esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, the selected complementary metasurface structures (cross, Jerusalem cross, and split ring resonator) were evaluated, and their sensitivity to planar geometry deformations was analyzed. Each structure was designed to have similar operating conditions--to have a resonance behavior (\(f_{\rm r}=5\) GHz) in the case of the reflection coefficient. Numerical models of the considered metasurfaces were developed, and designed structures were fabricated. The measured responses of fabricated structures were in very good agreement with the numerical modeled ones.  
During the proposed experiment, various deformations were applied to the previously designed structures. The main feature that was utilized for the evaluation was the resonant frequency \(f_{\rm r}\). Another one (less reliable) was the amplitude of the reflection coefficient in the resonant state \(|s_{11}(f_{\rm r})|\). Depending on the analysis type and the assumed criterion, various structures were optimal.  
In the case of the geometric parameters (\(w\), \(d\), and \(t\)) of the distortion experiment, the best one (less sensitive to any changes) was cross unit cell-based metasurface, and the worse one was the split ring resonator structure.  
If the external stress application experiment is considered (there is another mechanism for changing the geometrical parameters compared to the previous experiment, e.g., the slot expands in one direction, it narrows in the other), the cross structure is the most sensitive for such an influence. This means that, in the case of the current applications of metasurfaces, this structure should not be utilized in places where such planar deformations could happen. In the case of future applications (e.g., for the wireless measurement of 3D deformation), the CR structure would be applied.  
The observed discrepancy of the results in both experiments (resulting from different mechanisms of changing the set of geometric parameters describing the structure of the metasurface) led us to the following conclusion: the analysis of the metasurfaces' susceptibility to geometry changes should be performed concerning specific applications and the resulting restrictions/parameters.  
In future work, more structures will be evaluated, as well as non-planar deformations' influence on metasurface behavior will be investigated.  
P.L.--overall conception of the article, methodology, measurements, data analysis and writing the article; M.H.--metasurfaces design and numerical analysis. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Center, Poland (Narodowe Centrum Nauki, NCN), within the research project \"Investigation of the impact of irregularities and deformations of the metasurface structure on their reflection properties for electromagnetic waves in the microwave and terahertz frequency band\", grant number 2018/02/X/ST7/02459. The APC was funded by the Research Fund of the Faculty of Electrical Engineering (West Pomeranian University of Technology, Szczecin, Poland).  
The authors declare no conflict of interest.
# Inter-Session Repeatability of Marker-Less Motion Capture of Treadmill Running Gait
## 2 Materials and Methods
### Data Processing and Analysis
The timing of gait events derived from ML kinematics was compared to those from an instrumented TM for agreement using three metrics: (1) mean differences (MD), (2) Bland-Altman limits of agreement (LoA) over 95% confidence intervals, and (3) intra-class correlation coefficients (ICCs) with a two-way mixed effects model and mean of k measurements with absolute agreement. Correlations were classified as poor, moderate, good, and excellent for ICC values below 0.5, between 0.5 and 0.75, between 0.75 and 0.9, and over 0.9, respectively [24]. Two distinct reliability analyses (time series of full gait cycle and discrete variables) were conducted. Reliability across the entire gait cycle was assessed via intra-trial and inter-session variability based upon the work of [PERSON] et al. [25] and [PERSON] et al. [13] (Figure 4). Intra-trial reliability was defined as the average standard deviation across all the gait cycles (total > 30) within a trial. This average standard deviation within each trial was then averaged across all speeds and runners (n = 63) to arrive at an average intra-trial reliability value. Data from the first data collection session were used for this analysis. The intra-trial variability reflects the typical joint movement fluctuations that may be associated with TM running and ML motion capture. Inter-session variability was defined as the standard deviation of each joint angle across the time series (multiple gait cycles) for each speed and runner. Inter-session variability may reflect methodological fluctuations that arise from differences in camera calibration, participants' clothing, lighting, etc. A variability ratio was then computed as the quotient of the inter-session variability and the intra-trial variability. A ratio greater than one would indicate methodological variances between sessions that exceed the typical variance within a TM running trial. Figure 3: The foot strike gait event was determined for each football utilizing the vertical velocity of the whole-body center of mass. The data frame preceding the local minima (red and green circles) was tagged as foot strike alternating between left and right. The local minima represent the transition from downward trajectory of the COM to upward trajectory. As clinical running gait analysis typically focuses on discrete variables, inter-session reliability was also assessed for kinematic variables at foot contact, toe off, and peak joint angles during the stance phase. Reliability for these variables was assessed using ICC\({}_{3,1}\), standard error of measurement (SEM), and minimum detectable change (MDC). JASP software (ver. 0.16.4) was utilized for all statistical computations.
## 4 Results and Discussion
### Performance Analysis of Image Communication System  
For the experiments we have considered OFDM communication system for image transmission. Within the system, we have considered higher order M-QAM modulation to attain a high data rate, at the cost of added noise in the images. Additionally, we have considered various channel models and channel estimation techniques to evaluate the noise and image quality under different system conditions. Multipath AWGN channel and Rayleigh Fading channel with AWGN were used as channel models to simulate real-world environmental conditions. Simple LS and more complex MMSE channel estimation were used to evaluate the trade-off between image quality and system complexity for  
Figure 4: An overview of used case scenario utilizing Edge–Cloud collaboration for deep learning-based CV applications in (**a**) generic IoT ecosystem and (**b**) ITS environment within a Smart City.  
DL applications. Table 3 shows the simulation parameters of the OFDM-based image communication system.
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 2 The Origin of Radon
### Accumulation of Radon in Indoor Environments
#### 2.3.4 Groundwater and Water Supply Systems
The results of groundwater radionuclide concentrations were varied, with granitic lithologies showing the greatest activity. Metasediments, on the other hand, showed much lower values, which is consistent with the trends in soil uranium and radon concentrations. The absorbed dosage was discovered to be inversely linked to the uranium concentration of the rocks when it was measured with gamma spectrometers in close proximity to the rocks. This association shows that radon danger might be quickly estimated from the absorbed dosage. In their study, [PERSON] et al. (2019) investigated the variances between mineral and non-mineral water profiles through an analysis of hydrological and physicochemical properties, as well as radon concentrations [94]. This comprehensive study discovered distinctive features in two clusters of mineral water (C3 and C4), where high radon concentration and electric conductivity were found. In contrast, non-mineral water clusters (C1 and C2) had higher variation in altitude and precipitation. The results for the high-altitude collection points for the non-mineral water imply the importance of recharge in aquifer storage in cluster C2. The mineral clusters showed high physicochemical, radiological, and climatic attributes, further underlining the contrast with C1 and C2. They also found substantial differences in radon concentration within mineral water clusters. C3, for instance, demonstrated markedly low radon levels. This was attributed to a sampling issue caused by a closed circulation system, which led to the radon dissipating due to its short half-life. Moreover, their study illustrated the influence of multiple variables on radon production potential and contamination in groundwater using the PLS-PM model. Variables such as mineralogy texture, K\({}_{2}\)O content, Th content, and uranium content all played significant roles. This research contributed valuable insights for our understanding of how diverse factors influence the composition and safety of water sources, particularly the radon concentrations within them. Future work, as suggested by the authors, should integrate innovative modeling techniques to predict the flow and transport of radioactive contaminants.
# Comprehensive Evaluation of Lateral Performance of Innovative Post in Sand
## 1 Introduction
[PERSON] & [PERSON] [17] conducted a parametric study using finite element (FE) analysis to evaluate the effectiveness of steel fin pipe foundations (SFPFs) in resisting lateral load and reported that SFPFs demonstrated significantly higher lateral resistance than unfinned piles. [PERSON] et al. [12] mentioned that the predominant emphasis in existing research on finned piles has been directed toward enhancing the lateral load capacity of unfinned pile foundations, specifically those utilized for offshore wind turbines, typically characterized as pipe piles. It's worth noting that for various infrastructure projects such as sound walls, other types of piles like H-piles are often considered more suitable and practical. [PERSON] [13] conducted a full-scale monotonic and cyclic lateral loading on the IP (plate-modified H-pile) in clay. Plates with 950 mm length, 420 mm width and, 9 mm thickness were welded to the piles to form the IP. A total of 14 steel W8 \(\times\) 24 (W200 \(\times\) 36) H-piles were tested (4 H-piles, 8 single-paddled, and 2 double-paddled), with an embedded length of 3.5 m and with 1.35 m stickup. Two drilled shafts were also constructed and tested, and their response with that of the IPs. In addition, a limited parametric study was conducted using Lpile v2019 software. The study concluded that single-paddled piles have 22% higher capacity than the unpaddled pile. To better understand the behavior of the paddled H-piles in clayseoil, [PERSON] and [PERSON] [18] conducted a three-dimensional finite element analysis using PLAXIS 3D v20 to investigate the effect of soil consistency, paddle's length, and paddle's width on the lateral capacity of the PHPs. [PERSON] [19] tested PHPs in clay, however, the performance of the double paddle pile was affected by the installation disturbance (the operator swayed the piles laterally), and the performance of the IP system in sand was not evaluated. This work addresses the market's need for an enhanced foundation system tested in sandy soil. This study presents the results of a full-scale field-testing program that was conducted to investigate the lateral performance and capacity of double-paddled steel H-piles embedded in sand. In addition, a finite element model (FEM) was established using FE software PLAXIS 3D to simulate the test piles and was validated employing the field test results. The validated FE model was then used to conduct a parametric study to evaluate the performance of single-paddled and double-paddled IP systems under different soil conditions. In addition, the influence of the plate's width and length on the pile's lateral capacity was assessed. Subsequently, the behavior of the IP under lateral loading and its influence on surrounding soil are discussed. The outcomes of this study have the potential to establish design protocols for PHPs, and offer valuable insights for advancing engineering methodologies pertaining to foundation systems under lateral loading conditions.
## 2 Computational Methods
### Data Transfer Method  
In the coupled computation process, the method of conservative profile-preserving interpolation is utilized for data exchange across the coupling interface [14]. The data exchange process can be described as follows:  
\[u_{f}=Hu_{s} \tag{3}\]  
\[\delta u_{f}\cdot f_{a}=\delta u_{s}^{\mathrm{T}}\cdot f_{s} \tag{4}\]  
Figure 2: Computation results with different turbulence models.  
where in (3) and (4), \(u_{f}\) represents the displacement of the aerodynamic points; \(u_{s}\) represents the displacement of the structural points; \(H\) is the interpolation matrix; \(f_{a}\) denotes the aerodynamic loads; \(f_{s}\) represents the structural equivalent loads; and \(\delta u_{f},\ \delta u_{s}\) are the virtual displacements of the aerodynamic and structural points, respectively.  
If \(H\) is independent of the displacement, then we have  
\[\delta u_{f}=H\cdot\delta u_{s} \tag{5}\]  
Finally, the relationship between the structural equivalent loads and the aerodynamic loads is derived, which can be expressed as  
\[f_{s}=H^{T}\cdot f_{a} \tag{6}\]
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 2 Background
### Bloom Filters
Collisions occur when different elements map to the same bit position in the Bloom filter array. With larger \(N\), there are more available bit positions, leading to a lower FPR. * Number of entries (\(M\)): As the number of entries stored in the filter increases, the probability of encountering a false positive also rises. This is because a higher \(M\) increases the likelihood of multiple elements sharing the same hash values, leading to more collisions and a higher FPR. * Number of hash functions (\(K\)): Employing a greater number of hash functions disperses elements across the Bloom filter array more effectively. Each hash function maps the element to a potentially different bit position. More hash functions lowers the chance of collisions, and consequently reduces the FPR. By adjusting the values of \(M\), \(K\), and \(N\), developers can achieve a desired balance between the FPR and the space efficiency requirements of a specific application. A larger \(N\) offers a lower FPR but requires more space; conversely, a smaller \(N\) offers faster lookups but may lead to a higher FPR. Similarly, increasing \(K\) lowers the FPR but requires more  
Figure 2: Simple Bloom filter operation. computation during insertion and querying. Finding the optimal configuration depends on the specific application's priorities for space efficiency, query speed, and tolerable FPR. While standard Bloom filters offer significant advantages in terms of query speed and space efficiency, a key limitation is their inability to delete entries after insertion. This characteristic necessitates careful consideration of the data being stored and the potential membership changes throughout the application's life cycle. To address this limitation, extensive research has been conducted on extending the functionality of Bloom filters to allow removals without sacrificing performance [16]. Furthermore, efforts have been directed towards enhancing the space-efficient nature of Bloom filters. These advancements include the exploration of alternative data structures such as 2D and 3D arrays to potentially improve storage utilization [17].
## 2 Methodology
### Markov Direct Grey Model (the MDGM(2,1) Model)  
The Markov chain is employed to find when random fluctuations occur in the sample data. This algorithm uses transition matrices to divide the state and to calculate the probability, so the future evolution of the conditional probability depends on the current state of the system, but not on its history. For this reason, the Markov chain plays a remedial role in overcoming the limitation of the grey model and has become an interesting component in improvements to the capability of the grey forecasting model in recent works [9; 14; 16; 17; 18]. This literature indicates that the Markov chain has significantly increased the accuracy of the grey forecasting model.  
In this work, we first adopt predicted values of the DGM(2,1) model to conduct the Markov state transition. Then, transition matrices states are determined for each time step depending on the distribution of DGM(2,1) predicted series, and each state is an interval whose width is a fixed portion between the maximum and the minimum of the whole residual errors. The state partition is an important step in the Markov chain, however, there is no standard rule to divide these state intervals and, in general, the state partition is identified on the basis of the historical data and the research subject [19]. With those states, the state transition between two adjacent time steps can be defined. The transition probability from state \(i\) to state \(j\) after m step can be estimated using:  
\[P_{ij}^{(m)}=\frac{M_{ij}(m)}{Mi}\ (i,j=1,2\ldots m) \tag{6}\]  
where \(M_{ij}(m)\) is denoted by the number from state \(i\) to state \(m\) and \(M_{i}\) is the number of state \(i\). Thus, the \(m\) step of transition matrix is given as:  
\[R^{(m)}=\left[\begin{array}{cccc}p_{11}^{(m)}&p_{12}^{(m)}&\ldots&p_{1j}^{( m)}\\ p_{21}^{(m)}&p_{21}^{(m)}&\ldots&p_{21}^{(m)}\\ &&\ldots&\\ p_{i1}^{(m)}&p_{i1}^{(m)}&\ldots&p_{ij}^{(m)}\end{array}\right] \tag{7}\]  
Utilizing the \(R^{(m)}\) probability matrix to calculate the future transition state, the state probability for the next step is defined by probability in \(\pi\) vectors, denoted as \(\{e_{i}(t),i=1,2,\ldots\pi\}\) at time step \(t\). Set the center of the \(\pi\)-state as \(\{w_{i},(i=1,2,\pi)\}\). Then, the forecast values for the Markov modified models are obtained as:  
\[\hat{\pi}^{(0)}(t+1)=\hat{\pi}^{(0)}(t+1)+\sum_{i}^{\pi}e_{i}(t)w_{i} \tag{8}\]  
where \(w_{i}\) is the corresponding weigh for the state \(i\).
## 3 WEEE and ELV Management and Circular Economy
### Recovery and Destruction
#### 3.1.1 Incineration  
Incineration at temperatures over 1000 \({}^{\circ}\)C has been widely used for the destruction of ozone-depleting substances, such as CFCs and HCFCs. An advantage of incineration is that the generated energy can be harnessed for other processes. It has also been used for the incineration of the HFC R-23 (GWP 12,400), a high GWP compound that is a by-product of the manufacturing of the HCFC R-22 (GWP 1760), which is used as a refrigerant and in blends in foam blowing. Despite being in a phase-out process, the production of R-22 (GWP 1760) has increased, mostly due to its production in developing countries for direct use and due to its worldwide production as a component to make other products. Incineration has been used for the destruction of R-23 (GWP 12,400), but a recent study showed that the atmospheric concentrations of this gas reached very high levels in 2018, beyond what was projected considering the reported R-22 (GWP 1760) production [43]. This suggests that the R-23 (GWP 12,400) incineration facilities, mainly in developing countries, have been deactivated or are not working properly.  
The incineration technologies that were developed for the incineration of ozone-depleting substances (either in a dedicated process or in co-incineration with other waste) and of R-23 (GWP 12.400) have been used in some facilities for the degradation of other HFCs. The thermal stability of fluorinated compounds, such as HFCs, has been compared to the one of chlorinated compounds and it was concluded that fluorinated compounds can be destroyed with high efficiency by incineration [44]. The technologies currently in use include cement kilns, liquid injection incineration, municipal solid waste incineration, reactor cracking, and rotary kiln incineration, which have also been used with high potential for the destruction of HFCs, including R-23 (GWP 12,400), R-32 (GWP 677), R-125 (GWP 3170), R-134a (GWP 1300), and R-143a (GWP 4800) [39]. The technologies of gaseous/fume oxidation and porous thermal reactor have been approved for the destruction of HFCs since they were demonstrated to meet the performance criteria established by the UNEP TEAP Task Force on Destruction Technologies [39]. Moreover, alternative methods have been studied at pilot- and full-scale with high efficiency and low emissions. These include a gasification melting system in reductive conditions [45].  
Despite being effective for the destruction of HFCs and ozone-depleting substances, incineration processes contribute to global warming due to the release of CO\({}_{2}\), and generate harmful by-products. In this way, these processes need to be efficiently designed and operated and tightly controlled to keep emissions at minimum levels.
## 3 Control Design Methodology
### Geometric Model Description  
The robotic kinematic chain can be described using the Denavit-Hartenberg convention (DH) or the modified Denavit-Hartenberg (mDH) convention [19]. For each frame (corresponding to a joint), it is possible to assign dynamic properties such as mass, center of mass, and the inertia tensor. To compute these properties, a CAD model should be utilized to facilitate the calculation of inertial properties with respect to the different joint reference frames. Subsequently, a rigid body tree (RBT) model (a geometric model characterizing both the kinematics and inertial properties of the robot) can be designed. The authors propose, as an example, the adoption of CREO PTC [20] as CAD software to obtain the dynamic characteristics and MATLAB Robotic Toolbox to build the exoskeleton rigid body tree model, as shown in Figure 3a. This RBT model is imported into the Simulink model and utilized to real-time compute the direct and inverse kinematics and the gravity torques to be compensated at each joint. The aforementioned procedure is described in Figure 4.  
Figure 4: Workflow to develop the gravity torque estimation model: the dynamic model characterization for real-time gravity torque computation involves the utilization of various tools. In the CAD model of the prototype, the set of reference frames is redefined following the modified Denavit-Hartenberg (mDH) convention. Properties such as mass, center of mass, and inertia tensor for the different components of the device are then computed with respect to these reference frames. Using these dynamically derived properties, a rigid body tree model is constructed in MATLAB, adhering to the aforementioned mDH convention. This characterized model can be imported into the Gravity Torque function, which, in real-time, provides the joint torques necessary to maintain the robot at a specific configuration.  
Figure 3: (**a**) Rigid body tree representing the kinematic chain of the 6-degree-of-freedom prototype. The Modified Denavit-Hartenberg (mDH) convention was followed for describing the kinematics. Each reference frame, representing a rotational degree of freedom, is associated with the dynamic properties of the body, including mass, center of mass coordinates, and the inertia tensor. (**b**) Graphical comparison between the torques estimated by the model and the ones measured by SEAs sensors. During the test the exoskeleton was moved in position control without a user or a load. In the plot are reported, for each joint, the torques estimated by the SEAs and the ones real-time computed from the model through the robotic toolbox.