esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### CNN Architecture and Transfer Learning  
As depicted on Figure 2, our methodological approach consists of two parts: the VGG19 deep network [25] that provides the features extraction process on the OCTA image and a personalized densely connected network that represents the classification part.  
Figure 2: Our proposed modified VGG19 architecture. OCTA images are mapped through the VGG19 feature extractor to build discriminant 9 \(\times\) 9 feature maps that are used by the densely connected classifier to generate predicted probabilities and Grad-CAM heatmaps to visualize feature attribution for each class: No AMD (healthy choriocapillaris), advanced wet AMD (CNV), intermediate AMD (drusen/choriocapillaris impairment).  
VGG19 is a competition-winning model of the _ImageNet Large-Scale Visual Recognition Challenge_ (ILSVRC) [26], that has a sequential pipeline architecture consisting of 16 convolutional and 3 fully connected trainable layers including five max-pooling layers. In this work, only the VGG19 convolutional part is used as a feature extractor where convolution layers include 3 \(\times\) 3 convolution filters. A rectified linear unit (ReLU) activation function and a downsampling 2 \(\times\) 2 max-pooling operation follow each convolutional stack. This convolutional part provides vectorized feature maps used as input to the densely connected classifier.  
The densely connected classifier contains three fully connected layers, layers 1 and 2 are composed of 1024 nodes each. The last one consists of three nodes that provide the classification result into three types of OCTA images. A regularization dropout layer is included after the first dense layer to overcome overfitting the model by randomly dropping out 50% of the activations at that layer. ReLU activation function is used on the two first dense layers, whereas a softmax activation function is used on the last one.  
According to the huge number of learnable VGG19 parameters (144 million) and the limited amount of OCTA data in our training dataset, transfer learning from non-medical data is applied in our approach [27]. Therefore, learned knowledge from the ImageNet dataset [26; 28] is transferred to the model and adapted to our application by fine-tuning the convolutional part using OCTA images. The densely connected layers are trained from scratch on our OCTA data to classify OCTA images. Finally, feature maps from the last convolutional layer are mapped through the densely connected classifier to generate Gradient-weighted Class Activation Mapping (Grad-CAM) visualization [29]. The Grad-CAM produces a localization map that highlights the image's important features to the CNN for class predictions.  
Additionally, to assess the impact of our approach (transfer learning on modified VGG19 model) on CNV detection accuracy, we trained the original VGG19 model independently from scratch with random initialization on our OCTA data.
## 4 Results
### Overall Comparison of the Performed Experiments  
In order to compare the different experiments with regard to their potential use as an efficient thermal management system, the maximum heater temperature and the maximum cover temperature after 60 min of charging with 5 W are compared in Figure 11. The error bars indicate the measurement uncertainty of the thermocouples. The operation temperature upper limits of the electronic parts (80 \({}^{\circ}\)C) and the cover (43 \({}^{\circ}\)C) of a real tablet are drawn as dashed lines in Figure 11.  
The comparison illustrated in Figure 11 shows that the lowest peak temperature of the heater was obtained when the thermal spreader and the PCM package A was used. The maximum heater temperature after 60 min was reduced by 75 \({}^{\circ}\)C compared to the case where no PCM package and no thermal spreader were used. Furthermore, the results above clearly illustrate that the addition of a thermal spreader led to a drastic reduction of the temperature increase of the cover surface. The peak temperature of the cover was reduced by approximately 33 \({}^{\circ}\)C for all the experiments where a thermal spreader was used. Adding the thermal spreader increases the heat distribution in the radial direction and reduces the hot spot in line with the heater. Accordingly, the heat is spread over a larger surface when compared to the case when no spreader is used. That leads to more melting and accordingly more heat may be absorbed by the PCM, which leads to a smaller temperature increase of the cover surface.  
The effect of the thermal spreader may be analyzed by comparing the results of the experiment with PCM package A without a spreader and PCM package A with a spreader. Since the absolute  
Figure 11: Maximum temperature of the heater and the cover surface after 60 min of charging with 5 W for six different experiments.  
storage capacity of the cavity between the heater and the cover did not differ much between those two experiments (approximately 4106 J for PCM package A with a spreader and approximately 3600 J for PCM package without a spreader), the comparison allows a demonstration of the influence of thermal spreading. Figure 12a shows the temperatures of the lower surface of the PCM package (\(T_{L1}\)-\(T_{L5}\)) after 60 min against the radial distance of the thermocouples for both experiments. Figure 12b shows the resulting temperatures of the cover surface (\(T_{C}\)) after 60 min against the radial distance of the thermocouples for both experiments.  
Due to the presence of the thermal spreader, the heat was distributed in the radial direction, which led to a more uniform temperature profile along the radial distance to the center compared to the case where no spreader was used, as visible in Figure 12a. Moreover, the distribution of heat led to additional melting, which also led to a more uniform temperature distribution of the PCM package compared to the case without a spreader, as illustrated in Figure 12b.
## 4 Performance Evaluation  
### Simulation Parameters  
We opted for the Nakagami radio propagation model, which takes into account obstacles, to better reflect reality. We also used a real map to validate our contribution. In fact, owing to the VanetMobiSim tool, we imported maps from the US Census TIGER/Line database, which includes full coverage of the US, Puerto Rico, and many other cities. For our simulations, we considered a downtown-type traffic called TGR11001 that represents the District of Columbia, Washington [45]. The deployment of the vehicles is established by the intelligent lane-change pilot model (IDM LC), which makes the simulation in the network layer reliable.  
Figure 5 represents the used urban environment. The set of simulation parameters are given in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Propagation model & Nakagami \\ Bandwidth & 100 Mbps \\ Transmission range & 250 m \\ MAC protocol & IEEE 802.11p \\ Routing protocols & FzGR, GPSR-2P, GPSR-KP, and MNH-FGR \\ Real video file & Foreman.yuv with 300 frames YUV CIF format (352 \(\times\) 288) \\ Background traffic & CBR/UDP \\ Multimedia traffic & MPEG-4 \\ CBR packet size & 512 octets \\ CBR rate & 10 paquets/seconds \\ Simulation time & 300 s \\ Simulation range & 2500 m \(\times\) 2500 m \\ Number of vehicles & 20-50-100-150-200 \\ Mobility model & TIGER map \\ \hline Real map & District of Columbia (WA), IDM-LC \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 5: District of Columbia, Washington.
# Design and Application Space Exploration of a Domain-Specific Accelerator System
## 6 Results Analysis
### Performance Analysis
We are unable to make the statistics about the configuration cycles and data transmission cycles of TI DPS, but the computation cycles of DSP consumes 2.97 times more than the computation cycles of RASP, and 1.74 times more than the total execution cycles of RASP. Freescale T4240 does better in large-scale vector operations, but when it comes to complex operations such as FFT, matrix multiplication and matrix inversion, it is much worse than RASP due to its software implementation characteristics. Especially for matrix inversion with \(O(n^{3})\) algorithm complexity and complex control process, it consumes 2125 times more cycles than RASP. Thus, the total execution cycles are 219 times more than RASP. Taking the working frequency into account (RASP@1 GHz and [EMAIL_ADDRESS] GHz), RASP still gets a 146x speedup. As described in Section 4, when RASP executes a complex task, after the procedures of decomposing the task, constructing the API function, and compiling the API, a series of bit stream corresponding to each step is generated and stored in the external memory. Once the task starts, it can be regarded as processes in batch mode. Apart from the first step in the task, the data of the following steps to be processed come from the internal memory, so the data transmission time is eliminated. As mentioned in Section 3.2, the small chunk of data transmission is very inefficient. From Table 4 we can see that data transmission between the sub-algorithms occupies 25.36% of the total execution time. It means that compared with the independent execution mode, 25.36% of the whole execution time can be reduced owing to the on-chip scratchpad memory and our approach of complex task execution. Eliminating the data transmission between the tasks of STAP gives it a larger advantage compared with the other platforms. Therefore, the proposed system can not only yield considerable performance improvement with energy savings but is also capable of complex tasks.
## 3 Opportunistic Routing Protocol in WSNs
### Concept of MAC Protocol for Maintaining Duty Cycle  
S-MAC [1] was the first MAC protocol that introduced a duty cycle mechanism into energy-constrained sensor nodes. Figure 4 shows the packet transmission procedure of S-MAC. This protocol was proposed by [PERSON] et al. in 2002. The primary goal of S-MAC was energy conservation and self-configuration. In order to achieve these goals, the authors proposed three solutions:  
* Nodes periodically enter into a sleep mode (periodic listening and sleeping);
* Neighbor nodes compose a virtual cluster in order to auto-synchronize in the sleep schedule (collision and overhearing avoidance);
* Apply'message passing' to reduce contention latency for sensor network applications that require store-and-forward processing as data move through the network.  
However, the main drawbacks of S-MAC are the fixed duty cycle (not tunable), and that it is energy inefficient. Moreover, the major problem was that in order to wake up at the same time as the neighbor nodes, this scheme used a synchronization process whereby several control packets would need to be broadcasted, which is a huge source of energy wastage. These were the reasons that motivated researchers to create protocols compatible with asynchronous duty-cycled WSNs.  
Figure 3: A network with the link delivery probability of each node pair [4].
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
###### Abstract  
Nowadays, civil Global Navigation Satellite System (GNSS) signals are available in both L1 and L5 bands. A receiver does not need to acquire independently the signals in both bands coming from a same satellite, since their carrier Doppler and code delay are closely related. Therefore, the question of which one to acquire first rises naturally. Although the common thought would tell the L1 band signals which are narrowband, an accurate comparison has never been done, and the decision is not as easy as it seems. Indeed, L5 band signals have several advantages such as stronger power, lower carrier Doppler, or a pilot channel, unlike the Global Positioning System (GPS) L1 C/A signal. The goal of this paper is therefore to compare the acquisition of L1 and L5 bands signals (GPS L1 C/A and L5, Galileo E1 and E5a/b) to determine which one is more complex and by which factor, in terms of processing time and memory, considering hardware receivers and the parallel code search. The results show that overall the L5 band signals are more complex to acquire, but it depends strongly on the conditions. The E5 signal is always more complex to acquire than E1, while the L5 signal can have a complexity close to the L1 C/A in some cases. Moreover, precise assistance providing accurate Doppler could significantly reduce the L5 complexity below the L1 complexity.  
acquisition; complexity; Galileo; GPS; GNSS; hardware receiver +
Footnote †: journal: Sensors
## 3 Proposed GIS-Based Approach
### GIS-Based Input Data Preparation  
The procedure's first step consists of setting up the GIS-based input data using QGIS software [28]. QGIS is a free and open-source cross-platform desktop GIS application that supports viewing, editing, and analysis of geospatial data. QGIS allows users to analyze and edit spatial information and compose and export graphical maps. QGIS supports both vector layers and raster. On the one hand, vector data is stored as either point, line, or polygon features. On the other hand, multiple raster image formats are supported, and the software can georeference the images. QGIS integrates with other open-source GIS packages, including PostGIS, GRASS GIS, and MapServer. In addition, plugins written in Python or C++ extend QGIS's capabilities. Figure 5 shows the flowchart of the input data preparation procedure.  
Figure 4: Example of a two-dimension space Voronoi diagram for fifteen sites denoted by red squares.  
The proposed approach relies on dividing the municipality of Milan and Rozzano into polygons of the same dimension. As shown in Figure 6, using the QGIS tool called \"create grid\", a layer of 896 squares, dimensioning 500 m \(\times\) 500 m each, has been created and overlapped on the Google satellite image map. The grid has been later cut on the Milan and Rozzano municipality borders. Moreover, using the QGIS tool called \"centroids\", a point layer containing the centroids of the squares has been created.  
The squares and centroids layers are at the basis of the proposed approach. All the GIS information needed for the PSs planning, in the form of either punctual or polygonal layers, is first linked to the related squares and finally to its centroid. In the following, a detailed description of the input data is reported.  
Figure 5: Flowchart of the input data preparation procedure.  
Figure 6: Left picture: the considered 500 m \(\times\) 500 m grid overlapped on the Google satellite image map; right picture: centroids of each square.
# A Deep-Learning-Based Quality Control Evaluation Method for CT Phantom Images
###### Abstract  
Computed tomography (CT) is a rapid and precise medical imaging modality, but it poses the challenge of high radiation exposure to patients. To control this issue, stringent quality control (QC) evaluations are imperative for CT. One crucial aspect of CT QC involves the evaluation of phantom images, utilizing specifically designed phantoms for accuracy management and subsequent objective evaluation. However, CT QC has qualitative evaluation methods, particularly for evaluating spatial and contrast resolutions. To solve this problem, we propose a quality control method based on deep-learning object detection for quantitatively evaluating spatial and contrast resolutions, CT Attention You Only Look Once v8 (CTA-YOLOv8). First, we utilized the YOLOv8 network as the foundational model, optimizing it for enhanced accuracy. Second, we enhanced the network's capabilities by integrating the Convolutional Block Attention Module (CBAM) and Swin Transformers, tailored for phantom image evaluations. The CBAM module was employed internally to pinpoint the optimal position for achieving peak performance in CT QC data. Similarly, we fine-tuned the code and patch size of the Swin Transformer module to align it with YOLOv8, culminating in the identification of the optimal configuration. Our proposed CTA-YOLOv8 network showed superior agreement with qualitative evaluation methods, achieving accuracies of 92.03% and 97.56% for spatial and contrast resolution evaluations, respectively. Thus, we suggest that our method offers nearly equivalent performance to qualitative methods. The utilization of the CTA-YOLOv8 network in evaluating CT phantom images holds potential for setting a new standard in quantitative assessment methodologies.  
computed tomography; phantom image quality control; quantitative evaluation; artificial intelligence; deep learning Article
## 2 Edge Computing
### Security and Privacy Challenges  
Data transactions between front-end sensing devices and EC nodes are based on numerous wired and wireless communication standards. Similarly, data transmission between EC nodes and the cloud takes place over either public or private networks. However, these communication channels often lack robust security measures, rendering them susceptible to security and privacy threats [31; 32]. Key security concerns include authentication, access control, intrusion detection, and privacy [33]. Furthermore, the distributed nature of EC-enabled IoT networks broadens the attack surface, as each node, server, and communication channel presents potential opportunities for attackers to exploit vulnerabilities [34; 35]. EC introduces unique attack surfaces due to its distributed architecture and proximity to data sources [36].
## 2 Theoretical Foundations
### Current Status of Research on Users' Willingness to Use
Therefore, employing multiple models and research methods to empirically study the factors affecting user intention from a demand-side perspective is essential. The main objective of this study is to present a comprehensive and objective methodology specifically designed to provide design principles for a cultural education metaverse system. As highlighted in the previous discussion, existing research often relies on subjective methods such as expert interviews and questionnaires, which can introduce bias and result in subjective assessments. This study aims to bridge the divide between subjectivity and precision by integrating qualitative and quantitative research methods to address this gap. The study aims to utilize GT to uncover hidden patterns and connections within the rich interview data of users of the cultural education metaverse system. This inductive approach ensures that the assessment indicator system is comprehensive and reflects the actual experiences and perspectives of the target user groups. Additionally, the study intends to elucidate and predict end-user behaviors and intentions to accept and adopt new technologies by combining the UTAUT and TTF models. The methodology will construct a model of users' intention to use the cultural education metaverse system from two dimensions: user perception and technology acceptance. It will also explore the relationships between relevant factors, ultimately proposing correlations and their influence on users' intention to engage with the cultural education metaverse system.
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 7 Future Directions
### Relationship with Subsurface Temperature  
While many efforts have been devoted to the study of surface and air UHIs, little research has been done in terms of the detection and understanding of the subsurface urban heat island (SubUHI). Although UHI effects mostly originate near the ground surface, the resulting urban heat fluxes will propagate both upward into the atmosphere and downward into the subsurface [264]. Analysis of the SubUHI pattern is also useful for understanding the UHI dynamic across the entire vertical profile (i.e., from the atmospheric boundary layer to subsurface) [265]. The SubUHI is less studied and poorly understood mostly because of the lack of measurements provided by a suitable observation sensor network. Typically, the subsurface temperature (SubST) is driven by the heat flow from the Earth's interior and by the ground surface temperature [266; 264]. [PERSON] et al. [266] analyzed surface air temperature and borehole SubST records from a ground sensor campaign in Osaka, Japan. They found that air temperature heating is not as strong as the subsurface warming. Using two SubST observation stations in an urban and a rural area of Nanjing, China, a previous study showed that SubUHI intensity generally increased with increasing ground depth [267]. The analysis of temperature observations from a sensor network in shallow aquifers beneath six German cities showed that the derived SubUHI exhibited a more pronounced intensity with respect to the annual air UHI pattern [268]. The first attempt to detect the SubUHI by satellite remote sensing was described in [PERSON] et al. [265], where a three-time-scale model, able to produce SubST up to a depth of around 10 m, was applied to MODIS data. In this study, a comparison between SUHI and SubUHI was performed in Beijing, China, which showed that the land cover changes from natural to built-up surfaces have an impact on both LST and SubST. In addition, the time delay between SUHI and SubUHI in reaching the extreme intensities depends on the depth difference. Therefore, the use of satellite remote sensing for the simultaneous detection and analysis of surface and subsurface UHI patterns is a challenging opportunity that may become increasingly widespread in the future, through validation and calibration campaigns aimed to assess the reliability of satellite-derived SubST.
## 2 Materials and Methods
### Flash Profile (FP)  
The sensory analysis was performed using the Flash Profile (FP) method, as originally proposed by [PERSON] and [PERSON] [51] and further developed by [PERSON] and [PERSON] [52]. This technique is designed to rapidly characterize and differentiate products based on freely elicited attributes and subsequent individual ranking by trained panelists. Due to COVID-19 restrictions, the evaluation was conducted remotely, in accordance with established guidelines and best practices for at-home sensory testing, which have previously been successfully implemented in other sensory methodologies, such as focus groups [53; 54].  
A total of six trained panelists (aged 18-65), each with a minimum of one year of experience in the sensory evaluation of seaweed-containing foods, participated in this study. The panel was composed of students and academic staff from the Department of Chemistry at FCT NOVA, Caparica, Portugal.  
Each panelist received individually packaged samples of three cheese products, SHGC-C (control), SHGC-PP (enriched with _Palmaria palmata_), and SHGC-U (enriched with _Ulva_ sp.), each labeled with a three-digit random code. The samples were prepared as 2 \(\times\) 2 \(\times\) 2 cm cubes, maintained at 10 \(\pm\) 1 \({}^{\circ}\)C, and delivered under controlled cold chain conditions. A standardized instruction manual and evaluation forms were provided, and a preliminary online meeting was conducted to ensure methodological consistency.  
The Flash Profile (FP) test was conducted in two separate sessions:  
* Session 1 (Attribute Generation): All samples were accessed simultaneously. Panelists were asked to independently generate descriptive sensory attributes within the five sensory dimensions: appearance, aroma, flavor, texture, and aftertaste. Examples of terms generated included \"yellowish color\", \"marine odor\", \"fibrous texture\", \"salty taste\", and \"metallic aftertaste\".
* Session 2 (Ranking): After a consensus discussion via video call to finalize the attribute list, each panelist ranked the cheeses for each attribute using an ordinal ranking scale, independently per sensory dimension. The order of presentation was randomized for each panelist to minimize order effects. Between samples, panelists were instructed to pause for approximately 60 s and cleanse their palate with still water at room temperature.  
All evaluations were conducted individually in quiet home environments, free from external odors or distractions. Each session was supervised virtually by a trained researcher to ensure protocol compliance. The study was approved by the Research Ethics Committee (CEP), and all participants provided informed consent.