esa-sceva/satcom-chunk-collection huggingface dataset:
# Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation
## 4 Discussion
### How Working Conditions Affect Transfer Learning Performance  
To further understand how working conditions affect transfer learning performance, we analyzed the IMP performances of transfer experiments grouped by different working conditions. Figure 9 shows the IMP performance of transfer learning with different working conditions. Table 4 shows the information of experiments in Figure 9. Figure 9a,b show how fault conditions influence the performance of transfer learning. Figure 9c,d show how operating conditions influence the performance of transfer learning.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Figure** & **Label** & **Transfer (From To)** & **Operating Conditions** & **Fault Conditions** \\ \hline \multirow{3}{*}{Figure 9a} & E8 & FD001\(\rightarrow\)FD003 & 1\(\rightarrow\)1 & 1\(\rightarrow\)2 \\  & E2 & FD003\(\rightarrow\)FD001 & 1\(\rightarrow\)1 & 2\(\rightarrow\)1 \\ \hline \multirow{3}{*}{Figure 9b} & E11 & FD002\(\rightarrow\)FD004 & 6\(\rightarrow\)6 & 1\(\rightarrow\)2 \\  & E5 & FD004\(\rightarrow\)FD002 & 6\(\rightarrow\)6 & 2\(\rightarrow\)1 \\ \hline \multirow{3}{*}{Figure 9c} & E6 & FD001\(\rightarrow\)FD002 & 1\(\rightarrow\)6 & 1\(\rightarrow\)1 \\  & E3 & FD002\(\rightarrow\)FD001 & 6\(\rightarrow\)1 & 1\(\rightarrow\)1 \\ \hline \multirow{3}{*}{Figure 9d} & E12 & FD003\(\rightarrow\)FD004 & 1\(\rightarrow\)6 & 2\(\rightarrow\)2 \\  & E9 & FD004\(\rightarrow\)FD003 & 6\(\rightarrow\)1 & 2\(\rightarrow\)2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Information of experiments in Figure 9.  
Figure 8: Comparison of the IMP performances for transfer experiments.  
From Figure 9a,b, we found that when the fault conditions are considered, in both the transfer learning from single fault condition to multiple fault conditions (E8, E11) and the transfer learning from multiple fault conditions to single fault condition (E2, E5), the transfer learning scheme improved the prediction models of the target dataset.  
From Figure 9c,d, we found that when the operating conditions are considered, transfer learning from a single operating condition to multiple operating conditions (E6, E12) improved the prediction performance of the model on target dataset. However, transfer learning from the model trained on the multiple operating condition dataset to the single operating condition (E3, E9) had a detrimental effect on the prediction performance of the target model.  
From all the above results, we found that transfer learning under working conditions made effective performance improvements, except when transferring from multiple operating conditions to single operating condition (E3, E9).
# A Blockchain-Based Privacy-Preserving and Fair Data Transaction Model in IoT
## 3 Background Knowledge
### Verifiable Encrypted Signature  
The basic principle of verifiable encrypted signatures is as follows [36]: Data consumers encrypt the arbitrator's digital signature using their public key and verify if the signature truly exists in the ciphertext. Anyone can validate the validity of the signature via the arbitrator's provided public key. In the case of disputes, arbitrators can use their private keys to decrypt encrypted signatures and prevent traders from maliciously withholding digital signatures. Verifiable encrypted signatures effectively ensure fairness in online transactions and protect both parties from potential losses. The verifiable encrypted signature protocol comprises the following eight algorithms:  
1. \(Setup(1^{\lambda})\to pp\): Generates the open parameter \(pp\) by giving the parameter \(\lambda\).  
2. \(AdjKeyGen(pp)\rightarrow(APK,ASK)\): By disclosing the parameters \(pp\), generate the arbiter's key pair \((APK,ASK)\).
3. \(KeyGen(pp,APK)\rightarrow(pk_{i},sk_{i})\): Generating signer \(i\) by disclosing the parameters \(pp\) and the arbiter's public key \(APK\) of the key pair \((pk_{i},sk_{i})\).
4. \(Sign(pp,sk_{i},m)\rightarrow\sigma_{i}\): By disclosing the parameters \(pp\), private key \(sk_{i}\) and message \(m\), generating signer \(i\) of the digital signature \(\sigma_{i}\).
5. \(Verify(pp,pk_{i},m)\to 0/1\): Verify the validity of the digital signature \(\sigma_{i}\) by disclosing the parameters \(pp\), public key \(pk_{i}\) and message \(m\). If the algorithm outputs 1 it means that the signature is valid and outputs 0 it means that the signature is invalid.
6. \(VESSign(sk_{i},m,APK)\rightarrow\sigma_{i}^{VES}\): Generate a verifiable encrypted signature \(\sigma_{i}^{VES}\) by utilizing the private key \(sk_{i}\), message \(m\), and the arbiter's public key \(APK\).
7. \(VESSertify(pk_{i},m,APK,\sigma_{i}^{VES})\to 0/1\): By public key \(pk_{i}\), message \(m\), arbiter public key \(APK\) as well as the verifiable encrypted signature \(\sigma_{i}^{VES}\), verifying the validity of the verifiable encrypted signature \(\sigma_{i}^{VES}\). If the algorithm outputs 1, the cryptographic signature can be verified as valid, while an output of 0 indicates that it is invalid.
8. \(Adj(pk_{i},APK,ASK,m,\sigma_{i}^{VES})\sigma_{i}\): By public key \(pk_{i},APK\), the private key of the arbiter \(ASK\), message \(m\) and a verifiable encrypted signature \(\sigma_{i}^{VES}\) to obtain the digital signature \(\sigma_{i}\).
# Exploiting Outage and Error Probability
## 3 System Performance Analysis and Our Proposed Cooperative Routing Protocols
### Proposed Scheme 1: ACE
#### 3.5.1 Information Exchange Phase  
ACE is localization free routing protocol. Nodes are equipped with inexpensive depth sensors. Each node broadcasts its depth and residual energy information to all nodes in its transmission range via HELLO packet. Neighbour nodes are identified on the basis of depth information. A neighbor of node \"x\" is one whose depth difference from x lies within the transmission range of x. This process is repeated for all the nodes and information regarding local neighbours is stored in each node's database.  
Information exchange phase is repeated in the beginning of each time slot and neighbors and residual energy information is updated. This is because, once nodes start to die, neighbors and residual energy information may change and needs to be updated for the path establishment phase.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 3 Materials and Methods
### Data Processing, Analysis and Validation
#### 3.3.2 Semi-Automatic Mapping Based on SAR Data Processing  
* **SAR Amplitude classification (SAR_AC)**. The calibrated SAR (amplitude) image was classified to identify the water-covered area according to the criterion that soil covered by a quiet water table shows low amplitude and can be easily extracted from the image. We applied the classification to Sentinel-1 SLC for the Zaragoza, Po basin and Arahal areas. We also implemented a simple filter, available in the processing raster tool of QGIS software, to smooth local noise effects. Using empirical thresholds based on a visual approach, we classified the SAR data into three classes: (a) Low values of sigma naught (\(\sigma^{\circ}\)) (\(<-25/-20\) dB), i.e., water-covered areas (lakes or flooded areas), flat surface or shadow; (b) middle values (\(-20/-10\) dB), i.e., cultivated land or natural vegetation; and (c) high values (\(>-12\) dB), i.e., dense urban or forested areas or area with bare rocks. We used optical images to validate the accuracy of classification qualitatively. During the refinement step, the operator can separate the flooded area from another type of low-dB area with the help of ancillary data (e.g., permanent water body mask or optical images).
* **Backscattering variation (\(\Delta\sigma^{\circ}\))**. Two SAR images (one post-/co-flood and one pre-flood) were used for change detection analyses. SAR data, acquired in the SLC format, were radiometrically calibrated [66] to obtain the relevant \(\sigma^{\circ}\) maps, i.e., the surface backscattering maps. Calibrated SAR data were averaged through a multi-look operation, with one look in the azimuth direction and four looks in the range one, accurately co-registered [73] and, finally, geocoded [74]. Surface changes due to flooding were detected by calculating the log ratio between the post- and the pre-flood images. Thus, the \(\Delta\sigma^{\circ}\) map showing the temporal variation of surface backscattering from the pre- to the post-flooding phase was produced. For the classification of such a map, we applied an empirical threshold of the \(\Delta\sigma^{\circ}\) value that allows identifying as many flooded areas as possible and minimizes false detection errors.
# Schedulability Analysis in Fixed-Priority Real-Time Multicore Systems with Contention
## 5 Evaluation
### Simulations on a Real Platform  
Besides the simulation environment, a real platform was used to test the proposed contributions. We used a Zybo z7 (Digilent, Tokyo, Japan), which integrates a dual-core ARM Cortex-A9 processor.  
We implemented four real-time tasks in Ada language, which is a programming language used for critical software. In particular, we used the Ravenscar profile [34] to restrict the use of many tasking facilities so that the execution of the program was predictable. The parameters of the four tasks are detailed in Table 5 and were created based on a two-core scenario due to the board characteristics. In order to introduce interference, 50% of the tasks were broadcasting tasks.  
The code that executed the tasks consisted of loops with mathematical operations read and stored in a data array until the task execution time was completed.  
These tasks were allocated to the Zybo dual-core platform by the previously mentioned algorithm, WFDU (Figure 9).  
Figure 8: Percentage of cases that pass the proposed schedulability test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Id** & **C** & **D** & **T** & **1** \\ \hline
0 & 52 & 300 & 300 & 14 \\ \hline
1 & 11 & 300 & 300 & 0 \\ \hline
2 & 52 & 400 & 400 & 5 \\ \hline
3 & 11 & 400 & 400 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Task set parameters (ms) for the experimental use case.  
For this task set, the hyperperiod was \(H=1200\). Then, we calculated the \(WCRT\) measured on the platform and the \(WCRT^{ub}\) of each \(k\) activation. The results are shown in Table 6.  
From these results, we can deduce that the proposed upper bound is, in all cases, greater than the actual values measured on the platform. Apart from that, when all parameters in the system are integers, we may assume without loss of generality that all preemptions occur at integer time values. So, throughout this paper, we have assumed that all parameters are indeed integers. However, on a real platform using Ada, we make use of the package Ada.Real_Time, which has a reliable clock for real-time applications.
## 3 Related Work  
Securing communication in resource-constrained IoT environments, particularly in healthcare, has received increasing attention in recent years. Traditional encryption approaches, such as TLS or IPsec, offer robust security but remain impractical for low-power devices due to high computational overhead and memory consumption [1; 2; 3]. This limitation has motivated the development and evaluation of lightweight cryptographic algorithms optimized for embedded systems.
## 3 Field Test Method  
### Self-Balancing Static Load Test Inspection Principle  
The self-balancing method represents a novel approach for conducting static load tests on foundation piles. Different from traditional pile loading and anchor pile methods, this technique involves embedding a load box (O-cell) and reinforcing cage, which are welded together, inside the pile. The high-pressure oil pipe of the load box is then routed to the surface before pouring concrete into the pile. The load box is subsequently filled with oil and loaded using a high-pressure oil pump located on the ground. This force is transmitted to the pile, with the side resistance of the upper pile segment balancing the side and tip resistance of the lower segment to maintain the applied load. Given the considerable length of the test piles, the rated load capacity of the jack poses limitations on the use of a single load box, making its arrangement challenging. To address this issue, the double load box self-balancing test method was employed in this study. Furthermore, this method offers the following advantages: (1) It enables effective assessments of pile foundation bearing capacity before and after grouting. (2) The loading sequence of the double load box can be flexibly adjusted, allowing for the measurement of bearing capacity across different pile sections. In accordance with the design specifications, the upper load box (Upper O-cell) of test piles TP1 and TP2 was positioned 28.5 m from the pile end, while the lower load box (Lower O-cell) was located 3 m from the pile end. The equipment arrangement for the double load box self-balancing test method is depicted in Figure 2.  
Figure 1: Distribution profile of soil layers of test site and shaft instrumentation.
# Velocity-Aware Handover Self-Optimization Management for Next Generation Networks
## 4 Simulation and Performance Analysis
### Performance Evaluation
#### 4.2.2 Proposed Algorithm  
To analyze the performance of the proposed algorithm, simulations are performed under different mobile speeds. The proposed algorithm is compared with a dynamic algorithm and with those proposed by [PERSON] et al. [11] and [PERSON] et al. [9] algorithms. Figure 6 depicts the average HOP of all  
Figure 4: Average probability of HOPP vs. time.  
Figure 5: Average probability of HOPP vs. UE speeds.  
algorithms under different UE speeds. The proposed algorithm significantly reduces the average HOP compared with the three algorithms under all speeds. The overall average HOP obtained by the proposed algorithm is 70% less than that obtained by the other algorithms.  
Figure 7 illustrates the average HOPP probability with respect to the UE speeds over the entire simulation time. The simulation results demonstrate that the proposed algorithm obtains a lower HOP reduction rate than other algorithms under all UE speeds because the other algorithms do not effectively optimize the HCPs according to UE experience, especially when the UEs move near the cell edge. Consequently, the number of UHOs increase due to the high HOPP rate, especially when the UE moves in high speed. Moreover, a high rate of HOP may result in increased HOPP and HOF rates, where a low rate may decrease the HOPP rate.  
Figure 8 shows the average probability of RLF with respect to UE mobile speed, which presents the frequent rate of disconnection radio links between BSs and UE mobility. The average probability rate of RLF is obtained from the overall simulation time for each UE speed. The results imply that the  
Figure 6: Average HOP with varying UE speeds.  
Figure 7: Average probability of HOPP with varying UE speeds.  
proposed algorithm achieves a significant reduction in RLF compared with other algorithms under all mobile speed scenarios. The algorithm proposed by [PERSON] et al. achieves a higher RLF rate than other algorithms because only the HOM is adjusted according to UE speed. Therefore, both HCPs should be considered to manage the HO decision according to UE experience. In summary, the RLF rate of all compared algorithms gradually increases when the UE speed increases due to Doppler Effect and weak radio link connections. The proposed algorithm outperforms the other algorithms by more than 70%.
## 2 Study Background
### Gradient-Boosting Decision Tree
#### 2.1.2 Classification and Regression Tree  
CART is a basic classification and regression approach that consists of (decision) root nodes, leaf nodes, and directed edges, as shown in Figure 2[39; 40]. The root nodes denote features or attributes, while categories are shown by the leaf nodes [41]. In this paper, the CART for regression problems was used, whose core rationale is to identify the best partitioning point and confirm the output results of the leaf nodes. After the spatial region of the training set is effectively divided according to a particular method, the regression tree is then constructed by determining the values of each subregion. The specific process is as follows:  
(1) Determine the optimal value of the variable, \(j\), and the segmentation point, \(s\), to minimize Equation (1).  
\[\underset{j,s}{min}[\underset{c_{1}}{min}\sum_{x_{1}\in R_{1}(j,s)}\left(y_{i}- c_{1}\right)^{2}+\underset{c_{2}}{min}\sum_{x_{2}\in R_{2}(j,s)}\left(y_{i}-c_{2} \right)^{2}] \tag{1}\]  
where \(x_{i}\) and \(y_{i}\) represent the input and output variable, respectively; \(c_{1}\) and \(c_{2}\) are the mean values of the interval; \(R_{1}\) and \(R_{2}\) are some subregions of the input space.  
(2) As shown in Equations (2) and (3), the selected pair (\(j\), \(s\)) is used to divide the region and output the corresponding value:  
\[R_{1}(j,s)=\left\{x\Big{|}x^{(j)}\leq s\right\},R_{2}(j,s)=\left\{x\Big{|}x^{( j)}>s\right\} \tag{2}\]  
\[\hat{c_{\text{m}}}=\frac{1}{N_{\text{m}}}\sum_{x_{1}\in R_{\text{m}}(j,s)}y_{i },\,x\in R_{\text{m}},\,\,\text{m}=1,2 \tag{3}\]  
where \(x^{(j)}\) is the \(j\)-\(th\) variable, and \(s\) is the value it takes. The optimal value, \(\hat{c_{\text{m}}}\), is the mean of the corresponding output, \(y_{i}\), of all input instances, \(x_{i}\), on region \(R_{\text{m}}\).  
Repeat steps (1) and (2) until the termination condition is met. In addition, if the input space is divided into \(M\) regions, such as \(R_{1}\),\(R_{2}\)... \(R_{\text{m}}\), then a complete regression tree can be constructed according to Equation (4), as shown in Figure 2.  
\[\text{f}(x)=\sum_{\text{m}=1}^{M}\hat{c_{\text{m}}}I(x\in R_{\text{m}}) \tag{4}\]  
where \(I\) stands for the impurity of the decision tree.
## 4 Conclusions  
In this paper, we prepared the perovskites (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) and (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) via solvent evaporation. The perovskites (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) and (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) were well crystallized and oriented, and exhibited the layered structures with alternating organic and inorganic components. The absorption peaks were located at 392 nm (3.16 eV) and 516 nm (2.40 eV) respectively. PL intensity of the perovskite (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbBr\({}_{4}\) increased from 117 photon counts to 2195 photon counts, PLQY varied from 81.48% to 90.91%, and lifetime decreased from 0.75 ns to 0.50 ns when laser power increased from 10 mW to 100 mW. In contrast, PL intensity of the perovskite (C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)NH\({}_{3}\))\({}_{2}\)PbI\({}_{4}\) increased from 158 photon counts to 2771 photon counts, PLQY varied from 83.55% to 91.28%, and lifetime decreased from 0.67 ns to 0.46 ns when laser power increased from 10 mW to 100 mW. The fluence-dependence of PL intensity and lifetime with laser power is mainly due to the non-geminate recombination. The photoluminescence properties of organic inorganic hybrid perovskites can be tuned to a wide range, which has important applications in the optoelectronic field.  
[PERSON] carried out the experiment and wrote the manuscript; [PERSON] helped in revising the manuscript; [PERSON], L.X., F.Y. and [PERSON] helped in doing the experiment and analyzing the data.  
This research was supported by the National Natural Science Foundation of China (Grant No. 11204222), the Natural Science Foundation of Hubei Province, China (Grant No. 2013 CFB316, Grant No. 2014 CFB793), the Postgraduate Education Innovation Fund, Wuhan Institute of Technology (No. CX2016106).  
[PERSON] would like to thank for the support from Laboratory of Optical Information and Technology, Wuhan Institute of Technology; Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology.  
The authors declare no conflict of interest.  
Figure 10: Energy level diagram of the perovskite molecule. VB, valence band; CB, conduction band; ESS, electron surface states.
# Assessment of Lightweight Concrete Thermal Properties at Elevated Temperatures
## 1 Introduction
Between 400 and 600 \({}^{\circ}\)C, there is a dissociation of calcium hydroxide (Ca(OH)\({}_{2}\)) into calcium oxide (CaO) and water (H\({}_{2}\)O). At this point, there is a complete desiccation of the pore system. From this point on, the gradual mass loss is attributed to the release of carbon dioxide (CO\({}_{2}\)) from calcium carbonate (CaCO\({}_{3}\)) [22]. Therefore, specific heat is highly dependent on free water content. It is usually measured by means of differential scanning calorimetry (DSC) or differential thermal analysis (DTA) for temperatures above 600 \({}^{\circ}\)C [1]. Most research on LWC shows a significant variation in the thermal properties, which are affected by the fraction of lightweight aggregates and density [7; 23]. The addition of regular aggregates facilitates heat conduction. The introduction of internal pores affects thermal behavior, increasing the absorption of water into the lightweight aggregate and delaying the dehydration of the calcium hydroxide [4]. However, the standard EN 1992-1-2 [17] and the standard EN 1994-1-2 [18], hereafter referred to Eurocode 2 and Eurocode 4, respectively, set a constant value for specific heat of 840 J/(kg\(\cdot\)K) for any LWC, as well as a linear variation in thermal conductivity in the range of 20-800 \({}^{\circ}\)C.  
According to the literature, there are many studies on lightweight aggregates and their usage in lightweight systems. However, studies about the effect of elevated temperatures in LWC thermal properties are limited, and there is also limited guidance for undertaking standardized property tests at elevated temperatures [24]. Therefore, this work carries out research on the thermal conductivity and specific heat of various structural LWCs. Samples containing different proportions of aggregates are compared at ambient and elevated temperatures. To measure thermal properties, three different test methods are used: differential scanning calorimetry (DSC), laser flash analysis (LFA), and modifiedtransient plane source (MTPS). There are three main objectives of this research work. First, we seek to measure the specific heat (\(c_{p}\)) of the LWC over a wide temperature range (20-1000 \({}^{\circ}\)C); the measured \(c_{p}\) values of LWC using DSC and MTPS methods are compared. Second, we aim to study thermal conductivity (\(k\)) over a range of 20-300 \({}^{\circ}\)C in LWCs; for this purpose, MTPS and LFA methods are combined. Finally, we endeavor to propose a relationship between density, thermal conductivity, and temperature, as well as between specific heat and temperature.