esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrete HMM for Visualizing Domiciliary Human Activity Perception and Comprehension
## 2 Human Activity Comprehension Framework
### Human Activity Comprehension Module  
For an autonomous AI home robot, visual comprehension of user activities is a preliminary step to prepare for interacting with the user. Therefore, in the proposed work, the robot vision automatically comprehends the user's activity through a prior training set of known poses. There are two advantages to this approach, which include letting the HAC module acquire a more compact representation of the sequences, and secondly, overcoming the challenges of speed variations associated with different user activities.  
To reach the goal of the HAC module, four factors are carefully inspected in this work. First, the human profile images from the depth of the RGB-D sensor yield information from a total of 25 skeleton joints, wherein the joints represent different poses in the frames; however, our findings show that the information from only 10 skeleton joints is adequate for pose detection, since not all joints are equally informative due to the intrinsic noise of the sensor and the peculiarities of the human body. Secondly, the relative positions among skeletal points are transformed from a Cartesian coordinate system into a spherical coordinate system. The relative positions are extracted by the differences between positions of joints, wherein the differences should not be influenced by the position and orientation of the sensor or height/weight variations in user images in the spherical system. Thirdly, the features are quantized into vectors, since the feature set in each frame can be regarded as an observation symbol, such that the sequential observation from each frame in a video can be treated as a type of pose in terms of an observation sequence, and then, saved as the specific pose in the codebook. Finally, human activity recognition is performed through discrete hidden Markov models (D-HMMs) built from the sequences of training poses. A discrete HMM is trained for each activity on the poses for extracting as the observed symbols. Next, each activity sequence is encoded as a sequence of pose vocabularies for learning the HMMs, and then, the input testing sequence is estimated by scoring the models. Figure 1 shows the proposed D-HMM-based HAC framework.  
Figure 1: The proposed discrete HMM human activity comprehension framework.
## 6 Simulations
### Simulation Settings
Ignoring the inter-UE correlation changes caused by the reduction in the number of UEs, we randomly select 3000 or fewer UEs from the training set as labeled data to simulate the limited labeled data scenario for indoor positioning. We assign a label of 0 to the remaining data, treating them as unlabeled data. The student and teacher models are trained with a fixed ratio of 1:1 for unlabeled and labeled data. To maintain the fixed ratio of labeled and unlabeled data, a two-stream batch sampler is used to obtain a training batch, and a batch size of 256 is used for each stream. In the augmentation process, we select 5% of labeled data samples to operate amplitude scaling or temporal shifting and set the maximum scaling size \(a\) as 3 while the maximum translation size \(b\) is set to 5. We should keep the perturbed data as a small portion of the labeled data to ensure that critical positioning features are not completely destroyed. It should be noted that the perturbation here should operate in \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Clutter density, height, size & 0.6, 6 m, 2 m \\ Bandwidth & 100 M \\ TX power of total base station(BS) & 24 dBm \\ BS antenna configuration & (\(M^{a}\), \(N^{a}\), \(P\), \(M_{g}^{a}\), \(N_{g}^{a}\)) = (4, 4, 2, 1, 1) \\ Antenna height of user equipment(UE) & 1.5 m \\ BS height & 8 m \\ Carrier frequency & 3.5 GHz \\ Subcarrier spacing & 30 kHz for 100 MHz \\ Spatial Consistency & No \\ Synchronization between BS and UE & Ideal \\ Penetration loss & 0 dB \\ Number of floors & 1 \\ UE mobility & 3 km/h \\ Min BS-UE distance (two dimensional) & 0 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings for generating datasets. the training steps of each mini-batch instead of scaling the amplitude for the overall data first. For the reason that a large learning rate is prone to causing non-convergence or a gradient explosion phenomenon in the simulation, we carefully select an optimizer and a small learning rate. For the parameter setting of AMT, we set the ramp-up length \(T_{max}\) to 80, which means that \(w(T/T_{max})\) linearly increases to 1 within 80 epochs. To prevent training from degrading, \(w(T/T_{max})\) is set to 0 when the epoch starts from 0. We set the maximum value of the unsupervised weight \(w_{max}\) to 70. In addition, the smoothing coefficient \(\alpha\) of EMA is fixed at 0.97. The value of \(\alpha\) is set to ensure that the teacher model keeps up with the rapidly evolving student model by promptly disregarding erroneous prior values of the student model. The performance of the teacher model is considered to be the final result. The detailed parameter settings of AMT are concluded in Table 2.  
In our simulation experiments, positioning accuracy and convergence are used as the performance metrics of the proposed AMT model. In particular, we adopt 90%, 80%, 67%, and 50% points of the Cumulative Distribution Function (CDF) of the positioning accuracy to illustrate the performance gain in accordance with the evaluation methodology in 3 GPP TR38.857.
## 3 Main Objective and Research Method
A simplified DP algorithm is used for the whole trip, the factors that ensure reaching the destination on time are found, and are then applied to the next overlap in the complete DP algorithm. The simplified DP algorithm is based on the idea of only generating new alternatives when the speed limit changes for any reason, so the effect of the curve of dimensionality is not severe and execution time is reduced. Thus, during the trip, using V2V or V2I communications, the vehicle may receive information about any changes of speed limits or congestion in certain downstream road stretches, and optimal speed profile recalculation can be made in real time, firstly without considering road slopes to obtain the weighting factors and, secondly considering that road vertical profile and every speed limit type.
# An Efficient and Reliable Algorithm for Wireless Sensor Network
## 6 Data Forwarding in Extended ERASCA Protocol (EERASCA)
### Mathematical Analysis of Overhead (AO)  
The following three steps are followed in EERASCA:  
Step 1: At the beginning, the data should be moved from the source towards the mesh, the source node then forwards the data to IRMR of \(k\) hop. In the case of multicasting, the number of nodes in the group should be \(n+1\), however, in unicasting it should be \(n\).  
Step 2: When the transmitted data are reached to the mesh member, it is immediately multicasted/unicasted towards the IRMR.  
Step 3: After multicasting/unicasting, the desired receiver receives the data. Suppose that the mesh IRMR is a fraction \(\mu\) and in the multicast group when there are n members. Hence, IRMR within the mesh is \(\mu\times IRMR\). Likewise, several receivers (\(n\)) are equally distributed in the IRMR, and \((1-\mu)\) is on the edge of IRMR. Hence, the transmission rates for packet forwarding within EERASCA are:\[\begin{split}\text{AO}=k+n+1\times(IRMR-1)+k\times(1-\mu)\ n+k\times \mu n\\ \text{AO}=k+n+1\times(\mu IIRMR-1)+kn-k\mu n+k\ \mu n\\ \text{AO}=k+n+1\times(\mu IRMR-1)+kn\end{split} \tag{5}\]
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 3 Results
### Seasonal Variations  
To characterize the \(N_{e}\) seasonal variations, we divided the monthly data into daytime, post-sunet (LST = 19 h), and nighttime bins, and plotted these binned data as a function of time and magnetic latitude. Figure 11 shows the time series of \(N_{e}\) at 60, 80, 100, and 120 km. To the first order, the seasonal variation of daytime \(N_{e}\) at these altitudes is driven by solar radiation as the \(\chi\) maximum moves around the equator with season. The midlatitude maximums near the solstice months are clearly seen in the time series, whereas the seasonal minimums at the midlatitudes appear to be quite different. The minimum in the SH winter is much lower than that in the northern winter. The seasonal asymmetry in \(N_{e}\) is extended to the nighttime time series where the \(\chi\) effect can still be seen. The nighttime time series provides a better view of the seasonal variation of polar \(N_{e}\). Similar to the post-sunet result, the polar \(N_{e}\) has a larger concentration in the summer month, as expected for a stronger field-aligned current from the sunlit hemisphere [71; 72].  
A strong semiannual variation is observed in the nighttime \(D\)-region \(N_{e}\) with peaks near the equinox months. Several studies have suggested that the semiannual variation in the \(D\)-region could be induced by the NO concentration modulated by dynamic transport processes in the mesosphere and lower thermosphere [73; 74]. In this case, the modulated NO concentration and the enhanced nighttime ionization in the \(D\)-region are correlated with a peak around the equinox. Consistent with the semiannual variation reported in these earlier studies, the COSMIC-1 observations seem to have two peaks in the tropics, but with a slightly higher concentration in October than that in March.  
Figure 11: Seasonal variations of daytime (**left**), post-sunet (**middle**), and nighttime (**right**) \(N_{e}\) (in \(10^{10}\) m\({}^{-3}\)) at 70, 90, and 110 km.
## 3 Results
### Effects on Abdominal Wall Fat Pad, Liver, and Uterus Histopathology-Histomorphometry  
There was a significant (\(p\) < 0.01) increase in abdominal wall fat pad thickness and the mean adipocyte diameter in the OVX control mice (235.86% and 185.02%), which was attributed to significant tissue accumulation and adipocyte hypertrophy, respectively. However, there were significant differences in abdominal wall fat thickness and the mean adipocyte diameter with 200 mg/kg of PCP (\(-\)46.11% and \(-\)46.04%), 200 mg/kg of WC (\(-\)46.61% and \(-\)47.78%), and 100 mg/kg of WC (\(-\)33.96% and \(-\)35.68%) administered to OVX mice against the OVX control mice, respectively (Figures 8 and S1).  
It was determined that the percentage of fat transfer and the average hepatocyte diameter (1437.10% and 76.49%) increased significantly (\(p\) < 0.01) in the OVX control mice, respectively, due to lipid accumulation and steatosis. However, OVX mice given 200 mg/kg of PCP and 200 and 100 mg/kg of WC showed a decrease of \(-\)55.07%, \(-\)54.36%, and \(-\)38.96% in fat area and \(-\)31.32%, \(-\)31.47%, and \(-\)22.37% in the mean hepatocyte diameter compared to the OVX control group, respectively (Figures 8 and S2).  
In the OVX control group, the total uterine thickness, mucosal thickness, and the percentage of uterine glands in the mucosa were decreased (\(p\) < 0.01) (\(-\)76.90%, \(-\)76.26%, \(-\)69.25%, and \(-\)78.53%, respectively), due to estrogen-depletion-related atrophic changes. However, significant (\(p\) < 0.01 or \(p\) < 0.05) increases in all these parameters with 200 mg/kg of PCP (58.99%, 46.81%, 105.18%, and 144.61%), 200 mg/kg of WC (56.34%, 51.64%, 113.57%, and 162.50%), and 100 mg/kg of WC (38.03%, 27.92%, 61.01%, and 98.39%) administered to OVX mice compared to the OVX control group, respectively, showing a dose-dependent pattern (Figures 8 and S3).
## 4 Discussion and Future Trend
In other words, the laser speed effect is negligible and no influence is found on the fatigue lifetime of Ti-6 Al-4V specimens. In addition, the HIP process consists of heat treatment of AM parts under high pressure. It can reduce the number of residual stresses as well as the size of defects [97]. Application of the HIP treatment to evaluate its influence on multiaxial fatigue behavior of Ti-6 Al-4V materials [98]. To achieve an HCF life estimate using the Finite cell method (FCM) with an implementation particularly tailored to operate on \(\mu\)-CT images to conduct numerical studies on the as-built geometry [99]. CDM is used to estimate fatigue life using randomly distributed subsurface hydrogen pores compared with a homogeneous model [100]. CAD methods may create varied geometric characteristics, however, most performance control research uses uniform TPMS structures [101]. TPMS tissue engineering scaffolds or implants are highly addressed in recent research works. TPMS scaffolds provide mechanical, mass transfer, and cell growth benefits. As said, TPMS has promise in other interdisciplinary fields. Few mechanical studies use graded TPMS [102]. Too much study focuses on TPMS mechanical performance. Different materials' compression capabilities are explored. More research is required on TPMS heat and mass transmission, particularly heterogeneous and multiscale TPMS [103]. The design of cell topological shapes is still insufficient, and most of the literature is still studying the existing structures. The performance of cell structure is directly related to lattice structure, so it is necessary to design cell topology with a special performance. In the topology optimization of lattice structures, most of the research still focused on the strut sizes. The single optimization is not conducive to the design of lattice structures. Only from point of view of optimizing struts, the lattice structures cannot meet the specific requirements [104]. Deep learning, as one machine learning-based approach, was suggested to be an excellent tool for forecasting fatigue behavior and studying the effects of post-treatmentson additively constructed materials when combined with pre-training methods such as stacked auto-encoder [105].
Namely, as said above, the number of images with \"empty\" rail tracks in the SMART dataset is much larger than the number of images with obstacles, which is understandable, bearing in mind that in the real-world operational environment during the data recording field tests, obstacles mainly appeared on level crossings and did not appear on large, secured areas. Furthermore, the viewed distances during the data recording field tests were limited by the topology of railway terrain. Because of this, there was a need to augment the SMART dataset, in particular to expand it with images of long-range distant obstacles. In order to overcome the problem of not having a sufficient amount of real-world data, in this paper, a novel method is proposed for generation of synthetic images to augment the SMART dataset and so enable re-training of the CenterNet object detector with the augmented dataset to improve its object detection performances. The proposed novel augmentation scheme is a simple and efficient object-level data augmentation (ODA) method that improves the CNN model generalization for the railway obstacle detection domain. In order to achieve a context- and scale-aware data augmentation, the proposed data augmentation scheme includes a DL-based rail track detection as an essential step. The presented rail track detection method is a novel method based on using a state-of-the-art bounding box DL-based object detection, that detects parts of the rail tracks. For this purpose, the rail tracks in training images were annotated with multiple bounding boxes specifying rail tracks as objects consisting of multiple parts. Dealing with bounding box-based annotations and detection, the proposed rail track detection method is more efficient and faster than recent instance segmentation-based methods [16]. Additionally, as a DL-based method, it overcomes the shortcomings of established methods for rail track detection that are based on extraction of \"hand-crafted\" features, using traditional computer vision techniques [17]. Although focused on object-level data augmentation in the railway context, the proposed method can be used in any application where estimation of the object's distance is crucial for safety, such as autonomous driving [18]. The rest of the paper is organized as follows. In Section 2, an overview of related work focusing on generation of synthetic images and their use in the field of autonomous obstacle detection in railways is given. In Section 3, the proposed system for creation of synthetic images for training of a CNN model for obstacle detection in railways is presented. The experimental results are given in Section 4. The concluding remarks are given in the last section.
###### Abstract
Therefore, this is a critical aspect for consideration by farmers and industries interested in seed processing and commercial seed storage [30]. Furthermore, primed rice seed deterioration has been correlated with hampered starch metabolism or depletion of energy reserves [30; 31; 32], impaired antioxidative systems [33], and damaged cell structures [34], which destabilize selective permeability during storage. In addition, [PERSON] [30] suggested that primed rice seeds were more sensitive to high relative humidity and elevated temperatures, environmental factors that directly influence seed deterioration, than non-primed seeds [35]. Moreover, one of the limitations of most studies is that experiments are commonly performed on artificially aged seeds using short-term exposure to elevated temperature and humidity, which may fall short of a real aging process [22]. Several studies on rice have indicated variation in seed longevity between lines [36; 37] and varieties [10] and that priming efficiency is influenced by genetic diversity [38]. The aging of seeds leads to the loss of viability and a decrease in resistance to stresses during storage [7], including between sowing and germination in the soil [39]. In these conditions, seeds are subjected to stresses, including humidity and temperature fluctuations, which could affect the seed persistence and accelerate the deterioration of the seed [30] or result in seed death [39]. There is a need to understand the storability of primed seed for cultivar NERICA 4, one of the newly adopted varieties cultivated under the common farming conditions in Sub-Saharan Africa due to its superior comparability to local landraces in tolerance to harsh climatic conditions, tillering ability, and yield [2; 40]. The objective of the present study was to assess the vigor and longevity of rice cv. NERICA 4 primed seeds during short-term storage (120 days) in ambient conditions simulating the tropics and sub-tropics. Upscaling studies on understanding the longevity of primed seeds under storage would help in expanding their application under field conditions [41] and improve the predictability of primed seeds in storage.
## 4 Methodology
### Validation Methods  
The Root Mean Square Error (RMSE) is a widely used accuracy metric in regression tasks to evaluate the difference between the values predicted by a model and the actual values. To calculate the RMSE, first, the difference between the predicted and actual value for each data point is calculated, and then each difference is squared to prevent the cancellation of negative and positive errors. Subsequently, these squared values are averaged, and finally, the square root of the average is taken to adjust the errors to the original scale of the data. This metric is especially useful because it gives greater weight to larger errors, which is crucial in many practical contexts where large errors are particularly undesirable. The RMSE was calculated as shown in Equation (6).  
\[RMSE=\sqrt{\frac{1}{T}\sum_{t=1}^{n}(x_{t}-\hat{x}_{t})^{2}} \tag{6}\]  
where \(n\) is the total number of observations, \(x_{t}\) represents the actual values, and \(\hat{x}_{t}\) represents the predicted values.
# Fault Diagnosis of Rolling Bearings Based on Acoustic Signals in Strong Noise Environments
## 4 Simulation Verification Analysis
### Early Failure of Rolling Elements  
The sound signal of the early weak fault of the bearing rolling element was selected for analysis, as shown in Figure 38, and the noise reduction process was performed. The SBOA and CPO convergence curves obtained based on the minimum envelope entropy value are shown in Figures 39 and 40. After 26 iterations of the SBOA, FMD reached the optimal combination. The filter length \(L\) is 120, the frequency band cutting number \(K\) is 7, the mode number \(n\) is 7, and the cycle period \(m\) is 15. CPO was iterated 15 times, and VMD reached the optimal combination. The number of decomposition modes \(K\) is 7, and the penalty factor \(\alpha\) is 9697. The ISEI values of each mode are shown in Table 5.  
For further comparative analysis, the proposed methods S-FMD, C-VMD, EMD, CEEMD, and fixed-parameter FMD were used to perform noise reduction and decomposition on the simulated inner raceway fault signal and the measured sound signal of the inner raceway, outer raceway, and rolling element faults, and their ISEI values were selected respectively. The largest component was compared. The ISEI value is a comprehensive index obtained by merging kurtosis and the harmonic signal-to-noise ratio. Its size represents the degree of noise reduction effect of the bearing sound signal and the ratio of the bearing's fault characteristic frequency to the background noise energy. Different The ISEI values of the optimal components obtained by this method are shown in Figure 43.  
Different methods were applied for noise reduction to simulated inner raceway faults, measured inner raceway faults, measured outer raceway faults, and rolling element raceway faults. The component with the most fault-relevant information was then selected to calculate its ISEI. As illustrated in Figure 43, the ISEI value of the optimal component decomposed using the method proposed in this article is significantly higher than that of the best components obtained through the four methods: C-VMD, EMD, CEEMD,  
Figure 42: Envelope spectrum of V-IMF2 decomposed via parameter-optimized VMD (rolling element fault).  
Figure 43: ISEI value of the optimal component obtained by different methods.  
and fixed-parameter FMD. This finding demonstrates that the proposed method exhibits superior noise reduction and fault extraction capabilities in practical applications, as well as enhanced fault identification abilities, particularly in diagnosing weak faults.
# Feasibility Test and Analysis for Heavy Haul Freight Using WDPS Based on MIMO-OFDM
## 3 Measurement and Test
### WDPS Performance Measurement and Analysis
\begin{table}
\begin{tabular}{c l c c} \hline \hline  & \multicolumn{3}{c}{**Results**} \\ \cline{3-4}  & **Test Item** & \
\begin{tabular}{c} **Brake Equivalent** \\ **Response Time** \\ \end{tabular} &
\begin{tabular}{c} **Brake Release Times** \\ **(Measured)** \\ \end{tabular} \\ \hline \multirow{2}{*}{FSB} & Centralized control & 23.6 s & 25.4 s at 30 th freight \\  & Distributed control & 7.0 s & 8.8 s at 20 th freight \\ \hline \multirow{2}{*}{EB} & Centralized control & 5.9 s & 44.2 s at 30 th freight \\  & Distributed control & 3.6 s & 18.4 s at 20 th freight \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Braking test results. It was confirmed that even in the same tunnel section, there were many differences in performance depending on whether LOS was guaranteed. Based on the above analysis, it was confirmed that the communication environment (curve, gradient, etc.) is a major factor affecting WDPS performance for distributed control of heavy haul freight. Through such experiments, in order to ensure stable operation of heavy haul freight in the future, considering the topography of track, placement of transceivers and plans to improve antenna gains will be further studied.