esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterizing the Mechanical Performance of a Bare-Metal Stent with an Auxetic Cell Geometry
## 5 Conclusions  
Stents experience repetitive deformations due to the patient's routine activities, which may lead to failure. The bare-metal auxetic stent design evaluated in our study for mechanical behavior may improve longitudinal contraction, radial strength, and stent migration, and therefore may be a good candidate for clinical applications. However, although there are various in vitro studies on auxetic stents described in the literature, in vivo animal studies or clinical experiments on the biocompatibility of these stents have still not been performed yet. Therefore, further studies are needed to understand the benefits of auxetic stents.  
Conceptualization, S.K.B., H.L., M.B.G.J. and S.M.W.; methodology, S.K.B. and H.L.; software, S.K.B., H.L., A.A.K. and Y.C.; validation, S.K.B., H.L., M.B.G.J. and S.M.W. formal analysis, S.K.B., H.L., M.B.G.J. and S.M.W.; data curation, S.K.B., H.L., A.A.K. and Y.C.; writing--original draft preparation, S.K.B. and H.L.; writing--review and editing, S.K.B., H.L., M.B.G.J. and S.M.W.; project administration, S.K.B.; funding acquisition, S.K.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK), Grant No: 214 M377.  
Not applicable.  
The research facility and support of the Bursa Technical University, Bursa, Turkey is greatly acknowledged. The fabrication of samples was performed at the laboratories of University of Victoria, BC, Canada. The authors also acknowledge the peer reviewers and editors for their helpful suggestions.  
The authors declare no conflict of interest.  
Figure 6: Failed stent samples under: (**a**) three-point bending; (**b**) torsion; (**c**) longitudinal compression; and (**d**) lateral compression.
## 4 Software/Hardware Overview
### Hardware Overview  
The control of _Serpens_ is dependent on feedback from the actuators regarding position, velocity and torque. This feedback must be provided by the low-level controller of each actuator. Nowadays, there are several COTS actuators available in the market. However, only few commercial options provide precise current-based torque control and profile control for smooth motion planning. For the design of _Serpens_, the _Dynamixel XM430W-210T_, a COTS actuator produced by _ROBOTIS_ is selected for each joint module to meet these demanding requirements. This particular actuator provides the aforementioned data as well as additional feedback for temperature and input voltage. In addition to offering the required feedback, the _XM430W-210T_ has a study construction with full-metal gears and a metal body, while being able to deliver a stall torque of 3.0 Nm (at 12.0 V, 2.3 A) in a operating temperature of \(-5\)\({}^{\circ}\)-\(80\)\({}^{\circ}\)[28], which is considered sufficient in regards to the applications and the future development of _Serpens_. The chosen actuator communicates through a half duplex asynchronous serial Transistor-Transistor Logic (TTL) communication and also facilitate for daisy-chaining, which provides a simple connection structure for multiple actuators.For implementing the low-level control and the interception of feedback, a micro-controller is required at the joint level. Nowadays, different COTS options are available in the market. To facilitate the integration with the ROS-based architecture of _Serpens_, the _ROBOTIS OpenCM 9.04_ micro-controller is embedded in each joint module. The _OpenCM 9.04_ is a 32-bit _Cortex-M3_ core micro-controller compatible with ROS and with _Arduino_[32] software/hardware. This choice is also motivated by the limited physical space in the presented design of the joint module, therefore the form-factor of the _OpenCM 9.04_ (27 mm \(\times\) 66.5 mm) is a crucial parameter for the selection of this specific micro-controller for _Serpens_. In addition, since the high-level control can be centralised in a single-board computer (SBC) possibly located either in the head or in an external computer while the low-level control is distributed to the micro-controllers embedded in each joint module, the computing power provided by the _OpenCM 9.04_ is adequate for designated applications. The interface between the head module and each generic joint module is shown in Figure 8.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 2 Forecasting Method and Application
### LSSVM Optimized by Quantum Genetic Algorithm  
The Least Squares Support Vector Machine (LSSVM) replaces inequality constraints with equality constraints, regarding the sum of squared errors as experience losses of training set, transforming quadratic programming problems into linear equations [28]. The Radial Basis Function (RBF) is simple to calculate, requiring few parameters to be determined, and with strong generalization ability. The equation of the Radial Basis Function (RBF) is as follows:  
\[K(x_{i},x_{j})=\exp(-\frac{\|x_{i}-x_{j}\|^{2}}{2\sigma^{2}}) \tag{8}\]  
where \(\sigma\) is the kernel width. In this paper, the RBF kernel function is used as the kernel function of LSSVM. The regularization parameter \(\gamma\) and the parameter \(\sigma^{2}\) of the RBF kernel function have a great influence on the prediction accuracy of LSSVM. If the value of \(\gamma\) is too large or too small, the generalization ability of the system will be deteriorated. The value of \(\sigma^{2}\) will also affect the performance of the model. Therefore, choosing appropriate parameters can give LSSVM a good prediction effect. In this paper, the QGA is used to realize the adaptive selection of the parameters of LSSVM.  
The QGA is an intelligent optimization algorithm combining quantum computing and genetic algorithms, which was proposed by [PERSON] et al. [29]. The probability amplitude representation of qubits is applied to the coding of the chromosome so that a chromosome can express the superposition of multiple states. The operation of quantum revolving gate can update the chromosome, and thus the optimal solution of the goal can be achieved [30]. Compared with GA, QGA has the characteristics of small population size, strong optimization ability, high convergence speed and short calculation time [31].  
In GA, the commonly used coding methods for chromosomes are binary coding, real coding and symbol coding. In QGA, chromosomes are encoded using qubits [32].  
The state of a qubit can be expressed as:  
\[\left|\varphi\right\rangle=\alpha|0\rangle+\beta|1\rangle \tag{9}\]where, \(\alpha\) and \(\beta\) are complex constants and satisfy \(|\alpha|^{2}+\left|\beta\right|^{2}=1\). Qubits are used to store and express a gene. The gene can be in a \"0\" state or a \"1\" state, or any superposition state of them, which makes QGA have better diversity characteristics than GA. Multi-state genes encoded by qubits are shown in Equation (10).  
\[q_{j}^{t}=\left[\begin{array}{c|c|c|c|c|c|c|c|c|c
# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 3 System Model and Problem Formulation
### QuoTa Framework  
Different from existing work, QuoTa is equipped with both a quality detection module and a CPU frequency prediction module to mitigate the potential impact of malicious data owners and CPU frequency fluctuations on model convergence, respectively. The framework of QuoTa is given in Figure 1, as follows.  
* Step 1: The platform server broadcasts task specifications to each data owner, including (1) Data specifications: required for model training, such as pictures of cars, dogs, cats, birds, etc. Although the data provided by data owners are usually non-IID in a real scenario, the platform wants to find data owners whose data are equally distributed as far as possible for higher model accuracy (In general, the higher the degree of non-IID, the worse the quality of local model. Therefore, in model quality detection, for data owners with a high degree of non-IID, we will punish them and reduce their weight in model aggregation. More details will be shown in Section 4.1). (2) The bidding format that the data owner should submit to the server: the data owner's quotation, the data amount, and the rated CPU frequency. Note that the rated CPU frequency is known at the time of registration.
* Step 2: Each data owner submits a bid based on the bidding format and their spare resources. The bid submitted by a data owner \(i\) is: \(\mathcal{B}_{i}=\{b_{i},d_{i},fre_{i}\}\), where \(b_{i}\) is their claimed cost including computing and communication cost, \(d_{i}\) is their own data amount, and \(fre_{i}\) is their claimed rated CPU frequency.
* Step 3: Based on the received bidding information, the server selects winning a data owner's set within a limited budget, \(B^{t}\), based on their current reputation value and estimated CPU frequency \(f_{i}\) and then delivers the global model to selected data owners.
* Step 4: Each selected data owner uses its local resources to train the local model.
* Step 5: Each selected data owner uploads its local model parameter to the server.  
* Step 6: The server performs model quality detection on the uploaded local model, updates each data owner's reputation value, and uses reinforcement learning methods to estimate the CPU frequency of each data owner.
* Step 7: The server assigns different weights to different local models based on each data owner's quality indicator and then aggregates them into a global model.
* Step 8: The server pays reasonable monetary payment \(p_{i}(\mathcal{B})\) to each selected data owner \(i\).  
Let \(c_{i}\) denote each data owner's real cost. Thus, their utility is defined as \(u_{i}=p_{i}(\mathcal{B})-c_{i}\). The main notations are shown in Table 1. Next, we introduce data amount model and computing capability model, which affect the global model accuracy and model convergence time, respectively.
# Current-Sensing Topology with Multi Resistors in Parallel and Its Protection Circuit
## 3 Results  
### Experimental Circuit  
In order to verify the parallel current-sensing method and its protection circuit, a sensing circuit with five resistors was designed, as shown in Figure 13.  
There are two rectified mean-detection circuits and two comparison circuits in Figure 13. Define them as #1 and #2 for the circuits from top to bottom. The resistance values of the five sensing resistors and the current ranges covered are shown in Table 3.  
Three current ranges were covered by each sensing resistor. All \(I_{R}\) values were effective values. \(I_{PK}\) was the peak current covered by each sensing resistor. \(P_{R}\) is the rated power consumption for each resistor at the maximum covered current range. The design adopted a unified peak factor of 3. According to the value of the peak current, the maximum range  
Figure 13: Experiment circuit with 5 parallel sensing resistors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Resistor** & **Value (\(\Omega\))** & \(I_{R}\) **(A)** & \(R_{R}\) & \(I_{PK}\) **(A)** & \(P_{R}\) **(W)** \\ \hline \(R_{1}\) & 0.002 & 10/20/32 & 3.2 & 120 & 2.048 \\ \(R_{2}\) & 0.02 & 1.2/2.5/5 & 4.167 & 15 & 0.5 \\ \(R_{3}\) & 0.1 & 0.15/0.3/0.6 & 4 & 1.8 & 0.036 \\ \(R_{4}\) & 0.5 & 0.02/0.04/0.08 & 4 & 0.  
covered by \(R_{1}\) can be extended to 40 A. Due to the limitation of heat dissipation conditions for the sensing resistor, the maximum range is reduced to 32 A.  
In Table 3, the total range (ratio of maximum to minimum range for all resistors) is \(1.28\times 10^{4}\). Similar products that adopted multi sensing resistors in series have total range ratios of 333 and 4000, respectively [15,16]. In contrast, the parallel sensing method has advantages in expanding the dynamic range of current measurement. Through five parallel sensing resistors, the maximum range ratio \(R_{R}\) of a single resistor is only 4.167. The large dynamic range requirement of the power analyzer was divided by these sensing resistors. The performance requirements for amplification circuits were decreased. The hardware circuit is shown in Figure 14.  
In the hardware circuit, the whole acquisition module is shown and an enlarged image of the current-sensing board is included. Switches \(S_{1}\)\(\sim\)\(S_{5}\) were achieved through N-channel MOSFETs connected back to back, which have fast switching speed and low power consumption. Three wing-shaped heat sinks had been installed on the circuit board to improve the cooling capacity of the sensing resistors and switches. Two silicon carbide Schottky diodes \(D_{1}\) and \(D_{2}\) packaged in TO-247 were applied as the freewheeling diodes. They were installed on an aluminum heat sink on the back side.
## Materials and Methods  
### Materials  
The following reagents were purchased from commercial suppliers and, unless stated otherwise, were used as received. Acetonitrile, acryloyl chloride 97%, azobisisobutyronitrile 98% (AIBN), chloroform, ethylene glycol dimethacrylate (EGDMA), dichloromethane (DCM); 4-(dimethylamino)pyridine 99% (DMAP), divinylbenzene (DVB), hydrochloric acid 37%, methyl methacrylate (MMA) 99%, \(N\),\(N^{\prime}\)-disopropylcarbodiimide 99% (DIC), potassium dihydrogenphosphate, potassium hydroxide, sodium hydrogen phosphate, sodium hydroxide, and styrene (St) \(\geq\) 99% were obtained from Sigma-Aldrich (St. Louis, MO, USA). From other suppliers: 11-Aminoundecanoic acid 97% (Acros Organics, Geel, Belgium); ethyl acetate (J.T.Baker Chemicals, Phillipsburg, NJ, USA); deuterated dimethyl sulfoxide (DMSO-\(d_{6}\)) 99.8% Deutero, Kastellaun, Germany); hexadecane 98% (HD, Fluka Analytical, Seelze, Germany); (4-hydroxyphenyl)(dimethyl) sulfonium methyl sulfate (HPDMSMS, TCI Chemicals, Eschborn, Germany); and sodium polystyrene sulfonate (PES, Mutek, Herrsching, Germany); poly(diallyl dimethyl ammonium chloride) (PDADMAC, Mutek, Herrsching, Germany). Monomers were distilled under reduced pressure and stored under argon (Ar) at \(-20\)\({}^{\circ}\)C. All solvents used were of HPLC grade or higher. Phosphate buffers of pH 7.0 and 7.5 were prepared using potassium dihydrogenphosphate and sodium hydrogen phosphate solutions. Ultrapure water (TKA GenPure device, ThermoFisher Scientific, Waltham, MA, USA) was used to prepare all aqueous solutions and is hereby referred to as water.
###### Abstract  
Vertebral fracture (VF) may induce spinal cord injury that can lead to serious consequences which eventually may paralyze the entire or some parts of the body depending on the location and severity of the injury. Diagnosis of VFs is crucial at the initial stage, which may be challenging because of the subtle features, noise, and homogeneity present in the computed tomography (CT) images. In this study, Wide ResNet-40, DenseNet-121, and EfficientNet-B7 are chosen, fine-tuned, and used as base models, and a Bayesian-based probabilistic ensemble learning method is proposed for fracture detection in cervical spine CT images. The proposed method considers the prediction's uncertainty of the base models and combines the predictions obtained from them, to improve the overall performance significantly. This method assigns weights to the base learners, based on their performance and confidence about the prediction. To increase the robustness of the proposed model, custom data augmentation techniques are performed in the preprocessing step. This work utilizes 15,123 CT images from the RSNA-2022 C-spine fracture detection challenge and demonstrates superior performance compared to the individual base learners, and the other existing conventional ensemble methods. The proposed model also outperforms the best state-of-the-art (SOTA) model by 1.62%, 0.51%, and 1.29% in terms of accuracy, specificity, and sensitivity, respectively; furthermore, the AUC score of the best SOTA model is lagging by 5%. The overall accuracy, specificity, sensitivity, and F1-score of the proposed model are 94.62%, 93.51%, 95.29%, and 93.16%, respectively.  
C-spine injury; vertebral fracture; image classification; deep learning; ensemble learning; medical image analysis **MSC**: 68T07; 68T45
## 2 Materials and Methods
### Growth Assessment  
Cultures were analyzed daily by optical density measurement at 600 nm (OD\({}_{600}\)), in a Zuzi(r) spectrophotometer (Navarra, Spain) and microscopic observations (Zeiss AX10(r), Oberkochen, Germany). To determine biomass dry weight (DW), samples were filtered through 0.45-\(\upmu\)m cellulose filters (VWR, Amadora, Portugal), and washed with the same volume of an ammonium formate solution (61 g/L). Finally, they were dried at 120 \({}^{\circ}\)C in a Radwag(r) MA50.R moisture analyzer (Radom, Poland). The DW was calculated as represented in Equation (1).  
\[\text{DW}\left(\text{g L}^{-1}\right)=\frac{\left(\text{m}_{\text{f}}-\text{m} _{\text{i}}\right)}{\text{V}}. \tag{1}\]  
A correlation between DW and OD was obtained particularly for this strain as it is represented by Equation (2):  
\[\text{OD}\left(600\text{ nm}\right)=\left(1.8606\text{ }\times\text{ DW}\right)\text{ }-2.3048 \tag{2}\]  
Biomass productivity (P) was obtained by Equation (3) and specific growth rate (\(\upmu\)) by Equation (4), where DWf and DWi corresponded to the final and initial dry weights taken at the time points t\({}_{\text{f}}\) and t\({}_{\text{i}}\), respectively:  
\[\text{P}\left(\text{g L}^{-1}\right)=\frac{\text{DW}_{\text{f}}-\text{DW}_{ \text{i}}}{\text{t}_{\text{f}}-\text{t}_{\text{i}}} \tag{3}\]  
\[\upmu\left(\text{day}^{-1}\right)=\frac{\text{ln}\frac{\text{DW}_{\text{f}}}{ \text{DW}_{\text{i}}}}{\text{t}_{\text{f}}-\text{t}_{\text{i}}}, \tag{4}\]  
The maximum specific growth rate (\(\upmu_{\text{max}}\)) was calculated using Equation (4), but solely for the time interval of the exponential phase, i.e., considering the biomass concentration at the beginning and end of the exponential phase.
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 5 Policies
### Hyper-Cube Based Policies  
The policies are formalized by a number of rules, \(P_{p}\). The rules are formally defined as functions that assign decisions depending on subsets of conditions, C, that are either satisfied or not. The conditions are expressed either as subsets of states, \(S\), of the RF environment and radio characteristics or logical conditions of such states. A generic form of a rule can be formalized as:  
\[P_{p}:2^{C}\to D \tag{1}\]  
where the conditions \(C\) are combinations of set-based and logical conditions:  
\[C=S\cup L \tag{2}\]  
where \(S\) is an explicitly specified set of states, and \(L\) is a set of logical values; expressions of the form \(2^{L}\rightarrow\{T,F,U\}\), where \(T\) stands for logical _true_, \(F\) for _false_ and \(U\) for _unknown_.  
In a specific case, the sets can be intervals of dimensional values representing \(X\) and \(Y\) locations, with \(X_{i}\), \(Y_{i}\), time intervals \(T_{i}\), frequency ranges \(F_{i}\), transmit power \(W_{i}\) and other types of sets. An example of a spectrum access policy signature is:  
\[P_{p}:X_{i}\times Y_{i}\times T_{i}\times F_{i}\times W_{i}\times C_{i} \rightarrow\{a,d\} \tag{3}\]  
where \(\{a,d\}\) shows whether the policy is an _allowed policy_ or _restrictive policy_. \(P\) allows transmitting when the value is \(a\), while it disallows transmitting when it is \(d\).  
Examples of projections of this kind of policies are shown in Figure 8, where policy C is a restrictive policy, while the rest are allowed policies. This figure shows projections from 6D to 2D. In 6D, we refer to them as hypercubes or D-rectangles (in this case, D = 6).
# Diet, Polyphenols, and Human Evolution
## 4 Impact of the Diet Composition on Cognitive Functions
### Lipids  
The composition of the brain is 77-78% water, 10-12% lipids, 8% proteins, 1% carbohydrates, 2% soluble organic substances, and 1% salt [39]. Excluding water, lipids thus represent 50% of brain. More associations have been found for lipids than for any other components in studies relating to the influence of diet on brain function. The nervous system is the human tissue second in abundance in lipids and diversity in its composition. Some authors have associated the cognitive abilities acquired by human beings through evolution to the multiple functions of lipids in the nervous system: their structural role in biological membranes, participation as bioactive messengers involved in cell signaling and contribution to energy supply [40].  
Dietetic lipids can have both a positive and negative influence, as demonstrated in studies performed in humans or laboratory animals. Saturated fatty acids, which are mainly of animal origin, appear to promote a decline in cognitive abilities in elderly subjects [41; 42], as well as a reduction in the learning abilities in rodents [43; 44].  
On the other hand, an adequate intake of omega-3 fatty acids, and in particular docosahexaenoicenoic acid, DHA, seems to improve cognitive abilities in the elderly [38; 42; 45], including positive results in patients with mood disorders [46].  
Studies on the role of DHA in modulating cognitive properties have also identified its importance in the development of the nervous system of children [47; 48]. The current trend to supplement bottle feeding products with DHA comes from convincing evidence that a suitable intake of this molecule in infants and toddlers produces positive learning and behavioral outcomes [49]. DHA is present in high concentrations in fish, such as salmon, sardines, anchovies, mackerel and tuna (approximately 500-1900 mg/serving size). The DHA in such fish is beneficial for both the cardiovascular system and cognitive functions [45]. The Mediterranean diet is an excellent source of omega-3 fatty acids, both of vegetable and animal origin, and includes olives and their derivatives, seeds, and fish in smaller quantities.
# Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
## 3 Proposed Method
### Anomaly Scoring  
By minimizing objective function (15), the proposed framework can iteratively improve its ability to approximate the input attributed network better as the training converges. Instead of using objective function (15) as the indicator for anomaly scores, as seen in other graph autoencoder-based models, additional steps are introduced into the proposed model. For each node \(v_{i}\), its node degree in the original adjacency matrix is first obtained, denoted as \(D_{i}\). The next step takes the top-\(D_{i}\) values in the \(i\)-th row of the reconstructed \(\mathbf{A}^{rec}\) and sets these values to 1, while the remaining values in row \(i\) are set to 0. The updated reconstructed adjacency matrix is denoted as \(\widehat{A}^{rec}\), and the anomaly score for node \(v_{i}\) is determined according to the following formula:  
\[score(v_{i})=(1-\alpha)||A_{i}-\widehat{A}_{i}^{rec}||_{2}^{2}+\alpha||\hat{ \mathbf{X}}_{i}-\mathbf{X}_{i}^{rec}||_{2}^{2} \tag{16}\]  
Entities are ranked based on their anomaly scores, with a higher anomaly score indicating a greater likelihood of being considered anomalous.
## 4 Evaluation of Normalization Results
### Comparison of Each Pixel Normalization  
The ultimate purpose of radiometric normalization is to obtain spectral curve consistency at each pixel. In both R\({}^{2}\) and RMSE maps, hyperspectral library\(-\)based normalization methods have the lowest R\({}^{2}\) and the highest RMSE (Figure 8). Among the other methods, point\(-\)single and histogram matching have similar accuracy compared to polygon\(-\)single and polygon\(-\)multi. Except for pixels in the water spectrum (blue and dark blue in the R\({}^{2}\) histogram matching map) and artificial (brightly colored areas in the RMSE point\(-\)multi map), the pseudo\(-\)invariant polygon features\(-\)based normalization has significantly greater accuracy than other methods. Pseudo\(-\)invariant polygon features\(-\)based normalization has larger R\({}^{2}\) and smaller RMSE (Table 5). As the study area is an agricultural planting region, artificial pixels are few, slightly improving the accuracy of polygon\(-\)based normalization.