esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method  
### Overall Architecture  
Figure 1 is the overall framework of the proposed method. The main contributions of the proposed system are as follows: (1) two filtering methods are introduced into the conversion network and (2) an approach with sub-band cepstrum conversion and fusion is proposed. In this system, the input time domain signal was filtered through a temporal filter, and then acoustic features were extracted by WORLD [28], including Mel-cepstral coefficients (MCEPs), F0, and aperiodic components, which were treated with their corresponding recipes separately, as presented below. MCEPs (except for the energy feature) were converted by the proposed sub-band cepstrum conversion and fusion network. LogP0 was converted by equalizing the mean and the standard deviation of the source and target voice, which is a widely used method in VC. The aperiodic component was directly copied to perform as the corresponding parameters of the converted speech since previous research has shown that converting the aperiodic component does not make a statistically significant contribution toward improving the synthesized speech [15; 29]. After training the conversion model, the mean and variance of all target MCEPs and the converted MCEPs were calculated, and the target and conversion matrices \(T_{1}\) and \(T_{2}\) in Figure 1 were computed and stored, respectively. The detailed construction process of \(T_{1}\) and \(T_{2}\) is presented in Section 3.3. During the conversion phase, a global variance consistency filter was constructed using \(T_{1}\) and \(T_{2}\) for MCEPs filtering. The log-linear transform was applied to F0 conversion as follows:  
\[p_{t}{}^{(Y)}=\frac{p_{t}{}^{(X)}-u{}^{(X)}}{\sigma^{(X)}}\times\sigma^{(Y)}+u {}^{(Y)}, \tag{8}\]where \(p_{t}(\mathbf{X})\) and \(p_{t}(\mathbf{Y})\) are the original logF0 and the converted logF0, respectively. \(u^{(X)}\) and \(u^{(Y)}\) are the means, and \(\sigma^{(\mathbf{X})}\) and \(\sigma^{(\mathbf{\Omega})}\) are the standard deviations of logF0's of the training data for the source and the target speakers, respectively [2].
## 2 Battery ECM and Extended Battery ECM
### Extended Battery ECM  
To take into account the parametric variation due to the aging process, the relationship \(\frac{\text{d}P}{\text{d}t}\approx 0\) will be considered to build an extended battery ECM, where \(P\) is a general representation of the parameters such as R\({}_{0}\), R\({}_{1}\), R\({}_{2}\), C\({}_{1}\), C\({}_{2}\), and C\({}_{\text{n}}\). Details for the zero-time derivative condition for battery parameters can be found in [29]. Hence, the extended battery model is given in (3).  
It has been shown that the parameter variation of the second RC network during the aging process can be neglected [19]. Therefore, R\({}_{2}\) and C\({}_{2}\) are considered as constant. Hence, only the four parameters R\({}_{0}\), R\({}_{1}\), C\({}_{1}\), and C\({}_{\text{n}}\) are considered as additional state variables and will be estimated with the SOC.  
Figure 3: General form of the average open circuit voltage (OCV) curve (example adapted from [28], Copyright IEEE, 2013).  
The updating of these variables will cancel or at least mitigate the battery aging effect or temperature variation on the monitoring [23]. Finally, the extended battery model is as follows:  
\[\left\{\begin{array}{l}\frac{\mathrm{d}V_{\mathrm{J}}(t)}{\mathrm{d}t}=\frac{-1 }{\mathrm{R}_{1}(t)\mathrm{C}_{1}(t)}V_{1}(t)+\frac{1}{\mathrm{C}_{1}(t)}I_{ \mathrm{batt}}(t)\\ \frac{\mathrm{d}V_{2}(t)}{\mathrm{d}t}=\frac{-1}{\mathrm{R}_{2}\mathrm{C}_{2} }V_{2}(t)+\frac{1}{\mathrm{C}_{2}}I_{\mathrm{batt}}(t)\\ \frac{\mathrm{d}osc(t)}{\mathrm{d}t}=\frac{-\eta I_{\mathrm{batt}}(t)}{\mathrm{ C}_{n}(t)}\\ \frac{\mathrm{d}R_{0}(t)}{\mathrm{d}t}\approx 0\\ \frac{\mathrm{d}\mathrm{R}_{1}(t)}{\mathrm{d}t}\approx 0\\ \frac{\mathrm{d}\mathrm{C}_{1}(t)}{\mathrm{d}t}\approx 0\\ \frac{\mathrm{d}\mathrm{C}_{n}(t)}{\mathrm{d}t}\approx 0\\ \end{array}\right. \tag{3}\]  
\[V_{\mathrm{batt}}(t)=V_{\mathrm{OC}}(soc)-V_{1}(t)-V_{2}(t)-\mathrm{R}_{0}(t) \cdot I_{\mathrm{batt}}(t)\]
# Privacy-Preserving Handover Optimization Using Federated Learning and LSTM Networks
## 4 Results  
### Dataset  
The dataset used in our experiment is sourced from [20], this dataset offers extensive multi-source data suitable for predicting cellular traffic, providing crucial insights for developing and refining handover management algorithms. The dataset's comprehensive nature and real-world applicability make it an excellent foundation for validating the effectiveness of our proposed dynamic handover algorithm.  
This dataset was collected from the real 5G production network of a major Irish telecom operator, covering two mobility modes: static and driving. It includes various application scenarios, such as video streaming and file downloads. This dataset is particularly suitable for handover prediction and signal strength forecasting in mobile communication networks, as it contains rich key performance indicators, such as RSRP, Signal-to-Noise Ratio (SNR), and geographical information like UE mobility speed, longitude, and latitude, as shown in Table 1.  
The dataset is categorized into two modes: static and dynamic. Given that this study focuses on the 5G handover process, we specifically utilized the dataset collected under dynamic conditions. In the dynamic data collection, mobile devices were placed in vehicles moving through urban and suburban environments, capturing real-world mobility scenarios that are crucial for handover analysis. The velocity of the clients during the dynamic collection ranged from 0 to 57 kph, covering a range of typical vehicular speeds in daily traffic. This range provides a comprehensive basis for studying the impact of mobility on handover performance in 5G networks, as fluctuations in speed can significantly affect signal strength and the frequency of handover events.  
Figure 5: Our integral system flowchart.  
We utilized the dataset and allocated the data to 10 clients, ensuring that no training data were duplicated. Figure 6 illustrates the distribution of RSRP training data in each client, highlighting the diversity of the heterogeneous data. This diversity is crucial for ensuring the model's reliability.
# The Application of Fine Sand in Subgrades: A Review
## 2 Materials
### Dynamic Characteristics of Fine Sand
When higher load levels are applied to the pavement, stress hardening effects result in higher subgrade moduli response for sandy subgrade soils. However, fine-grained or plastic subgrade soils may not exhibit this trend, as the modulus response is expected to decrease with increasing vertical deviatoric stress, as described by Equation (3) as follows:  
\[E_{\text{subgrade}}=19729+322\text{g}_{\text{a}}-1.31\text{g}_{\text{m}}-5022 \text{H}_{\text{UAB/HMA}}+0.183\text{W}_{\text{Load}}-157\text{g}_{\text{a}} \cdot\text{g}_{\text{n}} \tag{3}\]  
where \(E_{\text{subgrade}}\) is the subgrade resilient modulus in psi, \(\text{g}_{\text{a}}\), \(\text{g}_{\text{m}}\), and \(\text{g}_{\text{n}}\) are parameters for fitting the gradation curve, \(\text{H}_{\text{UAB/HMA}}\) is the thickness ratio of the crushed stone base and asphalt concrete pavement, and \(\text{W}_{\text{Load}}\) is the load magnitude in lb. [PERSON] [14] conducted repeated load triaxial tests on uniformly compacted dry sand from the Arabian Gulf region, examining the relationship between permanent deformation of dry desert sand, stress conditions, and placement density. The authors also monitored the elastic strain occurring simultaneously with plastic strain and the elastic modulus. Furthermore, the constitutive relationships were established for utilization in conjunction with the \"elastic layering\" method to estimate the contribution of sand subgrades to permanent deformation in flexible pavement systems. The experiments showed that permanent axial strain is predominantly influenced by the magnitude of deviatoric stress, with elastic strain occurring simultaneously with permanent strain increasing with stress. A correlation between permanent strain and the number of load cycles was identified when the stress level was below 65% of the ultimate specimen strength, as described by Equation (4) as follows:  
\[\varepsilon^{p}=AN^{b} \tag{4}\]  
where \(\varepsilon^{p}\) is the permanent axial strain and \(N\) is the number of repeated loading cycles.
# Monitoring and Data Analysis in Small-Scale Landslide Physical Model
## 4 Results
### MEMS Accelerometers  
In the conducted series of tests of the sandy slope exposed to artificial rainfall, only the MEMS MXR9150M sensors were built-in in the model. The position of the accelerometers  
Figure 16: Diagrams of measured volumetric water content and temperature during tests at the following slope inclination and sensor depths: (**a**) 30\({}^{\circ}\) and 6 cm; (**b**) 30\({}^{\circ}\) and 18 cm; (**c**) 30\({}^{\circ}\) and 24 cm; (**d**) 35\({}^{\circ}\) and 6 cm; (**e**) 35\({}^{\circ}\) and 18 cm; (**f**) 35\({}^{\circ}\) and 24 cm; (**g**) 40\({}^{\circ}\) and 6 cm; (**h**) 40\({}^{\circ}\) and 18 cm; and (**i**) 40\({}^{\circ}\) and 24 cm. The depth of the sensors is 6, 18, and 24 cm from the surface, respectively. L, M, and H mark position of the sensors in part of the slope model, e.g., lower, middle, and upper part.  
was the same in all tests, all sensors along the central profile of the model: one sensor in the upper part of the slope, two sensors in the middle of the slope at different depths, and one sensor in the lower part of the slope. Data analysis for MEMS accelerometers is still ongoing, but several data communication problems occurred during the tests, and only a few data were obtained from the sensors. There are many reasons those can impact these problems, and additional tests are needed to improve these measurements. The MEMS AXDL345 accelerometers gave better results with less noise in later tests of small-scale slopes exposed to vibrations at shaking table (results are not presented here); therefore, the MEMS ADXL345 is preferred to be used in future tests.
## 1 Microgravity in Human Health
### Microgravity and Immune Response
Of note, microgravity might also affect inflammatory signalling in immune cells by acting on membrane homeostasis: indeed, membrane fluidity plays a role in cell physiology, and many soluble mediators that modulate immune functions can exert their effect through changes in membrane dynamics [43]. Furthermore, membrane fluidity can affect _per se_ immune cell functions by acting on T cell receptor (TCR) [44] and Toll-like receptor (TLR) signalling [45], as well as by controlling antigen presentation [46]--mostly by acting on the distribution and function of lipid rafts--and thus the immune response. In this context, microgravity can change membrane fluidity [47], suggesting that the Space environment might also affect lipid raft-mediated signalling in immune cells, which in turn might affect the production of pro-inflammatory mediators, bioactive lipids included. An additional point of action might be represented by the impairment of hematopoietic tissues, and of the maturation of immune cells: indeed, microgravity can induce atrophy of both bone marrow and thymus, which control generation and maturation of T and B cells [48]. Even though several groups recently investigated the molecular mechanisms behind Space flight-induced immune alterations, the full array of molecular actors behind them remains mostly elusive, with the biggest part of the published literature having focused on cytokines. Instead, the role of endogenous bioactive lipids, which are produced by the cells that orchestrate inflammation [49], has been poorly addressed. The latter molecules represent the fulcrum of the immune response, governing the initiation, extent and outcome of the inflammatory event, and understanding their role in the modifications elicited by microgravity on human immune networks represents a crucial piece of the puzzle that will ultimately allow safe Space travel in the near future.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 5 Conclusions  
In this paper, we proposed a novel self-supervised spatio-temporal graph learning model (SSTGL) to improve the GNN's potential for use in POI recommendations. In particular, we designed a model-agnostic self-supervised learning framework that took into account spatio-temporal prior knowledge. Based on the framework, we defined spatio-temporal aware data augmentation and pre-text tasks. Extensive experiments showed that SSTGL outperformed existing methods for the POI recommendation task.  
In future work, we will extend more views and more flexible data augmentation strategies under the self-supervised learning framework, and we will try to apply graph-based self-supervised learning to more complex tasks, including next POI recommendation and tour recommendation.  
Conceptualization, J.L. and H.G.; methodology, J.L. and C.S.; software, H.G. and Q.X.; validation, J.L. and H.C.; formal analysis, J.L.; investigation, J.L.; resources, C.S.; data curation, H.G.; writing--original draft preparation, J.L.; writing--review and editing, H.G. and C.S.; visualization, H.C.; supervision, C.S.; project administration, Q.X.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. U20B2045, U1936220, 62192784, 62172052, 62002029, 61772082).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 3: Hyper-parameter experiments for Foursquare dataset.  
Data Availability Statement: The data presented in this study are openly available in [19] and [[https://www.biendata.xyz/competition/smp2021_1/](https://www.biendata.xyz/competition/smp2021_1/)]([https://www.biendata.xyz/competition/smp2021_1/](https://www.biendata.xyz/competition/smp2021_1/)).
###### Abstract  
Due to its accuracy, simplicity, and other advantages, the Kalman filter method is one of the common algorithms to estimate the state-of-charge (SOC) of batteries. However, this method still has its shortcomings. The Kalman filter method is an algorithm designed for linear systems and requires precise mathematical models. Lithium-ion batteries are not linear systems, so the establishment of the battery equivalent circuit model (ECM) is necessary for SOC estimation. In this paper, an adaptive Kalman filter method and the battery Thevenin equivalent circuit are combined to estimate the SOC of an electric vehicle power battery dynamically. Firstly, the equivalent circuit model is studied, and the battery model suitable for SOC estimation is established. Then, the parameters of the corresponding battery charge and the discharge experimental detection model are designed. Finally, the adaptive Kalman filter method is applied to the model in the unknown interference noise environment and is also adopted to estimate the SOC of the battery online. The simulation results show that the proposed method can correct the SOC estimation error caused by the model error in real time. The estimation accuracy of the proposed method is higher than that of the Kalman filter method. The adaptive Kalman filter method also has a correction effect on the initial value error, which is suitable for online SOC estimation of power batteries. The experiment under the BBDST (Beijing Bus Dynamic Stress Test) working condition fully proves that the proposed SOC estimation algorithm can hold the satisfactory accuracy even in complex situations.  
SOC; adaptive Kalman filter; equivalent circuit model Article
Pyrolytic Modification of Avocado (_Persea americana_) Peel for the Enhancement of Cadmium(II) and Chromium(VI)  
Sorption Properties  
[PERSON]  
1  
biochar (pyrolyzed organic matter) was successfully used in water (polluted with metal ions) due to its various sophisticated properties like surface area and charge or hydrophobicity [2; 10; 11]. [PERSON] et al., in their work, reviewed various biomass-derived biochars to see whether they were good materials for the removal of toxic ions [12]. [PERSON] et al., in their work, used banana peel waste for producing biochar via pyrolysis for the removal of dye (methylene blue) from aqueous solutions [9]. A similar work was proposed by [PERSON] et al., where the authors used roots of _Ipomoa aquatica_ for methyl violet dye removal [13]. [PERSON] et al. carried out a test with the use of biochar produced from _Gigantochloa bamboo_ for methylene blue dye remediation [14]. By controlling pyrolytic parameters such as temperature, time of contact, and composition, it is possible to manipulate the physicochemical properties of organic materials, resulting in sorbents that exhibit high affinity, selectivity, and capacity for metal ions [15].  
This paper explores how pyrolytic modification can be applied to avocado peel to create new sorbent for the efficient remediation of Cd and Cr ions from wastewater and compares it to unmodified peel waste. The results were additionally evaluated by studying different isotherm and kinetic models showing the mechanisms concerning the sorption of Cd and Cr ions on both unmodified and pyrolytically modified avocado peel. Additionally, an assessment of the applicability of these materials in bioremediation processes is presented. In the future, the authors plan to use chemical modification of chosen fruit-peel-derived biochar to check adsorption properties against various heavy metal ions present in wastewater.
## 4 Theoretical Derivation  
### Mechanical Model and Basic Assumptions  
Figure 7 shows the engineering problem to be solved. The formation loss caused by the excavation of the circular shield tunnel causes the overlying strata to displace and then causes the underground pipeline to bend. As shown in Figure 8, the \(x\) coordinate is used to mark the position of each section along the pipeline. The theoretical derivation of this paper is based on the following basic assumptions and simplifications: tunnels and pipelines are buried horizontally in homogeneous isotropic strata, and their axes are perpendicular to each other. The foundation around the pipeline is simplified to the Pasternak foundation.
# Identical Parallel Machine Scheduling Considering Workload Smoothness Index
## 2 Literature Review
### Smoothness Index in the Assembly Line Environment  
[PERSON] and [PERSON] [15] are the first to introduce the smoothness index in the assembly line balancing problem, i.e., \(SI=\sqrt{\sum_{i\in\mathcal{M}}(W_{\max}-W_{i})^{2}}\), where \(W_{\max}\) is the given cycle time constraint of the assembly line. [PERSON] et al. [23] further present a new heuristic procedure based on the genetic algorithm for the assembly line balancing problem with the objective of maximizing the workload smoothness index. [PERSON] [24] use \(SI\) to measure the balance level of the working time between the workstations of the assembly line. [PERSON] et al. [25] investigate several objectives to smoothen the workload, and the proposed measures are systematically tested in a comprehensive computational study. Testing results suggest that workload smoothing is an essential task in mixed-model assembly lines. [PERSON] [26] proposes a novel method based on PSO to address a simple assembly line balancing problem (SALBP), minimizing the cycle time and the workload smoothing. [PERSON] and [PERSON] [27] consider the SALBP problem with a fixed number of workstations and prespecified cycle time. For the objective of minimizing the value of \(SI\), they develop several optimality properties and bounding mechanisms and then propose an efficient branch-and-bound algorithm. [PERSON] et al. [28] study an assembly line balancing problem where the factor of human energy consumption is included and the objective is to minimize the value of \(SI\)They propose several solution methodologies and make comparison of their performance via computational experiments.  
As shown above, the smoothness index \(SI\) has been well studied in the assembly line balancing problem. In this work, we adopt the smoothness index to evaluate the level of workload balance between the identical parallel machines in order to provide a most reasonable job scheduling solution in manufacturing decision making, considering the fairness of the workload.
## 4 Conclusions  
We investigated the S/X-band single-layer shared-aperture array antenna for the MFRs of military ships. The unit cell of the proposed antenna consisted of one S-band element and four X-band elements, which were printed on the same layer to prevent the blockage effect by the upper element in the stacked shared-aperture antenna. To improve the S-band antenna bandwidth, the S-band element consisted of one feeding patch and a pair of parasitic patches, which is a mutual complementary configuration to the X-band elements. The unit cell was extended to the 2 \(\times\) 2 array configuration to examine of the array gains and beam-steering performances. The antenna bandwidths of the S- and X-band elements were 13.6% and 13.4%, respectively. The array gains of the S- and X-bands in the bore-sight direction based on the measurement were 5.4 and 13.4 dBi, respectively. The maximum gain of the S-band element varied from 5.4 dBi to 3.5 dBi when the main beam was steered from 0\({}^{\circ}\) to 45\({}^{\circ}\). Under the same conditions, the bore-sight gain decreased from 13.4 dBi to 11.6 dBi in the X-band. The result demonstrated that the proposed shared-aperture antenna is suitable for MFR applications.  
Conceptualization, E.-Y.Y., D.J., C.-H.L. and H.C.; Formal analysis, E.-Y.Y., D.J. and H.C.; Investigation, E.-Y.Y. and D.J.; Methodology, E.-Y.Y., D.J., C.-H.L. and H.C.; Project administration, H.C.; Resources, E.-Y.Y., D.J., C.-H.L. and H.C.; Software, E.-Y. and D.J.; Supervision, H.C.; Validation, E.-Y.Y., D.J. and H.C.; Visualization, E.-Y.Y. and D.J.; Writing--original draft, E.-Y.Y. and D.J.; Writing--review & editing, E.-Y.Y., D.J. and H.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 11: Total active reflection coefficient and envelope correlation coefficient (**a**) total active reflection coefficients in the S-band; (**b**) total active reflection coefficients in the X-band; (**c**) envelope correlation coefficients in the S-band; (**d**) envelope correlation coefficients in the X-band.