esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Self-Attention Mechanism  
Conventional DCGAN typically uses convolutional operations to process images, which can only deal with local neighborhood information, making it difficult to establish long-range dependencies in images and generating high-resolution details with only fixed spatially localized points in low-resolution images. In contrast, SAM preserves the global integrity of the input sequences and effectively captures long-range dependencies across different locations [32]. Its introduction into DCGAN can generate more globally consistent feature representations, thus improving the performance of the generative network. Figure 4 illustrates the structure of the SAM module.  
SA decomposes the input into three branches, query (\(Q\)), key (\(K\)), and value (\(V\)):  
\[Q_{i}=W_{q}x_{i}, \tag{6}\]  
\[K_{i}=W_{k}x_{i}, \tag{7}\]  
\[V_{i}=W_{p}x_{i}. \tag{8}\]  
where \(W_{q}\), \(W_{k}\), \(W_{p}\) are the weighting matrices to be learned; \(Q_{i}\), \(K_{i}\), and \(V_{i}\) denote the spaces obtained by multiplying image features with the corresponding weights. \(Q_{i}\) is transposed  
Figure 4: Self-Attention Mechanism structure.  
Figure 3: Minibatch Discrimination network structure.  
and multiplied with \(K_{i}\) to obtain \(S_{ij}\), which is then normalized using the Softmax function to obtain the attention graph. The formula is as follows:  
\[S_{ij}=Q_{i}^{T}K_{j}, \tag{9}\]  
\[\rho_{ij}=\frac{\exp\left(S_{ij}\right)}{\sum_{i=1}^{N}\exp\left(S_{ij}\right)}. \tag{10}\]  
where \(\rho_{ij}\) denotes the degree of attention of the model to the position \(j\) at the position \(i\). The resulting attention map \(\rho_{ij}\) is multiplied by \(V_{i}\) to obtain the self-attention feature map. The formula is as follows:  
\[o_{i}=\sum_{i=1}^{N}\rho_{ij}V_{i}. \tag{11}\]  
The final output \(y_{j}\) represents a weighted representation of each input location in context, containing the information fusion and contextual relationships with other locations. The self-attentive feature map is multiplied by a scalar parameter, then weighted and summed with the original input to serve as input for the next hidden layer. The formula is as follows:  
\[y_{j}=\gamma o_{i}+x_{i}. \tag{12}\]  
where \(\gamma\) is a learnable scalar that is introduced to progressively assign weights to long-range information.
###### Abstract  
A very important task in Mobile Cognitive Radio Networks (MCRN) is to ensure that the system releases a given frequency when a Primary User (PU) is present, by maintaining the principle to not interfere with its activity within a cognitive radio system. Afterwards, a cognitive protocol must be set in order to change to another frequency channel that is available or shut down the service if there are no free channels to be found. The system must sense the frequency spectrum constantly through the energy detection method which is the most commonly used. However, this analysis takes place in the time domain and signals cannot be easily identified due to changes in modulation, power and distance from mobile users. The proposed system works with Gaussian Minimum Shift Keying (GMSK) and Orthogonal Frequency Division Multiplexing (OFDM) for systems from Global System for Mobile Communication (GSM) to 5G systems, the signals are analyzed in the frequency domain and the Renyi-Entropy method is used as a tool to distinguish the noise and the PU signal without prior knowledge of its features. The main contribution of this research is that uses a Software Defined Radio (SDR) system to implement a MCRN in order to measure the behavior of Primary and Secondary signals in both time and frequency using GNURadio and OpenBTS as software tools to allow a phone call service between two Secondary Users (SU). This allows to extract experimental results that are compared with simulations and theory using Renyi-entropy to detect signals from SU in GMSK and OFDM systems. It is concluded that the Renyi-Entropy detector has a higher performance than the conventional energy detector in the Additive White Gaussian Noise (AWGN) and Rayleigh channels. The system increases the detection probability (\(P_{\mathrm{D}}\)) to over 96% with a Signal to Noise Ratio (SNR) of 10 dB and starting 5 dB below energy sensing levels.  
mobile cognitive radio networks; Renyi entropy; software defined radio; spectrum sensing 2020222626; doi:10.3390/e22060626
## 3 Results
### Penetrometer Tests  
#### 3.2.1 Phase 1: Effect of Biopolymer Concentration on Crust Penetration Resistance  
_Medium-grained sand_. The results of the pocket penetrometer tests on medium-grained sand after the fifth wind tunnel cycle are presented in Figure 8 (see Table A4 for numerical values). The control group showed no penetration resistance (lower reading limit of pocket penetrometer = 0.5 N). By contrast, all the biopolymer treatments formed crusts, with the penetration resistance tending to increase as the concentration increased. While most biopolymer types achieved similar penetration resistances at lower concentrations, the differences became more distinct at higher concentrations. CS formed the strongest crusts at concentrations \(>\)0.75 wt%, followed by CMC and FBPC, while the XG-treated samples had comparably strong crusts at concentrations \(<\)0.38 wt%. In addition, the penetration resistance of XG-, WP-, CMC-, and CS-treated samples peaked at concentrations of 0.38, 1.0, 1.25, and 1.25 wt%, respectively, and stagnated or slightly dropped beyond these concentrations.  
_Fine-grained silica sand_. Figure 9 shows the results of pocket penetrometer tests performed on fine-grained silica sand after the fifth wind tunnel cycle. Similar to the medium-grained sand, the control group exhibited no measurable penetration resistance, while all the biopolymer treatments formed crusts, with the penetration resistance tending to increase at higher concentrations. The polysaccharide treatments (XG, CMC, and CS) generally displayed higher penetration resistances than the protein treatments did (WP and FBPC). XG formed relatively strong crusts at the lower tested concentrations and is only surpassed by CMC and CS at concentrations \(\geq\)0.75 wt% (XG \(\geq\) 0.38 wt%). The penetration resistance of WP- and FBPC-treated samples plateaued at concentrations of 0.50 and 0.75 wt%, respectively. In contrast, XG- and CMC-treated samples peaked at 0.63 and 1.50 wt%, respectively, after stagnating at lower concentrations.  
Figure 8: Penetration resistance of crusts after last wind tunnel test on day 28 on medium-grained sand. Penetration resistance tests were performed with six replicates (n = 6) with two penetrations per sample. The numerical data (mean (M) and standard deviation (SD)) are appended in Table A4.
## 4 Discussion
When the VPD increases to a certain threshold, its facilitating effect may convert to an inhibiting effect that reduces the productivity of vegetation in arid regions, such as by greatly increasing the mortality rate of forests [33] and significantly reducing food production [34]. Crops in arid regions require a good water-security strategy, and the relevance of VPD to crop growth in irrigated arid regions should be considered in food-security measures. Nevertheless, the growth of crops in arid regions is mainly ensured through soil-moisture assessments and timely irrigation. The relevance of VPD to crop growth in arid regions should be incorporated into the establishment of irrigation systems, further ensuring crop growth and improving the efficiency of water use. Similar suggestions have been made in the development of corn irrigation plans in Nebraska, USA, which aim to maintain crop yields while reducing irrigation water usage. The authors of that study attempted to reproduce the co-regulation pattern of VPD and soil moisture on crop growth using a validated hydraulically driven eco-system model [35]. Therefore, by considering the relevance of VPD on the growth process of crops in dryland areas, we could lessen the threat of drought events on crops growing in irrigation areas, and enhance humans' resilience to drought-stricken agricultural production in dryland areas. However, as climatic factors exert complex effects on terrestrial vegetation ecosystems [36], more spatial and temporal data are needed to accurately assess the relevance of VPD to vegetation growth. In addition, we need to further explore the VPD effect thresholds of different vegetation based on continuous monitoring and specialized experiments [32]. By elucidating the response mechanisms of different vegetation productivities to VPD under wet and dry conditions, we could better understand the role of VPD in terrestrial vegetation ecosystems.
## 3 Results and Discussion  
### Preparation and Characterization of Ethanol-Water Extracts of Rose Oil Industry Byproducts  
The byproducts of the rose essential oil industry retain biologically active substances (polyphenols, aroma compounds, glycosides) because the industrial distillation process does not result in complete exhaustion of the valuable substances present in the initial fresh material. These compounds can be further extracted with organic or water/organic reagents, which leads to further valorization of the byproducts [16]. The results, presented in Table 1, suggested that 70% ethanol-water solutions extracted the highest amount of polyphenonic compounds, which approximated 6.5 g/L. Since the antioxidant activity is strongly related to the presence of polyphenols [27], the highest antioxidant capacities for this extract as measured by ORAC and HORAC were not unexpected. The most important phenolic compounds in the extracts were investigated by liquid chromatography (Table 2). The results of the analyses (Tables 1 and 2) suggested that 70% and 30% ethanol solutions extract phenolic compounds to a greater extent, with the amount of naringenin, quercetin-3-\(\beta\)-glucoside (including quercetin), and catechin prevailing. Since the amounts of polyphenols present in the extracts are related to the quenching of free radicals, the 70% ethanolic extract exhibited the highest antioxidant activity determined by ORAC and HORAC assays [16]. The profile of polar volatile components in the three ethanol-water extracts was determined by GC-MS (Table 3). The highest amounts of aroma compounds in the extracts were observed for \(\beta\)-phenethyl alcohol (14.11 \(\pm\) 0.16% of total ion current (TIC)). \(\beta\)-citronellol (10.18 \(\pm\) 0.12% of TIC), nerol (6.15 \(\pm\) 0.11% of TIC), geraniol (4.67 \(\pm\) 0.12% of TIC), \(\beta\)-cubebene (5.95 \(\pm\) 0.15% of TIC), \(\beta\)-caryophyllene (3.84 \(\pm\) 0.10% of TIC), trans-nerolidol (7.37 \(\pm\) 0.08% of TIC), and geranyl acetate (2.99 \(\pm\) 0.11% of TIC). B-Phenethyl alcohol, _cis_- and _trans_-rose oxide, \(\beta\)-citronellol, nerol, and geraniol are characteristic of a specific dry, green, and flowery rose-like aroma. Their presence in the byproducts and, subsequently in the extracts, could contribute to the final aroma bouquet of the aromatized beverages, and enhance customer satisfaction with the final product. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Extract**} & \multirow{2}{*}{**Polyphenol Content, mg/L**} & \multicolumn{2}{c}{**Antioxidant Activity**} \\ \cline{3-4}  & & **ORAC, \(\mu\)mol TE/L** & **HORAC, \(\mu\)mol GAE/L** \\ \hline
30\% & 6029.0 \(\pm\) 150.5 b  & 97,845.6 \(\pm\) 1058.6 b  & 45,522.5 \(\pm\) 863.4 b  \\
70\% & 6506.1 \(\pm\) 137.9 a  & 108,674.9 \(\pm\) 968.3 a  & 63,219.3 \(\pm\) 915.4 a  \\
90\% & 3740.6 \(\pm\) 148.3 c  & 66,010.2 \(\pm\) 1058.8 c  & 40,448.8 \(\pm\) 886.2 c  \\ \hline \end{tabular}
\end{table} 
Table 1: Polyphenol content and antioxidant activity of ethanol-water ethanol-water extracts of rose oil industry byproducts.
# Commercial Hemp Seed Oils: A Multimethological Characterization
## 3 Results and Discussion
### Quality Parameters
#### 3.1.2 Oxidation Parameters: Peroxides Number and Anisidine Number  
Fatty acids oxidation can occur in two steps. First, unsaturated fatty acids react with oxygen, producing odorless peroxides that, in the second step, decompose in aldehydes, responsible for rancid taste. The degree of the first oxidation step is evaluated by peroxides number (P.N.) (EU Regulation (EU) 2015/1830); a limit of 20 meqO\({}_{2}\)/kg P.N. is reported for virgin and extra virgin olive oils. The peroxides value is useful for assessing the spoilage progression, especially in fresh oils or oil produced with low temperature technologies [41]. On the other hand, the anisidine number (A.N.) provides the assessment of the second oxidation step: an acceptable number for fat matrices is 10.  
The nine hemp seed oils here investigated showed a relevant variability for P.N. values, ranging from 4.32 to 22.14 meqO\({}_{2}\)/kg. Only sample 6 showed a P.N. value above 20 meqO\({}_{2}\)/kg. Again, as in the case of A%, these values are higher than the values from 1.94 to 7.20 meqO\({}_{2}\)/kg reported in the case of noncommercial samples [40; 42; 43] and Italian commercial hemp seed oils [7].  
All the investigated samples showed A.N. values ranging from 0.11 to 3.58 (below the limit), in agreement with literature data [44].
###### Abstract  
The use of recycled concrete aggregates (RCA) instead of natural aggregates in hot-mix asphalt mixtures is one of the ways to achieve energy savings and reduce carbon emissions in road engineering. However, the cement mortar on the surface of RCA adversely affects the adhesion properties between asphalt and aggregates, leading to a reduction in the performance characteristics of asphalt mixtures. In this study, a silane coupling agent (SCA) was employed to improve the adhesion properties of the RCA/asphalt interface. The enhancement mechanism of SCA on the RCA/asphalt interface was investigated from multiple perspectives, including macroscopic properties, interfacial microstructure, and nanoscale interfacial interactions. Firstly, the adhesion behavior and tensile strength of the interface between RCA and asphalt were determined through a boiling water test and direct tensile test, both before and after SCA modification. Secondly, scanning electron microscopy (SEM) was employed to observe the surface microstructure of RCA and the microstructure of the RCA/asphalt interface. Finally, the main component of mortar, calcium silicate hydrate (C-S-H), was taken as the research subject of investigation to examine the hydrogen bonding, interaction energy, and interface transition zone of the C-S-H/asphalt interface system using the molecular dynamics methodology. The results demonstrate a two-level enhancement in the adhesion performance of the interface at the macroscopic scale following SCA modification. The interface tensile strength increased by 72.2% and 119.7% under dry and wet conditions, respectively. At the microscopic scale, it was observed that the surface pores of RCA were repaired after SCA modification, resulting in a more tightly bonded interface between the RCA and asphalt. At the nanoscale, SCA modification reduces the hydrophilicity of the C-S-H surface, increases the interaction energy and water resistance of the C-S-H/asphalt interface, and enhances the weak interface transition zone between C-S-H and asphalt. This study provides a theoretical basis for using SCA to enhance the bond strength of the RCA/asphalt interface and lays the foundation for the application of RCA asphalt mixtures on highways.  
silane coupling agent; recycled concrete aggregates; RCA/asphal interface; interface reinforcement mechanism; molecular dynamic +
Footnote †: journal: Applied Sciences
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Cluster ID**} & \multicolumn{4}{c}{**CPA (Nautical Miles)**} & \multicolumn{4}{c}{**TCPA (Seconds)**} \\ \cline{2-9}  & **Danger** & **Threat** & **Caution** & **Attention** & **Danger** & **Threat** & **Caution** & **Attention** \\ \hline \#1 & 0.0 & 0.1352 & 0.4376 & 0.7549 & & & & \\ \#2 & 0.0 & 0.0712 & 0.7418 & 0.9997 & & & & \\ \#3 & 0.0 & 0.3005 & 0.7040 & 0.9494 & & & & \\ \#4 & 0.0 & 0.2677 & 0.7848 & 0.9996 & & & & \\ \#5 & 0.0 & 0.2021 & 0.7354 & 0.9892 & & & & \\ \#6 & 0.0 & 0.1936 & 0.6244 & 0.9618 & & & & \\ \#7 & 0.0 & 0.3336 & 0.6259 & 0.9182 & & & & \\ \#8 & 0.0 & 0.2225 & 0.6382 & 0.9822 & & & & \\ \#9 & 0.0 & 0.1938 & 0.6607 & 0.9861 & 0 & 120 & 240 & 360 \\ \#10 & 0.0 & 0.4122 & 0.6411 & 0.9445 & & & & \\ \#11 & 0.0 & 0.2886 & 0.4623 & 0.9004 & & & & \\ \#12 & 0.0 & 0.5340 & 0.7902 & 0.8500 & & & & \\ \#13 & 0.0 & 0.1323 & 0.5265 & 0.8603 & & & & \\ \#14 & 0.0 & 0.2100 & 0.4992 & 0.8359 & & & & \\ \#15 & 0.0 & 0.2704 & 0.8322 & 0.8679 & & & & \\ \#16 & 0.0 & 0.4503 & 0.6564 & 0.8625 & & & & \\ \#17 & 0.0 & 0.2546 & 0.6460 & 0.8664 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Range of CPA/TCPA by risk level. Figure 11: Simulation results of Pindex extraction: (**a**) real-time navigation data of target area, and (**b**) calculated Pindex visualization results. fact that these main input values are not fixed, and are rather formed differently depending on the regional characteristics. The primary limitations of this study are that clustering was performed for a single reference time and region-based data clustering was performed without considering the type and size of the vessel. To be used for real-time VTS tasks, the predicted Pindex input parameters (CPA, TCPA, and cluster ID) should be used to provide regional and global Pindex prediction values for VTSO.
## 3 Numerical Analysis of the Thermal Performance
### Approximation of the Temperature Fields
#### 3.2.2 Temperature Filed on Main Flank Face  
The temperature field on the main flank face is shown in Figure 6, where different colors represented the temperature distribution in the CFX simulation process. Following the above approach, the temperature field can be also approximately expressed via similar function models. Temperatures of the points located far away from the heat source are also assumed to be equal to the ambient temperature \(T_{air}\). Therefore, the temperature field on the main flank face can be approximately expressed as:  
\[\left\{\begin{array}{l}T_{f1}=P_{1}e^{P_{2}\sqrt{x_{1}^{2}+y_{1}^{2}}}+(T_{c }-P_{1})e^{P_{3}\sqrt{x_{1}^{2}+y_{1}^{2}}}\\ P_{1}=0.9224T_{c}-10.26e^{-0.3025a_{p}}-0.1566T_{c}a_{p}\\ P_{2}=-8.004+28.41a_{p}-46.52a_{p}^{2}+24.35a_{p}^{3}\\ P_{3}=-0.06248-0.04879T_{c}-0.0001278a_{p}+7.667\times 10^{-5}T_{c}a_{p} \end{array}\right. \tag{4}\]
# Detecting Grounding Grid Orientation: Transient Electromagnetic Approach
## 4 Performance Evaluation and Results' Analysis  
In this section, a performance study to demonstrate the viability of the proposed method for orientation detection of the grounding grid is conducted. The evaluation study was performed through simulations. Simulations were performed using Comsol Multiphysics 5.0, a Finite Element Method (FEM) based tool. Furthermore, the inversion calculations of the recorded magnetic field above the Earth's surface were performed in MATLAB, and the results of the calculations are presented graphically.
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 3 Materials and Methods
### Detection Scheme
#### 3.4.2 Temporal Window  
To further mitigate the number of false positives and ensure confidence consistency across frames, a temporal window was implemented. The process of this temporal window is illustrated in Figure 3. The primary objective of this temporal window is to ensure that the majority of detections involve weapons that consistently appear, rather than objects that may coincidentally resemble weapons.  
Figure 2: General scheme of the detection pipeline process.  
For each prediction passing through this temporal window, it undergoes a comparison with other stored bounding boxes using a distance metric (Euclidean distance). The bounding boxes are stored in a dictionary, where each key represents the coordinates of a bounding box, and the comparison is performed in two stages. First, the bounding boxes are filtered and ordered based on a predetermined maximum distance. Then, among the top five closest bounding boxes, the similarity of each bounding box is evaluated based on its area. If similarity is identified, the temporal window of that bounding box is updated by increasing the number of frames the object has appeared in. If no similarity is found between the bounding box and the top five closest bounding boxes, a new temporal window is generated for that bounding box, and the bounding box is not displayed.  
Furthermore, the temporal window incorporates a countdown mechanism and a confidence history, which are utilised to oversee the life cycle of each bounding box. The countdown mechanism involves decrementing a counter associated with each bounding box, which discards the bounding box once the counter reaches zero if no further detections are made. Conversely, the confidence history stores the confidence of each bounding box in order to calculate the mean value among similar bounding boxes. It should be noted that an object must appear in more than a specified number of frames and have a mean confidence exceeding a defined threshold in order for it to be displayed. This guarantees that only consistent detections are retained, thereby excluding spontaneous detections.