esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview on Different Types of Solar Cells: An Update
## 2 Organic Solar Cells  
### Advantages of Organic Solar Cells over Inorganic Solar Cells  
The advantages of organic solar cells (OSC) over inorganic solar cells are threefold [15; 16]. Firstly, in terms of the materials, limited silicon wafer technology leads to heavy, rigid, fragile, and bulky materials that are only available in a couple of colors. Conversely, the materials for OSC are light-weight, thin, flexible, and color-tunable, and can be customized to any design. Secondly, the fabrication process of inorganic solar cells is expensive, complicated, energy-intensive, must be conducted at high temperatures, and has limitations in the possible sizes of fabrications. On the other hand, OSC technology is cheap, simple, non-energy consuming, can be fabricated at ambient temperature, has no limitations in the size of fabrication, and has high speed production besides various possibilities of printing and coating fabrication techniques (such as inkjet, screen, and spray). Finally, the applications of inorganic solar cell are limited only to rooftops and open fields compared with the unlimited possibilities of OSC applications such as windows, screens, smart glasses, automotives, and the charging of mobile devices and laptops [17; 18; 19].
## 3 Results
### Mineralization Dynamics  
#### 3.3.1 Mineralization Dynamics of Soil Profiles  
Figure 5 illustrates the variations in CO\({}_{2}\) emission and cumulative mineralization of soil profiles across three types of steppes over the incubation period. Within the 60-day  
Figure 4: SOC content of soil aggregates of the three steppe types in the Loess Plateau. (**a**) Mechanical aggregates and (**b**) water-stable aggregates. Bars represent the standard error. Different letters indicate a significant difference between steppe types (\(p\) < 0.05). MS: meadow steppe; TS: typical steppe; DS: desert steppe.  
Figure 3: SOC and C\({}_{\text{M}}\) of the Loess Plateau section vary with steppe type. Bars represent the standard error. Different letters indicate a significant difference between steppe types (\(p\) < 0.05). Lowercase letters indicate SOC groups and uppercase letters indicate C\({}_{\text{M}}\) groups. MS: meadow steppe; TS: typical steppe; DS: desert steppe.  
incubation period, the order of soil organic carbon mineralization and cumulative mineralization among the steppe types was as follows: MS > TS > DS. Taking the 0-10 cm soil layer as an example, the cumulative mineralization amounts for the three steppe types were MS (1018.15 mg kg\({}^{-1}\)), TS (834.03 mg kg\({}^{-1}\)), and DS (499.86 mg kg\({}^{-1}\)). CO\({}_{2}\) emissions were initially rapid at the beginning of the incubation but gradually decreased over time. In the initial phase, the mineralization amounts for MS and TS were much higher than for DS. In the later stage from 10 to 60 days, the decreasing trend slowed down and then stabilized. The highest cumulative mineralization in the three steppe types was in MS (0-10) at 1018.15 mg kg\({}^{-1}\), while the lowest was in DS (50-100) at 328.16 mg kg\({}^{-1}\).  
In this study, SOC mineralization followed first-order kinetics, as only labile carbon was mineralized during the 60-day incubation period. Curve fitting parameters, as shown in Table 1, indicated that the turnover rate coefficient k was highest for MS and lowest for DS. The potential mineralizable carbon (Co) was highest for MS at 939.14 mg kg\({}^{-1}\) and lowest for DS at 446.28 mg kg\({}^{-1}\) (Table 2).
## 5 Experimental Results  
### Influence of Measurement Number  
In the experimental stage, we first studied the influence of different measurement numbers (m, for short) on the enhanced performance of our model. The m is set as 1024, 2048 and 4096. The average scores of multiple experiments are shown in Table 1. The loss function used during training is \(L_{alpha}\) (detailed in 5.2). Our model is abbreviated as SECS-U-Net. The experimental results show that a similar enhancement effect can be obtained when different m is used to train the model. Although there are differences in scores, they have little effect on the overall enhancement performance. The reason is that the loss function in this paper includes different training objectives, and they will play a role in model training at the same time. Moreover, ref. [22] proved that when m is large, the reconstruction error tends to stabilize, although there is no additional improvement in performance. Therefore, the m is set as 4096 in subsequent studies.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **PESQ** & **CSIG** & **CBAK** & **COVL** & **SSNR** & **STOI** \\ \hline Noisy & 1.97 & 3.34 & 2.44 & 2.63 & 1.68 & 0.916 \\ SECS-U-Net (\(L_{alpha}\), 1024) & 2.92 & 4.30 & 3.50 & 3.63 & 9.62 & 0.948 \\ SECS-U-Net (\(L_{alpha}\), 2048) & 2.90 & 4.28 & 3.48 & 3.61 & 9.49 & 0.947 \\ SECS-U-Net (\(L_{alpha}\), 4096) & 2.91 & 4.30 & 3.49 & 3.62 & 9.54 & 0.947 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Influence of different measurement number.
## 4 Conclusions  
In summary, P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite has been successfully synthesized by decorating Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanoflowers with P25 via one-step low temperature hydrothermal method. The new photocatalysts exhibit superior and durable photocatalytic activities for Rh-B photo-degradation. P25/Zn\({}_{0.15}\)Cd\({}_{0.85}\)S nanocomposite with P25:Cd ratio of 1:5 has high activity for the photo-degradation of Rh-B and high stability under visible light irradiation, and the rate of Rh-B degradation is much faster than those of pure CdS and all other tested samples. An explanation for the visible light induced photocatalytic activity of the coupled systems is provided, where the presence of P25 and Zn\({}_{0.15}\)Cd\({}_{0.85}\)S with different structures and energy levels for corresponding conduction and valence bands probably leads to the maximized photo-generated electrons and transport. Photo-generated holes and \({}^{\bullet}\)OH play a significant role in the degradation system, but electrons play the most important role.  
This work is supported by the National Nature Science Foundation of China (No. 11674258, 51506155), the National Nature Science Foundation of Hubei Province (No. 2014 CFB165, 2015 CKC898), the Fundamental Research Funds for the Central Universities (No. 2017I22 GX), Applied Basic Research Program of Wuhan (No. 20160101010020) and Ministry of Higher Education from Kingdom of Saudi Arabia. Thanks for the measurements supporting from Center for Materials Research and Analysis at Wuhan University of Technology (WUT).  
[PERSON] conceived and designed the experiment, analyzed the data and wrote the first draft, [PERSON] and [PERSON] gave relevant technical support, reviewed and edited the draft. All authors approved the manuscript. The authors declare no conflict of interest.
## 5 Results and Discussion
### Datasets  
Most datasets are imperfect, and the row samples employed to cover the application manners are insufficient in these cases. The most common DDoS datasets involve CI-CIDS2019, CICIDS2017, KDDCUP99, ISCX2012, Kyoto 2006+, and NSL-KDD, which researchers have significantly utilized for intrusion detection. In this work, we chose two datasets to validate our proposed DDoS classifier, which are:  
* DDoS attack SDN dataset (Mendeley Data): This set is an SDN-specific dataset created by the Mininet emulator and utilized by machine learning and deep learning algorithms for traffic classification. The project begins by constructing 10 Mininet topologies with switches connected to a single Ryu controller. It simulates a network for benign TCP, UDP, and ICMP traffic and malicious traffic, which consists of a TCP Syn attack, UDP Flood attack, and ICMP assault. The data collection contains 23 features, of which some are from the switches, and others are calculated, such as the Packet count, Switch-id, duration sec, byte count, Destination IP, Source IP, Port number, etc.
* Botnet dataset (UNSW_2018_IoT_Botnet): Even though several datasets have been proposed for detecting intrusions, most datasets are not updated and do not reflect actual data. The Canadian Institute for Cybersecurity addressed these issues by developing the Intrusion Detection Evaluation Dataset, ISCX-IDS 2012, and [25] generated by monitoring network activity for seven days. The labeled dataset consists of approximately 1,512,000,000 packets with 20 features. The primary characteristics of this dataset are discussed in [25] and include real, normal, and malicious streams comprising FTP, HTTP, IMAP, POP3, SMTP, and SSH protocols collected using real devices. All data are categorized and marked. The collected datasets contain a variety of intrusion kinds (Infiltrating, DoS, DDoS, and Brute Force SSH).
## 4 Results  
### Performance of ML Models  
The performance of each model on the validation and test data is summarized in Table 2. The RF model demonstrated the highest performance with 100% accuracy (ACC), true positive rate (TPR), true negative rate (TNR), and positive predictive value (PPV) for both the validation and test data. The MLP, LR, DT, SVM, and KNN models also exhibited excellent predictive performance, suggesting that various models are effective for stroke prediction. In contrast, the NB model exhibited a relatively lower performance, which may have been due to the assumption of independence among the feature variables not being met in the actual data.
## 2 Design and Method
### Optical Setup  
Optical measurements were performed using a confocal microscope system with the sample cooled down to 10 K. The excitation source was a 532 nm continuous wave laser. An objective of NA 0.7 was used to both excite the photoluminescence (PL) of the QD and collect the PL from the QD and out-couplers (shown as the red spots in the structure). A sketch of the detection part of the optical setup is shown in Figure 2. The spectra of the QD photoluminescence was recorded by a grating spectrometer equipped with a charge coupled device (CCD). This spectrometer was also used as a monochromator by changing mode to guide the light out of the exit slit. The light spots from the quantum dot and the output couplers were dispersed (shown as the inset picture). Then, only the photons corresponding to the exciton line of the QD moved into a HBT setup for the correlation function measurements. The HBT setup consisted of a 50:50 prism beam splitter, two identical single photon avalanche diodes (SPADs), and a single photon counting module. In addition, a pair of cylindrical mirrors were set before the beam splitter to separate the light spots from the QD and output couplers as much as possible. Then, two SPADs were adjusted to different heights to receive photons from different positions. The time correlated single photon counting module connected to the two SPADs measures the arrival times between photons on the two detectors and produces the second order correlation function.  
Figure 1: Design of the on-chip waveguide coupled single photon source.
## 7 Discussion  
### Effect of the Key Parameters on Energy Modeling  
Taking the relationship between horizontal flight velocity and the FPTLIR power as an example, Figure 21 shows a comparison of the full energy model with the simplified model that neglects propeller aerodynamics. From Figure 21, it can be seen that the FPTLIR power does not change monotonically with increasing velocity, but shows an initial decrease followed by an increase. Through analysis, it is observed that several factors contribute to this condition. Firstly, as the flight velocity increases, the power consumption decreases as the incoming velocity perpendicular to the propeller's plane of rotation increases, resulting in an increase in thrust. This effect is more pronounced at lower FPTLIR velocities. Once the power consumption of the FPTLIR reaches its minimum point, the increase in forward velocity results in a quadratic increase in airframe drag with respect to velocity. This has a negative effect on the FPTLIR performance, particularly at higher velocities, and subsequently results in increased power consumption. As the velocity increases, the pitch angle of the FPTLIR also increases, resulting in an increase in the power required to maintain vertical force equilibrium, again with a greater effect at high velocities. Figure 21 shows that neglecting propeller aerodynamics results in significant errors in the estimation of the model performance. This leads to an overestimation of energy consumption at low velocities and an underestimation at high velocities. In addition, deviations are observed in both the velocities corresponding to minimum FPTLIR power and maximum achievable velocities. As the application of the FPTLIR in complex natural environments continues to expand, it is particularly important to analyze in depth the effects of aerodynamic and environmental factors (especially wind) on its energy consumption and flight dynamics. Future research will analyze the FPTLIR in more complex environments, which will improve the applicability and accuracy of the model.
# Mechanical Properties Assessment of Low-Content Capsule-Based Self-Healing Structural Composites
## 3 Results and Discussion
### Fracture Results  
The Mode II fracture toughness values and the standard deviations of Carbon Fiber Reinforced Polymers (CFRPs) samples are presented in Figure 8a. The recovery of G\({}_{\text{IIC}}\) and the typical behavior of a representative sample are depicted in Figure 8b. It is observed that the capsules integration led to a 14% decrease in the G\({}_{\text{IIC}}\) compared to the reference material. Capsules are considered as voids which interrupt the integral structure of the composite. Furthermore, studies have revealed [31] that due to the fragile nature of the capsules, their contribution to the load bearing capability of epoxy is negligible, which can explain the observed reduction.  
Figure 8: _Cont._  
Figure 7: Microcapsules (**a**) Dimensional analysis and (**b**) size distribution.  
As far as the self-healing efficiency is concerned, the recovery in G\({}_{\text{IIC}}\) values reached 84% after the healing process compared to the modified samples. This significant recovery percentage is a clear indication of efficient interlocking between the capsules and the matrix material. Moreover, it can be attributable to the formation of a denser healed interlaminar region due to the polymerization of the healing system. In view of this, it is assumed that the selected low content of 10%wt. in combination with the highly active catalyst [18, 32] resulted in a minor knock down effect and a high healing efficiency.
## 6 Comparison  
A comparison of the proposed work with previous published similar works having super-wide bandwidths and similar applications is shown in Table 2. The proposed antenna shape (s-shape) is novel; it consists of stepped meander lines, quarter-wave transformer, and the defected ground structure to achieve a super-wide bandwidth. This is the first time that these techniques have been simultaneously applied. The bandwidth of the proposed structure covers all the required bands. The table shows that the proposed antenna has better performance (bandwidth, efficiency, gain, and bandwidth ratio) as compared to those described in the literature. The time-domain analysis of the antenna was good, showing less distortion loss during transmission and receiving.
## 4 Discussion
In the operational forecasting of river discharges, the catchment precipitation was originally estimated on the basis of the outputs from the INCA model. This routine has been changed during the past couple of years as the catchment precipitation estimated by means of the \(qPrec\) software was found to yield more realistic results than when based on the original concept of INCA. The results of the presented experiment with the water amount on the runway cannot be generalized at this stage. To do so, a more elaborate study is necessary, based on longer periods of observation and on a more complex (possibly non-linear) model with more predictors and with assumptions on the runoff of the water from the runway, the accumulation of the water on the runway from previous rainfalls, its evaporation, etc. The experiment indicated the potential for utilization of the QI-based QPE in aviation meteorology. Water levels on the runway can be practically estimated by the QI-based method at a multitude of airports within a given region, and consequently the pilot may have the chance of choosing to land at the airport with the least risky runway conditions. An even more elaborated model would, in principle, be able to cope with nowcasting of the runway conditions. Both software packages are under development, with a many potentially useful ideas for their further improvement in the near future. In \(qRad\), a fine tuning of the parametrization of the individual QIs is an ongoing process. Clearly, there is a possibility of extending the range of the involved QIs--currently, QIs based on bright-band detection and the radial components of wind data are in the testing phase. A further option to improve the \(qRad\) software is in taking advantage of specific characteristics of the dual polarization radars [24]. We also plan to test the possibility of the incorporation of X-band radars into the composites of C-band radars, which is a challenging task. We further intend to improve the _qPrec_ software, particularly regarding its statistical background. First, the probability of precipitation should be calculated on the basis of the Bayesian approach. Secondly, the current version of the _qPrec_ package is based on the hypothesis of normally distributed errors. This simplification should be revised in the future, and a more sophisticated model of error distribution should be implemented. More generally, the performance of the _qPrec_ software will be tested using a wider spectrum of precipitation-related characteristics. Beyond the CMAX product that has formed the basis of the calibration of the precipitation field so far, further radar products (e.g., CAPPI) and/or precipitation-related variables (satellite data, lightning characteristics, etc.) will be involved and tested. Both software packages are primarily intended for internal use at the SHMU. The _qRad_ software is also being used by the Hungarian Meteorological Service in the framework of an H-SAF product validation. Beyond this, no commercial distribution of the products is expected.
###### Abstract  
This work reports the preparation and detailed characterization of stannum indium sulfide (SnIn\({}_{4}\)S\({}_{8}\)) semiconductor photocatalyst for degradation of ethiofencarb (toxic insecticide) under visible-light irradiation. The as-prepared SnIn\({}_{4}\)S\({}_{8}\) showed catalytic efficiency of 98% in 24 h under optimal operating conditions (pH = 3, catalyst dosage of 0.5 g L\({}^{-1}\)). The photodegradation reaction followed pseudo-first-order kinetics. The major intermediates have been identified using gas chromatography/mass spectrometry. \({}^{\bullet}\)O\({}_{2}\)\({}^{-}\) and \({}^{\bullet}\)OH radicals appeared to be the primary active species in the degradation process as revealed by scavenger and electronic spin resonance studies, while photogenerated holes had a secondary role in this process. A plausible mechanism involving two routes was proposed for ethiofencarb degradation by SnIn\({}_{4}\)S\({}_{8}\) after identifying the major intermediate species: oxidative cleavage of the CH\({}_{2}\)-S and the amide bonds of the carbamate moiety. Lastly, SnIn\({}_{4}\)S\({}_{8}\) was found to be efficient, stable, and reusable in treating real water samples in three successive photodegradation experiments. This study demonstrates the prospect of SnIn\({}_{4}\)S\({}_{8}\) photocatalysis in treatment of natural and contaminated water from extremely toxic organic carbamates as ethiofencarb.  
photocatalysis; visible light; SnIn\({}_{4}\)S\({}_{8}\); ethiofencarb; active radicals; degradation mechanism +
Footnote †: journal: Physics Letters B
###### Abstract  
We have studied the interference caused by amplitude and phase distortions induced by rain in ultra-wideband communication systems designed for using amplitude modulation in GeoSurf future satellite constellations. The results concern radio links simulated with the synthetic storm technique at Spino d'Adda (Italy), Madrid (Spain) and Tampa (Florida), which are sites located in different climatic regions. The conclusions are (a) the three sites, although in different climatic zones, are practically indistinguishable; (b) the channel signal-to-noise ratio can be increased or decreased by interference with equal probability. Channel theoretical capacity loss, even in the worst case, is very limited and rain, therefore, does not cause significant linear distortions in ultra-wideband channels at millimeter waves; therefore, these channels could be used at millimeter waves.  
b achesd channel; GeoSurf constellations; interference; linear distortions; millimeter wavelengths; passband channel; rain attenuation; synthetic storm technique; time delay; ultra-wideband channels +
Footnote †: journal: _etlecom_  
0000-0002-3274-5000]M.M.M.
## 4 Results and Discussion
### Perceptual Evaluation Results  
Figure 5 depicts the results obtained from the MUSHRA test. First, results of grouped unstressed and stressed vowels (vow and 'vow) are analysed (i.e., the first two groups of boxplots). The baseline configuration (with expressive prosody, \(\mathrm{GSS_{N}}\) and \(\mathrm{VT_{N}}\)) obtains the lowest perceived emotional intensity (median score of 47 and 52 for happy unstressed and stressed vowels, respectively, and 54 and 52 for the aggressive ones).  
On the one hand, the incorporation of \(\mathrm{GSS_{E}}\) significantly increases the perceived emotional intensity of happy unstressed and stressed vowels by 6.4% and 11.5%, respectively (moving from 47 to 50 and from 52 to 58 points in the MUSHRA scale). Regarding the aggressive emotion, the contribution of \(\mathrm{GSS_{E}}\) is only significant for the stressed vowels, with an 19.2% increase (from 52 to 62). On the other hand, the sole contribution of the \(\mathrm{VT_{E}}\)  
Figure 4: Boxplots of the Kullback–Leibler distances from the analysed configurations to the target configuration (\(\mathrm{GSS_{E}VT_{E}}\)) for happy and aggressive vowels. Dotted lines represent the mean of the distributions, and whiskers are set to the 5 th and 95 th percentiles. The differences between configurations are statistically significant (\(p<0.01\)) except those marked with \({}^{*}\) (\(p<0.05\)) and ns (no significant).  
with respect to the baseline is only significant for the aggressive stressed vowels, increasing the perceived emotion by 23.1% (from 52 to 64).  
The best results are achieved when both \(\text{GSS}_{\text{E}}\) and \(\text{VT}_{\text{E}}\) are considered in the synthesis stage. The increase with respect to the baseline for unstressed and stressed happy vowels is 29.8% (from 47 to 61) and 23.1% (from 52 to 64), respectively, and 18.5% (from 54 to 64) and 32.78% (from 52 to 69) for the aggressive ones.  
With regard to the vowels, the contribution of \(\text{GSS}_{\text{E}}\) is significant for \([\text{i}]+[\text{i}]\) and \([\text{u}]+[\text{u}]\). In the latter, the increase is 4.2% (from 48 to 50) and 12.5% (from 56 to 63) for happy and aggressive speech, respectively. The higher differences are found in \([\text{i}]+[\text{i}]\), with increases of 22.4% (from 49 to 60) and 26.0% (from 50 to 63). Conversely, the contribution of \(\text{VT}_{\text{E}}\) is significant for happy and aggressive \([\text{a}]+[\text{a}]\), with increases of 25% (from 52 to 65) and 21.4% (from 56 to 68), respectively.