esa-sceva/satcom-chunk-collection huggingface dataset:
# Capacity and Delay Analysis for Large Social-Aware Mobile Ad Hoc Wireless Networks
###### Abstract  
Most mobile ad hoc wireless networks have social features. It is a fundamental problem how to understand the performances of social-aware mobile ad hoc wireless networks. In this paper, we consider a wireless network area, with restricted mobility model and rank-based social model. On this basis, we investigate the upper bound of throughput capacity in such networks using the protocol interference model. By tessellating the network area into cells spatially and dividing time into slots temporally, we propose a multi-hop relay and slots allocation scheduling strategy. Then, we derive the achieved throughput capacity under this strategy. Results show per-node throughput is related to parameter of social model and range of node motion. In addition, we also study the delay varies by queueing theory in such network. Finally, we discuss capacity-delay tradeoffs in such networks. These results are beneficial to the design of network protocols in large social-aware mobile ad hoc wireless networks.  
mobile ad hoc wireless networks; throughput; delay; social features +
Footnote †: journal: Applied
# Scattering or Pushing for Object Singulation in Cluttered Environment: Case Study with Soma Cube
## 4 Parameter Estimation and Simulator Based Dynamic Manipulation
### Quasi-Static Manipulation or Singulation by Pushing
#### 5.2.2 Pushing Algorithm  
Pushing algorithm is proposed as Figure 10. In the \"Select Target\" stage, we interpret the scene of camera. The image taken from RealSense Depth Camera SR305 (Intel, California, USA) is divided into several groups; group with several blocks in clutter and group with isolated blocks as shown in Figure 9. In \"Select Action\" stage, we figure out which approach to be used. If some blocks are contacting each other, then we will use the non-prehensile manipulation such as pushing to singular the blocks. Lastly, we remove the isolated Soma blocks one by one starting from the block nearest to the robot position.  
In pushing only singulation, the pushing will be performed with prehensile manipulation until all the blocks are completely singulated. When there are many possible pushings (as will be the case, for example, in the initial formation, and also later steps), we choose the overlapping contour region nearest to the end effector. After each pushing, the robot manipulator takes the image using real sense and process it to perform the next pushing. In Figure 11, the overlapping contour region between yellow and blue blocks is closest to the end effector. So, the first pushing will be along the green arrow between these two blocks as shown in Figure 11b. After each pushing, the resulting formation is analyzed as discussed before, and then the pushing is repeated just described, until all blocks are completely isolated.
## 2 Materials and Methods
### Data Acquisition and Treatment  
Energy-resolved soft TXM was performed at the MISTRAL beamline of the ALBA Synchrotron. For full details on the acquisition and analysis, please refer to previous publications [17; 18].  
Inside the microscope, samples were kept at cryogenic temperature (T \(<\) 110 K) and under high vacuum conditions during all the measurements. The cryogenic temperature helps minimizing radiation damage [1]. Transmission images (2 s exposure time, effective pixel size 10 nm, field of view 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m) were collected in step mode, varying the energy across the O K-edge and Mn L-edge with a variable spectral sampling (0.5-0.1 eV), depending on the energy width of the investigated features. A preliminary calibration of the absolute value of the energy was performed before the experiment using standard references samples at different energies along the available energy range of the beamline (CaCO\({}_{3}\), N\({}_{2}\), TiO\({}_{2}\), Mn\({}_{2}\)O\({}_{3}\), and Fe\({}_{2}\)O\({}_{3}\)). The objective zone plate lens (outermost zone width of 25 nm, 1500 zones) and the back-illuminated CCD detector (Pixis XO by Princeton Instruments with 1024 \(\times\) 1024 pixels and 13 nm pixel size) positions were automatically adjusted to maintain the sample in focus and constant magnification (1300\(\times\)). To obtain transmittance, two images are acquired at each energy, the transmitted intensity \(I\) and the incident intensity \(I_{0}\), which we call Flat Field. The obtained energy stack images are aligned taking as reference the first image.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 2 Materials and Methods
### Modelling  
Using the features selected and the classification model defined, we seek to fit our data to parametric and non-parametric distributions using both the whole data set and subsets of it. We also seek to understand correlation in the data, which would be a requirement for full modelling of the queueing system. We also look to identify message interdependence.  
We now explore the methods for parametric modelling in Section 2.2.1.
## 2 Theoretical Discussion
### Application of Track Stiffness for Calculation of Dynamic Factor
However, according to the Prud'Homme equation, the dynamic coefficient is the square root of the track bearing stiffness [19]. Refer to Figure 2 on the correlation between Eisenmann and Prud'Homme methods of dynamic factor calculation results with regards to track support stiffness and dynamic factor (track impact factor). Figure 2: Correlation between [PERSON] and Prud’Homme method [19]. As a result of comparing the dynamic coefficients of Prud'Homme and Eisenmann, in the case of Eisenmann, the dynamic coefficient is constant regardless of the track support stiffness. In the case of the low track bearing stiffness section, the dynamic coefficient of the Eisenmann method is large, but in the track bearing stiffness of about 120 kN/mm or more, the dynamic coefficient of the Prud'Homme equation is larger than that of Eisenmann. The comparison results shown here prompts a requirement to investigate the application of dynamic factor evaluation of tracks based on the different stiffness properties of the tracks.
# Curvature Sensing and Control of Soft Continuum Robots Using e-Textile Sensors
## 6 Conclusions  
In summary, this study introduces a novel soft e-textile resistive sensor paired with a deep convolutional neural network (CNN) specifically designed for real-time shape sensing in soft continuum robots. The CNN model is carefully crafted to handle the spatial continuities and inherent nonlinear characteristics of sensor data, significantly improving the ability to accurately detect complex shapes in soft continuum robotics. It demonstrates exceptional accuracy in determining the robot's curvature and planar angle of curvature. A Jacobian-based shape control scheme is employed to ensure the attainment of target shape parameters and the following of specified trajectories. The system's proficiency in real-time trajectory tracking, coupled with its low MAE values and precise shape control capability, highlights its effectiveness and reliability in control applications requiring high precision and responsiveness. Furthermore, the proposed Jacobian-based control for shape control of the continuum robot, utilizing the e-textile sensor and the CNN shape sensing model, showcases the system's robust and adaptive nature, ensuring accurate and responsive control across various conditions.  
Figure 13: End-effector tracking performance of the proposed Jacobian-based control to command the robot’s tip follow desired trajectory in (**a**) \(x\)- and (**b**) \(y\)-directions.  
We believe that our approach is scalable for sensing the robot's curvature, and extending it to multiple sections is straightforward. This is particularly feasible because the e-textile sensor provides an estimation of the robot's shape in terms of both curvature and the angle of the curvature plane. By stacking multiple sections of soft robots, each equipped with its own e-textile sensor, the curvature parameters of all sections can be estimated. Consequently, the overall shape of the robot can be derived using the forward kinematics equation. However, additional research is needed to extend this method to complex shape sensing, particularly involving variable curvatures of continuum robots. Future work could also focus on optimizing the e-textile sensor configurations to enhance sensitivity, durability, and response time. This includes investigating various materials, fabrication techniques, and sensor geometries.  
Conceptualization, E.V.G. and H.E.-H.; methodology, E.V.G. and H.E.-H.; software, E.V.G. and H.E.-H.; validation, E.V.G., I.H. and A.A.N.; formal analysis, investigation, I.H.; writing--original draft preparation, H.E.-H.; writing--review and editing, H.E.-H. and A.A.N.; visualization, I.H.; supervision, I.H.; project administration, H.E.-H.; funding acquisition, I.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The data that support the findings of this study are available from the last author upon reasonable request.  
The authors declare no conflicts of interest.
## 3 Interference Suppression through Beamforming Techniques
### Direction of Arrival of Targets under Study  
In previous sections, spatial interference suppression techniques were proven to improve targets' SNR. In this section, the angle estimation capability of the array will be demonstrated by taking advantage of the SNR increase to select the target's points in each CPI to perform Direction of Arrival (DoA) estimation.  
DoA has been performed by means of spatial filtering: generating beams in multiple directions and computing the angle where maximum target scatter is achieved. The estimated angles for the two targets studied in previous sections are shown in Figure 16, as well as the location of those targets in the real scenario during the acquisition time. Observing the estimated angle for the aerial target, it can be determined that, in the first CPIs, the estimation seems to be wrong, as it differs considerably from the angles estimated later on. This is due to the low target SNR achieved in those CPIs, as can be seen in Figure 14. Location of targets confirms the suspects that the farthest target is an aerial one in its approach to the Rota landing strip, but it also allows us to measure the real distance of targets from the PR: the maritime target is around 9 km from the PR, while the aerial one is at distances ranging from 26 km to 28 km from the PR.
## 3 Results
### Simulated Datasets
#### 3.2.2 Parameters Contributing to Tmax  
The PCA provided above enabled the discovery of a link between all PK parameters, particularly Tmax. The random forest method was then utilized to explore the contribution of each PK characteristic to Tmax. As in the case of actual data, the latter was accomplished separately using either AS or Cmax/Tmax. The Tmax estimates for each dataset were transformed into an ordinal scale by splitting into the corresponding quartiles of distribution (Table 13 in the Appendix A). Since the response variable (Tmax) was on an ordinal scale, the supervised RF method was able to be applied.  
Figure 7 depicts the application of the RF algorithm when the Cmax and Tmax are used. Three different conditions are analyzed depending on the sampling scheme (see Table 2) utilized, namely, sparse sampling (Figure 7a), typical sampling (Figure 7b), and dense sampling (Figure 7c). The left side of the graph refers to the variable importance of each PK parameter, while the plot on the right is the confusion matrix showing the predicting accuracy of the developed RF model.  
Regardless of the sampling design, all RF results coincided and showed that the major contributor to Tmax is always Cmax/Tmax. Lambda and AUCt, which represent elimination and absorption extent, made the least contribution. Then, depending on the sampling scheme, different performance was observed for the other metrics. For example, in the case of a sparse scheme, Cmax was the second-most important contributor (after Cmax/Tmax), followed by AUCp and Cmax/AUC. For the cases of typical and dense sampling schemes, the ratio Cmax/AUC exhibited a greater contribution compared to Cmax and AUCp. The confusion matrices showed high prediction accuracy for all three models, namely, 89.80% for the sparse sampling case (Figure 7a), 80.77% for the typical (Figure 7b), and again 80.77% (but with different individual values) for the dense design (Figure 7c).  
Figure 8 depicts the application of the RF algorithm when AS is used instead of Cmax and Tmax for the three scenarios of sampling schemes: sparse (Figure 8a), typical (Figure 8b), and dense (Figure 8c). The results obtained for AS are almost identical to those found above in the case of Cmax and Tmax. Again, the sampling frequency did not affect the behavior of AS, which was always found to be the major contributor to Tmax. All other PK parameters exert a lesser contribution to Tmax. Additionally, the prediction accuracy is high for the three RF models and equal to 83.67%, 73.08%, and 76.92% for the sparse, typical, and dense designs, respectively.  
Figure 7: Variable importance scores and confusion matrices, in the case of simulated data, using Tmax as the response variable. The three separate random forest models developed correspond to sparse (**a**), typical (**b**), and dense (**c**) sampling schemes. The descriptors used for random forest models refer to the Cmax/Tmax ratio and other pharmacokinetic parameters listed in Table 1. The quartiles of the Tmax distribution were used to define its four categories.
## 7 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **System Application** & **Sensing Device Performances** &
\begin{tabular}{c} **Implementation** \\ **Target** \\ \end{tabular} & **Time Performances** \\ \hline
[41] & \begin{tabular}{c} Depth map 3D \\ representation \\ \end{tabular} & \begin{tabular}{c} Asus Xtion (30 Hz) Kinect \\ camera (30 Hz) \\ \end{tabular} & \
\begin{tabular}{c} Intel Core i7 \\ 3.4 GHz CPU \\ \end{tabular} &
\begin{tabular}{c} 21.8 ms/frame \\ 170 MHz 15 fps \\ \end{tabular} \\
[48] & \begin{tabular}{c} Real-time 3D surface \\ model reconstruction \\ \end{tabular} & \begin{tabular}{c} Stereoscopic CCD cameras \\ \end{tabular} & \
\begin{tabular}{c} Xilinx Virtex-5 \\ FPGA (LX110T) \\ \end{tabular} &
\begin{tabular}{c} 170 MHz 15 fps \\ \end{tabular} \\
[36] & \begin{tabular}{c} Video-based \\ shape-from-shading \\ reconstruction \\ \end{tabular} & \begin{tabular}{c} Stereo digital cameras \\ \end{tabular} & \
\begin{tabular}{c} Xilinx Virtex-4 \\ FPGA (VLX 100) \\ \end{tabular} &
\begin{tabular}{c} 60 MHz 42 s/frame \\ \end{tabular} \\
[2] & \begin{tabular}{c} Real-time 3D \\ reconstruction of \\ stereo images \\ \end{tabular} & \begin{tabular}{c} Ten calibrated stereo \\ image pairs \\ \end{tabular} & \
\begin{tabular}{c} Virtex-2 Pro \\ FPGA (XC2 VP30) \\ \end{tabular} &
\begin{tabular}{c} 100 MHz 75 fps \\ \end{tabular} \\
\begin{tabular}{c} 30 fps \\ \end{tabular} \\
[50] & \begin{tabular}{c} Stereo matching for 3D \\ image generation \\ \end{tabular} & \begin{tabular}{c} 3 CCD cameras 320 \(\times\) 320 pixels \\ 33 MHz video rate \\ \end{tabular} & \begin{tabular}{c} ALTERA FPGA \\ (APEX20 KE) \\ \end{tabular} & \begin{tabular}{c} 86 MHz 0.19 ms/frame \\ \end{tabular} \\ Our system & \begin{tabular}{c} Real-time 3D shape \\ reconstruction from \\ stereo images \\ \end{tabular} & \begin{tabular}{c} Stereo catadioptric system \\ \end{tabular} & \
\begin{tabular}{c} Xilinx Virtex-6 \\ (VLX240T) \\ \end{tabular} &
\begin{tabular}{c} 275 MHz 30 fps \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stereo vision FPGA implementation performances.
# A Performance Evaluation for Software Defined Networks with P4
## 4 Experimental Design
### Traffic Design  
In order to mimic the Internet as closely as possible, traffic types such as Internet Control Message Protocol (ICMP), Transmission Control Protocol (TCP) [57], User Datagram Protocol (UDP) [58] and Content Delivery Network (CDN) were chosen. The traffic types were selected based on research presented by [PERSON] et al. [59], in which the authors focused on popular downloaded traffic. ICMP traffic is widely used as a measurement tool for troubleshooting, control and error messaging services. TCP can be considered as one of the main protocols of the IP suite. TCP is considered as one of the most trustworthy and reliable protocols utilised by applications on the internet. UDP is widely used to send messages with a minimum protocol mechanism. Based on the research presented by [PERSON] et al. [60] and the popularity of UDP applications, we included UDP traffic. A recent study by [PERSON] et al. [61] confirmed the network traffic trends for 2024, indicating that TCP and  
Figure 2: Topologies employed for this research, (**I**) multi-path topology (**II**) grid topology and (**III**) a Transit stub network model. (H denotes Host. Red indicates links with 1 Gbps, and black indicates links with 100 Mbps).  
UDP constitute the majority of Internet traffic. Finally, with the popularity and demand for CDN as indicated in the respective works of Cisco [8], [PERSON] et al. [62], and AppLogic [63], we have included a case study emulating the same. The study by AppLogic [63], published in 2025, reports that an average user consumes approximately 6 GB of CDN data per day. The distinct traffic types above were each examined both on an individual basis and a mixed basis to test and evaluate the performance of both _SDN+Ov5_ and _SDN+P4_ for the network topologies.  
Our experiments were carried out in two stages. The first stage, (Tier 1), involved one type of traffic at a time being run over the network, the types being ICMP, TCP, UDP or CDN, with each forming a single case study. The second stage, (Tier 2), involved all four types of traffic being run simultaneously over the network in topologies II and III.
# Building Penetration Losses at 3.5 GHz: Dependence on Polarization and Incidence Angle
## 1 Introduction
Despite building penetration losses usually tending to increase with incidence angle \(\theta\)[5; 6; 7; 14; 15; 16], some models do not even consider this angular dependence [4] or consider just a fixed term to account for non-perpendicular incidence, as in [17]:  
\[\mathrm{PL_{tw}} =\mathrm{PL_{npi}}-10\cdot\log_{10}\sum_{i=1}^{N}\left(\mathrm{p_{ i}}\cdot 10^{-0.1\ \mathrm{L_{material,i}}}\right) \tag{2}\]  
where \(\mathrm{PL_{npi}}\) denotes the non-perpendicular incidence additional losses, \(\mathrm{L_{material,i}}\) is the penetration loss of material i, \(\mathrm{p_{i}}\) is the proportion ratio of i-th materials and N is the number of materials. However, no values for \(\mathrm{PL_{npi}}\) are given in [17]. Other models include a generic angular variation of the losses in the form of (1--\(\cos\theta\))\({}^{2}\)[5; 6; 10; 15]. Finally, more complex models, which consider both the azimuth and elevation angular dependence of the building entry loss, have recently been developed [16]. Anyway, we have not found a specific study for penetration losses in the 3.3-4.2 GHz frequency band, considering both the polarization and angle of incidence. Mobile communications systems do not commonly use a single vertically polarized field, but orthogonal polarizations (none of them being strictly vertical, in many cases). A few previous studies [12; 18] have considered orthogonal polarizations, while the rest just deal with vertically polarized electric fields. Simulation results for wave transmission through double-glass windows presented in [19] show significant differences in the attenuation values for different polarizations and the angles of incidence, revealing the importance of considering both factors when analysing penetration losses. In this paper, we extend the study of building penetration losses to consider not just the different construction materials used on building facades, but also the polarization and incidence angular dependence of these losses. Measurements have been taken using directional antennas, which is reasonable as beamforming antennas are implemented for 5G communications. As shown in [20], directional antennas reduce the multipath contributions and increase the variation of the measured beam entry losses. Further reduction of other multipath contributions in the surrounding environment of the antennas is achieved by time gating. The remainder of this paper is organized as follows: in Section 2, we provide a theoretical background supporting the proposals; in Section 3, we describe the measurement system and the measurement environments, including the procedure we followed during the campaign; we present and discuss the measurement results in Section 4; and finally, we summarize the main conclusions in Section 5.
## 3 Results and Discussion
### Effect of Flow Rate on Seed Cysts and Cell Growth
lacustris_ during 72 h of photosynthetic cultivation in a well plate, with cysts treated at microfluidic flow rates ranging from 1.0 to 3.0 mL/min and a fixed loop number of 3. Up to 60 h, the treated cultures showed no significant growth enhancement compared to controls. However, after 72 h of incubation, cultures treated at 2.0 and 2.5 mL/min exhibited higher total cell number densities, reaching 23.5 \(\pm\) 1.9 \(\times\) 10\({}^{4}\) and 21.5 \(\pm\) 2.2 \(\times\) 10\({}^{4}\) cells/mL, respectively, representing increases of 17.5 and 7.6%, respectively, over the control group (20.0 \(\pm\) 0.9 \(\times\) 10\({}^{4}\) cells/mL). These results suggest that microfluidic collision treatment of seed cysts can enhance photosynthetic growth in _H. lacustris_ if carefully controlled, although the effects may appear later than expected. Based on the observed cell density, the optimal flow rate was selected as 2.0 mL/min for subsequent experiments.