esa-sceva/satcom-chunk-collection huggingface dataset:
# Chronic Inflammation in the Epidermis: A Mathematical Model
## 3 Results
### Dynamics of Two Bacterial Strains Model (M2 Model)
#### 3.2.5. Local Stability Analysis of Delay Differential Equations  
For simplicity, we consider the simplified version of the model for analysis by ignoring the good bacterial dynamics:  
\[\left\{\begin{aligned} &\frac{d}{dt}B_{1}(t)=rB_{1}(t)(1-\alpha_{11}B_{1 }(t))-\gamma A_{E}(t)B_{1}(t),\\ &\frac{d}{dt}A_{E}(t)=\beta B_{1}(t-\tau)-\delta A_{E}(t),\end{aligned}\right. \tag{44}\]  
Assume that \(\delta\gg 1\). Quasi steady state approximation is applied to (44) to obtain two-dimensional system:  
\[\frac{d}{dt}B_{1}(t)=rB_{1}(t)(1-\alpha_{11}B_{1}(t)-\beta_{11}B_{1}(t-\tau)) \tag{45}\]  
where \(\beta_{11}\) is explicitly given by:  
\[\beta_{11}=\frac{\gamma\beta}{r\delta}. \tag{46}\]  
The linearized system of (45), which is defined for \(E_{1}=(\bar{B}_{1},0)\), is given by:  
\[\left\{\begin{aligned} &\frac{d}{dt}x(t)=-r\bar{B}_{1}(\alpha_{11}x(t)+ \beta_{11}x(t-\tau))\end{aligned}\right. \tag{47}\]  
Characteristic equation \(P(z)=0\) defined for (47) is explicitly given by:  
\[P(z)=z+r\bar{B}_{1}\alpha_{11}+r\bar{B}_{1}\beta_{11}e^{-z\tau}=0. \tag{48}\]  
Let us investigate whether \(P(z)=0\) has a pair of pure imaginary roots \(z=\pm i\omega\), where without loss of generality, we can assume that \(\omega>0\). Then, the real and imaginary parts of \(P(+i\omega)=0\) are given by:  
\[r\bar{B}_{1}\alpha_{11}+r\bar{B}_{1}\beta_{11}\cos\omega\tau=0, \tag{49}\] \[r\bar{B}_{1}\beta_{11}\sin\omega\tau=\omega\]  
By adding the square of real and imaginary parts, we obtain that:  
\[\omega^{2}=(r\bar{B}_{1})^{2}(\beta_{11}^{2}-\alpha_{11}^{2}). \tag{50}\]  
The equality in (50) holds if and only if \(\beta_{11}>\alpha_{11}\). It follows from the first equation of (49) that:  
\[\tau=\frac{1}{\omega}\cos^{-1}\left(-\frac{\alpha_{11}}{\beta_{11}}\right). \tag{51}\]  
By numerical computations, critical time delay \(\tau^{*}\) at which a system undergoes Hopf bifurcation is determined for the parameter set in Figure 11. More precisely, \(\tau^{*}\simeq 1.418\). Figure 11 illustrates the dynamic changes of stability at a steady state in Equation (45) as the time delay (\(\tau\)) passes through the critical Hopf bifurcation point \(\tau^{*}\simeq 1.418\). The stable state of the equilibrium for smaller \(\tau^{*}\)'s (\(\tau=0.0\) (Figure 11A), \(1.0\) (Figure 11B) and \(1.417\) (Figure 11C)) becomes the unstable state around \(\tau^{*}\) (\(\tau=1.4181\) in Figure 11C).
## 3 Method
### Frame Selection  
To ensure efficient processing and accurate detection, the system utilizes a dynamic frame selection strategy tailored to the age of the chickens, based on video footage from CCTV cameras installed in broiler farms. This approach balances processing load and detection accuracy by adapting to the changing activity levels of chickens as they age.  
As shown in Figure 3, for younger chickens, less than 7 days old, frames are captured every 3 s to account for their higher activity levels and rapid movements. As the chickens age, the intervals are progressively shortened: for chickens between 7 and 14 days old, frames are captured every 2 s; for those between 14 and 21 days old, every 1 s; and for chickens between 21 and 28 days old, every 0.5 s. Finally, for chickens older than 28 days, frames are captured every 0.25 s to effectively monitor prolonged periods of inactivity, which could indicate the presence of dead chickens. These intervals were determined through experimental testing and interpolation, ensuring that the system remains responsive to behavioral changes, optimizing both detection accuracy and processing efficiency throughout the chickens' growth stages.  
The dynamic frame capture strategy significantly improves computational efficiency, allowing the system to reduce the number of frames processed without sacrificing detection accuracy. By adapting frame capture intervals based on the age of the chickens, the system optimizes resource usage, which is particularly important in large-scale farm operations where resource management is critical. This balance ensures that computational resources are allocated efficiently, monitoring more active younger chickens less frequently while closely observing older, more inactive chickens.
## 5 Results and Discussion
### Communication Protocol  
The protocol implemented in the three blocks of the system (_Smart Modules_, Central Nodes and _Monitoring Center_) has been checked in the lab by means of preliminary prototypes that represented a  
Figure 11: Evolution of solar radiation, supercapacitor voltage and XBee supply voltage during four days.  
Figure 10: _Smart Module_ attached to a PV panel during the tests.  
Figure 9: Current consumption during transmission (Channel 1). The signal on top is the wake-up period for the microcontroller in the _Smart Module_.  
network consisting of one _Monitoring Center_ and two Central Nodes: one including two _Smart Modules_ and the other one with only one _Smart Module_. This system succeeded in identifying all the nodes during start-up, after which the _Smart Modules_ periodically send the information to the _Monitoring Center_. Extension to a larger system only requires that the system parameters be changed.  
Also, a Matlab-based environment has been developed for testing purposes that complies with the features described above. This environment allows users to define the size of the PV plant considered and plot the evolution of relevant magnitudes associated to the whole plant, to a given tower or even to a simple panel. Figure 12 shows an example of the windows created for this purpose, where 97 PV panels from a solar tower are monitored. The window on the right allows users to define a particular period of time during which the power, voltage and current evolutions of any selected PV panel can be plotted.
# Aerial Robotic Solution for Detailed Inspection of Viaducts
## 3 Localization Solution
### Global Localization System
They are only refined twice as doing it more has not shown a substantial improvement of the result but an increase of computational load and computational time. Depending on whether the initial guess is reliable and if, at the instant of processing, the UAV is close to the structure, the RANSAC stage can be exchanged for the ICP algorithm directly to obtain better results. To identify these refinement stages, they are named ICP1, ICP2, and ICP3, with ICP1 being the one that can be exchanged for RANSAC, as explained before. Figure 7 shows a block diagram of the main steps of the algorithm. To improve this process, some assumptions are made that simplify the problem and work in all possible scenarios:  
* The total station 3D map is filtered manually so that it only contains information about the viaduct. This prevents unwanted objects from appearing. * The total station map's origin and the UAV's take-off point are aligned with the horizon, so they both have 0 degrees in pitch and roll. * The absolute yaw orientation of the UAV is known thanks to an onboard magnetometer. Therefore, the approximate rotation matrix between the LIDAR and the total station map is known beforehand. * The GNSS position of the total station map's origin is known approximately. If the drone has GNSS coverage at the take-off point, an initial guess for its position can also be obtained. The introduction of the above assumptions mainly corrects the orientation before starting the alignment process, thus reducing the problem to almost pure translation. In addition, even minimal GNSS coverage at the take-off point provides an initial guess that makes the problem converge more accurately and faster. In case no GNSS coverage is available, the approximate coordinate of the take-off point with respect to the total station 3D map can be entered manually. In addition to the above assumptions, in case of significant changes between reality and the reference map obtained with the total station due to catastrophes or severe structural failures, the discordant areas of the reference map should be removed. Alternatively, a map of the new state of the structure can be created with the same reference origin as the previous one.
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 4 Proposed Scheme
### Security Proofs
The IoT-cloud system then calculates _Cfj_ and uses \(\sigma j\) to create the audit key _Akj_, which is transmitted to the semi-trust server. The semi-trust server then puts _Akj_ aside to contrast it to _Vkij_ to guarantee that the file has been accurately stored. \(\square\)  
**Theorem 4**. : _Argues that the suggested system can supply key management._  
Proof of Theorem 4.: The suggested system argues that the smart device user obtains their key _Wk_ (\(Wk=H(UID\mid 1T\;P\;AID\mid\mid CSID)\)) and transmits it to the semi-trust server. The semi-trust server then employs the smart device user key _Wk_ to create two keys, _Ck_ and _Vk_. The opposition key _Ck_ is calculated by employing the cryptographic hash function (\(Ck=H(Wk\mid c)\)) and the verification key (_Vk_) _or_ (\(Vk=H(Wk\mid\mid v)\)). The semi-trust server then transmits the opposition key (_Ck_) to the IoT-cloud system throughout the integrity checking procedures. The IoT-cloud system then employs the opposition key (_Ck_) to calculate \(Cf\) (\(Cf=H(F\mid Ck)\)). Once _Cf_ has been determined, it can be employed to create the audit key _Ak_ (_Ak_ = \(\sigma\)_XOR_ _Cf_), which is transmitted back to the semi-trust server. Thus, this system can encourage key management. \(\square\)  
**Theorem 5**. : _Argues that the suggested system encourages privacy and confidentiality._  
Proof of Theorem 5.: A semi-trusted server can concurrently handle several integrity check tasks within multiple file restore requests using general integrity check procedures. Therefore, a semi-trusted server must keep the information private and confidential for every smart device user. Within the suggested system, the semi-trust server incorporates the file specification value _FILEID_ throughout the creation of the verification key _Vk_ (_Vki_ = \(H(Wk\mid\mid v\mid\mid F\;ILEID)\)) for every individual file of metadata. \(\square\)  
**Theorem 6**. : _Argues that the proposed system can protect against a replay attack._  
Proof of Theorem 6.: The hackers conduct repeated attacks by eavesdropping on the Ak (integrity checking key) transmission between the IoT-cloud system and the semi-trust server. When the transmission is complete, the hacker employs the _Ak_ (integrity checking key) to replicate the original copy of the IoT-cloud system when the semi-trust server re-requests to audit the file. This flaw within the proposed system can be solved by altering the integrity key _Ak_ after every integrity checking procedure. In addition, _Ak_ is a one-time key; thus, the hacker is unable to repeatedly circulate the integrity checking key. As a result, the hacker will not be able to complete this type of attack due to system protection measures. \(\square\)
## 6 Challenges and Future Research Scopes
### Security and Privacy  
Ensuring security and privacy is a significant challenge in edge cloud networks. Due to the cooperation of end, edge, and cloud resources and adoption of multiple technologies such as NFV and SDNs, end edge cloud networks not only inherit the security challenges of cloud computing but also introduce new ones. Adoption of virtualization technology in edge cloud environments creates VMs to share computation and storage. This brings concern of VM security in edge cloud as cloud computing. Since several applications run on shared infrastructure in end edge cloud networks, it is also crucial to address application-level security issues and proper separation of applications and data. Besides the software security, the highly distributed nature of edge cloud networks, which are closer to the users' premises, may increase the risk of physical attacks. As future edge clouds enable greater programmability and open platforms for third-party software contribution, managing potential risks across different stakeholders becomes essential [10; 13]. Another big challenge is that lightweight IoT end devices often lack sufficient resources to deploy comprehensive security mechanisms, which can make the edge servers more vulnerable. Moreover, publicly available applications could add new challenges of privacy and trust [85]. Several research papers proposed the use of AI to address unpredictable and complex security issues in edge cloud networks. [PERSON] and [PERSON] present a continual federated learning (CFL) approach to provide defense against attacks and an intrusion detection system (IDS) for SDN-based edge cloud networks [63]. [PERSON] et al. [86] proposed a hybrid autoencoder multilayer perceptron (AE-MLP) model for intrusion detection in edge computing with high accuracy. [PERSON] et al. [87] proposed a distributed denial of service (DDoS) attack mitigation method using blockchain for the IoT. Reference [88] introduced FloodDefender, a protocol-independent framework to protect SDN-aimed DoS attacks. FloodDefender employs three techniques: table-miss engineering, packet filter, and flow table management to address denial of service attacks on both the control plane and data plane. Federated machine learning (FML) has also shown potential to defend against security attacks on edge cloud networks. A weighted FL (WFL)-based DDoS detection approach is presented in [89]. References [90; 91] proposed a blockchain-enabled SDN architecture for secure IoT data transfer. Advanced persistence thread (ATP) is another vulnerable area for edge cloud networks. A model integrating FL and graph convolution network (GCN) for detecting APT in SDNs in presented in [92]. Therefore, AI-empowered security and privacy solutions are another area of future research in SDN-enabled end edge cloud networks.
## 4 Current Approaches to Open Challenges of F Estimations from Proximal to Airborne Scale
### Quality Check
#### 4.3.2 Ancillary Data for Post-Processing and F Retrieval  
F retrieval through the infilling of O2 absorption bands requires post-processing that involves (1) an accurate retrieval of the at-surface oxygen absorption features and (2) an F retrieval method that decouples the reflected and the emitted vegetation radiance. Section 4.1 discusses the impact of the atmosphere at different measurement altitudes. To appropriately account for this effect, some environmental parameters should be monitored to better parametrize the atmospheric state to later apply an atmospheric compensation or correction strategy. Besides the optical path from the target to the sensor, the molecular oxygen absorption band's depth depends on (among other parameters) air temperature and pressure [140]. Measuring surface pressure and temperature conditions for determining the effective optical path with the hypysometric equation [115] facilitates an improved characterization of the oxygen absorption features. Furthermore, as the diffuse component of the solar irradiance accounts for the aerosol scattering process, measuring both the global (total) and diffuse spectrally resolved solar irradiance components separately at the surface level is of great use to the validation of the atmospheric characterization assumed in the inversion or retrieval; i.e., not only theintegrated values are used to validate it, e.g., pyranometer measurements. Furthermore, a crucial step when implementing retrieval methods is to model how incoming radiance, vegetation radiance, and apparent reflectance vary in the absence of the O\({}_{2}\) absorption line. To do so, for FLD-based methods, a precise function that can interpolate incoming radiance, vegetation radiance, and apparent reflectance inside the absorption line needs to be defined. Accordingly, FLD-based methods require an accurate interpolation strategy that takes into account the following factors: (1) a good definition of the starting point of each absorption band; (2) a selection of points outside the absorption bands to perform the interpolation; and (3) an appropriate interpolation method. Alternatively, the spectral fitting method (SFM) decouples the emitted radiance (fluorescence) and reflected radiance by spectral curve fitting. The SFM relies on parametrized mathematical functions that represent reflectance and fluorescence within narrow spectral windows centered at oxygen absorption bands. F is generally represented by peak-like functions, such as Gaussian, Lorentzian, or Voigt, whereas canopy reflectance is usually described as second- and third-order polynomials or a more complex piecewise cubic spline [52]. Overall, there should be proper documentation for any (1) atmospheric correction strategy and (2) F retrieval strategy followed (i.e., model and formulations, assumptions and parametrizations; see [39] (in review, this issue) for more detailed information). Furthermore, these processes should be explained when presenting obtained F values from any given setup.
## 2 Problem Statement
### Visual Field Limited Time-Varying Characteristic Swarm  
Under the communication constraint, members in a swarm are not required to connect with other agents outside of some proximity, which defines the notation of communication-based neighborhood first presented by [PERSON] et al. [25]. The communication-based neighborhood of agent \(i\) is a set of teammates within a fixed radius \(R\) to the position of agent \(i\), which can be written as:  
\[\mathcal{N}(r_{i})=\{r_{j\in N,j\
eq i},\parallel p_{i}-p_{j}\parallel\leq R\} \tag{3}\]  
where \(\mathcal{N}\) is the communication-based neighborhood; \(N\) is the number of members in the swarm; \(r_{i}\) denotes agent \(i\); \(p_{i}\) and \(p_{j}\) are spacial positions of \(i\) and \(j\) agents, respectively; and \(R\) is the maximum communication radius. While the swarm is moving, the neighborhoods may change over time, which causes the whole swarm to be divided into several dynamically changing sub-swarms. [PERSON] al. defined those sub-swarms with the concept of Time-varying Characteristic Swarm (TVCS) [26]. The TVCS of agent \(i\) at time \(t\) can be represented as follows:  
\[\mathcal{S}^{t}(r_{i})=r_{i}\cup\{r_{j\in N,j\
eq i,}\parallel p_{i}^{t}-p_{j}^ {t}\parallel\leq R\} \tag{4}\]  
where \(\mathcal{S}^{t}(r_{i})\) represents the TVCS of agent \(i\). The number of members in one TVCS is obviously dynamically changing. At time \(t\), \(r_{i}\) is only able to communicate with other agents in \(\mathcal{S}^{t}(r_{i})\). In our case, the perception-based communication range is also limited by the visual field of each member, thus the definition of above TVCS changes to:  
\[\mathcal{S}^{t}_{v}(r_{i})=r_{i}\cup\{r_{j\in N,j\
eq i,}\parallel p_{i}^{t}-p _{j}^{t}\parallel\leq R\wedge\varphi_{ij}\in\mathcal{V}\} \tag{5}\]  
where \(\mathcal{S}^{t}_{v}(r_{i})\) is visual field limited TVCS (v-TVCS), \(\varphi_{ij}\) is the bearing angle of \(r_{j}\) in \(r_{i}\)'s frame, and \(\mathcal{V}_{i}\) is the visual field of agent \(i\). The illustration of v-TVCS is shown in Figure 2.
# Advances and Challenges in IoT-Based Smart Drug Delivery Systems: A Comprehensive Review
## 2 Internet of Things and Healthcare IoT
### Body Sensors  
Body sensors are the devices that collect the data and transmit them to the network. They can be small and compact and can be placed in various locations to gather data. Figure 4 shows body sensors that are commonly used in health management settings.  
Figure 3: Different tiers of communication in WBAN.  
Sensors integrated into drug delivery systems enable real-time monitoring of various physiological parameters, drug levels, or biomarkers. This data can be used to adjust drug dosing or personalise treatment regimens. For instance, glucose sensors can be combined with insulin pumps to create closed-loop systems (also known as artificial pancreas systems), where blood glucose levels are continuously monitored and insulin delivery is automatically adjusted based on the real-time measurements [13].
## 3 Results and Discussion
### Discriminant Analysis  
#### 3.5.1 Analysis Discrimination  
As a step further, machine learning (ML) was utilized to provide the discrimination of the cucumber peel samples based on their storage days. Since image analysis is a non-destructive technique, machine learning was utilized to demonstrate the utility of image analysis as a tool that can be employed by the industry. Specifically, discriminant analysis was applied to ascertain the most effective combinations of textural features extracted from colored and grayscale images of the cucumber peel, with the objective of grouping the cucumber samples based on different storage days. This was accomplished through the utilization of the Classification and Regression Decision-Tree classifier. Principal component analysis (PCA) illustrated a distinct separation and segregation of the samples across PC1, reflecting the variance derived from image textural features during the storage period. The scatter diagram (Figure 5) shows the discrimination amongst cucumber peel samples from days 1, 13, 22, and 28, with an overall discrimination accuracy of 90.0%, based on the features of mean skewness homogeneity and short-run emphasis. The combination of four features resulted in the creation of a high-performance machine learning system that was able to accurately classify cucumber samples based on their storage day. Specifically, it classified correctly 9, 9, 8 and 10 samples at days 1, 13, 22 and 28, respectively out of 10 samples.
###### Abstract  
Cellular communication systems support mobile phones positioning function for Enhanced-911 (E-911) location requirements, but the positioning accuracy is poor. The fifth-generation (5G) cellular communication system can use indoor distribution systems to provide accurate multiple time-difference-of-arrival (TDOA) and single time-of-arrival (TOA) measurements, which could significantly improve the indoor positioning ability. Unlike iterative localization algorithms for TDOA or TOA, the existing closed-form algorithms, such as the Chan-Ho algorithm, do not have convergence problems, but can only estimate position based on one kind of measurement. This paper proposes a closed-form localization algorithm for multiple TDOAs and single TOA measurements. The proposed algorithm estimates the final position result using three-step weighted least squares (WLSs). The first WLS provides an initial position for the last two steps. Then the algorithm uses two WLSs to estimate position based on heteroscedastic TDOA and TOA measurements. In addition, the geometric dilution of precision (GDOP) of the proposed hybrid TDOA and TOA positioning has been derived. The analysis of GDOP shows that the proposed hybrid positioning has lower GDOP than TDOA-only positioning, which means the proposed hybrid positioning has a higher accuracy limitation than TDOA-only positioning. The simulation shows that the proposed localization algorithm could have better performance than closed-form TDOA-only positioning methods, and the positioning accuracy could approximate Cramer-Rao lower bound (CRLB) when the TDOA measurement errors are small.  
Keywords: indoor positioning; 5G system; hybrid positioning; geometric dilution of precision; closed-form solution; Cramer-Rao lower bound +
Footnote †: journal: Applied