esa-sceva/satcom-chunk-collection huggingface dataset:
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 5 Results and Discussions
### Evaluation of Gas Leak Detection  
In this section, the algorithm for the gas leak detection and visually-enhancement of gas emanation is evaluated using three sets of experiments that investigate the performance of the algorithm and the whole system. First, the effectiveness and performance of the algorithm are assessed by using the collected data from Experiment A explained in Section 4.1 which is conducted in an isolated environment. The isolated environment provides an opportunity to evaluate the algorithm without the effect of other environmental parameters. The results of this experiment are shown in Figure 9. As shown in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Metrics** & **Original** & **AVS** & **L1-OP** & **PC-RF** & **OPC** \\ \hline \multirow{4}{*}{Scenario \#1} & PSNR (min–max) & 70.94–110.83 & 71.27–106.2 & 69.16–107.36 & **65.09–111.95** & 72.88–110.33 \\ \cline{2-7}  & ITF & 74.36 & 74.87 & 73.76 & **80.15** & 76.32 \\ \cline{2-7}  & ISI & 0.532 & 0.544 & 0.504 & **0.726** & 0.568 \\ \cline{2-7}  & AvSpeed & 0.1928 & 0.20 & 0.215 & 0.356 & **0.1526** \\ \hline \multirow{4}{*}{Scenario \#2} & PSNR (min–max) & 58.23–78.97 & **58.65–85.82** & 58.21–82.92 & 58.65–69.76 & – \\ \cline{2-7}  & ITF & 61.83 & 62.30 & **63.17** & 63.16 & – \\ \cline{2-7}  & ISI & 0.427 & 0.52 & **0.64** & 0.56 & – \\ \cline{2-7}  & AvSpeed & 1.559 & 1.05 & 0.565 & **0.41** & – \\ \hline \multirow{4}{*}{Scenario \#3} & PSNR (min–max) & 69.96–63.99 & 65.02–88.85 & – & **63.22–132.46** & 65.40–87.40 \\ \cline{2-7}  & ITF & 66.74 & 70.24 & – & **80.8** & 71.27 \\ \cline{2-7}  & ISI & 0.159 & 0.402 & – & **0.6** & 0.14 \\ \cline{2-7}  & AvSpeed & 1.06 & 0.5814 & – & **0.386** & 0.621 \\ \hline \multirow{4}{*}{Scenario \#4} & PSNR (min–max) & 58.43–68.5 & 63.46–71.56 & 59–69.65 & 59.46–91.82 & **62.24–248.9** \\ \cline{2-7}  & ITF & 64.47 & 66.72 & 64.56 & 68.63 & **81.91** \\ \cline{1-1} \cline{2-7}  & ISI & 0.1121 & 0.24 & 0.1220 & **0.2718** & 0.1281 \\ \cline{1-1} \cline{2-7}  & AvSpeed & 4.92 & 2.2356 & 1.6196 & 2.401 & **1.60** \\ \hline \hline \end{tabular}  
* PSNR: higher value indicates higher system accuracy. ITF: the higher shows smoother camera motions (perfect stabilization results infinity value. SSIM: higher value shows better performance. ISI: higher value shows higher similarity between frames. AvSpeed: the lower value shows more perceptual similarity.  
\end{table}
Table 5: The results of comparative analysis of candidate stabilization techniques. For each scenario, the best measurement for each metric is indicated in bold. The method abbreviation is defined only for reference simplifying.
## 2 Use Case Scenario: Fraud Detection  
With the increase of online merchants and e-commerce, fraud becomes a trillion dollar problem for the global economy with the loss of 3.5 to 4 trillion dollars per year, which makes about 5% of global GDP [13]. Several fraud detections and prevention companies are working to detect and prevent such fraud on time including BankCard USA, Kount, Ingenico, and fraud.net.  
The system of fraud.net is one of the world's leading peer production-based fraud prevention framework which aggregate and analyze large amounts of fraud data from thousands of online merchant in real time. This collaborative program is the largest merchant-led effort to combat online payment fraud costing US merchants an estimated amount of 20 billion dollars annually. It protects more than 2% of all the US-based e-commerce, and its clientele is growing very fast each year recently: This framework saves up to one million dollars a week for its customers by helping them detect and prevent fraud.  
The primary challenge of such platforms is to build and train a more significant number of more targeted and precise machine learning models to counter the effect of increasingly different and evolving forms of fraud. As the fraudster's strategy changes with time, the system should be able to evolve itself with the fraud evolution. There might be 100 different fraud schemes and each one with 100 different variations at any given day. To tackle these issues, the platform needs to have machine learning models and capabilities to identify and handle a new fraud scheme, as it pops up including its different variations.  
Latency-sensitive applications like fraud detection, traffic analysis, and media streaming need to adapt these capabilities and achieve a better tradeoff between lower latency and higher throughput. With this the goal of the system, the proposed system has different variations of the algorithm to achieve a better tradeoff between these, while taking SLA agreement into consideration. A generic fraud detection and prevention system architecture is shown in Figure 1. The core payment system process all the transaction and pass it to the main detection and prevention system. The system contains batch analytics, real-time analytics, predictive analytics, and interactive analytics modules which process the transactional data and produce alerts based on the analysis of the incoming workload. The alerts are then portrayed to the dashboard, and the system admin takes actions accordingly.  
Figure 1: Fraud detection and prevention system based on Amazon and Apache Flink.
# Biotechnological Approaches to Lowering the Ethanol Yield during Wine Fermentation
## 2 Genetic Improvement of _S. cerevisiae_
### Random Mutagenesis  
Genetic improvement by random mutagenesis involves the artificial increase in mutation rates by treatment with physical or chemical agents (UV radiation, X-rays, ethyl methane sulfonate, nitrosoguanidine) coupled with a suitable selection strategy [27]. To be effective, the intensity of the treatments must be lethal for most of the cells in the population. However, given the random nature of the process, the number of cells that must remain viable in order to capture the expected mutations is often still too high for strain-by-strain analysis. It is then critical to establish appropriate selection criteria that link selectable or detectable phenotypes to technologically relevant features of the mutant strains. While this seems like an easy task for some improvement outcomes--for example, those related to the robustness of yeast strains--improvements in wine quality, including the reduction of alcohol levels, require indirect and non-obvious selection criteria.  
An improved strain of _S. Dayanus_ was developed by random mutagenesis, and was marketed as a low ethanol producer (Oenoferm(r) LA-HOG from Erbsloh, Geisenheim, Germany). According to commercial information, the genetic improvement process involved two stages of chemical mutagenesis. The first one, with selection under hypersaline conditions, was intended to recover strains with an improved HOG (High Osmolarity Glycerol) response. In the second, the selection pressure was established with pyrazole, an inhibitor of alcohol dehydrogenase. The commercial strain was selected among the 40 strains recovered from this last step. During the fermentation of the Pinotoir grape juice, this strain produced 12.9 g/L glycerol and 94.6 g/L ethanol, compared to the 6.4 g/L and 102.4 g/L, respectively, produced by the parent strain.
# Survey of Blockchain-Based Applications for IoT
## 10 Decentralized AI and IoT
### Federated Learning with Blockchains in the IoT  
Federated learning is a decentralized machine learning approach that enables collaborative model training while maintaining data privacy. Instead of sharing raw data, it allows multiple parties to train models locally and only exchange model updates, ensuring sensitive information remains protected. This approach prevents data exposure while still enabling organizations to benefit from collective learning, making it particularly valuable in privacy-sensitive applications.  
Figure 15: Connecting data providers and analysts using blockchain.  
Blockchain-enabled federated learning (BFL) is an emerging approach to secure and decentralized federated learning, leveraging blockchains to maintain data integrity and accountability. However, BFL faces several critical challenges that can hinder its effectiveness. The first major challenge lies in the model validation process, which involves recruiting distributed validators to evaluate local models. This process is often slow and resource-intensive, creating a significant bottleneck in system performance. The second challenge is related to the incentive system, as blockchains are transparent by nature, which may inadvertently expose sensitive cost information from participants, posing privacy risks. Additionally, the practice of sharing local model updates without adequate protection can lead to data eavesdropping or inference attacks, compromising the confidentiality of participant data. To tackle these issues, [PERSON] et al. [65] proposed BIT-FL, a blockchain-enabled incentivized and secure federated learning framework. BIT-FL incorporates a loop-based sharded consensus algorithm to expedite model validation, a randomized incentive mechanism with differential privacy to protect cost information, and Gaussian noise to secure local model updates. Experimental evaluations demonstrate that BIT-FL not only enhances efficiency and privacy preservation but also improves robustness against adversarial attacks, making it highly suitable for real-world decentralized federated learning applications.  
By integrating federated learning into a permissioned blockchain, this approach enhances security during the data-sharing process without relying on centralized trust. The numerical results in [74] demonstrate that the blockchain-powered data-sharing scheme effectively safeguards sensitive information while enabling collaborative learning. The synergy between blockchains and federated learning offers a promising solution for secure and intelligent data exchange in the IoT. However, ensuring efficient data privacy protection through blockchains remains an open challenge, requiring further exploration of potential security threats and the development of more advanced privacy-preserving mechanisms.
## 4 Discussion
### Comparability  
In summary, we have found a great variety of implementation parameters and design factors, such as the virtual reality technology used (immersive vs. non immersive, general purpose vs. specific equipment, competitive vs. collaborative vs. cooperative, etc.); the game design factor (adaptability, rewards, narrative, etc.); the combination of virtual reality with conventional methods or not; sample size; duration and intensity of treatment; outcome measurement methods (SF-36, Fugl-Meyer, Wolf Motor Function, etc.); and the type of functions to measure (upper limb, lower limb, cognitive, activity of daily life, gait speed, etc.).  
This variety makes it difficult to compare the different studies and reduces the effectiveness of meta-analysis. Despite this heterogeneity, there are some valid conclusions, although there is still discussion about which characteristics of virtual reality rehabilitation programs are the key to success: specific environments for post-stroke rehabilitation show better outcomes than general purpose ones; a treatment period of 8 weeks or more is preceptive to obtaining relevant outcomes; VR can help at least in scalability needs in a context of scarce human resources and facilities to develop conventional therapies, increasing the therapy time quickly and with little investment; combining VR with conventional therapy presents better outcomes; and motivation can be improved, creating an enjoyable experience for the users, thereby preserving the usability and increasing both the adherence and effectiveness of the treatment.
# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 4 Polarimetric Dark Channel Dehazing System
### Polarization Calibration
\right] \tag{17}\]  
The elements in the matrix above are defined as follows:  
\(M_{p11}=g_{1}+g_{2}\)  
\(M_{p12}=M_{p21}=(g_{1}-g_{2})\cos 2\theta\)  
\(M_{p13}=M_{p31}=(g_{1}-g_{2})\sin 2\theta\)  
\(M_{p22}=(\sqrt{g_{1}}-\sqrt{g_{2}})^{2}\cos 2\theta+2\sqrt{g_{1}g_{2}}\)  
\(M_{p23}=M_{p32}=(\sqrt{g_{1}}-\sqrt{g_{2}})^{2}\cos 2\theta\sin 2\theta\)  
\(M_{p33}=(\sqrt{g_{1}}-\sqrt{g_{2}})^{2}\sin^{2}2\theta+2\sqrt{g_{1}g_{2}}\)  
\(M_{p44}=2\sqrt{g_{1}g_{2}}\)  
In the equations above, \(g_{1}\) and \(g_{2}\) represent the transmittance of the polarizers in their polarization direction and perpendicular direction, respectively. For ideal polarizers, \(g_{1}=1\) and \(g_{2}=0\). \(\theta\) is the angle between the polarization axis of the polarizer and the X-axis of the coordinate system.
## 3 Results and Discussion
### Potential Replication of Biomethane Production from Pig Manure  
Results of LIFE Smart Agromobility show that 1 kg of manure produces 2.79 litres of biomethane in the psychrophilic range (0-35 \({}^{\circ}\)C) with a Wobbe Index of 45.5 MJ/m\({}^{3}\). This results in a production of 35.2 Wh per kg of manure. This project manages 1300 tonnes of manure, so there is a total production of 45.8 MWh per year [19].  
This biomethane allows for covering 89,014 km/yr. This value was calculated by comparing diesel consumption with biomethane. Diesel consumption is around 5 litres per 100 km [41], and the low heating value of diesel is 10.28 kWh/L [42]. The calculated Wobbe index of biomethane of 45.5 MJ/m\({}^{3}\) resulted in the equivalence of 1.23 litres of diesel to 1 m\({}^{3}\) of biomethane. Considering that a car travels an average of 14,627 km annually [43,44], it can be calculated that a car requires 595.5 m\({}^{3}\) of biomethane annually. The plant produces 302 m\({}^{3}\)/month, which results in 89,014 km/year. This represents the annual distance travelled by six light-duty vehicles.  
During the production of biomethane, 0.11 kWh of electricity is consumed by pumps and the compressor to produce 1 MJ of biomethane, resulting in emissions of 31 g of CO\({}_{2}\)-eq per MJ of biomethane [45]. However, the CO\({}_{2}\) removed from biogas is not taken into account because it is considered as biogenic CO\({}_{2}\), according to the Environmental Footprint v3.1 methodology, supported by the European Union [46]. The amount of CO\({}_{2}\)-eq per MJ complies with European Regulation 2015/1513, which mandates that fuels must achieve at least a 60% reduction in emissions compared to the reference fuel to be considered sustainable [47]. The reference fuel emissions are defined as 94 g CO\({}_{2}\)-eq/MJ in European Regulation 2023/1185 [48]. This implies a reduction of 76%; therefore, this fuel could be considered sustainable.  
To estimate the biomethane production potential, primary data of LIFE Smart Agromobility was used, considering a mesophilic range (35 \({}^{\circ}\)C), as explained in Section 2.1.1. Furthermore, data from the Agricultural Census of the Spanish National Institute of Statistics was used to obtain the number of heads.  
Results of the potential from pig manure per province in Spain are shown in Figure 4, and in Appendix B, there is a detailed table of the potential of each province. Results indicate that Spain has 4.85 TWh/year of biomethane potential, most of it concentrated in the North-East of Spain in Catalonia (1346.82 GWh/year) and Aragon (642.84 GWh/year), and other regions stand out as well, such as Toledo (304.47 GWh/year) or Murcia (410.18 GWh/year). These results are in line with those of [PERSON] et al. [49], who found that most Spanish potential was concentrated in the North-East.
## 2 Materials and Methods
### Virtual Surgery Scenario for Nuss Procedure
#### 2.3.2 Virtual Surgery Scenario for Insertion Point and Shape Design of Nuss Bar  
The virtual surgery scenario for selecting the optimal insertion point and shape was set as follows [16]. Three points were selected based on the ribs around the sternum with the largest depression using the CT image and 3D model of Patient 1. As is shown in Figure 4, insertion point A is the sternum between the second and third ribs, insertion point B is between the third and fourth ribs, and insertion point C is between the fourth and fifth ribs.  
Figure 5 shows the chest wall shape expected after the Nuss procedure for each insertion point. The blue line on the CT image is the optimal Nuss bar shape, which was designed by referring to the PE shape type and the shape of a similar chest wall.  
To fabricate a patient-specific Nuss bar based on the patient's CT image, a computerized numerical control (CNC) bending machine that can machine every curved surface within the range of the Nuss bar curves must be first constructed. In this study, a CNC bending machine was constructed with the machining purpose.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Degree of Pectus Excavatum** & **Range of Haller Index** \\ \hline Normal chest & \textless{}2.0 \\ Mild excavatum & 2.0–3.2 \\ Moderate excavatum & 3.2–3.5 \\ Severe excavation & \textgreater{}3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of the Haller index.  
Figure 4: Virtual surgery scenario for insertion point and shape design of Nuss bar: (**a**) Nuss bar is inserted at A, B, and C points from 3D chest wall model; (**b**) CT axial images with insertion points A, B, and C.
# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 3 Experimental Evaluation
### Evaluation Methods for the Segmentation  
#### 3.1.1 Precision, Recall, and F1-Score Analysis  
Precision is the ratio of the number of positive pixels predicted correctly to all predictive positive pixels. Recall is the ratio of the number of correctly predicted positive pixels to the total of true positive pixels. Equations (2) and (3) define the precision and recall.  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
where \(TP\) is true positive, which is a pixel that is predicted to be positive and is actually positive; \(FP\) is false positive, which is a pixel predicted to be positive, but is actually negative; \(FN\) is false negative, which is a pixel predicted to be negative but is actually positive.  
The F1-score is a combination of precision and recall. Since precision and recall are in a trade-off relationship, they might have extreme values. Additionally, like the data used  
Figure 11: Sample image data were used to train the model. In each image, the image on the left is the tire depth image (.tif), and the image on the right is the image marked vent spew error.  
in this paper, when the imbalance between the number of normal classes and error classes is severe, it compensates for this. Equation (4) shows the F1-score.  
\[\text{F1 score}=2*\frac{Precision*Recall}{Precision+Recall} \tag{4}\]
# An Analysis of Rhythmic Patterns with Unsupervised Learning
## 2 The Compositional Hierarchical Model for Rhythm Modeling
### Learning and Inference
#### 2.2.2 Inference  
When a trained model is presented with new input data, the learned rhythmic patterns may be located in the input through the process of _inference_. Inference calculates part activations on the input data, bottom-up layer-by-layer, whereby the input data activates the layer \(\mathcal{L}_{0}\). For each occurrence of a rhythmic pattern (part activation), the scaling factor, encoded in \(A_{S}\), determines the length of the pattern, \(A_{T}\) determines its onset time and \(A_{M}\) its magnitude.  
Inference may be exact or approximate. In the latter case, two mechanisms, _hallucination_ and _inhibition_ can be used to tolerate for rhythmic irregularities in music.  
In exact inference, a part is activated only if all of its subparts are activated. Hallucination relaxes this condition and enables the model to produce activations even in the case of incomplete (missing, masked or damaged) input. The model generates activations of parts, which most fittingly cover the information present in the input signal, where fragments, which are not present, are hallucinated. The missing information is therefore extrapolated from the knowledge acquired during learning, encoded into the model structure. The amount of hallucination is governed by a threshold \(\tau_{H}\).  
Inhibition performs hypothesis refinement by reducing the number of part activations on individual layers. It provides a balancing factor in the model by reducing redundant activations. Although the learning algorithm penalizes parts redundantly covering the input signal, some redundant parts are retained. During inference, each layer may, therefore, produce multiple redundant activations covering the same information in the input signal. Inhibition removes activations of parts that have already been covered by stronger activations of other parts. The amount of removal can be controlled by the inhibition threshold \(\tau_{I}\).  
Approximate inference aids to the model's ability to find rhythmic patterns with deletions, changes or insertions, thus increasing its robustness. The inhibition mechanism has a more pronounced role in the rhythmic model, because of the relativity of the encoded patterns and, typically, high regularity of the input signal. Especially on the lower layers, a high number of activations representing simple straight rhythmic patterns emerges, typically out of a few simple parts activating at different scales and onsets. An example is given in Figure 8, which shows a series of activations of a simple \(\mathcal{L}_{1}\) part on a regular input signal. For clarity, only two different scales are shown in this example.  
Figure 8: The example shows overlapping activations of a single \(P_{1}^{1}\) part. Inhibition can be employed to efficiently reduce such overlapping activations.