esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed ML Attestation Method and Workflow  
### Dual-Token Attestation Method  
In the context of the attestation of ML models, we propose a dual EAT attestation method, as shown in Figure 2. We decouple the PSA attestation token from the ML-specific claims for the following reasons: the PSA token is typically generated by a secure partition manager component, which is usually provided by the vendor and is not intended for modification. Additionally, the ML-specific claims we propose at this time are not standardized. The PSA token attests to the device's identity and ownership, while the ML-EAT token provides evidence of the ML model and user application.  
The tokens are signed with different private keys (ECDSA-256) belonging to distinct entities. The first EAT token is signed with the SoC manufacturer's private keys, attesting to the chip's identity and integrity. The second EAT token is signed with the device manufacturer's keys or other third-party keys, provisioned during manufacturing. The second token is linked to the platform token via its hash (SHA256), which functions as a nonce. Additionally, the second token includes an authorization challenge from the verifier as one of its claims, ensuring freshness and preventing replay attacks.  
Furthermore, specific claims in the ML-EAT can be encrypted (AES-CBC-128), ensuring that sensitive information, such as the model architecture or proprietary details, remains confidential. This is particularly beneficial if the device participates in a collaborative federated learning environment, where multiple devices share updates without revealing sensitive data. In this context, the device needs to prove that its model architecture matches the one agreed upon by the federation, ensuring consistency and compatibility during the collaborative model training.
## 5 The SDRE Integral Type Servo Controller
### Design of SDW Matrix  
#### 5.3.1 Simulations in Order to Understand the Influence of SDW Matrix  
SDW matrix of state and SDW matrix of input can be expressed as follows:  
\[\mathbf{Q}(\tilde{\mathbf{x}})=\text{diag}(\mathbf{Q}_{\mathbf{q}_{e^{\prime}}}\mathbf{Q}_{\mathbf{ \omega}_{e^{\prime}}},\mathbf{Q}_{\mathbf{\phi}_{e^{\prime}}},\mathbf{Q}_{\mathbf{\Omega}_{e}}, \mathbf{Q}_{\mathbf{\Omega}_{e}},\mathbf{Q}_{\mathbf{q}_{e}}^{int},\mathbf{Q}_{\mathbf{\omega}_{e}}^{ int},\mathbf{Q}_{\mathbf{\phi}_{e^{\prime}}}^{int}),\ \ \mathbf{R}(\tilde{\mathbf{x}})=\text{diag}(\mathbf{R}_{\hat{\mathbf{\Omega}}},\mathbf{R}_{\mathbf{ \tau}_{\phi}}) \tag{41}\]  
where, \(\mathbf{Q}_{\mathbf{q}_{e}}\) is a SDW matrix for quaternion error \(\mathbf{q}_{e^{\prime}}\), \(\mathbf{Q}_{\mathbf{\omega}_{e}}\) is a SDW for satellite angular velocity error \(\mathbf{\omega}_{s}-\mathbf{\omega}_{r}\), \(\mathbf{Q}_{\mathbf{\phi}_{e}}\) is a SDW matrix for antenna joint angle error \(\mathbf{\phi}-\mathbf{\phi}_{r}\), \(\mathbf{Q}_{\mathbf{\Omega}_{e}}\) is a SDW matrix for RW wheel speed error \(\mathbf{\Omega}-\mathbf{\Omega}_{r}\), \(\mathbf{Q}_{\mathbf{q}_{e}}^{int}\) is a SDW matrix for integral value of quaternion error \(\int\mathbf{q}_{e}\text{d}t\), \(\mathbf{\omega}_{e}^{int}\) is a SDW matrix for integral value of satellite angular velocity error \(\int(\mathbf{\omega}_{s}-\mathbf{\omega}_{r})\text{d}t\), \(\mathbf{Q}_{\mathbf{\phi}_{e}}^{int}\) is a SDW matrix for integral value of antenna joint angle error \(\int(\mathbf{\phi}-\mathbf{\phi}_{r})\text{d}t\), \(\mathbf{R}_{\mathbf{\Omega}}\) is a SDW matrix for control input of RW angular acceleration \(\hat{\mathbf{\Omega}}\), \(\mathbf{R}_{\mathbf{\tau}_{\phi}}\) is a SDW matrix for control torque input of satellite actuator \(\mathbf{\tau}_{\phi}\).  
Firstly, in order to understand the influence of SDW matrix on characteristics of the SDRE servo control, we performed simulations with some fixed SDW matrix in two situation. In simulation I, we start the simulation with Initial RW wheel speed is zero. In simulation II, we start the simulation with assumption that RW has an initial wheel speed which is a disadvantageous condition for satellite attitude maneuver, and measuring error of RW wheel speed occurs. We show the simulation condition in Table 1. In Case 1, we have designed a large SDW matrix for the state error vector before the augment. In Case 2, we have designed a large SDW matrix for the integral of the state error vector. We show the parameters of SDW matrix in Table 2.
# Elimination Properties for a Probabilistic Scheduling Problem
## 4 Blocks of Tasks
### Blocks of Early Tasks  
**Definition 1**.: _Subpermutation of tasks \(\pi^{\mathcal{T}}\) in permutation \(\pi\in\Phi\) is called **blocks of early tasks** (in short \(\mathcal{T}\)**-block**), if:_  
1. _every task_ \(j\in\pi^{\mathcal{T}}\) _is early and_ \(d_{j}\geq E(\tilde{\mathcal{C}}_{\text{last}})\)_, where_ \(\tilde{\mathcal{C}}_{\text{last}}\) _is a random variable--date of completion of execution of the last task from_ \(\pi^{\mathcal{T}}\)_,_
2. \(\pi^{\mathcal{T}}\) _is the maximum subpermutation meeting the constraint (a)._  
**Corollary 1**.: _If \(\pi^{\mathcal{T}}=(\pi(a),\pi(a+1),\ldots,\pi(b))\) is a \(\mathcal{T}\)-block in a permutation \(\pi\), then_  
1. _an inequality_ \(\min\{d_{j}:\ j\in\pi^{\mathcal{T}}\}\geq E(\tilde{\mathcal{C}}_{\text{last}})\) _is fulfilled,_
2. _the cost of execution of tasks from_ \(\pi^{\mathcal{T}}\)_,_ \[\mathcal{L}(\pi^{\mathcal{T}})=\sum_{i=a}^{b}w_{\pi(i)}E(\tilde{\mathcal{T}}_{ \pi(i)})=0.\] (10)  
Using this property, we determine the first \(\mathcal{T}\)-block in permutation \(\pi\). After minor modifications (expected values of random variables \(E(\tilde{\mathcal{C}}_{i})\) instead of deterministic values \(C_{i}\)), one can apply the \(\mathcal{AT}\)**-block algorithm** presented in the work of [PERSON] [29]. The computational complexity of this algorithm is \(O(n)\). Considering further elements of permutation, after the last element of the first \(\mathcal{T}\)-block, we can determine the next block of early tasks.  
**Lemma 1**.: _If permutation \(\beta\) was generated from \(\pi\in\Phi\) by changing the order of elements in some block of early tasks in permutation \(\pi\) to_  
\[\mathcal{L}(\beta)=\mathcal{L}(\pi).\]  
Proof.: The proof results directly from the definition of the early task block and equality (10).
## 1 Introduction
For example, excavation works may lead to large daily ground settlement, while ground settlement may change slowly during the construction of tunnel structures in a foundation pit. Accordingly, a dynamic settlement prediction method should take into consideration such time-dependent influence factors to promote the prediction accuracy on daily ground settlement. However, existing machine learning-based methods generally fail to do so and thus cannot be used for the dynamic prediction of ground settlements in a direct way. Accordingly, this study intends to develop an ANN-based method for accurate and dynamic prediction of daily ground settlements surrounding a foundation pit. The selection of the ANN's input parameters takes into consideration not only static influence factors, such as soil properties and the geometry of a foundation pit, but also time-dependent influence factors, including construction stages and weather conditions, as well as previous daily settlement monitoring data. The proposed method is validated using data collected from a cut-and-cover highway tunnel project in western Hangzhou, China. This study is structured as follows. First, previous studies on the prediction of ground settlements induced by excavation or tunneling works are reviewed in the next section. Then, the establishment of an optimum artificial neural network is introduced with detailed step-by-step procedures in Section 3. Next, data collected from a real tunnel project are utilized to demonstrate the validity and applicability of the proposed approach in the Section 4. Finally, conclusions and future works are drawn in the Section 5.
# Path Planning under Force Control in Robotic Polishing of the Complex Curved Surfaces
###### Abstract  
Surface polishing is required in many manufacturing sectors. Currently, it demands a large amount of manual work, which is time-consuming, error-prone, and costly. Additionally, it creates hazards for the workers as it may result in many deadly respiratory diseases. Robotic polishing is the solution to these problems. It can improve productivity, eliminate the defects, and provides consistent product quality. In this paper, an effective approach is presented for the robotic polishing of the complex curved surfaces. The key part of the presented method is the tool path planning with controlled force and polishing parameters optimization evaluated using design of experiments (DOE). The tool path planning is aimed at improving the surface quality and the contact area per path. The constraints of joint limits and productivity are also considered. Moreover, its jerk avoidance strategy allows the robot to move swiftly while ensuring a smooth trajectory. The presented method is verified for the polishing of an eyeglass frame. A considerable improvement of 90% on the average roughness is achieved with the maximum acceptable roughness set at 0.02 \(\upmu\)m. The polishing operation takes just 79 secs and the average glossiness of 76 G is achieved on the final product along with the successful elimination of scratches on the surface.  
robotic polishing; path planning; force control; glossiness; surface roughness Article
# 6G Enabled Industrial Internet of Everything: Towards a Theoretical Framework
## 5 6 GIIoE System Paradigm
### Role of 6G Revolutionizing IIoE  
6G will become the \"eyes and ears\" of all IIoE systems since it provides real-time massive data collection and analysis. The pivotal role of 6G communications in IIoE, referred to as 6 GIIoEsystem, will empower and precipitate fifth industrial revolution mission and offers the following advantages to create significant value:* The extremely low latency, massive network capacity, high-speed (>100 Gbps) for massive data exchange hastens the realization of digitization towards personalization.
* Enables reducing operation time due to extremely low latency and increase in massive data throughput leading to enhance process productivity with quality control and leverage optimization process for expanded connected intelligent automation.
* Can attenuate the complexity of supply chain networks, and warehouse management, helping businesses to efficiently operate in dynamic industrial environments.
* Endows the next era of AI driven enterprises powered by 6G connected edge (cloud) computing percussing into massive volume of data to unleash insights that would otherwise not be attainable.
* Entrusting the sustainability of all industries managing the vast volume traffic to hyper e-commerce by enhancing 6G network accessibility at a faster pace accelerating online purchases, order placements, customer delivery and have the potential to disrupt on-site job functions through virtual meetings as well as remote working environment.
## 2 Materials and Methods
### Characterization of Saturated Nitrogen Plots  
Several studies have shown that nitrogen availability can be categorized into distinct stages of ecosystem response [16; 60; 61]. The first stage is documented as the 'pre-treatment stage', prior to any fertilization. Thereafter, the second stage of 'nitrogen loading' is characterised by increased nitrogen deposition, during which, ecosystem production responds positively to increased fertilization. Later, the third stage of 'early nitrogen saturation' is realised, as the ecosystem approaches initial nitrogen saturation; however, the associated negative effects are often subtle. [PERSON] et al. [16] documented several early indicators of nitrogen saturation. These include (1) elevated soil nitrate concentrations, (2) increased soil acidity, and (3) increased mineral cation leaching. The last stage--Stage 4--is documented as 'late nitrogen saturation', where significant levels of nitrogen saturation cause major ecosystem impacts. This considerably affects plant growth and species composition [16]. As such, detecting nitrogen saturation timeously within grasslands is crucial in maintaining optimal grassland health and longevity. Subsequently, this study explored stages 1 (pre-treatment) and 3 (early nitrogen saturation) to characterize grassland plots, which exhibited the initial signs of possible nitrogen saturation. Thus, a specific set of physiological criteria was used to characterize plots within the initial stages of nitrogen saturation. These criteria were centred around the early indicators of nitrogen saturation documented by [PERSON] et al. [16]. More precisely, grassland plots had to fulfil the entire set of physiological-based criteria to be considered nitrogen-saturated (Figure 2). In this regard, plots had to demonstrate a notable increase in nitrate (NO\({}_{3}\)\({}^{-}\)N) levels, a reduction in pH levels, and a reduction in either calcium and magnesium levels between stages 1 and 3. The resultant net changes for each of these variables across the growing season were calculated as the difference between stage 3 and stage 1. The nitrogen enrichment factor, which details the difference in nitrogen abundance between the substrate (i.e., soil nitrogen) and the product (i.e., foliar nitrogen) was also calculated using the equation below (Equation (1)) [62].  
\[Nitrogen\ Enrichment\ Factor=N_{Leaf}-N_{Soil} \tag{1}\]  
Figure 2: Flow diagram showing the physiological criteria used to identify grassland plots undergoing the initial stages of nitrogen saturation, as characterized by [PERSON] et al. [16].
## 3 Materials, Methods, and Findings
### Word Embeddings with LSTM Model  
Word embeddings are used to create semantic representations of texts. In this study, they are combined with the LSTM model to achieve effective results in the text classification tasks. In the training process, the model was trained for 20 epochs, and the Adam optimization algorithm was used. Figure 19 shows the complexity matrix of the Word Embeddings-LSTM model.  
The model correctly classified 96 instances in the \"no verbal violence\" class and incorrectly labelled only 6 instances as \"verbal violence present\" (false positive). In the \"verbal violence present\" class, the model made 89 correct predictions and misclassified 19 instances as \"no verbal violence\" (false negative). These results show that the overall accuracy rate of the model is 88.10%. The false positive rate is low at 5.88%, indicating that the model provides a reliable performance in the \"no verbal violence\" class. The false negative rate of 17.59% indicates that the model missed a few instances in the \"verbal violence present\" class.  
Figure 19: Word Embeddings-LSTM confusion matrix.  
Figure 18: CNN confusion matrix.
# EmbeddedPigDet--Fast and Accurate Pig Detection for Embedded Board Implementations
## 4 Experimental Results  
### Experimental Setup and Resources for the Experiment  
For the purpose of comparison, our individual pig detection experiments were conducted in the following PC as well as low-cost NVIDIA Jetson (NVIDIA, Santa Clara, CA, USA) environments: Ubuntu 16.04.2 LTS (Canonical Ltd., London, UK), OpenCV 4.1 for image processing [76], and  
* PC: Intel Core i5-9400F 2.90 GHz (Intel, Santa Clara, CA, USA), NVIDIA GeForce RTX2080 Ti (NVIDIA, Santa Clara, CA, USA), 32 GB RAM.
* Jetson TX-2 [77]: dual-core Denver 2 64-bit CPU and quad-core ARM A57 complex, NVIDIA Pascal(tm) architecture with 256 NVIDIA CUDA cores, 8 GB 128-bit LPDDR4.
* Jetson Nano [50]: quad-core ARM A57 complex, NVIDIA(tm) architecture with 128 NVIDIA CUDA cores, 4 GB 64-bit LPDDR4.  
We conducted the experiment in a 3.2 m tall and 2.0 m wide \(\times\) 4.9 m long pigsty at Chungbuk National University and installed a low-cost Intel RealSense camera (D435 model, Intel, Santa Clara, CA, USA) [56] on the ceiling to obtain the images. A total of nine pigs (Duroc \(\times\) Landrace \(\times\) Yorkshire) were raised in a pig room and the average initial body weight of each pig was (92.5 \(\pm\) 5.9) kg. We acquired color, infrared and depth images through the low-cost camera installed on the ceiling and each image had a resolution of 1280 \(\times\) 720, at 30 frames per second (fps). Figure 5 shows a pig room with a camera installed on the ceiling. To exclude the unnecessary region of the pig room, we set Region of Interest (RoI) as 608 \(\times\) 288.  
From the camera, we acquired 2904 training images and then trained EmbeddedPigYOLO (0.0001 for learning rate, 0.0005 for decay, 0.9 for momentum, leaky ReLU as the activation function, default anchor parameter and 20,000 for the iterations). Then, we obtained 1000 test images and conducted the test with light-weight image preprocessing and deep learning modules. The reported accuracy was the average of five-fold cross validation. Also, we implemented the proposed methods with YOLOv2 [74] and YOLOv3 [75], respectively (i.e., EmbeddedPigYOLO(v2) and EmbeddedPigDet(v2),  
Figure 5: Experimental setup with Intel RealSense low-cost camera.  
EmbeddedPigYOLO(v3) and EmbeddedPigDet(v3)). With COCO data set [78], YOLOv3 could improve meaningfully the accuracy of YOLOv2 with additional computational workload [79]. In pig detection, however, the accuracy of YOLOv3 was similar to that of YOLOv2 but YOLOv3 was much (i.e., by a factor of 2) slower than YOLOv2. In the following, therefore, we reported the performance of YOLOv2 related methods only.
# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
## 5 Conclusions  
The study presents a comprehensive evaluation of statistical methodologies and machine learning techniques applied to water quality prediction models. The analysis demonstrates that the integration of assumption and diagnostic tests, feature importance analysis, learning curve analysis, and PCA enhances model reliability and interpretability. The results indicate that dissolved oxygen (DO) and ammoniaacl nitrogen (AN) are the most influential water quality parameters, significantly impacting predictive accuracy. Additionally, the presence of heteroscedasticity and non-normal residuals highlights the need for robust data preprocessing techniques. The implementation of PCA effectively reduces dimensionality while preserving critical information, improving computational efficiency and model performance. These findings contribute to the development of more accurate and scalable water quality prediction models, supporting informed decision-making in environmental monitoring and resource management.  
Future research should focus on expanding the dataset to include a wider range of water quality parameters and geographical locations to improve model generalization. The incorporation of advanced machine learning techniques, such as deep learning and ensemble learning, may further enhance predictive accuracy and adaptability to complex data patterns. Additionally, exploring hybrid modeling approaches that integrate physical and statistical models could provide deeper insights into water quality dynamics.  
Conceptualization, W.Z.W.I. and N.A.A.A.; methodology, A.L. and W.Z.W.I.; validation, W.Z.W.I. and N.A.A.A.; investigation, A.L. and W.Z.W.I.; writing--original draft preparation, A.L.; writing--review and editing, W.Z.W.I. and N.A.A.A.; visualization, N.A.A.A. and W.Z.W.I.; supervision, W.Z.W.I. and N.A.A.A. All authors have read and agreed to the published version of the manuscript.  
This research is funded by a grant from Ministry of Higher Education, Malaysia, for Fundamental of Research Grant Scheme (FRGS/1/2024/WAS02/USIM/02/1), and the APC is funded by Multimedia University (MMUE/210013).  
All data are presented in the manuscript.  
We would like to acknowledge the support given by the RedTone Company, Universiti Sains Islam Malaysia and Multimedia University towards this project. The authors have reviewed and edited the output and take full responsibility for the content of this publication.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Pyramid Spatial-Temporal Graph Transformer for Skeleton-Based Action Recognition
###### Abstract  
Although graph convolutional networks (GCNs) have shown their demonstrated ability in skeleton-based action recognition, both the spatial and the temporal connections rely too much on the predefined skeleton graph, which imposes a fixed prior knowledge for the aggregation of high-level semantic information via the graph-based convolution. Some previous GCN-based works introduced dynamic topology (vertex connection relationships) to capture flexible spatial correlations from different actions. Then, the local relationships from both the spatial and temporal domains can be captured by diverse GCNs. This paper introduces a more straightforward and more effective backbone to obtain the spatial-temporal correlation between skeleton joints with a local-global alternation pyramid architecture for skeleton-based action recognition, namely the pyramid spatial-temporal graph transformer (PGT). The PGT consists of four stages with similar architecture but different scales: graph embedding and transformer blocks. We introduce two kinds of transformer blocks in our work: the spatial-temporal transformer block and joint transformer block. In the former, spatial-temporal separated attention (STSA) is proposed to calculate the connection of the global nodes of the graph. Due to the spatial-temporal transformer block, self-attention can be performed on skeleton graphs with long-range temporal and large-scale spatial aggregation. The joint transformer block flattens the tokens in both the spatial and temporal domains to jointly capture the overall spatial-temporal correlations. The PGT is evaluated on three public skeleton datasets: the NTU RGBD 60, NTU RGBD 120 and NW-UCLA datasets. Better or comparable performance with the state of the art (SOTA) shows the effectiveness of our work.  
skeleton-based action recognition; graph embedding; transformer; spatial-temporal attention; pyramid
# Untargeted Urinary Volatilomics Reveals Hexadecanal as a Potential Biomarker for Preeclampsia
## 2 Results
### Identified Volatile Metabolites in the Analyzed Urine Samples  
A metabolomic analysis of volatile organic compounds was performed on 91 urine samples consisting of 45 samples from women diagnosed with PE and 46 control samples. The analysis was performed using GC-MS with headspace solid-phase microextraction (HS-SPME), which led to the identification of a distinct metabolomic profile. In this study, 29 volatile organic metabolites out of a total of 107 detected compounds were identified in all urine samples of PE and control groups. GC-MS with HS-SPME analysis provided a detailed profile of the volatile organic metabolites present. The identified compounds are listed in Table 2.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Preeclampsia (N = 45)** & **Controls (N = 46)** & \(p\) \\ \hline Age, years & & & \\ Mean (SD) & 32.1 (4.4) & 32.6 (4.8) & 0.556 \\
95\% CI & 30.7–33.4 & 31.2–34.1 & \\ Parity & & & \\ Median (IQR, 25 th–75 th) & 1 (1, 1–2) & 2 (1, 1–2) & 0.003 \({}^{\star}\) \\
95\% CI & 1.2–1.7 & 1.6–2.1 & \\ Gestational age, weeks & & & \\ Median (IQR, 25 th–75 th) & 36 (9, 29–38) & 37 (4.5, 33.5–38) & 0.202 \\
95\% CI & 32.5–36.5 & 34.4–37.7 & \\ BMI, kg/m\({}^{2}\) & & & \\ Median (IQR, 25 th–75 th) & 26.35 (7.33, 23.88–31.21) & 24.51 (5.21, & \\
95\% CI & 26.1–29.4 & 23.5–26.1 & \\ Pregnancy weight gain, kg & & & \\ Median (IQR, 25 th–75 th) & 13 (10, 7–17) & 13 (6, 11–17) & 0.263 \\
95\% CI & 10.5–13.8 & 12.1–15.3 & \\ \hline \hline \end{tabular} BMI—body mass index. Data presented with mean (SD) or median (IQR, 25 th–75 th percentile), according to distribution and analyzed using Student’s \(t\)-test or Mann-Whitney \(U\) test. Gestational age pertaining to urine draw at PE onset or hospitalization of control group. \({}^{\star}p<0.05\).  
\end{table}
Table 1: Baseline clinical characteristics of the study participants.