esa-sceva/satcom-chunk-collection huggingface dataset:
# Calculation and Verification of the Real-Time Working Characteristics of a Viscous Coupling
## 5 Working-Characteristics Test of the VC  
In order to test the working characteristics of the VC, a VLSD, which sets the VC and the differential in one body, was designed and manufactured. The assembly layout and structure are shown in Figure 8.  
Figure 8: Prototype of the VLSD.  
Figure 7: The simulation result of working process of VC.  
The working-characteristics test of VLSD was carried out. The actual conditions of the test field are shown in Figure 9. The input speed of the axle was 1500 r/min, and the speed difference of two outputs remained 400 r/min. The limited-slip torque of the VLSD in the test is shown in Figure 10.  
As shown in Figure 10, the limited-slip torque decreased slightly during the first 30 s of the test. Then, the increasing trend grew fast, and reached 200 Nm at 60 s. The limited-slip torque reached the top at around 76 s, when the peak value was about 680 Nm.  
When comparing the two curves in Figure 10, apart from some differences between 40 s and 70 s, the trends of the torque curves are consistent with each other, and the time of the hump phenomenon's occurrence and the peak value of torque are basically the same on the two curves. So, the accuracy of the calculation model established in this paper is verified. The main reason for the differences is that the plates in the VC are movable during operation, which was ignored in the calculation model. This deficiency deserves further research.
# Crack Defect Detection Method for Plunge Pool Corridor Based on the Improved U-Net Network
## 5 Discussion
### The Synergy of Improved Modules  
The ablation experiments and visual analyses in Section 4.4 demonstrate that MIDConv and CDIAM achieve effective synergy via the complementarity of their feature extraction mechanisms. MIDConv adjusts convolution kernel offsets to enable dynamic expansion of the network's receptive field, improving the capability to capture features of irregularly shaped cracks. CDIAM employs channel grouping and global information calibration to enhance pixel-level semantic relationships in complex backgrounds and suppress interference from similar textures. In their combination, the dynamic spatial features provided by MIDConv at the encoder stage and the cross-dimensional semantic enhancement realized by CDIAM at the decoder stage form hierarchical complementarity, jointly resolving the issues of geometric feature extraction for irregular morphologies and semantic ambiguity in complex backgrounds. Quantitative results indicate that the joint use of the two modules raises MIoU to 84.44%, representing increases of 2.08% and 1.66% over single modules, respectively, which validates the positive impact of the two modules' synergy on improving crack detection accuracy.
## 3 Enhancing Supply Chain Security Through Blockchain Technology
### Security Advantages of Blockchain in Supply Chains
#### 3.1.3 Cryptographic Signatures  
Cryptographic signatures (digital signatures) are utilized to verify data origin and prevent unauthorized access [37]. Operating on the principle of asymmetric cryptography [38], this system encrypts data using a private key held by the signer. The resulting signed data can subsequently be validated by any verifier using a corresponding public key. If a logical outcome is obtained during the verification stage, the data are accepted as both authentic and unchanged. Consequently, by keeping the private key confidential, identity fraud and unauthorized alterations of documents are significantly prevented.  
In distributed ledger structures, such as blockchain, cryptographic signatures play a critical role in verifying the authenticity of blocks or transactions created by multiple authors. Each transaction or block is signed using the author's private key, and other network participants validate this signature with the author's public key, confirming both the source and the integrity of the data. Thus, trust is established among the network participants, ensuring overall system transparency [39]. A blockchain and IoT-integrated model has been proposed to enhance data privacy and transparency within supply chain management [40]. In the proposed model, IoT devices are employed to securely collect and store data, while cryptographic techniques and a permissioned blockchain network safeguard data privacy and integrity.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 3 Methodology
### Seismometer Self-Noise Model  
As previously discussed, the self-noise model of the seismometer can be expressed as follows:  
\[S_{EIN}(f)=\Big{[}S_{n_{m}}(f),S_{n_{ex}}(f),S_{n_{int}}(f),S_{n_{f_{l}}}(f),S_ {n_{da}}(f)\Big{]}\cdot\Big{[}H_{n_{m}}^{a_{i}}\;{}^{2}(s),H_{n_{ex}}^{a_{i}} \;{}^{2}(s),H_{n_{int}}^{a_{i}}\;{}^{2}(s),H_{n_{f_{l}}}^{a_{i}}\;{}^{2}(s),H _{n_{da}}^{a_{i}}\;{}^{2}(s)\Big{]}^{T}. \tag{46}\]  
where \(S_{EIN}(f)\) represents the PSD of the EIN in acceleration. For a typical VBB seismometer, \(S_{n_{int}}(f)\) corresponds to the acceleration noise PSD of the mechanical suspension pendulum, \(S_{n_{ex}}(f)\) represents the electronic noise in the forward path, \(S_{n_{int}}(f)\) accounts for the integrator noise in the feedback path, \(S_{n_{f_{l}}}(f)\) represents the force transducer noise in the feedback path, and \(S_{n_{da}}(f)\) is the differential amplifier noise (or can be seen as input noise of the output-stage device). According to Equations (19) and (22), each noise source can be modeled as a combination of band-limited Gaussian white noise and \(1/f\) noise.  
The terms \(H_{n_{m}}^{a_{i}}(s)\), \(H_{n_{ex}}^{a_{i}}(s)\), \(H_{n_{int}}^{a_{i}}(s)\), \(H_{n_{f_{l}}}^{a_{i}}(s)\), and \(H_{n_{da}}^{a_{i}}(s)\) are the noise transfer functions corresponding to each noise source, and their specific expressions are provided in Equations (23), (25), (33), (35) and (41). In this context, \(s\) represents the Laplace parameter, where \(s=j2\pi f\), with \(j\) being the imaginary unit and \(f\) the frequency.
## 4 Implementation
### Bridge
#### 4.3.1 Data Structure  
The Bridge processes libp2p communications, whose messages are encoded as UTF-8 byte strings; therefore, to be delivered via serial port, they must be first converted to hexadecimal and then into a series of bytes, appending a start and an end element. Moreover, the messages range in size from 20 to 2000 bytes, but the proposed Bluetooth transportation layer can only send 255 bytes, so messages need to be split in segments. To avoid saturating the channel, each segment is queued and broadcast with a specific delay. In addition to the message start and end characters, each segment contains a header that indicates the entire size of the unfragmented message, which is eventually processed by the receiver. Table 2 illustrates the internal structure of the frame, which contains the following fields:  
* Header (13 bytes): it contains the message header, which includes the information necessary to send the message through the OEC protocol.
* Dst (6 bytes): it contains the destination ID, which identifies the node to which the message is addressed.
* Start Char (2 bytes): it indicates the start of the message character, which serves as a delimiter so that the receiver knows where the message content begins.
* Data (1-227 bytes): payload of the message.
* Length (4 bytes): actual length of the message so that reception can determine whether the message has arrived complete.
* End Char (3 bytes): it indicates the end of the message character, which serves as a delimiter so that the receiver knows where the frame ends.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
13 bytes & 6 bytes & 2 bytes & 1–227 bytes & 4 bytes & 3 bytes \\ \hline \hline HEADER & DST & START CHAR & DATA & LENGTH & END CHAR \\ \hline \hline \end{tabular}
\end{table}
Table 2: Internal structure of the protocol data frame.
# Improved Ant Colony Algorithm for the Split Delivery Vehicle Routing Problem
## 5 Experimental Results  
To verify the effectiveness of the IACA in solving SDVRP, simulation experiments were conducted using Python code (v.3.7.0). The experimental environments were the Windows 10 operating system, an AMD 5800X processor, and 32 GB memory (Santa Clara,CA, USA). To test as comprehensively as possible, experiments were conducted on small, medium, and large datasets to compare the IACA with other algorithms.
# From Far-Field to Near-Field Micro- and Nanoparticle Optical Trapping
## 2 Conventional Optical Tweezers
### Ray Optics Approximation (\(r\gg\lambda\))
The resulting forces are:  
\[F_{scat}=F_{Z}=\sum_{i=1}^{N}\frac{n_{m}\cdot P_{i}}{c}\left[1+R_{i}\cos(2 \theta_{i})-\frac{T_{i}^{2}[\cos(2\theta_{i}-2r_{i})+R_{i}\cos(2\theta_{i})]}{ 1+R_{i}^{2}+2R_{i}\cos(2r_{i})}\right] \tag{2}\]  
and  
\[F_{grad}=F_{Y}=\sum_{i=1}^{N}\frac{n_{m}\cdot P_{i}}{c}\left[R_{i}\sin(2\theta _{i})-\frac{T_{i}^{2}[\sin(2\theta_{i}-2r_{i})+R_{i}\sin(2\theta_{i})]}{1+R_{i }^{2}+2R_{i}\cos(2r_{i})}\right], \tag{3}\]  
where the sum is over all \(N\) rays with power \(P_{i}\) each, interacting with the particle, and \(\theta_{i}\) and \(r_{i}\) are the incidence and refraction angles, respectively, as shown in Figure 2. The terms in the square brackets are the dimensionless _trapping efficiencies_, \(Q_{scat}\) and \(Q_{grad}\), and account for the efficiency of momentum transfer from the light ray to the particle. We also define the total trapping efficiency of the ray as \(Q_{ray}=\sqrt{Q_{scat}^{2}+Q_{grad}^{2}}\). The Fresnel coefficients, \(R\) and \(T\), depend on the polarisation of the incident rays. Therefore, the trapping efficiencies and the trapping forces will also be polarisation dependent. In Figure 3, the trapping efficiencies as a function of the ray's incident angle are plotted for circularly polarised light hitting a glass (\(n_{m}=1.6\)) sphere in water (\(n_{s}=1.33\)). We see that, for incident angles smaller than \(70^{\circ}\), the gradient force dominates, but as the incident angle increases, the scattering force becomes significant. This means that, for unfocussed or slightly focussed beams that have a small convergence angle, inevitably most of the rays (taking into account the Gaussian beam profile) will hit the surface of the particle with a large incident angle, \(\theta\), as shown in Figure 1a, thereby pushing the particle away. On the contrary, beams that are tightly focussed under a high NA objective lens, cause the rays to hit the surface of the particle with small incident angles (Figure 1b). As a result, the gradient forces strongly dominate over the scattering ones and a stable trap can be established, as [PERSON] et al. experimentally demonstrated [12]. Note that, in this case, the longitudinal component of the resulting forces always points towards a location slightly downstream of the beam's focal point,  
Figure 2: Calculation of the scattering and gradient forces acting on a Mie particle from a single ray, by taking into account geometrical optics and multiple reflection and transmission events, using Fresnel coefficients \(R\) and \(T\). leading to particle trapping close to, but not exactly at, the focal point. [PERSON]'s calculations [28] confirmed that, in order to create strong, single-beam traps, high convergence angles are required. For convergence angles smaller than \(\sim 30^{\circ}\), single-beam trapping is impossible. Instead, we can use two counter-propagating beams with the same characteristics, as shown in the bottom inset of Figure 1a, to cancel out the scattering forces [7].
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 3 Results
### Positioning
The mean error for RPSC1 with SmartNet was 0.114 m, with a standard deviation of 0.181 m and an RMSE of 0.214 m. In contrast, the worst performance was recorded with RPSC2 using IGN, with a mean error of 0.212 m, a standard deviation of 0.285 m, and an RMSE of 0.355 m. The relationship between the standard deviation and the mean value (coefficient of variation) also supports these observations: while IGN showed more consistent values in RPSC1 (CV = 0.98), this relationship degrades in RPSC2 (CV = 1.34), reflecting greater instability. Although SmartNet presents a higher CV in RPSC1 (1.59), this high value is a direct consequence of a small denominator (low mean error), which does not imply worse absolute stability. These results confirm that the SmartNet service offers more precise solutions globally and is less prone to extreme errors. In the context of this test, SmartNet has demonstrated a greater ability to maintain a fixed and precise RTK solution over time, while IGN appears more susceptible to fluctuations in link quality or satellite geometry. In all cases, a typical pattern of error evolution is observed, consisting of an initial acquisition phase, a transition towards stabilisation, and a subsequent low-error phase. However, the duration and regularity of these phases strongly depend on the service used. In conclusion, this test demonstrates that utilising the SmartNet service provides significant advantages in terms of precision, temporal stability, and convergence time for GNSS+RTK systems on mobile robotic platforms. In scenarios where precise, real-time positioning is required, such as autonomous tracking, assisted navigation, or swarm formation, these improvements can directly translate into greater operational safety and efficiency.
## 3 Experiments and Analyses
### Result Analyses
#### 3.3.1 Results of Ablation Experiments
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **BFP** & **DCN** & **EPSA** & \begin{tabular}{c} **ROI** \\ **Align** \\ \end{tabular} & **Type** & **Recall** & **Precision** & **mAP** & **F1** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Original \\ model \\ \end{tabular} } & \multirow{3}{*}{\(\times\)} & \multirow{3}{*}{\(\times\)} & & \multirow{3}{*}{\(\times\)} & SRBC & 0.686 & 0.761 & 0.731 & 0.722 \\  & & & & ARBC & 0.587 & 0.505 & 0.545 & 0.543 \\  & & & & MIX & 0.664 & 0.754 & 0.719 & 0.706 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Model 1 \\ \end{tabular} } & \multirow{3}{*}{\(\times\)} & \multirow{3}{*}{\(\times\)} & & \multirow{3}{*}{\(\surd\)} & SRBC & 0.772 & 0.811 & 0.793 & 0.791 \\  & & & & ARBC & 0.666 & 0.752 & 0.710 & 0.706 \\  & & & & MIX & 0.736 & 0.791 & 0.769 & 0.763 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Model 2 \\ \end{tabular} } & \multirow{3}{*}{\(\surd\)} & \multirow{3}{*}{\(\times\)} & & \multirow{3}{*}{\(\surd\)} & SRBC & 0.886 & 0.923 & 0.905 & 0.904 \\  & & & & ARBC & 0.782 & 0.865 & 0.823 & 0.821 \\  & & & & MIX & 0.838 & 0.897 & 0.874 & 0.866 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} BDE\_RCNN \\ \end{tabular} } & \multirow{3}{*}{\(\surd\)} & \multirow{3}{*}{\(\surd\)} & & \multirow{3}{*}{\(\surd\)} & SRBC & 0.895 & 0.942 & 0.919 & 0.918 \\  & & & & ARBC & 0.796 & 0.887 & 0.842 & 0.839 \\ \cline{1-1}  & & & & MIX & 0.868 & 0.921 & 0.895 & 0.894 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Index comparison results of four models. Figure 4: Detection results of four models. (**a**) Original model; (**b**) model 1; (**c**) model 2; (**d**) BDE_RCNN. to converge at 1750 iterations, as shown in Figure 5a-c, while BDE_RCNN converges at 600 iterations, as shown in Figure 5d. The BDE_RCNN method is superior to other models in terms of convergence times. Thus, with the reduction of loss curve, a higher level of training model can be obtained at fewer iterations. In addition, the learning rate curve of BDE_RCNN begins to decline when the iteration numbers reach about 250, as shown in Figure 5d. Compared with other models, BDE_RCNN more easily approaches the optimal model solution of training parameters in the training process.
# On Fast Converging Data-Selective Adaptive Filtering
## 2 Problem Description
### System Identification  
In the system identification, the desired signal can be formulated as  
\[d(k)=\mathbf{w}_{\mathrm{o}}^{T}\mathbf{x}(k)+n(k) \tag{26}\]  
where \(\mathbf{w}_{\mathrm{o}}\) is the optimal coefficient, \(\mathbf{x}(k)\) is the input vector and \(n(k)\) is the noise drawn from AWGN with zero mean and variance \(\sigma_{n}^{2}\). Therefore, the MSE can be expressed as:  
\[\xi(k) =\mathbb{E}[e^{2}(k)]=\mathbb{E}[n^{2}(k)]-2\mathbb{E}[n(k) \Delta\mathbf{w}^{T}(k)\mathbf{x}(k)] \tag{27}\] \[+\mathbb{E}[\Delta\mathbf{w}^{T}(k)\mathbf{x}(k)\mathbf{x}^{T}(k )\Delta\mathbf{w}(k)]\]  
where we define \(\Delta\mathbf{w}(k)=\mathbf{w}(k)-\mathbf{w}_{\mathrm{o}}\). Assuming that the noise and coefficients are uncorrelated, the second term in (27) is zero and we get the following expression  
\[\xi(k)=\sigma_{n}^{2}+\xi_{\mathrm{exc}}(k) \tag{28}\]  
where \(\xi_{\mathrm{exc}}(k)\) is the excess MSE and \(\mathbb{E}[n^{2}(k)]=\sigma_{n}^{2}\). As excess MSE tends to zero, the MSE expression for system identification, previously mentioned in (11), is rewritten as  
\[\xi(k)=\sigma_{\varepsilon}^{2}=(1+\rho)\sigma_{n}^{2}. \tag{29}\]
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 5 Research Challenges and Future Research Directions
### Timeliness Issues  
Challenge: Firstly, most existing algorithms emphasize detection performance and generalization but often overlook the model complexity and computational resources, which limits their application on mobile or low-power devices. For example, competitionsand databases have been released to boost research progress for FAS [27, 161, 165]. These challenges have focused on specific problems that affect the accuracy of the methods, such as multimodal information [162], 3D mask attacks [27], and surveillance scenarios [197]. However, constraints have been imposed on the model's compactness and efficiency. Secondly, transformer models (such as ViT [135] and Swin-Transformer [223]) suffer from large model sizes and require substantial computational resources. However, increasing accuracy often means more complex models, which can reduce real-time processing capability. Thirdly, there is a trade-off between detection accuracy and speed, thereby making it a key challenge to balance high accuracy with efficient algorithms. Lastly, real-time detection systems must adapt to various practical scenarios, such as lighting conditions, camera quality, and background changes. Existing models often need to improve in response to these dynamic environmental changes.  
Future Research Directions: Firstly, to enhance robustness and generalization in real-time detection, techniques like model compression and pruning can be employed to lightweight models should be designed and develop efficient FAS algorithms suitable for low-resource devices, thereby improving real-time performance. In recent years, several lightweight neural networks have been proposed, such as MobileNet-V2 [224], MobileViT [225], and EfficientFormer-V2. These networks will contribute to designing lightweight FAS algorithms. Secondly, online learning and adaptive systems should be researched to enable them to dynamically adjust in changing environments, thereby enhancing model performance and adaptability in complex scenarios.
## 3 Results and Discussion  
### GC-Qtof-Ms  
#### 3.1.1 Nontarget Analysis  
In total, approximately 40 kinds of VOCs were identified using screening protocol, all of which had match factor (MF) greater than 70 (Table S2). It can be seen that the identified compounds mainly included sulfur organic compounds, nitrogenous organic compounds, benzenes, terpenes, carbonyls, aromatics, and halogenated compounds. Some (toluene, chlorobenzene, 1,4-dichlorobenzene, p-cymene, and naphthalene) were assessed as health indicators and already included in list of target compounds. Due to the unpleasant odor description and low odor threshold concentrations (OTCs), VSOCs have previously reported and demonstrated as significant contributor to sewer [40] and sewage sludge composting plants [41]. There were two identified VSOCs (dimethyl disulfide and dimethyl trisulfide) to be selected as target compounds. Figure 1 showed an example of dimethyl disulfide (DMDS) emitted from influent wastewater (IW) detected using the nontarget analysis. At the expected retention time, five representative _m/z_ ions were automatically used. Figure 2 showed another example, the identification of dimethyl trisulfide (DMTs) was confirmed by the presence of 5 _m/z_ ions also in IW sample. Subsequently, each standard was used to confirm their existence. Then quantitative method of single-point calibration was applied to determine the concentrations of the analytes. DMDS and DMTS were only detected in wastewater samples before PSO with the concentrations ranged from 205.21 ng/L to 689.89 ng/L and from 20.57 ng/L to 70.35 ng/L, respectively. The maximum detected concentrations of DMDS and DMTS were higher than their OTCs, which are 0.0003 mg/kg and 0.00001 mg/kg [42]. DMDS has been recognized as key odorants [43], and it is also found to be the primary odorant in another study [31]. Thus, the presence of DMDS and DMTS in the wastewater during pre-treatment could be smelled by humans and caused odor problems.  
The mass accuracy and the ratios between the most abundant ion (Q) and every one of the other measured ions (qi) (Q/qi ratios) are the common problems in nontarget analysis. For example, Q/qi ratios did not accord with that of library mass spectrum, which might be ascribed to the complexity of wastewater matrix. The matrix complexity of environmental samples often leads to the determination errors, thus certain deviations in mass accuracy should be allowed for environmental samples [44].
## Acronyms  
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline ACN & Acetonitrile & LLLME & Liquid-liquid-liquid microextraction \\ AMMWCNT-PDMS & Amino-modified multi-walled & LLME & Liquid-liquid microextraction \\  & carbon nanotube-PDMS & & \\ APEOs & Alkylphenols ethoxylated & LODs & Limit of detections \\ APs & Alkylphenols & LOQs & Limit of quantifications \\ BPA & Bisphenol A & LPME & Liquid-phase microextraction \\ BUVSs & Benzotriazole UV stabilizers & MeOH & Methanol \\ CCL & Contaminant candidate list & MIPs & Molecular-imprinted polymers \\ CME & Capillary microextraction & MISPME & Molecular-imprinted solid phase \\ CNPrTEOS & Cyanpropyltriethoxysilane & MOF & Metal-organic framework \\ CNTS & Carbon nanotubes & MS & Mass spectrometry \\ CW/DVB & Carbowax/divinylbenzene & MS/MS & Tandem MS \\ CW/TPR & Carbowax/template resin & MWCNTs & Multi-wall carbon nanotubes \\ DAD & Diode array detector & NSAIDs & Non-steroidal anti-inflammatory drugs \\ DESI-MS & Desorption electrospray ionization & PAHs & Polyciclic aromatic hydrocarbons \\  & mass spectrometry & & \\ DI-SPME & Direct immersion solid phase & & \\ DI-SPME & microextraction & & \\ DLLME & Dispersive liquid-liquid & & \\  & microextraction & & \\ DLLME based on floating organic & & & \\  & droplet & & \\ DLPME & Dispersive liquid phase & & \\  & microextraction & & \\ DSDME & Directly-suspended droplet & & \\  & microextraction & & \\ dSPME & dual-SPME & PFCs & & \\ EDCs & Endocrine disruptor compounds & & & \\ \hline \hline \end{tabular}