esa-sceva/satcom-chunk-collection huggingface dataset:
# Constant-Beamwidth LCMV Beamformer with Rectangular Arrays
## 4 Constant-Beamwidth LCMV Beamformer with Rectangular Arrays  
Both the KP and LCKP beamforming methods have advantages and disadvantages. While the LCKP is only usable over linear arrays, beamformers achieved through it have (virtually) more sensors than there are available in the physical array, generally leading to better performance when combining different techniques. Meanwhile, the KP method can be applied to rectangular arrays, which on its own is beneficial, but it does not have the virtual utilization of more sensors. To benefit from the lack of symmetry of the rectangular arrays and to also be able to have more sensors available for each subarray's beamformer, we propose a generalization of the LCKP to URAs to take advantage of both methods, enabling virtual sensor augmentation while exploiting the rectangular array's symmetry.
## 2 Experimental Section
### Sorbent Tubes  
Active sampling onto sorbent tubes was performed _in situ_ using 30 cm VOC-Mole(tm) soil probes (Markes International Ltd.). _In situ_ sampling with this equipment allowed for the collection of highly volatile compounds that may otherwise be lost during sample transport and storage. The VOC-Mole(tm) soil probe was placed into the ground near the torso of each pig carcass at the beginning of the trial and a sorbent tube was affixed to the cap of the probe during sample collections. The probe contains subsurface holes that allow the soil vapour phase to diffuse into the probe. Pumped sampling was performed using a Field and Laboratory Emission Cell (FLECT(tm)). A Tenax TA/Carbograph 5 TD sorbent tube (Markes International Ltd.) was attached to the end cap of the VOC-Mole(tm) soil probe and air was drawn from the soil probe through the sampling tube using the air pump. A sample was collected at a rate of 100 mL/min for 30 min from the VOC-Mole(tm) soil probe at each experimental site (with carcass) and control site (no carcass). Sorbent choice was determined based on manufacturer's specifications. The range of compounds targeted using this sorbent combination is comparable to previous research [3,19,20]. Sample contamination and/or loss during transportation and storage was minimised using brass long-term storage caps according to product usage specifications by the manufacturer and according to the use of sorbent tubes in previous studies [3,24,25]. Tubes were capped, wrapped in aluminium foil and stored in an air-tight, sealed glass jar for transport to and from the field site. Select compounds collected on sorbent tubes have been previously validated in literature for stability up to 4 days [25] and were analysed within 48 h from collection. Internal standard injection of 5 \(\mu\)L of 20 ng/\(\mu\)L bromobenzene (GC grade, Sigma Aldrich) in methanol (HPLC grade, Sigma Aldrich) was performed using an automated syringe pipette. A Unity 2 TM Thermal Desorber (Markes International Ltd.) was used to desorb VOCs for analysis. Each tube was purged under dry nitrogen for 1 min, followed by thermal desorption at 300 \({}^{\circ}\)C for 4 min under a reverse flow of helium carrier gas. Desorbed VOCs were collected on a general purpose cold trap at \(-10\)\({}^{\circ}\)C containing a dual-sorbent bed of Tenax TA/Carbograph 1 TD. Cold trap desorption was performed for 3 min at 300 \({}^{\circ}\)C. A flow path temperature of 120 \({}^{\circ}\)C was used. A split flow of 20 mL/min was used at the cold trap.
## 3 Routing
### Routing in CRSN: Rising Issues
#### 3.2.3 Opportunistic Spectrum Access (OSA)  
OSA capabilities allow a node to maximize the utilization of channel user activity. CRSN nodes must be aware of the nature of primary user activity in such channels to efficiently perform OSA. The consequences of being oblivious to the user's activity, as in the case of traditional WSN routing that lacks OSA, include more packet drops, frequent communication black-out periods and heightened latency that arise when the protocols must wait for a transmission.
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 3 Strategies
### Effect of Substrate  
In addition to the different parameters mentioned above, the substrate also plays a vital role in the performance of PENGs. For example, [PERSON] et al. [110] demonstrated the effect of two different substrates (plastic and paper) on a ZnO nanowire/PVDF nanohybrid on piezoelectricity. They conducted a comparative study on nanogenerators with plastic and paper substrates. The plastic- and paper-based devices provided an output voltage of 0.04 V and 4.8 V, respectively, thus suggesting that the paper substrate was more efficient than plastic; this was attributed to the porosity of the paper. Paper substrates can efficiently transfer pressure to the piezoelectric structure. These analyses demonstrated that porous and soft substrates have a greater tendency to act as efficient substrates. This suggests that the choice of substrate is crucial for the development of higher-performance nanogenerators.
## 3 Results and Discussion
#### 3.1.6 Alkyl Glycos  
From cluster D in negative MN (Figure 2), four metabolites were detected exclusively in cucumber as formate adducts in peaks 44-47 that belong to the alkyl glycosides class (Table 1). Alkyl glycosides are formed of fatty alcohols glycosylated with sugars. In detail, peak 44, [M-H]\({}^{-}\) detected at \(m/z\) 367.1608, C\({}_{15}\)H\({}_{27}\)O\({}_{10}\)\({}^{-}\) which was identified as butanol-O-pentosyl-hexoside and yielded fragment ions at \(m/z\) 235, C\({}_{10}\)H\({}_{19}\)O\({}_{6}\)\({}^{-}\) and \(m/z\) 73, post sequential loss of both sugars followed by demethylation at \(m/z\) 59, while MSn ions at \(m/z\) 161, 113, 101, 71 belonged to hexosyl fragments (Figure S7) in good accordance with the literature [24] and databases. Similarly, peaks 45 and 46 were identified as benzyl-O-pentosyl-hexoside and hexanol-O-pentosyl-hexoside, respectively. Such compounds were previously detected in several fruits as in apples, anise, cumin, and others [15; 25; 26; 27]; however, according to our knowledge, this is the first report of their presence in Cucurbitaceae. The exact bioactivities of such class of compounds in these fruits have yet to be studied.
# Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems
## 1 Introduction and Related Work
### Context and Related Work
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Bandwidth** & **Range** & **Operating** & **Security** & **Area of Application** \\ \hline \multirow{4}{*}{Wi-Fi} & \multirow{4}{*}{\(>\)500 Mbps (802.11 ac)} & 50 m indoors, & \multirow{4}{*}{2.4 Ghz and 5 Ghz} & WPA2 & high-volume data \\  & & & (with stock & dependent attacks & transfer, \\  & & & equipment) & & reported) & e.g., notebooks, tablets, \\ \hline \multirow{4}{*}{Bluetooth} & \multirow{4}{*}{700 Kbs} & \multirow{4}{*}{\(<\)10 m} & 2.400–2.4835 GHz & \multirow{4}{*}{based on upper} & peripheral \\  & & & ISM band & layers & connectivity, \\ \cline{1-1}  & & & & & e.g., ear-buds, \\ \cline{1-1}  & & & & & sound-bars, smart \\ \hline \multirow{4}{*}{Audio} & \multirow{4}{*}{4-20 characters/second} & \multirow{4}{*}{\(<\)2 m (with the devices/experiments in this work)} & \multirow{4}{*}{any frequencies between 20 Hz and 20 kHz} & \multirow{4}{*}{less leakage from closed/isolated } & recently used as an \\  & & & & & intensive \\ \cline{1-1}  & & & & closed/isolated & communication \\ \cline{1-1}  & & & & & channel for: device \\ \cline{1-1}  & & & & & pairing [7; 8; 9], Wi-Fi \\ \cline{1-1}  & & & & & pairing [4] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with others wireless communication methods inside vehicular environments. As a summary of findings from related papers, the use of audio channels for data exchange has increasingly gained attention in recent years. The inaudible range above 16 kHz is of specific interest since it does not impede human hearing and the short range of audio signals may offer some security advantages since such channels are harder to eavesdrop (like other short-range interfaces as NFC). Moreover, the use of environment noise, not only from the audio domain but also vibrations recorded by accelerometers, is also attractive as it facilitates the pairing of devices that share a common context.
## 3 Materials and Methods
### Empirical Mode Decomposition (EMD)  
The method of EMD was introduced by [48] in 1998. The complex signal is divided into distinct oscillatory components that range in frequency from low to high using the well-known Hilbert-Huang transform (HHT) approach, leaving just a single monotone residue. There are two essential requirements for any IMF: (i) the upper and lower envelopes must have zero means, and (ii) the number of zero-crossings and the number of extrema must differ by one. With a signal \(y(t)\), the EMD method can successfully separate signals into their many components. This approach is often used for prediction problems simply because it is reliable, straightforward, and effective and doesn't depend on any major model assumptions [49; 50; 51]. The following is a description of the EMD process:  
Step 1: Identify every local extremum (local maximum and minimum) in the signal \(\{y_{i}\left(t\right)\}\).  
Step 2: Determine the upper signal envelope \(\{U\left(t\right)\}\), and lower envelope \(\{L\left(t\right)\}\).  
Step 3: To obtain the meaning of the upper and lower envelopes, i.e., \(M\left(t\right)\), join all the minima and maxima using the cubic spline interpolation approach.  
\[Mean(t)=\frac{U(t)+L(t)}{2} \tag{1}\]  
Step 4: In order to acquire the first component, the mean envelope determined in step 3 is subtracted from the actual signal, i.e.  
\[k_{1}(t)=y(t)-Mean(t) \tag{2}\]  
If \(k_{1}\left(t\right)\) satisfies the two requirements for the IMF as described above, it should be treated as the first IMF; otherwise, steps 1 through 4 are repeated by treating \(k_{1}\left(t\right)\) as a novel signal.  
Step 5: In order to produce \(r_{1}\left(t\right)\), the first IMF identified in step 4 will be subtracted from the signal, i.e.,  
\[r_{1}(t)=y(t)-k_{1}(t) \tag{3}\]  
Step 6: The filtering procedure from step 1 is used once more in this step, where \(r_{1}\left(t\right)\) is treated as a new signal. The aforementioned procedure will keep going till the final IMF is extracted from the signal. Finally, the real signal \(y\left(t\right)\) can be decomposed in such a way that the overall trend of the signal will be a smooth monotonic residue established in the final stage of EMD, mathematically:  
\[y(t)=\sum_{i=1}^{n}k_{i}(t)+r_{n} \tag{4}\]  
where \(k_{1}(t)\), \(k_{2}(t)\)... \(k_{n}(t)\) are several IMFs with varying frequencies that range from high to low, and \(r_{n}\) is the residual. Figure 1 depicts the flowchart for this decomposition.
## 1 Introduction
### Overall Methodology and Approach  
The approach helps to understand the impact of semi-harsh conditions on polymer viscoelasticity while flowing through porous media. The following steps or methodology was adopted to gather the data and draw conclusions upon:  
* Brine Preparation and Optimization: Three brines were prepared, focusing on the role of varying total dissolved solids and increasing amount of sulfate in seawater (SW).
* Polymer Diluted Solutions Preparation: Two diluted polymer solutions (2000 and 3000 ppm) were prepared in brines with different TDS and added amounts of sulfates, in order to investigate the impact of salinity and hardness.
* Rheological Measurements: An approach to characterize the polymer viscoelastic properties, focusing on steady shear viscosity, elongational viscosity, small-amplitude oscillatory shear (SAOS), and first normal stress difference (N\({}_{1}\)) measurements. Rheological measurements of diluted solution before and after core flooding helped to conclude the potential loss in viscoelastic properties around the injection well.
* Single-Phase Core Flooding Experiments: Diluted polymer solution injection was performed over increasing injection rates, to study the in situ flow dynamics of polymer solutions.
* Microfluidics: Polymer flooding (prepared in SW) with varying amounts of sulfates in oil-saturated micromodels helped in our understanding of which one, between salinity and hardness, has a dominating impact on in situ viscoelastic polymer response.
## 2 Materials and Methods
### Experimental Set-Up
#### 2.2.3 Real Samples Assays  
The proposed methodology was applied for monitoring the two UV filters in real matrices using the standard addition method (SAM). For tap, surface, estuarine, sea water, and wastewater assays, 25 mL of sample, previously filtered with paper filters (125 mm of diameter, Cat. No. 1001 125, Whatman, UK), were spiked with the working standards solutions (0.4-8.0 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\) L\({}^{-1}\) for PBS and 2.0-40.0 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\) L\({}^{-1}\) for BZ4) and performed in triplicate using the optimized procedure, as described before. Blank assays were also analyzed without spiking.
# Survey for Soil Sensing with IOT and Traditional Systems
## 4 Inference Method and Evaluation Metrics
### Method for Spectral Information Analysis
#### 4.1.2 Partial Least Square Regression  
Partial Least Square Regression is a modeling method commonly used in chemometrics for spectroscopy analysis [72, 73, 74]. It is used to build a predictive linear regression model with independent variables \(X\) (predictor) and dependent variables \(Y\) (responses). In this paper, the independent variable \(X\) comprises the original spectrum information collected from the photodiode, exhibiting high collinearity within each channel. The concentration level of a target macronutrient (e.g., nitrogen) serves as the dependent variable \(Y\) that needs to be predicted using the independent variables \(X\) and the PLS regression model. During the analysis process, both \(X\) and \(Y\) are decomposed into multiple principal components (PC) to form a linear combination of the original data, which is then projected onto a new space using latent variables [73, 74]. This process can be represented by Equations (13) and (14) [74].  
\[X =TP^{\mathrm{T}}+E \tag{13}\] \[Y =UQ^{\mathrm{T}}+F \tag{14}\]  
The predictor \(X\) is represented by an \(n\times m\) matrix, where \(n\) denotes the number of samples, and \(m\) corresponds to the number of available spectra. The response \(Y\) is an \(n\times p\) matrix, with \(p\) indicating the number of prediction targets. For example, \(p=1\) to investigate the relationship between the spectral matrix \(X\) and the responses \(Y\) associated with a single target, such as the concentration level of nitrogen. The error terms from each projection equation are denoted as \(E\) and \(F\). The score matrices of \(X\) and \(Y\) are represented by \(T\) and \(U\), respectively, both having dimensions of \(n\times l\). The loading matrices for \(X\) and \(Y\) are denoted as \(P\) and \(Q\), with dimensions of \(m\times l\) and \(p\times l\), respectively. Here, \(l\) represents the selected number of principal components, which convert the predictor \(X\) (spectral matrix) and the response \(Y\) into linear combinations of latent variables [73, 74]. The choice of principal components directly influences the precision and stability of the model. If the number of principal components is too small, some channels' spectral information will be restricted. Conversely, selecting too many principal components introduces additional noise that may affect the system's predictions. Therefore, selecting principal components aims to capture as much spectral information as possible while minimizing the impact of noise by choosing an appropriate number of latent variables. In the study by [32], the authors utilized the PLSR model on data obtained from LED reflection to estimate the concentration of nitrogen. They achieved a coefficient of determination (\(R^{2}\)) of 0.875 for the calibration set and 0.803 for the validation set.
# Comparative Study on Vibration Characteristics of Biaxial Carbon/Glass Hybrid Wind Turbine Blades
###### Abstract  
Endeavor to investigate the effect of carbon/glass hybrid ratio on blade flutter vibration characteristics. Based on the theory of strength of materials, the influence of carbon/glass hybrid ratio on the tensile strength of the sample under concentrated load was analyzed experimentally. The 10 KW wind turbine blade model was taken as the research object, and different carbon/glass hybrid ratios (2:6, 4:4, and 6:2) were selected as the blade reinforcement materials. The blade vibration characteristics were analyzed from three aspects: deformation displacement, blade mode, and stress distribution. The results showed that the blade hybrid ratio was between 6:2 and 4:4, and the maximum improvement in tensile performance was achieved. The hybrid ratios of 4:4 and 6:2 significantly improved the deformation resistance of the blades and required a shorter time to reach equilibrium. The blade stress was mainly concentrated at 1/3 of the blade, which was one of the main reasons for the phenomenon of blade waving.  
carbon/glass hybrid; tensile properties; vibration characteristics; deformation displacement; stress distribution +
Footnote †: journal: Applied sciences
# Multipath Interference Impact Due to Fiber Mode Coupling in C+L+S Multiband Transmission Reach
## 4 Transmission Reach Assessment
### System Parameters  
We have considered, as our reference scenario, a C+L+S MB optical transmission system using a bandwidth of 15.3 THz, which can accommodate 192 channels (64 channels per band), each operating at 64 Ghaud, with a channel spacing of 75 GHz and a 1 dBm channel launch power. Two other baud rate scenarios are also studied, the 32 and 128 Ghaud. Furthermore, two different 500 GHz bandgaps are considered, the first one between the L- and C-bands and the second one between the C- and S-bands, as in [5]. The optical transmission system considered is composed of a transmitter and a receiver connected by several spans of equal length (i.e., \(L_{span}=100\) km), as indicated in Figure 2. Different amplifier noise figures are considered for each band as in [5], and a 1 dB insertion loss is assumed for the band (de)multiplexer [28]. These parameters are summarized in Table 1.  
The optical G.654E SMF considered is characterized by a dispersion parameter of 21 ps/nm/km, a loss coefficient of 0.17 dB/km [14], and a Raman gain profile with \(C_{r}=0.018\) (W\(\cdot\)THz\(\cdot\)km)\({}^{-1}\)[29], as presented in Table 2. We additionally consider splice  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**System Parameters**} \\ \hline Bands & & C+L+S & \\ System bandwidth (THz) & & 15.3 & \\ Bandgaps (GHz) & & 500 & \\ Channel spacing (GHz) & 37.5 & 75 & 137.5 \\ Number of channels & 384 & 192 & 102 \\ Number of channels per band & 128 & 64 & 34 \\ Symbol rate (GBaud) & 32 & 64 & 128 \\ Span length (km) & & 100 & \\ \multicolumn{4}{c}{Spice loss (dB)} & 0.0043 to 0.068 \\ \multicolumn{4}{c}{Spice distance (km)} & 4.2 \\ Band (de)multiplexer loss (dB) & 1 & \\ Amplifier gain & compensate for span loss and ISRS effect \\ Amplifier noise figure & as in [5] & \\ Channel launch power (dBm) & \(-2\) & 1 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: C+L+S MB optical transmission system parameters.  
losses varying from 0.0043 to 0.068 dB every 4.2 km, which results in an MPI level, per span, that varies from \(-46\) dB/span to \(-28\) dB/span [14].
# Penetration Performance of Steel Cylinders in Sand Foundations
## 3 Finite Element Modelling
### Model Description  
The model was based on the test model. The steel cylinder was modelled as a rigid body with a diameter \(D\) of 100 mm and a wall thicknesses \(t\) of 1 mm, 2 mm, 3 mm and 4 mm, corresponding to the thickness to diameter ratios \(t/D\) of 0.01, 0.02, 0.03 and 0.04, respectively.  
The CEL model is demonstrated in Figure 5. The loading reference point was taken at the top and along the centerline of the cylinder, as illustrated in Figure 5b. To improve the computational efficiency, only a quarter model was used in this paper by taking advantage of the symmetry of the model. The soil domain had a radius of \(3.5D\) (i.e., 350 mm) and a depth of \(5.5D\) (i.e., 550 mm), ensuring a sufficient coverage without any boundary effect through the entire penetration analysis [35]. A void layer was prescribed with a height of \(0.5D\) (i.e., 50 mm) above the surface of the soil. The soil bottom was fixed vertical displacements while the soil side with cycles only allowed vertical displacements, and faces 1 and 2 (in Figure 5b) are axisymmetric boundaries.  
Figure 5: CEL model. (**a**) Steel cylinder model; (**b**) 3D calculation model; (**c**) mesh of the 3D CEL model (side view); (**d**) mesh of the 3D CEL model (top view).  
The entire soil domain was set as a Eulerian part, comprising linear reduced-integration Eulerian elements. A very fine mesh zone with a radius of 5\(D\)/8 (i.e., 62.5 mm) was set surrounding the steel cylinder with a minimum mesh size of \(t\)/6. The number of Eulerian elements was 5910464.  
The internal and external side wall-soil interfaces used the general contact algorithm obeying the Coulomb friction law to specify the cylinder-soil interfaces. The interface friction coefficient was set as 0.2.
## 3 Finite Element Analysis
### Finite Element Model  
Parametric solid models of wire rope were built using SpaceClaim Direct software, while the rest of the parameters of FE modeling were performed in ANSYS Workbench. Only a 20 mm long segment of the rope was modeled in order to reduce computational time (Figure 3). The model was discretized with 20-node brick solid elements. Frictionless contact between wires was defined because, for axially  
Figure 1: Equivalent load range.  
Figure 2: (a) 6 \(\times\) 7 fiber core wire rope; (b) 7 \(\times\) 7 wire rope; (c) 8 \(\times\) 7 fiber core wire rope.  
loaded rope, relative displacements between wires are negligible; consequently, friction does not add significantly to the stress strain evolution and can therefore be neglected in order to keep the model simple and effective [4,15]. Workbench allowed for a frictionless contact condition with no separation to be defined where little relative motion between the surfaces is allowed. Although the broken wire can, due to friction effects, resume its load once a certain distance from the rupture point is reached, the frictionless approach is employed here to account for the worst case scenario, where the rupture point is at a critical position for the load bearing ability of the wire.  
As for the boundary conditions, one end of the rope was fixed. On the other end, axial force was applied and circumferential displacements were constrained in order to prevent the rotation of the strands along the longitudinal axis, an approach proved to be valid by previous research [16]. A total force of 147 kN was applied, evenly distributed between the wires. The inner fiber cores of the \(6\times 7\) and \(8\times 7\) wire ropes were modeled as a surface that does not contribute to carrying the axial load and acts only as radial support for the strands. According to [16], perturbation due to boundary constraints are negligible for the FE model, which has a length equal to at least \(1/16\) of the helical pitch.  
Damage to the wire rope was modeled simply by suppressing individual wires or entire wire strands under the presumption that, once a wire is damaged, it can no longer carry the load and thus be neglected in the FE model. Figure 4 shows the \(6\times 7\) wire with full cross-section area, where one wire and one strand are suppressed.  
Figure 4: _Cont._  
Figure 3: FE model of **(a)**\(6\times 7\) fiber core wire rope; **(b)**\(7\times 7\) wire rope; **(c)**\(8\times 7\) fiber core wire rope.