esa-sceva/satcom-chunk-collection huggingface dataset:
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 2 Background
### WISCANet vs. Traditional Approaches  
Commercial radio products are typically implemented on either FPGAs or ASICs. These implementations offer high efficiency and performance, but are also time-consuming to develop, require specialized hardware knowledge, and typically incur significant one-time engineering costs. These embedded solutions are excellent for mass-produced technologies, but are neither viable nor accessible for conducting research on novel RF techniques. Commercial SDRs have made entry-level RF research more accessible and affordable, but often do not scale well to network applications or high-precision techniques. While the open source community has developed excellent tools to support these devices, these solutions are typically insufficient for intermediate to advanced RF research.  
WISCANet aims to address these shortcomings by providing a scalable control architecture for commercial SDR products. WISCANet allows users to quickly assemble SDR networks without requiring specialized hardware knowledge, and test over-the-air applications with minimal hardware configuration. By abstracting the hardware controls away from the user, WISCANet allows a non-expert user to deploy an OTA application by simply defining a baseband processing chain in a high level language. This technology reduces transition time between system design and OTA deployment, accelerates debugging and validation processes, and makes OTA experimentation more accessible to users that are not radio hardware experts.
## 4 Stability Analysis of the Slope of Roadbed Supported by Timber-Frame Beam-Bamboo Anchor Rod
### Maximum Shear Stress on the Slope  
The maximum shear stress of the slope is also one of the important indices to judge the stability of the slope. Figure 26a shows that when the slope is unsupported, the maximum shear stress peak is mainly concentrated in the ballast layer, and the top and foot of the slope are also subject to large shear stresses according to the potential sliding surface of the slope. At this time, the foot of the slope and the top of the slope are susceptible to shear damage. Figure 26b shows that when the slope is supported by bamboo anchors, the maximum shear stress of the slope decreases abruptly, and the peak shear stress decreases by 34.7%. In the slope supported by bamboo anchors, the maximum shear stress decreases abruptly, and the shear stress is mainly concentrated at the top of the slope below the loading point. It is an arc-shaped downward decay; the foot of the slope and the top of the slope shear stress decrease abruptly, and the probability of occurrence of shear damage is greatly reduced.  
In summary, when the slope is unsupported, the maximum shear stress distribution range fills the whole slope, shear damage easily occurs at the top and foot of the slope, and the stability of the slope is poor. Further, when the slope is supported by bamboo anchors and wooden frame beams, the maximum shear stress distribution range of the slope is rapidly reduced and mainly concentrates in the arc-shaped spreading attenuation below the loading point and attenuation mainly concentrates in the ballast layer and the foundation layer, the foot of the slope and the top of the slope shear stress is suddenly reduced, and the probability of shear damage is greatly reduced.  
Figure 25: Maximum shear strain on the slope. (**a**) Unsupported slope; (**b**) bamboo anchors for slope support.
## 2 Background
### Equalization and Channel Estimation
The optimal feedforward and feedback filter coefficients are, respectively, given by  
\[F_{k}^{l}=\frac{\kappa H_{k}^{*}}{\frac{1}{\gamma}+\left(1-(\rho_{blk}^{l-1})^ {2}\right)|H_{k}|^{2}}, \tag{19}\]  
and  
\[B_{k}^{l}=\rho_{blk}^{l-1}\left(F_{k}^{l}H_{k}-1\right), \tag{20}\]  
where \(\kappa\) denotes a normalized constant chosen to guarantee that \(\frac{1}{N}\sum_{k=0}^{N-1}F_{k}^{l}H_{k}=1\), and \(\rho_{blk}\) represents the correlation factor, a key parameter for reliable IB-DFE operation and accurate system performance [7; 22]. A challenging obstacle in any communication system for the success of the equalization procedure is the demanded necessity for the receiver to obtain the CSI and frequently updates on it, which is not an easy task due to the time variance and frequency selectivity of wireless channels. The pilot sequence length should, therefore, be optimized considering the CIR length with the objective of reducing channel estimation errors, the BER, and maximizing channel capacity [29]. Channel estimation techniques can be classified into two categories [19; 30]:  
* non-data-aided, where the CSI is obtained without the use of reference training signals, i.e., on the basis of the statistics of received signal sequences; and
* data-aided, which require added information, i.e., reference training signals that are included in the transmitted frame. Although non-data-aided techniques do not require reference signals, a large number of data must be collected in order to obtain reliable estimation channel samples. Hence, data-aided techniques, although the require additional frame overhead on the transmitted data that causes a decrease in spectral efficiency, can provide better performance, especially on fast-changing channel conditions. The simplest method for frequency channel estimation is to use block-type allocation and employ least-square (LS) estimation \(\hat{H}_{LS}\), where it is necessary for each \(k^{th}\) subcarrier to compute [11]  
\[\hat{H}_{LS_{k}}=\frac{Y_{k}}{X_{k}}=H_{k}+\frac{N_{k}}{X_{k}}. \tag{21}\]  
The LS estimator constitutes a low-complexity algorithm. However, the major disadvantage comes from noise enhancement. Since LS estimators do not generally require any channel statistics, the estimation is not perfect enough, especially in scenarios of very fast channel variations where system performance significantly deteriorates [18]. Nevertheless, it is essential to obtain for an initial coarse channel estimation.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Existing Energy-Saving Techniques
#### 2.3.3 Network Protocols and Communication Technologies  
Communication technologies, such as low-power WiFi, Bluetooth low energy (LE) [30], and low-power wide area network (LPWAN) [31], were developed to minimize energy consumption, making them ideal for IoT applications that require a long battery life. WiFi HaLow [32], one of the low-power WiFi versions, constructed based on the IEEE 802.11 ah standard [33] features high data transmission efficiency so as to diminish power consumption. Bluetooth LE, characterized by short communication intervals and quick connection establishment, reduces the power consumption while maintaining stable communication. LPWAN technology, allowing long-distance communication (several kilometers), reducesthe need for multiple relays or access points, making it suitable for long-range and low-data rate scenarios.
## 1 Introduction
However, as demonstrated for specific events analyzed over the Mediterranean region [7; 35], the GPM-CO offers new possibilities taking advantage of the larger channel assortment and higher spatial resolution of the GMI, and of the first spaceborne precipitation radar (in dual frequency) covering the mid-latitudes. In this study, 4 years of GMI and DPR observations (March 2014-February 2018) are used as proxies to select and characterize deep convective systems in the Mediterranean area. The goal of this study is to evidence how the GMI and DPR unique and unprecedented capabilities are often complementary in depicting unique features of deep convective systems, including overshooting tops and potential hailstorms. The non-sun synchronous GPM-CO orbit allows for the first time to analyze the diurnal cycle over the whole area, evidencing the different forcing mechanisms of deep convection over land and over sea in the different seasons. The dataset used and the methodology are described in Section 2, and the main results of the analysis are illustrated in Section 3, while the discussion and the conclusions are provided in Section 4.
## 7 Conclusions  
This article proposes a novel GFMC APF to address harmonic distortions in modern power grids with high renewable energy integration and nonlinear loads. By synergizing grid-forming converters' voltage/frequency regulation capabilities with advanced harmonic suppression, the GFMC APF overcomes the limitations of conventional solutions like passive filters and grid-following APFs. The system employs a three-tier control architecture: (1) a power control module using droop strategies for grid synchronization and power regulation, (2) a voltage/current regulation module combining a repetitive controller (RC) and PI controller to suppress periodic harmonics while ensuring steady-state stability, and (3) a harmonic instruction generation module that extracts harmonic components from the PCC via _dq_-frame transformation and filtering for precise compensation. Stability analysis, validated through impedance modeling and Bode plots, demonstrates that the RC effectively mitigates harmonics at integer multiples of the fundamental frequency, with optimal tuning (e.g., \(Q_{x}<1\)) balancing suppression and stability. Simulations confirm the GFMC APF's efficacy, reducing the PCC voltage THD to 3.59% and grid-injected current THD to 0.74%, compliant with IEEE Std. 519-2014. The system also enhances grid inertia, damping, and short-circuit capacity, ensuring sinusoidal waveforms and stable power dynamics under nonlinear loads. This integrated approach offers a scalable, cost-effective solution for improving power quality and stability in weak grids with high renewable penetration.  
Conceptualization, J.L.; methodology, J.L. and M.W.Q.; software, M.W.Q.; validation, M.W.Q., J.L., and J.F.; formal analysis, M.W.Q.; investigation, M.W.Q.; resources, J.L.; data curation, M.W.Q. and J.L.; writing--original draft preparation, M.W.Q.; writing--review and editing, M.W.Q. and J.F.; visualization, M.W.Q.; supervision, J.F.; project administration, J.F.; funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Guangdong Basic and Applied Basic Research Foundation under Grant Guangdong-Shenzhen Joint Youth Fund, Grant Number 2022A1515110422, in part by the National Natural Science Foundation of China, Grant Number 20221017-9, Grant Number 52377192, Grant Number 12411530110, and in part by the Department of Science and Technology of Shandong Province under Grant Shandong Province Youth Fund under ZR202210270088.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
Figure 16: Active and reactive power dynamics under non-stationary harmonic conditions.
# Bayesian Feature Fusion Using Factor Graph in Reduced Normal Form
## 2 Materials and Methods
### Model Architecture
#### 2.1.1 Face Detector  
The face detection has been implemented using YOLOv3 model [19], i.e., a deep neural network of 106 layers where the first 53 layers, called Darknet-53 and derived by Darknet-19 introduced in [20], are used as feature extractor. The major novelty of the YOLOv3, respect to the previous versions, is the capability of making detection on three different scales following the idea behind the feature pyramid paradigm [21]. YOLOv3 predicts 3 bounding boxes for each cell into which the image is divided. Each bounding box is described by \(5+Y_{C}\) parameters: two center coordinates, two dimensions, the objectness score (that express how confident is the model that that box contains an object) and a classification vector that describes the classification confidence for each of \(Y_{C}\) considered class.  
For our face detection problem, we used weights of a pretrained architecture on WIDER FACE Dataset [22] available at [23], where the only class of interest is \"face\".
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 2 Requirement Analysis
### Accuracy  
In general, the accuracy of a measurement system could be defined as \"how close\" the measurement is to its true value. This closeness is quantified by the distance between the measured and true value. In positioning systems, accuracy is usually given in three dimensions. Especially in applications where the vertical position is less relevant, as in vehicular applications where cars drive on roads, the 2D accuracy is used instead. Position accuracy is usually quantified by the Root Mean Squared Error (RMSE) or other conventions as the 95% confidence interval. When evaluating the accuracy of relative positioning systems for vehicles, it is common to differentiate between along-track and across-track accuracy. Especially in on-board ranging sensors, a differentiation between range accuracy and angular accuracy is made. Along with the relative position and relative velocity, it is useful for driver assistance applications to have information about the current uncertainty in each of the measurements. Sensor systems might have different precisions depending on the range towards the target vehicle, the view angle, the material the target is made of or the environmental conditions. A relative positioning system would ideally provide a highly accurate and bias-free relative position and relative velocity output in both the along- and across-track direction along with a measure of the current uncertainty of its estimation. [PERSON] and [PERSON] stated that 1 m position accuracy could be marginally acceptable for collision warning applications, while 50 cm would yield significantly better results [7].
## 4 The SECOM Standard
### Authentication  
Similarly to data integrity and confidentiality, in the SECOM implementation the authentication is achieved at three levels: through the adoption of an authentication and authorisation server, data protection, and a secure communication channel. Authentication is the process by which a user or service proves to a system its identity. When a user or a service wants to use the SECOM standard, it first has to authenticate itself through the authorisation server by providing the credentials and has to obtain a JWT access token for the microservices interfaces. The second level of authentication is through the data protection scheme, with the establishment of the shared key between the ship and the SCC. Parties exchange certificates signed by the CA, which is a trusted third party and can verify the authenticity of the messages. Finally, the third level of authentication is achieved through the SECOM secure communication channel, which provides service authentication on top of data integrity and confidentiality. It also provides a secure channel for the authentication of the functionalities exposed by each microservice.
# Detection of Impact Damage on PVA-ECC Beam Using Infrared Thermography
## 3 Experimental Setup and Procedure  
This section describes the experimental setup and demonstrates the impact damage detection based on active thermography. In addition, the temperature acquisition system will also be described in this section.
## 2 Materials and Methods
### Electromagnetic Induction (EMI)  
Electromagnetic surveys are based on the measurement of electrical conductivity and the magnetic permeability derived from the materials found in the subsurface using electromagnetic fields that are induced naturally or artificially in the study terrain. One interesting feature of the electromagnetic method is that it does not require direct contact with the ground being surveyed [29, 37]. The EMI method is rapid, non-invasive, and cost-effective, and is implemented using transmission (\(T_{x}\)) and receptor (\(T_{r}\)) coils, as described in detail by [22, 39, 51, 52].  
An alternating electrical current, which is produced by the transmission unit at a set frequency, passes through the transmission coil (\(T_{x}\)) to produce a primary, alternating magnetic field (\(H_{p}\)), which induces secondary electrical currents in the subsurface that generate a secondary magnetic field (\(H_{s}\)). Part of this magnetic field is captured by the receptor coil (\(T_{r}\)), which also receives part of the primary magnetic field through a reference cable. Thedifferences detected in the intensity, direction, and phase between these electromagnetic fields can be used to detect the presence of conductive bodies [51; 53].  
In general, the secondary magnetic field depends on the spacing of the coils (\(S\)), the operating frequency (\(\omega\)), and the conductivity of the terrain (\(\sigma\)). However, when operating at low induction numbers, where the product \(\omega\sigma S^{2}\) is less than 1000, the following equation can be employed [51]. These restrictions are incorporated into the EM 34-3 conductivity meter, in which the secondary magnetic field is defined by  
\[\frac{H_{S}}{H_{p}}=\frac{i\omega\mu_{0}\sigma S^{2}}{4} \tag{4}\]  
where \(H_{s}\) = the secondary magnetic field, \(H_{p}\) = the primary magnetic field, \(\omega=2\pi f\) (angular frequency), \(\mathrm{i}=\sqrt{-1},\mu_{0}=\) magnetic permeability of free space, \(\sigma=\) ground conductivity \(\left(\mathrm{mho.m}^{-1}\right)\), and \(S=\) intercoil spacing (m).  
The electrical conductivity varies according to the type of soil or rock, porosity, permeability, the degree of saturation, and the electrochemical properties of the liquids found in the pores, which is the most important factor in many cases [51].
# An Efficient Resource Scheduling Strategy for V2X Microservice Deployment in Edge Servers
## 2 The Resource Scheduling Model in Containerized Microservice
### Critical Factors in Resource Scheduling
#### 2.3.1 Microservice Calling Distance  
When a user sends a service request, a call chain will be formed on the server. All microservices in the call chain will work together to meet the user needs. Therefore, it is necessary to call the containers deployed in each host. The time taken by container calls in the same physical host will be significantly shorter than those across physical hosts. Therefore, in the process of container deployment and resource scheduling, the cross-physical host calls of the container should be as few as possible. In this way, it is possible to reduce the time for containers to transfer network calls between physical hosts and reduce network resource waste. We define Equation (7) to calculate the calling distance between containers to quantify the calls between containers.  
\[\left\{\begin{array}{cc}d\big{(}k_{i},k_{j}\big{)}=\left\{\begin{array}{ cc}1,&\text{calls across physical hosts between containers $i$ and $j$}\\ &0,\text{else}\end{array}\right.\\ D_{ij}=\sum\limits_{j=1}^{m-1}d\big{(}k_{i},k_{j}\big{)}\\ D_{i}=\operatorname*{D}_{ij}+D_{ji}\\ D=\sum\limits_{i=1}^{m}D_{i}\end{array}\right. \tag{7}\]  
\(d\big{(}k_{i},k_{j}\big{)}\) can quantify the calling relationship between containers \(i\) and \(j\). In Equation (7), we can use \(D_{ij}\) to calculate the sum of the calling distances of all containers starting from a particular container. Then, we use \(D_{i}\) to calculate the sum of the calling distances between all containers that have a dependency on container \(i\). Finally, we can obtain the sum of the calling distance of all containersthrough D. The smaller the value of D, the faster the microservice response month, and the higher the network resource utilization.
# Scheduling for Media Function Virtualization
###### Abstract  
Broadcasters are building studio architectures based on commercial off-the-shelf (COTS) IT hardware because of advantages such as cost reduction, ease of management, and upgradation. Media function virtualization (MFV) leverages IP networking to transport media streams between virtual media functions (VMRs), where they are processed. Media service deployment in an MFV environment entails solving the VMF-FG scheduling problem to ensure that the required broadcast quality guarantees are fulfilled. In this paper, we formulate the VMF-FG scheduling problem and propose a greedy-based algorithm to solve it. The evaluation of the algorithm is carried in terms of the end-to-end delay and VMF queuing delay. Moreover, the importance of VMF-FG decomposition in upgradation to higher-quality formats is also highlighted.  
virtualization; broadcasting; MFV; NFV; SMPTE +
Footnote †: journal:  
* [2021] 24 June 2021  
* [2021] 240 2  
standards are quite stricter than for networking services; for instance, a packet loss or even excessive delay in the arrival of a packet at the receiver can ruin the viewer experience. Deterministic networking (DetNet) can be exploited to provide end-to-end timing guarantees for the transport of media streams [6]. Specifically, DetNet mechanisms such as cycle specified queuing and forwarding (CSQF) allow scheduling packet transmissions along the network path that can fulfill the real-time requirements [7].  
Media services are represented using a directed graph, referred to as a virtual media function forwarding graph (VMF-FG). The deployment of a media service entails the mapping of the given VMF-FG to the underlying infrastructure; this is referred to as the VMF placement and chaining (VMF-PC) problem. The naive VMF-PC algorithm assumes best-effort networking for media transport between VMFs, and thus does not provide any guarantee on metrics such as packet loss, end-to-end delay, or jitter. Overprovisioning bandwidth for media streams could result in an improvement in these metrics, yet deterministic performance cannot be guaranteed. Therefore, the VMF-FG mapping process needs to incorporate scheduling of VMFs and virtual links to ensure broadcast-quality guarantees.  
In this paper, we first formulate the VMF-FG scheduling problem and then propose a greedy heuristic to solve it. The performance of the heuristic is evaluated by conducting numerical experiments.  
The rest of the paper is structured as follows. The context of the research is presented in Section 2, followed by related works in Section 3. The problem statement and the proposed heuristic are described in Section 4. The evaluation setup and results are described in Section 5. Finally, our conclusions and final remarks are summarized in Section 6.