esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 3 The Proposed e-RLRP Algorithm
### Reinforcement Learning Used in Routing Protocol
#### 3.1.1 Reward Propagation with Acknowledgment Message (ACK)  
The reward value is directly related to the receipt of the ACK. When a node wants to send a packet to a given destination it selects a neighbor from the existing one and sends the packet to that neighbor. After that, it waits for the corresponding ACK message, which contains meta-information about the received packet, and the reward value by the action of choosing this neighbor. This ACK message can return using a path different from the one used to send the corresponding packet.  
If the ACK is not received within a pre-defined time then the sender node sets a punishment, that is, a negative reward to the neighboring node to which the packet was  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**AMR-WB Operation Modes** & **Number of Bits** & **Bit-Rate (kbps)** & \(I_{e}\) & _Bpl_ \\ \hline
0 & 132 & 6.60 & 41 & ND \\
1 & 177 & 8.85 & 26 & ND \\
2 & 253 & 12.65 & 13 & 4.3 \\
3 & 285 & 14.25 & 10 & ND \\
4 & 317 & 15.85 & 7 & ND \\
5 & 365 & 18.25 & 5 & ND \\
6 & 397 & 19.85 & 3 & ND \\
7 & 461 & 23.05 & 1 & ND \\
8 & 477 & 23.85 & 8 & 4.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Adaptive Multi-Rate Wideband (AMR-WB) Operation Modes and their Bit-rates, \(I_{e}\) and _Bpl_ Values.  
forwarded. This negative value is set to \(-1\). If the ACK is not being received probably the neighboring node has gone offline. The neighboring may be experiencing hardware issues such as power outages, strong interference with wireless transmission or the node is overloaded with incoming traffic. Hence, it is consistent that this neighbor should be avoided in the future.  
If the ACK message is received on time a reward value will be provided within the message. If the value is high it means that the neighbor has a good way to the destination, the probability of choosing this neighbor in the future will increase. If the value is low it means that the chosen neighbor does not have a good route to that destination, because it has hardware problems, there may be many hops or the further links quality is weak. In this case the source node will slowly decrease the estimation value for this neighbor, which is likely to cause the node to later choose other neighbors.
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## Materials and Methods
### Virtual Force Sensor  
The external toque observer is established by the dynamic model for each axis, \(\tau_{a\ 6\text{-}1}\) is the actual torque produced by the driver, \(\tau_{pred\ 6\text{-}1}\) is the torque of the arm when there is no load estimated by the model, and the external torque \(\tau_{c\ 6\text{-}1}\) is obtained by the external toque observer. After \(\tau_{c\ 6\text{-}1}\) has been obtained, \(\tau_{c\ 6\text{-}1}\) is converted into the external force applied to the endpoint of the robotic arm through the inverse Jacobian transpose matrix \(\left(J^{\prime}\right)^{-1}\) and the virtual force sensor for the robotic arm is established in this way. Figure 5 is the flowchart of the virtual force sensor algorithm.  
\[\tau_{c\ 6\text{-}1}=\tau_{a\ 6\text{-}1}-\tau_{pred\ 6\text{-}1} \tag{3}\]  
\[Force_{6\text{-}1}=\left[\begin{array}{c}f_{x}\\ f_{y}\\ f_{z}\\ f_{\varnothing}\\ f_{\varnothing}\\ f_{\varphi}\end{array}\right]=\left(J^{\prime}\right)^{-1}\left[\begin{array}[] {c}\tau_{1}\\ \tau_{2}\\ \tau_{3}\\ \tau_{4}\\ \tau_{5}\\ \tau_{6}\end{array}\right] \tag{4}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Range** \\ \hline Max\_depth & 1–15 \\ Eta & 0.2, 0.1, 0.05, 0.01 \\ Min\_child\_weight & 0–6 \\ Subsample & 0.1–1 \\ Colsample\_bytes & 0.1–1 \\ Gamma & 0.1–1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The range of hyperparameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item\textbackslash{}Rank** & **1** & **2** & **3** \\ \hline Max\_depth & 10 & 10 & 8 \\ Eta & 0.01 & 0.1 & 0.1 \\ Min\_child\_weight & 0 & 5 & 0 \\ Subsample & 0.79 & 0.92 & 0.69 \\ Colsample\_bytes & 0.73 & 0.96 & 0.45 \\ Gamma & 0.08 & 0.31 & 0.63 \\ MSE & 0.00158 & 0.00161 & 0.00164 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bayesian optimization results.
## 4 Experiments and Evaluations
### Evaluation Method  
#### 4.3.1 Evaluation Criteria  
In order to evaluate the experiment objectively, the DR (detection rate) and ER (escape rate) were used. Their definitions are shown in the formulas below.  
\[DR=\frac{Number\ of\ malicious\ samples\ detected}{Total\ number\ of\ malicious\ samples} \tag{3}\]  
\[ER=\frac{Number\ of\ escaping\ Success}{Total\ number\ of\ malicious\ samples}=1-DR \tag{4}\]  
The ER (escape rate) reflects the proportion of the target detection model or tool classifying malicious samples into benign samples after escape transformation. The higher the proportion, the more vectors represent escape, which indicates that the defection of the model of defense adversarial attack is greater. The DR (detection rate), which reflects the escape detection model or tool, can still detect the proportion of malicious attack samples. The higher the detection rate, the stronger the ability of the model or tool to defend against attacks. The DR reflects the proportion of malicious attack samples after escape confusion, and the detection model or tool can still detect malicious attack samples. For example, the higher the detection rate, the stronger the ability of the model or tool to defend against the adversarial attack.
## 3 Simulation Analysis
### Simulation Results
#### 3.3.2 Six-Layer Model
Transformer vibration will inevitably have a strong impact on the surroundings, and the place where the vibration is the strongest, is in the transformer-centered small area, changing with the transformer's position. The intensity of the vibration that propagates to the edge will be greater than to the central position. When the transformer is at the edge position, such as position #6, the impact range of the vibration on the whole wall of the building is larger. Figure 31: Displacement nephogram of the whole building when the transformer is at position #1. Figure 30: Results of longitudinal monitoring points C1–C3 when the transformer is at position #3. Considering the influence caused by different positions of the transformer, specific points in the vertical direction are selected for comparison. The selected 17 points have been simulated and analyzed, here the result of the typical monitoring point 3 is given in Figure 33.  
From the Figure 33, the closer the transformer is to the monitoring point in the horizontal direction, the stronger the vibration is. By data contrasts between group (1, 4), (2, 5), (3, 6), it can be seen that when the transformer is at the edge, the vibration intensity of the monitoring point is only 50-60% of that when transformer is at the middle position due to vibration will be transmitted to the edge. Because of it, when the transformer is at central position, its impact range on the whole building is less than that at edge position. From the whole point of view, the best position for the transformer is the central area of the basement.
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images
## 3 Proposed Method
### Framework of Proposed Method  
The proposed method simultaneously achieves high hiding capacity and high compression efficiency. An outline of the method is illustrated in Figure 4. We used the G-EtC  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Hiding Capacity** & **Lossless Compression Performance** & **Flexible Restoration** \\ \hline Proposed Method & ✓ & ✓ & ✓ \\ RDH-EtC [30] & \(\times\) & ✓ & ✓ \\ RDH-MSB [29] & ✓ & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature comparison of the proposed method with related work.  
Figure 3: Block diagram of G-EtC method [39].  
method [39] for encryption. Therefore, this method could compress marked encrypted images using image coding standards because the interpixel correlation in each block is retained even after encryption and data hiding. In our method, reversibility is an essential condition for perfectly retrieving an original image. Thus, we had to use lossless compression algorithms such as JPEG-LS [37] and JPEG 2000 [38].  
A payload is then embedded using prediction-error expansion and histogram shifting (PEE-HS) [16] for each block used in encryption. Here, the prediction values are highly accurate due to the introduction of the median edge detection (MED) predictor, so high hiding capacity could be attained. However, the superior performance shown in Section 4 could not be obtained by simply using the PEE-HS algorithm. Thus, we modified the original algorithm so as to preserve the high prediction accuracy even after encryption using the EtC system. Consequently, the proposed method had significant advantages, i.e., the compatibility between high hiding capacity and good compression performance.  
The proposed method must conduct decryption after data extraction in order to restore an original image. Nevertheless, we could also decrypt a marked encrypted image without data extraction, thus obtaining a marked image containing a payload.
## 3 PDFT Simulations of Porphyrins on Surfaces
### Magnetic Couplings in Substrate Bound Porphyrins  
Porphyrins adsorbed on ferromagnetic substrates can act as excellent candidates for spintronic devices. The ferromagnetic substrates typically involve Co or Ni films deposited on metallic substrates. Mn, Fe, Co, Ni, and Cu porphyrins on Co and Ni substrates were studied to understand the magnetic coupling between metalloporphyrins and metallic surfaces. PDFT simulations are especially helpful to understand the magnetic properties of porphyrin/substrate systems. [PERSON] et al. [134] studied the adsorption of FeOEP on Ni and Co films bound to Cu(100) surface using X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD). The experiments were combined with PDFT simulations of corresponding porphyrin/substrate systems using GGA-PW91 [59] functional and DFT+U approach [68]. Binding energetics of CIFeOEP and FeOEP on Co and Ni substrates were determined, and it was noted that the porphyrin loses Cl\({}^{-}\) upon adsorption. PDFT simulations provided a deeper understanding of Fe-substrate exchange coupling from DOS, charge analysis and magnetization densities. A similar study with free base and Fe porphyrins on Co substrate was carried out by [PERSON] et al. [135] also using GGA-PW91 [59] functional and DFT+U approach [68]. The PDFT simulations were used to understand the origins of the substrate induced magnetic ordering of metalloporphyrins. It was demonstrated that FeOEP ferromagnetically exchange couples, while CIFeOEP antiferromagnetic couples with the substrate. The same research group also studied the magnetic coupling of FeOEP on \(c(2\times 2)\) oxygen reconstructed Co(100) surface [136] with the same PDFT methodology as their earlier studies and showed that FeOEP couples antiferromagnetically with oxygen reconstructed Co(100) surface.  
Magnetic coupling with Mn porphyrins on Co substrates was studied by [PERSON] et al. [137] using GGA-PW91 [59] functional and DFT+U approach [68]. The authors reported that Mn porphyrins can adsorb or chemisorb on the Co substrate with MnP-Co binding distances at 3.5 A and 2.1 A, respectively. This variable surface binding caused distinct magnetic exchange interactions between porphyrin and substrate, but it was found that Mn magnetic switching occurs at both binding distances. [PERSON] et al. [138] showed that ClMnTPP porphyrin involves in indirect magnetic coupling with Co substrate using PDFT and, STM, XAS, and XMCD studies. GGA-PBE [45] functional with Gaussian type orbitals and with DFT+U approach were used to determine the surface DOS and magnetic properties of ClMnTPP on Co substrate. It was found that if the chloride ion of the MnTPPCI molecule orients away (Co-Mn-Cl) from the Co surface, a weak ferromagnetic molecule-substrate coupling is observed. PDFT simulations with DFT+U approach were also used to understand the magnetic coupling interactions of Co porphyrins on Ni [139] and graphene [140] substrates.
# PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
## 2 Materials and Methods
### PadGAN
#### 2.3.1 Peak Information Maps  
Recently, latent space has flourished in the field of image generation [38]. Latent space can generate diverse high-resolution images [39] and can also be used for re-editing images by extracting latent space features [40; 41]. In order to introduce diversity into generated images, random Gaussian noise is commonly used as the input for extracting latent space features. However, unlike works focused on enhancing image diversity, this paper places high demands on the accuracy of generated image details. Therefore, random Gaussian noise as the input for latent space feature extraction may not be suitable.  
Max-pooling layers are widely used in image classification, segmentation, and other fields [42]. They can preserve texture features and edge information of images while reducing information redundancy. However, there is also a risk of losing important information. Due to the high demand for image details in end-to-end image-to-image translation tasks, max-pooling layers are rarely used to prevent information loss during training [43]. To reduce the risk of losing other information while preserving texture and other detailed information, we introduced the concept of peak information maps.  
Given the assumption that brain images from the same data site and the same species exhibit a certain degree of similarity, we perform a per-pixel maximum extraction operation on the low b-value brain images of macaques within the same site. All extracted maximum values are concatenated into a 3D image, which represents the peak information map of that site, as illustrated in Figure 2. Additionally, Equations (1) and (2) demonstrate this process. The peak information maps from different sites serve as inputs to the auxiliary network for the respective site's data, facilitating the extraction of latent space features.  
\[vox_{ij}=MAX(img_{i1}(vox_{j}),img_{i2}(vox_{j}),...img_{in}(vox_{j})) \tag{1}\]  
\[ref_{i}=PConcat(vox_{ij}),i=1,2,...,k,j=1,2,...,m. \tag{2}\]  
where \(vox_{ij}\) represents the \(j\)-th voxel selected at the \(i\)-th site, \(MAX(\cdot)\) represents the peak extraction operation, \(img_{in}(vox_{j})\) represents the \(j\)-th pixel of the \(n\)-th image at the \(i\)-th site, and \(ref_{i}\) represents the peak information map of the \(i\)-th site, of which there are \(k\) such peak information maps. \(PConcat(\cdot)\) represents the pixel concatenation operation, which concatenates individual pixels into the entire image. Iterate over all \(i\) and \(j\) values to obtain the peak information map for each site.  
Figure 2: Schematic diagram of peak information map. Image1, Image2, and Image3 represent three images within the same site. The green and blue rectangles represent the pixels of the image, where the blue rectangles represent the maximum pixel values at the same position in the three images. Concatenating the maximum value pixels at each position yields the peak information map.
## 5 Coupling Analysis of the ME Signal and EMR Signal
### Analysis of the Signal Coupling Effect
In the middle and late stages of the elastic deformation stage (A\({}^{\prime}\)B), the generated EMR remains at a consistent level with the ME signal. The main frequency of ME is 1.899 kHz, while the main frequency of EMR is 0.488 kHz. As the specimen transitions into the plastic deformation stage (BC), numerous EMR signal events emerge. The main frequency of ME becomes 2.812 kHz, and the main frequency of EMR is 1.999 kHz. When the specimen reaches its peak load, the amplitude of both the EMR and ME signals reaches a maximum before declining. Corresponding to the CD stage, the main frequency of ME is 2.814 kHz, and the main frequency of EMR is 2.164 kHz. Figure 9: Comparison of each stage and signal spectrum corresponding to the failure process of the J-4 specimen. (**a**) The corresponding stages of the whole process of failure of the J-4 specimen under load; (**b**) EMR signal spectrum corresponding to each stage of the J-4 specimen; (**c**) ME signal spectrum corresponding to each stage of the J-4 specimen. Using MATLAB 2021a software for calculations, a fast Fourier transform of the fifth channel data from the dolomite ME sensor is employed to generate the corresponding EMR signal spectrum diagram at different stages. Additionally, by comparing Figure 9b, c, it is observed that the frequency of the dolomite compression EMR signal is mainly concentrated within 20 kHz, while the frequency of the ME signal is primarily concentrated within 10 kHz. The amplitude intensity of the EMR signal gradually increases with the loading process. From elastic deformation to plastic deformation and, finally, into the post-failure stage, the frequency distribution of the dolomite EMR signal becomes increasingly concentrated around 2 kHz.
###### Abstract  
Stand-alone screens (SASs) are active sand control methods where compatible screens and slot sizes are selected through the sand retention test (SRT) to filter an unacceptable amount of sand produced from oil and gas wells. SRTs have been modelled in the laboratory using computer simulation to replicate experimental conditions and ensure that the selected screens are suitable for selected reservoirs. However, the SRT experimental setups and result analyses are not standardized. A few changes made to the experimental setup can cause a huge variation in results, leading to different plugging performance and sand retention analysis. Besides, conducting many laboratory experiments is expensive and time-consuming. Since the application of CNN in the petroleum industry attained promising results for both classification and regression problems, this method is proposed on SRT to reduce the time, cost, and effort to run the laboratory test by predicting the plugging performance and sand production. The application of deep learning has yet to be imposed in SRT. Therefore, in this study, a deep learning model using a one-dimensional convolutional neural network (1D-CNN) with adaptive moment estimation is developed to model the SRT with the aim of classifying plugging sign (screen plug, the screen does not plug) as well as to predict sand production and retained permeability using a varying sand distribution, SAS, screen slot size, and sand concentration as inputs. The performance of the proposed 1D-CNN model for the slurry test shows that the prediction of retained permeability and the classification of plugging sign achieved robust accuracy with more than a 90% value of R\({}^{2}\), while the prediction of sand production achieved 77% accuracy. In addition, the model for the sand pack test achieved 84% accuracy in predicting sand production. For comparative model performance, gradient boosting (GB), K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) were also modelled on the same datasets. The results showed that the proposed 1D-CNN model outperforms the other four machine learning models for both SRT tests in terms of prediction accuracy.  
adaptive moment estimation; deep learning; one-dimensional convolutional neural network; sand retention test; stand-alone screens +
Footnote †: journal: Applied Sciences
# Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot
## 1 Introduction  
Chatbots and dialogue systems are entering new areas of our lives. Quite recently, they have also been introduced to psychological and psychiatric therapies. Such a system, in order to have a therapeutic conversation with a patient, must be able to correctly recognize the patient's emotional state. In some cases, it is enough just to recognize the sentiment of the patient, i.e., to detect whether the patient's utterance has a positive or a negative emotional tinge, or carries no emotion at all. In other cases, to correctly lead the therapeutic dialogue, more detailed emotion and mood recognition must be performed.  
Our long-term target is to create a therapeutic dialogue system, working in Polish, able to hold empathetic conversations with patients. Most of the existing sentiment analysis approaches for Polish are opinion-based. They work either with short texts, e.g., as in message-level Twitter sentiment polarity classification, or with longer texts, e.g., when analyzing reviews of multiple aspects of restaurants or other services or goods. However, emotion-aware dialogue agents demand a different type of dataset, one that is more empathetic and multifaceted rather than just opinion-forming.  
The Polish language is under-resourced in regard to annotated empathetic texts. To fill in this gap, in our work, we made an attempt to build a Polish version of the dataset, using neural machine translation and English corpora as the source texts.  
In addition, we observed that in their experiments many researchers avoid taking into account the neutral emotional state, even though neutral utterances usually prevail during conversations. Since correctly distinguishing between neutral sentiment/emotion and any other emotional state is quite important in therapy, we decided to combine emotion-labeled texts with neutral ones. Next, we ran several experiments with sentiment polarityand emotion recognition for English and Polish, comparing the results between various classifiers and both languages.  
Our article is structured as follows: first, in Section 2, we briefly review the state of the art in the area of dialogue systems applied to mental health, sentiment and emotion recognition, and the existing related resources. Next, in Section 3, we describe how we created the corpora for our study. The experiments themselves are described in Section 4, followed by presentation of the results in Section 5. The article concludes with discussion of the results in Section 6 and a summary in Section 7.
# An Improved Multi-Chaotic Public Key Algorithm Based on Chebyshev Polynomials
## 2 Preliminary
### Chebyshev-ElGamal Public Key Cryptographic Algorithm (CEPKC)
#### 2.2.2 Parameter Selection in the Algorithm  
The selection of parameters is crucial to ensure the security of the CEPKC public key cryptographic algorithm, and the following principles [25; 52] are mainly considered:  
1. Selection of parameter \(N\): If \(N\) is a prime number, it is desirable to make \(N+1\) or \(N-1\) have no factors other than \(2\), in which case \(N\) is called a strong prime; if \(N\) is a composite number, \(N=\Pi_{i=1}^{\varphi}p_{i}^{k_{i}}\), where \(p_{i}\) is a prime number and it is preferable to have \(p_{i}\) as a strong prime, ideally \(\varphi\leq 3\). When properly chosen, the probability of small cycles occurring is minimized.
2. Principles for selecting parameter \(x\): For the above prime number \(N\), \(x\) is a parameter for the seed of \(T_{n}(x)\). Randomly choose an integer \(x\in Z_{N}^{*}\), where \(Z_{N}^{*}\) denotes the group on \(Z_{N}\), with \(x\
eq 1\). If \(N+1=\Pi_{i=1}^{\varphi}p_{i}^{k_{i}}\), where \(p_{i}\) is a prime number, \(i\in(1,\varphi)\)let \(n=\frac{N+1}{p_{1}}\) and compute \(T_{n}(x)\). If \(T_{n}(x)=1\) and \(p_{i}\
eq 2\), restart. Repeat until the desired parameter \(x\) is obtained. When \(N\) and \(x\) are appropriately chosen, the probability of small cycles is minimized, and the security of the finite field CEPKC encryption scheme can be effectively enhanced.