esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Flux Tower Data  
In the study site, there is an eddy covariance flux tower that belongs to the Guadarrama Meteorological Network [47; 48; 49].  
The flux tower provides data from 2017 to the present at a frequency of 10 Hz of several micrometeorological variables, including those for calculating CO\({}_{2}\) flux (Net Ecosystem exchange, NEE). This study collected and analyzed nearly three years of variables from January 2018 to July 2020 (Table 1). Firstly, raw files from the IRGASON (Campbell Scientific, Inc., Logan, UT, USA) every three hours were obtained for the study period, and the raw files were divided into 30-min files through the CardConvert 1.9 software (Campbell Scientific, Logan, UT, USA; hereafter CSI). Secondly, these files were processed with EddyPro 7.0.9 software (Li-Cor, Inc., Lincoln, NE, USA) to obtain the CO\({}_{2}\), water vapor, and sensible heat fluxes every 30 min for the study period. Half-hourly averaged fluxes were excluded when more than 10% of the raw data records were missing for any of the three components of wind velocity, temperature, and/or CO\({}_{2}\) concentration. To ensure the quality of EC flux data, we utilized quality control flags 0 and 1 from the outputs of EddyPro [50].  
Figure 1: Location of the eddy covariance flux tower in Spain (**a**), location (yellow star) of the flux tower in the village of El Escorial in Madrid (central Spain) at 40\({}^{\circ}\)34\({}^{\prime}\)56\({}^{\prime\prime}\) N and 4\({}^{\circ}\)08\({}^{\prime}\)16\({}^{\prime\prime}\) W (**b**) (images from © Google Earth (Landsat/Copernicus)), the eddy covariance flux tower (**c**) and scheme of equipment of flux tower (**d**).  
Data gaps were filled using the marginal distribution sampling technique described by [PERSON] et al. [51]. Flux partitioning into Ecosystem Respiration (Reco) and Gross Primary Production (GPP) was carried out according to the method by [PERSON] et al. [52]. Finally, minimum air temperature (T\({}_{\rm min}\)), soil water content at 4 cm depth (SWC), and tower GPP (GPP\({}_{\_}\)T) were averaged daily and then every 5 days to match the temporal resolution of remote sensing images. GPP\({}_{\_}\)T was then smoothed with a Savitzky-Golay filter [53] to reduce noise using a second-order polynomial with a smoothing window of seven and using the same filter as the MODIS and Sentinel-2 NDVI.
## 3 Results and Discussion
### Influence of Shear Temperature  
#### 3.2.1 Fluorescence Microscopy Test  
To investigate the influence of different shear temperatures on the microstructure of SBS-modified asphalt, samples prepared at 170, 180, and 190 \({}^{\circ}\)C were examined by fluorescence microscopy. The results are shown in Figure 5.  
According to Figure 5, most SBS polymers show a filamentous appearance and the rest were droplets. The SBS area ratio shows a rank as follows: 180 \({}^{\circ}\)C \(>\) 190 \({}^{\circ}\)C \(>\) 170 \({}^{\circ}\)C. It is important to prepare SBS-modified asphalt at the appropriate temperature. At low temperatures, the molecules are not sufficiently active, which results in a low degree of blending between SBS and the asphalt. At high temperatures, the SBS molecule might be susceptible to thermal oxidative degradation, which in turn affects the strength of the polymer network structure. In this sense, 180 \({}^{\circ}\)C seems to be an intermediate and suitable preparation temperature.
# Improved ADRC-Based Autonomous Vehicle Path-Tracking Control Study Considering Lateral Stability
## 4 The Path-Tracking Controller Design
### IESO for Vehicle Path Following  
According to the IESO, the disturbance state observer is designed to estimate the total of the internal and external disturbance acting on the vehicle system. From the 2-DOF dynamics model of the vehicle in Equation (4), Equations (23) and (24) can be obtained:  
\[\ddot{y}=f_{1}(\cdot)+b_{1}\delta_{f} \tag{23}\]  
where \(b_{1}=\frac{2C_{f}}{m}\), \(f_{1}(\cdot)=-\frac{2C_{f}}{m}\frac{v_{y}+l\dot{y}}{v_{x}}+\frac{2C_{f}}{m} \frac{l\dot{y}-v_{y}}{v_{x}}-v_{x}\dot{\varphi}+w_{1}(t)\); \(f_{1}(\cdot)\) denotes the total disturbances on the lateral motion channel, including external disturbance and model uncertainty; and \(w_{1}(t)\) denotes the value of external disturbance:  
\[\ddot{\varphi}=f_{2}(\cdot)+b_{2}\Delta M_{z} \tag{24}\]  
where \(b_{2}=\frac{1}{L}\), \(f_{2}(\cdot)=\frac{1}{L}(l_{f}F_{yf}-l_{r}F_{yr})+w_{2}(t)\); \(f_{2}(\cdot)\) denotes the total disturbances on the yaw motion channel, which include the external disturbance and model uncertainty; and \(w_{2}(t)\) denotes the value of external disturbance.  
Equations (23) and (24) are further rewritten as a second-order system, respectively, which have a single input and single output:  
\[\left\{\begin{array}{l}\dot{y}_{0}=v_{y}\\ \dot{v}_{y}=f_{1}(\cdot)+b_{1}\delta_{f}\\ y_{0}=y\end{array}\right. \tag{25}\]  
and  
\[\left\{\begin{array}{l}\dot{\varphi}_{0}=r\\ \dot{r}=f_{2}(\cdot)+b_{2}\Delta M_{z}\\ \varphi_{0}=\varphi\end{array}\right. \tag{26}\]  
Thereby, based on the IESO proposed in Section 3, two extended state observers can be constructed, as shown in Equations (27) and (28):  
\[\left\{\begin{array}{l}e_{1}=\dot{y}-y_{0}\\ \dot{\hat{y}}=\hat{v}_{y}-\beta_{11}e_{1}\\ \dot{v}_{y}=\hat{f}_{1}(\cdot)-\beta_{12}Fal_{11}(e_{1})+b_{1}\delta_{f}\\ \hat{f}_{1}(\cdot)=-\beta_{13}Fal_{12}(e_{1})\end{array}\right. \tag{27}\]  
and  
\[\left\{\begin{array}{l}e_{2}=\dot{\varphi}-\varphi_{0}\\ \dot{\varphi}=\hat{r}-\beta_{21}e_{2}\\ \dot{\hat{r}}=\hat{f}_{2}(\cdot)-\beta_{22}Fal_{21}(e_{2})+b_{2}\Delta M_{z}\\ \hat{f}_{2}(\cdot)=-\beta_{23}Fal_{22}(e_{2})\end{array}\right. \tag{28}\]  
Therefore, according to the content in Section 3, the value of total disturbance estimated by IESO can be derived as  
\[\hat{f}(\cdot)=\left[\begin{array}{cc}\hat{f}_{1}(\cdot)&\hat{f}_{2}(\cdot )\end{array}\right]^{T} \tag{29}\]
# Energy Performance of LR-FHSS: Analysis and Evaluation
## 5 Evaluation  
This section evaluates and discusses the three main energy performance metrics that we modeled in the previous section for an LR-FHSS ED: average current consumption, lifetime, and energy cost of data transmission. The section is divided into three main parts: one for each performance metric.  
Due to their equal characteristics in terms of maximum payload size and physical layer bit rate, the following analysis does not differentiate between DR8 and DR10 nor between DR9 and DR11. In the evaluation, we also include DR0 and DR5, the slowest and fastest mandatory LoRa DRs, respectively, for the sake of comparison with LR-FHSS. Duration values for \(T_{Tx}\) in these cases are extracted or derived from [20].
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 7 Discussion
### Prompting Strategies and Conversion Success (H1.1 and H1.2)  
Our results strongly support H1.1, demonstrating that structured prompting techniques like CoT and two-step conversion significantly outperform basic prompting. The high failure rates observed with basic prompting (Table 2) highlight its inadequacy for complex FHIR conversion due to a lack of guidance in adhering to FHIR standards. As hypothesized in H1.2, the two-step conversion method proved most effective, achieving near-perfect conversion rates with Gemini and significantly reducing the failure rate with Mistral. This underscores the importance of explicitly structuring the conversion process for LLMs, confirming that organizing information prior to FHIR mapping is crucial for accurate resource mapping and bundle construction. While CoT offered an improvement over basic prompting, it did not match the performance of the two-step approach. This discrepancy suggests that the CoT approach, while effective in breaking down complex tasks, may not be ideally suited for the precise and structured requirements of FHIR conversion. The model's ability to identify relevant information is not fully translated into accurate FHIR resource mapping, leading to incomplete or incorrect bundles. This finding aligns with the observations of [PERSON] et al. (2019), which demonstrated that LLMs struggle to self-correct reasoning without external feedback and may even experience performance degradation after self-correction attempts. In our context, the lack of explicit structuring in CoT prompts can be seen as a form of limited feedback, hindering the model's ability to consistently generate valid FHIR.
# An Algorithm to Assist the Robust Filter for Tightly Coupled RTK/INS Navigation System
## 4 Results
### The Performance of the TC RTK/INS Integration with the Improved Outlier Resistance
#### 4.4.1 Further Analysis of the Shinjuku Dataset
This fact demonstrates that it is feasible and valid to retain some detected outliers based on the CNR to resist excessive robustness.
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 5 Discussion  
### The Effects of the WIoU Loss Function on the Model  
The effects of using the WIoU loss function to detect old landslides are obviously better than those of using the CIoU loss function to detect old landslides, as shown in Table 4. After replacing the loss function, the model is significantly higher than the original model in all evaluation indexes. Figure 10 shows the training loss curves of the CIoU, WIoUv1, WIoUv2, and WIoUv3 networks, respectively. The loss curves decreased significantly at the beginning of the training and the decline trend began to slow down after 100 epochs. The total loss function converges to 0.39787, compared to the original loss function of YOLOv8. The improved loss function after the introduction of WIoU can make the network converge faster, so the improvement of the network loss function in this paper is reasonable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **CBAM** & **SE** & **SK** & **CIoU** & **WIoUv1** & **WIoUv2** & **WIoUv3** & **mAP (\%)** \\ \hline Base line & - & - & - & \(\surd\) & - & - & - & 62.8\% \\
1 & - & - & - & - & \(\surd\) & - & - & 68.3\% \\
2 & - & - & - & - & - & \(\surd\) & - & 67.2\% \\
3 & - & - & - & - & - & - & \(\surd\) & 67.9\% \\
4 & \(\surd\) & - & - & \(\surd\) & - & - & - & 65.8\% \\
5 & \(\surd\) & - & - & - & \(\surd\) & - & - & 68.2\% \\
6 & \(\surd\) & - & - & - & - & \(\surd\) & - & 73.8\% \\
7 & \(\surd\) & - & - & - & - & - & \(\surd\) & 70.3\% \\
8 & - & \(\surd\) & - & \(\surd\) & - & - & - & 63.7\% \\
9 & - & \(\surd\) & - & - & \(\surd\) & - & - & 63.4\% \\
10 & - & \(\surd\) & - & - & - & \(\surd\) & - & 70.6\% \\
11 & - & \(\surd\) & - & - & - & - & \(\surd\) & 66.1\% \\
12 & - & - & \(\surd\) & \(\surd\) & - & - & - & 64.6\% \\
13 & - & - & \(\surd\) & - & \(\surd\) & - & - & 70.9\% \\
14 & - & - & \(\surd\) & - & - & \(\surd\) & - & 70.7\% \\
15 & - & - & \(\surd\) & - & - & - & \(\surd\) & 64.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of ablation experiment results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** & **F1 (\%)** \\ \hline YOLOv8-CW1 & 76.7\% & 65.9\% & 68.2\% & 71.0\% \\ YOLOv8-CW2 & 80.1\% & 69.1\% & 73.8\% & 74.0\% \\ YOLOv8-CW3 & 79.0\% & 66.8\% & 70.3\% & 72.0\% \\ YOLOv8-SEW1 & 73.2\% & 68.3\% & 63.4\% & 71.0\% \\ YOLOv8-SEW2 & 75.6\% & 71.5\% & 70.6\% & 74.0\% \\ YOLOv8-SEW3 & 76.6\% & 63.4\% & 66.1\% & 69.0\% \\ YOLOv8-SKW1 & 74.9\% & 70.0\% & 70.9\% & 72.0\% \\ YOLOv8-SKW2 & 74.4\% & 70.3\% & 70.7\% & 72.0\% \\ YOLOv8-SKW3 & 73.3\% & 66.8\% & 64.7\% & 70.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The effects of different attentional mechanisms.
# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
###### Abstract  
In actual production, bearings are usually in a normal working state, which results in a lack of data for fault diagnosis (FD). Yet, the majority of existing studies on FD of rolling bearings focus on scenarios with ample fault data, while research on diagnosing small-sample bearings remains scarce. Therefore, this study presents an FD method for small-sample bearings, employing variational-mode decomposition and Symmetric Dot Pattern, combined with a pre-trained and fine-tuned Residual Network18 (VSDP-TLResNet18). The approach utilizes variational-mode decomposition (VMD) to break down the signal, determining the \(k\) value and the best Intrinsic-Mode Function (IMF) component based on center frequency and kurtosis criteria. Following this, the chosen IMF component is converted into a two-dimensional image using the Symmetric Dot Pattern (SDP) transform. In order to maximize the discrimination between two-dimensional fault images, Pearson correlation analysis is carried out on the parameters of SDP to select the optimal parameters. Finally, we use the pre-trained and fine-tuned method combined with ResNet18 for small-sample FD to improve the diagnosis accuracy of the model. Relative to alternative approaches, the suggested method demonstrates strong performance when dealing with small-sample FD.  
residual network18; small sample; pre-trained and fine-tuned; fault diagnosis; variational-mode decomposition; symmetric dot pattern +
Footnote †: journal: Applied Sciences
# Repellency of Veratraldehyde (3,4-Dimethoxy Benzaldehyde) against Mosquito Females and Tick Nymphs
## 2 Materials and Methods
### Repellency Assay  
#### 2.5.1 Laboratory Assay Using an Arm-in-Cage Test  
The repellency of veratraldehyde (0.25, 0.20, 0.10 and 0.03 mg/cm\({}^{2}\)) and its formulations (5, 7, 10 and 15% veratraldehyde) against _A. albopictus_ and _C. pipiens pallens_ was examined in a test cage (30 \(\times\) 30 \(\times\) 30 cm) containing 100-130 females. Females 5-7 days after emergence were used and were starved for over 12 h before testing. Appropriate quantities of the test materials, each in 100 \(\mu\)L of ethanol or 2 sprays of formulations, were directly applied evenly to the exposed skin of the back of the left or right hand through a rectangle (5 \(\times\) 5 cm) cut into the back part of a rubber glove. Controls received 100 \(\mu\)L of ethanol or 2 pump sprays of formulation without veratraldehyde. After air drying for 2 min, the treated hands of each volunteer were exposed to female _A. Albopictus_ and _C. pipiens pallens_ for 3 min and every 0.5 or 1 h until the test volunteer received a female bite. After one cycle was terminated, the hands of volunteers were exposed alternately tonew mosquito females. DEET (0.25, 0.20, 0.10 and 0.03 mg/cm\({}^{2}\)) and its formulation (20% DEET) served as a positive control for comparison in the repellency tests. The numbers of mosquito females biting the skin were recorded, and each assay was replicated more than three times.
## 3 Results and Discussion
### Analysis of Internal Crack Characteristics
#### 3.3.1 Evolutionary Characteristics of Ringtone Counting  
The positioning of the sound-emission data point can be comprehensively analyzed based on the location of the cracks within the cluster, rate of expansion, direction of the  
Figure 6: Evolution of macroscopic failure and horizontal displacement field on the surface of the sample.  
Figure 7: Maximum relative displacement variation in the sample.  
expansion, and process of fissure evolution. The clock count with the compression time-evolution rule can reflect the distribution of fissures at different stages, reflecting the appearance and expansion of microcracks. As shown in Figure 8, AE ringtone counting can be divided into four phases: compaction phase (I), elastic phase (II), unstable failure phase (III), and post-peak phase (IV). In combination with the localized characteristics of sound discharge events inside the sample, the clock count can be used to further analyze the rupture proliferation rule for the test. Figure 8 provides information on the relationship between the early damage of coal-cement stress and clock counts and the corresponding stage of the stress level.  
The clock-counting statistics for different early damages of coal-cement show that most acoustic releases occur within a short time prior to the breakup of the sample, resulting in a surge in acoustical releases when the peak intensity is reached. However, as the early damage increased from 0% to 40%, the time phase of the sound discharge began to evolve to the elastic phase, while the interference pressure was greater for the 60% condition, so that the cluster was compressed in the previous phase and had fewer new cracks.  
There are consistent patterns in the location of internal AE events in coal-grout rock samples: (1) During the initial compression deformation stage of coal-grout rock, the pores contained in the raw coal inside the rock bodies are compressed, resulting in a few new cracks and AE events, which are scattered within the rock samples. (2) During the  
Figure 8: Acoustic emission and stress characteristics of coal-slurry rock body under different early damage.  
elastic deformation stage, the rock body was subjected to compressive stress and produced small cracks. At this time, only a few AE events are generated, and their distribution is influenced by the uniformity of the coal body, and thus randomly distributed in the rock body sample. (3) After entering the elastic-plastic deformation stage, the coal-grout rock body yielded under high stress, generating more new cracks, which gradually expanded and connected with the original cracks, resulting in an increase in AE events. (4) When the stress exceeds the peak intensity, the AE activity of the rock body becomes abnormally active after entering the failure stage, and the concentration of cracks increases further. The slurry inside the rock body was also affected, producing a large number of cracks and accelerating rock fractures.
## 3 Research Methodology
### Modeling of Electric Vehicle Power Demand  
Power required by the EV (named as \(EV\_Dmd\)) is modelled from [50]. The request for the power charging of a single-vehicle at a particular duration \(t\) can be estimated as;  
\[P_{EV,t}=P_{EV,req}\ \times s_{t}\times w_{t}\times h_{t} \tag{11}\]  
where \(P_{EV,req}\) is the power anticipated by the battery for rising the \(SOC\) from the initial value (\(SOC_{0}\)) at the time of plugin to the final value (\(SOC_{max}\)) in an interval period \(\Delta t\). \(s_{t}\) is the signal for monitoring \(SOC\) of the vehicle battery. The \(w_{t}\)_and_\(h_{t}\) represent the campus working days with campus working hours, respectively.  
For random \(EV\_Dmd\), the displacement covered by the vehicle by the last charging (\(T_{d}\)) is generated randomly between 1 mile to the extreme EV journey (\(R_{t}\)). Based on journey distance (\(T_{r}\)), the gross energy accumulated by EV during the last journey range (\(E_{R}\)) is computed as;  
\[E_{R}=T_{d}\times E_{m} \tag{12}\]  
where \(E_{m}\) is energy utilization/mile for the given EV. From \(E_{m}\) and the gross battery capacity (\(Q_{bat}\)) of the EV, \(SOC_{0}\) is computed as;  
\[SOC_{0}=1-\frac{E_{R}}{Q_{bat}} \tag{13}\]  
Furthermore, at the start of the plugin, the parameter \(SOC_{0}\) applied to assess the power desired by the unit vehicle \((P_{EV,req}\) ) as given as:  
\[P_{EV,req}\ =\frac{(SOC_{max}-SOC_{0})\times Q_{bat}}{\Delta t} \tag{14}\]  
By inserting \(P_{EV,req}\) and decision signals \(s_{t},\ \ w_{t},\ and\ h_{t}\) in base equation, the power demand of unit vehicle at time \(t\), \(P_{EV,t}\) is computed. However, for a fleet of vehicles, \(P_{EV,t}\) is summed to achieve the accumulated \(EV\_Dmd\).  
\[EV\_Dmd,t=\left\{\begin{array}{c}\sum\limits_{n=1}^{N}P_{EV,t}^{n}\textit{ where }t\textit{ is the campus hours}\\ 0\textit{, else}\end{array}\right. \tag{15}\]  
where \(N\) is the maximum number of e-vehicles arrived at the station and \(P_{EV,t}^{n}\) is the required power by the \(nth\) car at a given time \(t\). Probability distribution was drawn based on an appropriately ample number of autonomous random variables i.e., inbound and outbound EV at the charging facility each with the set population mean \((\mu)\) and variance \((\sigma)\). Cascading the probability distribution function (pdf) of arrival and departure time \((A_{t}\textit{ and }D_{t})\), the allied pdf of \(D_{t}-A_{t}\) can originate, which is usually parking time. The pdf of the common parking staying time portrayed in Figure 9 as defined in [19].  
Figure 9: Hourly Probability Density Function of a fleet of vehicles. Reprinted with permission from ref. [19]. Copyright from IEEE.  
The flow chart of the power-desired model for a unit vehicle is marked in Figure 10.  
Figure 10: The flow path of the total demand of a fleet of electric vehicles.
## 5 Verification and Small-Scale Demonstration  
### General  
The activities envisioned for the proposed system are grouped into 2 main categories: (i) Edge Activities and (ii) IOTA powered Activities. The Edge Activities comprise (a) data aggregation; (b) metadata enrichment; (c) local rule-based decision making and (d) local control. Regarding the IOTA powered activities, the Iota Powered Nodes work in a collaboratively fashion, exchanging value for resources and/or services. IOTA serves as the infrastructure for the automated exchange of value in a publicly auditable fashion while enhancing trust between the parties. Clusters of nodes also cooperate, employing IOTA functionalities, such as the flash channels to transfer IOTA tokens offline using common transport layer protocols such as 802.15.4 and 802.15.1. or LoRa [45]. Indicatively, the IoT nodes could be further upgraded and exchange IOTA tokens in order to exchanges services. For example, they could improve the efficiency of their own rule-based system by exchanging data, or (given the proper infrastructure) procure and transfer electrical energy between stakeholders, or in case of internet failure, the request from their neighbors to act as gateways.