esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Concepts and Theory
### Tropospheric Scattering
#### 2.3.1 Scattering Models
As a result, although there are some differences in their reflectivity, both tropospheric forward and backward scattering occur simultaneously.
# Detection of Thermal Strain in Steel Rails with BOTDA
## 1 Introduction
This was attributed to the dominant thermal strain of the rail track that turned out to suppress the fiber jacket-induced nonlinear response of the Brillouin frequency shift, validating their applicability in distributed optical fiber strain sensing.
## 8 Future Work  
To overcome the limitations and challenges mentioned in Section 6, future research should focus on the following areas:  
* Broader Dataset Evaluation: Future work should include evaluating the Transformer-DNN and Autoencoder-CNN with a wider range of datasets, including NSL-KDD, KDDCup99, UNSW-NB15, and newer ones like CSE-CIC-IDS2018 and NF-ToN-IoT-v2, to assess the models' robustness, generalization capability, and effectiveness against emerging attack vectors.
* Data Preprocessing Refinement: The data preprocessing steps should be further refined and tailored for each specific dataset, to ensure optimal model performance. This will involve experimenting with different preprocessing techniques and understanding their impact on model outcomes. Detailed analyses of these preprocessing operations are thoroughly discussed in Sections 3.2 and 4.1 of this article.  
* Model Adaptation and Hyperparameter Optimization: Continued exploration of model adaptation techniques is necessary, with a focus on refining the hyperparameter optimization process for different datasets. This process should be systematically analyzed to identify the best practices for adapting the models to diverse data environments. These details are discussed in Section 3, particularly in Sections 3.3.4 and 3.3.5.
* Scalability and Computational Efficiency: Efforts should be made to optimize the models' computational efficiency and scalability, ensuring that they can handle larger datasets and more complex network traffic scenarios without compromising performance.  
Conceptualization, H.K. and M.M.; Methodology, H.K. and M.M.; Software, H.K. and M.M.; Validation, H.K. and M.M.; Writing--original draft, H.K. and M.M.; Supervision, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets used in our study, CICIDS2017 and NF-BoT-IoT-v2, are publicly available. Below are the URLs for the datasets: CICIDS2017: [[https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)]([https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)) (accessed on 16 January 2025), NF-BoT-IoT-v2: [[https://staff.itee.uq.edu.au/marius/NIIDS_datasets/#RA8](https://staff.itee.uq.edu.au/marius/NIIDS_datasets/#RA8)]([https://staff.itee.uq.edu.au/marius/NIIDS_datasets/#RA8](https://staff.itee.uq.edu.au/marius/NIIDS_datasets/#RA8)) (accessed on 16 January 2025).  
The authors declare no conflict of interest.
# Enabling Autonomous Agents for Mobile Wireless Sensor Networks
## 5 Implementation
### Battery Simulation of Network Operations
In order to estimate the battery drainage at each node due to network communication when the MWSN is stationary, we assume that during this phase all network nodes periodically transmit data to the AP using the controlled flooding algorithm, and then we compute \(P(n)\), the number of packets each node \(n\) is transmitting or forwarding during a network cycle. Thus, we can compute the battery drained by node n during every single network cycle as  
\[Drain(n)=K_{duty}+P(n)\times U_{p}(n)\qquad(mAh) \tag{1}\]  
where \(K_{duty}\) is a constant that accounts for the battery drainage due to the waking up and duty-cycling of the sensing nodes. Therefore, during a cycle and from a network perspective, all nodes send a packet to the AP, and intermediate nodes forward them according to the controlled flooding routing mechanism. As a result, nodes closer to the AP (which act as a data sink) experience higher energy consumption due to their higher number of packets forwards, and, consequently, they deplete first. The number of cycles each robot \(n\) lasts until depleted can be calculated by the difference between the current battery percentage and a critical level denoted by \(b_{l0}\) as  
\[Cycles(n)=\frac{0.01\times(b_{c}(n)-b_{l0})\times B_{cp}}{Drain(n)} \tag{2}\]  
This critical level \(b_{l0}\) is further explained in Section 5.2. For efficiency's sake, our network simulations follow an event-driven approach. Thus, network simulations advance in time until a node dies or when a node needs help in the Agents strategy, as stated in Equation (3). This is the minimum number of cycle value of \(N\) alive nodes. \[sim_{Cycles}=\min\{Cycles(0),Cycles(1),\ldots,Cycles(N-1)\} \tag{3}\]  
After determining the minimum number of simulation cycles for the most critical node, the corresponding battery subtraction is performed on all alive nodes according to their expected energy expenditure. Once this operation completes, the network simulation ends and the system behaves differently depending on the election of the strategy: SPF-only or Agents, according to Figure 2. So, the network simulation interval takes place between a topology change and the moment when Equation (3) is met; that is, when a robot requires assistance and consists of a certain number of cycles, as in Equation (2).
# Portable L-Band Radiometer (PoLRa): Design and Characterization
## 2 Hardware
### Back End and Processing  
The Linux microcontroller drives the switch, reads the temperature sensors, and samples the analog to digital converter (ADC), reading the power detector output signal. The switch has a settling time of less than 1 ms, and typically a full calibration cycle takes \(\sim\)69 ms, with 16 ms spent integrating each switch position, and the four temperature sensors are sampled during the four \(\sim\)1 ms switch position settling periods. The ADC is sampled at \(\sim\)2 kHz and 22 bits, and the RC time constant of the low pass filter is \(\tau\approx\) 1 ms. The ADC is capable of detecting \(<\)0.01 mV resolution, due to stable voltage regulation of the battery power source.  
The radiometer runs entirely on 5V DC, and consumes \(\sim\)0.7 A for a total of less than 4 W power consumption. The radiometer has no active temperature control, which was shown to be unnecessary to achieve the desired accuracies comparable to satellite-borne L-band radiometers. Instead, we rely on characterization of the physical-temperature dependence of the ACS. This characterization is presented in detail in Section 3.1. The calibration procedure of radiometer noise temperatures is also presented in Section 3.
# A Range-Extension Method for an Indoor Standard Device for Large-Scale Length Measurement
## 3 Range Extension of the Indoor Standard Device for Large-Scale Length Measurement
### Optical Path Setup in the Range-Extension Measuring System  
Figure 5 demonstrates how the measuring optical path is created by the laser interferometer measuring system. Upon traversing a corner reflector, the incident light fell on the reflector of the laser interferometer. After being refracted by the interferometer's reflector, the light was reflected by another corner reflector before entering the main unit of the laser interferometer. Overall, the incident light underwent two reflections to achieve the range-extension experiment of the laser interferometer.  
Figure 4: The optical path tests the corner reflector’s retroreflection parallelism.  
Figure 3: Structure of the hollow corner reflector.  
The range-extension experiment of the laser interferometer, as depicted in Figure 6, involved large-size corner reflectors with a diameter of 100 mm each, which served as the primary measuring device. By adjusting the positions of the corner reflectors and the reflector of the laser interferometer, we managed to extend the measuring range. The original reflectors were replaced by large-size corner reflectors, which allowed for the reception of more laser beams when reflecting over long distances. It can receive more laser beams when reflecting over long distances. In addition, the light reflectivity of mirror reflection is higher than that of transmission reflection.
# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 1 Introduction
The bounding box in the result of SOWP [10] and contains a large number of background regions (male skaters) in Frame 136, resulting in false tracking and the loss of real targets at Frame 396. The athletes in the sequence \"Bolt2\" are almost similar to each other in appearance, and their appearance changes significantly due to background clutter and deformation, which leads to SOWP [10], ACFN (Attentional Correlation Filter Network) [18] and MEEM (Multiple Experts Using Entropy Minimization) [19] mistakenly tracking other athletes; meanwhile, the trackers MUSTer (MUlti-Store Tracker) [20] and  
Figure 1: The illustration of background information redundancy in the bounding box caused by fast motion and background clutter, “Skating2-1” and “Bolt2” are from the OTB-100 (object tracking benchmark) dataset [17]. The compared visual tracking algorithms including the spatially-ordered and weighted patch descriptor (SOWP) [10], ACFN (Attentional Correlation Filter Network) [18], MEEM (Multiple Experts Using Entropy Minimization) [19], MUSTer (MUlti-Store Tracker) [20], TLD (Tracking-Learning-Detection) [21] and ours. TLD (Tracking-Learning-Detection) [21] drift. It is easy to see that our algorithm achieves accurate tracking under these challenges because MBD is used for calculating the similarity between the target and the background. The main contribution of this paper is that we present a novel MBD-based descriptor for visual tracking. Specifically, we propose to apply the MBD to calculate the weights of patches to construct the object descriptor for visual tracking, since MBD is more robust to pixel value fluctuation caused by motion or noise. Then, we adopt the boundary patches on each side in the bounding box as labeled background queries and calculate the MBD between the image patches and the seed patches; thus, the foreground target can be highlighted, and the background noise can be suppressed. Extensive experiments demonstrate that our proposed MBD-based descriptors can achieve more robust performances against other recent visual trackers when confronting fast motion and background clutter, as well as reduce drift effectively. The rest of this paper is organized as follows: Section 2 reviews the background and some state-of-the-art or classic methods related to our method. We will give a detailed introduction of our proposed algorithm in Section 3. Section 4 discusses experimental results including qualitative analysis and quantitative analysis. We draw the conclusion of this paper in Section 5.
## 3 Double Sky-Hook Control Algorithm  
### Conventional Sky-Hook Algorithm  
For the purpose of comparison between the conventional sky-hook algorithm and the double sky-hook algorithm, this section briefly summarizes the conventional one as follows: the force transmitted to the sprung mass through an ideal sky-hook damper is expressed as:  
\[F_{sprung}=B1_{sky}\cdot\dot{x}_{s} \tag{10}\]  
Figure 1: Quarter car model.  
Figure 2: Full car model.  
where \(B1_{sky}\) is the sky-hook damping coefficient and \(\ddot{x}_{s}\) is the sprung mass absolute vertical speed. In the real suspension configuration, however, the damping force is a function of a relative speed between a chassis and a wheel and is represented as  
\[F_{sprung}=B1_{d}\cdot\left(\dot{x}_{s}-\dot{x}_{u}\right) \tag{11}\]  
where \(\ddot{x}_{u}\) is the unsprung mass absolute vertical speed. The desirable damping coefficient \(B1_{d}\) that generates the same damping force as that of the ideal sky-hook damper is determined from Equations (1) and (2) as  
\[B1_{d}=\frac{B1_{sky}\cdot\dot{x}_{s}}{\dot{x}_{s}-\dot{x}_{u}} \tag{12}\]  
As discussed in the introduction, this theoretic sky-hook damper is effective only in the vicinity of the sprung mass resonance frequency and has a worse road-holding property than that of a passive damper.
# Adaptive Robust Fuzzy Impedance Control of an Electro-Hydraulic Actuator
## 5 Stability Analysis of the Whole System  
As Figure 4 shown, a fuzzy impedance controller and a nonlinear velocity controller are synthesized together in the whole cascade system. The parameters of impedance rules are predetermined. Thus, the impedance system (9) is a time invariant first-order system with positive coefficients. According to the concerned load system, the input \(e_{F}\) is bounded. Therefore, the impedance system is stable based on [PERSON]'s stability criterion.  
Defining a sliding function as  
\[s_{I}=m_{c}\ddot{e}_{x}+b_{c}\dot{e}_{x}-e_{F} \tag{38}\]  
it can be seen that the sliding function (38) generates the expected impedance behavior (46), when \(s_{I}\) vanishes [46]. The above definitions yield  
\[\begin{array}{ll}s_{I}&=m_{c}\left(\ddot{x}_{d}-\ddot{x}\right)+b_{c}\left( \dot{x}_{d}-\dot{x}\right)-e_{F}\\ &=m_{c}\left(-\dot{z}_{1}+\ddot{\overline{e}}_{x}\right)+b_{c}\left(-z_{1}+ \ddot{\overline{e}}_{x}\right)-e_{F}\\ &=-\left(m_{c}\dot{z}_{1}+b_{c}z_{1}\right)\end{array} \tag{39}\]  
Since the stability of ARC is already proved, \(z_{1}\) will converge to zero after a finite time only under parameter uncertainties. To make the force vary smoothly, the response of ARC is designed much faster than the fuzzy impedance controller. Thus, \(\ddot{x}_{s}\) and \(\dot{x}_{2}\) can be finally treated as small enough, since \(\dot{x}_{d}\) is always set as constant and the load is assumed to vary slowly in practice. Then, \(\dot{z}_{1}=\dot{x}_{2}-\ddot{x}_{s}\) can be considered small enough to make \(s_{I}\) converge to zero. Consequently, impedance performance can be guaranteed under certain conditions.  
Figure 4: Schematic diagram of the proposed ARFIC control strategy.
## 2 Materials and Methods
### Finite Element Simulation Model of Hydro-Pneumatic Spring Seal Based on ABAQUS
#### 2.3.1 Model Establishment  
A 2D axisymmetric model for the step seal ring, O-ring, hydro-pneumatic spring piston rod, and end cap was established based on the cross-sectional geometric dimensions of the GS seal ring, as shown in Figure 3a,b. Considering that the elastic modulus of the cylinder barrel and piston rod is significantly greater than that of PTFE and NBR, the cylinder barrel and piston rod were set as rigid bodies, and material properties were defined and assigned to each component before assembly.
# Operator-Based Adaptive Tracking Capacity Control in Complex Manufacturing Processes
## 2 Mathematical Model
\begin{table}
\begin{tabular}{c l} \hline \hline
**Variable** & **Description** \\ \hline \(X_{ji}^{I}(t)\) & Order input rate from the \(j\)th to the \(i\)th workstation for \(j,i\in\{1,\ldots,n\}\) \\ \(X_{i}^{I}(t)\) & Order input rate in the \(i\)th workstation for \(i\in\{1,\ldots,n\}\) \\ \(X_{ij}^{D}(t)\) & Order output rate from the \(i\)th to \(j\)th workstation for \(i,j\in\{1,\ldots,n\}\) \\ \(X_{i}^{O}(t)\) & Order output rate in \(i\)th workstation for \(i\in\{1,\ldots,n\}\) \\ \(p_{ij}\) & Order flow possibility from the \(i\)th to the \(j\)th workstation for \(i,j\in\{0,\ldots,n\}\) \\ \(p_{i0}\) & Order flow possibility from the \(i\)th workstation to final stage for \(i\in\{1,\ldots,n\}\) \\ \(n^{R}\) & Number of RMTs in the system \\ \(n_{i}^{D}\) & Number of DMTs in the \(i\)th workstation for \(i\in\{1,\ldots,n\}\) \\ \(v_{i}^{R}\) & Order output rate of DMTs in the \(i\)th workstation for \(i\in\{1,\ldots,n\}\) \\ \(x_{i}^{D}(t)\) & Disturbances in the \(i\)th workstation for \(i\in\{1,\ldots,n\}\) \\ \(u_{i}(t)\) & Number of RMTs in the \(i\)th workstation \(i\in\{1,\ldots,n\}\) \\ \(y_{i}(t)\) & WIP level in the \(i\)th workstation \(i\in\{1,\ldots,n\}\) \\ \(\tau_{1}\) & Transportation delay between workstations in \(X_{i}^{I}(t)\) \\ \(\tau_{2}\) & Reconfiguration delay of RMTs in \(u_{i}(t)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variables within a job shop system with RMTs. Furthermore, though the machine tools are assumed to be working at the maximum production rate, the capacity of the whole system is limited, and it depends on the number of RMTs in the job shop. For a job shop with \(n\) workstations and \(n^{R}\) RMTs, the number of RMTs at the \(j\)th workstation \(u_{i}(t)\) is a non-negative integer, which can be described with following constraints:  
\[u_{i}(t)\in\mathbb{N}_{0}\qquad\text{and}\qquad\sum\limits_{i=1}^{n}u_{i}(t) \leqslant n^{R}. \tag{6}\]  
For these constraints, the truncation \(\lfloor\cdot\rfloor\) and fractional approach [5] are utilized with  
\[\hat{u}_{i}(t)= \begin{cases}\lfloor u_{i}(t)\rfloor,&\text{if }\sum\limits_{i=1}^{n}u_{i}(t) \leq n^{R}\\ \left|\begin{array}{c}n^{R}\\ \sum\limits_{j=1}^{n}u_{j}(t)\end{array}\right|,&\text{else}.\end{cases} \]  
Therefore, when the sum of the RMTs exceeds \(n^{R}\), \(\hat{u}_{i}(t)\) is obtained with the fractional discrete value. Otherwise, the truncated value \(u_{i}(t)\) is chosen.
## 3 Results
### Total Porosity and tortuosity Factors  
The porosity of Cell 1 resulting from the 2D SEM image is 38.65% for the contact layer and 34.75% for the active layer and thus slightly higher than the 34.63% total porosity calculated from the \(\upmu\)-CT measurements. The total porosity resulting from the \(\upmu\)-CT is 32.69% for Cell 2. The 2D analysis results in a slightly lower porosity for the contact layer of 29.15% and a porosity of 32.91% for the active layer, confirming the visually determined compaction of the anode. Analogous results are obtained for Cells 3 and 5. The total porosity based on \(\upmu\)-CT increases between 1-5% depending on the treatment, as shown in Figure 8.  
In Table 2, the tortuosity factors for all cells are tabulated. The tortuosity factors increase at the beginning of the treatment (100 h) and relax with time.  
Figure 7: Microstructure development in electrochemically treated cells at 850 \({}^{\circ}\)C and 230 mA/cm\({}^{2}\) in dry fuel conditions after (**a**) 100 h and (**b**) 1000 h and in electrochemically treated cells at 850 \({}^{\circ}\)C and 230 mA/cm\({}^{2}\) in humidified fuel conditions after (**c**) 100 h and (**d**) 1000 h. All scale bars are 10 \(\upmu\)m.
# Analyzing the Impact of Variability and Uncertainty on Power System Flexibility
###### Abstract  
This study investigates the impact of variability and uncertainty on the flexibility of a power system. The variability and uncertainty make it harder to maintain the balance between load and generation. However, most existing studies on flexibility evaluation have not distinguished between the effects of variability and uncertainty. The countermeasures to address variability and uncertainty differ; thus, applying strategies individually tailored to variability and uncertainty is helpful for more efficient operation and planning of a power system. The first contribution of this study is in separating the variability and uncertainty, and determining which is more influential in terms of flexibility in specific system situations. A flexibility index, named the ramping capability shortage probability (RSP), is used to quantify the extent to which the variability and uncertainty affect the flexibility. The second contribution is to generate various scenarios for variability and uncertainty based on a modified IEEE-RTS-96, to evaluate the flexibility. The penetration level of renewable energy resources is kept the same in each scenario. The results of a sensitivity analysis show that variability is more effective than uncertainty for high and medium net loads.  
flexibility; ramping capability shortage probability; renewable energy resource; sensitivity analysis; uncertainty; variability +
Footnote †: journal: Applied Sciences
## 2 AST System Analysis and WSN Framework  
As shown in Figure 1, this paper presents a WSN framework for AST systems. The WSN based AST systems consists of a number of sensor nodes, several cluster head nodes, and additional optional wireless router nodes that help with data aggregation and transmission via wireless multi-hop. Each cluster head node associates some sensor node to create its own subnet for AST implementation in a certain area of the specimen. The monitoring and control of the WSN measurement system must be simultaneous with the AST loading facility. The next sections prove that this WSN framework could completely support low-power, multi-point, and heterogeneous operations with a distributed synchronization mechanism.  
In order to design an efficient WSN system based AST, it is important to understand the critical parameters and design requirements such as testing realizability, timeliness, scalability, and energy efficiency.  
* Structure strain changes under different testing loads are the main testing parameter in the fatigue and static tests. Because these testing results are used to evaluate the mechanical properties of the aircraft structure, the WSN based AST system should have sufficient precision for strain measurement, e.g. \(\pm\)0.1%. For large-scale specimens or a full-scale testing, the number of testing sensors could reach several hundred, therefore, every sensor node should be designed with multi-sensor input channels. In addition, WSN hardware systems must have anti-EMI  
Figure 1: The WSN framework for AST.  
capability, for the electromagnetic interference (EMI) resulting from other field equipment and the environment could adversely affect overall WSN measurements.
* Real-time data acquisition and transmission of strains at different sites on a specimen when a load is applied during the test are essential for realization of the testing function. When the testing engineer might want to query real-time data from some specific nodes to estimate the current status of the particular testing area of a specimen, features might be added to allow breaches in normal network operation to transmit control signals back to the sensing nodes. This could help in eliminating manual node debugging operations and extending testing functions.
* Over the duration of testing, some sensing nodes may fail or their batteries may become depleted. Also, a need may arise for installation of more sensing nodes to monitor particular processes and equipment more closely and precisely. The WSN should be scalable to accommodate changes in the number of nodes without affecting the entire system operation.
* Sensor nodes are autonomous devices that usually derive their power from a battery mounted on each node. It becomes necessary to have an inherent energy-saving means in every component of the WSN system to prolong the lifetime of each node in the network. All layers of the architecture are thus required to have built-in power awareness. DC power might also be used in the AST system to provide the energy for the WSN measurements, so a flexible energy supply should be designed for the WSN system based AST.