esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
If the photon energy is absorbed by the quantum dots and, instead of directly emitting light, the photon transfers to polypyrrole where excitons recombine, a similar effect to what is observed can occur. [PERSON] et al. have shown that the CBM (conduction band minimum) of PPy is higher than that of CdSe, and the VBM (valence band maximum) of PPy is lower than that of CdSe. However, the oxidation-induced energy level is close to the VBM of CdSe [37]. The energy level places around 0.4-0.7 eV from VBM of PPy (5.7 eV). In our experimental result (Figure 4d), the photoluminescence peak shifted from 592 to 567 nm, and the value corresponded to 0.09 eV. The calculated value was close to the extracted value from the experiment. In addition, the insertion or adsorption of CdSe quantum dots to polypyrrole could be regarded as doping of the conducting polymer, providing an additional energy state for excitons to recombine and emit light [38]. Further studies, such as exploration into the transient photoluminescence spectrum, should be conducted to clearly identify the mechanism. Finally, the current-voltage (I\({}_{\text{DS}}\)\(-\)V\({}_{\text{DS}}\)) characteristics were measured on a microchip where two Au electrodes (gap distance of 5 \(\upmu\)m) were bridged by a nanorod sample. The nanorod-based electronic devices were fabricated onto a SiO\({}_{2}\) substrate 300 nm thick by a conventional photolithographic  
Figure 4: (**a**) UV–vis absorption spectrum of CdSe nanoparticles. (**b**) Photoluminescence (PL) spectrum of CdSe nanoparticles. The excitation wavelength of PL is 500 nm. PL spectra with (**d**) and without (**c**) pyrrole monomer in CdSe and plating solution mixtures. technique. The gap between the electrodes of the device was 5 \(\upmu\)m and contacted with a gold (Au) electrode of 100 nm thickness (Figure 5a). Bare polypyrrole nanorods showed relatively low current with asymmetric \(\mathrm{I_{DS}-V_{DS}}\) characteristics, which resulted from the Au segment at one side of polypyrrole nanorods and contact resistance. In particular, because the PPy-Au segments were directly bonded, the Schottky characteristics were reflected, and a diode-shaped \(\mathrm{I_{DS}-V_{DS}}\) curve was shown on the bare polypyrrole nanorods [34]. Between the applied voltages \(-2\) V to \(+2\) V, the CdSe-embedded polypyrrole nanorods showed slightly higher current and quasi-linear ohm-like properties than the bare polypyrrole nanorods, which could be a result of increased carrier density by doping (Figure 5). However, above the applied voltage \(+2.5\) V the total current flow of CdSe-embedded polypyrrole nanorods was lower than that of the bare polypyrrole nanorods, which was probably due to the contact resistance of the embedded CdSe nanoparticles.
# A Novel RPL Algorithm Based on Chaotic Genetic Algorithm
## 4 Performance Evaluation
### Results and Discuss
Figure 4: Average end-to-end delay. Figure 3: Average success rate. (4) Average hop count  
The average hop count of RPL-CGA, ETXOF, 0.8 \(\times\) ETX + 0.2 \(\times\) RER, and 0.6 \(\times\) HC + 0.4 \(\times\) RER under several different node number circumstances are illustrated in Figure 7. The average hop count of RPL-CGA, ETXOF, 0.8 \(\times\) ETX + 0.2 \(\times\) RER, and 0.6 \(\times\) HC + 0.4 \(\times\) RER are almost the same at small node density, because the low node number in network restricts the choice of preferred parent. But with the increase of node number, the choice of a preferred parent is gradually unrestricted. Therefore, the hop count of ETXOF, 0.8 \(\times\) ETX + 0.2 \(\times\) RER, and 0.6 \(\times\) HC + 0.4 \(\times\) RER are much higher than that of RPL-CGA. Moreover, RPL-CGA can decrease hops from non-nodes to root dramatically. Figure 5: Average remaining energy. Figure 6: Average alive node number. Figure 7: Average hop count. (5) Average frequencies of preferred parent changing  
Average frequencies of preferred parent changing represent the stability of network topology. Figure 8 shows this statistical experimental indicator of RPL-CGA, ETXOF, 0.8 \(\times\) ETX + 0.2 \(\times\) RER, and 0.6 \(\times\) HC + 0.4 \(\times\) RER under several different node number circumstances. The average frequencies of preferred parent changing of ETXOF, RPL-CGA, 0.8 \(\times\) ETX + 0.2 \(\times\) RER, and 0.6 \(\times\) HC + 0.4 \(\times\) RER are not much different. So, on the one hand, RPL-CGA can significantly optimize several aspects of network performance; on the other, it can also guarantee the stability of network's topology. (6) Weighting factors (\(a_{1}\), \(a_{2}\), \(a_{3
## 6 Application to an Existing Bridge with Damage Detection Time-Based State Function
The CDFs obtained when inspections were applied at 6, 12, and 18 years are shown in Figure 7b. The \(p_{f}\) and \(\beta\) associated with the CDFs in Figure 7 are presented in Table 2. As shown in Figure 7 and Table 2, \(p_{f}\) estimated using the combination of KDE and GPD method was larger than those computed using MCS and KDE. \(\beta\) estimated using MCS was similar to that estimated using KDE. The difference between the CDFs at the state function value equal to zero (i.e., \(g=0\)) led to differences between the values of \(p_{f}\) (or \(\beta\)) based on MCS, KDE, and the combination of KDE and GPD. As shown in Figures 6 and 7, KDE appropriately represents the CDF based on MCS. As a result, there is no significant difference between the values of \(p_{f}\) (or \(\beta\)) based MCS and KDE. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inspection Application** & **Probability of Failure** & **MCS \({}^{*}\)** & **KDE** & **Combined KDE** \\
**Times** & \(p\)/**Reliability Index \(\beta\)** & & **MCS \({}^{*}\)** & **MCS \({}^{*}\)** & **MCS \({}^{*}\)** \\ \hline \(t_{insp1}=10\) years & \(p_{f}\) & 0.0034 & 0.0034 & 0.0037 \\  & \(\beta\) & 2.70 & 2.70 & 2.68 \\ \hline \(t_{insp1}=7\) years & \(p_{f}\) & \(3.40\times 10^{-5}\) & \(3.58\times 10^{-5}\) & \(5.13\times 10^{-5}\) \\ \(t_{insp2}=14\) years & \(\beta\) & 3.98 & 3.97 & 3.88 \\ \hline \(t_{insp1}=6\) years & \(p_{f}\) & \(2.00\times 10^{-6}\) & \(1.84\times 10^{-6}\) & \(3.00\times 10^{-6}\) \\ \(t_{insp3}=18\) years & \(\beta\) & 4.61 & 4.63 & 4.53 \\ \hline \hline \end{tabular}  
* Number of samples for MCS = \(10^{6}\).  
\end{table} 
Table 2: Probability of failure (\(p_{f}\)) and reliability index (\(\beta\)) associated with the damage detection time-based state function when MCS, KDE, and the combination of KDE and GPD are applied. Figure 5: PDFs of damage detection time-based state function values when the inspection is applied at 10 years: (**a**) entire representation; (**b**) representation for detail B.  
Figure 6: CDFs of damage detection time-based state function values when the inspection is applied at 10 years: (**a**) entire representation; (**b**) representation for detail C.
# Incorporating Code Structure and Quality in Deep Code Search
## 6 Discussions  
We take a brief discussion about the performance of SQ-DeepCS and other code search models in Section 5.4. We propose a new representation method of code snippets named program slice. Compared with API sequence used in DeepCS and AST used in NCS, program slice contains more structural information of code snippets. Program slice extracts common features based on AST, which is more in line with the programming idea of code snippets and makes SQ-DeepCS perform better than At-CodeSM. The introduction of the attention mechanism enables different parts of the code snippets to obtain different weights. The attention mechanism weights the quality of code snippets and gives higher weight to the high-quality parts of code snippets.  
Via comparison from Section 5.4, we find that limitations still exist in our research. Although the overall performance of our model is better than the existing methods, we find that in the results of some individual natural language queries, the accuracy of DeepCS, NCS, and At-CodeSM exceeds that of our model. In the experiment, we found that when the functions described by query statements are complex, the performance of the five  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Approach** & **S@1** & **S@5** & **S@10** & **P@1** & **P@5** & **P@10** & **MRR** \\ \hline DeepCS & 0.156 & 0.422 & 0.556 & 0.156 & 0.164 & 0.162 & 0.315 \\ A-DeepCS & 0.133 & 0.467 & 0.511 & 0.133 & 0.169 & 0.153 & 0.304 \\ P-DeepCS & 0.156 & 0.356 & 0.533 & 0.156 & 0.16 & 0.149 & 0.313 \\ PA-DeepCS & 0.2 & 0.444 & 0.644 & 0.2 & 0.182 & 0.187 & 0.348 \\ NCS & 0.156 & 0.356 & 0.511 & 0.156 & 0.147 & 0.142 & 0.251 \\ At-CodeSM & 0.178 & 0.467 & 0.578 & 0.178 & 0.178 & 0.16 & 0.323 \\ SQ-DeepCS & **0.222** & **0.511** & **0.644** & **0.222** & **0.182** & **0.171** & **0.374** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall accuracy of SQ-DeepCS and related approaches.  
Figure 7: The MRR of each epoch in validation set.  
methods is not satisfactory. We speculate that the function of many Golang methods collected in GitHub in the dataset is parameter setting or intermediate variables updating. These kinds of code snippets are less reusable and will thus affect search performance.
## 3 Cyclic Loading Tests
### Test Setup and Procedure  
The sketch map and photograph of the test setup for the dampers are illustrated in Figure 4. A 500 kN mechanical testing and simulation (MTS) machine was used to simulate the cyclic loads applied to the tested specimens.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & \multicolumn{3}{c}{**Weakened Segment**} & \multicolumn{3}{c}{**Horizontal Segment**} & \\  & **Length/mm** & **Width/mm** & **Length/mm** & **Width/mm** & **Thickness/mm** & **Angle** \\ \hline W30-O15\({}^{\circ}\) & 150 & 30 & 20 & 60 & 6 & 15\({}^{\circ}\) \\ W30-O30\({}^{\circ}\) & 150 & 30 & 20 & 60 & 6 & 30\({}^{\circ}\) \\ W20-O0\({}^{\circ}\)-LS & 150 & 20 & 20 & 60 & 6 & 0\({}^{\circ}\) \\ W30-O0\({}^{\circ}\)-LS & 150 & 30 & 20 & 60 & 6 & 15\({}^{\circ}\) \\ W30-O30\({}^{\circ}\)-LS & 150 & 30 & 20 & 60 & 6 & 30\({}^{\circ}\) \\ W30-O0\({}^{\circ}\)-LP & 150 & 30 & 20 & 60 & 6 & 0 \\ W30-O15\({}^{\circ}\)-LP & 150 & 30 & 20 & 60 & 6 & 15\({}^{\circ}\) \\ W30-O30\({}^{\circ}\)-LP & 150 & 30 & 20 & 60 & 6 & 30\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The design details of an oblique X-shaped metal damper.  
Figure 3: The design details of XMD: (**a**) specimen without an oblique angle; (**b**) specimen with an oblique angle; (**c**) specimen without stiffening ribs; (**d**) specimen with cover plates; (**e**) photo of specimens.  
For the convenience of conducting tests and obtaining test data accurately, two specimens with same design details were connected with a special steel beam and steel connector by bolts to test the hysteretic performance of X-shaped metal dampers, as shown in Figure 4. Additionally, the bottom of the special steel beam was anchored with the clamp of the MTS machine to simulate a rigid connection. The special steel beam was anchored with the top clamp of the MTS machine to apply cyclic loads. To reduce the deviation of test results, limiting devices were machined and arranged at the steel beam to constrain the out-of-plane behavior of the MTS machine with a spherical hinge connection.  
A displacement-controlled protocol was adopted in the tests, and each applied displacement was equal to the multiples of the yield displacement of the XMDs in accordance with ANSI/AASC 360-16 [37], as shown in Figure 5. Yield displacements could be approximately obtained based on theoretical analysis and numerical analysis, and the initial displacements for all specimens were approximately equal to 0.3 mm.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 2 Watermark System Model on 5G NR
### Improved Watermarking Scheme
#### 2.2.1 Improved Watermark Generation Scheme
 \tag{8}\]  
The user's signal after 16 QAM modulation is marked as \(s\), \(a_{1}\) is the value of the smallest modulus, \(a_{2}\) is the value of the second smallest modulus, and the mapping result is called a pseudo-user-bit, which is denoted as \(b^{\prime}\). The improved watermark generation scheme is shown in Figure 7. In Figure 7, Scheme I is the basic watermark generation scheme, which is described in Section 2.1; for comparison, it is still displayed here. Scheme II adds channel coding on this basis, and Scheme III is further improved, using a pseudo-user-bit instead of a user bit as the input of the hash algorithm. To distinguish them, in this paper, the watermark bits before encoding are called watermark source bits and are denoted as \(h_{s}\), and the watermark bits embedded in DMRS after encoding are called watermark embedded bits and are denoted as \(w\). \(b\) stands for user bits, and after modulation, the user's signal \(s\) is obtained, and then it is mapped to obtain the pseudo-user-bit, which is recorded as \(b^{\prime}\). The key is the shared key between legal communication parties. For the above three schemes, a unique watermark signal can be generated for each different data frame.
## 5 Simulation Results and Analysis
### Analysis and Comparison of the Proposed Localization Algorithm
#### 5.2.1 Simulations at Selected Point
It can be seen from those three figures that the positioning error of Chan-Ho and BiasRed would increases irregularly with the increase of the error after the error becomes larger. This may be caused by the error of the weighting matrix of those algorithms. Those two algorithms use WLS in the calculation process and ignore some items about TDOA ranging errors to build the weighting matrix. When TDOA ranging errors are small, the weighting matrix is closed to be right. However, when the ranging errors are large, the error of the weighting matrix would increase. The positioning error, caused by weighted matrix, is irregular. The proposed algorithm also has this problem, but the item  
Figure 11: Accuracy of different positioning method at point B; (**a**) positioning error MSE when the standard deviation of TOA ranging error is 0.3 m; (**b**) positioning error MSE when the standard deviation of TOA ranging error is 1.5 m.  
Figure 12: Accuracy of different positioning method at point C; (**a**) positioning error MSE when the standard deviation of TOA ranging error is 0.3 m; (**b**) positioning error MSE when the standard deviation of TOA ranging error is 1.5 m.  
about TOA measurement in weighting matrix is not related with ranging error. This may inhibit the irregularity of the positioning error when TOA ranging error is low. The positioning error of the proposed algorithm would also increases irregularly when TOA ranging error is high.
## 4 Experiment and Results
### Experiment Results and Analysis
#### 4.4.2 Radiomics Feature Selection Results  
Among the 669 radiomics features, a \(t\)-test was first performed to remove redundant features, resulting in 574 features with homoscedasticity. Subsequently, using the Least Absolute Shrinkage and Selection Operator (LASSO) method with 5-fold cross-validation, the optimal alpha parameter was determined (\(\alpha\) = 4.394 \(\times\) 10\({}^{-5}\)). This procedure led to the identification of the top 12 most significant features. To facilitate description and interpretation, these selected features were numbered, with their corresponding indices, image characterization properties, and weight values provided in Table 4.  
These 12 features, derived through various mathematical transformations and statistical methods, effectively capture the characteristics of lung nodules from multiple perspectives, including different frequency bands, spatial scales, and gray-level distributions. By introducing these features with descriptive characteristics, the interpretability of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
the proposed algorithm is significantly enhanced, as these features correspond to physical properties of lung nodules that have meaningful clinical significance.
# Amplitude Modeling of Specular Multipath Components for Robust Indoor Localization
## 3 Experiment  
### Experiment Setup  
An experiment was conducted in a laboratory room at the Graz University of Technology, as illustrated in Figure 1. The room consists of two plasterboard walls and two reinforced concrete walls (shown as black outer lines), three glass windows at the north wall (shown as thick gray lines), one whiteboard, and one wooden door at the south wall. In the following sections, we label these walls by east plasterboard (EPB), south wall (SW), west wall (WW), and north glass window (NGW), respectively.  
A measurement data set was acquired with an Ilmsens Ultra-Wideband M-sequence channel sounder [49], c.f. Figure 2(a), which provides a transmitted signal with 6.95 GHz carrier frequency to acquire a frequency band between approximately 3.5 and 10.5 GHz. The measurement principle is correlative channel sounding [50], in which a binary-code sequence with suitable correlation properties are transmitted and correlation with the known-code sequence is performed at the receiver to obtain the CIR.  
Dipole coin antennas, shown in Figure 2(b), have been used for the measurements. According to [51], the coin antenna has a very wide bandwidth ranging from 3 to 9 GHz and a nearly isotropic radiation pattern in the horizontal plane. As shown in Figure 1, a receive antenna was placed at a fixed position \(\mathbf{a}_{1}\), whereas a transmit antenna (at position \(\mathbf{p}\)) was moved along a trajectory segmented into 7 parts for easier interpretation. Both antennas were mounted on tripods at the same height, therefore only the co-polarized, azimuth radiation pattern of the antenna has an impact on the data. The raw measurements at the receiver port were filtered with an RRC pulse with center frequency 6.95 GHz, roll-off factor 0.5, and bandwidth \(1/T_{p}=2\) GHz to obtain the received signals corresponding to the model in (3).
# Security Audit of a Blockchain-Based Industrial Application Platform
## 4 Attacks and Countermeasures
### Attacks  
#### 4.1.1 Infrastructure- Hardware  
The blockchain is connected to systems to interact with production machines and user devices. The hardware on which this infrastructure runs can be attacked.  
Node Tampering
* _Condition monitoring_: the monitoring system is physically accessed and manipulated so that it stops monitoring or monitors the wrong machine, giving a false sense of security. The opposite is also possible (causing false alarms).
* _Legacy machine_: the machine connected to the network is physically accessed and tampered with. The hardware is modified by removing security features. Malicious control systems that affect the performance or reliability of the legacy equipment are inserted.
* _Network gateway_: the gateway for the attacker's next steps is physically accessed and manipulated. Malicious circuits that affect the performance or reliability of an electronic system are inserted.
* _OPC UA server_: the OPC UA server is physically accessed and manipulated to connect additional/incorrect input devices (e.g., simulate simple sensors or complete machines).
* _Sensor_: the door detection sensor is removed so that the door remains open while the machine is in operation. A new sensor is physically added to the network. For example, a malicious sensor could be added by replicating the identification of a sensor. This could lead to performance degradation or allow the attacker to inject spoofed data into the sensor network.  
Malicious Code Injection
* _Legacy machine_, _OPC UA server_, and sensor_: an attacker could gain access to any of these components by installing a malicious industrial add-in. When using debug functions (such as Joint Test Action Group (JTAG) pins that are still active), it is even possible to modify executed code and cause the device to malfunction. _These three targets are explained together because of their similarities._  
Sleep Denial Attack
* _Sensor_: attackers keep the battery-powered devices awake by feeding them with false inputs. This leads to the exhaustion of their batteries and thus to their shutdown.
## 6 Simulation Results
### Simulation Settings  
In the simulation, we assume that all the base stations are located at the center of the cells, and the users are randomly distributed within 200 m around the base stations, where the number of cells is \(M=3\) and the number of users in each cell is \(K_{m}=4\). The variance of the channel noise in the downlink multi-cell NOMA system is defined as \(\sigma_{n}^{2}=B_{tot}N_{0}/N\), where \(N_{0}=-170\)dBm is the noise spectral density. The maximum transmission power of the base station is \(p_{\max}=2\sim 12\)watt and the subchannel bandwidth is \(B_{n}=180\)kHz. The channel gain of the channel \(n\) between the user \(k\) and the base station \(m\) is represented as follows:  
\[H_{m,k}^{n}=10^{-(\text{PL}_{m,k}^{v}+\tau)/10}\left|h_{m,k}^{n}\right|^{2} \tag{31}\]  
where \(\text{PL}_{m,k}^{v}\) denotes the path loss from the base station \(m\) to the user \(k\) with \(v=3.2\) being the path loss exponent; \(\tau\) is the shadow fading, which is a normally distributed random variable with a mean of 0 and a standard deviation of 8; and \(h_{m,k}^{n}\) represents the fast fading for the communication between base the station \(m\) and the user \(k\) on the channel \(n\), which is an independent and identically distributed complex Gaussian random variable with zero mean and unit variance.  
In the proposed framework of the MDRL-UL, the single-agent neural network in the MDRLNN is equipped with an input layer, three fully connected hidden layers, and an output layer. To balance system performance and complexity, the number of layers \(L\) in the encoder of the ULNN is set to 3, and the dimension of the position is set to 512. Based on this, the complexity of each method is shown in Table 1, where \(T_{step}\) is the number of iterations, \(L\) is the number of layers, and \(V_{i}\) is the number of neurons in layer \(i\).  
Before the experiments, 750 pieces of channel gain information were collected for training and testing the MDRLNN and the ULNN in the downlink multi-cell NOMA system. The learning rate for the MDRLNN is set to 0.0008, and the learning rate for the ULNN is set to 0.0001.
# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 3 Results
### LSD (2D/3D) (Laboratory for Simulation Development)  
The LSD (2D/3D) (Laboratory for Simulation Development) excels in modelling dynamic community interactions through its pure C++ application programming interfaces granting modellers the freedom to implement various computational models. This tool focuses on equations, representing code snippets for updating model variables during simulations, enabling the modelling of adaptive agents [82]. It integrates socio-economic variables seamlessly, enhancing adaptability to diverse community scenarios. The LSD accommodates demographic factors and a range of data types, offering versatility in model development. While supporting scalability for different community sizes and addressing scalability challenges, details on participatory modelling, collaborative decision-making, and scenario planning support are unclear. The LSD enables sensitivity analysis and scenario testing but lacks explicit documentation on uncertainty assessment mechanisms. Although it allows for model error correction, information on feedback loops for long-term impact representation and monitoring and evaluating adaptation strategy effectiveness is scant. In summary, the LSD is a robust platform for dynamic community-based modelling, particularly in economic and social science simulations. Its strengths include flexibility in handling diverse data, modelling dynamic interactions, and incorporating learning and adaptation processes. However, further documentation is needed for a comprehensive evaluation of its capabilities in participatory modelling, scalability challenges, risk assessment, and feedback loop integration for effective community-based adaptation modelling.