esa-sceva/satcom-chunk-collection huggingface dataset:
# Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis
## 4 Experimental Verification
It can be seen that the high-frequency noise part of the signal from 3000 Hz to 5000 Hz has been removed, which achieves the purpose of noise reduction. In Figure 18, 148 Hz is closest to the fault signal 147.66 Hz; 230.6 Hz is closest to the fault signal 231.2 Hz. Considering that the bearing operating environment has errors, which are allowed, so the method proposed in this article is effective  
In Figure 18, the failure frequency of the reconstructed signal is not obvious. According to the method proposed in this paper, the reconstructed signal needs to be decomposed by the CEEMD  
Figure 16: MSE value of 10,000 sets of random noise signals (the signal length is consistent with the vibration signal). Figure 17: MSE value of each component of vibration signal decomposed by CEEMD. Figure 18: Vibration signal reconstruction. method, and the MSE value of each component signal be calculated. The MSE value is compared with the dynamic threshold value, and if there is an IMFs component signal greater than the threshold, it is removed, the remaining signal is reconstructed, and CEEMD decomposition is performed again. After multiple iterations, if it is finally obtained that there is no case where the MSE value of the component signal is greater than the threshold, then the iteration is terminated. The time and frequency domain diagrams of signal decomposition at the end of the iteration are shown in Figure 19. In Figure 19, the reconstructed vibration signal is decomposed into 14 layers, of which IMF1-IMF5 are high-frequency noise signals; IMF6 is the failure frequency of the inner ring of NJJ210 bearings; IMF7 is the failure frequency of the inner ring of NJJ405 bearings; IMF8-IMF14 are meaningless interference components. Therefore, fault features are extracted, which verifies the effectiveness of the proposed method. The relationship between the multi-scale sample entropy of each signal component and the dynamic threshold is shown in Figure 20. In Figure 20, the MSE values of IMF1-IMF14 are all smaller than the dynamic threshold. Figure 14:0 The time and frequency domain of reconstructed signal by CEEMD decomposition. (**a**) Time domain. (**b**) Frequency domain.
## 3 Methodology
### Feature Extraction Methods
#### 3.4.6 Convolutional Neural Networks (CNN)  
A CNN is used to process data that is in grid form, such as an image. It is made up of three main layers, the convolutional layer, the pooling layer, and the fully connected layer. Feature extraction is performed at the convolutional and pooling layers, whereas the fully connected layer performs mappings between the extracted features and target values. The convolutional layer is made up of low-dimensional filters that are multiplied by the image pixels by sliding it from left to right and top to bottom based on a stride value. A stride is the number of skips in pixel values for the filter to slide across the image. Each convolutional layer is followed by activation, and the Rectified Linear Unit (ReLU) is commonly used because it is fast and gives a better generalization. The image dimension is reduced when passed through a convolutional layer. The pooling layer is for further dimension reduction to decrease training time and increase immunity against noise, thereby preventing overfitting. Pooling could be carried out by summing the pixel values within a small grid (Sum Pooling), averaging (Average Pooling), or finding the maximum value (Max Pooling). Convolution layers and pooling layers are stacked for feature extraction. The deeper the network, the more sophisticated the process of extracting features. The first convolutional layer extracts features such as lines and edges, and deeper layers extract more detailed information. The fully connected layer is made up of neurons, as in the case of a Multiple Layer Perceptron, where two or more layers could be stacked, with the lastlayer forming the output layer. Regularization techniques, such as image augmentation, dropout, and batch normalization, are used to improve the accuracy of CNN [51; 52; 53]. The CNN used in this work is made up of five convolutional layers with batch normalization, and maximum pooling applied after each layer. The output of the last max pooling layer is passed to a fully connected layer with 177 neurons from which the features are extracted. The number of filters in the five consecutive convolutional layers are 24, 38, 83, 98, and 199, respectively, with square filters of sizes 2, 4, 4, 4, and 3, respectively. The maximum pooling layers used are of size 2. The complexity of the CNN is less compared to those of pretrained networks such as VGG-16, ResNet50V2, InceptionV3, EfficientNetB0, and so on. It, therefore, trains faster and occupies less memory space.
## 4 Materials and Methods
### Antioxidant Activity (ABTS\({}^{+}\) and DPPH Methods) and Total Polyphenols  
Measurement of antioxidants (AA) and total polyphenols (TPC) was done with an extract prepared as previously described by [PERSON] et al. [39] with some modifications. Briefly, 3 g of olive oil was mixed with 5 mL of methanol/water (80/20, _v/v_). The mixture was shaken for 2 min, and the hydrophilic phase was filtered with a GD/X 0.45 \(\upmu\)m cellulose acetate septa (25 mm, Sartorius, Madrid, Spain). This procedure was repeated twice with the lipophilic phases, and all the hydrophilic extracts were evaporated in a rotary evaporator at 35 \({}^{\circ}\)C. Finally, the residue was dissolved in 1.5 mL of methanol.  
DPPH radical (2,2-diphenyl-1-pirylhydrazyl) and ABTS\({}^{+}\) (azino-bis (3-ethylbenzothazoline-6-sulfonic acid) methods were used to evaluate the antioxidant activity (AA) of the olive oils. The DPPH was done as described by [PERSON] et al. [40], and the ABTS\({}^{+}\) as described by [PERSON] et al. [41] using a UV-visible spectrophotometer (Helios Gamma model, UVG 1002E; Helios, Cambridge, UK). Calibration curves (3.5-5.0 mmol Trolox L\({}^{-1}\)) with good linearity (R\({}^{2}\) \(\geq\) 0.999) were used for the quantification of the AA by both methods. Analyses were run in triplicate, and the results were expressed as mmol Trolox L\({}^{-1}\) of olive oil.  
Total phenolic content (TPC) was quantified using Folin-Ciocalteu reagent, as described by [PERSON] et al. [42]. Absorbance was measured using the same extract and spectrophotometer as in AA. Gallic acid was used to prepare calibration curves. This analysis was run in triplicate, and the results were expressed as gallic acid equivalents (GAE) L\({}^{-1}\) of olive oil. Gallic acid was used to facilitate comparison with previous studies.
# AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification
## 2 Background and Related Work
### Antivirus Detection Mechanisms
#### 2.1.2 Heuristic and Behavioral Monitoring  
Heuristic-based malware detection enhances the identification of previously unknown threats by analyzing anomalous patterns and behaviors that deviate from established baselines. This approach incorporates static analysis, which examines code without execution, and dynamic analysis, which monitors runtime behavior. Behavioral profiling further refines detection capabilities by identifying potentially malicious activities, such as unauthorized file encryption or irregular network interactions. While effective against zero-day threats, these techniques may yield false positives, erroneously classifying benign software as threats.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 2 Study Area and Data  
### Study Area and Field Measurements  
This study was conducted in a 5 km \(\times\) 5 km maize cropland region (centered at 38\({}^{\circ}\)51\({}^{\prime}\)N, 100\({}^{\circ}\)22\({}^{\prime}\)E) near Zhangye city, China (Figure 1). Zhangye is the largest maize breeding area in China, with an annual output of 450 million kilograms of maize seeds, accounting for more than 50% of China's annual consumption. The study area is flat with an average altitude of 1579 m. The study is characterized by a temperate continental climate. The total annual precipitation is 129 mm and annual average temperature is 6 \({}^{\circ}\)C. Croplands is the dominant land cover type and maize is the primary crop species. The growth stage of maize in our study area can be split into five stages: (1) souring stage after late April; (2) seedling stage from May until late June; (3) heading stage from late June to late July; (4) mature stage from August to late September; and (5) harvesting stage after late September. Figure 1 shows the location of the study area and the spatial distribution of six ESUs.  
A LAINet system was installed in our study area to provide temporally continuous LAI measurements along the year 2019. The effective LAI values are derived from the canopy gap fraction [37], calculated from the ratio between the below-canopy transmitted radiation and above-canopy total downward solar radiation [37]. The two radiation  
Figure 1: Sentinel-2 false-color RGB (8, 4, 3) composite of the study area on 30 July 2019. The yellow dots indicate the locations of the Elementary Sampling Units (ESUs).  
fluxes are automatically measured from above and below nodes, respectively, and the readings are collected by a central node and finally transferred to a remote data server through a general packet radio service network. The above and below nodes have the same hardware configuration and software functions except for their different shapes and the number of integrated optical sensors. In this study, four below nodes per ESU were deployed to measure below-canopy transmitted radiation at the ESU scale with a size of 30 m \(\times\) 30 m. Only one above node per ESU was deployed, assuming the homogeneity of the incoming solar radiation above the canopy. After the sampling design, the optical sensors are calibrated to ensure that all sensor readings are the same under the same downward radiation conditions, which is a guarantee of the correct measurement of canopy transmittance and the basis to calculate LAI. For details regarding the LAINet, please refer to [PERSON] et al. (2018). The implementation period of the LAINet in our study spanned from 1 June to 20 September 2019 (i.e., from day of year (DOY) 152 to 263), nearly covering the entire growing season of maize crop.
# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
###### Abstract  
Objectives: The overall approach towards Remotely Piloted Aerial System integration into a non-segregated airspace is that the unmanned vehicles should be able to fit into the current air traffic management system, thus meeting all the technical and regulatory requirements to be treated similar to any other airspace user. Such a requirement implies that unmanned aircraft operations should behave as close as possible to manned aviation or at least generate the most negligible possible negative impact on the system. From the air traffic management point of view, this implies that air traffic controllers should be capable of effectively handling different types of RPAS operating in a nominal state but also when suffering a potential contingency. This paper aims to analyse how air traffic controllers involved in managing unmanned aircraft integration into non-segregated airspace are impacted when an unmanned vehicle suffers a contingency. Participants: Six air traffic controllers were the test subjects, complemented by one RPAS pilot and several pseudo-pilots controlling the simulated manned traffic. The project collected real-time simulation data to develop specific indicators to determine how the controllers' workload increases while managing complex traffic scenarios, including a single RPAS. Study Method: We conducted exhaustive traffic flight simulations, recreating complex airspace scenarios, including various RPAS types and mission-oriented trajectories. The involved RPAS were subjected to two of the most relevant contingencies: loss of the command-and-control link and engine failure. The experiments were evaluated in different operational scenarios, including using autonomous communication technologies to help air traffic controllers track the RPAS operation. Findings: The results indicate that the air traffic controller's perception and workload are not affected beyond reason by the introduction of an unmanned aircraft as a new element into the non-segregated airspace, even when that aircraft suffers a contingency. The flight-intent technology increases situational awareness, leading to more efficient and safe airspace management. Additional simulations may need to be performed to evaluate the impact on airspace capacity, safety, and workload when various unmanned vehicles are simultaneously inserted.  
RPAS operations; non-segregated airspace; contingency management Article
## 5 Experimental Evaluation
### Functional Validation
#### 5.2.1 UDP/HTTP-Based Communications  
In this test case, both OBUs (1 and 2) communicate with the Edge Geoserver using UDP/HTTP protocol. At inception, OBU 1 and OBU 2 are registered in the Edge Geoserver and then mutually authenticated over HTTP(S). Subsequently, both OBUs can subscribe to an ROI that is based on the current geographical region. Subsequently, from the web interface of OBU 1, we trigger the transmission of a DENM message towards the Edge Geoserver (as a unicast UDP packet) to indicate an emulated hazardous event detection within the current virtual location of OBU 1. Consequently, the Edge Geoserver transmits a DENM towards OBU 2 with the information of the event reported by OBU 1, and the warning is displayed on the web interface of OBU 2. Similarly, but in the opposite direction, OBU 1 is notified by the Edge Geoserver upon the reception of a DENM transmitted by OBU 2.
## 2 Materials and Methods
### Methods
#### 2.2.7. Dissolution Capacity Measurement  
Four cubic samples of marble with a weight of 21.0 \(\pm\) 1.0 g and size of 2.0 \(\times\) 2.0 \(\times\) 2.0 cm were hydrophboized as described in Section 2.2.1. The initial weight (\(m_{0}\)) of every sample was measured with an accuracy of 0.0001 g. All samples were treated as described in Section 2.2.4 under ambient or reservoir conditions for 3 h. The volume of the liquid for the dissolution of one sample was equal to the area of the sample, multiplied by 2.5. The final weight (\(m_{f}\)) of the dry sample was measured. Dissolution capacity in g/g was calculated according to Equation (3). Actually, DC is the weight of marble which can be dissolved by 1 g of the CA in the solution.  
\[DC=\frac{m_{0}-m_{f}}{V_{L}\times(C_{EDTA}\times M_{EDTA}+C_{citric acid}\times M_{citric acid})+m_{L}\times\omega_{surfactant}} \tag{3}\]  
Figure 3: Experimental setup. 1—analytical balance, 2—beaker with imbibing liquid (octane or water), 3—cell, 4—tripod, 5—hoist, 6—thread, 7—100 mesh screen.  
\(V_{L}\) is the volume of treating solution, \(C_{EDTA}\) and \(C_{citric\, acid}\) are concentrations of EDTA-Na\({}_{2}\) and citric acid, respectively (0.4 M and 0.05 M), and \(M_{EDTA}\) and \(M_{citric\, acid}\) are molecular weights of EDTA-Na\({}_{2}\) and citric acid, respectively (358 and 192 g/mole). If surfactant is present, then the weight of the surfactant is added to the denominator.
## 3 Methods
### Feature Reduction  
Principal Component Analysis  
PCA is another technique that can be used for extracting the features through the filtering technique. It uses an orthogonal transformation for converting some observations of correlated variables to a group of uncorrelated variables [28]. It is an unsupervised method that calculates the linear mapping for accomplishing low size representation of original data, where there is a high amount of variance. The covariance of two variables \(X\) and \(Y\) is obtained while using the following equation:  
\[Cov(X,Y)=\frac{1}{n-1}\sum_{i=1}^{n}(Xi-\overline{x})(Yi-\overline{y}) \tag{10}\]  
The following processes take place in PCA:  
* The co-variance matrix of the data points is obtained.
* The individual eigen values are calculated and then sorted in decreasing order.
* The first k-eigen vector is selected and this will have k dimensions.
* The original set of dimensions is modified. i.e., the dimensions are transformed into k dimensions.
# Characterization and Water Content Estimation Method of Living Plant Leaves Using Terahertz Waves
## 2 Methods
### Material Characterization of Plant Leaves  
The Nicholson-Ross-Weir (NRW) method [25] is the most common technique in which the dielectric parameters \(\varepsilon_{r}\) and \(\mu_{r}\) of a planar material are extracted from a two-port vector NA measurement in which the transmission and reflection coefficients are obtained through the S-parameters. This method belongs to the category of frequency-by-frequency material extraction in which every point from the frequency sweep is used. In general, the NRW method generates both the complex permittivity \(\varepsilon_{r}=\varepsilon_{r}^{\prime\prime}-je_{r}^{\prime}\) and permeability, \(\mu_{r}=\mu_{r}^{\prime}-j\mu_{r}^{\prime\prime}\) of the specimen under test. Here, we assume that the leaves are non-magnetic and compute only the permittivity. One of the intrinsic problems of the NRW method is the periodicity of the phase of the electromagnetic wave that leads to ambiguous results. This problem has been discussed at length in other works [26; 27; 28]. To rectify this, we follow the step-wise approach in which the phase ambiguity is removed by using the phase delay information from the previous frequency point [29]. In this paper, we consider a plant leaf as a planar slab of thickness \(d\) which is positioned between two air-filled circular waveguides. With the help of an equivalent transmission line model, the reflection (\(\Gamma\)) and transmission (\(T\)) coefficients of a semi-infinite slab are expressed in terms of the measured s-parameters, \(S_{11}\) and \(S_{21}\) as [30],  
\[\Gamma=\chi\pm\sqrt{\chi^{2}-1}, T=\frac{S_{11}+S_{21}-\Gamma}{1-\left(S_{11}+S_{21}\right)\Gamma}, \tag{1}\]  
where the intermediate variable \(\chi\) is defined as \(\left(S_{11}^{2}-S_{21}^{2}+1\right)/2S_{11}\). In the case of the slab with a finite thickness \(d\), the transmission coefficient \(T\) can be described in terms of the propagation constant, \(\gamma\) as, \(T=\exp(-\gamma d)\), which can subsequently be written in the Euler form as \(|T|\exp(-j\phi)\) where \(\phi\) denotes the phase term. The propagation constant is then determined using [28],  
\[\gamma=\frac{1}{d}\big{\{}-\log(|T|)-j\phi+j2\pi n\big{\}}\text{ where }n\in\mathbb{Z} \tag{2}\]  
Figure 3: Real part of permittivity of coflea leaves at three different locations taken on days 1 and 4.  
which results in an infinite number of branches of the complex valued root due to the logarithmic function, demonstrated by the presence of the \(2\pi n\) term. The problem of selecting the proper branch is solved by the technique proposed in [29] in which at each frequency point, the phase delay information is recovered from the previous frequency point. If the phase difference, \(\phi_{i}-\phi_{i-1}<\pi\), the method ensures the current branch is selected. The permittivity is then calculated by [31],  
\[\varepsilon_{r}=\frac{\gamma}{\gamma_{0}}\left[\frac{1-\Gamma}{1+\Gamma} \right]. \tag{3}\]
# Blockchain Private File Storage-Sharing Method Based on IPFS
## 3 Methods
### Private File Save Process  
1. The file publisher sends the signed and encrypted named content packet to the IPFS node group in the model through the NDN network.
2. One of the nodes of the private IPFS constructed in the model accepts the data uploaded by the file publisher and verifies the user's identity through the user signature in the packet. After the authentication, the original ciphertext is uploaded to the private IPFS network for storage, and the unique hash value of the corresponding content is returned. The returned hash value is signed with the public key of the IPFS node.
3. Finally, the information received by the IPFS is simulated as a transaction and sent to the publisher's private blockchain node for storage. The transaction includes the content hash corresponding to the file, signature hash, uploader information and the corresponding time stamp. The ciphertext corresponding file hash is authenticated and stored between the nodes by the blockchain, thus achieving the guarantee of untamperable authenticity under the premise that the file is stored securely.
4. After the publisher node stores and packages related transactions, it synchronously backs up transactions on other nodes of the private chain using the Gossip algorithm.  
Ultimately on the blockchain, the guarantee of true consistency of information storage of files between all participating nodes is achieved.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 4 Wireless Technologies
### Trusted Wireless Technology  
Trusted Wireless technology developed by Phoenix Contact is a proprietary wireless alternative that bridges the gap between WirelessHART's suitability for industrial applications and WLAN long-distance capabilities. It operates on several frequency bands: 868 MHz, 900 MHz, and 2.4 GHz. It can handle data rates of up to 250 Kbps, which is suitable for IIoT applications [26].  
1. Network structure: Unlike Wi-Fi, Trusted Wireless supports mesh networking, allowing devices to serve as repeaters, thus enhancing communication range and reliability without direct paths to the base station (BS). Phoenix Contact, while not disclosing the routing protocol, introduced parent node black-listing and white-listing to optimize repeater roles and path planning to the BS. Furthermore, unlike WirelessHART, Trusted Wireless employs a more flexible, decentralized approach to network management using a parent-child relationship, as shown in Figure 2. Although the network design and data path are predefined, the parent-child zones mirror the Low-Energy Adaptive Clustering Hierarchy (LEACH) routing protocol's cluster head approach [26].
2. PHY and MAC Layer: For medium access, Trusted Wireless incorporates a Listen-Before-Talk (LBT) mechanism, which is the foundation of the Distributed Coordination Function (DCF) used in Wi-Fi, where devices sense the medium before transmitting to avoid collisions. Furthermore, like WirelessHART, Trusted Wireless employs FHSS as its transmission technology, hopping through up to 440 possible frequencies [26]. This makes the communication highly resilient to interference and less susceptible to jamming or interception, but compared to the OFDM and OFDMA used in Wi-Fi, it offers a lower data rate.
3. Security: Trusted Wireless secures industrial communication in a similar manner to WirelessHART by employing AES-128 encryption in CCM mode and frequency hopping; it also utilizes a Pre-Shared Key (PSK) system for authentication, mirroring aspects of WPA2's approach in Wi-Fi networks [26; 27]. Compared to WPA2 and WirelessHART, Trusted Wireless use of frequency hopping and PSK combines the principles of robust encryption with practical key management, aiming for a balance between security and operational efficiency in industrial wireless networks.