esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Experimental Results
### The First Heating Experiment
#### 5.1.1 Analysis of Experimental Results Using BSS  
This section analyzes the compensation effect of the BSS method. As shown in the reference [21], the BSS method minimizes the average residual between the stretched baseline and monitoring signals through a global stretching factor, thus achieving temperature compensation. This can be described by the following formula:  
\[\hat{\beta}_{\text{ms}}=\arg\min_{\beta}\{R\}, \tag{14}\]  
\[R=\int_{I_{1}}^{I_{2}}[u(t,T)-\hat{u}(t,T_{0},\hat{\beta})]^{2}\text{d}t, \tag{15}\]  
where \(\hat{\beta}_{\text{ms}}\) represents the optimal stretching factor, and \(T_{0}\) and \(T\) denote the signals at the initial and current temperatures, respectively. \(\hat{u}\) is the baseline signal after stretching, and \(R\) represents the mean square error between the two signals. This error serves as a measure of signal disparity and can be used as a damage index.  
Figure 13 shows the local phase diagram of the signals after compensation using the BSS. The BSS signal at position 1 (metal hoop 1) exhibits a slight phase lag compared to the high-temperature signal, while the BSS signal at position 2 (metal hoop 2) shows a greater degree of phase lag. The BSS signals at positions 3 and 4 (metal hoops 3 and 4) have similar phase lag levels. However, due to the inherent phase lag of signals in the temperature transition and high-temperature zones, the phase lag at positions 3 and 4 tends to decrease. The BSS phase at position 5 (metal hoop 5) is comparable to the high-temperature signal. Position 6 represents the end face signal, where the BSS processing results in a phase advancement compared to the test signal. Throughout the BSS processing, while some signals (e.g., at position 5) align well, most signals exhibit either a phase advancement (position 6) or a lag (positions 1, 2, 3, and 4).  
BSS applies a single stretching factor to transform signals, which is effective for uniform phase changes as already demonstrated. However, for signals with nonuniform phase changes--where some phases remain unchanged while others vary--the compensation effect of BSS is limited. This results in excessive compensation for the phase of signals in the nontemperature-variable region, while the phase compensation for signals in temperature-variable areas is insufficient.
## 5 DMME on an Application Example--\"Feed Axle\"
### Technical Understanding and Conceptualization
#### 5.2.3 Conceptualization  
As first step of the conception, the principal effects that influence the data-driven detection of marginal lubrication were worked out (Figure 7). The cause-effect chain starts with a marginal lubrication, which increases the friction in the axle. The higher the friction, the greater the required motor torque. This affects the magnitude of the motor current, which increases.  
There are relevant influences on the elements of the cause-effect chain. The manufacturing process on the machine also influence the motor current. Therefore, the influences of position, speed, acceleration, and varying inertia of the moving goods must be clearly separated from the effect of the lubricating condition. In addition, environmental influences such as ambient temperature play an important role. The lubrication behavior changes due to temperature-dependent viscosity.  
The drive system is embedded in a software environment. In addition to controlling the drive axle, the EAL/SIP interface is used for data acquisition by the \"IoT Gateway\" software which transfers data to the MySQL database. Figure 8 shows the acquisition concept for the parameters.  
The data acquisition starts with the internal sensors of the drive controller, which have a high resolution. The direct coupling of motor shaft and ball screw drive ensures a small positioning deviation of the motor encoder relative to the carriage position, as there is no backlash like with a common gearbox. The sampling rate of the data from the drive controller is limited to two milliseconds due to the used acquisition software \"Bosch Rexroth IoT Gateway\". The data is stored in a local MySQL database for testing the concept. Later, a cloud database can be used, as the software also supports this.  
As only data from the drive controller is collected, the approach for DM does not require synchronization of data sources. The drive controller assigns timestamps centrally to the measured data, which is stored on the database server by default in a nested JSON data format. Therefore, additional preprocessing of the data (deserialization) is necessary in preparation for data analysis. The necessary preprocessing must detect and handle errors in the data.
# Functional Analysis of Two Terpene Synthase Genes Isolated from the Flowers of _Hosta_ 'So Sweet'
## 3 Results
### In Vivo Localization of the Two TPSs  
Most monoterpene synthases are thought to be localized in plastids, while sesquiterpene synthases are generally thought to be localized in the cytosol. It is typically believed that monoterpene synthases and sesquiterpene synthases use GPP and FPP as their substrates, respectively [1, 6]. Bioinformatic analyses of the two _TPSs_ were performed using the online software TargetP 2.0, and the two _TPS_ genes were predicted with high probability to be chloroplast transport peptides, indicating the possibility of chloroplast localization. The reliability value of the prediction result was lower than the general value (>4), indicating low reliability. To test the above prediction, _Nicotiana benthamiana_ leaves were infected by _Agrobacterium_ cultures independently expressing the two _TPSs_[36]. The results of scanning laser confocal microscopy showed that Hs_TPS1_ was probably localized in plastids, whereas Hs_TPS2_ was localized in the cytosol (Figure 6).  
Figure 4: The total ion chromatogram of scent components emitted from the flowers of two different developmental floral stages. The stage of flower buds one day before full bloom (S2) and full-bloom flowers for one day (S3). 1 = \(\beta\)-Pinene; 2 = \(\alpha\)-Phellandrene; 3 = D-Limonene; 4 = Linalool; 5 = \(\beta\)-Phellandrene.  
Figure 5: Relative expression analysis was performed by qRT-PCR using _Actin_ as reference genes. (**a**) Relative expression levels of Hs_TPS1_; (**b**) Relative expression levels of Hs_TPS2_. The relative transcription level in tissue with the highest expression quantity was set to 1 (100%). Each bar represents the mean value \(\pm\)SE of three biological and three technical replicates (\(p\)-value \(<\) 0.05). The stage of flower buds two or three days before full bloom (S1); flower buds one day before full bloom (S2); full-bloom flowers for one day (S3); and flowers one day after full bloom (S4).  
Figure 6: Subcellular localization of Hs_TPS1_ and Hs_TPS2_ by transient transformation of _N. benthamiana_  
with _Agrobacteriumum_ carrying a pCamba 1300 expression vector, where the Hs_TPS1_ and Hs_TPS2_ coding sequence was fused with the GFP coding sequence, respectively. GFP, GFP fluorescence detected in the green channel; BF, brightfield image; Merged, merged green and BF channel images.
## 1 Introduction
The rise in water volume inside the vessel leads to an increase in pressure, resulting in the inactivation of microorganisms. This is mostly achieved by irreversibly damaging cell membranes and cell walls, which alters their permeability and destroys their functionality [8; 9]. When it comes to the application of the aforementioned processing technologies, previous studies from [PERSON] et al. (2023) have demonstrated the feasibility of HPP (400 and 500 MPa for 2 min) to extend the shelf-life of an apple-kiwi-carrot puree, as well as to retain some nutritional attributes such as ascorbic acid, total carotenoids, and antioxidant capacity [10]. A study analysing the effects of HPP (600 MPa, 15 min, 48 \({}^{\circ}\)C) and US (460 W/cm\({}^{2}\), 24 kHz, 15 min, 33 \(\pm\) 7 \({}^{\circ}\)C) on strawberry puree was performed by [PERSON] et al. (2017) who reported that HPP was better than US to retain antioxidant activity over a period of 30 days at 3 \({}^{\circ}\)C, while the later performed better to retain colour [11]. When it comes to PEF, [PERSON] et al. (2015) reported a reduction of 7.3 log units in _E. coli_ inoculated in strawberry puree after a PEF treatment at 30 kV/cm, 57.5 \({}^{\circ}\)C, 400 pulses/s and a pulse width of 1.8 \(\upmu\)s [12]. The objective of this study was to identify equivalent US, PEF and HPP processing conditions that can achieve inactivation of at least 5 log units of pathogenic-surrogated microorganisms (using _E. coli_ ATCC 25922 as a target microorganism) by conventional thermal pasteurization, and to evaluate the effectiveness of such processing conditions to ensure the safety during the shelf-life of Reineta Parda AP. In response to the practical demands of the food industry, our study emphasizes a practical approach, prioritizing the possible applicability of these technologies. By bridging the gap between innovative non-thermal technologies and their practical implementation in the food industry, particularly for Reineta Parda AP, we sought to provide insights that are directly relevant and readily applicable for food processors and manufacturers, as well as using the ready-to-implement quality assessment analyses, such as microbiological parameters, pH, colour, total soluble solids and water activity (a\({}_{\text{W}}\)), as well as a more fundamental parameter (antioxidant activity accessed by two different methods) that could be used as a possible marketing argument for this type of product for the cases where the processing technologies increase the antioxidant activity.
###### Abstract  
The study aimed to investigate the effects of pioglitazone hydrochloride (PGZ) and resveratrol (RES) on yellow-featureed broiler chickens. A total of 500 broiler chickens were randomly divided into four groups and fed a basic diet (control group) or a basic diet supplemented with 15 mg/kg PGZ, 400 mg/kg RES, or 15 mg/kg PGZ plus 400 mg/kg RES for 28 days. Compared with the control group, the PGZ and PGZ plus RES groups presented a significantly higher average daily gain and a decreased feed-to-gain ratio. Increases in the dressing percentage, semi-eviscerated yield, muscle intramuscular fat content, and C18:1n-9c, C18:3n-6, C20:3n-3, and monounsaturated fatty acid (MUFA) percentages were found in the PGZ plus RES group. Moreover, the diet supplemented with RES or PGZ plus RES increased the activities of catalase, glutathione peroxidase, and superoxide dismutase, and decreased the levels of reactive oxygen species of thigh muscle. Additionally, the mRNA abundance of peroxisome proliferator-activated receptor \(\gamma\) coactivator \(1\alpha\), fatty acid-binding protein 3, nuclear factor erythroid-2-related factor 2, and superoxide dismutase 1 was increased in the PGZ plus RES group. In conclusion, this study suggested that dietary supplementation of PGZ combined with RES improved the growth performance, the muscle intramuscular fat content, and antioxidant ability of yellow-featureed broiler chickens.  
Keywords:resveratrol; pioglitazone hydrochloride; fatty acid; meat quality; antioxidant ability; yellow-featureed broiler chicken +
Footnote †: journal: Applied
# Spectral Information Divergence-Driven Diffusion Networks for Hyperspectral Target Detection
## 3 Proposed Method  
This paper presents a novel HTD framework based on Diffusion joint spectral information divergence constraints. In this paper, the SID idea is integrated with Diffusion to propose a model for better representation of the background. As illustrated in Figure 1, the proposed deep learning method, based on diffusion constraints based on SID, comprises two primary modules: background learning and target comparison detection.
## Appendix E Details of the e-health subapplications and specific software deployment views.  
Figure 11 shows GitLab as a complete DevOps platform and e-Health-System-K8s-cluster nodepool-1 containing the deployed microservices. In general, the GitLab platform integrates GitLab projects into a Kubernetes cluster through components such as Helm-Tiller, Ingress, Cert-Manager, and GitLab Runner. The main element for CI/CD tasks is the GitLab Runner that interacts with the GitLab repository and executes CI/CD jobs for the deployment of applications to preproduction [36]. The final automatic deployment process carried out by GitLab of the subapplications leaves as a result the Ingress Service component that provides access to the services of the subapplications.  
The GitLab CI/CD pipeline for the integration and automatic deployment of system microservices was designed through a set of scripts with basic stages for building, testing, and deployment (gitlab-ci.yml file). When the developer code is pushed to the repository in GitLab, the GitLab triggers the CI/CD pipeline. The pipeline builds and stores the image in the GitLab container registry, runs the test, and invokes the GitLab Runner that automatically deploys the microservice on the Kubernetes cluster.  
Figure 14 shows the deployment of EMQ, Confluent (Kafka Broker and ZooKeeper), and Apache Spark on the e-Health-System-K88-cluster nodepool-2. These components were deployed as pod resources and service resources in the node pool. The control of the deployment was carried out via Kubernetes through the execution of YAML files and with the help of Helm-Tiller to obtain the components from Docker Hub.  
Figure 14: Detailed view of the deployment of the e-health subapplication microservices in the cloud from the GitLab repository.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Integrated Topologies
#### 3.3.1 Three-Port Modified Half-Bridge Converter  
Ref. [38] proposed a three-port DC-DC converter (TPC) with one input port from the solar array, one bidirectional port from the battery, and an isolated output port. The topology achieves zero-voltage switching for all semiconductors and reduces on-state losses by adopting synchronous rectification on the secondary side. A generalized method for modeling a three-port converter was also provided in the paper. However, the converter employs a complex control technique. Figure 17b shows the three-port modified half-bridge converter.
## 4 Trust Model
### Classification of Trust Types  
**(a) Vehicle historical trust:** A brief history of vehicle trust can be taken from the central system. However, when two vehicles interact, we used data packet forwarding (DFR) and control packet forwarding ratio (CFR) as trust factors. Also, we assign weights to these trust factors to monitor the overall historical trust of the targeted vehicle.  
**(b) Vehicle current trust:** A vehicle current trust is evaluated by the latest interactions among vehicles to predict future vehicle behavior using Bayesian network theory. It is computed by the latest interactions among vehicles to predict future vehicle behavior using Bayesian network theory. It is computed as a direct trust assessment based on physical vehicle interactions. Meanwhile, to accurately assess trust among vehicles, recommended or indirect trust is assessed with the help of novel 3 VSL, which also highlights the generated uncertainty of an event. The prior uncertainty of an event remains fix or unchanged.  
**(c) Route trust:** We choose to find the credibility of route in term of packets forwarding, and denoted as route trust value (RouteTV). When TW starts to discover a route, it must assure selected route authenticity. The route trust is calculated by combining the trust of intermediate nodes [36]. Also, the total route must be higher than the threshold level.
## 1 Introduction
The accuracy of an ocean simulation model depends on how well the model represents ocean dynamics, the quantity and quality of input data as well as its ability to simulate air sea interactions among other things [18]. The HYbrid Coordinate Ocean Model (HYCOM) ([[https://hycom.org](https://hycom.org)]([https://hycom.org](https://hycom.org)), accessed on 17 September 2021), used in this study, assimilates Argo float and Conductivity, Temperature and Depth (CTD) data for its salinity estimates. HYCOM has exhibited large errors near river discharge areas in the tropics and in boundary currents such as the Brazil-Mailvinas confluence and the Gulf Stream [19]. These errors may make the model a less than ideal choice for estimating SSS values in tropical discharge regions. Saildrone data can provide vital information regarding the extent of these errors in these locations and aid in the improvement of model output. Contemporary, high resolution in situ observations by saildrone uncrewed vehicles can resolve mesoscale and submesoscale variability observed in coastal regions and remain accurate at high latitudes, providing valuable data for process studies and validation of the satellite measurements [20]. The 'Elucidating the role of clouds- circulation coupling in climate-Ocean-Atmosphere component' (EUREC\({}^{4}\)A-OA) and the 'Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign' (ATOMIC, US) campaigns resulted in an unmatched observing effort in the western tropical Atlantic. In this campaign, three autonomous saildrone vehicles collected data over a 45-day period in regions east of Barbados (Figure 1). SSS collected by the saildrones during the campaign are used to assess the validity of SSS data retrieved from satellites and HYCOM products. Satellite SSS products as well as HYCOM model outputs are used extensively in areas that are lacking in situ measurements such as the region where the North Brazil Current occurs. Furthermore, HYCOM outputs are used as reference salinity fields for the calibration in SMAP products [21; 22]. This study validates SMAP SSS products at both 70 km and 40 km resolution as well as the HYCOM estimated SSS in the Western Tropical Atlantic. The validation is carried out using saildrone SSS data collected during the 45-day EUREC\({}^{4}\)A-OA/ATOMIC campaign. The paper is presented in the following manner. Section 2 introduces the EUREC\({}^{4}\)A campaign and the marine data collection platforms, the SMAP products, HYCOM and their respective datasets collocation and validation methodologies. Section 3 presents the results of the comparison between the satellite products, HYCOM, and saildrones datasets. Additionally, Section 3 highlights the investigation of a fresh tongue encountered during the campaign. Finally, the results are discussed in Section 4.
# Dynamic Community Detection Based on Evolutionary DeepWalk
## 2 Related Work
### Graph Embedding  
The research of graph embedding algorithms and models mainly includes static graph embedding algorithms and dynamic graph embedding algorithms. Static graph embedding algorithms mainly include matrix decomposition, random walk, deep learning, and other graph embedding methods [14]. Dynamic graph embedding algorithm includes matrix factorization, random-walk, deep learning, edge reconstruction [15].  
In the static graph embedding algorithms, the methods based on random walk expand the Word2 vec model in natural language processing and processes the node information in the network as word information. DeepWalk [13] and Node2 vec [16] are the main representative algorithms. The methods based on deep learning mainly adopt deep neural network technology using the encoder-decoder framework model. SDNE [17] is the main representative method, which considers the first-order and two-order of similarity of the graph, designs highly nonlinear functions, and optimizes the objective function to get the result of embeddings. However, for a network with millions of nodes, the efficiency of this method is relatively low.  
In the dynamic graph embedding algorithm, DynGEM [18] extends SDNE to the field of dynamic graph embedding. It uses a heuristic method to construct the first-order and second-order similarity of the networks, and introduces regular terms to prevent overfitting. DynGEM retains the embedding information at the previous moment so that the embedded model at the next moment can directly inherit the parameters of the model trained at the previous moment. However, considering only the embedded information from a previous snapshot network, other historical information may be ignored. Thus, [PERSON] et al. [19] put forward the Dyngraph2 vec model, which takes the network structure at time step \(t-l\), \(t-l+1\),..., \(t\) as historical information when computing the embedding matrix at time \(t+1\), to reflect the cross interaction of nodes in the whole time series and the nonlinear interaction in each temporal snapshot network. In addition, the model proposes three methods, including Dyngraph2 vecAE, Dyngraph2 vecRNN, and Dyngraph2 vecAERN.
## 1 Introduction  
### Basic Concepts  
In sub-surface drilling, critical or crisis events, such as unwanted influx into the borehole, usually arise not abruptly, but have rather a more gradual nature. For instance, the hazard potential of an unwanted influx into the borehole is small, as long as the influx rate affected by the permeability of the formation is low, and as long as the accumulated volume of the influx down the hole is small, and thus both have no further impact. If the influx rate increases while drilling deeper into a high-pressure gas formation, the chance for a crisis to emerge will increase too. Figure 1 demonstrates the importance of crisis prevention among the interactions in drilling disasters [1].  
Sensitivity Analysis (SA) allows for linking of normal operational events to the ones that may arise later to disrupt the overall system, i.e., crisis events. It is not always the case that operational events are understood when manually viewing the raw input data. Instead, a better understanding can be gained through analyzing features in the raw data. The next sections will explain the drilling events of interest and the types of features that can be analyzed. Moreover, it will be explained how those events can be linked using event-based Sensitivity Analysis.