esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Energy Input into Straw Formation  
The data on the RCRs and the energy parameters per crop type are presented in Table 2 [70; 71; 74; 75].  
In this study, the residue-to-crop ratios and the shares of energy used for the straw formation are expressed in percent. The results of our calculations are shown in Figure 3. The shares of energy used for straw formation range from 24% to 70% of the total energy inputs.  
The \(SR\) indicators for the actual data of wheat production reported in some articles are presented in Figure 4 [76, 77, 78, 79, 80]. The increase in yield results in the decrease in the SR.  
The impact of crop yield on the SR was studied using Equation (18). RCR functions for wheat, barley, and corn were taken from the results found by [PERSON] et al. [70]. Our calculations showed that the share of energy used for straw formation has a significant dependence on the yield and type of crop (Figure 5). Corn has a higher value than other cereal crops.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Crop**} & **Residue-to-Crop** & **Lower Heating Value of** & **Calorific Value of** \\  & **Ratio** & **Straw, MJ/kg** & **Grain, MJ/kg** \\ \hline Rapeseed & 1.1–1.7 & 27.7–29.0 & 14.7–19.1 \\ Wheat & 0.8–1.8 & 15.0–18.1 & 13.7–19.1 \\ Rye & 1.7–1.75 & 15.0–18.1 & 13.6–19.1 \\ Oats & 1.0–2.0 & 15.0–18.1 & 12.9–19.1 \\ Barley & 0.9–1.8 & 15.0–18.1 & 13.4–19.1 \\ Corn & 0.7–2.0 & 16.1 & 13.9–14.5 \\ Sunflower & 1.0–3.0 & 16.0 & 25.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Residue-to-crop ratios and energetic parameters of selected crops.  
Figure 4: Share of energy for wheat.  
Figure 3: The share of energy associated with straw formation.  
The energy consumption for straw formation and the energy consumption for straw collection were compared. [PERSON] et al. [45] reported that the energy consumption for straw collection ranged from 0.37 to 0.588 MJ per kg of straw. [PERSON] et al. [50] reported about 0.4256 MJ per kg of straw. However, if the energy consumption for straw formation is taken into account, the embodied energy ranges from 1.1 to 6.2 MJ/kg. Therefore, the energy consumption for straw formation may be up to 10-fold the energy consumption for straw collection, and it should be taken into account in an energy analysis of straw use.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## Appendix A.1.2 Beidou-2 IGSO Navigation Satellite RFI Data  
Beidou-2 IGSO navigation satellite runs in the geosynchronous orbit and works in L band. The L band receiver is set at the top of the building in Beijing Institute of Technology, China. The building is marked as the red point in Figure A3. Considering the sheltering effect, the possible coming area of RFIs obtained by the receiver is within the area marked as the blue lines in Figure A3.  
We obtain the Beidou-2 IGSO navigation satellite from the No.2 IGSO satellite. When there is no RFI, the received signal is shown in Figure A4a. The corresponding spectrum is shown in Figure A4b. In the absence of RFI, the signal is random, deriving from thermal noise. In Figure A4c,d, it shows the RFI signal in time and frequency domains, respectively. An obvious periodic RFI emerges in the figure. The detected RFI is a kind of pulse interference. Its pulse width is about 100 us and the pulse repeated frequency is nearly 900 Hz. The bandwidth of the RFI is about 4 MHz (wide spectrum part in Figure A4d). The equivalent antenna temperature is about 1573 K. There are some other high amplitude RFI signals existing in the spectrum (e.g., 100 Hz bandwidth). However, as their bandwidths are very small, their energies are small compared with the noise floor.  
If we add the detected RFI of Beidou-2 IGSO navigation satellite data into the GEO SAR case with a 2250 w average transmitted power, the SINR in the presence of the RFI is shown in Figure A5. The maximum SINR is about 15.7 dB, which shows little impact on GEO SAR imaging (compared with a 10 dB SINR requirement).
# Explainable Internet Traffic Classification
## 4 Experimental Results
### SVM Classifier
\begin{table}
\begin{tabular}{c c c c} \hline
**Feature** & **Class** & **TPR** & **FPR** \\ \hline \multirow{4}{*}{Statistical} & Mail & 0.75 & 0.06 \\  & Skype & 0.55 & 0.006 \\  & Firefox & 0.38 & 0.001 \\  & Safari & 0.81 & 0.12 \\  & BitTorrent & 0.95 & 0.003 \\  & Amule & 0.71 & 0.13 \\ \hline \multirow{4}{*}{Composite (\(H=3\))} & Mail & 0 & 0 \\  & Skype & 0.59 & 0.01 \\  & Firefox & 0 & 0.001 \\  & Safari & 0.86 & 0.31 \\  & BitTorrent & 0.89 & 0.001 \\  & Amule & 0.77 & 0.18 \\ \hline \multirow{4}{*}{Composite (\(H=5\))} & Mail & 0.19 & 0.002 \\  & Skype & 0.57 & 0.007 \\  & Firefox & 0.9 & 0.01 \\  & Safari & 0.86 & 0.3 \\  & BitTorrent & 0.96 & 0.002 \\  & Amule & 0.726 & 0.12 \\ \hline \multirow{4}{*}{Composite (\(H=10\))} & Mail & 0.9 & 0.004 \\  & Skype & 0.56 & 0.005 \\ \cline{1-1}  & Firefox & 0.96 & 0.001 \\ \cline{1-1}  & Safari & 0.89 & 0.005 \\ \cline{1-1}  & BitTorrent & 0.95 & 0.002 \\ \cline{1-1}  & Amule & 0.95 & 0.06 \\ \hline \end{tabular}
\end{table} 
Table 5: SVM: TPR and FPR over UniBS data set.
## 3 OLSR-Based Protocols
### P-Olsr  
Predictive-OLSR (P-OLSR) [28] is a variant of OLSR that predicts changes in the expected degree of wireless communication quality across UAV nodes in FANETs. It utilizes GPS data typically provided by the UAV's autopilot system. P-OLSR is the only routing technique currently supported on Linux for FANETs. Real-world testing and simulations of the MAC layer were utilized to determine the P-OLSR performance. For the studies, the authors [28] utilized a testbed comprised of two autonomous fixed-wing UAVs and a base station.  
The standard OLSR design does not take into account the quality of the wireless connection. A hop count measure is utilized for route selection, despite being insufficient for wireless mobile networks. The OLSR link-quality extension can take the quality of wireless networks into account by using the _expected transmission count_ (ETX) option [29]. The ETX metric is defined as:  
\[ETX(R)=\sum_{n\in R}ETX(n)=\sum_{n\in R}\frac{1}{\Phi(n)p(n)} \tag{4}\]  
where:  
* \(R\) is a network route connecting two UAV nodes, and \(n\) is a hop of the route \(R\);
* \(\Phi(n)\) is the possibility that a packet delivered over hop \(n\) would be successfully received and is also called the _forward receiving ratio_;
* \(p(n)\) is the possibility of successfully receiving the matching acknowledgment (ACK) packet and is also called the reverse receiving ratio;
* \(ETX\) calculates the number of transmissions and re-transmissions that will be required to deliver a packet from its source to its destination. Then, OLSR chooses the route with the lowest ETX, which is not always the option with the fewest hops [28].  
The network topology is continually changing, and a wireless connection between two UAVs might fail unexpectedly. The \(ETX\) measure may be insufficient in these networks since it is not reactive enough to track link fluctuations. To address this issue, the \(ETX\) measure was modified to consider the UAV's location and orientation in relation to its neighbors. The \(ETX(n)\) measure of the hop \(n\) between nodes \(i\) and \(j\) is weighted by a factor that compensates for the relative speed between \(i\) and \(j\), as shown below:  
\[ETX(n)=\frac{e^{n^{i/j}\beta}}{\Phi(n)p(n)} \tag{5}\]where \(v^{lj,i}\) is the relative speed between nodes \(i\) and \(j\) and \(\beta\) is a non-negative parameter. The ETX will be adjusted by a factor less than 1 when the UAVs \(i\) and \(j\) travel closer to one another and the relative speed turns negative. Alternatively, if UAVs \(i\) and \(j\) move apart from one another, the relative speed is positive, meaning that the ETX will be adjusted by a factor greater than 1 as long as this is the case. To put it another way, even if two nodes have the same values for \(\Phi\) and \(p\), it is preferable to hop between nodes that move closer to one another rather than ones that move farther away.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion
### Power Measurements and Path Loss Modeling
#### 5.1.5 Discussion of Path Loss Models
* The CI and FI models give the same result when \(L_{0}\) and \(\alpha\) are equal [38]. This can only occur in the LOS situation, as this is when both models could have the same behavior. * The CI and FI models are practically very similar in LOS measurements; in NLOS measurements, they vary significantly from each other. * For narrowband transmission, the slope in the NLOS-1 zone is very close to the free space propagation value with a small waveguide effect. For the NLOS-2 zone, the slope value increases considerably due to a large energy loss in diffraction at the R1 point. * For narrowband transmission and LoRa RSSI, in general, the power measurements performed have a very similar behavior between one technology and the other, with the nuance that LoRa has somewhat lower losses in the NLOS-2 zone. As shown in Figure 13, the received power curve of the narrowband transmission is very similar to that of the LoRa RSSI, previously raised by means of an offset. This offset is calculated as the difference in the mean of the narrowband transmission power measurements with the mean of the LoRa RSSI measurements. The error is calculated as the absolute value of the difference in each point of the narrowband transmission power measurement with the LoRa RSSI measurement after shifting the LoRa RSSI point cloud by adding the offset. * For the RSSI of LoRa and Zigbee, continuing with the previous point, we can observe how the behavior of the RSSI measurements of both LoRa and Zigbee is very similar to the power measurements of narrowband transmission, with the slight differences indicated above, so we can conclude that the RSSI measurements are reliable to make a propagation loss model. * For LoRa RSSI, the slope in the NLOS-1 zone is less close to the free space propagation, so some waveguide effect is present. In the NLOS-2 zone, the slope increases due to diffraction losses at the R1 point, but it is observed that the \(\beta\) value is considerably lower than in narrowband transmission due to the robustness of LoRa CSS modulation. * For Zigbee RSSI, the NLOS-1 slope is almost identical to the LoRa transmission because they experience the same waveguide effects. In the NLOS-2 zone, it was not possible to measure because the signal attenuated before reaching the R1 point due to the low power at which Zigbee transmits and its lower receive sensitivity. * For 5G SS-RSRP, it is the transmission that attenuates less with distance and suffers a very noticeable waveguide effect, seeing that its slope value is very close to 1 in the NLOS-1 zone for the FI model. On the other hand, in the NLOS-2 zone, it has a higher \(\beta\) slope than LoRa transmission and a very similar to narrowband transmission. Therefore, it is observed that 5G transmission shows the least slope in the NLOS-1 zone, while in the NLOS-2 zone, it shows a higher slope.
# Ice Production in Ross Ice Shelf Polynyas during 2017-2018 from Sentinel-1 SAR Images
## 4 Results
In some days when Kathabatic winds occurred continuously, such as in the periods of 17-22 June 2018 and 21-26 July 2018, the ice thicknesses were approximately 0.1-0.13 m, thinner than that of other times in the stable phase (Figure 5), because there was not enough time for sea ice to grow. So, the high-frequency of occurrence is one of the reasons for thin ice thickness, which also influences the ice production of each polynya event. In previous studies presented in Table 1, the polynya area was defined as the area with an ice thickness of less than 20 cm or the ice concentration of less than 75% from March to October [19; 23; 39], hereafter referred as the PMW method. The ice thickness and concentration were estimated from PMW based on PR36 and PR85 [23; 49]. In this study, the same PMW method is also used to estimate the average polynya area (March to October) in 2017 and 2018 based on AMSR2, and the results show that the annual average areas of RISP and MSP were 23,617 km\({}^{2}\) and 1782 km\({}^{2}\) in 2017, respectively, and 25,901 km\({}^{2}\) and 1874 km\({}^{2}\) in 2018, respectively (Table 2). The estimated polynya areas from PWM are much larger than the polynya areas from Sentinel-1 SAR in RSIP but much lower in MSP.
# Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding
## 2 Experimental Setup
### Experimental Design  
Table 1 presents the welding parameters adopted in this study. The fiber laser delivers 105 W power in pulse mode with an interval of 2 ms for each welding. The laser spot size is 50 um in diameter. To classify the welding quality for signal analysis and monitoring model development, the joint strength was measured after lap microwelding by a peeling test. The peeling test was performed for each welding spot on a sample, and the peeling force was obtained using a force transducer (Kistler 9217A) during the breaking up of a joint with equipment designed by the authors (Figure 2a); the peeling force used to break up the joint was measured and recorded throughout the test (Figure 2b).  
In a production line, loss of contact between the two layers of metal sheets might be caused by the deformation of the metal sheets or insufficient clamping force induced  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Laser Type** & **QCW Fiber Laser** \\ \hline Wave length (um) & 1.064 \\ \hline Laser power (W) & 105 \\ \hline Laser scan speed (mm/s) & 200 \\ \hline Frequency for laser (kHz) & 0.01 \\ \hline Time interval for pulse wave (ms) & 2 \\ \hline Spot size (um) & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Welding parameters.  
Figure 1: Laser welding research platform and fixture.  
by improper clamper design. Improper contact can yield low joint bonding strength or prevent joint generation. To simulate such conditions to generate different welding quality, the central clamp, the welding location, and two thin sheets of paper between the two layers of metal sheets were used to control the contact between the layers of metal sheets. The thickness of paper used to increase the gap was around 200 um. After the test, the peeling test was performed on each sample to confirm the final welding condition. Figure 3 illustrates the setups used to simulate the two types of contact between the metal sheet layers. To increase the possibility of obtaining improper contact between the layers, the central clamp bar was removed, and low torque (1.5 N) was applied to the screw. In addition, a welding location near the central parts of the fixture was selected. Two paper sheets, which did not cover the welding point, were placed between the metal sheets to help generate a gap. By contrast, to ensure proper contact between the layers of metal sheets, the welding location was moved near the clamp screw, the screw torque setting was high, and the central clamp bar was used. Table 2 presents a summary of the experimental parameters.
## 2 Materials and Method
### Principal Component Regression (PCR) Method
#### 2.2.2 Standardization  
Standardization is critical prior to PCA to align variables to zero-mean, unit-variance scales, ensuring equitable contribution to variance-based component extraction. Using Equation (1) [26], variables are transformed to eliminate scale-driven biases, enabling PCA-derived components to reflect authentic covariance patterns. In UCS prediction models, standardized inputs (mixture compositions, dosages, curing-time-dependent strengths) ensure latent relationships are isolated through dimensionality reduction, yielding mechanistic insights into stabilization processes while preserving regression accuracy. This safeguards against misinterpretation of variable importance, anchoring results to underlying physicochemical phenomena.  
\[X^{\prime}_{ij}=\frac{X_{ij}-\mu_{j}}{\sigma_{j}} \tag{1}\]  
where  
\(X_{ij}\) = the original value of the j-th soil property for the i-th sample  
\(\mu_{j}\) = mean of the j-th soil property across all samples  
\(\sigma_{j}\) = standard deviation of the j-th soil property  
\(X^{\prime}_{ij}\) = standardized value
## 1 Introduction
Thus, SRRS, together with nonlinear phase modulation, allows for the generation of broadband Stokes pulses with further compression to a pulse width significantly shorter than that of pump pulses. However, currently, the predominant technology for generating ultrabroadband Stokes pulses is based on the use of hollow fibers (HCFs) filled with gases. When pumped by femtosecond Yb laser pulses, this provides a spectrum broadening to 1.3 um in N\({}_{2}\)[24] and up to 2 um in N\({}_{2}\)O [25]. Nevertheless, this expensive and bulky technique is inferior to more traditional and convenient high-pressure gas cells. In this paper, for the first time to our knowledge, we report a highly efficient transient cascade SRRS in a hydrogen cell with high-energy output pulses ranging from ~1.1 to ~1.4 um, which was obtained using ~1.2 ps pump pulses from a Yb:YAG laser at 1030 nm. The spectral synthesis of multiple higher-order Stokes of cascade SRRS is demonstrated. Particular attention was paid to lowering the SRRS threshold, as well as expanding the spectral bandwidth of the rotational mode comb. The influence of polarization, energy, and the focusing conditions of the pump pulse, as well as the H\({}_{2}\) pressure, on the conversion efficiency and the evolution in the spectral envelope of SRRS and SVRS was studied. The circular polarization of pump pulses and seeding with a white-light supercontinuum (WLC) made it possible to suppress SVRS. High H\({}_{2}\) pressure ensured the generation of multiple higher-order Stokes of cascade SRRS corresponding to a shift of 587 cm\({}^{-1}\). As a result, a SRRS conversion efficiency of 52% was achieved and a Stokes comb energy exceeding 3 mJ. The possibility of compressing Stokes pulses with a synthesized SRRS spectrum up to ~14 fs at a central wavelength of 1.2 um is also discussed.
## 2 Basic Measurement Techniques for Localization in WSNs
### Distance Related Measurement
#### 2.2.3 Connectivity Based  
Connectivity based measurement is the simplest form of all the measurement techniques we have discussed so far. In this technique, a sensor is connected to another sensor if it is within the radio transmission radius of each other. Such measurement technique is treated as the binary measurement. In this technique, a sensor node is connected to another sensor node (binary 1) or not connected directly if it is outside the radio transmission range (binary 0). From one sensor to another sensor, the distance is thus represented as the hop count and various algorithms are applied to measure the average hop distance as accurately as possible [14]. This category of WSNs localization algorithm is popularly known as the range free localization algorithm.
# Development of a Staggered PCD End Mill for Carbon Fiber Reinforced Plastic
## 2 Design of Staggered PCD End Mill
### Analysis of Machining Defects  
When milling CFRP laminate under the extrusion of cutting force, the fiber in the top layer will not be restrained by the outside, so it is easy to delaminate the layer. If the fiber is not cut by the tool in time, it will remain in the edge of the workpiece surface and form burr defects. If the burr is caught in the cutting area of the tool and forcibly pulled off, a tearing defect will be formed. Milling burrs and tears often appear in the surface of the fiber layer. In the machining process, a combination of cutting, shearing and fracture along the fiber/matrix interface will be observed [29; 30; 31].  
Taking the upper surface of the workpiece, for example, as shown in Figures 4 and 5, when cutting edge 1 of the staggered PCD end mill is involved in milling, the upper surface is subjected to downward axial force, and the fiber layer near the upper surface can be cut without delamination due to the support of the CFRP sheet. When cutting edge 2 is involved in milling, the upper surface is subjected to upward milling force and the fiber layer near the upper surface layer is given to local cracking owing to loss of external support, but the milling range is small and the resulting defects do not affect the cutting performance. Cutting edge 3 is involved in milling in the same way as cutting edge 1 and it generates a downward cutting force and cuts the burr generated by cutting edge 2. In high-speed milling, the left-right alternative cutting edges work like scissors in the cutting process, and good surface quality can be obtained even in high-speed and large-feed conditions.
## 3 Research on an Improved CGAN Ground Grid Imaging Algorithm
### Improved CGAN Structure
#### 3.1.1 Generator Design
This paper adopts the CBAM (Convolutional Block Attention Module) attention mechanism to independently infer the attention map through the channel attention and spatial attention modules, thereby enhancing the transmission of feature information. As shown in Figure 7, the channel attention mechanism calculates the importance of different channels of the input feature map through global average pooling and maximum pooling, while the spatial attention mechanism assigns weights through the local information of the feature map. For the input feature map \(F\), the channel attention map \(M_{c}\) is calculated as follows:  
\[M_{c}(F)=\sigma(MLP(AvgPool(F))+MLP(MaxPool(F))) \tag{8}\]  
Among them, \(AvgPool\) and \(MaxPool\) distributions are the global average pooling and average maximum pooling operations, \(\sigma\) is the sigmoid activation function and \(MLP\) is a multi-layer perceptron with shared weights. The spatial attention map \(M_{s}\) is calculated by the \(7\times 7\) convolution kernel, and the specific expression is as follows:  
\[M_{s}(F)=\sigma\Big{(}f^{7\times 7}([AvgPool(F);MaxPool(F)])\Big{)} \tag{9}\]  
where \(f^{7\times 7}\) represents the \(7\times 7\) convolution operation of size, and \";\" represents feature concatenation. Finally, by multiplying the input features point by point, CBAM can effectively strengthen the weight distribution of conductivity change features, thereby optimizing the EIT reconstruction effect under the complex distribution of grounding grid corrosion. \[X_{out}=X+(M_{c}(F))\odot(M_{s}(F)\odot X)) \tag{10}\]  
\(\odot\) represents element-wise multiplication.