esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Adhesion and Hysteresis Friction of Rubber-Pavement System
## 1 Introduction  
Tire-pavement friction is one of the main factors that contribute to road safety. An adequate level of friction between the vehicle tire and pavement surface reduces the number of crashes, especially in wet pavement conditions [1]; therefore, an appropriate level of traction between rubber tires and pavements is essential for safe driving. Rubber is the principal component of vehicle tires. It is an elastomer that exhibits unique physical and chemical properties. It has low Young's modulus and high yield strain, as compared with other materials.  
It is documented that rubber generates three different forms of friction: adhesion, deformation, and wear. Previous studies have shown that the adhesion force is the most influential component in rubber friction in dry conditions and low speed [2; 3]. It is postulated that the adhesion between rubber tires and pavements is highly influenced by the surface free energy, which depends on the chemical composition of rubbers. Yet, to the best of the authors' knowledge, no studies have been carried out to investigate the effect of surface free energy of rubber on traction with pavements.  
The theory of rubber friction recognizes three major friction forces: adhesion force, bulk deformations (hysteresis), and cohesion losses due to the wearing of rubber [4]. The adhesion friction force is formed due to rubber-pavement interaction at a microtexture level and highly dependent on the true area of contact, and thus the adhesion friction is dominant for smooth contact at low speeds and dry conditions [3]. The hysteresis component of friction is caused by the bulk deformation of rubber material as it comes into contact with pavement asperities. Such deformation causes energy losses as rubber slides over the pavement surface. These energy losses are caused by the hystereticlosses in the rubber material due to loading and unloading [3]. The third major component of friction (cohesion losses) is caused by the wearing of rubber as it slides over the pavement surface. Pavement texture has an important role in the friction components--a recent study showed that higher pavement microtexture and macrotexture result in higher frictional properties [5]. The study also showed that pavement surfaces initially had lower frictional properties due to the thin film of asphalt coating the aggregates at the surface. The friction increased as the binder film was removed by traffic, exposing the microtexture. Then, the friction deceased due to polishing and abrasion of the aggregates at the surface.
# Two-Channel OTDM System for Data-Center Interconnects: A Review
###### Abstract  
It has been proposed to implement the \(>\)100 Gb/s data-center interconnects using a two-channel optical time-division multiplexed system with multilevel pulse-amplitude modulation. Unlike the conventional four-channel optical time-division multiplexed system which requires an expensive narrow pulse, the two-channel system can be implemented cost-effectively using a wide pulse (which can be simply generated using a single modulator). The two-channel system is expected to be practically available using an integrated transmitter in a chip due to the recent advances in photonics-integrated circuits. This paper reviews the current stage of research on a two-channel optical time-division multiplexed system and discusses possible research directions. Furthermore, it has been demonstrated that 200 Gb/s signals can be generated by using modulators with only 17.2 GHz bandwidth. Therefore, the use of the phase-alternating pulse can make the multiplexed signal robust to chromatic dispersion, enabling the 200 Gb/s 4-level pulse-amplitude-modulated signal to be transmitted over 1.9 km of standard single-mode fiber.  
Keywords:data-center interconnect; optical time-division multiplexing; pulse-amplitude modulation +
Footnote †: journal: Sensors
# False Alarm Reduction Method for Weakness Static Analysis Using BERT Model
## 3 False Alarm Reduction Method for Reliability Analysis of Static Analysis Results
### Weakness Static Analysis Alarm Classification  
An experimental environment for multiple static analyses was established, and when the training of the BERT-based line-level weakness analysis model was completed, a dataset was created. As shown in Table 4, the dataset has output analyzed by multiple static analysis tools (1: detected, 0: undetected), BWA (score) model on all test case files in each CWE and metadata provided by [PERSON] test suite C/C++ 1.3 version (present as a target through confirming the line of weakness which is defined in each test case file).  
In Table 4, at line 7, 5 out of 6 tools were analyzed through the multiple static analysis tools. The score obtained through the BWA model was 0.85 scores, and the line is likely to have a normal weakness (true alarm or true positive). Thus, it is possible to determine whether it is a true or a false alarm based on the results of multiple static analysis tools on a specific line and the scores obtained through the BWA model.  
In this study, this dataset was trained using the decision tree model [44]. Thus, the reliability of the weakness analysis results generated by several static analysis tools can be determined, and false alarms can be reduced by reclassifying the alarm of the weakness analyzer into a decision tree model based on the derived reliability.
# Spin-Orbital Transformation in a Tight Focus of an Optical Vortex with Circular Polarization
## 4 The SOC upon the Light Focusing  
Comparing (13) and (15), it can be seen that the sum of the total lengthwise projections of the OAM and SAM is conserved upon focusing. However, separately, the full longitudinal OAM and SAM are not conserved due to the SOC. The total SAM during focusing decreases and partially passes into the full OAM, which, in contrast, increases. It is especially evident when \(m=0\). In the launch plane, in this case, the total SAM is equal to the total beam power (1): \(\hat{S}_{z,0}=W\), and the total OAM is zero: \(\hat{L}_{z,0}=0\). During the focusing process, the total SAM will decrease and will be equal to (11) in the focus: \(\hat{S}_{z}=W_{0}-W_{2}\), and the total OAM will grow and will be equal to (12) in the focus: \(\hat{L}_{z}=2W_{2}+2W_{1}=W-(W_{0}-W_{2})\). Taking into account (9) and at \(\beta\approx\pi/2\), the full longitudinal SAM will decrease in focus compared to the initial plane by 2 times (\(\hat{S}_{z}=W_{0}-W_{2}\)=\(W/2\)), and the total longitudinal OAM will increase in focus compared to the initial plane, also by 2 times (\(\hat{L}_{z}=W/2\)). This means, that, in the limiting case (\(A(\psi)=1\), \(\beta=\pi/2\)) with a plane wave with right-hand circular polarization focusing, half of the total longitudinal SAM will transform into a longitudinal OAM. For a smaller numerical aperture (\(\beta<\pi/2\)) and for any other real amplitudes \(A(\psi)<1\), with field (1) focusing, a part less than half will pass from the SAM to the OAM. From (9), it can be found how much the SAM and OAM change due to the SOC for the case with a smaller numerical aperture, for instance, at \(\beta=\pi/3\) (\(m=0\), \(A(\psi)=1\)). Then, we get that SAM and OAM in the focus will be equal to \(\hat{S}_{z}=3W/4\) and \(\hat{L}_{z}=W/4\), respectively. That is, at a numerical aperture \(\sin\beta=\sqrt{3}/2\approx 0.87\), the limit value of the OAM that can occur in the focus of the field (1) is equal to one fourth of the total beam power.  
It is clear that similar formulas can also be obtained for an optical vortex with left-hand circular polarization. Then, for an optical vortex with topological charge \(n\) and left circular polarization, instead of (11), (12), and (13) we get:  
\[\begin{array}{l}\hat{S}_{z}=W_{2}-W_{0},\\ \hat{L}_{z}=mW_{0}+(m-2)W_{2}+2(m-1)W_{1},\\ \hat{S}_{z}+\hat{L}_{z}=(m-1)(W_{0}+W_{2}+2W_{1})=(m-1)W.\end{array} \tag{16}\]
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 8 Performance Analysis
### Communication Overhead  
As shown in Table 5, the protocol's communication costs scale with both dataset size and dimensionality, ranging from 97.2 MB (Wine, 2D) to 896.2 MB (Yeast, 8D) per iteration. The 8D configuration incurs 2.3\(\times\) higher bandwidth than 4D due to increased ciphertext dimensions, with a consistent expansion factor of 2.4\(\times\) compared to plaintext operations. Notably, batch processing reduces communication rounds by 35% for large datasets (\(n>1000\)), mitigating the overhead for high-dimensional cases like Yeast (8D). The results demonstrate a polynomial growth pattern in communication costs, where dimensionality has greater impact than sample size--evidenced by Wine's 3\(\times\) increase from 2D to 4D (97.2\(\rightarrow\)296.5 MB) versus [PERSON]'s 1.5\(\times\) increase from 4D to 8D (587.9\(\rightarrow\)896.2 MB). This trade-off between security (through homomorphic operations) and efficiency remains manageable for medium-scale clustering tasks.
# A Comprehensive Survey for Deep-Learning-Based
## 4 Abnormality Detection with Images over Long Range
### The Application of Satellites in Smart Grid Abnormality Detection
#### 4.1.1 Satellite Positioning Theory  
GNSS is a general term for satellite navigation systems in a broad sense, including satellite positioning systems with global coverage, satellite positioning systems with regional coverage, and space-based augmentation systems. GNSS system can be divided into three main parts--space satellite, ground control station, and user terminal--according to the differences in space and function. GNSS satellites send signals containing their position information to the ground all the time. The receiver receives satellite signals on the ground, decodes the satellite ephemeris, and measures the pseudo-range, carrier phase, and other measured values. Through these measured values, the position of the user receiver can be calculated. A GNSS positioning principle diagram is shown in Figure 4.  
Figure 3: Abnormality detection in a smart energy meter system based on Internet of things and sensor technology.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Satellite Data Types** & \begin{tabular}{c} **Commonly Used** \\ **Anomaly Detection Areas** \\ \end{tabular} & **Related Work** \\ \hline Satellite positioning data & \
\begin{tabular}{c} Tower pole tilt detection, \\ ground settlement detection, \\ inspection robot detection \\ \end{tabular} & [10; 11; 99; 90; 91; 92] \\ \hline Optical satellite images &
\begin{tabular}{c} Vegetation monitoring \\ \end{tabular} & [93; 94; 95; 12; 96; 97; 98; 99; 13; 14; 15; 16; 17; 18; 19; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38;  
According to different measurement values and positioning methods used, satellite positioning can be divided into positioning methods such as single-point positioning, precise single-point positioning, and RTK. Although GNSS can provide high-precision services globally, the environment has a more significant impact on the positioning. The use of GNSS for abnormality detection in the power grid is a challenging problem.
## 2 Materials and Methods
### Fermentation Trials
#### Yeast Species  
The commercial non-_Saccharomyces_ active dry yeast _Lachancea thermotolerans_, strain Kt 421 (Viniflora(r) CONCERTOT(tm), [PERSON], Hoersholm, Denmark), was used in sequential inoculation with _Saccharomyces cerevisiae_ (SIHA(r), Active Yeast 10, Eaton, Langenlonsheim, Germany) (L). Rehydration of 25 g/100 L of _L. thermotolerans_ was performed in unchlorinated tap water in ratio 1:10 at temperature 25 \({}^{\circ}\)C, according to manufacturer's instructions. Unsulfured grape must be added to yeast suspension in ratio 1:3, and after 20 min of acclimatization _L. thermotolerans_/must mixture was added in tanks. Addition of _Saccharomyces cerevisiae_ followed 24 h after _L. thermotolerans_. Fermentative control trial was _Saccharomyces cerevisiae_ (SIHA(r), Active Yeast 10) (S). The 30 g/100 L of _S. cerevisiae_ was added to warm water at 35 \({}^{\circ}\)C, gently stirred, and rehydrated for 20 min. Suspension was gradually acclimatized with addition of wine must be before inoculation. Spontaneous fermentation (N) was performed by yeasts naturally present in grapes. All fermentation trials were conducted in triplicate. The fermentation type used for wine production as well as their respective strains, suppliers, and fermentation conditions, are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \
\begin{tabular}{c} Fermentation \\ Yeast species \\ \end{tabular} &
\begin{tabular}{c} L \\ _Lachancea thermotolerans_ \\ _Saccharomyces cerevisiae_ \\ \end{tabular} & _Saccharomyces cerevisiae_ & Spontaneous yeasts \\ Strain & Kt 421 & 10 & Unknown \\ Suppliers & 10 & Siha, Active Yeast 10 & Natural \\ Timing of yeast inoculation & after destemming & after destemming & No \\ Temperature (\({}^{\circ}\)C) & 25.8–26.6 & 26.4–26.9 & 24.8–25.6 \\ Frequency of punch down & every 12 h/5 min & every 12 h/5 min & every 12 h/5 min \\ Duration of maceration & 7 days & 7 days & 7 days \\ \hline \hline \end{tabular}
\end{table}
Table 1: Yeast species and fermentation conditions in wine production.  
After completion of the primary aerobic phase of alcoholic fermentation, the wines were pressed off and decanted into 20 L wine glass balloons. The anaerobic completion of fermentation lasted three weeks. After this time, the wines were decanted, and 30 mg/L of SO\({}_{2}\) was added. The second decantation of the wines took place after two months when the samples were bottled in 750 mL bottles. Wine samples were analyzed in April 2021.
## 3 Results and Discussion
### Computational Modelling  
Computational modelling shows that MTX@UiO-66 in the tetrahedral holes is bound by pi-stacking with one linker and two hydrogen bonds, with a distance between the hydrogen of the MTX alkyl-OH to the ZrO cluster of 1.84 A and from the core OH to the ZrO cluster hydrogen of 2.13 A (Figure 10). In contrast, MTX is unable to fit within the tetrahedral pore of NH\({}_{2}\)-UiO-66 and binds only to the window, but is able to form hydrogen bonds with the two linker -NH\({}_{2}\) groups of 1.75 and 2.15 A, respectively. The octahedral pore is large enough to accommodate the MTX molecule fully. In UiO-66, MTX forms hydrogen bonds whereby all -OH groups bind to the oxygen atoms of ZrO clusters with O\(\ldots\)H distances of 1.95, 2.04, 2.14, and 2.27 A. In an NH\({}_{2}\)-functionalised octahedral pore, in NH\({}_{2}\)-UiO-66, the binding energy of MTX increases to 492 kJ/mol. One of the core -OH groups now binds to two -NH\({}_{2}\) functional groups (vs. a ZrO cluster), and there are additional weaker contacts between the MTX C=O, -NH groups, and -NH2 linkers. These findings are in agreement with the observed trends in release studies (Table 1). One molecule of MTX per unit cell of UiO-66 accounts for approximately 6.67 wt%, indicating that the NH\({}_{2}\)-UiO-66 has about three MTX molecules per unit cell, increasing to about eight in bare UiO-66 at the 1:1 loading ratio. This is consistent with being able to fit about two MTX molecules in each octahedral pore, MTX being just small enough to pass the tetrahedral pores. However, in the case of NH\({}_{2}\)-UiO-66, those tetrahedral pores are blocked easily, and therefore a drop in loading is observed at a higher concentration of MTX.  
Figure 10: Model showing interaction of MTX with UiO-66 and UiO-66-NH\({}_{2}\) in the (**A**,**B**) octahedral and (**C**,**D**) tetrahedral pores. Green bold lines showing the H-bond interaction between the MTX and the MOFs. Carbon atoms are indicated in grey, nitrogen atoms are blue, oxygen is red, small white spheres are hydrogen atoms and the large orange spheres represent zirconium.
## 5 Results Analysis  
Experiments were conducted using MATLAB/Simulink to validate the structural diagram of the control strategy for the FPPMSM under healthy conditions and an OCF, as depicted in Figures 5 and 6, respectively. The FPPMSM drive parameters are reported in Table 11. The proposed control algorithm for the motor was implemented on a digital signal processor (DSP) card (TMS320F379D), which produces PWM to control a five-phase voltage source inverter (VSI) that supplies the FPPMSM. The FPPMSM and VSI models were modeled in Simulink and simulated with a fixed step size of \(1\times 10^{-6}\) s. The generation of PWM is based on carrier pulse width modulation (CPWM), due to its easy and simple implementation for a five-phase motor spatially under an OCF. Therefore, the CPWM technique, dealing with modulation signals injected with zero-sequence signals, requires only a few mathematical calculations compared to other techniques. It uses the zero-sequence signal equation as follows [29]:  
\[v_{n0}(t)=-\frac{1}{2}(\max(v_{a}^{*},v_{b}^{*},v_{c}^{*},v_{d}^{*},v_{e}^{*}) +\min(v_{a}^{*},v_{b}^{*},v_{c}^{*},v_{d}^{*},v_{e}^{*})) \tag{42}\]  
The back-EMF of the motor exhibited a trapezoidal shape due to the presence of the third harmonic component, as shown in Figure 7. The fast Fourier transform analysis revealed that the third harmonic constituted 19.92% of the back-EMF spectrum. To evaluate the effectiveness of the proposed control strategy, we assessed it in healthy and faulty conditions, comparing the results with PI and traditional SMC (TSMC) control strategies as benchmarks, since they are the most commonly used control methods for FPPMSMs in the literature. The TSMC used in the results combined two methodologies proposed in [12; 13]. In [12], the authors advocated the use of a traditional sliding mode controller with the help of a PI controller in the control loop for the speed. In [13], the authors recommended the use of traditional sliding mode controllers in the control loops of the current. Additionally, to compare the performance of the MTO and MCL strategies, two performance metrics, namely torque ripples and speed fluctuations, were calculated as follows:  
\[T_{e_{ripple}}\%=\frac{(\max(T_{e})-\min(T_{e}))}{\text{mean}(T_{e})}\times 100,\quad\Omega_{f_{\text{circ}}}\%_{0}=\frac{(\max(\Omega)-\min(\Omega))}{ \text{mean}(\Omega)}\times 100 \tag{43}\]  
Figure 5: Proposed control diagram under healthy conditions.
# Nano-HPLC-HRMS Analysis to Evaluate Leptin Level in Milk Samples: A Pilot Study
## 2 Materials and Methods
### Sample Preparation and Purification  
Samples of human breast milk, of bovine milk, of bovine colostrum and of infant formulas were prepared and purified using the immunoaffinity process. Samples were processed using two different antibodies: monoclonal anti-LEP antibody produced in mouse with specific reactivity for human, and polyclonal anti-LEP antibody produced in rabbit specific for bovine. Therefore, the antibodies bound human and bovine leptin, respectively.  
To link the antibodies anti-LEP at the affinity columns of immobilization link we followed the procedure described in the manuals. We used both the antibodies anti-LEP at concentration of 4 ug/mL, suitable for the amount of leptin in breast milk [16; 17].  
When the affinity columns were ready and the antibody was linked, 300 uL of milk sample(s) were loaded and the incubation lasted over night at 4 \({}^{\circ}\)C under mechanical rotation. The columns were then washed and leptin was eluted in triplicate with 200 uL of glycine 0.2 M in hydrochloric acid, pH 2.5-3. 10 uL of Tris buffer 1 M in hydrochloric acid, pH 8.5-9, were added to neutralize the solution.  
Each sample was loaded, for each antibody, in triplicate using three different affinity columns, for a total of 900 uL of sample and 1.8 mL of elution solution, which were evaporated using a CentriVap Cold Trap with CentriVap Concentrator (Laboconco Co., Kansas City, Missouri, MO, United State of America). Samples were reconstituted in 50 uL 0.1% formic acid in water, vortexed and enzymatically digested with trypsin.  
To ensure an appropriate pH range for tryptic activity (pH 7-9), the reconstituted samples were diluted in ammonium bicarbonate 50 mM. Then DL-dithiothreitol, iodoacetamide, and trypsin (1/20 ratio) were added to the solution which was digested for 18 h at 37 \({}^{\circ}\)C. Samples were placed into low binding vial and analyzed with nano-HPLC-HRMS.  
To quantify leptin in human breast milk and infant formula samples, a calibration curve of the protein in bovine milk was prepared. Fresh whole cow milk was fortified with increasing amount of human leptin analytical standard (0-100-250-500-1000-2500-3000 ug mL\({}^{-1}\)). Calibration standards were processed as previously described.
## 4 Performance Evaluations
### Simulation Results
Figure 9: Impact of channel quality and external interference on the performance: (a) average end-to-end delay (The vertical lines shows the minimum and maximum delays for different channels), (b) energy consumption per delivered byte. All nodes send data to the sink.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 4 Self-Drilling Anchor Grouting Slope Simulation Analysis
### Analysis of the Supporting Performance of Self-Drilling Anchor Bolts
#### 4.4.2 Shear Strain Increment and Velocity Vector Analysis  
The distribution of the maximum shear strain increment in the slope soil reflects the stress state of the slope body. By analyzing the maximum shear strain increment distribution, potential sliding surfaces and possible instability modes of the slope soil can be identified. Numerical simulations were carried out using FLAC3D software, and the maximum shear strain increment under self-drilling anchor support was extracted, as shown in Figure 28.  
As shown in Figure 28, the maximum shear strain increment of the self-drilling anchors supporting the slope is still distributed at the toe of the slope. However, compared to the unsupported slope, its maximum value decreases from 0.312 to 0.148, representing a 52.6% reduction. This greatly alleviates the sliding tendency of the slope soil, and the shear zone of the slope under the self-drilling anchor bolt support is not significantly penetrated. Additionally, the velocity vector diagram shows that the velocities of the grid points on the potential sliding surface of the slope have significantly decreased. This indicates that the self-drilling anchors support system can improve the distribution of the maximum shear strain increment of the slope, reducing the risk of shear failure at the slope toe, thereby providing a good reinforcing effect on the slope.
# Incorporating Epistemic Uncertainties in Ship Operability Study
## 3 Results  
### Example Ship Description  
This study is presented for a model of a liner ship operating in the Adriatic Sea (Figure 1). The main particulars of the ship are given in Table 3. The main particulars of the ship model correspond to the features of the vessels within the \"larger ferry category\".  
Figure 1: Three-dimensional model of the ship used in this study.  
The shipping route between Ancona (Italy) and Split (Croatia) is assumed (Figure 2).  
The histogram of sea states along this shipping route is presented in Figure 3. Data in Figure 3 are obtained from the extensive analysis of sea states in the Adriatic Sea, using a reanalysis database [24].  
Based on the prior study of the shipping route, it is expected that the ship would only face waves from 45\({}^{\circ}\) and 135\({}^{\circ}\) directions, which correspond to the routes from Split to Ancona and vice versa.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Feature** & **Value** & **Measuring Unit** & **Description** \\ \hline \(L_{OA}\) & 114 & m & Length overall \\ \(L_{PP}\) & 103.2 & m & Length between \\ \(B\) & 18.7 & m & Breadth \\ \(T\) & 5 & m & Draft \\ \(V\) & 17 & kn & Nominal ship \\  & & & speed \\ \hline \end{tabular}
\end{table}
Table 3: Main particulars of the ship.  
Figure 2: Shipping route: Split (right)–Ancona (left).