esa-sceva/satcom-chunk-collection huggingface dataset:
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 2 Related Works
### Anchor-Based and Tracing-Based Methods
The mobile producer keeps on updating the anchor node with mobility update packets whenever there is movement. When the interest packet has been redirected to the anchor node, it uses the traces in the mobility update and forwards the pending interest packet. The solution aimed to reduce handoff delay and signaling overhead costs caused by the blind flooding of _TI_ and _TD_ in the Kite solution [27]. However, the solution ends with a path stretching longer than that of the Kite solution, which provides perfect triangular routing. Therefore, path optimization is not considered, and the anchor's placement becomes critical [7] and becomes a single point of failure. In the Kite solution [25], we can assume that a mobile producer is disconnected from the current router and directly reconnected to the new router. The mobile producer sends a _TI_ message with a special trace tag to the known location of the RS or the anchor router. The RS verifies and responds with a _TD_ message along the path that _TI_ is received, and the trace created along with the intermediate routers by updating the FIB tables between new router and the RS. When a consumer sends an interest packet to the RS requesting new or unsatisfied content, the RS forwards it to the new location of the mobile producer (new router) via the trace. The process of trace set-up continues whenever the producer moves to a new location. The Kite solution allows the mobile producer to keep the trace alive by exchanging _TI_ and _TD_. In the proposed OPMSS, when the mobile producer relocates to a new location, it configures and sends an _MI_ packet for a new name prefix update. The mobility tag is attached to the name prefix and is broadcasted to the FIB table's update domain. Upon receipt by the restricted domain control router, the router will determine the domains that need to rebroadcast the _MI_ packet based on the old prefix information. The routing plane then creates an optimal path between the consumer and the mobile producer.
# CommC: A Multi-Purpose COMModity Hardware Cluster
## 5 Applications, Use Cases, and Cost Analysis
### Cost Analysis
In contrast, CommC leverages existing commodity hardware, significantly reducing upfront investment costs while providing a scalable and flexible computing environment. This cost-effective approach makes high-performance computing more accessible to organizations that may otherwise struggle with the financial burden of traditional enterprise solutions. Discussing the performance of our design among popular cloud alternatives is a difficult task. The reason that gave birth to the proposed system design, apart from sustainability, was the high cost one must face in order to take advantage of such structures. However, when observing the hardware specifications of our implemented cluster and the alternatives in Table 4, one would expect that our approach should outperform Azure and Amazon Web Services due to higher CPU clock speed, and Google Cloud would be the best performer regarding tackling the tasks shown in Table 3. This statement by itself might be wrong. There are multiple factors that affect the performance of such systems, such as disk read/write speeds and network transfer speeds. In this study, we chose that each design would contain similar a SSD disk, in order to minimize the speed difference and the cost of the alternatives. Finally, in terms of networking, our commodity hardware is connected to a local network, which can sometimes be slower than virtual machines running on a single physical system. However, cloud providers do not guarantee that their virtual machines will be hosted on the same physical machine. They offer network plans for a fixed fee or based on the amount of transferred data, which will further elevate the cost. To sum up, this study does not attempt to compare the performance of our design versus cloud alternatives but offers a sustainable, highly customizable solution that can satisfy the needs of at least medium-sized organizations and enterprises.
# Research on a Simplified Estimation Method for Wheel Rolling Resistance on Unpaved Runways
## 2 Theory and Methods
### Validation of Simplified Resistance Estimation Equations  
Given the significant influence of rolling resistance on takeoff roll distance, the accuracy of the resistance equations was validated using theoretical takeoff performance calculations for a transport aircraft on unpaved runways. The aircraft's operational parameters include a maximum takeoff mass of 152 t, four engines each providing 120 kN of thrust, a wing area (\(S_{y}\)) of 300 m\({}^{2}\), air density (\(\rho\)) of 1.225 kg/m\({}^{3}\), and gravitational acceleration (\(g\)) of 10 m/s\({}^{2}\). Under idealized conditions, reference [30] calculates the theoretical takeoff roll distance of the aircraft on clay soil with 90% compaction density as 1085 m. Substituting resistance values derived from six simplified equations into this framework yielded the predicted roll distances shown in Figure 7.  
As illustrated in Figure 7, the calculated runway length exhibits an increasing trend with higher values of the exponent \(N\), tested across the range of 1.3-1.7. The optimal exponent \(N\) = 1.39, calibrated from field measurements using aircraft-load-simulated vehicle data, produced a roll distance of 936.52 m. While this result theoretically aligns closest with actual conditions at the clay test site, it deviates by 148.48 m (13.7%) from the reference value. In contrast, conventional models adopting \(N\) = 1.5, as suggested in references [20; 21; 22], reduced the discrepancy to 7.3%. Further analysis of expanded exponent values revealed varying error magnitudes:  
* \(N\) = 1.3; 16.9% deviation;  
Figure 6: Predictive capability of different fitting equations under various loads.  
\(N=1.4\): 13.3% deviation;
* \(N=1.6\): 4.0% deviation;
* \(N=1.7\): 44.3% deviation (rendering the prediction nonviable).  
These results highlight the critical sensitivity of takeoff performance predictions to exponent selection. The \(N=1.5\) model, despite its widespread adoption, introduces systematic overestimation errors, whereas the data-calibrated \(N=1.39\) provides a more contextually accurate baseline for cohesive soils. However, the 13.7% error underscores the influence of unmodeled factors such as soil moisture variability and dynamic load redistribution during high-speed taxing. This analysis emphasizes the necessity of site-specific exponent calibration to balance theoretical assumptions with field-measured nonlinearities, particularly for heavy aircraft operations on unpaved runways.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 2 Problem Formulation
### Objective Function
#### 2.1.1 Fuel Cost  
The fuel cost can be described by a quadratic polynomial formulation, as Formula (2) shows:  
\[F_{j,t}(P_{j,t})=a_{j}+b_{j}P_{j,t}+c_{j}P^{2}_{j,t} \tag{2}\]  
where \(a_{j}\), \(b_{j}\) and \(c_{j}\) are the fuel cost coefficients of the \(j\)th unit.
# Safety Assessment of Microwave Breast Imaging: Heating Analysis on Digital Breast Phantoms
## 3 Results  
The SAR distributions simulated under the conditions described above were extracted for each anatomical breast model. To quantify the level of conservativeness of the adiabatic heating assumption, the temperature increase in the healthy model #1 at 2 GHz with the antenna in position 1 was computed with the thermal transient solver of Sim4 Life, taking into account both diffusion and perfusion, with parameters from the IT'IS Foundation database (version 4.2) [25]. The computed values were everywhere lower than the ones provided by the method adopted in this work, with a peak of 2.46 \(\rm\upmu K\) against 7.42 \(\rm\upmu K\) \begin{table}
\begin{tabular}{c c c c} \hline
**Tissue** & **Specific Heat Capacity** & **Water Content** & **Mass Density** \\  & **(J**\(\cdot\)**kg\({}^{-1}\)**\(\cdot\)**K\({}^{-1}\))** & **(\%)** & **(kg**\(\cdot\)**m\({}^{-3}\))** \\ \hline Fat & 2300 & 50 & 911 \\ \hline Gland & 3000 & 60 & 1041 \\ \hline Skin & 3300 & 60 & 1109 \\ \hline Tumor & 3850 & 80 & 1179 \\ \hline \end{tabular}
\end{table} 
Table 1: Values of specific heat capacity, water content, and mass density of breast tissues. Peaks of temperature increase in the healthy breast model #1 deduced from the peaks of SAR with the horn antenna in the five considered positions at each selected frequency are reported in Table 2. Table 3 presents the results for model #2, which includes a benign anomaly highlighted in the table insets as red dots. Table 4 reports the values for breast model #3, which includes a malignant tumor, highlighted using red dots in the table insets. The obtained values for models #4 and #5 are shown in Tables 5 and 6, respectively. SAR distributions on the surface of the five exposed models are shown in Figure 5. The effect of the receiving Vivaldi antenna in the field distribution was tested for model #1, considering the horn antenna in position 3 transmitting at 6 GHz, which is the scenario showing the highest temperature increase in Table 2. Results are reported in Table 7. The sensitivity of the results with respect to variations in electrical and thermal properties of tissues was investigated for model #1, considering the horn antenna in position 3 transmitting at 6 GHz. Results are reported in Table 8. In the same scenario, SAR value sensitivity to mesh refinement was analyzed. The mesh density in the breast model domain was decreased by 50%, resulting in a 2 mm element size. Table 9 presents a comparison between temperature increases in this coarsened mesh assessment versus the original mesh configuration. The results suggest that any potential underestimation of SAR values when using a mesh dimension of 1 mm instead of 0.5 mm would not lead to harmful oversight of the actual temperature increases in tissue.
## 3 Design of the EASIST Transmission System
### The Controller
#### 3.2.2 Vector-Controlled Converter  
According to the principle of vector control, as shown in Figure 9, the current in the three-phase coordinate system of the asynchronous motor is equivalent to the current in the two-phase static coordinate system. And the two-phase static coordinate system is equal to the two-phase synchronous rotating coordinate system through coordinate transformation. If the coordinate system is rotated simultaneously with the core of the asynchronous motor, the DC motor model can be obtained; that is, the three-phase asynchronous AC motor is equivalent to the DC motor.  
In Figure 9, the corresponding VR\({}^{-1}\)-VR and 2s/3s-3s/2s cancel each other so that the input signal can be directly equivalent to the frequency signal of the motor. On this basis, when the two-phase synchronous rotating coordinate system is oriented according to the rotor flux, the mathematical model of the asynchronous motor can be obtained as follows [42,43]:  
\[\left\{\begin{array}{l}\psi_{r}=\frac{L_{m}}{L_{r}p+1}i_{sm}\\ \omega_{s}=\omega_{1}-\omega=\frac{L_{r}i_{sc}}{T_{r}\psi_{r}}\\ T_{e}=\frac{n_{p}L_{m}}{L_{r}}i_{st}\psi_{r}\end{array}\right.. \tag{10}\]  
where  
- \(\Psi_{r}\) is rotor flux;  
- \(i_{sm}\), \(i_{st}\) are stator current;  
- \(L_{r}\), \(L_{m}\) are rotor inductance and stator/rotor mutual inductance;  
- \(R_{r}\) is rotor resistance, \(L_{r}\) is rotor inductance, \(T_{r}=L_{r}/R_{r}\);  
- \(p\) is the differential operator;  
- \(\omega_{\rm s}\) is the angular velocity of the coordinate axis d-q relative to the rotor;  
- \(\omega_{1}\) is the angular velocity of the coordinate axis d-q relative to the stator;  
- \(\omega\) is the rotor angular velocity of the asynchronous motor;  
- \(n_{p}\) is the polar logarithm;  
- \(T_{e}\) is the motor torque [42].  
Figure 9: Schematic diagram of the vector control system [42].
## 2 Materials and Methods
### The Hydrogen Cyanide Determination  
Volumes of 100 \(\mu\)L of absorbent and 1 mL of ninhydrin reagent were pipetted into the quartz or plastic cuvette of the spectrophotometer. Immediately, the cuvette was covered with parafilm and shaken vigorously by inverting several times. The absorbance values at 493 nm were measured kinetically spectrophotometrically for 30 min, immediately after mixing the reagents. If the concentration of HCN in the blood is too high, the cyanide-containing absorbent may be diluted before measurements. Separately, other absorption measurements were also performed at certain times, such as 5 min, 10 min, 20 min, 30 min, and even 50 min. A reference solution was prepared each time by mixing 100 \(\upmu\)L of 2% K\({}_{2}\)CO\({}_{3}\) or 0.01 N KOH and 1 mL of 5 mg mL\({}^{-1}\) ninhydrin reagent directly in the spectrophotometric cuvette. The kinetics of the color reaction between cyanide ions and ninhydrin was followed against the reference solution at 493 nm in 1 cm\({}^{3}\) plastic cuvettes.  
The calibration curves were plotted with known concentrations of HCN either by the proposed kinetic spectrophotometric micro method or by the spectrophotometric reading of the absorbance of the cyanide adduct with ninhydrin at 584 nm and at a fixed time of 15 min [38]. In addition, the results of the proposed micro method with ninhydrin were statistically compared with those obtained by the reference method using chloramine T/barbituric acid-pyridine in pH 4.0 buffer solutions and at 30 \({}^{\circ}\)C [41]. The following solutions were prepared: (i) 0.2 M acetate buffer of pH 4.0; (ii) 0.50% (\(w/v\)) chloramine-T (Merck) in distilled water; and (iii) 0.150 M barbituric acid and 10% (\(v/v\)) pyridine were mixed and diluted to the mark in a 100 mL volumetric flask. The absorbance was read at 578 nm and 30 \({}^{\circ}\)C and compared with the slope values obtained by the proposed micro method.  
We studied method accuracy by measuring the HCN content of unfrozen whole blood samples fortified with a known amount of KCN.
## 4 Methods
### Compositional Data Analysis  
Some researchers pointed out that the presence of pseudo-correlation could lead to traditional statistical analysis methods overlooking the \"closure\" feature when analyzing data characteristics, which in turn results in incorrect correlations between elements [24; 25]. [PERSON] proposed the log-ratio transformation method for \"compositional data\", focusing on the proportional relationships between components rather than the components themselves, thus more accurately reflecting the relative changes between variables [16].  
Compositional data analysis involves first \"closing\" the data to ensure that the sum of element contents for all sampling points equals a constant value, followed by log-ratio transformation. The commonly used transformation methods include additive log-ratio transformation (alr), centered log-ratio transformation (clr), and isometric log-ratio transformation (ilr). The alr transformation requires selecting one component, then dividing this component by the other components and taking the natural logarithm. It is clear that the alr transformation can randomly lose a variable, and the transformed result is not unique. The clr transformation requires calculating the geometric mean of all component variables, dividing each component by this geometric mean, and then taking the natural logarithm. While this method does not lose any variables, it has an issue: the transformed data exhibits multicollinearity, making it unsuitable for robust covariance estimation [21; 22; 23; 26]. The ilr transformation's core is to define a component vector using a standard orthogonal basis, thereby creating a set of new component variables. This method resolves the multicollinearity issue of clr but still faces the problem of losing one variable, similar to the alr transformation.  
Among these methods, clr transformation has been widely applied to geochemical data processing and has been proven to be highly reliable by numerous researchers [18; 27; 28; 29; 30].  
Considering that the foundation for subsequent statistical analysis requires the data to meet normal distribution assumptions, and that compositional data transformation is a form of log-ratio transformation, it not only addresses the closure effect but also brings the data closer to meeting the normal distribution requirement. Therefore, all subsequent data processing is carried out using the data after compositional data transformation.
# An Improved GreenOFDM Scheme for PAPR Reduction
## 2 OFDM and PAPR  
OFDM is a multi-carrier modulation technique in which every time-domain symbol carries the sum of \(N\) overlapping orthogonal subcarriers mapped by \(\{A_{k}\}\) symbols (e.g., Quadrature Amplitude Modulation (QAM): QAM symbols) with \(0\leq k\leq N-1\).  
The OFDM base-band time-domain symbol is obtained by applying the Inverse Discrete Fourier Transform (IDFT) (Digitally implemented with an Inverse Fast Fourier Transform (IFFT)) to the frequency-domain inputs \(\{A_{k}\}\). The digital time-domain symbols are often oversampled to emulate the peaks that might appear in the analog domain and hence obtain a well-approximated digital version. An \(L.N\) points IFFT over  
\[\{X_{k}\}=\{A_{0}\,A_{1}\,\cdots\,A_{\frac{N}{2}-1}\,\underbrace{0\cdots 0}_{(L-1),N}\,A_{\frac{N}{2}}\,A_{\frac{N}{2}+1}\, \cdots\,A_{N-1}\}\]achieves the oversampling, where \(L\) is the oversampling factor. The OFDM base-band time-domain symbol is hence given by:  
\[x[n]=\frac{1}{\sqrt{N}}\sum_{k=0}^{LN-1}X_{k}\cdot\exp\left\{i2\pi\frac{kn}{LN}\right\} \tag{1}\]  
with \(0\leq n\leq N-1\) and \(i=\sqrt{-1}\). Generally an oversampling factor \(L=4\) is applied since it is enough to well approximate the analog symbols. Higher values will only add a higher computational complexity without substantial gain in the approximation.  
The PAPR of the oversampled OFDM symbol is given by:  
\[\text{PAPR}=\frac{\max_{0\leq n\leq LN-1}\left\{|x[n]|^{2}\right\}}{\mathbb{E} \left\{|x[n]|^{2}\right\}} \tag{2}\]  
where \(\mathbb{E}\{\cdot\}\) is the expectation and \(\mathbb{E}\left\{|x[n]|^{2}\right\}=\mathbb{E}\left\{|X_{k}|^{2}\right\}\) for \(X_{k}\) i.i.d. random variables (\(\{X_{k}\}\in 2^{n}\)-QAM with \(n=2m,m\in\mathbb{N}^{\star}\)). The PAPR of OFDM symbols is high because the numerator of Equation (2) is high. This is because _sometimes_ many modulated subcarriers \(X_{k}\cdot\exp\left\{i2\pi\frac{kn}{LN}\right\}\) add themselves in-phase generating high peaks. Furthermore, since the PAPR is a random variable it can be characterized by its Complementary Cumulative Distribution Function (CCDF), i.e., the probability that the PAPR of an OFDM symbol exceeds a predetermined value \(\gamma\). A semi-empirical approximation is given by [30]:  
\[\text{CCDF}_{\text{OFDM}}(\gamma)\approx 1-\left(1-\exp\left\{-\gamma\right\} \right)^{2.8N} \tag{3}\]
# Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework
## 3 Mathematical Model  
Given the Poisson arrival processes, the exponentially distributed service times, plus the RAC scheme, describe the system behavior using a \(S\)-dimensional Markov process  
\[\mathbf{X}(t)=(X_{1}(t),\ldots,X_{S}(t)),\quad t>0, \tag{8}\]  
where \(X_{s}(t)\), \(s\in\mathcal{S}\), represents the number of servicing requests at the \(s^{\text{th}}\) NSI at the time \(t\) over the system state space  
\[\Omega=\Big{\{}\mathbf{n}\in\mathbb{N}^{S}:(\mathbf{n}-\mathbf{N}^{\text{max} })\cdot\mathbf{j}\leq 0\wedge\mathbf{n}\cdot\mathbf{b}\leq C\Big{\}}, \tag{9}\]  
where \(\mathbb{N}^{S}\) represents the set of all \(S\)-dimensional vectors with natural elements. A depiction of considered Queueing System (QS) functioning is given by the scheme model in Figure 2.  
Consider the following main sets [35; 36; 37] of the state space \(\Omega\) for further investigation of the model. Let the blocking set, described as  
\[\Omega_{s}^{\text{block}}=\{\mathbf{n}\in\Omega:(\mathbf{n}-\mathbf{N}^{ \text{g}})\cdot\mathbf{e}_{s}\geq 0\wedge(\mathbf{n}+\mathbf{e}_{s})\cdot \mathbf{b}>C\},\quad s\in\mathcal{S}, \tag{10}\]  
collect all the system states, where an arriving request is bound to the blocking path at the \(s^{\text{th}}\) NSI. Let the direct admission set, described as  
\[\Omega_{s}^{\text{dad}}=\{\mathbf{n}\in\Omega:(\mathbf{n}-\mathbf{N}^{\text{ max}})\cdot\mathbf{e}_{s}<0\wedge(\mathbf{n}+\mathbf{e}_{s})\cdot\mathbf{b} \leq C\}, \tag{11}\]  
collect all the system states, where an arriving request is bound to the direct admission path at the \(s^{\text{th}}\) NSI. Let the via pre-emption admission set, described as  
\[\Omega_{s}^{\text{vpd}}=\{\mathbf{n}\in\Omega:(\mathbf{n}-\mathbf{N}^{\text{ g}})\cdot\mathbf{e}_{s}<0\wedge(\mathbf{n}+\mathbf{e}_{s})\cdot\mathbf{b}>C\}, \tag{12}\]  
collect all the system states, where an arriving request is bound to the via pre-emption admission path at the \(s^{\text{th}}\) NSI. Given the direct admission and via pre-emption admission sets, also describe the blocking set as  
\[\Omega_{s}^{\text{block}}=\Omega\setminus\Big{(}\Omega_{s}^{\text{dad}}\cup \Omega_{s}^{\text{vpd}}\Big{)}, \tag{13}\]  
Figure 2: Scheme model of considered QS with \(S\) NSIs at the 5G BS. where \(\Omega_{s}^{\text{add}}\cup\Omega_{s}^{\text{ppad}}\) represents the collection of all the system states that are in \(\Omega_{s}^{\text{add}}\) or in \(\Omega_{s}^{\text{vpad}}\), i.e., an arriving request is bound to any of the two admission paths at the \(s^{\text{th}}\) NSI.
### Data Detection and Import  
The scanners work by projecting a light source, such as a laser (with no heat or harmful effects) or structured light onto the patient's dental teeth and measuring the extent of the distortion which can be registered thanks to high-resolution cameras. The structural complexity of the teeth will create a distortion of the light beam, which will be analyzed and converted by dedicated software to create a map based on a point cloud. For each point, the software calculates the first two coordinates (x and y) while the third coordinate (z) is extrapolated by including the calculation of the distance from each camera. The software then triangulates the point clouds using mathematical models that analyze the data and create a final, precise 3D visualization of dento-gingival tissues.  
Although scanning systems have been introduced in the market with the aim to precisely reconstruct the 3D geometry of dental arches, the new generation of IOS have been integrated with high-resolution cameras with the aim to allow the detection of tooth color and shade matching as generally done with spectrophotometer or clinically by visual inspection. This opportunity is extremely helpful for clinicians since it is possible to combine intraoral scans and shade selection in the same digital registration for better efficiency and communication with lab technicians; however, there are only few IOS available that can directly make in-color impressions [16]. Although intraoral scanners showed high precision when used for shade selection, a recent review suggests that evidence on accuracy of intraoral scanners is inconclusive and that clinicians should control the potential factors affecting the accuracy of color detection such as environment light conditions or room illuminance.  
Concerning facial scanners, there are different methods for facial detection: photogrammetry, stereophotogrammetry, structured light scanning, and laser scanning. Photogrammetry and stereophotogrammetry are passive methods which means that the patient's face is scanned with two or more photographs. On the other hand, structured light scanning and laser scanning are active methods which means that sensors are used to capture light patterns via active triangulation. Figure 1 shows data acquisition from IOS and FS.