esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments Results
### Discussion  
According to the experimental results presented above, we can draw the following discussions.  
Firstly, active HSI has the potential to distinguish objects in similar color. Both the traditional classification model and the deep-learning-based model (SVM-RBF and proposed hybrid DenseNet) can achieve good classification performance for most classes.  
Secondly, owing to the significant spectral differences between most objects, extracting spectral features only is enough to obtain good classification results. However, for Class 6, spatial information is indispensable to get better results. The proposed hybrid DenseNet model can extract spectral and spatial features with the 1D and 2D branches, without destroying any features by the parallel double-branch structure. With this architecture, the hybrid DenseNet achieves better classification results compared with other methods or single-branch structure.  
Thirdly, for most classes, 10% of training samples are enough to get a good trained hybrid DenseNet model, but for Class 6, larger number of training samples are needed. Besides, there exists giant spectral redundancy in active HSI data. A total of 10 principle components can achieve a relatively good performance for most classes, except Class 6, which needs more principle components.  
According to the above discussions, we find that the classification performance under different situations is seriously affected by Class 6. Therefore, for Class 6, 11 spectra are randomly selected and shown in Figure 14. It is found that there is large spatial variability of spectral signatures within Class 6, which may be the reason causing the difficulty in classifying Class 6. The spatial variability may be caused by the highlights at some areas of the green plastic bottle cap.
## 5 Conclusions  
The metagenomic approach based on the sequencing of ITS2 region of fungal rDNA revealed changes in the fungal community associated with the ripening of sea buckthorn berries. Investigation of alpha and beta diversity of the fungal community, analysis of representative microbial ASVsshowed a clear separation among inhabitants of unripe (UB) and ripe berries (RB). Based on results of metagenomics analysis, unripe berry samples prevalently had _Aureobasidium_, _Taplrinua_, and _Cladosporium_ genera yeasts; while ripe berries were dominated by _Aureobasidium_ and _Metschnikovaia_. Potential biocontrol yeasts _Aureobasidium pullulans_ and _Cryptococcus wieringae_ were isolated from sea buckthorn surface at the early harvesting stage, while _Metschnikovaia pulcherrina_ and _Hanseniaspora warum_ were amplified from ripe berries and applied for the analysis of produced volatile organic compounds. Eleven esters, six alcohols, five volatile fatty acids, three ketones, two aromatics, and two compounds bearing both ester and aromatic moieties were isolated from the headspaces of four yeast species and identified by gas chromatography and mass spectrometry techniques. Principal component analysis revealed that volatile blends of all four yeast species were clearly separated from each other.  
The following are available online at [[http://www.mdpi.com/2076-2607/8/3/456/s1](http://www.mdpi.com/2076-2607/8/3/456/s1)]([http://www.mdpi.com/2076-2607/8/3/456/s1](http://www.mdpi.com/2076-2607/8/3/456/s1)), Figure S1: Rarefaction curves at a genetic distance of 3% for each sample, Table S1: Fungal taxonomy abundance count for sea buckthorn samples at different ripening stages from phylum to species level, Table S2: Relative abundance of the most common ASVs.  
Conceptualization, E.S., V.B., and R.M.; Collection of plant material, I.V.M., D.A., and B.R.; Metagenomic analysis of fungal microorganisms, I.V.-M., J.L., and B.R.; Bioinformatic analysis, J.L. and E.S.; Cultivable yeast isolation and identification, I.V.-M., R.S., Z.-S., and B.R.; Sampling and identification of volatiles, V.A., L.B.-C., and D.A.; Data interpretation, E.S., R.M., and V.B.; Funding acquisition, R.M.; Writing-- original draft, E.S., J.L., and R.M.; Writing--review and editing, E.S., B.R., R.M., and V.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Social Fund/European Regional Development Fund under grant agreement with the Research Council of Lithuania (LMTLT), grant number 09.3.3-LMT-K-712-01-0099.  
We would like to thank to [PERSON] for kind permission to collect sea buckthorn berries in his plantation.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Proposed Methods
### Construction of the Local Channel Attention Module LSEB (Local Squeeze-and-Excitation Block)  
The channel attention mechanism focuses on modelling the relationship between the channels, and uses the dependencies to strengthen the feature screening and extraction ability of the network. The network can filter out the useful information by capturing the global information, strengthening the extraction of this part of the features, and suppressing the useless features. The channel attention mechanism includes two operations, squeeze and excitation. Firstly, the global spatial features of each channel are compressed into a descriptor to represent the information of each channel. Then, the dependency of the channels is learned, the weights of the different channels are obtained, and the feature map is adjusted, according to the weight information. In this paper, based on the channel attention mechanism, the LSEB attention convolution block is constructed, and the structure of the LSEB module is shown in Figure 3. The module focuses on the local feature information of the images. Prior to the channel attention convolution, the LSEB module first divides the local image feature information into blocks of the same size. For the channel attention convolution operation, it connects the sigmoid activation function to study the nonlinear interaction between the channels. Finally, the output of the LSEB module is obtained by the information integration, which highlights the effective information and makes full use of the feature information.  
Figure 2: Structure diagram of the local channel attention residual convolutional neural network LSERNet.
# Development of Predictive Equations for Thermal Conductivity of Compost Bedding
## 3 Results and Discussion
### Prediction of Bed Compost Thermal Conductivity
Overall, the models proposed in this study to predict \(k\) based on MC and DC\({}_{\rm o}\) (both SC and DC) showed good generalization performance. The results are promising, considering that the modeled equations depend on variables that are subject to large variations in the field, such as moisture, applied pressure, the type of bed material, and depth.
## 4 Discussion
Polarization-dependent measurements can be realized in three ways: (1) by rotating the sample \(\pm 45^{\circ}\) by the robot, (2) dropping and re-picking the sample rotated by \(90^{\circ}\) (used for wood sample #1), and (3) by setting the polarizer angles manually to \(\pm 45^{\circ}\) (used for wood sample #2). With the robot arm, it is possible to scan a non-planar convex object of known surface shape by minimizing the distance of the sample to the aperture mask and choosing the orientation of the sample with respect to the incident beam. Furthermore, repeatable measurement, e.g., accounting for changing humidity conditions are possible. The higher jitter in the annual ring structure in the images for \(\alpha=0^{\circ}\) in Figure 8 indicates that the synchronization of spectrum collection is not perfect and demonstrates one limitation of our scanning setup. This may have led to a slightly lower resolution in the horizontal scanning direction compared to the vertical direction. The extraction of spectra from the available time domain spectrometer control software has its limitation with respect to achievable data rate as well as proper synchronization. However, for the images collected for this work, its influence on resolution is small compared to other factors. Today's state-of-the-art TDS systems may offer much faster spectrum collection including in time data storage. Here, it is even more important that the control software allows for low level hardware access or at least hardware clock/timestamp synchronization. In addition, the saving of spectra has to be optimized for reliability at high data rates with low latency. The rather long scanning times of 6 min to 20 min for our images are due to our specific TDS system and its limited access to the raw spectra. We see a high potential for shorter sample scanning times. With a newer system a speed up to a factor of five seems reasonable, provided that fast data storage is available. Spectrum collection of current TDS systems can exceed 50 spectra per second. Furthermore, an optimization of scanning speed, sample scan area, and aperture size in view of the targeted resolution allows an optimization of scanning times.
# Enhancing Diffusion-Based Music Generation Performance with LoRA
## 2 Related Work
### LoRA for Personalization and Subdomain Adaptation in Diffusion Models  
With the diffusion-based AudioLDM model providing a suitable backbone, this subsection investigates how LoRA and its extensions have been employed in diffusion models for fine-grained control and personalization. These advances motivate our proposed integration of LoRA into AudioLDM's text encoder for genre-specific adaptation.  
LoRA [10] has gained traction as a scalable PEFT method for diffusion-based generative models, enabling efficient adaptation through rank-constrained parameter injection. In the context of music generation, LoRA facilitates selective tuning of key modules--particularly the self-attention blocks in U-Net backbones--while keeping the majority of the model frozen. Recent studies have extended LoRA for personalization and subdomain control.  
DiffLoRA [17] employs a hypernetwork to generate LoRA weights from user-specific references, enabling sample-wise personalization in text-to-image models without additional fine-tuning. Blockwise-LoRA [18] introduces structural modularity by grouping LoRA weights into concept-specific blocks, enhancing controllability and compositionality. LoRA Fusion [19] further advances stylistic control by integrating multiple LoRA modules based on prompt-derived attention scores, allowing soft blending across learned domains. These methods vary in architectural strategy: DiffLoRA emphasizes dynamic generation of adaptation weights, Blockwise-LoRA focuses on hierarchical modularity, and LoRA Fusion enables multi-style interpolation via softmax-weighted key-query mechanisms. In this work, we extend these approaches to text-to-music generation by integrating LoRA into the text encoder of AudioLDM, targeting \(W_{q}\) and \(W_{v}\) projection matrices for subgenre-specific adaptation. This enables efficient fine-tuning without full model retraining, supporting genre conditioning, domain alignment, and personalization.  
These architectural innovations collectively demonstrate that LoRA is scalable and highly adaptable for use in domain-specific generation. In the context of text-to-music systems, this flexible control of model outputs is essential for respecting genre conventions, user intent, and stylistic coherence. Thus, LoRA-based techniques provide a compelling solution for personalized and expressive music generation in diffusion frameworks.
## 3 Comparative Analysis of Existing Evaluation Methods
### Applicability Analysis  
Overall, Formulae (7) and (18) perform well across different probability contours, particularly at low fines contents, with a high and balanced accuracy. Formula (8) adapts well to moderate fines content areas, whereas Formula (11) excels in non-liquefaction areas but exhibits a lower accuracy in liquefaction areas.  
These findings indicate the limitations in existing liquefaction evaluation formulae when handling high fines contents, necessitating further research and optimization to enhance their adaptability and accuracy.
# Development of a Mobile Buoy with Controllable Wings: Design, Dynamics Analysis and Experiments
## 4 Dynamics Simulation Results
### Dynamic Responses in Glider Mode
Meanwhile, as shown in Figure 11e,f, there are relatively large fluctuations in \(F_{x-total}\) and \(M_{total}\) at the initial moment and about 23 s due to the coupling of buoyancy, wing rotation and flow field. Figure 11g shows the impact of net buoyancy mass on the motion velocity, and it can be observed that the velocity increases with the increase in net buoyancy mass. Thus, reasonable control of \(m_{0}\) can regulate the period of the sawtooth motion. In addition, the effect of net buoyancy mass on motion trajectories, as shown in Figure 11h. It can be clearly seen that the diving depth and horizontal displacement gradually increase with the increases in net buoyancy mass, but the gliding attitude is almost constant, and the glide angle maintains at about \(42^{\circ}\). Finally, it can be seen from the simulation result that while both the control input, \(\alpha_{w}\) and \(m_{0}\), influence the dynamic responses of the buoy, net buoyancy mass has a larger impact on the buoy velocity, while wing rotation angle has a larger impact on mobile buoy's motion attitude. Figure 10: Dynamic responses under different wing rotation angles during the Glider mode, showing (**a**) forward velocity, (**b**) heave velocity, (**c**) angular velocity around the **Y**-axis direction (**d**) pitch angle, (**e**) force, (**f**) moment, (**g**) motion velocity and (**h**) motion trajectory. Figure 11: Dynamic responses under different net buoyancy masses during the Glider mode, showing (**a**) forward velocity, (**b**) heave velocity, (**c**) angular velocity around the \(\mathbf{Y}\)-axis direction (**d**) pitch angle, (**e**) force, (**f**) moment, (**g**) motion velocity and (**h**) motion trajectory.
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 7 TN Domain
### Transport SDN Fabric
Integrated in the E2E Transport Network Control Abstraction block (see Figure 17), this module would manage the workflows for the TN slice provision, as well as for its life cycle. Putting all the discussed solutions in the technology radar shown in Figure 9, we observe the following:  
* The deploy ring includes **IP domain SDN controller:****SBI** and **optics domain SDN controller:****SBI** solutions. With the emergence of the first Rel-15 commercial networks, some operators have recently started the deployment of SDN technology in transport networks, although not at large scale yet. For now, it is limited to IP/MPLS and optical/DWDM segments, and highly coupled with specific use cases. * The test ring covers **MW domain SDN controller:****SBI**, **Domain SDN controller:****NBI** and **E2E SDN controller:****internal logic** solutions. The operators are currently conducting trials on features from these three solutions, which are expected to be available in the medium term. * The explore ring captures the **E2E SDN controller:****NBI** solution (for the integration of transport SDN fabric with the rest of OSS assets) and the **Slicing-aware E2E SDN controller** solution (to integrate the slice semantics in the transport SDN fabric). According to the above rationale, it is clear that to get the targeted transport SDN architecture up and running in carrier networks, operators need to follow a staged approach where the full, E2E integration of slicing features constitutes the final stage. However, this does not mean we cannot have TN slicing meanwhile; indeed, as outlined in Section 7.1, there are multiple transport technologies available for slicing realization. What this really means is that the operator may not be able to have automation and programmability capabilities in the short and medium term when allocating and operating transport slices. The lack of SDN may force the operator to go for static provisioning and management operations, from the E2E management domain to technology-specific management systems, using traditional Command Line Interface (CLI) solutions with ad-hoc extensions to avoid vendor lock-in.
# IBA myQA SRS Detector for CyberKnife Robotic Radiosurgery Quality Assurance
## 3 Results
### Dose Rate Dependence  
The study measurements confirm the detector's response dependence on incident dose rate, corroborating the manufacturer's requirement for dose calibration specific to each dose rate of interest. myQA@SRS reading normalization to the ionization chamber signal shows that measured variations are only due to the dose rate fluctuations with SAD.  
As shown in Figure 7, the CMOS array/ion chamber ratio varies by 2.5% or less around each specific SAD value used by the treatment manipulator to irradiate trigeminal, intracranial, and body targets. Detector calibration can therefore be kept constant within the same treatment path and needs to be changed only when moving from one path to another.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{\
\begin{tabular}{c} **Nominal** \\ **Size** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Actual** \\ **Size** \\ **(mm)** \\ \end{tabular} } & \multicolumn{6}{c}{**myQA SRS**} & \multicolumn{2}{c}{\(\mid\)**Actual-**} \\ \cline{3-10}  & & **a** & **b** & **c** & **Average** & **Max I (a,b,c)** & **Min I (a,b,c)** & **Measured I** \\ \hline
5 & 5.2 & 5.0 & 5.0 & 5.1 & 5.0 & 0.1 & 0.0 & 0.2 \\ \hline
7.5 & 7.4 & 7.5 & 7.5 & 7.7 & 7.6 & 0.1 & 0.1 & 0.2 \\ \hline
10 & 9.8 & 9.9 & 9.9 & 10.0 & 10.0 & 0.1 & 0.0 & 0.2 \\ \hline
12.5 & 12.3 & 12.4 & 12.2 & 12.5 & 12.3 & 0.1 & 0.2 & 0.0 \\ \hline
15 & 14.7 & 14.7 & 14.8 & 14.8 & 14.8 & 0.1 & 0.0 & 0.1 \\ \hline
20 & 19.7 & 19.8 & 19.7 & 19.9 & 19.8 & 0.1 & 0.1 & 0.1 \\ \hline
25 & 24.8 & 24.9 & 24.8 & 24.8 & 24.8 & 0.0 & 0.0 & 0.0 \\ \hline
30 & 29.8 & 29.5 & 29.8 & 29.8 & 29.7 & 0.1 & 0.2 & 0.1 \\ \hline
35 & 34.6 & 34.5 & 34.6 & 34.6 & 34.6 & 0.0 & 0.0 & 0.0 \\ \hline
40 & 39.5 & 39.5 & 39.5 & 39.5 & 0.0 & 0.0 & 0.0 \\ \hline
50 & 49.5 & 49.5 & 49.1 & 49.3 & 49.3 & 0.2 & 0.2 & 0.2 \\ \hline
60 & 59.2 & 59.0 & 59.4 & 59.4 & 59.2 & 0.2 & 0.3 & 0.0 \\ \hline \end{tabular}
\end{table}
Table 1: Iris\({}^{\text{TM}}\) variable aperture collimator field size results. “Actual size” refers to the field size as measured at the time of Iris installation (as discussed in Section 2.3.4) and is considered the real aperture of the authors’ Institute Iris. Max \(\mid\)(a, b, c)-Average \(\mid\) column contains the maximum (minimum, in the following column) absolute value of the difference between each myQA@SRS measurement and the average of the three measured values (a, b, c). The column entitled \(\mid\)Actual-Measured \(\mid\) reports the absolute values of the difference between the average myQA@SRS measurement with respect to the actual field size.
# A Lightweight Visual Odometry Based on LK Optical Flow Tracking
## 2 System Description
### Pyramid-Based Sparse Optical Flow Tracking Matching
When the motion amplitude of pixels in the original image is large, the motion of these points is still small when observed at the higher levels of the pyramid, so that the algorithm can be applied to the scene where the camera moves rapidly, and accordingly, the robustness and efficiency of the LK optical flow method are improved. In this paper, the LK optical flow method is used to iteratively solve the motion vectors of key points in the pyramid image. As shown in Figure 4, when calculating the optical flow, it is started from the topmost image of the pyramid, and the predicted value of the key point in the current frame is obtained by the uniform motion model, which is used as the initial value of the top-level iteration to calculate the motion vector of the top-level. Then, the tracking result of the previous layer is used as the iterative initial value of the optical flow of the next layer and the motion vector of the layer is calculated. The process is repeated between the adjacent layers of the pyramid until the lowest layer of the pyramid (the original image) is reached, to obtain the final motion vector and achieve the key point matching between the adjacent images. To evaluate the tracking accuracy of key points and reduce the number of iterations of the optical flow solution, a judging criterion for stopping the iteration of the algorithm is set in this paper, based on the average residual of pixels in the neighborhood window of key points. The average grayscale difference of pixels in the neighborhood window of the corresponding key points between the adjacent images can be expressed via the following equation:  
\[\delta_{ave}=\frac{\sum\limits_{i=1}^{m}\left\|I_{r}(x_{i},y_{i})-I_{c}\left(x _{i}^{*}+d_{x},y_{i}^{*}+d_{y}\right)\right\|}{25} \tag{7}\]  
The termination condition of algorithm iteration is  
\[\delta_{ave}<\delta_{\min}||N_{iter}>N_{\max} \tag{8}\]  
where \(\delta_{\min}\) is the minimum value of the average grayscale difference of the pixels in the two neighborhood windows, \(N_{iter}\) is the current iteration number of the algorithm, \(N_{\max}\) is the maximum number of iterations allowed by the algorithm, and we set \(\delta_{\min}=0.02\), \(N_{\max}=10\) in the experiment. As described above, the difference between the VO studied in this paper with the ordinary optical flow method is that an initial value is provided for the pyramid-based LK optical flow tracking algorithm by the uniform motion model, and a termination condition for the algorithm iteration is set, which makes the algorithm to remain good adaptability in dealing with the large camera motion of the camera or the weak texture area. While ensuring the accuracy of the algorithm, the number of iterations is greatly reduced, and the robustness of the algorithm is effectively improved.
## 4 Data Analysis and Results
### Seismological Results
The three groups of the relocated aftershocks, if considered as a whole, appear to be well distributed on a plane, striking \(\sim\)N216\({}^{\circ}\) E and dipping \(\sim\)45\({}^{\circ}\) (Figure 10, red-dashed line), as determined by the least-squares fit on the hypocenters. This is quite compatible with focal mechanism solutions for the mainshock reported by most agencies, taking into account the WNW-dipping nodal plane (Table S1). Regarding specific spatial groups of the aftershock sequence, the best-fit plane for the northern cluster #3 strikes N181\({}^{\circ}\) E and dips 49\({}^{\circ}\) westwards (Figure 10, blue dashed line in profile c\({}_{1}\)-c\({}_{2}\)), whereas the mid-southern part (groups #2 and #4) has a least-squares plane that trends N216\({}^{\circ}\) E and dips 53\({}^{\circ}\) WNW (Figure 10, green-dashed line in profiles a\({}_{1}\)-a\({}_{2}\) and b\({}_{1}\)-b\({}_{2}\)). The more north-southwards orientation of the northern cluster's plane makes it more compatible with the Kastelli fault. The fault is steeply dipping at the surface (74\({}^{\circ}\)), but the aftershocks' distribution indicates a lower dip angle at the hypocentral depths of 6-9 km, implying lstricity of the fault. Cluster #3 is fairly more concentrated than group #4 in the south, which appears to include several smaller sub-clusters, some of which may be related to the activation of antithetic structures (e.g., Figure 10, black-dashed lines with a question mark in profile a\({}_{1}\)-a\({}_{2}\)). Normal faulting is indicated for the mainshock, the major aftershock, and several other aftershocks and foreshocks. However, oblique and strike-slip faulting is also observed for several events, including some foreshocks (Figure 10). Although the mainshock's focal mechanism is in line with the rest of the aftershocks, it is noted that its hypocenter, along with those of aftershocks that occurred before the deployment of the local network, is not well constrained at depth. Hence, it is reasonable to assume that its true focal depth should be deeper, i.e., between 6 and 14 km, which is an estimated range for its centroid (Table S1).