esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
RT-PCR analysis was carried out using the QuantiTect Virus Kit (Qiagen, Hilden, Germany), in a total volume of 25 \(\mu\)L containing 5 \(\mu\)L of 5 xQuantiTect Virus Mastermix, 0.25 \(\mu\)L of 100 xQuantiTect Virus RT Mix, 0.4 \(\mu\)M of each oligonucleotide, and 1 \(\mu\)L RNA. The HPV type-specific E6*I mRNA assay was used to detect the viral transcripts. This analysis amplifies a 65 \(\pm\) 75 base pair sequence of HPV and an 81 base pair sequence of ubiquitin C cDNA. Biotinylated amplification products are hybridized to ubC and HPV type-specific probes representing splice junction sequences on Luminex beads, followed by staining with streptavidin-phycoerythrin, and quantified in a Luminex analyzer. By using a splice product sequence as the detection probe, absolute specificity for RNA is ensured, eliminating the risk of false positivity of the assays using unspliced RNA, which comes from residual viral DNA and RNA from the preparation [23]. Expression of p16 was assessed by IHC in FFPE sections using the CINtec p16 Histology kit (Roche mtm laboratories AG, Mannheim, Germany) according to the instructions of the manufacturer. Positive p16 expression was defined as diffuse nuclear and cytoplasmic staining in 70% or more of the tumor cells. The validity of the p16 IHC staining result was assessed by evaluating the presence of p16 internal control staining. Discrepant results were re-checked by a pathologist, and the final classification of the staining was based on the majority consensus of the working group [24]. Competitive allele-specific Taqman PCR (CAST-PCR): FGFR mutations were detected by Competitive Allele-Specific Taqman(r) PCR technology (Thermo Fischer Scientific, Waltham, MA, USA), an assay performed in 384-well plates, in a total volume of 10 \(\mu\)L comprising 5 \(\mu\)L 2X Taqman Genotyping Mastermix, 0.2 \(\mu\)L 50X Exogenous IPC template DNA, 1 \(\mu\)L 10X Exogenous IPC mix, 1 \(\mu\)L Mutation Detection Assay, 1.8 \(\mu\)L deionized water and 20 ng DNA (in 1 \(\mu\)L). Applied Biosystems 7900 HT Fast Real-Time PCR System was used with the following set of conditions: 95 \({}^{\circ}\)C, 10 min followed by 5 cycles at 92 \({}^{\circ}\)C, 15 s and 58 \({}^{\circ}\)C, 1 min and 40 cycles at 92 \({}^{\circ}\)C for 15 s, and 60 \({}^{\circ}\)C for 1 min. SDS 2.3 Software was used to analyze the PCR result, together with Mutation Detection Software 2.0 (Thermo Fischer Scientific, Waltham, MA, USA). Ct was determined for exogenous IPC (Internal Passive Control) reagents added to each reaction to evaluate PCR failure or inhibition in a reaction. The Mutation Detection Assays Hs00000811_mu, Hs00000812_mu, Hs00001342_mu were used to detect variants p.R248C, p.S249C and p.K650Q in FGFR3 gene respectively, and Hs00001015_rf reference assay was used to identify the presence of wild-type FGFR3. All these assays were previously described in detail [19,20].
# A Thermogravimetric Analysis of Biomass Conversion to Biochar: Experimental and Kinetic Modeling
###### Abstract  
This study investigates the pyrolytic decomposition of apple and potato peel waste using thermogravimetric analysis (TGA). In addition, using Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS), the influence of pyrolysis temperature on the physicochemical characteristics and structural properties of biochar was studied. The degradation of biomass samples was studied between 25 \({}^{\circ}\)C and 800 \({}^{\circ}\)C. Although apple and potato peel decomposition present similar thermogravimetric profiles, there are some differences that can be evidenced from DTG curves. Potato peel showed one degradation peak in the range 205-375 \({}^{\circ}\)C with 50% weight loss; meanwhile, the apple peel exhibited two stages: one with a maximum at around 220 \({}^{\circ}\)C and about 38% weight loss caused by degradation of simple carbohydrates and a second peak between 280 \({}^{\circ}\)C and 380 \({}^{\circ}\)C with a maximum at 330 \({}^{\circ}\)C, having a weight loss of approximately 24%, attributed to cellulose degradation. To gain more insight into the phenomena involved in biomass conversion, the kinetics of the reaction were analyzed using thermal data collected in non-isothermal conditions with a constant heating rate of 5, 10, 20, or 30 \({}^{\circ}\)C /min. The kinetic analysis for each decomposed biomass (apple and potato) was carried out based on single-step and multi-step type techniques by combining the Arrhenius form of the decomposition rate constant with the mass action law. The multi-step approaches provided further insight into the degradation mechanisms for the whole range of the decomposition temperatures. The effect of temperature on biomass waste structure showed that the surface morphologies and surface functional groups of both samples are influenced by the pyrolysis temperature. A higher pyrolysis temperature of 800 \({}^{\circ}\)C results in the disappearance of the bands characteristic of the hydroxyl, aliphatic, ether, and ester functional groups, characteristic of a porous surface with increased adsorption capacity. Therefore, this study concludes that biomass waste samples (apple and potato) can produce high yields of biochar and are a potential ecological basis for a sustainable approach. The preliminary adsorption tests show a reasonably good nitrate removal capacity for our biochar samples.  
biomass waste; biochar; kinetic analysis; proximate analysis; thermal stability +
Footnote †: journal: _Sci._
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 2 Sensing Methods
### Semitensor-Product Compressed Sensing
The choice of matrix \(P\) is not limited, and matrix \(P\) can be any kind of matrix. When PTP is combined with CS, the high-dimension signal can be measured by low-dimension sampling. Hence, storage space is significantly reduced.
## 2 Mechanism  
In isotropic ferromagnetic materials, the relationship between \(\overset{\rightarrow}{B}\) and \(\overset{\rightarrow}{H}\) is both nonlinear and defined by hysteresis. \(\overset{\rightarrow}{B}\) is not a single-valued function of \(H\), and its variation also depends on the magnetization properties of the ferromagnetic material employed. The permeability \(\mu_{f}(\mu=f(H))\) will first reach a maximum with increasing magnetic field strength and then decrease due to saturation. When a material containing defects is magnetized by a static magnetic field, the magnetic lines of force are obstructed by the defect, allowing the magnetic lines of force to preferentially pass over the defect and become squeezed. The magnetic field around the defect is unevenly distributed, which is called magnetic field perturbation, as shown in Figure 1. The magnetic fields in the above-crack region and the far-crack region are \(\overset{\rightarrow}{H}_{DST}\) and \(\overset{\rightarrow}{H}_{mon}\), where \(H_{DST}>H_{non}\)[28]. According to the nonlinear \(\mu-H\) curve, the magnetic permeability of the above-crack area differs from that of the far-crack area, and it changes from \(\mu\) to \(\mu+\Delta\mu\), which is called magnetic permeability perturbation (MPP).  
As shown in Figure 1, DC magnetization is used to actively excite MPP. The pipe is rotated along the magnetization direction, and a fixed position probe loaded through AC excitation induces the eddy current field on the external wall of the pipe. The regions of MPP cause changes in eddy current distribution, resulting in changes in the secondary magnetic field received by the probe. In order to maximize the detection sensitivity of the signal, the receiving coils and excitation coils are distributed along the circumference of the pipe directly above the defect. The probe has a differential structure, which is oriented close and perpendicular to the sample to be tested.  
As shown in Figure 2, there is an axial defect in the internal wall of the pipe. Figure 2a,b show the MPP distribution of defects when the steel pipe is rotated to different circumferential positions, respectively. As shown in the red circle data in the figure,the MPP above the defect is lower than that of the near-defect area. If the perturbation region reaches the sensitivity range of the probe, the probe will immediately generate a differential signal.  
With respect to the steel pipe's (X-Y) cross-section, MPP occurs above the defects, and the MPP created by the internal wall defects is concentrated in the near-crack area. As shown in Figure 3, the MPP of region III is directly affected by the external diameter of the pipe. At the same time, the combined effect of the defect size, pipe diameter, and wall thickness determines the final MPP of region III.  
Figure 1: Schematic diagram of the MPPT principle.  
Figure 2: Schematic diagram of permeability distribution. (**a**) shows the MPP distribution of defects when the pipe is 0 degrees. (**b**) shows the MPP distribution of defects when the pipe is rotated to 20 degrees.
## 4 Numerical Examples
### A Single-DOF System  
In order to evaluate the accuracy of the proposed method, an undamped standard single-DOF system is firstly considered [19]. Its governing equation is:  
\[\ddot{u}(t)+\omega^{2}u(t)=0;u(0)=1,\dot{u}(0)=0 \tag{31}\]  
Letting \(\omega=\pi\), the exact solution can be written as:  
\[u(t)=\cos(\omega t) \tag{32}\]  
The free-v  
It can be observed that the Wilson method has a larger amplitude decay when the time step is a little longer, e.g., \(\Delta t\) = 0.2 s in this example, whereas the trapezoid rule, [PERSON] method, GGW method, and the proposed method can still maintain good accuracy. However, the Bathe method will bring a certain attenuation. Among all these methods, the proposed algorithm shows a lower period elongation. Furthermore, as shown in Table 1, the computational times of these methods are close to each other, thus the numerical efficiency of the proposed method can be proved.
## 2 Wind Power, Photovoltaic Power and Load Power Forecasting Model
### Stacking Ensemble Learning Model  
Stacking is an ensemble learning framework. Ensemble learning combines multiple learners to leverage their complementary strengths. It aggregates the outputs from multiple learners to achieve enhanced learning performance. The stacking ensemble learning model has a two-layer structure, as shown in Figure 1. The first layer uses multiple base learners to make predictions and obtains multiple sets of forecasting results. The second layer model is a meta-learner, which takes the outputs of the base learners as inputs and makes predictions again to obtain the final forecasting power. The base learners and meta-learner refer to different regression learning algorithms. As shown in Table 1, the base learners used in this study include the gradient boosting decision tree (GBDT) [24], XGBoost [25], the light gradient boosting machine (LGBM) [26], ridge regression (RR) [27], support vector regression (SVR) [28], and K-nearest neighbor (KNN) [29]. The meta-learner is linear regression (LR) [30]. The following subsections will briefly introduce the techniques used for the base learners and meta-learner, individually.  
Figure 1: The structure of stacking ensemble learning model used for power prediction.  
Due to the higher forecasting accuracy of the stacking ensemble learning model compared to the single algorithm model, this paper selects the stacking ensemble learning model to predict wind power, photovoltaic power and load power. In addition, it is noteworthy that the base learners and meta-learner adopted in stacking ensemble learning may be different depending on different forecasting objects.
## 3 Results
### Matrix Suction and Soil Water Characteristic Curve

Table 11: Fredholm and Xing SWCC equation parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Sample** & \begin{tabular}{c} **Water** \\ **Content [\%]** \\ \end{tabular}  & \begin{tabular}{c} **Gravimetric** \\ **Water Content** \\ \end{tabular}  & \ \begin{tabular}{c} **Matrix Section** \\ **[kPa]** \\ \end{tabular}  & \begin{tabular}{c} **Saturation [\%]** \\ \end{tabular}  \\ \hline
0\% (1) * & 4.08 & 0.0616 & 55890.41 & 12.96 \\
0\% (2) & 4.06 & 0.0601 & 51824.97 & 12.66 \\
3\% (1) & 5.42 & 0.0819 & 24913.01 & 17.24 \\
3\% (2) & 5.5  
The SWCC curve shown in Figure 12 exhibits unimodal behavior with a good fit to the measured suction values using the Fredholm and Xing equation. The curve followed a continuous trajectory without abrupt changes or trends, suggesting the formation of dual slopes or plateaus, indicative of bimodality. This pattern is consistent with the particle size distribution curve presented in Figure 9.  
Figure 11: (**a**) Whatman No. 42 filter paper sample; (**b**) filter paper in contact with the soil sample; (**c**) soil samples before storage; (**d**) soil and storage container to reach equilibrium in seven days; (**e**) storage of the samples in expanded polystyrene isothermal containers; (**f**) thermohygrometer used for temperature control. Figure 12: Soil water characteristic curve. The undisturbed samples did not display a clear trend because of the variability in the particle size distribution and plasticity with depth, as shown in Table 8. These conditions suggest the presence of distinct pore structures across different soil strata; hence, a single SWCC representing the entire 8-m sampling depth would not be truly representative. Moreover, it was not possible to extract a sufficient number of samples from each stratum to obtain suction values greater than 55.01 kPa, which was necessary to construct a well-defined bimodal curve. Additionally, the matric suction values of the undisturbed samples corresponded to a drying behavior, as the samples were collected in June, approximately one month after the peak annual rainfall period. In contrast, the reconstituted samples followed wetting behavior, as the material was initially air-dried and then rehydrated to reach the target moisture content. Accordingly, the SWCC was constructed exclusively using data from the reconstituted samples, representing wetting behavior. The data from the undisturbed samples were plotted on the same graph to allow comparison of suction behavior under both conditions and to highlight the variability observed in the trend of the undisturbed samples.
# Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables
## 4 On-Site Bridge Experiment  
### Experimental Introduction and Layout  
The Poyang Lake Bridge in Jiangxi, China, was selected as the experimental bridge, which has a total length of 3799 m and a deck width of 27.5 m. In addition, the length of the main bridge is 636 m, and the main bridge consists of four spans that are arranged across the border as 65 m + 123 m + 318 m + 130 m, as shown in Figure 13. In July 2019, during a routine inspection of the bridge, it was found that four bolts on the outside of the tensile and compression support on the downstream side of the auxiliary pier were broken. One week later, 4 bolts on the inner side of the tensile and compression support on the downstream side of the auxiliary pier broke and fell off, as shown in Figure 14. The failure of the steel plate anchor bolts on the downstream compression bearing caused the bearing to vacate and quit work. After a period of monitoring, the downstream support of the auxiliary pier was replaced from 10 October 2019, to 12 October 2019. During this period, auxiliary cable force monitoring was carried out on the cable numbered A15 on the downstream support.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Conditions** & **Maximum Stationary Phase Error (\%)** & **Maximum Mutation Point Error (\%)** & **Average Error (\%)** \\ \hline
1 & 8.009 & / & 3.479 \\
2 & 2.942 & 4.066 & 0.971 \\
3 & 4.864 & 7.369 & 2.288 \\ mean & 5.272 & 5.718 & 2.246 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Full-scale experimental error statistics table.  
Figure 14: Schematic diagram of support failure: (**a**) the bolt is off, (**b**) the cushion stone is damaged, (**c**) the lateral displacement (**d**) the broken bolt.  
Figure 13: the overview of Poyang bridge.  
The length of the A15 cable is 146.92 m, the weight per unit length is 60.1 (kg/m), the diameter is 125 mm, and the cable force of completion is 430 (t). When arranging on site, the acceleration sensor was placed at a distance of approximately 3 m from the bottom of the cable, and the sampling frequency was set to 500 Hz. Three working conditions were selected for this experiment; specifically, the state after the damaged support on the downstream side was removed, the state where the new support was installed, and the bolts were tightened, and the state after the new support was installed. Due to the high sampling frequency of the sensor and the large quantity of data, it required approximately 400 s to calculate the cable force per hour for the first two conditions, as shown in Table 5. The condition 2 indicates the repairing process, and the whole bridge stress was changing all the time. The condition 3 indicates that the whole repairing process finished, and the new stress state of whole bridge including the cable forces was stable.
## 2 Results
### Optimization of the HS-SPME Technique
#### 2.1.3 Sample Volume Analysis  
The analysis of the sample volume is a critical component of the optimization process. Although the statistical influence was not highlighted, the significance of the results is undeniable. The optimal sample volume was determined to be 6 mL. This choice arises as a strategic balance to achieve representative and accurate results.  
An optimal value of 6 mL was established to maximize performance and avoid unwanted interactions between the sample and the fiber. Contact between the sample and the fiber could lead to the inclusion of non-volatile compounds in the fiber or a significant decrease in the headspace. It is essential to emphasize that such interactions could potentially alter the results, deviating from the accurate volatile composition of CB [21].  
It is important to note that the choice of this value not only avoids immersion problems, which are characteristic of different HS-SPME methodologies. but addresses the inverse relationship between sample volume and headspace. As the sample volume increased, the headspace available for the volatilization of the compounds decreased. Consequently, the number of analyses that could be extracted from the headspace is reduced.  
This inverse relationship between the sample volume and the headspace can be explained by Equation (1) [17]:  
\[n\ =\ C_{e}^{\infty}v_{e}\ =\ C_{\phi}\frac{k_{sh}v_{s}v_{e}}{k_{sh}v_{e}+v_{s}}. \tag{1}\]  
where \(n\) represents the number of moles, \(C_{\phi}\) is the concentration of the analyte in the sample before extraction, \(v_{e}\) is the volume of the equilibrium phase, is the concentration of the analyte once equilibrium is reached in the equilibrium phase \(C_{e}^{\infty}\), \(k_{hs}\) is the equilibrium  
Figure 4: Chromatograms obtained from experiments E3 (extraction time = 50 min; sample volume = 4 mL; conditioning time = 15 min; fiber type = DVB/PDMS/CAR) and E41 (extraction time = 10 min; sample volume = 4 mL; conditioning time = 15 min; fiber type = DVB/PDMS/CAR) with interaction graphs of extraction times for the total area and number of peaks.  
constant a\(\Rightarrow\)b for the solid phase, \(v_{s}\) is the volume of the solid phase, and \(v_{m}\) is the sample volume.  
The equation establishes that the number of moles (\(n\)) is directly related to the sample volume and the concentration of volatile compounds in the headspace. As the sample volume increased, the other factors in the equation tended to approach one, increasing the concentration of analytes in the headspace.  
The optimal choice of 6 mL for the sample volume is presented as a coherent result with the literature. It supports the goal of obtaining representative and accurate results using HS-SPME methodology.
# Dual-Probe Near-Field Phaseless Antenna Measurement System on Board a UAV
###### Abstract  
On-site antenna measurement has been recently attracting an increasing interest in order to assess the antenna performance in real operational environments. The complexity and cost of these kind of measurements have been significantly cut down due to recent developments in unmanned aerial vehicles' (UAVs) hardware and antenna measurement post-processing techniques. In particular, the introduction of positioning and geo-referring subsystems capable of providing centimeter-level accuracy together with the use of phase retrieval techniques and near-field to far-field transformation algorithms, have enabled near field measurements using UAVs. This contribution presents an improved UAV-based on-site antenna measurement system. On the one hand, the simultaneous acquisition on two measurement surfaces has been introduced and calibrated properly, thus reducing geo-referring uncertainties and flight time. On the other hand, the positioning and geo-referring subsystem has been enhanced by means of a dual-band real time kinematics (RTK) unit. The system capabilities were validated by measuring an offset reflector antenna, and the results were compared with the measurements at the spherical range in the anechoic chamber and with the measurements collected with a previous version of the implemented system.  
on-site antenna measurement; dual probes; high directive antennas; dual-band Real Time Kinematics (RTK); phaseless techniques; Unmanned Aerial Vehicles (UAVs); antenna diagnostics +
Footnote †: journal: Sensors
Future Internet_**2013**, \(5\), 268-281; doi:10.3390/fi5020268  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
**Investigating the Tradeoffs between Power Consumption and Quality of Service in a Backbone Network**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) Computer Science Field, School of Architecture, Computing and Engineering (ACE), University of East London, London E16 2 RD, UK  
\({}^{2}\) ISN Group, Electrical & Electronic Engineering Department, Imperial College London, London SW7 2 BT, UK; E-Mails: [EMAIL_ADDRESS] (C.M.); [EMAIL_ADDRESS] (E.G.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +44-20-8223-7927.  
_Received: 8 February 2013; in revised form: 12 April 2013 / Accepted: 9 May 2013 / Published: 24 May 2013_  
**Abstract:** Energy saving in networks has traditionally focussed on reducing battery consumption through smart wireless network design. Recently, researchers have turned their attention to the energy cost and carbon emissions of the backbone network that both fixed and mobile communications depend on, proposing primarily mechanisms that turn equipments OFF or put them into deep sleep. This is an effective way of saving energy, provided that the nodes can return to working condition quickly, but it introduces increased delays and packet losses that directly affect the quality of communication experienced by the users. Here we investigate the associated tradeoffs between power consumption and quality of service in backbone networks that employ deep sleep energy savings. We examine these tradeoffs by conducting experiments on a real PC-based network topology, where nodes are put into deep sleep at random times and intervals, resulting in a continuously changing network with reduced total power consumption. The average power consumption, the packet loss and the average delay of this network are examined with respect to the average value of the ON rate and the ON/OFF cycle of the nodes.  
**Keywords:** power consumption; network performance; quality of service