esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Tensile Test Analysis  
There are three possible types of interactions for composites that have nanofiller in their structure (polymer-nanofiller, nanofiller-nanofiller, and interface-polymer interactions), but the mechanism and the magnitude of the load transfer between polymer matrices and the nanofiller are still unclear concerning mechanical properties.  
Considering the influence of carbon nanofillers on the macromechanical properties of nanocomposites, the main and most important tensile mechanical characteristics; ultimate (tensile) strength, elongation at ultimate strength and Young's modulus, have been compared. The dependence of ultimate strength as a function of filler content in MWCNT/PLA and GNP/PLA nanocomposites is shown in Figure 8a.  
The nanocomposites possessing GNP in the PLA matrix exhibit a trend of a slight decrease in ultimate strength as well as in elongation at a break when increasing the content filler. A possible reason for this could be a poor dispersion of graphene in the polymer volume and/or changes in polymer crystallinity due to the nanoparticles addition. The character of the curve describing nanocomposites with MWCNT/PLA is quite different. The incorporation of MWCNT significantly raises the tensile strength and elongation at ultimate strength parameters up to 6 wt% carbon filler content, see Figure 8. Above a 20% increase in the tensile strength of the MWCNT/PLA composite is observed at 6 wt% carbon nanotubes content compared to the neat PLA. The rising of carbon nanotubes loading from 1.5 wt% to 6 wt% enhances the elongation at ultimate strength, as can be seen in Figure 8b. This is due to the interfacial polymer-filler interaction related to the distinct hybrid structure between MWCNT  
Figure 8: Tensile strength (**a**) and elongation at ultimate strength (**b**) vs. carbon filler content for MWCNT/PLA and GNP/PLA nanocomposites.  
and polymer chains. The lower values of ultimate strength and elongation at ultimate strength for 9 wt% MWCNT/PLA nanocomposites could be explained by the high carbon nanotube concentration making the composite too crumbly and resulting in worst macromechanical properties.  
Young's modulus as a function of carbon filler content for both studied monofiler composite materials is presented in Figure 9. An insignificant impact has been observed concerning the nanocomposite elasticity by adding GNP in a PLA matrix. The Young's modulus is increasing in the case of carbon nanotubes loading up to 9 wt% filler content. The higher elasticity that could be seen for 9 wt% MWCNT/PLA nanocomposites is probably a result of good filler dispersion. The loadings above 3 wt% of graphene lead to a slightly lower Young's modulus of the nanocomposites. This Young's modulus reduction is insignificant and might be related to some negligible inhomogeneous distribution of carbon filler in the polymer, especially at 9 wt% GNP concentration.
###### Abstract  
Reducing radiation dose to patients without compromising imaging quality has been an important issue in the medical use of X-ray computed tomography (CT). In this study, based on the conceptual designs of different types of attenuation filters, the radiation doses to patients who undergo a typical head, thorax and abdomen scan using a cone-beam CT with different scanning protocols were simulated using the Monte Carlo method, and the isotropy of the noise power spectrum (NPS) of the reconstructed images was also calculated. Compared to the scanning protocol without attenuation and tube current modulation (TCM), the results showed that the fluid dynamic bowtie filter (FDB) combined with the TCM technique reduced the average organ dose by 70%, 34% and 60% for a typical head, thorax and abdomen scan, respectively, and the NPS isotropy of the reconstructed images was also significantly improved. Compared to most currently used static bowtie filters, the FDB has a higher potential to reduce the dose for patients undergoing CT scans. Further efforts are warranted to make the FDB technique clinically useful.  
CT; fluid dynamic bowtie filter; static bowtie filter; radiation dose; NPS isotropy +
Footnote †: journal: Medical Imaging and Image Quality Improvement  
beams and projections to provide more homogenous image noise and lower the patient dose [29; 30]. However, all filters can only be used in fan-beam CT, and their configurations are relatively complicated. To overcome the above shortcomings, [PERSON] et al. [27] proposed a conceptual design of a fluid dynamic bowtie filter (FDB) for both fan-beam and cone-beam CT (CBCT), which was expected to reduce the patient's dose and improve image quality. The proposed 3D bowtie consists of a highly attenuating bowtie (HB) filled with CeCl\({}_{3}\) liquid and a weakly attenuating bowtie (WB) immersed in the liquid. The HB aims at a balanced flux distribution on a detector array when no object is in the field of view (FOV). WB consists of air compensating for an object in the FOV and thus is a scaled-down version of the object. The WB can be rotated and translated synchronously with the source rotation and patient translation. Therefore, the overall flux balance is maintained in the detector array. It was expected that the 3D FDB could more efficiently reduce the patient dose and improve image quality.  
In this study, based on the conceptual design of the FDB proposed by [PERSON] et al. [27], a more comprehensive evaluation of dose reduction and image quality of the head, thorax and abdomen CBCT scans was performed using Monte Carlo simulations. The results of this study will be helpful in optimizing the design of FDB and promoting its clinical application.
# Configuration Design and Gait Planning of a Six-Bar Tensegrity Robot
## 2 Configuration Design of the Six-Bar Tensegrity Robot
### Singular Value Decomposition  
The vector \(\mathbf{t}\)-satisfying Equation (6) exists in the null space of the equilibrium matrix **A**. Since the equilibrium matrix **A** is not an invertible matrix, to obtain the ideal internal force vector, \(\mathbf{t}\), it needs to be decomposed by the singular value decomposition method:  
\[\mathbf{A}=\mathbf{U}\boldsymbol{\sigma}\mathbf{V}^{\text{T}} \tag{7}\]  
\[\mathbf{U}=\begin{bmatrix}u_{1}&u_{2}&\cdots&u_{m}\end{bmatrix} \tag{8}\]  
\[\boldsymbol{\mathbf{V}}=\begin{bmatrix}v_{1}&v_{2}&\cdots&v_{n}\end{bmatrix} \tag{9}\]  
\[\boldsymbol{\sigma}=\begin{bmatrix}\sigma_{1}&&\\ &\ddots&&\\ &&\sigma_{r}&\\ &&0\end{bmatrix} \tag{10}\]  
where \(\mathbf{U}\) is an orthogonal matrix of the order \(m\), \(\mathbf{V}\) is an orthogonal matrix of the order \(n\), and \(\boldsymbol{\sigma}\) is a non-negative singular value of the balanced matrix **A**.  
According to the properties of the matrix singular value decomposition, the singular value of the balanced matrix **A** is the positive square root of the nonzero eigenvalues of \(\mathbf{A}\mathbf{A}^{\text{T}}\) and \(\mathbf{A}^{\text{T}}\mathbf{A}\).  
Assuming that **U** is the eigenvector of \(\mathbf{AA^{T}}\), and **V** is the eigenvector of \(\mathbf{A^{T}A}\), from the properties of the matrix singular value decomposition, we can obtain the following:  
\[\mathbf{Av}_{i}=\mathbf{U}\mathbf{\sigma}\mathbf{V^{T}v}_{i}= \mathbf{U}\mathbf{\sigma}\left[\begin{array}{ccc}v_{i}&\cdots&v_{r_{n}}\\ \end{array}\right]^{\mathbf{T}}\mathbf{v}_{i}\] \[=\mathbf{U}\mathbf{\sigma}\left[\begin{array}{c}v_{1}^{\mathbf{ T}}\\ \vdots\\ v_{r_{n}}^{\mathbf{T}}\end{array}\right]\mathbf{v}_{i}=\mathbf{U}\mathbf{ \sigma}\left[\begin{array}{c}0\\ \vdots\\ 1\\ \vdots\\ 0\end{array}\right]=\left[\begin{array}{c}\sigma_{i}u_{i}\\ 0\end{array}\right] \tag{11}\]  
The following two formulas are obtained:  
\[\mathbf{Av}_{i}=\left\{\begin{array}{cc}\mathbf{\sigma}_{i}\mathbf{u}_{i}&i= 1,\ldots r\\ 0&i=r+1,\ldots r_{n}\end{array}\right. \tag{12}\]  
\[\mathbf{A^{T}v}_{i}=\left\{\begin{array}{cc}\mathbf{\sigma}_{i}\mathbf{u}_{i }&i=1,\ldots r\\ 0&i=r+1,\ldots r_{m}\end{array}\right. \tag{13}\]  
The matrices **U** and **V** can be divided into the following two parts:  
\[\left\{\begin{array}{ll}\mathbf{U}=\left[\begin{array}{cc}U_{r}&U_{r_{m}- r}\\ \end{array}\right],\\ \mathbf{U}_{r}=\left[\begin{array}{cc}u_{1}&\cdots&u_{r}\\ \end{array}\right],\\ \mathbf{U}_{r_{m}-r}=\left[\begin{array}{cc}u_{r+1}&\cdots&u_{r_{m}}\\ \end{array}\right]\end{array}\right. \tag{14}\]  
\[\left\{\begin{array}{ll}\mathbf{V}=\left[\begin{array}{cc}V_{r}&V_{r_{m}- r}\\ \end{array}\right],\\ \mathbf{V}_{r}=\left[\begin{array}{cc}v_{1}&\cdots&v_{r}\\ \end{array}\right],\\ \mathbf{V}_{r_{m}-r}=\left[\begin{array}{cc}v_{r+1}&\cdots&v_{r_{m}}\\ \end{array}\right]\end{array}\right. \tag{15}\]  
Combining Equations (5) and (12), the corresponding internal force vector, t, can be obtained.
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 2 Literature Review
The TeamAware platform leverages UAVs for visual scene analysis and integrates social media sensing for situational awareness in both natural and human-made disasters, improving coordination and response effectiveness [39]. The AiRMOUR project aims to enhance the safety and security of passenger drones in emergency medical services [40]. The TOAS project focuses on precision in herbicide application and crop weed management using UAVs, addressing pest infestations as a form of disruption [41]. Lastly, the PathoCERT project employs satellite data, social media analysis, and data processing to detect and assess water contamination, enhancing situational awareness in events such as earthquakes and floods [42; 43]. This exploration of past efforts has illuminated the research gap that AIRDROP inspires to address. While the reviewed literature highlights various initiatives in the field, it is evident that no single system comprehensively addresses the full spectrum of disaster management needs. AIRDROP's primary objective is to bridge these existing gaps by consolidating insights from previous efforts and offering a holistic disaster management approach that spans from preparedness to recovery for a wide range of natural and human-made disasters, effectively aligning itself with the academic endeavor for holistic methodologies in HL [44]. Table A1 in the Appendix A demonstrates a comprehensive juxtaposition of AIRDROP with the aforementioned relevant projects, demonstrating its broader range of disaster aspects and holistic approach across all disaster stages, distinguishing it from its predecessors. Recent research emphasizes the necessity of a strategy for utilizing technology across different HL operations and deployment phases to achieve real benefits [45]. Additionally, there is a growing body of research assessing the complementarity of drones with other digital technologies. However, comprehensive strategies that integrate these technologies across different HL operations and deployment phases are still lacking [5; 6; 16]. AIRDROP addresses these needs by providing a comprehensive platform that supports both pre- and post-event phases, ensuring continuity and effectiveness throughout the entire disaster management cycle across diverse disaster scenarios. In summary, the selection of AIRDROP as the focus of this research is driven by its potential to serve as a prototype for future disaster management systems, offering a scalable and integrated solution that addresses current gaps in both theoretical and practical aspects of HL.
## 2 Materials and Methods
### Methodology
#### 2.3.3 Rescaled Rang Analysis (R/S Analysis) and Hurst Index Method  
The Hurst index method can predict the future development trend according to the past trend of long time series data. The Hurst index method based on the R/S analysis method was first developed by a British hydrologist [45] in the study of Nile reservoir flow. It is widely used in hydrology, economics and climatology [46; 47], and has recently been applied to the study of vegetation cover change [21; 44; 47]. The calculation steps are as follows:  
\[\Delta NPP_{i}=NPP_{i}-NPP_{i-1}, \tag{9}\]  
\[\overline{\Delta NPP(m)}=\frac{\sum_{i=1}^{m}\Delta NPP_{i}}{m},\ m=1,2, \cdots,n \tag{10}\]  
\[X(t)=\sum_{i=1}^{m}\Big{(}\Delta NPP_{i}-\overline{\Delta NPP(m)}\Big{)},\ 1 \leq t\leq m \tag{11}\]  
\[R(m)=\underset{1\leq m\leq n}{max}X(t)-\underset{1\leq m\leq n}{min}X(t) \tag{12}\]  
\[S(m)=\sqrt{\frac{\sum_{i=1}^{m}\Big{(}\Delta NPP_{i}-\overline{\Delta NPP(m)} \Big{)}^{2}}{m}} \tag{13}\]\[\frac{R(m)}{S(m)}=(km)^{H} \tag{14}\]  
where \(NPP_{i}\) is the \(NPP\) value in year \(i\); \(k\) is constant; \(H\) is the Hurst index which can be calculated by linear fitting with the least squares in double logarithmic coordinate system (\(lnm,ln(r/s)\)). The norm of the Hurst index is 0\(\sim\)1. When 0 \(<H<\) 0.5, it indicates that the NPP will reveal a trend that is opposite of that in the past; when \(H=0.5\), it indicates that the change of NPP is mutually independent which is a random event; when 0.5 \(<H<\) 1, it indicates that there are long-term correlations and the NPP will be consistent with the changing trend before.
## 3 Results
### Test of SF Algorithm with PTB Diagnostic ECG Database
#### 3.2.1 Test 1: PLI Constant (Amplitude Test)  
The design of PLI constant (amplitude test) in Table 2 simulates five different settings of PLI amplitudes (50, 100, 200, 500, and 1000 \(\upmu\)V r.m.s.) corresponding to an input SNR of about 15, 10, 5, \(-\)5, and \(-\)10 dB (median value for 12 ECG leads) as deduced from the detailed statistical distributions in Figure 9. The SF algorithm leads to a considerable improvement in the output SNR to about 75, 70, 65, 55, and 50 dB, respectively, keeping the same level of the coefficient \(SNR_{imp}\) = 60 dB for all 12 ECG leads, notably not impacted by lead-specific amplitudes and waveforms.  
Figure 8: Performance of the SF algorithm with the optimal setting \(k_{i}=2^{-21}\) for an ECG example record (CAL20100 from CTS ECG dataset). The filter input (first trace), filter output (second trace), and filter error (third trace) are computed for three PLI settings in Table 1: (**a**) PLI constant, (**b**) PLI linear amplitude change, (**c**) PLI linear frequency change.  
The statistics of the SF errors in Figure 10 indicate a negligible dependency on the PLI amplitude in the full range (50-1000 \(\mu\)V r.m.s.), measured within a relatively narrow span of median values (min-max ranges) of RMSE = 0.8-1.5 \(\mu\)V (0.2-4 \(\mu\)V) and MAXE = 3-6.5 \(\mu\)V (1.5-12 \(\mu\)V) for all 12 ECG leads. We note that the ECG leads have different maximal errors, which are smallest for leads V1-V6 and largest for lead II (median MAXE elevation of 2 \(\mu\)V), although inter-lead RMSE differences are negligible (<0.3 \(\mu\)V).
## 3 Algorithm Design  
### EKF Algorithm  
The classical Kalman filter is an optimal recursive estimation prediction method widely used in dynamic linear system state estimation. In the actual operation of an electric vehicle's lithium battery, the system is highly nonlinear, and the extended Kalman filter algorithm can linearize this nonlinear system so as to estimate the battery's SoC. The filtering algorithm involves two steps: prediction and correction. Equation (6) has been derived from the [PERSON] battery equivalent model. In order to facilitate the operation of the algorithm later, the nonlinear system model (Equation (8)) is obtained by rewriting Equation (6) in compact form  
\[\left\{\begin{array}{l}\mathbf{x}_{k}=f(\mathbf{x}_{k-1},l_{k-1})+\mathbf{w}_{k-1}\\ z_{k}=h(\mathbf{x}_{k},l_{k})+v_{k}\end{array}\right. \tag{8}\]  
Figure 2: OCV-SoC fitting curve of battery.  
where \(f(\cdot)\) and \(h(\cdot)\) represent the state function and observation function of the system, respectively, and are the inputs of the system at a given time. From Equation (6), we can obtain \(\mathbf{x}_{k}=\left[\text{{SoC}}(k)\,\ U_{b}(k)\right]^{T}\), \(\mathbf{w}_{k-1}=\mathbf{w}(k-1)\ =\left[w_{1}(k-1)\,\ w_{2}(k-1)\ \right]^{T}\), and \(z_{k}=U_{L}(k)\), \(v_{k}=v(k)\).  
In order to facilitate further research on the EKF algorithm, Equation (2) can be used to create the state transition matrix of system \(\mathbf{A}\), noise matrix \(\mathbf{B}\), and observation matrix \(\mathbf{C}\), as shown in Equation (9) [25]. See Figure 3 for a specific flow chart of the EKF algorithm.  
\[\mathbf{A}=\left[\begin{array}{cc}1&0\\ 0&e^{-\frac{\Delta t}{R_{b}^{2}\hat{c}_{b}}}\end{array}\right],\mathbf{B}= \left[\begin{array}{c}-\frac{T_{k}\eta\Delta t}{Q}\\ R_{b}(1-e^{-\frac{\Delta t}{R_{b}^{2}\hat{c}_{b}}})\end{array}\right],\mathbf{C }_{k}=\frac{\partial h}{\partial x}|_{x=x_{k}}=\left[\frac{dU_{OCV}}{dSoC} \big{|}_{SoC=SoC(k)}\ -1\right] \tag{9}\]
## 4 Discussion
The ONIRY system seems to be a good alternative to the current algorithm, giving a chance for significant improvement of obstetric injury detection immediately after natural delivery. As the analysis on the re-analyzed dataset showed, with larger and more balanced data used to train the models, it is possible to increase the sensitivity of the method up to nearly 100%, which would eliminate the situation in which women leave the obstetric ward with undiagnosed sphincter muscle injuries. Additionally, impedance spectrometry can detect not only sphincter structural damage, but also its dysfunction (as the problem of gas and stool incontinence may not only be due to the tearing of the muscle, but also due to its stretching during delivery). Therefore, by assessing the occurrence of the problem by means of impedance spectrometry, it would be possible to immediately start treatment or rehabilitation (e.g., using exercises with biofeedback techniques or functional stimulation). However, it should be noted that the machine learning application was not intended to indicate the causes of sphincter malfunction. The pathology studied--obstetrical anal sphincter injury--may be related to a functional disorder of anal tone end contractility, with only one underlying pathophysiology mechanism, i.e., damage (due to straining) of the vulvar nerve. So far, we have not studied patients with other functional disorders of the anorectal area. The main limitations of the study are a small study group (however, the order of magnitude is similar to other pilot studies of medical devices) and strongly imbalanced data distribution. Therefore, we decided to use the post-processing (SMOTE) algorithm to enable a parallel branch to be further statistically analyzed. From the definition of the SMOTE method, we assume a larger \"re-analyzed\" dataset is coherent with the raw dataset. What is also important is that not all observations are synthetic. Those that are statistically depend on the original data. In addition, by using SMOTE, we did not change the distribution of classes out of the level of its occurrence in the population. As the subsequent studies on a larger group of patients are performed, such approach will not be needed. Additionally, research on a larger group (in particular with more patients with injury degrees of 3 and 4 as measured in the OASI classification) will also allow the use of other methods of data analysis, such as neural networks. According to the experience gained during this study, conclusions were drawn and improvements were made to the test procedure and device prototype: the ONIRY Probe was equipped with more electrodes to enable performing the study without the need to manually rotate the probe; the device software has also been equipped with a module for visualization, and preliminary interpretation of impedance data has been added. Of course, further research is still needed to evaluate the possibility to prospectively use ONIRY as an early screening tool shortly after a natural delivery.
## 4 Implementation  
We assume that the internet consists of multiple hosts, a portion of which are IoT devices and thus they are vulnerable to becoming part of a botnet. This botnet is controlled by a C&C server, which is a server that actually controls the botnet using lightweight commands, and targets a specific victim with a DDoS attack. Both the C&C server and the victim belong to the hosts that are not IoT devices.  
Let the network consist of multiple IoT devices, then the set of all IoT devices is denoted as:  
\[D=\{d_{1},...,d_{n}\} \tag{1}\]Additionally, let the set of all other hosts (non IoT devices) be:  
\[H=\{h_{1},...,h_{m}\} \tag{2}\]  
At each installation of IoT devices, an agent monitors the installed IoT devices. However, our approach can be installed in any number of gateways per installation or even in standalone devices directly connected to the internet. For the shake of simplicity we assume a uniform scheme, according to which there is one gateway per IoT installation. The set of agents is denoted as:  
\[A=\{a_{1},...a_{k}\},k\leq n \tag{3}\]  
Each agent \(a_{i}\) monitors a subset \(D^{i}\subseteq D,\forall i\) of the global set of IoT devices D. Specifically, the set of devices monitored by agent \(a_{i}\) is represented by:  
\[D^{i}=\{d^{i}_{1},...d^{i}_{p}\},p\leq n\text{ where }d^{i}_{j}\in D,\forall i,j. \tag{4}\]  
When a DDoS attack occurs, a host \(h_{c}\) acts as the C&C server and organizes an attack against a victim host \(h_{v}\). Host \(h_{c}\) takes control of a subset \(B\subseteq D\) of the IoT devices and commands them to collaboratively send high volumes of traffic to host \(h_{v}\). The devices that are part of the bot-net are denoted as  
\[B=\{b_{1},...b_{q}\},q\leq n. \tag{5}\]  
The infected devices usually correspond to multiple different agents, in order for the attacker to be able to utilize the upload bandwidth of multiple installations while at the same time keeping the attack less susceptible to detection.  
A sample network topology, consisting of three installations with three corresponding agents monitoring a varying number of devices, is depicted in Figure 1. Some of these devices (denoted in bold) are infected and comprise the botnet. A number of non IoT hosts also exist on the network and one of those has been selected as the victim host against which the DDoS attack is executed.  
Figure 1: This is an example of a network structure in the current model.
###### Abstract  
Remote sensing techniques are capable of mapping soil nutrient concentrations and preparing soil maps for long-term agricultural productivity and food security. Recently, hyperspectral imaging techniques have been widely used to quantify and map nitrogen levels in the soil in large areas. In this study, we employed a partial least square regression (PLSR) technique over PRISMA hyperspectral data on part of the Radhapuram area, Tirunleveli District, India to improve the accuracy of estimating soil nutrient levels. The results of the study show that the PLSR prediction accuracy rates using field observations provided the most accurate estimates of soil containing organic carbon (CC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK). Soil nutrient predictions were carried out using bands in visible near-infrared and near-infrared regions. Analysis of 150 bands using random analyses provided an R2 value of 0.970 and the PLSR technique performed best while using the combined bands in the VNIR+NIR regions. Based on the analyses, PRISMA hyperspectral images using spectral angle mapper (SAM) image classification provided a better map of the soil consisting of organic carbon. The research findings are important references for the prediction of soil nutrients with high accuracy.  
soil nutrients; spectroscopy; food security; regression; machine learning methods Article  
Nadu, India, through Precursors IperSpettrale Della Mission Applicative Hyperspectral Spectroscopy Appl. Sci. 202814186[[https://doi.org/10.3309/app14010186](https://doi.org/10.3309/app14010186)]([https://doi.org/10.3309/app14010186](https://doi.org/10.3309/app14010186))