esa-sceva/satcom-chunk-collection huggingface dataset:
# Grid Anchor Lane Detection Based on Attribute Correlation
## 3 Methods
### Calculation of the Grid Anchor Labels
#### 3.2.1 Two Intersections  
When a lane line intersects the boundaries of an anchor at two distinct points (Statements 10-15), two scenarios can arise based on the position of the lane line relative to the anchor:  
Figure 4: Illustration of three types of intersections between lane lines and the boundaries of anchors. (**a**) The intersection of the left lane boundary with the grid anchors; (**b**) The intersection of the vertical lane boundary with the grid anchors; (**c**) The intersection of the right lane boundary with the grid anchors. The yellow lines represent the lane boundaries, while the blue lines represent the boundaries of the grid anchors. The numbers indicate the identifiers of each grid anchor within the subfigures.  
Case 1: The lane line passes through the interior region of the anchor (e.g., Anchor 1 and Anchor 3 in Figure 4a and Anchor 2 and Anchor 4 in Figure 4b). In this case, the grid anchor is classified as a lane line, and the attribute values of the anchor are computed. The two intersection points are denoted as \(\left(x_{i,j}^{p1},y_{i,j}^{p1}\right)\) and \(\left(x_{i,j}^{p2},y_{i,j}^{p2}\right)\), where the intersection point with the larger y-coordinate is labeled as \(\left(x_{i,j}^{p},y_{i,j}^{p}\right)\). The direction attribute of the anchor can be determined from Table 2 using the coordinates of \(\left(x_{i,j}^{p},y_{i,j}^{p}\right)\).  
Based on the definitions of the offset and slope, the attribute values are calculated as follows:  
\[T_{n+3,i,j}^{s}=\frac{x_{i,j}^{p1}+x_{i,j}^{p2}}{2}-\frac{(2i+1)W}{2W_{s}} \tag{4}\]  
\[T_{n+4,i,j}^{s}=\frac{y_{i,j}^{p1}+y_{i,j}^{p2}}{2}-\frac{(2j+1)H}{2H_{s}} \tag{5}\]  
\[T_{n+5,i,j}^{s}=\frac{x_{i,j}^{p2}-x_{i,j}^{p1}}{y_{i,j}^{p2}-y_{i,j}^{p1}} \tag{6}\]  
Case 2: The lane line does not pass through the interior region of the anchor but instead lies precisely along the left or right boundary of the anchor (e.g., Figure 4c). In this scenario, the two intersection points are the two vertices of the anchor. Following the horizontal flooring rule, lane line features are assigned to the anchors on the right (e.g., Anchor 1 and Anchor 4 in Figure 4c), while the anchors on the left (e.g., Anchor 2 and Anchor 3 in Figure 4c) do not contain lane line features. Consequently, Anchors 1 and 4 in Figure 4c are classified as lane lines, and their attribute values are computed using the aforementioned method. Anchors 2 and 3 in Figure 4c are classified as background (Statements 11-13), and all their attribute values are set to 0.
# Reactance Regulation Using Coils with Perpendicular Magnetic Field in the Tubular Core design
## 3 Materials and Methods  
### Proposed Designs  
In this work, a specific method is proposed to design coils with perpendicular magnetic fields. Figure 2a,b show the schematic of the proposed design. Initially, the inner coil is wound in the toroidal direction to produce a toroidal shape. After that, a thin ribbon of ferromagnetic steel is wound on this toroidal shape in the poloidal direction to form the ferromagnetic core. The outer surface of the core should be isolated, and an external coil is wound in the poloidal direction (Figure 2b).  
Figure 2: Schematic representation of principle to create two perpendicular coils (**a**) internal winding covered with ferromagnetic ribbon, (**b**) external coil on the ferromagnetic core.  
The winding parameters of internal coil are: N = 260 turns, diameter of wire (d) = 1 mm, R\({}_{\rm DC}\) = 1.27 \(\Omega\), R\({}_{\rm 50 Hz}\) = 28.9 \(\Omega\), L\({}_{\rm 50 Hz}\) = 931 mH, whereas N = 1509 turns, d = 0.3 mm, R\({}_{\rm DC}\) = 21.1\(\Omega\), R\({}_{\rm 50 Hz}\) = 121.9\(\Omega\), L\({}_{\rm 50 Hz}\) = 836.6 mH, for the external coil. The ferromagnetic core is designed using multiple ribbon sections. Two closely placed segments are attached by micro-welding. The thickness of ribbon section is 0.1 mm. The degree of independence between perpendicular windings is studied analytically and experimentally in the following sections.
## 2 CoAP Overview
### Resource Discovery  
In machine-to-machine (M2M) applications where there are no humans in the loop, it is important to provide a way to discover resources offered by a constrained server. For HTTP Web Servers, the discovery of resources is typically called Web Linking [10]. The use of Web Linking for the description and discovery of resources hosted by constrained web servers (CoAP or HTTP) is specified by the CoRE Link Format- RFC 6690 [11]. A well-known relative URI \({}^{\ast}\)/.well-known/core\" is defined as a default entry-point for requesting a list of links to resources hosted by a server. Once the list of available resources is obtained from the server, the client can send further requests to obtain the value of a certain resource. The example in Figure 2 shows a client requesting the list of the available resources on the server (GET /.well-known/core). The returned list (in CoRE Link Format) shows that the server has, amongst others, a resource called /s/t that, when queried, returns the temperature in degrees Celsius. The client then requests the value of this resource (GET /s/t) and receives a plain text reply from the server with the value of the current temperature as payload of the message (23.5).  
Figure 2: An example of Constrained RESTful Environments (CoRE) direct resource discovery and Constrained Application Protocol (CoAP) request.  
However in many M2M scenarios, nodes might have long sleeping periods and thus making direct discovery of resources not practical. To solve this problem, the CoAP community is proposing to use CoRE Resource Directories (RD) that host descriptions of resources held on other servers [12]. This way a CoAP server can register its resources with one or more RDs. Clients in turn can discover these resources by performing lookups against an RD. For example the same resource discovery that was performed by using direct communication between the client and the server in Figure 2 can now be performed by using an RD as illustrated in Figure 3. For more details about the registration and lookup interfaces of Resource Directories we refer to [12].
# A Reconfigurable Radio-Frequency Converter IC in 0.18 \(\upmu\)m CMOS
## 3 System Architecture  
To realize the operation outlined in the previous section, a first prototype was designed and fabricated (RFID-I), but it did not fulfill the expected specifications. This work presents a re-design of the system (RFID-II) and its main elements, whose block diagram is shown in Figure 4. It consists of two independent variable frequency synthesizers to generate the LO references for up- and down-conversion, and two passive mixers. The frequency synthesizers, which are based on an integer-N phase-locked loop (PLL), share a common reference obtained from an external temperature-compensated crystal oscillator (TCXO) oscillating at 20 MHz.  
The system is fed with regulated supplies that are compensated in temperature in the range from 0 \({}^{\circ}\)C to 100 \({}^{\circ}\)C. The supplies use a 1.2 V reference voltage based on a band-gap architecture which is biased externally in the range from 2.7 V to 3.6 V. The regulator for the analog blocks has a low drop-out (LDO) architecture and provides a DC voltage output of 2.5 V whereas the regulator for the digital blocks has a conventional architecture and it provides a DC voltage of 1.8 V. They can give a maximum current of 70 mA and 20 mA by means of current supply circuits based on an operational trans-impedance amplifier (OTA) and high-swing cascode mirrors.  
The programming of the system is carried out by means of a serial inter-integrated circuit (I\({}^{2}\)C) interface operating on five 16-bit registers.  
Radio-frequency overlay applications demand very stringent characteristics in terms of noise and linearity to provide good quality signals to the end users. In the following sections, a summary of the main design considerations and characteristics of the main building blocks of the radio-frequency converter (the frequency synthesizer and the mixers) will be presented.  
Figure 3: Operation of the reconfigurable RF converter. A DVB-T signal sent in a triple-play scheme over GPON and encrypted using spectrum inversion is up-converted, inverted in spectrum and filtered for channel selection, and down-converted to be fed into a television set through its antenna input.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 5 Summary and Outlook
### Key Challenges
#### 5.2.2 The Element Spacing Leads to a Contradiction between the Gate Lobe and the Coupling  
In the optimization design of sparse planar arrays, it is imperative to address the challenge posed by the impact of array element spacing on the antenna pattern. Excessive spacing between elements can introduce grating lobes into the antenna pattern, while inadequate spacing leads to strong coupling effects that adversely affect the pattern. In particular, the optimization design of sparse cloth plane arrays is related to the sparsity of the array, the array element layout, and the antenna pattern. Consequently, achieving a balance between suppressing grating lobes and reducing coupling effects represents a key engineering challenge.  
Conceptualization, Y.H. and C.W.; methodology, Y.H.; software, C.W.; validation, Y.H. and C.W.; formal analysis, C.W.; investigation, C.W.; resources, C.W.; writing--original draft preparation, C.W.; writing--review and editing, Y.H.; visualization, Y.H.; supervision, Y.H.; project administration, Y.H.; funding acquisition, Y.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The dataset is not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### X-ray Diffraction of OPC and QFS-GPC Mixes  
The XRD results are presented in Figure 9. Figure 9 illustrates the results of the XRD configuration (20 = 10-75\({}^{\circ}\)) of the control (OPC) and QFS-GPC specimens observed after the ambient curing of 28 days. The reactions in OPC and QFS-GPC mixes arise in the exceedingly significant region in a range of 20 = 20-30\({}^{\circ}\). For the sample of the GPC-D mix, the sharp diversions peaks are observed in this region (20 = 20-30\({}^{\circ}\)) as compared to all other trials comprising OPC mix (control mix). These sharp peaks illustrate that the mix GPC-D is highly crystalline than other mixes of the present study. Similarly, the mixes of OPC and GPC-D in a range between 20 = 40-50\({}^{\circ}\) are illustrating the peaks which represent the development of crystalline phases while the samples of other mixes (GPC-A, GPC-B, GPC-C, and GPC-E) represent the formation of amorphous phases. A previous study on the elevated temperature of geopolymer concrete showed semi-crystalline allumino silicates before and after exposure to 200 \({}^{\circ}\)C. The development of crystalline phases was observed in this study, which is in agreement with the previous study [58].  
Figure 9: XRD pattern of OPC and QFS-GPC specimens.  
The restricted periodicity of particles (atoms) in the range of 20 = 60-70\({}^{\circ}\) also represents in GPC-C, GPC-D, and GPC-E samples due to the presence of QRD contents. So, it can be illustrated from the XRD diffractogram that an increase of QRD contents in all QFS-GPC mixes up to 15% increases the compressive strength due to the development of crystalline phases.
# Parametric Analysis of Outrigger Systems for High-Rise Buildings with Different Geometric Shapes
###### Abstract  
The increasing demand for efficient lateral load-resisting systems in high-rise construction necessitates the investigation of advanced structural solutions. Among many alternatives, outrigger systems are widely acknowledged as effective supplementary schemes for enhancing the strength and stability of tall buildings subjected to lateral loads. This work investigates whether the potential of such systems, well established for regular structures, also remains valid for the complex-shaped geometries that often characterize contemporary tall buildings. Tilted and twisted geometries are explored via the parametric variation of tilt and twist angles. The structural response, both with and without outriggers, is evaluated and compared to that of a regular geometry. The number, location, and relative stiffness of outriggers with respect to the inner core are also systematically varied to provide a comprehensive assessment. To facilitate the extensive parametric analysis, simplified analytical models are employed. Then, a selection of representative geometries are utilized to generate refined three-dimensional numerical models. A comparative survey between these two modeling approaches elucidates the accuracy and limitations of simplified methodologies, while providing insights into the structural behavior of outrigger systems. This work underscores the critical interaction between building configuration, outrigger location, and flexural stiffness in optimizing high-rise structural performance. The results reveal a significant influence of the building's morphology on the structural response, with major improvements exhibited by regular and tilted configurations. Conversely, twisted geometries can considerably alter global structural behavior depending on their degree of twist, potentially diminishing the outrigger's efficacy in mitigating lateral displacement and core base moment demands. By providing quantifiable insights into outrigger performance in complex-shaped structures, this research guides a more integrated architectural and structural approach in contemporary high-rise construction, leveraging an efficient simplified modeling framework for preliminary design.  
outrigger systems; twisted buildings; tilted buildings; complex-shaped tall buildings; simplified modeling +
Footnote †: journal: Applied Sciences
###### Abstract  
In this contribution, the authors perform a detailed analysis relating to the use of the multiconductor UIC (Union Internationale des Chemins de fer) cables as a broadband data transmission channel onboard regional trains. The analysis is performed evaluating the channel frequency response and the channel capacity as a function of the distance between communication devices, in the 2-30 MHz frequency range of broadband powerline communications. The per unit length (p.u.l.) parameters are measured and simulated in a finite element method (FEM) code, allowing accurate full multiconductor simulations to evaluate the attenuations of the selected channel for communication and the cross-talk between cables. This study has been conducted considering that there are other signals carried by the cable, during regular train operation, and their integrity needs to be taken into account. The results show that, as a matter of fact, UIC cables can be used to transmit high-speed data; this can be exploited for introducing additional facilities (i.e., infotainment, safety features and so on) onboard regional trains, at a reasonably low cost, since no new cabling harnesses need be installed.  
Keywords:data transmission; multiconductor cable; broadband communication; channel modeling; onboard communication +
Footnote †: journal: Accepted: 31 January 2019; Published: 4 February 2019
# Common Data Model and Database System Development for the Korea Biobank Network
## 5 Evaluation
### Results
#### 5.1.2 Banking Samples  
Figure 7 presents the top ten categories of collected samples. In 2019, the total number of specimen registrations was 706,491. Among them, serum was collected the most (229,942, (32.55%), alongside plasma (226,142, 32.01%) and buffy coat (79,507, 11.25%), which were also frequently collected. These three sample types accounted for more than 60% of the total number of specimens.  
The basic demographics of the study population are shown in Table 2. Among the total population (\(n\) = 65,754), the majority were male (\(n\) = 34,531, 52.52%), whereas 31,223 were female (47.48%). The majority of patients (\(n\) = 17,352, 26.39%) were 70 years old or older, followed by those aged 60 to 69 years (\(n\) = 15,563, 23.67%), and those aged 50 to 59 years (\(n\) = 12,010, 18.27%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Category** & **Total N (\%)** \\ \hline Sex & Male & 34,531 (52.52\%) \\  & Female & 31,223 (47.48\%) \\ \hline Age & Less than 10 & 2953 (4.49\%) \\  & 10 to 19 & 1603 (2.44\%) \\  & 20 to 29 & 2976 (4.53\%) \\  & 30 to 39 & 5866 (8.92\%) \\  & 40 to 49 & 7411 (11.27\%) \\  & 50 to 59 & 12,010 (18.27\%) \\  & 60 to 69 & 15,563 (23.67\%) \\  & 70 years or older & 17,352 (26.39\%) \\  & Unknown & 20 (0.03\%) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic demographics.  
Figure 7: Specim count.  
The 27 diseases or conditions can be classified into three types: (i) cancer-related diseases (10 diseases), (ii) non-cancer-related diseases (15 diseases), and (iii) healthy individuals and other diseases not included in the first two groups. Except for the other disease group (\(n\) = 24,951, 37.95%), ischemic heart disease (\(n\) = 5473, 8.32%) was received the most. The least common disease type was head and neck cancer (\(n\) = 104, 0.16%). Liver cancer and thyroid cancer-related information was collected by most hospitals (15 hospitals). In contrast, premature birth information was collected by the fewest number of hospitals (two hospitals).
## 4 Properties of \(\gamma\)-Graphs of Trees
### Cartesian Product and \(\gamma\)-Graphs of Trees
For convenience in the following proof if \(v\in sw(e)\) we will say \(v\) is _swapped by \(e\)_ and that \(e\)_suaps_\(v\). **Theorem 12**. : _Let \(G\) be a Cartesian product graph with \(G=G_{1}\,\square\,G_{2}\), \(G_{i}\
eq K_{1}\). Then \(G\) is the \(\gamma\)-graph of a tree \(T\) if and only if each \(G_{i}\) is the \(\gamma\)-graph of a tree \(T_{i}\)._  
**Proof. ** Suppose \(G=G_{1}\,\square\,G_{2}\), \(G_{1},G_{2}\
eq K_{1}\), is the \(\gamma\)-graph of a tree \(T\). Let \(e=U_{1}U_{2}\) be an edge of \(G_{1}\) and let \(e_{1}\) and \(e_{2}\) be any two edges associated with \(e\) in \(G\) on vertices \(V_{1}\) and \(V_{2}\) of \(G_{2}\) respectively. That is \(e_{1}=(U_{1},V_{1})(U_{2},V_{1})\) and \(e_{2}=(U_{1},V_{2})(U_{2},V_{2})\). As \(G\) is connected by Theorem 2, there is a path between \((U_{1},V_{1})\) and \((U_{1},V_{2})\) in \(G\), and considering only the edges associated with edges in \(G_{2}\) gives a path \(P\) between \(V_{1}\) and \(V_{2}\) in \(G_{2}\). Consider the subgraph \(e\,\square\,P\) of \(G\). It follows from Proposition 2 that every edge associated with \(e\) in \(e\,\square\,P\) swaps the same pair of vertices. As \(e_{1}\) and \(e_{2}\) were arbitrary edges associated with \(e\), then every edge associated with \(e\) in \(G\) swaps the same pair of vertices. By symmetry, this is true of every edge in \(G_{1}\) or \(G_{2}\). Let \((W,X)\) be a vertex of \(G\) and for \(i=1,2\), let \(E_{i}\) be the set of edges of \(G\) associated with the edges of \(G_{i}\). If a vertex \(a\in(W,X)\) is swapped by an edge in \(E_{2}\), it is not swapped by an edge in \(E_{1}\) by Proposition 2. Hence, form \(T_{1}\) by adding two leaves to any vertex in \((W,X)\) that is swapped by an edge in \(E_{2}\). Then it follows that \(T_{1}(\gamma)\cong G_{1}\). By symmetry, \(G_{2}\) is also the \(\gamma\)-graph of a tree. Conversely, suppose each \(G_{i}\) is the \(\gamma\)-graph of a tree \(T_{i}\). Let \(T\) be the graph formed by adding an edge between any stem of \(T_{1}\) and any stem of \(T_{2}\). It is easily verified that \(T(\gamma)\cong G\). \(\square\)
## 2 Materials and Methods
### Datasets and Performance Metrics  
We adopted the commonly used evaluation metrics in the literature of superpixel segmentation to evaluate superpixel segmentation algorithms, including boundary recall (BR), achievable segmentation accuracy (ASA), and under-segmentation error (USE). The BSDS 500 [33] consisted of 500 images with manually labeled ground truth segmentations. In the following experiments, the methods were evaluated using the average performance metrics of five hundred images in the BSDS 500 [33].  
Boundary recall (BR) measures the fraction of the ground truth boundary identifications. A high BR represents the better boundary adherence with respect to the ground truth boundaries. Given a ground truth boundary sets \(B(g)\) and the superpixel boundary sets \(B(s)\), then BR is defined as:  
\[BR=\frac{\text{TP}(B(g),B(s))}{\text{TP}(B(g),B(s))+\text{FN}(B(g),B(s))} \tag{8}\]  
Let FN (\(B(g)\), \(B(s)\)) and TP (\(B(g)\), \(B(s)\)) be the number of false negative and true positive boundary pixels. \(B(s)\) is matched to \(B(g)\) within a local neighbor with size \((2r+1)\times(2r+1)\). In practice, \(r\) is set to 1.  
Achievable segmentation accuracy (ASA) computes the upper bound of accuracy using superpixels as preprocessing step. Denote a ground truth segmentation of an image as \(\{G_{i}|i=1,2,\cdots,M\}\) and a superpixel segmentation as \(\{S_{i}|l=1,2,\cdots,K\}\). The ASA metric is defined as follows:  
\[\text{ASA}=\frac{\sum\limits_{S_{i}}\max_{G_{i}}\{|S_{l}\cap G_{i}|\}}{\sum \limits_{G_{i}}\lvert G_{i}\rvert} \tag{9}\]  
where \(\lvert.\rvert\) is the number of a superpixel in pixel. In general, a high ASA demonstrates that superpixels match the objects well in the image.  
Under-segmentation error (USE) measures the \"leakage\" of superpixels from the ground truth boundaries, which is calculated by:  
\[\text{USE}=\frac{1}{n}\sum\limits_{S_{l}}\left\lvert S_{l}-\text{argmax}_{G_ {i}}\lvert S_{l}\cap G_{i}\right\rvert \tag{10}\]  
where \(n\) is the total number of pixels in the image. Each superpixel was assigned to the ground truth segments with the largest overlap, and only the \"leakage\" with respect to other ground truth segments was considered. Therefore, USE implicitly also measures boundary adherence. A lower USE refers to that superpixels overlap with a ground truth segmentation more accurately.
## 3 Materials and Method
### Experimental Setup  
#### 3.4.1 Hardware and Software Platforms  
The experiments were conducted on an NVIDIA A100 GPU, a high-performance unit designed for artificial intelligence and high-performance computing, offering powerful computational capabilities, a large memory capacity (up to 80 GB), and Tensor cores for accelerating deep learning tasks. To enhance efficiency, the Multi-Instance GPU (MIG) feature was utilized for resource sharing and multi-tasking. On the software side, PyTorch 1.8 served as the primary framework for model construction and training, leveraging its dynamic computational graph and user-friendly API. CUDA and cuDNN were integrated to accelerate key operations like convolution and backpropagation, while NumPy 2.x, Pandas, and Matplotlib were employed for data processing and visualization. This hardware-software integration ensured efficient data handling, model training, and results analysis, supporting this study's experimental needs.