esa-sceva/satcom-chunk-collection huggingface dataset:
# Combinatorial Integral Approximation Decompositions for Mixed-Integer Optimal Control
## 3 Combinatorial Integral Approximation MILPs
### Norm Dependent MILP Formulation  
By introducing the auxiliary variable \(\theta\), we can reformulate (4) and (5) with the maximum norm.  
**Definition 7**.: _(CIAmax, SCIAmax) Let \(\mathbf{a}^{\ast}\) be the given optimal control solution of (OCP) and let the evaluated model function values \(\tilde{\boldsymbol{f}}\) be given as introduced in Definition 4. We define (CIAmax) as:_  
\[\min_{\theta\geqslant 0,\mathbf{w}\in\Omega_{M}} \theta\] (7a) \[\text{s.t.} \theta\geqslant\pm\sum_{j=0}^{l}(a^{\ast}_{i,j}-w_{,ij})\Delta_{j },\qquad\text{for }i\in[n_{\omega}],\quad l\in[M-1]_{0},\] (7b) _and (SCIAmax) as:_  
\[\min_{\theta\geqslant 0,\mathbf{w}\in\Omega_{M}} \theta \tag{8a}\] \[\text{s.t.} \theta\geqslant\pm\sum_{j=0}^{l}\sum_{i=1}^{n_{\omega}}(a^{\ast} _{i,j}-w_{,ij})\Delta_{j}\tilde{f}_{i,j,k},\qquad\text{for }k\in[n_{x}],\quad l\in[M-1]_{0}. \tag{8b}\]  
We introduce the MILP analogue formulations for the Manhattan norm with auxiliary variables \(s_{i,j}\geqslant 0,\ i\in[n_{\omega}],j\in[M-1]_{0}\). In this way, we specify the norm choices for Definition 6.  
**Definition 8**.: _(CIA1, SCIA1, \(\lambda\)CIA1) Consider \(\mathbf{a}^{*}\) as the given optimal control solution of (OCP). Let the evaluated model function values \(\tilde{\mathbf{f}}\) and dual variables \(\tilde{\mathbf{\lambda}}\) be given as introduced in Definition 4. Based on the auxiliary variables \(s_{i,l}\) we define (CIA1) as:_  
\[\min_{\theta\approx_{i,l}\geqslant 0,\text{\rm{w}}\in\Omega_{M}} \theta \tag{9}\] \[s.t. \theta\geqslant\sum_{i=1}^{n_{\omega}}s_{i,l}, \text{for }l\in[M-1]_{0},\] (10) \[s_{i,l}\geqslant\pm\sum_{j=0}^{l}(a_{i,j}^{*}-w_{i,j})\Delta_{j},\qquad\text{for }i\in[n_{\omega}],\quad l\in[M-1]_{0}. \tag{11}\]  
_With a different dimension for \(s_{k,l}\), we define (SCIA1) as:_  
\[\min_{\theta\approx_{k,l}\geqslant 0,\text{\rm{w}}\in\Omega_{M}} \theta \tag{12a}\] \[\text{s.t.}\quad\theta\geqslant\sum_{k=1}^{n_{x}}s_{k,l}, \text{for }l\in[M-1]_{0},\] (12b) \[s_{k,l}\geqslant\pm\sum_{j=0}^{l}\sum_{i=1}^{n_{\omega}}(a_{i,j }^{*}-w_{i,j})\Delta_{j}\tilde{f}_{i,j,k}, \text{for }k\in[n_{x}],\quad l\in[M-1]_{0}. \tag{12c}\]  
_Finally, we define (\(\mathbf{\lambda}\)CIA1) by modifying (12c) with the dual variables:_  
\[\min_{\theta\approx_{k,l}\geqslant 0,\text{\rm{w}}\in\Omega_{M}} \theta \tag{13a}\] \[s.t. \theta\geqslant\sum_{k=1}^{n_{x}}s_{k,l}, \text{for }l\in[M-1]_{0},\] (13b) \[s_{k,l}\geqslant\pm\lambda_{l,k}\cdot\sum_{j=0}^{l}\sum_{i=1}^{ n_{\omega}}(a_{i,j}^{*}-w_{i,j})\Delta_{j}\tilde{f}_{i,j,k}, \text{for }k\in[n_{x}],\,l\in[M-1]_{0}. \tag{13c}\]  
Of course, also other norms, such as the Euclidean norm, can be used. We do not consider them here because of the resulting nonlinearity of the constraints.
# Understanding the Light Detection in an Integrated Novel Electrophotonic Wavesensor Photodetector
## 3 Device Description  
A descriptive schematic of the proposed integrated circuit is presented in Figure 1. This shows the LEC, the waveguide, and the photodetector. As can be seen, the wavesensor is a MIS-like structure, with the insulator being simultaneously an integrated silicon nitride (Si\({}_{3}\)N\({}_{4}\)) planar waveguide core, extended into below the gate contact metal.  
In the scheme of Figure 1, the light is produced by an SRO LEC and injected into the silicon nitride waveguide core. The waveguide ends on the WS, and the gate metal is on the top of the nitride core. With this configuration, the light is absorbed directly by the photodetecting region as follows: since the buried SiO\({}_{2}\) lower cladding ends exactly at the edge of the WS, the radiation is transmitted with low losses until it reaches the gate area, and when the SiO\({}_{2}\) cladding reaches the edge of the Si substrate part of the WS, the change  
Figure 1: Representation of the integrated circuit with the waveguide from the LEC up to the MIS-like WS (a). 2D schematic of the wavesensor, that is a MIS-like photodetector with the waveguide core being monolithically integrated under the gate terminal (**b**).  
in refractive index allows the transmission of the light from the waveguide to the silicon, right below the gate. The photons transfer energy to the WS to produce electron-hole pairs, which causes drain current variations. Note that this is not a regular photonic or electronic device, as no external light input-output ports exist, and the only way to inject light is through the waveguide core.  
To estimate the viability of the concept and its detection characteristics, the phenomena taking place in the device were studied, in order to propose optimal fabrication parameters. Computational simulations were then carried out to confirm the viability and expected behavior of the WS.  
Following the process reported in [5], the substrate was a p-doped Silicon with acceptor concentration N\({}_{\text{A}}\) of \(1\times 10^{14}\) cm\({}^{-3}\). The distance between source and drain L, and the width of the transistor W, were, respectively, 5 \(\upmu\)m and 1 \(\upmu\)m. Drain and source regions were obtained through phosphor implantation prior to a thermal rediffusion at 1100 \({}^{\circ}\)C for 180 min. The nitride waveguide/gate insulator was obtained by chemical vapor deposition, and the thickness of the film was 140 nm. The metal used for the contacts was aluminum with thickness of 1 \(\upmu\)m. After the fabrication, electrical and optical simulations were performed. Substrate and source contacts were biased to 0 V, and the gate voltage was \(-5\) V. The drain voltage was varied from 0 V to 5 V. The light source was modeled using a uniform beam with wavelength (\(\lambda\)) of 600 nm and light power (\(P_{in}\)) varied from 0 nW to 50 nW, as reported for SRO-based light sources [12]. Figure 2 shows the 2D schematic of the WS.
# Blockchain-Based Multiple Authorities Attribute-Based Encryption for EHR Access Control Scheme
## 6 Scheme Analysis
### Performance Analysis
#### 6.3.4 Blockchain Simulation Experiments  
To verify the effectiveness of smart contracts, we test the performance of smart contracts using the private chain simulation of Ethereum.  
In this experiment, we set the number of authorities to five. As shown in Figure 8, the horizontal axis is the attributes managed by AAs and the vertical axis is the response latency of the smart contract. The AMC does not have any computation and only involves the assignment of attributes, so it does not grow linearly with the number of attributes but always remains around 0.45 s. The other three contracts run a Lagrangian interpolation algorithm each time the key is reconstructed, so the response latency of the PAKGC, the UAKGC, and the UACTGC grows slowly with the number of attributes. The other three contracts run a Lagrangian interpolation algorithm each time the key is reconstructed, so the response latency of the PAKGC, the UAKGC, and the UACTGC grows slowly with the number of attributes. At the number of attributes of 50, the response latencies of PAKGC, UAKGC, and UACTGC are 1.818 s, 1.702 s, and 1.659 s, respectively. Since it does not affect the user experience, it is acceptable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & \(\mathbf{GP}\) & \(\mathbf{SK}\) & \(\mathbf{CT}\) & \(\mathbf{CT^{\prime}}\) \\ \hline Qin [21] & \((2+U)|G_{1}|+|G_{2}|\) & \(2|G_{1}|+N_{u}|G_{2}|\) & \((2 Nattr+1)|G_{1}|+|G_{2}|+L_{v}\) & \(2|G_{2}|\) \\ TMACS [6] & \((2U+2N+n+10)|Z_{p}^{*}|\) & \((2N_{u}+5)|Z_{p}^{*}|\) & \((2N_{attr}+1)|G_{1}|+|G_{2}|\) & \(-\) \\ dec-ABE [7] & \(2|G_{1}|+|G_{2}|\) & \((N_{u}+3)|G_{1}|\) & \((2N_{attr}+2)|G_{1}|+(N_{attr}+1)|G_{2}|\) & \(|G_{2}|\) \\ BC-SABE [24] & \(12|G_{1}|+|G_{2}|+Z_{p}^{*}|\) & \(|Z_{p}^{*}|\) & \((3N_{attr}+2)|G_{1}|+|G_{2}|\) & \(-\) \\ O3-R-CP-ABE [26] & \(3|G_{1}|+|G_{2}|\) & \((N_{u}+2)|G_{1}|+|G_{2}|\) & \(N_{attr}|G_{1}|+|G_{2}|\) & \(|G_{2}|\) \\ Our scheme & \(3|G_{1}|+|G_{2}|\) & \(2|G_{1}|+|Z_{p}^{*}|\) & \((3N_{attr}+1)|G_{1}|+(N_{attr}+1)|G_{2}|\) & \(|G_{2}|\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Communication overhead.  
Figure 7: The comparison of our scheme with the TMACS scheme [6], dec-ABE scheme [7], and BC-SABE scheme [24] in terms of user decryption time.The comparison of user decryption time.
# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
### Main Attributes of the Research  
The object of the research is the sensor data.  
The subject of the research includes the neural network system for predicting anomalous data in sensor systems.  
The research aim is to develop a neural network system for predicting anomalous data in sensor systems, which will provide enhanced accuracy and speed in detecting data deviations, allowing for timely responses to potential failures and malfunctions.  
To achieve this aim, the following scientific and practical tasks were solved:  
1. Development of the proposed neural network system for predicting anomalous data in sensor systems.
2. Development of the preprocessor model.  
3 Development of the predictor model.
* 4 Development of the reconstructor model.
* 5 Development of the anomaly detector model.
* 6 Conducting a computational experiment involving the simulation of predictive anomalies for a sensor in a helicopter turboshaft engine (TE).  
The main contribution of this research lies in developing a neural network system for predicting anomalous data in sensor systems, which will, in turn, enhance the reliability and safety of critical systems, minimize economic losses by preventing emergencies, and optimize maintenance and operation processes. The developed system will be capable of adapting to changing conditions and system dynamics, providing more effective real-time control and monitoring.
# Developing an Intelligent Cellular Structure Design for a UAV Wireless Communication Topology
## 2 Related Studies
Authors in [32] presented a tethered platform for rapid restoration of communications links after a disaster using a Radial Basis Function (RBF) approach. A semi-adaptive cellular structure design is used across various terrains. Researchers in [33] presented a small cellular structure via adopting massive MIMO on a drone. It had acceptable results for small geographical areas, but might result in a harmful impact on a larger geographical area. Reinforcement Learning (RL) is considered in [34] to enhance cellular systems by focusing on jamming channels. Authors in [35] use a multi-agent deep reinforcement learning (DRL) for cellular UAV communications and trajectory design. Sensible results are obtained when a drone transmits data close to the cellular structure central, while performance decays when the drone moves towards the edge of the cellular structure. Table 1 shows a comparison between related studies against the proposed model. By analyzing the related studies and based on what has been presented in Table 1, the research gaps have been identified, so we draw our own research motivations to inform our own proposed model. There are unsolved issues that have been identified in the literature, such as fixed or predefined cellular designs with or without an AI framework to obtain an adaptive cellular structure design. Therefore, our proposed novel contribution is motivated by this to cover this gap. To our best knowledge and based on the comparative analysis, no work has been carried out to achieve automatic optimization of the cellular structure design from a UAV perspective using an AI framework. Clearly, our proposed model aims to develop an adaptive and intelligent cellular structure design for a UAV wireless communication topology using an AI framework, which is a noticeable shift from existing work in the literature. To achieve our aim, the following objectives need to be pursued:  
* Determining parameters that affect designing cellular structure designs from a UAV perspective and via using MIMO antenna;
* Developing an AI framework to optimize the cellular structure design automatically;
* Validating the proposed work in the case of a real urban and densely populated zone.
## 1 Introduction
To address the limitations of existing methods, recent research trends have focused on integrating physics-based simulation with deep learning to establish multimodal defect detection frameworks. Finite-element analysis (FEA) provides accurate modeling of heat conduction behavior under defined material properties and boundary conditions, offering a reliable physical foundation for understanding temperature field distributions. Meanwhile, end-to-end deep learning models such as the YOLO series demonstrate superior image recognition capabilities, enabling rapid localization of defect regions in complex images. By combining these two complementary approaches, it is possible to achieve collaborative modeling that bridges physical mechanisms and data-driven recognition, thereby enhancing both the interpretability and robustness of defect detection systems. This work proposes a multimodal detection framework for semiconductor packaging defects that integrates finite-element thermal simulation, infrared thermal imaging, and the YOLO11 deep learning detection network. The main contributions of this work are summarized as follows:  
1. A comprehensive finite-element model of a typical BGA package is constructed, incorporating representative defects such as missing solder balls and bridging. Both steady-state and transient thermal responses are analyzed to characterize defect-induced temperature anomalies;
2. An experimental infrared thermal imaging system is developed to capture real thermal images of defective packages. These measurements are compared with simulation results to validate the physical consistency of the thermal model;
3. A hybrid dataset, combining simulated and experimental thermal images, is constructed to train and evaluate the YOLO11 network. This integration significantly enhances the model's detection accuracy and generalization capability across simulated and real-world scenarios.
## 3 Materials and Methods
### BCR Calculation  
In this step, the extracted building footprints from the previous step were used to compute coverage ratios. Tabular data from the extracted building footprint layers were used for calculations. The BCR is defined as the percentage of the lot area covered by buildings (Equation (6)). The BCR was calculated at both the block scale and the parcel scale, with the site coverage including all the building footprints inside a block or a parcel. An assessment of 116 blocks and 267 parcels in Zone B was conducted to evaluate the accuracy of the BCR calculation based on the DL model.  
\[BCR(\%)=\frac{\text{Sb}}{\text{Sl}} \tag{6}\]  
\[Accuracy(\%)=\frac{|e BCR-a BCR|}{a BCR} \tag{7}\]  
where Sb is the area covered by a building, defined by the building footprint, and Sl is the area of the site where the building is located.  
In order to calculate the BCR, the building coverage area and block areas are required. Thanks to DL's building footprint extraction ability, it can facilitate the BCR estimation. The DL is appropriate for extracting building areas; however, it may not be appropriate for extracting parcel areas. The building's area is dependent on construction, and it can change time by time due to the expansion demand, while the parcels' area is fixed. Therefore, extracting building areas based on DL models is more reasonable than extracting parcel areas. Developing a DL model takes time and effort; thus, using the DL model to extract a constant variable like parcel area is not appropriate for DL nature. Besides, there are several sources of data that are used to acquire parcel areas more attainable than using DL, such as cadastral data, land development plans, etc. Therefore, in this study, we used cadastral data to extract parcel areas. The resulting BCR values for the blocks and parcels were then compared with the ground truth data to evaluate the model accuracy. Accuracy is the fit between extracted BCR (\(eBCR\)) and the actual BCR (\(aBCR\)) as shown in Equation (7). The low difference in the ratio between extracted BCR and the actual BCR indicates the high accuracy of the DL-based approach. This is the key point that we used to evaluate the effectiveness of the DL-based method in computing the BCR.
## 4 Discussion
### Interpretation of Key Findings  
The simulation results demonstrated a 13% reduction in infections over two months, with cases dropping from 352 to 308. This significant decrease highlights the potential effectiveness of moderate interventions in mitigating disease spread, even in areas with high socioeconomic vulnerability. The findings suggest that a balanced approach to pandemic management can yield positive results without resorting to more extreme measures like complete lockdowns, which may be particularly challenging to implement in vulnerable communities.  
From an economic perspective, the simulation revealed a stabilisation in tax revenue and improved the population's purchasing power. This indicates that the adopted measures struck a balance between controlling the spread of the virus and maintaining essential  
Figure 7: Average economic impact on Joana Bezerra Island over 60 days.  
economic activities. Achieving this balance is crucial in areas like Joana Bezerra Island, where extreme economic disruptions could severely affect the vulnerable population.  
Implications for Public Health Policy  
The results of this study have several important implications for public health policy, especially in the context of socioeconomically disadvantaged areas.  
1. Tailored Interventions: The effectiveness of moderate measures in reducing infection rates suggests that public health strategies can be tailored to vulnerable communities' specific needs and constraints without compromising their efficacy.
2. Economic Considerations: The observed economic stability underscores the importance of considering health and economic impacts when designing pandemic response strategies. This is particularly crucial in areas with high levels of informal employment and limited social safety nets.
3. Feasibility of Implementation: The focus on universal mask use and moderate social isolation presents a more feasible approach for communities where strict lockdowns may be impractical or economically devastating.
4. Data-Driven Decision Making: Using agent-based modelling with detailed epidemiological frameworks like SEPAI3R3O can provide valuable insights for policymakers, allowing for more informed and targeted interventions.
## 4 On-Board Unit Implementation
### V2X Communication Protocols
```
{ \"type\": \"denm\", \"origin\": \"on_board_application\", \"version\": \"1.0.0\", \"source_id\": \"obu_cttc_1\", \"timestep\": 1574778515425, \"message\": { \"protocol_version\": 1, \"station_id\": 225, \"management_container\": { \"action_id\": { \"originating_station_id\": 41, \"sequence_number\": 1 }, \"detection_time\": 503253332000, \"reference_time\": 503253330000, \"termination\": 1, \"event_position\": { \"latitude\": 486263556, \"longitude\": 224921234, \"altitude\": 20000 } },... \"situation_container\": { \"information_quality\": 1, \"event_type\": { \"cause\": 97, \"subcause\": 0 }, \"linked_cause\": { \"cause\": 1, \"subcause\": 1 } },
```  
Listing 1: ETSI ITS fully compliant ASN.1 encoded DENM payload in JSON format.
# The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry
## 3 Impact on _i_GNSS-R Altimetric Performance
### Precision Analysis  
The prediction of the altimetric precision is of primary importance for proper design and optimization of a GNSS-R instrument. In [17; 28], comprehensive models were proposed based on the Cramer-Rao Bound (CRB). In this work, a simpler and effective model proposed in [13] is adopted, in which the altimetric precision \(\sigma_{h}\) is expressed as a function of the SNR, the altimetric sensitivity, the number of samples incoherently averaged \(N_{\text{inc}}\) and the elevation angle \(\theta_{\text{SP}}\) as  
\[\sigma_{h}=\frac{1}{2s_{\text{ALT}}\sin\theta_{\text{SP}}\sqrt{N_{\text{inc}}} }\sqrt{\left(1+\frac{1}{SNR}\right)^{2}+\left(\frac{1}{SNR}\right)^{2}} \tag{7}\]  
Taking the scenario parameters in Table 2 and the instrument parameters from [29], the altimetric precision of the different cases are computed with Equation (7) for different receiver bandwidths as presented in Figure 8. By assuming an optimal receiver baseband bandwidth of 12 MHz, the altimetry precision are summarized in Table 3 for different geometries and incoherent average time. These analytical results predict that the altimetric precision is 1.5\(\sim\)1.7 times better when including the IM component, which should be hence incorporated in the precision budget of \(i\)GNSS-R altimetry systems.  
Figure 8: Altimetric precision at nadir and the edge of swath as a function of baseband bandwidth when considering the IM component. Case 1: without IM, Case 2: with IM. An incoherent average time of 1 s has been used in this example.  
Figure 7: Altimetric sensitivities as a function of baseband bandwidth when considering the IM component. Case 1: without IM, Case 2: with IM.
# A Time-Varying Filter for Doppler Compensation Applied to Underwater Acoustic OFDM
## 3 Doppler Compensation for Multicarrier Modulation
### Impact of Mobility on Signal Integrity
\tag{18}\]  
The discrete-form vector representation of the received signal as expressed by Equation (17) is convenient because it can be directly modelled using digital signal processing simulation tools. It is used in the next section to provide an estimate of the optimal time-scaling factor.
## 3 Results
### Analysis of the Driving Factors of Farmland Abandonment
#### 3.3.4 Abandoned Farmland Analysis in Residential Buffer Zones  
The distance between farmland and residential areas is a key factor influencing farmland abandonment. This phenomenon is affected by multiple variables, including cultivation costs, transportation and farming conditions, economic returns, agricultural production costs, socio-economic factors, policy subsidies, land transfer mechanisms, and other related factors. In this study, the Euclidean distance from residential areas is calculated and classified into several concentric buffer zones. These buffer zones are categorized into five ranges: 0-100 m, 100-300 m, 300-500 m, 500-700 m, and greater than 700 m. Figure 9a shows the distribution of perennial abandoned farmland across different distances from the residential area, while Figure 9b illustrates the distribution of seasonal abandoned farmland. By overlaying maps of abandoned farmland and residential area buffer zones, the proportion of abandoned farmland within each buffer zone is calculated, and the proportion of abandoned farmland within each residential buffer zone is also analyzed Figure 9c. The results indicate that for perennial abandonment, 25.98% of the abandoned farmland is located within the 100 m buffer zone of residential areas, with nearly 60% of the abandoned farmland located within the 100-500 m buffer zone. In the case of seasonal abandonment, 22.83% and 27.85% of abandoned farmland are found within the 100 m buffer zone in March and December, respectively, while 61% and 66% of abandoned farmland fall within the 100-500 m buffer zone during the same months. Regardless of whether the abandonment is perennial or seasonal, less than 5% of the abandoned farmland is located in buffer zones beyond 700 m. These results suggest a strong spatial correlation between abandoned farmland and residential areas, with proximity to residential areas playing a significant role in farmland abandonment.  
The farmland area, abandoned farmland area, and farmland abandonment rate within different buffer zones under the influence of factors such as distance to water systems, slope, road network density, and distance to residential areas are shown in Table 2.  
Figure 8: Spatial distribution of abandoned farmland around roads, as well as the ratio of cultivated land area and abandonment rate within the buffer zone. (**a**) Perennial abandonment of farmland. (**b**) Seasonal abandonment of farmland. (**c**) Cultivated land ratio and abandonment rate.