esa-sceva/satcom-chunk-collection huggingface dataset:
# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 1 Introduction
EPICNet [15] enhances point cloud features by utilizing image semantic information; however, it similarly suffers from inadequate interaction between local and global features in complex scenes, limiting fine-grained feature extraction. Such multimodal intermediate fusion methods exhibit insufficient interaction between local and global features, along with inadequate fine-grained feature extraction, thereby providing avenues for further optimization of multimodal intermediate fusion techniques. To address the aforementioned challenges, we propose a multi-modal 3D object detection algorithm with pointwise and voxelwise fusion (MPVF), which enhances multi-modal feature interaction and optimizes feature extraction strategies to improve detection precision and robustness. Our principal contributions are as follows:(1) We introduce a pointwise and voxelwise fusion (PVWF) module, which combines local features extracted by the pointwise fusion (PWF) module with global features derived from the voxelwise fusion (VWF) module. This integration enhances the interaction between multi-modal local and global features, improves the expressiveness of the fused features, and boosts the model's performance in detecting small objects and navigating complex scenes. (2) We design a more expressive feature extraction module, improved ResNet-101 and feature pyramid (IRFP), which comprises the improved ResNet-101 (IR) and feature pyramid (FP) modules. The IR module employs a group convolution strategy to incorporate high-level semantic features into both the PWF and VWF modules, thereby enhancing feature extraction efficiency. Concurrently, the FP module extracts rich, fine-grained features across multiple scales. The IRFP module consequently enhances the model's performance in complex scenarios, increasing both the accuracy and robustness of object detection. (3) Our experimental evaluations on the KITTI [16] dataset demonstrate that the MPVF algorithm achieves recognition accuracies of 85.12%, 48.61%, and 70.12% for cars, pedestrians, and cyclists, respectively, in moderate difficulty detection tasks. Our method surpasses other state-of-the-art detection models in terms of detection accuracy and false detection rates, thereby validating its effectiveness and practicality.
## 8 Results
### Ergodic Capacity Results
This is also true for the performance improvement in the proposed CA-based scheme compared to the single carrier scenario (i.e., where no CA is used) when the average model of effective SNR is considered, which is illustrated in Table 2 based on the results shown in Figure 7. In summary, it can thus be concluded that the mathematical model and closed-form expressions derived in Section 6 can correctly predict the trends observed by simulations in [22] and constitute a useful tool to analyse the performance of CA when used as a diversity technique and to determine the (qualitative) impact of various relevant parameters on the system performance. Figure 6: Ergodic capacity as a function of the number of CCs for various values of the spread parameter \(\varepsilon\) (average SNR = 10 dB, \(\varepsilon\) = 0.05). Figure 7: Ergodic capacity as a function of the number of CCs for various values of the bandwidth overhead parameter \(\alpha\) (average SNR = 10 dB, \(\varepsilon\) = 3 dB) (counterpart to Figure 2 based on the average model of effective SNR).
# A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
## 4 An RB RAIM Based on a Robust MM Estimation
### Fast Subsets Selection Based on the Characteristic Slope
Only the elements in matrix \(\mathbf{A}\) and \(\mathbf{S}\) then need to be used for numerical calculation, which considerably reduces the computation. The flow chart of the subset selection method based on the characteristic slope is shown in Figure 5. Set the termination threshold of the selection iteration, including the threshold \(\lambda\) of the change rate of the PDOP, the threshold PDOP\({}_{\text{T}}\) of the position dilution of precision, the protection threshold \(n_{sin}\) of the number of remaining satellites of a single constellation and the protection threshold \(n_{mul}\) of the number of remaining satellites of all constellations. The termination criteria for subset selection include the following aspects:  
* If \(PDOP_{S}>PDOP_{T}\), the process is terminated and the result is output;
* If \(\Delta PDOP>\lambda\), move the satellite back to \(S\) and take the set \(S\) as the selected subset, terminate the process and output the result;
* If the number of remaining satellites of the constellation meets the protection threshold \(n_{sin}\), mark the constellation as a protected one; if \(num_{con\_pro}=num_{con}\), take the current set as the selected subset, terminate the process and output the result;
* If \(num_{sat}\leq n_{mul}\), terminate the process and take the current set as the selected subset. Figure 5: Flow chart of subset selection method based on the characteristic slope. After the proposed fast subset selection, the number of subsets to be calculated by the LTS estimator is reduced by:  
\[\Delta ntum_{subset}=\frac{C_{n}^{h}-C_{n_{sd}}^{h}}{C_{n}^{h}}=\frac{n!(n_{sol} -h)!-n_{sol}!(n-h)!}{n!(n_{sol}-h)!}. \tag{28}\]  
With the decrease in \(h\), the effect of the proposed fast subset selection on the reduction of computation is more obvious. However, the number of satellites in each subset decreases accordingly so the termination threshold of the subset selection can be appropriately relaxed.
## 2 Testbed Setup and Measurement Methodology
### Experimental Setup for Timing Measurements
#### 2.1.1 Used Timing Tools  
The standard method of time acquisition in Linux are system calls such as _clock_gettime_ and _gettimeofday_ and (hereafter referred to as _System Call_). Until version 2.6 of Linux kernel, those functions were ordinary system calls, and afterwards they were reconstructed as virtual system calls to get rid of slow context switches and to give the possibility to get the timestamps through the memory mapping to the kernel space. Now, it is part of the VDSO library.  
Besides that _clock_gettime_ is implemented in VDSO system library call in Linux and is not performing a full context switched system call, there is a possibility to reduce its overhead even more. For this, a developed in term of our research HighPerTimer library uses direct access to TSC counter via RDTSCP instruction (hereafter referred to as _TSC_). All arithmetical operations were implemented as inline functions, thus the compiler maps it directly to assembler code without a function call overhead. The timers are internally kept as a single 64-bit counter and calculation of seconds/nanoseconds is performed only when the particular components are \"exposed\" to the calling functions component-wise.  
The Linux waiting operation is presented as the uSleep function. It puts the process into the scheduler's waiting queue and returns its state after the target time. This leads to large overheads in the scale below microseconds. One possible solution to reduce the miss time value is to use busy-wait loop when some atomic operation will be executed repetitively during the target time. This method is very efficient from the precision perspective; however, it takes 100% of CPU resources during sleep.  
The sleep from HighPerTimer library (_HPTSleep_) provides the hybrid solution to get the precision of busy-wait and low CPU utilization of System Calls. HPTSleep separates the waiting process into two phases to reduce the miss time value. Simplified scheme of this process is shown in Figure 2.  
In Phase 1, the process P0 will be set by uSleep function into the waiting queue for most of the target sleep time. The time of the first phase calculated as \(t_{target}-\frac{1}{HZ}\), where \(HZ\) is the frequency of interrupts in Linux, limited by 1000 Hz. This phase will be skipped if the target time is lower than \(\frac{1}{HZ}\). In Phase 2, the process will be executed in a busy-wait loop for the rest of the target time.  
Figure 2: Simplified scheme of executing processes with HPTSleep.  
This leads to the high precision which is comparable with pure busy wait loop. At the same time, this hybrid method takes only 1.5% of CPU resources [6].
## 3 Methodology
### Transfer of the Physical Field Variables  
As the position and topology of the mesh change after remeshing, the physical field variables of the old mesh are no longer applicable for the new mesh. Therefore, to ensure the continuity of the numerical calculations, new field values need to be interpolated according to the positions of the nodes.  
The physical field variables in the FIAC\({}^{3\text{D}}\) software are divided into node status information and mesh status information. In the FLAC\({}^{3\text{D}}\) software, there are functions for output and input physical field variables.  
For the transfer of the mesh field variables, for example, the transfer of the stress can be carried out in four steps:  
1. Transfer the stress of the old meshes to the old nodes to obtain the equivalent mesh physical field variables of the old nodes.
2. Determine the relationship between the new node and the old mesh. This step requires the new mesh node to be inside the old mesh.
3. Transfer the equivalent mesh stresses of the old nodes to the new nodes. The equivalent mesh stresses of the nodes and the shape function can be used to interpolate the value of the stresses at any position in the mesh; see Equations (1) and (2). \[u=[N]\bullet\left\{\begin{array}{c}u_{i}\\ u_{j}\\ u_{k}\\ u_{l}\end{array}\right\}\] (1) where \([N]\) is the shape function of the original mesh, and \(u_{i}\), \(u_{j}\), \(u_{k}\), and \(u_{l}\) are the physical field variables of the original mesh. \[[N_{0}]=\left[\begin{array}{cccc}1&x&y&z\end{array}\right]\bullet\left[ \begin{array}{cccc}1&x_{1}&y_{1}&z_{1}\\ 1&x_{2}&y_{2}&z_{2}\\ 1&x_{3}&y_{3}&z_{3}\\ 1&x_{4}&y_{4}&z_{4}\end{array}\right]\] (2) where \([N_{0}]\) is the shape function of the new node; \((x,y,z)\) are the coordinates of the new node; and \((x_{i},y_{i},z_{i})\) are the coordinates of the original node, \(i=1,2,3,4\).
4. Acquire the physical field variables of the new meshes; see Equation (3). \[\overline{\epsilon}_{i}^{Ele}=\frac{\left(\sum\limits_{j=1}^{4}\overline{ \epsilon}_{j}^{Node}\right)}{4}\] (3) where \(\overline{\epsilon}_{j}^{Node}\) is the equivalent physical field variables of node \(j\) of mesh \(i\); \(\overline{\epsilon}_{i}^{Ele}\) is the physical field variables of mesh \(i\).  
The node field variables can be transferred through the second and third steps described above.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 3 Methodology: Selected Query DETR (SQ-DETR)
### Advantages of SQ-DETR  
SQ-DETR enhances object detection by dynamically refining the query set throughout the detection process. Unlike traditional DETR-based models that rely on a fixed number of queries, SQ-DETR progressively reduces redundant queries while retaining the most informative ones. This adaptive approach improves detection efficiency and robustness without sacrificing accuracy.  
A key advantage of SQ-DETR is its impact on computational efficiency. By processing a progressively smaller query set at each decoder layer, SQ-DETR significantly reduces memory consumption and inference time. This makes it well suited for real-time applications and deployment on resource-constrained devices.  
Furthermore, SQ-DETR maintains strong detection performance while achieving substantial efficiency gains. By focusing on the most relevant queries, it enhances feature utilization and reduces unnecessary computations.  
As illustrated in Figure 3, our proposed query selection method strategically eliminates fewer critical object queries at each layer stage. Within the DETR decoder's internal operations, each layer refines the significance of queries through self-attention, elevating the scores of salient queries while diminishing those of less relevant ones. By selectively removing queries as depicted, we effectively prune redundant computations, leading to enhanced performance with reduced computational overhead. A closer examination of the figure reveals the removal of 25 queries per layer, indicating those queries that exhibit weaker mappings to objects and struggle to accurately predict specific classes.  
The modular design of the selection query module allows SQ-DETR to be seamlessly integrated into other DETR-based models. This adaptability extends its benefits to a wide range of Transformer-based object detection architectures, enabling broader applicability without requiring modifications to the backbone or encoder.  
Figure 3: Visualization of queries dropped by SQ-DETR.  
Artificial intelligence tools were utilized during the manuscript preparation process. Specifically, ChatGPT (OpenAI, GPT-4o) was used to assist with English language refinement and clarity.
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 5 Simulation and Evaluation of DEC and DEC-KM Protocols
### Simulation and Evaluation of DEC-KM
#### 5.2.2 Choosing Centroid Points for K-Means  
In the first step in Stage 2 of the DEC-KM algorithm, the K-means centroids are calculated in every cluster area obtained from DEC protocol and shown in Figure 4.  
Centroid points for selected cluster areas after the first K-means iteration are shown in Figure 5. The black square in each cluster area marks the centroid point of this area.
## 6 Results and Discussion
### Analysis of Conformation Tensor  
Figure 3 compares the individual components of the conformation tensor with the present model against the model of [PERSON] et al. (2018) and selected DNS data covering \(L^{2}\), \(Wi_{\tau_{0}}\) and \(Re_{\tau_{0}}\) variations (cases 16, 19, 20 and 26 in Table 1). as can be viewed in Figure 3, the \(C_{xx}\) predictions are consistent with the DNS data. The new closure for \(NLT_{xx}\) (see term III in Equation (26)) is responsible for the improved predictions and can capture the \(Re_{\tau_{0}}\), \(L^{2}\) and \(Wi_{\tau_{0}}\) variations with much greater simplicity, especially for increased Reynolds number (\(Re_{\tau_{0}}=590\)) compared with the model of [PERSON] et al. (2018).  
Figure 3 plots the \(C_{yy}\) component, showing good agreement with the DNS data and improving upon the most recent model, especially away from the wall. The important feature is the location of the value at the centre-line and the peak location which both show good improvement, especially for higher Reynolds numbers (\(Re_{\tau_{0}}=590\)). The improvements are a result of the new \(E_{\tau_{p}}\) closure (see Equation (33)) which directly impacts \(\varepsilon^{N}\) in the \(NLT_{yy}\) closure (see term I in Equation (26)). Figure 3 plots the \(C_{zz}\) component and shows an under-prediction due to the isotropic assumption used in the model of \(NLT_{ij}\), however, its impact is not significant.  
Figure 3: Comparison of the conformation tensor between DNS data (\(\times\) crosses) and predictions with the new model (continuum lines), and previous model (dash lines): (**a**) \(C_{xx}\); (**b**) \(C_{yy}\); (**c**) \(C_{zz}\) and (**d**) \(C_{xy}\). Each colour represents a different drag reduction regime: red (LDR, case 16); blue (IDR, case 19); green (HDR, case 20) and orange (very HDR, case 26. DNS data not available for \(C_{xy}\)).  
The model predictions of the \(C_{yy}\) term are important in capturing the features of the \(C_{xy}\) component. As can be observed in Figure 3d, the model is able to capture the near-wall region, which, according to the findings of [PERSON] et al. (2018), is the region of high chain dumbbell extension (limited to \(y^{+}<50\)) where the effect of \(C_{xy}\) acts towards the polymer shear stress.  
It is evident that the overall predictions of the individual conformation tensor components are improved compared to the model of [PERSON] et al. (2018). This is a result of the new \(NLT_{ij}\) and \(E_{\tau_{p}}\) closures developed in the present work, which allows more scope of predictability and increased numerical stability with simpler closures.
# Long-Term and Decadal Sea-Level Trends of the Baltic Sea Using Along-Track Satellite Altimetry
## 2 Methodology
Finally, the estimated (e.g., from SA data) and observed (e.g., from TG data) trends are statistically evaluated by using the root mean square error (RMSE):  
\[RMSE=\sqrt{\frac{1}{n}\sum_{t=1}^{n}(\beta_{t}^{o}-\beta_{t}^{o})^{2}} \tag{9}\]  
where \(n\) is the total number of estimated trends during the study period; \(\beta^{o}\) and \(\beta^{o}\) are estimated (e.g., by SA) and observed (e.g., by TG) trend values at time \(t\) (at different time windows), respectively. Note that the OLS-determined linear trend has its limitations, such as sensitivity to outliers, yielding extreme values and outliers which can skew the regression line and influence the estimated trend. Also, OLS assumes a linear relationship between the variables, which might not always represent the true nature of the sea level, especially over too-short periods. Hence, in this study, various non-linear methods, including the enveloping method [58] and sliding trend analysis [59; 60] were additionally tested. These alternative approaches provided similar trend estimates to the OLS results. Therefore, for the sake of the conciseness of this paper, only the OLS-based linear trend estimation results will be explained and discussed. Figure 2 shows the flowchart of the applied methodology and data processing steps. \[DT_{TG}(\varphi_{TG},\lambda_{TG},t_{i})=DT_{obs}(\varphi_{TG},\lambda_{TG},t_{i})+ VLM(\varphi_{TG},\lambda_{TG}).\ (t_{i}-t_{0})+\Delta N(\varphi_{TG},\lambda_{TG}).\ (t_{i}-t_{0}) \tag{1}\]0 Flowchart of the applied method.
# Stability and Control of Surrounding Rock of a Trapezoidal Roadway Retained with Hard Roof Cutting
## 2 Stability Analysis of the Roof with Top-Cutting
### Roof Cantilever Beam Model  
The stability analysis for the roof in a trapezoidal top-cutting roadway with inclined coal seam is an important prerequisite for studying the deformation characteristics and control mechanism of the surrounding rock. Based on the Cantilever Beam Theory [23; 24], this paper establishes a stability mechanics model for the roof in a trapezoidal top-cutting  
Figure 1: Layout of the 21,100 panel.  
Figure 2: Original support of the headgate.  
roadway with inclined coal seam, and analyzea the influence of coal seam dip angle, roof thickness, top-cutting height, top-cutting angle and other factors on roof stability. The mechanical model of the roof in the top-cutting roadway is shown in Figure 3.  
Assuming the thickness of the hard roof was \(m_{1}\), the top-cutting height was \(h\), and the uncut roof height was \(h_{1}\), then \(m_{1}=h+h_{1}\). According to the Elasticity Mechanics and Material Theory, the tensile stress of the uncut top portion can be calculated as follows:  
\[\sigma_{\text{tw}}=\frac{M_{\text{tw}}}{W_{\text{tw}}}=\frac{3\big{(}qx_{4}^{2 }+\rho gm_{1}x_{4}^{2}\big{)}}{h_{1}^{2}} \tag{1}\]  
where \(\sigma_{\text{tw}}\) was the tensile stress of the uncut roof; \(M_{\text{tw}}\) was the bending moment of the uncut roof; \(W_{\text{tw}}\) was the bending section coefficient of the uncut roof; \(q\) was the overburden load borne by the hard roof; \(X_{4}\) was the distance between the uncut roof and the end of the cantilever beam; and \(\rho\) was the density of the hard roof. \(X_{4}\), \(X_{5}\), \(h_{1}\) can be given as [13]:  
\[\left\{\begin{array}{l}x_{4}=x_{5}-x_{1}-x_{2}-x_{3}\\ x_{5}=l(\sqrt{\frac{2}{S^{2}}+\frac{3}{2}}-\frac{l}{S})\\ h_{1}=m_{1}-h\end{array}\right. \tag{2}\]  
where \(l\) was the periodic weighting length of the hard roof and \(S\) was the length of the panel. Then, according to the actual geology and mining conditions of the 21,100 panel, it can be calculated that \(X_{1}=2.15\) m, \(X_{2}=5.0\) m, \(X_{3}=htan\)\(\theta\), \(l=15\) m, \(S=185\) m, \(q=Gcos\)\(\varphi\), and \(\rho=2500\) kg/m\({}^{3}\). Putting the parameters into the above formula, the relationship between the tensile stress of the uncut roof and the roof thickness, coal seam dip angle, top-cutting height, and top-cutting angle can be presented as follows:  
\[\sigma_{\text{tw}}=\frac{(3G\cos\varphi+0.075\text{m}_{1})(15.28-h\tan\theta )^{2}}{\left(m_{1}-h\right)^{2}} \tag{3}\]  
where \(G\) was the gravity of the overlying rock layer, \(\varphi\) was the coal seam dip angle, and \(\theta\) was the top-cutting angle.  
In order to ensure that the hard roof can collapse along the cutting line after top-cutting, achieving a good cutting effect, the tensile stress in the uncut roof should be greater than its tensile strength:  
\[\sigma_{\text{tw}}=\frac{(3G\cos\varphi+0.075\text{m}_{1})(15.28-h\tan\theta )^{2}}{\left(m_{1}-h\right)^{2}}>\sigma_{\text{t}} \tag{4}\]  
Figure 3: Mechanical model of the roof in the top-cutting roadway.
## 3 Results and Discussion
### Functional Characteristics of BC\({}_{x}\)N\({}_{y}\) Films
#### 3.4.1 Optical Properties
Figure 10: Refractive index of BC\({}_{x}\)N\({}_{y}\) films as a function of the deposition temperature and CVD process type. The values of the optical band gap were estimated from [PERSON]'s plot [47]. With an increase in the film synthesis temperature, the band gap of LPCVD films varies in the ranges of 1.72-1.79 eV and 2.20-2.05 eV according to spectrophotometry and spectral ellipsometry, respectively (Figure 11b). The difference between the values obtained by the two optical methods is probably due to the effect of interference fringes on the results of calculations based on the transmission spectra. The PECVD films exhibit a slight decrease in the values of \(E_{\text{g}}\) from 5.28 to 4.92 eV with a rise in deposition temperature. This difference deals with variations in their chemical composition. The calculated values of band gaps are in good agreement with the reported values for BC\({}_{\text{x}}\) and BC\({}_{\text{x}}\)N\({}_{\text{y}}\) films. It is known from the literature that \(E_{\text{g}}\) values of BC\({}_{\text{x}}\) films deposited by magnetron sputtering of the B\({}_{\text{4}}\)C target lie in the interval of 1.1-2.8 eV [52]. The optical band gap of BC\({}_{\text{x}}\)N\({}_{\text{y}}\) films obtained by different CVD methods can vary over a wide range. With increasing deposition temperature from 373 to 973 K, \(E_{\text{g}}\) values decreased from 4.6 to 1.8 eV and from 4.9 to 1.2 for PECVD BC\({}_{\text{x}}\)N\({}_{\text{y}}\) films synthesized using mixtures of N-triethylborazine with NH\({}_{3}\) and He, respectively [63]. On the other hand, the E\({}_{\text{g}}\) value of the C-doped BN films decreased from 5.16 to 4.04 eV with increasing carbon content, since this contributed to the increase of \(\pi\) bonding originating from C=C bonds [94].