esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Improvement of the Auxiliary Surface Layout  
As mentioned in [19; 20], MAS becomes less accurate when the auxiliary surfaces are conformal to boundaries encompassing wedges. In particular, satisfaction of the boundary condition at midpoints (MPs) (see Figure 1) close to the tips is no longer sufficiently good. To overcome this complication, the auxiliary surface, defined by radius \(\rho_{aux}\) and using expressions analogous to (1-3), may be deformed so that ASs not only approach the tips closely, but become denser in the tip neighborhood as well. ASs may approach CPs following several patterns. In this work, two basic patterns were tested. Let \(M\) be the number of ASs to be moved. Let \(\rho_{m}\) be the polar radius of the \(m\)th AS (\(m=1,\,2,\,\dots,\,M\)), and let \(g\) be the maximum polar radius difference between the \(m\)th AS and the \(m\)th CP. Finally, let \(s\) be the proximity factor, defined in \([0,1]\), so that \(0\) corresponds to no approach and \(1\) corresponds to the maximum approach (resulting in coincident ASs and CPs). Then, the schemes proposed for the auxiliary surface deformation are defined in (8) as follows:  
\[\rho_{m}^{\prime}=\rho_{m}+g\varsigma\left(\frac{m}{M}\right)^{\
u}, \tag{8}\]  
where \(\
u=1\) for simple and \(\
u=2\) for progressive reach. The deformation effect is graphically described in Figure 3.  
Furthermore, in a way similar to [20], ASs and CPs, accordingly, should become denser close to the wedge tip. Again, there is no unique way to accomplish this. In this work, the scheme implemented multiplies the polar angle \(\varphi_{m}\) of the \(m\)th AS location by a factor \(D_{m}\), \(0<D_{start}\leq D_{m}\leq 1\), where \(D_{start}\) is user-defined. For example, in the first quadrant of the 'eye', \(D_{m}\) is defined as being close to \(0\) for ASs near the wedge tip, and close to 1 for ASs close to the vertical axis. For progressive densification, the scheme proposed is: \(\varphi_{m}^{\prime}=\varphi_{m}D_{m}^{2}\). Moreover, additional ASs may be superimposed to the already existing ones close to the tips if necessary. The combined effect of the tip approach and densification is depicted in Figure 4.
# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 2 The Proposed Hybrid Evaluating Method
### Models
#### 2.1.4 Analogy-Based Method  
The analogy-based method was first used for economic business forecasting in the 1920s ([PERSON] et al., 2017), a forecasting process was proposed so that the experts can use the process to conduct analogy. [PERSON] et al. (2017) carried out verification on an airport in Norway to compare the performances of elastic models and analogy-based methods, and found that under particular circumstances, analogy-based methods performed better. The employed process is shown below.  
Supposing that \(x_{it}\) is the APT of the studied airport at time \(t\), \(x_{it}\) is the APT of the analogical airport at time \(t\). The predicted result \(\hat{x}_{i(t+T)}\) can be obtained by the following steps.  
_Step 1_: Determining the target predicting time \(T\).  
_Step 2_: Determining the conditions for analogy. For the APT prediction, the analogical condition can be determined by referring to the airport with a similar APT and setting the allowed error \(\theta\). The analogical condition can be described as \(x_{it}*(1-\theta)\leq x_{it}\leq x_{it}*(1+\theta)\).  
_Step 3_: Filtering the airports which satisfy the analogical condition from a database and all the \(J\) satisfied airports are collected as a set \(D\).  
_Step 4_: Obtaining the airports' APT data from \(D\) as a collection \(X_{j(t+T)}\).  
_Step 5_: The predicted result can be calculated by:  
\[\hat{x}_{i(t+T)}=\frac{\sum_{j=1}^{J}x_{j(t+T)}}{J} \tag{26}\]
# Comparison of the Usefulness of CBCT and MRI in TMD Patients According to Clinical Symptoms and Age
###### Abstract  
Recently, the number of patients who visit the hospital with symptoms of temporomandibular joint disorders (TMD) has been gradually increasing, and the need for special imaging such as cone-beam computed tomography (CBCT) and magnetic resonance imaging (MRI) is increasing for accurate diagnosis and treatment. The purpose of this study was to help the image guideline by comparing the usefulness of CBCT and MRI according to the clinical symptoms of TMD patients and further examining whether the usefulness varies with age. A total of 473 temporomandibular joints (TMJs) with clinical symptoms of TMD who underwent both CBCT and MRI examinations were retrospectively reviewed. Clinical symptoms included pain, sound and limited mouth opening. The CBCT findings included sclerosis, flattening, erosion and osteophyte, while the MRI findings were defined as disc deformity, disc derangement or joint effusion. Joints were divided according to the presence of CBCT and MRI findings as follows: type I (neither CBCT nor MRI findings), type II (only CBCT findings), type III (only MRI findings) and type IV (both CBCT and MRI findings). We assessed the usefulness of the two imaging modalities by comparing the frequency of those four groups according to clinical symptoms and age. In TMD patients with the clinical symptoms, MRI and CBCT are complementary, but if it is difficult to choose the first of these two modalities, MRI is more recommended, and the younger the patient, the more the MRI is recommended.  
one-beam computed tomography; magnetic resonance imaging; temporomandibular joint; temporomandibular joint disorders +
Footnote †: journal: Medical  
time, and radiation dose, so it may be useful to have imaging guidelines. CBCT or CT is suitable for evaluating bony changes of the TMJ [3; 4; 5] while the MRI is useful for evaluating soft tissue changes and internal disc derangement [6]. Because of the low dose radiation and isotropic resolution of CBCT, CBCT is more recommended than CT for hard tissue evaluation of TMJ [4]. If a detailed clinical examination of TMD patients can distinguish between hard and soft tissue problems, in theory, an informed decision can be made to conduct an imaging work-up using CBCT (CT) or MRI. However, in reality, no matter how detailed the clinical examination, it is difficult to distinguish between hard tissue problems and soft tissue problems. It is not easy to determine which CBCT or MRI is better suited for diagnosis.  
Numerous studies have investigated the relationships between CBCT findings and clinical symptoms [7] and between MRI findings and clinical symptoms [8; 9; 10; 11; 12; 13]. However, there is a lack of imaging guidelines according to clinical symptoms, and few studies have reported age-based imaging guidelines.  
The purpose of this study was to evaluate CBCT and MRI findings in patients with TMD, to identify whether there were differences in the usefulness of these two imaging modalities according to clinical symptoms and age, and to help imaging guidelines.
# A Novel Self-Adaptive VM Consolidation Strategy Using Dynamic Multi-Thresholds in IaaS Clouds
## 6 Conclusions and Future Work  
In this paper, we present a novel self-adaptive VM consolidation strategy based on dynamic multi-thresholds (DMT) in IaaS Clouds. First, we define each parameter of the multi-thresholds, including CPU, RAM, Bandwidth, SLA, VM migrations, as well as a dynamic adjustment mechanism for these parameters of the threshold. After that, we improve the VM selection algorithm (MW-MVM) based on this dynamic multi-threshold, which fully utilizes the multi-dimensional parameters of our proposed dynamic threshold by predicting their future values. To further reduce the cost of VM migration and improve the efficiency of VM consolidation, a modified VM placement algorithm is then discussed (MW-BF). Finally, the experiments of the proposed strategy are performed on CloudSim, compared with the three benchmark strategies and one prediction strategy. The simulation results show that our proposed dynamic multi-thresholds strategy (DMT and DMT (MW)) for VM migration has a good performance, not only in lower SLA violation, but also in less energy cost and VM migrations. Particularly, these advantages of our proposed VM consolidation strategy are much superior than other strategies when the whole cloud system is at high or low workload, as well as the reduction in number of active hosts.  
Furthermore, since the experimental objects in this paper are only 50 heterogeneous servers, the number of PMs in the real cloud system is often as high as the tens of thousands. Thus, the effect of energy-efficiency and QoS for VM consideration using our proposed strategy will be much more obvious, which will help further decrease carbon dioxide and overall energy consumption in IaaS clouds. In the future, we plan to consider other different parameters in the multi-thresholds strategy and will further explore the impact weights of each parameter. At the same time, we will also focus on the modification of other VM selection and placement algorithms to further improve the migration-efficiency and energy-efficiency of VM consolidation in IaaS clouds.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data and provided technical support; [PERSON] contributed analysis tools and revised this paper; [PERSON] wrote the paper.  
This work is partially supported by the National Natural Science Foundation of China (NSFC) under grant No. 61572306, The National Key Research and Development Program of China (No. 2017 YFB0701600), Shanghai Innovation Action Plan Project under grant No. 16511101200, Shanghai University Material Genetic Engineering Institute (No. 14 DZ2261200), and the Japan Society for the Promotion of Science under Grants-In-Aid for Scientific Research 15H04678.  
The authors declare no conflict of interest.
## 4 Experimental Setup and Data Processing
### Data Processing  
In the experiment, we repeated measurements 30 times for each type of manufacturing defect, and the duration of a single measure was 5 s. The total sampling time was 150 s. The sampling time of each sample was 0.1 s, and each sample contained 2560 data points. Thus, the data set had 9000 samples, of which 7200 samples are the training set, and the remaining 1800 samples are the testing set. The number of samples for the training set and test set for six typical manufacturing defects are shown in Table 2. We randomly selected 20% of the training set data as the validation set. Each sample was transformed into a group of time-frequency images using CWTs and HHTs, simultaneously. The energy scales of CWT time-frequency images and the HHT time-frequency image are [0, 0.1] and [0, 0.01], respectively.
## 1 Introduction
The main contributions of this paper can be summarized as follows:  
1. From several initially assessed use cases, after careful analysis and consultation with ESA, we selected the two most prominent use cases to be analysed. The list of selected use-cases was subsequently also approved by ESA, which was mandatory in order to move forward. The selected use cases were: (1) opportunistic sharing of GSO/NGSO satellite and terrestrial systems in downlink Ka-band; and (2) opportunistic sharing of GSO/NGSO satellite and terrestrial systems in uplink Ka-band. The major criterion for selecting relevant dynamic use cases was the presence of three systems (GSO, NGSO, and terrestrial networks) operating in the same frequency band. In comparison, current research on applying DSS techniques in SATCOM is focused on use cases where only two systems share the spectrum. 2. To solve the interference and spectrum underutilization problems in selected use cases, two DSS techniques were presented: (1) DSS1--collaboration protocol; and (2) DSS2--decentralized spectrum sensing. 3. A baseline scheme with fixed spectrum assignment was defined for comparison of the DSS techniques in terms of performance according to the following key performance indicators (KPIs): spectrum utilization, aggregated system throughput, packet error rate (PER), latency, system uptime, control overhead, etc. 4. The first demonstration of distributed DSS techniques for efficient spectrum sharing across multiple satellite systems (with ESIMs) and terrestrial networks, while protecting GSO and NGSO downlink and uplink transmission as well as terrestrial links. The remainder of this paper is structured as follows: Section 2 presents an overview of state-of-the-art related to DSS and interference mitigation in SATCOM scenarios, Section 3 includes detailed information on the two selected use cases and problem statement, in Section 4 the proposed approach and requirements are defined, Section 5 comprises brief information on the proposed DSS techniques, Section 6 presents a simulation scenario and performance analysis of the DSS techniques against the baseline, and finally, Section 7 concludes the study.
# Sustainable Data-Driven Secured Optimization Using Dynamic Programming for Green Internet of Things
## 4 Simulations
### Results and Discussion
Furthermore, unlike most of the existing work, the communication links are monitored regularly, and whenever any malicious action is performed, appropriate alert messages are recorded in the forwarding tables. Accordingly, the SDS-GIoT model reduces the affected data packets over the wireless medium. As a result, the SDS-GIoT model efficiently tackles the misbehaving threats from the nodes and decreases the communication complexity for data transmission and resources management. Figure 6: Scenarios for varying IoT devices and data generation rates in terms of communication complexity. (**a**) communication complexity and IoT devices. (**b**) communication complexity and data generation rates. IoT devices and data generation rates. It is due to the following aspects. Firstly, intelligent computing for developing a sustainable system increases the strength of the chosen routes and efficiently balances the load distribution. Secondly, dynamic programming always tries to attempt the most reliable forwarders to route the system data toward the sink node using edge efficacy. Additionally, the dynamic decision effectively prevents the system from entering the incorrect states and controls the flooding of fake control packets. Such a process leads to reduce complexity and improves the system's lifetime. A comparison of the SDS-GIoT model with alternative studies is shown in Figure 8a,b. According to the revealed results, it was observed that the SDS-GIoT model significantly increases the efficient utilization of energy resources of the nodes by an average of 31% and 30% in terms of varying IoT devices and data generation rates. It has been found that as the IoT network grows, the network medium is also busy forwarding the huge nodes' data, which greatly decreases the performance of the network in terms of energy consumption. On the other hand, the SDS-GIoT model provides an intelligent solution using dynamic programming and explores the routes in the graphs for the formulation of the routing phase. Additionally, edge computing balances the load distribution for its closer nodes and transmits the data on time. The blockchain-based technology in the SDS-GIoT model also avoided the probability of route breaches and minimized the additional energy consumption in frequent route requests. Figure 8: Scenarios for varying IoT devices and data generation rates in terms of energy consumption. (**a**) energy consumption and IoT devices. (**b**) energy consumption and data generation rates. Figure 7: Scenarios for varying IoT devices and data generation rates in terms of no. of rounds. (**a**) Max no. of rounds and IoT devices. (**b**) Max no. of rounds and data generation rates.
# Deep Learning Optimisation of Static Malware Detection with Grid Search and Covering Arrays
### Paper Contribution  
1. The paper presents a novel approach to improving static malware detection using deep learning models with hyperparameter optimisation through covering arrays. The study demonstrates the feasibility of using cAgen in combination with grid search to find the optimal hyperparameter values. The findings show that this approach can significantly improve the accuracy of the baseline model for both the Ember and Kaggle datasets.
2. The study provides insights into the effects of different hyperparameters and their interactions on the performance of deep learning models for static malware detection.
3. It provides a framework for researchers and practitioners to improve the performance of deep learning models for malware detection through hyperparameter optimisation using covering arrays.
4. The results of this study have implications for the wider field of cybersecurity, where accurate malware detection is critical in protecting computer systems and networks.  
The targeted performance metric used in this work is accuracy. This follows many malware detection research papers and is very common practice for many deep learning applications. It is one of the major accepted performance criteria. To the best of our knowledge, no one has attempted to study the effects of different deep learning hyperparameters for malware detection. This is especially with both datasets Ember and Kaggle while utilising covering arrays and grid search approaches.
## 2 Radio Planning Aspects
### Digital Information  
The Murcia Region (South-West of Spain) is 11,296 km\({}^{2}\). The territory is organized into 45 municipalities, and each municipality delimits an area (municipal area) (see Figure 3). The geographical information has been obtained from the National Geographical Institute of Spain. A digital terrain model (DTM)--in a raster format [7]--with a 100 \(\times\) 100 m cell size (see Figure 3)--was used for the radioelectric coverage calculations in rural environments, which represents a compromise between accuracy and computation time. We can also observe the municipal areas as a vector layer with an associated attribute table that contains information (name, extension, population, etc.) in Figure 3[7].
## 4 Experiments
### Extended Experimentation
#### 4.4.2. Mistral 7B
Incorporation of Domain-Specific Datasets: A significant improvement was observed when ontology fine-tuning was expanded to include domain-specific datasets, which provided the model with richer contextual knowledge and helped mitigate early-stage errors in entity classification and relationship structuring. By grounding Mistral 7B in more domain-relevant examples, it was able to generate more coherent ontologies with improved logical consistency. 3. Expansion of Competency Questions: The number and complexity of competency questions used for model evaluation were increased in later iterations, providing a broader test set that reinforced correct interpretations of ontology structures. This iterative reinforcement led to improved model accuracy in reasoning-based ontology generation. 4. Iterative Fine-Tuning and Model Adaptation: The observed performance shift also highlights the cumulative effect of iterative fine-tuning, where each session builds upon prior refinements. While early fine-tuning attempts exposed model weaknesses, successive sessions reinforced correct classifications, improving coherence in ontology structuring. These findings suggest that fine-tuning LLMs for ontology engineering requires a dynamic and adaptive strategy. Performance can fluctuate significantly based on domain-specific datasets and the iterative refinement process. This highlights the importance of continuous optimization and specific domain data to maximize model effectiveness in domain-specific knowledge representation.
# Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression
## 4 Simulation and Analysis
### Compression Analysis  
The process of image steganography, transformation, and compression can be performed on any platform using any method. However, it is essential to have higher security and minimal storage space. Therefore, choosing an efficient and effective method will give the best results. Choosing a fast KT optimized for steganography and DWTs, along with wavelet packet compression of images, gives better results. To demonstrate the success of the proposed method, experiments were performed in MATLAB version R2017b, in which the data embedding, transformation, and compression work was carried out. MATLAB software is a tool that can be used to perform all mathematical operations, providing highly accurate results at every step. Windows 10 OS provides excellent support for productive and skillful research. KT lends an innovative flavor to steganography techniques, and transformations help protect steganography images. Additionally, the transformed image is compressed using lossless compression and decompressed again at the receiving end to the exact same resolution as the original image. The compressed images are stored in the database in less space, which helps in efficient storage.
# Simulation of Achievable Data Rates of Broadband Power Line Communication for Smart Metering
## 3 Performance of Broadband Power Line Communication--Related Works
### Repeaters Positioning in PLC Networks--Related Works  
The achievable rate of NB-PLC as a function of the number of repeaters was shown in a previous study [39]. This article focuses on strategies to efficiently use and operate repeaters via numerical results of a transmission model. In comparison, this article focuses on throughput simulation and repeater positioning in BPL for real topology together with on-field measurement for verification of simulation results.  
The need for repeaters in general depends heavily on the topology (number of branches, etc.), as was described in a previous study [40]. The influence of power line topological parameters on CTF was analysed in many previous works [31].  
In a previous study [41], the BPL performance simulation in NS-2 was conducted for two scenarios: single-hop transmission and 2-hop transmission using the repeater. In comparison, this article focuses on real topology with many repeaters.  
Evaluation of PLC Systems for Smart Metering Using Simulation Tools--State of the Art and Motivation  
PLC are very special due to their constraints [3; 11; 31]. For a power line it is quite hard to allow for prediction of the PLC behaviour. On the other hand the performance assessment via simulations for complex PLC networks with repeaters is an easy, fast, and cheap solution for understanding the grid configuration, influence of particular topological components, and performance possibilities.  
Simulation of these PLC systems using the advanced simulation tools, such as OPNET, OMNeT++, NS3, etc., has become trendy among the power system research community.  
A previous publication [42] introduced OPNET simulation for evaluation of Smart Metering Infrastructure based on PLC. Several scenarios of the Smart Metering infrastructure performance were evaluated. The PLC was considered for the transmission between local concentrators and smart meters. The PLC channel is considered as the bus-link in OPNET, because in the OPNET simulation tool the PLC channel is not designed. Therefore, the simulation does not respect the physical parameters of the power line, multipath propagation, noise, time and frequency dependency, maximum communication distances, and throughput for different PLC technologies and others.  
A cross-platform simulator for G3-PLC implemented in OMNeT++ has been presented in a previous study [43]. A simulator of in-home HomePlug-AV networks has been presented in another study [44]. Software called WITS for broadband PLC channel simulations was presented in another study [45]. The above-mentioned simulators focus mainly on simulations of the broadband PLC for in-home scenarios.  
The main representatives of simulators in MATLAB include the [PERSON] et al. simulator [46] and [PERSON] et al. simulator [47]. These simulators in MATLAB focus mainly on CTF simulations.