esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Network Architecture and Signal Model  
### Network Architecture  
Figure 1 shows the architecture of the integrated wireless sensor and multibeam satellite networks under the terrestrial interference. IWMSNNs are composed of sensor nodes (sensing), multibeam satellite (communication transmission) and remote monitoring hosts (data processing). The multibeam satellite covers multiple beams and multiple sensor fields connecting multiple satellite sinks per beam, while multiple TCNs may cause interference to the satellite. Obviously, multibeam satellite networks connect SNs to RMHs. The following provides more detail.  
Sensor nodes: SNs sense different dynamic physical quantities for different scenarios, which can translate the quantities into electrical signals through the appropriate devices [20]. The electrical signals can be digitalized and encapsulated into data packets and transmitted to RMHs through multibeam satellite networks.  
Multibeam satellite: The multibeam satellite can be considered as the important component of the IWMSMSNs to connect SNs and RMHs. In Figure 1, we see that the sinks transmit the sensing data, including audio, video, images and other data streams, from the SNs to the gateway station, which exchanges the information with the RMHs through the return link of the satellite. The return link of multibeam a geosynchronous earth orbit (GEO) satellite is assumed to be operated at the S/L band where the channels experience the Rician fading [33; 42]. The multibeam technology can improve the frequency and power efficiency, which reduces the required antenna aperture and power amplifier of sinks to lower the communication cost [42; 43].  
Remote monitoring hosts: RMHs mainly receive and process data packets from the SNs to obtain the required information.  
Terrestrial interference: The terrestrial interferences are mostly from the sidelobe interferences of the adjacent channel or co-channel from the terrestrial communication networks.
# Molecular Targets of Minor Cannabinoids in Breast Cancer: In Silico and In Vitro Studies
## 2 Results
### Effects of the Cannabinoids in Aromatase  
Since our docking analysis suggested that the seven minor cannabinoids selected had the potential to inhibit aromatase, a radiometric assay was performed to confirm these assumptions, using human placental microsomes, an aromatase enriched matrix [26]. The anti-aromatase activity of the compounds **CBG** and **CBN**, as well as **CBD**, was also evaluated in this assay. The reference AIs used in a clinic, Ana, Let, and Exe, were used as positive controls. Our results showed that none of the minor cannabinoids significantly inhibited aromatase, 12% (**CBDV**) being the highest anti-aromatase activity value determined (Table 4). As expected, **CBD**, at 10 \(\upmu\)M, presented an anti-aromatase activity of 83.23% (Table 4). In accordance with our previous studies [25; 91; 92; 26], the reference AIs induced a high anti-aromatase activity (>98%) (Table 4).  
Microsomes (20 \(\upmu\)g) were incubated with NADPH (15 mM), each cannabinoid (10 \(\upmu\)M), and [18-3H]-androstenedione (40 nM), during 15 min at 37 \({}^{\circ}\)C. Results are presented as a percentage of tritiated water released, in relation to the control, and are represented as the mean \(\pm\) SEM of three independent experiments carried out in triplicate. The reference AIs, Ana and Let, at 1 \(\upmu\)M, were used as positive controls.
## 4 Evaluation and Analysis
### Experimental Results
#### 4.3.6 Computational Performance  
The average computational cost of two proposed methods (i.e., MetricNet and MetricNet_3x3) and SAES [35] is compared in the following experiments. We test the computational cost of all module processes: (1) the feature extraction process of the neural networks, (2) the construction of the similarity matrices, and (3) the similarity calculation between image pairs. The Nordland dataset is utilized here because of its relatively long length. An Intel Xeon CPU E5-2678 v3 (2.50 GHz) and an NVIDIA GeForce Titan XP GPU are used here.  
As shown in Table 6, the feature extraction component takes more execution time than the other two processes. This is mainly because of the large number of computations in deep neural networks. Besides, due to the calculation of channel weighting, our methods take more time in feature extraction.  
Figure 15: Trajectories before and after the optimization based on the results of MetricNet (**left**), and examples of retrieval results of place recognition for queries on the KITTI dataset (**right**). The yellow boxes outline the information that changed in the query and matched images.  
Compared with SAES, the two proposed methods greatly reduce the computational cost of constructing the similarity matrix and calculating the similarity score. Moreover, MetricNet outperforms MetricNet_3x3 by up to 4.4% and 5.0% in these two processes, respectively. Though the effects of both proposed methods are considerable, MetricNet proved to be more robust in terms of efficiency. Note that the parameters and GFLOPs of MetricNet are 2.47 M and 1.03 G, respectively.  
In loop closure detection, it is significant to calculate the similarity score in real-time. With the increasing number of previous images in the database, the computational cost of pairwise image matching will gradually increase. Therefore, we further test the algorithms' computational performance when the frame index rises. As shown in Figure 16, as the number of previous images in the database grows, MetricNet achieves higher efficiency and better real-time performance.
## 1 Introduction  
During the last decades plasmonics attracted significant attention as the novel field pivoting the way for modern technologies, such as spectroscopy and sensing [1] and optical tweezers [2]. Surface plasmons (SPs) are introduced as the collective oscillations of the delocalized electrons presenting at metal-dielectric interfaces. SPs open the wide avenues to escape the diffraction limit of conventional optics [3] because of their capability to confine light in subwavelength dimensions with high efficiency. Doing so, the previous provides a fertile ground for a broad spectrum of applications ranging from surface enhanced spectroscopy [4], biomedical sensing [5] and solar cell photovoltaics [6] to optical antennas [7]. It is worthwhile noting that surface-plasmon-based circuits are established to bridge the disciplines of photonics and electronics at the nanoscale. The former allows to escape the current problems associated with the large size difference between the micrometer-scale bulky components of photonics and the nanometer-scale electronic chips.  
The resonant oscillations of free electrons at the interface of nanocomposite media due to optical radiations give rise to surface plasmon polaritons (SPPs) [8]. The propagation of SPPs in nanocomposites has been extensively studied in [9; 10; 11]. Composite media with metal nanoparticles are of particular importance aiming to create nanostructured metal-insulator systems and novel approaches of manipulating light based on them. The emerge of transparent conductive oxides (TCOs) has attracted tremendous interest within the scientific community. These stand for as the alternative approach for plasmonics [12] in the near-infrared region. In contrary to noble metals, TCOs such as indium tin oxide (ITO) demonstrate a great tunability of their optical and electronic properties [13]. The former is possible via doping and electric bias. The construction and fabrication of ultra-compact electroabsorption modulators [14] and for the proposal of new multimode modulator architectures [15] has benefited from the option of actively switching between a low-loss dielectric regime and a high-absorption plasmonic regime.  
In the present work, we discuss complex conductivity of the medium, which dramatically affects tunability of SPPs. The obtained results may have significant applications in storage and sensing devices. Herein, we provide a detailed study of SPPs characteristics at the interface separating a nanocomposite and hypercrystal. Engineering properties of the nanocomposite and that of hypercrystal, the SPPs properties are tuned significantly. We present a complete description of the SPPs dispersion relation with different controlling parameters. Our study is extended aiming to examine the complex conductivity dependent properties.
## 3 Research Methodology  
While vehicles traverse the urban traffic network, which is composed of multiple signalized intersections, drivers could manage vehicle idling themselves by turning off the engine while waiting for the traffic signal associated with turning direction. However, drivers do not have any ways to know how long their waiting time is until they pass the intersection, so a sophisticated technology needs to be applied to autonomously control vehicle idling without depending on human instincts.  
As seen in Section 2. Related Works, research efforts have not been made on the environmental benefits of autonomous vehicles under C-ITS, whose function is enhanced with automatic vehicle idling control at signalized intersection in the urban network, according to the best of our knowledge. Accordingly, this study creates a pivotal idling control method and evaluates its environmental benefits with microscopic simulation models, VISSIM, VISSIM COM, and MOVES.  
This study suggests a novel methodology of estimating CO2 emissions from vehicles waiting in the queue at signalized intersections in Figure 2. Specifically, this study needs to observe the spatial and temporal trajectories of all vehicles around signalized intersections to find idling situations, interactively implement control of vehicle idling, and accordingly estimate the CO2 emission, so the microscopic simulation models (i.e., mobility and emission) supported by the user interface are an important research means.  
Figure 2: Research framework with analytical modules.  
The microscopic mobility simulator, VISSIM, is typically used to investigate the impact of traffic infrastructure or operation alternatives on traffic flow or safety because it is faster, safer, and more cost-effective than a field experiment and implementation [45; 46; 47]. It used to be complemented with a user interface (i.e., VISSIM COM), capable of not only reading the data of the internal objects such as vehicles, links, evaluations, etc., but also modifying their attributes and properties like vehicle routing, speed, signal timing, etc., at a discrete time step during the running of a simulation [48; 49]. In addition, MOVES (motor vehicle emission simulator) is a representative microscopic emission simulator developed by the U.S. Environmental Protection Agency (U.S. EPA) and refers to the vehicle trajectories and vehicle specific power (\(VSP\)).
## 2 Materials and Methods
### Data Collection  
The data collection involved using temperature sensors (NTCs) that are standardly installed in the headlights of vehicles. The sensors were located on the circuit board of each light function, close to the LED. It is therefore important to note that the temperature results are not the air temperatures inside the headlamp. These sensors continuously recorded the temperature while the vehicle was in operation, providing a comprehensive picture of the thermal load under different operating conditions. The temperature conditions of the headlights were recorded and transmitted as a temperature histogram with a total of six temperature classes, which shows the duration in s of the activity of the temperature sensor in each of the six temperature ranges (see Table 1).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Temperature Class (Headlight)** & **Temperature Range in \({}^{\circ}\)C** \\ \hline
1 & \textgreater{}0 \\
2 & 0–40 \\
3 & 40–70 \\
4 & 70–90 \\
5 & 90–110 \\
6 & \textgreater{}110 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of the temperature class ranges for the evaluation of the sensors in the headlamps of the vehicles analyzed.  
As precise information on the exact ambient driving temperatures for each individual vehicle was not available, the countries were categorized into specific ambient temperature ranges. This categorization was based on the average annual temperature of the country using the library Meteostat [23]. The corresponding categorization of average annual temperatures is as follows.  
The data analysis enabled the calculation of the time proportion that each temperature sensor in the vehicle headlights spent in a specific temperature range. These temperature classes were expressed as a percentage and used to create temperature histograms. The histograms provide a visual representation of the temperature data distribution and symmetry, enabling the identification and quantification of the highest and maximum proportion of time in a particular temperature range.  
A one-year period was selected to gather vehicle data. To ensure a focused and relevant dataset, this study employed a rigorous selection criterion by limiting the vehicle mileage to the 25 th and 75 th quantiles. This approach allowed for a more accurate representation of typical vehicle usage and the associated thermal conditions of the headlights. The BEV vehicles travelled an average of 6123 km and the ICE vehicles 8927 km during this time, as shown in Figure 1. The period included both summer and winter conditions, providing a comprehensive view of the thermal load throughout the year.  
The data were divided into ambient temperature ranges, with a total of 8053 vehicles analyzed at temperatures under 15 \({}^{\circ}\)C, 8716 at 15-20 \({}^{\circ}\)C, 6020 at 20-25 \({}^{\circ}\)C, 1806 at 20-25 \({}^{\circ}\)C, and 1799 vehicles at ambient temperatures above 30 \({}^{\circ}\)C (see Figure 2).
# Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China
## 2 Materials and Methods
### System Design and Implementation  
The BMapSVF system embraces a font-end and back-end separation design (see Figure 3). The front-end integrates the progressive JavaScript framework Vue.js and its component library Element UI, embedding the Baidu Map JavaScript API for tasks like acquiring BSV. Meanwhile, the back-end Web API relies on the lightweight Python language server Web framework Flask. For the semantic segmentation of SVI, the system employs the PyTorch 1.11 CUDA version of the deep learning network library. To ensure efficient data transmission between the front-end and back-end and to enable real-time responsiveness in the processing workflow are facilitated by the incorporation of the new HTML5 WebSocket protocol. This protocol establishes full duplex real-time communication between the front-end and back-end upon the establishment of a single-session connection. This strategic choice significantly conserves server resources and bandwidth, ensuring the immediacy of the data [41]. In terms of data storage, the system employs the MySQL database, chosen for its open-source nature, support for multi-threading, provision of APIs for various programming languages, and the effectiveness of its built-in optimizer in enhancing query speed.
# _Separtial system innovation_
## 8 Selection Criteria, Data Cleaning and Collection  
The protocol began in August 2024. A total of 1209 papers were identified through searches using Scopus (816 papers, accessed on 31 August 2024) and WoS (393 papers, accessed on 31 August 2024), with the majority found on Scopus. To apply the aforementioned selection criteria, a data-cleaning process was necessary. Papers written in English on any application of XAI in process engineering, published in peer-reviewed international conference proceedings and journals, met the inclusion requirements for the SLR. Exclusion criteria involve articles related to the methodologies under study in disciplines other than process engineering (such as medicine or psychology), articles not published in peer-reviewed conference proceedings or journals, reviews or research perspectives, and duplicate papers. These inclusion and exclusion criteria guided the selection of articles.  
An initial cleaning was performed on each database by removing documents without a DOI to determine document eligibility. This resulted in the exclusion of 143 and 9 documents from Scopus and WoS, respectively, due to the absence of a DOI. Additionally, six papers from Scopus and three from WoS not written in English were excluded. This left 667 documents from Scopus and 381 from WoS. With a total exceeding 1000 documents, results from the search engines were combined, and duplicates were removed using the documents' DOI for verification. These initial database cleaning operations were carried out using Python's Pandas library.  
From the initial selection, 774 articles were chosen for screening. After reviewing the titles and abstracts, 332 articles were excluded because they were not related to the operational use of XAI but only referred to its general conceptualization and philosophical approach. An additional 60 documents were removed because they were reviews, surveys, books, perspectives, notes, or editorials. Out of the remaining 382 documents, another 293 were excluded for being outside the application domain (e.g., medicine, healthcare, psychology, software engineering), resulting in 89 documents. Subsequently, based on the authors' knowledge, an additional 11 relevant documents, which were not available in either the Scopus or WoS databases but are significant to the research, were included. This brought the total number of papers subjected to review to 100.  
Figure 3 presents a step-by-step flow diagram of this identification process, using a modified \"Preferred Reporting Items for Systematic Reviews and Meta-Analyses\" diagram reported in [57]. In this way, the 11.5% (89/774) of examined papers have been included in the study [58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87;
# Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics
## 1 Introduction
Furthermore, it also improved the total hit rate by 2% on average. The prefetch technology is essentially a memory access sequence-prediction technology, and the prefetch strategy should consider the memory access behavior of the application [11]. This section will analyze and summarize the patterns of the address sequence when the cache is missed, analyze the miss ratio in the cache, and design the prefetch strategy based on the analysis results. Figure 1 shows a comparison of the miss rates of various access patterns in the test program. As shown in Figure 1, the miss rate of irregular accesses is much larger than that of the regular access patterns. At the same time, the delay in accessing external memory caused by the misses of irregular access patterns is one of the main bottlenecks restricting the improvement of overall performance. Therefore, improving the performance of irregular prefetch patterns has a greater effect on improving the performance. To prevent performance degradation by irregular memory access streams from graph-processing workloads [12], we propose a DRAM buffer-based hybrid main memory architecture with a prefetching method based on an optimized locally weighted regression (LWR) algorithm. To address the challenges of traditional DRAM-based main memory limitation, increasing energy consumption, and the high-cost issue, our proposed solution introduces the DRAM-PCM-based hybrid main memory architecture based on our novel data management method. The design in this paper is a universal solution for all DRAM-based main memory architectures. Because the proposed model has the advantage of aggregating hot data, it can well serve the hybrid memory system that consists of non-volatile memory (NVM) with a relatively limited read-and-write lifetime. Itcan significantly increase the lifetime of non-volatile memory while reducing the memory miss rate. This paper makes the following contributions:  
1. We conducted an analysis of complicated and indirect memory access patterns from graph workloads to design a data prefetch algorithm by determining dynamic memory access patterns as a design application method utilizing big-data execution characteristics. 2. We designed an adaptive optimized regression prefetch scheme that can select from a dynamic set of prefetch engines by using a machine-learning approach. 3. We proposed a novel page-management mechanism that leverages the cost-effectiveness of PCM's characteristics by efficiently utilizing both DRAM and PCM. The subsequent sections of this paper are structured as follows: Section 2 provides an overview of the related research on prefetching methods and hybrid main memory architectures, while Section 3 presents our proposed DRAM-PCM hybrid memory model with the optimized linear regression algorithm. Section 4 conducts a comparative analysis of the proposed system with existing models. In Section 5, we present the experimental results obtained from our in-house simulation environments with various hybrid memory models.
###### Abstract
Although there are some hierarchical approaches, which are based on spatial processes, to measuring the levels of forest fragmentation [20; 21; 22], these were mainly used to estimate the status of forest fragmentation, and have rarely been used to study the impacts of fragmentation on ecological functions due to the difficulty of accessing field observation data for relevant fragmentation levels. In reality, the classification results of the fragmentation levels from these methods may have great potential in studies investigating the ecological effects of forest fragmentation, particularly those with the assistance of the remote sensing approach, to estimate ecological function indicators at large spatial scales. The processes of forest carbon uptake can be simply summarized as the absorption of atmospheric carbon dioxide by plants through photosynthesis [23]; gross primary productivity (GPP), a direct measurement of the total amount of carbon assimilated by vegetation, is generally considered as a reflection of vegetation carbon uptake. In addition, the vegetation carbon stock is highly related to the accumulation of annual GPP over a long period, which is mainly controlled by two factors--the carbon uptake period (CUP) and the maximum daily GPP (GPP\({}_{\text{max}}\)), which are associated with vegetation phenology and physiology, respectively [24; 25]. Both of these factors are affected by the climate and soil nutrient change caused by forest fragmentation [26; 27; 28]; therefore, detecting the variation in CUP and GPP\({}_{\text{max}}\) under different fragmentation levels is crucial for explaining the pathway or the mechanism of the impact of fragmentation on forest carbon uptake. In this study, forest landscapes in Zhejiang province, China, were selected as the study area in which to examine the relationship between forest fragmentation and GPP. Multiple sources of remote sensing data were applied to calculate forest fragmentation levels and to simulate vegetation GPP. We have proposed a basic assumption that the GPP and its two components--CUP and GPP\({}_{\text{max}}\)--vary between forest fragmentation levels. Based on the analysis of the variation in GPP, CUP, and GPP\({}_{\text{max}}\) with fragmentation levels, we would like to answer the following two scientific questions: (1) How does forest carbon uptake change with different fragmentation levels? (2) What is the main pathway affecting forest carbon uptake under different fragmentation levels?