esa-sceva/satcom-chunk-collection huggingface dataset:
# Visible Light Communication: An Investigation of LED Non-Linearity Effects on VLC Utilising C-OFDM
## 3 Simulation Results and Discussion  
The performance of the optical OFDM system was significantly impacted by LED non-linearity. The DC bias point was one of the key characteristics that the distortion it led to depended on. In this section, the results were considered in terms of BER performance and power penalty. The comparison that was evident between C-OFDM and fast OFDM was discussed. It is worth mentioning that the real comparison was between the C-OFDM and F-OFDM, as both used real transforms. However, the comparison of the conventional OFDM provided greater clarification.
## 1 Introduction
The polyhedra are formed by these points, where R points represent alternative values based on criteria, and P points indicate weighted criteria values. The method utilizes vectors defined by angles and lengths to establish the geometric relationships among these points, facilitating the decision-making process [44]. Notably, the ADAM method presents distinct advantages over other MCDM approaches: it is easily comprehensible, user-friendly, robust against an increasing number of criteria, highly intuitive, and exhibits minimal susceptibility to alterations in rankings. The method's simplicity is evident in its reliance on straightforward calculations involving the volumes of geometric bodies, specifically polyhedra. Despite being complex, these polyhedra entail straightforward volume computations, requiring only a fundamental knowledge of geometry [44; 45]. Consequently, the method proves easily applicable, with the complexity of determining alternative values remaining unaffected by an escalating number of criteria. Leveraging polyhedra volumes, which can be graphically represented with ease, facilitates the straightforward interpretation of results, enabling clear conclusions regarding the final ranking of alternatives. Furthermore, the method's intuitive nature is enhanced by visualizations of the obtained solutions. Method testing proves the remarkable stability of outcomes, with alterations in criterion weights causing only marginal shifts in results--indicating a low risk of altering alternative rankings [44; 45]. This is one of the youngest MCDM methods, but its applicability is well proven. [PERSON] et al. [45] analyzed the impact of digitization on achieving circularity in the agro-industry using a combination of a Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis, the ANP method, and the ADAM method. [PERSON] et al. [44] conducted an assessment of business models to select the best one and achieve a sustainable system of food production and consumption by combining the Best Worst Method (BWM) and the ADAM method. A review of existing research indicates a notable absence of any prior application involving the combination of SWARA and ADAM methods for material handling technology selection or any other problem. This represents an unexplored research gap that the current paper aims to address. This paper is organized as follows: In the introduction, the background of the cement production and handling problem is described, and based on that, an overview of relevant papers in the field of selection bulk material handling technologies and MCDM methods is provided. The second section outlines the methodology for evaluating these technologies. In the third section, a case study analysis resolves the technology selection issue for the defined alternative solutions and criteria. The fourth section discusses the solution, its practical and theoretical implications, and its limitations. The last section concludes the paper and suggests future research directions.
# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 2 Related Works
### High-Resolution Image Detection-Related Research  
There has been extensive research on high-resolution image detection and recognition in the field of satellite remote sensing. For example, in [18], buildings in remote sensing images captured by drones were successfully identified. In [19; 20], ships in Gaofen-3 SAR images were detected and identified. In [21], sparse representation was combined with various characteristics of remote sensing images for the first time, improving the accuracy of high-resolution image target recognition. Representatively, [PERSON]. [22] proposed the HRDNet network model designed for small target detection in high-resolution images. To improve detection speed, [PERSON]. [23] proposed a multi-GPU distributed computing method, optimizing performance for target detection tasks on 4K and 8K images. However, current research on high-resolution image detection primarily focuses on detecting conventional targets such as faces, pedestrians, buildings, ships, and aircraft [24]. These studies mainly focus on small target detection and typically deal with images of regular size ratios. Unlike target detection tasks, this study focuses on image recognition. The carriage line-scan images in this study are characterized by ultra-high resolution and extreme aspect ratios, with each image corresponding to an entire carriage. Thus, existing research results do not address the image recognition issues in this study.
# Development Trend of Electronic Nose Technology in Closed Cabins Gas Detection: A Review
## 3 Concept and Principle of the Electronic Nose  
The bulk detection of VOCs plays an important role inside enclosed cabins, as it has been a key issue for decades, but its detection methods are still stuck in the traditional manual detection with large gas Chromatographs [46]. The introduction of the electronic nose provides a new method for gas detection in closed cabins. The sensor array is an important part of the electronic nose The gas sensors used in the electronic nose are metal oxide semiconductor (MOS) [47], quartz crystal microbalance (QCM) [7], electrochemical (EC) [48], etc. Metal oxide semiconductor sensors are low cost and have high response sensitivity [49]. They are suitable for most electronic nose systems. We can evaluate the performance of the sensor from eight aspects, as shown in Figure 2. The progress and development of gas sensors have become the driving force for improving traditional detection methods. For different gases, electronic nose sensors can identify specific gases in closed cabins [16] and specific gas concentrations [29].  
Since the birth of electronic nose detection technology, its non-destructive detection process and the advantages of low-cost equipment [50] have led to its rapid development in gas detection. From a commercial point of view, the development of electronic noses employs pattern recognition systems to meet the requirements of designing different detection situations. As a result, the electronic nose is now developing in a more economical and portable direction to facilitate its application in practical situations. The detection limits of the electronic nose apply to the gas concentrations in closed cabins responsible for the national standards and regulations. The criterion to be reconsidered is the suitability of the enclosed cabin for gas detection, including direct assessment of the air inside the enclosed cabin. The re-recommended minimum concentrations for electronic nose performance  
Figure 2: Electronic nose sensor array main parameters.  
should not be evaluated based on the type of sensor array, but more consideration should be given to the environmental science acceptance of odor detection factors. Depending on the type of gas, a range of volatile components is released within the enclosed cabin. The lower concentrations of volatile gases within closed cabins can be achieved by adding pre-concentrator and by changing the electronic nose chamber design to achieve low concentration gas detection [17]. At present, the electronic nose has high feasibility as a volatile gas detection in closed cabins.  
This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.
## 3 Results and Discussion
### Sensitivity Analysis: Evaluating Predictor Variable Impacts on Thermal Loads
In a semi-arid climate like that of Marrakech (Figure 6), X6 demonstrates a relatively strong correlation with QHEAT (r = 0.90). X1, X2, X7, and X3 exhibit low to very low correlations, respectively. Other variables show no significant correlation. Regarding QCOOL, a moderate correlation is identified with X7 and X8, a minor correlation is present with X2 and X6, and even less is seen with X4 and X9. Other variables indicate a lack of correlation. In summary, for the three climates considered concerning QHEAT, it can be concluded that a strong correlation is evident with the air change rate (X6) (average r = 0.92). X1, X7, X2, and, to a lesser extent, X3 show low to very low correlations. No significant correlation is found for other variables. Regarding QCOOL, a moderate correlation is demonstrated by the east window-to-wall ratio (X8) (average r = 0.60), followed by X7 (average r = 0.52). X2, X6, X4, and X9 exhibit low to very low correlations, respectively, while others show no notable correlation. The inclusion of all available predictor variables at the outset was intentional and aligned with the methodology of performing a comprehensive feature evaluation. The purpose of the sensitivity analysis was twofold, as follows:* To quantify and rank the relative importance of each predictor variable with respect to its contribution to thermal load prediction. This is given in the form of filter feature selection analysis based only on the Pearson correlation between variables;
* To get information about the predictor variables with a strong impact on thermal loads;
* To support and validate the subsequent application of the Integral Feature Selection (IFS) method, which systematically eliminates redundant or non-informative features, and searches for optimal combinations of predictor variables can be employed to improve model performance and reduce complexity. By including all potential variables, even those with seemingly weak direct correlations, we ensured that no latent interactions or higher-order effects were prematurely excluded. In some machine learning algorithms, especially nonlinear ones, variables that are weakly correlated individually may still contribute to model accuracy through interactions. Consequently, the sensitivity analysis results helped justify the exclusion of certain features during the optimization phase and provided insights into the relative impact of each variable, which ultimately guided the development of a more robust and interpretable predictive model.
## 2 System Model
### Algorithm Model  
In this paper, the EDSAA based on MCRCS is proposed in Figure 2. There are \(N\) SUs and one PU system, which includes \(N\) PUs and one DC. The PUs have authorized spectra, which are used to form multiple complexes with SUs. Each SU passes the local energy detected and submits its own detection results to the DC through a unique signaling channel. The DC examines and compares these submitted results to determine the final available free spectrum and allocates the free spectrum to SUs. When the free spectrum is allocated, SUs can use adaptive modulation techniques to transmit data in the allocated spectrum. As shown in Figure 2, the gray portion represents the spectrum used by the PUs that was detected by DC; the black portion represents a small, fixed frequency band between the SU; and the white portion represents the free spectrum determined by the DC. We used \(b_{i}\)\((i=1,2,\ldots,N)\) to represent the allocated spectrum size of SUs, which is the strategy of game participants.  
The DC does not need to obtain the terminal information of the SUs at any moment. When the PUs need to use the spectrum, it will detect the terminal information of SUs, which does not return the spectrum in time. As a result, the channel resources can be maximally saved.  
Figure 1: System Components.  
Figure 2: Enhanced dynamic spectrum allocation algorithm (EDSAA) model.
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Research Perspective** & **Category** & **Literature** \\ \hline Routes and voyages & Single & [24; 25] \\ (periods) single/multi & Multi & [26] \\ \hline \multirow{4}{*}{Market segmentation criteria} & Container types (dry/refrigerated/20\({}^{\prime}\)/40\({}^{\prime}\)) & [28; 29; 30] \\ \cline{2-3}  & Shipper types (contract/general/emergency) & [31; 32] \\ \cline{2-3}  & Contract/spot markets & [31; 33] \\ \cline{2-3}  & Emergency/non-emergency markets & [16] \\ \hline \multirow{4}{*}{Booking strategy} & Bidding/bid-price control/discriminatory bidding strategy & [38] \\ \cline{2-3}  & Booking limit strategy & [39] \\ \cline{2-3}  & Delay-in-delivery strategy & [40] \\ \cline{2-3}  & Overbooking and delivery-postponed strategies & [17] \\ \hline Joint optimization problems & & \\ of SAPC and empty container repositioning & Shipping e-commerce platform & [18; 19; 41; 42; 43] \\ \hline \multirow{4}{*}{Booking strategy} & Multi-type containers and seasonal demand & \\ \cline{2-3}  & fluctuation estimation methods & [47] \\ \cline{1-1} \cline{2-3}  & A bi-level linear programming model & [8] \\ \cline{1-1} \cline{2-3}  & Ship schedule adjustment empty container & [1; 46] \\ \cline{1-1} \cline{2-3}  & inventory management & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the existing literature in the field of slot allocation. resource allocation predominantly employ the traditional \"offline booking\" model, which may not adequately address the dynamic nature of the container liner shipping market. Comparatively, an online booking system can better fit the volatile market, with the container liner shipping sector being exposed in recent years due to its advantages in dealing with market dynamics in real time. This paper brings novelties to our understanding of this area through the collaborative optimization of multi-cycle slot allocation and empty container repositioning under an online booking mode. A new mixed integer programming model is established based on market segmentation and delivery delay strategies. The commercial solver (Cplex) solves the small-scale numerical experiment, and a branch-and-cut algorithm solves large-scale examples. The contributions of this paper are summarized as follows: Firstly, a novel CSAE-CRP with a DP strategy based on online booking is proposed. It contributes to the new collaborative optimization on resource allocation, storage, and repositioning problems. Secondly, an online booking platform is introduced, with which the corresponding mixed integer linear programming (MILP) model is newly developed and no-show and discrimination pricing are incorporated and addressed. Lastly, a new branch-and-cut algorithm is proposed to resolve the MILP model.
## 4 Discussion
### Influence of Operating Parameters on the Adsorption of Lactic Acid onto GAC and Anionic Resins
#### 4.1.2 Effect of Temperature on Lactic Acid Adsorption  
The effect of temperature (303.15 to 353.15 K) shows an interesting trend for lactic acid adsorption from the model fermentation broth (Figure 2a). Increasing the temperature from 303.15 to 353.15 K decreased the adsorption capacity of GAC and RLX425 by \(\sim\)13% and \(\sim\)50%, respectively. Among the adsorbents tested in this study, the AMB400 resin was the most efficient adsorbent for lactic acid recovery, because of its operational flexibility at varying temperature ranges. [PERSON] et al. [25] reported that the lactic acid adsorption capacity decreased by \(\sim\)20% for the Amberlite(r) IRA-67 resin when the temperature increased from 293.15 to 323.15 K with an initial lactic acid concentration of 42.5 g/L. In another study, the adsorption capacity of the lactic acid onto zeolite molecular sieves decreased from 10.5 mg/g to 3.6 mg/g when the temperature increased from 299.15 K to 339.15 K with an initial lactic acid concentration varying between 14.2 and 16.1 g/L [40]. The decrease in lactic acid adsorption at higher temperatures is mainly due to weak interactions between the active sites of the adsorbent  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Resin/** & **Initial** & **Temperature** & **Initial Lactic Acid** & \(q_{e}\) **(mg Lactic** & & \\
**adsorbent** & **pH** & **(K)** & **Concentration** & **Acid/g** & **Acid/g** & **E (\%)** & **References** \\ \hline \multirow{4}{*}{GAC} & 2 & 303.15 & 50 & 175 & 35 & [22] \\  & 2 & 298.15 & 7 & 48 & 69 & [18] \\  & 2 & 303.15 & 22 & 121 & 54 & This study \\  & 2 & 303.15 & 4.8 & 38 & 81 & This study \\  & 2 & 353.15 & 4.7 & 35 & 76 & This study \\ \hline \multirow{4}{*}{RLX425} & 2.83 & 303.15 & 5.7 & 32 & 56 & [16] \\  & 2 & 303.15 & 22 & 60 & 27 & This study \\  & 2 & 303.15 & 4.8 & 17 & 35 & This study \\  & 2 & 353.15 & 4.7 & 9 & 18 & This study \\ \hline \multirow{4}{*}{AMB400} & 4 & 303.15 & 100 & 350 & 35 & [39] \\  & 5 & 298.15 & 88.7 & 197 & 22 & [20] \\  & 4.85 & 303.15 & 60 & 160 & 27 & [17] \\ \cline{1-1}  & 5 & 298.15 & 34 & 107 & 31 & [35] \\ \cline{1-1}  & 5 & 303.15 & 18 & 48 & 24 & This study \\ \cline{1-1}  & 5 & 303.15 & 5.4 & 31 & 57 & This study \\ \cline{1-1}  & 5 & 353.15 & 4.7 & 25 & 52 & This study \\ \hline \hline \end{tabular}  
* Notes: \(q_{e}\) = amount of lactic acid adsorbed onto the adsorbent at equilibrium (mg/g) and \(E\) = lactic acid recovery efficiency (%).  
\end{table}
Table 6: Lactic acid recovery from the model fermentation broth using GAC, RLX425 and AMB400 in batch adsorption experiments (adsorbent dose 10% \(w/v\)).  
and adsorbate molecules [41]. The extraction of acids by GAC and amine groups is shown to be exothermic and decreases with increase in temperature due to the following reasons: (i) decrease in the adsorption affinity towards adsorbate molecules; (ii) shifting the equilibrium towards the liquid phase; and (iii) promoting desorption as the reactions are exothermic in nature [42].
## 3 Case Study
### Algorithm and Computer Settings  
In our study, the calculations were carried out on a computer with Windows 10 for 64 bits, 8 Gigabytes of RAM and processor Intel Core i7-4510U 2-2.60 GHZ. Regarding the software, we used R and SPSS.  
Algorithm 1 and Figure 1 provide a summary of the methodology proposed for the dimension reduction, where we utilize rotation methods and disjoint components (a special type of the sparse technique). To compute the disjoint components, the algorithms proposed in [45; 46] were implemented. In the computational experiments conducted in this work, each algorithm was executed five times and the best solution was considered, which was obtained in eight seconds and three iterations during the processing.  
```
1:Collect the data in a matrix \(\mathbf{X}\) of order \(n\times p\), where \(n\) is the number of individuals and \(p\) is the number of variables.
2:Standardize \(\mathbf{X}\) subtracting the mean and dividing by the standard deviation of each variable.
3:Apply an FA determining the number \(q\) of factors (\(q<p\)) to be extracted from \(\mathbf{X}\) using some known method (for example, the scree test).
4:Compute \(q\) factors from \(\mathbf{X}\), fit the model with some known extraction method (for example, PCA), and go to Step 7 if the factor matrix \(\mathbf{F}\) is interpretable; otherwise, continue with Step 5.
5:Use rotation methods (data fit is kept) and go to Step 7 if the factor matrix \(\mathbf{F}\) is interpretable; otherwise, continue with Step 6.
6:Employ a DPCA computing \(q\) disjoint components from \(\mathbf{X}\) and fit the model based on the algorithms proposed in [45; 46].
7:Plot the reduced space of the variables.
8:Interpret the latent dimensions and conclude.
```  
**Algorithm 1** Method for dimension reduction.  
The methodology we propose seeks to facilitate the task of the data scientist. When performing a dimension reduction analysis, the most important objective is to interpret and characterize the latent dimensions. The first attempt is to carry out an FA using PCA to compute the latent dimensions (in general, any extraction method can be used).  
If the factorial matrix \(\mathbf{F}\) is not interpretable, we recommend to apply some rotation method to obtain an interpretable factorial matrix \(\mathbf{F}\), maintaining the fit of the model. If the non-interpretation problem persists, we suggest calculating disjoint components, losing a bit of fit in the model. We can see this procedure as a flow diagram in Figure 1. Computing disjoint components has the consequence of losing a bit of fit in the model,but the interpretation of the latent dimensions is guaranteed. The advantage of disjoint components can be seen in tables and figures presented in the next subsections.
## 2 The Phytochemistry of _Artemisia annua_ L. (Qinghao)
### Sesquiterpenenoids
#### 2.6.2.2. Germacranes and Elemanes  
Germacrene D **(392)** is the most abundant member of the germacrane class of sesquiterpenenes [22, 37, 46] and can constitute between 5-20% of the essential oil [19, 42, 43]. Germacrene A synthase, the enzyme which catalyzes the cyclization of FPP **(378)** to germacrene A **(390)**, has recently been isolated from the glandular trichomes of _A. annua_[137]. Compound **(395)** ([24703-35-3]) is the only example of a bicyclogermacrane sesquiterpene so far described from _A. annua_[19, 32, 43, 45, 74].  
\begin{table}
\begin{tabular}{|l|l|l|l|} \hline
**Name** & **Alternative Name(s)** & **CAS Number** & **References** \\ \hline (+)-Germacrene A **(390)** & Germacrene & [28028-64-0] & [74] \\  & (1([0]_E_,_4_(_E_))-Germacra-1([10),4,11-triene & \\ \hline Germacrene B **(391)** & (1([10]_E_,4_(_E_)-1(10),4,7(11)-Germacatriene & [15423-57-1] & [23, 28, 46] \\ \hline Germacrene D **(392)** & 1(10),4(15),5-Germacatriene & [23986-74-5] & [19, 23, 31-33, 36, 37, 42, 43, 45, 48, 66, 135] \\ \hline
1B-Hydroxy-4(15),5(_E_),10(14)-germacatriene **(393)** & & [9] \\ \hline Pregeijerence **(394)** & 11,12,13-tri-_nor_-1(10),4,6,-Germacatriene & [20082-17-1] & [41] \\  & 1,5-Dimethyl-1,5,7-cyclodextrinatriene & \\ \hline \end{tabular}
\end{table}
Table 31: Germacrane Sesquiterpenoids (1,7-Dimethyl-4-(1-methylethyl)cyclodecane, 9 CI).  
\begin{table}
\begin{tabular}{|l|l|l|} \hline
**Name** & **Alternative Name(s)** & **CAS Number** & **References** \\ \hline (+)-Germacrene A **(390)** & Germacrene & [28028-64-0] & [74] \\  & (1([0]_E_,_4_(_E_))-Germacra-1([10),4,11-triene & \\ \hline Germacrene B **(391)** & (1([10]_E_,4_(_E_)-1(10),4,7(11)-Germacatriene & [15423-57-1] & [23, 28, 46] \\ \hline Germacrene D **(392)** & 1(10),4(15),5-Germacatriene & [23986-74-5] & [19, 23, 31-33, 36, 37, 42, 43, 45, 48, 66, 135] \\ \hline
1B-Hydroxy-4(15),5(_E_),10(14)-germacatriene **(393)** & & [9] \\ \hline Pregeijerence **(394)** & 11,12,13-tri-_nor_-1(10),4,6,-Germacatriene & [20082-17-1] & [41] \\  & 1,5-Dimethyl-1,5,7-cyclodextrinatriene & \\ \hline \end{tabular}
\end{table}
Table 30: _Cont._The ten-membered germacrane ring frequently occurs with unsaturation at the 1(10) and 4-positions, as is apparent from Table 31. The six-membered ring in the elemane sesquiterpenenes (Table 32) is thought to be formed by Cope rearrangement of such 1(10),4-germacradienes. Elemanes may thus be artifacts which are introduced during the extraction and isolation process, especially, perhaps, as a result of the high temperatures often associated with GC-MS analysis (e.g., **397** may be formed thermally from **391**; and **396** may be formed from **390** by just such a pericyclic reaction).