esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 MAP Detection  
After applying the method used by [PERSON] and [PERSON] [26], we obtain the following target form:  
\[\mathbf{G}=\begin{bmatrix}rp&pl_{2}&rp\\ rl_{1}&l_{1}l_{2}&rl_{1}\\ rp&pl_{2}&rp\end{bmatrix}=\begin{bmatrix}p\\ l_{1}\\ p\end{bmatrix}[r&l_{2}&r], \tag{1}\]  
where \([p\;l_{1}\;p]^{T}\) is the vertical interference (VI) or intertrack interference (ITI) and \([r\;l_{2}\;r]\) is the horizontal interference (HI) or ISI. To remove the VI and HI, we  
\(R_{4}=-2p+l_{1}\); \(R_{5}=l_{1}\); \(R_{6}=2p+l_{1}\)]. For horizontal detection using the MAP algorithm, we calculated the conditional probability \(P(R_{i}\mid\mathbf{z})\) of the 6 symbols as follows:  
\[\begin{array}{rl}P(Ri\mid\mathbf{z})=\sum_{R_{i}}P(s^{\prime},\;s \mid\mathbf{z})&=\sum_{R_{i}}\frac{P(s^{\prime},\;s,\;\mathbf{z})}{P( \mathbf{z})}\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad=\sum_{R_{i }}\frac{P(s^{\prime},\;\mathbf{z}_{<k})P(\mathbf{z}_{k}\;,\;s\mid s^{\prime}) P(\mathbf{z}_{>k}\mid s)}{P(\mathbf{z})}\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad=\sum_{R_{i }}\frac{\alpha_{k-1}(s^{\prime})\gamma_{k}(s^{\prime},\;s)\beta_{k}(s)}{P( \mathbf{z})}\end{array} \tag{2}\]  
with \(i=1,\;2,\;\ldots,\;6\)  
where \(s^{\prime},\,s\), and \(\mathbf{z}\) are the previous state, current state, and received symbol of MAP detection in the proposed model, respectively. \(P(s^{\prime},\;\mathbf{z}_{<k})\) is the joint probability of the previously stated sequence received in the past; received sequence \(\mathbf{z}_{<k}\), \(P(\mathbf{z}_{k},\;s\mid s^{\prime})\) represents the conditional probability of the current state \(s\) and the current received symbol \(\mathbf{z}_{k}\) given the previous state \(s^{\prime}\); and \(P(\mathbf{z}_{>k}\mid s)\) is the conditional probability of the future received sequence \(\mathbf{z}_{>k}\) under the current state \(s\). We assigned \(\alpha_{k-1}(s^{\prime})=P(s^{\prime},\;\mathbf{z}_{<k})\), \(\gamma_{k}(s^{\prime},\;s)=P(\mathbf{z}_{k},\;s\mid s^{\prime})\), and \(\beta_{k}(s)=P(\mathbf{z}_{>k}\mid s)\) for simplification. The branch transition probability of the horizontal trellis \(\gamma_{k}(s^{\prime}\mid s)\) is estimated using the following equation.  
\[\gamma_{k}(s^{\prime},\;s)=P(z\mid Z_{i})P(R_{i})=\exp\left(-\frac{(z-Z_{i})^{ 2}}{2\sigma^{2}}\right)P_{e}(R_{i}) \tag{3}\]  
where \(z\) is the value of \(z\)[j,k], \(Z_{i}\) is the discrete value of \(z\) in the noiseless case (216 symbols on the trellis), \(P(z\mid Z_{i})\) is expressed by a Gaussian function, and \(P_{e}(R_{i})=P(R_{i})\) is the extrinsic information from the vertical detection, which helps create the feedback path. In the forward path, we set \(P_{e}(R_{i})=1/6\).
## 4 Results
### fNIRS Data  
Figure 6 illustrates the changes in HbO and HbR concentrations of CH10 over time during a typical task trial, and Figure 7 illustrates the cerebral activity of users during no sound, low sound, and high sound stimuli. The results of the two-way RMANOVA revealed that the main effect of sound approached significance [F(2,34) = 3.264, \(p\) = 0.051, \(\eta\)p2 = 0. 161], and a significant interaction effect between sound and brain area was observed [F(2,34) = 4.256, \(p\) = 0.022, \(\eta\)p2 = 0.200]. Analyzing the simple effect of sound, it was found that the high sound of the hair dryer induced a nearly higher \(\Delta\)HbO in users' left DLPFC compared to no sound (\(p\) = 0.053). However, no significant differences were detected in users' left DLPFC between low sound and high sound (\(p\) = 1) or between low sound and no sound (\(p\) = 0.105). Similarly, high sound induced a nearly higher \(\Delta\)HbO in users' right DLPFC than no sound (\(p\) = 0.076). Yet, no significant differences were observed in users' right DLPFC between low sound and high sound (\(p\) = 0.357) or between low sound and no sound (\(p\) = 1). In terms of brain area, the \(\Delta\)HbO in users' left DLPFC was higher than in users' right DLPFC when the product had low sound (\(p\) = 0.029). However, no significant differences were found between left DLPFC and right DLPFC when the product had high sound (\(p\) = 0.877) or no sound (\(p\) = 0.443).  
Figure 6: The changes in HbO and HbR concentrations of CH10 over time during a typical task trial.  
Figure 7: Cerebral activity of users during (**a**) no sound, (**b**) low sound, and (**c**) high sound stimulus. HbO = oxygenated hemoglobin; HbR = deoxygenated hemoglobin.  
The results of the two-way RMANOVA indicated that neither the main effect of brain area [F(1,17) = 0.465, \(p\) = 0.505, \(\eta\)p2 = 0.027], the main effect of sound [F(2,34) = 0.390, \(p\) = 0.680, \(\eta\)p2 = 0. 022], nor the interaction between sound and brain area [F(2,34) = 0.04, \(p\) = 0.961, \(\eta\)p2 = 0.002] were significant. Figure 8 illustrates the block-average \(\Delta\)HbO and \(\Delta\)HbR of DLPFC in different experimental conditions.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 3 Satellite Sensors for SUHI Studies
### Modis  
MODIS imagery has been the second popular data source for SUHI studies (25%). MODIS is a key instrument aboard on the Terra (1999) and Aqua (2002) satellites launched by NASA to scan the Earth's surface and atmosphere with a 36-band spectrometer and provide a global coverage every one to two days. MODIS records its images at a spatial resolution of 250 m (bands 1-2), 500 m (bands 3-7), and 1 km (bands 8-36). Thermal images are captured in the 1-km resolution. Due to the spatial resolution and swath dimension of MODIS--2330 km (cross track) by 10 km (along track at nadir)--its images are used for research of large study areas [2, 3, 4, 52, 75, 76, 53, 77, 6]. A significant benefit of MODIS is the range of quality-checked data products that are generated by the MODIS team. These data products are processed images, which can be directly used for various research purposes. Examples of such products that have been widely employed in SUHI studies include daytime and nighttime LST and emissivity data from MOD11C3, MOD11A1, and MOD11A2 [77]. The LST value in these products is retrieved by the generalized split-window algorithm [78]. In the current Collection-6 MODIS LST products, a new LST product based on the ASTER Temperature Emissivity Separation (TES) algorithm (MOD21) has also been released. It was claimed that the new data could provide increased sensitivity to a land cover change compared to other emissivity products [79]. However, the data have not been used in SUHI studies until now.
###### Abstract  
This paper introduces a control strategy to recover from various spin motions to level trim flight conditions, utilizing a fixed-time nonsingular terminal sliding mode approach to minimize altitude loss. Firstly, an integral terminal sliding mode surface is constructed to guarantee the tracking errors of the spin recovery system converge to the origin within a fixed time and avoid the singularity problem. Next, a fixed-time type reaching law is proposed to weaken the chattering phenomenon and guarantee terminal sliding hyperplanes converge to zero in a fixed time. Furthermore, the stability analysis indicates the fixed-time convergence stability of the spin recovery control system and provides an accurate calculation of the settling time. Finally, comparative simulations are performed to validate the effectiveness and superiority of the developed spin recovery control scheme.  
spin recovery; terminal sliding mode; fixed time control; altitude loss minimization Article
## 1 Introduction  
More than 1700 volatile organic compounds (VOCs) have been identified in the floral scents of flowering plants [1]. The richness of VOCs emitted by flowers is certainly very high. Floral VOCs, however, are not equally distributed across the phylogeny of flowering plants, so that the commonness and predominance of these compounds in floral scents varies widely among species. Common floral VOCs have a widespread phylogenetic distribution, which means that they are present in the floral scents of many species from different plant families. Plants generally synthesize and emit species-specific floral VOC mixtures to attract pollinators by mixing several of these common VOCs [2]. These specific floral VOC mixtures attract specialist pollinators that have evolved an innate preference for them [3] but also provide generalist pollinators with a reliable signal to effectively forage on their floral resources [4]. Some less common floral VOCs are only present in plants that are pollinated by specific pollinator groups with specific innate preferences for those VOCs [5; 6; 7]. Some of the rarest floral VOCs can constitute very specific and reliable communication channels between specialist pollinators and their host plants, even when they are emitted within a mixture that includes more common VOCs [3, 8].  
Most floral VOCs range from being relatively common to rare, but some are very common in the floral scents of species from all plant families. These volatiles, which are essentially obligate components of any floral scent, have great potential for attracting pollinators. The commonest floral VOCs, namely benzaldehyde, limonene, \(\beta\)-ocimene and linalool, are not only very common but are also predominant components of the floral scents of many species [9]. They belong to the two most prominent chemical groups of plant VOCs, the terpenoids and the benzenoids [10, 11].  
Here we review the current information on the importance of \(\beta\)-ocimene in plants, focusing especially on its biological functions and its ubiquitous presence in floral scents. We compiled abundant evidence from published studies supporting that \(\beta\)-ocimene may play a very relevant function as a general pollinator attractant. We further emphasize that \(\beta\)-ocimene may not only mediate pollinator attraction to flowers but plays several biological functions in plants, which vary depending on the organ and the time of emission.
# A Viscoelastic-Plastic Creep Model for Initial Damaged Coal Sample Affected by Loading Rate
## 2 Experimental Method and Procedure
### Initially Damaged Coal Sample Preparation Method  
The damage parameter \(D\) quantitatively characterizes the evolution of internal micro-defects, with its mathematical framework enabling the quantification of crack initiation, propagation, and damage accumulation dynamics, while revealing critical precursor signatures preceding structural instability [45; 46]. Based on energy conservation principles, the damage variable D is mathematically formulated as  
\[D=\sum_{k=1}^{i}U_{d}^{k}/(\sum_{k=1}^{i}U_{d}^{k}+U_{e}^{k}) \tag{1}\]  
where \(U_{d}^{k}\) is the plastic strain energy produced in the \(k\)th cycle, kJ, and \(U_{e}^{k}\) is the elastic strain energy produced in the \(k\)th cycle, kJ, which can be calculated by the area enclosed by the stress-strain curve.  
Based on the damaged coal specimen preparation methodology and the damage variable calculation formula (Equation (1)), the stress-strain curves of coal specimens with varying initial damage severity levels under cyclic loading-unloading conditions are illustrated in Figure 2[39].  
Experimental observations revealed that with increasing loading cycles, the elastic stored energy, plastic strain energy accumulation, and damage parameter \(D\) within specimens exhibited sustained growth, confirming the enhancing effect of cyclic loading on material degradation processes [15]. Based on the damage evolution model (Equation (1)), the \(D\)-values of coal specimens reached 0.312 and 0.448 after three and five loading cycles, respectively. To investigate damage rate sensitivity, a pre-damaged specimen system with \(D\) = 0.3 was established, and multi-rate triaxial mechanical tests were conducted, including conventional triaxial compression tests (TCT) at loading rates of 0.005, 0.01, 0.05, and 0.1 mm\(\cdot\)s\({}^{-1}\) (50, 100, 500, and 1000 microstrains\(\cdot\)s\({}^{-1}\)), as well as triaxial compression creep tests (TCCT) under sustained stress conditions. Generally, the study of strain rate laws typically involves using the loading displacement velocity for research. Therefore, in this paper, the term \"loading rate\" exclusively refers to the loading displacement velocity [48].  
Figure 2: Stress–strain curves of coal samples with initial damage.
# Learning and Compressing: Low-Rank Matrix Factorization for Deep Neural Network Compression
## 2 Related Works
### Optimal Rank Selection  
The DNN model consists of multiple layers played with different roles. Low-rank compression for DNN weight tensors requires optimal rank selection of the weight tensors at each layer. An inappropriate rank may lead to a significant drop in DNN performance. Therefore, the appropriate rank is the crucial factor for using matrix factorization to compress neural networks. In view of the above problem, one DNN model parameter-reduction technique [33], inspired by SVD was proposed. It used three factorized matrices instead of weight matrix and applied sparsity constraint to make entries of the center diagonal matrix zero. This method avoided retraining and solved for the optimal rank selection problems of the conventional low-rank MF approaches. Ref. [34] proposed a global optimization technique based on Bayesian optimization (BayesOpt), which could find the global optimal rank. The method disregarded the scale of the dataset and the structural characteristics of the network while making a good trade-off between computational complexity and accuracy. Ref. [35] proposed a method to minimize the complexity of the network while maintaining the required accuracy. The authors defined rank selection as a combinatorial optimization problem and introduced the space-restricted parameters to reduce the search space and obtain optimal ranks. Ref. [36] proposed a novel approach that stabilizes low-rank approximations of convolutional kernels and ensured efficient compression while maintaining the high-quality performance of the neural networks. Ref. [37] combined ranks with the inference runtime and adopted alternating optimization to solve it so that the network ran as fast as possible on the device while having the best performance (e.g., classification accuracy).  
The literature shows that existing rank-based DNN compression algorithms have two major drawbacks: First, a general rank-selection algorithm is lacking for general matrix factorization. Second, existing algorithms can not explicitly control the degree of compression. Each compression method may have its own unique accuracy-compression ratio. According to the above two shortcomings, this paper aims to develop a low-rank DNN compression algorithm that can solve the rank selection and freely control the degree of compression.
## 2 Antenna Structure  
Figure 1 shows the geometry modification of the \(5\times 5\) microstrip patch parasitic array using the cavity-backed slot. The proposed antenna is fabricated on a 100 mm \(\times\) 100 mm double-sided FR4 epoxy where a dielectric constant (\(\epsilon_{r}\)) and a substrate of thickness '\(d\)' are 4.4 and 0.8 mm, respectively. In order to obtain the optimized return loss and the widest beam radiation pattern, the antenna parameters and the positions of each patch element have been successfully simulated by means of the CST Microwave Studio simulation [30]. As shown in Table 1, the dimensions of the broad-beam configuration have been designed for the operating frequency of 5.0 GHz. The \(5\times 5\) square patch lengths '\(l_{p}\)' are printed on the upper side of an FR4 substrate. The rectangular slot length '\(l_{s}\)' and width '\(w_{s}\)' are etched in the center of the antenna ground plane and then covered with the rectangular metallic cavity. The slot itself must not resonate over the operating frequency bands of the antenna due to the radiation from this slot interfering from the patches. The length of the exciting probe '\(l\)' is installed at the cavity center. The center of the slot and the radiating patch are considered the origin point (0,0). The dimensions of the rectangular cavity are designed of the dominant mode (\(TE_{101}\)) with length '\(c_{x}\)', width '\(c_{y}\)', height '\(c_{z}\)', and the installing position '\(d_{y}\)', which is an important parameter to acquire the best return loss.
# Perspective on Double Pulsed Gas Metal Arc Welding
## 3 Advantages of DP-GMAW
### Improved Solidification Cracking Susceptibility  
[PERSON] et al. [12] found that a cracking-free weld joint was produced by DP-GMAW during welding of 8 mm thick magnesium alloy. [PERSON] et al. [28] noted that no cracks were detected in the fusion zone generated by DP-GMAW during welding of 30 Cr-4 Mo ferritic stainless steel. [PERSON] et al. [9] observed that DP-GMAW reduced cracking in the fusion zone compared with CP-GMAW. This performance was dependent on the thermal amplitude. [PERSON] et al. [20] found that the bead cracking ratio was reduced by DP-GMAW during welding of the A7N01 aluminum alloy. [PERSON] et al. [41] observed no cracking defect during welding of 5083 aluminum alloy. Figure 9 illustrates the solidification cracking rate versus the thermal frequency of CP-GMAW and DP-GMAW [17]. Here, cracking rate (%) is defined as the division of the crack length by the specimen length in the Houldcroft cracking test. The results indicate that the solidification cracking rate produced by DP-GMAW is less than CP-GMAW, regardless of the changes in the TF. The solidification cracking rate decreases with TF less than 30 Hz, and subsequently increases with a high TF once the TF exceeds 30 Hz. The optimized TF for the mitigation of solidification cracking ranges from 10-30 Hz.  
Figure 8: Porosity incidence versus thermal frequency of DP-GMAW with different welding speed (reproduced with permission from [40], Copyright Taylor & Francis, 1994).  
[PERSON] et al. [42] noted that finer grain dimension featured lower solidification cracking susceptibility during the solidification of AC7A aluminum alloy. [PERSON] et al. [43] found that cracking susceptibility of 6082 aluminum alloy decreased with finer grains when the grain dimension exceeded 25 \(\upmu\)m. Numerous investigations reveal that grain refinement of weld metal is an effective method for reducing the solidification cracking susceptibility [44; 45; 46]. Finer grain has a larger area of grain-boundary per unit volume, which produces a lower tensile strain generated by solidification shrinkage [42]. Finer grain can deform to accommodate contraction strains more easily [47]. The variation of grain orientation inhibits the growth of solidification cracking. Solidification cracking cannot grow long, because a crack has to change its direction periodically.  
Improved solidification cracking susceptibility is achieved due to refined solidification structures and variable grain orientation during DP-GMAW. As DP-GMAW method features microstructural refinement, reduced porosity incidence, and improved solidification cracking susceptibility, improvements in mechanical properties are reported in the literature. [PERSON] et al. [19; 48] found that both yield strength and ultimate tensile strength could be increased by using DP-GMAW during welding of 6061 aluminum alloy and 310S austenitic stainless steel. [PERSON] et al. [22] found that ultimate tensile strength could by increased by using DP-GMAW due to reduced porosity incidence.
## 5 Discussion
### Limitations and Future Research  
While the research has yielded notable findings, there remains room for further enhancement. (1) Since not all official data are released every year, and some data, such as the Shenzhen travel data, were used in previous years, the experimental results may have the problem of insufficient validity. In the future, we can consider releasing questionnaires and field trips to obtain the data with good representability. (2) In choosing the distance decay factor for the experiment, more reference is made to the experience of the existing related literature. The modified standardized gravity model is slightly different from the traditional gravity model in the shape of the curve, and the distance decay coefficient deserves further study. (3) The MM-G2 SFCA method proposed in this paper is based on a generalized accessibility approach, which is not limited to medical accessibility analysis; it can be extended to other areas of spatial accessibility analysis, offering the opportunity to play a broader role.
# Intra-Mode Decision Based on Lagrange Optimization Regarding Chroma Coding
## 5 Experimental Results  
To illustrate the coding efficiency of the proposed method, the VVC is modified by incorporating the improved Lagrange optimization scheme into the reference software VTM 22.1 [29] for comparison with the original version. Two aspects of algorithm verification are designed: an efficiency comparison between the proposed RDO method and the original RDO method and an analysis of the computational complexity of the proposed method relative to the original one. The experiments are conducted under the common test conditions specified in [30]. The video sequences, which belong to Class B/C/D/E, have diverse resolutions of 1920 \(\times\) 1080, 832 \(\times\) 480, 416 \(\times\) 240, and 1280 \(\times\) 720, as listed in Table 1. Each video sequence contains 300 coding frames. Here, the values of the quantization parameter are 22, 27, 32, and 37, respectively. The hardware platform used is Windows10 with an Intel Core i7-8700 CPU.