esa-sceva/satcom-chunk-collection huggingface dataset:
### LCA Applications and the US Federal Government
The attendees all had different perspectives on how their mission areas use LCA, but shared appreciation for the value of reusable LCA datasets. They recognized reusable, open datasets are an efficient way to share the specialized expertise of and communicate the science of each agency. A vision for a distributed network of LCA researchers and methods developers sharing LCA information through a common data interface emerged from that meeting. Since the meeting at USDA headquarters, the USDA LCA Commons expanded into a Federal LCA Commons, including participants from FHWA, NIST, US Forest Service, six DOE national labs, and Department of Defense. In 2018, DOE, USDA, and EPA signed a three-way Memorandum of Understanding, formalizing the commitment to coordinate on the development of LCA methods, and increase the public's access to reusable federal LCA research outputs. At the time of this publication, the Federal LCA Commons contains 18 LCI data collections available from 11 participating federal organizations. The Federal LCA Commons collaboratively developed three products that advance LCA interoperability and reuse: the US Electricity Baseline life cycle inventory (LCI) [7], the Federal Elementary Flow List [8], and the Federal LCA Commons data portal at www.lacommons.gov. The LCA Commons community-of-practice is currently a decentralized initiative, with independent research and policy organizations communicating to develop LCA resources of common interest. NAL currently provides repository services for discovery and access of LCA Commons data products and provides metadata guidance and support. Each participant in the community of practice shares their perspective on the suitable and necessary applications of LCA, and how LCA data products support policy and decision making. The community develops working principles for deciding on priorities for the initiative through open communication and decides how to leverage the existing mission and funding to advance the federal LCA capacity as a whole.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 3 ConvLSTM-DSConvGRU Hybrid Prediction Model
### Improved Hybrid Network  
To reduce the network model parameters and prevent the overfitting caused by many parameters, this study introduces depthwise separable convolution to reconstruct the ConvGRU network layer, which replaces the standard convolution in the ConvGRU layer with depthwise separable convolution and constructs the DSConvGRU. Many studies [30; 31; 32] have confirmed that the differences in structure between LSTM and a GRU result in different ways of processing input. Compared with LSTM, a GRU is faster and has fewer model parameters. Therefore, combining LSTM and GRU models can alleviate the gradient vanishing problem to a certain extent, while also speeding up model training and convergence. The performance of hybrid models obtained by combining LSTM and GRU models is also superior. Therefore, this study combined the ConvLSTM network layer and the DSConvGRU network layer to improve the prediction performance of the model, enhance the representation and generalization capabilities of the model, and reduce the risk of overfitting.  
This study developed a neural network based on an encoder-decoder architecture, and its detailed structure is shown in Figure 8. In equation \(a^{2}\times b\) next to the network layer in the figure, \(a^{2}\) is equivalent to \(a\times a\), indicating the size of the convolution kernel for that network layer, while \(b\) represents the number of convolution kernels for that network layer. The optimal values for the size and number of convolution kernels in the network layers of the model were determined through manual parameter tuning. The network structure consists of two parts:  
1. The encoder module consists of two ConvLSTM layers and one DSConvGRU layer. A BatchNorm layer is added after each of the network layers to regularise the output results and accelerate the training process while improving the model stability. The encoder is used to receive samples of WRF output variables and extract the spatiotemporal features of the variable sequence, compressing the high-dimensional input frame sequence into a low-dimensional vector that contains historical spatiotemporal information.
2. The decoder module consists of one DSConvGRU layer, two ConvLSTM layers, and one Conv3D layer. The DSConvGRU layer and ConvLSTM layers are used to decode the spatiotemporal feature vector output from the encoder module. Finally, the Conv3D layer compresses the last dimension of the decoded feature vector and unfolds it into an output frame sequence.
## 3 Distributed Method  
### Basic Knowledge  
(1) Graph Theory: A weighted directed graph \(G=(V,E)\) is composed of a set of nodes \(V=(v_{1},v_{2},\cdots,v_{n})\) and a set of arcs \(E=\left\{e_{ij}=(v_{i},v_{j})\right\}\subseteq V\times V\). Therein, the \(i\)th agent can receive information from \(j\)th agent if the corresponding arc \(e_{ij}\) exists. If agent \(i\) can get (send) information from (to) agent \(j\), then agent \(j\) is called in-neighbors (out-neighbors) of agent \(i\). The set of in-neighbors (out-neighbors) is denoted by \(N_{i}^{in}\) (\(N_{i}^{out}\)) with cardinality \(d_{i}^{in}\) (\(d_{i}^{out}\)). It is assumed that every agent has self-loops, which means every agent can send and receive information to itself. An undirected graph is connected if there exists an undirected path between any pairs of distinct nodes. A directed graph is strongly connected if there exists a path between any pair of two nodes with respect to the orientation of arcs. A nonnegative matrix is called row (or column) stochastic if all of its row-sums (or column-sums) are 1. In this paper, graph nodes represent the agents, and the graph arcs represent the communication links among agents.  
(2) Basic Definitions: Consider a CHP system \(G\) with \(n_{1}\) EOAs, \(n_{2}\) CGAs and \(n_{3}\) HOAs. Let the CHP system include two subgraphs \(G_{EC}\) and \(G_{HC}\), where \(G_{EC}\) composed of EOAs and CGAs, and \(G_{HC}\) composed of HOAs and CGAs, meanwhile the two subgraphs are both strongly connected digraphs. Moreover, define two matrices \(R\), \(S\) in subgraph \(G_{EC}\), where \(R\) is row stochastic and \(S\) is column stochastic. Let \(r_{i,j}=1/d_{i}^{in}\) if \(j\in N_{i}^{in}\), \(r_{i,j}=0\) if \(j\
otin N_{i}^{in}\), and \(s_{i,j}=1/d_{i}^{out}\) if \(i\in N_{j}^{out}\), \(s_{i,j}=0\) if \(i\
otin N_{j}^{out}\). Similarly, define two matrices \(\overline{R}\) and \(\overline{S}\) in subgraph \(G_{HC}\), where \(\overline{R}\) is row stochastic and \(\overline{S}\) is column stochastic. Let \(\overline{r}_{i,j}=1/\overline{d}_{i}^{in}\) if \(j\in\overline{N}_{i}^{in}\), \(\overline{r}_{i,j}=0\) if \(j\
otin\overline{N}_{i}^{in}\), and \(\overline{s}_{i,j}=1/\overline{d}_{j}^{out}\) if \(i\in\overline{N}_{j}^{out}\), \(\overline{s}_{i,j}=0\) if \(i\
otin\overline{N}_{j}^{out}\).  
(3) Consensus algorithm (or protocol): Based on the definitions discussed above, the first-order consensus protocol is given by:  
\[\chi_{i}\left(t+1\right)=\sum_{j\in N^{in}}r_{i,j}\chi_{j}\left(t\right) \tag{10}\]  
Since R is row stochastic, Equation (10) forms the first-order consensus protocol [26]. Each agent reaches the final common value \(\chi_{i}\left(\infty\right)\)\(=\)\(\omega^{T}\chi\left(0\right)\).
## 4 Result and Discussion
### Single Dynamic Impact Test
#### 4.1.4 Energy Absorption Performance  
The energy absorption performance of the material under impact can be obtained by integrating the area of the stress-strain curve. Figure 7 shows the influence of fiber content on the energy absorption performance at different strain rates. It can be seen that the influence of fiber content on the energy absorption performance was extremely significant. In the unmixed fiber group, the energy absorption performance of the specimen was extremely low, almost negligible, showing the typical brittle characteristics of concrete. After fiber incorporation, especially at a high strain rate, the energy absorption performance was significantly improved. When the fiber content was 2%, the energy absorption performance  
Figure 6: Effect of fiber content and orientation on peak strain.  
Figure 7: Influence of fiber content on energy absorption property in dynamic impact test (fiber non-oriented).  
was improved by 31.06% at the same strain rate. When the strain rate continued to increase, the energy absorption performance increased by 196.97%. When the content of fiber was increased to 4%, the maximum energy absorption performance of the specimen increased by 26.02% compared with 2% at a high strain rate. As the dosage continued to increase, the improvement effect on the energy absorption performance was not obvious, and the highest dosage was only 9.72% higher than that of U-SF4.  
From Figure 8, the variation of energy absorption in the fiber-oriented group was similar to that in the non-oriented group, but the overall energy absorption effect was improved on the basis of the non-oriented group. Under the condition of the same fiber content, the maximum energy absorption effect of the directional distributed fiber specimens increased by 10.2%, 10.73%, and \(-\)11.52%, respectively. Obviously, the energy absorption performance of the specimen decreased slightly compared with that of the non-oriented block at the highest fiber content, and the reasons were analyzed as follows: (1) In order to maintain the orientation of the fiber as much as possible, steps such as tamping and vibrating were skipped in the orientation pouring process of the fiber. A large number of holes and bubbles in the slurry were not effectively discharged, and the stability of the internal structure was slightly worse than that of the specimen with complete vibration. (2) In the case of a large amount of fiber, the disoriented fibers may have a better absorption effect on the bearing of dynamic impact loads.
## 2 The Elephant in the Room: Great Diversity
### Nb-IoT
#### 2.3.1 Global, Carrier-Grade Network  
The advent of the NB-IoT marks a significant milestone in the evolution of global, carrier-grade networks, offering a robust solution for extending IoT connectivity across a myriad of devices worldwide. Engineered to operate within the licensed spectrum, the NB-IoT benefits from the ease-of-use, reliability, and security that are inherent to carrier-managed networks, ensuring stable and secure communications for IoT devices, even in the most challenging environments. In addition, licensed bands augment its resilience to external attacks, QoS, and general control over the network's lowest layers.  
Its unique capacity to penetrate deep into buildings and underground areas, mainly due to its usual functioning on sub-gigahertz bands, makes it an ideal choice for a wide range of applications, from smart metering in utilities to asset tracking in logistics. Moreover, the NB-IoT's highly efficient power usage can extend devices' battery lives for years, reducing the maintenance costs associated with IoT deployments.  
As part of the global LTE standard, the NB-IoT enjoys widespread international support from telecom operators, facilitating seamless, cross-border IoT solutions and driving forward the vision of a fully interconnected world. The global reach and carrier-grade reliability of the NB-IoT stand as pillars for the next wave of IoT innovation, enabling a new realm of applications that were previously untenable due to connectivity and power constraints. In the specific case of Spain, where this research was carried out, the three largest carriers (Telefonica, Vodafone, Orange) provide virtually full NB-IoT coverage over the whole population. This fact is vital for enabling solutions for a wide variety of scenarios, especially considering that a single NB-IoT sector, usually three sectors per cell, can operate more than 100,000 devices [35].
## 3 Results and Discussion
### Determination of feloipine particle size  
Microscopic evaluation  
The size of crystals for each crystalline form was evaluated. The results of the performed measurements are presented in Table 9, Figure 5 for the macrocrystalline form and Figure 6 for the microcrystalline form.  
The feloipine macrocrystalline form had an average diameter 117.70 times larger than the microcrystalline form. Within the same type of particle size, the standard deviation was over 10%, the particle size having a great intravariability.  
Sieving and sorting method  
This test was applied under laboratory conditions only for the macrocrystalline sample as the microcrystalline sample had particles of diameter smaller (in the range of 6-50 \(\upmu\)m) than the smallest 32 \(\upmu\)m mesh sieve. The mass of each determined fraction was calculated and expressed as a percentage and the results are given in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Felodipine Crystalline Type** & \multicolumn{2}{c}{**Macrocrystalline Form**} & \multicolumn{2}{c}{**Microcrystalline Form**} \\ \hline
**Parameter** & **Particle Size (\(\upmu\)m)** & **Particle Surface Area (mm2)** & **Particle Size (\(\upmu\)m)** & **Particle Surface Area (mm2)** \\ \hline Average value & 3573.71 & 14.683 & 30.3621 & 0.924 \\ STDEV * & 1412.23 & 10.465 & 17.55609 & 5.298 \\ SAMPLE MEAN & 565.00 & 4.187 & 6.389642 & 2.576 \\ \hline \end{tabular}  
* STDEV—standard deviation.  
\end{table}
Table 9: Determination of medium particle size for feloipine, macro- and microcrystalline forms (performed on 24 samples of each type).  
Figure 4: Microscopic appearance of the particles of microcrystalline feloipine form (**a**) 4\(\times\); (**b**) 10\(\times\); (**c**) 40\(\times\); (**d**) 100\(\times\).  
Figure 5: Histogram of granulometric analysis of macrocrystalline felodipine.  
Figure 6: Histogram of granulometric analysis of microcrystalline felodipine.  
Figure 7: Mass histogram of granulometric analysis of macrocrystalline felodipine using the sieving and sorting method.  
_Volumetric characteristics of felodipine powders_  
The results regarding the volumetric characteristics of felodipine powders are represented in Table 10 and in Figure 8.  
The bulk density of the macrocrystalline form was 0.75 g/mL and the tapped density was 0.83 g/mL. For the microcrystalline sample, the bulk density of the powder was 0.29 g/mL, and 0.34 g/mL in the compacted state. It should be noted that in the tapped form, the density of microcrystalline felodipine powder was 2.5 times lower than that of the high crystallinity felodipine, revealing the high electrostatic charge of the particles.  
Microcrystalline felodipine had poor flowing and lower compressibility properties due to the electrostatic interaction forces of the microparticles, which was also observed on microscopic evaluation. On the contrary, the macrocrystalline felodipine powder had excellent flowability and compressibility, which is remarkable for a pure active ingredient [32; 33].
## 4 Experimental Results
### Classification Results
#### 4.4.1 Performance Evaluation of Single Dataset
A total of 491 (284 benign and 204 malignant) samples are used in the training stage whilst 245 (142 benign and 103 malignant) samples are used for testing. Testing samples are subsequently used in the training stage, and training samples are applied in the testing stage. The classification accuracy, sensitivity, specificity, and F-measure of the BCDR dataset were 97.82%, 98.57%, 96.81%, and 98.11% respectively. The proposed M-FD model obtains an excellent F-measure on the Mini-MIAS dataset, high specificity on DDSM and INbreast datasets, and excellent sensitivity on the BCDR dataset. The evaluation results showed that the proposed M-FD model obtains the best accuracy, sensitivity, and specificity on the INbreast dataset amongst the four datasets.
# Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging
## 3 Experimental Results and Discussion
### Discussion  
As the azimuth variance of equivalent range cannot be formatted in the RD domain, the approximation is adopted during the compensation of high order phase in the filter of \(H_{2}\big{(}f_{\eta}\big{)}\). The phase error of the compensation plays an important role in the AODF and the focusing precision, which can be expressed as:  
\[\Delta\Psi_{error}=\left(\sum_{i=3}^{\infty}\varphi_{i,LRWC}(R_{LRWC})-\sum_{ i=3}^{\infty}\varphi_{i,LRWC}(R_{0})\right)f_{\eta}^{i} \tag{36}\]  
According to the Equation (36), we do the simulations to inquire the potential limitations of GNLCSA. Figures 7 and 8 show the phase error of targets in the edge (the distance depart from the referenced azimuth position is 2 km), varying with the azimuth resolution or the squint angle at different wavelength.  
Figure 7: Phase error of targets in the edge (the distance departing from the referenced azimuth position is 2 km and the squint angle is 45\({}^{\circ}\)) varies with the azimuth resolution and wavelength. The blue solid line with ‘o’, the red dash line with ‘\(\star\)” and the cyan lineation line with ‘\(\
abla\)’ are the results with the wavelength equaling to 0.06 m, 0.03 m and 0.015 m, respectively.  
From Figures 7 and 8, it is obvious that the phase error decreases when the wavelength descends. In the process of SAR imaging, it can be neglected if the phase error is smaller than \(\pi/4\). In general, it can conclude that the GNLCSA is invalid in the occasions that the wavelength is greater than 0.06 m. Furthermore, the GNLCSA is unavailable for the applications of the low carrier frequency. It will greatly decrease the AODF and the focusing precision in those occasions. Contrastively, the GNLCSA can be applied in the occasions (the squint angle can be up to 60\({}^{\circ}\) and the azimuth resolution can be smaller than 0.8 m) when the wavelength is smaller than 0.015 m.
## 2 Related Works
### Model Compression  
There have been many works that propose to compress E2E ASR models. In general, existing works can be divided as: light-weight architecture design, model parameter pruning, data quantization and some other methods. And methods of different categories are usually orthogonal, which means they can easily be combined with each other.  
Light-weight Architecture Design. Light-weight architecture design aims at creating a base light-weight architecture itself from a pool of candidates. LightConv [11] and LiteTransformer [12] design light-weight Transformer variants, [13] propose to search light-weight encoder architectures with the help of NAS automatically.  
Model Pruning. Based on the designed architectures, model pruning can further remove redundant parameters of fixed architectures, and unstructured pruning and structured pruning are two major types of pruning. Even though unstructured pruning can effectively reduce model sizes [14; 15], it usually results in a negligible improvement in runtime memory or speed due to irregular pruning results. Structured pruning focuses on pruning unimportant structured blocks of weights [16; 31], which are friendly to real-worldimplementations and can indeed improve runtime memory or speed. And the weight magnitude [14] is the most commonly-used pruning criterion in existing methods.  
Data Quantization. Quantization refers to reducing the number of unique values required to represent model weights and activations [19; 20; 22], which allows to represent them using fewer bits, to reduce the memory footprint, and to lower the precision of the numerical calculations.  
Others Besides the above-mentioned pruning methods, there also existed some other ones, e.g., Knowledge Distillation [32] and Tensor Decomposition [17; 18; 33; 34]. These works also can obtain promising compression performance, and they can be incorporated with the above-mentioned ones in general.
## 4 Experimental Setup
### Uncertainty Analysis  
It is crucial when conducting experiments and analysing the data obtained to consider the potential sources of error. By doing so, the overall uncertainty linked to the measurements can be assessed in order to draw accurate conclusions and inferences from the experimental outcomes. Uncertainty analysis was conducted to identify and assess errors. The uncertainties associated with the experimental parameters can be influenced by several sources of error [42], including random fluctuations in the instruments used, the calibration of the test bed, the accuracy of observations, and the experimental methodology. If a parameter is measured directly, the measurement uncertainties are defined by the accuracies of the experimental instruments [43]. Conversely, for parameters measured by several devices, the measurement uncertainties are determined based on the principle of the mean square method [44] and the accuracies of the devices used:  
\[e_{R}=\left[\left(\frac{\partial f}{\partial x_{1}}e_{1}\right)^{2}+\left( \frac{\partial f}{\partial x_{2}}e_{2}\right)^{2}+...+\left(\frac{\partial f} {\partial x_{n}}e_{n}\right)^{2}\right]^{\frac{1}{2}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Apparatus** & **Specifications** \\ \hline \multirow{2}{*}{\(AST\) speed} & \multirow{2}{*}{Dynapar} & Measurement range: 0/3000 (r.p.m.) \\  & & Precision/resolution: 0.5 (pulses per revolution) \\ \hline \multirow{2}{*}{Power output} & \multirow{2}{*}{KPQA-01} & Measurement range: 0/1 (MW) \\  & & Precision/resolution: 0.5 (\%) \\ \hline \multirow{2}{*}{Water head} & \multirow{2}{*}{SITRANS LU150} & Measurement range: 0.25/5 (m) \\  & & Precision/resolution: 0.25 (\%) \\ \hline \multirow{2}{*}{Flow rate} & \multirow{2}{*}{KROHNE Optiflux 2100} & Measurement range: 0/400 (m\({}^{3}\)/s) \\  & & Precision/resolution: 0.3 (m\({}^{3}\)/s) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the measuring instruments used in the tests.  
where \(e_{R}\) is the measurement uncertainty of the parameter, \(f\) is the given function of the parameter, and \(e_{1}\), \(e_{2}\),..., \(e_{n}\) are the measurement uncertainties of the related measured parameters. All measuring instruments used in the experiment were periodically calibrated to ensure traceability of measurements. The results of the uncertainty analysis of the parameters measured and calculated in the experiment are shown in Table 3.  
Several criteria can be used to estimate the quality of the measurements. For example, the measurements can be considered as highly precise when the uncertainty of each parameter is less than 3% [43]. Other researchers use more restrictive criteria. Measurements are considered to be of high precision when the uncertainty of each parameter is less than 2% [45]. According to Table 2, the measurements herein can be considered as highly precise.