esa-sceva/satcom-chunk-collection huggingface dataset:
# A Combined Elevation Angle and C/N0 Weighting Method for GNSS PPP on Xiaomi MI8 Smartphones
## 4 Experimental Results and Discussion
### Quad-Constellation PPP Processing Strategy  
An undifferenced and uncombined observation model is adopted for the smartphone-based quad-constellation PPP [25; 26] to validate the proposed weighting method. The 2-Day predicted Global Ionospheric Map (GIM) products from the Chinese Academy of Sciences (CAS) are used as pseudo-observables to reduce the effect of ionospheric delay errors on the single-frequency observations [27; 28]. The satellite PCO (Phase Center Offset) and PCV (Phase Center Variation) are corrected using the International GNSS Service (IGS) products, whereas the smartphone PCO is corrected using a recommended value from the reference [29]. The detailed PPP processing strategy is shown in Table 1.  
The carrier phase observation precision is set to 0.01 m, whereas the pseudorange observation precision is set to 3 m [23]. In the quad-constellation PPP, the observation precision for different constellations and frequencies is different. We employ a four-order differential method [8; 32] to analyze the observation precision for different constellations and frequencies. On the basis of a comparative analysis, the initial pseudorange observation weight ratio is set to 5:1:7:7 for GPS/GLONASS/BDS/Galileo at the L1/G1/B1/E1 frequencies and 1:1 for GPS/Galileo at the L5/E5a frequencies. Since the carrier phase observation precision is similar, an identical weight strategy is used for the quad-constellation carrier phase observations at both frequencies. For observations at the L1/E1 and L5/E5a frequencies, their weight ratio is set to 1:2 for pseudorange and 2:1 for carrier phase.
## 3 Simulation Results  
### Frequency Response and Crosstalk  
Figures 9 and 10 show the transfer function magnitude between the \(n^{\text{th}}\) (with \(n=1\) to 7) hauled stock and the first one (where the transmitter modem is supposed to be placed) in the frequency range 2-30 MHz. In particular, the transmitter modem impedance is (as before) considered as a 50-\(\Omega\) resistance, while the termination resistances of the conductors (the ones defined in Equation (5)) are set either equal to the characteristic impedance of the line (around 86 \(\Omega\)) in Figure 9, or to an open circuit in Figure 10.  
It can be observed that, as it is easy to predict, attenuation increases with distance from the modem and with frequency. Additional information that can be drawn from Figures 9 and 10 is the following: Considering a transmission power of \(-\)55 dbm/Hz over the entire frequency band (a common value for PLC devices) and a background white Gaussian noise of \(-\)80 dbm/Hz (which represents a highly noisy channel), this leads to a signal-to-noise power ratio at the transmitter of +25 db. PLC systems are designed to work even in noisy conditions near to 0 db SNR at the receiver, so we obtain a maximum practicable attenuation of about \(-\)30 db; for this reason, as can be seen from Figures 9 and 10, a repeater is needed every three hauled stocks if the whole frequency band is needed. In addition, the different terminations of the line only affect the channel response for distances longer than 100 m, which are nearest to the termination, while the effect of the termination is negligible on the first three channels, corresponding to distances of 25 m, 50 m and 75 m. Therefore, considering that a repeater needs to be used every 75 m, the termination of the cable can be completely ignored for the communication purpose.  
Figures 11 and 12 show the crosstalk relating to conductor 7 (with 6 as common ground) when conductor 5 is excited, with the terminations of conductor 7 being set as the characteristic impedance  
Figure 10: Transfer function magnitudes between different hauled stocks, open circuit termination.  
Figure 9: Transfer function magnitudes between different hauled stocks, characteristic impedance termination.  
in Figure 11, and open circuit in Figure 12. Considering the magnitudes shown in the above figures, it is evident that for distances longer than 50 m the presence of the crosstalk signal is highly attenuated (more than 40 db in most frequency bands), whereas for the shortest distance of 25 m, the crosstalk attenuation less than 15 db, which is still acceptable taking into account the magnitudes of the signals described previously. As for the frequency responses, the effect of the termination on the crosstalk attenuation is significant only for distances longer than 100 m.
## 2 Materials and Methods
### Data Preparation  
#### 2.2.1 Field Observation Data  
A mechanistic model of CERES-Wheat to simulate crop growth was chose in this study. To calibrate and validate the model for use in our study region, field experiments were performed at the dryland farming and water saving agricultural experimental station, Hebei Academy of Agriculture and Forestry Science, which is located north of Hujiachi Town, in Shenzhou City (Figure 1). Seven field plots were used to derive the crop growth model. The field observations contained the important crop management information (i.e., the dates of planting and harvest, the planting depth and spacing, the planting density, irrigation dates and volumes, fertilization dates and volumes, phenological calendar) and observational yields.  
To acquire the growth conditions and validate the inverted LAI for winter wheat at a regional scale during different growth stages, 55 sample plots of the homogenous wheat areas (Figure 1) and seven key phenological stages were selected from March-May 2014: green-up stage (early March), jointing stage (late March), elongation stage (early April), booting stage (late April), heading stage (early May), anthesis stage (mid-May) and maturity (early June). The selected sample plots were located at least 100 m away from roads, residential areas or trees to avoid their influence on the measurements. The area of each sample plot was more than 90 m \(\times\) 90 m, with three 5 m \(\times\) 5 m areas measured in each plot, and a Trimble GeoXT3000 GPS from Trimble Navigation, Ltd., Sunnyvale, California, United States of  
Figure 1: Location of the study area, experimental and meteorological stations, sample plots and the spatial distribution of winter wheat in 2014.  
America (USA), was used to locate their position in the field. The wheat LAI were non-destructively measured using an LAI-2000 plant canopy analyzer from LI-COR Inc. (Lincoln, NE, USA) [59]. To prevent direct sunlight on the sensor and minimize the illumination conditions and boundary effects, all measurements were made with the Sun behind the operator, and the operator used a view restrictor of 45\({}^{\circ}\). Finally, LAI values from the three areas were averaged to represent the unique LAI value in each sample plot.  
The winter wheat yields in the 55 sample plots were manually measured after harvesting in early June of 2014.
## 3 Research Methodology
### Emission Scope
#### 3.1.3 System Boundary  
Since the study objective was to compare embodied carbon emissions from the concrete mix and the case studies, two system boundaries were considered. The first system boundary included emission processes involving the manufacture of traditional OPC concrete and sustainable Tex-crete concrete mixes. This system boundary includes energy consumption activities during material extraction and energy consumption during the conversion of waste materials to valuable raw material. The second system boundary included a cradle-to-gate boundary including carbon emissions from materials, equipment usage, and material transportation. Material transportation corresponds to the energy consumption during transportation of processed materials to the construction site, i.e., concrete and reinforcement.  
Figure 2 illustrates the two system boundaries considered for the current study.  
Total GHG emissions (kgCO\({}_{2}\)-eq) can be estimated from the following equation.  
\[E_{t}=\sum_{x=1}^{n}E_{x,GHG} \tag{1}\]  
\(E_{t}\) is the total GHG emissions and \(E_{x,GHG}\) is the GHG emissions from the \(x\)th emission source; m represents either materials, equipment usage, or transportation in the current study. Emissions from materials (E\({}_{\text{m}}\)) can be determined from Equation (2). \(Q_{i}\) is the  
Figure 1: Research methodology.  
amount of ith raw material used in the concrete mix design in kg and \(e_{im}\) is the carbon emission factor for ith raw material in kgCO\({}_{2}\)-eq/kg.  
\[E_{m}=\sum Q_{i}*e_{im} \tag{2}\]  
Equation (3) is used to determine GHG emissions from transportation. \(E_{tr}\) is the total carbon emissions from transportation, \(Ef_{j}\) is the GHG emission factor for fuel type (j) in kgCO\({}_{2}\)-eq/Mega Joules (MJ), and \(f_{\mathrm{T}}\) is the energy content factor for the transport mode T in MJ/tonne-km. \(d_{m}\) is the distance travelled by the vehicle in km, \(w_{i}\) is the weight of the ith material in kg.  
\[E_{tr}=\sum\frac{EF_{j}*f_{\mathrm{T}}*d_{m}*w_{i}}{1000} \tag{3}\]  
Previous studies have emphasized that the carbon emissions from construction equipment depends mainly on the fuel consumption [51]. Therefore, carbon emissions from construction (\(E_{EQ}\)) equipment can be estimated from the following equation.  
\[E_{EQ}=\frac{EF_{eq}*f_{eq}*h_{eq}}{1000} \tag{4}\]  
where \(EF_{eq}\) is the GHG emission factor for equipment \"\(eq\)\" in kgCO\({}_{2}\)-eq/kL\({}_{s}\)\(f_{eq}\) is the fuel consumption in litres/hour, and \(h_{eq}\) is the usage in hours.
###### Abstract  
In this work, we aim to characterize the effective scale height at the ionosphere F2-layer peak (\(H_{0}\)) by using in situ electron density (\(N_{\mathrm{e}}\)) observations by Langmuir Probes (LPs) onboard the China Seismo-Electromagnetic Satellite (CSES--01). CSES--01 is a sun-synchronous satellite orbiting at an altitude of \(\sim\)500 km, with descending and ascending nodes at \(\sim\)14:00 local time (LT) and \(\sim\)02:00 LT, respectively. Calibrated CSES--01 LPs \(N_{\mathrm{e}}\) observations for the years 2019-2021 provide information in the topside ionosphere, whereas the International Reference Ionosphere model (IRI) provides \(N_{\mathrm{e}}\) values at the F2-layer peak altitude for the same time and geographical coordinates as CSES--01. CSES--01 and IRI \(N_{\mathrm{e}}\) datasets are used as anchor points to infer \(H_{0}\) by assuming a linear scale height the topside representation given by the NeQuick model. COSMIC/FORMOSAT--3 (COSMIC--1) radio occultation (RO) data are used to constrain the vertical gradient of the effective scale height in the topside ionosphere in the linear approximation. With the CSES--01 dataset, we studied the global behavior of \(H_{0}\) for daytime (\(\sim\)14:00 LT) and nighttime (\(\sim\)02:00 LT) conditions, different seasons, and low solar activity. Results from CSES--01 observations are compared with those obtained through Swarm B satellite \(N_{\mathrm{e}}\)-calibrated measurements and validated against those from COSMIC--1 RO for similar diurnal, seasonal, and solar activity conditions. \(H_{0}\) values modeled by using CSES--01 and Swarm B-calibrated observations during daytime both agree with corresponding values obtained directly from COSMIC--1 RO profiles. Differently, \(H_{0}\) modeling for nighttime conditions deserves further investigation because values obtained from both CSES--01 and Swarm B-calibrated observations show remarkable and spatially localized differences compared to those obtained through COSMIC--1. Most of the \(H_{0}\) mismodeling for nighttime conditions can probably to be attributed to a sub-optimal spatial representation of the F2-layer peak density made by the underlying IRI model. For comparison, \(H_{0}\) values obtained with non-calibrated CSES--01 and Swarm B \(N_{\mathrm{e}}\) observations are also calculated and discussed. The methodology developed in this study for the topside effective scale height modeling turns out to be applicable not only to CSES--01 satellite data but to any in situ \(N_{\mathrm{e}}\) observation by low-Earth-orbit satellites orbiting in the topside ionosphere.  
topside ionosphere modeling; effective scale height; China Seismo-Electromagnetic Satellite (CSES--01); Swarm B satellite; Langmuir probes data; International Reference Ionosphere (IRI) model; NeQuick model +
Footnote †: journal: _Journal of the Optical Society of America_
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 4 Implementation and Experimental Results
### Prototype Implementation  
The _FW_ and the _FSL_ were implemented using the Go programming language (Golang) ([[https://golang.org/](https://golang.org/)]([https://golang.org/](https://golang.org/)) accessed on 15 November 2021). Golang is a fast-compiling language widely used by the industry. Golang provides concurrency by their built-in libraries. The _FW_ has been developed as a command-line tool with three commands that execute the four main functions of the _FW_. A summary of each command and its corresponding responsibilities is shown Table 1. Implementing the _FW_ as a command-line tool enables straightforward integrations with different fog node platforms and provides  
Figure 7: The _FSL_ processes flowchart.  
simple installation and update steps. In our prototype, we used the Kong Gateway ([[https://docs.konghq.com/gateway-oss/](https://docs.konghq.com/gateway-oss/)]([https://docs.konghq.com/gateway-oss/](https://docs.konghq.com/gateway-oss/)) accessed on 15 November 2021), a lightweight API gateway built on Nginx web server, as our main web server. The _FSL_ was developed as a Kong plugin with the Go Plugin Development Kit (PDK).  
The _FR_ was developed using the Django framework with Python 3.9. It was architected as a RESTful API that enables CRUD operations for fog nodes and fog services. An API Key authentication scheme was used for its simplicity. NATS was used as an event channel to publish the newly added or updated resources and for the _FW_ to subscribe to these updates. NATS was used in the industry for distributed systems and microservices to communicate together in an event/data streaming manner. The Fog Browser was developed using Python 3.9. The Fog Browser prototype was implemented as a simple web scraping script that fetched the static resources from nearby fog nodes if advised. All the components were packaged in containers using Docker images and automated using Docker Compose tools.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 4 Development of _Smart Metering_ Worldwide
### Asia-Pacific
#### 4.3.2 Japan  
Japan is betting on _Smart Metering_ solutions, especially after the Fukushima earthquake disaster, and they expect to deploy a total of 80 million \(SM\)s [46]. The Japanese government has set a target for about 80% of the nationwide electricity consumption to be monitored using \(SM\)s, phased from 2015 to 2020. By 2024, virtually all of Japan's roughly 80 million residential customers are expected to have an \(SM\) installed in their homes. The _Smart Metering_ plan deployment has been set with the aim of letting utilities see the demand in real time and adjust pricing accordingly, all without dispatching meter readers. The devices are also expected to encourage customers to save more energy [78]. One of the two main power utilities of Japan, TEPCO, announced that it was expanding its rollout program from 7 million to 27 million \(SM\)s--essentially, all the meters of their household customers. Installations began in the first half of 2014 and are to be completed by March 2021 [79]. On the other hand, KEPCO utility has already deployed about 2 million \(SM\)s. _Smart Metering_ deployments in Japan employ wireless technologies.  
Unlike most other nations, reliability is not considered to be an issue in Japan. The country has already undertaken significant generation and transmission infrastructure improvements as a result of investments beginning in the 1990s. A key focus area for Japan is the introduction of advanced integrated controls for demand side management and connectivity to the end-user [80].
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 3 System Performance Simulation
### System Performance in the Gaussian Channel
#### 3.1.2 System Performance with Carrier Frequency Offset
Finally, the comparison of the success rates of watermark bits and watermark authentication is shown in Figure 16. The transmission and authentication success rates of watermark bits in Scheme III under ideal carrier synchronization are also shown for comparison. In Figure 16, the success rates of watermark-bit transmission in Scheme II and Scheme III are higher than that in Scheme I and reach the same level as the ideal situation, which is due to the watermark coding and the symmetry of the watermark. When there is a frequency offset, the success rate of watermark authentication Scheme III can reach the same level as the ideal synchronization. However, in Scheme II, because the success rate of user-data transmission drops significantly, even if the success rate of watermark transmission can reach the level of ideal synchronization, the authentication success rate is lower than that under ideal synchronization. According to the above simulation results, in Scheme III, by coding the watermark bits, the bit error rate of watermark bits can be significantly reduced. By satisfying the symmetry of the watermark, the interference with user data can be reduced and the symbol error rate of user data can be reduced. By using pseudo-user-bits instead of user bits as the input of the hash operation, the success rate of watermark authentication can be guaranteed to reach the same level as the ideal synchronization when the received signal has no frequency offset. Therefore, Watermarking Scheme III has good anti-noise and anti-frequency-offset performance, and suppresses interference with the user data. Figure 15: Success rates of user-data transmission and watermark authentication with a frequency offset in the Gaussian channel.
# A Vision-Based Method for Simultaneous Instance Segmentation and Localization of Indoor Objects
## 3 System Overview and Method
### Architecture of Multitasking Network  
This paper proposes a multitasking network architecture that incorporates an attention mechanism by sequentially connecting the channel and spatial attention modules to extract shallow global features and focus on relevant regions during object detection, recognition, and semantic segmentation tasks. In comparison with conventional network models, this approach enhances feature utilization, optimizes computational resources, and mitigates noise impact.  
The multitasking network architecture can be divided into two stages: in the first stage, the image is scanned using a sliding window to generate a feature map and region proposal boxes; whereas in the second stage, these proposals are classified based on their features, bounding boxes are regressed, and masks are generated.  
Firstly, a deep convolutional neural network is utilized to extract low-level features and generate feature maps. As the depth of the network increases, more intricate image features are obtained. The regional proposal extraction network calculates ROIs that are subsequently mapped onto the feature map. To address misalignment issues, an ROI Align operation is incorporated. The bilinear interpolation method is employed to obtain the image value of the corresponding pixel position, thereby reconstructing spatial information and generating a fixed-size feature map. One branch connects to a fully connected network layer for classification and regression detection, while another branch performs semantic segmentation at the pixel level through FCN. Ultimately, multiple tasks are accomplished in this multi-tasking-CNN model as illustrated in Figure 2:  
The attention learning module consists of the channel attention module and the spatial attention module, which are concatenated to the network model to achieve discrimination in focus areas. The former includes global pooling, average pooling, a fully connected layer, and a Sigmoid activation function, while the latter comprises global pooling, average pooling, a convolutional layer, and a Sigmoid activation function. The feature extraction network employs ResNet50 as the feature extractor, which incorporates a residual module based on the VGG network architecture [26]. The lower layers detect low-level features such as edges and corners, while the higher layers capture more abstract semantic information. Once an image is processed by the backbone network, it generates a feature map that serves as input for subsequent stages. The loss function comprises classification error, detection error, and segmentation error:  
\[L=L_{cls}+L_{box}+L_{s}\]  
Figure 2: Composition structure of the multi-task network.  
where \(L_{cls}\) represents classification loss, \(L_{box}\) denotes regression loss, and \(L_{s}\) signifies segmentation error.
# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 2 Foundational Concepts
### Quantum Computing
Current superconducting and trapped-ion qubit systems exhibit coherence times ranging from microseconds to milliseconds, which severely limit the depth and complexity of quantum circuits that can be executed reliably. Quantum error correction (QEC) aims to mitigate these errors by encoding logical qubits across multiple physical qubits; however, implementing fault-tolerant QEC requires a massive overhead--potentially thousands of physical qubits per logical qubit--far beyond the capabilities of existing hardware. Scalability remains another key challenge, as present-day quantum processors, such as those developed by IBM, Google, and Rigetti, can only manage tens to a few hundred noisy qubits, whereas practical quantum advantage for machine learning applications likely demands thousands or even millions of high-fidelity qubits. Moreover, many quantum algorithms have yet to demonstrate a significant advantage over classical ML approaches, which have already solved numerous real-world problems with well-established frameworks. Individual quantum computations are often slower than their classical counterparts due to hardware limitations, and the high cost, fragility, and complexity of quantum hardware further complicate its widespread adoption. These challenges highlight the need for a balanced and realistic comparison between QC and classical ML, recognizing both the current limitations and the future potential of quantum-enhanced computation [22].