esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Energy Retrofit Measures in Hot-Summer-Cold-Winter Climates: A Case Study in Shanghai
## 3 Background Study: Building Energy Consumption and the Climatic Context in Shanghai
### Climate Context
Hence, it is crucial to have an insight into the impact of climate conditions on building energy consumption in this climate zone if appropriate retrofit solutions are to be determined. Documented climate data of Shanghai for the past 40 years (from 1970 to 2010) are summarised in Figure 4[32]. Figure 4a shows an ascending trend for the annual average temperature of Shanghai within the stipulated period. Before 1990, the rate of temperature increase was slow before it rapidly increased until 2010. Specifically, the annual average temperature increased from \(\sim\)15.4 \({}^{\circ}\)C to \(\sim\)16.4 \({}^{\circ}\)C after two decades from 1970. Post-1990, the temperature continuously increased to above 18 \({}^{\circ}\)C in 2007 [33]. Figure 4b shows a more vivid clarification of the temperature rise within a day for different periods. Within the recent decades, it was observed that the hourly temperature variation in Shanghai increased at a greater magnitude than in the past decades. Accordingly, building energy consumption was channelled towards a cooling load in this region of China, and vice versa [34]. Recent observation and simulation research show that climate change in this region was significantly affected by both external climate variability and internal oscillation [35]. While climate variability has been attributable to the climate features of the city, the internal oscillation was associated with the additive effect of climate variation and urban densification. Owing to the rise in urban residents from 58% in 1978 to about 90% recently [36], an increase in building density was expected. Consequently, this was stipulated to induce an urban heat island (UHI) effect, resulting in micro-(local) climate variation. An extensive study in Nanjing, a similar neighbouring city, speculated that the distance between the buildings rather than the building typology was the critical factor of urban micro-climates [13].
## 2 Piezo-Actuated FSM System
### Piezo-Actuated FSM System Design  
A flexure is used as the suspension system for the FSM, supporting the mover and the base plate, which are centrally bonded to the front and back sides of the mover. The flexure connects the static and dynamic components of the FSM and is mounted on its body. The flexure also prepresses the piezo-stack actuator assembly to enable dynamic operation. The stroke range of the actuators is expressed as follows:  
\[\Delta L\approx L_{0}\bigg{(}\frac{k_{A}}{k_{A}+k_{H}}\bigg{)}, \tag{1}\]  
where \(k_{A}\) is the stiffness of the actuator, \(k_{H}\) is the stiffness of the flexure hinge, and \(L_{0}\) is the range of the unstressed stack actuator. A stiffer flexure design increases the frequency of its structural modes, which is clearly beneficial from a control perspective. Thus, the PZT design has two conflicting aspects that must be considered. First, the PZT stack actuator must be adequately stiff to shift the main resonant mode to higher frequencies. Second, the actuator must be sufficiently compliant to avoid significantly reducing the stroke range.  
For the flexure design, a simulation-based parameter study was conducted with various materials and thickness configurations to investigate the tradeoff between range loss and the main resonance mode. To achieve a large system bandwidth, the main resonance mode with the optical mirror was targeted to exceed 400 Hz, while the range loss threshold due to prepressing was set as 5%. With stiffness values of \(k_{A}=12\) N/\(\upmu\)m and \(L_{0}=143\)\(\upmu\)m \(\pm\) 10% for the maximum range of the stack actuators, a maximum stiffness of 1.48 N/\(\upmu\)m was calculated according to Equation (1). As shown in Figure 2, a static mechanical analysis was performed to determine the stiffness \(k_{H}\) of each flexure configuration using ANSYS 2023 R1 Workbench (Ansys Inc., Washington County, PA, USA). A modal analysis revealing the structural modes of each flexure was conducted to attain the main resonance tip mode (equivalent to the tilt mode) of each configuration. The first two modes of the obtained flexure are shown in Figure 3. A main resonance mode (tip/tilt motion) appears at approximately 401 Hz. The overall specifications of piezo-actuated FSM with the optical mirror are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Identified Specification** & **Value** \\ \hline Stroke range & \(\pm\)5 mrad \\ Angular resolution & 1 \(\upmu\)rad \\ Mover diameter & 70 mm \\ Resonance frequency (tip) & 401.08 Hz \\ Resonance frequency (tilt) & 401.38 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dynamic specifications of the real plant.  
Figure 2: Assembled view of the piezo-actuated FSM without and with the optical mirror.
## 1 Introduction  
The development of eye-tracking technology has allowed for the quantification of visual search behaviour, cognitive thought and the analysis of human performance in the interpretation of stimuli [1, 2]. Progressing from the 19 th century, the advancement of computer-based eye-trackers prioritise a non-invasive passive approach to monitoring participants, facilitating a wide variety of studies across many disciplines [2, 3]. This recently has included the healthcare and medical fields, where stimuli interpretations can be studied--for example the interpretation of electrocardiograms by [PERSON] et al. [3, 4].  
In a separate discussion, the use of radiological images is an essential component of the medical industry, providing insight into patient wellbeing, medical conditions and potential treatment plans. Diagnostic error, defined as an \"incorrect, delayed or missed diagnos(i)s\" has the capacity to adversely impact patient wellbeing, safety and care [5, 6]. Rates of diagnostic error are consistent at around 3-5% annually (persistent irrespective of time and intervention), resulting in a global estimation of 40 million diagnostic errors annually [6]. As a major contributor to diagnostic processes, the radiological space can play a considerable role in diagnostic error, as shown by various intervention and literature into the field [5, 6, 7].  
Despite the research, documentation and attempted intervention of many techniques to reduce radiologic error [6, 7], there are some interesting discussions from a review by [PERSON] et al. [8] suggesting a place for eye-tracking technology in the advancement of professional experience and expertise in radiographic interpretation. There appears to be a lack of competency-based assessment in medical education for health professionals, as there have been challenges in developing educational material which has the capacity to \"create meaningful, relevant and repeatable outcome-based assessments for use in graduate medical education, residency and fellowships\" [8]. This suggested use of gaze-monitoring technology appears to be one potential method for the further understanding of radiology. Potential for continuous aptitude assessment, assessment of visual search behaviour and the establishment of assistive tool use are also considerable avenues which will be considered during this review. This thematic analysis is supported by [PERSON] et al. [9], who also consider eye-tracking technology as a potential tool for strength and weakness analysis of radiological interpretation.  
This systematic review aims to provide a primary understanding of the utilisation of eye-tracking and its potential to be used in the radiological space. The goal of this paper is to provide a review of the current literature and to contribute a summative analysis, providing an opportunity to discuss developments, future research areas and potential quantification of radiological interpretation styles and techniques.
# Agglomerative Clustering and Residual-VLAD Encoding for Human Action Recognition
## 3 Experimental Results and Discussion
### Performance Evaluation
#### 3.3.1 Codebook Size  
The codebook size affects the performance of the BoW pipeline. In the proposed scheme, three codebooks are formed. The global codebook using features from all the classes, a class-specific codebook using data from individual classes and the final codebook by combining the prior codebooks. Different combinations for both codebooks have been tried, for example, varying the size of the class-specific codebook while keeping the global codebook size fixed and vice versa. The size of the final codebook was generally kept as half of the combined codebooks. The number of clusters for the global codebook were varied from 50 to 500, while fixing the number of clusters for the class-specific codebook to two clusters per class as suggested by empirical evaluations. Figure 5 shows the results for HMDB51 split1 using features from ST-ResNet and ResNeXt-101. The best results are produced when the global codebook size is 200 for ResNeXt and 150 for ST-ResNet.
_Appl. Sci._**2015**, \(5\), 1494-1502; doi:10.3390/app5041494  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
**Luminescent Properties of Sr\({}_{4}\)Si\({}_{3}\)O\({}_{8}\)Cl\({}_{4}\):Eu\({}^{2+}\), Bi\({}^{3+}\) Phosphors for Near UV InGaN-Based Light-Emitting-Diodes**  
**[PERSON] 1,\({}^{\dagger}\), [PERSON] 2,\({}^{\dagger}\) and [PERSON] 2,\({}^{\star}\)**  
1 College of Chemistry and Chemical Engineering, Neijiang Normal University, Neijiang 641112, China; E-Mail: [EMAIL_ADDRESS]  
2 Key Laboratory of Comprehensive Utilization of Mineral Resources in Ethnic Regions, Joint Research Centre for International Cross-Border Ethnic Regions Biomass Clean Utilization in Yunnan, School of Chemistry & Environment, Yunnan Minzu University, Kunming 650500, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-871-6591-3043; Fax: +86-871-6591-0017.  
Academic Editor: [PERSON]  
_Received: 30 September 2015 / Accepted: 23 November 2015 / Published: 1 December 2015_  
**Abstract:** Sr\({}_{4}\)Si\({}_{3}\)O\({}_{8}\)Cl\({}_{4}\) co-doped with Eu\({}^{2+}\), Bi\({}^{3+}\) were prepared by the high temperature reaction. The structure and luminescent properties of Sr\({}_{4}\)Si\({}_{3}\)O\({}_{8}\)Cl\({}_{4}\):Eu\({}^{2+}\), Bi\({}^{3+}\) were investigated. With the introduction of Bi\({}^{3+}\), luminescent properties of these phosphors have been optimized. Compared with Sr\({}_{3.90}\)Si\({}_{3}\)O\({}_{8}\)Cl\({}_{4}\):0.10 Eu\({}^{2+}\), the blue-green phosphor Sr\({}_{3.50}\)Si\({}_{3}\)O\({}_{8}\)Cl\({}_{4}\):0.10 Eu\({}^{2+}\), 0.40 Bi\({}^{3+}\) shows stronger blue-green emission with broader excitation in near-UV range. Bright blue-green light from the LED means this phosphor can be observed by the naked eye. Hence, it may have an application in near UV LED chips.  
**Keywords:** luminescent properties; phosphors; light-emitting diodes; Sr\({}_{4}\)Si\({}_{3}\)O\({}_{8}\)Cl\({}_{4}\)
# LCAS-DetNet: A Ship Target Detection Network for Synthetic Aperture Radar Images
## 4 Results
### Ablation Study  
Figure 1 shows our proposed network architecture, which mainly has the advantages of two parts: (1) a cascade feature extractor composed of MLCA blocks and (2) \(\mathcal{L}_{Bbox}\) with a dynamic focusing mechanism. This section presents the results of ablation experiments, validating the efficacy of the MLCA mechanism and \(\mathcal{L}_{Bbox}\) with a dynamic focusing mechanism in the SAR ship detection tasks. Below is a description of the notations used:  
_Basic network_: a target detection network following the YOLOv8 framework without any MLCA mechanism or \(\mathcal{L}_{Bbox}\) with a dynamic focusing mechanism.  
_Basic network-MLCA_: improved basic network using a multi-scale cascade feature extractor composed of MLCA blocks.  
_Basic network-LOSS_: improved basic network using \(\mathcal{L}_{Bbox}\) with a dynamic focusing mechanism.  
_LCAS-DetNet_: a target detection network using a cascade feature extractor composed of MLCA blocks and using \(\mathcal{L}_{Bbox}\) with a dynamic focusing mechanism.  
As shown in Tables 4 and 5, adding MLCA and \(\mathcal{L}_{Bbox}\) with a dynamic focusing mechanism, respectively, or adding both at the same time, the performance of the network was significantly enhanced on both datasets. The first row is the basic network of YOLOv8, and the remaining rows are variants improved by MLCA and \(\mathcal{L}_{Bbox}\) with a dynamic focusing mechanism. The last line is our proposed LCAS-DetNet, which adopts the above two improvements simultaneously. The comprehensive evaluation index mAP showed an improvement of 6.94% and 2.51% on the HRSDI dataset and SSDD dataset.  
Our method focused on SAR images from a multi-location perspective and fused the semantic information extracted locally and globally to determine the location of ships. We believe that such fused information has a good guiding role in improving the spatial awareness of the network. We additionally employed a calculation method featuring a dynamic focusing mechanism to construct \(\mathcal{L}_{Bbox}\). This strategy mitigated the influence of dataset quality discrepancies on gradients, enabling the network to prioritize average-quality examples during backpropagation updates. By leveraging these approaches, our network boosted its detection accuracy.
# Bi-Stability in Flexure Beams: Introducing a Setup for Experimental Characterization
## 2 Materials and Methods
### Experiment Description  
The objective of the experiments is to characterize the mechanical behavior of the bi-stable topology described in Section 2.1. Through the tests, the bi-stability of the flexure beams of 55 mm length and five different thicknesses is characterized along with their critical force _CF_ required to snap (maximum input force), the snap-through point _STP_ (linear displacement before it snaps-through), their deformations, and their performance while put under compression five times consecutively. To that end, two experiments were performed:  
* Performance by thickness. Three samples of each thickness were tested under compression to measure the maximum required force (_CF_), the distances at which these forces are reached, their snap-through points (STP) and their deformation.
* Cycling loading. The same sample was subjected to compression five times to characterize its mechanical performance and determine an approximate behavior for real applications. In each test, the texture analyzer did the compression of the flexure beam, which was set back manually to the original position.  
To begin the experiments, the machine was configured to move the compression plate 35 mm downward at a speed of 1 mm/s. A 672045 compression plate was used to drive the sliding trigger until the mechanism snapped. A Samsung A8 smartphone was placed in front of the sample to record the tests.  
The software TexCalc 5(r) (v5.2.2.287, Perten Instruments, New South Wales, Australia) was used to obtain the data recorded from the machine. The measurements are plotted in a graph that shows the force in grams as a function of the displacement in millimeters, which represents the first part of the bi-stable curve. The software also generates CVS files with the load-displacement data, which was then processed in Matlab(r) (R2021a, MathWorks, Natick, MA, USA) using a median filter to reduce noise and generate the graphs shown in this paper.
## 3 Results
### Frequency Distribution of the Expanded Keywords  
As our method solely relies on vector similarity, it is capable of extracting even infrequently occurring keywords. As shown in Figure 2, approximately 60% of our extracted keywords occur less than five times in the entire data. Keyword extractions that utilize statistical measures [40] or the feature importance of classifiers [41] will not be as effective as our method in capturing these infrequent keywords. As they have less impact on the overall classification accuracy, they will simply be disregarded or be replaced by more frequent features or keywords. However, these infrequent keywords provide equally meaningful insight into EHR records, especially when the amount of data or standardization is limited. They capture different ways of expressing smoking status and might even represent typos in the expressions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods**********\# of Extracted****** & **1** & **5** & **10** & **20** \\ \hline Bag of Words & & & & \\ LSA & 49.63\% & 55.04\% & 57.79\% & 61.93\% \\ LDA & 43.69\% & 43.69\% & 43.69\% & 43.69\% \\ SPPMI (k = 1) & 90.67\% & 90.99\% & 91.30\% & 91.30\% \\ SPPMI (k = 5) & **91.20\%** & 89.93\% & 91.52\% & 91.83\% \\ SPPMI (k = 1) & 90.67\% & 91.20\% & 90.99\% & 91.41\% \\ SPPMI-SVD (k = 1, d = 100) & 90.88\% & 90.35\% & 90.56\% & 91.09\% \\ SPPMI-SVD (k = 1, d = 500) & 90.88\% & 90.88\% & 91.09\% & 91.94\% \\ SPPMI SVD (k = 1, d = 1000) & 90.46\% & 91.09\% & **92.15\%** & 92.79\% \\ SPPMI-SVD (k = 5, d = 100) & 90.56\% & **91.41\%** & 90.46\% & 91.41\% \\ SPPMI-SVD (k = 5, d = 500) & 90.56\% & 91.30\% & 91.41\% & 91.83\% \\ SPPMI-SVD (k = 5, d = 1000) & 90.56\% & 90.88\% & 91.94\% & 91.52\% \\ SPPMI SVD (k = 15, d = 100) & 91.09\% & 90.56\% & 90.35\% & 91.73\% \\ SPPMI-SVD (k = 15, d = 500) & 90.24\% & 90.56\% & 91.30\% & 91.09\% \\ SPPMI-SVD & & & & \\ (k = 15, d = 1000) & 90.77\% & 90.77\% & 91.30\% & 91.30\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of overall smoking status classification accuracy. All accuracies are reported in F1 scores.
# Benchmark Sea Trials on a 6-Meter Boat Powered by Kite
## 4 Post Processing
### Kite Related Equations  
At first, forces into front tether are computed in the heading reference frame instead of the frame of the boat:  
\[\underline{F}_{f}=\underline{\underline{M}}_{b},\underline{\underline{M}}_{b} \underline{F}_{m}^{(s)}. \tag{9}\]  
Then, back tether forces are added to front tether vector, to create the total kite force \(\underline{F}_{k}\) at kite attachment point \(A\) on the boat :  
\[\underline{F}_{k,A}=\frac{E_{f}}{\|\underline{F}_{f}\|}(\|\underline{F}_{f}\|+ F_{bl}+F_{br}). \tag{10}\]  
With the hypothesis of perfectly straight tethers with constant length, the total kite force vector at attachment point on the boat equals the opposite of the force generated by the kite at the kite position:  
\[\underline{F}_{k}=-\underline{F}_{k,A}. \tag{11}\]  
With the same assumption, the kite position \(\underline{P}_{k}\) and the kite velocity \(\underline{V}_{k}\) can also be computed with respect to the point \(A\) (kite attachment point):  
\[\underline{P}_{k}=\frac{\underline{F}_{f}}{\|\underline{F}_{f}\|}L_{t}. \tag{12}\]  
\[\underline{V}_{k}=\frac{dP_{k}}{dt} \tag{13}\]  
With the zero-mass assumption, the aerodynamic force \(\underline{F}_{a}\) generated by the kite is directly equal to the opposite of the force generated by the kite:  
\[\underline{F}_{a}=-\underline{F}_{k}. \tag{14}\]  
Knowing the relative wind vector at the altitude of the kite and the kite velocity, the apparent wind on the kite \(\underline{V}_{a}\) can be computed:  
\[\underline{V}_{a}=\underline{V}_{WR}-\underline{V}_{k}. \tag{15}\]  
This apparent wind vector allows us to define the aerodynamic reference frame \(R_{a}\), and is detailed in Section 2.6. The drag vector \(\underline{D}\) can be obtained by projecting the aerodynamic force \(\underline{F}_{a}\) on \(\underline{x}_{a}\), and the lift vector \(\underline{L}\) is then the difference between \(\underline{F}_{a}\)and \(\underline{D}\):  
\[\underline{D}=(\underline{F}_{a}\cdot\underline{x}_{a})\underline{x}_{a}, \tag{16}\]  
\[\underline{L}=\underline{F}_{a}-\underline{D}. \tag{17}\]  
The lift to drag ratio \(f\) is then obtained by computing the ratio of the norm of the lift and drag vector:  
\[f=\frac{L}{D}=\frac{\|\underline{L}\|}{\|\underline{D}\|}. \tag{18}\]  
Finally the lift coefficient \(C_{l}\) is also obtained, with \(A_{k}\) the kite area, and \(\rho\) the density of the air.  
\[C_{l}=\frac{\|\underline{L}\|}{\frac{1}{2}\rho A_{k}V_{a}^{2}}. \tag{19}\]
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 2 Materials and Methods
### Experiment and Data
#### 2.1.1 Testbed  
The test setup and its schematic representation are shown in Figure 1. It consisted of an electronically commutated DC motor mounted on an aluminum base plate using a steel bracket. A controller regulates the rotational speed of the motor, which can be continuously adjusted by varying the applied voltage, covering a range of approximately 300 to 2300 revolutions per minute (RPM). The DT9837 unit incorporates a frequency counter to digitize the rotor position signal, thereby providing accurate motor speed acquisition. A motor-driven shaft was connected to the second shaft of identical dimensions. A roller bearing, securely housed in a steel roller bearing block, supported the rotational movement of the shaft within this assembly. A 4-channel data acquisition system captures vibration signals using accelerometers attached to both the bearing block and motor mount. A 3D-printed unbalance holder is a nylon disc with axially symmetric recesses that accommodate inserted weights for controlled unbalance introduction and is positioned directly behind the roller bearing [22].
## 4 Ground Directional Pre-Grouting Reinforcement Technology  
### Advantages of Ground Directional Pre-Grouting Technology  
Compared with the traditional grouting reinforcement method, the advantages of ground directional grouting technology mainly lie in the following aspects: the construction environment, the construction time, and the grouting reinforcement effect.  
As shown in Figure 6 below, the construction space using ground grouting technology is not limited. The transportation cost of materials is low, and the transportation volume is large, and the mixing of materials can be completed on the ground. Moreover, the ground grouting technology can be utilized in all weather conditions, with high efficiency and a short construction period. Before the rock roadway excavation has begun, the grouting operation is carried out in advance to reinforce the rock mass and ensure the safety and stability of the excavation operation. Due to the wide range of the fault fracture zone, the  
Figure 5: Surrounding rock stress characteristic curve of the roadway crossing the fault fracture zone.  
diffusion range of underground grouting struggles to meet the requirements, while the ground grouting pressure is large, and the slurry diffusion range is wide, which can form a high-strength grouting consolidation body and provide a strong guarantee for the safety and stability of subsequent tunneling operations.
## 4 Evaluation
### Experimental Setup  
We set up an interactive environment based on the availability of resources, which includes three android smartphones (Samsung Galaxy S7), three iPhones (6s), three cell phone holders, and three tripod mounts. The MIRA application was installed on three android smartphones, and these were attached to classroom desks with the help of cell phone holders, which can be adjusted. The three iPhones attached to tripod mounts were used for audio and video recording of each user's interaction with MIRA. Complete sets of equipment were placed at three corners of the classroom, which include an android smartphone, cell phone holder, iPhone, tripod mount, classroom desk, and chair. Only three participants can interact with MIRA simultaneously in the design experimental setup. Therefore, we divided the participants into 11 groups (three members per group) based on their availability and feasibility. Each member of the group can interact with MIRA independently for an allocated time of 60 minutes while acting like a patient using the provided hints.