esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Radiation-Induced Bystander and Abscopal Effects
### First RIBE Studies at Synchrotrons  
Bystander effects are important in synchrotron radiation because tissues exposed to valley doses also receive the signals emitted by cells in the peak dose regions and vice versa. Research on the relevance of these bystander effects in synchrotron RT has a traceable history of no more than 14 years. The first study to suggest that bystander effects play an important role in synchrotron MRT was published by [PERSON] et al. [24]. In their experiments, the team irradiated bovine aortic endothelial cells and rat spinal cord with MRT. The results showed that the elimination of apoptotic cells and repair processes were faster than expected. This finding suggests that \"beneficial\" bystander factors are involved in tissue repair by promoting proliferation, migration, and differentiation of progenitor cells in the spinal cord. However, there are previous studies showing an increase in cell proliferation [92] and radiation-induced changes in the immune response of local immune cells and tumors [93], but these have not been associated with bystander effects.  
[PERSON] et. al. [94] provided further evidence for bystander effects by examining the response of the C6 glioma cell line to synchrotron micro-beams. The authors observed a higher number of DNA DSBs in cells adjacent to the irradiated area than would be expected from scattered radiation alone. They also noted cellular migration into the zones irradiated with peak doses. The team confirmed the role of RIBE by exposing unirradiated cells to the conditioned medium and observed a marked increase in 53 BP1 foci.  
[PERSON] et al. [95] found that normal human fibroblast WI-38 cells that had been exposed to synchrotron microbeams induced cell death in unirradiated cells. The authors confirmed their finding by treating WI-38 cells with aminoguanidine (an inhibitor of nitric oxide) before irradiation, which abolished the bystander effects and thus cell death. Another study published by the same group [96] showed that synchrotron-induced bystander effects were also observed in Chinese hamster V79 lung cells and that they could also be abolished by using nitric oxide inhibitors.  
The studies by [PERSON], [PERSON], and team [95; 96] also showed clonogenic bystander survival characterized by high cell killing at low doses followed by a marked escalation of survival with increasing doses. The authors described this response as a parabolic increase in bystander cell death. This is a well-known response in radiation biology and is known as low-dose hyper radiosensitivity (HRS) and increased radioresistance (IRR).
## 5 Discussion  
### Impacts of the Reference Data for DEM Selection  
The ICESat/GLAS data were considered as the optimal choice for DEM accuracy assessment, due to its high accuracy and wide coverage [15; 16; 17; 20; 21]. They have been used by DEM publishers for DEM error calibration [59], which might have an impact on the understanding of different DEM quality. The latest or upcoming release of more accurate altimetry data (e.g., ICESat-2, GEDI) [60], however, helps us to understand the DEM errors better. In here, we also selected corresponding ICESat/GLAS elevation points over the study area to analyze the impact of different reference data on DEM selection. Then, the DEM accuracy assessment method described in Section 4.1 was used to evaluate the overall accuracy of different DEMs (Figure 8). A comparison of the results is displayed in Figure 2, where the MEs of all datasets are negative, but the absolute MEs of DEMs are smaller than that of ICESat-2. This result can be attributed to that the ICESat/GLAS data contain the ground object height information (e.g., vegetation), but ICESat-2 measures the height close to the bare-earth surface elevations.  
Figure 8: Histogram plot of elevation errors for (**a**) SRTM1; (**b**) SRTM3; (**c**) ASTER; (**d**) AW3D30; (**e**) TDX30 and (**f**) TDX90.In addition, ASTER GDEM2 (~30 m spatial resolution) has the lowest accuracy among the six DEMs, which is in agreement with the results of Figure 2 and the study of [17]. AW3D30 DEM shows the best quality (RMSE = 6.9 m), followed by SRTM1 DEM, which conflicts the results shown in Section 4.1 and the results of [PERSON] et al. [24]. This is because AW3D30 DEM has been calibrated by the ICESat/GLAS altimetry data. When it is validated using the ICESat/GLAS data, the result will show a better but not true vertical accuracy. As a consequence, SRTM1 DEM should be recommended for the applications requiring accurate Earth surface description, as it has the best accuracy among the freely available medium-resolution DEMs. In addition, the newly released ICESat-2 is suggested as the reference height data for the DEM vertical accuracy assessment, due to its high accuracy and accessibility.
## 3 Results
### Focusing on People
#### 3.4.1. Introducing People in Assembly Operations  
Human operators are critical for competitive assembly systems when considering information flows, competence needs and the requirements for effectively making use of automation. In such an environment, human teams--rather than individuals, are key [190]. The role of operators depends strongly on the type of production system (e.g., high-volume production vs. low-volume high-variety). Traditional automation allows increased productivity, but it lacks the adaptability of human operators. The design of reconfigurable assembly systems by incorporating both machines and people can lead to cost-effective system flexibility and scalability. However, the collaboration between people and robots can create safety issues. These can be addressed in two clearly separated ways, according to [PERSON] et al.: (1) employing vision systems to stop robots; (2) robots so light and low force that they can be stoppedsafely by people. Safely increasing flexibility and efficiency in mixed-model assembly lines is one of the problems that Industry 4.0 technologies seek to address [54].  
**In conclusion**, the role of operators depends on the type of production system, and there is usually a trade-off between the increased productivity of automation and the adaptability of human operators. Reconfigurable, hybrid assembly systems that incorporate machines and people could lead to cost-effective flexibility and scalability. However, the collaboration between people and robots can also create safety issues.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 6 Challenges & Future Research Directions
### Optimizing Latency and Performance  
Next-generation 5G wireless networks differ significantly from conventional 3G/4G cellular systems, with more extreme performance and QoS requirements. Owing to the substantial open bandwidth in the mmWave spectrum, optimal channel modeling and resource allocation protocols are stringent. Until now, many multiplexing techniques have been implemented in wireless networks; however, to achieve low latency and high performance,  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Protocol Name** & **Spatial Reuse** & **Anti-Blockage** & **Targeted Wavelength** & **Key Idea** & **Limitations** \\ \hline
[69] & MD-MAC & Not supported supported & Not specified & 60 GHz & Using MDP guided predictability to address deafness problem & Poor performance when interferences are present \\ \hline
[70] & ALD-MAC & Not specified & Not specified & Not specified & Using ML algorithms for neighbor discovery over wireless nodes \\ \hline
[71] & DtD-MAC & Support-ted & Not specified & 60 GHz & Overcoming asymmetry-in-gain problem & No arrangement for channel state characteristics \\ \hline
[30; 56; 57] & ALOHA & Not specified & Not specified & 60 GHz & Making a transmission protocol into a basic and simple form & Completely discards the collision avoidance \\ \hline
[61; 62; 64] & TDD, TDMA & Not specified & Not specified & 60 GHz, 73 GHz & Using a single channel to execute the networking & Only a limited number of users can be assigned to a single channel \\ \hline
[52] & HetSNet & Support-ted & Not specified & 2 GHz, 28 GHz & Deployment of multi-hop routing & Requires large environment space for implementation, no efficient practical deployment data is available \\ \hline
[67] & PCDS & Support-ted & Not specified & 60 GHz & Near optimal delay and throughput & Only prioritizes popular content over important and rare contents \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of distributed MAC protocols.  
Figure 9: Challenges & future research directions.  
further research is needed on the existing multiple access protocols. As PHY layer technologies approach the Shannon capacity [72], optimizing the existing MAC layer protocols and searching for new, fast, and efficient MAC protocols which ensures data privacy and device security when devices are connected to public WPANs, are sorely needed to cope up with the 5G advancement challenges. Furthermore, there has been limited research into the usefulness of multi-hop paths in establishing QoS requirements at the MAC layer [73]. The majority of the known QoS-aware directional MAC techniques [74; 75] are restricted to single-hop wireless networks.
## 4 Model and Methodology of the Adaptive Protocol
### Energy Model  
Nodes adopt a periodic sleep/active work mode. The main energy consumption of nodes can be divided into two parts:  
1. Energy consumption in an active mode. We use \(\omega_{a}\), which denotes the energy consumption rate in this mode.
2. Energy consumption when nodes send and receive data.  
We adopt the typical WSN energy consumption model to calculate the energy consumption of sending, receiving, and aggregated data, which is given by Equations (2)-(4), respectively.  
\[E_{T}(l)=\left\{\begin{array}{l}l\times E_{elec}+l\times\varepsilon_{fs} \times d^{2},d<d_{0},\\ l\times E_{elec}+l\times\varepsilon_{mp}\times d^{4},d\geq d_{0},\end{array}\right. \tag{2}\]  
\[E_{R}(l)=l\times E_{elec}, \tag{3}\]  
\[E_{D}(l)=l\times E_{ec}, \tag{4}\]  
where \(E_{elec}\) represents the node's energy consumption when sending, and \(d_{0}\) represents the distance between nodes. When \(d<d_{0}\), the node energy consumption is in the normal loss mode; when \(d>d_{0}\), the node energy consumption is gradually reduced; \(\varepsilon_{fs}\) and \(\varepsilon_{amp}\) represent the energy at different distances; \(E_{ec}\) is the energy expended for computation; \(l\) is the length of the data frame.
## 2 Materials and Methods
### Analyses and Calculations  
The voltage (V) was acquired using a data acquisition system (Model 7700, Keithley Instruments Inc., Seoul, Korea) and recorded on a personal computer every 600 s. The polarization and power curves were acquired using linear sweep voltammetry (LSV) using a potentiostat (WMPG1000, WonATech Co., Seoul, Korea) at a scan rate of 0.1 mV/s. The polarization data used to acquire the MPD (W/m\({}^{2}\)) were normalized by the total anode area, internal resistance, and open circuit voltage. The net energy recovery (ER) was calculated based on the previously reported equation [23]. The voltage loss was calculated using the following equation;  
\[\mathrm{V_{loss}}\left(\%\right)=((\mathrm{V_{th}}-\mathrm{V_{ac}}))/\mathrm{ V_{th}}\times 100\]  
where, \(\mathrm{V_{loss}}\), \(\mathrm{V_{th}}\), and \(\mathrm{V_{a}}\) is the voltage loss, the theoretical voltage production of the stack system, and the actual voltage production, respectively.  
Principal component analysis (PCA) and Pearson correlation coefficients were calculated using SPSS Statistics (Ver 25, IBM CO., New York, NY, USA). The soluble COD (SCOD) was measured with the colorimetric method of the US EPA using a CODCr test kit (HS-CODCr-M, Humans Co., Daejeon, Korea).
# Numerical Integration-Based Performance Analysis of Cross-Eye Jamming Algorithm
## 4 Numerical Result of MSD  
This section  
Figure 6: MSD of cross-eye gain (\(\sigma_{\phi}=9^{\circ}\), first-order [PERSON] expansion).  
Figure 7: MSD of cross-eye gain (\(\sigma_{\phi}=10^{\circ}\), first-order [PERSON] expansion).  
Figure 8: MSD of cross-eye gain (\(\sigma_{\phi}=9^{\circ}\), second-order [PERSON] expansion).  
Figure 9: MSD of cross-eye gain (\(\sigma_{\phi}=10^{\circ}\), second-order [PERSON] expansion).  
Figure 11: MSD of cross-eye gain (\(\sigma_{a}=0.014\), first-order [PERSON] expansion).  
Figure 10: MSD of cross-eye gain (\(\sigma_{a}=0.012\), first-order [PERSON] expansion).  
The simulation MSD in Figures 6-13 are obtained from Equation (11). The first-order [PERSON] expansion-based simulation MSD and the first-order [PERSON] expansion-based analytic MSD in Figures 6, 7, 10 and 11 are obtained from Equations (13) and (15), respectively. The second-order [PERSON] expansion-based simulation MSD and the second-order [PERSON] expansion-based analytic MSD in Figures 8, 9, 12 and 13 are obtained from Equations (14) and (16), respectively. Finally, numerical integration-based MSD in Figures 6-13 is obtained from Equation (21). Simulation-based MSDs are obtained from Equation (12). The first-order [PERSON] expansion-based simulation MSD shows very good agreement with the first-order [PERSON] expansion-based analytic MSD. Similarly, the second-order [PERSON] expansion-based simulation MSD and the second-order [PERSON] expansion-based analytic MSD show very good agreement. The simulation-based MSD and the numerical integration-based MSD show good agreement.  
Figure 12: MSD of cross-eye gain (\(\sigma_{a}=0.012\), second-order [PERSON] expansion).  
Figure 13: MSD of cross-eye gain (\(\sigma_{a}=0.014\), second-order [PERSON] expansion).  
Table 2 shows the operation time of the Monte-Carlo simulation-based MSD, the analytically-derived MSD and the numerical integration- based MSD. The computational complexity of the Monte-Carlo simulation-based MSD increases with the increase of the number of repetitions in the Monte-Carlo simulation. On the other hand, the computational complexity of the analytic MSD and the numerical integration-based MSD are independent of the number of repetitions since analytic MSD and the numerical integration-based MSD are not obtained from the Monte-Carlo repetitions. The computational complexities of three MSDs are tabulated in Table 2. Comparing the analytic MSD and the numerical integration-based MSD shows the numerical integration-based MSD is more computationally intensive than the analytic MSD. Since the numerical integration-based MSD is implemented via double integration, it is more computationally intensive than analytic-based MSD. The integration-based MSD is associated with the MSD without approximation. On the other hand, the analytic MSD is associated with the first-order Taylor expansion approximated MSD.
# Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements
## 2 Problem Formulation
### A Heuristic Approach for the Best Measurement Selection  
The authors in [20] introduced a term _critical distance_, which is the distance between two sensors around which the performance of estimators based on hybrid RSS-TOA measurements should outperform both estimators based on RSS-only and TOA-only measurements. According to [20], the formula for calculating the critical distance is given as  
\[D=\frac{\sigma_{m_{i}}}{\sqrt{\text{e}^{\frac{\sigma_{m_{i}}^{2}}{2}}-1}}. \tag{25}\]  
However, the formula in (25) does not depend on the size of the area where the sensors are deployed, and it assumes perfect knowledge of the noise powers. The latter assumption might not stand in practice; thus, a different approach is considered here.  
From (1a) and (1b) respectively, the distance that best estimates \(\left\|\mathbf{x}-\mathbf{a}_{i}\right\|\) in the mean ML sense is  
\[\widehat{d}_{i}^{RSS}=10^{\frac{\eta_{0}-\eta_{0}\text{max}/2}{10 \gamma}}, \tag{26a}\] \[\widehat{d}_{i}^{TOA}=d_{i}-\beta_{\text{max}}/2. \tag{26b}\]  
By taking advantage of the estimates in (26a) and (26b), we can calculate the normalized relative difference between them as  
\[\epsilon=\frac{\left|\widehat{d}_{i}^{RSS}-\widehat{d}_{i}^{TOA}\right|}{\max \{\widehat{d}_{i}^{RSS},\widehat{d}_{i}^{TOA}\}}. \tag{27}\]  
As it is well known, RSS measurements are most valuable for short inter-sensor distances, while TOA ones bring advantage for long inter-sensor distances [1]. In order to exploit this fact, we make use of (27) to derive one of the following three choices:  
* \(\epsilon<\epsilon_{\text{min}}\): use TOA-only measurements;
* \(\epsilon_{\text{max}}\leq\epsilon\leq\epsilon_{\text{max}}\): use hybrid RSS-TOA measurements;
* \(\epsilon>\epsilon_{\text{max}}\): use RSS-only measurements.  
The values chosen for \(\epsilon_{\text{min}}\) and \(\epsilon_{\text{max}}\) in the above approach are entirely based on heuristics, but they make sense in what we consider to be a short and a long inter-sensor distance (as it will be seen in Section 5). Furthermore, our approach does not depend on the knowledge about the noise powers and it implicitly incorporates the size of the area, i.e., the length of links.
## 3 Results and Discussion  
### Bonding Strength  
The average maximum pulling force collected from the test is listed in Table 5, and the corresponding nominal bonding stress calculated using Equations (4)-(6) is shown in Table 6.  
Figure 11: Dynamic shear rheometer.  
It can be inferred from Tables 5 and 6 that the maximum pulling force and the nominal bonding stress was positively correlated with the powder-cement ratio. The difference between the nominal bonding stress of 1# fiber and 2# fiber when the powder cement ratio was 0.8, 1.0, 1.2, and 1.4 was about 5.6%, 1.0%, 13.1%, and 11.0% that of 2# fiber. The difference was mainly caused by the coupling agent [39] on the basalt fibers because the main difference of the chemical composition of the two fibers are the coupling agent on the fiber surface. The coupling agent acts as the bridge to connect the asphalt mortar and the basalt fiber and to disperse the stress in the asphalt mixtures from the asphalt material to the basalt fiber, making basalt fibers absorb the stress in the asphalt mixture more effectively. This is because the coupling agent of basalt fiber such as silane is a kind of organic material [40,41] and the original basalt fiber is inorganic because it is a mineral fiber made from nature basalt stone [42]. Thus, the connection between them is not good. Considering the fact that the asphalt is a kind of organic material [43], it is wise to adopt the coupling agent to modify the original basalt fiber to make it connect better with the asphalt material. Therefore, it is reasonable to adopt the nominal bonding stress to evaluate the bonding between the basalt fiber and the asphalt fiber because this is a very significant index. In the later section, the correlation between the nominal bonding stress and the rheological indexes was analyzed to verify the reliability of the nominal bonding stress calculated using this method.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 5 Security Evaluation
### Burrows-Abadi-Needham (BAN) Logic
#### 5.1.2 Logical Assumptions  
We made the following assumptions according to the nature of the protocol. First, the CN believes that the UE should control the HMAC key issued by themselves:  
\[C\mid\equiv U\mid\Rightarrow\ U\stackrel{{ K_{hmac}}}{{ \leftrightarrow}}C \tag{1}\]  
Second, since both the CN and the UE check the timestamp in the protocol, they should believe that the timestamps are fresh:  
\[C\mid\equiv\#(TS) \tag{2}\]  
\[U\mid\equiv\#(TS) \tag{3}\]  
Third, the CN and the UE should also believe that their locally generated keys are trustworthy to themselves:  
\[U\mid\equiv U\stackrel{{ K_{hmac}}}{{\leftrightarrow}}C \tag{4}\]  
\[U\mid\equiv a \tag{5}\]  
\[U\mid\equiv a\text{-}P \tag{6}\]  
\[C\mid\equiv b \tag{7}\]  
\[C\mid\equiv b\text{-}P \tag{8}\]  
Fourth, the UE should believe that the key generated by the CN is controlled and trusted by himself. Similarly, the CN should also believe that the keys generated by UE are controlled by himself:  
\[U\mid\equiv C\mid\Rightarrow\ b\text{-}P \tag{9}\]  
\[C\mid\equiv U\mid\Rightarrow a\text{-}P \tag{10}\]  
\[U\mid\equiv C\mid\equiv b \tag{11}\]  
\[C\mid\equiv U\mid\equiv a \tag{12}\]  
Fifth, the CN should believe the secret of the one-time hash sent from the UE by validating it with the records in the blockchain. Additionally, since it is only valid once, it can be viewed as a fresh nonce:  
\[C\mid\equiv U\stackrel{{ Y}}{{\leftrightarrow}}C \tag{13}\]  
\[C\mid\equiv\#(Y) \tag{14}\]  
Finally, since ECDH is used, it can be assumed that for the UE (\(U\)), the session key \(U\stackrel{{ K_{UL}}}{{\leftrightarrow}}C=a\text{-}b\text{-}P\) can be calculated with the received \(b\text{-}P\) and the locally generated \(a\). Similarly, for the CN (C), the session key \(U\stackrel{{ K_{UL}}}{{\leftrightarrow}}C=b\text{-}a\text{-}P\) can be calculated with the received \(a\text{-}P\) and the locally generated \(b\).
## 2 Materials and Methods
### Modeling Methodology
#### 2.4.3 Model Performance  
The model performance was evaluated in two aspects, i.e., model accuracy and model uncertainty. We evaluated the model accuracy in OOB and 10-fold CV with two performance indices (i.e., \(R^{2}\): coefficient of determination and RMSE). There is about one third data remaining after the bootstrap process, which can provide OOB internal validation. And the selected calibration dataset is subsequently divided into 10 folds. For each calculation, 9 folds are used to fit the model and the left is used to verify the result. The process is performed 10 times until each fold has been verified. The CV can reduce the model bias, and the validation result in OOB and CV can represent model predictive performance [52]. Two indices were calculated as the average of 50 repetitions with the following equations:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(O_{i}-P_{i})^{2}}{\sum_{i=1}^{n}(O_{i}-O_{a})^{ 2}} \tag{5}\]\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}{(P_{i}-O_{i})}^{2}} \tag{6}\]  
where \(n\) is the number of sampling points, \(P_{i}\) is the prediction of QRF, and \(O_{i}\) and \(O_{a}\) are the observation and the average of all observations, respectively.  
We also calculated the uncertainty value (\(U\)) for each validation point (4), and the average of \(U\) was used to represent the uncertainty of the model.  
\[U=\frac{CI_{95}-CI_{5}}{P_{a}} \tag{7}\]  
where \(Pa\) is the average prediction of 50 bootstraps and \(CI_{95}\) and \(CI_{5}\) are the 95% and 5% quantile of 50 bootstraps, respectively.  
Prediction interval coverage percentage (\(PICP\)) was used to estimate the percentage of observations falling into the 90% \(CI\) (7). When \(PICP\) was close to 0.9, the uncertainty was efficacious.  
\[PICP=\frac{\sum_{i=1}^{n}{\Big{(}O_{i_{low}}<O_{a}<O_{i_{up}}\Big{)}}}{\sum_{i =1}^{n}(O_{a})} \tag{8}\]  
where \(O_{a}\) is the observation and \(O_{i_{up}}\) and \(O_{i_{low}}\) are the values in the 95% and 5% quantile prediction in 50 times, respectively.