esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
All the samples were analyzed under a HORIBA JobinYvon HR800 LabRam \(\upmu\)-spectrometer (HORIBA Jobin Yvon, Paris, France), coupled with an Olympus BX41 microscope (Olympus, Tokyo, Japan). The used excitation source was a HeNe laser, 20 mW, working at 632.8 nm, and the detector was an air-cooled charge-coupled device (CCD). Spectrometer calibration was obtained checking position and intensity of the Si band at \(520.65\pm 0.05\) cm\({}^{-1}\). The analyses were performed with a timing of at least 2 cycles of 200 s, after introduction of D06 filter to prevent sample modification, and a confocal hole set at 200 \(\upmu\)m. ORIGIN software v 6.0 was applied on the recorded spectra. The asymmetric bands were treated with OPUS software to obtain fitting process after 13 points FFT smoothing of the spectrum and selecting a Lorentzian function and minimum number of component bands to obtain reproducible results with the minimal residual error.  
SEM analyses were carried out using an environmental SEM (E-SEM; model Quanta 200, FEI Company, Hillsboro, OR, USA), with a pressure of 90 Pa, an accelerating voltage of 20 kV, and a working distance of 10 mm. Observation of phases with different chemical composition on the fiber surface was easy thanks to the back-scattered electron detector (BSED) and the generated white-black contrasts, not so evident using secondary electrons (SE).  
For the six minerals, regulated as \"asbestos\", the studied samples were:  
* amosite from South Africa;
* UICC crocidolite reference sample from South Africa;
* tremolite from Brachiello (Val d'Ala, Piedmont region);
* anthophyllite from Bresimo mine (Trentino Alto Adige, Italy);
* actinolite certified by IOM (Institute of Occupational Medicine);
* UICC chrysotile reference sample \"B\" Canadian (NB #4173-111-1) from Quebec, Canada.  
The a.-d. minerals have been previously characterized with the traditionally used techniques (XRPD, SEM/EDS, TEM/EDS, and Analytical Electron Microscopy- AEM) and stated in [PERSON] et al.  
2004 [26], whereas for chrysotile, the crystal structure and chemistry was described in [PERSON] et al. 2016 [27].  
Oregon and Cappadocia erionite chemicals were determined by [PERSON] et al. 2006 [28], while North Dakota chemical composition was compared with Turkish samples in [PERSON] et al. 2011 [22].  
For each studied mineral phase, different spectra from various bundles of fibers were recorded in the spectral range 4000-100 cm\({}^{-1}\). When particles were observed on the fiber surface by means of the OM annexed to spectrometer, micro-Raman spectra were recorded directing the laser beam onto the grains.
###### Abstract  
In response to the growing demand for improved operational efficiency in road and bridge networks, constructing parallel bridges in complex river sections has become a crucial strategy. This study focuses on a parallel bridge construction project in the Jinan section of the lower Yellow River, conducting physical model tests to investigate the unique aspects of the impacts of different pier shapes and spans on the flow characteristics of sediment-laden rivers under real-world engineering scenarios. The experimental results demonstrate that the hydraulic physical model of this river section that was constructed is reliable, with a relative error of <20% in sediment deposition, in the simulation of sediment erosion and deposition, flow velocity patterns, water levels, and riverbed morphological changes during parallel bridge construction in bridge-clustered river sections. The newly constructed bridges have a limited influence on the overall regime of this river section, with their impacts on both banks remaining within controllable limits, and the river flow remains largely stable. In areas with denser pier arrangements, the phenomenon of backscatter upstream of the bridges is more pronounced, and under characteristic flood conditions, the newly built bridges amplify the water level differences between the upstream and downstream sections near the bridge sites. The ranges of influence of the water level drop downstream of the bridges increase, particularly in the main flow areas. Flow velocities generally increase in the main channel, while significant fluctuations are observed in the floodplain areas. Flood process experiments reveal that the slope at the junction between the main channel and the floodplain becomes gentler, with noticeable scourring in the main channel. After flood events, the river tends to evolve toward a U-shaped channel, posing certain safety risks to the piers located at the junction of the floodplains and the main channel. This research methodology can serve as a reference for studying flow characteristics in similar parallel bridge construction projects in river sections, and the findings hold significant implications for practical engineering.  
Keywords:parallel bridge construction; river model test; river simulation; river regime +
Footnote †: journal: Applied sciences
## 4 Research Results
### Influence of Rainwater on Measurement Using the UPV Testing Method
#### 4.1.3 The Testing Slab V3 SFRC  
Results of ANOVA for the last slab are summarized in Table 6 and Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **df** & **SS** & **MS** & & \\  & **Degrees of** & **Sum of** & **Mean Sum of** & **F-Statistics** & \(p\)**-Value** \\  & **Freedom** & **Squares** & **Squares** & & \\ \hline factor (rain) & 2 & 0.765 & 0.3825 & 2.97 & 0.0576 \\ \hline residuals & 72 & 9.274 & 0.1288 & & \\ \hline \multicolumn{5}{c}{Bartlett test of homogeneity of variances: K = 1.7547, \(p\)-value = 0.4159} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of the ANOVA for Slab V3.  
Figure 10: Boxplots of the ultrasonic wave transmission for Slab V2 and all rain levels (r0, r1 and r7).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Levels Compared** & **Difference** & **Confidence Interval** & \(p\)**-Value** \\ \hline r1–r0 & 0.308 & (0.1074, 0.5086) & 0.00131 \\ r7–r0 & \(-\)0.108 & (\(-\)0.3086, 0.0926) & 0.40639 \\ r7–r1 & \(-\)0.416 & (\(-\)0.6166, \(-\)0.2154) & 0.00001 \\ \hline \hline \end{tabular} From the pairwise comparison, it can be seen that the rain level r1 is different from the other two rain levels, as it is shown by the significant differences. The ultrasonic wave transmission at level r1 is greater than at levels r0 and r7.  
\end{table}
Table 4: Tukey multiple comparison of means for Slab V1.  
The ANOVA showed that the hypothesis of the same means was not rejected (\(p\)-value is greater than the significance level 0.05). All calculations were done in R [21].
## 3 Results
### The Definition of Quality Criteria and Indicators Analysis  
After selecting the features and pre-processing the data, the criterion for checking the quality status of the network was introduced. The evaluation criterion was the CCC index, which is obtained from the multiplication of a number of the most important indices. The histogram of this index was derived from 64,115 records and is illustrated in Figure 8.  
As expected, and as can be seen in the above diagram, the range of numerical values of CCC was between 0 and 100. Values close to 100 indicated higher quality. In Figure 8, there are two knees. Therefore, we divided the network status into three discrete categories. The first category had a CCC value higher than 94 and represented excellent quality. The second category had a CCC between 94 and 85 and represented acceptable quality. Several records fell into the third category, having a CCC less than 85. This category represented unacceptable quality of the network, and, therefore, our work priority was to focus on the data of the third category, the unacceptable category. From now on, we considered the records with CCC less than 85 as faults and identified the cause of the fault in these records.  
Before addressing the issue of identifying the cause of the fault, an analysis of the values of these indicators was conducted. To do this, we used classification algorithms and, especially, a decision tree. The reason for using the decision tree algorithm is this method's display ability and its good interpretation ability. In decision tree algorithms, the Quest method is used. This method had high accuracy for our provided data and this accuracy was obtained at a lower tree depth. Therefore, it avoided the problem of overfitting the model on the training data. The following tree separated the test data with 93.66% accuracy. The results were obtained with the help of Clementine software. It can be seen that among the 14 features used by experts in these networks to analyze the quality  
Figure 8: The histogram of CCC index value in one month of the examined data.  
Figure 7: The histogram of the dropped calls in the data.  
status, and among the seven indicators used in defining the quality criteria of the network, only three indicators, those of call interruption, interruption in the signaling channel, and the success rate in achieving the signaling channel, could estimate the network status (Figure 9).
# Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020
## 2 Materials and Methods
### Data Sources and Preprocessing  
#### 2.2.1 MODIS Products  
In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) products, specifically NDVI, EVI, and GPP, were used to monitor the vegetation because of their high-quality data sources ([https://e4 ftl01.cr.usgs.gov/](https://e4 ftl01.cr.usgs.gov/), accessed on 1 August 2023). The NDVI and EVI were using a 1-month temporal resolution and a 0.05\({}^{\circ}\)spatial resolution from the MOD13C2 product. To mitigate the impact of aerosol and atmospheric and molecular scattering, the maximum value composite (MVC) method was used to generate yearly NDVI and EVI datasets [31]. GPP data were sourced from the MOD17A2H product, featuring a temporal resolution of 8 days and a spatial resolution of 500 m. The monthly and yearly GPP data to achieve a spatial resolution of 0.05\({}^{\circ}\), starting from the 8-day data, were synthesized and resampled [32]. The period from 2002 to 2020 was considered in this study.  
Figure 1: Spatial pattern of grassland types (**a**) and climate class (**b**) in Inner Mongolia of China.
# Dynamics Analysis of Space Netted Pocket System Capturing Non-Cooperative Target
## 4 Dynamic Simulation and Results
### Parameters of the Service Spacecraft and Captured Target  
The parameters of the service spacecraft and captured target used in the simulation are presented in Table 1 and the parameters of the netted pocket system are presented in Table 2. The process of the service spacecraft capturing the target is designed as follows: at the beginning, the closing distance L between the ends of adjacent inflation rods is 5.78 m in the initial state; then, the netted pocket system is closed up and the final distance L between the ends of adjacent inflation rods is shortened to 2.1 m. After that, when the target is captured, the service spacecraft tows the target and the service spacecraft and the target motion together.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 6 Blockchain Potential in V2X Networks  
Recently, blockchain technology has gained a lot of attention in many fields, because of its ability to build trust in a decentralized manner. The potential of blockchain is huge. Blockchain can work in a decentralized, distributed and autonomous way without the need for third-party service providers or centralized authorities. A blockchain is simply a publicly distributed database with all the electronic transactions or events registered in a transparent ledger and shared among the participating members [51]. Every other event in the blockchain database is validated by the consensus mechanism in the network [52]. The blockchain offers privacy and anonymity of the user information, as each peer node holds the same copy of the ledger based on secure cryptography. The blockchain is used in various fields outside the domain of cryptocurrencies because of its unique features, such as immutability of data, public verification, transparency, auditability, security, and privacy [53]. Some of the application fields of blockchain are intelligent transport system [54], IoT systems [55], supply chain management, various types of industry [56], food supply chain [57], smart energy [58], healthcare [59], and many more.
## 5 Results of NSGA-II Optimization Approach
#### Summary of Results  
The optimization approach using the NSGA-II algorithm successfully identified an optimal project schedule by balancing cost, time, and resource constraints. After 200 generations, the best solution was determined using the weighted sum technique, with an optimal preference index (PI) of 0.16549756, a project duration of 274 days, and a total direct cost of USD 550,000. The analysis of the non-dominated set of solutions demonstrates that no single criterion (cost or time) could be improved without negatively impacting the other. By applying the optimal execution mode for each project activity (as detailed in Tables 7 and 8), resources for noncritical activities within their floating periods are redistributed effectively using our method. Comparing the obtained results (274 days and USD 632,200) with the contracted project duration and cost (285 days and USD 660,500), using the proposed method achieved a shorter project duration and lower cost. This confirms the effectiveness of NSGA-II in avoiding local minima and identifying a globally optimal solution. The method's ability to dynamically allocate resources while maintaining project constraints ensures an efficient and feasible scheduling solution.
# Recent Strategies for Environmental Remediation of Organochlorine Pesticides
## 4 Mechanism of Heterogeneous Photocatalysis  
The mechanisms of heterogeneous photocatalysis has been reported in several studies [63; 67; 68; 69; 70; 71]; only a summary of these mechanism will be highlighted in this review. All semiconductors (SC) have energy band with low empty conduction band (CB) and high occupied valence band (VB) in their electronic band structure. The separation between the CB and VB is called band gap energy. Upon the irradiation of a semiconductor photocatalyst with light energy which is equal to or more than its band gap energy (Eg), a pair of valence band hole (h\({}^{+}\)) and conduction band electron (e\({}^{-}\)) is formed as the electron is excited from VB to CB. The pair may either recombine or move to the surface of the semiconductor photocatalyst and react with acceptor or donor species. It should be noted that the prevention of the charge carrier recombination is important for photocatalyst reaction to proceed favorably. The reason being that fast recombination of the charge carriers will merely dissipate heat  
Figure 6: Properties of ideal photocatalysts.  
Holes of the valence band are strong oxidizing agent, which can oxidize the pesticides or attack the donor species. These attacked donor species can be readily oxidized to generate hydroxyl radicals from hydroxide ion at the surface or from water. On the contrary, electrons of the conduction band are reducing agent. They can react with dissolved oxygen to form superoxide anion radical O2\({}^{\bullet-}\). This may also lead to the formation of hydroxyl radicals. These radicals can oxidize pesticides on the surface of the SC, which eventually brings about the mineralization of the pesticides. The entire mechanism is presented in Figure 7 and summarized Equations (8)-(16).  
\[\text{SC}+\text{photon}\rightarrow\text{SC}(\text{e}^{-}_{\text{CB}})+\text{ SC}(\text{h}^{+}_{\text{VB}}) \tag{11}\]  
\[\text{H}_{2}\text{O}+\text{SC}(\text{h}^{+}_{\text{VB}})\rightarrow\text{H}^{ +}+\text{OH}^{\bullet}+\text{SC} \tag{12}\]  
\[\text{OH}^{-}+\text{SC}(\text{h}^{+}_{\text{VB}})\rightarrow\text{SC}+\text{OH}^ {\bullet} \tag{13}\]  
\[\text{SC}(\text{e}^{-}_{\text{CB}})+\text{O}_{2}\rightarrow\text{SC}+\text{O} _{2}{}^{\bullet} \tag{14}\]  
\[\text{O}_{2}\bullet-+\text{H}^{+}\rightarrow\text{HO}_{2}{}^{\bullet} \tag{15}\]  
\[\text{HO}_{2}\bullet+\text{H}^{+}+\text{SC}(\text{e}^{-}_{\text{CB}}) \rightarrow\text{H}_{2}\text{O}_{2}+\text{SC} \tag{16}\]  
\[\text{h}^{+}_{\text{VB}}+\text{Pesticide}\rightarrow\text{products of oxidation} \tag{17}\]  
\[\text{e}^{-}_{\text{CB}}+\text{Pesticide}\rightarrow\text{products of reduction} \tag{18}\]  
\[\text{OH}\bullet+\text{Pesticide}\rightarrow\text{products of degradation} \tag{19}\]
## 8 Experimental Results
### Cabled Experiments  
We performed cabled experiments to demonstrate the achievable ranging precision of the ToF estimation algorithm and Kalman filtering techniques. Two 4-antenna CHP2 users are connected by RF cables, attenuators, and a signal combiner to emulate an ideal LoS propagation channel. We subtract the mean of the resulting ToF estimates to highlight the variance and stability. These normalized ToF estimates are presented in Figure 19 for the interpolated correlator bank estimator and two extended Kalman filter formulations. The unfiltered correlation results maintain a ranging precision below 1.7 cm, while the extended Kalman filter(EKFs) drive the ranging precision as low as 0.3 cm at the cost of increased settling time.  
\begin{table}
\begin{tabular}{l r r r} \hline \hline
**Resource** & **Utilization** & **Available** & **\%** \\ \hline LUT & 171,436 & 274,080 & 62.55 \\ LUTRAM & 2967 & 144,000 & 02.06 \\ FF & 184,477 & 548,160 & 33.65 \\ BRAM & 402 & 912 & 44.08 \\ DSP & 1352 & 2520 & 53.65 \\ IO & 143 & 328 & 43.60 \\ GT & 1 & 24 & 04.17 \\ BUFG & 35 & 404 & 08.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: CHP2 logical resource utilization on ZynqMP XCZU9 EG.
## 1 Introduction
### Hybrid Localization Systems
Unfortunately, deploying such systems would require placing a large amount of sensors in fixed places, which makes them practically difficult to use in home environments. However covering with them selected spaces would not be problematic. Although, such sensors could be a good addition to radio-based systems, the author have not found any examples of employing them in hybrid localization schemes. The paper presents a novel hybrid localization concept combining Bluetooth Low Energy with ranging sensors. The presented solution is the effect of the works started in [35], where a concept was briefly described and tested with very simple experiments. The proposed system concept is intended mainly for indoor user localization in AAL solutions. The user is localized using a BLE system, which is supported with a few singular proximity sensors placed in the areas, where BLE-based localization is less accurate or are important from older person's safety point of view (e.g., a shower, near a stove). The results from both parts are fused using two types of hybrid localization algorithms. The presented concept was tested with simulations and experiments conducted in a typical fully furnished flat. The paper also includes the results of experimental assessment of VL53L1X [36] proximity sensor properties.
## 4 LoRaWAN-Based Outdoor Positioning in Application-Specific IoT Domains
### Smart City
In particular, an experimental setup mimicking real-world scenarios has been purposely implemented using the LoRaWAN infrastructure. In this way, the delay introduced by the communication infrastructure from the field up to the data sink at the application level has been evaluated. Ecotourism such as national forest parks is becoming increasingly popular. However, the accidents ratio as with getting lost and the occurrence of natural disasters is growing too. The large area of ecotourism makes LoRaWAN an acceptable communication technology for positioning. The authors in [132] propose a system for monitoring and assisting visitors of forest parks based on LoRaWAN for the development of a communication infrastructure capable of allowing communication. The proposed method is used to support an application for monitoring visitors' location.
## 2 DFIG-Based Wind Turbine System
### Fault Characteristics of DFIG
#### 2.3.2 Fault Current of the DFIG
Combining Equations (2), (3), and (5), the rotor flux after the fault can be obtained as follows:  
\[\overset{\rightarrow}{\psi}_{r0}=(L_{s}L_{r}-L_{m}{}^{2})\frac{2Q_{s}}{3L_{ m}U_{s}}+\frac{U_{s}L_{r}}{\omega_{s}L_{m}}+j(L_{s}L_{r}-L_{m}{}^{2})\frac{2P_{s}}{3L_{ m}U_{s}}, \tag{11}\]  
By substituting Equations (6) and (10) into Equation (2), the fault current \(\overset{\rightarrow}{i}_{s}\) of the DFIG stator can be expressed as  
\[\overset{\rightarrow}{i}_{s}=-\frac{AUL_{s}}{j\omega_{s}L_{r}}e^{-t/T_{r}{} ^{\prime}+j\theta}+[\frac{l_{m}}{L_{r}}f(s,R_{cb})-1]\frac{(1-A)U_{s}}{j\omega_ {s}L_{r}}e^{j(\omega_{s}t+\theta)}+\]  
\[\overset{\rightarrow}{\frac{l_{m}}{L_{r}}}(\overset{\rightarrow}{\psi}_{ r0}-f(s,R_{cb})\frac{(1-A)U_{s}}{j\omega_{s}}e^{i\theta}]e^{-t/T_{r}{}^{ \prime}+j\omega_{s}t} \tag{12}\]  
It can be seen in Equation (12) that the fault current of the DFIG stator consists of three parts: a DC component with the decay time constant of \(T_{s}^{\prime}\), a frequency component of \(\omega_{s}\), and a frequency component of \(\omega_{r}\) with a decay time constant of \(T_{r}^{\prime}\). Additionally, the frequency of the fault stator current is influenced by the components of the three different frequency currents. Additionally, the three different frequency currents are determined by the depth of the voltage dips, the resistance of the crowbar circuit, the rotor speed, and so on. Figure 2: Equivalent circuit to the DFIG with a crowbar circuit in d-q synchronous rotating reference frame. When the fault voltage of the DFIG stator dips to 0 pu, the rotor speed of the DFIG is set to be 1.2 pu, and the resistance of the crowbar circuit is set to be 0.001 ohms. Additionally, in this situation, the fault current of the DFIG stator can be obtained through simulation, as shown in Figure 3. The simulation result is consistent with the theoretical curve, which verifies the accuracy of the theoretical analysis. Additionally, it can be found in Figure 3 that the frequency of the fault current clearly shifted from 50 Hz to 60 Hz. Thus, during the fault period, the fault current of the DFIG had the characteristics of frequency offset.
## 3 Results
### The Spatial Patterns of the Relationship Between GPP (or ET) and SPEI  
The correlation between summer GPP anomaly (or ET anomaly) and SPEI from 2003 to 2015 had large spatial heterogeneity (Figure 5a,b). Both linear correlation slope (Figure 5a,b) and coefficients (Figure 5c,d) showed that GPP and ET had a positive correlation with SPEI inside the shrub, grassland and some barren/sparse vegetation, while having a negative correlation with SPEI in the ambient forest and croplands. It means drought induces the decreased GPP and ET for grassland and increased GPP and ET for cropland and forest in NADRs. Furthermore, the most significant positive correlated area (\(p<0.05\), \(0.05<p<0.10\)) occurred in the east grassland of Mongolia and middle Inner Mongolia, which was located in the semi-arid regions. The significant negative correlated areas (\(p<0.05\), \(0.05<p<0.10\)) were few, almost distributed in north mixed forest and northeast cropland, which are cold regions. Additionally, GPP and ET showed a minor different response to drought. For example, the significant correlation between ET and SPEI is also presented in the arid desert ecosystem, like Alashan aeolian sandy land (positive) and north of Tarim Basin (negative), where an insignificant correlation between GPP and SPEI was present.  
Figure 4: The frequency and trends of summer drought in the NADR from 2003 to 2015. (**a–c**) Drought frequency of moderate drought (MD, \(-1.5<\) SPEI \(<-1.0\)), severe drought (SD, \(-2.0<\) SPEI \(<-1.5\)) and extreme drought (ED, SPEI \(<-2.0\)); (**d**) the trends of SPEI; (**e**) times series of SPEI and the percentage area affected by droughts.