esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurements
### Measurement Scenario  
The measurements have been carried out in the second-floor corridor in the 18 th building, National Institute of Metrology, Beijing, China. The building is constructed of concrete with faculty  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{**Value**} \\ \hline Center Frequency & 3.6 GHz & 14.6 GHz \\ Beamwidth \(E\)-plane & 49\({}^{\circ}\) & 43\({}^{\circ}\) \\ Beamwidth \(H\)-plane & 38\({}^{\circ}\) & 34\({}^{\circ}\) \\ Gain \(E\)-plane & 7 dBi & 12 dBi \\ Gain \(H\)-plane & 9 dBi & 14 dBi \\ Sweep Bandwidth & & 0.8 GHz \\ Time Resolution & 1.25 ns \\ IF Bandwidth & & 300 Hz \\ Acquisition Time (6 sweeps\(\times\) 900 ms) & & 5.4 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measurement parameters.  
Figure 1: (color online) Radiation patterns of antennas at (a) 3.6 GHz; and (b) 14.6 GHz.  
offices and laboratories as shown in Figure 2. The corridor is 20 m in length, 2.4 m in width and 2.8 m in height. The side walls, floor and ceiling of the corridor are constructed of concrete. The floor is covered with ceramic tiles and the ceiling is covered with polystyrene tiles. The corridor has a number of wooden doors and some windows. In the measurements, the doors and the windows were closed and no people were moving around.  
The transmitter (TX) is fixed and denoted by the solid star in Figure 3 (top view). At the receiver side (RX), the antenna is moved along a straight line at 17 different locations, which are denoted as the solid circles in Figure 3. The TX and RX are placed in the middle of the corridor (the distances to both sides of the corridor are 1.2 m). The distance between the TX and RX (1) is 1.2 m. The distances between two adjacent RX locations are also 1.2 m. The transmitted and received antennas are vertically polarized in the elevation plane and fixed on adjustable tripods about 1.4 m above the ground. The height of the antenna was set to simulate the communication between two portable devices, and it will not vary much for most personal applications.  
In order to analyze the influence of antenna location (whether near the walls) on channel characteristics, we conducted another measurement in the same scenario. All the measurement settings were the same as the previous measurements, but the TX and RX antennas were placed 0.5 m from the wall. As shown in Figure 3, the TX and RX are denoted by the hollow star and hollow circles, respectively.  
Figure 3: Floor plan.  
Figure 2: Measurement environment.  
In order to achieve spatial averaging, five spatially separated positions were measured at each receiver location as suggested in [15]. One received position is at the center of one specific location, surrounded by the remaining four positions as shown in Figure 4 (top view); these positions are separated by about 0.17 m, which is about two wavelengths for 3.6 GHz and eight wavelengths for 14.6 GHz. Assuming that the phase of received signal is uniformly distributed, it is reasonable to average out the small-scale fading effect via these five positions [15] in the Ricean channel.
## 1 Introduction
This paper is organized as follows: after the introduction, in Section 2 the basic structure and operation principle of DLSRG are given, in Section 3 Mathematical modeling is given, in Section 4 the electromagnetic characteristics of DLSRG is analyzed, in Section 5 the power generation simulation experiment along with the simulation experiment results are presented, and finally, in Section 6, the conclusions are drawn.
# A Calibration Method for Time Dimension and Space Dimension of Streak Tube Imaging [PERSON]
This work presents a method to generate a time calibration array and an angle calibration array to separate the offset of a streak into a time dimension and a space dimension. The method aims to improve the range accuracy of the point cloud produced by STIL through the correction of the two calibration arrays.
## 1 Introduction and Background
A detailed review of various existing automatic and semi-automatic coastline extraction methods, along with their main advantages and limitations, is given in Table 1. Such methods have provided new momentum to the coastal mapping efforts, as they minimized required fieldwork activities and allowed semi-automated or automated mapping over large areas [47; 48]. Furthermore, the high temporal resolution of such imagery has simplified the multi-temporal quantification  
Figure 1: Coastal paradox demonstrated by the representation of Iz-Rava island group in [PERSON]’s Map of Northern Dalmatia, Amsterdam, c. 1620 [[PERSON], _Iaden sicum et Aeronua valgo Zara, Sebenico et Nona cum insulis adjacentibus in parte Dalmatie boreali_, Amsterdam, c. 1620 (National and University Library, Zagreb, Map and Atlas Collection, Novak Collection, Call number ZN-Z-XVII-JAN-1620b)] (**A**) and Worldview imagery, 2016 (**B**). of coastal changes [49]. However, the main disadvantage of the most existing methods is noises in spectral data (e.g., shadows or sea reflections) that can cause significant extraction errors [14]. Therefore, the main objectives of this study were to:  
1. develop and test a new method for coastline extraction from VHR models, which would overcome the above-mentioned problems;
2. determine the total number, area, and precise insular coastline length of all islands, islets, and rocks or rocks awash within the Iz-Rava island group using the automatic coastline mapping from WorldView2 (WV2)-derived models;
3. validate the accuracy of coastline extraction from WV-derived imagery and DSM in comparison to field-collected reference data. The Iz-Rava island group was selected because the Croatian coast is very indented, and as such, it serves as a very good study area for the validation of the newly developed coastal extraction tool (CET). According to the up-to-date, most detailed produced coastline mapping [50], Croatia has 1246 islands, islets, and rocks or rocks awash, with a total insular coastline length of 4398 km. However, this coastline mapping was based on the digitalization of coastlines from the official state topographic map created for the whole Republic of Croatia in 1:25,000 scale (TM25) [51]. Therefore, it can be assumed that the actual insular coastline length is significantly longer and that the total number of rocks and rocks awash is probably higher. Furthermore, WV stereo and multispectral imagery has not been, until now, used for automated mapping of coastline in Croatia.
## 2 Materials and Methods
### Data Preprocessing  
Each dataset in the SCA cohort was first segmented into one-minute intervals every hour, three hours prior to the onset of the SCA event (3 h, 2 h, and 1 h), then partitioned into one-minute intervals every 10 min prior to SCA onset (50 min, 40 min, etc.) and from 10 min into one-minute intervals every minute until the onset of SCA (10 min, 9 min, etc.). For the normal cohort, five random one-minute intervals were selected from the fourth hour of recording and isolated (See Figure 2 below).  
Figure 1: Block diagram of the methodology.  
Figure 2: Block diagram of the data segmentation process for the ECG recordings obtained from the SCA cohort.  
Both the normal and SCA cohort datasets were then converted from a double-channel ECG recording to a single-channel recording (see Figure 3). All ECG recordings were then subjected to baseline wander removal; a Butterworth band-pass filter with cutoff frequencies between 0.5 Hz and 45 Hz was applied to remove both baseline drift and high-frequency noise. The filtered ECG was then used in all subsequent steps with a sampling rate of 250 Hz (see Figure 3). All data preprocessing steps were conducted in MATLAB R2016a.
# Durability of Textile Reinforced Concrete: Existing Knowledge and Current Gaps
## 3 Factors Influencing the Durability and Mechanical Performance
### Sizing and Coating  
Sizing is added to the filament/fibre surface during the spinning process to reduce the damage potential during processing and handling and improve the fibres tensile efficiency. After sizing, the filament, bundle and fabric surfaces can be coated [8]. There is a large and growing body of literature investigating the effect of sizing and coating on the mechanical performance and durability of TRCs [48; 49; 50; 51; 52; 53]. The existing literature is still limited but shows sizing and coating could improve the bond between filaments and increase load-bearing performance [5; 38; 39]. The type of coating (e.g., epoxy, micro-sized silica or nano-sized silica) could also significantly influence the bond and mechanical properties of TRCs [53]. The coating can also affect the transport properties of TRC composite, hinder the chemical attack of the fabric in the alkaline environment, maintain the fabric's properties during the service life [5; 38; 39; 54], affect the thermomechanical behaviour of TRCs [13; 32; 37] or reduce the deposition of hydration products between the filaments [10].
# Considering the Optimization Design of Urban Bus Network Scheduling
## 5 Discussion  
This article proposes a synchronous optimization model for the bus network and scheduling, taking into account passengers' choices of different routes during travel. Compared to previous studies, this model further refines passengers' waiting time costs and comprehensively considers the total time cost for passengers. It also statistically analyzes passengers' choices of different routes to better reflect real-world scenarios. Synchronous optimization is more effective than staged optimization, but there are still shortcomings that need further improvement in future research. The improvements in future research can be made in several aspects:  
(1) Consideration of vehicle occupancy rates: The article assumes that passengers can all board the target route's vehicles while waiting and does not impose capacity limits on buses. Subsequent research can introduce restrictions on the capacity of buses, such that when buses reach their full capacity, passengers need to wait for the next bus. This consideration can be incorporated into the optimization process to account for bus occupancy rates.  
(2) Integration between buses and rail transit: The article only focuses on bus route deployment and does not optimize the relationship between bus routes and rail networks. With rapid development in rail transit, it has a significant impact on bus passenger flows, yet the study of the mutual influence between rail transit and buses is limited in this article. Future research can further analyze travel data between buses and rail transit, utilize bus operation data and subway ride data to study passenger flow changes, optimize the connection between buses and rail transit, and make timely adjustments to bus networks to maximize the use of public transit resources.