esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The mRNA and Protein Levels of Adipogenic Markers during Adipogenic Differentiation of hBM-MSCs  
The mRNA and protein levels of key adipogenic markers PPARy, C/EBP\(\upalpha\), and SREBP1c were determined in hBM-MSC adipocytes. Adipogenic inducement of the hBM-MSCs increased the levels of PPARy, C/EBP\(\upalpha\), and SREBP1c. Treatment with 10 \(\upmu\)M loliolide decreased mRNA and protein expression of all tested genes compared to untreated adipocytes (Figure 2A,B). Loliolide treated hBM-MSCs expressed 64.9%, 31.3%, and 73.2% less mRNA of genes PPARy, C/EBP\(\upalpha\), and SREBP1c, respectively, compared to the untreated control (Figure 2A). Treatment with loliolide also led to significant decrease in the protein levels of PPARy and C/EBP\(\upalpha\) along with a slightly inhibited level of SREBP1c (Figure 2B). The effect of loliolide on PPAR\(\upgamma\) signaling was also determined by fluorescence staining of the PPAR\(\upgamma\) protein. The hBM-MSC adipocytes expressed high levels of PPARy accumulated in the nucleus relative to DAPI staining (Figure 2C). Loliolide significantly decreased the PPAR\(\upgamma\) amount indicating, that it inhibited adipogenic differentiation of hBM-MSCs via suppressed PPARy activation.  
Figure 2: The effect of loliolide treatment on the mRNA and protein levels of adipogenic markers, PPARy, C/EBP\(\upalpha\), and SREBP1c in hBM-MSC adipocytes. The hBM-MSCs were differentiated into adipocytes with or without loliolide treatment. One group was left untreated and non-differentiated as the negative control. PPARy, C/EBP\(\upalpha\), and SREBP1c mRNA (**A**) and protein (**B**) expression levels were analyzed by RT-qPCR and Western blotting, respectively, at Day 10 of differentiation. \(\upbeta\)-actin was used as housekeeping gene and protein. Different letters (a–e) above the sample groups indicate that the difference between each group is statistically significant (\(p<0.05\)) compared to the untreated differentiated control. (**C**) Images of fluorescence stained hBM-MSCs at incubation Day 10 after adipogenesis inducement (except undifferentiated untreated group which was fed growth medium only), stained with anti-PPARy antibody (green) and DAPI (blue). Scale bar, 100 \(\upmu\)m.
## 4 Evaluation
### Results
#### 4.3.3 Results of Experiment 3
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(I_{\Delta t}\) & **Data Range** & \(L_{i}(\Delta t)>L_{j}(\Delta t)\) & \(L_{i}(\Delta t)<L_{j}(\Delta t)\) & **True Rate** & **False Rate** \\ \hline
0 & 2–928 & 107 & 820 & 88.5\% & 11.5\% \\
1 & 929–973 & 25 & 20 & 55.5\% & 44.5\% \\
0 & 974–980 & 7 & 0 & 0\% & 100\% \\
1 & 981–987 & 2 & 5 & 28.5\% & 71.5\% \\
0 & 988–1352 & 102 & 263 & 72.1\% & 27.9\% \\
1 & 1353–1391 & 20 & 19 & 51.2\% & 48.8\% \\
0 & 1392–1750 & 85 & 274 & 76.4\% & 23.6\% \\
1 & 1751–1815 & 38 & 27 & 58.4\% & 41.6\% \\
0 & 1816–2144 & 67 & 262 & 79.7\% & 20.3\% \\
1 & 2145–2173 & 23 & 6 & 79.3\% & 20.7\% \\
0 & 2174–3303 & 180 & 950 & 84.1\% & 15.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experiment 3_2’s evaluation result of the proposed PSI between path R5-6-8-10 and path R5-7-8-10.
## 4 Results and Discussion
### Sample Test Results of the Comprehensive Evaluation Model
#### 4.4.2 Comprehensive Evaluation Results and Analysis  
The score value corresponding to the comment set was set as \(S=\{s_{1},\,s_{2},\,s_{3},\,s_{4},\,s_{5}\}=\{1,2,3,4,5\}\); that is, values of 1, 2, 3, 4, and 5 were assigned to the planning level, standard level, integration level, optimization level, and leading level, respectively. A summary of the evaluation results is shown in Table 12.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Target Layer** & **Score** & **First-Level Index** & **Score** & **Second-Level Index** & **Score** \\ \hline \hline \multirow{8}{*}{Application maturity of enterprise AI technology} & \multicolumn{2}{c}{Intelligence strategy (IS)} & 4.1026 & Intelligence strategy (ISA) & 4.1026 \\ \cline{2-5}  & \multicolumn{2}{c}{Intelligence technology (IT)} & 4.7000 & \multicolumn{2}{c}{Intelligence technology level (ITA)} & 4.7000 \\ \cline{2-5}  & \multicolumn{2}{c}{} & & & \multicolumn{2}{c}{Film and television project planning and development (IBA)} & 3.3433 \\ \cline{1-1} \cline{2-5}  & \multicolumn{2}{c}{} & & \multicolumn{2}{c}{Film and television project production (IBB)} & 4.3276 \\ \cline{1-1} \cline{2-5}  & \multicolumn{2}{c}{} & & \multicolumn{2}{c}{Film and television project marketing (IBC)} & 4.5387 \\ \cline{1-1} \cline{2-5}  & \multicolumn{2}{c}{} & & \multicolumn{2}{c}{Organizational management mode (IOA)} & 3.5716 \\ \cline{1-1} \cline{2-5}  & \multicolumn{2}{c}{} & & \multicolumn{2}{c}{Corporate cultural conception (IOB)} & 4.7000 \\ \cline{1-1} \cline{2-5}  & \multicolumn{2}{c}{} & & \multicolumn{2}{c}{} \\ \end{tabular}
\end{table}
Table 12: The summary of the evaluation results for Enterprise Q.  
The average value of the overall level of application maturity of AI technology predicted by the experts according to their own experience and understanding of the situation of Enterprise Q was 4.1 points. The score calculated by the above evaluation model was 4.3328 points, which is relatively close to the expert's estimated score of the overall AI technology application maturity of Enterprise Q, indicating that the evaluation model constructed in this study is highly accurate.
# Intelligent Fault Diagnosis of Unbalanced Samples Using Optimized Generative Adversarial Network
## 2 Methods
### Brief Theory
#### 2.1.5 Parametric Rectified Linear Unit  
Rectified linear unit (ReLU) is a general activation function. It maintains the biological heuristics of the step function, but the derivative is non-zero when the input is positive, permitting gradient-based learning. The function \(ReLU\) is expressed by the following:  
\[ReLU(y)=\left\{\begin{array}{l l}y,&if\ \ y>0\\ 0,&if\ \ y\leq 0\end{array}\right. \tag{4}\]  
However, ReLU has the drawback that neurons with \(y\leq 0\) may not be activated, causing the related parameters to never be refreshed. Parametric ReLU (PReLU) [33] can adaptively learn the parameters of the corrected linear unit and avoid zero gradient, which is defined as follows:  
\[PReLU(y_{i})=\left\{\begin{array}{l l}y_{i},&if\ \ y_{i}>0\\ a_{i}y_{i},&if\ \ y_{i}\leq 0\end{array}\right. \tag{5}\]  
where \(y_{i}\) is the input of the \(i\)-th channel. \(a_{i}\) is a gradient for one layer as follows:  
\[\frac{\partial\xi}{\partial a_{i}}=\sum_{
# Relationship between Total Antioxidant Capacity, Cannabinoids and Terpenoids in Hops and Cannabis
## 4 Materials and Methods
### Microwave-Assisted Hot Air Drying of Hops and Cannabis (MAHD)  
Optimal MAHD conditions for cannabis and hop biomass identified previously were applied to this experiment [51]. MAHD was conducted in an automated laboratory-scale microwave oven with several modifications. Briefly, the main components were a 2450 MHz microwave generator (Gold Star 2M214, Seoul, South Korea) with adjustable power (0 to 750 W), waveguides, a three-port circulator, a manual three-stub tuner to match the load impedance, microwave couplers to measure forward and reflected power, a carbon load to absorb reflected power, and a microwave cavity made of brass (0.47 \(\times\) 0.47 \(\times\) 0.27 m) in which the samples were processed. In each experiment, approximately 100 g pre-frozen hops and cannabis inflorescence were placed in a nylon mesh sample holder tray (diameter = 0.21 m). The plant material was spread in one layer and placed inside the microwave cavity. Drying was performed until the sample reached a dry basis moisture content of 12%. Dried samples were transferred into a plastic bag and stored in a refrigerator at 5 \({}^{\circ}\)C before analyses. Drying was performed in triplicate under each condition.
## 2 Materials and Methods
### Methods
#### 2.5.1 Time-Series Phenological Pre-Processing  
The time-series data collected for this study are susceptible to various sources of disturbance, including diverse radiance conditions, background interference, instrumental noise, and atmospheric factors. Such abnormalities or implausible observations can obfuscate or even disrupt the underlying phenological patterns of the target crops. Consequently, the primary objective of the pre-processing phase is to mitigate the impact of the atmospheric, background and systematic noise while smoothing the time-series data, thus preparing it for subsequent phenological modeling. This pre-processing stage comprises two key components:  
1. Abnormal measurements elimination: To address abnormal measurements, a moving window abnormal elimination method was employed. This approach identifies measurements deviating by more than three times the standard deviation from the average value within an 11-day moving window (slightly longer than the interval of adjacent observed crop growth stages) and subsequently excludes them from the dataset [69].  
Time series smoothing: SIF and EVI time series underwent smoothing using a three-time Savitzky-Golay algorithm with an 11-day moving window [70]. This algorithm serves to attenuate off-season phenological signals while fitting a smoothing curve to the time-series observations.  
As illustrated in Figure 2, this pre-processing phase effectively transforms the raw data points into the smoothed dashed lines.  
The second aspect of this pre-processing section involves categorizing and compositing the datasets into intervals with different temporal resolutions, facilitating subsequent processing and evaluation. Initially, based on empirical knowledge of crop rotation detailed in Section 2.1, the minimum value between the day of the year (DOY) 100 and 200 serves as the rotation point. This division enables the segmentation of the smoothed time-series curve into two distinct growing seasons: winter wheat and maize. Notably, this step is exclusively applied to satellite data, because the in situ measurements were already classified. As depicted in Figure 2, the dashed lines in light blue indicate the rotation points derived through this procedure. Subsequently, the smoothed time-series curve is composited into data intervals spanning 3 days, 5 days, 7 days and 15 days, employing both maximum value composite (MVC) and average value composite (AVC) techniques at the midpoint of each interval [71]. Applying this step to MODIS EVI data is worth nothing, as the temporal resolution of the MODIS EVI product used in this study was 16 days and 8 days (when combined), and it had already undergone compositing.
# Spouted-Bed Gasification of Flame Retardant Textiles as a Potential Non-Conventional Biomass
## 3 Results
### Thermal Transitory and Gasification Temperature  
Preliminary experimentation using wood pellets was carried out with different LPG/air ratios to create a feasibility reaction, under controlled operations (combustion, pyrolysis or gasification). Generally, the initial stage involves the pre-heating of spouted bed, which occurs around 350 \({}^{\circ}\)C. The furnace thermal power was set at 11 kW/h with a minimal extra-stoichiometric air and suitable LPG flow rate [10]. Achieving this temperature assures the ignition point of prepared flame retardant cotton pellets.  
In order to attain the gasification phase, it is important to maintain high temperature. The temperature increment should expand progressively in the gasification plant, in order to expel the excess water/moisture present in the solid matrix. Sometimes, this operation takes up to a few hours and ought to revise for various cycles. The pre-heating temperature inside the reactor for gasification was reached to about 350 \({}^{\circ}\)C at 11 kW in 60 min, with a continuous but imitated addition of amounts of wood pellets (to avoid wastage of prepared pellets). High oxidizing atmosphere (\(\lambda\approx 1.20\), the stoichiometry ratio between gaseous fuel and oxygen) for total combustion of fed material, provides an additional energetic boost and enthalpy of the reaction inside the rig triggering immediate temperature increase [19]. Attaining the high temperatures, prepared flame retardant cotton pellets were fed.  
As shown in Figure 4, gasification of both the fed materials, flame retardant cotton pellets (before and after degradation) are in a regime of low-temperature gasification. The high-temperature gasification occurs at \(\sim\)\(>\)1200 \({}^{\circ}\)C, while \(\sim\)\(<\)1000 \({}^{\circ}\)C is considered as low-temperature gasification. In low-temperature gasification, air is utilized as gasifying medium, at times leads to undesired nitrogen into the generated gas [37].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Analytics [g/g (wt.\%)]** & **Pure Cotton** & \
\begin{tabular}{c} **Cotton with Flame** \\ **Retardant** \\ \end{tabular} &
\begin{tabular}{c} **Cotton after Flame** \\ **Retardant Removal** \\ \end{tabular} \\ \hline Moisture content & 8.43 & 2.42 & 2.63 \\ \hline Ash & 1.84 & 2.79 & 0.46 \\ \hline C & 44.45 & 42.41 & 43.88 \\ \hline H & 6.34 & 6.28 & 6.29 \\ \hline O & 47.12 & 47.01 & 48.35 \\ \hline N & 0.10 & 1.12 & 0.88 \\ \hline S & 0.19 & 0.36 & 0.12 \\ \hline Higher heat value (HHV, MJ/kg) & 15.88 & 16.26 & 15.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proximate analysis and calorific values of prepared pellets for gasification.
# Chiral Dielectric Metasurfaces for Highly Integrated, Broadband Circularly Polarized Antenna
## 2 Antenna Design
### Working Principle and Enhancement of ARBW  
Recent approaches for enhancement of AR bandwidth in metallic antennas have employed two pair slots, as explained in Reference [21]. Alternatively, dielectric resonators with high-permittivity are known to support electric and magnetic resonances of different orders [8; 10], in analogy with cylindrical nanostructures [18]. The near-field overlapping between nearby cylinders resembles the electronic-bands from well-localized atomic orbitals in the tight-binding model [26; 27] in electromagnetic platforms known as metasurfaces. Here, we use dielectric cylinders with a slanted slot at the center to produce integrable, highly efficient and broad ARBW CP antennas. An analogous concept was exploited in nanophotonic platforms to produce giant enhancement of chiroptical effects [18]. The concept is based on the coupling of the electric and magnetic modes of the resonators (considered to be dipoles in a qualitative approximation) which can be expressed as  
\[\tilde{\mathbf{p}}=\bar{\mathbf{x}}\tilde{\mathbf{E}}-i\tilde{C}\tilde{ \mathbf{B}},\qquad\tilde{\mathbf{m}}=\bar{\mathbf{x}}\tilde{\mathbf{B}}+i \tilde{C}\tilde{\mathbf{E}}, \tag{1}\]  
where \(\tilde{\mathbf{p}}\) and \(\tilde{\mathbf{m}}\) are the electric and magnetic dipoles, respectively. \(\bar{\mathbf{x}}\) and \(\bar{\mathbf{x}}\) are the complex electric polarizability and magnetic susceptibility, while \(\tilde{C}\) corresponds to the mixed electric-magnetic dipole polarizability. \(\tilde{\mathbf{E}}\) and \(\tilde{\mathbf{B}}\) are the complex electric and magnetic fields. The radiated electromagnetic energy has, therefore, a term \(\sim\tilde{G}^{\prime\prime}\text{Im}\left\{\tilde{\mathbf{E}}\cdot\tilde{ \mathbf{B}}\right\}\), where \(\tilde{G}^{\prime\prime}\) stands for the imaginary part of \(\tilde{C}\). It is just at the slanted sides \(l\)s where \(\tilde{\mathbf{E}}\) and \(\tilde{\mathbf{B}}\) become mixed [18], allowing the tuning of \(\tilde{C}\) through the tilting angle \(\alpha\) (see Figure 2d). The electromagnetic fields exciting the CDM are produced by a single narrow-band driven-patch antenna with truncated corners, thus being constantly fed by two orthogonal linearly polarized modes (AR \(\gtrsim 5\)), as will be shown later.
# Evaluating the Flexibility Benefits of Smart Grid Innovations in Transmission Networks
## 3 Use Case Scenario: Battery Integration in the Cypriot Power System
### Irre without Bess  
The BESS certainly enhances the flexibility of the Cyprus power system and therefore the insufficient ramping resource expectation (IRRE) metric was used for quantifying the enhancement of the Cyprus power system flexibility with the installation of the BESS. First, the IRRE for the years 2020-2025 without a BESS installed was calculated as a reference point (Figure 3). The results are based on the unit commitment resulting from the estimated load and RES generation provided by Transmission System Operator-Cyprus (TSOC).  
Figure 2: Flowchart for strategic decision-making through innovation integration.  
The generating power from the conventional units with and without the integration of the battery is illustrated in Figure 4a for the 80 MWh BESS capacity for 5 summer days in 2020. Also, the state of charge of the battery is presented in Figure 4b. Initially, it is assumed that the battery is fully discharged. Note that 80 MWh is the usable capacity of the battery, and we assume that there are no limitations about the minimum state of charge of the battery. The valley filling and peak shaving applications of the battery storage are obvious in Figure 4a. The integration of the battery storage manages to reduce the range between the maximum and the minimum produced power, with a great benefit on the operational cost of the system due to the fact that less generating units need to be committed and de-committed in order to satisfy the security and the technical constraints of the system.  
Figure 4: (**a**) Generating power from conventional power plants with and without the use of storage and (**b**) State of charge of the 80 MWh battery.  
Figure 3: IRRE without a BESS installed for the years 2020–2025.  
As illustrated in Figure 3, the IRRE follows an increasing trend over the six years. This is mainly due to the constantly increasing RES capacity in the 6 years as it is shown in Figure 5, which demands higher flexibility to compensate the bigger fluctuations of the net load. It is worth noting that no new conventional units are considered to be installed in this study.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 3 Methodology
### Proposed Algorithms
```
1:procedureClustering(\(dataStream\))
2:Step 1: Initialize \(\text{Dynamic Clustering Model with RL}\)
3: Let \(Q(s,a)\) represent the quality of action \(a\) in state \(s\)
4: Initialize \(Q(s,a)\) for all state-action pairs
5: Define reward function \(R(s,a)\) for evaluating clustering actions
6: Set learning rate \(\alpha\) and discount factor \(\gamma\)
7:Step 2: Deploy Self-Organizing Map (SOM) for Clustering
8: Initialize SOM with random weights \(W\)
9: Define neighborhood function \(h_{cl}(t)\) for neuron \(i\) at time \(t\)
10: Set SOM learning rate \(\eta\)
11:for each \(dataPoint\) in \(dataStream\)do
12: Step 3: Dynamic Clustering with SOM
13: Find Best Matching Unit (BMU) for \(dataPoint\) in SOM
14: Update weights \(W\) using \(h_{cl}(t)\) and \(\eta\)
15: Step 4: RL-based Optimization of Clustering
16: Observe state \(s\) (current clustering configuration)
17: Choose action \(a\) (adjusting clustering parameters) using \(\epsilon\)-greedy policy
18: Apply action \(a\), observe new state \(s^{\prime}\) and reward \(r\)
19: Update \(Q(s,a)\) using the Bellman equation:
20:\(Q(s,a)\gets Q(s,a)+\alpha[r+\gamma\max_{a^{\prime}}Q(s^{\prime},a^{\prime })-Q(s,a)]\)
21:Step 5: Store Clustered Data
22:\(clusterID\leftarrow\text{BMU index for dataPoint}\)
23: Store \(dataPoint\) in \(clusterID\)
24:endfor
25:endprocedure
```  
**Algorithm 1** TinyCleanEDF: Federated Learning for Data Cleaning and Anomaly Detection with Autoencoder-based Feature Extraction
CompressEdgeML, as described in Algorithm 3, is designed to introduce smart and adaptive data compression capabilities to the edge of IoT networks. This algorithm utilizes a neural network, denoted as \(NN\), specifically customized for data compression tasks. It is trained on sample data to efficiently identify and execute compression patterns, setting an initial compression ratio \(CR\) based on the characteristics of the training data. CompressEdgeML dynamically adapts its compression techniques to align with the current network conditions (represented as \(N_{\text{cond}}\)), ensuring an optimal balance between the quality of compression and operational efficiency. As data stream through the network, CompressEdgeML uses its neural network model to compress each datapoint. The algorithm continually updates the compression ratio in response to changing network bandwidth and storage capacities, ensuring that the size of the compressed data (\(|compressedData|\)) is always suitable for the network and storage constraints. The final step involves securely storing the compressed data in a specific storage system. This approach significantly enhances data management efficiency in IoT environments by reducing the size of data for transmission and storage while maintaining the integrity and usability of the information.
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 3 Results and Discussion  
To verify the generality and effectiveness of our deep learning acceleration core architecture, we used the Xilinx ZU15 EG chip to build a heterogeneous video processing system. The PL side of the ZU15 EG chip includes 26.2 Mb of on-chip storage resources, 3528 DSP48E2, and four external DDR4 chips on the PS side, with a maximum theoretical transfer bandwidth of 38.4 GB/s. This paper's data transfer bus is connected to the same AXI4 HP interface at 300 MHz, 128 bit width, and 4.8 GB/s theoretical maximum bandwidth.  
Figure 14: Diagram of clock follower and the output waveform of each point of the clock follower.  
Figure 13: DSP48E Cross-Clock Domain Design.  
Therefore, the on-chip DDR bandwidth is sufficient to support the data transfer bandwidth required for neural network computing. In addition, the input data for this paper comes from a real-time input video stream, so the hardware platform is equipped with a camera link chip for accessing the real-time video stream. The input video is processed by the interface chip, waiting for the acceleration core to infer. The detected results are sent from the PS to the PL side, overlaying the detected frame on the output video and displaying it through the video interface. The hardware we use is shown in Figure 15. We have used this hardware platform to implement and validate the deep learning acceleration core.  
In order to objectively evaluate the ability of our proposed method to handle the consistency problem of arithmetic power, speed, and resources, we make the following assumptions.  
1. Our proposed method will not be interrupted by unordered scheduling instructions during network inference tasks, which include parameter updates, recovery after video stream interruptions, and interruption exception handling.
2. The automatic optimization function of the synthesis tool is turned off since we have already performed manual optimization specifically for the proposed method. The secondary optimization of the automatic tool will affect the results.
3. When selecting other methods proposed in the literature for comparison, we try to select FPGA chips of the same architecture system because the internal structure of chips of different architectures is different, which will affect the evaluation of the effect of resource optimization.  
In Section 3.1, we illustrate the hardware implementation results and timing of the convolutional and fully connected layer pipelines in the computational scheduling kernel, list the hardware resources consumed by the computational scheduling kernel, and discuss the resource usage. In Section 3.2, we list the hardware resources consumed by the deep learning acceleration core and discuss the resource usage. In Section 3.3, we illustrate the actual performance of the deep learning acceleration core, compare it cross-sectionally with other similar lightweight acceleration cores, and discuss our method's advantages.