esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey of SNPs Associated with Total Number Born and Total Number Born Alive in Pig
## 2 SNPs Associated with Total Number Born Alive
As a result, 17 SNPs associated with NBAs were identified, 5 of these SNPs had a minor allele frequency less than 1%. The study of [PERSON] et al. [7] was the first to show an association with NBA for SNP rs81430147 (SSC11). All other SNPs were found in chromosome regions where candidate genes or QTLs affecting pig reproductive traits have already been identified. It should be noted that different SNPs were established for each breed cluster, and no associative communications were established simultaneously in two breeds. 13 SNPs were identified for Large White sows, and 4 SNPs for Landrace sows. In sows Landrace SNPs are localized on SSC7, SSC9, SSC11 and SSC16. Large White on SSC3, SSC5, SSC9, SSC10, SSC11 and SS18. The most significant effects on NBA of Large White sows are found for 4 SNPs: rs81379421 (SSC3), rs81417393 (SSC9), rs81242222 (SSC11) and rs81469701 (SSC18). Research carried out by [PERSON] et al. [22] on Large White pigs identified 6 SNPs associated with NBA located on SSC2, SSC3, SSC13, SSC14 and SSC18. The most significant effect was determined for SNP rs334867206 (SSC3), according to which pigs of AA genotype had more NBA, compared with analogues of GG genotype. [PERSON] et al. [26] investigated Large White and Landrace pigs and established 25 SNPs for the NBA. Of these, 11 SNPs located in SSC1 and SSCX were defined for Large White pigs, 14 SNPs for Landrace pigs in SSC2 and SSC6. It is remarkable that SNPs on SSCX showed significance only at the NBA for Large White sows. In the course of research on commercial pigs, [PERSON] et al. [25] established 2 SNPs (rs342908929 (SSC6) and rs324003968 (SSC15)) associated with NBA. According to the results of [PERSON] et al. [27] obtained from the sows of Duroc in the database presents 9 SNPs and all of them are localized on SSC6. [PERSON] et al. [24] studied the _IGFBP2_ (SSC15) and _IGFBP3_ (SSC18) genes in Berkshire pigs. Their results showed significant SNPs in these genes. This work also analyzed the expression levels of _IGFBP2_ and _IGFBP3_ mRNA in the endometrium in pigs of various genotypes. Homozygous GG pigs expressed higher levels of _IGFBP3_ mRNA in the endometrium than pigs of other genotypes, and a positive correlation was observed between litter size traits and _IGFBP3_ but not _IGFBP2_ expression level. These results suggest that SNPs in the _IGFBP2_ and the _IGFBP3_ gene are useful biomarkers for the little traits of pigs. According to the results of this work 2 SNPs are included in PigQTLdb rs45435330 on SSC15 as Genome map association for NBA and TNB.
## 3 Methodology
### Data Preprocessing
#### 3.2.3 Normalization  
Data scaling involves adjusting numerical values to fit within a specific range and is a critical preprocessing step in machine and deep learning. This process enhanced the efficiency and effectiveness of the training models by standardizing the data across all columns. One commonly used method for normalization is the MinMaxScaler, a popular tool available in the Scikit-learn library. Equation (2) [40] illustrates the procedure where each column is processed by subtracting its minimum value and then dividing by the range (maximum value minus minimum value) to normalize it. In Equation (2) [40], \(X\) represents the original value, min(\(X\)) is the minimum value in the column, and max(\(X\)) is the maximum value in the column. After evaluating several normalization techniques, MinMaxScaler was identified as the optimal choice for achieving the best results in our study. This normalization approach was applied to all features in the dataset to ensure consistent scaling across the entire dataset.  
\[X(scaled)=\frac{X-min(x)}{max(x)-min(x)} \tag{2}\]
# An Improved Evolutionary Multi-Objective Clustering Algorithm Based on Autoencoder
## 2 Proposed Algorithm
### Autoencoder (AE)  
Autoencoder is the part of AE-IEMOKC responsible for dimension reduction of the input datasets. It is a neural network based on deep learning and consists of an encoder and a decoder. The eight-layer neural network shown in Figure 1 is taken as an example. The encoder focuses on mapping the original dataset \(X\) to the low-dimensional embedding layer as the feature representation \(H\) of \(X\) through three hidden layer and one linear layer sequentially, which can be defined as a transformation:  
\[H=f(X)=W_{4}^{T}\phi\left(W_{3}^{T}\phi\left(W_{2}^{T}\phi\left(W_{1}^{T}X \right)\right)\right) \tag{1}\]  
Figure 1: Architecture of the proposed AE-IEMOKC.  
where \(\phi(\cdot)\) is a ReLU activation function [23] and \(\phi(\cdot)=\max(0,X)\); \(W_{1}\), \(W_{2}\), \(W_{3}\), and \(W_{4}\) are the weights of the encoder network. For simplicity, the bias term \(b_{i}\) for each layer in the formulation is dropped. The decoder focuses on transforming \(H\) to the reconstructed data \(X^{\prime}\) through three hidden layers and one linear layer sequentially, which can also be defined as a transformation:  
\[X^{\prime}=g(H)=W_{\delta}^{T}\phi\left(W_{\tau}^{T}\phi\left(W_{6}^{T}\phi \left(W_{5}^{T}H\right)\right)\right) \tag{2}\]  
where \(W_{5}\), \(W_{6}\), \(W_{7}\), and \(W_{8}\) are the wights of the decoder network. The autoencoder is able to learn the nonlinear feature of \(X\) by minimizing the reconstruction loss \(L_{\text{rec}}(X)\):  
\[L_{\text{rec}}(X)=\frac{1}{m}\underset{i=1}{\overset{m}{\sum}}\left\|X_{i}-X^ {\prime}{}_{i}\right\|^{2} \tag{3}\]  
where \(m\) is the amount of the original dataset \(X\). However, minimizing the reconstruction loss \(L_{\text{rec}}(X)\) contributes little to clustering [14]. Thus, a clustering loss \(L_{\text{cl}}(H)\) is considered together with the reconstruction loss \(L_{\text{rec}}(X)\), and the whole loss \(L(X,H)\) is defined as follows:  
\[L(X,H) =L_{\text{rec}}(X)+\lambda L_{\text{cl}}(H) \tag{4}\] \[\text{where }L_{\text{cl}}(H) =\left(\frac{10}{m^{d}\cdot^{d}}\sum\limits_{r=1}^{k^{*}}\sum \limits_{H_{i}\in C_{r}}\left\|H_{i}-m_{r}\right\|^{2}\right)^{2}\] \[m_{r} =\left(m_{r}^{1},m_{r}^{2},\ldots,m_{r}^{d^{*}}\right)\]  
where \(\lambda\geq 0\) is a parameter to balance the reconstruction loss and the clustering loss, \(k^{*}\) is the actual number of clusters of \(X\), \(d^{*}\) is the dimensionality of the embedding layer, \(m_{r}=\left(m_{r}^{1},m_{r}^{2},\ldots,m_{r}^{d^{*}}\right)\) denotes the \(r\)th cluster centroid of \(H\), and \(C_{r}\) denotes the collection of \(H\) in the \(r\)th cluster. By minimizing the loss \(L(X,H)\), the autoencoder is able to obtain the low-dimensional final \(H\), which maintains the nonlinear feature of \(X\) and is suitable for clustering.
## 2 Materials and Methods
### Characterizations and Physico-Mechanical Properties  
The chemical composition of all Al\({}_{2}\)O\({}_{3}\) commercial powders was determined using Energy-Dispersive X-ray Spectroscopy (EDX) (SHIMADZU, EDX 720, Kyoto, Japan). The qualitative phase analysis was accomplished by X-ray diffraction (XRD) using a Bruker-D2 Phase diffractometer (CuK\(\alpha\), 40 kV/30 mA). All XRD analyses were carried out at room temperature, with a goniometer speed of 2\({}^{\circ}\)/min, an angular step of 0.02\({}^{\circ}\) and a counting time of 0.6 s. The diffraction peaks observed were indexed using the Search Match(r) Program and the JCPDS database. The particle size distribution was measured by wet granulometric analysis (in defloculated dispersion) using a laser diffraction equipment (Cilas, 1064 LD).  
After infiltration, sample surface and cross-section were characterized by a scanning electron microscope (SEM) using SSX-550 Shimadzu (Kyoto, Japan). Vickers' microhardness was estimated using an HMV-G21D Tester from Shimadzu (Kyoto, Japan). Ten samples were used for each composition. The indentation load and time were 9.8 N and 15 s, respectively, which is recommended by ASTM C 1327-08. According to the ISO 6872:2015 standard, a universal testing machine of Instron model 5582 was employed to determine the three-point bending strength. Such testing was performed on 10 samples for each composition (sized to 30 \(\times\) 5 \(\times\) 4 mm\({}^{3}\)). The distance between the supports and test speed were 25 mm and 0.5 mm/min, respectively.  
Figure 2: Configuration of the homemade system designed for infiltration of PMMA into the sintered Al\({}_{2}\)O\({}_{3}\) commercial samples. (**a**) A lid of the stainless steel for vacuum chamber above which one iron magnet is placed, (**b**) iron magnet (150 mm \(\times\) 60 mm \(\times\) 15 mm; mass = 60 g) put over the lid, (**c**) a stainless steel vacuum chamber (diameter = 170 mm, height = 230 mm and thickness = 6 mm) inside which a beaker and samples are shown, and (**d**) the whole configuration of infiltration system connected to vacuum pressure adjustment.  
The Archimedes method was used to measure water absorption, apparent porosity and bulk density. Before immersion in distilled water, sintered samples were dried at 60 \({}^{\circ}\)C for 24 h to calculate their dry weight. Then, the samples were immersed in distilled water for 24 h to obtain the wet weight and the immersed weight. All samples were weighed on a Shimadzu balance, model AY 220.  
The \(t\)-test and Tukey media comparison, using a significance level of 5%, was used for statistical analysis of measured values.
## 3 Results and Discussion  
### Visual Inspections  
Figure 3 provides qualitative evidence of surface degradation under magnesium sulfate exposure; however, no quantitative image-based automated analysis [70] (e.g., crack mapping, edge loss quantification) was performed. Future studies should incorporate digital image analysis techniques to quantify degradation metrics, such as surface crack density and erosion index, which would allow for objective comparison between mixtures. The control mixture (M1) exhibited pronounced surface degradation at both 180 and 360 days of exposure. In contrast, the mixtures composed of 85% OPC and 15% CS showed relatively mild deterioration at 180 days. A thin white layer was observed on the surface, attributed to the formation of brucite (magnesium hydroxide), which commonly forms as a result of magnesium sulfate attack [71]. Micro-spectroscopic analyses by [PERSON] et al. [72] have reported that this white surface layer is not only composed of Mg(OH)2, but also contains magnesium carbonate (MgCO3) and magnesium silicate hydrate (M-S-(A)H) phases.  
At 360 days, the CS mixtures (M2, M3, M4, and M5) exhibited more pronounced signs of degradation, particularly at the edges of the specimens, with noticeable material loss and possibly a denser white layer. This is attributed to the progressive accumulation of reaction products from the MgSO4 attack, which increases crystallization pressure within the pores. When this internal pressure exceeds the tensile strength of the composite matrix, it triggers cracking and spalling of the mortar [73].  
Figure 3: Visual appearance of mixtures exposed in a 5% MgSO\({}_{4}\) solution for 180 and 360 days.
# Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms
## 2 Problem Formulation  
### Vehicle Modeling  
We first define a global coordinate frame \(\{U\}\) and a body frame \(\{B\}\) as shown in Figure 1. The kinematic equations of the vehicle are written as  
\[\dot{\mathbf{p}} =\mathbf{R}\mathbf{v} \tag{1}\] \[\dot{\psi} =\omega \tag{2}\]  
where \(\dot{\mathbf{p}}=[x,y]^{T}\) denotes the coordinates of its center of mass, \(\mathbf{v}=[u,v]^{T}\) represents linear velocity, \(\psi\) is the orientation of the vehicle, and its angular velocity is represented by \(\omega\). Moreover, the rotation matrix \(\mathbf{R}(\psi)\) is given by  
\[\mathbf{R}(\psi)=\begin{bmatrix}\cos(\psi)&-\sin(\psi)\\ \sin(\psi)&\cos(\psi)\end{bmatrix}. \tag{3}\]Its dynamic equations are  
\[\dot{\mathbf{v}} =-\mathbf{S}(\omega)\mathbf{v}+m^{-1}T\mathbf{n}_{1}+\mathbf{\Delta }_{v} \tag{4}\] \[\dot{\omega} =-J^{-1}\omega+J^{-1}\tau+\Delta_{\omega} \tag{5}\]  
where \(\mathbf{S}(\omega)\) is a skew symmetric matrix, given by  
\[\mathbf{S}(\omega)=\begin{bmatrix}0&-\omega\\ \omega&0\end{bmatrix}. \tag{6}\]  
\(\mathbf{n}_{1}=[1,\,0]^{T}\), \(m\) and \(J\) denote the car's mass and rotational inertial, respectively. The force used to make the car move forward is denoted by \(T\), and \(\tau\) represents the torque that can steer the vehicle. Unmodeled dynamic terms are represented by \(\mathbf{\Delta}_{v}\) and \(\Delta_{\omega}\). For more details about modeling surface vehicles, the reader is referred to [22].
# Model Checking Resiliency and Sustainability of In-Vehicle Network for Real-Time Authenticity
## 7 Conclusions  
CAN is the most common in-vehicle network system. The latest automobiles developed recently are equipped with numerous ECUs. The ECU over CAN bus can be a victim of security attacks leading to critical risks of vehicle safety. In particular, in case that the infotainment system of unwarranted third party vendor and driving control systems share CAN bus, the security risk is dramatically escalated.  
MAuth-CAN is a centralized authentication mechanism for CAN. In MAuth-CAN, the response timing is critical for the properties since a timeout works for the indication that a message passes authentication and ECUs accept a new message stored in its temporary queue when the timeout expires. MAuth-CAN utilizes two CAN controllers for fault-tolerance mechanism so that it continues its functionality under message flooding and bus-off attacks.  
This paper presents the formal proofs of resiliency and sustainability of MAuth-CAN authentication against message flooding and bus-off attacks where timing is critical to maintain such properties. Also, this paper shows how model checking, a formal verification technique, works for safety and security certificates of in-vehicle network. In this paper, we present a novel CAN model in a formal model, which captures CAN's timing behavior in MAC level of the data-link layer and can thus be used for verification of safety properties of other CAN applications. Using this CAN model, we perform formal verification for the sufficient conditions of those properties of MAuth-CAN.  
As conclusions, we show that MAuth-CAN authenticator is sufficiently resilient and sustainable against those two kinds of attacks if MAuth-CAN authenticator can handle two consecutive attack messages, the authentication time is less than the message transmission time, and MAuth-CAN authenticator uses two CAN controllers. Also, we conclude that 4 \(\times\) T\({}_{\text{D}}\) is the minimum and sufficient length of the timeout for ECUs to open incoming messages that have passed MAuth-CAN authentication. The experiment results from the implementation of MAuth-CAN are shown to be consistent with that propositions and conditions we have shown in this paper.  
Conceptualization, J.H.K., H.J.J. and I.L.; methodology, J.H.K.; validation, IL. and H.J.J.; writing--original draft preparation, J.H.K. and H.J.J.; writing--review and editing, IL.; supervision, IL.; project administration, IL.; funding acquisition, IL., J.H.K., H.J.J. and All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by NRF-2020R1A2C1014855, NRF-2018R1C1B5086261, and ONR N00014-17-1-2012 and ONR N00014-20-1-2744.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
###### Abstract
Multi-processing can be used in order to speed up the sequential processing but this becomes too inefficient in terms of memory usage. There are some advanced implementations being used in popular deep learning platforms to reduce information distortion caused by truncation/padding. TensorFlow [8] supports the cudnn_rnnv3 method (In tensorflow.python.ops.gen_cudnn_rnn_ops. However, it is not officially supported and cannot be found in the official documents.) method, which is a kind of RNN implementation backed by cuDNN [9]. Similarly, PyTorch [10] supports the PackedSequence class and related methods. Even if all the input sequences in a mini-batch are padded to have the target length (The padding is optional in PyTorch.) so they have some padded frames, these implementations compute the output sequences with the original frames only. In this paper, the frame corresponds to an element of the sequence. The computation can be executed in parallel (i.e., in mini-batch units). Therefore, each output sequence has the same length as the corresponding original input sequence. All the output sequences are then padded with zero to ensure these have the target length. For example, consider a sample input sequence \(\mathbf{X}=[\mathbf{X}_{1},\dots,\mathbf{X}_{t}]\) of length \(t\) in a mini-batch, and the target length is \(T(>t)\). Regardless of padding, the output sequence \(\mathbf{H}\) for \(\mathbf{X}\) is computed up to the time \(t\), that is, \(\mathbf{H}=[\mathbf{H}_{1},\dots,\mathbf{H}_{t}]\). Then, \(\mathbf{H}\) is padded with zero, that is, \(\mathbf{H}=[\mathbf{H}_{1},\dots,\mathbf{H}_{t},0_{t+1},\dots,0_{T}]\) where 0, is a zero-vector. However, there are still unnecessary values in the output sequences. Therefore, these implementations also cannot be free from information distortion. In this paper, we propose a method to remove the information distortion. After the output sequences are computed from the padded input sequence, the proposed method simply truncates the output sequence to have the same length as the original input sequence. Therefore, the proposed method can produce the same output sequence as the output sequence that would result from the original input sequence. We expect that the proposed method gives performance improvement on TD-SV tasks with some computing overhead. The remainder of this paper is organized as follows. Section 2 outlines the conventional method to handle variable length sequences for RNNs. Section 3 introduces the proposed method. Section 4 describes the experiments and their results. Finally, Section 5 concludes the paper.
## 3 Dynamic Transaction Confirmation Sharding Protocol
### Transaction Consensus and Review Mechanism  
The client broadcasts the transaction request message to the entire nodes. The remainder \(r\) obtained by the node according to the hash value of the transaction modulo \(k\) (the number of shards) is the serial number of the shard that processes the transaction. The Leader node sends the transaction confirmation threshold (hereinafter referred to as \(Tct\)) to the consensus nodes in the shard. The consensus node verifies the transaction information, adds the correct transaction to the transaction pool, and broadcasts a confirmation message. After the Leader node receives \(Tct\) confirmation messages, it queues the transaction into the encapsulation queue. When the epoch time is reached, the Leader node will encapsulate the transactions in the queue into a microblock and send the microblock information to the Master node. Figure 3 depicts the intra-shard consensus. After receiving a microblock, the Master node will verify the microblock and its internal transactions with all Leader nodes using the PBFT algorithm. On the premise that the consensus of \(k\) microblocks is completed, the whole network nodes adopt the bigblock synchronization state composed of all microblocks. Algorithm 2 uses pseudocode to describe the consensus steps of transactions in the subcommittee and consensus committee. The validator nodes receive a bigblock sent by the Master node and compare the content of the bigblock with the already verified transactions through their local transaction pool. If a validator node discovers a transaction submitted by a Leader node but has not been verified, the validator node will send a challenge message (including the microblock information to be reviewed and the position of the transaction to be reviewed ) to the Master node. The Master node forwards the received challenge message to the other \(k-1\) shards (excluding the initially processed shard) for verification. The Master node reaches a consensus on the verification results of the \(k-1\) shards in the consensus committee. If more than \(\frac{2k}{3}\) of the shards in the verification result consider the transaction incorrect, the Master node packages the challenge message and review result into a block and uploads the block to the alliance chain. During the implementation process, it is crucial for nodes to monitor the messages and blocks within the alliance chain network. This function plays a critical role in transaction consensus and on-chain blockchain operations. Figure 4 shows these key codes.
## 1 Introduction  
Hospital facilities provide valuable information for healthcare staff and patients. Such facilities preserve patient rights and accurate medical information that must be collected, kept, and used during diagnosis and treatment operations [1]. However, in some cases, this information is not kept in a secure system and, unfortunately, the number and severity of cyber attacks on medical record files are increasing. As safe and trustworthy health data transfer does not have systematic infrastructure support, much of this data sharing is still conducted manually or by fax or mail. This limited information resource affects sensitivity and secrecy, causing a delay in the provision of medical care [2]. Between 2009 and 2021, the United States Department of Health and Human Services reported more than 4419 cases of medical record breaches resulting in the exposure of 314,063,189 medical records [3; 4; 5]. Access control is an essential component of information systems and establishes security by verifying whether a user has the necessary rights to access the services they request. Current access control schemes face a lack of privacy, validation, and operation by third-party entities. One technological tool that can benefit hospital resource management is the emerging blockchain technology. Blockchains offer consistency and decentralization improve data security and integrity and alter entire sectors [6]. Since blockchains are secure, unchangeable, and decentralized, they can provide adequate data storage and access management. Blockchain technology can be a valuable tool for creating a smart healthcare network [7]. Owing to the properties of this technology, it can safeguard patient privacy and maintain and manage access to huge quantities of anonymous health data, thus allowing new studies and insights [8]. One of the most well-known and often-used aspectsof blockchain-based systems is the usage of smart contracts [9]. Smart contracts are a means to make the blockchain programmable in the context of blockchain and cryptocurrency.