esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Research Hotspots, Tendencies, and Orientations
#### 3.2.2 Content Analysis  
With the help of the Bibliometrix software's content analysis and word segmentation tools, a list of 271 noun phrases was identified, providing potential terms to describe research tendencies and orientations in the field of digital transportation. Following this, we screened the list using manual processing. To avoid omission due to word segmentation, 39 terms were summarized into three categories: unigram words, bigram words, and trigram words (consisting of one/two/three words, respectively). The content analysis using unigram, bigram, and trigram words illustrated in Table 8.  
Figure 6: Keyword Collaborations from 2015–2022 and the Evolution of TOP 20 Items.  
As shown in Table 8, unigram words analysis focuses on research objects (building, project, data), link processes (design, management), and development goals (digital, system). As for bigram words, they mainly focus on management links, including construction management, project management, and information management. The entire life cycle, smart city, and green development are all outlined as clear development goals, and AI technology is also a major research orientation. Finally, trigram words provide additional details on microscopic application scenarios and analysis objects, including active safety systems, ancillary building facilities, and indoor environmental quality.  
In general, transportation digitization research seems to be moving far from the applications of traditional technology, such as information theory (2017.8), construction (2019.1), and infrastructure (2019.8). It is more likely that researchers will integrate multiple technologies in order to build green, safe, and smart cities.
## 5 Experimental Results
### RQ3: Which Parameters Are Important for Improving SDP Performance?  
Figure 7 shows the correlation matrix between each variable and fitness value to answer RQ3. The correlation is displayed by color and size of circles at the intersection of the \(x\)-axis and \(y\)-axis indicating each variable. The color of a circle indicates positive correlation in blue, and negative correlation in red. The size of a circle indicates how good correlation is. The diagonal line has no meaning because it is related to the same variable. The following two facts were observed in particular in the matrix. The most important observation was that class_weight showed a positive correlation of about 0.4, and we could estimate that the class_weight was the one of influential parameter. On the other hand, as min_impurity_decrease showed the negative correlation of about 0.3. From this measurement, we excluded the variables min_impurity_decrease in our search space to improve performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CIR** & **CI** & **PrD** \\ \hline
**A1 (100:20)** & 0.28 & 0.55 & 0.83 \\ \hline
**A2 (100:40)** & 0.32 & 0.56 & 0.88 \\ \hline
**A3 (100:60)** & 0.39 & 0.53 & 0.92 \\ \hline
**A4 (100:80)** & 0.39 & 0.56 & 0.95 \\ \hline
**A5 (100:100)** & 0.39 & 0.56 & 0.95 \\ \hline
**B1 (100:20)** & 0.42 & 0.51 & 0.93 \\ \hline
**B2 (100:40)** & 0.43 & 0.51 & 0.94 \\ \hline
**B3 (100:60)** & 0.45 & 0.52 & 0.96 \\ \hline
**B4 (100:80)** & 0.46 & 0.53 & 0.99 \\ \hline
**B5 (100:100)** & 0.44 & 0.54 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Experimental results to answer RQ2.  
Figure 7: Correlation matrix analysis of parameters.  
Figure 8 plots the correlation between class weight and balance, which has positive correlation of 0.4. Figure 9 also plots the correlation between min_impurity_decrease and balance, which has negative correlation of 0.3. Both class_weight and min_impurity_decrease had weak correlation of less than 0.5.
# New Advances and Challenges of Fall Detection Systems: A Survey
## 3 Bird's Eye View of Fall Detection Research from 2014
### Evolution of Sensors
[13] put Kinect on-ceiling and let it face downwards to detect fall based on analyzing the raw depth data, which provided an automatic, privacy-preserving fall detection method for the indoor environment. Similar to using a ceiling-mounted 3D depth camera, [PERSON] et al. [21] analyzed the potential fall action by depth images. [PERSON] et al. [27] supposed that the available algorithms based on Kinect for fall detection are neither robust nor exhibit high sensitivity and specificity. They proposed an architecture to combine the accelerometer and Kinect. The accelerometer was used to indicate a potential fall, while Kinect was used to authenticate the eventual fall alert. Based on a similar idea, another work studies fall detection based on combining an accelerometer and Kinect [15]. It achieved good results according to different technical solutions. Third, the accelerometer firmly holds the second position after 2014. It also has an explicit change: accelerometer usually referred to a kind of specific device before 2014, while it mostly referred to the built-in component in the smart phone after 2014. Based on a smart phone and the third generation (3G) networks, [PERSON] et al. [19] proposed the fall accident detection and corresponding wide area rescue system. [PERSON] et al. [18] proposed a fall detection system based on smart phone. When detecting a fall event, the user location is tracked and notifications Short Message Service (SMS) and email are sent to a set of contacts. [PERSON] et al. [29] proposed a fall prediction system on smartphones. It could record users' gait datasets, and predict users' fall risk based on gait statuses. Although accelerators can only get motion information of part of the body, they are still often used in fall detection systems. It reflects that the researchers put ease of use in an important position. Furthermore, since the accelerometer is widely built in smartphone now, it can achieve fall detection and danger information alarm all in one device. The last important evolution with regard to the type of sensor used after 2014 is the adoption of Wi-Fi or radar as sensors to detect the fall. It does not use a new sensor but investigates fall detection from a brand new angle. The main idea of this kind of fall detection method is using wireless techniques to identify environment change and building the relationship between the wireless signal and human activities [12]. It not only allows users to perform daily activities naturally and continuously without wearing any devices on the body [30], but also protects users' privacy. This provides a new road leading to future.
# Improving Non-Autoregressive Machine Translation Using Sentence-Level Semantic Agreement
## 2 Background  
### Non-Autoregressive Translation  
By generating the target words in one shot, Non-Autoregressive Translation [4] is utilized to speed up the inference stage. The vanilla NAT model adapts a similar encoder-decoder framework to that used in autoregressive translation. Furthermore, the encoder used in the NAT model remains the same as the transformer [3]. Different from the autoregressive translation, the NAT model utilizes a bidirectional mask in the decoder, on which the non-autoregressive mechanism mainly relies. In addition, because the NAT model is unable to decide the length of a target sequence, as the autoregressive translation does effectively, we use a separate predictor to output the length.  
Using \(X=\{x_{1},x_{2},\ldots,x_{m}\}\) and \(Y=\{y_{1},y_{2},\ldots,y_{n}\}\) to denote the source and corresponding target sentences, the NAT model models the target sentence as an independent conditional probability:  
\[P(Y|X)=\prod_{t=1}^{N}p(y_{t}|X,\theta). \tag{1}\]  
In this way, the probability of a word at each position only depends on the representation of the source sequence, and the parameter \(\theta\) is learned with the cross-entropy by minimizing the negative log-likelihood as:  
\[\mathcal{L}(\theta)=-\sum_{t=1}^{T}\text{log}p(y_{t}|X;\theta). \tag{2}\]  
During inference, the word with the maximum probability at each position forms the final translation:  
\[\hat{y}_{t}=argmax(y_{t}|X;\theta). \tag{3}\]
## 2 Bimodal Information Collection System
### The Optimized Multispectral Sensing Module
Through extracting features from these selected specific bands in the blue band, green band, red band, red edge band, near-infrared band, the ability to specifically characterize the target will be strengthened, which would greatly facilitate the target identification. Based on the analysis above and market research, a multispectral sensor that meets our requirements above, the MS600 camera, was adopted in our study. It is composed of six single-band cameras. Each camera has its own optical path and photocarrier that records the data of the corresponding imaging band. Six spectral bands (shown in Table 1) could be acquired to form MS600 multispectral camera. Band selection should meet the requirements of large amounts of information and small correlation between bands. The camera was equipped with 1/3-inch sensor size, 1280 \(\times\) 960 pixels, global shutter, 1.5 s maximum capture rate, 7.5 cm ground pixel resolution at 120 m altitude, size \(77\times 72\times 47\) mm, weight 170 g, DLS (downwelling light sensor) and GPS module, each image can record GPS information in real-time. The image data collected by MS600 multispectral sensor were sets of DN values (remote sensing image pixel brightness value) of six bands, which were used to record the gray value of ground objects. Figure 4: The spectral relative reflectivity of green vegetation and green camouflage.
# A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin
## 2 Problem Formulation
Instead, it is a non-aligned irregular time series with non-aligned sampling times and non-fixed sampling intervals._  
Based on the above descriptions, the main objective of this article is as follows: Given a set of noisy, non-aligned and irregular observation sequences \(\left\{\big{(}\mathbf{y}_{n}^{irr},t_{n}\big{)}\right\}_{n=0}^{N}\) from a controlled system, we want to develop a VAE network model that can maximize the evidence lower bound (ELBO) given by  
\[\max_{\mathbf{W}_{cc},\mathbf{W}_{dc}}\mathbb{E}_{\mathbf{x}_{0}\sim p_{c}(\mathbf{x}_{0}) \left[\{\big{(}\mathbf{y}_{n}^{irr},t_{n}\big{)}\}_{n=0}^{N}\right]}\left[\log p_ {d}\Big{(}\hat{\mathbf{y}}_{0}^{irr},\cdots,\hat{\mathbf{y}}_{N}^{irr}\Big{|}\mathbf{x}_{0 }\Big{)}\right]-\mathrm{KL}\left[p_{e}\Big{(}\mathbf{x}_{0}\Big{|}\Big{\{}\Big{(} \mathbf{y}_{n}^{irr},t_{n}\Big{)}\Big{\}}_{n=0}^{N}\Big{)}\right]\left|\left|p(\mathbf{ x}_{0})\right|\right] \tag{2}\]  
to train the encoder network weights \(\mathbf{W}_{cc}\) and decoder network weights \(\mathbf{W}_{dc}\), as shown in Figure 1. \(\hat{\mathbf{y}}_{n}^{irr}\) is the estimated observation vector from the neural network model, corresponding to the original observation vector \(\mathbf{y}_{n}^{irr}\); \(p_{e}\) is the conditional distribution that the encoder network needs to approximate; \(p_{d}\) is the conditional distribution that the decoder needs to approximate; \(p(\mathbf{x}_{0})\) is the prior distribution. Once the optimal sequence reconstruction model is trained, it can predict observations at any desired time and produce the regular sequence needed \(\left\{(\mathbf{y}_{n}^{ir},t_{n}^{\prime})\right\}_{n=0}^{N^{\prime}}\) by the twin system. Here, \(\mathbf{y}_{n}^{\prime}\) is the estimated observation vector at desired time \(t_{n}^{\prime}\); \(N^{\prime}+1\) is the number of samples in the regular sequence. The next section will focus on the reconstruction method for noisy irregular sequences in controlled systems and propose a VAE network model based on PRN-NCDE to obtain regular sequences with higher fitting accuracy.
## 2 Related Work
### Calibration Methods
#### 2.4.2 AIT Pattern vs. openCV's Checkerboard  
As a first step in every calibration method, a calibration target is presented in front of a camera and images of this target are acquired with every camera.  
In order to facilitate better quality calibration with fewer acquisitions of the calibration target, the authors of [46] have developed a new central marker consisting of three asymmetric dots printed on an off-the-shelf high precision regular dot pattern calibration target (see Figure 2).  
The main advantage of a calibration target with such a central element is that its markers may fill the entire field of view of the camera as long as the central element is readable. Moreover, this central marker is designed to be robust and easy to recognize while providing information about image mirroring.  
In contrast, a typical checkerboard pattern is reflection symmetric, thus no information about mirroring is available. Furthermore, the openCV algorithm requires that the whole pattern is visible and that the user enters the dimensions of the board. In case of several cameras looking at the same scene such as the EBCA, it is particularly useful to have a pattern where only parts of the pattern need to be visible.  
AIT's pattern (see Figure 2) can been used in different sizes and for different resolutions. The central element (the three blue dots in Figure 2) should always be visible as well as one surrounding row of black dots, making 5 by 6 dots (as shown in the illustration) the smallest configuration for a stable detection. As a minimum requirement, the smaller dots should be resolved such that their diameter is at least 25 pixels wide, therefore the maximum number of black dots is only limited by the camera's number of pixels. The distance between two dots should be at least a single dot's diameter to allow for a stable distinction between neighboring dots.
## 2 Materials and Methods
### Raman Spectrum Denoising Module (RSDM)
Combining the relevant improvements mentioned above, the improved U-NET network proposed in this article can denoise spectral data very well. When the spectral data with noise is input into the network, the encoder compresses the features of the data, and then the decoder amplifies the features to the original size. During the encoding and decoding process, redundant noise information in the spectral data is eliminated, and at the same time, important features of the data are preserved.
## 5 Conclusions  
This study introduces the MJTAR protocol tailored for urban vehicular ad-hoc networks. MJTAR operates as a geographic routing protocol, leveraging 2-hop junction-based vehicle traffic density and curve metric distance to the destination. The protocol innovates by proposing an E-IFITS mechanism that estimates vehicle traffic conditions up to 2-hop junctions without dependence on fixed infrastructure. Additionally, it introduces the OMSS mechanism, employing an ant colony algorithm. Notably, MJTAR surpasses GSR and GyTAR in network performance, offering a probability-based algorithm to explore multiple junction paths with low latency and a high probability of packet delivery success.  
In future investigations, we aim to explore mechanisms for predicting vehicle traffic density using multi-junction-based machine-learning techniques. Furthermore, we intend to investigate forwarding node selection techniques that account for link quality to mitigate packet loss on roads between junctions. Finally, we plan to analyze the probability of packet collisions that may occur when multiple sources transmit packets to a destination in network simulations and study collision avoidance methods.  
Conceptualization, S.-W.L.; Methodology, S.-W.L.; Software, M.-A.K.; Formal analysis, K.-S.H.; Investigation, D.-K.K.; Conceptualization, H.C.; Supervision, H.C. All authors have read and agreed to the published version of the manuscript.  
[PERSON]: [PERSON], [PERSON] and [PERSON] were supported by LIG Nex1, and [PERSON] was was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(MOE)(2021 RIS-004).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare that this study received research funding from LIG Nex1, but there are no conflicts of interest.  
Figure 14: Bytes overhead.
# Discrimination of Aroma Characteristics for Cubeb Berries by Sensomics Approach with Chemometrics
## 3 Materials and Methods
### Hydro-Distillation (HD)  
All the air-dried cubeb berries samples (an amount of 100 g each) were subjected to hydro-distillation using a Clevenger-type apparatus to extract essential oil using the reported methods with some modifications [20; 32]. The Clevenger-type apparatus consisted of a 2000 mL glass flask, a vertical tube, a condenser, a measuring tube with stopcock, and a return tube. The return tube connected the bottom of the measuring tube to the vertical tube, which combined with the top of the condenser. The flask was filled with 1200 mL of distilled water and heated by an electric heating mantle. The extraction time was 4 h, after which no more essential oil was obtained. The vapor mixture of water-essential oil produced in the flask passed through the condenser and then the distillate was collected. The essential oil in the upper layer of the distillate was dried over anhydrous sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)) and stored at 4 \({}^{\circ}\)C until subsequent GC-MS analysis.
## 6 Design of an Intelligent Middleware Platform
### Virtual Device Layer  
This layer consists of virtual devices. A virtual device is a semantically and functionally enriched representation of a physical device. It uses web technology to provide a uniform interface to other devices and services. A block diagram of a virtual device is presented in Figure 3. A virtual device consists of front-end and back-end modules. The front-end module enables applications or services to access resources or services provided by a physical device via a standard web interface. The back-end module communicates with physical devices via device-specific protocols and mechanisms.  
A single virtual device may also represent multiple physical devices. For example, a virtual device may represent a sensor network that monitors a specific area and a smartphone that includes sensors. Virtual devices are divided into two categories: simple and container-based virtual devices.  
Simple virtual devices, such as virtual sensors or actuators, provide data collection or actuation services. A microservice can be used to implement a simple virtual device. A microservice or simple virtual device either runs on the physical device that it represents or on another physical device, such as an RPi or WiFi router. Figure 4 shows a Physical Sensor X is represented by a Virtual Sensor X hosted on a Raspberry PI computer. A microservice X is used to implement Virtual Sensor X.  
Figure 4: Implementation of a virtual device.  
Figure 3: Block diagram of a virtual device.  
A container-based virtual device represents a high-end physical device that provides task processing, data caching, or data storage services. A container-based virtual device hosts a container engine that executes containerized applications or microservices. Container technology [4] at the virtual device layer is used to address interoperability requirements. The architecture of a container-based physical device that also hosts containerized microservices is illustrated in Figure 5.
## 2 Conceptual Model and Test Method
### Scaling Theory  
To reasonably explore the influence of each parameter on the coupling cooling effect of ventilation and PTI, a scale model test platform was built. The heat exchange between the airflow and the high-temperature rock near the WF was studied in the coupled cooling experiments. Because of the large influence that pressure has on airflow, the Reynolds number and Archimedes number were primarily considered [8; 27]. When analyzing the heat transfer of airflow, the Prandtl number was almost constant [28; 29]. Furthermore, the impacts of thermal conductivity, buoyancy, inertia force, and convective heat transfer coefficient on the flow and temperature fields were taken into account [30]. Table 1 displays the key parameters used for the scaled model tests.
## 1 Introduction
### Aim of the Study  
The aim of this contribution is to propose an exportable method to determine the best time period/phenological phase for field-scale yield forecasting by S2. The method can be applied to any VI and crop type. In this case, we employed the NDVI [52; 53; 54], which is the most frequent index used for this purpose [55]. A wheat field in the Po River Valley (Northern Italy) is used as an example to show how the approach works. Fifteen variable-sized Max Value Composite (MVC) [56] periods, ranging from January to May, were composed and ranked according to their ability to explain the final yield. As a preliminary step, since the study area was located in a particularly humid region, an NDVI-based clear pixel procedure (NDVI-CPP) was also developed. This procedure leverages the difference in NDVI between cloud cover and vegetation to optimize cloud detection in Sentinel-2 images by fine-tuning the cloud probability (CP) mask threshold.
## 2 Measurement of Dynamic Stiffness  
As mentioned in Section 1, DS measurement techniques can be divided into two types, resonant and non-resonant. The latter is the one used in the present work and can be viewed as a subcase of the resonant method.  
The first approximation of the system of the resonant method is governed by [PERSON]'s second law as follows:  
\[F(t)=m\dot{x}(t)+c\dot{x}(t)+sx(t) \tag{4}\]  
where:  
* \(c\) is the damping rate of the spring, equivalent to the sample damping;
* \(s\) is the spring constant, and is equivalent to the sample stiffness;
* \(m\) is the mass, and corresponds to the mass of the loading plate;  
In the hypothesis of harmonic forces (\(F(t)=F_{0}\ e^{i\omega_{0}t}\)), the displacement \(x\) will be \(x(t)=x_{0}\ e^{i\omega_{0}t}\), and switching to the frequency domain, we can obtain the frequency response function of the system (_FRF_) \(H(\omega)\), which in this case corresponds to the compliance and is the reciprocal of the stiffness as follows:  
\[H(\omega)=\frac{x(\omega)}{F(\omega)}=\frac{1}{(s-m\omega^{2})+ic\omega} \tag{5}\]  
The test consists of the identification of the resonant angular frequency \(\omega_{r}\) of the system which depends on the sample stiffness and for which the compliance is at a maximum as follows:  
\[\omega_{r}\ =\ \sqrt{s/m} \tag{6}\]  
so that the stiffness \(k\) can be obtained by reversing the formula. However, the sample stiffness generally depends on frequency; thus, the obtained value of stiffness is valid only in the proximity of \(\omega_{r}\). Therefore, it is more correct to express stiffness as \(s(\omega_{r})\), making explicit the frequency dependency. This implies that, to correctly evaluate the stiffness of the sample in the entire frequency range of interest, multiple measurements must be performed by varying the load plate's mass \(m\).  
The non-resonant method can be seen as the resonant method in the limit of resonant frequency much higher than the frequency range of interest (5), \(\omega\to 0\) (or \(\omega\ll\omega_{r}\)) (\(m\ll\omega^{2}/s\)).  
In those conditions, the modulus of the DS can be expressed in terms of force and acceleration as follows:  
\[|s(\omega)|=\frac{|F(\omega)|}{|\bar{x}(\omega)|}\omega^{2} \tag{7}\]  
In contrast with the resonant method, there is no technical standard for non-resonant measurements to refer to, but the absence of the loading plate makes the method more practical and allows for it to also be applied to in situ pavements.