esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Test Results
### P3 Beam Results  
The P3 beam, a prestressed reinforced concrete beam subjected to hierarchical loading, initially exhibits no significant changes during the early loading stages. By combining Figure 7a-d and Table 5, it can be observed that at a load of 40 kN, multiple vertical cracks appear in the beam accompanied by weak fracturing noise. When loaded to 60 kN, the cracks at the mid-span of the P3 beam significantly widen and increase in number. At 70 kN, strain gauge 5 breaks, indicating that the bottom of the beam has cracked. This load is preliminarily determined as the critical load for the P3 beam. With a further increase in load, strain gauges 3 and 4 gradually lose their readings. At a load of 80 kN, a through-crack appears at the mid-span with a maximum width of 2 mm. Relative sliding between the steel reinforcement and the concrete is observed, accompanied by a brittle cracking sound. When loaded up to 85 kN, strain gauges 1 to 5 all lose their readings, and the concrete at the top of the mid-span is crushed with an increased frequency of explosive sounds. At this point, the P3 beam is considered to have experienced brittle failure.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Load (kN)**} & \multicolumn{5}{c}{**Strain**} & \multirow{2}{*}{**Deflection**} & \multirow{2}{*}{**Prestress**} \\ \cline{3-3} \cline{5-8}  & & **1** & **2** & **3** & **4** & **5** & **(mm)** & **(kN)** \\ \hline
1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 125.0 \\
2 & 20 & \(-342\) & \(-451\) & 285 & 589 & 831 & 3.147 & 126.0 \\
3 & 10 & \(-240\) & \(-357\) & 248 & 487 & 683 & 2.532 & 125.7 \\
4 & 40 & \(-1036\) & \(-1066\) & 1594 & 2528 & 3230 & 9.575 & 132.7 \\
5 & 10 & \(-359\) & \(-502\) & 730 & 1639 & 2411 & 4.067 & 128.1 \\
6 & 60 & \(-1818\) & \(-1622\) & — & — & — & 16.682 & 141.2 \\
7 & 10 & \(-513\) & \(-602\) & 2325 & 3726 & — & 5.272 & 129.2 \\
8 & 70 & \(-3240\) & \(-1948\) & — & — & — & 25.012 & 136.6 \\
9 & 10 & \(-1623\) & \(-866\) & — & — & — & 10.349 & 129.2 \\
10 & 74 & — & — & — & — & — & 30+ & 173.9 \\ \hline \hline \end{tabular} Note: “—” indicates that strain gauges have no indication.  
\end{table}
Table 4: P2 beam test data.  
Figure 7: P3 test beam bending failure process diagram. (**a**) pre-test; (**b**) multiple cracks; (**c**) midspan through-cracking; (**d**) top concrete crushed. The red lines and circle in the figure indicate the crack locations.
# Subwavelength-Cavity High-Gain Circularly Polarized Antenna with Planar Metamaterials
###### Abstract  
We present a specific subwavelength-cavity high-gain circularly polarized ultra-thin antenna made of planar metamaterials. The antenna is designed to operate at 2.80 GHz with a fixed thickness of approximately 1/6 of the operating wavelength in free space. The asymmetric unit cells of the metamaterial antenna exhibit two characteristics, namely, negative permeability and polarization selection. A linear-polarization micro-strip patch, which can realize circular polarization without a complicated feeding network, is embedded in the cavity as a feed. The circular polarization mode of the antenna can be changed by simply rotating the planar metamaterial horizontally. Simulations and experiments conducted on this antenna yielded results that are in good agreement with each other. This new subwavelength planar antenna can have potentially important applications in communication, early warning systems, and radio observation.  
metamaterial; antenna; subwavelength cavity; circular polarization; microwave +
Footnote †: journal:  
Article
# A Novel Predictable Rock Breaker Using Intelligent Hydraulic Control with ICT Convergences
## 1 Introduction  
Hydraulic breakers are widely used, typically attached to excavators in heavy industries and construction for the deactivation and removal of building structures, road demolition, water supply and drainage construction, foundation works, and the destruction of quarry stones. The breaking system is composed of the main body and the bracket which houses the system. Figure 1 shows the primary components and assembly of a hydraulic breaker. The main body consists of the head cap, cylinder, front head, and valve housing. The excavators discharge the pressurized hydraulic oil into the main valve, which is located at the top of the hydraulic breaker. The cylinder moves from the top to bottom and back and forth according to the oil pressure due to differences in the cross-sectional area. Lastly, the piston impacts the chisel, which makes direct contact with the rock to break it down.  
The means of the impact process have been developed and performed as manual preset strokes--either mechanically manual long strokes or short strokes. Recently, mechanical, automatic long and short strokes have been investigated, and are widely used in heavy industries [1; 2; 3].  
A few studies have reported the use of electro hydraulic control [4; 5; 6] for breaking systems [7; 8], as listed in Table 1. Moreover, there in an increasing demand for the improvement of construction equipment efficiency, coinciding with the emergence of new environmental regulation standards in many countries.  
A hydraulic breaker that can predict the strength of rock using an ICT-converged electronic auto-adjusted stroke was investigated and verified by practical operating field tests under conventional conditions so as to validate the proposed system's usability, durability, and efficiency.
# Application of Image Segmentation in Surface Water Extraction of Freshwater Lakes using Radar Data
## 1 Introduction
For instance, the Otsu Method has been applied on the cloud-free Landsat TM images for urban land cover detection, which focused on differentiating the urban land and nonurban land region in Haidian District of Beijing, China [27]. This research resulted in an accuracy of 84.83% for the Otsu Method, which was larger than the accuracy of 74% for the conventional postclassification change detection method [27]. Another study used the Otsu Method on the SAR data for the detection of oil spills over sea surfaces, which tried to find a threshold on the radar data to draw the edge of spilled oil film floating over the sea [28]. It examined the Penglai oil field and the Gulf of Dalian, resulting in an error rate of 3.0% on the Penglai oil field and an error rate of 13.0% on the Gulf of Dalian for the Otsu Method [28]. Even though the Otsu Method has already been widely applied in thresholding problems, it has been seldom used for surface water extraction. Furthermore, most previous studies do not provide algorithms and detailed scripts for implementation of the Otsu Method. Thus, we were interested in the application of the Otsu Method for surface water detection and providing reusable code for future implementation. Therefore, the objectives of the present work are to:  
1. Implement the Otsu Method and write reusable scripts to automatically select thresholds for surface water extraction using Sentinel-1 data on Google Earth Engine
2. Analyze the advantages and disadvantages of an unsupervised classifier from both mathematical and application perspectives
3. Contribute to the knowledge base of hydrological variation at Poyang lake by mapping surface water extent of the lake in January 2017, 2018, 2019 and 2020
## 1 Introduction  
Cloud Computing has seen one of the most radical shifts within Information Technology, and the shift is most apparent both in the move from migrating private networks to virtualized networks, as well as the move from private cloud systems onto public ones. Unfortunately these moves have not really changed the methods of providing security and robustness, and instead often rely on the addition of encryption keys or the implementation of multi-factor authentication techniques. Additionally, the public cloud still suffers from doubts around large-scale outage risks and other security concerns.  
One of the major risks in scaling existing systems into the Cloud is the current reliance on PKI (Public Key Infrastructure) to secure information, where there are risks around the possible existence of back-doors, front-doors, and a general lack of true understanding of encryption methods. Many encryption methods use a key pair, such as with Rivest Shamir and Adleman (RSA) algorithm, to protect the symmetric key which is then used to encrypt data into the Cloud. While most common methods are considered free of major vulnerabilities, provided the key size is sufficiently large to make it safe from brute force attacks, it is the loss of the private key which can cause major data loss concerns. Many public cloud-based systems are at risk of major data loss through private key loss (and thus data loss), especially through the growth of APTs (Advanced Persistent Threats), certificate cracking, and insider threats.  
The Future Internet thus requires in-built data protection, either with a strong protective shell, such as that provided by sticky policies, or with sharing methods which break data into secure fragments, and which have a strong enforcement policy to rebuild the data shares without relying on traditional encryption.
## 3 Methodology
### Comparison of Bathymetric Representation  
Comparisons of MDE differences and flow depth differences as input and output parameters of the hydrodynamic modelling were carried out. The first of the comparison processes allowed us to quantify the spatially distributed cross-sectional area reduction due to the non-availability of LiDAR data on submerged areas of the main channel. The second one allowed us to spatially assess the distributed differences in flow depth value, as well as to select the optimal model (\"LiDAR scenario\" plus \"best Manning's \(n\) value\") to simulate the flood hazard levels relative to the results of the original, or control, model.  
The errors in the bathymetric representation for the \"LiDAR scenario\" and \"real scenario\" were compared both using an arithmetic subtraction between the two models (Figure 4A) to obtain a difference value for each pixel of the model and by calculating the mean absolute error (MAE), as shown in Equation (2):  
\[\text{MAE}=\frac{\sum_{i=1}^{\text{n}}\lvert\text{H}_{\text{L}}-\text{H}_{ \text{r}}\rvert}{\text{n}_{\text{c}}} \tag{2}\]  
where H\({}_{\text{L}}\) is the elevation of the ith cell for the \"LiDAR scenario\" DEM, H\({}_{\text{r}}\) is the elevation of the ith cell for the \"real scenario\" DEM and n\({}_{\text{c}}\) is the number of cells used for the analysis.  
Figure 3: Basic conceptual diagram for the calibration of the parameter n, assuming that the flow rate (Q) is a function of the cross-sectional area of the channel (A\({}_{1}\) and A\({}_{2}\)) and the mean flow velocity (V\({}_{1}\) and V\({}_{2}\), which, in turn, is dependent on the Manning’s \(n\) values (n\({}_{1}\) and n\({}_{2}\))). The size of the symbols identifying the variables is directly related to the magnitude of the variables.
# Production and Processing of a Spherical Polybutylene Terephthalate Powder for Laser Sintering
## 3 Results and Discussion
### Tensile Tests  
Three tensile bars were tested. The results are summarized in Table 6. The parts produced with laser sintering exhibit brittle behavior, and suffer considerably in terms of tensile strength (\(\sigma_{\text{M}}\)) and elongation at break (LaB). It is typical for laser sintering materials to have a higher Young's modulus and much lower elongation at break than their injection molded counterparts, the same was found for  
Figure 11: (**a**) Glossy hot spots (indicated by the circles) appearing on the powder bed where the temperature is too high; (**b**) Parts that could be built and tested.  
Figure 10: (**a**) Sintered tensile bars in powder bed. Minimal curling is visible in the center; (**b**) Parts are moved on the powder bed by the recoater due to curling.  
laser sintered PBT parts by other researchers [13]. The drastically reduced tensile strength however indicates the laser sintering process is not the only factor influencing the mechanical properties. The polymer degradation that occurs during the powder production stages, which was confirmed by DSC and capillary rheometry, also plays an important role here.  
To compare properties, tensile bars made from different materials and with a variety of processes were tested. A set of specimens made from the unaltered granulate was cut out from an injection molded (IM) sheet. The next set of specimens was made by compression molding sheets with the produced powder as a starting material. The injection molded granulate shows properties corresponding to those reported on the material datasheet. As soon as the granulate is transformed to a powder, the strength and elongation at break deteriorate, regardless of how the specimens were fabricated. This behavior shows that the worsening of properties is not merely the effect of processing by laser sintering, but is mainly due to degradation in the powder fabrication process.
# Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
###### Abstract
[35] applied PSO to solve the ambiguity introduced by uncertainty at the locations of landmarks on the basis of the assumption that each pose hypothesis is represented by a particle. The quaternion-based PSO (Q-PSO) approach was presented in [36]. It fits a 3D model of the target object to depth images. The fitness function of this method is based on depth information only, and the quaternion formulation avoids singularities and the need for conversions between different rotation representations. PSO also implements adjustments for the regularization parameters and kernel parameters of a support vector machine [37] to improve the accuracy of facial detection. Ref. [38] applied PSO to impose kinematics constraints on the results of CNNs for implementing hierarchical hybrid hand PE. PSO was also used to introduce a cooperative target for PE with monocular vision [39], according to which the mathematical relationship between the distributed configurations of cooperative targets and the measurement accuracy was derived on the basis of the PnP principle. In the experiment conducted herein, a novel joint iterative PE and PSO method is introduced with the aim of improving the accuracy of satellite PE, which has great benefits for the development of space technology such as close proximity operations, active space debris removal, and on-orbit servicing. The main contributions of this work are as follows. 1. A new end-to-end satellite PE method is proposed; this approach jointly iterates between deep learning for the PE framework and PSO. This can improve the effectiveness of the model in terms of translating the position and orientation information of the target satellite;
2. The design of the position parameters of the PSO algorithm, which is effectively integrated with deep learning in the PE framework, reduces the error induced by complex input images, and improves the accuracy of the satellite pose inference process;
3. The proposed method is verified by testing it in different downstream computer vision tasks. The rest of the paper is organized as follows. In Section 2, the related works that briefly explain the deep learning technique for estimating spacecraft poses from photorealistic renderings [11] and the PSO algorithm is presented. Section 3 describes the utilized materials and the proposed method, which is the joint iterative method including deep learning-based spacecraft PE from photorealistic renderings and PSO, for improving the accuracy of the constructed model. The results are illustrated in Section 4. Conclusions and suggestions for future work are presented in Sections 5 and 6, respectively.
## 4 Security Analysis of the MAPP Protocol
### Security Requirement Analysis
#### 4.1.2 Message Authentication  
Message authentication represents proof that the message has not been changed during transmission. Our protocol provides message authentication by calculating a short bilinear pairing signature over each transmitted message. The signature calculation is done by hashing the message and mapping it to a point over the elliptic curve, then signing it with the sender's secret key. Verification of signature at receivers can provide the message authentication. In Equations (5), (7), and (8), each receiver in the different proposed methods can use the corresponding bilinear pairing equation to verify the signature, based on the received public key \(pk_{i}\), the zone generator \(g_{2 ZID}\) and the calculated signature \(\sigma_{i}\). Verifying the validity of \(e\) ( \(g_{2 ZID},\sigma_{i})=e(pk_{i},\ H(m))\) proves the message authenticity. If the verification failed at the receiver side, the message must be discarded.
## 4 MUSIC-Based 3D DoA Estimation Adopted to Multi-Mode Multi-Port Antennas
```
1:Input:\(\mathbf{y}(k)\in\mathbb{C}^{M\times 1}\)
2:Output:\([\mathbf{\hat{\phi}},\,\mathbf{\hat{\theta}}]\)
3:\(\mathbf{\hat{R}}=\frac{1}{K}\sum_{k=1}^{K}\mathbf{y}(k)\mathbf{y}(k)^{\mathrm{H}}\)
4:\(\mathbf{\hat{R}}=\mathbf{\Lambda}\mathbf{D}\mathbf{\Lambda}^{-1}\)
5: Determine the noise subspace \(\mathbf{\Lambda}_{n}\)
6:\(P_{\mathrm{MU}}(\phi,\theta)=\frac{1}{\left|\mathbf{a}(\phi,\theta)\mathbf{\Lambda}_{ n}\right|^{2}}\)
7: Find the peaks of \(P_{\mathrm{MU}}\)
```  
**Algorithm 1** MUSIC-based 3D DoA estimation adopted to multi-mode multi-port antennas
## 4 The Role of Dielectrophoresis in the Characterization of Skin Tumor Cells
### Clinical Applications
Not many studies used this kind of technique, but we mention the work of [PERSON] et al. [118] where traveling wave DEP was employed along with other particle separation techniques in order to manipulate human malignant cells. Finally, a fine-tunning potent technique is optically induced DEP, where, compared to classical DEP, the examiner can easily and quickly create or modify an electrode layout through the control of optical patterns, by acting as a virtual electrode. This method was successfully applied in microfluidic systems for the isolation and purification of rare cell species in clinical samples, such as CTCs [119] and CTC clusters [120], in a higher performance manner than their conventional counterparts. More recently, another promising approach consists of combining DEP with other methods of particle separation, such as magnetophoresis [121], photophoresis [122], and dielectrophoretic impedance measurement (DEPIM) [123]. Regarding skin tumor cells, the study conducted by [PERSON] et al. [114] demonstrated the efficacy of combining DEP with \begin{table}
\begin{tabular}{c c c} \hline \hline
**Skin Tumor Type** & **Main Findings** & **Reference** \\ \hline \multirow{4}{*}{Human malign melanocytes} & Cells’ behavior in DEP is markedly & \\  & dependent upon pigmentation & \\  & (melanin content), age, and drug & \\  & treatment (chlorpromazine) & \\ \hline \multirow{4}{*}{Melanoma Colo38 cells} & Separation of melanoma Colo 38 cells & \\  & from other cancer cells using & \\  & levitation principle in & \\  & software-controlled DEP & \\ \hline \multirow{4}{*}{Mouse melanoma B16F10 cells} & DEP has the ability to differentiate & \\  & between two malignant cells of the & \\  & same origin, based on their & \\  & melanin content & \\ \hline Human skin fibroblast and & Distinguishing healthy and cancer & \\   human malignant & cells using the microfluidic chamber & \\   melanocytes & DEP device & \\ \hline \multirow{4}{*}{A375 human skin cancer cells} & Assessment of the effects of various & \\  & parameters of nanosecond pulsed & \\  & electric fields combined with & \\  & multi-walled carbon nanotubes on & \\  & skin cancer cells viability & \\ \hline \multirow{4}{*}{A431 human skin cancer cells} & Separation of A431 cells from liquid & \\  & samples based on their & \\  & DEP parameters & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Clinical studies of DEP in skin cancers. metasurface-enhanced infrared reflection spectroscopy in order to detect and characterize A431 cells. These encouraging results in malignant cells originating from different cancerous tissues can be a good starting point for further research based on the field of skin cancer cells, including CTCs and other relevant tumor particles, based on DEP techniques.
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 4 Discussion
### The Comparison of the Performance Indexes  
Tables 12-15 present various performance indexes, such as integral error (IE);integral of the absolute error (IAE); integral of the square error (ISE); integral of time-weighted absolute error (ITAE); integral of time square error (ITASE) of PID and Dahlin controller, considering a 1000 s simulation time.  
Additionally, Tables 13 and 15 show the results of the performance evaluation for the Dahlin controller, for different values of the assigned time constant, \(1/\lambda\). The authors have determined the lower limit of the decrease in the desired time constant \(1/\lambda\), which is \(4\)_T_. Decreasing this constant below this limit, as the authors have noted, will not result in the desired behavior.  
Regarding the IE, IAE, ISE, ITAE, and ITASE parameters, the best results were almost always obtained using the modified Dahlin controller both in the ST and CT systems.
# Named Entity Recognition in the Field of Small Sample Electric Submersible Pump Based on FLAT
## 3 Named Entity Recognition Experiment
### Results and Analysis
#### 3.2.2 Comparison Experiment  
To evaluate the performance of the char-CNN-FLAT-CRF model in NER under low-resource conditions, this paper conducted comparative experiments. Figure 4 shows the accuracy and loss waveforms of the char-CNN-FLAT-CRF model and several mainstream NER models, including BERT-BiLSTM-CRF, BERT-FLAT, and BiLSTM-CRF, on the training and validation sets of a small-sample corpus of ESP. In the test set, three evaluation metrics were used: precision, recall, and F1 score. The experimental results are presented in Table 5.  
As shown in Figure 4, all the models exhibit a converging trend during the training process. However, significant differences in accuracy are observed among the models in the validation set. This phenomenon indicates the differences in the generalization ability of the models for NER, specifically their adaptability to nested entities and rare words in the context of ESP. Among these models, the char-CNN-FLAT-CRF model and the BERT-FLAT model demonstrate superior performance in this experiment.  
\begin{table}
\begin{tabular}{c c} \hline
**Model** & **Precision (\%)** \\ \hline char-CNN-FLAT-CRF & 86.16 \\ char-CNN-FLAT & 82.33 \\ \hline \end{tabular}
\end{table}
Table 4: Results of the ablation study.  
Figure 4: The accuracy and loss waveforms of multiple models on the training and validating sets: (**a**) Training accuracy waveform; (**b**) Validation accuracy waveform; (**c**) Training loss waveform; (**d**) Validation loss waveform.  
As shown in Table 5, in the NER task for ESP wells with limited data, the FLAT-based NER model outperforms the BiLSTM-based model. The analysis indicates that BERT and BiLSTM models require larger-scale and higher-quality corpora to effectively identify named entities, making them more suitable for tasks with abundant training data.  
In contrast, FLAT, by learning the relative relationships between characters and potential words, is able to better extract character and word features under low-resource conditions, thus demonstrating stronger recognition capabilities. Moreover, the char-CNN-based convolutional character and word embeddings applied in this study further enhance the performance of the FLAT model in low-resource tasks, giving it an edge over models like BERT that rely heavily on large training datasets.  
Additionally, the rule-matching mechanism introduced after CRF decoding in this study effectively improves the NER performance in the domain of ESP working conditions. It can better eliminate the interference from complex phrases and word groups in the corpus, thereby significantly enhancing the accuracy and robustness of named entity recognition.