esa-sceva/satcom-chunk-collection huggingface dataset:
# Global Regulatory Challenges for Medical Devices: Impact on Innovation and Market Access
## 4 Regulatory Frameworks of Major Markets for Medical Devices  
### European Union  
With the emergence of medical devices, a regulatory framework has been established to ensure the safety and efficacy of these products' availability within the Member States. In the EU, the regulation of medical devices is governed by the Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017, that came into force on 26 May 2021 and lays down strict rules on the safety, traceability and compliance of medical devices in Europe [11]. This regulation repeals the Directive 93/42/EEC on medical devices and the Directive 90/385/EEC on active implantable medical devices [11].  
The main bodies involved in regulating medical devices in Europe are the EC, the competent national authorities, and the Notified Bodies (NBs). The EC plays a crucial role in creating and overseeing the regulations and policies of the EU. It defines the standards that must be followed by medical device manufacturers and works with national authorities and NBs to ensure compliance. The national competent authorities are in turn responsible for overseeing the application of the regulations, ensuring that medical devices comply with the regulations in their respective countries. In addition, these authorities are also responsible for market surveillance, investigating incidents related to medical devices and taking appropriate action. Finally, NBs are independent organizations designated by EU member states to carry out conformity assessments of medical devices. They certify that the devices meet the requirements of European regulations before the devices can receive the Conformite Europeenne (CE) marker, which is required to market the device in Europe [34; 35; 36].  
In addition to these organizations, the European Medicines Agency (EMA) also plays a role in regulating medical devices. The EMA is a decentralized EU agency responsible for the scientific evaluation, supervision and safety monitoring of medicines developed by pharmaceutical companies for use in the EU [34]. Although the EMA is primarily focused on medicines, it also plays a role in regulating medical devices that are combined with medicines or considered high-risk [37].
# Blob Detection and Deep Learning for Leukemic Blood Image Analysis
## 5 Experimental Results
### Dataset Description: ALL-IDB  
ALL-IDB [19] is a public image dataset of peripheral blood samples from healthy individuals and leukemia patients. The experts collected these samples at the M. Tettamanti Research Centre for childhood leukemia and hematological diseases, Monza, Italy. The ALL-IDB database is composed of two versions: ALL-IDB1 and ALL-IDB2, and its images are in JPG format with 24 bit color depth.  
ALL-IDB1 is composed of 108 original RGB images captured with an optical laboratory microscope coupled with an Olympus Optical C2500L camera or a Canon PowerShot G5 camera. The first 33 images have a \(1712\times 1368\) resolution, while the remaining images have a \(2592\times 1944\) resolution. The images were taken with different magnifications of the microscope, ranging from 300 to 500, which brought the color and brightness differences. ALL-IDB1 presents full images containing either cells and agglomerates; therefore, it can be used to test the segmentation capability of algorithms, as well as image pre-processing methods or classification systems.  
ALL-IDB2 is a collection of cropped areas of interest of healthy and blast cells extracted from ALL-IDB1. It contains 260 images, and 50% of these represent lymphoblasts.  
We created an entirely new ground truth because ALL-IDB was designed to test the classification and not the segmentation performances. Moreover, to the best of our knowledge, no ground truths for segmentation evaluations are publicly available, not even from third parties.
## 2 Results and Discussion
### Taste Blueprinting by AI Testing Based on Sensomics  
In general, it can be assumed that the infusions with higher concentrations of flavan-3-ols and flavonol-3-o-glycosides (Ceylon and Darjeeling Testa Valley) are described by a more intense astringent sensation; however, to objectify the real contribution on the overall  
Figure 3: PCA results. (**a**) Score plot of the first and the second principal components (F1 F2 plane) based on key taste and quality compounds’ quantitative data determined in infusions prepared from tea leaves (Table 3). For Ceylon teas, an average profile obtained from four lots was considered; (**b**) loadings plot with the distribution of quantified analytes and centroids (yellow circles) for analyzed teas.  
taste perception of the seven flavan-3-ols and the seven flavonol-3-o-glycosides, beyond the other markers, concentration data were associated with taste thresholds (Table 3).  
Figure 4 visualizes, as spider diagrams, the dose-over-threshold (DoT, ratio of the concentration of each compound vs. its taste threshold) values, i.e., the taste blueprint profile of tea infusions. Key tastants are associated with their taste descriptors, e.g., bitter, p. (puckering) astringency, m.d/v (mouth-drying/velvety) astringency.  
Within the entire class of monitored polyphenols, flavonol-3-ol glycosides are the most relevant for the definition of the taste blueprint, evoking a strong mouth-drying and velvety-like sensation [23; 45]. In particular, _quercetin-3-o-rutinoside_, known as _rutin_, is the most representative marker, followed by _kaempfer-3-o-rutinoside_ and the other derivates of _quercetin_ (glucose and galactoside). _Rutin_ likely has a strong influence on the taste profile (upper part of the spider diagrams) since it is characterized by DoT values of three orders of magnitude higher than those of flavan-3-ols (very low taste thresholds, e.g., from 0.0009 mg/L for _rutin_ to 0.30 mg/L for the glycosidic compounds _quercetin_ and _kaempfer_). In addition, an important, although not primary, role for taste perception is also played by flavan-3-ols, especially those esterified with gallic acid; _epigallocatechingallate_ (EGCGG) is the most abundant _catechin_ present in black teas (85.35 mg/L in Ceylon infusion, 177.23 mg/L in Darjeeling Testa Valley tea) [14; 21]. The bitterness of the beverage is mainly related to the high content of caffeine, which, however, shows a low DoT value, being characterized by high TT.  
Figure 4: Spider diagrams with dose-over-threshold values (DoTs) illustrate the contribution of each key taste and quality marker to the sensory perception of selected teas. DoT values are reported on a logarithmic scale. Ceylon tea taste profile is reported as average values derived from four different lots.  
As a final consideration, the higher the concentration of flavonoidic markers, the more intense the astringency sensation after tea consumption; this outcome can be expected for tea samples from Ceylon, Darjeeling TV, Assam, and Kenya.
# De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction
## 4 Results Analysis and Discussion
### Performance of the Optimal Factors
For example, for the applied optimal factor, \(\alpha_{0}\) 4, the dynamic range improvement was larger than 20 dB when the noise level was lower than \(-\)60 dB while a 15-dB improvement was achieved using \(\alpha_{0}\) 4.25 at a noise level of \(-\)45 dB.
## 5 DASH7 Communication System Implementation
### The Receiving Process
#### 5.2.3 Time Synchronisation  
The demodulated data samples are passed through a digital filter that minimizes the effects of noise and the effects of Inter-Symbol Interference (ISI). The ISI can be caused when the filtered received pulses overlap. The best filter to maximize the signal-to-noise ratio (SNR) while minimizing the ISI is a matched filter (MF). A matched filter has a frequency response whose magnitude matches the magnitude of the frequency response of the transmitter's shape filter. Accordingly, for DASH7 signals, a Gaussian matched filter is deployed similar to the Gaussian shape filter which is implemented at the transmitter.  
Afterwards, the output of the shape filter is sampled periodically at the symbol rate which is achieved by downsampling the data by the factor SPS, at the precise sampling time instants \(t_{m}=mT_{symbol}+\tau\), where \(T_{symbol}\) is the symbol interval and \(\tau\) is a nominal time delay or timing offset that accounts for the propagation time of the signal from the transmitter to the receiver [26]. Note that in DASH7, the symbol interval is the same as the bit interval. To perform this periodic sampling, we require a symbol clock at the receiver to select the correct symbol from several received samples. The process of extracting such a clock signal at the receiver is usually called symbol synchronisation or timing recovery. Timing recovery is one of the most critical functions performed at the receiver of an asynchronous digital communication system. It is important to emphasize that the receiver must know both the transmitted symbol rate (\(1/T_{symbol}\)) and the time instant to extract the symbol from multiple received samples within each symbol interval \(T_{symbol}\).  
A clock signal can be extracted from the received data signal. Many techniques can be used at the receiver to achieve self-synchronisation. However, in this paper, we adopted the Mueller and Muller symbol synchronisation method [27]. The details of this method are out of this paper's scope.  
The time recovery process is the first stage after demodulating the received samples, as shown in Figure 14. In GNU Radio, the _Symbol Sync_ block provides several symbol synchronisation methods, including the Mueller and Muller method; every symbol synchronisation method has its specific parameters. Furthermore, the _Symbol Sync_ allows the definition of any type of match filter when using the Polyphase Filterbank (PF). The input of the _Symbol Sync_ are samples with a sample rate equal to \(1/T\). On the other hand, the output of the _Symbol Sync_ are symbols with a symbol rate of \(1/T_{symbol}\).
# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 5 Visual Saliency Prediction Datasets
### Dynamic Datasets  
The discussion in Section 4.2 has established the particularities of dynamic information and human attention and the limitation of eye movement equipment, which have led to difficulties in observing dynamic data. Incidentally, owing to the growth of application requirements, some large datasets have emerged in recent years. At present, the dynamic dataset mainly consists of the following:  
* DIEM dataset: The DIEM dataset was established in 2011 by [PERSON] et al. [99]. It contains a total of 84 videos, including advertisements, movie trailers, and documentaries, among others. A total of 50 observers have provided eye movement data through free viewing. The scene content and data scale are both limited.
* UCF-sports dataset: The UCF-sports dataset was established by [PERSON] et al. [100]. The dataset contains 150 videos, including 9 common sports categories. Different from the DIEM dataset, the observation object in the UCF-sports dataset is prompted by time-based actions in the video during the viewing process. The result is found to be purposeful.
* Hollywood-2 dataset: The Hollywood-2 dataset was also established in 2012 by [PERSON] et al. [100]. The dataset contains 1770 videos that are labeled according to 12 action categories, such as eating and running, among others. Unlike the UCF-sports dataset, the observation objects of the Hollywood-2 dataset are divided into three groups: free viewing, human action annotation, and video content annotation. The human-eye focus data are in the free viewing mode only and accounts for a small proportion of all of the data.
* DHF1K dataset: The DHF1K dataset was established by [PERSON] et al. [92] in 2018. The dataset consists of a total of 1000 video sequences watched by 17 observers and covers seven main categories and 150 scene sub-categories. The video contains 582,605 frameswith a total duration of 19,420 s. The DHF1K dataset also provides calibration for movement mode and number of objects, among others, thus providing convenience for studying high-level information of the dynamic attention mechanism.
* LEDOV dataset: The LEDOV dataset [101] was established by [PERSON] et al. in 2018. It includes daily activities, sports, social activities, art performances, and other content. A total of 538 videos, with a resolution of 720 px, contain a total of 179,336 frames of video and 5,058,178 gaze locations.  
For early dynamic models, the DIEM, Hollywood-2, and UCF-sports datasets were the three most widely used datasets in video saliency research. In recent years, with the continuous updating of datasets, there are more models also using the DHF1K database for training and testing. The DHF1K database has a huge amount of data and a wide range of application.
## 4 Performance Analysis
### Effective Capacity  
To evaluate the delay performance of some emerging wireless real-time applications under a quality of service (QoS) constraint, such as voice over IP and augmented reality/virtual reality, the effective capacity (EC), as an alternative performance metric of interest, has gained great attention. The EC is defined as the maximum constant arrival rate that a wireless fading channel can offer in a bid to ensure the QoS requirements. For wireless fading channels, the normalized EC can be expressed as  
\[\Re=-\frac{1}{A}\log_{2}\left(\mathbb{E}\Big{[}(1+\gamma)^{-A}\Big{]}\right)=- \frac{1}{A}\log_{2}\left(\int_{0}^{\infty}{(1+\gamma)^{-A}f_{\gamma}(\gamma)d \gamma}\right), \tag{51}\]  
where \(A=\theta TB\ln 2\), \(\theta\) denotes the QoS exponent characterizing the delay constraints, \(T\) is the block length, \(B\) represents the system bandwidth, and \(f_{\gamma}(\gamma)\) is the PDF of the instantaneous SNR (\(\gamma\)).
# Review of Traffic Assignment and Future Challenges
## 3 Traffic Assignment: Theoretical Background
### Environmental Concerns and Traffic Control  
The results of both \(STA\)[133] and \(DTA\)[10; 11] were used for the evaluation of the resulting pollution. This was applied not only to road traffic but also to transit [131]. Apart from the use of \(STA\) and \(DTA\) for environmental assessment, the problem of traffic assignment can be extended to control the traffic. Recall that the travel time is the central user's criteria in traffic assignment models. The equilibrium assignment results from a selfish choice that is not in the community's best interest (see the illustrative example presented in Section 2.4). Therefore, tolling is the lever for controlling the distribution of flows.  
The control was addressed in several papers to mitigate the cost of anarchy. However, the studied problems remain limited [4]. In contrast to the problem of having a known cost function for roads and distributing the flow accordingly, the control problem is based on a centralized optimization function for which the travel costs of roads are adjusted. More precisely, in [10] the authors suggest an approach where the dynamic system optimum with free flow is solved first. Then, the dynamic pricing is optimized after several iterations. More recently, Ref. [134] shows the impact of the design of incentive policy to minimize the price of anarchy in a congested network. This work was extended in [12] to compare between subsidies and tolls.  
Another control lever is the traffic light. A traffic-light plan can be used to either increase or decrease the travel time for a given road. However, such an approach poses the problem of modeling the travel time according to traffic-light plans. Moreover, it is widely admitted that this makes the problem more complicated to solve, requiring simplifications and artificial-intelligence (\(AI\)) techniques. One approach is to carry out several iterations in which the two problems, i.e., traffic assignment and traffic-light optimization, are dealt with in sequence. In the work presented in [135], the authors use several heuristic strategies [136] to optimize traffic lights while using MatSim [93] for traffic assignment. The authors of [137] suggest a navigation rule based on gene expression programming. The navigation rule allows vehicles to select the neighboring road. The traffic-light plan is optimized to maximize the throughput. In both papers, the traffic-light optimization aims to improve traffic flow according to the assignment result.
# Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology
## 6 Conclusions  
In this study, we built an efficient medical data management platform based on Fabric and implemented data uploading and querying for medical, operational behaviors, authorization, and other data through smart contracts. We designed data structures for medical data and operational behaviors data and proposed two data provenance methods, naive and DAG-based, to achieve full-process tracking or tracing of medical data. In our experiments, we tested the platform's data upload and query throughput and found that the platform meets the storage and query performance requirements for medical data management. Furthermore, we tested the provenance performance of naive and DAG-based methods under different data sizes and path lengths. The results show that the DAG-based method significantly outperforms the naive method in terms of TPS. For instance, with 200,000 records and path length 1, the DAG-forward and DAG-backward methods achieved a TPS of 21,099.91 and 21,767.52, respectively, while the naive method only achieved 537.20. Even with an increased data size of 1,000,000 records, the DAG-forward and DAG-backward methods maintained a high TPS of 20,815.98 and 19,981.06, compared to the naive method's 510.49.  
However, the DAG-based method requires more memory and additional storage space and necessitates extra mechanisms to prevent tampering of off-chain stored files. Therefore, in our future work, we plan to integrate a graph database into the platform's blockchain network to reduce storage and memory overheads of the DAG methods, simplify the data verification method, and further improve the performance of data provenance. In subsequent research, we will also consider more complex scenarios of user permission restrictions, design specific index structures for different constraints, and, thus, enhance the efficiency of data provenance under various constraints.  
X.M.: Conceptualization, Writing, Data analysis, Methodology, Validation, Data curation. C.L.: Conceptualization, Supervision, Investigation. Y.Z.: Conceptualization, Formal analysis, Writing--review and editing. G.Z.: Conceptualization, Investigation, Methodology. C.X.: Conceptualization, Resources, Supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Social Science Fund of China (22&ZD141).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing
## 2 Related Work
proposes a task migration method, using parked vehicles to reduce the network delay; the parked vehicle cannot provide a durable power output because its engine is in a stopped state [31]. With the use of a parked vehicle as a relay node, the network deployment method can improve network stability. Although the current research on fog computing and connected vehicle has become a relatively popular research direction, the literature combining the two is rare [32]. Further research is needed. Some researchers have also begun to adopt advanced technologies or strategies to address security mechanisms in industrial cooperation. [PERSON], [PERSON] et al. considering that potential attacking opportunities triggered by this progress are severely impacting the security fortress of current networks, especially in the edge access part, propose a Multi-dimensional Fine-grained Control (MFC) framework to strengthen safety and reliability in Radio Access Networks (RANs) [33]. An identifier mapping mechanism was designed to achieve network isolation, and the MFC framework is established to describe the model structure and implementation processes. Reference [34] found that the receiving buffer of artificial intelligence in the control area would also limit the throughput problem and proposed an Intelligent Collaborative Automation (SCA) scheme, to improve resource usage and overcome buffer limitations. The program provides comprehensive policy options by simplifying comparisons to provide details. [PERSON], [PERSON] et al. found that, in the existing adaptive oil drainage strategy, two aspects that need to be properly designed are ignored: the two-way interaction between vehicles may cause the stability of the strings to decrease, and the engine saturation constraint will cause the loss of cohesion [35]. The authors propose a novel adaptive platooning strategy handling these two crucial aspects. Uncertain heterogeneous rows should be homogenized to the reference dynamics. Engine constraints are addressed through suggested mechanisms, and this reference dynamics is \"less demanding\" by properly saturating their effects. The saturation action will prevent all vehicles in the row from reaching their engine limits, thereby maintaining the regularity of the vehicle's operation and making it more regular. Therefore, by analyzing and sorting out the current work, we find that most vehicle fog computing solutions face a single technology, with limited performance improvements and vehicle resource utilization needs to be improved. Faced with different scenarios, the high-speed mobility of the Internet of Vehicles and the complex and changing network environment have brought more uncertainty to the Internet of Vehicles. How to effectively improve the extension of vehicle fog computing and the utilization of vehicle resources has become an urgent problem and key question.
## 3 Preliminaries  
The network model used in this study is a WSN model in which N nodes are randomly allocated in a square sensing area (\(L\times L\)) centered on a BS. The BS has strong computing and network management capabilities and is equipped with more battery power or can be self-replenished through energy harvesting. Therefore, the BS has no energy limitation. On this basis, the following assumptions are made about the WSN.  
1. Each node has a unique identity document (ID), and all the nodes are evenly distributed in the monitoring area;
2. Nodes maintain stationary after the network deployment, and their energy is restricted and cannot be replenished;
3. The BS cannot move and can communicate with any node in the detection area;
4. Each node has multiple adjustable transmitting capacities and can choose the appropriate transmission power in terms of the communication distance;
5. The network adopts data fusion technology to decrease the amount of data transmission;
6. Every node does not have position perception capability;
7. Under a certain transmission power, an individual node can calculate the communication distance according to the received signal strength;
8. The simplified model proposed by [PERSON] et al. (2016) is adopted in our study for computing communication energy consumption in consideration of path loss.