esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
To address the dual constraints of caching capacity and spectrum resources in integrated UAV-assisted communication-caching-charging scenarios, this paper proposes and  
Figure 10: Sensitivity analysis of UAV revenue changes under different UAV altitudes.  
Figure 9: Sensitivity analysis of UAV revenue changes under different jitter radii.  
validates an effectiveness-verified cross-domain joint optimization framework through theoretical modeling and algorithmic design. By establishing a coupling model encompassing network topology, caching strategies, channel characteristics, and transmission energy consumption, the complex discrete optimization problem is decoupled into four subproblems: 3D UAV deployment, dynamic caching placement, NOMA user clustering, and power allocation. Corresponding optimization algorithms are developed. The particle swarm optimization algorithm iteratively searches for optimal UAV hovering positions to achieve adaptive spatial deployment. A Stackelberg-Bertrand-based caching strategy alleviates backhaul congestion during peak traffic through a dual-layer pricing-caching game between content providers and the UAV, prioritizing high-demand content. The simulated annealing algorithm enhances global stability through controlled stochastic perturbation in user grouping optimization. The Lagrange multiplier method is used to solve the power distribution of the subscriber channel, which gives the subscriber higher power distribution under the weak-channel condition and realizes fair power distribution. MATLAB simulations demonstrate that the proposed framework significantly outperforms benchmark algorithms in reducing transmission delay, conserving backhaul resources, and improving UAV economic returns. While current research focuses on single-UAV system optimization, future work will explore multi-UAV cooperative mechanisms and integrate content popularity prediction with user preference analysis into caching optimization to enhance dynamic adaptability of model.  
Conceptualization, Y.Z. and Q.Z.; Methodology, Y.Z.; Software, Y.Z.; Validation, Y.Z. and Q.Z.; Formal analysis, Y.Z.; Investigation, Y.Z.; Resources, Q.Z.; Data curation, Y.Z.; Writing--original draft, Y.Z.; Writing--review & editing, Y.Z.; Visualization, Y.Z.; Supervision, Q.Z.; Project administration, Q.Z.; Funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jiangsu Provincial Key Research and Development Program (grant BE2022068-2) and the National Natural Science Foundation of China (grant 92367302).  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Theoretical Methods
### Boundary Values of the Limited Width  
Due to the ground surface overload \(q\), \(z_{0}\) may not necessarily occur in the active state. In this case, \(z_{0}\) must be analyzed additionally. According to Equation (18), when \(z_{0}<0\), \(z_{0}=0\). When \(z_{0}>0\), \(0\leq\frac{c\cot\varphi\tan\delta-\gamma B/2}{qK_{aw}\prime}\frac{\tan\delta- \gamma B/2}{\tan\delta-\gamma B/2}\leq 1\). Thus, \(B\geq\frac{2c\cot\varphi}{\gamma\cot\delta}\), while \(c\cot\varphi\geq qK_{aw}\prime\). However, \(B\leq\frac{2c\cot\varphi}{\gamma\cot\delta}\) while \(c\cot\varphi<qK_{aw}\prime\). Therefore, the boundary for \(c\)-\(\varphi\) soil limited width \(B\) is:  
\[\left\{\begin{array}{l}\frac{2c\cot\varphi}{\gamma\cot\delta}\leq B\leq h\cot \beta\;(\text{when }c\cot\varphi\geq K_{aw}\prime q)\\ B\leq\frac{2c\cot\varphi}{\gamma\cot\delta}\;\text{and}\;B\leq h\cot\beta\;( \text{when }c\cot\varphi<K_{aw}\prime q)\end{array}\right.. \tag{19}\]  
Otherwise, the \(c\)-\(\varphi\) soil width is semi-infinite or beyond the calculation scope of this study for \(B<2c\cot\varphi/(\gamma\cot\delta)\) while \(c\cot\varphi\geq qK_{aw}\prime\).
# Machine Learning Algorithms: An Experimental Evaluation for Decision Support Systems
## 3 Machine Learning Algorithms and Techniques
### Logistic Regression  
Logistic Regression is a supervised algorithm that is often used for predictive analytics and modeling to understand the relationship between a dependent variable and one or more independent variables, wherein probabilities are estimated using a logistic regression equation [20]. The dependent variable is finite or categorical, whereas for binary regression, we have A or B options, and for multinomial regression, we have a range of finite options, for example A, B, C or D. Examples that use this technique can be found in [8; 9; 13; 14].
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 5 Conclusions  
The accuracy of vegetation indices in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems. In this study, remote sensing inversion and mapping of forest stand age in the Loess Plateau were carried out under consideration of the mechanism of the remote sensing of vegetation indices and the physiological function and canopy structure of the forest. The main conclusions are as follows:  
1. The canopy reflectance of different forest stand ages has a significant change pattern, and the older the forest stands, the lower the _NIR_ reflectance; the relationships between \(SR\), \(NIR_{v}\), \(NDVI\), \(GVI\) and forest stand age were more nonlinear than linear.
2. Principal component analysis (PCA) of canopy spectral information showed that \(SR\), \(NDVI\) and red edge (\(B^{5}\)) could explain 98% of all spectral information. \(SR\), \(NDVI\) and B5 were used to construct MLR and RF models, and the RF model was found to have high estimation accuracy (\(R^{2}=0.63\)).
3. The accuracy of the models was evaluated using reference data, and it was found that the accuracy of the RF model (\(R^{2}=0.63\)) was higher than that of the MLR model (\(R^{2}=0.61\)), but both models underestimated the forest stand age when the forest stand age was greater than 50a, which may be caused by the saturation of the reflectance of the old forest canopy. The RF model was used to generate a dataset of forest stand age, and it was found that the forest is dominated by young forests (\(<\)20a), accounting for 38.26% of the forest area. This study not only improves the method of forest stand age estimation, but also provides data support for vegetation construction in the Loess Plateau, which are the key data for carbon sink simulation of the regional ecosystem.  
**Author Contributions:** Conceptualization, X.W. and J.S.; methodology, X.W.; software, J.S., C.G. and C.W.; writing--original draft preparation, X.W.; writing--review and editing, F.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the China Postdoctoral Science Foundation, grant number 2023M732873; Fundamental Research Funds for Central Universities, grant number 2452022125; and National Natural Science Foundation of China, grant number 42207396.  
**Data Availability Statement:** The Sentinel-2 dataset for the period from January 2019 to December 2019 can be downloaded at [[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)) (accessed on 10 October 2023).  
**Acknowledgments:** The authors thank the anonymous reviewers and editors for their work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Weighted Wavelet Fusion Approach  
Underwater images acquired for hydraulic infrastructure inspection typically suffer from haze, color casts, low contrast, and blurred textures. To address these issues, we propose an enhancement pipeline based on weighted wavelet fusion. The enhancement process, as illustrated in Figure 1, comprises four stages:  
* Color correction: The raw RGB image is processed with MSRCR, followed by \(\gamma\)-correction, producing a brightness-balanced reference image \(I_{1}\).
* Contrast enhancement: The RGB image is also transformed to the CIELAB color space; the luminance channel \(L\) is enhanced with CLAHE and then recombined with the original \(a\) and \(b\) channels to form the contrast-enhanced image \(I_{2}\).
* Wavelet decomposition and weighting: Both \(I_{1}\) and \(I_{2}\) undergo discrete wavelet decomposition to separate low- and high-frequency sub-bands. The Laplacian energy of each sub-band is used as a saliency measure to compute spatially varying fusion weights.
* Wavelet fusion and reconstruction: The weighted sub-bands are fused and inverted via wavelet synthesis, yielding the final enhanced image.
## 2 Proposed Circuit and Operating Modes
### Voltage Control with Inverting Characteristics  
The role of the front-end diode bridge remains similar as discussed for the non-inverting operation. It is an absolute value converter that transforms the sinusoidal input voltage to its absolute form. The peak voltage across the reverse-biasing diodes of this bridge cannot exceed the peak value of the input voltage. Thus, the diode bridge always delivers a time-varying unidirectional voltage at the input of the transistor's bridge. This voltage is transformed to a regulated bidirectional voltage with boost characteristics. For the inverting operation, the control process for switching transistors \(Q_{1}\) and \(Q_{3}\) is inverted, as can be seen in Figure 7b. The PWM behavior of switching transistor \(Q_{4}\) for the non-inverting operation is changed to \(Q_{2}\) for a positive input and vice versa for a negative input. This means that the PWM control of switching transistors \(Q_{4}\) and \(Q_{2}\) stabilizes the positive and negative output for non-inverting and inverting operations, respectively. Figure 9a-d illustrate the transfer of power for a positive and negative regulated output voltage.  
Figure 9: Out-phase voltage boost operation during (**a**,**c**) shoot-through interval of positive and negative input voltage, respectively; (**b**,**d**) energy transfer interval of positive and negative input voltage, respectively.  
A similar approach as used for the non-inverting operation may be employed to compute the dynamic value of the inductor voltage and capacitor current during the PWM turn-on and -off period by using the KVL and KCL, respectively.  
\[L\frac{di_{id}}{dt}=kv_{s} \tag{11}\]  
\[C_{o}\frac{di_{c}}{dt}=-ki_{o} \tag{12}\]  
\[L\frac{di_{id}}{dt}=(1-k)v_{o}+(1-k)v_{s} \tag{13}\]  
\[C_{o}\frac{di_{o}}{dt}=(1-k)i_{s}+(k-1)i_{o} \tag{14}\]  
The dynamic values of the inductor current and capacitor voltage during the steady-state condition can be ignored due to the low variation in the required output frequency. Thus, the right-hand sides of Equations (11)-(14) are added, respectively, to obtain the voltage and current transfer ratio.  
\[\frac{V_{o}}{V_{s}}=\frac{-1}{1-k} \tag{15}\]  
\[\frac{I_{s}}{I_{o}}=\frac{1}{1-k} \tag{16}\]
## 4 Results
#### Interpretability with GradCAM  
To gain insight into the decision-making process of the ResNet-50 model, we used the Grad-CAM algorithm. This algorithm highlights the significant frequencies that are responsible for a given prediction. Additionally, we applied the t-SNE technique to visualize the activations in the last ten convolutional layers, as shown in Figure 9. Through t-SNE visualizations of activations, we can identify the convolutional layers that capture the most discriminative features relevant to the classification task. Layers that show well-separated clusters of activations indicate the learning of important features for distinguishing between different classes. Figure 9b demonstrates better cluster separation compared to the commonly used final layer. This suggests that the layer effectively captured the crucial features required for the classification task. Given the well-separated clusters of activations, this layer emerges as a strong candidate for generating meaningful visualizations. Therefore, by utilizing activations from this layer in our model, we can potentially enhance interpretability and gain valuable insights into the decision making process of our model.  
Figure 10 displays the attention maps generated using Grad-CAM. These maps provide insights into the specific spectral regions that the model considers essential for making predictions. By overlaying these heat maps onto the spectral plots of the test dataset, we can easily identify the frequencies that the model deems significant. In these visualizations, areas of high attention are indicated in red to highlight their importance, while areas of low attention are represented in blue. The model's attention is directed towards specific frequency ranges depending on the condition of the drill bit. For instance, when predicting a normal drill bit condition, Figure 10a, the model primarily focuses on frequencies within the range of 0-1000 Hz. Additionally, attention is also observed in the frequency range of 4000-6000 Hz, indicating the presence of distinctive spectral features that are crucial for identifying normal drill bit behavior. On the other hand, in scenarios involving abnormal drill conditions, Figure 10b, the model's attention shifts to different frequency ranges. Specifically, attention is concentrated within the frequency range of 2500-6000 Hz. This  
Figure 8: ResNet-50 model confusion matrix: green indicates true positives and true negatives while pink denotes false positives and false negatives.  
suggests that spectral characteristics within this range play a crucial role in distinguishing abnormal drill bit behavior from normal operation.
# Leveraging Static and Dynamic Wear Leveling to Prolong the Lifespan of Solid-State Drives
## 3 Greedy with Migration Pool
### Garbage Collection  
The Greedy-MP policy follows a two-step process to select victim blocks. In the first step, akin to the greedy policy, it selects the block with the highest invalidation ratio in the normal pool as a candidate victim block, termed _candidate1_. Subsequently, in the second step, it searches for blocks in the migration pool with higher invalidation ratios than _candidate1_. If no such blocks exist, _candidate1_ is chosen as the victim block. Conversely, if blocks with higher invalidation ratios, referred to as _candidates2_, are found, their erase counts are compared to that of _candidate1_ and the average erase count of all blocks.  
If the erase count of _candidates2_ is higher than that of _candidate1_ and also exceeds the average erase count of all blocks, _candidate1_ is still chosen as the victim block. This is because although _candidates2_ may have lower invalidation ratios, they are considered more worn-out relative to other blocks. However, if any _candidate2_ has been erased fewer times than _candidate1_ or if its erase count is lower than the average erase count of all blocks, it is selected as the victim block. This decision is based on the understanding that although these blocks are in the migration pool, they may not be significantly worn-out compared to other blocks due to migration performed relatively earlier.  
Consequently, migrated worn-out blocks are not selected as victim blocks until their erase counts become lower than those of candidates in the normal pool or fall below the average erase count of all blocks, irrespective of their invalidation ratios. This contributes to delay in transitioning to bad blocks.  
Once selected as victim blocks, they are utilized to handle write requests from the host after being erased. Once all clean pages are exhausted due to host write requests, the block is reinserted into the normal pool regardless of any prior migration, as it is no longer considered significantly worn-out compared to other blocks at that point. Figure 3 provides a pseudocode of the victim block selection process in garbage collection.
# Modified Index Policies for Multi-Armed Bandits with Network-like Markovian Dependencies
## 1 Introduction
### Application Context and Conceptual Validation in Network-like Scenarios  
While our primary contribution is theoretical, it is helpful to illustrate how this framework can be built on to conceptually extend to real network scenarios. Consider, for example, the following contexts:  
* Security [26]: Arms may represent intrusion detection strategies whose efficacy varies as an adversary's tactics evolve over time. Each state transition corresponds to a shift in the threat environment. Our Markovian MAB framework can guide strategic decisions to maintain robust defense while learning dynamically about evolving threats.
* Distributed Blockchain Systems [27]: Nodes or shards in a blockchain network might yield variable validation rewards depending on their state of congestion or consensus participation. The Markovian structure models the dynamic nature of node availability and network conditions in a way that would help a node operator choose where to allocate resources or which shard to support over time.
* QoS in Communication Networks [7]: Network links may fluctuate between high-quality, moderate, and poor states due to changing traffic patterns. By representing each link as a Markovian arm, our framework can assist in selecting the best channel at any given time in order to balance the exploration of uncertain but potentially high-quality links with the exploitation of known reliable ones.  
Figure 1: A sample example of two-arms of a multi-armed bandit. The first arm has two states and the second arm has one state.  
* IoT and System Administration [28]: IoT nodes or servers can transition between states that reflect varying processing loads or energy conditions. The Markovian MAB model helps a controller decide which node to query or utilize for computations, thereby maximizing long-term performance.  
In sum, while this work is focused on the theoretical aspects and fundamental results for up to three states, it offers a roadmap for future empirical explorations and practical implementations. The stylized simulation experiments that we show later serve as a preliminary demonstration and show that the theoretical principles hold in a controlled synthetic environment, thus setting the stage for subsequent research aiming at more comprehensive benchmarking in real-world network contexts.  
The remainder of the paper is structured as follows. Section 2 gives the related work. Section 3 presents the preliminaries. Section 4 shows the problem formulation. The index policy and its regret analysis are given in Section 5. Section 6 shows our numerical simulation results, and finally, Section 7 concludes the paper.
# Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
## 2 Methods  
The original WSI has a gigapixel resolution, which limits the use of a conventional CNN classifier scheme. Therefore, we adapted the patch-wise classification method. This section begins with a description of the dataset used in this study, followed by the patch preparation procedure for the training dataset. The proposed two-stage classification method is then elaborated, and the section ends with the implementation details.
# Gut Microbiota as an Endocrine Organ: Unveiling Its Role in Human Physiology and Health
## 3 Neurohormones and Their Role in Microbial Effects
### Neurohormones: Bridging the Gap between the Brain and Microbial Effects  
Gut microbiota can produce \"classical\" neurotransmitters derived from amino acids and gaseous neurotransmitters [38]. These neurotransmitters exert localised effects on gut physiology, such as motility and the release of intestinal hormones, as well as central effects on cognition and behaviour through the connection between the enteric nervous system and the brain. Gut microbes produce histamine [39], a monoamine synthesised from histidine decarboxylation. Recent findings by [PERSON] et al. [40] showed increased histamine-secreting microbes in the gut of asthma patients, suggesting a role in gut immunity. Serotonin, another neurotransmitter from amino acids, impacts gut motility and immunity [40], as discussed further. Gut microbes also produce \(\gamma\)-aminobutyric acid (GABA) and catecholamines (norepinephrine and dopamine) [41], influencing local and central physiology. Catecholamines from the microbiota likely do not affect the brain due to the blood-brain barrier [42]. Luminal gut dopamine may modulate water transport in the colon [43], but bacterial catecholamines' role in host physiology is under investigation.  
Additionally, gut microbes can release various gases, including nitrogen, oxygen, hydrogen, methane, and carbon monoxide [44]. [PERSON] et al. [45] elucidated that \"intestinal gases reflect the metabolic activity of gut microbiota in the gut\"; certain pathological conditions may exhibit variations in intestinal gas production. Besides their well-established metabolic functions, some gases produced by bacteria are considered neurotransmitters or \"gasotransmitters\", such as nitric oxide (NO) and hydrogen sulphide (H2S), both known to modulate gut physiology. For example, bacteria can generate NO via bacterial NO synthase enzymes (such as _Bacillus subtilis_) [45] and H2S from cysteine. These two gaseous neurotransmitters traverse the epithelium and modify gut function. Recently, a novel concept demonstrated that inhibiting proximal gut motility improves hyperglyaemia in type 2 diabetes. Given that NO and H2S exert tonic inhibition of smooth muscle cells, it is conceivable that the gut microbiome could directly influence gut muscle relaxation and thus participate in the control of glycaemia via this novel mechanism of action [46].
## 1 Introduction
The method for deriving a 1-D map describing the dynamics of the circuit is discussed at the base of the incident and reflected waves. Some analytical results are obtained by using the 1-D map. In [8; 9; 10], for a lossless transmission line circuit with a short at one side terminal and with a three-segment piecewise linear resistor function at another side terminal, the conditions are provided for the existence of the explicit function form of the incident and reflected wave transformed from the three-segment piecewise linear resistor function, the existence of the 1-D map, and the existence of the invariant interval of the 1-D map. Furthermore, in [8; 9; 10; 11], by using a 1-D map, analytical bifurcation results or chaotic states (except for _formal chaos_) are reported in terms of numerical simulation. However, _formal chaos_ and _observable chaos_ existence [13; 18; 20] have not been rigorously proven in such lossless transmission line circuits in [5; 6; 7; 8; 9; 10; 11]. In [22], by using interval arithmetic, the _formal chaos_ existence was proven in a lossless transmission line circuit, but the proof is very tedious. In this paper, from the above background, in Section 2, we consider a lossless transmission circuit with a dc bias voltage source in series with a load resistor at one side terminal and with a three-segment piecewise linear resistor at another side terminal. In Section 3, we summarize the method for deriving a 1-D map describing the behavior of the circuit. In Section 4, in order to provide a basis of chaotic application for the 1-D map (or the transmission circuit), we discuss _the mathematical definition of formal chaos_ and _the sufficient conditions of the existence of formal chaos_ for _generating_ 1-D maps. In Section 5, using Maple [23], we present an example of formal chaos existence and several examples of bifurcation behavior of 1-D maps. Using the degree of observability of chaotic states in terms of Lyapunov exponent, we discuss the observability of _formal chaos_ in such bifurcation processes. In the Conclusion section, we summarize the principal results and the future works. In particular, we mention the possibility of designing imperfect transmission lines with parasitic effects and nonidealities inside real integrated devices generating high frequency ranges and chaotic oscillations without the use of additional capacitors or inductors.