esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Crack Formation and Growth in Tunnel Linings Using Double-K Fracture Criterion
## 4 Numerical Experiments of the Fracture Behavior of Tunnel Liniags  
Owing to the existence of the fracture process zone, the fracture behavior of concrete linings become extremely complicated. In this section, the extended finite element method (XFEM) and fictitious crack model (FCM) are adopted to simulate the complicated fracture behavior of concrete lining. A specific code is developed using Matlab Language to perform calculations with the XFEM and FCM. Furthermore, the proposed theory of modified double-K fracture criterion is verified by analyzing the variation of the crack width and depth during the whole fracture process.  
The XFEM is a partition of the unity-based method, where the enrichment functions describing the discontinuity on both sides of a crack are incorporated locally into the standard finite element method. The geometry of internal cracks is separated from the finite element mesh, so there is no need to regenerate the mesh as the crack expands. This feature makes XFEM a powerful tool for analyzing crack propagation [32; 33; 34; 35].  
The FCM divides the concrete crack into two parts: physical crack and fictitious crack. The physical crack represents the traction-free crack, and the fictitious crack simplifies the fracture process zone into a separate line crack. Applying the stress softening relation of concrete to the fictitious crack can effectively calculate the complicated fracture behavior of concrete linings caused by the fracture process zone [21]. For more details on XFEM and FCM, the reader is referred to the following references [21; 36; 37; 38].
# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 4 Experiments
### Datasets and Metrics  
Our E\({}^{2}\)-VINS and compared methods (introduced in Section 4.1) were tested on two datasets, including the VIODE dataset (VIODE: [[https://github.com/kminoda/VIODE](https://github.com/kminoda/VIODE)]([https://github.com/kminoda/VIODE](https://github.com/kminoda/VIODE)), accessed on 27 September 2024) [42] and the ECMD dataset (The ECMD dataset: [[https://arclab-hku.github.io/ecmd/](https://arclab-hku.github.io/ecmd/)]([https://arclab-hku.github.io/ecmd/](https://arclab-hku.github.io/ecmd/)), accessed on 27 September 2024) [43]. The VIODE dataset [42] was recorded from a simulated UAV that navigates in challenging dynamic environments. Each dynamic environment includes four levels of dynamic complexity, progressively adding more moving objects. The ECMD dataset [43] is the first event-based SLAM dataset specifically focused on urbanized autonomous driving. It was recorded with a sensor platform consisting of various novel sensors, including two sets of stereo event cameras with distinct resolutions (640 \(\times\) 480, 346 \(\times\) 260), an infrared camera, stereo industrial cameras, three mechanical LiDARs (including two slanted LiDARs), an onboard inertial measurement unit (IMU), and two global navigation satellite system (GNSS) receivers. The ground truths were provided by a centimeter-level positioning system that combines the GNSS real-time kinematic (RTK) with the fiber optics gyroscope integrated inertial system as GNSS-RTK/INS. The ECMD dataset [43] was collected in various driving scenarios, including dense streets, urban areas, tunnels, highways, bridges, and suburbs. These sequences were recorded under daylight and nighttime, providing challenging situations for visual and LiDAR SLAM systems, e.g., dynamic objects, high-speed motion, repetitive scenarios, and HDR scenes.  
To assess the performance of the full SLAM method, we quantified pose estimation results using the mean position error (MPE, %). The MPE (%) is calculated as the mean position error (in meters) divided by the total length of the trajectory (in meters). We used the toolbox from [44] to evaluate the poses given by different SLAM solutions.
# Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
## 3 Results
### Comparison of Algorithms and Training Setup  
The validation of the proposed neural network for flap segmentation is based on the investigation of its performance compared to other state-of-the-art approaches that can perform flap recognition using a different architecture. In particular, we compare the proposed AER2U-Net on the uniquely generated flap dataset with the following segmentation algorithms: (i) U-Net [21], (ii) R2 UNet [36], and (iii) AttU-Net [33]. The aforementioned DL models were developed using the PyTorch library [41] in Python. The models were trained and evaluated on an NVIDIA GeForce RTX 4070 GPU. We trained the neural networks for 200 epochs, with early stopping criteria set to 10 epochs to avoid overfitting and using mini-batches of size 16. The training processes started from scratch by randomly initializing the weights of the networks. The models were trained end-to-end using the Adam algorithm with the default parameters set as \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\) and an initial learning rate of \(10^{-3}\).
## 5 Results and Discussion
### Emission Characteristics
#### 5.3.3 Smoke Emissions  
The variation in the opacity of the smoke for the different fuels is shown in Figure 12. It was observed that the density of the smoke increases with the increases in the engine load since the smoke depends largely on the engine power. HC emissions were reduced by increasing the percentage of biodiesel in the fuel. It was observed that the minimum and maximum smoke density produced was 1.88-16.06 HSU, 2-19.04 HSU, 2.12-22.02 HSU, 4.21-25 HSU, 5.69-27.98 HSU for PB10 SB5, PB5 SB5, PB10, PB5, and diesel, respectively.  
The reduction in HC emissions is mainly due to oxygen molecules in the chemical structure of the fuel, which produces complete combustion inside the chamber [23]. Due to this fact, biodiesel with the percentage of sunflower oil has the lowest HC emissions, since its oxygen content is higher compared to palm oil blends.  
Figure 11: (a) NOx and (b) HC, emissions for fuels tested.  
Figure 12: Smoke emissions for fuels tested.
## 1 Introduction
Nowadays, implant-supported rehabilitation has become common practice for tooth replacement due to trauma, dental caries, periodontal disease, or congenitally missing teeth. Interestingly, implant-supported overdentures were found to exert higher levels of the bite force than either root-retained overdentures or complete dentures [17]. In the course of dental implant treatment, occlusal overload can be one of the causes for implant failure [18; 19; 20]. Thus, a modified occlusal concept is recommended for implant-supported restorations, with a narrow occlusal table, a slight occlusal contact in the maximum intercuspation, and reduced occlusal forces during lateral movements [21; 22]. Unfortunately, minimizing occlusal forces over implant-supported restorations may produce an overload on adjacent teeth. [PERSON] et al. [23] showed that for splinted implants, traumatic occlusion in adjacent premolars was significantly higher, especially when maxillary teeth were measured with implants present in the opposing teeth. In addition, a number of other researchers reported a possible negative effect between implant-supported restorations and vertical root fractures (VRFs) or cracks in adjacent endodontically treated teeth [24; 25][PERSON] A case series analysis revealed that the majority of the adjacent teeth with VRFs were premolar or mandibular molar teeth and that all fractured teeth were restoredwith a crown and had a post present [24][PERSON] [PERSON] et al. [26] demonstrated that the requirement for restorative retreatment was significantly higher for teeth adjacent to implant restorations than for the contralateral controls. Similarly, the results of a retrospective study conducted by [PERSON] et al[PERSON] [27] revealed that the percentage of the loss of teeth adjacent to implant-supported restorations was higher than that of other posterior teeth. However, the question as to whether the bite force applied on teeth and implants in patients with dental implants is different to that in patients without dental implants remains unanswered. A precise clinical characterization of the biting force on teeth and implants will enable the treatment plan to be customized, with respect to choices of dental materials, occlusal scheme, contact points, and predictions of biting forces. To the best of our knowledge, there is very little information about this issue. Therefore, the aims of the current study are as following: (1) to evaluate the maximal bite force in patients with dental implants versus patients without dental implants, as measured by a digital bite force transducer (GM10); (2) to evaluate the influence of sex, age, and sleep/awake bruxsim on the maximal bite forces of the two groups. The null hypotheses are as follows: (1) no difference in the bite force applied to patients with or without dental implants was observed; (2) the maximal bite forces of the two groups were not affected by sex, age, and sleep/awake bruxism.
## 3 Results and Discussion  
### LAI Measurements  
In total, 532 LAI measurements were made, with single measurement points being omitted whenever the area turned out to be inaccessible because of high groundwater levels. This was the case only for measurement points in marsh areas in April and should not affect the results significantly.  
LAI values varied monthly (Figure 2). The median values for every single month illustrate the typical tendency of LAI reported in earlier studies [26,27]. The level of significance obtained in the F test shows strong seasonal LAI variability in the surveyed area.  
LAI values also varied by study site (Figure 3). LAI median values were higher in forests compared to the mixed forest-open sites. The highest LAI median values were obtained for the Dom site in the area, with a managed forest site habitat similar to that of a moderately moist soil mixed coniferous forest.
# On the Need for a General REST-Security Framework
## 5 Related Work Analysis
### [PERSON] et al.  
[PERSON] et al. [60] propose a scheme that signs and/or encrypts CoAP messages. This approach offers the options to encrypt a message, sign a message or sign as well as encrypt a message. However, the authors do not provide any policy, which specifies a list on the to be protected header entries. The proposed approach computes a signature and an encryption over the entire CoAP message including the payload and all present header entries. The resulting cipher-text and signature value are then stored in newly defined security headers which contain information on the security context such as the key type, whether the message is encrypted, signed or both and the destination. The latter information can refer to endpoints, i.e., client and server, or intermediate systems. A CoAP message may include one or multiple instances of these security header entries. Thus, the approach of [PERSON] et al. [60] allows to compute signatures and cipher text for multiple endpoints and intermediaries which enables an intermediate system to verify and decrypt traversing messages. However, the paper does not describe whether an intermediary is able sign and decrypt a message itself. This is an important property of a REST-ful security scheme in order to comply with the layered system constraint. This principle enables intermediate systems to interpret and transform the content of a message. The ability of intermediaries to sign and encrypt messages by themselves allows them to transform messages or parts of them and inform the endpoint that a distinct intermediary has processed certain message elements. Moreover, encrypting and signing the entire message without obeying a policy, which defines what headers are protected, violates the self-descriptive constraint. This prevents certain intermediate nodes from accessing and modifying a message. That is, the signature of the message is invalid if an intermediary changes the message, as no policy for describing the modification exists. The other drawback is that a completely encrypted message is not accessible by intermediate systems not possessing the required decryption key. Both scenarios may occur, as a lot of intermediaries are either transparent or reside outside organizational boundaries of the client and the server.
# Driving Safety Improved with Control of Magnetorheological Dampers in Vehicle Suspension
## 4 Simulation Results
### Skyhook Control of Vehicle Body Vibration Modes (Skyhook-2 DOF)  
The modification of Skyhook algorithm, denoted here as Skyhook-2 DOF, is dedicated to the mitigation of modes of vehicle body vibration, i.e., heave and pitch vertical velocities. For the sake of simplicity, Skyhook parameters for both state variables of the vehicle model were mutually set during the simulation, where the parameter corresponding to the pitch velocity was internally decreased twice, as it was described in Section 3. This variant of Skyhook algorithm focuses on heave and pitch velocities; thus, these state variables are analysed in Figure 15.  
The control parameters of the Skyhook-2 DOF, denoted as \(c_{sh2}\), ranged from 0 to 3000 Nsm\({}^{-1}\). The dependence of heave and pitch velocity characteristics on parameters of Skyhook-2 DOF is significantly different in comparison to cases of constant currents that are presented in Figure 10. Here, the resonance peak visible for the zero control current is clearly suppressed for the highest value of \(c_{sh2}\). The performance of Skyhook-2 DOF is most noticeable in the frequency range from 1 to 2.5 Hz, while the damping of vibrations at higher frequencies is slightly worse for higher values of the control parameter.  
Figure 14: Power spectral density characteristics of wheel normal forces for Skyhook control of suspension parts (Skyhook-1 DOF) and vehicle longitudinal velocity equal to 5 ms\({}^{-1}\): (**a**) front wheel, (**b**) rear wheel.  
Figure 15: The transmissibility characteristics of the body dynamics for Skyhook control of the vibration modes (Skyhook-2 DOF) at vehicle longitudinal velocity equal to 5 ms\({}^{-1}\): (**a**) heave velocity, (**b**) pitch velocity.  
Similar and significant improvement of damping can be seen in Figure 15b dedicated to the pitch velocity. The resonance peak that is located at frequency equal to 1.7 Hz is significantly decreased from approximately 6.0 to 3.5 \(\mathrm{rad}\cdot\mathrm{m}^{-1}\). No additional deterioration can be noticed for pitch transmissibility for greater values of \(c_{sh2}\).  
Additionally, PSDs of the front and rear wheel normal forces are presented for Skyhook-2 DOF in Figure 16. It can be noticed that, similarly to the Skyhook-1 DOF performance of Skyhook-2 DOF, is also significant in the case of suppressing variations of normal forces. Here, resonance peaks are suppressed twice for both front and rear wheels, while an additional increase of normal force PSD can be noticed for the front wheel in the frequency range from 2.5 to 5 Hz.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Computational Resource Management Based on RRH Clustering Techniques
#### 3.2.3 Interference-Aware RRH Clustering  
In [52], [PERSON] et al. presented an interference-aware clustering algorithm (IACA) for RRH clustering with the objective of minimizing the network power consumption, subject to a minimum throughput requirement. They designed the system model to perform the clustering within a few hours and divided the area into discrete zones using a square meshing method. The problem was formulated as a set partitioning problem and solved using a greedy heuristic solution with complexity reduction. The objective was to determine the RRHs that minimize the power consumption in accordance with the throughput constraints. The results of the heuristic revealed performances very close to the optimal solution and with less complexity. Although power saving is achieved using this approach, the energy efficiency was lower than that with the bin packing approach. Moreover, the RRH clustering strategy is designed considering a full buffer traffic model that does not take the traffic dynamicity into account.  
The work of [52] is extended in [48]. Here, the objective was to minimize the power consumption and to minimize the RRH re-association rate while forming a new cluster. This is because the re-association of RRHs with different BBUs may cause handover for the users connected to the RRH. Unlike the previous work, this work presented a two-stage heuristic solution. Here, the first stage decided the RRH re-association with a BBU when the minimum throughput requirements per user are not fulfilled. The second stage addressed the minimization of the number of active BBUs by moving RRHs to the BBU with a moderate throughput per user. A tradeoff between the power saving and re-association rate was illustrated via numerical results. That is, when the algorithm focused more on power saving, the re-associate rate increased, and vice versa.  
Extending [51], the authors formulated a joint user association and RRH clustering problem in [57] considering inter-cluster interference. They divided it further into two sub-problems to reduce complexity. The main goal was to maximize the network throughput and minimize the network power consumption. The RRH clustering problem was solved by an iterative, low complexity heuristic algorithm based on merge-and-split rules, in which the RRHs combined and organized themselves into independent clusters such that the network utility was maximized. This process was repeated until convergence, which implies that no more RRH clustering was required.
## 4 Efficient FFT Algorithms
### FFT Pruning  
The FFT pruning method was first proposed by [PERSON] [58] for eliminating operations on zero input and output values which are not required. Later, several improved FFT pruning algorithms were proposed [59; 60; 61; 62; 63]. [PERSON] _et al._[60] proposed an FFT algorithm for both input and output pruning. Since the computational structures of FFT and IFFT are same, most of these algorithms deal with FFT pruning. In all the pruning algorithms, the basic concept is to determine the index of the butterflies to be chosen for computations and those to be discarded. Thus, FFT computation time can be significantly reduced by pruning the operations on zero values in the discarded butterflies. Most of these algorithms are suitable for applications with specific zero-input or zero-output pattern distributions.  
He _et al._[63] applied the concept of FFT pruning to a multichannel OFDM system in order to achieve significant reduction of complexity. FFT pruning algorithm with frequency shift was proposed for the evaluation of comb spectrum in OFDM systems. As the comb spectrum shows considerable regularity, the proposed algorithm exploits the regularity for high efficiency. Also, the implementation of the proposed algorithm in a dedicated processor takes the advantage of the modularity of the algorithm. The assumption of zero values with a considerable regularity is not true for cognitive radio systems where subcarriers are switched off at random positions, based on the spectrum occupancy information and the sub channel condition. [PERSON] _et al._[64] proposed a general FFT pruning algorithm that operates on any zero-valued input distribution. An index matrix \(M_{i}\) is generated based on the input values to the FFT. For a radix-2 FFT algorithm with \(N\) levels (2\({}^{N}\) FFT points), the matrix \(M_{i}\) has \(N\) columns and 2\({}^{N}\) rows. Each element of the matrix corresponds to an addition/multiplication node of the FFT flow graph. If an element in the matrix is non-zero, the corresponding node has to be computed and if the element is zero, the corresponding node need not be computed. Thus, using the matrix the number of computations can be reduced. The reduction in computational complexity depends on the distribution of the non-zero inputs/outputs.
## 2 Materials and Methods
### Thermal Conductivity Measurements  
Thermal conductivity quantifies the ability of a material to transfer heat under a temperature gradient. It plays a crucial role in evaluating the thermal insulation properties of construction materials, where lower values are desirable for improved energy efficiency.  
Thermal conductivity measurements were performed at room temperature using a hot disk thermal constant analyzer (TPS 1500 Hot-Disk from Thermo-concept, Hot Disk AB, in Goteborg, Sweden). The conductivity measurement is based on the analysis of the temperature evolution of the sample, induced by a constant heat power, over time. A Kapton-type probe was placed between two identical samples with flat surfaces (Figure 4). All experiments were conducted with a heat power of 40 mW and an 80 s duration. This method is the one most widely used in the literature [28; 29] because of its several advantages, such as its fast characterization and adaptability to several types of materials. All tests were performed three times to ensure the reliability of the measurements.  
The thermal conductivity measurements were conducted using the hot disk method. The associated uncertainty of the instrument is \(\pm\)5%, which corresponds to \(\pm\)0.01 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\) for the range of values measured in this study [30]. To ensure accuracy, all measurements were performed under controlled conditions, including a stable environmental temperature of 20 \({}^{\circ}\)C. The uncertainty in the thermal conductivity values is primarily influenced by sample homogeneity and surface flatness, which were carefully addressed during sample preparation.  
The hot disk method is validated for a broad range of thermal conductivities (0.005-500 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\)), as reported in [30]. The materials investigated in this study fall within this range, ensuring accurate measurements.  
Figure 4: Hot disk setup used for the thermal conductivity measurements of the samples.  
Figure 3: Schematic representation of the different configurations of the plaster samples: (**a**) homogenized plaster with PCM (white circles) distributed within the matrix, (**b**) 3D-printed plaster with PCM (white circles) localized to within specific layers and (**c**) 3D-printed sample without PCM.