esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Previous Prediction
### Analysis of Deviation from Actual Results  
The analysis of the deviation from the actual results shows different tendencies, but one fact can be deduced from the overall analysis. The prediction of [PERSON] et al. is overall of quite high quality but shows the shortcomings of all predictions; it cannot predict all events and gets worse with time [26]. The comparisons of the first and second forecasts (for LNG-importing and LNG-exporting countries) shown in Tables 1 and 2 show negligible deviations from the data of the real situation and can be described as very accurate.  
The forecast accuracy is somewhat worse for LNG trading volumes (Table 3), where the deviation rises to 6.62% at the end of the reporting period, although it should be noted in particular that the error rate tends to increase over time.  
The situation in the LNG vessel market (Table 4) shows the same tendency, but in a much more pronounced form, with the forecast error in this case rising to 12.7%, which is significant. The reason for this behavior, according to [PERSON], is the oversupply in the market [43].  
The propulsion analysis represents a special case in which an unexpected loop occurred. In 2022, a new drive type, ME-GA, was added to the existing drive types. In a very short time, it took the top position on the order lists for ships, only for these engines to stop being sold in 2024; i.e., due to problems with environmental requirements, this concept was abandoned.  
The war between Russia and Ukraine has had a major impact on the entire LNG transportation network and has led to a significant increase in routes between Europe and the USA. This conflict has led to a steady demand for LNG in Europe, resulting in a corresponding development of European ports. However, according to the overall figures presented in Section 2 and the official data from the IGU World Reports, the BP Statistical Reviews, and the Environmental Impact Assessments, the war did not have a major impact on the growth of the LNG trade, but the question remains whether the halt in growth shown in Figure 3 is related to this problem [7; 28; 29; 44].  
In 2022, Europe increased its LNG imports by ship by 60%, compensating for the decline in gas supplies from Russian pipelines due to the problems in Ukraine. Thanks to this increase in imports by sea, Europe was able to maintain its energy security and meet its energy needs despite the supply disruptions caused by the crisis [45].  
Forecasts for the development of the LNG-powered shipping fleet are promising, as demand for LNG has increased significantly in areas with strict sulfur emission controls following the introduction of environmental regulations to improve air quality [46].  
These events demonstrate the need for more frequent market analysis and forecasting over a shorter period of time.
## 2 Methods
### Pre-Processing
#### 2.2.2 Gaussian Filtering  
The presence of considerable noise in the casting mold liquid surface images, caused by limits in the clarity of surveillance video, can have a negative impact on the accuracy of casting mold liquid surface segmentation. Gaussian filtering is applied to the dataset after region extraction to mitigate this noise and enhance image quality, as depicted in Figure 3b. Gaussian filtering efficiently attenuates noise in the image following a normal distribution, enhancing image quality while maintaining the overall image characteristics.  
Figure 3: Data pre-processing process. (**a**) Region extraction. (**b**) Gaussian filter. (**c**) Data augmentation.  
The essence of this method lies in the utilization of the Gaussian function, represented by the following formula:  
\[G(x,y)=\frac{1}{2\pi\sigma^{2}}e^{-\frac{x^{2}+y^{2}}{2\sigma^{2}}} \tag{1}\]  
In the equation, (\(x\), \(y\)) denotes the coordinates of pixels within the window and \(\sigma\) represents the standard deviation. By selecting an appropriate \(\sigma\) value, the specific Gaussian function can be determined.  
In Gaussian filtering, the value of the Gaussian function determines the pixel weight relative to the central pixel, with pixels farther from the center having lower weights. This strategy is utilized due to the higher probability of neighboring pixels in the image belonging to the same structure or texture, whereas pixels that are farther apart are more likely to belong to distinct structures or noise.  
Data Augmentation  
Because manually annotated semantic segmentation label images are scarce in this experiment, it is necessary to augment images in order to expand the diversity of data, improve the generalization of models, and reduce overfitting. Typical picture augmentation techniques include geometric changes, color alterations, and pixel transformations. This study used many augmentation techniques from the \"albumentations\" library [31]--including horizontal flipping, random cropping, random shifting and scaling, saturation transformation, RGB shifting, and motion blur--to enhance the dataset after Gaussian filtering, as depicted in Figure 3c.
# Robust Static Structural System Identification Using Rotations
## 2 Methodology
### Structural System Identification Using the Constrained Observability Method
Some single variables \(\{z_{g}\}\) may not appear in the unknown vector \(\{z\}\) of Equation (6). A new unknown vector \(\{z^{*}\}\) is obtained by adding \(\{z_{s}\}\) in \(\{z\}\). A null matrix \([\Omega]\) is introduced into the coefficient matrix so that the equations are not violated, as shown in Equation (8). \[[B^{*}]\cdot\{z^{*}\}=[B\;\Omega]\left\{ \begin{array}{c}z\\ z_{s}\end{array} \right\}=\{D\} \tag{8}\]  
The objective function (Equation (9)) of the optimization is to minimize the square sum of the residuals (unbalanced nodal forces) in Equation (8). \[f(\{z^{*}\})=\sum_{i=1}^{3N_{n}}\epsilon_{i}^{2} \tag{9}\]  
where \(\epsilon_{i}\) is the residual of the _ith_ equation in Equation (8), and \(N_{n}\) is the number of nodes. The procedure to carry out structural system identification by COM is summarized in Figure 1.  
In stage 1 (Steps 2-5), the observability equations are treated linearly such that the computation is much reduced when compared with nonlinear methods. All the steps in stage 1 are the same as those in structural system identification by NOM. In stage 2 (Step 7), nonlinearity is introduced by imposing constraints via optimization, with the aim of identifying more parameters. As some parameters have been identified in stage 1, the solution space for the optimization algorithm to explore in stage 2 is much reduced, which eases the computation and convergence issues. It is highlighted that the observability of the parameters can be analyzed in the 7-step procedure. The observability analysis can differentiate the meaningful estimations from those estimations obtained using numerical optimization. That is to say, the numerical estimations from optimization can always provide numerical values for all parameters, but some values are not meaningful as they are not observable due to the limited number or the limited spatial distribution of measurements.
## 2 Related Work
One of the major restrictions lies in the type and amount of information that could be extracted from raw data of transducers, e.g., cameras or IR-sensors. In this regard, several projects utilized previously modelled data of the environment, thus allowing isolation of the design and optimization of the human-machine interface from the data acquisition system. For example, some projects offer virtual-reality platforms which allow users to explore scenery prior to travelling, by means of an avatar controlled by the user and a virtualized sensory substitution device [22, 23]. In opposition to the pre-journey solutions of virtual reality, mixed reality promotes its usage in actual navigation scenarios. Several projects in this line of work implement sensory substitution interfaces using periodic measurements of the user's position and body movement [24]. Simultaneous Locating and Mapping technology (SLAM) from Google Tango eased the development of interfaces for BVI people's navigation, which required no additional infrastructure installation [25, 26]. This can be also applied to systems such as Google ARCore [27], which capture the motion of compatible smartphones through Concurrent Odometry and Mapping technology (COM). In this context, the developed prototype is a mixed-reality system which integrates previous and novel human-machine interfaces to assist BVI people in actual navigation scenarios. It relies on ARCore's COM, thus no in-situ installation is needed to track the users' movement. All required data of the surroundings are previously registered, which served to further improve previous sensory substitution algorithms; see Section 3.1.3.
###### Abstract  
Controlled experiments were conducted to examine the effect of fog on signal propagation in wireless communication and radar links operating in millimeter wavelengths. The experiments were carried out in a fog laboratory to verify theoretical results obtained from [PERSON]'s model. Attenuation and phase shifts of millimeter wave (mmW) radiation were measured, at different fog density characterized by the visibility distance and its water vapor content. Utilizing a vector network analyzer (VNA) enabled us to examine the actual atmospheric attenuation and the phase shift caused by the fog retardation. The experimental results demonstrate good agreement with the simulations even for very low visibility in highly dense fog. The study can be used to estimate link budget of mmW wireless links, including those allocated for the fifth generation (5G) of cellular networks.  
millimeter waves communications; 5 th generation of cellular communications (5G); the electromagnetic spectrum; antennas and propagation Article
## 3 Evaluation of Process Precision and Accuracy
### Decision Making on the Direction of Process Quality Improvement  
Based on the above-stated statistical testing results, there are two statistical tests that need to be performed to determine the direction of process improvement. The first is the test of process accuracy. Since the required quality level is six-sigma, the null hypothesis (\(H_{0}^{\prime\prime}\)) and the alternative hypothesis (\(H^{\prime\prime}\)) on the statistical test of precision index \(\delta\) are depicted as follows:  
\[H_{0}^{\prime}:\gamma\leq 1/6 \tag{46}\]  
\[H_{1}^{\prime}:\gamma>1/6 \tag{47}\]  
If the value of \(\gamma_{L}\) equal to 0.174 is bigger than \(1/6\), it is learned that the process variation is so large that the process must be improved. For the same reason, the required quality level is six-sigma. Then, the null hypothesis (\(H_{0}^{\prime\prime}\)) and the alternative hypothesis (\(H^{\prime\prime}\)) on the statistical test of accuracy index \(\delta\) are denoted as follows:  
\[H_{0}^{\prime\prime}:-0.25\leq\delta\leq 0.25 \tag{48}\]  
\[H_{1}^{\prime\prime}:\delta<-0.25\text{ }or\text{ }\delta>0.25 \tag{49}\]  
If the value of \([\delta_{L},\delta_{R}]=[0.405,0.481]\), then 0.405 is bigger than 0.25, indicating that the process is shifted to the right by more than 1.5 sigma. Therefore, some improvements must be made to help the process mean move toward the target value, and the process deviation must be controlled and fall within 1.5 sigma or less.
## 2 Materials and Methods
### Justification of the Topology Used for Synchronism  
This work focuses on the simultaneous capture of waveforms for the measurement of voltages and currents in a distributed manner. A decisive step in the design of a distributed measurement system based on TSN concerns the choice of a topology tailored to the type of application. The IEEE 802.1 AS profile compensated for the cable length between devices so that the advantages of an in-line topology for PV plants could be analysed.  
With the in-line topology, the host (TSN master) communicated directly with all of the devices in the system via a bus. The in-line topology is the simplest topology but is only possible with devices that have an integrated Ethernet switch compatible with IEEE 802.1Q to support Rapid Spanning Tree Protocol (RSTP) [22] and IEEE 802.1 AS to support TSN synchronisation. The RSTP specification guarantees the best path for data, but if a link is broken between two of the IEEE 802.1 AS compliant devices, data communication for synchronisation is interrupted. With this method, each device could be within 100 m, allowing for long distances to be reached with a single Ethernet cable.  
Synchronisation of multiple devices was based on packets that were transmitted over long distances with no delay in the signal propagation. Synchronisation of inputs and outputs using this profile was less than 1 us. The IEEE 1588-2008 standard's capability for distributed device synchronisation in smart grids can be enhanced with TSN in the range of tens of nanoseconds, depending on the selected topology. For two devices to synchronise, a common IEEE 802.1 AS compliant path must exist. If such a route exists, it is automatically detected and used.  
The IEEE 802.1 AS-2011 standard [19] builds on IEEE 1588-2008 [18] to maintain the synchronisation of all nodes within the IEEE 802.1 AS subnetwork. The standard defines so-called generic Precision Time Protocol (gPTP) domains [23], consisting of one or more systems that take into account the same time base and comply with the requirements of the standard.  
Some authors have devoted a line of research to evaluating the latency that can be achieved using TSN in a network with various levels of data transfer and they have compared these results with the UDP and TCP protocols [24]. In addition, they have analysed the possibility of generating waveforms for microgrid inverter synchronisation with TSN-synchronised clocks.
# Few-Shot Wideband Tympanometry Classification in
## 3 Results  
**Comparative experiment**. The GPGDA performance and those of the two baseline methods (FT and DAN) were assessed by F1-score and AUC on the WBT dataset and three different source datasets including _SynDataI_, _SynDataN_, and _SynDataM_ (Table 1). As judged by the F1-score and AUC, GPGDA yielded the best results in comparison with FT and DAN regardless of the source dataset types. Figure 3: Examples of WBT images in patients with tosclerosis and control subjects; (**a**): the WBT sample in a control subject; (**b**): the WBT sample in a patient. Visually, the WBT image shows a distinct absorbance peak (\(-\)200–0 daPa, \(<\)2000 Hz) in the control ear, while the peak is flattened in patients with tosclerosis. Moreover, it has to be noted that the performance gains of GPGDA over FT came at the cost of a considerable increase in training time. Since the GPGDA algorithm involved multiplication and inversion of matrices, its time complexity can be described by \(\mathcal{O}(n^{3})\) with the number of target data \(n\) when the ratio of synthetic/realistic data is kept constant. It means that the difference in training time between the proposed algorithm with FT can increase exponentially with the dataset size. Actually, for our settings, the training of a five-fold cross-validation for FT took about 4 min via a Tesla P100 GPU, while GPGDA took 25 min under the same conditions and identical GPU. However, the training was performed offline, and there was no significant difference for the inference time of these approaches (approximately 9 ms via an Intel CPU) in clinical practice since they adopted the same backbone network architecture. **Ablation experiment I: hyperparameter selection**. The proposed GPGDA algorithm included a hyperparameter \(\lambda\) for adjusting the weights of \(l^{m}\) and \(l^{c}\) in the final \(l^{GP}\) that was adopted to update the parameters of feature extractors. \(\lambda\) significantly influenced the performance of GPGDA as evaluated by AUC, regardless of the source dataset types (Table 2). Additionally, the optimal results were generally achieved at \(\lambda=0.5\) for the task of WBT-based diagnosis of otosclerosis. **Ablation experiment II: the roles of \(l^{GP}\) and \(l^{CE}_{r}\). The proposed GPGDA algorithm included two losses \(l^{GP}\) and \(l^{CE}_{r}\), which offered the gradient update flow for optimizing the parameters of feature extractors and those of classifiers during the training, respectively. The two losses played different roles in GPGDA (Table 3). The proposed algorithm had both losses enabled, and disabling either of them caused a drop in performance. The feature extractors deferred from the parameters in the previous pre-training on the synthetic source dataset when only \(l^{CE}_{r}\) was enabled (similar to the _fixed-feature extraction_ transfer
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 3 Scanning Directions
### Side View Imaging  
In side view transverse scanning, the light beam coming from an optical fiber is deflected at a certain angle by the presence of a prism, mirror, or a reflecting surface. The deflected light passes through a transparent optical window on the side of the probe. The optical fiber with the optics components attached to its distal end is rotated to scan the light beam in a circumferential pattern. In the first side-viewing OCT probe [15], a graded-index (GRIN) lens was attached to a single mode fiber (SMF) with a core diameter of 9 \(\upmu\)m. A right-angle microprism was attached to the GRIN lens, which deviated the light beam perpendicularly to the catheter axis. The distal end can be rotated using a drive motor [15; 57; 58] or a rotational stage [53] and scans the beam circumferentially. It is possible to deflect the light beam avoiding the prism by polishing the optical fiber at certain angle, and coating the surface with a reflecting material like aluminum or gold [58]. Even though such a design is easier to fabricate, it has a poor resolution. This limitation can be surpassed by splicing the GRIN lens and having no core fiber (NCF) at the tip of the SMF. NCF polished at a certain angle and coated with a reflecting metal layer gives a deflected beam with a Gaussian-shaped beam profile [59]. [PERSON] et al. used a polished ball-lens at the distal tip of an SMF to deflect and focus the light beam through an optical window. The rotation of the fiber is performed using a linear scanner [60].  
Some of the image artifacts related to the rotation of the fiber and the attached components are eliminated by rotating the microprism at the distal tip of the probe. In such a case, the prism is separated from the GRIN lens and actuated using a micromotor placed at the distal end [54; 61], providing the images with a finer resolution. The schematic of a catheter-based side viewing endoscope is shown in Figure 10(a). More recent side viewing imaging probes rely on the use of parabolic or cone-shaped mirrors at the distal tip. Such mirrors permit the incoming light from the fiber to be reflected in all directions, giving an omnidirectional side view. Such devices, called catadioptric probes, provide a panoramic field of view, obviating the need for rotation of the probe components [62].  
Figure 11: Schematic diagram: **(a)** side viewing probe; **(b)** forward viewing probe.
###### Abstract
In CECS 160 (general rules for the performance-based seismic design of buildings) [18], similar provisions are also recommended, and the formulas on the evaluation of structure modes are improved. A research report by the National Institute of Standards and Technology (NIST) provides modified coefficient curves, considering SSI based on the concept of traditional coefficient curves in seismic designs [19]. However, these methods are rarely adopted in practice for the following two reasons: The analysis involves the calculation of foundation impedance functions, which are too complex for engineering designs. In addition, the analysis considers only the beneficial effects of SSI and reduces base shear force by at most 30%, which is unsafe or lack of safety reverse. The foundation of a structure is usually comprised of various shapes, and it forms a group of multi-scatters in infinite half-space, which creates a complex scattering field under seismic excitations. In the past few decades, intensive effort has been put into its numerical simulations, such as raising the incident wave to a more shallow layer to reduce the range of the near-field [20], setting incident waves inside the artificial boundary to reduce the data storage on free-field motion and seismic loads [21], or carrying out a large-scale calculation, from earthquake sources to structure sites, through multi-scale simulation [22], and so on. However, up to now, the numerical simulations on SSI are still very costly and not widely available among civil engineers [23]. This paper investigates the possibility of SSI having detrimental effects using case studies on four buildings whose structural data, site data and earthquake records are available. The scattering field of the half-space is modeled by foundation impedances, which are obtained by a high-precision indirect boundary element method that we proposed in our previous research [24]. Under the earthquake excitations, kinematic interaction and inertial interaction are coupled for SSI, so some studies focus on the former in a half-space with a foundation [25; 26], or on the latter with a structure in a uniform half-space [27]. The SSI model, considering the two types of interactions, consists of an oscillator, a 3D rectangular foundation, and a multi-layered half-space. It is shown in the study that the shift in peak frequency between SSI system and fixed-base oscillator leads to the different seismic performances between them, so the paper also proposes a corresponding chart of peak frequency, or system frequency, between SSI and the fixed-base oscillator, to shed some light on SSI analysis in engineering seismic designs. The target parameters in the chart consist of the foundation embedment, soil-layer thickness, length-to-width ratio, and so on, to cater a variety of scenarios in coded seismic designs.
## 3 Biomechanics of Articular Cartilage
### AC Behaviour Under Cyclic Loading  
Articular cartilage, as a highly specialised tissue covering the articular surfaces of the bones in the synovial joints, plays an important role in transferring loads and ensuring smooth joint movement [233]. The response of AC to cyclic loading is crucial to understanding the mechanisms leading to damage and degenerative joint diseases [2].  
The action of repeated mechanical loading influences changes in the mechanical and structural properties of AC [233]. Studies have shown that with the increase in the number of loading cycles, tissue softening and structural damage in the form of cracks and defects in the extracellular matrix occur [233].  
In vitro experiments have confirmed that cyclic loading results in irreversible damage to AC cartilage, even without the participation of biological factors [233]. It follows that mechanical loading can initiate degenerative processes in the tissue.  
Cyclic loading also affects the friction properties of joint surfaces. Studies using animal models have found that long-term cyclic loading results in an increase in the coefficient of friction in the joint, which can lead to further AC damage [234].  
A schematic representation of the response of AC subjected to cyclic loading is presented in Figure 2 based on the work of [PERSON] et al. [235]. The graph illustrates the changes in AC deformation in response to repeated loading, revealing the characteristic features of the behaviour of AC under the influence of cyclic forces.  
The mechanical properties of AC are a result of its structure and the functions it performs. It is therefore fundamental to understand these aspects in order to further consider the influence of the joint environment, including synovium, on the maintenance of homeostasis and AC properties. This topic will be discussed in the next section.  
Figure 2: Schematic of deformation response of AC under cyclic loading.