esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
### Traditional Models  
Crash data often exhibit overdispersion and zero-inflation, which pose significant challenges for traditional statistical models like the Poisson and negative binomial models. Overdispersion occurs when the variance of crash counts exceeds the mean, leading to model misspecification and biased risk factor estimates. Zero-inflation refers to the high incidence of zero-crash data, where many observed counts are zero, skewing the distribution and complicating the modeling process. These characteristics make it difficult to accurately model crash data using conventional methods. Overdispersion and zero-inflation are common issues in traffic crash data due to the sporadic nature of crashes at specific locations and times. The Poisson model, which assumes that the mean and variance of crash counts are equal, is often inadequate for such data. This inadequacy can lead to underestimated standard errors and misleading inferences [9]. To address overdispersion, the negative binomial model introduces an extra parameter to account for the variance exceeding the mean. However, this model still struggles with zero-inflation, where the frequency of zero counts is higher than what the model predicts [10].  
The hurdle model is another approach that is used to address zero-inflated data. It consists of two parts: a binary component to model the occurrence of zeros, and a count component to model the positive counts. The hurdle model has been successfully applied in various studies to analyze crash data with excess zeros [11; 12; 13; 14]. This model is particularly useful when the data generation process for zeros is different from that for positive counts, allowing for a more flexible and accurate representation of the data. Moreover, advanced statistical techniques such as generalized estimating equations (GEEs) and hierarchical Bayesian models have been employed to handle the complexities of crash data [15]. GEEs account for the correlation between observations in longitudinal data, making them suitable for repeated measures of crash frequency over time. Hierarchical Bayesian models, on the other hand, allow for the incorporation of prior information and hierarchical structure in the data, providing robust estimates even in the presence of overdispersion and zero-inflation [16]. The Poisson-gamma model, a variant of the negative binomial model, introduces a gamma-distributed random effect to account for extra-Poisson variability [17]. This model can handle overdispersion better than the standard negative binomial model but still may not fully address zero-inflation. Another advanced approach is the use of finite mixture models, which assume that the population is composed of several subpopulations, each with its own distribution of crash counts [18].
## 3 Band Structure Governed Wave Packet Dynamics
Such an initial state models a situation, where the initial WP is injected onto the graphene sheet from a localized source. Figure 4: Time evolution of the probability density of a wave packet on the graphene surface with- and without defect. The band structure of the perfect crystal is incorporated into the kinetic energy operator, hence the potential is everywhere zero, except in the defect region. (**a–c**) Without defect. (**d–f**) With defect. The defect is shown by a blue spot. (**g**) Spectral distribution of the initial wave packet in the Brillouin zone (absolute value) is shown by red circles in the \(K\) and \(K^{\prime}\) points, superimposed on the graphene band structure. The radius of the red circles is \(\Delta k/2\), the intensity of the red color is proportional to the spectral weight at each \(\bigg{(}\overset{\rightarrow}{k}_{x},\overset{\rightarrow}{k}_{y}\bigg{)}\) points. (**h**) Scattering pattern, absolute value of the difference of the WPs shown on (**c,f**). The size of the calculation window is 23.04 nm. Several important effects can be seen in the time evolution of the probability density of the WP. Firstly, in the case of the unperturbed graphene surface, we can see an anisotropic (hexagonal) spreading of the WP. This effect is related to the spatially localized initial wave function and is in good agreement with our earlier calculations [18], where the initial WP was injected onto the pristine graphene surface from an STM tip. More recently, this effect was studied also with TB-DFT calculations, where a model gold STM tip was used [31], as well as with [PERSON]'s function formalism in a dual-probe STM setup [32]. In each case, long-range oscillation occurs resulting in an increased conductance in the armchair directions of the pristine material due to the localized source of the electrons. Secondly, an atomic scale modulation is presented on the WP. This is surprising at first glance, because this calculation does not directly have the atomic lattice as input. The atomic structure is, however, implicitly represented by the graphene dispersion relation. Thirdly, when the WP hits the local potential, the scattered WP has a hexagonal symmetry (anisotropic)--as it is best seen on the difference image in Figure 4h, though the local potential is cylindrically symmetric (isotropic). This effect, which can be termed as a \"generalized Huygens' principle\", is caused by the anisotropic nature of the graphene dispersion relation. [PERSON] principle states that all points of a wave front may be regarded as new sources of wavelets that expand in every direction at a rate that depends on their velocities [33]. In the case of an isotropic medium, the wavelets have a spherical shape (circles in 2D). If the value of these velocities, however, depends on the angle of propagation, then the wave front does not remain circular, and we obtain an anisotropic wave propagation [34].
In the development of the power industry, cost control has always been a key factor restricting innovative reform. The above four-wire MC control strategy generally requires a commutation detection circuit, which increases the cost. In addition, in order to obtain high quality output waveform with low harmonic distortion and reduce high switching losses caused by the high switching frequency. Soft switching is an option to reduce switching losses [15]. However, the soft switching method is limited by the duty cycle and load type. An ideal alternative is hybrid pulse width modulation (HPWM) with no significant switching losses to achieve high quality output waveforms [16]. This paper proposes a four-wire hybrid pulse width modulation strategy that does not require a commutation detection circuit for the HFL \(3\times 4\) MC topology. The compensation strategy is adopted to realize the separation of the fourth bridge leg from coupling of the front three bridge leg, so as to achieve independent control of the fourth bridge leg. Under the balanced load condition the compensation for the neutral potential deviation is zero, the modulation wave of the fourth bridge leg does not exist, and only the front three bridge leg operates. When the load is unbalanced, the fourth bridge leg can immediately compensate the neutral point potential deviation caused by the unbalanced load, the front three and the fourth bridge leg jointly control the voltage output. This method enhances the compatibility of \(3\times 3\) MC and \(3\times 4\) MC control, makes rational used of power switch and reduces the cost increase caused by switch loss. After applying the compensation method, the fourth bridge leg modulation wave is obtained. At this time, the advantages of the HPWM strategy under the three-wire MC can be completely applied to the four-wire MC. The method is simple in control, the safe commutation problem of the bidirectional switch tube in the four-wire MC topology can be effectively solved without adding any auxiliary commutation detection circuit. It simplifies the complexity of the system and achieves real low cost and high performance. On the control function, the application of the compensation strategy also speeds up the adjustment of the system under load fluctuations, improve the dynamic response performance, reduce the harmonic distortion rate, and greatly improve the capacity of power grid with unbalanced load. In terms of safety, in addition to the realization of safe commutation, the high-frequency transformer structure in the topology not only has the function of changing the range of the output voltage, but also solves the electrical isolation between the input and output side so that the inverter and personal safety are guaranteed [17; 18]. In terms of topology, it also has excellent control performance, and has a compact and reliable structure [19]. Therefore, the proposed strategy makes the HFL three-phase four-leg MC have a good prospect in practicality.
## 5 Results
### Performance Analysis of Discrete Wavelet Features  
The performance of discrete wavelet features was evaluated with SVM and the 1D-CNN deep neural network with gradient descent. Different kernel functions and various types of SVM were experimented with, as depicted in Table 3 and Figure 10, respectively. From the table and Figure, it was clearly shown that LS_SVM with polynomial kernel function performs better than other kernel functions and SVM types with an accuracy, sensitivity, and specificity of 97.2%, 94.5%, and 94.5%, respectively. Table 4 shows the comparison of the performance of the deep learning network with the best SVM classifier, where the former outperforms the latter with 98.8%, 97.3%, and 97.7% for accuracy, sensitivity, and specificity, respectively.
###### Abstract  
Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.  
indoor localization; smartphone sensors; pedestrian dead reckoning (PDR); Wi-Fi indoor positioning; sensor fusion frameworks; Kalman filter; location fingerprinting; trilateration; received signal strength indication (RSSI) +
Footnote †: journal: Applied Sciences
# Design and Implementation of Industrial Accident Detection Model Based on YOLOv4
## 4 Evaluation and Consideration
### Time Measurement  
This study evaluates the moving object collision scenario video, as this is the most complex and time-consuming situation to determine. First, we want to calculate the response time based on this video. Thus, 18,000 frames are read from the test video data, and the time required to start and end the risk determination is calculated as an average value. The average value of the time required for risk determination (including the time required for the object detection process) is the delay time. Figure 13 shows the response and delay times required to start the risk determination for each frame as graphs.  
In Figure 13, the response and delay times are displayed in units of 1000 frames for all 18,000 frames. The average response time is 15.94 ms and the average delay time is 18.43 ms. The average delay time is higher because YOLOv4 incorporates the object detection time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Training Data Preprocessing Hardware** & **Model Training Hardware** \\ \hline CPU & Intel i7-10700 (2.9 GHz, Core x16) & AMD Ryzen 7 5800\(\times\) (3.8 GHz, Core x16) \\ RAM & 16 GB & 32 GB \\ GPU & Nvidia GeForce GTX1660 & Nvidia GeForce RTX3080 \\ Storage & SSD\(-\)250 GB, HDD\(-\)4 TB & SSD\(-\)500 GB, HDD\(-\)6 TB \\ OS & Ubuntu 20.04.1 LTS & Ubuntu 20.04.5 LTS \\ Library & Docker 20.10.0, CVAT, Apache-dev, Git, & CUDA 11.2, cuDNN 8.1.0, Python 3.8.12, Numpy 1.21.4, \\  & Python 3.8.2, OpenH264 codec, Django & Tensorflow 2.7.0, darknet YOLOv4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Hardware specifications of training data preprocessing system and model training system.  
Figure 13: Response time (**left**) and delay time (**right**).  
We use the same test video to measure the total processing time for the risk determination (i.e., the total time from the start until the time the information directly labeled by a person and video frame are displayed on the screen). Then, we measure the time required to process the JSON file storing the video frames and risk events. JSON used two types of files: 1000 files and 18,000 frames of test video. Figure 14 provides a graph of the total processing time.  
As shown in Figure 14, the total processing time is graphed in units of 100 JSON files and takes an average of 0.23 s. We measure the time taken to read and process the JSON file containing the risk event because the model can be retrained according to industrial site environment changes when being used in the industrial field. This is to check in advance whether a hazardous situation in the industrial site is correctly determined and whether this model is suitable for actual deployment.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 6 Analysis and Discussion  
### Maximum Ratio Transmission  
To achieve reliable communication with low latency, i.e., no retransmissions, a high SNR is desired. If one has spatial diversity in the form of several transmit and/or receive antennas, as we have in our case, then it is shown that to maximize the SNR at time \(n\), one should use the linear precoder presented in [33]. Collect all uplink snapshots in the matrix  
\[\mathbf{H}_{n}=\left[r_{n}^{(1,13)},\ldots,r_{n}^{(12,13)}\right],\quad\in\mathbb{ C}^{(N_{\text{f}}\times H_{\text{a}}-1)} \tag{10}\]  
then, using the \(H_{\text{a}}\) long column vector \(\mathbf{e}=[1,1,\ldots,1]^{\text{T}}\) consisting of only ones  
\[\mathbf{H}_{n}^{\text{MRT}}=\frac{(\mathbf{H}_{n}^{*}\odot\mathbf{H}_{n})\mathbf{e}}{\|( \mathbf{H}_{n}^{*}\odot\mathbf{H}_{n})\mathbf{e}\|},\quad\in\mathbb{C}^{(N_{\text{f}} \times 1)} \tag{11}\]  
where the noise is assumed to be white Gaussian and uncorrelated with the signal.  
In Figure 8, two representative plots show the channel hardening effect from using distributed antennas; there are no more really deep fading dips. In the _ref_ scenario, we achieved an average array gain of 13.8 dB. In the _loop_ scenario, the average array gain was 14.4 dB. When we averaged all subcarriers, the results were similar, as shown in Figure 9. In the loop scenario, there were still variations in the received power levels in the order of 10 dB, and essentially all antennas experienced NLoS conditions at time 20 s and 58 s. However, despite the challenging propagation conditions, fading levels were small and the received power levels reasonably large. The key takeaway from these results is that there is much to gain from distributing antennas to combat small-scale and large-scale fading which enables reliable communication in challenging environments.
###### Abstract  
The presented paper examines the deployment of a cost-effective biofeedback system that provides anxiety awareness during online examination activities. Human anxiety is classified by evaluating biosignals related to skin conductance, skin temperature, and heart rate. The first aim of this study is to check the presented system performance. Thus, we test the convergent validity of the system regarding self-report measures of anxiety. Moreover, the system is validated against a commercial tool of anxiety detection. Fifteen (15) postgraduate students took part in the relevant psychometric test. The convergent validity of the system is found to be satisfactory. The second aim of this study is to identify the participant's personality dimensions according to Technology Readiness Index (TRI) which affects their academic performance and their real-time anxiety, as provided by the biofeedback device, during academic examinations. Thirty-five (35) postgraduate students, who were taking examinations in the form of synchronous online tests in the classroom for one of their lessons, took part in this stage of the research. The examined relationships are presented via a path model showing mainly that insecurity causes academic performance to decline, which in turn has a significant negative effect with increasing anxiety.  
technology readiness; academic performance; biofeedback in learning; student state anxiety Article
# Performance Analysis of a Cognitive RIS-NOMA in Wireless Sensor Network
## 3 Performance Analysis
### Outage Probability Analysis
\right)\frac{K\Delta_{M}(-1)^{l+a}}{(m+a)\Omega_{\mathcal{SD}}} \int\limits_{\frac{\eta_{th}}{\eta_{\mathcal{S}}}}^{\infty}\left[\frac{1}{ \Gamma(k_{m})}\gamma\biggl{(}k_{m},\frac{\theta_{m}^{*}}{w_{m}\eta_{th}}y \biggr{)}\right]^{m+a}e^{-\frac{l+1}{lS\mathcal{D}}y}dy \tag{25}\]  
Moreover, the integral in (25) is difficult to solve in closed form. Therefore, by applying the Gaussian-Chebyshev quadrature method [49], \(I_{2}\) can be approximated as  
\[I_{2} \approx\frac{K\pi^{2}}{\Omega_{\mathcal{SD}}4I}\prod\limits_{l=0} ^{K-1}\sum\limits_{a=0}^{M-m}\left( \begin{array}{c}K-1\\ l\end{array} \right)\frac{\Delta_{M}(-1)^{l+a}}{(m+a)}\] \[\times\sum\limits_{c=1}^{I}\sqrt{1-\varphi_{c}^{2}}\sec^{2} \biggl{(}\frac{(\varphi_{c}+1)\pi}{4}\biggr{)}\biggl{[}\frac{1}{\Gamma(k_{m})} \gamma\biggl{(}k_{m},\frac{\theta_{m}^{*}\varpi_{c}}{w_{m}\eta_{th}}\biggr{)} \biggr{]}^{m+a}e^{-\frac{l+1}{lS\mathcal{D}}\varpi_{c}} \tag{26}\]  
where \(\varphi_{c}=\cos\left(\frac{2c-1}{2I}\pi\right)\) and \(\varpi_{c}=\tan\left(\frac{(\varphi_{c}+1)\pi}{4}\right)+\frac{\eta_{th}}{\eta_ {\mathcal{S}}}\). By substituting (24) and (26) into (21), (20) can be obtained. This proof is completed.
## 6 Conclusions  
Using near-coincidental SAR imagery and altimetric data over three different winters in the Beaufort Sea, we showed that small-scale changes of a few hundred meters in sea ice surface related to leads, large ridges or ice floes of different sea ice types are recognizable by concurrent changes of altimetric waveforms. SAR imagery is a great asset to unambiguously identify the cause of these changes and to better understand the waveform behavior. SAR images thereby also facilitate the identification of regions potentially biased by the influence of thin ice features in older and thicker ice. Reliable freeboard estimates are possible for MYI floes in the order of 10 km in along-track and a few kilometers to both sides of the altimeter track. Freeboard estimates could be locally expanded to areas of similar signatures in the SAR image. The combination of information from SAR imagery and radar altimetry therefore has the potential to complete the picture of the sea ice situation on a local scale. SAR imagery however cannot solve the challenges imposed on freeboard to thickness retrieval by the properties of the snow layer [26], but improve the discrimination of ice types necessary for the calculation.  
Retracking methods and selection procedures of valid waveforms for freeboard estimation have been optimized for hemispherical and basin-scale sea ice thickness retrieval [3; 26]. Conversion of the here presented findings to useful additions to sea ice information from SAR imagery would benefit from the development of methods for locally optimized retrackers and improved waveform interpolation to better identify kinks in the leading edge of waveforms.  
Conceptualization, W.A, C.H. and L.E.B.E.; methodology, W.A.; validation, W.A., C.H. and L.E.B.E.; formal analysis, W.A.; investigation, W.A.; writing-original draft preparation, W.A.; writing-review and editing, W.A., C.H. and L.E.B.E.; visualization, W.A and C.H.; supervision, C.H and L.E.B.E.; project administration, L.E.B.E.; funding acquisition, L.E.B.E.  
This work is supported by the Swedish National Space Agency (dnr 164/18).  
The authors would like to thank the European Commission's Copernicus Programme and the European Space Agency for providing the satellite data. The authors thank the three anonymous reviewers whose comments greatly improved the quality of this manuscript.  
The authors declare no conflict of interest.
###### Abstract  
_Background and Objectives_: The current study aimed to compare the extent of bone growth achieved after guided tissue regeneration (GTR) based on the location and nature of the initial periodontal lesion. _Materials and Methods_: The study sample included 80 patients who underwent GTR at Kyungpook National University Dental Hospital since 2012 and were radiologically followed up for at least 2 years after treatment. The patients were divided into four groups based on the location (maxilla and mandible) and nature (furcation involvement and intrabony defects) of the lesion. Differences in bone levels before and after treatment were analyzed. Additionally, a comparison of outcomes based on the type of bone graft material used was conducted. Statistical analysis included a comparison of groups using a _t_-test, assuming equal variance. _Results_: The proportion of bone growth observed in the maxillary fraction, mandibular, maxillary intrabony, and mandibular intrabony lesions was 26%, 22%, 31%, and 34%, respectively. No statistically significant differences in the amount of bone fill were observed between the groups. However, a significant positive correlation was found between the initial depth of the bone defect and the amount of bone fill observed after treatment. _Conclusions_: Contrary to previous evidence, the current study observed no significant differences in the amount of bone fill after GTR based on the location and nature of the lesion. Furthermore, the amount of bone regenerated was directly proportional to the initial depth of the alveolar bone defect.  
Keywords:periodontal regeneration; guided tissue regeneration; bone fill; periodontal intrabony defect; duration involvement; radiographic comparison +
Footnote †: journal: Medical Imaging