esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Privacy-Preserving Collection of Health Data from a User's Wearable Device
## 3 Problem Definition, Background and Naive Solution
### Local Differential Privacy  
The basic concept of LDP involves perturbation of the original data by the data owner by adding random noise, and reporting the perturbed data to a data aggregator. This mechanism guarantees that the data owner's original data are not exposed to an external entity. LDP is defined as follows: A randomized algorithm \(\mathcal{A}\) satisfies \(\epsilon\)-LDP, if and only if for (1) all pairs of the data owner's local data \(v_{a}\) and \(v_{b}\), and (2) any output \(O\) of \(A\), the following equation is satisfied [3; 4; 5]:  
\[Pr[\mathcal{A}(v_{a})=O]\leq\epsilon^{\epsilon}\times Pr[\mathcal{A}(v_{b})=O].\]  
Here, \(Pr[\mathcal{A}(v_{a})=O]\) denotes the probability that the output of running a randomized algorithm \(\mathcal{A}\) with \(v_{a}\) is \(O\). The meaning of the aforementioned definition is that, regardless of the data an aggregator receives from a data owner, the aggregator cannot infer with, high confidence (which is controlled by the privacy budget \(\epsilon\)), whether the data owner has sent \(v_{a}\) or \(v_{b}\). This provides the data owner with plausible deniability. The parameter \(\epsilon\) is a privacy budget that controls the level of privacy. That is, small values of \(\epsilon\) ensure strong privacy, guaranteed by adding a comparatively larger amount of noise to the original data. By contrast, large values of \(\epsilon\) provide weak privacy protection by adding less noise to the original data.  
An important property regarding the privacy budget is the sequential composition property, which LDP follows to attain differential privacy. That is, the available privacy budget, \(\epsilon\), can be partitioned into \(n\) smaller privacy budgets, \(\epsilon_{1},\epsilon_{2},\cdots,\epsilon_{n}\), such that \(\epsilon=\sum_{h=1}^{n}\epsilon_{h}\) and the data owner uses each small privacy budget to report their local data to an aggregator.
## 3 Results and Discussion
### Managing Dynamicity and Cold Start  
The cold start and dynamicity in activity detection scenarios have been studied, e.g., the cold start setting in [PERSON] et al. [2], and the dynamic aspect of available sensors [22; 23]. The results indicate that the online learning approach is a rather robust approach for handling sensor dynamicity, including being rather straightforward with Naive Bayes, SVM, and k-NN to adapt the learning to the dynamicity, i.e., the removal and inclusion of sensor input. Obviously, interactive learning is logically sound for coping with cold start scenarios, both in settings with a true cold start (without any training data or trained model existing at the start) [2] and in settings with pre-training that has occurred by, for instance, using transfer learning (see more in Section 3.4). In any case, it requires a user to willingly provide user feedback immediately after the system with IDIVS has started to run. As mentioned, though, such a user needs to be an interactive service user but may also be an IDIVS teacher that takes responsibility for training the IDIVS directly after its deployment.
# Niching Grey Wolf Optimizer for Multimodal Optimization Problems
## 3 Result and Discussion
### Engineering Application of NGWO
#### 3.3.1 Welded Beam Design  
The objective of this case study is to minimize a welded beam's fabrication cost. It consists of four decision variables such as bar thickness (b), bar height (t), attached part length (l), and weld thickness (h), and seven inequality constraints (sheer and bending stress, side constraints, end deflection, buckling load). The problem is mathematically formulated as [43]:  
\[\mathrm{f(y)}=1.10471\mathrm{y}_{1}^{2}\mathrm{y}_{2}+0.04811\mathrm{y}_{3} \mathrm{y}_{4}(14+\mathrm{y}_{2}) \tag{34}\]  
\[\tau(\mathrm{y})=\sqrt{\ \tau_{1}^{2}+\tau_{1}\ \tau_{2}\frac{\mathrm{y}_{2}}{2 \mathrm{R}}+\tau_{2}^{2}} \tag{35}\]  
\[\tau_{1}=\frac{\mathrm{P}}{\sqrt{2}\mathrm{y}_{1}\mathrm{y}_{2}} \tag{36}\]  
\[\tau 2=\frac{\mathrm{MR}}{\mathrm{J}} \tag{37}\]  
\[\mathrm{M}=\mathrm{P}\Big{(}\mathrm{L}+\frac{\mathrm{y}_{2}}{2}\Big{)} \tag{38}\]  
\[\mathrm{R}=\sqrt{0.25*\left(\mathrm{y}_{2}^{2}+\left(\mathrm{y}_{1}+\mathrm{ y}_{3}\ \right)^{2}\right)} \tag{39}\]  
\[\mathrm{J}=2/\left\{\sqrt{2}\mathrm{y}_{1}\mathrm{y}_{2}\Bigg{[}\frac{\mathrm{ y}_{2}^{2}}{12}+\frac{(\mathrm{y}_{1}+\mathrm{y}_{3})2}{4}\Bigg{]}\right\} \tag{40}\]  
\[\sigma(\mathrm{y})=\frac{6\mathrm{PL}}{\mathrm{x}_{4}\mathrm{y}_{3}^{2}} \tag{41}\]  
\[\delta(\mathrm{y})=\frac{4\mathrm{PL}^{3}}{\mathrm{E}\mathrm{y}_{4}\mathrm{y} _{3}^{3}} \tag{42}\]  
\[\mathrm{P}_{\mathrm{c}}(\mathrm{y})=\frac{4.013\mathrm{E}\,\mathrm{y}_{3} \mathrm{y}_{4}^{3}}{6\mathrm{L}^{2}}\ (1-0.25\mathrm{y}_{3}\sqrt{\left(\frac{\mathrm{E}}{ \mathrm{G}}\right)/\mathrm{L}}) \tag{43}\]Here, \(\mathrm{P}=60000\mathrm{lb}\), \(\mathrm{L}=14\) inch, \(\delta_{\mathrm{max}}=0.25\) inch, \(\mathrm{E}=30\times 10^{6}\) psi, \(\mathrm{G}=12\times 10^{6}\) psi, \(\tau_{\mathrm{max}}=13600\) psi, \(\sigma_{\mathrm{max}}=30000\) psi. The inequality constraints are:  
\[\mathrm{c_{1}(y)}=\ \tau(y)-\ \tau_{\mathrm{max}}\leq 0 \tag{44}\]  
\[\mathrm{c_{2}(y)}=\ \sigma(y)-\ \sigma_{\mathrm{max}}\leq 0 \tag{45}\]  
\[\mathrm{c_{3}(y)}=\ y_{1}-\mathrm{y_{4}}\leq 0 \tag{46}\]  
\[\mathrm{c_{4}(y)}=1.10471y_{1}^{2}+0.04811y_{3}\mathrm{y_{4}(14+y_{2})}-5\leq 0 \tag{47}\]  
\[\mathrm{c_{5}(y)}=\ \delta(y)-\ \delta_{\mathrm{max}}\leq 0 \tag{48}\]  
\[\mathrm{c_{6}(y)}=\ \mathrm{p}-\mathrm{p_{c}(y)}\leq 0 \tag{49}\]  
\[\mathrm{c_{7}(y)}=0.125-y_{1}\leq 0 \tag{50}\]  
The decision variable bounds are:  
\[0.1\leq y_{1}\leq 2;\ 0.1\leq y_{2}\leq 10\ ;\ 0.1\leq y_{3}\leq 10;0.1\leq y_{4 }\leq 2\]
## 3 Results and Analysis
### Analysis of the Seismic Performance of the Frame  
#### 3.3.1 Factors Affecting Ductility  
1. Steels with different yield points  
The test piece KJ-1 was only equipped with an ordinary skeleton, while the test piece KJ-2 was equipped with a low yield point skeleton. All test conditions were the same except for the choice of steel. According to the analysis in Table 6, the ductility coefficient of specimen KJ-2 was 15.7% higher than that of specimen KJ-1. When the bearing capacity was not significantly reduced, the deformation of the structure was significantly improved. It showed that the use of low yield point reinforcement had a good effect on improving the ductility of the reinforced concrete frame.
# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
## 1 Introduction
On the other hand, TL utilizes knowledge from one domain to aid tasks in another related field. In bearing FD, we can pre-train the neural network model on a large dataset and subsequently transfer it for fine-tuning in the task of FD [23; 24; 25]. This method of pre-training and fine-tuning can make the model converge faster and still maintain good performance in the case of data scarcity [26; 27]. For example, [PERSON] et al. [28] used the TL strategy for small-sample FD. [PERSON] et al. [29] introduced a framework for FD using a dynamic model and TL to address the issue of small samples. [PERSON] et al. [30] used TL and a measurement network to realize cross-domain small-sample FD. [PERSON] et al. [31] introduced an FD method based on the finite element method and TL. [PERSON] et al. [32] proposed a multi-channel TL network based on graph attention (GAMTLN) to address the issue of small-sample FD. To sum up, aiming at the problem that bearing faults are limited and difficult to obtain in reality, we developed a method for FD in small-sample bearings using a two-dimensional image and ResNet18, employing a pre-trained and fine-tuned method. The primary contributions of this study are outlined as follows:  
1. The original vibration signal is broken down using VMD; the \(k\) value and IMF component of VMD decomposition are selected by using the center frequency and kurtosis criteria. 2. The IMF decomposed by VMD is successively generated into a picture through SDP transformation, and the Pearson correlation analysis method is used to select the optimal SDP parameters to increase the discrimination of a bearing fault image, which further improves the conversion effect from a vibration signal into a two-dimensional image. 3. The ResNet18 model with a pre-trained and fine-tuned method is used to efficiently learn and represent the extracted two-dimensional image features, which realizes more accurate and robust capture and recognition of bearing fault features. The remainder of this paper is structured as follows. Section 2 presents the fundamental concepts of VMD, SDP, and ResNet18. Section 3 outlines the detailed steps of the proposed FD framework. Section 4 verifies and compares some other methods through two datasets. Section 5 is the conclusion.
# A Parallel Approach for Multi-GNSS Ultra-Rapid Orbit Determination
###### Abstract  
Global Navigation Satellite System (GNSS) ultra-rapid orbit is critical for geoscience and real-time engineering applications. To improve the computational efficiency and the accuracy of predicted orbit, a parallel approach for multi-GNSS ultra-rapid orbit determination is proposed based on Message Passing Interface (MPI)/Open Multi Processing (OpenMP). This approach, compared with earlier efficient methods, can improve the efficiency of multi-GNSS ultra-rapid orbit solution without changing the original observation data and retaining the continuity and consistency of the original parameters to be estimated. To obtain high efficiency, three steps are involved in the approach. First and foremost, the normal equation construction is optimized in parallel based on MPI. Second, equivalent reduction of the estimated parameters is optimized using OpenMP parallel method. Third, multithreading is used for parallel orbit extrapolation. Thus, GNSS ultra-rapid orbit determination is comprehensively optimized in parallel, and the computation efficiency is greatly improved. Based on the data from MGEX and IGS stations, experiments are carried out to analyze the performance of the proposed approach in computational efficiency, accuracy and stability. The results show that the approach greatly improves the efficiency of satellite orbit determination. It can realize 1-h update frequency for the multi-GNSS ultra-rapid orbit determination using 88 stations with four-system observations. The accuracy of the GPS, GLONASS, Galileo and BDS ultra-rapid orbit with 1-h update frequency using the parallel approach is approximately 33.4%, 31.4%, 40.1% and 32.8% higher than that of the original orbit, respectively. The root mean squares (RMS) of GPS, GLONASS, Galileo and BDS predicted orbit are about 3.2 cm, 5.1 cm, 5.6 cm and 11.8 cm. Moreover, the orbit provided by the proposed method has a better stability. The precision loss of all parallel optimization can be negligible and the original correlation between the parameters is fully preserved.  
multi-GNSS; parallel computing; satellite orbit; computationally efficient +
Footnote †: journal:  
00 August 2021
## 3 Results and Discussion
### Asphalt Mixture Performance Analysis
#### 3.2.3 Low-Temperature Performance  
We evaluated the low-temperature performance of mixes using the three-point bend test. The results of the three-point bending tests of BJ200 and SSEA mixtures are shown in Figure 17.  
From Figure 17a-c, it can be seen that the flexural tensile strength and flexural tensile modulus of the SSEA mix gradually decrease and the flexural strain gradually increases with an increase in asphalt content. Comparing SSEA-5 with SSEA-1, the flexural tensile strength decreases by 23.9%, flexural tensile modulus decreases by 59.1%, and flexural strain increases by 86.1%. This indicates that the asphalt ratio greatly affects the low-temperature performance of SSEA, and the ability to resist cracking at low temperatures gradually increases as the asphalt ratio rises. This phenomenon is in line with the findings of [PERSON] et al. [49], who also pointed out that a higher proportion of epoxy resin and lower bitumen content help to improve the high-temperature performance of asphalt mixtures. However, as the proportion of bitumen increases, the temperature sensitivity of the material increases, leading to a decrease in its resistance to deformation.  
A comparison between SSEA mix and BJ200 shows that \(-\)10 \({}^{\circ}\)C flexural tensile strength and flexural strain are lower than BJ200. SSEA mixture bending strain accounts for 40.1-74.7% of the BJ200 bending strain, which indicates that SSEA mixture in the asphalt content of low-temperature performance is poor. When the asphalt content is higher, the  
Figure 17: Three-point bending test results for SSEA. (**a**) Bending strength, (**b**) bending strain, and (**c**) bending failure stiffness.  
bending and tensile strains gradually increase, and the low-temperature performance is improved. Still, there is a particular gap compared with the imported asphalt BJ200.
## 3 Simulation Analysis Using SSA for GREATEM Signal De-Noising
### De-Noising Simulation Analysis for GREATEM Signal with Measured Noise  
In the process of GREATEM system detection, the measured data often contains different types of noise. When the detection is near a transformer or under high-voltage transmission lines, the noise is mainly caused by the higher harmonic noise. The attenuation law of GREATEM signals is close to the exponent function. The early attenuation rate of the signal is fast, and its amplitude is large. The late measured data is similar to DC data, and its amplitude is 4-5 orders lower than that of the early period. Since the late data is weak, the noise interference of the late-stage signal is more serious. Firstly, we simulated and calculate the GREATEM signal with measured noise, as shown in Figure 6. We can see that the effect of measured noise on the late signal is more significant. Then we used SSA to de-noise the signal, as shown in Figure 7. The GREATEM signal is reconstructed according to the different singular values, and the noise components are separated from it.  
Figure 5: Different GREATEM signals and their singular spectra. (**a**) GREATEM Signal with white noise; (**b**) GREATEM Signal with power frequency and white noise; (**c**) Measured GREATEM data; (**d**) singular spectra of (**a**); (**e**) singular spectra of (**b**); (**f**) singular spectra of (**c**).  
Figure 6: GREATEM signal with measured noise and corresponding frequency spectrum.  
Figure 7a is the reconstructed GREATEM signal, and Figure 7b is the separated measured noise. Figure 8 shows the signal comparison before and after SSA de-noising. The \(\mathrm{Xn}\) is the calculated GREATEM signal with noise, the SSA is the reconstructed GREATEM signal by SSA, and \(\mathrm{Sn}\) is the calculated GREATEM signal without noise. After comparison with Figure 6, the reconstructed signal fits well with the calculated GREATEM signal. This verifies that the SSA algorithm could effectively separate the GREATEM simulation signal.  
From the change of the signal curve, the reconstructed signal is smooth and fits well with the original signal. We quantify the de-noising effect using SNR and RMSE. The expressions are as follows:  
\[SNR=10\times\log_{10}\frac{P_{signal}}{P_{noise}} \tag{14}\]  
\[RMSE=\sqrt{(\sum_{n=1}^{N}[\hat{\mathcal{V}}_{z}(n)-\hat{\mathcal{V}}_{z}(n) ]^{2}])/N} \tag{15}\]  
According to the formula (14) and (15), the SNR of the noisy synthetic signal is 34.8265 dB. After SSA, the SNR is increased to 65.8314 dB, adding nearly 31 dB. The RMSE before and after SSA are 3.6335 \(\times\) 10\({}^{-4}\) and 1.0002 \(\times\) 10\({}^{-5}\), respectively, verifying that the SSA algorithm has a significant de-noising effect on GREATEM signals.
## 3 Results and Discussion
Figure 6b shows the corresponding conversion efficiency of the MIR-DW. When the coupled pulse energy is low, for example, 170 pJ, the power of the MIR-DW in the output spectrum is relatively low. This results in a low conversion efficiency of 10.79%. As the coupled pulse energy increases, we find a clear structure of dual hump-shaped MIR-DWs in the output spectra. Although there are many oscillating structures in the conversion efficiency curve, it is almost always above 10% and has a peak value of 19.31% at 190 pJ. The reason is that the pump energy transferred to the MIR-DW between 2 and 3 \(\upmu\)m is high at first, then it begins to go down, whereas the energy of the MIR-DW at around 3.65 \(\upmu\)m is continuously increasing. Accompanied by this phenomenon, the generated MIR-DWs can act as a sub-pump in the normal dispersion region when its energy reaches the threshold. Then, the MIR-DWs start to shed energy to other frequencies through nonlinear coupling, such as SPM and cross-phase modulation (XPM) effects. When the pump energy is further increased, the spectral components in the anomalous dispersion region refresh the energy of MIR-DWs through both DW generation and four-wave mixing (FWM) effects. Consequently, the conversion efficiency is lifted again. This reciprocating process is consistent with the experimental results in [7]. As for the flatness of these spectra in Figure 6c, it has a similar oscillating structure. When the coupled pulse energy is 190 pJ, the flatness of this spectrum is as high as 6.31 dB ranging from 1.92 to 3.55 \(\upmu\)m, whereas for the coupled pulse energy below 190 pJ, the flatness is low due to the relative low power of the MIR-DW on the longer wavelength side. When the coupled pulse energy increases to 210 pJ, the dual hump-shaped MIR-DWs generation is more obvious, so the flatness decreases. As the coupled pulse energy increases further, the part of the spectra between the dual hump-shaped MIR-DWs is lifted; thus, the spectra is flattened. When the coupled pulse energy reaches 340 pJ, the value of the flatness becomes very large because of the unbalanced power distribution between the dual hump-shaped MIR-DWs. Figure 6: (**a**) The 3D waterfall of the output spectra at \(L=0.95\) mm with the full-width at half maxima \(\tau_{\mathrm{FWHM}}=75\) fs at different coupled pulse energies. (**b**) The conversion efficiency as a function of the coupled pulse energy. (**c**) The flatness as a function of the coupled pulse energy.
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
## 5 Performance Analysis of RICC  
### Energy Consumption of RICC  
Figure 8 is introduced to show the comparisons of energy consumptions between RICC and NCCC. As the previous analyses show, RICC and NCCC have the same largest energy consumption, so the network lifetime is maintained at the same level. However, in non-hotspot areas, RICC has a larger energy consumption than NCCC does, which is the consequence of increasing the data transmission power of the nodes. According to observation 1 in Section 4.1, when the transmission power of a node exceeds 0.1 J/Hop/Packet, the MFR does not significantly improve, which means that MFRs with different cluster sizes all reach their lower bounds, so we set an upper threshold for the power as 0.1 (J/Hop/Packet). But the energy utilization still cannot be perfectly balanced. The useof a more even energy utilization to achieve a better network performance is left for our future work. The corresponding energy used by nodes for delivering a packet per hop is given in Figure 9.  
The average enhanced energy utilization ratio of the network is shown in Figure 10; specifically, the ratio is improved by 59.4%-62.8%, which indicates a higher energy usage efficiency of RICC compared with NCCC.  
Figure 8: Total energy consumption of nodes.  
Figure 10: Average enhanced energy utilization ratios for different network sizes.  
Figure 9: Energy used for delivering a packet per hop.
## 6 Conclusions, Limitations, and Future Work
### Limitations  
Although our experiments showcased promising results for position-independent activity recognition using affordable wearable sensors, there are challenges to consider when deploying this deep learning model in real-world settings:  
* Computational limitations: The Att-ResBiGRU model requires up to 4 MB of memory, exceeding the capabilities of low-power wearable devices like smartwatches. To address this, model compression and quantization techniques are necessary for deployment on resource-constrained devices.
* Sensor drift: Sensor performance can degrade over time, leading to changes in signal distribution and potentially impacting the model's accuracy. Implementing mechanisms for recalibration or adaptive input normalization would enhance robustness.
* Battery life: Continuous sensor data collection for long-term activity tracking rapidly depletes battery life. Optimizing duty-cycling strategies is crucial to enable more extended monitoring periods.
* Privacy concerns: Transmitting raw, multi-dimensional sensor data raises privacy concerns, as it could reveal sensitive information about daily activities or underlying health conditions. Federated learning and selective feature-sharing approaches could mitigate these concerns and encourage user adoption
## 4 Experimental Results
### Inputs to Build VKGs: Ontology, Mappings, Queries, Databases, and Ontop
#### 4.4.4 Ontop Core  
The core part of Ontop is the SPARQL engine Quest, which is responsible for rewriting SPARQL queries over the virtual RDF graph and the domain-centric federated ontology into SQL queries over the relational database (see Table 3).  
Using Ontop Virtual Platform as Transformation Mapping Artifact for Data Integration and Data Interoperability Management  
Developing a transformation artifact using Ontop is essential for data integration and ensuring seamless interoperability. We described domain-centric ontology (e.g., seed ontology) as representing the relevant concepts and relationships within data sources. We also map data from each source, such as individual relational databases, to the corresponding ontological concepts and build a Federated Ontology (FO). Moreover, we utilized Ontop, which establishes mapping between elements in the data sources and the concept defined in the domain ontology (e.g., CVO ontological model). Additionally, Ontop applies transformation rules using mapping languages like R2 RML to convert and align data from their primitive format to the federated ontologies. These rules enable querying, transformation, and alignment with the domain ontology. The execution of this mapping phenomenon can be seen in Figure 8.