esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### HPLC Detection of HNS
#### 3.1.4 Determination of Sample Volume  
We assumed a retention time of 15 min. The mobile phase consisted of a 70% MeOH volume fraction and 30% ultrapure water, with a flow rate of 0.5 mL/min. The column temperature was set at 30 \({}^{\circ}\)C, and we tested injection volumes of 5 \(\upmu\)L, 10 \(\upmu\)L, 15 \(\upmu\)L, 20 \(\upmu\)L, and 25 \(\upmu\)L. As shown in Figure 5, chromatograms for other injection volumes were similar. Experimental results indicated that an injection volume of 10 \(\upmu\)L provided consistent retention times for DMF and HNS under dual-wavelength detection. This volume also exhibited good peak shape and reproducibility, and lacked extraneous peaks. Using this volume, we adjusted the analysis time to 6.5 min. This adjustment did not interfere with subsequent analyses and reduced the detection time for HNS by nearly 70%, significantly  
Figure 4: Retention times of DMF and HNS under dual-wavelength detection at different flow rates.  
Figure 3: Dual-wavelength high-performance liquid chromatography chromatograms of DMF and HNS at different flow rates, MeOH volume fraction of 70%: the mobile phase flow rates were tested at 0.4 mL/min (**a**) 226 nm (**b**) 271 nm.; the mobile phase flow rates were tested at 0.5 mL/min (**c**) 226 nm (**d**) 271 nm.  
lowering the cost of analysis. The performance with an injection volume of 15 \(\upmu\)L was similar to that with 10 \(\upmu\)L. However, reducing the injection volume extends the lifespan of the chromatographic column. To better protect the column, we ultimately selected an injection volume of 10 \(\upmu\)L.  
The optimal conditions for detecting HNS using high-performance liquid chromatography were determined through a series of experiments, as presented in Table 3.
# Additive Manufacturing in the Clothing Industry: Towards Sustainable New Business Models
## 2 Literature Review
### Literature Gap  
The literature review performed in this paper was conducted using academic publication search engines including Google Scholar and Scopus. The Google general search engine was also used to search for news articles and company webpages. The keywords used for the search were: clothing industry sustainability, textile industry sustainability, supply chain sustainability, fashion industry sustainability, additive manufacturing in the clothing industry, additive manufacturing in the apparel industry, additive manufacturing in the garment industry, additive manufacturing in the fashion industry, additive manufacturing in the textile industry, new business models fashion industry, new business models clothing industry, business model innovation, and new business models. The literature review began in November 2018 and was concluded in April 2021. The inclusion criteria for the articles were the presence of a case study related to the AM in the fashion industry, the study of sustainability of the AM, and the presentation of new business models enabled by AM.  
Although attention towards waste reduction in the clothing industry has increased during recent years [55], and some basic actions have been taken (efforts such as the use of sustainably grown cotton, plant-based polymers, and development of an institute for sustainable consumption to issue labels for products on the basis of carbon emission footprint [6]), there still is work to be done. In recent studies, [PERSON] et al. [83] studied the sustainability of clothing suppliers in India. They found several negative environmental and societal impacts, such as pollution and unfair competition amongst manufacturers of garments and child labor employment among manufacturing suppliers. [PERSON] and [PERSON] [84] provided an in-depth review of the current clothing industry's sustainability issues. We created Figure 1 to summarize the waste items and the byproducts of the textile and fashion industries presented in different publications [33; 84; 85].  
Moreover, studies investigating additive manufacturing suggest that AM is most suitable when the production batch size is small and the product is geometrically complex [62; 86]. [PERSON] and [PERSON] [87] pointed towards the possibility of an AM-enabled prosumer society; however, research exploring the impact of AM on clothing production practices is lacking. Additive manufacturing has begun to be used by a few fashion designers to produce high-end dresses and clothing pieces; however, to expand our understanding concerning the opportunities raised by these efforts and future possibilities, research is required. In this research, we aim to fill this gap.
# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 2 Preliminaries
### Arithmetic Coding
```
0: Marked images \(I_{1}^{\prime},I_{2}^{\prime}\)
0: Recovered original image \(O\) and hidden data \(m\)
1: Divide \(I_{1}^{\prime}\) and \(I_{2}^{\prime}\) into non-overlapping blocks of size \(16\times 16\)
2:for each block pair \((B_{1}^{1},B_{2}^{1})\)do
3: Read pixel pairs \(\{x_{1}^{1},x_{2}^{1},\ldots,x_{256}^{1}\}\) and \(\{x_{1}^{2},x_{2}^{2},\ldots,x_{256}^{2}\}\)
4: Read binary values from pixel positions \((x_{250},\ldots,x_{256})\) and convert to decimal length \(L\)
5:for\(k=1\) to \(L/2\)do\(\triangleright\) Extract data using RDH EMD
6: Compute \(f\leftarrow(x_{k}^{1}+x_{k}^{2}\cdot 2)\) mod \(5\)
7:\(f_{binary}=\left(\left\lfloor\frac{f}{2}\right\rfloor\cdot 2\right)+(f\mod 2)\)
8: Assign \(M(k)\gets f_{binary}\)
9:endfor
10:for\(j=1\) to \(L/2\)do\(\triangleright\) Restore original pixel \(x_{j}\)
11: Recover pixel value \(p_{j}\leftarrow\left\lfloor\frac{x_{j}^{1}\times x_{j}^{2}}{2}\right\rfloor\)
12: Replace corresponding pixels in blocks \(B_{1}^{1},B_{l}^{2}\)
13:endfor
14: Combine blocks to form images \(I_{1},I_{2}\)
15:endfor
16: Decompress extracted data using arithmetic decoding: \(M_{decompressed}\gets A_{D}(M)\)
17:for each pixel pair \((x_{j}^{1},x_{j}^{2})\)do
18: Construct codeword \(P_{j}=\{b_{j,\mathcal{I}}^{1},b_{j,\mathcal{E}}^{1},b_{j,\mathcal{E}}^{1},b_{ j,\mathcal{A}}^{1},b_{j,\mathcal{I}}^{2},b_{j,\mathcal{E}}^{2},b_{j,\mathcal{E}}^{2}\}\)
19: Convert \(M_{jj+2}\) to decimal \(l\gets b2d(M_{decompressed}^{l,j+2})\)
20: Flip bit at position \(l\) in \(P_{j}\)
21: Extract hidden bits \(m_{k:k+2}\gets H(P_{j}\oplus e_{l})\)
22:endfor
23: Combine all blocks to restore the original cover image \(O\)
24:return\(O,m\)
```  
**Algorithm 4** Data extraction and image recovery algorithm
## 3 Methodology
### Attention Block  
An input feature map \(I\in R^{C*H*W}\) can be reshaped in matrices \(K\in R^{C*(H*W)}\) and \(Q\in R^{(H*W)*C}\). The channel attention map \(A\in R^{C*C}\) is obtained by dividing \(K\) and \(Q\) by the factor \(\sqrt{C}\) and applying a softmax layer. This can be written as  
\[a_{ij}=Softmax\Big{(}\frac{f(I_{i},I_{j})}{\sqrt{C}}\Big{)} \tag{4}\]  
where \(a_{ij}\) refers to \(i\)th channel's influence on \(j\)th channel, while function \(f\) calculates their relationship.  
The channel statistics of a channel from an original feature map can be acquired with global average pooling (GAP), which can be expressed using  
\[g(I_{k})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}I_{k}(i,j) \tag{5}\]  
where \(k=1,2,...c\), \(I=[i_{1},i_{2},...,i_{c}]\) and \(g\) is GAP function.  
GAP is obtained as an attention vector and compresses global information, which allows feature dimensionality reduction and high-level feature extraction. This preserves salient features. Furthermore, by multiplying matrices \(A\) and \(V\), we obtain the result transformed into \(R^{C*1*1}\), which can be multiplied by the parameter \(\gamma\). We can then use the original feature map \(I\) to obtain the final output, which can be written as  
\[O_{j}=\gamma\sum_{i}^{C}(a_{ij}\cdot g(I_{k}))+\sigma_{\theta}I_{j}+b_{\theta} \tag{6}\]  
where \(\gamma\) starts from 0 and learns weight during training, \(\sigma_{\theta}\) refers to the weight of the \(1\times 1\) convolution, and \(b_{\theta}\) is the bias. Therefore, the final output is a weighted sum between feature maps obtained by convolution and those that came from attention with GAP.  
Since each channel corresponds to a specific semantic response, different channels have different contributions for acquiring useful feature information. By modeling the de  
Figure 2: The structure of the residual and squeeze and excitation blocks.  
pendencies of each channel and by adjusting features channel by channel, the network can selectively learn to identify which features contain useful information and which contain useless information, and can strengthen or suppress them accordingly. Therefore, addition of attention blocks into skip connections helps in eliminating redundant information and improves networks' representational power.  
Figure 3a shows the detailed block structure of the described attention block.
## 4 Results and Discussion
### Field Investigation  
The reliability of the proposed methods was validated via field investigation using a handheld Global Position System (GPS) device (GeoExplorer 6000) to locate landslides (Figure 14) and to produce a precise and reliable inventory map of the Cameron Highlands. The more detailed information (landside extent, source area, deposition, and volume) was obtained from in situ measurements which ultimately demonstrate the reliability of the produced inventory map in the field through use of a GeoExplorer 6000 handheld GPS. The results illustrated that the neural network techniques were able to detect true landslide locations which occurred in past years. Therefore, the results of this study verified that the proposed models can detect landslide locations and generate a reliable landslide inventory map.
## 3 Results
### Effectiveness, SOC, and COP during the Three-Fluids HEX Operating Mode  
Figure 10 summarizes the results obtained in the three-fluids HEX operating mode based on the SOC (energy level in the module), the effectiveness of the modules, and COP of the system at thermal equilibrium. The energy level (SOC) is higher in TES\({}_{1}\) than in TES\({}_{2}\) and TES\({}_{3}\). This is expected since thermal equilibrium was achieved at a lower temperature value in TES\({}_{1}\). The HTF net power is the highest in TES\({}_{1}\), followed by TES\({}_{2}\), and finally TES\({}_{3}\). All modules could achieve an effectiveness value higher than 0.5, and the highest effectiveness value of around 0.83 was obtained in TES\({}_{1}\), followed by TES\({}_{2}\) around 0.63, and finally TES\({}_{3}\) with 0.56. A COP around 4 was achieved for all the modules, although TES\({}_{1}\) and TES\({}_{2}\) showed the highest COP of 4.4, whereas the COP of TES\({}_{3}\) was around 4.
## 3 Method
### Suggested Method
#### 3.2.2 Extracting Radiomics Features  
Given multi-parametric MRI, we seek to extract another set of features that can capture different aspects of tumor characteristics than MSFI embedding. Radiomics features have already demonstrated a high correlation with pCR after nCRT for rectal cancer. Thus, we merge radiomics features with MSFI embedding to further improve pCR prediction performance. Using the pyradiomics package [40], we extract radiomics features from tumor areas in multiple MRI sequences, {T2, DWI/ADC, and CE}.  
Figure 4 shows 3740 radiomics features that were extracted from multiple MRI sequences, {T2, DWI/ADC, and CE}. From each MRI sequence, we extracted 2D/3D radiomics features on the tumor shape, voxel intensity histogram, texture of tumor areas, such as the gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), gray-level size-zone matrix (GLSZM), and gray-level dependence matrix (GLDM). In addition, we applied filters, such as log and wavelet transform, to each MRI sequence to extract higher-order statistical features on rectal tumor areas [41]. To extract more diverse textual features from T2, we applied a Gabor filter with four angles, {0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\), and 135\({}^{\circ}\)}.
# Associating Images with Sentences Using Recurrent Canonical Correlation Analysis
## 3 Association between Images and Sentences via Recurrent Canonical Correlation Analysis
### Sentence Representation Using Conventional LSTM-RNN  
Considering that sentences are naturally sequential data consisting of ordered words, and conventional LSTM-RNN can well model long-range contextual information, so we directly exploit the conventional LSTM-RNN to obtain sentence representations. As shown in Figure 1 (at the top), conventional LSTM-RNN orderly takes a word as the input to the corresponding hidden state at each time step, and all the hidden states propagate their values along the time axis. The hidden state at the last time step collects all the information in the sentence, which can thus be regarded as the representation of the entire sentence.  
We denote \(\left[\mathbf{w}_{1},\ldots,\mathbf{w}_{t},\ldots,\mathbf{w}_{T^{\prime}}\right]\) as a sentence associated with the image, \(\mathbf{w}_{t}\in\left\{0,1\right\}^{M}\) as the \(t\)-th word in the sentence, \(T^{\prime}\) as the number of words in the sentence (also the number of time steps in the conventional LSTM-RNN), \(M\) as the size of word vocabulary, and \(\left\{\mathbf{h}_{t}\in\mathbb{R}^{D_{y}}\right\}_{t=1,\ldots,T^{\prime}}\) as the hidden states at all time steps. Since the original representations of words are high-dimensional \"one-hot\" vectors, we embed them into low-dimensional real-valued vectors using an embedding matrix \(E\in\mathbb{R}^{U\times M}\). The details of the conventional LSTM-RNN can be formulated as follows:  
\[\mathbf{i}_{t}=\sigma(W_{\mathbf{w}\mathbf{i}}E\mathbf{w}_{t}+W_{\mathbf{h} \mathbf{i}}\mathbf{h}_{t-1}+\mathbf{b}_{\mathbf{i}}),\ \mathbf{f}_{t}=\sigma(W_{\mathbf{w}\mathbf{f}}E\mathbf{w}_{t}+W_{\mathbf{h} \mathbf{f}}\mathbf{h}_{t-1}+\mathbf{b}_{\mathbf{f}}) \tag{7}\]  
\[\mathbf{c}_{t}=\mathbf{f}_{t}\odot\mathbf{c}_{t-1}+\mathbf{i}_{t}\odot\tanh(W _{\mathbf{w}\mathbf{c}}E\mathbf{w}_{t}+W_{\mathbf{h}\mathbf{c}}\mathbf{h}_{t- 1}+\mathbf{b}_{\mathbf{c}}) \tag{8}\]  
\[\mathbf{o}_{t}=\sigma(W_{\mathbf{w}\mathbf{c}}E\mathbf{w}_{t}+W_{\mathbf{h} \mathbf{o}}\mathbf{h}_{t-1}+\mathbf{b}_{\mathbf{o}}),\ \mathbf{h}_{t}=\mathbf{o}_{t}\odot\tanh(\mathbf{c}_{t}) \tag{9}\]  
Different from the contextual attention-based LSTM-RNN, here we do not include any explicit definition of the initial memory state and the hidden state, similar to [38].
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 1 Introduction
This heterogeneity can play both positive and negative roles in plant remote sensing. It can cause a decrease in the ratio of signal to noise at the imaging of the reflected light; i.e., disturb the measurement. In contrast, the information about this spatial heterogeneity of plant characteristics in the field can be used for the analysis of the distribution of the action of stressors; i.e., this information can be the basis for using protection actions in damaged zones. Thus, the application of RIs has wide perspectives for plant investigations and remote sensing: (i) reflectance indices can be used for laboratory investigations of plant organisms in vivo; particularly, the development of the RI-based methods can simplify the analysis of physiological and biochemical processes in comparison with using complicated and expensive classical methods [10; 22]; (ii) measurements of RIs can be used for high-throughput plant monitoring in fields and greenhouses [22]; in the future, they can be an important part of automatic systems of plant cultivation. In our review, we considered the perspective and limitations of applying RIs for plant remote sensing. In Section 2, we described some optical methods of plant remote sensing, the main principles of scanning systems for measurements of reflectance in plants and canopy, and some mobile platforms for this remote sensing. Further, we described the main reflectance indices, including vegetation, water, pigment, and photosynthetic indices (Section 3). We focused on the relationship of growth, physiological, and biochemical characteristics with plant reflectance and on applications of these RIs. In Section 4, we discussed the methods of the corrections of RIs that are used for decreasing the influence of the environmental conditions on plant remote sensing.
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
## 3 System Architecture
### Inductive Link Design  
The required data rate for the downlink ranges from 0.2 Mbps to 4 Mbps for the wireless enabled visual prosthesis [15]. With the proposed system layout of placing the implant side of the wireless module just beneath the skin, the distance from beneath the skin to above the skin is below 10 mm [23]. An inductive link is the most suited type of link because of the low attenuation of magnetic fields by the skin tissue and the compactness of the coils [16]. To keep power consumption low, the carrier frequency is chosen to be below 20 MHz. Achieving sufficient bandwidth for the desired data rate can be challenging, especially in inductive links. This is generally attempted by greatly reducing the quality factor of the coils which leads to more power dissipation [11]. The transition region of the PSK modulated signal can be distorted if the transmission of the inductive system is  
Figure 3: Circuit schematic of the binary phase shift keying (BPSK) mixer.  
not flat enough in the passband. Creating such a (relatively) flat band can still be done without lowering the quality factor of the coils, by making the transmit and receive coils resonate at the same frequency. Furthermore, when the mutual coupling between the coils is high enough, the resulting coupled response gives two resonance peaks away from the resonance frequency of the individual coils, thereby creating a 'well' between peaks [28]. Figure 4 shows the ideal channel response for three values of the coupling factor. This well has a relatively flat passband to fit the data rate bandwidth. Empirically, it was found that the bandwidth is proportional to the coupling factor when the inductance is high enough (tens of microhenries), when the coupling factor is in range of 0.1-0.4, with a carrier frequency range of 0.1-30 MHz, and when the ohmic resistances of the coils are in the order of a few ohms [17]. When coupling factor is >0.4, the bandwidth is also proportional but with a slightly higher factor and mid-point.
## 3 Results and Discussion
### Model Fitting for Fermentation Temperature as an Input Variable
The generalized models fit the experimental data quite well and are acceptable (R\({}^{2}\) > 0.98, MAE 4.32-10.4); therefore, the models are applicable to estimate the SMY of husks, pulp and mucilage at any fermentation temperature between 21 \({}^{\circ}\)C and 37 \({}^{\circ}\)C. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**SubstrateModel**} & \multicolumn{3}{c}{**S**} & \multicolumn{3}{c}{\(R_{m}\)} & \multicolumn{3}{c}{\(\lambda\)} \\ \cline{2-11}  & \(c_{1}\) & \(c_{2}\) & \(\mathbf{R}^{2}\) & \(c_{3}\) & \(c_{4}\) & \(c_{5}\) & \(\mathbf{R}^{2}\) & \(c_{6}\) & \(c_{7}\) & \(\mathbf{R}^{2}\) \\ \hline \multirow{3}{*}{Husk} & LOG & 137.0 & 0.5 & 0.640 & 9.43 & 0.01 & 2.40 & 1.000 & 7.17 & 0.1 & 0.905 \\  & GOM & 130.3 & 0.8 & 0.999 & 6.42 & 0.58 & 5.93 & 1.000 & 7.3 & \(-\)0.1 & 0.985 \\ \multirow{3}{*}{Pulp} & LOG & 196.6 & 1.3 & 0.987 & \(-\)5.68 & 10.00 & 15.9 & 1.000 & 11.2 & \(-\)0.2 & 0.954 \\  & GOM & 286.7 & \(-\)1.0 & 0.977 & \(-\)2.44 & 6.36 & 12.4 & 0.999 & 3.47 & 0.02 & 0.570 \\ \multirow{3}{*}{Mucilage} & LOG & 248.9 & 1.1 & 0.646 & \(-\)14.3 & 19.60 & 24.7 & 1.000 & 11 & -0.2 & 0.937 \\ \cline{1-1}  & GOM & 334.8 & -1.0 & 0.978 & -50.9 & 56.90 & 55.8 & 1.000 & 4.96 & -0.1 & 0.536 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coefficients c\({}_{1}\)-c\({}_{7}\) for husks, pulp and mucilage, estimated for ultimate methane yield (S), maximum daily methane yield (\(R_{m}\)) and lag period (\(\lambda\)) for the modified Logistic model (LOG) and modified Gompertz model (GOM). Figure 4 shows residual values in the specific methane yield, representing the difference between the measured values and the predictions from the gLOG and gGOM model results for each data point in the time series. The maximal residual values from gLOG and gGOM model fittings are 20.6 and 34.9, respectively. The residual plots, both from gLOG and gGOM, showed no specific pattern in the independent variable time (t) despite a slight drift in the residuals from the nucilage. Thus, the residuals indicated no bias in the fitting of the models. Hence, the generalized models performed well in predicting the methane yields. Despite continuous methane production, both the LOG and GOM models estimated a negligible methane yield after 20 and 30 days from all sets of substrates fermented at 37 \({}^{\circ}\)C and 30 \({}^{\circ}\)C, respectively. Similar results by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] (2015) and [PERSON], [PERSON] and [PERSON] (2018) were reported. The reason could be attributed to the presence of slowly degradable substrate fractions, which neither the LOG nor the GOM models account for. Figure 3: Measured specific methane yield (SMY) of coffee husks, pulp and nucilage in the course of batch residence time (t) at different fermentation temperatures and curves fitted with the generalized modified Logistic model (gLOG) and generalized modified Gompertz model (gGOM); error bars show standard deviation, \(n=6\).