esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Results of the Estimation Obtained at Variable Current without Aging Effect
In addition, the \(R^{2}\) value of DNN was higher than that of the GRU and LSTM models, demonstrating the precision of that model for estimating the SoC. Figures 17 and 18 represent the training and validation losses of the GRU and LSTM models for simulation and experimental data. The convergence of the networks demonstrated the performance of the proposed models in learning the SoC estimation problem. Figures 19 and 20 show the offset between the reference SoC of each of the two types of data (experimental and simulation) and the predicted SoC. Although all three models were successful in estimating the SoC in both charge and discharge phases, the DNN model had a significant advantage over the GRU and LSTM models with a maximum error of 2.5% compared to 4.6% for the GRU and 5.5% for the LSTM. The difference in error between GRU and LSTM models came from the difference in the total number of parameters in the two models, which was 22,689 in the GRU versus 29,857 parameters in the LSTM model. The superiority of the DNN over the GRU and LSTM models in state-of-charge (SoC) estimation hinges on various factors, such as the nature of the problem at hand, the volume and quality of the training data, the model's complexity, and the optimization algorithm employed. A DNN exhibits enhanced capability in capturing intricate correlations between inputs and outputs, but it also has a higher risk of overfitting. Table 5 summarizes the advantages and disadvantages of the three prediction models for reliable battery SoC estimation. To examine the model performance under different conditions, a current profile that simulated real-life situations, like instantaneous braking and acceleration of electric vehicles, was used. Figure 17: Training and validation loss of the (**a**) gated recurrent unit (GRU) model and (**b**) long short-term memory (LSTM) model for experimental data. Figure 18: Training and validation loss of the (**a**) gated recurrent unit (GRU) model and (**b**) long short-term memory (LSTM) model for simulation data. Figure 9:0 Deep neural network estimated SoC vs. reference SoC and estimation error on the validation dataset.
## 3 Results
### Implementation and Visual Assessment of the Grad-CAM Technique  
Second, the heatmaps obtained through the grad-CAM technique have been used to establish if the classifier effectively makes its predictions based on the presence of dense areas in the mammogram. This fits into the more general purpose of assessing trust in predictions from our algorithm. The heatmap evaluation has been done qualitatively, which means by visually estimating if the highlighted regions in the heatmap correspond to the denser regions in the original image. The analysis consisted in visualizing and comparing the maps generated using the input images of the four classes. The maps have been produced for all the images in the test set and for all the four projections constituting the mammographic exam. In Figure 5, an example of a comparison of the heatmaps of the four density classes obtained from a model trained on right cranio-caudal projections is reported.  
The activated regions in the maps match reasonably well with the dense regions in the original mammogram for B, C and D classes. The grad-CAMs prove that the \"attention\" of the classifier is focused on the dense region as we expected. An important remark resulting from analyzing all the maps is that for class A mammograms the active area is almost always at the edge of the breast. This is reasonable because the A class is the one corresponding to the lowest density and it seems as if the classifier, not recognizing any dense region, focuses its attention on a different feature, such as the edge.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **AOUP** & **BIRADS** & **Uniform** \\  & **Test Set** & **Test Set** & **Test Set** \\ \hline \multirow{3}{*}{BIRADS Training set} & test accuracy (\%) & 79.1 & 83.1 & 73.6 \\  & recall (\%) & 75.2 & 80.1 & 73.6 \\  & precision (\%) & 82.6 & 87.9 & 79.0 \\ \hline \multirow{3}{*}{AOUP Training set} & test accuracy (\%) & 78.5 & 79.7 & 73.6 \\  & recall (\%) & 74.2 & 77.9 & 73.6 \\  & precision (\%) & 81.2 & 83.0 & 79.4 \\ \hline \multirow{3}{*}{Uniform Training set} & test accuracy (\%) & 72.8 & 72.9 & 77.8 \\  & recall (\%) & 78.9 & 79.9 & 77.8 \\ \cline{1-1}  & precision (\%) & 69.5 & 68.8 & 78.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Final results of CNN trained on different training set and tested on different test sets.
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 4 Simulation Results and Discussions  
In this section, simulations of the proposed Greenshields model-based fuzzy logic system for vehicle speed and travel-time prediction are conducted using traffic data from a specified segment of the Sun Yat-Sen Highway in Taiwan. These simulations aim to demonstrate the reliability and effectiveness of the developed fuzzy logic models. For comparison, a conventional model is also established using regression analysis. The proposed cascaded system for long-range travel-time prediction is likewise verified through simulation. The discussion of simulation results provides valuable insights that confirm the feasibility of the proposed system for vehicle speed prediction. The simulations ultimately validate the system's accuracy, its capability to enhance prediction performance, and its flexibility in constructing a cascaded fuzzy logic system for travel-time prediction across long-range routes composed of multiple highway segments.
## 4 Results and Discussion
From Figure 14, the case when injection combines with contraction, the growing values of \(\Phi_{2}\) increases the Nusselt number at the top wall. It also provides a similar increment for rising absolute \(\mathcal{A}\). Besides, the lower wall exhibits a drop in Nusselt number with the augmented absolute \(\mathcal{A}\). Clearly, the rate with which heat transfer occurs at the top wall reveals augmented values for the platelet shaped nanoparticles. The case where injection accompanied by dilation, Figure 15 exposed the deviations in Nu with the growing absolute \(\mathcal{A}\) both at the top and bottom walls. Again, it has been noticed that the platelet shaped nanocomposites have higher ability of transferring the heat as compared to cylinder or brick shaped tiny particles. Figure 16: Nusselt number for a numerous values of \(\alpha\); \(\mathcal{R}<0,\alpha<0\). Figure 14: Nusselt number for numerous values of \(\mathcal{A}\); \(\mathcal{R}>0,\alpha<0\). Figure 19: Nusselt number for numerous values of \(\mathcal{R}\); \(\mathcal{R}<0,\alpha>0\). With the growing \(\Phi_{2}\), one can clearly observe a decrement in the local heat transfer rate and it has been displayed in Figure 16 (suction/squeezing). However, an augmentation in the Nusselt number has been perceived with the growing volume fraction. Moreover, continuously squeezing the walls transmit a high amount of heat from both the walls. Also, platelet shaped tiny particles experiences a high amount of heat transfer as compared to others at the bottom wall, whereas, a reversed behavior has been seen at the top wall. In the subsequent case of injection/squeezing, Figure 17 has been plotted for the varying values of \(\alpha\) which displays the similar preceding results of Figure 16, i.e., \(Nu\) enhances at the both walls. Furthermore, the platelet shaped nanostructures possess high transmission of heat at the upper wall of the channel. For the case of squeezing/suction, Figure 18 has been sketched to see the consequences of rising \(\Phi_{2}\) along with absolute \(\mathcal{R}\) on the Nusselt number. One can notice a clear decline in \(\mathrm{Nu}_{\mathrm{u}}\) with increasing \(\Phi_{2}\), while an enhancement has been perceived with rising absolute \(\mathcal{R}\). Moreover, a reversed behavior was observed for \(\mathrm{Nu}_{\mathrm{l}}\) from the adjacent figure. Also, platelet shaped tiny particles shows a higher heat transfer rate at the bottom wall of the channel. For the case of dilation/suction, Figure 19 has been plotted to highlight the significances of rising \(\mathcal{R}\) and \(\Phi_{2}\) upon the Nusselt number. The rising values of \(\Phi_{2}\) depict an increment in the behavior of \(\mathrm{Nu}\) at the both walls. Likewise, the growing absolute \(\mathcal{R}\) simultaneously enhances the heat transmission rate at the top wall and it decreases at the bottom wall. Further, it is noteworthy that platelet shaped nanocomposites prove their ability of transferring the heat at the high extents and it can be observed at the both wall.
## 3 Results and Discussions
### Morphology and Characteristics of Silver Membrane
This thermal effect can trigger the decomposition of the PVP capping layer and the surface melting of Ag NPs. Both effects contributed to the sintering of the Ag NPs. This sintering process was dramatically more pronounced with an increase in laser power. Further increasing the laser power lead to the melting of Ag NPs because the melting temperature of nanoparticles is significantly lower than that of bulk material [24]. In this case, the temperature beneath the focal point at the PI surface can be lower than the decomposition temperature of PI, which can minimize damage to the PI substrate. This is  
Figure 2: The laser-scripted MS transmission line on a PI film. (**a**) The photo image of a MS line. (**b**) The microscope image of a silver membrane. The inset shows the cross-sectional view of the silver membrane. The scale bar in the inset indicates 10 μm. consistent with the experimental results, where the melting of Ag NPs was observed at a laser power of 100 mW, whereas the carbonization of the PI substrate occurred at a laser power above 250 mW. Ultimately, the sintering and melting of the Ag NPs could increase the conduction passes and, therefore, tune the resistance of the Ag membrane. The sheet resistance, which is crucial for RF devices, is plotted in Figure 4. When the Ag membrane was fabricated at a laser power of 37.5 mW, the sheet resistance was at a magnitude around \(10^{3}\)\(\Omega/\square\). The increase of laser power lead to a rapid drop in sheet resistance by two orders of magnitude, indicating the sintering of the Ag NPs. When the laser power exceeded 75 mW, the decrease of sheet resistance gradually slowed down, with a minimal average value of 0.25 \(\Omega/\square\) (200 mW). This is consistent with the formation of the continuous silver membrane via sintering and melting (shown in Figure 4c). The corresponding conductivity was \(10^{6}\) S/m (200 mW), which is comparable to the previous studies. The dramatic rise of the sheet resistance at 250 mW was mainly due to the break of the highly conductive path in the silver membrane via laser ablation. The electrons were transported through the porous carbon flacks with a significantly lower conductivity than the silver membrane. Consequently, it is possible to tune the sheet resistance of the silver membrane with a variation of the laser power. Figure 3: Scanning electron microscope images of laser-scripted silver membrane with different laser powers ((**a**) and (**b**): 50 mW, (**c**) and (**d**): 100 mW, (**e**) and (**f**): 150 mW, (**g**) and (**h**): 250 mW).
## 4 Engine Test Results and Discussions
### Engine Emission Characteristics
#### 4.3.2 Fuel Effects on HC and CO Emissions  
HC and CO emissions are from unburned fuel. They are lower but must be considered in China VI diesel engine technology. The effects of hydrocarbons on engine HC and CO emissions are shown in Figures 14 and 15.  
In Figure 14, the addition of hydrocarbons results in increased HC emissions. Diesel fuel always emits lower HC than blended fuels. Additionally, the greater the addition, the more HCs. The low boiling temperature and 10% distillation temperature are the reason for high HC emission, which can be seen in Tables 2 and 3. Among them, n-heptane is the lowest, which leads to the highest HC emission [26]. Dodecane and decalin follow the rule, but tetralin is an exception due to its stable molecular structure. Figure 15 shows the CO emissions. The highest CO under low load conditions is unrelated to its addition ratios of 20% and 50%. The carbon-carbon bond in tetralin is harder to break, which causes higher CO formation at low load conditions [27]. At higher load conditions, CO emissions follow the distillation temperature rule, similar to HC emissions. Diesel operation has the lowest CO emissions.  
Therefore, HC and CO emissions can be attributed to the fuel distillation temperature. The addition of the hydrocarbons reduces the distillation temperature and thus results in increased HC and CO emissions. Secondly, the molecular structure of the hydrocarbon has particular effects on CO emission.  
Figure 14: THC emissions versus engine loads at 1900 rpm.  
Figure 15: CO emissions versus engine loads at 1900 rpm.
## 3 System Models
### Formation Shape Model  
The USV team needs the geometry configuration of formation in the collective formation mission. Reasonable formation shapes of the USV team can increase the efficiency of formation task execution. To establish or maintain a specific geometric formation, it is necessary to establish a representative method of geometric formation. At present, there is no unified formation representative method to designate the formation of the USV team. In this paper, the formation shape matrix is established by combining with the formation description mode of chain guidance reference, defined as a \(4\times N\) formation shape matrix, \(F_{s}\) as shown in Equation (2). The matrix is adopted to represent the geometric relationship of USVs, where \(N\) is the number of USVs in the formation. In the matrix \(F_{s}\), the first row denotes the number of geometric nodes in the formation. The second and third rows represent the distance offsets between the USV and its immediate leader USV in \(x\)-axis and \(y\)-axis directions. The fourth row denotes the node number of the immediate leader of the USV in each formation node. \(N\) USVs form a geometric shape. The centric USV is the leader of the team, while the other USVs form the chain tracking one by one. Follower USVs keep the distance displacement \((d_{i}^{X^{\prime}},d_{i}^{Y^{\prime}})\) from their immediate leader. If one or several immediate leaders in the formation fail during the movement, it is necessary to reconstruct a formation according to the size \(N_{t}\) of good USVs and the first \(N_{t}\) columns of the formation shape matrix \(F_{s}\).  
\[F_{s}=\left[\begin{array}{ccccc}1&2&3&\dots&N\\ 0&\alpha d_{2}^{X^{\prime}}&\alpha d_{3}^{X^{\prime}}&\dots&\alpha d_{N}^{X^{ \prime}}\\ 0&\beta d_{2}^{Y^{\prime}}&\beta d_{3}^{Y^{\prime}}&\dots&\beta d_{N}^{Y}\\ 0&Il(n_{2})&Il(n_{3})&\dots&Il(n_{N-2})\end{array}\right] \tag{2}\]  
where \(d_{i}^{X^{\prime}}\) and \(d_{i}^{Y^{\prime}}\) are the horizontal and vertical distance of each follower-leader pair. \(\alpha\in(0,1]\) and \(\beta\in(0,1]\) are the expansion coefficients in horizontal and vertical directions, respectively. By adjusting the values of \(\alpha\) and \(\beta\), the horizontal and vertical expansion and contraction of the same formation can be realized. \(Il(n_{i})\) is the immediate leader of the USV located in the formation node \(n_{i}\).
# Mapping Radar Glacier Zones and Dry Snow Line in the Antarctic Peninsula Using Sentinel-1 Images
## 3 Methodology  
Due to the lack of in situ observation, RGZs and DSL derived from Sentinel-1 images are compared with SSM/I-derived snowmelt in this study. Snowmelt detected by the SSM/I is used to study the relationship between summer snowmelt and winter SAR backscatter. We also compare the summer snowmelt detected by the SSM/I and Sentinel-1. The SSM/I-derived melt area is used to validate the DSL mapped by winter Sentinel-1 mosaic. In this section, we present the melt detection method for the SSM/I. A practical method to classify RGZs is developed based on the simulations of a microwave scattering model.
## 3 Measurement, Control, and Communication Architecture
### Custom-Designed Hardware Components
#### 3.1.2 Wind Speed Measurement
Therefore, the raw ADC value is first converted into a voltage using:  
\[V=\frac{ADC_{value}\times V_{ref}}{1023} \tag{13}\]  
where \(V_{ref}\) is the reference voltage of the analog pin, typically 5 V in standard Arduino(r) boards. This calculated voltage is then used in the wind speed equation. To improve accuracy and filter out high-frequency noise, a moving average of multiple samples is typically applied. This approach enhances stability in the measured wind speed, particularly under turbulent or gusty conditions. Figure 4 illustrates the physical connection of the anemometer to the data acquisition system. The analog signal wire is connected to pin A3, and power is supplied via a regulated 12 V input. Ground reference is shared across the sensor, power source, and analog interface to ensure accurate signal referencing. The diagram simplifies the setup for  
Figure 4: Wind speed sensor connected to the Arduino® board. reproduction and clearly illustrates the signal path to pin A3, along with the power and ground wiring required for proper sensor operation.
# Orthogonal Chirp Division Multiplexing for Underwater Acoustic Communication
## 2 OCDM Basics
### OCDM in Matrix Form  
Equations (3) and (4) realize the basic concept of OCDM for analog signals. For a digital signal, OCDM modulation is performed by using Inverse Discrete Fresnel Transform (IDFnT), as shown in [14]. In fact, sampling (3) at \(mT/N\) yields:  
\[s_{m}^{k}=s_{k}\left(t\right)_{t=m\frac{T}{N}}=\sum_{n=0}^{N-1}x_{n}^{k}\psi_{n }\left(mT/N\right)=e^{j\frac{T}{N}}\sum_{n=0}^{N-1}x_{n}^{k}e^{-j\frac{T}{N} \left(m-n\right)^{2}} \tag{5}\]  
From the above expression, the concise matrix form of OCDM modulation can be expressed as:  
\[\mathbf{s}_{k}=\mathbf{\Phi}^{H}\mathbf{x}_{k} \tag{6}\]  
with \(\mathbf{s}_{k}=\left[s_{0}^{k}\ \...\ \ s_{N-1}^{k}\right]^{T}\), \(\mathbf{x}_{k}=\left[x_{0}^{k}\ \...\ \ x_{N-1}^{k}\right]^{T}\) and unitary matrix \(\mathbf{\Phi}\), which is the so-called Discrete Fresnel Transform (DFnT) matrix defined as follows for an even number \(N\):  
\[\{\mathbf{\Phi}\}_{m,n}=\frac{1}{\sqrt{N}}e^{-j\frac{T}{N}}e^{j\frac{T}{N} \left(m-n\right)^{2}} \tag{7}\]  
In the demodulation stage, a DFnT operation is invoked as follows:  
\[\mathbf{\hat{x}}_{k}=\mathbf{\Phi}\mathbf{s}_{k} \tag{8}\]
# Mutated Channelrhodopsins with Increased Sodium and Calcium Permeability
## 3 Results
### Mutation of the Aspartate in TM4 Influences the Ca\({}^{2+}\) Permeability  
As obvious impacts of mutation of the conserved aspartate in TM4 on ion selectivity were observed, we further compared the Ca\({}^{2+}\) permeability of these mutants, considering the importance of Ca\({}^{2+}\) in biological systems. Due to the existence of Ca\({}^{2+}\)-activated chloride channels in _Xenopus_ oocytes [23], BAPTA was injected into the oocyte to a final concentration of \(\sim\)10 mM, to block the Ca\({}^{2+}\)-induced chloride current (Figure 5). Then the photocurrents at \(-\)100 mV were measured in outside solution containing 80 mM CaCl\({}_{2}\) at pH 9.0. At \(-\)100 mV and pH 9, no net H\({}^{+}\) current could be observed and the inward photocurrent was then only from the Ca\({}^{2+}\) influx.  
ChR2 showed a robust composite photocurrent with 80 mM CaCl\({}_{2}\) at pH 9.0 and \(-\)100 mV, which was dramatically reduced to the pure Ca\({}^{2+}\) current after injection of 10 mM BAPTA (Figure 5a), as reported previously [2]. Both Chronos and ChR2 showed small Ca\({}^{2+}\) photocurrents (Figure 5a). ChR2 L132C (CatCh) showed an increased Ca\({}^{2+}\) photocurrent, compared to ChR2 (Figure 5), as previously reported [8]. Astonishingly, XXM also showed an increased Ca\({}^{2+}\) photocurrent, even higher than that of CatCh (Figure 5). \(Ps\)ChR D139H showed the highest Ca\({}^{2+}\) photocurrent, which on average was more than two times higher than that of CatCh (Figure 5b).
## 3 Mobile Network Security Evolution and the Evolution of Threat Landscape
### Implications for 5G Security  
The security strategies of mobile networks have critical implications for the development and implementation of security measures in 5G networks. A primary implication drawn from the historical successes is the persistent need for strong encryption and authentication measures. Given the expanded connectivity, higher speeds, and increased volume of data associated with 5G, the importance of these fundamental security measuresis heightened. More than ever, users need to be assured of the integrity and confidentiality of their data. Consequently, 5G security architectures are expected to incorporate even more advanced encryption algorithms and multifactor authentication methods that are robust against emerging threats [6; 37; 51].  
The pitfalls encountered during the evolution of mobile networks, notably during the transition to an all-IP network architecture in 4G, serve as a stark reminder of the importance of a holistic, multilayered approach to security [76]. Given the extensive connectivity, numerous devices, and diverse applications envisaged for 5G, the associated security challenges are even more complex [37; 51]. It necessitates an integrated security approach that combines traditional measures like encryption and authentication with cutting-edge measures such as network slicing, AI and ML algorithms for anomaly detection, and advanced privacy-preserving techniques. Network slicing, for instance, allows the separation of different services onto individual network slices, thus isolating critical services and limiting the potential impact of a security breach [66]. AI and ML algorithms can be employed to learn normal network behaviors, identify anomalies that could signal a potential security threat, and initiate appropriate responses [37]. Advanced privacy-preserving techniques can provide better protection for user data, addressing the ever-growing concerns over data privacy in the age of the IoT and big data. Another critical implication is the need for adaptability and continuous evolution of security measures to respond to the ever-changing threat landscape. Fifth-generation networks, given their extensive connectivity and diverse applications, are expected to face new and potentially unforeseen security threats [6; 51]. This necessitates proactive measures that can quickly identify, respond to, and neutralize such threats, demonstrating the need for dynamic, adaptive, and self-learning security systems.
## 2 Materials and Methods
### Biosurfactant Effect on Removal of Some Pollutants from Synthetic Wastewater
#### 2.6.3 Removal of Pollutants  
For the experiments, 450 mL concentrations of uncontaminated and contaminated synthetic wastewater with Pb\({}^{2+}\) and Cd\({}^{2+}\), respectively, were inoculated with 1% of each yeast strain having an initial concentration of 2 McFarland. To prepare the samples with a biosurfactant, 5% (\(v/v\)) biosurfactant was introduced in inoculated synthetic wastewater. Subsequently, all synthetic wastewater samples were incubated at 28 \(\pm\) 2 \({}^{\circ}\)C for 120 h. The removal efficiency of nutrients and heavy metals was determined initially and at the end of the experiments.  
The capacity to remove organic substances was evaluated by determining the chemical oxygen demand (COD), according to the ISO 6060 standard [35], using the Gerhardt Chemical Oxygen Analyzer equipment (C. Gerhardt GmbH & Co., Konigswinter, Germany).  
The ability to remove nitrate, nitrite, ammonium, phosphate, and sulfate ions was evaluated spectrophotometrically by using specific methods and reagents [36], and using the Hanna HI83300 Multiparameter Photometer (Hanna Instruments, Inc., Woonsocket, RI, USA).  
ELAN DRC-e ICP-MS (PerkinElmer, Waltham, MA, USA) equipment was employed to assess the efficiency of removing Pb\({}^{2+}\) and Cd\({}^{2+}\) ions. A quantitative evaluation was conducted for each element individually using the calibration curve method.