esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 2 Related Work
### Ad Exposure Effects and Consumer Engagement
Research conducted by [17] on YouTube review videos (totaling 600 h) identified features and patterns relevant to emotion (valence and arousal) and trustworthiness as the underlying dimensions of engagement. Several indicators of user engagement, including views, the ratio of likes to dislikes, and sentiment in comments, served as the ground truth. [PERSON]. [18] defined a set of video metrics (including video quality, time, and average percentage of video viewed) to model relative engagement, based on 5.3 million YouTube videos. Their results show that video engagement metrics are stable over time, with most of their variance explained by video context, topics, and channel information. Moreover, the study also found that the time spent watching is a better indicator of engagement than the number of views statistics, which is widely used in current studies of ad engagement. The advantage of these modeling approaches is that they are scalable, automatic, and, in several cases, can directly measure consumer engagement. However, developing robust predictive models for consumer engagement remains a challenge. Most of these approaches rely on user metadata (e.g., view count, frequency, and likeability data ) or video quality features (e.g., video resolution), and lack the reference ground truth of user-perceived engagement. A summary of these approaches is collected in Table 1. To the best of our knowledge, none of the existing approaches use features derived from the video itself, which is the goal of the present research. The video and multimedia descriptors are presented in the following sections. These descriptors were later used in the proposed model of consumer ad engagement, along with the referential data of perceived consumer engagement obtained from the observational study.
## 3 Results and Discussion
### Effect of Cooking on Volatiles' Profiles
Strecker aldehydes and/or nitrogen heterocyclic compounds have been repeatedly identified among the volatiles of thermally-dried or heat-extracted samples of _Pleurotus_[42], _Boletus edulis_ and _P. ostreatus_[77], _A. bisporus_[78], _Hericium erinacueus_[79], _Boletus edulis_[80], as well as in oils obtained from hydro distillation of _Pleurotus_ mushrooms [51; 52]. They were also present in cooked _P. ostreatus_[81], cooked wild mushrooms [15] and in _A. bisporus_ soup [82]. Interestingly, Strecker aldehydes and/or pyrazines were also detected in freeze-dried _M. importua_[17], dried _L. edodes_[54] fresh and freeze-dried truffles [29; 57]. In the present study, six and 17 pyrazines were detected in roasted _P. eryngii_ and _P. ostreatus_, with 3-ethyl-2,5-dimethyl pyrazine predominating at 8.76 and 21.22 ug/g f.w., followed by 2-ethyl-3,5-dimethylpyrazine, 2,5-dimethylpyrazine, 2-ethylmethylpyrazine and 3,5-diethyl-2-methylpyrazine at concentrations ranging between 2.9-6.0 ug/g f.w. (Table 3). Among the pyrazines determined in roasted _Pleurotus_ mushrooms, 3-ethyl-2,5-dimethylpyrazine is formed from the thermal treatment of alanine [66], methylpyrazine, ethylpyrazine, 2-ethyl-methylpyrazine, and 2,6-diethylpyrazine originate from the thermal treatment of serine and 2,5-dimethylpyrazine, 2,6-dimethylpyrazine, and 2-ethyl-3,5-dimethylpyrazine from the thermal treatment of threonine [74]. Finally, ethyldimethylpyrazines, also detected in the roasted _Pleurotus_ samples, originate from the Strecker degradation of methionine and are considered important flavor contributors in thermally processed foods [65]. Alanine, serine, threonine and methionine are among the amino acids reported in the _Pleurotus_ mushrooms studied [44]. It is noteworthy that due to the absence of thermal treatments, pyrazines, pyrrole, oxazole and sulfur compounds were detected only in the cooked samples (Table 3); hence, the volatile profiles of fresh mushrooms obtained in the present study can be considered as representative, in some degree, of the _P. eryngii_ and _P. ostreatus_ aroma. The sulfur compounds methional and dimethyl disulfide were detected in the roasted _Pleurotus_ samples (Table 3). Methional is the Strecker aldehyde of methionine and is eventually decomposed to form dimethyl disulfide, which contributes further to the overall flavor development [65; 67]. Methional is responsible for the aroma of cooked potatoes [67] and is the key aroma-active compound in cooked _T. matsutake_ mushrooms [55], while both sulfur compounds were reported in _A. bisporus_ soup [82].
# Rapid-Detection Sensor for Rice Grain Moisture Based on NIR Spectroscopy
## 3 Results and Discussion
### Characteristic Band Selection and Sensor Design
#### 3.1.2 The Principle of NIR Optical Detection  
The detection principles of conventional NIR detection instruments typically include the transmission method and the reflection method [33]. The transmission method is generally used to detect transparent liquid, but the rice grain samples are irregular and thick in shape. Hence, the transmission method was not suitable in this case, and the reflection method was adopted. In this paper, the optical detection principle of the sensor probe was mainly based on the following. When the light source irradiates the rice grain samples, the intensity of the reflected light signal that is collected by photosensitive components directly reflects the moisture content in the rice grain samples [34]. In addition, the application of a high-pass filter can reduce the noise signal interference and the focusing lens can enhance the reflection signal strength, as well as improve the instrument detection performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Wavebands**} & \multirow{2}{*}{**The Number of Variables**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Prediction Set**} \\ \cline{4-7}  & & & \(\mathbf{R_{c}^{2}}\) & \(\mathbf{R_{c}^{2}}\) & \(\mathbf{R_{p}^{2}}\) & **RMSEP (\%)** \\ \hline \multirow{2}{*}{950–1650 nm} & \multirow{2}{*}{350} & PLS & 0.989 & 0.57 & 0.970 & 0.8877 \\  & & CARS & 0.973 & 0.83 & **0.977** & 0.93 \\ \hline \multirow{2}{*}{1100–1250 nm} & \multirow{2}{*}{72} & PLS & 0.938 & 1.32 & 0.844 & 2.39 \\  & & CARS & 0.865 & 1.94 & 0.883 & 2.12 \\ \hline \multirow{2}{*}{1350–1550 nm} & \multirow{2}{*}{105} & PLS & 0.930 & 1.53 & 0.923 & 1.46 \\  & & CARS & 0.943 & 1.52 & 0.917 & 1.52 \\ \hline \multirow{2}{*}{1450 nm} & \multirow{2}{*}{1} & SLR & 0.845 & 1.51 & 0.832 & 1.68 \\ \hline \hline \end{tabular}  
* b: PLS (Partial Least Squares); CARS (Competitive Adaptive Reweighted Squares); RMSEC (root mean square error of the calibration); RMSEP (root mean square error of the prediction set).  
\end{table}
Table 2: The model performance of three bands by PLS and CARS.  
Figure 2: The model performance of 950–1650 nm modeled by Partial Least Squares (PLS) (**a**) and Competitive Adaptive Reweighted Squares (CARS) (**b**); The model performance of 1100–1250 nm Near-infrared spectroscopy (NIR) modeled by PLS (**c**) and CARS (**d**); The model performance of 1350-1550 nm NIR spectra modeled by PLS (**e**) and CARS (**f**). The model performance of 1450 nm NIR spectra modeled by SLR (**g**).
## 4 Existing Underwater System Models
### Underwater Energy Utilization Models  
The underwater energy model must consider the conditions required for acoustic communication in an underwater environment such as the frequency of the acoustic waves and its attenuation to computer energy consumption. The model used to estimate the minimal transmission power in marine acoustic communication is derived from the model in [40]. When transmitting data over a distance (d) from one ASN to another, the power level will be computed as  
\[\mathrm{P_{t}=P_{o}A(d,f)} \tag{1}\]  
where \"P\({}_{o}\)\" represents the minimal power required to receive data. Let A(d,f) represent the attenuation of underwater acoustic signals transmitted between two ASNs separated by a distance of d at frequency f. The A (d,f) can be expressed as  
\[\mathrm{A(d,f)=d^{k}a^{d}} \tag{2}\]  
The spreading factor for energy, denoted as \"k\", is typically set to 1.5 [27; 29; 34]. The value of \"a\" is determined by the absorption coefficient, which can be determined from the equation given below:  
\[\mathrm{a=10^{\alpha(f)/10}} \tag{3}\]  
[PERSON]'s formula is frequently used to calculate the formulation of \(\alpha\)(f).  
\[\alpha(f)=\frac{0.11\dot{f}^{2}}{\left(1+\dot{f}^{2}\right)}+\frac{44\dot{f}^{ 2}}{4100+\dot{f}^{2}}+0.003+2.75*10^{-4}\dot{f}^{2} \tag{4}\]  
The empirical formula provides estimates of absorption loss for frequencies ranging from 1 kHz to 1 MHz. Therefore, the consumed energy during the transmission can be calculated using the given mathematical equation:  
\[\mathrm{E_{tx}=T_{t}\cdot P_{t}} \tag{5}\]  
where \"T\({}_{t}\)\" is the period to send \"m\" bits at a data rate \"B\". Similarly, the energy required by an ASN in receiving \"m\" bits of data is given below:  
\[\mathrm{E_{rx}=T_{t}\;P_{r}} \tag{6}\]
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 4 DVB-T2/C2 Demodulator Architecture
### Mode Detection  
Detection of DVB-T2 signals is available by detecting the P1 symbol in the time domain. The P1 symbol transmitted in the start of each T2 frame includes information such as FFT sizes and MISO mode. Thus, if the P1 symbol is detected, information of FFT sizes to operate the demodulator in the DVB-T2 mode can be obtained. DVB-C2 uses a fixed 4K FFT. Thus, it is possible to detect the GI length and remove CFO and STO. The C2 preamble can be detected in the frequency domain through the FFT operation. Therefore, if the proposed demodulator is operated in the DVB-C2 mode at the beginning, the DVB-T2 or DVB-C2 signal can be detected in the time and frequency domains at the same time.  
An alternative method can be used for mode detection. DVB-C2 uses two GI lengths such as 1/128 and 1/64. Table 2 shows the number of GI samples used in DVB-T2. The 4K FFT mode of DVB-T2 does not use GI lengths of 1/128 and 1/64 used in DVB-C2. Therefore, this method can detect the operation mode of the demodulator in the time domain using the output value of the GI detector. If the operating mode is detected with time domain signals, the operation mode of the demodulator is quickly determined to make service support faster.  
Figure 9: Block diagram of the proposed DVB-T2/C2 demodulator.
# Detection of Decision-Making Manipulation in the Pairwise Comparison Method
## 3 Preliminaries
### PC Matrices  
In the PC method, a decision-maker, often an expert in a particular field, compares the alternatives in pairs. The easiest way to show these comparisons is in the form of an \(n\times n\) square matrix:  
\[\mathbf{C}=\big{[}c_{ij}\big{]}, \tag{1}\]  
where \(n\) denotes the order of the pairwise comparison \(PC\) matrix (the number of alternatives) and each \(c_{ij}\) element in the matrix \(\mathbf{C}\) indicates the result of the comparisons between the alternatives \(a_{i}\) and \(a_{j}\). The \(PC\) matrix \(\mathbf{C}\) has to be reciprocal, so for every \(i,j\in\{1,\dots,n\}\), the reverse comparison \(a_{j}\) to \(a_{i}\) gives  
\[c_{ji}=\frac{1}{c_{ij}}. \tag{2}\]  
Several methods allow us to calculate a ranking of \(n\) alternatives \(A=\{a_{1},\dots,a_{n}\}\) using \(\mathbf{C}\) as an input. Let us denote the ranking value of \(a_{i}\) as \(w(a_{i})\) and compose the priority vector as follows:  
\[w=[w(a_{1}),\dots,w(a_{n})]^{\mathsf{T}}. \tag{3}\]  
Although there are many methods to calculate the priority vector \(w\)[51; 52; 53], we will focus on two of probably the most popular of these [15]. The EVM (eigenvalue method) is proposed by [PERSON] [19] and the GMM (geometric mean method) is proposed by [PERSON] and [PERSON] [54].  
In the EVM [15], the priority vector \(w\) is calculated as a normalized principal eigenvector of \(\mathbf{C}\) such that \(\big{(}\|\cdot\|_{m}=1\) is the Manhattan norm) \(\|w\|=1\):\[w(a_{i})=\frac{\widehat{w}_{i}}{\sum_{j=1}^{n}\widehat{w}_{j}}, \tag{4}\]  
where \(\widehat{w}\) is the principal eigenvector of \(\mathbf{C}\), i.e., it satisfies the following equation:  
\[\mathbf{C}\widehat{w}=\lambda_{max}\widehat{w}. \tag{5}\]  
where \(\lambda_{max}\) is the principal eigenvalue.  
On the other hand, in the GMM, the priority of the \(i\)-th alternative is derived as a rescaled geometric mean of the \(i\)th row. Thus, the priority vector \(w\) consists of rescaled geometric means for all the rows of the matrix \(\mathbf{C}\):  
\[w=\varphi\left[\left(\prod_{j=1}^{n}c_{1j}\right)^{\frac{1}{n}},\ldots,\left( \prod_{j=1}^{n}c_{nj}\right)^{\frac{1}{n}}\right]^{T}, \tag{6}\]  
where \(\varphi\) is a scaling factor such that \(\|w\|=1\) and in the GMM method, the scaling factor is \(\varphi=\left(\sum_{i=1}^{n}\left(\prod_{j=1}^{n}c_{ij}\right)^{\frac{1}{n}} \right)^{-1}\). It is worth mentioning that the EVM and the GMM are equivalent if \(n=3\) ([54], p. 393) but may differ for \(n\geq 4\). The difference depends on the inconsistency. It can be shown that it is smaller the smaller the value taken by [PERSON]'s inconsistency index [55].
## 2 Microchip Laser
### Breadboard Results  
The results from the characterization of the optical cavities of the microchip laser are presented in Table 1. Table 1 is divided into four columns for each configuration (Conf. 1 to Conf. 4). Conf. 1 has an active medium with 1 mm of thickness and no Q-switch. Conf. 2 has an active medium 1.5 mm thick and no Q-switch. Since there is no Q-switching element inside the cavities, Conf. 1 and Conf. 2 have CW emission. Conf. 3 has an active medium 1 mm thick and a Q-switch inside the cavity. Conf. 4 has an active medium with 1.5 mm of thickness and a Q-switch as well. The addition of the Q-switch inside the cavities allows Conf. 3 and Conf. 4 to have pulse laser emissions.  
The cavity configuration chosen for the second setup of the microchip laser was Conf. 3, so the results will be focused on this one. The first step in the configuration of this cavity was determining the curve of the average output optical power versus the average optical pumping power. The slope efficiency of the laser was calculated to be around 8.5%. The shape of the pulse observed on the oscilloscope is shown in Figure 6, with the time division being in Figure 6a (10 ns), in Figure 6b (20 ms), and Figure 6c (5 ms).  
Figure 6a shows a single micro-pulse observed on the oscilloscope from the first setup of the microchip laser; Figure 6b is the burst emission, demonstrated in a viewing of three bursts; and Figure 6c is a single burst. The energy and peak power values taken from the first implementation of the microchip laser are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optical Element** & **Conf. 1** & **Conf. 2** & **Conf. 3** & **Conf. 4** \\ \hline AM thickness & 1 mm & 1.5 mm & 1 mm & 1.5 mm \\ \hline Q-switch & \(\bigstar\) & \(\bigstar\) & \(\bigstar\) & \(\bigstar\) \\ \hline  & & \multicolumn{3}{c}{Results} \\ \hline Efficiency & 16.0\% & 12.5\% & 8.5\% & 6.0\% \\ \hline Pumping & & & \\ threshold & 29.4 mW & 30.7 mW & 22.6 mW & 30.0 mW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Four cavity configurations from the first setup of the microchip laser.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Output optical power & 386.4 mW \\ \hline Pulse energy & 38.64 mW \\ \hline Pulse width & 11.40 ns \\ \hline Pulse peak power & 3185.4 W \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy and peak power values measured.  
Figure 7: Characterization of the resulting CW fixed optical cavity.  
From the emission spectrum, it was possible to conclude that the microchip laser presents a wavelength value around 1532 nm. The optical path calculations for the cavity length result in 6 \(\pm\) 1 mm. The resulting CW fixed optical cavity from the second setup was characterized by the curve of the average output optical power versus the average input optical power, shown in Figure 7.  
In Figure 7, there is a slope efficiency value of around 16.7% (taken from the linear regression slope). It is a slight increase from the Conf. 1 cavity assembled in the first implementation, which had a slope efficiency of 16.0%.
###### Abstract  
On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis is proposed in this paper. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. Finally, verification of the results on NASA #5 battery data sets demonstrates that the proposed method has better dynamic performance and higher accuracy than the three other popular methods.  
L 2018 8 2018 2  
regressive moving-average (ARMA) models [16]; (2) machine-learning technology, such as artificial neural networks (ANN) [17; 18; 19], support vector machine (SVM), or [4] relevance vector machine (RVM) [20; 21]; and (3) statistical technology such as the Bayesian method [22; 23] and Gaussian process regression (GPR) [24].  
Data-driven approaches are attracting more attention than the other two approaches, since they need neither long term accumulation of the related experience, nor accurate aging mechanism of battery degradation phenomenon. Instead, data-driven approaches only depend on historical data, and use data mining, pattern recognition, and machine-learning techniques to forecast the RUL of Li-ion battery. In existing data-driven approaches, most researchers directly adopt capacity or resistance as health indicators (HI) [16; 17; 18; 19; 20; 21; 22; 23; 24]. In real applications, it is hard to measure and monitor Li-ion battery capacity or resistance directly. Meanwhile, to realize on-line RUL prognosis, computationally-efficient data-driven approaches are preferable.  
A dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis for satellite Li-ion battery is proposed in this paper. The main contributions of this paper can be summarized as follows:  
* For measured discharge voltages, a novel polynomial-fitting-based HI is constructed to replace Li-ion capacity for Li-ion battery RUL prognosis.
* Considering on-line and multi-step prediction, a DLSTM with the Adam method, L2 regularization method, and incremental learning idea is proposed.  
The structure of this article is as follows. Section 2 briefly analyzes Li-ion battery data and clarifies the problem. Then, the proposed HI extraction and DLSTM methods are described in Section 3. Verification results are presented and analyzed in Section 4. Finally, Section 5 gives the conclusions and discusses future work.
# Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology
## 2 A New View Synthesis Technique Based on Latent Diffusion Models  
### Architectural Framework for IOBSPM  
The intelligent prediction technology for ore block morphology aims to perceive the overall shape of ore blocks in the mining area using a prediction model trained on simulation datasets. In autonomous shoveling scenarios, video or image data of the ore blocks ahead, along with corresponding pose information (3D spatial coordinates and angles), is captured by sensors mounted on the shoveling equipment. This enables the prediction of the ore block's shape from multiple angles. The specific implementation process is shown in Figure 1.  
The intelligent prediction technology for ore block morphology based on latent diffusion models consists of four main components: The first part involves using simulation software to create images of ore blocks and their poses as the target task dataset. This process aims to provide the model with a diverse range of ore block morphology samples, reducing the randomness in prediction results in real-world scenarios. The second part involves applying transfer learning to the diffusion model using the target task dataset. Through forward and reverse diffusion processes, the model continuously optimizes its loss function, with the core objective of adjusting the model's weights to better suit the application task of ore block prediction, thereby achieving a more refined application. The third part involves reducing the adverse effects of lighting and background to improve the accuracy of the predicted images. The ore block images are enhanced through methods such as background filling and brightness adjustment and then fed into the prediction model to generate new views from different perspectives, thereby enabling the intelligent prediction of ore block morphology. The fourth part involves using the generated multi-view images and pose information to allow for more detailed perception of the global features of the  
Figure 1: Implementation process of intelligent prediction of ore block morphology.  
target object, providing rich input data for grid-based 3D model reconstruction, and ultimately achieving a more visually expressive representation of the ore block morphology. Overall, the original views, predicted views, and 3D models of the ore blocks can provide more diverse information references for machines or remote operators.