esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Pot Experimental Design  
The experiments were conducted at University of Rzeszow (Poland). A one-factor pot experiment was carried out in the vegetation hall. In the vegetation hall the climate conditions were as follows: the average day/night temperature was 26/18 \({}^{\circ}\)C, day length was 16 h, relative humidity was around 50%-60%, and light intensity was 1400 mol-m\({}^{-2}\)-s\({}^{-1}\). External conditions, such as temperature and humidity, were controlled and a LED-based technology for lighting of plants during cultivation was used. Variable factors analyzed in the experiment were the effect of a dose of developed fertilizers (foliar and soil) on the growth and development of the Farm Gigant (FAO 250) maize plants variety-mass of one thousand seeds = 264 g. The pot experiment included the following objects: Control, without foliar and soil fertilization; Ff_1, Ff_2, Ff_3 on which the developed foliar fertilizer was applied in doses of 1, 2, and 4 kg ha\({}^{-1}\), respectively; and Sf_1, Sf_2, and Sf_3 on which the developed soil fertilizer was applied in doses of 30, 40, and 60 kg ha\({}^{-1}\), respectively. The pot experiment was carried out using 3 kg soil with loamy sand (USDA) and slightly acidic pH (pH H\({}_{2}\)O-6.52). The utilized soil had an organic matter content of 10.78% w/w and included macro-elements such as P (P\({}_{2}\)O\({}_{5}\), 11.2 mg), K (K\({}_{2}\)O, 4.39 mg), and Mg (29.66 mg) per 100 g of soil. The pot experiment was carried out in 10 replicates for each variant in a completely random order. During the experiment, a constant soil moisture of 50% of the maximum water holding capacity (WHC) was maintained. The application of the developed foliar fertilizer was carried out at an air temperature of 22 \({}^{\circ}\)C in the 6-leaf phase (BBCH 16), while the micro-granulate for initial fertilization was applied once, before souring the plants. Micro-granules with a diameter of 2-3 mm in the tested doses were applied at a depth of 4 cm below the maize grain. The solutions of the fertilizer doses tested were applied manually, dissolving the developed fertilizer in a volume of water corresponding to 200 dm\({}^{3}\) ha\({}^{-1}\). Measurements of selected gas exchange parameters, chlorophyll fluorescence, and relative chlorophyll content were carried out on the 14 th day after the fertilization treatment developed with a foliar product based on an alternative source of phosphorus. The measurements were conducted in the 9-leaf phase (BBCH19). Measurements of the parameters for plants fertilized with initial soil fertilizer were carried out at the same time.
# Duality of Biochar and Organic Manure Co-Compositing on Soil Heavy Metals and Enzymes Activity
## 3 Results and Discussion  
### Composition of Soil Amendments  
Table 1 indicates that the soil pH (6.2) was greater in chicken manure compared to other animal dung. NH\({}_{4}\)-N was greater (66.7 ppm) in chicken manure, whereas NO\({}_{3}\)-N concentration was higher (93 ppm) in biochar with P, Cd, Cr, Cu, Zn, Pb, As, and Ni concentrations among all the animal manures. K (1024 ppm) and C (50,623 ppm) were greater in inorganic and biochar treatments compared to other manures. Soil ure activity in vermicompost was also greater (1106 ug\({}^{-1}\) dry soil), whereas invertase activity was greater in all manures compared to the native soil (control). Acid phosphatase was greater in vermicompost and sewage sludge compared to chicken and horse manures. Alkalaline phosphatase was greater (524 ug g\({}^{-1}\) dry soil) in chicken manure compared to the other manures (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Soil** & **Sewage** & **Chicken** & **Horse** & **Vemi-** & **Native** & **Biochar** & **Inorganic** & **Organic** \\
**Properties** & **Sludge** & **Maure** & **Maure** & **Compost** & **Soil** & **Sei** & **Fertilizer** & **Fertilizer** \\ \hline Soil Water pH & 5.7 \(\pm\) 0.1 b & 6.2 \(\pm\) 0.3 a & 5.6 \(\pm\) 0.1 b & 5.7 \(\pm\) 0.1 b & 6.2 \(\pm\) 0.3 a & 6.4 \(\pm\) 0.0 a & 6.1 \(\pm\) 0.1 a & 5.8 \(\pm\) 0.1 b \\ N-NO\({}_{3}\), ppm & 20.1 \(\pm\) 3.5 d & 18.3 \(\pm\) 9.2 d & 25.0 \(\pm\) 6.0 c & 37.3 \(\pm\) 11.8 b & 20.7 \(\pm\) 4.5 c & 92.6 \(\pm\) 0.0 a & 79.3 \(\pm\) 8.4 b & 32.7 \(\pm\) 8.5 c \\ N-NH\({}_{4}\), ppm & 29.7 \(\pm\) 17 b & 66.7 \(\pm\) 6.4 a & 3.3 \(\pm\) 0.6 c & 3.7 \(\pm\) 0.6 c & 5.7 \(\pm\) 3.8 c & 35.8 \(\pm\) 0.2 b & 50.7 \(\pm\) 56.6 a & 47 \(\pm\) 23.5 a \\ P, ppm & 100.3 \(\pm\) 10.7 a & 89.3 \(\pm\) 6.6 a & 116.0 \(\pm\) 50 a & 8.7 \(\pm\) 9.5 a & 95.8 \(\pm\) 10.2 a & 42.8 \(\pm\) 0.3 b & 121.3 \(\pm\) 47.9 a & 94.8 \(\pm\) 10.3 a \\ K, ppm & 327.5 \(\pm\) 4.9 d & 483.8 \(\pm\) 74.8 b & 365.5 \(\pm\) 26 b & 557.3 \(\pm\) 79.8 b & 336.2 \(\pm\) 121 d & 22.4 \(\pm\) 4.0 e & 1024.0 \(\pm\) 0.38 a & 383.4 \(\pm\) 0.4 b \\ C, ppm & 1050.0 \(\pm\) 28 c & 1160.8 \(\pm\) 51.0 b & 1067.2 \(\pm\) 23 c & 1220.2 \(\pm\) 28 b & 1091.7 \(\pm\) 22 c & 50.62 \(\pm\) 5.5 s & 155.3 \(\pm\) 28.1 b & 1112.8 \(\pm\) 43.9 b \\ EC, us cm\({}^{-1}\) & 106.4 \(\pm\) 13 b & 95.8 \(\pm\) 14.6 b & 89.3 \(\pm\) 14.1 b & 122.3 \(\pm\) 8.6 b & 94.4 \(\pm\) 13.1 b & 40.3 \(\pm\) 5.9 a & 134.7 \(\pm\) 0.1 b & 27.9 \(\pm\) 0.1 c \\ Cd, ppm & 0.04 \(\pm\) 0.0 a & 0.04 \(\pm\) 0.0 a & 0.04 \(\pm\) 0.0 a & 0.04 \(\pm\) 0.0 a & 0.04 \(\pm\) 0.0 a & 0.04 \(\pm\) 0.0 a & 0.01 \(\pm\) 0.0 b & 0.05 \(\pm\) 0.0 a \\ Cr, ppm & 0.04 \(\pm\) 0.0 a & 0.04 \(\pm\) 0.0 a & 0.04 \(\pm\) 0.0 a & 0.04
## 3 Experimental Setup  
The experiment circuit is presented in Figures 3 and 4 for HVAC and HVDC, respectively. IEC60270 [24] is the basis for the experimentation procedure. A control desk (HV 9103) includes a peak, impulse, and DC voltmeter and a variable voltage supply. The control desk operates and controls the HVAC, HVDC, and impulse equipment. The supply voltage to HV9103 is 220-230 V, 50 Hz, and the regulating voltage is 0-220 V AC. During HVAC breakdown, a resistor (HV9121) is attached to safeguard the test transformer (HV 9105), which has an output of 220 V-100 kV in a single stage and 200 kV in two stages. The breakdown voltage is measured in real time by the voltmeter via a measuring capacitor (HV9141), as shown in Figure 3. The HVDC voltage up to 140 kV, 50 mA, and 50 Hz is generated via an experimental setup, as shown in Figure 4. The circuit consists of two HV rectifiers (HV9111) connected with a control desk (HV9103) and a smoothing capacitor (HV9112). A measuring resistor HV9113 measures the DC breakdown voltage. All the equipment can discharge itself except the smoothing capacitor, which is discharged through an external earthing switch.
# Evaluation of the User Adaptation in a BCI Game Environment +
## 3 Materials and Methods
### Materials
#### 3.1.4 OpenViBE  
The BCI-controlled game is employed on the OpenViBE platform, which is an open-source software for recording, processing and classifying EEG data either offline or in real-time. OpenViBE has several integrated algorithms, and it also provides drivers for BCI headsets to connect with other applications and engines.
## 4 Performance Evaluation Metrics
### Novel Performance Metrics  
These standard measures are supplemented by three unique assessment metrics that target multi-label DDoS detection and network resource optimisation during attack detection.  
Multi-label Sensitivity Index (MSI): The MSI measures the model's multi-label classification sensitivity. Instead of evaluating performance per class, MSI examines the model's ability to identify all relevant labels. This measure is crucial in multi-label DDoS attack identification when one instance might belong to numerous categories. Definition of MSI:  
\[\text{MSI}=\frac{1}{K}\sum_{k=1}^{K}\frac{TP_{k}}{TP_{k}+FN_{k}} \tag{23}\]  
K denotes the number of potential labels (or attack kinds), \(TP_{k}\) represents true positives, and \(FN_{k}\) represents false negatives. The MSI shows model sensitivity by aggregating how effectively the model recognises each attack type across all labels.  
The False Alarm Rate (FAR) is essential for assessing the model's capacity to differentiate DDoS attacks from regular network traffic. Fake positives are measured when the model misclassifies regular traffic as an attack. By marking innocent traffic as an attack, a high FAR causes needless network interruptions. For real-time DDoS detection systems, false positives may affect network performance and model credibility; hence, FAR must be minimised. FAR calculation:  
\[\text{FAR}=\frac{FP}{FP+TN} \tag{24}\]  
FP represents false positives (regular traffic misclassified as DDoS) and TN represents true negatives (regular traffic accurately classified). FAR quantifies how frequently the model misclassifies regular traffic, optimising DDoS detection systems.  
\(\Delta\mathcal{J}\) indicates cost function improvement, such as lower latency or congestion, computation time is the optimisation time, and memory consumption is the computational memory used. The OES measures how well the model improves performance while minimising resource use during optimisation.
# Chemical and Enzymatic Treatment of Hemp Biomass for Bioethanol Production
## 1 Introduction
Unfortunately, none of the lignocellulosic biomass pretreatment methods described above meets all these criteria at the same time. It is therefore necessary to subject the lignocellulosic biomass to pretreatment, which significantly affects the course of the further stages of bioethanol production i.e., enzymatic hydrolysis and fermentation process [11]. The alkaline pretreatment removes the acetyl and uronic acid groups present on the hemicellulose, thus increasing access for hemicellulose. This process can significantly improve in solubilizing lignin, showing less solubility of cellulose and hemicellulose. This pretreatment allows to increase the internal surface of cellulose, reduce the degree of polymerization and crystallinity and disrupt the structure of lignin [12]. The next stage is enzymatic hydrolysis, which determines the amount of simple sugars metabolized by yeast in the fermentation process. The decomposition of cellulose to simple sugars requires synergistic action of three types of cellulases: endoglucanases, cellobiohydrolases and \(\upbeta\)-glucosidase. The action of enzymes involves the attack on the cellulose by bonding with cellulose fibres in amorphous places, the cleavage of cellulosic chains, cutting off their considerable fragments, and then degrading them until the glucose polymer is obtained [13]. The last stage of bioethanol production is an ethanol fermentation. The ethanol fermentation process can be carried out in two ways; the first is separated hydrolysis and fermentation (SHF) while the other is simultaneous saccharification and fermentation (SSF). In SHF process the enzymes obtain the optimum at 50-60 \({}^{\circ}\)C and the pretreated biomass is first converted into fermentable sugar by cellulase. In SSF process the enzymes must be adapted to the temperature of the fermentation process 30-40 \({}^{\circ}\)C (the optimum temperature for _Saccharomyces cerevisiae_) and the pretreated biomass is converted into bioethanol in the presence of both enzymes and yeast in one bioreactor [14; 15; 16]. The aim of the study was to evaluate the pretreatment, enzymatic hydrolysis and fermentation process of hemp biomass during the preparation of the material for the production of bioethanol. The main goal is to increase the digestibility of maximum available sugars. Each chemical pretreatment and enzymatic hydrolysis has a specific effect on the cellulose, hemicellulose and lignin fraction. Thus, the most efficient methods and conditions should be chosen, allowing maximum cost reduction of the entire process.
## Applications of Continuous Wave Free Precession Sequences in Low-Field, Time-Domain NMR
###### Abstract  
This review discusses the theory and applications of the Continuous Wave Free Precession (CWFP) sequence in low-field, time-domain nuclear magnetic resonance (TD-NMR). CWFP is a special case of the Steady State Free Precession (SSFP) regime that is obtained when a train of radiofrequency pulses, separated by a time interval \(\mathrm{T_{p}}\) shorter than the effective transverse relaxation time (\(\mathrm{T_{2}}^{\star}\)), is applied to a sample. Unlike regular pulsed experiments, in the CWFP regime, the amplitude is not dependent on \(\mathrm{T_{1}}\). Therefore, \(\mathrm{T_{p}}\) should be as short as possible (limited by hardware). For \(\mathrm{T_{p}}<0.5\) ms, thousands of scans can be performed per second, and the signal to noise ratio can be enhanced by more than one order of magnitude. The amplitude of the CWFP signal is dependent on \(\mathrm{T_{1}}/\mathrm{T_{2}}\); therefore, it can be used in quantitative analyses for samples with a similar relaxation ratio. The time constant to reach the CWFP regime (\(\mathrm{T^{\star}}\)) is also dependent on relaxation times and flip angle (\(\theta\)). Therefore, \(\mathrm{T^{\star}}\) has been used as a single shot experiment to measure \(\mathrm{T_{1}}\) using a low flip angle (\(5^{\circ}\)) or \(\mathrm{T_{2}}\), using \(\theta=180^{\circ}\). For measuring \(\mathrm{T_{1}}\) and \(\mathrm{T_{2}}\) simultaneously in a single experiment, it is necessary to use \(\theta=90^{\circ}\), the values of \(\mathrm{T^{\star}}\) and \(\mathrm{M_{0}}\), and the magnitude of CWFP signal \(\mid\mathrm{M_{ss}}\mid\). Therefore, CWFP is an important sequence for TD-NMR, being an alternative to the Carr-Purcell-Meiboom-Gill sequence, which depends only on \(\mathrm{T_{2}}\). The use of CWFP for the improvement of the signal to noise ratio in quantitative and qualitative analyses and in relaxation measurements are presented and discussed.  
Keywords:time domain NMR; CWFP; SSFP; relaxation measurement +
Footnote †: journal: Applied
# IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies
## 3 Review of IoT-Enabled Smart City Components and Solutions
### Smart Industry and Production  
Smart industry and industry 4.0 define a transformation process in which IoT technologies, cyber-physical systems (CPS), M2M communication systems and cloud-based manufacturing [87] allow an innovative and less human-dependent productive environment [88]. Regarding the automation of goods supply chains, they can be easily tracked from the manufacturing process to final distribution using sensor technologies, such as RFID and NFC. Real-time information can be collected and analyzed for shipment tracking, as well as for the assessment of the quality and usability of products [14]. The smart industry and production domain includes all fields in which ICT leads to the automatization of the productive workflow, therefore also includes smart agriculture and farming, which addresses the challenge of sustainable food production. Smart agriculture systems often employ IoT devices to improve irrigation efficiency [89] and AI solutions are often deployed in IoT for agriculture, e.g., for crop monitoring, disease detection and data-driven crop supply management [7].  
Dublin Airport has employed smart industry 4.0 solutions to replace its baggage-handling systems in Terminal 2 [90]. In Shenzen, a smart industrial complex includes a series of smart factories that share technologies and provide efficient services (exploiting an ultra-high-speed Internet connection, next-generation wireless network, free Wi-Fi infrastructures and IoT devices connected via a common cloud platform) [3].
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 4 Efficiency and Robustness of the Approach  
Besides having verified the efficiency of the approach, the robustness of the procedure is studied here, also in view of a possible optimisation. In this section, simulations were performed considering the values of the nonlinear parameters used in simulations for Case 3 in the table of Figure 1, i.e., quadratic nonlinearity for both \(S\) and \(\lambda\).
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 3 Identity-Based Encryption Scheme
### Correctness and Parameters  
**Theorem 1**: ([21]) _When \(m\geq 2n\text{log}q\), \(\alpha\leq\left(\sigma\sqrt{m+1}\cdot\omega\big{(}\sqrt{\log n}\big{)}\right)^{-1}\), \(q\geq 5\sigma(m+1)\), the IBE scheme constructed in Section 3.1 is successfully decrypted with great probability._  
**Proof.** It can be obtained from the decryption formula  
\[\mathbf{s}_{id}^{\mathsf{T}}\cdot\mathbf{c}_{id} =\mathbf{s}_{id}^{\mathsf{T}}\left(\mathbf{A}_{id}^{\mathsf{T}}\mathbf{y}+\mu+ \mathbf{e}\right)\] \[=\mathbf{s}_{id}^{\mathsf{T}}\mathbf{A}_{id}^{\mathsf{T}}\mathbf{y}+\big{(} \mathbf{s}_{id}^{\mathsf{T}},\mu\big{)}+\big{<}\mathbf{s}_{id}^{\mathsf{T}}\mathbf{e}\] \[=\mathbf{b}\left[\frac{q}{2}\right]+\mathbf{s}_{id}^{\mathsf{T}}\mathbf{e}\]According to [21], when \(\alpha\leq\left(\sigma\sqrt{m+1}\cdot\omega\big{(}\sqrt{\log n}\big{)}\right)^{-1}\), \(q\geq 5\sigma(m+1)\) can satisfy with a great probability. Due to \(\langle s_{id}^{\mathsf{T}},e\rangle\leq\frac{q}{5}<\frac{q}{4}\), when \(\langle s_{id}^{\mathsf{T}},e\rangle<\frac{q}{4}\), if \(\mathbf{b}=1\), then \(\|\langle s_{id}^{\mathsf{T}},e_{id}\rangle-\frac{q}{2}\|<\frac{q}{4}\); If \(\mathbf{b}=0\), then \(\|\langle s_{id}^{\mathsf{T}},e_{id}\rangle\|<\frac{q}{4}\), obviously the decryption algorithm can successfully decrypt with great probability.  
According to the above analysis and Lemma 1, when \(\alpha\) and \(q\) reach the extreme value, respectively, there is \(\alpha\cdot q=\frac{5\sqrt{m+1}}{\omega\big{(}\sqrt{\log n}\big{)}}>\frac{5 \sqrt{2n\log q}}{\omega\big{(}\sqrt{\log n}\big{)}}>2\sqrt{n}\), satisfying the security requirements of LWE problem, that is \(aq>2\sqrt{n}\). To meet the above requirements, set scheme parameter \((m,q,\sigma,\alpha)\): \(m=2n\log q\), \(q=m^{\frac{3}{2}}\sqrt{n}\omega(\log n)\), \(\sigma=\sqrt{m}\omega(\log n)\), \(\alpha<\left(m\omega\Big{(}\log^{2}n\Big{)}\right)^{-1}\).\(\square\)
# Magnetorheological Fluid-Based Haptic Feedback Damper
## 4 Psychophysical Experiments
### Experiment 1  
The variable-damper keyboard device based on magnetorheological fluid needs to be user-oriented. In order to verify its usability, this experiment subjectively assessed the experience of using the device. The subjects reported the degree of comfort during using the device.  
**Subjects:** 15 volunteers were invited to conduct the experiment, involving 8 males and 7 females. The ages of the subjects were between 20 and 23 years old, and they had normal finger skin touch, had never participated in this or other related studies, and were not clear about the purpose of the experiment.  
**Process:** The experimental steps were as follows:  
1. The distance between the permanent magnets and the magnetorheological fluid layer was adjusted separately in the four damped buttons so that the distances were graded as 0 mm, 1.5 mm, 3 mm, and no magnet (the permanent magnet was removed in one button). The configuration is shown in Table 1 and Figure 17.
2. The volunteer pressed each of the four keys until the host computer output the key value represented by the key.
3. The volunteer's feedback on the distance between the permanent magnet and the magnetorheological fluid layer corresponding to the most comfortable key was recorded.  
**Results and analysis:** In the feedback given by the volunteers, more than 90% of the users were able to accurately distinguish the damping force levels. The statistical results are shown in the Table 2, which show that the degree of comfort was the highest when the NdFeB magnet was 1.5 mm away from the bottom of the magnetorheological fluid. This indicates that the magnetorheological fluid damping force could effectively improve the degree of comfort of the keys. Moreover, this damping force was adjustable to meet the needs of different users.
# Computational Evaluation of Turbulent Supersonic Jet Impinging on Inclined Plate
## 4 Results
The temperature level is non-dimensionalized by the inlet temperature \(T^{*}\). experimental flow field, with no bubble being present. In this case, the flow can be classified as Type III. A good agreement with experiments is also found at \(\theta=40^{\circ}\), where the plate shock forming at the upper side of the plate is predicted by both solutions, confirming what was found in [16]. According to these results, the flow can be classified as Type II (with bubble). Note, however, that the plate shock size seems thicker for DES than for unsteady RANS. Lastly, at \(\theta=50^{\circ}\), the flow type is still of Type II (with bubble). In fact, the intermediate tail shock and the stagnation bubble are clearly recognized in the present Schlieren pictures, consistent with the pressure peak reported in Figure 5. The presence of the bubble on the upper part of the impingement zone is more evident here than for previous numerical investigations [16]. Note, that the current results are fully consistent with the jet flow classifications reported in [14]. Overall, both approaches seem to correctly capture the essential flow structures, compared to experiments. However, DES is shown to predict some fluid flow structures more accurately, both near and away from the plate. For example, jet boundary and intermediate tail shock are captured with high detail by the hybrid model, whereas the traditional RANS model shows a lower level of resolution. Importantly, enhanced turbulence resolution is attained with DES by utilizing the same computational grid and, thus, with a limited increase in computational complexity. The DES approach, which partly resolves the turbulent fluctuations, is able to provide an estimation for the turbulent Reynolds stresses. A qualitative analysis of the normal stresses \(\langle u^{\prime 2}\rangle\) and \(\langle v^{\prime 2}\rangle\) is presented in Figure 9, where the stress level is non-dimensionalized by the inlet velocity squared \(U^{*2}\). The axial component is higher in magnitude, in agreement with previous numerical and experimental investigations [32; 33]. This fact can be attributed to the compressible effects at the exit of the under-expanded  
Figure 8: Schlieren pictures for different plate angles that are \(\theta=30^{\circ}\), \(40^{\circ}\), and \(50^{\circ}\) (from top to bottom): unsteady RANS (**left**) and DES (**middle**) solutions compared to experimental images (**right**) [12]. supersonic nozzle. Moreover, the amplitude of the velocity fluctuations in the axial direction tends to increase along the plate, as the inclination gets higher. Practically, a rise in the plate incidence leads to an increase in the level of disturbance for the impinging jet flow, resulting in a higher magnitude for the transversal component \(\langle v^{\prime 2}\rangle\) along the plate.