esa-sceva/satcom-chunk-collection huggingface dataset:
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Wireless Sensor Networks (WSN) Technology
#### 2.1.3 WSN Topology and Types  
The description of WSN topology is associated with the expected capabilities of sensor nodes, which enable the deployment of several numbers of nodes in a sensor field [44]. While four major types of topologies can be adapted for field deployment, such as, star, tree, point-to-point, and mesh topologies [36], well-planned handling of these arrangements are necessary for maintenance purpose [42]. Without a proper topology arrangement, maintenance-related issues become difficult to handle. These issues usually arise in deployment phases: pre-deployment and deployment phase, post-deployment phase, and redeployment of more nodes phase [42]. In some cases, the types of WSN are classified based on the field of application, such as health, industry, military, agriculture, etc. [45]. However, WSN types can be identified as terrestrial, underground, underwater, mobile, and multimedia wireless sensor networks [46].
## 1 Introduction
The regularized iterative reconstruction algorithm based on total difference minimization (TDM) combined with soft-threshold filtering has achieved a good effect for sparse-view CT reconstruction [26]. However, the total difference (TD) regularization only considers the sparsity of the image gradient in the horizontal and vertical directions, and the protection of the structure information is a little insufficient at the edge of the object. [PERSON] and [PERSON] considered a new regularization measure method based on compressed sampling, which constrains both the sparsity and continuity of the gradient and is called weighted total difference minimization (WTDM) [27]. According to their research, the hybrid compression sampling algorithm can obtain a high-precision image, and the edges in the image can be well restored. The weighted total difference minimization not only considers the sparsity constraint of the image gradient, but also considers the continuity constraint of the image gradient, which can effectively protect the gradient information in different directions of the image edge. In addition, the neutron projected data contain isolated noise, which affects the quality of a reconstructed image. Median root prior (MRP) regularization can suppress local non-monotonic structures and eliminate the influence of isolated noise on image quality [28; 29]. Inspired by MRP and WTDM regularization algorithms, this paper proposes a method that can simultaneously remove the isolated noise and effectively protect the gradient information in different directions. To make the soft threshold filter function adaptively adjust the threshold according to the image grayscale, the threshold rule is modified in this paper. The process of the proposed algorithm (simultaneous iterative reconstruction technique--modified weighted total difference minimization (SIRT-MWTDM)) in this paper is that the image reconstructed by using the FBP algorithm is used as the initial image of the SIRT-MWTDM algorithm. After each SIRT iteration, MRP and WTDM regularization operations are performed immediately. The loop is repeated until the stop condition is met. The main contribution of this paper is the improvement of the WTDMsoft threshold selection rules, and MRP and WTDM regularization are used in our algorithm. The SIRT-MWTDM algorithm is applied to the sparse angle reconstruction from the high-noise projections. The results of the simulation and neutron experiment show that our algorithm is superior to other algorithms in suppressing noise and improving image clarity. Moreover, it allows high-quality reconstructed images to be provided to the researchers of nuclear fuel elements in a reactor, which is very meaningful.
###### Abstract  
The aim of this study was to evaluate the absorption in a user's head of an electromagnetic field (EMF) emitted by the Wi-Fi and/or Bluetooth module of a wearable small Internet of Things (IoT) electronic device (emitting EMF of up to 100 mW), in order to test the hypothesis that EMF has an insignificant influence on humans, and to compare the levels of such EMF absorption in various scenarios when using this device. The modelled EMF source was a meandered inverted-F antenna (MIFA)-type antenna of the ESP32-WROOM-32 radio module used in wearable devices developed within the reported study. To quantify the EMF absorption, the specific energy absorption rate (SAR) values were calculated in a multi-layer ellipsoidal model of the human head (involving skin, fat, skull bones and brain layers). The obtained results show up to 10 times higher values of SAR from the MIFA located in the headband, in comparison to its location on the helmet. Only wearable IoT devices (similar in construction and way of use to the investigated device) emitting at below 3 mW equivalent isotropically radiated power (EIRP) from Wi-Fi/Bluetooth communications modules may be considered environmentally insignificant EMF sources.  
biomedical engineering; environmental engineering; Internet of Things (IoT); numerical simulations; radiofrequency sensor; occupational exposure; public health; specific energy absorption rate (SAR); wearables; wireless sensor network (WSN) +
Footnote †: journal: Sensors
## 2 Results
### Free Amino Acids in Watermelon Rind, Flesh, and Rind-Flesh Blends
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type** & **Amino Acids** & **0\% Rind** & **10\% Rind** & **20\% Rind** & **30\% Rind** & **100\% Rind** & **\(p\)-Values** \\ \hline E, bitter & Phe (Phenylalanine) & 14.23 d & 8.93 b & 11.46 c & 8.26 b & 2.69 a & \textless{}0.001 \\ E, bitter & Ile (Isooleucine) & 10.79 d & 7.62 b & 8.95 c & 6.99 b & 2.37 a & \textless{}0.001 \\ E, bitter & Val (Valine) & 8.78 d & 6.39 c & 6.61 c & 5.39 b & 3.24 a & \textless{}0.001 \\ E, bitter & Leu (Leucine) & 2.37 c & 1.86 bc & 1.95 bc & 1.59 b & 0.61 a & \textless{}0.001 \\ E, bitter & Met (Methionine) & 0.63 b & 0.79 bc & 1.00 c & 0.98 c & nd a & \textless{}0.001 \\ E, bitter & His (Histidine) & nd a & nd a & nd a & nd a & tr a & 0.190 \\ E, bitter & Lys (Lysine) & nd a & nd a & nd a & tr a & 0.390 \\ Bitter & Arg (Arginine) & 36.73 a & 38.13 b & 38.47 b & 39.30 c & 53.77 d & \textless{}0.001 \\ Unknown & Cit (Citrulline) & 39.73 a & 41.30 a & 44.43 b & 45.73 b & 61.43 c & \textless{}0.001 \\ Sweet & Ala (Alanine) & 14.42 d & 9.05 b & 12.13 c & 9.06 b & 6.78 a & \textless{}0.001 \\ Sweet & Ser (Serine) & 7.36 a & 8.71 a & 12.23 b & 12.40 b & 18.02 c & \textless{}0.001 \\ Sweet & Pro (Proline) & 6.07 d & 3.58 a & 5.08 c & 3.94 b & 3.31 a & \textless{}0.001 \\ Sweet & Gly (Glycine) & tr a & nd a & nd a & 0.27 a & 0.37 a & 0.042 \\ Umami & Asp (Asparatic acid) & 2.02 a & 1.59 a & 3.64 b & 3.40 b & 7.09 c & \textless{}0.001 \\ Tasteless & Asn (Asparagine) & 3.18 cd & 2.36 a & 3.32 d & 2.81 bc & 2.71 ab & 0.001 \\ Tasteless & Orn (Ornithine) & 0.26 a & 0.21 a & 0.16 a & 0.43 a & 1.71 b & 0.001 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{Different letters (a–d) within each row indicate significant differences of each amino acids among five samples according to one-way ANOVA and [PERSON]’s HSD test (\(\alpha\leq 0.05\)). E: essential amino acids; nd: not detected; tr: trace level, indicating two instances of no detection and one instance of small detection within the triplicate.} \\ \end{tabular}
\end{table} 
Table 1: Mean values (mg/100 g fresh weight) of free amino acids identified and quantified in watermmelon rind, flesh, and rind-flesh blends. Figure 1: Total free amino acids (sum of 16 amino acids, mg/100 g fresh weight) in watermmelon rind, flesh, and rind-flesh blends. Different letters (a–c) among five samples indicate significant differences according to one-way ANOVA and [PERSON]’s HSD test with \(p\leq 0.05\). taste. However, their concentrations were below their perception thresholds, for example, 435.5 mg/100 g for Arg in water [30]. The other seven quantified essential amino acids (Phe, Ile, Val, Leu, Met, His, and Lys) have a potential bitter taste, but their concentrations were also all below their thresholds. The concentrations for the four sweet associated amino acids (Ala, Ser, Pro, and Gly) were similarly below their thresholds. Lastly, Asp (umani-related) did not surpass its 53 mg/100 g in water threshold, either [30].
## 2 Theory
### Boundary Conditions  
In order to obtain the dispersion equation of axial propagation modes in a fluid-filled elastic tube, it is necessary to set reasonable boundary conditions.  
If the elastic tube wall is composed of multi-layer materials, at the solid 1-solid 2 interface, the continuous condition to be satisfied is that the displacement components \(u_{r}\), \(u_{\theta}\), \(u_{z}\) and the surface stress components \(\tau_{rr}\), \(\tau_{r\theta}\), \(\tau_{rz}\) in the solid 1 are equal to the corresponding components in the solid 2, as shown in Equation (15). Subscripts \(s1\) and \(s2\) represent the solid 1 and the solid 2, respectively, and \(r=r_{i}\) represents the interface.  
\[\begin{array}{l}\left[u_{rs1},u_{bs1},u_{zs1}\right]_{r=r_{i}}=\left[u_{rs2},u _{bs2},u_{zs2}\right]_{r=r_{i}}\\ \left[\tau_{rrs1},\tau_{rb1},\tau_{rzs1}\right]_{r=r_{i}}=\left[\tau_{rrs2}, \tau_{rb2},\tau_{rzs2}\right]_{r=r_{i}}\end{array} \tag{15}\]  
For the solid-fluid interface, if the viscosity of the fluid cannot be ignored, the continuous condition at the interface is that the displacement components and the surface stress components in the solid are equal to the corresponding components in the viscous fluid, as shown in Equation (16). Subscripts \(s\) and \(f\) represent the solid and the viscous fluid, respectively.  
\[\begin{array}{l}\left[u_{rs},u_{bs},u_{zs}\right]_{r=r_{i}}=\left[u_{rf},u_{ \theta f},u_{zf}\right]_{r=r_{i}}\\ \left[\tau_{rrs},\tau_{rbs},\tau_{rzs}\right]_{r=r_{i}}=\left[\tau_{rrf},\tau_ {r\theta f},\tau_{rzf}\right]_{r=r_{i}}\end{array} \tag{16}\]  
If the viscosity of the fluid is negligible, the boundary condition at the interface is a perfect slip boundary condition. That is, the radial component of the displacement of the solid and the fluid are equal, the circumferential component and the axial component of the displacement are discontinuous, and the surface stress components are still equal. The shear stress of the non-viscous fluid does not exist, that is, \(\tau_{r\theta f}=\tau_{rzf}=0\), as shown in Equation (17):  
\[\begin{array}{l}\left[u_{rs}\right]_{r=r_{i}}=\left[u_{rf}\right]_{r=r_{i}} \\ \left[\tau_{rrs},\tau_{rbs},\tau_{rzs}\right]_{r=r_{i}}=\left[\tau_{rrf},0,0 \right]_{r=r_{i}}\end{array} \tag{17}\]  
The outer tube wall satisfies the stress-free boundary condition, as shown in Equation (18). The subscript \(s\) represents the tube wall and \(r=r_{2}\) represents the outer tube wall.  
\[\left[\tau_{rrs},\tau_{rbs},\tau_{rzs}\right]_{r=r_{2}}=\left[0,0,0\right]_{r= r_{2}} \tag{18}\]  
In addition, for an infinitely long tube, the inner and outer wall of the tube should meet:  
\[\
abla\cdot\overrightarrow{\Psi}=0 \tag{19}\]  
The above Equations (15)-(19) constitute the boundary conditions that a fluid-filled elastic tube needs to be satisfied under different conditions. Expressions for these physical quantities in the above boundary conditions are given below.
# Stream: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection
## 3 System Design  
In this paper, we propose an integrated UAV maneuver detection system that visualizes and interpolates the UAV's trajectory calculated from UWB sensor data and detects the UAV's flight pattern using an attention mechanism. Our approach is structured to ensure the high accuracy and reliability of UAV flight pattern detection, which is paramount for the successful execution of UAV missions in complex environments. Our ultimate goal is to accurately detect the flight pattern of a UAV, and, for this, the quality of input data and the performance of the trained classification model are critically important. Figure 1 shows the process of the proposed overall system, which comprises three main stages: preprocessing, image-based interpolation, and UAV flight pattern detection using STEAM. First, a preprocessing stage generates a UAV trajectory based on data collected from UWB sensors and converts numerical data into images. This stage converts the numerical data into images, forming the foundation for subsequent analysis. The effectiveness of our system starts here, ensuring that the raw data are accurately transformed into a format suitable for deep learning applications.  
Second, an image-based interpolation stage performs interpolation of severe missing trajectory images with DNN. This approach effectively mitigates issues related to data quality degradation from external factors, like noise and data omissions. By using image data for interpolation rather than numerical data, we enhance both the data quality and the performance of the UAV abnormal maneuver detection model.  
Third is a STEAM stage that detects UAV flight patterns using a DNN classification model with an attention mechanism. STEAM uses a maximum-pooling-based attention strategy to precisely highlight key trajectory features, reducing model complexity and improving interpretability. Our experiments validate that STEAM surpasses traditional convolutional models in accuracy, particularly in UAV trajectory classification, and does so with less computational demand. In this section, we list the core technologies of the proposed system according to the process flow and provide specific explanations of how each technology is applied.
## 3 Results and Discussion
### Opportunities for Monitoring  
The conventional monitoring of PCBs in surface waters with pollutant concentrations close to the background level suggests two stages. The first stage includes water sampling in different seasons, from different depths, and at different stations, taking into account the presence of potential pollution sources, and determining the PCB concentrations in the samples The second stage includes analysis of the relationship between the detected concentrations of PCBs and sampling factors (year, season, station location, and depth of water layer). Based on the correlation analysis of the data, the congener(s) is (are) selected as indicators (indices) of the group with high positive correlations between the concentrations of congeners in the group. After that, during systematic monitoring, the concentrations of PCBs in a water body are estimated from the concentration of a congener selected as an indicator.  
Biomonitoring provides detection of congeners in a water body, whose concentrations are below the LOQ level of the method used, in particular, dl-PCBs. A biological object chosen as a bioindicator must meet all the criteria that bioindicator objects should have [16]. Based on the correlation analysis of the data, a congener is also selected in the groups with high positive correlations between the concentrations of dl-PCBs characterised by higher concentrations and, hence, higher reliability and accuracy of detection. Thereafter, the current monitoring of dl-PCBs is possible for one (several) selected congener(s), a sharp increase in the concentration of which may trigger a more detailed analysis of the monitoring results.
## 4 Experimental Results
### ISAR Imaging
The correlation and entropy values of the recovered ISAR images under different methods are summarized in Table 4. It can be seen that the proposed methods have a higher correction with the reference image and a lower entropy than their counterparts, and thereby exhibit a better capability of target-information extraction.
## 2 Materials and Methods
### Chemometric Tools
#### 2.2.2 Sequential and Orthogonalized Partial Least Squares Linear Discriminant Analysis (SO-PLS-LDA)  
Sequential and Orthogonalized-PLS is a multi-block regression approach developed to handle data matrices removing redundant information possibly present [48]. For two predictor blocks (\(\mathbf{X_{1}}\) and \(\mathbf{X_{2}}\)) and a response matrix \(y\), the algorithm can be ensemble in four steps:  
1. \(\mathbf{X_{1}}\) is used to estimate \(\boldsymbol{y}\) by PLS. Scores \(\mathbf{T_{x1}}\) and \(\boldsymbol{y}\)-residuals \(\mathbf{e}\) are calculated.
2. \(\mathbf{X_{2}}\) is orthogonalized with respect to \(\mathbf{T_{x1}}\), obtaining \(\mathbf{X_{2 orth}}\)
3. \(\mathbf{X_{2 orth}}\) is used to estimate \(\mathbf{e}\) by PLS.  
4. The equation \(\mathbf{y=X_{1}b+X_{2}c+f}\) is solved (\(\mathbf{b}\) and \(\mathbf{c}\) being the regression coefficients and \(\mathbf{f}\) the residuals).  
Sequential and Orthogonalized Partial Least Squares Linear Discriminant Analysis leans on SO-PLS, exploiting it for features reduction. In fact, in order to apply SO-PLS-LDA analysis [30] it is sufficient to create the SO-PLS model, and then applying LDA on the predicted \(\mathbf{y}\) or on the concatenated scores. For more details on SO-PLS-LDA the reader is referred to the works in [30; 49]. Calculations were made using in-house written functions running under Matlab, which are freely downloadable at [50].  
In the present work, SO-PLS-LDA has been used to distinguish Sorrento and Non-Sorrento walnuts, simultaneously handling data collected on the shell (\(\mathbf{X_{1}}\)) and on the kernel (\(\mathbf{X_{2}}\)).
## 3 Results
### RTqPCR, Cytochrome C Release, Reactive Oxygen Species Evaluation  
The molecular mechanisms modulated by _Azima tetracanttha_ natural extract were estimated utilizing qPCR-mediated analysis (Figure 1b), reactive oxygen species, and cytochrome c release. Our results show a significant increase in the CASP3, CASP7, APAF1 and BAX genes in _A. tetracanttha_ (_p_ < 0.05) methanol-extract-treated AGS and Kato-III cells (Figure 1b,c). Notably, we observed only a slight increase in the expression levels of the anti-apoptotic gene BCL2 after the _A. tetracanttha_ methanol extract treatment. The release of mitochondrial cytochrome c was found to be increased in the cells treated with _A. tetracanttha_-extract-treated cells (Figure 2). Together with this, the intracellular level of reactive oxygen species was also increased dose dependently (_p_ < 0.05) in both the cells (Figure 2).  
Figure 2: Changes in the reactive oxygen species levels and cytochrome C levels of Kato-III (**a**) and AGS cells (**b**) after _A. tetracanttha_ leaf methanol extract treatment. (*** indicate \(p\) < 0.001).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cell**} & \multirow{2}{*}{**Dosage**} & **Catalase** & **Reduced Glutathione** & \multicolumn{2}{c}{**Thiobaric Acid**} & \multirow{2}{*}{
\begin{tabular}{c} **Conjugated Dienes** \\ **(noles/mg Protein)** \\ \end{tabular} } \\  & & **(U/mg Protein)** & & **(noles/mg Protein)** & & **(noles/mg Protein)** \\ \hline \multirow{3}{*}{Kato-III} & Untreated & 21.6 \(\pm\) 1.03 & 3.77 \(\pm\) 0.19 & 1.35 \(\pm\) 0.11 & 22.5 \(\pm\) 2.4 \\  & 25 \(\mu\)g/mL & 34.2 \(\pm\) 1.19 * & 3.08 \(\pm\) 0.22 * & 3.21 \(\pm\) 0.41 * & 66.3 \(\pm\) 2.7 * \\  & 50 \(\mu\)g/mL & 51.7 \(\pm\) 2.04 ** & 2.62 \(\pm\) 0.18 *** & 4.64 \(\pm\) 0.34 ** & 82.8 \(\pm\) 3.1 *** \\ \hline \multirow{3}{*}{AGS} & Untreated & 44.1 \(\pm\) 2.82 & 2.51 \(\pm\) 0.26 & 2.46 \(\pm\) 0.20 & 36.1 \(\pm\) 2.9 \\  & 30 \(\mu\)g/mL & 68.3 \(\pm\) 3.55 * & 2.11 \(\pm\) 0.19 * & 3.65 \(\pm\) 0.16 * & 55.7 \(\pm\) 2.6 * \\ \cline{1-1}  & 60 \(\mu\)g/mL & 94.5 \(\pm\) 3.9 *** & 1.69 \(\pm\) 0.33 ** & 4.51 \(\pm\) 0.12 ** & 79.3 \(\pm\) 4.5 *** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cellular level of non-enzymatic redox markers and radical scavenging enzyme activities in gastric neoplastic cells treated with varying doses of methanol extract of _Azima tetracanttha_. The * indicates the significance variation by \(p\) < 0.05, ** indicates the variation of \(p\) < 0.01 and *** represents variation of \(p\) < 0.001.
## 2 Materials and Methods
### Field Samplings and Analytical Methods  
On 5 October 2018, soil was sampled at a depth of 0-0.2 m to obtain a more detailed chemical characterization (Table 1). Composite soil samples from the three blocks and treatment were stored in polyethylene bags protected from sunlight and immediately transferred to the laboratory. They were dried at room temperature, and the fine fraction (apparent diameter \(<\) 2 mm) was obtained after sieving. Oxidizable organic carbon was determined following the methodology described in [PERSON] and Black [28], and available P content was determined using the [PERSON] method (sodium bicarbonate-extractable P at pH 8.5) [29]. Available K was obtained by extraction with ammonium acetate 1N (pH = 7) [30] and quantified by atomic absorption spectrophotometry; the AAnalyst 200 analyzer (PerkinElmer, Shelton, CT, USA) was used. Total heavy metals were analyzed according to the UNE-EN 16174:2012 procedure [31].  
Furthermore, from additional samples in each plot, extractable Fe, Zn, Mn and Cu (microelements) plus Cd, Cr and Pb were obtained with a one-step extraction method with 0.005 M diethylenetriaminepentaacetic acid (DTPA) solution (1:2, _w:v_), as described in [32]. This extraction procedure focuses on detecting trace metals in the most labile fractions of the soil, which can lead to their potential absorption by plants [33], and it was especially developed for calcareous soils. The mentioned elements were quantified using inductively coupled plasma-mass spectrometry (ICP-MS) in a 7700x analyzer (Agilent Technologies, Santa Clara, CA, USA), following the standard [34].  
A sludge sample was taken every season before the application of the organic fertilizer; this was to adjust the rate of N to 170 kg N ha\({}^{-1}\) through an analysis in the laboratory using the Kjeldahl digestion and distillation method [35]. In 2019, the total N in sewage sludges was 61.2 g kg\({}^{-1}\) over dry matter. Total carbon was determined by ignition at 550 \({}^{\circ}\)C. Heavy metals, P and K in sludges were analyzed from microwave-digested samples [23] with aqua regia (3:1, _v:v_, HCl:HNO\({}_{3}\)) and quantified using inductively coupled plasma-mass spectrometry, as explained previously.  
In the 2019 maize harvest, plants from 0.75 m\({}^{2}\) of each plot (central part) were harvested and divided into three parts: the grain, the cob and the rest of the plant biomass. The different parts were weighed, dried, ground, digested and analyzed. From microwave-digested samples [23] using HNO\({}_{3}\) and H\({}_{2}\)O\({}_{2}\) (3:2:2, _v:v:v_, HNO\({}_{3}\):H\({}_{2}\)O\({}_{2}\):H\({}_{2}\)O) [36], Mn, Cr, Cd, Co, Pb were analyzed, while P, K, Mg, S, Fe, Zn, Cu were determined using HNO\({}_{3}\) and HCl (3:1:1, _v:vv_, HNO\({}_{3}\):HCl:H\({}_{2}\)O) [37]. All these elements were quantified using inductively coupled plasma-mass spectrometry, as indicated previously.