esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Recently, a largemulti-state prospective study of 259 US children with HUS as a complication of EHEC O157:H7 infection demonstrated unequivocally that exposure to antibiotics of any class in the first week of illness was independently associated with development of HUS and tripled the risk [18]. For the O157:H7 strain, antibiotics increase toxin production due to location of _stx_ genes within antibiotic-inducible resident lambdoidophages [19]. The recent outbreak in Germany with the Stx2-producing O104:H4 strain was notable for its high HUS rate at 22%, occurring overwhelmingly in adults (88%) and most of these being young women [12,15,20]. In this outbreak, use of antibiotics at one hospital center was beneficial, significantly reducing intestinal bacterial colonization duration and improving clinical outcome [21]. Unlike the more common O157:H7 strain, antibiotics reduced Stx secretion from this strain _in vitro_[22]. Argentina has the dubious distinction of having the highest incidence of pediatric diarrhea-associated HUS (13.9/100,000 population) and \(\sim\)20% of Argentina's pediatric kidney transplants are the result of EHEC infections [23]. Relative to other infectious disease outbreaks, the death rate from EHEC infection is fairly low at around 3%-5%, but morbidity associated with kidney injury is significant. About 12% of patients with diarrhea-associated HUS progress to end stage renal failure within 4 years and about 25% have long term renal impairment [24,25], putting them at high risk for other clinical complications as eventually encounter pregnancy, high blood pressure, diabetes, cardiovascular disease and other complications of aging. Because of the importance of HUS complications, considerable clinical and basic research has focused on this aspect of EHEC infections to define the onset of HUS and to identify cellular dysfunctions that contribute to the pathology. The intent is to identify clinical patterns and molecular biomarkers that identify those at highest risk in order to prevent or mitigate HUS. Not all cases of HUS are due to EHEC infections, and the similarities and distinctions between EHEC-induced HUS and related syndromes of atypical HUS and thrombotic thrombocytopenia pur pur purura can delay diagnosis and complicate clinical management of patients. Identifying effective non-antibiotic adjunctive therapeutics depends on understanding the molecular and pathophysiological mechanisms that contribute to organ injury, and in this respect, development of appropriate and representative animal models is critical. Both of these aspects are discussed below.
# A LoRa-Based Linear Sensor Network for Location Data in Underground Mining
## 5 Scalability  
To explore the scalability of the system as the number of relays and tags increase we simulate the system. In the analysis in the previous section we assumed an aggregate rate of traffic into each relay from the tags of \(\lambda\). With simulation we can explore more complex scenarios where the arrival rate into individual relays is not the same across the network. We use simulation to explore the performance of the network as the number of tags increases and for different distributions of tags. We also consider three distributions of tags throughout the network. First is where the number of tags at each relay is the same for all relays. This should approximately match our analysis in the previous section. Second is where the number of tags further from the headend is greater than those nearer, and third, the converse where the number of tags nearer the headend is greater than those further away.  
Our simulator is written using Matlab as a message passing system. In the simulation each relay and tag behaves as described in pseudo-code in Section 3.1.  
Each relay keeps track of the highest message number it has received from each tag. Any message it receives is broadcast provided the message number is greater than the highest recorded and provided the message TTL is greater than zero. Each relay only sees messages from its nearest neighbors. Upon receipt of a message the relay waits for the medium to go silent and then broadcasts its message after a random interval as described previously. The channel is assumed to be lossless. Given the  
Figure 3: Comparison of analytical and simulation models. Each tag generates one 30 Byte message every 60 s. Each relay forwards messages at service rate \(\mu=10\) per second.  
very good transmission characteristics we have observed of LoRa underground this is a reasonable assumption but exploring more lossy channel models is an area of potential future research.  
For the remainder of the paper we simulate messages being transmitted over LoRa relays configured to use Spreading Factor 7, bandwidth of 500 kHz and coding rate 1 giving a bit-rate of approximately 22 kbps. Messages generated by the tags are 30 bytes long and are generated once every 60 s.
# Classification of Marine Vessels with Multi-Feature Structure Fusion
## 3 Multi-feature Structure Fusion Based on Linear Discriminant Analysis
### Multi-feature Structure Fusion Based on Linear Discriminant Analysis
#### 3.3.3 Weight Matrix Generation after Feature Weighting  
We combine feature \(X_{1}\) and feature \(X_{2}\) by the proportional weighted stitching, that is \(X_{3}=\left[X_{1}*\beta_{1}X_{2}*\beta_{2}\right]^{T}\), here \(\beta_{1}=0.6\) and \(\beta_{2}=0.4\) is selected by cross-validation.  
To match the weighted spliced feature \(X_{3}\), the weight matrix \(W\) is produced by assigning each class weights in \(W_{3}\) to all samples of the corresponding class, as shown in Equation (14).  
\[W=\left[\begin{array}{c}\mathit{repmat}(W_{3},1,m_{1})\\ \mathit{repmat}(W_{3},2,m_{2})\\ \vdots\\ \mathit{repmat}(W_{3},c,m_{c})\end{array}\right]_{m_{c}} \tag{14}\]  
where the operator \(\mathit{repmat}(W_{3},k,m_{k})\) means that the \(kth\) row of matrix \(W_{3}\) is copied \(m_{k}\) times as \(m_{k}\) rows of matrix \(W\).  
The combined feature \(X_{3}\) and its internal structure \(W\) are constructed by the above method, and then Equation (8) is reformulated as Equation (15):  
\[\overline{X}_{3}W\overline{X}_{3}^{T}a=\lambda\overline{X}_{3}\overline{X}_{3 }^{T}a \tag{15}\]  
To solve Equation (15), the mapping matrix \(A=[a_{1},a_{2},\ldots,a_{c-1}]\) can be obtained by using the SRDA method proposed in [29], followed by the feature after structure fusion is \(Y=A^{T}X\).  
For the fusion feature, we calculated the mean of each class samples to get each class feature, and used the nearest neighbor method to determine the classification of each sample.
## 1 Introduction
The optical properties of silver nanoparticles are related to the localized plasmon resonance which, as an unusual phenomenon, is reserved only for a small group of nanostructures. Due to it, control of their properties that facilitates research is possible [24; 25; 26; 27]. On the other hand, the specific interaction between nanoparticles and biomolecules is willingly used in practice. Modern systems and materials containing metallic nanoparticles and biomolecules such as components of genetic information in the form of a monolayer or thin films can be used as accurate diagnostic sensors. The phenomenon of interaction between biomolecules and metal nanoparticles seems to be an interesting problem and covers at least several scientific disciplines such as the chemical and biological sciences. The adsorption process of organic molecules on surfaces of nanostructured solids and constructions with metal nanostructures is significant when systems have to be applied as biosensors [28; 29; 30], bionanotechnology systems, and biocatalysts. These applications are, many times, based on the phenomenon of surface-enhanced Raman spectroscopy (SERS), surface plasmon resonance spectroscopy (SPRS), and electrochemical and calorimetric detection methods [31; 32; 33]. On the contrary, specified biosensors may contain accurate molecules, such as proteins, genes, and their derivatives such as small segments of deoxyribonucleic or ribonucleic acid. Whereas oligonucleotides attach to their target, they generate a signal which is possible to measure. As follows, this type of biosensor can be used to detect specified genes and building blocks of genes related to viral infections, cancer, and other diseases. However, such advanced solutions require elementary studies on the synthesis and characterization of biomolecule-nanoparticle composites. The adsorption of organic molecules with biological relevance is still a great challenge due to their large sizes and complex structures. The goal of this work was to adsorb adenosine nitrogenous on a surface of porous activated carbon in the presence of silver nanoparticles as an accompanying substance. The adsorption process of a heterocyclic organic compound (nitrogenous base) was considered in terms of kinetic description. The interactions of the components with solid substrates, as well as the influence of the presence of a nitrogenous base on the morphology and size of silver nanoparticles, were considered and evaluated.
## 1 Introduction
Monitoring of terrestrial plant productivity is one of the key parameters in environmental resource management as it provides information on potential food resources and sources of wood for construction, fabrication and fuel [14]. For example, early indicators of crop health status are very valuable because management decisions can be made both by farmers at the field level but also by governments at the regional level to mitigate the economic and social impacts of yield variability. In addition, as climate and terrestrial ecosystems interact with and influence each other, vegetation productivity is also used as indicator for climate change effects [15]. Plant productivity is calculated as Net Primary Productivity (NPP), the difference between Gross Primary Productivity (GPP) and plant autotrophic respiration (Ra), which is the net carbon fixed by vegetation through photosynthesis [16]. At the global scale, terrestrial plant productivity is one of the most-modeled ecological parameters, with models that differ markedly in approach and complexity often yielding comparable estimates [17]. For example, a global 8-day MODIS product (MOD17A2) is available which models GPP at a 1 km resolution using a light-use efficiency model [18]. However, for regional applications (e.g., monitoring crop productivity) both the spatial and temporal resolution of this product is too coarse. In addition, this product has been developed for a global scale which means that several of the input parameters of the estimation model do not account for the local heterogeneity of land use and meteorological parameters [19-21]. Increased availability of real-time sensor data at the local scale could increase the understanding and detection of vegetation status of heterogeneous landscapes. The added value of a sensor web based approach would be that multi-source sensor streams can be integrated in the model. Standardized modeling results can be presented to the end-user and will supply information on the spatial distribution of vegetation productivity both in the actual situation (nowcasting) and for the near future (forecasting) [22]. In this study we have developed a sensor web based approach which combines earth observation and _in situ_ sensor data to derive regular products for vegetation productivity on a regional scale level. The approach is implemented in an automated processing facility which makes the products available through a dynamic web mapping service (WMS). Within the study a prototype application has been developed which provides daily maps of vegetation productivity for regional to national scale in the Netherlands. In the results section of this paper the spatial-temporal development of GPP over the Netherlands is presented. Finally, we assess the validity of the modeling results and discuss the limitations and opportunities for further development of the presented methodology.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.1 Resistance to Stolen Mobile Device Attacks  
We assume that adversary \(\mathcal{A}\) acquires a legal user's stolen mobile device. Under the adversary model in Section 3.4, \(\mathcal{A}\) can retrieve secret credentials through power analysis attacks, and \(\mathcal{A}\) seeks to determine the session key \(SK=h(UID_{i}||SI_{j})\oplus h(n_{i1}||n_{i2}||n_{i3})\). However, \(\mathcal{A}\) cannot calculate \(SK\) because \(\mathcal{A}\) does not know \(UI_{i}\) without the user's real identity \(ID_{i}\) and password \(PW_{i}\). Thus, our scheme offers resilience to stolen mobile device attacks.
## 3 Dynamic-Static Conversion Relationship
### Relation Establishment  
Table 1 presents the empirical equations and correlation coefficients between the static and dynamic elastic moduli obtained by previous researchers based on different types of rock samples. The empirical formulas obtained by [PERSON] et al. [33] and [PERSON] et al. [43] have the highest fitting degree, with a relationship coefficient (R\({}^{2}\) = 0.99). Overall, for different types of rocks, using nonlinear logarithmic or power-law relationships to predict the value of static elastic modulus is optimal, so the value of static elastic modulus can be optimally predicted from the correlation expression shown in the equation. In order to obtain our result, we researched a large amount of the literature published by researchers around the world [7; 9; 10; 29; 30; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57]. After analyzing and summarizing the experimental data they had obtained, we found that there are two forms of linear and nonlinear relationships between the dynamic and static moduli of elasticity. The equations presented in Table 1 can be categorized based on the independent variables used for predicting the static elastic modulus, which can be classified into seven fundamental models, as shown in Table 2. Among the 30 equations in Table 1, 24 relational expressions use the dynamic elastic modulus as the only independent variable (type I-III  
Figure 3: High-pressure and high-temperature triaxial test system, GCTS RTR-1500 [42]. IV-VI). Of these, 17 equations are linear regression-based (type I) [35; 58], 3 are power-law regression-based (type III), 3 are quadratic regression-based (type IV), and 1 is exponential regression-based (type VI). The remaining three equations employ dynamic modulus of elasticity and bulk density as independent variables, with logarithmic regression applied to these two variables (type II). One uses acoustic velocity as a variable (type V). A relatively rare type employs dynamic modulus and spatial attenuation as its variables (type VII). Two types employ P-wave velocity as the independent variable in power-law regression. The advantage of this relational expression (type V) is that it facilitates the acquisition of the necessary parameters (\(V_{\rm P}\)), thereby simplifying the testing necessary to determine the static elastic modulus, which is a dependent variable in all scenarios.
# Failure Life Prediction of Hub Bearing in Composite Tooling
## 3 Prediction of the Life of the Hub Bearing  
### System Model and Analysis  
The hub bearing that connects the disk narrow arm with the disk narrow is a special bearing used exclusively for the disk narrow. The overall shape of the disk narrow assembly was modeled to analyze the hub bearing. A finite element model [12] was applied to the disk narrow arm and the disk narrow. The shape of the element is a second order tetra mesh. The numbers of nodes and elements of the disk narrow are 4639 and 2148, respectively, and the numbers of nodes and elements of the disk narrow arm are 20,405 and 10,994, respectively, as shown in Figure 9. Six degrees of freedom (Fx, Fy, Fz, Mx, My, Mz) were the boundary conditions for the disk narrow arm connection to the frame and the shaft of the hub bearing [10; 11; 14]. Five degrees of freedom except for torque were set for disk narrow and housing of the outer raceway of the hub bearing.  
Figure 8: Loading condition of disk narrow assembly.  
Figure 7: Load amplitude history for disk narrow assembly.  
The equivalent load of the hub bearing according to the ISO 281 standard was calculated with Equation (2) and input loads in the x, y, and z directions derived in Section 2.3; the value was 1.98 kN [16].  
\[P=XF_{r}+YF_{a} \tag{2}\]  
where,  
\(P\): Equivalent load  
X: Radial load factor  
Y: Axial load factor  
\(F_{r}\): Radial load  
\(F_{a}\): Axial load
## 2 Materials and Methods
### Data and Statistical Analyses  
NIRS and pressure data were evaluated using MATLAB, version 9.10.0 (R2021a). The TSI signal as output by Ovysoft software (Version 3.2.72) was first smoothed using a Butterworth filter at the fourth-order and low-pass filter with a cut-off frequency of 0.2 Hz. Baseline TSI (TSIBase), was calculated as the average of 30 s before cuff occlusion or pHMI occlusion, respectively (Figure 6). The downslope of the TSI signal (TSIslope1) was calculated between the beginning of occlusion and the minimum TSI value during occlusion [49] by a linear regression equation and served as a measure of the O2 desaturation rate [42], whereas the upslope of the TSI signal (TSIslope2) immediately after deflation served as a measure of the O2 resaturation rate. TSIslope2 was calculated between the end of occlusion and the maximum TSI value after occlusion using the linear regression equation. TSIOvershoot was calculated as the delta between TSIBase and the maximum value of the TSI signal after deflation or opening of the pHMI, respectively. Mean pressure of the pHMI was calculated as the mean pressure signal of the pressure sensor during the occlusion period.  
All data were analyzed using IBM(r) SPSS Statistics Version 27. The significance level was set at \(p<0.05\). Mean pressure, TSIslope1, TSIslope2, and TSIOvershoot served as dependent variables. To address hypothesis no. (i) and (iii), \"type of occlusion\" was established as an independent five-step variable including arterial occlusion with pressure cuff, venous occlusion with pressure cuff, closed pHMI of the exoskeleton Lucy, closed pHMI of the exoskeleton Paexo Shoulder, and closed pHMI of Skelex 360. A repeated-measures ANOVA was performed, followed by pairwise comparisons with a Bonferroni correction of the alpha level to determine whether and which test modes differ from each other in resting position. In case of violation of sphericity, the correction according to Greenhouse-Geisser was used.  
To determine the effect of movement versus resting position on oxygen levels (hypothesis (ii)), a two-factorial ANOVA was performed with the inner-subject factors \"movement\" (2): resting vs. dynamic condition and \"pHMI design\" (3): Lucy, Paexo Shoulder, and Skelex 360 for all aforementioned dependent variables.  
Figure 5: Sequence of the dynamic task.  
Figure 6: Illustration for the calculation of NIRS key outcome parameters.
# Performance of Low-Height Railway Noise Barriers with Porous Materials
## 3 Experimental Characterisation of Porous Concrete
Each curve corresponds to the average between the two samples of same thickness, respectively, 4, 6 and 8 cm. It was observed that the increase in the thickness produces a shift in the sound absorption coefficient curve towards low frequencies. To predict the acoustic behaviour of porous concrete with different thicknesses and to represent these materials as the fluid-equivalent theory, the Horoshenkov and Swift model was used [33]. This model was derived assuming rigid frame granular media with a log-normal pore size distribution to predict the the characteristic impedance, \(\tilde{Z}_{c}\), and the wave number, \(\tilde{k}_{c}\), of porous concrete samples. It considers four macroscopic parameters to determine the acoustic behaviour: air flow resistivity, \(\sigma\), open porosity, \(\phi\), tortuosity, \(\tau\), and the standard deviation of the pore size, \(\sigma_{p}\). The inverse technique was performed using a genetic algorithm in which the objective function was based on the quadratic sum of errors between the analytical and experimental data, along a frequency range with \(nf\) discrete frequency values,  
\[\mathrm{OF}(\omega)=\sum_{i=1}^{nf}\left|\alpha_{\mathrm{ana}}-\alpha_{\mathrm{ exp}}\right|, \tag{5}\]  
where \(\alpha_{\mathrm{ana}}\) is the absorption coefficient obtained from the Horoshenkov and Swift model [33], and \(\alpha_{\mathrm{exp}}\) is the experimental absorption coefficient. These four macroscopic parameters were previously obtained in [23], and are presented in Table 2. The open  
Figure 7: Sound absorption behaviour of porous concrete. (**a**) Porous concrete built samples. (**b**) Average of the sound absorption coefficient for three different thicknesses: 4, 6, and 8 cm. (retrieved from [23]). porosity was the only macroscopic parameter experimentally determined, using the water saturation method. Figure 8 shows a comparison between the complex properties using the presented macroscopic parameters and those experimentally obtained through the two-cavity method for a sample with 4 cm. As observed in [34], an excellent agreement can be observed between the experimental data and the semi-phenomenological prediction, allowing us to represent and predict the porous concrete behaviour for different samples thicknesses and geometries.
## 3 System Model  
In this study, we propose the optimization of indoor positioning systems by integrating WiFi and PDR techniques using three different particle algorithms including the PSO, PF, and AO algorithms.  
The processing of the WiFi part includes two stages: offline and online. In the offline stage, the smartphone collects WiFi indoor signals based on reference points (RPs), which serve as fingerprint information. At RP positions, individuals collect WiFi signals from n access points and measure the received signal strength indicator (RSSI) values. The collected data are then preprocessed using a data filter. During this phase, a WiFi fingerprint database is established. The data are interpolated, and the fingerprint database is expanded using the inverse distance weighting (IDW) algorithm. In the online stage, the user moves within the indoor environment while their smartphone collects WiFi signals with corresponding RSSI values from multiple access points. The collected data are transmitted to the server for further processing and analysis. The RSSI information is first preprocessed by the data filter program, after which the PSO model is used to obtain an initial positioning result by going through the stages of initialization, IDW, fitness calculation, particle sorting, and WKNN.  
Additionally, PDR is employed to estimate the pedestrian's position. Accelerometer data are collected to measure acceleration, with a zero-velocity update (ZUPT) triggered upon detecting a zero-velocity point. Gyroscope data are gathered to obtain angular velocity. Step length and direction are estimated based on the acceleration and angular velocity data. The EKF is used for state estimation and error correction.  
Finally, the positioning data from WiFi and PDR part are fused using the AO-PF algorithm to obtain the final positioning result. First, the particle filter is initialized, and then, the state equation is generated using the PDR data. The observation equation is then generated based on the results of the PSO model in WiFi. The PF can generate N particles and then generate N new particles through the AO algorithm to reduce the risk of particle impoverishment. Then, the weights are updated based on all the particles, and finally, resampling is performed to complete the positioning. The flowchart of the AO-PSO-PF positioning algorithm is shown in Figure 1.