esa-sceva/satcom-chunk-collection huggingface dataset:
# Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems
## 3 Device Detection  
For a desired frame \(j\) and the \(l\)-th multipath, the hypothesis containing the information about the user and the device can be represented as  
**Hypothesis 1** (H1).: \(y_{(j,l)}=s_{(j,l)}+n_{(j,l)},\text{{absence of a device}}\)
**Hypothesis 2** (H2).: \(y_{(j,l)}=s_{(j,l)}+i_{(j,l)}+n_{(j,l)},\) _presence of a device_  
As the main objective is to determine whether another device is present or not, \(s_{(j,l)}\) does not convey any relevant information and it can be subtracted. Thus, yielding the following modified hypothesis  
**Hypothesis 3** (H3).: \(r_{(j,l)}=n_{(j,l)},\) _absence of a device_
**Hypothesis 4** (H4).: \(r_{(j,l)}=i_{(j,l)}+n_{(j,l)},\) _presence of a device,_  
where \(r_{(j,l)}=y_{(j,l)}-s_{(j,l)}\). Now, with the modified hypothesis we can easily determine the probability density function (pdf). The pdf of \(r_{(j,l)}\) given H1 can be obtained as  
\[p\Big{(}r_{(j,l)};\text{H}_{1}\Big{)}=\frac{1}{\sqrt{2\pi}\sigma}\exp\left(- \frac{\Big{(}r_{(j,l)}\Big{)}^{2}}{2\sigma^{2}}\right). \tag{9}\]  
While, the pdf of \(r_{(j,l)}\) given H2 is obtained as  
\[p\Big{(}r_{(j,l)};\text{H}_{2}\Big{)}=\frac{1}{\sqrt{2\pi}\sigma}\exp\left(- \frac{\Big{(}r_{(j,l)}-i_{(j,l)}\Big{)}^{2}}{2\sigma^{2}}\right). \tag{10}\]  
A likelihood ratio test, which is the ratio between the above two hypotheses yields\[\gamma_{(j,l)}=\frac{p\Big{(}r_{(j,l)};r_{\rm{H}_{2}}\Big{)}}{p\Big{(} r_{(j,l)};r_{\rm{H}_{1}}\Big{)}} = \frac{\exp\left(-\frac{\big{(}r_{(j,l)}-i_{(j,l)}\big{)}^{2}}{2\sigma ^{2}}\right)}{\exp\left(-\frac{\big{(}r_{(j,l)}\big{)}^{2}}{2\sigma^{2}}\right)} \tag{11}\] \[= \exp\left(\frac{2\Big{(}r_{(j,l)}\Big{)}\Big{(}i_{(j,l)}\Big{)}- \Big{(}i_{(j,l)}\Big{)}^{2}}{2\sigma^{2}}\right)\]  
As \(i_{(j,l)}\) is unknown, we need to replace it with an estimate. In the upcoming section, we will employ characteristic function (CF) to determine the moments of \(i_{(j,l)}\). As the device can enter the network at any moment of time, it is assumed to be uniformly distributed between the transmission of the frame. Furthermore, the impact will have an effect on \(K\) slots of transmission; therefore, a window-based approach is required which measures this effect. Assuming that the user is impacted over the duration of frame \(T_{f}\), and delay \(\tau_{m}\), then  
\[\Delta(m)=\frac{1}{T_{f}}\sum_{m=\tau_{m}-T_{f}/2}^{m=\tau_{m}+T _{f}/2}\gamma(m), \tag{12}\]  
where \(m=(jL+l)\). The effective threshold depending on length \(K\) will be given as  
\[\gamma=\frac{1}{K}\sum_{k=0}^{K}\gamma_{(k)}. \tag{13}\]  
Finally the presence of the device is determined as  
\[\Delta(m) \leq \gamma,\quad\text{absence of a device}\] \[\Delta(m) > \gamma,\quad\text{presence of a device}. \tag{14}\]  
Next, the moments of \(i_{(j,l)}\) are derived, which will be used for the device detection.
# A 77 GHz Transmit Array for In-Package Automotive Radar Applications
## 5 Discussion  
In the literature, several array-based solution examples for automotive radar applications like those in [1; 3; 4; 5] are available; however, they may suffer from larger feed network losses. If compared to other solutions based on a lens like the ones described in [6; 7], our proposed configuration is more compact and lightweight, while the reflect array proposed in [9] suffers from the blockage effect. Solutions based on transmit arrays can be considered good solutions for increasing the directivity of the antenna systems while maintaining a low weight and low profile. In Table 1, the configuration proposed in this work is compared with other transmit arrays taken from the recent literature. The aperture efficiency of the proposed transmit array compares well with other solutions, while its compact side dimension (W = 12 mm) makes it compatible with QFN package integration.
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 3 MPC in Biomedical Applications
### Cancer
#### 3.5.1 Oncolytic Viral Therapy
Figure 12 depicts a nonlinear MPC framework incorporating an impulsive control system for cancer tumor eradication using oncolytic viruses. Similar research [129] has explored such nonlinear modelling for real-time OVT estimation and control. This model represented virus injection in the form of an impulse. In order to correlate with the sampling time for tumour volume measurements, this impulse was initially generated every two days. The subject's state was estimated by feeding this discrete measurement into a hybrid extended Kalman filter (hEKF). This was followed by an impulsive nonlinear MPC (iNMPC) design to determine the optimal virus dosage for minimizing the number of tumour cells. The findings of this impulsive nonlinear MPC approach were compared to those obtained from prior studies [116, 121]. To calculate optimal viral doses, the suggested system employed a mathematical model that produced better and faster tumour regression than previous protocols. This model's effectiveness was also examined under different system uncertainties, such as dynamic biological parameters. This technique provided individualised therapy that was resilient to parameter and modelling uncertainties. The purpose of oncolytic virus treatment is to treat cancer by using viruses as therapeutic agents. There are several clinical difficulties that must be addressed before this potential therapy may be used, including dose issues, toxicity, and unknown tumour dynamics. In order to increase the knowledge of treatment outcomes and build better therapeutics, mathematical models can be employed to characterise the interactions between oncolytic viruses and cancer cells. As a result of the pressing need to enhance clinical results, a nonlinear estimate and control method based on impulsive control theory has been presented by the studies (discussed above) for determining the appropriate/optimal doses of viral injections.
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 3 Preliminaries  
### Blockchain  
To guarantee the integrity of the EHR and to enable efficient and secure access control, we employed blockchain in an EHR management system. Blockchain was first explained by [PERSON] in 2008 as a Bitcoin technique for an electronic cash system [11]. It is a decentralized peer-to-peer network operated by distributed nodes. In a blockchain network, every node contains ledgers with identical data. The data recorded in blocks cannot be modified and are resistant to external attacks. Hence, blockchain has garnered the attention of researchers in various fields [39; 40].  
Figure 1 illustrates the structure of a general blockchain. The blocks, which comprise transaction lists and block information, are linked in chronological order, and identical data are contained by the nodes participating in the network. After the data are saved in the blockchain, they cannot be altered. The hash of the previous block is stored in the next new block to guarantee the integrity of the previous data in the blockchain, and a digital signature generated via a public-secret key method enables the verification of nodes that are deploying the transaction. The time stamp and Merkle root enhance the data integrity and prevent forgery. The time stamp prevents the double-spending problem of blocks, whereas the Merkle root proves the integrity of transactions within each block [41]. Therefore, we can conclude that, unlike the conventional centralized database, blockchain provides data integrity by rendering information available to the public [42]. This transparency and distribution are vital to data integrity, rendering data forgery extremely difficult.  
Another critical function of the blockchain is smart contracts, which are digital contracts that are executed automatically once they fulfill the required condition [43]. This ideal contract obviates the requirement for a third party in transactions; furthermore, it is written and operated by all nodes in the distributed network without the control of a single entity once it is uploaded. We used smart contracts to upload and retrieve data from the blockchain and interplanetary file system.  
A decentralized application (DApp) enables interactions between users and the blockchain network. It is similar to the conventional application, which is structured  
Figure 1: Blockchain structure.  
with a front end and back end. However, compared with an application that executes the back end on a centralized server, the DApp is implemented on a distributed peer-to-peer network. Smart contracts are executed on a DApp, providing convenience to users who are willing to interact with the blockchain through smart contracts [44].  
In this paper, we employ Ethereum [12] to realize a blockchain-based EHR management system which protects the distributed ledgers and enables smart contracts. In addition, to monitor the blockchain, we build a DApp using Web-based programming.
## 3 Results and Discussion
### Effect of Fine Fraction Content on Cracking Resistance
#### 3.1.3 Displacement at 50% of Post-Peak Load (d\({}_{0.5\text{PostFmax}}\))  
As mentioned in Section 2, the displacement at 50% of post-peak load is directly related to the mixture ductility. As it can be observed in Figure 7, the results are consistent with the toughness results because they are both influenced by the ductility of the mixture.  
At a temperature of 20 \({}^{\circ}\)C, clear differences can be observed. Again, two main groups can be distinguished depending on the binder type. Mixtures manufactured with the polymer modified asphalt binder present higher ductility values due to the higher deformation ability of this binder. As the temperature drops, the curves tend to come together, especially at low temperatures where smaller differences between all the mixtures are observed due to the hardening process that asphalt binders suffer.  
Regarding the aggregate gradation, it is again noted that a lower content of fine aggregates leads to greater values, which indicate that the ductility of the mixture is strongly influenced by fine aggregate content.  
Figure 7: Displacement at 50% of post-peak load versus temperature.  
Equation (4) presents the relationship between the displacement at 50% of post-peak load, the temperature (T), the asphalt binder penetration at 25 \({}^{\circ}\)C (BG) and the fine fraction content (F).  
\[\begin{array}{c}d_{0.5 PostFmax}=-0.369+0.060\times T+0.033\times BG-0.015\times F \\ R^{2}=0.854\end{array} \tag{4}\]  
The statistical results are consistent with the graphical interpretation and a correlation could be established with a coefficient of determination of 0.854. So it can be concluded that there is a correlation between this cracking parameter and fine fraction content.
###### Abstract  
Landslide early warning systems (EWSs) have been widely used to reduce disaster losses. The effectiveness of a landslide EWS depends highly on the prediction methods, and it is difficult to correctly predict landslides in a timely manner. In this paper, we propose a real-time prediction method to provide real-time early warning of landslides by combining the Kalman filtering (KF), fast Fourier transform (FFT), and support vector machine (SVM) methods. We also designed a fast deploying monitoring system (FDMS) to monitor the displacement of landslides for real-time prediction. The FDMS can be quickly deployed compared to the existing system. This system also has high robustness due to the usage of the ad-hoc technique. The principle of this method is to extract the precursory features of the landslide from the surface displacement data obtained by the FDMS and, then, to train the KF-FFT-SVM model to make a prediction based on these precursory features. We applied this fast monitoring and real-time early warning system to the Bauge landslide, Tibet, China. The results showed that the KF-FFT-SVM model was able to provide real-time early warning for the Bauge landslide with high accuracy.  
fast monitoring; early warning; real-time; landslide +
Footnote †: journal: Sensors
# Cooperative Power-Domain NOMA Systems: An Overview
###### Abstract  
Interference has been a key roadblock against the effectively deployment of applications for end-users in wireless networks including fifth-generation (5G) and beyond fifth-generation (B5G) networks. Protocols and standards for various communication types have been established and utilised by the community in the last few years. However, interference remains a key challenge, preventing end-users from receiving the quality of service (QoS) expected for many 5G applications. The increased need for better data rates and more exposure to multimedia information lead to a non-orthogonal multiple access (NOMA) scheme that aims to enhance spectral efficiency and link additional applications employing successive interference cancellation and superposition coding mechanisms. Recent work suggests that the NOMA scheme performs better when combined with suitable wireless technologies specifically by incorporating antenna diversity including massive multiple-input multiple-output architecture, data rate fairness, energy efficiency, cooperative relaying, beamforming and equalization, network coding, and space-time coding. In this paper, we discuss several cooperative NOMA systems operating under the decode-and-forward and amplify-and-forward protocols. The paper provides an overview of power-domain NOMA-based cooperative communication, and also provides an outlook of future research directions of this area.  
Cooperative communication; successive interference cancellation; power-domain NOMA; precoder; equaliser; antenna diversity; outage probability +
Footnote †: journal: Sensors  
doi:10.3390/s22249652
## 3 Proposed Approach  
### Overview  
The algorithm steps of the proposed method are shown in Figure 3, and it is summarized as follows. As the first step, a fisheye image of the front view of a pallet robot is converted to a cubemap image. From the cubemap image, we generate a virtual pinhole camera image, Ground Plane Image (GPI), which is introduced in our previous study [23]. GPI contains only the images of the ground plane at the front of the robot as if a virtual pinhole camera is facing perpendicular to the ground plane, as shown in Figure 3. Ideally, GPI must be the ortho-rectified image captured from the virtual top-view camera. However, due to the image transformation error both from the fisheye to the cubemap and from cubemap to GPI, it is not perfectly ortho-rectified. The transformation error is mainly due to errors in fisheye lens modeling and errors in homography computation between the cubemap and GPI.  
To perfectly generate an ortho-rectified GPI, we also propose to transform the ground plain image again to the ortho-rectified image (ortho-rectified GPI). In ortho-rectified GPI, the ground plane is perfectly rectified as the top-view camera captures perpendicular to the ground. As the next step, we track image features in ortho-rectified GPI using the Kanade-Lucas-Tomashi (KLT) optical flow algorithm to determine the motion vector of the robot [24]. The motion vector between two GPIs is then scaled to determine the robot's motion in the metric space. Then, from the scaled vector, the velocity and steering angle of a virtual front wheel of the robot are estimated. Because the fisheye camera is mounted at the front of the robot, we employ a kinematic bicycle model for the motion estimation of the virtual front wheel of the robot [3].
# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 4 Experiments
### Setup and Parameter Settings  
Our tracking framework can be implemented in Windows or Linux operating system. The only thing worth noting is that one needs to install the Boost library first for running the k shortest paths optimization. The source code of the KSP library developed in C++ can be found at [54].  
For any computer configuration, processing a whole video sequence is possible in theory, but impractical, as its computing complexity would increase exponentially compared with a short sequence. Thus, we commonly split the sequence into batches first and then connect them according to some matching cues. The matching process adopts a pattern of slide window, described in detail in Figure 8. Such a batch processing mode can guarantee a global optimization solution within a batch of frames but it also must accept a delay as a tradeoff.  
Overall, compared with other relative approaches, our proposed method depends on very few parameters: only the granularity of the grid cells, the depth of KSP network, and the batch size. The batch size can be given according to the computing capability, while the other two parameters are determined by the consideration of the moving speed of players and the frame rate. In our experiments, we divide the court into discreted grids with a size of \(128\times 72\times 1\) as well as the depth of 1. In view of the running speed, the batch size is set as 100 frames and the number of matching range frames is defined as 50 in our experiments. Thus, in addition of the virtual source and sink nodes, 921,602 nodes  
Figure 8: Connect two tracking segments according to players’ positions within the matching range of \(l\) frames. For example, suppose the batch size is set as \(L\): both tracklet 1 and tracklet 2 contain \(L\) frames with \(l\) frames being overlapped. We call the overlapped \(l\) frames matching range frames. Then we calculate the distance between the two positions frame by frame within the matching range. If the distance is less than a given threshold, we call it a match. If not, a mismatch. Only if the matching number of times overpasses 80%, we then connect the two tracklets. Otherwise, we consider the current tracklet segment a new trajectory.  
Figure 7: Our own STU basketball dataset is collected with eight ground cameras evenly distributed around the court: (**a**) images captured from eight views; (**b**) the schematic diagram of camera installation.  
were the total for one batch. After the weights of all possible links are calculated by Formulas (11) and (12), a graph network is then constructed, as shown in Figure 2. It is not hard to see that the linking edges count of the graph mainly depends on the three parameters we have mentioned above: KSP network depth, grid number, and the batch size. More edges would results more time consumption in tracking process.
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 2 Methods and Data
### Data Screening of Strong Motion Records  
This study applies the following principles for screening strong motion records:  
1. Directional classification: Separate horizontal and vertical direction datasets were established. However, this study analyzes only the horizontal dataset;
2. Amplitude screening: Due to the minimal impact of records with low PGA on engineering seismic resistance outcomes, this study excludes data where PGA is less than 5 gal to reduce the interference from low-amplitude data;
3. Epicentral distance and station screening: This research focuses on attenuating moderate and minor earthquake strong motion records in the Loess Plateau region. Records with epicentral distances exceeding 100 km and those from non-surface stations are excluded;
4. Site type screening: Considering the limited number and uneven distribution of data from bedrock stations, which makes it challenging to statistically derive reliable predictive models for bedrock sites, records classified under the site type \"rock\" are excluded;
5. Anomalous data screening: Potential anomalous records are identified by plotting the distribution of PGA against magnitude and epicentral distance. Further waveform checks are performed using SeismoSignal software version 5.1.0 and records with obvious \"spikes\" or \"cut-offs\" are removed;
6. Magnitude screening: These records are excluded due to the insufficient number and uneven distribution of strong motion records with magnitudes less than \(M_{\rm S}\) 3.0. Additionally, records with magnitudes greater than \(M_{\rm S}\) 6.7 are excluded to focus on moderate and minor earthquakes.
# Security Analysis of the MQTT-SN Protocol for the Internet of Things
## 2 MQTT-SN Protocol
### Format of the Main Packets
#### 2.1.1 Connect Message  
This type of packet is used to connect MQTT-SN clients to the broker.  
Table 2 shows the format of a _Connect_-type packet, the different fields, and the octets they use. The function of these fields is as follows:  
* **Flags**: The most relevant ones for a _Connect_ packet:
* _Will_: It defines a message and a topic. In case of an error in the connection between the device and the broker, this message is sent to the chosen topic.
* _CleanSession_: Enabling this flag causes the client to forget previously subscribed topics. If it is not enabled, the broker computes the topics to which the client was subscribed and keeps them, using the _ClientID_ field.
* **ProtocolId**: It determines the version of the protocol being used.
* **Duration**: It contains the value of the keep alive timer of the connection.
* **ClientId**: It uniquely identifies a client connected to the MQTT-SN broker.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Common Fields** & **Description** \\ \hline Length & It determines the length of the packet. \\ MsgType & It defines the type of MQTT-SN packet. \\ Flags & It indicates additional options. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Brief description of the fields common to the different packets.  
Figure 2: MQTT -SN scheme.
# Basins of Convergence in a Multi-Perturbed CR3 BP +
## 3 Lagrange Points and Basins of Convergence
### Validating the Algorithm: The Sun-Earth System  
In this subsection, an analysis of the results is carried out to validate the implemented code. Given the modular construction of the dynamic model and assuming the independence hypothesis discussed in Section 2, the validations are presented separately. For this purpose, meshes with a resolution of \(500\times 500\) are used for the validation, without losing significant precision in the solution.  
All of the effects presented in the previous section can be validated against the literature except for perturbation due to natural satellites. This last effect was studied theoretically in [29] using a different formulation for a single satellite, but, to our knowledge, there are no data provided to compare. Furthermore, we note that our algorithm allows us to consider more than one satellite.