esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Training Approaches for Photovoltaic Forecasts by Means of Machine Learning
## 4 Case Study  
Experimental data for this study were taken from the laboratory SolarTechLab [37] located in Milano, Italy (coordinates: 45\({}^{\circ}\)30\({}^{\prime}\)10.588\({}^{\prime\prime}\) N; 9\({}^{\circ}\)9\({}^{\prime}\)23.677\({}^{\prime\prime}\) E). In 2014, the DC output power of a single PV module with the following characteristics was recorded:  
* PV technology: Silicon mono crystalline,
* Rated power (Net capacity of the PV module): 245 Wp,
* Azimuth: \(-6^{\circ}30^{\prime}\) (assuming 0\({}^{\circ}\) as south direction and counting clockwise),
* Solar panel tilt angle (\(\beta\)): 30\({}^{\circ}\),  
The monitoring activity of the PV system parameters lasted from 8 February to 14 December 2014, but the employable data, without interruptions and discontinuities, amount to 216 days. These 24-hourly samples were used as the database for the forecasting methods comparison.  
The PV module was linked to the electric grid by a micro-inverter ABB MICRO-0.25-I- OUTD [38], guaranteeing the optimization of the production. Its operating parameters--DC power included--were transmitted to a workstation for storage using a ZigBee protocol wireless connection, in real-time. An important issue that arises is how to avoid missing values and outliers. A suitable pre-processing procedure, which has already been developed and described in detail in [39], is applied here.  
The weather forecasts employed were delivered by a weather service each day at 11 a.m. of the day before the forecasted one, for the exact location of the PV plant. The historical hourly database of these parameters was used to train the network and includes the following parameters:  
* \(T_{amb}\) ambient temperature (\({}^{\circ}\)C),
* \(GH1\) global horizontal irradiance (W/m\({}^{2}\)),
* \(GPOA\) global irradiance on the plane of the array (W/m\({}^{2}\)),
* \(W_{s}\) wind speed (m/s),
* \(W_{d}\) wind direction (\({}^{\circ}\)),
* \(P\) pressure (hPa),
* \(R\) precipitation (mm),
* \(C_{c}\) cloud cover (%),
* \(C_{t}\) cloud type (Low/Medium/High).  
In addition to these parameters, in order to train the PHANN method, the local time \(LT\) (hh:mm) of the day and the Clear Sky Radiation model \(CSRM\) (W/m\({}^{2}\)) were also provided. These are the eleven inputs of the ANN. Regarding the specific settings of the ANN, exception made for the training database composition (as presented in Section 2), they were selected on the basis of a sensitivity analysis, as outlined in a previous study [23]. The ANN settings adopted in this study were:  
* neurons in the input layer: 11,
* neurons in the first hidden layer: 11,
* neurons in the second hidden layer: 5,
* neurons in the output layer: 1,
* training algorithm: Levenberg-Marquardt,
* activation function: sigmoid,
* number of trials in the ensemble forecast: 40.  
The same analysis is performed for the training Method A\({}^{\star}\) by comparing the results of \(\overline{NMAE}_{\%}\) in Figure 13 and the new error definition Equation (9) shown in Figure 14.
# Improved Video Action Recognition Based on Pyramid Pooling and Dual-Stream C3D Networks
## 2 Methodology
### Static and Dynamic Feature Extraction for Character Movement  
#### 2.1.1 First Frame Static Feature Extraction Based on CNN  
The static information in video frames can effectively reflect the characteristics of people or objects in the scene; therefore, extracting the features of the first frame of a video has become a common method. We use a convolutional neural network (CNN) to extract the features of the first frame of each video clip. CNNs perform well in image classification, so in this section, a neural network architecture is constructed based on the network design for image classification to extract the features of the first frame of a video. The network contains five convolutional layers, two pooling layers, two fully connected layers and one Softmax layer [4], and the outputs of the convolutional and fully connected layers are nonlinearly processed by the ReLU activation function, and finally, the Softmax layer outputs a 1000-dimensional vector as the static features of the video frame. Figure 2 illustrates the structure of this neural network.
# Experimental Study on Drying Characteristics of Alfalfa Hay Bales Using Hot Air Convection
## 3 Results and Discussion
### Analysis of Drying Characteristics for Different Bales and Optimization Proposal
Additionally, the frequency of loading and unloading will increase, leading to a rise in the non-drying time (time spent on loading and unloading operations) and higher labor intensity and costs. The fundamental reason for the uneven drying of bales is the varying resistance encountered by moisture during its movement, transfer, and evaporation within the bales. To address the issue of unevenness, it is necessary to artificially control the resistance in different parts of the bales, thereby regulating the drying speed of each part to achieve uniform drying. The corresponding structure can be designed with two separate air supply systems: one high-pressure system and one low-pressure system. The low-pressure system functions as before, blowing hot air into the upper and lower end faces of the bales through the air outlets. The high-pressure system, which is newly designed, includes an array of air cones installed on the lifting device. Each air cone has a sharp tip that can easily be inserted into the middle of the bale, with small holes on the side walls. The interior of the air cones is supplied with high-pressure hot air from the high-pressure system. During operation, after the bales are loaded, the hydraulic lifting system lowers, and the array of air cones is inserted into the bales until the air outlets clamp the two end faces of the bales. During drying, while the low-pressure system blows air onto the two end faces of the bales, the high-pressure system uses the array of air cones to intensify drying in the central area of the bales, where resistance is higher, thereby ensuring uniform drying and improving drying efficiency. This is also similar to the scheme proposed by [PERSON] [21]. Their thermodynamic simulations showed that inserting needles of hot air into hay bales can enhance the drying efficiency. These proposed schemes will be implemented and evaluated in future work.
# Investigation of the Inverse Magnus Effect on a Rotating Sphere in Hypersonic Rarefied Flow
## 3 Problem Description
### Flow Conditions  
In all simulations, the working gas is air, which is approximately treated as a mixture of nitrogen and oxygen. The fractions of N\({}_{2}\) molecules and O\({}_{2}\) molecules are 78.8% and 21.2%, respectively. The velocity of the freestream is \(V_{\infty}=2800\) m/s, and the thermodynamic parameters of the stream are similar to those of the atmosphere at the altitude of 100 km.  
Figure 4: Comparison of the DSMC results for the heat flux distribution on the surface of a cone and the corresponding experimental results [33]. The horizontal axis is the normalized distance along the cone surface measured from the nose of the cone, where \(R_{n}\) denotes the nose radius.  
Figure 3: DSMC grid and results of the hypersonic rarefied flow over a blunt cone: (**a**) computational grid; (**b**)streamlines on the plane of symmetry and contour of skin friction coefficient over the cone surface; (**c**) contour lines of the normalized density in comparison with the experimental measurements [32].  
The sphere with a radius \(R=0.1\) m (diameter \(D=0.2\) m) is rotating at a constant angular velocity \(\omega\). The axis of rotation is perpendicular to the freestream velocity. A dimensionless rotation parameter \(W\) is defined as the maximum linear velocity on the sphere to the magnitude of the freestream velocity, so \(W=\omega R/V_{\infty}\). Five different values of \(\omega\) are considered here, and the corresponding values of the rotation parameter \(W\) are 0, 0.25, 0.50, 0.75, and 1.00. For \(W=1.00\), \(\omega\) is 4456 revolutions per second. The wall is assumed to be isothermal at the temperature \(T_{\rm w}=300\) K, with full thermal accommodation \(\alpha=1\).  
Important parameters are listed in Table 1. The freestream Mach number is 10. The Knudsen number here is the ratio of the freestream mean free path \(\lambda_{\infty}\) to the diameter \(D\), and its value is 0.51, indicating a typical flow rarefaction in the transition regime.
## 2 Procedure for Bol Fatigue Analysis Using Schmidt-Neuper Method
### Reference Results  
This section describes the fundamental characteristics of the steel tower housing a 2 MW wind turbine. In addition, the cumulative fatigue damage of bolts is evaluated using the Schmidt-Neuper method. The tower is 72.37 m high and features a tubular design with variable cross-section and wall thickness along its height. An interior flange connection with 110 screws (M48) is investigated. The outer and inner diameters of the flange are 4.2 m and 3.8 m, respectively. As shown in Figure 3, the pitch circle diameter (PCD) of the screws is 3.98 m. The flange and tower wall thicknesses are 140 mm and 32 mm, respectively. The main specifications are 900 MPa yield strength and 1470 mm\({}^{2}\) stress area. The bolts are pretensioned with a force of \(F_{\text{V}}=926.1\) kN, and the stress under preload is 630 MPa, which corresponds to 70% of their bearing capacity. The washer's inner and outer diameters are 49.4 mm and 92.0 mm, respectively, and its thickness is 8 mm. Based on the aforementioned parameters, Figure 7 depicts the nonlinear relationship between the wall tension \(Z\) and bolt stress \(\sigma\).  
After obtaining the nonlinear relationship depicted in Figure 7 and combining it with the external time-variant loads \(M_{x}\), \(M_{y}\), and \(F_{z}\), the time-variant bolt stress can be easily derived using linear interpolation over the predetermined nonlinear curve. Sequentially,  
Figure 7: Nonlinear relationship between wall tension \(Z\) and bolt stress \(\sigma\).  
rainflow counting and fatigue damage accumulation are performed. For clarity, Figure 8 depicts the distribution of bolt damage accumulation over 20 years at 15\({}^{\circ}\) intervals.  
In Figure 8, the angular coordinates indicate the position of the tower within the horizontal section. The line connecting the 0\({}^{\circ}\) and 180\({}^{\circ}\) positions aligns with the direction of incoming turbulent winds, with 0\({}^{\circ}\) representing the upstream direction and 180\({}^{\circ}\) representing the downstream direction. The angles in Figure 8 represent the positions of the bolts on the flange. As observed in Figure 8, the radar diagram of bolt fatigue damage is asymmetrical. The extreme cumulative damage occurs near 0\({}^{\circ}\) or 180\({}^{\circ}\), implying that the bolt fatigue is predominantly caused by the bending moment. To acquire even more precise data, recalculation is performed by subdividing the region near 0\({}^{\circ}\), and the resultant fatigue damage distribution is displayed in Figure 9.  
From Figure 9, maximum damage appears to occur at \(-3.27^{\circ}\) (=\(-360^{\circ}\)/110) with a cumulative damage value of 0.292. The maximum damage does not exceed 1. The current  
Figure 8: Distribution of bolt damage accumulation over 20 years.  
Figure 9: Distribution of bolt fatigue damage near 0\({}^{\circ}\).  
design satisfies the fatigue strength criteria, as deduced from miner's rule [33]. Without additional explanation, the following section evaluates the bolt at \(-3.27^{\circ}\).
# Review of Studies on User Research Based on EEG and Eye Tracking
## 3 Indicators of User Research
### Eye-Tracking Index
#### 3.2.2 Data Indicators and Their Significance  
(1) First Fixation Time (TTFF)  
The TTFF represents the time required to view a specific AOI from the start of the stimulus, reflecting the attractiveness and salience of spatial elements. The TTFF can indicate stimulus-driven search with bottom-up cognitive inertia and attention-driven search with top-down information processing [35]. Although the TTFF is a basic indicator of eye tracking, it is very valuable because it can provide information about how certain aspects of the visual scene are prioritized.  
(2) First Fixation Duration (FFD)  
When exploring a visual scene with our eyes, we locate a part of the image through fixation. The first fixation duration reflects the duration (seconds) of the first time that an area of interest (AOI) is fixated, and reflects the distinguishability of spatial elements in the scene [36]. When this indicator is used together with the TTFF, it enhances the value of information conveyed as an indicator that first attracts attention in a scene. If the subject's TTFF is short and the first fixation duration is long, then that area is likely to be very eye-catching.  
(3) Average fixation duration  
The average fixation duration can tell us how long an area is viewed on average. If one image leads to a much longer average fixation duration than another image, there may be something worth exploring there. In addition, by comparing AOIs, the size of this metric can determine which areas are actually more important than other areas [37].  
(4) Dwell Time  
The dwell time refers to the total duration of attention to areas of interest (AOI) by subjects, which reflects the attractiveness of elements to viewers; that is, it is the total time spent focusing on an element once it is noticed.  
(5) Regression count  
The regression count reflects the participant's reprocessing of previous information. It provides information about how many times the participant returned their gaze to a specific target in a specific area of interest, which allows researchers to examine which areas repeatedly attract participants (regardless of whether they are good or bad), and which areas are seen and then moved [38]. Although eye tracking cannot indicate users' psychological feelings when looking at something, it can provide detailed processing data for research to analyze the attractiveness of things.
## 5 Discussion and Verification of the Entire CTE Model
### Total Value of Telecom Potential Obtained through CTE Model  
Table 9 shows the total potential results of the observed telecoms obtained through the CTE model.  
The total value that the CTE model can give for a telecom operator is eight (without the influence of feedback, and if they are positive, this amount may be higher). Considering the obtained result shown in Table 8, it is clear that the observed telecom has low potential. In addition to assessing potential, the CTE model also provides guidelines on how and in which direction a particular telecom should be developed. Each of the areas has a maximum value of 1 (noting that, theoretically, the value can be higher if it is an \"ideal\" telecom where an area has a value of 1, and with the use of feedback links or the implementation of Moderate Intelligence (AI), that area can have a value greater than 1).  
Here, the method of reading the value of the area will not be presented in detail, as this would require deeper analysis and clarification, but the classification by category for each of the areas is briefly listed as follows:  
* from 0 to (inclusive) 0.25--insufficient quality value
* from 0.25 to (inclusive) 0.5--satisfactory value
* from 0.5 to (inclusive) 0.75--good value
* from 0.75--to (including 0.9--a very good value
* from 0.9 to 1 (or over 1)--an excellent value  
According to the results in Table 8 and the previously defined distribution, this observed telecom should definitely develop and monitor new services, increase its impact in the digital environment and improve human resources activities, and we should definitely research ways to be less dependent on external influences on business.  
The overall rating shown in Table 8 is approximately 30.85% of the total maximum value (without the effect of feedback and forward links on the result), which gives an overall satisfactory rating for the observed telecom, but, with a little effort, this rating can be much improved, and, with long-term action on certain areas of the rating, can be even further improved, which will definitely bring a positive shift in telecom business, meaning more revenue, gain and greater user satisfaction.  
Of course, it is up to management to determine how and at what speed this will be done. In any case, this model provides an existing assessment of the potential, but also guidelines for the development and improvement of the observed telecom.
## 4 Experimental Results
### Discussion about the Hyper-Parameters of the Proposed Semi-Supervised Learning
#### 4.5.1 Different Numbers of Unlabeled Data in Self-Supervised Stage  
The impact of different amounts of unlabeled data in self-supervised learning was examined. In this experiment, the setups of self-supervised training, the predictor, and the projector were based on [21]. The batch size was set to 32, the base learning rate at \(6.25\times 10^{-3}\) with a cosine decay scheduler (but we fixed the learning rate of the predictor), the weight decay at \(1\times 10^{-4}\), all data augmentations (Random Rotation, Random Scale, and Random PadCrop) enabled, and the training epoch at 100 with an SGDM optimizer (\(momentum=0.9\)) for self-supervised training. In the fine-tuning stage, the initial learning rate of the classifier was set to 30 with a cosine decay scheduler [61], the weight decay at \(1\times 10^{-6}\), only Random Rotation augmentation enabled, the train batch size at 64 (the batch size is limited to the number of training data if the number of training data is smaller than the batch size), and the fine-tuning epoch at 100 using the SGDM optimizer (\(momentum=0.9\)).  
The results are shown in Table 5. This table clearly shows that using more unlabeled data for self-supervised training increases the accuracy of the fine-tuning stage. More data for training means being able to learn better visual representation, but it also requires collecting more data and more training time. Of course, it costs nothing to collect large amounts of unlabeled data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Optimal Accuracy (\%) \(\uparrow\)** & **Parameters** & **FLOPs** & \
\begin{tabular}{c} **Latency (ms) @** \\ **GPU \(\downarrow\)** \\ \end{tabular} &
\begin{tabular}{c} **Latency (ms) @** \\ **CPU \(\downarrow\)** \\ \end{tabular} \\ \hline
[20] & **99.01** & 128.86 M & 7.46 G & 8.83 & 127.89 \\
**MsvNetLite (ours)** & 98.84 & **1.70 M** & **0.16 G** & **6.96** & **19.27** \\ \hline
[14] & 94.26 & 33.94 M & 12.51 G & 13.36 & 137.88 \\
**FeatureNetLite (ours)** & **97.10** & **1.91 M** & **0.14 G** & 6.90 & 55.97 \\
[38] & 94.34 & 33.75 M & 6.40 G & 7.60 & 71.28 \\
[40] & 93.38 & 32.75 M & 10.70 G & 15.43 & 178.72 \\
[59] & 77.74 & 11.27 M & 0.73 G & **1.74** & **20.31** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison of optimal recognition accuracy (%), network size, and average inference latency with different approaches. The bold number is the best result in each column.
# Development of Wearable Textile MIMO Antenna for Sub-6 GHz Band New Radio 5G Applications
## 5 MIMO Antenna Diversity Parameters
### Channel Capacity Loss  
CCL is a crucial metric that plays a vital role in assessing MIMO channel capacity, serving to quantify correlation among closely spaced patch elements within MIMO systems. When working in multipath environments with a high signal-to-noise ratio, its calculation requires using Equation (4a).  
\[C_{loss}=-log_{2}^{|\Psi^{R}|}\] (4a) Assuming that \[\Psi^{R}\] represents an \[n\times n\] correlation matrix (i.e., \[n=2\] ), components of MIMO systems can be described by using one of the three equations shown here, and are expressed relative to S-parameters in Equations (4b)-(4e). \[\Psi_{11}=1-|S_{11}|^{2}-|S_{12}|^{2} \tag{4b}\] \[\Psi_{22}=1-|S_{21}|^{2}-|S_{22}|^{2}\] (4c) \[\Psi_{12}=-(S_{11}^{*}S_{12}+S_{21}^{*}S_{22})\] (4d) \[\Psi_{21}=-(S_{22}^{*}S_{21}+S_{12}^{*}S_{11}) \tag{4e}\]  
In MIMO systems, the primary objective is often to achieve a very low CCL to maximize channel capacity. For the specific MIMO antenna under consideration, the CCL threshold is established at 0.4 bits/s/Hz. As depicted in Figure 11b, the CCL consistently maintains a significantly low value (CCL < 0.13 bits/s/Hz) throughout the overall operating band. Consequently, the proposed irregular octagon-shaped MIMO antenna proves to be well-suited for applications in wearable systems.
# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
## 3 Results and Discussion
### Case II: With DG, without CVR and DLC  
Distributed generation technology has created an important role in smart grids due to its technical, environmental and economic benefits [2; 3; 4]. DG can improve reliability as well as provide some other technical benefits: reduced line losses, reactive power control, congestion mitigation at distribution and transmission level, increased system capacity, lower reserve margin requirements at low cost and shorter power outages [54].  
In this case, the optimal location of the distributed generator was established based on minimizing power losses of the entire test system. The best location was found to be bus number 21 [2; 54]. A Photovoltaic distributed generator was added to the determined bus location. The optimal capacity was found to be very large, namely 100 s MW, thus a smaller DG capacity was chosen to simulate a more realistic case [2; 54]. The capacity chosen was 10.233 MW. Figure 6 illustrates the output power of the PV system used in the grid-connected test system. It is worth mentioning that the output power of a PV array is influenced by solar irradiance. It is obvious that the output power of PV modules increases when the irradiance increases, and hence the capacity of PV will slightly increase [5].  
Figure 7 shows the voltage profile for the test system before and after the DG interconnection. It is observed that voltage the profile slightly increased; this small variation is due to the use of small capacity of PV compared to total system capacity. However, this integration of PV may alleviate voltage violations when CVR is applied.  
Figure 6: Output power of the interconnected PV.