esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Lane Control Strategy Based on Truck Platooning Simulation
### Analysis of the Lane Control Strategy  
Based on the highway truck lane restriction management strategy, this study investigated the conditions of truck platooning on exclusive lanes, and the effect of optimal speed control in the weaving section. Two representative scenarios were accordingly analyzed.  
Scenario I: The existing lane management method of the Nanjing-Shanghai highway is employed for the two outer lanes, i.e., the third and the fourth lanes. Thus the outermost fourth lane was selected as the exclusive lane for truck platooning, and the number of lanes designated for manual vehicle operation is reduced from four to three. Scenario I analyzes the impact of self-driving truck platooning in the weaving zone. Two comparative analyses were conducted: 1 comparison between opening and not opening the second lane for HDT driving under the lane dedicated to truck platooning; 2 comparison between truck platooning in the form of a queue, and that in the form of separation (a separated distance  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Vehicle Type**} & **Minimum** & **Maximum** & \multirow{2}{*}{**Average Speed**} & **85\% Vehicle** \\  & **Speed** & **Speed** & & **Speed** \\ \hline CAR & 46.6 & 120 & 84.2 & 96.4 \\ HDT & 34.2 & 91.5 & 60.1 & 72 \\ ATP & 36.5 & 94.66 & 65.3 & 77.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle speed distribution.  
between each two trucks), when passing through the weaving zone of ramps under the lane dedicated to truck platooning. Both analyses examined overall traffic efficiency and safety.  
Scenario II: This scenario aims improve traffic conditions of the highway bottleneck area, combined with the prior analysis of optimal speed control for truck platooning. Based on CVIS technology and the real-time acquisition of road traffic flow information, the optimal speed of truck platooning was obtained through the CTM, MPC, and multi-objective solution. Scenario II was subsequently set up to analyze the impact of optimal speed control with and without truck platooning under the dedicated lane strategy.  
For Scenarios I and II, the lanes that can be operated by different vehicle types are listed in Table 2.  
When the truck platooning lane is set up, the outermost exclusive lane on the basic section is reserved for truck platooning. However, this lane is shared with other vehicles in all other road sections. The flow rate input in the simulation was 1000 veh/h, with a minimum of 1000 veh/h and maximum of 7000 veh/h. The proportions of trucks to total traffic were set to 5%, 10%, 15%, and 20%, and the proportions of truck platooning to overall truck traffic were 10%, 30%, and 50%.
# Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
###### Abstract  
The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.  
e +
Footnote †: journal: Applied Sciences  
article
## 2 Materials and Methods
### Total Lipids Content
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No** &
\begin{tabular}{c} **Class of** \\ **Compound** \\ \end{tabular} & **Compound Name** & **Rt (min)** & **RI** & **Odor Description** \\ \hline
60 & Ketone & acetophenone & 12.82 & 1658 &
\begin{tabular}{c} Sweet, cherry pit, marzipan \\ and coumarinic, vanilla \\ nuance \\ \end{tabular} \\ \hline
61 & Terpene & pristane & 12.98 & 1667 & \\ \hline
62 & Pyrazine &
\begin{tabular}{c} 2,5-dimethyl-3-cis- \\ propenyrazine \\ \end{tabular} & 13.16 & 1677 & \\
* & & & & \\ \hline
63 & Alkane & heptadecane & 13.60 & 1700 & \\ \hline
64 & Aldehyde & 2,4-nonadienal & 13.74 & 1707 &
\begin{tabular}{c} Fatty, melon, waxy, green, leaf, \\ cucumber, tropical fruit \\ \end{tabular} \\ \hline
65 & Aldehyde & undecenal & 14.68 & 1756 & Fresh, fruity, orange peel \\ \hline
66 & Aldehyde & 3-dodecen-1-al & 14.68 & 1756 &
\begin{tabular}{c} Bitter orange, mandarin, \\ coriander \\ \end{tabular} \\ \hline
67 & Aldehyde & 2,4-decadienal & 14.92 & 1768 & Acid, greasy \\ \hline
68 & Terpene & phytane & 15.03 & 1774 & \\ \hline
69 & Alkane & octadecane & 15.53 & 1800 & \\ \hline
70 & FAME & methyl laurate & 15.65 & 1806 &
\begin{tabular}{c} Waxy, soapy, creamy, coconut, \\ with mushroom nuances \\ \end{tabular} \\ \hline
71 & Aldehyde & 2\(\AA\) decadienal & 15.81 & 1814 & Fatty, citrus, nutty \\ \hline
72 & Alkane & n-nonadecane & 17.49 & 1900 & Bland \\ \hline
73 & Alcohol & phenylethyl alcohol & 17.79 & 1916 &
\begin{tabular}{c} Sweet, floral, fresh and bready \\ with a rosey honey nuance \\ \end{tabular} \\ \hline
74 & Aldehyde & 2-phenyl-2-butenal & 18.23 & 1939 &
\begin{tabular}{c} Musty, floral, honey, powdery \\ and cocoa \\ \end{tabular} \\ \hline
75 & \
\begin{tabular}{c} Heterocyclic \\ compound \\ \end{tabular} & 2-acetylpyrrole * & 18.71 & 1964 &
\begin{tabular}{c} Musty, nutty-like with a \\ coumarin nuance \\ \end{tabular} \\ \hline
76 & Alcohol & 1-dodecanol & 18.89 & 1973 &
\begin{tabular}{c} Earthy, soapy, waxy, fatty, \\ honey, coconut \\ \end{tabular} \\ \hline
77 & Alkane & eicosane & 19.41 & 2000 & Waxy \\ \hline
78 & FAME & methyl myristate & 19.64 & 2012 & Fatty, waxy, petal \\ \hline
79 & Aldehyde &
\begin{tabular}{c} 5-methyl-2-phenyl-2- \\ hexenal \\ \end{tabular} & 20.64 & 2065 & \\ \hline
80 & Alkane & heneicosane & 21.30 & 2100 & \\ \hline
81 & FAME & methyl pentadecanoate & 21.58 & 2116 & \\ \hline
82 & Alkane & docosane & 23.10 & 2200 & \\ \hline
83 & FAME & methyl palmitate & 23.46 & 2221 & Oily, waxy, fatty, orris \\ \hline
84 & Alkane & tetracosane & 26.86 & 2400 & \\ \hline
85 & FAME & methyl stearate & 27.02 & 2413 & Oily, waxy \\ \hline
86 & FAME & methyl oleate & 27.36 & 2440 & Mild, fatty \\ \hline
87 & FAME & methyl linoleicate & 28.15 & 2503 & Oily, fatty, woody \\ \hline \end{tabular} Compounds tentatively identified by Wiley 275 and NIST 98 mass spectral libraries; FAME: fatty acid methyl ester; *: identified in roasted samples.  
\end{table} 
Table 1: _Cont._
## 2 Materials and Methods
### Methods
#### 2.2.5 Inverse Discrete Wavelet Transform for Multi-Resolution Analysis (MRA-IDWT)  
To reconstruct an enhanced image from the fusion results, we took four fused coefficients, \(AL\), \(HG\), \(VG\), and \(DG\), to be reconstructed using a single-level inverse discrete wavelet transform (MRA-IDWT). The same wavelet was used when we decomposed it into MRA-DWT and reconstructed it with MRA-IDWT. The reconstruction process started by up-sampling the fused coefficients (\(AL\), \(HG\), \(VG\), and \(DG\)) by introducing zeros between the samples at odd-indexed columns/rows to increase the image's resolution in both dimensions. The equivalent synthesis filters, previously employed in the DWT, were subsequently applied to these up-sampled coefficients. The inverse wavelet transform was first applied along the columns of the up-sampled coefficients to rebuild the intermediate row data, merging the \(AL\) with \(HG\) and \(VG\) with \(DG\). The procedure was then applied along the rows of the intermediate data to recreate the enhanced image completely.  
Figure 6: Example of results from proposed fusion (from **top** to **bottom**): \(AL\), \(HG\), \(VG\), \(DG\) with image fusion DWT/IDWT-IF using the mean-mean rule.
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 3 Recent Technical Advancements  
The Doppler radar theory and the demodulation techniques laid the foundation for noncontact microwave sensing, based on which more advanced techniques have been developed to resolve various issues that hinder the ubiquitous applications of Doppler radar in our daily lives.
## 6 Simulation Results and Discussion
### DRiPLOF's Performance in the Face of External Stressors
As such, DRiPLOF is able to deliver packets with relatively stable end-to-end delivery rates exceeding 90% when the root and the nodes closest to it are being jammed, compared to a median delivery rate of 65% and lower for a state-of-the-art multi-interface solution (POOF) and single-interface solutions (low- and high-rate MRHOF). Figure 10: _Cont._
# Compressed Communication Complexity of _Hamming Distance_
## 2 Preliminaries
### Joint Computation of Compressed String Problems  
In this paper, we also consider the communication complexity of the _Hamming distance_ problem between two compressed strings of equal length, which are compressed by LZ77 without self-references.  
**Problem 1** (_Hamming distance_ with non-self-referencing LZ77).:
**[PERSON]'s input**: \(\mathsf{LZN}(A)\) _for string_ \(A\) _of length_ \(n\)_._
**[PERSON]'s input**: \(\mathsf{LZN}(B)\) _for string_ \(B\) _of length_ \(n\)_._
**Goal**: _Both [PERSON] and [PERSON] obtain the value of_ \(\mathsf{d_{H}}(A,B)\)_._  
The following LCP problem for two strings compressed by non-self-referencing LZ77 has been considered by [PERSON] et al. [7].  
**Problem 2** (LCP with non-self-referencing LZ77).:
**[PERSON]'s input**: \(\mathsf{LZN}(A)\) _for string_ \(A\)_._
**[PERSON]'s input**: \(\mathsf{LZN}(B)\) _for string_ \(B\)_._
**Goal**: _Both [PERSON] and [PERSON] obtain the value of_ \(\mathsf{lcp}(A,B)\)_._  
[PERSON] et al. proposed the following protocol for a joint computation of the LCP of two strings compressed by non-self-referencing LZ77:  
**Theorem 1** ([7]).: _Suppose that the alphabet \(\Sigma\) and the length \(n\) of string \(A\) are known to both [PERSON] and [PERSON]. Then, there exists a randomized public-coin protocol which solves Problem 2 with communication complexity \(\langle O(\log z_{\ell}),O(\log\ell)\rangle\), where \(\ell=\mathsf{lcp}(A,B)\) and \(z_{\ell}=\mathsf{zn}(A[1..\ell])\)._  
The basic idea of [PERSON] et al.'s protocol [7] is as follows: In their protocol, the sequences of factors in the non-self-referencing LZ77 factorizations \(\mathsf{LZN}(A)\) and \(\mathsf{LZN}(B)\) are regarded as strings of respective lengths \(\mathsf{zn}(A)\) and \(\mathsf{zn}(B)\) over an alphabet \([1..n]\times[1..n]\times\Sigma\). Then, [PERSON] and [PERSON] jointly compute the LCP of \(\mathsf{LZN}(A)\) and \(\mathsf{LZN}(B)\), which gives them the first mismatching factors between \(\mathsf{LZN}(A)\) and \(\mathsf{LZN}(B)\). This LCP of \(\mathsf{LZN}(A)\) and \(\mathsf{LZN}(B)\) is computed by a randomized protocol for doubling-then-binary searches with \(O(\log z_{\ell})\) communication rounds. Finally, [PERSON] sends the information about her first mismatching factor to [PERSON], and he internally computes the LCP of \(A\) and \(B\). The total number of bits exchanged is bounded by \(O(\log\ell)\).  
In Section 3, we present our protocol for Problem 1 of jointly computing the _Hamming distance_ of two strings compressed by _non-self-referencing LZ77_. The scheme itself is a simple application of the LCP protocol of Theorem 1 for _non-self-referencing LZ77_, but our communication complexity analysis is based on non-trivial combinatorial properties of LZ77 factorization which, to our knowledge, were not previously known.
## 3 Results
### Research Questions' Answers
#### 3.2.7 RQ6  
What Aspects or Variables of Traffic Are Modeled?  
Comparing traffic analysis results under normal and flooded conditions can provide insight into the indirect impacts of floods on transportation networks. Generally, these results are related to travel time, travel distance, and street speeds, although some articles have examined other aspects of traffic. For instance, [PERSON] et al. [49], [PERSON] et al. [21], and [PERSON] et al. [50] assessed the impacts of flooding on the capacity of urban emergency responses, taking into account function losses from emergency stations to critical locations (such as care homes and sheltered accommodation) and changes to service area coverage by emergency services. Additionally, [PERSON] et al. [53] analyzed the number of interrupted trains and their duration of interruption as the response variable in their traffic modeling. Table 4 provides a summary of the traffic aspects analyzed in the articles and the number of articles that used them. Most of the articles focused on changes in private vehicle traffic, with travel time being the most commonly used traffic aspect in the literature concerning flooding situations affecting private transportation. In the case of emergency vehicles, access time and service area coverage are critical aspects that have recently garnered researchers' attention in flooding situations. The sole article that analyzed traffic in public transportation [53] focused on the train network and examined the number of interrupted trains and the duration of their interruption.
## 3 Results and Discussion
### Factors Affecting the Removal of 4-CP by the Combined MPHAC/PS System
#### 3.3.4 Effect of 4-CP Initial Concentration on 4-CP Adsorption and Degradation  
In addition to the initial pH of the solution, MPHAC dose and PS dose, the initial concentration of 4-CP is also important to take into account for a more efficient removal by the MPHAC/PS system. Figure 7 shows the effect of the initial 4-CP concentration on both its adsorption and degradation by the MPHAC/PS system.  
As illustrated, the removal of 4-CP decreased with an increase of the initial 4-CP concentration. This is attributed to several reasons: (i) by increasing the initial 4-CP concentration from 100 to 250 mg/L, the amount of PS and catalyst is maintained constant and the ability of the MPHAC/PS system to adsorb and degrade 4-CP decreases; (ii) the competition between PS and 4-CP for placement on the catalyst surface increases with increased initial 4-CP concentration [42]; and (iii) at higher concentrations of 4-CP, it is possible to produce reaction by products (e.g., phenol, hydroquinone, benzoquinone and malonic acid, as will be explained later on), which also use sulfate radicals for their reactions and reduce 4-CP degradation [26]. [PERSON] et al. [45] also observed a decrease of the removal efficiency of 2,4-dichlorophenol with the increase of its initial concentration. From Figure 7, it can also be seen that the increase of the initial concentration of 4-CP results in a reduction of the adsorption process with an increase of the degradation process. In fact, many of the active sites of the MPHAC are filled with 4-CP molecules as the 4-CP initial concentration increases; then, the relative portion of the adsorption process is reduced, thus the additional 4-CP molecules react with sulfate radicals, increasing the degradation process.  
From all the above performed experiments, optimum operating parameters permitting a high removal of 4-CP can be determined. The highest 4-CP removal factor, 99.5%, was registered for an initial 4-CP concentration of 100 mg/L, a MPHAC dose of 1250 mg/L, PS dose of 350 mg/L, and a solution pH of 5 for 60 min contact reaction time. [PERSON] et al. [45] studied the influence of the initial 2,4-dichlorophenol (2,4-DCP) concentration on  
Figure 6: Effect of PS dose on 4-CP removal factor by the MPHAC/PS system (initial 4-CP concentration: 100 mg/L; MPHAC dose: 1250 mg/L; and a solution pH of 5).  
the degradation efficiency of 2,4-DCP by means of PS activation using magnetic graphene oxide, and observed a decrease of the 2,4-DCP removal efficiency from 100% to 40% with the increase of the concentration of 2,4-DCP from 10 to 80 mg/L.
## 4 Results and Discussion
### Different Nano-TiO\({}_{2}\) Dosage  
Nano-TiO\({}_{2}\) emulsion (content of 5%) was added during the asphalt emulsification process. Among the emulsions, solid content nano-TiO\({}_{2}\) emulsion dosages were 10%, 20% and 30% (a higher content of solid nano-TiO\({}_{2}\) cannot be dispersed, and a lower dosage will lead to less photocatalytic effect). This section mainly investigated the exhaust degradation of different dosages of 5 nm nano-TiO\({}_{2}\) added in the mixture. The test results of two hours are shown in Figure 7.  
Through the analysis of experimental data, we can conclude that with the increase of the amount of nano-TiO\({}_{2}\), the degradation rate of exhaust gas of asphalt mixture tended to increase. However,  
Figure 6: The comparison chart of exhaust degradation different particle size mixture.  
Figure 7: Exhaust degradation effect in different content of nano-TiO\({}_{2}\).  
the degradation of CO and CO\({}_{2}\) in the exhaust gas was not obvious. With the increase of the amount of nano-TiO\({}_{2}\), the degradation rate curves of the two were almost flat. With the amount of nano-TiO\({}_{2}\) increased from 10% to 20%, the degradation rate of NO and HC greatly increased. Relatively, with the content of nano-TiO\({}_{2}\) increased from 20% to 30%, the increase rate decreased.
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 3 Results
### Sleep Quality  
A total of eleven studies assessed the accuracy of SRs in measuring sleep quality outcomes (Table 1). The mean nights of recording were 57 \(\pm\) 138, with a range from of one to 440 nights. The second generation of Oura Ring(r) was the most studied type of SR, in seven studies, and the first generation of Oura Ring(r) was analysed in one, while three focused on the third generation of Oura Ring(r). The control devices were the standard PSG in nine studies, actigraphy in one, and electroencephalography (EEG) in one (Figure 3). In three studies, funding was not specified, three studies were funded by public resources, two from both public and private fundings, one was an independent third-party evaluation, and in one, conflicts of interest emerged. The outcomes explored were: total sleep time (TST) in ten studies, rapid eye movement (REM) time in nine, light sleep time in seven, deep sleep time in seven, N1 and N2 sleep (equivalent to light sleep) in two, N3 sleep (equivalent to deep sleep) in two, Wake After Sleep Onset (WASO) in six, and Sleep Efficiency (SE) in four. All outcomes were analysed through Bland-Altman Plots. The studies' results demonstrate heterogeneity in the data, indicating that the estimates may not always be statistically accurate. A meta-analysis was conducted on three studies that analysed the accuracy of the second generation of Oura Ring(r) compared to PSG (Table 2). These meta  
Figure 2: PRISMA flowchart describing the selection process of the included studies.  
analyses showed a pooled mean bias for TST equal to \(-21.30\) min (SD = 5.4, LOA_L = \(-69.9\), LOA_U = 27.4, CI_Lm = \(-45\),916.4, CI_Um = 45,873.8), and a pooled mean bias for REM sleep equal to \(-18.2\) min (SD = 5.8, LOA_L = \(-33.3\), LOA_U = \(-3.1\); CI_Lm = \(-281.5\), CI_Um = \(245.1\)). The sensitivity analyses also showed the presence of strong uncertainty around the results for the TST outcome due to the width of the LoAs confidence intervals, without substantially altering the interpretation of the main analysis results. However, the sensitivity analysis for REM obtained by removing the results from [PERSON] et al. [42] showed a substantial decrease in the uncertainty of the results with a pooled mean bias of \(-20.8\) min (SD = 6.1; LoA_L = \(-32.9\); LoA_U = \(-8.8\); CI_Lm = \(-36.3\); CI_Um = \(-5.4\)), denoting a very low heterogeneity between the results of the two remaining studies for this outcome [44,50] (Table S3).
## 5 Validation
### Results of Validation--Link 1  
The validation results based on the selected MAE and NRMSE criteria for link 1 are summarized in Table 3. The model output for link 1 achieved the best result based on both statistical indicators at an initial joint angle of 10\({}^{\circ}\) (MAE = 1.0932 and NRMSE = 35.47%). However, even with the best model, the results represent that the agreement of the model output with the measured data is low and insufficient.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Description** & **Value and Unit** \\ \hline m\({}_{1}\) & Mass—link 1 & 5.914 kg \\ m\({}_{2}\) & Mass—link 2 & 3.437 kg \\ l\({}_{1}\) & Length—link 1 & 0.615 m \\ c\({}_{1}\) & Distance to the center of mass—link 1 & 0.293 m \\ c\({}_{2}\) & Distance to the center of mass—link 2 & 0.19 m \\ l\({}_{1}\) & Moment of inertia—link 1 & 0.357 kg\(\cdot\)m\({}^{2}\) \\ l\({}_{2}\) & Moment of inertia—link 2 & 0.013 kg\(\cdot\)m\({}^{2}\) \\ f\({}_{\text{c1}}\) & Viscous friction coefficient—link 1 & 0.35 N\(\cdot\)m\(\cdot\)s\(\cdot\)rad\({}^{-1}\) \\ f\({}_{\text{c2}}\) & Viscous friction coefficient—link 2 & 0.35 N\(\cdot\)m\(\cdot\)s\(\cdot\)rad\({}^{-1}\) \\ f\({}_{\text{d1}}\) & Coulomb friction magnitude—link 1 & 0.35 N\(\cdot\)m \\ f\({}_{\text{d2}}\) & Coulomb friction magnitude—link 2 & 0.35 N\(\cdot\)m \\ \hline \hline \end{tabular}
\end{table}
Table 2: The list of input parameters of the Simulink model.  
Figure 13: Model output of validation for the joint angle 10\({}^{\circ}\)—link 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Angle of Rotation** & **MAE** & **NRMSE** \\ \hline
10\({}^{\circ}\) & 1.0932 & 35.47\% \\
20\({}^{\circ}\) & 2.9180 & 20.29\% \\
30\({}^{\circ}\) & 3.2550 & 34.37\% \\
40\({}^{\circ}\) & 6.1717 & 16.69\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of validation for link 1.  
Figure 14: Model output of validation for the joint angle 20\({}^{\circ}\)—link 1.