esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fabrication, Measurement, and Results
### Measurement and Results  
Input impedance of all operating modes at the desired frequency of 35 GHz is essential. The return loss of the proposed antenna was measured with the network analyzer Agilent PAN N5442A. The antenna was fed with a 50 \(\Omega\) microstrip line, and the input impedance of the antenna was transformed by a transformer. The measured return losses of all modes were approximately 10 dB at the desired frequency of 35 GHz, as shown in Figure 7. Thus, the manufactured antenna has acceptable return loss performance.  
The radiating patterns of the proposed antenna are measured in the microwave chamber. As shown in Figure 8, the proposed pattern reconfigurable antenna can switch the radiating pattern among four operating modes by changing two RF MEMS switches states, and it can obtain three reconfigurable radiating patterns, i.e., left (approximately \(-20^{\circ}\)), middle (approximately \(0^{\circ}\)), and right (approximately \(20^{\circ}\)), respectively.  
Figure 8: _Cont._  
Figure 7: The measured return loss of the proposed pattern reconfigurable antenna in all states of the RF MEMS switches.  
Comparing the measured results, the calculated results, and the simulated results shown in Figures 4 and 5, good agreement is achieved. The measured results in Figure 8 have a back lobe because of the coupling between the antenna patches and the testing holder. The error in fabrication, such as the resolution of lithography, the residual polyimide, the thickness inhomogeneity, the asymmetry between the left RF MEMS switch and the right RF MEMS switch, and the conductor loss are the other primary causes for the discrepancies observed in the measurements. If the manufacturing process is high quality, these discrepancies will be alleviated.  
A comparison between the proposed pattern reconfigurable antenna and the available literatures was made, and the comparison results are shown in Table 5. The proposed antenna possesses a compact architecture structure and an acceptable reconfigurable angle range. It can be applied to a 5 th-generation (5G) mobile communication and satellite communication system.
# Ontology-Based Spatial Data Quality Assessment Framework
## 2 Materials and Methods
### Data Quality Elements  
ISO 19157:2023, formerly ISO 19157:2013, is a spatial data quality standard that defines the principles [27; 28]. It defines \"data quality elements\" by six components. \"Logical Consistency\", one of the six, is the focus of this study. Domain consistency (attribute conformance) and topological consistency are two sub-elements of logical consistency [28]. Some studies also define data quality elements in the literature. Topo-semantic consistency is a component within the broader concept of topological consistency [29]. For two objects, based on their semantics, it pertains to the accuracy of their topological relationships. In the study, the concept of topo-semantic consistency is preferred over the topological consistency mentioned in the standards, since it is conceptually more aligned with the study objective. Tables 1 and 2 list the quality elements that the study focused on. They are defined according to the most widely adopted rules in the specifications. The topo-semantic consistency element has several types that can be evaluated with the proposed framework. While \"must not overlap\" is used to determine whether the features of the same class spatially overlap or not, \"must be within\", \"must not overlap with\", and \"must not cross\" are used for topo-semantic validations between features of two or more classes.  
Domain consistency includes quality element types for finding the attributes of characteristics that have no value, a value that differs from specified fixed value, or a value outside a defined range of values.
# Hybrid Self-Protected Fiber-FSO WDM-PON System with Fiber Breakage Prevention
## 5 Conclusions  
We demonstrated a symmetric 24.3 Gbit/s OOK fiber-FSO WDM-PON system to deliver the baseband and FSO signal access. The proposed WDM-PON network also provided a self-restored mechanism to prevent fiber fault occurrence in the feeder and distribution fibers. To avoid WDM signal interference due to RB beat noise, we could separate the available wavelength bandwidth into A and B bands for fiber and FSO signal connections, respectively. Here, the CP and WF were used in the \(\mathrm{OLT_{A(B)}}\) and the WF and \(\mathrm{SW_{A(B)}}\) were applied in the \(\mathrm{ONU_{A}}\) and \(\mathrm{OWU_{B}}\) to produce the self-protected operation against the fiber breakpoint. Only an active \(\mathrm{SW_{A(B)}}\) device was employed in the \(\mathrm{ONU_{A}}\) and \(\mathrm{OWU_{B}}\) for fault protection. The corresponding \(\mathrm{ONU_{AN}}\) and \(\mathrm{OWU_{BN}}\) (\(N\) = 1, 2, 3,... ) were applied to serve as a group and connected to each other with protection fiber to route the fiber and FSO signal transmission, when the fiber breakage was occurred. In the experiment, 24 km fiber transmission, 1 km protection fiber link and 2 m free space link length were utilized in the proposed PON system to measure BER performances. The fiber and FSO signals of \(\mathrm{\lambda_{A1}}\) to \(\mathrm{\lambda_{A4}}\) and \(\mathrm{\lambda_{B1}}\) to \(\mathrm{\lambda_{B4}}\) were applied in the experiment. Here, the power sensitivities of \(\mathrm{\lambda_{A1}}\) to \(\mathrm{\lambda_{A4}}\) and \(\mathrm{\lambda_{B1}}\) to \(\mathrm{\lambda_{B4}}\) were obtained between \(-\)27.5 and \(-\)27.0 dBm and \(-\)27.0 and \(-\)26.5 dBm in normal states, respectively. When the fault occurred on the baseband and FSO link path, respectively, the obtained power sensitivities of \(\mathrm{\lambda_{A1}}\) to \(\mathrm{\lambda_{A4}}\) and \(\mathrm{\lambda_{B1}}\) to \(\mathrm{\lambda_{B4}}\) were from \(-\)27.0 to \(-\)29.0 dBm and \(-\)27.5 to \(-\)26.0 dBm, respectively. According to the presented self-restored fiber-FSO WDM-PON architecture, the achieved redundant powers of the eight selected wavelengths could reach the free space transmission length of 860 to 1000 m in normal condition or breakage of fiber. As a result, the proposed fiber-FSO WDM-PON not only provided both baseband and FSO signals, but also supported the self-protected mechanism against fiber breakage.  
**Author Contributions:** Methodology, C.-H.H.; validation, S.-Y.J. and S.-E.H.; experiment, C.-H.H., Y.-T.L. and L.-Y.C.; data curation, C.-H.H.; preparation, S.-Y.J.; writing--original draft, C.-H.Y.; review and editing, C.-H.Y. and C.-W.C.; supervision, S.-K.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministry of Science and Technology, Taiwan, grant number MOST-110-2221-E-035-058-MY2.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Object Detection-Based One-Shot Imitation Learning with an RGB-D Camera
## 3 Methodology
### Object Detection Network  
The output of object detection network is a probability map of the task-related semantic keypoint that is the center of the container in this case. The pixel with maximum value in this map is chosen as the semantic point and its pixel coordinates are inputted into the motion policy network. For one-shot learning framework, a demonstration trajectory is given. The first frame and last frame of the example trajectory are used to detection the task-related object, which is similar as [5]. Inputs of this object detection network are the RGB image of the whole environment, the first frame and last frame of the demonstration. Detailed structure of this network can be found in Figure 3. Trained encoder network is used to extract features of these images and these convolutional layers are fixed in the training phase. With the first frame and the last frame, the location of the task-related object is detected. Based on this location, two kernels are obtained by cropping the feature blocks of the start image with window  
Figure 2: The detailed structure of autoencoder.  
of size \(3\times 3\) and window of size \(5\times 5\). Two deep-wise cross correlation layers [6] are used for fusing these two kernels and the feature blocks of the environment respectively. After processes of some convolutional layers and deconvolutional layers, a probability map of the center of the container can be obtained. In other words, the target object is obtained firstly in the demonstration images and then exploit the feature of the target image to detection its location in the query image. This network is trained in two stages. The network is firstly trained to find the target object with the first and last frame. The prediction of this probability map is regarded as a classification problem and the cross-entropy loss is chosen as the loss function, which can be formulized as:  
\[\mathcal{L}_{1}=-\mathbf{M_{L}}log(\mathbf{M})-(1-\mathbf{M_{L}})log(1-\mathbf{M}) \tag{3}\]  
where \(\mathbf{M}\) is the predicted map and \(\mathbf{M_{L}}\) is the labeled map. In the labeled mask, the value of the pixels less than three pixels from the center of the container is set to one and the others are all zeros. L2 regularization is used in this training phase and the loss function is changed as:  
\[\mathcal{L}_{dt}=\lambda_{3}\mathcal{L}_{1}+\lambda_{2}\mathcal{L}_{reg} \tag{4}\]  
where the hyper-parameters \(\lambda_{3},\ \lambda_{2}\) are the weights of different terms. For the second training stage, the whole network is trained to predict the center of the container in the query image. Loss function is just same as Equations (3) and (4). After trained, the object detection network can output a probability map of the center of the container in the query image.
## 2 Results and Discussion
### Aroma Compounds of 15 Brands of EVOOs Extracted by SPME and SAFE
#### 2.1.3. Ketones  
In this study, 9 ketones were identified from the 15 brands of EVOOs (Table 1). Among them, 1-octen-3-one and 6-methyl-5-hepten-2-one, both with the mushroom note, were found in all the 15 brands of EVOOs. In the previous studies, 6-methyl-5-hepten-2-one was also reported in the virgin olive oil samples from Iran, Turkey, Spain, Tunisia [2, 11, 12, 15, 16, 20, 23, 27]. While 1-octen-3-one was only detected in 1 kind of Turkish olive oils until now [22], 3-Pentanone and 3,5-ocadien-2-one existed in the 15 brands of EVOOs comprehensively. 3-Pentanone showing ether note was also found in the samples of Spain, Greece, Tunisia [7, 15, 19, 22, 28], whereas 3,5-ocadien-2-one with fruit, fat, and mushroom note only appeared in the 3 kinds of Turkish olive oils in the previous investigations [23]. 1-Penten-3-one, 2-octanone, 2-nonanone, and acetophenone existed in few samples of the 15 brands of EVOOs.
## 2 Materials and Methods
### Adsorption Experiments  
The batch adsorption experiments were carried out using 50 mL of the solution containing a 50 mg L\({}^{-1}\) concentration of the pharmaceutical molecules. The effect of pH was studied in the range from 2 to 10, where HCl and NaOH solution adjusts pH. Afterward, a predetermined amount of the adsorbent (0.010-0.8 g) was mixed into the solution before sonication at ambient temperature for 5-180 min. The supernatant was centrifuged at 2000 rpm for 3 min. Then, the absorbance was measured using a UV-vis spectrophotometer (Shimadzu Uv-1900) at \(\lambda_{\text{max}}\) of each molecule at 224 and 288 nm for AT and PR, respectively. The results are averages of a minimum of 3 experiments.  
The percent removal of contaminant and the adsorption capacity was calculated using Equations (1) and (2).  
\[\text{Contaminant removal }\%=\frac{\text{C}_{0}-\text{C}_{\text{t}}}{\text{C}_{0}} \times 100 \tag{1}\]  
\[\text{The capacity for adsorption}=(\text{C}_{0}-\text{C}_{\text{t}})\times\frac{ \text{V}}{\text{M}} \tag{2}\]  
C\({}_{0}\) mg/L and C\({}_{\text{t}}\) mg/L are the initial concentration and concentration at time \"t\", respectively. \"V\" (mL) is the volume of (PR, AT), and \"m\" (mg) is the mass of the adsorbent.
# Directly Measuring the Pulse Front Distortion of High-Peak-Power Femtosecond Lasers
## 1 Introduction
According to the measured PFD, the effective pulse duration at the far field of this 200 TW laser was simulated to be about 49 fs, rather than the usually regarded 27 fs. As a result, the actually achieved focused intensity of this laser was only 55% of the ideal case (without PFD). It is worth noting that the degradation of focused intensity induced by PFD may be more serious for PW- and 10 PW-class femtosecond lasers with larger beam sizes.
# Efficient LDPC Encoder Design for IoT-Type Devices
## 2 Error Correction for IoT End-Node Devices  
We assume a communication model (Figure 1) in which a number of low-complexity IoT devices communicate with a central node--the gateway. Since we consider the gateway to possess great computational resources, an ECC scheme with a high decoding complexity can be considered for data blocks sent from devices to the gateway (the uplink direction). Therefore, LDPC coding seems to be an attractive option because of its great correction performance, which has the potential to save transmission power. However, in such a model, the LDPC encoder still needs to be implemented in an IoT node. In this article, we present the results of an investigation that aims to answer the question of how to optimize the LDPC encoder's implementation in a typical microcontroller, as well as to find out what the memory requirements and timing parameters of such a solution are.  
As shown in Figure 1, the presented solution is devoted to a microcontroller-based IoT device with any physical layer, wired or wireless, requiring computationally efficient and energy-efficient ECC. Therefore, the details of the protocol are abstracted as a digital modulation and communication channel in the system model considered. The developed coding system is utilized in an industrial implementation with a fiber-optic communication  
Figure 1: General architecture of IoT communication using LDPC codes for the error correction.  
link, with highly constrained optical power. This is an example of practical application of the presented ideas.  
IoT device nodes are typically based on small microcontrollers with limited resources and peripherals, such as a universal asynchronous receiver-transmitter (UART), SPI and I2C serial interfaces, etc. Some of these peripherals are configured to work with very-low-power modes; most of the time, IoT devices are in sleep mode, waiting for a wake-up, and then send a message upon the occurrence of some event. Considering ECC message encoding, the energy efficiency of such a message delivery is dependent on two factors:  
* The encoding time, which is connected with encoding complexity;
* The coding scheme gain, which is the possible reduction in transmission power due to the application of an effective ECC scheme.  
These two factors are likely contradictory because an effective ECC scheme that enables transmission power reduction usually has a high encoding--and especially decoding--complexity. Therefore, we propose the utilization of an LDPC coding scheme in the uplink direction, and we present how an LDPC encoder can be efficiently realized with a microcontroller. As presented in Figure 1, the assumed system model does not fix any specific ECC scheme for the downlink direction (from the gateway to the IoT node).
# Soil Moisture Retrieval by Integrating SAR and Optical Data over Winter Wheat Fields
## 2 Study Area and Data
### Satellite Data and In Situ SM Data  
The Sentinel-1 ascending SAR images were acquired on 16 April 2019, 28 April 2019, and 10 May 2019. The sentinel-1 SAR images are in VV and VH polarizations at an incidence angle that is close to 40\({}^{\circ}\), with the spatial resolution of 10 m. All of the cloud-free Sentinel-2 satellite optical images during 1 April 2019 to 31 May 2019 were collected, and the specific acquisition dates of the images are listed in Table 1. The data preprocessing of SAR images, such as the orbit correction, noise filtering, radiometric calibration, and geocoding, was carried out using the SNAP tool ([[http://step.esa.int/main/toolboxes/snap](http://step.esa.int/main/toolboxes/snap)]([http://step.esa.int/main/toolboxes/snap](http://step.esa.int/main/toolboxes/snap)), accessed on 1 January 2019). The Sentinel-2 images that were corrected for atmospheric effects were utilized to calculate the NDVI. Based on the method of time spline interpolation, the NDVI, on the same acquisition date as the SAR images, was obtained from the NDVI time-series data. Throughout the normal growth period of the plant, NDVI undergoes a smooth change. In addition, the temporal interpolation method used to obtain NDVI on the target date can partially reduce the uncertainty in the NDVI data itself which is caused by differences in atmospheric conditions at various dates. Therefore, the error introduced by the time interpolation method is ignored in this paper. The roads, water bodies, and buildings in the study area were masked. Finally, the Sentinel-1 and Sentinel-2 images were sampled to the resolution of 30 m for reducing the SAR speckle noise.  
Figure 1: Location of the study area and distribution of field observation points.  
The in situ SM measurements were carried out on 28 April 2019. The soil moisture of 68 total fields was measured using calibrated Time Domain Reflectometry probes (TDR) with 5 cm length probes which could easily measure the average value of SM from the depth of 0 cm to 5 cm. The size of each field is in the range of more than 50 m with uniform winter wheat growth. In each field, the SM was measured at 9 different positions with intervals of more than 15 m between two adjacent points, and the SM of one field is the average of the SM values measured at 9 points. To calibrate the measured value of TDR, a defined volume of soil was sampled and weighed at certain points, then, the soil sample was dried using an oven, and the weight was measured to obtain the volumetric moisture content of the sampled fields in cm\({}^{3}\cdot\)cm\({}^{-3}\). The maximum value of measured SM is 0.34 cm\({}^{3}\cdot\)cm\({}^{-3}\), the minimum is 0.07 cm\({}^{3}\cdot\)cm\({}^{-3}\) and the mean is about 0.18 cm\({}^{3}\cdot\)cm\({}^{-3}\).
# Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
## 2 Related Work
The authors state that TigerGraph is a native parallel graph database because its storage supports nodes, edges, and node properties in a way that promotes an engine that parses queries and analysis in massively parallel processing (MPP) and is highly scalable. TigerGraph's query language, GSQL, allows users to analyze graphs in sophisticated ways. The database has iterative algorithms, such as PageRank, loosely coupled components, shortest paths, and recommender systems. GSQL supports MapReduce and its graph is implemented accordingly to support parallel processing. Furthermore, TigerGraph has a free version and provides the necessary documentation for its use. The authors performed an experimental evaluation using a benchmark for MPP graph databases that allows the exploration of the multicore/single machine setting, analyzing data load, query execution time, and resource consumption. The study concludes that TigerGraph can handle a large volume of data with low node loading, query execution time, and reduced resource consumption. However, the study did not compare it with other databases. [PERSON] in Ref. [13] reviews the different graph databases and applications and compares their models based on selected properties. The databases analyzed were Neo4j, DEX, InfiniteGraph, Infogrid, HyperGraphDB, Trinity, and Titan. The study proves that most graph databases have different data structures which allow query APIs and other types of query languages in each graph database. However, there was no experimental evaluation of the databases. [PERSON] et al. in Ref. [14] present a study of existing graph databases that uses a new micro-benchmarking framework to provide insights into the performance of graph databases that goes beyond what macro-benchmarks can provide. The micro-benchmarking framework includes the most comprehensive set of queries and operators yet considered at the time of the paper's publication. The study used a large dataset to evaluate graph databases on synthetic and real data from different domains. The databases evaluated were: ArangoDB, BlazeGraph, Titan, OrientDB, Neo4j, Sparksee, and Sqlg. The study concludes that the use of microbenchmarks offers benefits to developers and researchers and can help them better understand the design, performance, and feature choices of graph databases. This paper differs from those of the other authors presented in this section, in that we used the LDBC SNB benchmark on the latest available versions of the four graph databases with the best score in the DB-Engines 2022 Ranking.
# Multipath Interference Impact Due to Fiber Mode Coupling in C+L+S Multiband Transmission Reach
## 4 Transmission Reach Assessment  
In this section, we compute the transmission reach, considering a C+L+S MB signal impaired by ASE noise, NLI, and MPI, modeled as described in Section 3. In Section 4.1, we present the system parameters, while in Section 4.2, we present and validate our procedure to assess the transmission reach without the MPI impact. Next, in Section 4.3, we assess the impact of the MPI in the transmission reach for two modulation formats, QPSK and 16-QAM, considering a 64 Ghaud symbol rate. Finally, in Section 4.4, we assess the impact of the symbol rate on the transmission reach by considering the 32 Ghaud and the 128 Ghaud scenarios.
## 1 Introduction
Lidar scans can more accurately retrieve vegetation canopy structural parameters [30]. The lidar scanning system scans the target by emitting laser light, and then compares the received signal (target echo) reflected from the target with the transmitted signal. After proper processing, the target's 3D point cloud data can be obtained. Relevant parameters of the target can be extracted based on the point cloud data. Due to the high-density point cloud data obtained by the ground-based lidar scanning system, the canopy structure parameters can be more accurately retrieved, and the horizontal and vertical information of the forest canopy can be detailed [31]. TLS can be used to directly measure tree height, canopy height, above-ground biomass, canopy coverage, forest density, volume, vertical structure, effective leaf area index (LAIe), and gap fraction. The LAIe is an important parameter for quantitatively characterizing the vegetation canopy structure [32; 33; 34; 35; 36]. Therefore, investigating patterns of LUE changes across different canopy structures can provide insights into how differences in forest structure affect LUE [11; 37]. In this study, we investigated the relationship between PRI and LUE, obtained from measurements of an AMSPEC spectrometer and eddy covariance (EC) system in a subtropical mixed forest in China. The objectives of this study are: (1) to use the BRDF model to correct the PRI observed by a multi-angle spectrometer; (2) to study the effects of environmental and climatic factors on the relationship between PRI and LUE, and analyze the changes in the relationship between the PRI and LUE; (3) to use the TLS data to extract the vegetation canopy structure parameters, and to determine the influence of the vegetation canopy structure on LUE.