esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effects of the Background on LAI Estimation
#### 3.2.2 Correlation Analysis between CIs and LAI  
To further analyze the influence of background elements on the relationship between CIs and LAI, we compared the Pearson correlations between CIs and LAI before and after image segmentation in key growth stages. Figure 6 presents the Pearson correlation trends between different CIs and LAI values at different growth stages. The Pearson correlation coefficients between segmented CIs and LAI were significantly higher than Retinex-corrected CIs. This indicated that image segmentation significantly improved the correlation between CIs and LAI. Taking the heading stage image as an example, the average Pearson coefficient between Retinex-corrected CIs and LAI was 0.27. After image segmentation, the correlation coefficient increased to 0.43, with an increase of 0.16. This demonstrated that image background plays a counteractive role in the estimation of LAI by CIs. Additionally, we found that after image segmentation, the correlation between CIs and LAI increased notably at the booting, heading, and filling stages.
## 4 Conclusions  
The untargeted metabolomics method was applied in a single injection of different samples of Spanish _Arctostaphylos wwa-ursi_ to perform a qualitative analysis of their volatile bioactive compounds. The novelty of this investigation is that for the first time VOC quality markers for the _Arctostaphylos wwa-ursi_ plant collected from various Spanish locations were determined and the influence of parameters such as geographical closeness, altitude and insolation of harvest areas have been demonstrated. The investigation was, focus mainly on antioxidant markers. As an analytical method, HS-SPME-GC-MS was optimized and applied. One-hundred seven different volatile compounds were detected and classified according to their structure. The most interesting detected compounds that potentially may be antioxidant markers were for example fumaratic acid 2-decyl dodecyl ester for Albarracin (Teruel), 4-(1-hydroxyallyl)-2-methoxyphenol for Chelva (Valencia), El Toro (Castellon) and both samples from Loarre (Huesca), dimethoxyphenyl-6-nitro-coumarin for Huetor (Granada) and Los Velez (Almeria) samples, 5-pentyl-1,3-benzenediol for Lierta (Huesca), and oxalic acid 6-ethyloct-3-yl ethyl ester for Pina de Montalgrao (Castellon).The chromatographic and chemometric analysis showed that the same _Arctostaphylos wwa-ursi_ collected from different locations have different antioxidant profiles. Parameters such as geographical closeness, solar radiation and altitude have been crucial for samples clustering. PCA, PLS-DA and Pearson correlation analysis connect individual compounds with AOX properties and total phenolic content. It was concluded that the extract from the plant from Pina de Montalgrao was rich in electron-donor compounds. The strongest AOX was obtained in the samples from Pina de Montalgrao and Loarre (collected in September) according to both the ORAC and DPPH methods. In contrast, the extract from Loarre (collected in September) was rich in free radical scavengers and
## 4 Algorithm Design
### Barotmetric Sensor Zero Drift Height Error Compensation  
Since barometric pressure sensors are susceptible to data fluctuations due to environmental influences, and barometric pressure sensors produce zero drift in a stable barometric pressure environment, in this paper, BP neural networks are trained to eliminate the effect of zero drift on height error.
## 3 Index Structure
### Index Updates  
As the objects in the sensor network move with time, the grid index needs to be updated so as to precisely store the object information. Here, we design an efficient mechanism to rapidly update the location information of objects. Consider the example in Figure 4, which shows how to process the updates of objects. In this example, at time 3, objects \(o_{1}\) and \(o_{2}\) move from the regions monitored by sensors \(s_{1}\) and \(s_{2}\) to the regions monitored by sensors \(s_{4}\) and \(s_{3}\), respectively. Due to the movement of objects \(o_{1}\) and \(o_{2}\), the following five tables of grid index need to be updated.  
* Updating \(T_{o}\) of object \(o_{1}\): when \(o_{1}\) moves, sensor \(s_{2}\) cannot monitor \(o_{1}\) while sensor \(s_{5}\) begins to monitor \(o_{1}\). Therefore, its table \(T_{o}\) is changed from (\(o_{1}\), {\(s_{1}\), \(s_{2}\), \(s_{4}\)}, 0, \(o_{1}\)-data) to (\(o_{1}\), {\(s_{1}\), \(s_{4}\), \(s_{5}\)}, 3, \(o_{1}\)-data).
* Updating \(T_{o}\) of object \(o_{2}\): once \(o_{2}\) moves, sensor \(s_{5}\) cannot monitor \(o_{2}\) but \(s_{6}\) can monitor \(o_{2}\). As such, the table \(T_{o}\) is updated from (\(o_{2}\), {\(s_{2}\), \(s_{3}\), \(s_{5}\)}, 0, \(o2\)-data) to (\(o_{2}\), {\(s_{2}\), \(s_{3}\), \(s_{6}\)}, 3, \(o_{2}\)-data).
* Updating \(T_{s}\) of sensor \(s_{2}\): due to the movement of \(o_{1}\), the table \(T_{s}\) of \(s_{2}\) needs to be updated. It is changed from (\(s_{2}\), 60, {\(o_{1}\), \(o_{2}\)} to (\(s_{2}\), 60, {\(o_{2}\)}).  
Figure 3: Grid index.  
* Updating \(T_{s}\) of sensor \(s_{5}\): because the locations of \(o_{1}\) and \(o_{2}\) are changed, the table \(T_{s}\) of \(s_{5}\) needs to be updated. It is changed from (\(s_{5}\), 60, \(\{o_{2}\}\)) to (\(s_{5}\), 60, \(\{o_{1}\}\)).
* Updating \(T_{s}\) of sensor \(s_{6}\): as object \(o_{2}\) moves, sensor \(s_{6}\)'s table \(T_{s}\) is updated. It is changed from (\(s_{6}\), 60, \(\{\)null\(\}\)) to (\(s_{6}\), 60, \(\{o_{2}\}\)).
# On the Lagged Diffusivity Method for the Solution of Nonlinear Finite Difference Systems
## 2 Discretization
### Time Discretization  
We now consider time discretization. Let us introduce a time spacing \(\Delta t\), which defines a series of time levels: the \(n\)-th time level \(t_{n}\) is defined by \(t_{n}=n\Delta t+t_{0}\), \(n=0,1,...\).  
The time derivatives of Equation (6) can be approximated by several different schemes. We could use, for example, the well-known \(\theta-\)method (e.g., see [13]), which is completely implicit when \(\theta\
eq 0\). An alternative, as noted in the Introduction, is given by IMEX methods, which have been employed to solve diffusion equations when convection or reaction terms can be treated explicitly.  
For instance, let us apply the \(\theta\)-method. Denoting by \(\mathbf{u}^{n}\) the approximation of \(\mathbf{u}(t_{n})\) (with \(\mathbf{u}(t_{n})\) solution of Equation (6) at \(t=t_{n}\)) and by \(\mathbf{s}^{n}\equiv\mathbf{s}(t_{n})\), we get:  
\[\begin{split}\frac{\mathbf{u}^{n+1}-\mathbf{u}^{n}}{\Delta t}=& \theta\left[-A(\mathbf{u}^{n+1})\mathbf{u}^{n+1}+\mathbf{b}(\mathbf{u}^{n+1})-\mathbf{G}(\mathbf{u}^{n +1})+\mathbf{s}^{(n+1)}\right]\\ +&(1-\theta)\left[-A(\mathbf{u}^{n})\mathbf{u}^{n}+\mathbf{b}( \mathbf{u}^{n})-\mathbf{G}(\mathbf{u}^{n})+\mathbf{s}^{n}\right]\end{split} \tag{8}\]  
for \(n=0,1,...\) and with \(0\leq\theta\leq 1\).  
By simple algebraic manipulations, we find that at each time level \(n=0,1,...\) the vector \(\mathbf{u}^{n+1}\) is given by the solution of the nonlinear algebraic system  
\[\mathbf{F}(\mathbf{u})=\left[I+\tau A(\mathbf{u})\right]\mathbf{u}-\tau\left[\mathbf{b}(\mathbf{u})- \mathbf{G}(\mathbf{u})\right]-\mathbf{w}=\mathbf{0}, \tag{9}\]  
where \(\tau=\Delta t\theta\), \(I\) is the \(\mu\times\mu\) identity matrix and \(\mathbf{w}=\mathbf{w}^{n}\in\mathbb{R}^{\mu}\) is a vector containing the known terms, defined as  
\[\mathbf{w}=\left[I-\Delta t(1-\theta)A(\mathbf{u}^{n})\right]\mathbf{u}^{n}+\Delta t(1- \theta)\left[\mathbf{b}(\mathbf{u}^{n})-\mathbf{G}(\mathbf{u}^{n})\right]+\Delta t\left[\theta \mathbf{s}^{n+1}+(1-\theta)\mathbf{s}^{n}\right].\]  
If we use an IMEX method, we similarly get a nonlinear algebraic system to solve at each time level. The difference is that some terms are treated explicitly. Thus, if, for instance, the reaction term is treated explicitly, at the time level \(n+1\) the mapping \(\mathbf{G}(\mathbf{u})\) is evaluated at time \(n\); \(\mathbf{G}(\mathbf{u})\) then becomes a known term which appears solely in the known vector \(\mathbf{w}\).  
Analogously, if the velocity is not linear, it could be useful to treat explicitly the convection term. In this case, the part of \(A(\mathbf{u})\) depending on \(\mathbf{\tilde{v}}\) would appear solely in the known vector \(\mathbf{w}\).
## 4 Outline and Methodology
### Evaluation of Consistency
#### 4.4.2 Spatial Consistency  
The pixel-by-pixel comparison method is here adopted in order to verify the accuracy of spatial positions. The spatial consistency of \"woodland\" is just considered in this study. First of all, the five types of classification datasets are divided into the two categories of \"woodland\" and \"non-woodland\" via a binarization processing method, with the five classification results of MCD12Q1 product, respectively, superimposed with those of the GlobeLand30 product in space. Following this process, four new types of classification data are obtained: \"woodland/woodland\", \"woodland/non-woodland\", \"non-woodland/woodland\" and \"non-woodland/non-woodland\". These new types depict the features of the spatial consistency of \"woodland\" between the two datasets. Secondly, the spatial similarity of the five MCD12Q1 classification results and those of the reference data is analyzed in the three sub-regions and two levels (provincial and regional), as expressed by the following formula [52].  
\[O=\left(\frac{A}{A+B+C}\right)\times 100\% \tag{3}\]  
where \(O\) is the spatial similarity coefficient, and \(A\), \(B\) and \(C\) are, respectively, the total number of pixels of three types of classification data: woodland/woodland, woodland/non-woodland, and non-woodland/woodland, respectively.
# LoRaWAN Behaviour Analysis through Dataset Traffic Investigation
## 2 LoRaWAN in a Nutshell  
LoRaWAN is a low-power, wide area networking protocol built on top of the LoRa radio modulation technique. It wirelessly connects simple devices to the Internet and manages communication between end-node devices and network gateways. Use of LoRaWAN in industrial spaces and smart cities is growing thanks to it is low cost, long-range and bi-directional communication, with very low power consumption.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 3 Mapping and Localization
### Multi-Robot Mapping  
With multiple robots deployed inside the cave, a framework is developed to maintain a master map by merging maps generated by each robot [36]. Each robot \(R^{i}\) generates its local map \(m_{i}\) and pose estimates \(p_{i}\) from its 3D Lidar scans. The local maps and pose  
Figure 8: (**a**) Cut section of the pit entrance model showing the trajectory of SphereX to land inside the pit; (**b**) Map generated during the pit entrance trajectory along with the estimated trajectory of the robot shown in red; (**c**) True and estimated trajectory of the robot along x, y, and z-direction; (**d**) True and estimated quaternions of the robot.  
estimates generated by each robot are then sent to a master robot that generates the master map \(M_{t}\) by performing pose-graph optimization. Once the pose-graph is optimized by the master, the trajectory updates \(u_{i}\) are transferred back to each robot which is then used to correct their local maps and pose estimates, as shown in Figure 9. We experimented with three robots deployed inside the cave model, as shown in Figure 10. Figure 10a-c show the individual maps generated by each robot 1, 2, and 3. Figure 10d shows the master map generated by using the multi-robot mapping framework.  
Figure 10: Map generated using the multi-robot mapping framework with 3 robots. (**a–c**) Map generated by robot 1, 2 and 3; (**d**) Master map.  
Figure 9: Framework for multi-robot mapping used to generate a master map \(M_{t}\) of the cave, using local maps generated by each robot.
## 2 Materials and Methods
### Leaf Rolling  
To measure the degree of leaf rolling, an image-based method was introduced to quantify the proportion of leaf rolling via the leaf rolling ratio (LRR). The LRR can be calculated as follows:  
\[\text{LRR}=\frac{\text{LA}_{\text{time}}}{\text{LA}_{\text{full}}} \tag{2}\]  
where \(\text{LA}_{\text{time}}\) is the leaf area projected in the image at the corresponding measurement time. Moreover, \(\text{LA}_{\text{full}}\) denotes the largest leaf area projected on the measurement day, which can be calculated from images obtained by pressing the corresponding leaves flat against a transparent plastic board.  
Images of ten representative leaves from each irrigation treatment plot were captured at two-hour intervals, from 08:00 to 16:00, on DOVs 225, 226, 227, and 234. The images were obtained by placing a plastic board underneath the leaves to stabilize them and then capturing images with a smartphone positioned perpendicular to the leaves. The LRR of each leaf was then calculated by comparing the observed projected area to the full expansion area.  
Figure 3 shows images of the two groups of maize leaves captured at various times throughout a single day, showing plots indicating an adequate water supply and those indicating water stress. In the well-watered plot, the leaves remained full and expanded from 08:00 to 18:00, with no significant rolling observed. In contrast, in the water-stressed plot, the leaves were relatively flat at 08:00 but began to roll inward from the main veins  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Active Fluorescence Parameter** & **Description** \\ \hline \(F_{m}^{\prime}\) & Maximum fluorescence under saturated pulse light \\  & measured during the day. \\ \(F_{s}\) & Steady-state fluorescence under actinic light \\  & measured during the day. \\  & Maximum fluorescence under saturated pulse light \\ \(F_{m}\) & measured at 2:00 midnight after full dark \\  & adaptation. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Active fluorescence observations measured with the Monitoring-PAM system.  
with increasing temperature and light intensity. This rolling intensified until approximately 14:00, resulting in tightly rolling, needle-like leaves. As the temperature and light intensity gradually decreased toward the evening, the leaves slowly unfurled but did not return to their fully expanded state by 18:00. A comparison of the relationships between the leaf morphology and the LRR revealed that the LRR effectively reflects the diurnal variations in leaf rolling under varying degrees of water stress.
## 5 Results and Discussion
Starting at approximately \(10^{-9}\) at a distance of 1.4 m, the BER increases to around \(10^{-4}\) at a distance of 1.8 m. Similarly for channel 2, data are successfully transmitted up to a salinity level of 35 ppt with the acceptable BER of \(10^{-3}\) at the distance of 1.80 m.  
Thus, Figure 6 shows that as the salinity level of the water increases, the BER values also increase, indicating a degradation in the communication performance of the UWOC system. Higher salinity levels introduce additional signal impairments, leading to a higher likelihood of errors in the transmitted data. Table 2 presents the comparison of our proposed UWOC with previous works. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Scheme**} & **Max** & \multirow{2}{*}{**Data Rate**} & **Our Contributions** \\  & & **Range** & & **and Novelty** \\ \hline
2023 [39] & NRZ & 3.06 m to 2.72 m & 500 Mbps–10 Gbps & Introduced PDM for higher data rates \\ \hline
2023 [40] & PPM & 10 m & Not reported & and improved \\ \hline
2023 [41] & OOK & 7 m & 5 Mbps & connectivity in \\ \hline
2023 [42] & OOK & 35 m & 5 Mbps–20 Mbps & varying underwater \\ \hline Our Work & NRZ/RZ & 10 m & 2 Gbps & conditions. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with the previous works. Figure 6: Measurement of BER at different salinity levels: (**a**) channel 1; (**b**) channel 2.
## 3 Results
### Web-Based Application  
Based on this model, we developed a web application. The front-end was implemented using Vue.js, and the model server was implemented using TorchServe. The model used for the web application was trained using all the data. The classification speed of the model processes 34 data per second, and the web application is capable of both single prediction and multi-prediction. On the single prediction page (Figure 3), the corresponding ICD-9 code and probability can be obtained by entering the name of the surgery and diagnosis. On the multi-prediction page (Figure 4), the ICD-9 codes and probabilities for multiple operative reports and diagnoses that can be obtained through the CSV file upload are presented.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**AP**} & \multicolumn{3}{c}{**F1**} & \multicolumn{3}{c}{**AUC**} \\ \cline{2-7}  & **Micro** & **Macro** & **Micro** & **Macro** & **Micro** & **Macro** \\ \hline
**Proposed Model** & Umls + * MC & 0.7672 & 0.4899 & 0.7415 & 0.3975 & 0.9842 & 0.9703 \\ \hline \hline \end{tabular}
* MC = Medical Character BERT.  
\end{table}
Table 6: Test performance of Proposed Models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ICD-9 Code**} & \multicolumn{3}{c}{**F1**} & \multicolumn{2}{c}{**Number of Data**} \\ \cline{2-5}  & **Umls** & **Character** & **in Test Set** \\ \hline \multirow{3}{*}{Majority labels} & 51.23 & **0.9324** & 0.9245 & 628 \\  & 06.4 & **0.9751** & 0.9638 & 264 \\  & 50.12 & **0.4096** & 0.3871 & 111 \\ \hline \multirow{3}{*}{Minority labels} & 46.43 & 0.7059 & **0.7200** & 27 \\  & 38.03 & 0.7600 & **0.8077** & 23 \\ \cline{1-1}  & 45.52 & 0.7857 & **0.8148** & 12 \\ \cline{1-1}  & 39.25 & 0.6000 & **0.6667** & 6 \\ \hline \hline \end{tabular}  
* ICD, International Classification of Diseases.  
\end{table}
Table 5: Comparison of the F1 score by labels.
## Nomenclature  
\begin{tabular}{l l} \(S\) & skin factor \\ \(k\) & permeability coefficient of far field zone, \(\mu\)M; \\ \(k_{d}\) & permeability coefficient of near-wellbore zone, \(\mu\)m\({}^{2}\) \\ \(\tau_{d}\) & drainage area radius, m \\ \(\tau_{w}\) & well radius, m \\ \(d\) & fractal dimension of pore space structure in bottom-hole zone \\ \(Ac\) & acid value \\ \(q\) & rate of acid composition injection, m\({}^{3}\)/s \\ \(D\) & diffusion coefficient, m\({}^{2}\)/s \\ \(h\) & perforated thickness of formation, m \\ \(b\) & a constant taken as equal to 1.7\(\cdot\)10\({}^{4}\) m\({}^{d-2}\) \\ \(V\) & volume of injected acid composition, m\({}^{3}\); \\ \(m\) & porosity, \%; \\ \(x\) & limestone content in rock, \% \\ \end{tabular}  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Oil flow Rate,**} & \multicolumn{2}{c}{**Cumulative Oil**} & \multicolumn{2}{c}{**Liquid Flow Rate,**} & \multicolumn{2}{c}{**Cumulative Liquid**} \\  & \multicolumn{2}{c}{**m\({}^{3}\)/day**} & \multicolumn{2}{c}{**Production, Thous. m\({}^{3}\)**} & \multicolumn{2}{c}{**m\({}^{3}\)/day**} & \multicolumn{2}{c}{**Production, Thous. m\({}^{3}\)**} \\ \cline{2-10}  & **Basic** & **Design** & **Basic** & **Design** & **Basic** & **Design** & **Basic** & **Design** \\ \hline January2019 & 1.7891 & 1.7891 & 49.4261 & 49.4261 & 3.2110 & 3.2110 & 59.9109 & 59.9109 \\ January2020 & 1.6641 & 6.2640 & 50.0560 & 50.9784 & 3.2110 & 19.0929 & 61.0861 & 64.1740 \\ January2021 & 1.5575 & 5.1407 & 50.6417 & 53.0355 & 3.2110 & 17.9362 & 62.2581 & 70.8852 \\ January2022 & 1.4647 & 4.4581 & 51.1918 & 54.7637 & 3.2110 & 17.4499 & 63.401 & 77.3300 \\ January2023 & 1.3641 & 4.0507 & 51.7069 & 56.3068 & 3.2110 & 17.2217 & 64.6021 & 83.6478 \\ January2024 & 1.2422 & 3.7704 & 52.1820 & 57.7304 & 3.2110 & 17.1556 & 65.7774 & 89.9339 \\ January2025 & 1.1376 & 3.5840 & 52.6136 & 59.0676 & 3.2110 & 17.2215 & 66.9494 & 96.2056 \\ January2026 & 1.0562 & 3.4548 & 53.0123 & 60.3496 & 3.2110 & 17.3781 & 68.1214 & 102.5209 \\ January2027 & 0.9885 & 3.3365 & 53.3841 & 61.5867 & 3.2110 & 17.5682 & 69.2934 & 108.9009 \\ January2028 & 0.9305 & 3.2241 & 53.7340 & 62.7857 & 3.2110 & 17.7579 & 70.4686 & 115.3689 \\ January2029 & 0.8836 & 3.1110 & 54.0639 & 63.9400 & 3.2110 & 17.9399 & 71.6407 & 121.8859 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of simulation of the basic and design variants.
## 7 Conclusions  
We investigated the contribution of MSFs to vocal emotion recognition of NVS in noisy and/or reverberant environments. To clarify the relationship between MSFs and previous results from vocal-emotion-recognition experiments in noisy and/or reverberant environments, we analyzed the results of those experiments regarding NVS, and then obtained modulation spectrograms of NVS by using an auditory-based modulation filterbank and extracted MSFs from modulation spectrograms. We then calculated the similarity between MSFs and the vocal-emotion-recognition results. The qualitative analysis between modulation spectrograms and discrimination index of the vocal-emotion-recognition results and the quantitative analysis between MSFs and these results indicate that  
1. MSFs can be used to explain the results of vocal emotion recognition in noisy and/or reverberant environments. Except for extremely high noisy reverberant environments, in daily environments, the contribution of MSFs in vocal emotion recognition is not affected by noise and reverberation.
2. There are two common MSFs (MSKT\({}_{k}\) (modulation spectral kurtosis) and MSTL\({}_{k}\) (modulation spectral tilt)) that have high similarity with the vocal-emotion-recognition results in all daily environments.  
MSFs, especially the two common MSFs, are considered to play an important role in vocal emotion recognition, and are considered to have the potential to be used in the explanation of nonlinguistic information perception by using CIs in noisy and/or reverberant environments, which still requires further research. For future work, due to the Fujitsu Japanese Emotional Speech Database used in this study being created using a professional actress (containing five clear acted emotions), these emotions can be considered the typical representations of diverse emotions, but they may differ from the emotional speech communication in our daily lives (containing ambiguous natural emotions). Therefore, it is necessary to use more natural emotion databases containing more emotions to examine the contribution of MSFs to vocal emotion recognition in daily communication.  
Conceptualization, T.G.; resources, Z.Z.; project administration, S.K. and M.U.; supervision, M.U.; writing--original draft, T.G.; writing--review and editing, S.K. and M.U. All authors have read and agreed to the published version of the manuscript.  
This research was funded by JST SPRING (JPMJSP2102), a Grant-in-Aid for Scientific Research (B) (21H03463), a Fund for the Promotion of Joint International Research (Fostering Joint International Research (B)) (20 KK0233), and the SCOPE Program of Ministry of Internal Affairs and Communications (201605002).  
The authors declare no conflict of interest.