esa-sceva/satcom-chunk-collection huggingface dataset:
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 3 The Proposed Approach
### Multi-Scale Feature Extraction  
#### 3.2.1 The PointConv Backbone  
We extract hierarchical point features using PointConv layers [38], which extend conventional convolutions to irregular 3D point sets. Formally, given an input point cloud \(\mathcal{X}=\{\mathbf{x}_{i}\}_{i=1}^{|\mathcal{X}|}\) with the associated per-point features \(\mathbf{F}_{\mathcal{X}}\in\mathbb{R}^{|\mathcal{X}|\times D_{\text{emb}}}\), each PointConv layer aggregates the local features as follows:  
\[\text{PointConv}(\mathbf{F}_{\mathcal{X}};\mathcal{X})_{(i_{j})}=\sum_{\mathbf{ x}_{j}\in\mathcal{N}(\mathbf{x}_{i})}S(\mathbf{x}_{j})\;\mathbf{W}_{(:,i)}\;( \mathbf{F}_{\mathcal{X}})_{j}, \tag{5}\]  
Figure 1: Overall architecture. We highlight three main components of our framework: (1) multi-scale feature extraction, (2) geometric keypoint attention, and (3) progressive flow refinement. With input pair of point cloud \(\mathcal{P}\) and \(\mathcal{Q}\), our model outputs the final corresponding displacements at coarsest level \(l=1,\mathbf{v}^{(1)}\) and \(\mathbf{u}^{(1)}\). The colors of the displacement vectors represent their angular orientation, facilitating the visual interpretation of flow directionality. Detailed descriptions of each module are provided in Section 3.  
where \(\mathcal{N}(\mathbf{x}_{i})\) denotes the local neighborhood of point \(\mathbf{x}_{i}\), \(S(\mathbf{x}_{j})\) is an inverse density scale factor computed via kernel density estimation, and \(\mathbf{W}\) represents the learnable convolutional weights conditioned on relative spatial coordinates. By stacking multiple PointConv blocks in a U-Net-style encoder-decoder [35], we generate a coarse-to-fine hierarchy of informative point feature embeddings.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 3 The Second-Generation Italian Smart Meter  
According to the Italian transposition of the new European Directive on energy efficiency, AEEGSI was made in charge of issuing a decision regarding the minimal functional requirements to be accomplished for second-generation smart meters (SM-2G). Thus, a public consultation took place for exploring the main features required, as well as the future-proof design criteria. The audience involved not only DSOs, but also retail suppliers, SM manufacturers, energy service companies, electricity industry associations, TLC regulator, standardization bodies, and telecommunications companies. Finally, the Regulatory Authority issued the decision 87/2016/R/eel and identified the minimal functional requirements for SM-2G and the expected performance levels for the new smart metering systems.  
The new SM-2G was conceived with the goal of improving services for customers, further reducing the practice of billing estimated consumption instead of actual consumption, and enhancing accuracy and precision of the metering data for all the customers connected in LV.  
The SM-2G communications features have two objectives: one is to bring raw metering data directly to the customers, while the other is to forward measurements to the corresponding DSO. The former stream comprises non-validated data, which are supposed to be used for provisioning services such as customer awareness and home automation. The latter stream conveys metering data to be used for billing operations, which, therefore, need a specific validation treatment by DSOs before being delivered to the retailer. Table 1 shows metering data managed by SM-2G, sampling data granularity, and a comparison with SM-1G for households and small business customers (up to 55 kW rated power).  
The regulatory decision is technology-neutral, hence the choice of the two communications technologies is left to the DSOs, with some constraints mainly regarding the usage of standardized technologies and frequency bands. It can vary from the consolidated PLC but also the newer Low Power Wide Area (LPWA) technologies, such as LoRa or SIGFOX, operating over unlicensed spectrum, or the forthcoming NB-IoT, operating over licensed spectrum.  
Figure 1 illustrates the new Italian electricity value chain after the SM-2G roll-out phase:
## 3 Results and Discussion
### Screening of Volatiles Mainly Effected by Dehydration Concentration  
In order to examine the relationship between the dehydration degree and the contents of volatiles, as well as to further screen the volatile compounds with content directly determined by the dehydration concentration effect, unary linear regression analysis (U-LRA) was employed between the juice volumes per 100 berries (JV100) (Figure 1f) and the contents of volatiles, expressed as \(\upmu\)g/L; the former was chosen as the independent variable and the latter was chosen as the dependent variable. The compounds with both R\({}^{2}\) > 0.9 and \(p\)-value of significance <0.01 are listed in Table 2. It should be noted that the JV100 directly represents the exact dehydration degree and concentration effect, with a lower JV100 indicating greater dehydration and concentration effect. Therefore, a negative correlation with the JV100 indicates a positive correlation with the dehydration degree.  
The U-LRA results indicated that thirteen compounds expressed as \(\upmu\)g/L were highly correlated with JV100 (R\({}^{2}\) > 0.9 and \(p\) < 0.01), twelve of which were negative and one positive. Six compounds in the free fraction were highly negatively correlated with JV100, namely, _trans_-2-hexenol, heptanol, octanol, benzyl alcohol, 1-octen-3-ol, 2-methylbutanal, and \(\alpha\)-terpinene. For the bound fraction, _trans_-2-heptenal, methyl salicate, 2-methyl-1-butanol, 3-methyl-1-butanol, and 1-octen-3-ol were highly negatively correlated with JV100. Regarding the total content of the free and bound forms, five compounds were highly negatively correlated with JV100, including pentanol, methyl salicate, 2-methyl-1-butanol, 1-octen-3-ol, and 2-methylbutanal. The results of U-LRA indicated that the increases in these volatiles were mainly caused by the concentration effect, and these compounds remained relatively stable during the non-destructive on-vine dehydration process, especially 1-octen-3-ol. The free and bound forms, as well as the total content, of 1-octen-3-ol were highly negatively correlated with JV100. Although 1-octen-3-ol--a biochemical marker of fungal infection with a mushroom note--is a key aroma component in icewines and noble-rot wines, it can also impart off-flavors to the wine when present at high levels [33; 42]. Therefore, the accumulation of this compound during the late-harvest process should be carefully monitored.  
Propyl octanoate, which exists only in free form, was positively correlated with JV100, and its content decreased gradually despite the dehydration concentration effect, indicating that the degradation of this compound was faster than the concentration and was determined by the dehydration level.
## 5 Experimental Setup
### Word Vectors  
A word2 vec (skip-gram) model was trained on 502,000 restaurant review sentences. These sentences were obtained from Yelp and SemEval-2016 Task 5 datasets. Effective training of our model relied on the tuning of different hyper parameters (e.g., context window, min word count). These parameters can highly influence the quality of word vectors. Five parameters were used to control the training of the word2 vec (skip-gram) model and their best values were chosen by hit and trial. Moreover, a summary of the values of parameters is shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Word size & 400 \\ Min word count & 1 \\ Number of workers & 5 \\ Context & 5 \\ Down sampling & \(1\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Word2 vec model tuned parameters.  
Dimension (D): This is used to control the size of the word embedding or vector. The size chosen for each word vector during training was \(D\) = 400. Thus, each \(\overrightarrow{w}\) is equal to the dimensions of \([1\times D]\).  
Minimum word count: In large text corpora there are always some words that are not very frequent and less meaningful. So, this parameter controls the minimum number of word counts that should be allowed for a word to be considered during the training (or learning) process. We used a minimum word count equal to 1 because we already had a small training dataset and wanted to have word vectors for the maximum number of words.  
Context: Context is actually the size of a window around each word. We used a context size of five words. It means that for any centered word \(w_{0}\) in a context size of five, we always had its left \(w_{-1}\), \(w_{-2}\) and right \(w_{1}\), \(w_{2}\) context. This is one of the most important parameters of the word2 vec model.  
Down Sampling: This parameter is used to control the most frequent words in the training text corpora. The most common range of down sampling lies between \([1\times 10^{-5},1\times 10^{-3}]\).  
Number of workers: This is the number of threads that we can manage to run on the machine for achieving the desired parallelism during the training process.  
We may not have all the word vectors against each word that falls under the domain of restaurant reviews. To address the missing word-vectors problem, we replaced them with zeros equal to the dimensions of the existing word vectors \(\overrightarrow{w}\).
## 6 Constant-Ductility Residual Displacement Ratio Spectra
### Limitations and Future Work  
While the proposed residual displacement spectra provide a practical framework for estimating post-earthquake displacements under sequential ground motions, the following limitations warrant explicit acknowledgment:  
1. Simplified Structural Idealization: the spectra are formulated under the assumption of bilinear hysteretic behavior, which inherently disregards critical nonlinear characteristics such as strength degradation, stiffness deterioration, and pinch effects. While the bilinear hysteresis assumption remains applicable to structures with stable energy-dissipating mechanisms (e.g., ductile steel systems), it may lead to biased predictions of residual displacements in systems exhibiting complex hysteresis, such as reinforced concrete structures.
2. Temporal and Socio-Resource Considerations: the current framework focuses solely on the mechanical aspects of structural repairaability and does not explicitly account for temporal factors, such as the interval between the mainshock and aftershock or the timeline of post-event resource allocation. While the spectra aid in prioritizing structures requiring urgent post-mainshock intervention (e.g., those exceeding displacement thresholds under aftershocks), the model overlooks socio-temporal dependencies critical to community-level recovery, including aftershock probability decay rates, repair resource availability, and reconstruction schedules. To address these gaps, future research will focus on the following: 1. Quantifying residual displacement demands for structures with complex hysteresis (e.g., strength degradation and pinch effects) under sequential excitations. 2. Integrating probabilistic aftershock timing models with socio-economic recovery metrics (e.g., infrastructure interdependencies, resource mobilization curves) to link mechanical damage thresholds to functional repairaability timelines.  
Figure 18: Comparison of [PERSON] model with sequence-based model: (**a**) \(\mu=2\); (**b**) \(\mu=4\); (**c**) \(\mu=6\).
## 2 Materials and Methods
### EoL Options for c-Si Solar Modules
#### 2.1.4 Recycling  
Different techniques for recycling solar modules are been developed for all PV technologies. Specifically, for c-Si, there are different possibilities for recycling and good results can be achieved with alternative or combined recycling processes. Generally, the first step is to mechanically remove the aluminium frame and the junction box from the rest of the module. The next stage is to delaminate or remove the encapsulant material, which is normally ethylene-vinyl-acetate (EVA). Several techniques can be used in this phase [2], including thermal [28; 29], and chemical (organic and inorganic) [20; 30; 31] and mechanical recycling processes [7].  
The most common process for recycling c-Si modules, which is commercially available in Europe [32], is based on a mechanical process for the extraction of the remaining materials of the module. However, the maximum amount of recovered materials from this process is currently about 80%, which is insufficient for future regulation requirements [33] and the value of the recovered resources is lower than that of the original raw materials [32]. Recently. the European company PV Cycle has achieved a recycling rate of 96% for c-Si PV modules using a new process that combines mechanical and thermal treatments [34].  
During the thermal process, the EVA and backsheet (Tedlar(r)) layers are burned, releasing potentially noxious and harmful emissions into the air. Therefore, in this study, we considered the treatment of the exhaust gas, as it is essential in the pursuit of an environmentally responsible process [28; 29]. The same assumption was made for the chemical process, as we considered that the backsheet is removed from the module after the chemical treatment. Assuming Tedlar(r) as the backsheet, it is demonstrated that it has a good thermal stability in the range of approximately \(-\)70-100 \({}^{\circ}\)C and loses its strength at 260-300 \({}^{\circ}\)C, which is not hot enough for the EVA to start to decompose and so the Tedlar can be separated first from EVA [28], however more experiments need to be done related to this temperature.
## 4 Results  
### Parameter Optimization Results  
The swarm size of the optimization algorithm plays a critical role in determining the precision of the optimization process. Different particle swarm sizes lead to distinct optimization outcomes. Generally, a swarm size within the range of 20 to 100 is deemed appropriate. In this study, swarm sizes of 20, 40, 60, 80, and 100 were utilized, each with 120 iterations, to optimize the parameter selection for XGBoost via the MPA.  
As depicted in Figure 6a, when the MPA is applied with varying swarm sizes, the fitness function achieves convergence after 90 iterations. It is worth highlighting that a swarm size of 60 results in the minimum fitness value (6.58 \(\times\) 10\({}^{-4}\)). By evaluating the \(R^{2}\), _RMSE_, _VAF_, and _MAE_ scores using Equations (9) to (12) (Figure 6b-e), it can be observed that when the swarm size is 60, the five evaluation metrics collectively achieve optimal performance. Specifically, the \(R^{2}\) score is 0.958, and the _VAF_ is 95.981, both higher than those of other swarm sizes, indicating a superior fitting performance. Meanwhile, the _MAE_ is 4.844, and the _RMSE_ is 7.033, which are lower than those of other swarm sizes, suggesting minimal regression analysis error.  
Figure 5: Analysis flowchart.  
Table 2 presents the XGBoost parameters (_num_trees_, _max_depth_, and _eta_) obtained when the swarm size is 60. These parameters yield the most favorable regression analysis results, validating the optimal performance achieved with a swarm size of 60.  
Figure 6: The result of parameter optimization. (The red font color represents the optimal values).
# Large Eddy Simulation and Dynamic Mode Decomposition of Turbulent Mixing Layers
## 3 Results and Discussions
### Characteristics of the Turbulent Mixing Layer
#### 3.1.1 Basic Characteristics of Turbulent Mixing Layer  
Figure 1(a) shows the vorticity thickness growth rate of the basic case. It can be seen from the figure that the growth process of the turbulent mixing layer mainly presents two  
different stages. In the first stage, the flow is in laminar flow state, and the growth rate is relatively small, and the growth curve is approximately linear. The second stage flow is in turbulent state after transition, and the growth rate of turbulent state is significantly higher than that of the first stage, and the growth curve is also approximately linear.  
The velocity center line of the turbulent mixing layer \(y_{c}(x)\) is obtained according to the following two equations:  
\[y_{c}(x)=y_{0.5}(x) \tag{29}\]  
\[\frac{\overline{U}(x,y_{a})-U_{2}}{U_{1}-U_{2}}=\alpha \tag{30}\]  
where \(\overline{U}\) is the average streamwise velocity.  
Figure 2b shows the velocity center line of the turbulent mixing layer, from which it can be seen that it has a significant bias to the low-speed side. Corresponding to the vorticity thickness growth curve in Figure 2a, there are two main sections in the velocity center line of Figure 2b, and the turning positions of the curves in the two graphs are the same, which indicates that the laminar/turbulent flow pattern also affects the degree of the velocity center line of the turbulent mixing layer to the low-speed side.  
The Reynolds shear stress is dimensionless as follows:  
\[\sigma_{xy}=\left\langle u^{\prime}v^{\prime}\right\rangle \tag{31}\]  
\[\hat{\sigma}_{xy}=\frac{\sigma_{xy}}{\Delta U^{2}} \tag{32}\]  
The normal position is dimensionless as follows:  
\[\varepsilon=\frac{y-y_{c}(x)}{\delta_{\omega}(x)} \tag{33}\]  
According to these two dimensionless quantities, the self-similarity of turbulent mixing layer can be observed. Figure 2c shows the \(\hat{\sigma}_{xy}-\varepsilon\) curve. According to the \(\hat{\sigma}_{xy}-\varepsilon\) curves of the five flow directions, the turbulent mixing layer is self-similar at about three-quarters of the flow direction in the physical computational domain. The Reynolds normal stress curves in \(x\) and \(y\) directions have similar phenomena, which are not provided here due to the length of paper.  
Figure 3 shows the vorticity evolution contours in the physical domain of the last FTC (41,300-42,000 steps, with 100 steps interval). It can be seen that the turbulent mixing layer begins to lose stability at about one third of the flow direction in the physical domain, and then generates vortex rolling, falling, pairing and merging.  
Figure 2: (**a**) Vorticity thickness growth rate; (**b**) center line of mixing layer; (**c**) Reynolds shear stress curve.
## 2 Preliminaries
### FBL Communication Performance  
To achieve low-latency transmission, the data packet is transmitted within the FBL regime, which means the FBL regime should be considered during the transmission phase.  
Following the FBL regime [13], the coding rate \(r_{k,t}\) for an additive white Gaussian noise channel between UE \(k\) and edge server in frame \(t\) is shown to have the following approximation:  
\[r_{k,t}=\mathcal{R}(\gamma_{k,t},\varepsilon_{k,t},n_{k,t})\approx\mathcal{C}( \gamma_{k,t})-\sqrt{\frac{V(\gamma_{k,t})}{n_{k,t}}}Q^{-1}(\varepsilon_{k,t}) \tag{6}\]  
where \(\gamma_{k,t}\) is the instantaneous SNR, \(\varepsilon_{k,t}\) is the error probability of transmission, \(n_{k,t}\) is the blocklength, \(\mathcal{C}(\gamma_{k,t})=\text{log}_{2}\left(1+\gamma_{k,t}\right)\) is the Shannon capacity, \(V(\gamma_{k,t})=1-\frac{1}{(1+\gamma_{k,t})^{2}}\) is the channel dispersion, \(Q(x)=\int_{x}^{\infty}\frac{1}{\sqrt{2\pi}}e^{\frac{-x^{2}}{2}}dt\) is the Gaussian Q-function, and \(Q^{-1}(x)\) is its inverse function. From (6), the error probability of the transmission \(\varepsilon_{k,t}\) can be expressed as:  
\[\varepsilon_{k,t}\approx\mathcal{Q}\left(\frac{\mathcal{C}(\gamma_{k,t})-r_{k,t}}{\sqrt{\frac{V(\gamma_{k,t})}{n_{k,t}}}}\right) \tag{7}\]  
For a data packet with packet size \(m_{k,t}\) transmitted at the UE \(k\) in frame \(t\), the coding rate \(r_{k,t}\) can also be expressed as:  
\[r_{k,t}=\frac{m_{k,t}}{n_{k,t}} \tag{8}\]From the analysis above, we know that allocating more blocklength for the transmission of a data packet will reduce the transmission error probability. However, it will also result in longer transmission times. Additionally, the error probability is affected by the time-varying CSI. Therefore, choosing the appropriate blocklength and transmission timing for data packets is important to achieve a trade-off between error probability and transmission time.
## 5 Efficiency and Finity of Computations
However, this is not the case, because the state of domain at a previously existing time step \(t\) may be affected by changes occurring at a newly introduced time step \(t^{\prime}\) that precedes \(t\). In comparing step 2-4, 10-12, and 16-18, one may observe that, when time steps greater than \(m\) are already present in the grounding at the time of steps 2-4, the corresponding rules are grounded and solved once at steps 2-4, then retracted at steps 10-12, and later regrounded and solved at steps 16-18. Undoubtedly, this process involves some degree of potential recomputation, but is necessary to ensure the satisfaction of the conditions of the Module Theorem. Retracting the rules is necessary to ensure that the necessary conditions are satisfied during the following expansion phase. Rather than using \(newtime/2\), one may be tempted to introduce new time steps by means of a relation \(newtime/1\) and correspondingly modified rule from (17):  
\[newtime(\ T+d\ )\leftarrow\Gamma(T). \tag{20}\]  
However, this rule may in some cases lead to the violation of the conditions of the Module Theorem. To understand how this may happen, consider time steps \(t_{1}<t_{2}<t_{3}\) greater than \(t\) and such that \(t_{1}\) and \(t_{2}\) are enabled (i.e., \(enabled(t_{1})\) and \(enabled(t_{2})\) both hold), and rules at both time steps require the introduction of time step \(t_{3}\) (i.e., \(newtime(t_{1},t_{3})\) and \(newtime(t_{2},t_{3})\) both hold). Suppose that a retraction phase occurs (steps 8-12) to a time step \(m\) such that \(t_{1}<m<t_{2}\). In the following expansion phase (steps 15-18), the ASP program will effectively consist of two modules: a module \(P_{1}\) containing ground rules corresponding to time steps \(0\) through \(m-1\), and a module \(P_{2}\) containing ground rules corresponding to time steps \(m\) and greater. It is not difficult to see that each module contains a ground instance of the above definition of \(newtime/1\) that defines \(newtime(t_{3})\). This violates the condition of the Module Theorem according to which \(O_{1}\cap O_{2}=\emptyset\). The use of relation \(newtime/2\) in place of \(newtime/1\) prevents this from happening, because module \(P_{1}\) defines \(newtime(t_{1},t_{3})\) and module \(P_{2}\) defines a different literal \(newtime(t_{2},t_{3})\). The soundness and completeness of \(Incremental^{h}\) are given by the following theorem. Theorem 4.1: _Given a program \(\Pi\), set \(p\) of pre-determined time steps, set \(O\) of observations, and time horizon \(h\). If \(clingo(\sigma(\Pi)\cup p\cup O)\) terminates, then the trajectory encoded by \(Incremental^{h}(p,O,h)\) coincides with the trajectory encoded by the answer set of \(clingo(\sigma(\Pi)\cup p\cup O)\)._  
Proof: The proof easily follows from Theorem 4.1. The key is to note that:  
* At each iteration, \(Incremental^{h}\) is guaranteed to introduce at least the same time step introduced by \(Incremental\);
* The retraction phase preserves all (ground) rules for time steps prior to \(m\).