esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Implementation
### Validation of the Green Water Loading  
[PERSON] et al. [45] conducted research on the green-water loads of various regular waves using a fixed FPSO model in a wave tank at the University of Sao Paulo. The experimental investigation was conducted in the wave flume of the Department of Naval Architecture and Ocean Engineering, University of Sao Paulo. As shown in Figure 3, the FPSO model employed in the experiment had a slightly inclined deck to facilitate water drainage after green-water events. A wave probe (WM01) was located on the upstream surface of the deck; the load cell used for measuring the impact force was located on a 20 mm \(\times\) 20 mm plate. The regular wave height was 0.088 m, the wave period was 0.97 s, and the wavelength was 1.46 m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Designation** & **Scale** \\ \hline Length (m) & 3.984 \(\pm\) 0.001 \\ Breakth (m) & 0.572 \(\pm\) 0.001 \\ Draft (m) & 0.163 \(\pm\) 0.001 \\ Displacement (kg) & 245.155 \\ Block coefficient (-) & 0.661 \\ Froude number (-) & 0.139 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main particulars of the model.  
Figure 2: Comparisons of heave and pitch time histories from present method and the method of [PERSON] et al. [43]: **(a)** Heave; **(b)** pitch.  
Figure 4 illustrates the comparison between the water elevations detected by WM01 and impact forces among the results obtained from the present solver, and the experimental data by [PERSON] et al. [45]. They noted that CFD simulations tend to slightly overestimate the maximum value, which could result from monitoring errors during testing, but such results remain satisfactory. These findings indicate that the present simulation methodology is highly effective in accurately simulating green-water events and loads.
## A MISO UCA Beamforming Dimmable LED System for Indoor Positioning  
**[PERSON] 1,*, [PERSON] 1 and [PERSON] 2**  
1 Wireless Information Security and Eco-Electronics Research Unit/National Electronics and Computer Technology Center, Bangkok 12120, Thailand;  
E-Mail: [EMAIL_ADDRESS]  
2 Radio Frequency (RF) Communications, Purdue University, Fort Wayne, IN 46805, USA;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed;  
E-Mail: [EMAIL_ADDRESS];  
Tel.: +66-256-467-00 (ext. 2550) or +66-886-288-820.  
_Received: 22 October 2013; in revised form: 18 December 2013 / Accepted: 10 January 2014 / Published: 29 January 2014_  
**Abstract:** The use of a multiple input single output (MISO) transmit beamforming system using dimmable light emitting arrays (LEAs) in the form of a uniform circular array (UCA) of transmitters is proposed in this paper. With this technique, visible light communications between a transmitter and a receiver (LED reader) can be achieved with excellent performance and the receiver's position can be estimated. A hexagonal lattice alignment of LED transmitters is deployed to reduce the coverage holes and the areas of overlapping radiation. As a result, the accuracy of the position estimation is better than when using a typical rectangular grid alignment. The dimming control is done with pulse width modulation (PWM) to obtain an optimal closed loop beamforming and minimum energy consumption with acceptable lighting.  
**Keywords:** indoor location awareness; optical wireless communications; adaptive beamforming
# Impact of One Additional Substitution on Player Load and Coaching Tactics in Elite Football
## 3 Results
### Coaches Survey  
The results of the questionnaire showed that coaches considered the current contingent of three substitutions in adult football too few (\(M_{\text{Q}3}=2.270\), \(SD=0.838\)) with a large effect size, \(t(36)=-5.295\), \(p<0.001\), \(d=-1.765\).  
Moreover, the coaches assessed an additional substitution in regular time as beneficial with regard to tactical influence of coaches on the match (\(M_{\text{Q}4.1}=4.297\), \(SD=1.664\)), \(t(36)=2.914\), \(p=0.006\), \(d=0.971\), load reduction during a match (\(M_{\text{Q}4.2}=4.216\), \(SD=1.475\)), \(t(36)=2.955\), \(p=0.005\), \(d=0.985\), load reduction during a season (\(M_{\text{Q}4.3}=4.00\), \(SD=1.580\)), \(t(36)=2.235\), \(p=0.032\), \(d=0.745\), and overall (\(M_{\text{Q}5}=3.757\), \(SD=1.480\)), \(t(36)=2.221\), \(p=0.033\), \(d=0.740\) (Figure 1).  
Coaches determined the additional substitution in extra time as beneficial regarding the aspects of tactical influence of coaches on the match (\(M_{\text{Q}6.1}=4.595\), \(SD=1.572\)), \(t(36)=4.237\), \(p<0.001\), \(d=1.412\), load reduction in one match (\(M_{\text{Q}6.2}=4.757\), \(SD=1.321\)), \(t(36)=5.787\), \(p<0.001\), \(d=1.929\) and overall (\(M_{\text{Q}7}=3.973\), \(SD=1.364\)), \(t(36)=2.109\), \(p=0.042\), \(d=0.703\). In contrast, regarding load reduction in one season (\(\text{M}_{\text{Q}6.3}=3.432\), \(SD=1.686\)), the additional substitution in extra time was judged as non-beneficial by the coaches, \(t(36)=-0.241\), \(p=0.811\), \(d=-0.080\) (Figure 2).  
Figure 1: Coaches survey: Mean of answers to questions four and five on additional substitution in regular time. Note: * signals a significantly (\(p\leq 0.05\)) higher mean value than the median (3.5) and thus the assessment of the additional substitution with regard to the queried aspect as beneficial by the coaches.  
The coaches indicated that an additional substitution would not lead to an increase in stalling the match (\(M_{Q8}=3.351\), \(SD=1.637\)), \(t(36)=-0.552\), \(p=0.584\), \(d=-0.184\). The coaches' responses also showed that the potential positive effects of an additional substitution would exceed the potential time consumption (\(M_{Q9}=4.378\), \(SD=1.341\)), \(t(36)=3.985\), \(p<0.001\), \(d=1.328\). In response to the question about the reasons for late substitutions, coaches indicated that caution against injuries (\(M_{Q10.1}=3.216\), \(SD=1.456\)), \(t(36)=-1.186\), \(p=0.243\), \(d=-0.395\), and time management (\(M_{Q10.3}=3.459\), \(SD=1.406\)) were less decisive factors, \(t(36)=-0.175\), \(p=0.862\), \(d=0.058\). In turn, the results showed that game strategy (\(M_{Q10.2}=4.162\), \(SD=1.302\)) was an important factor for substituting late, \(t(36)=3.094\), \(p=0.004\), \(d=1.031\). The coaches' responses indicated an increase of playing time for youth/young players if an additional substitution was admitted (\(M_{Q11}=4.514\), \(SD=1.283\)), \(t(36)=4.806\), \(p<0.001\), \(d=1.602\).
## 3 Methods and Data Sources
### Research Method
#### 3.1.2 ARIMA Model  
The \(ARIMA\) model was built by Python to predict and analyze the development trend of \"AI + education\" in Guizhou province and its cities (prefectures) in the future.  
The \(ARIMA\) model is one of the methods of time series prediction analysis. Many examples in the literature show that the \(ARIMA\) model has been widely used in the fields of economics, medicine, agriculture, and so on [25; 26]. It is usually represented by \(ARIMA(p,d,q)\), where \(AR\) represents the autoregressive process, \(MA\) represents the moving average process, \(p\) represents the number of autoregressive terms, \(d\) represents the number of different times, and \(q\) represents the number of moving average times [27]. The expression of the \(ARIMA(p,d,q)\) model is as follows:  
\[\left[1-\sum_{i=1}^{p}\phi_{i}L^{i}\right](1-L)^{d}X_{t}=\left[1+\sum_{i=1}^{q }\theta_{i}L^{i}\right]e_{t} \tag{2}\]  
where \(L\) is the hysteresis operator.  
After the establishment of the model, it is necessary to verify the goodness of fit of the model. Generally, the indexes used are Akaike information criterion (\(AIC\)) and root-mean-square error (\(RMSE\)). The smaller the indexes of the selected model parameters, the higher the goodness of fit of the model [28]. The calculation methods of the \(AIC\) and \(RMSE\) are as follows:  
\[AIC=-2\ln(L)+2k \tag{3}\]  
\[RMSE=\sqrt{\frac{1}{m}{\sum_{i=1}^{m}{\left(y_{predict(i)}-y_{(i)}\right)^{2}}}} \tag{4}\]  
where \(L\) is the maximum likelihood function under this model, \(k\) is the number of model parameters, \(m\) is the number of predicted times, \(y_{predict(i)}\) is the predicted value, and \(y_{(i)}\) is the true value.  
The modeling process of \(ARIMA\) is to determine the optimal model through the process of model identification and inspection after the original sequence is stabilized. The specific steps are as follows:  
1. Stabilization processing: The unit root test method is used to judge the stationarity of the selected data. If it is not stationary, it is necessary to carry out differential processing on the original data to make it stationarity (i.e., to determine the difference number \(d\));
2. Model recognition and order determination: Both the autoregressive order \(p\) and the moving average order \(q\) were tried to take values of 1-3 for parameter estimation, and the correlation coefficient of the model was preliminarily determined by the Akaike information criterion method. Generally, the smaller the \(AIC\) value, the higher the fitting degree, so the model with the smallest \(AIC\) was selected first;
3. Model testing: Use the \(RMSE\) to calculate the error value or to compare the predicted data with the original data over a while to evaluate the effect of model fitting and determine the optimal model parameters;
4. Forecasting: Use the determined optimal model to forecast and analyze the future time.
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 3 Handwritten Chinese Character Recognition
### Combination of Deep Learning and Traditional Techniques  
In addition to the mainstream use of deep learning methods to recognize HCC, some researchers have tried to combine traditional techniques to improve the performance of handwriting recognition systems further. The recognition process is shown in Figure 6. Specifically, for example, gradient features extracted by traditional techniques are fed into a deep network together with the original image [67]. In addition, features learned by deep networks are combined with traditional classifiers, such as MQDF [68]. However, since deep learning-based methods have enabled HCCR to achieve high recognition accuracy, only a few methods combining traditional techniques with deep learning networks have been developed in recent years.  
Figure 5: Schematic diagram of the HCCR algorithm based on zero-shot learning [66].  
[PERSON] et al. [69] fused MQDF with a deep belief network (DBN) as a new classifier cascaded model. When recognition confidence after using MQDF is below the threshold, DBN is used for re-identification to obtain the final recognition result. [PERSON] et al. [70] improved the model performance using specific domain knowledge. Specifically, they achieved 97.37% recognition accuracy on the ICDAR 2013 dataset in 2017 by integrating the traditional normalized cooperative directional decomposition feature map (directMap) and deep convolutional neural network (convNet) without data augmentation or model ensemble. In addition, the authors proposed an adaptive layer to reduce the mismatch between the specific source and test domains. New writing styles can be adapted to particular writers. Table 4 summarizes the recognition performance of different deep learning methods and some methods combining traditional techniques on the ICDAR 2013 dataset. Since some methods do not state their training time and testing speed in the literature, the comparison is more concerned with the recognition performance and model size of different methods.
## 2 IRS Technology Fundamentals
### Channel Model of IRS  
We consider the system model shown in Figure 4, where a BS communicates with a UE using an IRS-assisted network. It is assumed that the base station has \(M\) uniform linear array antennas and the IRS has \(N\) reflective elements. One user is considered in the services area having a single antenna. The received signal via IRS can be defined as [10]:  
\[\mathbf{y}_{irs}=\mathbf{h}_{u}\mathbf{\Psi H}_{b}\mathbf{x}+\mathbf{n} \tag{3}\]  
where \(\mathbf{y}_{irs}\) is the received signal at the UE, \(\mathbf{x}\in\mathbb{C}^{M\times 1}\) is the transmitted signal, \(\mathbf{H}_{b}\in\mathbb{C}^{N\times M}\) is the channel matrix from BS to IRS, \(\mathbf{h}_{u}\in\mathbb{C}^{1\times N}\) is the channel vector from IRS to UE, and \(n\sim\mathcal{CN}(0,\sigma^{2})\) is the additive white Gaussian noise (AWGN) at the user. \(\mathbf{\Psi}=\text{diag}(\mathbf{z})\in\mathbb{C}^{N\times N}\) is a diagonal matrix representing the phase shift values with \(\mathbf{z}=[\beta_{1}e^{\beta_{1}}\cdot\beta_{2}e^{\beta_{2}},\ldots\beta_{N }e^{\beta_{N}}]^{T}\in\mathbb{C}^{N\times 1}\). The total channel with direct communication can be formulated as [35]:  
\[\mathbf{y}_{\mathbf{t}}=\mathbf{h}_{u}\mathbf{\Psi H}_{b}\mathbf{x}+\mathbf{H}_{d} \mathbf{x}+\mathbf{n} \tag{4}\]  
where \(\mathbf{H}_{d}\in\mathbb{C}^{1\times M}\) is the direct channel between BS and UE. The BS-IRS channel \(\mathbf{H}_{b}\) can be written as follows:  
\[\mathbf{H}_{b}=\sqrt{\frac{MN}{P_{1}}}\sum_{p_{1}=1}^{P_{1}}\alpha_{p}^{1}\mathbf{ t}(\beta_{p}^{1},\theta_{p}^{1})\mathbf{r}(\beta_{p}^{1}) \tag{5}\]  
where \(\sqrt{\frac{MN}{P_{1}}}\  
\[\mathbf{t}(\beta,\theta)=\frac{1}{\sqrt{N}}\left[1,\ldots,e^{j\frac{2\pi}{\lambda}d_ {s}(n_{1}\cos(\theta)\sin(\beta)+n_{2}\sin(\theta))},\ldots,e^{j\frac{2\pi}{ \lambda}d_{s}((N_{1}-1)\cos(\theta)\sin(\beta)+(N_{2}-1)\sin(\theta))}\right]^ {T}, \tag{6}\]  
\[\mathbf{r}(\beta)=\frac{1}{\sqrt{M}}\left[1,\ldots,e^{j\frac{2\pi}{\lambda}d_ {s}(m\sin(\beta))},\ldots,e^{j\frac{2\pi}{\lambda}d_{s}((M-1)\sin(\phi))} \right]^{T}, \tag{7}\]  
where \(d_{r}\) is the elements spacing for IRS, \(d_{a}\) is the antennas spacing for BS, \(\lambda\) is the signal wavelength, and \(0\leq n_{1}<N_{1}\) and \(0\leq n_{2}<N_{2}\) denote the horizontal and vertical indices of IRS elements. For indexing the BS antenna, \(m\) is used, where \(0\leq m<M\). The channel matrix between the IRS and UE can be expressed as follows:  
\[\mathbf{h}_{u}=\sqrt{\frac{N}{P_{2}}}\sum_{p_{2}=1}^{P2}\mathbf{\alpha}_{p}^{2 }\mathbf{r_{2}}(\beta_{p}^{2},\theta_{p}^{2}), \tag{8}\]  
where \(\sqrt{\frac{N}{P_{2}}}\) is a normalization factor, \(P_{2}\) is the number of paths between IRS and UE, \(\mathbf{\alpha}_{p}^{2}\) is the complex gain for the \(p\)-th path, and \(\beta_{p}^{2}\) and \(\theta_{p}^{2}\) are the azimuth and elevation of angle of departure, respectively.
## 1 Introduction  
Aging is a complicated biological process that is controlled by a large number of genetic and environmental factors [1; 2; 3; 4]. Many theories have been proposed to account for the aging process, but none of them offers a fully acceptable explanation. Among all the theories, the free radical theory of aging, proposed by professor [PERSON] in 1956 [5], has been extensively investigated. This theory continues to be revised and remains a strong theory for the aging process until now. Free radical theory explains that aging is caused by cumulative oxidative stress from free radicals, resulting in cell death and, eventually, death of the organism [6]. Hence, this theory suggests that antioxidants that can sacrificially scavenge ROS (reactive oxygen species) and/or RNS (reactive nitrogen species) are effective for delaying the aging process. Indeed, early studies have indicated that plant-derived polyphenol antioxidants, including curcumin, quercetin, and resveratrol, show therapeutic potential for aging and aging-associated diseases [7; 8]. In addition, other synthetic flavonoids have been continuously reported as novel antioxidants exhibiting antiaging properties [9; 10; 11; 12].  
_Portulaca oleracea_ L. is a well-known annual weed, one that is widely distributed in temperate and tropical regions. This plant has been used for a long time, not only as an edible potherb, but also as a traditional medicine in many countries, being used to alleviate a wide range of illnesses [13]. In addition to phytochemical research, many pharmacological studies have been performed using the extracts or single compounds from _Portulaca oleracea_ L. to reveal their therapeutic properties.  
Interestingly, the extracts of this plant are known to possess not only a strong in vivo antioxidant capacity, but also anti-aging activity in a p-galactose-induced mice model [14; 15].  
Recently, oleracones, a novel series of flavonoids, were isolated from _Portulaca oleracea_ L. by Chinese researchers [16; 17]. They reported the potent antioxidative potential of oleracones, which allowed us to speculate that oleracones may also possess anti-aging properties [16; 17]. The current paper includes the concise sequential synthesis of oleracones and an evaluation of their possible lifespan-extension properties using _Caenorhabditis elegans_, an excellent experimental model to study aging. Among the reported oleracones, we planned to synthesize two homoisoflavones, oleracones D (**1**) and F (**2**), and a dihydrohalcone, olereacone E (**3**), which exhibit potent antioxidative effects (Figure 1).
## 1 Introduction
#### Integration of Metocean Forecast Data and Automation  
Metocean data interoperability development with integration using automation measures parameters such as surge height and wave height. This can also be addressed by using ML model techniques for efficient results. [PERSON] and [PERSON] [23] proposed surrogate time-independent modelling to evaluate Metocean conditions during hurricanes, which include peak wave period, peak wave direction, and storm surge using a Metocean database. The model was developed using a recurrent neural network (RNN), gated recurrent unit (GTU), and multilayer perception (MLP). [PERSON] et al. [24] proposed a prototype system that is capable of integrating Metocean model forecast and performance of ship data for modelling. In addition, the prototype allows for visualization of Metocean data after uploading from the system for evaluating the condition on each route.  
Several research studies have explored the needs of ML in the oil and gas industries. In this study, we investigate in depth the integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach.  
* A comparative analysis of the current uses of ML algorithms in the oil and gas industries is presented.
* The transformation and integration of existing Metocean architecture for the use of oil and gas data operators through automation is meticulously reviewed.
* A new model to be integrated with the existing Metocean data system using a machine-learning algorithm to monitor and interoperate with maximum performance is proposed.
## 3 Communication and Channel Modeling
### Channel Modeling
#### 3.1.5. Imperfect CSI  
Normally, the communication strategies and trajectory optimization are based on the knowledge of channel coefficients such as full and partial CSL. The main issue withUAV-enabled, RIS-assisted communication is imperfect CSI and channel estimation and beamforming in dynamic environments.  
A UAV-BS-based multiple RIS-assisted communication with outdated and imperfect CSI has been studied in [58]. In the case of multiple RISs, the problem is RIS allocation to a given user. The statistical distributions of instantaneous SNR under imperfect CSIs for both direct and composite fading links were derived using Rician fading. Using the cumulative distribution function (CDF) of SNR, series-based expressions for average coverage probability (ACP) and average bit error rate over the UAV's flight were obtained.  
To support ultra-reliable and low-latency communications (e.g., for IoTs and IoVs in B5G and 6G cellular networks [59]), short-packet transmissions are essential. In this light, a new framework for short-packet communications has been developed in [60], where an error probability bound for a given block length (BL) and coding rate have been derived. Followed by this, [PERSON], et al. investigated the application of RIS in energy harvesting in a UAV-RIS-IoT communication network with finite BL codes [61]. Using the derived statistics, the system's performance with finite block length codes was analyzed for average outage probability, block error rate, and goodput over the UAV's flight duration. The impact of factors such as imperfect CSI, number of RISs and reflecting elements, IoT locations, UAV altitude, and channel fading severity on UAV-RIS link performance was also investigated.
## 5 Wood-Based Panel Emissions
### Wood Panels Bonded without Adhesive
#### 5.2.3 VOC Emissions from Binderless Boards  
To our knowledge, only two studies published results concerning VOC emissions from binderless panels obtained by cooking-compression. The first one concerns binderless panels made from an extrusion-refined coriander straw and a deoiled coriander press cake [151, 152]. These cases were obtained by extrusion and were then thermopressed at 205 \({}^{\circ}\)C for 5 min under 21.6 MPa pressure. Acetaldehyde, linalool, camphor, and \(p\)-cymene were identified as the majority emitted compounds, with emission rates of around 31 \(\mu\)g m\({}^{-2}\) h\({}^{-1}\), 23 \(\mu\)g m\({}^{-2}\) h\({}^{-1}\), 9 \(\mu\)g m\({}^{-2}\) h\({}^{-1}\), and 4.4 \(\mu\)g m\({}^{-2}\) h\({}^{-1}\), respectively. Linalool and camphor emissions were attributed in particular to the residual presence of essential oil in the materials. Formaldehyde emissions were below the detection limit estimated at 0.2 \(\mu\)g m\({}^{-2}\) h\({}^{-1}\), while those of MDF and chipboard determined under the same conditions were, respectively, 42 and 84 \(\mu\)g m\({}^{-2}\) h\({}^{-1}\). For these two materials, formaldehyde was the main compound emitted.  
The second study concerns binderless boards produced from _Quercus robur_ under various processing parameters (time, pressure, temperature) to observe their impact on the board VOC emissions [101, 153]. VOC from these panels were studied in two ways. Firstly, emissions were measured in emission chambers. Secondly, the panels were pyrolyzed. Twenty-eight VOCs were identified by the pyrolysis of the binderless boards such as acetic acid, furfural, oak lactone, isoeigenol, vanillin, coniferaldehyde, and sinapaldehyde. Quantification of the VOC emissions led to the identification of acetaldehyde, formaldehyde,acetic acid, and furfural as the major emitted compounds at, respectively, 109 mg m\({}^{-2}\) h\({}^{-1}\), 44 mg m\({}^{-2}\) h\({}^{-1}\), 180 u\({}_{\text{Seq toluene}}\) m\({}^{-2}\) h\({}^{-1}\), and 126 u\({}_{\text{Seq toluene}}\) m\({}^{-2}\) h\({}^{-1}\).  
It clearly appears that binderless boards, although free of petrochemical or bio-based binders, can lead to emission of compounds known for their toxicity (formaldehyde, furfural, etc.) in significant quantities. It is therefore essential to take them into account as part of any strategy to develop such materials.