esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions and Discussion  
To realize noise reduction in one zone without changing the sound pressure in the other zone, three wideband ANC algorithms, based on the ACC, CLS, and SFR-ACC concepts are proposed in this paper. With a loudspeaker array and the white noise modeling method, dual-zone ANC with directivity is realized. Compared with the LS algorithm, the proposed three methods can reduce the noise in the control zone, as well as reduce the increase of sound power in the noncontrol zone. From 300 to 1200 Hz, the noise reduction in the control zone is more than 10 dB, and the increase of sound power in the noncontrol zone is less than 1 dB. Compared with the experimental results, the off-line simulation results (theoretical results) have a good agreement, which verifies that the algorithms proposed in this paper are effective and efficient. In addition, to acquire better noise reduction, the frequency range of modeling and calculating filter coefficients should be much wider than the frequency of interest.  
Considering the dual-zone ANC system proposed in this paper as one single unit, and assuming that one control system consists of many units, an arbitrary quiet zone and noncontrol zone can be obtained in space. Therefore, the distributed ANC can also be realized based on the algorithm and the ANC system proposed in this paper. Moreover, to acquire a robust ANC system, the method for choosing the regularization parameters is a valuable research issue which will be discussed in future work.  
Conceptualization, R.W., J.L., and X.W.; writing--original draft preparation, R.W.; writing--review and editing, X.W. and J.Y.; investigation, R.W.; supervision, X.W. and J.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 5: The contrasting results of the dual-zone ANC from 300 to 1200 Hz based on the algorithms proposed in this paper: (**a**) is the contrasting results of the theoretical and experimental reduction (**Re**); and (**b**) is the contrasting results of the theoretical and experimental increase of sound power (Isp).  
**Funding:** This research was supported by National Major Projects (No. 613427).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Considerations of General Health Risks Posed by Human Exposure to Aldehydic LOPs
#### 3.7.6 Comparative Evaluations of Health Risks Presented by Dietary LOPs and _Trans_-FAs  
Contributions by researchers concerning the provision of information on _trans_-fatty acid (TFA) levels in edible oils are of much value, especially since evidence relating TFA intake to the development and progression of cardiovascular diseases remains widespread, and their potential health risks in this context are currently considered to be more substantial than those presented by SFAs [105]. However, in view of these estimates, it is of much importance to note that, on a mole-for-mole basis, aldehydes arising from UFA oxidation are markedly more toxic than TFAs, although the estimated dietary intake of the latter is clearly much greater, or at least it will be in societies and countries which have not or not yet set regulatory legislation to remove them from their food supplies.  
Hence, without any rigorous control for the confounding deleterious health effects associated with the intake of aldehydes and further toxic LOPs, along with the concentrations of each of these toxins available in the dietary patterns considered, then such public health investigations targeting TFAs as'malefactor molecules', particularly those focused on their ability to promote CHDs, may be compromised. Moreover, in principle, TFAs may themselves be susceptible to peroxididative damage, followed by sequential fragmentation of any hydroperoxides arising therefrom to corresponding toxic secondary LOPs, including, but not exclusively, aldehydes.  
Despite some major conjecture in the literature available, there remains the possibility that the heating of CFOs according to frying practices may convert natural _cis_-configuration FAs to their corresponding TFA derivatives. One investigation reported a small increase in corn oil TFA levels following its exposure to stir-frying sessions [106].  
A recent WHO report [107] specifies that to date, 58 countries have introduced laws focused on the elimination of industrially generated TFAs from the global food supply, and this will protect an estimated 3.2 billion individuals against the harmful effects of these agents prior to the end of 2021. However, more than 100 countries are still required to act on this health-favourable incentive; moreover, 11/15 countries with the highest incidences of CHDs, apparently arising from TFA ingestion, are yet to take such actions.  
If that's the case for TFAs, what actions and regulatory legislation will be implemented in order to circumvent the deleterious generation and human ingestion of much more highly toxic dietary LOPs?
# Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion
## 2 Materials and Methods
### MLSE and MAP Equalizers  
In this section, the traditional maximum a posteriori probability (MAP) and the maximum likelihood sequence estimator (MLSE) are introduced.
# Physicochemical Properties and In Vivo Hepatoprotective Effect of Polysaccharides from Grape Pomace
## Materials and Methods
### Characterization of GPPs  
#### 2.4.1 Molecular Weight Determination  
The homogeneity of GPPs was detected with an HPLC system consisting of a waters e2695 platform (Waters, Milford, MA, USA) linked with an RI detector. The column was eluted using ultrapure water, and the flow rate was 0.6 mL/min using a TSK-gel G4000 PWxl column (Tosho, Tokyo, Japan). The average molecular weight was detectedusing a calibration curve, which was established using Dextran standards (known molecular weights: 11,600, 48,600, 80,900, 147,600, 273,000, 667,800, 1,185,000 and 2,150,000).
# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
## 2 Data and Methods
### Model Settings
#### 2.3.3. Deterioration Detection Approach  
In this study, we leverage the strengths of ensemble methods by amalgamation of the prediction outputs of our separately trained models for FNN and LSTM with an equal weighting of 0.5. Additionally, we investigate a wind turbine equipment health assessment method that leverages the prediction outputs of the ensemble model and compares it to the real outputs by calculating the root mean squared error (RMSE) and the root mean squared percentage error (RMSPE) that are defined in Equations (2) and (3) respectively.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\Sigma_{i=1}^{n}\big{(}Y_{i}-\hat{Y}_{i}\big{)}^{ 2}} \tag{2}\]  
\[\text{RMSPE}=\sqrt{\frac{\Sigma_{i=1}^{n}\big{(}Y_{i}-\hat{Y}_{i}\big{)}^{2}}{ \Sigma_{i=1}^{n}Y_{i}}} \tag{3}\]  
Our expectation is that the prediction model is trained on the good timeline. If the measured prediction errors increase then it means that the wind turbine power output diverges from the expected power output and the wind turbine performance has degraded. We can calculate RMSE and RMSPE cutoff limits based on the validation dataset for the good timeline. With these cutoff limits, we can detect a degradation in performance of the wind turbine from newly arriving data by comparing predicted and true power output values. An overview of the whole proposed method is shown in Figure 5.
## 3 Results and Discussion
### Implications for Management of the Oilfield Waste Pits in Desert Areas  
The research findings obtained from this study suggest that the environmental risk of the investigated oilfield waste pit was not significant. Under desert conditions, contamination of groundwater was unlikely, even when some localized liner failures took place. The oilfield was far away from human settlements. Therefore, the human health risk via ingestion and dermal contact is minimal. However, inhalation of petroleum hydrocarbon-containing dusts by oilfield workers is still likely, and it is necessary to take appropriate remedial action in order to reduce such a risk.  
A range of remediation technologies have been used to treat petroleum hydrocarbon-contaminated soils, including soil washing [27], thermal treatment [28], chemical treatment [29], bioremediation [30] and combined treatment methods [31; 32]. Soil washing is ineffective for heavily contaminated soils. Although thermal treatment is highly effective, it is very costly. Given that degradation-resistant long-chain fractions dominated the petroleum hydrocarbons in the sludge and contaminated soils, eliminating the petroleum hydrocarbons via chemical treatment could be extremely costly. Bioremediation is cost-effective in humid areas. However, the harsh environmental conditions encountered in  
Figure 7: Petroleum hydrocarbon composition of the sludge and the underlying soil.  
deserts do not favor bioremediation due to low soil moisture contents, high salinity and an insufficient supply of nutrients. From an environmental remediation point of view, it may be more cost-effective to use a soil-capping approach to limit human contact with the oily sludge and contaminated soils.  
Figure 8: Relationship of aliphatic fractions **(a)** C16–C21, **(b)** C21–C35, and **(c)** C35–C40 between the sludge and the surface soil.
# Design of an Underwater Acoustic Waveform and Integrated System for Communication and Detection
## 2 Waveform Design
### Performance Analysis
 \tag{12}\]Taking the 2-ary modulated fully responsive signal as an example, \(I_{i}=+1,\)\(-1,\)\(I_{2},\)\(I_{3},\)\(I_{j}=-1,\)\(+1,\)\(I_{2},\)\(I_{3}\). The first two symbol sequences are different, but the symbol sequences after the second symbol are the same, and the phase trajectories of the sequences coincide afterwards. Considering the difference sequence is \(I_{i}-I_{j}=\{2,\)\(-2,\)\(0,\)\(0.\)\(\ldots\)\(\ldots\)\(\}\), we can derive the upper bound of the binary fully responsive signal as  
\[d_{B}^{2}(h)=2\bigg{(}1-\frac{sin2\pi h}{2\pi h}\bigg{)},\ M=2 \tag{13}\]  
By extending this concept, we can obtain the upper bound for \(M\)-ary fully responsive signals as  
\[d_{B}^{2}(h)=\min_{1\ll k\ll M-i}\{(2\text{log}_{2}M)(1-\frac{\sin 2\text{ k}\pi\text{th}}{2\text{k}\pi\text{th}})\} \tag{14}\]  
Then, simulation analysis on the observed Euclidean distance and upper bound of the signal under the influence of \(M\), modulation index \(h\), and the correlation length \(L\) is conducted. Figure 4 presents upper bounds on the Euclidean distance obtained via simulation for modulation order \(M=2,\)\(4,\)\(8\) and response functions LREC and LRC versus the modulation index \(h\). These results show that the bounds increase with \(h\) until a given value and then they fluctuate. Further, the bounds generally increase with the radix. For \(M=2\), the upper bounds with LREC and LRC are similar when \(h\) is small, but for large values, the bounds with LREC are greater.
## 3. Designed Scheme
### Authentication and Reconstruction
#### 3.2.2 Authentication  
This step uses the previously described Pseudocode 1 to generate the bit sequence \(autentW\) from each channel of \(Ihw\), where \(autentW_{R}\), \(autentW_{G}\) and \(autentW_{B}\) are obtained. Each bit sequence is compared with each authentication watermark \(aut_{R}\), \(aut_{G}\) and \(aut_{B}\) resulting from Pseudocode 3 using the following equation:  
\[autentImg_{i,j}=\left\{\begin{array}{rl}255,&\textbf{if }autentW_{j+(i-1)\frac{B}{4}} \textbf{ is not }aut_{j+(i-1)\frac{B}{4}}\\ &0,\textbf{otherwise}\end{array}\right., \tag{3}\]  
for all \(i\) and \(j\) subject to \(1\leq i\leq A/4\) and \(1\leq j\leq B/4\). Each \(autentImg\) is then interpolated to size \(A\times B\).  
Once the previous steps have been performed, three reconstruction images (\(iRGB_{R}\), \(iRGB_{G}\) and \(iRGB_{B}\)) and three authentication images (\(autentImg_{R}\), \(autentImg_{G}\) and \(autentImg_{B}\)) are generated for each RGB channel and each LSB plane. A general authentication image is then computed by applying the bitwise OR operand to each of the authentication images, i.e., \(iAutent_{ij}=autentImg_{Ri_{j}}+autentImg_{Gi_{j}}+autentImg_{Bi_{j}}\), \(\forall i,j\), s.t. \(1\leq i\leq A\), \(1\leq j\leq B\). A point worth mentioning is that this operation is valid only for the 1-LSB embedding method. If the 2-LSB method is utilized, six watermarks for authentication and six for reconstruction should be generated. Analogously, the 3-LSB process requires nine watermarks for each case. Therefore, \(iAutent\) is obtained by applying the bitwise OR operation to the six or nine authentication images for the 2-LSB or 3-LSB method, respectively. Finally, the first level hierarchical authentication is performed on \(iAutent\), improving the tamper detection recognition.
## 5 Discussion
Notably, several methods were suggested to quantify value of trees in urbanized/near urbanized areas. These methods can be categorized into three approaches: the cost approach (e.g., Depreciated Replacement Cost, which assumes that the tree benefits can be reproduced by replacing the tree [71]), the market approach, which accounts for the trees' wholesale values [72], and the benefit-based approach, which is based on future stream of benefits that a tree might produce. While a plethora of such benefits were acknowledged in the literature (see [73] for an exhaustive description), not all have actually been quantified. In turn, quantified benefits include energy conservation (e.g., due to shading effects and reductions in the urban heat island), surface and storm-water runoff reductions, atmospheric carbon dioxide reductions, and increased property values. Finally, while some studies have found other substantial benefits such as lower levels of violence and stress in neighborhoods surrounded by trees, the _i-Tree_ model currently lacks quantification of these benefits [60; 74; 75]. However, we found the _i-Tree_ method to be the most appropriate considering our primary objectives, the locations of the trees, and the availability of data. Other benefit-based methods are based on variables that are more subjective, and thus, difficult to measure (e.g., historical significance). Nevertheless, as with many other methods, our method suffers from some limitations, specifically due to lack of accurate data (for example data on the exact condition of the trees). Future studies may improve the results obtained here by using more frequent high spatial resolution satellite schemes, providing more accurate information on trees' species and condition. In addition, a sensitivity analysis using the _i-Tree_ model may improve economic evaluation, accounting for uncertainty in economic loss estimates.
## 3 Results
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Vegetation Cover** & **Fire** & \multicolumn{2}{c}{**Multiple Regression Model**} \\ \hline \multirow{8}{*}{**P**} & \multirow{8}{*}{**UB**} & HA = 1136.464 \(-\) (232.733 \(\times\) Mn) + (19.395 \(\times\) Fe) \(-\) (1.311 \(\times\) Mg) \(R^{2}\) = 0.62 \\  & & DHA = 0.407 + (0.980 \(\times\) Mn) \(-\) (0.126 \(\times\) Fe) + (0.00244 \(\times\) Mg) \(R^{2}\) = 0.58 \\  & & \(\beta\)-GLU = \(-\)192.955 + (263.969 \(\times\) Mn) \(-\) (172.63 \(\times\) Fe) + (0.816 \(\times\) Mg) \(R^{2}\) = 0.84 \\  & & HA = \(-\)1647.699 + (280.039 \(\times\) Cu) \(-\) (124.302 \(\times\) Pb) + (380.605 \(\times\) pH) \(-\) (115.994 \(\times\) C) \(-\) (767.327 \(\times\) N) \(-\) (35.016 \(\times\) OH) \(R^{2}\) = 0.63 \\  & & DHA = \(-\)32.280 \(-\) (0.0869 \(\times\) Cu) \(+\) (0.0158 \(\times\) Pb) + (0.660 \(\times\) pH) \(-\) (0.0281 \(\times\) C) \(-\) (3.368 \(\times\) N) \(+\) \\  & & (0.0288 \(\times\) OH) \(R^{2}\) = 0.73 \\  & & \(\beta\)-GLU = 123.032 + (39.383 \(\times\) Cu) \(-\) (15.053 \(\times\) Pb) + (47.597 \(\times\) pH) \(-\) (14.792 \(\times\) C) \(-\) (510.130 \(\times\) N) \(-\) (11.591 \(\times\) OM) \(R^{2}\) = 0.85 \\ \hline \multirow{8}{*}{**HO**} & \multirow{8}{*}{**UB**} & HA = 2276.187 \(-\) (3.268 \(\times\) Mn) + (16.144 \(\times\) Fe) \(-\) (0.527 \(\times\) Mg) \(-\) (0.0698 \(\times\) Ca) \(-\) (2.135 \(\times\) Na) \(+\) (3.443 \(\times\) K) \(-\) (37.494 \(\times\) Al) + (1094.257 \(\times\) Cu) \(-\) (387.797 \(\times\) Pb) \(-\) (23.830 \(\times\) WC) \(-\) (26.092 \(\times\) pH) \(R^{2}\) = 0.79 \\  & & \(\beta\)-OTL = 34.487 + (0.298 \(\times\) Mn) + (0.0114 \(\times\) K) \(-\) (0.0277 \(\times\) Al) + (0.0130 \(\times\) WC) \(-\) (5.568 \(\times\) pH) \(R^{2}\) = 0.71 \\  & & \(\beta\)-GLU = 1851.453 + (18.433 \(\times\) Mn) + (20.033 \(\times\) Fe) + (0.171 \(\times\) Mg) \(-\) (0.00604 \(\times\) Ca) \(-\) (0.252 \(\times\) Na) \(+\) (0.167 \(\times\) K) \(-\) (21.172 \(\times\) Al) \(-\) (42.703 \(\times\) Cu) \(-\) (62.309 \(\times\) Pb) \(-\) (27.947 \(\times\) WC) \(-\) (152.040 \(\times\) pH) \(R^{2}\) = 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: **Results of multiple linear regression analyses for unburnt and burnt soils under different vegetation covers. ** Significant results of multiple linear regression analyses of soil enzymatic activities (hydrolase: HA, dehydrogenase: DHA and \(\beta\)-glucosidase: \(\beta\)-GLU) in relationship with soil abiotic properties (pH, water content: WC, total concentration of carbon and nitrogen: C and N, organic matter content: OM, available fraction of Al, Ca, Cu, Fe, K, Mg, Mn, Na, and Pb) in unburnt (UB) and burnt (B) soils covered by different vegetation cover (P: pine; HO: holm oak; BL: black locust; H: herbs). Italics indicate the adjusted \(R^{2}\) of each model.
## 4 Results
### Visualization for Interpreting Prediction Results  
Deep learning models have limitations in interpreting and reliability of the results because the process or basis for which the prediction is derived is opaque. To overcome this limitation, we determined which part of the image had the most influence on the final classification decision of the model through heatmap visualization. In this study, class activation map (CAM) was used, and the operation method of CAM is as follows. A total of \(k\) outputs can be obtained by applying global average pooling (GAP) to the last \(k\) feature maps obtained through the CNN layer. After linearly combining these values and applying softmax, the probability value is finally output. At this time, the activation map for class c is the same as Equation (4).  
\[A_{c}(i,j)=\sum_{k}w_{k}^{c}f_{k}(i,j) \tag{4}\]  
\(f_{k}(i,j)\) denotes the \(k\)-th feature image, and \(w_{k}^{c}\) denotes the weight from the \(k\)-th feature image \(f_{k}(i,j)\) to class \(c\). If each feature map \(f_{k}(i,j)\) is multiplied by the weight \(w_{k}^{c}\) for each class, a heatmap can be obtained with as many as \(k\) number of feature maps. By summing all these heatmap images pixel-wise, a single heatmap can be obtained, which is CAM [37]. The result of visualizing the part that most influences the model decision using CAM is shown in Figure 4. In Figure 4, it is observed that the parts that are important for predicting the class, such as a safety helmet or safety vest, are marked in red and have the most influence. Likewise, in the case of classes that indicate the risk of a fall accident, such as working near edges, the end of the facility is expressed in red. However, if the prediction is different from the actual class, it can be seen that areas that are completely unrelated to the class are displayed in red. Furthermore, a large portion of the image appears red or yellow. In other words, when compared to the correctly predicted image, a specific part did not affect the model's prediction; rather, a wide range of areas affected the model, causing confusion. As a result of examining erroneously predicted images, a case was found that could be confusing even to the naked eye, or a case where a risk factor could belong to more than one class. Through this visualization work, it was possible to objectively present the classification basis of the trained model and to improve the interpretation power of the model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Precision** & **Recall** & **F1 Score** \\ \hline A-type ladder outrigger & 100 & 100 & 100 \\ Movable scaffolding frame outrigger & 100 & 100 & 100 \\ Safety handrail & 100 & 100 & 100 \\ Scissor-type aerial work platform & 99.63 & 99.82 & 99.72 \\ Safety vest & 100 & 99.89 & 99.95 \\ Safety helmet & 99.74 & 99.65 & 99.70 \\ Working in pairs & 100 & 100 & 100 \\ Working near edges & 99.16 & 99.16 & 99.16 \\ Normal & 99.83 & 99.92 & 99.87 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance by class in Safety-MobileNet.
## 2 Mission and System
### SDOM: Separable De-Orbit Mechanism  
SDOM is a passive de-orbit system that was specifically developed for ALE-1. This system uses an aluminized polyimide thin film as an atmospheric drag sail and represents a new model of the de-orbit mechanism (DOM) that Nakashimada Engineering Works, Ltd. and Tohoku University have been collaboratively developing since 2010 [13; 14]. The appearance of the deployed DOM is illustrated in Figure 2. The SDOM system drags down satellites using the atmospheric drag that acts on the thin film, allowing satellites to re-enter the Earth's atmosphere. Several models of DOM have been already operated in orbit, providing us with important engineering findings [15; 16].  
SDOM consists of the following components:  
* A cylindrical container to ensure safety during rocket launch;
* A thin film deployment mechanism that acts as a drag sail to lower the orbit;
* A boom to keep the thin film away from the satellite so that it does not block the antenna or solar panels;
* A system to separate the drag sail from the container after reaching a predetermined target altitude.  
SDOM is designed to have five operational phases to ensure its proper functioning, as depicted in Figure 3 [5]. Phase 0 represents the safe storage of all components within the cylindrical body without deployment. In phase 1, the lid is opened. In phase 2, the boom is extended to position the drag sail mechanism 2.5 m away from the satellite's main structure. In phase 3, a 2.5 m \(\times\) 2.5 m thin film is deployed to initiate de-orbit. Upon reaching a predetermined target altitude (in the case of ALE-1, less than 400 km), the film and boom are separated on command from the ground station to prolong the satellite's orbital lifetime at that altitude (phase 4). The cylindrical container remains attached to the satellite body.  
Figure 2: Deployed configuration of DOM: De-Orbit Mechanism.