esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Earthquake Nowcasting  
[PERSON] et al. [33] proposed EQ nowcasting as a method for estimating the current state of fault systems in the sense that it determines the progress in the EQ cycle. To achieve this determination, one uses an EQ catalog to calculate from the'small' EQs, which are defined as those with magnitude \(M<M_{\lambda}\), but above a threshold \(M_{\sigma}\), i.e., of magnitude \(M\in[M_{\sigma},M_{\lambda})\), the level of hazard for 'large' \(M\geq M_{\lambda}\) EQs. The lower threshold \(M_{\sigma}\) is typically the completeness threshold of the EQ catalog used [33], while the EQ catalogs employed [116; 117; 117; 123; 124; 136] are global seismic catalogs such as the Advanced National Seismic System Composite Catalog or the NEIC PDE catalog. For such global catalogs, a \(M_{\sigma}=4.0\) magnitude threshold was considered [116; 117] for applications in EQ-prone areas outside the United States, such as those in Greece, Japan, and India. By employing the natural time concept, one counts the number \(n\) of small EQs that occur after a large EQ. In other words, \(n\) is the waiting (natural) time or interoccurrence natural time. The current number \(n(t)\) of small EQs, since the occurrence of the last strong EQ, is compared to the cumulative distribution function (CDF) of the interoccurrence natural time \(Prob[n<n(t)]\). For such an estimate of \(Prob[n<n(t)]\), it should have been ensured [33] that we had enough data to span at least 20 or more strong EQ cycles. In EQ nowcasting, the EQ potential score (EPS) equals the CDF value,  
\[\text{EPS}=Prob[n<n(t)], \tag{6}\]  
and is a measure of the level of current hazard, see Figure 3a. In References [116; 117; 123; 136], the seismic risk for various cities of the world was estimated through the following procedure: After calculating the CDF \(Prob[n<n(t)]\) within a large area, the number \(\tilde{n}\) of the small EQs around a city, i.e., those occurring within a circular region of epicentral distances \(r<R\) since the occurrence of the last strong EQ in this circular region, was found. Given that EQs exhibit ergodicity--see, e.g., References [137; 138]--[PERSON] et al. [117] suggested that the seismic risk around a city can be estimated by using the EPS corresponding to the current value of \(\tilde{n}\) by inserting \(n(t)=\tilde{n}\) in Equation (6).  
In the present study, the large area considered was N\({}_{23}^{52}\)E\({}_{5}^{50}\) which, for \(M_{\sigma}=4.0\) and \(M_{\lambda}=6.0\), led to the CDF shown in Figure 3a. A study of the empirical CDF comprising 123 EQ cycles in Figure 3a revealed that the fit with the Weibull distribution provided a good approximation with a root mean square of residuals equal to 0.0207; see Chapter 15 of [139]. This property of the Weibull distribution was in accordance with the results found by [PERSON] and [PERSON] [123] for Himalayan EQs.
## 4 EDETA Protocol Description  
In this section we present the EDETA (Energy-efficient aDaptative hiEarchical and robusT Architecture) protocol. It is based on a two-level architecture type: the first level is made up of clusters, and the second by a dynamic tree. The clusters are selected randomly and the network structure is recalculated after certain number of rounds.  
EDETA uses a calendar where events are scheduled based on wall-clock time. There are two kinds of nodes, cluster head (CH) nodes and leaf nodes (LF). Between cluster heads (CH), a tree structure is built to allow data communication to travel to a root sink. The protocol supports more than one sink in order to provide more scalability and some fault tolerant mechanisms.  
In the proposed system, with fixed cluster nodes, which are specially located in order to be easily powered (by means of energy-harvesting subsystems or directly from a mains line), the cluster head nodes feature virtually \"infinite\" energy. The engineer has to carefully plan the deployment and choose where to put the CH nodes and which type of power supply they will have.  
This approach allowed us to optimize the design of the leaf nodes, achieving a longer lasting, highly-competitive real WSN solution. EDETA's operation is divided into phases, the initialization phase and the normal operation phase. The duration of each phase is time constrained. This way, EDETA can be used in applications that require bounded response times.  
There are two variables that limit these phases' duration. The TIME_CONFIG variable limits the initialization phase and the TIME_SUPERFRAME variable limits one round of the normal operation phase. The normal operation phase has a limited number of rounds defined by the parameter MAX_INTRAROUNDS. Therefore the normal operation phase will have a maximum duration of MAX_INTRAROUNDS \(\times\) TIME_SUPERFRAME.
## 5 Geochemistry
### Rare Metal Contents  
The comparison between Jabal Sayid and Dayheen in terms of their rare metal contents, as shown in Figure 10, reveals some distinct variations in the composition of their pegmaties and peralkaline granites. Jabal Sayid pegmaties show the highest average contents of LREEs (4494 ppm), HRREs (2819 ppm), Y (4852 ppm), Th (1459 ppm), U (285 ppm), and Zn (1124 ppm) compared to Dayheen pegmaties. Dayheen pegmaties show the highest average contents of Zr (15,937 ppm), Nb (1445 ppm), and Ta (109 ppm). Furthermore, the Dayheen peralkaline granites have a higher average content of LREEs (581 ppm) and Nb (135 ppm) and lower contents of HREEs (98 ppm), Zr (1565 ppm), and Y (149 ppm) compared to the Jabal Sayid peralkaline granites. Th, U, Ta, and Zn contents are comparable between the peralkaline granites of both locations. The enrichment of these elements in the peralkaline granites and related pegmaties can be attributed to the presence of various mineral associations, as shown in Table 1.  
Figure 9: Chondrite-normalized REE patterns [30] for (**a**) the Dayheen peralkaline granites and pegmaties; (**b**) the Jabal Sayid peralkaline granites and pegmaties; (**c**) the Dayheen monozogranites; and (**d**) the Dayheen granodiorite–diorite association. Symbols as in Figure 6.
# Holographic Silicon Metasurfaces for Total Angular Momentum Demultiplexing Applications in Telecom
## 2 Materials and Methods
### Phase Pattern Calculation
In the presented work, the PBOE has been fabricated in the form of a pixelated metasurface of rotated subwavelength gratings (**a**), and designed for the demultiplexing of 7 OAM channels and circular polarization states (14 channels in total) over different OAM sets, centered in \(\ell=0\), with increasing OAM separation \(\Delta\ell=1\) (**b**), \(\Delta\ell=2\) (**c**), \(\Delta\ell=3\) (**d**). Three different channel configurations have been considered and tested: Linear array (**b**), regular polygon (heptagon) (**c**), semicircle (**d**). In this case, the two orthogonal circular polarizations experience opposite phase patterns:  
\[\Omega^{(+)}(u,v) =\arg\{\sum\limits_{j=1}^{n}c_{j}R_{j}^{*}\exp\Bigl{[}-i\ell_{j} \vartheta+i\alpha_{j}u+i\beta_{j}v\Bigr{]}\} \tag{6}\] \[\Omega^{(-)}(u,v) =\arg\{\sum\limits_{j=1}^{n}c_{j}R_{j}\exp\Bigl{[}+i\ell_{j} \vartheta-i\alpha_{j}u-i\beta_{j}v\Bigr{]}\}\]  
and their corresponding sets of intensity spots appear at symmetric coordinates in far field:  
\[ \begin{array}{l}x^{(-)}\Bigl{(}\ell_{j}\Bigr{)}=-x^{(+)}\Bigl{(}-\ell_{j} \Bigr{)}\\ y^{(-)}\Bigl{(}\ell_{j}\Bigr{)}=-y^{(+)}\Bigl{(}-\ell_{j}\Bigr{)}\end{array}  \tag{7}\]  
where the subscripts \((+)\) and \((-)\) stand for right-handed and left-handed circular polarizations, respectively. As expressed by Equation (7), a beam carrying OAM equal to \(\ell\) and right-handed circular polarization generates a bright spot at a position which is center-symmetric to the spot formed by the left-handed circularly-polarized state with opposite value of OAM. Hence, during the design of a metasurface performing demultiplexing over a properly-designed set of modes, particular attention should be paid to carefully choosing the spatial frequency carriers in order to prevent different channels from overlapping. A custom code has been developed in MATLAB(r) in order to compute the phase patterns for the selected set \(\{\ell_{j}\}\) of OAM values and the corresponding carriers frequencies \(\{(\alpha_{j},\beta_{j})\}\). The implemented algorithm is based on a successive computation of the integrals in Equation (2) and of the sum in Equation (1), implementing the fast Fourier transform algorithm and applying precise constrains, as explained in [5; 23], in particular phase quantization into 16 equally-spaced values in the range \([0,2\pi)\). In the following, three different configurations are presented and described. Each phase pattern performs the demultiplexing of circularly-polarized beams over 7 OAM values, for a total of 14 channels, with different OAM separation and far-field channel constellation: Linear array, regular polygon, semicircle.
# Data Lake Architecture for Smart Fish Farming Data-Driven Strategy
## 3 Big Data Enabling the Adoption of Fish Farming Data-Driven Strategies
It can also be noted that both batch and real-time processing can be adopted to make data accessible for analysis purposes. Processing includes operations such as selecting, joining, and aggregating datasets [23]. The AZ, also known as the access zone, exposes the preprocessed data and access to it is granted to data analysts. This zone provides a self-service data exploration for the analytics use cases (business intelligence, machine learning algorithms, statistical analysis, and reporting). Along with this, our proposed functional architecture for handling fish farming big data contains three main phases, namely, data generation and collection, data ingestion and treatment, and data analysis and exploration, as presented in the Figure 1 below:  
By concretizing this functional data handling architecture, the fish farming domain will not remain a traditional field but a data-driven domain, thanks to the possibility of analyzing and extracting valuable information from massive data gathered from multiple fish farms [24]. In other terms, the fish farming data lake will make data accessible for multiple users to perform analysis and not restrict access to only users with a solid technical background. Adopting a data-driven strategy will become possible, thus enhancing the whole domain.
# Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11 ah
## 6 Performance Evaluation and Discussion
### Dynamic Number of Stations  
In this section, we study the ability of TAROA to adapt to changes in the topology (i.e., number of associated stations) over time. The total traffic load, 1.275 Mbps for HT and 0.1425 Mbps for LT, is distributed among 1536 stations in the same way as mentioned in Section 6.2. The simulation only starts with 1024 associated stations and a traffic load of 0.85 Mbps for the HT scenario, and 1024 associated stations and a traffic load of 0.095 Mbps for LT scenario. A Poisson distribution then is used to model the arrival (i.e., association) and departure of stations, where a higher Poisson rate \(\lambda\) results in faster changes.  
Table 6 lists the average throughput of TAROA and EDCA/DCF when stations join and leave the network with Poisson rates 0.1, 1 and 5 every second. The results show that, as expected, EDCA/DCF adapts quite well to network dynamics. TAROA suffers more significantly, as it constantly needs to adapt the RAW configuration as the network topology changes. Moreover, estimating the transmission interval of a newly joined station takes a few packet transfers to converge. Nevertheless, TAROA shows resilience to topology change and only suffers significantly when the Poisson rate is 5, which corresponds to on average five stations joining and leaving the network every second. We argue that five stations joining and leaving the network every second is an unrealistically high number for the IoT scenario under investigation. As such, TAROA adapt well under realistic dynamic conditions. Overall, TAROA outperforms EDCA/DCF in all scenarios, except LT without hidden nodes. However, in this scenario (LT with 1024 stations and 0.095 Mbps), EDCA/DCF also outperformed TAROA in the static case. To summarize, even if EDCA/DCF is inherently more resilient to topology changes than a slotted mechanism like RAW, TAROA is still able to outperform EDCA/DCF under traffic conditions that correspond to static scenarios where TAROA is better. It should be pointed out that EDCA/DCF simulations with hidden nodes at Poisson rate 1 and 5, respectively, for LT scenarios use an association time-out of 10 s. This was done as the default association time-out of 0.5 s prevented stations using EDCA/DCF from properly associating with the AP in these two scenarios, which resulted in continuous performance degradation.  
Figure 9: Accuracy of transmission interval estimation with and without hidden nodes (H.N) for both HT and LT scenarios. (**a**) HT scenario; (**b**) LT scenario.
## 1 Introduction
However, there are too many assumptions about the first derivative of the disturbance and the existence of the thruster. The paper [23] develops a sliding mode controller based on proportional integral (PI) sliding mode control, in which an adaptive term is designed to effectively solve the problem of system chattering. The main drawback is that an integral term may cause the reduced stability margin due to phase lag [24]. In order to further simplify the model and avoid the underactuation problem, a novel methodology is developed in [25]. Unfortunately, the tracking accuracy is low due to the use of a simple control law that achieves asymptotic path following. Although there are various problems, the sliding mode control is still an effective control algorithm to deal with nonlinear control problems. From a practical perspective, the input saturation is unavoidable due to physical constraints in propulsion systems. If the input saturation is neglected in control design, it will lead to the instability of the system and even the collapse of the control system. In the presence of the input saturation, an adaptive impedance controller is developed for an n-link robotic manipulator in [26], where an auxiliary system is adopted to handle the input saturation. The paper [27] designs a novel robust adaptive ship trajectory tracking control law for surface ships, in which the input saturation is approximated using a Gaussian error function. Hence, the influence of input saturation should be considered in control design. Based on the discussion above, considering actuator saturation and modeling uncertainties, an adaptive sliding mode control approach is developed by employing sliding mode control, the MLP technique, backstepping method and the auxiliary system, which accurately makes the underactuated USV track a desired trajectory. The primary contributions of this paper are summarized as follows:  
1. A novel adaptive control strategy based on sliding mode control and backstepping method is presented for the underactuated USV, in which the hyperbolic tangent function and the neural shunting model are adopted to eliminate the chattering phenomenon and the \"explosion of complexity\" problem of the system, respectively. Comparing with the previous work [28], it is more effective to implement the control scheme in real practice. 2. Taking full account of the practical engineering, the input saturation is handled by designing an auxiliary system. The MLP technique and the adaptive technology are used to deal with unmodeled dynamics and unknown disturbance bounds, respectively, where the norm of all the weights is estimated instead of estimating each element. Only two weight-norm related parameters are required to be updated in the control law. This brief is organized as follows. In Section 2, problem formulation and preliminaries are presented. Section 3 illustrates a novel adaptive control scheme for the underactuated USV. The stability analysis of the whole system is given in Section 4. The simulation results to illustrate the proposed controller are shown in Section 5. Finally, Section 6 concludes this paper.
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 2 Related Work
### Testbed Implementation of a Hybrid Route Selection Scheme  
Universal software radio peripheral (USRP) testbeds have been deployed to perform route selection for 5G and cognitive radio networks. Together with an open-source software toolkit, namely GNU radio [48], signals are generated and various processes, including encoding, decoding, and modulation are performed. In this work, CenTri is implemented on a USRP/GNU radio testbed for 5G networks. While most experimental studies focus on data links and physical layer investigations, including interference mitigation [13; 14; 49], channel sensing [50], and power allocation [51; 52], this paper focuses on network layer implementation. There are six nodes used in [43; 44], eight nodes in [20], and ten nodes in [46].  
In [20; 43; 44; 46], the route selection scheme enables a SU to establish stable routes in the presence of PU activities in a multi-hop cognitive radio network. The routing metrics are channel availability [20; 44; 46; 53], channel quality [43; 44; 46], interference among SUs [43], and the number of hops of the route [20]. In [46], RL is embedded in each node to establish stable routes with the highest possible channel available time. The SU must perform channel sensing from time to time to measure channel availability based on the number of available channels and the number of route switches. The SUs can learn about the availability of white spaces via channel sensing in opportunistic channel access, or via direct communication with PUs in channel leasing. To implement a larger multi-hop network with more nodes (e.g., \(>\)10 nodes), the USRP/GNU radio units are connected to a single computer via a switch [46], which helps to reduce hardware and software delays. The proposed schemes have been shown to increase throughput [20; 43; 44] (or packet delivery rate [46]), as well as reduce the end-to-end delay [20; 43; 44], the number of channel switches [20; 46], the number of route breakages [46], and routing overhead caused by rerouting [43; 44].  
In this work, a heterogeneous network is considered in which there are up to eleven USRP/GNU radio nodes from different layers, including the macrocell and femtocell layers. Hence, the nodes have different characteristics in terms of the choice of operating channels, transmission power, and transmission range. Each node consists of a USRP/GNU radio unit, which serves as the RF front end, connected to a mini-computer Raspberry Pi3 B+, which is programmed to run CenTri. Thus, the testbed of this work extends those in the previous works [43; 44; 46], whereby the USRPs/GNU radio units are wire-connected with a single computer via a switch to emulate a common control channel. To the best of our knowledge, this is the first testbed implementation for a hybrid route selection scheme.
## 1 Introduction
[PERSON] [15] proposed a new multi-objective optimization approach by means of the ACSA to solve a complex environmental/economic dispatch problem of thermal generators in power systems. PACSA is proposed on the basis of ACSA, where 'P' stands for the parallel computing which can greatly reduce the running time of optimization operations through sacrificing hardware resources [16; 17]. In this article, PACSA is used to tune the parameters of the SMC, which combine the ACSA with parallel computing to improve the efficiency of the system. In this paper, an adjustable hydro-pneumatic suspension system of a rescue vehicle is taken as the object to study the proposed dynamic switch strategy between the ride comfort and handling stability. The load transfer ratio (LTR) is selected as the switch performance index, and it is calculated through a six-degrees-of-freedom (6-DOF) model. The ride comfort and handling stability controller of the hydro-pneumatic suspension are designed based on the SMC. The objective functions of parameters optimization of the SMC are obtained by means of analytic hierarchy process (AHP), and then the SMC's parameters are optimized by the PACSA. The research of the adjustable hydro-pneumatic suspension in this paper has highlights as follows. 1. The 6-DOF model of the adjustable hydro-pneumatic suspension based on the rescue vehicle is established, and the sliding mode controller is applied on the rescue vehicle because of its robust and easy realization. 2. A Switched SMC is proposed and built for the control of the adjustable hydro-pneumatic suspension system, which can address concerns on both ride comfort and handling stability under different road surfaces and driving conditions through switch actions, and the optimization ability of the proposed control strategy is proven compared with the conventional SMC based on MATLAB/Simulink. 4. PACSA is used to optimize the parameters of the Switched SMC, which is verified as more suitable for tuning the parameters of the SMC controller. The rest of this paper is organized as follows. Section 2 establishes the road model and the 6-DOF half-car model of the suspension system. Section 3 focuses on the switch control strategy of the hydro-pneumatic suspension. Section 4 describes the parameter tuning methods of the SMC. Section 5 carries out the simulation of the designed controllers, and the simulation results are analyzed. Section 6 is the conclusions.
# A Deep Learning-Based Password Security Evaluation Model
## 3 Methodology
### Phase 2: Password Feature Extraction
#### 3.2.1 Step 1: Feature Data Extraction  
The feature data should have no empty values for all passwords in the password list. In addition, each feature should not be related to each other since learning may become biased if each feature is related. In this paper, existing password security evaluation indicators are used to extract features. This is shown in Table 1. For example, the LUDS requirement evaluates security with a combination of uppercase letters, lowercase letters, numbers, and special symbols, and zxcvbn evaluates security based on frequently used passwords. In addition, Levenshtein distance evaluates security based on the number of letters that are different between the original letter string and the selected letter string.  
Figure 7: Process of extracting password features.  
In this respect, it can be seen that each password security evaluation indicator evaluates security by analyzing one password with a different criterion.  
The security evaluation score that is extracted with the security evaluation indicators is converted into a numeric form to be used as input data for the evaluation model. If Table 1 is examined, the LUDS requirement and zxcvbn are expressed in five steps, from 0 to 4, and the Levenshtein distance is expressed in two steps of 0 and 1. This follows the security evaluation classification criterion proposed by each security evaluation indicator. For example, the LUDS requirement classifies the security evaluation scores into five steps by using the addition and deduction indicators. zxcvbn measures the complexity of combining dictionary words with uppercase letters, lowercase letters, numbers, and special symbols and classifies them into five steps. Since Levenshtein distance is defined as the distance between two letter strings, it has a value of 0 or higher. However, if a large value is reflected as it is without a threshold in the model training process, distorted results may be obtained by being sharply biased to one side and affecting other values. Therefore, preprocessing that normalizes the values of the Levenshtein distance is needed. Since a password with a distance of 3 or more was classified as having strong security in an existing example that studied the security of passwords by using the Levenshtein distance, in this paper, a distance less than 3 is defined to be 0 and a distance of 3 or more is defined to be 1 [32; 33].