esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 1 Introduction  
Development of sustainable and zero-carbon-emission energy resources is considered one of the most demanding goals for the current world [1]. Within the vast field of green energy harvesting, an important one is bio-energy harvesting [2, 3]. Although such possibilities were proposed much earlier [4], this started to gain prominence in the early 2000s [5, 6, 7]. This relatively new research field is opening up opportunities for producing energy from the wide range of living creatures, including microorganisms [6] and macro-organisms [8], as well as from bio-hybrid organisms [9]. The application fields of these bio-energy harvesters are equally elaborated, ranging from industrial-scale energy production [10] to environmental monitoring [11, 12] and biomedical applications [13, 14].  
The first bio-energy harvesters reported in this review are known as microbial fuel cells (MFCs) which offer the possibility to become one of the next big industrial energy solutions [15, 16]. MFCs utilize catabolic metabolism of different microorganisms on a wide range of organic substrates [17] and turn them into micro bio-reactors generating electrical energy [18]. Extensive works have been performed on summarizing its various aspects like development in electrode configurations [19, 20, 21, 22], dedicated power management systems [23, 24], and cell separators [25, 26, 27]. With the prospects of becoming a next-generationlarge-scale power generation technology, they are already being used for powering up remote marine sensors [23], long-distance marine communications [28], simultaneous wastewater processing with auxiliary power supply [29], metal recovery processes [30] and in biosensors [31].  
The next biochemical energy harvester that has gained a considerable amount of popularity are enzyme-based biofuel cells (EBFCs) where synthetically produced enzyme molecules are immobilized on the electrodes for performing glucose oxidation and in turn power generation [32]. This particular type falls under the bio-hybrid energy harvesters [33] and is considered the promising natural resource for powering up implants and prosthetic devices [34]. Research on these implantable fuel cells is still at the animal trial level [35]. Simultaneously, investigations have also been started on testing the applicability of such cells from external body-fluids [13].  
The third and final bio-energy harvester discussed in this review is mechanical in nature. These biomechanical energy harvesters have a remarkable growing market for powering smart and wearable devices [36]. These harvesters are preferred for their non-invasive nature and easy-applicability in monitoring [37], diagnostic [38] and therapeutic [39] functions. They utilize mechanical energy produced from living animals and convert them into power solutions [40]. While their use for human application is eminent [41], they have also been used for powering other living bio-hybrid animals [42].  
In this work, we aim to offer a concise, functional summary of these three promising bioenergy harvesters and their progress in the recent years.
## 1 Introduction  
With the advancement of medical imaging and computer graphics technology, 3D liver image reconstructions play an increasingly significant role in medical education, technician training, and simulated surgery [1, 2]. However, acquiring complete and high-quality 3D liver CT images necessitates patients undergoing a full CT tomography procedure. Such data inherently involve patient privacy concerns and regional differences in medical equipment conditions. Additionally, the limited number of images for certain rare diseases makes it difficult for physicians to identify and learn from relevant cases.  
Recently, deep learning (DL)-based liver image analysis has been a great success in the medical diagnosis field [3, 4, 5]. However, DL-based methods require a large number of image samples for learning and training. Currently, collecting and labelling medical image samples is a labour-intensive task that can consume a lot of manpower and material resources, further limited by medical ethics and patient privacy issues. Therefore, it is very difficult to obtain real medical image samples at this stage, especially 3D medical images [6, 7, 8, 9]. Image generation techniques based on DL have made it possible to obtain more realistic segmented images, providing new methods. Nowadays, generative adversarial network (GAN)-derived image generation models have demonstrated remarkable results in the field of synthetic images [10,11], effectively addressing the issue of obtaining substantial amounts of medical image data for medical image synthesis.  
However, the previous GAN-based generation methods are unstable and have high uncertainty [12, 13, 14]. Furthermore, different from natural images, medical images often contain anatomical knowledge, i.e., prior clinical knowledge, and previous medical image synthesis methods fail to consider this knowledge in depth [6,15]. Furthermore, previous images have been mostly based on the generation of 2D medical images, with few methods focused on the generation of 3D medical images. In this paper, we propose a multi-task 3D generative adversarial network (GAN) with liver mask segmentation and liver generation tasks to generate 3D liver CT images using vascular segmentation labels (see Figure 1). The contributions of this paper can be summarized as the follows:  
* We propose a multi-task conditional generation adversarial model that synthesizes 3D liver CT images by inputting liver vessel labels to provide a priori anatomical structure information and using real liver segmentation labels for guidance. To the best of our knowledge, this is the first application for 3D liver CT image generation.
* We introduce a robust multi-gradient method to optimize multiple tasks, specifically by balancing the weights of individual tasks in a multi-task framework.
* Extensive experiments were conducted on the collected real data, and the experimental results show that the performance of our method outperforms existing state-of-the-art methods. In addition, this study demonstrates the possibility of using vascular images to synthesize images of the liver.
## 4 Results
### Texture Profile Analysis of Honey-Fortified Cow Yogurt Beverage  
In Table 5, our results illustrate the effect of various honey concentrations (1%, 3%, and 5%) on the average texture profile analytical parameters of probiotic cow yogurt beverage. As honey concentration increases, hardness decreases (from 71.87 to 60.70). This suggests that elevated honey concentrations reduce yogurt viscosity, likely due to the dilution of the protein matrix by the sugar content in honey. Our results correspond with those of ref. [34], who also examined the impact of pine honey on yogurt hardness via alterations to the casein network. Adhesiveness diminishes inversely as honey content escalates (from \(-\)7.13 to \(-\)0.97). This indicates that higher honey concentrations reduce the yogurt's adhesiveness, potentially owing to honey's influence on the yogurt's viscosity. These findings correspond with the study by ref. [54], which illustrated that honey's hygroscopic characteristics reduce adhesiveness via influencing water retention. Springiness significantly increases with higher honey concentrations (from 0.98 to 3.76). This suggests that the addition of honey improves the elasticity of yogurt, potentially due to modifications in the gel structure caused by honey. According to ref. [29], the integration of honey into the matrix augments springiness and boosts elasticity. The cohesiveness increases from 0.60 to 0.75 with higher honey concentrations, indicating an improvement in the yogurt's structural integrity, likely attributable to honey's interaction with proteins and water. Ref. [29] observed that cohesiveness is enhanced with higher honey concentrations as it strengthens the protein network. The gumminess increases with the concentration of honey, varying from 43.77 to 45.83. Despite the reduction in hardness, the increase in cohesiveness offsets this, leading to an overall enhancement in gumminess. Ref. [34] claimed that honey improves gumminess by increasing viscosity and promoting cross-linking. Chewiness markedly escalates with the concentration of honey (from 43.33 to 135.27). This demonstrates the synergistic effects of increased springiness and gumminess at higher honey concentrations, leading to a more intense chewing sensation. Moreover, ref. [29] noted that the addition of honey enhances chewiness by stabilizing gel properties. Resilience increases with honey concentration (from 0.31 to 0.41). This indicates that higher honey concentrations enhance the yogurt's resilience to deformation and improve its ability to regain its shape. Ref. [30] similarly found that honey improves resistance by strengthening the yogurt matrix. Honey affects the texture of probiotic yogurt by altering its firmness, stickiness, elasticity, and overall structural integrity. The increased degrees of springiness, cohesiveness, and chewiness suggest that honey enhances and strengthens the yogurt's gel network, leading to im
# IoT-Enabled Smart Cities: Evolution and Outlook
## 2 IoT-Enabled Smart Cities Landscape
### Santander  
The SmartSantander project [18] targeted the creation of a European experimental test facility for research and experimentation on architectures, key enabling technologies, services and applications for the IoT in the context of a smart city. The SmartSantander platform includes a continuously growing IoT infrastructure spread throughout the city of Santander (Spain) that currently encompasses more than 12,000 diverse sensors and other IoT devices (fixed and mobile sensor nodes, NFC tags, gateway devices, citizens' smartphones, etc.).  
The deployment, influenced by Santander Municipality's strategic smart-city service requirements is composed of around 3000 IEEE 802.15.4 devices, 200 devices (mainly gateways) equipped with 3G modules and 2000 joint NFC tag/QR code labels deployed both at static locations (storefronts, tourist-wise points, bus stops, etc.) as well as on-board mobile vehicles (buses, taxis). Moreover, smartphones belonging to citizens who have downloaded related Apps [19] are also part of the testbed infrastructure.  
On top of this sensing tier, the platform chosen to manage the management of the underlying infrastructure and the provision of services to end-users and/or application developers is based on FIWARE enablers [20]. Among all the different components of this platform the key one is the Orion Context Broker which exposes NGSlv2 standard interfaces [21] to enable real-time access to observations captured by any of the sensors deployed throughout the city.  
Over the deployed testbed and platform, several use cases and pilot services have been implemented:  
* **Environmental Monitoring:** IoT devices installed provide measurements of different environmental parameters, such as temperature, CO, noise, or air pollutants. Both static and mobile devices installed on vehicles such as buses and taxis retrieve environmental parameters.
* **Outdoor Parking Management and Guidance:** Parking sensors buried under the asphalt are installed in order to detect parking site availability in downtown outdoor parking zones. Several panels have been installed at the main streets' intersections in order to guide drivers towards the available free parking lots.
* **Traffic Intensity Monitoring:** Devices deployed at the main entrances to the city of Santander measure the main traffic condition parameters, such as traffic volumes, road occupancy, and vehicle speed or queue length.
* **Parks and gardens irrigation:** In order to make irrigation as efficient as possible, multiple sensors have been deployed in two green zones of the city to monitor irrigation-related parameters and serve this information to gardens' managers.
* **Waste management:** Paper and cardboard and plastics waste containers in the city are equipped with sensors which are helping to optimize the organization of collection trucks.
* **Participatory sensing:** Users can participate by reporting events or incidences occurring in the city, which will subsequently be propagated to other users who are subscribed to the respective type of events.
## 3 Our Proposed TCDE Scheme
### Constructing a Topographical Profile to Obtain an Initial Multimodal Distribution
#### 3.2.1 Constructing Contours of Topographical Profiles  
All individuals are used to construct the topographic contours. However, their distribution is often uneven, with some areas exhibiting significant gaps that require additional detail before contour plotting. In practice, to accurately represent the topographic surface, the space is uniformly divided into a set of raster grids. Missing data at unsampled raster points can be supplemented using Kriging interpolation.  
The Kriging interpolation method estimates the value of unknown points via the weighted summation of the known points. It mainly employs the positional relationship and degree of correlation between the sampling points and the estimated points. This is an unbiased and linear optimal statistical method. Let \(x_{1},x_{2},\ldots,x_{n}\) be a sampling point in the region and \(z(x_{1}),z(x_{2}),\ldots,z(x_{n})\) be the observed value of the corresponding sampling point. The estimated value \(z^{*}(x)\) of the regionalized variable at \(x\) is denoted as follows:  
\[z^{*}(x)=\sum_{i=1}^{n}\lambda_{i}z(x_{i}). \tag{8}\]  
where \(\lambda_{i}(i=1,2,\ldots,n)\) are the weight coefficients, and the values of each weight coefficient need to satisfy two conditions: unbiasedness and optimality.  
A block processing strategy is adopted in this study to balance the fineness of the topographic map and computational efficiency. The entire 2D search space is uniformly divided into 4 quadrants (i.e., 4 parts), and, inside each quadrant, a resolution of \(100\times 100\) is used to define grid points. Thus, the total number of interpolated points for the entire topographic map is 4 parts \(\times\) (\(100\times 100\) points/part) = 40,000 grid points. This approach ensures sufficient detail while effectively managing the computational load.
# Ultrasound Localization Microscopy in Liquid Metal Flows
## 2 Ultrasound Localization Microscopy
### Particle Localization and Tracking  
For the localization and tracking of the scattering particles, the software package _PtrPy_[45], which was developed in-house, was used. _PtrPy_ provides an integrated software tool that is based on the _Python_ package _trackpy_[46], in which the localization and linking of particle positions is conducted according to Crocker and Grier [47]. First, the background image is calculated and subtracted from each frame. Second, the centroid method is applied for the localization of the center of each particle from the B-Mode images, which is commonly used in ultrasound imaging microscopy [48]. Third, the identified particles are linked to trajectories through the estimation of the most probable location between consecutive frames using the nearest neighbor method. Trajectories that could not be followed for at least 250 ms are neglected. A memory of three frames is used until a localized particle can vanish and reappear nearby to compensate for fluctuations due to noise. Finally, the mean velocity is calculated from the scattered trajectories using interpolation to a regularly spaced grid. This results in a two-dimensional and two-component (2D2C) flow velocity map.  
Figure 2: Adjustments of the \(p\)-DAS beamformer to optimize localization and tracking using liquid metal echo data: **(a)** the data were beamformed with iterative values of \(p\), where \(p=1\) represented conventional DAS beamforming; **(b)** the evaluation of particle tracking: \(p=2\) showed the best compromise between the number of identified trajectories (822 per minute), the average trajectory length (49.6 frames) and the identified trajectories in the corner regions.
## 4 Fairness-Aware Optimal Transmission Scheme
### Deep Actor Networks and Critic Network  
In this case, through interacting with one critic network, several actor networks can be well-trained to determine the optimal actions under a dynamic state input. Since the gradient operation of the reward function may be NAN for some random experience tuples, we introduce a critic network to approximate it during the training process. As shown in Figure 2, the critic network is used to output approximate reward value for a specific power allocation scheme, and the actor networks are used to determine the transmit power levels of all sensors. Here, the ReLu function is used as the output activation function. The parameter set of the critic network is denoted by \(\theta^{\text{0}}\), and the parameter set of the actor network \(k\) is denoted by \(\theta^{\text{0}_{k}}\), \(k=1,\ 2,\cdots,\ M\). The input of all actor networks is the statistical channel state information \(\vec{s}_{t}\), which is defined as \([\alpha_{SR},\ \beta_{SR},\ \alpha_{RD_{1}},\ \beta_{RD_{1}},\ \alpha_{RD_{2}},\ \beta_{RD_{2}},\cdots,\ \alpha_{RD_{M}},\ \beta_{RD_{M}}]^{T}\). The input of the critic network consists of a state vector \(\vec{s}_{t}\) and action vector \(\vec{a}_{t}\), where \(\vec{a}_{t}\) is denoted by \([P_{1},\ P_{2},\cdots,\ P_{M}]^{T}\). More specifically, within each training iteration, the critic network can evaluate the output of the actor networks and then feed back an estimated reward value to them, based on which all the actor networks can improve their performance by performing gradient ascent. Also note that we use \(Q(\vec{s}_{t},\ \vec{a}_{t}|\theta^{\text{0}_{k}})\) to denote the output of the critic network and use \(\mu_{k}(\vec{s}_{t}|\theta^{\text{0}_{k}})\) to denote the output of the actor network \(k,\ k=1,\ 2,\cdots,\ M\), respectively. After a certain amount of iterations, all actor networks can be properly trained. While noting that the exact mathematical relationship between the input and output can be modeled by a deep neural network, approximate closed-form expressions for optimal transmit power levels can be obtained from well-trained actor networks.
# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 1 Introduction
This framework takes into account the unique challenges and requirements of health-care systems, aiming to ensure trustworthy decision-making and enhance the security and efficiency of data sharing. * Introducing a keyword-based trust management system that leverages the properties of fog nodes and their interactions. By utilizing keywords stored in a table on each node, the proposed system enables more accurate evaluation of trustworthiness and identification of malicious or untrusted nodes before data sharing occurs. This approach provides an additional layer of trust and enhances the security of fog computing environments. * Addressing a gap in the existing literature by combining trust management with network performance optimization. By integrating trust management into network optimization techniques, the proposed framework creates a holistic approach that enhances security and efficiency in fog computing environments. This integration allows for more flexible and secure interactions between fog nodes, leading to improved overall system performance. * An in-depth analysis of network performance parameters, such as latency and packet delivery ratio, using the iFogSim simulation tool. While the primary focus is on network performance, the results provide insights into the impact of network performance on trustworthiness and the need for optimization strategies to enhance overall system reliability.
# Passwordless Authentication Using a Combination of Cryptography, Steganography, and Biometrics
## 3 Methods
### Implementation
#### 3.2.4 Image Steganography  
Steganography provides the ability to hide data such that the existence of the data is unobvious [27]. The image uploaded during account creation by the user is the medium where the credentials for that particular account are stored, to be provided by the user via upload when there is a need to restore that account. The JavaScript code excerpts shown in Figure 6 describe the logic and process of embedding the credentials of an account in the image provided by the user for that account using Nodejs:  
* The image is converted into its binary format using the Jimp library [28].
* The user's typing ID (associated with their typing pattern) is encrypted using the server's public key. The public key for that account and the encrypted typing ID are then converted to their binary format. Each data is then joined into a long string of bits, as conversion to their binary format produces an array of bytes.
* Each bit from the string of bits from each group is then embedded into the image bytes using the least significant bit (LSB) steganography method. LSB involves replacing the right-most bit of each image byte with another bit from the data to be hidden [29]. The implication of this is that the number of bytes in the image must be at least the sum of all bits to be embedded.
* Once all the bits have been embedded in the LSB of the image, the new image is then reconstructed using the Jimp library; the new and original image will have no visual difference at the end of this process.  
Considering that the LSB method can be detected if an adversary is aware that steganography was employed, the goal of this method is not detection prevention since the public key is not a secret and the typing ID is encrypted; it is intended to offer ease of account recoverability. While users are allowed to reuse the same image for different accounts, only the original image is allowed; images pre-embedded with account details cannot be reused for another account. If a user uses the same image for all accounts, each image will be named differently. Users are also allowed to change the image name, provided they are able to identify which image belongs to which account.  
Figure 6: Embedding account public key in image using LSB steganography.