esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions
### Conclusions  
In this paper, we propose a novel IPSOGWO-MSVR indoor localization algorithm. It centers on position regression using MSVR and combines adaptive clustering and an autoencoder to process the raw CSI amplitude. Additionally, to further optimize performance, we introduce a hybrid optimality-seeking algorithm for parameter tuning. The results show that compared to the SVR algorithm, MSVR reduces the RMSE by 32% and 45.4% in the two scenarios, and has higher localization stability. In addition, our hybrid optimization search strategy successfully avoids falling into local optima and the training phase converges to the minimum RMSE at the 89 th and 79 th iterations, respectively. In Scenario 1, the optimal combination for the algorithm is(1, 11.8, 0.23), while in Scenario 2, the optimal combination is (0.2, 8.1, 0.47). Compared to current methods, our proposed method demonstrates clear advantages. In order to further improve the practicality and efficiency of the system, future research will focus on reducing the construction time of the fingerprint library, improving the speed of fingerprint matching, and exploring other optimization strategies. Also, given the challenges of data collection in practical applications, we plan to explore the use of mobile devices such as the Nexmon testbed for data collection.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data and wrote the paper. [PERSON] helped in writing the introduction and the related works and critically revised the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Key Research and Development Program of Anhui Province, grant number 202104a07020014; Major Science and Technology Projects of Anhui Province, grant number 202103a05020026; the Research Center of Mining Area Environmental and Disaster Cooperative Monitoring (Anhui University of Science and Technology), grant number KLAHEI202020; and the Key Scientific Research Project of Suzhou University, grant number 2021 yzd03.  
Not applicable.  
The data used in this article are not public.  
**Acknowledgments:** We gratefully acknowledge many important contributions from the authors of all the reports cited in our paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Depth Estimation from a Hierarchical Baseline Stereo with a Developed Light Field Camera
## 3 The Proposed Approach
### Hierarchical Baseline Stereo-Matching
#### 3.3.3 Fine Matching  
Fine matching plays an important role in recovering high-quality local details. Similar to subpixel interpolation in traditional stereo vision, fine matching is applied through sub-macro-pixel interpolation. Since coarse matching is conducted on a macro-pixel-wise basis, sub-macro-pixel interpolation computes pixel-wise matching. If a macro-pixel \(mp_{i}\) matches \(mp_{j}\) with the disparity \(d_{CM}\), the feature vector \(V^{t}\) for pixel-wise matching is recalculated by fusing \(mp_{j-1}\) and \(mp_{j+1}\) as  
\[\mathbf{V}^{t}(d)=\begin{cases}w_{1}V^{-}+(1-w_{1})V&d\geq d_{CM}^{-},d<d_{CM} \\ w_{2}V^{+}+(1-w_{2})V&d\geq d_{CM},d<d_{CM}^{+}\end{cases} \tag{4}\]  
where \(V^{-}\) and \(V^{+}\) are feature vectors of \(mp_{j-1}\) and \(mp_{j+1}\); \(d_{CM}^{+}=d_{CM}+1\), \(d_{CM}^{-}=d_{CM}-1\); if \(\|d-d_{CM}^{-}\|>0.5\), \(w_{1}=1-\|d-d_{CM}^{-}\|\), so do \(w_{2}\).  
Thus, matching is performed between \(V\) and \(V^{t}(d)\) using ZNCC, and the disparity is chosen by WTA. Finally, a pre-trained post-refinement network for bad-pixel correction [19] is directly applied without training. The hierarchical structure has been proven to be effective in eliminating matching ambiguities on textureless and specular reflection surfaces.
# Experimental Study on the Shear Strength of Silt Treated by Xanthan Gum during the Wetting Process
## 3 Results and Discussion
### Water Retention Characteristics Test  
Figure 12 shows the water retention characteristic curve of the XG-silt specimens. Figure 12(a),b show the drying and wetting curves, respectively, and Figure 13(a) shows the drying-wetting hysteresis curve. Figure 12(a),b show that the drying curves all shift to the upper right with increasing XG content, which reflects an increase in the water retention capacity of the treated specimens. Especially in the range of low suction, the water retention capacity of the silt significantly increases with increasing XG content. Because XG carries carboxylic acid (-COOH) and hydroxyl (-OH) with a negative charge, it can interact with cations in the soil particles to generate a capillary force, thus absorbing and retaining more water [46].  
Figure 12: Mercury intrusion pore size distribution: (**a**) cumulative curves; (**b**) differential curves.  
Figure 14 shows that the water retention capacity of the treated specimens during the drying process is stronger than that during the wetting process, which is related to the water retention hysteretic effect of the soil during drying [47; 48; 49]. The hysteretic circle area between the drying and wetting curves of the treated specimens also gradually increases with increasing XG content. The drying and wetting curves of specimens tend to be consistent in the high-suction range [50].  
The SEM and mercury intrusion test results show that more biofilms covered the surface of the soil particles with increasing XG content, and the bridging connections are found in the silt pores, which gradually filled with XG. The water retention capacity of the treated silt increased due to the strong water-retaining properties of XG [25; 26; 29; 51], and the hysteretic circle area between the drying and wetting curves also gradually increased. Previous experimental studies show that more water can be retained in soil pores after treatment with XG, which is consistent with the water retention characteristics test results of this study. Additionally, XG has a stronger water retention capacity than other polymers, which can improve the survival rate of vegetation in severely arid areas [19; 52].  
Figure 14: Drying–wetting hysteresis curve of silt with different XG contents.  
Figure 13: Water retention curves of silt with different XG contents: (**a**) drying process; (**b**) wetting process.
## 2 Principles of Eddy Current Testing and Probe Design  
### Principles of Eddy Current Testing  
Eddy current testing is based on the electromagnetic induction between the probe and the conductive material being tested [21]. The equivalent circuit diagram of the probe is shown in Figure 1, where the eddy current testing signal comes from the variation in induced voltage in the secondary coil. When an AC current \(I_{1}\) is passed through the excitation coil, under electromagnetic induction, an induced current \(I_{2}\) is generated in the detection coil. Conversely, \(I_{2}\) also affects the relationship between current and voltage in the excitation coil. Moreover, there is mutual inductance \(M_{12}\) (\(M_{21}\)) between the two coils.  
From Figure 1a, the output voltage \(U_{2}\) of the detection coil without sample is:  
\[U_{2}=Z_{2}I_{2}-j\omega M_{21}I_{1}, \tag{1}\]  
where \(Z_{2}\) is the impedance of the detection coil. According to the impedance formula, neglecting the capacitance due to the distribution of coil turns as follows:  
\[Z_{2}=R_{2}+j\omega L_{2}, \tag{2}\]  
Figure 1: Equivalent circuits of probe: (**a**) without sample; (**b**) with sample.  
From Figure 1b, the output voltage \(U_{2}\) of the detection coil with sample is:  
\[U_{2}=Z_{2}I_{2}-j\omega M_{21}I_{1}-j\omega M_{32}I_{3}, \tag{3}\]  
where \(I_{3}\) is the equivalent current of eddy currents induced in the sample by the coil, and \(M_{32}\) is the mutual inductance between the detection coil and the sample. From Equation (2), it can be observed that without a sample, the output voltage of the detection coil depends on the loop voltage after the coil generates the induced current and the voltage directly induced by the mutual inductance between the excitation coil and the detection coil. From Equation (3), the output voltage of the detection coil also includes the voltage generated by the mutual inductance \(M_{32}\) between the detection coil and the sample [22]. This part of the voltage signal can reflect defect information, which is the useful signal, while the voltage generated by the direct mutual inductance (\(M_{21}\)) between the excitation coil and the detection coil is considered the interference signal. If the interference signal is too strong, it may cause the effective signal to be submerged in noise, thereby reducing the detection sensitivity.  
In conclusion, in order to achieve the signal processing function of eddy current testing equipment and improve its sensitivity, it is necessary to reduce interference signals and increase useful signals. This means reducing the voltage generated by the direct mutual inductance between the excitation coil and the detection coil while increasing the voltage generated by the mutual inductance between the detection coil and the CFRP sample.
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 4 Simulation-Based Performance Metrics
### Case-Study 2: LoRa, Simulation-Based Results, Time Domain  
A chirp spread spectrum (CSS) system with a 125 kHz bandwidth and a spreading factor of 7 was used in the simulations. It was assumed that we have a single-floor square indoor area of 200 m \(\times\) 200 m size, in which \(N_{AN}\) access nodes are distributed uniformly, with \(N_{AN}\) between 3 and 100. Ten thousand Monte Carlo iterations were used to generate randomly the position of the ANs and of the IoT device. The positioning was based on TOA principle, where the TOA was estimated based on the correlation between the incoming signal and a reference CSS code. The results are shown in Figure 7 in terms of cumulative distribution function (CDF) of error, for a different number of LoRA access nodes, respectively. For three access nodes and at an SNR = \(-\)18 dB, the positioning error is higher than 50 m in more than 50% of cases. On the other hand, with 100 access nodes distributed in the 0.4 km\({}^{2}\) area, we can reach below 10 m accuracy in more than 50% of cases.
## 3 Results and Discussions
### Dimensionality Reduction through Important Data Feature Selection Using RF
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Locations**} & \multicolumn{2}{c}{**Proposed Hybrid RF-PS Model**} & \multicolumn{4}{c}{**Standard PS Model Coefficients**} & \multicolumn{4}{c}{**Standard Hybrid RBF-PS Model**} \\  & \multicolumn{2}{c}{**Coefficients**} & \multicolumn{4}{c}{**Coefficients**} & \multicolumn{4}{c}{**Coefficients**} \\ \hline
**Parameters** & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) \\ \hline Asaba 1 & 831.05 & 21.53 & \(-\)217.29 & \(-\)102.01 & 45.19 & 305.01 & \(-\)339.69 & 6.63 & 136.94 \\ \hline Asaba 2 & 502.79 & 22.54 & \(-\)123.06 & \(-\)1484.23 & 19.07 & 462.73 & \(-\)351.37 & 22.01 & 128.70 \\ \hline Asaba 3 & 78.16 & 22.94 & 1.09 & 1288.98 & 25.43 & \(-\)355.39 & 2201.98 & 23.23 & 621.04 \\ \hline Average & 470.66 & 22.37 & \(-\)113.08 & \(-\)406.09 & 29.90 & 137.45 & 503.64 & 17.29 & 18.47 \\ \hline Ontisha 1 & \(-\)235.18 & 39.37 & 87.14 & \(-\)497.57 & 39.70 & 157.86 & 1612.03 & 40.01 & 460.11 \\ \hline Ontisha 2 & 13.22 & 27.66 & \(-\)19.47 & 1341.66 & 24.50 & 369.47 & 1341.66 & 24.50 & \(-\)369.47 \\ \hline Ontisha 3 & 838.77 & 27.66 & 218.94 & 396.71 & 20.78 & 91.01 & \(-\)184.69 & 20.99 & 79.10 \\ \hline Average & 198.77 & 27.66 & \(-\)47.44 & 413.60 & 28.33 & \(-\)100.95 & 923.00 & 28.25 & 56.58 \\ \hline Awka 1 & \(-\)2631 & 27.62 & 790.42 & \(-\)175.93 & 27.40 & 71.52 & 1763.98 & 27.40 & 496.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Coefficients of the developed path loss model using the proposed hybrid RF-PS and other existing standard approaches.
## 3 Results
### Characteristics of the Sequencing Data  
As a result of quality control, an average of 77,311 high-quality sequences were obtained from each bacon sample (Table 1), which agreed with previous works in which the number of reads about each fermentation meat ranged from 33,983 to 170,428 [25]. All sequences were assembled to 1364 OTUs based on a 97% identity level. The indices of alpha diversity are shown in Table 2. Sufficient coverage of data was proven by up to 99% of the Good's coverage for all the samples. Although the amount of OTUs in each sample fluctuated obviously, ranging from 53 to 769, there was no difference between the basin, mountains, and plateau groups, owing to the total OTUs that were clustered to 1156, 1360, and 1298, respectively. In the meantime, the amount of shared OTUs among the three regions was up to 1101 (Figure 1), close to 81% of the total number of OTUs, indicating a high level of similarity in the bacteria compositions among the three groups. Traditional Sichuan bacon has developed its own style through long-term development, and the key formulations and manufacturing processes have remained almost unchanged since the Ming Dynasty, although a wide variety of bacons come from the food preferences of the different regions in Sichuan. This may have been the reason why the bacterial diversity of bacon in different regions was similar.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Groups**} \\ \cline{2-4}  & **Basin (\(\boldsymbol{n=14}\))** & **Mountains (\(\boldsymbol{n=15}\))** & **Plateau (\(\boldsymbol{n=10}\))** \\ \hline  & **Aw** & & \\ \hline Means \(\pm\) SE & 0.814 \(\pm\) 0.037 b  & 0.855 \(\pm\) 0.023 a  & 0.879 \(\pm\) 0.049 a  \\ Range & 0.761\(\sim\)0.924 & 0.825\(\sim\)0.912 & 0.828\(\sim\)0.974 \\ \hline  & \multicolumn{3}{c}{**pH**} \\ \hline Means \(\pm\) SE & 5.45 \(\pm\) 0.36 b  & 5.68 \(\pm\) 0.30 ab  & 5.92 \(\pm\) 0.25 a  \\ Range & 5.02\(\sim\)6.03 & 5.27\(\sim\)6.48 & 5.59–6.44 \\ \hline  & \multicolumn{3}{c}{**Salt content A**} \\ \hline Means \(\pm\) SE & 3.37 \(\pm\) 1.19 & 3.00 \(\pm\) 0.86 & 2.807 \(\pm\) 1.359 \\ Range & 1.88–5.24 & 1.87\(\sim\)4.95 & 1.01–5.53 \\ \hline  & \multicolumn{3}{c}{**Nitrite content B**} \\ \hline Means \(\pm\) SE & 1.12 \(\pm\) 0.80 b  & 4.80 \(\pm\) 3.46 a  & 2.57 \(\pm\) 2.15 b  \\ Range & 0.21–2.39 & 0.56–13.34 & 0.35–8.06 \\ \hline  & \multicolumn{3}{c}{**Peroxide value C**} \\ \hline Means \(\pm\) SE & 17.73 \(\pm\) 17.22 & 13.59 \(\pm\) 9.29 & 13.51 \(\pm\) 6.70 \\ Range & 1.54–61.70 & 3.40–34.00 & 7.59–26.58 \\ \hline \hline \end{tabular} Results are expressed as means with standard errors including 14, 15, and 10 samples, respectively, and each sample data were means of three replicates. A Expressed as g/100 g muscle. B Expressed as mg/kg dry basis. C Expressed as mg/kg dry basis. (\(\
u<\) 0.05).  
\end{table}
Table 2: Physicochemical indices in Sichuan bacon subjected to three groups of different regions.
# A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers
## 4 Decision Support System Workflow
### Data Pre-Processing  
The decision support system is fed with measurements collected by means of the acquisition platform described in Section 3. Each new data example is first directed to the data preprocessing service. At this stage, it is possible, e.g., to eliminate obviously  
Figure 1: Illustration of the LPG monitoring environment.  
Figure 2: General workflow of the decision support system (DSS) created.  
erroneous entries (e.g., data outside of the measurement range) or to reduce the frequency of data recording (by omitting some measurements).  
Several transformations applied at this stage are related to the domain in which the DSS created is applied. Figure 3 presents an example plot of LPG level measurements for over a year. It contains several artifacts that have to be addressed at an early stage of the transformations.  
One of the fundamental issues for a gas consumption metering and prediction system is the identification of deliveries. Tank refueling is visible on the chart in Figure 3 as a significant and instant increase of the gas level. When calculating gas consumption as the difference in gas level for a given day and the day before, this results in a large negative consumption value that should be set to zero. Typical delivery results in an increase of the gas level by more than 40% of the tank capacity; however, a smaller threshold value (e.g., 10%) should be set.  
Negative consumption, but of small value, can also result from the change in gas volume due to temperature changes. This phenomena is visible in Figure 3 between Months 7 and 10 as a very small increase in the gas level. In such a case, the gas consumption value is set to zero, and the gas level preceding the growth is retained in order to be used for further calculation of consumption in the next day. Another problem can be the low sensitivity of the gauge (resulting from its construction utilizing a float). This can cause only significant drops in the gas level in the tank to be registered. In such a case, the same conditions can result in significant gas consumption or a lack of consumption, which leads to an incoherent data set. This can take place especially in the case of small households with low gas consumption. The solution to this problem is averaging the gas consumption values with the use of a sliding window.  
Data initially processed are then sent to the data store. This data repository can be designed to store different data structures, and it can consist of different databases. Except measurements collected from LPG tanks, it can store weather data used to generate gas consumption models and to predict the value of gas consumption. The current weather conditions, such as temperature, could be collected by the LPG tanks. However, the temperature sensors mounted on tanks are not reliable because they are vulnerable to sunlight exposure. Additionally, weather data include weather forecasts, which are used to generate gas consumption prediction.
# Lawn Bonitation Value as a Function of Glycine-Complexed Iron Formulation Application
## 1 Introduction  
Grass species intended for lawn use should be characterized by high aesthetic values, uniform turf cover, and slow regrowth after moving [1, 2]. One of the essential features of lawn evaluation is the overall aesthetic aspect [3, 4]. The assessment of this feature's value and that of the regrowth rate constitute decisive factors for qualifying a variety of lawn grasses. The assessment of the general aspect is highly correlated with other traits, such as the turf, regrowth rate, or leaf perfection [4, 5].  
One of the many factors significantly influencing the appearance of a lawn is its degree of fertilization with macro- and microelements. An essential nutrient for all organisms is iron (Fe) [6]. Iron plays a vital role in plants, and it participates in many physiological processes, including its role as an activator of the biosynthesis of chlorophyll and many proteins [7, 8, 9]. In addition, it affects the proper growth and development of plants and ensures better crop quality parameters.  
In the case of lawn grasses, iron primarily affects a lawn's uniform and intense green color [10]. Iron deficiency leads to a reduction in the content of chlorophyll, a pigment that gives the grass its green color. Therefore, iron deficiency manifests as chlorotic symptoms (yellowing), which negatively affect a lawn's visual qualities. Additionally, iron deficiency can make a lawn less frost-resistant and more dehydrated in winter. It may be necessary to apply the essential minor nutrient Fe to turfgrass growing in soils with less than 2 1/2 ppm of extractable Fe [11]. An iron preparation used on a lawn is also an effective way to eliminate moss from the turf. The preparation not only eliminates the moss problem but also prevents its occurrence in the future.  
Iron deficiency in plants may result from the element's deficiency in the soil; alternatively, although the Fe content in the soil may be high, the available form of Fe\({}^{2+}\) may be low. This is because a large part of it is associated with soil components [7; 12], creating insoluble Fe\({}^{3+}\) bonds. Especially high pH soils are usually poor in the available form of Fe\({}^{2+}\)[8]. Iron availability is also lower in heavy, compact, and poorly permeable soils. Unfavorable humidity conditions, such as heavy rainfall or excessive watering, can also contribute to iron chlorosis on a lawn. Therefore, when there are unfavorable conditions for plant growth, and during the critical stages of this process, it becomes advisable to use appropriate macro- and microelements. The application of foliar iron (Fe) sprays is a standard method of correcting an Fe deficiency in crops [13]. Hence, this study aimed to evaluate the effect of the foliar application of glycine-complexed iron on the utility value of lawn turf under high pH and heavy soil conditions.
## 2 Numerical Model and Analysis  
### Analysis Model and Governing Equations  
The OBC/LDC-integrated power inverter charge/discharge module has a complex structure that includes various electronic components and circuits. The power inverter used in this study is composed of a top cover, main body, cooling plate, and bottom cover. The top and bottom covers are sealed together to protect the electronic components and circuit boards placed inside the module. The main body supports and protects the inner circuits and components. Further, the cooling plate, which is present in the middle, consists of top and bottom plates, and includes the flow multi-channel for cooling water that cools the heated electrical elements in the inverter; these top and bottom plates prevent the leakage of cooling water from the cooling plate. CFD analysis is used to examine the flow characteristics of this cooling water; however, for ease of analysis, the complex real model needs to be simplified based on the purpose of analysis. In particular, for this study, the analytical model can be simplified considering only the cooling water flow path, which is sufficient to examine flow behavior. The basic structure of the OBC/LDC-integrated power inverter used in this study is shown in Figure 1, and the specifications are shown in Table 1.  
The governing equations for flow analysis that are used to verify the flow characteristics of the cooling system include the following steady-state equations [17; 16]:  
\[\
abla\cdot\left(\vec{\rho\,u}\right)=0 \tag{1}\]  
\[\
abla\cdot\left(\vec{\rho\,u\,u}\right)=-\
abla P+\ \
abla\left(u\ \
abla\vec{u}\right) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Parameter** & **Specification** & **Unit** \\ \hline \multirow{4}{*}{Charging (OBC)} & Input Voltage & 85\(\sim\)265 & V, AC \\  & Input Current & 32 & A \\  & Output Current & 20 & A, DC \\  & Output Power & 7.2 & kW \\  & Power density & 1.44 & kW/L \\ \hline \multirow{4}{*}{Inverter (Bidirectional)} & Input Voltage & 240\(\sim\)450 & V, DC \\  & Output Voltage & 85\(\sim\)265 & V, AC \\ \cline{1-1}  & Output Power & 3.3 & kW \\ \hline Size & & \(250\times 180\times 110\) & mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the onboard charger/low-voltage DC-DC converter (OBC/LDC) power inverter system.  
Figure 1: Schematic structure of the OBC/LDC power inverter system with its cooling plate layout.  
\[\
abla\left(\rho k\overrightarrow{u}\right)=\
abla\left[\left(\mu+\frac{\mu_{t}}{ \sigma_{k}}\right)\
abla k\right]+G_{k}+G_{b}-\rho\varepsilon-Y_{M} \tag{3}\]  
\[\
abla\left(\rho\varepsilon\overrightarrow{u}\right)=\
abla\left[\left(\mu+ \frac{\mu_{t}}{\sigma_{k}}\right)\
abla\varepsilon\right]+C_{1\varepsilon} \frac{\varepsilon}{k}(G_{k}+C_{3\varepsilon}G_{b})-C_{2\varepsilon}\rho\frac {\varepsilon^{2}}{k} \tag{4}\]