esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The focus of this study was to compare the yolk flavor of eggs from laying hens of Chinese indigenous and commercial, based on detection of volatile compounds, fatty acids, and texture characteristics determination, using sensory evaluation, artificial sensors (electronic nose (E-nose), electronic tongue (E-tongue)), and gas chromatography-mass spectrometry (GC-MS). A total of 405 laying hens (Hy-Line Brown (\(n=135\)), Xueyu White (\(n=135\)), and Xinyang Blue (\(n=135\))) were used for the study, and 540 eggs (180 per breed) were collected within 48 h of being laid and used for sensory evaluation and the instrument detection of yolk flavor. Our research findings demonstrated significant breed differences for sensory attributes of egg yolk, based on sensory evaluation and instrument detection. The milk flavor, moisture, and compactness scores (\(p<0.05\)) of egg yolk from [PERSON] and Xinyang Blue were significantly higher than that of [PERSON]. The aroma preference scores of [PERSON] (\(p<0.05\)) were significantly higher, compared to [PERSON] and [PERSON]. The sensor responses of WIW and W2W from E-nose and STS from E-tongue analysis were significantly higher for egg yolks of [PERSON] (\(p<0.05\)), compared to that of [PERSON] and [PERSON]. Additionally, the sensor responses of umami from E-tongue analysis, was significantly higher for egg yolks of [PERSON] (\(p<0.05\)), compared to that of [PERSON] and Xinyang Blue. Besides, the contents of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, in egg yolk were positively correlated with egg flavor. The texture analyzer showed that springiness, gumminess, and hardness of Hy-Line Brown and Xueyu White (\(p<0.05\)) were significantly higher, compared to Xinyang Blue. The above findings demonstrate that the egg yolk from Chinese indigenous strain had better milky flavor, moisture, and compactness, as well as better texture. The egg yolk flavors were mainly due to presence of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, which would provide research direction on improvement in egg yolk flavor by nutrition. The current findings validate the strong correlation between the results of egg yolk flavor and texture, based on sensory evaluation, artificial sensors, and GC-MS. All these indicators would be beneficial for increased preference for egg yolk flavor by consumers and utilization by food processing industry, as well as a basis for the discrimination of eggs from different breeds of laying hens.  
egg flavor; sensory evaluation; electronic nose; electronic tongue; laying hens +
Footnote †: journal: Physics Letters B
## 3 The Proposed Hyper-CycleGAN
Compute the S\(\rightarrow\)T generator loss: \[\text{J}(\text{G}_{\text{S}\rightarrow\text{T}})=\frac{1}{\text{m}}\sum_{i=1}^{ \text{m}}\Bigl{(}\text{D}_{\text{T}}\Bigl{(}\text{G}_{\text{S}\rightarrow\text{ T}}\Bigl{(}\text{s}^{\text{(i)}}\Bigr{)}\Bigr{)}-1\Bigr{)}^{2}+\lambda_{\text{ cycle}}\text{J}_{\text{cycle}}\bigl{(}\text{S}\to\text{T}\bigr{)}\]
15. Update the generators
16. **Train the classifier:**
17. **for m = 1 to M do //** Classifier training sub-loop
18. Draw a minibatch of samples \(\{\text{x}^{\text{(}i)}\},\ldots,\text{x}^{\text{x}^{\text{(}k)}}\}\) from domain S to T
19. Compute the classifier loss: \[\text{L}_{\text{cls}}=\frac{1}{\text{k}}\sum_{i=1}^{\text{k}}\text{CrossEntropy} \Bigl{(}\Bigl{(}\text{t}\Bigl{(}\text{x}^{\text{(i)}}\Bigr{)},\text{y}^{\text{( i)}}\Bigr{)}\]
20. Update the classifier parameters to minimize L\({}_{\text{cls}}\)
21. **end for**
22. **end procedure During the classifier training phase, our focus shifted to harnessing the unique spectral characteristics inherent in hyperspectral data for effective classification tasks. Leveraging ResNet, specifically tailored for hyperspectral data analysis, we embarked on a journey to decode the complex spectral signatures embedded within our synthesized dataset. This dataset, meticulously crafted by the generator, served as the cornerstone of our classifier's training regimen. In the initial stages, as we laid the foundation for our ResNet model, we carefully initialized the weight parameters using the common random initialization method. This ensured that our classifier possessed the versatility to capture the rich and intricate spectral features encapsulated within hyperspectral data cubes. By imbuing our model with this innate adaptability, we equipped it with the capability to navigate the vast expanse of spectral space with precision and finesse. Optimization became paramount as we delved deeper into the training process. Here, we made a deliberate choice in favor of the Adam optimizer, renowned for its efficacy in handling the inherent complexities of high-dimensional data, such as hyperspectral imagery. With an initial learning rate meticulously set to \(1\times 10^{-4}\times 4\), our model embarked on a journey of exploration, gradually unraveling the subtle nuances embedded within the spectral domain. Throughout the arduous training journey, we adopted the batch training approach, allowing us to efficiently process large volumes of hyperspectral data while maintaining computational efficiency. This iterative process enabled our model to iteratively refine its understanding of the spectral landscape, iteratively honing its classification prowess with each passing epoch. Moreover, to safeguard against the looming specter of overfitting and ensure the generalization of our classifier, we implemented an early stopping strategy. With a patience value meticulously set to 15 epochs, our model navigated the delicate balance between convergence and generalization, ensuring optimal performance while mitigating the risk of premature convergence to suboptimal solutions.
###### Abstract  
Microfocusing X-rays direct high-density photons on crystal samples and can enhance the diffraction limit and quality of collected data. However, these intense X-rays can cause radiation damage to the sample, which often results in undesirable structural information. Accordingly, a data collection strategy that minimizes radiation damage is critical to obtaining accurate structural information. In this study, radiation damage in single-point data collection was investigated at two different X-ray exposure times (1 s and 100 ms) using microfocusing X-rays and a thaumatin crystal larger than the beam. The data collection statistics showed that the diffraction intensity of the Bragg peak did not gradually decrease until the crystal rotation reached 180\({}^{\circ}\), and it significantly decreased after exceeding this value. Thaumatin structures exposed to X-rays for 1 s (Thaumatin\({}^{18}\)) and 100 ms (Thaumatin\({}^{100\text{ms}}\)) were determined at 1.13 A resolution. The temperature factors for Asp60, Arg119, Lys163, and Lys187 of thaumatin were increased by radiation damage. Specific radiation damage was observed at the disulfide bond in Thaumatin\({}^{18}\) but was negligible in Thaumatin\({}^{100\text{ms}}\). Splitting and reprocessing Thaumatin\({}^{100\text{ms}}\) showed that electron density maps with minimal radiation damage can be obtained when using minimal data that satisfy the completeness, I/sigma, and CC1/2 parameters. These results expand our understanding of radiation damage phenomena in macromolecules and can be used for data collection applications.  
radiation damage; protein crystal; X-ray; disulfide bond; electron density map; thaumatin Article
## 4 Experimental Results and Analysis
### Analysis I: The Performance of Data Decomposition  
A comparison between the models without data composition and the proposed model is conducted to analyze the effectiveness of the data composition. The comparison is shown in Figure 10, and several discoveries can be made as follows.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**A2018**} & \multicolumn{2}{c}{**B2019**} \\  & **MAE** & **RMSE** & **MAE** & **RMSE** \\  & **(m/s)** & **(m/s)** & **(m/s)** & **(m/s)** \\ \hline PM & 0.503 & 0.676 & 0.310 & 0.434 \\ ARIMA & 0.501 & 0.670 & 0.308 & 0.429 \\ ELM & 0.501 & 0.669 & 0.309 & 0.431 \\ CNN & 0.499 & 0.669 & 0.309 & 0.429 \\ GRU & 0.499 & 0.667 & 0.307 & 0.428 \\ SSA-BRTD-GRU & 0.391 & 0.518 & 0.270 & 0.364 \\ SSA-QRTD-GRU & 0.372 & 0.494 & 0.261 & 0.353 \\ SSA-TRTD-GRU & 0.431 & 0.567 & 0.306 & 0.413 \\ SSA-TRTD-MR-GRU & **0.334** & **0.445** & **0.233** & **0.316** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The summary of prediction accuracies of models.  
Figure 9: Typical prediction of testing sets of A2018 and B2019 by the proposed model: (**a**) A2018; (**b**) B2019.  
Firstly, as shown in Figure 10 and Table 4, the PM model has the worst accuracy and the GRU model has the best accuracy among the models without data decomposition. However, differences between their accuracies are insignificant. To illustrate, the MAE and RMSE of PM that has the worst accuracy on the testing set of A2018 are 0.503 and 0.676 m/s, respectively, while those of GRU are 0.499 and 0.667 m/s. The accuracy of GRU is not significantly higher than that of PM. The reason behind this is the phenomenon of unpredictable noise embedding in the raw wind speed series, which can mask the characteristics of wind speed and then prevent the models from learning evolution patterns of wind speed. As a consequence of this discovery, PM is used to make initial predictions in the scheme of quasi real-time decomposition for its simplicity.  
Secondly, the proposed model with the help of data decomposition can achieve a significantly higher accuracy than the models without data decomposition. For example, the MAE and RMSE of SSA-TRTD-MR-GRU on the testing set of A2018 are 0.334 and 0.445 m/s, respectively, which are significantly smaller than those of GRU. The reason behind this superior performance is that data decomposition can extract the trend of the raw wind speed series and eliminate the unpredictable noise effectively, which makes it easier for the prediction model to learn latent mechanisms. In effect, all the models with data decomposition can have higher accuracy than the models without data decomposition. The aforementioned discoveries can be summarized as follows. GRU can predict the wind speed data more accurately than other models, and data decomposition can promote the accuracy of models.
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
## 3 Proposed Method
### Extended Mesh Fingerprinting Scheme Using [PERSON]'s Code  
#### 3.4.1 [PERSON]'s Fingerprint Code  
Among the various fingerprint codes against collusion attack, [PERSON] code [17] performs state-of-the-art performance. [PERSON] has proposed a randomized fingerprinting code based on the [PERSON] and [PERSON]'s scheme [4] that is provably secure against collusion attack. Compared with conventional methods, this code reduced the length of the code to the square of its length. Following [PERSON] [17], we have provides the formal definition of the fingerprint code below.  
A fingerprint code of length \(m\) for \(n\) users over the alphabet \(\Sigma\) is a distribution over the pairs \((X,\sigma)\), where \(X\) is an \(n\times m\) matrix over \(\Sigma\) and \(\sigma\) is an algorithm that takes a string \(y\in\Sigma^{m}\) (the pirated copy) as input, and produces a subset \(\sigma(y)\subseteq[n]:=1,2,\cdots,n\) (the set of accused users). For \(\phi=\
eq C\subseteq[n]\) a C-strategy is an algorithm \(\rho\) that takes the submatrix of \(X\) formed by the rows with indices in \(C\) as input, and produces a string \(y=\rho\in\Sigma^{m}\) as output \(1\) and satisfies the marking condition that, for all positions \(1\leq i\leq m\), if all the values \(X_{ij}\) for \(j\in C\) agree with some letter \(s\in\Sigma\) then \(y_{i}=s\). We say that a fingerprint codes is \(\epsilon\)-secure against coalitions of of size \(c\), if for any \(C\subseteq[n]\) of size \(|C|\leq c\) and for any C-strategy \(\rho\), the error probability,  
\[P[\sigma(\rho(X))=\phi\text{ or }\sigma(\rho(X))\
subseteq c] \tag{26}\]is at most \(\epsilon\). [PERSON]'s fingerprint code is an \(n\times m\) binary matrix, where \(n\) denotes number of users and \(m\) denotes the length of the fingerprint codes. The number of user \(n\), the size of collusion \(c\) and the error bound \(\epsilon\) are the key parameters that decide the size of the fingerprint code matrix.
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 3 Methodology
### The CNN-LSTM Hybrid Model
#### 3.4.3 Data Preprocessing and Segmentation  
To achieve the fixed length windows, one key requirement is to divide the standardized data into segments using a temporal sliding window algorithm. The algorithm divides the data into windows of signals, where a given window is associated with a specific activity and usually has one to a few seconds of observation data [18]. Two parameters were configured to achieve this: the size of the window and the shift [65]. The shift parameter helps create the overlap between windows as seen in Figure 4. In the case of a 50% shift, which is commonly used in much of the HAR literature, an overlap is created such that the first half of a window contains the observations from the last half of the previous window [32; 64; 65]. In this work, we used a window length of 32 (circa 0.75 s of sensor signals) and a 50% shift. A detailed analysis of the different window sizes and how we arrived at the choice of 32 window size can be found in Section 4.  
Figure 4: Sliding window with 50% overlaps.
# IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses
## 5 Intrusion Detection System (IDS) in IoT
### IDS Datasets Appropriate for IoT  
There are a good number of datasets available for the development and validation of IDSs. The most popular datasets used in the implementation of IoT-IDSs include NSL-KDD [117], the Bot-IoT [16], the Botnet [118], and the Android malware [119] datasets. The NSL-KDD dataset is designed to solve some of the inherent problems of the KDD'99 dataset. Thus, NSL-KDD eliminated redundant duplicate records, thereby significantly reducing the total number of records. The number of borderline (i.e., difficult) records were eliminated based on the inverse percentage so that the NSL-KDD dataset has far fewer borderline records than other datasets. Several papers focused on IoT intrusion detection have used this NSL-KDD and reported judicious and sensible results. The Android malware dataset (CICAndMal2017) contains malware and benign applications, proposed in [119]. The malware samples used to develop this dataset consist of Adware, Ransomware, Scareware, and Short Message Service (SMS) malware and include more than 80 network traffic features. The Bot-IoT is an IoT traffic-based dataset that contains more than 72,000,000
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Participants** & **Method** & **0.05** & **0.1** & **0.15** & **0.2** & **0.25** & **0.3** \\ \hline \multirow{3}{*}{1} & Approximate & 0.58(0.0071) & 0.53(0.015) & 0.41(0.02) & 0.31(0.025) & 0.24(0.022) & 0.19(0.02) \\ \cline{2-7}  & Sample & 0.57(0.021) & 0.46(0.018) & 0.35(0.019) & 0.27(0.019) & 0.21(0.017) & 0.17(0.015) \\ \hline \multirow{3}{*}{2} & Approximate & 0.58(0.023) & 0.49(0.039) & 0.37(0.037) & 0.27(0.031) & 0.2(0.025) & 0.16(0.02) \\ \cline{2-7}  & Sample & 0.55(0.04) & 0.42(0.045) & 0.31(0.039) & 0.23(0.03) & 0.18(0.023) & 0.14(0.018) \\ \hline \multirow{3}{*}{4} & Approximate & 0.58(0.012) & 0.5(0.033) & 0.39(0.042) & 0.29(0.041) & 0.22(0.035) & 0.17(0.029) \\ \cline{2-7}  & Sample & 0.57(0.023) & 0.44(0.031) & 0.34(0.041) & 0.26(0.039) & 0.2(0.033) & 0.16(0.028) \\ \hline \multirow{3}{*}{5} & Approximate & 0.58(0.023) & 0.54(0.025) & 0.44(0.025) & 0.35(0.021) & 0.27(0.018) & 0.22(0.016) \\ \cline{2-7}  & Sample & 0.58(0.053) & 0.48(0.048) & 0.37(0.042) & 0.29(0.033) & 0.23(0.025) & 0.19(0.019) \\ \hline \multirow{3}{*}{6} & Approximate & 0.59(0.0099) & 0.54(0.026) & 0.42(0.033) & 0.31(0.032) & 0.24(0.026) & 0.19(0.021) \\ \cline{2-7}  & Sample & 0.59(0.014) & 0.47(0.018) & 0.36(0.021) & 0.27(0.021) & 0.21(0.018) & 0.17(0.016) \\ \hline \multirow{3}{*}{8} & Approximate & 0.6(0.0069) & 0.56(0.02) & 0.45(0.037) & 0.34(0.039) & 0.26(0.034) & 0.21(0.027) \\ \cline{2-7}  & Sample & 0.61(0.017) & 0.49(0.025) & 0.38(0.034) & 0.3(0.033) & 0.23(0.029) & 0.19(0.023) \\ \hline \hline \end{tabular} Note: Mean (standard deviation) entropy values for each participant across six different threshold windows (\(r\)).  
\end{table} 
Table 3: Approximate and sample entropy values for each participant for the anterior-posterior center-of-pressure time series for eyes closed tandem dominant back. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Participants** & **Method** & **0.05** & **0.1** & **0.15** & **0.2** & **0.25** & **0.3** \\ \hline \multirow{3}{*}{1} & Approximate & 0.65(0.033) & 0.57(0.056) & 0.47(0.085) & 0.37(0.09) & 0.29(0.083) & 0.23(0.07) \\ \cline{2-7}  & Sample & 0.67(0.061) & 0.51(0.068) & 0.4(0.083) & 0.31(0.081) & 0.25(0.073) & 0.2(0.063) \\ \hline \multirow{3}{*}{2} & Approximate & 0.65(0.046) & 0.59(0.068) & 0.52(0.088) & 0.44(0.089) & 0.37(0.082) & 0.31(0.071) \\ \cline{2-7}  & Sample & 0.69(0.085) & 0.54(0.092) & 0.44(0.099) & 0.36(0.094) & 0.29(0.083) & 0.25(0.071) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Approximate and sample entropy values for each participant across six different threshold windows (\(r\)).
## 4 Discussion
Our method does not produce such accuracy, as the measurement error is estimated at less than \(1\) mm (the error was assessed by computing the error of movement of the upper marker when the conditions were steady, i.e., when the position of the marker in the camera coordinate system of reference was not changing). This may be considered a drawback, although the priority for us in developing this method was the easy and fast use as well as low invasiveness. However, we are aware that for orthodontic or prosthetic purposes, such accuracy is insufficient. Nonetheless, we believe that the proposed method may be used in different treatment stages, e.g., to evaluate physical therapies improving the range of motion and trajectory of mouth opening. After all, our method seems to also be much more objective than a visual examination. Another advantage of our method is that it may be applied in almost any condition. The measuring equipment is light and takes up little space. Therefore, an examination may be conducted in almost any place. There only has to be good lighting in the room. Other methods often use a few cameras, which need to be installed in relation to each other in specific positions [14; 18; 21; 35; 36]. Additionally, other methods often require the use of head-stabilizing elements [15; 35; 38]. These characteristics will often translate to specific requirements for the room, and the equipment itself will have to be permanently stored where examinations are performed. The most advanced methods available on the market, such as WinJaw or MODJAW, comprehensively assess jaw movements and are characterized by high accuracy and a three-dimensional motion analysis [40; 41; 42]. This is a clear advantage compared to the method proposed by us. However, these mentioned methods are justified for the work of a dentist, which requires great precision in planning orthodontic or prosthetic treatment. From the perspective of a physiotherapist, it is necessary to use a method that minimally interferes with muscle function and provides a quick preliminary diagnosis of the jaw movement pattern, allowing for subsequent evaluation of the effects of physiotherapy. In this regard, our proposed method appears to be sufficient, and due to its low cost, it may become a popular tool for assessing jaw movement.
# Power Prediction of Bifacial Si PV Module with Different Reflection Conditions on Rooftop
## 3 Proof of Theoretical Approach
### Application Test 2  
The power output of the bifacial module obtained through the theoretical prediction is compared with the measured data shown in Figures 22 and 23. When the irradiation was higher than 700 W/m\({}^{2}\), the average error rates were 3.93% and 2.21% on a day for Exp. 5 and Exp. 6, respectively. While Exp. 5 shows fluctuation of the data due to the intermittent shading, Exp. 6 assumed the solar irradiation is mostly less than 1000 W/m\({}^{2}\) at noon and the amount of solar irradiation would be incident uniformly. Under irradiation less than 700 W/m\({}^{2}\), however, the average error rates increase up to 12.8% in Exp. 5 and 17.8% in Exp. 6.  
In Figure 22, the maximum irradiation was 1002.3 W/m\({}^{2}\) from a pyranometer. The output of the bifacial module with a transparent back sheet was measured to be higher at every point than the one with an opaque white back sheet. In Figure 23, however, when the maximum irradiation is 970.9 W/m\({}^{2}\) and Exp. 6, there were some points where the output of the bifacial module with the transparent back sheet was lower than that of the white back sheet. This is because the bifacial module is highly influenced by the front side irradiation at low irradiance intensity, so that it is determined that the rear environment and materials are decided when considering the influence on the rear surface. However, because the output of the bifacial module is higher during most of the experiment time, it is better to make the rear side transparent when using a bifacial module.  
Figure 22: Output comparison between transparent and white back sheet on Exp. 5.  
In Exp. 5, the lower is the solar irradiation, the greater is the difference between the output obtained through modeling and the actual measured output. Accordingly, additional elements are required to predict the backside irradiance in Exp. 5. On the other hand, it is imperative to note that, if the bifacial module is connected with optimum capacity of ESS (Energy Storage System), it would lead to the increase of stability of the power network [30].
## 2 Materials and Methods
### Modeling
#### 2.1.2 Singularity Problem of SGCMGs  
When SGCMGs are in a singularity, the direction in which torque can be output degenerates to two dimensions or fewer, as shown in Figure 3. Thus, when the attitude maneuver direction and the degenerate torque direction are the same, this adversely affects the agility of maneuver. The singularity in the dimension of angular momentum (as shown in Figure 4a-c) is expressed as  
\[\text{rank}(\mathbf{A}(\delta))<3\;\;or\;\;\text{rank}(\mathbf{A}(\delta)\mathbf{A}( \delta)^{T})<3\;\Leftrightarrow\;m=\text{det}(\mathbf{A}(\delta)\mathbf{A}(\delta)^{T })=0 \tag{8}\]  
where \(m\) is a singularity parameter, indicating that it is closer to a singularity when it is closer to 0. In this paper, we assume a singularity when \(m\leq 0.3\). There are two types of singularities: internal and external. An internal singularity depends on the gimbal angle, and an external singularity occurs when the output torque is saturated at the performance limit. The external singularity is the outermost part of the singular surface, as shown in Figure 4a-c, and the internal singularity is found inside the external singularity.  
Furthermore, the shape of the singular surface differs depending on the skew angle, as shown in Figure 4a-c. In actual operation, \(\beta=54.74\) degrees (shown in Figure 4b) is often selected from the symmetry of the angular momentum. In conventional attitude control, the gimbal angular velocity input to the SGCMG system is calculated by solving the following steering law from the torque calculated in Equation (7):  
\[\dot{\delta}_{c}=\mathbf{A}(\delta)^{T}(\mathbf{A}(\delta)\mathbf{A}(\delta)^{T})^{-1}T_{c} \tag{9}\]  
In conventional singularity avoidance/escape steering laws, an error parameter or a null vector is added to Equation (9). Such laws have been studied because the Jacobian matrix is not a square matrix and rank degenerates in the singularity. However, as the gimbal steering is not taken into consideration in the attitude controller, the robustness may be adversely affected. Thus, the design of the singularity avoidance/escape steering laws must be considered by the attitude controller.