esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Maturity Groups (13.45)**} & \multicolumn{4}{c}{**Effect of Maturity (13.45)**} \\ \hline
**Variables** & **Early (\(u=12\))** & **On-Time (\(n=154\))** & **Late (\(n=6\))** & \(H\) & \(p\) & \(\eta_{P}^{2}\) \\ \hline FITNESS & & & & & & \\ V20-m (s) & 3.31 \(\pm\) 0.26 & 3.34 \(\pm\) 0.23 & 3.45 \(\pm\) 0.29 & 1.60 & 0.450 & 0.009 \\ TT (s) & 10.57 \(\pm\) 0.67 & 10.38 \(\pm\) 0.69 & 10.87 \(\pm\) 0.59 & 4.45 & 0.108 & 0.026 \\ CMJ Height (cm) & 30.4 \(\pm\) 2.9 & 30.0 \(\pm\) 5.2 & 28.5 \(\pm\) 4.7 & 0.60 & 0.743 & 0.004 \\ CMJ Power (w) & 904 \(\pm\) 93 *\# & 715 \(\pm\) 132 \(\infty\) & 477 \(\pm\) 45 & 33.51 & \(<\)0.001 & 0.165 \\ CMJ-S Height (cm) & 36.2 \(\pm\) 4.2 & 35.6 \(\pm\) 5.7 & 34.9 \(\pm\) 5.9 & 0.25 & 0.883 & 0.001 \\ CMJ-S Power (w) & 986 \(\pm\) 110 *\# & 779 \(\pm\) 139 \(\infty\) & 528 \(\pm\) 56 & 32.44 & \(<\)0.001 & 0.161 \\ SUM HG (kgf) & 80.4 \(\pm\) 12.3 \# & 70.9 \(\pm\) 14.4 \(\infty\) & 48.3 \(\pm\) 1.8 & 19.00 & \(<\)0.001 & 0.101 \\ MBT (m) & 5.49 \(\pm\) 0.77 \# & 4.99 \(\pm\) 0.71 \(\infty\) & 3.57 \(\pm\) 0.15 & 20.13 & \(<\)0.001 & 0.106 \\ Seat and reach (cm) & \(-\)4.8 \(\pm\) 7.2 & \(-\)1.0 \(\pm\) 7.9 & \(-\)0.8 \(\pm\) 5.3 & 2.52 & 0.284 & 0.015 \\ \hline GAME PERFORMANCE & & & & & \\ PIR/game & 2.46 \(\pm\) 2.44 & 2.18 \(\pm\) 3.42 & 0.43 \(\pm\) 2.42 & 2.31 & 0.314 & 0.014 \\ PIR/min & 0.10 \(\pm\) 0.40 & 0.13 \(\pm\) 0.23 & \(-\)0.02 \(\pm\) 0.20 & 3.33 & 0.190 & 0.019 \\ Points/game & 3.35 \(\pm\) 1.78 & 3.84 \(\pm\) 2.66 & 2.72 \(\pm\) 1.61 & 1.05 & 0.593 & 0.006 \\ Points/min & 0.23 \(\pm\) 0.11 & 0.26 \(\pm\) 0.15 & 0.18 \(\pm\) 0.09 & 1.77 & 0.412 & 0.010 \\ Rebounds/game & 3.36 \(\pm\) 1.67 & 2.78 \(\pm\) 1.89 & 1.70 \(\pm\) 0.85 & 4.15 & 0.126 & 0.024 \\ Minutes played/game & 13.4 \(\pm\) 3.2 & 14.0 \(\pm\) 3.9 & 14.2 \(\pm\) 2.8 & 0.33 & 0.848 & 0.002 \\ \hline \hline \end{tabular}  
* APHV, age at peak height velocity; BMI, body mass index; CA, chronological age; CMJ, countermovement jump; CMJ-S, countermovement jump with arms swing; MBT, medicine ball throw; SUM HG, sum of right and left handgrip; PIR, Performance Index Rating; TT, T-test; V20-m, speed 20 m test; %FM, fat mass percentage; FFM, fat-free mass.
* Significant difference between early and on-time mature players, \(p<\) 0.05. # Significant difference between early and late mature players, \(p<\) 0.05. # Significant difference between early and late mature players, \(p<\) 0.05. \(\Leftrightarrow\) Significant difference between on-time and late mature players, \(p<\) 0.05.  
\end{table}
# Measurement Uncertainty and Compliance Evaluation Applied to Natural Gas Moisture
## 2 Methodology
### ASTM D1142 Test Method, Approach 2  
[PERSON] proposed a relatively simple modification of [PERSON]'s law approach, where the water content of the sweet gas is calculated using the ideal expression of Equation (1) supplemented by a deviation factor [15], Equation (3):  
\[W=(760.4)\frac{P_{w}^{sat}}{P}+0.016016\times B \tag{3}\]  
where \(W\) is the water content (g/Nm\({}^{3}\)), \(P_{w}^{sat}\) is the saturation vapor pressure of pure water (MPa), \(P\) is the total system pressure (MPa), and \(B\) is given by Equation (4):  
\[logB=\frac{-1713.66}{T}+6.69449 \tag{4}\]where \(T\) is the dew point temperature (K) and the saturation vapor pressure can be calculated by Equation (5) [16]:  
\[ln\left(\frac{P_{w}^{sat}}{P_{c}}\right)=\frac{T_{c}}{T}\left(-7.85823r+1.83991r^{ 1.5}-11.7811r^{3}+22.6705r^{3.5}-1539393r^{4}+1.77516r^{7.5}\right) \tag{5}\]  
where \(T\) is the temperature (K), \(T_{c}\) is the critical water temperature (647.14 K), \(P_{c}\) is the critical water pressure (22.064 MPa), and \(\tau=1/T-T_{c}\). Thus, a simplified version of Equation (3) is Equation (6):  
\[W=\frac{A}{P}+B \tag{6}\]  
Referred to here as Section 2.2, ASTM D1142 Method 2 is included in ASTM Test Method 1142-95 (2021). The coefficients \(A\) and \(B\) (referenced to \(T_{b}\) = 520 R and \(P_{b}\) = 14.7 psia) are listed as a function of temperature in Table 2 for dew point temperatures ranging from \(-40\)\({}^{\circ}\)F to 440 \({}^{\circ}\)F. Although not explicitly stated in temperature due to the temperature dependence of \(A\) and \(B\), the corresponding dewpoint temperature can be solved iteratively given the water content. Although conveniently simplistic, neither ASTM method considers the actual gas composition. Furthermore, the range of data available for the specific volume of saturated water vapor (ASTM D1142 Method 1) or for the coefficients A and B (ASTM D1142 Method 2) is somewhat limited.
# Effect of PAS-LuxR Family Regulators on the Secondary Metabolism of _Streptomyces_
## 3 Regulation of Related Product Biosynthesis by PAS-LuxR Family Regulators
### Regulation of Polyene Macrolide Biosynthesis by PAS-LuxR Family Regulators
#### 3.1.3 Regulatory System Formed with PAS-LuxR and Other Family Regulators  
In addition to the regulatory system in Section 3.1.1 and Section 3.1.2, other families' regulators act in conjunction with PAS-LuxR family regulators. For example, the aureofuscus in _S. aureus_ has two regulators, namely, PAS-LuxR family regulator AurJ3M and RntB family regulator AurrI [45; 46]. In the _aurJ3M_ gene deletion strain, RT-PCR results showed that _aurB_, _aurC_, _aurG_, _aurF_, _aurS0_, _aurS1_, and _aurD_ are not transcribed, the transcript levels of _aurI_, _aurJ_, and _aurA_ genes are reduced compared with the wild-type strain, and almost no effect was observed on _aurR_, _aurE_, _aurH_, and _aurJ3M_ genes. Therefore, the deletion of _aurJ3M_ has no effect on the transcription of genes located downstream of AurJ3M, and AurJ3M regulates the biosynthesis of aureofuscin by affecting the transcription of the PKS gene _aurS0_. The amino acid transporter protein AurT regulates the biosynthesis of aureofuscin by affecting the secretion of PI factors [11], whose structural analogs activate the expression of related genes by binding to specific receptors within the gene cluster [47]. Hence, the production of aureofuscin in _S. aureus_ is coregulated by two regulators, namely, AurJ3M and AurT, from different transcription factor families. Reedsmycin is a nonglycosylated polyene macrolide produced by the marine _Streptomyces_ of \(S\). sp. CHQ-64. The antifungal activity of reedsmycin is comparable to that of nystatin in the inhibition of _Candida albicans_. The reedsmycin pathway has five regulators, namely the XRE family regulator RdmA, LuxR family regulators RdmC, RdmD, RdmE, and PAS-LuxR family regulator RdmF [48]. The regulatory mechanism of RdmF has not been reported.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 4 Experimentation  
### Dataset  
The dataset used to evaluate Retrieval-Augmented Generation (RAG) for Swahili Natural Language Processing (NLP) tasks was carefully curated to address the unique challenges of low-resource languages like Swahili [42]. It includes a diverse range of components designed to test RAG's ability to retrieve and generate accurate responses for conversational systems. The dataset composition is detailed in Table 2.  
The dataset comprises Swahili queries, paraphrased questions, multi-turn conversations, noisy text, and a set of external context chunks. Swahili queries include straightforward user-posed questions designed to evaluate the retrieval and generation processes. Paraphrased questions test semantic understanding and the system's ability to generalize across similar queries. Multi-turn conversations assess the ability to maintain context across multiple exchanges, while noisy text introduces real-world challenges like typos and informal syntax. The external context chunks serve as the knowledge base for retrieval, containing 5000 preprocessed chunks derived from Swahili documents. These chunks provide the external knowledge needed to generate contextually accurate responses. Unlike other components, the external chunks are not split into training, validation, or test sets since they are dynamically retrieved during the response generation process [43]. To ensure consistency and usability, the dataset was preprocessed by removing non-informative symbols, correcting orthographic errors, and segmenting longer text entries into smaller chunks suitable for retrieval. This preprocessing step enhances retrieval accuracy and ensures that the generator produces coherent and relevant outputs. This dataset highlights the distinction between fine-tuning and RAG. Fine-tuning focuses solely on direct mappings between Swahili queries and responses, while RAG leverages external knowledge to enrich the generator's ability to provide accurate and context-aware answers [44].
# BDS-3 Integrity Risk Modeling and Probability Evaluation
## 4 Result and Discussion
### Characterization of BDS-3 Signal-In-Space Ranging Errors
In contrast, GEO satellites have the worst SISRE accuracy, among which, PRN60 satellite has SISRE accuracy of 5.5 m (95%), obviously worse than the standard accuracy in the analyzed period. Figure 7: Monthly type mean statistical results of SISRE for BDS-3 GEO satellites (**top**), IGSOs (**middle**) and MEOs (**bottom**). SISRE denotes the rms global average SISE of the orbit and clock, SISRE (orb) denotes the orbit-only SISRE, SISRE (wul) stands for the SISRE at the worst user location, and SISRE (95%) stands for the 95 th percentile of the UREs.
## 2 Datasets
### Terra and Aqua MODIS Data  
The MODIS sensor is carried on Terra and Aqua, which are NASA Earth-Observing System (EOS) LEO satellites that pass through the equator at approximately 10:30 A.M. and 1:30 P.M. (local standard time, LST), respectively. MODIS Collection 6.1 (C6.1) L2 aerosol products, \"MOD04_L2\" and \"MYD04_L2,\" are adopted, in which each product file covers a 5-min interval. MODIS AOD retrievals at 500 nm are calculated by interpolating AOD retrievals at 470 and 550 nm based on the Angstrom exponent for the 470-550 nm wavelength pair to match Himawari-8 AHI AOD observations at a spectral band of 500 nm. Meanwhile, the merged Dark Target (DT) and Deep Blue (DB) AOD products can achieve larger completeness with reliable accuracy except for snow/ice cover. Therefore, three datasets with quality assurance flag values from 2 to 3 are extracted and incorporated into the interpolation process: \"Dark_Target_Deep_Blue_Combined\" at 550 nm, \"Effective_Optical_Depth_Best_Ocean\" at 470 and 550 nm, and \"Corrected_Optical_Depth_Land\" at 470 and 550 nm. The aforementioned MODIS aerosol products are available on Level-1 and the Atmosphere Archive and Distribution System (LAADS) at [[http://ladsweb.nascom.nasa.gov/](http://ladsweb.nascom.nasa.gov/)]([http://ladsweb.nascom.nasa.gov/](http://ladsweb.nascom.nasa.gov/)) (accessed on 5 March 2022).
## 9 Discussion
However, even in those cases where the maximum number of CCs that can be employed is constrained by practical limitations, the use of CA as a diversity technique can still provide substantial capacity improvements with respect to the single carrier scenario. This indicates that the proposed CA-based transmission scheme is beneficial under practical conditions. The secrecy performance of the considered CA-based transmission scheme was also evaluated as part of this work. In general, the use of CA as a diversity technique can improve the capacity not only of the links to legitimate users but also to undesired eavesdroppers. If both links experience similar SNR conditions, then the confidentiality of transmitted information may be compromised. However, such a situation should be unlikely in practical system implementations since, in a more realistic setup, the main link can typically be expected to experience a higher average SNR than the eavesdropper link, in particular with modern communication systems where the use of multiple antenna technologies and beamforming techniques are used to direct the transmitted signal towards the desired recipient. When the legitimate receiver in the main link experiences a higher average SNR than the eavesdropper link, then the use of CA can effectively result in an improvement in secrecy capacity as well. The observation above suggests that CA can in principle be safely used as a diversity technique to increase the user data rates and system ergodic capacity; however, when doing so, special attention should be paid to higher-layer techniques for communication confidentiality, since the secrecy capacity might in some unfavourable cases be potentially degraded when using CA as a diversity technique.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 4 Objectives and Advantages of Node Clustering in CR-WSNs
### Licensed User Protection  
The availability of electromagnetic spectrum has a geospatial correlation. The incumbent spectrum available in one geographic location may not be available in another geographic location. Hence, it is necessary for CR-WS nodes to share spectrum information among neighbors.  
Grouped CR-WS nodes deployed in the same geographic region may collect and share more information about the spectrum resource available in that particular region, which helps reduce the chances of PUs and SUs contending for the same channel at the same time. Therefore, clustering helps mitigate interference with the PUs, and protects the rights of PUs.
# Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
## 3 Multiview Temporal Alignment
### Multiview Autoencoder  
to regularize the training of the DNNs and to avoid naive solutions (e.g., \(\mathbf{f}(\mathbf{x})=\mathbf{g}(\mathbf{y})=\mathbf{c}\), for all \(\mathbf{x}\) and \(\mathbf{y}\), with \(\mathbf{c}\) being a constant vector), we propose the introduction of an auxiliary, autoencoder-based reconstruction loss. As shown in Figure 2, this loss function minimizes the mean squared error (MSE) between the DNNs' inputs \((\mathbf{x},\mathbf{y})\) and the reconstructed outputs \((\mathbf{\hat{x}}=\mathbf{f}^{-1}(f(\mathbf{x})),\mathbf{\hat{y}}=\mathbf{g}^{-1}(\mathbf{g}(\mathbf{y})))\), being \(\mathbf{f}^{-1}\) and \(\mathbf{g}^{-1}\) decoder networks that estimate the input feature vectors \(\mathbf{x}\) and \(\mathbf{y}\) from their latent projections \(\mathbf{z}^{x}\) and \(\mathbf{z}^{y}\), respectively, as follows:  
\[\mathcal{L}_{autoencoder}=\frac{1}{N}\Bigg{(}\sum_{i=1}^{N}\Big{\|} \mathbf{x}_{i}-\mathbf{f}^{-1}(\mathbf{f}(\mathbf{x}_{i}))\Big{\|}^{2}\] \[+\sum_{i=1}^{n}\Big{\|}\mathbf{y}_{i}-\mathbf{g}^{-1}(\mathbf{g}(\mathbf{y}_{i})) \Big{\|}^{2}\Bigg{)}. \tag{8}\]The weights of the DNNs in Figure 2 are now optimized by minimizing a weighted combination of the autoencoder-based loss in (8) and one of the similarity losses \(\mathcal{L}_{sim}\) described in Section 3.1, as follows:  
\[\mathcal{L}_{total}=\mathcal{L}_{sim}+\lambda\mathcal{L}_{autoencoder}, \tag{9}\]  
where the hyperparameter \(\lambda\) is set to 1 in this work for simplicity.
Remote Sensing from Different Sources for Crop Growth Monitoring in the Area of the Lower Northern Mississippi  
[PERSON]  
1 USDA-ARS Genetics and Sustainable Agriculture Research Unit, Mississippi State, MS 39762, USA 1  
[PERSON]  
1 USDA-ARS Genetics and Sustainable Agriculture Research Unit, Mississippi State, MS 39762, USA 1  
[PERSON]  
1 USDA-ARS Genetics and Sustainable Agriculture Research Unit, Mississippi State, MS 39762, USA 1  
[PERSON]  
2 Pont  
In observing crop fields, an SUAV is typically used to cover small fields, while satellite imagery and the imagery acquired from manned aircrafts can directly cover a large area of fields because their flight altitudes (or orbits) are much higher than sUAVs\({}^{\prime}\). Therefore, the spatial resolutions of the images from satellites and manned aircraft may be much lower than the images from sUAVs.  
In monitoring crop fields, different sources of remote sensing may be used depending on the purpose of research and management. Satellite imagery is mainly used for boundary delineation of regional or farm areas for determining land use and land cover. The imagery acquired from manned aircraft can be used for land use and land cover quantification, or, as the images acquired from sUAVs are, for field boundary delineation for detection and determination of plant traits and stresses. For a specific focus of concern, a source of remote sensing data can be used, and, at the same time, different sources of remote sensing data can be compared for optimal spatial, spectral, and temporal coordination. This paper overviews the use of remote sensing from different sources, especially airborne remote sensing from manned aircraft and UAVs, to monitor crop growth with three demonstration sites typical in the area of the lower northern Mississippi from the Mississippi Delta to the Black Prairie, one of the most important agricultural areas in the U.S., and indicates the capabilities of satellite remote sensing for crop field monitoring. Then, the issues and challenges are identified and discussed for future opportunities to integrate the remote sensing data from different sources to improve crop monitoring in this area and surrounding areas in the state of Mississippi and any other similar areas.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 7 Performances  
Here, we will discuss some main evaluation parameters of OPAs, which are FOVs, beamwidths and sidelobe suppression, modulation speeds, power consumption. It is important to note that one of critical factors determining FOVs, beamwidths and sidelobe suppression is the crosstalk and suppression among arrayed waveguide channels, and the efforts paid to reduce it should be reviewed first. For high-performance applications, large-scale OPAs are necessary. The scalability of OPAs will also be discussed. Finally, the calibration of OPAs to compensate for fabrication errors and mitigate crosstalk will be shown.
# Random Access for Underwater Acoustic Cellular Systems
## 3 RAP Design
### LTE-Based RAP
In summary, the LTE-based RAP is not appropriate for underwater acoustic cellular systems because it has a long symbol duration leading to high time selectivity, a small subcarrier spacing leading to CFO sensitivity, and a large time ambiguity leading to a small number of possible RAPs, although the computational complexity for RAP detection is small.
## 4 High-Resolution Images in Camera-Based WMSNs
### Image Stitching Technique
#### 4.1.1 Feature Matching  
At first, features must be extracted from image to match them at the next step. For feature extracting, [PERSON] and [PERSON] [38] image stitching method uses SIFT [39] algorithm which contains following stages:  
* _Scale-space extrema detection:_ The difference-of-Gaussian function is used to search over all scales and image locations and identify potential interest points that are invariant to scale and orientation.
* _Key-point localization:_ The potential interest points which are less stable and have low contrast or are poorly localized along an edge are rejected.
* _Orientation assignment:_ In this stage, each point is assigned one or more orientation based on local image gradient directions. SIFT features are scale and orientation invariant because all future operations are performed on image data that has been transformed relative to its orientation, scale, and location.
* _Key-point descriptor_: To compute descriptor vectors for key-points such that the descriptors are highly distinctive and invariant to the remaining variations like shape distortion and illumination, the local image gradients are measured at the selected scale in the region around each key-point.  
SIFT features are in a scale-space maxima/minima of a Difference of Gaussian (DOG) function. Each feature location has scale and orientation properties. Therefore, they have similarity-invariance in the frame which they are measured. The invariant descriptor is computed by accumulating local gradients in histograms of orientation. This causes the descriptor vector not to change with slight affine changes. This spatial accumulation is also important for shift invariance, since the interest point locations are typically only accurate in the 0-3 pixel range [42, 43]. Using gradients that eliminates bias and normalizing the descriptor vector that eliminates gain achieve lead to illumination invariance. Since SIFT features are invariant under rotation and scale changes, this system can handle images with varying orientation and zoom.
# IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving
## 3 The Proposed Approach
### Coordinate Systems and Notations
The exponential map between \(\mathbf{\theta}\) and \(\mathbf{R}\) can be formulated by using the Rodriguez formula:  
\[\mathbf{R}=\cos\theta\cdot\mathbf{I}+(1-\cos\theta)\cdot\mathbf{n}\cdot\mathbf{n}^{T}+\sin \theta\cdot\mathbf{n}^{\wedge}\,. \tag{3}\]  
The operator \(\wedge\) maps a vector to a skew-symmetric matrix as Equation (4), and the operator \(\vee\) maps a skew symmetric matrix back to a vector. \[\mathbf{\theta}^{\wedge}= \begin{bmatrix}\theta^{x}\\ \theta^{y}\\ \theta^{z}\end{bmatrix} ^{\wedge}= \begin{bmatrix}0&-\theta^{z}&\theta^{y}\\ \theta^{z}&0&-\theta^{x}\\ -\theta^{y}&\theta^{x}&0\end{bmatrix} . \tag{4}\]  
The notation of the data used in our system are specified as follows. Each type of data used in our system is composed of the timestamp and the data itself. The timestamp is defined as the time when the data is generated. \(t^{\text{B}}\) denotes the timestamp of the IMU measurement and \(t^{\text{L}}\) represents the timestamp of the lidar point clouds. \(f^{\text{L}}_{\text{k}}\) indicates all points generated from the lidar during \([t^{\text{L}}_{\text{k}-1},t^{\text{L}}_{\text{k}}]\). In addition, the \(i\)-th point generated by the \(b\)-th laser beam is denoted as \(\mathbf{X}^{\text{L}}_{(\text{i},\text{b},\text{k})}\) in the lidar coordinate system \(\{L\}\). The number of points received per laser beam is denoted as \(S_{b}\) and \(i\in[1,S_{b}]\).
# TraceGuard: Fine-Tuning Pre-Trained Model by Using Stego Images to Trace Its User
## 3 Proposed Approach
### Fingerprinting Pre-Trained Model
#### 3.4.2 Fine-Tuning for Uniqueness  
This step is conducted to achieve uniqueness of the fingerprint. \(D_{f}\), as a subset of \(D_{s}\), is selected from \(D_{s}\). We select \(K_{\text{em}}\) from \(K_{em}\in\{0,1\}^{\text{L}}\), designated for the \(j\)-th authorized user. \(E\) receives \(I_{co}\in D_{f}\) and embeds \(K_{\text{em}}\)\({}^{\text{j}}\) to generate the stego image \(I_{st}\). Then, \(I_{st}\) is sent to \(P_{f}\) and \(P_{sd}\), and embedding is obtained accordingly. Finally, the two embeddings are fed into \(D\) to extract \(K_{\text{ex}}\)\({}^{\text{j}}\) and \(K_{\text{ex}}^{\text{\prime}}\)\({}^{\text{j}}\), respectively.  
Two secret key extraction loss items compose the total loss for fine-tuning. One loss item, \(L_{k}\), is designed to evaluate the discrepancy between \(K_{\text{em}}\)\({}^{\text{j}}\) and \(K_{\text{ex}}\)\({}^{\text{j}}\), which are extracted from \(P_{f}\), and the other loss item, \(L_{k}^{\text{\prime}}\), is designed to evaluate the discrepancy between \(K_{\text{em}}\)\({}^{\text{j}}\) and \(K_{\text{ex}}^{\text{\prime}}\)\({}^{\text{j}}\), which are extracted from \(P_{sd}\). The incorporation of \(L_{k}^{\text{\prime}}\) aims to enhance the robustness of \(P_{f}\) against model stealing attacks. Thus, the pre-trained model is fine-tuned for uniqueness by minimizing the following loss:  
\[\begin{split}\mathcal{L}_{\text{uni}}=\lambda_{P_{f}}\mathcal{L} _{k}(K_{\text{em}}\text{'},K_{\text{ex}}\text{'})+\lambda_{P_{sd}}\mathcal{L} _{k}^{\text{\prime}}(K_{\text{ex}}^{\text{\prime}}\text{'},K_{\text{em}}^{ \text{j}})\\ \mathcal{L}_{k}(K_{\text{em}}^{\text{j}},K_{\text{ex}}^{\text{j}}) =\text{BCE}(K_{\text{em}}^{\text{j}},K_{\text{ex}}^{\text{j}})\\ \mathcal{L}_{k}^{\text{\prime}}(K_{\text{ex}}^{\text{\prime}}\text {'},K_{\text{em}}^{\text{j}})=\text{BCE}(K_{\text{ex}}^{\text{\prime}}\text{' },K_{\text{em}}^{\text{j}})\end{split} \tag{3}\]  
where \(\lambda_{P_{f}}\) and \(\lambda_{P_{sd}}\) are the weights of the two loss items.