esa-sceva/satcom-chunk-collection huggingface dataset:
# Magnetic Nanoparticles as Mediators for Magnetic Hyperthermia Therapy Applications: A Status Review
## 3 Results and Discussion
### Calorimetric Measurements
After a certain time, the curve reaches a maximum corresponding to the temperature, \(T_{\text{C}}\), already determined by thermomagnetic measurements. Table 4 lists the corresponding SAR values. In Figure 26, right graph [103], we determined the initial heating slopes (\(\Delta T/\Delta t\)) that we used to determine the values of SAR:  
\[\text{SAR}=\left(c_{\text{alloy}}\right)\left(\frac{\Delta T}{\Delta t}\right) \left(\frac{1}{x_{\text{Ni}}}\right), \tag{5}\]  
where \(x_{\text{Ni}}\) is 0.67, the calculation was based on the mass fraction of nickel in each sample. Using the heat capacities of \(c_{\text{Ni}}\) (0.444 J/gK) and \(c_{\text{Cu}}\) (0.390 J/gK), we calculate the heat capacity of the alloy, at a temperature of 20 \({}^{\circ}\)C, since all measurements had started at this value, defined by the temperature of the surrounding thermal bath. Table 5 lists the corresponding SAR values. \begin{table}
\begin{tabular}{c c} \hline \(H\) [kA/m] & SAR [mW/g] \\ \hline
16.9 & 4.3 \\
25.4 & 21.8 \\
33.8 & 35.8 \\
42.0 & 41.6 \\ \hline \end{tabular}
\end{table} 
Table 4: SAR values as a function of AMF amplitude for Ni\({}_{0.725}\)Cu\({}_{0.275}\) (microemulsion method). \begin{table}
\begin{tabular}{c c} \hline \hline
**Composition** & **SAR [W/g]** \\ \hline Ni\({}_{67.5}\)Cu\({}_{32.5}\) (sample A) & 0.60 \\ Ni\({}_{62.5}\)Cu\({}_{37.5}\) (sample B) & 0.36 \\ Ni\({}_{60}\)Cu\({}_{40}\) (sample C) & 0.12 \\ \hline \end{tabular}
\end{table} 
Table 5: Samples of composition Ni\({}_{1-x}\)Cu\({}_{x}\) (sol-gel method) and data of SAR measurements. To conduct calorimetric measurements, a solid powder sample was subjected to controlled heating. These measurements took place within a system capable of generating a nominal field strength of 2 kA/m and operating within a frequency range of 80 to 800 kHz. This setup allowed for precise characterization of the thermal properties and behavior of the sample under varying magnetic field conditions. Figure 27, left graph, shows the growth of the temperature of the powder sample in the calorimeter for different magnetic fields [55]. Remarkably, the Cu\({}_{27.5}\)Ni\({}_{72.5}\) MNPs exhibited a significant heating effect during our experiments. Under an applied magnetic field strength of 3.13 kA/m, we were able to achieve temperatures surpassing 40 \({}^{\circ}\)C.  
Figure 27, right graph, shows the temperature rise of the Cr\({}_{28}\)Ni\({}_{72}\) sample in the calorimeter at different magnetic field strengths [107]. The magnetic measurements of the solid powdered Cr\({}_{28}\)Ni\({}_{72}\) with a \(T_{\rm C}\) of 44 \({}^{\circ}\)C were performed in a system that generates an AMF (9.2-16.2 kA/m, 800 kHz).
## 1 Introduction
For instance, the spring frost on the southeastern Tibetan Plateau in 2019 affected over 158,000 people and disrupted the forage supply for 1.19 million livestock, resulting in economic losses amounting to 100 million Yuan [32]. This event provides a good case study for investigating the impacts of frost events on alpine vegetation and ecosystems, as well as for exploring the relationship between alpine vegetation, frost events, and environmental factors. Existing studies on frost in the Third Pole have primarily focused on examining anthropogenic impacts, attributing extreme events, and analyzing atmospheric circulation patterns [33; 34]. However, few studies have focused on the ecological effects of frost events in this region. To our knowledge, only one site-scale study reported a lower frequency of spring freezing events along the elevation gradient on the southeastern Third Pole, investigating their impact on the spring phenology of juvenile Smith fir trees [35]. However, no research to date has explored the risk of frost-induced damage to vegetation growth across the Third Pole during the growing season at the regional scale. This study aims to fill the knowledge gap in the response mechanism of alpine vegetation to frost events and climate factors in different seasons in the Third Pole. The main objectives include (1) assessing the spatial-temporal patterns and trends in frost days in the growing season in the Third Pole, (2) evaluating the spatial-temporal patterns and trends of the NDVI and GOSIF in different seasons in the Third Pole, and (3) examining the controlling mechanisms of frost days and climate factors on alpine vegetation.
## 4 Simulation Results  
The designed photovoltaic (PV) system was simulated using MATLAB to evaluate its performance under the climatic conditions of Ankara. The simulation was conducted on an hourly and monthly basis, incorporating real-world environmental data, including solar irradiance, temperature, and sunlight hours. The results were analyzed to compare the performance of three MPPT methods: ANN-based MPPT, RL-based MPPT, and the P and O MPPT method. The key metrics evaluated include PV energy production, battery SOC, and load reliability. This section presents the simulation results, accompanied by graphical representations, and provides detailed interpretations of each graph.
# Seismic Performance Assessment of Wide Pile-Supported
###### Abstract  
Pile-supported wharf (PSW) is one of the primary port structures and is often damaged by earthquakes. To mitigate the risk of seismic damage to a PSW, its seismic performance should be thoroughly assessed. This study aimed to examine the impact of ground displacement on the seismic performance of PSW with a mild soil slope. We performed soil-structure system finite element analysis targeting a wide PSW. The analysis is divided into two scenarios. In the first scenario, the PSW was modeled without regard for the soil slope, whereas the second scenario considered the soil slope. Two waveforms that matched the target spectral acceleration were used to study the effects of the waveform duration on the seismic response of PSW. The analysis results revealed substantial influences of soil slope displacement as well as differences in waveforms on PSW's seismic performance.
## 2 Data Source
### Data Collection and Supplement
 Note: SVM—support vector machines; FIS—fuzzy inference system; MI—mutual information; GPR—Gaussian process regression; ICA—mparallel competitive algorithm; PSO—particle swarm optimization; ANFS—adaptive neural-fuzzy inference system; FFA—firefly algorithm; GA—genetic algorithm; CSO—cat swarm optimization; ACO—ant colony optimization; BRT—boosted regression tree; BGAM—boosted generalized additive model; GWO—grey wolf optimization; GOA—grasshop optimizer algorithm; D—hole diameter (mm); H—bench height (mm); S—spacing (m); B—burden; PF—powder factor (kg/m3); ST—stemoming; E—elastic modulus (GPa); SDR—size of distributed rock (m); UCS—uniaxial compressive strength (MPa); UTS—uniaxial tensile strength (MPa); RQD—rock quality designation (%); Sch—Schmit laminar rebound number; Qe—total explosive amount (t); E——sub-grade drilling (mm); L—powder factor, spacing, burden, much pile's mean fragmentation size, and rock mass rating (RMR) with Equations (2) and (3) [36]. \[\text{E}=0.3752\times\text{UCS}+4.479 \tag{1}\]  
\[\text{FI}=0.03\times(\text{RMR}\times\text{PF}\times\text{SB})+0.73 \tag{2}\]  
\[\text{X}_{\text{B}}=\text{FI}\times\text{X}_{50} \tag{3}\]  
where the units of E, UCS, and Pf are GPa, MPa, and kg/m\({}^{3}\), respectively, RMR is [PERSON]'s rock mass rating, and FI is the Fragmentation Index. The FI is estimated using Equation (2), and the \(\text{X}_{\text{B}}\) is calculated using Equation (3). The RMR of the rock excavated by [PERSON] et al. [55] is taken as 50, which belongs to the 'Fair' class [56]. After supplementing the data, they are then converted to consistent input parameters with other datasets from [PERSON] [57] and [PERSON] [58] as required for this study. As a result, a small-scale dataset of 187 groups of in situ blasting data with seven variables including the spacing/burden ratio (S/B), bench height/burden ratio (H/B), burden/hole diameter ratio (B/D), stemming/burden ratio (T/B), PF, \(\text{X}_{\text{B}}\), and E was combined with fragmentation size data to generate a complete database.
# Dynamic Correction of the Influence of Long Measuring Path Irregularity in Antenna Tests
## 3 Analysis of the Electrical Length Control Method
In this case, \(\mathbf{x}=[\Gamma_{in},\varphi_{in}]\) is the vector describing the state of the system \(N=2\); \(\mathbf{p}=[S_{11},S_{12},S_{21},S_{22},\varphi_{11},\varphi_{12},\varphi_{21}, \varphi_{22},\Gamma_{G},\varphi_{G}\,,\,\Gamma_{Z},\varphi_{Z}\,]\) is the vector containing system parameters, \(M=12\). The following characteristics shown in Figure 3a-f are particularly interesting for the study of the microwave discontinuities influence on the electrical length control of the transmission path. Figure 3: _Cont._  
\(S_{11}=0\), but unfeasible on practice, or with \(S_{11}=-\Gamma_{Z}S_{12}S_{21}\cos(\varphi_{Z}+\varphi_{12}+\varphi_{21}-\varphi_{ 11})\). Section 4 of this present article examines the influence of microwave discontinuities reflections that randomly distributed along a transmission path. 2. Based on (15), a similar condition for the reflected wave from the load is \(d\varphi_{in}/d\varphi_{22}=0\). The possible solutions are: \(\Gamma_{Z}=0\), \(S_{22}=0\), \(S_{12}=0\) and \(S_{21}=0\). None of these conditions are met in real systems. Note that the condition:  
\[\Gamma_{Z}S_{12}S_{21}\cos(\varphi_{Z}+\varphi_{22})+2S_{11}\cos\theta-S_{11} \cos F=0\]  
can be approximately reduced to: \(\varphi_{Z}+\varphi_{22}=\frac{\pi}{2}+k\pi\), \(k=1,2\dots\)  
3. From (16) we obtain the condition for the maximum measuring accuracy the electrical length of the path \(d\varphi_{in}/d\varphi_{12}=d\varphi_{in}/d\varphi_{21}=1\). That is possible with \(S_{11}=0\) and \(\Gamma_{G}=0\). 4. To fully take into account the effect of the load reactance on the output signal phase in the proposed system, it is necessary that \(d\varphi_{in}/d\varphi_{Z}=2\). It can be seen from expression (17) that this is possible if two conditions are met at the same time:  
\[\left\{ \begin{array}{c}S_{11}=0\\ \cos(\varphi_{Z}+\varphi_{22})=\Gamma_{Z}S_{22}\end{array} \right.\]  
Thus, conditions have been obtained for achieving the maximum measuring accuracy the electrical length of a long microwave path with method of unmodulated reflection from the load. To improve the control accuracy of the electrical length, the most important conditions are:  
* eliminate the wave reflection from the signal source, which is achieved by adding a microwave isolator at its output;
* take into account during calibration the influence of microwave discontinuities randomly distributed along the channel on its input reflection coefficient.
## 5 Evaluation
### Hot/Cold-Aware Data Placement Structure Applied  
Figure 9a shows the BER comparison of the data placement structure by comparing the RBER aspect of retention error between the case without data placement and the case with data placement. Figure 9b shows the comparison of BER when ECC provided by default is applied to RBER. In addition, the RBER of Figure 9a was measured based on 9K P/E cycles of 3D TLC NAND flash memory [22]. Retention time, which is the \(x\)-axis of the graph, is a parameter indicating when a certain period has elapsed after data was saved, and was quantitatively evaluated by dividing it into four sections at two-week intervals from week 0 to week 6.  
Figure 9: Retention error according to retention time with hot/cold-aware data placement model and without data placement model. (**a**) comparison of retention error between the case without data placement and the case with data placement. (**b**) comparison of BER when ECC provided by default is applied to RBER.  
The red graph in Figure 9a shows the RBER of retention error for the existing data arrangement to which the data placement structure is not applied. The blue graph depicts the RBER of retention error when the data placement structure is used. As can be seen from the graph, data placement reduces RBER significantly, and the effect is more pronounced when the retention time is short. The reason for this is that as retention time increases, the likelihood of retention errors that occur also increases.  
The green graph in Figure 9b shows the BER when ECC, the data protection policy provided by SSD, is applied based on the red graph in Figure 9a. Conversely, the yellow graph depicts the BER when ECC is used, based on the blue graph in Figure 9a. Even when ECC is used, it is clear that the data placement structure has a lower BER. As the probability of occurrence of retention error increases with retention time, the BER increases with retention time for both the structure in which the data is relocated and the structure in which the data is not relocated.
# Managing Access to Confidential Documents: A Case Study of an Email Security Tool
## 5 Results
### Sharing Confidential Information
#### 5.3.1 Scenario 1: Employee Information Form with Human Resources  
In this scenario, we first asked the participants to review the employee information form, state whether they considered it to include sensitive information, and rate this form's information sensitivity. All participants stated that they considered this employee information form to include sensitive information. They provided many examples of included sensitive information, such as an employee ID, SSN, and contact information. We found that 21.1% of the participants rated this form's information sensitivity as \"moderately sensitive\", and 78.9% rated it as \"very sensitive\".  
After providing the hypothetical scenario, all participants performed this task by sending their forms to the HR's email address. Then, the participants rated their satisfaction with sharing their SSNs in the employee information form using their regular or standard email and secured email. Also, they were asked to rate their concerns regarding sharing their SSNs in the employee information form with HR using their regular or standard email and secured email.  
The Wilcoxon signed-ranks test showed that the participants' satisfaction with sharing SSNs in the employee information form via secured email was rated higher by the participants compared with sharing via standard email (Z = \(-\)3.6, \(p\) < 0.001 (Table 5)). Therefore, we found that all participants (100%) rated their satisfaction as moderately or very satisfied if they shared their SSNs with HR using secure email compared with standard email (26.4% rated it as moderately or very satisfied). Also, the test indicated that the users were more concerned if they shared their SSNs with HR via regular or standard email than secured email (Z = \(-\)3.8, \(p\) < 0.001 (Table 5)). Furthermore, 63.2% of the participants were not at all concerned if they shared their SSNs with HR using secure email compared with standard email (47.4% rated themselves as \"very concerned\").
# An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization
## 5 Expanded Experiments, Results, and Discussion
### Evaluation of TF-IDF and BERT Model Performance  
To evaluate the effectiveness of different word embedding techniques, we conducted a comparative analysis of TF-IDF with default parameters of scikit-learn's tfidfVectorizer and BERT models using cosine similarity across various scientific topics. Three article queries of the topics \"coronaviruses\", \"climate change effects\", and \"quantum computing advancements\" were implemented to the raw data of 10,000 articles described in Section 3. For the results, we examined the distribution of top research fields (Figure 2), result overlap (Figure 3), and similarity score distribution and decay (Figures 4 and 5). The patterns in the results demonstrated BERT's superiority in capturing semantic nuances and contextual relationships. When analyzing similarity scores across the three topics--\"coronaviruses\", \"climate change effects\", and \"quantum computing advancements\"--BERT consistently produced better results. For instance, in the \"coronaviruses\" query, BERT's similarity scores ranged from 0.55 to 0.80, while TF-IDF's scores rapidly dropped below 0.2 after the top few results (Figure 5). Similarly, for \"quantum computing advancements\", BERT maintained scores between 0.72 and 0.84, compared to TF-IDF's range of 0.22-0.38. BERT also showed a more gradual decline in similarity scores as the rank increased, indicating better relevance even for lower-ranked results. Both methods demonstrated high result diversity, but BERT's top results differed significantly from TF-IDF's, with only 14-23% overlap across queries (Figure 4). This low overlap suggests BERT's ability to identify relevant but less obviously related fields. For example, for the \"coronaviruses\" query, while TF-IDF heavily favored \"Physics\", BERT highlighted a broader range including \"Science & Technology--Other Topics\" and \"Virology\". These findings consistently demonstrate BERT's enhanced capability in understanding context and semantic relationships, making it particularly valuable for complex scientific literature search and recommendation systems where nuanced topic analysis is crucial. However, it is important to note that TF-IDF's execution time is significantly faster than BERT's, which could be a crucial factor in applications where the processing speed is a priority.  
Figure 2: The top fields/topics of three different queries using TF-IDF and BERT methods. The detailed fields/topics in each diagram are from the metadata of the retrieved articles.  
Figure 4: Distribution of similarity scores for query results of three different topics using TF-IDF and BERT.  
Figure 5: _Cont_.  
Figure 3: The overlap ratio between TF-IDF and BERT top 100 results for three different queries.
## 2 Proposed Framework and Experimental Methodology
### Proposed Encryption Workflow with Hybrid CPU-GPU Strategies
#### 2.5.4 Ping-Pong Processing  
The ping-pong processing technique, explained in Algorithm 4, pipelines data processing between the CPU and GPU. The CPU processes a chunk and sends it to the GPU while working on the next chunk, ensuring continuous data flow and maximizing resource utilization with total processing time \(T_{pipeline}=max(T_{CPU},\ T_{GPU})\).  
```
CPU and GPU alternately encrypt data chunks in a pipelined fashion, maintaining continuous processing flow and maximizing resource efficiency.
1. for each chunk \(i\) in data \(D\):
2. if\(i\backslash mod2=0\)then
3. CPU_encrypt(\(d_{i}\))
4. else
5. GPU_encrypt(\(d_{i}\))
```  
**Algorithm 4**Ping-pong CPU-GPU processing
# Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media
## 2 Material and Methods
### Nature-Inspired Optimization Algorithms
#### 2.2.3 Differential Evolution (DE)  
According to the differential evolution (DE) algorithm [26], a population (with NP size) of candidate solutions (agents) is initially randomly selected. New solutions are produced by adding the weighted difference between two population agents \(\big{(}\mathbf{x}_{r_{2}}^{t},\ \mathbf{x}_{r_{3}}^{t}\big{)}\) to a third one \(\big{(}\mathbf{x}_{r_{1}}^{t}\big{)}\). This operation is called mutation and its mathematical expression is as follows:  
\[\mathbf{y}_{i}^{t+1}=\mathbf{x}_{r_{1}}^{t}+F\big{(}\mathbf{x}_{r_{2}}^{t}-\mathbf{x}_{r_{3}}^{ t}\big{)}, \tag{5}\]  
where \(r_{1},r_{2},r_{3}\in\{1,2,3,\ldots,NP\}\) are randomly selected and \(F\in[0,2]\) is called the differential weight, which controls the amplification of the differential variation of Equation (5). Next, a crossover operator is applied to the resultant mutant agent subject to a crossover \(CR\in[0,1]\), according to the following rule:  
\[y_{ji}^{t+1}=\left\{\begin{array}{ll}y_{ji}^{t+1},\ if\ r_{j}<CR\ \text{or}\ j=randindex(i)\\ x_{ji}^{t},\ if\ r_{j}>CR\ \text{or}\ j\
eq randindex(i)\end{array}\right., \tag{6}\]  
where \(\mathbf{y}_{i}^{t+1}=\left\{y_{1i}^{t+1},y_{2i}^{t+1},\ldots,y_{mi}^{t+1}\right\}\) is the trial solution in the n-dimensional search space, \(r_{j}\in[0,1]\) is a random number, and \(randindex=\{1,2,3,\ldots,n\}\) is a random index. Finally, the produced solution \(\mathbf{y}_{i}^{t+1}\) is compared with its value in the previous iteration (generation) \(\mathbf{x}_{i}^{t}\) with respect to the objective function being minimized. If the new solution yields a smaller objective function value than its old value, then \(\mathbf{x}_{i}^{t+1}\) is set to \(\mathbf{y}_{i}^{t+1}\); otherwise, the old value \(\mathbf{x}_{i}^{t}\) is retained.  
The main advantages of the DE algorithm are its simplicity (few control parameters and simple operators) and the fast convergence to the global optimum.
# Seismic Response of Container Crane under Near-Field and Far-Field Ground Motions
## 2 Shaking Table Test of Container Crane
### Instrumentation  
Three demand parameters: strength, displacement, and acceleration, were evaluated in this study. Thirty-two strain gauges were installed on the scale crane's landside and seaside legs to determine the strength demand on the scale crane. The instrumentation plan is shown in Figure 2. The symbol \"S\" indicates the location of strain gauges, \"D\" shows the locations that were monitored by a camera to estimate the displacement of the scale crane, and \"A\" indicates the locations of accelerometers to determine acceleration. Internal forces due to seismic excitation were determined via strain gauges attached to the columns' top and bottom ends. The internal forces in the landslide legs were determined at four locations labeled S1 to S4, and that in the seaside legs were determined at four locations labeled S5 to S8. The strain gauges were attached to four sides of the column at each location, so two moments and an axial force could be determined from three strain gauges. However, if no strain gauges on each section were broken, the average of the estimated internal forces was  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Quantities** & **Symbol** & **Scale Factor** & **Quantities** & **Symbol** & **Scale Factor** \\ \hline Geometric length, \(l\) & \(S_{l}\) & 20 & Mass, \(m\) & \(S_{m}=S_{E}\cdot S_{l}^{2}\) & 400 \\ Elastic modulus, \(E\) & \(S_{E}\) & 1 & Time, \(t\) & \(S_{t}=S_{E}\cdot S_{l}^{1/2}\) & 4.472 \\ Acceleration, \(a\) & \(S_{a}\) & 1 & Moment of inertia, \(l\) & \(S_{l}=S_{l}^{1}\) & 160,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scale factors for designing the 1/20 scale crane.  
Figure 1: The 1/20 scale container crane.  
used. The strain gauges used in this testing were type FLA-5-11 of Tokyo Sokki Kenkyujo with a gauge length of 5 mm, gauge resistance of 120 \(\pm\) 0.5 \(\Omega\), and coefficient of thermal expansion of 11 \(\times\) 10\({}^{-6}\)/\({}^{\circ}\)C. The drift of the portal frame, an essential seismic response of a container crane, was determined at two locations, D1 and D2, using a camera. The NIKON D5500 camera with a 50-mm lens was used in this instance. The camera was focused on D1 and D2, where target patterns were attached to the scale container crane (see Figure 2). Three points for recording acceleration were A1, A2, and A3. A1 was the location on the trolley-boom girder, and nearest the gravity center of the scale container crane, A2 was the location at the intersection of the portal beam and the seaside column leg, and A3 was the location at the intersection of the portal beam and the landslide column leg. The acceleration of three points was recorded by using the triaxial accelerometer Kistler.