esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Action Evaluation Network
The algorithm can be summarized in Algorithm 2. ```
1:Initialize actor network and critic network for each agent \(i\in(1,N)\) with random parameters \(\theta,\phi\). Initialize action evaluation network with \(\psi\). Initialize predict step K, learning rate \(\alpha\), intrinsic factor \(\beta\). 2:for each episodes do
3: Initialize observation \(o\) from state \(S\)
4:for t=1,T do
5: Sample actions \(A=(a_{1},a_{2},\ldots,a_{N})\) from policies \(\pi_{1\ldots N}\)
6: Execute actions in \(E\), get reward \(R_{t}^{e}=\sum_{i=1}^{N}r_{i}^{i}\) and next observation \(o_{t+1}\)
7:endfor
8:for t=1,T in this episode do
9: Extract latent state \(L_{t}\)
10: Compute the influence of joint action \(C_{t}\)
11: Replace action \(a_{i}\) get \(C_{i,t}\)
12: Compute agent's continuous contribution \(r_{i,t}^{c}\)
13:endfor
14: Rewrite reward \(r_{t}=(1-\beta)r_{t}^{e}+\beta r_{t}^{c}\)
15: Compute gradient \(\
abla_{\theta}\), \(\
abla_{\phi}\), \(\
abla_{\phi}\). 16: Update policy and network \(\theta_{t+1}\leftarrow\theta_{t}-\alpha\
abla_{\theta}\), \(\phi_{t+1}\leftarrow\phi_{t}-\alpha\
abla_{\phi}\), \(\psi_{t+1}\leftarrow\psi_{t}-\alpha\
abla_{\phi}\)
17:endfor
```  
**Algorithm 2** Action evaluation  
Therefore, we can add \(r^{c}\) as an intrinsic reward to the update and optimization of the individual network. In this way, when the agent's policy selects actions, it considers a single step to obtain the largest environment reward and can obtain more future rewards. It makes the agent not only carry out its own greedy policy but also consider how to contribute to the whole environment. By balancing these two, agents can effectively achieve collaboration.
## 3 Method and Model  
This study develops a method that integrates an LLM with emotional representation for analyzing and scoring the emotional content in students' spoken English audio samples. Firstly, using the text and emotional labels from the IEMOCAP dataset, emotionally rich corresponding speech data is synthesized through the LLM. Subsequently, the synthesized speech data is combined with the audio from the IEMOCAP dataset and student spoken language samples to form a comprehensive dataset. Next, an emotion feature encoding network based on the transformer architecture is introduced to deeply extract emotional features from the audio. Finally, an English spoken language emotion evaluation network is designed, which enables the regression analysis and precise (accurate) scoring of students' emotional expression in spoken English. The overall framework is illustrated in Figure 1 and specifically includes the following three components:  
Step 1. LLM for emotion voice synthesis: the LLM, Typecast [27], is utilized in this paper to synthesize emotional voice by integrating textual content with emotional labels.  
This synthesized audio, combined with the IEMOCAP dataset audio and collected students' spoken language samples, is used to construct a comprehensive dataset for evaluating emotions in students' spoken language.  
Step 2. Emotion feature encoding network: The paper introduces an emotion feature encoding network based on the transformer model, aimed at deeply extracting the emotional spatial features from the audio. This network is capable of eliminating the influence of factors such as timbre, pitch, and content in the audio, ensuring that the emotional features are independent of other audio information. This independence enhances the accuracy of the evaluation.  
Step 3. Emotion evaluation network: The paper designs an English spoken language emotion evaluation network, aimed at achieving the accurate scoring of emotional expression in students' spoken English. For any input of student spoken audio (accompanied by emotion labels), the process begins by utilizing the corresponding text and emotion labels to generate TTS synthesized speech through the LLM. Subsequently, the actual student spoken audio and the TTS synthesized speech are separately input into the emotion feature encoding network, resulting in the corresponding emotion feature encodings. These two types of encodings, along with the emotion mean feature vectors of the IEMOCAP audio dataset corresponding to the emotion labels, are collectively fed into the emotion  
Figure 1: Architectural diagram of spoken English emotion analysis model.  
evaluation network. This process can be treated as a regression problem, where the emotion evaluation network is coarse-tuned through similarity calculations of emotional features extracted from the three types of audios and fine-tuned based on teachers' scoring. This method allows for the precise scoring of emotional expression in students' spoken language audio. For a detailed description of the training process, please refer to Section 3.3.
## 5 Result and Discussion  
### The Effect of Permutation Entropy  
In this article, we use permutation entropy to describe sequence complexity. Experiments have proved that low-complexity protein regions are often disordered. Permutation entropy has not been used to predict IDPS before, and it is used for the first time in our research. We calculated the permutation entropy of all ordered and disordered proteins in the data set DIS2209, and their probability density distribution is shown in Figure 3. It can be seen that there is a clear difference between ordered and disordered regions.  
Figure 3: Probability distribution diagram.  
In classification prediction, we can calculate the feature importance score of the predictive model, the score can highlight which features are important to the model and which features are not important to the model, helping us better understand the data and model. We show the feature importance of the five features in Figure 4. It can be seen that the feature importance of permutation entropy is higher than Shannon entropy and is basically the same as the preference of two amino acids.  
In order to ensure that permutation entropy plays a positive role in the prediction of intrinsically disordered proteins, we compared the prediction results with and without permutation entropy features in the two ensemble learning algorithms. The results are shown in Tables 3 and 4.  
By comparison, after adding permutation entropy to the two ensemble learning algorithms, the prediction results are significantly improved. In GBDT-PE, F1 and MCC have increased by 4% and 5%, respectively. In LightGBM-PE, the increase is the most obvious, F1 and MCC have increased by 9% and 6%, respectively. This is enough to show that permutation entropy plays a positive role in the prediction of inherent disordered proteins.
###### Abstract  
Load modifying gait retraining strategies, such as changing the foot progression angle (FPA) to toe-in and toe-out gait, are used for people with medial knee osteoarthritis. The FPA can be measured using a pressure sensitive walkway (PSW), but inertial measurement units (IMUs) are considered more suitable for clinical use. This study evaluated the reliability and validity of an IMU system, to measure FPA under different gait retraining strategies. Twenty healthy participants walked a 10-m-long path using different gait strategies (natural (2), toe-out gait (1), toe-in gait (1)) during four 90-s trials. FPA was measured simultaneously with IMUs and a PSW, the latter considered the reference standard. There was good and excellent reliability for the IMUs and PSW FPA measurements, respectively (ICC: IMU, 0.89; PSW, 0.97). Minimal detectable change (MDC) was 4.5\({}^{\circ}\) for the IMUs and 2.7\({}^{\circ}\) for the PSW. Repeated measures ANOVA indicated a significant effect of gait type on FPA (\(p<0.001\)), but not the measurement instrument (\(p=0.875\)). Bland-Altman plots demonstrated the good agreement of both systems for the baseline condition, though the IMUs seemed to consistently overestimate the FPA value compared to the PSW. In conclusion, IMUs are a reliable and valid measurement system for measuring FPA under different gait retraining strategies. The differences between the systems are significant for all gait strategies, so the systems should not be used interchangeably.  
gait analysis; foot progression angle; APDM; inertial measurement unit; toe-in; toe-out +
Footnote †: journal: Medical Imaging  
1
Footnote 1: Department of BioMechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands; [EMAIL_ADDRESS] (F.C.A.U.); [EMAIL_ADDRESS] (J.H.)  
2 Discipline of Physotherapy, Faculty of Medicine and Health, The University of Sydney, Sydney 2006, Australia; [EMAIL_ADDRESS]
## 3 Test Plan and Process
### Test Procedure  
The plan of this experiment was divided into three parts: (1) Friction pairs with different materials; (2) Single surface texture patterns; (3) Bi-composite surface texture configurations, as shown in Table 4. Although the three parts of the friction test were different in the surface structure and material of the friction pairs, the test steps were same. The specific test steps were as follows, as shown in Figure 3.  
Figure 3: Test process of friction pairs with different materials and different surface structures.  
1. According to Tables 2 and 4, different texture patterns were carved on the surface of SSiC by fiber laser machine.
2. All the specimens were cleaned and dried, and the surface morphologies of all specimens were measured and scanned by white-light interference three-dimensional profiler and SEM (scanning electron microscope). The average surface topography parameters were read after three consecutive scans in the white-light interference three-dimensional profiler.
3. The initial weight of the sealing rings (M120D) was measured and recorded.
4. According to the plan of the experiment and Table 4, friction pairs with different materials were carried out first.
5. The specimen was installed in the end surface friction and wear testing apparatus.
6. The friction test was started, and the relevant data were recorded, such as friction torque, friction coefficient, and surface temperature of the friction pairs. After the load, speed and running time were set. Friction coefficient and surface temperature of the friction pairs were dynamic measurements, and each test corresponds to only one set of the friction pairs (stationary ring and rotating ring), so every group friction pair was tested only once.
7. At the end of a group test, the specimen was removed, cleaned, and dried. The sealing ring of M120D was weighed, and then the surface topography parameters of the specimen were measured by white-light interference three-dimensional profiler and SEM.
8. At the end of the first part of the testing plan, repeat the test steps of (5)-(7) to test the second group of single surface texture patterns and the third group of bi-composite surface texture configurations.
9. The weight of the sealing ring (M120D) was measured before and after the test, that is, the sealing rings (M120D) in the 1-A1, 2-B1, 2-B2 and 2-B3 groups. To ensure the accuracy of the data, the sealing rings (M120D) were measured three times each time, and the average weight of these three measurements was taken as the test result.
## 2 Predictive Modeling Techniques
### C4.5 Decision Tree (DT) Model  
C4.5, introduced by [PERSON] [28], is a well-known algorithm, mostly employed for design decision trees. In general, in a decision tree, each branch node depicts a choice among a number of alternatives, and each leaf node denotes a classification or decision. An unknown (or test) instance is routed down the tree as per the values of the attributes in the successive nodes. When the instance reaches a leaf, it is classified as per the label designated to the corresponding leaf.  
In the initial step of model construction, a decision-tree induction algorithm is utilized to construct the tree. Numerous algorithms for decision-tree induction exist; they include Interactive dichotomizer version 3 (ID3) [27], commercial version 4.5 (C4.5) [28], and classification and regression tree (CART) [35]. C4.5 and CART are the most widely used decision tree algorithms in literature [36]. Therefore, this study uses the C4.5 decision tree approach to assess seismic soil liquefaction potential. C4.5 algorithm is an extension of the ID3 algorithm and uses the divide-and-conquer technique, whose key improvements incorporate pruning methodology and processing of missing values, numeric attributes, and noisy data [28]. A statistical test used in C4.5 for handling an attribute to each node in the tree also uses an entropy-based measure. The designated tribute is the one with the highest information gain ratio among attributes available at that tree construction point. The information gain ratio (\(A\), \(S\)) of an attribute '\(A\)' relative to the sample set '\(S\)' is represented as:  
\[\textit{Gain Ratio}\ (A,S)=\frac{\textit{Gain}\ (A,S)}{\textit{Split}\ Information\ (A,S)} \tag{4}\]  
where  
\[\textit{Gain}\ (A,S)=\textit{Ent}(S)-\sum_{a\in A}\frac{|S_{a}|}{|S|}\ \textit{Ent}(S_{a}) \tag{5}\]  
and  
\[\textit{Split}\ Information\ (A,S)=-\sum_{a\in A}\frac{S_{a}}{S}\ log2\frac{|S_{a}|}{|S|} \tag{6}\]  
where, '\(S_{a}\)' is the subset of '\(S\)' for which attribute '\(A\)' has value '\(a\)'. Clearly, the information gain ratio can be obtained directly for discrete-valued attributes.
## 2 Methods  
The solver used in each example below is explicit fourth order Runge-Kutta (RK4):  
\[x_{i}(n+1)=x_{i}(n)+\frac{h}{6}(k_{i1}+2k_{i2}+2k_{i3}+k_{i4}) \tag{6}\]  
\[k_{i1}=v_{i}(n) \tag{7}\]  
\[k_{i2}=v_{i}(n)+\frac{h}{2}k_{i1} \tag{8}\]  
\[k_{i3}=v_{i}(n)+\frac{h}{2}k_{i2} \tag{9}\]  
\[k_{i4}=v_{i}(n)+hk_{i3}. \tag{10}\]  
The subscript \(i\) in Equations (6)-(10) is to indicate that there can be \(N\) functions (\(f_{i}\)) with inputs \(v_{i}\)--which is some computable value. Each node (each ODE that we solve for) has only one output value (the updated state at time \(x_{i}(n+1)\), but the value \(v_{i}\), which we take to be an 'input' to the ODE, can be any parameterized combination, linear or not, of all the \(x_{i}\) states in the network; and, if desired, external input.  
The time-step \(h\) used here is simply the inverse of the sampling frequency: \(h=1/48,000\) for most of the examples shown below. RK4 is chosen because it is explicit, commonly used, and fourth order accurate. It is beyond the scope of this paper to describe the accuracy and numerical stability of RK4 and compare it to other, similar solvers. The curious reader may be referred to chapter 3 in [16] for a thorough investigation of this topic as it pertains to audio synthesis. It is worth noting, however, that RK4 does not always guarantee stability.  
Using a C language application programming interface (API) written by the author, a system of interconnected ODEs can be declared and (through the use of helper functions) passed to an implementation of RK4 that will solve each network node \(x_{i}(n+1)\) at every time-step. The API also allows for control objects (for adjusting function parameters) to be declared. These can then be manipulated in real-time while the audio engine is running. This API was used to create a Pure Data [17] (Pd) extern to produce the data shown in the examples below. As a creation argument, the extern takes the path to a shared object (a compiled C file with loadable functions) which specifies the functions to be solved, the network topology, and any required control inputs to adjust function parameters. The extern automatically creates the correct number of inlets and outlets and control values can be passed via Pd's messaging system. Having such an infrastructure for designing, controlling and solving systems such as that given in Equations (4) and (5) is a potentially powerful tool for computer musicians.  
This technique of declaring a system of inter-connected ODEs to a software application which will then compute the solution to each node on the network is referred to as 'unsampled digital synthesis' (UDS) in a recent paper given at the 2015 International Computer Music Conference [18]. The application of numerical solvers to ODEs is not new in digital audio. The technique has been used, for example, to model diodes in guitar distortion circuitry [19; 20]. Furthermore, such a scheme is essentially a subset of the more general family of techniques known as finite difference methods, whose use in digital audio applications is well studied [21].
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 2 Related Works
### The Adaptive Pixel Pair Matching (APPM) Technique  
In the proposed method, we use the APPM embedding technique to embed the authentication codes into quantized values of complex blocks. The limitation of APPM is that it can only embed at most 8 bits into a pair of quantized values, fortunately only 6 bits are required in our method. The principle of APPM [29] is to embed a digit of base \(\lambda\) into a pixel pair by referring to the reference table \(R_{\lambda}\), where \(R_{\lambda}\) is a table of size \(256\times 256\) filled with elements of integers in the range \([0,\ \lambda-1]\). Let \(R_{\lambda}(a,\ b)\) be the element located in \(a\)-th row and \(b\)-th column of \(R_{\lambda}\). \(R_{\lambda}(a,\ b)\) can be calculated by the following equation:  
\[R_{\lambda}(a,\ b)=(c_{\lambda}\times a+b)\bmod\lambda, \tag{1}\]  
where \(c_{\lambda}\) is a constant, and \(c_{64}=14\) in this paper. To embed a digit \(d_{\lambda}\) into \((a,\ b)\), first locate a pixel pair \((\hat{a},\ \hat{b})\) in the vicinity of \(R_{\lambda}(a,\ b)\) that satisfies \(R_{\lambda}(\hat{a},\ \hat{b})=d_{\lambda}\) and has the minimum distance to \((a,\ b)\). The located \((\hat{a},\ \hat{b})\) is then employed to replace \((a,\ b)\) and a marked pixel pair is obtained. When extracting the embedded digit \(d_{\lambda}\), since \(R_{\lambda}\) and \((\hat{a},\ \hat{b})\) are known, \(d_{\lambda}\) can be calculated by \(d_{\lambda}=R_{\lambda}(\hat{a},\ \hat{b})\). The schematic diagram of APPM is as shown in Figure 1a.  
Following is a simple example to illustrate the APPM embedding procedures. Figure 1b shows a partial reference table \(R_{64}\). Let \((a,\ b)=(53,\ 64)\) be the original pixel pair used to carry the digit \(d_{64}=26\) of base 64. Since \((52,\ 66)\) satisfies \(R_{64}(52,\ 66)=26\) and has the minimum distance to \((53,\ 64)\), the marked pixel pair \((\hat{a},\ \hat{b})=(52,\ 66)\) is located. To extract the embedded digit, we only need to locate the coordinate \((52,\ 66)\) in \(R_{64}\), and \(d_{64}=R_{64}(52,\ 66)=26\) can be obtained (see Figure 1b).
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 4 Combination of Multisource Data for Road Extraction
### Combination of Hyperspectral Images with Other Data for Road Extraction  
[PERSON] et al. [236] fused hyperspectral images and LiDAR data to map urban land use using a state-of-the-art CNN. To improve the speed of the network design, the same structure was used in both the hyperspectral and LiDAR branches. Each branch used a residual block to extract multiscale, parallel, and hierarchical features. The experimental results underlined the efficient road extraction performance of the proposed method. When only hyperspectral images and LiDAR data were used, the highway classification accuracies were found to be 65.35% and 42.08%, respectively. However, this highway classification accuracy increased to 80.89% when fused data was used. [PERSON] et al. [237] combined the LiDAR-based hyperspectral images obtained from the AVIRIS and DEM. First, a vector layer of polygons was constructed using the DEM data. Second, the buildings in the hyperspectral images were removed, and then the road and water were classified using a supervised classifier. The experimental results demonstrated that the performance of this classification process could be improved by using LiDAR data to remove the buildings from the hyperspectral image before the classification. The detection rate of the road was 91.3%, and the false alarm rate was 0. Two examples of multiple remote sensing data fusion are presented in [238]. One is the fusion of hyperspectral images with SAR images; this fused data can improve the detection accuracy of the target. The other is the fusion of hyperspectral images with high-resolution images. The spatial-spectral information of the target was fully analysed using the two combined data sources, and the identification accuracy was improved considerably.
## 2 Methods
### Evaluation Metrics
For the same set of pixels in the image, Jaccard index can be written by the following expression:  
\[Jaccard(T_{1},P_{1})=\frac{|T_{1}\cap P_{1}|}{|T_{1}+P_{1}|} \tag{13}\]
2. **Precision and Recall** Precision and recall are two other metrics used to measure the segmentation quality which are sensitive to under- and over-segmentation. High values of both precision and recall indicate that the boundaries in both segmentation agree in location and level of detail. Precision and recall can be written as \[Precision=\frac{TP}{TP+FP}\] (14) \[Recall=\frac{TP}{TP+FN}\] (15) where \(TP\) denotes true positive rate when a prediction-target mask pair has a score which exceeds some predefined threshold value; \(FP\) denotes the false positive rate when a predicted mask has no associated ground truth mask; and \(FN\) denotes the false negative rate when a ground truth mask has no associated predicted mask. 3. **Hausdorff distance (HD)**: Hausdorff distance (HD) measures the maximum distance between the two surfaces. Let \(S_{A}\) and \(S_{B}\) be surfaces corresponding to two binary segmentation masks, A and B, respectively. The Hausdorff distance (HD) is defined as \[HD=\max\Big{(}\max_{p\in S_{A}}d(p,S_{B}),\max_{q\in S_{B}}d(q,S_{A})\Big{)}\] (16) where \(d(p,S)=\min qeSd(p,q)\) is the minimum Euclidean distance of point \(p\) from the points \(q\in S\). 4. **Image Quality Metrics: PSNR:** The peak signal-to-noise ratio (PSNR) is the most commonly used quality assessment technique for determining the quality of lossy image compression codec reconstruction. The signal is the original data, and the noise is the error caused by the distortion. 5. **Clinical Indices:** To assess the performance of the ventricles, different indices have been used in the literature [54], such as left ventricular volume (LVV), left ventricular myocardial mass (LVM), stroke volume (SV), and ejection fraction (EF). The left ventricular volume (LVV) is defined as the volume enclosed by the LV blood pool and the myocardial mass is equal to the volume of the myocardium, multiplied by the density of the myocardium: \[\text{Myo-Mass}=\text{Myo-Volume (cm}^{3})\times 1.06\text{ (gram/cm}^{3})\] (17) Stroke volume (SV) is defined as the volume ejected during systole and is equal to the difference between the end-diastolic volume (EDV) and the end-systolic volume (ESV): \[SV=EDV-ESV\times 100\%\] (18)The ejection fraction (EF) is an important cardiac parameter quantifying the cardiac output and defined as the ratio of the SV to the EDV:  
\[EF=\frac{SV}{EDV}\times 100\% \tag{19}\]