esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Previous Work
The authors approximate a generalized router power consumption function based on those values and propose a modeling framework taking into consideration the tradeoffs between power and performance. In [27] the authors formulate green routing as a mixed integer optimization problem and consider several power models corresponding to different technologies and traffic matrices. They extend their work with a decentralized algorithm that switches links OFF in an IP-based network to reduce the system energy consumption ([28]). All these works identify the potential savings achieved by turning nodes OFF but ignore implementation obstacles such as the need for a mechanism that manages the network to limit packet losses and delays. In fact, it can be argued that any penalty in terms of QoS provided to users may have an indirect adverse affect on energy too. A user that has not been provided with sufficient QoS may need to repeat its use of the network and in doing so may incur additional energy costs. Thus in this paper, we examine the tradeoff between power and QoS by conducting experiments on a real, PC-based network topology and explore the challenges and issues that arise.
## 4 Case Example: Australian Public-Private Partnership (PPP) Schools
### Forecasting Analysis in \(t=25\) and Discussion
The distribution of \(A(t=25)\) and \(P(t\ =\ 25)\), i.e., shown as \(F_{AoDS,25}\) and \(F_{PSO,25}\), were expressed as Figure 8. The reliability index \(\theta\) at t = 25 years is 0.9305, thus the \(P_{f,t\ =\ 25}\ =\ 1-\varnothing(0.9305)\ =\ 17\%\). It infers the overlap area would expand with the operation year increasing. In the case of \(l_{2}\) is 20%, the service performance for PPP schools in Australia is counted as acceptable. In fact, the overload area will be affected by the position, mean value and dispersion of \(A(t)\) and \(P(t)\) curves. Therefore, in the case of almost unchanged of \(A(t)\), increasing \(P(t)\), shrine fluctuations of \(A(t)\) and \(P(t)\) due to uncertainty, can effectively reduce the probability of failure \(P_{f,t}\) and improve social PPPs service performance. This result indicates the decision-makers in both the private sector and government should give a significant voice to the end-user and their communities to meet not only current but also future social infrastructure needs, and better consider evolving the PPP contractual terms to further focus on end-user-oriental service output specifications in order to provide long-term flexibility to serve the end-user and community's needs. A key variant that influences the outcome detailed in Figure 8 relates to the forecasting result of the end-user's satisfaction, which is mainly affected by the student numbers. The relationship between student projections and end-users' perceived service quality appear closely linked. Thereby, it is a necessary step to forecast the end-user's need through forecasting student numbers during the preparation of PPP school contracts. Otherwise, this is the time for government to start major changes or modifications for the project when the probability of failure is above 20%, most of which would cost a lot more than if these had been considered in the contract. Figure 7: (**a**) Forecasting profile for \(A(t)\) of PPP schools at t = 25. (**b**) Forecasting profile for \(P(t)\) of PPP schools.
# Effect of Production Technique on Pilsner-Style Non-Alcoholic Beer (NAB) Chemistry and Flavor
## 2 Materials and Methods  
### Collection of Beer Samples  
Throughout this manuscript, the abbreviation NAB is used for beers < 0.5% ABV, and the term \"0.0\" NAB is used for beers with ethanol levels < 0.05% ABV. The study was based on an analysis of 20 NABs from 18 breweries (Table 1). Out of these 20 NABs, 18 were packaged in either 0.33 L or 0.5 L glass bottles (14 in brown glass bottles, 4 in green glass bottles) and these samples were purchased from local retailers. Two samples (RF1 and RF3) were packaged in PET bottles and these were purchased from two separate stores. Nineteen out of the 20 NAB were either marketed as \"non-alcoholic Pilsner beer\", \"non-alcoholic lager beer\", or their labels contained references such as \"bitter\" or \"extra bitter\", which are considered key attributes of Pilsner beer in Germany. Throughout purchasing it was ensured that the products had at least six more months before their \"best before date\", with the exception of RF1 and RF3 in PET. RF1 and RF3 had 5-month shelf lives and were tested approximately 4 weeks after packaging. The beer selection included four labeled as \"0.0%\" NAB, these were C1, C2, M1, and V2. The respective production technique of each NAB was requested by personal communication with the breweries. Nineteen out of the 20 NAB were brewed and bottled in Germany, which means these products were subject to the German beer purity law because they contained \"beer\" in their label. Therefore, these NABs could only be made with water, barley malt, and hop pellets/extracts [20]. Given this, it was assumed that the differences in the sensory and chemical properties of the NABs would primarily be a function of the processing technique.  
However, based on the results of previous studies, the specific inclusion of two NABs in this study is notable. Due to the previously reported [14; 15; 19] positive influence of hop aroma on NAB flavor, a non-alcoholic IPA (MI2) was included as a hop-forward reference to further investigate the influence of hops on NAB flavor and chemistry. Similarly, C1 was a NAB imported from the Netherlands and did not fall into the manufacturing practices of the German Purity law. C1 contained a \"natural aroma\". While adding an aromatic extract is not allowed by German brewing practice, similar to the addition of hops, it has been suggested that this approach can also be an effective approach to produce NAB with multifaceted flavor [21].  
The NABs selected for this study fell into five major categories with the following abbreviations being used throughout the paper: use of maltose-intolerant yeast (MI),  
restricted fermentation (RF), membrane dealcoholization (M), vacuum dealcoholization (V), combined/blended treatments (C).
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 8 Renewable Energy from Agricultural By-Products
### The Energy Potential of Grape Pomace and Distillation Stillage  
Grape pomace, a by-product of wine production, and distillation stillage, a by-product of brandy production, are rich in organic matter and have high calorific values [28]. The conversion of agro-industrial by-products into renewable energy sources represents a promising avenue for sustainable development. The study on biomass pellet production from wine by-products showcases the potential of grape pomace and distillation stillage as energy-rich materials:  
High energy content: distillation stillage exhibited a higher low heating value (>19 MJ kg\({}^{-1}\)) than grape pomace, making it a viable candidate for biomass energy production.  
Chemical composition: while both by-products have high volatile matter content, distillation stillage showed lower ash content and higher fixed carbon, contributing to its superior combustion properties.  
Environmental challenges: both materials exceeded ENplus(r) standards for ash, nitrogen, and sulfur content, necessitating pre-treatment strategies to reduce pollutant levels and improve combustion efficiency.
## 4 Beetle Swarm Optimization (BSO) Method
### Realization of the Proposed BSO
Once the sizes of all the beetles in a specified swarm are obtained, the estimated coordinates of the beetle \(i\)'s right and left antennas in the \(k\)th move can be calculated by using Equation (13), which can be further denoted as \(x_{R}^{\,i,k}\) and \(x_{L}^{\,i,k}\), respectively. The corresponding cost of the beetle \(i\)'s right and left antennas in the \(k\)th move would be \(f(x_{R}^{\,i,k})\) and \(f(x_{L}^{\,i,k})\) under the condition of Equation (7). Based on the cost values of the right and left antennas, this beetle \(i\)'s position in the \(k\)th move would be updated with Equation (12). The current global best cost \(G_{C}\) could then be updated based on the latest beetle's positions using Equation (7). \[\left\{ \begin{array}{l}x_{R}^{\,j,k}=x_{i}^{k-1}-\frac{d_{0}^{\,i,k-1}}{ \mathbf{b}}\cdot\overset{\rightarrow}{\mathbf{b}}\\ x_{L}^{\,i,k}=x_{i}^{k-1}+\frac{d_{0}^{\,i,k-1}}{2}\cdot\overset{ \rightarrow}{\mathbf{b}}\end{array} \right. \tag{13}\]  
Step 5: Output of global best solution. First, check whether the given termination condition \(T\) is satisfied or not; if not, go to Step 3 for the next iteration again. If it is satisfied, which means the beetle move counter \(k\) is larger than \(T\), the beetle positions with the least path cost combined with the current global best cost \(G_{C}\) in the last beetle move would be the desired output. The obtained output is the global best solution in the search space, which would then be used as the UAV's final waypoints. According to the flowchart and the pseudocode provided in Figures 6 and 7, only a few steps are needed to locate the global best solution for the UAV path-planning problem, which makes the proposed BSO method more efficient and practical.
## 5 Results and Discussion
Vapor generation is temperature-dependent (Equations (10)-(12)), and for this reason, temperature changes must be investigated. They are presented in Figure 9b as both experimental and computed curves. As in Figures 7 and 8, the arrows in Figure 9 identify the beginning of each wetting phase. In Figure 9a, a large increment of the amount of vapor was observed, as well as of temperature because the shape of the curves for \(\omega_{g}^{w}\) and temperature is identical. Upon drying, both temperature and the amount of vapor show a sharp decrease, which explains the fast drying observed in the simulation. The shapes of the numerical curves describing suction and the amount of vapor and temperature curves are identical, confirming that vapor generation and its equilibrium control the hydraulic response of the material. Figure 8: Comparison between numerical and experimental values for the degree of saturation in the cell. The arrows indicate the time steps when wetting was carried out. Considering Figure 9b, the comparison between the experimental and numerical curves shows important deviations. Indeed, in the test, the temperature increases in the initial drying to reach the room temperature, while it decreases in the simulation and only reaches the value measured in the room at the end of the second wetting. In addition, the amplitude of the temperature change interval is much larger in the simulation than in the room. This suggests that the value adopted for the thermal conductivity of the soil may be overestimated. It is worth noting that there are other equations alternative to Equation (15) for the definition of thermal conductivity as a function of the degree of saturation, also considering porosity, but no improvements were obtained when using them because the soil is closer to full saturation, and therefore, all expressions lead to values closer to the saturated value \(\lambda_{\text{sat}}\). For this reason, the value adopted for this constant (see Table 3) must be a relatively small value. In this case, the value adopted is near the minimum value measured in such highly porous organic materials [16; 17]. This result confirms the need to perform a more adequate calibration of this variable by conceiving adequate tests. Finally, for the suction variations and temperature intervals applied in the tests, porosity changes would be very small. Possible shrinkage would further decrease the permeability, with a small effect on the water retention curve and thermal conductivity. As discussed, this would contribute to decreasing further the water infiltration upon  
Figure 9: Analysis of vapor equilibrium considering (**a**) water vapor content computed, equal in the two tensiometers; (**b**) temperature evolution measured during the test and computed. The arrows mean wetting. wetting and increase the evaporation rate because the thermal conductivity would increase. Considering this and the fact that the porous tips were not simulated, at this stage, it can be considered that the thermohydromechanical analysis performed is sufficient for investigating this particular case.
## 4 Discussion
### Effects of CHA at NH4\({}^{+}\)-Enriched State (without DMPP Addition)
Within the many environmental constraints, WFPS, substrate NO\({}_{3}\)\({}^{-}\)-N concentrations, and the soil texture are known to play an important role in the fraction of N\({}_{2}\) over N\({}_{2}\)O emitted by denitrification (N\({}_{2}\)/N\({}_{2}\)O ratio). Lower N\({}_{2}\)/N\({}_{2}\)O ratios generally occur at lower WFPS, high substrate NO\({}_{3}\)\({}^{-}\)-N concentrations, and high clay content [76, 78]. This agrees perfectly with the conditions of CZ treatment during the experiment and helps to explain the observed shift toward emissions of N\({}_{2}\)O from partial denitrification in CZ. Unexpectedly, the higher N\({}_{2}\)O-N emissions in the CZ treatment are not according to NO\({}_{3}\)\({}^{-}\)-N consumption, and, unfortunately, in this case, the experimental data do not allow a mechanistic understanding of the origin of the accelerated N\({}_{2}\)O-N emissions. However, a previous incubation experiment in which the same soil amended with the same CZ\({}_{\text{p}}\) were tested over 16 days of incubation can provide some clues for the observed high emissions [10]. The authors found an immediate accumulation of NO\({}_{2}\)\({}^{-}\)-N in the soil during the first days of incubation before a drastic increase in NO\({}_{3}\)\({}^{-}\)-N net production. This NO\({}_{2}\)\({}^{-}\) accumulation was explained as a consequence of the high NH\({}_{4}\)+ level of the substrate (induced by CZ\({}_{\text{p}}\)), which is known to inhibit NO\({}_{2}\)\({}^{-}\) oxidation to NO\({}_{3}\)\({}^{-}\)[79, 80, 45, 46, 81]. Likely, the same NO\({}_{2}\)\({}^{-}\) accumulation may have occurred also in this experiment since the experimental conditions were comparable; however, NO\({}_{2}\)\({}^{-}\) was not measured. The NH\({}_{4}\)\({}^{+}\) inhibition of NO\({}_{2}\)\({}^{-}\) oxidation to NO\({}_{3}\)\({}^{-}\) could have caused the relatively low gross nitrification rates encountered (both in terms of gross production and consumption). Other evidence in the literature support this hypothesis, for example, in another incubation experiment performed by [22], CZ\({}_{\text{p}}\) addition to soil induced relatively higher NH\({}_{3}\) and NO\({}_{\text{x}}\) emissions. NH\({}_{3}\) is the substrate of the 1 st step of nitrification reaction where it is converted into first hydroxylamine and then into NO\({}_{2}\)\({}^{-}\). Higher NO\({}_{\text{x}}\) emissions measured by [22] are also supportive of the observed higher N\({}_{2}\)O emissions in the present experiment. This suggests that when O\({}_{2}\) levels were relatively low (WFPS of 65%) and NH\({}_{4}\)\({}^{+}\) levels were very high, the evolved N\({}_{2}\)O may have been generated by nitrifier-denitrification directly from the NO\({}_{2}\)\({}^{-}\)[82]. Nitrifier denitrification is a pathway of nitrification, where the oxidation of NH\({}_{3}\) to NO\({}_{2}\)\({}^{-}\) is then followed by its reduction to N\({}_{2}\)O (and then likely to N\({}_{2}\)) carried out by only one group of microorganisms (autotrophic NH\({}_{3}\)-oxidizers) [82].
## 4 Results and Discussion
### Influence of Measurement Accuracy on Discrimination Performance
Figure 7: Discrimination performance of proposed method versus different azimuth angle measurement accuracy. Based on the above experiment, the following conclusions can be drawn:  
* When the deception velocity is small, the higher the accuracy of the velocity measurement and angle measurement, the better the discrimination performance for the physical target. With the increase in deception velocity, the discrimination probability of the physical target gradually reaches near the expected probability and remains constant;
* The higher the accuracy of velocity and angle measurement, the greater the deception velocity and the better the discrimination performance for active false targets.
# Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances
## 4 More Modern Approaches with Larger Domains of Validity
### Parabolic Equation (PE)  
Acoustic losses at the rough ocean surface can be simulated utilizing a propagation code which can use as input an explicitly defined roughness profile. The PE (Parabolic equation [121; 122; 123]) model Ramsurf [124] is often used and enables to model shadowing from the surface roughness. Ramsurf is based on the split-step Pade method [121] (a pseudo-spectral numerical method for solving nonlinear partial differential equations).  
Firstly, based on the rough ocean surface of Gaussian spectrum, the validity of Ramsurf acoustic propagation model has been studied [125] by comparison with measurements after an explosion at receivers located on the ocean bottom around 1000 m depth. The height for the sea surface is from 1.5 m to 2 m. Transmission loss curve under a smooth sea surface and that under the rough sea surface are simulated respectively by Ramsurf. The sea surface reflection loss caused by the surface roughness is about 4 to 6 dB. The transmission loss simulated by Ramsurf model is very close to the experimental one. Kirchhoff approximation (KA) and small slope approximation (SSA) abilities to simulate the reflection loss are also compared: SSA and Ramsurf match well for very small grazing angles (<6\({}^{\circ}\)) for which KA breaks down. We nevertheless notice that the gap between KA and Ramsurf becomes less than 3 dB above 6\({}^{\circ}\): KA is thus inaccurate only in a very small region.  
Another study [126] focuses on the modeling of underwater acoustical reflection from a wind-roughened ocean surface and compares the surface loss between KA, a small-slope model SSA(2) and a stochastic modeling using Ramsurf. SSA(2) matches well with Ramsurf for the entire range of frequency and wind speed combinations studied (1.5-9 kHz, 5-12.5 m/s) and for sound incidence grazing angles from 1 to 11\({}^{\circ}\). KA can be used effectively from an angle about 2.5 to 7\({}^{\circ}\).  
An extension of the parabolic equation method to deal with forward scattering has recently been proposed for both Dirichlet and Neumann boundary conditions [127] but future works are needed to make the connection between forward and backward 3D scattering and treat other boundary conditions.
## 4 Results and Discussion
### Results
#### 4.1.5 Random Forest  
For the CIC-MalMem-2022 dataset, the accuracy result was 99.98%. As shown in Figure 33, the model correctly predicted 5808 malicious and 5803 benign instances. However, one malicious instance was incorrectly predicted as benign, and one benign instance was incorrectly predicted as malicious. For the CIC-IDS-2018 dataset, the accuracy result was 99.64%, with the confusion matrix shown in Figure 34. The model correctly predicted 2071 malicious and 4431 benign instances. Two malicious instances were incorrectly identified as benign, and twenty-four benign instances were incorrectly identified as malicious. For CIC-IDS-2017, the model achieved 99.40% accuracy; the confusion matrix in Figure 35 shows that the model correctly predicted 6287 malicious and 6985 benign instances. However, the model wrongly predicted 66 malicious samples as benign and 14 benign samples as malicious. The learning curve for CIC-MalMem-2022 in Figure 36 shows that the training score is close to 100%, which indicates that the Forest classifier can learn the training data effectively. The cross-validation score, however, is slightly lower than the training score. Figure 37 illustrates that the Random Forest model learns the CIC-IDS-2018 dataset effectively, since each cycle achieved 100% for learning; however, cross-validation shows a slightly lower percentage, starting at around 98.40% and declining to 96.1%. As shown in Figure 38, the CIC-IDS-2017 dataset's learning curve indicates the training score begins at 99.71% accuracy and decreases to 99.52. However, the cross-validation score is slightly lower than the training score: it starts at 99.39% and increases to 99.45%. The best parameter for the CIC-MalMem-2022 dataset is 20. The mean test score attained is 99.90%, as seen in Figure 39. Furthermore, the mean duration required to obtain these scores is 0.059 s. The analysis of Figure 40 reveals that the CIC-IDS-2018 dataset attains its best performance when the parameter value is set to 10. The selection of this criterion correlates with an average examination score of 99.20% and an average time taken of 0.056 s. Moreover, drawing upon the evidence shown in Figure 41, one may deduce that the most favorable value for CIC-IDS-2017 is 20. The aforementioned conclusion is substantiated by the average test score of 99.34% and the average score time of 0.248 s.  
Figure 32: Hyperparameter tuning for SVM on CIC-IDS-2017 dataset.  
Figure 34: Confusion matrix for RF on CIC-IDS-2018.  
Figure 35: Confusion matrix for RF on CIC-IDS-2017.  
Figure 33: Confusion matrix for RF on CIC-MalMem-2022.  
Figure 36: Learning curve for RF on CIC-MalMem-2022.  
Figure 37: Learning curve for RF on CIC-IDS-2018.  
Figure 38: Learning curve for RF on CIC-IDS-2017.
## 1 Introduction  
In the frame of developing novel active materials for electronic, optoelectronic and photonic devices, lead halide perovskites are currently receiving great attention due to their capability to combine an easy realization procedure, widely tunable electronic properties and promising performance as active materials for a wide range of devices. In particular, perovskite-based solar cells demonstrated an impressively fast power conversion efficiency increase, from 3.8% in TiO\({}_{2}\) sensitized photoelectrochemical cells [1], to the recent record value of 25.2% [2] in thin film solar cells.  
Lead halide perovskites also showed interesting photoluminescence (PL) and electroluminescence properties, which stimulated the research in light emitting devices, such as light emitting diodes (LEDs) [3].  
Moreover, also the first reports of room temperature amplified spontaneous emission (ASE) and high optical gain both in solution processed bulk polycrystalline thin films [4] and in perovskite nanocrystals (NCs) thin films [5] strongly stimulated further research aiming to develop laser devices exploiting lead halide perovskites as active materials.  
In addition, in this case, the development was very fast, leading to the first evidence of optically pumped lasing in microcavities [6] basically simultaneously to the first report on ASE, and to the first report of single mode lasing under continuous wave excitation [7] only three years later.  
Beyond these breakthroughs, wide research activity allowed many groups to propose approaches to improve the ASE and lasing properties addressing many aspects, like threshold reduction, wavelength range engineering, lasing cavity geometry and stability improvement.  
In this paper, we will review the main developments in the field of halide perovskite for lasers application and we will describe the state of the art and the future development perspectives.  
We will initially describe the main results on the investigation of ASE in perovskites thin film, we will then illustrate the developments and the state of the art of optically pumped lasers exploiting lead halide perovskites as active materials, and finally we will discuss the application perspectives to electrically and optically pumped laser devices.