esa-sceva/satcom-chunk-collection huggingface dataset:
# An Observer-Based Active Fault Tolerant Controller for Vehicle Suspension System
## 3 Robust \(H_{\infty}\) Fault-Tolerant Controller Design
### The Design of \(H_{\infty}\) Fault Compensation Controller  
Based on the above discussion in Section 2.2, the proposed \(H_{\infty}\) observer-based fault compensation controller can be summarized as Theorem 1.  
**Theorem 1.**_Consider the full-order state observer of active suspension system in (13), given a positive constant \(\beta\), if there exist positive matrices \(\mathbf{P}_{1}\) and \(\mathbf{Y}_{1}\) with appropriate dimensions, such that the following linear matrix inequalities (LMIs) hold:_  
\[\left[\begin{array}{ccc}\mathbf{A}_{ef}^{T}\mathbf{P}_{1}+\mathbf{P}_{1} \mathbf{A}_{ef}-\mathbf{C}_{e}^{T}\mathbf{Y}_{1}^{T}-\mathbf{Y}_{1}\mathbf{C} _{e}&\mathbf{P}_{1}\mathbf{F}_{e}-\mathbf{Y}_{1}\mathbf{D}_{e}&\mathbf{C}_{ef }^{T}\\ *&-\beta^{2}\mathbf{I}&0\\ *&*&-\mathbf{I}\end{array}\right]<0 \tag{17}\]  
_Thus, there exists a gain matrix \(\mathbf{L}=\mathbf{P}_{1}-^{1}\mathbf{Y}_{1}\) for the designed observer satisfying the following performance conditions:_  
_(a) The closed-loop system (13) is asymptotically stable_  
_(b) Under zero initial conditions, the \(H_{\infty}\) performance index \(\mid\mid T_{\tilde{f}u^{(g)}}\mid\mid\)\(<\beta\) is satisfied._  
_From Equation (10), the fault compensation control law is designed as:_  
\[u_{C}(t)=-M\mathbf{C}_{ef}\xi(t)=-M\hat{f}(t) \tag{18}\]  
**Proof.** By applying Lemma 1 to the closed-loop system (13), we have:  
\[\left[\begin{array}{ccc}(\mathbf{A}_{ef}-\mathbf{L}\mathbf{C}_{e})^{T} \mathbf{P}_{1}+\mathbf{P}_{1}(\mathbf{A}_{ef}-\mathbf{L}\mathbf{C}_{e})& \mathbf{P}_{1}(\mathbf{F}_{e}-\mathbf{L}\mathbf{D}_{e})&\mathbf{C}_{ef}^{T}\\ *&-\beta^{2}\mathbf{I}&\mathbf{0}\\ *&*&-\mathbf{I}\end{array}\right]<0 \tag{19}\]  
Based on Lemma 1, the closed-loop system (13) is asymptotically stable and the \(H_{\infty}\) performance index \(\mid\mid T_{\tilde{f}u^{(})}(\mathbf{s})\mid\)\(\mid\)\(<\beta\) is satisfied under a zero initial condition if and only if there exists \(\mathbf{P}_{1}>0\) such that inequality (19) holds, and (19) can be further written as:  
\[\left[\begin{array}{ccc}\mathbf{A}_{ef}\mathbf{P}_{1}+\mathbf{P}_{1}\mathbf{ A}_{ef}-\mathbf{C}_{e}^{T}\mathbf{L}^{T}\mathbf{P}_{1}-\mathbf{P}_{1}\mathbf{L} \mathbf{C}_{e}&\mathbf{P}_{1}\mathbf{F}_{e}-\mathbf{P}_{1}\mathbf{L}\mathbf{D} _{e}&\mathbf{C}_{ef}^{T}\\ *&-\beta^{2}\mathbf{I}&\mathbf{0}\\ *&*&-\mathbf{I}\end{array}\right]<0 \tag{20}\]  
Let \(\mathbf{Y}_{1}=\mathbf{P}_{1}\mathbf{L}\), we can get that (20) is equivalent to (17), and the gain matrix \(\mathbf{L}\) of the fault compensation observer is given by \(\mathbf{L}=\mathbf{P}_{1}-^{1}\mathbf{Y}_{1}\). The proof is completed. \(\square\)
###### Abstract  
In response to the twin development challenges of energy shortage and water-scarcity in worldwide arid to semi-arid regions with geothermal resources, a new combined power and freshwater generation system is proposed for geothermal energy utilization. In this system, a total-flow turbine (TF) is employed to be coupled with the traditional single-flash (SF) system and thereafter the coupled TF-SF system is investigated in this work. In addition to power generation, the exhaust steam from turbines are recovered to produce freshwater through condensation. Based on the novel designed system, the production of both power and water are studied under variable wellhead conditions, including variable wellhead pressures, temperatures, mass flowrates, and vapor qualities. The temperature of the separating point at which the total-flow expansion ends and the steam expansion starts is studied for optimal system output. In addition, the efficiency effects of the total-flow turbine on performance of the combined system is also investigated. The power generation comparison shows good power potential of the proposed TF-SF combined system. An effective total-flow turbine with an average efficiency of 65% can lead to an optimal power capacity, exceeding the traditional single-flash (SF) system by 23.7%. Moreover, more than 1/3 of total wellhead discharge can be recovered as desalinated freshwater by the naturally equipped condensation process of the power plant, showing extra benefit from geothermal energy utilization.  
geothermal energy; total-flow and single-flash (TF-SF) system; combine power and freshwater generation; parametric distributions; system performance and comparison +
Footnote †: journal: Applied Sciences
## 5 Discussion
### Proposed Pa-PCA-Ca Structure
In addition, the ML model in the Ca structure can take into account the underlying relationships among the inputs, unlike simple methods such as majority voting that were widely used in the literature [44]. The significance of employing a meta-model is also evident in Figure 6, where the lowest classification accuracies were obtained when only a single FC was utilized without the Ca structure. The entire methodology was developed and executed based on the capabilities of GEE. This platform offers extensive RS datasets, substantial computational resources, and several algorithms [26; 29]. GEE enables the processing of satellite data without requiring manual downloads. As the datasets and methods utilized in this study are publicly accessible within GEE, the proposed method has the potential to be implemented in large-scale and long-term studies thanks to the high-performance computing and parallel processing capabilities of GEE [31].
# Observing Pictures and Videos of Creative Products: An Eye Tracking Study
## Materials and Methods
### Procedure  
Two different sequences of alternated pictures and videos (see Figure 2) were arranged to be shown to Group_A and Group_B, respectively. Consequently, if a specific product was depicted as picture in the first sequence, the corresponding video was shown in the second sequence and vice versa. The stimuli have been sorted in increasing order of presumable difficulty of understanding. The choice of making participants observe a subset of pictures and videos, instead of e.g., pictures only or videos only, was aimed to minimize the dependence of results on people's typical eye scan paths, which are known to be largely idiosyncratic, e.g., [92]. This led, therefore, to design an experiment where the condition related to the form of representation, i.e., picture or video, has been randomized between subjects.  
At the beginning of each test, in order to allow the ET technology to acquire reliable data on the visual data of each participant, a calibration process was performed. Then, the authors read participants the following test instructions. \"You will see pictures or watch videos showing products for a few seconds. We ask you to observe them in silence. At the end of each display, the screen will turn black. As soon as the screen turns black, describe the product, its advantages/disadvantages and the characteristics that make it unusual. At the end of your explanation, nod, look at the center of the screen and we will move forward. So, to summarize, you have to describe the product, its advantages/disadvantages and the characteristics that make it unusual.\" In this way, the authors introduced a measure to make the participants' observations comparable, as they were assigned the same task. In addition, the presence of a quiz-like task was supposed to motivate participants' persistence in observing products and particularly those features relevant for the understanding of creative aspects.  
All the pictures were shown for eight seconds, so that the exposition time of videos and pictures was consistent. When every video and image disappeared, a black screen was shown to allow participants to describe the product they had just seen or watched, for which unlimited time was assigned. Actually, the first product shown was a trial picture aimed to verify the participants' understanding of the procedure.  
Figure 2: Procedure scheme, where the symbol P (V) means that the product shown is in the form of a picture (video).
# Review of Liquid Argon Detector Technologies in the Neutrino Sector
## 2 Single-Phase Experiments
### ProtoDUNE-SP
During normal operation of the ProtoDUNE-SP detector, the LAr undergoes continuous purification in a system of external filters containing molecular sieves and copper-coated catalysts, which act to remove water and oxygen contamination respectively [48], with recirculation pumps moving the LAr between the cryostat and filters. Gaseous argon from boil-off within the detector is also recycled back via an external condenser. This system has proved more than capable of achieving sustained high levels of purity, and therefore long electron lifetimes. The maximum drift time in the 3.6 m wide drift volumes is 2.25 ms [51], but even during early beam operation, lifetimes of 6 ms had already been achieved [49]. Within just a few weeks of continuous recirculation and purification, this had improved to consistently exceed 20 ms, achieving a maximum of approximately 89 ms [51]-corresponding to an impurity level of just 3.4 ppt \(O_{2}\) equivalent. (For comparison, the DUNE-SP specification calls for impurity levels of no more than 100 ppt.) Figure 28: The three sub-types of photon detection modules installed behind the wire planes of a ProtoDUNE-SP APA: (1) dip-coated light guides, (2) double-shift light guides, and (3) ARAPUCAs. The “top” of the APA (where the front-end electronics are situated) is at the top of the image. Adapted from [51]. The successful operation of ProtoDUNE-SP at CERN has proved that the underlying design of large single-phase LArTPCs is more than capable of satisfying the requirements for high precision physics analysis, with this experiment matching, and in many areas exceeding, the specifications and results expected from it. However, just as importantly for the purposes of DUNE-SP and future detectors, it has also shown that the methods of manufacturing, assembling, commissioning and operating such detectors are equally up to the task.
# Incorporating Setup Effects into the Reliability Analysis of Driven Piles
## 2 Basic Assessment Methods for Pile Setup  
Pile setup is the increase in axial bearing capacity of the pile driving into the soil with time. As a result, the ultimate resistance is divided into two components, \(R_{0}\) and \(R_{\text{setup}}\), as shown in the following equation:  
\[R=R_{0}+R_{\text{setup}} \tag{1}\]  
where \(R\) is the ultimate resistance; \(R_{0}\) is the initial ultimate resistance; \(R_{\text{setup}}\) is the setup resistance.  
Equation (1) emphasizes the importance of appropriately assessing setup resistance for reliability evaluation methods. Therefore, this was a topic that drew the attention of many practitioners and presented empirical relationships for predicting the pile setup. These empirical equations are listed in Table 1. Among existing equations, the logarithmic empirical relationship by [PERSON] [25] has been widely utilized to predict the pile setup, which is,  
\[R_{\text{setup}}=R_{0}A\log\frac{t}{t_{0}} \tag{2}\]  
where \(A\) is a variable that varies depending on the soil type; \(t\) is the time since the initial pile driving ended; \(t_{0}\) is the initial time.
## 4 Results and Discussion
### Geochemical Zoning
In the Eastern Deserts, the presence of factors F6 and F7 may indicate different geological features or chemical environments compared to other regions [61]. Figure 6: Feature Importance Stacked Plot. (**a**). Feature Importance Stacked Plot of Major Element; (**b**). Feature Importance Stacked Plot of Trace Element; (**c**). Feature Importance Stacked Plot of Rare Earth Element. Figure 8: Trace Element Factor Weight Distribution Map. Figure 7: Trace Element Background Value Clustering Heatmap. From the rare earth element importance stacking diagram (Figure 6b), it can be concluded that the significance of rare earth elements in different desert regions follows the order: Western > Central > Eastern. Deserts within the same large region share a common source area for their material supply. However, due to varying degrees of post-depostional weathering, the Eastern Deserts experience more intense chemical weathering, leading to a greater loss of rare earth elements [62; 63]. Based on the analysis of all the geochemical indicators, the twelve desert regions can be categorized into three geochemical zones: (I) Western Desert Regions (Kumutag, Gurbantunggut, Taklimakan, Qaidam) characterized by high carbonate rock content, low quartz and feldspar content, high trace element correlation and significance, and high rare earth element significance; (II) Central Desert Regions (Badain Jaran, Ulan Buhe, Mu Us, Hobq, Tengger) with moderate carbonate rock content, moderate quartz and feldspar content, average trace element correlation, and moderate rare earth element significance; and (III) Eastern Desert Regions (Hunshandak, Hobq, Hulun Buir) showing very low carbonate rock content, high quartz and feldspar content, low trace element correlation and significance, and low rare earth element significance. This classification aligns with previous research [47], which similarly divided the deserts of China into western, central, and eastern regions based on major element analysis.
## 2 Materials and Methods
### Gas Sensing Tests  
Before the electrical sensing tests, a crystallographic characterization of the ZnAl\({}_{2}\)O\({}_{4}\) powders calcined at 250 \({}^{\circ}\)C was carried out by X-ray powder diffraction using a Panalytical Empyren device with CuK\(\alpha\) radiation (\(\lambda\) = 1.546 A). The 20 continuous scan range was from 10 to 90\({}^{\circ}\) with steps of 0.026\({}^{\circ}\) at a rate of 30 s/step.  
The gas sensing tests were performed on the surface of pellets manufactured with the ZnAl\({}_{2}\)O\({}_{4}\) powders. For this, 0.3 g of the powders were compressed at a pressure of 11 tons for 5 min using a Simplex Ital Equip-25 tons hydraulic equipment. The dimensions of the pellets were 12 mm in diameter and 0.5 mm in thickness. Two ohmic contacts made from colloidal silver paint (Alfa Aesar, 99%, Mexico City, Mexico) were placed on the pellets' surface so that there was good contact between the surface and the electrons present during the experiments. The pellets were introduced into a small metal box (with a volume of 19 cm\({}^{3}\)) located inside the measuring chamber (with a capacity of 10\({}^{-3}\) torr). For the dynamic tests, the small box had two holes for the inlet and outlet of the test gas. One hole also allowed the introduction of the pellets' electrodes. The gas discharged from the metal box was evacuated by a system installed in the vacuum chamber. A Leybold TM20 electronic detector monitored the test gas partial pressure. The variations in electrical resistance were recorded with a Keithley 2001 multimeter coupled to a control and data acquisition system using the LabView software (National Instruments, Mexico City, Mexico). Mass flow regulators (Brooks Instruments) with a capacity of 2600 cm\({}^{3}\)/min (model GF100 CXKC-SH452.6L, Mexico City, Mexico) and 10 cm\({}^{3}\)/min (model GF100 CXKC-SH40010C, Mexico City, Mexico) were used to control the propane flows.
## 3 Channel Characteristics Comparisons
### Rms Ds  
RMS DS describes the time dispersion of MPCs, and it can be used to calculate the coherence bandwidth [34]. Furthermore, large DS may cause inter-symbol interference. The RMS DS can be calculated as follows [35]:  
\[\tau_{\text{RMS}}=\sqrt{\frac{\sum_{n=1}^{N}(\tau_{n}-\tau_{\text{mean}})^{2}P( \tau_{n})}{\sum_{n=1}^{N}P(\tau_{n})}}, \tag{8}\]  
\[\tau_{\text{mean}}=\frac{\sum_{n=1}^{N}\tau_{n}P(\tau_{n})}{\sum_{n=1}^{N}P( \tau_{n})}, \tag{9}\]  
where \(n\) is the index of paths, \(\tau_{n}\) and \(P(\tau_{n})\) are the delay and power of the \(n\)th path, and \(\tau_{\text{mean}}\) is the mean excess delay given by (9). Figure 10 presents the RMS DS for mmWave and VLC channels calculated by (8) and (9). To model the RMS DS, the distance-dependent model is selected as a candidate model in 3 GPP [5; 36]. We can model the RMS DS with the distance-dependent model as shown in [37],  
\[\tau_{\text{rms}}\propto d^{c}, \tag{10}\]  
where \(d\) is the distance between TX and RX, and \(c\) is a constant, which in this case is 0.15 and \(-\)0.04 for mmWave and VLC channels, respectively. Moreover, the confidence interval for \(c\) with the 95% confidence level is [0.141-0.159] and [\(-\)0.256-0.172] for mmWave and VLC channels, respectively. Note that the values of \(c\) in the confidence interval for mmWave are always positive, which indicates that the RMS DS will increase with distance for mmWave channels in this conference room. Moreover, this paper shows smaller RMS DS than that in [37] (28-38 ns). This is mainly because the environment in [37] is larger and more complicated, resulting in greater RMS DS. On the contrary, both positive and negative values appear in the confidence interval for VLC channels. This result indicates that RMS DS is little dependent on the distance for VLC channels in this conference room. This can be explained by the fact that the light rays experiencing complicated reflections are attenuated greatly and reflected in all directions, so that they can barely reach the RX, resulting in the small RMS DS in VLC bands.  
In Figure 11, we plot the cumulative distribution function (CDF) of RMS DS for mmWave and VLC channels, respectively. The \(\mu\) and \(\sigma\) parameters in normal fitting are \(-\)7.88 (\(10^{-7.88}\approx 13.18\) ns) and 0.1 for mmWave channels, while they are \(-\)8.13 (\(10^{-8.13}\approx 7.41\) ns) and 0.07, respectively, for VLC channels. This indicates that the delay dispersion is weaker in VLC bands than that in mmWave bands, i.e., propagation distances  
Figure 10: RMS DS fitting results for mmWave and VLC channels.  
of multipath rays are mostly close due to the few received multipath rays experiencing complicated reflections with a large excess delay in VLC bands.
## 3 All-Optical Ultrasound Transducers and Imaging
### Development of Transducers and Imaging
#### 3.1.1 Carbon-Based Transducers  
CNTs-PDMS nanocomposites are efficient photoacoustic transmitters and thus are promising choices for AOUSTs. Two types of AOUSTs have been demonstrated recently. (i) A CNTs-PDMS nanocomposite film and an etalon structure consisting of SiO\({}_{2}\)/TiO\({}_{2}\) distributed Bragg reflectors (DBRs) and an SU-8 spacer are integrated [88]. The DBR reflector allows an open spectral window for laser transmission that can be used for LGUS excitation of CNTs-PDMS nanocomposites. Compared with an Au mirror as a broadband reflector used in Reference [89], the DBR reflector circumvents the issue of little spectral window for LGUS excitation. A bandwidth of 27 MHz is obtained and a 2D transducer array is evaluated, showing the promise for high-resolution ultrasound imaging. (ii) An optical fiber with CNTs-PDMS coated on the end face of the fiber for LGUS transmission is combined with an FP etalon for ODUS sensing [90]. Strong ultrasound pressure of 21.5 MPa and a broad bandwidth of 39.8 MHz are demonstrated. AOUSI of an aorta is demonstrated to show the clinical potential.
## 2 Related Works
### FER Datasets  
#### 2.3.1 Real Face FER Datasets  
Numerous datasets have been proposed for research and development in FER, focusing on identifying and categorizing emotional expressions from human faces (see Table 3). The FER+ dataset [40], comprising 35,887 grayscale images distributed across seven emotional categories, is extensively used for the training and evaluation of FER models. Another significant contribution to the field is the EmotiW [43], an annual competition offering datasets tailored for FER tasks within unstructured settings. Unlike datasets artificially created in laboratory settings, these datasets consist of images and videos from real-life scenarios, proving invaluable for the development and assessment of FER systems. The BP4D dataset [44] provides high-resolution 3D videos capturing facial expressions from 41 participants, utilizing multiple synchronized cameras to support comprehensive temporal and spatial analysis. The MMI database [39] encompasses over 2900 videos and high-resolution images of facial expressions, accompanied by detailed annotations regarding Action Units (AUs) and fundamental emotions. Additionally, the SEWA dataset [47] centers on sentiment analysis and emotional recognition in naturalistic conditions, incorporating audiovisual recordings of genuine interactions annotated with continuous emotional dimensions such as arousal, valence, and liking. Emotional perception is inherently subjective, leading to variability in how different annotators interpret the same expressions. Such inconsistency in labeling introduces noise into the dataset, thereby diminishing the accuracy and reliability of emotion recognition models. This inconsistency affects the reliability of emotion classes, as emotions may overlap or be challenging to distinguish, especially between nuanced expressions such as \"fear\" and \"surprise\" [66]. Different datasets often use unique labeling conventions or categories, resulting in inconsistencies when comparing or merging datasets. For example, FER2013 may categorize emotions differently from MMI, which hinders the interoperability and integration of these datasets for broader FER applications [67]. In Figure 3, the same image in the FER2013 [40] dataset is labeled as \"fear\", while six annotators out of ten labeled it as sadness in FER+ [48].
## 3 Results
### Electronic Structure
% Ga structure where a monoclinic distortion formed, the 5f states for Pu-2 shifts right below the Fermi energy and a broadening of the Pu 6d states, which was also observed in the pure \(\delta\)-Pu when a monoclinic distortion formed [38], indicating a monoclinic structural distortion and the associated electronic structure response [33]. With the increase of Ga concentration to 1.852 at. % Ga, a similar monoclinic distortion appeared and there are no longer 5f states right below the Fermi energy as they have shifted to lower energies. There is still an observed broadening of the Pu-1 and Pu-2 5f and 6d states. At the 2.778 at. % Ga concentration, the Pu 5f states shifts even further below the Fermi energy with a decrease in the Pu 6d states for the atom closest to the Ga atom (Pu-1). Hence, the PDOS in Figure 9 suggests the stabilization of the fcc matrix by decreasing the Pu 6d states and promoting hybridization with the Ga 4p states further up in energy (approximately \(-1\) eV) when compared to the PDOS with no defect. We will now discuss the effects when the mono-vacancy is interacting with multiple Ga atoms in the 2.778 at. % Ga concentration. In the case where the mono-vacancy is located in between two Ga atoms at the first nearest neighboring distance, the PDOS shows an increase of the 5f states shifted right below the Fermi energy to promote Pu 6d and Ga 4p hybridization in this region for the Pu-1 and Pu-2 atoms (Figure 10). However, as indicated above, the fcc structure is locally preserved, but there is an energetic cost, as the formation energy increases to 1.57 eV. This is furthered highlighted when the mono-vacancy is in between three Ga atoms at the second nearest neighbor distance, there is no change to the Pu 5f states regardless of the distance from the Ga atom, and the Pu 6d states are similar to when there is no defect. The energetic cost is even higher in order to maintain the undistorted fcc lattice structure since the formation energy increased to 1.75 eV, thus implying that the fcc stability is most likely due to Ga-Ga repulsion [25]. Once the mono-vacancy is further out from the Ga, such as 4 nn distance, formation energy decreases to 1.10 eV, a monoclinic distortion forms, and the PDOS takes on the form as observed in pure \(\delta\)-Pu [38] and 0.926 at. % Ga case discussed above (Figure 9). Figure 8: Partial density of states for the 0.926, 1.852, and 2.778 at. % Ga concentrations with no defects. The Pu 5f6d states shown for Pu atoms at various distances from the Ga atom (Table 3).