esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Potential-Controlled Adsorption on CNT-Monoliths  
As a proof of concept, we studied the effect of applied potential on a model analyte's adsorption and desorption behavior, pictured in Figure 9. In potential step experiments, the potential-controlled adsorption behavior onto CNT monoliths could be proven. Untreated CNT monoliths showed initial adsorption of 10.7 \(\pm\) 4.7 \(\mu\)mol g\({}^{-1}\). Upon potential switch, a released amount of 6.2 \(\pm\) 0.6 \(\mu\)mol g\({}^{-1}\) could be detected, resulting in a release efficiency of 57%. In the following runs, the amount of adsorbed maleic acid firmly declined for the untreated monoliths. Changes on the monolith's surface and strongly bound analyte, not removed through the washing steps, might impair the repeated use of the electrodes and can lead to deviations of the measured concentrations. The availability of active binding sites is thereby drastically reduced. The released amount of analyte slightly increases  
Figure 8: Cyclic voltammetry of untreated and oxidized CNT-K monoliths.  
Figure 7: Chronoamperometry experiments for untreated (**a**) and oxidized (**b**) CNT-K monoliths. The calculated resulting mean current after EDL rearrangement for both materials is presented in (**c**).  
during the subsequent runs. The presence of unremoved analyte from the first run is thus confirmed.  
A major increase in adsorption capacity can be observed during all runs for oxidized monoliths, possibly due to an enlarged number of binding sites and enhanced current response. However, as the amount of released analyte upon potential inversion is reduced, another binding mechanism between the electrode's oxidized surface and the analyte might be revealed. This heterogeneity is often recognized for carbon nanotubes, as different sorption mechanisms, such as \(\pi\)-\(\pi\) bonds, hydrophobic interactions and hydrogen bonds, may act simultaneously [61]. Particularly for monoliths consisting of oxidized CNTs, electrosorption does not take place exclusively on the surface, aggravating the potential-dependent release of the analyte.  
Conventional electrosorption processes studied for the adsorption of smaller inorganic ions show similar adsorption capacities compared to our oxidized monoliths [62; 63; 64]. However, it has to be considered that the cited studies were conducted in flow-through setups, thus benefiting from an increased mass transport towards the electrode. In this context, the tested monoliths display good electrosorption properties even at weak applied potentials due to their high conductivity and SSA. Nonetheless, further investigation of the monoliths in a flow-through setup might improve the adsorption and desorption behavior due to mass transport effects.
###### Abstract  
This study investigates the potential of Plasma-Activated Water (PAW) technology for the production of nitrate fertilizer, focusing on the dielectric properties of plasma-treated water, such as the dielectric constant and dielectric loss factor. The research aims to elucidate the impact of plasma treatment and varying water flow rates on these properties. An experimental approach was employed wherein ion-free water was subjected to plasma treatment at different flow rates (0.5, 1.0, and 2.0 L/min) and durations (1, 2, and 3 h). The results reveal a marked enhancement in the dielectric properties of the water following plasma treatment, with the most significant improvements observed at a flow rate of 0.5 L per minute and a treatment duration of 3 h, and dielectric efficiencies of 97.82%, 97.21%, and 96.61% achieved at flow rates of 0.5, 1.0, and 2.0 L/min, respectively. These findings demonstrate that PAW technology enhances the efficiency of nitrate fertilizer production by optimizing energy storage and reducing energy losses. The study underscores the potential of PAW as a sustainable, environmentally benign alternative to conventional chemical fertilizers, contributing to more efficient and sustainable agricultural practices.  
Plasma-Activated Water (PAW); plasma technology; nitrate fertilizer; dielectric properties; sustainable agriculture +
Footnote †: journal: applied sciences
## 5 Conclusions  
In conclusion, the mESTARFM model enhances the capability of image fusion, which can blend multi-source remote-sensing images and produce new images with high spatial and temporal resolutions. Compared with ESTARFM, mESTARFM modifies the threshold of similar neighbor pixels selection and optimal moving window size and introduces land cover data in the calculation. These modifications are helpful for preserving more similar spatial details in the simulated image. In this paper, the updated image fusion model (mESTARFM) was tested by Landsat TM/ETM+ and MODIS; it may be also applicable to other similar instruments. The produced images are useful for research on natural resource management, land use/land cover changes, nature raster and ecological dynamics with high spatial and temporal resolutions.
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 4 Protocol
### ICRP (Internet-friendly Cryptographic Relay-detection Protocol)
#### 4.2.2 Active and Passive Modes  
Depending on the type and amount of data to be sent, we can distinguish two ways to use this protocol:  
1. Passive mode: we call Passive mode a long-term background use of the protocol. This mode is relevant in cases where \(\mathcal{S}\) and \(\mathcal{R}\) frequently exchange small amounts of data. In this case, a complete session of the protocol might be achieved over multiple exchanges. The protocol then passively keeps track of the overall number of sent packets and performs the verification when \(n\) packets have been sent.
2. Active mode: we call Active mode the execution of the protocol used specifically over the sending of one or several large files. This case is relevant when \(\mathcal{S}\) and \(\mathcal{R}\) exchange larger amounts of data on a more spread-out frequency. In this case, multiple sessions of the protocol might be achieved over a single fast-stream flow.  
Figure 13 illustrates how and when ICRP runs in both modes. It shows that, in Active mode, the verification phase must be performed concurrently with the measuring phase for performance's sake. This is not specifically required for Passive mode.  
Figure 11: Our protocol ICRP for traffic hijacking detection with verification performed by \(\mathcal{S}\).  
Figure 12: Our protocol ICRP for traffic hijacking detection with verification performed by \(\mathcal{R}\).
## 2 The Proposed Approach
### Prediction Model  
Convolutional neural networks (CNNs) use convolution filters to extract local and global features through sliding windows, and can learn spatial correlations of traffic flow variables nearby or in entire cities [22; 27; 35; 36; 37]. Long short-term memory neural networks (LSTM) were proposed by [PERSON] and [PERSON] in 1997 [38]. They can learn temporal relationships and dependencies from time series data and have been applied to short-term traffic prediction [5; 39; 40; 41]. Deep learning models combining CNNs and LSTMs are widely used in the literature regarding traffic flow prediction and can capture both the spatial correlations and temporal dependencies of traffic flow variables on road networks [5; 36; 42; 43; 44].  
On the basis of the popularity and performance of models combining CNNs and LSTMs in the existing literature, we propose a spatiotemporal traffic congestion network (STCN) based on CNNs and LSTMs to evaluate the effectiveness of our proposed representation framework for traffic congestion data. An overview of STCN's architecture is shown in Figure 3. STCN contains three main components. The first component consists of four CNNs and is used to learn spatial features and their correlations to traffic congestion between roads in a road network. The second consists of two LSTMs and is used to mine temporal dependencies across a series of historical traffic congestion data. Finally, the third has a full connection layer and a reshape operation to construct predicted traffic congestion in that road network.  
The first component takes a sequence of matrices in the form of \(C_{t}\) ordered chronologically as its input. The spatial features and correlations extracted by the first component are used as inputs to the second component. The output by the second component is processed by the third to predict traffic congestion levels and construct a traffic congestion map as output.  
Additionally, a max-pooling layer is used after each convolution layer to select representative features, and a batch-normalization layer to overcome internal covariate shift. A dropout layer is inserted before the full connection layer to prevent overfitting.
# Laser-Ultrasound-Based Grain Size Gauge for the Hot Strip Mill
## 3 Results and Discussion
### Online Measurements
The observant reader will notice a small periodicity with \(\sim\)9 periods along the length of the strips, which are the consequence of an earlier processing step in the hot strip mill. This is likely an effect of skid marks. As mentioned earlier, when the slab is heated in the re-heating furnace, it travels through the furnace from one side to the other on walking beams. These beams are actively cooled to survive the high temperature in the furnace, and one outcome of this is that the slabs are heated unevenly, giving rise to temperature deviations called skid marks. The use of the coil-box prior to the finishing mill neutralizes this temperature difference to the extent that the effect is barely noticeable for most materials. It is not seen in temperature measurements on the final strip but is detected by the LUS measurement. These new online grain-size measurements can enable a new paradigm for material development. The new data can be used as feedback information to the mill directly or for adjustments of the setup calculation to the next strip of the same grade. The data are also expected to be valuable as input when creating new material models and to result in more rapid development of new alloying concepts. Additionally, this new grain size gauge has the potential to improve quality control by identifying deviations in the production, to indicate if there are parts of the strip that should be directly internally recycled. This new information is also expected to be utilized as feedforward information in the later material processing steps. The grain size gauge currently assumes that the laser ultrasonic measurements are performed on a defect-free strips. The current hot strip mill produces material with high quality and high uniformity. Just before the laser ultrasonic measurement, the strip is de-scaled, removing the oxide scale which enables the grain size gauge to measure reliably. In less favorable conditions, however, defects in the strip such as inclusions and delaminations could deteriorate the measured response. Oxide scales that are not removed by the de-scaler could also have a negative effect on the measured frequency response. The effects of this oxide screening, inclusions or delaminations have not yet been observed in the measurements. However, the presence of any of the errors described above would only result in one faulty measurement since the steel strip is continuously moving in front of the LUS gauge. Furthermore, the model and algorithm only evaluate the grain size if the signal response has a high enough signal-to-noise ratio, which allows it to cope with the worst errors introduced by the unlikely defects.
# Real-Time Wave Energy Converter Control Using Instantaneous Frequency
## 2 Instantaneous Frequency Estimation
### Phase Locked Loop (PLL) Approach  
PLLs are widely used in control systems to synchronize the phase and frequency of an output signal with a reference input. Their applications range from low-frequency energy systems to high-frequency communications and semiconductor devices. PLLs are generally classified into different types based on their control structures and intended applications, with Type-1 and Type-2 PLLs being the most widely studied configurations.  
A Type-1 PLL consists of three key components: a phase detector (PD), a loop filter (LF), and a Voltage-Controlled Oscillator (VCO) [15]. The phase detector measures the phase difference between the input signal and the VCO output, generating an error signal. This error, processed through the loop filter, adjusts the VCO frequency to minimize the phase mismatch. Due to its simple proportional control mechanism, a Type-1 PLL is particularly well-suited for slowly varying signals, such as ocean wave propagation.  
Figure 1: Illustration of real-time instantaneous frequency estimation using a short-time window Hilbert transform.  
A conventional PLL consists of three key components:
* PD, which measures the phase difference between the input signal and the PLL's output.
* LF, which filters out high-frequency noise and smooths the control signal.
* VCO, which generates an adjustable frequency to track the input signal.  
For the cases analyzed in this paper, the instantaneous frequency is estimated using the enhanced PLL structure proposed by [16], as shown in Figure 2. This PLL improves phase detection and frequency tracking by directly estimating the phase and amplitude of the fundamental component of the input signal. Unlike conventional PLLs, this model directly estimates the phase and amplitude of the fundamental component of the input signal, improving robustness against noise and parameter variations. The loop filter gains were fine-tuned using the MATLAB Linearization Toolbox and PID tuner, ensuring optimal response time and robustness in tracking ocean wave frequencies.
# An Efficient SC-FDM Modulation Technique for a UAV Communication Link
## 6 Results Analysis
Figure 11: The peak-to-average power ratio (PAPR) performances of SC-FDM and orthogonal frequency division multiplexing (OFDM) for M-ary QAM modulation schemes: **(a)** 16-QAM; **(b)** 64-QAM. Figure 12: The probability of bit error of different modulation schemes for SC-FDM. Figure 13, the computation time of SC-FDM and OFDM modulation techniques depends on the number of subcarriers. In Table 3, we have shown the computation time of SC-FDM and OFDM modulation techniques. The computation time of SC-FDM ranged from 222 to 6653 ms with \(|N|\in[256,4096]\), while the range for OFDM was between 350 and 9723 ms. We can conclude that the computation time of SC-FDM is lower than that of OFDM.
## 3 Results and Discussion
### Device Performance Optimization of a PSC  
Based on the optimization of the ETL, HTL, and perovskite layer thicknesses, the thicknesses for each layer were 300 nm for ZnO, 1000 nm for PAL, and 60 nm for NiO\({}_{x}\). As shown in Figure 7a,b, the optimal V\({}_{\text{oc}}\), J\({}_{\text{sc}}\), FF, and PCE values simulated using each of these optimized layers increased from 1.03 to 1.05 V, 23.86 to 25.56 mA/cm\({}^{2}\), 67.27% to 75.75%, and 16.61% to 20.26%, respectively, when compared with those of the initial reference device structure. With this optimized structure, the effects of the PAL defect density and the interface defect density between ZnO/CsPbI\({}_{x}\)Br\({}_{3-x}\) and CsPbI\({}_{x}\)Br\({}_{3-x}\)/NiO\({}_{x}\) are also analyzed in the following section.  
Figure 6: Current density–voltage (_J_–_V_) curves for various NiO\({}_{x}\) thicknesses.  
Figure 7: Current density–voltage (\(J\)–\(V\)) and QE curves (**a**) before and (**b**) after thickness optimization.
## 5 Hybrid Localization Algorithms
### Summary  
As shown in Table 3, we compare the hybrid localization algorithms from following aspects:  
* Anchor type: Most hybrid localization algorithms can localize unknown nodes in deep water by using mobile anchor nodes (DETs and AUV), using hierarchical localization method (3 DUL) or other technology, such as magnetometer in LSUM. However, RSMB cannot localize deep water nodes by using one anchor node moving on the sea surface.
* Ranging method: Almost all the hybrid localization algorithms are rang-free except 3 DUL.  
Figure 13: The Localization Scheme with Directional Beacons (LDB).  
* Message communication: In most hybrid localization algorithms, unknown nodes stay silent.
* Time synchronization: All the hybrid localization algorithms do not need time synchronization.
* Localization coverage: Only UDB and LDB have smaller localization coverage.
* Localization time: Due to mobile anchor node's slow speed, localization time of 3 DUL, DUB and LDB are relatively longer. The localization time of 3D-MALS and RSMB depends on the beacons' sending interval.
* Localization accuracy: Due to sensor nodes' unpredictable movement in hybrid UWSNs, all the algorithms cannot provide accurate location information.
* Computational complexity: Computational complexity of all the localization algorithms is low.
* Energy consumption: In general, active message communication costs more energy than silent one.
A correctly designed MIP for dioxin could separate TCDD from interferent PCBs during pre-treatment steps. Among MIPs for dioxins, [PERSON] et al. [1] report the synthesis of a polymer specific for 2,3,7,8-tetrachlorobenzodioxin (TCDD) by covalent imprinting (MIP1). The procedure is very elegant, but it is laborious and time consuming. In the perspective of a wide use of MIP in SPE technology, the non-covalent approach appears more desirable. With this aim, we propose a non-covalent procedure for preparation of a molecularly imprinted polymer for TCDD (MIP2) in which a \"dummy\" template (2-amino-3,7,8-trichlorodibenzodioxin) is employed and the functional co-monomer is 2-methacryloyloxybenzoic acid. This scheme represents a significant simplification of the original one presented by [PERSON] et al.. Influence of template bleeding in dioxin analysis is avoided, since template used in the proposed non-covalent approach is only a close analogue of analyte.
# Forecasting the Spread of COVID-19 in Kuwait Using Compartmental and Logistic Regression Models
## 3 Results
### Prediction of the Spread of COVID-19 in Kuwait with Higher Reproduction Rate of \(R_{o}=2.5\)  
To estimate the impact of the current spread prevention measures applied by the government of Kuwait, such as the partial curfew, quarantine measures and social distancing, the SIR model was utilized. The simulations were carried out with a higher reproduction number of \(R_{o}=2.5\). A significant increase in the estimated epidemic size can be noted with 17,684 cumulative infections as presented in Figure 3 and Table 5. Compared with the current estimated reproduction number, it is apparent that the current prevention measures have resulted in a reduction of 76.12% of the estimated epidemic size. Figure 4 illustrates the impact of a high reproduction number on the peak value and ending phase of the epidemic. The ending phase is estimated to occur on 20 August 2020 and thus would result in a delay of approximately 11 weeks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Day.** & **Date** & **Confirmed Cases** & **Predicted Cases** & **Error Percentage** & **Confirmed Daily Cases** & **Predicted Daily** & **Error Percentage** \\ \hline
51 & 14 April 2020 & 1355 & 1343 & 0.89 & 55 & 102 & 85.45 \\ \hline
52 & 15 April 2020 & 1405 & 1449 & 3.13 & 50 & 106 & 112 \\ \hline
53 & 16 April 2020 & 1524 & 1559 & 2.30 & 119 & 110 & 7.56 \\ \hline
54 & 17 April 2020 & 1658 & 1673 & 0.90 & 134 & 114 & 14.93 \\ \hline
55 & 18 April 2020 & 1751 & 1789 & 2.17 & 93 & 116 & 24.73 \\ \hline
56 & 19 April 2020 & 1915 & 1907 & 0.42 & 164 & 118 & 28.05 \\ \hline
57 & 20 April 2020 & - & 2027 & - & - & 120 & - \\ \hline
58 & 21 April 2020 & - & 2147 & - & - & 120 & - \\ \hline
59 & 22 April 2020 & - & 2267 & - & - & 120 & - \\ \hline
60 & 23 April 2020 & - & 2386 & - & - & 119 & - \\ \hline
61 & 24 April 2020 & - & 2503 & - & - & 117 & - \\ \hline
62 & 25 April 2020 & - & 2618 & - & - & 115 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Short term forecasting for Kuwait.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Estimated epidemic size & 4223 cumulative cases \\ \hline Estimated epidemic rate (per day) & 0.113 \\ \hline Estimated peak date & 22-4-2020 \\ \hline Estimated peak day cases & 120 cases \\ \hline Estimated start of ending phase date & 15-6-2020 \\ \hline Root mean square error & 48 \\ \hline R\({}^{2}\) & 0.992 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimated simulation results of the logistics regression model.  
Figure 4: A comparison between the peaks of the current estimated reproduction number (\(R_{o}\) = 2.2) and a higher reproduction number of \(R_{o}\) = 2.5.  
Figure 3: A comparison between the prediction curves of the current estimated reproduction number (\(R_{o}\) = 2.2) and a higher reproduction number of \(R_{o}\) = 2.5.
## 2 Materials and Methods
### Lung Cancer  
Despite being the leading cause of cancer-related deaths in the entire population, lung cancer is exceedingly rare in AYA. Patients below 40 years of age make up only less than 1% of all lung cancer cases. In this age group, non-small cell lung cancer (NSCLC) predominates, with adenocarcinoma histology significantly more prevalent than in older adults. Probably due to better overall performance status, the prognosis in AYA with lung cancer is better than in older patients [262; 263]. There are no reports of exosomal Hsps detection being performed on AYA patients' tumors. However, some more general studies on the role of exosomal Hsps in lung cancer have been published.  
[PERSON] et al. reported that treatment of the lung carcinoma-derived cells with cytotoxic suberoylanilide hydroxamic acid (SAHA) resulted in posttranslational modification and increased exosomal release of Hsp60. They also suggested a possible role of this modified exosomal Hsp60 in immune antitumor stimulation [147]. [PERSON] et al. described a higher level of exosomal Hsp70 in lung, breast and ovarian cancer patient urine samples than in healthy voluntary donors and reported exosomal Hsp70 correlation with tumor progression [95]. Exosomal Hsps were also found in malignant ascites in a patient with lung adenocarcinoma [102]. [PERSON] et al. showed a significant correlation between Hsp70 serum levels and treatment response and elevated level of osteepontin--a biomarker related to hypoxia in patients with NSCLC. Post-therapeutic plasma exosomal Hsp70 promoted a positive clinical result, defined as a better response to radio and chemotherapy. This study did not investigate exosomal Hsp70 directly; however, plasma Hsp70 was detected in two fractions: free Hsp70 and lipid-bound Hsp70, the latter corresponding to the exosomal Hsp70 [148]. [PERSON] et al. reported a relationship between lung cancer cell (A549)-derived exosomal Hsp70 presence and induction of the pro-inflammatory phenotype in mesenchymal stem cells (MSCs) by triggering TLR2 signaling and activation of the NF-\(\kappa\)B pathway. This may facilitate lung cancer invasion and tumor growth [98]. Moreover, the elevated levels of Hsp70 and Hsp90 were detected in exosomes derived from Rab27a-overexpressing non-small cell lung carcinoma cells. These exosomes, via upregulating major histocompatibility complex class II (MHCII) and co-stimulatory molecules on dendritic cells, were found to elicit potent antitumor immune responses [149].  
Engineered exosomes have been also considered as a potential cargo for specific delivery of anticancer therapeutic drugs. Exosomes being loaded with Celastrol, which inhibits Hsp90 signaling pathways, showed higher antitumor potency in lung cancer cells. [264].