esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ranging Error Source Analysis and Modeling
### Other Models and Errors  
We use the ENU coordinate system as the navigation coordinate system. We use the navigation geographic coordinate system as the ENU coordinate system, taking into account the aircraft's flying height. Simultaneously, the Earth is considered to be a rotating ellipsoid. The error model of the gyroscope and LEO positioning receiver error model can be found in references [17; 37; 38]. Due to space limitations, we will not elaborate the model here.  
Other errors, such as tracking error, mainly involve code tracking error and carrier tracking errors, which are related to specific factors, such as the modulation code type, loop model, phase detector and loop bandwidth used by the system [39; 40]. However, the specific navigation design scheme based on the world's major LEO navigation enhancement systems has not been introduced, so we will learn from the relevant parameters of the existing navigation system for setting. In addition, regarding group wave delay and inter-channel differences, in cases, such as pure GLONASS and GNSS joint positioning, using multiple GNSS signals broadcast on different carrier frequencies, it is necessary to consider such measurement errors [41]. Other interference models can be found in references [42; 43]. Due to space limitations, we will not introduce them in detail here.
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Petri Net  
_Petri nets_ are directed bipartite graphs in which nodes are either classified as places (generally represented as circles) or transitions (generally represented as rectangles). Recalled from Section 5.4, in bipartite graphs, edges should connect nodes across the two disjoint groups. Edges pointing from a place to a transition are known as input places of a transition, and edges pointing from a transition to a place are known as output places of a transition. Places may acquire any number of tokens (depicts by dark filled circles as in Figure 16). If all input places connected to a transition have at least one token, then the transition is enabled.  
Once the transition is enabled and fires, it consumes the input places' tokens and produces tokens in output places [62]. If more than one transitions are simultaneously enabled, then the transitions can fire in any order. Thus, firing of transitions and executing the petri nets are nondeterministic (unless an execution policy is defined). This means, systems with concurrent behavior, such as some manufacturing systems, can benefit from being modeled as a petri net.  
[PERSON], [PERSON] [132] proposed an application of a particular type of petri net called as _timed event graph_ to choice-free manufacturing systems. In timed event graphs, a node can have only one incoming and outgoing edge, while timing feature can be considered [133; 134]. Moreover, timed event graph has been applied to model the job shop scheduling problem [135; 136].  
Figure 16 shows an example of a manufacturing system modeled by a petri net. This model includes six states denoted by places (circles), and six transitions which represent the changes between these states (rectangular). The states can be defined as follows: the process has not started, the process is being done by either machine 1 or machine 2, and the process is finished.
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 4 Simulation
### LED Panel Image Replication  
A highly realistic LED panel image replication method, shown in Figure 11, was applied in this study to provide fair analysis of the effect of the system parameters on the variances of LED features. The first step was to replicate the pixel coordinates of LEDs in the image. A pinhole camera model [12] shown in Figure 11a was used for this. The pixel coordinates of all LEDs in the image were obtained using the camera intrinsic and extrinsic parameters such as lens focal length, position, and pose, as well as the real world coordinates of the LEDs. The next step was to determine the greyscale of LED pixels in the image as shown in Figure 11b. The luminous exposure of the On and Off LED pixels and the image background pixels were determined from the LED and ambient light parameters and camera exposure settings. The Gaussian filter was then applied to replicate the blurriness phenomenon. Finally, gamma encoding was applied to calculate the greyscale of every pixel.  
Figure 10: Simulation procedure.
## 4 Experiments  
Within this section, a series of comparative experiments are conducted, encompassing three key facets: vehicle detection and edge point positioning, multi\(-\)vehicle tracking, and vehicle distance measurement, including yaw angle estimation. The performance evaluation of models is undertaken through a comprehensive analysis, utilizing two benchmark datasets: the KITTI dataset and a custom\(-\)created vehicle sensing dataset. The KITTI dataset comprises 7481 training images and 7518 test images, whereas the self\(-\)constructed vehicle sensing dataset incorporates 63 training videos and 52 test videos. This self-generated dataset stems from an onboard camera situated on the front windshield. Notably, these datasets offer a rich source of vehicle samples extracted from natural driving scenarios, replete with challenging conditions such as occlusion, shadow interference, and fluctuations in illumination. This inherent complexity underscores the difficulty faced by most sensing models in accurately extracting target information within such dynamic and intricate environments.
## Abbreviations  
ML Machine Learning  
MAE Mean Absolute Error  
RMSE Root Mean Square Error  
DNN Deep Neural Network  
ANN Artificial Neural Network  
ANFIS Adaptive Neuro-Fuzzy Inference Systems  
IQR Interquartile Range  
CDF Cumulative Distribution Function  
GPR Gaussian Process Regression  
LR Linear Regression  
KNN K-Nearest Neighbors  
MLP Multilayer Perceptrons  
RF Random Forest  
SVM Support Vector Machine  
SGD Stochastic Gradient Descent  
SS Stainless Steel  
ICP-OES Inductively Coupled Plasma Optical Emission Spectrometry
# The Applications of Magnetic Particle Imaging: From Cell to Body
## 5 Conclusions and Outlook  
The applications of MPI still need further exploration. According to its imaging characteristics, researchers applied MPI to problems in the field that the existing technology cannot solve well, or to problems in which MPI can further improve the current methods. Researchers have also tried to merge MPI with other imaging techniques and used the advantages of different techniques to improve imaging quality comprehensively. At present, MPI has enabled significant progress in various areas, such as cell tracking, tumor imaging, drug delivery, blood pool imaging, and visualized magnetic hyperthermia. More applications of MPI in the fields of diagnosis and treatment should be explored, taking advantage of characteristics of MPI such as high resolution, high sensitivity, unlimited penetration depth, linear quantitativity, real-time imaging, and no radioactivity [52].  
MPI scanners are currently not applied in the clinic. Both Wald and Magnetic Insight are trying to manufacture MPI instruments designed for the human body, but there are no finished products to date [5]. Moreover, tracers designed specifically for MPI also require further research. At present, the commonly used MPI tracers are contrast agents designed for MRI, such as Resovist (VivoTrax) and Feraheme [5]. Researchers are studying imaging tracers designed for multimodal imaging that include MPI and other imaging methods. However, among the crucial hindrances to the development of clinical MPI is that no tracer tailored to MPI physics is clinically approved [5]. Therefore, the development of MPI tracers for different diseases is a vital research direction for MPI development in the future.  
Although MPI research is still in its infancy, researchers have tried to explore the possibilities of MPI in different fields. With the further development of MPI devices and tracers, the clinical application of MPI will be expanded. Therefore, further exploration of MPI applications in the future will also be a primary research direction in this field.  
This work was supported by the Space Medical Experiment Project of China Manned Space Program (HYZHXM04004), National Natural Science Foundation of China (81471743).
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 4 Models
### Consumption Application Model  
Consumption Application Models [16] are used in activity _Generate Power Consumption Models_ of the proposed methodology (see Figure 3). These model are described in CPN and used to evaluate the power consumption of WSN applications implemented in nesC. To perform this task, it is necessary (i) to identify the nesC operators used in the application and convert them into Operator Models; (ii) map the nesC functions into Function Models; and, finally, (iii) create the Application Model, by  
Figure 3: Proposed methodology.  
joining all Functions Models with the Scheduler Model. In this way, different WSN applications have different models, and (consequently) different power consumptions.  
An Consumption Operator Model is a CPN model that represents the power consumption of a nesC operator. Figure 4 shows two Operator Models and each model has one place and one transition. For example, the operator model of _Operator A_ is represented by place _IN_ and transition _Operator_A_, while _Operator B_ is modelled as place _OP_B_ and transition _Operator_B_. When transition _Operator_A_ is triggered, it records the power consumption of the operator being modeled (_power_consumption(op_a)_). Beside that, it can indicate whether a feature is enabled/disabled (such as the radio) or if another implemented function must be performed. For example, the transition _Operator_B_ enables the radio.  
NesC loop statements (_for, while_ and _do-while_) and selection statements (_if-then-else_ and _switch-case_) have a operation models more complex to represent their behaviours as introduced in [16].  
As shown in Figure 4, operator models together compose a Consumption Function Model, which represents a function implemented in the application. The number of operator models used to describe a function model depends on the number of operators used in the function being modelled. For example, if a function has two operators (_Operator_A_ and _Operator_B_), its function model has two operator models connected by an arc (_Connector_). The function model starts and ends with places representing the beginning (_IN_) and the ending (_OUT_) of the function being modelled.  
After modelling all functions of the application, it is necessary to create a single model that orders their execution. This is the responsibility of the Consumption Scheduler Model (see Figure 5). This model has one transition for each Consumption Function Model created (_f1_, _f2_, _f3_, _..., _fn_); one transition called _Scheduler_ for managing the execution of the Function models; and three transitions to represent the states of the sensor node (_start_, _sleep_ and _end_).  
Figure 4: Generic Consumption Function Model.  
Figure 5: Consumption Scheduler Model.
# Intelligent Transmit Antenna Selection Schemes for High-Rate Fully Generalized Spatial Modulation
## 5 Discussion on the Simulation Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Selected Feature**} & **Length of the** & **Configuration 1** & **Configuration 2** \\  & **Feature Vector (\(N_{Q}\))** & **(\(N_{rx}\) = 2, \(N_{T}\) = 6)** & **(\(N_{rx}\) = 4, \(N_{T}\) = 10)** \\ \hline \(\left|h_{i,j}^{n}\right|\) & \(N_{rx}\times N_{T}\) & 12 & 40 \\ \(\measuredangle\left(h_{i,j}^{n}\right)\) & \(N_{rx}\times N_{T}\) & 12 & 40 \\ \(\left|h_{i,j}^{n}\right|^{2}\) & \(N_{rx}\times N_{T}\) & 12 & 40 \\ \(\left|h_{i,j}^{n}H_{i,j}^{n}\right|^{2}\) & \(N_{T}\times N_{T}\) & 36 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Complexity associated with different features based on the feature vector length. Figure 8 analyzes the ABERs of different TAS schemes for \(N_{T}\) = 5, \(N_{tx}\) = 4, \(N_{rx}\) = 4, and \(M\) = 4 configuration. It is found that SVM-based TAS outperforms FGSM-NTAS by \(\sim\)1.42 dB in terms of SNR gain. By increasing \(N_{T}\) to 6, the analysis is repeated in Figure 9. For traditional FD-TAS, the required SNR drops by \(\sim\)1.28 dB. For SVM, NB, KNN, and DT-based TAS systems, respectively, there is a reduction of \(\sim\)0.78 dB, \(\sim\)0.66 dB, \(\sim\)0.68 dB, and \(\sim\)0.68 dB. Figure 6: Comparison between ABER and SNR of FGSM for distinct TAS practices (\(N_{T}\) = 4, \(N_{tx}\) = 3, \(N_{rx}\) = 4, and \(M\) = 4). Figure 7: Comparison between ABER and SNR of FGSM for distinct TAS practices (\(N_{T}\) = 4, \(N_{tx}\) = 3, \(N_{rx}\) = 4, and \(M\) = 16). and \(\sim\)0.59 dB in the SNR requirements. In comparison to the FGSM-NTAS system, the SVM-based TAS technique gains \(\sim\)2.2 dB. Table 9 lists the SNR gain achieved by the proposed ML-based TAS practices over traditional FGSM-NTAS. Figure 8: Comparison between ABER and SNR of FGSM for distinct TAS practices (\(N_{T}\) = 5, \(N_{tx}\) = 4, \(N_{rx}\) = 4, and \(M\) = 4). Figure 9: Comparison between ABER and SNR of FGSM for distinct TAS practices (\(N_{T}\) = 6, \(N_{tx}\) = 4, \(N_{rx}\) = 4, and \(M\) = 4).
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 5 Algorithm Design
### Service Category Table Based Algorithm  
Algorithm 5 summarizes the strategies of the service category table based (SCTB) algorithm in this paper. If the user \(U_{i}\) wants to send a request to the LBSP. Firstly, he should aggregate other \(k-1\) users by using the request aggregation protocol. If there are some users who agree to form a clustering group together with the user \(U_{i}\), he will become the representative. Secondly, the representative uses variety judgment protocol to figure out the kinds of service category in the clustering group. Finally, from the comparison between (\(k\),\(l\)) and (\(th_{k}\),\(th_{l}\)), \(U_{i}\) chooses different strategies according to the following four kinds of situations:  
(i) \(k\geq th_{k}\) and \(l\geq th_{l}\), the clustering process is a success;  
(ii) \(k<th_{k}\) and \(l\geq th_{l}\), waiting for new users to join;  
(iii) \(k\geq th_{k}\) and \(l<th_{l}\); using filtering aggregation protocol for the subsequent processing;  
(iv) \(k<th_{k}\) and \(l<th_{l}\), using representative aggregation protocol for the subsequent processing.  
```
1:Broadcast the aggregating message;
2:Aggregate users' requests by using request aggregation protocol;
3:Figure out the current \(k\) and \(l\) by using variety judgment protocol;
4:if\(k\geq th_{k}\) and \(l\geq th_{l}\)then
5: The clustering process is a a success, the remaining work will be performed;
6:endif
7:while\(T>0\)do
8:if\(k<th_{k}\) and \(l\geq th_{l}\)then
9: Waiting for new users to join;
10:endif
11:if\(k\geq th_{k}\) and \(l<th_{l}\)then
12: Filtering aggregation protocol is adopted;
13:endif
14:if\(k<th_{k}\) and \(l<th_{l}\)then
15: Representative aggregation protocol is adopted;
16:endif
17:endwhile
```  
**Algorithm 5** Service Category Table Based (SCTB) algorithm
## 3 Stability Analysis for the Slipping Phase
### Transfer Function in the S-Plane  
The transfer function \(H(s)\) of the open loop, as shown in Figure 2, can be written as follows.  
\[H(s)=H_{p}(s)\cdot H_{c}(s)\cdot H_{z}(s)\cdot H_{d}(s) \tag{16}\]  
The transfer function \(H_{p}(s)\), whose input is the clutch transmitted torque \(\overline{T}_{c}(t)\) and whose output is the clutch slipping velocity \(\Delta\dot{\theta}_{c}(t)\) of the output shaft without including the zero-order holder and time delay, can be derived from (1) and (4), as follows.  
\[\begin{array}{l}H_{p}(s)=\frac{\Delta\dot{\theta}_{c}(s)}{T_{c}(s)}\\ =\frac{(I_{c}[s^{2}+c_{s}s+k_{c})}{I_{c}l_{c}s^{3}+(I_{c}c_{c}+I_{c}c_{c})s^{ 2}+(I_{c}[k_{c}+I_{c}k_{c})s}-\frac{(I_{s}s^{2}+c_{s}s+k_{c})}{I_{c}2I_{s}s^{3} +(I_{c}2c_{s}+I_{c}c_{s})s^{2}+(I_{c}2k_{c}+I_{c}k_{c})s}\end{array} \tag{17}\]  
The transfer function \(H_{c}(s)\) of the control algorithm, whose input is the slipping velocity error \(e(t)\) and whose output is the clutch transmitted torque \(\overline{T_{c}}(t)\), is derived by applying the Laplace transform to (13), as follows.  
\[H_{c}(s)=\frac{\overline{T_{c}}(s)}{e(s)}=\frac{k_{p}s+k_{i}}{s} \tag{18}\]  
The transfer function \(H_{z}(s)\) of the zero-order holder is derived by applying the Laplace transform to (15), as follows.  
\[H_{z}(s)=\frac{1-e^{-sT}}{s} \tag{19}\]  
The transfer function \(H_{d}(s)\) of the time-delay subsystem is written as follows.  
\[H_{d}(s)=e^{-\tau s} \tag{20}\]
## 4 Performance Analysis of Islanding Detection Methods
### Suitability for Multi-DGs System  
When the system contains multi DGs, the operation of DGs can be influenced due to the connection between different types of DGs. Based on that, the sensitivity of the detection method has to be assessed effectively. The selection of the islanding detection in the multi-DGs system can be affected by various factors, such as the generation type, reverse power flow presence, and the capacity generator ratio. For instance, if there is two DGs are connected to the same PCC but their capacities are different, then the DGs with the larger capacity can dominate the behavior when islanding happens. Moreover, if two DGs are connected in parallel with the same islanding detection method, unwanted tripping may occur due to the injections of disturbances. Therefore, the synchronization of disturbances is required to avoid such cases.
## 3 Proposed Model
### Feature Extraction Module  
The main goal of FEM, as shown in Figure 4, is to represent distinctive features of palmprint ROIs. Because the output of REM is a \(143\times 143\times 3\) palmprint ROI but the network input should be \(224\times 224\times 3\) pixel size, a pre-processing step is expected. To do this, the images are resized based on the network requirements.  
In the next step, a pre-trained network is used for the feature extraction task. As mentioned, before using transfer learning techniques in the feature extractor, the learning process starts based on the weights learned from the ImageNet database. We should fine-tune these pre-learned parameters on our pertinent database using our limited palmprint ROI images. In this way, we use a CNN architecture similar to Figure 1, remove the last layer (FC8), and then initialize it with the learned weights to implement parameters transfer. The advantage of this pre-trained architecture is that  
Figure 4: Feature extraction module.  
it has the superior capacity of the fully trained network. After that, we fine-tune this pre-trained architecture by passing palmprint ROIs through the pre-trained layers. It should be noted that the fine-tuning process is similar to the training process except that not all weight parameters should be fine-tuned because of the lack of training data. The effective way to fine-tune the pre-trained weights is to optimize only the last fully connected layer and keep the parameters of other layers frozen. To fine-tune these parameters, some superior optimizers are used to adjust the pre-trained weights automatically during training. The Adam optimizer [60] is a gradient-based first-order algorithm used to optimize stochastic objective functions. The optimizer updates exponential moving averages with the learning rate and the exponential decay rates \(\beta_{1}\) and \(\beta_{2}\).  
In order to evaluate this module and analysis fine-tuning process, the cost function of fine-tuning to minimize the difference between the target output and predicted output is calculated. To prevent overfitting, we use a logistic regression algorithm, L, using the function below:  
\[\sigma(z)=\frac{1}{1+e^{-z}} \tag{2}\]  
So, the cost function can be expressed as below:  
\[C(\Omega)=\frac{1}{m}\ \sum_{i=1}^{m}\left(\left|L(\Omega_{i}^{\prime},\Omega_{i} )\right|\right) \tag{3}\]  
where \(\Omega\) is the set of parameters that needs to be optimized during the fine-tuning process, \(\Omega^{\prime}\) is the set of predicted parameters, and \(m\) is the number of samples. It should be mentioned that the output is a fixed length 4096D vector for the different databases.
## 5 Hovering Attitude Control of the Unmanned Helicopter  
When the helicopter reaches the target point and hovers, the UAM enters the phase of hovering operation. Under the control of position, the helicopter's hovering position will be always constant. Since the UAM's overall center of gravity and moment of inertia will change with the running of the manipulator, the backstepping sliding mode controller for hovering attitude stability is proposed in this section. The expression converted by Equation (2) is  
\[I_{t}\dot{\omega}+\omega\times(I_{t}\omega)=\tau_{MR}+\tau_{TR} \tag{42}\]  
where \(I_{t}=diag\big{\{}I_{x},I_{y},I_{z}\big{\}}\) is the inertia tensor matrix of the entire UAM with the helicopter and the manipulator; \(\tau_{MR}\) denotes the torque generated by the main rotor; and \(\tau_{TR}\) represents the torque generated by the tail rotor. Then, Formula (42) can be specifically expressed as  
\[\dot{\omega}=I_{t}^{-1}[-\omega\times(I_{t}\omega)+CU_{\omega}+T] \tag{43}\]  
Note that \(U_{\omega}\in R^{3\times 1}\) is the control quantity, in addition, \(T\in R^{3\times 1}\) is a column vector and \(C\in R^{3\times 3}\) is the coefficient matrix on the change in the UAM's entire center of gravity.  
\[U_{\omega}=\begin{bmatrix}L_{mr}\\ M_{mr}\\ T_{tr}\end{bmatrix},C=\begin{bmatrix}1&0&H_{tr}\\ 0&1&0\\ 0&0&D_{tr}\end{bmatrix},T=\begin{bmatrix}0\\ 0\\ N_{mr}\end{bmatrix} \tag{44}\]  
where \(N_{mr}\) is the constant. When the manipulator is running, the change in each joint angle will account for the variation in the center of gravity (\(OM\)) and the inertia tensor matrix (\(I_{t}\)) of the entire system. Moreover, the variation in \(OM\) will further change the coefficient of \(D_{tr}\) and \(H_{tr}\) in matrix \(C\) as well as \(H_{mr}\) in Equation (13). Thus, the step of the analysis on how to find the relationship among \(I_{t},OM\) and the joint angles of the manipulator should be carried out first.
# Smart City Pilot Projects Using LoRa and IEEE802.15.4 Technologies
## 5 A Smart City Testbed: The Rigers Project
### Path-Loss Characterization via Experimentation
The above experiments have been replicated in the Navile district as follows. The gateway was located in the municipality building at 44 meters from the ground, while the LoRa device was located in different outdoor positions at 1 meter from the ground. Assuming the same one-slope path-loss model (1) adopted in the Saragozza district, the best-fit has been obtained with \(\alpha=3.84\). The experimental path-loss models derived above will be adopted in Section 5.2 to assess the success rate of point-to-multipoint LoRa transmissions in the two districts.