esa-sceva/satcom-chunk-collection huggingface dataset:
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 2 Related Work
### Diffusion Model  
In the past five years, diffusion models have seen significant advancements, particularly in image generation and generative modeling. These models, which generate data by reversing a diffusion process from noise to structure, have benefited from several key improvements. Early innovations focused on enhancing training stability and efficiency, exemplified by the work of [PERSON] et al., who introduced methods to accelerate the sampling process, making diffusion models more practical for high-quality image synthesis [52]. [PERSON] and [PERSON] further refined diffusion models by employing score-based techniques through stochastic differential equations, improving model performance and flexibility [53]. Conditional diffusion models have also become prominent, as demonstrated by [PERSON] et al., who showcased the ability to generate high-fidelity images from textual descriptions, broadening the applicability of diffusion models in controlled generative tasks [54].  
Recent advancements in diffusion models have focused on improving data generation quality and expanding their theoretical understanding. [PERSON] et al. introduced self-supervised signals to diffusion models, eliminating the need for large annotated datasets while generating semantically consistent images, proving especially useful on unbalanced data [55]. [PERSON] et al. explored the ability of diffusion models to generate human-like artistic drawings, showing that while diffusion models have made progress in artistic generation, they still lack the originality of human creations [56]. Meanwhile, [PERSON] et al. offered a theoretical analysis of diffusion models, demonstrating that they achieve minimax optimal rates for data distribution approximation and deepening the understanding of their efficiency in learning distributions [57]. These works collectively highlight diffusion models' versatility and growing maturity in terms of both practical applications and theoretical foundations.
## 3 Results and Discussion  
### Characterization of Sorghum Distillers Grain  
The compositions of raw materials have an important effect on the properties of the obtained biochar. Composition analyses of the SDG sample showed that it contained 24.6% \(\pm\) 0.7% starch, 19.6% \(\pm\) 0.4% cellulose, 14.9% \(\pm\) 0.5% protein, 4.4% \(\pm\) 0.2% crude lipid, and 6.4% \(\pm\) 0.3% ash. Although the SDG was the residue of sorghum after fermentation, it still had a high content of starch, which was mainly caused by the relatively low hydrolysis efficiency by microbes during fermentation [19; 27]. The high contents of cellulose and ash mainly come from the rice husk, which is added to loosen the solid sorghum medium and improve the heat transfer for fermentation and distillation [19]. The elemental analysis suggested that the SDG mainly contained C (44.1%), H (6.5%), and N (2.9%) because of its high starch, cellulose, and protein contents. Accordingly, the presence of high carbon element content makes the SDG a potential feedstock for biochar production.
# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
## 2 Low Power Wide Area Networks
### LoRaWAN  
Another widely used proprietary long range technology is LoRaWAN. Contrary to Sigfox, LoRaWAN applies a proprietary Chirp Spread Spectrum (CSS) modulation technique called LoRa, which stands for 'Long Range' [16]. In Europe, LoRa operates in the license free 433 and 868 MHz bands; in the US, 915 MHz is used. Symbols are encoded using a number of chirps, which causes the signal to be spread over a wider channel bandwidth. This technique reduces interference with other signals, protects the signal against jamming and cancels out multi-path and fading effects. The number of chirps is determined by the spreading factor, which can range from 7 to 12. By altering this parameter, a balance between data rate and range can be mediated. With a high spreading factor, long ranges can be achieved at the expense of a low 300 bit/s data rate. On the other hand, the data rate can be raised up to 37.5 kbit/s for shorter distances by using a low spreading factor. Because LoRaWAN supports multiple channels simultaneously, end-devices are able to exchange data for a longer period of time without violating duty cycle regulations. Subsequently, payloads of up to 250 B can be sent.  
Compared to Sigfox, LoRa works with a higher bandwidth which enables localization with Time Difference Of Arrival (TDoA). However, very accurate synchronization between the receiving base stations is required for such a method. Distances between the transmitter and the base stations are estimated based on the time of flight of a signal, a location estimation is obtained via triangulation. Semtech has implemented a proprietary geolocation feature in LoRaWAN, which is based on TDoA. The LoRa Alliance claims that this feature achieves an estimation error of 20 to 200 m, depending on the conditions [17]. [PERSON] et al evaluated a TDoA method, and concluded that it is possible to obtain a location accuracy of around 100 m for static transmitters [3]. Further research has to be conducted to improve the location accuracy for real-time tracking of dynamic targets.
# Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs
## 4 Performance Evaluation and Analysis
### Performance Trade-Offs  
Performance trade-offs of our proposed schemes and QUO VADIS are shown in Table 5. Gliders in QUO VADIS follow the sawtooth trajectory and wait for the best network topology, then they start communication. Future position and two uncertainty regions of gliders are estimated, to achieve a high delivery ratio, however at the cost of high end to end delay. In PAMP, gliders move randomly in the network area and periodically stay at sojourn positions. Transmission and reception of data are carried out at sojourn positions. PAMP achieves a high delivery ratio and less energy consumption as compared to QUO VADIS; however, the cost paid is high end to end delay. Co PAMP achieves a high delivery ratio as compared to both QUO VADIS and PAMP. In Co PAMP, cooperation is performed in which the two best neighboring gliders of the source act as relays and cooperate with the source glider to successfully deliver the data packet. The high delivery ratio in Co PAMP is achieved at the cost of high energy consumption and high end to end delay.
## 2 The Ionosphere and Polarization Diversity
Our team, after evaluating the polarization diversity for long-haul HF links between the Antarctic and Spain [20], is now considering the dual-polarized reception for a NVIS sensors network in order to decrease either the transmission power or the size of the antennas. In order to improve the robustness of the NVIS link and apply polarization diversity techniques, a combination of the different signals that arrive at the receiver is needed. There are multiple methods of diversity combining, each one presenting different characteristics and gains. We studied two different techniques: equal-gain combining, a method that sums all the received signals coherently, and selection combining, a technique that selects the strongest signal received (a higher signal-to-noise ratio (SNR)) and ignores the other.
## 1 Introduction  
In recent years, the scientific community has observed a growing interest in multiferroic materials, driven by their unique physical properties and promising applications. These materials are distinguished by the simultaneous presence of electric and magnetic fields, a characteristic stemming from their multiferroicity. This dual nature makes them suitable for advanced sensors and multi-state memory systems [1; 2; 3; 4; 5; 6]. The drive towards sustainable technology solutions has also placed multiferroics at the forefront of energy harvesting and photocatalysis research [7]. The ability of these materials to manipulate photo-generated charge carriers through their internal fields presents an innovative approach to enhancing the efficiency of solar energy devices [7; 8; 9; 10]. Notably, there is a specific emphasis on the quest for materials exhibiting the magnetoelectric effect, measured by the magnetoelectric coupling coefficient. This coefficient determines how strong the coupling between magnetic and electrical properties is [11; 12]. Multiferroics include various materials, among which an important group that has attracted great interest in recent years is materials with the Aurivilius-type structure [13; 14]. These materials are characterized by the general formula (M\({}_{2}\)O\({}_{2}\))\({}^{2+}\)(A\({}_{m-1}\)B\({}_{m}\)O\({}_{3m+1}\))\({}^{2-}\), where the M position is predominantly occupied by bismuth ions [15; 16]. The formulation indicates that these compounds consist of systematically arranged (Bi\({}_{2}\)O\({}_{2}\))\({}^{2+}\) layers and perovskite blocks (A\({}_{m-1}\)B\({}_{m}\)O\({}_{3m+1}\))\({}^{2-}\), with the parameter m denoting the number of such blocks situated between the bismuth oxides layers [17].  
The distinctive crystal lattice and intriguing physical properties of these materials are contingent upon the value of the Aurivillius phase number m [13; 18].  
The research focused on Bi\({}_{7}\)Fe\({}_{3}\)Ti\({}_{3}\)O\({}_{21}\) ceramics within the Bi\({}_{4}\)Ti\({}_{3}\)O\({}_{12}\)-BiFeO\({}_{3}\) system, incorporating varying concentrations of gadolinium ions (Gd\({}^{3+}\)). The premise was that these dopant ions would replace the (A\({}_{\text{m}-1}\)B\({}_{\text{m}}\)O\({}_{\text{3m+1}}\))\({}^{2-}\) structures within the perovskite blocks at the A position. Technological conditions, established through solid-state synthesis from simple oxides, were employed to synthesize novel ceramic materials Bi\({}_{7-\text{x}}\)Gd\({}_{\text{x}}\)Fe\({}_{3}\)Ti\({}_{3}\)O\({}_{21}\) with x values of (0, 0.2, 0.4, 0.6) (BGFT-x). Considering the ionic radii of gadolinium and bismuth (Table 1) [19], it was postulated that the substitution would alter the unit cell parameters, thereby influencing the inherent material properties. Subsequent measurements validated this hypothesis, revealing a modification in electric permittivity. The primary focus was on scrutinizing the impact of the modifier (Gd\({}^{3+}\)) on the electromagnetic coupling values within the tested multiferroic materials.
## 4 Results from the Literature Review
### Real-Time Mobility Management Systems  
Addressing urban challenges with AI-based traffic management solutions, cities are exploring real-time traffic management systems to optimize routing and ease congestion. These systems are supported by applications that allow users to select from various transportation modes and routes on a single platform (known as mobility on demand). This innovative traffic management approach aims to reduce congestion, enhance travel time reliability for commuters, and improve the economic efficiency of transport systems, which is particularly valuable during sudden economic disruptions [144]. In addition, AI-supported management, coupled with Internet of Things (IoT) capabilities, enables real-time route planning and reduces fuel consumption through congestion relief--making it an approach aligned not only with sustainable transport goals but also with environmental crisis management [145]. Studies suggest this innovative approach could also play a role in health crises, such as pandemics, by supporting social distancing and optimizing transport demand [146]. Moreover, this solution could support the coordination of ambulance traffic, providing significant public health benefits [147].
## 2 Dynamic Constitutive Model for 2024-T3 Aluminum Alloy  
### Strength Model  
Drawing on extensive experimental data and existing research findings [20; 21; 22], the strength model for metals under varying temperature, stress conditions, and strain rates is proposed as follows:  
\[\sigma_{eq}(\varepsilon,\xi,\dot{\varepsilon},T)=\left[A_{t} DIF+B_{t}\varepsilon^{n_{t}}\right]\left[1-\left(1-\gamma_{p}\right)\sqrt{1-\xi^{2}} \right]\times\left(1-T^{*m_{1}}\right)^{m_{2}} \tag{1}\]  
where \(\sigma_{eq}\),\(\varepsilon\) are equivalent stress and equivalent plastic strain, respectively. The values of material parameters \(A_{t},B_{t},n_{t}\) are determined by quasi-static tensile experiments. \(\gamma_{p}\) is defined as the ratio of shear equivalent stress to tensile equivalent stress. The Lode angle parameter \(\zeta\), related to the angle \(\theta\) of \(\pi\) plane (\(\xi=-\sin(3\theta)\), \(-\pi/6\leq\theta\leq\pi/6\)), is expressed as follows:  
\[\xi=\frac{27}{2}\frac{J_{3}}{\overline{\sigma}^{3}}=\frac{3\sqrt{3}J_{3}}{2J_{ 2}^{3/2}} \tag{2}\]  
where \(J_{2},J_{3}\) are the second and third invariant of the stress-deviator tensor. \(T^{*}\) (\(T^{*}=\left(T-T_{a}\right)/\left(T_{m}-T_{a}\right)\)) is the dimensionless temperature, with \(T,T_{a},T_{r}\) being the current, ambient, and melting temperatures, respectively. The material parameters \(m_{1},m_{2}\) can be experimentally determined through high-temperature experiments. The Dynamic Increase Factor (DIF), which describes the strain-rate effect on metals, is determined by both the magnitude of the strain rate and the plastic deformation of the metal, as expressed below [22]:  
\[DIF=\frac{(A_{t}+B_{t}\varepsilon_{x}{}^{n_{t}})DIF_{x}-B_{t}\varepsilon_{x}{}^ {n_{t}}}{A_{t}} \tag{3}\]  
where \(DIF_{x}\), the Dynamic Increase Factor at a given plastic-strain level, can be formulated as follows:  
\[DIF_{x}=\left[\frac{\text{atan}\Big{(}\log\frac{\dot{\varepsilon}}{\dot{ \varepsilon}_{0}}-W_{x}\Big{)}-\text{atan}\bigg{(}\log\frac{\dot{\varepsilon} _{quasi}}{\dot{\varepsilon}_{0}}-W_{x}\bigg{)}}{B_{y}}+1\right]\bigg{\{}\big{(} W_{y}-1\big{)}\text{tanh}\bigg{(}\bigg{(}\log\frac{\dot{\varepsilon}}{\dot{ \varepsilon}_{0}}-W_{x}\bigg{)}S\bigg{)}+W_{y}\bigg{\}} \tag{4}\]  
\(\dot{\varepsilon}_{0}\) denotes the reference strain rate, usually taken as \(\dot{\varepsilon}_{0}=1\text{s}^{-1}\), and \(\dot{\varepsilon}_{quasi}\) is the quasi-static strain rate, which is usually less than \(10^{-3}\text{s}^{-1}\).
## 2 Operation Principle for J-DBN-Based Equalization
### J-DBN Nonlinear Equalizers  
There are two main NN-based equalization schemes, namely the blind NN equalization and adaptive NN equalization algorithms. To avoid the residual error for equalizing  
Figure 1: (**a**) The structure of DBN, which can be approximated as a stack of RBMs. (**b**) The feedforward architecture of the \(j\)-th hidden neuron. (**c**) The reconstruction architecture of the \(j\)-th hidden neuron.  
nonlinear channels, the CMA equalizer can be combined with the NN algorithm. Meanwhile, DD-LMS combined with the NN equalization algorithm can further accelerate the convergence speed and ensure the optimum nonlinear decision. Our proposed J-DBN method, including an adaptive DBN-1-based equalizer and a blind DBN-2-based equalizer, is mainly based upon CMA and DD-LMS blind equalizers, as shown in Figure 2a.  
Figure 2b shows the detailed architecture of our proposed J-DBN equalizer, including two sequential DBNs. Each link between one visible layer and multiple hidden layers in J-DBN is associated with the weight value \(w_{ij}^{k}\), where \(k\) denotes the \(k\)-th hidden layer, and \(i\) and \(j\) represent the \(i\)-th node in the visible layer and the \(j\)-th node in the current hidden layer, respectively. The output of nonlinear neurons is summed as \(h_{j}^{k}=\sigma\left(\sum_{i=1}^{S}w_{ij}^{k}x_{i}\right)\), where \(S\) is defined as the length of input samples, and \(\sigma(\cdot)\) denotes the nonlinear active function between multiple hidden layers. The selection of a matching activation function is an important part of DBN construction.  
During the feedforward training process, the output of the \(j\)-th neuron in the other (\(k+1\))-th visible layer can be calculated as \(v_{j}^{k+1}=\sigma\left(\sum_{i=1}^{N}w_{ij}^{k+1}v_{j}^{k}\right)\), where \(v_{j}^{k}\) is the output of the \(j\)-th neuron cell in the \(k\)th hidden layer, and the total number of cells in this layer is \(N\). When \(k\) is increased as \(\lambda\), we define \(h_{j}^{L+1}\) as the final output signal calculated from the output layer. The DBN-1 and DBN-2 equalizers update the weights according to their respective cost functions, guaranteeing the optimal solution of the whole equalization process.
# Mini-Review: Modeling and Performance Analysis of Nanocarbon Interconnects
## 1 Introduction  
The breakthrough development of the semiconductor industry has revolutionized human society, from personal electronic gadgets, commercial and industrial equipment to military and aeronautical facilities. As predicted by [PERSON]'s law, the number of transistors within a chip doubles about every two years, while the cost comes down [1]. According to the International Technology Roadmap for Semiconductors (ITRS) projection, a 10 nm minimum feature size could support a tera-scale chip with a trillion transistors by 2020 [2]. Such phenomenal progress has been achieved through scaling of digital integrated circuit (IC) feature size to smaller physical dimensions.  
The ongoing miniaturization of the IC feature size has had a significant benefit in increasing the transistor speed. However, different from the transistor, the interconnect performance would be degraded due to the reduced conduction area and increased scattering probability for electrons [3; 4]. Under such circumstances, the chip performance is restricted by the shrinking interconnect dimensions on account of both the interconnect delay and the power dissipation. Moreover, the interconnect reliability has been becoming a more and more important problem, as the ampacity of conventional Cu wire cannot satisfy the increasingly stringent requirements [2; 5; 6]. Therefore, interconnects have become the major challenge in the design of modern ICs, thereby leading to the transition of IC technology from transistor-centric to interconnect-centric [7]. To address the interconnect challenges for next-generation ICs, diverse improvements of interconnect optimization and design methods have been reported, such as a low-k dielectric structure, three-dimensional integration, and inter-chip optical interconnects [8; 9; 10].  
Nanocarbon materials have attracted much attention since the carbon nanotube (CNT) was discovered by an arc-discharge evaporation method in 1991 [11]. Graphene, a Nobel Prize honored discovery, has further promoted the research in this field [12]. It was found that nanocarbon materials have many extraordinary physical properties. For example, the ultrahigh thermal conductivity of nanocarbon materials can help heat dissipation in high-density integrated systems [13; 14; 15]. The maximum current-carrying density of a CNT is more than two orders higher than that of Cu wires, thereby mitigating the electromigration-induced reliability problems [16]. It is natural to apply nanocarbon materials as an alternative option to potentially replace Cu for interconnects and passive devices in ICs [17; 18; 19; 20; 21; 22]. In recent years, there have been many publications in the literature devoted to the design, modeling/analysis, and fabrication/integration of nanocarbon interconnects. This paper seeks to review recent research efforts and progress in the nanocarbon interconnects. In particular, this paper focuses on the modeling and direct current (DC) performance analysis of nanocarbon interconnects for next-generation digital ICs.
# GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images
## 1 Introduction
The AD algorithm is particularly well-suited for multi-layer homogeneous tissue models with known optical properties, providing a quick and efficient solution, which may be, in turn, practical for real-time simulations, clinical applications, and large-scale studies. In recent years, the integration of graphics processing units (GPUs) has proven to be highly effective in reducing execution time and has revolutionized computational efficiency and accelerated processing, particularly in the field of medical imaging [16; 17; 18]. GPU platforms provide means to accelerate specific computational tasks and algorithms, surpassing the performance of central processing units (CPUs) while retaining a desirable level of flexibility [19]. In our study, we focus on implementing AD on GPUs to achieve enhanced computational speed; additionally, we augment GPU AD with the GPU Levenberg-Marquardt (LM) algorithm to extract object parameters from optical spectral data. The overall goal of our study was to address the practical challenges associated with analyzing hyperspectral images. We aimed to develop an algorithm that could deliver both computational speed and accuracy while yielding results comparable to the MC method. With this pragmatic focus, we sought to make the analysis of hyperspectral images more manageable.
## 3 The Analysis
### Effect of Data Duplication
#### 3.4.2 Average Delay  
By comparing Equations (18) and (22), it is proven that data duplication in time domain or in frequency domain requires the same spectrum allocation. However, the average delay are different. In time domain duplication, the transmission may be terminated when one transmission is received correctly, therefore, the average delay will be  
\[\overline{D_{t}}=\sum_{i=1}^{i=J}i\cdot\frac{d_{0}}{J}\cdot\epsilon^{i-1}(1- \epsilon). \tag{23}\]  
In frequency domain duplication, all transmissions end at the same time, i.e., the average delay will be  
\[\overline{D_{f}}=d_{0}. \tag{24}\]  
It is clear that the average delay of time domain duplication is smaller than the average delay of frequency domain duplication. However, in order to terminate the transmission in advance in time domain duplication, fast feedback mechanism is required to be implemented, which is out of the range of this work.
## 3 Results and Discussion  
After the functional validation, in view of the importance of achieving the maximum autonomy, the current consumption of each of the node types was measured in the field. The measurements were performed with a data acquisition card (USB-6008, National Instruments Corp., Austin, TX, USA) that measured the voltage drop at a 1 Ohm shunt resistor placed in series with the battery that powered the node. The analog input of the card was configured in differential mode in the range of \(\pm\)1 V and the acquisition was carried out in continuous mode at 1 kHz obtaining the averages every 0.25 s. In this mode, the card has a 12-bit resolution (equivalent to 0.48 mA) and an absolute precision at 25 \({}^{\circ}\)C of 1.53 mV (1.53 mA in current).  
For both type A and type B nodes, consumption tests were performed for the three possible operating modes, (i) the node connects to synchronize the date and time, collects data from the sensors and sends them to the server (this is the most energy-demanding operating mode and is therefore programmed to occur only every 24 h), (ii) the node collects data and sends them to the server (occurs every 30 min) and (iii) the node collects data and stores them in the memory card waiting for the next connection to the server (occurs in the middle of the period between sending events, i.e., 15 min later).  
Figure 4: Block diagram of type-B node.