esa-sceva/satcom-chunk-collection huggingface dataset:
# Wearable Sensors for Vital Signs Measurement: A Survey
## 3 Intelligent Prospect of Wearable Medical Sensors
### Intelligent Prospective Applications of Wearable Medical Sensors
#### 3.2.3 Prospects of Intelligence-Assisted Rehabilitation of Wearable Medical Sensors
(2020) proposed a scheme of dynamically selecting radio protocols in the energy-limited wearable Internet of Things medical system and considered using multiple radio protocols to send the physiological parameters sensed by patients to the server through local processing units (LPU). Through actual data and large-scale simulation, their results showed that compared with the existing schemes, the data rate increased by nearly 78% and the throughput increased by about 7% [87]. [PERSON] et al. (2021) proposed an intelligent feature-learning detection system (FLDS) based on deep learning combined with medical sensors for fetal congenital heart disease (CHD), a disease with a high mortality rate. A large number of their experiments showed that the accuracy of the proposed model algorithm could reach 91.9% [88]. [PERSON] et al. (2021) also used deep learning combined with medical IoT technology to predict and analyze diseases. It was found that Internet-of-Medical-Things (IoMT) sensors can be used for spreading, which could ensure timely disease prediction and form a healthcare system with advantages in both prediction and time performance [89]. [PERSON] et al. (2021) proposed a multi-modal medical image fusion method with deep learning, based on the characteristics of multi-modal medical images and the actual needs of medical diagnosis. It was found that the system can realize batch processing of images, and at the same time showed its superiority in terms of visual quality and various quantitative evaluation standards [90]. A review of the research of the above-mentioned scholars found that the combination of medical sensors and deep learning technology can predict human health, such as fetal congenital heart disease. However, most of the medical sensor systems proposed by the above scholars were tested in the laboratory or under simulated conditions, and the relevant technical models were not applied to businesses. A series of tests are still needed before they can be used in the actual medical field. Meantime, some scholars have conducted predictive analyses on brain diseases in the medical field. For example, the use of fuzzy clustering and neural networks to predict brain tumors [91], and the combination of Digital Twins technology and artificial intelligence technology to predict diseases [92] are all intelligent developments in the medical field and are of great significance to improving human health to avoid disease.
# Nanomaterials for Healthcare Biosensing Applications
## 3 Applications of Inorganic Nanomaterials in Healthcare Biosensing
A wide variety of inorganic nanomaterials have been used in healthcare diagnostics. In this review we mainly focus on the commonly utilized nanomaterials, including quantum dots, magnetic nanoparticles, noble metal nanoparticles, and nanostructures such as nanoshells, nanowires, and nanocages.
## 4 Results and Discussion
### Potential Health Hazards Assessment
#### 4.3.3 Absorbed Gamma Dose Rate (AGDR)  
Estimation of the excess absorbed gamma dose rate indoors caused by building materials (increment to that of outdoors) relies essentially upon: (1) the concentrations of the radioisotopes (\({}^{238}\)U, \({}^{232}\)Th, and \({}^{40}\)K), (2) the properties of these materials, and (3) design ways in dwellings [64]. As indicated by the European Commission [20], the extra AGDR  
Figure 8: Variation of I\({}_{\gamma}\) and I\({}_{\alpha}\) values for the investigated samples.  
(in nGy h\({}^{-1}\)) in the air within a room, owing to using the granite as superficial construction materials in its walls and floor, can be determined using the Equation (6) [28; 65]:  
\[\mathrm{AGDR}\left[\mathrm{nGy~{}h^{-1}}\right]=0.12\mathrm{AC_{Ra}}+0.14 \mathrm{AC_{Th}}+0.0096\mathrm{AC_{K}} \tag{6}\]  
Notably, the coefficients 0.12, 0.14, and 0.0096 in nGy h\({}^{-1}\)/Bq/kg included in Equation (6) are estimated according to the model of the standard room dimension (4 m \(\times\) 5m \(\times\) 2.8 m) designed by rectangular walls of concrete (20 cm in thickness and 2350 kg/m\({}^{-3}\) in density) and tiled with superficial material (3 cm thickness and 2600 kg/m\({}^{3}\) density) in walls and floor.  
The calculated values of the excess AGDR indoor originating from EISibai-Abu EITiyur granitic samples, when used as superficial building materials are given (Table 4) and compared to the recommended GAVs (Figure 9). They fluctuate between 13.09 to 26.72 nGy h\({}^{-1}\) with a mean of 18.26 \(\pm\) 0.89 nGy h\({}^{-1}\), which is less than their corresponding GAVs of 84 and 70 nGy h\({}^{-1}\) indoors UNSCEAR [21] and the European Commission European Commission Commission [20], respectively (Table 4 and Figure 9).
# Secure Delivery Scheme of Common Data Model for Decentralized Cloud Platforms
## 3 Results
### Our Encryption and Decryption Scheme for Delivering in SC-CDM
#### 3.4.3 Privacy Leakage Resilience of SC-CDM  
In constructing SC-CDM, since we are the scheme based on asymmetric key encryption, we prevent leakage of personal information in the integrity audit process. In the secure operation scheme, the public key of CDM consumer is maintained in SC-Blockchain.  
Figure 8 shows the result of the transaction log having a note CDM for registering public keys of CDM provider and CDM consumer in SC-Blockchain. CDM Separate Block Encryption/Decryption Algorithm (in Section 2.2.4) is used for building the encrypted CDM. The encrypted CDM is stored in IPFS with its metadata for signing and verifying. The transactions of new blocks can be verified by its hash value locally and the transactions from the IPFS network also can be verified. Transaction should be stored into IPFS. In the public-key-based solution, subsequent clients need to know the public key generated by the CDM uploader to audit the integrity. That is why subsequent clients can immediately learn who has a record. Figure 9 shows the transaction log related with consensus.  
Figure 10 shows the contents of the blockchain that consists of three blocks (0, 1, and 2) that are generated chronologically. The genesis block having is the first block in any blockchain-based protocol [39]. As can be seen in Figure 10, the block number 0 represents it. The block number 1  
Figure 8: The transaction log having a note CDM for registering public keys in SC-Blockchain.  
Figure 9: The transaction log related with consensus.  
consists of a set of four CDM transactions, t1 to t3, where t1 is happened before t2 and t3 occurs lastly in the causal relationship. The output of the hash function, H, is the digest, a kind of hash value. For the CDM, the transaction t1 is issued for registering the public key owned by the CDM provider. The transaction t2 is issued for registering the public key owned by the CDM consumer. The transaction t4 in the block b2 represents the delivery of a signed CDM by the CDM provider. It is accessed by the CDM consumer for handling the encrypted CDM. Eventually a signed CDM is verified by a private key of the CDM consumer in SC-CDM that is used as a part of the transaction t5. One block can be added to the blockchain at a time [40]. Each block is verified to ensure that it follows in sequence from the previous block. All CDM transaction records are kept in the blockchain and are shared with peers, including some members of a CDM service system who have joined in a blockchain network. The block of transactions is committed by the leader.
## 8 Technical Challenges for 6G Development
### Power Supply Issue  
Addressing power supply challenges is crucial for the success of 6G, aiming to seamlessly connect an extensive number of autonomous, low-power mobile devices. The current limitation of smartphone battery life to just one day poses a bottleneck in the evolution of mobile communication. Therefore, innovative power supply methods and streamlined signal processing architectures are imperative for the efficiency and growth of the 6G network.  
To tackle this issue, new architectures for mobile devices, particularly those utilizing wireless methods like energy harvesting and power transfer, can be developed. Simultaneously, adopting advanced energy-efficient wireless communication techniques is essential. For instance, designing low-complexity precoding and signal detection algorithms can enhance power efficiency, especially in ultra-high-dimension scenarios such as UM-MIMO multi-user setups. Furthermore, strategically integrating power supply methods with wireless transmission techniques, tailored to the requirements of mobile devices, is a key approach to realizing a future 6G network powered by mobile devices and ensuring energy autonomy across diverse conditions.
## 4 Experiments and Results  
### Experimental Data and Evaluation Criteria  
The feasibility of the proposed method was verified by testing seven datasets using C++ and MATLAB on a computer (i7-11700 CPU, 2.5 GHZ and 16 GB RAM), as shown in Figure 12. The Synthetic Point Cloud (Synth) is an indoor environmental simulation data from the datasets of the University of Zurich. Synth includes three irregular rooms and three doors. The real data includes two offices and one room. The first office (Office1) was scanned using a Trimble TX5 terrestrial laser scanner at the Beijing University of Civil Engineering and Architecture. Office1 consists of a long corridor, six rooms and 18 doors. The second office (Office2) is also from the University of Zurich datasets. Office2 consists of a corridor, five rooms and six doors. Another room (Room) is a private residence scanned using a mobile measurement system with a laser scanner (Hokuyo UTM-30 LX) [44]. Room consists of six rooms and four doors. In addition, we also selected TUB1, TUB2 second floor and UOM data from the ISPRS Benchmark Dataset. TUB1 data was captured using the Viametris iMS3D system with some missing wall boundaries. TUB1 includes 10 rooms and 23 doors. TUB2 data was captured using a Zeb-Revo sensor with low clutter. The second level of TUB2 includes 10 rooms and 28 doors. UOM data was captured using a Zeb-1 sensor acquired with a moderate level of clutter. UOM includes seven rooms and 14 doors. Three data sets in the Benchmark Dataset contain closed doors.  
In order to verify the experimental results, the reconstructed model is evaluated by the following three criteria: completeness; correctness; and accuracy. Completeness is defined as the proportion of reference data in the source data, calculated by Equation (7). Correctness is defined as the proportion of the source data in the reference data, calculated by Equation (8). Accuracy is defined as the geometric proximity of the source data to the reference data and is calculated by Equation (9).  
\[Completeness=\frac{Reference\cap Source}{Reference} \tag{7}\]  
\[Correctness=\frac{Reference\cap Source}{Source} \tag{8}\]  
\[Accuracy=|Distance(V_{i},S_{i})| \tag{9}\]  
where \(Source\) represents the source data, i.e., the door or 2D floor plane of the reconstructed model; \(Reference\) represents the reference data, i.e., the door or 2D floor plane of the original point cloud or ground truth model. \(Distance\) represents the Euclidean distance between data. \(V_{i}\) represents the point on the surface of the reconstructed model. \(S_{i}\) represents the closest point to \(V_{i}\) on the source data (point cloud/ground truth model).  
Figure 11: Local navigation network generation and adjustment. The yellow areas indicate the connecting transition space. The burgundy dots are the convex points. The pink dots are the concave points. The red dots and green dots are abstract nodes. The thick blue lines indicate the connection relationship between nodes. The pink lines are the wall segments intersecting with the navigation network.
# Dual Hybrid Attention Mechanism-Based U-Net for Building Segmentation in Remote Sensing Images
## 1 Introduction
To bridge this gap, we replace the original simple skip connection with the DHA module. By applying the DHA module to process shallow feature maps and fuse them with deeper feature maps, the network focuses more on foreground building information while disregarding the background interference. This refinement narrows the within-group gap between buildings, leading to improved segmentation accuracy. The rest of this paper is organized as follows. Section 2 begins with an overview of the DHA module, outlining its structure and key features. Subsequently, the proposed attention module is incorporated into U-Net, and a attention-based neural network is constructed for building segmentation in remote sensing images. Section 3 outlines the experimental setup, including details about the datasets used (Massachusetts Buildings Dataset [33] and Inria aerial image labeling dataset [34]), the experimental environment, and the evaluation metrics employed. In Section 4, we present the results of applying DHAU-Net to the segmentation of urban buildings. Finally, in Section 5, we draw conclusions based on extensive experiments.
## 3 Related Work
### Sticker Information Extraction  
Extracting information from product labels in industrial settings is a critical task that has garnered significant attention in recent years [23]. Numerous techniques and technologies have been devised to improve the efficiency and accuracy of information extraction, including OCR systems and various image and data processing methods [24].  
One such study is by [PERSON] et al. [25], who developed a low-cost OCR system grounded in deep learning. Their system accepted code text segment images from real products as inputs. However, their approach did not incorporate OD and failed to address the challenges of extracting information from complex sticker patterns, such as stickers with non-textual data. Another approach by [PERSON] and [PERSON] [26] involved developing an automated system for extracting pertinent information from medication container images. Their system utilized convolutional neural networks (CNNs) for sticker extraction and distortion correction, followed by OCR. Relevant information was obtained from the recognized text using natural language processing (NLP) techniques. Nonetheless, their system did not include OD and might not be suitable for extracting information from intricate real-world scenarios. [PERSON] et al. [27] proposed an automatic inventory tracking system that leverages computer vision and DL techniques. Their system locates, processes, and decodes label information, continuously updating the inventory database. Their approach consists of locating the label using an OD algorithm, decoding the barcode, and performing OCR. However, their system may struggle with the complexity of industrial product labels.  
In our proposed framework, we integrate OCR with OD based on [PERSON] [28]. This integration enables us to accurately extract all relevant data from product stickers, including text, barcodes, and QR codes. Furthermore, our framework is capable of handling multiple sticker patterns, aiming to become a robust solution for an industrial application.
# Enhancing Autonomous Vehicle Navigation with a Clothoid-Based Lateral Controller
## 6 Results and Discussion
### Tracking of a Dual-Lane-Change (DLC) Maneuver Trajectory  
This subsection covers the lateral control results for the dual-lane-change maneuver trajectory. This maneuver mimics common driving scenarios encountered on highways and freeways, making the test results more applicable to real-world driving conditions. It encompasses various driving scenarios within a single test, including straight stretches, gradual lane changes, and adjustments while in the other lane. This diversity challenges the vehicle's lateral control system to adapt to different curvature demands. Figure 13a displays a near-perfect overlap of the tracked coordinates with the intended path during a dual-lane-change (DLC) maneuver, indicating a high degree of accuracy from the lateral controller. The accompanying CTE versus time plot in Figure 13b further demonstrates the controller's tracking capability, with cross-track error values fluctuating within a narrow range (\(\pm 0.2\) m), suggesting that the vehicle remained close to the desired trajectory throughout the maneuver. The minor deviations in CTE are typical of dynamic driving scenarios and reflect the controller's ability to correct the vehicle's path effectively over time.  
Figure 12: (**a**) Tracked speed vs. arc length. (**b**) Steering angle vs. time plot for the 8-shaped-figure tracking.  
Figure 14b shows the steering angle over time, where the controller's output demonstrates a series of adjustments, reflecting active steering input to navigate the DLC trajectory. While the steering angles exhibit some peaks, suggesting responsive maneuvers to maintain the trajectory, the general trend indicates the controller's capability to output smooth steering commands appropriate for the speeds maintained, as shown in Figure 14a.
## 2 Related Work
### Distributed System for Information Management and Knowledge Distribution  
The work of [23] has proposed a probabilistic aggregation for knowledge generation. This approach uses a hierarchical aggregation technique called soft-state sketches. This technique is an extension of Flajolet-Martin sketches [24]. The fundamental characteristic of this approach lie in the fact that the aggregate information does not have a specific value of the monitored place, for instance, an average speed of a determined road. The aggregated information has, instead, a probabilistic value. The main benefit of this approach is the capability to combine the aggregated values, with the same context, for knowledge generation. However, this work lacks a VSM to perform knowledge generation task. Therefore, all vehicles would perform such a task, thereby generating highly redundant traffic of knowledge.  
[PERSON] et al. [25] have proposed an adaptive forwarding delay control, named catch-up, to gather aggregated local information from different sources for knowledge generation. To this goal, the forwarding speed of nearby information is dynamically adjusted. Thereby, each aggregate information can have one of the two types of adaptive delays, RUN (short) or WALK (long). The delay calculation is based on a distributed learning algorithm, in which each vehicle learns by means of local information. The main advantage of catch-up is the use of an adaptive forwarding delay for knowledge generation, as well as probabilistic aggregation. However, a disadvantage of this approach is that all vehicles can act as an information aggregator and knowledge generator, which can incur network overhead.  
Another solution is the data aggregation algorithm by restricting forwarders (DARF) [26]. This algorithm concentrates mainly on the selection of the vehicles that will continue the knowledge forwarding process, which was generated in the aggregation step. In order to do that, each vehicle receives one of the two available labels (forwarder or non-forwarder) according to the neighbourhood labels. As the name says, each label defines whether the vehicles will be a forwarder, or not, of the knowledge. The vehicle will be a non-forwarder if there is a forwarding vehicle immediately in front of and behind it. One of the advantages of DARF is the broadcast suppression mechanism applied during the knowledge distribution process, which is not applied in the above-mentioned works. However, it is possible to notice that there is no VSM to aggregate local information and generate the knowledge. In this way, it allows highly redundant traffic of knowledge in the network.  
All systems presented here have the same shortcoming, the absence of a VSM to carry out the tasks of information aggregation and knowledge generation. Without the selection mechanism, all the vehicles would perform such tasks, resulting in highly redundant traffic of knowledge in the network. This, consequently, will lead to high bandwidth consumption. Thus, the use of VSM contributes to improving this issue, which has not yet been addressed in the literature.
###### Abstract  
In our research, we propose a novel asymmetric multiple-image encryption method using a conjugate Dammann grating (CDG), which is based on the coherent beam combining (CBC) principle. The phase generated by the Dammann grating (DG) beam splitting system is processed and added to the image to be encrypted, and then, the ciphertexts and keys are generated by equal modulus decomposition (EMD). Decryption is to combine the beams through the CDG and collect the combined images in the far field. The proposed encryption scheme is flexible and thus extendable. CDG structure parameters, such as one period length of CDG, can be used as encryption key for the increase of the complexity. The Fresnel diffraction distance can also be used as an encryption key. The power of the combined beam is stronger than that of the single beam system, which is convenient for long-distance transmission and also easy to detect. Simulation results show that the proposed method is effective and efficient for asymmetric multiple-image encryption. Sensitivity analysis of CDG alignment has also been performed showing the robustness of the system. The influence of occlusion attack and noise attack on decryption are also discussed, which proves the stability of the system.  
multiple-image encryption; asymmetric cryptosystem; conjugate Dammann grating; equal modulus decomposition +
Footnote †: journal: Applied Sciences  
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