esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of Core Stability, Athletic Performance, and ACL Injury Risk across a Soccer Season
## 3 Results  
Descriptive results with median and interquartile range are presented below for core stability (Figure 3), performance (Figure 4), and ACL injury risk tests (Figure 5).  
For core stability tests, a significant effect of the measurement time was found for the CKCLEST (\(X^{2}(3)=55.5\), \(p<0.001\)). Pairwise comparison revealed a significant increase in touches between PRE and START (\(p<0.001\)) and START and MID (\(p=0.003\)). Moreover, the endurance index of the CKCLEST significantly evolved over the training season (\(X^{2}(3)=13.8\), \(p=0.003\)). In particular, a significant increase in the endurance index was found between MID and END (\(p=0.044\)). For the LQ-YBT, results indicated a significant difference over the season (\(X^{2}(3)=48.8\), \(p<0.001\)). In particular, the LQ-YBT score was larger at MID compared with START and END (\(p<0.001\)). No significant differences were found for the lateral step-down test.  
A significant main effect was found for the 20 m sprint (\(X^{2}(3)=36.8\), \(p<0.001\)). Pairwise comparison showed a significant reduced sprint time between PRE and START (\(p<0.001\)) and an increased time between START and MID (\(p<0.001\)). Jump height demonstrated a significant influence of the measurement time (\(X^{2}(3)=9.46\), \(p=0.024\)). Indeed, post hoc analysis revealed a decrease in jump height between PRE and START (\(p=0.009\)). Significant differences were also found for shooting speed and accuracy (\(X^{2}(3)=13.8\), \(p=0.003\) and \(X^{2}(3)=15.6\), \(p=0.001\), respectively). Speed was significantly lower at END compared to MID (\(p=0.031\)) and accuracy was reduced during pre-season training (\(p=0.013\)).  
Figure 3: Boxplot of core stability test results at each measurement time representing (**A**) CKCLEST score for sets of 15 s, (**B**) CKCLEST endurance index, (**C**) LQ-YBT composite score, and (**D**) lateral step-down score. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\) indicate significant difference between two measurement times.  
Regarding ACL injury risk tests, a significant decrease in TH knee valgus (X\({}^{2}\)(3) = 11.0, \(p\) = 0.012) and CMAS (X\({}^{2}\)(3) = 27.6, \(p\) < 0.001) score was noted. Knee angle and CMAS score were significantly smaller between START and MID (\(p\) = 0.013 and \(p\) = 0.004, respectively). No differences between measurement times were found for TH trunk ipsilateral flexion.  
No significant correlation was found between any core stability variations between measurement times and the respective variations in performance and injury risk variables.
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 4 Results
### Reactive BCI
#### 4.1.1 Home Automation and Control  
[PERSON] et al. [14] developed the HoloSSVEP system that utilizes the HoloLens HMD's AR camera combined with an eye tracker to control a smart home. To record the EEG signals, a g.Nautilus headset was employed consisting of three electrodes. The EEG signal was filtered using a bandpass filter between 1-35 Hz and then processed with canonical correlation analysis (CCA). To evaluate the experiment, 12 subjects tried to control 4 different systems (office lights, window blinds, a TV, and a music player) with four different control options. Classification accuracy was the evaluation metric applied for this work. Although the accuracy of the system was high, users were concerned about the comfort of wearing two headsets. With a similar objective, [PERSON] et al. [15; 29] implemented a home appliance control system by combining EEG-based BCI with HoloLens AR HMD. They tested four different stimulus types (three under an AR environment and one on an LCD monitor). A BioSemi ActiveTwo system with 33 electrodes was used to record the EEG data. Then, the EEG data were downsampled to 512 Hz, and a bandpass filter was applied between 2 and 54 Hz. The multivariate synchronization index (EMSI) was employed for the classification process. To evaluate their experiment, 17 participants took part in the online experiment, which consisted of controlling three home appliances with four available commands. Classification accuracy and information transfer rate (ITR) were the evaluation metrics for this experiment, with respective values of 92.8% and 37.4 bits/min. In a later attempt, they also integrated an eye tracker based on electrooculograms (EOGs) in their system. The system's performance and usability were assessed with 13 individuals over the age of 65. The EEG data were collected from 12 electrode positions using a BioSemi ActiveTwo system equipped with 12 active electrodes. Afterward, the data were downsampled at a rate of 512 Hz and subjected to bandpass filtering with cutoff frequencies of 2-54 Hz. An EMSI algorithm was used to classify the SSVEP responses. In this experiment, the evaluation criteria used were classification accuracy and ITR, achieving values of 88.8% and 34 bits/min, respectively.
# _k_-Labelsets Method for Multi-Label ECG Signal Classification Based on SE-ResNet
## 2 Materials and Methods
### Proposed Model Architecture  
The architecture of the model proposed in this study is shown in the Figure 3 and is basically based on the RAKEL algorithm [20]. In RAKEL algorithm, let \(L\) be a set of \(l\) labels in a multi-label classification problems. In the ECG signal classification problem in this study, \(L=5\). The RAKEL method creates \(m\)\(k\)-subsets of \(L\), such as \(L_{1}\), \(L_{2}\), \(\dots\), \(L_{m}\). The existing RAKEL model randomly selects \(m\) subsets, but since this study uses all subsets, \(m=\binom{5}{5}\). Original labels on all samples were translated into new labels based on labels in a set, resulting in one new label for each sample. Then, using samples with new labels, a single-label classifier is constructed. In this study, the SE-ResNet-34 model, which showed the best performance among 8 ResNet and SE-ResNet models, was used as a single-label classifier. All developed \(m\) SE-ResNet-34 models were put into the RAKEL model. The label set for a query sample is determined by combining the outputs of \(m\) classifiers. The method of combining outputs uses a soft voting method that sums all output values and passes through the sigmoid activation function.  
Figure 2: The SE-ResNet structure is configured by adding SE block to ResNet used in this study.
# Fatigue Crack Growth in Managing Steel Obtained by Selective Laser Melting
## 3 FCG Analysis Based on Plastic CTOD Range
### Low Cycle Fatigue Test  
Experimental tests were performed at room temperature and in laboratory air environment, on a DARTEC servo-hydraulic testing machine equipped with a 100 kN load cell. The tests were conducted under axial total strain-controlled mode, with sinusoidal waves, using a constant strain rate (da/dt) equal to 0.008 s-1, and total strain ratios (R\({}_{\varepsilon}\)) of zero and total strain amplitude (\(\Delta\varepsilon/2\)) equal to 0.8%. Specimens were produced according to the specifications outlined in ASTM E606 [16], with a gage section measuring 15 mm in length and 8 mm in diameter (Figure 4a).  
The final surface finishing was obtained by high-speed mechanical polishing using a sequence of silicon carbide paper grades, P600-grit, P1200-grit, and P2500-grit, followed by 3 \(\upmu\)m diamond paste. A 12.5-mm strain-gage extensometer was attached directly to the specimen gage section, using rubber bands, to assess the stress-strain relationship during the test. A total of 41 loading cycles were obtained to failure. 200 samples were collected for each loading cycle, using a PC-based acquisition system. Figure 4b shows the stress-strain curve obtained. The material exhibits a mild cyclic softening behaviour from the beginning of loading until about 80% of total life.
## 4 Simulation and Discussion
### Optimal Gate-ON Time  
In order to obtain the optimal gate-ON time, the background photon flux of different intensities is selected. Signal photons are independent variables, and the optimal gate-ON time is obtained by numerical simulation. The results are shown in Figure 6. When the signal photon flux and background photon flux are weak, the gate-ON time is the main factor affecting the performance of the SPAD receiver, and the shorter gate-ON time will affect the photons incidence, the optimal gate-ON time is the symbol time \(T_{C}\).  
The output photon count of SPAD has an exponential relationship with the signal photon. Further increasing the signal photon flux, the SPAD output does not exceed the maximum count. Although the gated mode reduces the incidence of signal photons, it has little effect on the actual counting output, and the appropriate gate-ON time can suppress the incidence of background photons and improve the BER performance.  
When the signal light flux is low and the background light flux is high, the lower bound of BER is affected by the background light. Appropriate gate-ON time needs to be set to reduce the influence of background photons on communication performance and improve the performance of SPAD. When the signal light flux and the background light flux are high, the appropriate gate-ON time can effectively reduce the signal photons and the background photons, so that the signal photons and the background photons reach the appropriate ratio to enhance the performance of the SPAD. The results of Figure 6 can be explained reasonably by the above situation.  
Figure 4: The influence of gated-ON time on the performance of time-gated SPAD. (**a**) \(N_{g}=16\). (**b**) \(N_{g}=64\).  
When the signal and background photon is low, the optimal external gate-ON time is symbol time. As the signal photon flux increases, the optimal gate-ON time is less. On the one hand, the SPAD has the upper limit of the photon count. Excessive signal photon incident does not change the maximum count, and the gated mode reduces the signal photon incident, but the signal photon flux is sufficient, and the BER performance is less affected; on the other hand, the gated mode reduces the incidence of the background photon, alleviates the counting error of the SPAD and enhances the BER performance. In other words, the gated mode reduces the signal photon flux, resulting in the loss of BER performance, but it also reduces the background photon flux and improves the BER performance. By weighing the performance loss and performance gain caused by the gated mode, the optimal gate-ON time is obtained to achieve the optimal performance of time-gated AQ-SPAD. When the background photons flux is high, the lower bound of the BER is determined by the background photons. The gated mode reduces the influence of the background light flux, improving the communication performance of the SPAD.  
Figure 5: Background photons and signal photons required to achieve the target BER. (**a**) \(N_{g}=16\). (**b**) \(N_{g}=64\).
# Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model
## 3 Results and Analysis
### Ablation Experiment
Traditional ELM models are relatively capable of capturing the relationship between humidity and ET\({}_{0}\) but less effective at capturing the more complex relationship between wind speed and ET\({}_{0}\). Adding multiple kernel functions in the MKELM model enhances the ability to handle complex nonlinear relationships, especially when wind speed features are included, showing a more significant improvement compared to traditional ELM models.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 4 Performance Evaluation
### Impact of Different Buffer of Nodes  
In this section, we consider the impact of buffer size on the delivery ratio, average latency and overhead ratio, respectively. We set the buffer size of the nodes from 10 MB to 100 MB, the results are shown in Figure 5.  
Figure 4: Comparison of different schemes. (**a**) Delivery ratio vs. node density. (**b**) Average latency vs. node density. (**c**) Overhead ratio vs. node density.  
From Figure 5a, we can see that when the buffer size increases, the delivery ratio of all four schemes increases accordingly. For RSNA, with the energy and buffer limited, we construct the delivery competency to predict the further relaying, and employ the forwarding willingness to jointly implement the message delivery decisions. Therefore, the delivery ratio of RSNA is highest. Compared with RSNA, the other three algorithms do not consider node attributes, so the delivery ratio is relatively lower.  
Figure 5b shows the comparison of average latency. Although the average latency of the four routing schemes decreases as the buffer size increases, the RSNA has the least average latency. For nodes in networks, the larger the buffer size, the more message copies can be retained, and the lower average latency. In Prophet, the nodes have to wait for an opportunity of higher delivery probability for message forwarding, thus the latency is the highest. Similarly, in Epidemic and SprayAndWait, the carrier nodes also need to wait for helper nodes, which increases the latency of message forwarding.  
As shown in Figure 5c, the overhead of RSNA is similar to SprayAndWait, much lower than that of Epidemic and Prophet. In SprayAndWait, the replications of any message are predefined, so the overhead ratio is also very low. Because the forwarding mechanism is based on flooding in Epidemic, it has a higher overhead than the other three routing algorithms.  
Figure 5: Comparison of different schemes. (**a**) Delivery ratio vs. buffer size. (**b**) Average latency vs. buffer size. (**c**) Overhead ratio vs. buffer size.
## 2 Simulation of InSAR Parameters, Power, Coherence and Phase
### Effect on Phase  
The influence of the forward scattering amplitude on phase, as depicted in Figure 14, shows that an increase in the forward scattering amplitude from 0.175 to 1.7548 corresponds with a rise in phase. Nonetheless, beyond the amount of 1.7548 (Phreal), additional increments in amplitude yield little alterations in phase.  
A comparison of this behavior with the Treuhaft model, commonly employed to elucidate the interaction between scattered electromagnetic waves and the medium's properties, reveals a similar trend. The Treuhaft model emphasizes the relationship between phase changes and scattering amplitude, especially in applications like radar interferometry and remote sensing [13; 20]. In this model, phase transitions are more pronounced at lower scattering amplitudes, whereas at elevated amplitudes (akin to the crucial value identified in the present study), the phase fluctuations stabilize. This comparison underscores the uniformity of the phase-amplitude relationship among various models, affirming that upon  
Figure 12: Forward scattering amplitude’s effect (p1–p9) on power.  
Figure 13: Forward scattering amplitude’s effect (p11–p18) on power.  
attaining a specific threshold (1.7548), the influence of augmenting the forward scattering amplitude on phase diminishes significantly, as anticipated by both empirical data and the [PERSON] model [20].
## 2 Methodology
### Biosignals Preprocessing  
**ECG**: The Kubios HRV software [54] is utilized for ECG preprocessing to quantify the HRV. It is an advanced software and extremely easy to use for HRV analysis. It consists of a mechanism for artifact correction (i.e., powerline interference, baseline wander and other noise components), trend component removal, an adaptive QRS detection and analysis for sample selection. The QRS detection algorithm in the Kubios HRV software was developed based on the Pan-Tompkins algorithm [55]. Figure 4a shows the patterns of the HRV.  
**EMG:** All the EMG signals were processed using MATLAB. There was a low amplitude voltage offset present in the hardware. The direct current (DC) power was eliminated. Then a bandpass filter was applied to the raw data to remove any movement artifacts with 20 Hz and 300 Hz cutoff frequencies. Since the raw EMG signal had positive and negative values [56], the full-wave rectification was applied to make sure the raw EMG signal did not have nearly zero mean and to analyze positive values only. The EMG signals linear envelope was then computed for further processing. Figure 4b shows the patterns of the EMG.
## 3 Materials and Methods
### Construction of a Privacy Named Entity Tag Set
Second, we refined the previously unified tag set into more specific categories to align with the personal information section. We divided PS_NAME into PS_NAME (name), PS_NICKNAME (nickname), and PS_ID (ID). The unique identification information, account number, and license plate number that were integrated under QT_OTHERS were subdivided into QT_RESIDENT_NUMBER (resident registration number), QT_ACCOUNT_NUMBER (account number), and QT_PLATE_NUMBER (license plate number), respectively. Third, we specified the tag sets that were previously vague in the personal information category. DT_OTHERS and OGG_OTHERS were specified as DT_BIRTH (date of birth) and OGG_CLUB (club/society). Fourth, we applied the remaining items similarly to the existing NER tag set and included QT_AGE (age), QT_LENGTH (height), TMI_EMAIL (email address), and OGG_EDUCATION (school). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Division** & **Named Entity Item** & **General Named Entity Tag Set** & **PNE Tag Set** \\ \hline
1 & Name & PS\_NAME & PS\_NAME \\
2 & Nickname & PS\_NAME & PS\_NICKNAME \\
3 & Date of birth & DT\_OTHERS & DT\_BIRTH \\
4 & Age & QT\_AGE & QT\_AGE \\
5 & Gender & - & CV\_SEX \\
6 & Height & QT\_LENGTH & QT\_LENGTH \\
7 & Weight & QT\_WEIGHT & QT\_WEIGHT \\
8 & Blood type & - & TM\_BLOOD\_TYPE \\
9 & Religion & OGG\_RELIGION & OGG\_RELIGION \\
10 & Nationality & LCP\_COUNTRY & LCP\_COUNTRY \\
11 & Club/society & OGG\_OTHERS & OGG\_CLUB \\
12 & Address & L\_IC & LC\_ADDRESS \\
13 & Place & LC, AF\_BUILDING & LC\_PLACE \\
14 & Resident registration number & QT\_OTHERS & QT\_RESIDENT\_NUMBER \\
15 & Alien number & QT\_OTHERS & QT\_ALIEN\_NUMBER \\
16 & Passport number & QT\_OTHERS & QT\_PASSPORT\_NUMBER \\
17 & Driver’s license number & QT\_OTHERS & QT\_DRIVER\_NUMBER \\
18 & Mobile phone number & QT\_PHONE & QT\_MOBILE \\
19 & General phone/FAX number & QT\_PHONE & QT\_PHONE \\
20 & Card number & QT\_OTHERS & QT\_CARD\_NUMBER \\
21 & Account number & QT\_OTHERS & QT\_ACCOUNT\_NUMBER \\
22 & Email address & TM\_EMAIL & TM\_EMAIL \\
23 & License plate number & QT\_OTHERS & QT\_PLATE\_NUMBER \\
24 & Workplace & OG & OG\_WORKPLACE \\
25 & Department & - & OG\_DEPARTMENT \\
26 & Job title/position & CV\_POSITION & CV\_POSITION \\
27 & School & OGG\_EDUCATION & OGG\_EDUCATION \\
28 & Grade & QT\_ORDER & QT\_GRADE \\
29 & Major & FD & FD\_MAJOR \\
30 & ID & PS\_NAME & PS\_ID \\
31 & URL & TMI\_SITE & TMI\_SITE \\
32 & IP information & - & QT\_IP \\
33 & Military unit & - & CV\_MILITARY\_CAMP \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Privacy named entity (PNE) tag set.
# A Seamless Image-Stitching Method Based on Human Visual Discrimination and Attention
## 5 Conclusions  
This paper has presented a stitching method for generating mosaic images consistent with the human visual system. The value of influence on stitching effects is quantified based on models of HVS. This method can locate a stitching line by quantifying the visual perceptions in overlapping regions, avoiding pixels which can produce a strong response to the human eyes, and the multi-band blending scheme is applied for smooth transition at the stitching line. Visual non-linearity, luminance difference, visual masking property and visual saliency are used to represent human visual discrimination and attention, and the weight map obtained through the combination reflects the human eye's perception of the image. Locating the stitching line in low-perception areas as much as possible can minimize the negative impact of discontinuities. To further prove the superiority of our proposed approach, we used images from the fields of architecture and remote sensing to compare our method with traditional methods. Experimental results show that the mosaic images processed by this method contain fewer detected stitching line and discontinuous edge pixels, which is more consistent with human visual perception. Different weight maps have a direct impact on the results. There are different situations in some applications, which may correspond to different optimal weight combinations. The quality of stitching images may be further improved if some deep-learning method is employed in the allocation of weights.  
Conceptualization, Z.S.; methodology, Q.C.; validation, Y.G.; writing--original draft preparation, Q.C.; formal analysis, P.W.; writing--review and editing, P.W. and Y.G.; project administration, Z.S.; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China under NO.61674115 and the National High Technology Research and Development Program of China under No.2012 AA012705. The authors declare no conflict of interest.
## 6 Security and Performance Analysis
### Implementation Result
#### 6.3.3 System Setup  
Exponentiations and pairing operations in the above algorithms for the encryption and decryption process account for a large portion of the computational overhead of CP-ABE systems; thus, we analyzed the encryption and decryption cost of our scheme. For the analysis purpose, we assumed that in our system, five attribute authorities are responsible for attribute management. In addition, it was assumed that each authority manages five attributes. Each attribute may have any one of the possible attribute values. We also implemented a secure signature scheme, which was used by CA to sign the user key, and AA use verified the algorithm to perform the verification of the key.
## 3 Results and Discussion
### EOP Hydrolysate Composition and Detoxification
Our results support this statement since, as seen in Table 1 and Figure 1, the treatment allows for very high reductions of HMF, furfural, and formic acid (100, 84, and 82%, respectively) and is able to significantly reduce the total phenol concentration (69%). On the contrary, acetic acid is only reduced on a very low level (only 4%), as activated charcoal detoxification is able to effectively reduce inhibitors, such as HMF, furfural, formic acid, and phenols, but there is not much evidence that this method is effective for acetic acid reduction [22]. Substantial furan and phenolic compound reductions by activated charcoal detoxification were also achieved from hydrolysates of the brewery's spent grain [23], sweet sorghum bagasse [24], olive tree pruning biomass [25], and corn pericarp [26]. Regarding the treatment with ion-exchange resin, as seen in Table 1 and Figure 1, a noticeable reduction of acetic acid content (37 versus 4%) and improvement in the results in phenolic compounds detoxification, compared with activated charcoal (85 versus 69%), was observed. Moreover, high reductions were also achieved in furans and formic acid (100% HMF, 69% furfural, and 80% formic acid). A high efficiency of ion-exchange resin detoxification to remove acetic acid and phenolic compounds was also observed in the detoxification of hydrolysates from rapeseed straw [19], cocoa pod husk [17], and corn cob [27]. Furthermore, considering sugar concentrations in the EOP hydrolysate before, and after, detoxification (Table 1), the two methods were effective for avoiding loss of sugars, with both cases lower than 8%. However, ion-exchange resin seemed to provide slightly better protection against sugar loss than activated charcoal. Taking into account the previous comments, the best method used in this work to reduce the presence of inhibitor compounds in EOP acid hydrolysate was the ion-exchange resin. Figure 1: Removal percentages of total sugars and inhibitor compounds in the acid hydrolysate after diverse detoxification methods. HMF: 5-hydroxymethylfurfural.