esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
The algorithm is validated on benchmark datasets in terms of accuracy. To address the issues of excessive power consumption, imbalanced load, and inefficient resource utilization, an efficient resource management algorithm is presented in [23]. The algorithm targets better resource utilization and load management to improve performance. The algorithm uses online resource prediction for each VM to reduce SLA violations and performance degradation. To achieve the desired goals VM placement and migration policies are used. Experiments on different workloads are performed to validate the performance of the proposed algorithm. Another algorithm [24] targets multi-variate resources utilization prediction in cloud data centers. The resources include CPU, memory, and network bandwidth. The algorithm uses a Convolutional Neural Network (CNN) and LSTM models for resource utilization prediction. Initially, the vector autoregression method is used to filter the linear interdependencies between the multi-variate data. In the next step, CNN and LSTM are used for prediction. The proposed model is evaluated with experimental results and comparative results in terms of accuracy are presented. VMs consolidation in cloud environments leads to resource wastage due to instability and high variability of resource utilization. To solve the issue, authors in [25] proposed a resource utilization prediction algorithm using support vector regression technique. The proposed method is best suitable for multi-attributes resources, particularly non-linear workloads. The algorithm is validated with experiments on real workloads and comparative results are presented. Despite many solutions in literature, resource utilization prediction of multi-variate resources still needs sophisticated solutions with improved accuracy and less execution time.
# Energy-Based Prediction of the Displacement of DCFP Bearings
## 2 State-of-the-Art Isolator Displacement Prediction  
Two of the main matters of concern during the design of the isolation systems were (I) whether the maximum displacement response of the isolation layer during the earthquake was within the capacity and (II) in the aspect of energy, whether the energy absorption capacity of the isolation system was enough in comparison to the input energy during the earthquake. In the design, nonlinear time-history analysis could be conveniently performed to obtain these values. However, in the early stages of the structural design, the structural configurations were not well defined. Thus, simplified prediction methods were generally introduced in various structural codes to obtain the design displacement or the energy demand of the isolation system under the specified seismic loads:  
1. Typical Simplified Prediction Methods for the Design Displacement of the Isolation System Introduced in ASCE (American Society of Civil Engineers), Euro Code and AIJ (Architectural Institute of Japan) [6; 7; 8].  
Figure 1: Composition of a double concave friction pendulum (DCFP) bearing.  
The methods introduced in ASCE and Euro Code are similar: they can predict the response displacement and the response force based on the simplified method of equivalent linear analysis under certain conditions. However, the two methods introduced in AIJ can give the tendency of the response reduction or the relation between the response force and displacement under various parameters by using the equivalent SDOF (Single Degree of Freedom) system or using energy balance, respectively.
# Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances
## 3 Historical Approximations  
This section deals with the perturbation and Kirchhoff approximations. Both have been extensively applied to rough surfaces and have their own strengths and weaknesses. Other approaches will be dealt with in later sections.  
Figure 2 shows the geometry used in this section for rough surface scattering.  
Figure 1: Exaggerated examples of rough surfaces generated for CIVA calculations with standard deviation of the height set to 1 mm for different kinds of rough surfaces: (**a–c**) linear profile for respective ratio \(L/\sigma=1\), \(L/\sigma=1.5\) (case of Figure 6) and \(L/\sigma=4.3\) (case of Figure 11); (**d**) planar for \(L/\sigma=1\); (**e**) cylindrical for \(L/\sigma=1\). Scales in mm.  
We consider a rough surface whose roughness is defined by z = \(h\)(x,y) where \(h\) is the height variation above or below the mean plane, z = 0. The xz plane contains the incident wavevector.
## 1 Introduction
Thus, although the trend in maritime communications is to modernize the infrastructure to allow the interconnection of ships and shore facilities through broadband communications, \"VHF voice digitalization, currently under studies, is one more expectation.\" [4]. Recent years have witnessed a dramatic increase in traffic and maritime activities, which has inevitably led to an increase in accidents and distress situations [8; 10]. Another consequence of this phenomenon is the exponential increase of both functional and non-functional noise sources, implicitly of the increasing agglomeration of the frequency spectrum. Given the particular importance of the providing adequate transmission of distress calls, distress messages, and distress traffic, the question is whether the emission classes currently used are sufficiently resistant to disturbances or whether other technical solutions, including the transition to digital communications, must be adopted. This information is particularly useful because a possible change must be adopted at the same time by all states signing the SOLAS convention, for both civilian and military ships, which are among the first ones to intervene in distress situations. In addition, for these ships, the bureaucratic circuit of endowment/modification programs is particularly long and difficult. An optimal planning of the use of electromagnetic spectrum in military naval operations, requires the knowledge of emission classes and frequencies used in search-and-rescue communications to eliminate the risk of disrupting these communications both by common channel, adjacent channel, or secondary reception channel disturbances. This paper aims to demonstrate by theoretical methods (analytical and numerical) the effectiveness of using analog systems in high-noise conditions--with a low signal-to-noise ratio (SNR), characteristic of the marine environment. The stability of analog systems employing amplitude (AM), frequency (FM), and phase (PM) modulations is investigated. The analyzed systems are currently under use in distress maritime communications. The paper is thus structured: Part 2 provides some technical details on how maritime communications are organized and regulated at international level; Part 3 is the main one and it performs a thorough analysis from the perspective of interference stability, on the behavior of analog modulation systems similar to those implemented in the communication systems on board ships and used in SAR operations. Part 4 verifies the theoretical results by simulatingthe analog systems in the Matlab-Simulink environment. Part 5 comprises the results discussion and Part 6 provides the paper conclusions.
## 1 Introduction
To improve the positioning accuracy in such anisotropic networks, we propose a multi-hop range-free localization method based on Least Square Regularized Regression, i.e., LSRR-LA (Least Square Regularized Regression based multi-hop range-free Localization Algorithm). In the case of LSRR-LA, we build a mapping model between geometric distances and hop counts by using Least Square Regularized Regression, and then we estimate the locations of unknown nodes in terms of this model. During the establishment step of this mapping model, structural risk minimization is obtained by tuning the weights between the empirical risk and a penalty item, thus ensuring enough stability in, and accuracy of, this mapping model. The rest of this paper is organized as follows. Related works are briefly introduced in Section 2. Section 3 describes the proposed LSRR-LA method in detail. Extensive simulation experiments are conducted to evaluate the performance of LSRR-LA and make comparisons with typical localization solutions, and the results are reported in Section 4. Finally, Section 5 gives some conclusions. Figure 1: An example of network anisotropy’s impact on the hop distance.
## 1 Introduction
Hence, it can easily interface with the applications and the data sources. It also has a higher offloading failure probability; therefore, the transmission reliability is decreased. Furthermore, it has less scalability in the search space. The EMSA algorithm [16] is highly elastic, costless, and trustworthy. Moreover, the information is quickly accessible by the users, and it is more reliable. Finally, it has high, virtually limitless storage capacity. Yet, it does not meet the bandwidth requirements and has a low maturity level. Furthermore, it does not have any loop-back connectivity or access control. PKI-based signatures [17] provide greater hardware redundancy and have automatic storage failover. However, the packet loss ratio is very high. In addition, the signal-to-noise ratio is very high during packet transmission. Ant colony optimization (ACO) [18] achieves excellent performance by balancing the network load, providing the increased security and integrity of the information over the network channel. However, it may result in considerable network delays, and it has a high overhead and low service quality in terms of cost, security, and latency. The Fibonacci cryptographic technique [19] can handle the network traffic and has lowcomputational complexity. However, it has a high consumption of network resources,poornode authentication, a high transmission time, and less caching ability. Hence, to resolve these challenges, a new reliable cloud data storage system was developed with optimization for high-quality service. Diverse approaches have been deployed to mitigate the cost function and increase the system's reliability [20]. During the storing process of cloud data, some critical issues are met, such as transmission and communication overhead. Scholars have implemented an effective data storage model to alleviate these challenges. Over the past few years, some experts have utilized multi-objective functions to lessen the storage cost and improve the data integrity, even considering the massive amount of data over the cloud network [21; 22]. Due to this approach, the best solutions have been obtained to solve the decisive issues to make the storage performance more effective. Moreover, different optimization algorithms such as Particle Swarm Optimization (PSO), the Grey Wolf Optimizer (GWO), etc., have been adopted. In [23], the multi-objective function was derived to maximize the resources
# Study of Image Classification Accuracy with Fourier Ptychography
## 1 Introduction
A multiple linear regression model is used to regress the relationship between independent variables including peak signal to noise ratio (PSNR), the structural similarity index (SSIM), and the dependent variable image classification accuracy. Based on the regression model, it becomes feasible to infer the image classification accuracy directly based on PSNR and SSIM rather than through the intensive and time-consuming deep learning-based image classification training. In comparison to deep learning image classification training, PSNR and SSIM are easier to calculate with lower computational cost, the proposed method then becomes useful for predicting the image classification accuracy of FPM-related visual tasks. Additionally, our research also provides insights into the general effectiveness of image classification accuracy following FPM reconstruction by comparing the FPM reconstruction with or without FPM reconstruction (also known as higher or lower resolution images). In conclusion, our contribution of using PSNR and SSIM to predict image classification accuracy is not only limited to the FPM technique but also a universal approach to other imaging techniques such as digital holography, optical scanning holography, and transport of intensity imaging [21; 22].
## 2 Proposed Finite Element Model
### Validation of Finite Element Model  
The finite element model proposed in this paper has been validated with the experimental results of test 1a presented by [PERSON] (2017) in his study \"Development of a Model for Estimation of Buried Large Diameter Thin-Walled Steel Pipe Deflection due to External Loads\" (see Figure 4).  
For the [PERSON]'s test (2017), a concrete box was previously built in the Center for Underground Infrastructure Research and Education (CUIRE) at Arlington University, and it consists of reinforced concrete walls and slab. Pea gravel bedding of 0.305 m height was placed at the floor of the soil box. Instrumented steel pipe piece was placed longitudinally along the length of the load cell and centrally along the width of the soil box.  
Figure 3: FEM analysis procedure of a buried pipe in a trench (2017).  
The pipe was embedded with native material as embedment. The embedment was placed in approximately nine layers compacted above 90% standard proctor density. Density measurement was taken through a nuclear density gage.  
A timber frame was constructed above the concrete box to provide 2.438 m high walls on the sides to facilitate additional surcharge load. The surcharge load due to compaction was achieved by 0.610 m of native material and the 2.438 m of pea gravel placed over the embedment.  
In [PERSON]'s test [16] the horizontal and vertical pipe deflection were measured by recording values u1 and u2 (inches), respectively, by means of convergence meters. Also, the micro circumferential deformations in different positions of the pipe were also measured using strain gauges.  
During the embedment phase there is a positive peaking deflection in the vertical diameter (u2) and a negative deflection in the horizontal diameter (u1). This behaviour is reversed during the backfill cover and surcharge loading when there is a reduction in the vertical diameter and an increase in the horizontal diameter.
## 4 Experimental Evaluation
### Efficiency Comparison  
In order to make a meaningful comparison, for each of our five datasets, we compare the efficiency of the best embedding method with that of the best similarity measure from Section 4.2.3, as follows:  
* **BlogCatalog:** as observed in Figure 6, NetMF with \(d=256\) (i.e., refer to Table 3) and SimRank* are the best embedding method and similarity measure showing highest accuracy, respectively. The execution time of NetMF with \(d=256\) is 6.76 (i.e., 1.85 from  
Figure 11: Efficiency of embedding methods to compute the similarity of nodes with all datasets.  
Table 16 plus 4.91 from Table 17), while the execution time of SimRank* is 4.38 (refer to Table 15); SimRank* shows almost 35% _better_ efficiency than NetMF.
* **Cora:** as observed in Figure 7, Line (i.e., with \(d\) = 512) and JPRank are the best embedding method and similarity measure, respectively. The execution time of Line when \(d\) = 512 is 60.31 (i.e., 34.62 + 25.69), while the execution time of JPRank with this dataset is 8.22; JPRank is almost 7.3 times more efficient than Line.
* **DBLP:** as observed in Figure 8, DeepWalk (i.e., with \(d\) = 128) and JPRank show the highest accuracies among embedding methods and similarity measures, respectively. The execution time of DeepWalk when \(d\) = 128 is 26.33 (i.e., 6.46 + 19.87) and that of JPRank is 8.41, which means that JPRank is 3.1 times _more_ efficient than DeepWalk.
* **TREC:** as observed in Figure 9, NetMF with \(d\) = 512 and SimRank* are the best embedding method and similarity measure, respectively. The execution time of the former method is 109.52 (i.e., 26.46+83.06) and that of the latter one is only 1.45, which means SimRank* is _significantly_ faster than NetMF.
* **Wikipedia:** as observed in Figure 10, DeepWalk with \(d\) = 64 and JacSim show the best accuracies among embedding methods and similarity measures, respectively. The execution time of DeepWalk when \(d\) = 64 is 17.10 (i.e., 16.20 + 0.90) and the execution time of JacSim is 55.29, which means DeepWalk is almost 3.2 times faster than JacSim.
# Development of a Social DTN for Message Communication between SNS Group Members
## 3 How to Build a Social DTN
### DTN Routing Protocol  
A reactive routing protocol has been designed by referring to the AODV (Ad-hoc On-Demand Distance Vector) protocol [18], which is a representative MANET (mobile ad-hoc network) protocol.  
Figure 3: Sequences for group information acquisition and community token sharing. (**a**) Group information acquisition; (**b**) Community token sharing.  
The aim of this design was that, since nodes that make up the DTN are mobile terminals, we wanted to reduce the consumption of their resources as much as possible. In other words, a route is established between nodes only when there is a request for message communication between these nodes. Only during communication, relay nodes need to hold resources for that communication. However, unlike MANET, mesh-network-based communication is not possible because Nearby Connections API [13] is used. Only ad hoc communication is possible. Therefore, we describe a function that stores, carries and forwards a message through repetitive connections and disconnections of an ad hoc communication link.
# Comparative Analysis of Image Classification Models for Norwegian Sign Language Recognition
## 1 Introduction  
An integral part of every human's life is communication. It is the way they express themselves and understand each other. For most humans, communication comes naturally. They use their ears as input and mouths as output. A less fortunate minority of humans cannot use their hearing or voice to communicate. They must rely on sign language to communicate, where visual movements and hand gestures are used.  
According to the World Health Organization, 5% of the world (430 million) struggle with hearing loss, which is likely to increase [1]. Seventy million out of those 430 million are completely deaf [2]. For the deaf and hard of hearing, it is often difficult to communicate with most of the population because a minority speaks sign language. This can cause issues in their daily lives that can be tough to deal with. Using machine learning models to create sign language translation models is one way to assist the deaf and hard of hearing [3].  
Typically, when humans do not speak the same language, they use a translator to help them understand each other. The same goes for someone who communicates through sign language when trying to understand an individual who uses oral communication. Sign interpreters are a common way to help the deaf and hard of hearing by translating for them [4]. This is an excellent service where individuals can receive aid from professional sign language interpreters in real-time. The issue with such services is availability because there is a lack of interpreters worldwide for this service always to be available [5]. Machine learning classification models can be implemented to complement sign interprets by recognizing sign language to assist the deaf and hard of hearing.  
Using machine learning models for sign language recognition is a well-researched topic. However, despite advancements in sign language recognition, there is a lack of research focused on NSL recognition. Therefore, further research is needed to determine the most effective classification model for NSL recognition to assist the deaf and hard of hearing in Norway and build a foundation for future work.  
Our paper compares machine learning classification models to determine which approach is most suitable for NSL recognition to address this issue. The research will use a custom-made dataset containing 24,300 images of 27 NSL alphabet performed by two signers using different backgrounds and lightning.  
However, it is essential to note some limitations of our study. The dataset includes images from only two signers, which may limit the model's ability to generalize across a diverse range of signers. Additionally, while our research provides valuable insights into recognizing NSL signs, it does not offer comprehensive guidelines for creating and implementing complete NSL recognition systems. The results obtained in this study should be seen as a foundation for further research and development.
## 1 Introduction  
Customer experience (CX) is one of the most important factors in the maintenance of a company's competitive advantage among its peers. Customers have greater power and influence with companies and have the means to validate them via websites or social networks, among others. Customers know more about companies' products, services, competitors, and prices, and are looking for unique and memorable experiences that accompany the delivery of systems, products, and services [1]. The relationship between companies and customers exists in various stages ranging from the initial awareness of a potential customer, through the shopping experience, until after the use of the product and/or service [2]. CX is a concept traditionally related to the study of marketing and services. Increasing interest in CX by the Human-Computer Interaction (HCI) community has recently arisen. CX extends the user experience (UX) concept. The ISO 9241 standard defines UX as \"The perceptions and responses of people, as a result of the anticipated use, of a product, system or service\" [3].  
In this work, we performed a systematic literature review of research published during the past 10 years (from 2010 to 2020), to identify customer experience definitions, influencing factors, and methods used to evaluate the context as it is applied to retail. Furthermore, we aimed to determine if heuristics exist to evaluate this concept. Because our interest in CX is founded in HCI, we focused our systematic literature review on references indexed in traditional computer science databases.  
The results obtained show a large number of definitions that explain the CX in different contexts with their respective dimensions. In addition, various methods are shown for the evaluation of the CX. However, many of these are not capable of comprehensively evaluating the CX.  
The article is organized as follows. Section 2 introduces the theoretical background. Section 3 presents the research questions and describes the methodology of the systematic literature review. Section 4 presents the studies selected, the results obtained, and the answers to the research questions. Finally, in Section 5, we present the conclusions and future work.