esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant Height Estimation in Corn Fields Based on Column Space Segmentation Algorithm
## 2 Materials and Methods
### Methods for Measuring Plant Height and Validation
#### 2.7.2 Validation of Measured Data  
Due to the large number of maize plants in the field, it was impractical to measure the heights of each plant manually. To validate the accuracy of the extracted point cloud height data, this study compared the extracted data of individual plants with the manually measured height data. The validation process involved the following steps:  
Figure 5: Flowchart of the extraction height.  
1. Sort and analyze curve consistency: Both datasets, the extracted point cloud height data and the manually measured height data, were sorted from low to high. The sorted data were then plotted on a curve graph to observe the consistency of their patterns;
2. Polar deviation definition and calculation: Polar deviation was defined as a metric to quantify the differences between the corresponding height values in the point cloud dataset and the manually measured dataset. It provides a normalized measure of the differences, allowing for comparison across datasets with varying ranges and distributions. If the curves were consistent, the polar deviation was calculated to assess the degree of similarity between the two datasets. If the curves were inconsistent, the raised parts of the curve were segmented for separate polar deviation calculations;
3. Further analysis based on polar deviation: If the polar deviation was close (indicating a high degree of similarity), the average height of both datasets was calculated in the same unit. If the polar deviation was significantly different, the deviation was analyzed in multiples of the unit;
4. Correlation analysis: For cases where the point cloud dataset contained more data points than the manually measured dataset, average heights were calculated in multiplier units. Correlation points between the two datasets, which referred to non-null values within the same unit interval, were extracted. After merging the null rows, the calculations were repeated. This process was continued until no further correlation points remained.  
The analysis of five test datasets demonstrated that the extreme differences between the point cloud height data and the manually measured data were minimal. Furthermore, the average heights calculated in the same units exhibited a high degree of consistency. These results validated the feasibility and accuracy of the maize population height extraction method using polar deviation as a metric to assess and quantify the consistency between the datasets. This method, based on multi-view geometric 3D reconstruction at the field scale, provides effective methodological support for high-throughput crop phenotype monitoring.
## 5 Discussion  
Our experimental results demonstrate that the proposed SLRBC can provide more accurate recognition performance by using a purpose-driven approach with the fusion of SoftLexicon and RoBERTa. We compare SLRBC with five baseline models: CNN-CRF [19]; BiLSTM-CRF [20]; BERT-BiLSTM-CRF [22]; SoftLexicon-BiLSTM-CRF [15]; and SoftLexicon-BERT [15]. By comparing CNN-CRF, BiLSTM-CRF, and BERT-BiLSTM-CRF, we prove the importance of lexical information in Chinese electronic medical record NER. Then, we compare SLRBC with SoftLexicon-BiLSTM-CRF and SoftLexicon-BERT, which belong to hybrid methods. The latter two only consider one or both representations, while our SLRBC considers basic character representation, SoftLexicon representation, and RoBERTa representation at the same time, thus achieving a more comprehensive representation. Moreover, the classical BiLSTM-CRF framework is adopted in our SLRBC, which helps to capture contextual information of entities more effectively.  
However, we also notice that SLRBC operates at a slower pace compared with other baseline models. This may be attributed to the fusion method adopted by the representation layer. In SLRBC, we use the simple concatenate method to fuse different representations. This approach preserves as much of the original information as possible but increases the dimensions of representation features. Although it is helpful to improve the performance of entity recognition, it also significantly increases the time and space cost. Therefore, different fusion methods can be explored to further improve SLRBC.
# Medical Applications of the GEMPix
## 8 On-Going and Future Developments
### Gemteq  
The GEMTEQ discussed in Section 6 is a version of the GEMPix specifically adapted for microdosimetry. The pixelated readout of the GEMTEQ is a key feature, which unlike the TEPC allows visualizing and analysing the full particle track. TEPCs are sealed detectors operating at low pressure so that the mean path length in the active gas volume is equivalent to typically a few \(\upmu\)m in tissue, simulating the cell size. The current version of GEMTEQ works with flushing propane-based TE gas at atmospheric pressure (the way GEMs are usually operated). We are currently designing a sealed version of the GEMTEQ (Figure 18), consisting of a vacuum chamber provided with a thin carbon window housing the detector, connected to a pumping station to adjust the operating pressure. The aim is to be able to run the GEMTEQ in sealed mode, to make it more portable, at pressure below atmospheric to achieve sub-micrometric spatial resolution and head towards microdosimetry at the sub-micron level.  
The spatial information obtained from the pixelated readout will be used to perform data analyses beyond standard microdosimetry, i.e., particle track analysis, and achieve track-structure microdosimetry. Upgrading the readout to the latest generation of Timepix (Timepix3 or Timepix4) will allow for measurements in a data-driven acquisition mode with simultaneous acquisition of charge and time-of-arrival information, for operation as a TPC. In this way, 3D particle tracks will be reconstructed and possibly track lengths and the change of the energy deposition along the track could be measured for each particle.
# The Application of Mesenchymal Stem Cells on Wound Repair and Regeneration
## 1 Introduction  
Regenerative medicine has developed tremendously in recent years, in fields like skin regeneration and wound healing. Age, traumas, tumors, congenital defects and other degenerative diseases are some of the reasons why this branch of medicine is becoming commonly used and under development [1, 2].  
Nowadays, diseases with difficult prognosis entail other approaches that can help improve outcomes. In addition, this approach can be used in a wide range of organ systems and contexts, namely dermal wounds [3, 4]. The main purpose is to replace or regenerate damaged tissues. Therefore, regenerative therapy applies differentiated cells, stem cells with specific characteristics, to regenerate damaged tissues [1, 2].  
Stem cells, being the future of regenerative medicine, are one of the most researched topics in the scientific community. These undifferentiated cells are capable of differentiation into different cell lines, as well as unlimited proliferation [1, 5].  
In matters of potency the only totipotent stem cells are those derived from the blastomere, being able to differentiate into every cell of the organism. The blastocyst-derived stem cells can differentiate into every cell, except the placenta, and are thus classified aspluripot. Cells originated from the fetus are considered multipotent, being capable of generating limited cell lineages [1,6].  
There are several types of stem cell and they are used due to their therapeutic potential, thought to derive essentially from their secreted cytokines [7,8]. These cells are capable of self-renewal and can be very useful in treating skin diseases where no other treatment was proven efficient, in chronic diseases and even in wound regeneration [7,8]. Stem cells can be used either directly in the damaged tissue or through scaffolds encapsulating these cells [1,9].  
Nowadays, skin diseases, specifically chronic wounds, require a lot of attention, as they are associated with very expensive and usually ineffective treatment processes. Therefore, the exploitation of the regenerative therapies' potential in this domain is very relevant [10].  
This review analyses the most relevant stem cell types used in skin regeneration and tissue engineering techniques used to deliver them. Also, a discussion on the importance and potential of the application of cell/scaffold-based therapies on wound healing, as well as other technologies like hydrogels and three-dimensional (3D) models, is addressed.
# Research Trends in Collaborative Drones
## 2 Distributed Computing with Drones
### Problem Description  
Network operators may require that drones broadcast messages announcing their identity and location while executing a given mission. Reasons can include identifying and isolating faulty drones, e.g., drones misbehaving due to failures or errors. Privacy issues deriving from the broadcast of unprotected identifiers on a wireless channel have been extensively reported in the literature. This calls for the use of the distributed computing paradigm to delegate the management of pseudonyms while guaranteeing anonymity [13]. Similar privacy-preserving authentication frameworks for Internet-connected vehicles have been proposed. Trends suggest that networked drones may interact with cloud services to generate, distribute, and report results. In such scenarios, edge cloud nodes connected to the cloud service and within the wireless range of collaborative dronesassure the mapping of pseudonyms to real identities. These nodes also correlate the collected information and communicate to the cooperative drones new findings, e.g., lists of misbehaving faulty drones.
# The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map
## 6 Conclusions  
A novel FCM learning approach for time series was proposed in this study. The model contains two stages. The first one is to construct an FCM model with the least square method, viz. LSFCM. The second one is to optimize the LSFCM with refinement of concepts to improve the prediction accuracy. Fuzzy c-means clustering is applied to fuzzify the given time series data set to automatically extract the FCM's concepts and fuzzy membership matrix. Two important contributions of the proposed method are learning the FCM with the least square method and refinement of concepts. The former can help the FCM learning eliminate the strenuous iterative computation. The latter can help the FCM obtain the optimal concepts by relocating the position of concepts. In addition, the stochastic strategy is applied to refine the concepts of the LSFCM. The influence of the parameters of the FCM on the prediction accuracy is analyzed. The number of concepts significantly impacts the prediction accuracy of the LSFCM prediction model. When the number of clusters is assigned to the optimal value, the ideal numerical prediction accuracy can be obtained. The parameters of activation function have no great effect on the prediction accuracy of the LSFCM model but have a big impact on the weight matrix. From the results of experimentation, LSFCM is a very competitive model for time series modeling and forecasting.  
Conceptualization, G.F.; formal analysis, G.F.; investigation, G.F. and W.L.; methodology, G.F. and J.Y.; supervision, J.Y.; validation, G.F., W.L. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 3 IoT Vulnerability Layers
### Hardware Vulnerabilities
#### 3.1.2 Hardware Reverse Engineering and Micro-Probing
Chip-level encryption with secure boot mechanisms ensures that even if the chip is physically accessed, its contents remain protected. Light sensors, power fluctuation detectors, and tamper-evident enclosures can be employed to thwart optical probing and laser fault injections. Additionally, fusing security-critical components after manufacturing can prevent reverse engineering by making chip-level access impractical [74].
## 3 Results and Discussion
### Antioxidant Capacity of Must and Wine
In white wine, flavonoids commonly constitute up to approximately 20% of the total polyphenol content, whereas in red wines, flavonoids account for over 85% of the phenolic substances. As flavonoids originate from the skins, seeds, and stems of grapes, their extraction yield during viniification is mainly influenced by factors such as temperature and length of skin contact. Therefore, the flavonoid content in red wine is approximately 20-30 times higher (>1000 mg/L) than in white wine [34; 35]. Some literature data [30; 33] indicate that white wines  
Figure 2: Antioxidant capacity (mmol Trolox/mL) by 2,2′-azinobis-(3-ethylbenzothazoline-6-sulfonic acid)] radical cation (ABTS®) (**A**) and ferric-reducing antioxidant power (FRAP) (**B**) in Aurore must and wine. F—wine after fermentation; W—wine after maturation. Yeast strain: 1—Spontaneous fermentation, 2—_S. cerevisiae_ SIHA® Cryarone, 3—_S. cerevisiae_ Challenge Arroma White, 4—_S. baganus_ SIHA® Active Yeast 4. Values (a, b, c) followed by the same letter were significantly different (\(p<0.05\)) according to [PERSON]’s test. are rich in polyphenols and have antioxidant properties similar to those of red wines and can therefore also help to prevent arteriosclerosis and coronary heart diseases. The antioxidant properties of the phenolic compounds have been widely studied but also the prooxidant effects of white wine on low-density-lipoprotein (LDL) oxidation have been reported [36]. Other investigators [37] found that white wine polyphenols were more effective than red wine polyphenols in the in vitro inhibition of LDL oxidation. For white wine polyphenols, the average Inhibitory Concentration (IC50) reached 1.7 uM, whereas it was 2.9 uM for red wines [37]. This finding was explained by differences in the polyphenolic composition of red and white wine, i.e., the predominance of polymeric phenols (tannins) in red wine and low-molecular-weight polyphenols in white wine. Additionally, some other factors are important, such as the absorption of a compound into blood circulation, metabolism and excretion, as these processes determine the health-promoting effects of polyphenols. It was often demonstrated that food-derived low-molecular-weight polyphenolic compounds are more beneficial due to their greater bioavailability and absorption. Current knowledge indicates that proanthocyanidin dimers and trimers are absorbed in their intact form and their absorption rates are less than 10% of (-)-epicatechin [38].
Sensors_**2012**, _12_, 806-838; doi:10.3390/s120100806  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Reliability and Availability Evaluation of Wireless Sensor Networks for Industrial Applications**  
**[PERSON] 1,\({}^{\star}\), [PERSON] 1, [PERSON] 2 and [PERSON] 3**  
\({}^{1}\) Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Campus Universitario 59078-900, Natal, Brazil; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) ISR, Department of Electrical and Computer Engineering, University of Porto, Porto 4200-465, Portugal; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) IDMEC, Department of Mechanical Engineering, University of Porto, Porto 4200-465, Portugal; E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +55-84-3215-3771; Fax: +55-84-3215-3738.  
_Received: 9 December 2011; in revised form: 5 January 2012 / Accepted: 10 January 2012 / Published: 12 January 2012_  
**Abstract:** Wireless Sensor Networks (WSN) currently represent the best candidate to be adopted as the communication solution for the last mile connection in process control and monitoring applications in industrial environments. Most of these applications have stringent dependability (reliability and availability) requirements, as a system failure may result in economic losses, put people in danger or lead to environmental damages. Among the different type of faults that can lead to a system failure, permanent faults on network devices have a major impact. They can hamper communications over long periods of time and consequently disturb, or even disable, control algorithms. The lack of a structured approach enabling the evaluation of permanent faults, prevents system designers to optimize decisions that minimize these occurrences. In this work we propose a methodology based on an automatic generation of a fault tree to evaluate the reliability and availability of Wireless Sensor Networks, when permanent faults occur on network devices. The proposal supports any topology, different levels of redundancy, network reconfigurations, criticality of devices and arbitrary failure conditions. The proposed methodology is particularly suitable for the design and validation of Wireless Sensor Networks when trying to optimize its reliability and availability requirements.  
keywords:dependability evaluation; wireless sensor networks; fault tree analysis; WirelessHART; ISA 100.11a +
Footnote †: journal: Journal of Power and Energy
## 2 Background
### Feedforward WaveNet Audio Effects Modeling Network--WaveNet  
The _WaveNet_ architecture corresponds to a feedforward variation of the original autoregressive model. For a regression task, such as nonlinear modeling, the predicted samples are not fed back into the model, but through a sliding input window, where the model predicts a set of samples in a single forward propagation. The feedforward Wavenet implementation is based on the architecture proposed in [9] and [46]. The model is divided into two parts: stack of dilated convolutions and a post-processing block. The model is depicted in Figure 2 and its structure is described in Table 3.  
Figure 2: Block diagram of the feedforward _WaveNet_: stack of dilated convolutional layers and the post-processing block.  
We use 2 stacks of 8 dilated convolutional layers with a dilation factor of 1, 2,..., 128 and 16 filters of size of 3. From Figure 1, prior to the stack of dilated convolutions, the input \(x\) is projected into 16 channels via a 3 \(\times\) 1 convolution. This in order to match the number of channels within the feature maps of the dilated convolutions.  
The stack of dilated convolutions processes the input feature map \(\mathbf{R}_{in}\) with 3 \(\times\) 1 gated convolutions and exponentially increasing dilation factors. This operation can be described by:  
\[z=\tanh(\mathbf{W}_{f}*\mathbf{R}_{in})\cdot\sigma(\mathbf{W}_{g}*\mathbf{R}_{in}) \tag{3}\]  
Where \(\mathbf{W}_{f}\) and \(\mathbf{W}_{g}\) are the filter and gated convolutional kernels, _tanh_ and \(\sigma\) the hyperbolic tangent and sigmoid functions and \(*\) and \(\cdot\) the operators for convolution and element-wise multiplication. The residual output connection \(\mathbf{R}_{out}\) and the skip connection \(\mathbf{S}\) are obtained via a 1 \(\times\) 1 convolution applied to \(z\). Thus, \(\mathbf{S}\) is sent to the post-processing block and \(\mathbf{R}_{out}\) is added to the current input matrix \(\mathbf{R}_{in}\), thus, resulting in the residual input feature map of the next dilated convolutional layer.  
The **post-processing block** consists in summing all the skip connections \(\mathbf{S}\) followed by a _ReLU_. Two final 3 \(\times\) 1 convolutions are applied to the resulting feature map, which contain 2048 and 256 filters and are separated by a _ReLU_. As a last step, a 1 \(\times\) 1 convolution is introduced in order to obtain the single-channel output audio \(\hat{y}\).  
Since the receptive field of the model is of 1022 samples, in order to output frames of 4096 samples, the input presented to the model consists of sliding frames of 5118 samples.
## 3 Materials and Methods
### DLCTC Model
#### 3.2.7 Softmax Layer  
In the proposed DLCTC model, the softmax layer plays a crucial role by converting the final feature representations into class probabilities for short text classification. After processing through the embedding and representation layers, the model outputs logits for each class, which the softmax function normalizes into probabilities that sum to 1. This normalization allows the model to determine the likelihood of a short text being classified as \"traffic-related\" or \"traffic-related\" by providing a probabilistic interpretation of the model's confidence. For instance, if the softmax layer outputs a higher probability for \"traffic-related\", the text is classified accordingly. This probabilistic output not only facilitates decision-making but also helps evaluate the model's performance and manage challenges such as data imbalance.
## 2 Materials and Methods  
### Soil Sampling and Soil Analysis  
Heavy metals contaminated agricultural soil was collected from the abandoned mine area near the Gahak mine in Gyeong-gi Province, Korea. Ten points of soil samples were collected using a hand auger and homogenized for a composite sample. After air dried, the soil was passed through a 2 mm sieve and stored in a polyethylene bottle until soil analysis.  
Soil pH, electrical conductivity (EC), total organic carbon (TOC), exchangeable cations (Ca, K, Mg, and Na) were analyzed, followed by the Method of Soil and Plant Analysis [17]. Soil particle distribution was analyzed using the wet sieve method [18]. Aqua regia solution (HNO\({}_{3}\):HCl = 1:3) was used to determine the total concentration of heavy metals (As, Cd, Cu, Ni, Pb, and Zn) in soil followed by ISO 11466 [19]. Sequential extraction analysis was performed to find out the binding form of heavy metals in the soil. In this study, the three-step BCR (The European Community Bureau of Reference) method devised by [PERSON] et al. [20] was used. According to the BCR method, carbonates binding form was examined using 0.11 M CH\({}_{3}\)COOH solution (agitation at 25 \({}^{\circ}\)C for 16 h). In addition, the Fe/Mn-binding form was determined using 0.1 M NH\({}_{2}\)OH-HCl (pH 2) solution (agitation at 25 \({}^{\circ}\)C for 16 h). After that, the organic/sulfide-binding form was analyzed using H\({}_{2}\)O\({}_{2}\) + 1 M NH\({}_{4}\)OAc (pH 2) solution at 85 \({}^{\circ}\)C until all organic matter was oxidized. The last residual form was determined using the aqua regia solution. Quantification of heavy metal concentration in solution was conducted using induced plasma coupled optical emission spectrophotometry (ICP-OES, Agilent, Santa Clara, CA, USA). Certified reference material (Montana Soil SRM2711, National Institute of Standards and Technology) and blank were examined to validate the quality control.
# Experimental Study of Wave-Induced Response of Piles in Seabed with Various Permeability
## 2 Testing Arrangements and Programs  
### Configuration of the Wave Flume  
The wave flume at Hohai University was used; Figure 1 shows the experimental layout and Table 1 lists all the tests. The wave flume had a length of 50 m, a width of 1 m (perpendicular to the page) and a height of 1.3 m. Regular waves were generated from the propeller at the left end and porous material was used at the right end to absorb the wave, thus reducing the wave reflection. The wave generator of the propeller was capable of generating regular waves with a wave period of 0.6 s-2.5 s and a maximum wave height of 0.2 m. A soil pit with a depth of 0.33 m below the flume bottom was located in the middle of the flume. Two trapezoid-shaped blocks were placed on the flume floor adjacent to the pit [21], so the depth of the soil sample was extended to 0.58 m, which was sufficient to accommodate the pile foundation and sensors for the experiments. The length of the block was 7.5 m and 5 m with a 1:10 sloping ramp. Thus, the water depth was 0.3 m and the wave pattern could be appropriately controlled.