esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Multi-DAG Satellite Network Task Scheduling Algorithm Based on Cache-Composite Priority
## 1 Introduction
The main focus of this paper is the multi-DAG task scheduling problem within the integrated Software-Defined Networking (SDN) satellite network architecture. In the DAG selection phase, we not only calculate task priorities but also incorporate the concept of fair scheduling to prevent potential excessive delays in the scheduling of low-priority DAG tasks. In addition, in multi-DAG task scheduling, there are some public subtasks, and these public subtasks can reuse the same processing results. In this study, we take advantage of the extensive coverage characteristics of satellite networks to repetitively use these public subtasks, thereby enhancing performance. The main contributions of this paper are as follows:  
* In the SDN satellite task deployment network architecture, the idea of splitting the tasks that the MEC server cannot afford into DAG subtasks is proposed. * The concept of public subtasks is introduced, combined with the SDN controller, adding a public task management module and reducing the system overhead caused by repetitive subtasks, thereby improving resource utilization. * This paper proposes a multi-DAG satellite network task scheduling algorithm based on composite priority. Compared with the Fairness scheduling algorithm and LB-HEFT scheduling algorithm, when introducing public subtasks, the task completion time and task energy consumption of this algorithm increase by nearly 20%. The remainder of this paper is organized as follows. In Section 2, the relevant research on DAG task scheduling is introduced. In Section 3, we propose a multi-DAG task scheduling algorithm based on cache-composite priority. In Section 4, we carry out a simulation. Section 5 presents a summary of the article and research prospects.
# Removal of the Previous Root Canal Filling Material for Retreatment: Implications and Techniques
## 1 Introduction
### Canal Transportation during Retreatment  
The safety of retreatment procedures is a matter of concern because of the risk of procedural accidents, including instrument fracture, zips, ledges and perforation. The occurrence of root canal transportation during filling removal and re-instrumentation has been evaluated, especially in curved canals and in the apical third [14,101]. Transportation can complicate proper cleaning, disinfection, and filling of the canal and, if unnoticed or not corrected, may evolve to more drastic accidents, including perforation and zips [102]. Furthermore, excessive dentin removal, especially in the most coronal parts of the root canal, may weaken the root structure and put the root at risk of fracture [103].  
Studies have shown a high frequency of canal transportation during retreatment [45,101,104,105]. For instance, a recent study revealed the occurrence of apical transportation in all mesial canals from mandibular molars retreated with either rotary multifile or single-file NiTi systems [105]. In general, the studies show a similar performance of the different NiTi systems in terms of centralization, including when compared with manual instruments [45,101,104,105]. However, any conclusion about a specific retreatment system/technique is premature because of the restricted number of studies on the subject.  
Many variables can interfere with canal transportation during retreatment, including canal anatomy, type of filling material, dentin hardness, preparation technique, operator experience, curvature angle, radius and position, and instrument geometry, motion, and alloy.
# Citrus Pest Identification Model Based on Improved ShuffleNet
## 2 Materials and Methods
### Improved SCHNet Model  
The dataset created for this study faced challenges, such as pest blending with the background, complex backgrounds in pest images, and variations in pest sizes. Therefore, ShuffleNet V2 1.0\(\times\) was selected as the backbone network and was adapted accordingly. The SCHNet architecture is illustrated in Figure 4. In this figure, Conv, Conv1, and Conv5 represent the convolution operations, Channel split denotes the channel separation, DWConv represents the depth-wise convolution, Concat indicates the channel concatenation, Channel Shuffle represents the channel shuffling, and MaxPool denotes the max pooling. Stage2, Stage3, and Stage4 comprise the enhanced basic and downsampling units stacked together. GlobalPool represents the global average pooling. FC represents the fully connected layer, PReLU denotes the activation function, and BCABM denotes the improved CBAM attention mechanism module.  
Figure 3: Basic module of ShuffleNet V2. (**a**) Basic unit (**b**) downsampling unit.
## 3 Simulations and Experiments
### Optical Experiments  
Optical experiments are carried out according to Section 2.5.2 in order to further certify the practicality of our proposed PC method. The schematic of optical experimental set-up is shown in Figure 10. The 671 nm laser is expanded and collimated by the beam expander and collimating lens. The phase-only holograms are loaded on the flat reflection-type phase-only LCOS-SLM with a beam splitter (BS) close to it. The resolution and the sampling intervals of the SLM is 1920 \(\times\) 1080 and 8 \(\upmu\)m, respectively. The reconstructed images are recorded by the CMOS camera which is 200 mm away from the SLM.  
Figure 8: Error of the diffraction results between PS (red line) and PC (blue line) method in amplitude with different flare angles of WRP. (**a**) 180\({}^{\circ}\), (**b**) 90\({}^{\circ}\), (**c**) 30\({}^{\circ}\) and (**d**) 7.5\({}^{\circ}\).  
Figure 9: Error of the diffraction results between PS (red line) and PC (blue line) method in phase with different flare angles of WRP. (**a**) 180\({}^{\circ}\), (**b**) 90\({}^{\circ}\), (**c**) 30\({}^{\circ}\) and (**d**) 7.5\({}^{\circ}\).  
Three pictures including \(USAF\), _cameraman_ and _peppers_ are used for optical experiments. Moreover, the optical reconstructed results are shown in Figure 11 where SCGHs with corresponding flare angles of 90\({}^{\circ}\) and 7.5\({}^{\circ}\) are shown as representatives. The SCGHs can be correctly reconstructed as shown in Figure 11a-f, which further validates the feasibility of the proposed method. However, the reconstructed images are with obvious speckle noise since one phase-only hologram is used for each reconstruction. Therefore, the time division multiplexing method is used as optimization to suppress the speckle noise and improve the image quality as shown in Figure 11g-l[23]. All the results are in accordance with our theoretical analysis.
## 3 Results and Discussion
### Functionalization of the Hydrophilic Block: MeO-PEG2000-PBA
ATR FT-IR (please see Figures S1 and S2 in Supplementary Material) shows characteristic peaks of PEG. In addition, the band at 1736 cm\({}^{-1}\) was ascribed to the vibration absorption of the carboxyl groups of the ester bonds in MeO-PEG-PBA, whereas the band due to B-O asymmetric stretching overlaps with PEG absorption around 1340 cm\({}^{-1}\).
## 1 Introduction
### Experimental Design--Measuring System LTS  
In this experiment, the LTS IL100 (Keyence) was used. Its technical characteristics are measuring range 70-130 mm, measuring spot diameter (0.400 \(\times\) 1.350 mm) depending on the distance to the workpiece, linearity \(\pm\)0.03 mm, repeatability 0.004 mm and accuracy of 0.001 mm. For the experiment, we developed a measurement system, the schematic of which is shown in Figure 2 and which contains these elements as shown in Figure 3.  
The measurement system also consists of a communication unit (DL-PN1 type DIN rail mount) with an amplifier (IL-1000, type for communication speed 500 kbps). The switch (Siemens-XB005) is used to connect the PLC via ethernet to a PC with software.  
For the PLC device, software was developed in the TIA portal program. As reported by [PERSON] et al. TIA Portal is also intended for the development of operator interfaces for machines and plants with operator panels (HMI) and for dispatching systems [38]. Profinet communication has been configured in Siemens TIA Portal (Figure 4).  
Figure 3: Measuring scheme of LTS, containing amplifier IL-1000, profinet module DL-PN1, PLC module Siemens 1511C.  
Figure 2: Measuring system LTS (Keyence—IL 100).  
It is very important that the data transfer from the PLC to PC is done via TCP communication with the TCP server and the client. The principle of data transfer is shown in Figure 5 also with the TCP communication part for data transfer from PLC to C#.  
An example of the main loop of the algorithm with an explanation of the basic principle can be found in Figure 6. The measurement data from the measurement process is stored in a PC as structured data in CSV format same as in articles by [PERSON] et al. [39; 40].  
Figure 4: TIA portal configuration and graph for experimental setup for stable pulses generation.  
Figure 5: Client communication in PLC system.  
Figure 6: Main loop example for data transfer to value in mm.  
Based on the design of the software for transformation and data processing, we can recommend the use of LTS directly in the manufacture of products on CNC machines. Part of the research was the design of the holder for LTS, its production on a 3D printer (as seen in Figure 7.)
# Deep Learning of Diffuse Optical Tomography Based on Time-Domain Radiative Transfer Equation
## 5 Discussion  
### Sampling Effect  
In the analyses presented in this paper, we employed datasets of all time steps for deep learning. A time step in our simulations is \(\Delta t=0.0006\) ns, while it is \(\Delta t=0.01\) ns in experiments using the phantom. One advantage of LSTM deep learning is to recognize global behaviors of temporal data. Utilizing this advantage, we conducted the LSTM deep learning study using only \(1/16\) of the simulation snapshots (16 times longer time steps). As a result, we confirmed that the our LSTM model works well even for such coarse-grained data. This allows us to speed up the deep learning dramatically, by adjusting the time step of the simulations to that of phantom experiments.  
In addition, in the present analysis, we have assumed noises of a Gaussian type for the training data. We have further investigated a different type of noise, which is a uniform one. As a result, we have confirmed that the training data with uniform noises give results similar to those with the Gaussian noises. Actually, if experiments are conducted many times, the noises in obtained data are expected to approach a Gaussian distribution. Indeed, the experimental data obtained in the phantom show Gaussian-like noises.  
Finally, in this paper, we have used 2D simulation data calculated with the radiation transfer solver TRINITY [11]. We have compared the present results to 3D Monte Carlo simulations, and confirmed that the present 2D models show a good agreement with 3D Monte Carlo simulations.  
Figure 11: Example for a multi-beam injection model. three absorbers, and three different incident beams. Each symbol corresponds to detected positions for each beam.  
Figure 10: Distributions of detection probabilities for an incident beam from S2 (**left panel**) and S1 (**right panel**): yellow domains highlight most probable positions for detection, while green domains correspond to moderate probabilities and blue domains correspond to low probabilities.
## 5 Open Research Issues and Challenges
### Networking with Cognitive Radio Capability  
In the CR paradigm, unlicensed secondary users can access some licensed parts of radio spectra. Some CR schemes have been studied for medical wireless body area networks [5,33]. Energy efficiency, collision avoidance and interference cancellation are the main goals in CRBANs for ubiquitous health monitoring. A considerable amount of current research is focusing on the MAC layer. Network layer and cross-layer design are other promising research areas that need to be addressed more extensively in the future.
## 4 Results
### Additional Comments  
The results of Section 4.2, for the 33-node system, were obtained after running the DCVSA algorithm with an average response of 1679.1267 seconds. Likewise, in this test system, the average response time was 4771.6179 seconds. The maximum difference in energy losses was 12.4286 kWh/day considering the characteristic demand curve and the generation variation of a PV system for both networks. This maximum difference can improve by increasing the sampling and the number of iterations for the DCVSA; since for systems, such as the 69-node, and those with more branches, having a higher number of variables results in a slower convergence.  
Regarding the response of the solution vectors for the 33-node system to reduce power and energy losses, it states that the location for reducing power losses was \(\{13,\ 24,\ 30\}\). In the same way, it denotes that the position for decreasing energy losses was \(\{14,\ 24,\ 30\}\), where the total installed power for PV systems is 2.9467 MW and 4.2687 MW, respectively. This means that the overall installed capacity required to reduce energy losses over a period of 24 h cannot be measured only taking into account the peak hour since an increase of 30.9686% is necessary concerning the total required during the 24 h.  
Likewise, in the 69-node system, it is outstanding to mention that the location nodes of the PV systems were different for the power and energy cases since in the first, it was \(\{11,\ 18,\ 61\}\), and in the second, it was \(\{17,\ 61,\ 64\}\). On the other hand, it displays that the capacity of the installed power to reduce losses in hour 19 was 2.6258 MW. Furthermore, for the decrease of energy losses, it was 3.3569 MW, which indicates that the installation of PV systems considering 24 hours a day should increase by 21.7785%.
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
### Related Work
Non-idealities present at the Tx and Rx chain include the phase noise of a local oscillator for in-phase and quadrature (IQ) mixer [15; 33; 34; 35; 36; 37], IQ mismatch (imbalance between gain and phase of I and Q branches of IQ mixer and image components) [24; 38; 39], receiver noise figure, ADC quantization noise [40] and digital to analog convertor (DAC) and ADC dynamic range [41; 42], power amplifier (PA) nonlinearity [3; 43; 44], and low noise amplifier (LNA) saturation [42; 45]. These non-idealities mentioned above are the major and foremost obstacle in perfect SI cancellation, and they cause residual SI. An iterative estimation and cancellation technique for nonlinear in-band full-duplex transceivers with IQ imbalances and amplifier nonlinearities has been presented in [46].
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 5 Discussion
### Advantages
#### 5.2.3 Reduced Impact of Attack and Downtime  
Customised rule creation is a key component of the suggested response solution, which reduces the impact of an attack and the ensuing downtime. It operates by identifying and eliminating specific attack traffic, thereby ensuring that normal network traffic remains uninterrupted. This refined technique prevents a single attack from disrupting the host traffic. In contrast, conventional methods without such tailored flows tend to respond by blocking all the traffic from the observed source. This results in a complete service outage for the accused host, demonstrating the distinct advantage of our approach over the other methods.
## 5 Analysis of Results
### Real-Time PWV Determination Validation
As shown in Figure 12, about 90% of the obtained real-time PWV's precision through GPS solutions is less 10 mm; also, the calculated PWVs by utilizing WHU and BKG products outperform the other products. For the precisions of the GLONASS-derived real-time PWVs, however, 90% of their values are less than 20 mm, outperforming BKG products; moreover, 90% of the errors in real-time PWVs resulting from Galileo processing models are less than 15 mm, with superior to GFZ and WHU products. For the BeiDou solutions, about 90% of the PWV errors range from 15 mm to 20 mm for GFZ and WHU products, respectively, with an exception for IGS products. Figure 11: STD values for real-time PWV derived from different PPP solutions. Figure 12: _Cont._
## 2 Materials and Methods  
### OCT/OCE System Configuration and Imaging Protocol  
The system setup is schematically shown in Figure 1. This OCE system consisted of two main parts, which are the signal detection part and the signal stimulation part. For signal detection, the PhS-OCT system employed a super luminescent diode (Thorlabs S5 FC series) for the laser source. The laser source had a central wavelength of 1310 nm, the spectral bandwidth was 85 nm and the axial resolution was 8.8 \(\upmu\)m theoretically. A 50 mm focal length objective lens was selected, which resulted in a lateral resolution of 16 \(\upmu\)m in air and 11.85 \(\upmu\)m in the oesophageal sample with a refraction index \(n=1.35\). The interfered signal was captured by a CCD camera (SUI-GL20448L; UTC Aerospace) with a 20,730 Hz sampling frequency.  
To stimulate the oesophageal tissue sample, a commercial electric-magnetic shaker (LDS V201, Bruel & Kjaer Sound and Vibration Measurement A/S, Teknikerbyen 28, DK-2830 Virum, Denmark) was employed. The shaker is driven by a signal generator (33220A.; Agilent Technologies, Santa Clara, CA 95051, USA) and a power amplifier (7724 DC-enabled AC power amplifier; AE Technon, Elkhart, IN 46516, USA) directly with a sinusoidal wave input signal to generate periodic stimulation to the sample. Based on the previous work carried out by our research group regarding the optimal stimulation frequency for vibrational OCE [26], the input sinusoidal signal was set at 650 Hz with a peak-to-peakamplitude of 30 milli-volts, which resulted in a vibration amplitude of 150-250 nm. The vibration amplitude was transmitted from the shaker to the sample and reference material via a solid-sample holder plate; the vibration amplitude was assumed to be uniformly distributed both in the sample and reference material layers.  
For the data acquisition protocol, it was set as the MB-scan mode. Each A-line scan represented depth information of the sample; 512 pixels were selected for depth information based on the current system; 512 A-line scans formed an M-scan. Thus, one cross-section of the sample consisted of 512 pixels \(\times\) 512 pixels. Each B-scan consisted of 512 M-scans. The scan protocol was controlled by the dual-axis galvanometer (Thorlabs, GVS002, New Jersey, USA) and the CCD camera trigger. Based on the current system setup, the actual scanning ranges in axial and lateral direction were 4.68 \(\upmu\)m/pixel and 6.25 \(\upmu\)m/pixel, respectively, in OCT structure image.
## 4 Exergy Destruction  
Exergy is the amount of work that can be done with a given amount of energy. Exergy is the highest theoretical useable energy that can be extracted from an energy resource or a substance that is subjected to an almost reversible technique from its starting condition until it is returned to its natural condition. Exergy is determined by a total of parameters that can be equal to zero (in an equilibrium state with the surroundings), and it is reliant on the relative state of a system and its ambient conditions [64]. Exergy is an indicator that measures how much a system deviates from equilibrium with its surroundings. Exergy is a term that encompasses both the qualitative and quantitative aspects of energy. Any irreversible occurrence results in exergy losses, resulting in process exergy destruction (loss of useful work) or elevated energy consumption from whichever source the energy was acquired from. The [PERSON] theorem connects exergy destruction to total entropy production in a system:  
\[\dot{E}=T_{0}\dot{S}_{gen} \tag{1}\]  
where \(\dot{E}\) is the exergy destruction, commonly known as loss of useful work, \(T_{0}\) is the environment's reference temperature, and \(\dot{S}_{gen}\) is the system's total entropy production.  
Limited numbers of scientists and researchers are reported in prediction and modelling of hydrogen energy demands. A hybrid energy system for electricity and hydrogen generation is theoretically conceived and implemented to an island, Bozcaada, by [PERSON] et al. [65]. Exergy, energy, and techniques are used to undertake a thermodynamic evalua  
Figure 2: Hydrogen Square (HydS): four corners model.  
tion. The Hybrid Optimization Model for Electric Renewable (HOMER) software examines the theoretical structure from the perspectives of energy load distributions and the economy. The study's analytical scenarios are based on the stand-alone island model. The converter, hydrogen tank, polymer electrolyte membrane fuel cell (PEMFC), electrolyser, wind turbines, and photovoltaic (PV) array are all included in the model. The magnitudes and locations of system inefficiencies are determined using exergy and energy assessments. The studies are mostly performed for the primary equipment under dynamically changing environmental and operational circumstances. There are also hourly distributions of varying exergy and energy rates shown. As a result, the PV array's daily average exergy and energy efficiencies are 14.26% and 13.31%, respectively. In addition, the efficiency values for the electrolyser equipment is calculated as exergy at 60.26% and energy at 59.68%, while the wind turbine is determined as exergy at 50.12% and energy at 46%, respectively.
## 3 Proposed Scheme: RSP-Based Message Authentication for VANET
### Message Authentication  
For \(V_{i}\), the neighbor set lists information about all neighbors, including their anonymous identities, pairing values, and the other vehicles that can help to pass messages. When \(V_{i}\) wants to broadcast a message, it will generate a polynomial function with all pairing values and a random key K, and then make the HMAC of the message and the time stamp with key K.  
\(F(x)=K+\prod(x-P_{Vi,Vj})\), where \(V_{j}\)s are the neighbors of \(V_{i}\), and the pairing values are \(P_{Vi,Vj}\).  
1. \(V_{i}\rightarrow\text{all: }(ID_{Ra},T_{t},ID_{Vi})\),\((M,T_{s})\),\((F(x),HMAC(M\;\|\;T_{S})_{K})\),(List of \(ID_{Vk}\)) where \(ID_{Vk}\) is the ID of the vehicle that is in the pairing value field of the neighbor set, and can help \(V_{i}\) to rebroadcast this message.
2. After \(V_{j}\) receives the message, \(V_{j}\) takes \(P_{Vj,Vi}\) from the neighbor set of \(V_{j}\); calculates \(F(P_{Vj,Vi})\) to obtain \(K^{\prime}\), and checks whether the \(HMAC(M\;\|\;T_{S})_{K}\), equals \(HMAC(M\;\|\;T_{S})_{K}\); if it does, then \(Vj\) authenticates this message; otherwise, \(V_{j}\) rejects this message.  
If \(V_{j}\) is in the list of \(ID_{Vk}\), then \(V_{j}\) will rebroadcast this message by a similar process.  
\begin{table}
\begin{tabular}{c c c} \hline
**Neighbor Vehicle** & **Pairing Value** & **Expiration Time** \\ \hline \((ID_{Rb},\,T_{t},\,ID_{Vj},\,DS_{Vi})\) & \(P_{Vi,Vj}\) & \(ET_{j}\) \\ \((ID_{Rc},\,T_{t-1},\,ID_{Vk},\,DS_{Vk}\) ) & \(P_{Vi,Vk}\) & \(ET_{k}\) \\ \((ID_{Rd},\,T_{t},\,ID_{Vi}\,,\,DS_{Vi})\) & \(ID_{Vi}\) & \(ET_{l}\) \\ \hline \end{tabular}
\end{table}
Table 4: Set of neighbors.