esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Achieving space-based gravitational wave detection requires the establishment of an interferometer constellation. It is necessary to establish and maintain stable laser interferometric links using the differential wavefront sensing (DWS) technnique. When the distant measurement beam experiences pointing jitter, it causes beam walk on the surface of the local detector. The reduced overlap between the local reference spot and the distant spot increases the nonlinear errors in the DWS technique, which need to be suppressed. Numerical analysis was conducted on the spatial beam interference signals of the DWS technique when the distant measurement beam experienced pointing jitter. An experimental measurement system was designed, and the beam walk was suppressed using a conjugate imaging system. The results show that within a range of 300 \(\upmu\)rad, the optical path with the imaging system can reduce measurement errors by at least 83%. This way also helps to reduce pointing jitter noise in inter-satellite links, thereby improving laser pointing control accuracy.This method would provide a valuable reference for future DWS measurement systems.  
beam walk; angle measurement error; differential wavefront sensing; inter-satellite laser link; laser pointing jitter Article
## 3 Results and Discussion
### Sequence, Physicochemical Nature and Bioactivity of Identified Peptides  
Most of the peptides identified in the 3-kDa permeate fraction were medium-to-large sized peptides having \(>\)7 amino acids, as shown in Table 4. Table 4 illustrates the biological activity of fragments of peptides identified by mass spectrometry, but not the activity of entire peptides. The average molecular weight of a seven-amino-acid-long peptide was 650 Da. The solubility profile of the peptides varied from being moderately hydrophobic (27% of the peptides analysed) to highly hydrophilic (73% of the peptides analysed), as measured using the ThermoFisher peptide analysing tool ([[https://www.thermofisher.com/ie/en/home/life-science/protein-biology/peptides-proteins/custom-peptide-synthesis-services/peptide-analyzing-tool.html](https://www.thermofisher.com/ie/en/home/life-science/protein-biology/peptides-proteins/custom-peptide-synthesis-services/peptide-analyzing-tool.html)]([https://www.thermofisher.com/ie/en/home/life-science/protein-biology/peptides-proteins/custom-peptide-synthesis-services/peptide-analyzing-tool.html](https://www.thermofisher.com/ie/en/home/life-science/protein-biology/peptides-proteins/custom-peptide-synthesis-services/peptide-analyzing-tool.html))). This tool does not state how hydrophobicity is calculated, but absolute amino acid-based scores are either based on Kyte and Doolittle [44] or the [PERSON] et al. [45] methods. Using BIOPEP-UWM (formerly BIOPEP) analysis ([[http://www.uwm.edu.pl/biochemia/index.php/en/biopep](http://www.uwm.edu.pl/biochemia/index.php/en/biopep)]([http://www.uwm.edu.pl/biochemia/index.php/en/biopep](http://www.uwm.edu.pl/biochemia/index.php/en/biopep)) accessed 20/01/2020), 91% of the peptides found in the 3-kDa permeate for hydrolysate KHN18 had previously identified DPP-IV and ACE-I inhibitory activities (e.g., EDGKNPDDDE, HGCGMHS, DPKGGGA). Correlations between ACE-I and DPP-IV inhibitory activity has been previously reported by scientists working on bioactive peptides [46]. Other bioactivities predicted for the mussel by-product hydrolysates using in silico analysis included antioxidant (e.g., VDDHHDDHHD), anti-amnestic (e.g., DHPLPGTD) and anti-thrombotic (e.g., GPPEGPGEPGSS) activities, as well as regulation of stomach mucosal activity (e.g., VGEPGPPPGP), immune-modulating (e.g., ATASILGY) and chemotactic activity (e.g., KPGPSHPGDSKA).
## 3 Nonlinear Dynamic Analysis
### FEA Model  
For the analysis, a five-story building with an irregular structure was chosen. It was constructed using reinforced concrete walls and columns, along with masonry infill walls. The building measures 17.6 m by 17.9 m and stands at a height of 23.2 m. The concrete staircases are located on the northern side of the building. The columns have different cross-sections, ranging from 25 cm by 25 cm to 35 cm by 35 cm, and the ceiling slabs are 25 cm thick. For further details, please refer to Figure 6, which includes the 4 th floor plan, cross-section, and the FEM model of the building.  
Figure 6: View of the building: (**a**) plan of the 4 th floor; (**b**) cross-section of the building; (**c**) FEM model No. 1—masonry and RC walls [23]; (**d**) FEM model No. 2—RC walls.  
The building was previously studied in [23]. The model of the selected building with irregularities was experimentally verified in [23]. The dynamic properties were measured using different excitation methods [23].  
This study details the numerical study of the nonlinear FEM model. The first variant of the model (model No. 1) corresponds to cellular masonry and RC-bearing walls. The second variant of the model (model No. 2) consists only of the RC-bearing walls. The first material creating the bearing walls of the building corresponds to the actual structure. The locations of the center of mass (CM) and the center of stiffness (CS) are shown in Figure 6.  
The FEM was applied to create a spatial model for the dynamic analysis of a building. The model involves all significant elements that affect the stiffness and mass distribution and determines the building's dynamic properties. Due to the irregularity of the load-bearing element positions, the construction of the model could not be simplified. The foundation level of the model is supported by fixed translation and rotational degrees of freedom. The structural model assumed for further analysis is presented in Figure 6. It is worth noting that the last floor of the building does not cover the entire roof area, as shown in Figure 6c,d. A detailed description of the model and dynamic in situ research on the building were presented in paper [23]. Dynamic in situ tests were conducted using several building excitations, including a modal hammer, traffic on a nearby street, and controlled rides of a light truck through the threshold release [23]. The measured and calculated eigenfrequencies are listed in Table 2 and presented in Figure 7.
## 5 Conclusions  
Based on SSDT, this paper deduced an MFC actuating force formula considering shear deformation, and drew its conclusion by verifying the simulation precision and applicability with reference to the experimental results in [29]:  
1. An MFC actuating force formula based on SSDT was presented. According to the MFC actuating force formula, the interfacial shear stress between the MFC and the plate structure takes on a type of hyperbolic sine function. The influence of the elasticity modulus and thickness of the plate structure on the MFC actuating force was analyzed, which indicated that the relationship between the MFC's total actuating force, the elasticity modulus and the thickness of the plate structure appears to be non-linear, and that under fixed voltages the MFC actuating force increases with the structural elasticity modulus and thickness, and then gradually tends to become stable.
2. The MFC laminated beam in [29] served as the object to analyze the MFC mean internal force distribution under different voltages. The result is that the MFC mean internal force distribution took on a type of hyperbolic cosine function, and the MFC internal force was smaller at the sticking border position, then increased rapidly in the center and tended to become stable. As for the P1 type of MFC, the MFC actuating force in the direction of the \(x\) axis is greater than that of the \(y\) axis; as for the P2 type of MFC, the MFC actuating forces are close in the directions of the \(x\) and \(y\) axes.
3. A simulation analysis was made for the two conditions in [29], and the comparison was conducted between the simulation results and the experimental results, which showed the simulation results of end displacement from the MFC actuating force formula based on SSDT were better than those based on CPT. This proves that with greater precision and applicability, the MFC actuating force formula based on SSDT can be further popularized into the areas of MFC vibration reduction and actuation for beam-plate structures.  
Conceptualization, J.Z. and J.T.; Formal analysis, J.Z.; Methodology, J.Z. and J.T.; Validation, J.Z. and J.T.; Data curation, Z.L., K.G. and H.X.; Writing--original draft, J.Z.  
This research was funded by the Natural Science Foundation of Hubei Province, grant number 2016 CFA020; the Project of Ministry of Science and Technology, grant number 2018 YFC0705601, the Fundamental Research Funds for the Central Universities, grant number 2019-YB-024.  
The authors declare no conflict of interest.
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 2 Project Overview  
A certain iron mine is located in Yueyang City, Hunan Province, and is engaged in the mining and beneficiation of iron ore. The mining method employed is open-pit mining. The mineral reserve in the mining area amounts to 320 million tons, with an annual production capacity of 2.33 million tons of iron ore. The ore types mainly include magnetite, hematite, and small amounts of limonite. The mine uses deep-hole bench blasting with continuous charge structures. The blasting parameters are shown in Figure 1. The blast hole inclination angle is 90\({}^{\circ}\), and the designed bench height (\(H\)) is 12 m. The actual specific charge (\(q\)) ranges between 0.33 kg/m\({}^{3}\) and 0.54 kg/m\({}^{3}\), the burden (\(B\)) ranges from 4.0 m to 4.8 m, the spacing (\(S\)) ranges from 5.0 m to 5.8 m, and the subdrilling (\(H_{0}\)) ranges from 0.5 m to 2.5 m. The wide range of blasting design parameter values indicates a strong reliance on subjective experience.
## 5 Analytical Result  
### Zircon U-Pb Dating  
This study presents the LA-ICP-MS zircon U-Pb dating results for four samples (FY-80418, FY-80618, FY-80703, FY80704) collected from the Fuyun-Qinghe area (see Table S1 for detailed results).  
Sample FY-80618, a volcanic breccia. Twenty-nine zircon grains, ranging from 100 to 200 \(\upmu\)m in length with length-to-width ratios of 1:1 to 2:1, were analyzed. These grains displayed euhedral to subhedral shapes and distinct oscillatory zoning in the cathodoluminescence (CL) images (Figure 5a). The Th and U concentrations varied from 10 to 914 ppm and 3 to 987 ppm, respectively, with Th/U ratios between 0.38 and 2.97 (excluding one outlier at 0.38), suggesting a magmatic origin [58]. The \({}^{206}\)Pb/\({}^{238}\)U ages of 18 concordant spots ranged from 368 to 394 Ma, producing a weighted mean age of 382.9 \(\pm\) 3.4 Ma (MSWD = 3.0), corresponding to the late Middle Devonian (Figure 6a).  
For sample FY-80618, a basalt, 29 zircon grains were analyzed. These grains measured 50 to 150 \(\upmu\)m in length, with length-to-width ratios of 1:5:1 to 2:1, and exhibited oscillatory zoning in the CL images (Figure 5b). The Th and U concentrations ranged from 51 to 1418 ppm and 83 to 1174 ppm, respectively, with Th/U ratios of 0.18 to 2.1 (mostly above 0.4). Eleven concordant spots yielded \({}^{206}\)Pb/\({}^{238}\)U ages between 398 and 421 Ma, with a weighted mean age of 401.7 \(\pm\) 4.7 Ma (MSWD = 2.6) (Figure 6b).  
Sample FY-80703, an andesite, was analyzed using 15 zircon grains. The grains ranged from 50 to 200 \(\upmu\)m in length, with length-to-width ratios of 1:1 to 3:1, and displayed oscillatory zoning in the CL images (Figure 5c). The Th and U concentrations varied from 57 to 277 ppm and 69 to 2236 ppm, respectively, with Th/U ratios of 0.09 to 1.26 (mostly exceeding 0.4, except for two points). Eleven concordant spots produced \({}^{206}\)Pb/\({}^{238}\)U ages ranging from 393 to 420 Ma, yielding a weighted mean age of 405.1 \(\pm\) 5.6 Ma (MSWD = 3.7), corresponding to the late Early Devonian (Figure 6c).  
Lastly, sample FY-80704, also an andesite, was analyzed using 15 zircon grains. These grains measured 50 to 200 \(\upmu\)m in length, with length-to-width ratios of 1:1 to 3:1, and exhibited oscillatory zoning in the CL images (Figure 5d). The Th and U concentrations ranged from 33 to 813 ppm and 56 to 758 ppm, respectively, with Th/U ratios of 0.44 to  
Figure 5: CL images of the zircons from the volcanic and volcanicalastic rocks in the Fuyun–Qinghe area (red circles represent U–Pb dating points while blue circles show Lu–Hf isotope points). (**a**) Sample FY-80418, (**b**) sample FY-80618, (**c**) sample FY-80703, (**d**) sample FY-80704.  
Seven concordant spots yielded \({}^{206}\)Pb/\({}^{238}\)U ages between 395 and 416 Ma, producing a weighted mean age of 404.8 \(\pm\) 6.7 Ma (MSWD = 2.9) (Figure 6d).
## 3 Mathematical Concept and Modeling
### Performance Metrics  
The performance of the proposed method is evaluated using several metrics, including classification accuracy, sensing error (SE), training accuracy, validation loss, validation accuracy, Matthew's correlation coefficient (MCC), F1 score, Jaccard index (JI), noise value prediction (NPV), and false discovery rate (FDR) [35; 36; 37]. Training accuracy measures the AMC model's performance on the training dataset, reflecting how well the model classifies or predicts modulation types in training samples. Validation loss quantifies the error between the predicted and target modulation types in the validation dataset, while validation accuracy represents the percentage of correctly classified instances in the validation set [38; 39]. The MCC metric, ranging from \(-1\) to \(+1\), assesses the effectiveness of binary classification models in AMC: \(+1\) indicates perfect predictions, \(0\) suggests random guessing, and \(-1\) implies completely incorrect predictions. The F1 score, which combines precision and recall, ranges from \(0\) to \(1\), with values close to \(1\) indicating better classification performance. The Jaccard index (JI) compares the similarity between predicted and actual modulation types, where \(0\) denotes complete similarity, and \(1\) indicates no similarity. The negative predictive value (NPV) measures the proportion of true negatives out of all negative predictions, indicating how often the model correctly identifies negative instances. The false discovery rate (FDR), on the other hand, represents the proportion of false positives out of all positive predictions, providing insight into the model's tendency to incorrectly label instances as positive [40]. Sensing error (SE) in AMC refers to detection inaccuracies that may lead to incorrect modulation classifications and is calculated as the average of the miss detection probability (\(P_{md}\)) and false alarm probability (\(P_{fa}\)) [41; 42].  
Figure 2: Algorithmic diagram depicting training and testing pipelines.
## 2 Materials and Methods
### User Testing
#### 2.3.2 User Data Collection and Processing  
Five male users (age: 57 \(\pm\) 11 years, mass: 97.5 \(\pm\) 8.7 kg) participated in and completed the study (Table 1). The users walked at 0.8 m/s during level-ground and ramp ascent/descent data collection. All gait trials were performed on an instrumented dual-belt treadmill (Bertec, Columbus, OH, USA), and an eight-camera-based 3D motion capture system (Qualisys AB, Gothenburg, Sweden) was used to track markers for defined body segments (400 Hz). A six DoF model was constructed, and data were processed and analyzed in Visual 3D (C-motion, Germantown, MD, USA). A fourth-order low-pass Butterworth filter was applied for the kinematic and kinetic data, with a cut-off at 6 Hz and 10 Hz, respectively [19]. All users wore their daily prosthetic socket. The prototype foot tested was the only alteration to each user's prosthetic set-up. The prosthetic foot was aligned for condition 1 (mid-stiffness) by a certified prosthetist with more than ten years of experience. The order of the stiffness conditions tested was randomized for each user who was blinded of the stiffness condition. The same VSA ankle unit was used for all trials. The change in the sole blade size and side was the only change to the ESR base foot. This modification was done to maintain the same foot length as the participant's prescribed foot. The treadmill ramp angle was set to 7.5\({}^{\circ}\) in accordance with previous ramp angle protocols [3; 20; 21]. The test time was set to allow the collection of 15 consecutive steps on each side at constant speed. The study was performed in two visits for each user. During the first session, each participant was fitted with the VSA prosthetic foot, which was aligned by the CPO. During the second visit, biomechanical data were collected. All users used the same type of shoe (Viking, Norway) during both test sessions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**User** & **A** & **B** & **C** & **D** & **E** \\ \hline Gender & Male & Male & Male & Male & Male \\ \hline Age (years) & 53 & 60 & 42 & 59 & 72 \\ \hline Height (cm) & 179 & 187 & 177 & 180 & 182 \\ \hline Mass (kg) & 103 & 82 & 100 & 100 & 102 \\ \hline Foot size (cm) & 27 & 27 & 26 & 27 & 27 \\ \hline BMI (kg/m\({}^{2}\)) & 32.1 & 24 & 31.9 & 30.9 & 30.8 \\ \hline Amputated side & Right & Right & Right & Left & Left \\ \hline Time since amputation (years) & 16 & 43 & 9 & 4 & 8 \\ \hline Prescribed prosthetic foot & Pro-Flex Pivot & Vari-Flex XC & Pro-Flex XC & Pro-Flex XC &
\begin{tabular}{c} Vari-Flex with \\ Quick Align \\ \end{tabular} \\ \hline Prosthetic foot stiffness category & Cat. 5 & Cat. 5 & Cat. 6 & Cat. 6 \\ \hline Cause of amputation & Trauma & Trauma & Trauma & Trauma \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the users enrolled in the study.
Sensors_**2014**, _14_, 10725-10752; doi:10.3390/s140610725  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Ubiquitous Sensor Network Platform for Integrating Smart Devices into the Semantic Sensor Web**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
1 Universidad Polit cnica de Madrid, GAPS-SSR, E.T.S.I. Telecomunicaci n, Avda. Complutense 30, 28040 Madrid, Spain; E-Mails: [EMAIL_ADDRESS] (A.S.I.); [EMAIL_ADDRESS] (L.A.H.G.)  
2 Telef onica I+D, Ronda de la Comunicaci n, s/n, 28050 Madrid, Spain; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-915-495-700 (ext. 4043); Fax: +34-913-367-356.  
_Received: 16 December 2013; in revised form: 3 June 2014 / Accepted: 10 June 2014 / Published: 18 June 2014_  
**Abstract:** Ongoing Sensor Web developments make a growing amount of heterogeneous sensor data available to smart devices. This is generating an increasing demand for homogeneous mechanisms to access, publish and share real-world information. This paper discusses, first, an architectural solution based on Next Generation Networks: a pilot Telco Ubiquitous Sensor Network (USN) Platform that embeds several OGC(r) Sensor Web services. This platform has already been deployed in large scale projects. Second, the USN-Platform is extended to explore a first approach to Semantic Sensor Web principles and technologies, so that smart devices can access Sensor Web data, allowing them also to share richer (semantically interpreted) information. An experimental scenario is presented: a smart car that consumes and produces real-world information which is integrated into the Semantic Sensor Web through a Telco USN-Platform. Performance tests revealed that observation publishing times with our experimental system were well within limits compatible with the adequate operation of smart safety assistance systems in vehicles. On the other hand, response times for complex queries on large repositories may be inappropriate for rapid reaction needs.  
**Keywords:** Sensor Web; Semantic Sensor Web; smart devices; Telco Platform; Ubiquitous Sensor Networks; next-generation networks
## 2 Time Series Forecasting for Energy Reduction
### Deep Learning for Time Series
#### 2.4.5. Sequence to Sequence (Seq2 Seq) Models  
Seq2 Seq models, introduced in [24], have proven to be very effective in learning from sequential data. The model aims to map an input sequence to another sequence. Both sequences can be of arbitrary lengths. Seq2 Seq models are behind applications such as Google Translate and many online chatbots as they are very good at translation.  
These models are based on an encoder-decoder structure, where both are some composition of RNNs. The encoder creates an embedded representation of the inputs in a vector, which is passed to the decoder as the initial state, to process the information and produce the outputs.  
The encoder is a stack of RNN units, in this case, LSTM units. Every LSTM unit takes an input \(x_{i}\), generates a hidden state, and passes the information to the subsequent cell. Finally, it produces a vector with the codified information of every timestep, which is the encoded vector. Figure 7 illustrates the Seq2 Seq model, where \(h_{i}\) represents the hidden states generated by the LSTM units.  
Figure 6: Gated Recurrent Unit Cell.  
The decoder is very similar to the encoder. It is also composed of LSTM units and is initialized with the encoded vector. Each unit takes the previous hidden state with the information provided by the encoder and produces an output \(y_{t}\) and its hidden state \(s_{t}\).  
A problem with this model is that the neural network needs to compress all the information from an input sequence into a fixed-length vector, and this becomes much harder with longer sequences, causing the network to forget the earlier parts of the sequence. To solve this problem, the Attention mechanism [25] was created.
# Mechanical Behavior of a Mine Tailing Stabilized with a Sustainable Binder
###### Abstract  
Mining is a primary sector for the national economy of many countries, but exploiting these natural resources causes negative impacts on the environment. Tailings produced during mining, called mine tailings, have to be disposed of, and for that purpose, they are often mixed with Portland cement to control environmental toxicity and improve their mechanical properties. However, the high environmental impacts of producing Portland cement are well known. In this sense, sustainable binders based on the alkaline activation of industrial wastes have been studied as an alternative to using Portland cement. This study focused on applying a sustainable binder based on the alkaline activation of fly ash to improve the mechanical performance of a mine tailing from a mine located in Portugal. Geotechnical tests and chemical analysis were conducted to characterize the mine tailing and fly ash used in the alkaline activation process. In addition, triaxial tests were performed to evaluate the mechanical performance of the specimens, with both natural and stabilized tailings. The developed study proved that stabilized tailing with activated fly ash shows promising mechanical performance showing that this approach can be an excellent alternative to using Portland cement.  
mine tailing; alkaline activation; fly ash; triaxial test Article