esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation and Evaluation  
### Implementation  
To focus on verifying the constant-time state search under the MSSA structure proposed in this article, we independently implement the packet forwarding pipeline used to verify the method in this paper, as shown in Figure 9. The pipeline implements the match-action table function by using the EM table lookup algorithm in the librte_table library provided by DPDK, and expands the table and entry structure to associate the corresponding table state area and flow state area. When the pipeline creates the table, the table state area and the flow state area are reserved according to the declaration. Furthermore, the pipeline uses the headroom in the DPDK's packet structure Mbuf as the packet state area. When Pipeline is first started, it creates a global state.  
Before processing the packet, the pipeline will obtain five addresses: packet header's base address, packet state area's base address, flow state area' base address of the packet matching entry, table state area's base address of the packet entering table, and the global state area's base address. These are useful for obtaining the packet field or state based on (type, offset, length) later on.  
Each type of state area is configured with a fixed size in the pipeline implementation. The greater the scope of packets shared by the state area, the more states that could be stored. As a result, we allocated 1 M bytes to the global state area, 1 K bytes to the table state area, 16 bytes to the entry state area, and 8 bytes to the packet state area.  
To process packets and state, the pipeline employs a set of atomic actions (Table 3). Arithmetic logic actions (and, or, not, assignment, etc.), forwarding actions (jump table, output, and flooding), and branch actions are all included (jump and compare jump).  
Figure 9: Pipeline architecture based on the DPDK framework that uses MSSA to record state.
## 4 Weighting Factor Estimation Method from Scheduling Results
### Inverse Optimization  
Inverse optimization is finding the input that generates the output, given the output of an optimization problem [35]. When applied to the weighting factor estimation problem, the output is the scheduling results, and the input is the weighting factors. We will describe how to estimate the weighting factors using the inverse optimization method. First, many schedule data, solved by changing the weighting factor from 0.1 to 0.9 by 0.1, are prepared. The scheduling problem is solved by giving an initial weighting factor. The weighting factors are updated to reduce the error between the resulting output and the desired output. The scheduling problem is solved again with the updated weighting factor. By repeating this, the weighting factors are estimated. The model is evaluated by the MSE between the estimated weighting factor and the true weighting factor. It is applied to both small- and large-scale problems. The outline of the inverse optimization algorithm is shown in Algorithm 1 [36].  
Figure 4: Outline of wrapper feature selection method.  
The input data are the initialized weighting factors. The weighting factor is initialized on line 1. On the 5 th line, the scheduling problem is solved after the initial value of the weighting factor is set. The optimal solutions \(\mathbf{x}_{\beta}\) are obtained at line 5. Exact solutions are obtained by a commercial solver for small-scale problems, and the approximate solutions are obtained for large-scale problems. In lines 6 and 7, the gradient of the weighting factor is computed, and the loss function is used for the gradient. Solutions \(\mathbf{x}_{\beta}^{true}\) with known weighting factor and optimal solutions \(\mathbf{x}_{\beta}\) are used to calculate the gradient. The loss function is calculated by solving some problems. The number of problems \(B\) on the 4 th line is set to 10. After that, the weighting factor is updated on line 9. When updating, the update width is divided by the number of problems and multiplied by the learning rate \(\alpha\). The iteration is executed with the maximum number of iterations \(I\), and the weighting factor output on the 11 th line is the result of the weighting estimation.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 3 Mapping and Localization
### Pit Entrance Phase  
Once the floor of the pit entrance is mapped, a suitable point is selected for the robots to land inside the pit from the surface. The suitable point will also be selected manually once the map generated during the pit survey phase is transmitted to a ground station. The robot then hops from the pit surface to the selected point on the pit floor using the soft-landing ballistic hopping mode. As the robot travels, a map of the pit entrance is created while estimating the pose (position and orientation) of the robot using the ICP-based Pose-Graph SLAM algorithm.  
Figure 8a shows the initial position and selected final position, along with the trajectory of the robot. Figure 8b shows the map generated during the pit entrance phase along with the estimated trajectory of the robot. Figure 8c,d show the actual and estimated position along the x, y, and z-axis, and the quaternion parameters of the robot, respectively. It can be seen that the position and orientation of the robot were successfully estimated using the ICP-based Pose-graph SLAM algorithm.
## 3 Laboratory Model Tests
### The Effect of Friction between the Backfill and Side Walls of the Test Box  
As previously noted, the front and back walls of the box were not lubricated. Therefore, the effect of friction between the backfill and the side walls on the load force should be taken into account. In order to estimate the increase in load due to such frictional resistance, a simple approach proposed by [PERSON] [95] was used.  
The resistance force (\(P_{F}\)) caused by the side wall friction can be estimated using the following formula, see [95]:  
\[P_{F}=a^{2}\tan\alpha\left[\tan\delta(1-\sin\phi)\left(\frac{a\gamma\tan\alpha }{3}+\frac{P_{E+F}}{aL}\right)+c_{\delta}\right], \tag{1}\]  
where  
\[a=\frac{H_{p}}{\tan\alpha}, \tag{2}\]  
\(\alpha\) is the angle of inclination of the slip surface to the horizontal plane, \(\phi\) is the friction angle of the sand, \(\delta\) is the angle of friction between the backfill and the side wall surface, and \(c_{\delta}\) is the resistance resulting from adhesion (in N/m\({}^{2}\)). \(P_{E+F}\) is the load force recorded during the experiment (in N), \(H_{E}\) is the height of the failure zone, and \(\Delta L\) denotes the width of the test box (both in m) (Figure 10).  
The value of the experimental load (\(P_{E}\)) reduced by the friction effect can be calculated using the following equation:  
\[P_{E}=P_{E+F}\left[1-\frac{a}{\Delta L}\tan\delta\tan\alpha(1-\sin\phi)\right] -a^{2}\tan\alpha\left[\frac{1}{3}(1-\sin\phi)a\gamma\tan\delta\tan\alpha+c_{ \delta}\right] \tag{3}\]  
The value of \(\delta\) was determined by the method presented in [96] (a \"tipping experiment\"). A layer of sand was placed on the surface of sheet glass, which was the same as that used for the side walls of the test box. Then the sheet glass was slowly tilted. The angle of tipping was gradually increased until the soil mass began to slide. This angle of inclination was taken as the angle of friction \(\delta\) between the sand and the glass surface.  
The analysis of the digital images captured during a similar test and processed by particle image velocimetry (PIV), presented in [96], showed that a deformation zone with a  
Figure 10: Triangular contact area between the sand and the side wall in the failure zone.  
shape similar to that of the failure zone formed even at an early loading stage. Therefore, it was assumed that the above relationship can be used to calculate the value of \(P_{E}\) at any stage of loading.  
The following data, corresponding to the experimental conditions, were used to calculate the value of the load (\(P_{E}\)) at seven different stages of experimental loading: \(P_{E+F}\): \(H_{E}\) = 0.5 m, \(\phi\) = 34.5\({}^{\circ}\), \(\gamma\) = 18.5 \(\times\) 103 N/m\({}^{3}\), \(\delta\) = 26\({}^{\circ}\), and \(c_{\delta}\) = 0 N/m\({}^{2}\). The results of these calculations are presented in Table 4.
## 2 Materials and Methods  
### LDPI-TOF-MSI Apparatus  
The home-built apparatus is shown in Figure 1. The mass spectrometry imaging system used in this study was integrated and constructed with multiple functional components, including a custom-designed linear time-of-flight-mass spectrometer (TOF-MS), vacuum system, laser system, and delay control system. An X-Y-Z stage was used for sample transfer during the imaging run [38]. A home-made TOF-MS system was used in this study. The details of the apparatus have been described previously, and only a brief introduction is given here [39].  
In this experiment, the laser light sources used included two nanosecond-scale solid-state lasers. The desorption laser, a 532 nm Nd:YAG solid-state laser (Minilite, Continuum, Inc., Milpitas, CA, USA) with a 10 Hz repetition rate, was focused on a small spot with a size of 0.5 mm\({}^{2}\) through a reflection and focusing lens. By controlling the pulse energy range within 0.5-3 mJ (10-60 mW/cm\({}^{2}\)), desorption of sample molecules can be achieved. The ionization laser source used in this experiment consisted of vacuum ultraviolet (VUV) 118 nm, 10.5 eV photons generated by nonlinear optical frequency mixing with another 1064 nm laser (INDI-40-10 YAG, Spectra Physics, Inc., Milpitas, CA, USA). Briefly, a 355 nm laser for ionization is generated through the frequency tripling crystal (FTC) of the fundamental frequency light produced by the 1064 nm laser, and the emitted light is focused by a focusing lens (f = 25 cm) before entering a homemade mixed gas cell. Specifically, this gas cell was filled with a high-purity nonlinear medium consisting of a phase-matched 1:10 Xe/Ar gas mixture at 160 Torr to generate 118 nm photons by the use of four-wave mixing and an MgF\({}_{2}\) lens (which can cause separation of the ultraviolet and VUV beams at the YAG third harmonic (n = 1.39 at 355 nm) and ninth harmonic (n = 1.67 at 118 nm) wavelengths), which are used for the output port. Finally, the 118 nm laser spot generated  
Figure 1: Schematic drawing of the LDPI-MSI system. Schematic diagram of the laser desorption positionization mass spectrometer imaging apparatus and the sample held with an X-Y-Z stage. The right panel represents the sample chamber.  
through this process can be focused to approximately 1 mm\({}^{2}\) above the sample. Then, the sample is completely ionized by coordination with the X-Y-Z platform [40]. As shown in Figure S1, the ion lenses are used to adjust and focus the ion beam so that ions can hit the detector more accurately. The ion signals were collected by a 22 mm diameter ternary position-sensitive detector (microchannel plate, MCP). The outputs corresponding to the ion signals were digitized, averaged, and recorded using a digital storage oscilloscope.
# A 2.4-GHz Fully-Integrated GaAs pHEMT Front-End Receiver for WLAN and Bluetooth Applications
## 2 Circuit Design
Considering the receiver will obtain large signal when it closes to a signal source such as base station, a bypass function is incorporated into the LNA module which enables the LNA to switch into loss mode [29]. In bypass path, the switch M\({}_{sw1}\), M\({}_{sw3}\), M\({}_{sw4}\) are in the on-states and M\({}_{sw2}\) is in the off-state, a series of two transistors with sized 90 \(\upmu\)m and T-type attenuation are employed to maintain the required loss of 7 dB while providing sufficient isolation between the input and output of the LNA. The operating state of four switches in receiving path has been extracted in Table 3.
## 3 Crack Propagation Related to Time-Dependent Behaviour in Railway Sleepers
### Crack Propagation Due to Creep  
Considering the crack propagation subject to creep, the load-crack length response for the sleeper due to creep in different time is evaluated as shown in Figure 7.  
Figure 7 indicates the effect of creep on flexural behaviour in terms of load-crack length response. Crack propagation with a loss of prestress due to creep is compared at 90, 180, 365, 3650 days. It is found that the loss of prestress due to creep over time can lead to greater crack length. In initial crack propagation, the crack length does not have notable difference between those cases. When the external loading increases more than 70 kN, the growth of crack length significantly increased for longer service life cases. The ultimate crack length due to creep at 3650 days is 9.73% longer than in a condition of no time-dependent loss.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time** & **Creep Loss** & \multicolumn{2}{c}{**Shrinkage Loss**} & \multicolumn{2}{c}{**Relaxation Loss**} & \multicolumn{2}{c}{**Total Loss**} \\ \hline
**(days)** & **(kN)** & **(\%)** & **(kN)** & **(\%)** & **(kN)** & **(\%)** & **(kN)** & **(\%)** \\ \hline
90 & 13.02 & 3.10 & 14.56 & 3.46 & 8.13 & 1.94 & 35.71 & 8.50 \\
180 & 15.65 & 3.73 & 17.68 & 4.21 & 9.06 & 2.16 & 42.38 & 10.09 \\
365 & 18.03 & 4.29 & 19.91 & 4.74 & 10.11 & 2.41 & 48.06 & 11.45 \\
3650 & 22.08 & 5.26 & 22.29 & 5.31 & 14.48 & 3.45 & 58.86 & 14.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Loss of prestress in railway sleepers due to time-dependent behaviour.  
Figure 6: Comparison of the crack propagation in the numerical and experimental results.
## 2 Materials and Methods
### Free Fatty Acid Analysis  
The lipid of the roasted lamb was extracted as described by [PERSON] et al. (2011) with some modifications. Total of 40 mL of mixed chloroform-methanol solution (2:1, _v/v_) was mixed with 3 g of ground roasted lamb. The mixture was homogenized at 4500 r/min for 10 s, holding for 60 min, and the homogenate solution was filtered through Whatman No. 4 filter paper. The filtrate was washed with 8 mL of solution (0.5 g/L CaCl\({}_{2}\) and 7.3 g/L NaCl) and centrifuged at 3000 r/min for 15 min. The total lipids were concentrated by a rotary evaporator at 45 \({}^{\circ}\)C. Free fatty acids were separated according to our previous method of [PERSON] et al. [22]. The lipid extracts were fractionated by passing 20 mg of lipids dissolved in 5 mL of CHCl\({}_{3}\): MeOH (2:1 \(v/v\)) through NH\({}_{2}\)-aminopropyl mini-columns. The mini-column first was washed with 2.0 mL of chloroform/isopropanol (2:1, \(v/v\)) to remove neutral lipids; then free fatty acids were obtained through eluting with 3.0 mL of 2% (\(w/w\)) acetic acid in diethyl ether. The free fatty acids and 10 \(\mu\)L of internal standard (heptadecanoic acid dissolved in hexane) were mixed with 1 mL 14% BF3/methanol (\(m/m\)). The mixture was methylated for 20 min at 50 \({}^{\circ}\)C in a water bath. After methylation, 4 mL of hexane was added into the mixture, shaken for 20 s and held for 1 h. Then methylated samples (1 \(\mu\)L) were analyzed using a gas chromatograph (GC-2010, Shimadzu, Kyoto, Japan) with a PEG-20 M capillary column (CP-Wax, 30 m \(\times\) 0.32 mm \(\times\) 0.25 \(\mu\)m). The oven temperature was maintained at 120 \({}^{\circ}\)C for 3 min, increased to 190 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, and then increased to 230 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, holding for 20 min. The detector and injection port temperature were maintained at 250 \({}^{\circ}\)C. The free fatty acids were identified by comparing the retention times of samples with those of standards and quantified with standard curve equations built with the standards of each fatty acid. The content of each fatty acid was expressed as mg/g dry matter.
## 2 Proposal of the New Protection Definition
### Main Results Achieved
Similarly to the analysis performed under interference conditions \(n-1\) and \(n+1\), for the received DTV power of \(-77\) dBm, the lowest PRavg values for interference on channels \(n-2\) and \(n+2\) were \(-51.16\) dB and \(-51.74\) dB, achieved with the 4G/LTE technology and **the 5G-RANGE** transceiver using GFDM, respectively. The highest values were also obtained with the same technologies, with 5G-RANGE as the interfering signal on channel \(n-2\) with a PRavg of \(-49.43\) dB and 4G/LTE technology as the interfering signal on channel \(n+2\) with a PRavg of \(-50.54\) dB. Additionally, during the test campaign, the PR was investigated in scenarios where it had two neighboring channels simultaneously around the DTV channel. For this, there was an adjustment in the test setup, including another interfering signal generation station, and combining the outputs of the variable attenuators of each one to work with two interfering signals. In these tests, signals 4G and 5G-RANGE in GFDM were used on channels \(n-1\) and \(n+1\), respectively. Table 3 shows the results of laboratory tests considering the incidence of two interfering signals simultaneously on channels \(n-1\) and \(n+1\). A 3 dB increase in the PR value is observed compared to the incidence of only one of the interferences at a time.
## 3 Results
### Cultural Differences by Demographic Variables
#### 3.2.3 Differences by Contractual Relationship (Own Staff/Contractor)  
Personnel in nuclear power plants score homogeneously in the safety and hazard perception scales, but show differences in the remaining survey scales. Own staff score higher than contractors in SCWE scales (own M = 5.07; contractor M = 4.47; \(\delta\) = 0.43), planning (own M = 5.69; contractor M = 5.24; \(\delta\) = 0.44), adaptive capacity (own M = 4.77; contractor M = 4.38; \(\delta\) = 0.31), and overall resilience (own M = 5.23; contractor M = 4.81; \(\delta\) = 0.40). That is, contractor personnel have a lower perception of blame-free environment and lower organizational resilience (Table 10, Figure 14).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Own staff (N = 1715)**} & \multicolumn{4}{c}{**Contractor (N = 2287)**} \\ \hline
**Scales** & **N** & **M** & **SD** & **N** & **M** & **SD** & **Cohen’s \&** \\ \hline Hazard perception & 1714 & 4.39 & 1.65 & 2286 & 4.10 & 1.66 & 0.18 \\ Safety & 1715 & 5.99 & 0.70 & 2287 & 5.85 & 0.78 & 0.19 \\ SCWE & 1715 & 5.07 & 1.42 & 2286 & 4.47 & 1.35 & 0.43 \\ Planning & 1376 & 5.69 & 0.95 & 2114 & 5.24 & 1.10 & 0.44 \\ Adaptive capacity & 1377 & 4.77 & 1.24 & 2113 & 4.38 & 1.30 & 0.31 \\ Overall resilience & 1377 & 5.23 & 1.02 & 2114 & 4.81 & 1.09 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Descriptive statistics of the scales by contractual relationship.  
Figure 12: Categorized BARS percentages for formalization, by location.  
Figure 13: Categorized BARS percentages for resources allocation, by location.  
BARS results are not homogeneous when it comes to contractual. On the one hand, some differences are statistically significant and have magnitudes with a small effect in terms of safety and norm and procedure quality (attention to safety: own M = 4.46; contract M = 4.20; t (96.62) = 2.66; \(p\) = 0.009; \(\delta=-0.38\)), (formalization: own M = 4.17; contract M = 3.80; t (33.30) = 1.80; \(p\) =
## 4 Discussion
### Strategies to Improve Gluten-Free Dough Rheology and Bread Characteristics
#### 4.1.4 Dry Heat Treatment of Gluten-Free Flours  
An interesting strategy is reported in [PERSON] (2019) [27], where the authors tested the use of dry heat to improve gluten-free doughs, bread, and cake, particularly those made with weak, substandard flours. The treatment led to protein denaturation and the partial gelatinization of starch granules,which induced an increase in gas retention capacity and dough expansion. A change in dough viscosity in cake-making was connected to the ability to transform from foam to sponge form, and reduced shrinkage during baking. In particular, the authors highlighted an increase in the volume of _kasutera_ cake by dry heating at 120 \({}^{\circ}\)C for 30 min. The same study found that heat-treated flour had increased resistance, viscosity, and stiffness. These factors increased dough elasticity, with positive effects on oven spring and loaf volume. The authors also investigated the effects of the treatment on sorghum flour, and found that heating produced bread with the highest specific volume, and the most cells per slice area. Improvements in structure, strength, and volume were related to the increased viscosity of sorghum flour. Additionally, cakes and bread made with dry-heated flours were more acceptable than controls in consumer testing.  
These findings are supported by [PERSON] et al. (2016) [28], who, additionally, highlighted that the pre-processing treatment seemed to be a particularly suitable way to improve the technological properties of gluten-free pasta produced with corn flour. Pasta produced with dry-heated corn flour had higher firmness, improved texture, and increased flavor after cooking, compared to pasta obtained by a conventional extruder.  
In conclusion, dry heat treatment seems to be a viable way to improve sorghum flour and sorghum-base doughs and products, with no pungent off-notes. Furthermore, in-depth studies of its effects on the structural behavior of flours are needed to better explain how heat alters flour and dough functionality, and how it may improve the quality of sorghum-based bakery products.
## 2 Materials and Methods
### Framework for GPP Generation
#### 2.3.3 Sensitivity Analysis of the Environmental Factors to the GPP Estimation  
The sensitivity of GPP to various environmental factors was evaluated using eight key variables: temperature (TEM), precipitation (PRE), solar radiation (SRA), normalized difference vegetation index (NDVI), CO\({}_{2}\), irradiance (I), land use and land cover (LUCC) and optimum temperature (TEMopt), and global sensitivity index was calculated using the EFAST method [32]. To perform the sensitivity analysis, the SimLab (version 2.2.1) software was employed, generating a sample size of N = 5000 using the EFAST extension method. The value intervals for the environmental factors used in the simulation are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Temperature Zone** & **Dry and Wet Areas** & **Natural Areas** \\ \hline III (warm temperate zone) & D (arid region) & IIID1 (Tarim and Turpan Basins) \\ \hline II (mid-temperate Zone) & D (arid region) & IIID2 (Alka and Hei Corridor) \\ \hline \multirow{4}{*}{HI (the plateau sub-cold zone)} & B (semi-humid region) & HIB1 (Golonaquh hilly plateau) \\ \cline{2-3}  & C (semi-arid region) & HIC1 (Qingnan Plateau wide valley) \\ \cline{2-3}  & HIC2 (Qingtang Plateau lake basin) \\ \cline{2-3}  & D (arid region) & HID1 (Kunlun alpine Plateau) \\ \hline \multirow{4}{*}{HII (the plateau temperate zone)} & A/B (humid/sub-humid region) & HIIA/B1 (Sichuan Xiang east high mountains and deep valleys) \\ \cline{2-3}  & C (semi-arid region) & HIC1 (Qingdong Qilian Mountain) \\ \cline{2-3}  & HIC2 (South Tibet mountains) \\ \cline{2-3}  & D (arid region) & HID1 (Qaidam Basin) \\ \cline{2-3}  & HID2 (the north Kunlun Mountains) \\ \cline{2-3}  & HID3 (Ali mountain) \\ \hline IV (Northern subtropical) & A (humid region) & IVA2 (Hanzhong basin) \\ \hline \multirow{2}{*}{V (mid-subtropical)} & A (humid region) & VA4 (Sichuan Basin) \\ \cline{2-3}  & A (humid region) & VA5 (yunnan plateau) \\ \cline{2-3}  & VA6 (the southern flank of the Eastern Himalaya) \\ \hline \end{tabular}
\end{table}
Table 3: Eco-geographic zoning of the Qinghai–Tibet Plateau.