esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Local Energy Markets
### Design of LEM
#### 2.1.1. Auction-Based Designs  
Auction-based designs are concerned with individual participants' bids and offer details and the subsequent matching by an LEM coordinator. The bidding process enables all the prosumers to directly access the LEM. Bidding quantity, bidding price, and bidding price-quantity are the three variants of auction-based LEM designs.  
Figure 7: LEM benefits and challenges and corresponding beneficial and affected stakeholders [48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59].  
Bidding quantity: LEM auctions based on bidding quantity allow prosumer participation through the bid and offer process, which only includes information on the amount of energy being demanded or offered. For participants, individual profit maximization through iterative quantity can be targeted, whereas an LEM coordinator will be responsible for residual load minimization by matching the quantity bids.  
Bidding price: In this case, the LEM coordinator determines the bidding prices and adjusts the prices in an iterative process until price equilibrium is achieved. The participating prosumer targets profit optimization through iterative prices, while the LEM coordinator determines and matches the pricing.  
Bidding price-quantity: Bidding based on simultaneous price and quantity matching does not necessitate any iterative approach, as other related valuable information is also available in the prosumers' bidding details. The participants are responsible for individually determining the constraints' price-quantity function, where the LEM coordinator must communicate the prosumers' price valuation, time-coupling constraints, and flexibility potential, along with the amount of energy being offered or demanded.
###### Abstract
If the absolute position and absolute velocities can be measured, a full-state feedback control can be designed, such as a linear quadratic regulator (LQR). Because it is impossible to measure absolute position and absolute velocity, linear quadratic Gaussian (LQG) control is often resorted to, which is actually a Kalman filter. [PERSON] et al. [26] proposed a negative acceleration feedback (NAF) control algorithm for an active tuned mass damper (ATMD) in a situation where the displacement of the proof mass is accurately controlled using an AC servo motor, and the accelerometer signal is measured. The air spring and VCA described above are actuators that generate force. A ball-screw mechanism can be used to generate the desired displacement. However, displacement generating actuators cannot be installed parallel to spring dampers because the ball-screw mechanism acts as a lock when it is not working. In other words, displacement type actuator cannot be used in parallel active type. So, in this case, the case of connecting the linear actuator to the spring-damper in series has to be considered, as shown in Figure 1d. The serial type shown in Figure 1d is structurally simpler in reality than the parallel type shown in Figure 1c. However, there are very few studies on vibration isolation systems using serial active systems compared to parallel active systems. As the two are different types of systems, a new control algorithm suitable for the serial active system is also needed. In this study, we are interested in the control of lateral vibrations of the vibration isolation table and assume that the displacement of the table can be measured, and the displacement of the base plate (see Figure 1d) can be accurately controlled. In addition, new control algorithms so called position-input and position-output (PIPO) controls have been developed to cope with the mentioned problems. Theoretical and experimental result show the efficacy of the proposed serial-type actuator and the control algorithm.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### 3D Bin Packing Problem  
Parallel to the CLP, the 3D Bin Packing Problem (3D-BPP) exhibits notable similarities while featuring two fundamental distinctions. The first point of divergence lies in load balancing, a trait often shared between the Pallet Loading Problem and 3D-BPP. In these scenarios, containers are generally not designed to accommodate unbalanced loads, necessitating a focus on equitable weight distribution [15].  
Conversely, the second notable deviation pertains exclusively to 3D-BPP. This discrepancy arises from the existence of two primary variants: the offline 3D-BPP, which bears semblance to the Pallet Loading Problem, and the online 3D-BPP. The latter represents a dynamic perspective where items arrive sequentially, and real-time calculations are required for the positioning of each item, as they are not known in advance. This dynamic approach contrasts with the offline version, where all items and container information are available in advance. These distinctions underscore the unique challenges and considerations associated with the 3D-BPP, setting it apart from its container and pallet variations [15].  
In a recent study [16], notable variations in the Online 3D-BPP have been introduced, aptly named \"Non-Deterministic Ordered Packing\" (NDOP) and \"Quasi-Online Packing\" (QOP). NDOP encapsulates the intricate task of handling irregularly shaped items arriving in an unpredictable order. In CLP scenarios, the sequence for item placement is pre-defined, yet NDOP introduces an element of uncertainty into this sequencing, increasing the complexity. The main challenge is to efficiently pack odd-shaped items into containers while dealing with the uncertainty of when each item will arrive.  
In contrast, QOP tackles scenarios where the order of item arrivals is unknown, but it retains some flexibility in the packing process. Unlike immediate online packing, QOP accommodates the non-deterministic arrival sequence, aiming to optimise the packing arrangement with a certain degree of adaptability. This concept addresses the challenge of efficiently placing items into containers when dealing with unpredictable arrival orders, striking a balance between adaptability and efficiency [16].
# A High-Resolution Magnetic Field Imaging System Based on the Unpackaged Hall Element Array
## 2 Materials and Methods
### Construction of the Magnetic Field Imaging System  
The core component that makes up the magnetic field imaging system is the magnetic sensing probe module, as shown in Figure 5. This module consists of an unpackaged Hall linear array and a main processing module that includes a microcontroller, 32 channel multiplexers, switch modules, flexible flat cables (FFC) connectors, etc. In order to prevent the magnetic field from interfering with the operation of the chips in the main processing module, the sensor probe and this module were placed on separate PCBs. Power and signals were transferred between PCBs through FFC. Voltage limiting and anti-reverse connection functions have been added to the power supply interface of the circuit module to enhance system reliability and stability.  
Figure 4: System block diagram of the magnetic field imaging system.  
Figure 5: A magnetic sensing probe module. It contains the unpackaged Hall linear array and the main processing module.  
Figure 6 presents the magnetic field imaging system. The Hall linear array board is fixed underneath the slide, which is driven by the slide through the ball screw. A limit switch is fitted on each side of the ball screw for determining the position of the slide table and protecting the device. In this system, the moving trajectory of the slide table is controlled by the written software program and the maximum moving range of this slide is 15 cm \(\times\) 15 cm. The X-Y axis motorized rail with \(\pm\)10 \(\upmu\)m repeated positioning accuracy, effectively reduces the movement error of the slide table, allowing the Hall linear array to accurately measure each point to be detected in a magnetic field area. The distance between the Hall elements without packaging enclosures and the samples can be less than 1 mm, which can better show the magnetic field distribution on the sample surface.
# Uncertainty-Aware Adaptive Multiscale U-Net for Low-Contrast Cardiac Image Segmentation
## 3 Experimental Results Analysis
### Uncertainty Estimation  
We have also employed the Laplacian approximation method to estimate model uncertainty to enable a more robust prediction of our model which can help experts make decisions. Figure 11 highlights the areas where the model exhibits lower confidence along with ground truth and predicted regions. It can be seen that boundary regions exhibit lower confidence because these regions often pose challenges due to overlapping intensities or unclear anatomical delineation. Most of the segmented regions demonstrate low uncertainty, which reflects the model's high confidence in its predictions for the majority of the structure. The integration of uncertainty estimation enhances interpretability by identifying regions that may require additional manual verification. This analysis underlines the robustness of the model and the value of uncertainty estimation in improving trust and decision-making in critical applications.  
Figure 12 illustrates the calibration error for different levels of data shift intensity across various corruptions, including rotation, Gaussian blur, Gaussian noise, hue, brightness, and contrast for the baseline model and model with Laplacian approximation. As the intensity of the data shifts increases, the calibration error for both methods generally rises, which reflects the growing challenge of maintaining reliability under more severe corruptions. The baseline model consistently exhibits higher calibration errors across all intensities, which indicates a lower ability to adapt to data shifts. In contrast, the base model with Laplacian approximation demonstrates significantly lower calibration errors. This trend is particularly noticeable at higher intensities, where the Bayesian model maintains a  
Figure 10: The visualization results of different models along with FP and FN.  
more stable error range compared to the wider spread of the baseline model. The results suggest that the Bayesian approach using Laplacian approximation can be explored to improve the model's confidence calibration.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 1 Introduction
### Amino-Acids neurotransmitters
#### 1.1.5 D-Serine  
D-serine is a molecule released by glial cells to act as a neurotransmitter or neuromodulator [45]. The concept of a functional role of D-configuration amino acids in higher organisms like mammals is relatively new, since they were thought to be only used in less complex living organism forms such as bacteria or insects [46; 47]. It is present in various parts of the brain and immunohistochemical studies have shown its presence in the rostral cerebral cortex, hippocampus, anterior olfactory nuclei, olfactory tubercule, corpus striatum, and amygdala [46; 47; 48; 49]. More precisely, D-serine is localized in the protoplasmic astrocytes of the grey matter that ensheath synapses [46; 47].  
Studies have shown that D-serine is involved in the glutamatergic hypothesis of schizophrenia [47]. Studies with schizophrenic patients showed that D-serine-based treatment seems to be effective for positive and negative symptoms [50; 51].
## 5 XAI Analysis for GNSS Signal Disruption Classification  
### Data Preparation  
The dataset recorded from the Skydel simulator containing I and Q raw data was processed based on user-defined specifications from the RF front end. The samples were then prepared in both domains: the time domain and the frequency domain (by computing the FFT). As described in the previous section, the statistical features were generated and pre-processed (removing the missing values and duplicate records). The resulting dataset was subsequently partitioned into training and testing datasets.
# An Assessment Model for Air Passenger Risk Classification
## 3 Case Study
### Results Analysis  
According to the final calculation results of the fuzzy comprehensive evaluation, it can be found that passenger A's degrees of membership with regards to high-risk, medium-risk and low-risk levels are 54.55%, 34.64% and 10.81%, respectively. Based on the maximum membership principle, it can be judged that passenger A is a high-risk passenger. Similarly, passenger B's degrees of membership with regards to high-risk, medium-risk and low-risk levels are 54.98%, 30.76% and 14.26%, respectively. Therefore, passenger B is also a high-risk passenger. Passenger C's degrees of membership with regards to high-risk, medium-risk and low-risk levels are 52.29%, 29.08% and 18.63%, respectively, so passenger C is a high-risk passenger. Passenger D's degrees of membership with regards to high-risk, medium-risk and low-risk levels are 29.94%, 38.23% and 31.83%, respectively, so passenger D is a medium-risk passenger. Passenger E's degrees of membership with regards to high-risk, medium-risk and low-risk levels are 27.49%, 38.85% and 33.66%, respectively. Therefore, passenger E is also a medium-risk passenger. Passenger F's degrees of membership with regards to high-risk, medium-risk and low-risk levels are 14.77%, 18.43% and 66.80%, respectively, so passenger F is a low-risk passenger. Passenger G's degrees of membership with regards to high-risk, medium-risk and low-risk levels are 13.16%, 16.27% and 70.57%, respectively. Therefore, passenger G is a low-risk passenger. Passenger H's degrees of membership with regards to high-risk, medium-risk and low-risk levels are 16.25%, 15.78% and 67.97%, respectively, so passenger H is a low-risk passenger.  
It can be seen from the results that the evaluation results of the established assessment model for the eight passengers are consistent with the basic assumptions, indicating that the model can objectively evaluate the risk of passengers and accurately distinguish passengers with different levels of risk. Compared with the traditional fuzzy comprehensive evaluation method, this model is easy to operate, and does not require experts to evaluate each index of passengers one by one. The improved calculation method of single factor evaluation vectors solves the problem that the fuzzy relation matrixes are difficult to determine, and realizes the risk classification assessment of passengers.
## 1 Introduction
The surface coverage and roughness were increased with an increasing concentration of graphene, and the cell attachment of hMSCs on graphene-chitosan (GC) substrates was promoted. Although the proliferation of hMSCs on a 0.05% GC substrate was better than that on substrates with a higher concentration, the osteogenic differentiation of hMSCs was slightly accelerated on substrates with higher graphene concentrations (i.e., 0.5% or 5%). To further confirm the effects of graphene on the initial cell behavior, the expression of integrin beta-1 and Cx43 was investigated. Interestingly, the expression levels were greatly increased in the presence of graphene, indicating that the initial cell-substrate interaction and cell-cell communication were enhanced by graphene, which might lead to the promotion of osteogenic differentiation. These findings demonstrate the advantages of graphene for achieving good biocompatibility and bioactivity, which can further promote cell functions and behaviors at the optimal concentration. Therefore, graphene has been gradually incorporated into the design of bone-related implants. [PERSON] et al. [23] investigated the osseointegration of a chemical vapor deposition (CVD) graphene-coated titanium alloy using rabbit models. The animals that received the CVD graphene-coated titanium alloy showed an improved bone volume, mineral apposition rate, and new bone formation rate due to enhanced surface biological activity. [PERSON] et al. [24] demonstrated that the coating of graphene on titanium might suppress bacterial activity, and the surfaces that were completely covered with graphene could prevent not only the adherence of bacteria but also biofilm formation. In this study, we propose a rational design for the fabrication of graphene-based hybrid dental implants using a facile spin-coating technique. To this end, the fabricated hybrid dental implant was formed into graphene-incorporated structures, which provided suitable surface properties for promoting osseointegration, such as increased wettability and roughness. We further demonstrate that the changed surface properties of the hybrid implant can control the functions of both mammalian and bacterial cells, offering a new design for a dental implant.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 3 Results
### Meta-Analysis Output (Forest Plot)  
The results of the meta-analysis, illustrated in Figure 2, summarize the standardized mean differences (SMDs) with 95% confidence intervals (CIs) across the included studies.  
The forest plot (Figure 2) provides a visual summary of effect sizes from individual studies, with blue markers representing SMD values and black error bars indicating 95% confidence intervals. The observed variability in effect sizes aligns with the moderate heterogeneity reported in the analysis, but the overall trend supports significant sprinting-induced changes in muscle activity and biomechanics.  
The overall pooled effect size (SMD) = 0.68 (95% CI: 0.42-0.94), \(p\) < 0.001, indicated a moderate-to-large effect of sprinting speed and fatigue on muscle activation and sprinting biomechanics. Heterogeneity (I\({}^{2}\) = 61%) suggests moderate variability among studies, likely due to differences in participant characteristics, experimental setups, and sprint conditions. No significant publication bias was detected ([PERSON]'s test: \(p\) = 0.14), confirming the robustness of these findings.  
Figure 2: Meta-analysis results of muscle activity and biomechanics of sprinting. Data were extracted from studies [2; 9; 13; 16; 20; 29; 30; 31; 32; 33; 34; 35].
## 5 Results
### System Usability Scale Results  
The System Usability Scale (SUS) developed by [PERSON] [21] is a simply yet powerful and highly utilized scale for assessing the usability of a new product or service [22]. SUS can be used for measuring the usability of any interface [22], while its \"_reliability using coefficient alpha ranged from 0.83 to 0.97_, _averaging around 0.91_\" in a number of studies analyzed in [23] (p. 582). It consists of 10 closed-type questions, or best statements, based on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree) and a formula for calculating the overall SUS score. The 10 questions are the following:  
Q1. 1 like to use this game frequently.  
Q2. 1 find this game to be more complicated than it should be.  
Q3. 1 think the game is simple and easy to use.  
Q4. 1 need technical support to use this game.  
Q5. 1 find the game functions smoothly and the functions are well-integrated.  
Q6. 1 think there are a lot of irregularities in the game.  
Q7. 1 think most people can learn this game quickly.  
Q8. 1 find this game to be time-consuming.  
Q9. 1 feel confident while using this game.  
Q10. 1 think there are a lot of things to learn before I can start using this game.  
During the evaluation, all students appeared to understand the questions well, and there was no indication that any participant had difficulty understanding them. No additional explanations were required, suggesting that the translated questionnaire was clear to the students. Analyzing students' responses in the SUS questionnaire (Table 3) reveals that most of them found the game enjoyable and rated their experience positively. Specifically, the usability score according to the SUS scale was 79.4%, which is considered to be good [22].  
Additionally, the vast majority of students (11 out of 13) agreed or strongly agreed that they intend to play the game again (Q1), and more than half of the students (8 out of 13) agreed or strongly agreed that the game is simple and easy to use (Q3). Just one student considered the game to be too time-consuming (Q8).
# uDMA: An Efficient User-Level DMA for NVMe SSDs +
###### Abstract  
The Non-Volatile Memory Express (NVMe) SSD provides high I/O performance for current computer systems, and direct memory access (DMA) is the critical enabling mechanism for direct I/O. However, the lengthy I/O stack becomes a new bottleneck that degrades the potential of NVMe SSD. This paper reveals that existing user-level DMA introduces additional overhead for pinning memory used by DMA from the user space. Moreover, it cannot adapt to I/O requests of different data sizes. This paper proposes an efficient and dynamically adaptive user-level DMA (uDMA) mechanism that can adapt to I/O requests for different data sizes and lighten the I/O software stack by amortizing per-request latency. The critical component of uDMA is the pinned memory pool, which avoids frequently pinning new memory blocks by reusing allocated and pinned memory blocks. In addition, it effectively connects the discrete pinned memory blocks by the scatter/gather lists, improving the utilization of the pinned memory pool. Compared with the latest user-level DMA method, uDMA has an improvement of at least 17% under various data sizes.  
NVMe SSD; user-level DMA; scatter/gather lists; pinned memory pool Article uDMA: An Efficient User-Level DMA for NVMe SSDs +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Efficient Support Vector Regression for Wideband DOA Estimation Using a Genetic Algorithm
## 4 Performance Analysis
### Comparison of Training and Testing Times  
The primary objective of the proposed algorithm is to reduce the computational complexity of the wideband SVR model, thereby significantly decreasing its training time. To validate the efficiency improvement, we conduct a comparative analysis with the CWSVR method. An ASUS computer with an Intel(R) Core (TM) I7-9750 CPU is used as the simulation platform. Figure 10 illustrates the variation in training time and average testing time of 500 test samples as the number of sub-bands increases.  
As shown in Figure 10, the training and testing times of the CWSVR method rise with the number of sub-bands. In contrast, the training and testing times of the proposed algorithm remain stable. When the number of sub-bands exceeds 40, our algorithm offers significant advantages in both training and testing times. This is attributed to the fact that the input feature dimension in the proposed algorithm does not increase with the number of sub-bands. As a result, the scale of the SVR model remains manageable, which not only reduces the computational complexity but also contributes to improved performance. This approach ensures both computational efficiency and effective handling of multiband signals, thereby facilitating faster training and enhancing the accuracy of the DOA estimation. It is  
Figure 9: The direction-finding results and errors of the LFM signal with the proposed algorithm: (**a**) direction-finding results; (**b**) estimation error.  
worth noting that as the sample size increases, the efficiency advantage of the proposed method becomes more apparent.