esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix B The Time Frequency Distribution of the SC-FDMA Signal  
Besides the single carrier pulse shaping 4-QAM signal, the time frequency distribution of the SC-FDMA signal is also discussed here. Let the sub-carrier spacing be \(\omega_{0}=2\pi/N\). For any subcarrier \(k\) of the signal \(x(n)\), the sum of adjacent subcarrier signals is expressed as  
\[\begin{split}\tilde{x}_{k}(n)=& x_{k}(n)+x_{k+1}(n )\\ =& X(k)e^{j\omega_{0}n}+X(k+1)e^{j(k+1)\omega_{0}n} \\ =&\left[X(k)+X(k+1)e^{j\omega_{0}n}\right]e^{j \tilde{\kappa}\omega_{0}n}\end{split} \tag{16}\]  
where \(X(k)\) is the data modulated on the \(k_{th}\) subcarrier. Define the real and imaginary part of \(X(k)+X(k+1)e^{i\omega_{0}n}\) as \(A_{k}(n)\) and \(B_{k}(n)\), respectively, where \(A_{k}(n)=X(k)+X(k+1)\cos(\omega_{0}n)\), \(B_{k}(n)=X(k+1)\sin(\omega_{0}n)\). Define \(\phi(n)=\operatorname{atan}\{B_{k}(n)/A_{k}(n)\}\), and then \(\tilde{x}_{k}(n)\) is reformulated as  
\[\tilde{x}_{k}(n)=\sqrt{A_{k}^{2}(n)+B_{k}^{2}(n)}e^{(j(k\omega_{0}n+\phi(n)))} \tag{17}\]  
The time-varying phase \(\phi(n)\) affects the center frequency of \(\tilde{x}_{k}(n)\). Define \(\
u(n)\) as the first order difference of \(\phi(n)\), \(\tilde{x}_{k}(n)\) is reformulated as  
\[\tilde{x}_{k}(n)=\sqrt{A_{k}^{2}(n)+B_{k}^{2}(n)}e^{(j((k\omega_{0}+\
u(n))n))} \tag{18}\]  
since \(\phi(n)=\operatorname{atan}\{B_{k}(n)/A_{k}(n)\}\), then \(\
u(n)\) is reformulated as (19), where \(D[\cdot]\) represents the first order difference.  
\[\
u(n)=D[\phi(n)]=\frac{1}{2\pi}\frac{D[B_{k}(n)]A_{k}(n)-D[A_{k}(n)]B_{k}(n)} {A_{k}^{2}(n)+B_{k}^{2}(n)} \tag{19}\]  
Equation (18) shows that the center frequency of each subcarrier is not constant but varies with time, which is due to the summation of adjacent subcarrier signals. From (19), the frequency deviation of each subcarrier is determined by \(A_{k}(n)\) and \(B_{k}(n)\), where \(A_{k}(n)\) and \(B_{k}(n)\) are associated with the data carried by subcarrier \(k\) and \(k+1\). Hence the frequency deviation is random and the sum of multiple sub-carriers will cause the bandwidth variations.  
Due to the randomness of the data modulated on each subcarrier, the spectrum of the SC-FDMA signal also has no regularity as shown in Figure 6. It would be a hard work to extract the effective pass region from the received signal, which has been contaminated by the noise. One solution is to determine the pass region by the energy distribution of the contaminated received signal. When the noise power is not strong enough to modify the desired pass region, we can use the time-frequency distribution of the received signal to represent the desired pass region. However, when the powers of interference and noise are high, they affect the desired pass region severely, and it is impossible to extract the effective pass region from the received signal. In summary, in an SC-FDMA system, although the SC-FDMA signal has a time-varying spectrum, it is possible to take the advantage of the time-varying filter only when the interference and noise are not too strong, which has been analyzed in our previous work [35].
# Impacts of Diverse Natural Products on Honey Bee Viral Loads and Health
## 2 Materials and Methods
### RNA Extraction, cDNA Synthesis, and qPCR  
RNA was extracted following the bulk total RNA extraction, first-strand cDNA synthesis, and real-time qPCR protocols outlined in [43]. Alternatively, total RNA was extracted from individual bees using TRIzol\({}^{\text{TM}}\) and then followed the protocol outlined in the BEECOOK for cDNA synthesis and qPCR [43]. For a subset of the samples, pooled bees were extracted using a modified RNAswift protocol [44] followed by DNase treatment, cDNA synthesis, and qPCR, as in [15]. The individual experimental details for RNA extraction, cDNA synthesis, and qPCR are described in [15] and all primers used in this study are described in the Supplemental Materials Table S1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Common Name (Scientific Name)** & **Level of Purity** & **Source** & **Conc. (ppm)** \\ \hline Astragalus (_Astragalus propinquus_) & Natural & Vitamin & \\  & & Shoppe\#VS-1637/1776483 & 100, 1000 \\ \hline Berberine chloride & 98\% & Sigma\#B3251 & 100 \\ \hline Beta-carotene & 95\% & Sigma\#C4582 & 2000 \\ \hline Cacao raw (_Tleobroma cacao_ L) & Natural & Whole raw cocoa beans (Peru) & 100 \\ \hline Cacao roasted (_Tleobroma cacao_ L) & Natural & Whole roasted cocoa beans (Peru) & 100 \\ \hline Calendula (_Calendula officinalis_) & Natural & Vitamin Shoppe \# \\  & & BB-1034/1083588 & 100, 1000 \\ \hline Cat’s Claw (_Ulacaria tomentosa_) & Natural & Vitamin Shoppe & \\  & & \#BB-1346/1098914 & 100, 1000 \\ \hline Carvacrol & 98\% & Sigma\#282197 & 100 \\ \hline Chrysin & 97\% & Sigma\#C80105 & 100 \\ \hline Cinnamic acid & 99\% & Sigma\#W228826 & 100 \\ \hline Curcumin (_Curcuma longa_) & 75\% & Sigma\#8.20354 & 100 \\ \hline Decanoic acid & 98\% & Sigma \#C1875 & 100 \\ \hline Echinacea (_Echinacea_) & Natural & Vitamin Shoppe & \\  & & \#VS-1625/1776038 & 100, 1000 \\ \hline Elderberry (_Sambucus_) & Natural & Vitamin Shoppe & \\  & & \#BB-1181/1681436 & 100, 1000 \\ \hline Garlic oil (_Allium sativum_) & Natural & Vitamin Shoppe & 100, 1000, 10,000 \\ \hline Ginger (_Zingiber officinal_) & Natural & Sigma\#W252204 & 100, 1000 \\ \hline Hesperidin & 80\% & Sigma\#H5254 & 100 \\ \hline Licorice (_Glycyrrhiz glabra_) & Natural & Vitamin Shoppe & \\  & & \#BB-1101/1087241 & 100, 1000 \\ \hline Limonene & 97\% & Sigma\#183164 & 100 \\ \hline Olive leaf (_Olea europaea_) & Natural & Vitamin Shoppe & 100, 1000 \\ \hline Oil of oregano (_Origanum vulgare_) & Natural & Vitamin Shoppe & \\  & & \#VS-1782/1484956 & 100, 1000 \\ \hline Thymol & 99\% & Sigma\#16254 & 10, 100 \\ \hline Tocopherol & FCG & Sigma\#V530066 & 100 \\ \hline Tyrosine low, high & 98\% & Sigma\#T3754 & 10, 100 \\ \hline Vanillin (_Vanilla planifolia_) & 99\% & Sigma\#V1104 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Natural product sourcing and tested concentrations.
## 3 Results and Discussion
### Tga  
Figure 5 shows the percentage of remaining mass vs. temperature curves and the derivative of the mass loss curve (DTG) obtained from TGA tests in oxidative and inert (nitrogen) atmospheres. Table 5 summarizes the main results of the TGA tests, namely the temperatures for the mass reductions of 5%, 10%, and 50% (often used to define the decomposition temperature) and the mass residue (in %).  
Figure 5: TGA results: the remaining mass (**a**,**b**) and the derivative of mass loss curve (DTG) (**c**,**d**) of BVE and BVEL resins as a function of temperature.  
In an oxidative environment, the remaining mass of the BVE and BVEL resins showed an almost identical two-step sigmoidal variation with increasing temperature (two peaks in DTG): while mass was reduced moderately at \(\sim\)300 \({}^{\circ}\)C, it dropped sharply between \(\sim\)300 \({}^{\circ}\)C and \(\sim\)420 \({}^{\circ}\)C, and then between \(\sim\)500 \({}^{\circ}\)C and \(\sim\)600 \({}^{\circ}\)C, due to the thermal decomposition of the resins with a slight lag (\(\sim\)10 \({}^{\circ}\)C) between the BVE and BVEL resins (the disturbance in the BVE curve at \(\sim\)500 \({}^{\circ}\)C, which occurred for the two samples, is likely due to smoke release during combustion of the resin). In an inert environment, the remaining mass curves of the BVE and BVEL resins almost overlapped, presenting a single drop (one peak in DTG) to between \(\sim\)300 \({}^{\circ}\)C and 420 \({}^{\circ}\)C.  
In an oxidative atmosphere, the decomposition temperature, defined for a mass reduction of 50% with respect to that at ambient temperature, is 387 \({}^{\circ}\)C and 393 \({}^{\circ}\)C for resins BVE and BVEL, respectively. This confirms the positive impact, even if slight, of introducing the complex aromatic lignin structures into the resin's polymeric chain. For the inert atmosphere, the decomposition temperature of both resins is 393 \({}^{\circ}\)C.
## 2 Forest Fire Detection by the Satellite data in China
### The Chinese Polar Orbiting Meteorological Satellite FY-1C/1D  
Chinese FY-1C/D satellites have a Multi-channel Visible and Infrared Scan Radiometer (MVISR) with ten spectral channels, similar to that of the NOAA/AVHRR instrument. The channels of MVISR onboard FY-1C/D satellites are useful for detecting forest fire and burned areas (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Channel**} & **Wavelength** & **Resolution** & **Dynamic** & **Detecting** & **Fire detection** \\  & **(\(\upmu\)m)** & **(km)** & **range** & **sensitivity** & **Fire detection** \\ \hline
1 & 0.58–0.68 & 1.1 & 0–90\% & S/N \(\geq\) 3 & Burnt area \\  & & & & (\(\rho\) = 0.5\%) & \\
2 & 0.84–0.89 & 1.1 & 0–90\% & S/N \(\geq\) 3 & Burnt area \\  & & & & (\(\rho\) = 0.5\%) & \\
3 & 3.55–3.95 & 1.1 & 190–340 K & NE\(\Delta T\)\(\leq\) 0.4 K & Hot-spot \\  & & & & (300 K) & \\
4 & 10.3–11.3 & 1.1 & 190–330 K & NE\(\Delta T\)\(\leq\) 0.22 K & Hot-spot \\  & & & & (300 K) & \\
5 & 11.5–12.5 & 1.1 & 190–330 K & NE\(\Delta T\)\(\leq\) 0.22 K & Burnt area \\  & & & & (300 K) & \\ \hline \end{tabular} S/N: signal to noise ratio; \(\rho\): reflectivity; NE\(\Delta T\): Noise-Equivalent Temperature Difference.  
\end{table}
Table 1: The channel characteristics for detecting forest fire by using MVISR sensor onboard FY-1C/1D satellite.  
Figure 1: Daxing’anling forest fire hot-spot and burnt area detection by using NOAA/AVHRR.  
Figure 2 presents a basic workflow of the forest fires detection based on the FY-1C/1D satellites data using brightness temperature and reflectance channels. The algorithm uses a combination of an absolute temperature threshold (315 K at Ch. 3, 3.55-3.95 \(\upmu\)m) and a series of contextual tests to detect active fires by using both the Ch. 3 and Ch. 4 (10.3-11.3 \(\upmu\)m). In addition, it incorporates warm background, cloud, land surface masking and several false alarm rejection tests. In Figure 2, the T3 and T4 stand for the bright temperature of channels 3 and 4 in FY-1C/D, respectively; and R2 is the reflectance of channel 2.
# Influence of Polypropylene Fiber on Tensile Property of a Cement-Polymer Based Thin Spray-On Liner
###### Abstract  
The influence of a polypropylene fiber on the tensile properties of a cement-polymer based thin spray-on liner (TSL) was investigated in this study. Two different contents of fiber were added to the liner, yielding two TSL groups. Tensile tests were performed (in accordance with the ASTM D638 standard) on the two groups of specimens as well as the control group at 1, 7, 14, and 28-day curing. The test result verified the large plasticity and low elastic modulus of the TSL compared with the fiber. SEM examination revealed that fibers lying parallel to the load direction ruptured or were pulled out from the matrix, which was beneficial to the tensile strength, but detrimental to the elongation because of their high stiffness. Other fibers lying perpendicular with the load direction were detrimental to both tensile strength and elongation through aggravating the propagation of the cracks. The tensile strength was improved by fiber incorporation, while the elongation was reduced at all curing. The influence of fibers on tensile toughness was uncertain since tensile toughness depended on strength as well as deformity.  
polypropylene fiber; tensile; cement polymer-based; thin spray-on liner; SEM +
Footnote †: journal: Applied Sciences
## 3 A Community Anomaly Detection Approach Based on Structural/Attribute Information
### Proposed Community Anomaly Detection Approach
#### 3.2.1 First Phase: Community Detection  
In the first phase, we partition the graph into communities. In our model, we use a very well-known modularity-based approach to detect the communities in the graph, called the Louvain Algorithm [19]. We use the Louvain algorithm for its fast performance. The algorithm initially assigns each node to a unique community and iteratively moves each node to its neighbor to maximize modularity gain. It stops when further modularity gain is impossible. The computation of gains in modularity is defined as:  
\[Gain-modularity=[\frac{S_{in}+L_{i,C}}{2w}-(\frac{S_{t}+L_{i}}{2w})^{2}]-[\frac{ S_{in}}{2w}-(\frac{S_{t}}{2w})^{2}-(\frac{L_{i}}{2w})^{2}] \tag{1}\]  
where \(S_{in}\) is the sum of edges weights inside the community \(C\), \(S_{t}\) is the sum of weights of edges connecting the different nodes in community \(C\), \(L_{i}\) is the sum of edges weights of the node \(i\), \(L_{i,C}\) is the sum of weights of edges connecting the node \(i\) by \(C\), and \(w\) denotes the sum of the weights of all the edges in the network.
## 4 Sensitivity Analysis of RPC-FST
### Sensitivity Analysis Based on Kriging
It can be expressed as:  
\[r^{T}(X)=\left\{R(X,X^{(1)}),R(X,X^{(2)})\dots R(X,X^{(m)})\right\} \tag{16}\]  
The predictive mean and variance for the Kriging model can be performed as follows:  
\[\mu_{gk}(X)=f^{T}(X)\hat{\beta}+r^{T}(X)R^{-1}(g-F\hat{\beta}) \tag{17}\] \[\sigma^{2}_{gk}(X)=\sigma^{2}\Big{\{}1-r^{T}(X)R^{-1}r(X)+[F^{T}R^ {-1}r(X)-f(X)]^{T}(F^{T}R^{-1}F)^{-1}[F^{T}R^{-1}r(X)-f(X)]\Big{\}}\]  
To make the output Kriging surrogate model more accurately predict the displacement of the RPC-PST column, the relative error-index \(\beta_{R}\) (Equation (18)) is used to evaluate the model [41]. \[\beta_{R}=\frac{g_{k}(X_{L})-g_{L}(X_{L})}{g_{L}(X_{L})} \tag{18}\]  
where \(X_{L}\) is a variable matrix composed of \(N_{L}\) samples generated by the LHS plan, \(g_{k}(X_{L})\) is the output by the updated Kriging surrogate model, and \(g_{L}(X_{L})\) is the output by the theory of FSI analysis. Based on the MCS method, the reliability can be expressed as:  
\[\frac{\partial P_{f}}{\partial\theta_{X_{i}}^{(k)}}=\int\dots\int_{R^{n}}I_{F} (x)\frac{\partial f_{X}(x)}{\partial\theta_{X_{i}}^{(k)}}\frac{1}{f_{X}(x)}f _{X}(x)dx=E[\frac{I_{F}(x)}{f_{x}(x)}\frac{\partial f_{X}(x)}{\partial\theta_ {X_{i}}^{(k)}}] \tag{19}\]  
The reliability sensitivity is obtained as Equation (20). \[\beta_{S}=\frac{\partial\hat{P}_{f}}{\partial\theta_{X_{i}}^{(k)}}=\frac{1}{ N_{MC}}\sum_{j=1}^{N_{MC}}\frac{I_{F}(X)}{\hat{f}_{X}(X)}\frac{\partial f_{X}(X)}{ \partial\theta_{X_{i}}^{(k)}} \tag{20}\]  
The sensitivity coefficient is calculated as follows:  
\[\text{cov}(\beta_{S})\approx\sqrt{\frac{\frac{1}{N_{MC}-1}\left(\frac{1}{N_{ MC}}\sum_{j=1}^{N_{MC}}\left[\frac{I_{F}(X)}{f_{X}(X)}\frac{\partial f_{X}(X)}{ \partial\theta_{X_{j}}^{(k)}}\right]^{2}-\left(\beta_{S}\right)^{2}\right)}{ \left|\beta_{S}\right|}} \tag{21}\]  
The sensitivity of the five input variables of steel tube thickness, RPC-FST column height, TNT weight, TNT distance, RPC-FST section diameter to the displacement error risk of RPC-FST columns, and sensitivity coefficient are evaluated by Equations (20) and (21). Due to the units of five random variables not being unified and the corresponding orders of magnitude of the five input variables being different, the sensitivity standardization calculation is required. After standardized distribution parameters \(\mu_{Xi}\) and \(\sigma_{Xi}\), the corresponding sensitivity can be expressed as Equation (22) [42, 43]. \[S_{\mu_{X_{i}}} =\frac{\partial P_{f}}{\partial\mu_{X_{i}}}\frac{\sigma_{X_{i}}}{ P_{f}} \tag{22}\] Figure 7:0  
The reliability sensitivity gradient of the \(i\)-th random variable to \(P_{f}\) is:  
Figure 7:1The sensitivity factor of the RPC-FST system can be obtained by standardizing the gradient of five parameters, as given in Equation (24). Figure 7:2  
where \(N_{\text{MC}}\) is the number of sample pools.
## 5 Major Equipment and Technologies  
In traditional agriculture, most farming measures are applied by heavy machinery, including harvesters, massive robots, and tractors. Several communications and remote sensing techniques are used to operate these machines. In advanced farming, for many crop-related practices such as irrigation, sowing, fertilizer application, and harvesting of crops, these machines and equipment are supported by GIS and GPS technologies, which are very accurate, efficient, and precise. Moreover, other agricultural measures (the site-specific management of crops) cannot replace these advanced techniques. The development of contemporary agriculture needs the efficiency of gathered information and generally has two parts [9]. First, remote sensing programs with multifunctional devices, including UAVs, aircraft, and satellites, are needed. Second, many other devices, such as ground and remote sensors, are required for particular functions at different locations. A GPS device can be used to identify the data collection spot's location, which can be used for location-specific processing.  
Over the past few decades, agriculture has shifted from small and medium-sized agriculture to highly industrialized and commercial agriculture. This shift plays an important role in advancing the agricultural industry like other developed industries because, in agriculture, data, control, and measurement are significant factors in accomplishing equality among production and cost to enhance outputs. For this purpose, the IoT is a fundamental approach for advancement and benefits in agriculture, which are well planned, managed, and automated. According to this information, between 2017 and 2022, the world's advanced agricultural industry is expected to grow by 19.3% in one year and reach USD 23.14 billion in 2022 [33].
# ULYSSES: Automated FreqUentLY ASked QueStions for KnowlEdge GraphS
## 3 Methodology
### The FAQGen Algorithm  
Next, based on the aforementioned ideas, we present the FAQGen algorithm for generating an FAQ list. The algorithm, shown in Algorithm 1, is given as input a query log \(Q\), a SPARQL endpoint, and a number \(n\) denoting the number of question-answer pairs to be included in the constructed FAQ.  
The algorithm starts by finding the \(n\) most frequent queries in the query log (line 1). Then, it initializes the FAQ list (line 2). Then, it iterates over the selected \(n\) most frequent queries in the query log (lines 3-8). For each query, it retrieves an answer from the corresponding SPARQL endpoint (line 4), it transforms both the query and the answer into a textual form, giving the proper prompts to the LLM models (lines 5-6), and, eventually, it adds the textual question-answer pair to the FAQ list. Finally, it returns the constructed FAQ list (line 9) for further visualization.  
The algorithm needs to iterate one time over all the queries in the log requiring \(O(|\mathcal{Q}|)\), in order to find the most frequent ones. Then, for the selected \(n\) queries it needs to answer them using the SPARQL endpoint. Although answering generic SPARQL queries is generally NP-complete, triple stores exploit advanced indexing and caching techniques and return an answer--even if it is incomplete--usually within 30 s. The same applies to LLMs, which typically return an answer within 1 to 10 s. Considering that the whole process needs to be repeated only for a limited number of queries, computing FAQ lists over large Knowledge Graphs exploiting our techniques is feasible and, as we shall show, gives high-quality results.  
```
1:\(m\)\(fQ\gets FindMostFrequentQuestions(Q,n)\)
2:\(FAQ\leftarrow\varnothing\)
3:for all\(q\in mfQ\)do
4:answer\(\leftarrow\) sendSPARQL\((endpoint,q)\)
5:textual\(Answer\leftarrow\)\(t\)\(t\)\(n\)\(fQ\)Text(answer)
6:textualQuestion\(\leftarrow\)\(t\)\(n\)\(fQ\)Text(\(q\))
7:\(FAQ\gets FAQ\cup\{\)textualQuestion\(,textualAnswer\}\)
8:endfor
9: Return FAQ
```  
**Algorithm 1** FAQGen(\(Q\), \(endpoint\), \(n\))
## 5 Discussion and Conclusions
Our findings highlight the differences between stations, in our case based on scheduled dwell times with regard to dwell time delay characteristics. Future studies can focus on determining a more detailed metric to characterize stations. An increased understanding in terms of how the type of station influences dwell times can help when faced with the choice as to where we should extend the dwell times. In line with this, our study uses data originating from Stockholm, and it is thus not possible to state whether our findings can be generalized for the entirety of Sweden or in an international context. Future studies should therefore focus on applying a similar method as conducted here to data originating from different geographical areas. Doing so allows for a comparison of the results and a deeper understanding of the effect of context-specific aspects such as the geographical location on the effects that passengers have on dwell times. In terms of data availability, we also see room for future studies. While this study was limited to data aggregated on a train level, future studies could include data on a door-by-door level to ensure that the effects of passengers at different doors, as well as the critical door, can be investigated. Such detailed data provide an important step in further understanding dwell times. Finally, we limit ourselves to the impact of passenger volumes on dwell times, excluding stations with a high likelihood of train interactions and crew changes. This does not mean that such instances are not relevant, however. It can be beneficial to study dwell times at such stations in the future, as this is relevant in understanding dwell time delays in general. Methodology, R.A.K. and C.-W.P. ; formal analysis, R.A.K. ; writing--original draft preparation, R.A.K. and C.-W.P. ; writing--review and editing, R.A.K. and C.-W.P. ; visualization, R.A.K. ; supervision, C.-W.P. ; funding acquisition, C.-W.P. All authors have read and agreed to the published version of the manuscript. This work was funded by K2, The Swedish Knowledge Centre for Public Transport. Not applicable. Research data are not shared due to confidentiality reasons. The authors would like to thank [PERSON] and [PERSON] for their help with providing and explaining the automatic passenger count data used in this study. The authors would also like to thank [PERSON] and [PERSON] for their valuable feedback on the manuscript. The earlier version of this manuscript was presented at the 9 th International Conference on Railway Operations Modelling and Analysis (ICROMA), and the authors would like to thank the anonymous reviewers there for their constructive comments.
## 4 Discussion  
### Optimal Balance for Loss Function  
To determine the optimal weighting strategy, we conducted ablation studies using each loss function individually and analyzed their influence on noise suppression and structural detail preservation.  
Our results revealed that MSE loss alone, despite its effectiveness in reducing pixel-wise errors, often led to oversmoothed reconstructions, potentially obscuring fine anatomical details. In contrast, the DSSIM loss significantly improved edge preservation and contrast by penalizing structural inconsistencies, ensuring that organ boundaries and fine textures were more accurately reconstructed. Additionally, PL loss contributed to perceptually realistic restorations by utilizing high-level feature similarities rather than solely relying on pixel-wise accuracy. These observations align with prior studies demonstrating that structural and perceptual losses play a key role in preserving the diagnostic information in LDCT imaging. Based on these insights, we adopted a detail-preserving loss weighting strategy with \(\gamma_{1}=0.2,\gamma_{2}=0.4,\gamma_{3}=0.4\). This configuration prioritizes the DSSIM and PL over the MSE, effectively balancing noise suppression and anatomical structure retention. Benchmark comparisons further validated the effectiveness of this approach, as GLAM-PViT outperformed state-of-the-art models (RED-CNN, TED-Net, DSC-GAN, DRL-EMP, BM3D) in preserving fine details while maintaining high PSNR and SSIM scores.