esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Time-Resolved Experiments  
Figure 2a shows the time-dependent intensity of the superlattice reflection at (\(-\)0.9 0 \(-\)2.1), which is a first-order satellite of the (\(-\)1 0 \(-\)2) peak, after excitation with 400 nm pulses at different fluences at an initial set temperature of 175 K. The data is normalized to the intensity before laser excitation. The time scale of the initial intensity reduction in the superlattice intensity is limited by the time resolution of 70 ps. We then see a second, slower relaxation over approximately 100 ns. Figure 2b shows the amplitude of the intensity reduction between 0.1 and 2 ns as a function of fluence. We see an approximately linear dependence of the intensity reduction for increasing fluences until 3.5 mJ/cm\({}^{2}\). For a fluence of 7.0 mJ/cm\({}^{2}\), the intensity reduction is nearly the same as for 3.5 mJ/cm\({}^{2}\), suggesting a saturation. Figure 2c shows the fluence dependence of the additional intensity reduction occurring subsequently. Here the intensity reduction is almost constant for fluences above 2.0 mJ/cm\({}^{2}\). These values were deduced from fitting the data to the model described in the discussion.  
Figure 1: Bragg reflection intensities on a logarithmic scale with satellites, shown along the L-direction. (**a**) reciprocal scan of the (\(-\)1 1 0) reflection with its first-order satellites, (**b**) reciprocal scan of the (\(-\)1 1 4) reflection with two orders of satellite reflections. (**c**) temperature dependence of the (\(-\)1 1 4) reflection over the stability range of the incommensurate phase (**d**) normalized logarithmic intensity of the first, \(\bullet\), and second, \(\blacksquare\), satellite order as a function of temperature, indicating the stability region of the two Fourier components of the incommensurability, with an exponential and linear fit to guide the eye, respectively.  
To investigate the dependence of the response at a fluence of 3.5 mJ/cm\({}^{2}\) on the depth of the probed volume, we vary the incidence angle of the X-rays and thereby changing the probe depth of the X-rays [34]. Figure 3a,b shows the time dependence at different incidence angles as well as the total intensity reduction as function of probing depth. At the lowest incidence angle of 0.4\({}^{\circ}\) (corresponding to 71 nm field penetration depth) we detect a complete suppression of the superlattice intensity within our sensitivity after approximately 50 ns. At higher incidence angles (and therefore deeper X-ray penetration depths) some superlattice intensity remains at late times.  
Figure 2: (**a**) Intensity of the superlattice reflection (\(-0.9\ 0.0\ -2.1\)) as a function of pump-probe delay for fluences \(\blacktriangledown\), \(\blacklozenge\), \(\blacklozenge\), \(\blacklozenge\) = 1, 2, 3.5, 7 mJ/cm\({}^{2}\), with a probing depth of 130 nm. Traces have been vertically offset for clarity. Red lines show the result of fitting the model described in the text to the data. (**b**) the amplitude of the first intensity reduction at intermediate times vs. fluence with a linear fit, and (**c**) the second at late times, with an exponential fit to guide the eye.
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
## 6 Conclusions  
This paper proposes an anti-jamming method and its implementation algorithm for GNSS receiver against sup-freedom interference. The major contributions are as follows:  
1. In term of sup-freedom anti-jamming performance, the characteristic of direction-sensitivity is revealed;
2. Making use of the direction-sensitivity, an anti-jamming method based on array antenna rotation is proposed showing in which interference suppression is improved by rotating the antenna at a certain angle.
3. In order to determine the optimal rotation angle efficiently, an implementation algorithm of variable-step iteration is proposed. Meanwhile, we derived the criteria of the iteration initial value and step selection, which improve the iteration efficiency by over 78.35%.  
There are several issues that should be taken into account in future works: (1) The algorithm presented in this paper assumes that the antenna array is ideal and ignores the mutual coupling of array elements and the inconsistency of RF channels. The influence of non-ideal factors will be further considered and the iterative formula will be modified accordingly when constructing the data covariance matrix. (2) A realistic scenario with live data will be needed to back up the findings. (3) Taking the receiver dynamic into account, the response speed of the antenna array servo structure needs to be considered. (4) It would be better to develop an equivalent algorithm in the data domain instead of adjusting the antenna attitude, which may avoid complex servo structures.  
[PERSON] performed the theoretical study, conducted the simulations, and wrote the manuscript; [PERSON] provided the methodology and revised the manuscript; [PERSON] helped with programming and revised the manuscript; [PERSON] provided conceptualizations and research suggestions. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Natural Science Foundation of China (NSFC), grants no. 62003354.  
The authors would like to thank the editors and reviewers for their efforts in supporting the publication of this paper.  
The authors declare no conflict of interest.  
Figure 13: The reasonability of selected iteration step coefficient. (**a**) The relationship between the average iteration number and interference intensities. (**b**) The relationship between efficiency improvements and interference intensities.
# Filter Pruning via Attention Consistency on Feature Maps
## 2 Related Work
### Weight-Based Filter Pruning  
Weight-based filter pruning methods are data-independent and they implement pruning strategies by analyzing the weights of the filters. Norm-based filter pruning methods use the criterion that the filters with smaller norms are not important to prune the filters. For instance, the by calculating the \(\ell_{1}\)-norm of filter weight, the L1 method [21] prunes the filter with a smaller \(\ell_{1}\)-norm of weight. Soft Filter Pruning (SFP) [22] first calculates the \(\ell_{2}\)-norm of the weights, then prunes those filters with the smaller weights norm in a soft manner. However, Filter Pruning via Geometric Median (FPGM) [23] indicates that the filters with smaller norms are not important and are not rigorous. According to the characteristics of the geometric median, FPGM finds that the information of the filters close to the geometric median can be represented by the remaining filters, which shows these filters can be pruned. Furthermore, Neuron Importance Score Propagation (NISP) [24] uses the properties of matrix power series to calculate the importance of a filter relative to all other filters, thus determining the unimportant filters. Furthermore, Spectral Clustering Filter Pruning (SCSP) [25] clusters the filters into K groups and sorts them according to their contributions, pruning the filters with smaller contributions.  
Nevertheless, the weight-based filter pruning methods lack the guidance of data prior information and data labels, hence achieving sub-optimal performance on some datasets.
# Multi-Granularity Dilated Transformer for Lung Nodule Classification via Local Focus Scheme
## 2 Related Work  
### Lung Nodule Classification  
Automated lung nodule classification algorithms have been extensively studied as the increasing threat of lung cancer. Similar to other computer vision tasks, lung nodule classification can be categorized into two aspects: hand-crafted and deep learning-based methods. The former traditionally use a SIFT descriptor [1] and Support Vector Machine [8] to predict the property of lung nodules. With the prosperous development of hardware technology, deep learning-based methods have obtained the predominant status. Convolutional Neural Networks (CNNs) is a widely used classifier. [PERSON] et al. [9] adopted three Residual CNN branches to learn multiple views of nodules and then merged the outputs of different views by multi-layer perceptron layers. [PERSON] et al. [10] proposed a 3D CNN to handle lung nodule risk stratification with volumetric information. Latterly, 3D dual path networks were developed [11] with auxiliary features (gradient boosting machine) to improve classification performance. [PERSON] et al. [12] subsequently proposed spatial and contextual attention modules to boost the representation quality. Moreover, researchers introduced principles of evolutionary computation into CNNs [13], which segments the nodules first and feeds the transformed features to CNNs for classification. In the train phase, we commonly need to design the hyperparameters of CNNs (e.g., kernel size, stride and padding, etc.), thus [PERSON] et al. [14] designed a Neural Architecture Search (NAS) scheme to select the optimal parameters of networks. ProCAN [15] enhanced the non-local network with curriculum learning to progressively improve the classification performance of lung nodules. The above CNNs-based methods can learn useful inductive bias but lack full global understanding, thus the transformer-based methods have advanced to capture global dependencies. DETR [16] used the regular self-attention scheme to learn pixel-wise relations, and Deformable DETR [17] with the deformable self-attention was designed to focus on the global attention of the objective region. Inspired by Deformable DETR [17], we propose the Deformable Dilated Transformer to capture the more discriminative global representation by calculating the self-attention of the objective regions.
###### Abstract  
Seismic data provides integral information in geophysical exploration, for locating hydrocarbon rich areas as well as for fracture monitoring during well stimulation. Because of its high frequency acquisition rate and dense spatial sampling, distributed acoustic sensing (DAS) has seen increasing application in microseismic monitoring. Given large volumes of data to be analyzed in real-time and impractical memory and storage requirements, fast compression and accurate interpretation methods are necessary for real-time monitoring campaigns using DAS. In response to the developments in data acquisition, we have created shifted-matrix decomposition (SMD) to compress seismic data by storing it into pairs of singular vectors coupled with shift vectors. This is achieved by shifting the columns of a matrix of seismic data before applying singular value decomposition (SVD) to it to extract a pair of singular vectors. The purpose of SMD is data denoising as well as compression, as reconstructing seismic data from its compressed form creates a denoised version of the original data. By analyzing the data in its compressed form, we can also run signal detection and velocity estimation analysis. Therefore, the developed algorithm can simultaneously compress and denoise seismic data while also analyzing compressed data to estimate signal presence and wave velocities. To show its efficiency, we compare SMD to local SVD and structure-oriented SVD, which are similar SVD-based methods used only for denoising seismic data. While the development of SMD is motivated by the increasing use of DAS, SMD can be applied to any seismic data obtained from a large number of receivers. For example, here we present initial applications of SMD to readily available marine seismic data.  
data compression; noise reduction; machine learning; singular value decomposition Article
# A Service-Efficient Proxy Mobile IPv6 Extension for IoT Domain
## 1 Introduction
The overall analysis and comparisons show that the present solution is more resource efficient, resource-friendly, reliable, and accurate in predicting the handover moment. The research contributions of this study are the following:  
* To provide a location-based extension for efficient utilization of resources in the IoT environment to accurately predict handover moment. * To provide an efficient buffering mechanism for storage efficiency in the IoT domain to handle packet-loss problems. * To achieve better signaling efficiency in the IoT environment by effectively using nMAG-ID. * To achieve efficient handover latency for the handover process during the mobility of resources in the IoT domain. The rest of the article is organized into the following sections. Section 2 provides details regarding the studies related to the presented problem. The proposed work, its mechanism, and its procedure are provided in Section 3. The setup for implementation and validating our extension is given in Section 4. Obtained results of the comparisons are presented in Section 5. Finally, the conclusion and future work are discussed in Section 6.
## 2 Pattern-Matching Algorithms Review
### Aho-Corasick Algorithm  
The automaton-based AC algorithm, which accepts fixed-size patterns as an input in the pattern-matching process, is widely used in string matching because of its easy implementation. The AC automaton is created specifically for the pattern set, and pattern detection is performed in the packet payload using this automaton. Figure 4 shows the functions that make up the AC automaton [9].  
As shown in Figure 4, the AC automaton has three functions a, b, and c, respectively. These functions are goto (g), failure (f), and output (o). State 0 is the initial state for the AC automaton. The transition from a state-character pair to another takes place with the function g. This transition can be successful or unsuccessful. The function f is used if it is not possible to switch to another state using a state-character pair. For example, Figure 4 shows a transition from State 4 to State 5 with the character e. However, if the current character in the packet payload is not equal to e, the g(4,e) transition will not occur, and a failure transition will take place with the f(4) = 1 failure function. Function \(o\) is utilized to determine whether there is a currently detected pattern. Accordingly, the indices of all detected patterns are stored using the output function. For example, if the current state is 5, the patterns are determined using the o(5) = {he,she} function.  
Let value n be the size of the scanned text, value k the total pattern number, value m the total character number in all patterns, and value z the total pattern number in the scanned text. In this case, the AC algorithm detects all patterns in O(n + m + z) time.  
Figure 3: DFA and NFA samples: (**a**) DFA and (**b**) NFA.  
The DPI-matching process is computationally intensive and time-consuming, as every byte in the packet payload is processed individually in the AC automaton. Also, storing all state transitions in different memory entries causes cache space to be useless for large state transition tables. As a result of this, the matching speed decreases in large pattern datasets. Compressing a transition table in order to use the cache efficiently is one of the research topics of the AC algorithm. While some improved AC algorithms [52; 53; 54; 55; 56; 57; 58] focus on reducing the memory space required for storing the automaton, some studies [59; 60; 61] focus on shortening the pattern-matching time. The study numbered [62] proposes a variable-stride AC automaton to reduce the number of memory accesses and energy consumption in the pattern-matching process.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 2 Introduction to LoRa and LoRaWAN  
This section gives a detailed overview of the technology, covering the LoRa physical layer as well as the LoRaWAN MAC layer open standard.
# A Novel Neural Network-Based Method for Medical Text Classification
## 3 Our Work
### Sentence Representation  
#### 3.1.1 Word Embedding  
Word embedding usually needs to transform words into vectors with the low-dimensional distribution. In fact, it maps words from vocabulary to corresponding vector of real values to capture the morphological, syntactic and semantic information of words. The Bag-Of-Words is also low-dimensional but there is a lack of context between words. To better represent the text content, we use the word2 vec method proposed by [PERSON] et al. [21] for word embedding in this paper. The skip-gram model is used in the word2 vec method for the task. Assume that a document has \(L\) sentences \(s_{i}\) and each sentence contains \(T_{i}\) words. \(x_{it}\) with \(t\in[1,T]\) represents the words in the \(i\)th sentence. Given a sentence with words \(w_{it}(t\in[1,T])\), our model embeds the words to vectors through an embedding matrix \(W_{e}\). The \(x_{it}\) is the vector representation of \(w_{it}\), which is formulated by (5).  
\[x_{it}=W_{e}w_{it}. \tag{5}\]  
In this paper, the dimensionality of each word vector is 300.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 4 Emulation Results and Discussion
### Impact of Satellite Constellation  
In order to have a better understanding of geometry of satellite constellation on the link performance, we depict in Figure 10 the average uplink BER for the two satellites constellations assuming case I access method is shown as a function of the latitudes of IoT ground devices. The BER is shown to follow the inverse trend of the satellite availability (as demonstrated previously in Figure 5, where the Walker-Star constellation has a higher BER at the equator and mid latitude locations in comparison to Walker-Delta. However, Walker-Delta provides a degraded performance as the latitude increases. This is because Walker-Delta constellations sacrifice the coverage at the poles to provide higher satellite density at lower latitude locations. Walker-Star constellations can match the performance at the equator by increasing the number of satellites or the satellite constellation altitude [12]. The average packet success rate for case I versus the number of satellites in the constellation is shown in Figure 11. Note that the Walker-Delta constellation example is deployed over \(\pm 60^{\circ}\) latitude, therefore the packet success rate of Walker-Star will inherently be higher when sampling the entire Earth surface.
## 4 Conclusions  
Thin films of tungsten oxide with significantly increased internal surface area were prepared by spin-coating using different polymers as structure-directing agents to influence the internal film structure and, thus, the electrochromic performance. The combination of PIB50-\(b\)-PEO45, a block copolymer that is known to form micelles under the present preparation conditions, and the homopolymer polyethylene glycol PEG 600 as templates led to WO3\(|\)up films with interconnected spherical mesopores desirable for the intercalation and deintercalation of charge-balancing Li\({}^{+}\) ions allowing high transmittance modulations between the bleached and the colored states of the films, short response times and high effective diffusion coefficients. The use of just PIB50-\(b\)-PEO45 provided mesoporous WO3\(|\)mp films but residual polymer fragments or the presence of widely isolated pores separated by rather thick walls of compact WO3 leading to trapping of ions similar to the compact WO3\(|\)comp films prepared without any additives. Thin films homogeneously containing interconnected micropores were obtained with PEG 400 (WO3\(|\)up400) or PEG 600 (WO3\(|\)up600) as additives, showing electrochromic characteristics almost as good as the WO3\(|\)ump films. Reversible and stable switching processes as well as a homogeneous distribution of the intercalated ions within the films were obtained for all three types of films that contained the interconnected micropores, WO3\(|\)up400, WO3\(|\)up600 and WO3\(|\)up. Furthermore, the use of PEG only leading to purely microporous films, i.e., to forego additional mesopores, allows a fabrication of tungsten oxide thin films at presumably lower cost and in an environmentally even more benign way favored by a considerably shorter heat treatment at even lower temperature. Nevertheless, the presence of both, micropores as well as interconnected mesopores of defined shape could still provide significant advantages in transmittance modulation, response times and coloration efficiency over films modified by just PEG as additive (WO3\(|\)up400, WO3\(|\)up600 as well as films reported in the literature [24; 27; 28]). These results are in excellent agreement with the enhanced Li\({}^{+}\) permeation found in corresponding TiO\({}_{2}\) films, also containing spherical mesopores (10-15 nm) connected by small mesopores (ca. 3 nm in diameter) [33]. In conclusion, films using a combination of 10-20 nm mesopores connected through micropores or small mesopores (i.e., \(<\)3 nm in diameter) can offer an attractive pathway towards facile diffusion of ions throughout the film network in particular for thicker films of even more intense coloration as long as the mesopores are well accessible for ions. The present study thus confirms that suitable porosity can indeed substantially improve Li\({}^{+}\)-based electrochromic properties.
###### Abstract  
As much as two-phase mixture models resolve more physics than single-phase homogeneous models, their inconsistent heat transfer predictions have limited their use in modelling nanofluid cooled minichannel heat sinks. This work investigates, addresses, and solves this key shortcoming, enabling reliable physically sound predictions of minichannel nanoflows, using the two-phase mixture model. It does so by applying the single-phase and the two-phase mixture model to a nine-passages rectangular minichannel, 3 mm deep and 1 mm wide, cooled by a 1% by volume suspension of Al\({}_{2}\)O\({}_{3}\) nanoparticles in water, over the Reynolds number range 92 to 455. By varying the volume fraction \(\alpha_{nf}\) of the second phase between 2% and 50%, under a constant heat flux of 16.67 W/cm\({}^{2}\) and 30 Celsius coolant inflow, it is shown that the two-phase mixture model predicts heat transfer coefficient, pressure loss, friction factor, exergy destruction rate, exergy expenditure rate, and second law efficiency values converging to the single-phase model ones at increasing \(\alpha_{nf}\). A two-phase mixture model defined with 1% second phase volume fraction and 100% nanoparticles volume fraction in the second phase breaks the Newtonian fluid assumption within the model and produces outlier predictions. By avoiding this unphysical regime, the two-phase mixture model matched experimental measurements of average heat transfer coefficient to within 1.76%. This has opened the way for using the two-phase mixture model with confidence to assess and resolve uneven nanoparticle dispersion effects and increase the thermal and mass transport performance of minichannels.  
michannel heat sink; microchannel backward facing step; nanofluid; two-phase mixture model; conjugate heat transfer model; computational fluid dynamics Article