esa-sceva/satcom-chunk-collection huggingface dataset:
# Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval
## 3 Experimental Results and Discussion
### Comparisons to the Second and Third Configurations  
In this set of experiments, we investigate the two other configurations for the vision and text backbones. We recall that the two other configurations are based also on transformers, except they differ on how they are pre-trained as mentioned in Table 4. The average recalls of the three configurations as depicted in Table 4 reveal that the first configuration based on CLIP is the best on all datasets and on both retrieval tasks. This clearly provides an indication that using models pre-trained on image-text matching tasks are more suitable for knowledge transfer compared with models pre-trained independently of image and text classification tasks.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Text-To-Image**} & \multicolumn{3}{c}{**Image-To-Text**} & \multirow{2}{*}{**Train Time**} \\ \cline{2-2} \cline{4-7}  & **R@1** & **R@5** & **R@10** & **R@1** & **R@5** & **R@10** \\ \hline TextRS & 24.55 & 65.66 & 80.60 & 24.08 & 66.04 & 80.78 & 5.8 h \\ \hline Merced & 19.33 & 64.00 & 91.42 & 19.04 & 53.33 & 77.61 & 7.9 h \\ \hline Sydney & 26.76 & 57.59 & 73.53 & 24.95 & 57.44 & 72.32 & 0.6 h \\ \hline RSICD & 9.14 & 28.96 & 44.59 & 10.70 & 29.64 & 41.53 & 102.7 h \\ \hline \hline \end{tabular}
\end{table}
Table 3: The retrieval performance in terms of R@K on different datasets.
## 4 Experiments and Analysis  
### Experimental Data and Evaluation Metrics  
The data used in this paper are divided into labeled datasets and unlabeled datasets. The labeled dataset is derived from the CCGS2019 evaluation task \"Medical Entity Recognition and Attribute Extraction for Chinese Electronic Medical Records\". Among them, the training set is 1000 Chinese medical texts, and the test set contains 379 Chinese medical  
Figure 2: Structure of Chinese medical entity recognition model based on semi-supervised learning.  
texts for evaluating the performance of the final model. The unlabeled data used the medical texts provided at the 6 th China Health Information Processing Conference, and a total of 3000 medical records were collected in the \"Love and Love Doctor\" medical network Chinese medical texts. As shown in Table 1, medical entities in the dataset are divided into six categories, namely disease and diagnosis, imaging examination, laboratory, operation, drug, and anatomy. As shown in Figure 3, the labeled medical text data are used as experimental data for subsequent experiments according to the BIO labeling method.  
We use the precision rate (\(P\)), recall rate (\(R\)), and F1 value (\(F1\)) to evaluate the performance of the model proposed in this paper. The accuracy rate measures the ability of the model to identify the correct entities, the recall rate measures the ability of the model to identify all entities in the dataset, and the \(F1\) value is the summed average of the accuracy rate and recall rate. The calculation formulae are as follows.  
\[P=\frac{TP}{TP+FP} \tag{2}\]  
\[R=\frac{TP}{TP+FN} \tag{3}\]  
\[F1=\frac{2\times P\times R}{P+R} \tag{4}\]
## 2 Materials and Methods
### Data Sets
#### 2.2.3 Ancillary GIS Data  
Fire perimeter data for the period January 2000-April 2017 were provided by the Department of Forestry and Fire Protection of California's Fire and Resource Assessment Program (FRAP, [[http://frap.fire.ca.gov/](http://frap.fire.ca.gov/)]([http://frap.fire.ca.gov/](http://frap.fire.ca.gov/))). The FRAP compiled this statewide spatial database of fire perimeters from BLM (US Bureau of Land Management), NPS (National Park Service), and USFS (US Forest Service) fires. The FRAP fire database represents the best digital record of fire history in California, and its data quality is higher than that of MODIS fire data or other data sets [77; 78]. The California State Wildlife Action Plan (SWAP; https://www. wildlife.ca.gov/SWAP) data were applied to identify the vegetation polygons of chaparral, CSS, grassland, forest, and developed land over Southern California [79]. The SWAP land cover data provides information on the distribution of vegetation types with high spatial resolution (30 m resolution) (Figure 2). We follow [80] and the SWAP data were aggregated to a 250 m \(\times\) 250 m resolution using majority statistics. This means that the vegetation type for each MODIS pixel was identified as the type that had the highest frequency at 30 m resolution in each 250 m \(\times\) 250 m grid. This approach better reflects the regional vegetation distribution than the nearest-neighbor interpolation, and is less affected by data noise and misclassifications [80]. Additionally, a rigorous data quality control was implemented by the California Department of Fish and Wildlife in generating the SWAP database and, thus, it is the \"best available\" land cover data for California at present [50]. Vegetation cover typical of chaparral is comprised of sclerophyllous, evergreen-leaved shrubs with hard stems and deep roots with a variety of species. CSS is dominated by softer-leaved, woody shrubs and sub-shrubs. The rooting zone is shallower than chaparral and the dominant shrubs may be summer deciduous or possess smaller leaves during the dry season. Grasslands are typically dominated by a variety of non-native annual grasses, which die in the summer months and the cover regenerates through seed germination in the winter. Lower-elevation stands of woodland-forests are dominated by evergreen and deciduous oak, while the higher elevations (>1300 m a.s.l.) are mainly occupied by conifer stands (e.g., pines and firs).
# Towards Flexible and Cognitive Production--Addressing the Production Challenges
## 7 Cognitive Communication and Safety on Shop Floors
### Safety Systems
#### 7.2.1 Safety of the Machines and Workers  
The system safety for dealing with failures is termed _functional safety_. The functional safety denotes that the system is free of machine-related failures. Safety-certified components should be used in such a manner that they are capable of detecting anomalies and can adequately reach a safe-state in case of internal and external failures. Integrated safety measures such as over/under-temperature and over/under-voltage monitoring [107], memory protections [108], code, timing consistency checks, and many more are ensuring reliability, credibility, and availability of the safety PLC.  
Safety in the system also means that the system is fault tolerant via redundancy in the system. The most common redundant architectures are shown in Figure 11. In the context of sensors, therefore, denotes having multiple sensors that measure the same physical phenomenon and thus deciding which of the values, if any, is valid. Moreover, future production lines will rely to a great extent on the data provided by sensors. This implies that sensory systems need to be reliable since they are the main source of information for process control and safety functions.  
Basic information on worker movement and behavior could be demanded via the IMU sensors in order to optimize the safety functions of the system. Localization in the current use-case is more dynamic and sophisticated. The head worn camera with visual processing will be used to detect the position and the gestures of the workers. It is also important to mention that the health status (e.g., heart rate, blood pressure, breathing, etc.) of the workers should play an important role in the future smart factories. These data will indicate the conditions of the workers and show how much stress they are experiencing. Human factors play a huge role in workplace safety, with fatigue and stress readily contributing to the number of accidents. With proper analysis of the health data, this number can be minimized.
## 5 Conclusions  
The present study is a complex presentation of the process of optimizing a planar Yagi Uda antenna considering also the effects of placing the resulting structure on the human body. The optimization process consists of the dielectric and geometry dimensions modification while aiming at a larger bandwidth and gain value for the resulting structure. Following the analysis and the data interpretation, the authors reached the conclusion that the optimized antenna has a thinner dielectric with better characteristics, a larger dimension for directors and two more conductor microstrips in order to increase the gain while maintaining the length and width of the initial structure. The results obtained from numerical modeling the proposed structure are compared to the measured results obtained experimentally with a cheaper and smaller VNA present on the market and the results are in accordance, thus determining the fact that the characteristics of antennas can be  
Figure 23: Electric field distribution on YZ transversal plane for a distance of 4 cm between antenna and tissues: (**a**) initial antenna; (**b**) optimized antenna.  
Figure 22: Magnetic field distribution on YZ transversal plane: (**a**) initial antenna; (**b**) optimized antenna.  
preliminary determined in controlled environments with smaller devices with a more easy and cost-effective process.  
In the last part of the paper, the authors analyzed the antenna from the human exposure point of view due to the numerous applications of this type of antenna, which is close to the human body. The study was conducted for the electric and magnetic field for radiation power density and also for the SAR values. Following the numerical modeling, it was determined that the antenna analyzed must be placed further from the human body in order for the values of the studied structures to be in accordance with the standards in force.  
Throughout the optimization process and the evaluation of the antenna's effects on the human body, the authors determined the steps for the optimization of the antenna, modeled each considered structure in HFSS, and measured the characteristic parameters of the antenna for validation. After reaching an optimum, the authors determined the effects it would have on the human body by modeling and interpreting the results accordingly to the standards in force.  
Conceptualization, C.C., C.P., A.G. and C.M.; methodology, C.C., A.G. and C.P.; validation, C.C., A.G., C.P., M.G. and S.A.; formal analysis, C.C.; investigation, C.C., C.P., A.G. and C.M.; resources, S.A. and M.G.; data curation, C.C., C.P., A.G. and C.M.; writing--original draft preparation, C.C.; writing--review and editing, C.C., A.G. and C.P.; visualization, S.A. and M.G.; supervision, C.M.; project administration, C.C.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Recent Advancements in Anaerobic Digestion and Gasification Technology
## 6 Feedback for Anaerobic Digestion  
The nutritional composition is regarded as a major factor that determines the suitability of any feedstock or substrate for biogas production. This nutritional composition affects the biogas yield, methane content, biodegradability, and degradation of the biomass kinetic involved [35]. These nutrients include carbohydrates, protein, and fats, as shown in Table 2. Interestingly, any feedstock for the anaerobic digestion process should be readily biodegradable and should not contain any toxic components that would impair the activities of microbes [36]. Table 2 shows the nutrient compositions of feedstocks with their methane yields and methane compositions.  
From Table 2, it is seen that lipids produced the highest methane yield of 1.01 m\({}^{3}\)/kgVS as well as a methane percentage composition of 70% compared with protein and carbohydrates. This implies that substrates that are rich in lipids hold great potential for methane production. Nevertheless, the use of such substrate should be closely monitored, as a drop in pH can be toxic to the microbial community because of degradation that releases long-chain fatty acids. Considering the feedstocks that are protein-rich, [PERSON] et al. [39] mentioned that these feedstocks have a high potential for methane yield. However, they increase the alkalinity of anaerobic digestion because of the degradation process that releases ammonium. This process enhances the digestate and inhibits the activities of the methanogens. Further, [PERSON] et al. [39] stated that this inhibition occurs because of the shift in equilibrium from ammonium to ammonia, usually in a concentration value of 53-1450 mg/L. On a broader scale, substrates or biogas production are classified into agricultural waste, industrial organic waste, and municipal waste.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Transport Layer
#### 5.4.1 End-to-End ACK  
According to the _staggered wakeup pattern_, STAs from ring 1 are the last ones to access to the channel and transmit their information. Once compared the data sources with the expected uplink traffic, the GW emits a broadcast message called _end-to-end acknowledgement_ (e2e ACK) with a list of acknowledged STAs. Figure 6 shows the e2e ACK operation at the end of every transmission window. Apart from being simple, quick and simultaneously listened by all network elements, e2e ACKs allow STAs to evaluate the state of their path to the GW and act consequently.  
Figure 6: Uplink data transmission phase in a multi-hop LPWAN running HARE protocol stack with two transmission windows and the network topology from Figure 7. Note the communication problems in the first transmission window between nodes \(N_{6}\) and \(N_{3}\).  
The e2e ACK also acts in the HARE protocol stack as an auxiliary mechanism for detecting lost ACKs. Thus, if a child remains awake as a result of not having listened to the ACK of its parent, it will go to sleep when detecting its own ID (and those of its children) in the list of acknowledged STAs from the e2e ACK. Only if at the end of all available transmission windows an STA has not correctly received any acknowledgement (either an ACK or an e2e ACK) will it discard its packet.
## 3 Gaussian Approximation  
Following the assumptions made in [11], the distributions of the messages involved in the LDPC iterative decoding process under appropriate hypotheses may be approximated as Gaussians. Since it may be observed that to totally specify a Gaussian only its mean and variance are needed, and during the iterative decoding process it is necessary to specify only the means and variances of a generic check node output message, \(u\), and of a generic variable node output message, \(v\). Furthermore, since in [11], assuming the validity of the symmetry condition, the variance \(\sigma^{2}\) was shown to be connected to the mean \(m\) by the relation \(\sigma^{2}=2m\), the means only can be kept in the computation process. The means of \(u\) and \(v\) have been denoted by \(m_{u}^{(l)}\) and \(m_{v}^{(l)}\) at the \(l\)-th iteration, respectively. Furthermore, the log-likelihood ratio (LLR) message \(u_{0}\) from the channel can be taken as Gaussian with a mean of \(m_{u_{0}}=2/\sigma_{n}^{2}\) and a variance of \(4/\sigma_{n}^{2}\), being \(\sigma_{n}^{2}=N_{0}/2\) the variance of the channel noise.  
The mean of the output of a degree-\(i\) variable node at the \(l^{th}\) iteration is:  
\[m_{v,i}^{(l)}=m_{u_{0}}+(i-1)m_{u}^{(l-1)} \tag{8}\]  
where \(m_{u_{0}}\) is the mean of \(u_{0}\) and \(m_{u}^{(l-1)}\) is the mean of \(u\) at the \((l-1)\)-th iteration.  
Defining \(\phi(x)\) as in Definition 1 in [11], the update rule for an irregular code becomes:\[m_{u,j}^{(l)}=\phi^{-1}\bigg{(}1-\bigg{[}1-\sum_{i=2}^{d_{l}}\lambda_{i}\phi(m_{v,j }^{(l)})\bigg{]}^{j-1}\bigg{)}. \tag{9}\]  
The output of a variable node is specified by its mean \(m_{u}^{(l)}\), that can be calculated as the linear combination of the means \(m_{u,j}^{(l)}\):  
\[m_{u}^{(l)}=\sum_{j=2}^{d_{s}}\rho_{j}\phi^{-1}\Big{(}1-\bigg{[}1-\sum_{i=2}^{ d_{l}}\lambda_{i}\phi\big{(}m_{u_{0}}+(i-1)m_{u}^{(l-1)}\big{)}\bigg{]}^{j-1} \Big{)}. \tag{10}\]  
Defining \(s=m_{u_{0}}\) and \(t_{l}=m_{u}^{(l)}\), Equation (10) may be rewritten as:  
\[t_{l}=f(s,t_{l-1}) \tag{11}\]  
where the function \(f(s,t)\) is expressed as:  
\[f_{j}(s,t):=\phi^{-1}\Big{(}1-\bigg{[}1-\sum_{i=2}^{d_{l}}\lambda_{i}\phi(s+(i -1)t)\bigg{]}^{j-1}\Big{)} \tag{12}\]  
\[f(s,t):=\sum_{j=2}^{d_{s}}\rho_{j}f_{j}(s,t). \tag{13}\]
## 2 Materials and Methods
### Chemicals  
All chemicals (cyclohexane, n-hexane, dichloromethane, acetone, sulfuric acid, Florisil, and sodium sulfate anhydrous [Na\({}_{2}\)SO\({}_{4}\)]) were of analytical grade and were purchased from Scharlau, Fluka (Germany). Deionized water was prepared using an Easy Pure UV instrument (0.05 \(\upmu\)S/cm; Barnstead(tm) GenPure(tm) Pro, Thermo Scientific, Dubuque, IA, USA).  
The dried fruit material used to test the content of compounds with each method was homogenized and freeze-dried in a LYO LAB 3000 apparatus.  
The following analytical standards were used for the analyses:  
* Deuterated PAHs standards (Semivolatile Internal Standard Mix) Sigma-Aldrich (Germany), naphthalene-D8 (NA D8), acenaphthene-D10 (AC D10), phenanthrene-D10 (PHE D10), chrysene-D12 (CHR D12), benzo[a]pyrene-D12 (BaP D12), and perylene-D12 (Per D12).  
The standard mixture of 16 PAHs in methanol: methylene chloride (1:1) (EPA Method 610 PAH Mixture, Merc, USA) included acenaphthene (AC), acenaphtylene (ACL), anthracene (AN), benz[a]anthracene (BaA), benzo[b]fluoranthene (BbFA), benzo[k]fluoranthene (BkFA), benzo[ghi]perylene (BghiP), benzo[a]pyrene (BaP), chrysene (CHR), dibenz[a,h]anthracene (DBahA), fluoranthene (FL), fluorene (FA), indeno [1,2,3-cd]pyrene (IP), naphthalene (NA) phenanthrene (PHE), and pyrene (PY).  
* Pesticides Surrogate Spike Mix solutions in acetone (4-8460, SUPELCO, Bellefonte, USA) included 2,4,5,6-Tetrachloro-m-xylene and 2,2',3,3',4,4',5,5',6,6'-PCB.
* Standard solutions containing indicators of PCB congeners: PCB 28, PCB 52, PCB 101, PCB 138, PCB 153, PCB 180 (6 PCB-Key Isomers LGC Ltd., NE 5575, Augsburg, Taufkirchen, Germany);  
dioxin-like PCBs: non-ortho PCBs (PCB 77, PCB 81, PCB 126, PCB 169) and mono-ortho PCBs (PCB 105, PCB 114, PCB 118, PCB 156, PCB 157) (CERTAN(tm) NE 90152 LGC Ltd., Teddington, UK).
# A Multi-Agent Deep Reinforcement Learning System for Government Interoperability
## 4 System Design
### Use Case Implementation
#### 4.7.4 Use Case 4: Water Contamination Crisis in a Municipality Scenario  
A water contamination crisis occurs in a municipality due to a leak in the industrial wastewater system, resulting in hazardous chemicals entering the public water supply. This leads to health concerns among citizens, including reports of illnesses caused by consuming contaminated water [35].  
1. **Request submission**: the **CitizenAgent** submits an urgent request to the **SecretariatAgent** reporting suspected contamination of the municipal water supply, complaining about an unusual odor, taste, or color in the tap water and cases of illness reported by healthcare facilities [36].
2. **Verification process**: The SecretariatAgent verifies the authenticity of the complaint by cross-referencing citizen reports with health department data [37]. The EventManagementAgent conducts water sample tests in municipal labs to confirm contamination and identify its source [38]. Meanwhile, the TreasuryAgent allocates resources for additional testing and implements immediate safety measures to mitigate potential risks [39].
3. **Safety checks**: The EventManagementAgent is responsible for shutting down the contaminated sections of the water supply system to prevent further exposure [40]. In response, emergency distribution of clean drinking water is organized in collaboration with the TreasuryAgent, who coordinates funding for water tanker services and bottled water supplies [41]. The MayorAgent oversees the implementation of safety protocols, including issuing public health advisories and coordinating with healthcare facilities to provide treatment to affected citizens [42]. Additionally, inspections are conducted at nearby industrial facilities to identify any potential leaks or regulatory violations.
4. **Resolution drafting**: The SecretariatAgent, in coordination with the Event ManagementAgent, drafts a resolution that outlines the following actions: repairing the damaged wastewater infrastructure and installing stricter monitoring systems; imposing fines or penalties on the responsible industrial facilities; and developing long-term measures, such as implementing water quality sensors and enacting stricter industrial waste management regulations. The MayorAgent then reviews the draft and approves it for implementation.
5. **Permit issuance**: The MayorAgent issues permits for the infrastructure repairs and the installation of advanced water treatment systems. Temporary permits are also granted for the deployment of water tankers and the establishment of emergency water stations. Meanwhile, the TreasuryAgent ensures that the necessary financial permits are in place to support the resolution plan.
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 3 Methods
### Fast Coherent Video Style Transfer
#### 3.2.2 Network Architecture Overview  
Figure 7 shows the details of the proposed gradient-based optimization network. In time step \(t\), there are four images in total which are used as inputs passed into the network which are per-frame stylized result \(f_{s}^{t}\), mask \(M^{t}\), warped image \(w^{t}\) and initial image \(\widehat{x}_{init}^{t}\). Coherent Losses force temporal consistency between adjacent outputs, and Perceptual Losses and Pixel Loss ensures that the image blurriness artefacts are reduced. The Coherent Losses contain an RGB-level loss and a Feature-level loss where the first one constrains the mean square error between RGB values of \(\widehat{x}^{t}\) and \(w^{t}\) and the second one restricts the mean square error between feature representations of them. The Perceptual Losses force differences between \(\widehat{x}^{t}\) and \(f_{s}^{t}\) inside the network to be reduced, and the Pixel Loss intends to keep the RGB values between \(\widehat{x}^{t}\) and \(f_{s}^{t}\). During each iteration, the generated image \(\widehat{x}^{t}\) gradually compensates for discontinuous texture points and updates features into the entire image. Specifically, the gradients computed from Total Loss are back propagated into the network, and the updated weights and biases inside of each CNN layer push \(\widehat{x}^{t}\) to grow into an image with more similarity to the inputs. In addition, the proposed gradient-based optimization network is much faster than previous methods found in the literature [23; 24] as it uses a new initial image \(\widehat{x}_{init}^{t}\). The reason for this is that the proposed optimization process needs much less iterations than previous methods [23; 24] using \(w^{t}\), since the initial image \(\widehat{x}_{init}^{t}\) has significantly reduced flow errors while \(w^{t}\) does not.