esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminaries
### REmesh Physical Array Architecture and Logical Topology Reconfiguration  
In an REmesh physical topology, the processor cores are located in a structure of size \(m\times(n+k)\), where \(k\) denotes the number of columns of redundant cores. Figure 3 shows a typical REmesh architecture, featuring a working core size of \(4\times 5\), with an additional column of redundant cores, resulting in a network of \(5\times 6\) routers. The REmesh physical array architecture and logical topology are detailed as follows:  
Figure 2(a) presents the REmesh physical topology, where the last column is designated for redundant cores. Within this REmesh physical architecture, each router is designed to connect with and only support one core. This design facilitates the creation of an efficient row-column formatted inter-chip interconnection network. Each box in the physical array symbolizes a core tasked with executing actual computational operations. The green circle represents the router responsible for packet forwarding and routing. The remaining rectangular boxes in different colors represent normal, faulty, and redundant cores, respectively. Figure 2(b) shows the REmesh logical topology of size \(4\times 4\) obtained by mapping Figure 2(a). There are no faulty cores in this logic topology. It is obtained by replacing the faulty core  
Figure 3: Physical array architecture and logical topology reconfiguration: (**a**) physical array architecture and (**b**) logical topology.  
Figure 2: Two-Dimensional REmesh architecture with redundancy.  
in Figure 3a with a fault-free core. By using the replacement operation, the regularity and completeness of the logic topology can be guaranteed, allowing the system to work even in the presence of faulty cores.  
From Figure 3, it is evident that the logical topology serves as a simplified equivalent of the physical topology. Faulty cores can be logically substituted by redundant fault-free cores located in the rightmost kth column. Since cores in the physical topology correspond to nodes within the same topology, the primary focus of this paper is on designing an efficient mapping strategy from nodes in the physical topology to those in the logical topology [35; 36].
## 4 GPSR Routing Mechanism
### Beaconing Interval Time  
In an ad hoc network, a beacon is a periodic exchange packet that carries node status information such as ID, velocity, position and other information required by routing protocols. In the original GPSR, the nodes are defined with a geographic beaconing mechanism, which advertise beacons for their own information announcements at regular intervals known as beacon interval times (i.e., at fixed beacon rate). When a node obtains a beacon packet from a neighbor node, it makes an entry for this neighbor's information in the neighbor list. In addition, to eliminate synchronization between neighbors' beacons, a maximum jitter equal to 0.5 of Beacon Interval Time (\(BIT\)) has been established for each beacon's transmission. Thereby, the mean inter-beacon interval time is uniformly distributed in the range (0:5 BIT, 1:5 BIT) [22]. Then, in accordance with (1), the node schedules a beaconing timer along the simulation time (\(SimTime\)).  
\[BT=SimTime+BIT+uniform(-1,\ 1)\times maxjitter \tag{1}\]  
where _maxjitter_ denotes the maximum allowable _jitter_.  
If no beacons are received from the X neighbor for a specified timeout neighbor validity (\(TNV\)), then the node is supposed to be out of range and will be removed from the list of neighbors. The \(TNV\) is calculated using Equation (2).  
\[TNV=4.5\times BIT \tag{2}\]
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 4 Routing Algorithms
### IoD Data Security
#### 4.5.1 Random Mobility Data Collection  
In this type of drone data collection, the mobile data collectors move randomly using a Markov model and collect data at the opportunity directly from sensor nodes. Then they transfer data to access points (APs). In [59], an improvement for routing protocol is proposed for mobility of the sink node because the sink's geological location update is restricted to a confined zone in order to reduce energy consumption. This reduces the generation of extensive data traffic due to the sink's movement. On one hand, the on-demand sink discovery exists for nodes, and on the other hand, the nodes are confined into zones. When the sink is not discovered, the zone is incrementally increased and updated until a network broadcast is initiated for the discovery of a possible route [56].
## 1 Introduction
### Aim and Outline  
For this reason, an enhanced image processing method is introduced that improves the sensitivity for detecting and localizing premature flow transitions. The image processing is based on the a priori knowledge about the wedge shapes of the premature flow transitions and the temperature gradient evaluation over both image dimensions. As a consequence of the improved subdivision of the laminar and turbulent flow regime areas, the enhanced flow transition localization enables to quantify the laminar flow reduction (LFR) with an increased accuracy.  
A description of the image processing for flow transition localization in thermographic images is given in Section 2, followed by the definition of the LFR as a figure of merit for the impact of surface disturbances on the boundary layer flow. In Section 3, the implementation of the image processing and the experimental setup of a field measurement on a wind turbine in operation for the thermographic flow visualization process is described. A simulated and measured thermographic image from a 1.5 MW wind turbine are used to verify and validate, respectively, the capabilities of the introduced image processing regarding the accurate flow transition localization and the determination of the LFR in the presence of premature transitions in Section 4. The article finishes with a conclusion and outlook in Section 5.
# The Framework of 6G Self-Evolving Networks and the Decision-Making Scheme for Massive IoT
## 3 The Distributed Task Offloading Scheme for Massive IoT
### System Model  
Considering the cloud-edge-device network framework in Figure 1, we pay attention to the task offloading and the resource allocation problem. We consider the network where one cloud server and \(N\) edge servers, denoted as \(\mathcal{N}=\{1,2,\cdots,N\}\), are deployed. \(K\) users are active and generate \(J\) service requests in the network, which can be respectively denoted as \(\mathcal{K}=\{1,2,\cdots,K\}\) and \(\mathcal{J}=\{1,2,\cdots,J\}\). Assume that one user generates only one service at a certain moment, and let \((D_{j}(t),F_{j}(t),T_{j}(t))\) denote the attributes of the service \(j\) at time \(t\), where \(D_{j}(t)\) is the data size, \(F_{j}(t)\) is the size of computing task, and \(T_{j}(t)\) indicates the maximum tolerable delay of the service \(j\).
## 2 Data Sources and Methods
### Auxiliary Data  
Except for the consideration of ozone precursors and meteorological elements, a multitude of datasets were employed in the development of the surface ozone estimation model tailored for the NCP. The compilation of datasets for the study comprised digital elevation model (DEM) information, NDVI derived from satellite data, land use and cover (LULC) classifications, demographic data reflecting population density (POPU), and the CAMS ozone reanalysis product (CAMS-O3). DEM data with a 30-m spatial resolution were obtained from the China Resources and Environmental Science Data Center (CRESDC). Concurrently, POPU data with a 1 km \(\times\) 1 km spatial resolution from 2019 to 2021, also derived from the CRESDC, were corrected based on the statistical yearbooks of each city for more accurate surface ozone estimation. The LULC data comprised nine ground-level attributes (LULC1-LULC9) from 2019 to 2021, with a spatial resolution of 30 m \(\times\) 30 m. Furthermore, NDVI data provided by the 16-day MOD13C1 product generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite were employed, featuring a spatial resolution of 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) and covering the period from 2019 to 2021 for model construction. To more accurately estimate the surface ozone in the NCP, the 1000 hPa O\({}_{3}\) data from the CAMS reanalysis product were selected as influential factors as they contain surface ozone information. In a treatment similar to CAMS-HCHO, the daily average concentration of CAMS-O\({}_{3}\) was calculated to be consistent with the daily MDA8. For the subsequent analysis of land surface ozone, all gathered explanatory variables, which included data on ozone precursors, meteorological conditions, DEM, POPU, LULC, and CAMS-O\({}_{3}\), were reprocessed to fit a 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) grid. Along the temporal dimension, given that the population and LULC data were gathered on an annual basis, the same set of population and LULC data was applied on a daily basis for every year to correspond with the daily MDA8 measurements. Similarly, a uniform dataset of NDVI values was used consistently for each calendar day over a 16-day period. Monitoring stations that lacked adequate sample sizes were omitted, and subsequently, the daily MDA8 time series data were aligned with the daily datasets of relevant explanatory variables to ensure a synchronized analysis.  
The dataset encompassed information on ozone precursors, meteorological conditions, DEM, NDVI, LULC, and POPU statistics across 133 separate monitoring sites. For each site, the corresponding data pairs were matched based on their geographical location and temporal correlation.
## 3 Theory
The term \(\mathrm{H}_{\mathrm{ii}}^{(\mathrm{j})}\) represents the receptance matrix, which can be determined by applying an excitation force at the internal measurement points \(\mathrm{X}_{\mathrm{i}}^{(\mathrm{j})}\) and measuring the resulting displacements at the same points. Similarly, \(\mathrm{H}_{\mathrm{ic}}^{(\mathrm{j})}\) is determined on isolated substructure j by applying an excitation force at the interface or connection measurement point \(\mathrm{X}_{\mathrm{c}}^{(\mathrm{i})}\) and measuring the resulting response at the internal point \(\mathrm{X}_{\mathrm{i}}^{(\mathrm{j})}\). Due to the symmetric nature of the receptance matrix, commonly referred to as reciprocity, \(\mathrm{H}_{\mathrm{ic}}^{\mathrm{j}}=\mathrm{H}_{\mathrm{ci}}^{\mathrm{j}}\). Assuming n arbitrary subsystems are coupled, the overall equations of motion for the dynamic system can be written as follows:  
\[ \begin{array}{c}\left[\mathrm{M}^{(\mathrm{i})}\quad\dots\quad 0\\ \vdots\quad\ddots\quad\vdots\\ 0\quad\dots\quad\mathrm{M}^{(\mathrm{i})}\end{array} \right] \begin{bmatrix} \dot{\mathrm{X}}^{(\mathrm{i})}\\ \vdots\\ \dot{\mathrm{x}}^{(\mathrm{i})}\end{bmatrix} + \begin{bmatrix}\mathrm{C}^{( \mathrm{i})}&\dots\quad 0\\ \vdots\quad\ddots\quad\vdots\\ 0\quad\dots\quad\mathrm{C}^{(\mathrm{i})}\end{bmatrix}\begin{bmatrix}\dot{ \mathrm{X}}^{(\mathrm{i})}\\ \vdots\\ \dot{\mathrm{x}}^{(\mathrm{i})}\end{bmatrix} + \begin{bmatrix}\mathrm{K}^{( \mathrm{i})}&\dots\quad 0\\ \vdots\quad\ddots\quad\vdots\\ 0\quad\dots\quad\mathrm{K}^{(\mathrm{i})}\end{bmatrix}\begin{bmatrix}\dot{ \mathrm{X}}^{(\mathrm{i})}\\ \vdots\\ \dot{\mathrm{x}}^{(\mathrm{i})}\end{bmatrix} = \begin{bmatrix}\mathrm{f}^{( \mathrm{i})}\\ \vdots\\ \dot{\mathrm{f}}^{(\mathrm{i})}\end{bmatrix} + \begin{bmatrix}\mathrm{g}^{( \mathrm{i})}\\ \dot{\mathrm{g}}^{(\mathrm{i})}\end{bmatrix}  \tag{5}\]  
This equation can be simplified as follows:  
\[[\mathrm{M}]\Big{\{}\dot{\dot{\mathrm{X}}}\Big{\}}+[\mathrm{C}]\Big{\{}\dot{ \mathrm{X}}\Big{\}}+[\mathrm{K}]\{\dot{\mathrm{X}}\}=\{\mathbf{f}\}+\{\mathbf{g}\} \tag{6}\]where \(\{\text{g}\}\) represents the additional unknown interface forces connecting the subsystems, ensuring equilibrium at the interface points. In the frequency domain, the dynamic equilibrium is expressed as follows:  
\[[\text{Z}(\omega)]\{\text{X}(\omega)\}=\{\text{F}(\omega)\}+\{\text{G}(\omega)\} \tag{7}\]  
In matrix notation, this can be expressed as follows:  
\[ \begin{bmatrix}\text{Z}^{(1)}&0&\cdots&0\\ 0&\text{Z}^{(2)}&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\ldots&\text{Z}^{(n)}\end{bmatrix}\begin{bmatrix}\text{X}^{(1)}\\ \text{X}^{(2)}\\ \vdots\\ \text{X}^{(n)}\end{bmatrix} =\begin{pmatrix}\text{F}^{(1)}\\ \text{F}^{(2)}\\ \vdots\\ \text{F}^{(n)}\end{bmatrix}+\begin{pmatrix}\text{G}^{(1)}\\ \text{G}^{(2)}\\ \vdots\\ \text{G}^{(n)}\end{bmatrix} \tag{8}\]  
Next, the assembly boundary conditions between subsystems must be applied. These constraints, along with compatibility equations, must be satisfied to represent the coupling condition. The next section of this paper discusses the approaches for coupling the substructures.
# The Vibration Response Characteristics of Neighboring Tunnels Induced by Shield Construction
###### Abstract
In numerical simulation studies, [PERSON] and [PERSON] (2020) used the two-dimensional numerical software Plaxis to analyze the construction process of twin tunnels and optimize their relative positions. Their findings indicated that soil settlement was minimized when the twin tunnels were horizontally aligned. In the same year, [PERSON] et al. (2021) employed a two-dimensional discrete element method (DEM) to investigate interactions between parallel twin tunnels, revealing that pre-strengthening the first tunnel significantly improved the stability of closely spaced twin tunnels. [PERSON] et al. (2018, 2020) developed a numerical model for twin tunnels that incorporated ground stress balance and shield tunneling. They analyzed how construction parameters, including excavation face support, grouting pressure, and grouting intervals, influenced ground settlement. [PERSON] et al. (2021) utilized boundary element methods in numerical simulations to assess the impact of tunnel vibrations on nearby buildings. These studies have established a foundational understanding of tunnel vibration responses. However, the relatively recent application of shield tunneling in urban subway engineering has resulted in an incomplete and unsystematic theoretical framework for studying vibrations induced by shield construction. [PERSON] et al. (2020) demonstrated that the two-dimensional particle flow method (PFC2D) effectively simulated discontinuities in coarse-grained alluvial soil, producing more accurate results. Since then, PFC has emerged as a crucial tool for investigating tunnel-soil interactions. This study builds on existing vibration theories to further deduce energy variations within tunnel segments during shield construction. The validity of these deductions is confirmed through field monitoring and numerical simulations. Additionally,the vibration response of tunnel lining segments in double-lane shield construction is thoroughly analyzed.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 1 Introduction
Here, node 3 works as PR as well as ST. Figure 1c shows the STNFD transmission, where node 6 (PT) transmits data to node 8 (PR) and PR does not have data to PT but a ST (node 7) has data for PT. Therefore, node 7 (ST) transmits data to PT. In this case, node 6 (PT) also acts as an SR.  
Figure 1: Different types of IBFD transmission: (**a**) BFD, (**b**) destination-based TNFD, (**c**) source-based TNFD. A MAC protocol named AdHoc-FDMAC for the ad hoc network using IBFD wireless communications has been proposed in this paper. The network structure of the proposed MAC is shown in Figure 2, where all the nodes are considered Full Duplex Nodes (FDNs) because of the advantages of FDNs over Half Duplex Nodes (HDNs). A conference paper has been published based on the basic idea of this proposed AdHoc-FDMAC [8]. Only BFD communications were considered in the paper [8]. However, in this paper, both source-based TNFD and destination-based TNFD communications along with BFD communications are considered. In addition, this paper shows different performance analyses, namely probability analysis, throughput analysis and routing time. The major contributions of this paper are given below:  
* An IBFD MAC protocol is proposed for the ad hoc network named AdHoc-FDMAC, where all nodes are FDNs. * This MAC describes all possible types of IBFD communications. * The performance analyses are performed in terms of probability analysis, throughput analysis and routing time. * The throughput of this AdHoc-FDMAC is compared with a recently published ad hoc MAC protocol as well as with the conventional HD communications. The AdHoc-FDMAC significantly outperforms the existing ad hoc MAC that uses IBFD communications. * The simulation result shows that the routing time is significantly lower than that of the conventional FD communications.
## 3 Materials and Methods
### Dataset and Experimental Settings
#### 3.5.2 Experimental Settings  
The segmentation and generation models in this article are trained based on the methods provided by the open-source projects YOLOv5 and Pix2 Pix. The rotation classification model and breast ultrasound report classification model proposed in this article are implemented based on the deep learning framework Pytorch. The GPU model used for training is NVIDIA Tesla K40, with a memory size of 12G. When training the classification network, the image size is adjusted to 224 \(\times\) 224. The optimizer used is Adam (the weight decay rate is \(1\times 10^{-4}\), the momentum is 0.9), the batch size is 12, the learning rate is \(1\times 10^{-3}\), iteration times are set to be 200 and the best-performing parameters are selected using a validation set after each iteration. Finally, the performance of the model was evaluated using a test set to obtain the performance indicators of the model.  
For the generative model, this article mainly evaluates it from two aspects. On the one hand, it evaluates its denoising effect. On the other hand, it compares the performance of breast ultrasound report classification models before and after adding the generative model. For classification models, we use accuracy, recall, AUC and \(F1\) score to evaluate them. \(TP\) (true positive) and \(TN\) (true negative) denote the accurate classification of positive and negative samples, respectively, while \(FP\) (false positive) and \(FN\) (false negative) represent the misclassification of samples. _Accuracy_ is the ratio of correct classifications, the formula for _Accuracy_ is Equation (7):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
_Recall_ is the proportion of true positives among actual positives, the formula for _Recall_ is Equation (8):  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
AUC measures classifier performance, ranging from 0.5 to 1. \(F1\) score is a harmonic mean of precision and recall, the formula for \(F1\) is Equation (9):  
\[F1=\frac{2\times TP}{2\times TP+FP+FN} \tag{9}\]
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 3 System Model for the Navigation System
### Measurement Model of the System
#### 3.2.5 Sonar Measurement Equation  
The sonar sensor and the barometric altimeter are often used to measure the altitude in the MUAV system. Because the sonar sensor has advantages of high precision, narrow measuring range and good stability, it can also meet the demand of indoor or low altitude flight. The barometric altimeter can be easily affected by environment, but it has a wide measuring range. So it can be used in the outdoor or high altitude flight. In some specific environments, these two kinds of sensors can be used in combination to acquire high reliability. The sonar measurement equation is described as Equation (8).  
\[y=kx+b \tag{8}\]  
where \(y\) is the output of the sonar, \(x\) is the true distance, \(k\) represents the coefficient of the first order, and \(b\) is the fixed distance error. The error coefficients \(k\) and \(b\) are acquired by laboratory calibration tests.
# A Large Area Pixelated Silicon Array Detector for Independent Transit In Vivo Dosimetry
## 2 Materials and Methods
### Percentage Depth Dose  
The percentage depth dose (PDD) measurement, along with all other measurements presented, used a 6 MV flattened beam delivered by a Clinac IX (Varian Medical Systems, Palo Alto, CA, USA) linear accelerator. The PDD measurements were performed in Solid Water (SW) (Promis Electro Optic, Wijchen, The Netherlands) with the source to surface distance (SSD) kept constant at 100 cm. The response of the MP987 central pixel irradiated with a field size of 10 \(\times\) 10 cm\({}^{2}\) at each measured depth (between 1.5 and 20 cm) was normalised to its response at d\({}_{\text{max}}\) (1.5 cm depth, reference conditions). The same measurements were made with a Farmer (Scanditronix/Wellhofer FC23-C) ionisation chamber (IC), used as a reference detector.
## 5 CFD in Extrusion Processes
### Prediction of Pressure, Temperature, and Shear Profiles
The practical value of simulating pressure, temperature, and shear profiles is well documented across industrial applications. CFD-based predictions support process optimization by enabling fine-tuning of screw speed, feed rate, moisture content, and barrel temperature to achieve desired structural and functional properties. They inform equipment design by guiding the selection and configuration of screw elements, die dimensions, and heating zones for consistent performance. Scale-up from laboratory to industrial scale--traditionally a trial-and-error task--benefits from CFD's ability to replicate flow, thermal, and stress behavior across different geometries and operating conditions. Furthermore, simulations assist in troubleshooting by revealing processing anomalies, such as recirculation zones, localized overheating, or pressure instabilities, which may otherwise go undetected [100]. Although CFD provides powerful insights, accurate prediction of these key profiles depends on several factors. Reliable rheological and thermophysical property data--measured under relevant shear and temperature conditions--are critical for defining model parameters. Mesh quality, numerical schemes, and solver settings must be carefully selected to resolve sharp gradients and complex boundary layers. Experimental validation using thermocouple data, pressure sensors, or torque measurements remains essential to ensure the credibility of simulation outputs. Advances in real-time CFD integration, machine learning-assisted modeling, and digital twin development are likely to further enhance the role of pressure, temperature, and shear simulations in intelligent extrusion process control [101; 102]. Figure 2: CFD-simulated shear rate distribution in rice-paste extrusion through syringe-type nozzles with diameters of 0.8, 1.0, and 1.2 mm. Total perpendicular deformation in a controlled environment temperature (**a**), and after structure-printing completion dependent on nozzle diameter (**b**). Source: [PERSON]; [PERSON] (2022) [99]. [Reproduced under CC BY 4.0 license from _Processes_. MDPI].