esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 4 Experimental Results and Discussion
### Evaluation on Image Degradation Modeling
On the contrary, IDFlow-SR achieved the best SR performance with a large margin, obtaining 2.94 dB/0.133/0.155 and 0.91 dB/0.048/0.161 better than baselineCNN-SR and Bicubic-SR in terms of PSNR/SSIM/LPIPS. IDFlow models the image degradation by learning the HR-LR conditional distribution, so it can generate more realistic HR-LR image pairs that help to promote SR performance on real-world images. Visual comparisons of these SR models trained with different image synthesis approaches are shown in Figures 5 and 6. The results generated by Bicubic-SR are blurry, with no difference in visual perception when compared with the input. Though DSGAN and K-ZSSR help to generate sharper results, there are obvious ringing artifacts around image edges in the SR results. In flat regions, these methods tend to amplify the noises, resulting in tiny irregular textures, which can be observed in Figure 6. Although baselineCNN supervisedly learns the degradation process, the model trained with baselineCNN produces the SR results with similar patterns as DSGAN and K-ZSSR. Both of these three methods utilized a CNN-based network. Once the parameters of convolution layers are fixed, the CNN only obtains a certain fixed degradation mode, without maintaining the stochastic nature of image degradation. Thus, they could not help the SR model to obtain convincing SR images, which also means that the image degradation process cannot be effectively modeled only with a single CNN model. In contrast, IDFlow-SR generates SR output that has clearer image patterns and better perception quality with no visible artifacts. By learning the HR-LR conditional distribution is more efficient than a deterministic mapping, demonstrating the superiority of IDFlow over the compared methods above.
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 3 Materials Classification  
CMCs are classified as composite materials and technical ceramics. Their composition includes ceramic fibers embedded in a ceramic matrix producing a reinforced fiber-reinforced material. Ceramic materials are known for their brittle nature, but CMCs are tougher than their ceramic constituents. This is due to the efficient design of the fiber-matrix interface, which is responsible for spotting and deflecting cracks in the matrix, protecting the fiber reinforcement from catastrophic failure [18,19]. The literature analysis indicated that CMCs could be classified into two basic composite categories: oxide CMCs and non-oxide CMCs.  
**Oxide CMCs** are oxidation-resistant materials and are used for applications with oxidizing environments, such as the hot section of turbine engine applications. Moreover, it has been found that the most used oxide composite base material families are alumina oxide and aluminosilicates [20,21]. These two base material families are then combined with several different ceramic and metallic fillers and reinforcements, which allow for the creation of the oxide CMCs. When both base material and filler reinforcement are oxide ceramic-based, an oxide-oxide CMC matrix is produced. A particular example is the combination of aluminosilicates with alumina/mullite fibers. Moreover, alumina can be successfully combined with boron carbide, silicon nitride, titanium carbide, and titanium dioxide filler reinforcements to create an oxide-CMC material. These material combinations will be discussed in greater detail in the following sections of this paper. Furthermore, oxide CMCs are considered to have better creep and corrosion resistance properties than non-oxide CMCs. Such properties have led to more research on this type of composite material in the past decade. Section 4 will focus on presenting the various types of oxide CMCs in more detail.  
**Non-oxide CMC** materials have been in the research spotlight due to their good mechanical and thermal properties. They are composed of a non-oxide base ceramic matrix material and a filler/reinforcement (ceramic, metallic, plastic, polymer, etc.). Literature highlights carbon, silicon carbide, titanium carbide, and tungsten carbide being used as base matrix materials. For every material base matrix, there are several filler/reinforcements materials combined to create a non-oxide CMC material. Non-oxide CMCs, when subject to elevated temperatures, have low oxidation and corrosion resistance, whereas oxide CMCs are more oxidation and creep resistant in high temperatures, which will be discussed in the sections below. Section 5 will focus on presenting the various types of non-oxide CMCs in more detail.
## 4 Materials and Methods
### Characterization of Sorbents  
The morphology of the sorbents was observed by SEM analysis (Phenom-ProX SEM Eindhoven-Netherlands-Thermo Fisher Scientific, Waltham, MA, USA), while the chemical composition was obtained by semi-quantitative EDX analysis (coupled with SEM). FT-IR spectra were collected for the sorbents at different stages of use (raw, after metal sorption, and after the fifth desorption). The samples were incorporated into KBr pellets and analyzed using an IR-Tracer 100 FT-IR (Shimadzu, Tokyo, Japan). The thermogravimetric properties were characterized using a Netzsch STA-449-F3 Jupiter thermal analyzer (Netzsch-Geratebau-HGmbh; Selb, Germany) under nitrogen, with a ramp of 10 \({}^{\circ}\)C min\({}^{-1}\). The BET-surface area and porous characteristics were collected through the acquisition of nitrogen adsorption-desorption isotherms (Micromeritics-TriStar, II-Norcross, USA-system, 77 K) using the BET and BJH equations. The samples were swept at 120 \({}^{\circ}\)C under N\({}_{2}\) for 4 h. The size of the sorbent particles was determined by TEM analysis (JEOL, 1010-JEOL Ltd., Tokyo, Japan). The pH\({}_{\text{p2c}}\) (pH of zero charge) was performed by the pH drift method [85]. The pH was measured using a compact pH ionometer, S220 Seven (Mettler-Toledo, Shanghai, China). Filtration was performed on the samples before the analysis using 1.2 \(\upmu\)m membranes. The elements were measured using ICP-AES (ICPS-7510, Shimadzu, Tokyo-Japan). The magnetic properties (M-H loop) were analyzed using a Lake Shore 7410 vibrating sample magnetometer (VSM, Lake Shore Cryotronics, Westervill, OH, USA). The elemental composition (C, S, N, H, and O weight contents) analysis was carried out on the sorbent using an element analyzer (CHNOS, Vario EL III, Elementar Analysensysteme GmbH, Sonaustra\(\bar{\text{e}}\)e, Langenselbold, Germany).
## 1 Introduction
In order to tackle these challenges, we propose an efficiency-aware incentive mechanism, FedNow, which enables edge servers to personally determine their local training rounds while motivating their participation without access to their true training strategies and private attributes. Our main contributions can be condensed as follows:  
* We propose an efficiency-aware incentive mechanism, FedNow, which allows strategic edge servers to personally decide their local training rounds to improve resource utilization while compensating their training costs. Compared to existing works that only focus on improving model performance or incentivizing client engagement, we jointly optimize the training efficiency and expenditure costs, forming a joint optimization objective. Moreover, we take the multi-dimensional heterogeneous attributes, decision-making, and incomplete information of edge servers into account, which makes our work more practical. * We model the optimization problem of FedNow as a mixed-integer nonlinear programming problem, which is nonconvex due to the involvement of mixed-integer variables and multiple incentive constraints. In order to solve this complex problem, we use contracts to determine the optimal payments for each type of edge server and design a scoring function to prioritize the selection of more efficient edge serversthat are able to provide more data at lower costs. Then, to demonstrate that FedNow strictly outperforms existing schemes in terms of joint optimization objectives, we theoretically derive sufficient conditions for making the above situation true. Based on that, by integrating the Monte Carlo method, FedNow can obtain near-optimal feasible solutions and model parameters within a finite number of training rounds. * We perform extensive experimental evaluations on one synthetic and three real datasets to demonstrate the advantages of FedNow in terms of resource utilization and training efficiency. The numerical results indicate that FedNow can converge at least 42.3% faster than our baselines when achieving the same model accuracy. Moreover, FedNow improves training efficiency and resource utilization by at least 15.53% and 22.78%, respectively. The remaining part of this paper is organized as follows. Section 2 provides a review of the related work. The system model and design objectives of FedNow are described in Section 3. Section 4 elaborates on the design and optimization details of our efficiency-aware incentive mechanism, and Section 5 shows our experimental evaluations. Section 6 draws the conclusions.
## 4 TL Approach
This is in line with an earlier work by these authors [63], in which they demonstrate that the accuracy increased from 69.3% to 77.3% for German and from 68.5% to 72.6% for Czech while using the Spanish dataset as the baseline. [PERSON] et al. [64] also observe that the Spanish dataset is generally a better baseline than the Czech and German datasets for PD classification. When Spanish is the base language, accuracy scores of around 90% are reached. When Czech is the base language, accuracy scores are lower and vary within a considerably wider range (60 to 80% accuracy). Specifically, these researchers experiment with moving fractions of the target language to the training data, and excluding these fractions, then, from the test data. Only30% has to be moved when German is the target language and Spanish is the base language to obtain an accuracy of approximately 90%. However, when Czech is the base language, the accuracy drops to 60%. [PERSON] et al. [65] employed an FL approach on specifically PC-GITA (Spanish), a German dataset with 176 subjects (88 PD and 88 HC), and a Czech dataset with 100 subjects (50 PD and 50 HC). They utilized a model consisting of four fully connected layers (1024, 256, 64, and 2) with Wav2 vec2.0 embeddings. The model achieved a high robustness across different languages and populations with accuracies of 83.2%, 78.9%, and 77.8% on the Spanish, German, and Czech datasets, respectively. Finally, there are other works in the literature which deal with the classification of several diseases using the same approach. In [25], the authors combine CNNs with TL among different diseases. A base model is trained on either [PERSON]'s or [PERSON]'s disease (HD), and then the model is tested on the other disease. The CNNs are more accurate in distinguishing between HD and non-HD, than PD and non-PD. Not surprisingly, the models pre-trained on HD tend to be more accurate than models that are pre-trained on PD. This shows again that some base models transfer better than others. In other words, similar to what is observed for TL among languages [63], HD seems to transfer better than PD [25]. Another study that uses TL among diseases is [42], which has already been mentioned in Section 3.2. Although several studies take a cross-pathological approach, more studies in the existing literature are cross-lingual. These cross-lingual studies are not limited to pre-trained DNN architectures and TL applications. For studies focused on deep feature extraction, a cross-lingual approach also seems to becoming more popular. In Section 5, it is discussed in greater detail.
# A Country Risk Assessment from the Perspective of Cybersecurity in Local Entities
## 1 Introduction
The outcome of this study was to identify potential weaknesses and prevent the escalation of privileges between public administrations. To this end, the following questions will be answered:  
* What is a citizen's perception of security with respect to the services published on the Internet by their local authorities? * What methodology could be employed to obtain the technical metrics of services exposed to the Internet in a simple manner? * How could a local public body be characterized based on competence variables? * How could the potential risk of local entities be incorporated into a country's overall risk assessment? \begin{table}
\begin{tabular}{c c c} \hline \hline
**Frameworks** & **Purpose** & **Developed by** \\ \hline CIS & \begin{tabular}{c} To develop and promote best practices, tools and guidelines to \\ enhance the security and resilience of Internet systems and critical \\ infrastructure against pervasive cyber threats \\ \end{tabular} & Center for Internet Security \\ \hline NIST 800-53 & \begin{tabular}{c} To provide a comprehensive set of security and privacy controls \\ that help organizations manage and mitigate risks associated with \\ information systems \\ \end{tabular} & National Institute of Standards and Technology \\ \hline PCI-DSS & \begin{tabular}{c} To ensure that all organizations that handle credit card transactions \\ maintain a secure environment to protect cardholder data from \\ breaches and fraud \\ \end{tabular} & Payment Card Industry Security \\ \hline ISO/IEC 27001 & \begin{tabular}{c} To provide a framework for establishing, implementing, \\ maintaining and continually improving an Information Security \\ Management System (ISMS) \\ \end{tabular} & \
\begin{tabular}{c} International Organization for \\ Standardization (ISO) and the \\ International Electrotechnical \\ Commission (IEC) \\ \end{tabular} \\ \hline CMMC &
\begin{tabular}{c} To enhance the cybersecurity posture of the defense industrial base \\ (DIB) and ensure that contractors and subcontractors adequately \\ protect sensitive information related to national security \\ \end{tabular} & U.S. Department of Defense (DoD) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some of the main security frameworks.
Specifically, we place an unmanned aerial vehicle in the orchard to collect citrus images from all directions around the citrus tree. These data are transmitted to the edge computing device Jetson nano via 4G data transfer. An improved target detection algorithm is deployed inside the device. To solve the problem that it is difficult to balance the accuracy and real-time of target detection algorithm in edge computing device, the state-of-the-art model was improved the structure of the model by adding a convolution module (CBAM) [26] with an attention mechanism;fusion of feature maps with different resolutions using adaptive parameters [27]; finally, with the accuracy guaranteed, we prune the model weights, add L2 regularization constraints to the batch normalization layer, delete and fine-tune the 30% redundant channels that occupy the minor information in the channels. In the experimental part, the feasibility of the proposed method proves by ablation experiments of the model itself and comparison experiments with other models. The main contributions of this paper are as follows:  
1. Combined with the advantages of the mobile operating platform and edge computing equipment, the improved deep learning target detection model is used to accurately and real-time detect the omni-directional citrus fruit image taken by UAV. 2. The target detection algorithm was improved and optimized. Attention mechanism, multi-layer feature adaptive fusion, and pruning optimization are adopted to improve the accuracy and reasoning speed of the model.
## 5 Optimization Method
### Algorithm Complexity  
The greedy heuristic described in Section 5.2, used to generate an initial solution, is of polynomial complexity. It starts with sorting the sets of clusters, demands, and PPs. Next, it processes the sets' elements iteratively, allocating the processing and transmission resources according to a first-fit policy.  
The CG procedure consists of two phases: (a) generating new columns by solving the PP problem and (b) solving the LP problem, as shown in Figure 3. The former processing phase is polynomial in time since it consists of calculating the reduced cost, using (27)-(29), for the paths in the set of candidate paths not included in the LP problem and selecting the paths of the highest cost. The latter phase involves solving an LP problem, where the computation times in practice increase linearly with the number of problem variables and constraints. Still, these two phases are performed in a loop until new columns cannot be found. The number of loop repetitions may be high, which is the main factor that makes the algorithm time-consuming when solving larger problem instances.  
Solving MILP in the last step of the algorithm is a complex task as the optimization problem considered is \(\mathcal{NP}\)-hard. Nevertheless, applying the CG procedure reduces the number of problem variables, facilitating solving the problem.  
Detailed computation times of the algorithm in different network scenarios are presented in Section 6.3.
## 2 Materials and Methods
### Experimental Design  
After the first experiment with African catfish juveniles, published by [PERSON] et al. [35], the experiment continued. The fish were moved into larger recirculating aquaculture systems to continue the study until achieving a market size of fish (weight of around 1 kg). At the start of the experiment, 240 fish were tagged (PIT-tags, BTS-ID\({}^{\otimes}\)) and unique four-digit codes were recorded. Then the fish were placed into each of four rearing tanks (\(n\) = 80 fish/group) in four separate RASs. Each RAS consisted of a submerged biofilter tank (volume 2500 L) with bio-elements (BT 10, Ratz), four circular rearing tanks (volume 650 L), and a 900 L pump tank with a drum filter (ECO15, DVS-FilterTechniek, Kerkrade, The Nederland). The tanks were supplied with a water flow of 6 L s\({}^{-1}\) and covered by a net to prevent fish jumping. Water circulation was maintained using a regulated pump (Jecod DCP 12000, Jebao Co., Ltd.). The water was heated to 24.0 \(\pm\) 0.1 \({}^{\circ}\)C using a 1.5 kW heater (900 EVO Aquatic Heater, Elecro Engineering, Hertfordshire, England). The oxygen concentration of outlet water was maintained at 63.0 \(\pm\) 2.0% (Secoh JDK400, Secoh, Japan) and a pH of about 7.0 \(\pm\) 0.04. As for the photoperiod, the natural light mode of the May-September summer season was set. The fish were acclimatized to these experimental conditions for two weeks.  
During this period, the fish were continuously fed with the experimental feed containing different HS contents: 0, 1, and 3, and 6% of food weight (HS0, HS1, and HS3 and HS6, respectively). Commercial feed (4.5 mm size of pellets, Aller Bona Float, Aller Aqua, Christiansfeld, Denmark) was spray coated with a commercial liquid feed additive Humafit (Humaty s.r.o., Czech Republic) containing HS from Siberian leanordite mineraloid in the required amounts. Then the HS-enriched pellets were dried in a laboratory dryer (UN 75,Memmert GmbH, Munchen, Germany) at 40 \({}^{\circ}\)C for 24 h. For more details on the proximate composition of the HS commercial product and experimental diets, see [35].  
The fish in each tank were fed by hand twice a day (8.00, 15.00). The daily food ratio was adjusted according to the manufacturer's instructions (1 to 2% of biomass). The tanks were cleaned of sediment daily and drained water (\(\sim\)1/3 of each rearing tank volume) was replaced with tap water.  
The water temperature, pH, and oxygen level were measured daily using a multimeter (HI-98194, Hanna Instruments, Italy). The levels of NH\({}_{4}\)\({}^{+}\), NH\({}_{3}\), NO\({}_{2}\)\({}^{-}\), and NO\({}_{3}\)\({}^{-}\) were checked regularly using an aquaculture multi-parameter photometer with pH meter (HI-83303, Hanna Instruments).
# Structural Health Monitoring for Prefabricated Building Envelope under Stress Tests
## 2 Materials and Methods
### Method
#### 2.1.2 Sensing Technologies Integration in Facade  
Structural sensing technologies are designed for integration in facade, studying their applicability during off-site facade manufacturing, as well as during installation.  
The integration of these sensors underwent a thorough examination involving an analysis of several key parameters. Dimensions were examined to determine compatibility and correctness for its application. Further consideration was given to the sensors' positioning, factoring in orientation and functionality to optimize data collection effectiveness. This included assessing the efficacy of data obtained, co-locating commercial sensors near Fiber Bragg Grating (FBG) sensors for direct comparative analysis, and situating sensors to monitor critical components of the facade, such as the center of transom width, the center of mullion height, and the center of the ventilated cavity. Additionally, the routing and length of cables connecting the sensors were analyzed to ensure efficient signal transmission and practical installation. The fixing system methodology was examined to guarantee stability and reliability in data acquisition. This comprehensive analysis contributes to an informed understanding of the integrated sensor system's performance within the context of the monitored facade. In Section 2.3, the final sensor integration designed for the testing activities is reported.
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 3 Materials and Methods
### \(kNN\) Search  
As it is a two-level structure that spatial indexing has proposed in this paper, the \(kNN\) searching algorithm is divided into two steps, octree searching and 3D R*-tree searching. Candidate 3D R*-trees are chosen by searching along octree, and then \(kNN\) are found by searching along 3D R*-tree.