esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Procedure
### Geometry, Computational Domain and Mesh  
The geometry used for the simulation corresponds to the geometry of the master mold used to make the microchannels device for experimental purposes. This device, together with the supporting experimental setup, isdescribed by [26]. Briefly, the polydimethylsiloxane (PDMS), which is a very suitable material for replica molding, was inserted into the acrylonitrile-butadiene-styrene (ABS) 3D-printed mold to cure in the shape of the mold depicted in Figure 1. After the curing period of the PDMS, the ABS mold was dissolved in an acetone bath, and finally we obtained the cleaned PDMS microfluidic heat sink. The dimensions of the master mold were also used for the _COMSOL Multiphysics 5.6\({}^{\circledR}\)_ software simulations.  
The mesh of the fluid domain was tuned for the flow studies, i.e., besides the tetrahedral elements that composed the majority of the mesh, a shell was created near the walls to enhance the reliability of the results in those regions [27], as shown in Figure 2.  
Figure 1: Schematic representation of the acrylonitrile–butadiene–styrene (ABS) master mold and respective dimensions (heat sink length, L; width, W; height, Z).  
In contrast to the flow distribution uniformity simulation, for heat transfer purposes, the entire dissipator had to be represented as a whole. Accordingly, the geometry had to include the PDMS channels' surroundings and their hollow region to allow the generation of distinct domains and different simulation modules in each one of them, as seen in Figure 4.  
The mesh comprises different regions, namely, the zone where the fluid flows, the solid region around that zone, and a localized transition in the boundary between both. The presence of two distinct domains requires the definition of the connection between  
Figure 4: Example of the geometry used in the heat transfer simulation, wherein both domains of the simulation can be seen.  
Figure 3: Details of the mesh in the collector.Highlighted: the inlet region and the transition between the collector and the channels.  
them, specifying the means of contact (a union in this case). In this way, the temperature changes continuously across the domain boundaries. An adaptation of the mesh to create a transition region on the inlet surface can be noted in Figure 4.  
The mesh with the best results in the convergence study was kept, and only one convergence study in the solid domain was performed, using the same points mentioned on the previous study. Details of the mesh are presented in Table 2.  
Mesh tests were also performed to select the best mesh for the heat transfer studies. The outlet temperature was calculated using the different meshes. The outlet temperature becomes independent of the mesh for meshes B5 and denser. Thus, the mesh B5 was selected, since the average quality of the elements is reasonably acceptable, and the mesh tests also demonstrated that a mesh with a higher number of elements will not strongly influence the final result of the simulation. Consequently, choosing the B5 mesh will also minimize the computational cost.
## 4 Experiments
### Dataset  
In this paper, we design a combined dataset consisting of IRMAS [23] and OpenMIC. It is important to note that the IRMAS training set is single-labeled, the IRMAS testing set is multi-labeled, and the samples in the IRMAS dataset vary in length. The music audio segments in the IRMAS dataset are stereo with a 44.1 KHz sampling rate and feature different musical styles. The recordings span several decades, resulting in varying audio qualities. The goal of IRMAS is to recognize instruments in audio segments, including cello, clarinet, flute, acoustic guitar, electric guitar, organ, piano, saxophone, trumpet, violin, and human voice. However, although the testing set reflects the performance of instrument recognition models on music samples of varying lengths, the IRMAS training set only contains single-instrument samples. To verify the effectiveness of the proposed MMGAT, we introduce the OpenMIC [24] dataset to create a training dataset that includes multiple instruments with different lengths. The resulting OpenMIC-IRMAS dataset consists of both training and testing datasets containing multi-labeled samples of varying lengths. The OpenMIC-IRMAS training set is a combination of the training datasets from OpenMIC andIRMAS. Specifically, we incorporate instances from the IRMAS dataset into the OpenMIC samples, creating music segments that range from 10 to 19 s in duration. In the experiments, the OpenMIC-IRMAS dataset is split into training and testing sets. We use the training set of OpenMIC-IRMAS to train the network, with 15% of the testing set reserved for validation. The attributes of the OpenMIC-IRMAS dataset are shown in Table 1.  
As Table 1 shows, the dataset is imbalanced, and the auxiliary classifier contains three classes.  
As evidenced in Table 2, the auxiliary classifier operates on higher taxonomic granularity compared to its principal counterpart. To holistically assess MMGAT's classification efficacy under this hierarchical paradigm, we employ rigorous multi-perspective evaluation:  
1. Class-imbalance-resistant micro-averaging for instrument-level fidelity;
2. Macro-averaging sensitive to rare classes for taxonomy-level fairness.  
Following standard evaluation protocols for multi-granular systems, these metrics are formally defined as per standard convention, ensuring direct comparability with prior hierarchical recognition frameworks.  
\[F1_{micro}=\frac{2P_{micro}R_{micro}}{P_{micro}+R_{micro}},\;\;F1_{macro}= \frac{2P_{macro}R_{macro}}{P_{macro}+R_{macro}}\] \[P_{micro}=\frac{\sum_{l=1}^{L}tp_{l}}{\sum_{l=1}^{L}(tp_{l}+fp_{ l})},\;\;R_{micro}=\frac{\sum_{l=1}^{L}tp_{l}}{\sum_{l=1}^{L}(tp_{l}+fn_{l})}\] \[P_{macro}=\frac{1}{L}\sum_{l=1}^{L}\frac{tp_{l}}{(tp_{l}+fp_{l})}, \;\;R_{macro}=\frac{tp_{l}}{L}\frac{tp_{l}}{(tp_{l}+fn_{l})}\]  
where \(L\) is the number of classes. \(tp_{l}\) is true positive. \(fp_{l}\) is false positive, and \(fn_{l}\) is false negative for each label.
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 5 Conclusions  
This study presented GCSG, an innovative hybrid deep learning model designed for precise load forecasting in Logical Range environments. The integration of Gramian Angular Field transformation with CNN-SENet spatial feature extraction and GRU-based  
Figure 7: Comparison of models under different datasets.  
temporal modeling was shown to be particularly effective in handling the complex, uneven workload distributions that are characteristic of Logical Range testing scenarios. Experimental validation demonstrated that GCSG achieves superior performance, with an \(R^{2}\) of 0.86, MAE of 4.5, and MSE of 34.5, representing significant improvements of 2.2%, 25.7%, and 40.4%, respectively, over conventional approaches (including BiLSTM, LSTM-ED, and other baselines).  
The model exhibited remarkable stability across varying operational conditions, maintaining consistent accuracy with input window sizes ranging from 16 to 40 and prediction steps spanning 1 to 29. This robustness was further verified through testing on multiple data partitioning schemes using different random seeds, confirming GCSG's reliable generalization capability.  
Looking ahead, two promising research directions emerge: extension to multi-device collaborative forecasting scenarios and optimization for edge computing deployment in order to enhance computational efficiency. These developments would further strengthen the model's applicability in large-scale Logical Range implementations while addressing potential latency constraints in time-sensitive applications.  
Conceptualization, H.C.; methodology, H.C.; writing--original draft preparation, H.C.; formal analysis, Z.D.; data curation, Z.D.; supervision, H.C. and Z.D. H.C. and Z.D. reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
All materials and data used are available from Xi'an University of Technology, China. The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.
## 2 Background
### IRL Concepts  
The notation MDP\(\backslash\)R is used to denote an MDP where the reward function \(R\) is not given. The IRL task consists in finding an \(R\) for which the agent's observed behaviour is optimal given an MDP\(\backslash\)R, usually working within a parametric class \(\{R_{\theta}:\theta\in\Theta\}\). The canonical approach to this is to use a linear approximation of \(R\) from _features_\(\phi(s,a)\in\mathbb{R}^{d}\) of each state and action, with weights \(\theta\in\mathbb{R}^{d}\), such that \(R(s,a)=\theta^{\sf T}\phi(s,a)\). This is explained in further detail, with alternative approaches, in Section 3.2. The idea of approximating utility (reward) as a linear function of subutilities (features) dates back to the work of [PERSON] and [PERSON] [36], used for opponent modelling in extensive form games (chess), and is closely related to earlier work in economics, such as in [37]. Often \(R\), \(\phi\), are functions of the state only, instead of state-action pairs. The extension to this setting is trivial if we adopt the state-action formulation. Under a policy, we have _feature expectations_ (expected discounted value of features) \(v^{\pi}\in\mathbb{R}^{d}\):  
\[v^{\pi}=\mathbb{E}_{(s_{i},a_{t})\sim d_{\pi,T,t}}[\sum_{t=0}^{\infty}\gamma^ {t}\phi(s_{t},a_{t})|D,T,\pi]. \tag{6}\]  
A feature matrix \(F\in\mathbb{R}^{d\times|\mathcal{S}\times\mathcal{A}|}\) can be employed to encapsulate the features for each state-action pair \(F_{(\cdot,s,a)}=\phi(s,a)\), resulting in \(v^{\pi}=F\mu^{\pi}\). Under a linear approximation of the rewards, \(V^{\pi}\) (Equation (1)) can alternatively be obtained from features (through linearity of expectations) with  
\[V^{\pi}=\theta^{\sf T}v^{\pi}=\theta^{\sf T}F\mu^{\pi}. \tag{7}\]Feature counts from a given trajectory \(\tau\) provide a compact representation of the trajectory  
\[\Phi_{j}=\Phi(\tau_{j})=\sum_{t=0}^{H_{j}}\gamma^{t}\phi(s_{t},a_{t})\in\mathbb{R }^{d}. \tag{8}\]  
The measurements of the behaviour of the agent of interest (here actor or expert) over time are given as demonstrations \(\mathcal{D}\), usually taking the form of an unordered set \(\mathcal{D}=\{\tau_{j}^{\pi}\}_{j=1}^{m}\) of sequential paths (i.e., trajectories) \(\tau_{j}^{\pi}=\left((s,a)_{t}\right)_{t=0}^{H_{j}}\) of length \(H_{j}+1\). Additional information may be provided in the demonstrations, including feature matrices, occupancies, etc.  
Empirical estimates of occupancy and feature expectations can be computed as average counts from the observed trajectories  
\[\bar{\mu}^{\pi}(s,a\mid\tau_{j})=\frac{1}{H_{j}+1}\sum_{t=0}^{H_{j}}\gamma^{t} \delta_{s_{t}s}\delta_{a_{t}a}, \tag{9}\]  
\[\bar{\sigma}^{\pi}=F\bar{\mu}^{\pi}. \tag{10}\]  
The above results all apply to mixed policies through linearity of expectations.
# A PEKS-Based NDN Strategy for Name Privacy
## 5 Experiments and Results  
In this section, we explain experiments and results. The first experiment shows that if a consumer wants to adopt a straight forward construction of the PEKS-based strategy, it needs to encrypt each name component by using a producer's public key. After PEKS ciphertext is computed, output elements, \(g^{r}\) and \(H_{2}\)(t) are concatenated by a 1 byte character to create a single string. Then the string becomes a ciphertext component \(C_{j}\) of an encrypted name \(/C_{1}/C_{2}/.../C_{k}\). Once parameters of PEKS are determined, the output size of the ciphertext is fixed. This means that the size of encrypted ciphertext generated by a consumer is the multiplication of the name size and the number of components. Furthermore, using PEKS scheme's algorithm multiple times is a painful process for a router in a network. Unlike NDN, PEKS-based NDN includes a hash function in the encryption process. It is possible to input multiple name components of plaintext names at a time. Such a construction reduces the size of the encrypted name and has less number of executions. Therefore, we can design a more efficient construction in which multiple name components rather than each name component are hashed in the encryption process. Hashing all name componentsexcept the producer's prefix component seems to yield the most efficient scheme. We discuss in Section 5.2 that such a whole-but-prefix name components encryption construction does not always provide the best efficiency. The purpose of the second experiment to show the caching ability of PEKS-based NDN and evaluation of download with respect to the cache hit ratio.
_Article_  
**Laboratory Study on the Performance of Asphalt Mixes Modified with a Novel Composite of Diatomite Powder and Lignin Fiber**  
**[PERSON] 1,2, [PERSON] 1,*(), [PERSON] 1,2, [PERSON] 1,**  
**[PERSON] 1 and [PERSON] 3**  
1 School of Human Settlements and Civil Engineering, Xi'an Jiao tong University, Xi'an 710054, China; [EMAIL_ADDRESS] (M.A.); [EMAIL_ADDRESS] (A.K.); [EMAIL_ADDRESS] (D.L.); [EMAIL_ADDRESS] (J.M.)
2 Department of Civil Engineering, Faculty of Engineering, Benha University, Benha 13512, Egypt
3 School of Earth Science and Land Resources, Chang'an University, Xi'an 710064, China; [EMAIL_ADDRESS]
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-1839-250-0205  
**Abstract:** The performance and the fundamental weaknesses of asphalt mix under environmental temperature and water effects have made researchers try to modify the asphalt mix properties by using the proper additives. For this reason, this paper aims to improve the anti-cracking performance and water stability of asphalt pavement by adding a novel composite of diatomite and lignin fiber in asphalt mixes. Four types of asphalt mixes, including control asphalt mix (CAM), diatomite modified asphalt mix (DMEM), lignin fiber modified asphalt mix (LFMAM), and diatomite-lignin fiber composite modified asphalt mix (DLFMAM) were prepared in the laboratory. Low-temperature bending test, Marshall Immersion test, and freeze-thaw splitting test were employed to evaluate the performance of the asphalt mixes. Results reveal that the use of the lignin fiber in reinforced asphalt mixes combined with diatomite led to an enhancement in the asphalt pavement performance more than the other three types of mixes. Diatomite has an important influence on the water damage resistance of asphalt mix more than lignin fiber. On the other hand, diatomite has a small effect on the anti-cracking performance; meanwhile, lignin fiber showed a significant improvement in the cracking resistance of asphalt mixes. DLFMAM has the best traveling performances among all asphalt mixes. Thus, this work provides a good reference for the design of composite asphalt mixes.  
**Keywords:** lignin fiber; diatomite powder; asphalt mixes; low-temperature cracking resistance; water stability
# Root Dilaceration and Dentigerous Cyst--A Cause-Effect Relationship?--\"Case-Series Study\"
## 1 Introduction
With a relative incidence among all three types of jaw cysts, it most commonly involves the mandibular third molar, followed by the maxillary canine and the maxillary third molar [45; 46]. There is also a dentigerous cyst of inflammatory origin, accompanying an immature tooth, caused by the inflammation from a non-vital deciduous tooth in the mixed dentition, involving especially mandibular premolars [47]. Generally, DCs are unicyclic and solitary, rarely bilateral or multiple in different areas of the jaws [43; 48]. Diagnosed unexpectedly (most frequently being asymptomatic) by an X-ray exam, DC appears radiographically as a unilocular radiolucen area around the crown of an impacted tooth surrounded by a well-defined sclero area [49; 50]. The new 3D imaging, CBCT, brings the great advantage of accurate evaluation of the size, shape, and structure of DC for a differential diagnosis. The most important objective of the treatment of DC is saving the impacted teeth, which depends on the depth and position of the impacted tooth [51; 52; 53]. As stated previously, DC can cause root ollacerations, but to our knowledge, there is no study about this association as the literature is limited to case reports [54; 55; 56]. Accordingly, our study aims to assess the association between dilaceration and dentigerous cyst as an etiologic factor through a clinical case series, as this association is scarcely discussed in the literature.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 4 Results
### Performance of the DR-JRC-CWG
In this scenario, the estimated target output signal-to-noise ratios (SNRs) of the JRC waveforms with different Doppler frequencies are illustrated in Figure 8. The simulation parameters of the JRC waveforms in Figure 8 are the same as those in Figure 7. The SNR is estimated by using the SNR estimation method in [47]. The input SNR of the JRC system is \(0\) dB in Figure 8. It can be seen that as the Doppler frequency increases, the output SNR of all the JRC waveforms decreases. However, the output SNR of the designed DR-JRC-CWG decreases slower than that of the other JRC waveforms. Furthermore, the output SNRs of the designed JRC-CWG and the DR-JRC-CWG are higher than those of the OPP-BPSK and the LFM-BPSK waveforms when \(f_{d}=0\). Hence, the DR-JRC-CWG has the best performance in terms of the output SNR. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**JRC Waveform** & \(\boldsymbol{w}\) & **PSLR (dB)** & \(f_{d}\) (Hz)** & **Velocity (m/s)** \\ \hline JRC-CWG-BPSK & - & \(-35\) & \(986.3\) & \(25.1\) \\ DR-JRC-CWG-BPSK & \(\mathbf{w}_{1}=\left[0.95,0.05\right]^{T}\) & \(-35\) & \(2513.0\) & \(63.9\) \\ DR-JRC-CWG-BPSK & \(\mathbf{w}_{2}=\left[0.9,0.1\right]^{T}\) & \(-35\) & \(2654.0\) & \(67.5\) \\ DR-JRC-CWG-16-PSK & \(\mathbf{w}_{2}=\left[0.9,0.1\right]^{T}\) & \(-35\) & \(2631.4\) & \(66.9\) \\ OPP-BPSK & - & \(-35\) & NA & NA \\ LFM-BPSK & - & \(-35\) & NA & NA \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PSLRs of the JRC waveforms and the corresponding velocities.
## 5 Experiments
### Evaluation of System Performance
#### 5.2.1 Static Positioning Experiment  
Considering that, in the actual work scene, manipulators often perform general movements to align with assembly targets, the initial pose error of the positioning experiment was set as  
\[[\Delta\chi,\Delta\theta]=[-226\text{ mm},224\text{ mm},386\text{ mm},30^{\circ},10^{\circ},10^{\circ}]\]  
The threshold of the boundary layer in the KF-SVSF model was defined as  
\[\psi_{S}=diag\Big{[}\begin{array}{cccccc}64&36&420&36&64&420&10&10\end{array} \Big{]}\]  
Table 5 gives the various steady-state errors of different strategies in positioning experiments, and these results are the average of five repeated experiments. Although the noise of image transmission and processing has a certain impact on the accuracy of target recognition, the improved PHUVS with the KF-SVSF method clearly performs better than other strategies in each comparison. This verifies the superiority of our improved strategy. Figure 14 is the change curve of the proposed improved PHUVS in terms of the image error and task function error. Figure 15 shows the angular velocity of each joint of the manipulator.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(\Delta\chi\) & \(\Delta\theta\) & \(\Delta I\) \\ \hline IBUVS & 1.08 & 0.023 & 4.14 \\ OPHUVS & 0.96 & 0.012 & 2.81 \\ IPHUVS with KF & 0.91 & 0.012 & 2.76 \\ IPHUVS with KF-SVSF & 0.72 & 0.011 & 2.59 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Steady-state error of different strategies in the static positioning experiment. \(\Delta\chi\) is in millimeters, \(\Delta\theta\) is in degrees and \(\Delta I\) is in pixels.  
Figure 14: Detailed result of the improved PHUVS with the KF-SVSF method in the static positioning experiment. (**a**) Image error norm. (**b**) Norm of error vector.  
Figure 13: Experiment platform and target.
## 3 Proposed Methodology
### Advantages of Fast Local Laplacian Filter  
The Fast Local Laplacian Filter (FLLF) offers several advantages, and its development has been motivated by the need for effective contrast enhancement in various applications. Some of its key advantages and motivations include:  
Preservation of Image Details: FLLF excels at enhancing image contrast while preserving important image details. It achieves this by selectively enhancing regions with low contrast, ensuring that fine details are retained and that edges remain sharp. This is crucial in applications where maintaining image fidelity is essential.  
Adaptive Enhancement: The filter is adaptive and its enhancement strength can be adjusted based on the characteristics of the input image. This adaptability makes it suitable for a wide range of images with varying contrast levels.  
Reduced Noise: FLLF effectively reduces image noise, resulting in cleaner and clearer images. This is particularly important in medical imaging, where noise can hinder the accurate interpretation of critical details.  
Improved Visual Quality: By enhancing contrast and reducing noise, FLLF significantly improves the overall visual quality of images. This can enhance user experience and aid in more accurate image analysis.  
Versatility: FLLF is a versatile technique that can be applied to various types of images, including medical images like coronary angiography. Its effectiveness in different domains makes it a valuable tool for researchers and professionals.  
Enhanced Diagnostic Capabilities: In medical imaging, the ability to enhance contrast and preserve image details is crucial for accurate diagnosis. FLLF's capabilities align with this need and can contribute to improved diagnostic accuracy.  
Quantitative Evaluation: The performance of FLLF can be quantitatively assessed using metrics like PSNR, MSE, RMSE, NC, and CoC. This allows for rigorous evaluation and comparison with other methods, demonstrating its superiority in contrast enhancement.  
Research Advancement: FLLF represents an advancement in the field of image processing and contrast enhancement. Its introduction opens up new possibilities for research and applications in image enhancement and analysis.
## 3 Results and Discussion
### Electrochemical Evaluation of Chitosan- and Chitosan/Carbodiimide Compound-Modification  
The electrode consisted of chitosan and carbodiimide compounds immobilized on the graphite felt surface. Prior to combining the modified electrode with the MES reactor, we checked the redox reactions of the chitosan- and carbodiimide compound-modified layers by cyclic voltammetry with electroactive species. Figure 4a shows the cyclic voltammograms (CVs) of a bare and a modified glassy carbon electrode (GCE) in 1 mM [Fe(CN)\({}_{6}\)]\({}^{3-}\) solution. In this experiment, all the electrodes were examined in 0.1 M sodium phosphate buffer (pH 7.0). The separation between the anodic (\(E_{\text{pa}}\)) and cathodic (\(E_{\text{pc}}\)) peak potentials (\(\Delta E_{\text{p}}\)) of bare GCE in [Fe(CN)\({}_{6}\)]\({}^{3-}\) solution was 0.28 V. On the other hand, the \(\Delta E_{\text{p}}\) values of chitosan and of chitosan/carbodiimide compound-modified GCE decreased to 0.23 and 0.11 V, respectively. The peak current densities (\(J_{\text{p}}\)) of the modified electrodes in the CVs increased and the midpoint potentials (\(E_{\text{m}}\)) shifted to the negative with the modification using chitosan and the chitosan/carbodiimide compound. [Fe(CN)\({}_{6}\)]\({}^{3-}\) ions might have been confined in positively charged layers (Table 1) through the force of electrostatic attraction [22]. From these results, we presume that the electrode coated with amine and amide functional groups might have facilitated the migration of negatively charged \(R\). _spharoides_ cells toward the electrode.
## 6 Performance Evaluation of ASC-RL  
Different methods are used to evaluate the performance of the RL-based scheme. Here, many parameters are used to verify performance evaluation, including joint Gaussian distributions and event correlation.