esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Stage Configuration of User-Side Hybrid Energy Storage Based on Fuzzy Optimization
## 2 Two-Stage Optimization Model of Hybrid Energy Storage on the User-Side  
### Stage 1: Supercapacitor Configuration  
#### 2.1.1 Model of Super Capacitor from the User's Perspective  
Taking the basic reference output as 1000 kW [21], the corresponding curve spectrum is solved by fast Fourier transform. In this paper, three common filtering algorithms are used to filter photovoltaic data: median filter (MF), wavelet transform (WT), and moving average filter (MAF) [22; 23]. Because MF is very effective in smoothing impulse noise, it can protect the sharp edge curve of photovoltaic output by selecting the appropriate point to replace the bad point with large fluctuations. [24] MF is selected to process photovoltaic data in this paper. The time and frequency spectra of each waveform are shown in Figure 2. It can be seen from the Figure 2 that among the three filter algorithms, the median filtering output is the smoothest. The proportion of high-frequency components after filtering is shown in Table 1. In this paper, the price of a capacitor per unit capacity is set as \(c_{SC_{E}}\), the price of a capacitor per unit power is set as \(c_{SC_{P}}\), and then the mathematical model is established from the user's point of view as follows.  
\[Min:C_{invest1}=c_{SC_{P}}\times P_{SC}+c_{SC_{E}}\times E_{SC} \tag{3}\]  
\[s.t.P_{SC,cha,t}\leq P_{SC}\times B_{SC,cha,t} \tag{4}\]  
\[P_{SC,dis,t}\leq P_{SC}\times B_{SC,dis,t} \tag{5}\]  
\[B_{SC,dis,t}+B_{SC,cha,t}\leq 1 \tag{6}\]  
\[SOC_{SC}(t+1)=SOC_{SC}(t)-\frac{\eta P_{SC}(t)\Delta t}{E_{SC}} \tag{7}\]  
\[SOC_{SCmin}<SOC_{SC}<SOC_{SCmax} \tag{8}\]  
\[SOC_{SCinitial}=SOC_{SCfinal} \tag{9}\]  
where \(P_{SC}\) is the rated power of SC, \(E_{SC}\) is the rated capacity of SC (discharge is positive, and charge is negative), and \(SOC_{SC}\) is the SOC of SC, which has upper and lower limits of \(SOC_{SCmax}\) and \(SOC_{SCmin}\), respectively. \(\eta\) is the charge-discharge coefficient, \(\Delta t\) is the time interval. \(B_{SC,cha,t}\) and \(B_{SC,dis,t}\) are boolean variables, representing charge and discharge state constraints, \(B_{SC,dis,t}=1\) if SC is in the discharge state, \(B_{SC,dis,t}=0\) if SC is in the charge state, and \(B_{SC,cha,t}\) is the same as \(B_{SC,dis,t}\). In the optimization model, the objective function indicates that the user wants capacitor investment to be as small as possible, and the constraints indicate that the charging state of the capacitor needs to be kept within the normal range. Capacitor charging and discharging upper limits are determined by the rated power of the capacitor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Primary Photovoltaic Output** & **Wavelet Transform** & **Moving Average Filtering** & **Median Filtering** \\ \hline
0.3981 & 0.3953 & 0.3235 & 0.3197 \\ \hline \hline \end{tabular}
\end{table}
Table 1: High-frequency ratio.
## 3 Cost and Benefit Models  
### Maintenance Costs  
Maintenance costs of equipment come from condition monitoring (CM), preventive maintenance, corrective maintenance (consequences of idling, minor stops and failure or breakdowns), and costs of upgrading or substitution of components (\(C_{u}\)). The cost associated with condition monitoring is:  
\[C_{cmt}(\mathbf{x})=L\cdot C_{hct}, \tag{18}\]  
where \(L\) represents the analysis period and \(C_{hct}\) the hourly cost of monitoring.  
Preventive maintenance costs are calculated using:  
\[C_{pm}(\mathbf{x})=\frac{L}{M}\cdot d_{pm}\cdot c_{hpm}, \tag{19}\]  
where \(d_{pm}\) and \(c_{hpm}\) represent, respectively, the mean time and the average hourly cost of performing preventive maintenance, and \(\mathbf{x}\) represents the vector of decision variables. For this model, it is worth noting that this cost will be incurred every PM action. In addition, in some preventive actions, it will also be necessary to add the cost of upgrading the component.  
The cost related to corrective maintenance is:  
\[C_{cm}(\mathbf{x})=u_{r}(\mathbf{x},\mathbf{M})\cdot\frac{L}{M}\cdot c_{hcm} \cdot d_{cm}, \tag{20}\]  
where \(d_{cm}\) is the mean time of performing corrective maintenance, \(c_{hcm}\) is the average hourly cost of performing corrective maintenance and \(u_{r}(\mathbf{x},M)\) is the time-dependent unreliability caused by discontinuity:  
\[u_{r}(\mathbf{x},\mathbf{M})=\rho+(1-\rho)\cdot\left(1-e^{-h^{*}M}\right), \tag{21}\]  
\(\rho\) being the probability of failure on demand, and \(h^{*}\) the averaged failure rate over the lifetime of a component (17).  
Finally, when the amount of maintenance activities allowed before changing a component is exceeded, the cost of upgrading a component (\(C_{u}\)) has to be considered. It is calculated by multiplying the amount of times a component has been changed (\(n_{c}\)) and the cost of upgrading or substituting a single component \(C_{c}\):  
\[C_{u}=C_{c}\cdot n_{c}, \tag{22}\]  
being  
\[n_{c}=\frac{L}{\sum_{k=1}^{c}M_{k}}. \tag{23}\]
# Sub-Band Backdoor Attack in Remote Sensing Imagery
## 8 Results of Sub-Band Backdoor Attack
### Results of Sub-Band BadNets Attack  
We applied the Score-CAM [52] method to identify the important bands for the target class 'River' in the EuroSat dataset, which are Red, Green, Blue, RE2, RE3, and Cirrus and the less important bands are Aerosol, RE1, NIR, RE4, WV, SWIR1, and SWIR2. We planted triggers in the important and less important bands, and performances were similar to those by all-bands BandNets, as shown in Table 6. The validation clean accuracy was 0.93, and ASRs were close to 100%. We do not repeat here to save space.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Trigger Size** & \multicolumn{2}{c}{**Important-Bands DCNN**} & \multicolumn{3}{c}{**Less-Important DCNN**} \\ \hline  & \(\mathbf{Acc_{Clean}}\) & \(\mathbf{Acc_{B}}\) & \(\mathbf{ASR}\) & \(\mathbf{Acc_{Clean}}\) & \(\mathbf{Acc_{B}}\) & \(\mathbf{ASR}\) \\ \hline \(2\times 2\) & & 0.92 & 0.99 & & 0.93 & 1.00 \\
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 3 The Gas Content of the Equivalent Layer
### The Basic Gas Content of the Equivalent Layer  
The fundamental gas content within the equivalent layer encompasses both the fundamental gas content inherent in the rock mass and the fundamental gas content present in the coal body, specifically:  
\[Q_{jh}=Q_{yj}+Q_{mj} \tag{3}\]  
where \(Q_{jh}\) is the basic gas content of the equivalent layer, m\({}^{3}\)/t, \(Q_{yj}\) denotes the basic gas content of the rock mass, m\({}^{3}\)/t, \(Q_{mj}\) denotes the basic gas content of the coal body, m\({}^{3}\)/t.  
Given the occurrence of gas reservoirs within rock formations, external gas primarily accumulates in a free state within medium to large pores (referred to as effective pores), which are conducive to gas flow. This is attributed to the impact of small pore water film or condensation blockage. Consequently, the amount of residual gas can be deemed negligible, encompassing the following scenarios:  
\[Q_{yj}\approx q_{yy} \tag{4}\]  
Coal seams are gas-producing layers, and gas is not affected by pore water film or condensation blockage. It can be stored in both adsorbed and free states in all pores. Therefore:  
\[Q_{mj}=q_{my}+q_{mc} \tag{5}\]  
where \(q_{my}\) denotes the emission amount of basic gas content in equivalent layer coal seams, m\({}^{3}\)/t, \(q_{mc}\) denotes the residual amount of basic gas content in equivalent layer coal seams, m\({}^{3}\)/t.  
Substitute Equations (4) and (5) into Formula (3), and the calculation formula for the basic gas content of the equivalent layer is:  
\[Q_{jh}=q_{my}+q_{mc}+q_{yy} \tag{6}\]Let \(q_{yy}+q_{my}=q_{dj}\). Where \(q_{dj}\) denotes equivalent layer basic gas emission, m\({}^{3}\)/t, hence, the Equation (6) can be expressed as follows:  
\[Q_{\rm{fh}}=q_{dj}+q_{mc} \tag{7}\]
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 3 A Decision Framework
### Cost of Different Telecommunication Technologies  
In this section we briefly discuss a cost for the three options in the proposed model: (a) TV White Space; (b) WiLD and (c) Satellite. Here are some examples of the cost/month/bitrate for each option:  
* Long distance Wi-Fi is not regulated. There is no license charge if the transmitter power is below 30 dBm. Upfront costs vary depending on the bitrate. The complete cost of transceivers for up to 10 s of Mbps is $200. In some instances, antennas need towers, which increase the cost significantly. Maintenance is roughly half the price of upfront cost per year, not including labor. There is an additional cost for repairs and management.
* TVWS spectrum is regulated in most countries. In that case, licenses must be negotiated, and they are often expensive. Common upfront cost is around $4000 per transceiver for rates below 1 Mbps. It is very likely that no towers will be necessary, and that is an advantage, although this depends on the foliage density at the location. Maintenance is similar to Wi-Fi, including staff to maintain equipment and system.
* Satellite is usually the most expensive option and is rarely affordable in a development project. Even if satellite could be accessed without paying a large fee, it is not sustainable and would not make sense in the business approach that we propose.  
In situations when there is a carrier, using the local carrier will have a cost/bps/month; however, maintenance cost will be significantly lower.
# Role of Functional Monomers upon the Properties of Bisphenol A Molecularly Imprinted Silica Films
## 2 Experimental Section
### Synthesis of BPA-Miprited Polymer (BPA-MIP) Films  
Two types of BPA-imprinted organosilica films (i.e., BPA-MIP\({}_{\text{MPTES}}\) and BPA-MIP\({}_{\text{DAMOT}}\)) were successfully prepared by directly spraying the precursor solutions on glass supports, according to a similar one-step procedure described by our group, with slight modifications [34]. In a typical batch for BPA-MIP preparation, two solutions were prepared separately at room temperature (RT considered 25 \({}^{\circ}\)C), the catalyst and the precursor solution. The catalyst consisted of an aqueous solution of 25% ammonia hydroxide (0.565 mL NH\({}_{4}\)OH/0.225 mL H\({}_{2}\)O). The precursor solution was prepared by dissolving the functional monomer DAMO-T or MPTES and the template BPA in 1 mL of ethanol under stirring (molar ratio monomer: BPA = 10:1). After stirring for 30 min at 200 rpm, in the precursor solution containing DAMO-T a structural monomer (0.13 mL TEOS) was also added to ensure the adhesion to the glass support. The precursor monomer solution was magnetically stirred for 1 h after which it was gradually added in the catalytic medium and mechanically stirred at 200 rpm, for 2 h (sol-gel time, tsg), when the partial hydrolysis reaction occurs, generating sols. The resulted solution was applied by directly spraying it onto cleaned glass supports to form crosslinked networks by alcohol/aldol polycondensation of sols. The obtained films were matureated for 48 h at 25 \({}^{\circ}\)C (until complete polycondensation), followed by curing for 48 h at 60 \({}^{\circ}\)C in an oven.  
The cured films were then washed two times according to the following procedure: step 1--washing with ethanol, for 8 h at room temperature, for removing unreacted monomers and step 2--extraction of BPA molecules with ethanol-acetic acid (9:1, \(v/v\)) for 4 h assisted by ultrasound (at 35 kHz frequency). As shown in Figure S1, a rigorous control of the supernatant solutions was performed by UV-Visible spectroscopy in order to assess the efficiency of the washing/extraction procedures. It can be observed in the second washing/extraction cycle that BPA was successfully extracted from the imprinted films (as confirmed by the absence of specific absorption peaks of BPA). Afterward, all films were dried at 60 \({}^{\circ}\)C overnight until constant weight and stored in dried conditions until use. By contrast, non-imprinted control films (noted by analogy NIP) were synthesized, conditioned, and washed respecting the same procedures as for the MIPs; the only difference was the absence of BPA from the synthesis.
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
## 5 Improved Modulations
### The b-Modulation  
After NIS, the b-modulation--sketched in Figure 3b--was proposed, which consists in mapping the QAM signal (or its standard Fourier transform) on the scattering data \(b(\lambda)\)[31]. The continuous nonlinear spectrum \(\rho(\lambda)\) is obtained (in the absence of discrete spectrum) through the following equations  
\[|a(\lambda)|=\sqrt{1-|b(\lambda)|^{2}} \tag{2}\]  
\[a(\lambda)=|a(\lambda)|\exp\left(j\mathcal{H}\left(\log(|a(\lambda)|)\right)\right) \tag{3}\]  
\[\rho(\lambda)=b(\lambda)/a(\lambda) \tag{4}\]  
with \(\mathcal{H}(\cdot)\) being the Hilbert transform. The main advantages of b-modulation are that (i) if the standard FNFT detection is used, the impact of noise is reduced, by avoiding the division by \(a(\lambda)\) at the RX, and (ii) the temporal duration of the signal is fully controlled, as shown in Figure 4b. Furthermore, a recent theoretical study on the impact of noise in the nonlinear frequency domain (after the FNFT) showed that, differently from the standard nonlinear spectrum modulation, the power spectral density of the noise tends to zero when the energy of the signal increases [33]. Unfortunately, the power of the signal is severely limited when using b-modulation, since \(|b(\lambda)|<1\) must hold for any \(\lambda\in\mathbb{R}\)[17; 31]. Theoretical bounds on the power in \(b\)-modulated systems (and for continuous spectrum modulation in the normal dispersion regime) have been recently given [34].
# Geographical Distribution of Global Radiation and Sunshine Duration over the Island of Cyprus
## 3 Results and Discussion
### Daily Values
#### 3.1.3 Daily Clearness Index and Relative Sunshine Duration
The average values of the daily clearness index ranged between 0.495 in January and 0.685 in July for the first group, while they ranged between 0.445 in January and 0.668 in July for the second group of the stations. The linear relationship between the annual mean \(K_{T}\) and elevation is demonstrated in Figure 13b. As it is indicated, the annual mean \(K_{T}\) decreased with elevation. Figure 13: (**a**). Monthly averages of clearness index (\(K_{T}\)) summarized by their location: group of coastal and inland plain stations and the group of semi-mountainous and mountainous stations. (**b**). Annual averages of clearness index (\(K_{T}\)) as a function of elevation (m). Table 6 shows the monthly average of global irradiation and the number of days in each month of the year according to the above classification. The average global irradiation was almost similar in both groups. The annual average of global irradiation was about 7 MJ/m\({}^{2}\) on cloudy days and about 21 MJ/m\({}^{2}\) on clear days. However, the annual number of cloudy days was 21 (about 6%) for the first group and almost twice (39) for the second group representing 11% of the annual number of days. The respective number for partially cloudy days was 121 and 149 days, while the days that were classified as clear days were 208 (57%) and 166 (45%), respectively. As it can be seen, the summer months were classified mainly as clear days. The discrepancy between the numbers of days in the corresponding to the four types of the days was a result of the fact of the different missing days in each of the month. The monthly average frequency of days according to their type gives an initial indication of the relative viability of different solar collector types. For example, concentrating solar collectors operate best under clear day conditions and, to a much lesser extent, under partially cloudy days. The major difference between day types was the significant reduction in the beam radiation, which also affects the global irradiation level. Regarding the global radiation, the reduction factor for partially cloudy days to that on clear days ranged from 0.73 to 0.78. Almost similar figures were obtained by [PERSON] and [PERSON] [46] at Beer Sheva in Israel. The monthly means and their standard deviations of the relative sunshine duration (\(\sigma\)) of four stations are presented in Table 7. The monthly means of the coastal and inland plain areas ranged between 0.510 and 0.920. The respective means for semi-mountainous stations ranged between 0.530 and 0.880, while at the higher locations they ranged between 0.485 and 0.810. Generally, the annual average decreased from 0.793 to 0.688 with an increase in elevation.
## 3 Proposed Methodology
### Joint Model Architecture  
The goal of this research is to develop a Speech Emotion Recognition (SER) model that combines both discrete and dimensional emotion representations.  
Joint models, similar to multi-task learning models, involve training a single model to perform multiple tasks simultaneously. Unlike multi-task learning models, which perform multiple unrelated tasks, the joint model combines tasks into a single unified model with closely related tasks or dependencies. The model optimizes a single joint objective function, capturing shared structures or dependencies between discrete and dimensional emotional representations, potentially improving performance compared to independent models.  
The proposed model architecture, illustrated in Figure 3, employs parallel feature extraction from multiple input channels, including Mel-Frequency Cepstral Coefficients (MFCC), Short Term Features (STF), Mel-spectrogram, and Raw Audio. These features are fed to a feature encoder network, which extracts latent space features from the input channels. The feature encoders are designed as follows:  
* One-dimensional Convolutional Neural Networks (IDCNNs) and Bidirectional Long Short-Term Memory (BiLSTM) encoders for both MFCC and STF features;
* A CNN-based feature encoder for Mel-spectrogram;
* A pre-trained model's latent feature encoder for the raw audio signal.  
Each of these feature encoders offers unique benefits, allowing the model to leverage their respective strengths and extract a comprehensive set of features that capture various emotional aspects of the speech signal. After feature extraction, the latent features pass through a self-attention mechanism, generating attention weights for each feature. These self-attention weights are input to a co-attention mechanism, learning the interdependencies between them.  
The co-attention weights are processed by a projection layer, splitting the output into two tasks: a categorical task for discrete emotional representations and a regression task for each dimensional value (valence, arousal, and dominance). The model is trained using a joint loss function, combining both categorical and regression loss functions. This approach enables the prediction of both discrete and dimensional emotion representations using a single unified network model.  
The block diagram in Figure 4 provides an overview of this joint architecture. The model receives input signals, processes them through the feature encoder, and generates outputs via the projection layer. The joint loss function, illustrated in Figure 4, combines both classification loss (for categorical emotions) and regression loss (for dimensional values). This unified approach allows the model to predict both types of emotional representations simultaneously, enhancing its versatility and performance in SER tasks. Figure 4 highlights how the model is structured to optimize both classification and regression tasks. By integrating discrete and dimensional representations within a single architecture, the model ensures a robust and comprehensive emotion recognition framework.
###### Abstract  
This work is aimed at showing the present capabilities and future potentialities of an imaging radar system that can be mounted onboard flexible aerial platforms, such as helicopters or small airplanes, and may operate in the UHF and VHF frequency bands as Sounder and as Synthetic Aperture Radar (SAR). More specifically, the Sounder operates at 165 MHz, whereas the SAR may operate either at 450 MHz or at 860 MHz. In the work, we present the first results relevant to a set of Sounder and SAR data collected by the radar during a helicopter-borne campaign conducted in 2018 over a desert area in Erfoud, Morocco, just after the conclusion of a system upgrading procedure. In particular, a first analysis of the focusing capabilities of the Sounder mode and of the polarimetric and interferometric capabilities of the SAR mode is conducted. The overall system, originally developed by CO.RIS.T.A. according to a ASI funding set up in 2010, has been upgraded in the frame of a contract signed in 2015 between ASI and different private and public Italian Research Institutes and Universities, namely CO.RIS.T.A., IREA-CNR, Politecnico di Milano and University of Trento.  
Synthetic Aperture Radar (SAR); Airborne SAR; Sounder; P-Band; helicopter-borne radar; UHF and VHF bands
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 3 Path-Tracking Controller  
The path-tracking controller architecture is shown in Figure 3. The error calculation module determines error information based on road data and planned trajectories. The mass estimator derives the vehicle's mass from controlled state parameters and provides this feedback to the controller. The MPC controller calculates the front wheel steering angle, and the steering angle compensation controller computes the compensation amount for the front wheel steering angle.
## 3 Results and Discussion  
Figure 3a shows some of the deposited layers on the flat substrate, and Figure 3b shows some on the existing wall obtained in these experiments. They all have good bead appearance with little spatters and no visible defects. In the middle segment of each deposited layer, the bead width (_W_) and the bead height (_H_) were measured (see Figure 3c) with the aid of a laser displacement scanner (0.01 mm repeatable precision, HG-C1030, Panasonic, Suzhou, China), and then the ratio of width to height (_RWTH_) was calculated, as given in Table 2. The variation of the bead geometry can be indicated by _RWTH_ since the cross-sectional area is a constant value. It can be seen that the three repeated experiments (Samples 1-3) show a maximum error of 0.74 mm in bead width and 0.23 mm in bead height. The discrepancy may be caused by the uncertainty of the molten pool solidification. Only the intermediate values (Sample 2) were recorded for the subsequent analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sample 1**} & \multicolumn{2}{c}{**Sample 2**} & \multicolumn{2}{c}{**Sample 3**} & \multicolumn{2}{c}{**Maximum Error**} \\ \cline{2-10}
**Run No.** & \begin{tabular}{c} **Width** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Height** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Width** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Height** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Width** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Height** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Width** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Height** \\ **(mm)** \\ \end{tabular} \\ \hline
1 & 9.27 & 3.27 & 9.34 & 3.23 & 9.48 & 3.16 & 0.21 & 0.11 \\
2 & 9.66 & 3.18 & 9.83 & 3.11 & 10.01 & 3.03 & 0.35 & 0.15 \\
3 & 10.55 & 3.09  
Figure 4 compares the bead width, bead height, and _RWTH_ between different groups of experiments. Figure 5 compares the bead cross section profiles fitted based on a parabola model [24], which is expressed by  
\[Y=-\frac{4H}{W^{2}}X^{2}+H. \tag{1}\]  
Figure 4: (**a**) Relation between bead width and wire feed rate (_WFR_) in Groups I–III; (**b**) Relation between bead height and _WFR_ in Groups I–III; (**c**) Relation between ratio of width to height (_RWTH_) and _WFR_ in Groups I–III.  
Figure 5: Bead cross section profile comparison between Groups I–III (**a**) when _WFR_ = 3 m/min; (**b**) when _WFR_ = 4 m/min; (**c**) when _WFR_ = 5 m/min.