esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
We, in this study, determined the geometric parameters of a single 1-3 piezo composite based on the simple procedure described in Figure 4. The approach assumed that the transducer with design parameters chosen by the maximum coupling value would produce the almost maximum acoustic pressure output. However, as shown in Figure 2b, the electromechanical coupling slightly changes in the span of from about 0.25 to 0.94 of the piezoelectric volume fractions. Therefore, we examined if the criteria to select the maximum coupling region would be reliable. For example, -5% reduction in the coupling value compared to the maximum makes two opposite scenarios; the first condition exhibits low acoustic impedance (i.e., reduction in the acoustic mismatch) (Figure 13a), yet the second one occupies a dense piezoelectric volume while compromising the acoustic impedance (Figure 13c). The thickness of the transducer was, in each simulation model, adjusted to correspond to the new speed of sound, caused by the change of electromechanical coupling. Figure 13a,c demonstrated the trade-off between the acoustic impedance and the piezo volume fraction. Therefore, the selection of the maximum electromechanical coupling can be a decent criterion for designing the FUS transducer design. Once the performance of a single 1-3 composite element is confirmed, the additional design effort can be made in terms of the matching, the backing, and the confocal design. For example, in Figure 12, the multi-element, confocal, 1-3 composite transducer was capable of delivering an acoustic pressure of approximately 23.6 MPa with a 100 V (AC) application. The corresponding acoustic intensity was over 188 \(\mathrm{W/cm^{2}}\) in a short duty cycle (i.e., 1%). The spatial resolution of the maximum pressure region was also improved by employing the arrayed FUS design and concentrating the acoustic energy at a spot. Therefore, the new therapeutic FUS device designed in this study can be used in new clinical applications, such as in precise, interstitial (or intravascular) histotripsy. In the future, we shall investigate miniaturized, confocal, 1-3 piezoelectric composite transducers for application to tissue ablation in an interstitial manner using high acoustic intensity with precise spatial resolution under a low electric input power.
## 3 Results
### Different Signal Reception
There is a big difference between the satellites used for the same measurements. This is particularly evident in the comparison between the Huawei P40 (Figure 7a) and the Huawei P20 (Figure 7b). In contrast to high-grade receivers and antennas, there is only a low C/N\({}_{0}\) dependence on satellite elevation in smartphone GNSS receivers and a large number of drops in C/N\({}_{0}\) that are unexpected for high elevations. These can be seen in Figure 7 for most smartphones. These results indicate that the commonly used elevation-dependent function may not be optimal for GNSS observation weighting and that a C/N\({}_{0}\)-dependent function, as already proposed [87], would be more appropriate. It is noticeable that the Xiaomi Mi 10 very often has interruptions in the paths of the active satellites. Therefore, it can be assumed that the smartphone's evaluation software  
Figure 5: Relative distribution of satellites in use by the smartphones studied. Blue indicates smartphones that have multi-frequency capabilities, and red indicates smartphones that can only receive the L1/G1/E1/B1l bands. Figure 6: (**a**) Correlation between the release date and the mean number of active satellites used by the smartphones in the whole experiment; (**b**) distribution of active satellites for the different frequency bands (different colors: different frequency bands). evaluates and changes the satellites used to determine the position much more differentially and dynamically.
## 3 IMS Data Characteristics
### Narcotic IMS Data Types  
The narcotic IMS data used in this paper obtained from the IMS detector of the target sample and the data consists of nine different types: eight types of narcotics and one normal type in which no narcotic exists. Various types of narcotics include amphetamine (Amp), morphine hydrochloride (Mor), fentanyl (Fen), alfentanil hydrochloride (Alf), MDMA hydrochloride (MDMA), ketamine hydrochloride (Ket), diazepam (Dia), and codeine phosphate hydrate (Cod). For all narcotics except alfentanil hydrochloride and standby state, data were acquired at 100 ng; for alfentanil hydrochloride, data were acquired at 300 ng. To acquire IMS data, one measurement was performed for 10 s. Data from one measurement consisted of one csv file. The number of data that made up all CSV files regardless of the narcotic type was 162,580. Looking at the reference of the IMS data graph in Table 2, there was a peak where the value of IMS data increased rapidly. The peak was a reactant ion peak (RIP), which was a separate ion signal, such as nitrogen that existed in large numbers in the air but did not correspond to the dangerous substance to be detected and occurred repeatedly during the data acquisition process. RIP was the criterion for dividing narcotic IMS data into multiple data [29].  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Target Sample** & **Concentration (ng)** & **Number (Times)** & **Reference** \\ \hline Amphetamine & 100 & 50 & **Response** \\ Morphine hydrochloride & 100 & 35 & **Frequency** \\ hydrate & 100 & 57 & **Frequency** \\ Fentanyl & 100 & 57 & **Frequency** \\ Alfentanil HCI & 300 & 55 & **Frequency** \\ MDMA HCI & 100 & 55 & **Frequency** \\ Ketamine HCI & 100 & 52 & **Frequency** \\ Diazepam & 100 & 74 & **Frequency** \\ Codeine phosphate & 100 & 45 & **Frequency** \\ hydrate & Normal state & — & 52 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Narcotic IMS data count, reference, and actual response data.
## 4 Results Discussion
### The Defect Prediction by Analytical Solution  
During selective laser melting, various process parameters have a great influence on the microstructure and mechanical properties of the parts, which in turn leads to internal defects such as lack of fusion, keyholing and balling effect. The defects produced in the process will cause porosity which will have a greater impact on mechanical properties and fatigue performance of parts [31; 32]. Therefore, how to predict and evaluate the possibility of the above-mentioned defects under a certain combination of process parameters is an important issue in production decision-making. Here, according to the evaluation criteria of defects, the calculation results of the analytical method proposed in this paper are used to predict the relationship between process parameters and defects.  
When melt pool does not sufficiently cover powder layer, it will result in lack of fusion. So the criteria for lack of fusion presented by [PERSON] [19] can be used.  
\[\left(\frac{H}{w}\right)^{2}+\left(\frac{L}{d}\right)^{2}\leq 1 \tag{15}\]  
where \(H\) is hatch spacing, \(w\) is the width of melt pool, \(L\) is the powder layer thickness and \(d\) is the depth of melt pool. With high energy deposited on powder, the metals vaporized will cause cavity in melted region, so the melt pool is narrow and elongate. For keyholing, the criterion is as following [33].  
\[\frac{\Delta H}{h_{\mathrm{s}}}>\frac{\pi T_{b}}{T_{m}} \tag{16}\]  
where \(\Delta H\) is energy distributed in unit volume, \(h_{\mathrm{s}}\) is enthalpy of melting, \(T_{b}\) is boiling temperature and \(T_{m}\) is melting temperature. Balling can cause high surface roughness and porosity, it can be determined by the following equation [34].  
\[\frac{\pi w}{l}>1 \tag{17}\]  
Based on the melt pool size predicted from analytical solution and the criterion Equation (8) for lack of fusion, Figure 7 showed the calculation result. From this figure, we can get the optimal process parameters to avoid lack of fusion.  
Figure 7: The calculation of Equation (15) for In718 (H is 100 \(\upmu\)m and L is 40 \(\upmu\)m).  
Based on the criterion of defects, the keyholing, lack of fusion and balling effect can be predicted by the estimation of melt pool and thermal distribution. From Figure 8, it can be found that the forming quality is different under the different combination of laser power and scanning speed. Moreover, the process parameters which will avoid the defects generation can be suggested by Figure 8.
## 3 Results and Discussion
### Effect of Fly Ash on Chloride Transportation
However, the pozzolanic reaction at the beginning of cement hydration is very slow because of the presence of less Ca(OH)\({}_{2}\). With the increase of curing age, the secondary hydration reaction between the activated ingredients, such as SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\), in fly ash, and Ca(OH)\({}_{2}\) produces C-S-H gel, calcium aluminate hydrate products, and so on. These products may absorb more free chloride ions, reducing the deposition of chloride ions in concrete. This also indicates that chloride binding capacity is improved with the addition of fly ash. These hydrates can fill large size pores in cement matrixes and thus reduce porosity, narrow pore diameter, and block the connectivity of the pores, all of which subsequently slow the diffusion and migration of chloride ions. In addition, 70% of fly ash particles are inert, compact hollow microsphere (cenospheres) with smooth surfaces [31,32], which are like activated nanomaterials that improve the microstructures of concrete. Consequently, a concrete with fly ash replacement has a much lower chloride deposition than normal concrete.
# Hardware Model Checking Algorithms and Techniques
## 7 Igr
### Cone Unwinding and Rewinding  
In standard interpolation, when finding a spurious counterexample, the current forward traversal is restarted from the initial states after the bad cone has been expanded by one step. IGR, instead, dynamically unwinds or rewinds the cone during forward traversal, in order to guarantee the refinement of some previously computed overapproximation of reachable states. The depth of the cone is therefore guided by the frames in the trace so that it can lead to a strengthening refinement for some of them.  
Supposing that, at a given point during the execution of IncritpModelChecking, the algorithm has computed a trace \(\mathbf{F_{k}}\), two approaches can be pursued with the goal of potentially expanding and/or refining \(\mathbf{F_{k}}\):  
**Cone Unwinding.**: When the forward traversal hits the cone, it starts a new traversal at an intermediate step in order to guarantee the refinement of the trace.
**Cone Rewinding.**: When the forward traversal hits the cone, it continues the traversal with iteratively smaller cones in order to refine and expand the trace.  
Overall, guided cone unwinding/rewinding allows IGR to dynamically tune the unrolling from the target and therefore to have better control over the precision of the computed overapproximations (interpolants). In this respect, standard interpolation is too rigid, as overapproximations are always strengthened expanding the cone by one and restarting the traversal from scratch.
## 2 Hybrid Numerical Model  
### Microscopic Chemical Reactions  
The study of gas discharge shows that there are many kinds of particles and associated excited states involved in physical and chemical reactions in the plasma of atmospheric streamer discharge. However, with the relatively low degrees of excitation, dissociation, and ionization of air, integrating all the particles and reactions into a chemical model is difficult and unnecessary. Based on the Pancheshnyi model [5], combined with [PERSON] [15], [PERSON] [21], [PERSON] [27], and our current research, a reaction group including e, N\({}_{2}\), N\({}_{2}\)\({}^{+}\), N\({}_{4}\)\({}^{+}\), N\({}_{2}\)O\({}^{+}\), O, O\({}_{2}\), O\({}_{3}\), O\({}_{2}\)\({}^{+}\), O\({}_{4}\)\({}^{+}\), O\({}_{2}\)\({}^{-}\), and O\({}^{-}\) is established, and a total of 35 plasma microscopic chemical reactions are involved in the reaction group. The equations of some microscopic reactions in the simulation are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Equations** & **Rates** \\ \hline \hline \(\begin{array}{c}\text{E}+\text{N}_{2}\rightarrow\text{N}_{2}+\text{e}\\ \text{e}+\text{O}_{2}\rightarrow\text{O}_{2}+\text{e}\\ \text{Impact ionization}\end{array}\) & \(\begin{array}{c}f_{1}(\varepsilon)\\ f_{2}(\varepsilon)\end{array}\) \\ \(\begin{array}{c}\text{e}+\text{N}_{2}\rightarrow\text{2e}+\text{N}_{2}^{+}\\ \text{e}+\text{O}_{2}\rightarrow\text{2e}+\text{O}_{2}^{+}\end{array}\) & \(\begin{array}{c}f_{3}(\varepsilon)\\ f_{4}(\varepsilon)\end{array}\) \\ \hline \multicolumn{2}{c}{Charge transfer between particles} \\ \(\begin{array}{c}\text{N}_{2}{}^{+}+2\text{N}_{2}\rightarrow\text{N}_{4}{}^{+}+ \text{N}_{2}\\ \text{N}_{2}{}^{+}+\text{N}_{2}+\text{O}_{2}\rightarrow\text{N}_{4}{}^{+}+ \text{O}_{2}\\ \text{N}_{4}{}^{+}+\text{O}_{2}\rightarrow\text{O}_{2}{}^{+}+2\text{N}_{2}\\ \text{O}_{2}{}^{+}\text{N}_{2}+\text{O}_{2}\rightarrow\text{O}_{4}{}^{+}+ \text{N}_{2}\\ \text{O}_{2}{}^{+}+\text{O}_{2}\rightarrow\text{O}_{4}{}^{+}+\text{O}_{2}\\ \text{O}_{2}{}^{+}+2\text{O}_{2}\rightarrow\text{O}_{4}{}^{+}+\text{O}_{2}\\ \end{array}\)} & \(\begin{array}{c}k_{5}=5\times 10^{-41}\\ k_{6}=5\times 10^{-41}\\ k_{7}=2.5\times 10^{-16}\\ k_{11}=1\times 10^{-15}\\ k_{12}=2.04\times 10^{-34}T^{-32}\\ k_{13}=2.04\times 10^{-34}T^{-3.2}\end{array}\) \\ \hline \multicolumn{2}{c}{Recombination Reaction} \\ \(\begin{array}{c}\text{e}+\text{O}_{4}{}^{+}\rightarrow\text{O}_{2}\\ \text{e}+\text{O}_{2}{}^{+}\rightarrow\text{2O}\\ \text{O}_{4}{}^{+}+\text{O}_{2}{}^{-}\rightarrow\text{3O}_{2}\\ \text{2e}+\text{N}_{2}{}^{+}\rightarrow\text{e}+\text{N}_{2}\\ \text{O}_{2}{}^{+}+\text{O}^{-}\rightarrow\text{O}+\text{O}_{2}\\ \end{array}\)} &  
In Table 1, \(k_{\rm i}\) is the reaction rate (The subscript i represents the reaction number), \(f_{\rm i}(\epsilon)\) is the collision cross section data, \(T\) is the background gas temperature, and \(T\)e is the electron temperature. The values of \(k_{\rm 1}\) to \(k_{\rm 4}\) depend on the calculation of collision cross sections derived from databases such as those of Biagi, Bordage, and Itikawa [28; 29].
## 7 Discussion of Portrait Drawing Evaluation
### Advantages of NCC  
The hair quality assessment is inherently challenging due to its complex texture, irregular patterns, and varying densities. Traditional evaluation methods often rely on subjective human judgments, which can lead to results that vary significantly between raters. To ensure an objective and quantifiable assessment, our proposal employs a pixel-wise similarity comparison using the _normalized cross-correlation (NCC)_ method. The advantages of the proposed method are as follows:  
* **Texture feature extraction:** The fine hair details are extracted by preprocessing both the reference image and the user's drawing using edge detection and eigenvalue-based texture analysis.
* **Pixel-by-pixel comparison:** The _NCC score_ is calculated by comparing corresponding pixels in the texture maps of both images, which ensures that even subtle differences in the texture and density are captured.
* **Numerical assessment:** The _NCC score_, ranging from \(-1\) to \(1\), provides an objective measure of the similarity. A higher score indicates a more accurate reproduction of the hair texture, while a lower score highlights discrepancies in the structure and shading.  
These steps ensure that our system provides a detailed and objective evaluation of the hair texture in the user's drawing. By combining edge detection, texture analysis, and _NCC_ scoring, the proposed system can effectively capture fine details and subtle variations in the hair density.
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 2 The Origin of Radon
### Accumulation of Radon in Indoor Environments
#### 2.3.5 Radon Solubility in Water  
When radon is present in soil or rock, it can diffuse into groundwater where it is retained due to its solubility. This dissolved radon can then be released into the atmosphere when the water is agitated, for example, during household activities such as showering or washing dishes. The solubility of radon in water is influenced by factors such as temperature and salinity. As a general rule, radon is more soluble in cooler and less saline waters. It is important to note that the ingestion of water containing dissolved radon poses a lesser health risk compared with inhalation of radon gas. The study by [PERSON] et al. (2017) focuses on various technical aspects related to the measurement of radon in water [95]. This work refers to international standards dedicated to these measurements, such as ISO 13164-3:2013, ISO 13164-4:2015, and ASTM D5072-09. One of the key findings of this study is that a significant radon loss (2-5%) was observed from polypropylene containers within just one day after sampling, while radon leakage was negligible from other types of containers. Additionally, the study highlights that the concentration of radon in spring and hand pump water is influenced by the concentration of \({}^{226}\)Ra in rocks and the abundance of \({}^{238}\)U minerals. Despite the relatively low solubility of radon in water, its activity level in groundwater can be high due to the high level of radon content. The study also reports that depleted levels of thorium and uranium, elements notorious for their relationship with radon, have been found. Yet perhaps the first, and therefore, very relevant approach to the subject is the analysis presented in the evaluation conducted by [PERSON], from the Department of Chemistry of the Wright State University (Dayton, OH, USA), in 1978 [96], which is schematized in Figure 1.  
The analysis provides a clear view of the relationship between temperature and the molar fraction of radon. The molar fraction of radon is a measure of its solubility, and upon analyzing the data, a distinct trend is observed: whereas the temperature increases, the molar fraction--and thus the solubility of radon--decreases. At the beginning of the line, at a temperature of 273.15 K, the molar fraction of radon is 4.217. This value consistently decreases as the temperature rises. At 373.15 K, the molar fraction of radon is only 0.672, significantly lower than the initial value. This negative trend indicates that radon is less soluble in water at higher temperatures. The decrease in radon solubility with increasing temperature may be due to the fact that, with increasing thermal energy, radon molecules have more energy to escape from the solution and enter the gas phase. This pattern is quite common for gases in solution and suggests that, under warmer conditions, it can be expected that there is less radon dissolved in water and more present in the gas phase.
## 2 Methodology
### RTAB-Map Method
Employing more Bayesian filters, the probabilistic update formula for S can be obtained (Equation (3)), where \(L^{t}=L_{-1}\),...,\(L_{t}\) denotes all nodes at time t, and the observation model \(P(S_{t}|L^{t})\) can be computed by the likelihood function \(\uparrow(S_{t}=j|L_{t})=P(L_{t}|S_{t}=j)\). The updated \(P(S_{t}|L^{t})\) is normalized and if \(P(S_{t}|L^{t})<T\), it means the loop closure is successful. The threshold T was determined experimentally. Finally, all the nodes and constraint edges are fed into the graph optimization module for global optimization, which corrects the odometry position of all the nodes globally. \[P(S_{t}|L^{t})=\eta P(L_{t}|S_{t})\sum_{i=1}^{t_{n}}P(S_{t}|S_{t-1}=i)P\Big{(} S_{t-1}=i\Big{|}L^{t-1}\Big{)} \tag{3}\]  
During online mapping, it is crucial to process data faster than it is inputted. However, as the mapping process continues, the time taken for closed-loop detection and graph optimization increases, which impacts real-time performance. To address this issue, RTAB-Map utilizes a memory management model [2] (Figure 3). The memory is divided into short-term memory (STM), working memory (WM), and long-term memory (LTM). STM serves as the initial storage space for new data in a new node with a fixed size. When STM reaches its capacity, the earliest nodes are moved to WM for closed-loop detection. After a certain time limit, some nodes are transferred to LTM. LTM stores less relevant nodes that do not significantly contribute to the global map in the short term and are not used outside of closed-loop detection and graph optimization. If a closed loop is detected between the current node and an old node in WM, neighboring nodes in LTM are relocated to WM to increase the chances of identifying the closed loop. This memory management mechanism ensures operational efficiency and enables RTAB-Map to support multi-session mapping.
## 6 Results and Discussions
### Network Performance Behaviour under DDoS Attacks  
Now, we analyse the network under DDoS attack conditions. For emulating a DDoS attack, IoT-DAG-1 transmits TCP and UDP traffic to the server, while IoT-DAG-2 transmits random TCP and UDP traffic to the server. The result is depicted in Figure 7. As observed  
Figure 6: SDN-based IoT network performance under normal operation conditions.  
Figure 7: SDN-based IoT network performance comparison under normal and DDoS attack conditions.  
in the results, during the DDoS attack with only two attackers, both TCP and UDP traffic suffer from the impact. For TCP traffic, the tunnel is out-of-service at most of the test period. For UDP traffic, even though it only requires extremely low bandwidth to transmit, a large proportion of traffic is discarded. Only 21% of periodic and 14% of random TCP requests, and 4% of periodic and 1% of random UDP requests are successfully transmitted to the far end. From the result in Wireshark, a RPi tries to resolve the far end MAC address using ARP protocol prior to packet forwarding, and it gives up after six attempts if fails to learn the destination MAC address. Therefore, packet delivery rate is not considered here, because most of the TCP/UDP requests are not even sent out from RPis during DDoS attacks due to the absence of MAC address information. It is important to note that random transmissions are more affected during a DDoS attack, so we can conclude that IoT event trigger applications are more vulnerable to attacks than periodical ones.