esa-sceva/satcom-chunk-collection huggingface dataset:
# FRP Bridges in the Flanders Region: Experiences from the C-Bridge Project
## 6 Structural Analysis
### Comparison with Structural Engineering Calculation Software  
In general, it can be said that the analytical calculation tool for the global analysis of an FRP bridge deck provides a good approximation of the deflection, the first natural frequency, and the stresses in the top and bottom flanges and webs. However, if local phenomena have a significant influence on the design, it may be necessary to switch to structural engineering software, which can be used to calculate the effects of the structural and material orthotropy on the deflection, especially in the transverse direction of the bridge deck. In what follows, the 2D calculation example from [41] is extended to a 3D analysis using a structural engineering software to assess the 3D effect, which is omitted in the analytical 2D method described in [41].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Calculation Note** & **Calculation Tool** & **Calculation Tool (Modified)** \\ \hline \multicolumn{4}{c}{Laminate properties} \\ \hline Longitudinal stiffness flanges, Ex [GPa] & 30.74 & 29.55 & 27.93 \\ Transverse stiffness flanges, Ey [GPa] & 14.08 & 13.23 & 14.16 \\ Shear modulus flanges, Gxy [GPa] & 4.39 & 4.38 & 4.63 \\ Longitudinal stiffness webs, Ex [GPa] & 15.74 & 17.86 & 15.24 \\ Transverse stiffness webs, Ey [GPa] & 15.74 & 17.86 & 15.24 \\ Shear modulus webs, Gxy [GPa] & 5.69 & 6.67 & 5.69 \\ \hline \multicolumn{4}{c}{Checks SLS} \\ \hline Natural frequency unloaded, ft0, unload, ft [Hz] & 2.61 & 3.37 & 3.28 \\ Deflection self-weight, wSW, ft [mm] & 39.00 & 43.00 & 39.50 \\ Deflection distributed load, wLC3, ft [mm] & 21.10 & 51.94 & 19.45 \\ Deflection service vehicle, wLC4, ft [mm] & 17.70 & 10.89 & 11.65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the calculation note and the (modified) calculation tool for the double-sided clamped FRP footbridge in Mortsel.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 2 System Model
### Multi-User Detector  
The receiver at the BS employs the ZF multiuser detector that performs a linear combination of the received samples in \(\mathbf{y}\) using a matrix \(\mathbf{W}\) of dimensions \(U\times A\), such that interference between users is completely eliminated, even though this may increase the noise variance. Thus, the ZF detector chooses \(\mathbf{W}\) so that \(\mathbf{W}\mathbf{H}=\mathbf{I}\), where \(\mathbf{I}\) is the identity matrix.  
Note that \(\mathbf{W}\) exists when \(\mathbf{H}\) is not range-deficient, that is, when \(A\geq U\). Further, \(\mathbf{W}\) exists when the columns of \(\mathbf{H}\) are linearly independent. This condition is fulfilled in the system model as long as the separation of the antennas in the BS and the random location of the users in the cell guarantees independent channel gains. (Theoretically, an antenna spacing of \(\lambda/2\) guarantees independent channel gains, where \(\lambda\) is the signal wavelength [5]). When \(A=U\), \(\mathbf{H}\) is a square matrix, and in that case the solution of the ZF criterion is unique and is \(\mathbf{W}=\mathbf{H}^{-1}\). On the other hand, when \(A>U\), there is an infinite number of matrices \(\mathbf{W}\) that satisfy that \(\mathbf{W}\mathbf{H}=\mathbf{I}\). In these cases, the ZF detector uses a matrix \(\mathbf{W}\) such that \(\mathbf{W}\mathbf{H}=\mathbf{I}\), which also minimizes the root mean square error, that is, \(\mathbb{E}[\|\mathbf{W}\mathbf{y}-\mathbf{s}\|^{2}]\)[36].  
Since \(\mathbf{H}\) presents sufficient statistics [4], \(\mathbf{W}\) can be decomposed into the product \(\mathbf{W}=\mathbf{X}\mathbf{H}^{H}\), where \(\mathbf{X}\) is a dimensional matrix \(U\times U\) to be determined next. Thus, the restriction \(\mathbf{W}\mathbf{H}=\mathbf{I}\) becomes \(\mathbf{X}\mathbf{H}^{H}\mathbf{H}=\mathbf{I}\). Since \(\mathbf{H}^{H}\mathbf{H}\) is invertible, it can then be determined that \(\mathbf{X}=(\mathbf{H}^{H}\mathbf{H})^{-1}\). Therefore,  
\[\mathbf{W}=\mathbf{H}^{\dagger}=(\mathbf{H}^{H}\mathbf{H})^{-1}\mathbf{H}^{H}, \tag{6}\]  
which is the Moore-Penrose pseudo-inverse matrix of \(\mathbf{H}\). If \(\mathbf{H}\) is square, then \(\mathbf{W}\) is reduced to \(\mathbf{H}^{-1}\).  
In the BS receiver, the linear transformation can be implemented via a filterbank [39]. For the presented scenario, the output of the ZF detector before the demapper (a structure used in the receiver to estimate the transmitted symbol based on the minimum distance criterion [36]) is obtained by applying  
\[\mathbf{z}=\mathbf{W}\mathbf{y}. \tag{7}\]
# A Design for SDN-Based Identifier-Locator Separation Architecture on IoT Networks
## Related Work
### Host-Based Identifier-Locator Separation Schemes  
For separating the ID and LOC roles of an IP address, several protocols have been proposed. These protocols are divided into host-based protocols such as Host Identity Protocol (HIP), shim6, and Mobile Oriented Future Internet (MOFI), and network-based protocols such as the Locator Identifier Separation Protocol (LISP) and DHT-based scheme [5; 6; 8; 16; 17; 18].  
HIP, a representative host-based ID-LOC separation protocol, introduced new namespaces for separating the ID and LOC of a host. It suggests to add a host identity layer between the IP layer and the transport layer. The host identity layer communicates with the upper layer using a host ID as an identifier and a lower layer using an IP address for the locator. This layer maintains the mapping information between identifiers and locators [5]. Shim6 provides site multihoming by Internet Protocol version 6 (IPv6) intermediation. It suggested a multihoming shim layer called I3 shim within the IP layer for the local agility with failover capabilities. The basic concept of Shim6 is similar to HIP, but it does not use a special namespace such as a host ID and locator in HIP. A host of Shim6 allows for multiple IPv6 address prefixes to continue existing communication [6]. MOFI also uses a host ID (HID) for ID and an LOC for a locator. However, MOFI uses two kinds of LOCs as a locally routable LOC that must only be locally unique in an access network (A-LOC) and an LOC that is used in the backbone network (B-LOC). Each access router (AR) within MOFI operates the LOC translation between the A-LOC and B-LOC. For the maintenance of HID-LOC mapping, the AR includes a local mapping controller (LMC) with a hash table and HID-LOC register (HLR). The HID-LOC mapping information are managed in ARs in a distributed manner [16; 17].  
The weakness of host-based protocols is that every host has to modify its protocol stack. It makes the deployment of hosts difficult in a network. Additionally, they need a centralized mapping server to the ID-LOC mapping, so it may cause a single point of failure [9].
# Automated Real-Time Assessment of Stay-Cable Serviceability Using Smart Sensors
## 1 Introduction
With vibration control, a serviceability assessment method was proposed using the first-passage failure probability based on the Korean design code, which specifies a range of acceptable amplitudes for cable vibration [4]. When the vibration of the subject cable was beyond the acceptable vibration level in terms of displacement, it was regarded as a stay-cable serviceability failure. This method is not appropriate for the real-time assessment of cable serviceability because the serviceability is determined based on the probability of serviceability failure during a given period of time. Another previous work evaluated the serviceability of footbridges under human-induced vibrations using long-term measurement data [25]. The serviceability of the bridge was considered a failure when the lateral and vertical accelerations of the structure exceeded the limits of comfortable acceleration provided by the guidelines. While this study demonstrates the serviceability assessment based on vibration data, real-time serviceability monitoring was not considered. Accordingly, in the present study, an automated real-time cable serviceability assessment system is developed using wireless smart sensors, which can monitor the serviceability of stay-cables effectively. Based on the design code from the United States [16], the serviceability failure of a stay-cable is defined as the case when the displacement of the cable in mid-span exceeds twice the diameter of the stay-cable with upper and lower bound. The serviceability assessment system for stay-cables proposed in this study is developed using the Raspberry Pi platform, which is an open-source single-board computer with low cost and low power consumption. The system features embedded on-board computation, including the acquisition of cable response using the MEMS accelerometer, estimation of displacement from the measured acceleration, serviceability assessment, and monitoring through wireless communication. Bridge inspectors can command the system through wireless communication, prompting it to send information related to the serviceability status of the stay-cable back to the bridge inspectors. A series of laboratory tests are conducted to verify the performance of the developed system. The rest of this paper is composed of four parts. First, the displacement estimation method usingacceleration data proposed by [PERSON] et al. (2019) is explained. Then, the developed wireless automated real-time cable serviceability assessment system is introduced in terms of hardware/software and gateway node/leaf node depending on its functions. Thereafter, the results from laboratory tests conducted to verify the performance of the assessment system are presented.
# Isolation of Circulating Tumor Cells from Glioblastoma Patients by Direct Immunomagnetic Targeting
## 3 Results
### Immunomagnetic Capture of GBM Cells  
To confirm that both MCAM and MCSP were suitable immunomagnetic targets for GBM cell isolation, we used LN-229 cells, due to MCAM and MCSP co-expression as determined by flow cytometry. Individually, targeting MCAM for cell isolation performed slightly better than MCSP targeting (26% versus 20% recovery) but there was a clear advantage of targeting both antigens in combination, which captured approximately 50% LN-229 cells (Figure 3).  
Figure 2: Glutamate aspartate transporter (GLAST) expression in GBM cells.  
The indicated antibodies were used to immunomagnetically recover LN-229 cells. Data from at least n = 2 independent experiments are presented. Horizontal error bars: mean range.  
We then tested our method for GBM-CTC isolation on 15 blood samples from 13 patients (samples from two patients were included at 2 independent time points approximately 3 months apart). The amount of blood available for CTC isolation was 27 mL (3 \(\times\) 9 mL) per patient. We successfully isolated 1-8 CTCs from 60% (9/15) of GBM patient blood draws when targeting the combination of MCAM and MCSP for CTC isolation (Figure 4A). Two-sided Fisher's exact test established significance (\(p\)-value = 0.0379) in isolating cells that are positive for GLAST and/or GFAP from GBM patients vs healthy blood donors. Importantly, a comparison of CTC identification by GFAP and GLAST staining across all patient samples confirmed that the combination of both identification antibodies increased CTC detection (Figure 4B,C).  
No correlation between CTC positivity and patient progression-free survival or overall survival was found in the small patient cohort (Figure 5).  
Figure 4: CTC isolation from GBM patient blood. Fifteen 27 mL patient blood samples from 13 GBM patients (P1-P13) were analyzed for GBM-circulating tumor cells (CTCs) using combined immunogenic targeting of MCAM and MCSP. CTCs were detected by \(\alpha\)GFAP and \(\alpha\)GLAST probing. (**A**) CTC counts for 13 patients/15 samples (patients with repeat samples are indicated -2). The same isolation protocol was used for 5 healthy donor blood samples (H1-H5) and detected no false positive “CTCs”. (**B**) Typical identification of a CTC (arrow) by nuclear staining (Hoechst), GLAST (green) and GFAP (red) positivity as well as lack of CD45 (pseudo color purple). (**C**) For the nine GBM-CTC positive patient blood draws, the mean of CTCs detected by either \(\alpha\)GFAP or \(\alpha\)GLAST probing alone or detected by double positivity is presented. Columns indicate the average and error bars standard deviations. * indicates significance (\(p\leq 0.05\)).  
Figure 3: GBM cell capture.  
CTC numbers were quantified for 13 patients and follow up progression-free survival (PFS) and overall survival (OS) were determined for each patient and are presented for CTC positive (red) and CTC negative patients (blue) as Kaplan-Meier graphs.
## 2 Basic Theory of Rheology  
The rheological models most commonly used to describe the fluidity of fresh cement paste are the Bingham and Herschel-Bulkley (H-B) models. In this investigation, the steady-state rheological parameters of fresh cement paste were obtained according to an RS-SST rheometer (Brookfield Company), then the values of these parameters were fitted based on Bingham, modified Bingham (M-B) and H-B models to analyse the rheological properties of fresh cement paste after CO\({}_{2}\) absorption. The corresponding basic rheological models are briefly introduced as follows.  
(1) Bingham model:  
The Bingham model (Expression (1)) is a relatively common rheological model used to describe the fluidity of fresh cement-based materials. If the external forces that the fluid bears are less than the yield stress, the fluid is characterised by plastic flow; otherwise, the fluid is characterised by viscous flow.  
\[\tau=\tau_{0}+\eta\gamma \tag{1}\]  
(2) Modified Bingham (MB) model:  
The Bingham model is an ideal model, while the MB model can more accurately characterise the rheology of cement-based materials. Its expression is  
\[\tau=\tau_{0}+\eta\gamma+\mathrm{c}\gamma^{2} \tag{2}\]  
(3) Herschel-Bulkley (H-B) model  
The H-B model is often used to describe the fluidity of fresh concrete, cement pastes and particle-containing suspensions. Compared with the Bingham model, the H-B model is more accurate. According to the H-B model, the plastic viscosity (PV) of fluid is mainly influenced by shear stress, which is mainly modelled by variations in a power exponent, usually denoted as \(n\). When \(n<1\), the shear stress decreases with increases in PV, thus leading to shear thinning. When \(n=1\), the fluid is characterised as a Bingham fluid. When \(n>1\), lower viscosity is obtained with higher shear stress at a lower shear rate, which results in shear thinning; and higher viscosity is obtained with higher shear stress at a higher shear rate, which results in shear thickening. The H-B model is expressed as follows:  
\[\tau=\tau_{0}+\mathrm{K}\gamma^{n} \tag{3}\]  
Rheological curves of the three rheological models are shown in Figure 1.
## 1 Introduction
### Path Planning with Unknown Obstacles  
Aside from the planned path for a HAUV, some unknown obstacles, such as animals, floating trash, and branches, could appear in real-time missions. As a consequence, a path replanning method is a requirement for an HAUV path planning framework. To replan a path, searching for a new collision-free spot near the obstacle in question and inserting it into the original path would be helpful. Finding nearby collision-free spots can be generalized as a k-nearest neighbors (KNN) problem. Our method solves the problem by reducing the KNN to a nearest neighbor search (NNS). The concept of KNN was firstly presented by [PERSON] and [PERSON] in 1951 [41]. Later, KNN was widely used in data mining, computer version, agriculture, etc. Many studies have been performed to prove the efficiency of the results of KNN algorithms [42; 43; 44]. When reduced to NNS, solutions are mainly based on three methods: space segmentation, hash algorithm, and graph theory. Space segmentation by trees and cluster algorithms perform well. A KD tree can be quickly constructed and used to search for a target (\(O(logN)\)) [45; 46], but inserting new elements into a KD tree may lead to unbalance. Cluster algorithms, k-means, GNAT, the anchor hierarchy, the cover tree, and spill-tree have been used to solve that problem, but these algorithms did not show decisive advantages over search trees [47]. In 1998, [PERSON] and [PERSON] presented LSH (locality sensitive hashing) [48], which can be used to cope with tremendous data. In recent years, many studies based on graph theory have been performed. [PERSON] et al. [49] built a neighborhood graph for data subsets to achieve efficient and accurate KNN graph construction.
## 3 Results and Discussion
### Pre-Classification Stage  
This stage could be performed using two types of architectures: multi-class and binary, which are explained below:  
1. Multi-class (\(q_{2}\) architecture). Two groups of features, \(c_{3}\) and \(c_{4}\), are used to decide if the current chromosome belongs to one of the three groups.
2. Binary (\(q_{3}\) architecture). The groups of features, \(c_{3}\) and \(c_{4}\), are used to decide if a chromosome: 1. Belongs to group 1 (G1) or does not belong to group 1 (NOTG1). 2. Belongs to group 2 (G2) or does not belong to group 2 (NOTG2). 3. Belongs to group 3 (G3) or does not belong to group 3 (NOTG3).  
Figure 7(a) shows the results of the multi-class classifiers. The accuracy for all these classifiers was close to 90%. In the other hand, the results of the binary classifiers are shown in Figure 7(b)-d, where it can be observed that most of the tested classifiers obtained accuracies close to 90%, except the SMO classifier in group 2, where non-optimized hyperparameters were used. In the other hand, the results of the binary classifiers are shown in Figure 7(b)-d, where it can be observed that all of the classifiers obtained accuracies close to 90%. Although, the best results were obtained using the \(q_{3}\) architecture (binary classification) and the feature set \(c_{3}\). It is worth noting that classifier with the best accuracy was the MLP.  
The purpose of this experiment was to pre-classify the chromosomes into three different groups, working with the feature sets \(c_{3}\) and \(c_{4}\). The group \(c_{3}\) is a vector of 131 features, while the group \(c_{4}\) is conformed of only 3. It was observed that the best results were obtained using the feature set \(c_{3}\), both in the binary and multiclass classifiers. The binary and multiclass classification schemes were compared  
Figure 8: Experiments 2 and 3. Pre-classification using multiclass classifiers (experiment 2) and the \(q_{2}\) architecture, and binary classifiers and the \(q_{3}\) architecture (experiment 3). (**a**) Pre-classification using multi-class classifiers (G1, G2, and G3). (**b**) Binary group 1, conformed by chromosomes 1 to 5. (**c**) Binary group 2, conformed by chromosomes 7 to 15. (**d**) Binary group 3, conformed by chromosomes 16 to 24.  
using the MLP classifier, since it gave the best results in both schemes. The minimum and maximum accuracies of both schemes are compared in Figure 9. The minimum and maximum percentages for the binary scheme are 73.16% and 98.13%, while the results for the multi-class scheme are 73.10% and 64.94%, respectively. This test showed that the binary classifiers obtained higher accuracies.
# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 6 Conclusions  
In this paper, we present a reputation-based decentralized spatial crowdsourcing framework for vehicular networks. Generated by historical behaviors and interactions, reputation can solve problems regarding truthfulness and continuity, and motivate vehicle users to provide high-quality crowdsourcing of road conditions. Next, the honesty behavior reputation update algorithm and low-reputation miner screening algorithm for the pool mining are utilized to improve the trustworthiness and efficiency. Through simulation study, the proposed method is shown to be capable of optimizing the revenue of miners and motivating high-quality crowdsourcing tasks of road conditions under a limited budget.  
Although the proposed blockchain-crowdsourcing model can improve the security performance and efficiency of the system, there are still some challenges to be overcome. For example, the threat of attacks (e.g., 51% attack and withholding block attack) brought about via rapid derivation of the chains; the balance of profit and consumption of joining the crowdsourcing tasks; and the synchronization of cross-chain transactions and currencies caused by fast-driving EVs and complex crowdsourcing transactions. These also represent the crucial points currently limiting the application of blockchain in vehicular networks. In the future, we will also work on optimizing the model to address the above issues.  
Conceptualization, W.G. and Y.S.; methodology, W.G. and Y.S.; software, W.G. and Y.S.; validation, W.G., Z.C. and Y.S.; formal analysis, W.G., Z.C. and Y.S.; investigation, W.G., Z.C., Y.S., X.G., T.H., J.L. and Y.L.; writing--original draft preparation, W.G. and Y.S.; writing--review and editing, W.G., Z.C. and X.G.; supervision, Z.C., X.G., T.H., J.L. and Y.L.; funding acquisition, Z.C. and X.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by NSFC under Grant 62071105, Innovation Capability Improvement Plan Project of Hebei Province (22567626H), CSC funding (202108130129), the joint project of China Mobile Research Institute& X-NET (2022H002), the 131 project, the fund project of Intelligent Terminal Key Laboratory of Sichuan Province (SCITLAB-1015).  
Not applicable.  
The authors declare no conflict of interest.
# BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading
## 7 Discussion
### Rollups  
Rollups are a popular development of the Layer 2 scaling strategy, which performs calculations off-chain, rolls many transactions up into a single batch, and sends it to the Ethereum Mainnet or its compatible chains (Layer 1) in a single action. They significantly decrease associated transaction processing times and gas fees and increase the system throughput simultaneously. Currently, there are two major types of rollups [46] used for scaling Ethereum: Zero-Knowledge Rollups (ZK Rollups), e.g., zkSync, StarkNet, Scroll, Polygon zkEVM, and Taiko, etc., and Optimistic Rollups, e.g., Arbitrum, Optimism, Metis Andromeda, and Boba Network, etc.  
The main difference between ZK and Optimistic rollups lies in how to finalize this batch of transactions. In ZK rollups, the Ethereum network verifies the correctness of the batch of transactions through cryptographic validity proofs (commonly called zero-knowledge proofs). Optimistic rollups, on the other hand, assume that off-chain transactions are valid unless proven otherwise. Hence, they rely on fraud proofs, a challenge to the submitted state to Ethereum.  
Using the proposed BPET framework, we built another decentralized electricity exchange system called zkDELX [47], which implemented a bilateral market on Polygon, Scroll, and Taiko zkEVMs. This market enables electric vehicle users to share their home chargers and exchange electricity in a peer-to-peer way to resolve the charging station shortage issue through the shared economy business model.