esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
In particular, most of the existing work focuses on minimizing economic costs or optimizing the metallurgical grade, but rarely addresses the RHF specific need to account for current inventory levels in order to reduce the frequency of blend ratio changes. They also overlook strict downstream compositional requirements and the fact that each raw material can only be adjusted within a fixed usage range. Although intelligent optimization algorithms have made some progress in solving formulation optimization problems, the optimization of RHF formulation schemes still faces challenges. Traditional formulation schemes based on human experience are usually computationally complex and struggle to balance multiple objectives during production, leading to fluctuations in product quality and reduced production efficiency. In recent years, the Human Learning Optimization (HLO) algorithm, as a novel meta-heuristic algorithm, has demonstrated significant potential in the field of complex engineering optimization. By simulating the mechanisms of random learning, individual learning, and social learning in human learning behavior [7], the algorithm has exhibited advantages in rapid convergence and global search capabilities for solving optimization problems. The research team has continuously improved the HLO framework and [PERSON] et al. introduced a relearning operator to enhance the solving capability for the multidimensional knapsack problem [24]. [PERSON] and [PERSON] proposed the HLO-PSO hybrid algorithm, which integrates particle swarm optimization strategies to enhance adaptability for workshop scheduling problems [25]. At the engineering application level, HLO has seen numerous improved algorithms, such as fuzzy PID optimal control for power plants [26], adaptive hybrid encoding for flame segmentation [27], the optimization of kernel parameters and regularization coefficients for furnace temperature prediction [28], the optimization of support vector regression (SVR) functions for mine blasting operations [29], and the construction of models to simulate the dynamic behavior of ultra-supercritical power plants [30]. These studies not only confirm the strong potential of HLO and its improved algorithms in complex engineering optimization problems, but also demonstrate that its learning mechanism can effectively coordinate the balance among multiple optimization objectives. This unique advantage in solving formulation optimization problems characterized by multi-objective conflicts provides both theoretical support and a technical pathway for the optimization of RHF formulation schemes. Human Learning Optimization (HLO) has demonstrated significant advantages across numerous industrial applications. By emulating human learning processes, HLO can incorporate the heuristic approaches that engineers use when addressing RHF blending challenges. Compared to other metaheuristic methods, HLO is particularly adept at generating strategies that closely align with real-world operational requirements.
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 5 Flexural Performance under Eccentric Load
### Parameters Analysis on Flexural Performance
#### 5.2.3 Influence of Steel Wedge Width  
Steel wedge width weakly affects the flexural performance of DC joints, as shown in Figure 21a; flexural performance increases with steel wedge width. When the width is 25 mm, the joints produce local buckling of the steel wedge at the later stage of loading. Flexural performance is undesirable when the width is 25 mm. As revealed in the relationship curves in Figure 21b, the correlation between steel wedge width and the moment resistance, initial rotational stiffness is linear and positive. The initial rotational stiffness and moment resistance increase by 2.5 MN\(\cdot\)m\(\cdot\)rad\({}^{-1}\) and 18.5 kN\(\cdot\)m, respectively, as the steel wedge width each increase by 5 mm, and their average increase rates are 4.7% and 8.2%, respectively.
## 2 Coding Metamaterials for RCS Reduction
### Coding Metamaterial Parametric Study
#### 2.3.3 Eight Lattices  
Only slight differences were visible for monostatic RCS values when compared to the six-lattice coding metamaterial designs, as demonstrated in Figure 8. Moreover, the three coding sequences exhibited almost identical RCS values for the whole 0 to 18 GHz frequency range. However, the scattering patterns of these sequences were manipulated and exhibited the lowest bistatic RCS value of \(-20.7\) dBm\({}^{2}\) for Coding Sequence 3. It was proven that the scattering patterns can be controlled by adopting various coding sequences. This will save time by writing coding sequences; otherwise, researchers must develop a new design if the constructed metamaterials fail to manifest desired properties. Table 8 illustrates the eight-lattice coding sequences row by row.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Coding Sequence** & **Row 1** & **Row 2** & **Row 3** & **Row 4** & **Row 5** & **Row 6** \\ \hline
1 & 110000 & 001111 & 110000 & 001111 & 110000 & 001111 \\
2 & 110010 & 101101 & 010000 & 111111 & 110010 & 010101 \\
3 & 110110 & 101001 & 010000 & 110010 & 010010 & 011101 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Coding sequences of \(n=6\) for each row.  
Figure 7: Monostatic RCS and bistatic scattering patterns (at 8 GHz) of three different coding metamaterials with six lattices.
# A New String Edit Distance and Applications
## 2 Restricted Forensic Levenshtein Distance
### Details of the RFL Algorithm
#### 2.3.1 A Perspective on Dynamic Programming  
From this point on, a working knowledge of the Levenshtein distance is assumed, with the following perspective. To compute the distance between strings \(a\) and \(b\) with unit edit costs, the base case is that one of the strings is empty. If \(a\) is the empty string then insert each character of \(b\), and the number of those insertions is the cost. If \(b\) is the empty string, delete each character of \(a\), and the number of those deletions is the cost. If the first characters of \(a\) and \(b\) are the same, then delete \(a[0]\) and \(b[0]\) for no cost and the overall distance will be the same as the Levenshtein distance between the remainder of the strings \(a[1:]\) and \(b[1:]\) (denoted as tail(\(a\)) and tail(\(b\)), respectively).  
In case the first characters of \(a\) and \(b\) do not match, the standard dynamic programming algorithm for Levenshtein distance effectively aligns the leading character of \(a\) with the leading character of \(b\), then modifies the leading character of \(a\) to match the leading character of \(b\). Once the leading characters of the strings match, the matching leading characters can be deleted for free.  
For example, in order to compute the Levenshtein distance from AGTCT to GACT, the A must be deleted and the first T switched to an A, for a cost of two. This eyeball calculation only works for simple examples, so Figure 1 illustrates this perspective on the classical Levenshtein algorithm.  
Note that in Figure 1 there could have been an additional T concatenated to the beginning of AGTCT and then immediately deleted. This would have been a valid edit,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Operations Accounted for** & **Classical** & **Naive** & **RFL** \\  & **Levenshtein** & **Forensic** & \\ \hline Single-character edits & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) \\ \hline Pure motif stutter & & \(\checkmark\) & \(\checkmark\) \\ \hline Distinguishing stutter and & & & \\ single-character edits & & & \\ \hline Multi- and single-character & & & \\ edit interactions & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Edits Accounted for in String Edit Distances.  
Figure 1: The steps to compute the Levenshtein distance from AGTCT to GACT.  
but not a minimal one. At each step the minimal cost must be taken, and this will be accomplished via dynamic programming. For a \(n_{a}\)-character string \(a\) and an \(n_{b}\)-character string \(b\), the dynamic programming algorithm constructs an \((n_{a}+1)\times(n_{b}+1)\) matrix, and overall it is \(O(n_{a}n_{b})\) in time complexity.
## 5 Performance Evaluations
### Results and Discussions
#### 5.3.4 Packet Drop Ratio  
Figure 8 shows the average packet drop ratio as a function of the number of nodes in a cluster. In the proposed scheme, the packet drop ratio was low as compared to the SEED protocol.  
Results show that in SEED, packet drop ratio increased with increase in the number of nodes in the cluster. The main reason is the selection criteria of a CH. In case of SEED, the CH was selected based on residual energy only without considering the storage capacity and computational capability of a CH. Therefore, when the buffer of CH became full, the packets drop ratio increased. In this case, more packets were dropped even though the CH had more energy. However, the proposed scheme considered not only energy but the storage capacity as well. Therefore, more packets were accommodated at CH and the chances of buffer overflow decreased. Furthermore, the proposed  
Figure 8: Packet drop ratio as a function of the number of nodes in a cluster.  
Figure 7: Number of control packets as a function of the number of rounds.  
scheme also considered the computational capability of CH. Meaning that packets were processed quickly which as a result decreased the packet drop ratio. Due to the consideration of computational capability in the CH selection process, the chances of buffer overflow due to high incoming packet rate decreased significantly. Consideration of these two parameters in addition to energy (e.g., storage and computational capability) increased the chances of CH to accommodate more packets which in turn reduced the packet drop ratio.  
Furthermore, situation(s) may arise where buffer overflow occur due to huge traffic volume. Despite the fact that the proposed scheme considered computational capability and storage capacity, it may fall short for heavy traffic scenarios, meaning that in heavy traffic conditions, CH may not accommodate incoming traffic and may result in packets drop. To overcome this situation, the proposed scheme utilized the additional features e.g., CH-acquaintanceship and CH-friendship. We evaluated the effect of CH-acquaintanceship and CH-Friendship features for heavy traffic scenarios by increasing the packet rate in the network. The packet generation rate was increased by 20 times (e.g., on average a node was transmitting 200 packets/s). As shown in Figure 8, the proposed scheme significantly reduced the packet drop ratio in case of heavy traffic conditions. The main reason is that the proposed scheme employed the CH-acquaintanceship and CH-friendship feature which allows the low resources CH to share its load with resourceful CH in its transmission range to reduce the packets drop ratio. Therefore, in the proposed scheme when there was a sudden increase in traffic load on the CH, it started sending the packets towards neighboring CH. By doing so, the CH's storage got free and there were rare chances of packet drop on that CH. In contrast, in SEED, the packet drop ratio increased abruptly, since no CH-acquaintanceship and CH-Friendship mechanism were employed in this case.
# Convergence Check Phase-Field Scheme for Modelling of Brittle and Ductile Fractures
## 5 Numerical Examples  
After derivation of the CCPF algorithm, the proposed scheme is verified on the usual benchmark examples from the literature. The verification consists of examples dealing with brittle and ductile fractures in monotonic loading regimes as well as problems of low-cyclic (LCF) and high-cyclic (HCF) fatigue. The results obtained were compared with the RCTRL algorithm. All examples were computed on the Supermicro workstation with 1 TB of RAM, 80 CPUs, and a clock speed of 3.1 GHz. The examples with brittle phenomena were used for the demonstration of the computational speed of the CCPF scheme compared to the RCTRL scheme. Hence, besides the numerical results and crack topology, computational time and number of iterations per increment were compared. After demonstrating the validity of the proposed CCPF framework on the problem of brittle fractures, an example considering elastoplastic behaviour and damage was tested. The last two benchmark problems deal with fatigue where the CT specimen was subjected to LCF and HCF regimes.
# Study and Analysis of Interference Signals of the LTE System of the GNSS Receiver
## 4 Simulation Studies
### Test Material and Research Methods  
The receiver, which is the test object, has 12 parallel tracking channels, which allows for the simultaneous reception of navigation information from up to 12 visible satellites. The signals received by the antenna of the receiver were supplied from the array of antennas.  
According to the manufacturer's data, this receiver determines the position with an error not exceeding 15 m. It also has the ability to work in DGPS (Differential Global Positioning System) mode, which increases the precision of its indications to 3-5 m, and can also use WAAS/EGNOS (Wide Area Augmentation System/European Geostationary Navigation Overlay Service), thanks to which its error does not exceed 3 m.  
This receiver provides information about the current position and time with a frequency of 1 Hz. The aim of the conducted tests was to check the repeatability of the position indicated by the receiver in static and dynamic conditions in the case of reception of the signal provided by LTE system base stations. During the conducted tests, both stationary and dynamic, the same measurement set was used, which consisted of the following elements:  
* The tested Garmin 16-HVS GPS receiver with wiring;
* A portable PC.  
These elements were connected by means of adapted cabling, using the popular RJ 45 and RS 232 connectors. For communication between the computer and the GNSS receiver, the Hyper Terminal program, which is an application of the Windows environment, was used.  
The research on the processing of navigation signals in the correlator of the GNSS receiver along with the reception of the LTE signal was carried out twice. The first attempt was made when the receiver was placed on the roof of a residential building with a height of about 10 m, i.e., in conditions of very good visibility of the celestial sphere. On the other hand, the second attempt was carried out under less favorable conditions. The tested receiver was mounted on the roof of the car cab, parked in a partially wooded place, and surrounded by a brick building, which significantly reduced the visibility of the celestial sphere.  
In both cases, the conducted stationary tests consisted of the continuous measurement of the position by the receiver for about 120 min. The obtained test results were recorded in the computer memory in the form of text files.
# System Design and Experimental Study of a Four-Roll Bending Machine
## 3 Design
### Roll Bending Simulation  
To verify the feasibility of roll bending forming for symmetric roller configurations, this study established an explicit dynamic analysis model using the ABAQUS platform. In the model, the trajectory lines of the side roller and bottom roller are arranged at a 25\({}^{\circ}\) inclination angle. The rollers are defined as 45 Cr alloy structural steel (modulus of elasticity 206 GPa, Poisson's ratio 0.30, density 7850 kg/m\({}^{3}\)), while the profile material is selected as aluminum alloy 6063 (modulus of elasticity 67.4 GPa, Poisson's ratio 0.33, density 2700 kg/m\({}^{3}\)). The contact friction coefficient between the rollers and the profile is defined as 0.5, and partial data on the relationship between yield stress and plastic strain for the profile material are shown in Table 2. Multi-step roll bending simulations were conducted by parametrically controlling the displacement of the side rollers (14 mm, 21 mm, and 26 mm). The meshing details are shown in Table 3 and Figure 11. As shown in Figure 11 and Table 4, the simulation results indicate: Within the specified displacement range, the roller system achieves stable progressive bending deformation, validating the geometric adaptability and process feasibility of the symmetric configuration. It is important to note that while these findings substantiate the rationality of the structural design, the actual bending accuracy requires further physical testing due to limitations inherent in the rigid body assumptions and idealized contact conditions of the simulation model.
# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 3 Novel Self-Adaptive VM Consolidation Strategy  
### Dynamic Multi-Thresholds (DMT)  
As mentioned earlier, different types of applications may share one physical resources, fixed thresholds are not suitable for environments with dynamic workloads. Thus, the system should adaptively adjust its behavior by considering different system workloads. Based on that, we propose a novel adjustment mechanism for threshold-based VM consolidation by comparing the current threshold of each defined parameter with their future value, which is predicted by forecasting each VM's future utilization on CPU, RAM and Network bandwidth. To further efficiently minimize the consumption of energy during each consolidation, two other parameters of SLA and VM migration costs are taken into consideration. Thus, our proposed dynamic multi-thresholds can be represented as follows:  
\[T=\left\langle T_{l},\ T_{u},\ T_{SLA},\ T_{m}\right\rangle \tag{1}\]  
The multi-thresholds \(T\) is defined by a combination threshold of lower utilization \(T_{l}\), upper utilization \(T_{u}\), SLA violation ratio \(T_{SLA}\) and the number of VM migration \(T_{m}\). Below \(t_{c},\ t_{r},t_{b}\) represents the lower thresholds of CPU utilization ratio, RAM utilization ratio and Network bandwidth consumption, respectively, while \(T_{c},\ T_{r},T_{b}\) represents the upper ones.  
\[T_{l}=\left\langle t_{c},\ t_{r},t_{b}\right\rangle,\ T_{u}=\left\langle T_{c},\ T_{r},T_{b}\right\rangle \tag{2}\]  
The adjustment of dynamic multi-thresholds to timely fit the whole cloud resources can be simplified into two main parameters, including \(T_{l}\) and \(T_{u}\). These two thresholds can be efficiently calculated by forecasting each future utilization ratio based on Linear Regression (Section 3.1.1). While the parameter of \(T_{SLA}\) can be viewed as a limitation factor of dynamic threshold by deciding whether to change current threshold or not (Section 3.1.2). \(T_{m}\) will be discussed to further improve the selection of VM in Section 3.2.
## 2 HSI: Basic Concepts
### HSI: Device Components
#### 2.1.3 Detectors  
After interacting with the sample, the light carrying the useful information will be acquired by the detector. Detectors convert photons into electrical signals so that the spectral signature can be interpreted. The most common solid-state detectors are charge-coupled devices (CCDs) and the complementary metal-oxide-semiconductor detectors (CMOS). CCD and CMOS sensors comprise many light-sensitive units (photodiodes) whose composition changes (silicon--Si, germanium--Ge, indium gallium arsenide--InGaAs, and mercury cadmium telluride--HgCdTe) according to the spectral range of detection [4; 10].  
Ge and InGaAs detectors have a suitable energy bandgap for detecting optical signals between 1000 and 1600 nm and 900 and 1700, respectively. Finally, HgCdTe detectors can cover a variety of infrared wavelength ranges, including short/mid- and long-wavelength IR. This versatility makes them suitable for applications ranging from remote sensing to thermal imaging. Silicon detectors are sensitive to UV, visible, and shortwave NIR spectra regions (400-1000 nm), they are cheap, can be miniaturized, and allow high-speed measurements with low noise and a good spectral response. For these reasons, silicon-based CCD cameras have been widely used in hyperspectral imaging systems for the inspection of agricultural products and food quality [4; 12].
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 4 Simulation Experiment
### Scenario 1  
This scenario involved a simulation of the GNSS signal being subjected to different degrees of interference or occlusion during the driving of an unmanned vehicle in obstructed spaces. The two time periods were as follows:  
Period 1: a time period between 250 s-400 s, where the GNSS signal was weakly interfered with or blocked, and a 10-fold measurement error was added to the GNSS positioning measurement, as shown in Equation (32):  
\[pos_{GNSS}=pos+10\cdot d_{GNSS}\cdot randn(6,1) \tag{32}\]  
Period 2: a time period between 600 s-750 s, where the GNSS signal was strongly interfered with or blocked, and a 20-fold measurement error was added to the GNSS positioning measurement, as shown in Equation (33):  
\[pos_{GNSS}=pos+20\cdot d_{GNSS}\cdot randn(6,1) \tag{33}\]  
Here, \(pos\) represents the true position information, \(d_{GNSS}\) represents the attitude error of the visual navigation, and \(randn(6,1)\) represents a \(6\times 1\) matrix of a standard normal distribution. The comparison between the improved AFKF proposed in this paper and a classic FKF is shown below. The results of the INS/GNSS/VNS integrated navigation position and velocity errors are shown in Figure 3a,b, and the three-axis position and velocity errors are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mean Error** & **Classic FKF** & **Improved AFKF** \\ \hline Position (E) & 6.7352 m & 2.5032 m \\ Position (N) & 7.7326 m & 3.0650 m \\ Position (U) & 8.9662 m & 2.7692 m \\ Velocity (E) & 0.1085 m/s & 0.0220 m/s \\ Velocity (N) & 0.1070 m/s & 0.0236 m/s \\ Velocity (U) & 0.1141 m/s & 0.0360 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Navigation error statistics results of classic FKF and improved AFKF.  
Figure 3: Comparison of navigation errors between improved AFKF and classic FKF: (**a**) Comparison of position errors. (**b**) Comparison of velocity errors.  
It can be seen that in periods 1 and 2, when the GNSS was subjected to different degrees of interference or obstruction, the improved AFKF proposed in this paper performed significantly better than the classic FKF. In all periods, compared with the classic FKF, the three-axis position average error of the improved AFKF decreased by 62.8%, 60.4%, 69.1%, and the three-axis velocity average error decreased by 79.7%, 77.9%, and 68.4%, respectively.