esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Resilient Recovery of the SN Based on Outer Synchronization
### Supply Network Model
In SN, there is not a one-to-one relationship, but a very complicated relationship. There are interactions between supply quantities, demand quantities, and inventory. The supply quantity from \(v_{i}\) to \(v_{j}\) is not the demand quantity form \(v_{j}\) to \(v_{i}\). Therefore, the state of the node is also adjusted by the state of the neighbor nodes with a supply relationship, using the second term. The set of neighbors of node \(v_{i}\) is denoted by \(N_{i}\). The neighbor of node \(v_{i}\) means that there is a supply relationship with the node \(v_{i}\). The number of nodes \(v_{j}\) satisfying \(j\in N_{i}\) shows how many nodes supply node \(v_{i}\). The supply relationship may be one-to-one, or several-to-one, which is expressed by the adjacency matrix \(G=[g_{ij}]\). When there is a business relationship between node \(v_{i}\) and \(v_{j}\), \(g_{ij}=1\), otherwise \(g_{ij}=0\). \(G\) is symmetric and non-negative. The inner coupling matrix \(\Gamma=diag(c_{1},c_{2},c_{3})\) is a diagonal matrix to indicate coupling strength: where \(c\) is the coupling coefficient; \(c_{1}\) represents the influence of neighbors of node \(x_{i}\) on the first component, demand quantities; \(c_{2}\) represents the influence of neighbors of node \(x_{i}\) on the second component, supply quantities; \(c_{3}\) represents the influence of neighbors of node \(x_{i}\) on the third component, inventory. The physical interpretation of the inner coupling matrix is the ability of mutual control between nodes. For example, there is a supply relationship between a raw material node \(x_{j}\) and a factory node \(x_{i}\). Since\(x_{j2}\)-\(x_{j1}\)). So, the supply quantities of the raw material node are coupled to the supply quantities of the factory node \(x_{i}\) (i.e., \(x_{j2}\)-\(x_{j1}\)), which expresses the connections between nodes in Equation (3). If the nodes are only interconnected through the demand relationship, then the nodes are coupled through the first component. In this scenario, \(\Gamma=diag(c,0,0)\). The configuration matrix \(A=[a_{ij}]\) is used, therein \(a_{ij}=-g_{ij}\) (\(i\
eq j\)), \(a_{ii}=\sum\limits_{j}^{N}g_{ij}\), then Equation (3) becomes Equation (4)  
\[\dot{x}_{i}(t)=f_{1i}(x_{i}(t),\varphi_{i})+f_{2i}(x_{i}(t),\varphi_{i}) \alpha+\sum\limits_{j\in N_{i}}a_{ij}\Gamma x_{j}(t) \tag{4}\]  
Therein, \(\alpha\) is the vector with scheduled parameters, \(\varphi_{i}\) is the scheduled status, \(f_{1i}\) is the function without parameters, and \(f_{2i}\) is the function with parameters. The validity of the proposed SN model has been presented in our previous study [30].
# An Empirical Evaluation of Document Embeddings and Similarity Metrics for Scientific Articles
## 8 Discussion  
The use of techniques from the Machine Learning set is sometimes justified by the results produced, ignoring, to a greater or lesser extent, formal or theoretical aspects. This is also because obtaining a curated corpus is often quite complex and time-consuming. The results presented throughout this chapter have shed light on the combination analyzed from both points of view: analytically and real-world scenarios.  
Task I, which has provided a piece of more analytical information about the measures (and embeddings), has shown that all the measures, except CS, are good ones. In general, all of them perform well with some non-remarkable imperfections, but for the case of CS + d2v with the thesis set, the problems become relevant, and we, therefore, would be cautious in the use of such measure for very long documents.  
With the information provided in Section 6, the best measures, from a theoretical standpoint, seem to be RWMD and WMD for our context; being nothing negligible to the results of SCS.  
On the other hand, the executions times show how the use of ELMo could become unjustified because other embeddings provide better results with a lower time cost. Another remarkable example is RWMD, which, albeit relaxing a constraint over WMD, it still achieves good results. Furthermore, it also takes much less computation time.  
The practical results, i.e., Task II, show examples of the mentioned casuistic at the beginning of this section. d2v + CS yielded a non very satisfactory result on very long  
Figure 10: Task II-Clustering using NRC achieves average results at most: small groups are separated in several cases. Colors are assigned automatically by the plotting algorithm.  
documents for Task I, but it turns out to be the best combination for Task II. It is the one with fewer clustering errors, at least for the used set, which the authors consider not trivial whatsoever.  
Task II shows also how the embeddings impact the measures' performance. The GV cases have different performance in comparison with the rest of the embeddings, and it seems to be caused by its statistical base. That is to say, GV works well making large clusters, which is expected behavior for statistical-based techniques.  
Concerning the other measures analyzed in Task II, i.e., SCS, RWMD, and NRC, the first two work similarly, being dependent on the embedding used. There are no clear arguments to opt for one or the other, except for the insignificant execution time of RWMD in comparison with the rest.  
Finally, NRC, the out-of-the-box selected measure for completeness, has, in general, a good performance. It is extremely fast, at least in comparison with the other ones, and monotonously decreasing, as depicted previously in the literature. Despite that, the clustering is one of the worst, probably because it does not work at a very detailed level with the context, as it seems to happen with the embeddings.  
All these results are summarized in a compact form in Table 5.
# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 3 Analysis of Experimental Data Characteristics
### Time-Frequency Characteristics of MEMS Data  
Figure 5a presents the time-domain response data collected by the MEMS sensors at measurement point 1 during the test. It is evident that in the direction of gravitational acceleration, the response signal fluctuates around 9.8 m/s\({}^{2}\) as sampling time increases, which is approximately consistent with the expected gravitational acceleration. In both the parallel and vertical truss directions, the response signals fluctuate around a mean value of zero as sampling time progresses. Figure 5b displays the root power spectral density plot for measurement point 1. In the parallel truss direction, distinct peaks appear at frequencies such as 2.14 Hz and 3.05 Hz; similarly, in the vertical truss direction, peaks are observed at 2.14 Hz and 2.82 Hz. These frequency points often correspond to the structure's potential modal frequencies.
## 3 Results and Discussion  
The SLM oscillator provides 7.8 ns laser pulses with single pulse energy of 9 mJ at 1064 nm wavelength. The repetition rate of the oscillator is adjustable from 1 to 10 Hz. Figure 4 shows the SLM seed pulse and compressed Stokes pulse we obtained in our experiment, respectively. The waveform of the oscillator output was given in Figure 4a showing a rather smooth shape. The diameters of the oscillator output is about 2.3 mm with angle of divergence less than 0.5 mrad. The temporal profile of the output was detected by a photodetector (UPD-40-UVIR-P; ALPHALAS GmbH, Gottingen, Germany) with rising time less than 40 ps, and recorded by an oscilloscope (806 Zi-A; Teledyne LeCory Inc., New York, NY, USA) at a bandwidth of 6 GHz. The compressed pulse profile with steep rising edge and pulse width around 450 ps is shown in Figure 4b.  
Figure 5 represents the experimental curve of output energy and compressed pulse width with the change of amplifier voltage, as well as the energy and pulse width stability (error bar). From the curve, we can see that the output energy is near linear growth from 650 to 850 V. One major reason is that with the increase of the injected energy, the SBS reflectivity is obviously improved. The reflectivity we obtained is higher than 75% when the voltage exceeds 650 V. Correspondingly, the output pulse width narrows down rapidly while improving the amplifier voltage from 350 to 650 V. This is because that, at this stage, the leading edge of the Stokes pulse is amplified greatly due to the fast growing gain of the compressor. However, from 700 V to higher voltage, no obvious pulse width compression or changes were observed. The mean reason for the increasing energy contributes little to the compression process is that the Stokes pulse width is close to the FC-770 medium phonon lifetime. The measured root mean square error (RMSE) of output energy at 850 V is less than 3%.  
Figure 6 represents the spatial beam characteristics of the output from polarizer P\({}_{3}\) in both near-field and far-field, which were measured by a Charge Coupled Device (CCD) (Camyu Co. Ltd., Chongqing, China). As shown in Figure 6a,b, the measured beam diameter of near-field is about 6.1 mm in \(x\)-direction and 5.9 mm in \(y\)-direction. The near-field beam divergence angle is less than 0.9 mrad. The 2D and 3D far-field laser intensity distribution profiles are given in Figure 6c,d, which obtained by a long focal length lens. Both the near-field and far-field show good beam quality.  
Figure 4: Temporal pulse profiles of the (a) SLM oscillator seed output; (b) SBS pulse width compressed output.  
Figure 5: The output energy and pulse width as a function of amplifier voltage.  
Figure 7 shows the measured beam radius under output energy of 200 mJ and pulse width around 450 ps at various distances from the lens with \(f=150\) mm. The transverse output beam profile was measured by using a 90/10 knife-edge technique. The result indicates a beam quality of \(M_{x}^{2}=1.80\), \(M_{y}^{2}=1.75\) in both directions perpendicular to the axis of propagation.
# Adaptive Authentication Protocol Based on Zero-Knowledge Proof
###### Abstract  
Authentication protocols are expanding their application scope in wireless information systems, among which are low-orbit satellite communication systems (LOSCS) for the OneWeb space Internet, automatic object identification systems using RFID, the Internet of Things, intelligent transportation systems (ITS), Vehicular Ad Hoc Network (VANET). This is due to the fact that authentication protocols effectively resist a number of attacks on wireless data transmission channels in these systems. The main disadvantage of most authentication protocols is the use of symmetric and asymmetric encryption systems to ensure high cryptographic strength. As a result, there is a problem in delivering keys to the sides of the prover and the verifier. At the same time, compromising of keys will lead to a decrease in the level of protection of the transmitted data. Zero-knowledge authentication protocols (ZKAP) are able to eliminate this disadvantage. However, most of these protocols use multiple rounds to authenticate the prover. Therefore, ZKAP, which has minimal time costs, is developed in the article. A scheme for adapting protocol parameters has been developed in this protocol to increase its efficiency. Reductions in the level of confidentiality allow us to reduce the time spent on the execution of the authentication protocol. This increases the volume of information traffic. At the same time, an increase in the confidentiality of the protocol entails an increase in the time needed for authentication of the prover, which reduces the volume of information traffic. The FPGA Artix-7 xc7a12 ticsg325-IL was used to estimate the time spent implementing the adaptive ZKAP protocol. Testing was performed for 32- and 64-bit adaptive authentication protocols.  
modular codes; low-orbit satellite communication systems; satellite identification system 20221
# Synthesis of Four-Link Initial Kinematic Chains with Spherical Pairs for Spatial Mechanisms
## 5 Discussion
Another area for improvement is in the handling of the boundary conditions and constraints of the system. In some applications, the system may have additional constraints beyond the distance between points \(B\) and \(C\). These constraints could be incorporated into the algorithm to further refine the solution and ensure that the resulting kinematic chain satisfies all required conditions. Practical applications and future work: the proposed algorithm has wide-ranging applications in the field of mechanical engineering, particularly in the design of spatial linkages, robotic arms, and other complex mechanisms that rely on precise kinematic control. Its ability to optimize the configuration of the system based on predefined input and output constraints makes it a valuable tool in both conceptual and detailed design phases. Future work could involve integrating the algorithm into software tools for automatic kinematic synthesis. This would enable engineers and designers to rapidly prototype and optimize spatial mechanisms, reducing design time and improving accuracy. Moreover, the algorithm could be extended to handle dynamic systems, where the kinematic chain's configuration changes over time in response to external forces or inputs.
## 2 Materials and Methods
### Batch Adsorption Experiments
#### 2.2.3 Analytical Methods  
The concentration of m-cresol in solution before and after adsorption was determined using the UV-Vis spectrophotometer at a wavelength of 270 nm. Calibration curves were constructed using standard m-cresol solutions in the range of 1-100 mg L\({}^{-1}\), and a correlation coefficient (R\({}^{2}\)) > 0.999 was achieved. The removal efficiency (\(\omega\), %) and adsorption capacity (\(q\), mg g\({}^{-1}\)) were calculated using the following equations.  
\[\omega= \frac{(C_{0}-C_{t})}{C_{0}}\times 100 \tag{1}\] \[q= \frac{(C_{0}-C_{e})\times V}{m} \tag{2}\]  
where \(C_{0}\) is the initial m-cresol concentration (mg L\({}^{-1}\)), \(C_{t}\) is the concentration at a given time \(t\) (mg L\({}^{-1}\)), \(C_{e}\) is the equilibrium concentration (mg L\({}^{-1}\)), \(V\) is the volume of the m-cresol solution (5 mL), and m is the mass of the XDA-1G resin (g). These calculations were used to evaluate the adsorption efficiency and capacity of resin under varying experimental conditions.  
Figure 1 shows a schematic illustration of the experimental workflow used in this study to evaluate m-cresol adsorption using XDA-1G resin. The process begins with resin preparation and continues through batch adsorption tests conducted under a fixed  
initial m-cresol concentration of 20 mg L\({}^{-1}\) to simulate high-strength industrial wastewater conditions. After adsorption, samples were separated, and residual m-cresol concentrations were analyzed using UV-Vis spectrophotometry. Detailed procedures, including structural characteristics, adsorption isotherms, kinetics, and thermodynamics, are provided in the Supplementary Materials.
# Distributed Reliable and Efficient Transmission Task Assignment for WSNs
## 5 Reliable and Efficient Task Allocation Algorithm for WSNs  
### Centralized Task Assignment  
As presented before, the aims at this research are to minimize the energy consumption of task allocation under the constraints of reliability and deadlines in the task allocation model problem presented in Section 3, where \(TG(V,E)\), \(RT(S,Ps)\) and \(M(t)\) are the three inputs as presented in Equation (11).  
TRETA is performed by the sink based on the given task reliability and deadline constraints through a global view, and its framework consists of five modules, enumerated as: data collection, task reliability evaluation, topology management, event management, and task assignment. The data collection module collects state information of each node, such as node ID (Identity Document ), node residual energy information, node neighbor, and packet loss rate information, among others; the node collection module stores network state information such as nodes, paths and task information (transmitted from the application layer) in the corresponding node table, topology information table, state information table, and task information table in the database for extraction by other modules; and the topology information table in the data collection module stores the global physical topology of the network.  
To establish a global view of the current network state, a topology management module in the framework of TRETA is designed. In addition to the global physical topology, the topology management module obtains the routing topology of the global network and then constructs a two-level global view.  
The event module manages events and triggers other module updates when a drive event occurs. To achieve reliable and efficient task allocation in WSNs, the driver events concerned include topology  
Figure 2: The multi-level global view of a wireless sensor network (WSN).  
change (nodes join or leave), as the reliability of the task is lower than the threshold and new tasks to be allocated.  
Based on the modules presented above, a reliable and efficient task allocation algorithm, namely TRETA, is proposed, as depicted in Figure 3.  
During the execution of the network and once the defined driving events occur, the event management module triggers the update of the data collection module, and the sink determines whether the reliability of the updated network is less than the target reliability. If yes, the task allocation module is notified to re-assign; otherwise, only the global view corresponding to the event is updated.
# The Importance of Modeling Path Choice Behavior in the Vehicle Routing Problem
## 2 Transport System Analysis
### Method Proposed  
The VRP depends on the PCP, and it cannot be studied by assuming an uncongested network and deterministic path choices. Figure 2 shows the dependency between the VRP and the PCP. The VRP provides the optimal solution, starting from objectives and constraints, and it is a component of the transport supply subsystem. The transport supply subsystem and the transport demand subsystem provide the input for the PCP, which takes place in the context of the assignment of travel demand to the transport networks; this interaction provides the behavioral constraint that must be respected by the VRP. There is a circular dependency between the VRP solution and the constraint provided by the PCP. Transport demand management policies, not included in this paper, also affect the behavioral constraint and it could be included in the context of the overall method.  
Decision-takers and analysts are the beneficiary of the proposed method. The decision-takers and companies at the policy level can evaluate the optimal VRP solution with a more accurate methodology for the PCP. The analysis can apply established solution (exact or heuristic) algorithms for the assignment and VRP solutions. The analysts can give more realistic solutions to the decision-makers for their decisions and the solutions can be assumed as an optimal routing strategy.  
Behavioral constraints assume a relevance considering the objectives of this paper. The behavioral constraint is modelled with the PCP in the assignment problem, is not linear, and is reported in Section 3. Starting from the path choice specification, in Section 4, the consolidated VRP is also specified considering the effect of the PCP within the VRP with a numerical example.
## 2 Analysis of the State of the Art
### [PERSON] and [PERSON]: The AMBI2 ONT Ontology
#### 2.2.2 Context Models Comparison
Following a similar perspective [PERSON] _et al._[34] analyze the user's participation in adaptive applications. Several concepts that should be taken into account regarding the user behavior and interaction with the application are presented (see Section 2.1.1.). Another similar approach regarding the user's more psychological aspects is presented by [PERSON] _et al._[35] Here the authors present a model for modeling user stress levels. This is related to the concept of Considerate Computing, term that it is explained in [58]. [PERSON] _et al._[43] also remark social environment as relevant for context modeling. More related to recommending environments, non explicit information about user's likes need to be computed. Similar approaches in these environments tend to avoid recommending systems intrinsic problems, like the so-called \"cold start problem\" [59, 60]. Another relevant point remarked by [PERSON] _et al._[43] are the user's tasks. Several authors consider activities relevant for modeling context [49, 51, 61]. Activities enrich context information about the user [21]. It is common to model user activities in the user model (see Table 1). Finally, and as occurs with user information, sometimes the collected data might lead to misunderstandings or non-trustworthy data. [PERSON] and [PERSON] [57] consider ambiguity and uncertainty in their work and presents an ontology-based process which allows to model them within a smart environment. Table 2 shows the differences between each reviewed solution for modeling context. It is difficult to gather all the approaches in a unique table. Therefore, Table 2 just emphasizes several differences, although these solutions have more characteristics modeled. For example, the work by [PERSON] and  
\begin{table}
\begin{tabular}{l l  
[PERSON] [57] is focused on modeling context uncertainty and vagueness due to the nature of context data from sensors. Besides and as it is deeply discussed in [40], modelling the context with ontologies offers the following advantages:  
* Ontologies are the most expressive approach to model context. * Composition and management of the model can be done in a distributed manner. * It is possible to partially validate the contextual knowledge. * One of the main strengths of ontologies is the simplicity to enact the normalization and formality of the model. Regarding the context-aware systems, we cannot go further without remarking the issues of gathering information from different sources or sensors. This information might sometimes be unreliable. Sensors can fail in the collecting process, they also can stop working due to several reasons, e.g., power or malfunction. What is more, every sensor \"speaks\" its language (this issue is addressed applying different techniques, like data fusion). Therefore, a process to evaluate the quality and trustworthiness of the collected information should be included in every context-based system as a method to avoid undesired results.
## 2 Motor Fault Diagnosis and Prediction  
### The Problem of Fault Diagnosis  
Fault detection and diagnosis in electric motors are key enablers for predictive maintenance strategies. As motor-driven systems become more complex and critical, the early identification of anomalies is essential to avoid costly failures and downtime. A typical diagnosis process is structured into the following four stages: signal acquisition, signal processing, fault detection, and fault classification [14,15].  
The first step involves acquiring signals such as current, voltage, or vibration, typically using non-invasive sensors. Once collected, these signals are preprocessed to enhance relevant features and suppress noise. In the detection stage, anomalies are identified based on deviations from expected patterns, which are then classified to determine fault type, severity, and location.  
Faults in electric motors manifest through changes in electrical behavior and are often subtle and masked by operational variability. This complexity necessitates advanced diagnostic methods capable of extracting meaningful patterns from noisy and dynamic data streams. The challenge lies not only in detecting these anomalies, but also in predicting their evolution over time [16].
# Underwater Wireless Sensor Networks Performance Comparison Utilizing Telnet and Superframe
## 4 Proposed Underwater Network Topology
The value of \(\text{d}_{\text{min}}\) is an important parameter in determining the size and shape of the clusters in the CB-UWSN, which will vary from 1 m to 3 m, based on the cluster node distance. It ensures that the sensor nodes within a cluster are in close proximity to one another, which can improve communication efficiency and reduce energy consumption.