esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analytical Method Validation  
Matrix influence was eliminated by setting the matrix standard. The results of quantitative analysis using the matrix external standard method showed that within the linear range (0.1-1 mg/kg), the peak area of Pyr showed a good linear relationship with the injection mass concentration in cucumber leaves and roots matrix, with a correlation coefficient (R\({}^{2}\)) better than 0.9996 (Table 5).  
In this study, the feasibility of the method was verified by adding a recovery test. The recovery and reproducibility experiments were carried out for the leaf and root samples in five replicates, at three fortification levels 0.01, 0.1, and 1 mg/kg, respectively. The results of the addition recovery test in Table 5 showed that the recoveries of Pyr were in the range from 74-81% for the cucumber leaves, and 87-97% for the roots, respectively. RSD was 0.54-7.36%, which satisfied the requirements for pesticide residue detection. The results  
Figure 6: LSM images of cucumber root, stem, and leaf, 3 d after carbon quantum dots-MSN treatment. Bar scales: 50 \(\upmu\)m.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Fortified Level** & **Average Recoveries** & **RSD** & **LOD** & **Linear Equation** & **R\({}^{2}\)** \\  & **(mg\(\cdot\)kg\({}^{-1}\))** & **(\%)** & **(\%)** & **(\(\upmu\)g\(\cdot\)kg\({}^{-1}\))** & **Linear Equation** & **R\({}^{2}\)** \\ \hline \multirow{3}{*}{Leaf} & 0.01 & 74 & 0.54 & \multirow{3}{*}{0.1} & \multirow{3}{*}{Y = 153,971,544 x + 1,292,542} & \multirow{3}{*}{0.9997} \\  & 0.1 & 75 & 4.16 & & & \\ \cline{1-1}  & 1 & 81 & 7.36 & & & \\ \hline \multirow{3}{*}{root} & 0.01 & 97 & 5.46 & \multirow{3}{*}{0.1} & \multirow{3}{*}{Y = 155,065,913 x + 16,332,063} & \multirow{3}{*}{0.9996} \\  & 0.1 & 90 & 5.62 & & & \\ \cline{1-1}  & 1 & 87 & 2.34 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average recoveries, RSD (\(n\) = 5), LOD linear equation, and determination coefficients (R\({}^{2}\)) of Pyr in leaves and roots.  
showed that the lowest detected concentration (LOD) of Pyr in the cucumber plants and the roots matrix was 0.1 \(\upmu\)g/kg, and the lowest added level was used to determine the quantitative limit (LOQ).
## 4 Discussion
### Limitations and Prospects  
There are two main shortcomings in this study. One is the analysis of NDVI changes mainly in the context of LULC, which may not accurately describe all the reasons for the changes. There are many factors that affect vegetation coverage change, such as climate, which is an important factor. However, this is not the starting point of this study. The second is that the NDVI data are not made up consecutive annual data, but rather selected for the years 2000, 2005, 2010, 2015, and 2020. The advantage is that they correspond to the LULC data, but the disadvantage is that they may not be able to describe in detail the NDVI changes during the transition period or seasonal or shorter-term vegetation responses, and some levels may show sudden increases or decreases.  
In the future, it is possible to consider (a) collecting continuous NDVI and LULC data for research, taking into account the impact of comprehensive factors such as climate. (b) At the same time, a more detailed analysis of the internal differences in coastal areas is needed, such as between rural and urban areas where there are different economic and environmental dynamics. (c) In addition, it is necessary to strengthen research on the effects of NDVI and LULC changes, such as their impact on the land surface temperature [78; 79; 80].
## 6 Carrier-Based Implementation
### CB-AZS Scheme  
This scheme uses the same ZSS signal given in (10), which is employed for CB-SVM but with the carriers defined in Table 6. For example, the reference signals of phases \(a\) and \(f\) in sector 1, which have the maximum and minimum envelope, are compared with the negative carrier wave, whereas the other reference signals are compared with the positive carrier. The same approach is extended to other sectors.
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 1 Introduction
[25] developed a robust adaptive federal unscented Kalman filter (RAFUKF) SINS/GNSS/VDM combined navigation algorithm. A federal unscented Kalman filter (FUFK) framework was developed to combine the filter variance with the f-parameter to construct a new ISF and to establish quantitative estimation fault detection mechanisms. [PERSON] et al. [26] proposed a robust adaptive localization algorithm that utilizes a robust equivalent weight estimation sub-filter and adaptively allocates the information sharing coefficient using the sub-filter state covariance. [PERSON] et al. [18] proposed a GPS-AOA-SINS integrated positioning scheme with fault detection inserted between the sub-filter and the main filter. The gain matrix of the fault sub-filter is adjusted in real-time to reduce its impact on the entire system. [PERSON] et al. [27] improved the filtering accuracy and system stability by constructing a confidence test model to effectively filter out faulty measurement information and adjusting the system noise covariance in real time. [PERSON] et al. [28] constructed a simplified state chi-square test(SSCST) for fault detection by using the residuals between actual and predicted observations, and they incorporated a time-varying decay factor into the sub-filters to enhance the stability of the system. However, most of these studies focused on specific scenarios and single-sensor anomalies, and they lack a discussion on complex and dynamic scenarios [16], as well as different types of sensor failures. In this paper, an improved adaptive federated filter (IAFKF) algorithm based on a federated Kalman filter was designed for the combination navigation system of INS/GNSS/VNS, which is used for unmanned vehicles in obstructed spaces. INS/GNSS and INS/VNS fusion navigation models are established. In the event of possible GNSS and VNS failures, a fault detection algorithm combining the residual chi-square and sliding-window average methods is proposed, which can better detect and isolate fault signals. An adaptive information sharing factor is also proposed to improve the positioning accuracy and fault tolerance by adjusting the error covariance matrix. Finally, the effectiveness of the proposed improved AFKF algorithm was verified through simulation experiments. The structure of this paper is as follows: In Section 2, a multi-source fusion model based on federated Kalman filtering is established by combining INS/GNSS and INS/VNS fusion navigation models. In Section 3, a fault detection method combining the residual chi-square test and sliding-window averaging and an adaptive information sharing factor are proposed. In Section 4, the effectiveness and real-time performance of the proposed integrated navigation algorithm are verified through simulation experiments. Finally, conclusions are drawn in Section 5.
## Appendix A Technical Details on Hyperparameter Optimization
In this case, a 3-fold cross-validation is used, which means that the dataset has been divided into three parts, and the model has been trained and validated three times for each combination of hyperparameters. After completing the Grid Search, the best-performing model is selected based on the chosen evaluation metric, which in this case was accuracy. The optimal parameters identified were: n_estimators = 300, max_depth = 30, min_samples_split = 2, min_samples_leaf = 1, and max_features ='sqrt'. The optimal parameters were selected based on straightforward logic, focusing on achieving model accuracy while avoiding overfitting:  
* n_estimators: Increasing the number of trees improves model performance by reducing variance. However, too many trees can increase computation time without significantly enhancing performance. A value of 300 was chosen as a good compromise between accuracy and computational efficiency. * max_features: Setting this parameter to'sqrt' (the square root of the total number of features) ensures that each tree is trained on a different subset of features. This approach increases the diversity among the trees, reducing their correlation and enhancing the model's robustness. * max_depth: A greater depth enables the model to capture more complex patterns in the data. However, excessive depth can lead to overfitting, where the model fits too closely to the training data and struggles to generalize to new data. A depth of 30 was selected to balance the ability to capture complex patterns while preventing overfitting. * min_samples_leaf and min_samples_split: These parameters help prevent overly specific subdivisions from being created that can lead to overfitting. By setting min_samples_leaf to 1 and min_samples_split to 2, each subdivision contains enough samples to be meaningful, thus improving the generalization of the model. Increasing the number of trees in the Random Forest (n_estimators = 300) and optimizing the max_depth (max_depth = 30) improves model metrics. This enhancement indicates that the model is now more accurate and reliable for predicting flood risk, resulting in reduced forecast errors and better explanations of data variability. Searching for the optimal parameters corresponding to the number of combinations specified in the grid required 216 iterations.
###### Abstract  
In small spaces in which typical cell towers cannot be constructed, distributed antenna systems (DASs) are the preferable approach for increasing network coverage, as they are a superior solution for congested, high-volume areas. However, due to their high cost and complexity in backhaul routing, reconfigurable intelligent surfaces (RISs) are a promising solution to overcome the major drawbacks of DAS systems while improving network coverage. Thus, this work investigated a correlation of execution in an RIS-aided system cell framework and DAS-aided system cell framework where a simple and precise structure for the performance measurement of area spectral efficiency (ASE) and energy efficiency (EE) under realistic channel presumptions was introduced. The analysis started with the downlink ergodic capacity with regards to the RIS framework and DAS framework under a generalized Nakagami-m fading channel with the presence of path-loss attenuation and interference from co-channel base stations (BSs), and was simplified further by utilizing a moment-generating function (MGF)-based approach. From the computed expression, the effects of traffic activity, EE and ASE were derived and analyzed for both systems in the presence of co-channel interference. The results were then verified by comparing them with Monte Carlo simulations, and the findings show that the two outcomes generally match. Based on these, it is demonstrated that the ASE performance of the RIS-assisted system in various traffic activity conditions outperforms the DAS-aided system; however, in high signal-to-noise (SNR) regions with full traffic activity, the ASEs are highest for both systems; by only 0.005 bits/s/Hz/km2.  
area spectral efficiency (ASE); co-channel interference (CCI); distributed antenna system (DAS); energy efficiency; ergodic capacity; reconfigurable intelligent surface (RIS); traffic activity factor Article
## 4 Discussions
Table 2 lists the RMSE and RRMSE values for a comparison with the measured values of \(f_{o}\)F\({}_{2}\) and predicted values of CCIR, URSI, and SRPM under different conditions, corresponding to two solar activity epochs (high and low), the four seasonal periods (Spring, Summer, Autumn and Winter), four local time periods (midnight, sunrise, noon, and sunset), and three latitude regions (high, middle, and low). These conclusions are drawn from Figure 12 and Table 2:  
1. The SRPM is superior to CCIR and URSI models in both high and low activity years. As well, the overall percentage error difference between IRI and SRPM during high solar activity is higher than that during low solar activity;  
2. Similarly, the SRPM is superior to CCIR and URSI models during four seasons. As well, the overall percentage error difference between IRI and SRPM is the maximum during spring and is the minimum during autumn. Specially, the variety trend of RMSE of CCIR is very similar to that of URSI during January to December as shown in Figure 12b;  
3. It is easy to see that the SRPM is superior to CCIR and URSI models during different time sectors as shown in Figure 12c. Specially, the overall RMSEs during afternoon are minimum than other time sectors. There are similar for three models. As well the overall percentage error difference  
Figure 12: Root-mean-square error between measured value and predicted value of IRI model (using both URSI and CCIR coefficients) and SRPM. (**a**) Root-mean-square error of three prediction method during high and low solar epochs; (**b**) root-mean-square error of three prediction method in 12 months of the total year; (**c**) root-mean-square error of three prediction method at 24 h of the total day; (**d**) root-mean-square error of three prediction method over high, middle and low latitude regions. from the test stations between IRI and SRPM is the maximum at the time sector of sunrise and is the minimum at the time sector of sunset;  
4. The trend of RMSE from the test stations for CCIR are very similar to that for URSI during January to December as shown in Figure 12d. The RMSEs for CCIR and URSI in middle latitude region are the minimum than other regions, while the RMSEs for SRPM in three regions are very similar;  
5. The average of RMSEs from the test stations for CCIR and URSI and SRPM are 1.082 MHz, 1.079 MHz, and 0.633 MHz respectively. As well, the average of the relative RMSE differences for CCIR and URSI and SRPM are 20.420%, 20.742%, 12.246% respectively. Especially, the SRPM model is better than the IRI model (CCIR and URSI) on average by a margin in the order of 8% (8.174% and 8.495%);  
In summary, the predicted results from the proposed model SRPM performed better than those from the IRI model including both the CCIR and URSI. In other words, the SRPM improves on the IRI model.
# Impact Fatigue of Viscoelastic Materials Subjected to Pounding
## 1 Introduction
In order to apply different types of impacts, researchers have proposed several testing machines, such as weight dropping machines [20, 21, 22], swinging pendulum machines [23], and rotary machines [24]. Influencing parameters, such as testing temperature [25], the number of impacts [23] and contact time [22], were studied. However, these studies focus mainly on the tensile fatigue of the viscoelastic material, while the viscoelastic material of the PTMD only undertakes the compressional impacts. Moreover, the samples investigated in the literature are manufactured as thin plates, and these studies mainly focus on the fracture process of the samples. These studies are all different from the dynamic loadings that a viscoelastic material will experience in the PTMD. Therefore, an impact-fatigue study on the viscoelastic material of the PTMD is still necessary. This paper presents an experimental study of the impact fatigue process of the viscoelastic material of the PTMD. The experimental device is based on a prototype PTMD used for the vibration control  
Figure 1: Schematic of (**a**) tuned mass damper (TMD); (**b**) pounding tuned mass damper (PTMD). of pipeline structures. It is specially designed to generate cyclic lateral impacts on the specimen. The bounding stiffness is employed to interpret the fatigue process of the viscoelastic material. **2. Impact-Fatigue Test**  
The impact-fatigue test is performed by using the experimental apparatus shown in Figure 2. The device consists of an L-shape structure, an exciter, an impact component, and sensors. The L-shape structure is made up of two steel rods and an added mass. The two steel rods are placed in the vertical and horizontal direction; their diameter is 12 mm. The added mass is fixed at the free end of the horizontal rod, and its mass is 1.5 kg. The exciter includes a motor (Pololu, Las Vegas, NV, USA) and an unbalanced mass. It can force the L-shape structure to oscillate in the vertical direction, and the horizontal rod will collide with the impact component under it. The specimen, which is adhered on the surface of the impact component, is a seven-layer 3 M VHB4936 dual adhesive viscoelastic material (Minnesota Mining and Manufacturing, St. Paul, MN, USA), which is also used in the actual PTMDs. Beneath the specimen, a load cell is installed to measure the bounding force. A laser sensor is placed 100 mm beneath the mass block to measure the displacement of the mass. The sampling frequency is 1 kHz. The testing temperature was kept around 20 \({}^{\circ}\)C.
## 2 Scenarios  
To illustrate the typical requirements, we use the following scenarios with some potential users such as [PERSON], a citizen concerned about the air quality around him, little [PERSON], the schoolboy that needs access to educational materials, [PERSON], the businessman running a taxi company, grandfather [PERSON] who requires continuous health monitoring device and the Benin Government that has installed distributed base stations in the city of Cotonou to sense the air quality as well as to gather the electrical consumption measurements. [PERSON] needs to know the position of his taxis even where there is no Internet access. Grandfather [PERSON] has a heart disease and is fitted with a heart rate monitoring device that can send alert when urgent assistance is required.  
These examples show the diverse needs that can all be met with a proper data communication network. In Section 3, we describe some of the previous related works. In Section 4, we deal with the requirements of the proposed Base Stations, while its general architecture is described in Section 5, with the proof of concept in Section 6. Performance tests are described in Section 7, and conclusions are in Section 8.  
Figure 3: Internet latency in Africa.
_applied_  
_sciences_  
_Article_  
**Fabrication of a Porous Three-Dimensional Scaffold with Interconnected Flow Channels: Co-Cultured Liver Cells and In Vitro Hemocompatibility Assessment**  
**[PERSON] 1,1,** [PERSON] 2,*,1,** [PERSON] 1, [PERSON] 1, [PERSON] 3, [PERSON] 3, [PERSON] 3, [PERSON] 2, [PERSON] 3 and [PERSON] 1**  
1 Institute of Industrial Science (IIS), University of Tokyo, 4-6-1 Komaba, Tokyo 153-8505, Japan;  
[EMAIL_ADDRESS] (M.L.); [EMAIL_ADDRESS] (M.N.); [EMAIL_ADDRESS] (M.S.);  
[EMAIL_ADDRESS] (T.N.)  
2 Department of Bioengineering, Graduate School of Engineering, University of Tokyo, 7-3-1 Hongo, Tokyo 13-8654, Japan; [EMAIL_ADDRESS]  
3 Department of Chemical System Engineering, Graduate School of Engineering, University of Tokyo, 7-3-1 Hongo, Tokyo 113-8654, Japan; [EMAIL_ADDRESS] (K.I.);  
[EMAIL_ADDRESS] (M.D.); [EMAIL_ADDRESS] (M.N.);  
[EMAIL_ADDRESS] (Y.S)  
4 Corresponded: [PERSON]+81-070-2646-4073  
\(\dagger\)Contributed equally to this work as co-first authors.  
**Abstract:** The development of large-scale human liver scaffolds equipped with interconnected flow channels in three-dimensional space offers a promising strategy for the advancement of liver tissue engineering. Tissue-engineered scaffold must be blood-compatible to address the demand for clinical transplantable liver tissue. Here, we demonstrate the construction of 3-D macro scaffold with interconnected flow channels using the selective laser sintering (SLS) fabrication method. The accuracy of the printed flow channels was ensured by the incorporation of polyglycolic acid (PGA) microparticles as porogens over the conventional method of NaCl salt leaching. The fabricated scaffold was populated with Hep G2, followed by endothelial with endothelial cells (ECs) grown under perfusion of culture medium for up to 10 days. The EC covered scaffold was perfused with platelet-rich plasma for the assessment of hemocompatibility to examine its antiplatelet adhesion properties. Both Hep G2-covered scaffolds exhibited a markedly different albumin production, glucose metabolism and lactate production when compared to EC-Hep G2-covered scaffold. Most importantly, EC-Hep G2-covered scaffold retained the antiplatelet adhesion property associated with the perfusion of platelet-rich plasma through the construct. These results show the potential of fabricating a 3-D scaffold with interconnected flow channels, enabling the perfusion of whole blood and circumventing the limitation of blood compatibility for engineering transplantable liver tissue.  
**Keywords:** selective laser sintering (SLS); PGA porogen; liver tissue; hemocompatibility; endothelium; antiplatelet adhesion
# Rapid Soil Tests for Assessing Soil Health
## 3 Results
### Step 1b: Extractability of Nutrients and Heavy Metals with 0.01 M CaCl\({}_{2}\)  
All essential macro- and micronutrients for plants (N, S, P, K, Mg, Na, Mn, Fe, Cu, Zn, Ni, B, and Mo) are being assessed through multi-nutrient extractions with 0.01 M CaCl\({}_{2}\) (apart from Ca and Cl). In addition, two elements that are essential for animals and humans have been included (Se, Co), together with pH and silicon (Si) ([PERSON] et al., 2022) [42]. Bio-available (heavy) metals are also routinely analyzed since 2024; extracted quantities commonly decrease in the following order: aluminum (Al) \(>\) titanium (Ti)-chromium (Cr)-vanadium (V) \(>\) arsenic (As) \(>\) cadmium (Cd) \(>\) chromium (Cr)-lead (Pb) \(>\) tin (Sn), although the order depends, in part, on soil pH (Figure 3, Table 2).  
Generally, the intensity of metal elements Al, Cd, Cr, Pb, and Ni was low in high-pH soils, and highly variable in low-pH soils (Figure 3). Conversely, the concentration of As was often low at low pH and showed a wide range as the pH increased. The other metal elements (Ti, V, and Sn) did not vary much with the soil pH status.
## 1 Introduction  
### Background  
An accurate Short-Term Load or Demand Forecast (STLF) system is essential to the establishment an effective power planning and generation system and to the real-time operation of utilities. By providing accurate prediction of demand, generators can produce optimal power levels and save energy resources while ensuring that utilities have enough time to prepare for scheduling and balancing the electrical grid and related systems.  
A balanced grid ensures a consistent supply of electrical power while accounting for demand and market factors, which ultimately lowers costs for consumers, reduces risk, and protects the utility provider [1, 2, 3, 4]. Accurate load forecasting allows for more efficient power markets and a better understanding of the demand profile while taking the power dynamics into account. In electrical engineering jargon, the term _load_ is commonly used to refer to _electricity demand_[5]. Throughout this paper, the terms _load_ and _electricity demand_ are used interchangeably.