esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Cranial Sites
Absorption at 1500 nm (the first overtone of NH, OH, and CH vibrations) corresponds to elevated water content that may be secondary to vasogenic edema from blood-brain barrier disruption or neuroinflammatory infiltrates, consistent with MRI studies showing transient T2 hyperintensities in acute lesions. However, no relapsing patient was recruited in the study; therefore, other factors should be invoked. The contribution of collagen deposition to scattering at this wavelength implicates reactive astrogliosis, a pathological feature of MS that promotes fibrotic scar formation [31]. The dual role of water and collagen in this spectral band underscores the utility of NIR spectroscopy in capturing dynamic tissue changes that conventional imaging may overlook. At 1800 nm (a wavelength identified from the brainstem site), the first overtone of SH and CH vibrations indicates possible alterations in oxidation processes and the metabolism of sulfur-containing proteins, which are critical for neuronal functionality. Hydrogen sulfide-induced S-sulfhydration regulates blood-brain barrier permeability and cerebral blood flow, and it intervenes in biological pathways, such as inflammatory response and oxidative stress [32]; in addition, protein S-sulfhydration regulates mitochondrial integrity, long-term potentiation, and calcium homeostasis [33], mechanisms found altered in MS patients. The spectral features found in the 1900-2000 nm range align with elevated lactate levels, a metabolite indicative of anaerobic glycolysis in metabolically stressed cells. In MS, this likely reflects activated microglia and macrophages within inflamed lesions, which shift to glycolytic metabolism under hypoxic conditions [34]. The simultaneous detection of hydroxyl group vibrations suggests concomitant oxidative stress, a mechanism implicated in both mitochondrial dysfunction and neurodegeneration [35]. These metabolic perturbations mirror magnetic resonance spectroscopy findings of lactate peaks in chronic active lesions [36], positioning NIR spectroscopy as a complementary tool for monitoring inflammatory activity. Changes in the spectral features at 2200-2300 nm may correspond to the loss of long-chain fatty acids in myelin membranes [37], a hallmark of MS demyelination. At 2200 nm, NH+OH combinations suggest changes in hydration levels and protein balance, while the signal at 2300 nm, linked to CH+CH combinations, highlights changes in lipid composition and membrane stability. Notably, the spectral shifts in this region may also arise from \(\beta\)-sheet-rich protein aggregates, such as amyloid-like deposits observed in progressive MS [38]. Finally, the 2400 nm signal, attributed to CH+CH and CH+CC vibrational combinations, suggests a reorganization of lipid components and modifications in cellular membrane stability. Overall, the multispectral approach employed here enables differentiation of MS-specific changes from age- or comorbidity-related confounding factors. These differences are driven by underlying physiological changes, including lipid and myelin integrity, neuronal density, oxidative stress, and water content fluctuations.
# UAV Communication Recovery under Meteorological Conditions
## 2 System Model
### Specific Attenuation Model for Rain, Fog, Snow, and Gases
#### 2.3.1 Rain  
Using the International Telecommunication Union (ITU) model, the specific attenuation due to rainfall is denoted as \(\gamma_{\rm rain}\) (dB/km) and it is related to the power law of the rainfall rate \(R_{\rm rain}\) (mm/h). It is written as [13]  
\[\gamma_{\rm rain}=kR_{\rm rain}^{\rm z}\quad({\rm dB/km}), \tag{6}\]  
where \(k\) and \(\alpha\) are determined by a series of coefficients provided by the ITU. Generally, \(R_{\rm rain}\) ranges from 0 to 100 mm/h.
## 2 Procedures in Deriving Rainfall Maps from Attenuation
### Determination of Baseline

Table 1: _Cont._[PERSON] et al. [65] compared a single-parameter baseline model based on a low-pass linear filter with a constant baseline model and found the former to have superior performance. Based on the temporal distribution of rainfall, the baseline can also be estimated as the median of all dry period attenuation over a long period prior to the wet period [45]. Furthermore, some baseline estimation algorithms have been developed that do not require dry and wet period classification. Assuming that the rain rate is a random process, [PERSON] et al. [66] demonstrated that information about the baseline is hidden in the minimum attenuation and suggested using a sliding window to estimate the baseline. For the median attenuation method described above, the baseline can be estimated as the median of all attenuations by utilizing a sufficiently large window without the need for dry/wet classification because the window can contain more dry-period samples [45; 47].
# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
## 2 Materials and Methods
### Sparse Dictionaries Model  
In the case of data matrices \(X^{s}_{m}\) and \(X^{t,l}_{m}\), which are formed by the class m samples \(x^{s}_{i,m}\), \(x^{t,l}_{j,m}\) in the source and target domains, the class m data constitution augmentation matrix is represented as \(X_{m}=\left[X^{s}_{m}\ X^{t,l}_{m}\right]\). The representation model of \(X_{m}\) in relation to the data matrix can be expressed as:  
\[\min_{D_{m}\mathcal{S}_{m}}\|X_{m}-D_{m}S_{m}\|_{F}^{2}+\lambda_{1}\|S_{k,m}\| _{1} \tag{1}\]  
Moreover, taking into account the augmentation strategy delineated in the literature [3], \(X^{t,u}_{k,m}\in\mathcal{R}^{d\times N^{t,u}_{k,m}}\) is employed to denote the data matrix composed of the pseudo-tagged samples selected in the target domain at the kth iteration and classified in class m. Here, \(N^{t,u}_{k,m}\) signifies the number of samples per class.  
Figure 2: Principle diagram of SSDT.  
Subsequently, the data matrix \(X_{k,m}=\left[X_{m}^{s}\ X_{m}^{t,l}\ X_{k,m}^{t,u}\right]\), the category dictionary matrix \(D_{k,m}\in\mathcal{R}^{d\times I_{m}}\) and the sparse representation coefficients \(S_{k,m}\in\mathcal{R}^{I_{m}\times(N_{m}^{t}+N_{m}^{t,u}+N_{k,m}^{t,u})}\) are defined, respectively. Thus, function (1) can be re-expressed as:  
\[\min_{D_{k,m},S_{k,m}}\|X_{k,m}-D_{k,m}S_{k,m}\|_{F}^{2}+\lambda_{1}\|S_{k,m}\|_ {1} \tag{2}\]  
At the \(k\)th iteration, the data matrix incremental expression is represented as \(X_{k,m}^{t,u}=\left[X_{k-1,m}^{t,u}\ \widetilde{X}_{k,m}^{t,u}\right]\), where \(\widetilde{X}_{k,m}^{t,u}\) refers to the selected sample of the current iteration. This indicates that the current data matrix builds upon prior results. The matrix denoting class m samples remains unchanged during subsequent selection. The incremental model proposed in this study increases the number of selected target domain samples in each category after every iteration, i.e., \(N_{k,m}^{t,u}=k\widetilde{N}_{m}^{t,u}\), where \(\widetilde{N}_{m}^{t,u}\) is a constant, obviating the need for inheritance relationships among the set of selected target domain samples.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AWGN & Additive white Gaussian noise \\ BWB-OFDM & Block-windowed burst OFDM \\ BER & Bit error rate \\ CAZAC & Constant-amplitude zero autocorrelation \\ CFR & Channel frequency response \\ CIR & Channel impulse response \\ CSI & Channel-state information \\ CP & Cyclic prefix \\ DFT & Discrete Fourier transform \\ FDE & Frequency-domain equalization \\ FFT & Fast Fourier transform \\ IB-DFE & Iterative block decision feedback equalization \\ IDFT & Inverse discrete Fourier transform \\ I/Q & Inphase and quadrature \\ ISI & Intersymbol interference \\ LTE & Long-term evolution \\ MMSE & Minimum mean square error \\ MIMO & Multiple-input multiple-output \\ OFDM & Orthogonal frequency-division multiplexing \\ PAPR & Peak-to-average Power Ratio \\ PSS & Primary Synchronization Signal \\ QPSK & Quadrature Phase Shift Keying \\ SRRC & Square-root-raised cosine \\ SNR & Signal-to-noise ratio \\ SISO & Single-input single-output \\ SDR & Software-defined radio \\ SSS & Secondary synchronization signal \\ TIBWB-OFDM & Time-interleaved block-windowed burst OFDM \\ ZC & Zadoff-Chu \\ ZP & Zero pad \\ ZF & Zero forcing \\ \end{tabular}
# Correlations Between Phenolic Composition and Perceived Astringency of Wines
## 3 Results and Discussion
### Correlations between Phenolic/Salivary Protein Interaction and Astringent Attributes  
Measuring the interaction and binding affinity between wine constituents and salivary proteins is more reliable for predicting the astringency of wines than determining the concentrations of astringent compounds. This is due to the fact that saliva fluid (containing salivary proteins) contributes directly to the perception of taste and mouthfeel. Therefore, the binding ability of phenolic compounds in wine samples to salivary proteins was determined and expressed as NTU turbidity units. In this context, the correlation of turbidity with astringent attributes was determined (Figure 4).  
The turbidity showed a strong positive correlation with perceived astringency (\(r\) = 0.8510, \(p\) \(<\) 0.0001). The TPC showed the highest correlation with turbidity (\(r\) = 0.8724, p \(<\) 0.0001) followed by the PTC (\(r\) = 0.7386, \(p\) \(<\) 0.0016), the TAC (\(r\) = 0.6496, \(p\) \(<\) 0.0088) and the PAC (\(r\) = 0.5081, \(p\) \(>\) 0.05). From the results, it was noted that the TPC and the PTC were strongly correlated in terms of their binding affinity to salivary proteins, which was consistent with their strong correlation with perceived astringency, as shown in Figure 2. The TAC showed a moderate correlation with turbidity, indicating that it contributed less to perceived astringency than the TPC and the PTC. On the other hand, although a moderate correlation was observed between the PAC and turbidity, this was not reliable due to the lack of statistical significance (\(p\) \(>\) 0.05).  
Interestingly, a moderate correlation was observed between the TAC and turbidity despite the weak correlation between the TAC and perceived astringency. We assumed that a number of anthocyanins bound to salivary proteins to form an anthocyanin-protein complex in a similar manner as proanthocyanidins. However, the binding affinity of anthocyanins is not enough for a significant impact on the perception of astringency. This result is in agreement with [PERSON] et al. [31] who determined the binding affinity of various forms of anthocyanins to BSA and salivary protein to evaluate the effect of anthocyanin-protein complex formation on in-mouth sensory properties. Their results indicated that anthocyanins formed an anthocyanin-protein complex, thereby inducing astringency and bitterness perceptions. Nevertheless, they assumed that proanthocyanidins (i.e., monomeric, oligomeric and polymeric flavan-3-ols) were the major contributors to an astringent sensation rather than anthocyanins.
## 6 Transport Layer in WMSN
### Reliability and Loss Recovery
RSTP incorporates TCP-ELN for the congestion control that uses transmission requests for packet losses caused by bit-errors and the classical TCP's congestion control mechanism for packet losses due to congestion. A transmission scheme is proposed in [61] for reliable transmission of images in wireless multimedia sensor network, which is based on two-dimensional discrete wavelet image transform (2D DWT) and semi-reliable transmission to achieve energy conservation. DWT divides the image into separable sub-bands of multi-resolution representations. For example, the image can be decomposed into 4 sub-bands (LL, LH, HL, and HH) where the LL sub-band has the half size version of the input image and contains the low-pass information, and the others contain high-pass information. The LL sub-band can also be transformed again to have more levels of resolution. Then each sub-band is compressed to reduce its size by using entropy coding for lossless compression (Lempel-Ziv-Welch, LZW is used here) because lossless compression is less complex and require less computation. As a result, image data can be divided into multiple packets of different priorities where lowest resolution image data packet has the highest priority and should be reliably transmitted. Other packets can be handled with a semi-reliable transmission policy in order to save energy. Semi-reliable transmission enables priority-based packet discarding by intermediate nodes according to their battery's energy state. Packets of certain priorities are only forwarded by intermediate nodes if their battery energy level is above a given threshold. Another framework that is based on multipath routing scheme is presented in [62], but this work considers offering reliability against bit-error from the wireless links only. The multipath multi-stream distributed reliability (MMDR) framework is proposed to reliably transport video content over WMSN by exploiting the features of multi-stream coding of video data and multipath routing. MMDR partitions the source encoded video data into multiple streams using one of the source coding techniques such as Layered Coding (LC), Multiple Description Coding (MDC), or Distributed Video Coding (DVC). Then Low Density Parity Check (LDPC) codes are used for channel coding the multiple video streams to compensate for the error prone wireless links. After that, MMDT will balance the video streams traffic over the available multipath route. Then at intermediate nodes, MMDR uses progressive error recovery algorithm (D-PERA) that tries to recover the bit errors by partially decoding the LDPC encoded packets.
## 4 Security Analysis
The level 1 secure channel can offer partial forward secrecy in the case of updating a node's key pair after a certain time. In contrast, triggering the ECQV scheme at level 2 security creates ephemeral key pairs, meaning that one session key corresponds to one key pair. A three-pass handshake can ensure perfect forward secrecy. 7. _Replay attack_: A replay attack is a form of network attack where an attacker intercepts and maliciously retransmits data that was previously recorded. A one-pass SKEMis susceptible to this kind of attack. [PERSON] addressed some solutions to this problem by assigning a session ID for every communication session or applying a time stamp [25]. Updating the key pair of a node is a method to mitigate the issue. Therefore, a secure channel at level 2 can completely prevent this kind of attack. * _Message modification attack_: As we discussed regarding the integrity property, if an attacker manages to capture and alter any part of a message being exchanged over a secure channel, it can result in different keys being extracted by both the sensor node and the server. This can also cause both parties to fail to agree upon the same session key. Consequently, the communication is meaningless and will not leak any critical information; therefore, the protocol is immune to modification attacks. The summary of the security features of our proposal is shown in Table 2, which also includes comparisons with other protocols.
## 4 Results and Discussion
### Occluded Environment
Table 3 consolidates the positioning result statistics for both stochastic models. For the Huawei Mate40 smartphone, the RMS of the DDPR-dependent model decreased by approximately 2 m in the N and E directions, and notably, by nearly 6 m in the U direction, when compared to the C/N0-dependent model. The positioning accuracy across these directions improved by 30%, 32%, and 34%, respectively. Similarly, for the Huawei P40 smartphone, the positioning accuracy in all three directions also saw an enhancement of 26%, 33%, and 24%, respectively. Moreover, a comparison between the whole constellation-based positioning (column 3) and the optimal satellite subset-based positioning (last column) revealed that even when the same C/N0-dependent stochastic model was utilized, the RMS values derived from the optimal satellite subset outshined those from the whole constellation in the N, E, and U directions for both smartphones. The comparison results differ from those in the above open environment, mainly due to the influence of significant errors such as multipath ones and NLOSs affecting some satellite signals in the harsh occluded environment. Figure 10: Time series of the positioning errors with different stochastic models in the occluded environment. environment. By selecting the satellite with the highest integrated C/N0 and a lower likelihood of significant errors, the impact of poor-quality observations on positioning performance is minimized to some extent. Simultaneously, improved positioning outcomes aid in constructing more accurate DDPRs, facilitating gross error detection and weight determination and further enhancing positioning efficacy.
## 2 Materials and Methods
### Modelling
Long-range Columbic interactions were calculated using the particle-mesh Ewald (PME) summation method [46]. The cutoff radius for LJ interactions and for short-range Columbic interactions was set to 1 nm. The leapfrog algorithm with a time step of 2 fs was used for integration of equations of motion. The simulations were conducted in the isothermal-isobaric ensemble (NPT) at 300 K and 1 atm using the velocity rescaling thermostat [47] (\(\tau_{t}\) = 0.1 ps) for temperature coupling and the Berendsen barostat [48] with the compressibility of 4.5 \(\times\) 105 bar\({}^{-1}\) (\(\tau_{p}\) = 2 ps) for pressure coupling. The initial configuration for the models were obtained by minimizing the total energy in the box, then equilibrated via isothermal ensemble (NVT) followed by NPT for each 2000 ps with a time step of 0.2 fs. Finally, the \begin{table}
\begin{tabular}{c c c c} \hline
**Bonds** & \(\mathbf{b}_{ij}\) **(nm)** & \(\mathbf{k}_{ij}^{b}\) **(kJ/mol nm\({}^{2}\))** \\ \hline CH–H & 0.1085 & 274,135.68 \\ CF–F & 0.1357 & 417,814.24 \\ CF–CH & 0.1534 & 258,487.52 \\ \hline
**Angles** & \(\theta_{ijk}^{0}\) **(deg)** & \(k_{ijk}^{\theta}\) **(kJ/mol rad\({}^{2}\))** \\ \hline F–CF–F & 105.27 & 1004.1600 \\ F–CF–CH & 107.74 & 753.1200 \\ CH–CF–CH & 118.24 & 671.9504 \\ H–CH–H & 109.27 & 322.1680 \\ H–CH–CF & 108.45 & 358.9872 \\ CF–CH–CF & 118.24 & 671.9504 \\ \hline
**Torsions (kJ/mol)** & \(k_{\phi}(1)\) & \(k_{\phi}(2)\) & \(k_{\phi}(3)\) & \(k_{\phi}(4)\) & \(k_{\phi}(5)\) & \(k_{\phi}(6)\) \\ \hline CF–CH–CF–CH & 1.65268 & 3.01248 & \(-\)1.58992 & \(-\)0.85772 & 1.77820 & \(-\)0.10460 \\ F–CF–CH–CF & 1.48532 & 1.44348 & \(-\)1.58992 & 0.58576 & 0.60668 & \(-\)0.10460 \\ \hline \end{tabular}
\end{table} 
Table 2: Force field parameters for polyvinylidene fluoride (PVDF) [40; 41]. systems were carried out with a production run of 100 ns. All trajectories were visualized using Visual Molecular Dynamics, version 1.9.3 [49].
# Large-Area Coverage Path Planning Method Based on Vehicle-UAV Collaboration
## 5 Conclusions  
In this paper, a coverage path planning method based on vehicle-UAV collaboration is proposed for large areas. The core idea of the proposed method is adopting a divide-and-conquer strategy to divide a large area into small areas, and then completing efficient coverage scanning tasks through the collaborative cooperation of vehicles and UAVs. The supply points are generated and adjusted based on the construction of regular hexagons and a Voronoi diagram, and the segmentation and adjustment of sub-areas are also achieved during this procedure. The vehicle paths are constructed based on the classical ACO algorithm, providing an efficient way to traverse all supply points within the coverage area. The classic zigzag coverage path method is adopted to fill the contours of each sub-area, and the UAV paths collaborate with vehicle supply points using few switching points. The simulation experiments verify the effectiveness and feasibility of the proposed vehicle-UAV collaboration coverage path planning method, and the comparative experiments demonstrate that the proposed method excels at large-scale coverage path planning scenarios, and achieves a significant improvement in coverage efficiency.  
Conceptualization, methodology, N.Z. and Q.Z.; software, B.Z.; validation, B.Z., J.F. and L.Y.; formal analysis, investigation, C.G.; resources, N.Z.; data curation, Q.Z.; writing--original draft preparation, writing--review and editing, visualization, N.Z. and B.Z.; supervision, Q.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Program of Song Shan Laboratory (included in the management of Major Science and Technology Program of Henan Province, Grant No. 221100211000-01).  
Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Coverage Path Planning Methods**} & \multicolumn{3}{c}{**Total Path Length (m)**} \\ \cline{2-3}  & **Small Area Case** & **Large Area Case** \\ \hline The Proposed Coverage Path Planning Method Based & & \\ on Vehicle–UAV Collaboration & & 202,626 & 820,678 \\ The Classic Zigzag Coverage Path Planning Method & & \\ Based Entirely on UAV & & 224,943 & 1,127,467 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical results of comparative experiments on different coverage path methods.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Acknowledgments: Thanks to the anonymous reviewers for their thorough review and their instructive comments and suggestions. Conflicts of Interest: The authors declare no conflicts of interest. Abbreviations The following abbreviations are used in this manuscript:
# A Review on Design Considerations and Connection Techniques in Modular Composite Construction
## 3 Area of Research
### Connections in Modular Construction Scenario
Cast in situ connections were rated lowest, given their extensive on-site workload, requirements for formwork preparation, and alignment issues, rendering them less favourable for modular assembly where efficiency is critical. Maintenance considerations were assessed based on anticipated long-term care. Cast in situ connections demanded the least maintenance due to their monolithic nature, which removes weak points. Grounded coupler connections necessitated moderate maintenance, primarily for grout integrity assessments in high-seismic contexts. In contrast, embedded steel plates and bolted connections required the most maintenance, as regular bolt tensioning and corrosion protection are essential to sustain their long-term performance. In summary, the qualitative ratings depicted in the radar chart offer a comprehensive view of the strengths and weaknesses of each connection method in the context of modular construction. Grouted coupler connections present the optimal balance between seismic performance, durability, and installation ease, rendering them a favoured option for modular structures. Embedded steel plates and bolted connections demonstrate superiority in construction speed and flexibility, albeit with the necessity for diligent maintenance. Cast in situ connections deliver enhanced seismic resilience; however, they are encumbered by on-site workload and prolonged construction durations. Choosing an appropriate connection method should be informed by project-specific criteria, weighing structural performance, efficiency, and cost implications.