esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experimental Setup
#### 4.1.2 Datasets  
MovieLens-100K, MovieLens-1M [38], Lastfm-2K [39] and Foursquare [40] are frequently employed datasets in recommender systems. The two MovieLens datasets contain user ratings of movies from one to five from the MovieLens website, with each user rating at least 20 movies. The Lastfm-2k dataset, sourced from Last.fm, one of the largest music platforms globally, records user interactions with artists' works. The Foursquare dataset is retrieved from the Foursquare application's public API, encompassing numerous user check-ins, social connections and venue ratings. These four are real-world datasets that better align with the data distribution in real-world FRSs. They exhibit diverse properties, such as size, domain and sparsity. Moreover, they are all widely used datasets in the evaluation of recommendation models.  
Focused on implicit feedback recommendation scenarios, we convert the ratings in each dataset into implicit data, where a rating of 1 indicates user interaction with an item.  
We filter out users with fewer than five historic interactions following [34] to simplify dataset partitioning, and the characteristics of the four datasets are detailed in Table 3.  
For the dataset split, we employ the leave-one-out method which is widely used in the literature of recommendation [37]. For each user, we assign their most recent interaction as the test set and allocate all other interactions to the training set. Additionally, we designate the latest interaction within the training set as the validation set to identify optimal hyperparameters. For the final evaluation, we follow the established strategy outlined by [37; 41], which involves randomly selecting 99 unobserved items for each user and ranking the test item among these 100 items. Additionally, we adopt the experimental setup of [PERSON] et al. [37], where we randomly sample four negative samples for each positive sample in training set.
# Impact Performance of Helmholtz Self-Excited Oscillation Waterjets Used for Underground Mining
## 3 Results and Discussion  
### Velocity Distribution  
Figure 7 shows the time evolution of the velocity contour. The velocity field inside the chamber is a series of water slugs which is quite different from uniform flow field of continuous jets. The jets flow through the Helmholtz oscillator, forming pressure feedback and backflow vortex on both sides of the cavity. The pressure pulsates as water flows from the downstream nozzle. The maximum velocity reached as high as 1.4 times of inlet velocity. When the jet acts on the target through a certain target distance, its kinetic energy converts into pressure energy, thus the axial velocity gradually drops to 0, and the flow turns to radial distribution along the wall.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mesh** & **Nodes** & \(\mathbf{u_{\text{av}}/u_{0}}\) \\ \hline Case 1 (Coarse) & 62,000 & 0.9417 \\ Case 2 (Medium) & 91,000 & 0.9372 \\ Case 3 (Fine) & 127,000 & 0.9371 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the mesh independence.  
Figure 5: Computational mesh.  
Figure 6: Validation.  
The mean velocity distributions are shown in Figure 8 when \(L_{\text{c}}/d_{1}=2\), \(D_{\text{c}}/d_{1}=8\), \(\alpha=120^{\circ}\), \(P_{\text{o}}=20\) MPa. Figure 8a shows the non-dimensional velocity magnitude at different axial locations. The velocity at jet axis gradually attenuates along the direction of jet flow. When it reaches the wall surface (\(S=0\) mm), velocity near the wall drops sharply because of the shear layer, so the non-dimensional axial velocity is about 0.09 and the influence radius of the jet increases to \(3.0d_{2}\). Figure 8b shows the non-dimensional velocity magnitude at different radial locations. At the radial direction, as the jets diffuse along the wall, the velocity decreases gradually. The velocity decreasesrapidly near the wall due to shear layer effect. The further away from the jet axis, the faster the velocity decreases. When the radial distance is about \(9d_{2}\), there is no change in velocity.
## 3 The New Power System
### Battery Sizing and Weight Considerations  
The battery must be sized by considering not only the limitations in terms of gravimetric energy density but also in terms of power density [34]. Assuming a depth of discharge of 80% and a gravimetric energy density of 200 Wh/kg for the battery, the battery mass ranges between 10 kg for the power assist mode and 128 kg for the all-electric case. As a compromise, the battery size will be set equal to 10 kWh (62.5 kg) because, in case of engine failure, the aircraft can easily complete takeoff and ascent and then descend to land. Since the maximum battery power in Figure 9 is about 76 kW, the battery must have a power density of about 1200 W/kg, a value in line with today's high-power lithium battery technology [33].  
At this stage, an accurate weight analysis of the proposed system has not yet been performed, even if for each component, the weight can be estimated from the literature data of power densities for modern electric machines. However, it is not correct to evaluate the weight of a hybridization scheme by simply adding the weight of the extra components to the original weight of the aircraft [35]. First, the starter motor/generator and the battery of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Specification** & \
\begin{tabular}{c} **Toyota Hybrid** \\ **System (THS)** \\ \end{tabular} &
\begin{tabular}{c} **Honda Hybrid** \\ **System (HHS)** \\ \end{tabular} \\ \hline \multirow{2}{*}{Wankel engine} & Peak power & 70 kW @ 7000 rpm & 70 kW @ 7000 rpm \\  & Displacement & 654 cm\({}^{3}\) & 654 cm\({}^{3}\) \\  & Fuel & Avgas & Avgas \\ EM1 & Max. power & 30 kW & 30 kW \\ EM2 & Max. power & 50 kW & 70 kW \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of the old and new powertrains.  
Figure 10: Engine efficiency for the series -hybrid, power assist, and thermal modes.  
the conventional configuration are not needed anymore to turn on the engine and generate on-board electricity. In the case under analysis, moreover, the proposed power system makes use of a very light Wankel engine, and the fuel saving can help reduce the size of the fuel tanks. Additionally, note that, according to Italian legislation, the takeoff weight must be kept under 450 kg, which is also the actual takeoff weight of the reference vehicle used in this investigation. Therefore, the retrofitting of the Freccia would not be feasible even with a limited increase in the powertrain weight. For this reason, the design of a new prototype ultralight aircraft will be the goal of the next step of this project, which will also include a reduction in the weight and bulk of the proposed system and the choice of battery.
# Coal Mine Water Inflow Prediction Model Based on Multi-Factor Pearson Correlation Analysis
## 2 Methods
### Bidirectional Long Short-Term Memory Network  
Due to the fact that a single long short-term memory (LSTM) network can only consider the impact of the preceding sequence data on current data, and cannot provide feedback from subsequent data to the preceding sequence, a variant of LSTM, bidirectional LSTM (BiLSTM), is proposed, which adds a backpropagation layer [21; 22]. BiLSTM can simultaneously utilize historical data and future information for prediction, improving the predictive ability of the model. As shown in Figure 2, BiLSTM is composed of an input layer, BiLSTM layer, fully connected (FC) layer, and output layer.
## 3 Materials and Methods
### Model Construction
#### 3.2.6 ETS Model  
The ETS model is a statistical model widely used in time-series forecasting, especially for processing data with obvious seasonality and trend [40]. The ETS model can capture the patterns in the data more accurately by decomposing the time series into three parts: error, trend, and seasonality. In ETS (AAM), AAM means:  
* Error: The error is additive (A), which is suitable for situations where the error remains relatively stable at different levels.
* Trend: The trend component is additive (A), which is suitable for time series with relatively linear trend changes.
* Seasonality: The seasonal component is multiplicative (M), which is applicable when seasonal fluctuations change with changes in the level.  
The ETS (AAM) model has good adaptability and flexibility. It uses the maximum likelihood method to estimate parameters. Especially in the sales forecast of FMCG, it can effectively capture seasonal fluctuations and provide accurate sales forecasts.
## 4 Oscillator Design and Measurement Results  
In this study, a low-phase-noise X-band oscillator was designed using a new high-Q_L resonator, as shown in Figure 7. As shown in the figure, an oscillator was used for the series feedback structure and self-bias method, and the series feedback at the source was chosen as an oscillator, which provides the negative resistance (\(-\)R) to the gate of a GaAs-MESFET.  
Figure 6: Fabrication and experimental results of the proposed resonator: (**a**) simulation and measurement results; (**b**) fabrication.  
Figure 7: _Cont_.  
The oscillator is composed of a \(\lambda_{\rm g}/4\) transmission line (microstrip line) and a radial stub for a DC-bias circuit providing RF power to a wideband open-loaded circuit. The proposed high-\(Q_{\rm L}\) resonator is connected to the gate (G) of the GaAs-MSFET. V\({}_{\rm D}\), R\({}_{\rm D}\), and Rg are the drain (D) bias voltage and drain (D) resistance; further, R\({}_{\rm S}\) and C\({}_{\rm S}\) are the source (S) resistance and source capacitance, respectively.  
L\({}_{\rm D}\) and L\({}_{\rm S}\) are the RF-check (RFC) in the drain (D) and source (S), respectively. Radial stubs are the RFC circuits in the drain (D) and source (S) loads. The gate (G) bias is self-biased, and a capacitor (C\({}_{\rm by}\)) is used only for by-pass C for the bias. The fabricated oscillator was used as the Teflon substrate, and the dielectric constant (\(\varepsilon_{\rm r}\)), height (h), and thickness (\(t\)) of the Teflon substrate were 2.55, 0.54 mm, and 0.018 mm. The fabrication was performed by wet etching, which is used for negative film development.  
The measurement results of the new oscillator are shown in Figure 8, in which the oscillation frequency is 10 GHz. The phase noise of the oscillator is observed to be \(-138.13\) dBc/Hz at an offset frequency of 100 kHz. Thus, the Q\({}_{\rm L}\) of the resonator has a considerable effect on the phase noise performance of the oscillator. Additionally, the output power and amount of second harmonic suppression for the X-band oscillator are 18.66 dBm and \(-34.40\) dBc under bias conditions of V\({}_{\rm DS}\) = 3 V and I\({}_{\rm D}\) = 40 mA, respectively. The figure of merit (FOM) is given by Equation (20), which is \(-195.7\) dBc/Hz at a 100 kHz offset, where the \(L\)_foffset_ is the phase noise in the dBc/Hz at the offset frequency, and \(f_{offset}\) is from the carrier (oscillation) frequency,\(f_{0}\). P\({}_{\rm DC}\) is the DC power dissipation in mW [13].  
\[\rm FOM=-20 log\left(\frac{f_{0}}{f_{offset}}\right)+L\Big{\{}f_{offset}\Big{\}}+ 10 log\left(\frac{P_{\rm DC}}{1\;{\rm mW}}\right) \tag{20}\]  
Figure 7: Designed oscillator: (a) equivalent circuit, (b) structure (layout), (c) fabrication.  
Figure 8: _Cont_.  
An Agilent E4440A PSA series spectrum analyzer was used to measure the oscillations. The phase noise and output power of the fabricated oscillator were compared with those of oscillators developed in previous studies, as shown in Table 3.
# Comparison of Three Biological Control Models of _Pycnoporus__sanguineus_ on Phytopathogenic Fungi
## 2 Materials and Methods
### Model 3: Antifungal Effectiveness Tests of Full P. sanguineus Extract  
Antifungal activity tests were conducted on the phytopathogens _B. cinerea_ and _F. oxysporum_ under controlled conditions using a culture medium supplemented with a complete extract of _P. sanguineus_ mycelia, and they were compared to the commercial products. The growth, separation, and dehydration of _P. sanguineus_ mycelia were carried out in the same way as described for the second model. A total of 6.2 g of _P. sanguineus_ dehydrated mycelium was obtained, which was subsequently pulverized using a Nutribullet(r). The obtained powder was added to the PDA at a concentration of 24.8 mg/mL, which was filtered through 0.22 \(\upmu\)m Millipore filters and then sterilized at 120 \({}^{\circ}\)C for 15 min. In the same way, PDA with captan and benomyl were used as contrast treatments at concentrations of 2 mg/mL and 0.52 mg/mL, respectively, as well as a control group with PDA without any additives. Subsequently, the antifungal activity tests were carried out on _B. cinerea_ and _F. oxysporum_ under controlled conditions. The following treatments were performed, with 5 replications each: _P. sanguineus_ extract vs. _B. cinerea_, _P. sanguineus_ extract vs. _F. oxysporum_, captan vs. _B. cinerea_, captan vs. _F. oxysporum_, benomyl vs. _B. cinerea_,  
Figure 1: (**a**) The UV–vis spectrum (C4H8O2) and (**b**) high-performance liquid chromatography (CH3 CN/H2O) of cinnabarin present in the extract of _P. sanguineus_.  
benomyl vs. _F. oxysporum_, as well as a culture medium without additives vs. _B. cinerea_ and _F. oxysporum_, respectively. The inoculums were isolated from previously mature and growing cultures. The characteristics evaluated were the GR and the PIRG for the effectiveness of the complete extract of _P. sanguineus_, and the same procedure was followed for the captan and benomyl treatments.
## 2 The Working Principle of CFST Ductile Column Energy Dissipation Device (CFST Dampers)
### Lateral Stiffness of CFST Dampers
#### 2.1.3 Yield Stage of Rigid Support at Both Ends of Energy-Dissipating Columns  
As shown in Section 2.1.2, when the frame columns at both ends yield, the overall stiffness of the structure can be calculated by Formula (12).  
\[K_{3}=K_{sc22}+2K_{c2} \tag{12}\]  
where \(K_{c2}\) is the post-yield stiffness of a single column of frame column, which refers to reference [17].  
\[F_{3y}=F_{2y}+K_{3}\big{(}\Delta_{3y}-\Delta_{2y}\big{)} \tag{13}\]  
where \(\Delta_{3y}\) is the overall displacement of the frame corresponding to the yielding of the supports at both ends of the energy-dissipating columns, which can be calculated by Formula (14).  
\[\Delta_{3y}=\frac{nV_{scy}}{K_{sc1}}+\frac{V_{z}-nV_{scy}}{K_{sc2}} \tag{14}\]  
where \(V_{z}\) is the shear corresponding to the support at both ends of the energy-dissipating columns when yielding, which refers to reference [17].  
From the working principle of the CFST dampers mentioned above, it can be seen that the relevant parameters of the CFST column, such as height \(h\), outer diameter \(D\), wall thickness \(t\) of the steel pipe, slenderness ratio \(\lambda\), and the number of CFST columns \(m\), are the main design parameters of the CFST dampers. In order to effectively maximize the advantages of the CFST damper, it is necessary to ensure that it has certain initial stiffness, and the yielding of the CFST column must occur before the yielding of the reinforced concrete column. By adjusting the relevant parameters of the CFST column in the CFST dampers, such as height \(h\), outer diameter \(D\), wall thickness \(t\) of the steel pipe, slenderness ratio \(\lambda\), and the number of CFST columns \(m\), the stiffness of the first story of the masonry structure with RC frames can be easily adjusted to make it more reasonable.
## 4 Case Study for SU Allocation in 0.4 kV PTLs According to the Developed Methodology
### Effect of Sectionalization and Automatic Reclosing in the Event of Unstable Failure  
Equipping the _SU_ with a microcontroller control unit capable of realizing an automatic reclosing function allows for a reduction in three constituents of PS restoration time. They are time for obtaining information (t\({}_{\text{obt. infor.}}\)), time for recognizing information (t\({}_{\text{rec. infor.}}\)),and time for connecting the repaired equipment (\(\text{t}_{\text{harmonic}}\)). If the PTL failure is unstable, PS restoration time could be reduced to the value of recloser time delay of SU contactor (in the experimental sample it is set equal to 0.5 min, or 0.0083 h), that is:  
\[\text{t}_{\text{restor.unst.dam.MCUSU}}\ =\ 0.0083\ \text{h}\]  
The reduction in PS restoration time would be:  
\[\text{t}_{\text{resotor.unst.dam.MCUSU}}\big{/}_{\text{resotor.unst.dam.MCUSU}} \ =\ 1.84\big{/}0.0083\ =\ 221.7\ \text{times}\]  
Therefore, in case of the unstable failure, the application of SU with automatic reclosing function would enable us to reduce PS restoration time slightly more than 221 times. The damage would decrease significantly for consumers S30-S43 approaching USD 0.1, whereas the liquidated damage for consumers S1-S29 would amount to the same USD 70.93.
## 4 Discussion
### Performance Analysis
#### 4.2.1 Experimental Results of DSFF-DenseNet-DT  
We experimented with the proposed DSFF-DenseNet-DT method to conduct ten experimental runs in order to avoid results obtained by chance in the experiment. The average value of ten experimental runs was taken as the final experimental result. In Table 4, the experimental findings are displayed. The DSFF-DenseNet-DT method can effectively identify flotation working conditions and has good robustness. Its average accuracy, precision and recall of flotation working condition recognition exceeded 92.67%, and the F1 score reached 0.94, of which the recognition accuracy, precision and recall of class C1 reached 100%, and the F1 score reached 1. Figure 13 shows the confusion matrix of DSFF-DenseNet-DT on a self-built dataset. The confusion matrix's diagonal value is the biggest, as can be seen, indicating that DSFF-DenseNet-DT can accurately identify the flotation working conditions.
## 3 Simulation, Results, and Discussion
### Bended Structure  
#### 3.2.1 Effect of the Cylinder's Radius  
In an effort to obtain the highest possible sensitivity factor, various modifications were made to the original configuration. The flat structure was bent into a cylindrical form, and the original size remained unchanged (Figure 8a). Simulations were performed for different values of the cylinder radius R. The results for a test tube of radius R = 3.75 mm are presented in Figure 8b. The response S\({}_{21}\) was affected by the bended form of the sensing structure. The frequency band increased proportionally with smaller bending radii. It can be seen that the frequency interval between the upper (100% water) and lower (0% water) water concentrations increases with the decrease in the bending radius for 10 mm, 7 mm, and the minimum possible value of 5 mm. Beyond that, the structure is no longer practically  
Figure 6: **S\({}_{21}\)** parameter simulations of (**a**) water, ethanol, and air samples of normal glass tube design compared to measurements [41]; (**b**) water–ethanol concentration of the designed parallel test tube compared to measurements.  
Figure 7: Q factor and resonant frequency versus water–ethanol concentration of (**a**) normal glass tube design compared to measurements [41], and (**b**) the designed parallel test tube compared to measurements.  
feasible, due to structure deformation; consequently, the sensitivity increases, and reaches its maximum.  
Bending the structure on a smaller cylinder radius improves and increases the sensor's sensitivity. Table 2 summarizes the main obtained results.
# Magnetic Solid-Phase Extraction of Organic Compounds Based on Graphene Oxide Nanocomposites
## 3 Conclusions  
Due to the sufficient surface area and its superparamagnetic properties, magnetic graphene oxide has successfully been employed for the extraction of a wide variety of organic compounds from environmental, food, biological and agricultural samples. The surface of magnetic GO/Fe\({}_{3}\)O\({}_{4}\) nanocomposite contains a large amount of hydroxyl and carboxyl groups, which assist the interaction between the sorbent and organic molecules through \(\pi\)-\(\pi\) stacking, hydrophobic interaction as well as hydrogen bonding. In most MSPE methods, the GO/Fe\({}_{3}\)O\({}_{4}\) sorbent was found to be reusable after the desorption step, indicating satisfactory stability of the materials. Moreover, the synthetic procedures for the preparation of magnetic graphene oxide are simple, mild, rapid and economic.  
The main limitation of graphene oxide-based adsorbents is aggregation and restacking of the nanosheets, which may lead to blocking of the active adsorption sites of the sorbent, as well as to the reduction of its specific surface area. A wide variety of functional groups have been employed to overcome this problem. Enhancement of the dispersibility of magnetic graphene oxide in hydrophobic media can be also achieved through functionalization with appropriate chemical groups. Functionalization of magnetic graphene oxide has been also employed in order to enhance the extraction efficiency of the nanocomposite by introducing compatible chemical molecules with high surface area. Moreover, by using functional groups that can selectively bind with various organic, inorganic and biological guest molecules such as \(\beta\)-cyclodextrintrin, the selectivity of the extraction towards the target analyte can be significantly improved.  
Microporous organic polymers, molecular imprinted polymers, metal-organic frameworks, and zeolitic imidazole frameworks have been used in order to create nanocomposites with extraordinary properties. The resulted materials combined the advantages of magnetic graphene oxide and the chosen material and exhibited sufficient surface area, great extraction efficiency, ease in handling and separation and good stability.  
Finally, modification of magnetic graphene oxide with ionic liquid and deep eutectic solvents can significantly enhance the extraction efficiency of the sorbent. In this case, the modification process is simple, rapid, and environmentally friendly since no toxic organic solvents are required. Moreover, magnetic procedures are energy and time saving, and they require only small quantities of solvents during extraction and elution steps; therefore, they play a key role in green analytical chemistry.  
The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number: 138).  
The authors declare no conflict of interest.