esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Networks Routing Using an Arrow-Based Description
## 1 Introduction  
This paper is the extended version of the reference [1] presented at MOCAST 2020 conference. The routing algorithm and its extension is suitable for static ZigBee and mesh networks [2; 3; 4; 5].  
Many types of networks deployed for every-day use including home networks, static sensor networks, etc. can be modeled as a connected planar graph, described using either its node or its cycle description.  
The networks are cyclically connected such that a cycle description of the associated graph is possible.  
Routing is an essential feature of any communication network. Almost any networks-related standard, including those by IEEE Zigbee IEEE 802.15.4, recommends the existence of routing protocols. The cornerstone of routing is the ability to find paths between two given end nodes. Most of such algorithms rely on node hopping and are based on the node description of the graph associated to the network [2; 3; 4; 5]. We note that many types of networks deployed in everyday use (including home networks, static sensor networks, etc.) can be modeled as a connected planar graph.  
In a previous paper [6], it has been shown that a routing algorithm can be developed using the cycle description of the underlying graph. This method has several advantages over algorithms based on node-hopping, such as its lack of back-tracking.  
However, one disadvantage of the method presented in [6] was that the data obtained as the result of the algorithm are difficult to process. In this paper, we address this shortcoming by means of a new \"arrows\" model for the graph. This model significantly improves the path-finding algorithm based on cycles mergers.  
The paper is organized as follows:  
Section 2 introduces the basic notations used in the paper;  
Section 3 describes an arrow-description of the graph;  
Section 4 describes the use of the arrow algebra;Section 5 describes the routing algorithm using arrows;  
Section 6 is dedicated to a more detailed description of the algorithm;  
Section 7 is dedicated to conclusions and future work.
## 3 Results
### Tolerance Index and Bioavailability  
Increasing the biochar rate increased the tolerance index at each Cd concentration level (Figure 4). The highest tolerance index was observed with B5 (Figure 4a). However, B1 and B3 also significantly improved the tolerance index, but 5% addition had the best effect at all levels of Cd pollution (\(p<0.05\)). All biochar amendments decreased the bioavailability of Cd, which was most significant at biochar amendment rates of 3% and 5% and partly significant at 1% biochar application (\(p<0.05\)) (Figure 4b). The lowest bioavailability of Cd was generally observed at a biochar application rate of 5% (Figure 4b). Generally, the bioavailability of Cd in all treatments increased with an increasing Cd level.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Percent of** & **pH** & **EC** & \multirow{2}{*}{**OC (\%)**} & **CEC** & \multirow{2}{*}{**N**} & **P** & **K** \\
**Biochar** & & **(dS m\({}^{-1}\))** & & & **kg\({}^{-1}\)** & **(mg kg\({}^{-1}\))** & **(mg kg\({}^{-1}\))** \\ \hline
0 & 6.4 \(\pm\) 0.02 b & 0.31 \(\pm\) 0.02 a & 1.1 \(\pm\) 0.02 b & 8.11 \(\pm\) 1.02 c & 0.15 \(\pm\) 0.01 c & 12.1 \(\pm\) 1.2 c & 8.3 \(\pm\) 1.2 c \\ (Control) & 6.5 \(\pm\) 0.01 b & 0.27 \(\pm\) 0.05 a & 1.2 \(\pm\) 0.04 b & 12.31 \(\pm\) 1.01 b & 0.4 \(\pm\) 0.03 b & 21.1 \(\pm\) 2.1 b & 15.4 \(\pm\) 1.1 b \\
3\% & 7.17 \(\pm\) 0.03 a & 0.26 \(\pm\) 0.02 a & 1.57 \(\pm\) 0.01 a & 13.35 \(\pm\) 1.03 ab & 0.5 \(\pm\) 0.05 b & 35.5 \(\pm\) 3.3 a & 29.5 \(\pm\) 1.3 a \\
5\% & 7.45 \(\pm\) 0.02 a & 0.23 \(\pm\) 0.04 a & 1.92 \(\pm\) 0.03 a & 14.68 \(\pm\) 0.44 a & 0.91 \(\pm\) 0.08 a & 39.6 \(\pm\) 3.9 a & 32.7 \(\pm\) 0.4 a \\ \hline \hline \end{tabular}
\end{table}
Table 4: Soil chemical properties (mean values \(\pm\) SE) in presence of various percent of biochar.  
Figure 3: Effect of different levels of biochar on: (**a**) leaf biomass; (**b**) stem biomass; (**c**) root biomass; and (**d**) total biomass in oak seedlings at different levels of Cd contamination (bars represent means +/- one standard deviation). The dashed line represents the control (no biochar, no Cd), and the term “Polluted soil” indicates pots with the Cd but without biochar. Different letters represent significant differences between treatments. The star indicates a significant difference between treatments and control.
## 3 Proposed PBFT Architecture and Model
### Consensus Process of Proposed PBFT
#### 3.3.1 Clustering Stage  
PBFT suffers from scalability issues as the number of nodes in a blockchain network increases [14]. The proposed PBFT addresses these issues by implementing a layered network structure that utilizes location-based clustering and dynamic group management. Location-based clustering is effective in reducing communication latency within blockchain networks. Generally, the propagation delay increases as the distance between two communicating nodes increases. Therefore, the overall communication latency increases. Proper clustering and grouping management of network participant nodes is crucial for minimizing communication delays. When clustering, it is possible to manage nodes at the national or city levels. Clustering nodes at the city level in the blockchain network would result in an increased number of CPNs managing each cluster. In such cases, broadcasting consensus messages and aggregating results can lead to considerable overhead. In contrast, clustering at the national level can reduce the potential overhead for CPNs, given the relatively smaller number of clusters involved. However, this approach may result in the formation of multiple groups within each cluster, increasing the consensus time for each group. Consequently, achieving a good balance between the total number of clusters and groups is crucial. Figure 3 illustrates a blockchain network structured based on continental locations, comprising five clusters. Each CPN and GPN can manage up to two subgroups within the example structure, as shown in Figure 2.  
As shown in Figure 3, the consensus process based on PBFT is conducted with these groups as the fundamental units by utilizing the nodes within each cluster to form groups. Such clustering ensures that relatively distant nodes do not exchange messages during the consensus process. Consequently, this approach can significantly reduce the time required for achieving consensus. The proposed PBFT method ensures that nodes other than the CPN of each cluster cannot communicate with nodes from different clusters during the consensus process. In cases where the number of participating nodes in terms of countries or cities is very small, nodes can be evenly distributed among multiple clusters based on their physical locations. However, a significant disparity in the number of nodes among specific clusters may lead to substantial differences in the time required for consensus within each cluster. During the clustering stage, it is essential to cluster nodes participating in the blockchain network based on their location information so that each cluster has an evenly distributed number of nodes as much as possible.
## 3 Results
### Characterization of the Synthesized ZnO NPs
#### 3.1.2 UV-Vis Spectral Analysis  
To further verify the results, the formation and stability of the synthesized ZnO NPs were determined by measuring the absorption spectrum in the 200-800 nm wavelength range against the aqueous extract of _C. spinosa_ L. as the reference. As seen in Figure 2, the maximum absorption peaks at 374 nm confirmed the green synthesized ZnO NPs. Furthermore, a peak with 290 nm was observed in the absorption spectra of the aqueous extract of _C. spinosa_ L. The obtained absorption peaks in the visible range are formed from the excitation of free electrons due to the color change [28]. The position and shape of the absorption peaks of metallic nanoparticles are strongly related to particle size and morphology. A sharp, single absorption peak corresponds to spherical nanoparticles [29]. Metal ions are transformed into metal nanoparticles by the phytochemicals that are found in the plant extract. As a result, the plant extract has stabilizing and reducing capabilities. UV-Vis spectroscopy was used to observe the progression of this reaction [30]. When electromagnetic waves interacted with electron conduction band oscillation in the spectrum of UV-visible spectroscopy, peak absorption was coupled by the surface plasmon resonance (SPR), accurately simulating metal ion reduction and nanoparticle creation. Because there are numerous OH groups available, it is possible to make nanoparticles. The fruits of _C. spinosa_ L. contain large amounts of alkaloids such as capparisine A, capparisine B, and capparisine C, as well as other flavonoid compounds such as rutin, quercetin, and tetrahydroquinoline acid, which are known for their ability to reduce metal ions and act as stabilizing agents in the nanoscale dimension [22]. These compounds are also known to be antioxidants and free of harmful substances.  
Figure 1: The visual appearance of the synthesized ZnO NPs using C. spinosa L. fruit extract: (**a**) fruit extract solution; (**b**) zinc acetate; and (**c**) mixture of zinc acetate and fruit extract.
## 2 Materials and Methods
### Volatile Compound Analysis  
Volatile compounds were determined using gas chromatography-mass spectroscopy (GC-MS) with solid-phase micro-extraction (SPME). A sample of 2 g was placed in a 15 mL vial and mixed with 1 g NaCl and 2.5 \(\mathrm{\SIUnitSymbolMicro m}\) of an internal standard (4-methyl-2-pentanol, 0.5 mL/L in distilled water). In the SPME extraction procedure, after equilibrating the sample in a water bath at 60 \({}^{\circ}\)C for 30 min, the SPME fiber (2 cm-50/30 mm; DVB/Carboxen/PDMS, Stable Flex Supelco, Bellefonte, PA, USA) was inserted into the vial for 30 min at 60 \({}^{\circ}\)C. At the end of the period, absorption of volatile components into the fiber was achieved. The fiber was then thermally desorbed at 250 \({}^{\circ}\)C for 10 min in the GC port. A polar Stabilwax column (60 m length \(\times\) 0.32 mm i.d. \(\times\) 0.25 \(\upmu\)m thickness; Restek, Bellefonte, PA, USA) was used for separation, with helium (99.9%) as the carrier gas at a flow rate of 3 mL/min. The oven temperature was programmed to start at 40 \({}^{\circ}\)C for 1 min, then increased from 40 to 100 \({}^{\circ}\)C at a rate of 7 \({}^{\circ}\)C/min and held for 5 min, followed by 180 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C/min and held for 1 min, and finally increased to 250 \({}^{\circ}\)C at a rate of 15 \({}^{\circ}\)C/min and held for 4 min. The volatiles were quantified as \(\upmu\)g/L by comparing peak areas to the internal standard, and their identity was confirmed by comparing mass spectra to library databases (Wiley 6 and FFNSC; Flavours and Fragrances of Natural and Synthetic Compounds).
# Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil
## 1 Introduction
TDLAS is an absorption spectroscopy technique that is the most direct and efficient way of detecting molecules. It has achieved remarkable results in atmospheric environmental monitoring and combustion detection [24; 25]. TDLAS is also more resistant to interference than photoacoustic spectroscopy. In the field of DGA, [PERSON], [PERSON] et al. [26] used TDLAS to measure acetylene at the \(\mu\)L/L level in the laboratory. [PERSON] et al. [27] measured methane in a field environment, with a detection error of 4 \(\mu\)L/L. [PERSON] et al. [28] developed a CH\({}_{4}\)/C\({}_{2}\)H\({}_{6}\) sensor with a detection range of 100 \(\mu\)L/L. In addition, [PERSON] et al. [29] used a four-laser device to detect four kinds of hydrocarbon gases, due to the fact that a small amount of the gas species reflects limited information regarding the transformer status. However, carbon monoxide and carbon dioxide are characteristic gases that contain transformer condition information that cannot be ignored. In summary, this paper investigates and presents an online detection setup for dissolved gases in transformer insulating oil based on headspace degassing and tunable laser absorption spectroscopy. The oil-gas separation system degases large volumes of insulating oil samples at low pressure, constant temperature, and low frequency agitation. The gas detection system uses a near-infrared wavelength for detection. The characteristic wavelengths of the gases are selected appropriately to reduce cross interference. Therefore, six target gases can be accurately detected by three lasers. Detection sensitivity is improved by the wavelength modulation technique and a long optical path structure (optical path length = 12 m). The complexity of the optical path is reduced by the common optical path formed by the time division multiplexing (TDM) technology. After the setup was completed, the performance was verified experimentally, and detection results are used to analyze the transformer status.
# Studies and Rejection of Intercrystal Crosstalk on FPGA in a High-Energy Photon-Counting System
## 6 Conclusions  
The intercrystal scatter phenomenon has a bad effect on image quality and is significant in our designed high-energy large-container-inspection \({}^{60}\)Co \(\gamma\)-ray photon-counting system, and this has been verified both theoretically and experimentally. To solve this problem, we analyzed the characters of intercrystal scatter events, taking into account their energy, position, and time relationships, which can be determined with photon-counting detectors. The analysis shows that intercrystal scatter events mainly distribute in the adjacent detector units, with time gaps of around 5 ns. Then, the proper time and detector window widths are selected and applied to reject the intercrystal scatter events. The algorithm is implemented on the FPGA and can switch the detector window width according to the applied scenarios.  
The simulation on an SP 250 mm Fe model validates the effectiveness of the proposed approach, with the criterion of \(\Delta\)Det \(\leq\) 2 functioning better in this low-count-rate scenario, reducing the RMSE from 4.328 to 3.273. Finally, the laboratory test shows that with the application of the algorithm of \(\Delta\)Det \(\leq\) 2, the photon-counting system under an 100 Ci intensity and 24 m/min scan speed can reach 280 mm Fe SP, which is 60-70 mm Fe more than that of the current integration system, proving the advantages of the algorithm we proposed. Further, FPGAs enable the real-time timestamping of detected signals with a sub-nanosecond resolution, thus having the potential to be applied to more coincidence event discrimination tasks (e.g., PET imaging), positioning them as a pivotal enabler for next-generation scientific and industrial systems.  
Conceptualization, H.Z. and J.C.; methodology, H.Z.; software, H.Z.; validation, J.C., H.Z. and Z.W.; investigation, J.C., S.J. and H.Z.; writing--original draft preparation, H.Z.; writing--review and editing, H.Z., S.J. and J.C.; visualization, S.X., S.J. and H.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Hefei Meyer Optoelectronic Technology Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Analysis of the Influence of Flood on the L4 Combination Observation of GPS and GLONASS Satellites
###### Abstract
In terms of CNR observation, the relationship between multipath error and CNR is illustrated by [PERSON] et al. [14], and a new method based on CNR is proposed to detect the multipath effect for GPS satellites. Similar methods can also be found in [PERSON] and [PERSON] [15], [PERSON] et al. [16], and [PERSON] et al. [8]. Considering that the multipath error performs more seriously during the flood, [PERSON] et al. investigated the influence of flood on the CNR of GPS satellites, the results indicate that the CNR during the flood performs lower than that without flood [17; 18]. In addition, [PERSON] et al. demonstrated that not only the CNR of GPS satellites was affected during the flood, but also the CNR of GLONASS satellites [11]. Based on previous references, the CNR related to multipath error is significantly affected during floods. Considering that some of the low-cost receivers cannot provide the CNR observation, thus, the L4 combination observation is also used to reflect the multipath error. However, the influence of flood on L4 combination observation has not been analyzed. L4 combination observation is obtained by making the difference between L1 and L2 frequency [19]. Considering that L4 is an ionosphere-free geometry-free linear combination, thus, it is widely used for studying ionospheric disturbance, multipath effect, and parameter retrieval [20; 21; 22]. Based on the triple frequency signal of GPS satellite, an improved L4 linear combination of geometry-free phase observations is developed to detect snow depth by [PERSON] et al. [23]. Meanwhile, [PERSON] et al. proposed using the L4 observation of the GLONASS satellite to detect the snow depth [24]. By considering the influence of regional ionosphere disturbance, [PERSON] et al. developed a new method using L4 observation to establish TEC (Total Electron Content) model [22]. In addition, [PERSON] et al. presented a new sea-surface altimetry method by using dual-frequency ionosphere-free carrier phase L4 combination observation [25]. Based on the above analysis, it can be found that the L4 observation can be used in many applications, thus, it is necessary to analyze the influence of flood on L4 observation. Taking the above into account, the relationship between multipath error, flood, and L4 combination observation is illustrated in theory. Then, the influence of flood on L4 combination observation of GPS and GLONASS satellites is analyzed in depth. In order to ensure the accuracy of comparison results, the satellite ground track repeat period of GPS and GLONASS is presented in the sky plot. In addition, the influence of other elements such as rainfall and hardware device is also investigated.
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 2 Materials  
### Study Area  
The state of Sao Paulo is the largest Brazilian sugarcane producer. Since 2006, the satellite images have been used to monitor the sugarcane conditions [8]. The state is located in the south-east of the country and contains 90% of the sugarcane plantings of southern and central Brazil with an area of more than 5.5 mln ha [36]. The state is divided into 645 municipalities and 15 administrative regions, eleven of which grow sugarcane to produce sugar and ethanol. In the rest of the administrative regions (Baixada Santista, Registro, Sao Jose dos Campos, and Sao Paulo) sugarcane is not grown on a commercial scale due to unfavourable climate and terrain features [8].  
Vector mask of the sugarcane fields provided by local farmers was used to form a reference sample for 2018. The sample contains 30 fields with an area from 7 up to 419 ha (total area is 3745 ha) located in the municipalities of Aracatuba, Arco-iris, Bentode Abreu, Clementina, Guararapes, Iaciri, Inubia Paulista, Lourdes, Lucelia, Osvaldo Cruz, Parapua, Piacatu, Rinopolis, Rubiacea, Salmourao, Santo Antonioda Alegria, Santopolis do Aguapei, and Valparaiso. Figure 1 shows the location of Sao Paulo, its municipalities, and the study site location as well as some examples of the reference sample fields.
## 2 Related Work  
In recent years, advances in image dehazing can be divided into two main phases, which can be categorized into initial techniques based on a prior assumptions about the image, and deep learning-based dehazing methods that utilize neural network models for end-to-end mapping.
## 1 Introduction  
Brain processes in response to continuous speech have been investigated through the acquisition of brain electrical activity. Electroencephalography (EEG) has facilitated the understanding of underlying brain activity with high temporal resolution [1; 2; 3]. Event-related potential (ERP), which is generally computed by averaging EEG signals over trials, has been widely used to extract time-locked neural activities evoked by short-duration speech tokens (e.g., words, syllables, and phonemes) [4; 5]. However, using short tokens as stimuli limits our understanding of how the brain processes continuous speech [6].  
System identification (SI) is a more direct method to investigate neural entrainment to continuous stimuli by modeling how stimulus features are mapped to neural responses [7]. The SI model, which considers the human brain a linear time-invariant (LTI) system, has been used to understand brain functions in response to specific visual and auditory stimuli [8; 9; 10; 11]. In these studies, reverse correlation was commonly used to characterize the temporal response function (TRF) in an LTI system [12]. This approach, which implements reverse correlation by computing the cross-correlation between the input and output of the system, has been employed to investigate neural entrainment to continuous speech stimuli [13; 14; 15] as well as to continuous visual stimuli [16; 17]. Although this approach has proven able to investigate neural encoding in response to continuous stimuli, it is more appropriate for stochastic process stimuli (e.g., Gaussian white noise) than for slow-modulating and non-stationary stimuli, such as continuous speech [18].  
However, natural stimuli for humans rarely exhibit the characteristics of stochastic processes. To address cases involving modeling of the TRF to slow-modulating and non-stationary stimuli, normalized reverse correlation has been considered, which effectively estimates the spectral-temporal receptive field (STRF) of brain neurons for more complex stimuli [19]. Although this normalized reverse correlation can map slow-modulating and non-stationary stimuli to neural responses, problems of matrix inversion and overfitting due to noise along the low-variance dimensions of datasets remain [20]. Regularized linear regression (i.e., ridge regression), developed initially to investigate neural responses to pure-tone stimuli in the primary auditory cortex, has been employed to address these problems [21]. However, incorrect selection of the regularization parameter of ridge regression can result in an inaccurate model [22]. Thus, the intensive optimization of the regularization parameter should be performed with ridge regression. To overcome this inefficiency, in this study, we proposed a non-parametric and computationally efficient approach to model linearization between stimuli and neural responses. The estimated model was developed using a detrended cross-correlation function. The computational efficiency while analyzing EEG data in response to continuous speech stimuli was compared between our approach and the ridge regression method.