esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Programmed Diagnostic Tools (PDTs) for Polysomnogram (PSG) Analysis  
PDTs are based on artificial intelligence, namely conventional machine learning and DL techniques.  
Unfortunately, machine learning is often subjected to the curse of dimensionality or p(features) >> n(samples), a common problem that surfaces when training machine learning models with high dimensional data, such as PSG recordings, which cause the model to overfit [26]. Hence, data reduction step such as feature extraction is helpful for traditional machine learning algorithms to prevent overfitting problem when handling high dimensional PSG recordings [27]. Often, the feature extraction from PSG recordings is done manually through experience and acquired skills of human observers [27; 28]. The extracted features are fed to standard machine learning models, such as the support vector machine or random forest classifiers, to classify PSG recordings into respective sleep stages. However, this explicit feature engineering involves converting PSG recordings to a low dimensional vector which can result in information loss [27]. Furthermore, the aforementioned machine learning techniques are not ideally suited to handle high dimensional data because of the lack of depth to capture relevant relationships between the covariates in large volumes of data. As a result, standard machine learning is limited in its ability to reliably classify PSG recordings with high precision and accuracy.  
On the other hand, DL models can be trained with raw PSG recordings without the need for information reduction [27]. DL techniques attempt to train the model, such that they learn how to make sense of data on their own by extracting features automatically from PSG signals. The extracted knowledge determines the inference. When a large data volume is available, DL models often outperform machine learning models because they can utilize all the available information and make accurate predictions [29]. In addition, DL models learn features that are useful to make inference and neglect those that are not. Hence, DL techniques are more suitable than traditional machine learning techniques when dealing with high-dimensional PSG signals and are thus considered state-of-art methods for automated sleep stage classification.
# Surface Charging on Insulating Films with Different Thicknesses in UPS
## 3 Results and Discussion
### Work Function Analysis  
The determination of the material work function (WF) through valence band analysis constitutes a principal application of UPS. Current methodologies for thin-film characterization predominantly adopt the conductor-based WF calculation framework: WF = hv - (E\({}_{\text{Cutoff}}-\) E\({}_{\text{Fermi}}\)), where E\({}_{\text{Cutoff}}\) and E\({}_{\text{Fermi}}\) correspond to the secondary electron cutoff edge and Fermi edge in the spectra, respectively. However, the validity of this conventional approach for progressively thickened films requires systematic verification due to charging effects.  
To unambiguously distinguish the spectrometer cutoff from the sample cutoff, a negative bias voltage (\(-\)5 V) was applied during subsequent measurements. While this methodology demonstrates proven efficacy for conductive specimens, its applicability to thickness-dependent insulating films remains insufficiently explored. UPS analyses with bias voltage were performed on both samples (Figure 6a,b) throughout the sputtering process. Cross-referencing with unbiased measurements (Figure 2b,d) revealed comparable spectral evolution trends: the three-regime behavior of Sample 1 and the two-regime behavior of Sample 2 remained consistent under biased conditions.  
The spectral shifts induced by the negative bias were quantitatively analyzed through Fermi edge displacement measurements (Figure 6c). Notably, the biasing efficiency exhibited pronounced substrate resistivity dependence: Sample 1 maintained an approximate \(-\)5 V shift across the film thickness range (<50 nm), whereas Sample 2 preserved this magnitude of shift only within a reduced thickness threshold (<16 nm). This thickness-dependent divergence highlights the influence of substrate resistivity in the biasing effectiveness for insulating films.  
The derived WF values calculated using the standard formula are presented in Figure 6d. For Sample 1 (low-resistivity substrate), the bias voltage effectively counteracts charging across the 10-50 nm thickness range, yielding reliable WF measurements. Beyond 50 nm, however, the weakening of the bias efficacy makes the cutoff edges of the spectrometer and sample overlap, artificially inflating WF values. In contrast, Sample 2 (high-resistivity substrate) demonstrates limited biasing effectiveness, with WF values exhibiting an upward deviation beyond 20 nm thickness. The competition between biasing effectiveness (Figure 6c) and charging-induced surface potentials (Figure 5) directly affects the credibility of the WF measurement results. While increasing bias voltage may temporarily alleviate this issue, its utility diminishes as film thickness grows and insulating properties dominate. Beyond a critical thickness threshold, where bias influence becomes negligible, conventional WF determination methods based on the framework lose validity.
# A Novel DV-HOP and APIT Localization Algorithm with BAT-SA Algorithm
## 4 Simulation Results  
The network region is bounded by a 1000 m radius. The position of nearby nodes can be determined viaa normal DVHOP algorithm; however, it does so with increased latency and inaccuracy. The proposed Bat with SA methods DVHOP and APIT canbe used to acquire the performance metrics parameters and outcomes. In terms of latency, the anchor shift illustrates randomization [18]. The performance of the DV-HOP and APIT algorithms with and without the Bat-SA localization approach is compared based on the results of the simulation. When assessing performance, the following variables are considered:
## 2 Investigations Focusing on Overall Accumulated-Bed Characteristics
### Investigations with Single-Sized and Single-Signed Solid Particles
#### 2.1.2 Experimental Results and Discussion  
Based on the experimental observations, it was initially confirmed that particle volume had no obvious impact on the final bed shape [23]. Then, through a series of experiments with \(V_{\text{P}}=5\) L, it was verified that, possibly due to the more significant impact of particle jets on the accumulated particle beds, more concave and flatter particle beds (i.e., particle beds with lower mound height, smaller repose angle, larger dimple area, and volume) could be attained when smaller-sized particles or a nozzle with larger diameter or higher placement were employed. In addition, it was confirmed that particle-shaped relevant characteristics (such as sphericity) could also effectively affect DBF behavior. Figure 5 shows the effect of particle shape. From this figure, it is observable that for non-spherical particles, potentially due to the smaller impacts of the falling particles on the accumulated particle beds and slower dissipation in lateral directions resulting from larger inter-particle friction [29], more particles accumulated on the bed center, leading to comparable higher particle mounds than those for the spherical cases. However, regarding the effect of particle density, the variations of \(H_{\text{m}}\), \(A_{\text{dim}}\), and \(V_{\text{dim}}\) were found to be non-monotonous, although the repose angle seemed to decrease with the increase in particle density.  
In short, owing to these experiments employing single-sized and single-shaped particles, the fundamental characteristics of DBF were investigated, and the basic influences of particle size, nozzle height, and nozzle diameter on the overall accumulated-bed characteristics were revealed. Quantitative parametric analyses were performed, generally based on inter-particle interactions. Nevertheless, the effect of particle sphericity and density was not comprehensively identified. Moreover, more detailed characteristics and mechanisms during the DBF process (such as the particle-fluid interactions) were not comprehensively captured and explored.  
Figure 4: Schematic illustrations of key quantities for representing the accumulated-bed characteristics. (**a**) Particle mound/bed height and repose angle; (**b**) dimple area; and (**c**) dimple volume [29].
## 2 Materials and Methods  
### Study Site  
We analyzed the 2020 warm-temperate, mixed forest ecosystem data from the national positioning observation and research station at the Henan Xiaolangdi Earth Critical Zone National Research Station on the Middle Yellow River (35\({}^{\circ}\)01'45''/N, 112\({}^{\circ}\)28''08''/E). The site is composed of Chinese cork oak, arborvitae (_Platycladus orientalis_ L.) and locust (_Robinia pseudoacacia_ L.), with Chinese cork oak accounting for more than 80%. The forest's age at the time the data were collected was 47 years, the average plant height was 8 m and the stand density was 998 trees/hm\({}^{2}\). Understory shrubs were dominated by _Grevia biloba_ G. and _Vitex negundo_ L., with thick litter layer and less herbs. The soil composition was mainly brown soil and limestone-weathered parent-material-leaching brown soil. The soil structure was poor: soil gravel content was high and the average soil depth was 50 cm. The station is located in Ijyuan City, Henan Province, in the Taihang Mountains, in the middle reaches of the Yellow River Basin, at an altitude of 410 m. It has a typical temperate monsoon climate, with a hot and rainy period. The average precipitation between June and September is 438.0 mm, accounting for 68.3% of the annual precipitation. The average annual temperature is 12.4 \({}^{\circ}\)C, the annual sunshine duration is 2367.7 h, the average annual precipitation is 641.7 mm, the annual evaporation is 1400 mm and the frost-free period is 220-230 days.
## 4 Results  
### E-Nose Response Signal  
Figure 5 shows the original E-nose response signals of different categories of samples. Before the sample odor is introduced (0-10 s), as all the sensors are exposed in the ambient air at this time, they are in their stable state. When pumping the sample odor into the detection chamber (10-20 s), the resistance values of the gas sensors drop down quickly. After the sample odor is cut off and the ambient gas is subsequently flushed into the detection chamber (20-210 s), all the gas sensors could gradually return back to their initial state. Those response behaviors indicated that the E-nose has a good sensitivity and desorption ability for the experimental gases.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Sensor No.** & **Main Detected Target Gas** \\ \hline S1 & VOCs \\ S2 & Aldehydes, hydrogen sulfide \\ S3 & VOCs, carbon monoxide, ammonia \\ S4 & Benzene, methylbenzene, ethylene \\ S5 & Aldehydes, olefin \\ S6 & Air quality control, acetic acid, acetone \\ S7 & Alkane, ethanol, acetone \\ S8 & Aldehydes, ethylbenzene, methane \\ S9 & Air quality control, hydrogen \\ S10 & Ethanol, ammonia, hydrogen sulfide \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the gas sensors in the E-nose system.  
Figure 4: Schematic of the electronic nose system used in the current work.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Model Candidates
#### 3.3.1 Proximity-Based Algorithms  
Proximity-based algorithms use proximity or distance in hyperspace to classify each data point. It estimates the distance of one data point relative to its surroundings. Thus, the proximate sample number of surroundings is crucial for the result.  
Local Outlier Factor (LOF) is one of the first famous local anomaly detection algorithms [23]. The LOF is essentially a ratio of local densities. It examines the local density of any sample in relation to its neighbor. Local means it depends on the object's isolation in its surroundings. It considers a significantly lower density than others as an outlier [24].  
Cluster-Based Local Outlier Factor (CBLOF) adopts a clustering algorithm (e.g., k-Means) to determine the area density in the dataset. Afterwards, it performs a density estimation for each cluster. The size of the cluster and the distance to the nearest large cluster define the anomaly score [25].
## 2 Results
### Divergence Time Estimation  
The molecular data analysis was conducted on the plastome and the SNPs (Geneious-0.05 MS) datasets with reduced sampling (only one sample was kept for each species). Although the two datasets generated different overall _Clematis_ phylogenetic frameworks, they yielded very similar divergence time estimates of sect. _Tubulosae_ (Figures 3 and S2). The molecular dating of the SNPs (Geneious-0.05 MS) data also generated a slightly different topology with the SNPs ML tree in that the Korean species _C. urticifolia_ was sister to the other island species in the Bayesian dating analysis (Figure 3), whereas _C. tubulosa_ showed to be sister to the other island species in the ML analysis (Figure 2C). The stem age of sect. _Tubulosae_ was estimated to be in the Pliocene (Geneious-0.05 MS: 4.06 Mya, 95% HPD of 3.07-5.03 Mya; Platsome: 4.01 Mya, 95% HPD of 3.02-5.04 Mya), and the crown age was in the Quaternary (Geneious-0.05 MS: 1.84 Mya, 95% HPD of 1.21-2.59 Mya; Platsome: 1.86 Mya, 95% HPD of 1.30-2.53 Mya). The stem (Geneious-0.05 MS: 1.61 Mya, 95% HPD of 1.06-2.31 Mya; Platsome: 1.86 Mya, 95% HPD of 1.30-2.53 Mya) and crown (Geneious-0.05 MS: 1.36 Mya, 95% HPD of 0.88-1.97 Mya; Platsome: 1.17 Mya, 95% HPD of 0.74-1.62 Mya) ages of the island species were estimated to be late in the glacial cycles of the Quaternary.  
Figure 3: BEAST chronogram of _Clematis_ sect. _Tubulosae_ constructed by the SNPs sequences (Geneious-0.05 MS, only one sample was kept for each species). The mean divergence times (Mya) and 95% high posterior density (HPD) are shown at the branches. Climatic events, including aridification of Central Asia (pink shade), the establishment of a monsoon system (blue shade), and a global average \(\xi^{18}\)O curve (leaf-hand axis) derived from benthic foraminifera which mirror the major global temperature trends during the 23 Mya (red line) [7; 62; 63] are shown below the tree. Samples of sect. _Tubulosae_ are highlighted with color, as in Figure 2. Blue triangles are calibration points.
# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 3 Results
### Internet of Things Monitoring System Test and Analysis
#### 3.2.1 System Packet Loss Rate Test  
The forest fire monitoring data from two forest farms from 1 July 2021 to 30 September 2021 were downloaded onto the monitoring data management platform to perform a statistical analysis. According to the 30 min collection cycle, the number of monitoring data packets was 3572, and the number of data packets received by the platform was 3358 for Longshan and 2725 for Longyandong. As the SIM card of Longyandong Forest Farm gateway was not recharged in time, 785 data were lost between 0:00 on 15 July 2021 and 10:00 on 5 August 2021. Table 3 presents the statistics for the packet loss rate data. The packet loss rate of the forest environment monitoring network of Longshan Forest Farm was calculated at 5.99%, and the packet loss rate of the forest environment monitoring network of Longyandong Forest Farm was 23.7%. If the data loss period of Longyandong Forest Farm had not been considered, the gateway of Longyandong Forest Farm would have sent 2787 packets, with a packet loss rate of2.22%.  
The analysis found that the packet loss rate for Longshan Forest Farm was higher than that of Longyandong Forest Farm. The environment data monitoring node for Longshan Forest Farm was deployed close to the gateway node. However, due to the dense tree growth in the deployment environment, the difference in geographical locations and altitudes between the two nodes, and the weak signal strength of the SIM card, the gateway node was often disconnected and lost several data packets. However, although a height drop and housing block occurred between the environment information monitoring node and gateway node in Longyandong Forest Farm, many nodes were deployed, improving the data transmission link, overcoming the effect of geographical factors, and effectively reducing the packet loss rate. The results indicate that the hardware device and embedded routing protocol can be applied to an unattended situation in a forest environment monitoring field to ensure long-term stable system operation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Forest Farm** & **Longshan** & **Longyandong** \\ \hline Number of local packets sent & 3572 & 3572 \\ Number of packets received by the platform & 3358 & 2725 \\ Packet loss rate & 5.99\% & 23.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Packet loss rate statistics.
# Modeling Population Dynamics and Assessing Ecological Impacts of Lampreys via Sex Ratio Regulation
## 7 Sensitivity Analysis  
This paper is mainly based on the Runge-Kutta algorithm to solve the model of the system of differential equations corresponding to the population change in lampreys, and some of the parameters are approximations of the ideal state. Nonetheless, the situation tends to be more intricate, particularly in the case of lampreys, a distinct species whose gender is influenced by their growth rate. Therefore, the practicality, scientific validity, and stability of this model should be further considered.  
Specifically, considering that the hatching success of lampreys is closely related to the environmental temperature as well as the amount of food, this test perturbed the temperature and the amount of food within a certain range. To evaluate the model's sensitivity, a comparison was made of the population trends of lampreys at various levels of disturbance over time.  
Figure 16 shows the changes in the survival environment temperature of the lamprey population under different levels of disturbance. It can be found that in the short term, to a certain extent, the higher the temperature, the larger the population size and the more rapid the growth, while the lower the temperature, the smaller the population size and the slower the growth. The main reason is that the survival rate during the incubation period is closely related to the temperature, but in the long term, the difference in the size of different populations at the time of stabilization is relatively small. This also further validates the reasonableness of the present model, that when the external environment is suitable, the population size of lampreys is mainly limited by the environmental capacity.  
Figure 17 shows the variation of the lamprey population under different levels of disturbance of the living environment food. It can be found that the richer the amount of food, the larger the size of its population. However, following approximately 35 years of growth, the disparity among various populations becomes less pronounced.  
Figure 16: Sensitivity of the model under the condition of applying different disturbances \(\alpha\) to the ambient temperature.
## 3 SIC Detection Based on Interference Estimation Coefficient  
As shown in Figure 5, the signals of User 1 and User 2 are encoded in the power domain by the base station, and the users use a SIC receiver to detect their own signals. The traditional SIC detection algorithm, which directly carries out interference reconstruction, does not consider the nonlinear effect of the device. In this paper, the interference coefficient was firstly introduced to further accurately estimate the interference and reduce the error propagation. The interference coefficient was estimated by regression using the least squares (LS) and long short-term memory recurrent neural networks (LSTM) algorithms. We used \(\beta\) to describe the interference estimation coefficient in this paper.  
As shown in Figure 5, the two users are assigned different transmitting powers. The user with low channel gain is assigned a high transmitting power, and the superimposed signal is transmitted by the base station. As User 1 has a low channel gain, high power is allocated to User 1, and the signal is detected directly. The high power of User 1 leads to strong interference for User 2, so it is necessary to estimate the interference of User 1 to User 2. Next, we discuss how to estimate  
for interference coefficient estimation of the whole NOMA-VLC system [67; 68; 69]. As shown in Figure 6, LSTM is a special kind of recurrent neural network that is capable of learning long dependencies. Therefore, the LSTM-SIC algorithm is proposed based on the recurrent neural network, which uses the first 12 training sequences within a frame to forecast the following 3 interference estimation coefficients. The interference coefficient can be obtained by LSTM to reconstruct interference signal and substantially reduce the overhead of the training sequence.  
As shown in Figure 7, the LSTM algorithm can be used to predict the interference coefficient. It can be seen that after neural network prediction training, the predicted value and the real value tended to be the same, as seen in Figure 8a, and the cumulative error function of the prediction network converged well after iteration, as seen in Figure 8b.  
Figure 6: Four interacting neural network layers in the repetition module of the LSTM.  
Figure 7: Prediction results of training of interference coefficient.
# A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications
## 2 Methods and Results
### Antenna Simulation in Anatomical Models
#### 2.3.4 Total Radiated Power (TRP)  
The value of total radiated power (TRP) in the male anatomical model was estimated to be \(-15\) dBm at 402.5 MHz and \(-16\) dBm at 2.45 GHz. In the female anatomical model TRP was calculated \(-9\) dBm at 402.5 MHz and \(-14\) dBm at 2.45 GHz when the input power was 1 W.  
It was observed that the gain and the total radiated power (TRP) were somewhat higher in the female model. This may be due to the anatomical differences between the male and female body. In the male body, the pancreas is covered by the stomach and liver. In contrast, in the female body, the pancreas is covered only by the small intestine. Thus, the radiated power has fewer obstacles as it is propagated to the external receiver.
## 5 Results and Discussion
It is important to emphasize that the sniffers used in these experiments have only one radio interface and, consequently, they cannot receive and send packets at the same time. This contributes to the monitoring network to capture less than 100% of packets sent by the target network. Therefore, the results should be better if the sniffers had two radio interfaces. Figure 14: Energy consumed by each sniffer _vs_. number of sniffers. The results presented in this section demonstrate that EPMOSt considerably reduces the energy consumption of the monitoring network, when it is compared with the monitoring systems described in Section 2 (which do not use the election mechanism of sniffers). Besides, EPMOSt keeps the percentage of captured packets close to the values obtained without the use of the election mechanism. These results prove the viability of using EPMOSt when there is a need of monitoring a WSN in a real scenario over long periods.