esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The measurements above show the effective minority carrier lifetime (\(\tau_{eff}\)) that was obtained for the different passivation cases. It should be noted that back surface passivation was not performed in this experiment which is a reason for the low carrier lifetime readings of the samples, thus the reading gives only an indication of the front lifetime. The minority carrier lifetime could be expressed as [26]:  
\[\frac{1}{\tau_{eff}}=\frac{1}{\tau_{bulk}}+\frac{\left(S_{eff}^{F}+S_{eff}^{B} \right)}{d}\]  
where \(\tau_{bulk}\) is the bulk Shockley-Read-Hall (SRH) lifetime, \(S_{eff}^{F}\) and \(S_{eff}^{B}\) are the effective surface recombination velocity at the front surface and at the back surface, respectively, and d is the wafer thickness. It has been demonstrated that by increasing the surface area the front surface recombination velocity increases [26]. Since the combination of SiO\({}_{2}\)/SiN provided the highest carrier lifetime, this means that it provided the lowest surface recombination velocity. Previous results have  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Planar** & **RIE** & **MACE** \\ \hline Average & & & \\ Carrier & 17.8 \(\mu\)s & 14.6 \(\mu\)s & 11.1 \(\mu\)s \\ Lifetime & & & \\ \hline Image & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mapping of the Carrier Lifetime of a sample passivated with SiO\({}_{2}\)/SiN.  
Figure 7: The carrier lifetime of the untreated samples and the samples treated with SiO\({}_{2}\)/SiN.  
demonstrated that SiO\({}_{2}\) passivation reduces the Si-SiO\({}_{2}\) interface state density which improves the surface recombination [27]. It has been also noted that high Auger recombination near the surface plays a key role in the lifetime of the carriers [27]. The RIE and MACE processes lead to dislocation and dangling bonds which also lead to severe Shockley-Read-Hall (SRH) recombination [27]. Thus the high recombination (low carrier lifetime) is due to surface, Auger, and SRH recombination. SiO\({}_{2}\) passivates the surface and SiN provides bulk passivation because during the deposition of SiN, hydrogen, which is found in the deposition chamber, diffuses in the material. The presence of hydrogen reduces the defect state density and suppresses the Auger recombination near the surface and the SRH recombination in the bulk of the material. Thus, when SiO\({}_{2}\) and SiN are deposited together the highest carrier lifetime is obtained since the surface dislocations are passivated by SiO\({}_{2}\) and the dangling bonds in the bulk of the material bond with hydrogen due to SiN deposition. It also must be noted that in almost all of the measurements the MACE pillars had a carrier lifetime greater than the RIE pillars and this is due to the fact that the micropillar surface formed by RIE may be damaged as a result of the plasma. However, the MACE process results in less damage especially when the ratio of HF and H\({}_{2}\)O\({}_{2}\) is controlled [28].
## 2 Theoretical Derivation of PIC Model
### Contact Pressure Caused by Cable Bending
For example, taking \(\omega=1/3\), we can calculate the distribution ratio for each layer of wire based on the above formula, resulting in its transmission coefficients, which are listed in the Table 1 below:  
Figure 10: Contact pressure distribution caused by cable bending. During the vibration process, the curvature radius of the wire changes with time, and it can be obtained as follows:  
\[\rho_{0}(x,t)=\left|\frac{\left(1+\left(\frac{\partial u(x,t)}{\partial x}\right) ^{2}\right)^{3/2}}{\frac{\partial^{2}u(x,t)}{\partial^{2}x}}\right| \tag{14}\]  
It can be assumed that the curvature radius of the central wire is the same as the curvature radius of the cable. Therefore, at different positions on the cross-section, the curvature radius of the wire is given by:  
\[\rho_{(i,j)}=\rho_{0}\pm iD_{w} \tag{15}\]  
where \(\rho_{0}\) is the curvature radius of the central wire, \(i\) is the number of layers of wires, and \(D_{w}\) is the diameter of the wire. Layer-by-layer superposition leads to the vertical pressure caused by the bending of the wire as follows:  
\[p_{v(i,j)}^{(k)}=\sum\frac{T_{(a,b)}}{\rho_{0}+R}\cdot\beta_{(c,d)} \tag{16}\]  
Therefore, the contact pressure between wires caused by bending is given by:  
\[q_{3(i,j)}^{(k)}=\frac{p_{v(i,j)}^{(k)}}{\sqrt{3}}=\frac{\sum\frac{T_{(a,b)}}{ \rho_{0}+R}\cdot\beta_{(c,d)}}{\sqrt{3}} \tag{17}\]  
For example, for wire (2,1) in Figure 11, when calculating the contact pressure at the contact point \(\langle 2\rangle\), it is necessary to superimpose the contact pressure distribution values contributed by the wires within the upper conical region. For ease of calculation, let us expand Equation (17) using \(q_{3(2,1)}^{(2)}\) as an example:  
\[q_{3(2,1)}^{(2)}\cdot\sqrt{3}= \frac{T_{(5,1)}}{\rho_{0}+5\sqrt{3}r}\cdot\beta_{(3,1)}+\frac{T_{( 5,2)}}{\rho_{0}+5\sqrt{3}r}\cdot\beta_{(3,2)}+\frac{T_{(5,3)}}{\rho_{0}+5 \sqrt{3}r}\cdot\beta_{(3,3)}\] \[+\frac{T_{(4,1)}}{\rho_{0}+4\sqrt{3}r}\cdot\beta_{(2,1)}+\frac{T_ {(4,2)}}{\rho_{0}+4\sqrt{3}r}\cdot\beta_{(2,2)}\] \[+\frac{T_{(3,1)}}{\rho_{0}+3\sqrt{3}r}\cdot\beta_{(1,1)}\]  
By substituting the tensile forces \(T\), cable curvature radius \(\rho_{0}\), wire radius \(r\), and the transmission ratio of force between different wires \(\beta_{(i,j)}\) into the equation, the magnitude of the contact pressure can be calculated. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Layer** & **x** & **0** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
1 & & 1.000 & — & — & — & — & — & — & — \\
2 & & — & 0.500 & — & — & — & — & — \\
3 & & 0.500 & — & 0.417 & — & — & — & — \\
4 & y & — & 0.319 & — & 0.347 & — & — & — \\
5 & & 0.106 & — & 0.324 & — & 0.289 & — & — \\
6 & & — & 0.107 & — & 0.318 & — & 0.241 & — \\
7 & & 0.036 & — & 0.142 & — & 0.305 & — & 0.201 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Transmission coefficients across different layers.
## 4 Results and Analysis
### Stabilised Approach: Analysis  
#### 4.3.1 Stabilised Approach: Fleiss' Kappa  
Assisting in looking at agreement among participants, Fleiss' kappa was used here to see how pilots responded to stabilised approach scenarios. If there is agreement, then it demonstrates the similarity in responses to different situations, as well as similarity in understanding among pilots.  
The participant responses ranged between a slight to fair agreement regarding the stabilised approach questions. For question 26a, the most common answer was \"proceed with the landing, relying on your skills to handle the gusty conditions\". Notably, no pilots chose to attempt to find a closer, albeit less familiar, alternative landing site. When asked what influenced their decision-making choice here, pilots mainly referenced their confidence in personal skill and aircraft capability. This was followed by weather, then distance to the nearest safe landing site and fuel status, which are some of the many factors that can impact pilot decision-making [24].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **33 Pilots** & **18 Pilots** \\ \hline \(\chi^{2}\) & 8.89 & 3.66 \\ Degrees of freedom & 3 & 3 \\ \(p\)-value & 0.0308 & 0.3006 \\ Conclusion & Reject & Accept \\ \hline \hline \end{tabular}
\end{table}
Table 6: Chi-squared results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Question** & **P\({}_{\mathbf{o}}\)** & **P\({}_{\mathbf{e}}\)** & **Kappa** \\ \hline (26a \({}^{2}\)) Based solely on the change given above, what is your immediate decision? 1  & 0.355 & 0.096 & 0.287 \\ (26b \({}^{2}\)) Which factor influences your decision the most? 1  & 0.238 & 0.068 & 0.182 \\ (29 \({}^{2}\)) In the event of an unstable approach or descent, what is your course of action? & 0.364 & 0.079 & 0.310 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Stabilised approach Fleiss’ kappa.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Licences (18 Pilots)** & **Expected** & **Observed** \\ \hline Recreational Pilot Permit & 0.69 & 0 \\ PPL & 11.93 & 9 \\ CPL & 4.03 & 7 \\ ATPL & 7.34 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Chi-squared (18 pilots).  
Comparatively, question 29 had a slightly stronger agreement among the pilots, with the majority opting to \"go-around and attempt the approach again once stabilised\", followed by \"adjust the pitch and power to return to the desired glide path and speed\". No pilots selected the option to prioritise finding an alternate place to land safely.
## 3 Femtosecond Laser Direct Writing of Gradient Index Flat Lens
### Optical Losses Pre- and Post-Irradiation  
Another important feature when developing such a new manufacturing approach is the optical losses induced by the FLDW technique. Consequently, we measured the optical transmission (\(\Delta\lambda\in[0.2;\,20]\) \(\upmu\)m) of our material before and after laser irradiation, using a 1.5 mm diameter disk as a test sample. Here, we chose the laser energy from the results given in Figure 7 to implement a \(\uppi\) rad phase change at 550 nm for the sake of simplicity. Indeed, \(\uppi\) rad is a basic phase shift value particularly used in phase mask optics (binary annular phase masks, binary Fresnel lenses, etc.).  
Figure 8 represents the spectral transmission of the GeS2 glass material pre- and post-irradiation. We noticed that the difference before and after irradiation was quite negligible (typically \(<\) 1% transmission change). As a result, the writing of a disk with a phase shift of \(\uppi\) rad did not affect the transmission. The results shown in Figure 8 allowed us to consider good conservation of the optical transmission even after imprinting some significant refractive index changes, even if, obviously, a higher phase variation will surely imply a more significant decrease of the optical transmission of the related optical components, mostly related to significant light scattering effects.
## 4 Methods
### CBAM Module
#### 4.2.2 Spatial Attention Module  
The module showed in Figure 6 is designed to learn the correlations between different spatial positions ([PERSON] et al., 2020). It reshapes the feature map along the channel dimension into a two-dimensional matrix. Then, by passing through a convolutional layer and an activation function, spatial weights are generated. Finally, by multiplying the spatial weights with the feature map, a new feature map is obtained.  
Figure 5: Channel attention module.  
Figure 6: Spatial attention module.  
In convolutional neural networks, features from different channels and spatial positions have varying importance for different tasks. For instance, in image classification tasks, features from different colour channels have varying abilities to distinguish between different categories. In image segmentation tasks, features from different positions have different capabilities for dividing different regions [30]. Similarly, in the time series regression problem of continuous blood pressure estimation, signals from different channels such as PPG [31], its derivative PPG, and PPG\" have different degrees of correlation with blood pressure. The channel attention module can be used to adjust their respective weights. As the values at specific time steps in dynamic blood pressure signals, SBP and DBP also have varying degrees of correlation with the amplitudes of PPG, PPG', and PPG\" at different time steps [32]. The time dimension can be viewed as a spatial dimension, and the spatial attention module can be used to weigh signals at different time steps. By adaptively adjusting the feature weights of different channels and time positions, the expressive ability of the model can be improved, resulting in higher accuracy of the final blood pressure estimation.
# An Analysis of MPTCP Congestion Control
## 4 Multi-Homing Solutions
### Network Layer  
There are different proposals to enable multi-homing in the network layer. Some of the common methods are summarised in this subsection.  
* **Site-Wide Multi-homing:** This method refers to when one LAN is connected to the Internet via different transit providers (see Figure 2). Hosts in such a network tend to be single-homed while the gateway router is responsible for multi-pathing. The router can shift end-hosts TCP flows between different transit providers without breaking any connection. Site-wide multi-homing provides redundancy, load balancing and allows policy enforcement. However, the scheduler makes decisions based on the current network condition and preferences rather than the hosts needs [1].
* **Mobile IPv4 and IPv6 [14]:** Mobile IP uses entities called the home agent and foreign agent to create a tunnel to mobile hosts (see Figure 3). Mobile IP allows mobile hosts to change IP addresses without disrupting/changing the connection. As can be seen in Figure 3, when a mobile node connects to a new gateway (the foreign agent), the mobile node registers a care-of address with the home agent. The care-of address is usually assigned to the logical end point of the tunnel between the home and the foreign network (mainly the foreign agent). The care-of address can be reused for different visiting hosts. When a mobile device shifts from one foreign agent to another one, the home agent will forward data to the foreign agent to end the data to the mobile host. However, the topology relies heavily on traffic passing through the foreign agent as well as the home agent at least once. Further, while mobile nodes can change their own IPs, there is no support for resource pooling and hence bandwidth remains unchanged.
* **Site Multi-homing by IPv6 Intermediation (Shim6) [15]** Shim6 is a pure end-host based solution which allows IPv6 nodes to have multiple addresses. Shim6 improvesload balancing and redundancy. However, it does not provide simultaneous use of multiple paths and is restricted to IPv6 networks.
## 3 Results  
### Thermophysical Modeling Results  
#### 3.1.1 Thermophysical Modeling Results for the Frozen Rock Mass  
Figure 7 shows contour plots constructed in the plane secant to the channel, including the axis of this channel (radius = 0), to visualize changes over a period of 10 years in the temperature distribution in the channel. The physical conditions of the filtering water and frozen rock are given by the previously described initial and boundary conditions for well t3 (see Section 2.4).  
The leftmost plot in Figure 7 shows contour lines corresponding to a temperature value of \(-0.5\)\({}^{\circ}\)C over time. The values correspond to time points in the simulation; for example: 0 corresponds to 0 years and 245 days (i.e., 2 September of the 1 st year); 1 corresponds to 1 year and 245 days (i.e., 2 September of the 2 nd year), and so on until the value of 9, which corresponds to 9 years and 245 days (i.e., 2 September of the 10 th year) after the start of the simulation.  
The middle and right plots (Figure 7) show the modeled temperature distribution in the frozen rock at two points in time: 245 days (2 September of the 1 st year) for the middle plot and 3530 days (2 September, 10 th year) for the right plot. The temperature values in the plots are shown by the color bar on the right-hand side in Figure 7. There is a noticeable thawing of the rock from the surface and the upper part of the filtration channel (15 m downward movement of the 0 \({}^{\circ}\)C contour over 10 years).  
The amount of heat transferred by warm water to the frozen rock over 10 years is found to be 207.8 GJ, i.e., an average of 20.78 GJ per year. Calculating the corresponding average channel power yields a value of 659.01 W, with an average specific heat flux of 21.26 W/m\({}^{2}\).  
Figure 7: Contour plots of temperature distribution in the frozen rock (well t3) at different points of time. The simulation period is 10 years. The ambient rock temperature is \(-4\)\({}^{\circ}\)C and the velocity of water flowing into the cylindrical channel is 100 m/day.
# Study on Microstructure of Cemented Organic Soil in Dianchi Lake, China
## 3 Test Results and Analysis
### UCS Test Results and Analysis of Cemented Soil  
The UCS test results will not easily reflect the dominant influence of the two variable factors on the strength of the decoupled sample when the HA content and cement rate are low. Therefore, a confidence interval is set for the test results. Among them, the confidence interval of the HA content is 10-30%, and the confidence interval of the cement ratio is 10-25%.  
Figure 7: Cohesive soil aggregates.  
Figure 9 shows the relation between UCS and the cement ratio. When the humic acid content is fixed, the UCS of the cemented soil sample increases with the cement ratio. It is worth noting that when the humic acid content is 20% and the cement ratio is lower than 15%, the UCS of the cemented soil increases slowly. It shows that when the humic acid content is high, the solidification effect of cement on organic soil is limited. However, the increase in cement ratio makes the cement hydration products gradually increase, and the influence of humic acid on the cement hydration reaction is gradually weakened. Its UCS increases with the increase in cement ratio. Therefore, the increase in cement ratio can weaken the negative effect of humic acid on the UCS of cemented soil.  
Figure 10 shows the relation curve between UCS and humic acid (HA) content of the cemented soil sample. At the same cement ratio, the UCS of the cemented soil sample decreases with increased humic acid content. Especially when the cement ratio is less than 20% and the humic acid content is more than 15%, the UCS of the cemented soil sample decreases significantly.  
Figure 8: Humic acid aggregates.  
Figure 9: Relation curve between UCS and cement ratio.
## 3 Methods
### Data Integration  
The procedure for integrating the various data sources and establishing a forest fire risk map is shown in Figure 4. In this procedure, the parameters are integrated into a model incorporating both natural and social classes of information. Natural classes include the following: the forest type (forest map 1:100,000) established by remote sensing methods, elevation, topographic slope, slope direction, river density (analyzed from the terrain map 1:100,000), total annual precipitation, maximum temperature/year, minimum humidity/year, and the prevailing wind direction (extracted from 33-year meteorological data). Social information consists of the population distribution and the distance to roads, sourced from provincial statistics.
## 3 System Architecture
### Detailed System Implementation
#### 3.4.3 Geospatial Processing Implementation  
Dynamic zone generation employs DBSCAN clustering on geographic coordinates using the haversine metric for accurate Earth-surface distances. The algorithm's epsilon parameter (neighborhood radius) adapts based on listing density, which is calculated as the 5 th percentile of nearest neighbor distances. This adaptive approach ensures consistent cluster quality across varying urban densities.  
Integration with OSRM (open-source routing machine) provides realistic travel time estimates. The system maintains a local OSRM processing OpenStreetMap (London, UK) data for Timisoara, updated monthly. Route calculations use the CH (Contraction Hierarchies) algorithm for sub-millisecond query times on the city-scale road network [16]. Walking time estimates include a 1.2\(\times\) multiplier for real-world factors like traffic signals and path indirectness, validated against Google Maps walking directions with 89% accuracy.  
The 89% consistency rate was established through a comprehensive validation study conducted over 4 weeks in February 2025 using 480 origin-destination pairs sampled from our listing dataset. The validation framework employed stratified sampling across spatial and temporal dimensions, with 40 paths from each of 12 zones to the nearest university campus, measured at three key times daily (9 am, 3 pm, and 6 pm) to capture traffic signal variations. The distance distribution included 160 short paths under 1 km, 240 medium-length paths between 1 and 3 km, and 80 long paths exceeding 3 km. Each path comparison followed a standardized protocol: first calculating OSRM routes using the foot profile with OSM data, then querying the Google Maps Directions API with walking mode and specified departure times, and finally validating a subset of 48 paths through GPS tracking with five volunteer students.  
The error distribution analysis, defining consistency as OSRM estimates within \(\pm\)15% of Google Maps times, revealed that 61.2% of estimates fell within a \(\pm\)5% (accurate match), 19.4% showed a \(\pm\)5-10% deviation due to traffic signal timing differences, 8.4% had a \(\pm\)10-15% variance from unmapped pedestrian shortcuts, 7.1% showed a \(\pm\)15-20% differences due to construction or temporary obstacles, and only 3.9% exceeded a 20% error due to outdated OSM data. Statistical validation yielded a mean absolute error of 1.73 min, a root mean square error of 2.41 min, a Pearson correlation of r = 0.94 (\(p<0.001\)), and Bland-Altman 95% limits of agreement between \(-\)4.8 and +5.2 min. The 1.2\(\times\) multiplier applied to raw OSRM calculations compensates for systematic underestimation in congested areas, improving central zone accuracy from 86% to 91% where most student housing is concentrated.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 3 Materials and Methods
### Model
#### 3.4.1 Hardware and Software Environment  
The experiment was executed using the Ubuntu 18.04 64-bit operating system, PyCharm Professional for remote connection to the RIG server, Python 3.10 programming language, and several libraries such as TensorFlow, Keras, NumPy, Pandas, and Scikit-Learn. Table 7 provides detailed hardware information about the RIG server.  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline  & **Flow Duration** & **Flow Pkts/s** & **Flow Byts/s** & **Fwd Pkts/s** & **Bwd Pkts/s** \\ \hline count & \(5.000000\times 10^{4}\) & \(5.000000\times 10^{4}\) & \(5.000000\times 10^{4}\) & \(5.000000\times 10^{4}\) & \(5.000000\times 10^{4}\) \\ mean & \(-2.387424\times 10^{-17}\) & \(1.136868\times 10^{-18}\) & \(-5.400557\times 10^{-18}\) & \(-7.389644\times 10^{-18}\) & \(-2.273737\times 10^{-18}\) \\ std & \(1.000010\times 10^{0}\) & \(1.000010\times 10^{0}\) & \(1.000010\times 10^{0}\) & \(1.000010\times 10^{0}\) & \(1.000010\times 10^{0}\) \\ min & \(-5.253012\times 10^{-1}\) & \(-1.000130\times 10^{-1}\) & \(-4.345196\times 10^{-2}\) & \(-5.722901\times 10^{-2}\) & \(-1.127359\times 10^{-1}\) \\
25\% & \(-5.200939\times 10^{-1}\) & \(-9.993990\times 10^{-2}\) & \(-4.344642\times 10^{-2}\) & \(-5.722901\times 10^{-2}\) & \(-1.126813\times 10^{-1}\) \\
50\% & \(-4.405559\times 10^{-1}\) & \(-9.982524\times 10^{-2}\) & \(-4.342988\times 10^{-2}\) & \(-5.714464\times 10^{-2}\) & \(-1.125122\times 10^{-1}\) \\
75\% & \(-4.341171\times 10^{-1}\) & \(-9.591224\times 10^{-2}\) & \(-4.287239\times 10^{-2}\) & \(-5.707213\times 10^{-2}\) & \(-1.089045\times 10^{-1}\) \\ max & \(2.576183\times 10^{0}\) & \(4.561686\times 10^{1}\) & \(1.236910\times 10^{2}\) & \(3.544473\times 10^{1}\) & \(4.542805\times 10^{1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Descriptive statistics.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**No.** & **Feature Name** & **Description** \\ \hline
1 & Flow Duration & Duration of the flow in Microsecond \\
2 & Flow Pkts/s & Number of flow bytes per second \\
3 & Flow Bts/s & Number of flow packets per second \\
4 & Fwd Pkts/s & Number of forward packets per second \\
5 & Bwd Pkts/s & Number of backward packets per second \\ \hline \hline \end{tabular}
\end{table}
Table 5: Description of features.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Deep Learning(DL) Methods  
The DL algorithm has strong ability of feature extraction and data representation, and has made advanced achievements in medical image processing. The application of DL techniques in the field of image fusion has emerged as an active topic in the last three years.