esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results of Data Training and Discussion
### Neural Network Modeling
#### 3.3.4 F1-Score  
The F1-score combines accuracy and recall into a unified measure. It is particularly useful in scenarios where both metrics are significant and we are striving for a balance between them. It is calculated using Equation (7):  
\[F1-score=2\;*\;\frac{Precision\;*\;Recall}{Precision+Recall} \tag{7}\]In our case:  
\[F1-score=2\,*\,\,\frac{0.946\,*\,0.935}{0.946+0.935}\approx 0.941 \tag{8}\]  
The model predictions were compared with the actual classes in these matrices to analyze their performance. Each matrix was divided into four quadrants representing the four possible combinations of predictions and actual classes: true positive (TP), false positive (FP), true negative (TN), and false negative (FN).  
Starting with the overall confusion matrix (Figure 2(a)), we can observe that in 342 cases, the model correctly predicted the absence of kinesiophobia (TP), while in 282 cases, it correctly predicted the presence of kinesiophobia (TN). However, there are also 20 cases where the model incorrectly predicted the absence of kinesiophobia when it was present (FN), and 16 cases where it incorrectly predicted the presence of kinesiophobia when it was not present (FP).  
Analyzing the confusion matrix of the training set (Figure 2(b)), we observe that the model achieved higher accuracy in predicting the absence of kinesiophobia, with 251 cases correctly classified as negative (TP) and 197 cases correctly classified as positive (TN). However, there were also 6 cases incorrectly classified as negative (FN) and 8 cases incorrectly classified as positive (FP).  
Figure 3: Confusion matrix: (**a**) overall confusion matrix, (**b**) training set, (**c**) validation set, (**d**) validation set.  
Looking at the confusion matrix of the validation set (Figure 3c), we see a similar trend to the training set, albeit with slightly lower performance. There is a higher proportion of cases correctly classified as negative (TP = 47, TN = 42), but also a higher proportion of cases incorrectly classified as positive (FP = 4) and negative (FN = 6) compared to the training set.  
Finally, analyzing the confusion matrix of the test set (Figure 3d), we observe that the performance is similar to the validation set, with reasonably good accuracy in predicting the absence of kinesophobia (TP = 44; TN = 43), but a non-negligible number of cases incorrectly classified as positive (FP = 6) and negative (FN = 6).
## 4 Materials and Methods
### Mass Spectrometry Analysis (MALDI-ToF MS and MS/MS)  
The confirmation of the molecular ion was conducted using matrix-assisted laser desorption-ionization mass spectrometry (MALDI-ToF MS), employing two distinct matrices: HCCA and DHB. In this context, a Bruker Ultraflex MALDI ToF/ToF mass spectrometer was used. Peptide mapping was assessed by using a semi-saturated DHB solution (36 mg/mL) mixed with a 3:2 ratio of ACN: 0.1% TFA (in MilliQ water). In addition,a supra-saturated HCCA matrix solution was prepared in the same solvent. Following room-temperature drying, the sample was co-crystallized with each matrix in a 1:1 ratio on a 348-spot target plate. MALDI-ToF MS spectra were acquired in positive ion mode under defined conditions: 25 kV acceleration voltage, a delay of 140 ns, 50% grid voltage, and a low mass gate of 500 Da (500-1400 Da mass range). For MS calibration, a Bruker Peptide Calibration STD II containing nine reference peptides from 700 to 3500 Da was employed. Furthermore, the specific molecular ion underwent tandem mass spectrometry (MS/MS) using collision-induced dissociation (CID). The investigation of peptide fragmentation aimed to ensure precise data acquisition.
## 4 Materials and Methods
### Headspace Solid-Phase Microextraction  
The VOCs emitted were captured using HS-SPME. A 50/30 \(\upmu\)m divinylbenzene/ carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber (Supelco, Bellefonte, PA, USA) was selected for its broad adsorption range, covering both polar and non-polar compounds. A new SPME fiber was used and preconditioned by heating it to 270 \({}^{\circ}\)C for 1 h to remove any potential contaminants, ensuring sample integrity. The same SPME fiber was then used to perform all the analyses (blanks and samples). The SPME fiber was preconditioned by heating it to 270 \({}^{\circ}\)C for 1 h to remove any potential contaminants, ensuring sample integrity.  
To enhance headspace VOC concentration, the books were positioned upright with their central pages opened and slightly fanned out to maximize surface exposure within the chamber. The selected pages exhibited the highest textual density; neither volume included illustrations or distinct ink formulations. The selected pages exhibited the highest textual density; neither volume included illustrations or distinct ink formulations. The HS-SPME extraction was carried out at 20 \({}^{\circ}\)C for 24 h. This duration was selected based on the procedure described in [10], where a 24-h extraction was demonstrated to yield reproducible and representative signals across a wide range of VOCs.  
Prior to sampling, each desiccator was evacuated to remove any potential environmental contaminants and then sealed and left undisturbed for 24 h at an ambient temperature of 20 \({}^{\circ}\)C for a blank exposure.
# Experimental Study on Small-Strain Shear Modulus of Unsaturated Sility-Fine Sand
## 5 Conclusions  
In this work, based on an improved BE-RC test system for use in unsaturated soil, the small-strain shear modulus of unsaturated sand was investigated in the whole range of small strain (\(10^{-6}\)-\(10^{-3}\)) and the evolution law of the small-strain shear modulus and the internal particle-water-gas interaction mechanism, varying with the saturation level, are also elaborated upon using the dehumidification path. The main conclusions are as follows:  
1. In the range of small strain, with an increase in strain, the shear modulus of sand under different net stress levels gradually decays and tends to remain at a stable value under large strain. The higher the stress level, the faster decay of the shear modulus. At the same shear strain, the shear modulus of sand does not vary monotonically with the saturation but reaches the maximum value at optimum saturation.
2. An improved prediction model for the \(G_{max}\) of sand considering saturation variation was proposed, by which the continuity problem in the model at the optimum saturation was solved.
3. Based on the meso-scale morphological evolution of internal pore water in sand particles and the variation in intergranular stress caused by capillary action, the curve of \(G_{max}\) versus saturation was divided into three stages: the boundary effect stage, where saturation changes have almost no effect on the \(G_{max}\); the transition stage, where capillary action plays a significant role and the \(G_{max}\) gradually increases with decreasing saturation and reaches a maximum at the optimum saturation; the unsaturated residual stage, where the intergranular stress rapidly decreases and the \(G_{max}\) decreases to the level in the saturation state.  
[PERSON]; wrote the manuscript. [PERSON] and [PERSON]; developed the BE-RC test system for unsaturated soils. [PERSON] and [PERSON]; measured the soil-water characteristic curve of silty-fine sand in Hangzhou Bay, China. [PERSON] and [PERSON]; performed the combined BE-RC tests. [PERSON] and [PERSON]; wrote and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NATURAL SCIENCE FOUNDATION OF CHINA, grant number 51979269 and 52127815.  
The data presented in this study are available on request from the corresponding author.  
The authors declare that they have no conflict of interest.
## 2 Analysis of Modulation Process  
Before obtaining the roll angle from GPS signals, it is necessary to clarify the modulation process during antenna rotation, in order to figure out the true roll angle from the phases of roll modulation signals.
## 2 Materials and Methods  
Experiments were performed in two different phases that consisted of testing the kinematic accuracy of VMG30 recordings (Phase I) and studying VMG30 detection sensitivity when contacting objects (Phase II) while performing a set of tasks. VMG30 and CyberGlove II (both right gloves with 16 DoFs and 18 DoFs, respectively) were used to acquire data during the experiment.  
Both gloves recorded exactly the same 16 DoFs, according to the manufacturers, but CyberGlove also recorded flexion and deviation of the wrist. For this reason, this 16 DoFs present in both gloves were calibrated using the same validated across-subject calibration protocol [1] developed by our research group. This calibration protocol only has to be performed when acquiring the glove, contrarily to the calibration protocol recommended by the manufacturers, which has to be performed for each participant recorded. This protocol consisted of 44 recordings: static postures controlled with wood accessories (to obtain gauge gains) and controlled movements (to apply kinematic cross coupling corrections). Both gloves were calibrated by recruiting six participants with hand sizes between 160 mm and 200 mm, selected to achieve a sample representative of the average adult hand length. Participants were not the same for both gloves; therefore, a total of 12 participants took part in the calibration experiments, with a mean hand length of 177.2 (\(\pm\)12.3) mm for CyberGlove trials and 179.8 (\(\pm\)15.3) mm for VMG30 trials. The calibration procedure was performed three times for each participant, and the mean gains and coefficients from the calibration were defined as the mean values obtained across all the participants and trials for each glove.  
All the experiments were approved by the Research Ethics Committee with Human Beings (formerly Deontology Committee) of Universitat Jaume I (Spain), reference number CD/31/2019. All the participants were previously informed about the characteristics of the experiment and gave their written consent to participate.  
Figure 2: Same participant wearing a CyberGlove (left hand) and a VMG30 (right hand).
## 1 Introduction  
The greater tuberosity (GT) fracture is one of the common proximal humerus fractures. Around 20% of patients with proximal humerus fractures have GT fractures [1]. The GT is the attachment site for the supraspinatus, infraspinatus, and trees minor muscles of the rotator cuff. Shoulder functions deteriorate after the GT fracture has occurred. Avulsion-type and split-type GT fractures in which the fractured fragments are separated require orthopedic surgery for fracture reduction and fixation [2]. Locking plates or screw fixation are common treatment methods for the greater tuberosity fracture [3; 4; 5; 6]. However, the risk of the axillary nerve and deltoid muscle damage is increased during the treatment with locking plate fixation [3; 5; 6; 7].  
Studies conducted in the past have evaluated and compared various treatment methods for the GT fracture through clinical trials [3; 4; 5; 6], in vitro experiments [8; 9; 10; 11; 12; 13; 14], and computational simulations [15; 16; 17; 18]. There are, however, variations in fracture pattern and individual condition of patients, bone mineral density and geometry, and size of specimens that may affect the evaluation of various fixation strategies [19; 20; 21]. Furthermore, the shoulder joint has a high degree of mobility. Therefore, it is essential to consider shoulder movements as well when evaluating various fixation strategies for GT fractures. Biomechanical behaviors in the different fields of modern medicine have been extensively investigated by means of computer-aided design and finite element method [22; 23; 24; 25]. With the use of these techniques in the present study, the humerus GT fracture model with plate fixation was analyzed under mechanical testing loading and realistic rotator cuff muscle loading.  
The objective of this study was to compare, biomechanically, the effects of shoulder rehabilitation activities on the humerus GT fracture stabilized by a locking plate with rotator cuff function. A three-dimensional finite element model of the GT fractured humerus with a locking plate was modeled and analyzed numerically. To understand the impact of shoulder rehabilitation activities on plate fixation for GT fracture, we considered three types of shoulder rehabilitation activities including abduction, flexion, and horizontal flexion (Figure 1). The numerical model with a single traction force condition was validated by biomechanical tests. The validated numerical model was used to apply rotator cuff muscle forces to calculate displacement at the fracture site, bone stress, plate stress, and mean screw stress. The null hypothesis was that there would be no difference in the biomechanical behavior of the shoulder joint with GT fracture fixation with locking plates for the different rehabilitation activities.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 2 Background and Related Work
### OpenMote Hardware  
The measurements presented in this paper are performed using OpenMote, a modular open-hardware ecosystem designed for the industrial IoT [3]. The platform was developed at UC Berkeley and is designed to efficiently implement IoT standards such as 6 TiSCH.  
OpenMote-CC2538 is the core of the OpenMote hardware ecosystem. It is the most important component, and other components (e.g., the OpenBattery) are considered to be extensions of it. It features a Texas Instruments CC2538 System-on-a-Chip (SoC) that consists of a 32-MHz micro-controller with 32 kB of RAM and an IEEE 802.15.4-compliant 2.4 GHz radio.  
The OpenUSB version used in this article has a CC1200 radio chip. Unlike the CC2538, which has a 2.4 GHz radio, the CC1200 is a radio transceiver that operates in the 900-MHz range, e.g., the 868 MHz band in Europe. This allows for longer-range communication between the modes. As OpenUSB only holds the CC1200 radio transceiver, it needs to be connected to OpenMote-CC2538, which holds the microprocessor to control it.  
Currently, a new board called OpenMote B is being released. OpenMote B will be a next-generation, dual-band OpenMote device [4]. It provides a dual-radio interface for short- and longer range communication, combined on one board.
###### Abstract
A comprehensive study of all these parameters and their inter-relationships among them is necessary to understand VOO shelf-life. In the last decade, numerous authors have focused on tracking different compounds and parameters of VOO during its shelf life. The majority of these studies have aimed to monitor the changes in the quality parameters of VOO during the storage under different conditions and with different types of containers [5; 21]. Other authors have centered their studies on the loss of VOO healthy compounds, such as phenols, during the shelf life under different storage conditions [22; 23; 24]. Due to the changes in sensory characteristics that take place during shelf-life, some of the studies were based on volatile analysis during storage at different conditions [3; 8; 25; 26]. Some of these studies do not use conditions that are commonly used in a supermarket, where oils are exposed to room temperature and light/dark cycles of 12 h. Therefore, currently a disparity of results is observed when the volatile compounds and other parameters are evaluated during the storage of VOOs, with a resulting difficulty in the interpretation of oxidation processes at moderate conditions. Aroma has a strong influence on the consumer's rejection or acceptability of VOOs that have been stored for several months [7]. For this reason, stablishing a link between the chemical changes taking place in the oils during the storage and the sensory changes during this time is needed to predict better the 'best before' date of the oil. The aim of this study was to evaluate the VOO quality changes generated by the oxidation process to which the oil is inevitably subjected during its storage. Thus, a long storage experiment of 27 months was performed, period during which VOOs from three different cultivars were exposed under moderate conditions simulating a supermarket scenario. When moderate conditions of temperature and light intensity are used, the changes on sensory properties can be subtle and difficult to interpret. In order to facilitate this interpretation, in addition to the volatile composition and sensory assessment, chemical parameters directly or indirectly related to virgin olive oil quality were analyzed month by month. Furthermore, the oxidative stabilities of fresh oils evaluated with accelerated procedures (Rancimat and mesh cell-FTIR) were also considered. With all this information, the moments in which a remarkable sensory change take place was identified and explained by changesproduced in the volatile composition and the other indexes of quality. Finally, the results of stability assessment on the fresh samples carried out by Rancimat and mesh-cell FTIR were analyzed according to the development of the off-flavors during the storage of the four monocultivar samples.
# Knowledge Embedding Relation Network for Small Data Defect Detection
###### Abstract  
In industrial vision, the lack of defect samples is one of the key constraints of depth vision quality inspection. This paper mainly studies defect detection under a small training set, trying to reduce the dependence of the model on defect samples by using normal samples. Therefore, we propose a Knowledge-Embedding Relational Network. We propose a Knowledge-Embedding Relational Network (KRN): firstly, unsupervised clustering and convolution features are used to model the knowledge of normal samples; at the same time, based on CNN feature extraction assisted by image segmentation, the conv feature is obtained from the backbone network; then, we build the relationship between knowledge and prediction samples through covariance, embed the knowledge, further mine the correlation using gram operation, normalize the power of the high-order features obtained by covariance, and finally send them to the prediction network. Our KRN has three attractive characteristics: (I) Knowledge Modeling uses the unsupervised clustering algorithm to statistically model the standard samples so as to reduce the dependence of the model on defect data. (II) Covariance-based Knowledge Embedding and the Gram Operation capture the second-order statistics of knowledge features and predicted image features to deeply mine the robust correlation. (III) Power Normalizing suppresses the burstiness of covariance module learning and the complexity of the feature space. KRN outperformed several advanced baselines in small training sets on the DAGM 2007, KSDD, and Steel datasets.  
defect detect; small training set; knowledge embedding relational network; gram operation +
Footnote †: journal: Applied sciences
## 7 Results
### Results for Individual Classifiers
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{_Others_} & \multicolumn{4}{c}{_Normal_} \\ \cline{2-9}  & **Sens \%** & **Spe \%** & **Acc \%** & **Pre \%** & **Sens \%** & **Spe \%** & **Acc \%** & **Pre \%** \\ \hline
**Classification Algorithms** & _Others_ & **Global** & **Global** & **Global** & **Normal** & **Global** & **Global** \\ \hline \(L2 RLR\) & & & & & & & & \\ Test & 94.80 \(\pm\) 0.55 & 98.79 \(\pm\) 0.03 & 97.91 \(\pm\) 0.12 & 96.29 \(\pm\) 0.98 & 97.63 \(\pm\) 0.12 & 96.61 \(\pm\) 0.4 & 97.17 \(\pm\) 0.27 & 97.56 \(\pm\) 0.68 \\ \hline \(ANNC\) & & & & & & & \\ Test & 96.87 \(\pm\) 1.06 & 99.55 \(\pm\) 0.08 & 98.96 \(\pm\) 0.21 & 98.45 \(\pm\) 0.39 & 98.98 \(\pm\) 0.13 & 97.72 \(\pm\) 0.56 & 98.40 \(\pm\) 0.27 & 98.39 \(\pm\) 0.22 \\ \hline \(BAG\) & & & & & & & \\ Test & 95.66 \(\pm\) 0.31 & 99.87 \(\pm\) 0.02 & 98.95 \(\pm\) 0.07 & 99.60 \(\pm\) 0.10 & 99.39 \(\pm\) 0.19 & 97.61 \(\pm\) 0.09 & 98.57 \(\pm\) 1.31 & 97.71 \(\pm\) 0.04 \\ \hline \(KNN\) & & & & & & & \\ Test & 97.69 \(\pm\) 0.21 & 99.40 \(\pm\) 0.04 & 99.03 \(\pm\) 0.05 & 97.92 \(\pm\) 0.16 & 99.07 \(\pm\) 0.08 & 98.29 \(\pm\) 0.10 & 98.71 \(\pm\) 0.06 & 98.66 \(\pm\) 0.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for individual algorithms \(ANNC\), \(BAG\), \(L2 RLR\), and \(KNN\) using as input the \(TFRI\) used to characterize and detect _Others_ and _Normal_ classes. Comparing the results offered by the different algorithms, there is an important variation in the results for the sensitivities of \(VF\) and \(VT\) depending on the algorithm. For instance, if the sensitivity level for \(VF\) is high, the sensitivity for \(VT\) decreases. It can be observed that the \(KNN\) classifier achieves the best performance for the proposed methodology due to the adequate detection and discrimination capacity of \(VF\) when compared against the rest of classes. However, it has a high execution time because the \(KNN\) algorithm requires many iterations to calculate the closest distances. For the \(ANNC\) algorithm, the classes are separated by means of a surface that maximizes the margin among them, with the least number of training errors, having a computational cost much lower than that obtained with \(KNN\). The \(L2 RLR\) algorithm has less time of execution because it is based on probabilities. The Bagging creates its individual classifiers by training a system of classification on different bootstrap samples of the training set, thus retrieving a higher run time than the rest of the classifiers (except for \(KNN\)).
## 2 System Model
### Propagation Model  
The propagation model is inspired from [19; 24], wherein the average path loss model for air-to-ground communication can be given below in terms of the line of sight (LoS) links and non-LoS (NLoS) links.  
\[\begin{split} L_{\text{LoS}}^{k}=20\log\left(\frac{4\pi f_{c}d_{k}} {2}\right)+\eta_{\text{LoS}},\\ L_{\text{NLoS}}^{k}=20\log\left(\frac{4\pi f_{c}d_{k}}{2}\right)+ \eta_{\text{NLoS}},\end{split} \tag{1}\]  
where \(f_{c}\) is the carrier frequency and \(d_{k}\) is the Euclidian distance between the UAV and user \(k\), \(c\) is the speed of light, \(\eta_{\text{LoS}}\) and \(\eta_{\text{NLoS}}\) are the mean value of the excessive path loss for LoS and NLoS, respectively. The probability of LoS link is given as,  
\[P_{\text{LoS}}^{k}(\theta_{k})=\frac{1}{1+\psi\exp(-\varsigma(\theta_{k}-\psi ))}, \tag{2}\]  
where \(\psi\) and \(\varsigma\) are constant values depend on the environment, \(\theta_{k}=\frac{180}{\pi}\arcsin(\frac{h_{d}}{d_{k}})\) is the elevation angle. Besides, the probability of NLoS link can be calculated as  
\[P_{\text{NLoS}}^{k}(\theta_{k})=1-P_{\text{LoS}}^{k}(\theta_{k}). \tag{3}\]  
Therefore, the average path loss can be expressed as  
\[\begin{split} L_{k}(h_{d},\theta_{k})=20\log\left(\frac{4\pi f_{c }d_{k}}{2}\right)+P_{\text{LoS}}^{k}(\theta_{k})\eta_{\text{LoS}}+\\ P_{\text{NLoS}}^{k}(\theta_{k})\eta_{\text{NLoS}}.\end{split} \tag{4}\]