esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### MIRDcell Software  
The MIRD Committee developed a software application, called MIRDcell ([[http://mirdcell.nims.rutgers.edu/](http://mirdcell.nims.rutgers.edu/)]([http://mirdcell.nims.rutgers.edu/](http://mirdcell.nims.rutgers.edu/)), accessed on 18 January 2024), which calculates the distribution of radiation dose and predicts the surviving fraction of cell populations [44]. The user can select the size, shape, and geometry of the cell population, the distribution of radioactivity, and the source of radiation. The available sources of radiation are predefined MIRD radionuclides, monoenergetic particles, or a radionuclide created by the user. The continuous spectrum and the mean energy of \(\beta\)-emitters are also available options. The cellular model used in MIRDcell consists of two concentric homogeneous spheres of unit density. The external sphere represents the cell with radius \(\mathrm{R_{C}}\) and the other one is the nucleus with radius \(\mathrm{R_{N}}\), respectively. The radioactivity is distributed in the source region of the cell, nucleus, cytoplasm, or cell surface [45]. The target region and the size of the radius of the cell and nucleus are selected by the user. The Software also includes a functionality that creates a cell population of any shape and of 2 or 3 dimensions, and calculates the dose distribution among the labeled cells. After the selection of these parameters, MIRDcell calculates the S factor of target region and the survival fraction based on the linear quadratic model. Importantly, MIRDcell calculates the S factor using the Cole-Howell analytic range-energy relations, Equations (5)-(7) and (10)-(13), for electron energies below 100 keV, and numerical data from Equations (8) and (9) for energies above 100 keV [41]. In the present work, for comparative purposes, (self-irradiation) S factors for monoenergetic electrons with initial energy 1 keV-4 MeV were calculated for cell spheres with different radii using the MIRDcell V2.0.16 version.
# A Framework for Pedestrian Attribute Recognition Using Deep Learning
## 3 Proposed Approach
### Mask R-CNN Object Detector  
The first stage of the pedestrian attribute recognition framework is the Mask R-CNN object detector. The purpose of this stage is to extract the pedestrians from a surveillance scenario for further analysis. Pedestrian detection has so many applications in the real world, i.e., smart vehicles, robotics, security [40]. Mask-RCNN or Mask Region-Based Convolutional Neural Network in [41] was developed as a result of a more in-depth investigation of deep learning for pedestrian detection. The overview of the Mask RCNN object detector is shown in Figure 3. It is a double stage CNN architecture. The first stage is responsible for proposing object regions. The second stage is responsible for providing the masked regions of the objects, the bounding box of the objects, and the classification of the objects. Each stage of the Mask R-CNN is explained briefly in the following:  
* **First Stage** Backbone: The first stage of Mask R-CNN consists of Backbone and RPN. The Backbone of the Mask R-CNN is responsible for extracting the features of a given input image. ResNet 50 CNN architecture is used in the Backbone to extract the features. The extracted features are passed to the second stage via the ROI layer. Additionally, the features are also forwarded to the RPN network.  
**RPN:** Region Proposal Network(RPN) is responsible for proposing regions of the location of an object. Several convolution layers are used to predict the probability of an object presence using the softmax function. The bounding box of the objects is also predicted in the RPN. The RPN provides the objects to the ROI align layer, passing the individual objects to the second stage.  
Figure 3: Overview of the Mask R-CNN Object Detector.  
Figure 2: Steps of proposed pedestrian attribute recognition framework.  
* **Second Stage**  
**Branches:** The objects from the ROI align layers are passed to the Branches stage. This stage has three parts, i.e., Mask, Coordinates, Category. The Mask part provides the masked regions of an object. Similarly, the Coordinate part provides the object's bounding box, and the Category part provides the object's class name.  
Figure 4 shows an example of Mask RCNN in real-world scenario. The image source is available in [25].
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
## 3 Preliminaries
### MFrLFM Representation for RGB Color Image  
Fractional-order polynomials are not restricted to integer-order values compared with other similar orthogonal moments, which give it a high ability to represent the fine details in an image rather than their rivals' orthogonal polynomials of integer order [49]. These descriptors are also invariant to geometrical transformations (rotation, scaling, and translation). It is defined over a unit circle as follows:  
\[MFrM_{pq}(fc)=\frac{2p+1}{2\pi}\int_{0}^{2\pi}\int_{0}^{1}fc(r,\theta)\big{[} W_{pq}(r,\theta)\big{]}^{*}rdrd\theta \tag{5}\]  
where \(W_{pq}\) represents the essential functions of the Legendre-Fourier moments of fractional orders.  
The function \(fc(r,\theta)\) denotes the RGB color images, the asterisk symbol \"**\" refers to the complex conjugate; \(p=|q|=0,1,2,3,4,5,6\ldots\ldots\ldots\infty\).  
The multi-channel fractional-order moments for all RGB color channels of the input image are computed using  
\[f_{CR}(r,\ \theta),f_{CG}\ (r,\ \theta)\ \text{and}\ f_{CG}(r,\ \theta)\]  
Therefore, the original image is reconstructed by the reliance on the orthogonal property [50] of the fractional-order LFMs as follows:  
\[f_{C}^{recons}(r,\theta)=\sum_{p=0}^{pmax}\sum_{q=-qmax}^{qmax}MFrM_{pq}(f_{c} )W_{pq}(r,\theta) \tag{6}\]  
where \(f_{C}^{recons}(r,\theta)\) refer to the reconstructed image elements \(R\), \(G\), \(B\) of  
\[f_{CR}^{recons}(r,\ \theta),\ f_{CG}^{recons}(r,\ \theta)\ \text{and}\ f_{CB}^{recons}(r,\ \theta)\]
## 2 System Architecture
### Medication Adherence: Use Case Requirements  
Given the outcome of the above-mentioned surveys, the assistance provision in medication adherence activities comprised a high priority use case for the users and the caregivers. In accordance with this, the robot should:  
* be aware of the medication schedule of the user;
* provide reminders to the user through the communication modalities before a medication session;
* be able to locate, detect and fetch the pill box, especially when the latter is placed in high places difficult to be reached by the user;
* monitor and assess the progress of the medication adherence activity;
* be able to place the pill box back to its storage position;
* establish communication with external person in cases were medication process has not been completed successfully;
* complete the assistance provision for the medication adherence scenario in a coherent and structured manner, with sufficient repeatability.  
Figure 1: The RAMCIP robot engaged in a real medication adherence assistive task with a user in an actual home environment.
## 3 Short-Reach Systems Classification and Enabling Technologies
### Data Center Network Requirements and Challenges
#### 3.2.5 Latency  
As previously mentioned, DCNs have hundreds of thousands of servers that need to communicate with each other by means of low-latency and high-performance interconnection networks [74]. Furthermore, a considerable amount of computation and data storage is usually executed in DCNs [75]. Therefore, based on the associated supercomputing, as well as other delay-sensitive cloud applications/services, DCNs demand more stringent latency than the long-haul systems. Furthermore, short-reach systems normally experience small propagation delays and comprise fewer link components, which can incur relatively less signal delay. Consequently, for short-reach systems, the main element that incurs significant latency is the forward error correction (FEC). Typically, the FEC latency can be between 15 and 150 \(\upmu\)s, making it the key contributor to the total system latency. Based on this, complicated soft decision-FEC (SD-FEC) with iterative decoding methods is not usually considered as an effective option. Therefore, either a standard hard decision-FEC (HD-FEC) or other innovative low-latency codes are more applicable to the short-reach systems. It should be noted that the implementation will also impose more stringent requirements regarding the pre-FEC BER and receiver sensitivity [35].
## 3 CE Methods and System Model  
CE is an important technique in OFDM architecture [26]. CE is explicitly defined as the description of a mathematically modelled channel. CE Algorithms are usually used to determine the channel impulse response or frequency response. Figure 1 shows the overall concept of CE. Channel estimator specification requirements include minimization of mean square error (MSE) and computational complexity. CE can be classified into PACE, blind channel estimation (BCE), and decision-directed channel estimation (DDCE). PACE is the most common method of transmitting a known signal from a transmitter, where pilot means the reference signal used by both a transmitter and a receiver. This can be extended to any wireless communication system and is of very low computational complexity. The key drawback, however, is the decrease in the transmission rate because non-data symbols (pilots) are added. One design challenge for PACE is thus to jointly minimize the pilot numbers while precisely estimating the channel.  
The current research focuses on reaching and evaluating CE techniques such as the proposed DL-based peephole LSTM model, DL-based GRU model, and DL-based LSTM model for 5G channel models using PACE. The proposed method's performance will be compared to the LS and MMSE estimations.
## 3 Analysis of Temperature Field of Frozen Wall  
### Mathematical Model of Freezing Temperature Field  
The determination of freezing temperature field is a transient heat conduction problem with internal heat source and phase transition interface movement. The differential equations governing the two-dimensional (2D) freezing temperature field in the cartesian coordinate system are shown as follows:  
In the unfrozen areas,  
\[\rho C_{u}\frac{\partial T_{u}}{\partial t}=k_{u}\bigg{(}\frac{\partial^{2}T_ {u}}{\partial x^{2}}+\frac{\partial^{2}T_{u}}{\partial y^{2}}\bigg{)} \tag{2}\]  
In the frozen areas,  
\[\rho C_{f}\frac{\partial T_{f}}{\partial t}=k_{f}\bigg{(}\frac{\partial^{2}T_ {f}}{\partial x^{2}}+\frac{\partial^{2}T_{f}}{\partial y^{2}}\bigg{)} \tag{3}\]  
At the phase transition interface,  
\[\frac{k_{f}}{\cos\varphi+\sin\varphi}\bigg{(}\frac{\partial T_{f}}{\partial x }+\frac{\partial T_{f}}{\partial y}\bigg{)}-\frac{k_{u}}{\cos\varphi+\sin \varphi}\bigg{(}\frac{\partial T_{u}}{\partial x}+\frac{\partial T_{u}}{ \partial y}\bigg{)}=\psi\frac{d\xi_{N}(x_{N},y_{N})}{dt} \tag{4}\]  
\[T_{f}=T_{u} \tag{5}\]  
where \(\rho\) is the density of soil, kg/m\({}^{3}\); \(T_{u}\) and \(T_{f}\) are the temperatures of the unfrozen and frozen regions, respectively, \({}^{\circ}\)C; \(C_{u}\) and \(C_{f}\) are the specific heat capacities of unfrozen soil and frozen soil, respectively, J/(kg\(\cdot\)K); \(k_{u}\) and \(k_{f}\) are the thermal conductivities of unfrozen soil and frozen soil, respectively, W/(m\(\cdot\)K); \(x\), \(y\) is plane position coordinates, \(m\); \(\varphi\) is the circumferential coordinate with the freezing hole center as the coordinate origin, rad; \((x_{N},y_{N})\) is the coordinate of the frozen front, m; \(\xi_{N}\) is the position of the frozen front, \(m\); \(\psi\) is the latent heat of phase transformation per unit volume of rock and soil mass, J/m\({}^{3}\); \(t\) is the time, \(s\).  
The initial condition of soil temperature field is  
\[T|_{t=0}=T_{0} \tag{6}\]  
where \(T_{0}\) is the initial temperature of soil, \({}^{\circ}\)C.  
The boundary conditions of soil temperature field are as follows:  
\[T|_{(x_{p},y_{p})}=T_{c}(t) \tag{7}\]  
\[T(t)|_{(x=\infty\alpha\alpha y=\infty)}=T_{0} \tag{8}\]  
where(\(x_{p},y_{p}\)) is the freezing holes boundary coordinates, m; \(T_{c}(t)\) is the real-time temperature of the freezing holes, \({}^{\circ}\)C.  
Equations (2)-(8) constitute the definite solution of the freezing temperature field in 2D cartesian coordinate system.
# Practical V2I Secure Communication Schemes for Heterogeneous VANETs
## 4 Four Heterogeneous Signcryption Schemes for V2I Communication Scenarios
### Pki\(\rightarrow\)IBC One-to-Many Signcryption (OMHSC-I)  
The **Setup** is the same as the above MOHSC-I algorithm, except the **PKI-KG** and **IBC-KG** algorithms have slight changes, i.e., the sender RSU's public/secret key \(pk=xP/sk=x\) in PKI and \(n\) receivers \(V_{i}\)'s key pairs \(\left[pk_{ri}=H_{1}(ID_{ri})=Q_{ri},sk_{ri}=sQ_{ri}\right]_{i=1}^{n}\) in IBC.  
**Signcrypt**: Taking **params**, a random message \(m\), the RSU's secret key \(sk\), multiple receivers' public keys \(\left[pk_{ri}\right]_{i=1}^{n}\) as input, the algorithm performs the following steps (repeat 2, 3 steps \(n\) times):  
1. Choose a random \(r\in Z_{q}^{*}\) and compute \(R=r\cdot pk\);
2. Compute \(k_{i}=e(r\cdot sk\cdot P_{pub},pk_{ri})\), \(c_{i}=m\oplus H_{2}(k_{i})\);
3. Calculate \(h_{i}=H_{3}(pk,pk_{ri},R,c_{i})\), \(S_{i}=sk\cdot h_{i}P_{pub}\).
4. Broadcast \(\sigma=(R,c_{1},\ldots,c_{n},S_{1},\ldots,S_{n},\Omega)\) to \(\left\{V_{i}\right\}_{i=1}^{n}\), where \(\Omega\) is a label which includes message about how \(c_{i}\) and \(S_{i}\) are associated with the receivers.  
**Unsigncrypt**: Any receiver \(V_{i}\) takes \((R,c_{i},S_{i})\) from \(\sigma\) according to the label \(\Omega\). Input \(V_{i}\)'s secret keys \(sk_{ri}\), the sender's \(pk\) and **params**, then the algorithm executes the following procedures:  
1. Compute \(h_{i}=H_{3}(pk,pk_{ri},R,c_{i})\);
2. Verify \[e(S_{i},P)=e(h_{i}\cdot pk,P_{pub});\] (3) If (3) is established, emits _true_ which means \(\sigma\) is valid. Or else, emits _false_ and abort.
3. Calculate \[k_{i}^{\prime}=e(R,sk_{ri});\] (4)
4. Compute \(m=c_{i}\oplus H_{2}(k_{i}^{\prime})\), and get the message \(m\).
## 4 Results and Discussion
### Effect of Material's Stiffness Ratio on the Factor of Safety (FoS)  
The stiffness ratio represents the different characteristics between the stiffness of the geogrid with respect to the stiffness of the materials. In this study, the elastic modulus of embankment (E\({}_{\mathrm{m}}\)), clay (E\({}_{\mathrm{c}}\)) and the granular columns (E\({}_{\mathrm{col}}\)) was kept constant, and the stiffness of the geogrid (E\({}_{\mathrm{g}}\)) was changed (Table 3) to represent the stiffness ratio corresponding to each one of them as follows, E\({}_{\mathrm{g}}\)/E\({}_{\mathrm{m}}\), E\({}_{\mathrm{g}}\)/E\({}_{\mathrm{c}}\), and E\({}_{\mathrm{g}}\)/E\({}_{\mathrm{col}}\), respectively.  
Figures 10-12 show that at the relatively high angle of embankment slope (\(\beta\)). It was observed that factor of safety (FoS) values increases with an increase in the stiffness ratio of the geogrid with respect to the other materials (i.e., embankment, clay, and column). On the other hand, very minor effect for the area replacement ratio (Ar %) was recorded on the factor of safety in the high stiffness values. At low angle of embankment slope (\(\beta\)) values, the stiffness ratio values have led to positive effects on the stability of the  
Figure 7: Soil—column interaction at the shear plane. (**a**) Normalized height vs. normalized lateral displacement. (**b**) Normalized shear strength vs. normalized soil movement in embedment ratio L/D = 8.  
embankment. The amount of increase (as comparing to high \(\beta\) values) was found to be higher. The reason behind this is that the geogrid increases the length of the slip surface and increases the friction along the shear plane. Furthermore, in high stiffness values, the geogrid granular columns GECs contribute to delay the plastic (yielding behavior) in the soil [8] by mobilizing higher forces (stresses) [22].
## 4 Control Application of Mechatronic Devices: Adaptive Controller
### System Identification  
To implement an adaptive controller, it is necessary to have access to the inputs and outputs, labeled in Figure 3 as \(u(n)\) and \(y(n)\). With these acquired data, the online identification block obtains an estimated mathematical model of the real plant, denoted as \(\theta_{plant}(n)\) in Figure 3. The estimated model of the plant can be obtained using several algorithms; one of the most commonly employed is the recursive minimum square method [19]. The controller parameters estimator uses the identified plant model to adjust the controller gains and obtain the desired behavior according to the estimated plant. Therefore, it performs a dynamically adjusted controlled system, providing a controller with better robustness against aging, disturbances, and minor mechanical problems.  
For the algorithm performance and paper/article purpose, a first-order model, with the following transfer function, is chosen:  
\[G_{p}(s)=\frac{A}{s+B}, \tag{1}\]  
where \(A\) and \(B\) are the parameters to be identified.
## 2 Linear or Linear-like Formulations of Nonlinear Systems  
This section reviews some common techniques to transform a nonlinear system into a linear or linear-like system, which includes Jacobian linearization, feedback linearization, state-dependent coefficient (SDC) parameterization, and Takagi-Sugeno (T-S) fuzzy modeling.  
A nonlinear system is considered as  
\[\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x})+\mathbf{g}(\mathbf{x})\mathbf{u},\ \mathbf{y}=\mathbf{h}(\mathbf{x}) \tag{1}\]  
where \(\mathbf{x}\) is a state vector; \(\mathbf{u}\) is a control input vector; \(\mathbf{y}\) is an output vector; \(\mathbf{f}(\mathbf{x})\), \(\mathbf{g}(\mathbf{x})\), and \(\mathbf{h}(\mathbf{x})\) are nonlinear functions of the states and time.  
The following subsection will present several schemes transforming Equation (1) into the linear or linear-like systems as the form:  
\[\dot{\mathbf{z}}=\mathbf{A}\mathbf{z}+\mathbf{B}\mathbf{v},\ \mathbf{w}= \mathbf{C}\mathbf{z} \tag{2}\]  
where \(\mathbf{z}\), \(\mathbf{v}\) and \(\mathbf{w}\) are new state, control and output vectors, respectively; \(\mathbf{A}\), \(\mathbf{B}\) and \(\mathbf{C}\) are matrices derived from Equation (1). If they are not functions of \(\mathbf{z}\), Equation (2) is a linear system. In contrast, Equation (2) is linear-like system.
## 4 Performance Analysis
### Anti-Interception Performance  
In this subsection, the anti-interception performance of the proposed MP-WFRFT- and SM-aided DM scheme is investigated. We assume the LUs know the SM mapping in advance and Eves cannot wiretap any number information. However, before performing the proposed scheme, we also need to exchange the MP-WFRFT parameters between the transmitter station and the LUs through a secure channel. In a practical application scenario, the Eve may know the MP-WFRFT modulation and intercept them with imperfect parameters. Therefore, it is necessary to analyze the effect of the leakage of MP-WFRFT parameters on the secrecy performance of our proposed scheme. Here, the actual MP-WFRFT parameters can be expressed as  
\[\left[\begin{array}{c}\hat{\alpha}\\ \hat{\mathbf{m}}\\ \hat{\mathbf{c}}\end{array}\right]=\left[\begin{array}{c}-\alpha_{s}\\ \mathbf{m}_{s}\\ \mathbf{c}_{s}\end{array}\right]+\left[\begin{array}{c}\Delta\alpha\\ \Delta\mathbf{m}\\ \Delta\mathbf{c}\end{array}\right] \tag{25}\]  
where \(\Delta\alpha,\Delta\mathbf{m}=[\Delta m_{1},\Delta m_{2},\Delta m_{3},\Delta m_{4 }]^{T}\), and \(\Delta\mathbf{c}=[\Delta c_{1},\Delta c_{2},\Delta c_{3},\Delta c_{4}]^{T}\) are the mismatched errors. The detailed simulations and analysis are given in Section 5 for investigating the impact of these nine MP-WFRFT mismatched parameters on secrecy performance.
# Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building
## 7 Conclusions  
In this paper, we presented a practical approach of event inference for automatic control in IoT-enabled smart building environment. The proposal consists of unified API development, knowledge base and inference engine. The event inference models based on sensor observations was separated into deterministic and probabilistic. Therefore, two types of models: finite state machine (FSMs) and bayesian network (BN) have been used for capturing the state transition and sensor data fusion. As opposed to earlier straightforward machine learning-based methods, our proposal focused on the conditional conjunction and transition of two correlated events, for which a graph model of the physical environment was considered necessary.  
To tackle the problem of the sizes of time interval (\(\Delta t\)) observations were too small to derive accurate results, MCMC sampling process has been utilized for approximating the time intervals (\(\Delta t\)). Specifically, linear regression of local distances between two space entities on the ontology graph has been leveraged for the optimization of the sampling process. The proposal has been implemented in a real smart building environment and 78-days data collection of the state on light and elevator has been conducted for evaluation. Event conjunctions on the light and elevator have been utilized for further inferring room occupancy and indoor users' trajectories.  
To show the usability of the proposal, we extracted the knowledge of users' waiting time on the elevator. The FSM mapping result of elevator-using demonstrated the probability of users' waiting event was 56.7%, with the total waiting time during the evaluation was 3085 s and average waiting time was 11.55 s. The numerical results demonstrated the potential of automatic control for zero-waiting on elevator based on knowledge inference on event conjunction in smart building.  
**Author Contributions:** Writing--original draft preparation, H.G.; conceptualization, X.P.; supervision, N.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
The model evaluation compares both models' predictive accuracy, considering statistical metrics. The analysis sheds light on storm surge dynamics, aiding coastal protection planning. By analyzing historical storm data, the study's insights into the intensity of typhoons and tropical cyclones enable better anticipation of potential future weather-related challenges. This knowledge can guide disaster response strategies, aid in the assessment of vulnerabilities, and inform land-use planning in coastal regions.
## 4 Discussion
### SIF and GPP Relationship and Environmental Drivers
[46] reported that GPP and SIF tend to decline along a climatic gradient from cold and humid regions to hot and arid regions. Their study also found that under equivalent temperature conditions, increased precipitation can elevate the GPP/SIF ratio. This is consistent with our observations, where frequent precipitation in August likelydisrupted direct coupling between SIF and GPP, leading to a lower correlation compared to the drier months of September and October.