esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Subject, Device, Data Collection and Experiments
### Device  
As mentioned earlier, the subject of this study is a patient with severe lower extremity edema with flat feet (Figure 1a). In order to understand his plantar pressure, this study designed a pair of self-made pressure sensor insoles specifically for his plantar pressure distribution. The pair of self-made pressure sensor insoles has left and right feet, and each insole is equipped with six pressure resistance sensors.  
In order to effectively grasp the patient's foot information, the position of the six sensors on each foot is very important. In this study, the MP-5 ink footprint device (Figure 1b) was used to capture patient footprints (Figure 1c). In this footprint, the deeper the mark is, the higher the pressure in that area. Ink footprint is a cheap, simple and non-invasive method. The footprints obtained in this way can not only provide for the study of foot structure, but can also be used to diagnose pathological conditions of flat feet [13; 14].  
According to the footprint, it was observed that the patient's greatest pressure was on the hind feet, and the second largest pressure was on the hallmark and forefoot. After discussing with the doctor, the research team decided to place the six sensors on each foot as shown in Figure 2a: hallux (L1, R1), forefoot (L2, L3, R2, R3), midfoot (L4, L5, R4, R5) and hind feet (L6, R6). Figure 2b shows a pair of pressure-sensing shoes designed by our team for the subject.  
Figure 1: (**a**) The patient. (**b**) An MP-5 ink footprint device. (**c**) A patient’s footprint image obtained from the MP-5 ink footprint device.  
In addition, the subject had a significant collapse of the soles of his feet due to edema of his lower extremities (the so-called flat family phenomenon). The research team hoped to use different height foot pads and arches to explore whether they can effectively improve plantar distribution. In response to this problem, the research team used a 3D printer to design three insoles of different heights (0 mm, 3 mm and 6 mm); each insole consists of 18 identical texture granules on the sole [15], as shown in Figure 3. In addition, an artificial arch support (7 cm long, 1.5 cm wide and 1.5 cm high) was made for each foot.
## 1 Introduction
### The Problem of Radio-Frequency Interference  
Microwave radiometers are among the most sensitive instruments, as they are designed to fulfill stringent requirements. This implies that any unexpected signal, apart from the radiometric noise, present at the same radiometric band under analysis, causes a bias on the final power measurement.  
Passive radiometric bands are usually protected by the international regulations, which forbid any electromagnetic emission in these bands. However, MWR suffers from Radio-Frequency Interference (RFI) in many bands, boosted by the pervasive use of wireless communications. RFI signals are those that are intentionally or unintentionally emitted in protected frequency bands, which degrade the performance of the receivers. There are a huge number documented cases of RFI in MWR applications. Two examples of them are the case of SMOS described in [12], and the case of AMSR-E described in [13].  
In the recent years, many studies have been done in order to solve the problem of RFI in MWR. Algorithms for RFI localization are being developed to find and switch the RFI sources off (e.g., [14]). Another approach to solve the RFI problem is the development of RFI detection and mitigation techniques, in order to deal with the RFI problem from the receiver side. There are many different approaches for RFI detection and mitigation that have been developed for microwave radiometry such as time-frequency blanking [15; 16], normality tests [17; 18] or polarimetric analysis [19; 20] among others.
# Modeling of Downlink Interference in Massive MIMO 5G Macro-Cell +
## 4 Simulation Studies
### Assumptions
In this case, we use the same approach as in the second simulator for the 3 GPP model. The MPM simulator was validated at every stage of its evolution. In many of our papers, we showed its verification based on measurement data and comparison with other propagation models, e.g., [34; 35; 40]. Generating a huge number of propagation paths, we obtained an average result for the MPM based on only one simulation run. In this case, analysis of the confidence intervals of the obtained results is not possible. To achieve this aim, we have to follow an approach similar to that used in the 3 GPP simulator, i.e., we must run multiple simulations for a small number of random propagation paths in accordance with the Monte Carlo process. We want to highlight that the calculation time for the Monte Carlo approach is definitely shorter for the MPM than the 3 GPP simulator. Simulation results for the 3 GPP channel model have been obtained by a proprietary system-level simulator, also implemented in a MATLAB environment according to the block diagram depicted in Figure 3. A crucial part of this simulator is the implementation of the full 3D statistical channel model, as defined by the 3 GPP in [28]. From the perspective of the results presented in this paper, the essential parts of the simulator used are the antenna model and fast fading models, based on Sections 7.3 and 7.5 of [28], respectively. As a UMa scenario has been assumed in this study, the most important statistical parameters of angular spread can be found in Table 7.5-6 Part-1 of [28]. This simulator is maintained and used to provide system-level simulation results as contributions to current 3 GPP standardization works, as well as research studies, e.g., [37]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline carrier frequency & 3.5 GHz \\ distance \(D\) between gNodeB (Tx) and UE (Rx) & (100, 200, 500) m \\ height of gNodeB (Tx) antenna & 25 m \\ height of UE (Rx) antenna & 1.5 m \\ gain of single antenna element & 6.4 dBi \\ HPBW of single antenna element & 90\({}^{\circ}\) for H, 65\({}^{\circ}\) for V \\ spacing between antenna elements & 0.5 of wavelength for H, 0.7 of wavelength for V \\ front to back ratio & 30 dB \\ antenna array of gNodeB (Tx) & 8 \(\times\) 8 \\ antenna array for UE (Rx) & 1 \(\times\) 1 \\ range of angular separation \(\Delta\)e in horizontal & \\ plane between reference and interfering beams & from 0\({}^{\circ}\) to 60\({}^{\circ}\), with step of 1\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main simulation parameters.
## 3 Results
### Correlation Analysis  
For the two cyanobacterial samples, the correlations between the six parameters are summarized in Figure 3C and displayed in red and blue. The darker color indicates a stronger correlation. Positive correlations between the two values are indicated in red and negative correlations in blue (Figure 3C). The H\({}_{2}\)O\({}_{2}\) concentration in _M. aeruginosa_ was positively correlated with the duration of the dark treatment period (_r_ = 0.76). In contrast, Chl-a (_r_ = \(-\)0.50) and carotenoids (_r_ = \(-\)0.80) decreased with an increase in the duration of dark treatment. In _P. foetida_, Fv/Fm was significantly negatively correlated with the dark treatment period (_r_ = \(-\)0.8), H\({}_{2}\)O\({}_{2}\) concentration (_r_ = \(-\)0.4), and carotenoid content (_r_ = \(-\)0.70). At the same time, H\({}_{2}\)O\({}_{2}\) concentration was positively correlated with the dark treatment period (_r_ = 0.82) and carotenoid content (_r_ = 0.78). In both cyanobacteria, chlorophylls and carotenoids showed an extremely high correlation (_r_ = 0.61 for _M. aeruginosa_ and \(r\) = 0.91 for _P. foetida_).  
A more positive response to an increasing duration of dark treatment was exhibited by _P. foetida_ than _M. aeruginosa_. The main differences in the responses of the two cyanobacteria to stress were highlighted by changes in Chl-a and carotenoid content.  
Figure 3: Supplementary experimental results and correlation analysis. The H\({}_{2}\)O\({}_{2}\) levels in the two cyanobacteria were significantly different when grouped by their dark treatment period (**A**). The growth of re-cultured _M. aeruginosa_ after a 13-month dark treatment period was slower compared to healthy cyanobacteria (**B**). Correlation analysis between specific parameters is presented in (**C**). Error bars represent standard errors. The differences were considered significant at (**) \(p\) < 0.01 (_t_-test). The correlation between parameters is expressed by the Pearson correlation coefficient \(r\), in which \(r\) > 0 means positive correlation and \(r\) < 0 means negative correlation.
## 4 Optimal One-Shot Estimators
### First-Order Models  
Here, we assume that the relative propagation delay \(\tau\) and time offset \(T\) between two nodes \(A\) and \(B\) follow a first-order Markov model. This implies that node \(B\) moves with constant radial velocity \(\tau\) and the clocks on the two radios have constant relative frequency offset \(\hat{T}\). During the two-way cycle \(\{(n-1),(n)\}\), the delay and offset in frame \((n)\) are expressed as functions of the previous frame \((n-1)\) as  
\[\tau^{\,(n)} =\tau^{\,(n-1)}+\dot{\tau}^{\,(n-1)}\,I_{A}^{\,(n-1)}, \tag{6}\] \[T^{\,(n)} =T^{\,(n-1)}+\dot{T}^{\,(n-1)}\,I_{A}^{\,(n-1)}, \tag{7}\]  
where \(I_{A}^{\,(n-1)}\) is the separation between the two frames. By substituting Equation (1) and some simplifying algebra, we estimate the first-order frame length \(I_{A}^{\,1,(n-1)}\) as  
\[I_{A}^{\,1,(n-1)}=\frac{t_{B,Tx}^{\,(n)}-t_{A,Tx}^{\,(n-1)}+T^{\,(n-1)}}{1- \dot{T}^{\,(n-1)}}. \tag{8}\]  
Using Equations (6)-(8), we can simplify Equations (1) and (2) into a system of linear equations in \(\tau^{\,(n-1)}\) and \(T^{\,(n-1)}\).  
\[\tau^{\,(n-1)} -\,T^{\,(n-1)} =\,\delta^{(n-1)}, \tag{9}\] \[\varepsilon^{(n-1)}\,\tau^{\,(n-1)}+\,\zeta^{(n-1)}\,T^{\,(n-1)} =\,\eta^{\,(n-1)}, \tag{10}\]  
where \(\delta\), \(\varepsilon\), \(\zeta\), and \(\eta\) are scalar coefficients defined as:  
\[\delta^{(n-1)} =t_{B,Rx}^{\,(n-1)}-t_{A,Tx}^{\,(n-1)}, \tag{11}\] \[\varepsilon^{(n-1)} =L_{A}^{\,(n-1)}+t_{B,Tx}^{\,(n)}-t_{A,Tx}^{\,(n-1)}+T^{\,(n-3)},\] (12) \[\zeta^{(n-1)} =L_{A}^{\,(n-1)}+t_{A,Rx}^{\,(n)}-t_{A,Tx}^{\,(n-1)}-\tau^{\,(n-3 )},\] (13) \[\eta^{(n-1)} =t_{A,Rx}^{\,(n)}\,(\,T^{\,(n-3)}+L_{A}^{\,(n-1)}\,)\,+t_{B,Tx}^ {\,(n)}\,(\,\tau^{\,(n-3)}\] (14) \[\quad-L_{A}^{\,(n-1)}\,)\,-t_{A,Tx}^{\,(n-1)}\,(\,\tau^{\,(n-3)} +T^{\,(n-3)}\,).\]  
Using Equations (9) and (10), we can construct closed-form estimates of \(T\) and \(\tau\) at node \(A\) for frame \((n-1)\):  
\[\tau^{\,(n-1)} =\frac{\eta^{\,(n-1)}+\zeta^{(n-1)}\delta^{(n-1)}}{\varepsilon^{( n-1)}+\zeta^{(n-1)}}, \tag{15}\] \[T^{\,(n-1)} =\frac{\eta^{\,(n-1)}-\varepsilon^{(n-1)}\delta^{(n-1)}}{\varepsilon ^{(n-1)}+\zeta^{(n-1)}}. \tag{16}\]  
Since our two-way ranging configuration is symmetrical, the delay and offset estimators defined in Equations (15) and (16) can be implemented at both nodes \(A\) and \(B\) with a trivial change of variables or by following the same process outlined in this section with the appropriate substitutions. We therefore exclude the equivalent derivations for node \(B\) for brevity for the remainder of the manuscript.
# An Overview of Lidar Imaging Systems for Autonomous Vehicles
## 2 Basics of Lidar Imaging
### Imaging Strategies  
Once the three main measurement strategies used in lidar imaging systems have been presented, it is worth noting all of them have been presented as pointwise measurements. However, lidar images of interest are always 3D point clouds, which achieve accurate representations of fields of view as large as 360\({}^{\circ}\) around the object of interest. A number of strategies have been proposed in order to build lidar images out of the repetition of point measurements, but they can essentially be grouped into three different families: scanning components of different types, detector arrays, and mixed approaches. Scanning systems are used to sweep a broad number of angular positions of the field of view of interest using some beam steering component, while detector arrays exploit the capabilities of electronic integration of detectors to create an array of receiving elements, each one capturing illumination from separate angular sections of the scene to deliver a \(t_{O}F\) value for each individual detector. Some of the strategies have also been successfully combined with each other, depending on the measurement approach or requirements, and are briefly discussed in a section devoted to mixed approaches.
# Multi-Agent Simulation Approach for Modular Integrated Construction Supply Chain
## 4 Results and Discussion
### Limitations and Future Research Directions  
While this study offers valuable insights, several limitations must be acknowledged. The model assumes a fixed demand for both modules and components, which does not fully capture the variability and uncertainty present in real-world scenarios. Additionally, it does not consider real-time traffic data or weather conditions, both of which significantly impact transportation logistics. Ignoring these elements can lead to less accurate simulations and suboptimal logistical planning. Also, in this model, we have not taken into account multi-modal transport (e.g., train, air, river and sea, and vehicles) which could optimize logistics. Furthermore, the current model does not evaluate the environmental impact of different scenarios, thus ignoring important sustainability factors such as carbon emissions and energy consumption. Lastly, the simulation is based on a specific geographic area, which may not be representative of other regions with different logistical challenges, regulatory environments, and infrastructure conditions.  
Building on the insights gained from this study, several avenues for future research and practical advancements in the MiC supply chain can be identified. Future studies should incorporate real-time traffic data, weather conditions, and dynamic demand variations to enhance the accuracy and responsiveness of logistics models. Leveraging real-time information can help anticipate and mitigate disruptions, ensuring smoother supply chain operations. Additionally, incorporating environmental impact assessments into the logistics models can provide a more holistic evaluation of the benefits of MiC. Moreover, utilizing advanced optimization techniques, such as machine learning algorithms and genetic algorithms, can further refine logistics planning. These methods can identify the most efficient routes, schedules, and resource allocations under various constraints and uncertainties. The integration of IoT and blockchain technologies can enhance transparency, traceability, and coordination among stakeholders. IoT devices can provide the real-time tracking and monitoring of logistics operations, while blockchain can ensure secure and immutable data sharing across the supply chain. Finally, developing digital twin models of the MiC supply chain can enable real-time simulation and predictive analytics. Digital twins can provide a virtual replica of the physical supply chain, allowing stakeholders to test different scenarios, optimize processes, and make informed decisions.
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 3 General Operation States of Underwater Gliders
### Surface Comms State  
In Figure 5, the schematic diagram of the vehicle in the surface comms state is shown. In this state of the operating process, the vehicle is in the state of rest, floating on the surface with a section protruding out of the water so that it can communicate with the command center via satellite; sending information about the operating cycle, its position and receiving instructions from the pilot to continue with the mission or to wait to be recovered.  
Figure 4: Schematic diagram of Inflection state of an underwater glider.  
Since a section of the vehicle is out of the water, the volume \(V_{S}\) that was previously submerged no longer provides buoyancy in this state, for which it has to be compensated by the VBS. The compensated buoyancy force \(F^{\prime}_{B}\) can be calculated by Equation (2):  
\[F^{\prime}_{B}=(V_{0}+V_{d}-V_{S})\rho_{s}g=mg. \tag{2}\]  
Substituting the value of the reference volume \(V_{0}\) from Equation (1), the required volume to be displaced \(V_{d0}\) by the VBS for the surface comms state is obtained as shown in (3):  
\[V_{d0}=V_{S}+m(\rho_{d}-\rho_{s})/\rho_{d}\rho_{s}. \tag{3}\]  
Therefore, if the density of the fluid \(\rho_{s}\) and \(\rho_{d}\), the mass of the balanced vehicle \(m\), and the required volume out of the water \(V_{S}\) are known, the volume \(V_{d0}\) required to maintain the vehicle in the surface comms state can be calculated from neutral buoyancy to execute satellite communication with the command center.
# Inter-UAV Routing Scheme Testbeds
## 1 Introduction
Support for the following mobility models is inherent in the NS-3 network simulator:  
* Constant Position Mobility Model (NS-3 specific mobility model)
* Constant Velocity Mobility Model (NS-3 specific mobility model)
* Constant Acceleration Mobility Model (NS-3 specific mobility model)
* Gauss Markov Mobility Model [18; 19]
* Hierarchical Mobility Model (NS-3 specific mobility model; a combination of specified \"parent\" and \"child\" mobility models)
* 2D Random Direction Mobility Model [19]
* 2D Random Walk Mobility Model [20]
* Random Waypoint Mobility Model [21]
* Steady State Random Waypoint Mobility Model (NS-3 specific mobility model; uses stationary instead of random distribution for initial speed, pause and position)
* Waypoint Mobility Model (NS-3 specific mobility model)  
For the majority of the existing routing protocols, a discreet event network simulator such as the NS series may prove sufficient. However, non-classically layered schemes cannot be as effectively simulated due to inherent limitations of said software. To address this issue, there was developed the FINS framework capable of considering flexibility in the layer stack and thus enable nonadjacent interlayer data exchange during simulation [22; 23; 24]. Opnet Modeler has also been used by researchers to evaluate routing schemes of both classical and cross-layer architectures in numerous occasions. Another approach to evaluating routing schemes' efficiency, is directly using them in a testbed-network, usually comprised of Raspberry Pi modules. Great examples of research incorporating hardware-based testbeds are [25; 26; 27; 28]. Despite the existence of related work in the field of ad-hoc routing and testbed implementation, no published papers present freely available testbed frameworks. The present paper aims to fill this void by:  
* Engaging in FANET-specific routing protocol and mobility model analysis
* Proposing a universal routing scheme testbed
* Making said universal testbed completely open-source for other researchers to utilize and improve  
Table 1 compares the present work to already existing surveys and testbed-oriented papers. The present paper is focused not only on surveying protocols but also on proposing a testbed. Thus, only papers of a similar nature were considered for the following comparison. Our approach proves to be the most complete in covering ad hoc routing and providing an evaluation platform open for all interested researchers to use. The layout of the rest of this paper is as follows; Section 2 analyzes and explains matters of inter-UAV routing, while considering some of the most important and \"influential\" routing protocols. Section 3 is divided into two subsections; one surveying existing routing scheme testbeds (Section 3.1) and one proposing the aforementioned prototype testbed (Section 3.2). Section 4 is dedicated to the analysis of the proposed universal routing testbed and its modes of function: standalone, supplementary and stationary mode. Each of the aforementioned modes are explained in full detail in Sections 4.1-4.3 respectively.
## Data and Methods
### Object Detection
#### 3.4.2 BDD100k Objects  
The BDD100K dataset [35] is the largest driving video dataset with over 100 K videos and 10 diverse tasks to evaluate. It possesses geographic, environmental, and weather diversity, which is useful for training models that cope with the rapid changes of a street scene. For the object detection task, the dataset consists of 10 object classes and contains bounding box annotations for 100K images from different scenarios, such as streets, tunnels, gas stations, parking lots, residential, and high-ways under diverse conditions at different times of day and night in real traffic urban street and is split into training, validation, and testing subsets with a 0.7/0.1/0.2 split, respectively. More specifically, a neural network based on the Cascade R-CNN architecture [33] and pre-trained on the aforementioned training subset of the dataset is used to detect cars, trucks, buses, motorcycles, traffic lights, and traffic signs. The pre-trained model achieves 0.563 mAP on the validation and 0.534 mAP on the testing subset at 0.5 IoU threshold. These performance scores are quite satisfactory, as stated in public benchmarks ([[https://github.com/SysCV/bdd100k-models/tree/main/det](https://github.com/SysCV/bdd100k-models/tree/main/det)]([https://github.com/SysCV/bdd100k-models/tree/main/det](https://github.com/SysCV/bdd100k-models/tree/main/det)), date accessed 10 December 2022).
# Development of an Eco-Friendly Head Impact Protection Device
## 3 Results
### Biomechanical Analysis  
In order to perform the injury risk analysis, the YEAHM model developed at the University of Aveiro was employed [39; 40]. The three Cartesian components of linear acceleration (curves in Figure 9 for 20 J) were used to drive the FE head model. These impacts were simulated using a rigid headform and a rigid wall with the headband in between attenuating the impact. These conditions are far from realistic leading to overestimation of peak accelerations, which is the main limitation of this analysis. Bearing this in mind, the following consideration focused on the lower energy impact, 20 J of energy, in which the headbands performance showed larger differences.  
Brain response was checked in terms of strain, pressure and von Mises stress. Using a set of thresholds reported in the literature, such as the ones condensed in [41], the probability of Mild Traumatic Brain Injury (MTBI), Diffuse Axonal Injury (DAI) and concussion occurring was evaluated. Some of these values refer to a single area of the brain like the corpus callosum (Figure 11), where it was possible to determine a 50% probability to occur concussion and DAI. Basically, almost the entire corpus callosum was under an excessive level of deformation, reaching a maximum principal strain higher than 0.21.  
Table 10 summarises the results of this numerical evaluation. It presents the brain volume percentage where the injury thresholds were exceeded: (i) 18 kPa of von Mises stress results in a 50% probability to occur MTBI; (ii) 173 kPa and 235 kPa of hydrostatic pressure for moderate and severe TBI, respectively; (iii) 0.18 of maximum principal strain for 50% probability of DAI and concussion in the corpus callosum.  
The numerical results computed with the FE head model revealed the commercial C-headband as the best performer. The injury risk associated with this headband was always lower than the others. Nevertheless, the sustainable alternative, NL10 agglomerated cork, performed better than the A and B headbands, being lighter than the C-headband. Therefore, although the best performance was achieved by a headband composed by synthetic materials, the natural option proved to be an excellent alternative by having the closest performance to headband C. Additionally, this headband was not optimised, being designed to have similar conditions to the commercial products. Therefore, it is still possible to improve the performance of NL10 headband by carrying out a parametric study based on its geometrical design.  
Figure 11: Sagittal plane depicting the maximum principal strain mapping (threshold of 0.21).