esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study on Load Monitoring Approaches
## 4 Load Monitoring Approaches and Cybersecurity  
The various load monitoring approaches (ILM and NILM) are energy monitoring techniques that can pose cybersecurity risks if the data is compromised or misused. These risks include data breaches, data tampering, identity theft, data hijacking, and denial-of-service attacks. To ensure the security of ILM and NILM data, it is important to implement security measures that can ensure data integrity and confidentiality, such as cryptography [62], access management, and data verification.  
Here are some of the cybersecurity-related aspects of load monitoring that should be considered:  
Data privacy: Load monitoring approaches rely on the collection and analysis of energy consumption data, which can potentially contain sensitive information about individuals and their daily habits. It is critical to ensure that this data is stored securely and that only authorized individuals have access to it.  
Network Security: ILM and NILM systems often rely on a network of sensors and devices to collect and transmit data. These devices can be vulnerable to cyberattacks, making it important to implement appropriate security measures, such as firewalls, intrusion detection systems and encryption protocols, to protect the network.  
Authentication and authorization: Access to the ILM and NILM system and data should be limited to authorized users only. Strong authentication and authorization controls are essential to prevent unauthorized access to the system.  
Data Integrity: The accuracy and reliability of the data collected by ILM and NILM is critical to its effective use. Any tampering with the data can lead to incorrect conclusions and decisions. It is therefore important to ensure that data is not manipulated or altered in any way.  
Physical Security: Sensors and devices used for NILM must be physically secured to prevent unauthorized access or tampering. Access to the equipment must be limited to authorized personnel.
## 2 Principle and Overall Design of Frequency Stabilization System
### Overall Design of Frequency Stabilization System  
The structure block diagram of the frequency stabilization system is shown in Figure 4a. The 780 nm ECDL is used, and the supporting control box includes temperature, current, and PZT control modules, which adjust the temperature, current, and external cavity length of ECDL, respectively, to tune the laser frequency. The computer adjusts the temperature and current through the serial port, and the PZT scanning voltage is output from the DAQ card (16-bit A/D, 16-bit D/A).  
In the optical path part, an optical isolator (OI) prevents the effect of optical feedback on frequency stability. The light from the ECDL is converted into spatial light through a polarization-maintaining fiber (PMF). The half-wave plate (HWP) and polarizing beam splitter (PBS) structure adjust the splitting ratio of the emitted light and light used for frequency stabilization. The cylindrical transparent glass cell (\(L=75\) mm, \(\Phi=25.4\) mm) is filled with naturally abundant Rb atoms, and its temperature is stabilized at 23 \({}^{\circ}\)C by a temperature controller. 90:10 (T:R) generates pump light with strong transmission and probe light with weak reflection. 50:50 (T:R) reflects the pump light vertically into the Rb cell, so that it coincides with the probe light. A photodetector (PD) receives the light intensity of the light passing through the Rb cell. The physical diagram of the frequency stabilization system is shown in Figure 4b.  
In the adjustment of laser frequency, regulating the temperature response is slow and regulating the current easily causes the laser mode to hop, while the PZT has the characteristics of a short response time and fine regulation scale [26]. Therefore, in this experiment, the laser temperature is stabilized at a constant value, the laser current is set at a suitable position, and the PZT voltage is adjusted to realize the frequency scanning. When the pump light and probe light powers are approximately about 0.08 mW and 0.3 mW, respectively, the SAS signal can be observed in Figure 4c. The laser frequency adjustment is realized by outputting the PZT scanning signal through the D/A terminal of DAQ. The electrical signal on the PD is collected at the A/D terminal, and through a series of signal processing, the PZT reference voltage is obtained when the frequency is stabilized, which is fed back to the PZT control module to regulate the PZT voltage and the laser frequency stabilization. The interactive interface of the frequency stabilization system is shown in Figure 4c.  
Figure 3: (**a**) Primary, (**b**) secondary, and (**c**) tertiary differential curves of Rb atomic SAS.  
Figure 4: (**a**) Block diagram of the frequency stabilization system structure. (**b**) The physical diagram of the frequency stabilization system. (**c**) The interactive interface of the frequency stabilization system.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 3 Architectural Challenges
### Energy Conservation  
In multiple papers, energy conservation in LoRa networks has been studied, most of which address the LoRa and LoRaWAN parameters and the tradeoff between energy consumption, reliability, and distance [17; 18; 19; 20]. Minimizing data transmission is a practical energy-saving approach, especially in our proposed architecture that relies on slow data links. Techniques to achieve this includes compression and data pre-processing, and filter ing. This paper proposes using external ULP timers to replace the modern microcontrollers' built-in ULP modes, as presented in Section 4.7. This approach enables nano-Ampere sleep currents for the IoT device.  
Energy efficiency in LoRa based networks, especially when using the LoRaWAN protocol, is a well-researched topic. An energy consumption model based on LoRa and LoRaWAN is described in [17]. The authors study the impact of LoRaWAN parameters such as acknowledged transmission, spreading factor, coding rate, payload size, and communication range on the sensor node consumption. Their model is based on LoRaWAN Class A. The optimization study showed a tradeoff between the LoRaWAN communication range, the spreading factor, and the transmission power. This optimization study is very interesting to choose and configure LoRa/LoRaWAN parameters [17]. Energy efficiency optimization of LoRaWAN using an open-source simulator is presented in [18].  
Optimization strategy in LoRa networks can be done by transmission parameter selection. There are 7620 possible parameter combinations [19]. It is possible to use different network parameters, which result in acceptable link quality but with energy consumption that differs 100 times [19]. The LoRaWAN specification does not provide configuration selection mechanisms; thus, most implementations use static parameters. For high communication ranges (greater than 10 km), the transmission power must be fixed to 20 dBm with Spreading Factor (SF) set to 12 [17].  
Energy efficiency for multihop LoRa networks was studied in [20]. The authors used an adaptation of the TSCH MAC protocol for a long-range operation which addresses most of the challenges created in multihop LoRa communications. A time division mechanism synchronizes nodes' wake-ups for constrained nodes. They also implement a channel hopping mechanism to diversify transmissions over different channels in a power-efficient manner.
## 5 IoT Adoption Challenges
### Technological Challenges
#### 5.1.1 Security and Privacy  
While IoT improves an organization's productivity and performance, it introduces various security and privacy issues [4; 27; 43; 45; 54; 65; 78; 82; 84], as an increasing number and variety of connected devices are being integrated into organizations [17; 48; 71; 73]. The privacy threats associated with IoT are manifested by unauthorized function execution and the leakage of personal data and sensitive user information [1; 3; 70; 72; 33]. Simultaneously, IoT security risks involve different attacks on the software, hardware, and networks [39; 41; 60], such as denial of service [31], compromised or spoiled devices [23], and data or information modification/loss [22; 59]. In addition, IoT implementation requires cooperation between several partners, raising concerns regarding data ownership and protection [29; 36; 57]. Consequently, IoT security implications are posing significant barriers to wider adoption within organizations for several reasons. Firstly, the potential security threat escalates due to the lack of common standards and architecture for IoT security [9; 46; 69; 86]. Secondly, IoT networks comprise various devices, platforms, communication mediums, and protocols, leading to the emergence of many layers that require different security systems [70]. Thirdly, most IoT communications are wireless, making an attack relatively easy [52; 79; 53]. Finally, IoT systems are extremely dynamic [2], resulting in the emergence of new cyberattacks [24; 63].
## 5 Evaluation of Keypoint Matching
### Environment of the Evaluation
#### 5.2.3 Implementation  
The PatchGAN patch size was set to 128 \(\times\) 128 pixels, with a batch size of 32. NVIDIA Tesla V100 GPU was used for parallel processing, and Adam [41] optimization was used to train the generator and discriminator, with a learning rate of 10\({}^{-3}\).  
Each training time was 36 h. The training time represented the time when the L\({}_{1}\) losses of the generators were low enough. The loss curves for each dataset are shown in Figure 8.  
SIFT [24] was used as the keypoint detector and descriptor, and the parameters were the default values of OpenCV [42] version 3.4.3. To remove the false corresponding points [36], the threshold values of the scale and gradient direction were set to two octave layers and 5 degrees, respectively.  
The template window sizes of the DLSC, HOPC, and CFOG were set to 100 \(\times\) 100 pixels, and the distance threshold of the corresponding points was set to 1.5 pixels. This indicated that corresponding points with distances greater than 1.5 pixels we re considered outliers. The same threshold was applied to all the other methods.  
Figure 8: Loss curves of each data set. Blue lines show training, and orange lines show validation.  
An edge-enhancement filter based on the Laplacian filter was used, which is given by the following:  
\[F_{edge}(\mathbf{I})=\left[\begin{array}{ccc}-v&-v&-v\\ -v&1+8v&-v\\ -v&-v&-v\end{array}\right]\mathbf{I}, \tag{8}\]  
where \(v\) is a parameter for setting the strength of the edge enhancement. In this experiment, \(v=0.1\) was used because it revealed good results in preliminary experiments.
# Green Ultrasound-Assisted Extraction of Podophyllotoxin from _Juniperus scopulorum_ Needles
###### Abstract  
Background--Podophyllotoxin, a polyphenonic compound with major applications in cancer treatment, is in short supply, as its source is now an endangered plant species. It is therefore essential to find another available plant that produces high levels of podophyllotoxin. Some _Juniperus_ species are known to contain podophyllotoxin, more exactly within their needles, but are still unused in this regard. Objective--The aim is to develop an efficient green ultrasound-assisted extraction protocol for podophyllotoxin from commercially available _Juniperus scopulorum_ varieties. Methodology--To highlight optimal extraction conditions, a single-factor experiment was performed to identify the parameters (extraction duration, frequency, temperature extraction, preliminary grinding, and plant material concentration) influencing extraction. A multifactorial approach using the Box-Behnken design was then applied to determine the exact optimal extraction conditions. Results--The conditions for extracting the maximum amount of podophyllotoxin consist of 51.5 min of extraction time, in ethanol at 69.3%, and at 58.8 \({}^{\circ}\)C. This is the highest podophyllotoxin extraction yield ever obtained from _Juniperus scopulorum_ needles. Compared with the reference protocol for the extracting of podophyllotoxin from _Juniperus_, ultrasonic extraction is an eco-friendly protocol, safe for humans, and the podophyllotoxin extracted by this method has much greater stability, allowing its purification. The analyses also indicate a variation in the podophyllotoxin content of _Juniperus scopulorum_ needles, (from 7.02 to 10.34 mg/g DW) depending on the variety and year of purchase. Conclusion--Extraction in ethanol at 69.3% and at 58.8 \({}^{\circ}\)C for 51.5 min allows the extraction of up to 10.34 mg podophyllotoxin/g from freeze-dried _Juniperus scopulorum_ needles.  
green extraction; _Juniperus scopulorum_ needles; podophyllotoxin; ultrasonic extraction
# FREDA: Few-Shot Relation Extraction Based on Data Augmentation
## 4 Experiment
### Ablation Experiments  
In this section, to analyze the various components of the model that played their part, we conducted ablation experiments on the FewRel1.0 dataset using an encoder of BERT as shown in Table 4. SimpleFSRE was the baseline of our model. Then, the relation information descriptions were removed, and the relation triple information drawn from the support set samples was added to the experiments. Finally, we added the interactive attention module, focusing on the parts of the query set that were highly interactive with each instance in the support set and relational triple information, respectively, with highly interactive instances receiving higher coefficient values. The test set results of the experiments are submitted to the Codalab website for evaluation, ensuring the fairness and credibility of the results.  
Table 4 shows that the test results improve significantly in the k-way-1 setting when only the relation triple information is added but improve less in the 5-way-5-shot setting  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **5-Way-1-Shot** & **5-Way-5-Shot** & **10-Way-1-Shot** & **10-Way-5-Shot** & **Average** \\ \hline SimpleFSRE & 91.29/94.42 & 94.05/96.37 & 86.09/90.73 & 89.68/93.47 & 93.74 \\ +Triple & 92.30/94.73 & 94.65/96.41 & 85.38/91.39 & 90.47/92.94 & 93.86 \\ FREDA & 92.23/94.86 & 94.59/96.52 & 85.99/91.66 & 90.50/93.23 & 94.06 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The results of ablation experiments.  
Figure 6: Comparison plot of the BERT (CP) model with additional pretraining added, where FREDA (CP) is our model. The values in the graph are the accuracy of each model in the test set.  
and drop in the 10-way-5-shot setting instead. This suggests that our model is more suitable in a less sample setting. When the interactive attention module is added, our model improves in all four settings compared to adding only the relation triple information but still relatively more in the K = 1 setting. The analysis of this ablation experiment shows that the addition of both the relational triad information and the interactive attention module helped our model.
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 3 Results
### Maximum Mooring Tension Response  
The maximum mooring response was predicted by the ANN model using training data; the results were then compared with the FEM model's results (Figure 12). The prediction of the top tension responses of the spar platform was obtained by considering a wave height of 12.65 m and a wave period of 11.39 s. The ANN model's predictions aligned with the FEM findings.  
Table 6 presents the statistical analysis of the mooring line tension response of the spar platform concerning the FEM and ANN results. The results of the ANN model are suitable for predicting the top tension response in terms of newly chosen mechanical parameters and environmental forces (Figure 13). The top tension response for the 3000 s time history was predicted by the ANN model and compared with the FEM model results for a wave height of 11.30 m and a wave period of 14.15 s.  
Figure 11: Pitch response time history predicted for wave height of 12.65 m and wave period of 11.39 s.  
Figure 12: Mooring line tension response predicted using training data by ANN and FEM models for 12.65 m wave height and 11.39 s wave period.
## 4 Case Study  
### Example Road Network  
This article takes the city of Hefei in Anhui Province as an example, with a jurisdiction area of 1339 square kilometers. As of the end of June 2024, the number of electric vehicles has reached 295,600, and it is expected that the number of electric vehicles will exceed 500,000 by the end of 2025. According to statistics from June 2024, a total of 210,000 charging infrastructure facilities have been built in the city, of which only 23,000 are public charging facilities. By categorizing and calculating the requirements for charging infrastructure in electric vehicles, it is expected that a total of 22,000 new charging facilities will be added by the end of 2025.  
The government department plans to develop a resort here, so new charging facilities need to be added. As shown in Figure 4, by analyzing the main road network structure of the selected area, it can be seen that the road intersections mainly form a quadrilateral grid. Therefore, the field grid can serve as the basic unit of road network composition, and the entire road network can be abstracted as a combination of multiple field grids. Given the high complexity of the actual road network and the complexity of data acquisition, this paper describes the study and design of a simplified road network model based on the grid structure for simulation.  
To study the layout scheme of urban charging stations and traveler behavior, simulation experiments were conducted using a nine-node simplified road network, as shown in Figure 5. The road network includes a pair of starting points (O, D). Charging stations are located at nodes A, B, C, D, E, F, and G, with a site selection status of 0-1 decision variables. The number of charging stations at each station must meet the capacity constraints of [3,10].  
Figure 4: Road network near the resort area.  
There are six paths in this road network:  
\begin{tabular}{l l} Path 1: L1--L2--L9--L12 & Path 2: L1--L8--L4--L12 \\ Path 3: L1--L8--L11--L6 & Path 4: L7--L3--L4--L12 \\ Path 5: L7--L3--L11--L6 & Path 6: L7--L10--L5--L6 \\ \end{tabular}
# Virtual Source Array-Based Multiple Time-Reversal Focusing
###### Abstract  
Time reversal (TR) is the process of generating a spatio-temporal focus at a probe source (PS) location by transmitting a time-reversed version of a received signal. While TR focusing requires the PS for a coherent acoustic focus at its origin, the requirement of the PS has been partially relaxed by the introduction of the concept of a virtual source array (VSA) (_J. Acoust. Soc. Am._**2009**, _125_, 3828-3834). A VSA can serve as a remote platform or lens and redirect a focused field to a selected location beyond the VSA for which the field is assumed as a homogeneous medium with constant sound speed. The objective of this study is to extend VSA-based single TR focusing to simultaneous multiple focusing. This is achieved using the optimization theory by employing the multiple constraints method derived from a constraint matrix, which consists of appropriately synchronized transfer functions. Through numerical simulations, it is found that simultaneous multiple focusing can be achieved with distortionless response at selected multiple locations, and its performance degrades in the presence of sound speed mismatch. For achieving robust multiple focusing in the mismatch environment, singular value decomposition is applied to obtain the weight vector (i.e., backpropagation vector) that best approximates the column vectors of the constraint matrix. Numerical simulation results show that VSA-based multiple TR focusing using SVD is not a method to simultaneously focus on multiple locations, but a method of constructing a field which robustly passes through multiple locations in sound speed mismatch environment.  
time-reversal mirror; virtual source array; multiple focusing Article
# Pedestrian Re-Identification Algorithm Based on Unmanned Aerial Vehicle Imagery
## 6 Conclusions  
The MNTReID model proposed in this paper demonstrates significant effectiveness in pedestrian re-identification, particularly for complex behaviors observed from UAVs in multi-view scenarios. By combining a MU-GCN with a non-local attention mechanism, the model enhances both local and global feature extraction. The multi-scale graph convolutional network captures detailed pedestrian features at different scales, while the non-local attention mechanism incorporates global context and reduces the impact of background noise. This ensures high identification accuracy even in dynamic and varied multi-view environments. Experimental results show that the mAP and Rank-1 metrics of MNTReID outperform other pedestrian re-identification methods across different datasets, validating its effectiveness on UAV aerial images. Furthermore, through experiments with different flight altitudes, we find that the model can achieve a relatively good recognition effect when the flight altitude is lower than 40 m.  
However, the model proposed in this paper has a large number of parameters, which makes direct deployment on UAVs challenging. Currently, the model relies on communication between the UAV and the ground station for real-time data transmission. Future research will focus on reducing the model size to enable more efficient real-time deployment. Additionally, the approach demonstrates significant potential for real-world applications, including UAV surveillance, intelligent transportation, and border patrol.  
L.S.: conceptualization, methodology, formal analysis, survey, resources, data management, validation, writing--original draft. X.J.: conceptualization,methodology, formal analysis, investigation, data management, experimental validation, writing--original draft, writing--review and editing. J.H.: supervision, methodology, survey, resources, validation. J.Y.: software, formal analysis, visualization. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding
###### Abstract  
Layered silicates, as bitumen modifiers, have received increasing attention. The main objective of this study was to evaluate the influence of layered silicates on bitumen properties. For this study, montmorillonite (MMT), rectorite (REC), organic montmorillonite (OMMT), and organic rectorite (OREC) were selected. The layered structure type of layered silicates was characterized by SEM (scanning electron microscope) and XRD (X-ray diffraction diffractometer). Tests for determining high-temperature properties included viscosity, DSR (dynamic shear rheometer), and TG (thermogravinetry) tests, and studies for determining the low-temperature properties were conducted by BBR (bending beam rheometer) and DSC (differential scanning calorimetry) tests. Our results show that MMT, REC, OMMT, and OREC were all intercalated structures. OREC had the largest \(d_{001}\) interlayer space, followed by REC, OMMT, and MMT. OREC improved the high-temperature property of virgin bitumen more effectively than OMMT. Meanwhile, REC-modified bitumen exhibited a high-temperature property similar to OMMT-modified bitumen. When compared with REC and OREC, MMT and OMMT were less efficient in reducing the low-temperature properties of virgin bitumen, and OMMT was the least efficient. Therefore, it can be concluded that the nature of pristine layered silicates has a great impact on the high- and low-temperature properties of bitumen. Moreover, organic treatment can simultaneously improve the high- and low-temperature properties of layered silicate-modified bitumens.  
bitumen; layered silicate; montmorillonite; rectorite; structure; property +
Footnote †: journal: Applied Sciences