esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Applications of Fuzzy Logic Control for Refrigeration Systems
## 3 Fuzzy Driver Applications on RACs
### Fuzzy Control in Domestic Refrigerators  
There is very little information in the literature regarding the application of fuzzy logic control in domestic refrigeration systems. Among the works found, [PERSON] et al. [62] developed a controller model based on fuzzy logic and neural networks to improve the performance of the internal temperature of the refrigerator. Employing the controller, it was also possible to reduce the variation of the internal temperature. [PERSON] [63] concluded that fuzzy controls can reduce energy consumption by 3% by regulating compressor duty cycles and also represent a good alternative to replace thermostatic control. [PERSON] and [PERSON] [64] proposed a controller with Mamdani inference for the temperature in a domestic refrigerator with a variable speed compressor; this method was designed to make the transition from the analog control to the digital control of the refrigerators. [PERSON] and [PERSON] [65] developed a controller for the temperature and humidity of a refrigerator; through simulation they verified that the fuzzy control saved operating costs and at the same time had fewer fluctuations in the internal temperature. [PERSON] et al. [66] proposed an alternative method for the fuzzy controller, combining it with genetic algorithms and comparing it with the PID control. Using a third-order discrete-state system, they calculated the air temperature in the refrigerator and determined that the combination of genetic algorithms with fuzzy logic exhibited better thermal behavior and, consequently, the temperature of the setpoint was reached quickly, thus reducing energy consumption by 0.3957 kWh. [PERSON] et al. [67] implemented a fuzzy controller in which, as the main contribution, the opening of the doors of the refrigerator was considered, integrating this habit of use in the rules of the controller. The authors concluded that the diffuse controller along with the incorporation of the habit of using reduced energy consumption by 3% compared to the conventional controller. Although the application and development of fuzzy controllers for home refrigerators are sparse, ample research opportunity presents itself. For example, integration with classic controllers or neural networks allows improvements in thermal and energy behaviors. Additionally, the integration of the usage habits to the controllers would allow the achievement of greater savings in energy consumption and better quality of food products.
# Deep Learning Methods in Image Matting: A Survey
## 4 Deep Learning Methods
### Trimap-Free Methods
#### 4.2.2 Dual-Decoder Structure  
To achieve a trimap-free method without using a semantic segmentation network to generate trimaps, researchers have designed a one-stage dual-stream network structure. This parallel decoder network structure makes the model simpler and easier to train compared with the two-stage network [8; 10; 22; 31] while achieving equally precise visual effects. [PERSON] et al. [18] extracted semantic features from the image using a dual decoder and predicted the foreground and background separately. As the semantic segmentation results are coarse, the authors designed a network that blends the foreground and background to produce a refined alpha matte without generating a trimap, which achieves excellent results. In contrast to [18], which predicted the foreground and background, [PERSON] et al. [36] proposed a division of the matting task into two simultaneous subtasks: a high-level semantic segmentation alongside the preservation of low-level structural features. The two decoders respectively learn semantic features to distinguish the foreground and background and extract low-level structures to preserve details. The two decoders work together to complete the matting task. [PERSON] et al. [35] share a similar idea to [PERSON] et al. [36] through the use of two decoders for the semantic learning and detail extraction of the transition region. The authors added a guidance module to provide guidance for detail prediction using semantic features, achieving interaction between detail and semantic features. This enables the network to generate an alpha matte with excellent detail effects in the transition region.
# Modernising Receiver Operating Characteristic (ROC) Curves +
### Article Organisation  
This work aims to demonstrate that many of the limitations inherent to traditional ROC curves (exemplified with the case of pregnancy testing in Section 3.1) can be tackled thanks to significant advances in the meantime in modern test theory, such as measurement system analysis (MSA, Section 3.2) and item response theory (IRT, Section 3.3), which have developed in parallel with the venerable ROC tool (Section 2).  
The linearisation of ROC curves is a major part of the modernisation effort, together with MSA and IRT as proposed earlier (Section 3.4.1), and together with the new approach presented here (Section 3.4.2).  
Section 4 contains the results of the application of the novel methods, presented in Section 3, to the pregnancy testing case study. The work culminates in Section 4.5 with an assessment of the functioning of the new metrics in comparison with more traditional metrics in judging the relative performance of devices, such as the popular area under curve metric (explained in Appendix A.1). Section 4 concludes with an appraisal of measurement uncertainties and limitations in the work so far.  
The article closes with a discussion, some recommendations and a conclusion in Section 5.
## Appendix A  
Figure 11 presents the profiles for sampling Sentinel-2 and GF-1 data mentioned in Section 5.1. Specifically, the profiles extend from the westernmost to the easternmost point of the island, with profiles generated at 1 km intervals. Sampling points are evenly distributed along each profile at 10 m intervals.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 3 Smart Grid Communication  
From the previous section we can see that SGs are highly dependent on information flow and communication between different entities in different networks. Communication is one of enabling technologies of SG. As the number of sensors increase, the amount of data coming to and from the utility increases.
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 2 Internet of Healthcare Things (IoHT)
### IoHT Components  
An IoHT implementation can simply consist of a single sensor device like a heartbeat monitor connected to a mobile device via Bluetooth. The mobile device should have a specific application to generate alarms after analyzing data. It could be complex, consisting of multiple sensors, intermediate devices, and centralized servers connected with a central management system. The medical Emergency Response Team (ERT) monitors the patient's health conditions and responds in the case of any emergency.  
Figure 3 shows a generic IoHT implementation where different devices at different layers communicate with each other with the help of various protocols. Intermediate devices like cell phones or PDAs communicate with sensors/actuators using short-range protocols and with healthcare servers with the help of the Internet. Servers take the appropriate actions by updating the database or contacting the relevant physicians or quick response teams in case of an emergency. It is a simple architecture used in IoT application development [41]. Each connected device has limitations like limited processing, memory, and battery resources, etc., and the applications are developed for better utilization of these limited resources. The reference architecture model shown in Figure 3 highlights a layered approach for describing IoHT functionalities and their problems. In addition, the layered architecture is used for identifying IoHT data security and privacy issues in technical and legal aspects. This five-layer architecture helps to identify IoHT data security and privacy issues at different layers, what type of devices and entities (medical team, physician, and emergency services) are involved at a specific layer, and what threat vectors are involved at each layer.  
Figure 2: IoHT Architecture Classification.  
Here, we will discuss each layer with its components.
# Improving Decodability of Polar Codes by Adding Noise
## 3 Conventional Schemes  
In this section, we first provide the perturbation decoding principle. Then, we briefly summarize the recent related works, including the PB-SSC decoding algorithm [33] and the PP-SSC decoding algorithm [35].
## 3 Numerical Analysis
### Grid Systems and Operating Conditions  
To reduce the simulation result error, a grid dependency test was conducted. This process was carried out six times. As shown in Figure 3a, the x-axis of the graph is the number of elements, and the y-axis is the total heat flux. Starting with about 110,000 elements, the number of elements was increased by about 100,000 and the results were analyzed. As a result, there was an error in the previous three cases. After about 140,000 elements, it was determined that the total heat flux converged to about 13.5 W/m\({}^{2}\). Therefore, numerical analysis was performed on about 140,000 elements in consideration of time and efficiency. Additionally, eight inflation layers were arranged to precisely observe the flow pattern around the permanent magnets and the wall of the glass tube. Finally, mesh configurations in these conditions are shown in Figure 3b. The operating conditions and boundary conditions of the simulation were basically set to the same as those of the experiment. As shown Figure 3c, the heating pad temperature T\({}_{\text{h}}\) (=60 \({}^{\circ}\)C) at the lower part of the glass tube and the initial temperature T\({}_{\text{i}}\) (=19.5 \({}^{\circ}\)C) of the ferrofluid were set as in the experiment, and the magnetic flux density (\(M\)) of the permanent magnet was set to 0.325 T. Since the effect of the outside temperature was not large in the actual experiment, the boundary condition of the glass tube except for the heating pad were assumed to be walls in the simulation. Then, numerical analysis was performed.
## 3 Factors Influencing U-WSN Design
### Architectures of U-WSN
#### 3.1.6 UAV-Satellite Link (U-SL)  
In some applications, the UAV is connected to satellite. The communication range is long. To achieve the long distance connectivity, the Random Access (RA), hybrid schemes and Demand Assignment (DA) schemes are usually used. In Figure 2, we provide a summary and comparison of their technologies and applications.
# Routing Strategies for Isochronal-Evolution Random Matching Network
###### Abstract  
In order to abstract away a network model from some real-world networks, such as navigation satellite networks and mobile call networks, we proposed an Isochronal-Evolution Random Matching Network (IERMN) model. An IERMN is a dynamic network that evolves isochronally and has a collection of edges that are pairwise disjoint at any point in time. We then investigated the traffic dynamics in IERMNs whose main research topic is packet transmission. When a vertex of an IERMN plans a path for a packet, it is permitted to delay the sending of the packet to make the path shorter. We designed a routing decision-making algorithm for vertices based on replanning. Since the IERMN has a specific topology, we developed two suitable routing strategies: the Least Delay Path with Minimum Hop (LDPMH) routing strategy and the Least Hop Path with Minimum Delay (LHPMD) routing strategy. An LDPMH is planned by a binary search tree and an LHPMD is planned by an ordered tree. The simulation results show that the LHPMD routing strategy outperformed the LDPMH routing strategy in terms of the critical packet generation rate, number of delivered packets, packet delivery ratio, and average posterior path lengths.  
isochronal-evolution random matching network; routing strategy; traffic dynamics; path planning; complex dynamic network +
Footnote †: journal:  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-000  
phone at one time. The MCN is also an IEN with the slot being 1 s. Although there have been some studies on MCNs [8; 9; 10], very few studies have paid attention to the fact that any two edges of the MCN have no vertex in common.
## 2 Materials and Methods
### Aroma Compounds Analysis
#### 2.5.3 Gas Chromatography-Quadrupole Mass Spectrometry (GC-qMS)  
An Agilent 6890 gas chromatograph coupled with an Agilent 5975 mass spectrometry (Agilent Technologies, Santa Clara, CA, USA) was used to separate volatile compounds in the passion fruit wine according to a published method [21]. An HP-INNOWAX capillary column (60 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m thickness, J&W Scientific, Folsom, CA, USA) was used to separate volatile compounds through a split-less inlet mode under a flow rate of helium (carrier gas) at 1 mL/min. The gradient of oven temperature was programed as follows: 50 \({}^{\circ}\)C held for 1 min, then increased to 220 \({}^{\circ}\)C at a 3 \({}^{\circ}\)C/min rate, held at 220 \({}^{\circ}\)C for 5 min, then increased to 250 \({}^{\circ}\)C at a 5 \({}^{\circ}\)C/min rate and finally held 250 \({}^{\circ}\)C for 5 min. The electron impact mode was used in mass spectrometer with a voltage of 70 eV ionization energy. The mass scan was carried out in a range of _m/z_ 20 to 450 under a selective ion mode. Retention indices were calculated using a C7-C24 n-alkane series (Supelco, Bellefonte, PA, USA) under the same chromatographic condition. Volatile compounds with commercially available standards were identified by comparing their mass spectrum and retention indices with the NIST11 library and their reference standard. Volatile compounds without the reference standards were tentatively identified through comparing their mass spectrum and retention index with the library of NIST11 [23].  
For quantitation, the volatile standards were dissolved into a passion fruit wine model solution consisting of 12% _v/o_ ethanol, 10 g/L glucose and 9 g/L tartaric acid. The model solution was extracted using the same extraction procedure as the passion fruit wine samples and analyzed under the same chromatographic conditions. The volatile compounds with the reference standard were quantified using the reference standard, whereas the quantitation of the volatiles without the available standard was carried out by the standard that possessed the similar carbon atoms numbers or chemical structure [21; 22; 24]. Each sample was analyzed on GC-qMS in duplicate.
## 2 Methodology
### Support Vector Regression  
The support vector regression (SVR) model was proposed by [PERSON] et al. in 1995 and has been widely used in nonlinear problem solving [35; 36]. In SVR models, sample data are divided into a fitting sample and predicting sample. The fitting sample is then mapped to a high-dimensional feature space. The best fitting effect is obtained in the space of the optimal decision function model and the validation sample is used to validate the analytical model results. The regression function for SVR is:  
\[f(x)=\left\langle W\cdot\Phi(x)\right\rangle+b \tag{6}\]  
where \(W\) is the weight vector, \(\Phi(x)\) is a nonlinear mapping from the input space to the output space, \(b\) is bias. Transforming the estimation function into a function minimization problem by the \(\varepsilon\) insensitive loss function:  
\[R_{\text{min}}=\frac{1}{2}\|W\|^{2}+C{\sum_{i=1}^{m}}\left(\xi_{i}+{\xi_{i}}^{ *}\right) \tag{7}\]  
The constraint conditions are as follows:  
\[\left\{\begin{array}{l}W^{T}\Phi(x_{i})+b_{i}-y_{i}\leq\varepsilon+\xi_{i}; \\ y_{i}-W^{T}\Phi(x_{i})-b_{i}\leq\varepsilon+\xi_{i};\\ \xi_{i},\xi_{i}^{*}\geq 0,i=1,\ldots,l.\end{array}\right. \tag{8}\]  
Figure 2: Long short-term memory (LSTM) module contains four interacting layers.  
where \(C\) is the penalty factor; \(\xi_{i}\) and \(\xi_{i}\)* are relaxation factors. The Lagrange multiplier is introduced at last, and converted into an equivalent dual problem based on the Wolf duality theory:  
\[\min\frac{1}{2}(\alpha_{i}-\alpha_{i}\text{*})^{T}Q(\alpha_{i}-\alpha_{i}\text{* })+\varepsilon\sum_{i=1}^{l}\left(\alpha_{i}+\alpha_{i}\text{*}\right)+\sum_{i= 1}^{l}y_{i}(\alpha_{i}-\alpha_{i}\text{*}) \tag{9}\]  
The constraint conditions are expressed in Equation (10):  
\[\left\{\begin{array}{l}\sum\limits_{i=1}^{l}\left(\alpha_{i}+\alpha_{i} \text{*}\right)=0\\ 0\leq\alpha_{i}\text{*}_{i}\text{*}\leq C,I=1,2\ldots l.\end{array}\right. \tag{10}\]  
where \(Q_{ij}=k(x_{i},x_{j})=\Phi(x_{i})^{T}\Phi(x_{j})\). The SVR prediction model can be obtained by quadratic programming:  
\[f(x,\alpha_{i}\text{*},\alpha_{i})=\sum_{i=1}^{l}\left(\alpha_{i}\text{*}- \alpha_{i}\right)K(x_{i},x)+b \tag{11}\]  
where \(K(x_{i},x)\) is the kernel function of SVR.  
Linear kernel, polynomial kernel, radial basis kernel function (RBF), and sigmoid function [37] are four commonly used kernel functions for SVR algorithm. With a wide convergence domain, the RBF is widely used [21; 38; 39]. Therefore, the RBF is adopted as the kernel function for the SVR model in this paper.
# Transformer: An OS-Supported Reconfigurable Hybrid Memory Architecture
###### Abstract  
_Non-volatile memories_ (NVMs) have aroused vast interest in hybrid memory systems due to their promising features of byte-addressability, high storage density, low cost per byte, and near-zero standby energy consumption. However, since NVMs have limited write endurance, high write latency, and high write energy consumption, it is still challenging to directly replace traditional _dynamic random access memory_ (DRAM) with NVMs. Many studies propose to utilize NVM and DRAM in a hybrid memory system, and explore sophisticated memory management schemes to alleviate the impact of slow NVM on the performance of applications. A few studies architected DRAM and NVM in a cache/memory hierarchy. However, the storage and performance overhead of the cache metadata (i.e., tags) management is rather expensive in this hierarchical architecture. Some other studies architected NVM and DRAM in a single (flat) address space to form a parallel architecture. However, the hot page monitoring and migration are critical for the performance of applications in this architecture. In this paper, we propose Transformer, an OS-supported reconfigurable hybrid memory architecture to efficiently use DRAM and NVM without redesigning the hardware architecture. To identify frequently accessed (hot) memory pages for migration, we propose to count the number of page accesses in OSes by sampling the access bit of pages periodically. We further migrate the identified hot pages from NVM to DRAM to improve the performance of hybrid memory system. More importantly, Transformer can simulate a hierarchical hybrid memory architecture while DRAM and NVM are physically managed in a flat address space, and can dynamically shift the logical memory architecture between parallel and hierarchical architectures according to applications' memory access patterns. Experimental results show that Transformer can improve the application performance by 62% on average (up to 2.7\(\times\)) compared with an NVM-only system, and can also improve performance by up to 79% and 42% (21% and 24% on average) compared with hierarchical and parallel architectures, respectively.  
n _Applied_  
15 December 2022  
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# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 2 From State-of-the-Art SDR Quality Assessment Metrics to HDR/WCG Quality Assessment Metrics
### Adapting SDR Metrics to HDR/WCG Images  
For adapting SDR metrics to HDR/WCG images, the reference and distorted images are first converted in a perceptually linear color space. A remapping function is then applied. Finally the SDR metrics is used to determine the quality score. Figure 1 presents the diagram of the proposed method.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Name** & **Color** & **Reference** & **Main Principle** \\ \hline
**PSNR** & & & Ratio between the range of the signal and the mean square error \\ \hline \(\overline{\Delta\text{E}}\) & ✓ & & Mean of the color difference metrics \\ \hline \(\overline{\Delta\text{E}^{S}}\) & ✓ & [PERSON] et al. [26] & Mean of the color difference metrics by considering the blurring effect of the HVS. Also known as S-CIELab \\ \hline SSIM & & [PERSON] et al. [8] & Metrics based on the comparison of three characteristics of the images: the luminance, the contrast and the structure \\ \hline SSIMc & ✓ & [PERSON] et al. [27] & Linear combination of the SSIM applied on the three components Y\({}^{\prime}\), Cr and Cb of the images. \\ \hline
**CSSIM** & ✓ & [PERSON] et al. [28] & Combination of SSIM and \(\Delta\text{E}^{S}\) \\ \hline
**MS-SSIM** & & [PERSON] et al. [10] & Multi-scale SSIM \\ \hline FSIM & & [PERSON] et al. [29] & Comparison of the phase congruency and the gradient magnitude \\ \hline FSIMc & ✓ & [PERSON] et al. [29] & Color extension of FSIM. Adds two comparisons corresponding to the two chrominance components \\ \hline
**PSNR-HVS-M** & & [PERSON] et al. [30] & PSNR on the DCT blocks of the images using CSF and visual masking \\ \hline
**PSNR-HMA** & & [PERSON] et al. [31] & Improvement of the PSNR-HVS-M. Takes into account the particularities of the mean shift and the contrast change distortions \\ \hline
**PSNR-HMAC** & ✓ & [PERSON] et al. [31] & Linear combination of the PSNR-HMA on the three components Y\({}^{\prime}\), Cr and Cb of the images. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected SDR quality metrics.