esa-sceva/satcom-chunk-collection huggingface dataset:
# Network Pharmacology-Based Study on the Efficacy and Mechanism of _Lonicera japonica_ Thunberg
## 2 Materials and Methods  
### Compound and Target Gene Collection  
In order to construct an LJ network, constituents of LJ were collected from public databases. Compounds of LJ were collected through the Oriental Advanced Searching Integrated System (OASIS) database ([[https://oasis.kiom.re.kr/](https://oasis.kiom.re.kr/)]([https://oasis.kiom.re.kr/](https://oasis.kiom.re.kr/)), accessed on 15 June 2022). OASIS is a Korean medical database which contains traditional medicine studies and herbal information. It provides biochemical information for 478 herbs, including LJ. Based on the OASIS database, 26 active compounds of LJ, including loganin, sieroside and quercetin, were identified along with their molecular formulae and PubChem Compound identification numbers (CIDs) (Table 1). Targets, which are chemical genes, associated with each compound were collected through the PubChem database ([[https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/)]([https://pubchem.ncbi.nlm.nih.gov/](https://pubchem.ncbi.nlm.nih.gov/)), accessed on 15 June 2022) [19]. The PubChem database contains various items of chemical information, such as names, molecular formulae, structures and other identifiers. A total of 1662 target genes were identified as co-occurring with the compounds (Table S1). Out of the 1662 targets, after removing duplicates, 359 targets were sorted via the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database ([[http://www.string-db.org/](http://www.string-db.org/)]([http://www.string-db.org/](http://www.string-db.org/)), accessed on 15 June 2022) with a score \(\geq\)0.7, which represents a high confidence rate [20]. In total, 359 targets were selected as potential targets of LJ and used to construct the LJ network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compound Name** & **Molecule Formula** & **PubChem CID** \\ \hline Centauroside & C\({}_{34}\)H\({}_{46}\)O\({}_{19}\) & 102183195 \\ Demethylseqanol & C\({}_{16}\)H\({}_{24}\)O\({}_{10}\) & 11617742 \\
5-O-caffeoylquinic acid & C\({}_{16}\)H\({}_{18}\)O\({}_{9}\) & 12310830 \\ Ketologanin & C\({}_{16}\)H\({}_{22}\)O\({}_{10}\) & 12311348 \\
1,4-di-O-caffeoylquinic acid & C\({}_{25}\)H\({}_{24}\)O\({}_{12}\) & 12358846 \\ Sweroside & C\({}_{16}\)H\({}_{22}\)O\({}_{9}\) & 161036 \\ Secologanin acid & C\({}_{17}\)H\({}_{24}\)O\({}_{10}\) & 161276 \\ Secoxyloganin & C\({}_{17}\)H\({}_{24}\)O\({}_{11}\) & 162868 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Active compounds of _Lonicera japonica_ Thunberg (LJ). The 26 compounds and their PubChem compound identification numbers (CIDs) based on the Oriental Advanced Searching Integrated System (OASIS) database.
## 3 Proposed Routing Algorithm
### Operation of the Radio-Aware Routing Algorithm
#### 3.2.4 The fast re-route scheme  
In the proposed algorithm, the route was determined by the air time cost, but lossy situation still occurs when a next target node fails. At the moment, even the proposed algorithm cannot send data to the failed node without the fast re-route scheme, because the sender S has not updated the route failure information if the node error occurs at unspecified time. This route failure problem can be promptly fetched by using the fast re-route scheme. When a node detects the route failure, the route failure flag was set to 1. Then, the fast re-route scheme promptly starts to select alternative route that will replace the failed route.  
Figure 3: The flow chart of fast re-route scheme.  
The route failure flag is set by the RTS/CTS mechanism of the 802.11b MAC CSMA/CA protocol [28]. The RTS/CTS is a probing process before sending data to avoid interference among neighbors and hidden terminal problem. Since the utilization of RTS (Request To Send) and CTS (Clear To Send) packets gives advantages to the cross-layer design, the RTS/CTS mechanism is used to detect route failure in RA.  
Figure 3 shows the process of the fast re-route scheme of RA. The sender [PERSON] selects a target node N to forward sensing data based on RA. Before [PERSON] forwards the data, [PERSON] broadcasts the RTS packet to neighbors to check a route for making a connection between S and N. If N receives the RTS from [PERSON] successfully, [PERSON] sends the CTS to [PERSON] as a response to the RTS. At that time, [PERSON] checks the recipient of CTS from N until the retransmission of RTS exceeds the retry limit. If [PERSON] does not receive the CTS from [PERSON], [PERSON] updates the route failure flag of N in its neighbor table. After that, [PERSON] starts the algorithm to determine an alternative node instead of N to re-route packets.  
In this case, the route failure flag is included to decide an alternative node in addition to the air time cost and direction. The failure node, N was removed from the list of candidate nodes, and [PERSON] chooses another node which has the best air time cost among remained candidate nodes in the table. After [PERSON] selects a new target node, [PERSON] starts to run the fast re-route scheme repeatedly until the data transmission succeeds.
## 3 Dynamic Response Characteristics of the TVSG
### Active Power Steady-State Deviation Issue  
According to the self-synchronization characteristics of the TVSG during grid-connected operation [30], when the TVSG operates in a steady state, the frequency of its output voltage is determined by the grid frequency. If the grid frequency fluctuates, the grid-connected output frequency of the TVSG will deviate from the rated frequency, resulting in a frequency deviation. The active power output of the TVSG during steady-state operation is defined by Equation (8).  
\[\Delta P_{\text{es}}=\lim_{s\to 0}\bigl{(}G_{\text{PP}}(s)\Delta P_{\text{ref}} +G_{\text{P}\omega}(s)\Delta\omega_{\text{g}}\bigr{)}=\Delta P_{\text{ref}}-(D _{\omega}\omega_{0}+K_{\omega})\Delta\omega_{\text{g}} \tag{8}\]  
Equation (8) shows that when the grid frequency varies, the coupling of damping characteristics and PFR in the TVSG system can be represented as a droop control loop. The damping coefficient \(D_{\omega}\) can further exacerbate the steady-state deviation of the TVSG's active power. The unit step response characteristics of the TVSG to given power disturbances and grid frequency disturbances are illustrated in Figure 4. The main circuit parameters are listed in Table 1.  
Figure 4: Transient response characteristics of TVSG under different control parameters: **(a)** The response curve of \(G_{\text{PP}}(s)\) (\(D_{\omega}=7.6\)); **(b)** The response curve of \(G_{\text{P}\omega}(s)\) (\(D_{\omega}=7.6\)); **(c)** The response curve of \(G_{\text{PP}}(s)\) (\(J=1.01\)); **(d)** The response curve of \(G_{\text{P}\omega}(s)\) (\(J=1.01\)).  
Figure 3: The trend of pole distribution in APL small-signal model of TVSG.  
In practical engineering, to ensure that the power system has adequate inertia support, a larger virtual inertia coefficient \(J_{\omega}\) is usually selected. Figure 4a,b demonstrate that an increase in \(J_{\omega}\) intensifies the dynamic oscillation of the TVSG's active power output. Hence, a larger damping coefficient \(D_{\omega}\) is required to meet the system's need for power oscillation suppression. Figure 4c,d show that increasing \(D_{\omega}\) significantly enhances the TVSG control's effectiveness in suppressing transient oscillations of the system's active power, while gradually mitigating the power overshoot issue under grid frequency disturbances. However, increasing \(D_{\omega}\) also exacerbates the steady-state deviation of the TVSG's active power. Therefore, the parameters \(J_{\omega}\) and \(D_{\omega}\) in TVSG control cannot be simultaneously optimized for both dynamic response and steady-state output power accuracy, necessitating a compromise in their determination.
## 2 System Model
### Conventional Learning  
The optimization of the linear predictor \(V_{f}\) in (2) can be formulated as a supervised learning problem as it will be detailed in Section 3. In conventional learning, the predictor \(V_{f}\) is designed separately in each frame \(f\) based on the corresponding dataset \(\mathcal{Z}_{f}^{\mathrm{tr}}\). In order for this predictor \(V_{f}\) to generalize well to slots in the same frame \(f\) outside the training set, it is necessary to have a sufficiently large number of training slots, \(L^{\mathrm{tr}}\)[48; 49].
# MHBase: A Distributed Real-Time Query Scheme for Meteorological Data Based on HBase
## 2 Related work  
### Distributed Architecture  
The Apache Hadoop is a framework that allows for distributed processing of massive data across clusters of computers using simple programming models. Its distributed file system, named HDFS, is the open source implementation of GFS (Google File System) [11]. HDFS follows a master-slave structure, in which multiple datanodes are under the control of one \"namenode\". A namenode is mainly responsible for managing metadata about where the files are stored and for what purpose as well as also preserving the mappings between files and the corresponding data blocks in memory. \"Datanodes\" are used to store the actual data and report block lists back to the namenode termly. Therefore, Hadoop is highly fault-tolerant and is designed to be deployed on low-cost hardware. It also provides high throughput access to application data and is extremely suitable for applications that have large data sets.  
HBase is an open-source, distributed, non-relational database [12] on top of HDFS. Data are stored in HBase by (key,value)pairs. The key represents the row identifier and the value contains the row attributes. Data in HBase are modeled as a multidimensional map in which values are located by four required parameters, tablename, rowkey, family, and timestamp, and one optional parameter, qualifier. The key-value pair could be expressed as:  
\[value=Map\;(tablename,\;rowkey,\;family,\;[qualifier,]\;timestamp) \tag{1}\]  
These kinds of data are ultimately stored in HDFS to shield the heterogeneity of the underlying system to make the cluster more reliable and the consistency of data is ensured by Zookeeper [13], a centralized service for maintaining configuration information, naming, providing distributed synchronization and group services. Figure 1 demonstrates the relationship between Hadoop and HBase.  
There is a root table named \"-ROOT-\" and a catalog table named \".META.\" in HBase. -ROOT-keeps track of where the.META. table is, while the.META. table keeps a list of all _RegionServers_ in the HBase cluster. To locate entries in three times, -ROOT- will never be divided into two. All these HBase tables are persisted into HDFS. HBase ensures load balancing via both horizontal and vertical types of data partitioning and, based on that, takes advantage of -ROOT- and.META. to locate specific entries.
## 2 AR/VR Applications
### Automotive Industry  
AR/VR technology has a huge impact on the entire end-to-end process involved in the design, production, sale, and even marketing of automobiles. Moreover, the usage of the 5G network makes the adoption of these futuristic AR/VR technologies for high-speed data transfers through smartphones and Internet penetration with very low latency easier. The classical robotics methods used for risk assessment in road safety have been dominated by the usage of AR/VR technologies in autonomous vehicles to enhance road safety and improve the general driving experience [43]. Three types of AR displays, namely heads up displays (HUDs), HMDs, and heads down displays (HDDs), have been deployed for car navigation systems to guide the users with navigation information [44].  
To develop autonomous vehicles (AVs), an extensive amount of training and testing of various AI approaches is required, which are expensive and time-consuming. AR/VR has been considered as the most-innovative tool to overcome these limitations due to the requirement of different simulation models to generate extensive data and test the developed AI algorithms. Their models replace existing simulation-based research on AV platforms such as MATLAB and CarSim for the evaluation human behaviors and road environments to estimate the potential safety issues of AVs [45; 46]. With the advancement of VR devices, multiple implementations of HMDs were shown in [34; 47; 48]. To imitate the human driver's decision-making when driving an AV, reinforcement learning (RL) has been used to evaluate risky scenarios by finding the optimized steering angles and velocity to reduce the risk of collision.
# Leveraging Large Language Models for Departmental Classification of Medical Records
## 6 Discussion
### Ethical and Privacy Concerns  
One of the most critical aspects of deploying medical classifiers is ensuring compliance with data privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States. This study successfully implemented data anonymization techniques to protect sensitive patient information, a necessary step in any medical application. However, the fast-evolving nature of privacy concerns in digital health requires continuous monitoring and periodic updates to the system's privacy protocols to address new challenges [31]. Regular audits and adherence to global data protection standards, such as the General Data Protection Regulation (GDPR) in Europe, are essential to maintain user trust and prevent data breaches. Additionally, ethical considerations, including the potential for bias in classifier responses, must be carefully managed to avoid discrepancies in care [32].
## 2 Materials and Methods
### Data
#### 2.2.9 Gimms NDVI  
The normalized difference vegetation index (NDVI) is a widely used remote sensing index and is used to describe vegetation greenness. It is calculated as the difference between visible and near-infrared reflectance of vegetation. The latest version (3 g.v1) of the GlobalInventory Modeling and Mapping Studies NDVI (GIMMS NDVI) dataset was used to calculate the correlation with SPEI in this study [68; 69]. This long-term NDVI time series (8 km pixel size, available twice monthly) spans the period July 1981 to December 2015. This dataset was created using reflectance data obtained from the GIMMS project using channels 1 and 2 of the Advanced Very High Resolution Radiometer (AVHRR). This product was re-gridded to the CCDAS resolution.
# A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data
## 3 Methodology
### Evaluation of Detection  
Ten-fold cross validation was used to assess performance of the tool. To evaluate the goodness of detection, certainties were used as scores. For voice data-based detection, a score for the observation \(\mathbf{x}\) is given by the probability of the dominant class at a terminal node the \(\mathbf{x}\) is assigned to. For query data-based detection, the certainty obtained using Equation (5) was used as score.  
We evaluated performance of a classifier using the following measures: (a) the detection error trade-off (DET) curve and the EER; (b) the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The DET, EER, ROC, and AUC measures were computed using an interpolated version of the ROC through a pool adjacent violators algorithm, namely the ROC convex hull method (see the BOSARIS toolkit [33]).  
Various thresholds can be used to convert a soft decision into a hard one and the overall performance of a detector can be conveniently represented by the ROC or DET curve. Due to the logarithmic scale, the DET curves enable comparison of several models at a glance easier than the ROC curves [34]. Nevertheless, measurements of AUC from ROC can be valuable for various comparisons.  
A convenient way to compare the accuracy of models with different DET (ROC) curves is the equilibrium point, usually known as EER. EER is the point where the DET (ROC) curve intersects the diagonal and: (a) miss rate (false positive rate) = false alarm rate (false negative rate), for DET; or (b) sensitivity (true positive rate) = specificity (true negative rate), for ROC.
# Ultrasonic Nondestructive Testing Image Enhancement Model Based on Super-Resolution Imaging
## 4 Results and Discussion  
In this paper, we will use the experimental equipment shown in Figure 6 to verify the model, reconstruct the image using the most conventional two-fold super-resolution reconstruction method, and use the classic super-resolution models (EDSR [18], DBPN [20], MZSR [21]) for comparison. These models have shown good performance in super-resolution imaging of general image data sets, so we choose these models to compare with the models in this paper. PSNR and SSIM are used as the evaluation criteria for the model. The experimental results are shown in Table 2 below. All experiments were conducted using Pytorch 1.13.0, Pycharm 2024 on NVIDIA RTX 2060 super.  
From Table 2, we can see that the proposed model has a good advantage in ultrasonic image super-resolution and is significantly better than other models in terms of PSNR and SSIM indicators. The model has a good advantage in ultrasonic image super-resolution and such high-resolution images provide a good raw data basis for the final defect detection.  
In order to test the model more intuitively, this paper selects more typical images as the standard for comparative experiments and enlarges the details in the same image for easy observation. The final experimental results are shown in Figures 7-10 below. Because  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Methods** & **PSNR** & **SSIM** \\ \hline EDSR & 29.371 & 0.903 \\ DBPN & 30.681 & 0.924 \\ MZSR & 27.663 & 0.877 \\ Ours & 33.122 & 0.955 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of each model.  
there are two kinds of defects in the whole data, we used images of two types of defects (holes and slits) to demonstrate the results.  
From Figures 7 and 10, we can see that in terms of the overall image, the model can perform the super-resolution imaging task very well. Figures 8 and 10 show the reconstruction results of each model. The enlarged image on the left is the area in the red frame of the original image. From Figures 8 and 10, we can see that for the local area in the HR image, the model results of this paper are more realistic in detail boundary processing, the boundaries are clearer, and there is no granular performance in image coherence. It is also significantly better than the existing model in qualitative analysis and shows good model performance in ultrasound super-resolution imaging tasks.  
Figure 8: A local magnified image of the defective part of the hole defect.  
Figure 7: Complete picture of the processing results for each model for the hole defect.  
Through the above qualitative and quantitative analysis, it can be concluded that the model is superior to EDSR, DBPN, and MZSR models in terms of PSNR and SSIM indicators and also shows relatively superior performance in qualitative visual indicators, proving that the super-resolution model using up- and down-sampling layers and multi-layer residual networks is effective. This model can handle ultrasound super-resolution imaging problems well.
## 3 Medium Access Control Layer
### Time-Based Schemes
#### 3.3.1 Scheduled-Based Schemes
However, in slotted aloha, the transmission is deferred to the beginning of each time slot. Hence, each node is obliged to schedule the beginning of its transmissions at the beginning of each time slot. Nevertheless, because slots are not assigned as in pure TDMA, collisions may also occur if different nodes select the same slot to transmit. However, given the space uncertainty of the underwater acoustic medium, the performance of this protocol is degraded to that of pure aloha. In [73], the authors try to cope with this problem by adding extra guard time in the time slots, achieving 17%-100% better throughput results than the original slotted aloha in an underwater medium. The original floor acquisition multiple access (FAMA), which is introduced in Section 3.3.2., requires long RTS and CTS packets in order to guarantee that the data packets will be transmitted collision-free [83]. However, in the underwater acoustic channel, where transmissions are expensive, excessively large control packets might be too energy expensive. In order to reduce these high energy costs, slotted FAMA uses time slots, in the same way as slotted aloha, to reduce the control packet size [74]. The slot length is equal to the maximum propagation delay plus the transmission time of a CTS packet, which assures that only control packets may collide and that the transmission of data packets is collision-free. Another approach is the one proposed in [75], in which TDMA is used, but nodes are able to adaptively identify who can transmit at the same time without causing collision. In order to do so, nodes calculate a list of neighbors and share it by piggybacking it to their outgoing packets. Upon reception of a new list of neighbors, each node updates its connectivity matrix, and based on this, the node then decides whether or not it is able to safely transmit in the next slot. A different proposal for low-duty-cycle underwater communication networks is given in [76]. This protocol sets up an adaptive TDMA schedule. Nodes first exchange SYNCpackets within their transmission periods and learn their neighbors. Consequently, a node knows when it should wake up to hear a transmission and when there are no transmissions, so that it can remain in sleep mode. Cluster-based on-demand time sharing (COD-TS) [77] proposes a different solution. Nodes are organized into clusters, and the cluster heads are the nodes in charge of assigning the slots for the next communication round. At the beginning of each round, the cluster head populates the schedule, and each node sends its request to transmit at the end of the communication round. In addition, cluster heads communicate among themselves in order to avoid collisions with neighboring clusters.