esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## 3 Multi-Phase Adaptive Recoding
### Protocol Design
The intermediate node sends the BID-phase pair \((1,1)\) after knowing that the source node has finished sending phase 2 of the first batch. In the figure, one phase 1 packet of the first batch forwarded by the intermediate node is lost. After receiving the feedback from the destination node, the intermediate node sends a phase 2 packet of this batch and the systematic packets for BID 3 phase 1 are deferred by one timeslot.
## 3 Results
### Survival Outcomes  
Patients underwent restaging imaging 12 weeks after completion of treatment to measure their response to treatment. Thirty-five (70%) patients achieved a complete response (CR) to treatment, 13 (26%) patients had a partial response (PR), and two (4%) patients had progression of disease (POD)/persistent disease at the main center site. Thirty-seven (84%) patients achieved a CR, five (11%) patients had a PR, and two (5%) patients had POD/persistent disease at the satellite sites. The patients with POD/persistent disease were subsequently treated with pembrolizumab at the main cancer site, or further lines of chemotherapy (carboplatin/paclitaxel, carboplatin/gemcitabine, cetuximab) followed by nivolumab or pembrolizumab at the satellite sites.  
With a follow-up of 140 months (\(\sim\)12 years), median OS was not yet reached for both the main campus and the satellite sites (Figure 2A). Between both sites, there was no significant difference in OS (\(p=0.22\)) and PFS (\(p=0.88\)) (Figure 2A,B). No significant difference in two-year (47 [94%] vs. 37 [84%] patients, \(p=0.12\)) and five-year (46 [92%] vs. 37 [84%] patients, \(p=0.23\)) PFS was observed between the main campus and the satellite sites, respectively (Table 2).  
Figure 2: (**A**) Kaplan-Meier curve for overall survival by treatment site and (**B**) Kaplan-Meier curve for progression-free survival by treatment site.
# Effect of Planting _Portulaca oleraca_ L. on Improvement of Salt-Affected Soils
###### Abstract  
Saline-alkali land is a critical factor limiting agricultural production and ecological restoration. Utilizing salt-tolerant plants for bioremediation represents an environmentally friendly and sustainable approach to soil management. This study employed the highly salt-tolerant crop _Portulaca oleraca_ L. cv. \"Su Ma Chi Xiian 3\" as the test material. A plot experiment was established in coastal saline soils with planting _P. a- oleraca_ (P) and no planting (CK) under three blocks with the different salt levels (S1: 2.16 g/kg; S2: 4.08 g/kg; S3: 5.43 g/kg) to systematically evaluate its salt accumulation capacity and effects on soil physicochemical properties. The results demonstrated that _P. oleraca_ exhibited adaptability across all three salinity levels, with aboveground biomass following the trend PS2 > PS3 > PS1. The ash salt contents removed through harvesting were 1.29, 2.03, and 1.74 t/ha, respectively, in PS1, PS2, and PS3. Compared to no planting, a significant reduction in bulk density was observed in the 0-10 and 10-20 cm soil layers (\(p\) < 0.05). A significant increase in porosity by 9.72%, 16.29%, and 12.61% was found under PS1, PS2, and PS3, respectively, in the 0-10 cm soil layer. Soil salinity decreased by 34.20%, 50.23%, and 48.26%, in the 0-10 cm soil layer and by 14.43%, 32.30%, and 26.42% in the 10-20 cm soil layer under PSI, PS2, and PS3, respectively. The pH exhibited a significant reduction under the planting treatment in the 0-10 cm layer. A significant increase in organic matter content by 13.70%, 12.44%, and 13.55%, under PS1, PS2, and PS3, respectively, was observed in the 0-10 cm soil layer. The activities of invertase and urease were significantly enhanced in the 0-10 and 10-20 cm soil layers, and the activity of alkaline phosphatase also exhibited a significant increase in the 0-10 cm layer under the planting treatment. This study indicated that cultivating _P. oleraca_ could effectively facilitate the improvement of coastal saline soils by optimizing soil structure, reducing salinity, increasing organic matter, and activating the soil enzyme system, thereby providing theoretical and technical foundations for ecological restoration and sustainable agricultural utilization of saline-alkali lands.  
hologytic plants; phytoremediation; soil reclamation; saline-alkali land
# Combining Supervised and Unsupervised Fuzzy Learning Algorithms for Robust Diabetes Diagnosis
###### Abstract
It is found that the original 21 detectors on the highway have significant correlation among them and FCM based unsupervised learning component extends the mutual independence of the input of neural network (fuzzy clusters) so that the final prediction performance is revealed as better than any type of single neural network classifiers (neural network, recurrent neural network (RNN), and long short-term memory (LSTM)). In this paper, we propose a hierarchical combination method between fuzzy supervised and fuzzy unsupervised learning algorithm. For unsupervised learning component, we choose most popular standard FCM as other approaches, but we adopt fuzzy max-min neural network (FMM) [21] as the supervised classifier. FMM constructs an N-dimensional hyperbox defined by its Max and Min point. In classification, hyperbox fuzzy sets are aggregated to form a single fuzzy set class. Learning in FMM consists of creating and expanding/contracting hyperboxes in a pattern space. If well-designed, FMM has online learning ability in one-pass through the data samples, and can overcome overlapped classes, and having nonlinear separability [22]. To demonstrate the effect of such hierarchical combination of FCM and FMM, we chose the diabetes prediction problem, which has multiple open databases. Diabetes is a chronic metabolic disease due to a high level of sugar in the blood over a lengthy period. Although it has severe complications, early detection of the disease can reduce the risk factor and severity of the disease. However, the robust and accurate prediction of diabetes is challenging since there are significant outliers/missing values in the diabetes datasets with limited number of known labeled data [23].
###### Abstract  
Internal and external faults in a power transformer are discriminated in this paper using an algorithm based on a combination of a discrete wavelet transform (DWT) and a probabilistic neural network (PNN). DWT decomposes high-frequency fault components using the maximum coefficients of a 1/4 cycle DWT as input patterns for the training process in a decision algorithm. A division algorithm between a zero sequence of post-fault differential current waveforms and the differential current coefficient in the 1/4 cycle DWT is used to detect the maximum ratio and faults. The simulation system uses various study cases based on Thailand's electricity transmission and distribution systems. The simulation results demonstrated that the PNN and BPNN are effectively implemented and perform fault detection with satisfactory accuracy. However, the PNN method is most suitable for detecting internal and external faults, and the maximum coefficient algorithm is the most effective in detecting the fault. This study will be useful in differential protection for power transformers.  
Keywords:difference relay; fault detection; power transformer; wavelet transform (WT); probabilistic neural network (PNN) +
Footnote †: journal: Applied Sciences
# Smart Parking: A Literature Review from the Technological Perspective
###### Abstract  
The development and high growth of the Internet of Things (IoT) have improved quality of life and strengthened different areas in society. Many cities worldwide are looking forward to becoming smart. One of the most popular use cases in smart cities is the implementation of smart parking solutions, as they allow people to optimize time, reduce fuel consumption, and carbon dioxide emissions. Smart parking solutions have a defined architecture with particular components (sensors, communication protocols, and software solutions). Although there are only three components that compose a smart parking solution, it is important to mention that each component has many types that can be used in the deployment of these solutions. This paper identifies the most used types of every component and highlights usage trends in the established analysis period. It provides a complementary perspective and represents a very useful source of information. The scientific community could use this information to decide regarding the selection of types of components to implement a smart parking solution. For this purpose, herein we review several works related to smart parking solutions deployment. To achieve this goal, a semi-cyclic adaptation of the action research methodology combined with a systematic review is used to select papers related to the subject of study. The most relevant papers were reviewed to identify subcategories for each component; these classifications are presented in tables to mark the relevance of each paper accordingly. Trends of usage in terms of sensors, protocols and software solutions are analyzed and discussed in every section. In addition to the trends of usage, this paper determines a guide of complementary features from the type of components that should be considered when implementing a smart parking solution.  
s  
smart parking; sensors; LPWAN; networking; smart cities +
Footnote †: journal: Applied Sciences
## Appendix D Lower Bound on the Volume of the Hamming Ball  
**Lemma A1  
where \((a)\) holds, since we let  
\[\text{Res}(n)\triangleq\frac{e^{\lambda_{1}(n)-\lambda_{2}(\lfloor nk \rfloor)-\lambda_{2}\left(n\left(1-\frac{\lfloor nk\rfloor}{n}\right)\right)}}{ \sqrt{2\pi\lfloor nk\rfloor\left(1-\frac{\lfloor nk\rfloor}{n}\right)}}, \tag{100}\]  
and \((b)\) holds, since  
\[\frac{e^{\lfloor nk\rfloor}\cdot e^{n\left(1-\frac{\lfloor nk \rfloor}{n}\right)}}{e^{n}}=1. \tag{101}\]  
Next, we proceed to bound the Hamming ball as follows: Observe that the volume of Hamming ball as provided in (100) is lower bounded by the Binomial coefficient for the largest index, i.e., \(j=\lfloor n\varepsilon\rfloor\). Therefore,  
\[\text{Vol}\big{(}\mathcal{B}_{\aleph_{0}}(n,r)\big{)} \triangleq\sum_{j=0}^{\lfloor nk\rfloor}\binom{n}{j}(q-1)^{j}\] \[\geq\binom{n}{\lfloor n\varepsilon\rfloor}(q-1)^{\lfloor nk \rfloor}\] \[>\frac{(q-1)^{\lfloor nk\rfloor}}{\left(\frac{\lfloor n \varepsilon\rfloor}{n}\right)^{\lfloor nk\rfloor}\cdot\left(1-\frac{ \lfloor nk\rfloor}{n}\right)^{n\left(1-\frac{\lfloor nk\rfloor}{n}\right)}} \cdot\text{Res}(n) \tag{102}\] \[=q^{\log_{q}\left(\frac{(q-1)^{\lfloor nk\rfloor}}{\left(\frac{ \lfloor nk\rfloor}{n}\right)^{\lfloor nk\rfloor}\cdot\left(1-\frac{\lfloor nk \rfloor}{n}\right)^{n\left(1-\frac{\lfloor nk\rfloor}{n}\right)}}\right)+\log_ {q}\text{Res}(n)}\] \[=q^{\lfloor nk\rfloor\log_{q}(q-1)-\lfloor nk\rfloor\log_{q} \frac{\lfloor nx\rfloor}{n}-n\left(1-\frac{\lfloor nk\rfloor}{n}\right)\log_ {q}\left(1-\frac{\lfloor nk\rfloor}{n}\right)+\log_{q}\text{Res}(n)}\] \[=q^{n\left(\frac{\lfloor nk\rfloor}{n}\right)+\log_{q}\text{Res} (n)}.\]  
Now, by letting \(\lambda_{1}(n)=0\) and \(\lambda_{2}(n)=1/(12n)\), we obtain  
\[\text{Res}(n)=\frac{e^{-\frac{1}{12\lfloor nk\rfloor}-\frac{1}{ n-\lfloor nk\rfloor}}}{\sqrt{2\pi\lfloor nk\rfloor\left(1-\frac{\lfloor nk \rfloor}{n}\right)}}\stackrel{{(a)}}{{\leq}}\frac{e^{-\frac{1}{ 12\lfloor nk\rfloor}-\frac{1}{n-\lfloor nk\rfloor}}}{\sqrt{2\pi\lfloor n \varepsilon\rfloor\left(1-\varepsilon\right)}}\stackrel{{(b)}}{{ =}}K(\varepsilon)\lfloor n\varepsilon\rfloor^{-\frac{1}{12\lfloor nk \rfloor}-\frac{1}{n-\lfloor nk\rfloor}}, \tag{103}\]  
where \((a)\) follows for sufficiently large \(n\), since \(\lfloor n\varepsilon\rfloor\leq n\varepsilon\) and \((b)\) holds by setting \(K(\varepsilon)\triangleq\frac{1}{\sqrt{2\pi(1-\varepsilon)}}\). Therefore,  
\[\log_{q}\text{Res}(n)=\log_{q}K(\varepsilon)-\frac{1}{2}\log_{q} \lfloor n\varepsilon\rfloor-\frac{1}{12\lfloor n\varepsilon\rfloor}-\frac{1} {n-\lfloor n\varepsilon\rfloor}=o(\log_{q}n), \tag{104}\]  
which implies that  
\[\lim_{n\to\infty}\frac{\log_{q}\text{Res}(n)}{\log_{q}n}=0. \tag{105}\]Thereby,  
\[\text{Vol}\big{(}\mathcal{B}_{\mathbf{x}_{0}}(n,r)\big{)}\triangleq\sum_{j=0}^{ \lfloor nr\rfloor}\binom{n}{j}(q-1)^{j}\geq q^{nH_{q}\left(\frac{\lfloor nr \rfloor}{n}\right)+o(\log q\,n)}. \tag{100}\]  
\(\square\)
# Feasibility of LoRa for Smart Home Indoor Localization
## 3 Experiment and Evaluation
### Localization Performance  
Finally, we evaluate the performance of LoRa in estimating the location of tags in an indoor environment. We use the same data as the previous evaluation and run a trilateration algorithm on the range measurements using least square estimation (LSE) method similar with [71], solved by quasi-Newton BFGS method, with the following cost figure:  
\[\hat{T}=\underset{x,y}{argmin}\sum_{i}^{n}\Big{(}d_{i}(x,y)-d_{i}\Big{)}^{2}\]  
where \(\hat{d_{i}}\) is the measured distance with anchor \(i\) and \(d_{i}(x,y)\) is the parametric (with \(x\) and \(y\) coordinates) distance with anchor \(i\) as follows:  
\[d_{i}(x,y)=\sqrt{(x-x_{i})^{2}+(y-y_{i})^{2}} \tag{2}\]  
We run the algorithm for each 5 measurements from all anchors to estimate one tag location. From the 2000 measurements between each tag and anchor, we get 400 location estimates for each tag. And then, we calculate the Euclidean distance between the ground truth location and estimated location of each tag to get the localization error.  
Table 3 is the summary of localization error and CDF or localization errors in Figure 7. Tags 4 and 5 have errors below 1.6 m, while other tags (1, 2, and 3) have errors up to 3.2 m. The reason for the larger error of these tags is intuitive from the ranging performance results. Tag 1 has large ranging errors with Anchor 2, Tag 2 has large ranging errors with Anchors 2 and 3, and Tag 3 has large ranging errors with Anchor 2. On average, our localization error is better than 2.75 m, even in NLOS.  
One thing to note is that even though the localization error goes up to 3.2 m (lower accuracy), the precision is still very high. The standard deviation remains below 25 cm.
# Light-Controlled Microbots in Biomedical Application: A Review
## 4 Aspects of Design
### Manipulation of Microbots
#### 4.1.1 Optical Tweezers (OT)
\[F_{\mathrm{grad}}=\frac{2\pi\alpha}{cn_{m}^{2}}\
abla I_{0} \tag{2}\]  
where, \(\alpha=n_{m}^{2}a^{3}\left(\frac{m^{2}-1}{m^{2}+2}\right)\)\(\alpha\) is the polarizability of the sphere. The gradient force is proportional to the intensity gradient and points up the gradient when \(m>1\). However, when the wavelength of the laser is similar to the dimension of the particle (\(a=0.1-10\lambda\)), then neither of the two above-mentioned calculations are accepted, rather, a more complete theory is required to describe the forces. Most of the trapping laser beams use a wavelength of 1000 nm, as a result, many of the trapping particles of interest fall in this region (yeast, bacteria, or fabricated microbots at the micron scale). In this particular range, the theoretical calculations of the trapping force do not provide the physical intuition of optical trapping. To trap multiple particles in a single setup, a Spatial Light Modulator (SLM) is used to reflect the incident laser spot into a pattern before it enters the objective. SLM can create multiple patterns using Liquid Crystal on Silicon (LCoS) technology. In LCoS, multiple liquid crystal layers are sandwiched in between a cover glass/transparent ITO (Indium Tin Oxide) and an electrode layer. The electrode layer consists of multiple pixels and when a voltage is applied between the ITO and the electrode layer, the liquid crystal layers orient them accordingly to the direction of the electric field. This provides SLM the capability of creating a pattern only by reflecting light in the SLM panel [138]. Figure 5 shows the working principle of conventional optical tweezers along with the schematic and portable modular optical tweezers. Traditional OT systems employ an inverted microscope with a bulky design that is adequate for in vitro applications but lacks the practicality of in vivo considerations. A recent study has introduced all-fiber modular optical tweezers (AFMOTs), which can optically trap biological cells with reliability and freely move on a sample substrate [133]. They have successfully demonstrated the separation of the nontumorigenic breast epithelial cell line (MCF10A) from its cancerous PTEN mutants (MCF10 PTEN-/-). This technology has the potential to pave the way for a feasible and practical targeted drug delivery system in vivo. They even demonstrated the  
trapping of red blood cells in a living mouse. This takes the applicability of the actuation of small particles within the body using light one step further.
# Design and Analysis of a Planar Six-Bar Crank-Driven Leg Mechanism for Walking Robots
## 4 Kinematic Analysis
### Position Analysis  
The vector loop equation of the four-bar \(O_{2}ABO_{4}\) can be written as  
\[\overset{\rightarrow}{R_{2}}+\overset{\rightarrow}{R_{3}}-\overset{ \rightarrow}{R_{4}}-\overset{\rightarrow}{R_{1}}=0 \tag{1}\]  
Writing each of the above vectors in complex form leads to Equation (2):  
\[ae^{i\theta_{2}}+be^{i\theta_{3}}-be^{i\theta_{4}}-de^{i\theta_{1}}=0 \tag{2}\]  
The independent variable is \(\theta_{2}\) and it will be controlled with a motor. To solve Equation (2), we must substitute the Euler equivalents for the \(e^{i\theta}\) terms and then separate the resulting Cartesian-form vector equation into two scalar equations which can be solved simultaneously for \(\theta_{3}\) and \(\theta_{4}\).  
\[\theta_{3}=2\text{tan}^{-1}\left(\frac{-E+\sqrt{E^{2}-4 DF}}{2D}\right) \tag{3}\]  
\[\theta_{4}=2\text{tan}^{-1}\left(\frac{-B+\sqrt{B^{2}-4 AC}}{2A}\right) \tag{4}\]  
where  
\[A=-k_{1}+(1-k_{2})\text{cos}\,\theta_{2}+k_{3} \tag{5}\]  
\[B=-2\text{sin}\,\theta_{2} \tag{6}\]  
\[C=k_{1}-(k_{2}+1)\text{cos}\,\theta_{2}+k_{3} \tag{7}\]  
\[D=-k_{1}+(k_{4}+1)\text{cos}\,\theta_{2}+k_{5} \tag{8}\]  
Figure 4: Vector loops of the mechanism considered for synthesis.  
\[E=-2\sin\theta_{2} \tag{9}\]  
\[F=k_{1}+(k_{4}-1)\cos\theta_{2}+k_{5} \tag{10}\]  
\[k_{1}=\frac{d}{a},\quad k_{2}=\frac{d}{c},\quad k_{3}=\frac{a^{2}-b^{2}+c^{2}+d^{ 2}}{2 ac}, \tag{11}\]  
\[k_{4}=\frac{d}{b},\quad k_{5}=\frac{-a^{2}-b^{2}-d^{2}+c^{2}}{2 ab} \tag{12}\]  
Due to the geometric properties of the pantograph (\(O_{4}BCD\)), certain relationships between the angles of the links hold true. The angles \(\theta_{6}\) and \(\theta_{4}\) are equal because link 6 and link 4 are symmetrical within the pantograph structure. This symmetry ensures that both links maintain the same angle relative to the reference axis. Similarly, the angles \(\theta_{5}\) and \(\theta^{\prime}{}_{3}=\theta_{3}-\gamma\) are equal because the pantograph's geometry enforces mirrored movement, keeping these angles consistent with each other.  
\[\theta_{5}=\theta^{\prime}{}_{3}=\theta_{3}-\gamma \tag{13}\]  
\[\theta_{6}=\theta_{4} \tag{14}\]  
The vector position equation of the foot contact point P can be written in the form  
\[\overset{\rightarrow}{R}_{p}=O_{2}^{\rightarrow}A+\overset{\rightarrow}{ AC}-\overset{\rightarrow}{DC}+\overset{\rightarrow}{DP} \tag{15}\]  
By expanding the above expression and separating the real and imaginary parts, we can obtain the coordinates of the foot, \(P_{x}\) and \(P_{y}\):  
\[P_{x}=a\cos\theta_{2}+l\cos(\theta_{3}-\gamma)-c\cos\theta_{4}+f\cos\left( \theta_{4}+\beta\right) \tag{16}\]  
\[P_{y}=a\sin\theta_{2}+l\sin(\theta_{3}-\gamma)-c\sin\theta_{4}+f\sin\left( \theta_{4}+\beta\right) \tag{17}\]
# Hybrid Crow Search Algorithm-LSTM System for Enhanced Stock Price Forecasting
## 2 Materials and Methods
### Data Generation
#### 2.1.3 VMD (Variation Mode Decomposition)  
VMD, proposed by [PERSON] and [PERSON] (2013), is a signal decomposition method that decomposes the original signal \(f(t)\) into a set of modal functions \(u_{k}(t)\) and searches for the solutions of \(u_{k}\) and the corresponding set of frequencies of different modes (\(\omega_{k}\)). The original signal is decomposed into \(K\) components, where \(\{u_{k}\}=\{u_{1},\ldots\ldots,u_{k}\}\) is a set of modal functions; \(\{\omega_{k}\}=\{\omega_{1},\ldots\ldots,\omega_{k}\}\) is the set of corresponding frequencies of the different modes; \(\lambda\) is the Lagrange multiplier, and \(f(\omega)\) is the original signal \(f(t)\) in the frequency domain. The formula involves three hyperparameters: \(\alpha\) (Alpha): This parameter controls the bandwidth of the modes. A higher value of \(\alpha\) enforces more compact modes in the frequency domain, leading to smoother components. Conversely, a lower \(\alpha\) allows for broader modes, capturing more detailed variations in the signal. \(K\): This parameter determines the number of modes into which the signal is decomposed. Tolerance (\(\varepsilon\)): This parameter serves as a stopping criterion for the iterative optimization process. The values of \(u_{k}\) and \(\omega_{k}\) are updated as follows:  
1. Initialize \(\{u_{k}^{1}\}\), \(\{\omega_{k}^{1}\}\), \(\lambda^{1}\), and \(n\) as zero;
2. Step 2. Update \(u_{k}\) as follows: \[u_{k}^{n+1}(\omega)\leftarrow\ \frac{f(\omega)-\sum_{i<k}u_{i}^{n+1}(\omega)- \sum_{i>k}u_{i}^{n}(\omega)+\frac{\lambda^{n}(\omega)}{2}}{1+2\alpha\big{(} \omega-\omega_{k}^{n}\big{)}^{2}}.\] (1)
3. Update \(\omega_{k}\) as follows: \[\omega_{k}^{n+1}\leftarrow\ \frac{\int_{0}^{\infty}\omega\Big{|}u_{k}^{n+1}( \omega)\Big{|}^{2}d\omega}{\int_{0}^{\infty}\Big{|}u_{k}^{n+1}(\omega)\Big{|} ^{2}d\omega}.\] (2)4. Update \(\lambda\) as follows: \[\lambda^{n+1}(\omega)\leftarrow\lambda^{n}(\omega)+\tau\Bigg{(}f(\omega)-\sum_{k}u _{k}^{n+1}(\omega)\Bigg{)}.\] (3)
5. Unless \(\sum_{k}\frac{\left\|u_{k}^{n+1}-u_{k}^{n}\right\|\binom{2}{2}}{\left\|u_{k}^{ n}\right\|\binom{2}{2}}<\varepsilon\) is satisfied or the maximum number of iterations is reached, return to Step 2;
6. Output \(K\) modal functions. Many scholars have explored VMD for financial data prediction [24], ECG detection [25], stock trend prediction [26], and carbon price prediction [27]. Next, we provide an example to demonstrate the processed results, as shown in Figure 1. The hyperparameters used are as follows:  
* alpha = 1500;
* tau = 0.01;
* K = 4 (the signal is decomposed into four modes, with the residual representing the remaining value).  
Since this method cannot decompose the signal completely, the stock chosen for the example is 0050, a well-known ETF stock in Taiwan. The currency used is the New Taiwan Dollar (TWD).  
Figure 1: Decomposition of the Input Signal into Intrinsic Mode Functions (IMFs) and Residual.
## 2 Materials and Methods
### Near-Miss Incident Data  
The University of Agriculture and Technology, Japan, has collected near-miss incident data consisting of video recordings, vehicle travel speeds, accelerations, braking signals, and locations on a map [24; 25]. These data were obtained from drive recorders installed on taxis (ego vehicles) [24]. A drive recorder consisting of a camera and three-dimensionalaccelerometers was installed on the inner side of the front window, with the angle of the camera fixed in the forward direction. The camera captured the front view at the rate of 30 frames per second. The data collection is triggered by the sudden braking by a driver at >0.5 G deceleration; the recorder captures data 10 s before and 5 s after the trigger incident. In the current study, we focused on near-miss incidents where a right-turning vehicle traveling on the left side of the road equipped with a drive recorder (hereafter referred to as an ego vehicle) was about to collide with pedestrians. We studied 112 video data (from 2005 to 2019) of right-turning vehicle-pedestrian near-miss incidents captured by drive recorders.