esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging Retrieval-Augmented Generation for Automated Smart Home Orchestration
## 3 System Overview
### Architectural Design and Components
#### 3.3.2 Data Storage and Integration Layer  
This layer manages all data related to devices and their bundles, ensuring that the orchestrator can efficiently retrieve necessary information for decision-making and code generation. It consists of two main components:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Field** & **Data Type** & **Description** & **Example** \\ \hline name & String & Commercial name of the device & _lightswitch3000_ \\ ID & Number & Number ID of the device & _0252525_ \\ manufacturer & String & Real name of the manufacturer & _Apple Inc._ \\ model & String & Model of the device & _J3000-pt-2024_ \\ SW\_version & String & Version of the device’s firmware & _3.4_ \\ suggested\_area & String & Recommended space area of use & _Livingroom_ \\ main\_feature & String & Main feature of the device & _Light dimming_ \\ configuration\_url & URI & Driver/Bundle’s URL & _[[https://devioc.url.apple](https://devioc.url.apple)]([https://devioc.url.apple](https://devioc.url.apple)). com/j3000-pt-2024_ \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of payload fields in the discovery message.  
* Vector Database: The orchestrator utilises a vector database to store embeddings of device information. By converting device attributes into embeddings, the system enables context-aware searches (a form of similarity search), which is crucial for matching user requests to relevant devices. The vectorization process concatenates key attributes of each device (such as the user or device ID, room, device name, and listed capabilities) into a single descriptive string. This string is then passed through a pre-trained Sentence Transformer model to produce a numeric embedding vector. The embedding captures semantic relationships. For example, devices in the _\"living room\"_ with the capability _\"light\"_ will have vectors positioned such that a query embedding for \"living room lamp\" finds them as nearest neighbours. Each resulting vector is stored with a unique ID and associated metadata (the raw attributes). When the orchestrator later needs to find devices for a user command, it embeds the query in the same way and asks the database for the closest matches, retrieving the IDs of the most relevant devices. This vector-based approach greatly enhances the system's ability to select devices intelligently based on context rather than requiring exact keyword matches.
* Relational Database: Alongside the vector store, a relational database is used for maintaining detailed device and bundle information in tables. The schema includes tables such as _device_info_, where static info like device model, the interface package and class name, and available method names are stored; and _device_usage_, which is used to track the real-time usage status of each device and other dynamic states.  
By separating concerns this layer provides a robust foundation for the orchestrator. It ensures both speed via vector search and accuracy via consistent relational data in the system's operation.
## 1 Introduction
### Joint Bayesian for Modeling View Variance  
When dealing with view change problems, several appearance-based approaches are proposed: (1) the view transformation model (VTM) [23; 24]; (2) the view-invariant feature-based approaches [21; 25]; and (3) multiview gallery-based approaches [26; 27]. On the OULP dataset, VTM-based methods are widely used: [24] proposed a generative approach which is a kind of VTM-based methods and makes use of transformation consistency measures (TCM+); [23] further proposed a quality-dependent VTM (wQVTM). Recently, a view-invariant feature-based approach (GEINet) [21] was proposed and achieved the best performance. We introduce Joint Bayesian [28] to model the view variance which differs from the above approaches. For comparison, the unsupervised Nearest Neighbor classifier based on euclidean distance (NN) is also adopted as a baseline method. In order to evaluate the compactness of DeepGait, PCA is used to project the representation into lower dimensions. Furthermore, we choose the right \(K=256\) components to strike a balance between recognition performance and computational complexity when using Joint Bayesian.
# A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
## 3 Sector-Matching PHD Filter
### Initialization and Prediction  
Within the \(k\)th scanning cycle, the RFSs of the newborn target is expressed in Gaussian mixture form as shown in Equation (35):  
\[\gamma_{\mathbf{t}_{\gamma,k}^{i}}(\mathbf{x})=\sum_{i=1}^{J_{\gamma,k}}\omega_{\gamma,k}^{i}\mathcal{N}\Big{(}\mathbf{x};\Big{(}\mathbf{m}_{\gamma,k}^{i},\mathbf{t}_{\gamma,k }^{i}\Big{)},\mathbf{P}_{\gamma,k}^{i}\Big{)} \tag{32}\]  
Each newborn Gaussian component's sampling time \(\mathbf{t}_{\gamma,k}^{i}\) is obtained according to Equation (32). According to Equation (35), the initial intensity, \(D_{0|0}\), is described as \(\Big{\{}\Big{(}\mathbf{m}_{\gamma,0}^{i},\mathbf{t}_{\gamma,0}^{i}\Big{)},\omega_{ \gamma,0}^{i},\mathbf{P}_{\gamma,0}^{i}\Big{\}}_{i=1}^{J_{\gamma,0}}\).  
The posterior probability density, \(D_{k-1|k-1}(\mathbf{x})\), at the end of the \(k-1\)th scan can be approximated as: \(\Big{\{}\Big{(}\mathbf{m}_{k-1}^{j},\mathbf{t}_{k-1}\Big{)},\omega_{k-1}^{j},\mathbf{P}_{k -1}^{j}\Big{\}}_{j=1}^{J_{k-1}}\), where \(t_{k-1}=(k-1)T\) is the timestamp of the surviving Gaussian component at time \(k-1\). The predicted density at the end of \(k\)th scan is:  
\[D_{k|k-1}(x)=\bigcup_{i=1}^{|\mathbf{Z}_{k}|}D_{\mathbf{t}_{\parallel}^{i}|k-1}(\mathbf{x}) \tag{33}\]\[D_{t_{k}^{i}|k-1}(\mathbf{x})=\left\{(\mathbf{m}_{t_{k}^{i}|k-1}^{j},t_{k-1}^{i}),\omega_{t _{k}^{i}|k-1}^{j},\mathbf{P}_{t_{k}^{i}|k-1}^{j}\right\}_{j=1}^{I_{t_{k}^{i}|k-1}} \tag{34}\]  
where \(I_{t_{k}^{i}|k-1}=I_{k-1}+J_{\gamma_{k}^{i}t_{k}^{i}}\), \(I_{k-1}\) is the number of components at the \(k-1\)th scanning cycle and \(I_{\gamma_{k}^{i}t_{k}^{i}}\) is the number of newborn components at time \(t_{k}^{i}\).  
Without considering spawned targets, Equations (5)-(8) are rewritten as:  
\[D_{t_{k}^{i}|k-1}(\mathbf{x})=D_{S,t_{k}^{i}|k-1}(\mathbf{x})+\gamma_{t_{k}^{i}}(\mathbf{x })=\sum_{j=1}^{I_{t_{k}^{i}|k-1}}\omega_{t_{k}^{i}|k-1}^{j}\mathcal{N}\bigg{(} \mathbf{x};\bigg{(}\mathbf{m}_{t_{k}^{i}|k-1}^{j},t_{k-1}^{j}\bigg{)},\mathbf{P}_{t_{k}^{i }|k-1}^{j}\bigg{)} \tag{35}\]  
\[D_{S,t_{k}^{i}|k-1}(\mathbf{x})\mathbf{\&}=p_{S,t_{k}^{i}}\times\sum_{j=1}^{I_{k-1}} \omega_{t_{k}^{i}|k-1}^{j}\mathcal{N}\bigg{(}\mathbf{x};\bigg{(}\mathbf{m}_{S,t_{k}^{ i}|k-1}^{j},t_{k-1}^{j}\bigg{)},\mathbf{P}_{S,t_{k}^{i}|k-1}^{j}\bigg{)} \tag{36}\]  
\[\mathbf{m}_{S,t_{k}^{i}|k-1}^{j}\mathbf{\&}=F_{t_{k}^{i}|k-1}\mathbf{m}_{k-1}^{j} \tag{37}\]  
\[\mathbf{P}_{S,t_{k}^{i}|k-1}^{j}\mathbf{\&}=F_{t_{k}^{i}|k-1}\mathbf{P}_{k-1}^{j}\mathbf{F}_{t _{k}^{i}|k-1}^{j}+\mathbf{Q}_{t_{k}^{i}|k-1} \tag{38}\]  
\[\omega_{t_{k}^{i}|k-1}^{j}=p_{S,t_{k}^{i}}\omega_{k-1}^{j} \tag{39}\]  
where the parameters in \(\mathbf{F}_{t_{k}^{i}|k-1}\) indicate the time interval is \(t_{k}^{i}-t_{k-1}^{j}\).
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Security Attributes of ECSSP-SC
#### 6.1.3 Integrity  
If an _Attacker_\(A\) somehow alters any component \(c\), \(T\), or \(w\) in the message sent by the \(USR\), then this modification will be easily identified by the \(SVR\), since the key \(K^{\prime}\) retrieved by the \(SVR\) will not be same as the key \(K\) generated by the \(USR\). In turn, \(T^{\prime}\
eq T\), and the session is terminated by the \(SVR\), since the authentication fails. Similarly, when an _Attacker_\(A\) alters the component \(A_{S}\) of the message sent by the \(SVR\), it will be easily detected by the \(USR\), since \(A_{S}{}^{\prime}\) computed by the \(USR\) will be different from \(A_{S}\) and the authentication of \(SVR\) will fail. Therefore, the current session will be dismissed by \(USR\). Thus,an attacker cannot fool the \(USR\) or \(SVR\) after modifying the messages, and hence, the ECSSP-SC also fulfills the integrity of messages.
# SC-YOLOv8 Network with Soft-Pooling and Attention for Elevator Passenger Detection
## 4 Experiments
### Experiment Results
This substantiates the effectiveness of the proposed network in this paper. To validate the generalization ability of the improved YOLOv8 network in different scenarios, experiments were conducted using the publicly available CrowdHuman dataset [28], which focuses on dense pedestrian scenes. The detailed experimental results are presented in Table 5. From the observations in Table 5, it is evident that the improved YOLOv8 network maintains excellent performance in bounding box regression for pedestrian detection in dense scenes. Compared to Faster-RCNN, YOLOv3, YOLOv5, and YOLOv8, the improved YOLOv8 network exhibits superior performance, highlighting its significant advantage in pedestrian detection within dense scenes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **SCConv** & **Dual-Branch SPP-Fast** & **SPM-CBMA** & **P (\%)** \\ \hline
1 & & & 91.77 \\
2 & ✓ & & 92.86 \\
3 & ✓ & ✓ & 93.18 \\
4 & ✓ & ✓ & ✓ & 94.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of evaluation of each module in the ablation experiment. Figure 20: Our network visualization results. \"Params\" and \"FLOPs\" are two important metrics used in deep learning to evaluate the complexity and computational requirements of neural network models. \"Params\" typically refers to the number of parameters in a neural network model. The number of parameters is a measure of the complexity of the model and indicates how much information the model can store. \"FLOPs\" stands for floating point operations, and it measures the computational complexity of a neural network model. FLOPs represent the number of floating-point arithmetic operations that the model performs when processing input data. Params quantify the number of learnable parameters in the model, while FLOPs quantify the computational workload of the model. As shown in Table 6, the proposed improved YOLOv8 network exhibits an increase of 2.3 M parameters and 5.4 B FLOPs relative to the YOLOv8 network. This is primarily attributed to the additional parameters and computational load introduced by the attention mechanism and soft-pooling technique.
# Application of Modeling and Simulation in a Self-Reprogrammable Prototype of a Manufacturing System
## 1 Introduction
The successful implementation of DT concepts relies on a well-structured simulation model and a high level of connectivity between physical and virtual environments. This integration allows the real-world system to benefit from insights generated by simulation models, thereby enhancing decision-making in production scheduling [21; 25]. In this regard, [PERSON] et al. (2020) highlight that the adoption of Industry 4.0 necessitates the digitalization of the entire manufacturing system, leading to improvements in productivity, efficiency, flexibility, and agility--key factors for enabling mass customization, which is essential for modern factories [6; 15]. The use of digital models, in conjunction with optimization tools, offers significant benefits for supporting decision-making in manufacturing. One notable advantage is observed in production scheduling when simulation models incorporate Genetic Algorithm techniques [26; 27]. Despite the benefits of using simulation and Digital Twins (DT) to aid decision-making in manufacturing, the widespread application of DT remains challenging due to the complexities of real-time data exchange and its implementation as an autonomous optimization tool [28]. This paper proposes a technical architecture for developing a simulation engine capable of real-time data exchange between a manufacturing system and its digital counterpart. The primary objective is to create an integrated environment that seamlessly connects physical and virtual domains, enabling automatic fault detection, optimization, and corrective actions without human intervention. This study focuses on developing a prototype that integrates simulation, production management, data collection, and optimization tools, leveraging the principles of DT. This research contributes to bridging the gap between theoretical advancements and practical applications of DT. Furthermore, it explores the concepts of self-reprogramming and autonomy, particularly emphasizing a DT's ability to dynamically adapt its model in real time and respond to changes in the physical environment [16]. The remainder of this paper is structured as follows: Section 2 explores the concept of DT in detail. Section 3 describes the research methodology, including the classification and stages of the investigation. Section 4 presents the prototype design, encompassing the management system perspective, prototype layers, logical programming model, and physical resources. Section 5 discusses the results of the simulation tests and evaluates the physical performance of the system. Finally, Section 6 concludes the study by summarizing key findings and providing insights for future research.
## 4 Performance Evaluation
If a MECS decides to redirect its tasks if its load is higher than \(\tilde{p}\), it requires exchanging its load information with neighboring MECSs. All four methods that are used for performance comparison in our experiments use \(\tilde{p}\) for the threshold value. Thus, each MECS exchanges its load information with other MECSs at each time slot and calculates the average load to decide whether to redirect its tasks. Therefore, they have the same communication overhead. The computational complexity of our method can be derived as follows. In our method, when the load of a MECS is higher than the average load, the MECS selects a set of target MECSs whose loads are lower than its load. Then, the MECS redistributes its tasks to the target MECSs in order in proportion to the load differences between itself and the target MECSs. Thus, the computational complexity of LMLBR is \(O(|N|log|N|)\) for sorting the target MECSs in order. In RR, when the load of a MECS is higher than the average load, the MECS randomly selects a target MECS to redistribute the tasks. Thus, the computational complexity of RR is \(O(1)\). However, considering the results in terms of the quality of the service provided by a MEC system, the proposed method outperforms RR in terms of the rate of tasks completed within the delay constraints, while being comparable to RR with respect to the task blocking rate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{\lambda}\)} & \multicolumn{2}{c}{**Q Size = 20**} & \multicolumn{2}{c}{**Q Size = 10**} & \multicolumn{2}{c}{**Q Size = 5**} \\ \cline{2-7}  & **RR** & **LMLBR** & **RR** & **LMLBR** & **RR** & **LMLBR** \\ \hline
0.5 & 0 & 0 & 0 & 0 & 0.0247 & 0.0521 \\
0.7 & 0 & 0 & 0.0021 & 0.0024 & 0.0491 & 0.0605 \\
0.9 & 0.0004 & 0.0001 & 0.0051 & 0.0035 & 0.0792 & 0.0666 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Task blocking rate versus queue size. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\(\mathbf{\lambda}\)} & \multicolumn{2}{c}{**Q Size = 20**} & \multicolumn{2}{c}{**Q Size = 10**} & \multicolumn{2}{c}{**Q Size = 5**} \\ \cline{2-7}  & **RR** & **LMLBR** & **RR** & **LMLBR** & **RR** & **LMLBR** \\ \hline
0.5 & 0.7641 & 0.8528 & 0.7892 & 0.8532 & 0.7745 & 0.8437 \\
0.7 & 0.6652 & 0.7936 & 0.7213 & 0.8225 & 0.7154 & 0.8097 \\
0.9 & 0.5596 & 0.7423 & 0.6113 & 0.7612 & 0.614 & 0.7301 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Rate of tasks completed within the delay constraints versus queue size.
## 5 Results
### Multi-Objective Optimization
#### 5.1.2 Design of Experiment and Surrogate Model  
The first step of any population-based optimization is to determine an initial set of sample points by means of design of experiments (DOE) [36]. There are many kinds of DOE methods, including a full factorial design, the Latin hypercube design (LHD), mixture design techniques, and combined design [37]. LHD achieves uniform sampling in multidimensional space and thereby was utilized to sample an initial training set for thesurrogate model in this study. Figure 13 illustrates the 20 uniformly distributed sample points obtained by LHD, and Table 3 exhibits the response values obtained from the simulation at the sample points.  
Surrogate models have been widely used in design optimization to address the prohibitive computational costs arising from the direct coupling of optimization algorithms and finite element analysis. There are several popular surrogate models, including PRS, Kriging, RBF, artificial neural network (ANN), etc. Analyzing the accuracy of each method from the perspective of problem dimensionality, PRS and RBF work well in low dimensions, and Kriging can handle problems of higher dimensions (<50). When faced with a tremendous dimensional problem (~10,000), ANN may be the best choice [38; 39]. In this study, the Kriging model, as one of the most representative and promising models, is utilized due to its good approximation ability to nonlinear functions and unique error estimation function  
global accuracy, are used to evaluate the error of the surrogate model. These metrics are defined as follows:  
\[MRE=\frac{\sum\lvert(\widetilde{y}_{i}-y_{i})/y_{i}\rvert}{n}\times 100\% \tag{9}\]  
\[RMSE=\sqrt{\frac{\sum\left(\widetilde{y}_{i}-y_{i}\right)^{2}}{n}} \tag{10}\]  
where \(n\) represents the number of sample points, \(y_{i}\) and \(\widetilde{y}_{i}\) represent the FE result and the corresponding predicted value at each point \(i\), respectively, and \(\overline{y}\) represents the average of the objective responses of the FE calculation. Generally, the model's accuracy increases with the decreasing \(MRE\) and \(RMSE\).  
The error analysis of the established surrogate model needs to be evaluated by new sample points. They are also randomly sampled and different from the original sample points, as the error of the surrogate model at the original sample points is zero. [PERSON] et al. [41] suggested taking \(0.25n\) samples for testing. Five new sample points are used to verify the accuracy of the surrogate model, and the error analysis results are shown in Table 4. Both \(MRE\) and \(RMSE\) are small, indicating that the established surrogate model predicts the target response well and can be adopted for subsequent multi-objective optimization.
# Energy Efficiency of D2D Multi-User Cooperation
###### Abstract  
The Device-to-Device (D2D) communication system is an important part of heterogeneous networks. It has great potential to improve spectrum efficiency, throughput and energy efficiency cooperation of multiple D2D users with the advantage of direct communication. When cooperating, D2D users expend extraordinary energy to relay data to other D2D users. Hence, the remaining energy of D2D users determines the life of the system. This paper proposes a cooperation scheme for multiple D2D users who reuse the orthogonal spectrum and are interested in the same data by aiming to solve the energy problem of D2D users. Considering both energy availability and the Signal to Noise Ratio (SNR) of each D2D user, the Kuhn-Munkres algorithm is introduced in the cooperation scheme to solve relay selection problems. Thus, the cooperation issue is transformed into a maximum weighted matching (MWM) problem. In order to enhance energy efficiency without the deterioration of Quality of Service (QoS), the link outage probability is derived according to the Shannon Equation by considering the data rate and delay. The simulation studies the relationships among the number of cooperative users, the length of shared data, the number of data packets and energy efficiency.  
D2D communication; multiple user cooperation; energy efficiency; outage probability Article
## 4 Fabrication and Experimental Verification  
Fabrication and testing of a single-fed RDRA MIMO antenna using a single feed mechanism are carried out. Al\({}_{2}\)O\({}_{3}\) ceramic material \(e\tau\) = 9.8 was used to make the dielectric resonator, which was subsequently placed on the PEC ground plane. in a microwave, chamber to quantify scattering parameters (\(\left|\text{S11}\right|\) measurement), and the EMT-24 SATIMO measuring system was used to calculate other parameters, such as axial ratio, pattern radiation, and realized gain. Traditional feeds (for example, probe connector, Coaxial probe feed, and microstrip line) consume extra downsides as compared to conformal copper feeds, because of this e can be used instead. Air breaks, which produce frequency discrepancies, are difficult with coaxial feedings. Conformal strips, In parallel, can be easily attached to the SMA connectors [30]. Conformal feeds help reduce air gaps because it is comprised of adhesive glue copper (Cu) duct tape that attaches to the MIMO RDRA side swiftly and securely.
# Reliability-Aware SPICE Compatible Compact Modeling of IGZO Inverters on a Flexible Substrate
## 3 Result and Discussion  
### Analysis Process  
The analysis process for the inverter simulation is shown in Figure 2. The load and driving transistors will be under electrical stress depending on the driving condition of the device. The two types of electrical stress that can be applied to each transistor (Figure 2a) are current stress (CS) and positive bias stress (PBS). The electrical stress will affect \(V_{T}\), and the \(V_{T}\) change will result in the voltage transfer curve (VTC) of the inverter. In general, when the gate insulator is composed of SiO\({}_{2}\), the cause of the positive \(V_{T}\) shift due to the PBS condition can be explained by electron trapping (Figure 2b,c) [24; 25; 26; 27]. However, when Al\({}_{2}\)O\({}_{3}\) is deposited by low-temperature ALD, the AlO-H bonding tends to have weak bonding that can lead to a negative \(V_{T}\) shift. This is known as a hydrogen migration phenomenon as a result of the movement of H\({}^{+}\) ions (Figure 2b,c) [18; 19; 20; 21; 22; 23]. The movement of electrons and hydrogens when the positive voltage is applied to the gate electrode is represented in Figure 2b.  
In this experiment, the effects of electrical stress were measured and analyzed in accordance with the physical states of flat and bending conditions. The results showed that negative and positive shifts of \(V_{T}\) were mixed because of the aforementioned electron trapping and hydrogen migration phenomena. In particular, in the case of AC driving, where the stress/recovery situation is repeated, the parameters \(\Delta V_{T}\) and \(\tau\) were extracted using the SEF Equation (1). The compact model was mounted on HSPICE, and the load/driver transistors for AC simulation and its consistency were verified for the change of \(V_{T}\), as shown in Figure 2d.  
\[V_{T}=\Delta V_{T0}\bigg{(}1-e^{-(\frac{t}{\tau})^{\beta}}\bigg{)} \tag{1}\]  
Figure 2: Analysis process for the inverter simulation. (**a**) Two types of electrical stress can be applied to load and driver transistors: positive bias stress (PBS) and current stress (CS). (**b**) Schematic of electron trapping and hydrogen migration in an IGZO transistor. (**c**) Threshold voltage shift (\(\Delta V_{T}\)) upon electron trapping and hydrogen migration. (**d**) Concept of AC inverter simulation based on a compact model of \(\Delta V_{T}\) in load and driver transistors.
## 3 Construction of Mathematical Model  
The mathematical model of the proposed BDS-3 Inter-Satellite Link and Connected Element Interferometry-based measurement method for cislunar space targets mainly includes four parts: (i) Dynamical constraint equations for non-cooperative targets in cislunar space; (ii) BDS-based interplanetary link for irradiation of non-cooperative spacecraft in cislunar space; (iii) Transmission loss equation of BDS-3 inter-satellite link signal in cislunar space; (iv) CEI-based precision measurements of targets in cislunar space. The derivation and analysis of the above mathematical model will be described as follows.