esa-sceva/satcom-chunk-collection huggingface dataset:
# Crop Yield Prediction Based on Agrometeorological Indexes and Remote Sensing Data
## 4 Results
### Yield Estimations Models Built in Jilin and Liaoning Provinces  
#### 4.2.1 Models Built Using Variables Selected by Random Forest  
Based on the first method described in Section 3.2, for each variable group and each province, 24 yield estimation models were established and validated (Figure 4). In Jilin Province, the accuracy of the model established by the GPP variable group fluctuated continuously. The increase in independent variables does not significantly improve the overall accuracy of the yield estimation models in Jilin Province. For the GPP variable group, the MARE values of the 24 models ranged from 0.0993 to 0.1104, the R\({}^{2}\) values ranged from 0.7410 to 0.7703, and the CCC values ranged 0.8730 to 0.9093, respectively (Figure 4a). For the NDVI variable group, the MARE values of the 24 models ranged from 0.0925 to 0.1086, the R\({}^{2}\) values ranged from 0.7410 to 0.7703, and the CCC values ranged from 0.8730 to 0.9093, respectively (Figure 4b). Overall, taking all three validation measurements (MARE, R\({}^{2}\), and CCC) into account, when the number of independent variables was 24, the accuracy of the model reached the highest for each variable group in Jilin Province.  
In Liaoning Province, the accuracy of the yield estimation models increases with increases in the number of independent variables. For the GPP variable group, the MARE values of the 24 models ranged from 0.1326 to 0.1679, the R\({}^{2}\) values ranged from 0.5766 to 0.7148, and the CCC values ranged from 0.7176 to 0.8592, respectively (Figure 4c). For the NDVI variable group, the MARE values of the 24 models ranged from 0.1242 to 0.1480, the R\({}^{2}\) values ranged from 0.6552 to 0.7466, and the CCC values ranged from 0.7641 to 0.8918, respectively (Figure 4d). The accuracy of the GPP variable group reached its highest when the number of independent variables was 16, and the accuracy of the NDVI variable group reached its highest when the number of independent variables was 17.  
Figure 4: The accuracy of yield estimation models built using variables selected by random forest (**(a)** GPP group in Jilin Province, **(b)** NDVI group in Jilin Province, **(c)** GPP group in Liaoning Province, and **(d)** NDVI group in Liaoning Province).
## 2 Materials and Methods  
### Location, Treatments and Weather Conditions  
The experiment was set up in an olive grove located in the coastal region of Croatia, with a plant protection program under Integrated Pest Management [37]. Fully matured trees from two olive cultivars (Cv.), Istarska Bjelica (IB) and Leccino (L), were explored. Four trees per cultivar represented each treatment with a total of 32 trees included in the trial. For tree maintenance, standard agricultural practices for olive groves in Croatia were followed, which implies two main fertilizations during the inter-harvest period [36].  
In accordance with our previous research [36], different levels of Silicon foliar Applications were conducted 15, 30 and 45 days after anthesis. Each application consisted of an untreated control (C) with different silicon concentrations within Si1 (0.55 g Si/L), Si2 (1.1 g Si/L) and Si3 (2.2 g Si/L) treatment. Olive trees were sprayed until runoff with approximately 5-6 L per tree.  
In May, during the flowering phase, temperatures were within the normal range, with only a slight deviation of 0.2 \({}^{\circ}\)C below the multiyear average. This period was exceptionally dry, with rainfall measuring a total of 30.2 mm, which represented a 49% decrease in average rainfall. Early fruit development in June occurred under very warm conditions, with temperatures 2.4 \({}^{\circ}\)C above the multiyear average. Additionally, the weather was extremely dry, with only 2.9 mm of rainfall, marking a 6% deviation from the norm. Conditions in July were similar to those in June, maintaining high temperatures, while rainfall returned to the normal range at 37.3 mm.  
During the pre-harvest period of the trial year, the weather conditions generally aligned with the 30-year average rainfall for the area. In August, 32.2 mm of rain was recorded, followed by 55.3 mm in September and 76.3 mm in October. However, temperature fluctuations were noted compared to the multiyear averages. In August and September, temperatures increased by 1.2 \({}^{\circ}\)C and 1.6 \({}^{\circ}\)C, respectively, while October saw a decrease of 0.9 \({}^{\circ}\)C compared to the multiyear average (Croatian Meteorological and Hydrological Service, www.meteo.hr, accessed on 20 November2024).
## 3 Results
### Verification of Algorithm Set-Up  
The algorithm setup encompasses several key parameters, including the maximum number of reflections and the types of reflective surfaces. These parameters are systematically verified to ensure the functionality and reliability of the algorithm. By testing various configurations and observing the corresponding outputs, the settings that enhance the accuracy of the noise simulations are optimised, thereby confirming the algorithm's  
Figure 6: (**a**) Floor plan of the factory (Furniture Painting and Assembly factory in Klang Malaysia) with pictures (A1/A2—Painting booth, B1/B2—Sanding booth, C1—Assembly booth); (**b**) drawing of floor plan divided into grids; (**c**) 3D model of the floor plan’s outline.  
robustness in predicting sound propagation within the defined environment. The noise outside the building is not considered here and is assumed to be an enclosed space.  
(A) Max number of reflections  
As outlined in the Section 2, the number of reflections was incrementally increased from zero to assess the impact on the resultant Sound Pressure Level (SPL). Figure 7 illustrates the ray diagrams corresponding to each number of reflections utilised in this verification process. Figure 8 presents a line graph depicting the collected data, which indicates that the results stabilise at two reflections. The testing revealed that the resultant SPL remains consistent across three iterations, from two to four maximum reflections. Consequently, it can be concluded that any additional reflections beyond four contribute negligibly to the results. Therefore, the maximum number of reflections is established at four for subsequent simulations.  
Figure 7 further visualises the ray diagrams for each number of reflections, while Figure 8 illustrates the relationship between the resultant SPL and the number of reflections.  
(B) Reflection surface  
The simulation was conducted using various surface types in MATLAB to evaluate their effects on the resultant Sound Pressure Levels (SPLs). Figure 9 presents a bar graph comparing SPL 1, SPL 2, and the resultant SPL obtained from the verification phase for  
Figure 8: Resultant SPL vs. max number of reflections graph.  
Figure 7: Three-dimensional version of ray diagram of the simulation when reflection is changed from 0, 1, 2, 3 and 4.  
each material used. The results demonstrate slight variations in SPL based on the sound absorption coefficients of the materials. The materials utilised for the floors, ceilings, and walls can be adjusted accordingly within the algorithm. Furthermore, should additional sound-absorbing materials be implemented in the factory, the algorithm can be modified to incorporate these materials, enhancing its adaptability.  
In conclusion, the type of surface material significantly influences sound absorption properties. Therefore, factories can effectively reduce noise levels by optimising material choices, contributing to a safer work environment. The algorithm's capacity to predict noise levels in the presence of sound-absorbing materials allows for proactive noise management strategies within industrial settings.
## 4 Applications of CDs in Cancer Theranosis
### Cancer Therapy via Drug Delivery, PTT, PDT, PTT+PDT, siRNA Delivery, SDT, and Gas Therapy
#### 4.2.7 Gas Therapy  
The gas therapy employs endogenous sagotransmitters (i.e., NO, H\({}_{2}\)S, and CO) to modulate various biological functions within the tumor microenvironment [65; 193; 194; 195]. To date, the L-arginine (Arg) CD study remains the primary example of CD-mediated gas therapy in animal models [65; 195]. [PERSON] et al. developed L-Arg-based CDs (Arg-dots) using L-Arg as the precursor in a microwave method [65]. The Arg-dots were shown to generate NO in H\({}_{2}\)O\({}_{2}\)-rich tumor environments. In vivo evaluations on human female breast cancer adriamycin (ADR) drug-resistant cell line (MCF-7/ADR) tumor-bearing mice revealed that the Arg-dots group exhibited a significant anti-tumor efficacy compared to the control (i.e., PBS) and ADR groups, with the Arg-dots+ADR group showing further suppressed tumor growth over 21 days (Figure 16a,b). This study demonstrates the efficacy of gas therapy on cancer and highlights the superior effectiveness of the combined treatment of Arg-dots and ADR relative to ADR and Arg-dots alone.  
Figure 16: In vivo evaluation of tumor growth inhibition in MCF-7/ADR tumor-bearing mice following treatment with gas therapy: (**a**) the tumor volume growth curves up to 22 days and (**b**) photographs of excised tumors from each treatment group after 22 days [65].  
Figure 15: In vivo evaluation of anti-tumor performance of RB-CDs@RGD in C6 tumor-bearing nude mice using SDT: (**a**) tumor volume growth curves up to 14 days and (**b**) photographs of excised tumor tissues after 14 days. Student’s \(t\)-test (**\(p<0.01\)). \(p<0.05\) is considered to be statistically significant [192].
# Image Transmission over Cognitive Radio Networks for Smart Grid Applications
## 3 Proposed MAC Layer for Image Streaming over CRSNs
### Proposed Objective Function  
It is desirable to \(E(\tau_{ETT}^{i})\) be maximized so that the transmitter node has the highest transmission in the current channel. However, the system's performance must be such that the average time overhead \(E(\tau_{OH}^{i})\) is also minimized. In order to jointly maximize \(E(\tau_{ETT}^{i})\) and minimize \(E(\tau_{OH}^{i})\), we define the objective function \(f\) as a ratio of these two functions as indicated in Equation (16):  
\[f=\frac{E(\tau_{OH}^{i})}{E(\tau_{ETT}^{i})}\Rightarrow min(f)=min(\frac{E( \tau_{OH}^{i})}{E(\tau_{ETT}^{i})}) \tag{16}\]  
So we can write Equation (17):  
\[\frac{E(\tau_{OH}^{i})}{E(\tau_{ETT}^{i})}=\frac{L_{i}\frac{(1-L_{i}^{X})}{(1 -L_{i})}T_{S}+\tau_{P}^{i}\frac{1-L_{i}^{2X}}{1+L_{i}}}{\tau_{P}^{i}(\frac{(1 -L_{i}^{X})}{(1-L_{i})}-\frac{L_{i}+L_{i}^{2X}}{1+L_{i}})} \tag{17}\]  
By replacing the Equations \(P_{f}^{*}=(1-P_{f})\) and \(L_{i}=(1-P_{f})e^{-\lambda_{i}t_{P}^{i}}\) applying the constraints related to the problem variables, the final mathematical presentation of our proposed optimization model is obtained as follows:\[\begin{split} min(\frac{E(\tau_{OH}^{i})}{E(\tau_{ETT}^{i})})=min( \frac{P_{f}^{*}e^{-\lambda_{i}t_{p}^{i}\frac{(1-(P_{f}^{*}e^{-\lambda_{i}t_{p}^{ i}})X)}{(1-P_{f}^{*}e^{-\lambda_{i}t_{p}^{i}})}}T_{S}+\tau_{p}^{i}\frac{(1-(P_{f}^{*}e^{- \lambda_{i}t_{p}^{i}})^{2X})}{1+P_{f}^{*}e^{-\lambda_{i}t_{p}^{i}}}})\\ \tau_{p}^{i}(\frac{(1-(P_{f}^{*}e^{-\lambda_{i}t_{p}^{i}X})}{(1-P_ {f}^{*}e^{-\lambda_{i}t_{p}^{i}})}-\frac{P_{f}^{*}e^{-\lambda_{i}t_{p}^{i}+(P_ {f}^{*}e^{-\lambda_{i}t_{p}^{i}})^{2X}}}{1+P_{f}^{*}e^{-\lambda_{i}t_{p}^{i}}}) )\\ P_{f}^{*}=(1-P_{f})\\ S.t:(1)\,t_{b}\leq\tau_{p}^{i}\leq D_{m},\\ (2)\,1\leq X\leq[\frac{D_{m}}{T_{S}+t_{b}}],\\ (3)\,X(\tau_{p}^{i}+T_{S})\leq D_{m}\\ (4)\,X(\tau_{p}^{i})\leq\tau_{I}.\end{split} \tag{18}\]  
In the following, Figure 6 shows the changes of \(E(\tau_{OH}^{i})/E(\tau_{ETT}^{i})\) in terms of X and \(\tau_{p}^{i}\) in two subfigures with considering all parameters and restrictions in the Equation (18).
# Maximizing the Capacity of Edge Networks with Multicasting
## 9 Conclusions  
To solve the problem that traditional unicast communication schemes greatly add pressure to edge networks, this study proposes that multicasting can improve the capacity of an edge caching system. Multicast communication transmits the same content to many receivers at once, and can effectively alleviate the traffic-load pressure on the edge network because a separate link need not be established for each user. Using this feature, we constructed a cooperative transmission multicast tree, where a content node is selected as the root node. This reduced the cooperative transmission distance for content and improved the network capacity. Given the last-hop multicasting and complete \(k\)-ary multicasting, a lower and upper bound on the system capacity is proposed. The simulation results verify the superiority of the proposed scheme. In future, more effective content distribution methods and root node selection schemes will be investigated to improve the edge system performance in multicasting session scenarios.  
Conceptualization, P.Y.; methodology, S.L.; software, M.L.; validation, C.L. and X.Z.; writing--original draft preparation, P.Y.; writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the National Natural Science Foundation of China under Grant No. 62072159 and No. 61902112, the Science and Technology Research Project of Henan province under Grant No. 222102210011 and No. 232102211061.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
While the mesh fabrication process involves iterative adjustments and comparative analysis of samples post-production, the absence of a comprehensive database for satellite mesh fabrics often results in prolonged development cycles and increased production costs due to the lack of clear criteria for process optimization. Therefore, further theoretical analysis and design optimization research are essential to supplement the fundamental data required for efficient design. In this study, as shown in Figure 1, optical properties were measured using five mesh specimens with different combinations of knitting parameters (OPI, knitting pattern, and number of twists), tailored to meet the electrical and mechanical requirements of the antenna. Additionally, to investigate the variation in transmittance based on the initial tension and direction between the mesh and antenna rib structure, three specimens with different tension directions were analyzed. Based on the measurement results, a thermal model of the mesh reflector was constructed, and an on-orbit thermal analysis was performed to analyze the thermal characteristics and sensitivity of the reflector depending on the mesh parameters when the reflector was exposed to on-orbit thermal environments. Furthermore, we investigated the influence of the deployed solar panels, which is a factor affecting the thermal distortion of the reflector. Figure 1: Methodology flowchart illustrating the major milestones of this study.
## 3 Experimental Results
### Ablation Study  
In this subsection, an ablation is performed to find the quantitative comparisons for the load identification results when from the input of the high- and low-frequency components using wavelet transform and those from the input in the time domain. We choose to use the random loads data in Section 3.2.1 when accelerometers are symmetrically distributed, i.e., the sensor distribution in Figure 4a, in this ablation. For the case of \"time-domain\", the acceleration measurements in the time domain of all six sensors are set to be the input of the network, while for the case of \"ours\", the proposed method is applied. Despite the difference in network inputs, the other details on network design and network training would remain the same. Table 10 shows the quantitative comparisons on the metrics.  
From the comparisons in Table 10, it can be seen that by using the decomposed components as the network input, the proposed approach would obtain a better load identification results in terms of a higher CC and a lower RMSE. The feature extraction in the decomposed components would be helpful for the load identification. However, it should be noted that the computational loads of the proposed method are larger than those of the method using time domain inputs.
# A Precise Prediction of Tunnel Deformation Caused by Circular Foundation Pit Excavation
###### Abstract  
In comparison with tetragonal retaining structures, circular retaining structures have an advantage in terms of controlling the deformation caused by foundation excavation, and are a reasonable choice in engineering practice. Many results have been obtained regarding the effect of tetragonal excavation on the deformation of an adjacent tunnel. Nevertheless, a sufficient understanding of the circular excavation's effect on the deformation of an adjacent tunnel is currently lacking. Therefore, this study focused on the problem of precise predicting tunnel deformation below a circular excavation. A numerical model was established to calculate the tunnel deformation caused by the circular excavation. An advanced nonlinear constitutive model, known as a hypoplasticity model, which can capture path-dependent and strain-dependent soil stiffness even at small strains, was adopted. The models and their associated parameters were calibrated by centrifuge test results reported in the literature. The deformation mechanism was revealed, and the calculated results were compared with those obtained with a square excavation and the same excavation amount. The differences between the deformations caused by these two types of excavation shapes were analyzed. It was found that under equal excavation area conditions, the excavation-induced deformations of the metro tunnel below a circular excavation were approximately 1.18-1.22 times greater than those below a square excavation. The maximum tunnel tensile bending strain caused by the circular excavation was 32% smaller than that caused by the square excavation. By comparing with the measured results, it is proved that the proposed numerical method can provide effective reference for engineers to analyze soil-structure problems.  
advanced model; precise prediction; circular foundation pit; tunnel deformation +
Footnote †: journal: Applied Sciences
## 3 Results
### Fall vs. NFall Groups  
No differences were observed regarding the bioimpedance analysis (BIA) between fallers and non-fallers. Data from the stabilometric test showed a significant difference between Fall and NFall groups regarding the sway area with the open eyes (OE) condition and the maximum speed of oscillation (or sway speed) at OE (respectively \(p\) = 0.016 and \(p\) = 0.003). No difference was found in path length for OE (\(p\) = 0.06) or CE conditions in any group comparison. Fall and NFall groups did not differ in the six-minute walking test (6 MWT) nor in the 10 m walking test (10 MWT). Statistical significance has been detected in the left propulsion at 10 MWT only (\(p\) = 0.03). As for the timed up and go (TUG) test, significant differences were found between Fall and NFall groups in the (i) duration of initial rotation (IR) during TUG test (\(p\) = 0.038), (ii) duration of final rotation (FR) during TUG test (\(p\) = 0.015), (iii) speed of initial rotation during TUG test (\(p\) = 0.019), and (iv) speed of final rotation during TUG test (\(p\) = 0.048) (Table 3).
# Potential Aroma Chemical Fingerprint of Oxidised Coffee Note by HS-SPME-GC-MS and Machine Learning
## 1 Introduction
Furthermore, due to the complexity and dynamics of the chemistry involved, coffee degradation studies have mainly been conducted on a single species, package, or condition [22]. Systematic studies in this direction on different species, packagings, and materials have been conducted only in recent years [7, 20, 23]. Therefore, defining the oxidation chemical footprint of coffee can be an objective valuable tool to be used in screening as a support to the sensory panel in testing new and more sustainable packaging. In these perspectives, \begin{table}
\begin{tabular}{l l l} \hline
**Compound Ratio (Indices)** & **Trend over Time** & **Reference** \\ \hline Acetone/2,3-butanedione & Increase & [16, 21] \\ \hline
2-Butanone/2-methylfuran & Increase & [20] \\ \hline
2-Methylfuran/methanol (M/M) & Increase & [3, 5, 16, 21] \\ \hline
2,3-Butanedione/2-methylfuran & Increase & [16, 20, 21] \\ \hline Dimethyl disulphide/methanediol & Increase & [20] \\ \hline
2-Butanone/methanethiol & Increase & [20] \\ \hline Acetone/propanal & Increase & [16, 17, 18, 19, 20, 21] \\ \hline Thiophene/propanal & Increase & [16, 21] \\ \hline Thiophene/2,3-butanedione & Increase & [16, 21] \\ \hline
2-Methyl butanal/2,3-butanedione & Increase & [16] \\ \hline
2-Methyl butanal/propanal & Increase & [16] \\ \hline
2-Methylfuran/2-butanone (M/B) & Decrease & [3, 16, 21] \\ \hline
2-Methylfuran/2,3-butanedione (M/BD) & Decrease & [3, 16] \\ \hline Methanethiol/hexanal (MT/HE) & Decrease & [3] \\ \hline
2,3-Butanedione/2-methylfuran & Decrease & [21] \\ \hline \end{tabular}
\end{table} 
Table 1: Indices of coffee ageing from a literature survey, data adapted from references column. machine learning tools play a key role in extracting and describing relevant information encrypted in complex data by using different algorithms and visual tools [24; 25; 26; 27; 28]. This study investigates the volatilome of good-quality coffee (from now \"good\" for short) and oxidised coffee (i.e., _Coffea arabica_ (arabica) and _Coffea canephona_ (robusta)) in different packagings (i.e., standard with aluminium barrier and Eco-caps) by combining HS-SPME-GC-MS/FPD with machine learning to define a potential fingerprint describing the oxidised note of roasted coffee.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## Abbreviations  
The following abbreviations are used in this manuscript:  
AD Alzheimer's Disease  
AP Interoposterior  
AV Acceleration Variance  
BESS Balance Error Scoring System  
BBS Berg Balance Score  
CoM Center of Mass  
CTSIB Clinical Test of Sensory Interaction and Balance  
DTW Dynamic Time Warping  
EMG Electromyography  
FA Friedreich's ataxia  
FGA Functional Gait Assessment  
FoG Freezing of Gait  
FRT Functional Reach Test  
GVI Gait Variability Index  
IMU Inertial Measurement Unit  
ISB International Society of Biomechanics  
KNN K-Nearest Neighbors  
LyE Lyapunov Exponent  
MAE Mean Absolute Error  
ML Mediolateral  
MS Multiple Sclerosis  
PCC Pearson Correlation Coefficient  
PD Parkinson's Disease  
RMSE Root Mean Square Error  
RoM Range of Motion  
SEM Standard Error of Measurement  
SEMG Surface Electromyography  
SL Step length  
SLST Single Leg Stance Test  
STA Soft Tissue Artifact  
STD Standard Deviation  
STS Sit to Stand  
SVM Support Vector Machine  
TUG Time Up and Go  
WF Walking Frequency