esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Adaptive Time-Frequency Analysis
### Variational Mode Decomposition and Optimization Models
#### 3.2.1 Variational Mode Decomposition
Moreover, while the parameters of VMD, such as the penalty factor and bandwidth constraint, necessitate manual calibration, their controllability opens up avenues for extensive optimization in signal processing. Third, by formulating the signal decomposition as an optimization problem and achieving an optimal solution through iteration, VMD ensures algorithm stability and demonstrates strong noise resistance, effectively reducing noise-induced perturbations in decomposition results. For example, in the domain of bearing fault diagnosis, VMD adeptly isolates fault characteristic frequencies, preserving high decomposition accuracy even in noisy environments. Conversely, VMD is not without its limitations in structural health signal processing. First, its efficacy is contingent upon the judicious selection of parameters. Despite its adaptive prowess, the performance of VMD is significantly influenced by the choice of parameters, including the penalty factor (\(\alpha\)) and the number of modes (\(K\)). The determination of these parameters often necessitates empirical insight or refinement through optimization algorithms, thereby escalating the complexity and application challenge of the algorithm. For instance, in the analysis of acoustic emission signals emanating from centrifugal pump cavitation, inappropriate parameter configurations may engender inaccurate decomposition results, thereby impeding the extraction of fault features [89]. Second, VMD is encumbered by computational complexity and time costs. While its iterative optimization process ensures decomposition stability, it also imposes substantial computational demands. When confronted with prolonged signals or high-sampling-rate data, the computational onus of VMD escalates markedly, thereby constraining its applicability in real-time monitoring systems. Finally, VMD is susceptible to the influences of noise and spurious components. Notwithstanding its noise resistance, decomposition results may still be corrupted in high-noise environments. Furthermore, the decomposition process of VMD may introduce spurious components, which not only exacerbate the complexity of signal processing but may also obscure genuine fault features.
## 3 The October 2016 Seismic Crisis  
The 2016 October seismic crisis involved a series of seismic events that affected a large area of Italian territory located along the central Apennines, between the Region of Lazio (L), Umbria (U), Marche (M) and Abruzzo (A), from August 2016 to January 2017. The earthquake characteristics are expressed in terms of magnitude, in order to have an overall measure of the energy released during the seismic crises.  
The first strong earthquake occurred on 24 August (Mw = 6), with the epicentre positioned in the Tronto Valley, between the historical centres of Arquata del Tronto (M) and Accumoli (L) [25]. The village of Amatrice was utterly destroyed, causing hundreds of casualties and people left injured and homeless. During the earthquake swarm, two strong aftershocks took place: the first on 26 October (Mw = 5.9) with epicentre in the Umbria-Marche borders, between the villages of the province of Macerata di Visco (M), Ussita (M) and Castelsantangelo sul Nera (M), while a second on 30 October (Mw = 6.5) with epicentre between the cities of Norcia (U) and Preci (U) [26]. This last event caused the collapse of several buildings and above all churches in a wide area, such as the basilica of San Benedetto in Norcia and San Salvatore's church in Campi Bas  
Figure 1: Methodological framework adopted in the present work.  
30 October did not cause any casualties in Campi Alto di Norcia, because residents were evacuated from the city immediately after the previous seismic events. Other aftershocks took place in January 2017 with a lower magnitude. Table 1 shows the evolution of the 2016-2017 earthquake sequence, identifying the position of all the mainshocks and the epicentre areas.  
The main shocks, that occurred in Campi Alto between 26 and 31 October 2016, are extrapolated from the earthquake's strong ground motion and post-processed. Table 2 shows the maximum peak ground acceleration (PGA) registered by the Campi station for the different seismic events analysed. It is not correct to compare single PGAs with forecasted shakings (PGA with 10% probability of exceedance in 50 years), since probabilistic models cannot be validated (or rejected) on the basis of a single event, but this process is useful to understand the consistency of the model suggested by the Italian Code [27]. In this sense, the maximum PGA is much higher than the one generally used for the structural analysis of the buildings located in this area and, therefore, provide important information about the action to which the structures were subjected and the resulting damage.
## 3 Relevant Research in Wind Farm Design and Optimization
### Sub-Modeling
#### 3.2.2 Structural Fatigue and Degradation Modeling  
Most of the published works on the WFDO problem have ignored both the environmental and the mechanical factors that produce unfavorable effects on the energy conversion efficiency such as the WT aero-structural loading and structural fatigue degradation (which is commonly related to O&M costs). Although the effects of TI and unsteady aerodynamics on structural fatigue loading have been observed and have been the subject of several studies [59,290,302], a general model to account for its overall effects has not yet been developed and validated. The reason is probably related to the limited state of knowledge on their effects and the impossibility of describing them through simple mathematical expressions.  
The only existing approach to model structural fatigue and degradation impacts in the wind farm financial balance was proposed by [PERSON] [166,279]. [PERSON]'s model rely on the accurate estimation of the mean accumulated equivalent fatigue load (\(L_{eq}\) [Pa]) for each WT in the wind farm and for each of the WT structural members, which can be calculated, as shown in Equation (42), by summing all possible contributions from individual load cases and while taking into account the probability of occurrence of each load case and the total number of equivalent load cycles the WT will undergo during its expected operational lifetime:  
\[L_{eq}=\left[\frac{1}{n_{eq,L}}\sum\
olimits_{j=1}^{n_{WD}}\sum\
olimits_{i =1}^{n_{WS}}R_{eq}\big{(}U_{i,j}\big{)}^{m}n_{eq}\ p\big{(}U_{i,j}\big{)}n_{T} \ \Delta_{n_{WD}}\Delta_{n_{WS}}\right]^{\frac{1}{m}} \tag{42}\]  
where \(R_{eq}\) [Pa] is the equivalent load for the load case [279], defined by the mean wind speed \(i\) in the direction \(j\) (\(U_{i,j}\)), \(m\) is the Wohler exponent [29] for the WT member considered, \(n_{eq}\) is the number of 600 s cycles corresponding to \(R_{eq}(U)\). \(p(U)\) is the probability for the load case, \(n_{T}\) is the number of 10-min cycles corresponding to the wind farm expected lifetime, \(n_{eq,L}\) is the lifetime equivalent number of cycles. \(n_{WD}\) and \(n_{WS}\) are defined as the number of discretized wind directions and wind speeds during the calculation. \(\Delta_{n_{WD}}\) and \(\Delta_{n_{WS}}\) are defined as wind direction and wind speed differentials.  
As suggested by [PERSON] _et al._[166], a simplified approach can be implemented, in which an extensive database of generic individual load cases is pre-calculated using aero-elastic simulations [303] and subsequently used during the WFDO process. The database should encompass several different situations in which the WT under consideration operate under the wake of another WT (e.g., different free-stream inflow conditions, turbulence intensities, WT spacings, _etc._). The computed structural degradation can be used during the calculation of the expected O&M and fatigue degradation costs, as discussed in Section 3.2.6.
# Retrieval of Similar Evolution Patterns from Satellite Image Time Series +
## 2 Materials and Methods  
In this paper, the SITS analysis is approached through time series information retrieval, which is the process of searching inside a collection of data. The proposed search process starts with a query provided by the end-user, whose aim is to identify evolutions that are similar (i.e., have similar behavior) to the selected query. Firstly, the proposed method determines the degree of similarity between a specific query and the rest of the spatio-temporal evolutions. This step is performed by computing the DTW measure between the query and the rest of the evolutions. The DTW score is expected to be small for similar evolutions and large for non-similar ones. The retrieval process consists of deciding whether a certain evolution is similar to the given query or not. In this sense, the retrieval process can be seen as a problem of separating the spatio-temporal evolutions into two classes (i.e., _similar_ versus _non-similar_ with respect to a given query). The class identification is performed via Bayesian decision theory and it is based on determining the optimal threshold that separates relevant from irrelevant evolution patterns with respect to a given query.  
Specifically, the proposed approach is a two-step process: (1) compute DTW distances and (2) find the optimal threshold. The process is summarized in Figure 2 and starts by computing a DTW distance image containing all the DTW dissimilarity scores with respect to a fixed query. After applying the optimal threshold over the DTW distance image, the final result is a binary map which aids in delimiting similar spatio-temporal evolutions.
# A New Multi-Objective Unit Commitment Model
## 3 Methodology
### Initialize Tie-Line  
A good initialization strategy of tie-line could lead to an improved result when performing the following procedures. The initialization strategy follows rules.  
1. The tie-line constraint (10) and system constraints should be met. To ensure the UC problems in subsystems are still feasible, the increase of generation in subsystem should meet constraint (5), which is denoted as \(RC_{j,t}\) for subsystem \(j\) in (17).
2. The total generation level in any pairs of subsystems depends on the total capability in each subsystem. \[\frac{\text{Capability}_{\text{A}}}{\text{Capability}_{\text{B}}}=\frac{Generation _{\text{A}}}{Generation_{\text{B}}}\] (12) The priority of these conditions is 1, 2. The initialized tie-line could be obtained by solving the following problem. \[\min\sum_{t}\sum_{j}\left(\left(D_{j,t}+\sum_{i}p_{j,t}^{tie} \right)-D_{i,t}^{init}\right)^{2}\] (13) \[s.t.p_{i,t,j}^{tie}=0,i\in\{1,...,M\},t\in\{1,...,T\}\] (14) \[p_{i,t,j}^{tie}+p_{i,t,t}^{tie}=0,i,j\in\{1,...,M\},t\in\{1,...,T\}\] (15) \[|p_{i,j,t}^{tie}|\leq P_{i,j}^{max},i,j\in\{1,...,M\},t\in\{1,...,T\}\] (16) \[\sum_{j}p_{j,t,t}^{tie}\leq RC_{j,t},i,j\in\{1,...,M\},t\in\{1,...,T\}\] (17)  
where \(p_{i,j,t}^{tie}\) is the power flow of tie-line from subsystem \(i\) to subsystem \(j\) at time \(t\), consequently, \(\sum_{i}p_{j,t,t}^{tie}\) is the generation which will be added to the subsystem. \(P_{i,j}^{max}\) is the tie-line limit, if there is no tie-line between subsystem i and j, \(P_{i,j}^{tie}\) could be set to 0. \(D_{i,t}^{init}\) is constant calculated based on rule 2, which stands for the recommended load in subsystem \(i\) during time interval \(t\).  
Solving (13) can be easy as there is no time-dependent constraint and all variables \(p_{j,t,t}^{tie}\) are continuous. Hence the initialized tie-line could be obtained by satisfying the recommended load as well as system constraints.
# Three-Dimensional Broadband and Isotropic Double-Mesh Twin-Wire Media for Meta-Lenses
## 2 Dispersion Analysis of the Double-Mesh Twin-Wire Structure
The period is found to determine the upper limit of operating frequency, beyond which the effective refractive index starts to be dispersive. This frequency dependency is attributed to the fact that the structure can no longer be homogenized into an effective medium for its larger periodicity in comparison to the wavelength at higher frequencies. It is worth noting that the period is not easily varied throughout a graded index lens. In Figure 3b, the effective refractive index for different wire diameters \(d\) of the double-wire mesh structure is presented. Changing the wire diameter changes the capacitance of the structure. However, the wire diameter offers only moderate control of the refractive index and cannot be used to design lenses that require a large refractive index range. The effective refractive index for different amplitudes \(A\) of the double-wire mesh structure is presented in Figure 3c. \(A\) refers to the amplitude of the undulations of the metallic wires. Therefore, when \(A=0\), the wires are straight. The effective refractive index elevates with larger values of \(A\) owing to the increased phase delay induced by the sinusoidal meander that enforces the wave to transmit along a longer path, or, in other words, this length increases the inductance. Therefore, when \(A=0\), the value of the effective refractive index is the smallest, thus it is regarded as the design reference. As shown in Figure 3c, changing \(A\) alone from \(0.1p\) to \(0.4p\) gives an index range of around \(1.4\), readily sufficient for implementing a Luneburg lens profile \(n(r)=n_{0}\sqrt{2-(r/R)^{2}}\) (\(R\) being the lens radius and \(n_{0}\) the refractive index of the background material, same as that at the periphery of the lens) [46], as discussed in Section 3. Following the same rationale, this range can be further extended for other profiles such as Maxwell fisheye lenses by introducing additional meander turns within the unit cell, i.e., increasing \(N\).
# An Optimized Load Balancing Using Firefly Algorithm in Flying Ad-Hoc Network
## 4 Results and Discussion
Node priority is based on a threshold value route selection which is in terms of fuel and if a node meets the threshold criteria to participate in routing. To avoid a node becoming a bottleneck, the optimal information capacity of a metric node concerning traffic and remaining fuel is employed. Packet Delivery Ratio (PDR): A network generates certain data packets, which are then delivered through a routing mechanism. A data packet is considered delivered when it is received in full and without loss by the destination node. Packet delivery ratio = (all packets received by the receiver successfully \(\times\) 100)/all packets produced by the senders  
In the simulation results analysis, we discovered that the network connectivity giving a packet delivery ratio of more than 95% is dependent on the network characteristics of the GPMOR protocol as opposed to the GPSR protocol. Figure 12 shows the simulation results of a network of varied nodes with the transmission power of a flying node configured. Figure 10: Delay analysis of flying nodes. Figure 13 depicts the analysis of a network in connection with throughput when the number of UAVs, area sizes, and speed is varied. The X-axis denotes the time of simulation in m/s, whereas the Y-axis denotes throughput in bits per second (bps). The network's throughput grew as the number of UAVs increased, as did its performance. Thus, when UAVs' speeds were reduced to 0 m/s and 12 m/s, the GPMOR protocol beat the GPSR protocol, with the number of UAVs growing to almost 100, as shown in Figure 13. This is because GPMOR allocates time slots for packet transfers to prevent network congestion.
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
## 3 Results and Discussion
### Experimental Setup
#### 3.1.6 Federated Learning Method Parameter Setting  
We compared FLGQM with FedAvg, median, Krum, and trimmed mean. Among them, FedAvg is the most popular method in non-adversarial environments. The median, Krum, and trimmed mean are three Byzantine robust federated learning methods. These methods all satisfy the general framework. Hence, all of them share the following parameters: \(K,\ \tau,\ R_{l},\ R_{g},\ b\,\) and \(\beta\). The specific definitions and explanations of these parameters can be found in Table 2. Specifically, based on previous work [16], we set \(\tau=K\), i.e., all clients participate in the federated learning process in each round. Also, we set the local iteration round \(R_{l}=5\) and the global iteration round \(E_{g}=300\). We set \(b=16\) for MNIST and \(b=64\) for CIFAR-10.  
In addition to the common parameters mentioned above, different methods have different additional parameters. Krum has the parameter \(f\) and the trimmed mean has the parameter \(\mathcal{K}\), which represents the upper limit of malicious clients. We set \(f=\mathcal{K}=\mathcal{M}\), i.e., giving them enough advantage in both algorithms to let them know the number of all malicious clients. For the FLGQM method proposed in this paper, we set \(\tau_{a}=0.8\) and \(\tau_{a}=0.2\). In the geometric metric score stage, for MNIST, we set the hyperparameters to \(\mu=1\), \(\omega=0\), \(\gamma=2\), and for CIFAR-10, we set the hyperparameters to \(\mu=2\), \(\omega=1\), \(\gamma=4\)
## 1 Introduction  
The shearer is an important piece of equipment at the fully mechanized coal mining face (Figure 1). The pick is the core component of the cutting unit as the key device of the shearer [1, 2]. During automatic and intelligent cutting at the coal mining face, the complex geological conditions, such as the fold structure, fault structure and magmatic intrusion, induce distinct occurrence conditions of coal rock seams (gangue, inclusion, or fault in the coal and rock), and the difficulty in the identification of complex coal-rock interfaces at the coal mining face [3, 4, 5]. Complex occurrence conditions, shearer vibration, rotary cutting of the helix drum and uneven track will cause the wear deterioration and even fracture of the pick when cutting the coal wall [6, 7], which greatly affects the safety and reliability of the pick, service life of the drum and production efficiency of the shearer. Therefore, it is essential to master occurrence conditions of coal rock seams, mechanical coal-rock breakage by shearer picks, shearer pick forces during cutting, the coal-rock cutting mechanism and service safety evaluation of the shearer pick. However, the correlations between complex geological conditions and wear deterioration/safety and reliability evaluation of shearer picks have not been reported yet. The reviews on the cutting loads, cutting mechanisms, quantitative evaluation of wear deterioration and service safety evaluation of shearer picks are of great significance for establishing the wear deterioration evaluation system of shearer picks and obtaining the influencing factors of wear deterioration of shearer picks. Meanwhile, research results can promote automatic and intelligent cutting of coal rock seams, reduce the equipment fault rate and improve production efficiency of the shearer. However, this article is a comprehensive review of selected solutions on the evaluation of coal-rock cutting performances of shearer picks under complex geological conditions. The results can be applied in cases of the complex geological conditions in countries and regions concerned in the present study.  
The aim of the present study is to present the evaluation of coal-rock cutting performances of shearer picks under complex geological conditions, nevertheless, due to the volume of the article, only the most important solutions published in recent years are presented. Section 2 shows the occurrence conditions of coal rock seams. Mechanical coal-rock breakage of coal rock by shearer picks is introduced in Section 3. In Section 4, the research progress on shearer pick forces during coal-rock cutting is presented. Section 5 exhibits the coal-rock cutting mechanism. The service safety evaluation of the pick based on wear and fatigue is reviewed in Section 6. Finally, the research prospect is proposed in Section 7.
## 5 Approximately Optimal Steiner Tree Algorithm
### Weight-Modified MST and Pruning  
Finally, we introduce a modification of the edge weights before constructing an MST on that new graph that will be pruned into an approximate Steiner tree. These remaining steps stem from a centralized algorithm first proposed by [13], whose steps can be implemented efficiently in the distributed setting, as in [2]. We first modify the edge weights as follows:  
Partition the edges \(E\) into three sets: _tree edges_\(E_{F}\), as in Definition 9, which are part of the edge set of the SPF; _intra-tree edges_\(E_{IT}\), which are incident on two nodes in the same tree \(T_{i}\) of the SPF; and _inter-tree edges_\(E_{XT}\), which are incident on two nodes in different trees of the SPF. Ensuring that each node knows which of these edges it belongs to can be accomplished in one round by having each node send its neighbors the ID of the terminal it chose as the root of the tree in the SPF it is part of. The edge weights are then modified as follows, denoting the modified weights as \(W^{\prime}\):  
(i): For \(e=(u,v)\in E_{T},W^{\prime}(u,v):=0\)  
(ii): For \(e=(u,v)\in E_{IT},W^{\prime}(u,v):=\infty\)  
(iii): For \(e=(u,v)\in E_{XT},W^{\prime}(u,v):=d(u,Z_{u})+W(u,v)+d(v,Z_{v})\),  
noting that \(d_{G}(u,s(u))\) is the shortest-path distance in \(G\) from \(u\) to its closest Steiner terminal.  
Next, we find a minimum spanning tree on the graph \(G^{\prime}=(V,E,W^{\prime})\), for which we may implement the classical \(\mathcal{O}(1)\) round algorithm proposed by [6]. On a high level, this constant-round complexity is achieved by sparsification techniques, reducing MST instances to sparse ones, and then solving those efficiently. We skip the details here and refer the interested reader to [6]. After this step, each node knows which of its edges are part of this weight-modified MST, as well as the parent-child relationships in the tree for those edges.  
Finally, we prune this MST by removing non-terminal leaf nodes and the corresponding edges. This is conducted by each node \(v\) sending the ID of its parent in the MST to every other node in the graph. As a result, each node can locally compute the entire MST and then decide whether or not it connects two Steiner terminals. If it does, it decides it is part of the Steiner tree; otherwise, it broadcasts that it is to be pruned. Each node that has not been pruned then registers the edges connecting it to non-pruned neighbors as part of the Steiner tree. This pruning step takes 2 rounds and up to \(n^{2}+n\) classical messages.