esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Radar-Based HAR with Deep Learning  
[PERSON] et al. [11] were the first to apply DCNN technology to radar-based human activity recognition. They used time-frequency images directly as input and successfully distinguished seven human activities with an accuracy of 90.9%, and human versus non-human targets with an accuracy of 97.6%. They treated the classification of time-frequency images as an image recognition problem, a method that has been widely adopted in subsequent research and become the mainstream approach today. When discussing CNN-based recognition methods, it is important to note that CNNs are particularly suitable for processing data rich in spatial features. However, radar data and human activity recognition typically involve time series analysis. Therefore, some researchers believe that models should be designed to leverage this temporal feature and handle radar time-series data. To address this, [PERSON] et al. [12] designed a segmented convolutional gated recurrent neural network. This structure computes implicit features from each time segment of the micro-Doppler images through convolution and then uses bidirectional GRUs to further extract features based on the current time step. This method can recognize and locate activities of varying durations, offering a finer temporal resolution and better generalization.  
RTMs and RDMs can provide additional perspectives for explaining human activities. By combining complementary information from multiple domains, a more comprehensive representation of activities can be captured, enhancing the model's ability to distinguish similar actions. [PERSON] et al. [13], based on a parallel CNN structure and SE attention mechanism, fused TDM and RTM spectrograms. Their experimental results demonstrated that multi-domain feature fusion can effectively improve the accuracy of radar-based human activity recognition. [PERSON] et al. [14], using MobileNet-V3, fused a RTM, TDM, and RDM at the data level, feature level, and decision level to complete their study on fall detection. Through experimental comparisons, they found that data fusion and decision fusion methods both outperformed single-domain approaches, with decision fusion achieving the best results under the same conditions.  
Transformers have demonstrated excellent performance in handling long-range dependencies in sequential data, which is crucial for human activity recognition tasks where actions are distributed over time. [PERSON] et al. (2018), based on a RTM, proposed a swin transformer encoder that combines cosine similarity attention and patch overlap to capture deep spatiotemporal features of human activity spectrograms. Through experimental comparisons, this method achieved a better performance than traditional CNNs and RNNs. Similarly, [PERSON] et al. (2018) first used parallel CNNs to extract features from the RTM and TDM separately, then fused these features and fed them into a swin transformer for classification, enabling comprehensive extraction and modeling of the spatiotemporal features of radar spectrograms.
# Farm Monitoring System with Drones and Optical Camera Communication
## 1 Introduction
The proposed system offers high-density sensing at a low cost due to the use of LED modules, which significantly reduces the cost of wireless communication. Thanks to the use of visible light, OCC does not require a license for operation, making it a cost-effective alternative to traditional radio-based communication systems. To efficiently collect densely distributed sensor data, we propose a trajectory control algorithm for the receiving drone. The proposed algorithm calculates the flight route and speed based on the locations and sizes of the light sources and the specifications of the receiver camera. The primary contribution of this paper is the development of a low-cost, high-density sensing system utilizing existing agricultural drones. The proposed system leverages OCC technology to enable efficient data collection from sensor nodes distributed across farmland. This system is particularly suitable for deployment in areas with limited infrastructure or radio communication constraints. We present preliminary experimental results from a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, demonstrating the feasibility of the proposed system. We implemented a system where drones automatically collect data from sensor nodes distributed across farmland. An Arduino UNO microcontroller was used to manage the sensor nodes, and a DJI Mavic 2 Pro drone served as the data receiver. The experimental results confirmed that OCC-based data transmission was successful, with minimal errors, allowing for effective collection of sensor data. The rest of this paper is organized as follows. Section 2 summarizes related research relevant to this study. In Section 3, we introduce the proposed system. We explain the overall flow of the proposed system and provide the flight route calculation method in the proposed algorithm. Then, Section 4 provides the performance of the proposed algorithm using computer simulations and the effectiveness of the algorithm. Experimental results are presented in Section 5. We implemented a program for the drone to move autonomously and evaluated the feasibility of the proposed system. Finally, Section 6 summarizes the conclusions of this paper.
# Calibration of a Rotary Encoder and a Polygon Using a Two-Autocollimator Method
## 3 Experiments
### Uncertainty Evaluation
The En-value for pitch angle deviations of the 24-sided polygon is shown as follows:  
\[E_{\text{n}}=\frac{\left|\beta_{\text{two-autocollimator}}-\beta_{\text{ shift-angle}}\right|}{\sqrt{U_{\text{two-autocollimator}}^{2}-U_{\text{shift-angle}}^{2}}} \tag{14}\]  
where \(\beta_{\text{two-autocollimator}}\) is a pitch angle deviation using the two-autocollimator method with the expanded uncertainty \(U_{\text{two-autocollimator}}\). \(\beta_{\text{shift-angle}}\) is a calibration result using the shift-angle-method, and \(U_{\text{shift-angle}}\) is the expanded uncertainty of the shift-angle-method. According to the reference paper [24], the expanded uncertainty of the shift-angle-method \(U_{\text{shift-angle}}\) is 0.15\". Figure 5 shows the different calibration results between the two-autocollimator method and the shift-angle-method. The maximum En-value is 0.58, which is less than 1. The En-value for angle errors of SelfA rotary encoder is shown as follows:  
\[E_{\text{n}}=\frac{\left|\alpha(\theta)-\mu\right|}{\sqrt{U_{\text{two-autocollimator}}^{2}-U_{\text{self- calibration}}^{2}}} \tag{15}\]  
where \(\alpha\) (\(\theta\)) is an angle error using the two-autocollimator method with the expanded uncertainty \(U_{\text{two-autocollimator}}\), \(\mu\) is a self-calibration result, and \(U_{\text{self-calibration}}\) is the expanded uncertainty of self-calibration. According to the reference paper [24], the expanded uncertainty of self-calibration \(U_{\text{self-calibration}}\) is 0.02\". Figure 7 shows the different calibration results between self-calibration and the two-autocollimator method. The maximum En-value is 0.59, which is less than 1. The En-values for the angle error and the pitch angle deviation mean that the proposed two-autocollimator method is reliable.
# Effect of Different Gradings of Lightweight Aggregates on the Properties of Concrete
## 3 Numerical Modeling and Characterization
### Probabilistic Characterization Method and CT Measurement
#### 3.2.2 X-ray CT Measurement  
Several approaches, such as scanning electron microscopy (SEM) and optical microscope (OM), can be used to identify the material characteristics. Although they have many advantages, these methods have critical limits to investigate the material characteristics and the inner structures without damagingthe samples. To overcome the limitations, X-ray micro-computed tomography (CT), a non-destructive method, is used here to investigate the inner structures of the LWC specimens. Using CT images, the spatial and size distributions of LWAs within the specimens can be investigated without destructing the samples.  
In this study, an X-ray radioscopy measurement tool developed to observe the material structure is utilized [46]. For more effective investigation of the LWA distribution in the specimen, an imaging processing for converting the original 8-bit (grayscale) to a binary (black and white) image is conducted, as shown in Figure 4. For the imaging, the image processing toolbox in MATLAB (R2016a) is used here. In this figure, Figure 4a is the original 8-bit CT image of the region of interest (ROI). The image in Figure 4a is composed of 1400 \(\times\) 1400 pixels with 27.5 \(\upmu\)m pixel size, which is sufficient to describe the LWAs. Each pixel in the 8-bit grayscale image is represented by a value between 0 and 255, where 0 is black and 255 is white. For the binarization, a proper threshold value should be selected to classify aggregate and matrix from the original CT image, and the threshold is chosen by using the Otsu method [47] as well as mix design. Here, a set of pixels smaller than the threshold value is considered as an aggregate with the white, while the black represents the matrix part of the specimen including pores, as shown in the binary image in Figure 4b. To classify each aggregate, the watershed segmentation algorithm [48] is adopted here as shown in Figure 4c. Then, the 3D image in Figure 4d is generated by stacking a series of binary images, and the characteristics of the LWAs in the specimens are examined numerically using the obtained 3D images.
## 4 Results and Discussion
### Visual Efflorescence
#### 4.3.2 Effect of Alkali Equivalent on Visual Efflorescence  
Figure 12 shows the visual efflorescence changes of RPSG with different alkali equivalents at 1 day, 3 days and 7 days. It can be seen from Figure 12 that after 1 day of immersion, the specimens RP-S30-M1.3N3 and RP-S30-M1.3N6 did not produce obvious crystalline substances, while the specimens RP-S30-M1.3N9, RP-S30-M1.3N12 and RP-S30-M1.3N15 have a little crystalline material on the surface. After 3 days of immersion, crystalline substances appeared on the surface and surrounding of all the specimens, and the most crystalline substances appeared in the specimens RP-S30-M1.3N3 and RP-S30-M1.3N15. After 7 days of immersion, visual efflorescence of the specimens was more obvious. The activity of RP and slag is weaker at a low alkali equivalent, and the specimen has smaller strength and larger capillary porosity (as shown in Figure 9), so the rate of efflorescence of specimen RP-S30-M1.3N3 is faster. However, the specimen RP-S30-M1.3N15 with a high alkali equivalent also had an obvious efflorescence phenomenon after immersion in water for 1 day, and the degree of efflorescence was high. The reason for this is that specimens with a high alkali equivalent are not stable and the crystallization pressure generated by the excess residual alkali in the pores causes cracks to form in the top and bottom of the specimen. This leads to rapid and severe efflorescence, which eventually leads to the rupture of the specimen. According to the mass loss results in Figure 12, it can be seen that the relationship between efflorescence and alkali equivalent is not an absolute positive correlation. At lower alkali equivalents, the degree of efflorescence decreases as the alkali equivalent increases. For the specimens with high alkali equivalents, the higher the alkali equivalent, the higher the degree of efflorescence. In this paper, the 6 wt.% alkali equivalent specimen has the least efflorescence. From the results of the analysis based on Figure 9, it can be seen that the capillary porosity of the specimens RP-S30-M1.3N6, RP-S30-M1.3N9 and RP-S30-M1.3N12 is basically the same, but the relationship between the degree of efflorescence is: RP-S30-M1.3N12 \(>\) RP-S30-M1.3N9 \(>\) RP-S30-M1.3N6. This is because the modified sodium silicate solution with high alkali equivalent has a higher concentration of alkali metal ions.
## 1 Introduction
### Parameters in the Reflection Asymmetric Case  
Scatterers with non-reflection symmetric structures are often characterized by the polarization helicity \(\tau\). Helix scattering often occurs at places with complex man-made structures where double-bounce scattering is usually strong. In general cases, the existence of target helicity can add the detection of urban man-made buildings. When assuming that the scatterer has a significant target helicity, the corresponding coherency matrix can be shown as follows:  
\[\mathbf{T}=\mathbf{Q}(2\theta)^{\rm T}\left[\begin{array}{ccc}t_{1}\cos^{2}\ 2\tau&t_{3}\cos\ 2\tau&\dot{ \underline{t}}_{1}\sin\ 4\tau\\ t_{3}^{*}\cos\ 2\tau&t_{2}&\dot{\underline{t}}_{3}^{*}\sin\ 2\tau\\ -\frac{t}{2}t_{1}^{*}\sin\ 4\tau&-\dot{\underline{t}}_{3}\sin\ 2\tau&t_{1} \sin^{2}\ 2\tau\\ \end{array}\right]\mathbf{Q}(2\theta), \tag{5}\]  
where \(t_{1}=\langle|h+v|^{2}\rangle/2\), \(t_{2}=\langle|h-v|^{2}\rangle/2\), and \(t_{3}=\langle(h+v)(h-v)^{*}\rangle/2\). The effect of target orientation can be compensated by the target de-orientation procedure [15]. We assume the coherency matrix to be with the position of \(0^{\circ}\) orientation angle. By considering the scattering asymmetric parameter \(\tau\), the two parameters \(\alpha_{\rm B}\) and \(\alpha_{\rm av}\) in (2) and (3) has the following forms, as shown in (6) and (7), respectively:  
\[\tan(\alpha_{\rm B})=\frac{T_{22}+T_{33}}{T_{11}}=\frac{\langle|h-v|^{2} \rangle}{\langle|h+v|^{2}\rangle}\frac{1}{\cos^{2}\ 2\tau}+\tan^{2}\ 2\tau \tag{6}\]  
\[\tan(\alpha_{\rm av})=\Biggl{(}\frac{|\rho_{r}(\tau)-1|^{2}}{|\rho_{r}(\tau)+ 1|^{2}}\Biggr{)} \tag{7}\]  
where:  
\[\rho_{r}(\tau)=\sqrt{\frac{\langle\left|S_{\rm VV}(\tau)\right|^{2}\rangle}{ \langle\left|S_{\rm HH}(\tau)\right|^{2}\rangle}}e^{j(angle(S_{\rm VV}(\tau)S_ {\rm HH}^{*}(\tau)))},\]\[\left\{\begin{array}{cc}S_{\text{VV}}(\tau)=-h\sin^{2}\ \tau+v\cos^{2}\ \tau\\ S_{\text{HH}}(\tau)=h\cos^{2}\ \tau-v\sin^{2}\ \tau\end{array}\right..\]  
Then, the difference between (6) and (7) can be used to analyse the effect of helicity \(\tau\) on the parameter \(\Delta\alpha_{\text{B}}\), as follows:  
\[\Delta\alpha_{\text{B}}=\alpha_{B}-\alpha_{\text{av}}=\text{atan}\Bigg{(} \frac{\langle|h-v|^{2}\rangle}{\langle|h+v|^{2}\rangle}\frac{1}{\cos^{2}\ 2\tau}+\tan^{2}\ 2\tau \Bigg{)}-\text{atan}\Bigg{(}\frac{|\rho_{r}(\tau)-1|^{2}}{|\rho_{r}(\tau)+1|^{2 }}\Bigg{)} \tag{8}\]  
We can observe that \(\Delta\alpha_{\text{B}}\) increases with \(\tau\). When backscatter is contributed by returns from helix type scatterers (reflection asymmetric scatterers), \(\Delta\alpha_{\text{B}}\) is much closer to zero than those from reflection symmetric scatterers which are with the property of \(\tau=0\). For the symmetric case where \(\tau=0\), when \(r\) decrease, \(\Delta\alpha_{\text{B}}\) approaches to \(-45^{\circ}\).
# Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks
## 3 Modeling and Performance Analysis  
To analyze the total cost (registration and paging cost) on radio channels, we defined the following notations:  
U: The location registration cost for one registration  
C\({}_{\text{U}}\): The location registration cost in an hour  
V: The paging cost for one cell  
C\({}_{\text{P}}\): The paging cost in an hour  
T\({}_{\text{c}}\): The interarrival time between two calls (r.v., T\({}_{\text{c}}\)\(\sim\) Exp[\(\lambda_{\text{c}}\)], E(T\({}_{\text{c}}\)) = 1/\(\lambda_{\text{c}}\))  
T\({}_{\text{m}}\): The time spent in a cell (r.v., E(T\({}_{\text{m}}\)) = 1/\(\lambda_{\text{m}}\))  
\(f_{m}^{*}(s)\): The Laplace-Stieltjes Transform for \(T_{m}\) (=\(\int_{I=0}^{\infty}e^{-st}f_{m}(t)dt\))  
R\({}_{\text{m}}\): The remaing time from the call occurrence to the time the UE moves out of the cell (r.v.)  
Adopting the additional nature of Poisson processes [24], we assumed that there are incoming calls with mean \(1/\lambda_{in}\) and outgoing calls with mean \(1/\lambda_{out}\) from total calls with mean \(1/\lambda_{c}=1/(\lambda_{in}+\lambda_{out})\).  
For each case of urban and rural areas, we define the following states to obtain the registration cost as shown in Figure 2 (urban area) and Figure 3 (rural area), respectively.  
State S\({}_{1}\): The state that UE is in the initial cell, where the user requires registration and forms new TAL. In an urban case, due to random walk assumption, the state S\({}_{1}\) changes to S\({}_{3}\) after the user moves to the center cell (cell 3). For the rural case, if the user moves to cell 2 or cell 3, the S\({}_{1}\) changes to S\({}_{4}\). On the other hand, when the user moves into outer cells which are not included in the same TAL, the state S\({}_{1}\) does not change and another registration is requested.  
State S\({}_{2}\): The state that UE is in central cells (cell 3 in urban area, and cell 2 or 3 in rural area) depicted as S\({}_{2}\).  
State S\({}_{3}\): The state that UE is in outer cells.  
State S\({}_{4}\): The state that UE is in two outer cells (cell 1 or 5) in the rural environment.  
State S\({}_{5}\): The state that UE is bottom and top cells (cell 7 or 9) in the rural case.  
State S\({}_{0}\): The state that a call occurs to/from the UE and the cell is changed to central cell. Note that state S\({}_{0}\) is related to implicit registration [4; 7; 25]. That is, when an incoming or outgoing call to/from the UE occurs, the UE's location can be updated without additional registration cost.  
Based on the states' definition, Markov-chain models can be obtained for urban and rural areas as shown in Figures 2 and 3, respectively.  
Figure 2: State transition diagram for an urban environment (small sector, \(60^{\circ}\)). (a) State of a small sector (\(60^{\circ}\)). (b) State transition diagram of a small sector.
## 1 Introduction
### Literature Review
In [24], wideband channel characteristics, for example, the channel gain coefficient, DS, and Ricean K factor at the 3-4 GHz and 38-40 GHz bands, were analyzed and modeled. In [25], the Volcano ray-tracing channel simulation was tuned and benchmarked against field measurements at two frequencies relevant to 5G industrial networks: 3.7 GHz and 28 GHz. Large-scale parameters, such as the PDP, and the CDF of the RMS DS were explored. In [26], the author performed simultaneous multi-band ultra-wideband dual-polarized double-directional measurements at the sub-6 GHz (6.75 GHz), mmWave (74.25 GHz), and THz (305.27 GHz) bands in a small machine room. In [27], channel measurements were conducted at 28 GHz, 38 GHz, 132 GHz, and 220 GHz in four industrial scenarios. The channel characteristics were extracted and modeled, including the path loss, K factor, RMS DS, and AS. However, the existing research on the comparison of multi-band channel characteristics in industrial scenarios only considers a few electronic wave bands, and there is no fair comparison of the channel characteristics covering the sub-6 GHz, mmWave, THz, and visible light bands in the same industrial scenario. The impact of frequencies on the channel characteristics of the industrial scenario is still unknown. To sum up, some studies have been conducted on the channel characteristics for industrial scenarios in various bands. However, the research on each band has only focused on a few specific frequency points, for example, 28 GHz and 142 GHz in the mmWave band and 300 GHz in the THz band. Moreover, there is a particular lack of research on industrial scenario channels in the visible light band. Furthermore, it can be found that some influencing factors could have a significant impact on the channel characteristics of the industrial scenario, such as the environment density and antenna height. Therefore, in order to accurately describe the impact of the frequency on the channel characteristics of industrial scenarios, it is necessary to conduct channel characteristic research covering the sub-6 GHz to visible light bands with a unified antenna height in the same industrial scenario.
# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 2 Types of Data
### Quantitative Data
#### 2.1.1 Normal Distribution  
Normal distribution is the most common type of distribution, assumed in nearly all types of statistical analyses and continuous populations. It is symmetric about the mean, suggesting that data closer to the mean are more frequent in occurrence than data far from the mean [34]. Normal distribution appears as a bell curve, and it is represented by two parameters: the mean and the standard deviation. Also, in normal distribution, 68% of observations lie within one standard deviation of the mean, 95% lie within two standard deviations of the mean, and 99.9% lie within three standard deviations from the mean. A very strong theorem based on normal distribution is the \"central limit theorem\", which states that under appropriate conditions, the distribution of a sample variable approximates a normal distribution as the sample size becomes larger [32]. However, how it can be known if a data set comes from a normal population? If the sample is reasonably large (\(n>30\) is a good rule of thumb), a histogram may give a good indication, peaking in the center and decreasing less or more along the sides; this is a good sign of normality [35]. For small samples, it is more difficult to tell whether the observations are normally distributed; however, statistical tests such as the Kolmogorov-Smirnov test and Shapiro-Wilk tests exist to test normality. The null hypothesis of the tests states that collected data are normally distributed, meaning that if the \(p\)-value of the test is not significant, the null hypothesis is accepted; thus the data are considered normally distributed. Some authors suggest that the Shapiro-Wilk test is more appropriate for small sample sizes, whereas the Kolmogorov-Smirnov test is used for large data sets [7].
## 2 System Model
Finally, the channel capacity of the \(\text{SS}_{i}\rightarrow\text{RIS}\rightarrow\text{SS}_{j}\) link can be formulated as  
\[C_{\text{SS}_{i}\text{SS}_{j}}=(1-\alpha)\log_{2}\Big{(}1+\gamma_{\text{SS}_{i }\text{SS}_{j}}\Big{)}. \tag{14}\]