esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning
## 3 Methodology
### Average Action Re-Labelling for Metabolic Rate Extraction  
An average re-labelling process on the classes of the action recognition model is considered here. In this re-labelling process, each action is assigned a metabolic rate that is based on those provided by [29]. By doing this, a novel method can be used to compute the metabolic rate over a period of time. Here, in this work, an average metabolic rate is considered over a period of time for several reasons. One of them is the possibility of the misclassification of an action that would likely results in an incorrect relabelling of the metabolic rate. Such an error can be reduced by taking the average of the metabolic rate over a period of time. This gives an opportunity to control the thermal model over that period based on the metabolic rate of the occupant in addition to temperature, humidity, and other parameters. To formulate this relation, let \(t\), \(A_{t}\), and \(m_{t}\) be time, actions and the metabolic rate variables respectively. In a period of time from \([t_{1},t_{2}]\) a sequence of actions can be performed \(A_{t1}\) to \(A_{t2}\), which are associated with a sequence of metabolic rate. As a result, temperature \(T\) can be changed in different ways either at each single action per unit of time or as a function of the average of the actions over a period of time. In this work, the average of the metabolic rates for different actions over a period of time is considered. This will help to keep the thermal model as close as possible to achieving occupant comfort-ability. This can be formulated as:  
\[M_{t_{1},t_{2}}=\frac{1}{N}\sum_{t=t_{1}}^{t_{2}}A_{t}(m_{t}) \tag{1}\]  
where \(M_{t1,t2}\) is the resulted metabolic rate over a specific period of time, \(N\) is the number of processed actions over period \([t_{1},t_{2}]\).  
Table 1 includes some daily actions that are used in the action recognition model during sitting and standing poses, with its correlated metabolic rate based on [29]. These actions are considered in terms of fuzzy temperature control to show the differences that could result when each occupant has its own metabolic rate.
## 3 Optimization Results
### Results for Scenarios 1 and 2
Regarding efficiency, in terms of process operation and ergonomic risk reduction, the simulations (b) and (c) are efficient, and the choice for implementation should fall on the solution that best meets the requirements of the production process. That is, if the production process is more focused on customizing the finished product, simulation (b) will be more appropriate, since the semi-finished product warehouse (zone 12) is located near the bottling area (zone 10), the area of packaging (zone 14), and the shipping area (zone 15). In the case of a production process of large quantities of products with a low level of variety or customization, the simulation (c) will be more efficient once the finished product warehouse (zone 13) is located in the vicinity of the area of bottling (zone 10), the packaging area (zone 14), and the dispatch area (zone 15). In both simulations (b) and (c), the packaging materials warehouse is further away from the bottling area, which can be easily overcome by good planning and timely transfer of materials. On the other hand, the staging areas are located close to the batching area (zone 9) which, in turn, is located adjacent to the bottling area (zone 10), thus closing the production cycle. Figure 4: (**a**) Scenario 1; (**b**) scenario 2; (**c**) scenario 2. Solutions obtained to optimize a production area layout in terms of general and ergonomic risk reduction, including the wine heating process (production of Madeira wine).
## 3 The Proposed Architecture  
The SePCS system architecture as shown in Figure 1 is composed of four subsystems, including Data Center, Information Managers, Resource Broker and Mobile Nodes. The first three together are called the Monitoring/Collection Information Server (MCI Server for short), and are associated with each of the others through a backbone network. Data Center is a database system that records resource information for the system. Information Manager is responsible for collecting, computing and classifying resources. Resource Broker takes charge of allocating resources to user requests. Mobile nodes connect themselves to the MCI Server through access points (APs for short) to acquire and provide grid resources. The relationship between Resource Broker and Information Manager is shown in Figure 2 in which we can see that Resource Broker consists of three global queues that are used to keep different types of user requests, and Scheduler, which allocates grid resources to the corresponding jobs. Information Manager comprises Collector, Front Unit, Calculator and Mobile Agents. Their functions will be defined and described later.  
Figure 1: The SePCS system architecture.
## 4 Experimental Setup
### Datasets Sizes, Splits, and Image Dimensions  
We evaluate our model using a combination of real-world and synthetic datasets to ensure robustness and generalization:  
* LOL Dataset [8]: Contains 500 paired low-/normal-light images captured with dual-exposure setups in varied lighting conditions. We use the standard split of 485 images for training and 15 for testing, as specified in the dataset documentation. Each image has a resolution of 600 \(\times\) 400 pixels, resized to 256 \(\times\) 256 or 512 \(\times\) 512 during preprocessing.
* SID Dataset [11]: Provides raw short- and long-exposure image pairs from Sony and Fuji sensors. Following common practice, we preprocess raw data into sRGB using the authors' pipeline and use the Sony subset for training and testing. For SID, we follow common practice and use only the Sony subset, consisting of 509 training pairs and 41 testing pairs. Raw data is processed into sRGB format using the pipeline provided by the dataset authors. Images are cropped and resized to 512 \(\times\) 512 during preprocessing.
* Synthetic Dataset: To enhance training diversity, we generate synthetic low-light images by applying gamma correction (\(\gamma\in\) [2.0, 5.0]) and additive Gaussian noise (\(\sigma\in\) [0.01, 0.05]) to clean images sourced from the DIV2K and BSD500 datasets. This process yields a synthetic dataset comprising 2000 training pairs and 500 validation pairs, with all images uniformly resized to 256 \(\times\) 256 pixels. As described in Section 3.4, all images are resized to 256 \(\times\) 256 or 512 \(\times\) 512 and normalized to the \([0,1]\) range during preprocessing.
## 2 Occurrence of Patulin in Apple Juice and OTA in Coffee, Beer and Win  
### Occurrence of patulin in apple juice  
Patulin-producing fungal strains have been isolated from a variety of fruits including apples, grapes, cherries, crab apples, pears, apricots, strawettes, nectarines, track mulberries, white mulberries, lingonberries, peaches and plums [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. The presence of patulin-producing fungi does not necessarily guarantee patulin production and patulin production is usually, but not exclusively, associated with apple soft rot and blue mold rot, must commonly caused by _P. expansum_[29; 33]. This fungus, which is the most common of apple rot in the apple industry [44] has been shown to naturally cause blue-rot in apples [30; 31].  
Patulin production within units and their products has been investigated and often appears to be dependent on factors such as water activity (aw), temperature, pH, and other chemical characteristics intrinsic to fruits [45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 7  
Zealand [58], Netherlands [59], Sweden [60], USA [33, 61, 62], Turkey [63, 64, 65], Australia [66, 67], Iran [68, 69], Saudi Arabia [70], Argentina [71], Brazil [72, 73, 74, 75, 76], Japan [77], Italy [78, 79, 80], India [81], Austria [82], Belgium [83, 84], Greece [85] and other countries, as presented in Table 1.
# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## 5 Conclusions  
In conclusion, this research embarked on an exploration of drone-based survey missions, leveraging advanced technologies and methodologies to enhance the efficiency and effectiveness of data collection processes. The deployment of a single DJI Mini 3 Pro drone, orchestrated through the Dronelink application, demonstrated the capability to capture precise images of a designated area with remarkable accuracy. The subsequent analysis using the 'Recognize' app showcased the successful identification of objects within the surveyed region.  
Building upon this foundation, the research extended its inquiry to the collaborative use of multiple drones. The DT Drones application facilitated the strategic distribution of five drones around a specified target through the implementation of Delaunay triangulation. This geometric algorithm optimized the spatial arrangement of drones, resulting in comprehensive coverage and superior detection capabilities. The comparative analysis revealed that this collaborative approach outperformed the single drone deployment, especially in terms of object identification. The findings underscore the significance of automated mission planning and geometric algorithms in optimizing drone-based surveys. The integration of Dronelink for mission automation and Delaunay triangulation for spatial distribution holds promise for a myriad of applications, ranging from environmental monitoring to infrastructure inspection. The success of this approach suggests its potential scalability and adaptability to diverse terrains and survey objectives.  
As the research journey concludes, the implications of these findings extend beyond the current study. The methodologies employed offer valuable insights for researchers,practitioners, and industries seeking to harness the full potential of drone technology in data collection and analysis. Future research endeavors may delve deeper into refining the proposed methodologies, exploring variations in terrain, and investigating the scalability of these approaches for large-scale surveys.  
In essence, this research contributes to the evolving landscape of remote sensing and spatial data collection, providing a framework for the thoughtful integration of advanced technologies in drone-based surveys. The success of both single and collaborative drone deployments highlights the dynamic capabilities of these platforms, paving the way for continued advancements in the field of geographical data acquisition.  
Writing--original draft preparation, A.A.; writing--review and editing, C.C.; supervision, C.C. and P.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors acknowledge the allocation of computing resources by The University of Sussex.  
The authors declare no conflicts of interest.
###### Abstract  
Visual place recognition (VPR) is crucial for enabling autonomous agents to accurately localize themselves within a known environment. While existing methods leverage neural networks to enhance performance and robustness, they often suffer from the limited representation power of local feature extractors. To address this limitation, we propose CriSALAD, a novel VPR model that integrates visual foundation models (VFMs) and cross-image information to improve feature extraction robustness. Specifically, we adapt pre-trained VFMs for VPR by incorporating a parameter-efficient adapter inspired by Xception, ensuring effective task adaptation while preserving computational efficiency. Additionally, we employ the Sinkhorn Algorithm for Locally Aggregated Descriptors (SALAD) as a global descriptor to enhance place recognition accuracy. Furthermore, we introduce a transformer-like cross-image encoder that facilitates information sharing between neighboring images, thus enhancing feature representations. We evaluate CriSALAD on multiple publicly available place recognition datasets, achieving promising performance with a recall@1 of 89.3% on the Nordland dataset, while the closest rival achieves only 76.2%. CriSALAD outperforms both baseline models and advanced VFM-based VPR approaches.  
visual place recognition; visual localization; transfer learning; feature aggregation 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 2018-2018. External Links: Document Cited by: SS1.
# In Vitro Simulation of Human Colonic Fermentation:
## 4 Available Colonic In Vitro Fermentation Models
### Static Colonic Fermentation Models
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Main Objective** & **Study Subject** & **References** \\ \hline \hline \multirow{6}{*}{To assay the prebiotic effect of polysaccharides and oligosaccharides on gut microbiota and their metabolites} & Polysaccharides and oligosaccharideschariides from: -Fruits: Mulberry fruit. -Fungi: _Craterellus cornucipoides._ -Algae: _Sacchararia japonica_, and _Cordinalina piluffera_. -Plants: flowers of _Cmellina sinensis_ and _A. tequilana Weber var._ [30; 31; 32; 36; 37; 38; 39; 40; 41; 42] \\  & -Grains: Tibetan hull-less barley. -Bacteria: _Weissella cibaria_ and Bifidobacteria. -Beans: Red kidney bean. -Tea: Fuzhuan brick tea. \\ \hline \hline \multirow{6}{*}{To assay the prebiotic effect of some **polyphenols** on gut microbiota and their metabolites.} & Different polyphenols from: -Foods: cereal, meat, and dairy. -Drinks: coffee, tea, wine, and different juices. -New snacks: Pineapple snack bars. -Industrial residues: Persimmon and blueberry residues. -Pomaces: Blueberry pomace. -Dairy, egg, fish, and meat products after boiling, frying, grilling, and roasting. -Plant food products (alcoholic drinks, cereals, cocoa, coffee, fruits, legumes, nuts, oils, tubers, and vegetables) after boiling, frying, grilling, roasting, and toasting. -Enzymatic modification of the cell wall integrity: cotyled cells from pinto bean seeds. -Extrusion: whole grain oats and wheat bran. -Particle size manipulation: maize bran, cellulose, and whole grain oat flakes. -Cheonggukang: a Korean traditional fermented soybean soup. -Edible insects: black field cricket nymphs, grass grub larvae, and wax moth larvae. -Moring oleifera Lam. Leaves -Probiotic salami. -Tibetan hull-less barley and refined Tibetan hull-less barley. -Spent coffee grounds. -Sun-dried raisms. -Additives such as: -Dietary emulsifies: sodium CMC, P80, soy lecithin, sophorolipids, and rhamnipids. \\ \hline \hline \end{tabular}  
* To assay the **gut microbiota metabolism** in response to food components.
* \end{table} 
Table 1: Applicability of static in vitro colonic models in the food science and technology field from 2011 to 2021. Despite of the fact that static models are useful tools to study gut microbiota-food interactions, they have limitations when it comes to mimicking gut fermentation as a dynamic process [7; 11]; for that reason, great effort has been made to develop dynamic colonic methods.
## 2 Materials and Methods
### Methodology
#### 2.5.6 Model Evaluation Methods  
To assess the proposed model, evaluation techniques were used [42]. The diagnostic ability of the model was represented by the ROC. The ROC portrayed the specificity curve against sensitivity. The area under the curve (AUC) depicted the prediction values. The prediction was better when the area under the curve was larger [43]. The prediction ability of RNN model was determined based on ACC, recall, F1-score, precision, threat score (TS), CSI, and FM, and can be expressed as follows [44; 45; 46]:  
\[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+\text{FN}} \tag{8}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Optimized Parameter** & **Suitable Values for RNN** & **Description** \\ \hline Epoch & 100 & 100 times train the sample \\ \hline Batch size & 64 & Size of each data batch \\ \hline Drop out & 0 & Reduce overfitting \\ \hline Optimizer & Adam & Adaptive moment estimation \\ \hline Learning rate & Cross-entropy & Used for binary classification \\ \hline Loss function & 0.001 & Weight updating speed \\ \hline \hline \end{tabular}
\end{table}
Table 2: RNN model parameters and importance.  
Figure 4: Overall methodological flowchart.  
\[\text{F1 score}=\frac{2\times\text{TP}}{2\times\text{TP}+\text{FP}+\text{FN}} \tag{10}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{11}\]  
\[\text{CSI}=\frac{\text{TP}}{\text{TP}+\text{FN}+\text{FP}} \tag{12}\]  
\[\text{FM}=\sqrt{\frac{\text{TP}}{\text{TP}+\text{FN}}.\frac{\text{TP}}{\text{ TP}+\text{FP}}} \tag{13}\]  
Hereby, true positive (TP) and true negative (TN) show the correct classification of earthquakes and non-earthquakes. Similarly, false-positive (FP) and false-negative (FN) indicate misclassified earthquakes and non-earthquakes, respectively. For accuracy, the values of every statistical performance must be between 0 and 1. Moreover, the higher the ACC performance values, recall, F1-score, and CSI, the better the model works (Table 3). Moreover, MCC value were estimated for perfect predictions (1), random predictions (0), and inconsistency (1).
# Study on Band Gap and Sound Insulation Characteristics of an Adjustable Helmholtz Resonator
## 2 Structural Design and Band Gap Analysis  
### Structural Design  
The cross section of the cell is shown in Figure 2 (\(x\), \(y\) is the direction of periodic arrangement). As shown in the figure, the framework of the cell is a square structure; the inner cavity of the Helmholtz resonator is divided into two parts by a movable diaphragm. The smaller upper cavity is cavity I, and the larger lower cavity is cavity II. Cavity I and cavity II are connected to the outer cavity through a labyrinth tube. The design of the labyrinth tube makes full use of the folding and coiling of the space, which greatly increases the length of the tube, fully extends the propagation distance of sound waves in the structure and effectively reduces the lower limit of the low-frequency band gap. Moreover, the volume of the two cavities can be adjusted by changing the length of the telescopic screw, so as to change the structure configuration. This design can achieve the purpose of adjustable band gap and active control of low-frequency noise. The structural parameters are as follows: the length of the framework is \(l\), the thickness of the wall and the diaphragm is \(d\), the total height of the inner cavity is \(2h\), the height of the telescopic screw is \(h\), the width of the labyrinth tube is \(s\), the length of the labyrinth tube is \(l_{1}\), the length of the screw is \(b\), the volume of cavity I is \(V_{1}\), the volume of cavity II is \(V_{2}\) and the lattice constant is \(a\).
# A Survey of Generative AI for Detecting Pedophilia Crimes
## 5 Benefits of Generative AI in Detecting Pedophilia
### Spotting Online Predators  
Generative AI offers a promising solution for identifying and detecting online predators by leveraging its ability to analyze large datasets, recognize intricate patterns, and generate novel content, which is primarily useful for creating realistic synthetic training data, such as simulated grooming conversations, to augment sparse or imbalanced datasets and build more robust detection models. As an example, [PERSON] et al. [29] implemented a SimCSE-based contrastive embedding model with RoBERTa encoders--a powerful architecture from the generative AI domain--to extract sentence-level features from grooming conversations. They used these features to train an SVM classifier that achieved 99% accuracy in identifying predatory chats. This demonstrates how LLMs can be repurposed as reliable detectors.  
By analyzing communication trends, language use, and online activity, AI can help identify individuals involved in grooming activities or seeking to exploit children for sexual purposes.  
This proactive approach enables timely intervention, providing an opportunity to protect children from harm before it occurs. In addition to identifying existing cases of exploitation, generative AI also plays a role in uncovering new tactics used by predators, ultimately increasing the effectiveness of child protection strategies [6].
## 5 Chicane Commissioning
### CTR Measurement  
The goal of the CTR measurement is to find the booster phase corresponding to the maximum bunch compression, which provides the maximum CTR pulse energy. The fully compressed-bunch booster phases are expected to benchmark the simulations for the bunch compressor performance. In the space-charge tracking simulation with the CSR effects, the beam emitted by a Gaussian laser with a root-mean-squared (rms) pulse length of 3.4 ps is fully compressed from the rms bunch length of \(\sim\)4 to \(<\)0.73 ps or 0.22 mm for a beam momentum of 17 MeV/c and bunch charge below 200 pC. Then, the fully compressed beam is sent to hit an aluminum plate and radiates the CTR, which is detected by a THz pyroelectric detector. Note that the detector can detect signals with a wavelength between 200 and 500 \(\upmu\)m while supporting the maximum repetition rate of 1000 pps and the minimum detectable energies in the order of 50 nJ [17].  
Throughout the CTR measurement, beam momentum is always set to 17 MeV for every momentum chirp, while the corresponding chicane's dipole magnet currents from the commissioning result are also fixed. Thus, the beam is first set up for the momentum of 17 MeV/c at the MMMG booster phase. Then, the booster phase offset is set in order to generate momentum chirp, where the beam momentum is reduced. Finally, the booster RF gradient is increased to achieve the momentum of 17 MeV/c again. Note that this momentum-chirp-generation procedure is similar to the simulation via program ASTRA in [2] and the momentum chirp measurement in [10].  
After the preparation of the beam momentum and momentum chirp, the beam is transported downstream to the chicane and transversely focused to a CTR measurement station consisting of the aluminum plate target and the pyroelectric detector. Figure 9 displays a plot of CTR pulse energy measured via the pyroelectric detector as a function of the booster phase for bunch charges of 30, 50, 100, and 200 pC. It also shows that the maximum CTR pulse energy from different bunch charges corresponds to different booster phases. Note that the bunch charge higher than 200 pC could be challenging for the CTR measurement of the fully compressed bunch due to the longitudinal space charge effects. In this case, the fully compressed bunch is decompressed as it propagates to the CTR measurement station. Therefore, the CTR measurement of the higher bunch charge should not be conducted for the fully compressed-bunch booster phase benchmarking.  
Figure 8: Dipole magnet currents resulted from the commissioning method without beam position measurement throughout chicane as a function of beam momentum.  
Finally, the booster phase which provides the maximum CTR pulse energy in Figure 10 is benchmarked with the simulation in [2]. Figure 10 also shows simulation results including CSR effects from different programs, which are IMPACT-T and OCELOT. Moreover, the experimental results show a similar trend to the simulation results. This suggests that the bunch compressor properties from the commissioning method are similar to the simulations.