esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Plant Material and Growth Conditions  
Wheat (_Tritcium aestivum_ L) var. Kronstad F2004 was transformed using _Agrobacteriumium tumefaciens_-mediated transformation with two different constructs: pGreen rd29A::TPS1-TPS2 or pGreen 35::TPS1-TPS2, using the stress-inducible rd29A promoter and the CaMV35S promoter for the constitutive expression of the transgene, respectively [22; 24; 25]. These two constructs include the bar gene for phosphinothricin (PPT) selection. Mature wheat embryos were extracted from the seeds under aseptic conditions using a scalpel blade and were cut longitudinally and transversely. Embryos were placed on CM4C semi-solid medium (pH 5.8 with 0.5 mg/L 2,4-D, 200 uM acetosyrringone, and 2.4 mg/L picloram), individually inoculated with 10 uL of active _A. tumefaciens_ LBA-4404 culture and incubated for 5 days in darkness at 26 \({}^{\circ}\)C. To remove the bacteria, embryos were washed 3 times with a washing solution (1.95 g/L MES; 100 mg/L ascorbic acid; 150 mg/L citric acid; 40 g/L maltose; 110 g/L sucrose; 0.02% Tween 20; 100 mg/L ascorbic acid; 150 mg/L citric acid; pH 5.8) for 15 min at 26 \({}^{\circ}\)C with mild stirring. Later, embryos were washed for 1 h with a solution containing 300 mg/L cefotaxime, 200 mg/L meropenem, and 10% PPM (plant preservative mixture). Sterile filter paper was used to remove the excess solution.  
Infected embryos were cultivated in CM4C semi-solid medium (pH 5.8 with 0.5 mg/L 2,4-D; 2.4 mg/L picloram; 750 uL/L PPM; 100 mg ascorbic acid; 150 mg citric acid; 50 mg meropenem; and 100 mg cefotaxime) for four weeks at 26 \({}^{\circ}\)C in darkness until the embryonic call were generated. Subsequently, the calli were transferred to MMSOC-2 media (4.4 g/L MS salts; 40 g/L maltose; 100 mg/L hydrolyzed casein; 1.95 g/L MES; 0.2 mg/L 2,4-D; 2.4 mg/L picloram; 750 uL/L PPM; 100 mg ascorbic acid; 150 mg citric acid; 50 mg meropenem; 100 mg cefotaxime; 3 mg/L PPT; 7 g/L agar; pH 5.8) for two weeks in darkness at 26 \({}^{\circ}\)C. After that, the calli were incubated on MMSOC media (3 g/L MS salts; 40 g/L maltose; 100 mg/L hydrolyzed casein; 1.95 g/L MES; 750 uL/L PPM; 100 mg/L ascorbic acid; 150 mg/L citric acid; 50 mg meropenem; 100 mg cefotaxime; 3 mg/L PPT;7 g/L agar; pH 5.6) for 4 additional weeks under a 16 h light/8 h darkness photoperiod at 26 \({}^{\circ}\)C. Surviving root-forming plantlets (T0 generation) were transferred to pots and tested to PPT resistance. The transgenic plants were grown to maturity in a greenhouse.  
Non-transformed and transgenic wheat seeds were placed in Petri dishes with filter paper sown in sterile water to induce germination. After root emergence, seedlings were exposed to light to promote photosynthetic activity. Once the first leaf was developed, plants were transferred to pots filled with a mixture of soil and peat moss (1:1) in greenhouse conditions at temperatures of 20-25 \({}^{\circ}\)C during the day and 10-15 \({}^{\circ}\)C at night, with constant watering.
# Broadband Generalized Sidelobe Canceler Beamforming Applied to Ultrasonic Imaging
## 4 Results
### Circular Cyst Experiments  
Echo data for the circular cyst and scattering points in a high speckle noise environment are obtained. Several scattering points are set around the cyst, which have different and lower scattering coefficients to increase the background speckle noise [26; 27]. Beamforming responses after different beamformings are shown in Figure 5. Figure 6 shows the lateral variation of the beamforming responses.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Beamforming** & **FWHM (mm)** & **PSL (dB)** \\ \hline DAS & 1.50 & \(-\)15.4 \\ SA & 1.11 & \(-\)30.2 \\ GSC & 0.60 & \(-\)41.8 \\ Broadband-GSC & 0.63 & \(-\)44.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Full width at half maximum (FWHM) and peak sidelobe level (PSL) of the scattering point at depth z = 41 mm.  
Figure 5: Simulated circular cyst and scattering points imaging in high speckle noise environment. (**a**) DAS; (**b**) SA; (**c**) GSC; (**d**) Broadband-GSC.  
The lateral resolution of the scattering point at (x, z) = (-4, 46.5) mm is 1.71, 1.29, 0.85 and 0.73 mm after DAS, SA, Broadband-GSC and GSC, respectively. Results show that the Broadband-GSC still can improve the lateral resolution compared with DAS and SA during the high speckle noise environment. Broadband-GSC improves the FWHM by 34.1% and 50.3% compared with SA and DAS, respectively. The results are the same as point targets experiments, which validate the effectiveness of the proposed beamforming.  
Comparing the lateral variation after each beamforming at z = 32 mm, Broadband-GSC has almost the same result as DAS. Meanwhile, GSC generates the similar result as SA. The reason is that scatters do not have obviously higher signal-to-noise ratio (SNR) than background. The system's SNR also influences the performance of the adaptive beamforming. Reference [28] indicates that the performance of the adaptive beamforming is dependent on the system's SNR. When the system's SNR decreases, imaging quality after adaptive beamforming will rapidly decrease. Consequently, the advantages of combining plane wave emission with Broadband-GSC to improve both the system's SNR and lateral resolution will be discussed.
# A Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks
## 3 Methodology  
In the current research, we followed the steps shown in Figure 1. We first explained the definition of 'consistency' in machine learning, which we aimed to explore. Then, we conducted a literature review by highlighting two notable research works [20,21] that have been conducted. As the next step, we explained the mathematical intuition behind consistent approximators. After that, we presented the mathematical construction of the proposed RNN formula, which aims to provide consistent estimates independent of the number of input increments. To evaluate the proposed approach, we used both synthetic and real-world datasets.  
To test the proposed approach, we synthetically generated data points in one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) spaces. We then applied various functions to these data points to generate target data points for the NN architectures we aim to train. The expectation is for the NN architecture to learn these underlying mapping functions and provide consistent estimates when the inputs change.  
In sequential learning, three major neural network (NN) structures can be encountered: convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Although all these sequential learning approaches were tested on the generated datasets, for reasons of brevity, we only present and compare our results obtained with Consistent RNN (ConsRNN)-based NN architectures with the results from the Gated Recurrent Unit (GRU)-based NN architectures because this model outperformed the others. Additionally, including the Transformer-based NN architectures, none of the trained models were able to provide consistent estimates other than the ConsRNN-based NN architecture. For further details, the published data and code can be checked.  
We trained standard RNN-based architectures, such as GRU and LSTM, on the generated data points, along with the proposed RNN Cell-based NN architectures. The goal was  
Figure 1: The comprehensive research process followed in this study, outlining the key stages and the methodology employed to achieve the research objectives.  
to train these architectures with a sequence consisting small number of increments, \(\approx\)100, while testing their ability to predict outputs with sequences consisting of large numbers of increments in the same domain, i.e., \(<\)1000. All the details of the data generation process and the training results are provided in the Experiments section. The same testing procedure was applied to real-world datasets, such as Forming Limit Curve (FLC) predictions and strain-stress predictions. The results are evaluated both qualitatively and quantitatively and discussed in detail. The strengths and weaknesses of the proposed approach are highlighted. In the concluding remarks, avenues for further research are outlined, and the primary contributions of this study are scrutinized.
Due to the complex influence factors on NH\({}_{3}\) emissions, such as the housing system, manure storage, and environmental control technique, it is hard to collect representative data [19, 20], [PERSON] et al. [21] used the RAINS model methodology to estimate the NH\({}_{3}\) emission from laying hen farms in the NCP, which was 0.43 g/d-hen. This result could not fully reflect the diurnal variation and housing system differences to understand the basic NH\({}_{3}\) emission and develop cost-effective mitigation strategies or techniques. Besides, with the development of manure cleaning technology in China, the manure belt has gradually replaced the slurry scraper to remove manure outside, which can greatly reduce NH\({}_{3}\) concentration inside the house [22]. However, the studies concerning NH\({}_{3}\) concentrations and emissions from laying hen houses with manure belts are still very limited in China [12, 23, 24]. Conventional cages (CC) are commonly used in the laying hen industry of China. In recent years, different designs of laying hen housing systems have been developed to improve the animal welfare and productivities, such as the aviary system (AV, for laying hens) and the natural mating colony cage (NM, for layer breeding), which may greatly affect NH\({}_{3}\) emissions [19, 20]. The AV, a welfare-friendly cage-free system, allows the hens to freely access wire-mesh floors at different heights and perform more natural behaviors inside. It is typically equipped with a manure belt beneath each wire-mesh floor to timely remove manure out of the house with the purpose of improving indoor air quality. The NM is an alternative of CC, or single floor system, for breeder birds. Compared with CC with artificial insemination, NM has a bigger activity space per bird and a lower stocking density for the breed hen and cock in the same cage, which allows the natural mating of the birds and thus could substantially save labor. Currently, studies on indoor air quality and emissions from the hen houses installed with different systems can rarely be found in China. It is still highly needed to study NH\({}_{3}\) emissions from different housing systems to provide a base for the emission abatement. This study measured NH\({}_{3}\) concentrations and calculated the emissions from CC, AV, and NM housing systems for laying hens in the NCP during summer with the following objectives: (1) to qualify their NH\({}_{3}\) emission levels in summer and characterize their diurnal variations; (2) to investigate the impact of the ventilation rate (VR), temperature, relative humidity (RH) and manure removal on NH\({}_{3}\) concentrations and emissions; and (3) to analyze the differences of NH\({}_{3}\) concentrations and emissions among the three types of laying hen houses.
## 1 Introduction  
Various participants are involved in the construction process, including clients, contractors, and technical supervisors. A project develops according to the achievement of goals that considers the requirements of the client and also ensures the qualifications of the chosen contractor and suppliers. A successful project requires appropriate contractor selection. Failing to select an adequate contractor often leads to price changes, a delayed project schedule, and substandard quality. However, the prequalified bidding process is the only step to regulate the selection of the qualified contractor, and each client conceives the evaluation criteria differently.  
Most project leaders in construction underestimate and neglect the importance of the contractor selection process [1; 2]. It is challenging to analyze a wide variety of uncertainties presented by conditional considerations in decision making. Problems in selecting contractors include insufficient time for execution, complex procedures, and poor information channels [3]. The evaluation is usually performed by the general contractor's management based on intuition and experience [4]. In most cases, a selection process called \"prequalification\" is used to screen out contractors with inferior skills, leaving the contracting officer to select only from the remaining qualified bidders.  
The prequalification process attempts to identify a group of eligible contractors after bids are received to avoid wasting resources evaluating contractors. In the current literature, prequalification is defined more generally [5; 6; 7; 8; 9], according to which the process includes contractor registration and project-specific prequalification. Registration may include an initial evaluation and periodic updates to higher-level labor categories from a list of registered contractors according to their capabilities [10].  
Therefore, it is essential for contractors to implement a systematic mechanism to improve the performance of the bidding process for construction projects. A multicriteria approach solves this problem by allowing contractors to be evaluated on their financial and technological aspects, quality standards, technical performance, and other tangible and intangible characteristics. This helps to reduce the number of unsuccessful tenders [11], integrates prequalification in the selection of candidates, introduces a second standard to study contractors, and generates a final evaluation, taking into account the total tender cost to propose the best bidder [12]. In this study, a new contractor selection model is proposed for owners that extends beyond the traditional contractor management model.  
The remainder of this paper is organized as follows: Section 2 reviews the literature on contractor evaluation models and the prequalification criteria. Section 3 describes the interpretive structural modeling methodology for solving the problem. Section 4 reveals the implementation of the proposed approach to identify the key criteria in contractor prequalification. Sections 5 and 6 analyze and discuss the results, respectively. Finally, Section 7 draws conclusions.
## 5 Curriculum Data Scheduling
### Principles of Curriculum Learning at the Network Edge  
Curriculum, a learning strategy, refers to a structured set of content and learning experiences. In the realm of ML, curriculum learning is a concept inspired by the way humans learn new information progressively, from simpler instances to more complex ones. In the context of DML, we aim to design a training process in a way that starts with easier tasks and gradually moves to more difficult tasks. This learning approach has been proven to be helpful for improving the utility of global models [41].  
To apply curriculum learning at the network edge, for any smartphone \(n\), we summarize the following key principles:  
1. Score functions should depend on the global model: Any instance in \(\mathbf{d}_{n}\) is mapped to a numerical value by a score function. As (8) aims to maximize the utility of the global model, this score function should solely rely on the global model.  
2. Pacing functions should be monotonically increasing: The number of data samples scheduled per round is determined by a pacing function. Intuitively, it becomes more difficult to improve the performance as the global model converges. Therefore, more data should be scheduled for the later rounds to push the performance of the global model.
3. The difficulty level should be progressively increased: As model training progresses through successive rounds, the average difficulty level of \(d_{n,r}\) is expected to increase with the round number \(r\). Furthermore, \(d_{n,r}\) should be sorted such that the difficulty level of instances is also progressively increasing during local training.
4. The amount of data learned per round should be optimized: In a typical edge system, latency should be considered a key QoS factor. Therefore, data samples processed per round should be controlled and optimized; otherwise, curriculum learning may not be practical for the network edge.
# Research on Synthetic Aperture Radar Processing for the Spaceborne Sliding Spotlight Mode
## 4 Convolution Backprojection Algorithm for Sliding Spotlight SAR  
### CBP Algorithm  
The Convolution BackProjection (CBP) algorithm is a point-by-point imaging algorithm, which is a point-to-point image reconstruction process. The CBP algorithm can be artificially set according to the resolution requirements and the actual situation for different modes and different frequency bands. No matter how great the range migration is, the CBP algorithm can accumulate its energy for each point along its own migration curve [20]. The process is shown in Figure 5:  
Step 1: Construct a ground pixel grid point  
According to the resolution requirement, the pixel grid points of different pixel intervals are constructed in the ground imaging area, and the azimuth and distance coordinates of each pixel are recorded, to guarantee that the resolution in azimuth and range domain of the two-dimensional pixel unit are basically matched.  
Figure 5: Flow chart of CBP algorithm.  
Step 2: Reverse-projection  
1. Range pulse compression  
There is no need for multi-points accumulation after pulse compression, all oversampling points are left.
2. Determination of the beam coverage of ground pixels  
According to the corresponding azimuth of each pixel, the range coordinates, the antenna position and azimuth beam width of each pulse, it can be judged which of the pixels is within the coverage of the pulse beam and recorded.  
The judgement is based on the two window functions in the echo expression of the ground target. The point target located in the two window functions can be covered by the pulse beam:  
\[w_{1}=\text{rect}\bigg{(}\frac{\hat{t}-\frac{2R(t_{m};R_{B})}{c}}{T_{r}}\bigg{)}. \tag{19}\]  
\[w_{2}=\text{rect}\bigg{(}\frac{Avt_{m}-x}{X}\bigg{)} \tag{20}\]  
3. Pixel-by-pixel reverse-projection  
Then we calculate the distance between each pixel in the imaging area and the corresponding antenna position of the pulse, and then the range data is interpolated according to the distance to obtain the different energy contribution of the pulse to the different pixels covered by the pulse. For the same pixel, the energy from different pulses of its contribution needs coherent accumulation.  
In the time domain imaging algorithm such as CBP, the ground pixel are set artificially based on the resolution requirements and the actual situation, and the interval can be slightly smaller than the resolution generally, according to the desired image geometric direction.
# Alkali-Activated Materials with Pre-Treated Municipal Solid Waste Incinerator Bottom Ash
## 4 Conclusions  
From the above results, it is possible to conclude that:  
* The chemical and mineralogical characterization of MIBA showed that it has potential to be activated, but the high aluminum content is problematic in alkali-activated materials due to the formation of hydrogen gas.
* The pre-treatment was effective at reducing the amount of pure aluminum available to react, thereby allowing the production of mortars with adequate dimensional stability;  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mix** & **Water Absorption by Immersion (\%)** & **Standard Deviation** \\ \hline
0.0 M & 13.4 & 0.14 \\
0.25 M & 13.1 & 0.09 \\
0.5 M & 14.8 & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Water absorption by immersion of the mortars with the CAA design.  
Figure 15: Capillary absorption coefficients of CAA mortars: (**a**) between 10 and 90 min; and (**b**) between 90 and 4320 min.  
* FA improved the workability of mixes in the fresh state, regardless of the activator design. Nonetheless, the mixes with the CAA design presented a higher fluidity since they had the lowest sodium silicate content;
* MIBA led to a less dense material due to the foaming reaction between the residual aluminum with the alkaline activator. This caused a higher porosity, lower mechanical strength, and higher water absorption, especially in contents equal to or greater than 75%. However, mixes with a maximum of 50% MIBA content did not show an excessive deterioration in the properties evaluated;
* Carbonation substantially improved the mechanical properties of AAM, especially in those with higher contents of MIBA, as it is a precursor with a high calcium content;
* Shrinkage of the sealed and unsealed specimens is influenced by thermal curing and storage conditions; therefore, the sealed specimens that experienced minimal exchange of water with the environment exhibited higher shrinkage associated with the internal reactions within the AAM matrix;
* All mixes presented sodium migration when performing the capillary test, the magnitude of which was greater in mixes with a greater amount of MIBA.  
Conceptualization, R.V.S.; methodology, R.V.S.; formal analysis, Y.A.; investigation, Y.A.; resources, R.V.S.; writing--original draft preparation, Y.A.; writing--review and editing, R.V.S. and J.d.B.; supervision, R.V.S. and J.d.B.; project administration, R.V.S.; funding acquisition, R.V.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FCT--Foundation for Science and Technology, through research projects PTDC/ECI-CON/29196/2017 (RInPolyCrete) and EXPL/ECI-EGC/0288/2021 (ECO2 Alkrete).  
Not applicable.  
The authors acknowledge the support of the CERIS Research Institute, IST, University of Lisbon.  
The authors declare no conflict of interest.