esa-sceva/satcom-chunk-collection huggingface dataset:
# Vnode: Low-Overhead Transparent Tracing of Node.js-Based Microservice Architectures
## 2 Basic Concepts
### Distributed Tracing  
Distributed tracing is a strategy for collecting execution data from modern systems, particularly microservice architectures. It traces the lifecycle of a request as it passes through all the nodes in the system. Distributed tracing provides a hierarchical view of the trace, where one can observe the time a request spends on each service [7]. Distributed tracing was introduced to address the complexities of distributed architectures, which are not suited to traditional debugging and tracing methods. In microservice environments, components need to interact to produce a result. In other words, when a request is issued from a particular node, it may need to interact with several other services in the infrastructure to return the result. In this context, when an issue arises along its path, it is necessary to identify where the bottleneck occurs to resolve the problem.  
Traditional tracing techniques are not suitable because they are generally used for debugging monolithic applications. In the case of microservices, tracing involves injecting a trace context into the request headers to identify it throughout its lifecycle. In this way, the request can be properly aligned and hierarchized according to the service level it invokes over time. The various calls made by microservices during their interactions can thus be traced to understand the overall performance of the system [6].  
Distributed tracers such as Google Dapper [7] and Zipkin [24] have revolutionized technology by offering the ability to collect data from each request, their execution times, and the causalities between services in the infrastructure. Furthermore, analyzing the collected trace data can be complex. Distributed tracing systems generate a large amount of data, and interpreting these data often requires advanced data analysis skills and a deep understanding of the system's architecture [13]. Distributed tracing continues to evolve, with new improvements and integration into increasingly sophisticated tools and platforms. Therefore, it is necessary to propose tools that address shortcomings and bring more flexibility to the ecosystem.
## 4 Experimental Procedure and Analysis of Results
### Experimental Setup
#### 4.1.3 The Structures and Hyperparameters of LSTM-GARCH-Family Combined Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**GARCH (p,q,r)** & **Error Distribution** & \(p\)**-Value** & **AIC** & **SC** & **HQ** \\ \hline \multirow{2}{*}{GARCH (1,1,0)} & Student’s t & 0.0000 & \(-\)0.122576 & \(-\)0.028818 & \(-\)0.084697 \\  & GED & 0.0000 & \(-\)0.103389 & \(-\)0.009631 & \(-\)0.065510 \\ \multirow{2}{*}{GARCH (2,1,0)} & Student’s t & 0.0000 & \(-\)0.113682 & \(-\)0.0040289 & \(-\)0.069491 \\  & GED & 0.0000 & \(-\)0.095590 & 0.013794 & \(-\)0.051399 \\ \multirow{2}{*}{GARCH (1,2,0)} & Student’s t & 0.0000 & \(-\)0.121924 & \(-\)0.015540 & \(-\)0.080733 \\  & GED & 0.0000 & \(-\)0.096971 & 0.012413 & \(-\)0.052779 \\ \multirow{2}{*}{GARCH (2,2,0)} & Student’s t & 0.0000 & \(-\)0.117193 & 0.007817 & \(-\)0.066689 \\  & GED & 0.0000 & \(-\)0.087844 & 0.037166 & \(-\)0.037340 \\ \multirow{2}{*}{GARCH (3,1,0)} & Student’s t & 0.0000 & \(-\)0.109819 & 0.015191 & \(-\)0.059315 \\ \multirow{2}{*}{GARCH (3,2,0)} & GED & 0.0000 & \(-\)0.091065 & 0.033946 & \(-\)0.040560 \\  & Student’s t & 0.0000 & \(-\)0.127149 & 0.013488 & \(-\)0.070331 \\ \multirow{2}{*}{GARCH (3,2,0)} & GED & 0.0000 & \(-\)0.099995 & 0.040642 & \(-\)0.043177 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AIC, SC, and HQ results of the GARCH model with different parameters. neurons already introduced in the discussion of parameter settings, we set the numbers of neurons in the two LSTM layers of the single combined models to 25 and 10, and the numbers of nodes in the fully connected layers to 5 and 1. Similarly, single-combination models reach eda state of convergence after 100 epochs. Dual-model prediction. The two GARCH-family models were combined with the LSTM model to obtain the dual-combination models LSTM-GE, LSTM-GP, and LSTM-EP. We adjusted hyperparameters such as the number of neurons in the LSTM layer of the model to construct a well-performing garlic prediction model. The final numbers of neurons in the LSTM layers were chosen to be 30 and 10, respectively; and the numbers of nodes in the fully connected layers were 5 and 1, respectively. Triple-combination model prediction. The three GARCH-family models were combined with the LSTM model to obtain the triple combination model LSTM-GEP. The final numbers of neurons in the LSTM layers were chosen to be 30 and 15, respectively; and the numbers of nodes in the fully connected layer were 5 and 1, respectively.
# Yeast-Based Magnetic Biocomposite for Efficient Sorption of Organic Pollutants
## 3 Result and Discussion
### Methylene Blue Adsorption  
#### 3.2.1 Adsorption of MB onto YL-M-L-AC  
The research carried out on the process of removing the MB dye from the liquid phase using the YL-M-L-AC biocomposite allowed for the conclusion that the time after which the process equilibrium is established is 24 h. Figure 7 graphically presents the research results of the dependence of the sorption capacity on the time of operation and sorption process, sorption capacity, and MB removal at different initial MB concentrations. The sorption course (Figure 7A) shows that YL-M-L-AC removes MB in three stages. The first stage (up to 1 h) is responsible for the fastest MB removal due to the largest number of free active sites, including the most easily accessible ones [59]. At this stage, the greatest increase in sorption capacity occurred, from 10.13 mg/g to 26.96 mg/g. The second stage is observed in the range from 2 to 12 h. During this stage, a slowdown in the removal of methylene blue was noticed (q\({}_{\text{e}}\) = 31.26 mg/g) related to the occupation of active sites by MB molecules. After 12 h from the start of the process, a third stage was observed in which the MB removal process began to stabilize, reaching equilibrium after 24 h, resulting in a q\({}_{\text{e}}\) of 32.10 mg/g. After running the process for some time, the adsorption capacity will remain constant due to the active sites saturation. Therefore, the optimal contact time for adsorption equilibrium studies is considered to be the period during which the adsorption capacity remains constant [60]. The effect of the initial MB concentration on the sorption capacity and removal percentage was examined (Figure 7B,C). As can be seen, when the MB concentration in the solution increased from 50 to 250 mg/dm\({}^{3}\), the MB adsorption capacity of YL-M-L-AC increased by almost 380% from 8.49 to 32.10 mg/g. The maximum sorption capacity of the YL-M-L-AC biocomposite was determined to be 32.10 mg/g, indicating that the initial MB concentration was the driving force for the dye removal process [61].  
Figure 7: Adsorption of MB on YL-M-L-AC: (**A**) C\({}_{0}\) = 250 mg/dm\({}^{3}\), T = 25 \({}^{\circ}\)C (adsorption process), (**B**) t = 24 h (C\({}_{0}\) impact studies), T = 25 \({}^{\circ}\)C, (**C**) t = 24 h, T = 25 \({}^{\circ}\)C (degree of MB removal).  
A comparison of the experimental adsorption capacity of the YL-M-L-AC biocomposite with various adsorbents is presented in Table 2. To date, various alginate-based materials containing various component materials, including magnetic particles, have been used in MB adsorption, which has led to the defining of different adsorption efficiencies. According to the data in Table 2, YL-M-L-AC shows better performance compared to some other adsorbents, suggesting that it is a promising material for MB removal from aqueous solutions.
## 2 Conceptual Development of Structural Design
### PCB Holder (Spacers)  
Internal subsystem components, mostly PCB boards, are inserted into these standard slots using a set of special PCB holders (spacers) made of softer plastic material to protect the sensitive surface of the PCB. The spacer removes the need for long screw rods to mount the internal subsystems, as in the case of conventional CubeSat design. The spacers' width is derived from the defined slot width. The spacers also have a cutout slit according to the thickness of each PCB or component, as shown in Figure 6. They are attached to the PCBs at four corners by just applying a small push force for a snap-fit. The tolerance of the cutout on the spacers should correspond to that of the PCB/components' thickness tolerance. Similarly, the tolerance of the spacers' width should also correspond to the tolerance of the slot width on the structure.  
Figure 4: Exploded view of the Slot-based 3U structural model.  
Figure 5: Slot plate with standardized slot width for 3U model.  
Regarding the spacer material, different plastic materials were assessed. However, PEEK (polyether ether ketone) was chosen due to its high strength and stiffness.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 4 Experiments on Trajectory Planning and Tracking of CMRRs
### Experiments on Trajectory Tracking of CMRRs  
The mobile robot platform and test environment used for the test are still the same as those used for the trajectory planning test. A number of scenarios are designed by issuing straight line and sinusoidal curve type trajectories and selecting different terrains and locations in the simulated tunnel to investigate the effect of the MPC tracking algorithm in the real environment. The robot localization information is still given by the LOAM algorithm. The results of PID trajectory tracking were also tested to verify the superiority of the MPC trajectory tracking method proposed in this paper.
## 3 Materials and Methods
### Classification Using SVM  
SVM is a supervised learning method that is used to solve problems in classification and regression. It is an effective learning method in high-dimensional spaces and when the number of dimensions is larger than the number of samples. Hyperplanes can be constructed in high-dimensional or infinite-dimensional spaces [32; 33]. Intuitively, separation is achieved by the hyperplane with the largest distance to the nearest training data point in the class--the so-called margin-maximizing hyperplane. In this study, we implemented it in scikit-learn support vector classification (SVC) [34]. The main objective of SVM is to find the hyperplane in the feature space that can easily separate the classes [19], which needs to solve the following constraint problem:  
\[\max\alpha\underset{i=1}{\overset{n}{\sum}}\alpha_{i}\ -\frac{1}{2}\ \sum_{i=1}^{n}\sum_{j=1}^{n}\alpha_{i}\alpha_{j}y_{i}y_{j}K(x_{i},\ x_{j}) \tag{2}\]  
subject to  
\[\sum_{i=1}^{n}{y_{i}}^{T}\alpha_{i}=1,\ 0\leq\alpha_{i}\leq C,i=1,\ \ldots,\ n\ \forall\ i=1,2,3,...,n \tag{3}\]The final discriminate function takes the following form:  
\[f(x)=\sum_{i=1}^{n}\alpha_{i}K(x_{i},\ x_{j})+b \tag{4}\]  
where \(b\) is the bias term.  
SVM can be used for both binary classification problems and multiclass classification problems [35]. For multiclass problems, there are two different approaches: (i) one-vs-one approach and (ii) one-vs-all approach. In the case of this study, we have used a one-vs-all approach.
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 6 Types of Information Transmitted
### Video Conferencing  
Advancements in information and communications technology has led to the increased use of video conferencing in telemedicine [123]. One such scenario was a telemedicine system designed for both rural and urban areas as a result of the dearth of medical personnel in rural areas of Iraq, called Mobile Field Hospital (MFH) [124]. In this scenario, intranet connections were established using GPRS devices (mobile phones) and personal computers. This allows a medical doctor in a remote hospital (MFH) to then perform a video conference with a specialist doctor in another location using GPRS within the network. In another example, telemedicine video conferencing was used at a mining station in Australia located about 1800 km away from the specialist clinic; the video conferencing unit was connected via an Integrated Services Digital Network (ISDN) and provided a data rate of 128 kbps [125].  
In their design for a telemedicine framework, [34] used both Advanced Encryption Standard (AES) and the Rivest-Shamir-Adleman (RSA) algorithm for data encryption. The encrypted video and audio were transmitted using Real-time Transport Protocol (RTP) over User Datagram Protocol (UDP). In [2], video conferencing was achieved through Voice over Internet Protocol (VoIP) using the MPEG and MPEG2 file formats.
## 4 Discussion
### Uncertainties and Limitations  
While this study has revealed significant insights into the effects of climatic factors and vegetation restoration on ET and its components in the HLS, certain uncertainties and limitations remain. Firstly, the parameter settings of the PT-JPL model and the quality of input data could influence the simulation results. Future research should focus on further optimizing the model parameters and employing higher-resolution observational data for validation [52]. Secondly, this study focuses on the effects of climate factors and vegetation restoration on ET, while the effects of other potential factors (human activities) on ET have not been fully considered. Future research should comprehensively consider the combined effects of multiple factors to understand the driving mechanism of ET more comprehensively [53]. Finally, the time scale of this study is 2000-2018, and the time span is relatively short. Future research should consider climate change and vegetation dynamics on a longer time scale in order to better predict future trends.
# Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
## 5 Experimental Result  
There were 840 items of surge arrester PD data extracted in this paper, including 210 data items for each of the four surge arrester fault types. After SDP, each type had 210 SDP diagrams, of which 150 images were taken as training samples and 60 were taken as test samples for CNN recognition.
## 2 Materials and Methods
### Numerical Methodology  
Figure 3 shows a schematic diagram of the numerical methodology used in this work to investigate the thermal performance of the CPC solar collectors described in Section 2.1. Firstly, an optical analysis is performed using the ray tracer software Tonatiuh [27] to obtain the energy and spatial distribution of rays (photons) intersecting the surface of the absorber tube, for a certain day, from 6 a.m. to 6 p.m. (Figure 3a). Secondly, the data from the ray-tracing analysis is processed using MATLAB [28] to obtain the energy distribution (surface heat flux) on the CPC absorber surface (Figure 3b). Thirdly, a coupled FEM-CFD simulation is built in Abaqus [29], in which the power (energy) is input on the absorber FEM model, which is coupled with the CFD model of the water running through the absorber (tube) (Figure 3c). The thermal FEM-CFD analysis allows for the estimation of the temperature of the water. Each step of the simulation methodology is described in detail in the following sections.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 4 Proposed Method PDTR
### Updating the Probabilistic Routing Phase
#### 4.3.2 New-Parent Probabilistic Selection  
The node needing new-parent \(n_{np}\) has to probabilistically select a new parent among the candidate parents set \(CP_{np}\); this selection is achieved according to the updated probabilistic routing table, defined by the update of the initial probabilistic routing table. For the nodes in \(CP_{np}\) at the time when \(n_{th}\) (parent node of \(n_{np}\)) loses \(\ell_{e}\) of its energy, the update is calculated by Equation (12) as following:  
\[\overline{h}\left(n_{np,j}\right)=\frac{\left(1-\tilde{h}\left(n_{np,j}\right) \right)^{\alpha}}{\sum_{l=1}^{C_{np}}\left(1-\tilde{h}\left(n_{np,j}\right) \right)^{\alpha}}\;,\]  
\[\overline{d}_{np,j}=\frac{\left(1-\tilde{d}_{np,j}\right)^{\beta}}{\sum_{l=1} ^{C_{np}}\left(1-\tilde{d}_{np,l}\right)^{\beta}}\;\;and\]  
\[\overline{E}_{np,j}=\frac{\overline{E}_{np,j}\;^{\gamma}}{\sum_{l=1}^{C_{np}} \overline{E}_{np,l}\;^{\gamma}},\;\forall n_{j}\in CP_{np},\;\left|CP_{np} \right|=C_{np}.\]  
\[\overline{\varphi}_{(np,j)}=\frac{\tilde{\varphi}_{(np,j)}}{\sum_{n_{j}\bigcup CP _{np}}\tilde{\varphi}_{(np,j)}}=\frac{\left(1-\tilde{h}\left(n_{np,j}\right) \right)^{\alpha}\left(1-\tilde{d}_{np,j}\right)^{\beta}\overline{E}_{np,j}\;^ {\gamma}}{\sum_{l=1}^{C_{np}}\left(1-\tilde{h}\left(n_{np,j}\right)\right)^{ \alpha}\left(1-\tilde{d}_{np,j}\right)^{\beta}\overline{E}_{np,l}\;^{\gamma}},\]  
**Remark 4**.: _Hops-count distribution and the residual energy distribution must be updated, because they may change after the packets transmission process, while the transmission distance distribution has to be updated not due to the fact that it changes by time (the nodes are statics), but due to the fact that the neighboring nodes set of the node needing new-parent \(n_{np}\) changes, which requests a transmission distance distribution update._4.3.3. Updating the Hops-Count for the Sub-Tree (\(n_{np}\)) Nodes  
In each time any node \(n_{np}\) changes its parent, and the hop-count of its new-parent is different from the hop-count of its old-parent, the hop-count of the all nodes belonging to the Sub-tree (\(n_{np}\)) has to be updated according to Equation (18), thus the update of the hops-count distribution and the update of the probabilistic routing table by Equation (12) are completed.  
After linking the node needing new-parent \(n_{np}\) to its new-parent \(n_{p}\), if \(h\big{(}n_{p}\big{)}\
eq h(n_{h})\), then for each child node and its parent node in Sub-tree (\(n_{np}\)), the hop-count should be updated by the following Equation (18).  
\[h\left(n_{np}\right)=h\left(n_{p}\right)+1\text{ and }h(child\ node)=h(parent\ node)+1 \tag{18}\]
# Research on a Support System for Automatic Ship Navigation in Fairway
###### Abstract  
In previous investigations, controllers for the track-keeping of ships were designed with the assumption of constant ship speed. However, when navigating in a fairway area, the ship's speed is usually decreased to prepare for berthing. The existing track-keeping systems, which are applied when the ship navigates in the open sea with a constant ship speed, cannot be used to navigate the ship in the fairway. In this article, a support system is proposed for ship navigation in the fairway. This system performs three tasks. First, the ship is automatically controlled by regulating the rudder to follow planned tracks. Second, the ship's speed is reduced step by step to approach the berth area at a low speed. Finally, at low speed, when the ship's rudder is not effective enough to control the ship's heading to a desired angle, the ship's heading is adjusted appropriately by the bow thruster before changing the control mode into the automatic berthing system. By the proposed system, the automatic systems can be combined to obtain a fully automatic system for ship control. To validate the effectiveness of this proposed system for automatic ship navigation in the fairway, numerical simulations were conducted with a training ship model.  
automatic ship navigation; fuzzy logic; fairway; bow thruster; wind disturbance +
Footnote †: journal:  
1
# Study on Preparation and Properties of Sintered Brick from Multi-Source Solid Waste
## 3 Results and Discussions
### Influence Law of Weathering Resistance  
#### 3.2.1 MGB Sintered Brick  
(1) Influence of temperature rise rate and holding time on the weathering resistance of sintered brick.  
As can be seen from Figure 9, with a temperature rise rate of 5 \({}^{\circ}\)C/min and 10 \({}^{\circ}\)C/min, the water absorption rate did not change significantly. With an increase in the temperature rise rate, the sintered brick water absorption rate began to significantly increase, which due to the higher temperature rise rate, will lead to an acceleration of the sintered brick water volatilization rate; the limited pore channel has not met the need for water evaporation as brick billets inside a large number of tiny channels that have disconnected or formed external pores, which in turn leads to sintered brick water absorption rate increases. During the sintering process, a series of physical and chemical reactions occur inside the sintered brick. Waste glass slag and other substances become solid-fused during the sintering process, filling the internal pores of the sintered brick and causing the volume of the brick to shrink. At the same time, the internal structure of the brick is destroyed, forming a network structure of interconnected particles, in which the pores are gradually filled and the volume contraction becomes more significant, eventually leading to a reduction in water absorption.  
(2) Influence law of sintering temperature on the weathering resistance of sintered brick.  
Figure 10 shows the effect law of sintering temperature on the water absorption rate and saturation coefficient of sintered brick. From the figure, it can be seen that the water absorption rate of sintered brick decreases with the increase in sintering temperature. When the sintering temperature was 950 \({}^{\circ}\)C, the water absorption rate was 13.56%. When the sintering temperature reached 1050 \({}^{\circ}\)C, the water absorption rate and saturation coefficient basically reached the minimum value. As the temperature continued to increase, the water absorption rate and saturation coefficient remained relatively constant. This is because as the sintered brick increases with the sintering temperature, more mineral components inside the brick melt, internal pores and connected pores fill, and the degree of brick densification  
Figure 9: Effect of heating rate and holding time on weathering resistance of MGB sintered brick.  
increases; when the temperature reaches 1050 \({}^{\circ}\)C, the internal physicochemical reaction of the sintered brick basically reaches a stable state.