esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Result and Analysis
### Influence of the Transformer Layer  
To investigate the effect of transformer layer depth on classification performance, we conducted a series of experiments across four steganographic datasets. Transformer depth is critical for capturing hierarchical structures and modeling long-range dependencies, which are essential for extracting subtle steganographic patterns. While deeper networks can enhance representational capacity, they also introduce additional computational costs. This analysis aims to identify an optimal depth that balances accuracy and efficiency. The experimental results are summarized in Figure 3.  
As shown in Figure 3, increasing the number of transformer layers generally leads to improved classification accuracy across all datasets, although the performance gains diminish beyond a certain depth. On the Geiser dataset [17], accuracy reaches a maximum of 92.53% with five layers, with the most significant improvement observed between one and two layers. For the Miao (\(\eta=1\)) dataset [18], performance peaks at 86.71% with five layers,  
Figure 2: Impact of embedding dimension on detection accuracy across multiple datasets.  
stabilizing after three layers. Similarly, the Miao (\(\eta=2\)) dataset [18] achieves 92.42% accuracy, with marginal improvements beyond three layers. In contrast, the Miao (\(\eta=4\)) dataset [18], characterized by higher embedding complexity, benefits more from deeper architectures, achieving 94.81% accuracy with five layers. These findings suggest that, while increased transformer depth can enhance detection performance, simpler datasets can achieve near-optimal accuracy with two to three layers, whereas more complex cases necessitate deeper models for effective feature extraction.
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPPThird Generation Partnership Project  
5G Fifth Generation  
CPPController Placement Problem  
GEOGeostationary Orbit  
gNBgNodeBs  
GPPGateway Placement Problem  
ISTNsIntegrated Satellite-Terrestrial Networks  
JPKMJoint Partition-Based K-Means  
NFVNetwork Function Virtualization  
OEAOptimal Enumeration Algorithm
## 6 Numerical Examples
### Numerical Example 1: Single Edge-Notched Tension Test
An alternate approach, which is highly effective in determining accurate gSIFs [276], may be applied when the domain is star convex with regards to the crack tip, and is introduced here as a high fidelity reference solution (SBFEM hi-fi). Although by hp-refinement on the boundary, the gSIFs are accurately determined utilizing only a few DOFs, and thus minimal computational resources, this approach is only applicable to crack propagation in a select few cases, such as in this symmetric tension test, where the crack path remains straight. The SIFs obtained by discrete crack methods coincide to the fourth significant figure. For comparison purposes, the tension test is also performed using the cohesive phase field method shown in Equation (92). The fracture response in this case depends on the shape parameter \(p\), which controls the shape of cohesive stress-crack opening curve. Increasing the value of \(p\) enables faster degradation of stresses as soon as the critical stress limit is reached, however, too large \(p\) may lead to poor convergence. Figure 16 shows the dependence of load-displacement responses and critical loads on the choice of shape parameter \(p\). The length-scale parameter \(l_{o}\) for each case is chosen based on its upper bound value in Equation (69). A cohesive phase-field model is highly useful when the size of fracture process zone (FPZ) is large enough, and the Griffith's description of purely brittle fracture becomes inadequate [250]. In such cases, the numerical phase-field model can be calibrated with the specific material responses by making an optimal choice for the parameter \(p\). Figure 11: Tension test geometry, material parameters, loading and boundary conditions. Figure 12: Tension test load-deflection curves. Figure 13: Tension test phase field evolution for (**a**) \(u\) = 0.0057 mm (**b**) \(u\) = 0.00585 mm (**c**) \(u\) = 0.00595 mm, with displacement increment \(\Delta u=1\times 10^{-6}\) mm.
## 4 Performance Evaluation
### Results and Analysis
#### 4.4.1 Results Considering the Freespace Channel  
As discussed in Section 3, the freespace propagation model does not hold for the radio channel conditions present in highway scenarios as measured for V2V communications [37]. However, it is an important scenario commonly used for evaluation purposes [55]. As no fading is introduced when considering the freespace channel, every transmitted packet will reach the transceiver nominal radio range. Thus, for this channel the collision probability for broadcast packets is higher than that observed when considering a fading channel. Therefore, the effect of the packets collisions on the routing strategy performance can be observed with more detail when assuming freespace propagation. The routing metrics obtained when increasing the maximum acceleration range, \(\alpha\), for two different densities, \(\lambda\), are shown in Figures 7-9. It can be seen in Figure 7(a) that the packet delivery ratio (PDR) is above 98% for both protocols. Because of the transmitting radio range is deterministic for freespace propagation, any packet loss is caused by collisions or erroneous predictions for both protocols. Thus, as Figure 7(a) shows, both kinds of packet losses are adequately handled by the particular redundancy strategy implemented in DDOR and FPBR. Similar conclusions can be drawn for the number of hops (NH), the end to end delay (EED) and the average delay per hop as shown in Figure 7(b), Figure 8(a,b) respectively. In fact, it can be readily seen in Figures 7 and 8 that both protocols perform similarly well when considering the PDR, NH, EED, and delay per hop metrics.  
The network and the MAC overhead metrics are presented in Figure 9. According with Figure 9(a), DDOR generates fewer control packets than FPBR at the network layer. This behavior is observed because DDOR implements its redundancy strategy at the MAC layer by using the RTS/CTS mechanism. On the other hand, Figure 9(b) shows that FPBR yields better MAC overhead metrics than DDOR. This behavior is observed because the RTS/CTS mechanism used in DDOR needs a minimum of three additional packets to transmit a single data packet.  
Figure 8: Delay per hop and end-to-end delay (EED) obtained with FPBR and DDOR when increasing the maximum acceleration, \(\alpha\). Two different vehicle densities were considered for these plots: \(\lambda=133\) and \(\lambda=33\). (**a**) Delay per hop _vs. a._ (**b**) EED _vs. a._  
Figure 7: Packet delivery ratio (PDR) and number of hops (NH) obtained with FPBR and DDOR when increasing the maximum acceleration, \(\alpha\). Two different vehicle densities were considered for these plots: \(\lambda=133\) and \(\lambda=33\). (**a**) PDR _vs. a_; (**b**) Number of Hops _vs. a._
## 3 Results
### The Analytical Figures of Merit  
The chromatogram obtained under the optimized conditions (Figure 7) shows the successful separation and detection of the five PAEs, namely, DBP, DIBP, BBP, DPP and DEHP, in water. The analytical figures of merit of the prepared COF-TPB-DMTP-coated fiber for the SPME of PAEs are summarized in Table 2, namely, the linear range, correlation coefficient (R\({}^{2}\)), limits of detection (LODs), limits of quantification (LOQs), inter-day and fiber-to-fiber relative standard deviations (RSDs), and enrichment factors (EFs). The EFs for the PAEs ranged from 268 to 2657. The LODs (S/N = 3) and LOQs (S/N = 10) were in the ranges of 0.04-0.44 \(\mathrm{\SIUnitSymbolMicro m}\mathrm{\SIUnitSymbolMicro m}^{-1}\) and 0.13-1.46 \(\mathrm{\SIUnitSymbolMicro m}\mathrm{\SIUnitSymbolMicro m}^{-1}\), respectively. The linear range for  
Figure 6: The effects of extraction parameters on the desorption efficiency for the PAEs: (**a**) desorption time (conditions: extraction time, 30 min; extraction temperature, 30 \({}^{\circ}\)C; desorption temperature, 280 \({}^{\circ}\)C; NaCl concentration, 5%; agitation speed, 250 rpm); (**b**) desorption temperature (conditions: extraction time, 30 min; extraction temperature, 30 \({}^{\circ}\)C; desorption time, 2 min; NaCl concentration, 5%; agitation speed, 250 rpm). The extraction solution contained DIBP 30 \(\mathrm{\SIUnitSymbolMicro m}\mathrm{\SIUnitSymbolMicro m}^{-1}\), DBP and DPP 10 \(\mathrm{\SIUnitSymbolMicro m}\mathrm{\SIUnitSymbolMicro m}^{-1}\), and BBP and DEHP 50 \(\mathrm{\SIUnitSymbolMicro m}\mathrm{\SIUnitSymbolMicro m}^{-1}\). the PAEs varied from 0.04 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) to 500 \(\upmu\)g\(\cdot\)mL\({}^{-1}\). The inter-day RSDs for the five days of replicate extractions of the PAEs ranged from 4.1% to 12.9%. The intra-day RSDs of the PAEs repeatedly extracted during the three periods of the day ranged from 3.9% to 14.1%. The RSDs for fiber-to-fiber obtained on three parallelly prepared fibers were less than 9.9%. In addition, the TPB-DMTP-GS-SSF showed a high enrichment efficiency for DIBP, DBP, DPP and BBP in water. The adsorption selectivity of the TPB-DMTP-GS-SSF was tested by the extraction of several organic compounds with different polarities and conjugation effects, including n-octadecane, n-eicosane, n-heneicosane, DIBP, DBP, DBP, DBP, DEHP, benzene, methylbenzene, aniline and o-toluidine (Figure 8a). It can be seen that the TPB-DMTP-GS-SSF offered excellent extraction efficiency and selectivity for the PAEs. The EF of aniline and o-toluidine was higher than those of the straight chain alkanes, benzene and toluene. The phenomena could be attributed to the \(\pi\)-\(\pi\) stacking interactions on the benzene ring. The results highlight the potential of the TPB-DMTP-GS-SSF for the extraction of the polar organic compounds. As shown in Figure 8b, no significant loss of extraction efficiency
# Towards Prediction of Immersive Virtual Reality Image Quality of Experience and Quality of Service
## 1 Introduction and Related Works
A particular challenge in objectively determined quality metrics is the requirement to have the original unimpaired medium available for comparisons, which is not practical for real-time service evaluation and adjustments at the network edge. Image quality assessments that are non-referential have attracted recent research efforts, as well; see, e.g., [12]. In this article, we rely on the BRISQUEimage impairment [13] as the QoS indicator, which we successfully employed in past research efforts [2]. A similar dataset that presents immersive VR videos is described in [14]. As described in [15], significant challenges for active experimentation for the QoE determination in VR scenarios remain. A potential path to eliminate the QoE active human-in-the-loop requirement without indirect approximation via QoS metrics is enabled through developments in the domain of human-computer interaction. The focus of this article is on enabling a passive human-in-the-loop for immersive multimedia presentation scenarios. Multimedia presentation, however, is only one particular example of these types of feedback loops. Current developments also include haptic feedback evaluations, which represent additional challenges due to the different types of human sensory system stimuli required [16; 17]. The utilization of physiological signals has attracted research interests, and common examples include Electroencephalography (EEG), skin conductivity or heart rate [18]. The direct utilization of EEG measurements has attracted research interests in the QoE domain [19] and has successfully been evaluated in traditional content consumption settings; see, e.g., [20; 21; 22]. Two common approaches to the utilization of the EEG data are given by (i) a direct employment of EEG measurements at 300-500 ms after a stimulus (e.g., media display or quality changes) and (ii) in the context of steady-state evoked potentials [23]. We follow these two approaches by employing values directly, as well as homogenized via z-scores across the experimental sessions.
## 6 Comparison Between KANs and Classical Models  
### KAN-B vs. Usui Model  
The classical flank wear model is the Usui Adhesion Wear Model (hereinafter \"Usui Model\"), which describes tool wear under high-temperature/pressure-turning conditions, based on the adhesion wear mechanism between tool and workpiece materials:  
\[\frac{dW}{dt}=a\cdot\sigma\cdot V\cdot e^{-b/T} \tag{18}\]  
where \(dW/dt\) is the wear rate; \(a\), \(b\) are material constants that depend on the tool and workpiece materials; \(\sigma\) is the normal stress at the contact surface; \(V\) is the sliding velocity of the chip relative to the tool; and \(T\) is the interfacial temperature at the contact surface.  
The Usui Model is a physics-based approach that can also be used with finite element simulations. However, precise material constants \(a\) and \(b\) must be determined experimentally. Moreover, adhesive wear is not the dominant mechanism under all cutting conditions. Mechanisms such as abrasive wear, diffusion wear, and chemical wear are not considered.  
Taking Formula (6), \(y_{fw}=0.1|10.7x_{1}+2.2x_{2}-6.0|\), as an example, its differences from the Usui Model are discussed as follows. The mathematical formula derived by KAN-B suggests that the effect of speed (\(x_{3}\)) is either indirectly reflected through other variables or insignificant for flank wear under the given cutting conditions. The feed rate (\(x_{1}\)) and depth of cut (\(x_{2}\)) are modeled via a combination of linear terms and absolute values. In contrast, the Usui Model explicitly incorporates speed (\(V\)), temperature (\(T\)), and stress (\(\sigma\)) through terms like the exponential \(e^{-b/T}\), which quantifies the temperature effect on wear.  
The formula obtained by KAN-B reflects the mathematical relationships between the physical variables specific to the current dataset. Although it does not rely on prior physicalknowledge or laws, it provides a white-box interpretable expression. It offers a key insight: researchers can conduct modeling studies for diverse specific machining conditions.  
For instance, in high-speed milling of titanium alloys, a KAN model might reveal the asymmetric effect of axial cutting depth on wear. When the tool overhang is large, negative coefficient terms in the model could reflect critical chatter effects caused by reduced system stiffness. If KANs generate an absolute value function involving a \"stiffness threshold,\" the stable operating domain of the process system can be mathematically determined.  
The iterative research paradigm of \"phenomenological modeling-physical mechanism tracing\" provides a new pathway for unraveling the mechanisms of complex manufacturing processes.
# A Simplified Pointing Model for Alt-Az Telescopes
## 3 The Methodology for Creating the Telescope Pointing Model
### Calculate Azimuth and Elevation Angles  
Since the pointing of the alt-az telescope is based on the local geodetic horizontal coordinate system, the right ascension and declination of the image center should be transformed to the local geodetic horizontal coordinate system to obtain the true azimuth and true elevation angles of the telescope pointing. The calculation is realized using a standard astronomical program [11] with input parameters of the image observation time (universal time), the telescope's geographical latitude and longitude, the elevation of the mounting site, and the temperature and atmospheric pressure during the observation.  
In the calculation process, atmospheric refraction must be corrected, especially for low elevation angles. The atmospheric refraction \(R\)[12] at sea level can be expressed in minutes of arc as follows:  
\[R=1/\tan(E+7.31/(E+4.4)), \tag{1}\]  
where \(E\) is the apparent elevation angle. The effect of refraction increases when the atmospheric pressure increases or when the temperature decreases. Therefore, a correction factor should be multiplied to \(R\). If the pressure at the telescope mounting site is \(P\) millibars, and the air temperature is \(T\) Kelvin, the factors is \((P/1010)\)\(\times\)\((283/T)\). Note that the above formula is only applicable for \(E\) > 15\({}^{\circ}\). Our PAST telescope also operates at \(E\) > 15\({}^{\circ}\). The final true elevation angle is obtained by subtracting \(R\).  
By subtracting the input telescope azimuth angle from the true azimuth angle and subtracting the input telescope elevation angle from the true elevation angle, the azimuth angle error matrix and the elevation angle error matrix can be obtained, respectively. Ten sky images are captured for each pointing direction, from which ten azimuth angle errors and elevation angle errors can be derived. Then, we use the averaged values to represent the pointing errors at that direction. The uncertainties represented by the 95% confidence intervals for the azimuth axis and the elevation axis are 1.5 arcs and 1.7 arcs, respectively. These uncertainties might be derived from the wind disturbance,  
Figure 4: An example sky map captured at an elevation angle of 50 degrees and azimuth angle of 100 degrees. The exposure time is 0.2 s. The size of the image is 2048 \(\times\) 2048 pixels. The stars identified by the Astrometry.net system are marked by the circles. The right ascension and declination of the image center (marked by “+”) are 19 h 58 m 35.221 s and +36\({}^{\circ}\)13\({}^{\prime}\)54.080\({}^{\prime\prime}\), respectively.  
the mechanical imperfections, and the sensor inaccuracies, and have little influence on the pointing model.
## 2 Background
### RFID System Overview  
RFID is the system containing tags and readers, and it is applied to automatically identify and track tags attached to objects. Figure 1 shows some terminologies applied during the identification process. A frame is the duration from the moment a reader begins recognizing all tags within its reading range to the time it finishes all recognitions. Let \(f_{i}\) denote the \(i\)-th frame. A frame contains a set of slots. A slot is the duration when a reader transmits a triggering (feedback) signal to the tags and then the tags respond with their IDs to the reader. Let \(S_{i,j}\) denote the \(j\)-th slot in the \(i\)-th frame. Notably, the status of a slot can be classified as idle, success, and collision. An idle slot indicates that the reader does not receive any signal from tags. The success slot means that the reader can successfully decode one or more tag IDs from the signal. A collision slot is the slot where the reader receives the signals but cannot decode any information from them. As shown in Figure 2, a collision is generally caused by that two or more tags choose the same slot to send message to the reader.  
During the identification process, the reader periodically broadcasts a query or some information with regarding the frame in the first slot of the frame. The tags within radio coverage range of the reader will select one slot and response their own ID in this slot if their ID matches the query. Ideally, the number of slots within a frame should equal to the number of the tags within the coverage of a reader in order to maximize the bandwidth usage. Otherwise, some unused slots will exist if the number of slots is larger than the number of the tags, or the tags will suffer serious collisions if the number of slots is fewer than the number of the tags. Therefore, the RFID reader has to estimate the number of the tags to decide an appropriate frame size.
# Development of a Structural Monitoring System for Cable Bridges by Using Seismic Accelerometers
###### Abstract  
In this study, a structural health monitoring system for cable-stayed bridges is developed. In the system, condition assessment of the structure is performed based on measured records from seismic accelerometers. Response indices are defined to monitor structural safety and serviceability and derived from the measured acceleration data. The derivation process of the indices is structured to follow the transformation from the raw data to the final outcome. The process includes, noise filtering, baseline correction, numerical integration, and calculation of relative differences. The system is packed as a condition assessment program, which consists of four major process of the structural health evaluation: (i) format conversion of the raw data, (ii) noise filtering, (iii) generation of response indices, and (iv) condition evaluation. An example set of limit states is presented to evaluate the structural condition of the test-bed cable-stayed bridge.  
structural health monitoring; cable-stayed bridge; seismic accelerometer; response indices 1
# Neural Network-Based Reference Block Quality Enhancement for Motion Compensation Prediction
## 2 Related Work
The reference CUs, and the original CUs corresponding to different fractional filters, are made into different datasets for network training. All convolution kernels in the network are extracted and compressed into a \(13\times 13\) matrix to replace the DCTIF for MC. This approach can effectively reduce the complexity of the network; it can finally achieve BD rate savings of 0.77%, 1.27%, and 2.25% on average under RA, LDB, and LDP configurations, respectively, compared to H.266/VVC. [PERSON] [17] used neural networks to enhance inter hyprediction and replaced bidirectional optical flow (BDOF) with networks. Compared with H.266/VVC, it saves 1.4% and 0.58% of the BD rate under RA and LDB configurations, respectively. [PERSON] [18] combined DL with affine motion compensation. This method takes the reference region, the predicted region, and the motion vectors of sub-CUs of affine motion compensation as the inputs of the network, and then outputs the final predicted region. The authors trained 76 networks for different QPs and CU sizes. It finally achieved a \(-1.77\%\) BD rate compared to H.266/VVC under LDP configuration. In [19], [PERSON] used LSTM [25] to enhance the reference frame to generate a virtual frame. This method achieves a BD rate reduction of 0.58% compared to H.266/VVC under the LDP configuration. Most of the above-mentioned methods use a neural network to replace the DCTIF. Unlike these methods, we embedded the RBENN as a module into inter prediction, which could be used together with these methods. To maximize the coding efficiency, we only enhanced the CU blocks whose size was larger than \(16\times 16\), and also enhanced the CU blocks before affine motion compensation.
## 2 Materials and Methods
### Analysis of TAEM and TAEE by HPLC and LC-MS/MS  
TAEM and TAEE were analyzed by a Waters 1525 series HPLC system (Waters, Milford, MA, USA) coupled with a Waters 2487 Absorbance Detector (Waters, Milford, MA, USA) using a C\({}_{18}\) column (4.6 \(\times\) 250 mm, SymmetryShield\({}^{\text{TM}}\) RP, Waters, Milford, MA, USA). The mobile phase consisted of 0.1% AcOH/H\({}_{2}\)O (A)-Acetonitrile (B), the gradient program was set as: 0-10 min, 10% B; 10-40 min, 10-50% B; 40-50 min, 50% B; 50-85 min, 50-100% B; 85-115 min, 100% B; 115-120 min, 100-10% B. The flowrate was 1.0 mL/min, the UV detection wavelength was set at 254 nm, the operating temperature was maintained at 25 \({}^{\circ}\)C. The concentration of the samples was 2 mg/mL.  
A C\({}_{18}\) column (5 \(\upmu\)m 250 \(\times\) 4.6 mm, Diamonsil C\({}_{18}\) (2), Dikma, Beijing, China) was used and the samples were analyzed by Thermo Scientific Dionex Ultimate 3000 system (Thermo Scientific Dionex, Waltham, MA, USA) and Thermo Scientific LCQ Fleet (Thermo Scientific Dionex, Waltham, MA, USA) liquid chromatography-mass spectrometry. The gradient program was set as above. The system operated in the positive and negative ionization modes of electrospray ionization (ESI). The total ion chromatogram (TIC) and mass spectrogram were recorded. Based on the obtained molecular ions (_m/z_), and matching that with literature, the components could be inferred.