esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Effects of Different Mode Bond-Slip Fracture
### Investigation of Different Bond-Slip Failure Modes Based on FEA
#### 4.3.2 Splitting Failure Mode
In the analysis, the rotational deformation angle of the base reaches 0.03 at a 3.5% drift, with the corresponding rotational deformation accounting for approximately 85% of the total deformation. Consistent with the experiment, the RC column featuring small-diameter, high-density bar arrangements transition from the bending deformation mode to the rocking deformation mode during the hysteretic loading process. This is because a 2D model is used in this study, and all the reinforcements on one side were simulated with only one bar in the FE model. Therefore, when the bond failure occurs, resulting in a decrease in load-carrying capacity, bond failure occurs simultaneously in all reinforcements on one side However, only some of the reinforcing bars may undergo splitting failure and be pulled out of the footing, while the remaining bars still retain some load-carrying capacity. This is also the reason why in the simulation of the hysteresis curve the load capacity of the RC column on one side does not gradually decrease but drops suddenly after reaching the maximum load capacity. Further studies based on FE simulation analysis using 3D models are required to verify partial bond-slip failure on RC column performance. Table 4 presents a comparison of the maximum and minimum lateral force from the experimental and analytical results for Case-1, which employs the pull-out model, and Case-2, which employs the splitting model. For Case-1, the error range for the maximum and minimum horizontal loads is approximately 8% to 11% after  
Figure 31: Reproducibility of rotational deformation (Case-2). (**a**) Rotational Deformation Drift History. (**b**) Rotational Deformation Step History. Figure 30: Damage to Axial Reinforcements at Base of Columns by Analysis for Splitting slip model (Case-2). (**a**) South Side. (**b**) North Side. model. Meanwhile, for Case-2, which adopts the splitting model, the error rate for the maximum and minimum horizontal loads is maintained below 3%. As previously mentioned, although the pinching phenomenon in Case-1 is challenging to quantify, the analytical results reproduce the characteristics of the experimental results well when comparing the hysteresis curve contours. For Case-2, while the accuracy of the hysteresis curve reproduction can be further improved, the pinching phenomenon in the hysteresis curve, the asymmetric rotational deformation history, and the significantly different reinforcement strain histories in the north and south have been well reproduced to some extent. Therefore, we believe that the two bond-slip models proposed in this study, based on the form of bond failure, are both correct and effective.
## 3 Results and Discussion
### Analysis of Heavy Metals  
Tests for trace metals were carried out on 12 samples of mineral water collected in August 2018. The obtained results showed very different concentrations in the analyzed mineral waters. Table 6 presents the results of the metal composition tests carried out on the natural bottled water samples selected for the tests. \begin{table}
\begin{tabular}{c c} \hline
**Locality** & **Effective Dose Rate (\(\upmu\)Sva\({}^{-1}\))** \\ \hline Poland & 3.92 \(\pm\) 1.34 \\ \hline Japan [54] & 5.6 \\ \hline Portugal [55] & 4.27 \\ \hline Turkey [56] & 4.62 \\ \hline Iran [57] & 150 \\ \hline World [58] & 290 \\ \hline \end{tabular}
\end{table} 
Table 5: Average annual doses effective for adults from uptake with drinking water in mineral water samples. Figure 6: Annual effective dose for adults from the consumption of individual mineral waters. The obtained results are shown in Figures 7-13. The performed tests showed that in the mineral water samples selected for analysis, zinc concentrations ranged from 287.5 \(\upmu\)g/L in Amita water to 1300 \(\upmu\)g/L in Naleczowianka water. It should be noted that requirements for drinking water are not specified for zinc. Trace metals, defined as micronutrients and standardized in the MH Regulation of 2017, are copper, nickel, chromium, lead, cadmium, and mercury. The highest concentration of copper was in Slotwinka water, 400 \(\upmu\)g/L, and the lowest, 31.3 \(\upmu\)g/L, in Zywiecki-Krysztal water. It should be noted that in all mineral waters the value of 2 \(\upmu\)g/L Cu, specified in the ordinance of the [PERSON] of Health of 2017, was exceeded. Nickel concentrations ranged from 3.6 \(\upmu\)g/L in Laguna water to 37 \(\upmu\)g/L in Slotwinka water. In the case of nickel, the normalized values (20 \(\upmu\)g/L) were exceeded in only two waters: Slotwinka and Jozefowianka (31.8 \(\upmu\)g/L). The concentration of chromium and cadmium in all waters was below the acceptable value of 50 and 5 \(\upmu\)g/L, respectively. For Cr and Cd, the lowest and the highest concentrations were found for the same mineral waters: Jozefowianka, 0.7 and 3.4 \(\upmu\)g/L, respectively, and Slotwinka, 2.8 and 46.8 \(\upmu\)g/L, respectively. Mercury concentration in 11 waters was below the standard value (1.0 \(\upmu\)g/L). Only in the Amita water did we find an exceedance, 1.4 \(\upmu\)g/L. The greatest exceedances of the permissible value (10.0 \(\upmu\)g/L) were recorded for lead. The concentration range for Pb was 5.2-41.8 \(\upmu\)g/L. The highest concentrations were found in the following waters: Zuber--41.8 \(\upmu\)g/L, Jozefowianka--37.0 \(\upmu\)g/L, and Slotwinka--39.8 \(\upmu\)g/L. The best water for its low lead content was Franciszek.
## 4 Data Transformation Impact on Gray Values Compression
### Context Tree Weighting  
The context tree weighting method was first described by [PERSON], [PERSON], and [PERSON] in their 1995 article [48]. Context Tree Weighting (CTW for short) is an effective implementation of weighting the distribution of codes. The main components of the CTW codec are the CTW model estimator and the entropy encoder. The CTW model estimator is responsible for modeling the input stream, i.e., evaluating the probability of the next symbol occurring, while the entropy encoder uses the predicted probabilities to compress the input stream.  
A very important part of the CTW algorithm is the context tree, which is dynamically created during encoding and decoding. The context is defined as all previous symbols of an input stream that is being encoded. Each context that occurs is stored as a path in a contextual tree. The encoding process consists of four stages:  
* Searching for a path in the contextual tree that matches the current context.
* In every node in the contextual tree, the probability of the next symbol \(P_{e}\) is predicted using data that are stored in the node itself (estimated probability is calculated using _Krichevski-Trofimov_ estimation method or with _Zero-Redundancy_ estimation method);
* Calculating the weighted probability \(P_{w}\) on all \(P_{e}\) values;
* Sending the weighted probability to entropy coder.
# DIKWP-TRIZ: A Revolution on Traditional TRIZ Towards Invention for Artificial Consciousness
## 4 Mapping and Coverage Analysis of TRIZ Rules in DIKWP
### Mapping the 40 TRIZ Principles to the DIKWP Model
#### 4.1.8 Information (I) \(\rightarrow\) Knowledge (K)  
This process represents the cognitive subject's transformation of structured Information semantics into organized Knowledge. It highlights the importance of transitioning from Information to Knowledge and the application of this Knowledge in new informational domains. This transformation is a fundamental aspect of constructing Knowledge semantics. Below are several TRIZ principles related to this transformation process and their specific applications:  
* **Principle 15: Dynamism** The Information represents different semantics, and as information updates, the complete semantics corresponding to its knowledge represent our gradually clearer understanding of the world. Therefore, the transformation of information into knowledge represents the continuous expansion of the integrity of knowledge boundaries. And the dynamism of Principle 15 corresponds well to this point. For example, many studies on the prediction and characteristics of protein molecules are constantly expanding human understanding of the field of biomolecules [70; 71]. In order to record such dynamics, researchers have constructed corresponding databases to record these dynamic changes [72].
* **Principle 24: Intermediary** This principle in the conversion refers to the use of frameworks or models to transform information into knowledge, serving as a bridge between raw information and structured understanding. For example, in the field of education, developing a curriculum as a framework to guide student learning can help integrate dispersed learning materials into a systematic knowledge system [73].
## 2 Nutritional Characteristics  
### Approximate Composition of Artichoke  
There is a large variability in nutrients between the different botanical parts of artichoke (head, bracts, leaves and stem) [17; 18]. Genetic [19], harvest [20; 21], geographic [22] and environmental [23] factors also contribute to this variation. According to [PERSON] et al. [24], artichoke provides 79.73 kcal per 100 g edible portion, with it being considered a low-calorie food due to its high moisture content (approximately 80%) [25; 26].  
Carbohydrates represent up to 56.62 g/100 g (Table 1) of the dry weight (DW) of artichoke. However, a large part of this constitutes dietary fiber [27] (Section 2.4). Free sugars of a low molecular weight constitute a very small fraction of the total carbohydrates, with sucrose being the most abundant one followed by glucose and fructose [28].  
Artichoke head has a protein content of 24.27% DW (Table 1). However, the content of this macronutrient varies in other parts of the artichoke considered as by-products, with it being lower in leaves (8.74%) [27] and bracts (10.35%) [26]. In terms of amino acid composition, essential amino acids represent about 41.04% of the total amino acids, with aromatic amino acids (phenylalanine and proline) being the most abundant followed by the branched amino acids valine and leucine [29]. On the other hand, artichoke is able to fulfil the requirements of all amino acids, with it being a better source of essential amino acids than other vegetables such as carrots, tomatoes, cauliflower, corn or beans [30].  
Figure 1: (**A**) Cultivars of the “Blanca de Tudela” variety in the region of Murcia, Spain; (**B**) cross-section of an immature artichoke head; (**C**) mature artichoke inflorescence [7].  
The fat content of artichoke is 2-4% DW, with it being lower in the edible flower and higher in the leaves [26]. In addition, artichoke is characterized by a fatty acid profile rich in polyunsaturated fatty acids (PUFAs), representing almost half of the total fatty acids (44.5%). The most abundant unsaturated fatty acid is linoleic acid, with 55.20 mg per 100 g edible portion followed by palmitic acid (34.80 mg/100 g) and \(\alpha\)-linolenic acid (20.40 mg/100 g) [32]. Additionally, the edible part of artichoke displays a good PUFAs/SFAs (0.84) and n-6/n-3 (4.64) ratio [24], being in accordance with the parameters recommended by the Food and Agriculture Organization (FAO) [33]. Finally, triterpenes (fardiol and taraxasterol) and sesquiterpenes were found to be the major class of lipophilic components of cultivated cardoon and artichoke leaves [34]. Sesquiterpenes are responsible for up to 80% of the bitter taste of artichoke [35], with cyanoropicrin being the most abundant one [36]. These bioactive compounds have been reported as having anti-inflammatory and anti-hyperlipidemic activity [37].
## 5 Experimental Verification  
In order to further verify the effectiveness of the PQDs classification method proposed in this paper, an experimental platform for PQDs detection and classification is built. The overall scheme is shown in Figure 8. The experimental platform is built as shown in Figure 9. The experimental equipment table is shown in Table 6. The microcomputer relay protection test device is used to simulate the disturbance source to generate the disturbance signal, and the three-phase voltage is processed by the signal acquisition circuit to obtain the disturbances that conform to the chip voltage. The UKF-KELM classification method is applied to the hardware platform to classify the disturbances. The experimental part analyzes seven kinds of disturbances, such as voltage swell, voltage sag, voltage pulse, harmonics, harmonics + swell, harmonics + sag, and harmonics + pulse. The effective value of the output normal voltage is set to 30 V, and the frequency is 50 Hz.  
\begin{table}
\begin{tabular}{c c c} \hline  & **UKF-ELM** & **UKF-KELM** \\ \hline Training time/s & 0.0015 & 0.0026 \\ Test time/s & 0.0018 & 0.0401 \\ \hline \end{tabular}
\end{table}
Table 5: Algorithm training time.  
Figure 7: The success rate of voltage swell detection and recognition in different noise environments.  
17 V. The type of disturbance in Figure 10c is harmonics + swell, including third, fifth, and seventh harmonics. When harmonics occur, the amplitudes are stable near 15 V, 11 V, and 7 V, respectively, and the maximum fundamental voltage is up to 65 V. The disturbance type in Figure 10d is voltage swell, which does not contain harmonics, and the maximum voltage reaches around 59 V. The disturbance type in Figure 10e is pulse, without harmonics, and the maximum voltage impact value reaches about 170 V. The disturbance type of Figure 10f is harmonics + sag, including third, fifth, and seventh harmonics. When harmonics occur, the amplitudes are stable near 6 V, 4 V, and 2 V, respectively, and the minimum value of fundamental voltage drops to near 24 V. The disturbance type of Figure 10g is harmonics + pulse, including third, fifth, and seventh harmonics. When harmonics occur, the amplitudes are stable near 10 V, 6 V, and 4 V, respectively, and the fundamental voltage impulse value is near 120 V.  
The success rate of hardware platform classification is shown in Table 7. It can be seen from the table that the accuracy of UKF-KELM is very high when judging a single disturbance type, and it still maintains high accuracy in the face of complex multi-disturbance situations. The average success rate of UKF-KELM classification is 98%, which verifies the effectiveness of the PQDs classification method proposed in this paper.  
Figure 10: _Cont._
# Optimization Scheme for Modulation of Data Transmission Module in Endoscopic Capsule
## 3 Performance Analysis
### Complexity Analysis Comparison with MDCSK  
This section presents a comparison of the hardware computational complexity of MIP-MDCSK with that of the corresponding MDCSK schemes, all of which operate at the same modulation order (M). The details of the components involved in the transmission and reception processes for MIP-MDCSK, MCS-DCSK, and MDCSK are summarized in Tables 3 and 4, respectively. To achieve M-ary modulation, all schemes require the use of filtration twice, both at the transmitter and receiver, in order to generate orthogonal signals. Additionally, the complexity of MIP-MDCSK and MCS-DCSK systems is increased due to the implementation of position and Walsh code selection techniques, making them more intricate than the MDCSK scheme. Furthermore, the delay units in the transmitter and multipliers in the receiver of MIP-MDCSK are more sophisticated compared to those in the other schemes. As a result, the increased computational complexity in MIP-MDCSK is a trade-off for its enhanced BER performance.
# Cumulative Impact of Wind Farm Noise
## 3 Materials and Methods
The following equation presents the method of calculating the noise level of a single turbine in a receiver [63]:  
\[\text{Lp}=\text{Lw}-\text{Adv}-\text{Aatm}-\text{Agr}-\text{Abar}-\text{ Amisc}-\text{Cmet}\]  
Lp--the calculated noise (dB);  
Lw--a wind turbine sound pressure (dB);  
Adv--the attenuation due to geometrical divergence;  
Aatm--the attenuation due to atmospheric absorption;  
Agr--the attenuation due to ground effect;  
Abar--the attenuation due to a barrier;  
Amisc--the attenuation due to miscellaneous other effects;  
Cmet--meteorological correction. Since this is a conservative method, the following attenuation effects have not been taken into account: Agr, Abar, Amisc, and Cmet, so the total level of noise produced by a single wind turbine is calculated in the following manner:  
\[\text{Lp}=\text{Lw}-\text{Adv}-\text{Aatm}\]  
\[\text{Lp}=\text{Lw}-10\text{ log}(2\pi\text{r}^{2})-\text{ar}\]  
r--source-receiver distance (m);  
a--the atmospheric absorption (dB/m). The cumulative noise level of multiple wind turbines in a receiver is calculated as  
\[\text{Lcum}=10\text{ log}(\Sigma 10\text{ (Lp}/10))\]  
Having in mind the type of terrain in which the two wind farms used for modeling and presented here are situated (porous ground), this effect is negligible. If the modeling was done on extremely hard ground or an area covered in concrete, this effect would definitely be included in the calculation. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Zone**} & \multirow{2}{*}{**Purpose of Space**} & \multicolumn{2}{c}{**Noise Level, LAeq (dBA)**} \\ \cline{3-4}  & & **Day and Night * **} & \multirow{2}{*}{**Night**} \\ \hline
1 & & & \\  & heritage sites, large farms & 50 & 40 \\ \hline
2 & Tourist destinations, camping zones, school areas & 50 & 45 \\ \hline
3 & Strictly residential areas & 55 & 45 \\ \hline
4 & & & \\  & & &Basic parameters that need to be entered into the software for calculating the noise level are presented at the beginning of this section. Different calculations are possible depending on the input data (wind speed--average or maximum, different turbine types, etc.). In this way, a clear picture of noise level changes corresponding to different scenarios is provided, enabling proper decision-making in optimizing the project's development. A detailed explanation of the impact of the reflected sound in modeling by means of WindPro is given in the software manual [63]. The data generated by such modeling can be further analyzed to determine the average level of noise and identify all the areas in which the noise exceeds the recommended levels. Such information can be used in developing strategies for mitigating wind farm noise effects, such as adjusting the orientation of turbines or erecting noise barriers.
# Automatic Detection of Construction Workers' Helmet Wear Based on Lightweight Deep Learning
###### Abstract
To solve this problem, we designed an end-to-end one-stage convolutional neural network that achieves high accuracy while significantly reducing computational cost and can meet the real-time performance required for real-world detection. The main contributions of this paper are as follows:  
* The proposed object detection network for helmet-wearing utilizes GhostNet, a lightweight network, as the backbone feature extraction network. It uses its cheap operation to make the model lighter overall while ensuring efficient automatic feature extraction. * In the feature processing stage, we designed multiscale segmentation and feature fusion network (MSFFN) to improve the algorithm's robustness in detecting objects at different scales. In contrast, the design of the feature fusion network can enrich the diversity of helmet features, which is beneficial to the accuracy of helmet detection when the distance changes, viewpoint changes, and occlusion phenomena occur. * Our proposed lightweight residual convolutional attention network version 2 (LRCA-Netv2) is an improvement of the spatial attention module LRCA-Net proposed in our previous work. The main idea of the improvement is that by fusing the combined features along with the horizontal and vertical directions and then weighting the attention separately, such an operation can establish dependencies between the more distant features using precise location information. It has a good performance improvement over the previous module. * The mAP and FPS of the proposed lightweight helmet-wearing detection network evaluated on the combined dataset reach 93.5% and 42, respectively, improving our model in execution speed and accuracy compared to other methods.
## Materials and Methods  
### Patients and Sample Collection  
The study group consisted of 42 children aged from 6- to 18-years who sought treatment in the Pedodontics clinic of the Carol Davila University of Medicine and Pharmacy in Bucharest. Dental pulps (\(n=42\)) were collected from the permanent teeth of these patients (one tooth/patient). All parents/caregivers gave their informed consent for the collection of dental pulp from their children, according to the protocol approved by the ethics committee of Carol Davila University of Medicine and Pharmacy, document No. 5751/05.03.2021. Only healthy cooperative patients without any medical conditions were included in the study. Uncooperative subjects, or subjects whose caregiver did not sign the informed consent, were excluded. From each patient included in the study, dental pulp from a single tooth was collected. Clinically symptomatic, irreversibly inflamed dental pulp samples (experimental group, \(n=23\)) were collected from teeth with a history of prolonged or spontaneous lingering pain, extreme pain response to thermal stimuli (cold), sensibility on percussion, caries, and/or restorations on the tooth surface, from patients who sought emergency treatment. Radiographic examination did not reveal periapical pathosis, or periodontal diseases. Normal, healthy pulp samples (control group, \(n=19\)) were collected from clinically healthy permanent teeth extracted for orthodontic reasons. Anamnesis, clinical, and radiographic examinations were performed for the exclusion of pain history, caries, restorations, or any type of fracture.  
All dental pulp samples were collected in aseptic conditions. Before infiltration for local anaesthesia, all teeth were professionally cleaned of dental plaque, and teeth surfaces were also cleaned with 70% alcohol. A pulpectomy of teeth with irreversible pulpitis was performed only after the placement of a rubber dam. Caries and the roof of the pulp chamber were removed, and inflamed dental pulp tissues were collected using barbed broaches (VDW, GmbH, Munich, Germany). Irrigation of the endodontic space during pulp collection was performed only with sterile saline solution. Healthy teeth extracted for orthodontic reasons were grooved longitudinally using a #557 diamond bur and split using an elevator. Dental pulp was removed using a spoon excavator (LM Dental, Planmeca Group, Parainen, Finland). Only dental pulp samples that were removed in a single piece were used. All pulp samples were placed in Eppendorf Tubes containing 5 mL of saline solution and were stored at \(-70\)\({}^{\circ}\)C until use.
# High Performance Receiver Design for RX Carrier Aggregation
## 3 LNTA Topologies and Simulation Results
### Two-Stage LNTA  
Two-stage LNA is shown in Figure 7. The second stage is sized smaller so as not to load the first stage of the LNA. CA1 is a single-stage design, and its performance is comparable to the non-CA case.  
Figure 5: Receiver front-end architecture with two CA support. LNTA: low-noise trans-conductance amplifier.  
Figure 6: Three-coil BALUN LNTA.  
On the other hand, the CA2 path has two-stage active blocks, and even with large degeneration in 2 nd stage, linearity is poor due to the cascaded design approach [10]. NF for CA2 is primarily set by that of first stage design. Table 1 summarizes the simulation results with the two-stage LNTA approach. All of the performances are comparable to the reference (SISO), with the exception of CA2 path linearity (\(1 IP_{3}\)).