esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Method
### Experimental Environment
The parameters for the 12 light sources used in the experiment are detailed in Table 1. To accurately measure the CCT and illuminance levels produced under each lighting condition, we employed the Konica Minolta CL-500A Chroma meter. The measured parameters, reflecting the precise lighting conditions achieved, are presented in Table 2. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Spectrum**} & \multirow{2}{*}{\begin{tabular}{c} \(E_{h}\) **in lux** \\ **(\(\pm\)2\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **CCT in K** \\ **(\(\pm\)3\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\(Duv\)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} \(u^{\prime}\) **(\(\pm\)0.015)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **\(v^{\prime}\) **(\(\pm\)0.015)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **IES TM30–20** \\ **R\({}_{f}\)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **IES TM30–20** \\ **R\({}_{g}\)** \\ \end{tabular} } \\ \hline SPD1 & 50 & 3081 & \(-\)0.0162 & 0.2539 & 0.4972 & 82 & 100 \\ SPD2 & 103 & 3015 & \(-\)0.0154 & 0.2556 & 0.4995 & 81 & 100 \\ SPD3 & 152 & 3038 & \(-\)0.0152 & 0.2548 & 0.4994 & 82 & 99 \\ SPD4 & 198 & 3046 & \(-\)0.0153 & 0.2546 & 0.4992 & 81 & 99 \\ SPD5 & 50 & 3965 & \(-\)0.0212 & 0.2376 & 0.4760 & 81 & 100 \\ SPD6 & 105 & 3974 & \(-\)0.0212 & 0.2375 & 0.4758 & 81 & 100 \\ SPD7 & 156 & 3943 & \(-\)0.0211 & 0.2379 & 0.4764 & 81 & 100 \\ SPD8 & 200 & 3949 & \(-\)0.0211 & 0.2378 & 0.4763 & 81 & 100 \\ SPD9 & 49 & 5019 & \(-\)0.0211 & 0.2261 & 0.4619 & 79 & 101 \\ SPD10 & 102 & 5068 & \(-\)0.0212 & 0.2257 & 0.4612 & 79 & 101 \\ SPD11 & 149 & 5084 & \(-\)0.0212 & 0.2256 & 0.4609 & 79 & 100 \\ SPD12 & 196 & 5082 & \(-\)0.0215 & 0.2259 & 0.4607 & 79 & 101 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Photometric and colorimetric data of the illumination Spectral Power Distributions (SPDs) used in the experiment. Figure 1: Laboratory diagram (blue labels indicate indirect light).
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
###### Abstract  
In this article, an innovative approach for magnetic data communication is presented. For this purpose, the receiver coil of a conventional magneto-inductive communication system is replaced by a high-sensitivity wideband magnetic field sensor. The results show decisive advantages offered by sensitive magnetic field sensors, including a higher communication range for small receiver units. This approach supports numerous mobile applications where receiver size is limited, possibly in conjunction with multiple detectors. Numerical results are supported by a prototype implementation employing an anisotropic magneto-resistive sensor.  
Keywords:wireless communication; magnetic induction communication; mobile sensing systems; magnetic sensors; RF-challenging environments +
Footnote †: journal: Sensors
# Fermentation Characteristics and Microbiota during the
## 3 Results
### Microbial Diversity in M. aquaticum Silage
aquaticum_ silage after 60 days of ensiling are shown in Figure 4. At the phylum level, after 60 d of fermentation, the main phylum in the control silage were Basidiomycota (55.6%), Ascomycota (29.4%), Mortierellomycota (0.8%), and Mucromycota (0.1%). Similarly, the LP-treated silage was predominated by Basidiomycota (56.6%) and Ascomycota (27.9%), while the abundance of Ascomycota was highest in FH-treated silage (83.6%) and followed by LB-treated silage (63.0%). The abundance of Basidiomycota was observed in FH-treated silage (12.5%) and LB-treated silage (21.8%). [PERSON] et al. [29] also found that Ascomycota had a high abundance, and then was followed by Basidiomycota in silages. However, [PERSON] et al. [27] found that Ascomycota had the highest abundance at the phylum level in the control silage after 60 days of ensiling, whereas Basidiomycota was still the dominant phylum in the LAB-treated silage. This result was different from our finding that Basidiomyc was the dominant phylum in the control silage of _M. aquaticum_. At the genus level, the abundance of _Saitozygous_ was higher in LP-treated silage (48.9%) and silage treated with CK (45.4%), than that in LB-treated silage (14.8%) and FH-treated silage (9.3%). _Saitozygous_ was reported to suppress acetic acid fermentation and is an integral component of soil and epiphytic plants [30; 31; 32]. Although more information on the fungal microbiota of silage is lacking, the roles of these fungi in forage ensiling are not known. Figure 4: The epiphytic fungal community in _M. aquaticum_ silage after 60 d of ensiling. The bacterial communities are shown at the phylum level (**A**) and the genus level (**B**). CK, control; LB, _Lactobacillus buchneri_; LP, _Lactobacillus plantarum_; and FH, combination of LB and LP.
## 3 Results and Discussion
### Descriptive Sensory Analysis  
An appropriate performance of the panel was observed with a good reproducibility by the end of the orientation sessions. Sixteen attributes (odor, basic tastes, flavor, texture, and defects) were used to fully describe the smoothies and are presented in Table 4. The smoothies prepared with _Mollar de Elche_ pomegranate juice presented higher intensity of the fruit puree (F, J or Q) and pear odor, while the _Wonderful_ smoothies had more intense pomegranate odor. The use of figs intensified the grape and pomegranate odor notes, while jujubes increased the pear notes. Regarding the basic tastes, _Mollar de Elche_ pomegranate smoothies led to sweeter notes compared to the scores of _Wonderful_ which were defined as sourer samples. With respect to the fruit puree, the use of figs intensified the sweetness (Table 4). Regarding to flavor attributes, similar trends as odor attributes was observed. _Wonderful_ samples led to higher scores of pomegranate compared to _Mollar de Elche_ smoothies that presented higher intensities of fruit puree (F, J or Q). In accordance with our results, previous study indicated that the use of _Wonderful_ juices in pomegranate products enhanced pomegranate notes [16]. The fiberness and consistency were the texture attributes evaluated and highly significant differences were observed, especially in the product fiberness. The use of jujubes and quintecs led to the highest intensities of fiberness and consistency, respectively. Therefore, the type of fruit puree used had an important effect on fiberness, with jujubes leading to the highest intensity and quintecs to intermediate values. Finally, it is important to highlight that the factor having the lowest influence on the sensory profile was the ratio puree:juice, which only influenced the consistency (with the 60:40 products being the most consistent ones). Recently, it was observed that the pectin content increased when using the highest content of fruit puree in smoothies [5]. Pectin has techno-functional characteristics which enhance texture of the smoothies by the reaction of certain water-soluble pectic substances with Ca ions to form some Ca pectates [5]. The puree:juice ratio can, therefore, be adjusted accordingly to the consistency preferred by the consumers. The price of the fruits used for the puree should be also be taken into consideration in order to produce a smoothie with high qualitative characteristics, but at affordable price. It is worth mentioning that a previous study indicated that consumer's overall liking in a jujube fruit consumer study was highly correlated with jujube flavor (high intensity), sweetness (high intensity), and bitterness (low intensity) [23]. In our study, the highest jujube notes and sweetness was found for the ratio 40J:60 PM. It represents a good starting point for further development/exploitation of novel smoothies. Similar trend was found in dried pomegranate girls, where the consumer's overall liking was linked with fresh pomegranate flavor [16].
# A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion
## 4 Experiment and Analysis
### Objective Assessment of Image Quality  
#### 4.2.1 Lightness Order Error (LOE)  
Objective indicators are crucial for evaluating the performance of fusion algorithms. In this study, we use three objective evaluation indicators to evaluate different methods, including lightness order error (LOE) [15], visual information fidelity (VIF) [28], and the natural image quality evaluator (NIQE) [29]. Among them, LOE is an important indicator to judge the image enhancement algorithm, which is defined as:  
\[\text{LOE}=\frac{1}{n}\sum_{i=1}^{n}RD(x) \tag{15}\]  
Figure 10: Enhanced effect of ”wardrobe” image.  
Figure 9: Enhanced effect of ”gym” image.  
\[RD(x)=\sum_{j=1}^{n}U(L(x),L(y)\oplus U(L_{e}(x),L_{e}(y) \tag{16}\]  
where \(n\) is the number of pixels, \(L(x)\) represents the lightness component at location \(x\) of the input images, \(\oplus\) stands for exclusive-or operator, \(L_{e}(x)\) represents the lightness component at location \(x\) of the enhanced images, and \(U(p,q)\) is a function that returns \(1\) if \(p\geq q\), 0 otherwise. A smaller LOE value indicates better brightness naturalness. Quantitative comparisons of the performance among the different methods are presented in Table 1. It is evident from Table 1 that our enhanced images have the smallest LOE values, demonstrating the best brightness naturalness.
## 5 Conclusions  
The study found that with Photovoltaic Aquaculture Systems (PV-AQS) it is possible to reduce energy costs by 50%, without the need for large investments. The multi-criteria analysis (technical, economic and environmental) jointly indicated that they are a viable and sustainable solution for implementation.  
The current data allow the establishing of the fact that the cost per kilowatt of a PV-AQS represents an opportunity, so it is recommended to know the benefits of On Grid-PV to plan its construction with the support of analyzed financial institutions that facilitate credits for its realization. This is based on the fact that there is a real possibility, and they are susceptible to access capital, which has an investment recovery period of less than five years.  
The data on costs in Mexico per Kwp of USD are useful for all types of farms and future designs for the estimation of construction costs for small ARELs, SMEs and industrial farms. The characteristics of the farms are very varied, and the only common factor is that most of them require energy, since shrimp, carp, Tllapia, catfish or other species farms all use electrical energy, mainly for pumps and oxygenation. Therefore, with the information contained in the document, it was demonstrated that photovoltaic technology is underutilized in the sector and that it is replicable in any farm in the production of any species. For the design of an On Grid-PV for any production system, it is only required to perform an energy diagnosis and its geographical location.
## 6 Conclusions  
1. Based on the study on the tower deviation error formation mechanism in the process of cable lifting, the related formulas of the arch rib elevation change caused by the change of tower as the temporary structure state is deduced. The studies show that the arch rib hanging on it has a linkage effect due to tower stress deformation, and greatly influences the precision of arch girder suspension.
2. Traditionally, the tower deviation is controlled by arranging a large number of steel strands and manually pulling the steel strands, which wastes a lot of steel strand materials and labor. Now, the cost is greatly reduced by the technology proposed in this paper.
3. Under normal operation, the influence of active regulation on the stress of each component of the structure is not significant. This means that active regulation has an obvious influence on the lateral displacement of the tower, but its sensitivity to the internal force and stress of the tower is very low, and the tower is in a safe state.
4. The technology, having been applied to world-class arch bridges, can actively and timely apply pressure to the tower top, correct the deviation of tower in the process of cable lifting, reduce the deviation value, and finally reduce the impact on the precision of the arch girder suspension. Furthermore, it can realize the real-time control of the tower maximum deviation within the scope of \(\pm\)2 cm. The research results explore and improve the CFST arch bridge construction technology, and provide a construction reference for the tower deviation control of the same types of bridges and more long-span CFST arch bridge with intergraded main cable and buckle. With the development of positioning technology, the measurement accuracy of tower offset will be higher, and this technology will be increasingly widely used.  
Conceptualization, N.D. and M.Y.; methodology, M.Y.; data analysis X.Y.; writing--original draft preparation, M.Y and X.Y.; writing--review and editing, M.Y. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Nanning City \"Yongjiang Project\" funded project (2018-01-04), National Natural Science Foundation of China (grant number 51868006), and Nanning Excellent Young Scientist Program (grant number RC20180108).  
Not applicable.  
The testing and analysis data used to support the findings of this study are included within the article.  
The authors declare no conflict of interest.
## 2 Latent Space Based Learning
### Prediction Model  
We aim to obtain a prediction model that minimizes the reconstruction error \(\sum_{uij}\left(s_{uij}-\hat{s}_{uij}\right)^{2}\) of the sentiment strength scores over all users, items, and opinion-words. For minimizing the above expression our model uses the Alternating Least Squares (ALS) method to reconstruct user opinions--that is, \(\mathbf{t}_{uii}\) vectors--not included in \(S\). This factorization collects patterns of taste among similar reviewers generating automatic predictions for a given user.  
Specifically, we subject the input matrix \(\mathbf{R}\in\mathbb{R}^{N\times D}\) to an ALS factorization [30] of the form \(\mathbf{R}\approx\mathbf{PQ}^{\text{T}}\) in order to estimate the missing reviews. Here, \(\mathbf{P}\in\mathbb{R}^{N\times K}\) and \(\mathbf{Q}\in\mathbb{R}^{D\times K}\), where \(K\in\mathbb{N}\) is the number of latent factors or features [8]--a predefined constant typically of the order of ten. Any sentiment score \(s_{uij}\) can then be approximated by the scalar product \(\hat{s}_{uij}:=\mathbf{p}_{u}^{\text{T}}\mathbf{q}_{ij}\) (recall that \(\hat{s}_{uij}\) is the prediction), where \(\mathbf{p}_{u}\in\mathbb{R}^{K\times 1}\) is the \(u\)-th row of \(\mathbf{P}\) and \(\mathbf{q}_{ij}\in\mathbb{R}^{K\times 1}\) the \((\sum_{1}^{l}D_{k}+j)\)-th row of \(\mathbf{Q}\). The procedure monotonically minimizes the following quadratic loss function until convergence is reached  
\[\langle\mathbf{P},\mathbf{Q}\rangle:=\text{arg min}_{\mathbf{P},\mathbf{Q}} \sum_{(u,i)\in S}\left(\lambda\mathbf{p}_{u}^{\text{T}}\mathbf{p}_{u}+\sum_{ j=1}^{D_{i}}\left(\varepsilon_{uij}^{2}+\lambda\mathbf{q}_{ij}^{\text{T}} \mathbf{q}_{ij}\right)\right),\]  
where \(\varepsilon_{uij}:=s_{uij}-\hat{s}_{uij}\). Here, \(\lambda\) denotes the regularization parameter that prevents overfitting and plays a crucial role in balancing the training error and the size of the solution (see Section 4 for further details).
## 2 Related Work
### Recommendation Algorithms  
#### 2.3.1 Collaborative Filtering  
In the field of recommendation, Collaborative Filtering (CF) [18] is a kind of classic algorithm. Its basic principle is to determine the degree of similarity between the recommended objects based on various pieces of similarity information, in order to recommend content that the recommended objects may like. If Collaborative Filtering is classified according to neighborhood-based data, it can be divided into User-Based Collaborative Filtering (UserCF) and Item-Based Collaborative Filtering (ItemCF). UserCF determines the user's neighbors by calculating the similarity of access items between users, and recommends users based on their items' access records. ItemCF determines the relevance of items by calculating the similarity of user visits between them, and recommends them based on the similarity between items that the users has visited and other items that have not been visited. There is a certain difference in the scope of application between these two methods. UserCF is mainly used to recommend items to users who share common interests and hobbies. Therefore, it has group and social characteristics. From a technical perspective, it is necessary to maintain a similarity matrix of users, which is suitable for situations where users are stable but items are updated quickly. ItemCF recommends items to users that are similar to their previous likes, thus better reflecting their interests. From a technical perspective, it is necessary to maintain a similarity matrix of items, which is suitable for situations wherein items are updated slowly. Overall, the effectiveness of using collaborative filtering alone is limited. If it is mixed with other types of algorithms, better results can be achieved.
## 2 ISF Coefficient Quantization in AMR-WB
### Linear Prediction Analysis  
In a linear prediction, a Levinson-Durbin algorithm is used to compute the 16 th order LPC, \(a_{i}\), of a linear prediction filter, defined as  
\[\frac{1}{A(z)}=\frac{1}{1+\sum\limits_{i=1}^{16}a_{i}z^{-i}}. \tag{1}\]  
Subsequently, the LPC parameters are converted into the immittance spectral pair (ISP) coefficients for the purposes of parametric quantization and interpolation. The ISP coefficients are defined as the roots of the following two polynomials\[F_{1}^{\prime}(z) =A(z)+z^{-16}A(z^{-1}), \tag{2}\] \[F_{2}^{\prime}(z) =A(z)-z^{-16}A(z^{-1}). \tag{3}\]  
\(F_{1}^{\prime}(z)\) and \(F_{2}^{\prime}(z)\) are symmetric and antisymmetric polynomials, respectively. It can be proven that all the roots of such two polynomials lie and alternate successively on a unit circle in the \(z\)-domain. Also, \(F_{2}^{\prime}(z)\) has two roots at \(z=1\) (\(\omega=0\)) and \(z=-1\) (\(\omega=\pi\)). Such two roots are eliminated by introducing the following polynomials, with eight and seven conjugate roots respectively on the unit circle, expressed as  
\[F_{1}(z)=F_{1}^{\prime}(z)=(1+a[16])\prod_{i=0,2,\ldots,14}\,(1-2q_{i}z^{-1}+z ^{-2}), \tag{4}\]  
\[F_{2}(z)=\frac{F_{2}^{\prime}(z)}{(1-z^{-2})}=(1-a[16])\prod_{i=1,3,\ldots,13 }\,(1-2q_{i}z^{-1}+z^{-2}), \tag{5}\]  
where the coefficients \(q_{i}\) are referred to as the ISPs in the cosine domain and \(a\)[16] is the last predictor coefficient. A Chebyshev polynomial is used to solve (4) and (5). Finally, derived from the ISP coefficients, 16 th order ISF coefficients \(\omega_{i}\) can be obtained by taking the transformation \(\omega_{i}=\arccos(q_{i})\).
## 5 Application Results and Analysis
### Simulated Signal
Besides, NewEBWS outperforms SNRBWS in MSE and NCC indexes, and the advantage of SNRBWS over NewEBWS method in MAE index gets smaller when the energy proportion of the coefficient with maximum absolute value in the coefficients gets smaller.
# Polar Code Parameter Recognition Algorithm Based on Dual Space
## 4 Numerical Simulation
### Influence of Code Rate  
A recognition rate with lengths of 128, 256, and 512 is simulated when the code rate is different. The number of codewords was 2000, the  
As shown in the figures, when the code rate decreases, the recognition performance is improved, but this performance improvement decreases with increasing code length. This is because when the code length is constant, the dimension of the dual space increases as the code rate decreases, and the calculation of the statistic is closer to the theoretical value, so the recognition performance is improved. Otherwise, the increase in the code length will increase the total code weight of the dual-space vector, making the calculation of the statistic more susceptible to bit errors. This slowly offsets the performance improvement brought about by the reduction of the code rate. The longer the code length, the more obvious the offset, and the recognition performance curves of different code rates become increasingly closer.
## 4 Study on Mining Layout Pattern Based on Numerical Simulation
### Control Effect of Rockburst under the Layout Pattern of Small Panel and Large Coal Pillar  
After adopting the layout pattern of a small panel and a large coal pillar, the inclined mining scale of the third panel is 550 m, and the panel-isolated coal pillar's width is 200 m between the second and third panels, as shown in Figure 1. According to theoretical analysis and numerical simulation conclusions, under the current panel mining scale, the reasonable width for the panel-isolated coal pillar should be between 210 m and 270 m. The actual width of the coal pillar left between the second and third panels is less than the reasonable width, leading to a higher degree of stress concentration in the panel-isolated coal pillar area, posing a risk of rockburst during the mining process in the third panel. Based on the distribution characteristics of microseismic events greater than \(1\times 10^{3}\) J in the panel-isolated coal pillar area between the second and third panels during the monitoring period from September to December 2021, the plane distribution map of microseismic events in the isolated coal pillar area is shown in Figure 15. By December 2021, working face 205 (LW205) had completed mining, and working face 301 (LW301) had advanced 852 m, in the later stages of mining. During this stage, there were a lot of microseismic events on the side of the isolated coal pillar near working face 301 (LW301), and there were large-energy microseismic events in the elastic region of the coal pillar.  
This indicates that with the advancement of working face 301, the goat area in the third panel expands, and the stress on the panel-isolated coal pillar area increases. Combined with the mining disturbance to the panel-isolated coal pillar, there were many large-energy microseismic events in the panel-isolated coal pillar area, extending into the central elastic area. Therefore, under the current layout pattern of a small panel and a large coal pillar, the isolation protection effect of the panel-isolated coal pillars is limited, and there is still a certain risk of rockburst. Appropriate unloading and prevention of rockburst measures need to be taken.