esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Hierarchical Density-Based Spatial Clustering Algorithm for Streaming Applications
## 3 Methodology
### Excess of Mass Calculation  
Finally, the excess of mass is calculated for the condensed hierarchy tree and a suitable cut is made. These are the clusters in the hierarchy with the lowest epsilon values, and they indicate clusters that cannot be divided up any more with regards to the minimum size of clusters. A new measure is developed to measure the persistence of clusters. This is denoted by \(\gamma=\frac{1}{distance}\). So, for each cluster, the \(\gamma_{birth}\) and the \(\gamma_{death}\) are the values when the cluster splits off and becomes its own cluster. The cluster stability is then computed using equation [21]:  
\[\Sigma_{p\in cluster}(\gamma_{p}-\gamma_{birth}) \tag{5}\]  
Then, in reverse topological order, the sum of stabilities of the child clusters are computed and are compared to the stability of the cluster. If the sum of the child clusters' stabilities is larger than the cluster's stability, the cluster's stability is equal to the sum of the child clusters' stabilities. If the cluster's stability is larger than the total of its offspring, the cluster is declared a chosen cluster, and all of its descendants are unselected. We call the current collection of chosen clusters our flat clustering and return it once we reach the  
Figure 4: [PERSON]’s algorithm pseudocode.  
root node [21]. To make this explicit, [PERSON], as well as [PERSON] and [PERSON], discuss the concept of mass excess where the C is a subset of a domain function f and the excess of mass of C at level is  
\[E(C,\lambda)=\int_{\mathcal{C}_{\lambda}}(f(x)-\lambda)dx\text{ where }C_{\lambda}=\{x\in C|f(x)\geq\lambda\} \tag{6}\]  
So, for a cluster tree of \(f\), the definition of excess of mass of a cluster \(C_{i}\) that exists at level \(\lambda_{C_{i}}\) of the cluster tree is \(E(C,\lambda)=\int_{\mathcal{C}_{\lambda}}(f(x)-\lambda_{min}(C_{i}))dx\) for \(\lambda=\lambda_{min}(C_{i})\) and \(C_{i_{1}},C_{i_{2}},...C_{i_{k}}\), are the children of \(C_{i}\). Therefore, the final excess of mass equation can be expressed as \(E_{R}(C_{i})=E(C_{i})-\sum_{j=1}^{k}E(C_{i_{j}})\). The \(\lambda\) at which the cluster persists maximally is taken as the cutoff value for that level set. There could be multiple \(\lambda\) depending on the scale of the cluster persistence. This way, clusters of different densities can be identified correctly.
# A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
## 5 Testing the MEC Model with FEA
### MEC Model Verification
On the other hand, in the MEC model, the mentioned flux distributions are represented only by three permeances (\(P_{ub}\), \(P_{mb}\), and \(P_{lb}\)) along the vertical direction for the pole region and only by two permeances (\(P_{hl1}\), \(P_{hl2}\)) for the inner-slot leakage flux.
# Controller-Pilot Data Link Communication Security
## 5 Potential Countermeasures  
A critical factor defining the requirements for CPDLC system security is its limited available bandwidth. According to [20] CPDLC allows for up 31.5 kb/s and in practice the actual performance of the data link can drop down to only 4 kb/s. Any proposal for CPDLC protocol security has to take into account this limitation on the available bandwidth, and should avoid extra overheads.  
In what follows we discuss several solutions to improve CPDLC security, taking into consideration the limitations on the system resources.
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 6 Prototyping and Testing
### Obstacle Detection Results  
For the purpose of implementing a reliable framework for assistive maintenance, we addressed a particular focus to the real-time response of the obstacle detection system. In order to evaluate it, we used as Key Performance Indicator (KPI) the mesh processing time in relation to the number of points and triangles.  
We set the quantization parameter for mesh simplification (also used as the histogram bin size for ground-plane extraction) to \(\delta=10\) cm (see Section 4.1). The percentage of elevations used to detect the histogram peak associated to the ground-plane was set to \(p=10\%\). Among the retrieved mesh-graph components, we considered obstacles to be those lying within 1 m from the floor-plane.  
Table 1 reports some results on the measured computation times for the tasks of mesh simplification, ground-plane extraction and object detection, in relation to the number of vertices (before and after reduction), triangles and detected obstacles. Measurements were collected sequentially, as the mesh acquired by the AR device grows while the environment is being explored. From these results, it is evident how mesh simplification helps to stabilize the total computation time: while the number of vertices in the non-processed mesh grows from thousands to tens of thousands, the overall time required for processing the scene remains below 100 ms, on average. Additionally, the fraction of computation time needed for detecting the mesh-graph components is most of the time negligible. It may surprise to observe that the number of detected obstacles do not follow a strictly non-decreasing trend, but this is in fact perfectly in line with the way HoloLens creates and refines environmental meshes: since new acquisitions are constantly being incorporated into the current model, it happens quite often to see an object being first acquired as multiple disconnected components and then being completed within the next few frames. The bandwidth and the latency of the 5G network, together with their counterpart in the 802.11 ac WLAN, did not introduce any delay both in the real-time bidirectional data streaming to and from the HoloLens. An exhaustive 5G transmission speed and latency tests were not performed since the whole use-case was tested in an experimental 5G setup with limited performances with respect to the final operative deployment; however, the scene processing together with the remote call interaction and the proper hologram placement was always seamless and no disruptive delays occurred.
## 3 Recent Developments and Progress
### Environmental Remote Sensing by GNSS-R
#### 3.5.3 Vegetation Monitoring  
The detection of vegetation, including the monitoring of plant water content, is crucial for ensuring healthy crop growth and for the timely identification and resolution of issues. An accurate evaluation of this water content could enhance the precision of soil moisture estimates. With the ongoing advancements in GNSS technology, a dense network of stations has emerged globally, along with an increasing quantity of satellite signals received, aiding in the development of a plant monitoring network. GNSS signals operate within the microwave spectrum and are particularly sensitive to factors such as surface roughness and water content. While these signals assist in monitoring plant water content, they are also influenced by both surface roughness and soil moisture. Consequently, it is essential to utilize techniques that mitigate these interfering variables when estimating plant water content. [PERSON] et al. [55] first proposed the use of the multipath effects of GPS receivers to monitor soil moisture, which laid the foundation for GPS reflection measurement to monitor plant water content. [PERSON] et al. [56] used airborne GNSS-R to measure soil moisture in farmland, and correctly inverted the spatial variation of soil moisture with the flight trajectory of the machine, but the inversion results were lower than the measured values. [PERSON] et al. [57] described the relationship between vegetation growth and ground reflection multipath near GPS stations and showed that in two agricultural test sites, vegetation height and water content were negatively correlated with the intensity of ground reflection multipath signals measured by high-precision GPS stations. [PERSON] et al. [58] designed an antenna suitable for GNSS reflection measurements and experiments showed suitable vegetation monitoring in most agricultural environments.
# Protein Dynamics in F-like Bacterial Conjugation
## 2 Dynamics of Proteins Involved in the Regulation of Bacterial Conjugation
### FinO and FinP  
The RNA binding protein FinO features a disordered N-terminal region from Met1-Lys25 that prevented the protein's crystallization, the remainder of the 186 residue protein from the F-like plasmid R6-5 was crystallized and solved by X-ray diffraction (Figure 2A) [25]. The structure of FinO is highly elongated and mainly \(\alpha\)-helical, with flexible N- and C-terminal helices extending from a well-structured central region composed of five helices and two \(\beta\)-hairpins. The terminal helices are highly positively charged and have been shown to bind RNA through proteolytic assays [28, 29, 30, 32]. The stem loop II (SLII) region of FinP and its 3\({}^{\prime}\) nucleotides have been shown to bind these respective surfaces with high affinity to increase the stability of the transcript. As well, it is predicted that this positions the RNA, such that _tra_] RNA can be bound in a predicted kissing complex [28]. While no crystal structures of a FinO-RNA complex have been solved, a low-resolution model of the FinO-SLII complex has been presented using small angle X-ray scattering (SAXS) and corroborated with various assays (Figure 2B) [28].  
The flexible N-terminus that was removed for proper crystallization was determined to be functional in promoting strand exchange of the RNA with _tra_] RNA transcripts through uncoiling stem loops [29, 30]. The \(\alpha_{1}\) helix has been shown to be especially important in promoting strand exchange as it features a positively charged surface and many key hydrophobic residues, as seen through point mutation assays. The presence of disordered regions in RNA chaperone proteins is common and not mere coincidence for FinO; while the N-terminal region appears to be dispensable for the in vivo stability of FinP, deletions of the region strongly reduce the ability of FinO to repress conjugation [29; 33; 34]. FinO\({}_{45-186}\) exhibits no inhibition of conjugation, even though this N-terminal truncation mutant was demonstrated to protect FinP against degradation similarly to the full-length protein [29]. This demonstrates the often-overlooked importance of highly dynamic regions; targeting this region with an antibiotic could result in the uncontrollable expression of an F-like T4 SS that would greatly encumber bacterial cellular processes.
## 3 Results and Discussion
### Single-Phase Flow Results  
To compare the degree of the nonrotating property among the magnetic particles, the nonmagnetic particles, the theoretical velocities, and the vorticities inside the pipe were calculated. The calculation conditions were an inlet average velocity of 0.67 m/s, Re 2000, and single-phase and laminar flow. Figure 8 shows the magnetic, nonmagnetic, and theoretical velocity profiles. On the \(x\)-axis, 0 is the center of the nozzle, and 1.0 is the wall of the nozzle. The \(x\)-axis was nondimensionalized to half the width of the solenoid nozzle. The velocity was nondimensionalized to the average velocity. The velocity profile of the magnetic particle affected by the external magnetic field is flat in the middle. This result shows a flow pattern similar to that found by [PERSON] et al. [29]. The area of deceleration was expected to be within 200 \(\upmu\)m of the wall. The nonmagnetic particle was also affected by the external magnetic field. This result is similar to those of [PERSON] et al. [20], [PERSON] et al. [21], and [PERSON] et al. [22]. This LIF-PIV setup could not measure at less than 200 \(\upmu\)m from the wall due to the cone-shaped focal area of the objective lens. This phenomenon will be supplemented in further research. Equation (5) is an analytical profile equation for the theoretical study:  
\[u(r)\ =\ 2V_{avg}\left(1-\frac{r^{2}}{R^{2}}\right). \tag{5}\]  
In this equation, \(u\) is the velocity, \(r\) is the location of the nozzle, \(V_{avg}\) is the average velocity, and \(R\) is half the width of the nozzle.  
Figure 7: A raw image of the bubbly flow. Captured in the Davis PIV analysis program.
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 7 Conclusions  
The flexibility and modularity of HLF, while beneficial for custom deployments, introduce attack surfaces when the configuration access is not tightly controlled. This study systematically evaluated DoS attack vectors in HLF, demonstrating that both external adversaries and malicious insiders can exploit its modular architecture, configurable consensus, and identity management model to degrade performance and availability.  
While the results of this study are based on the HLF ecosystem, similar vulnerabilities likely exist in other enterprise blockchain platforms that rely on modular architectures and complex configuration layers (e.g., Corda, Quorum, or Besu). This calls for a shift from performance-centric tuning to the threat-aware engineering of blockchain deployments. The bottom line is that even in permissioned blockchain systems that assume trust among participants, security-through-configuration alone is insufficient.
# Bi-Objective Power Optimization of Radio Stripe Uplink Communications
## 1 Introduction  
The conventional massive multiple-input-multiple-output (mMIMO) fifth-generation (5G) communication systems are cell-centric, in which a single base station (BS) contains the electronic components of all antennas while serving the user equipment (UE) in a cell. This configuration has low-capacity requirements in the fronthaul along with the possibility of covering large areas. The UE are multiplexed in the spatial domain through the reception of very directive signals, a technique known as beamforming. mMIMO also explores multipath propagation to boost overall data rates, spectral efficiency (SE) and power efficiency by increasing the number of data streams that are simultaneously transmitted (spatial multiplexing) or by improving reliability of the communication link through a redundant data transmission (spatial diversity) [1; 2; 3]. The channel hardening and favorable propagation conditions allow for the implementation of simple precoding and equalization techniques [2; 3]. However, the network performance is bounded by the inter-cell interference [3; 4]. This means that the spatial multiplexing of UE requires more complex techniques to separate the data streams. Additionally, due to the constraints on the number of radio frequency chains and in order to meet the traffic requirements for the next generation of communications, new mMIMO network configurations are being proposed [5; 6].  
The combination of mMIMO with cloud radio access network leads to the concept of distributed mMIMO which aims to provide an UE-centric approach allowing an ubiquitous service experience to all UE, reducing inter-cell interference. A practical distributed mMIMO system is known as cell-free (CF) mMIMO [4; 7; 8; 9]. In CF mMIMO, the antennas are grouped in access points (APs) that are geographically spread out. These APs jointly cooperate in transmission and reception by having an independent fronthaul link and power supply [9]. In this network configuration, the UEs are served by the same time and frequency resources. Nevertheless, the main bottleneck for a practical implementation of a CF mMIMO network is the need for a the large fronthaul capacity and signaling requirements along with a high network implementation cost (with a huge density of long cables) of the connections from APs to the central processing unit (CPU). This way, practical CF mMIMO systems with decentralized processing algorithms have been recently proposed [10; 11; 12].  
The radio stripe (RS) concept [10; 11; 12] is a realistic implementation of a CF mMIMO network. It consists of a stripe, connected to a CPU, where a high number of antennas are connected and cooperate phase-coherently. The APs are sequentially located inside the same cable, providing synchronization, data transfer and power supply via a shared link [12], thereby avoiding the need for dedicated fronthaul links between each AP and the corresponding CPU.
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
###### Abstract  
This paper investigates the possibility to improve target detection capability in a DVB-T-based passive radar sensor by jointly exploiting multiple digital television channels broadcast by the same transmitter of opportunity. Based on the remarkable results obtained by such a multi-frequency approach using other signals of opportunity (i.e., FM radio broadcast transmissions), we propose appropriate modifications to the previously devised signal processing techniques for them to be effective in the newly considered scenarios. The resulting processing schemes are extensively applied against experimental DVB-T-based passive radar data pertaining to different surveillance applications. The obtained results clearly show the effectiveness of the proposed multi-frequency approaches and demonstrate their suitability for application in the considered scenarios.  
passive radar; multi-frequency; target detection; non-coherent integration; DVB-T signals +
Footnote †: journal: Sensors
# A Sigfox Energy Consumption Model
## 4 Modeling Sigfox Device Current Consumption  
This section presents models of crucial energy performance parameters of Sigfox, such as device current consumption, device lifetime (for battery-operated devices), and energy efficiency of data delivery. A device periodically transmits an uplink data message (e.g., a message that carries a sensor reading) is assumed. The models consider the impact of frame losses. The section is divided in two subsections, which offer the aforementioned models for unidirectional and bidirectional transactions, respectively. All Sigfox physical layer bit rates are considered.
## 4 Near-Space High-Resolution and Wide-Swath Imaging
### Potentials and Challenges
Some reconstruction algorithms have been proposed by other authors, e.g., [80]. In this paper, another simple reconstruction algorithm is derived. A block diagram for the reconstruction from the three sub-aperture signals is shown in Figure 8 (left). This algorithm is based on considering the data acquisition in a multi-aperture near-space SAR as a linear system with multiple channels, each can be described by a linear filter. From the sampling theorem, we know that the sampled signal spectrum \(X_{s}(f)\) is the sum of the unsampled signal spectrum, \(X_{0}(f)\), that is repeated every \(f_{s}\) Hz with \(f_{s}\) the sampling frequency(samples/sec.). That is:  
\[X_{s}(f)=f_{s}\sum_{n=-\infty}^{\infty}X_{0}(f-nf_{s}) \tag{13}\]  
If \(f_{s}\geq 2B_{d}\), the replicated spectra do not overlap, and the original spectrum can be regenerated by chopping \(X_{s}(f)\) off above \(f_{s}/2\). Thus, \(X_{0}(f)\) can be reproduced from \(X_{s}(f)\) through an ideal low-pass filter that has a cutoff frequency of \(f_{s}/2\). In this way, subsequent data processing involves interpolating the data from each channel. We then have:  
\[y_{s}(n)=x_{s}(\frac{n}{N_{0}}) \tag{14}\]  
where \(N_{0}\) is the interpolation scale. Correspondingly, its spectrum is represented by  
\[Y_{s}(f)=\sum_{n=-\infty}^{\infty}y_{s}(n)e^{-j2\pi fn}=X_{s}(N_{0}f) \tag{15}\]  
Then, the linear filters can be derived as  
\[H_{k}(f)=N_{0} \tag{16}\]  
where \(f_{ck}\) is the corresponding Doppler centroid. Finally, the filtered signals can then be combined coherently, as shown in Figure 10 (right). In this way, the capability of ambiguity suppression allows for improved resolution and an enlarged swath. Note that this technique is also useful for ambiguity suppression in a distributed near-space SAR with multiple transmitters or/and receivers. However, any cross-track separation of the receivers will introduce additional phase in the received signals, which has to be compensated. Moreover, the joint processing these data will then lead to a nonlinear combination of cross-track interferometry [81].
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 2 Related Works
Each rendering node renders different scenes and the fusion node combines the pixel depth maps and scene images rendered by the rendering nodes into a final image [20]. This research solves the problem of parallel rendering between rendering nodes but it does not address the issue of returning the final image to the user terminal. [PERSON] et al. proposed an architecture called a highly parallel multi-task parallel rendering system suitable for processing large-scale complex scenes, capable of executing multiple rendering tasks simultaneously and supporting multi-screen display [21]. This architecture implements the process of returning rendered images to user terminals, but the returned images are result images rendered separately by each process without compositing the images rendered by each process. The complete rendering result is physically spliced and displayed to users through multiple display devices. [PERSON] et al. designed a data compression algorithm for a parallel rendering system based on InfiniBand. The proposed parallel rendering system consists of server nodes, display nodes, and rendering nodes controlled by service nodes, which return images rendered by rendering nodes to display nodes [22]. This research solves the problem of compositing parallel rendered images and returning them to display nodes, but its display nodes request scene images to be displayed periodically under server control without achieving real-time interaction with scenes. Despite extensive discussions on real-time interactive methods for the large-scale parallel visualization of flow fields, there is still a need for further research to address the management challenges of efficient data access and network communication for such datasets. In this regard, this paper proposes a data-partitioning strategy based on grid segmentation and designs job management, data communication, and human-machine real-time interactive coordination methods. These steps gradually achieve the real-time interactive exploration of large-scale flow-field data.