esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Factors Affecting the Total Amount of Selenium (Se) in Soil and the Various Forms of Se
The interplay of these factors leads to the reduction in selenate to selenite in the soil, thereby diminishing the content of effective Se components and the overall bioavailability of Se. The soil's OM content in the study area ranged from 21.92 g/kg to 127.66 g/kg, with an average of 38.95 g/kg (Table 5). Several hypotheses exist regarding the binding mechanisms of Se oxyanions to soil's OM, including the formation of Se-OM-mineral ternary complexes, partial anoxic zones, and an increase in sorption sites [52]. Research by [53] confirmed that Ca\({}^{2+}\) released from carbonate bedrock may enhance the stability of OM in the soil. Given the widespread distribution of carbonate rocks in the study area, the high solubility of carbonate minerals contributes to the distinct hydrothermal characteristics observed in the chemical composition of karst groundwater. [PERSON]'s study [32] confirmed that the hydrothermal composition of the groundwater in the Zunyi area was primarily composed of Ca\({}^{2+}\), Mg\({}^{2+}\), HCO\({}_{3}\)\({}^{-}\), and SO\({}_{4}\)\({}^{2-}\) ions [54]. The release of a substantial amount of Ca\({}^{2+}\) ions in the study area significantly enhances the stability of the soil's OM, leading to the fixation of a large amount of effective Se in the soil's OM. The clay content in the study area ranged from 50.73% to 76.19%, with an average of 63.33% (Table 5). Clay is recognized as a significant soil variable that enhances Se retention in soil. Typically, fine particles exhibit greater exchange capacity and stronger adsorption capacity compared to coarse particles [55]. The substantial clay content in the soil of the study area suggests that it has the capacity to adsorb a considerable amount of Se, resulting in the transformation of soluble Se into insoluble forms. Generally, in southwest karst regions characterized by a relatively low pH and relatively high soil OM and clay contents, while the total Se content in the soil is elevated, the available Se content remains low.
## 3 Results  
### Results of Muscle Force Estimation  
Figure 6 shows the muscle forces (\(\%\)F\({}_{max}\)) estimated by the inverse dynamics computation of swallowing and the static optimization, which were normalized by the maximum forces as below:  
\[\%\text{F}_{max}=\frac{f_{e}}{f_{max}} \tag{12}\]  
where \(f_{e}\) and \(f_{max}\) are the estimated force and the maximum force of muscle, calculated by Equations (3) and (9), respectively. The figure also shows the three characteristic points defined in Figure 2 to compare the muscle activities with the movements of the hyoid bone and thyroid cartilage. Figure 7 shows the onset and duration of the muscle activity, which was derived from Figure 6. The activation level was defined as values higher than \(1\%\) of the maximum force, and the contrast coloring changed depending on the muscle force. The results suggest which muscle mainly contributes to swallowing, and they confirmed that the activities of the muscles correlate with the movements of the hyoid bone and thyroid cartilage.  
Figures 2, 6 and 7 show that all suprahyoid muscles (PMH, AMH, GH, AD, PD, and SH) and an upper part of the middle pharyngeal constructor muscle (PG1) begin to generate contractile forces to pull the hyoid bone in the postero-superior direction in the first phase between \(P_{\text{s}}\) and \(P_{1}\). Here, ST also contributes a counterbalancing force to the thyroid cartilage, preventing it from being pulled in the posterior direction by the passive force of TH and thyrohyoid membrane, which are stretched. SP seems to contribute to maintaining the posture of thyroid cartilage in this period. In the second phase, between \(P_{1}\) and \(P_{2}\), GH, AD, PD, and SH generate larger forces to move the hyoid in an antero-superior direction. Here, TH, SP, and a part of inferior pharyngeal constrictor (PT1 and PT2) begin to generate force to pull the thyroid upward and slightly forward. After reaching the maximum displacement at \(1.73\) s, in the last phase between \(P_{2}\) and \(P_{\text{e}}\), the later pharyngeal phase, the activation of all muscles are declined, and the hyoid bone and thyroid cartilage return to the resting position by the passive forces of the stretching muscles and membranes.
# Understanding Neutrophil Dynamics during COVID-19 Infection
## 4 Numerical Results
### The Role of Tissue Damage on Immune Resolution  
All the stability results presented above assumed that the tissue damage function can be ignored by setting \(\alpha=0\). To determine how relaxing this condition affects dynamics we investigate the short- and long-term behavior of total neutrophils \(n(t)+a(t)\) and proinflammatory cytokines \(c(t)\) when we vary (i) the frequency of oscillations \(p\in\{1,10,0.1\}\), (ii) the number of tissue damage cycles \(A\in\{1,10,0.1\}\), and (iii) the magnitude effect that tissue damage has on the proinflammatory cytokine recruitment rate \(\alpha\in\{1,10,0.1\}\). We start with parameters and initial conditions in Tables 1 and 2 (severe case), and \(\phi~{}=~{}0.046<\phi^{critical}\), which results in resolution of inflammation for the baseline assumptions \(p=A=\alpha=1\). We then increase or decrease one of the tissue damage parameters \(\{p,A,\alpha\}\) while leaving the other two unchanged. We find that the frequency of oscillations \(p\) has no significant effect of the short- and long-term \(n(t)+a(t)\) and \(c(t)\) dynamics (see Figure 8A). The number of tissue damage cycles \(A\), however, has a significant effect on the length of inflammation, with many rounds of tissue damage resulting in prolonged inflammation which persists for longer than 100 days for \(A=10\) (see Figure 8B, red curves) and is resolved in 50 days for \(A=1\) (see Figure 8B, black curves). The long-term dynamics, however, do not change with the no-inflammation equilibrium state being reached regardless of \(A\). Last, the magnitude of tissue damage, \(\alpha\), has a transient effect of both \(n(t)+a(t)\) and \(c(t)\) dynamics. In particular, a large \(\alpha=10\) leads to a large transient increase in both total neutrophils and proinflammatory markers over the first 20 days (see Figure 8C, red curves), while a small \(\alpha=0.1\) has a low overall effect (see Figure 8C, blue curves). Interestingly, the transient effect is prolonged for low \(\alpha\) and decays faster for high \(\alpha\).  
Figure 8: **Dynamics for different \(f(t)\) functions.** Dynamics of model Equation (3) over 100 days for (**A**) \(p=0.1\) (blue line), \(p=1\) (black line), \(p=10\) (red line); (**B**) \(A=0.1\) (blue line), \(A=1\) (black line), \(A=10\) (red line); and (**C**) \(\alpha=0.1\) (blue line), \(\alpha=1\) (black line), \(\alpha=10\) (red line). The other parameters are given in Table 1 and Table 2 (severe case) and \(\phi=0.047\).
# Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
## 2 Materials and Methods
### Cross-Validation between the Frozen and FFPE, and between Different Cancer Types  
The main purpose of this study was to characterize the generalizability of the deep learning-based classifiers for normal/tumor tissues. First, we cross-validated the classification results of the classifiers for the frozen and FFPE tissues. Therefore, the frozen and FFPE WSIs for a cancer type were classified by the FFPE and frozen classifiers, respectively, for the same cancer type. Then, the classifiers for the FFPE tissues were applied to classify FFPE WSIs of different types of cancers.
# Method for Viewing Real-World Scenes while Recording Video
## 2 Related Studies  
### Object Tracking Technology: Minimum Output Sum of Squared Error (MOSSE)  
Visual tracking has received a significant amount of attention in recent years. Visual tracking technology can be regarded as important and has many practical applications in video processing [5]. [PERSON] et al. insisted that if a target is located in one frame of a video, it is recommended to track that object in subsequent frames [5]. If the target is successfully tracked, it provides more information about the activity of the target. Because tracking is easier than detection, tracking algorithms require fewer computational resources than an object detector. Many types of tracking technologies have been suggested that can check changes in the target appearance and track the complex motions of the target, including  
Figure 1: People view events through the mobile phone screen rather than the real-world event itself.  
incremental visual tracking [6], robust fragment-based tracking [7], graph-based discriminative learning [8], and multiple instance learning [9]. These tracking technologies are effective [10]; however, they are not easy to apply and involve complex appearance models and optimization algorithms. In addition, it is difficult to maintain the 25-30 fps produced by many modern cameras. To address the above issues, [PERSON] et al. proposed a new type of correlation filter (CF) called MOSSE [5]. This technology, when initialized, produces stable correlation filters using a single frame. The research of [PERSON] et al. showed that simpler MOSSE CFs can solve visual tracking problems more effectively than heavy weight classifiers, complex appearance models, and stochastic search techniques [5]. MOSSE provides an effective algorithm that is accurate, easy to implement, and much faster than other technologies. The advantage of a MOSSE filter is its robustness when dealing with variations in lighting, scale, pose, and non-rigid deformations during processing at 669 fps. In addition, it supports tracker pausing when the object disappears and resumes where it left off when the object reappears [5].
# The Application of Spectral Entropy to P-Wave Detection in Continuous Seismogram Analysis
## 1 Introduction
In the current phase of the investigation, spectral entropy is introduced as an additional indicator to enhance the sensitivity and reliability of the detection model. At the initial stage of the project, spectral entropy was intended to serve as a supporting parameter within the system for P-wave detection. However, as this study progressed, it became clear that spectral entropy could be a valuable standalone analytical method for seismological tasks. The decision to demonstrate its applicability in this way was made to highlight its potential beyond its use within the framework of neural network models. The results obtained are thus presented and discussed independently in this dedicated publication.
## 2 Materials and Methods
### Feature Extraction
#### 2.3.5 Motion Parameters  
Despite the majority of relevant studies consider motion as part of exclusion criteria (as stated in the introduction), we incorporate subject motion as a feature (for the individuals whose motion was on average less than 0.2 mm during acquisition) [28]. On this premise, the movement parameters could be assessed based on their relevance in the FS and classification precedures, while expanding the subject pool as much as possible. Motion features were estimated by utilizing the framewise displacement (FD) provided from each datacenter (2 features corresponding to FD mean value and percentage of slices with FD over 0.2 mm). Additionally, the rate of change in the BOLD signal (DVARS), corresponding to the temporal derivative of time courses and the variance over voxels of root mean square, was also calculated and incorporated (1 feature).
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 2 Preliminaries
### Complexity Assumptions  
* DDH assumption: Given a cyclic group \(G=\langle g\rangle\) of prime order \(q\). For any probability polynomial-time (PPT) adversary \(\mathcal{A}\), given a tuple \((q,g,g^{x},g^{y})\) for random elements \(x,y\in Z_{q}^{*}\), the advantage for \(\left|\Pr[\mathcal{A}(g,q,g^{x},g^{y},h_{b}]=b-\frac{1}{2}\right|\) is negligible, where \(b\in\{0,1\}\), \(h_{0}=g^{z},h_{1}=g^{ab}\), \(z\in Z_{q}^{*}\).
* CDH assumption: Given a cyclic group \(G=\langle g\rangle\) of prime order \(q\). For any PPT adversary \(\mathcal{A}\), given a tuple \((q,g,g^{x},g^{y})\) for random elements \(x,y\in Z_{q}^{*}\), the advantage of computing \(g^{xy}\) is negligible.  
In order to protect the privacy of electricity consumption data \(m\), we employ an additive homomorphic variant of the ElGamal cryptosystem (under the DDH assumption, the ElGamal encryption scheme is semantic secure [36]) to encrypt it and generate the ciphertext \((C_{1},C_{2})\). Before sending the \((C_{1},C_{2})\) to the next level, in order to enable the receiver to check whether the \((C_{1},C_{2})\) has been tampered with or forged during public channel transmission, we use the BLS signature algorithm (under the CDH assumption, the BLS signature scheme was proven to be secure [37]) to generate the signature \(\sigma\) of \((C_{1},C_{2})\)and send \((C_{1},C_{2},\sigma)\) to the receiver. According to the structure of the BLS signature, after receiving \((C_{1},C_{2},\sigma)\), the receiver uses bilinear maps to check whether it is valid. The relationship between Sections 2.1-2.3 is shown in Figure 1.
## 4 Discussion
### Synergistic Effect of Anmonia Volatilization Rate Among Concentration, Temperature, and pH
However, in our research, we ignored the influence of soil minerals and microbial processes and focused on the following chemical reaction processes:  
\[\text{NH}_{4}\text{+OH}^{-}<\Rightarrow\text{NH}_{3}\cdot\text{H}_{2}\text{O }<\Rightarrow\text{NH}_{3}\text{(liquid)}+\text{H}_{2}\text{O}.\]  
When focusing on the chemical conversion process in paddy field water and the volatilization diffusion process at the gas-liquid interface, we found that the correlation between pH, temperature, c(NH\({}_{4}\)+), and ammonia volatilization amount became obvious. As the source of nitrogen in ammonia, the amount of ammonia volatilization is inevitably positively correlated with the concentration of ammonium ions. However, due to their different physical and chemical properties, various nitrogen fertilizers also have different ammonia volatilization intensities [33]. Temperature and pH show a significant promoting effect on ammonia volatilization in the paddy ecosystem. Temperature, as a promoting factor in most chemical reactions, also has a highly significant positive correlation with the amount of ammonia volatilization. As the temperature increases, the diffusion rates of NH\({}_{3}\) and NH\({}_{4}\)+ increase. Under constant pH conditions, within the range of 5 \({}^{\circ}\)C to 35 \({}^{\circ}\)C, for every 10 \({}^{\circ}\)C rise in temperature, the proportion of NH\({}_{3}\) in paddy water increases twice [13]. A high temperature can also reduce the solubility of NH\({}_{3}\) in the liquid phase, promoting the release of ammonia into the atmosphere. On the other hand, in the liquid phase, ammonium ions with a positive charge can combine with the hydroxide ions, transform into hydrated ammonia, and evaporate in the form of ammonia gas in an alkaline environment. However,if the paddy water is acidic, ammonium ions cannot convert into hydrated ammonia, naturally inhibiting the volatilization of ammonia gas [34]. In a field experiment, ammonia nitrogen concentration, temperature, and pH can be obtained conveniently through online monitoring instruments. The ammonia volatilization rate (milligrams per hour) under these conditions can be easily estimated using this formula, and the cumulative ammonia volatilization amount in a certain period can be calculated by adjusting the sampling frequency per hour. The quantitative relationship between pH, temperature, ammonium nitrogen concentration, and ammonia volatilization provides the possibility for quickly estimating the amount of ammonia volatilization.
# Nickel (Ni\({}^{2+}\)) Removal from Water Using Gellan Gum-Sand Mixture as a Filter Material
## Materials and Experimental Preparation
### Experimental Process
#### 2.2.4 Adsorption Isotherm and Kinetic Study  
Hydraulic Conductivity Analysis  
The hydraulic conductivity is calculated using [PERSON]'s law:  
\[k=\frac{V\cdot L}{A\cdot h\cdot t}, \tag{3}\]  
where \(V\) is the volume of water collected, \(L\) is the height of HBL, \(A\) is the area of the soil specimen, \(h\) is the head difference, and \(t\) is the time required to obtain \(V\).  
\(L\) is the height of HBL because the presence in HBL is the main factor governing the reduction in hydraulic conductivity.  
Nickel Analysis  
The concentration of nickel ions in the solution was measured using inductively coupled plasma-optical emission spectrometry (Agilent ICP-OES 5110, SCLA, USA), calibrated by the standard metal solution. The amount of nickel uptake (mg/g) was calculated using the following equation:  
\[q_{e}=\frac{\left(C_{i}-C_{f}\right)\cdot V}{1000\cdot W}, \tag{4}\]  
where \(C_{i}\) and \(C_{f}\) are the nickel ion concentration at the initial and final stages, respectively (mg/L), \(W\) is the amount of adsorbent (g), and \(V\) is the volume of solution (mL). The removal efficiency (\(R\), %) via nickel adsorption is calculated as follows:  
\[R=\frac{C_{i}-C_{f}}{C_{i}}\cdot 100. \tag{5}\]  
Surface Morphology  
Scanning electron microscopy (SEM-SU5000, Hitachi, Japan) was performed on GG-treated sand samples after Ni\({}^{2+}\) adsorption. The morphologies of the samples were observed using energy-dispersive X-ray spectroscopy (EDS) for element characterization. SEM-EDS was performed on ten points per sample to minimize deviations. In addition, the samples were coated with platinum before analysis to enhance the electrical conductivity of the surface.
## 2 Background and Related Work
### Background
#### 2.1.4 Sensor Technologies in Autonomous Vehicles  
Autonomous vehicles rely on various sensor technologies, which are integral to safety and connectivity systems. This section focuses on key sensors used in autonomous vehicles, particularly in relation to the attack vulnerabilities that will be discussed later.  
* LiDAR: Light Detection and Ranging (LiDAR) provides a 360-degree view using laser channels. It maps the surrounding environment in 3D using mechanical, semisolid-state, and solid-state systems [3].
* Radar: Millimeter-wave radar detects non-transparent materials and supports functions such as blind spot detection and parking assistance [3].
* GPS: The Global Positioning System enables geographical location tracking by communicating with satellites. Its openness makes it vulnerable to cyberattacks [4].
* Magnetic Encoders: These measure angular velocity using magnetoresistance or Hall ICs and are essential for the Anti-lock Braking System (ABS) and TPMS systems [4].
* TPMS: Tire Pressure Monitoring Systems include four sensors and an ECU. They transmit tire pressure data securely by filtering out unauthorized IDs [4].
* Camera: Cameras support object detection, traffic sign recognition, parking, collision avoidance, and night vision capabilities [4].  
Figure 4: Vehicle diagnostics.  
Figure 3: V2X communication.  
* Ultrasonic Sensors: These detect nearby obstacles and are primarily used for low-speed operations like parking [4].
# Toward the Generation of an Isolated TW-Attosecond X-ray Pulse in XFEL
## 3 Methods with Current Modulation
### Current Modulation Method  
The distance between two electrons with different energies changes in a chicane-type bunch compressor, which is composed of four dipole magnets. A high-energy electron passes the chicane with a short path length, whereas a low-energy one passes through the chicane with a longer path. This path length difference results in the density modulation of an electron beam after passing the chicane. This means that intentional energy modulation is needed for a density modulation by a chicane.  
Figure 2: **(a)** Schematic drawing for the tilting method in [PERSON] et al. [39] for amplification. The blue solid line represents the longitudinal direction of an electron beam. The green dashed line shows the good trajectory region (GTR). The red lines show the amplification process of the TRP. **(b)** Simulation results for radiated power in the tilt method. The different colors in (b) represent the different offset of the electron beam at the entrance of the undulator. See [PERSON] et al. [39] for details. Reproduced from [PERSON] et al. [39] with permission.  
A method of modulating the electron beam energy by using a laser pulse in a wiggler has been proposed, as shown in Figure 3[20]. A few-cycle laser pulse co-propagates with an electron beam in the wiggler. By the interaction with the electric field of a laser pulse under the magnetic field of a wiggler, some electrons gain energies and others lose energies, so that the energy modulation resembles the pattern of the electric field of the laser, as shown in the inset in the middle of Figure 3. This energy-modulated electron beam experiences density modulation when it passes through a chicane, as shown in the inset on the right of Figure 3. This simple idea initiated proposals in the accelerator physics community to realize a TW-as XFEL, as described in the following sections.
## 4 Experiments and Discussion
### Ablation Studies
#### 4.3.1 Ablation Study on Local Attention Mechanism
\begin{table}
\begin{tabular}{c c} \hline \hline
**Encoder** & **Accuracy** \\ \hline Transformer encoder & 0.7719 \(\pm\) 0.0049 \\ DAT encoder with \(D=1\) & 0.8073 \(\pm\) 0.0047 \\ DAT encoder with \(D=2\) & **0.8368 \(\pm\) 0.0038** \\ DAT encoder with \(D=3\) & 0.8191 \(\pm\) 0.0067 \\ DAT encoder with \(D=4\) & 0.8075 \(\pm\) 0.0084 \\ DAT encoder with \(D=5\) & 0.8079 \(\pm\) 0.0045 \\ DAT encoder with \(D=6\) & 0.8032 \(\pm\) 0.0030 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quantitative comparison with different encoder. Figure 3: Network structure comparison. (**a**) The original Transformer network structure; (**b**) dual attention Transformer encoder. fectiveness in distinguishing between different types of malware. The model demonstrated high F1-scores in categories such as Trojan-FakeAV (0.96) and Net-Worm (0.92), indicating its proficiency in classifying instances from these categories. The results can be seen in the confusion matrix in Figure 4. There are two categories that are significantly lighter in color. However, the model still faced challenges in accurately classifying P2P-Worm, Trojan-Downloader, and Trojan-Ransom categories, as reflected by lower F1-scores (0.12, 0.45, and 0.58, respectively).