esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### MBN Measurements
These carbides are missing in the near-surface region in comparison to the unaffected bulk. Again, the depth of the region in which missing carbides can be observed is greater for the positions of higher MBN. The significance of these precipitates is demonstrated in \[v_{p}=\frac{dM}{dt}=(H-H_{c})^{q} \tag{1}\]6 The difference between MBN in the demagnetized and magnetized states is only minor when the surface is free of these fine carbides (as indicated by the evolution of the black and red evolutions in Figure 14). DWs can move easily, the pinning strength of the matrix is low, and the effect of the magnetic coupling due to the presence of the remnant magnetization is only minor. As soon as the decarbartized layer becomes thinner and the region with fine carbides prevails within the MBN sensing depth, the effect of the remnant magnetization is mixed with the hindering effect of carbides, and the valuable difference in MBN evolution between the magnetized and demagnetized states can be found for the lower magnetizing voltages. Expressed in other words, the weaker magnetizing field is not able to overcome the remnant magnetization, and the superimposing contribution of the pinning sites is expressed in terms of fine carbides. Moreover, it can also be noted that the evolutions for the regions emitting higher MBN saturate earlier (at the lower magnetizing field linked to the magnetizing voltages), and the saturated values are remarkably higher than those for the regions emitting lower MBN due to the different contribution of carbides within the MBN sensing depth (the estimated sensing depth is about 0.1 mm in this particular case [45]). The aforementioned topic is closely related to the components used in the automotive, petrochemical, and/or civil industries. Compositional heterogeneity, especially in multiphase steels [46] or complex phase steels [47], might be detected. In addition, the heterogeneity in the dual-phase steel structure [48] used, especially in the automotive industry or maraging steels [49], can potentially be optimized using magnetic testing.
## 1 Introduction
Elimination of phosphate from water can be done using various forms of iron, such as sponge iron, goethite, akagneite, and iron ore. These iron-based adsorbents showed high phosphate adsorption capabilities, but have a longer removal time and hydraulic retention time. In the case of sponge iron, it showed 90% phosphate removal in 12 h [35], and iron ore showed 97% phosphate removal in 15 days [36]. Synthetic goethite has 40-100% phosphate removal capacity, but it takes 2-8 h. Likewise, akagneite showed 15-100% removal efficiency in 4-8 h [37]. Biogenic iron oxide exhibited 100% removal, but the time required was 24 h [38]. Many research teams tried to resolve this problem by using different iron sources. [PERSON] et al. (2016) [39], synthesized Iron-ZSM-5 material in which the iron source was AIC (ammonium iron citrate). It possessed regenerative properties which have potential application in wastewater treatment and catalysis. Because of the sharp reduction in the available phosphorus resource, both phosphorus removal and recovery are considered in the wastewater treatment process [40]. Unfortunately, very few studies have achieved to handle this issue. In addition, there is a lack of adequate research on the mechanism of phosphate adsorption-desorption by zeolite. In the current work, we conducted a phosphate adsorption-desorption cycle experiment and focused on the first adsorption-desorption cycle and tried to analyze the activity of different elements of Iron-Zeolite-A (Iron-Z-A) with respect to phosphate. Spectroscopic and microscopic techniques such as XRD, SEM-EDS, EPR, FT-IR, XPS, and Raman were used to explain the prime mechanism behind adsorption-desorption of phosphate vividly. Kinetics and isotherm models were performed to evaluate the performance of adsorption-desorption.
## 1 Introduction
In other words, the important question raised here is this: Can seasonal changes in \(T\) and \(p\) generate a significant false SIF signal that, if not appropriately compensated, mimics seasonal patterns related to temperature-dependent physiological responses expressed with vegetation SIF? Altogether, this work aims to address all of these issues within a theoretical framework by using simulated data with the MODerate resolution atmospheric TRANsmission (MODTRAN) ([PERSON] et al., 2014) atmospheric Radiative Transfer Model (RTM). The remainder of this paper is organized as follows. Section 2 describes the relationship between the measured at-sensor and at-surface solar irradiance and up-welling canopy radiances. Section 3 briefly reviews the FLD-family and SFM SIF retrieval approaches and modifies these standard formulations to introduce a first order O\({}_{2}\) transmittance correction and analyses their limitations. We then propose to adapt a classical airborne atmospheric correction scheme for proximal sensing data, thereby compensating for O\({}_{2}\) effects while also accounting for effects due to the instrument's spectral resolution through the use of a radiative transfer model. Section 4 is dedicated to the results: (1) supporting the need to compensate for O\({}_{2}\) absorption when retrieving SIF in proximal sensing; (2) reporting the expected SIF accuracy when a first order O\({}_{2}\) compensation is applied to either the FLD or SFM retrievals for different instrument resolutions and sensor heights; and (3) reporting the expected SIF accuracy when a classical airborne atmospheric correction scheme is applied to compensate for the O\({}_{2}\) transmittance. Section 5 examines the O\({}_{2}\) transmittance dependency on \(T\) and \(p\) conditions and its potential impact when monitoring long temporal series. Section 6 discusses the main results found in this paper in a broader scientific context in view of experimental field campaigns and satellite validation activities. Finally, our main conclusions are summarized in Section 7.
## 5 Discussions  
Traditional construction information classification systems, such as Uniclass2015 [6] and OmniClass [43], are structured using a facet approach. Uniclass comprises 10 tables including complexes, activities, entities, and elements. OmniClass defines 15 tables, including construction entities, spaces, and elements, according to function. Each table can be used independently or in combination to classify specific types of information, offering the advantage of categorizing complex subjects. However, accurately implementing precise construction information requires a thorough understanding of the relationships between classes in various tables and can therefore be challenging.  
Consistent information transmission is crucial in relation to the construction data generated throughout the phases of road construction, spanning from design to construction and maintenance. For instance, during the road construction process, tasks such as commencement, completion, and maintenance are executed based on electronic design drawings. Although design drawings evolve into construction-specific drawings, such as as-built, design-change, and completion drawings, maintenance activities often rely on the original design drawings stored by the client. This results in a discontinuity, such that construction information is not consistently conveyed.  
By contrast, the MESH construction information classification system, which connects construction information generated on the construction site, such as objects and activities, through a hierarchical structure, clarifies the sequential relationships between objects and activities. This enables effective process and payment management. The MESH allows for the representation of all situations and information arising in road construction sites by defining JOBs using creating a 1:N relationship between them through the dualization and hierarchization of construction objects and activities. Consequently, the MESH ensures the consistency and continuity of construction information throughout the entire lifecycle of infrastructure facilities.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 3 Signal Processing and Design
### Data Component
#### 3.2.1 Bit Rate  
To quantify the amount of data we can transmit over a CSS signal, we need to evaluate the number of bits transmitted per chirp period. This depends on the selected CSS modulation, e.g., BOK-CSS transmits one bit per (symbol) chirp period. Meanwhile, with a FSK-CSS signal, we work with a constellation of \(M=2^{\text{SF}}\) symbols, with SF as the spreading  
Figure 2: Minimum chirp period needed to obtain target sensitivity performance (PD \(>0.9\) @ PFA = \(10^{-5}\)) for different bandwidths as a function of CN0 and \(\{N_{\text{sat}},N_{\text{vis}}\}=\{50,5\}\) satellites.  
factor. The spreading factor denotes the number of bits per chirp period, and in a LoRa-like manner, it is given by [19]  
\[\text{SF}\doteq\log_{2}(BT_{\text{c}}), \tag{6}\]  
where \(T_{\text{c}}\) is the chirp period, denoting the symbol duration and selected to achieve sensitivity performance. Then, the BOK- and FSK-CSS bit rates are respectively given by  
\[R_{\text{b}}^{(b)}\doteq\frac{1}{T_{\text{c}}}\quad\text{and}\quad R_{\text{b} }^{(f)}\doteq\frac{\text{SF}}{T_{\text{c}}}. \tag{7}\]
# Review on Silver Nanoparticles as a Novel Class of Antibacterial Solutions
## 4 Silver Nanoparticles and Their Antibacterial Role
### Factors Impacting the Antibacterial Activity of Silver Nanoparticles
#### 4.1.1 Nanoparticle Size  
Numerous studies show that there is an inverse correlation between dimensions of nano silver particles (5-9 nm) and their antibacterial activity. [PERSON] et al found that there is significant increase in antibacterial activity especially below 10 nm [45]. The smaller dimensions of nano particles are associated with easier bacterial cell's wall penetration, and more intense destruction by reactive oxygen species (ROS) accumulation. Also, recent studies suggest the small and medium-size functionalized silver nanoparticles severely impact mitochondrial electron transport, phagocytosis, autophagy, organelle integrity and organization (e.g., microtubules). Pronounced transcriptional responses were also observed for medium-size silver nanoparticles [49].  
Due to their small hydrodynamic diameter (less than 6 nm), the silver nanoparticles can easily pass through the kidneys, being eliminated from the body through the urinary system, which greatly reduced the risk of long-term toxicity [45].
# A New Ice Accretion Model for Aircraft Icing Based on Phase-Field Method
## 4 Discussion  
Within the framework of this study, the ability of the phase-field method in aircraft icing calculation was confirmed, and a phase-field icing model and its icing code was developed. Like normal phase-field models, the phase-field icing model treats multi-phase fluids as one fluid with variable material properties.  
At present, ice accretion models are mainly divided into the Messinger icing model and shallow water film icing model. In the Messinger icing model, the mass and energy conservation equations of the control volume are established based on the surface liquid water to solve the equilibrium temperature of the control volume, and then the amount of ice accretion is calculated. The shallow water film icing model is based on the distribution of liquid water on the surface, and the ice accretion model is established based on the movement of the water film. The ice shape predicted for glaze ice and mixed ice needs to be improved. Compared with the two kinds of ice accretion models above, the phase-field icing model takes an ice-water mixture as the research object, and establishes the ice accretion model based on the phase-field method.  
The flight state and weather conditions of the aircraft will affect the ice shape and the freezing level. The flight state includes flight speed and angle of attack (AOA). The meteorological conditions include the ambient temperature, liquid water content (LVC), mean volumetric diameters (MVD) and ice time. In order to verify the accuracy and applicability of the phase-field icing model, eight different icing conditions are selected, in which the incoming Mach number is from 0.197 to 0.313, the LWC is from 0.5 g/m\({}^{3}\) to 2.1 g/m\({}^{3}\), the incoming temperature is from 244.8 K to 267.55 K, the icing time is from 300 s to 600 s, the AOA is from 0\({}^{\circ}\) to 4\({}^{\circ}\) and the MVD is from 19 \(\upmu\)m to 30 \(\upmu\)m. The predicted ice types include crime ice, glaze ice and mixed ice. For any icing condition, the ice shape and its characteristic points are more consistent with the experimental ice shape than those provided by LEWICE.  
Although the correctness and effectiveness of the phase-field icing model to predict the two-dimensional icing ice shape under different icing conditions have been verified, the correctness of the model when predicting the three-dimensional icing shape needs to be developed further. At the same time, the current phase-field icing model is only a two-dimensional phase-field problem, which can be developed into a three-phase problem of ice water and air in the future.
## 3 Microseismic Evolution Characteristics of Rockburst Formation Process
### Special Types of Rockbursts
#### 3.3.1 Structure-Type Rockburst
[17] used the energy ratio method to study the fracture types of microseismic events in the process of three consecutive strong rockbursts along the structural plane. The proportion of shear fracture events was 72.76%, and the proportion of tensile failure events was only 2.44%. This shows that the formation process of a structure-type rockburst is mainly shear fracture. The above studies showed that the existence of structural planes significantly influences the proportion of events of different fracture types in the process of rockburst formation. Therefore, in the early warning of rockbursts, the type of rockburst can be judged in advance by analyzing the proportion of events of different fracture types, for a more accurate early warning. [PERSON] et al. [47] used the energy ratio method to study the formation process of rockbursts in underground caverns and found that the \(E_{S}/E_{P}\) value of microseismic events before a rockburst tends to decrease evidently, that is, the proportion of tensile failure events before the rockburst increases significantly. Therefore, the rockburst can be predicted by the change in the proportion of different failure types in the process of rockburst formation. The typical characteristics of strainburst and structure-type rockbursts in different studies are summarized, as shown in Table 1.
###### Abstract  
We report a significant enhancement in the electrical transport and photoconductivity of ZnO/ZnS core/shell nanowires (NWs) compared to those of ZnO NWs via the application of compressive strain. Under a compressive strain of \(-0.15\%\), the output current of the ZnO/ZnS core/shell NWs increases by 91.1% compared to that under the no-strain condition, whereas that of the ZnO NWs under the same condition is 42.7%. The significant increase in the output current of the ZnO/ZnS core/shell NWs is attributed to the type-II band alignment and strain-induced piezopotential changes at the junction interface, which induce a reduction in the barrier height to enable efficient charge carrier transport. Furthermore, under UV illumination and a compressive strain of \(-0.15\%\), although the photocurrent of the ZnO/ZnS core/shell NWs increases by 4.5 times compared to that of the ZnO NWs, the relative increase in the photocurrent of the ZnO/ZnS core/shell NWs is 11.7% compared to that under the no-strain condition, while the photocurrent of the ZnO NWs increases by 32.3% under the same condition. A decrease in the increase rate in the photocurrent of the ZnO/ZnS core/shell NWs with a change in strain under UV light compared to that under the dark condition can be explained by the piezoelectric screening effect induced by photogenerated carriers. By calculating the change in the Schottky barrier height (SBH), we demonstrate that the piezoelectric potential with a change in strain decreased the SBH, thus increasing the current level. Lastly, we propose a mechanism of the piezotronic and piezo-phototronic effects under applied strain and their effects on energy-band diagrams.  
ZnO/ZnS core/shell nanowire; ZnO nanowire; piezotronic effect; piezo-phototronic effect; type-II band alignment Article
# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 2 Materials and Methods
### Convolutional LSTM (ConvLSTM) Network Structure  
The Convolutional LSTM (ConvLSTM) [21] network structure changes the input to an M \(\times\) N grid with P number of channels per grid compared to the traditional LSTM network, so the input to the network is a 3D tensor. The authors of this paper also gave the formula for the ConvLSTM structure, as in Equation (1).  
\[\begin{split} i_{t}=\sigma(W_{xi}*X_{t}+W_{hi}*H_{t-1}+W_{ci} \circ C_{t-1}+b_{i})\\ f_{t}=\sigma\left(W_{xf}*X_{t}+W_{hf}*H_{t-1}+W_{cf}\circ C_{t-1}+ b_{f}\right)\\ C_{t}=f_{t}\circ c_{t-1}+i_{t}\circ\tanh\left(W_{xc}*X_{t}+W_{gc}*H_ {t-1}+b_{c}\right)\\ o_{t}=\sigma(W_{xo}*X_{t}+W_{ho}*H_{t-1}+W_{co}\circ C_{t}+b_{o}) \\ C_{t}=o_{t}\circ tanh(C_{t})\end{split} \tag{1}\]  
In the formula, the asterisk represents the convolution operation. ConvLSTM combines the advantages of convolutional neural networks and recurrent neural networks, which  
Figure 2: UNet network structure adapted with permission from Ref. [14]. Copyright 2015, copyright Ronneberger.  
makes it suitable for spatio-temporal data and can be used in video ground semantic segmentation tasks. Its structure is shown in Figure 3.
###### Abstract  
This paper presents a novel Time Difference of Arrival-based approach suitable for single-anchor positioning systems, implemented by phase wrapping-impaired array antenna. With the latter being a typical occurrence in large Switched Beam Antenna (SBA) operating in the low microwave range. The proposed method takes advantage from the large bandwidth of radio link, established between the anchor and the positioning target, by generating an unambiguous equivalent phase relationship between antenna array elements. The technique is validated by adopting a relatively large SBA antenna operating in the 4.75-6.25 GHz bandwidth, and capable to position a target in a 3D domain. Experimental data, carried out in the 4-7 GHz frequency bandwidth, show that by dealing properly with the inherent constraint of phase wrapping issues, it is possible to get a significant improvement on the elevation angle with respect to methods not capable to deal with phase reconstruction and thus operating in a phase-less context. Combining range and angle errors, the associated cumulative distribution function error in 90% of cases shows an error of 0.13 m.  
localization; TDoA; wideband microwave electronics; Switched Beam Antenna; phase wrapping-impaired +
Footnote †: journal: Electronics  
Article  
A Novel TDoA-Based Method for 3D Combined Localization Techniques Using an Ultra-Wideband Phase Wrapping-Impaired Switched Beam Antenna
## 4 Results and Discussion
### Impact of Reply Rate and RE on DME Capacity  
International civil aviation organization (ICAO) recommends that reply efficiency should be more than 70% and reply rate no less than 2700 ppps to ensure accuracy of distant measuring [1]. Transponder can transmit distant measuring pulses with \(\lambda_{t}=2700-169=2531\) ppps pulse density only besides identification pulses as mentioned above. Under the limitations, DME capacity and the corresponding parameters can be obtained from Figure 6 and some of them are listed in Table 2 to compare.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reply Rate (ppps)** & **JTIDS** & **Mode** & \(R_{e}\) & **Number of Aircraft** \\ \hline \multirow{4}{*}{2700} & \multirow{2}{*}{Yes} & X & 71.4\% & **159** \\  & & Y & 52.5\% & 216 \\ \cline{2-5}  & & X & 75.3\% & **154** \\  & & Y & 55.3\% & 210 \\ \hline \multirow{2}{*}{2848} & \multirow{2}{*}{Yes} & X & & 171 \\  & & Y & **100** \\ \cline{1-1}  & & X & 200 \\ \cline{1-1}  & & X & **117** \\ \hline \hline \end{tabular}
\end{table}
Table 2: DME Capacity under the limitations of reply rate and reply efficiency.  
Figure 7: Difference of reply efficiency for different modes vs. number of aircraft.  
Under the limitation of 2700 ppps reply rate, in mode X, DME capacities are 159 and 154 when there is JTIDS interference or not, respectively. Corresponding reply efficiencies are 71.4% and 75.3%; they are more than 70%, so 159 and 154 are the valid capacities of DME in mode X. DME capacities in mode Y are 216 and 210, they look like more than those in mode X; but they are invalid values because their corresponding reply efficiencies are 52.5% and 55.3%, they are less than 70%. Under the limitation of 70% reply efficiency, DME valid capacities in mode Y are 100 and 117 actually, since their corresponding reply rates are 1748 and 1964, respectively; and they are less than 2700. It looks like a strange result that DME capacity in mode X interfered by JTIDS (159) is more than that without interference (154). However, we should not look DME capacity only, simultaneously, we should combine the corresponding RE. RE interfered by JTIDS (75.3%) is more than without interference (71.4%). So we should consider the two limitations of reply efficiency and reply rate at the same time when calculating DME capacity.
## 3 Results
### ML Models' Performance  
Table 2 showcases the specificity, sensitivity, and accuracy metrics achieved by each ML model when using 10-fold cross validation on the dataset as it is (with no oversampling whatsoever). Overall, the prediction model based on LightGBM can be considered to have performed the best. It achieved the best results in terms of specificity and overall accuracy. Specifically, it predicted multiglandular cases with an accuracy of 66.67% (tied for best with the CatBoost based model) and achieved an accuracy of 83.58%. On the other hand, the SVM based model was the most accurate at predicting adenoma cases, with a sensitivity of 91.59%.  
Table 3 showcases the specificity, sensitivity, and accuracy metrics achieved by each ML model. Overall, the prediction model based on LightGBM is considered to be the optimal one for the particular dataset. It achieved the best results on all metrics compared to the other models. Specifically, it predicted single gland (adenoma) cases accurately in 86.92% and multiglandular cases in 81.48%, and achieved an overall accuracy (after being trained on an oversampled dataset) of 85.82%. Additionally, Figure 2 depicts the confusion matrices for each prediction model.  
Furthermore, the Shapiro-Wilk test confirmed that all features conform to a normal distribution. Our statistical analysis revealed that the only feature of statistical significance was the number of deficiencies (\(p\) = 0.000). Importantly, the other parameters did not demonstrate significance, with \(p\)-values exceeding 0.05, indicating their limited statistical impact on distinguishing between adenoma and multiglandular conditions.