esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Theoretical Analyses
### Theoretical Model
\tag{17}\]  
where \(x_{1}\), \(\dot{x}_{1}\), and \(\ddot{x}_{1}\) denote the displacement, velocity, and acceleration of the primary structure, respectively; \(x_{2}\), \(\dot{x}_{2}\), and \(\ddot{x}_{2}\) denote the displacement, velocity, and acceleration of the accessory structure, respectively; \(P_{1}\) and \(P_{2}\) denote the time-variant external exciting force applied to the primary structure and the accessory structure, respectively; \(M_{1}\) and \(M_{2}\) denote the coupled mass of the accessory structure and the accessory structure, respectively, which are the sums of the structural and added masses of the corresponding structure, i.e., \(M_{j}=M_{sj}+M_{aj}\) (\(j=1,2\)); \(K_{1}\) and \(C_{1}\) denote the stiffness and damping of the primary structure, respectively; \(K_{2}\) and \(C_{2}\) represent the stiffness and damping between the accessory structure and the primary structure, respectively; \(K_{3}\) and \(C_{3}\) represent the stiffness and  
Figure 8: The comparison of horizontal and vertical displacements of a typical guide wall block. **(a)** Horizontal displacement; **(b)** vertical displacement. Figure 7: The modified two-degrees-of-freedom theoretical model for the guide wall: the associated-forced coupled vibration (AFCV) system. coupled mass ratio  
\[\mu=\frac{M_{1}}{M_{2}}\]  
frequency ratio  
\[\lambda_{ab}=\frac{\omega_{a}}{\omega_{b}}\ (a,b=0,1,2,3)\]  
where \(\omega_{j}\) (\(j\) = 1,2,3) denotes the natural frequency of different structure in different system  
\[\omega_{1}=\sqrt{\frac{K_{1}}{M_{1}}},\omega_{2}=\sqrt{\frac{K_{2}}{M_{2}}}, \omega_{3}=\sqrt{\frac{K_{3}}{M_{2}}}\]  
and damping ratio  
\[\xi_{1}=\frac{C_{1}}{2M_{1}\omega_{1}},\,\xi_{2}=\frac{C_{2}}{2M_{2}\omega_{2} },\,\xi_{3}=\frac{C_{3}}{2M_{2}\omega_{3}}\]  
In this AFCV system, the natural frequency and damping ratio of the accessory structure are coupled values depending on \(M_{2}\), \(K_{2}\), \(K_{3}\), \(C_{2}\) and \(C_{3}\), which are too complicated for calculation. Therefore, \(\omega_{2}\) and \(\omega_{3}\) are defined to represent the decoupled nature frequencies of the accessory structure in the AV system and the independent FV system. Similarly, \(\xi_{2}\) and \(\xi_{3}\) are decoupled damping ratios of AV and FV systems. To investigate the mechanism of guide wall vibration, the amplitude coefficient of the accessory structure is calculated to analyze its dynamic response in the AFCV system, and the aforementioned two kinds of vibrations are taken into account and a comparison is made to explore their contribution to the coupled vibration.
# Time Series Visualization and Forecasting from Australian Building and Construction Statistics
## 3 Methodology
### Time Series Forecasting  
The data obtained from the ABS website are a set of time series data. In addition to visualizing them in the interactive tool (Section 3.3), we performed forecasting, based on the historical time series records from the processed ABS statistics. We explored both traditional non-deep learning-based method and deep learning method. Specifically, we model the problem as an multivariate time series forecasting problem that, given historical time series values and related features, we use to forecast the values of the future. Consider \(n\) time series variables \(\{y_{1t},\ldots,y_{nt}\}\). A multivariate time series is the \((n\times 1)\) vector time series \(\{\mathbf{Y_{i}}\}\), where the \(i\)-th row of \(\{\mathbf{Y_{i}}\}\) is \(\{y_{it}\}\). That is, for any time, \(t\), \(\{\mathbf{Y_{i}}\}=(y_{1t},\ldots,y_{nt})\). Our task is to predict a specific \(\{y_{it}\}\) (i.e., the interested statistic value) for \(t\)+1, \(t\)+2,..., by given \(\{\mathbf{Y_{i}}\}\).  
The long short-term memory (LSTM) is a deep learning model that is commonly adopted in time series forecasting, due to its power of modelling sequence data, and time series is a typical type of time series data. LSTM is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. Most of the current LSTM time series forecasting methods make forecasting based on only the time series itself, that is, using the historical values of the same time series to predict its next values. In our work, we tend to involve more relevant and informative information to aid the prediction. Specifically, we adopt the economic statistics from ABS and build a LSTM-based multivariate time series forecasting model.
# How Successful Is Transfer Learning for Detecting Anorexia on Social Media?
## 6 Error Analysis
### Words Included in the Vocabularies of the Pre-Trained Models  
Table 3 shows how many words are included for each previously used vocabulary model and how many are not in the SAD dataset. The BETO vocabulary has the greatest coverage in the SAD corpus since it includes over 6000 unique tokens. Specifically, we find words from the corpus that are not contained in other model vocabularies such as _gorda_ (fat), _delgada_ (thin), _luchar_ (fight), _desafio_ (challenge), _enfermo_ (sick), _animo_ (courage), _musculo_ (muscle), _reducciion_ (reduction), _desayuno_ (breakfast) and _nutricion_ (nutrition). All these words have important meanings in the context of anorexia, including descriptions of the physical characteristics, foods, moods, and so on.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Anorexia**} & \multicolumn{3}{c}{**Control**} & \multicolumn{3}{c}{**Macro-Avg**} \\ \cline{2-10}
**Model** & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** & **P (\%)** & **R (\%)** & **F1 (\%)** \\ \hline LSTM & 89.05 & 92.61 & 90.75 & 93.10 & 89.65 & 91.31 & 91.08 & 91.13 & 91.03 \\ BiLSTM & 87.78 & 93.54 & 90.53 & 93.90 & 81.16 & 90.90 & 90.84 & 90.85 & 90.71 \\ CNN & 88.85 & 93.37 & 90.95 & 93.89 & 89.20 & 91.38 & 91.37 & 91.29 & 91.16 \\ \hline XLM-100 & 91.96 & 93.41 & 92.67 & 93.98 & 92.59 & 93.27 & 92.98 & 93.00 & 92.97 \\ M-BERT & 91.77 & 93.73 & 92.72 & 94.21 & 92.34 & 93.24 & 93.00 & 93.03 & 92.99 \\ XLM-17 & 92.51 & 93.14 & 92.81 & 93.77 & 93.17 & 93.45 & 93.14 & 93.15 & 93.13 \\ BETO & 93.31 & 94.45 & 93.87 & 94.94 & 93.85 & 94.39 & 94.12 & 94.15 & 94.13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results obtained on the SAD corpus.
## 4 CTP and Probabilistic Routing
### Triangular Distribution Function  
In order to model the distribution of the possible routes to the sink we investigated the use of a discretized version of the Triangular Distribution Function (TDF) as suggested in [20]. The Triangular Distribution is typically used as a subjective description of a population for which there is only limited sample data. It is based on a knowledge of the minimum and maximum and an inspired guess as to what the modal value might be.  
\[f(x|a,b,c)=\begin{cases}\frac{2(x-a)}{(b-a)(c-a)}&\text{for }a\leq x\leq c\\ \frac{2(b-x)}{(b-a)(b-c)}&\text{for }c\leq x\leq b\\ 0&\text{otherwise}\end{cases}\]  
\(a\), \(b\) and \(c\) parameters determine the form of the probability density function. Parameter \(a\) represents the lower limit, \(c\) represents the mode and \(b\) the upper limit. For basic CTP the distribution function uses \(a=b=c\) with the value of 1 indicating that there is only one possible neighbour eligible. When multiple paths to the sink are considered, the \(b\) parameter indicates the number of candidate neighbourseligible for transmission. Having characterized the Probability Density Function it is quite simple to generate random variates that follow the distribution.  
In order to determine the candidate node for transmission, instead of selecting the node with minimum ETX as CTP does, nodes are logically sorted by ascending ETX. The node with lowest ETX will be considered the first candidate, and other nodes will be considered subsequent candidates according to their ETX. The neighbours to transmit are selected according to the Probability Distribution Function of the Triangular Distribution (see Figures 1 and 2).  
Before each transmission each node computes a value \(\mu\) drawn from the uniform distribution in the interval \((0,1)\). The neighbour to transmit to is calculated as the floor of \(X\), a triangular distributed random variate given by:  
Figure 1: Plot of different probability density function of the Triangular Distribution. The X axis indicate the number of eligible neighbours.  
Figure 2: Discrete Probability Function for Triangular Distribution. For 3 neighbours the probabilities are distributed following the Probability Density Function of the TD.  
\[X=b\times(1-\sqrt{1-\mu})\]  
This schema distributes transmissions amongst candidate paths but giving more chances to transmit to well connected nodes.
# Automatic Fruits Freshness Classification Using CNN and Transfer Learning
## 4 Simulation Results
### Comparison  
In this section, we present the comparison of our developed method with several other methods on same datasets. We compare our work with three recently reported fruit freshness classification methods [5; 19; 20; 23; 24; 25]. For fair comparison, we use the same training strategy as reported by above-described methods. Table 3 shows the mean accuracy comparison. From Table 3, a few important observations are listed below.  
* On Dataset 1, the YOLO based method [5] reports 97% fruit freshness classification accuracy. On this dataset, our proposed method yields 98.2% classification accuracy.
* On Dataset 2, our developed method yields 99.8% classification accuracy and beats all the compared methods. In this dataset, the ResNet-50 based method [24] also reported an encouraging classification accuracy of 98.89%. Moreover, dataset [20] reported detailed classification results by implementing several architectures with the mean outcome of 88.77% fruit freshness classification.
* In [25], Dataset 3 is introduced. To the best of our knowledge, none of the work reports accuracy on this dataset. On this dataset, our developed method yields 99.3% fruit freshness classification. On the whole, our developed method achieves a mean accuracy of 99.1% on all three fruits datasets.
* We believe that our findings are encouraging and will be useful for various fruit packing industries. At advanced level, our method can also be used to know the fruits freshness level when they are growing with the tree.
## 3 Proof of Stability
### Bounded-Input Bounded-Output Proof  
As \(\boldsymbol{y}^{*}\) is a given constant vector and \(\boldsymbol{e}(k)\) is bounded, we can obtain the boundedness of the output \(\boldsymbol{y}(k)\) and because \(\boldsymbol{\hat{\Phi}_{c}}(k)\) is bounded, we can always identify a positive number to make the following formula valid.  
\[\left\|\boldsymbol{I}-\frac{\rho\boldsymbol{\Phi}_{c}(k)\boldsymbol{\hat{\Phi }_{c}}^{T}(k)}{\lambda+\|\boldsymbol{\hat{\Phi}_{c}}(k)\|^{2}}\right\|_{v} \leq M_{2} \tag{12}\]  
Using Equations (7), (11) and (12), we obtain:  
\[\begin{array}{l}\left\|\boldsymbol{u}(k)\right\|_{v}\leq\left\|\boldsymbol{ u}(k)-\boldsymbol{u}(k-1)\right\|_{v}+\left\|\boldsymbol{u}(k-1)\right\|_{v} \\ \leq\left\|\boldsymbol{u}(k)-\boldsymbol{u}(k-1)\right\|_{v}+\left\| \boldsymbol{u}(k-1)-\boldsymbol{u}(k-2)\right\|_{v}+\left\|\boldsymbol{u}(k-2 )\right\|_{v}\\ \leq\left\|\Delta\boldsymbol{u}(k)\right\|_{v}+\left\|\Delta\boldsymbol{u}(k-1 )\right\|_{v}+\ldots+\left\|\Delta\boldsymbol{u}(1)\right\|_{v}+\left\| \boldsymbol{u}(0)\right\|_{v}\\ \leq M_{2}(\|\boldsymbol{e}(k)\|+\|\boldsymbol{e}(k-1)\|+\ldots+\|\boldsymbol{ e}(2)\|+\|\boldsymbol{e}(1)\|)+\left\|\boldsymbol{u}(0)\right\|_{v}\\ \leq M_{2}(d_{2}^{k-1}\|\boldsymbol{e}(1)\|+d_{2}^{k-2}\|\boldsymbol{e}(1)\|+ \ldots+d_{2}\|\boldsymbol{e}(1)\|+\|\boldsymbol{e}(1)\|)+\left\|\boldsymbol{u }(0)\right\|_{v}\\ \leq M_{2}\frac{1}{1-d_{2}}\|\boldsymbol{e}(1)\|+\|\boldsymbol{u}(0)\|_{v} \end{array} \tag{13}\]  
Therefore, the system bounded-input bounded-output is proven and the closed-loop system is bounded-input bounded-output stable.
# Data Hiding Method for Color AMBTC Compressed Images Using Color Difference
## 1 Introduction
Instead of increasing the number of quantization levels per block, one bit per block can be compressed more than the conventional ABMTC by using a common bitmap and 7-bit quantization levels. In other words, the image can be represented with about 95 bits per block. Specifically, to compress three bitmaps into one bitmap, we extract the luminance from RGB and use k-means [17] to classify the pixels in the block into three groups to generate a common bitmap per block. The performance of this method can be confirmed by experimental results. In this paper, we introduce a method for efficient DH based on our proposed color AMBTC. For efficient DH, we use the Optimal Pixel Adjustment Process (OPAP) method [18]. Through the experiment, we can confirm the superiority of the color AMBTC based on k-means. The contributions we proposed to color ABMTC and DH methods are as follows. First, the color AMBTC method using k-means enables improving the image quality dramatically. Second, the image compression performance was improved by 1 bit per block. Third, we showed how to use OPAP to hide enough secret data while reducing the loss of the cover image. The rest of this paper is organized as follows: Section 2 reviews the BTC series used to compress gray images and the BTC series related to color image compression. The encoding process of color AMBTC and generation of the common bit-plane are discussed in Section 3. In Section 4, the proposed color AMBTC method and the data hiding method based on it are introduced. Section 5 are the experimental result. Section 6 draws some conclusions.
## 5 The Molecular Mechanisms Behind the Resistance of Biofilms to Antimicrobial Agents
### Persister Cells
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mechanism** & **Description** & **Examples** & **Citation** \\ \hline Physical Barrier of the & EPS matrix in biofilms functions as a physical & EPS in biofilms of & \\  & obstacle, hence diminishing the infiltration of & _P. aeruginosa, S. aureus_, and & [115; 122] \\ Biofilm Matrix & & antimicrobial agents. & _S. mutans_ restrict the & [115; 122] \\  & & generation of antibiotics. & \\ \hline Diminished Rate of Growth & Bacteria within biofilms frequently exhibit & Cells in biofilms of & \\  & reduced growth rates and diminished & _S. epidermidis, K. pneumoniae_, & \\  & metabolic activity, rendering them less & and Mycobacterium TB & [12; 125] \\ and Metabolic Activity & vulnerable to antibiotics that specifically & exhibit slow growth and are & \\  & target rapidly proliferating cells. & resistant to many medications. & \\ \hline Genetic Recombination & Biofilms facilitate genetic diversity via DNA & Genetic alterations in the & \\  & alterations, resulting in variations that could & biofilms of _A. baumannii_ and & \\  & and Variation & potentially exhibit greater resistance & _P. aeruginosa_ contribute to the & [114; 135] \\  & & to therapies. & development of & \\ \hline Stress Responses and & Bacteria in biofilms possess the ability to & Adaptive reaction to several & \\  & adjust and acquire resistance mechanisms as & stressors, such as physical & \\  & adaptive Resistance & a reaction to environmental stressors, such as & pressure, hypochlorous acid, & [137; 139; 168] \\  & & the existence of antibiotics. & and exposure to antibiotics. & \\ \hline Persister Cells & Biofilms consist of ’persister cells’ that are in & Survival of persister cells in & \\  & a dormant state and possess a high level of & biofilms of _C. albicans_, & \\  & tolerance to antimicrobial agents. These cells & _E. faecalis_, and S. aureus_ & [12; 156; 157] \\  & have the ability to reestablish the population & & \\  & after undergoing treatment. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mechanisms and illustrations of biofilm resistance to antimicrobial agents.
# Source Symbol Purging-Based Distributed Conditional Arithmetic Coding
## 4 Simulation Results
### Decoding Performance  
In order to evaluate the decoding performance of SPDCAC, the proposed scheme will be compared with several other DSC schemes. Those DSC schemes used for comparison with SPDCAC in this experiment are DJSCAC and the DSC scheme based on irregular LDPC code, respectively. The DSC scheme based on LDPC code use the approach proposed in [13]. After DSC schemes encoding the same source sequence at the same code rate, the same side information sequence is used to help the decoding of these schemes. Both DJSCAC and SPDCAC use the same forbidden interval \(\mu=0.01\). DJSCAC controls the code rate by adjusting the maximum admissible range of \(\alpha\) in (6). During the coding process for each symbol, \(\alpha\) is firstly calculated by (6), if its value exceeds a predetermined threshold, set \(\alpha\) to this threshold; otherwise, \(\alpha\) remains unchanged. In this experiment, the maximum width of the decoding tree in the MAP algorithm used by DJSCAC and SPDCAC is limited to 256.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Encoding Approach**} & \multicolumn{4}{c}{**RMCR (Bit/Symbol)**} \\ \cline{2-5}  & \(P_{t}=0.9\) & \(P_{t}=0.8\) & \(P_{t}=0.7\) & \(P_{t}=0.6\) \\ \hline AC & 0.4844 & 0.734 & 0.898 & 0.962 \\ DJSCAC & 0.3437 & 0.4687 & 0.4765 & 0.4922 \\ SPDCAC & 0.2422 & 0.3828 & 0.4238 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The RMCR of SPDCAC and DJSCAC when coding different sources.  
Figure 8 shows the decoding performance of different schemes when coding the same source at different code rates. It can be seen that in some code rate cases, SPDCAC can exhibits a better BER performance than that of DJSCAC. Compared with the DSC scheme based on the LDPC code, SPDCAC exhibits a better BER performance when \(H(X|Y)\) is larger, and this advantage is more significant when the code rate is lower.  
The decoding performance of a LDPC code-based DSC scheme is related to the number of parity bits (or the block size). Therefore, when the length of the source sequence is large, and \(H(X|Y)\) is small, the decoder can easily become lossless when decoding results. However, the BER performance of the DSC scheme based on LDPC codes will deteriorate rapidly as \(H(X|Y)\) increases. This shows that it is difficult for an LDPC code-based DSC scheme to ensure a better decoding performance when the correlation strength between \(X\) and \(Y\) is weak. Although the DSC scheme based on the LDPC code will ensure a better decoding performance when coding the source with weaker internal correlation strength, in the case of coding a source with strong internal correlation strength, the BER performance of the DSC scheme based on the LDPC code can only be better than that of SPDCAC when \(N\) is large and \(H(X|Y)\) is small. However, when the internal correlation strength of the source is strong and \(N\) is small, the decoding performance of the DSC scheme based on the LDPC code is completely inferior to that of SPDCAC.
## 2 Materials and Methods
### Preparation and Characterization of Chitosan Microparticles Loaded with Relaxing Peptide
#### 2.2.1 Particle Size Distribution and Surface Charge  
Particle size distribution (mean diameter and polydispersity index) and zeta potential were both determined by dynamic light scattering and electrophoretic light scattering, respectively, using a Malvern Zetasizer Nano ZS instrument (Malvern Instruments Ltd., Worcestershire, UK). The analysis was performed at room temperature using samples appropriately diluted in 0.1 M NaCl saline solution. For each sample, the mean size of three determinations was calculated. Values reported are the mean \(\pm\) standard deviation of at least three different batches of nanoparticles.
## 3 Results and Discussion
The difference between this result and ours might be due to the fact that, in our case, we included coarse material in the analysis; this coarse material could contain higher percentages of organic matter. The characteristics of the water that is drained by the river could also play a role in the relationship between [Hg]\({}_{\rm w}\) and POC. [PERSON] et al. [15] found no significant correlation between [Hg]\({}_{\rm w}\) and POC for floodplain lakes of the Amazonian river. However, these lakes contained water classified as white or black water, while the Itenez river is considered a clear water river. The fact that not all authors find a positive correlation between particulate mercury and particulate organic matter can also be due to regional differences and types of soil. Therefore, our data can contribute to a wider analysis of which conditions give rise to such a positive relationship. Samples were taken in two seasons of the same year, at the beginning of the low water season and by the end of the low water season. The relationships between suspended particulate matter, particulate organic matter, and particulate mercury by weight [Hg]\({}_{\rm w}\) did not show seasonal variations. The possible terrigenous nature of particles could explain the seasonal invariance of these relationships. The nature of the bonds between mercury and particulate suspended matter could affect mercury's fate and the possibilities of its methylation. Our results suggest that particulate mercury binds to organic carbon. However, mercury adsorption has also been related to aluminum and iron oxyhydroxides [3; 27], which, in turn, are closely associated with organic matter [17]. Therefore, further studies are necessary to establish if the correlation of particulate mercury with POC is due to direct binding of mercury with organic matter or binding with the oxyhydroxides associated with this organic matter. Moreover, several studies have shown that mercury binding to organic matter is mostly through thiols, sulfides, and disulfides [25; 28]. Consequently, the nature of the particulate organic matter might influence the amount of captured mercury. A study on sediment cores taken from the Titicaca lake has shown that capturing mercury on solids depends more on quality than quantity of organic matter. There is a positive correlation between bound mercury and reduced sulfur content [29]. Quantification of reduced sulfur in SPM could help establish the nature of the bonds between mercury and organic matter. Figure 4: Relationships between particulate organic carbon (POC) and suspended particulate matter (SPM) (**A**), [Hg]\({}_{\rm w}\) and SPM (**B**) and [Hg]\({}_{\rm w}\) and POC (**C**) for samples taken in June (closed squares) and September (open squares).