esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Ensemble Learning Models
#### 2.4.2 Multi-Grained Cascade Forest  
Inspired by deep network learning, the Multi-Grained Cascade Forest (gcForest) algorithm is proposed. The gcForest generates a deep forest with three characteristics: layer-by-layer processing, intramodel feature transformation, and sufficient model complexity. The gcForest model divides training into two stages, Multi-Grained Scanning and Cascade Forest. Multi-Grained Scanning generates features, and Cascade Forest generates prediction results through multiple cascades of forests [24]. The gcForest has fewer parameters than deep neural networks and has considerable robustness to hyperparameter settings. In order to better adapt to the low-dimensional characteristics of credit loan data, we abandoned Multi-Grained Scanning. The structure of the gcForest model used in this experiment is shown in Figure 2. The base learners used by the model are the XGB Classifier, Random Forest Classifier, Extra-Tree Classifier, and logistic regression. Early_stopping_rounds is set to 3. The maximum number of layers is set to 100.
## 7 Recently Proposed Intelligent Techniques for Power Systems
### Wavelets with Fuzzy Logic  
A novel method for locating and categorizing faults in transmission lines has been presented by [147] and is based on a mix of wavelet and fuzzy approaches. Fuzzy logic was used to extract significant features from wavelet MRA coefficients to determine the fault's precise position in the lines. Dynamic properties of fault signals (line current) are retrieved using wavelet MRA coefficients. Wavelet and neuro-fuzzy-based fault-finding techniques have been addressed by [148] for an integrated transmission system. The neuro-fuzzy system for fault localization was divided into two sections: one to determine the location of faults in overhead power lines and the other for subterranean cables. Wavelet transform approximates the coefficients of current and voltage signals, and two FIR filters remove the DC offset components of current signals. The fault was located by comparing a predetermined value with the summation of the D1 coefficient of current over half a cycle. A novel method for classifying problems in power transmission systems utilizing a mixed fuzzy logic and wavelet approach has been developed [149]. The approach's inherent benefit was the capacity to deal with uncertainty caused by continually changing power system factors.
###### Abstract  
Few-shot object detection (FSOD) based on fine-tuning is essential for analyzing optical remote sensing images. However, existing methods mainly focus on natural images and overlook the scale variations in remote sensing images, leading to feature confusion among foreground instances of different classes. Additionally, since only a subset of instances are labeled in FSOD training data, the model might mistakenly treat unlabeled instances as background, leading to confusion between foreground features and background features, particularly those of novel classes. The preceding phenomenon indicates that severe feature confusion in remote sensing FSOD hampers the ability of the model to accurately classify and localize instances. To address these issues, this paper proposes a two-stage FSOD framework based on transfer learning via pseudo-sample generation and feature enhancement (PSGFE), including pseudo-sample generation module (PSGM) and feature enhancement module (FEM). The former reduces the feature confusion between foreground and background by generating pseudo-samples for unannotated background areas. The latter dynamically captures and enhances multi-scale features on the region of interest (ROI), and extracts unique core information for each class to eliminate the feature confusion among foreground instances of different classes. Our method has been validated on the optical remote sensing datasets DIOR and RSOD. It demonstrates superior performance compared to existing methods.  
few-shot object detection; remote sensing images; pseudo-sample generation; feature enhancement +
Footnote †: journal: Applied sciences  
Article
## 3 Adaptive Control Method
### Adaptive Control Termination with the LMS Algorithm  
To express the working process of the adaptive algorithm for cancellation of the transmission-reflection wave, the signal-handling process is expressed in the frequency domain, as shown in Figure 4.  
The adaptive algorithm principle is driving the signal in the next time in accordance with the signal in the last moment. Hence, the subscript in bracket is the time mark. The output of termination at frequency downstream from Equation (17) can be shown as:  
\[Y_{(n)}(w)=X_{3(n)}(w)W_{(n)}(w). \tag{22}\]  
Figure 4: Signal-handling process of adaptive control system.  
The signal at hydrophones 3 and 4 can be expressed as:  
\[X_{3(n)}(w) =X_{(n)}(w)H_{13}(w)+Y_{(n)}(w)H_{23}(w), \tag{23}\] \[X_{4(n)}(w) =X_{(n)}(w)H_{14}(w)+Y_{(n)}(w)H_{24}(w), \tag{24}\]  
where \(X(w)\) is the primary source signal, \(H_{13}\) is the actual transfer function between the primary source and hydrophone 3, and \(H_{23}\) is the actual transfer function between adaptive termination and hydrophone 3. \(H_{14}\) is the transfer function between the primary source and hydrophone 4, and \(H_{24}\) is the transfer function between adaptive termination and hydrophone 4. Then, the error signal is given as:  
\[E_{(n)}(w)=X_{4(n)}(w)-X_{3(n)}(w)e^{-jk(x_{4}-x_{3})}, \tag{25}\]  
where \(k\) is the complex wave-number, which can be obtained by \(k=w/c\), where is the sound frequency and \(c\) is the sound velocity. MSE can be given as:  
\[\varepsilon=\Big{|}E_{(n)}(w)\Big{|}^{2}=\Big{[}X_{4(n)}(w)-X_{3(n)}(w)e^{-jk( x_{4}-x_{3})}\Big{]}^{2}. \tag{26}\]  
The refreshment method of the weighting vector is the steepest descent method, and the iterative equation of the weight coefficient vector in this algorithm can be described as:  
\[W_{(n+1)}(w)=W_{(n)}(W)-\
abla_{(n)}(w), \tag{27}\]  
and the algorithm gradient can be obtained from  
\[\
abla_{(n)}(w) =\frac{\partial E_{(n)}(\varepsilon)}{\partial W_{(n)}} \tag{28}\] \[=-2E[E_{(n)}(w)X_{3(n)}(w)]\]  
Although the above equations for deriving the correct weight vector utilize transfer functions, such as \(H_{13},H_{14},H_{23},\) and \(H_{24}\), we do not need to measure these transfer functions in actual application. We can obtain the error signal \(E_{(n)}\) and input of the LMS algorithm \(X_{3(n)}\) through hydrophones 3 and 4 without measuring the transfer functions, which prevents the error caused by inaccurate transfer functions.
## 4 Analysis of Test Results for Diversion Function Pipe Segment Models
### Analysis of Total Stresses on Both Sides of the Pipe Segment under Different Differential Pressures
#### 4.4.2 Variation in Total Stress on Both Sides of the Pipe Segment at Different Depths  
Figure 20a-f illustrates that the total stress on the left and right sides of the pipe segment is proportional to the depth. As the depth increases, the total stress around the pipe segment also increases in a linear manner. The comparison of the two curves reveals that the total stress difference between the left and right sides of the pipe segment at different depths is also different. Furthermore, the total stress difference between the left and right sides of the pipe segment at the deepest depth is also larger. The unsymmetrical pressure effect of the pipe segment is also more pronounced, with a corresponding increase in deformation. The total stress on the left side of the pipe segment is larger than that on the right side. An increase in differential pressure results in a corresponding increase in the total stress difference between the two sides of the pipe segment. The lower part of the pipe segment is particularly susceptible to the bias effect, which can lead to a greater degree of deformation. Consequently, by reducing the total stress difference between the two sides of the pipe segment through the utilization of the diversion function, the issue of unsymmetrical pressure on both the sides of the pipe segment can be alleviated.  
Figure 19: Variation in total stress at each measurement point.
## 5 Results and Discussion
Testers point out that the proposed methodology increases the efficiency as the same tool includes all the necessary steps for the real commissioning of the cell, integrating all types of robots and collaborative applications. The intermediate virtual commissioning includes all the minimum details, and the immersive VR visualization gives a sense of total realism (sense of presence). Moreover, this solution is safe, dynamic, and cost-effective. Potential applications can be found in different industries. Thus, a very likely outcome is extending these results to introduce robots in the manufacturing processes of multiple industries and to increase their efficiency. In this sense, the next steps of this work will focus on more complex manufacturing processes, and extend the capabilities to conduct data analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Robot Manufacters** & **Commercial Sim. Tools with VR** & **DT Based on VR** \\ \hline Low investment & 2 & 1 & 3 \\ Multi-robot & 1 & 3 & 3 \\ Human-robot collab. & 1 & 1 & 3 \\ Immersive & 1 & 3 & 3 \\ Customization & 1 & 2 & 3 \\ Training & 1 & 2 & 3 \\ Versatility & 1 & 2 & 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative between simulation tools and the proposed approach. In the current Industry 4.0 revolution, where manufacturing technologies are continuously changing in order to achieve personalized products, in contrast with the traditional serial production, intelligent automation is a core element to increase the productivity and to improve the competitiveness of the industry. Robots are the future of the industry, and thus the design, the commissioning, and the operation of the robotized cells are critical to achieving success. The proposed approach demonstrates that the synergies between Industry 4.0 technologies, such as digital twins, virtual reality, and collaborative robotics, enable working at new levels and parallel environments which have not been accomplished yet. The future of manufacturing requires the interaction between humans and multiple types of robots, and between physical and virtual scenarios. Each manufacturing process will have its digital twin not only for visualizing or controlling, but also for continuous improving. Conceptualization, L.P.; methodology, L.P.; validation, L.P., S.R.-J., and N.R. ; writing--original draft, L.P.; writing--review and editing, L.P., R.U., and D.F.G. All authors have read and agreed to the published version of the manuscript. This research was funded by Gobierno del Principado de Asturias Programa Asturias grant number IDI/2018/00063, Robots 4.0. The authors would like to thank [PERSON] and [PERSON] for their programming support. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l}
3D & Three-Dimensional \\ AGV & Automatic Guided Vehicle \\ CPPS & Cyber-Physical Production Systems \\ DT & Digital Twin \\ HMI & Human-Machine Interface \\ PLC & Programmable Logic Controller \\ QR & Quick Response \\ ROS & Robot Operating System \\ VR & Virtual Reality \\ \end{tabular}
## 2 System Model and Bayesian Filter
### Bayesian Gaussian Recursive Filter  
The Bayesian recursive solution to the problem above can be divided into a prediction step and a measurement update step. At the prediction step, the mean and associated covariance of the Gaussian predictive density can be obtained as follows:  
\[\hat{\mathbf{x}}_{k|k-1}=\int_{\mathbb{R}^{n_{x}}}f(\mathbf{x}_{k-1})N\Big{(}\mathbf{x}_{k-1 };\hat{\mathbf{x}}_{k-1|k-1},\mathbf{P}_{k-1|k-1}\Big{)}d\mathbf{x}_{k-1}, \tag{2}\]  
\[\begin{array}{ll}\mathbf{P}_{k|k-1}&=\int_{\mathbb{R}^{n_{x}}}f(\mathbf{x}_{k-1})f^{ T}(\mathbf{x}_{k-1})N\Big{(}\mathbf{x}_{k-1};\hat{\mathbf{x}}_{k-1|k-1},\mathbf{P}_{k-1|k-1} \Big{)}d\mathbf{x}_{k-1}\\ &-\hat{\mathbf{x}}_{k|k-1}\hat{\mathbf{x}}_{k|k-1}^{T}+\mathbf{Q}_{k-1}\end{array}, \tag{3}\]  
At the measurement update step, \(D_{k-1}\) is denoted as the history of input measurement. Then, the predicted measurement density can be represented as follows:  
\[p(\mathbf{z}_{k}|D_{k-1})=N\Big{(}\mathbf{z}_{k};\hat{\mathbf{z}}_{k|k-1},\mathbf{P}_{zz,k|k-1 }\Big{)}, \tag{4}\]  
\(\hat{\mathbf{z}}_{k|k-1}\) is the predicted measurement, which is predicted based on time index \(k-1\) and the measurement model. \(\hat{\mathbf{z}}_{k|k-1}\) and its associated innovation covariance \(\mathbf{P}_{zz,k|k-1}\) are given by the following:  
\[\hat{\mathbf{z}}_{k|k-1}=\int_{\mathbb{R}^{n_{x}}}h(\mathbf{x}_{k})N\Big{(}\mathbf{x}_{k}; \hat{\mathbf{x}}_{k|k-1},\mathbf{P}_{k|k-1}\Big{)}d\mathbf{x}_{k}, \tag{5}\]  
\[\mathbf{P}_{zz,k|k-1}=\int_{\mathbb{R}^{n_{x}}}h(\mathbf{x}_{k})h^{T}(\mathbf{x}_{k})N \Big{(}\mathbf{x}_{k};\hat{\mathbf{x}}_{k|k-1},\mathbf{P}_{k|k-1}\Big{)}d\mathbf{x}_{k}-\hat{ \mathbf{z}}_{k|k-1}\hat{\mathbf{z}}_{k|k-1}^{T}+\mathbf{R}_{k}, \tag{6}\]  
and the cross-covariance is as follows:  
\[\mathbf{P}_{zz,k|k-1}=\int_{\mathbb{R}^{n_{x}}}\mathbf{x}_{k}h^{T}(\mathbf{x}_{k})N\Big{(} \mathbf{x}_{k};\hat{\mathbf{x}}_{k|k-1},\mathbf{P}_{k|k-1}\Big{)}d\mathbf{x}_{k}-\hat{\mathbf{x}}_ {k|k-1}\hat{\mathbf{z}}_{k|k-1}^{T}. \tag{7}\]  
Once the new measurement is received, the filter computes the posterior density, yielding the following:  
\[p(\mathbf{x}_{k}|D_{k})=N\Big{(}\mathbf{x}_{k};\hat{\mathbf{x}}_{k|k},\mathbf{P}_{k|k}\Big{)}, \tag{8}\]  
where  
\[\hat{\mathbf{x}}_{k|k}=\hat{\mathbf{x}}_{k|k-1}+\mathbf{K}_{k}(\mathbf{z}_{k}-\hat{\mathbf{z}}_{k |k-1}), \tag{9}\]  
\[\mathbf{P}_{k|k}=\mathbf{P}_{k|k-1}-\mathbf{K}_{\mathbf{P}zz,k|k-1}\mathbf{K}_{k}^{T}, \tag{10}\]  
\[\mathbf{K}_{k}=\mathbf{P}_{xz,k|k-1}\mathbf{P}_{zz,k|k-1}^{-1} \tag{11}\]
## 2 Related Works
Data from all these sensors were fused using a sigma-point Kalman smoother to determine the human user's indoor position. Their study presents superior results compared to using the extended Kalman filter and a commercially available positioning tool. [PERSON] et al. [25] proposed the use of a support vector machine (SVM) and an improved Kalman filter (KF) to estimate a target position based on RSSI. They applied the SVM in order to produce the estimated positions which are enhanced using the Kalman filter. However, [PERSON] et al. report that the combination of SVM and the KF introduces a higher run-time. The proposed approach in our study only uses only a deep learning-trained model and does not exhibit real-time positioning delays except during the training of the deep learning model. [PERSON] et al. [26] proposed a robust extended Kalman filter (REKF) algorithm which estimates the positions of sensors which are attached to mobile robots in an indoor environment. This applications for which the approach was proposed are delay tolerant networks (DTNs). The approach of enabling sensor node mobility was done to eliminate the processing constraints of static sensor nodes and the need for static reference beacons. The positioning results show that the proposed REKF achieved localization accuracy within 1 m in the indoor environment. [PERSON] et al. [13] used a CWT to extract time-frequency features from the transformed image and used a CNN to train and learn the patterns. A K-nearest neighbors (KNN) model was then applied to define the target location. The method achieved good performance compared to some existing models. The work in [13] is the closest match to an existing feature extraction approach using the CWT for indoor localization compared to the idea in this paper. The difference is that this paper introduces the concept of data augmentation in order to further improve on the localization performance exhibited by the CWT feature set without data augmentation. Additionally, extensive experimentation was done to evaluate the performance of the augmented CWT features by applying Morlet and Morse. Various data augmentation approaches have been developed to improve on the performance of deep learning models. Some of these include local averaging [14] and jittering [27] for time series data. In this paper, jitter-caused additive Gaussian noise is used as a tool for augmenting the RSSI signal. Table 1 shows a summary of existing feature set formulations used in indoor localization studies. Each of the feature set formulations is characterized in terms of the technical dataset composition and whether its augmented or not.
# Health IoT Threats: Survey of Risks and Vulnerabilities
## 4 Results
### Overview of IoT Security and Privacy in Healthcare
#### 4.1.2 The Network Layer
Encryption provides an additional layer of security that makes it more difficult for attackers to access and read the transmitted data. Healthcare IoT devices are also susceptible to distributed denial of service (DDoS) attacks, which can overwhelm network resources and cause downtime for critical medical systems [121]. DDoS attacks can result in service disruptions, and in healthcare, such disruptions can be life-threatening. According to [36], DoS attacks can be a crucial problem for multidomain infrastructure and, therefore, for IoT medical applications. Healthcare IoT systems must have appropriate security controls in place to detect and mitigate DDoS attacks promptly. Legacy devices and outdated software pose a significant security risk to healthcare IoT systems, as they often lack security patches and updates [54]. These devices and software can have known vulnerabilities that attackers can exploit to gain unauthorized access to sensitive patient data. Healthcare organizations must take appropriate measures to ensure that legacy devices and software are updated and protected against known vulnerabilities. The use of mobile devices by healthcare providers to access IoT systems outside of the secure hospital network creates a significant security risk. Mobile devices can be infected with malware, targeted by phishing attacks, and can be vulnerable to network eavesdropping [122]. Healthcare organizations should ensure that mobile devices used to access IoT systems are appropriately secured and use secure communication protocols to communicate with IoT devices. Several studies have identified vulnerabilities in the network layer of healthcare IoT systems that cybercriminals can exploit to launch various attacks. For example, a study by [PERSON] et al. [101] identified vulnerabilities in the network layer that could be exploited to launch attacks, such as replay attacks, man-in-the-middle attacks, and message injection attacks. Similarly, another study by [PERSON] et al. [123] reported on vulnerabilities in the ZigBee communication protocol, which is widely used in healthcare IoT systems. The researchers found that the lack of encryption and authentication mechanisms in ZigBee networks made them vulnerable to a range of attacks, including traffic sniffing, DoS, and spoofing attacks. Some of the security and privacy issues of medical IoT in the network layer are presented and defined in Table 5.  
To mitigate these risks, healthcare organizations must implement appropriate security controls such as encryption, authentication, and access control mechanisms [124]. Healthcare organizations should also use up-to-date software and security patches, conduct regular security assessments and testing, and ensure that legacy devices and software are updated and protected against known vulnerabilities. By remaining vigilant and proactive, healthcare organizations can prevent cyberattacks and safeguard the privacy and confidentiality of patient data.
## 4 Mathematical Programming Relaxations
### Parametric Surrogate Relaxations  
To reduce the number of variables in the MILP relaxation \(LB\), a parametric surrogate relaxation considers only the outages with an index \(k\leqslant k^{0}\) with the previous MILP formulation only for cycles \(k\leqslant k^{0}\), and an aggregation of cycles \(k>k^{0}\) in one production cycle without outages. This gives rise to the following MILP formulation where constraints \(M^{ordo}_{k^{0}}d\geqslant b^{ordo}_{k^{0}}\) gather the truncated constraints (CT14-CT21) considering only variables \(d_{i,k,w}\) with index \(k\leqslant k^{0}\). We define quantities \(\Delta^{\prime}_{i}=\max_{k>k^{0}}(\mathbf{Bo}_{i,k^{m}_{i}}-\mathbf{Bo}_{i,k^ {0}})\) to ensure that, for all \(k>k^{0}\), \(\mathbf{Bo}_{i,k^{m}_{i}}-\mathbf{Bo}_{i,k^{0}}\leqslant\Delta^{\prime}_{i}\).  
\[LB(k^{0})=\min_{d\in\{0,1\}^{N},p,n\geqslant 0} \sum_{i,k}\mathbf{C}^{\prime}_{i,k}r_{i,k}+\sum_{j,s,t}\pi_{s} \mathbf{C}^{p}_{j,w}\mathbf{F}_{t}\,p_{j,s,t}^{\mathsf{T}1}-\sum_{i,s}\pi_{s} \mathbf{C}^{end}_{i,s}x^{end}_{i,s} \tag{23}\] \[\forall i,k, d_{i,k+1}\leqslant d_{i,k}\] (24) \[\forall i,k\leqslant k^{0}, d_{i,k}=d_{i,k,W}\] (25) \[M^{ordo}_{k^{0}}d\geqslant b^{ordo}_{k^{0}}\] (26) \[\forall i,k, \mathbf{Rmin}_{i,k}\;d_{i,k}\leqslant r_{i,k}\leqslant\mathbf{R max}_{i,k}\;d_{i,k}\] (27) \[\forall i,s, x^{init}_{i,0,s}=\mathbf{Xi}_{i}\] (28) \[\forall i,s,k\leqslant k^{0} x^{init}_{i,k}\leqslant\mathbf{Smax}_{i,k}\] (29) \[\forall i,s,k\leqslant k^{0},w x^{fin}_{i,k,s}=x^{init}_{i,k,s}-\sum_{t}\mathbf{F}_{t}\,p_{i,k,s,t}^{ \mathsf{T}2}\] (30) \[\forall i,s,k<k^{0}, x^{fin}_{i,k,s}\leqslant\mathbf{A}_{i,k+1}+(\mathbf{Smax}_{i,k}- \mathbf{A}_{i,k+1})\;(1-d_{i,k+1,W})\] (31) \[\forall i,s,k\leqslant k^{0} x^{init}_{i,k,s}-\mathbf{Bo}_{i,k}=r_{i,k}+\frac{\mathbf{Q}_{i,k}- \mathbf{Q}_{i,k}}{\mathbf{Q}_{i,k}}(x^{fin}_{i,k-1,s}-\mathbf{Bo}_{i,k-1})\] (32) \[\forall i,s,k\leqslant k^{0} x^{fin}_{i,s}\leqslant\mathbf{\chi}^{fin}_{i,k,s}+\mathbf{S}_{i}(d_{i,k,W}-d_{i,k+1,W})\] (33) \[\forall t,j,s, \mathbf{Pmin}^{s}_{j,t}\leqslant p_{j,s,t}^{\mathsf{T}1}\leqslant \mathbf{Pmax}^{s}_{j,t}\] (34) \[\forall t,i,k\leqslant k^{0},s, p_{i,k,s,t}^{\mathsf{T}2}\leqslant\mathbf{Pmax}^{s}_{j,t}(d_{i,k,w -\mathbf{Bo}_{i,k}}-d_{i,k+1,w_{t}})\] (35) \[\forall s,t, \sum_{i,k\leqslant k^{0}}p_{i,k,s,t}^{\mathsf{T}2}+\sum_{j}p_{j,s,t }^{\mathsf{T}1}=\mathbf{D}_{s,t}\] (36) \[\forall i,k\leqslant k^{0},s, x^{end}_{i,s}\leqslant x^{init}_{i,k,s}-\sum_{t}\mathbf{F}_{t}\;p_{i,k,t}+ \mathbf{Smax}_{i,k}\left(1+d_{i,k+1}-d_{i,k}\right)\] (37) \[\forall i,s, x^{end}_{i,s}\leqslant\Delta^{\prime}_{i}+x^{init}_{i,k,s}+\sum_{k >k^{0}}r_{i,k}-\sum_{t}\mathbf{F}_{t}\;p_{i,k^{0},t}+\mathbf{Smax}_{i,k}\left(1 -d_{i,k^{0}}\right) \tag{38}\]As mentioned and proven in [17], this MILP formulation gives lower bounds for the EURO/ROADEF Challenge 2010:  
Proposition 1: _For all \(k^{0}\in\mathcal{K}\), \(LB(k^{0})\leqslant LB\). Hence, each dual bound for \(LB(k^{0})\) is a dual bound for the EURO/ROADEF Challenge 2010._
# Formation Control Algorithm of Multi-UAV-Based Network Infrastructure
## 5 System Evaluation
### Application Scenario  
We employed a simple application model that consistently requests the fixed amount of data rate, which is referred to \(r_{m_{i}}\) in this paper. Following the objective of the UAV-based network infrastructure, all the terrestrial devices try to connect to the backbone network by the gateway (i.e., GCS) and one of the UAVs. We installed PacketSink at the gateway and measured the overall throughput of the network.