esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Based Intelligent Monitoring System Applying RNN
###### Abstract  
In this paper, we propose an intelligent monitoring framework based on the Internet of Things (IoT) by applying a Recurrent Neural Network (RNN) for the predictive maintenance of a biobanking system. RNN, which is one of the deep learning models, is used for time series data. It is called a sequence model because it processes inputs and outputs in sequence units. The proposed framework measures the internal temperature of the cryogenic freezer and the temperature of each component simultaneously, monitors the internal temperatures of internal and middle layers in real time, sends the sensing temperature data to the server, and performs predictive learning. Thus, it is possible to support the intelligent predictive maintenance of the biobank by performing a time series data analysis of the temperature sensor using RNN. Among RNN methods, a simple RNN has a longer-term dependency problem; therefore, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which have higher learning performance, are selected. To support the intelligent predictive maintenance of the biobank, both the LSTM and GRU models were constructed, and comparative experiments were performed. The proposed system can ensure the safety of bio-resources by performing predictive maintenance using RNN and provide an accurate status of the biobank in real-time. In addition, before an abnormal situation occurs, it is possible to respond immediately to emergencies that may damage biological resources.  
IoT-based intelligent monitoring predictive maintenance; RNN; LSTM; GRU +
Footnote †: journal: Applied Sciences
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 7 Performance Evaluation  
In this section, the performance achieved by the proposed SACAR algorithms is evaluated through simulations on realistic scenarios. At first, the definition of the considered scenarios and their parameters setting are provided. Next, a brief description of the algorithms that served as benchmarks is presented. Then, a performance analysis considering different network metrics, such as delivery probability, overhead ratio, average latency, and average number of hops, is carried out over each scenario. Finally, an analysis of the impact of the number of nodes with goals and the ones with skills on the network performance is also detailed.
# Experimental and Simulation Identification of Xanthohumol as an Inhibitor and Substrate of ABCB1
## 3 Results  
### XN Inhibited the Efflux Functions of ABCB1 in MCF-7/ADR Cells  
Our previous works have revealed XN exhibits significant synergistic effects with DOX [8]. To determine whether XN could modulate ABCB1 efflux function, we measured the intracellular DOX concentration when treated or untreated with XN using flow cytometry. Our results showed that there was only a little intracellular DOX when treated with XN, whereas XN concentration-dependently increased DOX accumulation intracellularly (Figure 2A), suggesting that XN was able to decrease DOX efflux.  
We further confirmed that XN inhibited ABCB1 transport functions via evaluation of the efflux of the ABCB1 substrate, Rho123, from MCF-7/ADR cells. As shown in Figure 2B, XN could significantly and concentration-dependently increase the intracellular Rho123 concentration, emphasizing that XN could inhibit the efflux function of ABCB1.
## 3 Results and Discussion
### Method Validation
#### 3.2.5 Accuracy  
To determine the precision, the % recovery values of the test substances at three corresponding concentration levels were calculated. The results are listed in Table 4 and range from 97.1-102.0%.
## 1 Introduction
Static tests offer a major limitation because instruments are loaded at only one specific section. Dynamic testing definitively overcomes this limitation by simulating the dynamic in-and-out movements (pecking motion, brushing, stroking) of an endodontic treatment, representing a more clinically relevant setup. The trend is toward an amplitude of 3 mm with a frequency of about 0.5 Hz, Ideally, all procedures should be performed at body temperature [27], because the temperature during testing significantly influences the time to fracture of the instruments due to the specific metallurgy and different A\({}_{\text{f}}\) temperatures of the alloys [28; 29; 30; 31]. Nevertheless, no further details concerning the congruency of the simulated artificial root canals and the diameter of the instruments have been proposed. There is a consensus that instruments should rotate freely without having friction to the artificial canals walls in order to prevent additional torsional load. However, a looser fit leads to less constant trajectories of load during testing [32]. Since [PERSON] et al. expressed a need for standardization for cyclic fatigue testing of NiTi rotary instruments to ensure uniformity of methodology and to allow comparable results more than 10 years ago, nothing noteworthy has happened [20]. An exception is the latest presentation of a new test machine allowing a real-time measurement of bending force of the instruments when performing cyclic fatigue tests [33]. Only few efforts have been made to create more standardized artificial canals [34; 35], and many authors did not specify the diameters. The influence of the congruency between the instrument and the testing tube/artificial canal is still unclear. Thus, the aim of the study was to investigate the influence of different artificial root canal sizes on the fracture resistance of NiTi instruments when performing cyclic fatigue tests. The null hypothesis (H0) tested was that the size and the corresponding congruence of the matching artificial canals have no impact on the fracture resistance of the tested instruments.
# Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
## 5 Experimental Results
### Offline Train
#### 5.2.3 Performance vs. Learning Rate  
Figure 9 shows the impacts of different learning rates on the model accuracy and loss. The learning rate is one of the key parameters for training neural networks. Each color represents a learning rate. The \(x\)-axis is the epoch; the \(y\)-axis is the accuracy rate and the loss. The purple line is the accuracy rate and loss with a learning rate of 0.01. With the increase in the number of epochs, the accuracy rate and loss curve change become unstable, because the learning rate controls the range of weight parameters after updating each epoch. The higher the learning rate, the greater the weight update range. This achieved the minimum value of the loss function. The weight parameter values continue to diverge at both ends of the extremely optimal value. Therefore, with the increase in the number of epochs, the loss does not decrease and the accuracy does not increase. When training to 10 epochs, the accuracy is 0.85 and the loss is 0.47.  
Figure 8: The model performance of different optimizers.  
The red line shows the accuracy and loss with a learning rate of 0.001. It can be seen from the figure that the loss with a learning rate of 0.001 is significantly more stable than the loss curve with a learning rate of 0.01. The loss decreases from 0.15 to 0.01, and the accuracy increases from 0.95 to 0.98. However, because the learning rate decreases, the parameter update speed slows down. When the number of iterations is in the interval (1, 2), the loss decreases. However, with the increase in the number of epochs, the model loss and accuracy remain unchanged, so it spends more training resources to obtain the optimal parameter value.  
The cyan line is the accuracy and loss curve with a learning rate of 0.0001. It can be seen from the figure that when the epoch number is 2, the accuracy increases from 0.82 to 0.96, and the loss rate decreases from 0.52 to 0.14. Relative to the learning rate of 0.001, a learning rate of 0.0001 is better for rapidly finding the convergence direction. When the number of epochs is 10, the accuracy rate is 0.99 and the loss rate is 0.01. Through the above analysis, we selected a learning rate of 0.0001 for this paper. According to the above parameter, the CNAT model accuracy reached 0.98.  
After the parameter configuration of the CNAT model, we retrained the model, saved the trained model, and utilized it in the online detection module.
# Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table
## 4 Optimization of Robotic Deburring
### Tracking the Toolpath
#### 4.2.1 Rotary Table Constraints  
The deburring toolpath consists of cutting and non-cutting motions. During non-cutting motions, the tool is not in contact with the workpiece. The tool moves to a clearance where no collisions are possible, and a large rotation of the rotary table can be allowed. During non-cutting motions, unrestricted table rotation is permitted, in the following interval:  
\[rot_{t,n}\in(-\pi,\pi) \tag{17}\]  
During cutting motions, the tool is in contact with the workpiece, which means that excessive table rotation can cause damage to the workpiece. To keep the inaccuracies caused by the relative motion of the workpiece and the tool within an acceptable range, the maximum allowable table rotation is set based on the deburring accuracy tolerance. Depending on the current toolpath segment \(c_{t}\ =\ \left[x_{t},\ y_{t},\ z_{t}\right]\), the relative motion between the tool and the workpiece can be expressed as the Euclidean distance between two points, as follows:  
\[dist\ =\ \sqrt{\left(x_{t+1}-x_{t}\right)^{2}+\left(y_{t+t}-y_{t}\right)^{2}+ \left(z_{t+1}-z_{t}\right)^{2}} \tag{18}\]  
where  
\[c_{t+1}(x,y,z)\ =\ r_{z}{\cdot}c_{t}(x,y,z) \tag{19}\]  
and \(r_{z}\) is the counterclockwise rotation around the \(Z\)-axis.  
For an acceptable deburring accuracy of less than 3\({\cdot}10^{-5}\) m, the maximum rotation of the rotary table at each toolpath segment must be in the following interval:  
\[rot_{t,c}\in\left(-\pi{\cdot}10^{-5},\pi{\cdot}10^{-5}\right) \tag{20}\]  
If the current toolpath segment is at the outer edge of the table, the maximum relative motion between the workpiece and the tool is 2.4880\({\cdot}10^{-5}\) m.
## 5 Implementation of the Proposed Method
### Template Preprocessing Phase  
A template preprocessing phase determines the multi-resolution hybrid-projection information for the object of interest in the template image \(P\). The precise steps are presented as follows:  
1. The template image \(P\) is input specified manually.
2. The multi-resolution template image \(P_{l}\) is constructed, where \(l=0,1,\ldots,L\), \(l\) represents several image pyramid levels and \(L\) is the maximum image pyramid level.
3. Ring-projection transform is used to determine ring-projection values \(C_{P,l}(r)\) within different image pyramid levels, where \(r\in[R_{l,\min},R_{l,\max}]\).
4. After the ring-projection transform process, the variance-project values \(\sigma_{P,l}(r)\) for each image pyramid level are determined through variance-project transform, where \(r\in[R_{l,\min},R_{l,\max}]\).
5. The hybrid-projection values \(H_{P,l}(r)\) for each image pyramid level are determined using ring-projection values \(C_{P,l}(r)\), variance-projection values, \(\sigma_{P,l}(r)\) and the corresponding weighting coefficients for the ring projection \(w_{m,l}(r)\) and variance projection \(w_{\sigma,l}(r)\). The weighting coefficients, \(w_{m,l}(r)\) and \(w_{\sigma,l}(r)\), are calculated by image entropy.
6. An appropriate threshold of similarity, \(T_{\min,l}\), for different inspection conditions is identified. The levels of the image pyramid, weighting coefficients, hybrid projection values, and threshold are used for the online-alignment phase of the image pyramid technique, and the similarity measure with the image pyramid search technique, respectively.
## 2 Current Technology
### Application Framework  
During our research, several gaps were found in the various telemedicine application models [13]. Despite a decreasing trend, a majority segment of users still face difficulty in using these systems, as shown by the publications associated with these models. As stated in the first paragraph, modifying the design core of telemedicine systems to prioritize the end-user's needs requires an immediate adjustment to the project framework. The user-centered design model and other models analyzed [14] differ in that the former prioritizes usability over factors such as sales logic, production logic, process planning, and disposal/recycling. The project steps required for producing any application system based on the user-centered logic are shown in Figure 1[15], with the necessary features for each step listed in the second column. The planning stage (PSS planning) requires improved reliability, user involvement, and data security for product features. The implementation step (PSS project) must simplify interactions, improve reliability, optimize resources, enable short development cycles, provide flexible interfaces, ensure interoperability among various systems and technologies, and offer autonomy for product characteristics. Finally, the design step Figure 2 is crucial for developing or modifying the PSS planning step and necessitates meeting requirements for resource optimization, short development cycles, innovation, cross-sectoral knowledge creation, real-time data exchange, and closed-loop operations for the macro category of data communication.
# SDN-Based Secure Common Emergency Service for Railway and Road Co-Existence Scenarios
## 4 Implementation of the Proposed SDN-Based Service for Rail and Road Coexistence
### Emergency Data Packet Duplication  
The developed ONOS application ensures that emergency data packets can be differentiated from non-emergency data packets and, when an emergency or critical situation occurs, both the emergency servers, i.e., RailEmer and CarEmer receive the emergency message [23]. For the demonstration purpose, we used the UDP protocol to send and receive emergency and non-emergency data packets. Figure 6 shows the emergency data packet VLAN tagging and duplication mechanism of the emergency data packet. In the given Figure 6, Train1 and Car1 are connected to access point Ap1. Suppose Car1 is stuck at the level crossing or has some issue and Train1 is about to reach the level crossing.  
In this considered situation, the emergency data packet is generated by Car1. When this emergency data packet comes to access point Ap1, the SDN application tags this data packet with VLAN ID 5 and forwards it to the SDN controller. The SDN controller duplicates the emergency data packet and sends the original data packet to access point Ap1. Along with these, the controller installed the forwarding rules using the OpenFlow13 protocol at the access point Ap1, switch S11, and switch S22. In this selected scenario the original emergency data packet is sent to the car emergency server (CarEmer) since the emergency message is initiated by Car1. The SDN application replaces the destination IP and MAC address of the duplicate emergency data packet with the IP and MAC address of the rail emergency server (RailEmer) and sends this duplicate emergency data packet to RailEmer. In Figure 6, it can be seen that an emergency data packet is duplicated and distributed to both emergency servers. In this manner, the SDN-based application informs the emergency servers of both domains about the critical situation in the railway and road coexistence scenario.
## 2 Materials and Methods
### Electron Backscatter Diffraction  
Sample preparation for electron backscatter diffraction (EBSD) observation involved grinding and polishing. Sheets were cut from the TD plane of the fabricated samples for EBSD analysis. The TD plane was selected for observation, as it is commonly used for rolled samples. This plane enables examination of the microstructure through the thickness, providing insights into the role of shear deformation predominantly occurring at the top. The specimens were then mechanically polished and etched in a 2 wt% picric acid solution in ethanol. The microstructural and crystallographic characteristics of the steel samples were analyzed using EBSD in a field-emission scanning electron microscope (Hitachi S-4300 FESEM, Hitachi, Tokyo, Japan) using a working distance of 20 mm and a step size of 2.5 \(\upmu\)m. EBSD data were cross-referenced with a ferrite phase database and further analyzed using the TSL-OIM software, 7.3.0.
# Spatial Unmasking Effect on Speech Reception Threshold in the Median Plane
###### Abstract  
This study examined whether the spatial unmasking effect operates on speech reception thresholds (SRTs) in the median plane. SRTs were measured using an adaptive staircase procedure, with target speech sentences and speech-shaped noise maskers presented via loudspeakers at \(-30^{\circ}\), \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\) and \(90^{\circ}\). Results indicated a significant median plane spatial unmasking effect, with the largest SRT gain obtained for the \(-30^{\circ}\) elevation of the masker. Head-related transfer function analysis suggests that the result is associated with the energy weighting of the ear-input signal of the masker at upper-mid frequencies relative to the maskee.  
spatial unmasking; speech reception threshold; median plane +
Footnote †: journal: Medical Image Communication
# Quantifying Soil Moisture Impacts on Water Use Efficiency in Terrestrial Ecosystems of China
## 3 Results
### Spatiotemporal Variation of WUE  
In terms of the growing season, the range of _WUE_ in all terrestrial ecosystems was 0.02-19.26 g C/Kg H\({}_{2}\)O, with an average value of 1.05 g C/Kg H\({}_{2}\)O and a standard deviation of 1.45 g C/Kg H\({}_{2}\)O. Furthermore, the high-value areas of _WUE_ were mainly located in the arid regions of Northwest China (Figure 3a). The spatial variation in _WUE_ in the different climatic zone was also distinct (Figure 3c). The sub-rigid zone in the Qinghai-Tibet Plateau(PSF) and the temperate zone in the Qinghai-Tibet Plateau (PTMP) had the highest _WUE_, with maximum values of 1.80 g C/Kg H\({}_{2}\)O and 1.03 g C/Kg H\({}_{2}\)O, respectively. The north subtropics (NST) had the lowest _WUE_ with a value of 0.48 g C/Kg H\({}_{2}\)O. The _WUE_ in the warm temperate zone (WTMP), middle temperate zone (MTMP), cold temperate zone (CTMP), south subtropics zone (SST),marginal tropical(MT) zone, and middle subtropics zone (MST) was0.98, 0.87, 0.64, 0.60, 0.55, and 0.51 g C/Kg H\({}_{2}\)O, respectively. In general, the change in _WUE_ was correlated with the _SM_ (coefficient of determination: \(R\) = 0.56), and a higher _SM_ usually led to a lower WUE (Figure 3d).  
Figure 2: Accuracy verification of net primary production (_NPP_), evapotranspiration (_ET_), and soil moisture data used in this study. (**a**) Scatter plot between the estimated and measured monthly _NPP_ from 12 flux towers from 2003 to 2010; (**b**) scatter plot between the monthly _ET_ data from the Global Land Data Assimilation System (GLDAS) dataset and the measured monthly _ET_ data from 12 flux towers from 2003 to 2010; (**c**) scatter plot between the monthly volumetric soil moisture in the root zone (0–100 cm soil layer) from the GLDAS dataset and the _SM_ dataset based on microwave sensing from 2002 to 2011; (**d**) scatter plot between the estimated (in this study) and GLDAS soil moisture data in the root zone (0–100 cm soil layer) of the study area.  
During the growing season, _WUE_ showed a downward trend in 43.99% of the total area. Among these areas, _WUE_ showed extremely significant (Sig = 0.99), significant (Sig = 0.95), and insignificant (Sig = 0.90) downward trends in 35.03%, 6.11%, and 2.58% of the total terrestrial ecosystem area, respectively. In contrast, _WUE_ also showed an increasing trend in 34.91% of the total area of the terrestrial ecosystem. Among these areas, the percentage with extremely significant, significant, and insignificant increases in _WUE_ was 28.07%, 4.94% and 1.91%, respectively. Thus, in the past 30 years, the _WUE_ of the terrestrial ecosystem has generally shown a downward trend, especially in the arid areas of Northwest China, the Mongolian Plateau, and the southern Qinghai-Tibet Plateau. _WUE_ also showed a significant downward trend in some areas of the monsoon region of China (Figure 3b).
# Intelligent Detection Methods of Electrical Connection Faults in RF Circuits
## 1 Introduction
Sets of values of reflection and transmission parameters were calculated under various conditions to train the algorithms, and the classification performance was analyzed and compared. The diagnostic capability, applicability, time complexity, and learning cost of the three algorithms are discussed in the following sections.
# The Detection of Long-Chain Bio-Markers Using Atomic Force Microscopy
## 3 Results  
### Abiotic Mineral Surfaces  
The discrimination of bio-polymer coatings and pure minerals was tested in a series of samples. A pure mineral was first run to show the force curve of a surface without biological materials present. As an analog of a clean mineral, Figure 2 shows the AFM-FS of a quartz surface. The AFM-FS force distance curve for quartz is qualitatively similar to other minerals observed by others [16]. This includes mica, basalt, palagonite, calcite, gypsum, glass and graphite. It should be noted that hydration layers may dominate the surface of many inorganic, minerals and this has been recently reviewed [17]. While the magnitude of detachment force is influenced by surface water layers, the detachment distance is relatively short range (<1 nm) relative to the chain length of bio-molecules.  
Figure 2: Atomic force microscope-force spectroscopy (AFM-FS) detachment plot of quartz showing the deflection of the cantilever as a function of distance. There is an abrupt force change when the tip is detached from the surface as it is withdrawn.