esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Localization Methods
### Use of UAV Sensor  
In the fingerprint-based localization introduced in the previous section, in general, the greater the number of sensors, the greater the amount of information in the fingerprints and the more accurate the localization. However, the use of fixed sensors increases the installation cost. Therefore, as shown in Figure 3, following the approach of [15], this study proposes the use of UAVs as sensors, which can move freely in the air, thereby substantially extending the dimensions of the location fingerprint database at a low cost. Also, even when a fixed sensor is out of sight of the target, the UAV can move to maintain an LoS condition and collect more reliable location fingerprints. As a result, improved localization accuracy can be expected, and highly efficient localization can be achieved at lower cost with a small number of sensors. In summary, this paper employs a single UAV working as a sensor node to collect RF fingerprints and estimate the location of illegal emitters at different discrete positions on the UAV's flying trajectory. Taking advantage of the UAV's mobility freedom, this is equivalent to the deployment of multiple fixed sensors with only a single UAV. Also, since there is only a single UAV sensor in our proposed system, there is no need for information sharing among sensors to estimate the location of a target illegal emitter. This is another advantage of our proposed UAV-based localization sensor system. It should be noted that, considering the limited power budget of a UAV, this paper restricts the RF fingerprints to only the RSSI measured at the UAV sensor since obtaining other types of RF fingerprints like the TDOA and AOA will increase the signal processing cost and circuitry size and therefore increase the power consumption at the UAV [4].  
Figure 2: Two phases of fingerprint-based localization method.  
However, when using UAV sensors, it is necessary to determine their flight paths. Since flight paths that enable highly accurate localization depend on the propagation environment, a system that can optimize flight paths in any environment is required. In this paper, we propose such a system to optimize the flight path for the scenario of moving a UAV sensor and compare the localization accuracy with that of the conventional case where fixed sensors are used.
## 2 Acquisition of Tunnel Face Images
### Stitching and Fusion of Partitioned Photography Images
#### 2.2.1 Image Stitching of Tunnel Work Face Partitions  
As shown in Figure 6, the blue part of the floor partition image overlaps with the red part of the right arch foot partition image. This overlapping area needs to be stitched together.  
The image stitching process uses the SURF (Speeded-Up Robust Features) algorithm, which performs faster compared to other algorithms [33]. First, the Hessian matrix of the image is calculated according to Equation (1):  
\[H(f(x,y))=\left[\begin{array}{cc}\frac{\partial^{2}f}{\partial x^{2}}&\frac{ \partial^{2}f}{\partial x\partial y}\\ \frac{\partial^{2}f}{\partial x\partial y}&\frac{\partial^{2}f}{\partial y^{2} }\end{array}\right] \tag{1}\]  
where \(H(0)\) is the Hessian matrix, and \(f(x,y)\) is the color value at the image coordinates \((x,y)\)  
Figure 5: Onsite digital image shooting plan. (Each number corresponds to a part of the tunnel face that is divided.  
Figure 6: Image overlapping area.  
Next, the determinant of the Hessian matrix is calculated using Equation (2) to obtain the local extremum points of the pixels, which are used as the SURF feature points of the image.  
\[\text{det}(H)=\frac{\partial^{2}}{\partial x^{2}}\frac{\partial^{2}f}{\partial y ^{2}}-\left(\frac{\partial^{2}f}{\partial x\partial y}\right) \tag{2}\]  
where \(H\) is the Hessian matrix, and \(f\) is the color value at the image coordinates \((x,y)\).  
After obtaining the feature points of the reference image and the matching image, the similarity of the feature points is calculated using the Euclidean distance criterion shown in Equation (3):  
\[l=\sqrt{\sum_{i=1}^{n}(X_{1}(i)-X_{2}(i))^{2}} \tag{3}\]  
where \(l\) is the distance between the two points, \(n\) is the dimension of the feature points, \(X_{1}\) is the descriptor vector of the feature point in the reference image, and \(X_{2}\) is the descriptor vector of the feature point in the matching image.  
When the distance is less than the set threshold (found to be optimally between 0.6 and 0.8), the two feature points are considered successfully matched.
# Efficient Use of GPU Memory for Large-Scale Deep Learning Model Training
## 1 Introduction
In addition, the access patterns of the three types of deep learning data are similar to those that can be managed with memory advise. Therefore, it is possible to efficiently utilize GPU memory by setting three types of deep learning data as appropriate memory advise. We conducted an experiment to train a large-scale model, BERT-Large [18], by applying our optimized memory advise scheme, and analyze the results of the experiment to suggest efficient memory advise. * Second, we apply CUDA Unified Memory to PyTorch and set different memory advises for three types of data required for deep learning. It is difficult to train a large-scale model due to the limitation of single GPU memory. Existing PyTorch does not support CUDA Unified Memory technology, so it is impossible to perform deep learning if a memory size larger than the available GPU memory is required to perform deep learning. Most of the deep learning models that achieve high accuracy are very large, such as BERT. Therefore, we apply CUDA Unified Memory technology by modifying PyTorch, an open source deep learning framework, to train large-scale learning models that can achieve high accuracy. In addition, we use the modified PyTorch to verify efficient memory advise through an experiment in which we actually perform large-scale model training. We trained BERT-Large, a large-scale deep learning model, using PyTorch to which CUDA Unified Memory technology is applied. When the mini-batch size is 32 and the max sequence size is 256, we confirmed that the training time is reduced by up to 9.4% by using appropriate memory advise. Using vanilla PyTorch, it is possible to train BERT-Large in mini-batches of size up to 16 with a GPU with 24 GB of memory. However, if our proposed scheme is applied to PyTorch, training can be performed with a mini-batch of size 32, which was impossible to train with vanilla PyTorch. The rest of the paper is organized as follows. In Section 2, we describe the CUDA Unified Memory and how to use CUDA Unified Memory efficiently. The data management scheme we propose for efficient deep learning is described in detail in Section 3. In addition, Section 4 describes how we modified PyTorch to apply CUDA Unified Memory. In Section 5, we analyze the performance of the proposed GPU memory utilization method. We present the related work in Section 6. Finally, we conclude our paper in Section 7.
## 3 Results
### HRV Data  
HRV data were analyzed from only 12 participants due to data quality issues regarding one participant. SDNN values between pre- and post-intervention conditions increased for all 12 participants, while RMSSD values of 10 participants increased and results from 2 participants stated a decrease. SI values decreased for 11 participants, while the data from one participant stated a slight increase.  
Figure 5: Screenshot from Binaural Odyssey application, featuring an element labeled fast white line in this article.  
Statistical analysis of HR, SDNN, RMSSD, and SI values in three different conditions were conducted with repeated measures ANOVAs. Mean values of these variables can be seen in Table 1. Regarding SDNN, RMSSD, and SI values, the mean scores between conditions were statistically significantly different. SDNN _[F(2,22) = 21.888, p < 0.0005]_, RMSSD _[F(2,22) = 10.086, p = 0.001]_, SI _[F(1.243,13.675) = 14.077, p = 0.001]_. Regarding HR, no statistically significant difference between conditions was found. HR _[F(2,22) = 5.114, p = 0.015]_. However, we can observe a slight decrease in HR mean values between the baseline and intervention measures. This could be explained by the fact that participants had recently arrived at the research location prior to the baseline measurement, and for some, the heart rate could be still slightly elevated from the preparations for the study, while in intervention and post-intervention measurements, the participant had been sitting down for a longer time due to the design of the research. In addition, any possible initial nervousness regarding the research participation in general is likely to be most visible in the baseline measurement.  
Paired samples _t_-tests were conducted to determine the effect of VR intervention on HRV regarding values of SDNN, RMSSD, and Stress Index (SI). When comparing the values between baseline and post-intervention conditions, the results of the paired sample _t_-tests showed that the mean differences of SDNN _[Mean difference = \(-\)6.47 (\(\pm\)5.06 SD)]_, RMSSD _[Mean difference = \(-\)3.38 (\(\pm\)5.03 SD)]_, and Stress Index _[Mean difference= +2.05 (\(\pm\)1.66 SD)]_ were all statistically significant at the 0.05 level of significance _[SDNN t(11) = \(-\)4.43, p = 0.001; RMSSD t(11) = \(-\)2.33, p = 0.040; SI t(11) = 4.26, p = 0.001]_. This indicates increased activity of the parasympathetic nervous system and decreased activity of the sympathetic nervous system, which can imply reduced stress levels and increased feeling of relaxation.  
HRV data analysis reveals that, while using the application, the SDNN, RMSSD, and SI values all indicate increased sympathetic nervous system activity in comparison to both the baseline and post-intervention measurements, which implies a stressful experience. Furthermore, the HRV data indicates an increased level of relaxation and decreased level of stress after the experiment in comparison to the baseline values. In other words, while the experience itself seems to have been intensive, it produced a relaxing effect.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 2 Preliminaries and Related Work
### LT Encoding Process  
Assume that \(k\) input symbols, \(\{u_{1},u_{2},u_{3},\ldots,u_{k}\}\), are to be transmitted: the transmitter determines and sends a potentially limitless stream of encoded symbols, as shown in Algorithm 1[9]. In the algorithm, \(\{u_{m1},u_{m2},\ldots,u_{md}\}\) are \(d\) input symbols chosen uniformly from the \(k\) input symbols, and \(c_{i}\) is the encoded symbol at step \(i\). As can be seen from the algorithm, the encoding process keeps generating encoded symbols until a positive acknowledgment (ACK) is collected from the receiver. This is how the LT codes adjust their codes depending on the channel's characteristics. For instance, channels with a high loss rate require more encoded symbols, producing a low code rate. At the same time, channels with a low loss rate require fewer encoded symbols, resulting in a high code rate.  
```
Input: Source symbols \(\{u_{1},u_{2},u_{3},\ldots,u_{k}\}\), \(i=1\); while(There is no ACK from the receiver)do  Choose a random degree (\(d\)) depending on the degree distribution;  Choose \(d\) source symbols, uniformly at random, from the \(k\) source symbols, \(u_{m1},u_{m2},\ldots,u_{md}\);  Perform bitwise XOR on the chosen source symbols to generate the encoded symbol \(c_{i}=u_{m1}\oplus u_{m2}\oplus\ldots\oplus u_{md}\);  Send the encoded symbol (\(c_{i}\)) to the receiver; \(i++\);  end while
```  
**Algorithm 1**Encoding Process of LT Codes  
After applying Algorithm 1 on the \(k\) source symbols, \(n=k(1+\varepsilon)\) encoded symbols are generated on the fly, as shown in Figure 2, where \(\varepsilon\) is the code overhead.  
Figure 2: The encoding process of LT codes, where \(k\) source symbols (circles) are used to generate \(n\) encoded symbols (squares).
# River Water Quality Monitoring Using LoRa-Based IoT
## 5 Results
### Energy Consumption  
As the IoT Node is to be operated with a battery, energy consumption measurements were carried out, with the Arduino in energy saving mode, defining the consumption in each phase of a work cycle of this subsystem: sensor reading (phase A), LoRa module transmitting (phase B) and Arduino in power down mode (phase C). See Figure 30.  
And this leads to the definition of the IoT energy consumption in one hour (9):  
\[Consuption_{(IoT\ Node)}=\frac{102*\ T_{A}+176*\ T_{B}+52*\ T_{\text{C}}}{T_{A}+T_ {B}+T_{\text{C}}}\ [\text{mAh}] \tag{9}\]  
where:  
* \(T_{A}\) is phase A: sensors reading time [s];
* \(T_{B}\) is phase B: \(T_{packet}\) [s];
* \(T_{\text{C}}\) is phase C: Arduino powerDown (sleep) time [s].  
Figure 29: Dashboard, historical page, data and log files.  
\(T_{A}\) is fixed (0.6 s), \(T_{B}\) is the LoRa \(T_{packet}\) and \(T_{C}\) is the powerDown time configured on the Arduino. It is thus possible to predict the duration of a given battery or calculate its capacity for a required duration of operation.  
Phase C consumption could still be minimized by removing the signaling LEDs from this subsystem.
# Federated Reinforcement Learning Based AANs with LEO Satellites and UAVs
## 2 Preliminaries and Related Works
### Deep Reinforcement Learning  
Reinforcement learning (RL) is a mathematical framework for computing devices to perform learning autonomously based on experience, and the core of RL is learning through interactions with the environment [20; 21]. In RL, an agent selects an action using the policy, that is the basis of selecting the action, and performs the chosen action in the environment. Then, the agent observes the state of the changing environment and obtains the rewards from the environment. By repeating the above process, the agent continues to update the policy so that a better action can be selected. The best order of actions is determined by the rewards provided by the environment, and the purpose of the agent is to learn the optimal policy that maximizes the expected compensation values. The algorithm or mechanism for the agent to perform learning depends on the RL method.  
In relatively simple environments, it is possible to define all states that an agent can have. However, for complex problems, it is hard to predict all states and to consider all possible actions in each state. To overcome this limitation, deep reinforcement learning (DRL) was defined by applying deep neural networks to existing reinforcement learning [22]. The DRL trains the natural network based on the state and reward resulted from the action to find the optimal action based on the state. In recent years, various DRL algorithms have been proposed, and there many DRL algorithms, such as Deep Q network (DQN) [23], Deep Detergent Policy Gradient (DDPG) [24], Asynchronous Advantage Actor-Critic (A3C) [25], Trust Region Policy Optimization (TRPO) [26], Proximal Policy Optimization (PPO) [27], Soft Actor-Critic (SAC) [28], etc.
## 5 Conclusions  
This paper has discussed a case study on the characteristics of deformation and the control technology of deep roadways under broken high ground stress and rock mass in Jinchuan No. 2 Mine. On the basis of a detailed field investigation, the deformation failure types and characteristics in the roadway under primary support have been outlined. Additionally, a numerical simulation and field monitoring were performed to assess the influence of the improved support in the test roadway. The main conclusions of the research are as follows:  
1. The roadway deformation failure types could be sorted into six types, including roof cracking, floor heaving, unsymmetrical pressure, roof falling, side cracking and rib spalling, among which rib spalling is the most severe. Moreover, the main features of roadway deformation include prominent time effects and significant space effects, and the maximum principal stress plays a dominant role.
2. The broken rock zone test found that zonal disintegration phenomena occur in the test roadway and there are three broken rock zones whose depths are approximately 0-1.8, 2-2.8, and 3-4.8 m. The zonal disintegration proves that the anchoring method used in the original support is not feasible.
3. An improved combined supporting scheme called the \"multistage anchorage + CFST\" has been put forward and assessed. The supporting structures and surrounding rock mass formed a hierarchical support system which could cooperate and work together. The results of the numerical simulation and field monitoring indicate the effectiveness of the new support scheme's control effect.  
Data curation, G.L.; Formal analysis, G.L., H.Z. and F.M.; Methodology, G.L., and F.M.; Software, G.L. and J.G.; Writing--original draft, G.L. and J.G. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the National Natural Science Foundation of China (Grant Nos. 41831293, 41877274, and 41772341). Grateful appreciation is expressed for the support.  
The authors would like to express their sincere gratitude to Jinchuan mine for their data supports. In addition, the authors are grateful to assigned editor and three anonymous reviewers for their enthusiastic help and valuable comments which have greatly improved this paper.  
The authors declare no conflict of interest.  
Figure 23: Schematic of the test roadway before and after the novel support scheme: (**a**) Construction period, (**b**) Current situation.
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
## 2 Early Endosome-to-Golgi Transport of STx and STx1
### Other Regulators  
Other factors that are involved in the early endosome-to-Golgi transport of STxB/STx1B include V-ATPase and protein kinases. Inhibition of V-ATPase using bafilomycin A or siRNA-mediated knockdown of its subunit ATP6V0A2 reduces the entry of STxB/STx1B into the Golgi [51]. Upon chemical inhibition or siRNA knockdown of PKC8, there is a decrease in the overlap of STxB/STx1B with the Golgi marker giantin, and an increase with the early endosome marker EEA1 [50]. Similarly, inhibition or knockdown of p38 also causes a reduction in the overlap between STxB/STx1B and giantin [49].
# Mathematical Modelling of the Effects of Plasma Treatment on the Diffusivity of Biofilm
## 2 Materials and Methods
### Development of a Mathematical Model of CHX Diffusion in a Biofilm
From the value of \(D_{e}\), we can estimate the biofilm porosity, which will give us an idea on the extent of biofilm disruption by the plasma treatment.
## Applications
#### 3.1.3 Federated Transfer Learning (FTL)  
Federated Transfer Learning is used when datasets have different feature spaces and only partially overlap in sample ID space [44]. This type of FL leverages transfer learning techniques to transfer knowledge from a source domain (with abundant labeled data) to a target domain (with limited labeled data) [45]. FTL is useful when collaborating organizations have different but related data, allowing them to benefit from each other's data without direct access.  
**Applications**  
* Cross-domain recommendation systems: Using user data from different services to improve personalized recommendations.
* Cross-organization collaborations: Enhancing machine learning models by leveraging different feature sets from collaborating organizations.
## 3 System Design
### Multicast Tree Generation Procedure
#### 3.5.1 Topology Gather  
Topology gather is conducted at the beginning of CCSM. The ASR and CR send the status report message to MMC periodically, and MMC therefore maintains a core network topology. The topology information in MMC have lifetime. Once the lifetime is expired, it will trigger the topology gather procedure. When a new CR/ASR joins in the core network which are infrequent or one CR crashes, acquiring topology is triggered to notify MMC. The topology information consists of device ID, interface ID, relevant interfaces list, and link status information such as bandwidth, delay, jitter and so on.
# Effect of Pore Size Distribution and Amination on Adsorption Capacities of Polymeric Adsorbents
## Materials and Methods  
### Chemicals  
Divinylbenzene (DVB, 83.5%) and divinylbenzene (DVB, 50.0%) was purchased from Shandong Dongda Chemicals Company (Shandong, China) which were used as monomers. Toluene (>99.7%, Shanghai Chemical Reagent Company), liquid wax (>99.5%, Shanghai Chemical Reagent Company), n-heptane (>99.7%, Shandong Dongda Chemicals Company) and hexadecanol (>99.7%, Shanghai Chemical Reagent Company) were used as porogen. Gelatin (Photographic grade) was purchased from Yancheng Dafeng Gelatin Company (Jiangsu, China). Benzoyl peroxide (>99.7%, Shanghai Chemical Reagent Company) was used as an initiator. Zinc chloride (>99.7%) as the catalyst for chloromethylation were purchased from Shanghai Chemical Reagent Company. Chloromethyl ether (>99.7%, Shanghai Chemical Reagent Company), chloroform (>99.7%, Nanjing Chemical Reagent Company) and methylal (>99.7%, Shanghai Chemical Reagent Company) were used as the swelling agent. Hexamethylenetramine (>99.7%), methylamine aqueous solution (25%) and dimethylamine aqueous solution (33%) used for amino-modified were all purchased from Nanjing Chemical Reagent Company. HCI and NaOH were obtained from Shanghai Chemical Reagent Company. Phenol (>99.7%), 1-amino-4-bromoanthraquinone-2 sulfonic acid (ABS acid, >99.7%), and reactive brilliant blue 4 (RBBA, >99.5%) used as adsorbates were also purchased from Nanjing Chemical Reagent Company. Their structure and molecular sizes of them were estimated by WinMopac7.21, and the results are shown in Figure S1.