esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Weighted CNN-LSTM with Sliding Window Fusion for RFFE Final Test Yield Prediction
###### Abstract  
In semiconductor manufacturing, the final testing phase is critical for ensuring chip quality and operational efficiency. Accurate yield prediction at this stage optimizes testing workflows, boosts production efficiency, and enhances quality control. However, existing research primarily focuses on wafer-level yield prediction, leaving the unique challenges of final testing--such as test condition variability and complex failure patterns--insufficiently addressed. This is especially critical for Radio Frequency Front-End (RFFE) chips, where high precision is essential, highlighting the need for a specialized prediction approach. In our study, a rigorous RF correlation parameter selection process was applied, leveraging metrics such as [PERSON]'s correlation coefficient and variance inflation factors to identify key RF-related features, such as multiple frequency-point PAE measurements and other critical electrical parameters, that directly influence final test yield. To overcome the limitations of traditional methods, this study proposes a multistrategy dynamic weighted fusion model for yield prediction. The proposed approach combines convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with sliding window averaging to capture both local features and long-term dependencies in RFFE test data, while employing a learnable weighting mechanism to dynamically fuse outputs from multiple submodels for enhanced prediction accuracy. It further incorporates incremental training to adapt to shifting production conditions and utilizes principal component analysis (PCA) in data preprocessing to reduce dimensionality and address multicollinearity. Evaluated on a dataset of over 24 million RFFE chips, the proposed model achieved a Mean Absolute Error (MAE) below 0.84% and a Root Mean Square Error (RMSE) of 1.24%, outperforming single models by reducing MAE and RMSE by 7.69% and 13.29%, respectively. These results demonstrate the high accuracy and adaptability of the fusion model in predicting semiconductor final test yield.  
RFFE chips; CNN-LSTM; dynamic weighted fusion; yield prediction; PCA; RF correlation analysis 2025  
2
# A Complete Key Management Scheme for LoRaWAN v1.1 +
## 5 Centralized Key Management Scheme for LoRaWAN  
To address the above-mentioned issues, a proposed complete Centralized Key Management (CKM) scheme is outlined in the paper. This CKM scheme has an IoT infrastructure of four components, the end nodes, the network servers, the application servers and the CKMS (Centralized Key Management Server). The CKMS is a trusted agent only dealing with the whole lifecycle of the key management, i.e., the CKMS is handling the key generation/derivation, updating, backup/recovery, and key revocation. It is a trusted agency that can be players such as government, highly reputable financial institutions or registered key management centers. The application user can trust the CKMS to do all the key management work for them in a transparent way. The CKM has four sub-schemes to address the problems in LoRaWAN v1.1.
## Appendix A Modeling of the Wind/PV Hybrid System  
### Modeling of a PV  
Photovoltaic panels are composed of multiple distinct cells connected in series, parallel, or in a combination of both. They transform sunlight into electricity through the PV effect [33]. The energy generated by a photovoltaic panel over a given period depends on a set of factors [2; 3; 34]. The main factors include the panel's surface area, its peak power, and the irradiation influenced by factors, such as location, season, sunlight exposure, and weather conditions. The energy produced by a photovoltaic panel can be estimated using the following formula [3]:  
\[E_{ph}=P_{r}F_{0}(\frac{G_{eff}}{G_{0}})P_{c}, \tag{1}\]  
where:  
\(P_{r}\): Performance ratio (mean value 0.72 or 0.75, with 0.75 for optimal panel orientation);  
\(F_{0}\): Factor that takes losses into account;  
\(G_{eff}\): Effective annual irradiance of the incident (W/m\({}^{2}\));  
\(G_{0}\): Irradiance under standard conditions (1000 W/m\({}^{2}\));  
\(P_{c}\): Rated power under standard conditions provided by the manufacturer \((W)\).
# A Mobile Solution for Enhancing Tourist Safety in Warm and Humid Destinations
## 4 Result
### The T3S Effectiveness Testing
#### 4.3.1 Test for the Accuracy of the T3S in Obtaining and Providing Data  
The T3S system has been meticulously designed to deliver comprehensive information about tourist attractions, particularly concerning health safety factors that may contribute to heat stroke, dehydration, and heart attacks. These factors encompass temperature, humidity level, atmospheric conditions, pulse rate, heart rate, body temperature, and blood pressure.  
Based on the data presented in Table 8, the accuracy percentage signifies the extent to which the application aligns with the actual weather conditions, with a perfect match denoted by 100%. Meanwhile, the response time, measured in seconds, represents the duration it takes for the application to retrieve and process the information upon activation. For instance, the application demonstrates 100% accuracy in reporting the current temperature, indicating its correspondence with the real-time temperature data. Additionally, the response time for temperature retrieval and processing stands at 0.002 s, reflecting the efficiency of the application in accessing and analyzing information from internet sources.  
Similarly, the application demonstrates 100% accuracy in reporting humidity and weather conditions, with response times of 0.001 s and 0.000 s, respectively. This guarantees the provision of swift and precise information about current weather conditions to users. The average accuracy and response time for all weather parameters are 100% and 0.001 s, respectively. Consequently, the application ensures the reliable delivery of up-to-date weather information to users in a timely manner.
## 4 Results and Discussion
### Linear Static Finite Element Analysis Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Primary Arch (Main Arch)**} \\ \hline  & \multicolumn{3}{c}{**Truss**} & \multicolumn{3}{c}{**Transverse Bracing**} \\  & **Chord** & **Bracing** & **Vertical** & **Chord** & **Web** \\ \hline Size & 700 WB173 & 310 UC118 & 150 UC23.4 & 400 WC303 & 150 UC30.0 \\ Length (mm) & 6530 & 13,474 & 6036 & 8000 & 8000 \\
1.35G & 539, \(-\)1379 & 850, \(-\)1443 & 78, \(-\)136 & 1174, \(-\)1105 & 102, \(-\)23 \\
1.2G \(+\) 1.5Q & \(0,-\)5493 & 2142, \(-\)2828 & 545, 0 & 4422, \(-\)4090 & 443, 0 \\
1.2G \(+\) & \(Q+W_{orth}\) & 0, \(-\)4074 & 1692, \(-\)2337 & 389, 0 & 3268, \(-\)3088 & 343, \(-\)9 \\
1.2G \(+\) & \(Q+W_{up}\) & 0, \(-\)4174 & 1664, \(-\)2290 & 393, 0 & 3315, \(-\)3149 & 341, \(-\)7 \\
1.2G \(+\) & \(Q+W_{down}\) & 0, \(-\)4040 & 1703, \(-\)2350 & 385, 0 & 3270, \(-\)3078 & 352, \(-\)7 \\ \hline
**Maximum** & **539, \(-\)5493** & **2142, \(-\)2828** & **545, \(-\)136** & **4422, 4090** & **443, \(-\)23** \\
**Axial Capacity** & **6180, \(-\)5938** & **4214, \(-\)2859** & **837, \(-\)966** & **10,843, \(-\)8695** & **1084, \(-\)1251** \\
**Utilisation** & **0.09, 0.93** & **0.51, 0.99** & **0.65, 0.14** & **0.41, 0.47** & **0.41, 0.02** \\ \hline \hline \end{tabular} Note: Compressive forces are shown as negative and tensile forces as positive.  
\end{table} 
Table 12: Analysis results of axial forces and member selection for primary arch (main arch) component (kN). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Primary Arch (Main Arch)**} \\ \hline  & \multicolumn{3}{c}{**Truss**} & \multicolumn{3}{c}{**Transverse Bracing**} \\  & **Chord** & **Bracing** & **Vertical** & **Chord** & **Web** \\ \hline Size & 700 WB173 & 310 UC118 & 150 UC23.4 & 400 WC303 & 150 UC30.0 \\ Length (mm) & 6530 & 13,474 & 6036 & 8000 & 8000 \\
1.35G & 539, \(-\)1379 & 850, \(-\)1443 & 78, \(-\)136 & 1174, \(-\)1105 & 102, \(-\)23 \\
1.2G \(+\) & \(1.5Q\) & \(0,-\)5493 & 2142, \(-\)2828 & 545, 0 & 4422, \(-\)4090 & 443, 0 \\
1.2G \(+\) & \(Q+W_{orth}\) & 0, \(-\)4074 & 1692, \(-\)2337 & 389, 0 & 3268, \(-\)3088 & 343, \(-\)9 \\
1.2G \(+\) & \(Q+W_{up}\) & 0, \(-\)4174 & 1664, \(-\)2290 & 393, 0 & 3315, \(-\)3149 & 341, \(-\)7 \\
1.2G \(+\) & \(Q+W_{down}\) & 0, \(-\)4040 & 1703, \(-\)2350 & 385, 0 & 3270, \(-\)3078 & 352, \(-\)7 \\ \hline
**Maximum** & **539, \(-\)5493** & **2142, \(-\)2828** & **545, \(-\)136** & **4422, 4090** & **443, \(-\)23** \\
**Axial Capacity** & **6180, \(-\)5938** & **4214, \(-\)2859** & **837, \(-\)966** & **10,843, \(-\)8695** & **1084, \(-\)1251** \\
**Utilisation** & **0.09, 0.93** & **0.51, 0.99** & **0.65, 0.14** & **0.41, 0.47** & **0.41, 0.02** \\ \hline \hline \end{tabular} Note: Compressive forces are shown as negative and tensile forces as positive.  
\end{table} 
Table 13: Analysis results of bending moments and member selection for primary arch (main arch) component (kNm).
## 8 Challenges and Opportunities
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Challenge** & **Description** \\ \hline Computational efficiency and scalability & High-fidelity simulators like Isaac Sim and Gazebo require significant computational resources, limiting real-time performance and accessibility [19; 22]. Ensuring that behaviors simulated in environments like Gazebo and Isaac Sim translate effectively to real-world deployment remains a key challenge [95]. \\ Interoperability and standardization & The lack of standardized APIs and middleware complicates system integration across different hardware and software platforms, increasing development overhead [17; 59; 96]. \\ Latency and synchronization issues & High-frequency data exchange in distributed environments introduces delays, impacting real-time control and sensor-actuator synchronization [17; 19]. \\ Human–robot interaction (HRI) modeling & Simulating both the physical and cognitive aspects of human–robot interactions in real-time remains an open problem [20]. \\ \multirow{2}{*}{Learning curve and accessibility} & Simplifying simulator setup and configuration can lower the entry barrier for researchers and developers without extensive robotics expertise. Some simulators, such as Unity and CoppeliaSim, offer more accessible learning environments, while Isaac Sim and MuJoCo require advanced configuration knowledge [19; 40]. \\ \multirow{2}{*}{Physics fidelity} & Simulators like Isaac Sim and MuJoCo provide high-fidelity physics modeling for robotic manipulation and dynamic interaction tasks. In contrast, lighter simulators like CoppeliaSim prioritize efficiency at the cost of reduced physical accuracy [19; 22]. \\ \multirow{2}{*}{Gazebo and Isaac Sim are deeply integrated with ROS, enabling streamlined transitions from simulation to real-world deployment. Unity and Unreal Engine, while powerful in visualization, require additional middleware to interface effectively with robotics frameworks [20; 89]. \\ \multirow{2}{*}{Integration with robotics frameworks} & \multirow{2}{*}{Integration with robotics frameworks} \\  & \\ \end{tabular}
\end{table} 
Table 10: Challenges in Robotics Simulation.
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 8 Performance Evaluation of the Proposed FL-IDS
### Data Distribution Experiments  
To evaluate the efficacy of the FL approach relative to a centralized approach, three preliminary experiments were conducted with varying data distributions (IID and non-IID). Each experiment was conducted over a wide range of rounds, and the resulting model was subsequently evaluated on the server side. The following experimental settings were implemented:  
* **Experiment 1:** data were distributed following an IID. The data were split into five samples with stratification--one sample for each client.
* **Experiment 2:** Data were distributed following a non-IID, with an increasing number of clients participating in the FT only with non-malicious samples.
* **Experiment 3:** data were distributed following a non-IID, with each client storing a particular type of attack.  
The purpose of these experiments was to evaluate the effect of data distribution on performance and the number of rounds required to converge. Experiment 1 reported numeric results, serving as the baseline experiment to assess the impact of decentralization. Experiments 2 and 3 only presented final results due to the large amount of collected data. However, the full data can be found at [73].
## 2 SGX-Based TEE
### Basic Concept About SGX
#### 2.1.1 Isolated Execution  
The isolation of physical memory is the primary condition for isolated execution. A protected contiguous memory space called Enclave Page Cache (EPC) is allocated in the memory and is physically isolated from other parts of the system. The data stored in the EPC are automatically encrypted by the CPU when written to memory and decrypted when read back into the CPU cache. The size of the EPC is limited, at 93 MB in the SGX version 1 [30], which leads to some performance degradation issues [31]. Special hardware instructions, Ecall and Ocall, are required to enter and exit the enclave. Furthermore, SGX records the allocation of each EPC page in the Enclave Page Cache Map (EPCM) and manages the access and modification of these pages. When any operation attempts to access the enclave, the CPU will sequentially check the permissions of the external access process, the validity of the EPC address, and the EPCM to ensure address space isolation.
## Data and Methods
### SMAP FT Classification Agreement  
The SMAP FT classification accuracy was evaluated against independent surface air temperature (SAT) based FT estimates from in situ global weather stations. The WMO global weather station daily SAT measurements were obtained from the National Weather Service NCEI Global Summary of the Day [39] and are independent from the GMAO global model reanalysis surface temperatures (Tsurf) used for the SMAP FT algorithm calibration. A single representative station closest to the center of a grid cell was selected when two or more stations were located within the same cell. The SAT daily minimum (SATmin) and maximum (SATmax) records for the selected stations were used to define the daily frozen (T \(<\) 0 \({}^{\circ}\)C) and non-frozen (T \(\geq\) 0 \({}^{\circ}\)C) temperature conditions. The SATmin and SATmax based FT estimates for each station were then compared with the respective SMAP AM and PM FT classification results from the overlying grid cell, assuming that the local timing of daily SATmin and SATmax occurs near the satellite equatorial crossing times [1,11]. Approximately 5000 WMO global weather stations were selected for the 2016-2017 period and used for the SMAP FT validation assessment. Differences between the in situ station SAT based FT conditions and corresponding SMAP FT state from the overlying grid cells were used to define the global daily FT spatial classification agreement, expressed as a percentage (%) of the number of stations and overlying SMAP grid cells with identical FT classification results relative to the total WMO station cells represented within the global domain on an annual and daily basis. The use of weather station SAT measurements for SMAP FT validation benefits from the large number of weather stations that are globally available, while a similar approach was successfully used to validate other satellite global FT records derived from higher frequency (K-band) Tb retrievals [11]. However, the SMAP L-band Tb retrievals are expected to show enhanced sensitivity to surface soil FT, which may differ from SAT conditions [7,18].  
Figure 1: Correlation (R) between the Soil Moisture Active Passive (SMAP) daily vertically polarized brightness temperature (Tbv) and GMAO surface temperature for 2017; the FT-SCV domain encompasses grid cells where \(|\)R\(|\)\(>\) 0.5.
# FUXYA2020: A Low-Cost Homemade Portable EDXRF Spectrometer for Cultural Heritage Applications
## 1 Introduction
The system features a high efficiency for a wide range of elements down to sodium, thanks to the use of two tubes with different anodes and to the presence of a continuous helium flow in front of the tubes and the detector, with the declared aim to overcome the usually limited performance of compact XRF systems for in situ analyses. An interesting project was presented in [PERSON] et al. [39], where a low-cost portable system for in situ elemental mapping by XRF was developed. The basic components (i.e., X-ray tube and detector) are very close to ours, but the final aim, and thus the design, are highly different, because, for a good scanning system, more attention must be given to the positioning system, the acquisition time and the ability to reconstruct elemental maps with good spatial resolution, while quantitative analyses is not considered. Some homemade micro-XRF spectrometers were also studied [40], but their discussion is out of the scope of the present paper. The aim of the FUXYA2020 (fluorescence X-ray analysis) project was to create a low-cost portable prototype for EDXRF analysis that was compact, lightweight, easily transportable, and attachable to a tripod for convenient positioning. In the FUXYA system, the two most expensive elements are obviously represented by the X-ray source and the detection unit. These are mandatory for a reliable spectrometer, but with a homemade project it is possible to spread the purchasing on different periods. In the following, we will discuss the design, the choice of elements and the performances of FUXYA2020 when it is used for the characterization of cultural heritage materials, namely pigments, ceramics, and metal alloys. Despite its basic features, the FUXYA spectrometer demonstrates its capability for work with rapid and reliable characterization on cultural heritage materials whenever commercial devices are unaffordable.
###### Abstract
Therefore, the main contribution of this paper is a mathematical modeling to solve the problem of static and multistage TNEP that simultaneously incorporates the effect of active power losses and series capacitive compensation (SCC). The proposed methodology for computing power losses is divided into two phases: In the first phase, a PL is solved to calculate the bus angles; in the second phase, the power losses of the transmission lines are calculated from the results obtained in the first phase. These losses are distributed as loads on the power system buses. Once the losses are added, a new PL is executed to obtain the results of the power flows, dispatched generation, load shedding, and the new voltage angles. To solve the TNEP problem, a highly efficient genetic algorithm (HEGA) based on the approach presented in [43] is employed. In addition, the VGCH algorithm is implemented to make the configuration proposed by the proposed algorithm feasible in each generational cycle, if such configuration is unfeasible. This paper is organized as follows: Section 2 presents the mathematical models for static and multistage TNEP, as well as the mathematical expressions used to compute active power losses of transmission lines. Section 3 presents the HEGA implemented to solve the TNEP problem, as well as the VGCH algorithm. Section 4 presents the results obtained by the proposed methodology for different test power systems, Section 5 presents a critical analysis of the results, and finally, conclusions are presented in Section 6.
## 2 Related Work
### Deep Learning-Based Time Series Forecasting  
Time series forecasting encompasses a broad spectrum of applications, ranging from financial markets [25] and traffic [26] to weather prediction. While traditional statistical methods such as ARIMA and VAR remain valuable for their interpretability, deep learning approaches have emerged as the dominant paradigm, offering superior performance in handling complex temporal and spatial patterns [27].  
The evolution of deep learning models for time series analysis has been marked by significant architectural innovations. RNNs have been the primary choice for handling sequential data for the past several years, including NLP [28] and time series analysis [29]. Improved RNN architectures such as GRU [30] and LSTM [31] have addressed the fundamental limitations of vanilla RNNs in capturing long-range dependencies. These architectures have demonstrated remarkable successes [32], including in weather-related time series applications. However, there exists a significant computational time assumption due to the autoregressive structure of RNNs, and they also suffer from the gradient vanishing problem. To address these issues, SegRNN introduces parallel prediction mechanisms [33], enabling the model to predict multiple time steps in parallel. This approach improves both forecasting accuracy and computational efficiency.  
Nowadays, the transformer architecture has gained significant attention in long sequence modeling [34] thanks to its multi-head self-attention mechanism, which has demonstrated remarkable ability to capture long-term dependencies and avoid the gradient vanishing problem that arises in RNNs. Consequently, transformers have shown great promise in sequential tasks such as time series forecasting. Although the attention mechanism demonstrates superior performance in long-term temporal modeling, it has drawbacks in terms of model complexity and computational cost [35]. Indeed, numerous methods have been proposed to address these issues, such as treating split patches or whole sequences as tokens in PatchTST [36] and iTransformer [37]. Another research line focuses on utilizing intricate temporal patterns within time series by leveraging techniques such as seasonal trend decomposition [38] and nonstationary compensation [39] to improve forecasting accuracy.  
Recent developments in time series forecasting have also explored alternative architectures. MLP-based models such as DLinear [40] offer simple yet effective solutions for time series prediction, achieving comparable performance while requiring significantly fewer computational resources.  
However, the existing deep learning-based time series forecasting models still lack the ability to capture the specific physical features of weather time series, including the strict values constraints of meteorological variables and their interdependencies, which may lead to suboptimal performance on weather time series prediction tasks. Therefore, in this paper we propose a generic deep learning plug-and-play module to help deep learning-based time series forecasting models to address these challenges effectively and efficiently.