esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Performance Comparison  
Assessing the overall effectiveness of a model presents a significant challenge, as it requires comparing different models using the same dataset. Therefore, we evaluate the efficacy of the proposed model through a comparative analysis with other models using the CSI-HAR and StanWiFi datasets. The comparative results are provided in Table 6. Our study demonstrates that the proposed CNN-GRU-AttNet model outperforms other models  
Figure 11: The accuracy and loss metrics of the CNN-GRU-AttNet model on the StanWiFi dataset: **(a)** train and validation accuracy curves; **(b)** train and validation loss curves.  
Figure 10: Confusion matrix of the proposed model on the StanWiFi dataset.  
on the CSI-HAR dataset in terms of recognition capability. The CNN-GRU-AttNet model achieved an average accuracy of 99.62%, precision of 99.61%, recall of 99.61%, and F1-score of 99.61%. Furthermore, our proposed CNN-GRU-AttNet model exhibits a remarkable accuracy improvement of 4.62% compared to the leading-edge model currently available on the CSI-HAR dataset.  
The proposed CNN-GRU-AttNet model has demonstrated a superior level of accuracy, achieving a score of 98.66% on the StanWiFi dataset, which represents a 0.66% improvement over the best state-of-the-art performance [33]. Upon analyzing the performance of various existing models presented in Table 6, it is evident that our proposed CNN-GRU-AttNet outperforms all others in terms of recognition outcomes on both datasets. This observed enhancement can be attributed to the recently suggested framework, which adeptly captures and utilizes spatial and temporal characteristics extracted from unprocessed CSI data for the purpose of HAR.
## 5 Experimental Progress
### NRRS Task Design  
Since the test constellation does not possess inter-satellite links, the ground station is used as a relay to simulate inter-satellite data transmission for near real-time remote sensing. The NRRS experiment is demonstrated in Figure 12 with the following steps:  
1. The user accesses the satellite Internet based on the STOM terminal and submits remote sensing demands.
2. The satellite GS2 forwards the remote sensing requirements to the gateway station.
3. The ICRS application system responds to user requirements, carries out task planning, and gives feedback on task status.
4. The TT&C station uploads the shooting commands to the remote sensing satellite BP01 through.
5. The satellite BP01 starts attitude maneuver 450 s in advance and starts imaging after attitude adjustment.
6. The remote sensing satellite observes the target and sends images to the data receiving station simultaneously.
7. The data receiving station uploads the image data to the ICRS application system for near real-time processing and original image analysis, then refreshes the task status.
8. The gateway station forwards the processed image data to the satellite AP03.
9. The user accesses the satellite Internet and ICRS application system again to obtain the images.  
Figure 11: The SNR results and communication time of the STOM terminal.  
Figure 9: Evolution of the satellite attitude and angular velocity during staring communication (green line: roll, yellow line: pitch, blue line: yaw).  
It is worth noting that the satellites cannot perform in-orbit processing and intersatellite transmission. Therefore, in this experiment, the original data are forwarded to the data receiving station for processing, and then distributed through another satellite to replace the process of in-orbit processing and inter-satellite transfer. In addition, since the data receiving station cannot process data, the original image files are uploaded to the ICRS application system for processing. Since the original files are approximately 2 GB, the connection speed between the terrestrial network and data receiving station is limited to 100 Mbps. If the image can be processed at the data receiving station, the NRRS process can be further reduced by 2-3 min.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 2 Background
### FPGAs in Wearable Medical Devices
#### 2.3.3 Low-Power FPGA Families
Further comparisons are provided in Table 1.  
Microchip IGLOO2 and IGLOO Families: Microchip's IGLOO2 and IGLOO Nano FPGAs utilize flash-based technology, resulting in extremely low static power consumption. This flash-based architecture eliminates the need for external configuration memory, allowing the device to be instantly operational and retain its configuration even when powered off. Both devices are equipped with an ultra low-power static mode known as the flash-freeze power mode, which allows the device to enter and exit this state in under 1 us, consuming nano power. This technology streamlines power management by avoiding the need to turn off voltages, I/Os, or clocks at the system level, while I/Os can maintain their states during flash-freeze mode. The IGLOO2 device, built on a 65 nm process, features a flash-freeze mode, while the IGLOO Nano device, built on a 130 nm process, also offers an additional sleep mode. Further details are listed in Table 1.  
GOWIN Semiconductor and QuickLogic Low-Power Families: Apart from these renowned manufacturers, other vendors like GOWIN Semiconductor and QuickLogic also offer low-power FPGAs. The GOWIN GW1 NZ series FPGAs are non-volatile flash-based devices known for their ultra-low power consumption, instant-on capability, and suitability for mobile and wearable applications. They also feature a low-power mode which operates at a core voltage of 0.9V. In contrast, QuickLogic's PolarPro-3 FPGAs utilize SRAM technology, offering high flexibility and reprogrammability, making them ideal for battery-powered applications. These FPGAs achieve ultra-low static power consumption of about 55 uA, enhancing the battery life of devices. Both series are optimized for low power, but they leverage different technologies to meet their power efficiency goals. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Massufacturer** & **PFGA Family** & **Device** & **Process** & **Tools** & **Tools** & **Logic Resource** & **RAM** & **Mon Backs** & **Core** & **Static Current** & **Features** \\ \hline  & Sparstra-7 [23, 24] & XC78 & 26 ms & SLAM & 6000\(\times\) LEs & 180 & 10-DISP Slos & 0.95V/1.0 EV & 32 mA\({}^{1}\) & Lower power consumption than previous generation, \\ \hline AMD (Milton) & Auto-7 [23, 25] & XC742T & 26 ms & SLAM & 12,800\(\times\) LCs & 720 & 40\(\times\) DSPR1 & 0.95V/1.0 EV & 43 mA\({}^{1}\) & Partial non-sequentialization, voltage scaling, power gating, and clock aging.
# EMD-Shannon Entropy-Based Methodology to Detect Incipient Damages in a Truss Structure
## 4 Experimentation
### Study Cases  
Corrosion is considered one of the main mechanisms that can damage a civil structure, especially truss structures. It is characterized by degrading the materials, mainly metals, that constitute a structure, resulting in weakness of the structural members, which can produce the collapse of the structure in certain cases; hence, corrosion identification in its early stage is of vital importance [62]. Corrosion can take years to degrade the material; hence, in order to accelerate the process, the bar element extremes of the bridge were immersed in hydrochloric acid, generating external corrosion damage. The corrosion damage was produced gradually in the bar element with the aim of observing the behavior of a bridge under four levels of damage. The healthy element presents a diameter of 19 mm. The damaged elements present reductions in their diameters of 1 mm, 3 mm, 5 mm, and 8 mm, representing incipient, light, moderate, and severe levels of damage respectively (see Figure 5). It should be pointed out that the damage levels are selected considering the minimum and maximum amount of diameter reduction that can be induced without compromising the member integrity, and ensuring a uniformly-distributed separation [3; 5; 58; 63].  
Figure 4: Measured vibration signals in Ax, Ay, and Az for (**a**) healthy condition and (**b**) incipient damage in first-bay.  
Figure 5: Bar element with a healthy condition (19 mm of diameter in all sections) and bar elements with the different levels of corrosion (1 mm, 3 mm, 5 mm, and 8 mm of reduction in the diameter).
## 5 Conclusions  
In heterogeneous V2X, intelligent congestion control techniques are important for providing efficient network services. The emphasis of this paper is on congestion control. For the dynamic nature of vehicle movement and the diversity of network service types in vehicle communications, the paper proposes an ICCDRL as a solution to address the diverse needs of network applications of terminal devices, so as to ultimately achieve on-demand driven congestion control in V2X networks. First, traditional congestion control is discussed in this paper. A congestion control model based on DRL is proposed using [PERSON]'s stochastic memoryless property to design a reasonable state space, action space and reward function. Second, the main goal of this phase of the study was to obtain the minimum cost as well as to define the overhead weights and congestion sensitivity factors \(\lambda_{TDL}\). The QoS parameters of different services in vehicle communications were collected first, and then the minimum cost was calculated according to the different importance of the services. Finally, the simulation platform was built in ns-3, and the experimental results verify the high performance of the ICCDRL algorithm proposed in the paper.  
The key advantage of ICCDRL is to differentiate the importance and congestion sensitivity factors of different services based on QoS weights, using PPO2 to learn from historical experience and using a large amount of historical QoS data as a training set to  
Figure 15: Comparison of convergence speed.  
optimize the model. In addition, because the Agent of the ICCDRL algorithm combines the design idea of on-demand driven service, the congestion window changes more smoothly and is not too aggressive, unlike other DRL-based congestion control algorithms, showing a very good performance. In response to the simulation results, further optimization algorithms will be proposed to improve the slow-start threshold in the future, hoping to achieve further improvement of transmission performance in V2X.  
[PERSON] conceived and designed the whole system; [PERSON] and [PERSON] designed the algorithm and model; [PERSON] and [PERSON] conducted the experiment and analyzed the data; [PERSON] wrote the research paper. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Industrial research project of Science and Technology Department of Shaanxi Province (Grant No. 2016 KTZDGY4-09), key research and development plan project of Shaanxi Science and Technology Department (Grant No. 2017 ZDDXM-GY-016), and the project of Innovation and Entrepreneurship Training Program for College Students at the national level (Grant No. S202110702110; S202110702089), and the Research project on teaching reform of education in Shaanxi province (Grant No. 20 JGY016, 17 JZ004, 17 JY015), and the Characteristic disciplines in Education department of Shaanxi province (Grant No. 080901), the Pre-research Project of 13 th Five-year Equipment Development (41402020202).  
Not applicable.  
The authors declare no conflict of interest.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 2 Background and Related Works
### Background
#### 2.1.6 Searchable Encryption Vulnerability  
* Keyword Guessing Attack (KGA): one of the most serious PEKS security issues is its vulnerability against off-line Keyword Guessing Attacks (KGA). An adversary can encrypt the candidates' keywords by using the public key of the receiver and identifying the ciphertext that fits the intended trapdoor. This allows the adversary to retrieve the keyword concealed in the trapdoor to breach the privacy of the users. Such attacks rely on the keywords' observations that are selected and receivers typically scan for files using well-known keywords [25].
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 4 Parallel Edge Deblocking Filter Architecture (PEDBF)
### Boundary Strength Computation Unit  
The boundary strength computation unit computes the BS value, as shown in Figure 5. The BS value range for the HEVC coding standard is between 0 and 2. The BS processing unit receives control signals regarding the pixel block received from the external buffer, such as whether the received pixel block is the left, right, top, or bottom edge of the frame, if it is inter/intracoded, and if its transform coefficients are not zero. The BS value is 0 if the block of pixel data read from the external buffer is a part of the left or top edge of a frame. In addition, the BS value is 0 if the data are not a part of the left or top edge of a frame, the two neighboring 8 \(\times\) 8 blocks are not intracoded, if the block has nonzero transform coefficients, and if the motion vector is less than 4. The BS value is 1 if the transform coefficients of the block are nonzero and the adjacent blocks are not intracoded. The BS value is also 1 if the adjacent blocks are not intracoded and if the block does not have nonzero transform coefficients, and if the motion vector is higher than or equal to 4. The BS value is 2 if any of the above conditions are not met. DBF is triggered based on the calculated BS value. If the generated value of BS is 0, no filtering is performed; if the determined value is 1, a weak/normal filter is used; and if the stipulated value is 2, a strong filter is used.  
Figure 5: Boundary strength computation unit.
## 1 Introduction  
Transportation, zero emissions, economy, governance and environment [1, 2] are the smart city key functions that drive the population density where one of the visions is to support coherent and seamless mobile connectivity. With the versatility of mobile devices and their up-scaling numbers, there is a need to devise some statistical methods to cope with the Fifth Generation 5G trends of communication where the Energy Savings (ES) and CO2 reductions are highly important [3]. Due to the exponential increase in user demands, the requirements for data-intensive wireless services are also boosted at an unprecedented rate, encumbering current wireless systems and providers [4]. Remedies are currently under immense discussion and development to satisfy ubiquitous up-scaling demands, which are further expected to rise manifold [5]. 5G compared to earlier generations not only addresses physical layer problems but also introduces modern applications being run by the use of Machine Learning (ML) methods. Hence, the deployment of Ultra-Dense Small Cells (UDSCs) is on a direct collision path with the challenges in modern smart city concept such as transportation,zero emissions, economy, governance and environment [3; 6] that impacts directly the vision of 5G, as shown in Figure 1. This is because UDSCs are power hungry network elements that satisfy user demands by \"always ON\" strategy. Consumed power has a direct relation to the total amount of energy being drawn coupled with CO2 emissions and operational costs (OPEX). Small Cells (SCs) as compared to Macros Cells (MCs) have relatively low energy consumption; however, the always ON approach in Heterogeneous Network (HetNet) architecture with densely deployed SCs increases overall energy consumption [3; 7; 8].  
The high energy consumption of SCs is due to the circuit power component (load independent power consumption) that enacts a much larger portion of over-all energy consumption [9]. The various reasons that load component gets overloaded include in-efficient use of SCs drawing unnecessary power and incorrect mobility management of daily passengers ridership across deployed SCs. As a result, with the concept of UDSC deployments, the need for CO2 reductions driven by ES schemes using Reinforcement Learning (RL) and statistical mobility methods driven by Machine Learning (ML) algorithms will be even more compelling.
## 4 Discussion  
### Positive Effect of Ecological Restoration on Mercury Mobility  
In this study, the results showed very distinct mercury behaviors based on sites that were not solely related to vegetation density. Indeed, the three least covered sites (Bel1, Yao1 and Yao2) presented very different speciation results with nearly 50% non-mobile mercury for Bel1 and less than 10% non-mobile mercury for Yao1 and Yao2. In addition, the results showed a significant increase in mobile and toxic mercury for sites Yao1 and Yao2 with more than 40% compared to about 25% of mobile mercury for Bel1. The presence of tree cover did not positively impact the mobility of mercury on the Bel3 site compared to adjacent plot Bel2, which showed a higher proportion of semi-extractable and non-mobile mercury and less mobile mercury. However, mercury speciation over the oldest restored area of Yao3 showed a very significant decrease in mobility and potential toxicity of mercury with 90% of mercury present in non-extractable forms.  
While HCL/ethanol extraction represents more of a mobility potential linked to leaching under acidic conditions, water extraction, CaCl\({}_{2}\) and EDTA represent more direct mobility close to natural conditions in soils. The F1, F2 and F3 extractions showed the lowest percentage of easily mobile mercury for the Yao3 site and the highest for Yao1 with 1 and 8%, respectively. While the low mobility of mercury species for the Yao3 site are close to those for the Asturias [51], Almaden [52] and Usagre [53] mines in Spain, the Cuyuni Basin [54] in Venezuela, old mining mines in Mexico [55], in the Yaoni region of French Guiana [16], and the mercury mobility and toxicity for Yao1 were significantly higher and comparable to mobility at some Wanshan mining sites in China [56]. Depending on rainfall, these sites can go into suboxic or even anoxic conditions and these results show that the pool of mercury available for rapid methylation diverges very strongly between the rehabilitated sites.  
The results indicate the contribution of a dense cover of fabaceae on the Yao3 site to modify mercury speciation compared to the unrestored Yao1 and Yao2 sites. At this site, despite a higher total mercury content than at the other rehabilitated sites, most of the mercury corresponds to very stable chemical species with low mobility and high stability, such as amalgamated elemental mercury, strongly bound mercury (mineralized and metal-oxyhydroxides) and species in the form of sulphides and hydroxide oxides. For the restored site Bel3, the results are more contrasted, but are possibly due to the more recent restoration and more limited contributions in organic matter in only four years of vegetation recovery.
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 2 Materials and Methods  
### Vineyards Sampling  
The study research was based on the dataset already published [31]. A sample of 3686 vineyards was randomly selected from Google Earth (Google LLC, Mountain View, CA, USA) across the Italian territory. Figure 1 depicts the actual distribution of the sampled vineyards. Two kinds of data were attributed to each vineyard, i.e., (I) structural parameters describing the field's shape and geographic information [31], and (II) biomass developing evolution evaluated through RS techniques. The methodology was planned to identify which items from the I variables significantly affect the II variables. Figure 2 outlines the study workflow.  
Figure 1: Geographical distribution of the sampled vineyards (pink points).
# Numerical Simulation of Tunnel Face Support Pressure in Layered Soft Ground
## 2 Numerical Modeling of the TBM Face Support Pressure
### The Definition of the Support Pressure of the Excavation Face in an Unstable Condition  
In order to analyze the influence of different composite strata on ground settlement, the support pressure of the excavation face should be determined. In this study, the stress control method was used to determine the support pressure of the excavation face when failure occurs. The essence of the stress control method is to gradually reduce the applied pressure until the failure occurs, and the pressure applied before the failure point is the minimum excavation face support pressure. In this numerical simulation, the maximum displacement point in the excavation face is taken as the control point, as shown in Figure 2(a). By recording the displacement of the control point at each pressure drop increment, the pressure-displacement curve is obtained, as shown in Figure 2(b). When the applied support pressure decreases, the displacement of the control point increases. When the curve becomes horizontal, it means that damage has occurred. Therefore, before reaching this point of failure, the support pressure applied is the limiting support pressure required for the excavation face. When the soil is completely destroyed, the displacement of the characteristic part will change abruptly, and the finite element calculation may not converge. Therefore, we continue to reduce the support pressure until the model calculation does not converge, at which time the soil displacement is considered to be infinite. Until then, all numerical calculations are convergent.
# Conversation-Based School Building Inspection Support System
## 6 Conclusions  
In this study, we digitalized the process of building inspections and developed a conversation-based system for notifying and guiding assessors to complete their inspection tasks. The system is composed of the notification module, the conversation module, and the display module. The notification module (N-module) is activated by a routine inspection schedule or by receiving information when an earthquake occurs, subsequently sending notifications to the registered assessors of the school via IM applications and thus triggering the building inspections. The conversation module (C-module) interacts with assessors via IM applications as a chatbot that guides assessors to specify inspection targets, describe the damage, and eventually complete the inspection task. The display module (D-module) reads inspection reports from critical schools, analyzes the data, and presents summaries and detailed reports via an interactive dashboard for managers. We implemented the system at the main campus of the National Taiwan University of Science and Technology as a case study. By adapting smart device technology, this study provides a novel solution featuring aggressive notifications and a guiding interface for nonprofessional assessors. The conversation-based system developed in this study reduces the complexity of building inspections, the inconvenience of the paperwork process, and the inefficiency of management.  
**Author Contributions:** Conceptualization, M.-H.T.; methodology, M.-H.T., H.-Y.C., and L.-Y.L.; software, H.-Y.C. and L.-Y.L.; validation, M.-H.T., H.-Y.C., and L.-Y.L.; formal analysis, M.-H.T.; investigation, M.-H.T., H.-Y.C., and L.-Y.L.; resources, M.-H.T.; data curation, M.-H.T. and H.-Y.C.; writing-original draft preparation, H.-Y.C. and L.-Y.L.; writing-review and editing, M.-H.T. and H.-Y.C.; visualization, H.-Y.C.; supervision, M.-H.T.; project administration, M.-H.T.; funding acquisition, M.-H.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by the Taiwan Building Technology Center from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.  
**Acknowledgments:** The authors are grateful to [PERSON] of Soochow University, Taiwan, and [PERSON] of Da-Feng Elementary School, New Taipei City, Taiwan, for their useful advice. The authors are also grateful to [PERSON], [PERSON], and [PERSON] for their contribution to the system implementation.  
**Conflicts of Interest:** The authors declare no conflict of interest.