esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Development and Validation of the Platform
### Experimental Platform Setup
Additionally, the core database contains associations between the DNN and the slices, making it easier to apply different Quality-of-Service (QoS) parameters to UEs, which is crucial for real-time IoT use cases. Application orchestration across the two locations is handled by a Cloud-based orchestration platform. To enable the seamless deployment and management of Edge applications, a Kubernetes cluster is deployed at each Edge node, serving as the essential environment for hosting either entire applications or components of distributed applications. This setup ensures that any application intended to run at the Edge is appropriately deployed within the Kubernetes environment. The orchestrator manages the platform and application deployment, allowing for automatic and streamlined operation. Through this orchestration system, applications are dynamically deployed, scaled, and monitored across Edge sites, optimizing both energy efficiency and performance. This automation simplifies the overall functioning of the platform, enabling real-time adjustments to resource allocation based on current demands. An integrated AI module further enhances the orchestration process by analyzing continuous streams of data, allowing for advanced automation through the application of AI and ML techniques, as explained in Section 3.2.
## 3 Proposed Design Method for Dual-Mode PUF
### Circuit-Level Techniques
#### 3.1.4 ABB  
The SRAM cell failures are not only affected by intra-die variations, but also by inter-die variations. This can be understood by analysing the change in \(V_{read}\) and \(V_{tripRD}\) with an inter-die \(V_{th}\) shift (\(\Delta V_{thINTER}\)) of the cell transistors [18]. As shown in Figure 11, when \(V_{BIT}\) = \"1\",  
\[\begin{split}& V_{tripRD}=V_{trip}(0)+\Delta V_{thINTER}\frac{ \sqrt{\beta_{M1}/\beta_{M2}}-1}{\sqrt{\beta_{M1}/\beta_{M2}}+1},\\ & V_{read}=V_{trip}(0)-\Delta V_{thINTER}\left(1\pm\frac{\sqrt{ \beta_{MA}/\beta_{M5}}}{\sqrt{1+\beta_{MA}/\beta_{M5}}}\right).\end{split} \tag{5}\]  
Figure 10: Implementation of adaptive negative bit-line circuit.  
Figure 9: Dynamic power supply based on the selection of read or write operation.  
With a negative \(\Delta V_{thINTER}\), \(V_{read}\) increases but \(V_{tripRD}\) decreases [18]. This will reduce (\(V_{tripRD}-V_{read}\)), thus degrading the read stability. When \(\Delta V_{thINTER}\) is positive, write failure will increase due to the weakening of access transistors. Such stability degradation can be eliminated by employing the ABB technique proposed in [18]. Take n-channel metal-oxide-semiconductor field-effect transistor (NMOS) as an example: the body bias (BB) can be utilized to adjust \(V_{th}\) as  
\[V_{th}=V_{th0}+\gamma(\sqrt{2\varphi_{B}+V_{SB}}-\sqrt{2\varphi_{B}}), \tag{6}\]  
where \(\gamma\) is the body effect parameter, \(\varphi_{B}\) is a physical parameter and \(V_{SB}\) is the voltage between the source and body.  
\(V_{th}\) of M1 to M6 can be adjusted by their respective body bias voltages \(V_{B1}\)-\(V_{B6}\). For both NMOS and PMOS in general, reverse body bias (RBB) increases \(V_{th}\) whereas forward body bias (FBB) reduces \(V_{th}\)[19]. According to Equation (5), the increase of \(V_{th}\) with RBB leads to higher (\(V_{tripRD}-V_{read}\)), which, in turn, reduces the read failure probability. Conversely, the reduction of \(V_{th}\) with FBB increases the driving capability of the access transistors, which leads to reduced write time and improved write ability.  
Even though ABB can be applied to reduce the read and write failures, it is not without limitations. The ideal case of biasing each transistor independently is not practical due to a large amount of voltage supplies and area overhead [20]. As a practical expedient, the same body bias can be applied to all cells to mitigate the degraded cell stability against inter-die variations. In addition, unlike PMOS BB, which can be applied in a standard dual-well process, the application of BB to NMOS requires a triple-well process [18]. If cost permits, it is preferable to adjust the BBs of both PMOS and NMOS transistors to mitigate the inter-die variations. Under the constraint of manufacturing cost, BB can only be applied to PMOS transistors to reduce the read and write failures despite less efficiency [18]. Since the inter-die \(V_{th}\)s of PMOS and NMOS may move in different directions, it is also desirable to control the BB of the two types of transistors individually.
# Internet of Nano-Things, Things and Everything: Future Growth Trends
## 6 Challenges and Impediments to IoT
### Automatic Discovery of Resources  
Due to the wide diversity of IoT objects and devices, a universal protocol for the automatic discovery of resources must be created. This IoT application-specific protocol cannot, however, be just a simple modification of SIP (session initiation protocol). In traditional computing the end users are more aware of the software and resources of their computing devices. This is not the case with IoT devices, which, being autonomous, must carry out automatic discovery of available resources, without human intervention. This is particularly acute for the millions of IoT devices likely to be deployed in Smart Cities. This approach is in direct contrast to mobile platform applications usually under the full control of the user.  
To aid the automatic discovery of resources and to help the IoT end user, different value-added services can be created. These services can extend beyond the normal IoT daily functions to include: semantics and data identification services, automatic configuration management, device registration/deregistration, service advertising and device semantic integration. Thus, for this process to be a complete success, IoT objects need to be discoverable and allow themselves to be discovered within the IoT ecosystem, with full mutual exchange of device capability information. Device prioritization also needs to occur, hence implying the ranking of IoT objects along the IoT network chain. This entails how far and wide an IoT needs to be detectable to make its presence known to the network initially. The discovery process can be event-based, one-time only, on a publisher-subscriber basis and indicating whether it is a home device or an IIoT device.  
Resource discovery, though essential for an IoT device, needs to take into account the power consumption from the limited IoT power supply. Thus, IoT devices, through their power conserving nature are mostly in a dormant state and only \"wake-up\" when required, e.g., an IoT fire sensor only alerting the user via the Internet upon actual smoke detection: in the dormant state, this IoT fire sensor will be asleep and undiscoverable via the web. CIoT devices in the home are often behind a fire-walled gateway, thus they are not discoverable by web crawlers.  
IoT devices may also be connected by low power radio links, often using shared electromagnetic spectrum suffering high levels of attenuation, interference, multi-path effects and distortion. This often means loss of connection and hence multiple attempts to reconnect during a session.
## 2 Ball Convergence  
The concept of metric regularity that follows plays a role in the ball convergence of NIPA. Definition 1 ([1]): We say that a set valued operator \(Q:D\rightrightarrows\mathbb{R}^{i}\) is metrically regular at \(y_{2}\in D\) for \(y_{1}\in\mathbb{R}^{i}\), if for \(y_{1}\in Q(y_{2})\), the graph of \(Q\) is closed (locally) at \((y_{1},y_{2})\). Moreover, there exist constants \(\lambda,\alpha,\beta>0\) such that  
\[U(y_{2},\alpha)\subset D\text{ and }d(w,Q^{-1}(z))\leq\lambda d(z,Q(w))\text{ for all }(w,z)\in U(y_{1},\alpha)\times U(y_{2},\beta).\]  
To avoid repetitions more details and properties of the standard concepts developed can be found in [1, 2, 5, 6] and the references therein. Let \(x^{*}\in K\) be such that \(0\in F(x^{*})+H(x^{*})\). The following Lipschitz-type conditions are useful. Definition 2: Suppose: there exists \(\ell_{0}>0\) such that  
\[\|F^{\prime}(x)-F^{\prime}(x^{*})\|\leq\ell_{0}\|x-x^{*}\|\text{ for each }x\in D. \tag{2}\]  
Then, we say operator \(F^{\prime}\) is center-Lipschitz on \(D\). \[\text{Consider }D_{0}:=D\cap U(x^{*},\tfrac{1}{t_{0}}).\]  
Definition 3: Suppose: there exists \(\ell>0\) such that  
\[\|F^{\prime}(x)-F^{\prime}(y)\|\leq\ell\|x-y\|\text{ for each }x,y\in D_{0}. \tag{3}\]  
Then, we say operator \(F^{\prime}\) is restricted-Lipschitz on \(D_{0}\). **Definition 4**. : _Suppose: there exists \(\ell_{1}>0\) such that_  
\[\|F^{\prime}(x)-F^{\prime}(y)\|\leq\ell_{1}\|x-y\|\ \text{for each}\ x,y\in D. \tag{4}\]  
_Then, we say operator \(F^{\prime}\) is Lipschitz on \(D\)._  
Consider the partial linearization of \(F+H\) at some \(x\in D\)[6], \(T_{F+H}(x,\cdot):D\rightrightarrows\mathbb{R}^{i}\) to be given as  
\[T_{F+H}(x,z):=F(x)+F^{\prime}(x)(z-x)+H(z).\]  
Let \(\rho:=\sup\{s\geq 0:U(x^{*},s)\subseteq D\}\). **Remark 1**. : _Condition (4) was used in the proof of the main result ([6], Theorem 2). But it turns out that (2) and (3) can be used instead. We have that_  
\[D_{0}\subseteq D, \tag{5}\]  
_resulting_  
\[\ell_{0}\leq\ell_{1} \tag{6}\]  
_and_  
\[\ell\leq\ell_{1}, \tag{7}\]  
_where \(\frac{\ell_{0}}{\ell_{1}}\) can be small (arbitrarily) [9; 10; 11]. The iterates \(\{x_{n}\}\) belong in \(D_{0}\) which is a more accurate region than \(D\) used in [6] (see also the numerical section). That is why we obtain smaller Lipschitz constants. Then, we also suppose_  
\[\ell_{0}\leq\ell; \tag{8}\]  
_otherwise our results hold with \(\ell_{0}\) replacing \(\ell\)._  
_If one uses (2) where it is needed and (3) everywhere else instead of \(\ell_{1}\), in the proof of Theorem 2 in [6], we can prove our main ball convergence result for NIPA:_  
**Theorem 1**.
## 3 Results and Discussion
### X-Ray Fluorescence (XRF)  
To analyze the elemental composition of the modified sample, X-ray fluorescence analysis was conducted to determine the presence of the dopant element. The results, presented in Table 6, indicate that platinum (Pt) was detected in quantities closely matching the initially calculated composition.
# The ConTAD Project: Detection of Multi-Container Loss for Safety of Navigation +
## 4 Closing the Technological Gap: The ConTAD Approach
### Detection of Container Loss  
The first challenge is detecting container loss at sea. Since common tracking units are already equipped with acceleration or shock sensors, these can be utilized by a detection algorithm. Methods to avoid wrong alerts need to be implemented.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 5 Discussion
In addition, although the experimental satellite in this paper is only GF3, the proposed MOEA can also be applied to multi-satellite optimization problems, for example, constellation optimization [48], multi-satellite joint scheduling [49], etc. For different application problems, the goal of an MOEA is always to find the best solution to the problem. In addition to the imaging satellite task planning problem for large-scale mapping studied in this paper, there are many large-scale multi-objective optimization problems in the real world, for example, feature selection in machine learning [50; 51], neural network training [52], drone-assisted camera network [53], routing problems [54; 55], etc. The proposed method can also be explored for its value in different fields. The proposed ECL-INS-LMOA focuses on innovating new search strategies, but the environmental selection strategy adopts the methods in existing papers. In the future, the environment selection strategy will be designed to enhance the performance of MOEAs. In addition, the regional mapping we are currently researching only focuses on obtaining orthophoto images of one region. In the future, we will focus on obtaining regional stereo imaging products and multi-satellite multi-region task planning problems. In summary, we will construct different imaging satellite task planning models and design corresponding solving algorithms based on specific imaging satellite task planning problems in practical applications.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 3 Background
### Handover Challenges
#### 3.5.1 Adjacent Cell Scheduling  
Handover enables seamless connectivity between UEs and BSs during movement. To improve the quality of HOs, a process enlists and schedules all the adjacent cells available based on the UEs measurement report. This scheduling process may become time-consuming, affecting the HO decision process and quality. Therefore, scheduling and minimizing adjacent cells is mandatory to improve the HO decision process and quality. 3 GPP introduced and standardized Neighbor Cell List (NCL) and Automatic Neighbor Relation (ANR) procedures in [122] to improve the operation and maintenance of HOs in wireless networks. The target BSs can now be constrained to specific neighbour cells with only specific quality parameters to avoid handover failure, which NCL provides. However, due to small cells and HetNets, adjacent cell scheduling list size may become large, affecting the handover decision as the searching time will be increased to select the suitable target cell. Several studies have been done by the authors [123; 124; 125; 126; 127; 128] to enhance this scheduling and minimization process of neighbour lists to improve HO performance as well as to lower signal overhead, and energy consumption.
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 2 Methods
### Machine Learning for Aquatic Vegetation Classification
#### 2.2.5 Generalizing Over Multiple Species  
Although image classification based on two classes (_Hydrilla_ and Other) achieved satisfactory accuracy, classification across multiple plant species or classes was pursued to produce even better results with increased utility. To enable treatment of multiple species and enhance model generalization, hydroacoustic imagery of _Cabomba_ (Figure 14c) and Coontail (Figure 14d, _Creatophyllum demersum_), two common aquatic plant varieties, was collected and the DNN was trained on four classes. The data set was divided into training, validation, and test sets with 657, 80, and 80 images, respectively, of each class. Equal numbers of images in each class during training ensured prevention of sample bias; any significant difference was found to affect the classification probabilities. Cross verification on the test set following parameter tuning confirmed/ensured adequate model generalization.
## 4 Results
### Evaluation Metrics  
In this subsection, we define and discuss some of the metrics currently used to evaluate the segmentation algorithm performance. In particular, we analyze the effectiveness, robustness, and accuracy of the segmentation method by means of confusion matrix-based metrics.  
* True Positive (TP): This metric represents the number of pixels that are correctly identified as belonging to the tumor region.
* False Positive (FP): This refers to the number of pixels that are incorrectly classified as tumors when they actually belong to the background.
* True Negative (TN): Refers to the number of background pixels correctly identified as background.
* False Negative (FN): Represents the number of tumor pixels incorrectly classified as background.  
The proposed method is evaluated against various quantitative metrics. These metrics include Accuracy, Sensitivity, Specificity, Dice Coefficient (DC), Volumetric Overlap Error (VOE), Relative Volume Difference (RVD), and Average Symmetric Surface Distance (ASSD).
## 3 Proposed Architecture and Algorithm
### Data Preparation  
ECG refers to the heart in each cardiac cycle, in which tracings of the pacemaker, atrial and ventricular function successively excited one by one, along with the bioelectrical changes monitored in the ECG, lead to the graphics of the various forms of potential changes detected from the surface (referred to as ECG) [19]. The ECG provides objective indicators of when the heart is excited about an occurrence, its spread and the recovery process. The ECG shows the electrical activity of the excited heart, and it has an important reference value in basic functions of the heart and pathology research. The ECG can be used to analyze and identify a variety of arrhythmias; it can also reflect the extent and development of myocardial damage and atrial and ventricular function and structural condition [20]. It has reference value in guiding cardiac surgery and suggests the necessary drug treatment. The standard ECG leads to electrocardiogram waves, named by the Dutch physiologist [PERSON], the inventor of the ECG [21]. He divided one cardiac cycle into P, Q, R, S, and T waves.  
Figure 2: Decomposition level.  
There are currently three internationally recognized ECG databases which can be used as a standard, namely, the Massachusetts Institute of Technology's MIT-BIH Arrhythmia Database, the AHA database of the American Heart Association (AHA) and the European ST-T ECG database [22]. In this paper, we selected the ECG data from the MIT-BIH Arrhythmia database. This database includes 48 groups, with two-lead ECG recordings for half an hour, a total of up to 24 h of information. This database contains 47 individuals' ECG information (datasets ID 201 and 202 are duplicated, so we select different signal segments for our test); the subjects consist of 25 men aged between 32 to 89 and 22 women aged from 23 to 89. These ECG data have a sampling rate of 360 Hz and a 12-bit binary representation. Each ECG signal is first adjusted to have zero mean to eliminate any DC offset.
## 4 Comparison of Systems Discussed
### Application of Techniques and Technologies in Visually Impaired Navigation
Figure 9: Spatial recognition of the area around the user. captured in sample sets, while maintaining the representative robustness of the raw data. The data fusion technique for navigation differs from that used in mapping by applying a weighted average that recognizes the context for using or not using a given sensor. These data are compared with the LWPL map and a graph created by the Dijkstra algorithm representing the route. The navigation itself is performed by combining the pedestrian dead reckoning (PDR) algorithm with the Kalman Filter to make the PDR iterative and eliminate the accumulated errors generated by the inertial sensors. Figure 10 shows the architecture of the indoor navigation system for the visually impaired built in this research. The algorithms were embedded in a prototype in the shape of glasses built according to the specifications suggested by the visually impaired users themselves and tested by a group of 20 people with different degrees of disability, ranging from low vision to the total blind. The margin of error between the position informed by the system and the reference position was 0.108 m and an average deviation of 0.186 rad.
# Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
## 3 Improved Deep Principal Component Analysis Method
### Sensitivity Analysis  
The size of the covariance represents the amount of information retained by the method, so the larger the covariance, the larger the amount of retained information, which is expressed as follows:  
\[S=\frac{1}{N-1}(X_{11}+X_{12})^{T}(X_{11}+X_{12}) \tag{12}\]  
Figure 2: The process of ID-PCA.  
The obtained covariance can be expressed as:  
\[\begin{split}& S=S_{11}+S_{12}\\ &=\sum_{k=1}^{3}S_{2k}+S_{24}\\ &=\sum_{k=1}^{2^{2}-1}S_{jk}+S_{j,2^{j}}\end{split} \tag{13}\]  
According to Formula (13), it can be obtained that \(\sum_{j=1}^{2^{i}-1}S_{i,j}>\ldots>\sum_{j=1}^{3}S_{i,j}>S_{11}\). Since the covariance matrix describes all the information about the relationship between dimensions, it can be concluded that as the order increases, the information retained by ID-PCA also increases. Therefore, compared with the Improved-Principal Component Analysis (I-PCA) method, the ID-PCA method can retain more sample information. This method can mine the hidden fault information in the samples discarded by I-PCA so as to achieve the purpose of early fault detection.  
As shown in Figure 3, by projecting the original dataset into the third-order pivot space and residual space, the following conclusions can be drawn. For the first-order projection based on the depth principal component analysis method, that is, the projection of I-PCA, its residual space is \(X_{12}\); for the second-order projection based on the ID-PCA method, its residual space is \(X_{24}\); finally, order projection's residual space is \(X_{38}\). From Figure 3, \(X_{12}>X_{24}>X_{38}\) can be derived. Therefore, it can be concluded that as the order increases, the residual space of the deep principal component analysis method becomes smaller and smaller, i.e., the principal component space contains more and more information, which can detect weak early faults. Considering the computational complexity, this paper only calculates the third-order subspace cutoff.
## 4 Discussion
### Identification of Significant Parameters
The global count indicates that, overall and for extreme errors (max and min), the most relevant parameter is \(\alpha_{d}\), followed by \(\alpha_{k}\). Very similar tables (not shown for brevity) are obtained for \(u_{1,\text{error}}\), \(v_{1,\text{error}}\), \(\Delta_{1,\text{error}}\), \(\
abla_{1,\text{error}}\), \(V_{\text{S1,error}}\) and \(V_{\text{DI,error}}\). However, for \(a_{1,\text{error}}\), the most relevant parameter is \(T_{1}\) followed by \(\alpha_{d}\). The information in Table 4 is used to produce Table 6 in the following way: first, the error results for the case considered are sorted according to the variables with the highest and second highest \(c_{v}\) listed in Table 4, thus obtaining 16 independent data subsets, each one associated with a specific value of the first and second parameters. Then the corresponding statistic (min, \(x^{10}\), \(x^{90}\) and max) for each subset is found. For instance, the top left sub-table in Table 6 refers to the min of \(\Delta_{1,\text{error,first}}\); for this variable and story, the most influential parameters (as discussed) are \(\alpha_{k}\) and \(\alpha_{d}\); the four different values for \(\alpha_{k}\) (0.20, 0.40, 0.60, 0.80) are listed as row labels (left), and the four different values for \(\alpha_{d}\) (0, 1/3, 2/3, 1) are listed as column headers (above). The min of \(\Delta_{1,\text{error,first}}\) for all systems with \(\alpha_{k}=0.40\) and \(\alpha_{d}=1/3\) can be read in the table as \(-0.15\). The procedure has been repeated for all shear-type cases; average values between cases obtained are within \(\pm 10\%\) variation of those in Table 6. Thus, given a system with known properties, the error bounds for classical modal analysis can be estimated. For instance, the un-conservative error (min at root can be kept almost negligible by choosing a constant damper distribution (\(\alpha_{\text{d}}=1\)), regardless of the amount of added damping \(\xi_{a1}\). However, if \(\alpha_{\text{d}}=1/3\), the maximum acceptable inherent damping for a \(-0.15\) error threshold would be \(10\%\) (resulting in a min \(\Delta_{1,\text{error,root}}=-0.14\)). It is worth noticing, however, that values of parameters beneficial for some specific target may be simultaneously detrimental for other. For example, the adoption of \(\alpha_{d}=1\), while ensuring that the min \(\Delta_{1,\text{error,root}}\) is negligible (above \(-0.01\)), is linked to considerable un-conservative errors for the first story (min \(\Delta_{1,\text{error,first}}\) up to \(-0.41\)) and over-conservative errors for the roof (max \(\Delta_{1,\text{error,root}}\) up to \(+2.78\)). This proves the difficulty of proposing simple rules involving the parameters considered, to keep the error under control. Similar conclusions are obtained for RSA-SRSS and RSA-CQC errors.