esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation and Analysis
### Discussion of Electric Field Distribution  
The normalized electric field distribution of the two-dimensional and three-dimensional models of the laser was simulated by the finite element method. The simulation results are shown in Figures 2 and 3. It can be seen from Figure 2a that the field enhancement effect at the air groove between the nanowire and the graphene is remarkable, and the high localization of the energy constraint is achieved. This is because the continuous movement of electrons on the surface of the metal film causes a strong light field to be generated there, and at the air gap between the metal Ag and the nanowire, the semiconductor nanowire waveguide mode is coupled with the surface plasmon mode of the metal interface, resulting in a localized effect of partial energy. Figure 2b is a normalized electric field distribution of the horizontal serif in Figure 2a. It can be seen from Figure 3 that under  
Figure 1: **(a)** The block diagrams of the nanolaser. **(b)** The section plan of the nanolaser.  
the excitation of the pump light, which is based by a titanium sapphire laser with a center wavelength of 405 nm, a repetition rate of 10 kHz, and a pulse width of 100 fs [41], the SP wave generated on the metal surface is coupled into the CdS nanowire through the graphene and the air gap. The CdS nanowire is used as the gain medium and also the FP resonator, so the partial SP waveguide and the high gain dielectric waveguide are coupled and oscillated from both ends of the nanowire after oscillation amplification in the cavity.
# Non-Invasive Glucose Measurement Technique Based on Time-of-Flight
## 2 Operating Principle
This insight aligns with findings by [PERSON] et al., who established a correlation between blood glucose concentration and the tissue's reduced scattering coefficient [10]. Furthermore, studies by [PERSON] et al. demonstrated the capability of optical coherence tomography (OCT--a technique dependent on scattering) to assess glucose-induced changes in tissue scattering properties with a high specificity, suggesting its utility for glucose sensing applications [11]. These observations are further supported by the work of [PERSON] et al., who investigated glucose-induced alterations in light transport through blood, illustrating the significance of scattering dynamics in non-invasive glucose sensors [12]. This body of work collectively underscores the critical role of light scattering in non-invasive glucose sensing technologies. However, the works available up to now were created using high complexity and cost equipment, usually only available in a laboratory setting. By leveraging these optical phenomena, as well as the low cost of the shelf sensing element, the presented sensor represents a promising step toward accurate, non-invasive glucose monitoring in clinical and daily use. Figure 1: Simplified schematic of a heterodyne ToF sensor.
## 3 Methods  
Based on the above research, the implementation process of spacecraft anomaly detection based on a knowledge graph is shown in Figure 3.  
According to the technical route outlined above, the research process in this paper can be roughly divided into three parts.  
Figure 3: Technical route of the spacecraft anomaly detection method based on a knowledge graph.  
The first part involves the construction of a knowledge graph. By integrating materials from various sources, experts design and summarize concepts to create an ontology model for the knowledge graph. Next, knowledge extraction is conducted to initially construct the knowledge graph.  
The second part is focused on extracting spacecraft feature data. This process includes data cleaning, feature selection, and data classification. The resulting feature data are then integrated with the knowledge graph to form a spacecraft anomaly detection knowledge graph that incorporates feature data.  
The third part deals with the anomaly detection of spacecraft telemetry data. First, static attribute data matching is performed based on sensors for spacecraft telemetry data. After generating reference data from the knowledge graph, dynamic attribute data matching is carried out. Anomaly detection is then conducted based on the data-matching results. Upon discovering anomalous telemetry data, the anomaly detection algorithm generates detection results. These results are analyzed to assess the quality of the spacecraft knowledge graph.
## 3 HKE Spectra and Spectral Energy Budgets
### Evolutions of the HKE Spectra
From Figure 6, it is clear that in the troposphere, the HKE is dominated by the RKE over almost all wavelengths. Considering that the TC is characterized by intensive rotation, this is to be expected. Therefore, the tropospheric spectral shape of HKE is determined by the RKE spectrum to a large extent, and the gradual swelling of the HKE spectrum during the intensification of Lekima is induced by the variation of the RKE spectrum. This means that the unique arc-like spectrum for TC in the troposphere stems from the rotational mode of TC motion. When it comes to the lower stratosphere, the RKE spectrum becomes as flat as the DKE spectrum and the HKE tends to be dominated by the divergent mode, probably because that the vortex mode of the TC which is remarkable in the troposphere does not extend upward to higher heights, and the remnant of the TC outflow layer we examine here is strongly divergent. As both the RKE and the DKE have flat spectral shape with a slope of \(-\)5/3, the resultant lower-stratospheric HKE spectrum also inherits this feature.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 5 Protocol and Interfaces
### Orchestration Interfaces
#### 5.1.1 Provision SFC  
The provisioning function interface receives an NSD file that we have formed as a simplified pseudo-YAML file format supporting both the ETSI NFV and the TOSCA [22] standard. Here, the messages are compressed in a simplified proprietary manner to visualize the content of the configuration exchange. Hence, the messages do not match syntactically with the YAML format (Figure 15). The relevant information elements in the messages are:  
* The Virtual Network Function Descriptor (VNFD), which in this prototype describes the instantiated Virtual Machines global identifier (VNF-ID). The VNFD also includes a description of whether the VNF is a normal VNF or an encrypting VNF (EVNF). Additionally, it includes a new Preshared Key variable (Key), which is a field that must be standardized. This is suggested to be standardized in the TOSCA VNF Configurable Property name-space [22].
* The Virtual Network Function Forwarding Graph Descriptor (VNFFGD), which describes the SFC. The format of the VNFGD needs to include both the inner and the outer SFCs. For Proof of concept purposes, we simplify the orchestration message to one new custom file descriptor as pseudo-YAML (Figure 15).
# Topology Control of Low-Connection UAV Laser Network with Virtual Nodes
## 5 Problems and Optimization
### Planarity  
In topology control of ad hoc networks that use radio frequency as a means of communication, it is often required that the generated topology graph is planar. This facilitates the operation of some geometry-based routing algorithms and reduces communication interference between nodes. Although the high directivity of laser communication sig  
Figure 3: Unmatched nodes.  
nificantly reduces communication interference between nodes, the excellent properties of planar graphs still make it a worthwhile goal to pursue.  
In the initial algorithm, the use of random initialization in graph matching algorithms often leads to intersecting pairs of nodes, which is one of the reasons the topology control graph may not be planar. Furthermore, during the mapping of the virtual subgraph back to the original graph, the mapping algorithm does not consider the positional relationships between nodes, causing the mapped edges to intersect. This section will address these issues with optimizations, with the goal of obtaining a superior topological structure.  
Firstly, addressing the issue of nodes crossing due to random initialization in the graph matching algorithm, let us consider the intersecting graph (Figure 4). According to the pigeonhole principle, in a quadrilateral with vertices A, B, C, and D, there must be at least one angle that is not less than 90 degrees, which implies that at least one diagonal is longer than any of the sides of the quadrilateral. If we select the shorter edges as the objects for initialization, we can minimize the occurrence of intersections.  
Secondly, regarding the mapping process, paired nodes can divide the range of virtual nodes into two semicircles. Crossing edges often occur when two nodes are connected to nodes outside their respective semicircles. Since, after topology control, the angle between the edges connected to the virtual nodes in the virtual subgraph is no less than \(\pi/3\)[18], the number of connections within any semicircle, without considering the boundary of the semicircle, is always no more than 3. Therefore, by only considering the virtual nodes within their respective semicircles for pairing, the occurrence of crossing edges can be prevented.
# On Construction of a Campus Outdoor Air and Water Quality Monitoring System Using LoRaWAN
## 4 Experimental Results
### Gateway Receiving Sensitivity  
Because there are numerous models and evaluations of radio signal propagation at the frequencies utilized by LoRa in diverse situations, this testing focuses on the decoding performance of LoRa receivers. As shown in Figures 17 and 18, they sent two different types of packets to the gateway, 1500 and 2000, which were then recorded as Received Signal Strength Indicators (RSSI) of received packets from a LoRa device (logging points). All packets were transmitted with a bandwidth of 125 kHz and a coding rate of 4 out of 5 using this configuration.  
Figure 16: The energy consumption comparison charts with a WiFi module under a 15 m distance.  
Figure 15: The energy consumption comparison charts with the LoRa module under a 1.7 km distance.  
For minimizing the distance covered before reaching low RSSIs, the device's broadcast power was set to the minimum (2 dBm, with a 3-dBi antenna). The distance between the logging stations and gateway when packets started to be dropped was in the order of 1400 m. Figure 19 illustrates the actual results of sensitivity receiving.  
Figure 19: The Actual Results of Sensitivity Receiving.  
Figure 17: Receiver sensitivity data collection in the 915 kHz Channel.
###### Abstract  
Single-phase direct frequency converters are gaining attraction at the research and academic level as they are rapidly getting space over conventional multistage converters. The converters developed with a rectification and inversion process using a DC-link level are examples of multistage converters with some serious concerns such as an increase in the overall weight, losses, and cost. They also suffer from the low-reliability issue due to the issues involved with DC-link capacitors and problems linked with electromagnetic interference (EMI) caused by high-frequency pulse width modulation (PWM) switching. These problems are addressed with line frequency switching cyclo-converters. In these converters, the power quality of the output voltage is improved by governing the amplitude of some selected output pulses or half-cycles. For this purpose, a low-frequency multiple tapping transformer may be used to obtain various voltage levels. However, its use is the main source of increased overall weight, losses, cost, and volume. In transformer eliminated topologies, high-frequency PWM control can be employed to control the magnitude of some selected half cycles of the output voltage. However, this approach may arise some problems related to EMI. In both control techniques, the attention is focused on the power quality of the output voltage only. The concern for the input current is ignored and not yet analyzed. This is one of the critical power quality concerns and requires further investigation. The magnitude control of the output half-cycles causes the variation in the amplitude of some half cycles of the input currents. As a result, all half cycles of the input current become non-symmetric. It generates harmonics that are always of low frequency and cannot be easily filtered out. It results in a high value of the harmonic factor (HF) of the input current. The improvement in the power quality of the output voltages severally degrades the power quality of the input currents. In this research, this problem is investigated with mathematically computed harmonic coefficients with a pulse selective approach. Also, a simple single-phase cycloconverter is introduced to improve the power quality index of the input current. The overall analysis is supported by the results obtained from a Simulink-based environment and a practically constructed prototype.  
multistage converters; frequency converters; electromagnetic interference; cycloconverter; power quality; harmonic factor +
Footnote †: journal: _Sci_
## 3 The Proposed Coverage Flight Path Planning Algorithm
### Sensor Footprints Sweeps Fitting in Free Spaces of the Area of Interest  
UAV carries a specific tool to cover the AOI in coverage missions. The tool can be a visual sensor, transmitter, digital camera, or spray tank depending upon the mission. In this work, we assume that UAV carries a visual sensor to cover the AOI during coverage. The attached tool has different sensing characteristics and varying footprint sizes depending upon the UAV height. If the UAV altitude is low, the footprint size is small and image resolution is high. In contrast, when the UAV altitude is high, the footprint size is bigger, and resolution is low. The sensing parameters are adjusted and usually taken at the start of the mission to achieve the desired objectives. UAV generally flies at constant altitude in coverage missions. However, in some cases, it pays attention to the priority of regions. The important regions are covered with low altitudes and vice versa. The sensor footprint \(F\) is of rectangular shape with fixed length \(F_{l}\) and width \(F_{w}\), respectively. However, the size of the footprint keeps changing with the UAV altitude during the mission. After determining the sensor footprint size on the ground as shown in Figure 5(a), we fit \(N\) sensor footprints' sweeps in such a way that maximal coverage can be achieved with fewer footprints' sweeps.  
Figure 5: Overview of the coverage path obtained through two different sweep directions.  
Figure 6: Overview of the sensor footprint on ground and footprint sweep in the AOI.  
The \(N\) sensor footprints fitted in the AOI can be mathematically expressed as a set as shown in Equation (1):  
\[R=\{F_{1},F_{2},F_{3},\ldots,F_{N}\}. \tag{1}\]  
Each footprint sweep has two parts: one is on the ground (i.e., 2D in rectangular form) and the other is from a certain height (i.e., 3D in a line segment form) similar to the sample shown in Figure 6b. The 3D one passes from the middle of the ground one and it is used as an actual path on which UAV moves during the mission. Considering the footprint as line segment, the start point \(a_{i}\) and endpoint \(b_{i}\) of footprint \(i\) are given as:  
\[F_{i}=(a_{i},b_{i})=((x_{a_{i}},y_{a_{i}},z_{a_{i}}),(x_{b_{i}},y_{b_{i}},z_{b_ {i}})). \tag{2}\]  
After fitting the footprints' sweeps, we find the midpoint of each footprint line segment part utilizing \(a\) and \(b\) values to find the visiting sequence of footprints' sweeps. The midpoint \(m_{i}\) of the \(i\)th footprint \(F_{i}\) can be computed using the \(a_{i}\) and \(b_{i}\) co-ordinates values. A pictorial overview of a footprint in rectangle form and sensor footprint sweep is shown in Figure 6b.
# Development and Validation of Universal 3D Blast Fragmentation Model
## 3 Three-Dimensional Fragmentation Model
### Primary Blocks in the Rock Mass and Model Geometry  
Proper blast fragmentation analysis requires that geological features are accounted for a significant impact on results. The model considers this by introducing a discrete fracture network (DFN) that describes the spatial distribution of preexisting joints in the rock mass that will impact pressure wave propagation in the rock mass. DFN captures orientation, size, and spacing between joints and is a well-known method in rock engineering used to represent the structural properties of the rock mass.  
The model requires that the initial \"solid\" block is defined at the first step, and then, DFN is introduced for analysis. The goal is to obtain as representative as possible a primary block size distribution, which is performed by splitting the initial \"solid\" block by fractures in DFN. Firstly, fractures contained within DFN are sorted in descending order by size. The first fracture in the sequence splits the solid block completely, resulting in two newly formedblocks. The subsequent fracture in sequence splits the block, inside of which its centroid is located. This procedure repeats for all fractures in DFN. There is the second mode of splitting, where fractures are sorted in the same manner, but each fracture splits all the blocks it has intersected with. Which mode should be used is determined by returning to the structural setup of the certain rock mass. The result is a set of primary blocks distributed in space that is further to be fragmented by explosive charges that pass through them.  
As previously said, only blocks intersecting with the borehole and containing an explosive charge within will be fragmented by explosive detonation. Figure 9 illustrates the process of model geometry preparation, where the block to be mined is split by preexisting joints, and a set of primary blocks is defined. In the next step, explosive charges are incorporated into the model. As can be seen, some of the primary blocks do not or only partially contain an explosive charge inside them.  
Each explosive charge is defined by explosive properties (density and detonation velocity) that determine the pressure inside the blast hole. The geometry of the blasting pattern defines the position of each charge. Each charge is labeled by its number in the initiation sequence within the pattern.  
Figure 8: Fragmentation model input diagram.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 4 Discussion
### Generalization of CTAFNet on Small Dataset  
In order to verify the generalization of CTAFNet on small datasets, this section compares CTAFNet with nine methods on the widely used the Salinas dataset, and the results are shown in Table 11. The results demonstrate that the proposed CTAFNet method achieves the best performance, with 94.62% pixel accuracy and 95.70% mIoU, indicating that CTAFNet generalizes well to small datasets. In addition, Deeplab v3+, which performed well on the three large datasets, performed poorly on the Salinas dataset. It may be because the Deeplab v3+ has a large number of parameters, and overfitting occurs when there are fewer training samples.
## 4 Experiments and Analysis
### Experimental Environment  
All computational tasks in this research were executed on a dedicated server utilizing an AMD EPYC 7502 processing unit paired with an NVIDIA GeForce RTX 3090 graphics card (24 GB VRAM), supported by 16 GB RAM to manage computational workloads. The system operated on a Linux-based platform. For accelerated neural network operations, the implementation leveraged the PyTorch 2.2.2 framework integrated with CUDA 12.1 acceleration and Python 3.10 runtime environment.  
The optimization protocol employed the AdamW algorithm initialized with a learning rate of 0.001429, accompanied by momentum stabilization at 0.9 and a weight decay coefficient of 0.0005. Training procedures spanned 300 complete iterations of the dataset with mini-batch processing configured to 16 samples per batch.
## 4 Results and Discussion
### Level 3: Machine Learning  
4.3.1. Time Series Regression  
This subsection aims to answer RQ3.1 (Can fuel consumption and carbon dioxide emission data and other input metrics be utilized to predict outputs in upcoming years in Canada?). To determine which Machine Learning models can be used for predicting fuel consumption and carbon dioxide emission, different experiments were conducted, as presented below.  
Figure 15: Heat map for Chi-square of Independence tests between all features.  
Firstly, all the input features from the dataset are used to calculate their mean values over time, as shown in Figure 16.  
Secondly, using the correlation results from Section 4.2.3, this study builds the following models to predict the fuel consumption (in city, highway, and total) and CO\({}_{2}\) emissions of an average vehicle in Canada in the four upcoming years.  
* Persistence models (using walk-forward validation);
* Autoregression models (using autoregression function by statsmodels);
* Optimized autoregression model (using walk-forward over time steps).  
The prediction results of these models are presented in Figure 17 and Table 11.  
It can be observed from Table 11 that the autoregression model always has the highest RMSE. The optimized autoregression model has lower values, while the persistence model has the lowest values. The persistence model predicts that total fuel consumption and CO\({}_{2}\) emission will increase in the next four years. However, fuel consumption in the city is projected to decline, while the data in highways are expected to grow firmly.  
The rest of the following Machine Learning models have been constructed to answer RQ3.2 (Is it possible to build Machine Learning models that use vehicle specifications data to predict their fuel consumption and carbon dioxide emission?).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Metric** & **Persistence Model** & **Autoregression Model** & **Optimized Autoregression Model** \\ \hline Total Fuel Consumption & 0.002 & 0.026 & 0.026 \\ Fuel Consumption in City & 0.004 & 0.045 & 0.044 \\ Fuel Consumption in Highway & 0.002 & 0.097 & 0.068 \\ CO\({}_{2}\) Emission & 1.287 & 3.412 & 2.178 \\ \hline \end{tabular}
\end{table}
Table 11: Root Means Square Error (RMSE) of different regression models.  
Figure 16: All input metrics over time.