esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Main Contributions of This Work  
While most of the previous studies highlight the viability of the vertical farm model, only a few works try to find solutions to very important aspects in real world scenarios, such as optimization of resources (water, fertilizers, etc.... ) and minimization of operational and energy costs. This can be achieved by introducing both customized light recipes and sensor systems to monitor the plants during the whole growth cycle.  
In this sense, a work related to the present research is the one from [PERSON] et al. [26], in which the effect of different light recipes composed of uncommon LED lights (Hyper Red, Deep Blue and White) on the growth performance of basil was evaluated through statistical methods. In this related work, the better light recipe for basil growth was indicated as follows: ratio HR:DB = 3:1, distance equal to 65 cm and exclusion of White LEDs. For the present work the same experimental conditions were kept, such as type of soil or type of LEDs, but in contrast to this previous work, the basil growth was examined not only at the end of the growth time, but also at several intermediate times, in order to calculate optimized LED light recipes for each time period [26].  
In fact, the main hypothesis of this study is that basil needs different LED conditions at different stages of its growth to promote its overall growth efficiency in vertical farms, since in others studies the fact emerges that the addition of White LEDs has a relevant influence on the fresh and dry weights of basil and other species such as broccoli, cabbage and potato [27; 28; 29]. In addition, as opposed to the great majority of the studies found in the literature, the Design of Experiments (DoE) techniques have been applied with two main purposes: (i) to rationally design and limit the number of experiments required in order to collect the data concerning basil growth, and (ii) to quantitatively calculate a tailored light recipe for every growth step in the plant's life (five-days each) through analysis of variance (ANOVA) and multivariate linear regression. In this sense, this work will merge experimental work for data collection with mathematical model calculation based on real data. Finally, the results of the present work, in contrast to the related work and previous literature, will generate specific mathematical models that can be employed to promote different canopy properties (alone or in combination with others) at different times of plant growth, thereafter, giving to the final user a great control in vertical farm cultivation. In fact, in this study, mutual interaction between variables, such as type of LED and intensities, is quantitatively estimated, in contrast to previous literature where variables were only considered separately.
## 4 Response Reconstruction for an Actual Continuous Bridge
### Information on the Continuous Bridge and Monitoring System  
The Fuchang Bridge, located in Hebei Province, China, comprises two distinct dual bridges on the left and right sides, supported by rubber bearings. Both of these bridges are typical, prestressed, concrete, continuous beam bridges. The left-side bridge is a prestressed continuous box-girder bridge comprising four continuous spans (\(32\) m + \(32\) m + \(37\) m + \(32\) m), totaling 133 m in length, as illustrated in Figure 8[45]. The right-side bridge, measuring 187.2 m in length, is composed of two separate structures: one is a prefabricated, prestressed, concrete, continuous T-beam bridge, and the other is a prestressed, continuous box-girder bridge. A health monitoring system was installed on the fourth span of the left-side bridge, with twelve strain gauges placed at three cross-sections (02, 03, and 04). The strain data were sampled at a frequency of 50 Hz during the monitoring period from March 2018 to December 2018.  
Figure 7: Reconstruction of high-frequency responses at 1/4 beam in four scenarios. Dashed line, high-pass filtered high-frequency responses from the finite element simulation result; dot line, reconstruction result from Equation (8b).
## 3 A Brief Overview of AHL Systems in _Burkholderia_
### Burkholderia cepacia _Complex (BCC)_
ambifaria_} & _-/-_ & M31 & \(\textasci{}\textasci{}\textasci{}\textasci{}\textImportantly, the QS systems in _B. cenocepacia_ are interrelated with each other; CepR regulates _cepl_ and _ccilR_ genes, while CciR negatively regulates the expression of _cepl_ thus allowing for a negative regulatory feedback loop on the _cepl/R_ system [55]. The solo/orphan _ceplR_2 which lacks the associated cognate _luxI_-AHL synthase gene, negatively regulates itself and is also negatively regulated by CciR [57]. Figure 2 shows an example of the regulatory pattern observed in BCC members. Studies on _B. cenocepacia_ have shown that the production of AHL is also influenced by a neighboring gene downstream from _cepl_[56]. This was identified as ORF BCAM1871, it is co-transcribed with _cepl_ and is regulated by CepR. Orthologs of the ORF BCAM1871 contain a domain of 3-hydroxy-3-methyl-glutaryl-CoA reductase family (HMG-CoA reductase). Its genomic location is conserved in all BCC members. Previous studies suggest that BCAM1871 alone cannot activate AHL synthesis, but it enhances the rate of AHL production [56]. Apart from the BCC group,  
Figure 3: Cladogram of the orthologs of ORF BCAM1871, encoding a protein with an HMG-CoA domain in _Burkholderia_ spp. The orthologs are well separated into _cepacia_ and _pseudomallei_ groups. The RMI (\(\,\bar{\pi}\,\bar{u}\,\bar{l}\,\) ) motive is flanked by conserved genes on both sides in members of both _cepacia_ and _pseudomallei_ groups. The numbers on the tree branches indicate bootstrap values (%). Figure 2: QS regulatory circuits in _Burkholderia cenocepacia_ J2315 [54]. the orthologs of ORF BCAM1871 are also conserved in _B. mallei_ and _B. pseudomallei_, it flanks downstream to _luxI_ homologs, and the corresponding systems are flanked upstream by an Mg transporter gene. (_i.e._, the M1 topology (RMI) is complemented by two conserved flanking genes in these species) (Figure 3). Circular map representations reveal that the chromosomal arrangements of the AHL QS genes in BCC members are relatively well conserved with respect to the potential OriC (Figure 4).
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
###### Abstract  
Much research has focused on classification within a closed set of emitters, while emitters outside this closed set are misclassified. This paper proposes an open-set recognition model based on prototypical networks and extreme value theory to solve the problem of specific emitter identification in open-set scenes and further improve the recognition accuracy and robustness. Firstly, a one-dimensional convolutional neural network was designed for recognizing 1/Q signals, and a squeeze-and-excitation block with an attention mechanism was added to the network to increase the weights of the feature channels with high efficiency. Meanwhile, the recognition was improved by group convolution and channel shuffle. Then, the network was trained with the joint loss function based on prototype learning to complete the separation of intra-class signals and the aggregation of inter-class signals in the feature space. After the training, the Weibull model was fitted for pre-defined classes by incorporating the extreme value theory. Finally, the classification results were obtained according to the known classes and the Weibull model, effectively completing the open-set recognition. The simulation results showed that the proposed model had a higher recognition performance and robustness compared with other classical models for signals collected from five ZigBee and ten USRP 310 devices.  
specific emitter identification; prototypical networks; open-set recognition; extreme value theory +
Footnote †: journal: Applied
## 2 Gravity Calculation Method
### Gravitational Acceleration and Coordinate Transformation  
As the spacecraft's gravitational acceleration is normally no longer on the radius directions for irregular asteroid, in the calculations in this paper, some transformations are needed from spherical coordinates to Cartesian coordinates. The gravitational acceleration and related symbols \(\
abla U=[U_{x}U_{y}U_{z}]^{T}=[a_{x}a_{y}a_{z}]^{T}\) are  
\[U_{x}=\frac{\partial U(r,\phi,\theta)}{\partial x} = \frac{x}{r}\frac{\partial U(r,\phi,\theta)}{\partial r}-\frac{xz}{ r^{2}\sqrt{x^{2}+y^{2}}}\frac{\partial U(r,\phi,\theta)}{\partial\phi}-\frac{y}{x^{2}+y^ {2}}\frac{\partial U(r,\phi,\theta)}{\partial\theta}, \tag{7}\] \[U_{y}=\frac{\partial U(r,\phi,\theta)}{\partial y} = \frac{y}{r}\frac{\partial U(r,\phi,\theta)}{\partial r}-\frac{yz}{ r^{2}\sqrt{x^{2}+y^{2}}}\frac{\partial U(r,\phi,\theta)}{\partial\phi}+\frac{x}{x^{2}+y^ {2}}\frac{\partial U(r,\phi,\theta)}{\partial\theta},\] (8) \[U_{z}=\frac{\partial U(r,\phi,\theta)}{\partial z} = \frac{z}{r}\frac{\partial U(r,\phi,\theta)}{\partial r}+\frac{ \sqrt{x^{2}+y^{2}}}{r^{2}}\frac{\partial U(r,\phi,\theta)}{\partial\phi}. \tag{9}\]  
where \(\tan\theta=y/x\sin\phi=z/r\), \(r=\sqrt{x^{2}+y^{2}+z^{2}}\). But the magnitude square of the gravitational acceleration \(\mathbf{a}\) can be simplified as  
\[\mathbf{a^{T}a}=[\
abla U]^{2} = (\frac{\partial U}{\partial x})^{2}+(\frac{\partial U}{\partial y })^{2}+(\frac{\partial U}{\partial z})^{2}, \tag{10}\] \[= \left(\frac{\partial U}{\partial r}\right)^{2}+(\frac{\partial U }{\partial\phi})^{2}\cdot\frac{1}{r^{2}}+(\frac{\partial U}{\partial\theta})^{ 2}\cdot\frac{1}{r^{2}\cos^{2}\phi}. \tag{11}\]
# One-Dimensional Modeling of the Pressure Loss in Concrete Pumping and Experimental Verification
## 5 Analysis of Results
### Analysis of Experimental Results
The calculated pressure loss is small for the following reasons. First, the resistance term consists of the frictional resistance between the concrete and the pipe wall during the conveying process and the bonding force inside the concrete. Second, the concrete pumping process is regarded as the overall movement of the plunger flow. The frictional resistance is concentrated in the contact area between the concrete and the pipe wall, ignoring the blocking effect on the mortar in the lubrication layer produced by the coarse aggregate particles. The pressure loss calculated by the one-dimensional flow model is relatively close to the experimental value, and the relative difference is only 20.2%. The results indicate that the source of pressure loss considered in the construction of the one-dimensional flow model is basically the same as that in the real situation. The pressure loss mainly comes from the frictional resistance generated by the relative motion of the mortar in each layer in the lubrication layer, as well as from the large number of  
Figure 6: On-site experimental testing and experimental results. vortices generated by large aggregate particles, and the large wall roughness blocking effect increases the pressure loss. In this model, the transition zone between the lubrication layer and the plunger layer is not considered. Therefore, the pressure loss generated in this part is neglected, resulting in the pressure loss calculated by the one-dimensional flow model being smaller than the experimental value. In addition, in the calculation of the pressure loss values by the empirical formulas and the one-dimensional flow model, the pumping concrete is assumed to be in an ideal state, and its flow state is assumed to be a constant flow. However, the actual pumping process of concrete involves intermittent plunger movement, and its flow state is a nonconstant flow. The concrete close to the exit area of the ram pump is more affected by the impact of the ram pump and the gravity return of the concrete, resulting in a high pressure loss. In contrast, the exit area at the far end of the ram pump is less affected and has a relatively small pressure loss per unit length. Combined with engineering experience, in the area near the pumping exit area, the fluctuation degree of the concrete flow with time is small. However, due to the pumping space, cost, and other factors, the pumping outlet concrete flow in engineering practice has not reached the ideal constant state. In other words, the experimentally measured pressure loss is slightly greater than the value in the ideal state of pressure currently.
## 3 Discussion
Metal mesh integrated with thin film, especially IZTO/Ag/IZTO, provides low resistance, flexibility, and high transparency at the cost of a complex fabrication process.
# A Data-Driven Forecasting Strategy to Predict Continuous Hourly Energy Demand in Smart Buildings
## 3 Methodology and Approach
### Forecasting Models Training, Tunning, and Execution

Table 1: Forecasting models architectures and hyper-parameters. The chosen values previously mentioned varying from the default values set by the separate libraries since they appeared to perform better. For models that were created with the scikit-learn library, grid search and randomized search [34] were used as parameter search techniques to obtain the best combinations of parameters. For other models, the best parameter combinations were obtained through backtesting. It should be noted that for models that do not natively support multi-target forecastings, such as MLR and XGBoost, a multi-output function was used which allowed this to be possible. When the final model was already trained, the prediction of the year 2019 was made with each of the forecasting models and then evaluated by different accuracy metrics.
## 2 Modelling and Theoretical Analysis for Power Flow Gradients  
### Power Flow Regulation Theories by the OSIV of UPFC  
The general UPFC embedded power system is shown in (a) of Figure 1, and the structure of UPFC is encircled by dotted red lines. The UPFC is mainly constituted by two back to back converters which are linked together by one common dc capacitor. The converter connected to Bus1 is generally named the shunt converter, which can maintain the voltages of Bus1 and dc link constant. The other back-to back converter is known as the series converter, which can insert a varying voltage source into the transmission lines. The schematic diagram of UPFC embedded system based on specific real circuit is shown in (a) of Figure 1.  
As is known, the regulations for the power flows in the system by UPFC are dominated by the OSIV of the UPFC. According to the phasor diagram in (b) of Figure 1, there are two regulation variables of OSIV containing the magnitude of OSIV and the phase angle \(\theta\) of OSIV. When the magnitude of OSIV changes, or the phase angle \(\theta\) of OSIV rotates, the phasor of the voltage \(v_{2}\) of Bus2 will also vary, thus the active and reactive power flows in the system changes accordingly. Here, one special regulation mode is assumed as the specific value combinations of the magnitude of OSIV and the phase angle \(\theta\) of OSIV. When the two regulation variables are being regulated individually or comprehensively, there will be multiple regulation modes by OSIV the regulation effectiveness or efficiencies for power flows may also be different by various regulation modes of the two variables of OSIV.  
Moreover, as the phasor of the voltage \(V_{1}\) of Bus1 is assumed constant and fixed at the initial position in (b) of Figure 1, when the system operation condition changes, meaning the phase difference \(\delta\) and the voltage of \(v_{\mathrm{r}}\) vary by some extent. In the case that even the magnitude and phase angle of OSIV are kept unchanged, the current \(I_{1}\) through the transmission line will also vary, resulting that the power flows in the system change accordingly.  
Upon the above discussions, the power flows in the system may change with the magnitude of \(v_{12}\), the phase angle \(\theta\) of \(v_{12}\), the phase difference \(\delta\) and the magnitude of \(v_{\mathrm{r}}\). As the magnitude of \(v_{\mathrm{r}}\) usually varies within small limited ranges, only the former three variables are assumed as the characteristic independent variables. For convenience, the magnitude of \(v_{12}\), the phase angle \(\theta\) of \(v_{12}\), the phase difference \(\delta\) are assumed as CIV1, CIV2, and CIV3, respectively.  
Figure 1: The structure and phasor diagrams of the unified power flow controller (UPFC): (**a**) Schematic diagram of the UPFC embedded system; (**b**) Phasor diagram of the UPFC embedded system.
###### Abstract  
This paper presents a comprehensive on-site decision-making framework for assessing the structural integrity of a jacket-type offshore platform in the Gulf of Mexico, installed at a water depth of 50 m. Six critical analyses--(i) static operation and storm, (ii) dynamic storm, (iii) strength-level seismic, (iv) seismic ductility (pushover), (v) maximum wave resistance (pushover), and (vi) spectral fatigue--are performed using SACS V16 software to capture both linear and nonlinear interactions among the soil, piles, and superstructure. The environmental conditions include multi-directional wind, waves, currents, and seismic loads. In the static linear analyses (i, ii, and iii), the overall results confirm that the unity checks (UCs) for structural members, tubular joints, and piles remain below allowable thresholds (UC \(<\) 1.0), thus meeting API RP 2A-WSD, AASC, IMCA, and Pemex P.2.0130.01-2015 standards for different load demands. However, these three analyses also show hydrostatic collapse due to water pressure on submerged elements, which is mitigated by installing stiffening rings in the tubular components. The dynamic analyses (ii and iii) reveal how generalized mass and mass participation factors influence structural behavior by generating various vibration modes with different periods. They also include a load comparison under different damping values, selecting the most unfavorable scenario. The nonlinear analyses (iv and v) provide collapse factors (Cr = 8.53 and RSR = 2.68) that exceed the minimum requirements; these analyses pinpoint the onset of plasticization in specific elements, identify their collapse mechanism, and illustrate corresponding load-displacement curves. Finally, spectral fatigue assessments indicate that most tubular joints meet or exceed their design life, except for one joint (node 370). This joint's service life extends from 9.3 years to 27.0 years by applying a burr grinding weld-profiling technique, making it compliant with the fatigue criteria. By systematically combining linear, nonlinear, and fatigue-based analyses, the proposed framework enables robust multi-hazard verification of marine platforms. It provides operators and engineers with clear strategies for reinforcing existing structures and guiding future developments to ensure safe long-term performance.  
Footnote †: journal: Applied sciences  
_Article_
# Antennas and Propagation: A Sensor Approach
###### Abstract  
Antennas are essentially transducers, as they convert electromagnetic fields into signals and vice versa. Moreover, remote sensing or sensor networks cannot be imagined without antennas, and radiowave propagation in complex environments is a crucial aspect for the operation of such systems. New technologies such as 5G generate further perspectives for sensor networks and raise additional challenges to antenna design.  
This Special Issue gathers topics of utmost interest in the field of antennas and propagation, such as:  
* New directions and challenges in antenna design and propagation;
* Innovative antenna technologies for space applications;
* Metamaterial, metasurface and other periodic structures;
* Antennas for 5G;
* Electromagnetic field measurements and remote sensing applications.  
We originally invited the authors who contributed to the 2020 International Workshop on Antenna Technology, held in Bucharest (Romania) on 25-28 March, to submit thoroughly extended versions of their work. Surprisingly, half of the submissions to the Special Issue through the deadline were not conference paper extensions but completely new contributions.  
2021
## 2 Related Work
### Neural Network Model Architecture for NER  
In 2015, [PERSON] et al. [5] proposed the utilization of a Bi-LSTM network for past and future input features for sequence tagging task and CRF for sentence-level tag information to produce higher tagging accuracy. Compared with other state-of-the-art NER network architectures for the CoNLL-2003 (English) corpus, their NER model with both Senna embedding and gazetteer features achieved superior performance of 90.10% F1. Since then, the combination of Bi-LSTM and CRF has been popularly accepted as an effective network architecture for sequence tagging tasks, including NER.  
Later, [PERSON] et al. [7] proposed a neural network for NER task, consisting of Bi-LSTM and CRF with character-based word representations learned from the supervised corpus and unsupervised word representations learned from unannotated corpora. They utilized word embedding, which was pre-trained using skip-n-gram and character embedding based on a lookup table.  
[PERSON] and [PERSON] [6] introduce a novel neural network architecture that benefits from both GloVe-based word embedding, and character-level features, by using a combination of Bi-LSTM, CNN and CRF. Similarly, [PERSON] et al. [22] presented a deep neural network architecture based on Bi-LSTM and CRF. They utilized GloVe word embedding models  
Figure 1: Further pre-training Pipeline of BERT language model.  
and Bi-LSTM-based character level embeddings for biomedical named entities recognition. They showed that the proposed deep neural network model using word and character-level embeddings outperforms significantly well compared to only word-level embeddings, which implies that character-level embeddings can help with out-of-vocabulary words and misspelled words, different forms of the same entity, etc. As a result, the character-level representation can infer a representation of unknown words in the training data and increase the Bi-LSTM model performance [22].  
By reflecting such previous research results, we construct a neural network model based on Bi-LSTM and CRF architecture, and imposing input feature vectors obtained from concatenating contextual word embedding, POS feature and character-level feature representations.