esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Functionality of Key Modules
### Local Fatigue Assessment
#### 3.4.2 Theory of Critical Distances
Figure 22: Comparison of blunt notched V-plate (mesh ‘K1V_H10_A1_PH90_RHO1_E_TE’) and circular hole-in-plate (mesh ‘circle_fine_quad_R1 mm_v1’): (a) Absolute max principal stress; and (b) Displacement in the x-axis. If an increased evaluation arc-length is set for the TCD, the obtained stresses will decrease, due to the reduced stress concentration effect. Figure 21 characterizes the Point Method stress distribution at a distance of \(L_{m}=1\) mm, which implies a finite life fatigue assessment. For the circular hole-in-plate, the mean values were \(\overline{\sigma}_{TCD,M2,CIR}=1.29\) MPa and \(\overline{\sigma}_{TCD,M3,CIR}=1.31\) MPa, while the standard deviations were \(\hat{\sigma}_{TCD,M2,CIR}=0.01\) MPa and \(\hat{\sigma}_{TCD,M3,CIR}=0.01\) MPa. In the case of the elliptical hole-in-plate, the mean values were \(\overline{\sigma}_{TCD,M2,ELL}=1.39\) MPa and \(\overline{\sigma}_{TCD,M3,ELL}=1.40\) MPa, while the standard deviations were \(\hat{\sigma}_{TCD,M2,ELL}=0.15\) MPa and \(\hat{\sigma}_{TCD,M3,ELL}=0.15\) MPa. The elongated defect led to mean values of \(\overline{\sigma}_{TCD,M2,DEF}=2.46\) MPa and \(\overline{\sigma}_{TCD,M3,DEF}=2.45\) MPa, while the standard deviations were \(\hat{\sigma}_{TCD,M2,DEF}=0.35\) MPa and \(\hat{\sigma}_{TCD,M3,DEF}=0.34\) MPa. The elongated defect contour still presented the highest Point Method stress distribution, which is expected as the elongated defect maximal extent is about ten times the circular hole; thus, the stress concentration is more pronounced, even at an increased evaluation distance. Finally, the effect of stress condition--namely, plane stress vs. plane strain--on the examined planar defects was studied. The evaluation distance was set to \(L_{m}=a_{0}/2\), making the plane stress results directly comparable to Figure 20. Statistical evaluation of the Point Method stress results yielded values with similar scatter indices; thus, only the mean values are given in the following. For the circular hole-in-plate, the mean values were \(\overline{\sigma}_{TCD,M2,CIR}=2.41\) MPa and \(\overline{\sigma}_{TCD,M3,CIR}=2.43\) MPa. The elliptical hole-in-plate led to \(\overline{\sigma}_{TCD,M2,ELL}=3.69\) MPa and \(\overline{\sigma}_{TCD,M3,ELL}=2.81\) MPa. The elongated defect exhibited the highest mean Point Method stress values, according to \(\overline{\sigma}_{TCD,M2,DEF}=4.27\) MPa and \(\overline{\sigma}_{TCD,M3,DEF}=4.45\) MPa. In general, the evaluated Point Method stresses were minorly affected by the choice of element formulation. In the case of plane stress, the obtained Point Method stress results were about 7% higher than those for plane strain formulation. The difference was more pronounced when the notch stress concentration increased, which is expected, due to continuum mechanics.
## 7 Experimental Studies
### Result and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Spatial Features**} & \multicolumn{4}{c}{**Database**} \\ \cline{3-5}  & & **YTC**[51] & **ICT-BBT**[52] & **ICT-PB**[52] \\ \hline \multirow{3}{*}{Ours—Jeffrey} & Regional & 36.33\% & 57.60\% & **37.50\%** \\  & Global & 38.33\% & **67.80\%** & 22.50\% \\  & Combination & **44.50\%** & 65.40\% & 36.83\% \\ \hline \multirow{3}{*}{Ours—LEM} & Regional & 50.33\% & 64.00\% & 40.67\% \\  & Global & **67.33\%** & **79.60\%** & 47.33\% \\  & Combination & 62.67\% & 73.20\% & **48.60\%** \\ \hline \multirow{3}{*}{Ours—Stein} & Regional & 73.00\% & 76.80\% & 61.83\% \\  & Global & 76.00\% & **92.60\%** & 65.67\% \\  & Combination & **79.00\%** & 86.80\% & **71.17\%** \\ \hline \multirow{3}{*}{Ours—AIRM} & Regional & 69.50\% & 75.00\% & 59.83\% \\  & Global & 76.83\% & 82.00\% & 62.50\% \\ \cline{1-1}  & Combination & **82.33\%** & **87.80\%** & **68.20\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Recognition results with global spatial features and regional spatial features. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Database**} \\ \cline{2-4}  & **YTC**[51] & **ICT-BBT**[52] & **ICT-PB**[52] \\ \hline \multirow{2}{*}{RieCovDs—AIRM [39]} & 69.33\% & 66.65\% & 58.33\% \\ \multirow{2}{*}{RieCovDs—LEM [39]} & 69.63\% & 68.60\% & 56.67\% \\ \multirow{2}{*}{AidCovDs—AIRM [40]} & 73.94\% & 54.90\% & 61.67\% \\ \multirow{2}{*}{AidCovDs—LEM [40]} & 77.12\% & 61.10\% & 57.67\% \\  & LEML [42] & 74.00\% & 78.20\% & 49.33\% \\ \multirow{2}{*}{SPDML—AIRM [43]} & 74.67\% & 78.20\% & 52.00\% \\ \multirow{2}{*}{SPDML—[PERSON] [43]} & 75.00\% & 79.20\% & 52.33\% \\ \multirow{2}{*}{SPDSL—LEM [44]} & 78.33\% & 75.20\% & 55.31\% \\ \multirow{2}{*}{SPDSL—AIRM [44]} & 80.50\% & 72.40\% & 57.50\% \\  & DALG [45] & 78.67\% & 86.89\% & 56.83\% \\ \multirow{2}{*}{CERML [41]} & **82.63\%** & 85.00\% & 66.77\% \\ \hline \multirow{2}{*}{Ours—Stein} & 79.00\% & 86.80\% & **71.17\%** \\ \multirow{2}{*}{Ours—AIRM} & **82.33\%** & **87.80\%** & 68.20\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recognition results of comparison algorithms. eling of ROIs in our proposed method. Moreover, focusing more on ROIs but not global information helps in reducing interference from facial obstructions. By controlling the cumulative error of motion estimation, ROIs will be skipped in time when encountering facial obstructions. Although RieCovDs also extract features from image regions, the position and numbers of regions is unchangeable, ignoring the dynamic change of the recognition target in the video. Moreover, although RieCovDs is based on image regions, it actually still extracts global information from frame images with regions covering the whole picture.
# Semantic Segmentation of Brain Tumors Using a Local-Global Attention Model
## 4 Experiments and Results
### Datasets  
We carried out experiments on two public brain tumor datasets: the BraTS2023-GLI dataset and BraTS2024-GLI dataset. These datasets contained MRI images from multiple medical centers, covering patients of various ages and tumor types. They provided good benchmarks for developing glioma segmentation algorithms, as all the images had been precisely annotated and approved by experienced neuroradiologists.
# Review of Recent Type-2 Fuzzy Controller Applications
## 3 Review of IT2 FLSs
### Membership Functions Used in T2 FLC Applications  
After analyzing all of the papers presented in this section, we consider that is important to mention the types of membership functions that are used in the fuzzy models and what profits did they give. We can observe that in 15 percent of the papers, only triangular MFs are used. On the other hand, 33 percent of the papers make a combination of MFs; using triangular MFs on the center and trapezoidal MFs at the ends. In another case, 40 percent of the papers only use Gaussian MFs in the fuzzy controllers. In two percent of the papers, triangular MFs at the center and Gaussian MFs at the ends are used; also, another two percent uses the elliptic MFs. Finally, in eight percent of the papers, the type of MFs is unknown, as it is not indicated by the authors. In reviewing the developed controllers in the literature, we find that in many cases, triangular membership functions are used when there are software or hardware limitations, e.g., when the fuzzy controller needs to be implemented on a circuit, which is more easily done with triangular functions that do not require a lot of memory resources in hardware. In other cases, when possible, Gaussian functions are used because they provide more continuous performance, but require more memory (space) resources. In applications where the fuzzy controllers could be implemented in software and there is no problem with space and time limitations, Gaussian or combinations with other membership functions can be used.
## 2 Materials and Methods
### InSAR Dataset and Processing  
To assess the general stability of the test site, the specific characteristics of localized instability areas and the effectiveness of recently implemented remedial works, we set up a multiscale and multi-level procedure combining different monitoring methodologies (Figure 3).  
As a leading active remote sensing technique, InSAR techniques were proven to be an effective tool in long-term and slow deformation monitoring [29]. In this study, after the InSAR deformation velocity map was obtained, a statistical method (hotspot analysis) was employed to extract the hotspot from the sparsely distributed PSs, which can exclude the redundant information and indicate deformation concentrated area. Then, according to the landslides inventory data, the landslide edges were delineated as polygons and overlaid on the ground displacement rate map to target the suspected active landslides. For controlled landslides showing deformation trends, analysis of ground-based monitoring data and field investigations were conducted to verify the overall effect of the engineering remedial works.  
Equipped with a C-band synthetic aperture radar, Sentinel-1 provides continuous images in various weather conditions at an almost global coverage scale. To obtain the ground deformation  
Figure 3: Flow chart illustrating the adopted methodology.  
velocity map for the study area, 70 Sentinel-1 images covering a time span between June 2015 and June 2018 were collected and selected for PSInSAR analyses. More than 980,000 PSs with coherence level above 0.60 were obtained, thus ensuring a sufficient spatial coverage and reliable information about the deformation characteristics (Figure 4). The SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) covering the study area was used in the generation of differential interferograms and the processing procedure employed the PSInSAR technique [30; 31]. First, the precision track parameters file and the DEM of the study area were used for image cropping and registration. Then, the differential interference process was conducted by SARscape 5.2.1 at ENVI 5.3 environment. Finally, the obtained LOS InSAR ground displacement rate was projected at the steepest slope direction to better retrieve the ground deformation trend.  
The above mentioned InSAR analysis process was relatively easy, flexible, and accessible, with totally free satellite radar images and a short machine processing time. Such a method is a reasonable basis for the following regional ground surface stability analysis.
# Efficient Parallel Implementation of CTR Mode of ARX-Based Block Ciphers on ARMv8 Microcontrollers
## 3 Related Works
### Parallel Implementation of CTR Mode of Operation on the NEON Engine
#### 4.2.1. LEA-CTR Optimization
Steps 17-20 are the round operations for \(X_{2,0}\) word affected by the variable counter in Round 3, and Steps 21-25 are the round operation process for the word affected by the variable counter in Round 2. In Steps 26-29, the word including the counter performs round operations. In Step 30, the round keys for one round are loaded into the vector registers. In Steps 31-32, one round operation is reduced by looking up the last pre-computation table. After Round 4, it is the same as the parallel implementation of LEA. In LEA-192 and LEA-256, only the number of rounds is different from that of LEA-128, so it is possible to optimize CTR mode in the same way as the above procedure. ```
1:v0-v3(Plaintexts), v26-v28(Table), v29-v31(Roundkeys)
2:v0-v3(Ciphertexts)
3:Loading the RK and the table, (x1: RK Address, x2: Pre-computation table Address)
4:LD1 {v29.4e-v31.4s}, [x1], #8
5:LD1 {v26.4s-v28.4s}, [x2], #8
6:1 Round Function
7:EOR v3.16b, v3.16b, v29.16b
8:ADD v2.16b, v3.16b, v26.16b
9:SHL v3.4s, v24.4s, #29
10:SRI v3.4s, v24.4s, #3
11:2 Round Function
12:EOR v24.16b, v3.16b, v30.16b
13:ADD v24.4s, v24.4s, v27.4s
14:SRI v3.4s, v24.4s, #5
15:LD1 {v2.4s-v28.4s}, [x2], #8
16:L01 {v2.64s-v28.4s}, [x2], #8
17:RO r3.4s, v24.4s, v28.4s
18:ADD v24.4s, v24.4s, v28.4s
19:SHL v3.4s, v24.4s, #27
20:SRI v3.4s, v24.4s, #5
21:Loading the RK and table
22:LD1 {v2.94s-v31.4s}, [x1], #8
23:LD1 {v2.64s-v28.4s}, [x2], #8
23:Round Function
24:EOR v24.16b, v0.16b, v30.16b
25:ADD v24.4s, v24.4s, v26.4s
26:SHL v1.4s, v24.4s, #29
27:SRI v1.4s, v24.4s, #3
28:EOR v0.16b, v0.16b, v31.16b
29:EOR v24.4s, v3.4s, v29.4s
```  
**Algorithm 5** Parallel implementation of LEA-128 CTR mode optimization. Figure 11: Proposed LEA-CTR mode optimization through pre-computation for the initial four rounds. 23: ADD v24.4s, v24.4s, v0.4s
24: SHL v0.4s, v24.4s, #27
25: SRI v0.4s, v24.4s, #5
26: EOR v3.16b, v3.16b, v31.16b
27: ADD v24.4s, v27.4s, v3.4s
28: SHL v3.4s, v24.4s, #9
29: SRI v3.4s, v24.4s, #23  
Landing the RX
30: LD1 {v28.4s-v31.4s}, [x1], #64  
4 Round Function
31: EOR v24.16b, v1.16b, v30.16b
32: ADD v24.4s, v24.4s, v28.4s
# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 5 Deep Learning Approaches
### Odometry Estimation
The corrected pose is then applied to the initial transformation assumption, yielding the final odometry between the two point clouds and resulting in ego-transformations at each investigated scale. Finally, [PERSON] et al. [91] implemented Milli-RIO, a radar based odometry method based on point cloud allignment, A NDT [40] is used in an unscented Kalman-Filter (UKF) approach for point cloud alignment with a sophisticated point matching procedure. A RNN is implemented in the form of a LSTM layer as a motion model. Given a state of the system as  
\[\mathbf{x}_{t}=[\mathbf{p}_{t},\mathbf{q}_{t},\mathbf{v}_{t},\mathbf{b}_{t}^{a}]^{T} \tag{7}\]  
with \(\mathbf{x}_{t}\) being the state, \(\mathbf{p}_{t}\) the position, \(\mathbf{q}_{t}\) the rotation, \(\mathbf{v}_{t}\) the velocity, and \(\mathbf{b}_{t}^{a}\) bias of angular velocity at time \(t\) in Equation (7). Assuming a transition function \(f(\cdot)\) for the sensor motion model and a constant bias for the bias of the angular velocity, the system state between consecutive scans is given by  
\[\mathbf{x}_{t}=\left[\mathbf{p}_{t-1}+f(\mathbf{x}_{t-1}),\mathbf{q}_{t}\cdot\Delta\mathbf{q},\mathbf{ v}_{t-1},\mathbf{b}_{t-1}^{a}\right]^{T} \tag{8}\]  
A bidirectional-LSTM with 256 nodes is used to approximate the transition function \(f(\mathbf{x}_{t-1})\) in Equation (8). A bidirectional LSTM is chosen, as it should caption the forward and backward dependencies of the motion model. The network is trained with ground truth trajectories and the predicted state is fed into the UKF. As seen, deep learning methods for radar-based odometry focus on either spinning or automotive radar data, depending on the available representation. The processing steps and implementations differ depending on the representation. For automotive radar, the data are generally represented as a sparse detection point cloud. The RCS and Doppler values are used as further features of each detection. The trend for automotive radars tends toward predicting the flow of all detections in a scene, and thus predicting the ego-motion via stationary objects. A variety of different layers, including PointNet-style feature enhancers, linear, warping, and cost-volume layers, are used to best approximate this transformation. For spinning radar data, the focus is on identifying relevant features and keypoints from image-like radar scans. These models are able to operate in a polar or cartesian space, taking advantage of the denser representation of spinning radar. CNNs and transformer architectures are integrated for spatial feature extraction, while LSTM and GRU layers are implemented to establish a temporal connection between the spatial features. For both radar setup configurations, the focus is still on finding a 2D transformation matrix \(T\in SE(2)\). Most papers also utilize some masking module in which irrelevant matches or features are filter out. Only recent models try to utilize 4D automotive radars to find a 3D ego-transformation matrix \(T\in SE(3)\).
## 3 Results and Discussion
### Comparative Experiment Results and Analysis
From Figure 8, it can be seen that the proposed method achieves the highest alignment in the visualization results, indicating that the predicted homography matrix is the most accurate and has the smallest error compared to the actual homography matrix. This performance surpasses that of the other seven methods. Next, the impact of different offsets on various methods is analyzed. When generating test data, different offset values, \(\rho\), are set, representing the range of random perturbations of the four corner points. The smaller the offset, \(\rho\), the less distortion in the homography-transformed image, and vice versa. In this experiment, the offset, \(\rho\), is chosen from the range [10; 60]. The error results of different methods are shown in Table 1. It can be seen that, under the same offset value, the proposed method has the lowest homography estimation error. As the input image offset increases, the error of HomographyNet increases  
Figure 8: Visualization results of different homography estimation methods. significantly, while PFNet performs relatively better. The two traditional feature-based methods, despite having larger errors, show relatively smaller increases in error changes due to their reliance on feature-point extraction. The error of the unimproved unsupervised homography estimation network is also significantly higher than that of the proposed method. Although the new unsupervised homography estimation methods, C-AHomE and biHomE, exhibit relatively low error estimates, their stability significantly decreases as the perturbation range increases, falling short of the stability demonstrated by the method proposed in this paper. This indicates that the proposed method performs well, even when the input images have a low overlap rate, and demonstrates greater robustness to different scaling, rotation, and other viewpoint changes.
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 3 Reception of the Composite Signal
### Tracking of the BOC Signal
The normalized incoherent early-delayed amplitude methodology is adopted in the loop phase discriminator, as shown in Equation (16):  
\[\Delta\tau=\left(1-\frac{d}{2}\right)\cdot\frac{E-L}{E+L} \tag{16}\]  
where \(d\) is the early-delayed correlation spacing and \(E\) and \(L\) are the amplitudes of the early and delayed branches, respectively, as shown in Equations (17) and (18):  
\[E=\sqrt{W_{IIE}^{2}+W_{IQE}^{2}+W_{QIE}^{2}+W_{QQE}^{2}} \tag{17}\]  
Figure 7:0  
where \(W_{IIE}\), \(W_{IQE}\), \(W_{QIE}\), and \(W_{QQE}\) are the integration and dumping results for the four early branches and \(W_{IIL}\), \(W_{IQL}\), \(W_{QIL}\), and \(W_{QQL}\) represent the integration and dumping results for the four delayed branches. Notably, the carrier tracking loop, subcarrier tracking loop and code tracking loop are independent of each other and do not interfere with each other.
## 2 Background  
This paper highlights the benefits of FL, self-clustering, and in-network processing, while utilizing kNN to collaboratively recognize patterns with physically sparse IoT devices.  
The following sections provide background information on FL, LEACH, kNN, and voting approaches. FL integrates with IoT networks by enabling a decentralized framework that leverages local processing capabilities at the IoT nodes. Each IoT node, equipped with minimal computational resources, trains a local model using its collected data and transmits only model updates (e.g., weights or gradients) to a central server or cluster head. This process avoids transmitting raw data, addressing both energy efficiency and privacy concerns. A typical implementation involves the following steps:  
1. **Data Collection and Local Training:** IoT nodes collect data from their environment (e.g., temperature, motion) and perform local training using lightweight machine learning models, such as decision trees or small neural networks.
2. **Model Aggregation:** Cluster heads, selected using energy-efficient clustering protocols such as LEACH, aggregate model updates from nodes within their cluster. This reduces communication overhead by limiting transmissions to short distances.
3. **Global Model Update:** The aggregated updates are sent to a central base-station or edge server, which refines the global model. This model is then redistributed to IoT nodes for the next training round.  
For example, in an environmental monitoring application, sensors distributed across a region can collaboratively train a model to predict air quality. Each sensor locally processes its data and sends only model updates to the cluster head, which aggregates them before forwarding to a base-station for final updates. This hierarchy ensures energy-efficient operation and scalability.  
The TFNN approach further simplifies FL integration by combining in-network data aggregation and classification. By leveraging a kNN classifier at the cluster head level, TFNN enables single-round classification, bypassing the need for multiple iterations. This hierarchical design demonstrates the practical feasibility of FL in resource-constrained IoT networks. This integration ensures that sensor networks can achieve distributed intelligence while maintaining energy efficiency, privacy, and low latency, making FL a practical solution for modern IoT applications.
# Code-Switching in Automatic Speech Recognition: The Issues and Future Directions
## 4 Results of Review
#### 4.2.3 Evaluation Metrics  
In much of the research on bilingual and multilingual, the most common form of evaluation metrics is the word error rate (WER). This evaluation metric is common for bilingual and multilingual research for languages that use the Roman alphabet. The WER is a standard metric that allows researchers to compare their current work with previous works and provides the degree of improvement in their system's ability to recognise speech with CS. It also allows the comparison of performance among the different languages. For instance, English [28; 45] performs better than other languages, and bilingual and multilingual models have a lower WER than the monolingual model [5; 30; 32; 34; 36; 39; 44; 46; 56; 57; 61].  
However, according to [PERSON] et al. [30], conventional WER is insufficient to measure CS languages' performance due to ambiguities in transcription, misspellings, and the use of words from two different writing systems. Such errors cause the WER of an ASR system to be higher than it should be and complicate its evaluation. On top of that, these errors make it difficult to determine the modelling errors resulting from CS language and acoustic models [30].  
While WER is the primary measurement metric in ASR, some research uses character error rates (CER) [26; 27; 52; 53], especially the research involving English-Chinese CS, as the writing symbols differ between the two languages. Mixed error rate (MER) was applied in ref. [40], which applies WER to English and CER to Mandarin.  
The recognition performance of the CS bilingual and multilingual ASR systems uses the WER (30 papers), CER [27; 29; 30; 58; 59], CS ratio [6; 40; 52], MER/confusion matrix [2; 31; 32; 40; 50] and phoneme error rate (PER) [31; 56].  
We have analysed the performance of CS ASR systems in terms of their phone set arrangements as combining, mapping, or merging. Figure 2 depicts the overall results of the evaluation of ASR performance in recognising speech with CS conducted in the selected papers.  
As shown in Figure 2, the research that applies the combining of the phone set for CS [4; 6; 26; 29; 32; 39; 47; 49; 51; 52; 59] used WER, CER, and MER for measuring the performance of the ASR system. The existing work reported an error of 10% to 65% for WER, 5% to 37% for CER, and 37% to 38% for MER. The research that applied the mapping  
Figure 2: The overall results of the evaluation of ASR performance in recognizing speech with CS conducted in the selected papers.  
of phone sets [5, 40, 48, 56] has reported an error of 28% to 57% for WER, 5% to 37% for PER, and 37% to 38% for MER. Meanwhile the research on merging the phone set for CS [4, 6, 26, 29, 32, 39, 41, 47, 51, 52, 59], has reported an error of 23 to 43% for WER, 44% to 48% for CER, and 30% to 71% for MER.