esa-sceva/satcom-chunk-collection huggingface dataset:
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Definition of \(\bar{S}\)  
We introduce an extended entropy formulation in this work, which we termed the Extended Non-Equilibrium Entropy \(\bar{S}\). This expression extends the conventional definition of entropy to a wider range of physical variables, providing a general thermodynamic state function that is suitable for non-equilibrium systems. In particular, the Extended Non-Equilibrium Entropy \(\bar{S}\) is methodically designed to capture constraints on the momentum and angular momentum of the system in addition to its internal energy and temperature. In instances when standard descriptors might not be sufficient, this elaborate dependency plays a crucial role in describing the complex dynamics and thermodynamic subtleties of systems operating far from equilibrium, which allows for the incorporation of additional constraints that are relevant in non-equilibrium scenarios:  
\[\bar{S}=\sum_{\alpha=1}^{N} \bigg{[}S^{(\alpha)}\Big{(}E^{(\alpha)}-\frac{\big{(}p^{(\alpha )}\big{)}^{2}}{2m^{(\alpha)}}-\frac{\big{(}I^{(\alpha)}\big{)}^{2}}{2I^{( \alpha)}}\] \[-q^{(\alpha)}V^{(\alpha)}+q^{(\alpha)}(\mathbf{A}^{(\alpha)}\cdot \mathbf{v}^{(\alpha)})-U^{(\alpha)}_{\text{mec}}\Big{)}\] \[+\Big{(}\mathbf{a}\cdot\mathbf{p}^{(\alpha)}+\mathbf{b}\cdot( \mathbf{r}^{(\alpha)}\times\mathbf{p}^{(\alpha)})+J^{(\alpha)}\Big{)}\bigg{]}, \tag{8}\]  
where  
* \(S^{(\alpha)}\) denotes the entropy contribution from the \(\alpha\)-th particle or subsystem, reflecting the individual disorder or randomness contribution;
* \(E^{(\alpha)}\) signifies the total energy of the \(\alpha\)-th particle or subsystem;
* \(p^{(\alpha)}\) and \(m^{(\alpha)}\) represent the momentum and mass, respectively;
* \(J^{(\alpha)}\) and \(I^{(\alpha)}\) refer to the angular momentum and moment of inertia;
* \(q^{(\alpha)}\) represents the charge;
* \(V^{(\alpha)}\) and \(\mathbf{A}^{(\alpha)}\) are the scalar and vector potentials;
* \(\mathbf{v}^{(\alpha)}\) denotes the velocity;
* \(U^{(\alpha)}_{\text{mec}}\) symbolizes external mechanical energy contributions;* **a** and **b** are vectors of Lagrange multipliers related to translational and rotational velocities. The index \(\alpha\) is employed throughout our equations to denote the applicability of our entropy formulation to specific particles or subsystems within a larger system. This designation serves multiple purposes: 1. It emphasizes the microscopic foundation of our thermodynamic analysis, allowing for the detailed tracking of individual contributions to the system's overall entropy. 2. It reminds us of the potential for statistical or collective constructions, facilitating a transition from a microscopic to a macroscopic perspective when necessary. 3. It assures the flexibility of our approach, indicating that while the \(\alpha\) index is crucial for a nuanced understanding, it can be abstracted away or summarized for problems where a granular level of detail is not required.
## 2 Methods
### Overall Framework  
As illustrated in Figure 3, the framework can be divided into two stages. The first stage is the self-supervised pretraining stage, which inputs unlabeled WBMs. First, data preprocessing is performed in this stage, including WBM denoising and resizing. Then, momentum contrast learning is used to train the two encoders using queries and keys generated from two augmented views of the input WBMs. The query and key encoder extract feature representations of queries and keys and output encoded embedding. A WBM dictionary called \"queue\" is introduced in momentum contrastive learning to reuse the encoded keys from the previous batches. Finally, the query and the corresponding key embedding are positive pairs. The query embedding and the queue are negative pairs for calculating the contrastive loss. Only the parameters of the query encoder are updated by backpropagation. The parameters of the key encoder are updated by the momentum mechanism.  
The second stage is the fine-tuning stage, which employs labeled WBMs to fine-tune the pretrained encoder for classification. Similar to the pretraining stage, data preprocessing is carried out initially. During the fine-tuning process, a prototypical network is utilized for classification. Ultimately, a well-trained defect classification model is obtained.
# Monocular Depth Estimation Using Res-UNet with an Attention Model
## 3 Proposed Method
### Proposed Model
#### 3.2.2 Attention Block  
Attention in fully convolutional neural networks (CNNs) has been introduced for applications such as image captioning [42], machine translation [43], and classification [44]. Currently, attention gates are frequently employed in natural-image analysis, knowledge graphs, and language processing.  
In CNNs, attention is implemented using one of two methods: post-hoc network analysis and trainable attention mechanisms. The former technique has mostly been used to gain access to network reasoning for the job of visual-object recognition [22; 45]. Similarly, trainable attention is categorized as hard attention and soft attention. In our work, we adopted a soft trainable attention mechanism as it can be easily adopted in UNets.  
1. **Hard attention** (Stochastic): Model training is more challenging in hard attention because of iterative region proposal and cropping [46]. These models are frequently non-differentiable and require reinforcement learning for parameter updates. Hard attention has been highly adopted in trainable transformers in recent times, as these models use iterative region proposal and cropping during feature learning.
2. **Soft attention** (Deterministic): Soft attention is probabilistic and uses normal back-propagation. These attention mechanisms do not require Monte Carlo or repeated random sampling. Soft attention is used to highlight the only relevant activation during training. It reduces the computational recourses wasted on irrelevant activations and provides a better generalization of the network. Additive soft attention has recently been used in image categorization and sentence-to-sentence translation [42; 47]. Mathematically, soft attention can be written as follows. \[E_{p(s_{t}|a)}[z_{t}]=\sum_{i=1}^{L}\alpha_{t,\ i}\ a_{i}\] (3)  
Here, \(E_{p(s_{t}|a)}\) denotes deterministic soft attention, \(s_{t}\) is the attention location, \(z_{t}\) is a context vector, \(\alpha\) represents the weights, \(L\) corresponds to the features extracted at a different image location \(i\).  
The expectation of context vector \(z_{t}\) is taken directly and deterministic attention is modeled by computing a soft attention weighted annotated vector \(\sum_{i=1}^{L}\alpha_{t,i}\ a_{i}\), as proposedby [PERSON] et al. (2018). This is equivalent to introducing a softly weighted context \(\alpha\), into the system. The learning in attention is end-to-end with conventional backpropagation because the entire model is differentiable ([PERSON] et al., 2014).
## 6 Open Issues and Future Research Directions
### 3D Scenarios  
The majority of UAV routings are often placed on a 2D surface, even though UAVs move in 3D space. The major issue in 3D UAV routing is managing the mobility of the UAV nodes. To improve routing efficiency in multi-UAV networks, UAVs must communicate in 3D space while considering critical characteristics. Design swarms of UAVs in 3D UAV networks lead to a plethora of novel application scenarios. For instance, research focuses on developing a 3D mobility model with a smooth trajectory in FANETs [121].
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 7 Potential Real-World Applications
### 5G-and-Beyond Communications Systems  
In next-generation wireless networks, the proposed model can enhance modulation recognition, facilitating adaptive modulation techniques that optimize data rates and reliability under varying conditions [49]. [PERSON] et al. [72] propose a novel deep-learning architecture that addresses the challenges for identifying modulation types in dynamic and heterogeneous 5G environments, particularly in non-cooperative scenarios. In [73], a deep-learning-based modulation classifier is designed explicitly for communications networks beyond 5G. Their research emphasizes the need for real-time modulation recognition to accommodate a broader range of signals, showcasing that hyperparameter optimization and diverse data formats can enhance classification effectiveness.
## 4 Methods
### The HySperx Data Preprocessing  
During data collection, a white reference panel and a gray calibration panel (both with measured reflectance spectra) were placed in front of the samples for calibration purposes. The white panel is made of a material that reflects almost 100% in the spectral range of 0.35-2.5 \(\upmu\)m. An average dark current frame was subtracted from frames of each image, and the Flat-Field Method calculates the relative reflectance by dividing the spectral value of each pixel by the value of the white reference panel to convert the raw data to reflectance.  
The reflectance data should be filtered to improve the S/N. The Savitzky-Golay filtering (SG) is a widely used technique in modern hyperspectral remote sensing studies [28; 29; 30]. The principle of SG is the least square filtering based on moving window [31]. The moving window contains the point to be smoothed in the center of the window with multiple adjacent points on either side of the center point. All the points in the window are used to perform the least squares fitting, but only the central point (i = 0) is smoothed (Figure 8). It works especially well when the typical band of the signal is narrow, and the height and width of the spectrum can well be preserved. The filtering effect of the spectrum varies with the size of the moving window and the order of the polynomial. A larger window can produce better smoothing effect, but may lose the tiny absorption features (Figure 9b). A higher order polynomial can achieve a better smoothing without attenuation of data features [32; 33]. In this paper, the moving window size of 5 points and 2 polynomial order is adopted for the hyperspectral images (Figure 9a).  
Figure 8: The principle of Savitzky–Golay filtering.
# Modeling and Parameter Calibration of Morchella Seed Based on Discrete Element Method
## 2 Materials and Methods  
### Agronomic Requirements for Cultivating Morchella Seeds  
The agronomic requirements for cultivating Morchella seeds involve several key steps. First, wheat and corn grains are selected as the primary substrate or nutrient source. These grains must be thoroughly cleaned and sterilized to provide an environment rich in carbohydrates and essential nutrients. During inoculation, the spores must be introduced aseptically into the substrate to ensure proper attachment and nutrient absorption. Under optimal temperature and humidity conditions, the spores begin to germinate, forming mycelium that quickly spreads through the substrate, creating a mycelial network. As the mycelium grows, it is critical to strictly control environmental factors, including temperature, humidity, and oxygen supply, to support the healthy development of the mycelial clumps. Finally, under favorable conditions, the mycelium develops into fruiting bodies, completing the lifecycle from spores to mature Morchella mushrooms. This structured process ensures the successful cultivation of Morchella and highlights the importance of maintaining precise environmental conditions throughout the growth stages.
## 2 Design Principles  
### Mechanical Model of the Three-Dimensional Seismic Isolation Device  
The three-dimensional seismic isolation system includes horizontal, bidirectional, and vertical isolation. In the design of the isolation device, a symmetric design is considered for the horizontal bidirectional aspect; therefore, the mechanical model of the three-dimensional seismic isolation device is simplified to a unidirectional horizontal and vertical model, as shown in Figure 1.  
Figure 1: Simplified mechanical model of a three-dimensional isolation device.  
In this mechanical model, \(m_{1}\) represents the mass of the isolated object, while \(m_{2}\) denotes the mass of the three-dimensional seismic isolation device. The parameters \(k_{1}\) and \(k_{2}\), as well as \(c_{1}\) and \(c_{2}\), reflect the stiffness and damping characteristics of the isolation device coupled with the lower load-bearing structure in the vertical and horizontal directions, respectively. Similarly, \(k_{3}\) and \(k_{4}\), along with \(c_{3}\) and \(c_{4}\), represent the stiffness and damping characteristics of the isolation device coupled with the upper isolated object in the vertical and horizontal directions.  
The degrees of freedom of the isolated object in the horizontal and vertical directions are denoted by \(x_{1}\) and \(y_{1}\), respectively, while the degrees of freedom of the three-dimensional seismic isolation device in the horizontal and vertical directions are represented by \(x_{2}\) and \(y_{2}\). The motion differential equations are listed as shown in Equation (1).  
\[\begin{bmatrix}m_{1}&0&0&0\\ 0&m_{2}&0&0\\ 0&0&m_{1}&0\\ 0&0&0&m_{2}\end{bmatrix}\begin{bmatrix}\ddot{x}_{1}\\ \ddot{x}_{2}\\ \ddot{y}_{1}\\ \ddot{y}_{2}\end{bmatrix}+\begin{bmatrix}c_{4}&-c_{4}&0&0\\ -c_{4}&c_{2}+c_{4}&0&0\\ 0&0&c_{3}&-c_{3}\\ 0&0&-c_{3}&c_{1}+c_{3}\end{bmatrix}\begin{bmatrix}\dot{x}_{1}\\ \dot{x}_{2}\\ \dot{y}_{1}\\ \dot{y}_{2}\end{bmatrix}+\begin{bmatrix}k_{4}&-k_{4}&0&0\\ -k_{4}&k_{2}+k_{4}&0&0\\ 0&0&k_{3}&-k_{3}\\ 0&0&-k_{3}&k_{1}+k_{3}\end{bmatrix}\begin{bmatrix}x_{2}\\ y_{1}\\ y_{2}\end{bmatrix}=\begin{bmatrix}0\\ m_{2}a_{1}(t)\\ 0\\ m_{2}a_{2}(t)\end{bmatrix} \tag{1}\]  
The dynamic response at any given time can be calculated using the Newmark method based on Equation (1).
## 4 The Relationship between the Interpretation Process and Scale
### Scale Downgrading  
Once the observer's interpretation settles onto a specific scale, a selection of the relevant symbols forming the description has been established. Accounting for the frequency of each symbol leads to the construction of the symbol probability distribution which characterizes the description. If these probabilities are ordered, a so-called symbol probability profile can be obtained. These profiles are by themselves descriptions of the system observed, represented by a shape of \(D-1\) degrees of freedom, where \(D\) is the symbolic diversity. When the symbol selection obeys the minimal entropy criterion--that is when the observation is performed according to the Fundamental Scale--the profile shape may serve for identity purposes due to the uniqueness condition of the minimal entropy probability distribution.  
In order to use the ranked probability profiles for identification purposes, or to compare systems observed at different scales, it may be convenient to '_simplify_' the frequency profile while preserving its overall shape. This is done by replacing groups of points in the original profile by single points. The selection of point sets to be replaced must be done considering the density of points in each profile segment, in order to keep the same level of detail and significance in all sectors of the profile; this is the basis for the formulation of the scale downgrading process presented in [14].  
This procedure for downgrading the language scale is useful given the frequent requirement for expressing descriptions at the same scale or to show any description's profile shape, at any arbitrarily selected scale. Figure 1 shows the profile shape representing the [PERSON]'s 9 th Symphony 3 rd Movement, recorded as a MIDI file. Figure 1a shows the frequency profile of the 2828 fundamental symbols which describe the MIDI file at the lowest entropy. Figure 1b,c shows the profile with scale downgraded to 513 and 65 symbols respectively.
## 3 Modified CNN Based on Dropout and the SGD Optimizer
### Modified Convolutional Neural Network Based on Dropout and the SGD Optimizer  
On the basis of the design of the CNN structure, the Leaky ReLU activation function, and the overfitting prevention method that is based on dropout and the SGD, the Modified Convolutional Neural Network based on Dropout and the SGD Optimizer (MCNN-DS) is developed, which has the following main processes.  
* Step 1: Pretrain the filter, and initialize the filter size pixel as \(P_{1}\times P_{2}\).
* Step 2: Enter the image dataset for training. Process the image of the training set into the same picture as the filter size, and read the data to form the image data matrix \(\mathbf{X}\).
* Step 3: Initialize the weight \(w^{(l)}{}_{ij}\) and bias \(b_{i}\) and invoke the kernel function _def Kernel()_ provided by TensorFlow to initialize parallel operations.
* Step 4: The Conv2d is used for two-dimensional convolution operation to obtain the first layer convolution feature matrix \(\mathbf{X}^{(1)}\).
* Step 5: The first layer convolution feature matrix \(\mathbf{X}^{(1)}\) is used as the input data of the pool layer. Use Formula (5) for the pool operation to obtain the feature matrix \(\mathbf{X}^{(2)}\).
* Step 6: Use the SGD optimizer function expressed in Formula (4) to derive the learning rate of the top-down tuning optimizer, and use the weights in TensorFlow and the update-biased interface to update the weight \(w_{i}\) and the bias \(b_{i}\), thus obtaining the feature matrix \(\mathbf{X}^{(3)}\).  
* Step 7: Generate the second convolution following Steps 4, 5, and 6 to derive the feature matrix \(\mathbf{X}^{(4)}\).
* Step 8: Merge the feature matrix \(\mathbf{X}^{(4)}\) into a column vector as the input of the neuron at the full-joint layer, multiply it with the weight matrix plus the bias, and then use the Leaky ReLU activation function to obtain the eigenvector \(\mathbf{b_{1}}\).
* Step 9: Use the eigenvector of the fully connected stratum as the input of the dropout layer, compute the output probability of the neuron in the dropout layer using Formula (6), and the eigenvector \(\mathbf{b_{2}}\) is obtained.
* Step 10: Use the eigenvector \(\mathbf{b_{2}}\) as the input and the Softmax classifier [25] output to achieve the results.  
Of note, in the convolution feature calculation for step 4 and step 5, the step length is 2 and the margin is set to 0. The pooling operation uses a 3 \(\times\) 3 matrix on the basis of a policy previously presented in the literature [26] to ensure that the image input and output after feature extraction have the same size. The Leaky ReLU activation function is used in each step to activate the neuron.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 3 Discussions and Outlook
### Improvement of Electrical Conductivity and Durability  
When flexible graphene electrodes are used for ECG monitoring on the skin, although the electrode itself is flexible, more and more cracks appear in the graphene after repeated stretching, increasing electrode impedance. This will further affect the monitoring quality of ECG signals under long-term use. To solve this problem, a flexible ECG electrode based on silver nanowire bridging the graphene has been proposed [89]. By imitating the plasmodesmata in higher plants, silver nanowires (AgNWs) were used to bridge laser-scribed graphene oxide (LSG) nanoflakes (Figure 6a). In this way, LSG nanoflakes remain interconnected by AgNWs after the flexible electrode is stretched during wearing, which stabilizes impedance and improves durability, increasing suitability for long-term ECG monitoring. In addition to the above doping method, electrical conductivity and durability can also be improved by improving the physical structure of the electrode. As shown in Figure 6b, styrene-butadiene foam was used as the skeleton structure of the graphene electrode. The realized three-dimensional porous graphene electrode is made of soft and durable styrene-butadiene rubber micro-balls wrapped with graphene nanoflakes, creating a dense and resilient conductive network. In addition, the various modified graphene electrodes mentioned above are also typical methods to improve electrical conductivity and durability [68; 69; 71; 72; 74].
## 4 Primary Findings
### Holistic Analysis of Findings
#### 4.1.6 Impact of I4.0  
The sixth category investigated the impact of I4.0. All interviewees assessed the average effect of I4.0 tools based on their implemented projects. As a result, a significant percentage of respondents (60%) highlighted an improved business performance (Figure 7). Twenty percent of respondents forecast a high probability of improvements in the long run, but, due to the immaturity of implemented I4.0 projects, it is too early to measure their performance. Thirteen percent of the interviewees indicated that they did not measure the performance at all. Finally, only one company had closed a project because of low returns.  
The impact of I4.0 tools is mostly positive yet occasionally negligible. Most of the respondents indicated that I4.0 solutions improved their business performance in terms of efficiency, quality control, and strategic management, and, in some cases, already increased the company's net income (Q24). Moreover, I4.0 projects have a positive impact on society [70]. One of the interviewees stated that the ongoing smart city transformation project in Nur-Sultan helped to increase road safety (Q25). The social impact might become an additional motivation to invest in I4.0 projects for many companies.  
Despite the numerous successful projects, sometimes the results of the I4.0 implementation might be insignificant. One interviewee company downgraded the ERP system because of its financial inefficiency. They switched to a simpler ShareFile and 1C: Accounting software. Another respondent completely abandoned the implementation project due to the lack of performance improvements (Q26). Section 4.1.3, critical factors, examined the  
Figure 6: Established international partnerships among Kazakhstan companies.  
Figure 7: The impact of Industry 4.0 on companies in Kazakhstan.  
reasons for the failure of 14.0 projects. Companies must be aware of implementation barriers indicated in Section 4.1.2, 14.0 implementation, and possess resources to overcome them.  
Lastly, one of the interviewees stated that the fourth industrial revolution does not inherently bring prosperity. It just creates opportunities to improve the performance of businesses and governments.  
Changes happen, and I4.0 catalyzes this opportunity. Companies that take the opportunity will be winners. (Interviewee 2)
## 3 Discussion
### Industry 4.0 and Logistics 4.0
Industry 4.0 technologies adopted by logistics operations will enable new features that facilitate the optimization of resources and energy, such as the constant transparency of the complete supply chain, for all participants in the process.