esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Measurement Campaign
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Setup** & **Equipment** & **Description** \\ \hline \multirow{8}{*}{**Transmitter @28 GHz**} & Signal generator SMB100A from Rohde \& Schwarz, Munich, Germany & Signal generator up to 20 GHz. The transmitted power has been set at 14 dBm \\ \cline{2-3}  & Frequency multiplier FDA-K/28 from & Frequency multiplier connected to the signal generator to increase the CW transmitted signal up to 28 GHz \\ \cline{2-3}  & Ka-band pyramidal horn antenna, model & The antenna offers 20 dBi nominal gain and a typical half power beamwidth of 14 degrees on the E-plane and 16 degrees on the H-plane \\ \hline \multirow{8}{*}{**Receiver @28 GHz**} & Spectrum analyzer N9952A 50 GHZ & Portable spectrum analyzer up to 50 GHz (see Figure 6 for reference) \\ \cline{2-3}  & Ka-band omnidirectional antenna Model & The antenna model is equipped with a low noise amplifier (LNA) of 30 dBi \\ \cline{2-3}  & Signal generator SMB100A from Rohde \& Schwarz & Signal generator up to 20 GHz. The transmitted power has been set at 5 dBm \\ \cline{2-3}  & Frequency multiplier FES-12 from Farran Technologies & Frequency multiplier connected to the signal generator to increase the CW transmitted signal up to 60 GHz \\ \cline{2-3}  & E-band pyramidal horn antenna & 23 dBi nominal gain and a typical half power beamwidth of 10 degrees on the E-plane and 11 degrees on the H-plane \\ \hline \multirow{8}{*}{**Receiver @60 GHz**} & Spectrum analyzer N9952A 50 GHZ & Portable spectrum analyzer up to 50 GHz (see Figure 7 for reference) \\ \cline{2-3}  & FieldFox from Keysight Technologies & (see Figure 7 for reference) \\ \cline{2-3}  & WR-12 Harmonic Mixer Module from & The Harmonic Mixer Module is designed specifically for handheld spectrum analyzers (see Figure 7 for reference) \\ \cline{2-3}  & E-band pyramidal horn antenna & 23 dBi nominal gain and a typical half power beamwidth of 10 degrees on the E-plane and 11 degrees on the H-plane \\ \hline \end{tabular}
\end{table} 
Table 5: Measurement transmitter/receiver setup summaries for both frequencies under analysis. Figure 6: Measurement setup at 28 GHz frequency on the first and second floors of the library: (a) transmitter and (b) receiver. Figure 7: Measurement setup at 60 GHz frequency on the first and second floors of the library: (a) transmitter and (b) receiver. For the second setup case (transmitter antenna placed on the second floor), the antenna is aligned with different fixed receiver points on the first floor with the aid of the antenna positioner and the laser pointer in order to perform the different measurements for each operating frequency. Figure 9 shows the considered transmitter antenna position on the second floor and the different measured receiver points for both frequencies.
# Enzymatic Degumming of Soybean Oil for Raw Material Preparation in BioFuel Production
## 3 Results
### Study of Enzymatic Degumming Duration on Degumming Efficiency  
A study to determine the effect of enzymatic degumming duration on the efficiency of degumming of 1 kg of crude soybean oil was conducted. The reaction time determines how complete the hydrolysis of phospholipids is. The experiments investigated the reaction from 30 min to 5 h. The level of phosphorus and free fatty acids in the final product was determined, the results are given in Table 6. Degumming was carried out at fixed values of pH = 6.9-7.1; T = 57 \({}^{\circ}\)C; amount of enzyme Purifine(r) 3G 100 ppm; 2.5% water by weight of oil.  
The optimal enzymatic degumming time was 2 h, during which the phosphorus level decreased to 9.2 ppm, and the fatty acids remained stable (acid value 0.84 mg KOH/g oil). Increasing the duration to 3 h or more slightly improved the efficiency of dephosphorylation but was accompanied by an increase in fatty acids and increased energy costs.
## 4 Experiments and Results
### Hydra Heads Performance
#### 4.2.1 Abnormality Classification
These results suggest that freezing the weights of the trunk while learning the weights of the task-specific head can lead to better overall performance as compared to the traditional approach. Specifically, using the weights of the SISR model improves generalization and decreases the number of training parameters (see Section 4.3) for the classification head, which leads to enhanced overall performance. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Accuracy** & **AUC** & **Sensitivity** & **Specificity** & **Precision** & **F-Score** \\ \hline Baseline (HR) & 0.8283 & 0.8502 & 0.7800 & 0.8776 & 0.8667 & 0.8211 \\ \hline Baseline (scale 2) & 0.8182 & 0.8586 & 0.7600 & 0.8561 & 0.8636 & 0.8085 \\ _Hydra_ Head (scale 2) & 0.8512 & 0.8905 & 0.7700 & 0.9082 & 0.8953 & 0.8280 \\ \hline Baseline (scale 3) & 0.7980 & 0.8492 & 0.6800 & 0.8475 & 0.8947 & 0.7727 \\ _Hydra_ Head (scale 3) & 0.8384 & 0.8748 & 0.7500 & 0.9184 & 0.9000 & 0.8000 \\ \hline Baseline (scale 4) & 0.7929 & 0.8632 & 0.6900 & 0.8980 & 0.8734 & 0.7709 \\ _Hydra_ Head (scale 4) & 0.8219 & 0.8416 & 0.7100 & 0.9388 & 0.9189 & 0.7816 \\ \hline Baseline (scale 8) & 0.7727 & 0.8537 & 0.7000 & 0.8469 & 0.8041 & 0.7568 \\ _Hydra_ Head (scale 8) & 0.8081 & 0.8608 & 0.7800 & 0.8761 & 0.8235 & 0.7991 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of CXR abnormality classification using the baseline model and _Hydra_ head.
# Study on Shear Resistance and Structural Performance of Corrugated Steel-Concrete Composite Deck
###### Abstract
In 2018, [PERSON] presented the results of tests on continuous composite concrete slabs with trapezoidal steel decking. The study evaluated longitudinal shear failure, deflection, and redistribution of bending moment, proposing a finite element model [20]. In 2021, [PERSON], [PERSON], and [PERSON] studied adhesive bonding connections, showing that their behavior resembles that of mechanical connectors. The study highlighted their structural ductility and potential advantages [21]. [PERSON]. [PERSON] et al. validated a 3D numerical model using experimental tests and proposed shear stress and ductility criteria for an innovative shear connector. The load-carrying capacity of composite beams using bolted shear connectors was influenced by various factors [22]. [PERSON] et al. experimented with bolted shear connectors on composite deck slabs, finding that dovetailed profiled composite slabs exhibited higher resistance [23]. Nowadays, the traffic is more developed, and the requirements of the bridge deck plate, which is one of the main components of the bridge deck system, are becoming increasingly high [24]. The traditional reinforced concrete bridge deck plate is gradually unable to meet the demand of traffic volume due to its own limitations of self-weight and poor ductility, crack resistance, and shear resistance [2]. At present, there is less research on the form of the bridge deck slab, which is a combination of undulating steel and concrete. Therefore, this paper investigates the shear performance and slip of the new composite bridge deck structure, which uses the corrugated steel plate as the base plate, and analyzes the shear performance and slip of the composite bridge deck based on the structure of pegs and PBL perforated steel plate as the shear connection key. The shear performance of the composite deck is analyzed based on the structure of shear connection keys such as pegs and PBL perforated steel plates. The advantages of the composite decks in terms of stiffness, self-weight, load-bearing capacity, shear resistance, slip resistance, etc., are discussed to provide a theoretical reference for practical engineering applications.
## 4 Validation and Analysis of Results
### Kinetic Analysis of the Source Distribution of Sand-Embedded Particles in Road Embankments
#### 4.2.2 Source Analysis of Mixed Grain Size Sand Buried Particles  
Due to the microscopic scale, this paper aims to investigate the effect of different particle size compositions on the source distribution of sand-buried particles in a mixture of particle sizes. Therefore, the parameters for modeling sand grains using the particle modeling algorithm based on the SPH method in this study are shown in Table 2 below:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Computational Domain** & **Particle Diameter** & **Percentage of Particles** \\ \hline
190 mm \(\times\) 90 mm & R = 0.00005 m & 100\% \\ \hline
190 mm \(\times\) 90 mm & R = 0.00005 m, R = 0.00010 m, & 50\%, 30\%, 20\% \\ \hline
190 mm \(\times\) 90 mm & R = 0.00005 m, R = 0.00010 m, & \\ R = 0.00015 m, R = 0.00020 m, & 45\%, 25\%, 15\%, 10\%, 5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mixed grain size sand parameters.  
Figure 17: Comparison of sand velocity distribution.  
As shown in Figure 18, the particle source distribution of sand-embedded particles from the embankment. We utilize a backtracking algorithm to track the source of sand-buried particles in mixed-grain wind-sand streams. It can be found that the peak point of the sand-buried particle source gradually shifts to the right as the number of mixed particle size species increases. This is because the more significant the diameter of the sand particles, the more kinetic energy is required for the particles to make the jump and the longer it takes for the wind-sand flow to reach equilibrium.  
According to Figure 18, the peak of the source of sand-buried particles can be made out, and a windbreak wall is provided at the peak thereof. The windbreak embankment sand concentration distribution is shown in Figure 19. We can find that no sand particles are present on the embankment surface, and the concentration of sand particles at the foot of the leeward slope in the windward slope map is reduced to less than 10% below. The correctness of utilizing a particle backtracking algorithm to track the source of sand-buried particles and then installing a retaining wall for sand-blocking measures is illustrated. It is worth noting that the concentration of sand particles at the leeward slopes increases with the increase in the particle size species.  
Figure 19: Distribution of windbreak embankment sand concentration.  
Figure 18: Particle source distribution of sand-embedded particles in road embankment.
# Regulation of the _Pseudomonas syringae_ Type III Secretion System by Host Environment Signals
## 4 Additional Global Regulators of the T3 SS in _P. syringae_
### Modulation of T3 SS Gene Expression by RsmA RNA-Binding Proteins  
The RsmA protein family is implicated in the regulation of T3 SS gene expression in _P. syringae_, as well as various traits related to plant-microb interactions in _P. protogens_ CHA0 (alternatively _P. fluorescens_ CHA0), a nonpathogenic rhizosphere bacterium [182; 183; 63; 179; 180; 181; 182]. RsmA proteins are a family of regulatory mRNA-binding proteins that modulate the abundance of their target transcripts. The modulation of RsmA protein activity by the GacSA two-component system (reviewed in Section 3.3) has been extensively characterized in CHA0, and this regulatory network is often referred to as the Gac-Rsm system [182]. RsmA proteins can impact target mRNAs either negatively, i.e., by inhibiting the translation of bound mRNA transcripts, or positively, i.e., by stabilizing the target mRNA [183; 184; 185; 186; 187]. DC3000 possesses five distinct RsmA proteins (RsmA1-5), alternatively termed CsrA1-5 in some publications [188; 132; 189]. In a recent study, both RsmA2 and RsmA3 were required for full T3 SS expression and virulence on host tomato [188; 189]. Homologs of RsmA2 and RsmA3 were similarly required for virulence of _P. syringae_ pv. _phaseolicola_ 1448A [190]. However, a separate study of RsmA1-5 functions in DC3000 concluded that RsmA2 and RsmA3 were not required for T3 SS deployment, and that RsmA3 appeared to function as a repressor of T3 SS expression [132]. Additional experiments to resolve the exact mechanism(s) of T3 SS regulation by RsmA2 and RsmA3 may be helpful to clarify these apparently conflicting findings.  
In _Pseudomonas_ spp., GacA neutralizes the regulatory effects of the RsmA protein family by directing the expression of the _rsm_ family of small non-coding RNAs (ncRNAs) [181; 191; 192]. The _rsm_ ncRNAs function as molecular sponges to titrate RsmA protein, preventing RsmA interaction with target mRNAs [179; 180]. In DC3000, seven _rsm_ ncRNAs (_rsmY_, _rsmX1_-5, and _rsmZ_) are expressed in a GacA-dependent manner [189; 192]. Based on the recent characterization of GacA as a negative regulator of T3 SS gene expression in DC3000, GacA-mediated expression of _rsmXYZ_ may deactivate RsmA2 and/or RsmA3, preventing these regulatory proteins from activating T3 SS expression [188; 140; 189]. However, this model conflicts with other experimental findings of RsmA function in DC3000, and has yet to be experimentally evaluated [132]. An Rsm-independent branch of the GacSA regulon may also exist [122; 190], which may provide an alternative pathway to negative regulation of the T3 SS.
## 2 Materials and Methods
### Data
\[\text{SPI}= A_{S}\times\tan(\beta) \tag{2}\] \[\text{TRI}= \text{DEM}_{\text{max}}-\text{DEM}_{\text{min}}\] (3) \[\text{STI}= \left(\frac{A_{S}}{22.13}\right)^{0.6}\times\left(\frac{\sin\beta}{ 0.0896}\right)^{1.3} \tag{4}\]  
where the specific catchment area \(A_{S}\) refers to the catchment area per unit width, \(\beta\) represents the slope, and \(\text{DEM}_{\text{max}}\) and \(\text{DEM}_{\text{min}}\) denote the maximum and minimum values of the pixel elevation, respectively. This study uses 2064 landslide points, which serve as positive samples. An equal number of random points were also generated within the study area to act as negative samples. To avoid sampling areas potentially affected by landslides, all negative samples within a 500 m buffer of known landslide points were excluded. Additionally, all points located within water bodies were removed, as such areas typically lack the geomorphological conditions necessary for landslide occurrence. To address the different measurement units and spatial resolutions of each conditioning factor, it is essential to standardize the data to eliminate any dimensional differences that could affect the model results. As such, the raster resolution was standardized to 1 km \(\times\) 1 km. Using well-defined positive and negative samples, along with a multisource database structure, specific target data were extracted at various levels to construct the training and prediction samples for the model. Accurate discretization of the data is also critical, as it directly influences the quality and depth of subsequent analyses [50]. This study accounts for the characteristics of each factor's data and categorizes most conditioning factors into five or six levels (Table 2). The spatial distributions of 13 conditioning factors, excluding EAP, are illustrated in Figure 3.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Conditioning Factor** & **Class** & **Classification Standard** \\ \hline
**DEM/(m)** & 6 & \textless{}100; 100–200; 200–400; 400–600; 600–800; \textgreater{}800 \\
**Slope/(\({}^{\circ}\))** & 6 & \textless{}2\({}^{\circ}\); 2–5\({}^{\circ}\); 5–10\({}^{\circ}\); 10–15\({}^{\circ}\); 15–20\({}^{\circ}\); \textgreater{}20\({}^{\circ}\) \\
**Slope aspect** & 9 & North; northeast; east; southeast; south; southwest; west; northwest;  
Figure 3: Spatial distributions of the different influencing factors considered in this work: (**a**) slope, (**b**) distance to rivers, (**c**) TWI, (**d**) DEM, (**e**) SPI, (**f**) NDVI, (**g**) STI, (**h**) slope aspect, (**i**) distance to faults, (**j**) TRI, (**k**) distance to roads, (**l**) lithology, and (**m**) soil texture.
## 3 Case Study: Recorder
### Analysis by Synthesis Model
Moreover, considering that computational optimizers deal with minimization problems more efficiently, we set our objective to output the minus absolute coefficient (Equation (2)). In that way, the objective is introduced to the optimizer (in this work we use [PERSON], see Section 3.3) which is searching for a set of variables to minimize the objective and thus, maximize the correlation coefficient. In this work, the objective function leads to a non-convex optimization where the optimizer is looking for a global minimum. Thus, the number of iterations needs to be quite big to ensure good results. The objective function takes two inputs: (i) the synthesized digital signal generated by the PM and (ii) therecorded reference signal generated by the recorder. Our model's goal is to create a model with the best signals' match in terms of physical properties. The reason we made this choice is that maximizing the resemblance of the reference with the synthesized signals in terms of physical properties would, consequently, maximize the resemblance in terms of perceptual properties. \[\rho_{S,R}=\frac{\text{cov}(S,R)}{\sigma_{S}\sigma_{R}} \tag{1}\]  
\[\text{Obj}=-|\rho_{S,R}| \tag{2}\]  
After determining the set of the correction factors, the boundaries, and the objective, the next step is to put the optimizer into practice. In this work, our focus is to find the optimal correction factors to tweak the algorithm's parameters in order for the synthesized signal to be as close as possible to the relative instrument's signal. The optimizer, at every iteration, is trying a new set of variables for the modifiable parameters of the recorder's PM that generates a signal (synthesized signal) to be compared (correlation coefficient) with the recorded signal (goal signal). The optimizer will minimize the objective function for all the possible notes (fingerings) and output an optimal set of correction factors.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 5 Space-Efficient Information Bottleneck Successive Cancellation List Decoder
### The Role of Translation Tables  
The output of all the decoding table lookups in the structure of an information bottleneck SCL decoder has the same alphabet \(t_{i,j}\in\{0,1,\ldots,|\mathcal{T}|-1\}\). The purpose of translation tables is to decipher the cluster index \(t_{i}\), an abstract quantity obtained from a decoding table lookup, at the decision level into LLR \(L_{u_{i}}(t_{i})\), i.e., its meaning for the bit \(u_{i}\). The reason for requiring a distinct translation table for each bit lies in the fact that the translation tables of two qualitatively different bit channels translate the same cluster index into an LLR value differently. Figure 9 shows the translation tables for bits \(u_{14},u_{83}\), and \(u_{124}\) of an information bottleneck SCL decoder with \(|\mathcal{T}|=16\) generated for a half-rate polar code with \(N=128\) and design \(E_{b}/N_{0}=3\) dB. In the figure, cluster indices are given along the x-axis, while the LLR values they correspond to are given along the y-axis. It can be seen that \(L_{u_{14}}(t_{14})\
eq L_{u_{83}}(t_{83})\
eq L_{u_{124}}(t_{124})\) if \(t_{14},t_{83}\), and \(t_{124}\) take the same numerical value, e.g., \(t_{14}=t_{83}=t_{124}=0\). Further, the same cluster index translates to different LLR magnitudes not only for frozen and information bits, but also for different information bits. However, one notices that \(L_{u_{14}}(t_{14})\
eq L_{u_{83}}(t_{83})\) for \(t_{14}=1\) and \(t_{83}=5\). Similarly, \(L_{u_{14}}(t_{14})\
eq L_{u_{124}}(t_{124})\) for \(t_{14}=2\) and \(t_{124}=7\). We need to somehow _align_ all the translation tables such that a cluster index \(t\) translates to the same LLR value regardless of the bit channel \(i\), \(0\leq i<N\), for which it is used.
## 4 Discussion
### Mechanical Properties  
The microstructural differences influenced the mechanical properties of fabricated samples to a great extent. The SLM processed material had a superior UTS reaching 1320 MPa, which is the highest value encountered in the literature next to 1270 MPa [86] and the lowest elongation (2-3%). According to the available literature, this phenomenon may be caused by two independent factors. First of all, from the previous research of the authors [37], the results suggest that oxygen addition plays a significant role in solution hardening of technical-grade titanium. Pronounced strengthening effects arise from athermal shuffling of the interstitial O atoms by the screw dislocations, impeding line defect motion [87]. Nevertheless, low oxygen concentration or its interaction with substitutional solutes may enhance strength with minimal deterioration impact on ductility [88; 89]. Secondly, large heating gradients during the manufacturing process prevent the formation of the \(\beta\)-phase, causing supersaturation of the material and internal stresses [90] at the atomic level, seen as wide XRD peaks (Figure 9). However, its elongation was just between 2 and 3%. Poor ductility of the SLM-fabricated sample has been connected with its high porosity [37] and sharp acicular \(\alpha^{\prime}\) precipitates. The density of the 15-mm sample was small due to the presence of pores and regions with powder, which did not melt properly. This effect showed that the parameters optimized for smaller preliminary samples(Figure 2) could not be strictly transferred to larger sizes (Figure 3). The EBM-fabricated samples and conventionally-fabricated heat-treated samples met the UTS and elongation requirements from the ASTM F136 standard. The UTS strength was above 900 MPa, and the elongation about 12%. Thanks to their microstructure, high residual stress and oxygen strengthening [37], the SLM-fabricated material had much higher hardness than the EBM-fabricated and the wrought Ti-6 Al-4V materials. The larger epitaxially-grown grains produced in the samples fabricated by the SLM method than in the samples obtained by other methods have not caused any larger hardness anisotropy in the \(XY\) and \(XZ\) directions than in the EBM fabricated materials. The surface of the tensile samples after the tests revealed some differences. The EBM material had numerous homogenous dimples with a high specific surface area. The dimples are typical for microvoid coalescence, a high microscopic fracture mechanism observed in ductile metals. On the other hand, SLM samples presented topography terraces and shallower dimples. This implies the existence of easy intergranular fracture paths along selected plains, most probably \(\alpha^{\prime}\) martensite needles. According to [91], a more suitable structure is the bimodal \(\alpha+\beta\) that can dissipate the fracture energy in a larger volume of the material.