esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Optimal Control of Treadmill Shock Absorption Based on Ground Reaction Force Constraint
## 5 Experimental Data  
### Establishment of the Dataset  
The dataset is established by continuously changing the air pressure values of the airbags in the active damping control treadmill while measuring the ground reaction force (GRF) at a constant speed. The air pressure value corresponding to the minimum GRF at a specific speed is considered the optimal pressure for that speed. Following this pattern, the air pressure values corresponding to the minimum GRF at different speeds are measured to create the dataset. The dataset includes graphical representations of certain data (Figure 5a shows the air pressure value corresponding to the minimum GRF at a speed of 4 km/h, while Figure 5b shows the same for a speed of 10 km/h). From Figure 5a, it can be seen that at a speed of 4 km/h, the air pressure value of 140 kPa corresponds to the minimum foot pressure value (GRF). Similarly, from Figure 5b, at a speed of 10 km/h, the air pressure value of 80 kPa corresponds to the minimum foot pressure value (GRF).
# Spot Weld Inspections Using Active Thermography
## 2 Materials and Methods
### Excitation Methods  
Different excitation techniques are used in order to find the optimal measurement setup for spot-weld inspections. The used techniques can be divided by the heating mechanics used: light heating and inductive heating. Heating the samples using halogen lamps is performed using lock-in thermography and pulse thermography.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Sample** & **1A** & **2A** & **3A** & **4A** & **5A** & **1B** & **2B** & **3B** & **4B** \\ \hline Current [kA] & 8 & 6 & 3.5 & 5 & 16 & 8 & 6 & 3.5 & 5 \\ Weldtime [ms] & 400 & 200 & 600 & 200 & 300 & 400 & 200 & 600 & 200 \\ Force [kN] & 3 & 2.6 & 3 & 3.5 & 2.8 & 3 & 2.6 & 3 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used to generate the samples.
# Joint Client and Resource Optimization for Federated Learning in Wireless IoT Networks
## 1 Introduction
To solve the problem efficiently, the reformulation and decoupling are adopted, and the optimal resource allocation can be achieved by using an online algorithm. Nevertheless, the amount of local training data is not considered in the client scheduling. Another joint optimization of client selection and resource allocation for wireless FL is studied in [16], where the target is to maximize the total average number of active clients and transmission time. The Lyapunov optimization is employed to achieve an online-manner solution. Similarly, in [17], a joint optimization of client scheduling and resource allocation for hierarchical FL is investigated. The formulation simultaneously captures the uncertainty of the wireless channel and the weight gradient. However, in these former works, the joint optimization mainly focuses on the wireless channel and resource allocation without considering the amount of local training data in each client. In [18], both the bandwidth and power allocation are considered in the wireless resource optimization, and the objective is to maximize the number of accessed clients. However, in these existing works, the objective functions treat all nodes equally, and do not involve the amount of the training data in the client, which is worth being considered, because, in practice, the amount of collected data for each IoT node varies. Motivated by these observations, we investigate the joint optimization of client selection and wireless resource allocation in FL-aided IoT networks, and the major contributions are as follows. * We developed a joint optimization framework for FL in wireless IoT networks. Specifically, the framework supports client selection and wireless resource allocation, which includes the power and bandwidth allocation of the clients. In the framework, the objective function takes both the amount of training data and wireless resource consumption into consideration. * We solve the formulated problem by using an alternative direction-based algorithm. In the algorithm, the primal problem is decomposed and transformed, and then, by combining and solving the constraints simultaneously, we derive the iteration equation for the optimal power and bandwidth. After that, the optimal client selection can also be achieved using a greedy algorithm. * We conduct extensive simulations using the MNIST to examine the effectiveness of the proposed joint optimization in utility of the network, accuracy rate and energy consumption. The rest of the paper is organized as follows. Section 2 introduces the system model. Section 3 describes the problem formulation. The proposed algorithm is then presented in Section 4. The simulation results are discussed in Section 5. Finally, the conclusion is drawn in Section 6.
## 2 Results and Discussion
### Comparison of Headspace Extraction Methods
#### 2.1.6. Dynamic Headspace Combining Multiple Car/PDMS SPME Fibers and Tenax (D-HS-MultiSPME/Tenax)  
Considering the complementarity of Tenax and Car/PDMS from SPME fibers towards the volatile compounds of human milk, the last strategy consisted of combining multiple SPME fibers and Tenax in a single cartridge under a dynamic extraction procedure.  
The volatile profile obtained from this D-HS-MultiSPME/Tenax extraction procedure was associated with the highest TPA compared to all other extraction procedures in the present study. Moreover, this method detects the highest number of compounds (\(n=53\)). This performance can be explained by the largest quantity of sorbents used in this method and the complementarity of Car-PDMS and Tenax polarity towards milk volatiles.  
This volatile profile is characterized by a large proportion of aldehydes (52%), terpenes (20%), alcohols (10%) and ketones (9%). The relative proportion of these chemical classes is very close to those obtained with the two other dynamic extraction procedures. Although detected to a small extent, esters and N-heterocycles are better detected with the D-HS-MultiSPME/Tenax procedure than with the other methods tested here. Moreover, this method identified one additional compound, the 2-methylpyrrole (LRI: 1575), not previously detected with the D-HS-Tenax or the D-HS-MultiSPME methods. From this chemical class, only pyrrole was previously identified in human milk [25]. This chemical class was largely reported in roasted food products [8] and may be traced to diet.  
Furthermore, this method efficiently recovers carboxylic acids, which were poorly extracted with Tenax only. Therefore, the association of the Car-PDMS SPME fibers with Tenax in the one-step D-HS-MultiSPME/Tenax procedure combines a large range of sorbent polarity and allows the powerful extraction of compounds from a large range of chemical classes, including carboxylic acids. Hence, it results in a more comprehensive profile than those obtained with the methods assessed previously.
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 2 Background
### Corrosion Detection Process
#### 2.3.2 Image Pre-Processing  
The second process is image pre-processing. Pre-processing is used to enhance the quality of raw images. Generally, raw images contains a great deal of unwanted background noise. As a result, several pre-processing techniques can be applied, such as histogram equalization, morphological operations, and median filters. These techniques can reduce and eliminate noise, distortion, and interference in the raw images.
# SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection
## 3 Methodology
### SPD-Conv Module  
The accuracy of object detection models is generally high when recognizing medium and large objects. However, the model's accuracy diminishes rapidly when it comes to identifying tiny objects in remote-sensing images. This decline can be attributed to the strided convolution module in the YOLOv8s backbone network. Although this module enlarges the receptive field and reduces parameter computation through downsampling, it inadvertently results in the loss of fine-grained information and less efficient feature representations. In contrast, SPD-Conv [51], instead of employing stride convolution layers and pooling layers, uses a space-to-depth (SPD) layer and a non-stride convolution layer. The SPD layer downsamples the feature map while preserving all information in the channel dimension, effectively eliminating information loss. This design strategy enhances the model's training performance by improving the network's learning capability.  
Processing of feature maps by SPD-Conv module when scale = 2, as depicted in Figure 1, the unprocessed feature map is shown in Figure 1a, Figure 1b indicates that the feature map is divided equally into four categories in the dimension of space. One color indicates one category, the vectors of the same color are concatenated together in the dimension of the space; that is, the feature map of arbitrary size S\(\times\) S \(\times\) C\({}_{1}\) is downsampled to generate four feature maps of size S/2 \(\times\) S/2 \(\times\) C\({}_{1}\), as illustrated in Figure 1c. Subsequently,these four feature maps are concatenated along the C\({}_{1}\) dimension, resulting in a feature map of size S/2 \(\times\) S/2 \(\times\) 4C\({}_{1}\), as illustrated in Figure 1d.  
Following the SPD feature transformation layer, a feature map of size S/2 \(\times\) S/2 \(\times\) C\({}_{2}\), possessing the necessary number of channels C\({}_{2}\) for the subsequent module, is obtained using nonstrided convolution, as illustrated in Figure 1e. This process effectively preserves all discriminative feature information.
# Multidisciplinary Applications of AI in Dentistry: Bibliometric Review
## 1 Introduction  
Artificial Intelligence (AI) has significantly reshaped modern healthcare, introducing groundbreaking enhancements in patient care and medical practice [1]. Its integration into diverse medical fields has not only extended human capabilities but also improved efficiency and accuracy in clinical settings [1]. In this technological surge, the field of dentistry has not remained untouched by the AI wave, witnessing transformative changes in various aspects of dental practice [2]. The integration of digital technologies in dentistry is advancing the frontiers of precision medicine at an unprecedented pace [3].  
While the field of dentistry has made considerable strides with the adoption of AI, certain challenges persist. Variability in diagnostic precision and the subjectivity inherent in treatment planning are prominent concerns that require attention [4]. AI stands as a robust solution to these issues, promising to strengthen decision-making processes, bring uniformity to clinical practices, and improve the quality of patient care outcomes.  
The growing volume of academic literature underscores the significant advancements in the theoretical and practical utilization of AI within dentistry. AI's applications are extensive and profound. This includes not only diagnosing oral pathologies, such as cancerous lesions, periodontal and periapical diseases, and caries, but also the conception of individualized treatment strategies in orthodontics, and the enhancement of precision in oral surgeries through guidance and positioning techniques [5; 6; 7].  
The goal of this study is to thoroughly review and synthesize a broad spectrum of literature from 2000 to 2023, applying a bibliometric approach to provide a comprehensive overview of AI's applications and impact in dentistry. The focus extends to understanding emerging trends and pinpointing significant contributions and advancements in the field; additionally, to showcase how AI technologies are effectively being utilized to enhance diagnostic accuracy, treatment planning, and prediction of treatment outcomes.  
The expected outcome of our work was to provide a comprehensive overview of AI applications within dentistry, offer valuable contributions to the scientific discourse, and inform further research and developments in this transformative field.
## 3 Damage Mechanisms of the Roadway's Surrounding Rock under Dynamic Pressure
### Analysis of the Damage Mechanism of the Roadway's Surrounding Rock under Dynamic Pressure  
By establishing a mechanical model of a roadway under nonuniform, dynamic pressure, a more in-depth study was conducted of the morphological evolution and distribution range of the surrounding rock's plastic zone under the influence of mining. Combined with the deformation characteristics of roadways under dynamic pressure, we have inferred that the primary factors contributing to significant deformation and failure of roadways subjected to dynamic pressure are as follows:  
1. Original rock stress conditions. The gas drainage roadway is buried approximately 400 m deep and is subject to relatively large stress on the original rock. After the excavation of the roadway, there has been a shift in the stress state of the surrounding rock from three-dimensional to unidirectional. Consequently, the surrounding rock is susceptible to damage and displays indications of localized fragmentation and loosening.
2. Development of fissures in surrounding rocks. The fractures in the broken surrounding rock develop rapidly under the influence of mining, eventually causing severe plastic damage. In addition, there are often various interlayers inside coal seams. The  
Figure 6: Distribution of the plastic zone subjected to horizontal dynamic pressure.  
shear and tensile properties of the interlayers are low, and they are prone to damage and sliding, resulting in layered protrusions.
3. Surrounding rock stress environment. During the mining process of close coal seams, the severe mine pressure caused by the excavation of the upper group of coal will seriously damage the stability of the gas drainage roadway and cause serious plastic damage. Under the influence of the advanced support pressure of the mining working face, the rock surrounding the roadway will be seriously damaged, and anchor cables cannot resist the pressure. In addition, the interlayer dislocation of broken surrounding rock will most likely shear anchor cables and other supporting components, leading to support failure and ultimately causing overall roadway instability and damage.
# Utilizing Energy Storage for Reliability Solutions in Active Distribution Systems
## 1 Introduction
Authors in Reference [13] have analyzed the reliability and economic aspect of the microgrid with renewable energy resources and storage from the system planning perspective. Works in References [3; 8; 9; 11; 13] either only considers supply interruptions or the methods proposed are not readily applicable in the context of utility-scale distribution network. References [5; 6; 7; 12] have taken into account the voltage sag/swell, however, generalized methods are lacking to incorporate the intermittency of renewable DGs and energy limitation of storages. Thus, the above-reported literature lacks the systematic approach to assess the role of ESS to mitigate short- and long-duration reliability events at a different level of a distribution system. Such a comprehensive assessment provides a basis to examine the cost-effectiveness of reliability-centric upgrades at the customer and system planning level. In this regard, this work explores the role of ESS, along with other resources, to mitigate reliability events that arise from random failures in the distribution network. Scenario-based probabilistic modeling of reliability event mitigation with the ESS is presented, which is integrated into the reliability evaluation framework. The proposed approach is efficient in assessing the reliability solutions with ESS considering storage technology type, power/energy rating, hardware availability, presence of other distributed energy resources, etc. The possible use of other resources to alleviate reliability/power quality issues is also discussed. A range of case studies is performed considering the ESS at an end-user premise, and the utility/microgrid-scale ESS to examine their potential to provide reliability solutions. Finally, valuable insights into the efficient utilization of ESS are provided based on findings.
## 3 Information Leakage Analysis of Merkle Tree-Based Authentication Schemes
### Previous Schemes and Their Vulnerabilities
#### 3.2.4 Merkle Tree-Based Authentication with Transmission in Encrypted Form
A comparison of the computation time required for Merkle tree-based authentication for different data sizes is presented in Figure 7, in which _Public setting_ refers to the time required for parameter generation by the trusted authority for Diffie-Hellman key agreement. As this approach is also based on a Merkle tree, the computation time increases as the volume of data increases. In addition, computational load for the prover and the verifier is also similar to that of the Merkle tree-based approach. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **100 B** & **1 KB** & **10 KB** & **100 KB** & **1 MB** & 10 MB & **100 MB** & **1 GB** \\ \hline \multirow{2}{*}{Seed generation} & 0.00065 & 0.00075 & 0.00092 & 0.00223 & 0.00313 & 0.00420 & 0.00822 & 0.01068 \\  & (0.00016) & (0.00018) & (0.00020) & (0.00061) & (0.00037) & (0.00054) & (0.00101) & (0.00119) \\ \hline \multirow{2}{*}{Pseudorandom bitstring generation} & 0.01106 & 0.01417 & 0.03730 & 0.28324 & 4.12432 & 44.29721 & 450.26368 & 4690.52351 \\  & (0.00085) & (0.00133) & (0.00351) & (0.02986) & (0.79537) & (8.69847) & (89.4971) & (488.92123) \\ \hline \multirow{2}{*}{Proof generation} & 0.04765 & 0.57035 & 7.20359 & 84.95004 & 1058.25365 & 12,523.93617 & 139,315.96462 & 1,666,976.31436 \\  & (0.00148) & (0.01614) & (0.09508) & (0.86894) & (0.23808) & (8.65474) & (236.08956) & (2663.86754) \\ \hline \multirow{2}{*}{Verification} & 0.00018 & 0.00019 & 0.00022 & 0.00032 & 0.00036 & 0.00036 & 0.00036 & 0.00100 \\  & (0.00014) & (0.00014) & (0.00013) & (0.00013) & (0.00013) & (0.00013) & (0.00014) & (0.00023) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average computation time (ms) for authentication based on a hardcore function by data size (standard deviation in parentheses). However, public setting in the initial stage requires a relatively high computation time even for 1 GB of data (although it is executed only once and requires a constant amount of time). Encryption, decryption, and key agreement also increase the computation time for both the verifier and the prover for exchanges of challenges and proofs, respectively. The detailed experiment results are summarized in Table 4. For 1 MB of data, key agreement accounts for 46.9% and 99.3% of the computation time for the prover and the verifier, respectively, while encryption and decryption uses only 0.2% and 0.5%, respectively.
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
###### Abstract  
Fault detection and isolation with redundant strapdown inertial measurement unit is critical for ensuring the reliability of the guidance or navigation system in the fields of both aeronautics and astronautics. Although the parity space approach is used widely, it cannot detect the soft fault which affects navigation performance under pulse quantization. This paper develops the three-channel filters to detect the soft fault and conducts theoretical implementation. The constraint conditions of their parameters are explored and the influence of the weight of different ratios is analyzed. The Monte Carlo simulation is carried out in order to verify the validity of the fault detection and isolation method. The simulation results and their analysis provide a theoretical reference for fault detection and isolation with redundant strapdown inertial measurement unit.  
fault detection and isolation; redundant inertial measurement unit; quantization error; three-channel filter
# Business Process Configuration according to Data Dependency Specification
## 5 Results: Transformation Applied to the Trip Planner Example
### Empirical Evaluation  
Since the generated imperative only depends on the declarative model, it is created only once at design time. The transformation performed involves a COP and two own-developed algorithms. Therefore, the critical points of our proposal lies in the resolution of the COP and these algorithms.  
Regarding the resolution of a COP, we use one of the existing commercial tools to evaluate the COP, since the COP resolution has remained a known problem studied by researchers in the area over several decades, and is supported by an important set of tools used in real problems. In general, the time to find the solution of a COP depends on: (1) the type of constraints (lineal, polynomial); (2) the number of constraints; and (3) the number and type of the variables. It is possible to find an analysis in [52] on the complexity of the NP-complete constraint problem resolution according to these  
Figure 13: Trip Planner Model Result.  
characteristics. The complexity of resolution of CSPs depends on the number of possible solutions of the problem, and on whether it is neither under constraint nor unsolvable. The most complex of these problems are those that are neither under constraint nor unsolvable. For these reasons, no affirmation can be given concerning the efficiency in a generic way of our proposal, since our declarative specification enables any type and any number of constraints; therefore the evaluation time depends on the specific problem. Depending on the number of constraints associated with a COP, and the number of variables, the COP evaluation time remains variable.  
On the one hand, in relation to Algorithms 1 and 2, the computational order is linear since it only depends on the number of activities. On the other hand, Algorithm 2 defines two recursive functions which are related between each other. The main idea of this algorithm is to apply a Divide-and-Conquer Method by means of reducing the problem in sub-problems, being the smallest problem, in our case, a single activity. Therefore, in both the best and the worst cases, the computational order is linear since it depends on the number of activities either they have a parallel or sequential relationships.  
With the aim of performing the evaluation, both algorithms are executed over a set of 50 generated test cases (see Table 3). Each test case establishes a different number of activities with a set of \(\mathbb{PDI}\) and constraints that relate them. On the one hand, as explained before, the number of activities determines the complexity of solving the problem. On the other hand, the number of \(\mathbb{PDI}\) and the constraints determine the structure of the final imperative business process.  
Figure 14 shows the computing time needed to solve both algorithms. The test cases are measured using a PC with an Intel Core i7-2675 QM CPU with a 2.2 GHz processor and a 8 GB of RAM.
## 4 Discussion  
Table 1 shows a performance summary and comparison with previously reported works. The noise efficiency factor (NEF) is calculated with current consumption, input-referred noise, and bandwidth for comparison of those amplifier performances [15]. The equation for calculating NEF can be expressed as Equation (9).  
\[\text{NEF}=\ V_{rms,in}\cdot\sqrt{\frac{2\cdot I_{tot}}{\pi\cdot UI_{T}\text{-} 4 kT\cdot BW}} \tag{9}\]  
\(V_{rms,in}\) is the input-referred noise, \(I_{tot}\) is the amplifier's total current consumption, \(4 kT\) is the thermal voltage, and \(BW\) is the amplifier bandwidth.  
The NEF of the proposed amplifier is 3.98 with amplifier GBW and noise integration bandwidth of 3 MHz, which is similar and comparable to previously reported works. The measured input-referred noise of 5.43 nV/Hz shows that the implementation of a BJT as the input of the amplifier can improve the noise performance of the amplifier.  
Figure 11: Slew rate measurement result of the proposed chopper operational amplifier.  
Figure 12: Transient measurement result of the proposed chopper operational amplifier with a buffer configuration.  
Figure 13: Transient measurement result of the proposed chopper operational amplifier with an inverting amplifier configuration.