esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The massive multiple-input multiple-output systems (M-MIMO) and orthogonal frequency-division multiplexing (OFDM) are considered to be some of the most promising key techniques in the emerging 5G and advanced wireless communication systems nowadays. Not only are the benefits of applying M-MIMO and OFDM for broadband communication well known, but using them for the application of the Internet of Things (IoT) requires a large amount of wireless transmission, which is a developing topic. However, its high complexity becomes a problem when there are numerous antennas. In this paper, we provide an effective two-stage multiuser detector (MUD) with the assistance of the accelerated over-relaxation (AOR) iterative algorithm and Chebyshev acceleration for the uplink of M-MIMO OFDM systems to achieve a better balance between bit error rate (BER) performance and computational complexity. The first stage of the receiver consists of an accelerated over-relaxation (AOR)-based estimator and is intended to yield a rough initial estimate of the relaxation factor \(\omega\), the acceleration parameter \(\gamma\), and transmitted symbols. In the second stage, the Chebyshev acceleration method is used for detection, and a more precise signal is produced through efficient iterative estimation. Additionally, we call this proposed scheme Chebyshev-accelerated over-relaxation (CAOR) detection. Conducted simulations show that the developed receiver, with a modest computational load, can provide superior performance compared with previous works, especially in the MU M-MIMO uplink environments.  
multiuser massive MIMO (MU M-MIMO); Internet of Things (IoT); 5G (fifth generation); orthogonal frequency-division multiplexing (OFDM); accelerated over-relaxation; Chebyshev acceleration; Chebyshev-accelerated over-relaxation (CAOR) detection Article
## 1 Introduction
Our framework introduces several key innovations: a lightweight blockchain consensus mechanism specifically designed for IoMT networks, an adaptive aggregation function for privacy-preserving federated learning, and intelligent resource allocation through reinforcement learning [13]. As illustrated in Figure 1, EdgeGuard introduces a secure blockchain layer that enables safe collaborative learning while preventing information leakage of patient data. The main contributions of this work are as follows:  
* **Privacy-preserving federated learning architecture:** A novel adaptive aggregation mechanism that enables secure model training across distributed healthcare institutions [14]. This architecture incorporates differential privacy techniques and secure aggregation protocols, ensuring patient privacy while optimizing model performance through quality-aware aggregation. * **IoMT-optimized blockchain consensus:** A lightweight consensus mechanism specifically engineered for resource-constrained medical devices, providing robust security guarantees while maintaining efficiency. This mechanism ensures data integrity and creates immutable audit trails for regulatory compliance. * **Intelligent resource management:*
* Advanced optimization techniques for heterogeneous IoMT environments include the following:
* A dynamic model complexity adaptation based on available computational resources. * Adaptive learning rate scheduling, considering resource constraints. * Quality-aware device selection for optimal federated learning rounds. * Efficient model update compression for bandwidth-constrained scenarios. * **Comprehensive performance framework:** A multi-dimensional evaluation framework that assesses not only diagnostic accuracy but also computational efficiency, communication effectiveness, energy consumption, and fairness across diverse IoMT devices, ensuring practical deployability in real-world healthcare settings. This paper is structured as follows: Section 2 discusses the related work and Section 3 presents EdgeGuard's system formulation, including the system model, assumptions, threat model, problem statement, and design goals. Section 3 describes the proposed EdgeGuard framework in detail. Section 4 explains the operational design and algorithm analysis. Section 5 discusses the experimental setup, results, and analysis, and Section 6 concludes the paper.
## 6 Results: Challenges Related to the Implementation of a Digital Twin  
The future needs, identified during the interviews, that should be addressed to achieve a comprehensive digital twin for geometry assurance, are listed below. They can be divided into four different categories: system level, simulation working process, management issues, and education. The needs are further discussed in Section 7.  
System-Level  
1. The major future needing identified at all the companies is a fully updated 3D digital model developing through all the phases in the product realization process. This is outlined in Figure 7. All activities and changes that affect the geometry of a part should also be brought back to the 3D digital model of the product, securing an always-updated and true geometrical representation of the actual produced part. This is today not the case at the companies in the study.  
The 3D model should be updated when new information is available during the product realization process. Besides changes in geometry, locator positions, tolerances, and inspection point positions should also be linked to and updated in the 3D model.  
Simulation Working Process
2. Assembly models for variation simulations done in the early concept phases should be reused and refined during the planning phase. Today, usually new models are created, due to differences in modeling structures and working procedures. This is not only ineffective, but it can also lead to inaccurate information and misunderstandings due to model differences.
3. The decomposition of final requirements to critical dimensions on the parts, done in the early concept phase, should be reused to a greater extent during inspection preparation in the planning phase.
4. Simulation must be seen as one of the customers to inspection data, and the inspection should be adapted to this. This is necessary to provide high-quality input to the digital model and to achieve a complete digital twin.
5. An individual digital twin of the product should be developed/kept for following-up purposes and for predicting maintenance.
6. \"Virtual trust\" needs to improve.  
Management Issues
2. The design engineers need access to the production inspection data.
3. There should be a generic project manning for all projects to secure that all aspects (including geometry assurance) are fully covered. This is not related to the digital twin concept but is included for completeness.
4. Communication within the companies needs to improve. The design engineers are seldom willing to change their concepts to fit production capacity.
5. People's willingness to change is sometimes low and needs to improve.
## 1 Introduction
Moreover, the InSAR technique shows its potential in large-scale deformation monitoring, e.g., on a regional scale or national scale with the increased availability of free-of-charge and commercial SAR datasets [22; 23; 24]. Although the conventional ground-based methods including global positioning systems (GPS) and leveling can provide precise measurements, the lack of such measurements makes it difficult to verify the InSAR measurements. Numerous studies have been carried out for the land subsidence monitoring over YRD in recent years [9; 10; 11; 12; 25; 26]. However, a comprehensive and multi-dimensional surface deformation mapping over the YRD has not been done and the causal factors of the geohazards have not been fully explored. Moreover, there is a lack of knowledge of the spatial extent, magnitude, and temporal evolution of land subsidence and the causal factor analysis in the entire Shandong peninsula, which impedes the governments in making precise decisions concerning hazard prevention and mitigation. In this study, comprehensive and detailed surface deformation measurements over the entire Shandong peninsula are conducted. A time series InSAR technique with multi-satellite and multi-track SAR datasets from 2016 to 2019 is employed. Two-dimensional (2-D) ground deformation in YRD is retrieved by the Multidimensional SBAS (MSBAS) technique. The temporal evolution of land subsidence and spatial deformation distribution of localized subsidence zones are also revealed. Finally, the causal factors associated with the localized subsidence are discussed.
## 4 Design of the PV Voltage Dynamics
### Implementation of the Complete Control Structure  
An efficient implementation of the complete control structure requires reformulating the switching function (41) to reduce the mathematical operations. Therefore, the mathematical expression of the switching function is condensed as \(\Psi=i_{1}\cdot(2-d)+i_{2}\cdot(1-d)-i_{pv}-i_{r}\). Moreover, from (22), it is calculated that \((2-d)=1+\frac{v_{pv}}{v_{b}}\) and \((1-d)=\frac{v_{pv}}{v_{b}}\). Using those simplifications, the block diagram of the complete control structure is presented in Figure 11, where the switching circuit was previously designed in Figure 9. The block diagram shows the variables needed to be measured: the PV voltage is used in the calculation of the P&O algorithm, voltage controller, and SMC; the PV current is used in the calculation of both the P&O algorithm and SMC; while the output voltage \(v_{b}\) and the inductor currents (\(i_{1}\) and \(i_{2}\)) are used for the SMC calculation.  
Such an implementation of Figure 11 can be performed using a digital embedded processor, such as the TMS320F28335 from Texas Instruments [29], which enables to implement the complete block diagram into a single device. However, the calculation of \(\Psi\) can also be performed using analog hardware: the additions, subtractions, integrations, and static gains (\(k_{p}\) and \(k_{i}\)) can be implemented with operational amplifiers, while the static value can be set with a Zener diode as depicted in the switching circuit of Figure 9. The multiplication and division operations can be implemented with analog multipliers and dividers, which are available as integrated circuits such as the AD533 [30], RC4200 [31], and HA-2557 [32]. It is important to note that the P&O algorithm requires a digital device;thus, calculating \(\Psi\) inside the digital processor is a much more convenient solution since no additional hardware is required.
# Building Information Modelling Facility Management (BIM-FM)
## 6 Barriers Affecting the Implementation of BIM-FM
### Lack of Alignment with FM  
The lack of FM alignment with other sectors, particularly the BIM and AEC sectors, may be attributed to a lack of understanding of FM by other sectors. A common misconception of FM held by the AEC sector appears to be that too much information is created during the design and construction phases of an asset. Therefore, the AEC sector assumes it must be reduced to a manageable amount for FM at the handover stage to enable leaner data storage [74]. This mindset may be due to the fact that having surplus information and models of insufficient quality causes functional error when the information is exported from BIM to FM [75], and the easiest fix is to reduce the amount of information. However, the lack of standardisation regarding information requirements for BIM-FM means the resulting models often fail to contain important information whilst simultaneously containing redundant information [64]. In this respect, it is worth remembering that management decisions are not (and should not be) made in isolation. The interrelatedness of management data and decisions is evidenced by the Global Forum on Maintenance and Asset Management [39], who identify thirty-nine subject areas of AM and provide some examples of their (non-constant) interrelationships. The complexity of decisions is also acknowledged by [PERSON] and [PERSON] [76], who used fuzzy Multi-Criteria Decision Making (MCDM) [77] to account for the range of qualitative and semi-quantitative data that they suggest informs maintenance decisions. A practical BIM-FM example is provided by [PERSON] et al. [42], who created a platform for BIM-FM of a cultural heritage asset for onsite condition assessment and management. To achieve this aim, the BIM-FM system included condition information, financial information, and the impact of potential work on the cultural value. Whilst limited to a single use case, it demonstrates that a single FM activity requires multiple data inputs.  
There is abundant information generated at the construction stage, which is theoretically useful for FM. However, the associated high cost of manipulating, storing and managing these data should be justified [6]. Consequently, it appears more appropriate to suggest that the information from the design and construction phase must be filtered to the small selection relevant to FM and supplemented with additional data unique to FM. This implies that the recommendations of works, such as that by [PERSON] et al., [PERSON] and [PERSON] and [PERSON] et al. [21; 49; 59], which suggest that the general lack of information for FM in BIM should be overcome by greater FM involvement in the design and construction phase are once again oversimplifying and misunderstanding the situation. There also needs to be a greater effort to integrate BIM and FM sectors. [PERSON] et al. [11] found that even with FM involvement at the design stage, for information requirement development, the process was limited by the FM practitioners' lack of BIM knowledge.
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 6 Conclusions  
In this paper, we proposed a Location-Based Deferred Broadcast (LBDB) scheme for interest forwarding in ad-hoc NDN. Location information of the forwarding node and the data sources are used to derive waiting time for the timers that set up the forwarding priority and reduce broadcast collisions. As such, the proposed scheme selects the forwarding nodes to reduce data delivery delay by reducing transmission overhead. The effectiveness of timer mechanism is validated through ORBIT testbed experimentation. The LBDB is fully implemented in V-NDN network architecture using ndnSIM. The simulation results show that the use of two timers has proven to improve the overhead, the average number of hops and delay while maintaining an average satisfaction ratio. Such an improvement can be particular important for CAVs in live-death situations in terms of the data acquisition delay. The future work will extend this method for mobile scenarios in IoT where energy consumption is a priority.  
Conceptualization, M.K. and X.H.; Data curation, M.K.; Formal analysis, M.K.; Funding acquisition, X.H.; Investigation, M.K.; Methodology, M.K.; Project administration, X.H.; Resources, X.H.; Software, M.K.; Supervision, X.H.; Writing--original draft, M.K.; Writing--review & editing, X.H.  
[PERSON]'s work is supported partly by the National Science Foundation under Grant No. 1719062. Any opinions, findings, and conclusions or recommendations expressed in this article are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.  
The authors declare no conflict of interest.
## 2 Contributions of This Research  
In this study, we proposed the MIMO-COOK modulation, which reduces the bit error rate (BER) and improves the data rate as compared with the conventional COOK modulation. Our scheme has the following advantages:  
* Frame rate variation support: frame rate variations have an important effect on the OCC system; packets can be missed when decoding data on the receiver side. Most people believe that the camera frame rate is constantly similar to the camera specifications (e.g., 30 fps or 1000 fps). It is very difficult to synchronize the transmitter (Tx) and the receiver (Rx) depending on the technical parameters of the different cameras. The sequence number (SN) will improve the system performance by checking whether it has a frame rate larger than the packet rate of the transmitter.
* Detection of missing packets: we used two consecutive images captured by the camera to easily detect each missing packet by comparing two SNs when the length of the SN exceeded a certain value. A comparison of the SN and Ab bit to detect missing packets in the COOK scheme is presented in Section 4.
* Data merger algorithm: in our experiment, we proposed this algorithm for each data sequence by compacting the packages into it in the exact order. In this scheme, we used the SN to combine the data packets such as the Ab bit.
* Improve system performance as compared with the conventional COOK: a matched filter is the optimal linear filter for maximizing the SNR. For determining the on/off thresholds in images, the matched filter is superior to the conventional zero-crossing filter. The SNR is maximized;therefore, the communication distance also is increased, and we can achieve the desired BER. This will be shown in Section 3.3.
* Improved data rate: using multi-LEDs, the data rate will be increased as compared with the conventional COOK. Multi-LEDs are used to transmit data; therefore, RoI signaling is chosen to detect quickly the light sources in images corresponding to each LED.
* Improve the communication distance: by using the matched filter to detect the start of frame and decode data, the communication distance is improved than the conventional COOK (using Zero Crossing) because of its SNR improvement.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 5 The Calibration of Extrinsic Parameters
### Solution of Extrinsic Parameters  
According to Expressions (19) and (21), for PTZ cameras conform to Figure 1b, when the horizontal rotation of the left and right cameras is \(\alpha\) degrees, the pitch rotation of the left and right cameras is \(\beta\) degrees, and the zoom value is \(z\). The rotation and translation matrices of the camera coordinate system relative to its original position are:  
\[\begin{split}\textbf{R}_{rot}^{l}=\textbf{R}_{v-\alpha}^{l} \textbf{R}_{h-\beta}^{l}\\ \textbf{T}_{rot-z}^{l}=\textbf{R}_{v-\alpha}^{l}\textbf{T}_{h- \beta}^{l}+\textbf{T}_{v-\alpha}^{l}+\textbf{R}_{v-\alpha}^{l}\textbf{R}_{h- \beta}^{l}\textbf{T}_{z}\\ \textbf{R}_{rot}^{r}=\textbf{R}_{v-\alpha}^{r}\textbf{R}_{h- \beta}^{r}\\ \textbf{T}_{rot-z}^{r}=\textbf{R}_{v-\alpha}^{r}\textbf{T}_{h- \beta}^{r}+\textbf{T}_{v-\alpha}^{r}+\textbf{R}_{v-\alpha}^{r}\textbf{R}_{h- \beta}^{r}\textbf{T}_{z}\end{split} \tag{22}\]For PTZ cameras conforming to Figure 1c, the rotation and translation matrices are:  
\[\begin{array}{l}\mathbf{R}_{rot}^{l}=\mathbf{R}_{h-\beta}^{l}\mathbf{R}_{v- \alpha}^{l}\\ \mathbf{T}_{rot-z}^{l}=\mathbf{R}_{h-\beta}^{l}\mathbf{T}_{t-\alpha}^{l}+ \mathbf{T}_{h-\beta}^{l}+\mathbf{R}_{h-\beta}^{l}\mathbf{R}_{v-\alpha}^{l} \mathbf{T}_{z}\\ \mathbf{R}_{rot}^{r}=\mathbf{R}_{h-\beta}^{r}\mathbf{R}_{v-\alpha}^{r}\\ \mathbf{T}_{rot-z}^{r}=\mathbf{R}_{h-\beta}^{r}\mathbf{T}_{v-\alpha}^{r}+ \mathbf{T}_{h-\beta}^{r}+\mathbf{R}_{h-\beta}^{r}\mathbf{R}_{v-\alpha}^{r} \mathbf{T}_{z}\end{array} \tag{23}\]  
According to the definition of extrinsic parameters (Expression (2)) and the analysis in Section 3.2, the extrinsic parameters of dual-PTZ cameras are:  
\[\begin{array}{l}\mathbf{R}^{l-r}=\mathbf{R}_{0}^{l-r}(\mathbf{R}_{rot}^{r}) ^{-1}\mathbf{R}_{rot}^{l}\\ \mathbf{T}^{l-r}=(\mathbf{R}_{rot}^{r})^{-1}(\mathbf{R}_{0}^{l-r}\mathbf{T}_ {rot}^{l}+\mathbf{T}_{0}^{l-r}-\mathbf{T}_{rot}^{r})\end{array} \tag{24}\]  
where \(\mathbf{R}_{0}^{l-r}\) and \(\mathbf{T}_{0}^{l-r}\) are the rotation matrix and the translation vector in the initial position, respectively, which can be easily calibrated with [PERSON]'s method.
# Design of Chaotic Interleaver Based on Duffing Map for Turbo Code
## 2 Parallel Concatenated Convolutional Codes  
In 1993, [PERSON] et al. [1] introduced the parallel concatenated convolutional codes (PCCC) with interleaver and named them turbo codes. These codes are based on two recursive systematic convolutional (RSC) encoders in parallel, but an interleaver precedes the sec ond RSC. The binary information sequence of \(L\) length is expressed by \(u=(u_{0},\ \ldots,\ u_{L-1})\) in the first coder input, while \(u\prime=(u\prime_{0},\ldots,\ uv_{L-1})\) describes the permuted version of the same sequence in the second coder input. Meanwhile, \(c^{1}=(u,\ p^{1})\) and \(c^{2}=(u\prime,\ p^{2})\) are code words from the first and second components codes of the turbo code, respectively. The length of the codeword for the first and second component code is \(N_{1}\) and \(N_{2}\), respectively.  
Figure 1 presents the formation of the output code word \(c\) based on the concatenation in Equation (1), omitting the permuted information symbols \(u^{\prime}\).  
\[c=\left(u,\ p^{1},\ p^{2}\right) \tag{1}\]  
The rate of the resulting code \(R\) is given by Equation (2).  
\[R=\frac{L}{N_{1}+N_{2}-L} \tag{2}\]  
The concatenation rule of the interleaver \(\pi\) is defined by Equation (3):  
\[\pi=(\pi(0),\ldots\pi(L-1)) \tag{3}\]  
where \(\pi(i)\), \(i=0,1,\ldots L-1\) corresponds to the \(0,1,\ldots L-1\) indexes.  
The relation between \(u_{i}\) and \(u^{\prime}_{i}\) is given by Equation (4):  
\[u\prime_{\pi(i)}=u_{i},\ \forall\ i=0,\ 1,\ \ldots,\ L-1. \tag{4}\]
# Post-Disaster Recovery Monitoring with Google Earth Engine
## 2 Materials and Methods
### Methods  
The developed approach for post-disaster recovery assessment has three main steps (Figure 2): (i) land cover classification of the pre-disaster, event time, and post-disaster images, (ii) change detection of pre-disaster and event time classified images to obtain the damage map, and (iii) change analysis of the post-disaster classified images and the damage map to obtain the recovery maps at T2-T4. Generating the land cover maps from the Landsat 7 and 8 satellite images with GEE consisted of three main stages: image composite generation, image classification, and accuracy assessment.