esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Error Analysis and Misclassifications  
Error analysis is a critical aspect of evaluating deepfake detection models, providing insights into their strengths and weaknesses in classifying real and manipulated content. Confusion matrices for the three models--Xception, ResNet-50, and VGG16--are presented in Figures 3-5, offering a breakdown of the number of true positives (\(TP\)), true negatives (\(TN\)), false positives (\(FP\)), and false negatives (\(FN\)) for the DFDC test set.  
The Xception model demonstrates balanced performance, with low \(FP\) and \(FN\) rates, indicating its robustness in correctly identifying both real and fake samples. In contrast, ResNet-50 exhibits higher false positive rates, where real frames are misclassified as fake, potentially undermining its reliability in real-world scenarios. VGG16, while achieving relatively high overall accuracy, shows a tendency toward false negatives, particularly in videos with subtle manipulations.  
Figure 3: Confusion matrix for Xception on the DFDC dataset. Xception demonstrates balanced performance with a low false positive rate and high recall, effectively capturing deepfake-specific features.  
Figure 2: Cross-dataset generalization with 10% fine tuning on FaceForensics++.  
These patterns suggest that each model has distinct limitations in handling specific deepfake artifacts, such as lighting inconsistencies, expression mismatches, and subtle edge blurring. Such insights are crucial for refining detection strategies and enhancing the models' practical deployment in diverse environments.  
Consolidated Observations:  
* Xception: Exels in both precision and recall, making it a robust choice for applications requiring high reliability. It achieves a low false positive rate (10.9%) and high recall (90.5%).
* ResNet-50: The higher false positive rate (27.3%) highlights a need for improved feature extraction or better generalization techniques. While it offers faster inference, it struggles with correctly identifying real samples.  
Figure 4: Confusion matrix for ResNet-50 on the DFDC dataset. ResNet-50 exhibits higher false positives, misclassifying real frames as fake, impacting its precision.  
Figure 5: Confusion matrix for VGG16 on the DFDC Dataset. VGG16 achieves balanced performance but faces challenges with subtle manipulations, leading to occasional false negatives.  
* VGG16: Balances performance but exhibits occasional difficulties with subtle manipulations, leading to a false negative rate of 12.2%. This trade-off may require additional fine tuning or ensemble integration for high-precision applications.  
By analyzing these confusion matrices, it is evident that while all models have strengths in detecting manipulated content, specific trade-offs in error types (_FP_ vs. _FN_) are critical for selecting models tailored to particular use cases. Future research should aim to address these limitations through adversarial training, data augmentation, or hybrid model approaches.
# Development of Highly Sensitive Temperature Microsensors for Localized Measurements
## 2 Materials and Methods
### Electrical Characterization Setup  
After successful fabrication, temperature measurements with the fabricated sensors were performed. The apparatus used comprised a programable current DC source (Yokogawa 7651) for supplying the sensors (excitation) when characterizing their single performance, a hotplate (Prazitherm type PZ28-2, 1100 W) and a peltier module as heating sources to control the temperature in the sensors, a digital multimeter (Agilent 34410A 61/2) connected to a computer, a custom-made software to continuously read the electric signals from the sensors and a picoammeter (Keithley 6487) for reading, with precision, the current flowing by the sensor (Figure 7).  
Figure 5: Schematic diagram of temperature microsensors patterning process: (**a**) glass wafer; (**b**) LOR1A-SB200 lift-off resist; (**c**) AZ4110 positive photoresist; (**d**) UV exposure; (**e1**) developed resist and (**e2**) LOR; (**f**) platinum deposition; (**g**) removal of the lift-off resist; and (**h**) deposition of SiO\({}_{2}\).  
Figure 6: **(a)** Photographs of the fabricated temperature microsensors and (**b**) detail of the different windings (line width and spacing of 10 \(\upmu\)m).
## 2 Materials and Methods
### Statistical Analysis  
All results are expressed as a mean \(\pm\) standard error (Mean \(\pm\) SEM) of a minimum of three independent experiments performed in triplicate. Statistical analysis was performed using software SPSS(r) v.21 (IBM, NY, USA). To evaluate if quantitative variables follow a normal distribution the Shapiro-Wilk test was used. When the distribution was normal, parametric tests were used, otherwise, nonparametric tests were applied. Two sample comparisons were assessed either by the Student's t-test or by resorting to the Mann-Whitney test according to the need of using parametric or non-parametric tests. Comparison of the quantitative variables between more than two groups was obtained using Kruskal-Wallis (non-parametric test) or using the ANOVA test (parametric test). Multiple comparisonscorrections were performed considering the Bonferroni procedure. Metabolic activity and protein content were normalized to non-treated same-cell line controls. This procedure allowed for the creation of dose-response curves and calculation of the treatment duration that inhibits metabolic activity and protein rate by 50%, analogous to IC50 (half maximal inhibitory concentration) for each cell line. Results were analyzed and processed using the software Origin Pro 8.0 (OriginLab, Northampton, MA, USA) and adjusted to a sigmoidal curve. Confidence intervals of 95% were obtained for each cell line.
# Generalized Paxos Made Byzantine
## 4 Crash Fault Tolerant Protocol
### Agreement Protocol
#### 4.1.1 Classic Ballots
The acceptors reply to _phase 2a_ messages by sending _phase 2b_ messages to the learners, containing the ballot and the proposal from the leader. After receiving \(N-f\) votes for a sequence, a learner learns it by extracting the commands that are not contained in his _learned_ sequence and appending them in order. Please note that for a sequence to be learned, a learner does not have to receive \(N-f\) votes for the exact same sequence but for equivalence sequences (in accordance to our previous definition of equivalence). ```
1:\(ballot_{l}=0,proposals=\bot,accepted=\bot\)
2:upon trigger_next_ballot(\(type\)) do
3:\(ballot_{l}+=1\)
4:\(\textsc{send}(BALLOT,ballot_{l},type)\) to proposers
5:if\(type=fast\)then
6:\(\textsc{send}(FAST,ballot_{l},view)\) to acceptors
7:else
8:\(\textsc{send}(P1A,ballot_{l},view)\) to acceptors
9:
10:upon receive(\(PROPOSE,prop\)) from proposer \(p_{i}\)do
11:if\(\textsc{isUniversallyCommutative}(prop)\)then
12:\(\textsc{send}(P1A,ballot_{l},prop)\) to acceptors
13:else
14:\(proposals=proposals\bullet prop\)
15:
16:upon receive(\(P1B,ballot,bala_{a},vals_{a}\)) from acceptor \(a\)do
17:if\(ballot_{a}=ballot_{l}\)then
18:\(accepted[ballot_{l}][a]=\langle bala_{a},vals_{a}\rangle\)
19:if\(\#(accepted[ballot_{l}])\geq N-f\)then
20:\(\textsc{phase\_2a}()\)
21:
22:functionphase_2a()
23:\(votes=\bot\)
24:\(k=-1\)
25:for\(a\)in\(acceptors\)do
26:\(bal_{a}=accepted[ballot_{l}][a][0]\)
27:\(val_{a}=accepted[ballot_{l}][a][1]\)
28:if\(bal_{a}>k\)then
29:\(k=bal_{a}\)
30:\(votes=\bot\)
31:elseif\(bal_{a}=k\)then
32:\(votes[val_{a}]+=1\)
33:if\(votes[val_{a}]>f\)then
34:\(maxTried_{l}=val_{a}\)
35:break
36:for\(a\)in\(acceptors\)do
37:\(maxTried_{l}=\textsc{merge\_sequences}(maxTried_{l},accepted[ballot_{l}][a])\)
38:\(maxTried_{l}=maxTried_{l}\bullet proposals\)
39:\(\textsc{send}(P2A\_CLASSIC,ballot_{l},maxTried_{l})\) to acceptors
40:\(proposals=\bot\)
41:\(maxTried_{l}=\bot\)
42:endfunction
```  
**Algorithm 3** Generalized Paxos--Leader 1
# A Regionalization Approach Based on the Comparison of Different Clustering Techniques
## 5 Conclusions  
To our knowledge, this is the first quantitative attempt to conduct an environmental regionalization of the study area. We utilized a range of algorithms, each presenting its own advantages and limitations. The process of selecting the most appropriate method that closely aligns with the reference regionalization proved to be a valuable learning experience. We identified several positive results when comparing our results with the reference regionalization; the spatial and environmental coherence of the regions generated by the K-means++ algorithm, which incorporates spatial contiguity through a third-degree polynomial, underscores the analytical robustness of our approach. This method enabled us to achieve a certain level of comparability, particularly at broader classification scales, such as Olson biomes, though not at finer scales, such as ecoregions.  
We conclude that the regionalization produced by K-means++ is reliable for high-level environmental classifications. It has shown that the regions obtained are aligned in some way to one of the most used regionalizations in the scientific community and has shown robustness in the construction of regions generally. Consequently, this regionalization can serve as a valuable tool for environmental research, for example, in the identification of climatic patterns that influence the presence/absence of species or in predictive distribution models. The results obtained from the federated model, or Infomap, have demonstrated robustness in the construction of regions, highlighting above all how the hybridization of models can be a powerful tool for clustering. In contrast, other models such as DBSCAN have not achieved an adequate spatial ordering; therefore, it would be worth evaluating updates to this algorithm to assess its performance, such as HDBSCAN.  
Conceptualization, J.L.A.C. and J.P.G.-M.; methodology, J.L.A.C.; validation, J.L.A.C. and J.P.G.-M.; investigation, J.L.A.C.; formal analysis, J.L.A.C.; data curation, J.L.A.C.; writing--original draft preparation, J.L.A.C.; writing--review and editing, J.L.A.C. and J.P.G.-M.; visualization, J.L.A.C.; project administration, J.L.A.C.; funding acquisition, J.L.A.C. and J.P.G.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Thanks to [PERSON], National Museum of Natural Sciences of Spain, for his knowledge and contributions.  
The authors declare no conflicts of interest.
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 2 System Description
### Embedded BTB HVDC Siting in Meshed System  
When replacing the existing AC line of the system with HVDC, the equivalent impedance of the power system increases, and the magnitude of the fault current decreases accordingly. For short circuit analysis, HVDC normally operates as a PQ bus at both ends for load flow calculation, whereas the fault current mode acts as if the current source is connected to both ends as shown in Figure 1[24].  
The three-phase fault current is calculated as the sum of the system Thevenin impedance of the fault point and the fault impedance formed between the fault point and the ground divided by the voltage that was applied just before the fault occurred. In other words, it is  
Figure 1: HVDC fault calculation model (PQ bus mode).  
which an open circuit voltage source having a voltage applied to a point just before a failure of a short circuit formed at the time of failure is connected as in Equation (1):  
\[I_{fault}^{n}=\frac{V_{prefault}^{n}}{Z_{Th}^{n}+Z_{fault}^{n}}, \tag{1}\]  
in which  
\(I_{fault}^{n}\) is a fault current at the fault point \(n\);  
\(V_{prefault}^{n}\) is a pre-fault voltage at the fault point \(n\);  
\(Z_{Th}^{n}\) is a thevenin impedance at the fault point \(n\); and  
\(Z_{fault}^{n}\) is a fault impedance at the fault point \(n\);  
In Equation (1), \(V_{prefault}^{n}\) and \(Z_{fault}^{n}\) cannot be controlled arbitrarily, and when the AC line is replaced with BTB HVDC, \(Z_{Th}^{n}\) can be controlled according to the position. Studies have been conducted to find the position of HVDC to reduce the fault current caused by the increase of \(Z_{Th}^{n}\). In this paper, using the HVDC decision algorithm defined in [23], the HVDC placement is chosen. Although it is possible to use the location selection criteria to reduce the fault current of the entire system or to reduce the number of fault current exceeding points, the location of HVDC is selected based on the fault current reduction of the most severe buses.
## 4 Case Studies
### Discussion  
It is worth mentioning that the case studies were conducted in a real-world operational environment: the provincial control center. Because SAC belongs to the category of off-policy reinforcement learning, it has better sample utilization efficiency, requiring less time to train models for real-time use compared to other well-known competitors like proximal policy optimization (PPO). Thus, only the SAC algorithm is adopted in the real-world environment due to the limited computing resources and time allowance for fine-training the RL models.  
Before deploying the proposed SAC-based control system in the control center, the control center has automatic voltage control (AVC) system running 24\({}^{\star}\)7 to regulate system voltage profiles and transmission losses. However, due to the complexity of obtaining precise full-system information and models, limitations are observed in deriving optimal control actions to mitigate operational risks. So, the demonstrated case studies did reflect scenarios where RL actions are added on top of the traditional AVC logic, achieving superior performance over existing approaches. To further verify the effectiveness of SAC agents, sensitivity analysis was conducted by training and testing the SAC agents against various conditions. Below are two examples of SAC agents completing control tasks with a 0.8% and 1.0% relaxation of voltage limits, both demonstrating similar and satisfactory control performance in regulating voltage profiles.  
The above case studies have demonstrated the feasibility and effectiveness of the proposed SAC-based method in a real operational environment: a provincial control center. In the design and testing phase, several key issues need to be addressed to achieve resilience and robustness in the control strategies, including the following:  
1. Given the importance of valid samples when training good RL agents, it is essential to periodically include new samples in the SAC agent training, from either a real-time EMS system or planning cases, considering various types of disturbances that can  
Figure 7: SAC agents’ performance considering relaxation of the voltage limits (**upper**: 0.8% relaxation; **lower**: 1.0% relaxation).  
capture major changes in the power system. From the authors' experience, it is a good practice to update the model training daily.
2. Once the SAC agent is trained with satisfactory performance, adoption of the agent for use in real time is rapid, typically within dozens of ms. However, it is very important to ensure the quality of the input samples when constructing the state space of the SAC agent before obtaining control strategies.
3. The proposed method in this paper mainly tackles the regulation of system voltage violation, line overloading, and system losses. When extending this approach towards more complicated control tasks in daily operation with different objectives (sometimes with conflict), using multiple RL agents can be a good research direction, considering the tradeoff among different control objectives.
## 2 Materials and Methods
### Development of Models
#### 2.5.4 Adaptive Boosting  
Adaptive Boosting or AdaBoost (AB) belongs to the set of Ensemble Methods, specifically to the family of boosting methods, in which the base estimators are built sequentially, and the goal is to reduce the bias of the combined estimator. The motivation is to combine several weak models to generate a powerful ensemble. In contrast with averaging methods, boosting methods usually work best with weak models, namely shallow trees in this case. A sequence of weak learners is fitted on repeatedly modified versions of the data. The predictions from all of them are then combined through a weighted sum to produce the final prediction. The data modifications at each boosting step consist of applying weights \(\omega_{1},\omega_{2},\ldots,\omega_{N}\) to each of the training samples (\(x_{i},y_{i}\)) [71]. Initially, all the weights are all set to \(\omega_{i}=1/N\), so that the first step simply trains a weak learner in the usual manner. For each successive iteration, the sample weights are individually modified, and the algorithm is reapplied to the weighted data. The training examples that were incorrectly predicted by the model induced at the previous step have their weights increased, whereas the weights are decreased for the ones predicted correctly. As iterations proceed, examples that are difficult to predict receive an increasing influence. Each subsequent weak learner is forced to concentrate on the examples that were missed by the previous ones in the sequence.
# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
## 4 Digital Technologies in the Water System: Key Drivers of Water 4.0
### Artificial Intelligence (AI)
The framework advances the leak detection strategy by alleviating the data requirements, guiding optimal sensor placement, and locating leakage via WDN leak zone partitions. The proposed system is scalable and its applicability to various WDNs prove its potential for sustainable management of WDNs with 95% detection accuracy. The studies conducted in [97] present a machine learning approach that helps to identify leak locations based on pressure sensor measurements. A random forest classifier is used for small-sized and medium-sized benchmark networks. The presented results show that the proposed methodology can be successfully used for leak localization using data obtained from numerical simulations even for sparse sensor placement. The authors in [98] use expert knowledge and data-driven models for leak detection and localization in WDNs. Analysis performed on a Barcelona WDN dataset with both real and simulated leaks showed that the proposed solution can improve the leak detection and localization. In [PERSON] et al. [99], an adaptive intelligent dynamic technique was developed for water resource planning. A computational intelligent system was proposed by [100] for leak localization in WDNs. The effectiveness of the proposed system was demonstrated on Modena WDN data, and the results obtained show that the proposed intelligent system gives a satisfactory performance in terms of leak detection, leak size estimation, and localization. By leveraging on digital tools for automation of water network management operations, a long-term holistic vision of an integrated water network management could be created. This will act as a central system of record and control of water assets. Thus, AI is one of the emerging technologies needed in Water 4.0 to enhance the operational management of water supply systems. Several efforts are ongoing to build algorithms with smart sensors and artificial neural networks that will dynamically strategize water  
Figure 9: A framework for the application of AI for water quality monitoring [90]. operations more intelligently. Prediction models for future water demand, robotic sensors in water systems, and block chain technologies to cater for financial transactions related to meter billing are feasible with appropriate application of AI techniques.
# Dense Robust 3D Reconstruction and Measurement for 3D Printing Process Based on Vision
## 3 The Matching Method Based on the FFT-SIFT-AKAKAE
### Feature Extraction Based on AKAZE Algorithm  
Although the SIFT algorithm has good scale rotation invariance and robustness, the core of the algorithm is the scale space constructed by Gaussian filtering template with different scale parameters. Such linear filtering method will blur the whole image both in detail information and noise, resulting in the missing of contour information of the image. The accuracy of 3D reconstruction will be affected when it is applied to 3D printed parts. Referring to the reference [23], this paper introduces an accelerated KAZE algorithm, namely AKAZE algorithm. AKAZE realizes Fast Explicit Diffusion (FFD) embedded in the pyramid framework, improves the speed of feature detection in nonlinear scale space, and well preserves the boundary information of the target object. The process of AKAZE algorithm is nonlinear diffusion filtering, construction of nonlinear scale space, and generation of MLDB descriptor.  
1. The change of image brightness in different scale space can be described by nonlinear diffusion filter, and the divergence factor of flow function is used to control the change of image brightness: \[\frac{\partial L}{\partial t}=div(c(x,y,t)\cdot\
abla I)\] (7) In the formula, the symbol \(\
abla\) and \(div\) represent the solution of gradient and divergence operation respectively, the \(L\) matrix represents the brightness of the image, and the conduction diffusion function is represented by \(c\), so that the image can obtain the diffusion self-adaptability of local structure. The fast display diffusion method FFD is used to accelerate the solution of nonlinear partial differential equations.
2. In the process of constructing the nonlinear scale space, the images processed at different levels in each group of AKAZE have the same resolution as the original images. The FED algorithm is used to calculate the nonlinear diffusion image sequence.
3. The generation process of MLDB descriptor is as follows. Firstly, the main direction of the feature point is determined. The feature points centered within the radius of \(6\sigma\), the neighborhood of the point at which a first order differential \(L_{x}\) and are Gaussian weighted. The neighborhood of the feature point is traversed in a 60\({}^{\circ}\) sector. Accumulation of all of the weighted vectors will accumulate and the highest fan area direction as the main direction of feature points, as shown in Figure 3. The surrounding area of the feature points in accordance with the grid division, and the main direction of the feature points based on neighborhood rotate operation, after the rotation to interval within the grid resampling discrete point value, and through the calculation of horizontal and vertical direction of the scatter of grey value and gradient values, generate descriptor with rotation invariance.
## 2 Materials and Methods
### Imagery and Climate Data Collection
#### 2.2.3 Climate Data  
Daily total rainfall (mm) and mean air temperature (\({}^{\circ}\)C) were obtained from the Agro-Meteorological Grid Square Data, NARO [40] ([[https://amu.rd.naro.go.jp/](https://amu.rd.naro.go.jp/)]([https://amu.rd.naro.go.jp/](https://amu.rd.naro.go.jp/)), accessed on 23 October 2020). Daily data were downloaded for the months of the soybean growing season (July-October) in 2018-2020. The meteorological data had an approximately 1 km\({}^{2}\) resolution based on the reference mesh area used by the data provider. Ideally, higher-resolution data for the climate would have been utilized to match the resolution of the satellite imagery and to understand the spatial variations more precisely. However, such data were not available without in situ measurement; thus, we used these data as a substitute for the meteorological data.