esa-sceva/satcom-chunk-collection huggingface dataset:
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 2 System Model
### The Receiver
#### 2.3.4 Received Data Estimation  
In the VLC-MIMO techniques, the performance cannot be optimized as a stable modulation form. In addition, just LoS channel gains through the transmitter to the receiver are utilized as channel matrix coefficients. However, the non-LoS is proposed to show the difference between the BER performance of OFDM and wavelet-OFDM. The nonlinearity and limited bandwidth properties of LEDs have not been considered to achieve the significant enhancement of channel diversity. To decode and recover data from the obtained signals, a channel estimation matrix technique is used. MIMO de-multiplexing is performed because of its low complication, zero-forcing (ZF) is implemented using basic channel observation [10]. Therefore, in order to get the transferred data estimation \(\text{X}_{\text{est}}\) and decompose the detected streams of Y, a matrix inversion and multiplication should be required:  
\[\text{X}_{\text{est}}=\text{H}^{-1}\text{Y}. \tag{17}\]  
After ZF based MIMO de-multiplexing, we have [11]:  
\[\widetilde{\text{x}}=\text{H}^{-1}\text{y}=\ \text{R}\ \text{P}_{\text{opt}}\ \xi,\text{HA}\ \text{x}+\text{H}^{-1}\ \text{n}, \tag{18}\]  
where H is the channel matrix for the 4 \(\times\) 4 SDM-VLC system given by Equation (11) and n is the additive noise vector. Likewise, \(\text{n}_{\text{i}}\) (i = 1, 2, \(\cdots\cdots\), N) is represented as a zero-mean real-valued AWGN through variance \(\sigma^{2}\ _{\text{ni}}\). Both shot and thermal noises, given by Equations (7) and (8), respectively, are jointly added to total noise. The notation \((.)^{-1}\) indicates the inverse operator. diag(.) symbolized to the diagonal items, which are the results, are given within the rounded brackets. All blocks except the DWT-OFDM ones are explained in Ref. [22]. Here, we focus on DWT-OFDM blocks in detail.
# Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
## 2 Band Selection with Sparse Space Clustering  
This section summarizes the classical Sparse Space Clustering (SSC) method [28; 29] for hyperspectral data analysis given the HSI dataset \(H\in\mathbb{R}^{m\times n\times b}\), where \(m,n\), and \(b\) denote the width, height, and the number of bands, respectively. We reshape the original HSI cube \(H\) into matrix form \(X\), where \(X=(x_{1},x_{2},\ldots,x_{b})\in\mathbb{R}^{m\times b}\). The SSC method assumes that all bands (i.e., band vectors) of the \(X\) lie in a union of low-dimensional subspace, and a linear or affine combination of several bands can represent every band vectors. Accordingly, each band vector \(x_{i}\in\mathbb{R}^{mn},\ i=1,2,\...,b\) can be represented as  
\[x_{i}=z_{1i}x_{1}+z_{2i}x_{2}+\ldots+z_{bi}x_{b}, \tag{1}\]  
where \(z_{ij}\) in (1) is the \(ij\) th element of matrix \(Z\in\mathbb{R}^{b\times b}\). It should be noted that \(x_{i}\) cannot be represented by itself, which indicates that \(z_{ii}=0,\ i=1,2,...b\).  
We combine \(b\) band vectors into a matrix format so that (1) can be organized as  
\[X=XZ,\ diag(Z)=0, \tag{2}\]  
where \(Z\) is the coefficient matrix of all band vectors and \(diag(Z)\) denotes the diagonal vector of matrix \(Z\). By minimizing the following optimization Problem (3), the solution of (2) can be found [29].  
\[Z=argmin\|Z\|_{q}^{2}\ \text{s.t.}\ \text{X}\ =\ \text{XZ},\ \text{diag}(\text{Z})\ =\ 0. \tag{3}\]  
where \(\|Z\|_{q}\) represents the \(l_{q}\) norm of \(Z\), which is defined as \(\|Z\|_{q}=\left(\sum_{i=1}^{b}\sum_{j=1}^{b}|z_{ij}|^{q}\right)^{\frac{1}{q}}\). For \(q=0\), the \(l_{0}\) norm counts the number of nonzero elements of \(Z\), and it helps to maintain sparse property of \(Z\). The \(l_{1}\) norm is usually considered as a relaxation \(l_{0}\) norm.  
Then, the sparse matrix \(\hat{Z}\) is used to construct the similarity matrix \(W\), that is, \(W=(\hat{Z}+\hat{Z}^{T})/2\). With \(W\), the spectral clustering method [30] clusters all band vectors into their underlying subspaces.  
We convert the band selection problem into the general form  
\[min\|X-XZ\|_{q}^{2}+\lambda J(Z) \tag{4}\]  
where \(J(\cdot)\) represents the regularized term and \(\lambda>0\) is a regularized parameter. The regularized term \(J(Z)\) can depend on certain properties of the data, such as geometrical information and physical meaning [22].
## 3 Results and Discussion
Owing to having the same channel electrostatic field environment, the leakage current (Ioff) values of our proposed FinFET are almost the same as the traditional FinFET under five different working modes [1]. For the five working states, because the gate control capacity levels differ, the Vth, SS, and DIBL values also differ, as shown in Table 1. These parameters are critical to the performance of the devices; thus, extensive knowledge of these parameters is essential. Among the various FinFETs, the SOI FinFET shows a suppressed leakage current and superior SCEs [9]. It limits the depletion region of the source-drain region, which can improve the leakage current, SCE, and sub-threshold characteristics of the device [10]. Tremendous progress has been made recently regarding the extremely thin SOI material technology [11], as well as other advances; thus, their significance is apparent. For our proposed structural device, the output characteristic curve, i.e., the Id-Vd curve, is also presented, as shown in Figure 8, which represents the curves under different working conditions. One state in Figure 8 shows the situation in which a single gate is turned on (MODEL1). Here, the side gate is turned on, which is the curve mode of MODEL1. For MODEL2 in Figure 8, the top gate (gate2) is open and the other gates are closed. This is a novel control mode with different states. The relevant output characteristic Id-Vd curve in this state is presented. In Figure 8, MODEL3 shows the double-gate control. In this state, the top gate (gate2) is open and one of the side gates (gate1 or gate3) is open. The Id-Vd curve under MODEL4 is also shown in Figure 8. This state is still in the double-gate control; however, the top gate (gate2) is closed, whereas the side gates are in the same open state. At this time, gate1 and gate3 are simultaneously opened, which results in MODEL4, with gate2 being closed and gate1 and gate3 being open. Finally, Figure 8 also shows a state in which gate1, gate2, and gate3 are on, and this corresponds to the working-state mode of MODEL5. In this mode, the corresponding transfer characteristic curve is presented. A specific comparison of these multiple modes of operation is also shown in Figure 8.  
The different output characteristic curves under the five working states are similar based on the aforementioned analysis process when describing the transfer characteristic curve. However, the curves vary owing to the different couplings under the multiple-gate control structure. This also shows the good adaptability of the device in multiple working states from the statistical data used for the characteristic curve. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MODEL** & **Vth (mV)** & **Idsat (10\({}^{-5}\)A)** & **Ioff (10\({}^{-9}\)A)** & **SS (mV/dec)** & **DIBL (mV/V)** \\ \hline MODEL1 & 501.6 & 1.50 & 5.39 & 166.5 & 74.2 \\ \hline MODEL2 & 757.9 & 0.471 & 6.54 & 167.1 & 86.2 \\ \hline MODEL3 & 459.6 & 1.72 & 3.08 & 153.8 & 66.0 \\ \hline MODEL4 & 338.2 & 2.00 & 1.55 & 96.9 & 55.2 \\ \hline MODEL5 & 319 & 2.03 & 1.55 & 91.8
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 5 Related Works
The proposed protocol presents better performance than traditional offloading techniques, but there is no discussion about privacy, the impact of the added signaling, or energy consumption on the user's side. The literature has widely discussed solutions to the problems introduced by small cells, but, in general, only a few objectively show the negative effects related to the appearance of hotspots. In general, the deployment of small cells and the use of MIMO techniques demand investments in site-planning and infrastructure, and the latter usually requires channel estimation. FFR is still relevant as an efficient approach to mitigate inter-cell interference in mobile networks [33; 34; 35]. Besides, recent studies still point out that FFR can be combined with different techniques to reduce interference, such as beamforming [34]. Hotspot scenarios are consistently studied for their importance regarding current and future mobile networks [36; 37]. Still, no work has been found in the use of FFR as the primary solution to improve performance on hotspot scenarios without using small cells. FFR can be a good alternative for its simplicity and efficiency, even in heterogeneous scenarios with high user density [38]. This is one of the arguments that motivates the scientific hypothesis of this work.
# CT Scanning of Structural Characteristics of Glacial Till in Moxi River Basin, Sichuan Province
## 4 Results
### Meso-Structure Characteristics of Intact Glacial Till
#### 4.1.4 Particle Orientation  
The orientation of glacial till particles not only affects its engineering properties, but also reflects its stress history. At present, the statistical methods of particle orientation in soil mainly include the orientation rose diagram, average orientation direction and principal orientation angle [28; 29; 30]. The paper uses the directional rose diagram method to count the particle direction of the glacial till samples in the X, Y and Z sections.  
In the process of particle orientation statistics, the CT scanning image is first subjected to \"despeckle\" processing, which is to remove the independent small area blocks generated by the SEM image after the binarization process. Generally speaking, these blocks are composed of only a few or a dozen pixels (depending on the resolution of the scanned image), and they do not represent the actual particle shape, so these parts of the blocks should be excluded from the statistical analysis; then, the CT scanning image is binarized by Matlab software. Finally, a coordinate system is established in Matlab, and the 0\({}^{\circ}\) directions of the X, Y, and Z sections are specified as the positive directions of the Y, X, and Z axes. Due to the symmetry of the particle direction, we take 15\({}^{\circ}\) as the interval, divide 0\({}^{\circ}\)-180\({}^{\circ}\) into 12 intervals, and count the number of particles in each interval. The results of the particle direction statistics of the X, Y, and Z sections are shown in Figure 17.  
The particle's long axis direction distribution in the X and Y longitudinal section directions in the glacial till sample is similar, the distribution is uniform and there is no obvious preferred orientation (Figure 17). The orientation of the particle long axis direction in the Z-section direction is more obvious. However, the distribution of the particle's long axis direction in the longitudinal and transverse directions is different.
# World-Models for Bitrate Streaming
## 5 Results and Discussion  
### Baselines  
For bitrate streaming, this work uses an implementation of the Advantage Actor-Critic (A2C) algorithm from Stable-Baselines [32], a collection of implementations of common deep reinforcement learning agents. We obtained a copy of the Park authors' bitrate streaming agent in order to validate the implemented agent used in this project. In correspondence, the authors noted that they added traces to the repository during publication and the results may have slight differences from the scores in the original paper. We performed a combination of grid search and refinement by hand on parameters for the value coefficient, starting entropy coefficient, entropy decay, and optimization algorithm. The entropy was decayed by the entropy rate every 264 steps and stops decaying once the entropy reaches zero. Figure 4 shows the training curve for the final model-free A2C agent trained entirely inside the Park environment.  
Every 26,400 steps, the policy of the agent is evaluated for 10,000 actions. The solid blue line represents the average score at each evaluation step and the shaded portion highlights the standard deviation. The curve is jagged because the environment is stochastic, as evaluating the agent's performance regularly with a high number of iterations impedes training. Through correspondence with the Park authors [7], we received their implementations for the buffer-based and RobustMPC heuristics. A random agent in the environment receives a score of \(-5729.3\pm 4250.1\). The provided Buffer-based and RobustMPC agents receive average scores of 237.7 \(\pm\) 435.4 and 286.2 \(\pm\) 502.5 over 1000 episodes, respectively.
# Depth Image Completion through Iterative Low-Pass Filtering
## 1 Introduction
However, dedicated-design networks still require further studies to tackle unknown scenes and complex distributed surfaces that do not exist in the dataset. In light of these challenges, we introduce a novel depth completion methodology centered on frequency-domain manipulation. Spatial characteristics inherently encompass both the surface structure and noise, posing a formidable task in addressing both aspects effectively. To surmount this challenge, we have devised a method that robustly restores depth information within the frequency domain through an Iterative Low-Pass Filter (ILF). The ILF is designed to handle scenarios of single-shot chances and complex surfaces. We also provide an optimization characteristic of ILF using L1 norm analysis. As we process sparse and noisy depth data using our approach, noise is systematically eliminated, yielding a rich and accurate depth profile in each iteration. The resulting depth image encompasses the correct depth data, thereby resolving the longstanding issue of missing and noisy data.
## 2 Methodology
### EEG Acquisitions during Fabric Stimulations  
After obtaining ethical approval from the Human Subjects Ethics Sub-committee (HSESC) of the Hong Kong Polytechnic University, twelve survivors of chronic stroke were recruited as the \"stroke group\", and fifteen unimpaired participants were recruited as the \"control group\", whose demographic details are listed in Table 1. The inclusion criteria of stroke group were: (1) individuals must be at least six months after the singular and unilateral brain lesion due to stroke; (2) the lesions occurring due to stroke were experienced in the subcortical area, to ensure the detectable EEG from the cortical area. All unimpaired participants were right-handed. No significant difference was found in age between the stroke and control groups (_p_ > 0.05) by the independent _t_-test after verifying normality evaluations via the Shapiro-Wilk test [45].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Measure** & **Stroke Group (_n_ = 12_)** & **Control Group (_n_ = 15_)** \\ \hline Age in years & \(55.1\pm 16.0\) & \(46.4\pm 17.4\) \\ Gender (male/female) & \(11/1\) & \(5/10\) \\ Stroke type (ischemic/hemorrhagic) & \(10/2\) & - \\ Affected side (right/left) & \(6/6\) & - \\ Years since stroke & \(14.9\pm 5.8\) & - \\ FMA (upper extremity) & \(42.5\pm 15.2\) & - \\ FMA (light touch on forearm) & \(1\pm 0\) & - \\ MAS (elbow) & \(1.1\pm 0.7\) & - \\ \hline \hline \end{tabular} Note: Data are given as mean \(\pm\) standard deviation. MAS: Modified Ashworth scale; FMA: Fugl–Meyer assessment [28].  
\end{table}
Table 1: Demographic characteristics and clinical scores of the stroke and control groups [28].  
The experimental setup and protocol for the fabric fine tactile stimulation are shown in Figure 1. The three types of fabrics, i.e., cotton, nylon, and wool of the same size and different textural properties, were alternatively placed on the skin surface of the ventral forearm of the upper limb, i.e., a single stimulation trial (Figure 1c). Each trial consisted of a 30 s baseline measurement, i.e., no fabric stimulation to the skin, followed by alternative stimuli with the three different fabrics in a random sequence for 13 s stimulation with each fabric and 60 s gaps in between. The stimulation trial was repeated thrice for each target forearm. The whole brain EEG with 64 channels (BP-01830, Brain Products Inc., Gilching, Germany) based on the 10-20 system [46] was captured during the stimulation trials at a sampling frequency of 1000 Hz. Each subject was required to stay awake and calm during the EEG measurements while wearing ear plugs and an eye mask, whose purpose was to minimize visual and audio disturbances from the environment. The detailed experimental procedure is described in [28].
## 3 Panoptic Segmentation
### Top-Down Approach to Panoptic Segmentation  
The PS is classified into top-down, bottom-up, single-path approaches, and other approaches [39]. But many deep learning methods follow the top-down approach, and this section elaborates the understanding of this approach. It is a simple approach of object detection and segmentation and is categorized into two stages namely one-stage and two-stage. In a one-stage approach, a one-stage detector is used to remove the proposals generation and make use of an anchor-free approach to perform object detection. The two-stage approach on the other hand, performs the proposals generation as its first step and then post-processing is done in the next step to achieve segmentation. Instance segmentation uses a Mask R-CNN [38] in this two-stage approach as seen in Figure 7.  
Figure 5: Architecture of a unified panoptic segmentation network [7].  
Figure 6: Efficient panoptic Segmentation Network architecture [37].  
The instance segmentation is built on a faster R-CNN, and it can be seen from Figure 7 that the ROI Pooling is substituted by ROI Align and minimizes the quantization errors. The technique is simple and straightforward, and based on the results of object detection by fast R-CNN, the Mask R-CNN utilizes a mask using smaller fully connected CNN to obtain the segmentation mask on a pixel-by-pixel basis along with the bounding boxes, output class, and mask logits. The issue lies not only in addressing conflicts between different branches such as the inconsistent class label predictions in both semantic and instance segmentation, but also within branches, addressing issues of overlapping and occlusion.  
A single network is used by PS to combine the predictions of semantic and instance segmentation branches that are trained together using heuristics called the JSIS-Net (Joint semantic and instance segmentation) network [40]. This network architecture (seen in Figure 8) uses a feature extractor (ResNet-50 in this case) shared between the semantic and instance segmentation branch. Mask R-CNN is used by the instance segmentation to generate pixel clusters that are combined to obtain a normalized mask. However, a pyramid pooling module is used by the semantic segmentation branch to extract feature maps and to reshape the size prediction of the image input. The semantic branch's predicted 'thing' class is now substituted with the 'thing' class predicted from the instance branch. Subsequently, the network merges the predicted'stuff' class from the semantic branch and the predicted 'thing' class from the instance branch to perform PS. This network architecture successfully resolves the differences between the semantic and instance segmentation branches via a post-processing module and overlooks the conflicts of intra-branch which is overlapping and occlusion.
## 3 Results and Discussion
### Influence of NH\({}_{3}\) and NO\({}_{x}\) on SOA at High RH (Experiments: Tol-5 and Tol-6)
#### 3.3.2 Influence of NH\({}_{3}\), NOx on SOA Light Absorption at 80% RH
First, NH\({}_{3}\) clearly promotes SOA browning because both B\({}_{\rm abs}\) and MAC increase with the NH\({}_{3}\) concentration under both NP and NR conditions. Second, the scattering of toluene SOA was thought to be synergistically promoted by NH\({}_{3}\) via NH\({}_{4}\)NO\({}_{3}\) formation under the NP regime (i.e., in low NO\({}_{x}\) scenarios), which is consistent with [PERSON] et al. [47]. Conversely, NH\({}_{3}\) did not influence the scattering properties of toluene SOA under the NR regime. Finally, the presence of NH\({}_{3}\) led to higher B\({}_{\rm scat}\) and lower B\({}_{\rm abs}\) values under low NO\({}_{x}\) conditions, with the reverse being true under high NO\({}_{x}\) conditions. These results indicate that NH\({}_{3}\) increases the O\(\circ\)C content of the SOA under low NO\({}_{x}\) conditions and the N\(\circ\)C ratio of the SOA under high NO\({}_{x}\) conditions. Figure 11: Summary of the effects of NH\({}_{3}\) on the optical characteristics of toluene SOA, i.e., MAC MSC, B\({}_{\rm abs}\) and B\({}_{\rm scat}\) at 80% RH under low and high NO\({}_{x}\) conditions (data are plotted using a log scale).
## 2 HHT Theory and Signal Recognition
### HHT Theory
#### 2.1.2 The Hilbert Spectral Analysis  
The Hilbert spectral analysis method [37] can more intuitively reflect the change characteristics of the instantaneous frequency of nonlinear signals over time. Firstly, every IMF component of the signal is subject to the Hilbert transform, and the corresponding analytic signal \(A[c_{i}(t)]\) is obtained as follows:  
\[A[c_{i}(t)]=c_{i}(t)+j[\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{c_{i}(t)}{t- \tau}d\tau]=\alpha_{i}(t)e^{i\theta_{i}(t)} \tag{10}\]  
where the instantaneous amplitude and instantaneous phase are as follows:  
\[\alpha_{i}(t)=\sqrt{c_{i}^{2}(t)+H^{2}[c_{i}(t)]} \tag{11}\]  
\[\theta_{i}(t)=\arctan\frac{H[c_{i}(t)]}{c_{i}(t)} \tag{12}\]  
where the instantaneous frequency is as follows:  
\[\omega_{i}(t)=\frac{d\theta_{i}(t)}{dt} \tag{13}\]  
Then, the IMF component \(c_{i}(t)\) can be expressed as:  
\[c_{i}(t)=\text{Re}[\alpha_{i}(t)\exp(j2\pi\int f_{i}(t)dt)] \tag{14}\]  
Show \(\alpha_{i}(t)\) on the time-frequency plane, and the Hilbert spectrum of \(c_{i}(t)\) is obtained.  
\[H_{i}(t,f)=\begin{cases}\alpha_{i}(t),f=f_{i}(t)\\ 0,\ \ \ f\
eq f_{i}(t)\end{cases} \tag{15}\]  
We finally obtain the analysis signal of the Hilbert spectrum.  
\[x(t)=\text{Re}[\sum_{i=1}^{n}\alpha_{i}(t)\exp(j2\pi\int f_{i}(t)dt)] \tag{16}\]
## 4 Discussion
reported that the terpene cyclase _tarA_ is involved in the production of the sesquiterpene compound tricinolinol acids (TRAs) in _Trichoderma hypoxylon_[52]. The BLAST analysis showed that TPS1 shared a sequence similarity of 76.88% with QNC71690.1 from _T. hypoxylon_ at the amino acid level. Further phylogenetic analysis also revealed their homology (Figure 2). Considering the TRA synthesis pathway [52], it is hypothesized that TPS1 may function similarly to TraA proteins as a terpene cyclase involved in sesquiterpenoid synthesis. The bioinformatic analysis indicated that the TPS1 protein belongs to the terpene cyclase-like 2 subgroup and contains an encoded terpene cyclase, three metal sites, and a hydrogen bond bound to the pyrophosphate anion. Referring to previous studies, a putative biosynthetic pathway for the formation of TPS1 was proposed: TPS1 acts as a terpene cyclase with a building block of farnesyl pyrophosphate (FPP) to produce beta-cedrene. It is worth noting that a single terpene synthase may generate multiple products; for instance, the \(\gamma\)-humulene synthase from North American fir has the capacity to produce 52 different sesquiterpenes, possibly due to distinct binding orientations within the substrate-binding DDXXD structural domain [53]. Therefore, if a reaction permits the formation of multiple intermediates, it is more likely that some side products requiring fewer steps or shorter reaction times than the main product will be formed [54]. As far as we know, the biological functions of fungal sesquiterpenes in relation to themselves or fungal-plant interactions remains incomplete. Current research primarily focuses on sesquiterpenes in plants, which produce terpenoids which function as phytotoxic and repellent compounds against phytophagous insects. For instance, Melia azedarach releases neemin, a furan triterpen, to deter insects such as _Pieris occidentalis_ and _Muccania separata_[55; 56]. Fungal sesquiterpenes have similar functions but have been less extensively studied [57].