esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### GC-MS Profile of mVOCs  
Bacteria were cultured in sealed 50 mL vials (sealed with metal crimp caps fitted with rubber septa to ensure that they were gastight) containing 10 mL of solid Hoagland medium with 0.7% agar and 3% sucrose, and subjected to different levels of stress (0, 75, 100 mM NaCl). These vials were inoculated with 500 uL suspension of the strains under study. All components were autoclaved individually prior to inoculation. The _E. coli_ DH5\(\alpha\) strain was included as a negative control. In addition, a control chromatographic profile was obtained from the culture medium, without bacterial growth, in order to identify the presence of volatile compounds in the selected system. Vials were placed in an oven at 28 \({}^{\circ}\)C for three days until carrying out the VOC analysis. Adsorption was programmed for 30 min at 40 \({}^{\circ}\)C, and SPME flex fibers (divinylbenzene/carbouxan/Polydimethylsiloxane (DVB/CAR/PDMS) (Supelco) were desorbed at 210 \({}^{\circ}\)C for 1 min at the GC-MS injection port [65].  
VOC analyses were performed on a Perkin Elmer Clarus 600 GC-MS using a DB5 column (60 m \(\times\) 250 um \(\times\) 0.25 um; J & W Scientific, Folsom, CA, USA) coupled to a mass analyzer with helium carrier gas at a constant flow rate of 1.0 mL/min. Volatiles absorbed by the SPME fiber were desorbed in the heated (250 \({}^{\circ}\)C) GC inlet for 60 s using splitless injection. The column temperature was programmed to start at 33 \({}^{\circ}\)C for 3 min, and heated at 10 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C and then at 40 \({}^{\circ}\)C/min from 180 to 220 \({}^{\circ}\)C (total run time = 18.7 min). Ions were generated using electron ionization (EI) (70 eV) and acquired at 4 scans/s over \(m/z\) 40-500. Volatile components were identified by comparison of retention times and recorded mass spectra against the NIST database, with an acceptance of similarity (SI) \(\geq\) 800.
## 3 Numerical Results
### OP Dependence on the Number of Interfering Cores
In [20], a maximum acceptable ICXT level of around \(-23\) dB is required to achieve an OP \(=10^{-4}\), with one interfering core and \(r=0\). Hence, the penalty regarding the maximum acceptable ICXT when comparing our system to the above-mentioned OOK systems is around \(0.3\) dB [20] and \(6\) dB [19]. From these results, it is possible to infer that doubling the number of interfering cores leads to a reduction of the maximum acceptable ICXT level per interfering core of about \(3\) dB. It is also possible to observe that the lowest maximum acceptable ICXT level per interfering core is about \(-29.5\) dB, with four interfering cores, \(|S_{mn}\cdot R_{s}|=0.01\) and \(r=0\). Therefore, it may be inferred that the maximum acceptable ICXT level per interfering core would be around \(-32.5\) dB for eight interfering cores. As the maximum number of interfering cores effectively affecting the performance is typically \(8\), then an ICXT level per interfering core not exceeding around \(-32.5\) dB leads to an OP lower than \(10^{-4}\) in the amplified PAM4 link studied in this work.
## 4 Performance of Waste-Plastic-Modified Bitumen Binder and Mixture
### Characteristics of Plastic-Modified Bitumen Binder
#### 4.1.3 Effect of Bitumen Modification Mixing Parameters and Plastic Particle Size  
Stable dispersion and storage are significantly influenced by mixing factors such as plastic dose, mixing temperature, shear rate, and mixing duration [46]. The conditions of blending, namely temperature, shear rate, and mixing duration, substantially influence performance [21]. Optimal blending generally occurs at temperatures ranging from 150 to 180 \({}^{\circ}\)C, with shear rates above 3000 rpm for a minimum duration of 90 min [46; 47]. Elevated temperatures pose a threat to plastic degradation or bitumen oxidation [21].  
Plastic waste with smaller particle sizes was observed to enhance phase distribution and binder stability. Microscopic analysis verified a more uniform polymer dispersion in contrast to blends containing bigger plastic particles [46]. Smaller plastic particles (<1 mm) enhance dispersion, minimizing coalescence and phase separation [11]. Conversely, larger particle sizes increase the risk of agglomeration, adversely affecting mechanical performance and storage stability [46].  
Recent microscopic analyses further validate the impact of plastic particle size and type on dispersion within bitumen. Ref. [46] performed fluorescence microscopy (FM) imaging of plastic-modified bitumen and discovered that LDPE and LLDPE had uniform dispersion with small particle sizes (average \(\sim\)4 \(\upmu\)m) and high particle counts (-7456 for LDPE), suggesting enhanced compatibility attributed to their reduced crystallinity. Conversely, HDPE exhibited larger particles (average 11 \(\upmu\)m, peaking at 98 \(\upmu\)m) and a decreased particle count (536), suggesting inferior compatibility due to its elevated crystallinity and rigid domains. Moreover, PP-modified samples exhibited considerable agglomeration, with droplet sizes nearly tenfold larger than PE, signifying inadequate dispersion and restricted contact with bitumen. Figure 2 delineates these microstructural variations, emphasizing the significance of plastic type and particle size in attaining uniform dispersion and improving compatibility with bitumen.  
Table 3 presents an extensive overview of the several waste plastics utilized in bitumen modification, including their standard dosages, mixing circumstances, and related performance effects.  
Figure 2: FM images of plastic-modified bitumen showing dispersion differences for LDPE (L), LLDPE (LL), HDPE (H), and PP (P). Adapted from [46].
## 3 Results and Discussion  
### Setting Time  
The setting time of OPC and OPC-CSA binary blended cement was evaluated under the effects of NC and AS, as shown in Figure 2. Regarding the single OPC system, without the addition of AS, the setting time was decreased with the increased dosage of NC, which could be caused by the accelerated hydration process through the nucleation effect of NC. Moreover, with the combined addition of NC and AS, the setting time was significantly reduced. Regarding the OPC-CSA binary blended cement, an obvious reduction in setting time was found, which was attributed to the rapid setting property of CSA. Similar to the condition found in OPC, a decreased setting time was observed with the increased dosage of NC. Moreover, a significantly reduced setting time was found in OPC-CSA binary blended cement with the combined addition of NC and AS. The setting time of the binary blended cement can be controlled under 1 h.  
Figure 2: Setting time of the single OPC system and OPC-CSA binary blended cement with or without the addition of NC and AS; (**a**,**b**) single OPC; (**c**,**d**) OPC-CSA.
## 3 Methodology
### InSAR-RiskLSTM Framework  
In this section, we present the InSAR-RiskLSTM framework Mixture of Experts, designed to integrate advanced spatial and temporal modeling techniques for precise railway deformation risk prediction. This framework comprises three core components: the Spatial Attention Encoder, the Temporal Risk Predictor based on LSTM, and the Feature Fusion Mechanism. These components work synergistically to extract meaningful spatiotemporal features, model sequential dependencies, and synthesize information for accurate risk assessment.
# Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization
## 3 Proposed Methodology
### Assessment of Security
#### 3.3.4 GPU Computing-Based Acceleration  
In reality, there are additional acceleration techniques for ECDSA or the Hash function in V2X data transmission networks to achieve higher efficiency. According to [38], the specialized hardware accelerator can do 1500 and 2000 ECDSA signature and validation calculations per second, respectively. The author of [41] implements ECDSA on an Intel i7 CPU, at a speed of less than 0.1 ms for a single signing or validation process. However, because of performance of these computations is greatly dependent on hardware configuration, it is appropriate to develop a viable solution based on reasonable hardware estimates. For example, equipping each vehicle with a specialized high-performance security calculator is expensive. Or, for architectural or power usage reasons, the Intel i7 CPU is unlikely to be used in cars. The architecture should take into account the hardware resources that are available on today's vehicles. One viable option is to do the computation operations on System on Chip (SoC) platforms, which are often found in embedded systems such as smartphones. As a result, SoC designs are already being used in today's automobiles:Vehicles using Nvidia Drive \(PX\) (really Nvidia Tegra X1 [42]) systems were used to speed up AI algorithms [43].  
The Nvidia Tegra X1 is a conventional SoC design that includes a GPU, a visual encoder/decoder, DSP, and dashboard monitor, as well as a single part of systems RAM and a CPU that acts as control system and coordinators. As illustrated in Figure 11, this article construct the solution for V2X transmission systems using a minimized SoC structure with only the essential parts. The CPU is a computation core that also serves as a controller for other parts such as the GPU and communication modules. Information in the system RAM can be calculated by GPU, that can also be controlled by the CPU and units for communication. Small computation processes can then be assigned to various units for numerous complicated processes for overlapping the processing period for improved execution.
# Exploring Benefits of Using Blending Splines as Transition Curves
## 2 Preliminaries
### Transition Curves  
In railway design, the transition curves fulfill the role of connecting straight sections to curved sections, and curved sections of different radii [27]. The task of the transition curve is to gradually decrease/increase the radius of the curvature from radius \(R_{1}\) to \(R_{2}\), and to provide a change in superelevation following the same behaviour as the curvature, usually by linear, Bloss, sine, or cosine functions. These properties are important in order to counteract a sudden jerk in the centrifugal forces, and instead gently introduce them over the course of the transition curve. The main advantages of using transition curves are [28,29]:  
* Providing a comfortable ride for passengers.
* Providing a safer ride for passengers.
* Enabling the vehicle to drive at a higher speed.
* Reducing the wear and tear on wheels and rails, thus, decreasing the maintenance and repair costs.  
As mentioned in the introduction, the clothoid is the most used transition curve today. One of the first discoveries of the clothoid as a transition curve in railways was by [PERSON] in [30], who was among the first to approach the transition problem mathematically. A clothoid (of length \(l\) and end radius \(r\)) is a spiral defined parametrically as  
\[\binom{x}{y}=\binom{C(t)}{S(t)}, \tag{4}\]  
Figure 1: Two local curves (\(l_{k}\) and \(l_{k+1}\)) blended together to create a blending spline (red curve).  
Figure 2: Plots of possible blending functions.  
where \(C(t)\) and \(S(t)\) are the Fresnel integrals,  
\[\begin{split} C(t)&=\frac{1}{a}\int_{0}^{t}\cos\left( \frac{\pi}{2}u^{2}\right)du,\\ S(t)&=\frac{1}{a}\int_{0}^{t}\sin\left(\frac{\pi}{2}u ^{2}\right)du,\end{split} \tag{5}\]  
with \(a=\sqrt{\frac{1}{\pi t}}\) being a scaling factor, and \(\hat{t}=at,\ -\infty<t<\infty\). A main reason for the clothoid's popularity as a transition curve is its curvature, which changes linearly with the curve length.
# A 3D Agent-Based Model of Lung Fibrosis
## 2 Materials and Methods
### Hybrid Multi-Agent-Based Model  
To build our hybrid multi-agent-based model, we follow the bottom-up approach: we gather observations found in the literature, then use this data to define the properties and behaviors on the microscale (i.e., on the agent level) and finally observe where these lead to on the macroscale. At any given time t, an agent is fully defined by the set of all its attributes that together determine its state. To specify the attributes of our agents and the interfaces to read and write them, we implement the class _alveolarDuct_, which extends the BioDynaMo _Cell_ class. Each agent in our simulations is therefore an instance of _alveolarDuct_, whose data members store the initial and current number of cells for each cell type (see Table 2). Essential data members such as the cell position and diameter, as well as the methods to access them, are inherited from the base class _Cell_.  
Figure 3: Time evolution of the average MMP (**a**) and TIMP (**b**) concentrations in a stationary simplified model simulation with different \(\Delta t\) values.  
As soon as a new agent is added to the simulation, it gets assigned its behaviors. Computationally, this involves defining a list of sequential operations (examples can be found in Figure 1B from [43]), encapsulating them in a named structure, and, finally, adding the structure to a vector of behaviors that is attached to the agent. After a time step \(\Delta t\) (the same used for the update of the concentration of the extracellular substances), each agent runs through all its behaviors; that is, it performs all the actions listed in each of them. Since all the agents in our simulations are alveolar ducts, they run the same behaviors at each time step. Nevertheless, since many behaviors depend on the local concentration of the mediators and different agents perceive different microenvironments, they evolve and act differently throughout the simulation.  
The behaviors implemented in our model can be categorized, as shown in Table 3. As mentioned above, the behaviors share the same template but differ in the core section, which defines the actions that the agents must perform. Their implementation within the ABM is heavily influenced by the model of idiopathic pulmonary fibrosis, published in [48] as a mathematical formulation, where many aspects of the IPF are provided. Unless otherwise stated, concentration-dependent rates follow Michaelis-Menten kinetics  
\[S=\frac{\lambda*C}{C+K} \tag{2}\]  
where \(\lambda\) represents the maximum value of the rate, \(C\) is the local concentration of an extracellular substance, and \(K\) is the concentration at which the rate reaches half of its maximum value. Agents can directly measure the local concentration (i.e., that of the diffusion box they are located in) of a substance at each time step and use this quantity to determine the variable rates of their behaviors. These measurements must be repeated at every time step as the extracellular environment evolves throughout the simulation.  
The following subsections briefly outline each behavior.
## 3 Results
### Breakage Mode Discrimination
Figure 15: Relationship between critical normal diameter \(d_{\text{cr}}\) and cone angle \(\theta\). Therefore, breakage modes can be discriminated for cone-hemispherical gypsum by critical contact diameter \(d_{\text{crl}}\) and \(d_{\text{cru}}\) evaluated by formula (3). When contact diameter \(d\) is smaller than or equal to the lower critical contact diameter \(d_{\text{crl}}\), the particle will crush as peeling under normal contact. When contact diameter \(d\) is larger than or equal to the upper critical contact diameter \(d_{\text{cru}}\), the particle will crush as splitting under normal contact. Otherwise, the particle will crush as peeling-splitting. The discrimination can be described as:  
\[\left\{ \begin{array}{ll}d\leq d_{\text{crl}}&\text{Peeling}\\ d_{\text{crl}}<d<d_{\text{cru}}&\text{Peeling-splitting}\\ d\geq d_{\text{cru}}&\text{Splitting}\\ \end{array} \right. \tag{4}\]  
where \(d_{\text{crl}}\) is the lower critical contact diameter; \(d_{\text{cru}}\) is the upper critical contact diameter. The formulas (3) and (4) can be used to estimate the breakage modes of cone-hemispherical gypsum particles according to particle shape.
# A Review of Research on Secure Aggregation for Federated Learning
## 3 Privacy-Preserving Mechanisms
### Homomorphic Encryption
Choose a set of prime moduli \(\{q_{0},q_{1},\ldots,q_{L}\}\) to control noise growth. Define the scaling factor \(\Delta\) to map floating-point numbers to integer polynomials. Select a noise distribution, such as a discrete Gaussian distribution, to generate a small noise polynomial \(e\). (2) Generating keys: Generate private key \(s\in R_{q}\), where \(s\) is a small polynomial randomly selected from the ring. Generate public keys \((a,b)\),\(b=-(as+e)\bmod q\), where a is a random polynomial, and \(e\) is noise. 2. Data encoding: (1) Data vectorization: Encodes the plaintext data \(z=(z_{0},z_{1},\ldots,z_{N/2-1})\) to be encrypted by the user as a polynomial \(m(X)=z_{0}+z_{1}X+\cdots+z_{N/2-1}X^{N/2-1}\). (2) Scaling polynomials: Scale the polynomial by a factor of \(\Delta\), \(m^{\prime}(X)=round(m(X)\Delta)\)
3. Encryption: Generate ciphertext \(c=(c_{0},c_{1})\), \(c_{0}=br+m^{\prime}(X)+e_{0}\bmod q\), \(c_{1}=ar+e_{1}\bmod q\), \(r\) is a random polynomial, \(e_{1}\), \(e_{2}\) are noise polynomials. 4. Decryption: Decrypt the ciphertext \(c=(c_{0},c_{1})\) using private key \(s\),\(m^{\prime}(X)=c_{0}+c_{1}\bmod q\). 5. Plaintext recovery: \(m(X)=m^{\prime}(X)/\Delta\), after which it is converted to plaintext data \(z=(z_{0},z_{1},\ldots,z_{N/2-1})\). The above three homomorphic encryption schemes are relatively mature and have distinct application scenarios. Paillier and ElGamal are PHE schemes, and they are generally more efficient than the CKKS scheme. Paillier supports additive homomorphism properties, offering higher precision in additive aggregation and lower algorithmic complexity. ElGamal supports multiplicative homomorphism properties, making it suitable for weighted multiplications and key exchange scenarios. Unlike Paillier and ElGamal, CKKS supports the direct encryption of real numbers, making it well-suited for high-precision model training tasks.
# Design and Study of a New Wave Actuator for a Boat
## 2 Mathematical Modeling of Wave Actuator
### Hydromechanical Modeling  
First, the wave dynamics are analyzed to understand how wave forces apply to the boat's bottom. A free decay of the boat motion in still water is analyzed. After an upward vertical displacement, the boat is released, allowing the motions to die out freely. The characteristics of the vertical motion of the boat are determined by the solid mass of the boat and the hydromechanical loads on the boat. Applying [PERSON]'s second law to the heaving boat:  
\[F_{h}=-a\ddot{z}-b\dot{z}-cz \tag{2}\]  
where  
\(\ddot{z},\dot{z},z\) is the boat motion (m/s\({}^{2}\), m/s, m);  
\(a\) is the hydrodynamic mass coefficient of the boat (Ns\({}^{2}\)/m = kg);  
\(b\) is the hydrodynamic damping coefficient of the boat (Ns/m = kg/s);  
\(c\) is the restoring spring coefficient of the boat (N/m = kg/s\({}^{2}\)).  
Vertical oscillation of the boat caused by waves can be expressed in the following form:  
\[z=z_{0}\sin\omega t\dot{z}=\frac{dz}{dt}=z_{0}\omega\cos\omega t\ddot{z}=\frac {d\dot{z}}{dt}=-z_{0}\omega^{2}\sin\omega t \tag{3}\]  
Figure 1: The relationship between motion and waves on a boat.  
The hydrodynamic mass, also known as added mass, is a coefficient that represents the extra mass of water moved by the boat as it oscillates in waves. This added mass creates a standing wave pattern around the boat without losing energy. To calculate it, the strip theory method is used, which involves dividing the submerged part of the boat into thin sections (or strips) along its length. The added mass is then determined by summing up the contributions from each section, as shown in the following equation [21]:  
\[A_{ij}=\int_{-L/2}^{L/2}\mu_{ij}dx\Rightarrow a=A_{33}=\int_{-L/2}^{L/2}A_{33} ^{(2D)}(y,z)dx \tag{4}\]  
where  
\(\mu_{ij}=A_{33}^{(2D)}=4.75\rho(2L)^{2}\) is the added mass coefficients of 2D bodies;  
\(L\) is the length of the boat (m).  
Similarly, the damping coefficient, which accounts for energy loss due to water resistance, is calculated by integrating the damping contributions of these sections along the boat's length as follows:  
\[B_{ij}=\int_{-L/2}^{L/2}\lambda_{ij}dx\Rightarrow b=B_{33}=2\
u(m+a)\ with\ v=0.2 \tag{5}\]  
where \(m\) is the solid mass of the boat (kg).  
For a boat floating freely on water, the restoring force--acting like a \"spring\"--only applies to its up-and-down motion, known as heave motion. This restoring effect helps the boat return to its balanced position after being displaced by waves. The strength of this restoring force is calculated using the following equation:  
\[\text{The heave oscillation}:c=\rho gA_{wl} \tag{6}\]  
where  
\(A_{wl}\) is the water plane area (m\({}^{2}\)).
## 4 Proposed System Models for Cardiovascular Disease Prediction
### Data Balancing Using Proximity Weighted Random Affine Shadow Sampling  
Data balancing is one of the most important steps in preprocessing. Data imbalancing issues occur when the instances of one class are higher than the instances of other class. There are two classes in the dataset: majority class and minority class. In the dataset, the majority class contains more data instances than the minority class, resulting in an imbalanced dataset. If the dataset is not balanced, then it leads to poor and inefficient performance of the model.  
Data imbalance is one of the major issues in real-world datasets [43]. Two different types of data balancing techniques are used to address data imbalance issue, i.e., undersampling and oversampling. In undersampling, majority class instances are reduced to make them equal to the minority class instances. In this case, most of the information is lost. In oversampling, minority class instances are increased by creating synthetic samples.  
Various methods are used for data balancing. However, it is crucial to carefully perform balancing to avoid information loss. The HDHI dataset is highly imbalanced such that class 0 has 229,787 participants who do not have heart disease and class 1 has 23,893 participants who have heart disease. These number highlights that only a small number of participants have heart disease. In this paper, we have used the ProWRAS data balancing technique to balance the ratio of minority and majority classes.  
Proximity Weighted Random Affine Shadow Sampling  
ProWRAS is an oversampling technique that cluster the data points of minority class. The clusters are formed based on the distance the data points have from the majority class. Weights are assigned to each cluster. A large weight is given to the cluster that is close to the majority class. Weights determine the number of synthetic samples from each cluster. To avoid overlapping, majority class synthetic samples having low variance are generated in borderline clusters [44].  
max-conv and net-conv are two parameters depending on whether the model offers four oversampling schemes. max-conv is used to select the number of shadowsamples from which one synthetic data point is generated. The oversampling technique is designed so that data are balanced between both classes. Multiple classifiers can produce efficient and accurate results [45].
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 3 Soundfield Recording and Reproduction
### Soundfield Reproduction  
Spatial soundfield reproduction aims to create an immersive soundfield over a predefined spatial region so that the listener inside the region can experience a realistic but virtual replication of the original soundfield. This is achieved by controlling the placement of a set of loudspeakers usually put on the boundary that encloses the spatial region of interest and deriving the signals emitted from the loudspeakers. loudspeaker array design and audio processing are two key aspects to control sound radiation and to deal with the complexity and uncertainty associated with soundfield reproduction.