esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiment Setup
### Coordinate System and Measurement Accuracy  
The fuselage of the fixed rotor system is used as the construction axis coordinate system. The origin is at the center of the rotor shaft. The x and y axes are parallel to the plane of the rotor, and the z axis is perpendicular to the plane and points upward, as shown in Figure 6. The sensitivity of the single-axis acceleration sensor EA-YD-185 is 100 mV/g. The sensitivity of the three-axis acceleration sensor EA-YD-3193-5 is 50 mV/g. The sampling frequency is set to \(0\sim 500\) Hz, the sampling point is 2048, and the frequency resolution is 0.245 Hz. After the original acceleration data are acquired, they are first transformed into the frequency domain spectrum by a fast Fourier transform (FFT) process. The time domain acquisition and frequency domain transform process are shown in Figure 6. The fuselage vibration acceleration of the model-scale rigid rotor is measured in the x, y, and z directions. Based on the vibration acceleration test data, the influence of the blade adjustment parameters (counterweight, pitch, trailing-edge flap) on the rigid-rotor dynamic balance is analyzed.
## 4 Joint Preprocessing Techniques for MU-Network MIMO
### Mu-Tdma  
In MU-JTDMA, the collaborative BSs together serve each of their CEUs at a different time slot. Thus, it is free from multi-user interference at the cost of spectral efficiency. In [21], it was shown that the receiver side (say CEU \(u\)) equivalently observes a \(K^{(u)}\) equivalent parallel channels:  
\[Y_{k}^{(u)}=\sigma_{k}^{(u)}d_{k}^{(u)}+W_{k}^{(u)},\;k=0,1,\ldots,K^{(u)}-1, \tag{19}\]  
where \(\sigma_{k}^{(u)}\) is the \(k\)th non-zero singular value of aggregate channel matrix \(\boldsymbol{\Lambda}^{(u)}\) and \(W_{k}^{(u)}\) denotes the received noise.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 2 System Architecture
### The Radio Environment Map  
To ensure optimal performance, a REM is used to map the wireless environment and identify areas where signal interference or coverage gaps might occur [17]. By analyzing the radio signal distribution across the industrial environment, the REM helps determine the optimal placement of sensor nodes and relays and ensures reliable communication paths. This reduces packet loss and improves overall network reliability [18]. During the initial setup, the proposed system utilizes the REM to determine optimal locations for sensor deployment, ensuring that the wireless network remains stable and reliable. By carefully mapping out the signal strength in different areas, the system can ensure that each sensor has a strong and reliable connection to the network, minimizing packet loss and improving overall network reliability.
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 4 Tasking Offloading Algorithm Based on DRL
### The MDP Framework
#### 4.1.3 Reward Function  
The reward function has been proved to be beneficial for finding an optimal action strategy. Thus, the reward must be set appropriately to match the efficient learning process [32]. Note that there exists a situation where tasks are not completed and dropped at the deadline \(\pi_{m}\), so we use a negative reward to this situation, and when tasks have been completed before \(\pi_{m}\), we use a relatively positive reward to present it. Then, the reward function is denoted as:  
\[r_{t}=\begin{cases}-\pi_{m},&\text{if $u_{m}(t)$ been dropped}\\ R-C_{m}(t),&\text{if $u_{m}(t)$ been processed,}\end{cases} \tag{11}\]where \(R\) is a constant. Moreover, we define a long-term accumulative reward as: \(R_{t}=\sum_{i=0}^{T}\zeta^{t}r^{t}\). Here, there is a discount factor, \(\zeta\in[0,1]\). Furthermore, the agent is determined to maximize the accumulative reward for minimizing the total delay through a long period, \(T\).
## 2 Materials and Methods
### Data
#### 2.1.3 GEBA & BSRN  
GEBA is a database of ground-based measurements, located at the Institute for Climate and Atmospheric Sciences ETH Zurich (Zurich, Switzerland). It provides mean monthly values of 15 energy flux components (global, diffuse and direct shortwave radiation, surface albedo, longwave downward and upward radiation, etc.) measured from 2500 globally distributed stations, with 500,000 monthly values. Many stations provide measurements since the 1950s and some of them back to the 1930s. Their accuracy, in terms of relative random measurement error, has been estimated by [19] at 5% of the monthly means, and 2% of the yearly means. SSR monthly data, mostly measured by pyranometers, are collected from 1397 stations for the time period 1980-2017 [85; 86]. The GEBA data are available at no cost for bona fide research after registration at [87].  
The BSRN is a project of the Radiation Panel of the GEWEX under the World Climate Research Programme (WCRP). Its goal is to provide radiation measurements of high quality and high temporal resolution (one to three minutes) at sites (presently 73) located in various climate zones around the world since January 1992. These measurements are reported to the BSRN Archive at the Alfred Wegener Institute (AWI) in Bremerhaven, Germany. In this study SSR measurements taken from 73 BSRN stations for the time period 1992-2020 are utilized. The BSRN sites can measure the direct shortwave flux (measured with a pyrheliometer) and the diffuse shortwave flux (measured with a shaded pyranometer), apart from the global shortwave flux (measured with a pyranometer). The quality of the BSRN solar and thermal radiation data is reported in [88]. Using single pyranometers in conjunction with the component sum method at BSRN sites [85], and considering long term averaging, an accuracy up to 5 Wm\({}^{-2}\) (~2% for 24-h mean solar irradiance) has been estimated, meeting the BSRN specifications under optimal observing conditions. The mean monthly BSRN data used for the evaluation of MERRA-2 SSR and GDB are computed from daily data, which are in turn calculated from the instantaneous (minute level) data. Care is taken to ensure the representativeness and accuracy of the computed monthly mean BSRN data. To this aim, if more than 25% of the total instantaneous measurements during the daytime of one day are missing, the daily integrated SSR values are excluded. Then, in the next step of the computation of BSRN monthly SSR fluxes, if more than five daily SSR data are missing in one month, the monthly SSR data are excluded from the comparison. The BSRN data were freely downloaded via PANGEA or ftp at [89]. The global distribution of the originally available GEBA and BSRN stations shown in Figure S1. The stations, especially the GEBA ones, cover almost the entire globe (continental areas) with a better or worse density, whereas there is a dense network of stations in Europe and Eastern Asia.
## 3 Methods for Image Captioning
### Visual Models
#### 3.1.1 Feature Vector Using Convolutional Neural Network (CNN)  
Different CNN models, such as VGGNet, ResNet, and Inception-V3, each with a different number of layers and filters but sharing the same fundamental architecture, can extract the image's features.  
**GoogLeNet.** The GoogLeNet [37] architecture employs various techniques, including global average pooling and \(1\times 1\) convolution, to build a deeper architecture. Its creators lowered the 60 million AlexNet-proposed parameters to just 4 million, comprising 22 layers. With a top-5 error rate of 6.67%, which was extremely similar to human performance, it took first place in ILSVRC. Inception-V3 [38] is a different GoogLeNet variant. Compared to its predecessors, it has 42 layers and a decreased error rate.  
**VGGNet.** [PERSON] and [PERSON] [39] proposed VGGNet in 2014 based on a Convolutional Neural Network architecture. The input to the VGGNet is a 224 \(\times\) 224 RGB image. The preprocessing layer calculates mean image values derived for the complete ImageNet training. The mean image values are subtracted from the RGB input image. Then, this input image goes to the convolution layers. Convolution layers are stacked and then applied to the training images. The VGG16 architecture has 13 convolutional layers and three fully connected layers. Another VGGNet version, VGG19, contains 19 weight layers, including the same five pooling layers and 16 convolutional layers with three fully connected layers each.  
**Residual Neural Network (ResNet).** On the ImageNet dataset, ResNet [40] took first place with a top-5 error rate of 3.57%, outperforming human performance. ResNet's new architecture is based on two key ideas: skip connections (gated recurrent units) and heavy batch normalization. These two methods made it possible to train the model with 152 layers, less complex than VGGNet [39]. Another popular ResNet variant is DenseNet [41], which makes additional connections to address the vanishing gradients problem.  
Figure 4: The general workflow of encoder-decoder image captioning mainly follows the following steps: visual model, visual encoding, and language model, and it is trained with different training strategies.
## 7 Future Directions
### Standardization and Regulation  
To successfully deploy THz communication systems, the unified standardization of frequency bands and policies across nations is necessary. There is a need for various stakeholders, researchers, and policymakers to embark on joint initiatives to develop framework specifications that would enhance compatibility across various frontiers to allow optimal utilization of the spectrum.
# Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting
## 7 Simulation Results and Analysis
### Average System Capacity under Time Delay Error CSI  
As shown in Figure 3, in nonideal CSI, the capacity of several typical algorithms is compared when configured as [2,2,4,13,8]. In time delay error CSI, the proposed algorithm has more room for handling interference and delay error, and minimizes the effects of interference and delay errors, further combining power allocation and phase alignment to improve signal quality. So, the proposed algorithm is better than the literature [17] MAX-SINR-SCEK algorithm. The literature [11] adopts MMSE and LSE high-order predicting channel, and although it can improve the channel quality under the delay error CSI to a great extent, the improvement is relatively limited and its performance is inferior to the proposed algorithm.  
Figure 2: The average system capacity comparison under the ideal CSI.
# Improved Design of Imaging System for Online Detection of Large-Sized Step-Shaft Runout Errors
## 2 Principle and Simulation Analysis
### Imaging Optical System Design
#### 2.2.3 Tolerance Analysis  
The wavelength of imaging optical system tolerance analysis is set to 658 nm, the surface tolerance module in the radius of curvature is set to an aperture of 2, thickness tolerance is set to 0.025 mm, lens surface X,Y eccentricity tolerance due to rotationally symmetrical structures is set to 0.008 mm, the same surface X,Y axis tilt is set to 0.0133 degrees, and the surface irregularity is set to an aperture of 0.25. Because the manufacturing process will affect the size of said difference, it is necessary to set the component tolerance of 0.008 X,Y eccentricity, 0.0133 degrees of the X,Y axis tilt. To ensure the ability of the lens to refract light for processing in accordance with the design of the optical focal length of  
Figure 10: Comparison of MTF and dot plot.  
the lens, lens refractive index tolerance is set to 0.0008, and Abbe tolerance is set to 0.08%. In other words, the Q4 grade tolerance standard is selected for tolerance analysis.  
After tolerance analysis of the optical system, the total reduction in MTF in the worst case is not more than 0.3, meaning that that the photodetector PSD receiving the target can still distinguish the resolution of the optical system. This imaging system adopts a sensitivity analysis method to carry out tolerance analysis. Using the geometric average transfer function of a 42 lp/mm optical system as the standard, 200 Monte Carlo samples are adopted, and the geometric average transfer function value obtained is 0.43520936. The analysis results are shown in Table 4.  
As can be seen from Table 4, the tolerance analysis results show that 90% of the mean geometric MTF transfer function is better than 0.33013253. According to the experience of optical manufacturing, 90% of the MTF results of the tolerance analysis are better than 0.15, and thus meet the design requirements. Therefore, the tolerance analysis results shown in this paper meet the design requirements.
## 3 Experimental Section
### Analysis of Bubble Effect on UOCC Link
#### 3.3.2 Influence Analysis of Different Bubble Size  
Secondly, to investigate the effect of different air bubble sizes, we captured two cases from the images taken with the CMOS camera. One case was that the bubble size was larger than the duration of bit \"1\", as shown in Figure 8a. Large bubbles obstruct the optical beam and cause a deep fade, which was reflected in the insert image as irregular shadow areas. These shadow pixels should not be selected for decoding, as it would lead to the misjudgment of \"1\" as \"0\". Nevertheless, due to the distortion of the bubbles under the UOCC link, a transmission zone was formed at its center. In addition, owing to the rolling shutter effect (RSE) of CMOS, in each row of the image, it recorded the same data signal of the LED. Therefore, bit \"1\" could still be recovered correctly as long as the appropriate area was selected (such as M region in Figure 8a). Another situation was that the bright fringe was wider than the bubble size (Figure 8b). As is observed, the level of fading also decreased because the optical signal was less susceptible to serious obstruction so more light reached the image sensor. This means that more available areas could be used for decoding.
## 3 Methods
### Navigation Risk Assessment Model
#### 3.2.3 Weighting the Criteria Using CRITIC
The final weight of each criterion is combined with the accident statistics (see Table 2) using Equation (14):\[\mathrm{w}_{z}=\frac{w_{z}^{c}\times Q_{z}}{\sum_{m=1}^{k}w_{m}^{c}\times Q_{m}}, \tag{14}\]  
where \(\mathrm{w}_{z}\) represents the final weight of criterion \(z\), and \(Q_{z}\) represents the normalized statistics of criterion \(z\), obtained from Table 2.  
The correlation coefficients of the five criteria are shown in Table 3. The high correlation coefficient between the wind and wave criteria indicates a certain correlation between wind and waves in accidents. The weak negative correlation between sea fog and wind reflects that it is not easy to generate sea fog in gale weather. The correlations between ocean currents with the other factors are small reflecting that the ocean currents are less affected by other environmental factors. The weighting results are shown in Table 4 in descending order: sea fog, waves, wind, depth, and currents. The navigational risk value is calculated through Equation (15):  
\[\mathrm{R}_{i}=\sum_{z=1}^{k}w_{z}\times x_{i,z}^{\prime}, \tag{15}\]  
where \(\mathrm{R}_{i}\) represents the navigational risk value in grid \(i\), and \(x_{i,z}^{\prime}\) represents the standardized value of criterion \(z\) in grid \(i\). The calculated navigational risk values are standardized using Equation (7). After standardization, the final navigational risk assessment values are obtained, which are divided into four levels: [0, 0.25), [0.25, 0.5), [0.5, 0.75), and [0.75, 1], representing \"safe\", \"a little safer\", \"a little dangerous\", and \"dangerous\", respectively.
# Reconstruction of Land and Marine Features by Seismic and Surface Geomorphology Techniques
## 4 Seismic Sedimentology
As part of the seismic sedimentology techniques, seismic geomorphology needs to be  
Figure 2: 3D view of dip angle (**right** images) and depth root-mean-square (RMS) amplitude slice from 3D seismic reflection data (**left** images) of (**a**) Type 1, (**b**) Type 2, (**c**) Type 3, and (**d**) Type 4 channel complexes in the Canterbury Basin, offshore of New Zealand [46]. Note that the black dashed line is the channel outline, while the red dashed line is the area of the contourie deposit. integrated (complementary) and evaluated with other seismic sedimentology techniques (i.e., the ninety-degree phasing of seismic data, seismic lithology, and seismic slicing) for maximum benefit on the extracting geology, geophysics, and reservoir information out of 3D seismic reflection data [24, 26, 43]. A study conducted by [PERSON] et al. [112] revealed that paleo-landscape geomorphological attributes can be extracted from seismic data acquired by MBES. By analyzing these data, the authors noticed that it was possible to identify the geometrical position of a beach rock located at 45 m of water depth (Figure 3).
# Application of Convolutional Neural Networks in Visual Feedback of Movable Camera Mounting Control
## 3 Results
### Optimal CNN Estimator  
CNN estimators, the architecture of which was described in Section 2.2, were trained for various learning parameters. The following variables were considered: the learning rate \(\alpha\in\{0.0001,0.0005,0.001,0.005\}\), the size of the batch \(\in\{32,64,128,216\}\), and different early stopping rules [31]. The most optimal results, in terms of high accuracy, low loss function value, and proper generalization throughout the learning sets, were obtained for the following set of parameters:  
* learning rate \(\alpha=0.0005\),
* batch size = 64,
* early stopping: learning was terminated when for 20 epochs, the accuracy of the development set did not increase by 0.5%.  
The values of the loss function and the accuracy achieved as a function of the epoch number are presented in Figure 7. In the case of both estimators, the early stopping rule was met before 60 epochs. No overfitting was observed, as the performance was similar for the training and development sets.  
The accuracy achieved from the development and training sets is combined with a test set in Table 5. Both estimators provided satisfactory results, as they correctly classified head orientation with high accuracy. The horizontal estimator acted better, and the reason is directly related to the range of head movements. Previous studies have shown that the normal functional range of motion of the neck during daily activities is higher in the horizontal than in the vertical direction [32]. The same conclusion can be drawn from the collected images. The accuracy of vertical CNN was reduced due to insignificant changes between the central and slight orientations in this plane of motion.  
Figure 7: The results for all learning sets for both estimators: horizontal and vertical. (**a**) Loss function (horizontal). (**b**) Loss function (vertical). (**c**) Accuracy (horizontal). (**d**) Accuracy (vertical).  
It is worth pointing out the accuracy gap between training and test sets. The main reason lies in a slightly different distribution of the data in the sets. The test set comprised web camera images that were of better quality (higher resolution, more information, less blurry) than the images gathered from volunteers. The images that were classified incorrectly in the training set by the estimators were mostly of the lowest resolution, and/or the person collecting the image did not follow the instructions of data acquisition precisely.
###### Abstract
Technicians and scientists who run the observatory and many other experiments must be instructed during the summer campaign, and they often have no prior experience with lasers or lidars. However, they are essential for running the system, acquiring data, cleaning the view window, and performing other tasks. During the last few years, lidar alignment and data acquisition have been performed remotely. This implies that the technicians can remain in their office to operate the observatory, without having to go outside at temperatures that can be as low as \(-70\)\({}^{\circ}\)C with bad visibility and strong winds to reach the observatory at a distance of 300-400 m. If necessary, the lidar can be remotely controlled from our institute in Italy. This paper has the following structure. In Section 2, the remotely controlled lidar system is described, followed by a description of the measurement protocols, the preprocessing of the raw data, and the methods to determine the detection and the classification of the PSCs. Subsequently, the results of the observations in 2021 are presented as an example of the PSC observations throughout the years 2014-2023. They will be discussed together with the coincident measurements of the satellite-borne CALIOP lidar. A short overview of the possible formation processes reported in the literature is presented, as well as the strong temperature dependence of PSC formation. The concluding remarks deal with the comparison of ground-based and satellite-borne instruments in general and the detection limits of both systems. Also, the seasonal variation in the observed PSCs will be discussed in relation to the local temperatures measured with radio sondes and integrated with NCEP data.
## 3 Results
### Binary Regression Analysis  
The procedure, focusing on the final attempts created during open play, modeled organized attacks as the response and treated counter-attacks as the reference category. The Omnibus test is statistically significant (likelihood ratio chi-square = 95.073, df = 13, \(p\) < 0.001), indicating that the model with the selected predictors is statistically superior to the null model. The Hosmer-Lemeshow test was not statistically significant (chi-square = 7.255, df = 8, \(p\) = 0.509), indicating that the model fits the data well, with no significant discrepancies. Additional metrics included [PERSON]'s Information Criterion (AIC) = 152.192, Cox-Snell R square = 0.409, and Nagelkerke R square = 0.553.  
Table 8 reveals several key findings. First, teams that are winning tend to make more final attempts through counter-attacks as opposed to organized attacks when compared to teams that are losing (\(p\) = 0.049, OR = 1/0.318 = 3.145). Second, the defensive, pre-defensive, and pre-offensive sectors demonstrated a greater propensity for organized attacks than the offensive sector (\(p\) = 0.002, OR = 22.068; \(p\) = 0.002, OR = 19.977; and \(p\) = 0.007, OR = 14.613, respectively). Third, the presence of initial pressure (compared to the absence of initial pressure) favors the creation of counter-attacks over organized attacks (\(p\) = 0.025, OR = 1/0.304 = 3.289). Finally, when a penetrative action occurred within the first three seconds of possession, there was an increased likelihood of observing counter-attacks compared to situations where a non-penetrative action took place (\(p\) < 0.001, OR = 1/0.031 = 32.258).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pair** & **Chi-Square** & **df** & \(p\) \\ \hline Defensive vs. pre-defensive & 0.225 & 1 & 0.635 \\ Defensive vs. pre-offensive & 0.038 & 1 & 0.845 \\ Defensive vs. offensive & 16.056 & 1 & \textless{}0.001 \\ Pre-defensive vs. pre-offensive & 0.45 & 1 & 0.503 \\ Pre-defensive vs. offensive & 19.753 & 1 & \textless{}0.001 \\ Pre-offensive vs. offensive & 14.629 & 1 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Post hoc tests for the indicator initial sector.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Type of Play** & **Cases** & **Mean Rank** & **Median** & **Mann–Whitney U** & **Z** & \(p\) \\ \hline \multirow{2}{*}{Attack duration} & Open play & 180 & 189.4 & 15.66 & \multirow{2}{*}{7217.5} & \multirow{2}{*}{\(-6.479\)} & \multirow{2}{*}{\(<\)0.001} \\  & Set play & 139 & 121.92 & 7.00 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Mann–Whitney U test for attack duration.