soil, the rate of the number of plants that grew two months after sowing (PI) was lower than the germination rate (Ge) observed at two weeks, although the difference was not significant. This decrease indicates that during this period there was either no new germination and some plant died or the rate of new germination was lower than the death rate.  
The addition of biochar to the contaminated soil resulted in a significant increase in the germination rate in the RA4B and RA8B treatments with respect to RA, with no significant differences between the doses of amendment applied (Figure 3). Incubation time did not affect plant performance. In fact, the number of plants two months after sowing did not vary with respect to the initial germination in the soil amended with 4% (RA4B) and 8% (RA8B) biochar, suggesting that the treatments did not condition plant survival.  
At the end of the experiment, no significant differences in the number of live plants were observed between the NC soil and the RA soil amended with biochar, irrespective of the amendment dose (Figure 3). Indeed, in NC, RA4B, and RA8B the survival rate of _Trifolium pratense_ varied between 94 and 98% while in RA it was 70%.  
Figure 3: Germination rates (Ge) at two weeks and rate of number of plants present at the end of the experiment (PI) in the uncontaminated soil (NC), in the unamended contaminated soil (RA) and in RA soil amended with biochar at 4% (RA4B) and 8% (RA8B). Error bars represent standard deviation. Different letters indicate significant differences (\(p<0.05\)) between treatments by [PERSON]’s post-hoc test.
###### Abstract  
Coastal development such as sea reclamation, port terminals, and breakwater construction has significantly altered the southwestern coastline of Laizhou Bay, changing the regional hydrodynamic environment. To explore how tidal range and tidal prism respond to these 20-year coastline changes, this paper selected the southwestern coastline data of Laizhou Bay in 2000 and 2020, established a 2-D tidal model, and studied the impact of the 20-year coastline changes on tidal range and tidal prism in this sea area. The results show that during the 20 years from 2000 to 2020, most of the bay area's coastline was in a growth trend of advancing toward the sea, 85.3% of the cross-sections were growth areas, 11.9% were dynamic stable areas, and there was almost no erosion area. Affected by reclamation activities, the area of the southwestern part of Laizhou Bay decreased by 11.66%, the coastline increased by 63.27%, and the center of mass moved 2.22 km to the northeast. The reclamation of tidal flats caused the potential energy of tidal waves in the bay to increase, the maximum possible tidal wave in the southwestern part of the bay showed an increasing trend, and the bay top between Weifang Port and Guangli Port increased particularly significantly, with a maximum increase of 22 cm. The spring tide period, near tide period and average tidal prism in the southwestern bay decreased by 4.79%, 3.29%, and 4.24%, respectively. The reclamation of tidal flats is the main reason for the decrease in tidal prism in the southwestern part of the bay.  
Laizhou Bay; coastline changes; tidal range; tidal prism +
Footnote †: journal: Applied sciences