esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Mixed Traffic Flow Efficiency and Safety on Hard-Shoulder-Running Freeways
## 2 Materials and Methods
### Vehicle Modeling and Parameters  
To examine the characteristics of traffic flow incorporating HSR on the freeway, we employed a simulation-based approach utilizing PTV VISSIM 2022 software, which serves as the benchmark for microscopic traffic and transportation planning, rooted in modeling and simulation techniques [24; 25]. Both the car-following behaviors of the CAVs and HDVs are characterized by the Wiedemann-99 model, which includes four driving regimes [26], and the lane change behaviors are modeled using the free lane selection model, which is an algorithmic approach based on predefined rules for lateral vehicle movements [27]. The parameters for CAVs and HDVs are specified using the Wiedemann-99 model and the lane change model within the VISSIM software, as detailed in Table 1. The description of each parameter is shown in [28]. After calibrating the parameters, the results are within the acceptable range. Compared to the real traffic flow, the error of the model running results in the peak period and flat peak period is 2.0% and 6.8%, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **CAVs** & **HDVs** \\ \hline Car-following model & Wiedemann-99 & Wiedemann-99 \\ CC0 & 1.00 m & 1.50 m \\ CC1 & following a HDV: 1.1 s; & 1.52 s [29] \\ CC2 & 0.00 m & 4.00 m \\ CC3 & \(-\)6.00 & \(-\)8.00 \\ CC4 & \(-\)0.10 & \(-\)0.35 \\ CC5 & 0.10 & 0.35 \\ CC6 & 0.00 & 11.44 \\ CC7 & 0.10 m/s\({}^{2}\) & 0.25 m/s\({}^{2}\) \\ CC8 & 4.00 m/s\({}^{2}\) & 3.50 m/s\({}^{2}\) \\ CC9 & 2.00 m/s\({}^{2}\) & 1.50 m/s\({}^{2}\) \\ lane-change model & Free lane selection & Free lane selection \\ min. headway (front/rear) & 0.5 m & 0.5 m \\ move to slower lane if collision time is above & 11 s & 11 s \\ safety distance reduction factor & 0.75 & 0.6 \\ maximum deceleration for cooperative braking & \(-\)6.00 m/s\({}^{2}\) & \(-\)3.00 m/s\({}^{2}\) \\ coop. lane change/max. speed difference & 10.80 km/h & - \\ coop. lane change/max. collision time & 10.00 s & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters within VISSIM for CAVs and HDVs.  
Figure 1: The road section of the study area. The arrows represent the direction of traffic flow; solid lines denote the edges of traffic lanes, and dashed lines mark where drivers can cross the separators between lanes traveling in the same direction.
# Monitoring Perishable Commodities Using Cellular IoT:
## 3 Results and Discussion  
### Cloud and Alarms  
The interface developed for computers and mobile devices is shown in Figures 9a and 9b, respectively, which show the results of all environmental parameters and geolocation measured by the system and recorded on the server for two weeks. The first shows the packet counter at the top left, graphs with the environmental parameters in the central part and a map indicating the location at the bottom. The second shows the instantaneous values of these parameters together with a table to visualize the history of these parameters. It can be seen that these are simple and intuitive interfaces that show all the necessary information.  
Figure 9: Interface developed for (**a**) computers and (**b**) mobile devices, together with (**c**) screenshots of the alarms received via Telegram to the mobile phone.  
On the other hand, in Figure 9c, screenshots of the alarms received via Telegram to the mobile phone in cases where the maximum and minimum thresholds configured according to Table 5 were exceeded are shown. With this, the correct functioning of the data recording in the cloud and the sending of the alarms was also verified.
## 5 Results  
To measure the evolution of tracking, we use the metric \(D_{100}=\|\mathbf{\tilde{p}}-\mathbf{p}_{s}\|\), a derivative of the \(D_{25}\) metric used in [40]. \(\mathbf{p}_{s}(t)\) is the target's position, and \(D_{100}\) is the distance between the center of gravity and the position of the source. Subscript 100 defines the fact that 100% of the swarm elements are taken into account. If \(D_{100}>>r_{max}\cdot r_{eq}\), we will logically consider that the target tracking is \"bad\" and failed. If \(0<D_{100}<r_{max}\cdot r_{eq}\), we consider that the tracking is \"good\".
## 3 Experimental Test Procedure
### Data Processing  
Obtained data from MS Trimble SX10 used for the processing were images, point clouds, and a job file. The job file contained all metadata for a single job in an SX10; in this case, metadata about the captured images were extracted. Data processing was done using MATLAB and the code developed by the authors. The code was used to compare the data from two different epochs. For each epoch, an image, corresponding point cloud, and image metadata were loaded.  
Data processing consisted of fusing image and scan data into RGB+D images for each epoch. Using image matching algorithms (SURF, BRISK, ORB and KAZE), identical points on images from different epochs were identified. Displacements for matched identical points are then calculated. Based on LVDT measurements, the wooden beam is numerically modeled as a reference for testing calculated displacement from RGB+D.
# Features of Structure and Absorption in the Jet-Launching Region of M87
## 1 Introduction  
M87 is a nearby (D\(\sim\)16.7 Mpc, [1]) FRI radio galaxy which harbors a central black hole with a mass reported from 2.4 to 7.2 \(\times\) 10\({}^{9}\) M\({}_{\rm sun}\)[2; 3; 4]. Profiting from its proximity, the angular resolution of ground-based Very Long Baseline Interferometry (VLBI) now is down to a few times of the angular size of schwarzschild radii (\(R_{s}\)) of the super massive black hole (SMBH) in M87. In recent years, more and more facts of the vicinity of the SMBH in M87 are unveiled by high angular-resolution VLBI observations especially at millimeter wavelength. e.g., the accurate position of SMBH in M87 is constrained by [5]; the central compact radio source in M87 is imaged directly by [6].  
M87 is a prototype to study the AGN jet-launching region as well as the vicinity of the SMBH. The structure of the jet in its launching region has been determined with VLBI monitoring, that smears out the variability details by stacking multi-epoch observations at 22, 43 and 86 GHz [7; 8; 9]: the limb-brightened jet structure starts at a projected distance down to \(7R_{s}\) to the SMBH, with an apparent opening angle wider than 100\({}^{\circ}\)[9; 10]; as the limb-brightened jet propagates outward, it has to experience multiple expansions followed with subsequent recollimations [8; 9] i.e., \"collimation regimes\" and finally reaches an equilibrium parabolic expansion in several thousand \(R_{s}\), and keeps the shape until 10\({}^{5}R_{s}\)[11; 12]. Besides the motion along the jet, the jet flow rotates around the axis clockwisely, and the toroidal component of magnetic fieldis found close to the core by VLBI polarimetry [8]. Additionally, the counterjet is proved to be existent and with a limb-brightened structure as well [8].  
In this work, we present images of the radio jet in M87 on a scale within 10 mas, i.e., a few thousand \(R_{s}\), captured by very long baseline array (VLBA) at 22, 43 and 86 GHz quasi-simultaneously. Based on these images with high dynamical ranges, we analyze the transverse jet structure and obtain the most accurate spectral-index maps of the jet in M87 so far, and for the first time, we compare the results of the two analyses and find spatial associations between the structural features and the absorption features. We then make discussions on these associations. We also discuss the cause of the temporal jet morphology at the observed epoch. Throughout this work, we use cosmological parameters \(H_{0}=67.8\) km s\({}^{-1}\) Mpc\({}^{-1}\), \(\Omega_{M}=0.308\), \(\Omega_{\Lambda}=0.692\), then 1 pc corresponds to 11.3 mas, and \(R_{s}\approx 7\times 10^{-4}\) pc = 7.9 \(\upmu\)as if we take \(\mathrm{M_{BH}}=7.2\times 10^{9}\) M\({}_{\mathrm{sun}}\).
## 4 Conclusions  
This work dealt with affordable SDRs without clock synchronization, in an attempt to explore their possible ability to perform free-space measurements, to be used in the estimation of electromagnetic permittivity.  
Despite the intrinsic limitations of the hardware, due to the low cost of the prototype setup, we believe that the study has led to some interesting results for the permittivity estimation. The proposed technique relies on the numerical simulation of the measurement configuration, which is used as a sort of calibration stage, leaving to the comparison with the actual measured data the task of recovering the values of the DUT parameters. So, the technique could be used in different environments and configurations just by changing some of the computational parameters. Several expedients have been explored to address the limitations of the setup and, in the perspective of a cost-benefit compromise, the experimental data turned out to be consistent with simulations and with other references.  
This indicates that a sufficient amount of data exploited in conjunction with a computational model can, under certain circumstances, compensate for the absence of synchrony between Tx and Rx and overcome other drawbacks of a low-cost setup.  
The paper provides a preliminary study focused on many aspects of the characterization problem (setup, software, hardware, model, electromagnetics). Each of these aspects deserves a deeper investigation in future works including: (a) the free-space calibration; (b) optimization of parameters like the permittivity of the MUT, the properties of the floor and \(w_{0}\); (c) improving the computational model, possibly in 3D; (d) developing the uncertainty analysis.  
Conceptualization, F.J.F.G. and E.J.D.S.; Methodology, F.J.F.G. and E.J.D.S.; Software, F.J.F.G. and L.A.F.; Validation, F.J.F.G., A.B. and E.J.D.S.; Formal analysis, F.J.F.G., A.B. and E.J.D.S.; Investigation, F.J.F.G. and L.A.F.; Resources, F.J.F.G. and L.A.F.; Data curation, F.J.F.G.; Writing--original draft, F.J.F.G.; Writing--review & editing, F.J.F.G., A.B., L.A.F. and E.J.D.S.; Supervision, E.J.D.S.; Project administration, F.J.F.G. and AB. All authors have read and agreed to the published version of the manuscript.  
This research was funded by: Ministero dello Sviluppo Economico, within the program UE-FESR, PON \"Imprese e competivita\" 2014-2020, under the project \"IDROS\" (Impiego di Droni per la Ricerca nel sottOSuolo) grant number F/050187/02/X32; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq) and Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG) under grant number APQ-03382-17.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 1 Introduction
This MFXI has the potential to be incorporated into an imaging device that could easily be employed at the patient's bedside in the ED, Urgent Care setting, and austere medical settings. The MXFI device is small, light, and can easily be taken to the patient's bedside for POCT. As a first step in investigating the clinical potential of the MFXI for POCT in the emergency and urgent care setting, the investigators created a glass foreign body/chicken wing model to compare the relative accuracy of MFXI to POCUS in detecting small glass FBs in wounds. The hypothesis was that MFXI images allow non-radiologist, emergency medicine clinicians to more accurately identify small and very small FBs than POCUS utilized by these same Emergency Medicine clinicians. The clinicians regularly read plain X-rays as part of their clinical practice but did not have specific radiology training. They had been trained and certified as POCUS operators and had significant Emergency Medicine experience using POCUS in their practice. MFXI provides sufficient resolution for the detection of small glass fragments. It is particularly advantageous in comparison to ultrasound imaging. This is illustrated in Figure 1. These images were taken during the early phase of our study. Examples of glass fragments are shown. The MFXI image clearly shows seven glass fragments. The ultrasound image of the same sample only shows one fragment.
## 4 Performance Evaluation  
In this section, the performance of the F-HHT will be studied and comparisons with other methods and the theoretical limit will be provided. For comparison purposes, the standard and widely utilized least squares (LS) method was chosen, along with two earlier HHT methods: random HHT (R-HHT) and hybrid HHT (H-HHT). The performances of the localization methods will be investigated through simulations. First, the test setup will be introduced, followed by demonstrative examples to illustrate the operation of the F-HHT. The performance comparisons include accuracy, speed, computational cost, and also robustness. Finally, the performance of the proposed method will be illustrated with real measurements.
# Design of a Single-Stage Transformerless Buck-Boost Inverter for Electric Vehicle Chargers
## 1 Introduction  
Dc/ac power electronic converters, i.e., inverters, have found widespread applications in the areas of renewable generation [1; 2; 3; 4; 5], power conditioning [6; 7; 8; 9], energy storage systems [10], and electric vehicles (EVs) [11; 12]. Generally, buck-type inverters, such as the well-known H-bridge inverters, are employed to achieve the objective of dc to ac power conversion [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. However, due to the inherent voltage step-down characteristic of buck converters [13], the peak ac output voltage for the buck-type inverter should always be lower than its input dc-link voltage. This requirement imposes several limitations on the design and operation of buck-type inverters. For example, in applications where the input dc voltage is equivalent to or lower than the output ac voltage, one additional stage of dc/dc power converter is necessary to boost up the dc-link voltage. Otherwise, the undesirable over modulation of buck-type inverters may occur, which will disrupt the system from normal operation [6].  
Although the adoption of another dc/dc converter into the dc/ac power conversion system with low dc-link voltages, e.g., photovoltaic generation [1], is a feasible and widely used solution, this approach will inevitably increase the system cost and complexity. It is therefore highly desirable that the inverter can operate without any limitations on the relationships between its dc voltage and ac voltage. As an interesting attempt, [PERSON] proposed a novel inverter, named the z-source inverter, which simultaneously achieves the objectives of dc to ac conversion and voltage boost [14]. Nevertheless, it necessitates extra filter components and complicated control algorithms.  
As an alternative simple solution, a single-stage transformerless buck-boost inverter is proposed in this paper. By directly extending the operating principles of dc/dc buck-boost converter into dc/ac applications, the proposed inverter can be designed and operated in a straight-forward way, as will be discussed in the following sections.
# Performance Analysis of a 3D Wireless Massively Parallel Computer
## 2 Ball Computer Architecture
### 3D Hexagonal Topology  
A 3D hexagonal topology is chosen to implement the ABC concept. In a 3D hexagonal topology (Figure 3b) each node (other than edges and corners) has 12 neighbours. A face-centred cubic (FCC) formation has geometrical symmetry and reduces the space occupied by the network. It is shown that it is the most compact formation of spheres in a hexagonal topology. Gauss [50] has proven that the packing density of the spheres in FCC is \(\frac{\pi}{3*\sqrt{2}}\approx 0.74048\). There is still more than 25% of the volume available for cooling with a single contiguous coolant space.  
Table 1 shows that on-chip radio devices are the best technologies for the ABC. Apart from energy consumption, the main problem with radio devices is packet collision caused by the hidden-node problem. A multi-channel scheme is used to stop packets corrupting each other. Each node has eight radios tuned to different channels. A network partitioning and channel assignment algorithm guarantees no collision between packets.  
Those algorithms are designed to eliminate packet collision and at the same time minimise the overall number of channels. The first algorithm partitions the network into zones. Inside a zone  
Figure 3: The topology of a ball computer in (a) 2D and (b) 3D.  
there would be no hidden nodes. Zones in 2D and 3D grids look like a triangle and a pyramid, respectively (Figures 4 and 5).  
The second algorithm assigns channels to zones so that there is no signal interference between any two zones and at the same time the number of channels is minimal. These two algorithms guarantee that there would be no packet collision either inside a zone or between zones.  
Depending on the range of radio communication the number of the zones may vary. Figure 6 compares the number of hops in a 5D direct network (like IBM's Blue Gene) with a 3D ABC platform with two different radio ranges. The performance of ABC is highly dependent on the number of neighbours each node can contact directly. Two cases are compared in Figure 6. The blue graph represents the case in which each node only communicates with its direct neighbours. The radio range is bigger in the red graph so that it can directly contact its immediate neighbours as well as their respective neighbours. For small and medium-size networks (fewer than 8000 nodes), the number of hops in a 3D ABC is lower than in a 5D network.  
Figure 4: The process of forming a zone. The top three images show three neighbour nodes and their radio ranges. The bottom left image shows the overlap of those ranges which makes a common pink region in middle bottom image. The channel inclusion (green) and exclusion (red) areas are defined in bottom left image.  
Figure 5: A 3D grid of nodes and the partitions around a node (Z1 to Z8).
# Path Planning and Real-Time Collision Avoidance Based on the Essential Visibility Graph
## 2 Dubins' Path Generation in the Absence of Obstacles  
Consider two points \(A=(x_{A};y_{A})\) and \(B=(x_{B};y_{B})\), namely the starting and target point, and two heading angles \(\psi_{A}\) and \(\psi_{B}\) used to define the current starting point and the desired final direction, respectively.  
To obtain a path compliant with aircraft performance limits, connecting \(A\) with \(B\) with given heading angles, the trajectory must be smooth and continuous with bounds on curvature to account for the minimum flight turning radius \(R_{min}\). The quality of the path is measured in terms of path length, in order to rate the fuel consumption.  
In 2D unconstrained environments, [PERSON] algorithm [10] is able to provide optimal and smooth paths, which are composed of straight lines and circular arcs only. Optimality, in the absence of obstacles, is guaranteed under the assumption that the distance between \(A\) and \(B\) is more than \(2R_{min}\). Consequently the path is composed of three pieces: an arc of circumference with the radius equal to \(R_{min}\), a straight line, and another circular arc with the same radius.  
The optimal path is obtained with the following geometrical construction.  
* Consider the circumferences, \(\mathfrak{C}_{ALeft}\) and \(\mathfrak{C}_{Right}\) (\(\mathfrak{C}_{BLeft}\) and \(\mathfrak{C}_{BRight}\)), with radius \(R_{min}\) to the left and right of the starting (final) point \(A\) (\(B\)) and having at this point a tangent described by \(\psi_{A}\) (\(\psi_{B}\)).
* For each pair of circumferences (\(\mathfrak{C}_{ARRight}\), \(\mathfrak{C}_{BRight}\)), (\(\mathfrak{C}_{ARRight}\), \(\mathfrak{C}_{BLeft}\)), (\(\mathfrak{C}_{ALeft}\), \(\mathfrak{C}_{BLeft}\)), and (\(\mathfrak{C}_{ALeft}\), \(\mathfrak{C}_{BRight}\)), four common tangents can be used to connect one circumference to the other, but only one is compatible with the starting direction \(\psi_{A}\) and the final direction \(\psi_{B}\).
* Along the above tangents compatible with \(\psi_{A}\) and \(\psi_{B}\), four feasible paths can be computed, namely RSR (Right Straight Right) for (\(\mathfrak{C}_{A_{Right}},\mathfrak{C}_{B_{Right}}\)), RSL (Right Straight Left) for (\(\mathfrak{C}_{A_{Right}},\mathfrak{C}_{B_{Left}}\)), LSL (Left Straight left) for (\(\mathfrak{C}_{A_{Left}},\mathfrak{C}_{B_{Left}}\)), and LSR (Left Straight Right) for (\(\mathfrak{C}_{A_{Left}},\mathfrak{C}_{B_{Right}}\)).
* The optimal path is the shortest among RSR, RSL, LSL, and LSR.  
Figure 1 provides an example of how [PERSON] concept is applied. Assuming \(A=(-1\) km; \(-1\) km) and \(B=(1\) km; \(1\) km) with \(\psi_{A}=-2\pi/3\) rad and \(\psi_{B}=-\pi/3\) rad and a turning radius \(R_{min}=250\) m it shows the four feasible paths, namely RSR, RSL, LSL, and LSR. In this case, the shortest one is the RSR path.
## 7 Robustness
### Evaluation Metrics and Baseline Performance  
Before discussing specific attack scenarios, it is essential to establish the significance hierarchy of the evaluation metrics used in this study. For forensic watermarking applications, Bit Error Rate (BER) is the primary metric due to its direct measurement of watermark extraction accuracy, which is essential for recipient identification in evidence leakage scenarios. Lower BER values indicate more accurate watermark recovery, with BER = 0 representing perfect extraction. Normalised Cross-Correlation (NC) is a complementary measure used to quantify the similarity between the original and extracted watermarks on a scale from 0 to 1, where values approaching 1 indicate higher fidelity. Bit Error Quality (BEQ) provides a perceptual assessment that combines extraction accuracy with visual quality preservation, which is important for maintaining evidential integrity.  
The baseline performance establishes the benchmark against which attack resilience is assessed. The proposed method achieves BER = 0 in all datasets tested under no-attack conditions, with NC = 1.0 and PSNR values ranging from 46.13 dB to 49.37 dB. This baseline demonstrates perfect watermark recovery and preserves high image quality in the absence of distortions.