esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions  
In this paper, we presented a new method, called automatic multiscale-based peak detection (AMPD), for automatic detection of peaks in noisy periodic and quasi-periodic signals. Compared to traditional methods for peak detection, the proposed AMPD algorithm has the following advantages: (i) no free parameter values have to be chosen by the user prior to analysis; (ii) peaks in periodic and quasi-periodic signals can be detected; and (iii) the peak detection efficiency is fairly robust against high and low frequency noise. Hence, the presented method results in a high efficiency in detection of peaks.  
Using simulated and real-world signals, the usefulness of the AMPD algorithm was evaluated and demonstrated. For signals with high amplitude low frequency noise, the performance of the algorithm can be optimized by high-pass filtering the signal prior to analysis. However, the high-pass filtering is not necessary for most of the signals as demonstrated.  
For future work it would be interesting (i) to further explore the information given in the LMS in order to find, for example, additional rules for the automatic identification of peaks; (ii) to evaluate the performance of the AMPD algorithm compared to other peak detection algorithms available; and (iii) to use and test the algorithm in real world applications.
# A Source Code Similarity Based on Siamese Neural Network
## 3 WICE-SNN Framework
### Details of WICE-SNN
#### 3.2.1 WICE Layer  
_Word embedding:_ Word2 Vec is a widely word embedding model applied in NLP. Word2 vec learns a vector representation for each word using a simple neural network. There have two network architectures: continuous bag-of-words (CBoW) model and skip-gram model. CBoW model uses the average of a context vector, also known as an input vector, to predict a word with softmax weight. Skip-gram model uses a single word to predict surrounding words. They consists of an input layer, a projection layer (hidden layer), and an output layer. Formally, a code snippet \(c\) is split into a word sequences, \(c=\left\{v_{1},v_{2},\ldots,v_{N}\right\}\), where \(N\) is the number of words in code snippet \(c\). The input of CBoW model is \(\left\{x_{1},x_{2},\ldots,x_{k}\right\}\), \(x_{i}\in R^{1\times N}\), is the one-hot vector of \(v_{i}\), window size is \(k\), vocabulary size is \(N\). The converted vector \(u_{i}\) for one-hot vector of \(v_{i}\) is expressed as:  
\[u_{i}=x_{i}\times W \tag{1}\]  
where weight matrix \(W\in R^{N\times d}\) are the paremeters of the embedding layer and \(u_{i}\in R^{1\times d}\), where \(d\) is the dimension of hidden layer. Output layer is a log probability vector. Each word vector is trained to maximize the probability of neighboring words. The word which has the largest probability in the vector is the output word. As a result, the representation of code snippet is transformed into a matrix \(U\) which is a part of the weight matrix \(W\).  
_Composition in Statistical Information:_ When using word vectors to represent code snippet, the weight of all word vectors are equally. But, we know the contribution of each word in code is different. For example, there are three main types of vocabulary in source codes, reserved words, user-defined words and operations. Reserved words that represent data types, control structures, library files and others; second, user-defined variable names, function names, etc.; third, various operation symbols. For source code snippets, the weight should reflect the structure of source code. In order to improve the effect of code presentation, the words of control structure should have different coefficients and multiplied by the word frequency of it. So, the vector of code snippet is represented as \(U=\left\{f_{1}\cdot w_{1},\ldots,f_{i}\cdot w_{i},\ldots,f_{m}\cdot w_{m} \right\}^{\prime}\), where \(w_{i}\in W\) is the \(i\)-th line of embedding matrix \(W\), \(\left\{f_{1},f_{2},\ldots f_{m}\right\}\) is the TF-IDF value of each word, m is the number of words in code snippet.
# 3 MSF: A Multi-Modal Adaptation of the 6 TiSCH Minimal Scheduling Function for the Industrial IoT
## 2 IPv6 over the TSCH Mode of IEEE 802.15.4
### 6 TiSCH Architecture Basics
#### 2.1.2 The Link Layer
MSF's minimal slotframe, which it borrows from RFC 8180 [4], consists of a single cell with slot and channel offsets of zero. This minimal cell is a shared advertising cell and can be used to transmit/receive all frame types, but it is mostly used for broadcast traffic, including TSCH Enhanced Beacons (EBs), as broadcast traffic is not allowed on cells of other slotframes. MSF prohibits overlap with the minimal cell. In the autonomous slotframe, a node permanently installs a single receive (Rx) cell, the slot- and channel offsets of which are hashes of its own link-layer address. Under certain conditions, a node may also add (and later remove) a shared Tx cell to a neighbor using the same hashing functions to derive the cell's offsets from its neighbor's link-layer address. The cells within this slotframe are referred to as autonomous Rx and Tx cells, respectively. The autonomous Rx cell can only overlap with autonomous Tx cells, whereas autonomous Tx cells can overlap with all types of cells except the minimal cell. As long as a node has at least one negotiated Tx cell towards a neighbor, having an autonomous Tx cell towards it is forbidden. In addition, an autonomous Tx cell must be removed when there are no more packets for the corresponding neighbor. MSF is designed to work with RPL in the sense that a node must add at least one negotiated Tx cell towards a parent when it becomes preferred and must remove all negotiated cells (Tx or Rx) towards a parent as soon as it is no longer preferred. Negotiated cells are exclusively managed through a set of two-step 6P transactions, all of which (except CLEAR transactions) can only be initiated by a node sending a 6P request to its preferred parent. That is, when a node switches its preferred parent, it initiates the appropriate 6P transactions (at appropriate times), and it does so using autonomous cells (applies to both the old and new preferred parent) until it has its first negotiated Tx cell (applies to the new preferred parent only), after which all unicast traffic to the new preferred parent is sent across the negotiated Tx cells the node has added (it may have negotiated multiple Tx cells at once), including further 6P traffic. When, which type of, and how many negotiated cells are added and/or removed is subject to many rules (see RFC 9033 [7]). Negotiated cells are not shared.
# A Setting Optimization Ensemble for a Distributed Power Grid Protective Relay
## 3 Independent Factor Evaluation Model
### The Operational Step of IFE  
The primary objective of IFE is to confer statistically independent and non-Gaussian properties upon the independent component \(X\) through the identification of a suitable transformation matrix \(W\). Different IFE methods are categorized based on distinct objective functions, encompassing the maximum likelihood estimation method and the minimum mutual information method. Among these approaches, the FastIFE algorithm swiftly resolves independent components under specified constraints by utilizing negative entropy as a non-Gaussian measure and maximizing it as an objective function. This method exhibits notable advantages over alternative techniques, particularly in terms of its rapid convergence rate, making it well suited for large-scale datasets. The specific operational steps are outlined as follows:  
1. Calculate \(\overline{Y}\), the mean of \(Y\), and calculate the mean removed \(X^{*}\) according to the following formula: \[X^{*}=X-\overline{Y}\] (17)
2. Find \(F=cov\big{(}\,X^{*},\,X^{*}\,\,T\big{)}\), the covariance matrix of \(X^{*}\). Calculate the eigenvalue diagonal matrix \(\Lambda\) and the eigenvector \(\xi\). Let \(P=\Lambda^{-1/2}\xi^{T}\). The processing data \(R\) are obtained from Equation (18): \[R=P\times X^{*}\] (18)
3. Randomly initialize the matrix \(W^{(0)}\), setting the modulus of each column vector in it as 1. Set \(k\) as the iteration indicator, and let \(k=0\).
4. The numerical iterative calculation for \(w^{(k)}\) is carried out: \[w^{(k)}=\xi\Big{\{}RF\Big{[}w^{(k-1)T}_{d}R\Big{]}\Big{\}}-\xi\Big{\{}RF\Big{[} w^{(k-1)T}_{d}R\Big{]}\Big{\}}\times w^{(k-1)}_{d}\] (19) where \(F\) is a hyperbolic tangent function and \(f=F^{\prime}\), which means \(f\) is the first derivative of \(F\) with respect to \(t\), then \[F(t)=\tanh(t)=\frac{e^{t}-e^{-t}}{e^{t}+e^{-t}}\] (20) \[f(t)=\frac{4e^{2t}}{\left(e^{2t}+1\right)^{2}}\] (21)(5) Orthogonalize and standardize the matrix \(W\):  
\[w_{j}^{(k)}\rightarrow\sum_{i=1}^{m}({w_{j}^{(k)}}^{\prime}w_{i})w_{i} \tag{22}\]  
\[w_{j}^{(k)}\rightarrow\frac{w_{j}^{(k)}}{\left|\left|w_{j}^{(k)}\right|\right|} \tag{23}\]  
(6) If \(\left|w_{j}^{(k)\top}w_{j}^{(k-1)}-1\right|<\varepsilon\), then \(d=d+1\), or else judge that it does not converge and return to Step (3).  
(7) If \(d=m\), derive the matrix \(W\). Then, the independent component \(X\) is obtained according to Formula (16).  
In conclusion, independent factor evaluation, as a potent signal processing method, demonstrates outstanding performance in handling mixed signals. The detailed operational process highlights that IFE establishes the groundwork for the precise separation of independent signal components through steps such as data preprocessing, centralization, whitening, and more. The selection of an appropriate objective function, such as maximizing the non-Gaussianity of the independent component, coupled with an optimized solution method, contributes to the overall efficiency of the process with fast computational performance and convergence speed.
## 2 Materials and Methods
### Antimicrobial Activity (Zone of Inhibition)  
The antimicrobial activity of dried films of p(HEMA) and drugs-loaded p(HEMA) was determined using three different bacterial strains: _S. aureus_ ATCC 6538, _E. coli_ ATCC 8739, and _P. aeruginosa_ ATCC 9027. The bacteria were thawed and incubated for 18 h at 37 \({}^{\circ}\)C in an Erlenmeyer flask containing nutrient broth (NB). After 18 h bacteria were harvested by centrifugation and re-suspended in new NB until the optical density (OD 540 nm) was equivalent to 1 \(\times\) 10\({}^{8}\) CFU mL\({}^{-1}\). The microorganisms were streaked on an MHA agar plate. Once the agar had set and cooled, discs of the drug-loaded hydrogels (diameter 5.58 mm, thickness 0.79 mm) were placed by sterile forceps on the surface of the inoculated MHA plates and incubated at 37 \({}^{\circ}\)C for 18 h. The diameter of the inhibition  
\begin{table}
\begin{tabular}{c c c} \hline
**Formulation** & **Antimicrobial Agent (g)** & **2-HEMA (g)** \\ \hline F1 (control) & & 9.8 \\ F2 & 0.05 AMP & 9.75 \\ F3 & 0.05 LVX & 9.75 \\ F4 & AMP: LVX (1:1) & \\  & 0.025.0.025 & 9.75 \\ F5 & AMP: LVX (3:1) & \\  & 0.0375:0.0125 & \\  & AMP: LVX (1:3) & \\  & 0.0125: 0.0375 & 9.75 \\ \hline \end{tabular}
\end{table}
Table 1: Formulations, components and ratios of the prepared hydrogel films.  
zone (clear zone indicating no bacterial growth) was measured daily for 14 days using a ruler caliber [28]. The experiment was set twice for each microorganism (at least 6 different discs were included in each experiment).
# Prediction Models for Diabetes in Children and Adolescents: A Review
## 3 Prediction Models for Type 2 Diabetes (T2D)
### Genetic predisposition  
The majority of children and adolescents with T2D have a family history of the disease. It is a highly heritable condition, with 90% of children and youth affected having a first- or second-degree relative who also has T2D [65]: in the TODAY study [17], it is stated that 59.6% of adolescents with T2D had a first-degree relative with diabetes, while 89% had a grandparent affected by the condition. This elevated risk highlights the role of genetic predisposition, environmental factors, and intrauterine influences in the development of T2D. T2D is often diagnosticated in the additional influence is with children born small for gestational age (<2500 g), newborn macrosomia of diabetic mothers (>4000 g), and premature adrenarche in girls (public hair appearing before the age of 8 years). An increase in this metabolic disease and also hypertension was reported in boys with a lower birth weight, which supports the theory of programming conditions from the intrauterine life [66].  
Finally, in [67], it is stated that obesity combined with genetic predisposition and/or family history is the main risk factor for T2 DM in children and adolescents.
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 3 3 GPP 5G Technology for TSN
### Support for Integration with TSN  
Various interworking architecture were considered for 3 GPP 5 GS interworking with TSN [27]. Currently, 5 GS operates as a single TSN bridge and is connected to the TSN bridge of multiple TSN domains to provide service. As shown in Figure 3, a new function was added to the control plane and user plane in 5 GS to interwork with the TSN bridge. 3 GPP 5 GS defines the translator functionality for interworking with TSN [28], including device-side TSN device (DS-TT) and network-side TSN translator (NW-TT) for the user plane and TSN application function (AF) for the control plane. DS-TT and NW-TT perform the role of a bridge port, and TSN AF interlocks with the CNC of the TSN system to perform control for the TSN service. Within 5 GS, the TSN AF can control DS-TT/NW-TT through 5 GS signaling.  
With 5 GS acting as a TSN bridge, one logical bridge per UPF can be formed. Here, the 5 GS bridge comprises UPF ports, UE-UPF tunnels, and DS-TT ports. For TSN traffic transmission within 5 GS, the UPF manages traffic transmission between port pairs by mapping one PDU session to one DS-TT port. For 5 GS to operate as a TSN bridge, bridge information should be configured first in 5 GS, and then, the 5 GS bridge information should be reported to the CNC of the TSN network. 5 GS receives the bridge configuration information from the CNC and maps it to 5 GS Quality of Service (QoS) information (e.g., 5G QoS Identification (5 QI) and time-sensitive communication assistance information  
\begin{table}
\begin{tabular}{c c} \hline \multicolumn{2}{c}{**5G Enablers for Industry 4.0**} \\ \hline
1 & Support for time-sensitive communications by integrating the 5 GS with IEEE TSN. \\ \hline
2 & Support for non-public networks. \\ \hline
3 & Support for a 5G-LAN type service. \\ \hline \end{tabular}
\end{table}
Table 1: 5 GS support for industrial IoT (3 GPP Release 16) [26].  
Figure 2: Centralized network configuration (CNC) model.  
(TSCAI)). The TSN AF communicates with the CNC in 5 GS, transmitting port and bridge information to DS-TT/NW-TT.
# Real-Time Object Tracking Algorithm Based on Siamese Network
## 4 Experiments
### State-of-the-Art Comparison  
As we all know, OTB (Object Tracking Benchmark) and VOT (Visual Object Tracking) are very comprehensive to the test of tracker, and they are universal platform for evaluating computer vision algorithm. Therefore, the proposed SiamFP is compared with the most advanced tracker on OTB and VOT benchmarks. Traditionally, the tracking speed exceeding 25 FPS is considered as real-time, and the tracking speed of SiamFP can reach 50 FPS.
# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
## 3 ST-GCN
### Constructing Skeleton Graph  
The information in the skeleton spatial-temporal graph includes the number of human joints \(N\) and frames contained in the input video sequence \(T\). A skeletal spatial-temporal graph is constructed with \(G=(V,E)\) as the input to the ST-GCN; the set \(E\) represents the edge of the skeleton graph. Thus, the set of matrix eigenvalues of \(N\) joints under \(T\) frames is denoted by \(V=\{v_{tri}|t=1,\ldots,T,i=1,\ldots,N\}\)[34], where \(v_{tri}\) is the upper \(i\)-th skeletal point of the \(t\)-th frame.  
Two types of edges exist in the skeletal spatial-temporal graph [35], one of which is the spatial edge \(E_{S}=\left\{v_{tri}v_{tj}|(i,j)\in H\right\}\), which corresponds to the natural connection of joints. The other is the temporal edge \(E_{F}=\left\{v_{tri}v_{(t+1)i}\right\}\), which connects the same joints in successive time steps, where \(v_{tj}\) is the upper \(j\)-th skeletal point of the \(t\)-th frame. On this basis, a multilayer spatial-temporal graph convolution is constructed, which can make the information merge along the spatial and temporal dimensions. Figure 2 is the constructed spatial-temporal graph.  
Figure 1: The human action recognition process of ST-GCN.  
Figure 2: The spatial–temporal graph. Orange dots denote body joints. The blue line represents the inner edge between the naturally connected body joints.
###### Abstract  
The rapid expansion of cities in tropical regions has triggered a series of problems such as the destruction of rare natural resources and decreases in the environmental resource carrying capacity and ecological security, which seriously threaten the sustainable development of tropical cities. In this study, the city of Sanya, Hainan, China, is taken as an example. A bottom-line ecological security pattern is constructed based on the remote sensing ecological index (RSEI) and the patch-generating land use simulation (PLUS) for urban growth boundary (UGB) delineation. The results show that Sanya has a good ecological background, but the overall ecological quality decreased from 2014 to 2018 due to the expansion of construction in hot spot areas. Under the natural growth scenario, the urban built-up area in Sanya in 2030 will be 73.81% greater than in 2018, mainly occupying a large amount of agricultural and ecological space, and urban expansion will not be effectively suppressed. Delineation of the UGBs combined with the ecological constraints can effectively protect the regional ecological security and control the urban sprawl, which is relatively consistent with the current planning. The results of this study demonstrate that the RSEI-PLUS-based UGB delineation perspective has a high scientific and applicability, and they provide a reference for the coordinated ecological-economic sustainable development of ecologically fragile cities in tropical areas.  
ecological constraints; PLUS; urban growth boundary; tropical city +
Footnote †: journal:  
Article  
Incorporating Ecological Constraints into the Simulations of Tropical Urban Growth Boundaries: A Case Study of Sanya City on Hainan Island, China.
It is well known that processing of Urals vacuum residue is associated with high sediment formation [11; 20], and for that reason the severity of operation in the hydrocracker is relatively low when Urals vacuum residue is processed, which results in a lower conversion [11; 20]. Our earlier research [17] has shown that the vacuum residues with a higher saturate content have a higher inclination to form sediment during hydrocracking. For that reason, they are processed at a lower reaction severity and therefore converted to a lesser extent. The Urals and Siberian Light vacuum residues have relatively high saturate content, which makes them difficult to process at a higher severity in the commercial hydrocracker. In contrast, the vacuum residues from Middle Eastern crude oils have a lower saturate content and a lower tendency to sediment formation, which allows operation at a higher reaction severity, leading to a higher conversion. This is well illustrated in Figures 1 and 2. In order to understand what was behind the considerable improvement in commercial EBVRHC unit performance, reaching 82 wt.% conversion during processing difficult feeds such as Urals vacuum residue, an intercriteria analysis evaluation was performed similar to our previous study [16]. The intercriteria analysis (ICrA) was created in the Institute  
Figure 1: The effect of crude oil type on the conversion level in ebulated bed vacuum residue hydrocracking at the same product stability (product sediment content). (Adapted with permission from Ref. [21] Copyright 2014, copyright Elsevier) (This data is taken from the licensor of the commercial ebulated bed vacuum residue hydrocracker under study.). Figure 2: Dependence of hydrocracked vacuum residue sediment content on the crude oil type processed. (This data is taken from the operation of the commercial ebulated bed vacuum residue hydrocracker under study). for biophysics and biomedical engineering, Bulgarian academy of sciences (BAS) as a tool to support decision making in multiobject multicriteria problems [22; 23; 24; 22]. It found successful applications in medicine, biology, economics, physics, etc., and it can be considered a component of the artificial intelligence toolkit [25]. It was also successfully applied in several studies in the field of chemistry and technology of petroleum [26; 27; 28; 16; 2]. A detailed explanation of the essence of ICrA applied in the field of petroleum processing can be found in our recent research [16]. In this study, ICrA evaluation is applied to the data from the commercial ebullated bed vacuum residue hydrocracker that processes vacuum residues derived from three crude oils: Urals, Siberian Light, and Basrah Heavy. The aim of this study is to identify the factors contributing to conversion enhancement from 70 to 82 wt.% while maintaining sediment formation and fouling rates within reasonable limits.
# Zero-Shot Image Classification with Rectified Embedding Vectors Using a Caption Generator
## 3 Proposed Method
### Learning Joint-Embedding Space  
As depicted in Figure 2, the training stage for the joint-embedding space consists of two flows. First, a CNN model is applied to the image data, and the extracted feature is placed on the joint-embedding space through two trainable fully connected layers. The output of the image processing flow is written as \(\varphi_{I}(I_{i})\). Second, an LSTM network is applied to the textual description of the given input images, and the text features extracted from the trainable LSTM network are again placed in the joint-embedding space through the trainable fully connected layer. The text processing flow output is written as \(\varphi_{T}(T_{i})\). The vectors from different flows are placed on the same embedding space; thus, their inner product can be calculated as follows:  
\[s_{ij}=\varphi_{I}(I_{i})\cdot\varphi_{T}\big{(}T_{j}\big{)}. \tag{1}\]  
Figure 2: The overall process of the proposed method. In the training stage, images and text pairs are processed through feature extraction encoders (CNN and LSTM) with fully connected layers to construct a joint-embedding space using matching loss. In the test stage, a rectified vector is created through a weighted sum of image-feature vectors and text-feature vectors in the joint-embedding space, and it is compared to the test class prototype vectors. Additionally, images and generated captions can be used for the augmentation of text descriptions in the training stage.  
Then, the matching loss for all image-text pairs \((I_{i},T_{i})\) in a minibatch subset \(B\) is defined as follows:  
\[L_{mat}=\sum_{i=1}^{|B|}\sum_{j\
eq i}max\big{(}0,s_{ij}-s_{ii}+margin\big{)}, \tag{2}\]  
where \(s_{ij}\) is the score value of the inner product calculated using the mismatched image-text pair \((j\
eq i)\), and the \(margin\) is a positive constant value for stable training. Learning with this loss function updates the embedding function to increase the matched score value \(s_{ii}\) and decrease the mismatched score value \(s_{ij}\) so that the embedding space can represent a semantic correlation between the image and text.