esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 3 Classification and Evolution of Interference Management Strategies in B5G Networks
### Classification and Analysis of Interference Management Strategies in B5G Networks
#### 3.2.3 Inter-Cell Interference
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Issue** & **Methodologies** & **Advantages** & **Limitations/Future Works** & **Ref.** \\ \hline LB with ICI. & CRE with ABS. & Reduced ICI and improved load distribution across cells. & Integration with AI-based scheduling in HetNets will be considered. & [128] \\ \hline Interference-robust RACH detection. & DL model for NPRACH in multi-cell UL. & Better access success under interference-heavy NB-IoT settings. & Needs validation for non-Gaussian noise and energy-constrained IoT deployments. & [129] \\ \hline To mitigate the ICI and improve the satisfaction of users. & Mean cell packet delay outage ratio-based-resource allocation scheme. & Enhancing the QoE of the proposed system. & \(\bullet\) The optimum value of minimum BW will be considered. & [130] \\  & & & \(\bullet\) The optimal tradeoff regarding the BW allocation to the donor cell will be considered for enhancing the beneficiary cell’s assistance without compromising the donor cell’s QoE. & [131] \\ \hline Mitigate the dominant inter-cell-interference. & Novel IM technique based on MRP. & Maximizing the ST. & The dynamic configuration techniques that maximize the rank used by each user in the network will be considered. & [132] \\ \hline To enhance the performance of the edge user in UL HetNets. & UL ICI elimination scheme based on SCMA. & Minimizing the BER of the proposed scenario. & The DOL transmission was not considered. & [132] \\ \hline To optimize the balance between enhancing spatial gain and augmenting interference robustness. & An inter-cell RAC technique. & Maximizing the ST by 65\% compared with conventional schemes. & The UL transmission & [133] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of previous studies of ICI in literature.
###### Abstract
The approach to robustness analysis of the DTPN models in this study is different from the existing reachability analysis methods that rely on abstraction of state space of timed Petri nets [9; 10]. Our approach is based on the concept of model transformation in Model Driven approach (MDA) [11], which transforms DTPNs into spatial-temporal networks (STNs) to enable the compact representation and efficient search for solutions based on mathematical programming/optimization tools. As this study focuses on assessing the impact of resource failures on temporary properties based on model transformation, this study is different from [12; 13], which focus on robust control and scheduling based on timed, extended reachability graphs. This paper is different from [8; 14] as it focuses on the robustness property of CPS. In addition, the problem studied in this paper considers order requirements with multiple types of product demands instead of a single type of product demands. This paper is different from [15] as it includes important properties, algorithms, rigorous proof of properties of CPS, complexity analysis and verification of complexity analysis results by experiments. In summary, the contributions of this paper include: (1) proposing a formal timed model to capture resource failures in CPS, (2) formulating a feasibility problem for nominal CPS and a robustness analysis problem to analyze the impact of resource failures on temporal properties, (3) developing algorithms for the problems based on model transformation and (4) analyzing complexity of the proposed algorithms and verifying the computational feasibility of the proposed approach. The structure of this paper is as follows. A literature review and the robustness analysis problems in CPS will be introduced in Sections 2 and 3, respectively. In Section 4, construction of STNs and analysis of the nominal CPS based on STNs will be presented. In Section 5, the impact of resource failures on CPS will be analyzed. In Section 6, we will illustrate the proposed method by examples and present the results. We will discuss the results in Section 7 and conclude this paper in Section 8.
## 1 Introduction
Since implementing a configurable backplane to larger platforms such as 3U or 6U CubeSats has not been achieved before, several challenges such as managing communication between the existing standard bus system and multiple mission payloads, as well as meeting the mission requirements of complex payloads, are extensively covered in this study. In addition, the contribution of this paper is that it presents a 3U configurable backplane designed to manage several missions on the limited number of available electrical interfaces of a standard bus. The design concept could benefit satellite developers who provide hosted payload services where bus resources are maximized to accommodate as many payloads as possible. This paper also demonstrates the modifications in a bus system necessary to scale up and handle complex missions in a W6U CubeSat. The CubeSat was deployed from the ISS in March 2022, and it has been successfully supporting the execution of the missions. This paper is composed of five sections. Section 2 discusses the standard 1U bus system. It also discusses two backplane designs in different CubeSat platforms--a 3U backplane prototype for multiple payloads and a backplane for a W6U CubeSat with complex mission requirements. Section 3 discusses the tests conducted and the results of the two backplane designs. Lastly, Sections 4 and 5 present the Discussion and Conclusion.
# Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control +
## 4 Stability Analysis
### Force Control Stability
#### 4.2.1 Force Along \(n\)-Axis  
The forces along \(n\)-axis is controlled by translational pose command along \(\mathbf{n}\), from (13) we have  
\[f_{n}=k_{tn}n+k_{j}\eta_{t}\, \tag{30}\]  
where \(k_{j}=\frac{d_{x}k_{H}}{\|d\|^{2}}\). If we substitute the pose controller in (30), assuming the gravity term compensated by feedback linearizing term, the dynamics will be:  
\[m_{n}\dot{n}+k_{tn}n=-k_{p}\dot{n}-k_{d}\dot{n}\, \tag{31}\]  
where \(m_{n}=m\) is derived from (1). Substituting \(n\) and its derivatives, in the left hand side of (31), with \(f_{n}\) from (30), and control terms, in the right hand side, with (12) we obtain:  
\[m_{f}\ddot{f}_{n}+b_{f}\dot{f}_{n}+k_{f}f_{n}=-k_{1}\int_{0}^{t}\dot{f}_{n}-k_ {2}\ddot{f}_{n}+h_{n}(\ddot{\eta}_{t},\dot{\eta}_{t},\eta_{t})\, \tag{32}\]  
where \(m_{f}=m_{n}k_{tn}^{-1}\), \(b_{f}=k_{d}k_{tn}^{-1}\), \(k_{f}=(1+k_{p}k_{tn}^{-1})\), \(h_{n}=(m_{n}k_{tn}^{-1}k_{ijn}\ddot{\eta}_{t}+k_{d}k_{tn}^{-1}k_{ijn}\dot{\eta}_ {t}+(1+k_{p})k_{tn}^{-1}k_{ijn}\dot{\eta}_{j})\), and \(k_{1}=k_{p}k_{lf}\), \(k_{2}=k_{p}k_{pf}\).  
One can express (32) in the frequency domain by introducing the controller transfer function \(C(s)=k_{2}+\frac{k_{1}}{s}\) and plant transfer function \(G(s)=1/(m_{f}s^{2}+b_{f}s+k_{f})\), where (\(s=\sigma+j\omega\)). The system output \(f(s)\) is then obtained as:  
\[f(s)=\frac{G(s)C(s)}{1+G(s)C(s)}f_{d}(s)+\frac{1}{1+G(s)C(s)}h(s). \tag{33}\]  
For the stability of the system, the characteristic polynomial of the system, that is, \(s(m_{f}s^{2}+b_{f}s+k_{f})+(k_{2}s+k_{1})\), must have all roots with a real negative part and, to achieve this, according to the Routh-Hurwitz stability criterion it is required that:  
\[k_{pf}+\frac{1}{k_{p}}+\frac{1}{k_{tn}}>\frac{m}{k_{D}}k_{lf}. \tag{34}\]  
Choosing sufficiently high PD gains and an appropriately low I-gain for slowly-varying force commands, that is, \(s\to 0\), we obtain \(\frac{G(s)C(s)}{1+G(s)C(s)}\to 1\), \(\frac{1}{1+G(s)C(s)}\to 0\), thus \(f\to f_{d}\).
# A Review of Recent Advancements in Knock Detection in Spark Ignition Engines
## 3 Advancements in Knock Detection  
Despite knock being fairly established and understood, it is still a critical factor that must be accounted for in SI engine design. As such, there have been continual advances in the measuring and assessment of engine knock. Knock research has progressed significantly since [PERSON]'s first measurement of knocking pressure signals, with better sensors and analysis algorithms. This section will detail the most recent advances.  
Generally, these advances can be categorized into two primary categories, advances in sensors and advances in processing. While some of the advancements discussed in these sections are related to new sensors, the bulk of the advancements relate to advances in processing techniques. In particular, many studies are focused on the use of machine learning algorithms to more accurately process the different signals associated with knock. Machine learning involves the use of algorithms and statistical models to analyze and extract meaningful patterns from each signal. This approach enables the development of systems that can automatically learn and adapt to complex signal characteristics, including those with high levels of noise, such as those that are used for detecting knock.
## 4 Operation of the System Implemented
### Node Identification  
In a WSN likely to include many _Smart Modules_, synchronization is a key issue. Appropriate identification of each of the nodes must be carried out so that problems do not arise later during steady-state operation. Thus, a protocol is defined that detects all the nodes in the system, provides them with a unique name and clearly identifies their location in the PV plant.  
All the communication modules have predetermined default values when they are first powered up. This makes it possible to establish the initial communication between all of them. An example of a possible identification of two Central Nodes by the _Monitoring Center_ is sketched in Table 1, which shows how two Central Nodes are detected on the default network (PAN = 1,000/CH = 10) and provided with new parameters to operate on the network to be used (PAN = 335/CH = 12).  
\begin{table}
\begin{tabular}{c|c|c} \hline
**Monitoring Center** & **Central Node 1** & **Central Node 2** \\ \hline Turn-on, Addr = 0, & & \\ Pan = 1,000/Ch = 10 & & \\ \hline Gets data from PC, num towers\(\leftarrow\)2 & & \\ \hline  & Turn-on, Addr = 1, & \\  & Pan = 1,000/Ch = 10 & \\ \hline Detects new module, found\(\leftarrow\)1 & & \\ \hline Starts communication on default PAN, & & \\ Addr0\(\rightarrow\)Addr1 & & \\ \hline Sends new parameters & Gets new parameters, & \\  & Addr\(\leftarrow\)–001, Pan\(\leftarrow\)–335/Ch\(\leftarrow\)–12 & \\ \hline Waits for new modules, & & \\ found \(<\) num towers & & \\ \hline  & Turn-on, Addr = 1, & \\  & Pan = 1,000/Ch = 10 & \\ \hline Detects new module, found\(\leftarrow\)2 & & \\ \hline Starts communication on default PAN, & & \\ Addr0\(\rightarrow\)Addr1 & & \\ \hline  & & \\ \hline Sends new parameters & & \\  & & Addr\(\leftarrow\)–002, \\  & Pan\(\leftarrow\)–335/Ch\(\leftarrow\)–12 & \\ \hline Identification complete, & & \\ Found = num towers & & \\ \hline Moves to active PAN, Addr\(\leftarrow\)–000, & & \\ Pan\(\leftarrow\)–335/Ch\(\leftarrow\)–12 & & \\ \hline \end{tabular}
\end{table}
Table 1: Node identification at start-up.  
A similar procedure will be followed to have each of the Central Nodes identify all the _Smart Modules_ in their tower. From this point on, polling will be implemented so that the information captured at the _Smart Modules_ in the PV panels can be sent over to the main computer in the solar farm.
## 5 Dynamic Analysis
### Mechanical Stop Model  
To avoid the singular configuration at the TDC and changes in the mechanism configuration, a mechanical stop has been introduced. The mechanical stop should be made of soft material to reduce unnecessary shocks to the mechanism structure. The mechanical stop is modeled as a one degree of freedom system that is activated as soon as \(x_{sup}<x_{stop}\), in which \(x_{sup}\) is the coordinate of the surface of the slider and \(x_{stop}\) is the coordinate of the mechanical stop when the slider is not in contact with it (see Figure 8).  
The force transferred by the mechanical stop to the mechanism is:  
\[F_{stop}=k_{stop}\big{(}x_{sup}-x_{0}\big{)}^{\epsilon}+f\big{(}x_{sup},x_{0}, h_{0},x_{1},h_{1}\big{)}\dot{x} \tag{28}\]  
where:  
* \(k_{stop}\) is the stiffness of the mechanical stop;
* \(x_{sup}\) is the coordinate of the surface of the slider that impacts the mechanical stop;
* \(x_{0}\) (=\(x_{stop}\)) and \(x_{1}\) are the coordinates of the limits of the mechanical stop;
* \(e\) is the exponent in (28), which is null in the case of the Kelvin-Voigt model;
* \(h_{0}\) and \(h_{1}\) are the limits of the function \(f\).  
Function \(f\) is described by a polynomial function, whose normalized shape is shown in Figure 9:  
\[f\big{(}x_{sup},x_{0},h_{0},x_{1},h_{1}\big{)}=\left\{\begin{array}{ll}h_{0} &x_{sup}\geq x_{0}\\ h_{0}+a\Delta^{2}(3-2\Delta)&x_{1}\leq x_{sup}\leq x_{0}\\ h_{1}&x_{sup}\leq x_{1}\end{array}\right. \tag{29}\]  
where:  
\[a=h_{1}-h_{0}\ \ \,\ \ \ \Delta=\frac{x_{sup}-x_{0}}{x_{1}-x_{0}} \tag{30}\]  
Figure 8: Model of the mechanical stop of the mechanism.
## 4 Discussion  
This study proposed a Velostat-film-based pressure sensor system to solve the problems of the non-detection of pressure changes and saturation of measured values in the existing pressure change measurement system to determine the locomotion movement intentions of transfemoral prosthetic users. The intention detection system based on the measurement of internal pressure changes in the socket, which was previously studied to detect the intentions of locomotion movements of prosthetic users, presents a problem in terms of not detecting pressure changes in an amputated section with low muscle mass. In addition, there is a problem in which the linear sensing pressure range of the sensor is lower than the internal pressure range of the socket, resulting in the early saturation of the pressure measurement. In this study, it was possible to fabricate a wide type of sensor. A pressure sensor system designed to eliminate the problems of not detecting pressure changes and the saturation of measured values is proposed, and its fabrication guidelines are presented. The system proposed in this study has a different pressure measurement range depending on the size; it is based on the Velostat film, which can be fabricated in various sizes and shapes and is fabricated in consideration of individual pressure ranges depending on the user. Through this study, it was confirmed that a sensible pressure change of 1 V or more occurred due to the elimination of the problem whereby the pressure change cannot be measured in an area with low muscle mass. In addition, it was verified that a linear output was generated within the required measurement range by addressing the problem of the early saturation of pressure measurements due to the low linear sensing pressure range of the sensor. A pressure sensor with the improved detection of pressure changes and the reduced early saturation of measured values was applied to four positions inside the socket to confirm the possibility of detecting six movements: standing up, locomotion, stair climbing, stair descending, sitting, and standing. However, since its durability has not been verified, it is necessary to confirm its long-term availability in the future. In addition, the threshold value of the operating signal defined in this study will need to be converted into a standardized equation to increase the classification performance.
# Enhancing Indoor Localization with Room-to-Room Transition Time: A Multi-Dataset Study
## 3 Material and Method
### Online (Learning-and-Test) Phase
#### 3.2.7 Linear SVM  
Linear SVM (Linear Support Vector Machine) [54] is an ML model for classification on a linearly separable dataset. Its main purpose is to classify data points into two classes by creating a decision boundary between two different classes. Linear SVM aims to achieve the maximum margin (distance) when building the delimiter. This means finding the widest gap between the two classes. The marginal classification objective increases the ability to accurately classify new observations and generally improves the generalization ability of the model.
# Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots
## 2 The Probabilistic BR Loss Model
### CBP Determination of New and Handover Calls  
The CBP of new calls in the stop phase, \(B_{s,new}\), can be determined via:  
\[B_{s,new}=(1-p_{s,new})\sum_{n=C-t_{new}}^{C-1}P(0,n)+P(0,C) \tag{12}\]  
In the case of the \"strict\" BR policy, the same CBP formula can be adopted, assuming that \(p_{s,new}=0\) for \(j=C-t_{new}\),..., \(C-1\).  
In the moving phase, call blocking of new calls occurs only when there are no available b.u. Consequently, the CBP determination of new calls in the moving phase, \(B_{m,new}\), can be based on the following formula:  
\[B_{m,new}=P(1,C) \tag{13}\]  
Based on (12) and (13), the total CBP of new calls, \(B_{new}\), can be determined via:  
\[B_{new}=B_{s,new}+B_{m,new} \tag{14}\]  
The CBP of handover calls in the stop phase, \(B_{s,h}\), can be determined via:  
\[B_{s,h}=P(0,C) \tag{15}\]The CBP of handover calls given stop phase, \(B_{s,h}^{*}\), can be determined via:  
\[B_{s,h}^{*}=\frac{B_{s,h}}{\sum\limits_{n=0}^{C}P(0,n)} \tag{16}\]  
Finally, the link utilization, \(U\), can be computed via the following formula:  
\[U=\sum\limits_{i=0}^{1}\sum\limits_{n=0}^{C}nP(i,n) \tag{17}\]  
A tutorial example at the end of this paper (see Appendix A) presents in detail all necessary calculations for the determination of \(B_{s,new}\),\(B_{m,new}\),\(B_{s,h}\) and \(U\).
# A Crowd Cooperative Spectrum Sensing Algorithm Using a Non-Ideal Channel
## Appendix A  
Proof for Proposition 1: It is obvious that the feasible domain of the optimization problem \(P1\) is a convex set, and then we prove that the objective function is a convex function.  
The necessary and sufficient condition for a function to be a convex function is that the Laplasse operator is not less than 0 [23], i.e.,  
\[\Delta(-utility)\geq 0 \tag{10}\]  
For the sake of simplicity, without losing the generality, we set \(p(u_{1})=p(u_{0})=0.5\), i.e., the probability of the existence of the main user is 0.5. (10) can be simplified as follows:  
\[utility_{i}=0.5\cdot[(k-k^{\prime})(p_{i}-2p_{i}p_{e}+2p_{f}p_{e}+1-p_{f})]-c \cdot t_{i}-c_{s}\cdot s_{i}. \tag{11}\]  
So the Laplasse operator of the objective function can be expressed as follows:  
\[\Delta(-utility_{i})=\frac{\partial^{2}(-utility_{i})}{\partial t_{i}^{2}}+ \frac{\partial^{2}(-utility_{i})}{\partial s_{i}^{2}}. \tag{12}\]  
First calculate \(\frac{\partial^{2}(-utility_{i})}{\partial t_{i}^{2}}\), and we can get:  
\[\frac{\frac{\partial^{2}(-utility_{i})}{\partial t_{i}^{2}}}{\frac{\partial t _{i}^{2}}{\partial t_{i}^{2}}}=\frac{1}{2}(k-k^{\prime})(1-2p_{e})(\frac{1}{ 2}\cdot s\cdot(t_{i}f_{s})^{-3/2}e^{-m^{2}/2}+m\cdot s\cdot(t_{i}f_{s})^{-1/2} e^{-m^{2}/2}\frac{-\frac{1}{2}(t_{i}f_{s})^{-1/2}\cdot\gamma_{i}}{\sqrt{2 \gamma_{i}+1}}), \tag{13}\]  
where  
\[m=\frac{Q^{-1}(p_{f})-\sqrt{t_{i}f_{s}}\gamma_{i}}{\sqrt{2\gamma_{i}+1}},\;s =\frac{1}{2}\cdot\frac{\gamma_{i}}{\sqrt{4\pi\gamma_{i}+2\pi}}.\]  
Because the signal-to-noise ratio \(\gamma_{i}\) is positive and therefore \(s>0\). Because \((t_{i}f_{s})^{-3/2}\cdot e^{-m^{2}/2}>0\), so \(-\frac{1}{2}\cdot s\cdot(t_{i}f_{s})^{-3/2}\cdot e^{-m^{2}/2}<0\), i.e., \(m\leq 0\). So  
\[m\cdot s\cdot(t_{i}f_{s})^{-1/2}\cdot e^{-m^{2}/2}\cdot\frac{-\frac{1}{2}(t_ {i}f_{s})^{-1/2}\cdot\gamma_{ji}}{\sqrt{2\gamma_{ji}+1}}>0. \tag{14}\]  
According to the definition and (5), we can get \(k-k^{\prime}>0\) and \(p_{e}\leq 0.5\). So  
\[\frac{\partial^{2}(-utility_{i})}{\partial n_{ji}^{2}}\geq 0. \tag{15}\]  
Then calculate \(\frac{\partial^{2}(-utility_{i})}{\partial s_{i}^{2}}\), and we can get:  
\[\frac{\partial^{2}(-utility_{i})}{\partial s_{i}^{2}}=(\frac{pl}{n})^{2}(k-k^{ \prime})(p_{i}-p_{f})e^{-s_{i}\cdot pl/n}, \tag{16}\]  
where \(n\) indicates noise power, and \(pl\) indicates path loss. Set target false alarm probability \(p_{f}\) less than 0.5. Because \(\frac{1}{n^{2}}\cdot e^{-s_{i}/n}>0\), we can get:  
\[\frac{\partial^{2}(-utility_{i})}{\partial s_{i}^{2}}\geq 0. \tag{17}\]Thus, we can get:  
\[\Delta(-utility_{i})=\frac{\partial^{2}(-utility_{i})}{\partial t_{i}^{2}}+\frac{ \partial^{2}(-utility_{i})}{\partial s_{i}^{2}}\geq 0. \tag{20}\]  
Hence objective function is a convex function. Optimization problem \(P1\) is a convex optimization problem. Proof complete.
## 3 Experiments Evaluation
### Algorithm Behavior on Simulated Data
#### 3.1.1 Recovery on Signal Sparsity  
In the first experiment, we investigate how signal sparsity affects the success of recovery. Here we fix the CS measurement ratio \(\rho_{cs}\) as 15%, 30%, and 50%, respectively, and manually tune the signal sparsity from 0% to 30%. Figure 1 shows the fact that for truly sparse signal \(s\), even a very small ratio of CS measurements could recover \(s\) from the low-rank background signal. For example, a _25-sparse_ signal can be successfully recovered from only 150 CS measurements.
## 4 Experiments and Results  
### Dataset  
The NEU [45] surface defect dataset contains 1800 grayscale images of hot-rolled steel strips, each with a resolution of 200 \(\times\) 200 pixels. Six common steel surface defects are presented in the dataset, namely rolled-in scale, plaque, crack, pitting surface, inclusions, and scratches. Unfortunately, the bounding box annotations provided in the original dataset are not applicable for the task of semantic segmentation. To address this issue, the NEU-seg dataset was created by [45], which provides pixel-level annotations for three typical defects (inclusions, patches, and scratches) using the labeling tool LabelMe. The NEU-seg dataset consists of 3630 training images and 840 testing images.  
In a real-world production environment, it is often challenging to acquire plenty of diverse defect images and their corresponding labels due to operational and equipment  
Figure 5: Details of the two attention modules: (**a**) the channel attention module; and (**b**) the spatial attention module.  
constraints. To address the over-fitting issue caused by limited training data, we employ a data augmentation operator during the training process. To align with the network's training requirements, we transform each original image to 256 \(\times\) 256 pixels, and then augment the processed image by rotating it by 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\). At the same time, we use the same augmentation to process the ground truth.