esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Plant Biomass  
The SynCom significantly increased lettuce biomass when inoculated in roots and leaves (\(p\) < 0.05; Figure 3). The total plant biomass in RL was 31.1% (0.62 \(\pm\) 0.03 g) higher (\(p\) < 0.05) compared to non-inoculated plants (0.47 \(\pm\) 0.04 g Figure 3a). On the other hand, the application of the PGPR synthetic consortium to roots or leaves did not affect biomass production. The total dry weight in R (0.47 \(\pm\) 0.03 g) and L (0.45 \(\pm\) 0.05 g) was similar to non-inoculated plants and significantly lower (\(p\) < 0.05 in both treatments) compared to RL (Figure 3a). Although inoculation with the PGPR synthetic consortium did not affect total dry weight in R and L, significant changes in biomass partitioning between leaves and roots occurred. While leaf biomass in R (0.37 \(\pm\) 0.02 g), L (0.32 \(\pm\) 0.04 g), and RL (0.51 \(\pm\) 0.03 g) was similar to non-inoculated plants (0.39 \(\pm\) 0.03 g), inoculation methods differently affected leaf biomass (Figure 3b): in R and L, leaf dry weight was significantly lower (\(p\) < 0.05 and \(p\) < 0.01 respectively) compared to RL. Root biomass significantly increased (\(p\) < 0.001) in L (0.127 \(\pm\) 0.01 g) compared to non-inoculated plants (0.078 \(\pm\) 0.01 g; Figure 3c). The root biomass observed in R and RL inoculated plants was respectively equal to 0.102 \(\pm\) 0.01 g and 0.097 \(\pm\) 0.01 g, not dissimilar from C and L (Figure 3c). Inoculation with the SynCom significantly affected biomass allocation (\(p\) < 0.01; Figure 3d). The root-to-shoot ratio was significantly (\(p\) < 0.01) higher in L (0.40 \(\pm\) 0.02 g g\({}^{-1}\)) compared to non-inoculated plants (0.20 \(\pm\) 0.01 g g\({}^{-1}\)). Moreover, the ratio was significantly lower (and similar to C) in RL (0.19 \(\pm\) 0.01 g g\({}^{-1}\)) compared to L (\(p\) < 0.001) and R (0.28 \(\pm\) 0.02 g g\({}^{-1}\); \(p\) < 0.01) (Figure 3d).  
Figure 2: Neighbor-joining consensus cladogram based on the alignment of 16S rRNA gene sequences of the N-fixing isolates from _P. Ientiscus_ L. (bold) and related taxa of the _Bacilluscale_ family. The sequences were aligned with CLUSTAL Omega, the cladogram was constructed in Geneious Prime, and bootstrap values (shown in percentage next to node) were calculated from 1000 resampling. Values in small font indicate the attributes of node height. The tree is rooted to 16S rRNA from _Escherichia coli_ (accession number J01859).
# Trajectory-Tracking Control of Unmanned Vehicles Based on Adaptive Variable Parameter MPC
## 2 Unmanned Vehicle Trajectory-Tracking Model
### Unmanned Vehicle Trajectory-Tracking Model  
To represent the relationship between the unmanned vehicle and the reference trajectory, a trajectory-tracking model was established [20], as shown in Figure 2. In Figure 2: \(L\) is the pre-sighting distance, \(\psi_{d}\) is the desired heading angle, \(e_{y}\) is the distance between the center of mass point of the vehicle and the pre-sighting point \(d_{p}\) on the reference trajectory, and \(e_{d}\) is the distance between the center of mass of the vehicle and the nearest point \(d\) on the reference trajectory.  
According to the relative position relationship between the vehicle and the reference trajectory, the trajectory-tracking model based on single-point pre-sighting can be obtained as  
\[\left\{\begin{array}{l}\dot{e}_{y}=v_{y}+v_{x}e_{\varphi}+L\dot{\varphi}\\ \dot{e}_{\varphi}=\dot{\varphi}-\dot{\psi}_{d}=\dot{\varphi}-\kappa_{d}v_{x} \end{array}\right. \tag{4}\]  
where \(e_{y}\) is the lateral position error, \(e_{\varphi}\) is the heading angle error, \(\kappa_{d}\) is the road curvature of the reference trajectory, and \(v_{x}\) is the longitudinal vehicle speed. The pre-sight distance \(L\) is set to adaptively adjust the distance size with the longitudinal vehicle speed [21], denoted as  
\[L=v_{x}\Delta t,L_{\min}\leq L\leq L_{\max} \tag{5}\]  
where \(\Delta t=N_{p}T\), \(N_{p}\) is the MPC controller prediction time domain, and \(T\) is the controller sampling time. Take the state variable as \(x=\Big{[}\begin{array}{cccccc}v_{y}&\dot{\varphi}&\dot{\varphi}&\dot{ \varphi}&e_{y}&e_{\varphi}\end{array}\Big{]}^{T}\), so that the control variable \(u=\delta_{f}\), the output variable is \(y=\big{[}\dot{\varphi}&e_{y}&e_{\varphi}\big{]}^{T}\), and \(w\) is the disturbance variable. Combining Equations (3) and (4) gives the state equation of the trajectory-tracking model of the unmanned vehicle as  
\[\left\{\begin{array}{l}\dot{x}=Ax+Bu+w\\ y=Cx\end{array}\right. \tag{6}\]  
When designing the controller, it is difficult to adapt the tire force calculated using linear stiffness to changes in road adhesion and load transfer. Therefore, it is necessary to adaptively adjust the lateral stiffness of the controller based on the real-time tire lateral force.
# An Innovative Adaptive-Dimension Accordion Honeycomb for Circumferential Deformation
## Mechanical Property Analysis
### Honeycomb Stiffness \(k^{mn}\)  
In Equation (39), the stiffness of the unit cell is related to the corresponding radius \(r\); therefore, the properties of the honeycomb is uneven. In this paper, \(k^{mn}\) and \(k_{ij}\) represent the stiffness of the \(\{m,n\}\) honeycomb and the \((i,j)\) cell, respectively. Based on (i) and (ii), \(k_{ij}\) is independent of the index \(j\); thus, \(k_{i}\) can be used instead.  
For an \(\{m,1\}\) honeycomb, where only one layer of cells exists along the radial direction, all cells have the same \(\Delta_{\theta}\) during circumferential deformation. Hence, the radial cell can be represented equivalently as parallel springs, as depicted in Figure 6.  
Figure 6: Parallel spring equivalent diagram.  
Thus, the circumferential stiffness of the \(\{m,1\}\) honeycomb is the sum of the stiffness of unit cells, and the formulation is as follows:  
\[k^{m1}=\sum_{i=1}^{m}k_{i} \tag{40}\]  
where \(k_{i}\) represents the stiffness of the \((i,1)\) cell.  
For a \(\{1,n\}\) honeycomb with only one layer of cells along the circumferential direction, all cells are congruent according to (ii), having the same circumferential stiffness. Therefore, the circumferential cell can be represented equivalently with series springs, as depicted in Figure 7.  
The formulation of circumferential stiffness of the \(\{1,n\}\) honeycomb can be expressed as  
\[k^{1n}=\frac{k_{1}}{n} \tag{41}\]  
By combining Equations (40) and (41), the circumferential stiffness of \(\{m,n\}\) honeycomb can be expressed as  
\[k^{mn}=\frac{\sum_{i=1}^{m}k_{i}}{n} \tag{42}\]
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM) of Composites  
SEM is employed for the evaluation of the extent of interfacial adhesion between fibers and the matrix, while these observations support the results obtained from the mechanical tests. Figure 8 displays the SEM microphotographs of the composites' fractured surfaces.  
Figure 8: SEM microphotographs of the composites’ fractured surfaces: (**a,e,i**) for ramie; (**b,f,j**) for hemp; (**c,g,k**) for flax; and (**d,h,l**) for kenaf, filled with filler content of 10 wt.%, 20 wt.%, and 30 wt.%, respectively.  
As clearly seen in the SEM microphotographs of the composites, pull-out fibers and fiber-matrix debonding can be observed. The fibers have been detached from the polymer matrix during deformation, due to the insufficient interfacial adhesion between the fibers and the polymer matrix. This poor interfacial adhesion between fibers and matrices leads to the low wettability of fibers in the polymer matrix and to the impaired mechanical properties of manufactured composites [12], since no effective load transfer takes place. In general, when interface adhesion is poor, the capabilities of polymer-reinforcing agents cannot be exploited to the full extent.
## 2 Experimental Set-Up
### Measurement Scenarios  
We carried out measurements in four types of macrocell environments: open, wooded, suburban, and urban areas (Figure 2). These zones could be typical environments for 5G cellular deployments. The open areas selected were located on the Campus of the University of Vigo. In the wooded environment we could find different types of trees between the Tx and Rx that could cause minimal or strong signal shadowing. The wooded environment could show the effects of the radio link blockage by the vegetation. The suburban environment consisted of typical streets and roads with two- to three-story houses and buildings that lined the streets. The urban scenario was characterized by the presence of street canyons with buildings of 5-10 floors.  
Figure 1: Pictures of the (**a**) Tx and (**b**) Rx.  
Figure 2: Measurement scenarios.  
During the measurement, we kept the Rx end at a fixed position over the environment obstacles while we moved the Tx end to several locations. These are given in Figure 3. For the open environment, the line-of-sight (LOS) condition was met at all measurement locations, while, for the other three environments, we had both LOS and non-line-of-sight (NLOS) locations.  
A schematic illustration of the measurement set-up if given in Figure 4, and the distance details are given in Table 2.  
Figure 3: Location of Tx and Rx in the various environments: (**a**) wooded; (**b**) open; (**c**) suburban; (**d**) urban. Maps from Google, TerraMetrics.  
At each location of the mobile end, we carried out an angular scanning by placing the antenna on a pan-&-tilt system. We scanned 360\({}^{\circ}\) in azimuth with steps of 15\({}^{\circ}\) and five elevation AOAs relative to the horizon: 8\({}^{\circ}\), 24\({}^{\circ}\), 40\({}^{\circ}\), 64\({}^{\circ}\), and 72\({}^{\circ}\).  
For each Tx position, elevation, and azimuth AOA, three of these CIRs were averaged to estimate the Power Delay Profile (PDP).
###### Abstract  
Digital breast tomosynthesis (DBT) is a highly promising 3D imaging modality for breast diagnosis. Tissue overlapping is a challenge with traditional 2D mammograms; however, since digital breast tomosynthesis can obtain three-dimensional images, tissue overlapping is reduced, making it easier for radiologists to detect abnormalities and resulting in improved and more accurate diagnosis. In this study, a new computer-aided multi-class diagnosis system is proposed that integrates DBT augmentation and colour feature map with a modified deep learning architecture (Mod_AlexNet). To the proposed modified deep learning architecture (Mod AlexNet), an optimization layer with multiple high performing optimizers is incorporated so that it can be evaluated and optimised using various optimization techniques. Two experimental scenarios are applied, the first scenario proposed a computer-aided diagnosis (CAD) model that integrated DBT augmentation, image enhancement techniques and colour feature mapping with six deep learning models for feature extraction, including ResNet-18, AlexNet, GoogleNet, MobileNetV2, VGG-16 and DenseNet-201, to efficiently classify DBT slices. The second scenario compared the performance of the newly proposed Mod_AlexNet architecture and traditional AlexNet, using several optimization techniques and different evaluation performance metrics were computed. The optimization techniques included adaptive moment estimation (Adam), root mean squared propagation (RMSProp), and stochastic gradient descent with momentum (SGDM), for different batch sizes, including 32, 64 and 512. Experiments have been conducted on a large benchmark dataset of breast tomography scans. The performance of the first scenario was compared in terms of accuracy, precision, sensitivity, specificity, runtime, and f1-score. While in the second scenario, performance was compared in terms of training accuracy, training loss, and test accuracy. In the first scenario, results demonstrated that AlexNet reported improvement rates of 1.69%, 5.13%, 6.13%, 4.79% and 1.6%, compared to ResNet-18, MobileNetV2, GoogleNet, DenseNet-201 and VGG16, respectively. Experimental analysis with different optimization techniques and batch sizes demonstrated that the proposed Mod_AlexNet architecture outperformed AlexNet in terms of test accuracy with improvement rates of 3.23%, 1.79% and 1.34% when compared using SGDM, Adam, and RMSProp optimizers, respectively.  
Digital Breast Tomosynthesis; Augmentation; Deep Learning; Breast Cancer; Colour Feature Mapping; Image Classification
## 5 Discussion
The findings of this study are relevant for the specific case studies that have been considered, but they are affected by some limitations when extrapolating the results to other contexts. The first aspect to be highlighted is the fact that they are related to a pre-pandemic situation, in which remote working was not seen as a common opportunity but rather limited to workers that had specific needs. An update of this study, which is currently being done, may provide additional insights about the differences that have arisen after the forced COVID-19 remote working models. Another limitation is related to the fact that remote working was chosen by a limited number of employees, which may not reflect the average characteristics of the workforce. Thus, when estimating the potential broader impact of increasing remote working share it is important to analyze specific aspects, including the average distance from home to office, the transport mode that is being used and the specific technology. Finally, it should be borne in mind that the results on consumption and avoided emissions presented in this paper do not take into account, being calculated for the period 2015-2018, the evolution of the characteristics of the average vehicle fleet and the use of electric vehicles, both cars and electric bicycles and scooters. Similarly, in some cases the authors have used average figures for the vehicle characteristics due to the impossibility of having all of the required technical information from the survey.
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
## 6 Contiki-NG  
[PERSON] et al. [37] note that IoT deployments use a wide variety heterogeneous hardware. The authors highlight that developers of IoT devices can significantly benefit from having an operating system that abstracts the underlying hardware and provides features such as memory management. Consequently, integrating our bootstrapping solution in an operating system can make it easier for developers to adopt it. Therefore, we implement it on a variant of the popular Contiki OS [38].  
Contiki is an open-source lightweight operating system for resource-constrained devices. It provides Internet connectivity for sensors, microcontrollers, and other low-cost devices over standard IPv4 and IPv6. Additionally, Contiki supports various modern, low-power wireless protocols, such as IPv6 over Low-Power Wireless Personal Area Networks (6 LoWPAN), Routing Protocol for Low-Power and Lossy Networks (RPL), and Constrained Application Protocol (CoAP) [39].  
Contiki-NG [40] is an independently developed fork of Contiki OS. It aims to improve the original codebase by only focusing on standard-based IPv6 communication, supporting modern IoT platforms, improving documentation, and modernizing the structure of the code [41]. Because Contiki OS is no longer actively maintained, we instead chose Contiki-NG for our implementation. In the next few sub-sections, we present some important characteristics of Contiki-NG.
## 1 Introduction  
### Background  
Accurate and reliable vehicle speed measurement is a fundamental component of intelligent transportation systems (ITSs), serving critical applications such as traffic flow analysis, congestion monitoring, incident detection, and automated enforcement [1]. Among the commonly used methods for speed measurement, time delay in the literature [2, 3, 4]) estimation (TPE) between two spatially separated sensors remains one of the most intuitive and robust strategies, especially for point-based systems [5, 6]. This approach estimates the travel time of a vehicle between two sensor units to compute speed from the known baseline distance. However, the performance of TPE critically depends on the quality and consistency of the signals acquired from both sensors.  
Various technologies have been employed for vehicle detection and TPE, including radar, LiDAR, magnetic loops, and video cameras [7, 8, 9, 10]. These methods, which are supposed to be accurate, often suffer from high deployment costs, power demands, or environmental limitations. In contrast, pyroelectric infrared (PIR) sensors provide a low-cost, passive, and energy-efficient alternative, particularly suited for scenarios where minimal infrastructure is preferred or energy constraints exist [11; 12].  
PIR sensors detect changes in infrared radiation, typically emitted by heating objects such as vehicles when they pass through the sensors' fields of view (FOV) and convert them into electrical signals. When a group of two or more PIR sensors are placed along a lane at a known distance apart, the time period or time interval between their response signals can be used to estimate vehicle speed. Despite their simplicity, PIR-based speed measurement systems face several challenges for TPE: variability in sensor response time due to differing thermal or electrical time constants; environmental noise and reflections, which degrade the signal-to-noise ratio; limited temporal resolution, which hinders precision in fast or closely spaced events.  
These challenges lead to the need for a deeper understanding of how sensor imperfections and signal characteristics affect the accuracy of TPE, particularly through systematic error analysis.
## 4 Discussion
### Stress Field Analysis  
Building on prior research regarding compressive stress, this study advances understanding using a novel model. Section 3.2 shows that rising bp increases dissection compressive stress; this is consistent with previous findings [15; 16; 17]. Notably, hypertension significantly impacts the stress difference between the TL and FL, which varies with the position on the dissection and the cardiac cycle stage. This stress difference, influenced by maximum systolic pressure and mean aortic bp, drives FL expansion and aortic rupture, key factors in AD progression. This aligns with clinical observations in AD patients and [PERSON]'s research [34].  
When the von Mises stress on the vascular wall exceeds its mechanical failure strength, the inner membrane tears, with the maximum von Mises stress location being critical [35; 36]. Section 3.3 reveals that under hypertension, the intimal flap experiences high stress (over \(1\times 10^{4}\) Pa) during systole, a 30% increase compared to normal bp. Stress concentration occurs at the intimal flap-vascular wall junction. These findings confirm that hypertension exacerbates intimal flap damage near the tear, separates the distal intimal flap from the media, and enlarges the FL, all accelerating AD progression. Beyond stress considerations, the resultant deformation of the intimal flap is another critical factor influenced by hypertension.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Blood** & **Observation** & **Mean \(\pm\) Standard** & & \\
**Pressure** & **Node** & **Deviation/mm** & & \\ \hline \multirow{3}{*}{High} & proximal node & 8.2033 \(\pm\) 1.0136 & 6.2564 & 10.6450 \\  & distal node & 7.7732 \(\pm\) 1.1528 & 5.9016 & 10.3850 \\  & intermediate node & 7.4394 \(\pm\) 2.1500 & 0.8004 & 10.2920 \\  & proximal node & 6.3538 \(\pm\) 0.7567 & 4.8648 & 8.2431 \\  & distal node & 6.0881 \(\pm\) 0.8894 & 4.7038 & 8.1334 \\  & intermediate node & 5.8543 \(\pm\) 1.3679 & 1.6623 & 7.8678 \\  & proximal node & 4.5327 \(\pm\) 0.5581 & 3.4733 & 6.0340 \\  & distal node & 4.3692 \(\pm\) 0.6529 & 3.3475 & 5.8987 \\  & intermediate node & 4.3681 \(\pm\) 0.7836 & 2.0199 & 5.5504 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The maximum, minimum, and mean displacement of different nodes of the intimal flap.
# Pore Structure of Coals by Mercury Intrusion, N\({}_{2}\) Adsorption and NMR: A Comparative Study
## 1 Introduction  
Coal generally has the characteristics of dual pore structure (matrix pore and fracture), low permeability, large pore surface area, and strong adsorption capability [1]. The matrix pore division scheme is diverse. The International Union of Applied Chemistry (IUPAC) [2] proposed a pore size classification: micropores <2 nm in diameter; mesopores 2-50 nm in diameter; macropores >50 nm in diameter. To better understand the effects of pore structure on both gas adsorption capacity and flow capability, the classification from [PERSON] [3] for coal pore size is proposed: super micropores (<10 nm), micropores (10-10\({}^{2}\) nm), mesopores (10\({}^{2}\)-10\({}^{3}\) nm), and macropores (10\({}^{2}\)-10\({}^{3}\) nm). In this work, the [PERSON]'s classification is used. The main super micropores, micropores, and mesopores occur as a part of the coal matrix, providing extremely large internal surface area with a strong affinity to certain gasses, such as CH\({}_{4}\) and CO\({}_{2}\). About 95% of the total gas may be stored in the coal matrix in the adsorbed form. Hence, pore properties of coals are of great importance to gas storage and flow behavior in coal. Methods for studying the pore structure in coalbed methane (CBM) reservoir mainly include mercury intrusion porosimetry (MIP), low temperature nitrogen adsorption (N\({}_{2}\)GA), [4] and low field nuclear magnetic resonance (NMR) [5] experiments. The NMR technique has been widelyapplied due to its nondestructive and simplicity. Especially, the quantitative assessment of pore surface area, PS/VD (pore size/volume distribution) and pore shape plays an important role for understanding gas adsorption capacity and flow behavior.  
In this work, our study is focused on the evolution characterization of pores, pore size/volume distribution, and pore structures of 16 coal samples ranging from lignite to anthracite, considering the effect of coalification on pore structure. Furthermore, the comparison of pore characteristics results from different techniques is conducted. Therefore, this study should be helpful for technique optimization for assessing pore structure and the evaluation of CBM resources and reservoir properties.