esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Empirical Studies to Evaluate Liquefaction Resistance of Partially Saturated Sands
## 4 Experimental Results
### Parameters Affecting Liquefaction Behavior of Partially Saturated Sands
#### 4.3.1 Effective Confining Stress  
The effect of the initial effective confining stress on the liquefaction behavior of the sands was examined using the results from the tests in which the sand specimens were isotropically consolidated under different stress levels (\(\sigma_{c}^{\prime}=25\), 50, or 100 kPa) while the other testing parameters (e.g., \(S_{r}\), \(u_{0}\), and \(D_{r}\)) were analogous.  
Figure 7 shows the liquefaction-resistance curves for the nearly/fully saturated sand specimens. It can be seen that the CSR at a given \(N_{liq}\) decreased as the \(\sigma_{c}^{\prime}\) increased from 50 to 100 kPa. This implied that under comparable loading and experimental conditions, the resistance of the nearly or fully saturated sands to liquefaction was reduced with an increase in the initial effective confining stress. This result agreed with many research findings in the field of soil liquefaction [59; 60].  
Figure 8 exhibits the cyclic liquefaction-resistance curves obtained from two sets of tests conducted at two different degrees of saturation (e.g., \(S_{r}=94\)-\(96\%\) and \(S_{r}=84\)-\(86\%\)). It appeared that for a constant relative density, back pressure, and degree of saturation, the CSR at a specific \(N_{liq}\) increased as the initial effective confining stress was increased from 25 kPa to 100 kPa. This indicated that the partially saturated sand specimens were more resistant to liquefaction at high effective confining stresses. Similar observations were made in previous studies [17; 41].
###### Abstract  
Demands of standalone dehumidification systems have been increasing in order to realize energy savings in air-conditioning processes. In a desiccant dehumidification system, the water vapor from the moist air undergoes a phase change phenomenon, this being from vapor to adsorbed phase, a process analogous to latent heat exchange. The energy exchange involved in such a process is often significant--up to 80% of the total energy exchange. In this study, the influence of the phase change phenomena involved in a desiccant dehumidification system was evaluated experimentally, along with the performance investigation under low desorption air temperatures of 308, 318, 328, 338, and 345 K. The system was driven by a constant adsorption temperature of 293 K. The dehumidification ability, latent heat ratio, and latent effectiveness were employed as key performance indexes. The results showed that with the increased desorption temperature, the latent heat ratio decreased, whereas the dehumidification ability and latent effectiveness increased. The highest latent heat ratio was found to be 0.61 at the desorption temperature of 308 K, whereas the highest latent effectiveness was obtained at the desorption temperature of 345 K. A suitable temperature for the effective and efficient dehumidification was observed to be 318 K for the current system.  
adsorption; dehumidification; effectiveness; latent heat; performance +
Footnote †: journal: Applied Physics Letters B
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 2 Medical Imaging Applications
### Nonlinear Microscopy
#### 2.3.2 Higher Harmonic Generation Microscopy  
Another kind of nonlinear microscopy method is based on the use of nonlinear scattering from the tissue molecules. The laser light interacts with the tissue and causes an electromagnetic field to interact with the tissue's electric cloud, generating oscillating electric dipoles. In non-centro symmetric molecules, the output light beam generated by moving from the transition state to the ground state is a distorted version of the exciting light beam, which creates the harmonics. Analogous to the multiphoton microscopy, the higher energy density required to excite the molecule is possible at the focal point. The harmonic generation is a coherent process, and the electric field radiated by the different molecules interfere with each other. Thus, it requires an ordered tissue structure where the aligned molecules can construct interference [10].  
Similar to the multiphoton process, the harmonic generation comprises second, third, and higher harmonic generation depending on the number of photons exciting the molecules. The energy of the output light beam is given by sum of the energies of the incoming light beams hitting the tissue surface. In second harmonic generation (SHG), the output photon has a wavelength that is double that of the two hitting photons. In other words, the incoming light from the laser induces a nonlinear polarization in the structure, which then generates a coherent wave with a frequency double that of incoming light. In third harmonic generation (THG), the output wave frequency is three times that of the exciting light beam [10]. The microscopic probe for the harmonic generation process is similar to that of a multiphoton microscopy. The only difference is the presence of a narrow band-pass filter, in front of the detector and centered at the SHG wavelength to block the residual excitation light [30].  
This kind of microscopy is mainly used to image collagen fibers, myosin in muscle fibers, and microtubules in axons because of their ordered tissue structures [25]. Due to the similarity of SHG and TPEF, these techniques are often used together. A large number of SHG-based nonlinear microscopic devices are presented in the literature for imaging different tissue structures and diagnosis [31; 32]. [PERSON] et al. imaged living neuroblastoma cells using both SHG and TPEF techniques. Both techniques provide images with very fine resolution as can be seen in Figure 7[31].
## 5 Validation and Results  
We deployed a testbed to validate the systems described in this paper, emulating a real smart city infrastructure. Figure 14 shows the main components of the testbed, which are described in Section 5.1. In Section 5.2, we present the results of the performance evaluation of the dynamic AR marker.
# Enhancing Reliability Analysis with Multisource Data:
## 1 Introduction
### Research Scope  
Many studies have been conducted on condition predictions of bridge elements using various models. However, a major portion of previous studies only focused on selected bridge element types such as deck [6; 17] or a small number of selected bridges located at a certain geographical location [3]. Therefore, it is important and necessary to understand the interdependencies between NBI bridge attributes and respective bridge capacities, and determine a more generalized methodology for quantifying the reliability of bridges using live loads available in the WIM data. The scope of this paper includes conducting a reliability analysis of highway bridges in Georgia. To understand the correlation between NBI attributes and bridge capacities, modern machine learning algorithms [17; 18; 19; 20] are employed. This study uses a XGBoost algorithm [21] to understand the interdependencies between load ratings (LR) and several bridge element condition ratings (CR). XGBoost is known for its capability to handle imbalanced data and high prediction accuracy. However, such gradient boosting methods are not easy to interpret. Therefore, to enhance interpretability of results, Shapley additive explanation plots (SHAP) will be used to visualize the impact of predictors on a target variable. SHAP is a framework built on the game theory and determines the effects of attributes through local interpretability [22].  
Therefore, in this study, a correlation between selected NBI attributes and bridge capacities is evaluated by a XGBoost algorithm, and the results are compared with CatBoost [23] algorithms. The aim of this exercise is to examine if the maximum load carrying capacity determined as a part of operating load rating is a consistent indicator which reflects bridge conditions. Furthermore, vehicle weight distributions observed between 2019 and 2021 are used to evaluate reliability indices of bridges using the maximum load carrying capacities available in the NBI database. It is observed that the number of heavyweight vehicles has been growing rapidly over the past few years. Yet, bridge owners are not provided with the live load data while making bridge maintenance decisions, which ultimately leads to an adverse selection problem, undermining the hidden risk of a bridge failure. The issue of adverse selection in bridge maintenance needs to be addressed as it is linked to the public safety and accessibility and thus needs to be mitigated by providing reliable information.
## 3 Results
### Results of Measurements and Correlation Analysis
#### 3.2.2 Results of PLSR Model Prediction  
Figure 12 shows the variation of \(AIC\) with the number of PCFs. The optimal number of PCFs can be selected when the \(AIC\) value is small. Thus, two PCFs were used in the PLSR prediction model for cohesion, and three PCFs were used for the PLSR model of the internal friction angle. The results of the cohesion and internal friction angle predicted by PLSR are illustrated in Figure 13.  
For soil cohesion (Figure 13a), the performance of the PLSR model for the calibration set is \(R_{C}{}^{2}\) = 0.76, \(RMSE_{C}\) = 4.85, which is not satisfactory; however, its \(R_{V}{}^{2}\) on the validation set is 0.82, greater than 0.8, showing good predictive ability. For the internal friction angle (Figure 13b), the PLSR model evaluation indexes \(R_{C}{}^{2}\) and \(R_{V}{}^{2}\) are both 0.68, far less than 0.80, indicating the predictive reliability of the model in both the calibration set and validation set is inadequate.  
Figure 11: Soil shearing parameters prediction of the BPNN models. (**a**) Cohesion; (**b**) internal friction angle.  
Figure 12: Effects of number of hidden layer neurons on correlation coefficient \(R_{C}{}^{2}\) in BPNN models.
## 5 Experiments and Results  
In this section, we provide the details of implementations, such as different parameters and the configuration set-ups during the experiments. After that, the results of the experiments with the proposed method, along with the comparisons made with state-of-the-art networks, are demonstrated. Finally, we showed the robustness and transferability of the proposed method by applying it to another dataset from an entirely different geographical region.
# Representing Aspectual Meaning in Sentence: Computational Modeling Based on Chinese
###### Abstract  
Situation types can be viewed as the foundation of representation of sentence meaning. Noting that situation types cannot be determined by verbs alone, recent studies often focus on situation type prediction in terms of the combination of different linguistic constituents at the sentence level instead of lexically marked situation types. However, in languages with a fully marked aspectual system, such as Mandarin Chinese, such an approach may miss the opportunity of leveraging lexical aspects as well as other distribution-based lexical cues of event types. Currently, there is a lack of resources and methods for the identification and validation of the lexical aspect, and this issue is particularly severe for Chinese. From a computational linguistics perspective, the main reason for this shortage stems from the absence of a verified lexical aspect classification system, and consequently, a gold-standard dataset annotated according to this classification system. Additionally, owing to the lack of such a high-quality dataset, it remains unclear whether semantic models, including large general-purpose language models, can actually capture this important yet complex semantic information. As a result, the true realization of lexical aspect analysis cannot be achieved. To address these two problems, this paper sets out two objectives. First, we aim to construct a high-quality lexical aspect dataset. Since the classification of the lexical aspect depends on how it interacts with aspectual markers, we establish a scientific classification and data construction process through the selection of vocabulary items, the compilation of co-occurrence frequency matrices, and hierarchical clustering. Second, based on the constructed dataset, we separately evaluate the ability of linguistic features and large language model word embeddings to identify lexical aspect categories in order to (1) verify the capacity of semantic models to infer complex semantics and (2) achieve high-accuracy prediction of lexical aspects. Our final classification accuracy is 72.05%, representing the best result reported thus far.  
Keywords:lexical aspect; situation TYPE; dataset evaluation +
Footnote †: journal: Applied Sciences
## 4 Antenna Array
### Normal Array
#### 4.1.1 Uniform Amplitude and Phase Distribution  
For this case, each element is fed with equal power level and phase, thus achieving a broadside radiation. The same phase value is guaranteed by means of self-manufactured cables with identical electrical length. Figure 8 shows a comparison between simulation and measurement of the co-polar patterns for both polarizations. Co-polar and cross-polar patterns for \(\varphi=90^{\circ}\) are depicted in Figure 9. The measured gain is 11.1 dB and 11.7 dB for the horizontal and vertical polarization, respectively. It differs 1.7 dB from the simulated values, since the power divider used to obtain a uniform amplitude distribution for each antenna element and the cable losses are not considered within the calibration process. This includes the insertion losses related to the power divider and the inherent power losses given by the unused output of the power divider that is matched with a 50 \(\Omega\) load. Precisely, due to the slight phase and amplitude unbalance at the outputs of the power divider [38], the pattern at \(\varphi=90^{\circ}\) is marginally assymetric, being more visible for the vertical polarization. However, it can be seen that the measurements agree very well with the simulated values.  
The co-polar and cross-polar patterns for \(\varphi=90^{\circ}\) are depicted in Figure 9. The measured cross-polarization suppression, 23 dB and 28 dB for the horizontal and vertical polarization, respectively, is lower than the simulated data. This difference can be certainly due to different reasons, especially the accuracy of the measurement, since the cross-polarization values are mainly below \(-20\) dB, which leads to an extremely sensitive measurement. Other aspects such as the fabrication and manufacturing tolerances should also be considered. Nevertheless, it can be seen that the measured patterns meet the expected results very well.  
Figure 8: Comparison simulation and measurement @ \(f_{0}\) = 1.325 GHz: co-polar patterns. Normal array. Uniform amplitude and phase distribution.  
Figure 7: Manufactured normal array.
## 3 Analytical Methods and Applications
### Discussion of Analysis Methods  
Our review has indicated six major analytical methods of geospatial analysis and geographical information science used in IoT research. For each technique, various IoT-based projects and initiatives using this technique have been identified. Table 1 maps the different IoT research areas identified, together with the geospatial analytical method used, according to the surveyed work. As the table shows, geometric measures and basic analytical methods were the ones mostly used, while the IoT areas of health and disease detection, transportation, wildlife monitoring, agriculture and environmental research were the ones that used the largest variety of the different geospatial analytical methods recorded.  
Figure 8: An example interpolation (adapted from [31]).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**IoT Area** & **Geometric Measures** & **Data Mining** & **Basic Analytical Operations** & **Basic Analytical Methods** & **Network Analysis** & **Surface Analysis \& **Coestatistics** \\ \hline Tourism & x & x & - & x & - & - \\ \hline Utility networks & x & x & - & - & x & - \\ \hline Disaster monitoring & x & - & x & - & - & x \\ \hline Health and disease detection & x & x & - & x & - & x \\ \hline Transportation & x & x & - & x & x & - \\ \hline Logistics and assets & - & x & - & - & x & - \\ \hline Wildlife monitoring & - & x & - & x & x & x \\ \hline Agriculture & x & - & - & x & x & x \\ \hline Crime prediction & - & - & - & x & - & - \\ \hline Sports and gaming & x & - & - & x & - & - \\ \hline Environment & - & - & x & x & x & x \\ \hline \hline \end{tabular}
\end{table}
Table 1: IoT research areas and geospatial analytical methods.  
Although each paper has been categorized in the most representative category it belongs, the majority of identified research work involved (additionally) some type of basic geometric measures (i.e., distance, adjacency, connectivity, and proximity) or basic analytical operations (i.e., buffering, overlays, and map algebra). In relation to map algebra, some of the surveyed projects employed spatial relationships such as _within_ or _nearby_ of some raster area, especially tourism services [20; 51] and cultural heritage-related visits [57]. Fusion of information from various sources was also observed, combined with geospatial analysis, such as the case of predicting the future direction of an on-going fire [73]. Analysis of time series and trajectories has also been adopted, in projects related to logistics [15], or wildlife and animal monitoring [59; 60].
## 3 Research Approach
### Study Area  
The study area is located in the Sikkim Himalayas, often referred to as the \"Third Pole\" due to its extensive glacial coverage. This region is part of the Eastern Indian Himalayan Region (IHR), which is crucial for the freshwater supply in South and East Asia. The glaciers of the Sikkim Himalayas, highly sensitive to global climate changes, cover over 436 km\({}^{2}\), and their retreat has led to the formation of moraine-damped glacial lakes. Currently, there are approximately 320 glacial lakes in the region, 85 of which have formed recently due to glacial retreat and ice melt. Gurudongmar Lake exemplifies this process, having formed through the retreat of glaciers in the Chhombo Chhu watershed of the upper Teesta basin. The lake is flanked by bedrock and moraine deposits [27,28]. The Sikkim Himalayas are positioned at the convergence of the Central and Eastern Himalayas, creating a complex climatic zone influenced by the Indian Summer Monsoon (ISM), Westerlies, and Easterlies. Long-term climate data from Gangtok indicate a significant rise in minimum temperatures, increasing by approximately 0.036 \({}^{\circ}\)C annually from 1961 to 2017.
# Power System Voltage Correction Scheme Based on Adaptive Horizon Model Predictive Control
## 4 Voltage Correction Scheme Based on AH-MPC  
The structure of the proposed scheme is shown in Figure 4. The system measurements are collected and processed by a state estimator in the control center. The results are then used to initialize the state variable of the system model and predict the future system trajectory. Compared with a standard MPC controller, the byproduct of the voltage prediction results are used to update the horizon parameter settings. With the numerical simulation results of \(\hat{V}^{*}\) and the trajectory sensitivity information already available, the calculation of the horizon parameters based on Equations (13)-(17) requires solving a set of linear algebraic equations, which involves little extra computation.  
With adaptive horizons applied, the objective function (6) and constraint (8) in the standard MPC-based optimization model are rewritten as:  
\[\min(\sum_{k=1}^{N_{\text{pu}}}(\mathbf{V}_{\text{r}}-\hat{\mathbf{V}}_{k})\mathbf{Q}(\mathbf{ V}_{\text{r}}-\hat{\mathbf{V}}_{k})^{\text{T}}+\sum_{k=1}^{N_{\text{cr}}}\Delta\mathbf{u}_{k }\mathbf{R}\Delta\mathbf{u}_{k}^{\text{T}}) \tag{20}\]  
\[\mathbf{V}_{\min}\leq\hat{\mathbf{V}}_{N_{\text{pu}}}\leq\mathbf{V}_{\max} \tag{21}\]  
\[N_{\text{p}(n+1)}=N_{\text{cr}}=t_{\text{cr}}/t_{\text{s}} \tag{22}\]  
where \(N_{\text{pu}}\) and \(N_{\text{cr}}\) are the number of prediction steps and control steps applied in the \(n\)-th optimization.  
Figure 5 shows the flow chart for the voltage correction scheme based on AH-MPC. The procedure of the proposed scheme in the \(n\)-th optimization include the following.  
* At a sampling point \(t_{n}\), trapezoidal numerical simulation is used to obtain the predicted system trajectory within the prediction horizon [\(t_{n}\), \(t_{n}+t_{\text{pn}}\)].
* The trajectory sensitivity is obtained as a by-product of the first step.  
Figure 3: Determination of adaptive horizon parameters.  
* Based on the predicted system trajectory and the trajectory sensitivity results, the adaptive horizon parameters are determined as is introduced in Section 3.
* The optimal control model is formulated according to Equations (7), (9), (10), (20), (21) and (22).
* By solving the optimal control model, the first step of the so computed control sequence is applied in the system at \(t_{n}+t_{\mathrm{s}}\).
* The above procedure is repeated at the initial time of the (\(n+1\))-th optimization.  
If disturbances are detected by phase measurement unit (PMU) measurements and monitoring systems, all the parameters are initialized at the next sampling point.  
Figure 4: Structure of the voltage correction scheme based on adaptive horizon model predictive control (AH-MPC).  
Figure 5: Flow chart for the voltage correction scheme based on AH-MPC.
## 2 Materials and Methods  
In the present work all acquisitions were performed using a Symbia IntevoTM system (Siemens, Erlangen, Germany) provided with the novel xSPECT software, specifically dedicated to quantitative imaging. The software uses CT as the frame-of-reference for image reconstruction, preserving the resolution of the CT matrix and permitting a more accurate alignment of both data sets during reconstruction. The resulting reconstructed xSPECT image is created in a CT equivalent slice-by-slice DICOM format.  
Since the photopeak of \({}^{99\text{m}}\)Tc is at 140 keV, low-energy high-resolution (LEHR) collimators were used for all phantoms. To simulate clinical conditions, images were acquired using the acquisition workflow currently adopted by our Institute for patients undergoing RE treatment, i.e., 256 \(\times\) 256 matrix (2.39 mm\({}^{3}\) voxel size), angular step of 5\({}^{\circ}\) over 360\({}^{\circ}\) (75 frames) and a 15% energy window centered at 140 keV. Acquisitions were made with the phantoms in the center of the field of view. Raw data were corrected both for attenuation and scatter. Furthermore, data were decay corrected to the acquisition time.  
The activities used for the preparation of the phantoms were measured in the Calibrator of Eliza hot-cell (COMECER S.p.a), traceable to the Italian National Institute of Ionizing Radiation Metrology for the geometry being measured (accuracy within \(\pm\)5% at k = 2 level, as recommended by AAPM report 181 [18]).  
The work is structured into four logically sequential steps, as follows:  
1. Assessment of gamma camera calibration factor using a NIST traceable \({}^{57}\)Co point source;
2. Selection of reconstruction method: using the calibration factor derived from the previous step, data acquisition of a homogeneous cylindrical phantom was performed in order to assess the best reconstruction method. Two algorithms were tested (the OSEM and OSCG algorithm) using the built-in quantitative xSPECT package. The selected algorithm was then used to reconstruct raw data in step (3) and (4);
3. Determination of recovery coefficient: acquisition of the NEMA-IEC phantom provided with 6 fillable spheres was carried out in order to assess recovery coefficients for small volumes;
4. Method validation: using information elicited from the previous steps, the quantification procedure was validated using a 3D-printed anthropomorphic phantom provided with fillable spheres simulating hepatic lesions.  
A summary of experimental materials and methods described in the present work is shown in Figure 1.  
Figure 1: Experimental materials and methods described in the present work.