esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Structure Measured Temperature and Deformation Analysis
### Temperature Field Test Data and Analysis  
To fully obtain the temperature field of the structure under the condition of periodic climate change, the temperature observation time is set to one year, that is, January to December 2023. For bridge structures subjected to solar radiation and atmospheric temperature changes, the most unfavorable vertical positive temperature difference can be generated in summer when solar radiation is intense, and the most unfavorable vertical negative temperature difference can be generated under the effect of sudden atmospheric cooling in winter [16]. According to the annual temperature test data, the temperature measurement point data from 00:00 to 23:29 on 17 August 2023 were selected as the summer analysis data. The maximum ambient temperature inside and outside the box girder was 37.9 \({}^{\circ}\)C and 36.4 \({}^{\circ}\)C, respectively. The temperature measurement point data from 00:00 to 23:29 on 18 January 2023 is selected as the winter analysis data. The minimum ambient temperature inside and outside the box girder is 4 \({}^{\circ}\)C and 6.3 \({}^{\circ}\)C, respectively. Using the measuring point of the concrete bridge panel in the middle section of the main span, the obtained data are shown in Figure 16.  
As can be seen from Figure 16, during the coldest month in winter and the hottest month in summer, the ambient temperature presents an obvious cosine function form, and the maximum temperature difference is 2.1 \({}^{\circ}\)C and 7.5 \({}^{\circ}\)C, respectively. During the period from 0:00 to 5:00, the temperature gradually decreases, while at 6:00 to 15:00 the temperature gradually rises and reaches the maximum at 15:00. After 15:00, as the sun gradually goes down, the temperature begins to slowly decrease, eventually reaching a new thermal equilibrium. Due to the limited temperature measurement points in the box, all located at the bottom of the steel box girder, the overall temperature changes gently, and the maximum value appears at around 15:00 in the afternoon, which is consistent with the results of the temperature field simulation.
## 6 Challenges and Future Trends in LEV Charging
### Use of Wide Band-Gap Electronics  
As the concept of fast charging in LEVs grows in relevance among researchers, special emphasis must be placed on the type of semiconductor switches used in power electronic converters. This is to ensure safe and satisfactory performance of these switches at high current. Currently, silicon-based MOSFETs and IGBTs are the most used switches for EV charging applications. However, it is expected that wide band-gap semiconductor-based switches--such as silicon carbide (SiC) and gallium nitride (GaN)--will replace their silicon-based counterparts eventually [114]. This is due to their capability for operation at high frequencies and higher voltage handling capability [115].  
Wide band-gap semiconductor switches are found to be suitable for operation at higher frequencies. As a result, there is considerable reduction in the size of passive components such as inductors. In conventional silicon semiconductors, the switching losses increase with increasing switching frequency, thus placing a limit on minimum inductor size. On the contrary, wide band-gap semiconductor devices reduce the possibility of this trade-off [116].  
The authors of [117] carried out a comprehensive study to evaluate the advantages of wide band-gap devices over silicon devices when used in DC-DC converters. It was observed that DC-DC converters based on wide band-gap devices displayed improved efficiency at higher switching frequencies. Furthermore, it is also observed that switching losses are considerably reduced in wide band-gap device-based converters--even at high frequency operation. In this way, the authors validated the superiority of wide band-gap devices for EV charging applications.  
Owing to their advantages, recent publications have started exploring wide band-gap device-based chargers for conventional EVs [118; 119]--with their usage of SiC-based switches--and other applications [120]. Performance of such devices has been validated in these works through improved power density and efficiency. Most importantly, there is scope for use of these devices in converters for LEV chargers, especially in on-board implementations. Works such as [89; 90; 121] have used GaN switches for on-board converters. However, cost remains a major hurdle in the adoption of these switches. Another point to note is that the on-state resistance of a SiC MOSFET is marginally lower than a conventional Si MOSFET of similar rating [122; 123]. Thus, there is little benefit in using wide bandgap switches at very low power applications. However, there are other aspects where wide bandgap switches have an edge over conventional switches, such as lower input capacitance, resulting in lower gate drive losses [124].
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 6 Experiments and Results
### Attack Scenarios  
In order to evaluate the proposed model, seven attack scenarios were simulated in the testbed. These were common network-based attacks, targeting the networking part of the ICPSs such as packets, protocols or production processes [49]. This section details these attacks as follows:  
* **Eavesdropping attack.** This aims to eavesdrop on production processes using MITM technology. It works as a relay, which forwards packets between master and slave devices and keeps a copy of the traffic to infer production processes.
* **Tampering attack.** This attack is similar to the previous one. The main difference is that attackers modify the operation values. They aim to disturb the production process.
* **Selective forwarding attack.** This attack uses MITM technology to forward certain operation types and drop other types. Its goal is to disturb the production process, as in the tampering attack.
* **Denial-of-Service (DoS) attack.** This tries to slow down or interrupt systems by flooding packets or consuming massive computational resources.  
Figure 5: The framework of the testbed, which contains one master device, two Modbus slave devices, an attacker PC and the proposed detection model. The network switch connects all components. All packets that go through the switch are mirrored to the anomaly detection system for anomaly detection.  
* **Replay attacks.** This attack replays a certain copy of the operations to mislead the control program and disturb the production process.
* **Unauthorised operation.** The operation whose function type \(FN\) has not previously been observed. This could be caused by the attackers using MITM or malware programs to manipulate slave devices.
* **Unauthorised device.** The master or slave device has never been observed in the training dataset. This anomaly could be caused by an attacker connecting his/her tools to the network.  
For eavesdropping, tampering and selective forwarding attack scenarios that rely on the MITM technology to intercept and forward packets, the operation time interval or the response time increases. In this work, this increment was measured as \(T_{diff}\). As the last two defence lines rely on this time increment to detect malicious operations, this work controlled the range of \(T_{diff}\) to evaluate the model's sensitivity. Specifically, this work used three ranges: \([10,20)\), \([20,50)\) and \([50,100]\). In this work, the man-in-the-middle was used to control the operation time interval and the response time variants, where we controlled the forwarding (processing) latency using the _sleep_ command.  
This work collected 15,000 benign operations to train the model. A total of 137,000 benign and 15,000 malicious operations were collected for each attack type to evaluate the detection performance.
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 4 Results and Discussion
### Performance Parameters, Evaluation and Comparison
#### 4.1.2 Precision  
The second metric used for ProSenAD performance evaluation is precision, also known as PPV. It is a more scrutinized evaluation metric as compared to the CA. It calculates the correctly predicted positive instances over all positive instances predicted by the model, which can also be explained as the quantification of accurate classification of positive class using the \(TP\) and \(FP\) parameters. Equation (2) is typically used to calculate the precision of a model.  
\[\frac{TP}{TP+FP} \tag{2}\]  
Figure 11 shows the average precision/PPV results of ProSenAD, GAN-C, ML-LGBM, GB, XGBoost, and baseline LGBM. GAN-C shows the highest difference in performance in the protocol-specific attack scenario, with a precision of 0.84. The proposed model outperforms it by 15%. Next, GB performs the poorest among all benchmarks (except for the GAN-C in protocol-specific attack detection) with an average precision of 0.883 in protocol-specific attack scenario and 0.853 in SN-inherited attack scenario. ProSenAD shows improvement in both scenarios with an improved performance of 10.7% and 12%, respectively, for this benchmark. Next, the XGBoost shows an average precision of 0.905 in the first attack scenario, and 0.893 in the second attack scenario. ProSenAD outperforms it by 8.5% and 8%, respectively. The proposed model also shows better PPV in comparison with the LGBM for both attack scenarios, with an increase of 5.3% for protocol-specific attack detection and 5.1% for SN-inherited attack detection. The difference between the performance of the proposed model and ML-LGBM is almost similar, but the latter is designed only for the detection of one type of attack, i.e., the protocol-specific attack. It does not address SN-inherited attacks. Therefore, ProSenAD outperforms it in terms of number and simultaneity of attack detection. Overall, the proposed model shows improved performance as shown through the comparative analysis.  
Figure 10: Comparison of classification accuracy.
# Structure-Enhanced Prompt Learning for Graph-Based Code Vulnerability Detection
## 3 Methodology
### Vulnerability Detection
#### 3.4.1 Model Training  
The training process, illustrated in Figure 8, takes three inputs: the target function (\(f_{\text{anchor}}\)), the structurally enhanced positive prompt (\(f_{\text{positive}}\)) and the structurally enhanced negative prompt (\(f_{\text{negative}}\)). These inputs are projected into a shared feature space, where their relationships are optimized using a joint loss function that combines Triplet Loss and Cross-Entropy Loss.  
Figure 8: The vulnerability detection process.  
Figure 7: Triplet Loss: enhancing similarity within types, differentiating across types.  
To learn meaningful embeddings for \(f_{\text{anchor}}\), we accumulate the gradients for each input function and backpropagate them to update the network parameters. This ensures that structurally similar functions remain close in the learned space, while distinct functions are pushed apart.  
To quantify the relative similarity between samples, we define a loss function that operates on an anchor (\(a\)), a positive example (\(p\)) and a negative example (\(n\)), with a predefined margin \(m=0.7\). This criterion ensures that positive pairs are closer than negative pairs in the embedding space. The Triplet Loss component is formulated as  
\[L_{triplet}(a,p,n)=max\{0,(sim(a_{i},n_{i})-sim(a_{i},p_{i}))+m\} \tag{14}\]  
where the distance \(sim(x_{i},y_{i})\) is given by  
\[sim(x_{i},y_{j})=\frac{x_{i}\cdot y_{j}}{|x_{i}|\cdot|y_{j}|} \tag{15}\]  
To enhance classification accuracy, Cross-Entropy Loss is also applied, ensuring that embeddings align with the correct class labels. The final joint loss function is defined as  
\[L_{joint}=\lambda L_{triplet}+(1-\lambda)L_{CE} \tag{16}\]  
By setting \(\lambda=0.2\), we prioritize classification accuracy while still leveraging metric learning to refine the embedding space. This joint optimization effectively captures structural similarities, reduces misclassification errors and improves vulnerability detection performance.
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 2 Sensing Methods
### Other Sensing Methods  
Traditional compressed sensing associates sparse signals with a common sensing matrix regardless sparse domain. However, the performance of the sensing matrix is very problematic. Especially when the sensing matrix is a partial orthogonal sensing matrix, sensing fails because the signal is sparse in some transform domains. This problem is mainly because of the consistency of the sensing matrix with the sparsity matrix. [PERSON] et al. proposed a robust sensing approach that multiplied the sensing  
Figure 3: Difference between traditional matrix multiplication and semitensor matrix multiplication. Traditional matrix multiplication should meet limitations of matrix dimensions. Column number in matrix \(A\) must be equal to row number in matrix \(x\). The theory of semitensor product (STP) breaks through this limitation, able to perform matrix multiplication when two matrices do not meet the dimension-matching condition [31].  
matrix by the inverse matrix of the sparsity matrix in the sensing step [34]. The operation process of the random sensing matrix, especially the partial orthogonal sensing matrix, was improved.  
So far, the sensing schemes of common CS theoretical models consist of random isolated measurements whose elements are randomly generated variables. [PERSON] et al. introduced the concept of measurement blocks [35]. In their scheme, the measurements of the sensing scheme were no longer a set of isolated measurements, but a set of measurements that might represent any shape (for example, parallel or radial lines).  
[PERSON] et al. proposed another CS construction approach without randomness [36]. Their matrices had low incoherence. In order to obtain a CS matrix with low incoherence, an identity matrix with coherence 0 could be used as part of the CS matrix. In the meantime, they added vectors into their scheme under the basis of low coherence. Their CS matrix was given by the following:  
\[A=[E|v_{1},v_{2},v_{3},\cdots,v_{n-m}], \tag{23}\]  
where \(E\), \(m\times m\) identity matrix; \(v_{i}\), \(m\)-dimensional appended vector with low coherence; and the dimension of \(A\) was \(m\times n\), where \(m\ll n\). Compared to existing random matrices, the matrix constructed by this approach could achieve higher recovery accuracy. However, there were still two problems to be solved: failure in some cases and the compression-ratio increment.
# Comparison of Current Five-Point Cylindicity Error Separation Techniques
## 2 Error Separation Models for In Situ Measurement of Cylindricity
### Spiral Scan EST
Similar to the parallel scan EST, the initial zero-setting value \(D_{1}\)-\(D_{5}\) of Probes 1-5 is also allocated a value of zero or the nominal radius, which will not affect the spiral line profile on the cylinder. Applying the IDFT to \(\mathbf{R}(p)\)\((p=0,1,\cdots,N\times M-1)\), we may obtain \(\rho(i)=\mathrm{IDFT}[\mathbf{R}(p)]\)\((i,p=0,1,\cdots,N\times M-1)\), indicating the spiral line profile on the cylinder. A reconstructed cylindrical profile should be:  
\[r(z,\ddot{i})=\rho(i)(i=0,1,\cdots,N\times M-1);\;z=i\times\frac{t}{N};\; \ddot{i}=\mathrm{remainder}(i/N)\;(\ddot{i}=0,1,\cdots,N-1). \tag{11}\]  
It's worth noting that, different from the parallel scan EST, the spiral scan EST measures the one-dimensional straightness of the spiral profile on the cylinder, and reconstructs an integral cylindrical profile. It is difficult to distinguish the radius deviation and out-of-roundness of each cross-section, as well as the profile of the spatial curved median line of the cylinder.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 4 The Proposed 5 GSBA Scheme  
### Motivation  
The existing 5G authentication protocol, 5G-AKA, is a vulnerable protocol that creates a vast burden to the AUSF. Since there will be more connected devices in the future, attackers are likely to launch DoS and DDoS attacks to flood the AUSF and other 5G authentication entities. However, as there could also be some essential utilities using 5G, the 5G network has to be stable at all times. Therefore, to prevent DoS and DDoS attacks from paralyzing the 5G network, there is an urgent and critical need to design an authentication protocol that ensures no DoS and DDoS attacks can be successful. This protocol should work in a decentralized manner, such that it will not suffer from a single point of failure due to request flooding. Given all these constraints, we believe that an authentication protocol combined with a blockchain would be an ideal solution. Although a blockchain could introduce more overhead during database synchronizations, it helps reduce the opportunity of system overloading by decentralizing authentication tasks. In this paper, we propose a 5 GSBA scheme that uses a blockchain to decentralize the subscription repository from UDM to all gNBs, such that authentication tasks can be decentralized to the gNBs. By doing so, we can prevent DoS and DDoS attacks from impacting the quality of service (QoS) of the entire 5G network.
## 3 Results and Discussion
### Asymmetrical Flow Field-Flow Fractionation  
It is well known that immunoglobulins are susceptible to spontaneous decomposition during storage [32]. However, the rate of this process depends on many factors [33; 34; 35; 36]. It was necessary to confirm the presence of fragments of the antibodies in the initial solution because these fragments could be formed only during electrophoresis. Therefore, we additionally investigated the fraction 10-30 kDa obtained after centrifugation of a monoclonal antibody preparation (clone 2H3C6) by a nondestructive method--asymmetrical flow field-flow fractionation (AF4). Using the Micro BCA Protein Assay Kit, we have shown that the content of IgG fragments in the obtained fraction 10-30 kDa was 1.7% (by weight) from the initial IgG content. The fraction was additionally concentrated 20 times by centrifugation through 3 kDa filters. The obtained sensograms (Figure 6) confirmed the presence of a fraction of protein molecules with a molecular weight lower than that of BSA (66 kDa). The main peak of the AF4 sensogram for the studied preparation accords to IgG fragments with a molecular weight about 25 kDa, which we associated with IgG light chains (L). A minor peak in the 50 kDa region was also detected, which may be associated with partial transmission of IgG heavy chains by Amicon Ultra 30 kDa filters or by aggregation of IgG light chains.  
Figure 5: Results of SDS-electrophoresis of Mab 2H3C6 in a polyacrylamide gel.  
Figure 6: Asymmetrical flow field-flow fractionation of the fraction 10–30 kDa obtained after centrifugation of a monoclonal antibody preparation (clone 2H3C6; black curve), and BSA as a reference protein (red curve). The fraction was additionally concentrated 20 times by centrifugation through 3 kDa filters.
# Optimization of Virtual Loudspeakers for Spatial Room Acoustics Reproduction with Headphones
###### Abstract  
The use of headphones in reproducing spatial sound is becoming more and more popular. For instance, virtual reality applications often use head-tracking to keep the binaurally reproduced auditory environment stable and to improve externalization. Here, we study one spatial sound reproduction method over headphones, in particular the positioning of the virtual loudspeakers. The paper presents an algorithm that optimizes the positioning of virtual reproduction loudspeakers to reduce the computational cost in head-tracked real-time rendering. The listening test results suggest that listeners could discriminate the optimized loudspeaker arrays for renderings that reproduced a relatively simple acoustic conditions, but optimized array was not significantly different from equally spaced array for a reproduction of a more complex case. Moreover, the optimization seems to change the perceived openness and timbre, according to the verbal feedback of the test subjects.  
Spatial audio; Spatial sound reproduction; SDM; Headphone reproduction; Optimization 2017
## 3 A Case Study for Validating the Proposed Model and Process
### Boundary Conditions for Static Solution  
A multi-step analysis was used to model the crack growth over a large number of cycles. Crack growth was assumed to be quasi-static such that the crack growth could be divided into a series of linear static load steps. In each step, loads were applied at the outermost nodes of the upper and lower pin holes.  
Figure 5: Compact Tension Specim Geometry used in Experiments by [PERSON] et al. [23].  
For the experiments, the authors updated the loading to maintain a near-constant stress intensity factor at the crack tip and this method was repeated in simulation so as to establish similar boundary conditions in the experimental work of [PERSON] (2018). The experimental crack growth data for the modified CT specimen was obtained utilizing a constant stress intensity factor range at the crack tip and a loading program was created for the test apparatus which maintained a constant \(\Delta K_{I}\) value of 20 MPa (m)\({}^{1/2}\). Loading was updated multiple times throughout the analysis so as to maintain a near constant stress intensity factor at the crack tip.
# Low-Complexity High-Throughput QC-LDPC Decoder for 5G New Radio Wireless Communication
###### Abstract  
This paper presents a pipelined layered quasi-cyclic low-density parity-check (QC-LDPC) decoder architecture targeting low-complexity, high-throughput, and efficient use of hardware resources compliant with the specifications of 5G new radio (NR) wireless communication standard. First, a combined min-sum (CMS) decoding algorithm, which is a combination of the offset min-sum and the original min-sum algorithm, is proposed. Then, a low-complexity and high-throughput pipelined layered QC-LDPC decoder architecture for enhanced mobile broadband specifications in 5G NR wireless standards based on CMS algorithm with pipeline layered scheduling is presented. Enhanced versions of check node-based processor architectures are proposed to improve the complexity of the LDPC decoders. An efficient minimum-finder for the check node unit architecture that reduces the hardware required for the computation of the first two minima is introduced. Moreover, a low complexity a posteriori information update unit architecture, which only requires one adder array for their operations, is presented. The proposed architecture shows significant improvements in terms of area and throughput compared to other QC-LDPC decoder architectures available in the literature.  
5G new radio standard; quasi-cyclic low-density parity-check (QC-LDPC) codes; channel coding; decoder architectures