esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Comparison of Prediction Accuracy of Classical and Penalized Methods Using CV as Criterion
The performance of ALASSO and NNG depends on initial estimates, which are less reliable when data contain limited information, thus negatively affecting their prediction accuracy. RLASSO has two tuning parameters that also require sufficient information for accurate estimation. These factors may explain why lasso is often preferred in challenging conditions [51]. Conversely, in scenarios with low correlation and high SNR, NNG, ALASSO, and RLASSO outperformed the lasso, for the reasons previously explained. In summary, lasso performed best in prediction accuracy in scenarios with limited information, whereas classical methods as well as NNG, ALASSO, and RLASSO performed best in scenarios with sufficient information. Additionally, NNG, ALASSO, and RLASSO outperformed classical methods in scenarios with low SNR. Among NNG, ALASSO, and RLASSO, no single approach consistently outperformed the others. In scenarios with sufficient information, NNG, ALASSO, RLASSO, or classical methods may be used, as they yield similar predictions. However, classical methods may be preferred due to their tendency to produce simpler models.
## 2 Related Work
### FQT  
Full Quantization Training (FQT) represents the next frontier in the evolution of quantization techniques, where not only the weights and activations but also the gradients are quantized during the training process [34; 35; 36; 37; 38; 39; 40; 41]. This comprehensive approach to quantization aims to fully leverage the benefits of low precision throughout the entire training process, potentially leading to significant improvements in both training efficiency and model deployment [13].  
The primary motivation behind FQT is to extend the benefits of low precision to the backward pass of training, where gradients are calculated and used to update the model's weights. By quantizing gradients, FQT reduces the memory bandwidth and computational requirements of training, which can be particularly beneficial for large-scale models trained on distributed systems [42]. However, this approach also introduces additional challenges, particularly in terms of numerical stability, as low-precision gradients can lead to inaccuracies in the weight updates, potentially hindering the convergence of the model [8].  
To address these challenges, FQT methods often employ a variety of techniques to stabilize the training process. For example, dynamic scaling of gradients is a common approach, where the gradients are scaled to a higher precision during critical stages of training to prevent numerical errors [43; 44]. Additionally, gradient clipping is frequently used to limit the range of gradient values, reducing the risk of instability during training. These techniques are crucial for ensuring that FQT can achieve competitive performance while maintaining the efficiency benefits of low-precision training.  
Recent research in FQT has also explored the use of mixed-precision training, where different parts of the network are trained with different levels of precision [9; 45]. This approach allows for a more flexible and adaptive training process, where the precision can be adjusted based on the specific needs of each layer or operation within the network [46].
# MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
## 2 Related Work  
For decades, the implementations of machine learning algorithms on low-cost embedded systems have been vigorously investigated. Due to the limited computing resources on embedded systems, these approaches tend to lack outstanding performance. However, FPGA is an effective solution for machine learning algorithms [13]. [PERSON] et al. [14] proposed a decision tree hardware architecture based on FPGA. It improves data throughput and resource utilization efficiency by utilizing parallel binary decision trees and a pipeline. A 3.5\(\times\) computing speed is achieved while only 2952 Look-up Tables (LUTs) of resources are consumed via an 8-parallelism 4-stage pipeline. [PERSON] et al. [15] proposed a binary classification architecture based on SVM and k-NN. Over 10\(\times\) computing speed and 200\(\times\) power-delay are obtained as compared with ARM A53. [PERSON] et al. [16] proposed a 4-stage, low-power SVM pipeline architecture capable of achieving 98% of accuracy on over 30 classification tasks. It consumes only 1315 LUTs of resources and operates at a system frequency of 50 MHz. The aforementioned works introduce the deployment of MLalgorithms on FPGA platforms, but there are still deficiencies: 1. None of the works integrate the mainstream ML algorithms for FPGA deployment; 2. None of the works compare the final results horizontally with other embedded platforms of various IoT terminals.  
Due to the high inherent parallelism in FPGAs, more advanced machine learning algorithms, such as neural networks, are widely studied. [PERSON] et al. [17] proposed a Deep Neural Network (DNN) based architecture for classification tasks on low-power FPGA platforms. They thoroughly compared the performance with STM32 ARM MCU and designed a general communication interface for accelerators, such as SPI and UART. The entire acceleration process consumes only 25.459 mW of power with a latency of 1.99 s. [PERSON] et al. [18] proposed a Deep Learning Accelerating Unit (DLAU) that accelerates neural networks, e.g., DNN and CNN. Additionally, they developed an AXI-Lite interface for the acceleration unit to enhance its versatility. In general, the DLAU outperforms Cortex-A7 by 67%. [PERSON] et al. [19] developed a resource-constrained CNN accelerator for IoT endpoint SoCs that does not require any DSP resources. With a total resource overhead of 4901 LUTs, the data throughput reaches 6.54 GOP (Giga Operation per second). The above works present neural network-based FPGA deployments, but none of them is designed for low-cost FPGAs, which are oftentimes the most prevalent platforms used for IoT endpoints.  
Although the previous works succeed in increasing the capability of endpoint computing by implementing only one or two machine learning algorithms on FPGAs, further analyses and comprehensive comparisons across low-cost FPGA platforms, as well as the integration of more commonly used machine learning algorithms are still required.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 3 Watermark Embedding and Extraction Algorithm  
### Video Watermark Embedding Algorithm  
The video watermark embedding algorithm framework in this paper is shown in Figure 3. The algorithm can be applied to color video. First all I-frames and their chromaticity subblocks are extracted for color video sequences in the compression process, and the subblocks to be embedded are selected according to the \(C\)-value size of each subblock; Then, according to the characteristics of the video and watermark, the modulation mode is determined adaptively; after modulation by the third non-zero quantized DCT coefficient of the subblock, the watermark is embedded; The watermarks are replayed back to the I frame and the H.264 encoding is continued. Finally, the H.264 encoded video containing watermark is outputted.
## Appendix A
### Quant GAN Training Procedure  
The QGAN training pseudocode in Algorithm A1 below is based on open-source code from [PERSON] et al. [12] retrieved on 8 August 2020 at [[https://www.techatbloomberg.com/machine-learning-finance-workshop-2020/](https://www.techatbloomberg.com/machine-learning-finance-workshop-2020/)]([https://www.techatbloomberg.com/machine-learning-finance-workshop-2020/](https://www.techatbloomberg.com/machine-learning-finance-workshop-2020/)). Figure 6 shows the implemented topology. Figure A5 and Algorithm A1 below demonstrate the QGAN training procedure.  
It was noted that the open-source implementation in pytorch promotes training convergence by using batch normalisation [64] to reduce the covariate shift in hidden layers and spectral weight normalisation [65] to stabilise network training by rescaling weight tensors by their spectral norms. The Adam optimisation algorithm [67] was used to optimise the TCNs.  
```
Inputs:Real data \(X\in\mathbb{R}^{N_{X}\times T}\), minibatch size \(M\in\mathbb{N}\), max epochs \(E\in\mathbb{N}\) TCN hyperparameters: # of temporal blocks \(L\), inner blocks per temporal block \(B\), kernel size \(K\), dilation factor \(D\), skip layer dimensionality \(N_{skip}\) temporal block dimensions \(\mathfrak{L}=\{N_{1},\dots,N_{L}\}\), inner block dimensions \(\mathfrak{B}=\{N_{l_{i}}\}_{i=1}^{B}\forall l=\{1,\dots,L\}\) Topology:Discriminator TCN: \(D(X\in\mathbb{R}^{N_{X}\times T_{RS(D)}};\theta_{d},\mathfrak{L},\mathfrak{B},N_{skip},K,D,\phi)\mapsto[0,1]\) Generator TCN: \(G(Z\in\mathbb{R}^{N_{Z}\times(T_{RS(D)}+T_{RS(G)}-1)};\theta_{g},\mathfrak{L},\mathfrak{B},N_{skip},K,D,\phi))\mapsto\mathbb{R}^{N_{X}\times T_{RS(D)}}\) \(D\) learning rate \(\alpha_{d}\), \(G\) learning rate \(\alpha_{g}\) Outputs:Optimal TCN parameters: \(\theta_{g}^{*},\theta_{d}^{*}\) Synthetic Data: \(\tilde{X}\in\mathbb{R}^{N_{X}\times T}\)
1 init params \(\theta_{g},\theta_{d}\) given topology \(L,B,K,D,N_{skip},\mathfrak{L},\mathfrak{B}\) for\(e=1\) to \(E\):do // sample minibatch of real data \(\{X_{i}\}_{i=1}^{M}\) // generate minibatch of fake data Draw minibatch of noise prior \(\{Z_{i}\}_{i=1}^{M}\) Simulate \(\{\tilde{X}_{i}\}_{i=1}^{M},\tilde{X}_{i}=G(Z;\theta_{g})\) // current \(\theta_{g}\) // stopping condition(s): if properties of \(\tilde{X}\) or G, \(D\) loss conditions met ifstopping condition(s) met:then break else // Train generator: // Estimate \(\mathbb{G}\) minibatch gradient: // current \(\theta_{d}\) // Desecond generator gradient \(\theta_{g}\leftarrow\theta_{g}-\alpha_{g}\cdot\Delta_{\theta_{g}}\) // Train discriminator: // Estimate \(\mathbb{D}\) minibatch gradient \(\Delta_{\theta_{d}}\leftarrow\
abla_{\theta_{d}}\frac{1}{M}\sum_{i=1}^{M} \log D_{\theta_{d}}(X_{i})+\log\big{(}1-D_{\theta_{d}}(\tilde{X}_{i})\big{)}\) // Ascent discriminator gradient \(\theta_{d}\leftarrow\theta_{d}+\alpha_{d}\cdot\Delta_{\theta_{d}}\) // Generate synthetic paths \(\tilde{X}=G_{\theta_{g}}(Z)\) return\(\theta_{g}^{*},\theta_{d}^{*},\tilde{X}\)
```  
**Algorithm A1**Quant GAN training [12]
# Architectural Design for Digital Twin Networks
## 4 Functional DTN Implementation
### DTN Description
#### 4.1.2 Graph Algorithms Facet  
The DTN is improved by integrating graph features and algorithms into the Click architecture. This enhancement uses the open-source C++ graph library LEMON [31] to implement a directed graph based on the topology.
# Hybrid Renewable Systems for Small Energy Communities: What Is the Best Solution?
## 2 Methods and Developments
### Model Deployment  
#### 2.2.1 Small Energy Community Characteristic  
In this study, a small community in the northern part of Portugal, Marruge, was selected as the site for the setup implementation of a small hybrid energy solution (Figure 6). The model's simulation algorithm is set to an hourly timestep. Two approaches were delineated for this study: stand-alone and grid-connected.  
The village of Marruge, located in the Caramulo Mountains, provides an ideal setting for studying a grid-connected energy system due to its accessible location, as it is neither an isolated nor a highly remote community. Marruge features an agricultural area spanning approximately 8 hectares, developed into terraces. While this system stabilizes soil, reduces water runoff, and minimizes erosion, it also poses challenges, such as making the use of heavy machinery difficult. Traditionally, the land has been used to cultivate cereals, fruits, legumes, and vegetables. The village uses an irrigation system based on uneven gutters fed by local springs. However, several risks threaten the sustainability of Marruge, including the aging population, fire hazards, limited accessibility, and the loss of natural habitats due to the spread of eucalyptus trees.  
Despite these challenges, Marruge holds significant potential for development, particularly in sustainable rural tourism focused on health, nature, and well-being. The village's rich biodiversity, favorable microclimate, and strong cultural heritage, including traditional mills, granaries, and schist houses, make it an attractive destination. Its location in Serra do Caramulo and the presence of abundant water sources further enhance its appeal. Marruge could also benefit from investment partnerships with regional companies interested in social and environmental responsibility, alongside institutional support and a growing demand for sustainable tourism. These factors present a valuable opportunity for revitalizing the village while conserving its natural and cultural assets, providing new hybrid energy solutions able to incentive new water and energy nexus allocations.
# An Established Theory of Digital Twin Model for Tunnel Construction Safety Assessment
## 4 Establishment of DT Construction Model
### Analysis of Content Covered by Virtual Model
#### 4.1.1 Determine the Modeling Content and Its Accuracy Level  
At this stage, the digital twin model and its modeling levels are determined according to the influence of different elements in the construction process on construction safety. The definition and classification of modeling accuracy are conducted through the hierarchical  
Figure 2: Two phases of creating the digital twin model.  
method of the black box, gray box, and white box. Among them, the white box indicates the construction elements that need to be described as completely as possible; the gray box indicates the construction elements that need to be or can be only partially described; the black box shows the features that can only be described. Finally, the knowledge model is established through modeling behavior and rule dimensions, which endows the digital twin model with the ability to simulate dynamic construction behavior. The specific process can be divided into the following:  
1. Establish coverage of construction elements that must be modeled. Experts' experience can be used to analyze and determine the construction elements that significantly impact the construction safety assessment. To reduce effort and digital models' complexity, the work should avoid modeling non-impact or low-impact construction elements. The premise is that the model can complete the simulation and simulation of the construction process.
2. Define the modeling level for each construction element. The modeling level should focus on the importance of construction elements, the comprehensiveness of available information, and the data characteristics of construction elements. Ideally, all construction elements should be defined to white-box precision. However, for some construction elements, not all the information data significantly influences the construction process's safety management. Some component information is incomplete, so it can be established at the gray-box level. The data, which has little influence on construction safety, can be established at the black-box level. It is worth noting that the data at the black-box level are still indispensable in ensuring the integrity of the construction twin model.
3. Finally, according to the construction process of the actual project, the virtual models of each construction element are integrated to obtain a complete digital twin model of the construction process. Based on the establishment of the knowledge-dimensional twin model, the twin model is given the ability and rules to evolve to be mapped and matched with the actual construction process in real time.  
The specific steps are shown in the Figure 3.  
Figure 3: Actions and sub-actions of phase 1.
# Assessment of Imaging Protocol and Patients Radiation Exposure in Computed Tomography Colonography
## 3 Results and Discussion
However, the potential dose reduction depends on the proper choice of imaging factors and images' standard quality [26]. In the Philips CT machine, the noise level can be adjusted to obtain image contrast based on patient size. ATCM is activated using Dose Right, automatic current selection (ACS), to get acceptable image quality by variation in tube current around the patient, and the z-direction. For this, technologist intervention is necessary. The mAs per CT slice need proper adjustment since drift from the initial setting overtime is expected [26; 27]. In comparison with the GE CT machine, the ATCM sustains a similar noise level during image acquisition. A reference image from a standard phantom is needed, using GE AutomA 3D has z-axis (AutomA) and angular modulation (SmartmA), with the mA being changed for each quadrant during every rotation [28]. To avoid overexposure the technologist role is crucial in selecting a noise reference based on patient weight [29]. From the results presented in Table 3A,B, the ATCM may not be adjusted by the technologist, which leads to a higher radiation dose. Figure 1A shows direct relationship between tube current (mA) and the CTDI\({}_{\text{vol}}\) (mGy) and effective dose (mSv). CTDI\({}_{\text{vol}}\) and effective dose increase with tube current value. According to the ACR recommendation, the standard effective dose per procedure is 10 mSv. The American Association of Physicist in Medicine (AAPM) recommends a 3.0 to 6.0 mGy range of CTDI\({}_{\text{vol}}\) for an average sized patient (70-90 kg) (AAPM, 2011). The AAPM recommended the CTDI\({}_{\text{vol}}\) value of 2-4 mGy and 5-9 mGy according to patient weight ranges, 50-70 kg and 90-120 kg, respectively. Figure 1B shows the relation between the CTIDvol (mGy) and the DLP (mGy.cm) per CTC procedure. Wide variation is observed in DLP (mGy.cm) value as CTDI\({}_{\text{vol}}\) increases (mGy). Both dose descriptors are patient dose indices that calculate patient dose per slice volume and per procedure, respectively. Although patient doses differ according to patient size, in agreement with current study, previous studies have shown that patient dose can fluctuate by as much as a factor of two through patient size for a similar type of CT procedure to obtain radiographic images of comparable quality. [PERSON] and [PERSON] [30] reported a variation in patients' doses ranging from 10 to 100 fold variances in DLP (mGy.cm) for CT procedures acquired for the typical clinical
# Automated Code-Smell Detection in Microservices Through Static Analysis: A Case Study
## 5 Proposed Solution to Detect Code Smells
### Shared Persistency  
Shared persistency happens when two or more modules of the application share the same relational database. An example of this can be seen in Figure 2. This is detected by parsing the application's configuration files and finding the submodules' persistence settings location. For example, in a Spring Boot application, the application YAML file is parsed for the datasource URL. Then, the persistence of each module is compared to the others to find shared datasources.
## 3 Results
### Case 4 (24 February 2015)  
On 24 February 2015, geomagnetic conditions were quiet (minimum Dst \(\sim-\)25 nT; daily SSN: 44). Around 22:30 UT, SF was observed at Pruhonice and San Vito, as illustrated in Figure 5a. Specifically, Pruhonice exhibited an MSF ionogram signature, while San Vito registered an FSF signature. Over Pruhonice, a southeastward oblique plasma trace was noted, supported by MSTID propagation, as shown in Figure 5b. The corresponding ROTI map (Figure 5c) recorded fluctuations of about 0.2 TECU/min over Pruhonice and 0.15 TECU/min over San Vito under the SF activity.  
Figure 5: (a) observation of SF events over Pruhonice and San Vito at 22:30 UT; (**b**) MSTID in GPS d-TEC map (pink markers indicate locations of Pruhonice and San Vito); (**c**) ROTI fluctuations observed over Pruhonice and San Vito; (**d**) Swarm B track over Pruhonice and San Vito; and (**e**) latitude Ne and (**f**) dNe profiles of Swarm B over Pruhonice and San Vito latitude.  
The latitude Ne profile of Swarm B, shown in Figure 5e, reveals a pronounced south-to-north gradient over the Pruhonice latitude around 22:21 UT, while a less steep northward gradient was registered over San Vito. Figure 5f highlights the topside irregularity signatures coinciding with SF occurrences at both locations. Over Pruhonice, where MSF developed with an hmF2 of 400 km, dNe fluctuations were approximately 2 \(\times\) 10\({}^{4}\) elec/cm\({}^{3}\). In contrast, FSF was noted over San Vito with an hmF2 of about 360 km, accompanied by a lower dNe fluctuation of 0.5 \(\times\) 10\({}^{4}\) elec/cm\({}^{3}\).  
These observations suggest that bottomside ionospheric irregularities can be uplifted and subsequently develop into FSF. [PERSON] et al. 2022 [30] described the asymmetric geomagnetic and electric field lines facilitating this upward motion, causing the irregularities to cascade and initiate small-scale disturbances in the topside ionosphere. They noted that medium- and large-scale irregularities observed in Swarm data are not directly attributable to any specific bottomside structures.  
This case study highlights a strong association between ROTI fluctuations and fluctuations in the latitude Ne profile of Swarm under the influence of SF activity. [PERSON] et al. 2016 [34] also reported consistent results, demonstrating a robust relationship between GNSS-derived ROTI measurements and concurrent in situ Ne data from the Swarm LP. Their findings confirmed a notable similarity in the seasonal and longitudinal patterns of topside ionospheric irregularities.