esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 6 Evaluation
### Database Decision Evaluation  
The aim of this evaluation was to find out how accurate the chatbot is at selecting the right database to answer the user's question. For this, 282 questions and correct database selection pairs were created by hand, covering all databases from Table 2. In order to be able to evaluate whether the bot is also able to correctly recognize that it cannot answer a question with the available databases, the test data set also contains questions that are not related to one of the databases. Consequently, the correct choice for these questions was expected to be \"None\". Only the _Json LLM_ was used for the evaluation, as only this part of the chatbot is responsible for the database selection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Details**} & \multicolumn{3}{c}{**Graphs**} \\ \cline{2-4}  & **Movie** & **Northwind** & **CLEVR** \\ \hline \# Nodes & 171 & 1.035 & 287 \\ \hline \# Relationships & 253 & 3.139 & 337 \\ \hline \# Distinct Relation. types & 6 & 4 & 1 \\ \hline \# Distinct Properties & 8 & 47 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Graphs used for evaluation.  
The schema for database selection was structured as follows: \"Given the [Database Descriptions] and [question], determine which database is best suited for the task.\" In the evaluation dataset, the ground truth answers were the names of the respective databases, serving as the benchmark for accuracy in database selection.  
The options available for selection were \"MovieDatabase, Northwind, CLEVR, None\". For each of the databases, a short and concise description of their content was given in the corresponding descriptor. The selection accuracy is defined by the number of correctly selected databases divided by the sum of all questions. As can be seen in Table 3, the chatbot, or rather the _[PERSON]_, is very good at selecting the correct database for the user's question, reaching a overall selection accuracy of 96.45%. The data set for CLEVR alone is a little less accurate in comparison. After closer examination, the reason behind this is that some of the questions in this test data set do not contain any keywords from the CLEVR database description, which prevents the model from finding an assignment to a descriptor. In addition, due to the fact that CLEVR was generated synthetically, none of the station names or subway lines have an equivalent in the real world that the LLM could reference from its internal knowledge. This is different in the movie dataset. Here, the model can conclude from its training data that, for example, the person \"[PERSON]\" is an actor and that a question containing this person can be answered with the help of the movie graph.
# TARCS: A Topology Change Aware-Based Routing Protocol Choosing Scheme of FANETs
## 2 Related Work
### Discussion  
The mobility aware methods noted above can be classified into the following categories:  
* Neighbor node or link mobility awareness [10; 11; 12; 13; 14; 15; 16; 17; 19; 20].
* Neighbor node mobility awareness combined with congestion level perception [11; 12; 15].
* Neighbor node mobility trend prediction [12].  
These mobility aware methods can also be divided into location aided [11; 13; 14; 16; 17] and non-location aided [10; 12; 15; 19; 20]. The former refers to estimating the neighbor node/path mobility by means of information from auxiliary devices, such as global positioning system (GPS) receivers, with the advantage of simple and fast calculation and the disadvantage of introducing GPS errors and new interference. The latter usually measures the position or speed of the neighbor node according to the power or the Doppler shift of the received signal. Although no error or other interference is introduced, the direction and distance of the neighbor node are not accurately determined.  
The processing method after mobility awareness can be summarized into the following:  
* Selecting stable node/link during route discovery [10; 11; 12; 13; 17; 19].
* Excluding or preventing high mobility nodes from participating in route discovery [15; 16].
* Node mobility classification and decision making [14].  
The mobility awareness methods, the processing methods and the proposed TARCS are summarized in Figure 2.  
However, the methods in the above literature are only limited to measuring node mobility or link stability caused by mobility, without involving accurate definition and accurate measurement of topology changes.  
In this paper, the methods of perception and perceived result processing are different. In terms of the sensing method, the above literature uses only a single parameter (velocity or distance) for perception, but TCD is used here to quantify the topological changes between nodes from multiple aspects, including distance, velocity, direction and neighbors' number, and to accurately reflect the topological changes. As to the subsequent processing method, in this paper, after determining the node's mobility mode based on the perceived result, the routing protocol is re-selected according to the current mobility mode characteristics. Essentially this is a routing protocol optimization selection strategy.
## 1 Introduction
RIPS was also used to measure the temperature of mixtures of gases by comparing the time-domain signal shape with the predictions of available collisional models [24]. Rotational wave packets were also measured by detection of the probe beam deflection [25] using high-energy excitation, with amplified sources delivering from several hundred microjoules to several millijoules. All the above-cited works detect the refractive effect of the rotational coherence, even though spectral effects were also measured and modeled by measuring the probe spectrum at selected pump-probe delay times [8, 26, 27]. However, to the best of our knowledge, the time-dependent spectral ISRS signal showing the rotational revivals structure has never been reported before. In this paper, we show that a suitably arranged ISRS setup with spectral detection, equipped with a single-oscillator ultrashort source delivering few nanojoule energy and less than a milliwatt average power, can be used to measure the rotational wave packet revivals in N\({}_{2}\) at different pressures, with a measurement time down to 100 s. From these measurements, we obtain a low-resolution rotational spectrum of the sample, which we use to derive the temperature with a certain accuracy without the need for collisional linewidth models. While these values might still not be suitable for practical use of the technique, they are not limited by intrinsic or fundamental issues, and therefore can be substantially improved by the further tuning of the components of the experimental setup. We believe these findings pave the way for low-cost exploitation of coherent pure-rotational spectroscopy of gases, with important applications ranging from fundamental molecular physics studies to remote, non-intrusive, temperature measurements.
## 2 Materials and Methods
### Drying  
Convection drying (CD) was carried out in a prototype laboratory dryer at the Department of Food Engineering and Production Organization of the Institute of Food Sciences of the Warsaw University of Life Sciences (Figure 1). The leaves (approximately 20 g) were placed on a sieve with a load of 0.7 kg/m\({}^{2}\), and each time, they occupied the same area. The air flow was 0.5 m/s and was parallel to the samples. The ambient air humidity was approximately 55%. The drying was carried out until the water content was below 0.1 kg/kg d.m. The drying was carried out at the following temperatures: 40-50, 60, 70 \({}^{\circ}\)C. The kinetics of the drying process were examined by interval measurement and the recording of the mass of the tested samples in the computer memory. Each drying was performed in 2 repetitions.  
Microwave-convection drying (M-C) was carried out in a prototype laboratory dryer at the Department of Food Engineering and Production Organization, the Institute of Food Sciences, the Warsaw University of Life Sciences (Figure 2). The leaves were dried on a rotating sieve with a load of 0.7 kg/m\({}^{2}\), and the material occupied the same surface each time. The air flow was 0.5 m/s and was perpendicular to the samples; the microwave power (with a typical frequency of 2450 MHz) was 100, 200, and 300 W, respectively; and the temperature of the drying air was at the level of 30 and 40 \({}^{\circ}\)C. The drying was carried out until the water content was below 0.1 kg/kg d.m. The dryer allowed for interval measurement and the recording of the mass of the tested samples in the computer memory. The drying was performed in two repetitions.  
Figure 1: Scheme of a convection dryer: 1—fans; 2—heating element; 3—control section; 4—recording computer; 5—balance; 6—drying chamber.  
Figure 2: Scheme of a microwave–convection dryer: 1. recording computer; 2. control computer; 3. magnetron; 4. air flow control; 5. drying chamber; 6. scale; 7. the rotating mechanism of the drying sieve; 8. perforated sieve; 9. Fan.  
The drying curves were plotted as a function of dimensionless moisture ratio (MR) during drying. The moisture ratio was calculated as follows:  
\[\text{MR}=(\text{u}_{\tau}-\text{u}_{\text{e}})/(\text{u}_{0}-\text{u}_{\text{e}}) \tag{1}\]  
where u\({}_{\text{e}}\) is the equilibrium moisture content (g H\({}_{2}\)O\(\cdot\)g\({}^{-1}\) dm), u\({}_{0}\) is the initial moisture content (g H\({}_{2}\)O\(\cdot\)g\({}^{-1}\) dm), and u\({}_{\tau}\) is the moisture content at each moment of the process (g H\({}_{2}\)O\(\cdot\)g\({}^{-1}\) dm).  
Table Curve 2D v5.01 software was used to compute the effective water diffusion coefficient. The simplified equation of [PERSON]'s second law of diffusion (Table 1) for an infinite plate was used [30].  
\[\text{MR}=(8/\pi^{2})\cdot\exp((-\pi^{2}\text{D}_{\text{eff}}\,\tau)/(4\text{L} ^{2})) \tag{2}\]  
where \(\tau\)--time (s); D\({}_{\text{eff}}\)--effective water diffusion coefficient (m\({}^{2}\cdot\)s\({}^{-1}\)); and L--half of the material thickness (m).
# Comparative Analysis of Fatty Acids Concentration in Liver and Muscle Tissues of Rats and Mice
## 1 Introduction
The analysis demonstrates distinct lipidomic fingerprints associated with various tissues, highlighting the unique metabolic functionalities and cellular structures of each tissue type. The study emphasizes the importance of understanding tissue-specific lipidomics for interpreting metabolic states and disease mechanisms [13]. The findings derived from metabolic investigations frequently entail a comparative analysis between rats and mice, organisms characterized by numerous shared traits, particularly in lipid metabolism. Nevertheless, notable distinctions between them manifest in certain facets. For both rats and mice, the liver is the primary site for de novo lipogenesis (DNL), the process of creating fatty acids from acetyl-CoA, predominantly derived from glucose, but the rate of fatty acid synthesis in the liver can vary between the two species, influenced by factors such as diet and hormonal regulation. Mice generally have a higher rate of de novo lipogenesis in the liver compared to rats [14]. Rodents, including rats and mice, primarily use glucose as the precursor for fatty acid synthesis, unlike other species that may use acetic acid or other substrates [15]. Rodent adipocytes, which would include those from rats and mice, are noted for their broad responsiveness to various endocrine signals, which can significantly influence lipid metabolism. This includes a wide array of hormonal responses that regulate anabolic and catabolic processes in lipid metabolism [16]. Understanding the contribution of these particular fatty acid classes in living organisms is crucial to explaining the intricate connections between dietary intake, metabolic pathways, and health outcomes. This article aims to present an analysis of the content and composition of SFA, MUFA, and PUFA in both liver and skeletal muscle tissues of rats and mice, which are the most common animal models used in lipid metabolism research. By delineating the lipidomic landscapes of crucial metabolic organs such as the liver on skeletal muscles, we aim to uncover potential variations that could underpin observed differences in metabolic phenotypes between rats and mice. Additionally, elucidating the species-specific lipid signatures may shed light on the adaptability of rodent models to dietary interventions, pharmacokinetics, and metabolic surgery, providing essential context for interpreting experimental outcomes and extrapolating findings to human physiology. The outcomes of this research may have implications for refining experimental design, data interpretation, and translational relevance, ultimately advancing our understanding of the intricate interplay between diet, metabolism, and health.
# A Low-Cost Evaluation Tool for Synchronization Methods in Three-Phase Power Systems
## 3 Results
### Evaluation of Synchronization Algorithms
#### 3.2.2 Case Study 2: Frequency Changes  
The dynamics of the estimated amplitude and frequency when varying the reference frequency are shown in Figures 17 and 18. Figure 17 shows the results for a grid without harmonics, and Figure 18 shows the results for a grid with the harmonics content. In particular, for the variation in \(t_{0}\), the estimated frequency can be evaluated by obtaining the OS and \(t_{s}\) from the dynamic response (Figures 17b and 18b). Table 11 shows \(t_{s}\) for DSOGI-PLL with LPF. The results confirm the behavior of the SRF-PLL algorithm under distorted grid conditions and the dynamics of the estimated frequency of the DSOGI-PLL algorithm reported in [8; 29], respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Measure**} & \multicolumn{3}{c}{**SRF-PLL**} & \multicolumn{2}{c}{**DSOGI-PLL with LPF**} & \\ \cline{3-6}  & & **Without** & **With** & **Without** & **With** & **With** \\  & & **Harmonics** & **Harmonics** & **Harmonics** & **Harmonics** & **Harmonics** \\  & & **(See Figure 15)** & **(See Figure 16)** & **(See Figure 15)** & **(See Figure 16)** & **(See Figure 16)** & **(See Figure 16)** \\ \hline \(v_{sal,o}(t)\) in a steady state (SS) & ME & 11.7 & 10.3 & 12.5 & 12.8 & mV \\  & RMSE & 60.4 & 53.2 & 32.6 & 33.1 & mV \\ \hline \(A_{1,o}(t)\) in the transient state (TS) & OS & - & - & - & - & p.u. \\  & \(t_{s}\) & 20.2 & 18.2 & 21.8 & 13.2 & ms \\ \hline  & Phase angle & 16.29 & & 20.54 & \(\circ\) \\  & \(t_{ex}\) & 33.17 & & 63.61 & \(\upmu\)s \\ \hline \end{tabular}
\end{table}
Table 10: Summary of evaluation methods for case 1, change in \(t_{0}\).  
Figure 17: The performance of the SRF-PLL (left column) and DSOGI-PLL with LPF (right column) algorithms with the proposed evaluation tool. Case study 2, frequency change without harmonic content: (**a**) references and estimated signals; (**b**) reference, estimation, and error of frequency in change \(t_{0}\), measuring \(t_{s}\) and OS in \(f_{1\omega}(t)\) with \(\Delta f_{1}(t)\).  
Figure 18: The performance of the SRF-PLL (left column) and DSOGI-PLL with LPF (right column) algorithms with the proposed evaluation tool. Case study 2, frequency variation with the harmonic content: (**a**) references and estimated signals; (**b**) reference, estimation, and error of frequency in change \(t_{0}\), measuring \(t_{s}\) and OS in \(f_{1\omega}(t)\) with \(\Delta f_{1}(t)\).
# HVdc Circuit Breakers: Prospects and Challenges
## 2 High Voltage Direct Current Networks
#### Distinctiveness of HVdc Breakers  
DC limiting reactor is a major component of an HVdc circuit breaker. It is an inherently bulky element of the breaker that is expected to handle maximum dc fault current without getting itself damaged. When the limiting reactor is connected in an M-HVdc grid, the line impedance, as well as the grid stability is considerably affected. Moreover, an increase in transient voltage is observed at the breaker terminals. Apart from this, the fault current may face an initial rate of rising for both external as well as internal faults. The series impedance between the fault location and the converter is lower in an external fault as compared to an internal fault. Initially, for the first few milliseconds, an internal fault might give a higher fault current than an external fault. As for the protection of dc transmission lines, the external fault is considered outside of the protection zone defined by the dc reactor's boundary. That is why, the impact of external fault is mitigated due to the inductor's boundary effect and a smaller fault current is noticed under external fault as compared to an internal fault [86; 87]. However, after several milliseconds of fault inception, due to lower series impedance, there will be a higher rise in the current level in case of an external fault than an internal fault. The HVdc breakers are designed to interrupt the fault current in the first few milliseconds only. An aggregated model of dc breaker is shown in Figure 1.  
Figure 1: An aggregated model for the HVdc breaker.
## 6 Evaluation
### Comparison with Existing Automated Sample Construction Methods
#### 6.2.2 Performance Comparison under Linux  
We conducted experiments on A3C-Linux under environment W. These experiments are mainly about the performance of A3C-Linux, aiming to prove that A3C occupies minimal equipment resources and network resources and is better than the existing methods. We still use Wireshark as a baseline. It is worth noting that Wireshark does not have labeling capabilities, whereas A3C-Linux contains automated labeling, so A3C-Linux does more operations than Wireshark.  
We first experimented on the occupation of network resources. Note that network resources in this experiment refer to the resources available to the end system rather than the actual upper limit of network resources. Improper design of the network sniffer tool may cause packet blocking. Therefore, the network sniffer's read-and-write operations on traffic may affect the obtaining of network resources on the end system, which needs to be tested.  
We have carried out several experiments on the same file under environment W. We transferred a 569 MB file to a Linux server using SFTP and compared the network speeds of the Linux server in only A3C, only Wireshark, or no capture. The experimental results (average of all the experiments) are shown in Figure 9. The x-axis means the downloading time consumption, while the y-axis represents the size of the already-downloaded file.  
It can be seen from the results that the transfer rate with A3C enabled is slightly lower than without any capture enabled but higher than with Wireshark. Generally, the transmission speed difference between the three scenarios is insignificant. A3C and Wireshark have little impact on network resource acquisition and do not block packet transmission. The results indicate that A3C does not have any impact on the acquisition of network resources, which is the guarantee of normal service performance.  
Then, we conducted experiments on the usage of OS resources. The system resource usage is calculated when only A3C is enabled, only Wireshark is enabled, and neither is enabled. This part of the experiment was carried out in the Linux environment, and a software installation package (about 250 MB) was taken as the sample. The system resource  
Figure 9: Comparison of network resource occupation in three situations.  
occupation was monitored during the downloading process. The experimental results are shown in Figure 10.  
The results show that A3C has a slightly low overall consumption of equipment resources. A3C has a sudden high CPU consumption when it is just started because it needs to apply for some resources from the operating system in advance and conduct PP Mapping initialization. However, with the continuous operation of the program, the resources consumed by A3C are negligible compared with the system resources consumed without capture. The Wireshark consumes more CPU power than A3C to some extent.
###### Abstract  
This study presents the analysis and comparison of Wi-Fi coverage modeling for a hotspot using deterministic and empirical propagation models developed by researchers from the Universidad de Las Americas in Quito, Ecuador. Signal intensity measurements were taken from both the hotspot and the repeater at various locations within the Checa parish using a Raspberry Pi and a Global Positioning System (GPS). To assess the accuracy of the models, heat maps were generated using Matlab (R2023A). The results showed that the adjusted model, comparing the received signal levels of the hotspot with the Stanford University Interim Propagation Model (SU), exhibited a significant error margin, especially at distances below 60 m. However, starting at \(-70\) dBm and beyond 60 m, the sampled data aligned better with the adjusted model. The discrepancy in the heatmaps was explained by the hotspot's higher transmission power compared to the Wi-Fi repeater. Furthermore, the reception levels of the hotspot were low near the transmitter, which led to new measurements being taken with the Wi-Fi repeater (Raspberry Pi 3). With the new measurements, the adjusted model using logarithmic regression showed a better fit, particularly in the range from \(-40\) dBm to \(-98\) dBm, with a path loss exponent of 8.96. This demonstrated a significant improvement in prediction accuracy, particularly at short distances. The results emphasize the importance of using tools such as Matlab and reference models to optimize network planning, providing the Universidad de Las Americas with a valuable tool to generate heat maps in areas with characteristics similar to those of Checa in the context of their community outreach programs. This approach could be crucial for future research and optimization of community Wi-Fi networks in similar environments.  
coverage modeling; power level; linear regression; access points; hotspot; sampling; heat maps; wireless communications +
Footnote †: journal: Medical Imaging  
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