esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Stability and Robustness  
Stability can be assessed from closed-loop characteristic polynomial  
\[1+G_{P}G_{CY}=0, \tag{25}\]  
\begin{table}
\begin{tabular}{c c c} \hline \(b\) & \(K_{i}\) & \(K_{P}\) \\ \hline
0 & 0.0359 & 0.2679 \\
0.2 & 0.0343 & 0.2671 \\
0.5 & 0.0259 & 0.2630 \\
0.7 & 0.0170 & 0.2585 \\
0.9 & 0.0061 & 0.2530 \\ \hline \end{tabular}
\end{table}
Table 1: PI controller parameters.  
Figure 3: Process \(G_{P}\) closed-loop responses. Comparison between different values of \(b\).  
where process model \(G_{P}\) is given by Expression (8), and controller \(G_{CY}\) by Expression (7). It follows that  
\[A_{0}K_{I}+s(A_{0}K_{P}-A_{1}K_{I})+s^{2}(A_{2}K_{I}-A_{1}K_{P}+1)+s^{3}(A_{2}K_{ P}-A_{3}K_{I})+s^{4}(A_{4}K_{I}-A_{3}K_{P})+\cdots=0. \tag{26}\]  
Considering Condition (25), the necessary (but not sufficient) condition that all poles of the characteristic polynomial are in the left half plane is that coefficients of Equation (26) are positive. Consequently, the necessary conditions for closed-loop stability are  
\[\begin{array}{c}A_{0}K_{I}\geq 0\\ A_{0}K_{P}-A_{1}K_{I}\geq 0\\ A_{2}K_{I}-A_{1}K_{P}\geq-1\\ (-1)^{i}(A_{i}K_{I}-A_{i-1}K_{P})\geq 0;\ i=3,4,\ldots\end{array} \tag{27}\]  
Sufficient stability conditions might be established by testing the Routh determinants. However, introducing pure time-delays into the process transfer function makes such analysis difficult or even impossible. Therefore, closed-loop stability and robustness were studied in detail on the following process model:  
\[G(s)=\frac{K_{P\!R}e^{-T_{delay}s}}{s\Big{(}1\ +\ a_{1}s\ +\ \alpha a_{1}^{2}s^{2} \Big{)}}. \tag{28}\]  
The process (\(G(s)\)) represents the first- or second-order integrating system with or without time-delay. The stability and robustness of the closed-loop system when applying the proposed tuning method depend on the ratio between parameter \(a_{1}\) and \(T_{delay}\), parameter \(\alpha\), and on chosen parameter \(b\). Processes with real poles have \(\alpha\leq 0.25\).  
The closed-loop system is stable for any chosen time-delay and controller parameter \(b\) (between 0 and 1) if ratio \(\alpha<0.869\). Therefore, the closed-loop response is stable for all processes with real poles, or with moderate complex conjugate poles (up to \(\alpha<0.869\)). Stability improves by increasing time-delay and controller parameter \(b\) (see Figure 4).  
Figure 4: Stability margin for process \(G(s)\) at different values of controller parameter \(b\).
## 8 Discussion
### Limitations and Future Work  
Despite these improvements, the following limitations are recognized in this study:  
First, to focus on the identification of the technological issues, the experts' validation was conducted on a completed bridge rather than a bridge under construction. The experiments did not reveal all potential problems that may occur while managing a large-scale infrastructure site. The existence of materials, temporary facilities, and equipment on such sites can create occlusions and blind spots, hampering projections and lines of sight. Furthermore, the structure itself will change as construction progresses, creating issues for the ideal placement of the QR codes. Such factors need to be accounted for in the future development of AROS.  
Secondly, the length of the bridge used in the experiment was 20 m, which is relatively small compared to common civil infrastructure bridges. The number of triangle meshes of a larger bridge will likely be higher, so securing projection stability solely using the specified simplification criteria may not be feasible. This may be solved by varying the Level of Detail (LOD) by projection distance, as shown in Figure 11. For projections over 50 m, the entire model will be projected, so models with low LOD will be projected, thereby significantly increasing the simplification rate. Conversely, close-up projections should have higher levels of LOD, thus showing details of the model with decreased simplification rates.  
Figure 11: Changes in the level of details (LOD) with a change in the projection distance.  
This approach requires storing in advance BIM models with different LODs in AROS and displaying the appropriate model depending on the projection distance. Further studies are required to determine and formalize the appropriate LODs in relation to project distances.  
Finally, limitations were observed when using ARWorldMap for improved placement accuracy. Projection time lags and intermittent loss of stored anchors occurred when traveling distances of over 15 m. These issues occurred due to the burden of having to collect and process the point cloud data required by ARWorldMap. As mentioned in Section 6.1, AROS requires significant computing power for processing the triangle meshes, leaving little free space to process the point cloud data. [45] identified such limitations in using ARWorldMap on tablets or smartphones.  
One solution would be to segment the scan regions and thereby reduce the amount of point cloud data needed per anchoring cycle. Therefore, any future research includes devising a suitable segmentation approach optimized for large civil structures. For tasks that do not require exact projections, e.g., projecting an inspection checklist, opting to neutralize ARWorldMap and rely solely on the QR code should also relieve the requirement for processing power.
## 4 Understanding Centralised Identity Systems
### LDAP: The Directory-Based Approach
#### 4.5.3 Security of LDAP  
LDAP provides several advantages for managing user identity and authentication, such as streamlining user administration and access control, while also offering a hierarchical structure that simplifies directory entry organization and retrieval. LDAP's interoperability ensures compatibility across different systems and platforms.  
However, LDAP is not without its flaws. One significant security issue is that LDAP transmits credentials in plaintext by default, posing a serious security risk unless SSL/TLS encryption is employed. Moreover, LDAP configurations can be complex, requiring careful planning and execution. As directory sizes grow, scalability concerns can lead to performance degradation. As such, it is crucial to address LDAP's inherent security risks [26; 27].
## 3 Experimental Results
### Performance Comparisons  
The comparison of the different algorithms in Table 2 identified the proposed CNN model as the most efficient approach for identifying the functional networks of interest on healthy subjects. In addition to the comparison presented in Table 2, we extended our effort to implement other well-known CNN architectures such as VGG16, ResNet, and DenseNet on our dataset. However, the performance of these models was recorded in the range of 50% to 55% on healthy data and, therefore, was perceived to be unreliable. The observed difficulty was in the dimension of the original images and the total number of images in our dataset. The typical image size for well-known CNN architectures for computer vision (such as VGG16, ResNet, and DenseNet) is considered to be at 224 pixels \(\times\) 224 pixels, as they are mainly designed to work on the ImageNet database [53]. Our original images are in multi-channel format and therefore have a size of 42 pixels \(\times\) 51 pixels \(\times\) 34 (width, height, channel). In order to adjust the image size, a bi-cubic interpolation has been used to up-sample image size by a factor of 4. This up-sampling reduced the quality of images and caused a significant drop in the performance of the models. On the other hand, the number of training images is much lower than the number of parameters in the well-known CNN architecture, leading the model to over-fit and reducing the model performance.
###### Abstract  
Optimization of process parameters for manufacturing of individual surfaces and the product as a whole is one of the key tasks of technological preparation of production. Processing of individual design elements of the product is a structurally complex process, which is a clear sequence of actions characterized by a set of parameters. The effectiveness of the optimization process directly depends on the level of detail of the process under study and the optimal choice of targets and control parameters. Thus, for a single design element of the product, the control task is reduced to the structural and parametric optimization of its processing. For this purpose, it is necessary to develop a hierarchical model of structural and parametric optimization of the processing of individual design elements of the product. The paper presents a structural hierarchical model of the design element forming process, describing the sequence of actions and intermediate states of the object at different control levels. The model is based on the structural decomposition of the process and includes three levels: technological processing route, technological transition, and working stroke. For each level, theoretical-multiple models, vector optimization criteria, control parameters, and boundary conditions are defined. The article also demonstrates the application of the model on the example of optimization for the process of shaping a group of threaded holes, where the Pareto-optimization method was used to obtain optimal machining parameters that reduced the labor intensity with an acceptable increase in error and surface roughness. The paper presents the results of controlling the machining process of a group of threaded holes by means of structural optimization of the machining process route and parametric optimization of cutting parameters for each working stroke. Optimal parameters of the machining process for the investigated group of threaded holes are achieved by reducing the number of technological transitions within the technological route of machining, as well as optimizing the value of cutting speed set in the process of tapping with a tapsetter. Thus, as a result of the optimization, the total labor intensity of individual transitions decreased by 18.3%, with an increase in processing error by 12.1%, and deterioration of the roughness parameter by 13.2%, which satisfies the initial conditions of optimization. The obtained results prove the effectiveness of the developed hierarchical model of controlling the process of machining individual design elements of the product through structural and parametric optimization of its individual stages.  
**Keywords:** forming; design element; structural hierarchical model; Pareto-optimization; technological route; technological transition; working stroke
# Review on BCI Virtual Rehabilitation and Remote Technology Based on EEG for Assistive Devices
## 4 Source of EEG Information and Data Handling
### Motor/Imagery Physionet  
Motor Imagery Dataset by Physionet (Physionet) is a database consisting of 1500 recordings of 1-2 min obtained from 109 volunteers on 64 EEG channels using the BCI2000 system [19]. This database was used in the previous classification studies [20] with different features extracted from the raw information. Each subject performed 14 experimental runs with the following characteristics: two one-minute baseline runs (one with eyes open, one with eyes closed) and three experimental runs of two minutes of each of the following four tasks:  
1. Open and close the left or right fist.
2. Imagine opening or closing the left or right fist.
3. Open and close both fists or both feet.
4. Imagine opening and closing both fists or both feet.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 2 The Real-Time Metaverse
### The Role of Advanced Technology  
The realization of the real-time metaverse hinges on several key technological advancements. One of the most important is the ability to capture, transmit, and process data in real-time. This requires a high-speed, low-latency infrastructure, such as 5G networks and edge computing, which allows sensor data to be processed locally or close to the source, reducing transmission delays [49]. Additionally, powerful AI models are necessary to analyze and interpret the incoming data, ensuring it is accurately reflected in the virtual environment. AI also plays a crucial role in managing the complexity of real-time environments [50]. Machine learning algorithms can help predict how objects will move or change over time, making the real-time metaverse more fluid and reducing the impact of any delays in data transmission. Furthermore, AI-driven data fusion techniques allow the system to combine data from multiple sensors [51] into a unified 3D model.
# Enhancing Weather Forecasting Integrating LSTM and GA
## 4 Results and Discussion
### Results Discussion  
The evaluation metrics obtained for each proposed case were compared with two benchmark methods: XGBoost and transformer. Similarly to the proposed cases, a GA was also applied to these methods. The average numerical results obtained for each case and for the benchmark method, are presented in Table 7. The evaluation metrics represent each metric's average daily value according to the time range studied. Figure 11: Last 15 days of the testing set (blue) and the predicted data (red) for 15 days ahead in the future for (**a**) daily average temperature, (**b**) daily average wind direction, (**c**) daily average wind speed, (**d**) daily instant wind speed, and (**e**) daily solar radiation predicted values (red), using an optimized LSTM.
# Coordinated Control of Virtual Power Plants to Improve Power System Short-Term Dynamics
## 1 Introduction
### Literature Review  
The primary purpose of a VPP is to optimize the performance of its constituent parts by coordinating the production and consumption [5]. Similar to VPPs, the concept of microgrid has been introduced as a solution for the reliable aggregation of DERs, ESS and controllable loads [6]. Therefore, in this paper, the VPP is modeled as a microgrid that is connected to the Transmission Grid (TG) through a Point of Connection (POC) [7], with the inclusion of Distributed Generators (DGs), voltage-dependent loads and ESS. Although VPPs are not microgrids, some control techniques originally designed for microgrids can be also applied to VPPs, and vice versa. In this vein, the literature review below includes works that discuss the short-term dynamics and control of microgrids.
## 2 Related Works
### Area Coverage Optimization Approaches
Our proposed approach addresses the area coverage problem of DSN consisting of static and rotatable sensor nodes. With the objective of improving coverage ratio and reducing redundancy ratio by sensing direction rotation, we set up a multi-objective model in order to achieve high coverage ratio and low redundancy ratio. We next utilize an IAPSO to search for optimal sensing direction group to achieve multi-objective optimization for DSN. Compared to existing approaches [21; 22], our proposed area coverage optimization approach can be suitable for multiply covered area where the negative correlation between coverage ratio and redundancy ratio is not obvious, and the IAPSO can effectively avoid local optima to some degree.
# Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control
## 4 Control Analysis
### LQR Control  
For this system, we utilize an LQR method with an optimal control regulator that tracks a reference trajectory better than traditional controllers such as PID. This method is beneficial when we linearize nonlinear systems about defined equilibrium points. While the LQR method may be limited to linear systems, they can be highly effective tools for realizing exponentially stable controllers used in linearized nonlinear systems. LQR, by definition, gives the optimal state-feedback law that minimizes particular quadratic objective functions.  
A basic LQR control system model for the monowheel has been implemented by writing code in the MATLAB environment, along with implementing that code on the model developed on MATLAB Simulink with \(Q=400\times diag([1000,1,10,1])\) and R = 0.05; the step response is shown in Figure 5 with \(t_{r}=1.35\) s, \(t_{s}=2.94\) s.  
Figure 4: Pole placement control.  
It is important to note that the monowheel would be subjected to a wide range of masses when used for real-world applications, as well as when different people try to ride it. The system needs to maintain dynamic stability and self-balancing properties for a wide variation in masses. The previously established LQR control model is accordingly altered, which was also performed in the MATLAB program. The current model can sustain riders with weights ranging from 12 kg to 180 kg while in motion. However, the very smooth responses are available in the range of 30 kg to 180 kg, assuming the frame can sustain such weights. The step responses for different weights are shown in Figure 6a-d.  
The plant model in MATLAB Simulink represents the state-space equations as blocks. Likewise, the controller and sensor designs were developed from first principles without  
Figure 5: Step response for LQR control.  
Figure 6: Step responses at various masses. (**a**) Weight = 10 kg. (**b**) Weight = 40 kg. (**c**) Weight = 120 kg. (**d**) Weight = 180 kg.  
the use of any predefined blocks. This system accepts position \(x\) as its reference input. Figure 7 shows the responses of all four states of the system. Only one of the states (\(x\)) contributes to making the system controllable.
# Advancement of the DRPE Encryption Algorithm for Phase CGHs by Random Pixel Shuffling
## 1 Introduction
In Section 2, various image encryption techniques are described, with a particular focus on the DRPE algorithm. In Section 3, we describe the complete workflow, starting from the generation of a 3D scene using computer graphics and its storage in a CGH, followed by the encryption and decryption processes, and concluding with its final visualization. This work places particular emphasis on proposals related to the encryption phase. The novelty of this approach lies in the fact that it allows, if desired, the use of a single key for both the pixel permutation process and the phase modification, generating the encrypted CGH, which serves as the ciphertext. Section 4 presents results and discussion and performance analysis of our methods. Finally, Section 5 highlights the main conclusions of this work.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 6 Security Analysis and Formal Verification
### TLA+ Correctness Verification
#### 6.2.3 Correctness of Evaluable Model-Sharing Model
Definition 5.8: System specification:  
The system specification includes initial state and sequential operation execution:  
TPSpec ==  
TPInit/ |[TPNext]_<<RN_State,PN_State,PN_Prepared,Modle_State,BC_State,msgs>>  
Definition 5.9: Model invariants:  
1. Model training invariant: PN must be in training-prepared state with FL model initialized before training: Inv_PNTrainModelAfterPrepared==/PN_State=\"TrainPrepared\"/Modle_State =\"False\"
2. Model evaluation invariant: BC must receive messages from all evaluation-prepared nodes before committing: Inv_BCCommitEvaluatedMsgAfterPrepared==/PN_Prepared=PN
3. Model aggregation invariant: BC must receive messages from all aggregation-prepared nodes before committing: Inv_BCCommitAggregatedMsgAfterPrepared==/PN_Prepared=PN  
Verification results: The TLA+ model checker validated the formal specification (see Figure 8). All invariants passed with no deadlocks in the state space, confirming the evaluable model-sharing model's correctness.
# Thermal Interaction and Cooling of Electronic Device with Chiplet 2.5D Integration
## 3 Simulation Verification
### The Effect of the Interposer  
The fixed spacing between the two chiplets is 0.1 mm, with the varying thermal conductivity of the interposer being 5 W/mK, 40 W/mK, 70 W/mK, 110 W/mK, and 148 W/mK, while keeping other parameters constant. Figure 8 shows the relationship between chiplet temperature rise and different interposers. As the thermal conductivity of the interposer decreases, the temperature rise of the passive chip decreases, while the temperature rise of the active chip increases. The temperature difference within the passive chip does not always increase or decrease. This is consistent with the trend predicted by the distribution parameter model.
# Tourist Recommender Systems Based on Emotion Recognition--A Scientometric Review
## 7 Clusters Mapping  
This section analyzes the co-occurrence mapping to identify the themes related to SR and tourism's transversal axes. For this purpose, the dataset preprocessed with SientoPy (which unifies Scopus and WoS) was used to generate a network map with the VOSViewer tool [240]. Initially, the author keyword co-occurrence map was created by setting up a thesaurus file to combine standard terms of technologies and algorithms to implement recommenders based on emotions. Also, 35 words unrelated to the theme described were filtered. The network map formed five clusters from selecting 52 keywords merged based on the co-occurrence links' total strength values. The merged network characterizes the thematic areas' development over time (2000 to 2019), showing the most meaningful traces of the related research documents (see Figure 9). Each point represents a node in the network, and the lines connecting the nodes are co-occurrence links. The five clusters show homogeneity with the thematic categories considered in the sections preceding.  
* The first red cluster focuses on implementing machine learning algorithms to recognize emotions based on physiological data from wearable devices [11; 12; 36; 184; 187] and social networks' affective data [15; 16; 49; 63; 46]. The emerging IoT topic encourages collecting large datasets analyzed in big data architectures that support smart tourism applications [94; 22; 95] and health care recommenders [24; 25; 198; 206; 208].
* The second green cluster considers the implementation of on-line product recommender systems [6; 14; 15; 16; 49; 63; 67; 46], tourism recommenders [4; 48; 53; 61; 113; 114; 115], and user modeling using clustering algorithms [116; 64; 112]. The pop-up theme is oriented to the recommendation of interest points based on data from social networks [7; 13; 50; 99; 100; 101; 116].
* The third purple cluster emphasizes sentiment analysis using data mining algorithms to process and extract contextual features in social network datasets [154; 155; 43; 77; 82; 155]. The emerging topic of deep learning applied to the recommendation based on emotions [156; 9; 36; 82; 159] is highlighted.
* The fourth yellow cluster establishes the relationship between collaborative filtering and semantic web techniques in the definition of user-profiles and the construction of the recommender systems' ontologies. [4; 54; 80; 81; 82; 137]. An emerging approach is content-based filtering that leverages the knowledge base in the recommendation process [50; 51; 52; 53; 45].
* The fifth blue cluster is oriented to implementing recommenders and context-sensitive mobile applications supported in the ubiquitous computing infrastructure [126; 127; 128; 130; 131; 132; 133; 89; 130; 131; 133]. It is worth highlighting the importance of the user's context in the planning of tourist trips [104; 105; 110; 111; 112].
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### Topologies of WSNs
#### 2.3.2 Mesh Topology  
This topology uses a multihop network scheme to cover all connected sensor nodes, that is, the signal goes from one sensor node to another until it reaches the base station. The communication range of each node is not limited by distance; we can, however, add an intermediate sensor node to extend it [45]. The main advantage of this topology is that it maintains connectivity in the network, but its major disadvantage lies in the number of jumps that increase from one communication to another, increasing energy consumption in the network.