esa-sceva/satcom-chunk-collection huggingface dataset:
## A Comprehensive Study of Data Collection Schemes Using Mobile Snks in Wireless Sensor Networks  
**[PERSON], [PERSON] *, [PERSON] and**  
**[PERSON]**  
Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor 81310, Malaysia;  
E-Mails: [EMAIL_ADDRESS] (A.W.K.); [EMAIL_ADDRESS] (M.H.A.); [EMAIL_ADDRESS] (J.I.B.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +607-553-8761; Fax: +607-553-8822.  
_Received: 12 December 2013; in revised form: 22 January 2014 / Accepted: 30 January 2014 / Published: 5 February 2014_  
**Abstract:** Recently sink mobility has been exploited in numerous schemes to prolong the lifetime of wireless sensor networks (WSNs). Contrary to traditional WSNs where sensory data from sensor field is ultimately sent to a static sink, mobile sink-based approaches alleviate energy-holes issues thereby facilitating balanced energy consumption among nodes. In mobility scenarios, nodes need to keep track of the latest location of mobile sinks for data delivery. However, frequent propagation of sink topological updates undermines the energy conservation goal and therefore should be controlled. Furthermore, controlled propagation of sinks' topological updates affects the performance of routing strategies thereby increasing data delivery latency and reducing packet delivery ratios. This paper presents a taxonomy of various data collection/dissemination schemes that exploit sink mobility. Based on how sink mobility is exploited in the sensor field, we classify existing schemes into three classes, namely path constrained, path unconstrained, and controlled sink mobility-based schemes. We also organize existing schemes based on their primary goals and provide a comparative study to aid readers in selecting the appropriate scheme in accordance with their particular intended applications and network dynamics. Finally, we conclude our discussion with the identification of some unresolved issues in pursuit of data delivery to a mobile sink.  
**Keywords:** sink mobility; energy consumption; data delivery latency; packet delivery ratio; wireless sensor networks
## 2 Description of the Problem  
The scheduling problem considered in this study can be described as follows: A set of independent jobs \(J_{i}\) (\(i=1,2,\ldots,n\)) has to be scheduled on a single machine without interruption or preemption. All the jobs are available for processing at time zero, machine breakdown does not occur, and the machine can process only one job at a time. Each job \(J_{i}\) has a positive processing time \(p_{i}\) and a due date \(d_{i}\). Jobs are classified into \(F\) families. Each family \(l\) has \(n_{l}\) jobs (\(l=1,2,\ldots,F\)) such that \(n_{1}+n_{2}+\ldots n_{F}=n\). (\(i\)) denotes the family of job \(i\). Here, the family setup time, \(s_{ij}\), is determined by job \(j\) that is scheduled to be performed after job \(i\) by the machine, with \(f(i)\
eq f(j)\). If jobs \(i\) and \(j\) belong to the same family (\(f(i)=f(j)\)), there is no setup time, that is, \(s_{ij}\) is zero. The following notations are used to describe the SMSDFS problem and the proposed DILA in the next section:  
\(i\) = job index, \(i\) = 1,2,3,..., \(n\)  
\(p_{i}\) = processing time of job \(i\)  
\(l\) = family index, \(l\) = 1,2,3,..., \(F\)  
(\(i\)) = the family of job \(i\)\(s_{ij}=\) family sequence-dependent setup time of job \(j\) is processed immediately after job \(i\)\(d_{i}=\) due date of job \(i\)\(P1=\) population \(1\)\(P2=\) population \(2\)\(P_{num1}=\) the number of members in \(P1\)\(P_{num2}=\) the number of members in \(P2\)\(\Pi_{1p}=\) the solution \(p\) in \(P1\)\(\Pi_{2p}=\) the solution \(p\) in \(P2\)\(\Pi^{best}=\) the best solution in \(P1\) and \(P2\)\(\Pi^{best}=\) the local best solution in \(P1\)\(\Pi^{best}_{2}=\) the local best solution in \(P2\)\(V_{1p}=\) the value of the member \(p\) in \(P1\)\(V_{2p}=\) the value of the member \(p\) in \(P2\)\(V^{best}_{1}=\) the value of the local best solution in \(P1\)\(V^{best}_{2}=\) the value of the local best solution in \(P2\)\(V^{best}=\) the value of the global best solution in \(P1\) and \(P2\)\(\Pi^{VND}=\) the solution generated by variable neighborhood descent (VND) algorithm \(V^{VND}=\) the value of the solution generated by VND algorithm \(c1,\ c2=\) the values for controlling the probabilities of executing the member-added function and member-removed function \(C_{i}=\) completion time of job \(i\)\(T_{i}=\)\(\begin{cases}(C_{i}-\mathrm{d}_{i})&\text{if }(C_{i}-\mathrm{d}_{i})>0,\\ 0\text{ otherwise.}\end{cases}\)  
The scheduling problem considered in this paper is to minimize the total tardiness. The objective can be defined as below:  
Objective = Minimize \(\sum_{i=1}^{n}T_{i}\).
## 5 Experimentation, Results, and Analysis
### Results  
Evaluation with Baselines  
Table 6 provides a comprehensive evaluation of our proposed DCAT model against several baseline models across different datasets. A numerical analysis of the metrics reveals that DCAT consistently outperforms the baseline models.  
\begin{table}
\begin{tabular}{l c c} \hline
**Metric** & **Weighting Factor** & **Rationale** \\ \hline BLEU & \(\delta=0.5\) & Linguistic accuracy \\ METEOR & \(\epsilon=0.3\) & Advanced linguistic features \\ CA & \(\zeta=0.2\) & Contextual relevance \\ \hline \end{tabular}
\end{table}
Table 5: Composite Evaluation Score (CES) weighting factors.  
* **Flickr8k Dataset:** DCAT achieves a BLEU score of 0.7425, METEOR score of 0.4391, CES score of 0.6279, and CA score of 0.4182. These scores are notably higher compared to the next best-performing model, AoANet, which has BLEU, METEOR, CES, and CA scores of 0.7135, 0.4304, 0.5935, and 0.3427, respectively.
* **Flickr30k Dataset:** On this dataset, DCAT registers BLEU, METEOR, CES, and CA scores of 0.7388, 0.4356, 0.6243, and 0.4129, respectively. Again, these are superior to the AoANet model, which has respective scores of 0.7117, 0.4288, 0.5916, and 0.3411.
* **MSCOCO Dataset:** For the MSCOCO dataset, DCAT maintains its superior performance with BLEU, METEOR, CES, and CA scores of 0.7366, 0.4329, 0.6215, and 0.4096. The closest competitor, AoANet, lags behind with scores of 0.7099, 0.4272, 0.5897, and 0.3395.  
The analysis clearly shows that DCAT is consistently the top-performing model across all datasets and metrics. The margin of improvement in metrics like BLEU and METEOR ranges from 0.02 to 0.03, which is statistically significant. The CA metric, designed to measure contextual richness, also indicates that DCAT is better at capturing context in the generated captions.
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 2 Underwater Integrated Navigation System  
### System Structure  
As mentioned in the previous section, auxiliary sensors are necessary to assist the SINS for high accuracy. In an underwater integrated navigation system, commonly used aiding equipment include DVL, magnetic compass (MCP), pressure sensor (PS), global positioning system (GPS), acoustic positioning system (APS) and geophysical navigation system (GNS, such as terrain aided navigation and gravity aided navigation). Unfortunately, the GPS signals are unavailable under water, so the vehicle with GPS needs to surface regularly. APS requires the vehicle to be placed within the coverage area of transponders, which limits the operation range of the vehicle. Similarly, GNS restricts the vehicle to work in the area where certain geography information is known. The DVL, without the need to use external sources, is a popular sensor to assist the SINS in underwater environment. Unfortunately, the observability of heading is low for SINS/DVL integrated navigation system, therefore a MCP is adopted to provide heading information. In addition, a PS is used to provide height (or depth) information due to the divergence of the height from the SINS and the low accuracy of the vertical velocity from the DVL [4,22]. Accordingly, SINS/DVL/MCP/PS integrated navigation system is established to perform independent underwater navigation.  
Figure 2 shows the system structure of the SINS/DVL/MCP/PS integrated navigation system. Because of its less computational effort and high fault-tolerance, the federated filter is adopted to fuse the multi-sensor data. The SINS is the reference system providing continuous velocity, attitude and position information. Three local filters implemented with KF are composed of the DVL, the MCP and the PS assisting the SINS, respectively. The measurement deviation between the SINS and the assistant navigation observer is given to the corresponding local filter as input. Since the DVL provides the velocity measurements in the body frame (\(b\) frame), the outputs of the DVL need to be transformed to the navigation frame (\(n\) frame) by the attitude matrix obtained from the SINS.
## 2 Methodology  
Figure 1 depicts an overall view of the steps carried out to meet the purpose of the current study. Each task is detailed in the next sections. Three well-known accuracy criteria used for evaluating the quality of the performance of the models are the root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R\({}^{2}\)) which are expressed as follows:  
\[RMSE=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}{\left[(Y_{i_{observed}}-Y_{i_{ predicted}})\right]}}^{2}} \tag{1}\]  
\[MAE=\frac{1}{N}{\sum\limits_{l=1}^{N}{\left|Y_{i_{observed}}-Y_{i_{predicted}} \right|}} \tag{2}\]  
\[\text{R}^{2}=1-\frac{\sum\limits_{i=1}^{N}{\left(Y_{i_{predicted}}-Y_{i_{observed }}\right)^{2}}}{\sum\limits_{i=1}^{N}{\left(Y_{i_{observed}}-\overline{Y}_{ observed}\right)^{2}}}, \tag{3}\]  
where \(Y_{i_{predicted}}\) and \(Y_{i_{observed}}\) symbolize the predicted and observed SCCs, respectively. Also, \(\overline{Y}_{observed}\) is the average of the observed SCCs, and \(N\) denotes the number of samples.
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 5 Policies
### Policy Engine and Inference Store  
Policies are interpreted by the PE component. The most important function is to infer whether a request for transmission can be granted or not. The case of \"allow\" is rather simple; the Controller returns a permission (allow) to Software Control, which then can start transmitting. In the case it is denied, there may be a bigger issue that should be resolved by the PE. For instance, if the request cannot be granted for transmitting at this time and at this power, the question is: what should the Controller do? If the only response from PE is allow or deny, the Controller may need to attempt many different requests (assuming it knows how to generate them) before one is finally granted, if at all. To avoid this \"long-loop\" scenario, the PE can produce a list of transmission opportunities that would be allowed in the current state and given the current policy.  
Internally, to generate opportunities, the Controller uses the Planner (see Section 6.4) and VIStology's Inference Store. The Inference Store (IS) is a component that combines the power of BaseVISor's OWL inference capability [17] with an in-memory triple store that exposes a SPARQL 1.1 interface [16]. Upon changes to the triple store (assertion of new facts), BaseVISor is automatically triggered and injects implicit facts into the store. Thus, at query time, all facts (explicitly asserted and inferred) are immediately available.  
IS stores the currently active policy (provided by the PM) in its ontological form. Each policy is represented in OWL according to the structure that is partially represented in Figure 7. The top-level class--SpectrumAccessPolicy--can be marked as _active_, and the PM ensures that exactly one policy is active at any given time. The policy comprises a number of SpectrumAccessRules that are related to it via the _hasRule_ object property. Each rule _goverris_ exactly one SAPHyperspace, which represents a multi-dimensional space defined by frequency range, time interval, a geospatial region and other sets, as described in Section 5. The concrete definitions of these dimensions are imported from the pre-existing ontologies: SpectralSPARQL [13] and OGC GeoSPARQL [14]. Each SpectrumAccessRule either allows or denies access to the SAPHyperspace that it governs, which is modeled in the ontology with two subclasses: AllowedSpectrumAccessRule and DeniedSpectrumAccessRule. Instances of the AllowedSpectrumAccessRule can specify the maximum transmit power (defined in SpectralSPARQL) that is allowed within the governed hyperspace. The ontology was designed using the best ontology engineering practices, i.e., modularity and reuse, which allow for a rapid development and support the integration of the policy data with other sources of information in future scenarios.
## 4 Modeling of Physicochemical Parameters and Their Impact on Thermal Processes
### Accounting for Chemical Reactions During Thermal Processes  
Thermal processing in the food industry extends beyond mere heat transfer, involving complex chemical reactions that influence both the sensory properties and the safety of food products. A key example is the Maillard reaction, a non-enzymatic browning process that occurs between reducing sugars and amino acids. This reaction is highly temperature-dependent and follows intricate pathways that are sensitive to both time and temperature conditions [139]. The Maillard reaction contributes to the development of characteristic flavours, aromas, and the desirable brown colour in cooked foods. To model this reaction accurately within thermal processes, it is essential to incorporate detailed kinetic models that account for the thermally activated nature of the reaction [29].  
The reaction rate can be expressed using an Arrhenius-type equation (Equation (18)):  
\[k=A\times e^{-\frac{E_{a}}{kT}} \tag{18}\]  
where \(k\) is the rate constant, \(A\) is the pre-exponential factor, \(E_{a}\) is the activation energy, \(R\) is the universal gas constant, and \(T\) is the absolute temperature. This equation reflects the temperature dependence of the Maillard reaction rate, with \(k\) increasing exponentially as the temperature rises, thereby affecting the quality and safety of the final product [34]. In parallel, protein denaturation is another crucial chemical process occurring during thermal treatment. As the temperature increases, protein molecules undergo conformational changes that result in the loss of tertiary structures, leading to altered solubility and emulsifying properties. These transformations significantly affect food texture, water retention, and other functional characteristics. The kinetics of protein denaturation can also be described using an Arrhenius model, where the rate of denaturation is similarly dependent on temperature [140; 141]. To accurately simulate these chemical transformations, CFD is coupled with reaction kinetics models, enabling the prediction of heat transfer, mass transfer, and the evolution of chemical reactions under different thermal conditions. The CFD simulations solve the governing equations of fluid flow, energy conservation, and species transport. These include the Navier-Stokes equations for momentum conservation,the energy equation for heat transfer, and species conservation equations for chemical reaction modeling. By incorporating reaction rates into these simulations, it is possible to simulate the concurrent effects of thermal processes and chemical reactions on food quality [74; 142].
## 3 Results and Discussion: Experimental Tests
### Fresh Performance: Slump Flow  
The slump flows of all the mixes, shown in Figure 2, were between 750 mm and 850 mm. The results met the initially established slump-flow requirement (SF3 slump-flow class), despite the 100% coarse RA incorporated in all the mixes, the angular forms of which tended to reduce concrete workability [43].  
The addition of the fine fraction of RA increased the slump flow in an approximately linear way, possibly due to its higher fines content than rounded siliceous sand (Figure 1). Thus, the irregular shape of fine RA was no longer a problem, having addressed its higher water absorption in the mix design [36]. The positive effect of fine RA was not carried over to RA powder, whose use led to the lowest slump flows, some coinciding with the minimum required value of 750 mm. A plausible explanation may be that the more irregular shape and lower density of this aggregate powder in comparison with limestone aggregate powders hindered the dragging of the larger aggregate particles, thus reducing the SCC flow, as some other researchers also noted [50]. The smaller particle size of the limestone  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Aggregate** & **F0** & **F50** & **F100** & **L0** & **L50** & **L100** & **R0** & **R50** & **R100** \\ \hline Limestone filler & 165 & 165 & 165 & 0 & 0 & 0 & 0 & 0 & 0 \\ Limestone & 0 & 0 & 0 & 335 & 335 & 335 & 0 & 0 & 0 \\ powder & 0 & 0 & 0 & 0 & 0 & 0 & 305 & 305 & 305 \\ RA powder & 0 & 0 & 0 & 0 & 0 & 0 & 305 & 305 & 305 \\ Siliceous sand & 1100 & 550 & 0 & 940 & 475 & 0 & 940 & 475 & 0 \\ Fine RA & 0 & 505 & 1010 & 0 & 435 & 865 & 0 & 435 & 865 \\ Coarse RA & 530 & 530 & 530 & 530 & 530 & 530 & 530 & 530 & 530 \\ Viscosity & 2.30 & 2.30 & 2.30 & 2.30 & 2.30 & 2.30 & 2.30 & 2.30 & 2.30 \\ regulator & & 4.50 & 4.50 & 4.50 & 4.50 & 4.50 & 4.50 & 4.50 & 4.50 \\ Plasticizer & 185 & 210 & 235 & 185 & 210 & 235 & 200 & 220 & 245 \\ Cement & 300 & 300 & 300 & 300 & 300 & 300 & 300 & 300 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SCC composition.  
Figure 2: Slump flow of SCC mixes.  
filler <0.063 mm compared to the limestone powder 0/0.5 mm also increased the slump flow of the resulting SCC.
# Dynamic Viscosity and Transverse Ultrasonic Attenuation of Engineering Materials
## 3 Results and Discussion
### Attenuation Behavior of Non-Ferrous Metals and Alloys
In three singl \(\times\) phase Ni alloys, Monel 400, Inconel 600 and 625, C\({}_{\rm dt}\) increased over C\({}_{\rm d}\) and their variations were large, ranging from 20 to 180 dB/m/MHz. Lower values of Inconel 625 alloy appear to come from cold work. Similar changes were found for Inconel 718 and 738 alloys, but hardening is presumably from precipitation hardening. Known heat treatment history is needed for further exploration. One single crystal Ni\({}_{3}\)Al sample provided C\({}_{\rm d}\) and C\({}_{\rm dt}\) values and the latter was nearly twice larger, as in many of Ni-base alloy results. Using an Inconel 738 plate, directional anisotropy of C\({}_{\rm d}\) and C\({}_{\rm dt}\) was evaluated. Since C\({}_{\rm d}\) for the L orientation showed the frequency-squared dependence, direct comparison is difficult. However, the overall behavior appears to be identical to most other directional tests conducted. That is, the attenuation becomes higher in the order of L, T, and S orientation. This means that this plate underwent mechanical processing after casting. In some Al alloy bars, however, extrusion was apparently used and S orientation was similar to T orientation without showing the highest attenuation. The final group includes two Ti alloys, Ti-6-4 and Ti-6-6-2 and tungsten (W). The C\({}_{\rm d}\) and C\({}_{\rm dt}\) values for Ti-6-4 and tungsten were comparable, while these for Ti-6-6-2 showed the usual factor of two increase for the transverse mode. Similar C\({}_{\rm d}\) and C\({}_{\rm dt}\) values were also found in seven other tests for Cu alloys (N44, N45, N64, N103), Ni (N72), and Inconel 738 (N109, N110). Tungsten is another metal beside Al with elastic isotropy. Its high hardness should suppress dislocation damping, but its attenuation values were comparable to Al alloys at 12 to 13 dB/m/MHz. In fact, C\({}_{\rm dt}\) values of Al alloys were often higher than W. This finding does support the dislocation bow-out mechanism for damping [13]. Reasons for comparable attenuation between the two vibration modes and the absence of hardness effect need further studies.
## 2 Materials and Methods
### Non-Uniquely-Decodable Data Coding
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(d=\) & **4** & **6** & **8** & **10** & **12** & **14** & **16** & **20** \\ \hline Overhead per bit & & & & & & & & \\ \(\frac{d-1}{d\cdot 2^{d-1}}\approx\) & 0.094 & 0.026 & 0.007 & 0.002 & \(1.1\times 10^{-4}\) & \(4.4\times 10^{-4}\) & \(2.8\times 10^{-5}\) & \(1.8\times 10^{-6}\) \\ \hline Payload per bit & & & & & & & & \\ \(1-\frac{2}{d}+\frac{2(d+1)}{d\cdot 2^{d}}\approx\) & 0.656 & 0.703 & 0.759 & 0.802 & 0.834 & 0.857 & 0.875 & 0.900 \\ \hline Dis.Info. per bit & & & & & & & & \\ \(\frac{2}{d}-\frac{4}{d\cdot 2^{d}}\approx\) & 0.438 & 0.323 & 0.248 & 0.200 & 0.167 & 0.143 & 0.125 & 0.100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The payload, disambiguation information, and overhead bits per each original bit introduced by the proposed non-UD coding for some selected \(d\) values.
## 1 Introduction
Due to the complex local geological conditions, unpredictable site conditions and other factors of seismic propagation it is difficult for traditional regression methods to achieve predictions consistent with the observations [25]. To establish a reasonable regression relationship for a local site, more effort is needed to seek more accurate methods for predicting ground motion characteristics. With the continuous rapid development of artificial intelligence methods, more and more scholars are committed to applying various machine learning techniques to ground motion prediction [29]. These methods are applied to the problems solved by traditional regression methods. Recently, new algorithms, such as the Bayesian method, clustering methods and neural network methods have been gradually developed for ground motion predictions [30; 31]. For example, a new method of seismic site classification was proposed using HVSR [32] curves and a neural network [33]. For the aspect of attenuation prediction, [PERSON] and [PERSON] [29] proposed an effective generalized learning network for nonparametric spatial modeling to predict the ground motion attenuation law for Wenchuan earthquake in China. The M5 tree method [6], heterogeneous Bayesian learning and the back-propagation neural network (BPNN) were used by [PERSON] and [PERSON] [34] and [PERSON] and [PERSON] [6; 29], respectively, to predict the ground motion, improving the previous learning algorithm and providing a solid support for the automatic prediction of basin ground motion. As a new machine learning algorithm, the convolutional neural network (CNN) with convolution layers overcomes the disadvantages of traditional BPNNs. Compared with the typical machine learning models such as BPNNs, the CNN has dimension reduction [35] and strong feature extraction ability, fuses multiple inputs and converges fast. Furthermore,it can extract some important features more effectively. In the feature extraction stage, the CNN can learn directly from the original input data and optimize the features in the training stage, then grasp the feature information more accurately. As real seismic records in a local domain contain many kinds of potential seismic characteristics, the combination of multiple features when training a CNN is likely to result in a better method. It can be seen that using the CNN method to predict the amplification characteristics of ground motion offers a new direction in this research field. In this paper, based on the growing number of ground motion data for the densely distributed local stations and the detailed geology data for Lower Hutt Valley, New Zealand, the CNN method was applied to establish novel prediction models to predict the surface seismic amplification factors in this local area. The seismic observation records of 44 earthquakes from seven stations were selected, and seven CNN models were created. The CNN models were used to identify the surface amplification factors for the basin with strong earthquake observation data.