esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Vegetative Growth  
As shown in Table 3, the morphological features, such as plant height, stem diameter, and leaf area, of the tomato plants were highly affected when the plants were grown under high-salinity treatment (S3) compared with low-salinity treatment (S1, \(\sim\)3.0 dS m\({}^{-1}\)). A significant impact of salinity on tomato plant height was observed across the implemented treatments (Figure 1). The mean plant height reached a maximum value of 193.6 cm under the low-salinity (S1, \(\sim\)2.5 dS m\({}^{-1}\)) treatment, and a minimum height of 126.9 cm was observed for the high-salinity treatment (S3, \(\sim\)9.0 dS m\({}^{-1}\)). A similar trend was noted with regard to stem diameter and leaf area. At a low-salinity level (S1), the leaf area was recorded as 16 m\({}^{2}\) plant\({}^{-1}\), which was 40.3% higher than the leaf area (11.4 m\({}^{2}\) plant\({}^{-1}\)) recorded at S3 salinity level (EC\(\sim\)9.0 dS m\({}^{-1}\)). A lower (10.2 mm) value of stem diameter was observed when the plants were treated at S3 salinity level, which was 36.2% higher compared with the control (S1) treatment. Overall, the moderate salinity (S2) treatment was found to be on par with the S1 treatment with respect to morphological features. Moreover, the mean difference in the value of leaf area and stem diameter across the first and second seasons was 19% and 22%, respectively, under low-salinity water treatment.  
The responses of the plants in terms of morphological parameters to the tested nutrient recipes N1 and N2 produced more or less the same values. A maximum plant height of 171.3 cm was obtained for N2, which was 11% higher compared with the control (N1). This was followed by N3 (160.4-153.3 cm) and N4 (157-161.3 cm). The maximum stem diameter reached up to 12.7 mm in the control (N1), while reductions of 2.4%, 3.6%, and 7.2% in stem-diameter values were observed for the N2, N3, and N4 recipes, respectively. Unlike stem diameter, leaf area was found to be superior at 14.5 m\({}^{2}\) plant\({}^{-1}\) in plants treated with N1 and N2, and a slight reduction was observed for the N3 (14.2 m\({}^{2}\) plant\({}^{-1}\)) and N4 (13.8 m\({}^{2}\) plant\({}^{-1}\)) -treated plants. The results for season 1 showed that the vegetative growth parameters, including plant height, stem diameter, and leaf area, were moderately significantly higher (P = 0.03) under a combination of moderate salinity (S2) and N2 recipe compared with other treatments, but they were not superior to the control (i.e., combination of S1 and N1). However, in season 2, the mean vegetative growth parameters were  
Figure 1: Morphological parameters of tomato plants under different salinity (S) stress and nutrient recipes (N): (**a**) tomato plant height, (**b**) stem diameter, and (**c**) leaf area.; The columns with different lowercase letters are significantly different (\(p\) < 0.05).  
somewhat superior to the control (N1) but comparable to the S1 treatments. The results also indicated that the performance of the grafted plants was better than the non-grafted plants.
## 3 MATLAB and ADAMS Co-Simulation  
### The Single DOF Model of AVRS in the Vertical Direction  
Due to the complex coupling characteristics of the parameters in the system having six degrees of freedom, the AVRS should be transformed into six independent subsystems. To conduct the co-simulation, ADAMS and MATLAB were employed to establish a single DOF model of AVRS in the vertical direction to study the characteristics of FFC and FBC. In this study, ADAMS software was used to simulate the main structure of the system, which is supported by four parallel steel springs, and MATLAB was used to build the control system. The vertical driving force was established at the center of each spring to simulate the vertical linear motor in the actual system. By applying white noise excitation to the base, the passive velocity transmissibility curve, which is critical for model validation, was acquired and is shown in Figure 3.  
The black dotted line represents the passive transfer function curve that was obtained from the sweep frequency test of the ADAMS model. The curve is consistent with the  
Figure 3: Validation of consistency between calculated transfer function (TF) and ADAMS model, and contrast with the LMS measured result.  
mathematical transfer function model shown by the green line in the same figure. The red line is the experimental transmissibility curve that was vertically acquired by external VSE-15D sensors and the LMS-SCADAS test system. The results demonstrate that the simulation model can still well fit the realistic system below 40 Hz even considering the influence of the system's high-frequency noise and high-order modes.
# Exploring the Potential of Sensing for Breast Cancer Detection
## 5 Biosensors for Breast Cancer Detection
### Colorimetric Biosensor  
A colorimetric biosensor can detect the existence of different biomolecules or chemical compounds by using the concept of color change in a solution [97; 98; 99]. In a real experiment, a biosensor contains several sensing elements that can interact simultaneously with the specific analyte, viz., by breaking chemical bonds, generating new bonds, or any chemical reaction that ultimately leads to a color change in the solution or material, and these results are converted by a signal transducer into measurable data.  
In [97], the authors consider HERCEPTIN as an antibody for detecting the HER2/neu receptor, one of the most remarkable biomarkers for breast cancer. For this purpose, the authors constructed a micro-calorimeter by considering micro-electromechanical technology and observed that (a) there was a peak output voltage due to the binding reaction of HERCEPTIN with HER2/neu receptor as well as the temperature difference; (b) the reaction was developed only in hot junctions; (c) the output voltage linearly (nearly) increased with HERCEPTIN quantities; (d) at the edge of the cell membrane, the intensity was dramatically increased, which means the antibody HERCEPTIN developed bonding near the cell membrane of the biomarker. In [98], the authors constructed a sandwich-type Bi2 Se3-AuNPs calorimetric biosensor which has a high LOD of \(10^{-18}\) M and a dynamic range of \(10^{-18}\) M-\(10^{-12}\) M. This calorimetric biosensor was developed under consideration of a three-step multiple signal amplification strategy for early detection of the BRCA1 mutation.
## 3 Results and Discussion
### Confusion Matrix Evaluation  
A confusion matrix serves as a comprehensive framework to evaluate the performance of a classification model against a labeled test dataset. It reports the number of correct and incorrect predictions, categorized as true positives (TPs), false negatives (FNs), false positives (FPs), and true negatives (TNs), and provides insight into the strengths and weaknesses of model performance [38]. This tool is particularly valuable in assessing how well a model performs across different classes, especially in binary classification problems such as landslide vs. non-landslide classification. The classification reports and visual confusion matrices for the selected models (RF, LR, and XGBoost) are presented in Figure 12, while their numerical values and derived metrics are summarized in Table 3.  
To provide a more comprehensive assessment beyond global metrics such as accuracy and AUC, we conducted an in-depth evaluation of confusion matrix-derived indicators. Among the models, the Random Forest classifier demonstrated the highest recall of 90.91%, corresponding to the lowest false-negative rate (FNR) of 9.09%, indicating superior sensitivity to actual landslide occurrences. XGBoost achieved a comparable recall of 89.58% with an FNR of 10.42%, also showing strong capability in detecting hazard-prone zones. In contrast, LR exhibited a lower recall of 82.64% and a higher FNR of 17.36%, suggesting a greater risk of underreporting landslide-susceptible areas. These findings emphasize the importance of incorporating class-specific metrics, particularly recall and FNR, into model evaluation protocols, as relying solely on aggregate measures may obscure critical performance deficiencies. In the context of landslide susceptibility mapping, minimizing false negatives is particularly important, as undetected high-risk zones can lead to severe socio-economic and infrastructural consequences
# Reconstruction Optimization Algorithm of 3D Temperature Distribution Based on Tucker Decomposition
## 4 Reconstruction Parameters Optimization of Tucker Decomposition
### Influence of Core Tensor Dimension  
In the previously published article, the dimension of the core vector was been optimized [9]. This paper uses this calculation method to determine the core tensor dimension of 9 \(\times\) 4 \(\times\) 5 \(\times\) 3. Using the optimized core tensor, the data under five experimental conditions are reconstructed, and the effect of the existence of noise with SNR = 20 dB is analyzed. The results are shown in Figure 7.  
It can be seen from Figure 7 that under the five test conditions, the optimization effect of the core tensor dimension is equally applicable to the measurement with or without noise. The optimized reconstruction algorithm has high reconstruction accuracy and good noise immunity.
## 4 Conclusions  
In order to forecast the energy consumption of China accurately, considering the main driving factors of energy consumption, the novel model of EEMD-ISFLA-LSSVM is proposed in this article. The prediction accuracy of energy consumption is influenced by various factors. In this article, first considering population, GDP, industrial structure (the proportion of the second industry added value), energy consumption structure, energy intensity, carbon emissions intensity, total imports and exports and other influencing factors of energy consumption, the main driving factors of energy consumption are screened as the model input according to the sorting of grey relational degrees to realize feature dimension reduction. Then, the original energy consumption sequence of China is decomposed into multiple sub sequences by Ensemble Empirical Mode Decomposition for de-noising. Next, the ISFLA-LSSVM model is adopted to forecast each subsequence, and the prediction sequences are reconstructed to obtain the forecasting result. After that, we make an empirical analysis for energy consumption prediction, and four models (ISFLA-LSSVM, SFLA-LSSVM, LSSVM, and BP neural network) are selected to compare with the EEMD-ISFLA-LSSVM model, which proves the practicability and validity of the EEMD-ISFLA-LSSVM model for energy consumption forecasting in China. Finally, the EEMD-ISFLA-LSSVM model is adopted to forecast energy consumption in China from 2018 to 2022, and according to the forecasting results, it can be seen that China's energy consumption from 2018 to 2022 will have a trend of significant growth.  
In this research activity, [PERSON] got involved in the data collection and preprocessing phase, [PERSON] in model constructing, [PERSON] in empirical research, results analysis and discussion, and manuscript preparation. All authors have approved the submitted manuscript.  
Figure 11: The forecasting results of energy consumption from 2018 to 2022 in China.  
**Funding:** This research was funded by [the Natural Science Foundation of China] grant number [71471059], [the Fundamental Research Funds for the Central Universities] grant number [2018 ZD14] and [the 111 Project] grant number [B18021].  
**Acknowledgments:** This research was supported by the Natural Science Foundation of China (Project No. 71471059), \"the Fundamental Research Funds for the Central Universities (2018 ZD14)\", and \"the 111 Project (B18021)\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optical Boundaries for LED-Based Indoor Positioning System
## 3 Optical Boundary Definition
### Noise Determination for the System Model  
To determine the effect of the aforementioned design parameters on positioning for the system model considered, the bit error rate (BER) is required. The BER is derived from the relationship between the BER and signal to noise ratio (SNR). The SNR is given in Reference [82] by  
\[\text{SNR}=\frac{(\mathcal{R}P_{r})^{2}}{\sigma_{t}^{2}} \tag{4}\]  
where \(\mathcal{R}\) is the responsivity of the photodetector, and \(\sigma_{t}\) is the total noise in the receiver system which is given as  
\[\sigma_{t}^{2}=\sigma_{s}^{2}+\sigma_{th}^{2} \tag{5}\]  
where \(\sigma_{s}\) and \(\sigma_{th}\) are the shot noise and thermal noise, respectively, as described in Reference [82]. On-off keying (OOK) modulation is used to determine the total noise value in this system experimentally by computing the \(Q\)-factor given in Reference [83] by  
\[Q=\frac{v_{n}-v_{f}}{\sigma_{n}+\sigma_{f}} \tag{6}\]  
Figure 4: Received optical power from LED\({}_{2}\) for a horizontally moving receiver when tilted at \(0^{\circ},20^{\circ},\)\(40^{\circ},\) and \(60^{\circ}.\)where \(v_{n}\) and \(v_{f}\) are the on and off voltage levels and \(\sigma_{n}\) and \(\sigma_{f}\) are the noise deviation at the on and off voltage levels of the OOK modulated pulse. Laboratory measurements of \(v_{n}\), \(v_{f}\), \(\sigma_{n}\), and \(\sigma_{f}\) are taken at height \(h\) to compute \(Q\). From the value of \(Q\), the BER is calculated by  
\[\text{BER}=\frac{1}{2}\left[1-\text{erf}\left(\frac{Q}{\sqrt{2}}\right)\right]. \tag{7}\]  
Given that for OOK, from Reference [84], \(\text{BER}=\mathcal{Q}(\sqrt{\text{SNR}})\) where \(\mathcal{Q}(\cdot)\) is the Q-function which is defined as  
\[\mathcal{Q}(\
u)=\frac{1}{\sqrt{2\pi}}\int_{\
u}^{\infty}\exp\left(-\frac{u^{ 2}}{2}\right)du=\frac{1}{2}-\frac{1}{2}\,\text{erf}\left(\frac{\
u}{\sqrt{2}}\right) \tag{8}\]  
for a random variable \(\
u\). By comparing (7) and (8) we can write  
\[BER=\mathcal{Q}(Q) \tag{9}\]  
and by substituting (9) into (4), the total noise in the system is given by  
\[\sigma_{t}^{2}=\frac{(\mathcal{R}P_{r})^{2}}{Q^{2}}. \tag{10}\]
## 2 The Role of Dynamic Capabilities of Controlling in Shaping Organizational Performance
**_There is a relation between dynamic capabilities of controlling and quality of controlling._What's more, it is known that the longer controlling is implemented in the organization, the stronger it influences the results obtained due to its implementation related to the functioning of the organization as a whole. Controlling time of use is strongly positively related with all results obtained due to controlling implementation [37]. Therefore, assuming that the time of controlling use in an organization may, in simple terms, be a measure of its excellence (because it is assumed that both controlling solutions and, consequently, the products (services) it offers are of higher quality--as a result of undertaken improvement activities), it can be assumed that controlling has the ability for the very fast reaction to the changes occurring in economic reality. It allows for constant development of new solutions and facilitating business management increasingly well. Hence, it gives possibilities to ensure continued existence of organization. Therefore, the following hypothesis can be formulated:  
**Hypothesis 2 (H2). **_There is a relation between dynamic capabilities of controlling and organizational performance._  
It is also natural that the quality of controlling influences organizational performance. The hypothesis relating to the above statement is formulated in the study to analyze the mediation model of controlling dynamic capabilities influence on organizational performance and has already been de facto repeatedly confirmed in the previous studies of the authors [24; 37; 43; 44]. It will be also verified in this research:  
**Hypothesis 3 (H3). **_There is a relation between quality of controlling and organizational performance._  
In the context of the relations described above, it seems that there is a need to comprehensively explain the mechanism of the impact of controlling dynamic capabilities on organizational performance, while analyzing the mediating role of quality of controlling. It will allow to verify and more comprehensively explain the mechanism of controlling dynamic capabilities influence on organizational performance. It seems that controllers, using their controlling solutions and providing managers with high-quality products (services), affect the job performance of employees and managers [45], which ultimately and indirectly affects organizational performance [45]. Therefore, in the light of the above, the main hypothesis should be formulated:  
**Hypothesis 4 (H4). **_Dynamic capabilities of controlling influence organizational performance through quality of controlling._  
The diagram illustrating the adopted research hypotheses is presented in Figure 1.  
Figure 1: Mediation model of DC of controlling influence on organizational performance; _source: own work_.
## 3 Electricity Consumption Behavior Classification Based on CLB-BER
### Improved CLUBS Clustering
#### 3.2.1 Split Phase  
Initially, the algorithm begins with the entire dataset within a single cluster S, which is subsequently inserted into a priority queue Q for iterative segmentation. As long as Q is not empty, the algorithm extracts a block B, dividing it into two sub-blocks. Should this division prove valid, these sub-blocks supplant B in Q; if not, B stands as the definitive block for that iteration. To optimize the segmentation, it is essential to evaluate the marginal distribution for each dimension \(i\) within block B, so it is necessary to compute the functions \(C_{B}^{i}:\ \mathbb{R}\to N\) and \(LS_{B}^{i}:\mathbb{R}\overset{I}{\rightarrow}\mathbb{R}^{d}\), as shown in Equations (9) and (10).  
\[C_{B}^{i}(x)=|p\in B\wedge p[i]=x| \tag{9}\]  
\[LS_{B}^{i}(x)=\sum_{p\in B\wedge p[i]=x}p \tag{10}\]  
We aggregate all the points \(p\) within block \(B\), where the coordinates of the \(i\)-th dimension equal \(x\). This aggregation can be depicted as either a graph or an array.  
Minimizing the segmentation of WCSS can be achieved by performing a linear scan of the graph or array. The Kalinsky-Harabas (CH) index is used to assess the quality of clustering. After each segmentation, the CH value is recalculated. If the CH value increases, the splitting phase is deemed valid and proceeds; if the CH value decreases below 70%, the splitting is stopped.  
\[WCSS(C)=\sum_{i=1}^{d}\left(Q_{i}-\frac{S_{i}^{2}}{n}\right) \tag{11}\]  
where \(S_{i}=\sum_{p\in C}p_{i}\) and \(Q_{i}=\sum_{p\in C}p_{i}^{2}\), respectively, denote the sum of the \(i\)-th coordinates and the sum of the squares of \(i\)-th coordinates of the points in C.  
Let C = \(\{C_{1},\ldots,C_{k}\}\) be a set of clusters over a dataset \(D=\{p_{1},\ldots,p_{n}\}\), where the \(i\)-th (\(1\leq i\leq k\)) cluster has \(n_{i}\) points and centroid \(\overline{\epsilon_{i}}\), and let m denote the centroid for the whole dataset \(D\). The BCSS (Between-Clusters Sum of Squares) of C is defined as  
\[BCSS(\mathbf{C})=\sum_{i=1}^{k}n_{i}\times\parallel\overline{\epsilon_{i}}- \overset{-}{\mathbf{m}}\parallel^{2} \tag{12}\]  
\[CH-index(C)=\frac{BCSS(C)}{WCSS(C)}\times\frac{n-k}{k-1} \tag{13}\]  
\[\Delta WCSS\left(\mathbf{C}^{ij}\right)=\frac{n_{i}\cdot n_{j}}{n_{i}+n_{j}} \sum_{k=1}^{d}\left(\frac{S_{i,k}}{n_{i}}-\frac{S_{j,k}}{n_{j}}\right)^{2} \tag{14}\]  
where \(S_{i,k}\left(S_{j,k}\right)\) is the sum of the \(k\)-th coordinate (i.e., dimension) of all the points belonging to the cluster \(C_{i}\left(C_{j}\right)\). This equation can be used to determine the increase in WCSS when a pair of clusters is merged into a single one.
# Volume Loss Assessment with MT-InSAR during Tunnel Construction in the City of Naples (Italy)
## 4 Results
### Volume Loss Assessment and Settlement Prediction for the Second Excavation  
For volume loss assessment, monitored displacements from Figure 11a are converted into vertical settlements according to Equation (8), and then projected onto cross sections orthogonal to the first excavated tunnel alignment, as before. This time, Equation (15) is used to fit the observed settlements, in order to retrieve information on the volume loss V\({}_{\text{L}}\).  
In Figure 13, the fitting on nine cross sections considered is presented. The correlation coefficients R\({}^{2}\) are considerably high for all sections except for C and H. Nevertheless, all volume losses V\({}_{\text{L}}\) obtained from the best fit are in the expected range of 0.5% to 1% for TBM excavation. The maximum settlements d\({}_{\text{v}}\) from the best fit are 9.0, 12.5, 7.0, 7.8, 7.4, 6.1, 6.1, 6.5, and 7.7 mm for sections A to I, respectively. To retrieve the actual maximum settlement w, these values should be divided by the \(\psi\)\({}_{\text{max}}\), which, for \(\vartheta\) = 50\({}^{\circ}\), \(\gamma\) = 55\({}^{\circ}\), and K = 0.25 \(\div\) 0.45, is equal to 1.01 \(\div\) 1.04.  
The values of volume loss V\({}_{\text{L}}\) and inflection point distance i\({}_{\text{x}}\) assessed from settlements occurring during the first excavation are used as input parameters to predict the settlement trough as a result of the second excavation.  
Using the superimposition of the effects, the final settlement profiles are finally obtained. The latter are compared with the MT-InSAR monitored data from 2 July 2020 to 29 January 2022 in Figure 14. This figure shows that the settlements induced by the second excavation are compatible with the volume loss V\({}_{\text{L}}\) obtained by measuring the settlement trough during the first excavation.  
Figure 13: First excavation monitored settlements dv (black dots) vs. Gaussian fit (solid red line, Equation (15)). Sections A to I refer respectively to those in Figures 10 and 11a,b.  
The predicted shape of settlement profiles (red lines in Figure 14) agrees overall with the monitored data, as well as the magnitude of settlements, in all sections except for A, E, and H, where the monitored ones are higher between the tunnel centerlines. The possible deviation from the first excavation may be due to different ground conditions or TBM operations.