esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Procedures for Sustainable Structural Concretes Using Wastes and Industrial By-Products
## 4 Discussion
### Mechanical Properties
#### 4.2.1 Compressive Behavior of EAFS Mixtures
As it was already verified for high \(q\) values that the strength and elastic modulus tends to decrease, this deduction is possible by using the equation referred to in Figure 15 for EAFS aggregate mixtures. The second-degree polynomial equation also shows that from a certain value of \(q\) the strength and elastic modulus will not increase any more but must decrease if \(q\) becomes too low. The \(w/p\) can be correlated to the distribution modulus, \(q\), characteristic compressive strength, \(f_{\alpha k}\), and average elasticity modulus at 28 days, \(E_{cm}\), as shown in Figures 16 and 17, respectively. For this type of aggregate the mixtures show, as expected, that for greater \(q\) values the \(w/p\) increase according to a linear relation represented in Figure 16. Moreover, for high \(w/p\) values, the characteristic compressive strength and elasticity modulus will be smaller, according to the linear relation shown in Figure 12.0 It should be noted that, the linear regression has a maximum coefficient of determination (\(R^{2}\)) of 62% for the relation of \(q\) with \(w/p\) ratio (Figure 16), and of 71% and 56% for the relation of the characteristic compressive strength with \(w/p\) ratio and elasticity modulus with \(w/p\) ratio, respectively. That means that there is a small deviation of the \(w/p\) ratio values of the EAFS mixtures from the optimal \(w/p\) ratio. However, it can be stated that this difference is due to the different amounts of EAFS particle size fineness that the different EAFS grain size fractions have, that is, it may be necessary to increase or decrease the \(w/p\) ratio so that different EAFS  
Figure 12.1 Mechanical behavior as function of the amount of EAFS aggregates at 28 days: (**a**) characteristic compressive strength, \(f_{\alpha k}\); (**b**) average elasticity modulus, \(E_{cm}\). Figure 12.2 Evolution of the mechanical behavior as function of distribution modulus, \(q\), at 28 days: (**a**) characteristic compressive strength, \(f_{\alpha k}\); (**b**) average elasticity modulus, \(E_{cm}\). compositions reach the same workability as the reference mixtures, keeping the amount of _SP_ equal between them. In conclusion it can be said that, according to the modified [PERSON] and Andersen particle packing model and within the limits of the current investigation, parameters such as \(w/p\) and \(q\) may be approximately deduced adopting the linear and second-degree polynomial equations described before. These expressions are valid within the domain \(q\in\) [0.15, 0.35] and it may be assumed that similar relationships may be obtained by conducting similar studies for alternative materials. The quantity of EAFS waste aggregates can additionally be reflected as shown in Figure 12.3
# Haze-Aware Attention Network for Single-Image Dehazing
## 2 Related Work
### Deep Learning-Based Image Dehazing  
Due to the inability of prior-based dehazing methods to adapt well to all haze scenes, recent dehazing efforts have moved away from using priors. Some end-to-end networks directly estimated haze-free images [2; 3; 14; 23; 24; 25; 26; 27]. AECNet, SFNet, and others use a U-shaped structure, which has been proven to be superior for haze removal. These methods have achieved some results, but they perform poorly in dehazing real images. Recently, transformers [28; 29; 30; 31; 32] have been used in image tasks due to their advantage in capturing long-range relationships. However, their computational complexity increases quadratically with resolution, thereby making them unsuitable for pixel-to-pixel tasks like dehazing. Moreover, these methods lack theoretical interpretability. Therefore, instead of using transformers, we developed our own more efficient attention mechanism based on physical priors.
## 3 Results
### Simulations, Case Studies, and Real-World Applications
#### 3.3.2 Originality  
Simulations and case studies have revealed the significant impact of geographic, technological, and contextual factors on the implementation of energy systems. In islanded and remote microgrids, hierarchical EMS solutions address the unique challenges of intermittent RES and limited connectivity. The use of specialized real-time controllers for energy storage in floating PV systems exemplifies how tailored solutions can address site-specific constraints while ensuring energy reliability [52]. In urban settings, DAO-based energy management frameworks have demonstrated their ability to meet the complex demands of densely populated areas. By incorporating autonomous scheduling and energy-sharing mechanisms, these systems optimize resource use while addressing privacy concerns and potential communication disruptions. This approach highlights the critical role of advanced algorithms in achieving decentralized, resilient energy networks [54]. In isolated environments, P2P trading frameworks tailored for low-voltage nanogrids have demonstrated innovative strategies for managing energy exchanges between prosumers.  
A secondary control method ensures the stability of power-sharing arrangements, leveraging the droop control and adaptive voltage regulation to maintain reliable operation in resource-constrained systems [56]. Similarly, the integration of V2X technologies into P2P trading frameworks showcases the potential of EVs to support dynamic energy management in urban microgrids. These frameworks balance grid constraints with EV battery safety, enabling efficient trading without compromising operational limits [21]. Geographic and political factors further influence the implementation of energy systems. In Hong Kong, for example, demand-side flexibility strategies have been integrated with P2P trading to address regional energy challenges. These systems align with local environmental goals and regulatory frameworks, demonstrating how localized solutions can enhance sustainability while maintaining compatibility with the grid infrastructure [55].
# Performance Analysis of Hybrid Kinematic Mechanism for Fusion Reactor Maintenance
## 3 HKM Trajectory Planning
### Joint Space Trajectory Planning  
With the designed Cartesian space trajectory planning, the discrete inverse kinematic solutions for each driving joint can be obtained. Here, the polynomial interpolation method for interpolation and fitting of discrete data are used. To simplify the computation and avoid excessive acceleration during velocity change, the cubic polynomial interpolation method was used to fit the discrete joint space inverse kinematic solutions.  
The cubic polynomial interpolation method, as a commonly used interpolation method, has a continuous position and velocity curves and a variable acceleration, but the accelera  
Figure 4: Left outboard BB position (**a**); lifting left outboard BB trajectory (**b**).  
Figure 5: Left inboard BB position (**a**); lifting left inboard BB trajectory (**b**).  
tion is not necessarily continuous. Consider the case of an interpolation between two data points, whose mathematical expression is the following:  
\[q(t)=a_{0}+a_{1}(t-a_{0})+a_{2}(t-a_{0})^{2}+a_{3}(t-a_{0})^{3},\ t_{0}\leq t\leq t _{1}, \tag{29}\]  
where \(a_{0},\ a_{1},\ a_{2},\ a_{3}\) are the coefficients to be determined.  
Consider the case where two data points are given for interpolation if the position and velocity information \((q_{0},\ q_{1},\ \
u_{0},\ \
u_{1})\) at the initial moment \(t_{0}\) and the final moment \(t_{1}\) are given, and let \(h=q_{1}-q_{0},\ T=t_{1}-t_{0}\). Then, these parameters can be calculated using the following equations:  
\[\left\{\begin{aligned} a_{0}&=q_{0}\\ a_{1}&=v_{0}\\ a_{2}&=\frac{3t-(2v_{0}+v_{1})T}{T^{2}}\\ a_{3}&=\frac{-2t+(v_{1}+v_{1})T}{T^{3}}\\ \end{aligned}\right.. \tag{30}\]  
For the case of interpolation given a series of \(n\) data points, the entire interpolation curve can be computed sequentially by using the above equation for both adjacent data points. Then, the continuous inverse kinematic solution of each driving joint of the HKM can be obtained as shown in Figures 6 and 7.
# Framework for Social Media Analysis Based on Hashtag Research
## 3 Social Media Analysis Based on Hashtag Research Framework
### Limitation of Framework  
In the field of knowledge representation, it is necessary to draw attention to the limits of this method:  
**Regional Differences**--for an analysis that focuses on identifying attitudes without regional differences, see references [13; 54]. It is necessary to draw attention to this field in terms of research limitation. Regional differences mainly affect research in two areas of a disproportionate number of reports. An example is reference [14], which identified differences in corporate social responsibility based on the selected hashtag #CSR between developed and developing countries. In this study, 24,339 messages (21.42%) were selected from a basic set of 113,628 Instagram posts, which contain information about the location of the message. Of these, 9712 messages were sent from developed countries and 14,627 from developing countries. If the analysis was created from a global perspective, the results would be affected by more communications from the region of developing countries, which are about 50% more than from developed countries. See Table 1.  
**Focus only on hashtags**--the whole SMAHR framework is focused only on analysis through a specific part of the content, i.e., hashtags. Hashtags do not have to fully cover the meaning of the message, but they are used to express or emphasize something that is not expressed in the text of the message, such as emotions, mood, location, and/or political inclination [60]. Analyses that focus on text content without hashtag analysis have the same limitations. When interpreting the results, it is necessary to draw attention to this limitation in terms of research limitation.  
**Selected social network**--each social network has different target users. There is a difference in users' age structure, the use of technologies by which users connect (mobile vs. desktop), education, etc. These factors affect the resulting attitudes of users.
## 3 Materials and Methods
### Data Analysis  
The data were processed and analysed using the IBM Statistical Package for the Social Sciences 23.0 (SPSS). The K-means clustering procedure was used with the Elbow method to determine the optimal number of clusters. An independent sample \(t\)-test was performed to examine differences among the segments related to PLB, FBI, and SAT at the item level. The assumption of homogeneity of variances was satisfied, as indicated by [PERSON]'s test (\(p>0.05\)). For normality, skewness and kurtosis tests were used. The values satisfied the cut-off level of \(\pm 2.58\)[78,79]. The data had no outliers, as assessed by inspecting a boxplot for values greater than 1.5 [79]. Internal consistency for all three constructs received satisfactory alpha values (\(\alpha\geq 0.7\), [79,80]): perceived immersion (\(\alpha=0.7\)), perceived learning benefits (\(\alpha=0.84\)), and future behavioural intentions (\(\alpha=0.82\)).
## 3 Research Model Design
### Variable Selection and Data Sources  
#### 3.3.1 Variable Selection  
The explained variable in this article is the level of agricultural green development, which is calculated using the gray correlation method. The core explanatory variable of this article is agricultural science and technology innovation. As far as its process is concerned, agricultural science and technology innovation is the result of the interaction and coordination among relevant elements, such as science, technology and organization. It progresses through basic research, applied research and the demonstration and extension of agricultural scientific research. Therefore, this article uses agricultural technology research and development and agricultural technology application to measure the level of agricultural technology innovation, specifically expressed as the number of agricultural patent authorizations and agricultural technology market turnover.  
With reference to related research results, in this study, the following control variables were added: fiscal support for agriculture (FSA), expressed as the proportion of local financial expenditure on agriculture, forestry and water affairs in the sown area of crops (unit: yuan/hm2); agricultural machinery input intensity (MII), expressed as the proportion of the total power of agricultural machinery in the total sown area of crops (unit: kW/hm2); the agricultural disaster rate (ADR), expressed as the proportion of the affected area of crops in the total sown area of crops (unit: %); crop planting structure (CPS), expressed as the proportion of the planted area of food crops in the planted area of crops, excluding the planted area of food crops (unit: %); multiple cropping index (MCI), expressed as the proportion of the total planted area of crops in the cultivated area (unit: %).
# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
## 3 Transformer Architectures
### TransIQ-Complex  
We have developed a novel strategy for working with complex (\(I\) and \(Q\)) information in our tokenization module, inspired by the DeepFIR [20] model. This model has shown its effectiveness in AMR tasks by using a complex CNN layer with complex weights and biases. Considering the composite nature of the input data, featuring both real and imaginary components, the incorporation of a complex convolutional neural network (CNN) layer within our tokenization module emerges as a key strategy for enhanced data handling. This enhancement aligns with the TransIQ architecture, as illustrated in Figure 4, while integrating two essential modifications. Primarily, the model processes the input signal as a singular channel entity, diverging from the conventional dual-channel data approach. Specifically, although the input signal originally consists of two channels, one for the \(I\) and another for the \(Q\) components, utilizing a complex convolutional layer allows for the integration of these channels into a unified one-channel input, thereby resizing the signal's dimension to \(n\times 1\). Following initial segmentation into shorter tokens of length \(l\), the dimensionality of each token after applying sliding window is maintained at \(l\times 1\). A second modification is the inclusion of a complex convolutional layer in place of a standard CNN in the architecture shown in Figure 4. Subsequently, each token undergoes processing via a  
Figure 4: TransIQ architecture. In the tokenization module of this architecture, the input signal is segmented into tokens. Each token then undergoes one-dimensional convolutional before being processed by the Transformer-encoder module.  
complex convolutional layer that individually extracts features from the singular-channel data, generating an output with \(N_{c}\) output channels. Thus, this tokenization method transforms the one-dimensional vector of the signal sequence into a multidimensional feature representation.
# Enhancing Task-Oriented Dialogue Modeling through Coreference-Enhanced Contrastive Pre-Training
## 4 Experiment
### Experimental Setup  
All benchmarks are conducted in PyTorch on servers featuring Tesla V100 GPUs, equipped with 32 GB of memory. We utilize the Adam optimizer for model training and employ a learning rate schedule, gradually reducing it from \(2\times 10^{-5}\) to \(1\times 10^{-5}\) for downstream tasks. In detail, the learning rate is set to \(1\times 10^{-5}\). The pre-training batch size is set to 32, and we use a graph encoder with 5 layers of GIN and 768 hidden units.  
When selecting metrics for various downstream tasks, we tailor our choices based on the task's nature and dataset characteristics. For intent recognition, we evaluate using  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **\#Dialogue** & **\#Utterance** \\ \hline MetaWOZ [23] & 37,884 & 432,036 \\ Schema [24] & 22,825 & 463,284 \\ Taskmaster [25] & 13,215 & 303,066 \\ MultiWOZ2.1 [26] & 10,420 & 71,410 \\ MSR-E2E [27] & 10,087 & 74,686 \\ SMD [28] & 3031 & 15,928 \\ Frames [29] & 1369 & 19,986 \\ WOZ [30] & 1200 & 5012 \\ CamRest676 [31] & 676 & 2744 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of task-oriented dialogue datasets used in the pre-training phase.  
out-of-scope intent recall and accuracy (Acc) on all data, in-domain intents, and out-of-scope intents. The accuracy provides a general performance overview, while recall focuses on identifying positive instances, crucial for imbalanced datasets. This ensures the model performs well overall and effectively identifies minority class instances.  
For dialogue act prediction, we use micro-F1 and macro-F1 scores across three datasets. Micro-F1 averages the precision and recall scores for each dialogue act class, emphasizing the overall performance. Macro-F1 averages the F1 scores for each class individually, ensuring the performance on the less frequent classes is not overshadowed by the more common ones.  
For dialogue state tracking, we use joint accuracy and slot accuracy. Joint accuracy evaluates the alignment between the predicted and actual dialogue states at every turn, requiring precise correspondence for all domain-slot combinations. The slot accuracy assesses the agreement of each domain-slot-value triplet with its ground truth label.
# Inelastic X-ray Scattering as a Probe of Terahertz Phonon Propagation in Nanoparticle Suspensions
## 7 Looking Ahead  
As an extension of the studies discussed in this paper, one could consider systems of NPs interacting in a controlled fashion. In this respect, at Brookhaven National Lab, a group led by [PERSON] developed a new method to assemble nanoparticles (NPs) into ordered structures using polyhedral DNA frames [47]. These structures can be integrated by encapsulating NPs with grafted DNA strands [48]. The symmetry of the vertices determines the structure's coordination and overall geometry, while the linkage scaffold is uncoupled from the characteristics of the NPs embedded in it. The 3D DNA frameworks can be \"converted\" by promoting inorganic material growth on DNA struts. This procedure allows for the creation of several inorganic lattice frameworks made of silica [49], superconducting niobium [50], silicon carbide [51], and more. The size of the voids can be regulated through the growth time, allowing control of the scaffold's void ratio from 20% to 100% (bulk solid). Preliminary measurements were performed on these systems at APS-Argonne National Laboratory and NSLS-II-Brookhaven National Laboratory using inelastic X-ray scattering. The main result of such a measurement is summarized in Figure 13. Although the features observed in the measured line shape look promising, the scattering signal still needs sensible improvement. Specifically, an enhancement in the statistical accuracy and signal-to-background ratio would require larger samples and optimized sample+substrate arrangements.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** I warmly acknowledge [PERSON] for the help with the figure production and the stimulating discussions.  
**Conflicts of Interest:** The author declares no conflicts of interest.  
Figure 13: (**Panel A**) IXS spectrum of a SiO\({}_{2}\) nanoscaffold compared with the best fitting line shapes (see the legend). (**Panel B**) High-resolution electron microscopy image of one of the silica nano-platforms used as a sample for the IXS measurements (see text). (**Panel C**) Microscope image of the nano-scaffold assembly and related holder used to perform the measurement.
## 2 Usual Rotor Faults in Conventional Machines
### PMSM Faults: Demagnetization  
Permanent magnet synchronous machines (PMSMs) are widely used in LSHT applications, relying on a high pole count and strong rare-earth magnets to provide high torque density [42; 43; 44]. Moreover, pairing the features of PMSMs with the adoption of tooth-coil winding (TCW) configurations has proven to provide several advantages over the distributed winding (DW) counterparts [45]: short end-winding turns, low cogging torque, compactness, and a high copper space factor, which makes it possible to improve the machine power/weight ratio.  
Nevertheless, the PMSM rotor can suffer from rotor irreversible demagnetization, which can lead to performance penalizations. This arises as a combination of various phenomena of a thermal, electrical, and mechanical nature. The uniform demagnetization (UD) case is described as the even demagnetization of all rotor magnets where only an amplitude decrease in the airgap flux density is observed [46]. The most likely reason for uniform demagnetization in all poles is the operation at excessive temperature. On the other hand, partial demagnetization (PD) can arise from different phenomena such as a combination of high temperature operation and demagnetizing MMF applied in the \(dq\) rotating stator frame. Figure 4 compares the magnetic flux distribution of UD and PD, respectively.  
Figure 4: Types of demagnetization in electrical machines: (**a**) position of magnetization probes; (**b**) machine with uniform demagnetization; (**c**) machine with partial demagnetization.  
Figure 3: Different misalignment types: (**a**) reference faultless machine; (**b**) parallel misalignment; (**c**) angular misalignment.  
A common case of partial demagnetization is the so-called trailing edge demagnetization which occurs at the edges of the magnets opposite to the direction of rotation [47]. This is accentuated for motors operating with a high load angle (i.e., the difference between the stator magnetic axis and the rotor q-axis). All demagnetization cases dramatically affect the PMSM performance. Furthermore, undetected demagnetization in its early stages can rapidly escalate the severity of failure. This progression is attributed to the resultant increased current consumption, which leads to higher joule losses and a subsequent rise in temperature within the coils and magnets.
## 3 Wavelet Analysis
### Wavelet Coherence Analysis  
Wavelet coherence is mainly used to analyze the correlation of two time series, \(x(t)\) and \(y(t)\), at a specific spatial scale and time. The wavelet coherence of the two time series is expressed as follows [33]:  
\[W_{xy}^{2}(a,b)=\frac{\left|\overline{W}_{xy}(a,b)\right|^{2}}{\left|\overline {W}_{x}(a,b)\right|^{2}}\left|\overline{W}_{y}(a,b)\right|^{2}} \tag{4}\]  
where \(W_{xy}(a,b)\) is the coherence coefficient of two time series, \(x(t)\) and \(y(t)\); \(\overline{W}_{xy}(a,b)\) is a smoothed cross-spectrum of \(x(t)\) and \(y(t)\) sequences; and \(\overline{W}_{x}(a,b)\) and \(\overline{W}_{y}(a,b)\) are the smoothed wavelet spectra of time series \(x(t)\) and \(y(t)\), respectively. The value of the coherence coefficient ranges from 0 to 1. A larger value indicates stronger coherence, while a smaller value indicates weaker coherence.