esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 2 Internet of Things and Healthcare IoT
### Healthcare IoT  
Healthcare IoT, or the internet of things in healthcare, is the integration of connected technology in the medical industry to collect and exchange data for improved patient care and better decision making. Examples of healthcare IoT devices include wearable fitness trackers, smart pills, and remote supervision devices. The data collected is used by healthcare providers to diagnose the patient's health and plan their care, leading to more personalised, effective, and efficient treatment. Additionally, healthcare IoT can reduce costs and increase efficiency within the healthcare system by reducing the need for in-person appointments. Figure 2 shows different elements in an IoT-based system [9].  
Figure 2: Elements of IoT-based system.  
Wireless sensor network (WSN): Wireless sensor networks (WSNs) collect data from the environment using small, low-power sensors with wireless communication. They are used in industrial, agricultural, and environmental monitoring systems and measure physical parameters such as temperature, humidity, pressure, motion, and sound. The data are wirelessly transmitted to a central unit for analysis, making it efficient. WSNs serve as sensors, providing data to IoT platforms. Continuous monitoring through WBANs can detect emergencies and diseases early, improving healthcare for people with disabilities and children. Data are wirelessly sent to a monitoring device, which analyses and controls drug delivery, ensuring patients receive the required medication. WBANs benefit drug delivery by improving patient compliance, reducing costs, and increasing healthcare accessibility [10]. Wireless body area networks (WBANs) are a type of wireless sensor network (WSN) system that employs wearable or implanted sensors to collect real-time data on a patient's physiological parameters and clinical metrics [10]. These data are wirelessly transmitted to a central monitoring device that analyses it and remotely controls the drug delivery system, ensuring patients receive the required medication in the right dosage at the right time. WBANs provide numerous benefits in drug delivery, including improved patient compliance, reduced healthcare costs, and increased accessibility to healthcare services [11]. However, their implementation presents challenges such as security, privacy, and interoperability. WBANs can be implemented in wearable or implanted drug delivery systems, with the former delivering medication through a patch or infusion and the latter delivering medication directly to the target area. The communication in a WBAN typically consists of three tiers [12], as given in Figure 3.
## 2 Materials and Methods
### Characterization of the Nanoparticles
#### 2.7.1 Transmission Electron Microscopy (TEM) and Energy Dispersive X-ray Analysis (EDX)  
Characterization of the samples was performed via high-resolution transmission electron microscopy (HRTEM) using a JEOL JEM-2100 electron microscope (Tokyo, Japan) operating at 200 kV and 0.23 nm resolution. The electron source used was a lanthanum hexaboride (LaB6) filament. The sizes of the nanoparticles and data analysis of their sizes were achieved using Image J software v1.53t (National Institutes of Health, Bethesda, MD, USA) T. The samples were also analysed via EDX (X-Max 80 mm silicon drift detector (SDD), Oxford Instruments, High Wycombe Buckinghamshire, UK) using INCA v 4.15 software (Oxford Instruments, High Wycombe Buckinghamshire, UK) to determine their elemental compositions in order to verify whether the metals under investigation were present.  
Sample preparation was conducted as follows; a 5 \(\upmu\)L drop of each sample was pipetted onto a carbon coated copper TEM grid and allowed to stand at room temperature for 2 min. The excess fluid was removed using filter paper and thereafter a 5 \(\upmu\)L drop of distilled water was added and once again removed using filter paper to wash off any excess media. The grids were then air dried at room temperature for at least 2 h before being analysed.
## 2 Simulation Method of Three-Dimensional DFN Based on Monte-Carlo Technique
### Construction of Probability Models for Geological Parameters of Rock Fractures
#### 2.1.2 Model of Fracture Dimension  
The mean trace length is an important parameter reflecting the dimensions of fractures. It is related to the statistical distribution of the diameter of the fracture in the Baecher disk model. Log-normal and negative exponential distributions are typically used to model the distribution of the trace length of a fracture. Given that field observations have shown that the number of fractures increases significantly with the reduction in the trace length, we conclude that the negative exponential distribution is more consistent with the actual distribution of the trace length of fractures. [PERSON] [6] established the relationship between the distribution of trace lengths of fractures and the distribution of disk diameters in the Baecher disk model for both measured line sampling and statistical window sampling, respectively. [PERSON] and [PERSON] [26] further proposed an equation for estimating the statistical parameters of the distribution of the diameter of fractures by using the measured trace lengths in the case of a negative exponential distribution,  
\[\overline{D}=\frac{\pi}{8}\overline{l} \tag{2}\]  
where \(\overline{D}\) is the mean diameter of fractures, and \(\overline{l}\) is their mean trace length.  
We assume that the expected areas of the rectangular and disk fractures are equal to establish a relationship between the lengths of the edges of the rectangular fracture and the diameter of the disk fracture. Specifically, the two side lengths of the rectangular fracture are set to be independent, and both satisfy a negative exponential distribution with parameter \(\overline{E}\). Then, according to the previous assumptions, we can obtain the following:  
\[\overline{E}=\frac{\sqrt{\pi}}{2}\overline{D} \tag{3}\]  
By substituting the above equation into the negative exponential distribution, the two side lengths \(a_{i}\) and \(b_{i}\) of the rectangular fracture can be acquired as follows:  
\[\left\{\begin{array}{l} a_{i}=-\frac{\sqrt{\pi}}{2}\overline{D} \ln(1-R_{i})\\ b_{i}=-\frac{\sqrt{\pi}}{2}\overline{D}\ln(1-R_{i})\end{array}\right. \tag{4}\]  
where \(R_{i}\) is a random variable that follows a uniform distribution in the interval [0, 1].
# Path Planning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorithm
###### Abstract  
The artificial potential field algorithm has been widely applied to mobile robots and robotic arms due to its advantage of enabling simple and efficient path planning in unknown environments. However, solving the local minimum problem is an essential task and is still being studied. Among current methods, the technique using the virtual hill concept is reliable and suitable for real-time path planning because it does not create a new local minimum and provides lower complexity. However, in the previous study, the shape of the obstacles was not considered in determining the robot's direction at the moment it is trapped in a local minimum. For this reason, longer or blocked paths are sometimes selected. In this study, we propose an enhanced virtual hill algorithm to reduce errors in selecting the driving direction and improve the efficiency of robot movement. In the local minimum area, a dead-end algorithm is also proposed that allows the robot to return without entering deeply when it encounters a dead end.  
mobile robots; artificial potential field; local minima problem; enhanced virtual hill; dead-end algorithm +
Footnote †: journal: Applied  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# A Novel Methodology for Planning Urban Road Safety Interventions
## 3 Novel Methodology
### Cost-Benefit Analysis for Road Safety Interventions
#### 3.4.3 Estimation of Intervention Costs  
Estimating intervention costs and assessing economic feasibility are essential to ensuring a project's sustainability and alignment with its objectives, and it can be carried out through a parametric approach during preliminary project phases. The parametric estimation method is particularly effective for evaluating various project options quickly and efficiently. It leverages standardized parameters to estimate intervention costs without requiring detailed quantitative analysis of every component. This allows for a rapid preliminary cost assessment, aiding in selecting options that are both economically feasible and technically sound.  
Parametric estimation uses indicators derived from historical data and official price lists to calculate total costs based on project quantities. Common indicators include unit costs per square meter, cubic meter, or installed unit, depending on the type of intervention. Principal cost categories considered in the parametric estimation are as follows:  
* Materials and labor, calculated using unit costs applied to projected quantities;
* Site management costs, such as setup, maintenance, and lighting, typically calculated as a percentage of total project costs;* General project expenses covering design, project management, and other associated costs, estimated at 13-17% of total project costs [30], with an additional 10% for contractor profit [31]. The parametric estimation provides a reliable preliminary cost assessment, ensuring economic viability and supporting informed decision-making during early planning stages.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 2 Positioning Architecture  
The positioning architecture presented in this article is based on an INS as a master system and an EKF-based Doppler positioning system as a slave system. The slave system estimates the position, velocity, and attitude of the vehicle using inertial data of a MEMS-based IMU and Doppler measurements of the multi-constellation software-defined receiver (MC-SDR) designed in the LASSENA laboratory of the ETS university located in Montreal, Canada. The MC-SDR utilized in this study is completely discussed in [6]. The receiver is able to provide pseudorange rate measurements of several satellites from different LEO constellations. Furthermore, the INS alignment system is accounted for the online calibration of the master INS using the error measurements and the IMU date.  
To have more accurate error estimation in the INS alignment system, an IMU rotation simulator was provided. The simulated rotation actuator rotates the IMU in an already-defined sequence, which is discussed in Section 3.2. The main purpose of this actuator is to increase the observability rank of the error state-space model included in the alignment system. Figure 1 shows the block diagram of the proposed method. Finally, after estimating the error of position, velocity, and attitude, as well as the IMU bias vector, the IMU data were corrected, and the INS was calibrated. It should be mentioned that the alignment system estimates the error states using a Kalman filter (KF). The error measurements for this system are provided by subtracting the estimated states from master and slave systems. In the following parts, the master INS kinematics, the EKF-based slave system, and the KF-based alignment method as well as the rotation technique are discussed in detail.
# Identifying Synthetic Faces through GAN Inversion and Biometric Traits Analysis
## 4 Experimental Setup and Analysis of Results
### Metrics-Based Analysis  
First, we perform a similarity analysis between each image and its reconstructed counterpart. We considered the following metrics:  
* Mean Squared Error (MSE): Computes the distance pixel-wise between the two images \[MSE(\mathbf{x_{l}},\mathbf{x_{R}})=\frac{1}{MN}\sum_{n=1}^{N}\sum_{m=1}^{M} \sum_{k=1}^{3}(\mathbf{x_{l}}(n,m,k)-\mathbf{x_{R}}(n,m,k))^{2}\] where \(M_{\text{,}}N\) are the dimensions of the image (equal for both images); \(\mathbf{x_{l}}\) is the RGB input image and its \(\mathbf{x_{R}}\) reconstructed version.
* Structure Similarity Index Method (SSIM): it is a perception-based model that takes into account the mean values and the variances of the two images \[SSIM(\mathbf{x_{l}},\mathbf{x_{R}})=\frac{(2\mu_{\text{I}}\mu_{\text{R}}+c_{1} )(2\sigma_{\text{IR}}+c_{2})}{(\mu_{\text{I}}^{2}+\mu_{\text{R}}^{2}+c_{1})( \sigma_{\text{I}}^{2}+\sigma_{\text{R}}^{2}+c_{2})}\] with \(\mu_{\text{I}}\), \(\mu_{\text{R}}\) being the mean values of the two images, \(\sigma_{\text{I}}\), \(\sigma_{\text{R}}\) variance of the two images, and \(c_{1}\) and \(c_{2}\) being stabilization factors.
* Learned Perceptual Image Patch Similarity (LPIPS) ([[https://github.com/richzhang/PerceptualSimilarity](https://github.com/richzhang/PerceptualSimilarity)]([https://github.com/richzhang/PerceptualSimilarity](https://github.com/richzhang/PerceptualSimilarity)) (accessed on 30 December 2022)): it is proposed in [31] and used in [4] for the same purpose; it computes the similarity between the activations of two image patches for some pre-defined network. A low LPIPS score means that the image patches are perceptually similar.  
The results are reported in Figure 7. We can notice that SSIM histograms do not show a clear distinction among different clusters. Indeed, SSIM is sensitive to the perceivable changes in terms of structural information, which are usually not noticeable in GAN-generated images. On the contrary, we observe that pairs deriving from real images yield generally higher MSEs than the ones derived from synthetic faces (red histogram), making it evident that reconstructing a pointwise equal image of an unknown target is much more difficult. The same happens for the LPIPS metrics, where, following what was observed in [4], the SG2 images yield a higher similarity with their reconstructed counterparts.  
Moreover, real images belonging to different datasets lead to different distributions, both in terms of LPIPS and MSE. In particular, it is interesting to observe that _FFHQ_ images (blue histogram) present significantly lower values concerning other sources of real images: this may be related to the fact that those images were included in the training set of StyleGAN2, and thus, they are known to the generator.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & \(\mathbf{x_{l}}\) & \(\mathbf{x_{R}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.
## 2 Related Work
### OpenFlow-Based Schemes  
As a popular technology in software-defined networking, OpenFlow is used by researchers as the basis to achieve more innovative integrations. [PERSON] et al. [17] used the calculation of the link threshold congestion to perform global dynamic rerouting and transferred the largest flow from the link that exceeded the threshold. Therefore, two algorithms called DLBS are proposed to adapt to different tree structures. This method with load balancing as the goal improves the utilization of network bandwidth. [PERSON] et al. introduced SDN/OpenFlow technology into the complex and difficult-to-control ops network and proposed an OF-OPS network structure combined with the OpenFlow control process. Through the perception of competition for tasks in the network, the routing/rerouting strategy is combined to distribute traffic under the premise of load balancing [26].  
With the development of cloud computing, the problem of data center traffic transmission has also become a research hotspot. Hedera [27] is a representative centralized control scheduling system to dynamically manage the flow and effectively improve the utilization of resources. [PERSON]. [28] proposed a data-intensive framework Vays, combining Coflows and effective heuristics (SEBF) to improve network utilization and ensure freedom of starvation. [PERSON] proposes SlickFlow [29], a flexible routing method based on OpenFlow for fast error recovery. This mechanism simplifies the forwarding task in the switch and can effectively recover from failures. In [30], considering that streaming media requires a lot of resources, [PERSON] et al. develop QPlow, a platform that standardizes this feedback loop and initiates a variety of control policies over it. The experiments on YouTube verify its high-quality service guarantee.  
In summary, the existing algorithms based on flow scheduling cannot actively control network congestion. The static algorithm has insufficient scalability, only executing the scheduling algorithm at the beginning of the transmission, which cannot adapt well to the real-time changing network state. The existing dynamic load-balanced algorithms can provide adjustment locally or even globally, but they can only adjust a network that has reached the performance bottleneck after the fact, according to the feedback mechanism. A single strategy cannot achieve the goal well, so a composite adaptive method is needed. Our work focuses on dynamic load-balancing algorithms for proactive congestion control.
## 5 Resistance Properties  
Compression test results for isotropic and anisotropic MRE under different applied forces and magnetic fields were initially obtained to evaluate the electrical resistivity of MRE, which explained the behaviour of the electric charge carrier in the MRE. Figure 8 shows the magnitude of resistance versus applied force for various magnetic fields. The hardness of the sample was Shore 57A for isotropic MRE and Shore 58A for anisotropic MRE. The experiment was conducted by applying a weight between 1 and 5 kg. The magnetic field also was varied between 0 T (off-state) and 0.1 T (on-state). It can be seen that the resistance was decreased exponentially as the weight changed by an increment of 1 kg for both the isotropic and anisotropic samples at off- and on-state conditions.  
By applying load to the MRE, the electrical resistance decreased for both the isotropic and anisotropic MRE. This can be referred to as the piezoresistance effect [27]. This phenomenon might be because the cobalt particle chains become closer between each particle. So, the decrease of the resistance will increase the electrical contact between the cobalt particles. The resistance of the anisotropic MRE is lower than that of the isotropic MRE. This is because the chain-like structure inside the MRE has made the electricity flow more efficiently. Indeed, the particle alignment also plays a big role in electric circulation in MRE. In addition, it can also be related to the MR effect, which shows that the anisotropic MRE has a higher MR effect than the isotropic sample. The higher modulus change can be attributed to the greater strength interaction between particles causing less resistance.  
In addition, when applying a magnetic field to 0.1 T, the MRE results in a larger reduction in the resistance for both MRE. The magnetic field causes the cobalt particles instantaneously to become magnetic dipoles; they follow the direction of magnetic field lines, therefore forming a network of  
Figure 8: Resistance reduction of MRE in the magnetic field 0 (off-state) and 0.1 T (on-state).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **MR Effect (\%)** \\ \hline Isotropic & 27.77 \\ Anisotropic & 71.42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The MR effect on the storage modulus of the MRE.  
parallel chains inside the MRE. By combining the applied force and magnetic field, the resistance of the MRE decreases with applied force.  
The resistance changes at the very beginning, and gradually, the resistance reaches a nearly constant value. The correlation between magnetoresistance and piezoresistance in MRE is ascribable to the deformation of the material. This phenomenon also was discovered and consistent with the findings of [PERSON] et al., who verified that by applying force and a magnetic field, the resistance would decrease [20].
# Precise Point Positioning Using Triple GNSS Constellations in Various Modes
## 4 Real-Time PPP Satellite Clock Corrections  
IGS launched its real-time service (RTS) in April 2013, which provides the users with real-time satellite orbit and clock corrections. At present, the IGS RTS uses a network of 130 globally distributed real-time tracking stations (IGS, 2016). Generally, the IGS satellite orbit and clock corrections are available to users with a delay based on the stated accuracies of the corrections, which are intended to be used in the post-processed positioning mode, e.g., the final IGS orbit and clock corrections have a delay of about 14 days. The IGS produced \"ultra rapid\" precise satellite correction products, which can be used in near real-time and real-time positioning; however, the prediction part of these corrections are based on earlier observations and are significantly less accurate than the other IGS products [26; 38].  
The IGS RTS produces and publishes real-time GNSS orbit and clock corrections, which are streamed to users in the Radio Technical Commission for Maritime services (RTCM) format. The RTCM State Space Representation (SSR) format is capable of supporting sub-decimeter RT-PPP anywhere in the world. Currently, the RTS products are offered for the GPS, Galileo, and GLONASS constellations. Table 1 outlines the IGS RTS products, their formats and frequency [25; 26].  
In order to access the RTS-IGS data streams that contain the satellite orbit and clock corrections, an NTRIP client application must be used. The Bundesamt fur Kartographie und Geodasie (BKG) NTRIP Client (BNC) version 2.11.1 is used to access these data streams. BKG Ntrip Client (BNC) is an open source application that support a variety of GNSS positioning applications [38].  
IGS01/IGC01 precise satellite orbit and clock corrections are computed using a single epoch GPS combination. The solution epochs in this product are completely independent of each other, which has the advantage that the full accuracy is available as soon as product generation starts. While the IGS02 precise satellite orbit and clock corrections is extracted from one of the incoming ultra-rapid solutions. Both of the IGS02 and IGS03 use Kalman filtering and require a few minutes to converge to their full accuracy. The major difference between IGS03 and IGS02 is that the former includes GNSS corrections in addition to GPS [39].