esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Results and Discussion
### Load-Slip Relationship
Furthermore, the results of specimens with steel bars were similar for self-compacting geopolymer concrete (SCGC), geopolymer concrete, and conventional concrete of 40 MPa strength [53; 54; 55; 56]. The outlier values in some of the specimens in this series of experiments were due to the shutting down of the UTM midway through the experiment. This study provides promising results for the use of basalt bars as a reinforcement alternative to conventional steel bars. Moreover, the self-compacting geopolymer concrete, with its advantageous features--including strength, flowability, lack of need for vibration, and ambient curing properties--would make the BFRP-reinforced specimens more efficient to use easily in real life scenarios [1; 2; 7; 53; 54; 55; 56]. The current experimental investigation finds that while studying the causes of bond behaviour, it is important to consider other parameters, such as fibre volume in each of the bundles for basalt or any other kind of FRP bars, as these may influence the bond behaviour. The reinforcement sectional area, surface roughness of the rebar, bar diameter, and friction at the bar-concrete interface are other factors that have been found to affect the bond behaviour of pull-out specimens [19; 57; 58; 52]. Bonding can also be affected by surface treatment of the bars, and by the country of origin/production of the bars. Hence, the results of this study hold true only for the current bar characteristics, and more studies are required to standardize basalt FRP bars' bond behaviour.
## 2 Related Work  
### Deep-Learning-Based CD Methods  
In recent years, deep learning technology has become a new favorite in the field of CD [39; 40], especially convolutional neural networks (CNNs). These deep-learning-based methods can be roughly summarized into two categories, i.e., image-level methods and patch-level methods.  
(1) Image-level methods: This category of method is to acquire semantic change information by analyzing a complete bitemporal image at a time [41]. Hence, that usually requires a large number of pairs of manually labeled training image pairs. For example, [PERSON] et al. proposed a Siamese U-Net with shared weights to acquire a building change map in an end-to-end manner [42]. [PERSON] et al. devised a local-global pyramid network for building CD in [43]. In [44], a spatial-temporal attention-based network based on self-attention mechanism was applied to mine deep robust features for large image-to-image CD datasets. Although these approaches can achieve competitive performance, they often not only require a large number of manually labeled paired images to train the network, but also cost more storage space and computational resources.  
(2) Patch-level methods: Different from image-level methods, this type of method indicates using local pixel patches or superpixels as analysis units, and capturing feature representations through convolution or fully connection to achieve CD. In the early stages, [PERSON] et al. proposed a novel CD method based on deep learning [45], which can avoid the effect of the DI to provide a better change detection performance. In [46], a Gabor-based PCANet (GaborPCANet) was promoted for CD in SAR images, which utilizes PCA filters as convolutional filters to capture the image features. A convolutional-wavelet neural network (CWNN) was devised to detect sea ice change detection from SAR images in [47]. [PERSON] et al. developed a semisupervised multiple CD approach, which can detect multiple changes using only a very limited samples by training a generative adversarial network [48]. This approach introduces dual-tree complex wavelet transform into CNNs to reduce the effect of the speckle noise, thus improving detection performance. However, these methods based on local pixel patches are still limited by the selection of regular windows. To alleviate this limitation, superpixels-based CD methods have received attention, which aim to use superpixels as analysis units to capture more representative features through CNNs. To achieve this, recent methods have made further efforts. For instance, [PERSON] et al.  
presented a superpixel-based difference representation learning to extract semantic change information between bitemporal images [49]. In [50], an end-to-end superpixel-enhanced CD network was designed, which combines an adaptive superpixel merging module to mine difference information for CD. Other methods refer to [51; 52; 53].
# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 3 Algorithms
### Unsupervised Feature Extraction Based on Autoencoder  
The purpose of the first stage, the unsupervised learning process, was to fully use unlabeled data to initially train the model. The required trained model could accurately extract general features in unlabeled samples. We ensured the model had basic clustering capabilities for both labeled and unlabeled categories.  
In this study, an autoencoder was constructed to perform feature extraction on trajectory data. Autoencoders are commonly used in trajectory clustering and feature extraction [14]. Its structure is shown in Figure 4.  
The autoencoder generally consisted of an encoder and a decoder. The purpose of the encoder was to compress the input data into a latent space representation; the decoder aimed to reconstruct the input data represented by this latent space and express the input information as much as possible.  
The purpose of the entire network was to make the output and input as close as possible so that the intermediate feature vector could be guaranteed to correspond well to the input data. Therefore, as long as the error between the input and output was small enough, the entire network achieved the purpose of compressing the input space. The most commonly used function was the MSE (mean squared error) loss function, shown in Equation (4).  
\[Loss=\frac{1}{n}{\sum_{n}(y\_true_{i}-y\_pred_{i})}^{2} \tag{4}\]  
In the formula, \(y\_true\) represents the input sample, and \(y\_pred\) represents the restored sample of the output.  
RNN network was the mainstream neural network model for processing time series data. The trajectory sequence was typical time series data [15], and the time series feature was the key piece of information for distinguishing the trajectory type [16]. Therefore, this study used the GRU [17] network structure, a variant of the RNN [18], to build a sequential autoencoder, shown in Figure 5.  
In Figure 5, the GRU layer was used to read the timing features of the trajectory. The RepeatVector layer was used to regenerate the time series from the extracted feature vectors and reconstruct the input data using a subsequent  
Figure 4: Autoencoder structure.  
During model training, the input data was normalized. Zero padding was performed to keep the input data length consistent. Taken from the encoder intermediate layer output as the trajectory feature vector. In general clustering algorithms, this feature was used to calculate the similarity for clustering, but because the model lacked guidance, the autoencoder could only blindly extract features during feature extraction. Under the semi-supervised condition of this study, the label data was used to further fine-tune the model in the next stage and improve the model feature extraction effect.
## 4 OWC Technologies for the 5G, 6G, and IoT Solutions
### Fulfilling the Service Quality Characteristics
Therefore, no additional energy is consumed by an LED transmitter \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Spectral Category/Sub-Category**} & \multicolumn{2}{c}{**Frequency Range**} \\ \hline RF & Very low–super high frequency & 3 kHz to 30 GHz \\ (3 kHz to 300 GHz) & Microwave & 225 MHz to 100 GHz \\  & Millimeter wave & 30–300 GHz \\ \hline \multirow{8}{*}{Optical (300 GHz to 30 GHz)} & Far infrared & 0.3–20 THz \\  & IR & Thermal infrared & 20–100 THz \\  & (300 GHz to 394.7 THz) & Short-wavelength infrared & 100–214.3 THz \\  & & Near infrared & 214.3–394.7 THz \\ \cline{2-3}  & & Red & 394.7–491.8 THz \\  & & Orange & 491.8–507.6 THz \\  & VL & Yellow & 507.6–526.3 THz \\  & (394.7–833.3 THz) & Green & 526.3–600 THz \\  & & Blue & 600–666.7 THz \\  & & Violet & 666.7–833.3 THz \\ \cline{2-3}  & & Near ultraviolet & 0.750–1 THz \\  & & Middle ultraviolet & 1–1.5 THz \\  & UV & Far ultraviolet & 1.5–2.459 THz \\  & (750 THz to 30 GHz) & Hydrogen Lyman-alpha & 2.459–2.479 THz \\  & & Extreme ultraviolet & 2.479–30 THz \\  & & Vacuum ultraviolet & 1.5–30 THz \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RF and optical spectra [2; 3; 4; 5; 6; 21; 29]. if it is used for illumination as well. Compared to RF sensors, LED sensors consume very little energy. The OWC technologies can provide communication systems that consume very little power; hence, the OWC-based communication technologies can provide energy-efficient communication systems that are an important requirement for the 5 GB and IoT deployments. _Reliable connectivity:_ A reliable connectivity is an important criterion for any kind of communication system. The OWC systems assure a very high level of SNR, especially for indoor users. Even for outdoor scenarios, the OCC can provide non-interference communication and a high SNR. Moreover, a stable performance is achievable even when the communication distance increases. The FSO also provides a good SNR level for outdoor long-distance communications. The OWC networks yield an opportunity of providing an extra tier network for indoor users, which surely increases the reliability of a communication system. Therefore, the OWC systems can increase the connectivity reliability for users in the 5G/6G and IoT networks. _Ultra-high security:_ The OWC technologies can provide a secure communication, as is required by the 5G, 6G, and IoT networks. The OWC signal cannot penetrate an obstacle; therefore, outside people cannot hack the information. It is impossible for a network hacker device that is outside to pick up the inside optical signal. The information can be exchanged in a highly secured manner, especially for health purposes. Hence, the OWC systems offer a higher level of security for the 5G/6G and IoT networks.
###### Abstract  
The presented work studies the processes of synthesis of ZnO microstructures using atmospheric-pressure microwave nitrogen plasma and investigates their photocatalytic activity in the processes of degradation of 2.4-dinitrophenol and the antibiotic ciprofloxacin when irradiated with sunlight. The work proposes an effective method for formation of photosensitive ZnO powders. Due to the features of plasma treatment in the open atmosphere of zinc metal microparticles, ZnO structures are formed with sizes from hundreds of nanometers to several micrometers with various micromorphologies. The lattice parameters of ZnO structures are characteristic of a hexagonal unit with a = 3.258 A and c = 5.21 A, volume 47.95 A\({}^{3}\). The size of the crystallites is 48 nm. The plasma treatment was performed by means of a 2.45-GHz plasmatron at a power input of 1 kW in nitrogen flow at a rate of 1-10 L/min. Zn microparticles were injected into the microwave plasma at a mass rate of 20 g/min. High photocativity was demonstrated (rate constants 0.036 min\({}^{-1}\) and 0.051 min\({}^{-1}\)) of synthesized ZnO structures during photo-degradation of 2,4-dinitrophenol and ciprofloxacin, respectively, when exposed to solar radiation. Photo-active structures of ZnO synthesized using microwave plasma can find application in processes of mineralization of toxic organic compounds. Structures of ZnO synthesized using microwave plasma can find application in processes of mineralization of toxic organic compounds, and also in scintillation detectors, phosphors.  
microwave plasma; zinc oxide; antibiotic; dinitrophenol; solar radiation; photocatalysts +
Footnote †: journal: Applied  
0023
## 4 The Deployment of WDM-PON Protection Schemes
### Considered Presumptive Scenarios for RING WDM-PON Protection Schemes  
The RING protection scheme of the WDM-PON network is considered for the protection provisioning in an effective way in terms of costs. Advanced users demand a reliable connection, and it is expected that network operators will provide a continual access to telecommunication network services. Thus, a network reliability securing by implementation of the protection scheme is a substantial request. Optical fibers in a ring topology demand higher attention for failure-less signal transmission comparing to other network topologies. A ring topology decreases costs due to localization and sharing options of the same transmission channels in working and protection optical fibers. This topology can be also used for the TDM-PON or hybrid PON networks [19; 23].  
For practical utilization by network operators, the following presumptive scenarios of the RING protection scheme are considered:  
* The protected access network is designed for the fiber failure protection and the Rayleigh backscattering noise suppression. It provides a possibility for improvement the network scalability and also for increasing the channel capacity per ONU unit. In this protection scheme, there exist two optical paths--working and protection. In a case of the fiber failure, optical modules can protect and restore communication channels by the use of appropriate optical switches for immediate traffic protection providing [23];
* The protected metro-access network utilizes a combination of network topologies. A network is composed of duplicated feeder fibers in the ring topology, whereby two synchronous optical switches are used for selecting either the working or the protection mode. This protection scheme does not provide a protection of the distribution network part. An enormous data traffic up to 1 Tbit/s can be processed, an extra high number of users can be supported and a large geographical area can be covered in this protection scheme. Any failure on the physical layer can cause a huge data damageaffecting end users; therefore, an effective control system for detecting and protecting of failures must be implemented. This protection scheme ensures the high network availability with lower costs comparing to other protection schemes considered for ring network topologies [27].
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 1 Introduction
More recently, instrument error inventories for microwave sounders have been established [13]. Nevertheless, the assumption that observation errors are independent and uncorrelated is not valid for any observatory. In practice, observation error correlations are minimized by thinning the dataset or'super-obbed' samples, which improves overall model skill by mitigating the effects of correlated error, which may be interpreted by the model as a real signal [16; 17; 18]. The premise of not fully exploiting observation data is unappealing, considering that the process of acquiring these data usually requires the considerable capital expense of building and flying remote sensing satellites. The highest utility of dense observations in space and time occurs when the application (i.e., the model) resolution matches the observation resolution. For many imagers and sounders, there may be a reasonable case that thinning is an appropriate way to scale dense observation data to match model gridding. For CYGNSS, however, thinning the data is an especially unpalatable solution, as the peak utility of CYGNSS data is during the relatively uncommon occurrence that a specular point passes through the eyewall of a tropical cyclone. Thinning may miss this observation or misrepresent the structure of the storm in the model. By a similar token,'super-obbing' is not a practicable remedy because tracks are one dimensional and collocated observations are potentially hours apart. Blending data from a wide temporal window risks misrepresenting the dynamics of the tropical cyclone and negates the fast-revisit utility of the observatory. Estimating the correlated error structure for observations can improve model skills in NWP [6; 14; 19; 20; 21]. A number of methods have been demonstrated to estimate the observation error correlation matrix [22; 23], most notably [PERSON] diagnostic [24]. Our work does not estimate the full observation error correlation matrix that is typical of these prior works. Instead, we provide a first-principle, bottoms-up, tunable engineering model for how the CYGNSS instrumentation itself can cause observations to contain correlated errors. This correlation model is validated and tuned against empirical observation data during a period when two CYGNSS assets were in a specific orbital geometry where observations nearly coincided in space and time. We further discuss the challenges and opportunities that result from this correlated error model, as well as highlight the potential for applicability to future assimilation investigations.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 3 Experimental Results and Analysis
### Data Preprocessing  
In the data preprocessing and cleaning stage, the first step is to integrate the original wave height records collected in different years to build a unified and comprehensive dataset. In the process of processing the wave height data, invalid or abnormal records with a wave height equal to 99 are strictly identified and eliminated to ensure the reliability and consistency of the data and lay a solid foundation for subsequent analysis.  
The first four columns of the data are the measurement times that correspond to the data. For the data in the first four columns (the four columns of the dataset represent the specific timestamps associated with each corresponding observation), each data point is assigned an accurate timestamp to accurately index. To solve the inevitable missing value problem, we use linear interpolation to fill in the gaps to maintain the continuity of the time series.  
Given that the original wave height data presents a skewed distribution, which may affect the effectiveness of subsequent decomposition and modeling, we perform a natural  
Figure 4: Distribution of wave height data.  
logarithmic transformation on the processed values. This transformation effectively reduces the skewed distribution, making the distribution more symmetric and more conducive to statistical analysis. In addition, in order to reduce the impact of high-frequency noise and improve signal clarity, we also use a smoothing filter. As shown in Figure 5, the original wave height data shows violent fluctuations, and high-frequency peaks and troughs appear frequently, indicating that there may be noise or transient disturbances. After filtering, the amplitude of these fluctuations is significantly reduced, the high-frequency components are attenuated, and the overall trend of the data is smoother and easier to interpret. This improvement helps to decompose more accurately and efficiently in the subsequent modeling steps.
# Potential Possibilities of Using Peat, Humic Substances, and Sulfurous Waters in Cosmetology
## 4 Medicinal Waters
### Biological Activity of Sulphurus Water
#### 4.3.2 Antioxidant Activity  
Studies were performed to investigate the effects of long-term exposure to sulfur sources on oxidative stress and antioxidant biomarker responses in people living near sulfur sources. The studied area was Al-Hammah sulfurous springs, which are located in the northern part of the Jordan Rift Valley and host many sulfurous springs. The study involved 250 people aged 18 to 45 who did not have hypertension, diabetes problems, or chronic diseases and were not taking any medications. The control group samples were collected from 250 volunteers living in the Der-Allah region, which has a similar mean sea level (MSL) and geographic features but without sulfurous springs exposure. The study group did not experience headaches, nausea, or breathing problems. Moreover, the studies showed that the study group had lower values of total oxidative stress (TOS) and oxidative stress index (OSI) than in the control group. Total antioxidant capacity (TAC) and total nitric oxide (NOX) levels were higher in sulfur spring residents compared to controls. There was also no effect of living near sulfur springs on the level of oxygen saturation (SPO\({}_{2}\)) or heart rate. The studies indicated that exposure to sulfur sources increases antioxidant capacity and reduces the level of oxidative stress (Table 5) [128].  
Studies were carried out on a group of patients to evaluate sulfur baths on oxidative stress. The experimental group consisted of 19 people who used sulfur baths for 3 weeks. Diagnostic tests were performed before the start of therapy and after completion. Compared to the control group, the studies confirmed a reduction in peroxide concentrations and SOD activities (Table 5) [129].  
[PERSON] et al. conducted tests on the antigenotoxicity of sulfurous water. The studies were performed using single-cell alkaline gel electrophoresis (SCGE) (comet assay). DNA damage was significantly inhibited by natural water with H\({}_{2}\)S concentrations of 5.0 and 2.5 \(\upmu\)g/mL (Table 5) [130].
## 5 Impact Assessments on Estimated Parameters at Collocated Stations
### Impact of Backward Smoothing on Adaptable RT/NRT Solutions  
The new strategy was evaluated using multi-constellation GBM and COM products. Dual-station differences were calculated for the ZTD and horizontal linear gradients when considering various delays (5-60 min) applied for the backward smoothing. In other words, the dependency on delay characterizes a possible improvement due to the increased latency of the product. Figure 13 shows the statistics for both collocated stations using the standalone GPS, and multi-constellation (GPS + GLO and GPS + GLO + GAL) solutions. The positive impact is observed when comparing the backward smoothing and the Kalman filter, and it increases steadily for both the ZTD and the east horizontal gradient (the north gradient is not shown here, but similar to the east gradient) along with the delay for triggering the backward smoothing. The impact becomes significant after the 20-min delay and is larger for the collocated stations at Matera. A very similar impact is observed for multi-constellations and GPS-only solutions and resulting in smaller discrepancies of the tropospheric parameters at both collocated stations.