esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Intensity Whole-Body Vibration: A Useful Adjuvant in Managing Obesity? A Pilot Study
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Factor** & **F** & **\(P\)** & **Partial \(\eta^{2}\)** \\ \hline \multirow{3}{*}{**Mass [kg]**} & Time & 2.26 & 0.14 & 0.03 \\  & Group & 1.74 & 0.17 & 0.07 \\  & Group \(\times\) Time & 0.01 & 0.99 & 0.0003 \\ \hline \multirow{3}{*}{**BMI [kg/m\({}^{2}\)]**} & Time & 0.08 & 0.78 & 0.001 \\  & Group & 0.58 & 0.63 & 0.02 \\  & Group \(\times\) Time & 0.86 & 0.47 & 0.034 \\ \hline \multirow{3}{*}{**Abdomen**} & Time & 1.43 & 0.24 & 0.02 \\  & Group & **6.59** & **0.001** & **0.22** \\  & Group \(\times\) Time & 0.06 & 0.98 & 0.002 \\ \hline \multirow{3}{*}{**PAS [mm/hg]**} & Time & **25.8** & **0.29 \(\times\) 10\({}^{-5}\)** & **0.26** \\  & Group & 1.09 & 0.36 & 0.04 \\  & Group \(\times\) Time & 0.35 & 0.79 & 0.01 \\ \hline \multirow{3}{*}{**PAD [mm/hg]**} & Time & **11.24** & **0.001** & **0.14** \\  & Group & 3.95 & 0.01 & 0.04 \\  & Group \(\times\) Time & 1.02 & 0.39 & 0.04 \\ \hline \multirow{3}{*}{**FC [bpm]**} & Time & **15.3** & **0.21 \(\times\) 10\({}^{-3}\)** & **0.17** \\  & Group & 0.32 & 0.81 & 0.01 \\  & Group \(\times\) Time & 0.75 & 0.53 & 0.03 \\ \hline \multirow{3}{*}{**FFM [kg]**} & Time & 0.27 & 0.61 & 0.004 \\  & Group & 0.57 & 0.64 & 0.02 \\  & Group \(\times\) Time & 0.03 & 0.99 & 0.001 \\ \hline \multirow{3}{*}{**CHO [mg/dL]**} & Time & 0.57 & 0.45 & 0.008 \\  & Group & 0.18 & 0.91 & 0.007 \\  & Group \(\times\) Time & 0.09 & 0.97 & 0.003 \\ \hline \multirow{3}{*}{**CHO [mg/dL]**} & Time & **21.6** & **0.17 \(\times\) 10\({}^{-4}\)** & **0.24** \\  & Group & 0.15 & 0.93 & 0.007 \\  & Group \(\times\) Time & 1.89 & 0.14 & 0.08 \\ \hline \multirow{3}{*}{**HDL [mg/dL]**} & Time & 4.01 & 0.05 & 0.05 \\  & Group & 1.7 & 0.18 & 0.06 \\  & Group \(\times\) Time & 0.19 & 0.91 & 0.007 \\ \hline \multirow{3}{*}{**LDL [mg/dL]**} & Time & **14.1** & **0.34 \(\times\) 10\({}^{-3}\)** & **0.16** \\  & Group & 0.74 & 0.53 & 0.03 \\  & Group \(\times\) Time & 1.29 & 0.28 & 0.05 \\ \hline \multirow{3}{*}{**HB1 Ac [\%]**} & Time & 3.22 & 0.08 & 0.04 \\  & Group & 2.29 & 0.09 & 0.09 \\  & Group \(\times\) Time & 0.03 & 0.99 & 0.001 \\ \hline \multirow{3}{*}{**Insulin [mU/L]**} & Time & 2.56 & 0.11 & 0.03 \\  & Group & 1.29 & 0.28 & 0.05 \\  & Group \(\times\) Time & 1.13 & 0.34 & 0.04 \\ \hline \multirow{3}{*}{**Glucose [mg/dL]**} & Time & **7.71** & **0.007** & **0.09** \\  & Group & 0.74 & 0.53 & 0.03 \\ \cline{1-1}  & Group \(\times\) Time & 2.14 & 0.1 & 0.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metabolic and body composition characteristics of the four groups and relative significance levels and effect size (partial \(\eta^{2}\)). Statistically significant data are highlighted in bold. PAS, systolic blood pressure; PAD, diastolic blood pressure; FC, heart rate; FFM, fat free mass in Kg; FFM%, fat-free mass in %; CHO, total cholesterol; HDL, cholesterol; LDL, cholesterol; HDL, cholesterol; HD1 Ac, glycated hemoglobin; insulin; glucose.
## 1 Introduction  
Enhancing the data to highlight key aspects is one of the main goals in the analysis of aeromagnetic data. In addition to providing information on lithological changes and structural trends, aeromagnetic prospecting also defines lateral changes in susceptibility contrast. Gradients themselves can be used to infer the properties of subsurface structures.  
From a mining point of view, the Bou Azzer inlier hosts two important mines: the cobalt mine of Bou Azzer associated with the ophiolitic complex whose mineralization is in the form of lenses and veins, and the mine of Bleida marked by the presence of significant copper indices in the form of lenses.  
Aeromagnetic data are analysed using gradient tensor and other operators to identify many positions of the geological borders. The greatest amplitudes like horizontal gradientswhich can be replaced by horizontal components of gradient tensor were employed by [1] with the intention of identifying geologic near-vertical borders from aeromagnetic anomalies. The boundaries displayed the highest values signifying sharp disparities in lateral susceptibility. Gradiometers have enabled the recent ability to conduct measurements of all the tensor elements. For the purpose of distinguishing structural characteristics from potential field gradient tensor data, many mapping approaches have been devised. Researchers in [2] calculated the gravity-magnetic tensor invariants across a sophisticated model simulating a basement surface with both horizontal and vertical faults.  
In the interest of estimating source depth, ref. [3] demonstrated that the greater negative eigenvalue is analogous to the greatest negative bend acquired from typical potential field anomalies. Geologic contacts were identified [4] by means of the horizontal gravity-magnetic gradient tensor. Horizontal gravity gradients that have been seen or calculated from magnetic data are used to define the eigenvalues of the Hessian matrix in [5; 6; 7] as described by a linear feature analysis. Refs. [8; 9] imaged subsurface geology using invariants derived from horizontal gravity gradient components.  
A method for using the gravity-magnetic gradient tensor was developed by [8]. According to [10], the eigenvector of the entire tensor gravity-magnetic gradient matrix that corresponds to the least eigenvalue may be used to predict the striking direction of quasi 2D entities. According to [11], the sub-vertical plugs, dikes, or diatremes connected to alkaline encroachments are imaged by the rotational and horizontal invariants of the gravity-magnetic gradient tensor. The different tensor components were merged into invariants by [12], who also specified information on geologic contact and body shape.  
To identify the margins of anomalous sources, we compute and map strike lineaments (\(\Theta\)) using processed rotational invariants (I\({}_{0}\), I\({}_{1}\), I\({}_{2}\)) and horizontal invariants (I\({}_{\text{h}1}\), I\({}_{\text{h}2}\)). This technique was used to construct the gradient tensor of the aeromagnetic data from Bou Azzer.
# Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT
## 4 Security and Performance Analysis
### Security Analysis
#### 4.1.4. Dynamic Group Management  
The proposed dynamic group management scheme supports the dynamic cluster header allocation of LEACH routing protocol. The LEACH routing protocol improves the energy efficiency by forming new groups and changing the cluster headers of a group on a continuous basis. However, the existing studies on sensor management were designed based on the network topology in which cluster headers were dedicated. The proposed scheme is different from the method to manage a group using one single group key. It continues to generate information transmission session keys based on polynomial  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Blundo’s Protocol** & **Zhang’s Protocol** & **Singlee’s Protocol** & **Proposed Scheme** \\ \hline Relay attack & Not Secure & Not Secure & Secure & Secure \\ Replay attack & Not Secure & Not Secure & Not Secure & Secure \\ Eavesdropping & Secure & Secure & Not Secure & Secure \\ Leaked key & Not Secure & Secure & Secure & Secure \\ Sensor nodes anonymity & Not-supported & Not-supported & Not-supported & Supported \\ Dynamic group management & Not-supported & Not-supported & Not-supported & Supported \\ Mutual authentication & Not-supported & Supported & Not-supported & Supported \\ Forward security and Error detection & Not-supported & Supported & Supported & Supported \\ Mafia attack success probability & - & - & (1/2)’n & 2*{1/(4*n-th Node)}’n \\ Terrorist attack success probability & - & - & (1/2)’n & Node)}’(n*n-th Node) \\ \hline \end{tabular}
\end{table}
Table 2: Comparative security analysis.  
operation by receiving field function seed values from BS. As for group authentication, the proposed scheme utilizes simultaneous authentication that mixes information to be used for the authentication and key derivation between group sensors. That is to say, it conducts simultaneous authentication for group authentication in a way that it mixes the values to be used for authentication and the values to be used for key derivation rather than the way of authenticating a group by matching the key values. As a result, the proposed scheme supports the allocation of dynamic cluster headers and groups through the aforementioned way of group authentication.
## 7 Conclusions  
In conclusion of this work, the performance of a short-reach \((2\times 2)\) optical MIMO system with PAM-2 and PAM-4 constellations is demonstrated. This work aims to compute the effectiveness of the jointly computed pre- and post-equalizer within the power and cost constraints. The experimental results from this study reveal that the system performance is improved by \(\approx\)2.5 dB while utilizing the proposed joint-PPE with PAM-2 and PAM-4 over PE-only. Additionally, when a performance evaluation between the conventional PPE scheme and the joint-PPE is conducted, a significant (\(\approx\)1.5 times) improvement is observed with a constant number of PPE coefficients in both PPE schemes. These results support the hypothesis that the optimally designed PPE coefficients can improve the BER performance of an MMF-based optical system. The findings have implications for increasing the spectral efficiency of critical dispersion-impaired transmission links, and emphasize the need for cost-effectiveness and simple implementation. Future research should focus on optimizing the PPE coefficients with channel robustness. Additionally, the potential of PPE schemes with an MMF link should be explored in the area of quantum communication utilizing quantum key distribution mechanisms [33; 34; 35]. In summary, this work adds valuable insight into the optimization and utilization of the joint-PPE with the HOM formats in an optical MIMO system consisting of an MMF link.  
Conceptualization, J.S. and A.A.; methodology, J.S. and A.A.; software, J.S.; validation, A.A.; formal analysis, J.S.; investigation, J.S.; resources, J.S. and A.A.; data curation, J.S.; writing--original draft preparation, J.S.; writing--review and editing, A.A.; visualization, J.S.; supervision, A.A.; project administration, A.A.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
The authors declare no conflict of interest.
###### Abstract  
Thermal energy storage (TES) systems are key components for concentrated solar power plants to improve their dispatchability and for shifting the energy production efficiently to high revenue periods. The commercial state of the art is the molten salt two tank storage technology. However, this TES confronts some issues like freezing and decomposition, which require continued technical attention. Furthermore, the molten salt itself is very expensive compared to other storage materials. A TES option that possesses a high cost reduction potential and the ability to increase the whole power plant efficiency is the regenerator-type energy storage. Here, a packed bed inventory of waste metallurgical slag from electric arc furnace (EAF) can achieve further cost reduction. Despite previous studies regarding the use of steelmaking slag as an inventory material for thermal energy storages, there are still basic questions to be answered. This work presents experimental thermal performance and thermo-structural stability studies of slag-based TES, obtained during the European project REslag. The EAF slag and different insulation options were tested for their thermomechanical strength in a uniaxial compression test rig. The thermal cyclic behavior was investigated in a pilot TES plant with temperatures up to 700 \({}^{\circ}\)C. The experimental results confirm the suitability of steelmaking slag as thermal energy storage inventory material. Furthermore, a comparison of experimental and simulation model results shows an agreement of over 90%.  
Keywords:regenerator storage; thermal energy storage (TES); slag; concentrated solar power (CSP); thermo-structural stability; thermal performance tests +
Footnote †: journal: Applied Physics Letters
# A Petri Net Approach for Business Process Modeling and Simulation
## 3 Related Work
### ProM  
Process Mining Workbench is an open source framework used in the field of process mining to analyze and visualize business process data. Its characteristics include [29; 30; 31]:  
* **Extensibility and Modularity:** ProM is designed in a modular way, which means that it is possible to add and combine different plugins to adapt it to specific needs.
* **Variety of Process Mining Algorithms:** ProM includes a wide range of algorithms for process discovery and analysis, including model discovery, conformance checking, and rule discovery, among others.
* **Process Visualization:** ProM offers visualization tools to graphically represent business processes, which helps to better understand the flow of activities and events.
* **Model Discovery:** ProM can help to create process models from event logs. This includes the generation of flowcharts and Petri net models, among others.
* **Performance Analysis:** certain versions of ProM include tools to assess process performance, such as bottleneck detection and identification of areas for improvement.  
However, as with any tool or software there are features that certain users might consider to be drawbacks or limitations; for example [32; 33]:  
* **Learning Curve:** ProM may have a steep learning curve for users unfamiliar with process mining or the software itself, and advanced functionalities may require a solid understanding of the underlying concepts.
* **Data Format Compatibility:** ProM can be picky with regard to the data formats it can handle. Users may need to prepare their data in a specific way before they can be used in the tool.
* **Process Log Generator:** ProM randomly generates models based on user-supplied criteria and can provide a related simulated event log; however, it does not provide a way to modify the generated model or change simulation options.
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 4 Results
### Principal Features of the Proposed Method  
The principal features of the proposed method to improve the passband of the Chebyshev sharpened comb filters from the literature are summarized as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(C_{21}\) & \(C_{22}\) & \(\delta_{2}\)**dB** & \(N_{2}\) \\ \hline \(2^{-1}\) + \(2^{-5}\) & \(2^{-2}\) + \(2^{-6}\) & \(0.0307\) & \(11\) \\ \(2^{-1}\)-\(2^{-5}\) + \(2^{-8}\) & \(2^{-1}\) + \(2^{-7}\) & \(0.0276\) & \(12\) \\ \(2^{-1}\)-\(2^{-5}\) + \(2^{-8}\) & \(2^{-1}\) + \(2^{-7}\) + \(2^{-9}\) & \(0.0273\) & \(13\) \\ \(2^{-1}\)-\(2^{-5}\) + \(2^{-8}\) + \(2^{-10}\) & \(2^{-1}\) + \(2^{-7}\)-\(2^{-11}\) & \(0.0266\) & \(14\) \\ \(2^{-1}\) + \(2^{-5}\) + \(2^{-7}\)-\(2^{-9}\)-\(2^{-11}\) & \(2^{-1}\) + \(2^{-7}\)-\(2^{-9}\) + \(2^{-11}\) & \(0.0262\) & \(16\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: SPT compensator \(C_{2}\) coefficients, passband deviations and the number of adders.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(C_{31}\) & \(C_{32}\) & \(\delta_{3}\)**[dB]** & \(N_{3}\) \\ \hline \(2^{0}\)-\(2^{-3}\) & \(2^{1}\)-\(2^{-3}\) & \(0.0776\) & \(9\) \\ \(2^{0}\)-\(2^{-3}\) + \(2^{-7}\) & \(2^{1}\)-\(2^{-3}\) & \(0.0601\) & \(11\) \\ \(  
* The method includes the compensation of Coleman Chebyshev comb filters, which have a favorite characteristic of providing high and equal attenuations in all folding bands.
* The choice of narrowband and wideband compensators depends on the passband of interest. For the wideband case, there is a choice between two compensators depending on the number of adders and the compensation quality.
* All designs have the option of optimal multiplier and multiplierless designs.
* Design flexibility presented as a trade-off between the number of adders and the quality of compensations.
* Simplicity of design since there is only one parameter in the narrowband case and two parameters in the wideband case to optimize.
* The comparisons with the methods from the literature demonstrate the advantages of the proposed method.
# Model Predictive Evolutionary Temperature Control via Neural-Network-Based Digital Twins
## 2 Materials and Methods
### Digital Twin
#### 2.3.2 Training Protocol  
Determining optimal hyperparameters for training neural networks can pose a challenge and often necessitates an empirical approach. In our case, extensive testing was conducted, leading to the derivation of the following list of hyperparameters:  
* The batch size was set to 16.
* The optimizer employed was Adam, utilizing a default initial learning rate of 0.001.
* A learning rate decay scheme was employed, wherein \(lr_{t}=lr_{t-1}\times 0.99\) was initiated after the tenth epoch, with decay continuing until a minimum value of 0.000001 was reached.
* Training was conducted for 800 epochs on an NVIDIA GeForce RTX 3080 GPU. Early stopping was implemented with a patience of 100 epochs.
* One hundred copies of the fan settings vector were utilized.  
The selection of an appropriate loss function significantly influences the performance of the model. In this study, the mean absolute percentage error (MAPE) was adopted, with the following conventions: \(\frac{0}{0}=0\) and \(\frac{q}{0}=\infty\) for all \(a\
eq 0\)[39]. Equation (1) demonstrates the calculation of the MAPE loss, where \(n\) denotes the number of pixels in the image, \(p\) represents the predicted value for a given pixel, and \(gt\) signifies the ground truth value.  
\[LOSS_{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\frac{(p_{i}-gt_{i})}{gt_{i}}*100 \tag{1}\]Preliminary tests indicated that utilizing MAPE as the loss function yielded significantly improved the performance in comparison to the mean absolute error (MAE) or mean squared error (MSE) for both the training and validation datasets.  
To maximize the utilization of all available data, the sequence length was limited to two, considering the duration of the experiments in the dataset as described in Table 1. For instance, an experiment comprising 8 frames contributed a single input-ground truth sequence pair, while 9 frames resulted in 2 pairs, and so forth. To prevent the model from memorizing the order of entries in the dataset, all sequence pairs were randomly shuffled.
## 2 Materials and Methods
### Measurements
The installation of two different receivers on the USV resulted from the fact that the dedicated accuracy of determining a position's coordinates by the navigation multi-GNSS receiver was 2.5 m (CEP (Circular Error Probable), \(p=0.50\)), while the geodetic receiver using the VRSNet.pl network enabled the performance of measurements in real time with an accuracy of 2-5 cm (\(p=0.95\)) in both a horizontal and vertical plane. The geodetic GNSS receiver was a reference receiver based on which the XTE value was determined. The navigation multi-GNSS receiver was installed at the front of the mounting frame connecting both hulls of the vessel, and the geodetic receiver was placed on a 1 metre long pole attached to the rear part of the mounting frame using a special grip. The data recorded at the time of the measurements by  
Figure 7: Sounding profiles designed in two variants: parallel (**a**,**b**) and spiral (**c**,**d**), along with the actual plotted USV route. Measurement points were recorded by a geodetic GNSS receiver (sky-blue colour). the geodetic receiver were recorded on the controller's internal card, while the position of the navigation receiver was sent in real time to a laptop with u-center software installed by u-blox (Figure 8).
## 4 Experimental Results
### Discussion  
The cross-domain tests illustrated that mIBEA is one of the worst poor-performing algorithms for this group of problems, while GDE3 is one of the top-performing algorithms. Thus, the inclusion of mIBEA and GDE3 in the LLH pool increases the difficulty of choosing the best algorithm for hyper-heuristics. In the previous studies, mainly three algorithms were considered in the LLH pool: IBEA (one of the worst algorithms in our study), SPEA2 (the best one in this study), and NSGAII.  
MOABHH and HHRL performed quite well, showing clearly superior results when compared to HHLA and HHCF. Both MOABHH and HHRL removed the poor performing LLHs at the beginning of the search, letting the best LLH run more time than others. HHLA and HHCF, however, kept trying poor-performing LLHs. HHRL removed the poor performing LLHs right away, without giving a proper chance to them during its initialization process, but those LLHs might have performed well in the later stages of the search process. On the other hand, MOABHH has kept all the algorithms running in parallel and removing a percentage of the offspring generation from the worse algorithms. This increases the MOABHH capability of exploring the search and avoiding the chance of removing an LLH with potential good performance in the later stages from the LLH pool in the beginning of the execution.
## 4 State of Art about Data Availability in L2 Rollups
### On-Chain (Rollup Mode)
#### 4.1.1 Calldata  
In a smart contract, calldata is a read-only data area that works similarly to memory and is used to pass arguments to a function. Therefore, calldata remains on-chain as a part of the Ethereum chain history logs and is not stored as a part of the Ethereum state. Since calldata does not change the Ethereum state, it results in a cost-effective way to store information. Zk-rollups employ calldata to publish compressed transaction data on-chain; the rollup operator creates a new batch by calling the relevant function in the contract and passing the bytes array encoded data as payload to the function arguments. \(T_{1},T_{2},\ldots T_{n}\) are the individual transactions in the rollup batch. The rollup operator computes proof \(\pi\) and the state transition \(S\) of batch \(B\). The calldata for \(B\) is as follows:  
\[B_{S}=CE(T_{1},T_{2},\ldots T_{n}) \tag{4}\]  
where \(CE\) is the function that compresses the transaction data and encodes the compressed data in a byte array. \(B_{S}\) along with proof \(\pi\) are then passed as parameters in the function call \(F_{verify}\) of the rollup smart contract \(R_{S}\). An L1 smart contract verifies the proof, updates the state on-chain, and finalizes the batch.  
\[F_{verify}(\pi,B_{S})\rightarrow\text{Updated state in contract }R_{S} \tag{5}\]  
Loopering [30], a decentralized exchange, employs Ethereum calldata for data availability and zk-SNARK for verification. This ensures that all the essential data for verification are available without preserving the entire transaction history on-chain. In contrast, the Immutable X [31], a zk-rollup-based NFT solution, employs zk-STARK for proof generation and posts the state differences as calldata in L1 or off-chain. Conversely, Optimism [27] is an optimistic rollup employing the calldata or blob to ensure data availability. The batch includes the transaction data and the result of the state transition to ensure verification during the dispute period. Indeed, optimistic rollups assume that the transaction batch is valid unless someone challenges the validity of the batch.