esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 3 The Theory of Shop Scheduling Problems
### Shop Layouts
#### 3.2.7 Cellular Manufacturing System (CMS)  
_The cellular manufacturing systems_ (CMS) have been introduced as an efficient layout in the philosophy of _just-in-time_ and _lean manufacturing_ which concerned with optimizing processes for time and value. To meet increasing demand for mid-volume and mid-diversity product mixes, CMS is designed as a hybrid of job shops and flow lines. CMS have cells containing grouped machines (or operators, workstations, etc.) that process jobs [80, 81]. Jobs are grouped in part-families and ideally do not leave their cell [80].  
The scheduling of cellular manufacturing is often referred to as flow shop group scheduling [82] and sometimes distinguishes inter-cell scheduling from intra-cell scheduling [80, 83]. The scheduling problem includes cell formation, assigning machines to each cell, and general task scheduling [84].
## 2 Related Works
[40] leveraged the NSL-KDD dataset to show the application of decision tree models in detecting APT attacks with high detection accuracy. [PERSON] et al. [39] used an ensemble deep learning-based cyber-attack detection method specifically designed for industrial control systems; the outcome of their work yielded good results, but they recommended the use of real-time datasets with properly defined attack types. In [41], the authors use a new approach to determine DDoS attack patterns on SCADA systems using machine learning applied three machine learning techniques using the KDDCup'99 dataset, but recommend using a real-time SCADA dataset for improved results. The characteristics, features, and operations of Stuxnet and APTs were reviewed by [42], in addition to highlighting their recent trends, attack features, and prediction of future attacks. [PERSON] and [PERSON] [43] investigated packet pilfering, injection attacks through HTTP, and command injection attacks. They were focused on DDoS flood attacks without the capability of modifying packets sent by other hosts as in the case of MitM attacks. [PERSON] et al. [44] in their work, Deep Learning-Aided Cyber-Attack Detection in Power Transmission Systems, noted the need to consider multiple sources of uncertainty and variations from subsystems renewable energy, smart grids, and so on. Furthermore, [PERSON] et al. [45] in their Survey of Attack Projection, Prediction, and Forecasting in Cyber Security, emphasized the need for attack forecasting to minimize the impact on systems. Researchers had used different machine learning approaches with datasets such as NSL-KDD and KDD CUP'99 to address the issue of cyber-attacks in the past. These datasets have existed for a long time and may not be a full representation of modern-day dynamic attack modes. Hence, the products of such research may not be relied upon for the detection of recent amorphous attacks. Consequently, recent works have either emphasized the use of facility-specific datasets, as argued in this paper, or the use of recent and updated datasets such as the WUSTL-2018 [35], ORNL PowerGrid [46], and the TON_IoT datasets [47]. Table 1 summarizes the recent related works and research gaps that motivated this work. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Reference** & **Major Findings** & **Limitations** \\ \hline \multirow{4}{*}{Pu et al. [16] positives} & \multirow{4}{*}{Unsupervised clustering-based anomaly detection method to minimize false positives} & i. Identical and repeated NSL-KDD datasets which affect the learning ability of the algorithm and the final output \\  & & of the algorithm and the final output \\ \cline{1-1}  & & ii. possibility of generating lots of false positives which could deceive real network traffics \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related Studies and identified research gaps.
# Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study
## 3 Testbed Design and Test Cases
### Testbed Design
#### 3.1.3 SDR Environment  
We use an SDR connected to a laptop to implement eavesdropping, running the free GNU Radio toolkit. We chose GNU Radio due to the comprehensive support of the LoRa modulation libraries by this platform. Specifically, we use the Gr-lora library [36] to capture on-air communication. As the SDR, we chose the RTL-SDR USB device [37], which, albeit being a relatively low-cost solution, covers the frequency band from 24 MHz up to 1766 MHz. The SDR has been equipped with an omnidirectional antenna with a gain of 3 dBi.
# Prevalence and Predictors of Osteoporosis Among the Chinese Population in Klang Valley, Malaysia
## 2 Materials and Methods
BMI was classified as underweight (<18.5 kg/m\({}^{2}\)), normal (18.5-24.9 kg/m\({}^{2}\)), and overweight (>24.9 kg/m\({}^{2}\)) for those aged <65 years [16]. Conversely, for subjects aged >65 years, a BMI <22 kg/m\({}^{2}\) was classified as underweight, 22-27 kg/m\({}^{2}\) as normal, and >27 kg/m\({}^{2}\) as overweight [17]. Subjects' waist circumference was measured between the lowest rib margin and the iliac crest while they maintained their standing position [18]. \begin{table}
\begin{tabular}{c c} \hline
**Inclusion Criteria** & **Exclusion Criteria** \\ \hline Malaysian aged \(\geq\)40 years & Previously diagnosed with metabolic bone diseases ([PERSON]’s disease, \\  & osteogenesis imperfect, osteomalacia, rickets) \\ \hline  & Diagnosed with conditions that alter bone metabolism \\  & (hypo-/hyper-calcemia, hypo-/hyper-thyroidism, \\  & hypo-/hyper-gonadism) \\ \cline{2-3}  & Receiving therapeutic agents that alter bone metabolism (thiazide \\  & diuretics, glucocorticoids, thyroid supplements, anticonvulsants, \\  & antidepressants, osteoporosis treatment agents, etc.) \\ \cline{2-3}  & Having mobility problems, need a walking aid, fractured six months \\  & prior to the screening date, having metal implants at the calcaneus, hip, \\  & spine, or femoral neck \\ \hline \end{tabular}
\end{table} 
Table 1: Inclusion and exclusion criteria used for this study. BMD at the lumbar spine (anteroposterior, L1-L4) and left hip, lean body mass, and fat mass were assessed using total body dual-energy X-ray absorptiometry (DXA) (Hologic Discovery QDR Wi densitometer, Hologic, MA, USA). The BMD values (in g/cm\({}^{2}\)) were converted to a T-score based on the reference values of the Singaporean Chinese population. Bone health status of the subjects was classified according to the World Health Organization (WHO) criteria into osteoporosis (T-score \(\leq\)-2.5), osteopenia (T-score \(\leq\)-1 and \(>\)-2.5), and normal (T-score \(>\)-1.0). The DXA measurement was performed by an experienced technologist. Daily calibration was conducted using a phantom. The short-term in vivo coefficient of variation for this device was 1.8% and 1.2% for lumbar spine and total hip, respectively.
## 3 Critical State Model for Clean Sand  
In the work of [PERSON] et al. [14], the NCL was used as a key tool to distinguish between loose and dense sand, using as a reference control line (RCL). Therefore, in this case, the terms loose and dense refer to sands that are looser or denser than the RCL. Due to changes in the RCL and CSL, some revisions were required for the model presented in [14]. Before describing the revisions in detail, a clear summary of sand behavior for both loose and dense conditions is stated as follows:  
1. Sand on RCL exhibits only volume contraction during shearing, and the stress ratio \(\eta=q/p\) reaches its maximum value at the critical state, where \(\eta=M_{c}\) and \(M_{c}\) is the slope of the critical state line on \(p\)-\(q\) plane.
2. Sand that is looser than the RCL (sheared from a point above the RCL) exhibits only volume contraction, but it has a larger amount than sand on the RCL, it finally reaches the same maximum stress ratio as sand on the RCL at \(\eta=M_{c}\).
3. Sand that is denser than the RCL (sheared from a point below the RCL) exhibits volume contraction in early stage, followed by dilation (the stress ratio is greater than the maximum stress ratio of sand on the RCL, i.e., \((\eta=M_{f})>M_{c}\), and finally converges to the stress ratio \(\eta=M_{c}\).  
It is evident that both sand on RCL and sand looser than RCL have no peak but reach the same maximum stress ratio at the critical state, while a peak exists in dense sand during shearing before reaching the critical state. Figure 9 depicts the concept mentioned above.  
Figure 8: Shape and location of the CSL and RCL: (**a**) CSL is derived from the NCL; (**b**) RCL is derived from the CSL.  
Figure 9: Schematic drained stress–strain curves of sand at different initial state.
## 1 Introduction
The traditional operator-monitored approach is therefore unsuitable for industry 4.0 and hinders targeted lean manufacturing. It has been proven that a proper lubrication regime acts as a damping system that reduces the overall vibration of a machine [13; 14]. It can therefore be hypothesized that the vibrational behavior of a machine will inevitably change as its lubrication condition deteriorates. In general, when the operating condition is healthy, the machine generates relatively low vibrations. The opposite occurs when the lubrication condition deteriorates. Vibration analysis has therefore shown feasible potential for use in condition monitoring and fault diagnosis of industrial machines. [PERSON] et al. [15] showed that vibrational analysis accurately performed fault diagnosis and provided insights to improve the fatigue designs of wind turbines. [PERSON] et al. [16] developed an online condition monitoring technique capable of identifying cracks in a rotor's shaft by monitoring the vibrational changes in the rotor's dynamic behavior. Although vibrational analysis has thus far proven to be a powerful tool for condition monitoring, it has been mainly used for the monitoring of rotating machines. This study explored the use of vibration analysis for condition monitoring of a toggle clamping system of a plastic injection molding machine. Two commonly used vibration analysis health indicators (HI) were initially implemented to observe their abilities to isolate vibrational signatures indicative of lubricant degradation in the toggle clamping system. These two health indicators (HI) included a time domain-based root mean square (RMS) health indicator, and a frequency domain-based frequency center (FC) health indicator. Due to their limitations, we derived a third, structural response-based shock response spectrum health indicator. This final approach proved to be the most suitable for monitoring the lubrication condition of the toggle clamping system.
## 4 Example Verification
### Feature Vector Selection
According to the above methods, the input eigenvectors with 41 parameters, i.e., the vibration of the oil receiver in the Y direction, the vibration of the stator frame in the Y direction, the vibration of the combined bearing in the Y direction, the vibration of the water guide bearing in the Y direction, the temperatures of the thrust bearing bushes from No.1 to No.12, the stator coil temperatures from No.1 to No.12, air cooler hot air temperatures from No.1 to No.6, stator core temperatures from No.1 to No.12, and the unit flow, are preliminarily obtained. To reduce the influence of redundant variables, the above parameters are further analyzed. The time-domain variation curves of some monitoring parameters are shown in Figure 4.  
From Figure 4, these monitoring parameters are highly correlated, indicating that they have the same roles in reflecting the operation state of the unit. Therefore, one of the parameters is selected as the input feature. Through linear correlation analysis, nine parameters, including the vibration of oil receiver, the stator vibration in the Y direction, the vibration of combined bearing in the Y direction, the vibration of water guide bearing in the Y direction, the temperature of No.1 forward thrust bearing bush, the temperature of No.1 stator coil, the temperature of No.1 air cooler hot air temperature, the temperature of No.1 stator core and unit flow rate, are selected as final input eigenvectors.
# Performance of Strengthened Accelerated Oscillator Damper for Vibration Control of Bridges
## 2 Topological Configuration and Motion Equations of the SAOD-LS
### Validation of SAOD-LS Motion Equation and Its Performance Comparison with AOD  
To assess the correctness of the SAOD-LS equation of motion and to compare its damping effect with that of AOD, we utilize Example 1, whose parameters are listed in Table 1.  
In Example 1, the theoretical displacement solution of \(m_{1}\), designated as \(x_{1}\)-Case 1, is derived using Equation (9). To validate the motion equation of SAOD-LS, we utilize the FE approach, and the result is denoted as \(x_{1}\)-Case 2. In this FE modeling, \(m_{1}\) and \(m_{2}\) are represented using mass elements; the connecting rod is represented using a link element. Regarding the modeling method and boundary conditions, the one terminal of \(k_{1}\) and \(c_{1}\) is connected to \(m_{1}\), while another terminal is connected to the ground. The transmission device is emulated by controlling the forced displacement relationship between \(m_{1}\) and \(m_{2}\). The one terminal of \(k_{2}\) and \(c_{2}\) is connected to \(m_{1}\), while another terminal is connected to \(m_{2}\). Additionally, the time-history displacement results of AOD, obtained through the FE method, are labeled as \(x_{1}\)-Case 3. The parameters for Case 3 are detailed in Table 1, with the only distinction being that Case 3 employs the AOD structure instead of the SAOD-LS used in Case 1 and Case 2. The results are visually represented in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \(m_{1}\) (kg) & \(m_{2}\) (kg) & \(k_{1}\) (N/m) & \(k_{2}\) (N/m) & \(c_{1}\) (Ns/m) & \(c_{2}\) (Ns/m) & \(x_{1}\) (0) (m) & \(v_{1}\) (0) (m/s) & \(f_{load}\) (Hz) & \(a\) (m/s\({}^{2}\)) & \(r\) \\ \hline
1000 & 40 & 100,000 & 4000 & 100 & 40 & 0 & 0 & 1.27 & –3.8 & 4 \\ \hline \end{tabular}
\end{table}
Table 1: Values of parameters in Example 1.  
The maximum discrepancy between \(x_{1}\)-Case 1 and \(x_{1}\)-Case 2 is 0.143 mm. The consistency observed between Case 1 and Case 2 suggests that both the SAOD-LS motion equation and its analytical solution are accurate. Additionally, only in Case 1 and Case 2 of this paper, the peak displacement of \(m_{1}\) in SAOD-LS is noticeably smaller compared to that of AOD. Only by adjusting the AOD connection mode, without any modifications to the model parameters, can we achieve this reduction. Specifically, by comparing Equation (5) from this study with Equation (10) from [39], one can observe that the amplification factor for \(c_{2}\) in C of SAOD-LS has been enlarged from \(r^{2}\) to \((r+1)^{2}\), similarly to the augmentation of \(k_{2}\) in \(K\).
## 2 Islanding Detection Methods
### Computational Intelligent Based Methods  
The accuracy of islanding detection can be increased using signal processing techniques. However, when the DG system is more complex, signal processing methods fail to completely minimize the NDZ. In such cases, the performance can be enhanced by adding intelligence to the islanding detection relay. Computational intelligent methods can be used for islanding detection by handling multiple parameters simultaneously. The selection of threshold values is not required using those methods, but a large computational burden exists. Below is a discussion of the computational intelligent-based methods.
# Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network
## 3 Route Nonuniformity Path Modelling
### How to Measure Route Feature Values for RA Calculation  
Since connected time and disconnected time characterize nonuniformity of the route, they are called \"route feature values\" in this paper. Methods of measuring route feature values for AODV and OLSR are described below. The algorithm used to calculate RA is also presented.
## 3 Polymerization Reactions within AAO Templates
### Radical Polymerization
The authors selected imidazolium-based ionic monomer 1-octyl-3-vinylimidazolium bis(trifluoromentanesulfonyl) imide ([OVIM][NTf2]) [129] as the sterically hindered ionic monomer and found noticeable differences between nanomembranes and high-pressure systems, although, for both systems, a significant increase in the control of the reaction and the polyelectrolyte properties was found. Moreover, they also found that the molecular weight increased when the reaction was carried out in AAO nanocavities of 35 and 150 nm in diameter, although the dispersity was slightly broader (D: 2.2-2.7) compared to bulk polymerization. Copolymerization of butyl methacrylate and 2-hydroxyethyl acrylate. [PERSON] et al. extended the study of polymerization reactions in confinement within restricted geometries of AAO templates to the free radical copolymerization of butyl methacrylate (BMA) and 2-hydroxyethyl acrylate (HEA) monomers in order to obtain BMA-HEA copolymer nanos  
Figure 8: (**a**) Raw isothermal DSC data. (**b**) Time evolution of the monomer conversion determined from DSC measurements. As an inset in panel (**b**), the dependence of the polymerization rate constant vs. pore diameter is presented [128]. Copyright 2016. Reproduced with permission from the publisher: RSC. tructures with tunable mechanical characteristics and swelling/wetting properties [130; 131]. For an initial molar composition of 0.45/0.55, with AIBN as the initiator, at 70 \({}^{\circ}\)C, the copolymerization reaction within AAO templates of 70 nm diameter led to high conversion (100%) (Figure 9). Moreover, the BMA-HEA copolymer obtained under confinement showed a similar molecular composition to the initial one. The reactivity ratios of the copolymerization reaction were determined for the P(BMA/HEA) copolymer synthesized in confinement at different molar compositions. HEA is the most reactive monomer in the copolymerization reaction in confinement and in bulk, however, in the restricted geometry of the AAO template, the reactivity ratios shift slightly away from the preferential incorporation of HEA. In addition, [PERSON]'s modulus and wetting behaviors were significantly modified, and the obtained BMA-HEA copolymer nanopillar structures showed interesting soft surface and significant swelling capacity.
## 2 Quantum Key Distribution
### Vulnerabilities in Protocol Implementation  
BB84 security proofs assume several idealities, including perfect on-demand single photon sources, lossless quantum transmission, perfect transmitter-receiver basis alignment, and perfect single photon detection [18]. However, these security assumptions are not valid when building real-world systems which deviate from theoretical protocols [2]. For example, reliable on-demand single photon sources are not currently available nor are they expected in the near term [1]. Therefore, most QKD systems attenuate classical laser pulses down from millions of photons to weak coherent pulses with an average photon number less than one. More specifically, the number of photons contained in the pulse is represented using a Poisson distribution with a low (i.e., <1) Mean Photon Number (MPN)  
\[P(n|\mu)=\frac{\mu^{n}e^{-\mu}}{n!}, \tag{1}\]  
where \(\mu\) is the average number of photons in a pulse (i.e., \(\mu\) is the MPN) and \(n\) represents the number of photons in the pulse (i.e., \(n=0,\ 1,\ 2,\ 3,\ \dots,\ \mathrm{N}\)). For example, with a typical MPN, \(\mu=0.5\), nearly 60% of the pulses have zero photons, 30% of the pulses have one photon, and 9% of the pulses have two or more photons. This means nearly 23% of the non-empty pulses emitted by Alice are non-ideal multiphoton pulses which leak information about the \"unconditionally secure\" QKD-generated secret key to eavesdroppers. This introduces a significant security vulnerability into the QKD protocol.  
\begin{table}
\begin{tabular}{c c c|c c} \hline \multicolumn{3}{c|}{**Alice Prepares**} & \multicolumn{2}{c}{**Bob Measures**} \\ \hline
**Bit** & **Basis** & **Prepared State** & **Basis** & **Result** \\ \hline
0 & \(\oplus\) & \(|\leftrightarrow\) & \(\oplus\) & 0 \\
1 & \(\oplus\) & \(|\updownarrow\rangle\) & \(\oplus\) & 1 \\
0 & \(\oplus\) & \(|\leftrightarrow\rangle\) & \(\otimes\) & _random_ \\
1 & \(\oplus\) & \(|\updownarrow\rangle\) & \(\otimes\) & _random_ \\
0 & \(\otimes\) & \(|\
warrow\) & \(\oplus\) & _random_ \\
1 & \(\otimes\) & \(|\
warrow\) & \(\oplus\) & _random_ \\
0 & \(\otimes\) & \(|\
warrow\) & \(\otimes\) & 0 \\
1 & \(\otimes\) & \(|\
warrow\) & \(\otimes\) & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Example BB84 Protocol.
## 2 Related Work
Building on the insights and limitations identified above, this paper introduces a new clustering scheme that integrates UAV-assisted data collection and WPT to address key challenges in WSN-based smart irrigation systems, including energy efficiency, hotspot mitigation, and network longevity, as outlined in the following contributions. 1. New clustering scheme: The proposed scheme divides the network into two distinct regions; standard nodes in the central region and advanced nodes in the outer region, further organized into four clusters. This design balances energy consumption and optimizes data aggregation. 2. UAV-assisted data collection and charging: the scheme leverages a UAV as a mobile sink to traverse the network, collect data from optimized clusters, and wirelessly charge helper nodes using WPT, thereby enhancing network longevity. 3. Hotspot problem mitigation: by optimizing data collection rates and adaptively selecting cluster heads based on available energy, the proposed scheme effectively reduces energy imbalances and extends the lifetime the network. 4. Performance evaluation through simulations: extensive simulations demonstrate that the proposed scheme outperforms conventional clustering and energy management techniques, achieving lower energy consumption, a reduced number of dead nodes, and improved network lifetime. 5. Application in smart irrigation: the scheme is applied to a smart irrigation system, where it maintains stable soil moisture levels and reduces water consumption by approximately 20% compared to conventional irrigation methods.