esa-sceva/satcom-chunk-collection huggingface dataset:
# Entropy Optimization in MHD Nanofluid Flow over an Exponential Stretching Sheet
## 4 Method of Solution
### Collocation
}^{Ns-1}\mathbf{d}_{iq}^{(m)}I\right)F_{q,\epsilon+1}^{(m)}\] \[+c_{7,e}\mathbf{D}^{2}T_{i,\epsilon+1}^{(m)}=RR_{3,e}, \tag{24}\]where the bold variable coefficients represent diagonal matrices  
\[RR_{1,e} =R_{1,e}-\left(\mathbf{a}_{\mathbf{d},\mathbf{e}}\mathbf{d}_{iN_{ \mathrm{S}}}^{(m)}(\mathbf{D})+\mathbf{a}_{\mathbf{s},\mathbf{e}}\mathbf{d}_{ iN_{\mathrm{S}}}^{(m)}I\right)F_{N_{\mathrm{s}},e^{\prime}}^{(m)},\] \[RR_{2,e} =R_{2,e}-\mathbf{b}_{3,\mathbf{e}}\mathbf{d}_{iN_{\mathrm{S}}}^{(m )}T_{N_{\mathrm{s}},e}^{(m)}-\mathbf{b}_{7,\mathbf{e}}\mathbf{d}_{iN_{\mathrm{S }}}^{(m)}T_{N_{\mathrm{e}},e^{\prime}}^{(m)}\quad\text{and}\] \[RR_{3,e} =R_{3,e}-\mathbf{c}_{3,\mathbf{e}}\mathbf{d}_{iN_{\mathrm{S}}}^{(m )}H_{N_{\mathrm{s}},e}^{(m)}-\mathbf{c}_{6,\mathbf{e}}\mathbf{d}_{iN_{\mathrm{S }}}^{(m)}F_{N_{\mathrm{s}},e^{\prime}}^{(m)} \tag{25}\]  
and I is an \((Nx+1)\times(Nx+1)\) identity matrix. The equation is then expressed in matrix form as  
\[\left[ \begin{array}{ccc}A11_{i,j}&A12_{i,j}&A13_{i,j}\\ A21_{i,j}&A22_{i,j}&A23_{i,j}\\ A31_{i,j}&A32_{i,j}&A33_{i,j}\end{array} \right]\left[ \begin{array}{c}F_{i}^{ (m)}\\ T_{i}^{(m)}\\ P_{i}^{(m)}\end{array} \right]=\left[ \begin{array}{c}RR_{1,e}^{(m)}\\ RR_{2,e}^{(m)}\\ RR_{3,e}^{(m)}\end{array} \right] \tag{26}\]  
where  
\[A11_{i,j} =\mathbf{D}^{3}+\mathbf{a}_{1,\mathbf{e}}\mathbf{D}^{2}+\mathbf{ a}_{2,\mathbf{e}}\mathbf{D}+\mathbf{a}_{3,\mathbf{e}}I+\mathbf{a}_{4, \mathbf{e}}\mathbf{d}_{ii}^{(m)}(\mathbf{D})+\mathbf{a}_{5,\mathbf{e}}\mathbf{ d}_{ii}^{(m)}I,\quad A11_{i,j}=\mathbf{a}_{4,\mathbf{e}}\mathbf{d}_{ij}^{(m)}( \mathbf{D})+\mathbf{a}_{5,\mathbf{e}}\mathbf{d}_{ij}^{(m)}I\] \[A12_{i,j} =\mathbf{a}_{6,\mathbf{e}}\mathbf{D}+\mathbf{a}_{7,\mathbf{e}}I, \qquad A13_{i,j}=a_{8,\mathbf{e}}I,\qquad A12_{i,j}=A13_{i,j}=A23_{i,j}= \mathbf{0}\] \[A21_{i,j} =\mathbf{b}_{4,\mathbf{e}}\mathbf{D}^{2}+\mathbf{b}_{5,\mathbf{e }}\mathbf{D}+\mathbf{b}_{6,\mathbf{e}}I+\mathbf{b}_{7,\mathbf{e}}\mathbf{d}_{ ii}^{(m)}I,\qquad A21_{i,j}=\mathbf{b}_{7,\mathbf{e}}\mathbf{d}_{ij}^{(m)}I\] \[A22_{i,j} =b_{0,\mathbf{e}}\mathbf{D}^{2}+\mathbf{b}_{1,\mathbf{e}}\mathbf{ D}+b_{2,\mathbf{e}}I+\mathbf{b}_{3,\mathbf{e}}\mathbf{d}_{ii}^{(m)}I\qquad A22_{i,j} =\mathbf{b}_{3,\mathbf{e}}\mathbf{d}_{ij}^{(m)}I,\qquad A22_{i,i}=\mathbf{b}_{ 8,\mathbf{e}}\mathbf{D},\] \[A31_{i,j} =\mathbf{c}_{4,\mathbf{e}}\mathbf{D}+\mathbf{c}_{5,\mathbf{e}}I+ \mathbf{c}_{6,\mathbf{e}}\mathbf{d}_{ii}^{(m)}I,\qquad A31_{i,j}=\mathbf{c}_{6,\mathbf{e}}\mathbf{d}_{ij}^{(m)}I\qquad A32_{i,i}=c_{7,\mathbf{e}}\mathbf{D}^ {2},\] \[A33_{i,j} =\mathbf{D}^{2}+\mathbf{c}_{1,\mathbf{e}}\mathbf{D}+c_{2,\mathbf{ e}}I+\mathbf{c}_{3,\mathbf{e}}\mathbf{d}_{ii}^{(m)}I\qquad A33_{i,j}=\mathbf{c}_{3, \mathbf{e}}\mathbf{d}_{ij}^{(m)}I, \tag{27}\]  
Here, \(A_{rs}(i,i)\) is the diagonal of each matrix \(A_{rs}(i,j)\), where \(r=s=1,2,3\).
## 6 Case Study
### Workcell Setup and Batch Order Building  
Figure 10 illustrates the interaction between the holons proposed in the case study. This interaction is a result of registering the operational resources into the workcell and building the product orders and the batch list. In the first four lines of Figure 10a, we can see two workers and two IRs are registering sequentially to the operational resources list, by sending an ACL messages with \"AGREE\" communication act to the orchestrator agent. The orchestrator can distinguish between the AGREE message received from the worker or the robot from the message conversation-id field which can be seen in Figure 10b. For example, a conversation-id \"WorkerRegistration\" has been used for a worker registering while \"RobotRegistration\" has been used for a robot registration.  
Figure 10: (**a**) Workcell Setup and Batch Order Building; (**b**) ACL Message holds the Worker Registration; (**c**) ACL Message holds the product details.  
In line 5, 6, 7, and 8 of Figure 10a, we can see four different pump orders with IDs (CP:1, CP:2, CP:3, CP:4) are sent from the pump agent (i.e., the Product holon) to the batch agent (i.e., the Task holon). Each customized pump order is encapsulated in an INFORM message which can be seen in details in Figure 10c. The ACL message content field contains the different features of the pump order CP:1 plus the required quantity. All the pump orders are stored in the batch agent and can be modified and arranged by the TH interface. Then it can be sent to the orchestrator agent as showed in Figure 10a line 9, 10, 11, and 12. The batch agent sent the four customized pump orders in a form of PROPOGATE messages, then it sent a PROPOSE message which is shown in Figure 10a line 13 to indicate the end of this batch order.
## 5 Conclusions  
In this paper, we propose a joint power allocation and relay selection algorithm based on TS relay architecture, which aims to maximize the max-min bidirectional instantaneous information rate. The system model can be widely applied into the fields of 5G, wireless sensor, internet of things, cognitive radio and wireless body area network. By using the balancing information rate criterion, the closed-form of the optimal power allocation can be obtained. Then, the proposed time allocation scheme can get the optimal TS factor. At last, the relay with the largest instantaneous information rate of the set is selected as the optimal cooperative relay. Energy harvesting technology can solve the energy problem in energy-constrained nodes and reflect the concepts of green environmental protection and sustainable development. The proposed algorithm can make up the loss of the information rate for energy transmission. Simulation results show that the proposed scheme provides effective performance improvement over the traditional schemes. However, energy harvesting technology may cause loss of spectral efficiency. Therefore, combining full-duplex technology will be the direction for future research.  
Conceptualization, S.X.; data curation, S.X.; formal analysis, S.X.; funding acquisition, X.S.; methodology, S.X.; project administration, X.S.; software, S.X.; supervision, X.S.; validation, S.X. and Z.X.; visualization, S.X.; writing--original draft, S.X.; writing--review and editing, X.S., S.X., Z.X. and X.H.  
This work was supported by the National Nature Science Foundation of China under Grant No. 61473066 and No. 61601109, and the Fundamental Research Funds for the Central Universities under Grant No. NI52305001.  
The authors thank the anonymous reviewers for their insightful comments that helped improve the quality of this study.  
The authors declare no conflict of interest.
# Optimization of Linear Quantization for General and Effective Low Bit-Width Network Compression
## 3 Two-Stage Quantization Method
### Clustering  
Data clustering is the process of grouping similar data vectors into multiple sets. Recently, clustering algorithms have been applied to a wide range of problems, including model compression and data analysis. This paper first presents application clustering for the optimization of quantization algorithms. Due to the bell-shaped distribution of the network weights, the uniform partition of the traditional linear quantization cannot distinguish the weights located in the middle part of the number axis when the bit-width is smaller than 5 bits, resulting in a significant decrease in the accuracy of the neural network. As shown in Figure 6, the accuracy of the model will have a huge decrease when the quantization bit-width is under 5 bits. This paper uses K-means to cluster the weights of each layer of the pre-training network so that the cluster set will not be changed when mapping.
## 1 Introduction
### Motivation--Why BPL?
#### 1.1.1. BPL for Transformer Substation Automation  
In upcoming years, roll-outs of smart technologies such as AMM (Automated Meter Management), smart substations, sensoric systems and others are expected. On the level of substations, usage of the optical networks would be an ideal solution. However, in historical city centres, deployment of optical networks is complicated and slow. Sometimes radio frequency (RF) or BPL is the only possible solution. BPL would be used in this transition period until dense optical networks will cover all the substations in such areas. After that it can be turned into a backup communication channel for optical network outage maintenance, fiber cuts, etc.  
Recent research [14] has also introduced the possibility of analyzing powerline cable conditions and enabling detailed evaluation of the actual physical state of the interconnected nodes.
## 3 Results and Discussion
### Zeta Potential and Bacterial Cell Size Determination  
The described results clearly present that C\({}_{55}\)H\({}_{40}\)O\({}_{34}\) interacts strongly with _P. aeruginosa_ cells. Such interactions can modify bacterial Zeta potential and their cell size. In  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature [K]** & \(\Delta\)**H [k] \(\times\) mol\({}^{-1}\)]** & \(\Delta\)S [k] \(\times\) mol\({}^{-1}\) K\({}^{-1}\)]** & \(\Delta\)**G [k] \(\times\) mol\({}^{-1}\)]** \\ \hline
296 & & & \(-29.892\) \\
303 & 56.689 & 0.293 & \(-31.939\) \\
310 & & & \(-33.987\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermodynamic parameters of C\({}_{55}\)H\({}_{40}\)O\({}_{34}\)–protein bonds of _P. aeruginosa_ membranes.  
Figure 6: Van ’t [PERSON] affinity diagram of C\({}_{55}\)H\({}_{40}\)O\({}_{34}\) for _P. aeruginosa_ membrane proteins.  
order to study if C\({}_{55}\)H\({}_{40}\)O\({}_{34}\) can induce changes at the _P. aeruginosa_ size as well as surface Zeta potential, these two values have been measured, and the results are demonstrated in Table 3. It should be emphasized that the Zeta potential is an important physicochemical parameter that plays a crucial role in the interactions of microorganisms with various ions and molecules. It also provides information on membrane permeability, biofilm formation, and aggregation under the influence of various compounds [45].  
The results indicated that C\({}_{55}\)H\({}_{40}\)O\({}_{34}\) slightly decreased the Zeta potential of the studied bacteria compared to the control bacteria but generally did not induce the changes in _P. aeruginosa_'s diameter. Small alterations of _P. aeruginosa_ size were observed in the presence of C\({}_{55}\)H\({}_{40}\)O\({}_{34}\), which are negligible compared to the control. The other studies also observed Zeta potential changes in bacteria cells under the influence of polyphenolic compounds. For instance, [46] demonstrated that ferulic acid, rosmarinic acid, and epigallocatechin gallate affect the Zeta potential, reducing adhesion in _P. aeruginosa_, _Bacillus cereus_, and _Staphylococcus aureus_. Tannins revealed also the ability to change Zeta potential in _S. mutans_ cells [16] as well as in _S. aureus_ cells [11].
# A Trajectory Optimisation-Based Incremental Learning Strategy for Learning from Demonstration
## 3 Research Methodology  
The methodology in this research consists of the following steps: (i) segmentation and extended demonstration; (ii) DMP modelling and incremental learning update. The framework of this section is shown in Figure 2. Details are given below.  
Before introducing the methodology of this paper, it is necessary to state the superiority of the designed method. Traditional trajectory segmentation methods mainly include subjective decision making based on trajectory geometric features [26; 27] and adaptive segmentation based on neural networks [28]. In the optimised 1D-SEG developed in this paper, both the geometric features of the trajectory and a rigorous mathematical process are considered. This can make the segmentation results more objective. Compared to other trajectory generation algorithms like rapidly exploring random tree (RRT) [29], the advantage of PRM lies in the ease of optimising sampling points, as it constructs a roadmap to facilitate finding better trajectories in complex environments. In addition, the flat structure and rapid update strategy of the BLS do not require extensive training time, making it suitable for quickly building models.
## 3 Fusion Precomputed AES Encryption Algorithm
### Precomputation-Based S-Box Construction
Return (mul(x, rand_mult) + rand_add) & 0 xFF. 4. Define the diffusion function diffusion(x):  
Input a byte x, apply a custom \(5\times 5\) filter for differential diffusion. Use a \(5\times 5\) filter template, with the central pixel having the highest weight and the surrounding pixels having lower weights. Apply the filter to each byte value, enhancing the differences between the bytes and increasing randomness. Return the byte value after the differential diffusion. 5. Define the function to generate the S-box():  
Initialize an empty list s-box. For i from 0 to 255:  
If i = 0, set inv_i = 0. Otherwise, compute inv_i = EXP[255 - LOG[i]]. Pass inv_i through affineTransform() to obtain the transformed value transformed. Pass transformed through diffusion() to obtain the byte value after differential diffusion. Append the diffused result to the s-box list. To perform byte substitution via a lookup table operation, simply look up the corresponding output byte of the S-box by the value of the input byte. For example, in Table 1, suppose the input byte is 0x63. First, split it into two 4-bit bytes: six and three. Then, convert six and three to row and column indexes, respectively. In this example, the row index value of six corresponds to a decimal value of seven (its hexadecimal is initially 0), and the column index of three corresponds to a decimal value of four. Next, look for the value 0 xCD in the S-box with index (7, 4), and this value is the replaced output byte. The inverse S-box is the same. The process of byte replacement through the lookup table operation can significantly reduce the overhead in time. Only the value of the input byte as an index is input into the S-box to find the corresponding replacement value. In this way, the whole algorithm realizes the process of generating S-boxes and inverse S-boxes in AES. In this paper, through operations such as exponential domain arithmetic and differential diffusion, the nonlinear characteristics and diffusion effect of S-boxes are enhanced, and the security of the AES algorithm is improved. And the The improvement in this paper is mainly reflected in the S-box generation process, but this process is only executed in the initialization phase and its time complexity is O(1), which has limited impact on the efficiency of the encryption process. Compared with traditional AES encryption, the complexity of the encryption stage is O(\(R\times N\)), where R is the number of rounds and N is the size of the plaintext block. It is mainly composed of Subbytes, Shifrows, Mixcolumns and Addroundkey, so the runtime performance is basically the same as that of traditional AES. On the whole, the improved method improves the security by enhancing the randomness and resistance to differential attack of the S-box. Although the initialization cost is slightly higher, it is suitable for high security scenarios.
## 3 Methods and Data Sources
### K-means Cluster Analysis  
Cluster Analysis is a method of classifying individuals based on their own characteristics. The principle of cluster analysis is that individuals in the same class have greater similarity, and individuals in different classes have greater differences. Among many clustering methods, K-means has become a widely used clustering algorithm because of its fast clustering speed and clear cluster structure [33; 34].  
Specifically, K-means is based on the principle of the minimum distance to the cluster center under a given number of clusters and objective function, and the observation values are allocated to the clusters where the centers are located to form the first iteration. K category is formed. Calculate the mean value of each variable based on the observations that comprise each category. The n means values in each class form k points in the n-dimensional space, which is the class center of the second iteration. Through multiple  
Figure 1: Flowchart of the mechanism by which the productive services sector affects carbon emissions.  
iterations, when the objective function converges to the threshold, the clustering scheme is completed [35; 36]. The specific process is as follows:  
1. The system randomly selects \(k\) cluster centroid points as \(u_{1}\), \(u_{2}\),..., \(u_{k}\)\(\in\)\(R^{n}\);
2. For each observation value \(i\), calculate the distance from the center of mass and classify it: \[c^{(i)}\)\(=\)\(\text{argmin}_{j}||x^{(i)}\)\(-\)\(u_{j}||^{2}\] (2)
3. For each type \(j\), recalculate the centroid: \[u_{j}=\frac{\sum\limits_{i=1}^{m}1\Big{\{}x^{(i)}=j\Big{\}}x^{(i)}}{\sum \limits_{i=1}^{m}1\{c^{(i)}=j\}}\] (3)  
In the above formula, k is the number of clusters. After many calculations, it is found that 5 categories of k have good characteristic distribution directivity; \(c\) is the clustering unit \(i\) and the closest category among the k categories; \(u_{j}\) represents the centroid point.
# Competitive Detection of Volatile Compounds from Food Degradation by a Zinc Oxide Sensor
## 1 Introduction
The ZnO sensor was initially exposed to the three pure compounds and subsequently to binary and ternary mixtures of the three compounds, simulating a degradation process, to compare the responses and evaluate the effect of the interference. The results showed that the interaction of the three volatiles on the ZnO surface interferes in the hexanal detection, reducing the sensor's sensitivity in comparison to the response to the vapors of the pure component. A comparative analysis of the transient response, however, showed that the electrical resistance of the ZnO sensor in the presence of hexanal vapors had a very rapid decrease that was not observed with the other compounds. Therefore, in a detection device based on a single ZnO sensor, the intensity of the signal, which is related to the vapor concentration, should not be the only parameter that is relied on, but the combination with the decrease rate of the sensor's resistance, which is distinctive of hexanal, has to be considered so to improve the reliability of the response.
# Survey on Data Hiding Based on Block Truncation Coding
## 3 Survey of Related Works
### Non-Reversible Data Hiding
#### 3.1.4 Error Correction Coding (ECC)  
[PERSON] et al. [39] enlarged the cover image using the nearest neighbor interpolation algorithm and converted it to AMBTC, and proposed a method for efficiently hiding large amounts of data by applying the HC (7,4) code to this cover image. It seems that their method is superior to the existing DH method in terms of efficiency. Hamming Code (HC) is a single Error-Correcting linear block Code (ECC) with a minimum distance of three for all the codewords. In HC \((n,k)\), \(n\) is the length of the codeword, \(k\) is the number of information bits, and \((n-k)\) is the number of parity bits. Let \(x\) be a \(k\)-bit information word, and the \(n\)-bit codewords \(y\) are generated by using \(y=xG\), where \(G\) is the \(k\times n\) matrix. Let \(e=y-\tilde{y}\) be the error vector that determines whether an error occurred while sending \(y\). The single error \(e\) could be corrected by using the syndrome \(S=\tilde{y}\cdot H^{T}\), where the syndrome \(S\) denotes the position of the error in the codeword. HC (7,4) is used to hide 3 bits for every 7-bit pixel group of the bitmap, and BM in trios is a codeword.  
[PERSON] and [PERSON] [40] introduced a way to hide data using Hamming code. However, this method had problems in efficiency compared to the existing methods. In order to improve the problems of [PERSON] and [PERSON] [40], [PERSON] et al. [41] use an optimized Hamming code lookup table to minimize errors in data hiding. The performance of the proposed method is good in terms of DH capacity and quality.  
[PERSON] et al. [42] proposed a new AMBTC-based DH technique using Hamming distance and pixel value difference method. This method first preprocesses the original image with a smoothing filter so that the quality can be maintained even after AMBTC compression. After that, 8 bits of secret data are inserted into the bit plane of the less complex block using Hamming distance calculation.  
**Example 5**.: _When trio is \((\mu_{0},\mu_{1},BM)=\) (146, 149, 0011011101000), the first 7 bits of BM are \(y=[0\ 0\ 1\ 1\ 0\ 1\ 0]\) as the codeword. For the codeword \(y\), first find the syndrome \(S=(H\oplus y^{T})\ mod\ 2=[0\ 0\ 1]\). When the secret bit \(d=[1\ 1\ 1]\), in order to hide the secret bits \(d\) in the codeword, a new \(S^{\prime}=S\oplus d^{T}=[0\ 0\ 1]\oplus[1\ 1\ 1]^{T}=[1\ 1\ 0]=6\) is obtained, and then the 6 th pixel of \(y^{\prime}=[0\ 0\ 1\ 1\ 0\ 0\ 1]\) is flipped. The codeword \(y^{\prime}\) has hidden bits \(d\). In case that this trio is transmitted to the receiving side, it will directly obtain 3-bit information from the first 7 pixels of the trio's BM by using HC (7, 4). That is, \(S=(H\oplus y^{T})\ mod\ 2=[1\ 1\ 1]\)._
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 2 Related Works
### Landmark Standardization  
The most straight-forward standardization is the scaling normalization where the coordinates are normalized into a value range of 0 and 1, based on the max and min values (Equation (1)). However, this method can correlate to the face size compared to z-score standardization.  
\[x_{norm}=\frac{x-x_{min}}{x_{max}} \tag{1}\]  
Z-score standardization is a statistical method that transforms a variable to have a mean of 0 and a standard deviation of 1. This can be useful for comparing values of the variable across different samples or populations, or for comparing the variable to a normal distribution.  
In the context of facial analysis, z-score standardization can be applied to facial landmarks, which are points on the face that correspond to specific features such as the eyes, nose, and mouth. By standardizing the coordinates of these landmarks, it is possible to compare the relative positions of the landmarks across different faces or to detect subtle changes.  
Research in [33] evaluated the effect of standardizing imaging features on the accuracy of a radiomics analysis for predicting the histology of non-small cell lung cancer (NSCLC). A total of 476 imaging features were extracted from each database and standardized using min-max normalization, z-score normalization, and whitening from principal component analysis. The results showed that all of the standardization strategies improved the accuracy of the histology prediction, with the highest accuracy achieved using z-score normalization.  
Another case of using z-score standardization as explained in [34] occurs in measuring telomere lengths across different studies or runs, because raw values of rTL (Relative Telomere Length) can be influenced by technical and contextual factors, and are not directly comparable. Z-score standardization helped to mitigate this problem by expressing the data in standard deviations, which are directly comparable between studies.  
Likewise, un-normalized landmarks can be influenced by many factors such as input image dimension, size of the face, and detected location. This can affect the robustness of algorithms that are designed to work with various devices and people. In MediaPipe Facemesh [31], the landmarks are normalized using the width and height of the image, which again may not be ideal for comparative purposes.  
In the context of machine learning, comparing K-means clustering results on infectious diseases datasets showed that the z-score standardization method is more effective and efficient than the min-max and decimal scaling standardization methods [35]. A study in [36], on the other hand, pointed out that conventional z-score standardization may not be optimal for certain clustering analyses, along with the performances of modified z-score standardizations.  
Overall, [33; 34; 35; 36], strongly suggest that z-score is the optimal, robust standardization for meta analysis of facial landmarks.