esa-sceva/satcom-chunk-collection huggingface dataset:
# On-Road Driver Emotion Recognition Using Facial Expression
## 5 Results and Discussion  
### Training Details  
The datasets adopted for the source domain training were FER and CK+, and dataset adopted for the target domain training was the on-road driver facial expression dataset collected in this study. FERDERnet adopts the data augmentation re-sampling module (ABR) for the target original long-tailed dataset to alleviate data imbalance. Hence, the original long-tailed dataset was input into the face detection module (FD) of FERDERnet to generate grayscale faces as shown in Figure 5. It was then input into the ABR module, which outputs the augmented re-sampled dataset (effective images: 15,523; resolution: 160 \(\times\) 160, GRAY).  
The augmented and re-sampled dataset produced by the ABR module containing 15,523 images was fed into the ER module for model training. The target dataset was divided into a training set with 12,418 images and a test set with 3105 images. The performance in the target dataset was the result of performing stratified cross-validation, which has better performance in small, imbalanced datasets.  
Figure 4: Collected on-road driver facial expression dataset. Each clip contains the driver facial expression and road scenario synchronously; the driver facial expression is induced by various scenarios during day (the first three columns) and night (the fourth column).  
Figure 3: Screenshot of the developed annotation tool called “DriverEmotionLabelTool”, used to annotate the video sequences of the on-road driver facial expression dataset.  
This study employs the Adam optimizer with a learning rate of \(10^{-4}\). To confirm the training optimizer for the proposed FERDERnet, an experiment with different training algorithms was conducted. Five different optimizers were chosen, namely Adam, SGD, RMSprop, Adagrad, and Adamax. FERDERnet_R (FERDERnet with resnet50 backbone) was trained using the above five optimizers. As can be seen from Table 1 and Figure 6, the Adam optimizer obtained the best accuracy as well as the best F1-score. As for the training time, each optimizer required a similar time. Therefore, the Adam optimizer was confirmed to be the FERDERnet optimizer. With the Adam optimizer and cross-entropy loss, training FERDERnet executed 150 epochs for the source dataset and 50 epochs for the target dataset. For backbone details, in each backbone this research adopted, the batch normal layer was placed between convolution layer and ReLU layer, and dropout was only adopted for the full connect layer, with the dropout rate set to 0.5 for all five backbone networks. The model implementation was done using Pytorch. The model was trained and tested on a server with an Intel Xeon E5-2678 [EMAIL_ADDRESS] GHz CPU and a NVIDIA GeForce RTX 2080 Ti GPU.
## 4 Discussion
### Generalizability and Scalability Across Educational Levels  
Another critical strength of the proposed framework is its suitability for diverse educational settings, from small primary schools to sprawling multi-campus universities. Modularity--in both hardware (IoT sensors, edge devices) and software layers (predictive maintenance, scheduling, resource allocation, asset tracking)--enables institutions to adopt selected functions aligned with their immediate demands and technical readiness. For example, a remote high school may introduce AI-driven scheduling tools to minimise frequent classroom conflicts. At the same time, a large urban research university might roll out full-scale predictive analytics encompassing IT infrastructure, science labs, and energy systems [37]. The emphasis on open protocols and wireless communication (Wi-Fi 6, 5G) further bolsters scalability; expansions typically require simply adding additional sensors or server nodes to handle higher data loads. Flexibility is crucial for building a \"smart campus\", particularly given that some institutions (e.g., rurals) struggle with legacy infrastructure or constrained budgets. The multi-layer architecture in Figure 1 enables targeted, layer-specific tactics that translate the framework to low-resource campuses. For example, leveraging a neutral-host micro-cell with network slicing permits several rural schools to share radio and core resources; In [66], the techno-economic analysis reports a 35-45% reduction in annualised cost per user relative to single-tenant roll-outs. This slice-as-a-service model preserves the high-reliability back-haul assumed in Figure 1 (centre).  
Key closed-loop functions--predictive maintenance as shown in Figure 2) and timetable repair (Figure 5)--can execute on sub-10 W single-board devices located on-site; only lightweight model updates are synchronised, allowing operation during back-haul outages without disrupting the data flow arrows shown in Figure 1.  
Digital-inclusion work by [67] shows that coupling zero-rated mobile access with concise, practice-oriented micro-MOCCs for teachers increased LMS engagement among vulnerable learners by nearly one-third. Mirroring that finding, the dashboard in Figure 7 is implemented with progressive enhancement (HTML-only first, full 3D digital-twin view when bandwidth permits) and is accompanied by a two-step training path--online orientation before equipment arrival and quarterly in-person clinics (half-day refreshers every three months for the school's designated \"digital custodian\"). Embedding neutral-host slicing, edge-first execution, and blended capacity-building into the deployment roadmap ensures that inclusion is operational rather than aspirational, fully consistent with the modular logic of the proposed framework in this research. Lastly, smaller colleges could rely heavily on edge computing solutions or a cloud subscription service rather than maintaining their own data centres [68].
###### Abstract  
Machine learning (ML) algorithms have been developed for cost performance prediction in the form of single-point estimates where they provide only a definitive value. This approach, however, often overlooks the vital influence project complexity exerts on estimation accuracy. This study addresses this limitation by presenting ML models that include interval predictions and integrating a complexity index that accounts for project size and duration. Utilizing a database of 122 infrastructure projects from public works departments totaling HKD 5465 billion (equivalent to USD 701 billion), this study involved training and evaluating seven ML algorithms. Artificial neural networks (ANNs) were identified as the most effective, and the complexity index integration increased the R\({}^{2}\) for ANN-based single-point estimation from 0.808 to 0.889. In addition, methods such as bootstrapping and Monte Carlo dropout were employed for interval predictions, which resulted in significant improvements in prediction accuracy when the complexity index was incorporated. These findings not only advance the theoretical framework of ML algorithms for cost contingency prediction by implementing interval predictions but also provide practitioners with improved ML-based tools for more accurate infrastructure project cost performance predictions.  
artificial neural networks (ANNs); complexity index; cost contingency; infrastructure project; interval prediction Article
## 5 Methodology
### Methods  
In this section, we explore the methods employed to establish a viable communication link. Synchronization facilitated the detection of the start frame delimiter, while an additional guard time was implemented to enhance the effectiveness of symbol detection. Finally, we present a detailed description of the procedures used for identifying the transmitter's pixel location and demodulating symbols.
# Distributed Architectures and Constellations for \(\gamma\)-ray Burst Science
## 4 The Role of Distributed Architectures in Tomorrow's Multi-Messenger Astrophysics
### An Incomplete List of Pathfinder/Precursor Missions
#### 4.3.3 Gali  
A new concept for identifying the direction of GRBs was suggested recently by [PERSON] et al. [34]. The concept was named GALI (GAmma-ray burst Localizing Instrument). Its basic idea is to use numerous small scintillators (e.g., 1 \(\times\) 1 \(\times\) 1 cm\({}^{3}\) cubes) in a 3D array utilizing their mutual shielding. Consequently, the relative \(\gamma\)-photon count of each scintillator varies strongly with the direction of the burst. In a sense, GALI can be thought of as a coded-mask detector, but where the mask itself has detecting elements. Moreover, the detector (and mask) have no preferred direction, and thus provide full-sky coverage, as opposed to coded-mask instruments. A configuration such as CAMELOT, benefits from the SiPMs, which occupy little volume; hence, they enable the compact packing of the scintillators. As with GRBAlpha/CAMELOT, the SiPMs are radiation-sensitive, and need to be protected. A GALI laboratory prototype was successfully tested, and a flight model to be launched to the International Space Station (ISS) is being built. The laboratory prototype is shown in Figure 4.  
The GALI concept can be scaled to any size, and thus can fit many platforms. Clearly, larger versions will be more sensitive, and, more importantly, they will provide a better angular resolution. An advantage of the GALI configuration is the reduced sky background on the inner scintillators, which will light up only for GRBs in specific directions. This allows for a high signal-to-background ratio on these scintillators, and the exploitation of soft \(\gamma\)-rays below 50 keV for directionality. The aforementioned flight model will consist of 350 scintillators, occupying a total volume of merely \(\sim\)1 L. Simulations show that even such a small detector can identify the direction of a burst down to approximately \(\pm\)2\({}^{\circ}\) for 1 s GRBs with a 10 keV-1 MeV flux of 10 ph cm\({}^{-2}\) s\({}^{-1}\), \(\pm\)5\({}^{\circ}\) for 5 ph cm\({}^{-2}\) s\({}^{-1}\), and \(\pm\)10\({}^{\circ}\) for 2.5 ph cm\({}^{-2}\) s\({}^{-1}\)[34]. Although GALI can operate onboard a single satellite, it can also be incorporated into a distributed satellite architecture to enhance the sky coverage and directional capabilities of the entire constellation.
## 4 Soil Moisture Retrieval from Reflection Measurements: A Background on the Discipline
### LHCP-Based Soil Moisture Retrieval
It results in being:  
\[\frac{SNR_{refl,l}}{SNR_{dir}}=\frac{R^{2}}{(R_{A}+R_{B})^{2}}\cdot\left(| \Gamma_{vv}\left(\epsilon_{r},\theta\right)|+|\Gamma_{hh}\left(\epsilon_{r}, \theta\right)|\right)^{2}\cdot C \tag{5}\]  
where \(C=\frac{G_{refl,l}^{r}}{P_{N,l}^{r}}\cdot\frac{P_{N,dir}^{r}}{G_{dir}^{r}}\) depends on the hardware differences in the receiving chains, mainly due to antennas and RF filtering gains. The actual value of \(C\) must be determined with a calibration. One of the more robust ways to calibrate the system for soil moisture purposes is the on-water calibration, used for example in [34], through multiple over-water overflights. This is because the expected reflected power over water is well known given the incidence angle, while over the terrain the uncertainty is higher, due to the imperfect knowledge of the soil composition and its inherent dis-homogeneity. In order to have a more accurate calibration, a measurement campaign should be done _in situ_ with other sensors (hygrometers), for different soil types in different moisture conditions. This would involve the need of performing measurements for a long time, in order to have reliable measurements, and to compare all of the obtained results with the other sensors in the terrain. However, for the application at hand, the on-water calibration is proven to be quite an effective low-cost solution [34]. After the calibration, the dielectric constant \(\epsilon_{r}\) in Equation (5) is solvable via numerical routines, given the knowledge of \(R\), \(R_{A}\), \(R_{B}\) and \(\theta\). It has to be noted that from Equation (5), only \(|\epsilon_{r}|\) can be evaluated: in order to get the full soil moisture information, the real and imaginary parts of \(\epsilon_{r}\) need to be separated, which is possible considering empirical dielectric models, such as the one proposed in [31].
# Design and Performance Analysis of BDS-3 Integrity Concept
## 3 Integrity Risk Probability Distribution
### Integrity Risk Tree Model  
The risk tree method is a graphical model of expressing the logical relationship between a particular failure condition and the causes or failures leading to this condition. It is an application of fault tree analysis being used in the aerospace industry [21].  
The BDS satellite SIS integrity failure comes from the space segment and the ground segment. The bottom events of the space segment failure include the satellite clock anomaly, and the satellite signal and data anomaly (specifically, the transmitting power anomaly, the message data anomaly, the code-carrier inconsistency, and the signal distortion).  
The bottom events of the ground segment failure include the satellite orbit processing anomaly, the satellite clock processing anomaly, the ephemeris fitting anomaly, the data input anomaly, the orbit and time synchronization processing equipment anomaly, the monitoring station (MS) data anomaly (including the transmission link failure between the MS and the master control station (MCS)), and the message upload anomaly (including the upload failure caused by control instruction faults and configuration faults).  
Based on the above-mentioned bottom events, an integrity risk tree can be constructed to systematically analyze and evaluate the satellite service failure probability of BDS, as shown in Figure 1.  
There are a total of nine bottom events in Figure 1, including two in the space segment and seven in the ground segment. The probability of the top event is the known \(P_{\text{sat}}\). The occurrence of each bottom event could lead to the occurrence of the top event. The relationship between them is a simple probability addition, which can be expressed as following:  
\[P_{\text{sat}}=\sum_{i=1}^{9}P_{\text{event}_{i}} \tag{9}\]  
where \(P_{\text{event}_{i}}(i=1,2,\ldots 9)\) denotes the probability of each bottom event in the integrity risk tree.
## 6 Results and Discussion
### Performance Metrics
#### 6.1.2 Accuracy (Acc)  
It is measured by dividing the number of correctly labeled images by the total number of test images [4]. Equation (10) depicts single class accuracy measurements  
\[Accuracy=\frac{TruePositive+True\,Negative}{True\,Positive+True\,Negative+ False\,Positive+False\,Negative} \tag{10}\]
# Research on Improving Optical Field Distribution of TOSA Based on 3D Defocusing Coupling
###### Abstract  
The four-direction performance of a TOSA and power tested with different jumpers are affected by the optical field distribution of the TOSA. In order to improve the optical field distribution of a TOSA, this paper analyzes the factors affecting optical field distribution in the core and cladding of fiber and establishes a 3D defocusing coupling model to improve the uniform distribution of the optical field. The verification between the 2D coupling model and 3D defocusing coupling model shows that four-direction performance with 3D defocusing coupling is less than 1.0 dB, and the difference of power tested with different jumpers is not more than 0.5 dB. The results are better than that of a TOSA with a conventional coupling model, and the reliability and yield of the TOSA are higher. The 3D defocusing coupling model has practical guiding significance and economic value in TOSA production.  
coupling model; four-direction performance; 3D defocusing coupling model; optical field
###### Abstract  
Self-localization is one of the most challenging problems for deploying micro autonomous underwater vehicles (\(\mu\)AUV) in confined underwater environments. This paper extends a recently-developed self-localization method that is based on the attenuation of electro-magnetic waves, to the \(\mu\)AUV domain. We demonstrate a compact, low-cost architecture that is able to perform all signal processing steps present in the original method. The system is passive with one-way signal transmission and scales to possibly large \(\mu\)AUV fleets. It is based on the spherical localization concept. We present results from static and dynamic position estimation experiments and discuss the tradeoffs of the system.  
localization; micro autonomous underwater vehicles; swarm robotics; software-defined radio +
Footnote †: journal: Sensors
# Transmitter and Receiver Circuits for a High-Speed Polymer Fiber-Based PAM-4 Communication Link
## 1 Introduction
In this process, transistors can operate at an emitter current density of up to 12 mA/\(\upmu\)m\({}^{2}\) with a maximum collector-emitter voltage of 2 V. Measurements on a transistor with 10 \(\upmu\)m emitter length indicated an extracted \(f_{\rm t}\) and \(f_{\rm max}\) of 350 and 600 GHz, as well as a minimum noise figure of 4.3 dB [5]. The structure of this paper is divided as follows: after the introduction in Section 1, information about the design of the RF-DAC-based PAM-4 modulator is given in Section 2. Section 3 covers the design of the power detector. The combined measurement results are presented in Section 4. A discussion and conclusion are given in Section 5.
# Secure and Privacy-Protected Bioinformation Implementation in Air Passenger Transport Based on DLT
## 1 Introduction
The scientific novelty of this research in the aviation service industry is mainly the following:  
1. Although digitized biometrics are currently available for applications at each stage of the journey, integrating this digitized information from different stages and achieving meaningful applications remains a challenge. This article proposes a framework that combines the use of biometrics and DLT to improve data sharing between multiple parties in air passenger transport and facilitate passenger operations within airports. The framework enhances coordination and cooperation leading to smoother operations and a better overall travelling experience. 2. The framework, in realizing the vision of One ID, can meet the security requirements of information sharing in the aviation services industry. Its decentralized nature and permission management features ensure data security, preventing unauthorized access and increasing the level of protection of travelers' sensitive data. 3. In addition to the theoretical proposal, this research applies the framework to two possible specific service scenarios to demonstrate how both scenarios can facilitate service expansion and improve operational efficiency through the framework. The first scenario involves solving the baggage mismatch problem and the second scenario involves the sharing of Advance Passenger Information (API) data between airlines and border authorities. These two specific application scenarios validate the effectiveness of introducing biometric identification and DLT technology in improving service and operational efficiency. They demonstrate the feasibility and practical value of these technologies in real-world applications. The remaining contents of this article are organized as follows. Section 2 describes the related work of facial recognition and information sharing in the aviation transport industry; Section 3 discusses the proposed framework using Corda and facial information identification technology; Section 4 presents a scenario in which this framework can help with the baggage mismatch problem; Section 5 presents another scenario in which this framework can improve API data sharing between airlines and border authorities; and Sections 6 and 7 conclude this article with a discussion of the advantages and conveniences that the bioinformatic identification technology and the DLT framework can bring to both organizations and passengers. It also suggests future research directions for this research and related work that can be improved. The research hopes to provide valuable insights into the potential of DLT in air passenger transport.
## 4 Methods for Determining the Author of a Natural Language Text
### Deep Neural Networks
#### 4.2.1 Long Short-Term Memory  
LSTM is a successful modification of the classical RNN, which avoids the problem of vanishing or exploding gradients. This is due to the fact that the semantic weights of the LSTM model are the same for all time steps during error backpropagation. Therefore, the signal becomes too weak (exponentially decreases) or too strong (exponentially increases). This is the problem that LSTM solves.  
The LSTM model contains the following elements:  
* Forget Gate \"\(t\)\"--an NN with sigmoid;
* Candidate layer \"C\"--an NN with Tanh;
* Input Gate \"I\"--an NN with sigmoid;
* Output Gate \"O\"--an NN with sigmoid;
* Hidden state \"H\"--an vector;
* Memory state \"C\"--an vector.  
Then the time step \(t\) is considered. The input to the LSTM cell is the current input vector \(\mathbf{X}_{t}\), the previous hidden state \(H_{t-1}\), and the previous memory state \(C_{t-1}\). The cell outputs are the current hidden state \(H_{t}\) and the current memory state \(C_{t}\). The following formulas are used to calculate outputs:  
\[f_{t}=\sigma(\mathbf{X}_{t}*\mathbf{U}_{\mathbf{f}}+H_{t-1}*\mathbf{W}_{t}), \tag{12}\]  
\[\overline{C_{t}}=tanh(\mathbf{X}_{t}*\mathbf{U}_{\mathbf{c}}+H_{t-1}*\mathbf{W} _{\mathbf{c}}), \tag{13}\]\[I_{t}=\sigma(\mathbf{X}_{t}*\mathbf{U}_{\mathbf{i}}+H_{t}*\mathbf{W}_{\mathbf{i}}), \tag{14}\]  
\[O_{t}=\sigma(\mathbf{X}_{t}*\mathbf{U}_{\mathbf{o}}+H_{t-1}*\mathbf{W}_{\mathbf{o }}), \tag{15}\]  
where \(\mathbf{X}_{t}\)--the input vector; \(H_{t-1}\)--the hidden state of the previous cell; \(C_{t-1}\)--the memory state of the previous cell; \(H_{t}\)--the hidden state of the current cell; \(C_{t}\)--the memory state of the current cell at time \(t\); \(\mathbf{W}\), \(\mathbf{U}\) are the weight vectors for the forget gate \(f_{t}\)), the gate of candidates, i.e., an input and output gates; \(\sigma\)--sigmoidal function; _tanh_--tangential function.  
The most important role is the state of memory \(\overline{C_{t}}\). It is the state in which the input context is stored. It changes dynamically depending on the need to add or remove information. If the value of the forget gate is 0, then the previous state is completely forgotten; if equal to 1, then it is completely transferred to the cell. With the current state of \(C_{t}\) memory, a new one can be calculated:  
\[C_{t}=f_{t}*C_{t-1}+I_{t}*\overline{C}_{t}. \tag{16}\]  
Then it is necessary to calculate the output from the hidden state \(H\) at time \(t\). It will be based on memory state:  
\[H_{t}=O_{t}*tanh(C_{t}), \tag{17}\]  
Received \(C_{t}\) and \(H_{t}\) are transferred to the next time step, and the process is repeated.
## 2 Materials and Methods
### Batch Adsorption Experiments
#### 2.2.2 Effect of Contact Time and Temperature  
To investigate the adsorption kinetics and thermodynamics of m-cresol, batch experiments were conducted using 0.15 g of resin at four different temperatures: 30 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 60 \({}^{\circ}\)C. A total of 40 clean 15 mL centrifuge tubes were prepared and properly labeled. For each test, 5 mL of m-cresol solution (20 g L\({}^{-1}\)) was added to a tube containing the specified amount of resin. The tubes were then placed in a thermostatic shaker maintained at 150 rpm. Samples were collected at predetermined time intervals: 5, 10, 20, 30, 60, 120, 240, 480, 720, and 1440 min. After agitation, each sample was immediately centrifuged and filtered through a 0.45 \(\upmu\)m membrane. The supernatants were subsequently analyzed for residual m-cresol concentration. All sampling was performed in a consistent and repeatable manner, ensuring that the delay between stopping agitation and measuring each sample was uniform across all timepoints. To minimize potential error at early timepoints, we acknowledge that pipette sampling combined with in-line filtration would offer greater precision for kinetic studies, and we plan to adopt this improved method in future work.