esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Options C and D assess the energy savings at the facility level, when the ECM cannot not easily measured in isolation from the rest of the building. Option C assesses savings by analyzing utility bills before and after the implementation of the ECM. Option D uses simulations of equipment or facilities, when base year or post-retrofit data are unreliable or unavailable [13]. These four options of A, B, C and D are recommended by the IPMVP to measure saved energy. However, every methodology has its limits in implementations, as indicated in Table 1.  
Options C and D require more time and skill and are more costly to measure than options A and B [13]. Option C recommends a methodology to report saved-energy for the entire facility. By analyzing meter data, option C gives reliable evidence to show energy savings. It is a widely employed method, since it provides a maximum measurement boundary. However, this methodology has limited application, as the expected savings must exceed 10%. This restricts the range of application range for option C for most existing buildings to only an elementary saving project _i.e._ the energy saving effect cannot be less than 10% [12]. Option D is an alternative methodology with the same measurement boundary. It uses computer simulation software to predict the energy for the facility. A simulation model must be calibrated so that it predicts an energy pattern that approximately matches the actual metered data. However, simulated data must be interpreted by experts and some facilities may not have access to such expertise, so option D is of limited benefit to end users [12]. Therefore, all four IPMVP options have their limitations, especially those that cannot provide an actual system boundary for the section wherein savings are to be measure. Conditions which vary in a predictable fashion are normally included within the basic mathematical model used for routine adjustments. Unexpected or one-time changes may require non-routine adjustments, normally called simple Baseline Adjustments. Examples of situations that often need Baseline Adjustments are: (i) changes in the amount of space being heated or air conditioned; (ii) changes in the amount or use of equipment; (iii) changes in environmental conditions (lighting levels, set-point temperatures, _etc._) for the sake of standards compliance; and (iv), changes in occupancy, schedule, or throughput [12]. The facility can be a building or even an entire complex of buildings. Equipment is the machinery or tools inside the facility. In this study, option A and B only focus on equipment; option C and D consider the entire facility or building, but option C lacks accuracy.
# Multi-Media and Multi-Band Based Adaptation Layer Techniques for Underwater Sensor Networks
## 6 Related Studies Based on Multi-Band and Multi-Media Communication Technologies
### Multi-Media Underwater Communication  
[56] For underwater acoustic communication, MC-UWMAC was designed. This protocol is developed based on low power and multiple channels. Collision-free communication is guaranteed. [57] The MAC protocol for underwater acoustic communication was proposed to solve the presence of noise sources around the region covered by network. Noise-aware MAC (NA-MAC) is a protocol that improves the ability of nodes; it was developed with a multi-band modem using the frequency band to detect increases of in-band noise.  
NA-MAC working flow [57]:  
1. START: In the starting stage, the default-band can be used by the nodes.
2. ALERT: In this stage, the nodes should be aware of increases in noise, so that they might change to a new frequency band.
3. TRACKING: In this stage, the nodes exchange their information with neighboring nodes using PREQ (packet used for noise level request) and PRES (packet used to replay to noise level request) to get the updates about the noise level.
4. NEW-CHANNEL: The node changes the current band and transmits through a new band.
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 3 Biocompatible 3D Printing Materials
### Biomaterial Inks
#### 3.2.3 Ceramics  
Ceramic materials are a mixture of inorganic salts, including calcium and phosphate, which are used for bone and dental applications due to their osteoconductivity. On their own, ceramics are very brittle, which makes them difficult to handle and implant; therefore, in biofabrication, ceramics are combined with a polymeric binder for extrusion bioprinting or 3D powder printing [49; 75]. Commonly printed ceramics include tricalcium phosphate (TCP), hydroxyapatite (HAp), bi-phasic calcium phosphate (BCP), poly (methyl methacrylate) (PMMA) and biogas. Tetrac calcium phosphate (TTCP) is a promising candidate for bone replacement and has been shown to be highly resorbable at low pH. A study by [PERSON] et al. showed the use of TTCP with a phytic acid binder. Following printing, and post-hardening with additional binder solution, TTCP was still the most abundant ceramic phase, but an amorphous calcium phosphate phase was also present, indicating a reaction between the ceramic powder and binder [76]. Ceramics have been used to functionalize PCL by mixing the powdered ceramic and polymer, producing a liquid melt and then extruding filaments to produce a multi-layered scaffold [77]. In this study, PCL blended with natural ceramics (decellularized bone matrix and Bio-Oss, a bovine derived bone mineral) outperformed synthetic ceramics (HAp and TCP) regarding osteoinductive capability [77].
# An Intelligent Measurement Method and System for Vehicle Passing Angles
## 5 Discussion  
The automated measurement of the passing angles of vehicles has been developed slowly due to environmental constraints, making it challenging to address disputes encountered in actual engineering measurement. In this paper, we introduced a wheeled robot equipped with a LIDAR and a camera into the measuring process, taking into account various interference factors in data collection and calculation. Our system has been successfully deployed and has played a role in data comparison during enterprise bidding processes.  
In this study, the results of the overall point cloud have proven the effectiveness of the method, as they exhibit few errors and little drift, as shown in Figures 12, 14 and 15. This is achieved by reducing the proportion of ground points that do not contribute to frame matching, avoiding the selection of unstable feature points during the feature extraction process and smoothing the odometry. These measures effectively reduce matching errors, inaccuracies and significant drift. We conducted simulation experiments to verify the accuracy of the computational method and performed field experiments to validate the feasibility and robustness of the proposed method and system. The calculated passing angles in simulation had a norm error of 0.06252% for the approach angle, 0.01575% for the departure angle and 0.003987% for the ramp breakover angle compared to the true value. Additionally, we conducted three groups of experiments using the system, each consisting of ten repetitions of the calculation, resulting in variances of 0.12407 in the approach angle, 0.48747 in the departure angle and 0.69804 in the ramp breakover angle. Based on the simulated and experimental results, the method and system were validated.  
The measurement system and method proposed in this paper enable the efficient acquisition of the point cloud of a vehicle body and compute the passing angles with high data consistency. This system, coupled with the proposed method, reduces manual labor, improves measurement efficiency and mitigates data controversy caused by manual experience-based selection of target points for measurement. Moreover, the method proposed in this paper also brings reference to the intelligent measurement of geometric parameters for various kinds of large equipment.
## 2 Related Works
### Security Enhancement Strategies for Satellite Networks  
A number of strategies have been investigated to improve satellite network security. The implementations of security protocols, threat mitigation techniques, and security assessment methods are some of the ways that prior research suggests for enhancing security. [PERSON] et al. [15] analyzed cascading failures in LEO networks and proposed a model to enhance network resilience. However, the model primarily focused on satellite failures and did not fully account for other factors influencing network robustness. [PERSON] et al. [33] utilized space digital twin (SDT) technology to create a virtual ISTN environment, which served as a foundation for security assessments and vulnerability detection. However, the complexity of the SDT model development limited its ability to reflect network characteristics accurately. [PERSON] [34] conducted a comprehensive study on software-defined satellite networks (SDSNs) and leveraged SDNs' centralized management and reconfiguration capabilities to address the dynamic topology challenges of satellite networks. [PERSON] et al. [35] applied blockchain technology to satellite networks, while [PERSON] et al. [36] integrated quantum technologies, and [PERSON] et al. [37] implemented federated learning. However, these studies lack a detailed security-focused approach, limiting their applicability from a cybersecurity perspective. Researchers have also proposed various strategies for enhancing security, providing valuable insights into different aspects of satellite network protection. These studies have made significant contributions to the field, particularly in qualitative security assessments and the analysis of individual threat vectors. However, systematic threat modeling and quantitative security evaluation for satellite networks remain relatively underdeveloped. Although existing research has provided fundamental advancements, a structured framework for analyzing multisegment attack propagation and risk quantification has yet to be fully established. To advance these efforts, this study introduces a TV-HARM-based threat modeling approach and conducts a quantitative security assessment to comprehensively evaluate the security posture of satellite networks.  
By incorporating multisegment interactions, protocol-level vulnerabilities, and structured security metrics, our approach establishes a systematic framework for security evaluation, enabling a more precise and practical assessment of satellite network security risks. This structured methodology not only builds upon existing research but also enhances security analysis by integrating multilayered-attack propagation modeling and quantitative risk evaluation.
## 2 Materials and Methods
### Inclusion/Exclusion Criteria and Pre-Processing  
In the present study, [PERSON]'s brain parcellation scheme (CC200) [27] was obtained with inclusion criteria encompassing individuals who had moved on average less than 0.2 mm during the acquisition, resulting in 883 samples [28].  
Pre-processing included structural and functional procedures performed on the fMRI data acquired. Structural pre-processing followed the steps of brain extraction, normalization to the MNI152 standard space (1 mm\({}^{3}\) isotropic) with linear algorithms and segmentation of the brain tissue with respect to the CC200 parcellation scheme. Functional pre-processing involved brain extraction, slice timing correction, global mean intensity normalization to 1000, motion correction, nuisance signal regression, band-pass filtering in the range 0.01-0.1 Hz, and finally, registration to the MNI152 standard space. The resulted data were then utilized to extract the BOLD time series of each region. The above-mentioned procedure is described thoroughly in the ABIDE Pipeline for the Analysis of Connectomes [29].  
On the available data the BOLD time series of the 18 brain regions constituting the DMN were further inspected to identify confounding elements [30; 31]. As such, the individuals' data where one of the BOLD time series was zero (due to the calculation of the correlation of a zero element) were removed from subsequent analysis, leading to the final sample size of 871 individuals. Of note is that the portion between ASD and TD individuals included was approximately 1.18, meaning the two groups were represented by almost equal instances, avoiding the usage of imbalanced ASD and TD groups.
###### Abstract  
Studies on virtual-to-realistic image style transfer have been conducted to minimize the difference between virtual simulators and real-world environments and improve the training of artificial intelligence (AI)-based autonomous driving models using virtual simulators. However, when applying an image style transfer network architecture that achieves good performance using land-based data for autonomous vehicles to marine data for autonomous vessels, structures such as horizon lines and autonomous vessel shapes often lose their structural consistency. Marine data exhibit substantial environmental complexity, which depends on the size, position, and direction of the vessels because there are no lanes such as those for cars, and the colors of the sky and ocean are similar. To overcome these limitations, we propose a virtual-to-realistic marine image style transfer method using horizon-targeted loss for marine data. Horizon-targeted loss helps distinguish the structure of the horizon within the input and output images by comparing the segmented shape. Additionally, the design of the proposed network architecture involves a one-to-many style mapping technique, which is based on the multimodal style transfer method to generate marine images of diverse styles using a single network. Experiments demonstrate that the proposed method preserves the structural shapes on the horizon more accurately than existing algorithms. Moreover, the object detection accuracy using various augmented training data was higher than that observed in the case of training using only virtual data. The proposed method allows us to generate realistic data to train AI models of vision-based autonomous vessels by actualizing and augmenting virtual images acquired from virtual autonomous vessel simulators.  
style transfer; autonomous vessels; horizon targeted loss +
Footnote †: journal: Applied  
Article  
Horizon Targeted Loss-Based Diverse Realistic Marine Image Generation Method Using a Multimodal Style Transfer Network for Training Autonomous Vessels
## 4 Discussion
The rationale behind an OTA is to limit the disconnection and reconnection of the abutment from the fixture, which is shown to possibly negatively affect marginal bone levels [49]. However, the topic is still controversial: in fact, some papers seem to associate OTA technique with improved marginal bone stability when compared to traditional restorative procedures [20; 21; 22; 49; 50; 51], whilst others failed to find significant advantages [23; 52; 53]. Another limitation is the short observation period, which might fail to capture progressive bone loss. Most of the initial adaptive peri-implant bone loss occurs in the first 12 months of prosthetic function, and it is strongly related to the risk of future peri-implantitis [7]. However, while the long-term survival rates of single implants are quite high, at 5 years, the cumulative rate of implants showing \(>\)2 mm of bone loss can reach 5.2%, and the soft tissue complications rate can reach 7.1% [54]. [PERSON] et al. [55] found that immediate abutments significantly reduce the amount of early bone loss at 1 month afterothesis delivery on sub-crestal implants. However, no difference is found with traditional abutments at 12 months. It also appears that bone gain was obtained around traditional implant-level restorations. Conversely, a slight bone gain was observed in the test group of the present study, where the average bone remodeling at 6 months was \(-\)0.72 mm, but was \(-\)0.60 mm at 12 months. On the other hand, [PERSON] et al. [50] found that the OTA significantly reduced the early bone loss around equi-crestal implants. Therefore, it appears that minimal disturbance of soft tissues might be of greater importance for equi-crestal implants. The bone loss observed by [PERSON] et al. [50] around implants with traditional prosthetic workflow was 1.21 mm on average, higher than the one observed in the present group. This, again, could be due to differences in implant design. The choice to use the definitive GFA in the present study was due to the manufacturing company's recommendations, and to make the impression stage easier. However, it would be interesting to replicate in a future trial both protocols of [PERSON] et al. and [PERSON] et al. (2019); [PERSON] et al. (2020). Nevertheless, the authors believe it would be misleading to separately consider every single factor associated with implant failure or success. A more global comprehension of different clinical parameters interacting together with the peri-implant hard and soft tissues would be a more effective way to understand the crucial balance between patient biology and the implant [PERSON] et al. (2020); [PERSON] et al. (2020); [PERSON] et al. (2020). The results of our study confirm the null hypothesis. It would be interesting to replicate the study by adjusting the apico-coronal position of the implant based on the soft tissue height, in order to verify the direct correlation between STH (soft tissue height) and MBM (marginal bone modification). In this scenario, the test group should ideally achieve MBM approaching zero and, consequently, BL equal to zero.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 4 Case Studies
### Case Study 2: A 4U CubeSat  
In the second case study, a fictitious scenario is considered in which the constellation consists of four 1U CubeSats, collectively referred to as CS4. These satellites are initially stacked in a configuration similar to the PQ4 satellite shown in Figure 6b. Each unit, denoted as CS1, has a mass of 2 kg and a deployable panel with an area of 0.01 m\({}^{2}\). Using the same configurations presented in Table 4 but scaled for mass and area yields \(\beta_{Max}\) and \(\beta_{Min}\) values of 114.4 and 57.2 kg m\({}^{-2}\), respectively, for the CS2 satellites. In the case of the CS1 satellites, the corresponding values are 57.2 and 28.6 kg m\({}^{-2}\), respectively.  
The deployment strategy begins at the CS4 stage, where the satellite maintains its 4U CubeSat form factor. Its deployment follows a \(k_{max}\) = 2 stage deployment process, starting with the initial launch of a CS4 satellite at \(k=0\). After an initial disengagement maneuver, the CS4 satellite splits into two CS2 satellites, marking the start of stage \(k=1\) and phased to some degree. The process is then repeated, culminating in four CS1s.  
Figure 12: Semi-major axis comparison between the leading satellite and the reference.
# Dynamic Inversion Method for Concrete Gravity Dam on Soft Rock Foundation
While static inversion provides efficient one-time estimation, it fails to capture temporal parameter evolution and may yield less accurate results for long-term monitoring. [PERSON] et al. introduced a Bayesian optimization framework combined with deep learning to invert dynamic material parameters of high arch dams under discharge excitation, demonstrating the potential of probabilistic methods in dynamic assessment [16]. [PERSON] et al. proposed an improved Autoformer-based deep learning model for multistep and multifeature deformation prediction of the concrete dam, achieving higher accuracy in capturing long-term temporal patterns and varying environmental factors [17]. Additionally, [PERSON] et al. developed a dynamic deformation prediction model for dams based on a time attention mechanism, enhancing the model's ability to focus on temporal features and improving prediction accuracy [18]. [PERSON] et al. presented an LSTM-based anomaly detection model for arch dam deformation, effectively identifying abnormal structural behavior through predictive modeling [19]. Moreover, a study by [PERSON] et al. [20] introduced a hybrid SSA-LSTM model, combining the sparrow search algorithm with long short-term memory networks to optimize model parameters and improve the accuracy of dam deformation predictions. These studies highlight a growing trend toward integratingdata-driven techniques with numerical simulations to better reflect the time-varying nature of dam behavior. This study proposes an improved particle swarm optimization (PSO) method for dynamic parameter inversion for the gravity dam on a soft rock foundation. In contrast to conventional static inversion methods, the proposed method systematically incorporates real-time monitoring data to characterize the time-dependent degradation of the foundation's elastic modulus. A finite element model is established to simulate the dam response under operational conditions, with inversion results rigorously validated against field displacement measurements. The principal novelty of this work resides in its explicit consideration of time-dependent material property deterioration within the inversion framework, enabling more realistic characterization of the dam behavior. The results provide valuable insights for the long-term safety assessment and maintenance of gravity dams, advancing the field of structural health monitoring for hydraulic infrastructures.
# A Review: Sample Preparation and Chromatographic Technologies for Detection of Aflatoxins in Foods
###### Abstract  
As a class of mycotoxins with regulatory and public health significance, aflat (e.g., aflatoxin B1, B2, G1 and G2) have attracted unparalleled attention from government, academia and industry due to their chronic and acute toxicity. Aflatoxins are secondary metabolites of various _Aspergillus_ species, which are ubiquitous in the environment and can grow on a variety of crops whereby accumulation is impacted by climate influences. Consumption of foods and feeds contaminated by aflat are hazardous to human and animal health, hence the detection and quantification of aflat in foods and feeds is a priority from the viewpoint of food safety. Since the first purification and identification of aflat from feeds in the 1960s, there have been continuous efforts to develop sensitive and rapid methods for the determination of aflat. This review aims to provide a comprehensive overview on advances in aflat analysis and highlights the importance of sample pretreatments, homogenization and various cleanup strategies used in the determination of aflat. The use of liquid-liquid extraction (LLE), supercritical fluid extraction (SFE), solid phase extraction (SPE) and immunoaffinity column clean-up (IAC) and dilute and shoot for enhancing extraction efficiency and clean-up are discussed. Furthermore, the analytical techniques such as gas chromatography (GC), liquid chromatography (LC), mass spectrometry (MS), capillary electrophoresis (CE) and thin-layer chromatography (TLC) are compared in terms of identification, quantitation and throughput. Lastly, with the emergence of new techniques, the review culminates with prospects of promising technologies for aflatoxin analysis in the foreseeable future.  
aflatoxins; chromatography; sample preparation 2020, 12, 539; doi:10.3390/toxins12090539