esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Design and Implementation of swHPFM
#### 2.2.3. Serpentine Register Communication
To complete the serpentine register communication, first, the thread numbers of the serpentine CPE are reordered by mapping the number of CPE threads to snake-type number to build the list of neighbors that must be communicated. The neighbor list builder pseudo-code is presented as Algorithm 1, in which _row_front_ denotes the thread number of the previous CPE, _row_back_ represents the thread number of the next CPE, _column_front_ is the previous column of the CPE, and _column_back_ denotes the thread number of the next column of the CPE. When the serpentine neighbor list is built, the data distribution will change accordingly, as illustrated in Figure 10.  
```
Initialize the original thread ID of CPE  Get the thread number of the current CPE, _thread_id_ \(\leftarrow\) athread_get_id(-1);  Obtain the row number of the CPE, _row_ \(\leftarrow\) thread_id_ / 8;  ifrow_%2==0:  serpentine_column \(\leftarrow\) 7-column;  else serpentine_column \(\leftarrow\) column;  end if  Update the thread ID of the serpentine CPEs, _Serpentine_CPE_thread \(\leftarrow\) row*8+_serpentine_column_;  row_front \(\leftarrow\) serpentine_column_ \(-1\);  row_back \(\leftarrow\) serpentine_column +1; ifrow_front==-1:  column_front \(\leftarrow\) row+1; end if ifrow_back==8:  column_back \(\leftarrow\) row-1; end if
```  
**Algorithm 1**Serpentine Neighbor List Builder. Finally, register communication was executed according to the neighbor list. Since the calculation relies on the forward and backward data, it is necessary to communicate forward and backward. The pseudo-code of the serpentine register communication is presented, as shown in Algorithm 2. In this way, diagonal communication can be executed directly, without intermediate routing registers, and the register communications can be effectively reduced.
# Optimization Design for Sparse Planar Array in Satellite Communications
## 4 Sparse Planar Array Optimization Design Method
### Sparse Area Array Optimization Based on Analysis Methods
Furthermore, the proposed method exhibited a high level of efficiency, with a simulation time of 4.3 s, which can facilitate the acquisition of low sidelobe levels.
## 3 Proposed Model
### Extracting the Extrinsic Information for Serial MAP Detection  
According to Section 2.2, the output of the VI/HI estimators is the vector with the below forms.  
HI estimator:  
\[\mathbf{v}=\Big{[}v[j,k+1]\quad v[j,k]\quad v[j,k-1]\Big{]}, \tag{40}\]  
VI estimator:  
\[\mathbf{v}=\Big{[}v[j+1,k]\quad v[j,k]\quad v[j-1,k]\Big{]}, \tag{41}\]  
In this study, because we exploit the extrinsic information for horizontal MAP detection, in serial MAP detection, we only use the results of the HI estimator in (38). With (38), we can collect all the values of \(v[j,k]\in\{R_{1}=-2p-l_{1};\)\(R_{2}=-l_{1};\)\(R_{3}=2p-l_{1};\)\(R_{4}=-2p+l_{1};\)\(R_{5}=l_{1};\)\(R_{6}=2p+l_{1}\}\). Then, to calculate the extrinsic information when the value of \(v[j,k]=R_{i}\), we used the below equation to convert the values of \(v[j,k]\) into probabilities of \(R_{i}\) and other symbols.  
\[\left\{\begin{aligned} P_{0}(R_{i})=\alpha\quad 0\leq\alpha\leq 1 \\ P_{0}\big{(}R_{q}\big{)}=\frac{1-\alpha}{5}\text{ with }q\
eq i \end{aligned}\right. \tag{42}\]  
From (35), we give the value of \(P_{0}(R_{i})\) to \(P_{e}(R_{i})\) (\(P_{0}(R_{i})=P_{e}(R_{i})\)). Regularly, \(P_{e}(R_{i})\), in conventional methods without feedback from vertical detection, is equal to 1 or the same probabilities for all symbols. This leads to the low performance of horizontal detection in an initialized loop when there is no feedback from vertical detection. However, with our proposed model, we can supply the extrinsic information by using \(P_{0}(R_{i})\) for the horizontal detection in the initialized loop and improve the performance of system. In addition, \(\alpha\) is the ratio to extract the extrinsic information. Simultaneously, to find out the value of \(\alpha\), we implemented the survey as described in Section 4.  
For extrinsic information from the vertical detection, we consider Table 2 to convert the output of the vertical detection into the probabilities of symbols \(R_{i}\).  
After detecting the initialized loop, we can obtain extrinsic information from the vertical detection. Thus, at the first loop, we supplied the results of \(P_{e}(R_{i})\) from Table 2 to (35) to improve the performance of horizontal MAP detection.  
In Figure 3, the signal \(z[j,k]\), contains both the original signal and 2D interference. The algorithm in Section 2.2 helps isolate the signal affected only by 1D interference, which closely resembles \(b[j,k]\). Next, we modified the output of the \"VI/HI Estimator\" to obtain probability values, which were then supplied as extrinsic information for the horizontal MAP detection.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(R_{i}\) & \(P_{e}(R_{i})\) \\ \hline \(-l_{1}-2p\) & \(P_{a}(-1)P_{a}(-1)P_{a}(-1)\) \\ \(-l_{1}\) & \(P_{a}(1)P_{a}(-1)P_{a}(-1)+P_{a}(-1)P_{a}(-1)P_{a}(1)\) \\ \(-l_{1}+2p\) & \(P_{a}(1)P_{a}(-1)P_{a}(
## 6 Use-Case Examples  
Positioning of devices in indoor environments was a focus area of 3 GPP Release 16 [87], and further enhancements are expected in Release 17 [88]. With the fast advances of 5G standardization, several new indoor positioning use cases are brought along. They fall into the following categories:  
1. Location-based services-related use cases include AR- and VR-scenarios, telepresence and wearables for entertainment [89] and educational [90] reasons, as well as smart advertising [91], navigation, building occupancy-count estimation[92], and social networking [14].
2. Industrial use cases include trolley location, waste management, container handling, manufacturing, warehousing, and Industrial Internet of Things (IIoT) [93; 94; 95; 89].
3. eHealth-related use cases are split to positioning of people and medical equipment, including patient location, remote health care, and remote surgery [96; 97; 98].
4. Emergency and mission critical use cases are related to emergency services, first responders, alerting nearby responders, emergency vehicle and equipment location [14].
5. Road-related use cases include traffic monitoring, management and control, V2X, car and bike sharing, as well as flow control in transportation hubs and public transportation [99].
6. Rail, maritime, and aerial use cases are related to asset tracking (wagon and container), drones (transport and inspection), mining, and underwater and aerial unmanned vehicles [100; 101; 102; 103].  
Expectations for 5G- and 6G-based positioning depend on the application area. A comprehensive summary of positioning requirements for different use cases can be found in [87]. This standard defines positioning requirements, such as horizontal and vertical accuracy, availability, heading, and latency for some typical scenarios, given that the UE moves slower than a given speed.
# Ohmic Baking of Gluten-Free Bread: Role of Starch and Flour on Batter Properties
## 1 Introduction  
GF bread formulations have been continuously developed to overcome most of the problems that arise due to the absence of gluten, such as low bread volume, friable crumb, and poor mouthfeel. The most recent approach to improve GF bread quality is applying a non-conventional baking technology: ohmic heating. Ohmic heating is a volumetric heating method that passes an electrical current through food, resulting in fast and uniform heating [1]. Recently, this technology has been successfully applied for baking GF bread, improving bread volume and pore properties [2]. However, fundamental knowledge of how each ingredient contributes to increasing GF bread quality using ohmic heating is still missing.  
It is known that the structure and, therefore, quality of GF bread is mainly controlled by starch, which has a significant effect on the batter viscosity and stability [3, 4]. Since starch is the main component in a GF formulation, the GF batter commonly requires more water compared to wheat bread. [PERSON] et al. [5] highlighted that water content in GF bread controlled the starch gelatinization, which significantly affected gas retention. Prior studies investigated the influence of water on GF bread quality in various ranges [5, 6, 7]. Results indicated that water content dramatically influenced the rheological behavior of GF batter. However, an inadequate consensus on determining suitable rheological properties to obtain an optimal GF bread quality has been observed, as research so far has found contradicting and unclear effects.  
Viscosity is one of the rheological properties that significantly influences GF bread quality. In particular, it is positively correlated to bread volume [8; 9], which is usually attributed to a higher gas holding capacity and foam stability of the batter. However, an exceeding viscosity will limit the gas expansion ability of the batter during proofing. Similarly, low viscosity cannot retain the gas cells in the batter due to easy dissipation, resulting in low bread volume [10; 11]. Previous studies have not been able to indicate an adequate range of batter viscosities that are required to obtain high-quality GF bread.  
If bread was to be baked using ohmic heating, detailed knowledge about the rheological behavior of GF batter is crucial. Ohmic heating relies on the ability of ions to move within the food matrix, which in turn is affected by the viscosity of the batter. This has been seen in previous studies before, as high viscosities have led to lower heating rates [12; 13]. Since the rheological behavior of GF batter is majorly influenced by the starch properties and water content [6; 7; 8], this study aimed to investigate the role of GF starch (and flour) from different sources (tuber and cereal), as well as the rheological behavior of GF batter and the final bread quality after baking with ohmic heating. The attempt was to thoroughly understand the interaction between the rheological properties and ohmic baking and to define a suitable viscosity range for this processing approach.
## 4 Discussion
Its fracture energy regularization strategy minimizes mesh sensitivity and produces consistent load-displacement behavior and crack propagation paths, representing an improvement over earlier DVIB implementations [55; 63]. These advancements firmly place the model within the domain of mesoscale fracture mechanics and reinforce its applicability to tunnel-stability problems [71]. Mesh sensitivity analyses confirmed that when the fracture energy was properly calibrated, the resulting load-displacement curves and crack trajectories were consistent across the discretization levels. The displacement trends around the tunnel crowns and sidewalls corresponded well with the expected excavation-induced stress redistribution, thus further validating the physical accuracy of the model. These results underscore the benefits of a potential-driven elastoplastic framework for tunnel-deformation analysis. Unlike conventional continuum models, which assume material homogeneity and require complex remeshing, the SW-DVIB model naturally captures the initiation and evolution of brittle failure. Its ability to represent material anisotropy and post-peak softening behavior without mesh dependence makes it particularly suitable for the design and assessment of tunnel support systems in brittle and strain-softening geological environments. Despite its strengths, this study has several limitations. The simulations were limited to two dimensions, which restricted their ability to capture the full three-dimensional stress redistribution and out-of-plane effects. Although Poisson's ratio effects were explored, further sensitivity analyses targeting other parameters such as fracture energy, yield stretch, and angular stiffness are necessary. In addition, the computational performance of the model was not explicitly evaluated. Quantifying runtime and memory demands is essential for assessing their feasibility in large-scale applications. To address these limitations, future research should include (1) comprehensive model validation, including direct comparative studies with continuum or lattice-based methods,systematic mesh sensitivity analyses, and verification against experimental data, such as the CSCBD tests from [PERSON] et al. [69]; (2) expanded parametric investigations, including full sensitivity and ablation analyses of Poisson's ratio, fracture energy regularization, yield stretch, angular stiffness, and the impact of rock bolt optimization strategies on tunnel stability; (3) methodological enhancements, including the extension of the model to three-dimensional simulations for realistic tunnel geometries and stress redistributions, and the evaluation of computational complexity in terms of runtime and memory efficiency for large-scale geomechanical applications. These initiatives will further establish the SW-DVIB framework as a robust tool for simulating complex failure mechanisms and enhancing the predictive capabilities of tunnel stability assessments in geotechnical engineering.
# Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method
## 5 Conclusions  
A novel solution based on signal processing is proposed in this work to address the problem of coherent noise in guided wave testing using the SSP technique. The main concern was to identify the limitations of SSP in terms of sensitivity and resolution when two features are close to each other.  
Therefore, a synthesised signal has been created to identify the limitations of SSP in terms of establishing the smallest defect that can be detected, and to establish the resolution limitation when the location of a defect is close to a dominant feature. It was demonstrated that the SSP technique with optimum parameters successfully identified defects down to 2% CSA, and enhanced the SNR of the received signal. In addition, a threshold of 50 cm has been defined, below which the temporal resolution will be significantly reduced. The outcome was then experimentally validated for an eight-inch pipe containing two saw cut defects using the Teletest system, where it achieved a comparable result. To sum up, it is demonstrated throughout this work that the proposed method using a polarity thresholding algorithm has the potential to improve the sensitivity and spatial resolution of GWT in terms of SNR (by up to 30 dB), by detecting smaller defects (down to 2% CSA) with a resolution threshold of 50 cm between the two features. Thus, this work shows that SSP, as implemented here, could be applied for pipeline inspection using the GWT technique.  
Further work on this subject should focus on validation of this technique with field data, which are usually more complex and contain different types of defects. Furthermore, the sensitivity of this algorithm could be investigated for coated and buried pipelines, where the attenuation rates are sufficiently high that they cause a major reduction in guided wave test capability.  
[PERSON] conducted the synthesised modelling, analysis, programming, experiments, and drafted the manuscript; [PERSON] helped edit the manuscript; [PERSON]-[PERSON] contributed to reviewing the manuscript.  
This research was funded by the National Structural Integrity Research Centre (NSIRC), managed by TWI with the partnership of Brunel University.  
The authors gratefully acknowledge TWI and PI Ltd. for granting access to the Teletest equipment.  
The authors declare no conflict of interest.
# Squat Lifting Imposes Higher Peak Joint and Muscle Loading Compared to Stoop Lifting
###### Abstract  
(1) Background: Yearly, more than 40% of the European employees suffer from work-related musculoskeletal disorders. Still, ergonomic guidelines defining optimal lifting techniques to decrease work-related musculoskeletal disorders (WMSDs) has not been unambiguously defined. Therefore, this study investigates if recommended squat lifting imposes lower musculoskeletal loading than stoop lifting while using a complex full body musculoskeletal OpenSim model. (2) Methods: Ten healthy participants lifted two different weights using both lifting techniques. 3D marker trajectories and ground reaction forces were used as input to calculate joint angles, moments and power using a full body musculoskeletal model with articulated lumbar spine. In addition, the muscle activity of nine different muscles was measured to investigate muscle effort when lifting. (3) Results: Peak moments and peak joint power in LSS1 were not different between the squat and the stoop, but higher peak moments and peak power in the hip, knee, elbow and shoulder were found during squat lifting. Moment impulses in LSS1 were higher during stoop lifting. This is reflected in higher peak electromyography (EMG) but lower muscle effort in prior described muscles during the squat. (4) Conclusions: Squat lifting imposes higher peak full body musculoskeletal loading but similar low back loading compared to stoop lifting, as reflected in peak moments, peak power, and peak EMG.  
Keywords:lifting technique; stoop; squat; work-related musculoskeletal disorders; musculoskeletal modeling; spine; shoulder; back loading +
Footnote †: journal: Applied
# Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya
## 4 Results
### Spectral Variable Importance for Crop Systems Mapping  
The backward variable selection method, applied on the RF variable importance ranking, resulted in selecting 15 RapidEye spectral variables (Figure 4) that were found to be the most relevant for mapping mono- and mixed maize cropping systems after crop masking. Using the LULC map result (Figure 2), nine and six spectral variables, respectively, were selected as important variables from the two RapidEye images, captured during the stem elongation and the flowering development stages, respectively (Figure 4). Moreover, most of the selected variables from both observation periods were the RapidEye spectral wavebands themselves. In general, all five RapidEye bands (blue, green, red, red-edge and near infrared) were selected as useful spectral features for classifying the two maize cropping systems, while only five indices (RE1_NDVI, RE1_NDRE, RE2_DVledge, RE1_LCI and RE1_SIPE3) were useful for separating different maize cropping systems (Figure 4).  
Figure 4: Mean decrease in accuracy of the 15 most important input variables that were selected using the random forest backward feature elimination function and the.632+ bootstrapping function on importance ranking.  
Figure 3: Random forest (RF) _mtry_ and _ntree_ optimization grid for the land use land cover (LULC) classification result (**a**) and for the mono- and mixed maize cropping systems mapping result (**b**) using the internal out-of-bag (OOB) error rate of RF resulting from a grid search with a tenfold cross validation setting.
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Streaming Video Analytics and Edge Computing (2017-2019)  
To overcome the latency and scalability limitations of cloud-only batch processing, VBDA systems began incorporating edge computing and streaming analytics. This evolution led to hybrid cloud-edge architectures, where edge devices performed real-time inference, while cloud platforms handled model training and archival storage. Stream processing tools such as Apache Kafka ([[https://kafka.apache.org](https://kafka.apache.org)]([https://kafka.apache.org](https://kafka.apache.org)), accessed on 15 May 2025) and Apache Flink ([[https://flink.apache.org](https://flink.apache.org)]([https://flink.apache.org](https://flink.apache.org)), accessed on 13 May 2025) became foundational for enabling low-latency video ingestion and analytics pipelines [28]. At the same time, hardware accelerators like NVIDIA Jetson (NVIDIA Corporation, Santa Clara, CA, USA) and Intel Movidius (Intel Corporation, Santa Clara, CA, USA) enabled efficient on-device video processing [2].  
The introduction of YOLOv3 in 2018 further enhanced real-time object detection capabilities, reinforcing the practicality of edge-first analytics [29]. Complementary tools such as Elasticsearch for semantic indexing and graph databases for contextual correlation, advanced video content organization, and retrieval. Emerging technologies like ONNX-promoted AI model portability across heterogeneous hardware. While early 5G adoption helped reduce communication latency. Nevertheless, coordinating distributed inference, handling heterogeneous edge hardware, and achieving seamless edge-cloud orchestration continued to present formidable challenges.
## 2 Related Works
### ANN-to-SNN Conversion of the Transformer Architecture  
The Transformer architecture [29] and its variants have achieved excellent results in fields such as natural language processing (NLP), computer vision, and speech processing. By converting a pre-trained ANN-Transformer into an SNN-Transformer, it is possible to fully utilize the energy efficiency advantages of SNNs while preserving the performance of ANNs [51]. Some studies have theoretically proven the effectiveness of this combination.  
An ANN-to-SNN conversion method for Transformers was provided by [41], which can convert the Transformer model into SNNs. However, only simple integrate and fire (IF) neurons were used to replace the ReLU activation function after multi-head self-attention to achieve network conversion, without addressing the implementation of self-attention mechanisms in SNNs. To overcome this challenge, the MST model proposed by [PERSON] et al. [42] successfully introduced the self-attention mechanism into SNNs by pruning the self-attention module of Transformers and designing new spiking neurons to simulate activation functions like ReLU. However, the converted self-attention mechanism still retains the Softmax function from ANNs, and a complete conversion of self-attention in SNNs has not yet been achieved. The research on the above ANN-to-SNN conversion methods has focused mainly on converting simple activation functions, such as ReLU and Sigmoid. Complex activation functions like Softmax in Transformers still remain difficult to simulate using spiking neurons.  
To simulate complex activation functions, the FS-neuron [52] uses time encoding with spike patterns to convert more complex activation functions, requiring only a few time steps and significantly improving latency and throughput. According to their report, it can be applied to almost any activation function. However, due to the lack of a fixed function curve for the Softmax activation function, the trainable FS-neuron cannot simulate it. Therefore, simulating the Softmax activation function with spiking neurons to achieve low-latency ANN-to-SNN conversion for SNN-Transformer models remains an ongoing challenge.