esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Reducing the Uncertainty at the Output of Kalman Filter by Post Processing
### Reducing the Uncertainty at the Output of Kalman Filter
#### 3.3.2 Wavelet-Based Polynomial Fitting
As in the case of the polynomial fitting methods presented in the previous section, the principal inconvenience of the proposed wavelet-based polynomial fitting method is the necessity to know a priori the degree of the fitting polynomial, r-1, to deduce r--the regularity of the MW that must be used for polynomial part extraction. This inconvenience could be eliminated using local polynomial approximations (LPA) [25].
# Kinematics Model Optimization Algorithm for Six Degrees of Freedom Parallel Platform
## 3 Results
### Physical Test
The algorithm can successfully complete the calculation of the forward kinematics solution of the parallel platform. According to the experimental process in Figure 6, select 10 attitude data points in the parallel platform control center to test the error of the MPR-NR forward solution algorithm. The result is shown in Figure 8.  
It can be seen from Figure 8 that the angle error is in the order of \(10^{-3}\), and the displacement error is in the order of \(10^{-4}\). Therefore, the correctness of the operation of the MPR-NR algorithm can be proved. The main reason for the error is that the length value of the robotic arm shown in Figure 7 retains only one decimal place after the decimal point, resulting in a truncation error. Based on the Eigen open-source matrix operation library, the MPR-NR algorithm is implemented in C++ code under the Qt platform. The code was successfully deployed to the host computer of the parallel platform control center. Use the MPR-NR algorithm to calculate the attitude of the parallel platform. The attitude closed-loop task in the workspace of the parallel platform runs normally, which further proves the stability and reliability of the algorithm.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 4 Proposed Method
### On the Receivers' Side
#### 4.2.2 Receivers with Full Authorization  
For fully authorized receivers, they not only possess key A that contains parameters to generate the measurement matrix \(A\), but also possess key H that contains parameters to generate the concealing matrix \(H\), and these two keys work together as symmetric decryption keys. The details of data reconstruction process are shown as below.  
Step 1: To begin with, from Equations (10) and (12), we can get the transformed encrypted and concealed signal \(y_{c}^{*}\in\mathbb{R}^{M\times 1}\)  
\[y_{c}^{*}=\frac{y_{c}-\beta a}{\alpha}=\Phi^{*}x. \tag{16}\]  
Step 2: The OMP algorithm could be exploited to obtain the estimate of \(x\).  
\[\overset{\wedge}{x}=arg\underset{x}{min}\left\|x\right\|_{1}\quad subject\ to \quad y_{c}^{*}=\Phi^{*}x=(A+H)\Psi^{\mathsf{T}}x \tag{17}\]  
Step 3: Finally, an inverse transformation of \(x=\Psi s\) is performed to obtain the estimate of the original signal \(s\).  
\[\overset{\wedge}{s}=\Psi^{\mathsf{T}}\overset{\wedge}{x} \tag{18}\]  
Eavesdroppers may manage to complete the same operations as normal receivers after they capture transmission data that are processed by the proposed method. However,because they do not possess secret keys, which act as essential resource for reconstruction, they cannot obtain the available information sent by senders. When considering the key distribution issue that could be applied in IoT, there are several existing solutions [42; 43; 44]. Therefore, such issues are not discussed in this paper.
## 3 The Proposed EATS-MOBFS Method for the 2L-VRPSC
### Framework of the Solution Mechanism
#### 3.1.3 Adaptive Weight Adjustment  
In this study, each neighborhood operator is assigned a weight, and a self-adaptive mechanism is utilized to select neighborhood operators periodically. The weight of each neighborhood operator is based on the score, which measures how well the neighborhood operator has performed in the previous iterations (predefined by \(p_{u}\) iterations). After \(p_{u}\) iterations, the weight of each neighborhood operator is initially identical and calculated as follows:  
\[\omega(i)=\left\{\begin{array}{ll}(1-r)\omega(i),&\text{if }\theta(i))=0\\ (1-r)\omega(i)+r\frac{\pi(i)}{\theta(i)},&\text{otherwise}\end{array}\right. \tag{13}\]  
where the reaction factor \(r\) controls the influence of the recent success of an operator on its weight. \(\theta(i)\) denotes the number of times the neighborhood operator \(i\) has been used during the \(p_{u}\) iterations. \(\pi(i)\) is the score of neighborhood operator \(i\), which is initially set to zero and updated according to its performance during each iteration as follows: (i) increase \(\delta_{1}\) if the chosen neighborhood operator generates a new solution that is better than the  
Figure 2: Move types used in the EATS algorithm.  
current best solution; (ii) increase \(\delta_{2}\) if the chosen neighborhood operator generates a better solution compared to the current one.
## 3 The Clustering Protocol
### Cluster Setup  
By adding the two cluster average distance \(D_{avg}\), and the variance \(\delta^{2}\) of the distance set of two clusters in the cluster setup process, we can construct a cluster setup factor. The two clusterscorresponding to the largest cluster-merging factor can be merged until the number of clusters reaches the pre-set number of clusters \(k\). Compared to the original AGNES algorithm, the algorithm has smaller distance difference between the nodes in the two clusters, and therefore the energy consumption of the nodes is more uniform during data transmission. The detail procedure of this phase is given by the pseudo-code in Algorithm 1.  
```
Inputs: (1) \(n\) objects (2) \(k\)--number of clusters Result:\(k\) clusters of different sizes
1: Each object constitutes an initial cluster;
2: Current cluster number \(k^{\prime}=n\);
3: while (\(k^{\prime}>k\))
4: for \(i=1\to k^{\prime}\)
5: for \(j=1\to k^{\prime}\)
6: if \(i\sim=j\)
7: calculate the distance of the two nodes in the two clusters \(C_{i}\) and \(C_{j}\);
8: build a distance set \(D\) and obtain the distance mean \(D_{avg}\) and the variance \(\delta^{2}\);
9: calculate the clustering factor of clusters \(C_{i}\) and \(C_{j}\) \[F_{ij}(D_{avg},\delta^{2})=\frac{1}{D_{avg}+0.5\delta^{2}}\]
10: end if
11: end for
12: end for
13: combine the two clusters corresponding to \(F_{max}\)
14: \(k^{\prime}\)-;
15: end while
```  
**Algorithm 1** Cluster Setup Algorithm
## 4 Discussion  
### Comparison of Different Text Fusion Methods  
To demonstrate the efficacy of the proposed approach of fusing the textual semantics and latent visual features of sketches in the context of the sketch generation task, we conducted a comparative analysis with two other cutting-edge single-stage text fusion methodologies, namely DF-GAN [24] (denoted as \"DF\") and DiverGAN [26] (denoted as \"Diver\"), within the framework of the DSTF-GAN. DF utilizes two MLPs to predict language-conditioned channel-wise scaling parameters \(\gamma\) and shifting parameters \(\theta\), which are generated from the concatenation of semantic vectors \(T\) and noise vectors \(\mathbf{z}\), to operate the feature maps. Diver utilizes attention mechanisms to derive reshape operators at both the channel and pixel levels, thereby facilitating the re-weighting of the visual feature maps. We replaced the text fusion module in the DSTF-GAN with the \"DF\" and \"Diver\" fusion techniques, while keeping other components unchanged, and evaluated the effectiveness of the proposed text semantic fusion method using quantitative metrics, including FID and the newly introduced CM precision. The Frechet Inception Distance (FID) [37] was employed to evaluate the different fusion methods. The FID measures the quality of the generated images by computing the Frechet distance between the distribution of generated samples and the distribution of actual data. A lower FID value indicates that the synthetic images closely resemble the real images, reflecting their visual quality. The FID is calculated as  
\[FID(r,f)=\left\|\mu_{r}-\mu_{f}\right\|_{2}^{2}+Tr\left(\sum_{r}+\sum_{f}-2 \left(\sum_{r}\sum_{f}\right)^{1/2}\right) \tag{27}\]  
Figure 14: Comparative representative examples of generated sketches and renderings with existing buildings. The generated sketches and existing buildings are out of the training dataset, and the corresponding renderings are similar to the main features of some existing buildings. where \(\sum\) and \(\mu\) represent the covariance and feature-wise mean of samples, respectively, and \(f\) denotes the real and synthetic images, respectively. While FID evaluates the visual quality of generated sketches, it does not assess the semantic alignment between generated sketches and their corresponding textual descriptions. To address this limitation, we introduced contrastive matching precision (CM precision) as a complementary metric for evaluating text-to-conceptual sketch synthesis. To calculate the CM precision, we used the pre-trained CTIMM to measure the percentage of correct matches between the generated sketches and their corresponding input textual descriptions.
## 2 Architecture  
The gateway bridges that connect physical sensing networks with traditional communication networks, M2M gateways can realize the conversion between different types of sensor networks and different types of communication networks. M2M gateways can also realize such functions as data forwarding, protocol conversion, and control management. The structure of the RESTful M2M gateway system can be divided into three layers, _i.e._, device resource abstraction layer, resource management and control layer, and service interface layer. The RESTful M2M gateway adopts a resource-oriented architecture, which uses the URI design of the gateway resources. The structure is shown in Figure 1.  
_Resource abstraction layer._ The main functions of resource abstraction are to shield the differences between physical devices (including the differences between devices in terms of implementation details and the differences between transmission protocols), and to provide with a unified framework for obtaining the device data and operating various devices, which adopts the OSGi technology to create a  
Figure 1: RESTful M2M architecture.  
uniform protocols management framework with the Dependence Inversion Principle to access the different physical devices.  
_Resource management layer._ Resource control management is the core functions for realizing the M2M gateway, which includes the registration, resource management, physical devices configuration management, log management, event management, data processing and storage and data acquisition. Also the data traffic congestion and scheduling algorithm is implemented.  
_Open service interface layer._ Open service interface is to encapsulate various types of resource in an M2M gateway or capabilities provided by the M2M gateway to form Web services for applications. The M2M gateway can provide the data and capabilities to the terminal device, which is connected to the gateway, and can support M2M applications for directly reading device data and sending control commands through a REST interface on the Internet.  
When the gateway receives the data request sent by the client, it parses the data, matches the data with those in the resource operation table, and completes the positioning operation. Then, the resource-mapping module implements the mapping between the application level and the physical level for the resource, _i.e._, mapping to the physical address in the physical module. By searching and matching the resource status description table and the interface description table in the resource management module, the data request is converted to a standard protocol data packet, and the data packet is issued to a GPRS communication module through the established data path. When the communication module receives the data request packet, and sends a reply packet to the gateway. The gateway parses this reply packet, converts it to realistic and effective data, and then updates the status of the system resource. Additionally, it encapsulates the information with a specified data format and returns it to the client. Thus, the client receives the monitoring and control of the sensor data.
## 1 Introduction
Finally, the IPHP was methylated to IPMP. It is worth noting that the common point of the two hypotheses is both of them suggested that the final step of MPs' biosynthesis is the HPs being methylated to MPs [24; 25; 26], which had been confirmed in grape. An O-methyltransferase transferase (OMT), which is capable of methylating HPs to MPs, has been isolated and purified, and four OMT genes, including _VvOMT1_, _VvOMT2_, _VvOMT3_, and _VvOMT4_, had been identified in wine grapes [27; 28; 29]. The key difference between the two hypotheses is that the \"Amino acid amidation pathway\" hypothesized that the MPs' biosynthesis begins with the amidation of a branched-chain amino acid [22], whereas the \"Amino acid condensation pathway\" hypothesized that the first step of MPs' biosynthesis is a two-amino-acid condensation reaction to form a cyclic dipeptide [23]. Therefore, according to the \"Amino acid condensation pathway\", the biosynthesis of IBMP begins in the condensation of leucine with glycine, which forms 2,5-dicarbonyl-3-isobutyl-piperazine (DCIP) (Figure 1). However, to date, except for the final step, the other information about MPs biosynthesis pathway still remains in the hypothesis stage. Once the results of relevant research are different from the general cognition [30; 31; 32], it is difficult to explain from the mechanism level. Our previous study had explored the effect of the assumed precursor L-Leu and the possible key intermediate AMPA on IBMP biosynthesis in grape berry according to the \"Amino acid amidation pathway\" [22]. Here, we reported a further study on IBMP biosynthesis in wine grapes based on the \"Amino acid condensation pathway\" [23]. The DCIP that was proposed as the possible key intermediate of IBMP biosynthesis was incorporated into the Cabernet Sauvignon grapes at pre-veraison in _situ_. In order to avoid the influence of year difference on the results of the field experiments, the IBMP content in grape berries and the relative expression levels of VvOMTs in berry skins were monitored during the experimental period over two consecutive years (2020-2021). The effect of DCIP on IBMP accumulation in grape berry and the relative VvOMT genes expression pattern of berry skins was evaluated. The results would further broaden the understanding of IBMP biosynthesis pathway in wine grape.
## 4 Proposed Collision Warning System and V2V Communications
### Message Format  
The proposed data format is shown in Figure 6. It is composed of the number of detected objects (\(N\)), sensor accuracies (\(A\)), confidence (\(L\)), and trajectory data. The sensor accuracy is in 4-byte floating point format which contains vision sensor accuracy. The rest are used to set uncertainty of confidences and calculate (26) and (27). Next, the size of confidence part varies depending on the predetermined list of classes, \(\Theta_{C}\), and we allocate 2 bytes of short data type for each class. The position of the detected object is 24 bytes long consisting of longitude \((x)\), latitude \((y)\), and height \((z)\), which follows the GPS coordination. We use the float data type of 4 bytes for conveying velocity information. The aforementioned field is repeated for \(N\) times, thus the size of the entire packet is given by \(13+N(2\times L+28)\) bytes.  
Next, we consider the latency of transmitting vehicle information in the proposed message format. Suppose we use a standard SAE 2735 Basic Safety Message (BSM) format [66] which contains ID, position, speed, heading, etc. We will set the basic BSM to be 500 bytes long, which consists of 300 bytes for the default size of BSM [67; 68] plus reserved space for other application such as high-end security. If the system classifies 16 categories and detects 20 objects \((L=16,N=20)\), the proposed entire safety message is about 1.7 Kbytes, which takes only a few milliseconds in existing V2V communications such as DSRC [11].  
Figure 6: Proposed message format.
# Evaluation of Yukmiijihwang-Tang as a Novel Preventive Agent in Ovalbumin-Induced Murine Asthma  
[PERSON]  
1 Department of Veterinary Pathology, College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Republic of Korea; [EMAIL_ADDRESS] (J.-Y.S.)  
[PERSON]  
1 Department of Veterinary Pathology, College of Veterinary Medicine, Chungnam National University, Daejeon 34134, Republic of Korea; [EMAIL_ADDRESS] (J.-Y.S.)  
[PERSON]  
2 KM Convergence Research Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero,  
[PERSON]  
2 KM Convergence Research Division, Korea Institute of Oriental Medicine, 1672 Yuseong-daero,
exposed to environmental triggers are particularly vulnerable to oxidative stress-induced damage [6]. Oxidative stress is implicated in airway inflammation, bronchial hyperresponsiveness, and tissue remodeling; it not only exacerbates acute symptoms but may also contribute to chronicity [7]. Indeed, oxidative stress is considered a key determinant of asthma manifestation.  
While asthma may benefit by avoiding triggers, such as allergens and respiratory irritants, the incorporation of long-acting beta agonists (LABAs) or anti-leukotriene agents alongside inhaled corticosteroids is advised if symptoms persist [8]. The current therapeutic approaches rely heavily on corticosteroids and generally aim to alleviate symptoms rather than directly addressing the underlying causes. Despite the emergence of diverse therapeutic options, the increasing prevalence of asthma and its associated mortality emphasize the lack of a fundamental treatment addressing the pathogenesis of this condition [9]. It is increasingly acknowledged that new treatments must be developed to address the mechanisms underlying asthma.  
Yukmiijhwang-tang (YJT; also called Liu wei di huang tang in China) is an herbal remedy that is widely utilized in Asia for nourishing the body and preventing/managing diverse diseases [10]. YJT is composed of the extracts of six herbs: Rehmannia radix, Dioscoreae radix, Corni fructus, Hoelen, Mountain cortex radices, and Alismatis radix [11]. Numerous investigations have explored the pharmacological impacts of YJT, including its effects on xerostomia [12], Alzheimer's disease [13], and type 2 diabetes mellitus [14]. In vivo, YJT was shown to mitigate bone loss in a rat model of ovariectomy-induced osteoporosis [15]. In the context of benign prostatic hyperplasia, YJT significantly reduced both prostate weights and dihydrotestosterone levels in the serum and prostates of experimental rats [16]. However, no previous study has examined the effectiveness of YJT in treating asthma. Here, we evaluated the curative impact of YJT in a murine model of asthma caused by ovalbumin and explored possible fundamental mechanisms.
## 2 Related Works
### Cell Decomposition-Based Method  
All of the floor map segmentation-based methods tend to project raw data onto an x-y plane and ignore wall information. Although the room segmentation results may be good, indoor walls are difficult to accurately reconstruct because of a high level of missing and noisy data at the boundaries. Furthermore, certain works [4; 27; 28; 29; 31; 32; 34] can only address buildings under a restrictive Manhattan world assumption, i.e., exactly three orthogonal directions: one for floors and two for walls [1]. Although this assumption may hold true for many indoor environments, many architectural elements of real-world buildings deviate from the strong Manhattan world assumption.  
Different from floor map segmentation-based methods, a space partitioning and cell labeling method based on a graph-cut operation was proposed to address these issues [1; 14; 21; 39; 40; 41]. The space is first partitioned into a cell decomposition [48; 49] via extracted wall lines, and then the labels of each cell are confirmed by an energy minimization approach. However, this method has a drawback regarding the complete reconstruction of long corridors [1; 21; 40]. After partitioning the floor space via extended wall lines, long corridors will be segmented into several sections. Thus, the labeling algorithm will separate these regions by implausible walls that are not part of the building's true walls [21; 40].
## 4 Numerical Examples  
In this section, we give numerical results on some test functions to compare the efficiency of the proposed methods (\(5^{th}\)_3 pVS_, \(10^{th}\)_4 pVS_ and \(20^{th}\)_5 pVS_) with \(3^{rd}\)_3 pVS_ and [PERSON]'s method (\(2^{nd}\)NR). Numerical computations have been carried out in the MATLAB software rounding to 1000 significant digits. Depending on the precision of the computer, we use the stopping criteria for the iterative process \(E_{n}=|x_{n}-x_{n-1}|<\epsilon\) where \(\epsilon=10^{-100}\). Let \(N\) be the number of iterations required for convergence. The test functions and their simple zeros are given below:  
\[\begin{split}& f_{1}(x)=\sin\left(2\cos x\right)-1-x^{2}+e^{\sin(x^ {3})},\quad x^{*}=-0.7848959876612125352...\\ & f_{2}(x)=x^{3}+4x^{2}-10,\hskip 42.679134 ptx^{*}=1.36523001341 40968457...\\ & f_{3}(x)=(x+2)e^{x}-1,\hskip 42.679134 ptx^{*}=-0.44285440100238 85831...\\ & f_{4}(x)=\sqrt{x}-\cos x,\hskip 42.679134 ptx^{*}=0.64171437 08728826583...\end{split}\]  
Table 1 shows the corresponding results for \(f_{1}(x)\) to \(f_{4}(x)\). It can be found that the \(20^{th}\)_5 pVS_ I.F. converge in less iterations with the least error \(E_{N}\) for the functions and their starting points considered. This I.F. takes at most half the number of iterations than that of the \(2^{nd}NR\) I.F. to converge. The number of iterations and the error are smaller when we choose a starting point close to the root.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|} \hline \(f(x)\) & \(x_{0}\) & \(2^{nd}NR\) & \(3^{rd}3 pVS\) & \(5^{th}3 pVS\) & \(10^{th}4 pVS\) & \(20^{th}5 pVS\) \\ \hline  & & \(N\) & \(E_{N}\) & \(N\) & \(E_{N}\) & \(N\) & \(E_{N}\) & \(N\) & \(E_{N}\) \\ \hline \(f_{1}(x)\) & \(-0.7\) & 7 & 1.09e\(-\)074 & 5 & 2.58e\(-\)094 & 4 & 1.04e\(-\)111 & 3 & 6.96e\(-\)077 & 3 & 3.31e\(-\)299 \\ \hline  & \(-1\) & 7 & 9.07e\(-\)061 & 5 & 1.65e\(-\)076 & 4 & 9.04e\(-\)095 & 3 & 8.96e\(-\)068 & 3 & 5.99e\(-\)249 \\ \hline \(f_{2}(x)\) & 1.6 & 7 & 7.80e\(-\)063 & 5 & 2.07e\(-\)079 & 4 & 5.53e\(-\)097 & 3 & 2.53e\(-\)080 & 3 & 9.88e\(-\)324 \\ \hline  & \(2.5\) & 8 & 3.21e\(-\)053 & 6 & 1.29e\(-\)100 & 5 & 1.01e\(-\)173 & 4 & 2.57e\(-\)302 & 3 & 1.024e\(-\)125 \\ \hline \(f_{3}(x)\) & \(-0.3\) & 7 & 7.80e\(-\)066 & 5 & 3.63e\(-\)083 & 4 & 1.62e\(-\)102 & 3 & 4.90e\(-\)071 & 3 & 1.18e\(-\)282 \\ \hline  & \(-0.8\) & 8 & 4.34e\(-\)073 & 6 & 6.08e\(-\)138 & 5 & 2.24e\(-\)144 & 4 & 7.38 e\(-\)190 & 3 & 3.88e\(-\)092 \\ \hline \(f_{4}(x)\) & 0.7 & 6 & 1.41e\(-\)067 & 4 & 5.59e\(-\)057 & 4 & 9.12e\(-\)222 & 3 & 1.08e\(-\)108 & 3 & 0 \\ \hline  & \(2\) & 7 & 1.52e\(-\)062 & 5 & 8.42e\(-\)087 & 4 & 1.38e\(-\)069 & 4 & 9.78e\(-\)238 & 3 & 3.70e\(-\)123 \\ \hline \end{tabular}
\end{table}
Table 1: Results for the \(5^{th}\)_3 pVS_, \(10^{th}\)_4 pVS_ and \(20^{th}\)_5 pVS_ Iterative Functions (I.F.s) for \(f_{1}(x)\)-\(f_{4}(x)\) along with \(2^{nd}NR\) and \(3^{rd}\)_3 pVS_ I.F.s