esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposes a delay-driven optical interconnect Low Earth Orbit satellite constellation time-sensitive business routing and scheduling strategy named PSFRS, which is suitable for large-scale connections in a massive Low Earth Orbit constellation. Firstly, it introduces a parallel subnet long-distance transmission scheme based on optical interconnects to achieve low-latency data packet transmission and large-scale connections. Secondly, by incorporating CQF gate scheduling from TSN deterministic transmission technology to determine the transmission delay of a single satellite, this is then extended globally, transforming the transmission scheduling of the entire constellation into an FJSP problem. Finally, reinforcement learning is introduced for problem solving, where reinforcement learning is used to replan the scheduling of well-segmented inter-subnet routes. Simulation results in an LEO satellite constellation environment demonstrate that our proposed optical interconnect routing and scheduling strategy can rapidly compute inter-satellite visibility, effectively solve routing scheduling within the constellation, and improve transmission success rates while significantly reducing end-to-end delays by simultaneously considering load and latency. Furthermore, through comparative experiments, we validate the effectiveness and accuracy of the proposed evaluation standard model. In future research, through collaboration with the Optical Communication Laboratory at Shanghai University, we will further expand the model mentioned in this paper. Our team plans to include ground stations of PON networks in the research scope, study various paths for time-sensitive business transmission, and, ultimately, build an integrated air-ground-space time-sensitive business transmission model. Overall, the findings of this paper are crucial for assessing and optimizing performance in cross-span transmissions of time-sensitive services within large-scale LEO satellite constellations, providing a foundation for future research and development in optical interconnect constellation networks.  
**Author Contributions:** Conceptualization, B.C. and X.F.; methodology, X.F.; validation, X.F. and Y.H.; formal analysis, B.C.; investigation, X.F.; writing--original draft preparation, X.F. and Y.H.; writing--review and editing, X.F. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by The National Key Research and Development Program of China (2021 YFB2900800).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 SAR Payload Unit Design
### Power Supply Unit  
As portrayed in Figure 16, the implemented PSU has a mass of 5.5 kg and a DC power consumption of 300 W. The external configuration and an exploded view of the PSU are displayed in Figure 16a,b, respectively. The SAR payload separately configured the PSU to supply stable power to the DCU, RFU, and EFE units. The PSU received unregulated +28 V power from the battery and provided +28, \(\pm\)10, and +5 V power to the SAR payload units.
# Experimental Study and Parameter Optimization of a Magnetic Coupled Piezoelectric Energy Harvester
## 3 Experimental Platform Setup  
In order to verify the lumped parameter model for the magnetic coupled piezoelectric energy harvester and investigate its characteristics, a corresponding experimental platform is designed. The experimental platform setup is presented is Figure 4. The experimental setup consists of an electromagnetic shaker (YE-5, Yiyang, Beijing, China), a signal generator (AFG3152C, Tektronix, Beaverton, OR, USA) to produce a range of vibration frequencies and a high-power amplifier (ATA-3080, Agitek, Xi'an, China) to create a cyclic force of the required magnitude. An acceleration sensor (AD100T, X&K, Hebei, China) is fixed on the base to measure the acceleration. The electrode of the piezoelectric material is connected with a rectifier bridge energy harvesting circuit, as shown in Figure 4b, and the load output voltage is observed through an oscilloscope (DS4012, Rigol, Beijing, China). At the same time, the oscilloscope also observes the vibration acceleration to ensure that the excitation amplitude is maintained the same at different frequencies.  
The experiment prototype is shown in Figure 5. The devised harvester prototype consists of two cantilever beams with magnets, a proof mass and a base frame. Since the lumped parameter model is used in this paper, a mass must be added at the tip of the beam to ensure the accuracy of the model for frequency prediction [35]. The auxiliary beam uses a magnet as a mass and no other masses are installed. In order to make the piezoelectric beam have a lower resonance frequency, its thickness is designed to be thinner and an additional mass is added. The mass is composed of aluminum, because aluminum has no magnetic properties and does not affect the distribution of the magnetic field.  
The piezoelectric cantilever beam is manufactured using aluminum alloy material (6061T) and commercially available piezoelectric material composed of macro fiber composite (MFC-M8514-P2, Smart Material Corp, Sarasota, FL, USA). The MFC is glued to the beam with epoxy adhesive (DP460, 3M, St. Paul, MN, USA). The magnets (Nd-Fe-B) are attached to the beam, which is easy for replacement during the experiment. The experimental frame is made of acrylic material and its  
Figure 4: Experimental platform setup diagrams: (**a**) Components connection; (**b**) Energy Harvesting Circuit.  
Figure 5: Experimental prototype: (**a**) Overview of the platform; (**b**) Close-up of the harvester.  
spacing adjustment can be carried out by changing the intermediate block. Some parameters of the devised harvester are identified as shown in Table 3.
# Towards the Detection of GPS Spoofing Attacks against Drones by Analyzing Camera's Video Stream
## 2 Related Work
The main disadvantage of these solutions is that they will only work with drones that have cellular communication capabilities; in contrast, our solution relies on a camera, which is a more common drone component. Methods for navigating without GPS have also been proposed, such as MVP [17], a method for navigating in locations such as tunnels and underpasses. The main idea is to extract magnetic fingerprints from geomagnetic field anomalies and compare the measurements against a magnetic map. This method will be difficult to apply in open places and without prior knowledge of the area. Other research suggested countermeasures that use existing hardware. Several studies proposed methods that use motion sensors and compasses to detect GPS spoofing attacks. For example, one study [18] presented a method that uses gyroscope measurements to verify GPS measurements; its drawbacks are that the sensor's measurements suffer from false negative/positive errors and must be calibrated in advance. Another method [19] uses an on-board camera and the inertial measurement unit (IMU) to obtain the velocity and position of the drone to detect unexpected changes in the flight path. In this case, the drawbacks are the same as the previous method; additionally, this method relies on two sensors: the camera and IMU. Another study [20] proposed two methods which must be used together for GPS spoofing detection. Here, the difference in acceleration between the GPS receiver and the accelerometer is used to detect GPS spoofing. This solution's disadvantage is that the accelerometer can be affected by external factors, and flying or driving conditions can affect GPS spoofing detection performance. A deep learning-based solution which uses images from satellites and compares them to images from a drone'scamera to determine whether the locations match was proposed by [5]; its disadvantage is that it requires initial preparation in the flight area. Moreover, every GPS point in the area must be covered, a requirement which increases the size of the precompiled database significantly. Another study presented DIAT [2], which verifies the data/signals received from a drone's sensors with data from other nearby drones in order to detect compromised measurements. Its drawback is that it needs to support and protect drone-to-drone communication, which requires development of the communication protocol. While various countermeasures against GPS spoofing attacks have been suggested, they all have disadvantages. A recent SoK paper [1] identified GPS spoofing attacks against civilian drones as a scientific gap that cannot be prevented by any existing mechanisms with a high technological readiness level [21].
# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 2 Related Work
### Heterogeneous Information Network Embedding-Based Recommendation  
In natural and social systems, interacting components form interconnected networks, which can be referred to as complex information networks. These networks are homogeneous, containing the same type of objects and links; however, most real systems contain multiple types of interacting components, and as such can be modeled as heterogeneous information networks (abbr. HIN). HIN consist of multiple types of nodes and links, and have been proposed as a powerful information modeling method [24] able to naturally model complex objects and their rich relations. Figure 1 shows the HIN of two course datasets. HIN embedding, which aims to embed multiple types of nodes into a low-dimensional space, has received growing attention as well.  
Heterogeneous information networks can naturally model complex objects and their rich relations in recommender systems, as in such systems objects are of different types and links among objects represent different relations. In [25], the authors propose an approach to evaluate the similarity of items or users in HIN. In [26], the authors proposed an MF-based recommendation method that uses the entity similarity calculated on a heterogeneous information network based on a meta-path algorithm. In [27], a collaborative filtering method on a weighted heterogeneous information network was proposed. This method is constructed by connecting users and items with the same rating. It can flexibly integrate heterogeneous information to make recommendations through weighted meta-paths and weighted integration methods. In order to take full advantage of the relationship heterogeneity in information networks, the authors of [28] introduced metapath-based latent features to represent the connectivity between users and items along different types of paths.  
The network embedding approach is more resistant to sparse and noisy data. Considerable research has been done on representation learning for HIN. Broadly speaking, the existing works on HIN embedding can be categorized into four types: random walk-based methods, decomposition-based methods, deep neural network-based methods, and task-specific methods. In [6], an HIN embedding-based approach for recommendation was proposed. In [29], the authors exploited the attention-guided walk model in a heterogeneous information network to selectively sample discriminative attributes and representative explanation meta-paths to explain the recommendations.
## 2 Materials and Methods
Applying higher-order line models such as the Galatry model or the speed-dependent Voigt profile might further reduce the residual ([PERSON], 2017; [PERSON], 2018); however, the required spectral line parameters for solving these models are not yet available in the corresponding spectral line databases ([PERSON], 2018). From Figure 3, the signal-to-noise ratio (SNR) can be calculated for every scan where the signal is defined as the peak absorption (\(OD_{peak}\)) and the noise is defined by the standard deviation (\(1\sigma\)) of the absolute residual. This leads to SNR (\(1\sigma\)) values of 791 and 154 for high and low water vapor concentrations, respectively. Dividing the measured water mole fractions of 1314 ppm and 73 ppm by the calculated SNR, a single scan H\({}_{2}\)O resolution of 1.7 ppm and 0.47 ppm, or a path length and measurement bandwidth normalized resolution of 21.6 ppm m Hz\({}^{-0.5}\) and 6.0 ppm m Hz\({}^{-0.5}\), can be derived for high and low concentrations, respectively. To assess the temporal stability of the measurement system and the evaluation method, 100 consecutive scans were detected under constant conditions and evaluated. To cover the expected measuring range for the single droplet drying experiments, the measuring system was flushed with dehumidified air from the house mains (upper detection range) and with pure nitrogen (lower detection range). For the upper detection range, this results in an average water mole fraction of 1259 ppm with a standard deviation of 1.0 ppm, whereas a mean water mole fraction of 17 ppm with a standard deviation of 1.0 ppm was determined for the lower detection range. The maximum possible water mass fraction that can be detected with the present setup is 2775 ppm with a single scan SNR of 220.
# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme
### Preprocessing Data
#### 3.2.4 Socio-Environmental Determinants of Housing Prices  
The socio-environmental context of a region plays a pivotal role in determining real estate values. Specifically, properties in areas that boast high quality public services, such as esteemed educational institutions and healthcare facilities, and have robust transportation networks often fetch higher prices than those in areas lacking these amenities. Conversely, areas deficient in public transportation or quality public services typically experience diminished real estate demand, leading to reduced property prices [58; 59; 60]. Recognizing the importance of these determinants, we have incorporated the condition of local infrastructure and amenities surrounding a property into our dataset under the term 'local living facilities'.  
Educational institutions are paramount in homebuying decisions, especially for families with school-age children. The proximity of schools to a residence is often a primary consideration for such families. In light of this, our dataset includes the count of elementary and middle schools within a 500 m radius of the property, represented as 'ELET_SCH' (Elementary School) and 'MDL_SCH' (Middle School). Furthermore, urban amenities such as parks and libraries, which elevate quality of life, have a bearing on property prices. We have quantified the number of urban parks and libraries within a 500 m radius of the property and integrated these data as variables named 'CNT_PARK' (Count of Park) and 'CNT_LIB' (Count of Library) in our dataset.  
After incorporating these socio-environmental determinants, another crucial variable that comes to mind is the average income by region. It is generally understood that owning property in affluent areas requires a proportionate income. Thus, we expected a strong correlation between the average regional income and property prices. To substantiate this, we analyzed the total reported salary by region based on the end-of-year tax settlement of earned income. This analysis yielded the average annual salary per person by region, which we represented in our dataset as the variable 'INC_BY_REG' (Income By Region). This addition further enriched our understanding of the interplay between property prices and regional income.
## 3 Design Challenges of MAC Protocol for IBSN
### Requirements of MAC Protocol Design for IBSN
#### 3.1.2 Reliability  
Reliability in wireless communication refers to the number of successful receptions of packets and the number of packets transmitted through a wireless link between two nodes. The wireless link between two nodes in IBSN is highly deteriorated by the conductive human tissues and dynamic human activities. The MAC protocol should have additional mechanisms, such as acknowledgment schemes, and beacon synchronization schemes to overcome the dynamic link quality and to increase the reliability [28]. Enhanced reliability, such as integrity and authenticity of the data packets, is achieved by adding redundant information bits in the form of a cyclic redundancy check (CRC) and message integrity code (MIC) in the IEEE 802.15.6 standard. Thus, there is generally a trade-off between a higher reliability and throughput. Priority nodes sending medical data should be ensured of reliability as it  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Major Features of Efficient IBSN MAC** & **Acceptable Value for Implanted Medical Devices** \\ \hline Throughput & up to 200 KBPS for medical devices \\  & up to 4 Mbps for non-medical devices \\ \hline Latency & up to 100 ms in life critical implants up to 2 s in monitoring medical devices \\ \hline Bandwidth & 300 KHz MICS band 100 MHz in 2.4 GHz ISM band 1.74 MHz in 433 MHz ISM band \\ \hline Duty cycling & less than 0.01\% in MICS band medical devices no restriction if Listen before talk is incorporated \\ \hline Interference mitigation & CRC, frequency agility are recommended for safety purposes \\ \hline \hline \end{tabular}
\end{table}
Table 5: Features of MAC protocols as suggested by IEEE 802.15.6.  
contains life-critical information in IBSN. An efficient MAC protocol will have an optimum reliability by adapting to the application scenarios and the context of data.
## 3 Experimental Results
### Comparative Experiments
#### 3.4.4 PR Curve Analysis and Confusion Matrix Analysis  
Figure 9(a),b display the performance of each class in the model. In Figure 9(a), the curve obtained by sequentially adding the improved modules to the baseline model is shown. This curve reflects the trade-off between accuracy and recall of the object detection results at different confidence levels. Generally, the area under the P-R curve (AP, Average Precision) is widely used to evaluate the performance of object detection models, with higher AP values typically indicating better performance. It is clearly observable from Figure 9(a) that the blue curve effectively integrates the advantages of several other curves, indicating that our model maintains higher precision and recall across different working points, thus improving overall performance. Figure 9(b) presents a normalized confusion matrix, which provides the accuracy percentage for each class. The results show that the recognition performance is particularly good for insulators and defects.  
Figure 9: Loss function comparison.
# Continuous-Wave THz Imaging for Biomedical Samples
## 2 CW THz Single-Point Scanning Imaging  
The modern CW THz single-point scanning imaging system has become one of the main measurement methods due to the advantage of high SNR, high spatial resolution, and the simple principle of detection in the proposed CW THz imaging methods. CW THz single-point scanning imaging geometry can be divided into transmission and reflection mode measurement.
## 3 Definition of Optimization Problem
### Particle Swarm Optimization  
[PERSON] and [PERSON] proposed the Particle Swarm Optimization (PSO) algorithm by simulating the collective behavior of bird flocks [29; 30]. In the iterative process, they constantly adjust their position \(z\) and speed \(v\) based on the individual optimal solution \(p_{best}\) and the group optimal solution \(g_{best}\), so as to gradually approach the global optimal solution. The evolution process of particle swarm is:  
\[\left\{\begin{array}{l}v_{i}^{t+1}=\omega\cdot v_{i}^{t}+c_{1}r_{1}\Big{[}p_ {i,best}^{t}-z_{i}^{t}\Big{]}+c_{2}r_{2}\big{[}g_{best}^{t}-z_{i}^{t}\big{]}\\ z_{i}^{t+1}=z_{i}^{t}+v_{i}^{t+1}\end{array}\right. \tag{11}\]  
where \(t\) is the number of evolutionary generations, \(r_{1}\) and \(r_{2}\) are random numbers between \([0,1]\), \(c_{1}\) and \(c_{2}\) are the learning factors, and \(\omega\) is the inertia weight factor.  
And \(z_{i}\) and \(v_{i}\) and satisfied:  
\[\left\{\begin{array}{l}z_{i}\in[z_{\min},z_{\max}]\\ v_{i}\in[v_{\min},v_{\max}]\end{array}\right. \tag{12}\]  
where \(z_{\max}\) and \(z_{\min}\) are the maximum and minimum limits of the space where the particles are located. \(v_{\max}\) and \(v_{\min}\) are the maximum and minimum velocities of particles.  
In PSO algorithm, the key to adjust the local search ability and global search ability is the design of inertia weight \(\omega\). For complex high-dimensional function optimization problems, [PERSON] et al. [31] proposed an exponential decreasing inertia weight strategy (EDIW) to balance global and local search capabilities. The EDIW strategy is as follows:  
\[\omega(t)=\omega_{\min}+(\omega_{\max}-\omega_{\min})\cdot\exp\left[-20\times \left(\frac{t}{T}\right)^{6}\right] \tag{13}\]where \(\omega_{\rm max}\) and \(\omega_{\rm min}\) are the maximum and minimum inertia weights. \(T\) represents the maximum evolution generation. Taking \(\omega_{\rm max}\) = 0.9, \(\omega_{\rm min}\) = 0.4, the change curve of EDIW is shown in Figure 6.  
The EDIW strategy maintains a large inertia weight at the beginning of the iteration, which enhances the global search ability. Once the approximate position of the optimal solution is located, the local is finely explored, so that the global search ability is well balanced with the local exploitation ability.
# Investigation of the Behaviors of Methanol Spray Impingement and Wall Wetting
## 1 Introduction
However, the current time resolution of the weighing method is still low, and it cannot be applied to the boundary conditions that easily accelerate the evaporation of liquid film, such as a high-temperature wall or high-temperature environment. At present, the refractive index matching (RIM) method proposed by [PERSON] et al. [20] is a potential method to measure the adhesive fuel film thickness. [PERSON] et al. [21] used the RIM method to study wall wetting. The results showed that the peak thickness of liquid film decreases with the increase in ambient temperature. Ambient pressure and orifice diameter did not significantly affect the adhesion ratio of spray. [PERSON] et al. [22, 23] found that the increase of the adhesive fuel film volume is related to the increase of soot emission. In addition, the increase in injection pressure increased the fuel adhesion mass, area and maximum liquid film thickness on the wall, while the increase in ambient temperature led to the opposite result [10, 11, 24]. [PERSON] et al. [25] studied the spray and adhesive fuel film characteristics of n-hexane injected by a five-hole GDI injector through the RIM method and found that flash boiling can occur at low ambient pressure, and the fuel quality deposited on the wall usually increases with the increase of ambient pressure. As mentioned above, the research of spray impingement and adhesive fuel film is mostly focused on high-pressure injectors, and the fuel studied is mostly gasoline and diesel or its components. However, there are few studies on the characteristics of spray impingement and the wall wetting of methanol, one of the most promising alternative fuels for the internal combustion engine. Methanol spray impingement and wall wetting in the intake port of the PFI engine significantly influences the mixture formation process, and, thereby, the combustion process. In this study, an optical experiment platform of spray impingement was built, and the diffusion backlight method and the refractive index matching method were used to capture the spray impingement process and the development process of adhesive fuel film of a methanol PFI nozzle through a high-speed camera. In addition, an image processing program matched with the experimental platform was developed to quantify and analyze the characteristics of spray impingement and fuel adhesion formation. This study is helpful in further improving the accuracy of the numerical methanol engine model and optimizing the injection strategy of the methanol engine.