esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Blind Deconvolution Based on Self-Supervised Machine Learning
## 3 Experiments
### Synthetic Experiment  
In this section, we demonstrate the effectiveness of the proposed SSL-BD method on synthetic data. We obtain the synthetic seismic data by convolving the reflectivity profile generated from a part of the Marmousi2 model [10] and the Ricker wavelet with a dominant frequency of 30 Hz and a phase of 30\({}^{\circ}\). The reflectivity profile has 192 seismic traces, each trace has 800 time sampling points, and the sampling interval is 1 ms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Part of Marmousi2** & **CNPC Data** & **iErsk3D Data** \\ \hline SSL-D & 589.2 s & 1856.1 s & 4424.3 s \\ SSL-BD & 1965.7 s & 5558.3 s & 13,590.3 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Runtimes of the SSL-D and SSL-BD with regard to three datasets.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Evaluation Metric** & **Formula** \\ \hline Mean absolute error (MAE) & \(MAE=\frac{1}{N_{\text{tr}}}\frac{1}{L}\sum_{j=1}^{N_{\text{tr}}}\sum_{i=1}^{L} |\text{Actual}_{i,j}-\text{Predicted}_{i,j}|\) \\ \hline Mean squared error (MSE) & \(MSE=\frac{1}{N_{\text{tr}}}\frac{1}{L}\sum_{j=1}^{N_{\text{tr}}}\sum_{i=1}^{L} (\text{Actual}_{i,j}-\text{Predicted}_{i,j})^{2}\) \\ \hline Mean absolute relative error (MARE) & \(MARE=\frac{1}{N_{\text{tr}}}\frac{1}{L}\sum_{j=1}^{N_{\text{tr}}}\sum_{i=1}^{L} \big{|}\frac{\text{Actual}_{i,j}-\text{Predicted}_{i,j}}{\text{Actual}_{i,j}} \big{|}\) \\ \hline Mean square relative error (MSE) & \(MSRE=\frac{1}{N_{\text{tr}}}\frac{1}{L}\sum_{j=1}^{N_{\text{tr}}}\sum_{i=1}^{L} \big{(}\frac{\text{Actual}_{i,j}-\text{Predicted}_{i,j}}{\text{Actual}_{i,j}} \big{)}^{2}\) \\ \hline Structure similarity (SSIM) & \(SSIM=\frac{(2-\text{Actual}_{\text{avg}}\text{Predict}_{\text{avg}}+\text{c}_{ \text{i}})(2\text{Covariance}_{\text{actual, predict}_{\text{avg}}+\text{c}_{ \text{i}})}}{(\text{Actual}_{\text{avg}}+\text{Predicted}_{\text{avg}}+\text{c}_{ \text{i}})(\text{Actual}_{\text{avg}}+\text{Predicted}_{\text{avg}}+\text{c}_{ \text{i}})}\) \\ \hline Peak signal-to-noise ratio (PSNR) & \(PSNR=10\lg(\frac{\text{Actual}_{\text{avg}}^{2}}{\frac{1}{N_{\text{tr}}}\frac{1} {L}\sum_{j=1}^{N_{\text{tr}}}\sum_{i=1}^{L}\sum_{i=1}^{L}\left|\text{Actual}_{i,j }-\text{Predicted}_{i,j}\right|^{2}})\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Evaluation metrics for comparison of different results.  
Figure 8: Two-dimensional synthetic test of part of Marmousi2 model. (**a**) Input seismic data. (**b**) True reflectivity profile. (**c**) Predicted reflectivity profile of SSL-D [23]. (**d**) Predicted reflectivity profile of the proposed SSL-BD method.  
Figure 7: Two-dimensional synthetic test of part of Marmousi2 model. (**a**) Amplitude spectra of true wavelet. (**b**) Amplitude spectra of wavelet estimated by the proposed SSL-BD method. (**c**) Phase spectra of true wavelet. (**d**) Phase spectra of wavelet estimated by the proposed SSL-BD method.  
Figure 9: Detailed comparison of a single trace. (**a**) True reflectivity sequence. (**b**) Predicted reflectivity sequence.
## 4 Methodology
### Unmanned Ariel Vehicles (UAVs)
#### 4.4.2 UAV-to-UAV Based Intrusion Detection Mechanism  
During data collection, the UAVs communicate with each other and exchange trajectory information along with other parameters, such as residual energy. This information is shared periodically, assuming that UAVs have sufficient energy to bear the communication cost. The UAVs move in the form of a fleet; where each UAV is covering a different path but is connected with other UAVs through a wireless connection. The entire UAV communication is logged for subsequent use in model training.  
The proposed intrusion detection model is trained on the collected data at the fog broker and deployed at UAVs and attempts to identify these attacks. The proposed architecture consists of multiple stages, i.e., data prepossessing, feature engineering, and intrusion detection. First, a dataset is gathered to evaluate the performance of the system. A \(k\)-means-based cluster sampling technique is used to create a highly representative subset of the data while preventing class imbalance. The dataset is treated throughout the feature engineering process to eliminate redundant and unnecessary features using information gain-based and correlation-based feature selection approaches, and the kernel principal component analysis model is used to further decrease the dimensions and noisy features.  
It is suggested that a hyperparameter-based optimization IDS be used to effectively identify both known and unidentified intrusions as discussed in [56]. The system is composed of several tiers, where the first tier consists of four tree-based machine learners namely decision tree, random forest, extra tree, and extreme gradient boost, which are used to identify known attacks. By integrating the output of the four base learners from the first tier and optimizing the learners, the second tier uses a stacking ensemble model and the Bayesian optimization-tree-structured Parzen estimator approach to further increase the intrusion detection accuracy. An anomaly-based intrusion detection system is built in the next stage to identify unknown attacks. The cluster-labeling \(k\)-means model is used as the 3 rd tier of the intrusion detection system to successfully differentiate attack data from regular samples. The Bayesian optimization-Gaussian process technique and two biased classifiers make up the fourth tier of the IDS, which is utilized to improve the model and decrease classification errors in the \(CL\)-\(k\)-means. Each test sample's detection outcome is ultimately reported, and it may be a known attack with a type, an unidentified attack, or typical benign traffic.
# Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling
## 3 Sources of Electromagnetic Leakage
### Wireless Keyboards and Mice
It should be noted that wireless connections are usually protected by a widely used encryption algorithm, Advanced Encryption Standard--AES [4; 60]. Wireless devices should use a secure connection-only mode, and an anti-rollback feature for security-based device firmware upgrades should be considered [61; 62].
# A Smart Collaborative Routing Protocol for Reliable Data Diffusion in IoT Scenarios
## 3 The Smart Collaborative Routing Protocol for IoT
### Spreading the Query Command in the Event Area  
After the query command is transmitted to the event area, the GIN protocol utilizes the iterative geographic forwarding strategy to spread the command in the event area. The node that first received the query command divides the area into multiple sub-areas and forwards the query command to the central location of all sub-areas. In each sub-area, the node closest to the center of the area can receive the query command. Then it will subdivide the sub-areas into more sub-areas and forward the command. When a sub-area has no nodes, the entire iteration process is finished and query command is diffused to the total event area.
# CFD Based Non-Dimensional Characterization of Energy Dissipation Due to Verticle Slosh
###### Abstract
Previous aeroelastic studies into the influence of slosh damping focus on the general trends and changes in flutter boundaries due to various tank fill-level configurations [14]. These campaigns neglected the vertical motion of the tank, employing seismic shaking tables to provide the excitation to the structure. This article furthers on existing work in a number of respects. First, weakly compressible volume of fluid (VoF) based CFD [15; 16] is experimentally validated, predicting violent slosh induced energy dissipation to with-in 2% of the experimental values. The CFD also uncovers that the main source of slosh induced energy dissipation is due liquid impact. Next, the SDOF system is non-dimensionalised such that slosh induced energy dissipation may for the first time be expressed accurately in terms of Froude number and liquid-gas ratio via CFD generated curve fits. These in turn for the first time point to the possibility that slosh induced energy dissipation may be expressed as a linear function of tank excitation kinetic energy. Finally, it is demonstrated how the newly fitted non-dimensional scaling laws may be used with ease to estimate the slosh induced damping of ideally scaled experiments. Note that the development of the aforementioned scaling laws is impossible via laboratory size experimental campaigns due to limitations in available fluids. As such, for this purpose we employ the rigorously validated Elemental(r) volume-of-fluid (VoF) CFD software. This paper is organised as follows:  
1. We validate the accuracy of the CFD code Elemental(r) for modelling violent vertical slosh physics relevant to this article. 2. We detail the CFD based energy budget used to quantify effects of scaling non-dimensional properties on the system. 3. We present a non-dimensional analysis of a SDOF tank under vertical slosh, isolating the functional relationship characterising slosh induced energy dissipation. 4. We define the non-dimensional parameter space of interest for the problem under consideration (to include both experimental and full scales). Finally, we develop novel scaling-laws which correlate the slosh induced energy dissipation as a function of the identified non-dimensional parameters. This is done via curve fitting of CFD generated data. 6. The developed novel scaling laws are finally applied to quantify ideal (representative of full scale aircraft) experimental slosh induced energy dissipation.
# A Survey on Variable Neighborhood Search for Sustainable Logistics
## 4 Results and Findings  
Figure 1 illustrates the growth in the number of papers related to the topic of this study. As shown, there has been a significant increase in publications since 2019, underscoring the growing importance of VNS in the field of sustainable logistics. Note that the year 2024 presents a small decrease due to the date of the present study.  
Regarding the journals where these studies are published, most of them belong to the areas of operations research, computer science, and engineering. The two most frequent journals are _Computers & Operations Research_ and _Computers & Industrial Engineering_. Figure 2 shows the concept cloud created according to the number of papers published in each journal.  
In terms of the optimization problem addressed, we have identified five primary types: routing, location, scheduling, location/routing, scheduling/routing, and network design. Additionally, a few papers focus on smaller categories such as inventory, lot sizing, and layout, which have been grouped under the category \"Others\". As shown in Figure 3, the vast majority of papers using variants of VNS for sustainable logistics deal with routing problems, followed by scheduling.  
Figure 1: Number of publications per year.  
In the following sections, we provide a more detailed analysis of the hybridization of VNS with other methods, explore multiobjective approaches that incorporate VNS, examine the consideration of uncertainty and dynamism in the studies, and conclude with an analysis of sustainability. For a more synthesized analysis, Appendix A includes a summary of the papers selected in this review and its characteristics.
## 4 Conclusions  
To summarize, we studied nanoparticles of the half-doped CMR manganites Ho\({}_{0.5}\)Ca\({}_{0.5}\)MnO\({}_{3}\), of about 20 nm in size, to investigate the role of reduced dimensions on the stability of the charge and orbital ordered CE-type antiferromagnetic structure characteristic of the bulk. We conclude that, contrary to what happens in most half-doped rare earth CMR manganites at this length scale, charge ordering and antiferromagnetism in Ho\({}_{0.5}\)Ca\({}_{0.5}\)MnO\({}_{3}\) are not suppressed. The proposed magnetic structure at the nanosize is based on a supercell metrically coincident with the bulk one, however, hosting a larger fraction of Zener double-exchange ferromagnetic interactions among Mn species. This result counts Ho\({}_{0.5}\)Ca\({}_{0.5}\)MnO\({}_{3}\) among those few half- or nearly half-doped CMR manganites, such as Sm\({}_{1-x}\) Ca\({}_{x}\)MnO\({}_{3}\) (\(\sim\) 0.5) [50], in which the antiferromagnetic ground state is so robust that the Zener double exchange cannot emerge as a long-range interaction even by reducing the grain size to the nanoscale. An open issue remains concerning the nature of the transition seen around 40 K, which Ho\({}_{0.5}\)Ca\({}_{0.5}\)MnO\({}_{3}\) nanoparticles have in common with their bulk counterpart and with several other half-doped CMR phases. The question of whether such a transition is intrinsic--if it may be triggered by the rare-earth-manganese interaction, or if it is due to magnetic impurities--deserves further investigations.  
Conceptualization, A.G.L., F.C., M.F. and D.P.; methodology, A.G.L., F.C.; validation, A.G.L., F.C., G.M., M.F., D.P. and E.P.; formal analysis, A.G.L., F.C. and E.P.; investigation, A.G.L., F.C., E.P. and G.M.; resources, M.F., D.P. and G.M.; data curation, A.G.L., F.C. and E.P.; writing--original draft preparation, A.G.L., F.C.; writing--review and editing, A.G.L., G.M., M.F., E.P., D.P. and F.C.; visualization, A.G.L., F.C.; supervision, A.G.L.; project administration, A.G.L., M.F., D.P. and F.C.; funding acquisition, A.G.L. All authors have read and agreed to the published version of the manuscript.  
G.M. acknowledges financial support from the PON AIM program (project AIM1809115--Activity No. 3--line 2.1).  
The data presented in this study are available upon request from the corresponding authors.  
Figure 11: Real part of the AC magnetization as a function of temperature at three selected frequencies for the micrometric sample. Data were collected using a null DC magnetic field and an AC magnetic field H\({}_{\rm AC}\) = 3.5 Oe.  
**Acknowledgments:** We thank [PERSON] for the X-ray fluorescence analysis and [PERSON] for his help in the synthesis and characterization of the samples.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
This means that the majority of the observations of several pulsar related phenomena-such as mode switching, subpulse drifting, nulling, etc., are prone to be flawed due to the impact of ionospheric variations. Knowing how strong the ionospheric fluctuations impact for the pulsar's signal (particularly observed with a single LOFAR station) are, our intention was to invert the well-known problem of ionospheric calibration to obtain some information about the ionosphere fluctuations in the LOFAR pulsar's signal. Such an inversion is currently known and performed during the calibration of interferometric observation [28], however until now it is only made for imaging mode of observation, not in a single station beamformed mode, as presented in our study. In our work we found that ionospheric fluctuations behavior described with widely-used GNSS-based indices such as ROT and ROTI have a clear counterpart in the pulsars' radio flux intensity registered by LOFAR. We chose the rate of TEC as an ionospheric indicator, as it is commonly used for investigation of different irregularities' structures within the plasma density. One can note clear correspondence between diminishing pul  
Figure 12: The figure shows individual pulses collected in an isometric view (**a**), which corresponds to data presented in Figure 4. A different method of data reduction was used-not 10 s integrated profiles were taken, but single pulses. In (**b**) panelthe average pulsar profile from adding 190 individual pulses is shown. In the (**c**) part an example of a single pulse is presented. sar's peak intensity observed with LOFAR during night and the ionospheric irregularities auroral oval spread towards the mid-latitudes (observed e.g., with ROTI, as in Figure 11). However, that correspondence may be questioned due to the ROTI long averaging time, it finds confirmation in the anti-correlation between ROT values and pulsar's peak intensity observed during LOFAR-GNSS signal paths close-ups. Our work is the one of the first to take up this topic and thanks to the tools created, subsequent analyzes of a much larger sample will be conducted in close future. The results obtained in our research encourage us to analyze such effects for a much larger number of pulsars in the near future.
## 3 System and Channel Model
### Interference Analysis  
There are two types of dynamic wireless channels that need to be modeled in the context of communication within an RIS-assisted multi-user ad-hoc network. It includes the wireless channel between i-th transmitter \((D_{i}^{t})\) to m-th RIS using j-th RB, \(\mathbf{g}_{imj}(t)\), with \(i\in[1,2,\ldots,N]\), \(m\in[1,2,\ldots,M]\), \(j\in[1,2,\ldots,J]\), and the wireless channel from m-th RIS to i-th receiver \((D_{i}^{t})\) using j-th RB \(\mathbf{f}_{imj}^{H}(t)\). Specifically, these two types of dynamic wireless channels can be modeled mathematically as follows.  
\(D_{i}^{t}\)-_RIS wireless channel model:_  
\[\mathbf{g}_{imj}(t)=\sqrt{\beta_{im}(t)}\times\mathbf{a}(\phi_{RIS},\theta_{ RIS},t)\times\mathbf{a}^{H}(\phi_{D_{i}^{t}}\theta_{D_{i}^{t}},t) \tag{4}\]  
where \(\sqrt{\beta_{im}(t)}\) denotes the time-varying \(D_{i}^{t}\)-RIS channel gain; \(\mathbf{a}(\phi_{D_{i}^{t}}\theta_{D_{i}^{t}},t)\) and \(\mathbf{a}(\phi_{RIS},\theta_{RIS},t)\) represent the multi-antenna array response vectors that are used for data transmission from \(D_{i}^{t}\) to RIS, respectively, with \(\mathbf{a}(\phi_{D_{i}^{t}},\theta_{D_{i}^{t}},t)=[a_{1}(\phi_{D_{i}^{t}}, \theta_{D_{i}^{t}},t),\ldots,a_{N}(\phi_{D_{i}^{t}},\theta_{D_{i}^{t}},t)]^{T}\in \mathbb{C}^{N\times 1}\) and \(\mathbf{a}(\phi_{RIS},\theta_{RIS},t)=[a_{1}(\phi_{RIS},\theta_{RIS},t),\ldots,a_{M}(\phi_{RIS},\theta_{RIS},t)]^{T}\in\mathbb{C}^{M\times 1}\).  
_RIS-D\({}_{i}^{t}\) wireless channel model:_  
\[\mathbf{f}_{imj}(t)=\sqrt{\beta_{mi}(t)}\times\mathbf{a}^{H}(\phi_{mi},\theta _{mi},t) \tag{5}\]  
where \(\sqrt{\beta_{mi}(t)}\) describes the time-varying channel gain from RIS to \(D_{i}^{t}\) at time \(t\); \(\mathbf{a}(\phi_{mi},\theta_{mi},t)\) is the multi-antenna array response vector used for data transmission from the RIS to \(D_{i}^{t}\) with \(\mathbf{a}(\phi_{mi},\theta_{mi},t)=[a_{1}(\phi_{mi},\theta_{mi},t),\ldots,a_{ M}(\phi_{mi},\theta_{mi},t)]^{T}\in\mathbb{C}^{M\times 1}\).  
Then, the time-varying signal-to-interference-plus-noise ratio (SINR) at the \(i\)-th receiver (\(Rx\)) with the assistance of the \(m\)-th RIS on \(RB_{j}\) is obtained as Equation (6). Here, \(D_{I}\) represents the set of device-to-device (D2D) pairs to which \(RB_{j}\) is allocated. Only D2D pairs that share the same j-th \(RB_{j}\) will be interfered with by each other, and there are a total of K D2D pairs in the set \(D_{j}\).  
\[\gamma_{i,j,m}(t)=\frac{|\mathbf{W}_{i}(t)(\mathbf{h}_{i,j}^{H}(t)+\mathbf{f} _{i,m,j}^{H}(t)\mathbf{\Theta}_{i,m,j}(t)\mathbf{g}_{i,m,j}(t))|^{2}}{\sum_{d_ {k}\in D_{j},k\
eq i}^{K}|\mathbf{W}_{k}(t)(\mathbf{g}_{k,i,j}^{H}(t)+\mathbf{ f}_{k,i,m,j}^{H}(t)\mathbf{\Theta}_{i,m,j}(t)\mathbf{
# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 4 Performance Evaluation
### Experiment Setup  
To validate the performance of the proposed TDDRMF framework in workload prediction tasks, we conduct experiments on a dataset tracked by the Alibaba cluster. Load data from some consecutive time periods are extracted for collation and preprocessing to ensure consistency and reliability. In the first two experiments, the dataset is divided into 80% for training and the rest for testing. Subsequent experiments are conducted in a special way to simulate a cold start environment.  
We compare TDDRMF with a variety of methods, including ARIMA [10], RF [35], LSTM [36], TRMF [37], and TAMF [23]. The evaluation focuses on three aspects: prediction accuracy, training efficiency, and robustness. Prediction accuracy is measured using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Coefficient of Determination (\(R^{2}\)). Training efficiency is evaluated based on training time, while robustness is assessed under cold-start scenarios.  
The specific computer configuration is as follows: processor Intel(R) Core(TM) i7-10510U CPU at 1.80 GHz (2.30 GHz Turbo); RAM 16.0 GB; storage 512 GB SSD; Windows 10 64-bit operating system. All models are implemented using the PyTorch framework. To ensure reliability, each experiment is repeated five times, and the average results are reported to minimize the impact of random variations. The experiments in this article are conducted on a locally available computer.  
Since we use RMSE as our primary prediction-error metric in all experiments, the dynamic update mechanism directly compares the per-timestep RMSE against the threshold \(\epsilon\) to decide whether to trigger local retraining. This maintains consistency with our evaluation and reduces hyperparameter complexity. All key parameters (regularization coefficients \(\lambda_{1},\lambda_{2}\) and error-detection threshold \(\epsilon\)) are selected via a two-stage grid search on a 5% validation subset as follows. Initially, we search over broad ranges (\(\lambda_{1},\lambda_{2}\in[10^{-4},10^{2}],\epsilon\in[0.01,10]\)) with coarse intervals to identify promising regions. Then, we perform fine-grained tuning within narrowed ranges around the best-performing values to select the parameters that minimize RMSE. Final values are as follows: \(\lambda_{1}=3*10^{-1}\), \(\lambda_{2}=1*10^{-1}\), and \(\epsilon=0.3\). All reported results use these settings, except RQ2 which uses \(\epsilon=0.5\).
###### Abstract  
**(1) Background:** The paper focuses on foot biomechanics in static situations. The aim was to determine the distribution of the load exerted by the human body on the ground in order to establish reference points on the foot for correct human body posture. **(2) Methods:** A model was developed to describe the body weight-ground relationship, consisting of a support platform and a part imitating the rest of the human body. Experiments consisted of tilting the general centre of gravity from the maximum forward through midfoot, a passive, neutral position, to the maximum backwards while maintaining balance. The ground load was measured in each position. **(3) Results:** The loads of the front and rear parts of the support platform and the resultant load force at different degrees of body tilt were calculated. It has been shown that at the maximum inclination of the body to the extreme support point, the entire weight falls on this point. For the neutral position (in the Basic Balance Point), the load on the front and rear parts of the support platform was 26% and 74%, and 40% and 60% for the passive position (in the Passive Balance Point). **(4) Conclusions:** The distribution of body weight on the ground is determined by the projection of the general centre of gravity on the ground through the feet. The resultant ground reaction force defines both the magnitude and direction of the load exerted on the support platform. Ground reaction forces associated with body weight were assessed at five anatomical points of the foot: the forefoot, rearfoot, midfoot, and the Passive and Basic Balance Point. In an upright standing posture, the projection of the general centre of gravity fluctuates between the Passive and Basic Balance Point, corresponding to the passive and neutral positions, respectively. Only in the neutral position, the body's weight, as concentrated in the general centre of gravity, falls on the axis of the upper ankle joint and distributes the load between the forefoot and rearfoot. Determining the correct distribution of foot loads may serve in the future to study abnormalities in human body posture  
doi biomechanics; centre of gravity; torque; ankle joint; weight-bearing foot; foot as a support platform