esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Phenomena Related to Overlimiting Mass Transfer Mechanisms
#### 3.2.2 Current-Voltage Curves  
Different current-voltage curves showcasing the predominance of different mass transfer mechanisms are presented in Figure 7. More specifically, Figure 7a highlights the effect of salt concentration on the threshold membrane voltage drop at which activation of electroconvection occurs. In congruence with the evolution of the chronopotentiograms obtained for Cr\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) (see Figure 6a), overlimiting currents in these curves are caused by electroconvection. An earlier onset of electroconvection is desired in order to reduce energy losses associated with the operation at overlimiting currents. The results obtained indicate that higher concentrations of multivalent ions can reduce the length of the plateau region, which is in congruence with the findings of previous studies [13; 14; 23]. However, the simultaneous development of shifts in the speciation within the membrane system makes difficult to extrapolate this conclusion to all type of electrolytes and concentration ranges. Figure 7b shows an example where the contrary effect is observed: the plateau length is shorter for the more diluted solutions. In this case, the easier formation of precipitates at higher concentrations implies extended plateaus. The measurement of the pH at the cathodic compartments also proves that the type of overlimiting mass transfer mechanism can differ depending on the salt concentration. The pH at the cathodic compartment remained almost constant during the experiments conducted with \(5\times 10^{-3}\) M NiSO\({}_{4}\), which is in agreement with the predominant role of electroconvection observed in Figure 6c. On the contrary, after the first deposition of precipitates taking place with \(2\times 10^{-2}\) M NiSO\({}_{4}\), the high transmembrane voltage drop, resulting from the precipitate layer, promotes the dissociation of water and the pH in the cathodic compartment drops abruptly. These results also indicate that an early onset of electroconvection does not avoid the formation of precipitates but can induce a change in the structure of the deposited precipitates which suppresses the dissociation of water molecules at the membrane/precipitate junction.  
Figure 7: (**a**) Normalized current-voltage curves obtained for different solutions of Cr\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) and (**b**) for different concentrations of NiSO\({}_{4}\) where different overlimiting mass transfer mechanisms occur.
## 5 Results and Discussion
### The ResQhot 2.0 Assembly
#### 5.2.6 Neck Securing Device Module  
The neck securing device (NSD) features three main components/mechanisms: (1) A frame designed to be as compact as possible and encase all the safety mechanisms implemented on the NSD; (2) inflatable components that are the main protection against any cervical spine and head injuries during the loading procedure; and (3) a sliding rail mechanism that guides the NSD when it moves up or down along the belt.  
The NSD frame was designed to cover the casualty's head and neck up to the shoulders, as illustrated in Figure 7c. The frame's design allows sufficient space between the casualty's head, neck, and the frame in order to prevent any direct contact. The NSD slides along the bed to the ground and slides back up around the bed end. For that reason, the back of the device is curved to allow a smooth up and down motion (see Figure 12a).  
The NSD inflatable components consist of three separate inflatable modules: One lifts and supports the back of the head, and two support the neck, as shown in Figure 12b. The inflatable modules are inflated by means of a pressurised air chamber that is controlled by a solenoid valve. In order to fully support the neck and head, the inflatables curve around the neck to stabilise the head and prevent unwanted movement. The neck support modules support the neck from each side, and during inflation, the modules slightly lift the neck and support it from the bottom. This allows the head to bend back slightly to facilitate unblocking the respiratory tracts. Figure 12c shows the inflatable components of the NSD module.  
Figure 12: Design of the NSD module. The NSD module features three main components: An NSD frame, inflatable components, and a sliding guide mechanism. (**a**) The design has a curved shape to enable smooth loading up and down during the operation. (**b**) Design of the NSD inflatable components that support the casualty’s head and neck during the extraction procedure. (**c**) The NSD module. (**d**) The NSD sliding rail mechanism that allows it to slide up and down along the stretcher bed module.  
In order to guide the NSD up and down the stretcher bed, a pair of sliding guides were developed. They consist of two long rails attached to each side of the stretcher bed, a rod that slides up and down along the rails, and two pin joints connecting each side of the NSD frame to the sliding rod. Figure 11(d) shows the NSD sliding rail mechanism.
## 3 Numerical Analysis and Verification
### Governing Equations  
In the model, the heat exchange between the air and the outer surface of the surrounding rock causes a temperature change in the surrounding rock at a certain depth, and heat conduction exists in the deep surrounding rock due to the temperature boundary at the bottom. There are three ways of exchanging energy: heat conduction, convective heat transfer, and heat radiation. Relative to the other two, the heat transferred by heat radiation is negligible [18; 29; 30]. Equation (2) is the governing equation of unsteady heat conduction. Equations (2a), (2b), (2c), and (2d) are the boundary conditions [24].  
\[\rho c_{p}\frac{\partial T}{\partial t}=\lambda\bigg{(}\frac{\partial^{2}T}{ \partial x^{2}}+\frac{\partial^{2}T}{\partial y^{2}}\bigg{)}\ \ \left(\in\Omega\,,\;t>0\right) \tag{2}\]  
\[T(x,y,0)=T_{0}\ \ \left(\in\Omega\,,\;t=0\right) \tag{2a}\] \[T(x,y,t)=T_{1}\ \ \left(\in\Gamma_{1}\,,\;t>0\right)\] (2b) \[-\lambda\frac{\partial T}{\partial y}=h\Big{(}T-T_{f}\Big{)}\ \ \left(\in\Gamma_{3},\;t>0\right)\] (2c) \[-\lambda\frac{\partial T}{\partial x}=h\Big{(}T-T_{f}\Big{)}\ \ \left(\in\Gamma_{2}+\Gamma_{4}\,,\;t>0\right) \tag{2d}\]  
where \(\rho\) is the density of the surrounding rock, kg\(\cdot\)m\({}^{-3}\); \(c_{p}\) is the specific heat capacity of the airflow, \(\cdot\)(kg\(\cdot\)C)\({}^{-1}\); \(T\) denotes the temperature of the surrounding rock, \({}^{\circ}\)C; \(t\) is time, \(s\); \(\lambda\) is the heat conductivity, W\(\cdot\)(m\(\cdot\)C)\({}^{-1}\); \(h\) is the surface heat transfer coefficient, W\(\cdot\)(m\({}^{2}\cdot\)C)\({}^{-1}\); and \(T_{f}\) is the ambient temperature, \({}^{\circ}\)C. \(\Gamma_{1}\) is the Dirichlet (first-type) boundary condition, \(\Gamma_{2}\), \(\Gamma_{3}\) and \(\Gamma_{4}\) are the Robin (third-type) boundary conditions, and \(\Gamma=\Gamma_{1}+\Gamma_{2}+\Gamma_{3}+\Gamma_{4}\) is the boundary of the whole region \(\Omega\), as shown in Figure 7.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 6 Performance Evaluation and Benchmarking  
### Metrics for Evaluating VLC-Based IPS  
#### 6.1.1 Accuracy  
Euclidean distance is a straightforward metric that measures the straight-line distance between the estimated and true position. The formulation can be represented as follows:  
\[d=\sqrt{(\mathbf{p}_{est}-\mathbf{p}_{true})^{2}} \tag{6}\]  
where \(\mathbf{p}_{est}\) and \(\mathbf{p}_{true}\) are the estimated and true positions, respectively.
## 3 Automatic Power Management System
### Power Error-Tolerance  
The measured power fluctuates around the power reference because of various factors such as random variation of wind speed, noise measurement in sensors and the continuous activity of the pitch actuator. The logic of the automatic switching system is based on constant step changes of the power reference following the supervisor's request. Therefore, without any power error tolerance around the power reference, the banks will switch ON and OFF each time there is an error between the measured power and reference one as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Conditions** & **Generated power (\(P_{gen}\))** \\ \hline Q & \(\geq\)0.1 W \\ R & \(\geq\)0.2 W \\ S & \(\geq\)0.3 W \\ T & \(\geq\)0.4 W \\ U & \(\geq\)0.5 W \\ V & \(\geq\)0.6 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conditions of the generated power.  
It can be seen from Table 2 that the bank's switch will automatically go OFF when the available power is smaller than the reference one, which is practically incorrect for a small error because there is sufficient power to illuminate the bank.  
In order to overcome this problem, an error-dependent threshold for automatic bank switches can be added to the logic of the automatic switching system. The appropriate power threshold \(P_{tr}\) is the lower end of acceptable power for the number of manually switched ON banks. The added logic subtracts the programmed allowable error from the original threshold as  
\[P_{tr}=P_{ref}-\varepsilon \tag{4}\]  
where \(P_{ref}\) is the power reference based on the number of banks manually switched ON by the supervisor, and \(\varepsilon\) is the error tolerance.  
The acceptable power range \(P_{a}\) for the number of banks ON is given by  
\[P_{a}=P_{ref}\pm\varepsilon \tag{5}\]  
Therefore, if the generated power \(P_{gen}\) is changing in the range of \(P_{a}\), the automatic switch(s) is (are) considered ON and the bank(s) is (are) illuminated.
## 2 The Proposed Methods
### Parseval's Theorem  
If a discrete sampling signal \(X[n]\) is regarded as a current, the energy consumed by the current through a 1 \(\Omega\) resistance would be equal to the sum of the squares of the Fourier spectrum coefficients. This is known as [PERSON]'s theorem, as shown in Equation (8):  
\[\frac{1}{N}\sum_{n=N}\bigl{|}X[n]\bigr{|}^{2}=\sum_{ik=N}\lvert a_{k}\rvert^{2} \tag{8}\]  
where, \(N\) is the sampling period and \(a_{k}\) is the spectrum coefficient.  
This theorem is applied to the wavelet transform. If the scaling function and the mother wave have an orthogonal basis, [PERSON]'s theorem can be used to explain the relationship between the energy of the signal and the energy possessed by each order of wavelet coefficients, that is, it can allow the signal energy to be cut in the time domain and frequency domain of the wavelet transform.  
The energy contained in [PERSON]'s theorem is divided with time \(k\) and scale \(j\), as shown in Equation (9) derived from Equations (3) and (8):  
\[\frac{1}{N}\sum_{i}\bigl{|}X[i]\bigr{|}^{2}=\frac{1}{N_{j}}\sum_{k}\bigl{|}cA _{jk}\bigr{|}^{2}+\sum_{j=1}^{j}\biggl{(}\frac{1}{N_{j}}\sum_{ik}\bigl{|}cD_{ j,k}\bigr{|}^{2}\biggr{)} \tag{9}\]  
Figure 2: Flow chart of discrete wavelet transform (DWT) with multi-level analysis.  
The former term in the above formula represents the average power generated by the decomposed signal, which is similar to the original signal component, while the latter term is the sum of the average power of multiple analyses of each detail change.
# Popular Biochar as an Alternative Substrate for Curly Endive Cultivated in a Soilless System
###### Abstract  
Imminent necessity for eco-friendly and low-cost substitutes to peat is a defiance in the soilless plant cultivation systems. Wood biochar could entirely or partly substitute peat as a plant growing constituent to produce vegetables. Nevertheless, knowledge concerning potential plant performance of leafy green vegetables grown on wood biochar is restricted. The present study assessed the main physicochemical traits of various growing media constituted by decreasing the content of peat and by increasing the percentages of poplar wood biochar. Yield, nutritional and functional properties of curly endive plants cultivated in a protected environment were also tested. Biochar was pyrolyzed from poplar (_Populus nigra_ L.) at 450 or 700 \({}^{\circ}\)C for 48 h. Increasing biochar concentration and pyrolysis temperature resulted in higher pH, EC and K content of the growing mediums. Biochar was also effective in increasing particle density and bulk density. Biochar at 70% and pyrolysis temperature of 450 \({}^{\circ}\)C significantly increased head fresh weight by 47.4%, head height by 24.9%, stem diameter by 21.5% and number of leaves by 80.8%, respectively compared with the control (100% peat). Head dry matter content, root dry matter content, SSC, ascorbic acid and total phenolic were also significantly affected by this treatment. Furthermore, the addition of biochar and the use of higher pyrolysis temperature decreased N leaves concentration. This represents a particularly important target for leafy green vegetables healthiness.  
Keywords:polar wood biochar; pyrolysis temperature; _Cichorium endivia_ L. var. _crispum_; soilless cultivation; substrate mixtures; quality traits +
Footnote †: journal: Applied Sciences
# BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification
###### Abstract  
Brain tumors pose significant health challenges worldwide, with glioblastoma being one of the most aggressive forms. The accurate determination of the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is crucial for personalized treatment strategies. However, traditional methods are labor-intensive and time-consuming. This paper proposes a novel multi-modal approach, BTDNet, that leverages multi-parametric MRI scans, including FLAIR, T1 wCE, and T2 3D volumes, to predict the MGMT promoter methylation status. BTDNet's main contribution involves addressing two main challenges: the variable volume lengths (i.e., each volume consists of a different number of slices) and the volume-level annotations (i.e., the whole 3D volume is annotated and not the independent slices that it consists of). BTDNet consists of four components: (i) data augmentation (which performs geometric transformations, convex combinations of data pairs, and test-time data augmentation); (ii) 3D analysis (which performs global analysis through a CNN-RNN); (iii) routing (which contains a mask layer that handles variable input feature lengths); and (iv) modality fusion (which effectively enhances data representation, reduces ambiguities, and mitigates data scarcity). The proposed method outperformed state-of-the-art methods in the RSNA-ASNR-MICCAI BraTS 2021 Challenge by at least 3.3% in terms of the F1 score, offering a promising avenue for enhancing brain tumor diagnosis and treatment.  
RSNA-ASNR-MICCAI BraTS 2021 Challenge; brain tumor radiogenomic classification; prediction of MGMT promoter methylation status; BTDNet; multimodal approach; routing; mask layer; MixAugment; multi-class focal loss; volume-level annotations; variable-length data
## 3 Results and Discussion
### Analysis and Discussion
#### 3.2.4 Flexural Strength Analysis (FS)  
The results of FS (R\({}_{3}\)) analysis of variance are shown in the third row of Table 7. R-squared and Adj. R-squared are both close to 1, and Adeq. Precision is greater than 4, which indicates that the fitted model is significant. According to the variance results in Table 8, X\({}_{2}\), X\({}_{3}\), X\({}_{2\times 3}\), (X\({}_{1}\))\({}^{2}\) and (X\({}_{2}\))\({}^{2}\) are the significant factors of the FS quadratic model. According to the least square method, the regression coefficients of each factor were determined, and a reasonable second-order FS polynomial equation was established according to actual factors:  
\[\text{FS}=3.75-0.058X_{1}+0.12X_{2}-0.22X_{3}+0.18X_{2}X_{3}-0.23(X_{1})^{2}-0. 33(X_{2})^{2} \tag{4}\]  
The diagnostic data points of the statistical model in Figure 5 are approximately linear sets, indicating that the model has high significance.  
Figure 6 is a three-dimensional response surface diagram of FS, revealing the relationship between WBR, BFC, RC and FS, and the interaction between WBR, BFC, and RC. As shown in Figure 6, when WBR and RC increased, the change trend of FS of RBFC was similar to that of CS. This is because the influence mechanism of WBR and RS on FS is similar to that on CS. On the other hand, with the increase of BFC, the FS also showed an obvious trend of first increasing and then decreasing. When the BFC was small, the basalt fiber had to be pulled out of concrete matrix during concrete crack propagation stage and the fiber was even broken. This process required a lot of energy absorption, which slowed down the crack propagation process, thereby increasing the flexural strength of concrete. However, when the BFC was large, basalt fibers tended to agglomerate in concrete, which increased the internal defects of concrete and adversely affected the flexural strength of concrete. From the comparative analysis in Figure 6 and the results of variance analysis in Table 7, it can be seen that BFC has the most significant effects on the FS of RBFC.  
Figure 5: Normal plot of residuals for FS.
## 5 Experimental Results and Discussion
### Performance Metric and Baseline Method  
In our proposed work, recognition rate is reported at FAR = 0.1% and used as the baseline feature descriptor for performance comparison. For matching, 1D-Log Gabor with the matching operator Adaptive Hamming Distance is used same as our previous work [53]. Our method shows recognition accuracy of 95.63%, 99.41% and 93.17% and FRR 4.27%, 0.49% and 6.73% on three databases: CASIA-Iris-Thousand, UBIRIS.v2 and LG2200, respectively. Quantitative results between the previous methods and our proposed method are shown in (Table 3).
# VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm
## 3 Proposed VSFCM Algorithm  
In this section, a new cluster initialization method named the cut-off distance-induced clustering initialization (CDCI) is proposed first. Based on the CDCI, the viewpoint-driven subspace fuzzy c-means algorithm (VSFCM) is established subsequently, which has introduced the subspace clustering mode and fuzzy feature weight processing mechanism  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Advantage** & **Disadvantage** \\ \hline FCM & Classic algorithm can automatically find & Sensitive to cluster center initialization, poor \\  & cluster centers. & noise immunity. \\ \hline V-FCM & Simplified clustering process, fast convergence. & Poor noise immunity, sensitive to cluster center \\  & & initialization. \\ \hline DPC & Can quickly determine cluster centers. & The density radius is difficult to determine; cluster centers are not easy to obtain, and there are often \\ \hline SCAD & Fuzzy weighted index is introduced to obtain & Sensitive to cluster center initialization. \\  & better weight value. & \\ \hline ESSC & Taking the distance between clusters into account, using entropy information. & Sensitive to cluster center initialization, more \\ \hline FRFCM & Select important features by weighting, and reduce feature dimension by discarding unimportant features. & Sensitive to cluster center initialization without \\ \hline DVPFCM & With the help of new viewpoints and typical & \\  & values, there is relatively stronger robustness; & Processing high-dimensional data appears weak, \\  & there is a better initialization strategy. & and the cut-off distance is not perfect. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of advantages and disadvantages of each algorithm.  
and combined with the separation formula between clusters optimized with the viewpoint. Its algorithm idea is shown in Figure 1.
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 5 Results and Discussion
### Actual Wave Energy Harvesting Plant Experiment  
Similar to the wave tank study, we conducted two distinct experiments on the collected dataset. Each sample in the dataset was labeled either strong, mild or weak in accordance with the output power. Wave patterns that generated more than 50% of the maximum output power were considered strong. When the power was between 10% and 50% of the maximum output, the wave pattern was labeled mild. If it was less than 10%, it was labeled weak. The results of the experiments are presented below.
# Computing a Clique Tree with the Algorithm Maximal Label Search
## 2 Preliminaries
### Algorithms MLS and MLSM
```
input : a connected graph \(G\) and a labeling structure \(\mathcal{L}=(L,\preceq,l_{0},Inc)\) output : an MEO \(\alpha=(x_{1},\ldots,x_{n})\) of \(G\) and the associated minimal triangulation \(H=G_{\alpha}^{+}\) \(H\gets G\); \(V^{\prime}\leftarrow\emptyset\); initialize all labels as \(l_{0}\); for\(i=n\)down to \(1\)do  Choose a vertex \(x\) in \(V\setminus V^{\prime}\) whose label is maximal; \(Y\leftarrow\emptyset\); foreach\(y\) in \(V\setminus(V^{\prime}\cup\{x\})\)do ifthere is a path \(\mu\) of length \(\geq 1\) in \(G(V\setminus V^{\prime})\) between \(x\) and \(y\) such that for each internal vertex \(z\) of \(\mu\) label\((z)\prec label(y)\)then \(Y\gets Y+\{y\}\); \(E(H)\gets E(H)\cup\{xy\}\);  end if foreach\(y\) in \(Y\)do \(label(y)\gets Inc(label(y),i)\);  end for \(V^{\prime}\gets V^{\prime}\cup\{x\}\);  end for
```  
**Algorithm 3**MLSM. **Notation 1**. : _Let \(G\) be a graph; let \(\alpha\) be an ordering of \(G\); let \(i\in[1,n]\); and let \(y\in V\)._  
* \(N_{G}^{\alpha,i}(y)=\{z\in N_{G}(y)\mid\alpha^{-1}(z)>i\}\)_,_
* \(N_{G}^{\alpha+}(y)=N_{G}^{\alpha,\alpha^{-1}(y)}(y)=\{z\in N_{G}(y)\mid\alpha^ {-1}(z)>\alpha^{-1}(y)\}\)_,_
* \(N_{G}^{\alpha+}[y]=N_{G}^{\alpha+}(y)\cup\{y\}=\{z\in N_{G}[y]\mid\alpha^{-1}( z)\geq\alpha^{-1}(y)\}\)_,_
* _a generator of a minimal separator_ \(S\) _(resp. maximal clique_ \(K\)_) of_ \(G\) _w.r.t._ \(\alpha\) _is a vertex_ \(x\) _of_ \(G\) _such that_ \(S=N_{G}^{\alpha+}(x)\) _(resp._ \(K=N_{G}^{\alpha+}[x]\)_)._  
If \(\alpha\) is a PEO of a chordal graph \(H\), then each minimal separator of \(H\) has at least one generator w.r.t. \(\alpha\)[25], and each maximal clique of \(H\) clearly has exactly one generator w.r.t. \(\alpha\), which is the vertex \(x\) of \(K\) with minimum \(\alpha^{-1}(x)\). **Lemma 1**. : _In an execution of MLS, for each \(i\) in \([1,n-1]\) and each \(y,z\) in \(V\) such that \(\alpha^{-1}(y)\leq i+1\) and \(\alpha^{-1}(z)\leq i+1\), at the beginning of iteration \(i\) (choosing vertex \(x_{i}\)) of the **for** loop,_  
1. _If_ \(N_{G}^{\alpha,i}(y)\subset N_{G}^{\alpha,i}(z)\)_, then_ \(\mathit{label}(y)\prec\mathit{label}(z)\)_,_
2. _If_ \(N_{G}^{\alpha,i}(y)\subseteq N_{G}^{\alpha,i}(z)\)_, then_ \(\mathit{label}(y)\preceq\mathit{label}(z)\)_._  
Proof. : (i) If \(N_{G}^{\alpha,i}(y)\subset N_{G}^{\alpha,i}(z)\), then \(\alpha^{-1}(N_{G}^{\alpha,i}(y))\subset\alpha^{-1}(N_{G}^{\alpha,i}(z))\), and therefore, by IC, \(\mathit{label}(y)=\mathit{lab}_{\mathcal{L}}(\alpha^{-1}(N_{G}^{\alpha,i}(y) ))\prec\mathit{lab}_{\mathcal{L}}(\alpha^{-1}(N_{G}^{\alpha,i}(z)))=\mathit{ label}(z)\). The proof of (ii) is similar.
## 2 Materials and Methods
### System's Ability to Solve the Problem of Carrier Phase Ambiguity (SSR)  
Each sample from the obtained data contained status information from the NMEA sentence of a specific RTK receiver. The information could include one of the following three values: \"FIX\"--when the sample belonged to a period of a known integer value of the carrier phase, \"FLOAT\"--when the sample belonged to a period known only to the decimal of the carrier phase value and \"GNSS\"--when the sample belonged to a period when the RTK receiver was not able to process the correction signal. The \(SSR\) ratio, expressing the RTK ability to solve the problem of ambiguity of the integer part of the carrier phase, was calculated as follows:  
System status ratio (\(SSR\))--ability of the system to solve the problem of integer phase ambiguity (%):  
\[SSR=\frac{n_{f}}{n_{w}}*100, \tag{1}\]  
where \(n_{f}\)--dataset size of the samples with solved integer phase ambiguity (samples of the \"FIX\" state epoch) (-), and \(n_{w}\)--dataset size of the measured samples (samples of the whole measurement) (-).