esa-sceva/satcom-chunk-collection huggingface dataset:
# Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy
## 2 Ensemble Machine Learning (ML) Models
### Base Learner  
#### 2.3.1 Random Forest  
Random forest is an ensemble algorithm based on classification and regression trees, proposed by [PERSON] by combining the bagging method and the Random Subspace method [35]. By combining multiple individual trees to form a forest, random forest can achieve more reliable prediction results. The randomness of the random forest algorithm is mainly reflected in two aspects: first, samples are drawn from the original dataset with replacement using bootstrap sampling, forming training subsets to train decision trees. Next, the Random Subspace method is used to randomly select the feature variables required for each node split in the decision trees, generating multiple decision trees. Finally, the predictions of all decision trees are aggregated to produce the final prediction result. The random forest method requires few parameters to be tuned during implementation, and its randomness effectively prevents overfitting issues, making it widely used in the prediction of blasting effects [36; 37; 38]. The principle formula for generating random forests is as follows:  
\[\text{RF(x)}=\frac{1}{\text{N}_{\text{trees}}}\sum_{i=1}^{\text{N}_{\text{ trees}}}\text{f}_{i}(\text{x}) \tag{1}\]  
where \(RF(x)\) is the prediction result of the random forest method on sample \(x\); \(N_{\text{trees}}\) represents the number of decision trees in a random forest; \(f_{i}(x)\) indicates the prediction result of the _ith_ decision tree on sample \(x\).  
Figure 2: Stacking strategy schematic diagram.
###### Abstract  
In recent years, substantial hydrocarbon discoveries have been documented in Member 4 of the Yingcheng Formation (Ying IV) in the northern Songliao Basin. To fully understand the developmental characteristics of the Yingcheng Formation source rocks and clarify the main controlling factors of their formation, this study focuses on typical wells in the Xujiaweizi Fault Depression. By integrating core data, organic geochemistry, elemental geochemistry, and paleoenvironmental parameter reconstruction, we determined the paleoenvironment, key controls, and organic matter enrichment model during the deposition of Ying IV. The results show that the total organic carbon (TOC) content of the source rocks ranges from 1.46% to 4.34% (average 2.65%). The paleoclimate during deposition was predominantly warm and humid, with low oxygen, reduced water conditions, freshwater-to-brackish salinity, and a deep-lake environment. Paleoproductivity was moderate to high. Relationship analysis indicates that TOC content was jointly controlled by paleoclimate (warm and humid conditions promoting biological proliferation) and paleoproductivity (nutrient supply from volcanic activity and terrigenous clastics). The positive feedback between elevated productivity under warm-humid conditions and deep-lake reducing environments led to organic matter enrichment in the source rocks of Ying IV, following a productivity-controlled model. This study provides critical geological insights for deep natural gas exploration in the Songliao Basin.  
Keywords:Xujiaweizi fault depression; member 4 of the Yingcheng formation; source rocks; paleoenvironment reconstruction; organic matter enrichment +
Footnote †: journal: Applied sciences
## 3 Preliminaries
### Adversary Model  
This work introduces the \"Dolev-Yao (DY) model [44]\" and \"Canetti-Krawczyk (CK) model [45]\". We evaluated the security of schemes based on these adversary models. The detailed assumptions are described below:  
* Adversary \(\mathcal{A}\) can eavesdrop, intercept, delete, forge, and resend messages exchanged via a public channel [46].
* Adversary \(\mathcal{A}\) can obtain a stolen mobile user device and retrieve the secret values stored on it through a power analysis attack.
* Adversary \(\mathcal{A}\) can register as a legitimate user on the \(CS\) and participate in mutual authentication.
* Adversary \(\mathcal{A}\) can obtain session-specific ephemeral secrets and long-term private keys of the \(CS\) after the session termination.
* Adversary \(\mathcal{A}\) can try to perform privileged insider, DoS, and impersonation attacks, among others.
# A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor
## 1 Introduction
Conventional MPPT techniques cannot deal with partially shaded conditions (PSCs) because they cannot distinguish local MPP and global MPP (GMPP). To deal with PSC, specially designed MPPT algorithms are typically required, these methods can be categorized into (1) soft computing (SC) related techniques and (2) two-stage methods. For the first category, particle swarm optimization [20], grey wolf optimization (GWO) [21], transfer reinforcement learning approach [22], and Q-learning algorithm [23] are applied to find the real GMPP. However, the complexity of these algorithms raises questions about its practical implementation into a low-cost microcontroller. For the second category, a scan is initially conducted to find all the possible \"candidate intervals\" of GMPP, and a second stage is then utilized to find the exact GMPP location. Among this category, [PERSON] et al. [24] employed a fixed-spacing segmentation, [PERSON] and [PERSON] [25] utilized a tentative simulation, [PERSON] et al. [26] used restrictive voltage windows, and [PERSON] et al. [27] applied a search-and-skip process to find the candidate interval. In the second stage, P&O [24; 25; 26] and INC [27] are utilized. Overall the search for the GMPP in this category generally involves a candidate interval locating method and an approach to maintain the GMPP which is simply identical to the local MPP tracking algorithm. Consequently, the proposed technique can also be integrated with some developed candidate interval locating method, which will be left as a future work for this paper, to track the GMPP. As such, this study proposes a novel VSS INC MPPT method with an adaptive scaling factor. The proposed technique is similar to the conventional adaptive VSS algorithm, but with one additional feature that incorporates an adaptive mechanism that guarantees the optimal selection of the scaling factor according to the operating conditions. To the best of the author's knowledge, this is the first research work that constantly adapts the scaling factor according to the irradiance levels. Besides, the proposed method is simple and can easily be integrated into the original SGS firmware. Firstly, intensive simulations are utilized to find the corresponding scaling factor that serves to optimize system performance. Secondly, the irradiance estimation method proposed in [19] is employed to estimate the current irradiance level. Based on the acquired irradiance level, an appropriate scaling factor is adopted and then the VSS INC MPPT technique is performed. The proposed method has the advantages of easy implementation and simple calculation. Besides, optimal performance can be guaranteed under fast-changing environments. Comparing with conventional adaptive VSS INC MPPT technique, the proposed method can improve both the transient response and steady-state oscillation.
## 3 Vision-Based Approach Using a Fisheye Lens Monocular Camera
### Setup and the Obtained First Dataset
The total computational time for 2214 images was 182,241 s. The average computational time per frame was 85.11 s. The computational times differed depending on the respective action patterns. The shortest and longest were, respectively, 57.98 s in SP1 and 135.99 s in ST2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Specifications** \\ \hline OS & Windows 10 Professional 64 bit; Microsoft Corp. \\ CPU & Core i5-6200 (2.30 GHz); Intel Corp. \\ Memory & 8192 MB \\ GPU & HD Graphics 520; Intel Corp. \\ VRAM & 128 MB \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Major specifications of the computer used for this experiment. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Behavior** & **Subset** & **Capture** & **Total Images** & **Valid Images** & **Conversion** \\
**Pattern** & **Name** & **Time (s)** & **(Frames)** & **(Frames)** & **Ratio** \\ \hline ET & ET1 & 21 & 218 & 218 & 1.00 \\  & ET2 & 20 & 209 & 209 & 1.00 \\ RD & RD1 & 20 & 200 & 200 & 1.00 \\  & RD2 & 20 & 200 & 200 & 1.00 \\ SP & SP1 & 20 & 202 & 202 & 1.00 \\  & SP2 & 20 & 202 & 202 & 1.00 \\ LC & LC1 & 20 & 202 & 202 & 1.00 \\  & LC2 & 20 & 204 & 172 & 0.84 \\ ST & ST1 & 18 & 181 & 138 & 0.76 \\  & ST2 & 16 & 160 & 100 & 0.63 \\ \hline Total & & 199 & 1978 & 1843 & 0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Details of our original benchmark dataset. Figure 2 depicts sample images of ET, RD, SP, LC, and ST. In each image, the left, center, and right panels, respectively, depict the original, pose detection, and feature vector sub-images. Except for ST, occlusion occurred because of a desk. Although the subject's legs were not included, the skeleton position of the upper buttocks was estimated. Although the laptop display occluded the arms, the second joints were estimated. Images show that differences between the ET and SP postures are slight. The ET and SP images depict that differences between the postures are slight. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Behavior** & **Subset** & **Processing** & **Total Images** & **Second per** \\
**Pattern** & **Name** & **Time (s)** & **(Frame)** & **Frame (s)** \\ \hline ET & ET1 & 20,949 & 218 & 96.10 \\  & ET2 & 21,659 & 209 & 103.63 \\ RD & RD1 & 22,992 & 200 & 114.96 \\  & RD2 & 12,056 & 200 & 60.28 \\ SP & SP1 & 11,711 & 202 & 57.98 \\  & SP2 & 11,757 & 202 & 58.20 \\ LC & LC1 & 11,982 & 202 & 59.32 \\  & LC2 & 11,750 & 172 & 68.31 \\ ST & ST1 & 18,409 & 138 & 133.40 \\  & ST2 & 13,599 & 100 & 135.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Details of processing used to extract feature vectors using OpenPose. Figure 2: Sample images of eating (ET), reading (RD), operating a smartphone (SP), operating a laptop computer (LC), and sitting (ST).
# Airglow Observation and Statistical Analysis of Plasma Bubbles over China
## 2 Processing Method for Airglow Images  
The optical brightness of the original airglow image is quite low, that is, the airglow image has a low grayscale value, so we cannot observe the optical features of plasma bubbles from the original airglow image. Plasma bubbles mainly appear in the ionospheric F-region. The data of plasma bubbles can be mainly obtained from the airglow emissions of 630 nm wavelength, which mainly lies in the area below the peak height of the ionospheric F2 layer, with the height range of 250-300 km. The airglow imager is mainly composed of fish-eye lens, filter, CCD camera, etc., and the filter has a narrow bandwidth of 2.0 nm, which can better filter out other background emissions, and obtain the airglow information at a wavelength of 630 nm. To investigate the morphological and drift features of plasma bubbles, it is necessary to perform a series of image processing on the original image. The processing method of airglow images generally includes image enhancement, azimuth correction, and image projection [21]. Figure 1 illustrates the processing steps for airglow images of plasma bubbles.
# Treatment Selection for Early to Intermediate Hepatocellular Carcinoma
## 6 Application of Hepatic Arterial Infusion Chemotherapy (HAIC)  
HAIC, a method that introduces anti-cancer drugs directly into the tumor-feeding artery, is traditionally used in Japan for the treatment of intermediate to advanced-stage HCC and is one of the recommended therapies in the Japanese guidelines [11]. There are no randomized controlled trials to show the effect of HAIC compared to placebo, and the objective response rates based on observatory studies vary (0-71%) [11]. Nonetheless, doctors who are familiar with HAIC have often experienced good responses in some HCC cases.  
We compared the outcome of 476 patients with HCC who underwent HAIC with 5-fluorouracilacil and cisplatin with that of 1466 patients who did not receive active therapy. We used the database of primary liver cancer registered by the Liver Cancer Study Group of Japan [27]. The results of the multivariate analysis, as well as propensity score-matched analysis clearly show the efficacy of HAIC. Recently, an additive effect of HAIC on sorafenibib-treated patients was examined in the SILIUS RCT study [28]. The regimen of HAIC was low-dose cisplatin and fluorouracil. Although no difference in OS was observed between HAIC/sorafenib and sorafenibib monotherapy, a possible survival benefit was demonstrated in patients with a tumor thrombus at a major portal vein.  
In addition, HAIC is less harmful than TACE or MTA, so it can be used for patients with poor liver function [29]. Early to intermediate-stage HCC includes patients with Child-Pugh B grade. MTA cannot be used for these patients, and sometimes there is hesitation in treatment with TACE because of the fear of inducing liver failure. HAIC can be used for these patients. Concrete survival benefits of HAIC in non-advanced HCC must be proven with further examinations. Nonetheless, it is worth considering the use of less harmful HAIC at least once in patients with intermediate-stage HCC.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 4 Improved DQN Algorithm
### Reward Function of HDQN Algorithm  
In the data transmission phase, the BS receives the coordinates of all cluster heads. Subsequently, a DRL method is employed to determine the optimal data transmission route. In this scenario, the Agent aims to identify the best path by taking a series of actions in different states and receiving corresponding rewards to find the optimal data transmission route. The definitions of state \(s_{t}\), action \(a_{t}\), and reward \(r_{t}\) at the current time \(t\) are as follows:  
1. State \(s_{t}\): The state \(s_{t}\) includes the coordinates and remaining energy of heterogeneous ordinary nodes and cluster heads, the number of relay nodes involved in transmitting data from the cluster heads to the BS, and the header of the data packet.
2. Action \(a_{t}\): The action \(a_{t}\) corresponds to the selection of the next-hop route.
3. Reward \(r_{t}\): The reward function is defined as follows: \[R_{N}=\omega_{1}D(n)+\omega_{2}E(n)-\omega_{3}C(n)\] (18) where \(D(n)\) represents the distance to the next-hop relay node. The goal is to select the node that is closest to both the current node and the BS as the next-hop node. In this paper, the proposed combination reward for distance and step size is given by \[D(n)=\mu d_{n}+(1-\mu)r_{d}\] (19) where \(d_{n}\) represents the distance between the source node \(M_{so}\) and the destination node \(M_{de}\) and \(r_{d}=-\frac{d_{Mo_{B}}}{d_{Mo_{B}}B}\) is the distance reward. \(d_{Mo_{B}}=\sqrt{x_{Mo_{B}}2^{+}y_{Mo_{B}}2^{2}}\) is the distance from source node \(M_{so}\) to BS. \(d_{Mo_{B}}=\sqrt{x_{Mo_{B}}2^{+}y_{Mo_{B}}2^{2}}\) is the distance from the destination node \(M_{de}\) to BS. \(\mu\) and \(1-\mu\) represent the weights of \(d_{n}\) and \(r_{n}\), respectively.  
\(E(n)\) reflects the remaining energy of the next-hop relay node, and nodes with higher energy are more likely to be selected. Super nodes typically have a stronger remaining energy level compared to other nodes, so the probability of being selected as relay nodes is also higher.  
\(C(n)\) represents the number of relay nodes in the data transmission route. The higher the value of \(C(n)\), the lower the probability of choosing this transmission line. This parameter is designed to ensure that fewer relay nodes are involved in alternative data transmission lines, while also balancing the participation of other heterogeneous nodes in the transmission process, minimizing the overall energy consumption of HWSNs. Assuming \(N_{CH}\) is the number of all cluster heads, the number of relay node participants in a candidate data transmission line can be represented as \(\sum\limits_{i=1}^{N_{CH}}CH_{\_part_{i}}\). \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) are the weight ratios of \(D(n)\), \(E(n)\), and \(C(n)\), respectively, with the aim of achieving energy balance and extending the network lifetime. In this paper, \(\omega_{1}=\omega_{2}=\omega_{3}\) and \(\omega_{1}+\omega_{2}+\omega_{3}=1\).
# Drug-Food Interactions with a Focus on Mediterranean Diet
## 2 Pharmacological Mechanisms of Drug-Food Interactions
### Pharmacokine Drug-Food Interactions (PD-DFIs)  
Pharmacodynamic interactions refer to modulation of the pharmacological action of a drug in the site of action or in secondary tissues that can induce ADRs and side effects. PD-DFIs are usually related with additive, synergistic, or antagonistic effects from food compounds on the pharmacological pathways of a drug. One of the most well-known DFIs is for foods containing tyramine (i.e., wine and cheese). These foods are to be avoided from patients treated with monoamine oxidase inhibitors (MAOIs) [42]. Another typical example is the potential modulation of anticoagulating action of coumarin analogues (warfarin, acenocoumarol, phenprocoumon) from vitamin K (Vit-K) [106]. Vit-K1 (dihydroquinone, KH2) is the necessary cofactor for activation of the clotting factors, thus coumarin analogues and Vit-K are antagonizing each other. Foods rich in Vit-K such as kale, collard greens, broccoli, spinach, cabbage, and lettuce should be consumed from patients under anticoagulation treatment keeping in mind potential alterations in international normalized ration (INR). The general guidance is that patients need to sustain a stable diet, so the daily intake of Vit-K must remain constant throughout their treatment [107; 108]. Similarly to Vit-K, special precautions should be taken for diets rich in minerals such as Potassium (K\({}^{+}\)) in cases of cardiovascular diseases treated with K\({}^{+}\) sparing diuretics (spironolactone, amiloride, triamterene etc.) or agents acting on the renin-angiotensin system (i.e., angiotensin-converting enzyme inhibitors, ACEs, angiotensin receptor blockers, ARBs, etc.). In this respect, fruits such as bananas, oranges, and apricots, and vegetables such as spinach, potatoes, mushrooms, and peas should be consumed keeping in mind that high intake may lead to hyperkaliemia resulting in cardiac side effects and arrhythmias [109; 110].
## 5 Feasibility Study
### Performance Evaluation of Raspberry Pi Boards as Asterisk Server for LTC Applications
#### 5.1.4 Transcoding VoIP Testing Overview  
An overview of transcoding VoIP testing procedures is shown in Figure 8. Transcoding is the process of decoding an already encoded digital audio signal, making some alteration to its content, and encoding it again using another format. Transcoding is a process-intensive operation that consumes more resources than passthrough. In the transcoded VoIP testing, we utilized another Raspberry Pi board. The second board was equipped with a bare-metal Asterisk server, without the need for other applications or services running on the PoT gateway. The embedded Asterisk servers on the boards were interconnected using two distinct SIP trunks. The SIP trunks were configured such that they used different audio codecs. SIP trunk 1 utilized ulaw audio codec, and SIP trunk 2 utilizes g722 audio codec. We used these codecs because they are popular, open-source, and can be freely utilized by any VoIP system. The second Raspberry Pi board was used to run the test script. The test script called an MOH extension on the board (Raspberry Pi 2). However, the script forces the call to go through SIP trunk 1, which utilizes ulaw audio codec. Once the call was received at Raspberry Pi 1 (PoT gateway), it went through the extensionsdefined in the extensions.conf configuration file on the PoT gateway to find a match. The extension definition of MOH on the PoT gateway was defined such that it went through SIP trunk 2. Since the audio codecs of the incoming channel (SIP trunk 1) and the outgoing channel (SIP trunk 2) were different, transcoding was performed on the PoT gateway prior to passing the call back to Raspberry Pi 2 through SIP trunk 2. Therefore, we could monitor the performance of the PoT gateway while carrying out transcoding operations. Since the test script was running on Raspberry Pi 2, we utilized os and subprocess Python libraries to send remote Linux bash commands from Raspberry Pi 2 to the PoT gateway. These commands were used to retrieve information about the performance measurements of the PoT gateway (%CPU utilization and load average).  
Figure 6: The flowchart of the proposed performance testing algorithm.  
Figure 7: Passthrough VoIP testing overview.
## 5 The Proposal of a Tiered Observation Approach  
Here, we investigate the methane observation capabilities of Sentinel-2, GF5-02-AHSI, and EnMAP, and how the combination of their coverage and resolution can be used for the hierarchical monitoring of methane leaks, achieving a complete process from large-scale rapid detection to high-precision localization and quantification. Sentinel-2 provides high-frequency, large-scale preliminary monitoring, while EnMAP and GF5-02-AHSI utilize their higher ground pixel resolution and spectral resolution to confirm leaks and provide precise emission quantification. This hierarchical approach improves detection efficiency, reduces costs, and enables rapid responses to short-term leak events. This method can also be applied to other satellite data. The hierarchical methane leak observation method proposed in this study (Figure 11) integrates different satellites and ground-based instruments, as well as auxiliary information sources, to achieve the more comprehensive monitoring of methane leaks. The method also emphasizes the use of information from different (targeted) satellite platforms and supporting information such as reports, news, satellite-observed combustion, and ground-based air quality observations. Particularly in cases where ground observations and auxiliary information are unavailable, such as in remote areas, the satellite remote sensing component of the hierarchical observation method becomes crucial.