esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The identification of the most pertinent site parameters to classify soils in terms of their amplification of seismic ground motions is still of prime interest to earthquake engineering and codes. This study investigates many options for improving soil classifications in order to reduce the deviation between \"exact\" predictions using wave propagation and the method used in seismic codes based on amplification (site) factors. To this end, an exhaustive parametric study is carried out to obtain nonlinear responses of sets of 324 clay and sand columns and to constitute the database for neuronal network methods used to predict the regression equations of the amplification factors in terms of seismic and site parameters. A wide variety of parameters and their combinations are considered in the study, namely, soil depth, shear wave velocity, the stiffness of the underlaying bedrock, and the intensity and frequency content of the seismic excitation. A database of \(A\)Fs for 324 nonlinear soil profiles of sand and clay under multiple records with different intensities and frequency contents is obtained by wave propagation, where soil nonlinearity is accounted for through the equivalent linear model and an iterative procedure. Then, a Generalized Regression Neural Network (GRNN) is used on the obtained database to determine the most significant parameters affecting the \(A\)Fs. A second neural network, the Radial Basis Function (RBF) network, is used to develop simple and practical prediction equations. Both the whole period range and specific short-, mid-, and long-period ranges associated with the \(A\)Fs, \(\mathrm{F_{a}}\), \(\mathrm{F_{v}}\), and \(\mathrm{F_{I}}\), respectively, are considered. The results indicate that the amplification factor of an arbitrary soil profile can be satisfactorily approximated with a limited number of sites and the seismic record parameters (two to six). The best parameter pair is (\(PGA\); resonance frequency, \(f_{0}\)), which leads to a standard deviation reduction of at least 65%. For improved performance, we propose the practical triplet \((PGA;V_{S0};f_{0})\) with \(V_{S30}\) being the average shear wave velocity within the upper 30 m of soil below the foundation. Most other relevant results include the fact that the \(A\)Fs for long periods (\(\mathrm{F_{I}}\)) can be significantly higher than those for short or mid periods for soft soils. Finally, it is recommended to further refine this study by including additional soil parameters such as spatial configuration and by adopting more refined soil models.  
**Keywords:** nonlinear site response; site proxies; neural network; seismic response; site factor
## 2 Related Work
### RAMI 4.0 Architecture  
At present, two main reference architectures, RAMI 4.0 and IIRA, are in the process of formalization and standardization.  
The RAMI 4.0 (Reference Architecture Model Industry 4.0) architecture [13] was developed by BITCOM (the German Association for IT, Telecommunications, and New Media), VDMA (Mechanical Engineering Industry Association), and ZWEI (German Electrical and Electronic Manufacturers' Association). It is about to become an international standard (IEC PAS 63088) [11]. Its 6 layers--from top to bottom: business layer, functional layer, information layer, communication layer, integration layer, and asset layer--provide an overview of the main aspects of Industry 4.0. It considers two important stages of the product life cycle, the development and production stages. Regarding the level of detail, different hierarchical levels are addressed, from the product level to the extended enterprise level (* Connected word =).
## 4 Conclusions  
Effective methods for isolating analytes from biological matrices should facilitate the determination of low concentrations of endogenous substances in biological samples. The extremely low concentrations of catecholamines and their metabolites, including DHPG, MHPG, and VMA, along with their diverse physicochemical properties, necessitate a careful selection of extraction procedures and parameters. This study presents the first application of a new 3D-printed DSPE system for extracting compounds from the norepinephrine metabolic pathway (DHPG, MHPG, and HVA) from biological samples, comparing it with two different extraction methods based on LLE and SPE. Given the low concentrations of analytes in the sample matrix, we also focused on the potential for signal amplification, expressed as the enhancement factor, achieved during sample preparation. Among the LLE techniques, the greatest signal amplification was achieved through a modified double process at a sample pH of 5.8. The most efficient procedures for all analytes utilized C18 columns with methanol as the eluent. The results obtained from the new 3D-printed DSPE system with Styrene-DVB sorbent and acetone as the desorbent confirmed the significant potential of this solution for effective extraction and signal amplification for all tested analytes, reaching up to a 3-fold increase. Additionally, the developed MEKC method, supported by the proposed 3D-printed DSPE system with Styrene-DVB sorbent, can serve as an offline preconcentration technique for the simultaneous isolation and determination of these catechol compounds in urine samples for diagnostic purposes.  
Conceptualization, I.O. and M.B.; methodology, N.H., D.K. and I.O.; software, I.O., N.H. and P.K.; formal analysis, I.O., M.B. and P.K.; investigation, I.O., P.K. and M.B.; resources, I.O. and D.K.; data curation, I.O.; writing--original draft preparation, I.O.; writing--review and editing, I.O. and P.K.; visualization, I.O. and N.H.; supervision, I.O.; project administration, I.O. and T.B.; funding acquisition, I.O. and T.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Center Poland by the grant number 2018/31/D/ST4/03970.  
Not applicable  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
## 1 Introduction
Previous studies have reported that the LSTM shows particularly high performance when predicting biomechanical parameters using motion sequenced sensor data while developing a machine learning model in the biomechanical field. Therefore, in this study, we evaluated the performance of LSTM for predicting a joint moment based on plantar pressure data collected from a wearable insole sensor during a lifting task. The purpose of this study is to propose a system that predicts lower extremities and L5/S1 joint moment using machine learning model and plantar pressures from an insole sensor system. In detail, we developed a new deep learning structure by combining classifier and regressor as one network to estimate the lower extremities and L5/S1 moments. Additionally, we evaluated the usefulness of the proposed machine learning model by applying to various lifting conditions.
# Adaptive Clutter Intelligent Suppression Method Based on Deep Reinforcement Learning
## 6 Conclusions  
In the complex clutter background, the clutter center frequency is not fixed, and the spectral width is large, which leads to the performance degradation of the traditional adaptive clutter suppression method. To address the above problems, this paper proposesan intelligent clutter suppression method based on deep reinforcement learning (DRL). By constructing a filter parameter decision model of the deep Q-network (DQN), establishing a reward and punishment mechanism, and introducing a greedy strategy, the agent continuously interacts with the environment and uses the learning results to optimize the filter parameters in real time, so that the filter's notch can be \"more accurate\", \"deeper\", and \"wider\" to adapt to different clutter strength environments. The experimental results show that the proposed algorithm can still effectively adapt to different clutter strength environments in the complex clutter background, thus significantly improving the clutter suppression effect, and the performance is better in the strong clutter.  
Based on the research in this paper, future work will focus on further optimizing the compensation of the blind speed phenomenon. Specifically, we plan to introduce variable pulse repetition frequency (PRF) as an optimization method to address the impact of the blind speed phenomenon on the clutter suppression algorithm. By dynamically adjusting the pulse repetition frequency, we hope to be able to better adapt to changes in the target speed, thereby improving the adaptability and accuracy of the algorithm in complex environments.  
In addition, it is worth pointing out that the clutter simulation data in this paper are modeled based on the parameters of the phased-array radar, and the corresponding experimental radar parameters also rely on the phased-array radar. The clutter suppression method is mainly applied to phased array radar and has a strong application background. However, our results are also generalizable, and it is expected that the method can be effectively applied to mechanically scanned radars and other types of radars. Regarding the adaptation to these radar systems, further testing and validation are required in future work. This will be an important direction for our further research.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 7 Simulation--Real Topology  
### Topology Description  
Figure 9 shows the real topology with the position of Smart Meters in supply points. The node n1 is a data concentrator located in MV/LV transformer substations with LTE network for connection to the data center. The node n1 is a PLC HeadEnd modem and the other nodes are Smart Meters in supply points, fuse boxes, and the transition between underground and overhead power lines.  
The real topology is located in a rural area (small village) with only family houses. The no. 21, 22, 23, 24, 25, and 28 points are fuse boxes, other points are Smart Meters with BPL modems in supply points. The points from no. 2 to no. 17 are terraced houses.  
Figure 8: Channel capacity for changing branch terminal load impedance.  
Five types of power line cables were considered: underground cables NAYY 150 SE, AYKY 3 \(\times\) 120 + 70, and AYKY 4 \(\times\) 50, and overhead cables AlFe 4 \(\times\) 50 and AES 4 \(\times\) 120.  
The distance between n1 (HeadEnd) and the farthest node n32 is 835 m.
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 2 Mobile Ad Hoc Networks (MANETs)
### Target MANET-Applications and -Scenarios
#### 2.3.1 Disaster Response  
Our first scenario class focuses on the case where existing infrastructure is unavailable [19], be it temporarily (as in the case of, e.g., a power outage) or permanently (such as, for example, when a hurricane has wiped out the cell tower network of a Caribbean Island). In this application, we assume some ongoing coordinated effort that includes many field units operating in uncertain terrain that need to be connected through a gateway to the global IT infrastructure or some operational headquarters [31]. This also covers the case where a MANET is deployed to serve as temporary infrastructure.  
In such a setting, the demand is likely to move as rescue efforts sweep across the deployment area or as parts of the local infrastructure successively come back online. The MANET must respond to this shifting demand by dynamic node re-allocation.
## 4 M2M Communication
### Constrained Application Protocol (CoAP)
CoAP base specifications identify DTLS [115] and IPsec [116] as mechanisms to offer data origin authentication, integrity and replay protection, and encryption for the CoAP messages. In addition to these, an alternative [117] to IPsec and DTLS has been presented.
## 3 Problem Statement and the Dimension Reduction with Iovp  
### Reduced Rank Methods and the Projection Matrix  
The filter \(\mathbf{\omega}_{k}[i]\) in Equation (10) can be estimated efficiently via the RLS algorithm; however, the convergence and tracking behaviours depend on \(M\) and on the eigenvalue spread of \(\mathbf{R}\). Reduced-dimension methods are introduced to speed up the convergence of beamforming algorithms and to reduce their computational complexity [5; 8]. A reduced-rank algorithm must extract the most important features of the processed data by performing dimensionality reduction. This transformation is carried out by applying a matrix \(\mathbf{S}_{D}\in\mathbb{C}^{M\times D}\) on the received data as given by:  
\[\bar{\mathbf{x}}[i]=\mathbf{S}_{D}^{H}\mathbf{x}[i] \tag{14}\]  
where, in what follows, we denote the \(D\)-dimensional terms with a \"bar\" sign. The obtained received vector \(\bar{\mathbf{x}}[i]\) is the new input to a filter given by the vector \(\bar{\mathbf{\omega}}=[\bar{\omega}_{1},\bar{\omega}_{2},\ldots,\bar{\omega}_{D}]^ {T}\), and the resulting filter output is:  
\[\bar{y}_{k}[i]=\bar{\mathbf{\omega}}_{k}^{H}\bar{\mathbf{x}}[i] \tag{15}\]  
In order to design the reduced-rank filter \(\bar{\mathbf{\omega}}_{k}\), from Equation (6), we consider the following optimization problem:  
\[\begin{split}&\min\mathcal{J}_{\text{\tiny reduced-rank}}(\bar{\mathbf{ \omega}}_{k}[i])=\bar{\mathbf{\omega}}_{k}^{H}[i]\bar{\mathbf{R}}\bar{\mathbf{\omega}}_{k}[i ],\\ &\text{subject to}\qquad\bar{\mathbf{\omega}}_{k}^{H}[i]\bar{\mathbf{a}}( \theta_{k})=1\end{split} \tag{16}\]  
The solution to the above problem is:  
\[\bar{\mathbf{\omega}}_{k}[i]=\frac{\bar{\mathbf{R}}^{-1}\bar{\mathbf{a}}_{k}(\theta_{k})} {\bar{\mathbf{a}}_{k}^{H}(\theta_{k})\bar{\mathbf{R}}^{-1}\bar{\mathbf{a}}_{k}(\theta_{k})} \tag{17}\]  
where the dimensional reduced covariance matrix is given by \(\bar{\mathbf{R}}=E\left[\bar{\mathbf{x}}[i]\bar{\mathbf{x}}^{H}[i]\right]=\mathbf{S}_{D}^{H} \mathbf{R}\mathbf{S}_{D}\) and the reduced-rank steering vector is obtained by \(\bar{\mathbf{a}}(\theta_{k})=\mathbf{S}_{D}^{H}\mathbf{a}(\theta_{k})\), where \(E[\cdot]\) denotes the expectation operation. The above contents show how a projection matrix \(\mathbf{S}_{D}\) can be used to perform dimensionality reduction on the received signal and resulting in improved convergence and tracking performance over the full-rank filter listed in Equations (6) and (10).
# Retrofitting a Building's Envelope: Sustainability Performance of ETICS with ICB or EPS
## 5 Results
### Comparative 3E Performance of ETICS with ICB and Expanded Polystyrene (EPS)
#### 5.2.2 Comparison of the Energy Savings in Heating and Cooling of ETICS with ICB or EPS  
The results achieved in the comparison of the economic balance concerning the \"environmental impact savings\" demonstrate that the use of the maximum thickness (9 cm) of insulation in ETICS with ICB or EPS on the external surface of these walls can result in a carbon saving in comparison with the reference alternatives, from 24% to 31%. Similar results were achieved for the \"environmental impact savings\" of consumption of PE-NRe for the energy of heating and cooling during the study period. Thus, analysing the C2C PE-NRe and GWP with energy for heating and cooling, it was found that the alternatives with ETICS with ICB or EPS present from 13% to 26% lower impacts than reference ones.  
These \"environmental impact and economic savings\" during the B6 substage are expressed in this study per m\({}^{2}\) of the external wall of the flat chosen, but the corresponding savings provided by  
Figure 8: C2C consumption of non-renewable primary energy (PE-NRe, in MJ, without energy for heating and cooling)/m\({}^{2}\) of the external wall alternatives of ETICS with ICB and EPS.  
Figure 7: C2C Global Warming Potential (GWP, in kg CO\({}_{2}\) eq, without energy for heating and cooling)/m\({}^{2}\) of the external wall alternatives of ETICS with ICB and EPS.  
the implementation of this energy renovation in Portugal or in other countries can be extrapolated depending on the thermal performance characteristics of the majority of existing buildings.
## 1 Introduction
### Motivation of the Research  
Today, amidst the gradual depletion of mining resources, more and more mine enterprises are moving to remote locations with no electric grid access. In remote sites, fossil fuels including diesel and natural gas are the main sources of energy. In 2017, 18,364 k tonne of diesel is consumed in mining: about 18% of the total industrial share [2]. Diesel emissions are the major contributors to the CO\({}_{2}\) accumulation in the earth's atmosphere by adding 123 ppm between the pre-industrial era and 2016 [16]. In mining, 71% of the emission is from fossil fuels while 29% is due to electricity usage [17]. In addition to its environmental impacts, diesel dependence also created an escalation in the cost of electricity generation from diesel generators. Utility cost of electricity generation from a diesel generator can be as high as 0.76 USD/kWh for some remote locations excluding carbon tax [18]. Thermal duties of refrigeration units in deep mines are typically in the range of 0.5 and 20 MW [14,19]. The use of diesel generators to generate this huge energy is a financial and an environmental concern. The motivation of the research is the need to lower the high cost of energy that remote mines are currently using in addition to their higher environmental footprints. The penetration of renewable energy in mine chilling application not only assists to break the complicated relationship of mining and renewable energy, but also improves the public perception of environmental consciousness towards mining.
# Simplified Pushover Analysis for Rapid Assessment of Shear-Type Frames
## 4 Validation of the Proposed Simplified Mechanical Model
### Application of the Proposed Model  
Three reinforced concrete frames with a varying number of stories are considered for the comparative application of the proposed method. The frames considered are designed for gravity loads only, according to common practice in Italy in the pre-1980s, before the introduction and enforcement of seismic codes and capacity design. A common failure mechanism found in such gravity-load-designed structures is column-swaying, due to the presence of strong beams and weak columns, which results in a shear-type overall behavior.  
Figure 5 shows various details of the RC frames such as: frames geometry, element cross sections, longitudinal, and shear reinforcement. The concrete and reinforcing steel strength were 18 and 370 MPa, respectively, which were typically used in Italy in the 1970s. Static nonlinear pushover analysis was conducted using SAP2000 software [31] and the displacements at the attainment of three limit states DLS, LLS, and CLS were retrieved for  
Figure 5: Geometry and sectional details of the case study frames.  
each case, consistent with the criteria adopted for the simplified method. Figure 6 shows the comparison of the displacement quantities at DLS, LLS, and CLS obtained through the accurate pushover analyses and the proposed simplified analytical model. It is evident from the displacement comparison at various limit states that the proposed model provides a satisfactory match with the full pushover analysis. The error in terms of drift at various limit states between the two methods for the three case studies is shown in Table 2, where (+) and (-) signs stand for overestimation and underestimation, respectively. Based on the results, the error is deemed to be acceptable. Moreover, it is observed that in case 1 the damage concentration occurs at the first story, while for cases 2 and 3 the damage is concentrated at the third story. It should also be noted that the stiffness is generally higher using the simplified method, except for LLS and CLS of case 3. The mechanical model performs well to capture the deformed shape at the elastic limit (DLS) and subsequently at the inelastic range (LLS and CLS) and the formation of soft stories. Furthermore, in Figure 7, the pushover curves of the RC frames in terms of base shear-top displacement obtained from the two methods are compared, showing a satisfactory correspondence.  
Figure 6: Comparison of the displacement profiles from the full pushover analysis (dashed lines) and the proposed analytical method (thick lines). From above: Case 1 (3 storeys), Case 2 (5 storeys), and Case 3 (6 storeys).
# An Approach to Incorporating Implicit Knowledge in Object Detection Models
## 3 Problem Formulation and Preliminaries
### Information Loss in the Object Detection Model
#### 3.1.2 Information Loss Caused by Multi-Tasking  
In addition to sampling information loss, multi-task learning is another significant factor leading to information loss in single-stage object detection models. In multi-task learning, the model needs to handle multiple tasks simultaneously, which may result in mutual interference between tasks, leading to information loss, as shown in Figure 3.  
Figure 3: Schematic diagram of multi-task single-stage object detection model.  
Figure 2: Visualization of feature maps at different layers in neural networks. (**a**) Input images; (**b**) shallow layer feature maps; (**c**) deep layer feature maps.  
Currently, the main approach to address this issue is by designing loss functions to balance the importance of each task. Different tasks may have varying learning speeds and difficulties, and excessive focus on one task may lead to a decline in performance in others. During model training, to adapt to multiple tasks, the model may prioritize certain tasks while sacrificing the learning effectiveness for others, thereby causing information loss.  
The shared underlying network structure in multi-task learning may also have an impact on the feature representation for each task. The shared representation may not be optimal for every task, causing some tasks to inadequately express their specific information, resulting in information loss.
## 2 Materials and Methods
### Data Source and Processing
#### 2.2.2 Remote Sensing Data  
Remote sensing data included eight-day land surface temperature (LST) data (MOD11A2), monthly synthetic normalized difference vegetation index (NDVI) data (MOD13A3), annual synthetic land cover (LC) data (MCD12Q1), eight-day synthetic GPP data (MOD17A2 HGF), and eight-day synthetic ET data (MOD16A2 GF), which are all derived from the EOS data center of the NASA Land Processes Distributed Active Archive Center (LPDAAC) in the  
Figure 1: Provinces (**a**) and land cover types (**b**) in the Huang–Huai–Hai Plain (HHHP) and administrative divisions of China (**c**).  
United States. MOD11A2 misses two images in 2001, but it has no effect on synthetic monthly-scale LST data. MCD12Q1 lacks the data of 2020. Owing to the small change in MCD12Q1, the data of 2019 were selected to simulate the data of the 2020 year. All remote sensing data were preprocessed using the MODIS data (Table 1) processing software MRT for image stitching, resampling (uniform spatial resolution of 1000 m), and projection conversion. The time range was from 2001 to 2020, and the spatial range was the HHHP.
# Bounce: A High Performance Satellite-Based Blockchain System
## 3 Bounce Protocol
### Trust Model
#### 3.3.1 Public Key Infrastructure  
1. All components ( satellite Bounce Units, Sending Station, and Broadcast Ground Station) have access to a public key infrastructure (PKI), managed by Mission Control. The result is that each Component knows the public key of all other Components.
2. The Mission Control Administrators will determine for each Component \(A\) whether \(A\) is a Sending Station, Broadcast Ground Station, or Bounce Unit. Some terrestrial components may take on several roles.
3. For fault tolerance reasons, there will be several satellites. Mission Control will determine which satellite is responsible for which time slot. During each time slot, the responsible satellite will create a Commit Record containing an array of roots of zero or more Merkle Trees that will be added to the blockchain. All Components know these slot assignments.