esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Topologies of Wireless Sensor Networks for Health Monitoring of a Cultural Heritage Site  
[PERSON] 1  
1 Instituto de Tecnologias Fisicas y de la Informacion \"[PERSON]\" (ITEFI) CSIC, C/Serrano, 144, 28006 Madrid, Spain; [EMAIL_ADDRESS]  
[PERSON] 2  
2 CEI Campus Moncloa, UCM-UPM y CSIC, 28040 Madrid, Spain; [EMAIL_ADDRESS]  
[PERSON] 1  
1 Instituto de Tecnologias Fisicas y de la Informacion \"[PERSON]\" (ITEFI) CSIC, C/Serrano, 144, 28006 Madrid, Spain; [EMAIL_ADDRESS]  
[PERSON] 2  
2 CEI Campus Moncloa, UCM-UPM y CSIC, 28040 Madrid, Spain; mi.martinez.garrido@csic.  
types of networks [11; 12; 13]. To face the problem of testing WSNs, different articles have also been found in the literature [14; 15]. Therefore, a preliminary analysis about which topology is more adequate should be carried out in this application scenario. This paper provides a performance evaluation of tree and mesh routing topologies of WSNs in a cultural heritage site. The historical heritage site selected was the San Juan Bautista church in Talamanca de Jarama (Madrid, Spain). The tree topology used was the collection tree protocol (CTP) and the mesh topology was the XMESH provided by MEMSIC (Andover, MA, USA). These topologies were selected because they are the most commonly used [16; 17; 18; 19; 20; 21; 22; 23]. An experimental study of WSN power efficiency using the XMESH routing topology was reported in [23].  
The BLIP, the Berkeley Low-power IP stack, and TinyRPL implementations in TinyOS 2.x were used to evaluate the performance of the newly proposed standards and compare them with CTP [24]. Three protocols for WSNs (i.e., CTP, MultiHopLQI and BLIP 6 LowPAN) were experimentally compared to assess the performances in terms of latency and packet loss [25]. In this work, CTP exhibited minimum latency and the best reliability. Previous studies compared mesh and tree topologies, but only in a simulated scenario [26; 27].  
To test which routing topology was better for this kind of application, the WSNs were first deployed on the upper floor of the CAEND institute in Arganda del Rey (Madrid, Spain) simulating the church deployment. This deployment is representative of a typical building interior with numerous walls and no direct line of sight between the WSN elements. In the cultural heritage site studied in this work there were almost no fixed obstacles and, therefore, direct line of sight between the WSN elements. Both scenarios had a rather similar arrangement; the motes were distributed in an elongated rectangular surface, forming two parallel lines with the base station to one end. This arrangement is very common in buildings, such as warehouses, churches, offices, etc. Motes were tested point to point and several communication parameters were measured, such as the received signal strength indication (RSSI), the throughput, and the transmission bandwidth. It was found that this network was appropriate for this kind of deployment. We, therefore, decided to carry out a more comprehensive study to determine which topology was preferred in a particular scenario.
# Recent Advances in Sample Preparation for Cosmetics and Personal Care Products Analysis
## 2 Sample Preparation Strategies for Cosmetics Analysis
### Pressurized Liquid Extraction (PLE)  
PLE is an advanced extraction technique that operates at high temperature and pressure. The elevated temperature enhances the solubility of the analytes, breaking the matrix-analyte interactions, and thus increasing the mass transfer. A clean-up step is frequently performed at the same time as extraction (in-cell clean-up) by adding sorbents at the bottom of the extraction cell [16]. Although it requires solvent volumes similar to those of UAE, PLE provides shorter extraction times, and the currently commercially available instruments present a high degree of automatization.  
PLE has been applied to determine UV filters, fragrances, preservatives, perfluorooctanoic acid (PFOA) and its derivates [55; 56; 57; 58; 59], and for the simultaneous extraction of musks, preservatives and plasticizers [60; 61] in a broad range of cosmetics and personal care products, including those intended for babies and children. The main advantage of PLE is that it drastically reduces the extraction time compared to traditional techniques. For example, only 5 min are needed to extract preservatives, obtaining recoveries ranging from 75 to 119% [60]--whereas UAE or SLE require between 10 and 30 min [28; 32]. In addition, depending on the instrumentally available extraction cells, it can be easily miniaturized, reducing the sample size by up to five times and the extraction volume up to two times [55]. Analytical determination after PLE has usually been performed by LC-MS/MS for the most polar compounds such as UV filters or PFOA derivatives [55; 59], whereas a derivatization step, mainly acetylation with acetic anhydride and pyridine, was simultaneously performed with the extraction before GC-MS analysis for the most polar target analytes' determination such as UV filters and preservatives [56; 61].
## 4 Benchmark Databases and Performance Evaluation Metrics
### Dataset Used
#### 4.1.10 NDSec-1  
[PERSON] et al. [198] produced this dataset in 2016. A variety of cyberattacks are included, such as a botnet, brute force attacks (FTP, HTTP, and SSH), HTTP flood, SYN flood, UDP flood, exploits, port scanning, spoofing, and SQL injection. There are a total of 5838 records in this dataset. From these records, 3558 records (or 60.1%) are categorized as normal traffic and 2380 records (or 39.9%) as attack traffic.
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
## 6 Results and Discussion
Furthermore, we can see that \(ESC\) is approximately constant over the entire range of the power allocation factor, which is owing to the fact that the rate of increase in \(EC_{x_{1}}\) is approximately the same as the rate of decline in \(EC_{x_{2}}\).
# Activation of Persulfate for Groundwater Remediation:
## 4 Factors Affecting the Application of Persulfate in ISCO  
For practical field applications, many factors affect the efficiency of persulfate in degrading target organic pollutants. As is shown in Figure 2, the main influencing factors to be considered in field applications of persulfate are the dosage of persulfate, the persistence and the radius of influence of persulfate, and the physicochemical properties of soil, pH and dissolved ions in groundwater (Figure 2).
## 2 Materials and Methods
### Markers of Interest and Data Analysis  
Since the wearable-sensor system (the fall-trigger) and the main part of the air-pad unit could only be placed within a vast, the markers of interest (possible sensors' positions) were those on the trunk: 7 th cervical vertebrae (C7), clavicle, sternum, left shoulder (LSho), right shoulder (R Sho), 10 th thoracic vertebrae (T10), left scapula/back (LB), right scapula/back (RB), lower left back (LLB), and lower right back (LRB) (Figure 6). Kinematic data (i.e., max speed, max acceleration, and the duration of fall) were quantified by the 15-segment model. The kinematic data obtained were analyzed using SPSS Statistics v.22.0 (SPSS Inc., Chicago, IL, USA). Results were presented using descriptive statistics. Further, for developing a practical device, the focuses would be on convenience (minimal sensors required) and reliability (avoiding the cumulative Type I and II error rate from complicated statistical analyses). Therefore, independent \(t\)-tests were applied to contrast differences between fall and non-fall  
Figure 4: Placement of the 42 reflective markers for full-body biomechanical modeling [19; 20; 21]: (a) a front view; (b) a back view. The asymmetric placement of the markers on limbs was used for distinguishing the left and right side of the body during motion capture.  
Figure 5: Biomechanical modeling based on 3D motion capture—a typical fall to the left.  
situations, aiming at identifying one or two potential sensitive locations for the development. Statistical significance was defined as \(p<0.05\).  
In order to minimize the air-pad system (i.e., minimizing the amount of air needed for a protection), the short should consist of three air-pads, left, right, and rear. Therefore, the other function of the wearable sensor-system should be to identify the fall direction. As such only one of the three pads would be inflated during a fall.
## 3 Results  
### Correlations between Vegetation Indices (Vis) and GPP  
We analyzed the relationships between satellite VIs and midday GPP within individual sites to assess the effects of temporal variation within sites (separate regression lines for each site in Figure 2), and across all sites to assess the between-ecosystem type generality (dashed lines in Figure 2). The within site relationships between GPP and both EVI and NIR\({}_{\text{v}}\) were tighter than those for NDVI (Supplementary Material 3). The within site relationships were weakest in the three sites that were dominated by evergreens (oak-pine, sierra mixed conifer, and subalpine forests), especially for NDVI Comparatively weak temporal relationships at evergreen sites could be expected given the low seasonal variation in canopy biophysical properties [11]. The temporal relationships between GPP and both EVI and NIR\({}_{\text{v}}\) had more consistent intercepts and slopes between sites than did those with NDVI (Figure 2, Table S3). This resulted in stronger across-site relationships for EVI and NIR\({}_{\text{v}}\) than for NDVI.
## 2 Proposed Methodology
### Waveform Imaging Using a Modified Recurrence Plot
However, for the proposed RP with no random noise, \(w(x)\), and \(2w(x)\) random noise, the yellow area showed a relatively small change; results were 36.41%, 36.50%, and 36.39%, respectively. Therefore, it was concluded that for another type of signal that emulates dynamic resistance waveforms, the proposed RP is sensitive to amplitude changes of the simulated waveform, while being robust to random noise. In summary, an identical conclusion can be drawn from both simulation studies, which examined two different types of waveform signals. Figure 10: Simulated dynamic resistance signals with random noise. Figure 9: RP images of simulated dynamic resistance signals: (**a**) original RP of \(f_{1}(x)\), (**b**) original RP of \(f_{2}(x)\), (**c**) original RP of \(f_{3}(x)\), (**d**) proposed RP of \(f_{1}(x)\), (**e**) proposed RP of \(f_{2}(x)\), and (**f**) proposed RP of \(f_{3}(x)\). Figure 11: RP images of simulated noisy dynamic resistance signals: (**a**) original RP of \(f_{2}(x)\), (**b**) original RP of \(f_{2}(x)+\omega(x)\), (**c**) original RP of \(f_{2}(x)+2\omega(x)\), (**d**) proposed RP of \(f_{2}(x)\), (**e**) proposed RP of \(f_{2}(x)+\omega(x)\), and (**f**) proposed RP of \(f_{2}(x)+2\omega(x)\). Figure 12: Performance comparison of the original and proposed RPs using a simulated dynamic resistance waveform: (**a**) the change of yellow area as the waveform amplitude increases and (**b**) the change of yellow area as the random noise increases. RPs reveal different patterns (i.e., topology and texture) for phase space trajectories with randomness, periodicity, chaos, and trend as shown in Table 3. For example, single isolated points indicate that strong fluctuation exists in the process. Another example is that diagonal lines parallel to the line of identity (LOI) represent that the evolution of the states is similar at different epochs; the process can be deterministic. RP structures should be quantified with more objective criteria. [PERSON] et al. (2018) states that \"the visual interpretation of RPs requires some experience\". It is challenging to interpret the image of RP structures by naked human eyes. Moreover, interpretation results can be changed since personal experiences are different.
## 3 Results and Discussion
### Adsorption Tests
#### 3.1.1 Addition of MWCNTs
This trend is particularly evident in the case of Cu\({}^{2+}\) and Ni\({}^{2+}\), where a significant enhancement was achieved by adding the MWCNTs. The low adsorption perceived in the case of Zn\({}^{2+}\) can be related to the low electronegativity and higher mobility of this metal compared to the other heavy metals tested. Furthermore, the combination of soil with MWCNTs resulted in higher adsorption capacity when compared to the MWCNTs alone, except, again, in the case of zinc for which very similar values were obtained in the three situations tested. In general, the results reported in this study lead us to conclude that mixing MWCNTs with soil can effectively increase the amount of HMs immobilized. Thus, it is possible to say that this strategy can be a valid option to be used in the remediation of contaminated soils, as well as in stabilization processes, especially taking into consideration the very low amounts of MWCNTs required. When comparing the results with the authors' previous work [31], where it was used a non-ionic surfactant, the results are in general better, except in the case of zinc for which similar results were obtained. This shows once again that the anionic surfactant (SDBS) has a positive impact in the immobilization process, even when applied in a lower concentration than the non-ionic one (almost 100 times smaller).
## 4 Structure of a Palmprint Recognition System
### Evaluation Performances  
The valid accuracy and equal error rate (EER) serve as widely accepted metrics for evaluating the performance of biometric systems. These metrics are fundamental in judging the effectiveness of such systems and are commonly utilized in the field. Valid accuracy assesses the overall correctness of the system in authenticating users, indicating its capability to accurately identify legitimate users. Conversely, the EER stand as a pivotal metric in evaluating biometric system performance. It pinpoints the precise operating condition where the false acceptance rate (FAR) and false rejection rate (FRR) converge equally, signifying an equilibrium assessment of the system performance.  
The valid accuracy is the percentage of correctly accepted genuine instances or positive matches in the biometric system, which can be obtained through:  
\[Valid-accuracy=\frac{Number\ of\ true\ acceptances}{Total\ number\ of\ genuine\ instances}\times 100 \tag{1}\]  
where:  
True acceptances: the number of instances where the biometric system correctly accepts a genuine user.  
Total number of genuine instances: the total number of instances where a genuine user attempts authentication.  
The EER serves as an equilibrium point between the two error rates, which allows the FAR and FRR to intersect.  
\[EER=\left(FAR+FRR\right)/2 \tag{2}\]  
where:  
\[FAR=\frac{Number\ of\ false\ acceptances}{Total\ number\ of\ impostor\ attempts}\times 100 \tag{3}\]  
\[FRR=\frac{Number\ of\ false\ rejections}{Total\ number\ of\ genuine\ attempts}\times 100 \tag{4}\]  
It is important to note that lower EER values indicate better performance in terms of balancing the false acceptance rate (FAR) and false rejection rate (FRR).  
In an ideal system, the recognition rate would be 100% and the EER would be 0%. However, in practice, there is often a trade-off between these two metrics, and system designers aim to find a balance that meets the requirements of the specific application.
## 3 Distributed Target Tracking Systems
### Estimation Adjustment  
In order to adjust angle information, the three-layer perceptron neural network is considered. Based on the proposed network architecture, the goal of training is to maximize the correlation \(C\) between the signal of the hidden neuron and the residual output error [27]:  
\[C=\sum_{j=1}^{p}|\sum_{q=1}^{Q}(S(z_{h}^{q})-S_{av})(\delta_{j}^{q}-\Delta_{j})| \tag{2}\]  
where \(\mathcal{S}(\cdot)\) is the signal function, \(S_{av}=\frac{1}{Q}\sum_{q=1}^{Q}S(z_{h}^{q})\), \(\Delta_{j}=\frac{1}{Q}\sum_{q=1}^{Q}\delta_{j}^{q}\), \(z_{h}^{q}\) is the signal of the hidden neuron \(h\) in response to input pattern \(X_{q}\), \(\delta_{j}^{q}\) is the familiar scaled output error at neuron \(j\), \(\Delta_{j}\) is the average scaled error on the entire pattern set. Accordingly, for selecting the network parameters (weights and biases) that best approximate a given function, the backpropagation learning algorithm is considered to minimize the mean square error performance \(\mathcal{E}\).  
Figure 4 illustrates the perception network architecture. Note that \(J\) represents the number of input neurons, which may denote the number of received messages from neighboring sensors, the AOA measurement of the estimated target, the desired value of sub-cluster size \(n\), and the minimum angle coverage area of a tasking sensor with a right-hand-side angle boundary and a left-hand-side angle boundary (two brown lines as shown in Figure 5(top)), where the initial sample area is located within the coverage area. \(U^{1}\) denotes the number of hidden neurons. In the output layer, \(U^{2}\) represents the number of neurons, which may denote the network approximation results. Moreover, let IW and LW be the input weight matrix and layer weight matrix for the hidden layer and the output layer, respectively. Let \(b^{1}\) and \(b^{2}\) be bias vectors for the hidden layer and the output layer, respectively. Established upon the developed neuron network model, the behavior of the TCAT scheme may be abstracted with sensible settings, which is further discussed in Section 6.  
Figure 4: The three-layer perception network architecture for analyzing the performance of the TCAT.
###### Abstract  
Herein, the oxidation behaviour of refractory (HfCo)\({}_{\bf 100-x}\)(NbMo)\({}_{\bf x}\) (x = 0; 10; 25; 40; 75; 100 (at.%)) high-entropy alloys with a bcc+B2 structure subjected to cyclic oxidation at 1000 \({}^{\circ}\)C was studied. The single-phase B2-ordered HfCo alloy demonstrated the best spallation resistance and retained a pristine form after 100 h. The oxidation kinetics of the HfCo alloy was near-parabolic, accompanied by the formation of external HfO\({}_{2}\) or CoO layers after 1 or 100 h, respectively. Additions of (NbMo)\({}_{\bf x}\) deteriorated the spallation resistance (x \(\leq\) 25 at.%) or led to complete disintegration (x \(>\) 25 at.%). Among the (NbMo)-containing alloys, the (HfCo)\({}_{\bf 00}\)(NbMo)\({}_{\bf 10}\) alloy with the dual-phase bcc+B2 structure showed the most promising oxidation resistance. This alloy withstood cyclic oxidation up to 15 h with a mass gain close to the HfCo alloy and survived 100 h without changes in geometry of the specimen. Unlike the HfCo alloy, in the (HfCo)\({}_{\bf 00}\)(NbMo)\({}_{\bf 10}\) alloy, the external CoO layer was found already after 1 h. The effect of chemical and phase compositions on the formation of certain oxides was discussed. Comparison with the other refractory high-entropy alloys was also presented.  
refractory high-entropy alloys; oxidation behaviour; bcc+B2 structure; spallation resistance
## 3 Computational Methods
### Proposed SCN-EXT Method
It should be emphasized once again that, in the SCN-EXT network, we employed the basic components with the same hyperparameters from the SCN network (i.e., components were initialized with the recommended settings from [10]). By adding \(J-1\) new local appearance and spatial configuration components, the proposed SCN-EXT network is able to learn \(J^{2}-1\) functions more than the original SCN network. Each such component (i.e., neural network) has independent training parameters, and can, thus, learn a subset of the targeted landmarks. On the other hand, compared to the base SCN network, the number of free parameters in SCN-EXT grows linearly with the number of components used. Landmarks in a training set are not separated into groups (e.g., with respect to an individual anatomical feature or with respect to the neighboring position), so a benefit of utilizing several components in the SCN-EXT is that they can optimize for self-determined  
Figure 3: A rough block diagram of the proposed extended SpatialConfiguration-Net (SCN-EXT). and overlapping groups of landmarks. Each component (neural network) needs to estimate only a fraction of all the targeted landmarks, and multiple networks can cooperate on the same landmark. An idea in our solution is similar to the so-called grouped convolutions, where the channels in a single convolution layer are grouped together. Each group of channels is processed by a separate set of convolution kernels without overlap between the groups. This has a similar advantage as our proposed approach: a moderate increase of free parameters despite a greater increase of convolutional filters. Increasing the capacity of the CNN network considerably, i.e., the ability to learn several new functions, by a small increase of the number of degrees of freedom (DoF), thus provides more stable learning by using the same training set.
## 2 Materials and Methods
### Image and Feature Processing  
3D Slicer Software (version 4.11) was used for image visualization and segmentation [24]. Both the primary tumor (PT) and the largest lymph node (LN) were delineated on DWI at \(b\) = 800 mm\({}^{2}\)/s by two HN radiologists in consensus ([PERSON] and [PERSON]), usingT2-weighted images as a guide. Each contour mask was rigidly propagated on both ADC and D maps and saved as a DICOM file.  
Diffusion maps and structure set files were imported into S-IBEX software [25] to calculate the radiomic features. It is a standardized version of IBEX (image biomarker explorer) software [26], adapted and validated consistently with the guidelines of the Image Biomarker Standardization Initiative (IBSI) [27].  
Eight families of features were extracted from a 3D analysis of both ADC and D maps: Intensity Direct (39 features), Intensity Histogram (13 features), Gray Level Co-occurrence Matrix or GLCM (25 features), Gray Level Distant Zone Matrix (GLDMM) (25 features), Gray Level Run Length Matrix (GLRLM) (16 features), Gray Level Size Zone Matrix (16 features), Neighboring Gray Level Dependence (16 features), and Morphology (16 features), for a total of 157 features for each PT and LN.  
A detailed list of the features and the image pre-processing steps (interpolation, re-segmentation, intensity discretization) are reported in Supplemental Tables S1 and S2.  
The outliers, i.e., data that were more than three median absolute deviations away from the median, were removed from the value distribution of each feature to improve the reliability of radiomics [28]. To remove the dependency of some features from the lesion volume, the Spearman rank correlation (Rho) test was used and, in case of highly positive or negative correlation (Rho > 0.85 or Rho < -0.85), their values were normalized for the corresponding PT/LN volume.  
The harmonization between 1.5 and 3T systems was performed at the feature level using the ComBat algorithm [29] via the package neuroCombat in R Studio [RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA, USA, URL: [[http://www.rstudio.com](http://www.rstudio.com)]([http://www.rstudio.com](http://www.rstudio.com)) (accessed on 22 September 2021)].
# Property Analysis of Slag Composite Concrete Using a Kinetic-Thermodynamic Hydration Model
## 2 Hydration-Based Property Evaluation Model of Binary Blends of Cement-Slag
### Summary of the Proposed Model  
The Gibbs energy minimization program (GEM-Selektor) is widely used for analyzing the phase assemblage, pore solution composition, and durability of cement-based materials [16; 17; 27]. The strength development of hydrating binary cement-slag closely relates to the phase assemblage of the hydration products [16; 17; 27].  
This paper proposes an integrated kinetic-thermodynamic model of binary blends of cement-slag. As shown in Figure 1, the reaction extent of slag can be determined based on the results of the experimental study of the hydration heat of binary blends of cement-slag.  
Moreover, the reaction extent of slag and cement are used as the input parameters of a Gibbs energy minimization (GEM) thermodynamic equilibrium model. The contents of the hydration products are determined. Moreover, strength development is evaluated using the contents of the products of hydration.