esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Construction of the DHR Sensor  
Considering the data in Figure 1, we chose the commercial Nanolplus ICL with a center wavelength of 3345 nm. The dynamic range of the ICL current is 20-100 mA, and the corresponding wavelength is 3344-3355 nm. The operating temperature is set at 20 \({}^{\circ}\)C with a typical output power of ca. 17.2 mW. The laser is driven with a lab-made circuit using the WTC3293 temperature control module (Wavelength Electronics, Bozeman, MT, USA). The ICL is mounted on a standard TO66 package equipped with a collimating lens. The outgoing spot is a beam 3 mm in diameter with Gaussian power distribution. The selected microphone is the full directional capacitive microphone (Shenzhen Yingge AcousticsTechnology Company, Shenzhen, China). The microphone has a sensitivity of \(-26\pm 3\) dB. The DHR photoacoustic cell is made of 304 stainless steel (Figure 2). Both the main tubes and capillaries are 50 mm in length, and the inner diameters of them are 5 mm and 2 mm, respectively. The total volume of the photoacoustic cell is approximately 2.5 mL. Four calcium fluoride disks with a thickness of 0.5 mm are used as optical windows sealed at both ends of the two main tubes. Although only one main channel of the photoacoustic cell is illuminated by the laser at the Helmholtz resonance frequency, both channels produce acoustic signals of the same size in the opposite phase [27,28].  
Figure 3 illustrates the procedure of gas detection. The sample gas with a certain concentration was prepared by mixing the standard gas (balanced with nitrogen) and high-purity nitrogen through two mass flowmeters. The photoacoustic cell was then purged with an excess of the sample gas to ensure that the photoacoustic cell was completely filled with the sample gas to be measured, and the pressure in the photoacoustic cell was controlled to ambient pressure. The ICL was wavelength-modulated by combing a triangle wave of 0.25 Hz and a sine wave at the cell resonance frequency. The amplitude of the triangle wave was 40-100 mA, i.e., the absorption wavelengths of the three sample gases were all covered with a complete scan. The signal acquisition was accomplished by the two microphones on the two channels. The signals were differentially amplified by a preamplifier with a gain of 100 and passed onto the lock-in amplifier (OE 1022, Sine Scientific Instrument, Guangzhou, China). All the lock-in amplified data were obtained with an integration time of 10 ms, and the output signal was collected by the VK701 data acquisition board (Vk  
Figure 1: The line strengths of methane, ethylene, and ethane in 3344–3350 nm simulated using the HITRAN database.  
Figure 2: (a) Schematic and (b) photo of the DHR photoacoustic cell.
# Cryptographic Key Management in Delay Tolerant Networks: A Survey
## 5 Alternative Key Management Taxonomies in DTNs  
### Require or Not Trusted Third Party (TTP)  
Key management solutions can also be categorised based on whether they require a TTP or not. A TTP can be used for key management services such as key generation, key distribution or translation or keying material and certification [66]. Traditionally, in continuously connected networks the most proven practice for the key management is to contact an online TTP. Although the approach that imposes a TTP is secure and resilient, it is also not scalable for DTNs. In fact, DTNs require a different approach for handling key management. This is because every pair of nodes has to obtain keys from the online TTP, something that can not be guaranteed in DTNs with intermittent connectivity. Moreover, this approach has sizable communication overhead, which is unwelcome for DTNs. Also, the TTP constitutes a single point of failure. On the other hand, works that are self-organised may not have the aforementioned problems, but are applicable only to small size networks due to the computational overhead produced. Most of the works that do not require TTP tried to solve the security initialisation problem as an alternative solution, while most of the works that require TTP attempted to solve the key establishment, without considering the open issue of security initialisation. Contributions that require a TTP increase the communication overhead, while those which do not rely on a TTP produce additional computation overhead. As a result, both approaches can not always apply in such a hostile environment. Below, we categorize the majority of the works included in this survey based on the existence or not of TTP.  
Figure 2: DTN key management timeline.  
* _Require TTP_--Works such as [45; 46; 47; 48; 49] are based on PKI solutions mandate a TTP, and thus a CA. Works such as [12; 24; 25; 26; 40; 41] that are founded on IBC solutions, require a TTP too, namely the PKG. In addition, schemes in group key management that rely on LKH such as [52; 54], necessitate a TTP known as Key Distribution Center (KDC). Moreover, the work in [42], which is based on CPK, eliminates the need for online TTP and only needs an off-line PKG.
* _No TTP is required_--Works that rely on PGP [33; 50] and two-channel cryptography [34] are self-organised [30; 35] and do not require a TTP.
# On-Board Flickering Pixel Dynamic Suppression Method Based on Multi-Feature Fusion
###### Abstract  
The blind pixel suppression is the key preprocess to guarantee the real-time space-based infrared point target (IRPT) detection and tracking. Meanwhile, flickering pixels, as one of the blind pixels, is hard to suppress because of randomness. At present, common methods adopting a single feature generally need to accumulate dozens or hundreds of frames to ensure detection accuracy, which cannot update flickering pixels frequently. However, with low detection frequency, the flickering pixels are easily miss detected. In this paper, we propose an on-board flickering pixel dynamic suppression method based on multi-feature fusion. The visual and motion features of flickering pixels are extracted from the result of IRPT detection and tracking. Then, the confidence of flickering pixel evaluation strategy and selection mechanism of flickering pixel are introduced to fuse the above features, which achieves accurate flickering pixel suppression using a dozen frames. The experimental results evaluated on the real image of four scenarios show that the blind pixel false detection rate of the proposed method is no more than 1.02%. Meanwhile, evaluated on the simulated image, the flickering pixel miss suppression rate is no more than 2.38%, and the flickering pixel false suppression rate is 0. The proposed method could be an addition to most other IRPT detection methods, which guarantees the near-real-time and reliability of on-board IRPT detection applications.  
flickering pixel suppression; infrared point target; multi-feature fusion +
Footnote †: journal: Applied  
on Multi-Feature Fusion  
Footnote †: journal: Applied
## 2 Feature Extraction Method for Axis Motion Trajectories
### Axis Trajectory Signal Purification and Denoising  
Before purifying and denoising the data, considering the large number of data in a single sample of the original signal, sample segmentation is performed on the signals in both directions along the time series for easier subsequent calculations. The segmented samples are synthesized to obtain the axis trajectory sample signal.  
Figure 2a shows the axis trajectory signal synthesized from the time domain original vibration displacement signals in two directions. It can be observed that the axis trajectory pattern is unclear, the curves are disordered, and there is significant noise interference.  
After the sample segmentation of the original signal, single sample signals are taken, and a Hankel matrix is constructed using the maximum dimension method. SVD is performed to obtain the singular values arranged in descending order, as shown in Figure 2b. Starting from the sixth singular value, the singular values remain relatively stable. The first six singular values are selected to reconstruct the signal, and the same process is applied to the single sample signals in both directions.  
The reconstructed signals in both directions are then synthesized into a single sample axis trajectory. The results of purification and denoising are shown in Figure 2c. Compared to the original axis trajectory pattern, the purified and denoised single sample axis trajectory pattern retains the key geometric features of the axis trajectory to the greatest extent, facilitating the subsequent feature extraction.
## 5 Experiments and Results
### Estimating the Repeal-Flooding Boundary  
Figure 10 shows the signaling overhead versus distance of F&C with the repeal-flooding boundary. The experiments consisted of one node searching for a route to each of the remaining 499 nodes, in this case. As a reference, Figure 10a shows the case in which each node manually learned about its optimized repeal-flooding boundary value in advance. This case represents the best performance F&C can achieve and a benchmark for comparison. In Figure 10b, nodes use only the first two resource searches, using the unbounded version of the F&C protocol, to estimate their repeal-flooding boundary, as discussed in Section 4.1.3 (the two routes were selected randomly). Once every node estimated its repeal-flooding boundary, its value remained constant for the remaining 497 resource searches. Figure 10b shows that it took F&C 182,113 signaling packets for one node to find a route to the other 499 nodes under these conditions. This result represents 27 percent fewer signaling packets compared with traditional flooding. Figure 10c,d show the signaling overhead versus the distance of F&C after nodes use the first 5 and 10 resource searches only to estimate their repeal-flooding boundary, respectively. Figure 10c shows how F&C achieved a 35 percent signaling reduction compared with traditional flooding, which represents only two percent higher than the performance when nodes use the optimized repeal-flooding boundary (see Figure 10a). This signaling reduction is the result of nodes estimating a more accurate repeal-flooding boundary value. Figure 10d, on the other hand, did not improve the results from Figure 10c, even if the nodes used the first ten resource searches to estimate their repeal-flooding boundary. Looking into the source and target nodes' location for resource search 6 to 10 in Figure 10d, we found that they corresponded to shorter routes when only a few nodes updated their repeal-flooding boundary compared with the value they had after five resource searches. This result is a reminder that to estimate an accurate repeal-flooding boundary, the length of the route of a resource search is more important than the number of resource searches. Overall, Figure 9 clearly shows the ability of F&C to quickly achieve an optimized performance with a minimal number of previous resource searches.
# Carbon Nano-Onion Peroxidase Composite Biosensor for Electrochemical Detection of 2,4-D and 2,4,5-T
## 3 Results and Discussion
### Analysis of Real Samples  
The applicability of the biosensors in real samples was tested using soil and water samples (Table 3). 2,4-D was detected in a soil sample obtained from a local barley crop one day after the application of the herbicide. It can be seen that the concentrations obtained with the biosensor and with a commercial ELISA are in good agreement, indicating a good performance of the biosensor and similar to conventional doses used in European soils [10]. In the case of 2,4,5-T, due to the impossibility to obtain real samples contaminated with this herbicide, the CNO modified biosensor was tested in river water samples collected from the Francoli river in Tarragona and spiked with 2,4,5-T. As can be seen in Table 3, the recovery concentration obtained is 108%, demonstrating the suitability of the developed surface chemistry for detection in complex samples and could find application in agriculture to monitor the presence and environmental fate of phenoxy herbicides.
## 4 Performance Evaluation
### Test in Urban Area
Figure 14: Position error cumulative distribution of the positioning results in Segment 1. Figure 13: Positioning results of open loop tracking in Segment 1. The trajectories obtained with the open loop tracking with different configurations for Segment 2 are shown in Figure 15. The position accuracy of DET with small grid sizes is highly degraded. Owing to the poor detection performance caused by the low \(C/N_{0}\), false peak detection frequently occurs which will extremely distort the position results especially when the grid size is small. The maximum error reaches about 80 m. For DIS, the position error is also larger since the huge noise in the signal destroys the estimation results. As the grid size is broadened to 0.1 chip for DET, the positioning performance becomes better. However, when the grid size of DET is 0.2 chip, the error is a little larger due to the incremental uncertainty within the grid. ACSA-OPTA shows remarkable improvements relative to the other configurations. As \(C/N_{0}\) is varying intensely, the ACSA-OPTA can adaptively adjust the grid size to the optimal one so that it can enhance the measurement accuracy in all cases. Figure 16 illustrates the position error cumulative distribution of the results in Segment 2. It shows that the performance of ACSA-OLTA is outstanding compared with the traditional open loop tracking methods whose configurations of grid sizes and measurement calculation method are fixed. 70% of the positioning results have error smaller than 20 m and less than 10% positioning results have errors larger than 40 m. In addition, the positioning errors of DET with the grid size of 0.02 chip and 0.05 chip and  
Figure 15: Positioning results of open loop tracking in Segment 2. DIS with the size of 0.1 chip are extremely increased. More than 30% position results have error larger than 40 m. On the other hand, large numbers of positioning results computed from DET with the grid size of 0.1 chip have the errors in the range from 15 m to 40 m due to the fact that it can avoid the false detection to some extent but the uncertainty will distort the accuracy as well. Continuously enlarging the grid size to 0.2 chip, the performance of DET is degraded as well and more of the position results have the error larger than 20 m. Although it can eliminate the false peak detection in most time, the uncertainty in the grid is really large so that the accuracy is also distorted.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Supervised Learning
#### 4.1.8 Uav  
In [63], a supervised deep learning approach is proposed for UAV systems. The proposed model uses a Clustering-based Two-layered (CBTL) algorithm for addressing this joint caching and trajectory prediction issue. Then, a DL approach of a CNN is used to enhanced make fast decisions online. This approach aims to maximize the network's throughput by jointly optimizing cache and trajectory. Simulation results show the effectiveness of the proposed approach in terms of accuracy. In [64] an ANN-based algorithm is proposed, to detect GPS spoofing signals in UAV systems. The results show high detection accuracy of spoofing signals and can reduce possible false alarms in the UAV system. In [65], the authors propose a SVM-based supervised approach for detecting jamming, spoofing and intrusion attacks in UAV systems. The proposed model shows high accuracy in detecting any attacks, reassuring safer UAV systems against cyber security attacks. The authors in [66] proposed a supervised ANN approach combined with an evolutionary algorithm, to predict the Received Signal Strength (RSS) in a UAV system. Moreover, in [67] an ensemble approach is selected, which exhibits satisfactory results in terms of performance and accuracy. Table 6 reports some supervised ML models used for B5G/6G problems.
## 1 Introduction
All this affects accuracy of ET\({}_{0}\) estimation. In data scarce areas, [PERSON] et al. (2015) established ET\({}_{0}\) estimation BP models with only temperature input in Hexi Corridor, Northwest China and found that their root mean square error (RMSE) was increased by 23%, compared with Hargreaves equation [26]. [PERSON] et al. (2015) found that the BP neural network method only with temperature data increased RMSE and mean absolute error (MAE) in the North China Plain (NCP) compared with support vector regression (SVR) models [27]. Recently, various optimized algorithms were developed to improve the performance of neural networks. Genetic algorithm (GA) is an optimized algorithm simulating the nature of biological genetics in a population to search for the best individuals as optimal weights and thresholds for neural networks [28]. GA has been shown to have the capacity to optimize learning machine model using limited data. In Gyeong Sangbuk, South Korea, GA optimized neural networks models were evaluated in predicting the daily ET\({}_{0}\) and it was found that GA optimization methods were able to estimate daily ET\({}_{0}\) from limited weather data [29]. In Southwest China, [PERSON] et al. (2022) evaluated the performance of extreme learning machine (ELM), a new learning feedforward neural network, GA optimized ELM and empirical models for estimating daily ET\({}_{0}\) and found that GA-ELM was the most efficient method for predicting daily ET\({}_{0}\) using T\({}_{\max}\), T\({}_{\min}\) and R\({}_{\rm a}\) data [30]. The accuracy of GA optimized BP model in ET\({}_{0}\) prediction also proved higher than that of empirical models under the same parameter input combinations in Yangtze-Huaiihe River Basin, China [31]. It is obvious that GA optimization models have been a hot spot in the application of machine learning. China covers a variety of climate zones and has obvious alternation of seasons. Previous studies of ET\({}_{0}\) estimation mainly concentrated on a specific time scale in particular regions of China. This paper used GA optimized BP neural network models to estimate ET\({}_{0}\) from 14 locations across China over five climatic zones. Coefficients of determination between meteorological variables and ET\({}_{0}\) were calculated. Data input combinations were determined based on r\({}^{2}\) > 0.50 and r\({}^{2}\) > 0.70, respectively. This study attempted to comprehensively compare BP and GABP models for modeling ET\({}_{0}\) using different input combinations across various climatic zones and seasons in China. We hypothesized that GABP models significantly improved model performances across various climatic zones and seasons. This study aims to provide an optimized model for local farmers and policy-makers to accurately estimate and predict potential evapotranspiration and crop water consumption.
# Trusted Composition of Internet of Medical Things over Imperfect Networks
## 4 Clinical Example System  
To demonstrate how the trusted composition of IoMT may be achieved across an imperfect network, a clinical use case is presented in this section, and will be used throughout the paper. The use case involves automated monitoring of a patient-controlled opioid infusion after a surgical procedure. As depicted in Figure 1, the patient is connected to a _respiration monitor_ to measure breathing rate, and a _pulse oximeter_ to measure blood oxygen (SpO2) and heart rate. In order to administer opioids for pain management, the patient is attached to a _patient-controlled analgesic_ (PCA) infusion pump through an infusion, usually set into a vein on the back of a hand. With a PCA pump, a patient receives opioid infusion at a basal rate, but can also request an additional bolus by pressing a button connected to the PCA pump. The typical PCA pump operates as a stand-alone device.  
Overdoses are possible, even with the built-in hazard mitigation. One of the main causes of these overdoses is the PCA pump's ignorance of the patient's vital signs, which are tracked by devices like a pulse oximeter and respiration monitor. When the monitoring devices do not communicate with the PCA pump, infusion may continue even if the patient's health is entering respiratory depression.  
Several studies have investigated the integration of medical devices in a closed-loop with a controlling application to coordinate among the monitoring devices and the PCApump [4; 5]. Opioid infusion can be reduced or halted based on vital signs monitored by devices like a pulse oximeter or respiration monitor. However, these studies assume devices communicate through a perfect communication network. The IoMT-based automated opioid infusion system we are considering also takes into account the fact that there is a potential for data loss, unauthenticated access, and information delay due to imperfect communication networks.
###### Abstract  
This study focuses on addressing the pollution caused by Ni in water. To enhance the removal efficiency of Ni\({}^{2+}\), atapulgite (ATP) from Linze County, Gansu Province, China, was used as a carrier to prepare atapulgite loaded with nanoscale zero-valent iron (nZVI@ATP) via a liquid-phase reduction. This approach aims to mitigate the aggregation and oxidation tendencies of nZVI, thereby improving its performance in Ni\({}^{2+}\) removal. The results revealed that nZVI@ATP exhibited a mesoporous structure with a specific surface area and an average pore size of 51.79 m\({}^{2}\)/g and 9.22 nm. Notably, nZVI@ATP showed a remarkably reduced agglomeration phenomenon. In addition, nZVI@ATP demonstrated a considerably more excellent adsorption performance for Ni\({}^{2+}\) than raw ATP and pure nZVI, as its highest adsorption capacity was 143.20 mg/g when the iron-ATP ratio was 2:1 (initial concentration: 200 mg/L, initial pH: 5, temperature: 298 K, and dosing amount: 1 g/L). The adsorption of Ni\({}^{2+}\) by nZVI@ATP followed the quasi-secondary kinetic model, and the removal rate of Ni\({}^{2+}\) was inversely proportional to the initial concentration and directly proportional to the dosage. The adsorption capacity tended to increase and then decrease as the pH increased. The removal mechanism of Ni\({}^{2+}\) by nZVI@ATP involved adsorption, reduction, and precipitation, with the significant mechanism being the induced Ni(OH)\({}_{2}\) precipitation on the nZVI@ATP surface.  
annoscale zero-valent iron; attapulgite; Ni\({}^{2+}\) removal; adsorption mechanism