esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Human beings are subjected to radiations coming from natural and artificial sources in their living environments [1]. Natural radioactivity is due to the presence of cosmogenic and primordial radionuclides in the Earth's crust [2]. Artificial fallout radionuclides, such as \({}^{137}\)Cs, are derived mainly from global nuclear tests conducted between the mid 1940s and the 1980s, as well as from nuclear accidents [3]. In addition to its ionizing effects, \({}^{137}\)Cs may be toxic; it can undergo bioconcentration and bioaccumulation and adversely impact human and ecosystem health [4].  
There are three ways of exposure to ionizing radiations for humans: external gamma rays, inhalation of radon and other radioactive nuclides and ingestion of radioisotopes through food and water [5]. For the latter, in particular, the natural radioactivity in food comes mainly from \({}^{40}\)K; uranium and thorium daughter products are usually present in traces [6; 7; 8]. Ingested or inhaled radionuclides are distributed among body organs (according to the metabolism of the element involved and the organs), normally exhibiting varying radio sensitivities [9].  
Salt is one of the most important compound in the life of organisms as it is necessary for humans, animals and plants; due to its importance for large cells of the body and metabolism in the cell, it is a necessity for survival. It can be extracted from the sea and from mines and, with its derivative, it is largely used in many industries [10]. The human body needs about 10 g of salt daily, so the analysis of food salt in terms of chemical and radioactive contamination is extremely important to safeguard the human health [11]. Consuming contaminated food increases the amount of radioactivity and chemical contamination inside an individual, and therefore, it increases the health risks associated with radiation exposure and metals pollution [12; 13; 14]. The exact health effects depend on the type and the quantity of ingested contaminants [15; 16].  
In this article, nine different samples of food salt (Cyprus black, Himalayan pink, Hawaii red, iodized, hyposodic iodized, Maldon smoked sea, common sea, Breton sea and Persia blue), available at the Italy markets for their wide use in the various dietary regimes typical of the Italian population, were chosen to be analyzed to identify and quantify natural (\({}^{40}\)K) and artificial (\({}^{137}\)Cs) gamma-emitting radionuclides with HPGe gamma spectrometry. In order to evaluate any possible radioactive contamination and to estimate the effective dose due to the salt ingestion, metals (Cu, As, Cd, Hg and Pb) with Inductively Coupled Plasma Mass Spectrometry (ICP-MS) were used to assess any possible chemical pollution.  
Food salts available at Italy markets have never been analyzed in terms of radioisotopes and metals contamination; so, as a consequence, the health risks for the Italian population have never been evaluated. Thus, no similar reports are present in the literature.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 5 Results
### Static Inversion
#### 5.1.2 Importance of Consistency between Time Periods Covered  
Inversion 01 was carried out using interferogram S1 A2, which does not cover the pre-eruptive period. The other inversions all used S1 A, which covers the same period as that spanned by the other interferograms. We find that S1 A consistently always gives explained data values between 8% and 12% higher than S1 A2 (Table 1). When inverting S1 A2, a posteriori computation of the explained data on S1 A, which was not used in the inversion, indicates a better fit than that of S1 A2. This clearly confirms that the data in S1 A2 are inconsistent with the other InSAR data. Given that S1 A2 does not cover the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Inversion** & & **Inv01** & **Inv02a** & **Inv02b** & **Inv03** & **Inv04a** & **Inv04b** & **Inv05** \\ \hline  & **Family** & **F1** & **F2** & **F2** & **F1** & **F2** & **F1** \\ \hline
**Inv01** & F1 & X & 129 & 124 & 109 & **90** & 139 & **132** \\
**Inv02a \({}^{\star}\)** & F2 & 121 & X & **107** & **167** & 150 & **169** & 177 \\
**Inv2b** & F2 & 105 & **110** & X & **80** & 153 & **78** & 194 \\
**Inv03** & F2 & 104 & **153** & **72** & X & 157 & **51** & 202 \\
**Inv04a** & F1 & **85** & 141 & 168 & 167 & X & 185 & **63** \\
**Inv04b** & F2 & 116 & **144** & **61** & **46** & 164 & X & 209 \\
**Inv05** & F1 & **132** & 168 & 216 & 209 & **62** & 214 & X \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average distances (in meters) between pairs of meshes. \({}^{\star}\) is the preferred model presented in [PERSON] et al. [16]. Distances in bold are for models belonging to the same family.  
Figure 7: Methods for computing the average distance between two meshes, illustrating why \(D_{AB}\) is slightly different from \(D_{BA}\).  
pre-eruptive period, it is likely that the other interferograms, which do cover this pre-eruptive period (S1 D, S1 A, CSK D and CSK A) integrate a certain degree of pre-eruptive displacement.
# Investigation into the Uniformization of Proton Beams for FLASH Therapy
## 2 Materials and Methods
### The Physical Principle of Beam Uniformization Using Nonlinear Magnets
#### 2.1.1 Multipole Magnet
The magnetic field gradient of the two pairs of octupole magnets is 2978 T/m\({}^{3}\) to \(-\)1910 T/m\({}^{3}\), while the gradients for the octupole and dodecapole magnet components are 2978 T/m\({}^{3}\) to \(-\)150,000 T/m\({}^{5}\) and \(-\)1910 T/m\({}^{3}\) to \(-\)1,370,000 T/m\({}^{5}\), respectively. \begin{table}
\begin{tabular}{c c} \hline \hline
**Octupole Field** & **Dodecapole Field** \\ \hline \(B_{Y}=\frac{1}{6}G(x^{3}-3 xy^{2})\) & \(B_{Y}=M(x^{5}+5y^{4}x-10y^{2}x^{3})\) \\ \(B_{x}=\frac{1}{6}G(3x^{2}y-y^{3})\) & \(B_{x}=M(5 yx^{4}-10y^{3}x^{2}+y^{5})\) \\ \(G=\frac{qB_{0}}{p_{0}^{2}}b_{4}\) & \(M=\frac{qB_{0}}{p_{0}^{2}}b_{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The magnetic field strength of the octupole field and dodecapole field in the X and Y directions. From Figure 2, it is evident that the dodecapole component can eliminate the \"peak\" effect caused by the octupole magnet, resulting in a flatter beam and an increased uniform region. This is attributed to the addition of higher-order magnetic field components, which increase the steepness of the beam spot, compensating for the effects produced by the lower-order components. Figure 3 is the cross-section view of multipole magnet.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
###### Abstract  
In this paper, we propose a delay- and power-efficient, multi-user, low-power wireless local area network (WLAN) communication scheme for Internet of Things (IoT) WLAN devices. Extremely low-power operation is one of the key requirements of emerging IoT devices. However, the current duty-cycle-based power saving approach may incur large access delay times owing to the trade-offs between the power consumption and the access delay. In order to reduce this delay and enhance the power-saving performance, wake-up receiver-based schemes have been proposed. However, because wake-up receiver-based schemes do not consider multiuser operation in dense communication environments, large delays are inevitable in the case of multiuser operation. In order to provide extremely low-power operation and under 1-mW standby power with reduced delay, we employed the optimized multiuser transmission scheduling of IEEE 802.11 ax in the proposed scheme and proper enhanced distributed channel access (EDCA) parameter settings. This is with the aim to reduce the delay caused by long wake-up times, and to avoid collisions caused by simultaneous transmission in uplink multiuser scenario. By using the proposed scheme, simultaneous IoT communication with multiple mobile IoT devices is possible while providing low-power operation. Simulation results verified the outstanding delay performance of the proposed scheme.  
enhanced distributed channel access (EDCA); IEEE 802.11; Internet of Things; orthogonal frequency division multiple access (OFDMA); wake-up receiver +
Footnote †: journal: Applied Sciences
# Seasonal Pollution Levels and Heavy Metal Contamination in the Jukskei River, South Africa
###### Abstract  
Monitoring river systems is crucial for understanding and managing water resources, predicting natural disasters, and maintaining ecological balance. Assessment of heavy metal pollution derived valuable data which are critical for the environmental management and regulatory compliance of the Jukskei River. Heavy elements were evaluated in the Jukskei River for seasonal impact, potential health risks, and contamination level with concentration levels ranging from 6900 mg/kg iron (Fe) to 0.85 mg/kg cadmium (Cd) in the dry sampling season and 6900 mg/kg Fe to 0.26 mg/kg Cd in the wet season. Enrichment factor analysis indicated high contamination levels of Fe and Pb in both dry and wet seasons. Moreover, pollution indicators revealed extremely high contamination of geo-accumulation and enrichment factors in the downstream to upstream in both seasons with a mild contamination factor for mercury (Hg). Principal Component Analysis revealed anthropogenic sources of arsenic (As), Cd, and Pb due to wastewater and agricultural pesticide application while Thorium (Th), uranium (U) and Hg were attributed as a results of gold mining activities. ANOVA and Pearson correlation analysis showed a high and moderate link between As-Pb, Cd-Pd, and As-Hg, which are significantly correlated. The potential ecological risk index assessment revealed a significant impact of heavy metals on the freshwater ecosystem.  
heavy metals; source apportionment; multivariate analysis; principal component analysis; ecological risk index; pollution assessment
# Study on Single-Event Transient Hardness of Semi-Enclosed Gate NMOS
## 4 Analysis of the Impact Mechanism of Different Incident Positions on Single-Event Transients  
To thoroughly validate the improved resistance to Single-Event Transients (SETs) of semi-enclosed gate NMOS compared to conventional NMOS, we employed Cadence Virtuoso layout design software(the version number is 6.1.7) to model both conventional NMOS and semi-enclosed gate NMOS devices [34]. Relevant parasitic parameters were extracted, and SET simulation analysis was conducted using the double-exponential current source method. As shown in Figure 14, (a) represents an inverter composed of conventional NMOS, while (b) illustrates the semi-enclosed-gate NMOS inverter structure; all modeling parameters were adopted from the minimum-size parameters in the SMIC 55 nm process library [35].  
From the simulated voltage pulse curves shown in Figure 15, it can be analyzed that the semi-enclosed gate NMOS inverter exhibits an output voltage pulse width of 100 ps under single-event transient effects, while the traditional NMOS inverter shows 112 ps. The voltage peak of the semi-enclosed gate is \(-\)5.968 mV compared to \(-\)6.812 mV for the traditional NMOS. Additionally, the voltage recovery time to steady state for the semi-enclosed gate inverter is 1.224 ns, whereas the traditional NMOS requires 1.414 ns. These results demonstrate that using semi-enclosed gate NMOS as the fundamental circuit unit provides superior performance in terms of voltage peak suppression, pulse width reduction, and stabilization time mitigation caused by single-event transients when compared to conventional NMOS structures.  
Figure 14: Structural diagrams of semi-enclosed gate NMOS inverters and conventional NMOS inverters.
## 3 Automated Operational Modal Analysis Based on Modal Uncertainty
### Clustering  
A check table \(t_{ij}\) is constructed to judge whether there are two identical modes in the collection of filtered modes. The frequency and MAC index are compared for two distinct modes.  
\[\begin{split}\left|\mu_{f_{i}}-\mu_{f_{j}}\right|& \leq z(p)\sigma_{f_{j}}\\ \mu_{MAC_{j}}-z(p)\sigma_{MAC_{j}}&\leq\mathrm{ MAC}\Big{(}\mu_{\phi_{i}},\ \mu_{\phi_{j}}\Big{)}\end{split} \tag{11}\]  
If both inequalities of Equation (11) are satisfied, \(t_{ij}\) is _True_ or \(t_{ij}\) is _False_. \(t_{ij}\) determines whether the \(i\)-th mode is identical to the \(j\)-th mode, whereas \(t_{ji}\) determines whether the \(j\)-th mode is identical to the \(i\)-th mode. Note that \(t_{ij}\) is not mandatorily equal to \(t_{ji}\). \(z(p)\), as the inverse function of the cumulative density function of a standard normal distribution, is a function providing the probabilistic parameter corresponding to the cumulative probability \(p\) from the standard normal distribution. In this study, a cumulative probability of 99% is adopted. However, \(t_{ij}\) does not show significant change even for a cumulative probability of 95% because, for two modes considered identical, the two modes must exhibit practically similar standard deviations. Consequently, the sensitivity to the cumulative probability \(p\) appears to be insignificant. In addition, since this corresponds to a two-sided test for the frequency, and the one-sided test for the MAC index, \(z(p)\) of the one-sided test should be used for the MAC index. However, as the sensitivity is low with regard to \(z(p)\), \(z(p)\) of the two-sided test is used. If the decision about whether two modes are identical is made using the standard deviation, the approach can be adapted to modes with different variabilities.  
\(\tilde{t}_{ij}=t_{ij}\ \wedge\ t_{ji}\) is then constructed from the as-obtained check table, where \(\wedge\) stands for the conjunction or logical multiplication (AND). \(\tilde{t}_{ij}\) is _True_ only when the two modes are identical, which allows the removal of modes with relatively large variability. The process clustering the identical modes by \(\tilde{t}_{ij}\) is as follows (Figure 2). Cluster \(C_{c}^{(1)}\) of the modes for which the \(i\)-th row of \(\tilde{t}_{ij}\) is _True_ is searched. Then, cluster \(C_{c}^{(2)}\) of the modes for which \(\tilde{t}_{ij}\) is _True_ for the row of the searched modes in \(C_{c}^{(1)}\) is extracted. \(C_{c}\) is constructed by the combination of \(C_{c}^{(1)}\) and \(C_{c}^{(2)}\) (\(C_{c}^{(1)}\cup C_{c}^{(2)}\)). If this process is repeated until the absence of change in \(C_{c}\), the final cluster \(C_{c}=C_{c}^{(1)}\cup C_{c}^{(2)}\cup\cdots\) of identical modes can be constructed. If the process is repeated with other modes that are not included in \(C_{c}\), a number \(n_{c}\) of clusters \(C_{c}\) (\(c=1,\cdots,n_{c}\)) can be formed.
# Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System
## 3 Hierarchical Synchronization Control Strategy Design  
In order to solve the synchronization control problem of the independent rear wheel steering actuator, motivated by the virtual shaft control algorithm, a hierarchical synchronization control  
Figure 2: The kinematic model of four wheel steering with two degrees of freedom.  
strategy based on the virtual rear axle steering dynamics model is proposed. The block diagram of the proposed control strategy of ARIS system is shown in Figure 3.  
The upper controller in the left dotted box of Figure 3 adopts the virtual synchronization controller to reduce the synchronization error between rear wheels. The upper controller consists of three parts. Firstly, according to the vehicle dynamics, the reference steering angle of the rear wheel is obtained, which is shown in Equation (6). Then, the dynamic model of the virtual rear axle steering mechanism (VRSM) including the synchronization control force and the external disturbance force is established, which is shown in Equation (9). Finally, the target tracking steering angles of the left and right rear axle wheels, \(\delta_{r1}\) and \(\delta_{r2}\), are redistributed according to the Ackermann kinematic model, which is shown in Equation (12).  
Considering the nonlinearity, uncertainty and external disturbance of the ARIS system, the lower controller adopts the LADRC controller to realize each steering wheel angle tracking control. The disturbance inside and outside the ARIS system is observed by the linear extended state observer (LESO) in the LADRC controller and can be compensated to the designed lower controller to reduce the influence of external disturbances on the control system.
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 5 Simulation and Analysis
### Simulation Results
#### 5.2.4 Packet Loss  
In this set of experiments, the packet loss due to the sinkholes is measured. This is an indicator of how fast the detection process has occurred. However, for a fair comparison, TBIDS depends mainly on the detection period set at the sink node during its configuration time. In this set of experiments, the detection is assumed to be done with each message received. Besides, adding more than one sink node enhances the detection accuracy, as seen before. Therefore, it has been found that it is beneficial to examine the proposed algorithms with the worst sinkhole scenario and the best sink node scenario. It is assumed that there are five sinkholes and five sink nodes with a fixed number of sensors, 500 and 15,000 nodes. However, after investigating S-LEACH and MS-LEACH, it has been found that once CHs are compromised, no message will be sent to the sink node or at least modified by the adversary. So, both algorithms are excluded from those sets of experiments.  
Figure 18a shows the packet loss for 500 nodes network with five sink nodes and five sinkholes. The packet loss for TBIDS, MBIDS, and ABC is 2%, 15%, and 30%, respectively. This is also an indicator of the speed of sinkhole detection. The same results are confirmed in Figure 18b, where a large-scale sensor network is with 15,000 nodes, five sink nodes, and five sinkholes. However, it has been noticed that TBIDS shows a fixed packet loss percentage, and the ABC packet loss is 45%, and MBIDS increases to 25%.  
Figure 17: Lifetime for large-scale networks, 8000 sensor nodes, with 10, 20, and 30 sink nodes.  
Figure 19 shows the results of another set of experiments in which 8000 sensor nodes are deployed in the monitored field with a different number of sink nodes (10, 20, and 30). The figure shows significant improvement in the packet loss percentage, where TBIDS reaches almost 1% when 30 sink nodes are used. Besides, ABC algorithm performance enhances from 55% to 37%, which is an immense achievement.
## 3 DCS Scheme Families
### Geographic Hash Table  
_Geographic Hash Table (GHT)_[1] was the first DCS scheme proposed in 2002 by [PERSON], _et al._ The motivation for this research was to make effective use of the vast amount of data gathered by large-scale sensor networks using scalable, self-organizing and energy-efficient data dissemination algorithms. [PERSON], _et al._ use a Distributed Hash Table (DHT) [20] in order to hash keys, for example event name or type, into geographic co-ordinates and store this event in sensor nodes in a geographic location closer to the co-ordinates. Greedy Perimeter Stateless Routing (GPSR) [21] was used to store and/or retrieve data from a sensor node. GHT uses the function _put_ (_k_, _d_), where \(k\) is a hash key used as the destination geographical location and \(d\) is the data, to forward a data packet to the location \(k\) using GPSR. The closest sensor node to the geographical location \(k\) is chosen as the home node, where data is stored for this event type. Similarly, when a consumer wants to consume/query data of an event type (for this particular case e.g., _humidity_), GHT again maps the event type to the hash key \(k\) and uses _get(k)_ to forward the query to the corresponding spatial/geographical location \(k\). The home node then replies by providing stored data for that event type. In Figure 1, sensors are represented by '\(\star\)'. A producer node senses a value and forwards it to home node denoted by '\(\mathbf{O}\)'. In turn, another consumer node uses the same hash function and retrieves the stored data from the home node. When storing data, represented by '\(\cdot\cdot\)\(\cdot\)', the producer node sends data to the target node and in the retrieval process, denoted by '\(\mathbf{<\)\(\mathbf{>}\)', the query is first forwarded to the home node and replies are then sent back to consumer.
## 4 Discussion
### The Possible Impact of Retrieval Density in High Latitude Regions  
As shown in Figure 10, there are large gaps of satellite XCO\({}_{2}\) retrievals in high latitude regions, especially during the winter season due to snow cover in these regions and very small signal noise ratio in observation. For analyzing the XCO\({}_{2}\) SCA, the availability of retrievals around the peak and trough of the cycle is important. Fortunately, the ACOS-GOSAT XCO\({}_{2}\) retrievals cover the peak and trough of the XCO\({}_{2}\) cycle, as indicated by CT2015 simulations, suggesting that our data-driven mapping method can generally reproduce the SCAs in these regions from the satellite retrievals. However, at high latitude boreal regions, the ACOS-GOSAT retrievals may also be affected by systematic retrieval errors [(14; 68)], so the SCA values at these high latitude regions, as shown in Figure 3, should be interpreted with caution. Further validations should be carried out with more TCCON data available in the future.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 4 Experimental Results and Discussions
### Feature Comparisons  
In Table 4, feature comparisons among the proposed method and other related methods are given. Different from other methods, the carrier images of our method are color halftone images. In addition, our method has real reversibility and provides a high embedding capacity for halftone images, although less redundancy exists in encrypted color halftone images.
## Materials and Methods
### Measurement of Soil Moisture Content by Optical Multispectral Sensor  
Previous studies have estimated SM using passive microwave sensors by observing the brightness temperature of a land surface through sensors and leveraging its relationship with temperature. Since SM is calculated according to the physical properties of the land surface, it is quite accurate. However, it has a coarser spatial resolution than the optical sensor. The total area of South Korea is 106,286 km\({}^{2}\). Since the AMSR-based SM products have a spatial resolution of 25 km, the entire country could be represented in 170 pixels only. Because the 0.5-km-resolution product of the optical sensor can represent the area with approximately 425,000 pixels, the passive microwave sensor product faces limitations in analyzing physical properties for cases such as this study. To overcome the limitations, we applied a method for calculating volumetric SM using optical sensor products presented by [PERSON] et al. [24]. The optical sensor used in this study is MODIS. Specifically, MOD11A1 LST and MOD43A3 land surface albedo were used.  
\[\rm\Delta LST=LST_{daytime}-LST_{nighttime}, \tag{3}\]  
\[\rm ATI=\frac{1-\alpha}{\rm\Delta LST}, \tag{4}\]  
\[\rm SM=\frac{ATI_{i}-ATI_{min}}{ATI_{max}-ATI_{min}}, \tag{5}\]  
where \(\rm\Delta LST\) is the difference between \(\rm LST_{daytime}\) and \(\rm LST_{nighttime}\), \(\rm LST_{daytime}\) is the land surface temperature during daytime, and \(\rm LST_{nighttime}\) is the temperature during nighttime. ATI, which stands for apparent thermal inertia, is a physical property of materials describing their impedance to temperature change. \(\alpha\) is the land surface albedo and 1- \(\alpha\) represents physically absorbance. Volumetric SM (m\({}^{3}\) m\({}^{-3}\)) can be calculated by normalizing the maximum and minimum ATI values.