esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Results and Discussion
### Load vs. Displacement  
Figure 11 displays the load-displacement curves of the UHPC and GRC panel specimens with varying curved section thicknesses.  
As shown in Figure 11, the six sets of curves exhibited a similar overall trend. At the beginning, the displacement grew proportionally to the test load. After concrete cracking, the curves transitioned from the linear stage to the nonlinear stage. The load-displacement curves deviated from a straight line, and the stiffness of panel specimens decreased. After reaching the ultimate load, the test load decreased with the consecutive increase in displacement. The bearing capacity of all UHPC panel specimens decreased gradually after the peak point, indicating their superior ductility. In contrast, when the curved section thicknesses exceeded 34 mm, the GRC panels saw a brittle failure with a rapid decrease in load after the peak point. This is because the delamination effect  
Figure 9: Test setup and apparatus: (**a**) Schematic diagram (side); (**b**) Test setup (front).  
of glass fibers became more evident as the curved section thickness increased, leading to the detachment of a thin GRC laminate in the panel specimen. At the same curved section thicknesses, the UHPC panel specimens exhibited longer linear stages and greater slopes in their load-displacement curves than the GRC panel specimens. Furthermore, the ultimate loads of the UHPC panels were always greater than those of the GRC panels. This can be attributed to the higher matrix strength, greater modulus of elasticity, and better distribution of short steel fibers.  
Figure 10: Failure details of panels: (**a**) Crack pattern at initial loading stage (inside view of curve section); (**b**) crack penetration at failure stage; (**c**) failure mode of UHPC decorative panels; (**d**) failure mode of GRC decorative panels; (**e**) failure mode of UHPC tension specimens; and (**f**) failure mode of GRC tension specimens.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 3 Air Pollution Monitoring Equipment
### Particulate Matter Sensor
#### 3.2.4 Optical Analyzers [91]
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Measurement Method** & **Advantages** & **Disadvantages** & **Accuracy** \\ \hline Tapered Element & & A heater must be used which leads to lose of semi-volatile material. & \\ Oscillating Merobalance (TEOM) analyzers & Provide real time (\(<\)1 h) data with high precision. & No usually with large size, heavy weight and high cost. & \(\pm\)0.5 \(\upmu\)g/m\({}^{3}\) \\ \hline \multirow{4}{*}{\(\beta\)-attenuation analyzers (BAM)} & & A radioactive source is used. If heater is used some semi-volatile material may be lost. Need to replace the paper filter periodically. Usually with large size, heavy weight and high cost. & \(\pm\)1.0 \(\upmu\)g/m\({}^{3}\) \\ \hline \multirow{4}{*}{Black smoke method} & Simple, robust and inexpensive. Easy to maintain. Short sample time (in minutes). & Measure the darkness rather than the mass concentration of the particulate matters. Darkness-mass factor may change from time to time and location to location. & \\ \hline \multirow{4}{*}{Optical analyzers} & Small, light weight and usually battery operated. Short sample time (in seconds or minutes). & Depends on some assumptions of particle characteristics (e.g. each particle is perfect bean-like shape). These assumptions may be different from time to time and location to location. & Depends on the analyzer type and usually not specifically declared by the manufacture. \\ \hline \end{tabular}
\end{table} 
Table 8: Comparison of four types of particulate matter (PM) measurement methods.
# Axial-Symmetric Diffraction Radiation Antenna with a Very Narrow Funnel-Shaped Directional Diagram
## 3 Frequency Domain Representations
For calculating the radiation pattern \(D(\vartheta,k,M)\), it is necessary to solve the initial boundary value problem (1), and then, using the exact radiation condition [25; 32; 34], from which absorbing condition (10) follows, to determine, by the values of \(U(g,t)\) on the arc \(r=L\), the values of \(U(g,t)\) and \(\mathbf{E}_{\text{tg}}(M,\vartheta,t)\) (\(E_{\vartheta}(M,\vartheta,t)\) for \(TE_{0}\)-waves or \(E_{\vartheta}(M,\vartheta,t)\) for \(TM_{0}\)-waves (see formulas (1.19) in [31]) on the arc \(r=M\), and, finally, to use the transformation \(\widetilde{f}(k)\leftrightarrow f(t)\).
## 3 Proposed Model
### Generator Architecture  
The generator can be divided into two parts joined by a skip connection: an encoder and a decoder. The skip connection bridges the deep feature of the decoder with the shallow feature of the encoder, allowing the encoder to refer to the decoder when extracting image features after convolution, which ensures a higher-quality restoration. In the small structure of the entire network, the batch normalization layer [37] is used for normalization operations so that the entire network's gradient layer does not vanish easily. LeakyReLU [38; 39] was adopted as each network's activation layer in place of ReLU[40] and Adam [41] was employed as the optimizer. A dynamic learning rate strategy was implemented to halve the learning rates among 500, 750, 875, and 950 training iterations. The overall generator architecture is illustrated in Figure 1.
# Robust Bluetooth AoA Estimation for Indoor Localization Using Particle Filter Fusion
## 2 Array Model
\(N_{x}\in\mathbb{C}^{1\times N}\), \(N_{y}\in\mathbb{C}^{1\times N}\) are the received noise, and the value of the orientation matrix \(A_{x}\in\mathbb{C}^{M\times 1}\) is:  
\[A_{x}= \begin{bmatrix}1&1&\ldots&1\\ e^{j2\pi d\text{cos}\,\theta_{1}\text{sin}\,\varpi_{1}/\lambda}&e^{j2\pi d\text {cos}\,\theta_{2}\text{sin}\,\varpi_{2}/\lambda}&\ldots&e^{j2\pi d\text{cos }\,\theta_{k}\text{sin}\,\varpi_{K}/\lambda}\\ \vdots&\vdots&\ddots&\vdots\\ e^{j2\pi(M-1)d\text{cos}\,\theta_{1}\text{sin}\,\varpi_{1}/\lambda}&e^{j2\pi( M-1)d\text{cos}\,\theta_{2}\text{sin}\,\varpi_{2}/\lambda}&e^{j2\pi(M-1)d\text{cos}\, \theta_{k}\text{sin}\,\varpi_{K}/\lambda}\end{bmatrix}  \tag{7}\]  
Since the x-axis and y-axis share the array at the origin, the orientation matrix \(A_{y}\in\mathbb{C}^{(M-1)\times 1}\) is represented as:  
\[A_{y}= \begin{bmatrix}1&1&\ldots&1\\ e^{j2\pi d\text{cos}\,\theta_{1}\text{sin}\,\varpi_{1}/\lambda}&e^{j2\pi d \text{cos}\,\theta_{2}\text{sin}\,\varpi_{2}/\lambda}&\ldots&e^{j2\pi d\text{ cos}\,\theta_{k}\text{sin}\,\varpi_{K}/\lambda}\\ \vdots&\vdots&\ddots&\vdots\\ e^{j2\pi(M-1)d\text{cos}\,\theta_{1}\text{sin}\,\varpi_{1}/\lambda}&e^{j2\pi( M-1)d\text{cos}\,\theta_{2}\text{sin}\,\varpi_{2}/\lambda}&e^{j2\pi(M-1)d\text{cos}\, \theta_{k}\text{sin}\,\varpi_{K}/\lambda}\end{bmatrix}  \tag{8}\]\(A_{x}\) and \(A_{y}\) are combined, as indicated below:  
\[A= \begin{bmatrix}A_{x}\\ A_{y}\end{bmatrix} =[a_{1},a_{2},\dots,a_{K}] \tag{9}\]  
Jointly, the corresponding representation of the signals received by the whole array can be obtained as follows:  
\[G=AS(t)+N(t) \tag{10}\]  
where \(S(t)\) and \(N(t)\) represent the \(K\) source signals and additive noise at time \(t\), denoted as follows:  
\[S(t)=[s_{1}(t),s_{2}(t),\dots,s_{K}(t)]^{T} \tag{11}\] \[N(t)=[n_{1}(t),n_{2}(t),\dots,n_{M}(t)]^{T}\]
# TinyGS vs. SatNOGS: A Comparative Analysis of Open-Source Satellite Ground Station Networks
## 3 SatNOGS
### SatNOGS Introduction
Afterward, the network will be responsible for observing the characteristics of the satellite present in the SatNOGS DB and selecting the most suitable GS to carry out this communication according to its frequency range and availability within the selected time interval. Then, it will present the most suitable GS to the user. From all of the GSs presented, the user selects the desired GS, GSx (the user can select more than one GS), and then a \"Job\", Jobx, is placed in the queue of GSx jobs according to the order of arrival on the network. Then, the Linux computer periodically asks the network if it has observations scheduled and, if so, it executes them in order. After all of the tasks/jobs that were in front of Jobx are executed, Jobx will start executing, and the GSx will communicate with the satellite selected by the user at the beginning. Subsequently, the GSx will receive the data and send it to the network. In the end, the user who requested the GSx and any other user can observe the data collected from the satellite, for example, via the web network [12; 13].
## 5 Drug Delivery Systems
### Liposomes  
Liposomes are spherical vesicles comprising one or several enclosing phospholipid bilayers, usually in an internal aqueous phase. Their main advantage is the ability to carry both hydrophilic and hydrophobic substances. The former is dissolved in the internal aqueous compartment, whereas the latter is in the lipid bilayer--Eos--as predominantly hydrophobic molecules are transported in the lipid bilayer [72]. So far, the liposomes used to encapsulate EOs include phosphatidylcholine, cholesterol, and lecithin. A couple of studies have shown that encapsulating EOs in liposomes helps extend their antimicrobial, antioxidant, and cytotoxic properties [73; 74; 72]. Despite the improved antimicrobial and antioxidant activities, studies have reported a couple of problems that hinder the medicinal application of this drug delivery system. Liposomes are unstable concerning pH and were also shown to have poor loading capacity and low physical stability. Additionally, the manufacturing process of liposomes has quite high costs. Despite these drawbacks, liposomes are experimentally researched to prevent food spoilage [75]. It has also been insinuated that double coating or other carrier techniques can help overcome these difficulties [72], a topic that is currently being researched experimentally [74].  
There are many reports in the available literature on the use of liposomes loaded with EOs in medical applications. For example, [PERSON] et al. [76] tested the antimicrobial activity of _Zataria multiflora_ EO loaded into nanoliposomes against enterohaemorrhagic _E. coli_. Not only were the authors able to demonstrate lower MIC values of this method compared to the EO or carrier alone, but they also showed that it had a positive effect on _stx2_ gene expression. In turn, [PERSON] et al. [77] demonstrated that cinnamon EO loaded into liposomes showed potent antibacterial and antibiofilm properties against _S. aureus_, as evidenced by transmission electron microscopy. The authors reported the MIC values of 25 ug/mL compared to 50 ug/mL when the EOs were used alone. The results are consistent with other experiments like [78; 79].
## 3 Results and Discussion  
### Caffeine and Chlorogenic Acid in Cold Brew Samples  
This study demonstrated that caffeine and chlorogenic acid can be one of the basic analytical parameters in cold brew. Table 2 shows the results of bioactive coffee ingredients from 81 trials of popular beverages betweenthe coffee roasting coffee profile and extraction condition. Samples were formed in a matrix of three roast profiles \(\times\) three extraction times \(\times\) three extraction temperatures. Depending on the direction of technology development, the raw material rich in bioactive compounds may make the process less efficient and decrease coffee's antioxidant capacity, stimulating properties or giving them the right quality. \begin{table}
\begin{tabular}{c c c c c} \hline
**Compound** & **MRM Transition _m/z_ (Q1-> Q3)** & **Q1 (V)** & **CE (V)** & **Q3 (V)** \\ \hline \multirow{3}{*}{Caffeine} & 195.2\(\rightarrow\) 138.10 & \(-\)10.0 & \(-\)22 & \(-\)22.0 \\ \cline{2-5}  & 195.2\(\rightarrow\) 42.15 & \(-\)10.0 & \(-\)36 & \(-\)15.0 \\ \hline \multirow{3}{*}{Chlorogenic acid} & 355.2\(\rightarrow\) 163.05 & \(-\)19.0 & \(-\)15 & \(-\)27.0 \\ \cline{2-5}  & 355.2\(\rightarrow\) 89.10 & \(-\)11.0 & \(-\)55 & \(-\)30.0 \\ \hline \end{tabular}
\end{table} 
Table 1: MRM analysis parameters for caffeine and chlorogenic acid. Dybkowskaetal. [8] reported polyphenol levels depending on the origin and roasting conditions of the green bean. Researchers prepared hot (temp. 94 \(\pm\) 2 \({}^{\circ}\)C, extraction time 5 min) drinks from 1 g of roasted coffee where the measured concentration of polyphenols was 21-43 mg. For calculations in the design experiment performed with cold brew beverages, only the content of chlorogenic acid--the main representative of coffee phenolic acids--was considered. The analysis suggests that, depending on the cold brew preparation method, the total content of polyphenols (along with chlorogenic acid isomers, phenolic acid derivatives and melanoid complexes with antioxidant properties) in the beverage may be higher than in hot brewed coffees. As seen from Figure 1, a relatively high mean value content of chlorogenic acid was determined in the product made at 5 \({}^{\circ}\)C and 15 \({}^{\circ}\)C in the relationship between coffee roasting profile and extraction temperature. The difference between these results is not statistically important. It seems that an ambient temperature below 25 \({}^{\circ}\)C for cold extraction has a protective effect on changes in their composition due to, among other things, the inhibition
# Addressing the Potential of L5/E5a Signals for Road ITS Applications in GNSS-Harsh Environments +
## 3 Analysis
### Satellites' Visibility, C/N0 and Multipath
Figure 4: OnePlus Nord 2 5G: (**a**) Skylplot of the observed satellites on L1/E1/B1 (solid marks) and on L5/E5a (empty marks), (**b**) L1/E1/B1 and (**c**) L5/E5a/B2a C/N0 elevation-based (grey: elevation \(\leq 15^{\circ}\), blue: elevation \(>15^{\circ}\)) series (modified from _GNSSAnalysisApp_ and _RTKLIB_).
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 4 Proposed Deep Learning Model
### Enhancing Small Object Detection  
Small object detection is critical in this application, as medical vials are often compact and visually similar. To improve the model's ability to detect such objects, we integrated Spatial Pyramid Pooling (SPP) into the neck of the model. The SPP module enables the extraction of multi-scale features by applying pooling at different kernel sizes:  
\[F_{SPP}=\sum_{k}P_{k}(F), \tag{6}\]  
In Equation (6) \(P_{k}(F)\) denotes pooling at kernel size \(k\), and \(F_{SPP}\) represents the aggregated feature map. In this work, we used pooling sizes of \(5\times 5\), \(9\times 9\), and \(13\times 13\), ensuring that objects of varying sizes and distances can be effectively recognized.
## 5 Numerical Examples  
In this section, the quality of the mesh generation algorithm is evaluated through a series of CPFEM simulations performed on a Grade 91 ferritic-martensitic steel and a Zircaloy-2 alloy (constitutive models are summarized in Appendices A and B). The polycrystalline microstructure was reconstructed virtually using DREAM.3D, assuming an average grain size of 33 \(\upmu\)m [48], a random crystal texture, \(\mu=3.5\), \(\sigma=0.1\), and a primary equiaxed shape. Multiple two-dimensional image slices were extracted from the resulting microstructure and transformed into NURBS models using the image processing algorithms presented in Section 2. The PolyCISAMR algorithm introduced in Section 3 was then employed to generate the conforming meshes in the form of input files for the Warp3D CPFE code [56]. Because this code only performs 3D analyses, the meshes were first extruded into a single layer of quadratic wedge elements and subdivided into three quadratic tetrahedral elements for the CPFE analysis.  
In the following simulations, the boundary conditions on the CPFEM models are consistent: symmetry is applied to the left, lower, and back faces of the unit cell, traction-free conditions are applied to the top \(y\) and front \(z\) faces, and a constant axial true strain rate of 0.01 1/hr is applied to the right \(x\) face. This strain rate is larger than the slower creep strain rates of 0.0001 1/hr for which the model was calibrated; however, the macroscale stress-strain curves produced from the model at this rate are on a similar order of magnitude as the yield stress of 300 to 400 MPa for Gr91 at this temperature and strain rate [48]. The time step size varies from 0.02 to 0.05 hr during the analysis.
# Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles
## 3 Our Method
### Problem Formulation  
In this section, we briefly describe the fact descriptions, law articles, charges, terms of penalty and legal judgment prediction, as well as the related notations and terminology, as Figure 3:  
* Fact description: The fact description is a part of an Judicial decision document which mainly describes the time and place of the case, the defendant, what illegal activities were carried out, etc., and is noted as \(FD\).
* Law article: Law articles are the basis for convictions and each charge has at least one law article. The law articles are noted as \(L=\{LA_{1},LA_{2},\dots,LA_{n}\}\). In the dataset used in the paper, the maximum number is 118.
* Charge: The charges are the items that need to be predicted. The maximum number is 130.
* Term of penalty: The terms of penalty are the items that need to be predicted. The maximum number is 11.
* Legal judgment prediction: The LJP task is to obtain the legal judgment based on the fact description of the case and our task is to train a model \(F\) to predict the LJP LJP result (applicable law articles, charges and terms of penalty).
## 4 Numerical Results
In this regime with a massive number of antennas, the cross-UT channels (\(\mathbf{G}_{1}\) and \(\mathbf{G}_{2}\) in Figure 1) become almost orthogonal to the AP to relaying UT channels (\(\mathbf{h}_{2}\) and \(\mathbf{h}_{1}\) in Figure 1), and thus, the zero forcing operations in Algorithm 1 and Algorithm 2 do not help much. Instead, the UT transmit beamforming of  
Figure 2: The average sum throughput in bits per second per hertz (BPS) comparison over the relaying UT transmit power with different AP transmit powers (\(P_{s}\)). Here, \(M=3\). Algorithm 2 becomes ineffective as they simply point to the one of target UTs, respectively. This handicap of Algorithm 2 is reflected as the degradation of its performance at the large \(M\) regime. Figure 4: The average sum throughput in bits per second per hertz (BPS) comparison over the number of UT antenna set with different AP transmit powers (\(P_{s}\)) and different relaying UT transmit powers (\(P_{r}\)). Figure 3: The average sum throughput in bits per second per hertz (BPS) comparison over the AP transmit power with different relaying UT transmit powers (\(P_{r}\)). Here, \(M=3\).
## 1 Introduction
### EEG Signal Acquisition  
The electroencephalographic signal represents the electrical potentials recorded by electrodes placed on the scalp, which fluctuate due to the synchronous activity of millions of neurons in the brain. These fluctuations in electrical potentials are caused by the ionic current flows associated with the neural processes, reflecting the underlying neuronal activity within the cerebral cortex [5].  
The contact point of the electrode with the subject's head picks up electrical signals that represent the collective activity of many neurons within the area of the brain being monitored. These signals are not limited to a single neuron but rather reflect the synchronized firing of numerous neurons involved in the neural processes, which are associated with a particular cognitive or motor task being performed by the subject.  
The electroencephalogram is a system composed of multiple electronic components designed to acquire, amplify, and transmit the electrical signals, which represent the brain's neural activity. However, the conditions in which the EEG system operates can present challenges in accurately capturing the low-voltage signals due to the use of amplification and signal processing techniques.
## 4 Research Results: The Resettlement Communities as an Opportunity for Renewal
### GIS Spatial Analysis Results  
In this transformation, densification can be considered to increase land-use efficiency, renew the obsolescent stock, and provide housing to more diversified inhabitants. The analysis of the parameter of the density of the 176 resettlement communities in Suzhou already shows a small trend of densification over 30 years. In the early years of urbanization, the units were smaller, and the FAR was lower, less than 1.3; the cases with a FAR higher than 1.5 were realized after 1999, but in the 2000s, FARs higher than 2 and lower than 1.5 coexisted. After 2008, almost no communities had a FAR lower than 1.5. Since 2016, nearly all cases have had a FAR higher than 2, with some also reaching more than 3, and units becoming larger (Figure 3).  
According to the FAR calculation via GIS (Figure 4a), 42% of the resettlement communities have a FAR between 1.0 and 1.5; a total of 35% have a FAR between 1.6 and 2; a total of 19% have a FAR higher than 2; and 4% have a FAR lower than 1. The analysis does not show a general rule in the distribution of the resettlement communities in the city, and neither can reasons be found for the different FARs of the cases, if not a higher FAR in proximity to the edges of Gusu, the oldest district of Suzhou, and a lower FAR in the most distant ones from Gusu.  
For the house conditions calculation (Figure 4b), the areas with the highest housing conditions are located around Jinji Lake and Dushu Lake in the industrial park, at the junction of Gusu District and the industrial park, near Xinqu Road, and exhibit a typical centrality, with lower housing conditions occurring further to the periphery. Compared with areas with high housing prices, areas with lower housing conditions have wider coverage and cover the complete urban development boundaries. Low-density resettlement communities tend to concentrate in areas with slightly higher urban housing conditions.  
For community accessibility analysis (Figure 4c), the highest area is located in the area west of Jinji Lake in the industrial park and at the junction of the ancient city, as well as the area around Dushu Lake. Similar to housing prices, it shows a clear centrality. However, compared with housing prices, its coverage area is relatively small, located in the city's core area. In addition, there is no obvious consistency with reachability for low-density and high-density resettlement communities.  
According to the integration analysis of accessibility and house conditions superposition (Figure 4d), the area with high house conditions and accessibility is the area where the industrial park is close to Gusu District, which has a strong centrality in the overall distribution. However, because the two are not completely superimposed in space, the core area radiates from the industrial park to the new area, forming a larger radiation area.  
Figure 3: Average resettlement communities’ FAR change from 1983–2019 in Suzhou city. (The dashed line is the trend of the column chart).