esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Classification
#### 2.4.4 Accuracy Assessment and Confusion Matrix  
Accuracy assessments were performed using the \"create accuracy assessment points\" and \"compute confusion matrix\" tools in ArcGIS Pro. A sampling strategy of 250 equalized stratified random (ESR) points was used so that each class had either 62 (for four-class burn maps) or 84 (for three-class harvest maps) reference points. The accuracy assessment points tool projected an equal number of points for each class (ESR) onto the classified output, which generated a table with columns for (a) point ID, (b) classified as, and (c) ground truth. Each point was then examined using the georeferenced orthomosaic as a reference [37] to determine its true land cover for which the corresponding class value was entered into the \"ground truth\" column. Once the table was filled with both the \"classified as\" and \"ground truth\" values, the \"compute confusion matrix\" tool was used to generate a sample error matrix table. Then, all the sample error matrix tables were converted into estimated population error matrix tables [77; 78], from which per-class and map-level classification accuracies and efficacies were derived [79] (see Section 3). Image classification efficacies were computed to compare the classification effectiveness between the two different classification schemas. The map-level image classification efficacies (MICE) were used for this comparison.
# Data-Free Area Detection and Evaluation for Marine Satellite Data Products
## 1 Introduction
New methods are provided for researchers to study data-free areas of marine products.
# A Review of Current Trends, Techniques, and Challenges in Large Language Models (LLMs)
## 2 Language Models and Attention Mechanism
### Attention-Based RNN Models  
As stated in [23], using an RNN encoder-decoder architecture for NMT tasks, the fixed-length encoded vector generated by the encoder became a performance bottleneck. The performance of such an RNN-based encoder-decoder model deteriorated rapidly as the length of an input sentence increased. It also assigned more importance to the later tokens in the input sequence than the ones appearing earlier in the sequence. Such an architecture, also called global attention, failed to capture local context and longer dependencies adequately. Additionally, as RNNs are sequential, they prohibited parallelization, resulting in longer training time. To overcome this issue, Ref. [23] proposed _attention mechanism_ in the decoder, which automatically soft-searches relevant context from the input sentence required to predict the target word. The vectors of these context words and the previously  
Figure 2: Multihead attention mechanism.  
generated (target) words are used to predict the current target word. As a result, the attention mechanism helped align and translate the input and output jointly.  
Unlike the traditional encoder-decoder RNN model, the self-attention mechanism does not encode the entire input sequence into a fixed single vector. The input sentence is therefore not squashed into a single fixed-length vector, where the decoder has flexibility to attend to more than one hidden state of the encoder. Additionally, in the attention mechanism, only a subset of encoded vectors of the input sequence are chosen adaptively during the decoding. The attention mechanism gives more weight or attention to the part of the input sequence that is relevant to the target. As a result, it allows capturing dependencies from the information spread throughout the sequence irrespective of the distance between the tokens. Furthermore, as the decoder is empowered with the attention mechanism, the encoder is relieved from the burden of encoding the input into a fixed-size vector. Paper [23] shows how this joint learning of alignment and translation improves performance over the basic encoder-decoder approach, especially over longer sentences.
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
## 1 Introduction
To overcome these problems, a two-stage KF-based tracking loop is proposed. This scheme involves both coarse tracking and fine tracking. The coarse tracking stage refines the acquisition results and reduces the large frequency errors, while the fine tracking stage adopts the KF-based PLL to track. An important part between the coarse tracking and the fine tracking is the judgment of the bit synchronization. If the bit synchronization is not successful, the receiver will not enter the fine tracking stage and coarse tracking will continue. In this way, the data bit signs are known in the fine tracking stage. Hence, the coherent integration time can be increased in the fine tracking stage to track weaker signals. In addition, the proposed method can use the coarse tracking results to initialize the KF parameters. Accordingly, the proposed tracking algorithm not only implements KF-based tracking to improve the tracking sensitivity, but also employs two-stage tracking to converge rapidly. The remainder of this paper is organized as follows. In the Section 2, the GNSS signal model is presented. Then, the Section 3 describes the conventional two-stage tracking method. In the Section 4, the proposed two-stage KF-based tracking architecture is described in detail. In the Section 5, simulations are performed to evaluate the tracking sensitivity, convergence speed, and weak dynamic signal performance. Finally, the conclusions are presented in the Section 6.
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
###### Abstract  
This paper introduces the working principle of the inductive power transfer (IPT) system from the perspective of the electromagnetic field. Using [PERSON]'s equations, the analytical solution for the electromagnetic field, synthesized by the primary and secondary circular coils in an IPT system, is deduced in detail to obtain the electric field in the IPT system, and the derivation process is easy to understand for researchers engaged in IPT. The final solutions are obtained by combining analytical derivation and the numerical integration method to find the induced voltage in the secondary coil. Finally, by comparison, the simulation results from the finite element software are in a good agreement with those from the analytical analysis. Moreover, an IPT system is set up to validate the analytical and simulation results, and the maximal relative error is under 6% in different working conditions, which shows that it is feasible to understand the working principle of IPT systems from the viewpoint of the electromagnetic field.  
Keywords:IPT; time-harmonic electromagnetic field; analytical solution +
Footnote †: journal: Applied Sciences
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Channel Model
#### 4.1.2 Reflected Light  
Since the light can be reflected by walls or other objects and then captured by the receiver, we have the aggregate received power given by  
\[P_{r}=P_{rd}+P_{rnon-d}=G_{d}\cdot P_{t}+\int_{wall}P_{t}\;dG_{ref} \tag{3}\]  
where  
\[G_{ref}=\left\{\begin{array}{cc}\frac{(m+1)A}{2\pi^{2}D_{t}{}^{2}D_{t}{}^{2} }\cdot\rho\cdot A_{wall}\cos^{m}(\phi)T(\theta)\cdot\\ g(\theta)\cos(\theta)\cos(\alpha)\cos(\beta)\\ \text{ if }\left|\frac{\theta}{FOV}\right|\leq 1,\\ 0\\ \text{ if }\left|\frac{\theta}{FOV}\right|>1\end{array}\right. \tag{4}\]  
with the parameters being illustrated in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Explication** \\ \hline \(m\) & Lambertian parameter with \(m=-\log 2/\log\Bigl{(}\cos\phi_{\frac{1}{2}}\Bigr{)}\) \\ \(\phi\) & Angle of irradiance \\ \(\phi_{\frac{1}{2}}\) & Half-power angle of LED \\ \(\theta\) & Angle of incidence \\ FOV & Angle of a field of view at the receiver \\ A & Effective area of the photodiode \\ \(D_{d}\) & Direct distance between transmitter and receiver \\ \(T(\theta)\) & Filter gain \\ \(g(\theta)\) & Concentrator gain \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters for direct light channel model.  
Taking into consideration factors like background light intensity or other light sources, the above expression (3) can be expanded to  
\[P_{r}=P_{rd}+P_{mon\text{-}d}+P_{background} \tag{5}\]  
where \(P_{background}\) is used to denote the additional light intensity. Interested readers are also referred to [49] and the references therein for further details of VLC channel models.
# Towards Efficiently Provisioning 5G Core Network Slice Based on Resource and Topology Attributes
## 4 Heuristic 5G Core Network Slice Provisioning Algorithm Design
### Node Ranking Based on Network Resource Attributes and Topology Attributes
#### 4.1.3 Degree Centrality  
In an undirected graph, the degree centrality of a node indicates the ratio of the number of its adjacent links to the total number of links in the graph, i.e., normalized degree centrality.  
\[DC(v_{i})=\frac{\sum\limits_{v_{j}}a_{ij}}{|V|-1}, \tag{14}\]where \(a_{ij}\) is 1 if node \(v_{i}\) and node \(v_{j}\) are connected by a link; otherwise, it is 0. The degree centrality measures the local topological importance of the node in the network. The greater it is, the more connected the node is and the more likely it is to be selected.
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 3 Cases for Investigation
### Selected Jamming Incidents
#### 3.2.2 Case 2  
The second case is from TA10 on 31 October 2019. In Figure 5, it is seen that, in this incident, all frequencies and constellations are affected. The incident lasts about 50 min, with the first 25 min being the worst. Especially, the approximately 10-min heavy impact period (HIP), marked by the red box in Figure 5, is interesting, as this represents a worst-case scenario, where all signals are heavily degraded. For simplicity, only Galileo E1 and E5a are shown in the figure.  
What is interesting about this case is that most of the jamming incidents registered by the TAPAS network are of exactly this kind, affecting primarily GPS L1 and Galileo E1 [8]. Therefore, analyzing this case is relevant in assessing how vulnerable the current GNSS infrastructure is. It will also show how much is gained by using GLONASS or multifrequency solutions in the case of single-band L1 jamming.  
Figure 4: C/N\({}_{0}\) values for GPS L1 observations during Case 1, an L1 jamming incident registered at TA03. The incident lasts approximately 1 h and 10 min (from 11:18 to 12:28).
## 4 The 1 mm Resolution Clinical PET System
#### Limitations of 1 mm\({}^{3}\) Spatial Resolution Clinical PET Systems  
Achieving 1 mm\({}^{3}\) spatial resolution in clinical PET systems is a major step forward, but some important challenges remain, especially those related to the choice of radiotracer and how it is delivered to the body. One key factor is the positron range--the distance a positron travels in tissue before it interacts with a nearby electron and annihilates. In case of high-energy positron emitters such as \({}^{68}\)Ga or \({}^{124}\)I, this range can exceed 1 mm in soft tissue, causing a mismatch between where the radiotracer molecule is located and where the annihilation event occurs [18; 23; 24; 25; 76; 77]. This effect can lead to image blurring and minimize the benefit of high-resolution hardware.  
Another challenge involves how the radiotracer is administered. Systemic delivery methods, such as intravenous injection, may lead to widespread tracer distribution or non-specific uptake, especially in areas with high blood flow or inflammation. This can make it harder to detect small lesions, even with advanced imaging systems.  
Figure 8: Readout electronics of the 1 mm\({}^{3}\) resolution clinical PET scanner: providing a comprehensive view from front-end to back-end boards, including high-voltage biasing circuitry for APDs [50]. Schematic illustration of the readout electronics (**A**). Assembled and fabricated readout boards (**B**).
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 6 Evaluation
### Performance Evaluation  
To ensure practical application in real-world scenarios and to identify any potential bottlenecks in the system, the performance times during the execution of all evaluation tasks have been measured (see Table 6). To evaluate the performance of our approach, we use the average duration (in seconds) of all observations as the primary performance measure. Mathematically, the performance measure is defined as  
\[\text{Performance Measure}(\bar{x})=\frac{1}{n}\sum_{i=1}^{n}x_{i}\]  
where \(x_{i}\) represents the duration (in seconds) for the \(i\)-th observation, and \(n\) is the total number of observations. This was gathered simultaneously during the previous evaluation techniques in the previous sections, starting with the database selection times for each database, including Movie, CLEVR, Northwind, and None if no applicable database is the correct reference for the model. Then, the chat history evaluation times were measured with increasing complexity from zero-step reasoning to three-step reasoning. Lastly, the times of zero-shot and few-shot were compared, differentiating between query generation time, query execution time, and answer generation time. Indeed, the evaluation provided differentiated insights into the performance of our system. As for the database selection task, the CLEVR dataset revealed slightly higher durations compared to the other datasets, which is consistent with the previous findings of slightly lower accuracy. When examining the reasoning tasks, we observed a trend where increased context led to slightly longer durations.  
However, the model showed stable performance in scenarios with extended context lengths. The three-step reasoning trials showed that the model could maintain efficient processing times despite the added complexity. During zero-shot prompting, query generation was the most time-consuming process, confirming the general assumption, that the initial context setup requires a high amount of resources. In contrast, query execution was performed in near real-time, demonstrating the model's efficiency in translating queries into database actions. Notably, performance time improvements were observed, as expected, in the few-shot query scenarios. The model used previous examples to optimize query generation, resulting in a reduced duration for subsequent tasks.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Metrics**} & \multicolumn{2}{c}{**Selection Option**} \\ \cline{2-3}  & **0-Shot Prompt.** & **Few-Shot Prompt.** \\ \hline Sel. Accuracy (\%) & 61.00 & 92.80 \\ \hline Syntax Errors & 48 & 0 \\ \hline Semantic Errors & 20 & 0 \\ \hline \# Questions & 500 & 500 \\ \hline \end{tabular}
\end{table}
Table 5: CQL query generation evaluation result.
# Hand Trajectory Recognition by Radar with a Finite-State Machine and a Bi-LSTM
## 3 Methodology
### Complex Trajectory Gesture Recognition
#### 3.2.1 Data Preprocessing  
The radar IF signal can be divided into fast time dimension, slow time dimension, and receiving channel dimension. The fast time dimension represents a single chirp, with the phase of the chirp containing the target distance information which can be obtained by performing one-dimensional FFT on this dimension. The slow time dimension is the accumulation of multiple chirps, and the phase difference information between different chirps contains the target velocity information which can be obtained by performing one-dimensional FFT on this dimension. Finally, the phase difference between different channels contains the target angle information. Capon processing to the channel dimensions can obtain the azimuth and elevation angle information of the gesture. Therefore, the selected gesture features, distance, azimuth, and elevation information are fused into a three-dimensional feature block as input to the neural network for classification and recognition.  
Since the duration between the start and end positions of the gestures varies, the number of chirps collected for different gestures also differs. To ensure that the size of the data input into the neural network remains consistent, a sliding window is employed to segment the gesture data. Each intercepted sliding window corresponds to a frame of data, with the length of the sliding window corresponding to the number of pulses within a frame. During the experiment, the frame rate of a complete gesture motion was set to 10 frames, each containing 100 chirp pulses. The process of segmentation using sliding windows is illustrated in Figure 14.
## 2 Radio Communication Technologies
### The Main Characteristics of LPWAN Technologies
#### 2.2.1 Device Power Consumption  
To ensure the low power consumption of the SigFox modules, the differential binary phase-shift keying (D-BPSK) modulation with a fixed bandwidth of 100 Hz is used. With this, the data rate is 100 bps (for Europe) or 600 bps (for the U.S.). D-BPSK is also a part of the ultra-narrowband (UNB) modulation group [11]. SigFox technology is battery-powered using small packets with an on-air frame of 26 bytes for 12 bytes of payload. As a result, the protocol is lighter and reduced, which causes less consumption of energy, increasing the network capacity, so that the modules can support data transmission for decades [11; 12].  
Additionally, LoRaWAN technology was designed considering that LoRa end devices are powered with batteries, meaning that the power usage must be as low as possible. These devices may be either fixed or mobile, meaning they do not connect with a specific gateway. In the star LoRaWAN topology, several end devices transmit and broadcast to one or more given gateways. The servers of all gateways are connected in the back-end and automatically decide on which gateway to handle the received packets. The focus is on battery-powered end devices, but the long listening times required for package arrival and the huge amounts of transmissions at the nodes quickly increase the power consumption. For this matter, LoRaWAN defines different classes of energy usage, namely A, B, and C, based on the sensor's power usage constraints:  
1. Devices that have a basic set of mandatory characteristics;
2. Devices that implement scheduled listening windows;
3. Devices used for bidirectional and any-time communication [9].  
In general, the end devices in most of the LPWAN technologies used today, such as Sigfox, LoRaWAN, and NB-IoT, are mostly in sleep mode as long as the application needs a considerable reduction of the amount of consumed energy, which increases the end device lifetime. NB-IoT communication is synchronous while handling quality of service tasks, which increases the energy consumption and reduces the end device lifetime. However, this additional usage of energy helps NB-IoT to provide low latency during IoT connectivity [10].
# Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners
## 2 Dose Calculation Algorithm for Photon Beams
### Dose Spread Kernel  
Monte Carlo (MC) simulation is the most practical technique for the calculation of energy deposition kernels. Kernels for monoenergetic photons in water have been calculated by [PERSON] at al. [9], [PERSON] at al. [8] and [PERSON] at al. [10,11]. Different types of kernels, depending on the irradiation geometry [12,13] and technique [14, 15, 16], have been defined.  
The approach used for the computation of the previously introduced DSK considers an incoming monoenergetic, infinitely narrow photon beam that is perpendicularly incident on a homogeneous medium (e.g., water). The beam is forced to interact in a fixed point of the target volume. All subsequent interaction events are simulated via the MC method [11], including secondary particles paths, depicting a detailed energy deposition pattern. The DSK is nothing more than a pre-calculated function providing the average energy fraction (carried by a single incoming photon) absorbed in each small volume element around the primary interaction site. Each dose deposition value is calculated as the arithmetic average of many simulation events of the order of millions.  
The DSK calculated in water can be rescaled to approximate the DSK in another homogeneous medium with an identical atomic number but different density [8]:  
\[k_{m}(\mathbf{r})=\left(\frac{\rho_{m}}{\rho_{w}}\right)^{3}k_{w}\left(\frac{\rho _{m}}{\rho_{w}}\mathbf{r}\right) \tag{6}\]  
where \(\rho_{w}\) and \(\rho_{m}\) are water and medium density, respectively. [PERSON] et al. [9] introduced a density scaling method to be directly applied to the DSK. It assumed that energy transport through the kernel from the interaction point \(\mathbf{s}\) to the interest point P at \(\mathbf{r}\) occurs along a straight line: introducing ray tracing into the kernel. Along each ray, the contribution of the kernel is scaled with the average electron density encountered along the line connecting \(\mathbf{s}\) and \(\mathbf{r}\). This leads to a distortion of the kernel shape. The size of the scaled kernel appears to be shrunk in the direction of high-density local inhomogeneity or stretched in the direction of low-density local inhomogeneity.  
An efficient way to deal with the DSK approach is provided by the collapsed cone convolution (CCC) algorithm, which was introduced in 1989 by [PERSON] [17] and is one of the most widespread algorithms in treatment planning systems (TPS). In this approach, the convolution is performed by assuming that all energy is released into coaxial cones of equal solid angle and, from volume elements on the cone axis, is rectilinearly transported, attenuated and deposited in elements on the axis. Density scaling of the kernels is performed during the convolution procedure to account for the inhomogeneity in the irradiated volume. The CCC approach offers good agreement with performed measures, except when a lateral charged particle equilibrium is missing in low-density media.