esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Feature Extraction  
Extracting representative features from sensor data is essential to improve the prediction capabilities of the ML algorithm. In this work, we use a sliding-window-based technique to extract statistical features from preprocessed data provided by the data integration module. We partition each day into 12 time slots of two hours each. For each time slot, we compute the following features.  
* The time duration spent performing each activity type, i.e., _stationary_, _walking_, _running_, or _unknown_.
* The time duration of each detected sound type, i.e., _silence_, _voice_, _noise_, or _unknown_.
* The time duration during which the smartphone was _locked_.
* The time duration during which the smartphone was in a _dark_ environment.
* The time duration during which the user was engaged in a _conversation_.  
In the above features, all the time durations are measured in minutes. We also collect the number of hours slept during the previous night, as well as features regarding the distance traveled during the morning (7 a.m.-1 p.m.), afternoon (1 p.m.-8 p.m.), and night (8 p.m.-7 a.m.), respectively. In these features, the traveled distance is measured in kilometers.  
The above features represent the context information extracted during the day from the smartphone user's activities. Hence, a user's day is characterized by 135 contextual features.  
Finally, the ground truth regarding _sleep quality_ is represented by a real number in [1; 4], where 1 represents very good sleep quality, and 4 represents very bad sleep quality. As explained before, the ground truth is collected from a selected set of users by means of questionnaires.
## 3 Modeling of Metal Artifacts in X-ray CT
### Results of Modeling Metal Artifacts
#### 3.2.2 Objects of the Second Group  
As an example, we chose an Al cylinder 250 mm in diameter with a central cylindrical cavity 100 mm in diameter and twelve cylindrical inserts 40 mm in diameter with densities from 0.2 g/cm\({}^{3}\) to 2.4 g/cm\({}^{3}\) in increments of 0.2 g/cm\({}^{3}\).  
Figure 4 shows the corresponding reconstructed slices.  
For asymmetric objects, metallic artifacts appear as streaks [7; 8; 25; 27; 47; 48]. For the simulated object and \(h_{\rm f}\) = 0 mm and \(k_{\rm ADC}\) = 16, increasing the maximum energy to 300 keV  
Figure 4: Reconstructed slices of a 250 mm aluminum cylinder with cylindrical inserts.  
Figure 3: Reconstructed slices of an aluminum pipe with a diameter of 250 mm and a wall thickness of 75 mm, \(k_{\rm ADC}\) = 16.  
leads to a decrease in streaks artifacts. The same effect for X-rays with \(E_{\rm max}=150\) keV is achieved by increasing the ADC bit depth to \(k_{\rm ADC}=20\) and using 5 mm thick copper pre-filters. Moreover, the simultaneous increase of the ADC bit depth and pre-filtering most effectively eliminate metal artifacts.  
For the object under study, the correct choice of the parameters of the X-ray CT system made it possible to reduce the number of metal artifacts, as in the first two examples.
# Logistics Service Provider Lifecycle Model in Industry 4.0: A Review
## 4 Concept of Logistic Service Provider Lifecycle Model (LSLM)
### Sub-Criteria of Real-Time Information Sharing  
Real-time information sharing has been performed to operate and manage the activities of logistics services [3; 26]. Improving lead times on-time delivery, reducing cost, and fulfilling customer satisfaction afford a client trust that the activities along the supply chain can satisfy their customer requirements [30; 110; 111]. Infrastructure will be important to implement information sharing such as ERP systems, RFID, cloud technology, blockchain, and GPS [85; 105; 106]. The linkage interaction in the supply chain with information sharing increases customer conviction and obligation (trust and commitment). [PERSON] and [PERSON] demonstrated that three aspects of information sharing are significant for improving efficiency in the supply chain [26; 55]. In high-level management in companies, information sharing, tracking and traceability, real-time automation, monitoring, cloud technology, and big data have been used through implementing logistics operation management, which is relevant in the vertical and horizontal management of strategies, tactics, and operations [26; 84; 110]. Hence, the effect of information sharing is a phenomenon to satisfy customers to sustain their alignment; high investment in infrastructure to fulfil customers is a gap of a break-even point between cost and reliability [76; 112].
## 5 Conclusions  
1. This paper takes the perilous rock on the soil slope as the research object, assuming that the foundation soil is homogeneous and isotropic, the damping ratio is less than 1, and the deformation is linear elastic deformation in the amplitude range. The vibration model of the perilous rock on the slope was simplified into a vibration model with multiple degrees of freedom. Based on the dynamic theory, the quantitative relationship among the natural vibration frequency, mass, side length, deformation modulus of the foundation soil, and buried depth of the perilous rock on the soil slope was established; that is, the dynamic characteristic model of the perilous rock on the slope was set up.
2. The indoor model tests were carried out with models of different mass, different side lengths, and different buried depths. The experimental findings demonstrate that as the buried depth of perilous rock increases the frequency of its natural vibrations grows steadily in a quadratic fashion. The natural amplitude vibrations decrease quadratically as the mass of potentially perilous rock increases. Table 3 shows that the dynamic characteristic model of the perilous rock developed in this research is accurate, as predicted by the tests.
3. Combined with the dynamic characteristic model and limit equilibrium model of the perilous rock on the soil slope, the evaluation model of perilous rock stability based on the natural vibration frequency was established, and the model was verified to be correct by a field test. Based on the research results, the buried depth of the perilous rock can be calculated indirectly by measuring its natural vibration frequency. Its safety coefficient on the soil slope can be calculated. In this way, these calculations offer the theoretical basis and technical support for early identification, stability evaluation, and monitoring and early warning.  
Figure 9: Perilous rocks. (**a**) A is a perilous rock on the open-top landslide; (**b**) B is a perilous rock on the open-top landslide near A.  
**Author Contributions:** [PERSON] and [PERSON] contributed to the conception of the study and performed the experiment; [PERSON] and [PERSON] were responsible for data collection; [PERSON], [PERSON] and [PERSON] made significant edits to the manuscript; [PERSON], [PERSON] and [PERSON] were in charge of making important revisions to the manuscript; [PERSON] and [PERSON] helped perform the analysis with constructive discussions. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. U1704243) and the Natural Science Foundation granted by the Department of Education, Anhui Province (No. KJ2020A0235).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussions
### Evaluation of LIFs and Modeling Criteria  
The results of the IGR test indicated that with an IGR > 0, all of the selected LIFs were important and would not introduce any problem in the modeling process (Figure 6). The results of the multicollinearity test showed that none of the LIFs had a collinearity problem (Figure 6). In addition to the other factors affecting the accuracy of the models discussed above, the model results were also influenced by the training data in terms of the number of samples, which played a vital role in tuning the models. As a rule of thumb, [PERSON] et al. [71] found that a minimum of 10 \(\times\) d \(\times\) C samples are required to tune models with 'C' classes in d-dimensional problems. Therefore, in this study, the number of training samples used (280) was above the minimum requirement, and these were further used to tune and train the models to achieve training accuracies of 0.866, 0.854, 0.760, 0.892, and 0.707 for RF, SVM, maxENT, GBM, and LR, respectively.  
The modeling results of RF, SVM, maxENT, GBM, and LR, obtained using training data, were classified into five different classes. The spatial distributions of the landslide susceptibility classes shown in Figure 8 present the varying nature of the results in different models. Therefore, to check the similarities in the prediction results of the models in the area, a cross-correlation between the model outputs using validation data was performed, and it was found that RF-SVM, RF-GBM, and SVM-GBM had the most-correlated models in this study, with correlation values of 0.993, 0.981, and 0.985, respectively (Figure 11a). This verifies that the three models, RF, SVM, and GBM, performed very well, with approximately similar accuracy in the area.  
Figure 11: Model comparison: (a) correlation between models, (b) spider graph of SVM vs. slope, aspect.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 2 Related Work
### Message Filtering  
In threshold detection-based secure synchronization methods, a suspicious node would be isolated once it is voted as an attacker. These methods are unfavorable for masquerade attacks. The message filtering technique sifts out the trusted messages to compute clock parameters rather than isolating the suspicious ones crudely. Linear clock characteristic promotes the design basis of message filtering based secure time synchronization.  
[PERSON] et al. [21] proposed RTSP to defend against the Sybil attack. As the time messages from the same node obey a linear relationship, namely, a conformance relationship, RTSP employs a graph theoretical approach to filter suspicious messages in the bufferinstead of identifying the node. Finally, the anomaly detection process is evolved into a dynamic programming approach to find the maximum clique, namely, valid time messages. To further reduce the security defense costs, [PERSON] et al. (2019) utilized the timestamp correlation among different nodes and the uniqueness of a node's clock skew to detect invalid information rather than isolating suspicious nodes. Although the two protocols ([PERSON] et al., 2019; [PERSON] et al., 2019) can defend against Sybil attacks, they are applied to fully distributed networks and are not suitable for centralized networks with a time source.
## 5 Conclusions  
Considering the unmeasured satellite Li-ion battery capacity and dynamic demand of RUL prognosis, a DLSTM-based, indirect RUL prognosis method is provided in this paper. This method can extract an indirect HI which can replace battery capacity and realize Li-ion battery on-line RUL prognosis. The verification results on NASA #5 battery data sets demonstrate the feasibility and effectiveness of the proposed method.  
However, the operation conditions and environment are constant in this paper. The Li-ion battery RUL, based on different operations and environments, will be considered in future research. Furthermore, the management of uncertainties is also important for the PHM of Li-ion battery. Its main purpose is to analyze the influence of various uncertainties, and provide a confidence coefficient for predictions. In future, Bayesian theory, particle filter, or other uncertainty technologies will be considered and added to realize the management of uncertainties of Li-ion batteries.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & _RMSE_ & _MAE_ & _RULerror_ \\ \hline DLSTM & 0.0119 & 0.0087 & 2 \\ LSTM & 0.0413 & 0.0348 & 20 \\ ANN & 0.0334 & 0.0303 & \(-4\) \\ ARMA & 0.0356 & 0.0302 & \(-5\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction performance comparison of four methods.  
Figure 6: Comparison of four different methods for satellite Li-ion battery RUL prognosis: (**a**) DLSTM for RUL prognosis; (**b**) LSTM for RUL prognostic; (**c**) ANN for RUL prognosis; (**d**) ARMA for RUL prognosis.  
**Author Contributions:** [PERSON] provided methodology, validation and writing-original draft preparation; [PERSON] gave conceptualization, writing-review & editing and supervision; [PERSON] carried out the investigation; [PERSON] and [PERSON] provided funding support.  
**Funding:** This research was funded by Nature Science Foundation of China under Grant 61873122 and 61673206; Equipment Pre-research National Defense Science Technology Key Laboratory Foundation under Grant 61422080307; Key Laboratory of Spacecraft Fault Diagnosis and Maintenance on Orbit; Postgraduate Research & Practice Innovation Program of Jiangsu Province under Grant KYCX18_0300.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Power Up: Comparing PAPE Responses in Free-Weight vs. Flywheel Resistance Training Systems
###### Abstract  
Background: Post-activation potentiation enhancement (PAPE) is a physiological phenomenon that results in an acute improvement in muscular performance following a resistance training protocol, whose effects have been widely studied. However, due to methodological challenges in matching training stimuli, the results comparing the PAPE effect of strength training with flywheel or free-weight systems show inconsistencies in the literature. Since mean-propulsive velocity (MPV) is the most used parameter for monitoring free-weight intensity in real time, the aim of the study was to analyze the PAPE effect of a resistance training bout using free-weight and flywheel devices, matching intensities through MPV. Methods: Thirty physically active subjects participated in this crossover study, which aimed to compare the PAPE effect between a three-set, six-repetition half-squat exercise with free-weight and flywheel devices. Training intensity was matched in both groups through MPV. Vertical jump and sprint tests were conducted pre- and 4 min post-intervention. Results: There was no group x time interaction for the jump and sprint (\(p>0.05\)). Vertical jump decreased post-exercise (\(p<0.001\)). Conclusions: When training intensity in free-weight and flywheel devices is matched through MPV, the acute performance alterations will be similarly matched, with no PAPE benefits observed at least 4 min post-exercise.  
f Mywheel; post-activation potentiation; jump; sprint; performance; fatigue; resistance training Article
# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 2 Measurement Data  
The district heating data of the city of Jyvaskyla, Finland, from January 2013 was used for the parameter estimation of the models and for the optimization simulations. The data was provided by the local heating utility and included the hourly measured heat consumption (\(P\)) and volume (\(V\)) for individual buildings together with the hourly measured outdoor temperature (\(T_{out}\)). The same outdoor temperature measurements were used for all the buildings as local outdoor temperature data were not available. Outdoor temperature range was from \(-26.7\)\({}^{\circ}\)C to \(+2.1\)\({}^{\circ}\)C with median value being \(-6.5\)\({}^{\circ}\)C. For the simulation studies, the 201 largest district heat consumers among the apartment buildings, schools, and commercial, public and office buildings were chosen as these kinds of buildings are the most likely to be included in the DSM. The combined heat consumption of these 201 buildings amounted to 32% of the total heat consumption of the Jyvaskyla district heating system. Measurement data for the buildings is presented in Table 1.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 3 Systematic Errors  
Systematic errors are derived from coherent phase variations external to the desired phase. For terrain mapping, the topographic phase is the desired component, while the deformation phase component is desired for deformation monitoring applications. Systematic errors come from the systematic phase shifts which occur in interferograms. The phase shifts generated by undesirable coherent phase variations superimpose noise onto interferograms and generate unwanted phase fringes. If the additional fringes are misinterpreted as the desired topographic/deformation phase component, then the resulting measurement would include the contribution from these redundant phase shifts and undermine the accuracy of the measured values. For example, if the InSAR measurement captured five fringes for a deformation mapping application, but only three of the fringes were caused by actual surface deformation (the desired signal) and the other two fringes were caused by tropospheric delay (the undesired signal), the measured deformation accuracy would be reduced as two fringes were misinterpreted as true deformation phase variation.  
In this paper, systematic errors are classified into three categories based on the origin of the phase shift: viewing geometry (from space), propagation path (atmosphere), and scatterer motions (ground surface). The viewing geometry consists of three phase components: (1) flat-Earth phase, (2) orbital phase, and (3) topographic phase. The propagation path consists of three components: (1) ionospheric advance, (2) tropospheric delay, and (3) liquid water delay. Scatterer movement accounts for (1) scatterer phase shift and (2) surface movement.
## 4 Parametric Analysis  
The optimization task has been performed by altering the dimensions of the structures in the antenna design. Different dimensions of the parameters have different levels of impact on the radiation characteristics of the design. In this section, those parameters of the design will be discussed that have a significant effect on the antenna's characteristics.  
Radius of the feed-plane circle (R3): The change in the values of the radius (R3) of the circular structure along with half-disk parasitic cover changes both the return loss and resonating frequency. Figure 6a depicts these variations with the change in the radius R3. As can be seen, an R3 value of 1.1 mm gives the optimal value of S\({}_{11}\).  
Figure 4: Radiating and parasitic patch equivalent circuit.  
Figure 5: E field distribution analysis between radiating and parasitic patch.  
Length of stub in the ground plane (St): As the length of the stub is increased, the effective length of the radiating surface also increases, which causes the resonant frequency to shift to lower values. However, optimal results are attained with St = 2.00 mm, as seen in Figure 6b.  
Width of ground plane surface (W): The radiating effective length is also increased by increasing the width of the ground plane (W). The optimum results for S\({}_{11}\) are achieved with W = 8.3 mm, as shown in Figure 7a.  
Figure 6: Optimization of S\({}_{11}\) and resonant frequency using (a) R3 and (b) St.  
Figure 7: Optimization of S\({}_{11}\) with (a) W and (b) Sh.  
Length of inset feed (slit on both sides of the feedline, Sh): The slit placed on either side of the feedline has a strong influence on the radiation characteristics of the design. This technique can be used to improve and fine-tune the antenna's characteristics. As seen in Figure 7b, a variation in the length of Sh influences the resonant frequency, \(S_{11}\), and the bandwidth. The optimum value for our configuration is achieved at Sh = 1.02 mm.  
Based on the above discussion, we can clearly infer that the proposed structure can be made to resonate over a wide range of frequencies with minor alterations of the dimensional parameters, like R3, St, W, and Sh.
# Emulating Software-Defined Disaggregated Optical Networks in a Containerized Framework
## 2 Proposed Architecture for SDN-DON Emulation
### Advanced Automatic Deployment of Emulated SDN DON
#### 2.2.2 Kubernetes-Based Agent Management
This approach is totally in line with network slicing, essential for 5G-aware deployments [40], being this fact another solid reason to choose Kubernetes as agent manager in a SDN DON emulation framework. Note that the server-client architecture, in correspondence with the Kubernetes master nodes (also illustrated with blue dashed arrows in Figure 2a), coexists with the client-server structure used in NETCONF protocol in correspondence with SDN controller-optical agent container (illustrated in solid red arrows in Figure 2). Concerning Kubernetes integration as part of the Advanced Automatic Deployment of Emulated SDN DON oversees agents' management. First, the configuration for creating containers (e.g., agents) can be performed by three different ways: via script by using the _kubctl_ service, manually from dashboard or remotely through the Kubernetes API  
Figure 3: The Kubernetes master nodes-based architecture. Once the containers are up and running in one or multiple pods, the Kubernetes Master is in charge of managing the computational resources according to the workload and the specifications stated in the configuration phase.