esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of OCC Indoor Positioning System Based on Flat Panel Light and Angle Sensor Assistance
## 5 Experimental Results and Analysis
### Fuzzy Localization Experimental Results  
The results of pre-processing and optimization of the acquired images are shown in Figure 13. The image enhancement is very effective, and finally the LED-ID is successfully identified.  
Figure 12: Experimental setup of the LP-VLP system.  
Figure 13: The result of each process of image processing of the acquired image: (**a**) original image; (**b**) binary image after threshold segmentation; (**c**) ROI boundary profile identification chart; (**d**) the corresponding area of the flat lamp split out in the original figure; (**e**) image after tilt correction; (**f**) image after adaptive lighting compensation; (**g**) image after histogram equalization; (**h**) image after edge recognition.  
We conducted an experimental analysis of the recognition decoding rate of the images by the control variable method. The variables are the shooting angle and the height of the phone from the flat lamp. The specific experiments are as follows: adjust the horizontal angle of the cell phone to 0 degrees, 15 degrees, 30 degrees, and 45 degrees, respectively (the tilt angle is too large to shoot); control the flat panel light within the field of view (FoV) of the cell phone (remove the incomplete image of the flat panel light); use the front camera of the cell phone to capture images. The vertical distance between the cell phone and the flat panel light is 80-200 cm (the closest distance is 80 cm, otherwise the cell phone FoV cannot accommodate the complete image of the flat panel light), 80 images are captured at every 20 cm interval (20 images per angle), and the control exposure parameters are \(-\)1 EV, the flat area is within 300 \(\times\) 300 cm, the size of the flat panel light is 30 \(\times\) 60 cm, and the LED-ID value is 133,724. The image algorithm proposed in this paper is used to optimize the nearly 500 images collected by uniformly processing and decoding, and the success rate of decoding in each case is counted. Figure 14 shows the results of the experiment. As can be seen from the figure, due to the large size of the flat panel light, the vertical distance between the cell phone and the flat panel light has little influence on the recognition rate, and the decoding rate is 100% when shooting at 0 degrees. The horizontal angle of the cell phone when shooting has a greater impact. Analyzing the reason, it can be seen that the tilt angle is too large; the area of the graphic code in the image is too small; the tilt correction algorithm is not easy to recover the image information, resulting in the failure of the decoding code; and the decoding code effect is the worst when the vertical distance is 45 degrees, which is 91.4%. The overall recognition rate of the whole system is 96.4%, which indicates that the VLP system for flat panel lights proposed in this paper has a certain feasibility.
## 5 Efficiency: from Material to System  
### Concepts  
The principle diagram of a perfect sorption heat storage system using solid-water vapor chemisorption is presented in Figure 3. The 2 curves present the solid/water vapor equilibrium of the sorbent and the vapor/liquid equilibrium of the water sorbate. Under the solid/water vapor line, the sorbent is under the \(A_{solid}\) form. Above the solid/water vapor line, the sorbent is under the \((Av\)H\({}_{2}\)O)\({}_{solid}\) form. Basically, the two main concepts of system design are closed and open.  
The concept of closed chemical sorption thermal energy storage system is given in Figure 4. At the beginning, sorbent is under the \((Av\)H\({}_{2}\)O)\({}_{solid}\) form. During the storage step, \(Q_{in}\) heat is transferred to the solid at the temperature \(T_{in}\). Therefore, dehydration occurs, and water vapor pressure is increasing. Consequently, the gas moves from the material to the condenser where the pressure is \(P_{h}\). The vapor condensates (in the condenser) and heat of condensation is released, \(Q_{cond}\). During the release step, liquid is evaporated (in the evaporator) at temperature \(T_{e}\), requiring energy, \(Q_{evap}\). The pressure in the evaporator is higher than in the solid and a vapor flow stands. Thermal energy \(Q_{out}\) is then released during the sorption reaction in the solid.  
The other option consists of using an open system; a principle diagram of such system is given in Figure 5. The concept consists of using dry air (inert from the reaction point of view) and water vapor (i.e., humid air). During the storage step, the humid air is passing through the material under the \((Av\)H\({}_{2}\)O)\({}_{solid}\) form. \(Q_{in}\) thermal energy is then transferred to the solid at the temperature \(T_{in}\), resulting  
Figure 4: Closed heat storage system—quantities refer to Figure 3—reproduced from [1], copyright permission: Elsevier, 2019.  
Figure 3: Theoretical Clausius–Clapeyron chemical sorption cycle – reproduced from [1], copyright permission: Elsevier, 2019.  
in the dehydration process and a potential heat of condensation in the carrier, \(Q_{cond}\). During the release step, humid air is passing through the material under the \(A_{solid}\) form and may requires a quantity of heat to evaporate liquid water, \(Q_{crap}\). The water vapor is sorbed and a quantity of thermal energy \(Q_{out}\) is transferred to the humid air.  
In a closed system, a water tank with a heat exchanger is necessary. It behaves either like a condenser, or like an evaporator, depending on the storage or release step. In an open system, it is necessary to find the water vapor. In most of the publications, it is stated that this humidity can come from inside the building. However, calculations show that it is not enough water vapor, especially in wintertime when outside specific humidity is very low.
## 2 Adaptability of FDP to PV Station Access
### Simulation Conclusion and Analysis  
The simulation circuit model is built using PSCAD 4.2 as shown in Figure 1. The system-side synchronous generator operates in two modes: a strong or weak system [29]. Figures 2 and 3 analyze the action criteria waveforms and braking current phase angles under different power systems and fault resistances for A-phase ground, BC-phase, and internal faults. Figures 5 and 6 study, respectively, waveforms of action criteria and phase angle of braking current under different power systems and fault resistances when phase-A-to-ground and phase-B-to-phase-C internal faults occur.  
Figure 5: FDP performance (A-phase ground and internal faults). (**a**) Different power grid systems; (**b**) different fault resistances.  
From Figures 5a and 6a, it can be observed that FDP operates accurately when faults occur in a strong system and also operates reliably during earth faults in a weak system. But the phenomenon of refusal to operate is very likely to occur when a short-circuit fault occurs [26], such as K < 0.8.  
From Figures 5b and 6b, FDP operates reliably under different fault resistances in a strong system. As fault resistance increases, the action criterion gradually rises. The protection operates reliably during earth faults. However, the protection sensitivity decreases during phase-to-phase faults.  
Figure 7 examines the waveforms of the action criterion, the phase angle of the braking current, and the influence of CT saturation in a strong system during external faults on the PV and system sides. In Figure 7a, severe CT saturation on the PV side causes K\({}_{\text{A}}\) to increase significantly, exceeding 0.8, but protection fails to act. In Figure 7b, protection does not operate under serious CT saturation on the system side. The transmission line carries weak current from the PV side, and due to its low power capacity, CT saturation is minimal. K\({}_{\text{A}}\) increases slightly but remains well below 0.8.  
Figure 6: FDP performance (BC-phase and internal fault). (**a**) Different power grid systems; (**b**) different fault resistances.  
Figure 7: FDP performance (A-phase ground and external fault). (**a**) PV-side external faults; (**b**) system-side external faults.
# IFDA: Intermittent Fault Diagnosis Algorithm for Augmented Cubes Under the PMC Model
###### Abstract  
Fault diagnosis technology is a crucial technique for ensuring the reliability of multiprocessor systems. Many previous studies have paid close attention to the permanent faults of systems while ignoring the rise of intermittent faults. Meanwhile, there is a lack of a rapid diagnostic algorithm tailored for intermittent faults. In this paper, we propose multiple theorems to evaluate the intermittent fault diagnosability of different topologies under the \(PMC\) model. Through these theorems, we demonstrate that the intermittent fault diagnosability of an n-dimensional augmented cube (\(AQ_{n}\)) is \((2n-2)\) when \(n\) is greater than or equal to 4. Furthermore, we present a fast intermittent fault diagnosis algorithm, which is named as \(IFDA\), to identify the processors with intermittent fault in the networks. Finally, we evaluate the performance of the algorithm in terms of the parameters Accuracy and Precision. The simulation experimental results show that the algorithm \(IFDA\) has good performance and efficiency.  
PMC model; intermittent faults; fault diagnosis algorithm; augmented cubes; multiprocessor systems 2020 IEEEexample:BSTcontrol
# Steel Corrosion Behavior in Light Weight Fly-Ash Based Alkali Activated Mortars
## 2 Materials and Methods  
### Materials  
In this study, EFA-Fuller(r) low-calcium fly ashes (Class F according to ASTM C618 standard) sourced by Bau Mineral (Herten, Germany) were applied as precursor. Their chemical composition, measured by inductively coupled plasma optical emission spectroscopy (ICP-OES) is reported as oxides (in wt%), as follows: 58.6 SiO\({}_{2}\), 23.0 Al\({}_{2}\)O\({}_{3}\), 0.9 TiO\({}_{2}\), 6.1 Fe\({}_{2}\)O\({}_{3}\), 5.9 CaO, 1.8 MgO, 1.1 K\({}_{2}\)O, 0.9 Na\({}_{2}\)O, 0.9 SO\({}_{3}\), with a loss of ignition of 2.7 wt%. To perform alkali activation, sodium silicate and an 8M sodium hydroxide (ACS reagent NaOH pellets (97%, sourced by Sigma Aldrich, Milan, Italy) aqueous solution were used as activators. The sodium silicate solution (Reoflux B) was supplied by Ingesil S.r.l (Verona, Italy) and it is characterized by the following chemical composition: 29.86%SiO\({}_{2}\), 14.43% Na\({}_{2}\)O, 55.71% H\({}_{2}\)O, with SiO\({}_{2}\)/Na\({}_{2}\)O ratio equal to 2.07. To prepare the alkali activated mortars, a normalized silica sand (complied with the EN 196-1 standard) or expanded perlite, both with a fixed grain size distribution (d\({}_{\text{max}}\) = 2 mm), were used as fine aggregates. In addition, a 30 vol% hydrogen peroxide solution (ACS reagent H\({}_{2}\)O\({}_{2}\) solution sourced by Sigma Aldrich, Milan, Italy) was applied as a foaming agent for the preparation of light weight mortar samples.  
Steel plate (1 mm-thick) specimens applied in this study showed the following chemical composition (in wt%), measured by Glow Discharge Optical Emission Spectroscopy (GDOES, Spectruma Analitik GDA 650, Milan, Italy): 0.04 C, 0.22 Mn, 99.66 Fe with S, P, Cr and Ni in traces. The steel surface was sand blasted before sample preparation, producing a controlled surface finishing characterized by the following roughness parameters: R\({}_{\text{a}}\) (arithmetic mean deviation of the roughness profile) equal to 4.3 \(\pm\) 0.6 \(\upmu\)m and R\({}_{\text{q}}\) (root-mean-square deviation of the roughness profile) equal to 5.6 \(\pm\) 0.7 \(\upmu\)m.
## 5 Discussion
### Implications for Indoor-Outdoor Campus Navigation App Design
#### 5.4.3 Improved Location Search  
Navigation systems should provide different options for improved location search. Some of the options for improved location search suggested by participants in this study include: \"Option to automatically put current location as starting point instead of a manually entering the location\", \"Floor maps within the building and option to find the building by name\", \"If there was room search option in Google Maps it would have saved some time\".  
Wayfinding focuses on searching the route from the user's current position to the target destination. Therefore, it is not only important to detect the user's position, but providing different options related to route guidance and search is also important [82]. [PERSON] and [PERSON] [79] suggested providing a self-locating icon and using a dialogue box with text showing \"your current location\" in the beginning to prompt users to use the self-locating icon. According to [PERSON] and [PERSON] [82], AR technology can be used in the mobile-based indoor navigation system to detect the user's position and display AR navigation directions on top of real-world camera view to provide an immersive navigation experience and guide the user to the destination. [PERSON] et al. [83] focused on providing a personalized, customized navigation menu bar, one click to open and search on demand functions for 3D visualization indoor navigation system. [PERSON] et al. [73] proposed including a universal search function in wayfinding systems to enable users to search for things specifically instead of by category, which may be difficult to use and frustrating.  
[PERSON] and [PERSON] [79] indicated that smartphone-based indoor navigation interfaces must also provide options such as multiple choices of route planning and reminders. Moreover, in the present study, participants suggested that the navigation app should provide alternate route options. Some of the comments include: \"Show alternate routes\", \"Quickest path illustrating on MazeMap\", \"It should have alternative options as well. I could not find the second building, so I think it should be more specified towards the building name\".
## 3 Results
### Fall Risk Assessment System
Figure 9: Confusion matrix of the fall risk assessment algorithm. belongs to the group of fall risk assessment systems, but it stands out with a precision of 94.4% and an accuracy of 99.1%. Thanks to the fact that its methodology is closer to fall detection systems since it uses indicators extracted from walking and does not require the patient to perform any type of exercise or unusual task. This makes its application faster, more accurate, and imperceptible. In addition, only 13% of the studies conducted to date have a population over 50 years of age [47], so there could be significant differences in the use of their information on older adults. This factor was considered of great importance at the time of gathering the population of participants, having a group made up only of adults over 60 years old, which allows the system to focus on working with people who are within this population group. The promising results of the proposed system allow for future plans, such as adapting the system for continuous use in the clinic, where patients can be assessed outside the test environment, i.e., while moving between areas of the clinic. This will allow staff to monitor the fall risk of each patient passing through the assessment area, allowing intervention to be anticipated for those at high risk of falling. However, for this, it is necessary to work with some limitations since the study had a population with a Tinetti score between 19 and 28 points, so there are no cases with a high risk of falling. Therefore, it is necessary to expand the current database by seeking the participation of people over 60 years of age with scores below 19, allowing the software to consider all ranges of the Tinetti scale. It is also suggested that the video recording method be modified to avoid the use of the treadmill so that direct assessment on a firm floor is possible since few older adults are accustomed to using treadmills. In addition, the software was designed to work in a test area, so several considerations must be made when placing the system in a common area. There may be unforeseen events such as obstructions, poor lighting consistency, clothing inappropriate for limb detection, and the presence of multiple people in the area, among many other situations that cannot be anticipated or avoided.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 3 Fair Editions of Decision Trees
### SCAFF Fair Tree Classifier  
A novel fair Decision Tree classifier that optimizes both classification performance and fairness, measured through strong demographic parity, is presented in [13]. The authors introduce a new Decision Tree splitting criterion, SCAFF (Splitting Criterion AUC for Fairness), which simultaneously optimizes classification performance using ROC-AUC and fairness using strong demographic parity. Extensive experiments are conducted on real-world datasets, including the Adult Income Dataset and the Bank Marketing Dataset, comparing SCAFF against standard Decision Trees and the Discrimination-Aware Decision Tree [6]. The results demonstrate that SCAFF consistently outperforms previous fairness-aware Decision Trees, achieving higher fairness with minimal accuracy loss (2-4% reduction) compared to non-fair models.
## 6 Analyzing the Graph Database-Based and IFC-Based Construction Management Information Model
### 4D Visualization  
The 4D visualization has two purposes. In the process of making a schedule, the visualization of the schedules allows the project team to check the schedule for completeness and ensure that sequencing and constructability requirements are satisfied [23]. During the actual construction, visualization of the construction process will present the delay tasks by extracting delay information and marking different colors, which will facilitate the project team's control of the delay risks. In this section, Microsoft Project 2013 and Navisworks 2021 are used to visualize the information model with time information. Project and Navisworks are the most commonly used management software in construction management, but the IFC format is not well supported. Therefore, it makes the use of construction management information very inefficient and inconvenient. This framework proposes a method to display a process model extracting from the IFC-based information model in the 4D effect. The specific implementation process is as follows.  
Initially, all the IfcTask entities in the created IFC information model are fetched automatically from the graph database's retrieval algorithm. Next, the same method is used to extract the information of IfcCostItem entities, Pset_ScheduleNetworkAnalysis, Pset_Critical Path, and PSet_DelayAnalysis corresponding to each IfcTask entity. Afterward, all this retrieval information is written into Microsoft Project 2013. Finally, importing the Revit building model and Project files into Navisworks achieves the 4D display of construction information management.
## 3 Methodology
### Calculation of Structural Similarity Index/Peak Signal-to-Noise Ratio  
Access to the original and received videos makes computing the SSIM/PSNR feasible. An objective evaluation of transmission quality can be obtained by computing the SSIM/PSNR between the original and received video.
# A Displacement Controlled Fatigue Test Method for Additively Manufactured Materials
###### Abstract  
A novel adaptive displacement-controlled test setup was developed for fatigue testing on mini specimens. In property characterization of additive manufacturing materials, mini specimens are preferred due to the specimen preparation, and manufacturing cost but mini specimens demonstrate higher fatigue strength than standard specimens due to the lower probability of material defects resulting in fatigue. In this study, a dual gauge section Krousse type mini specimen was designed to conduct fatigue tests on additively manufactured materials. The large surface area of the specimen with a constant stress distribution and increased control volume as the gauge section may capture all different types of surface and microstructural defects of the material. A fully reversed bending (R = \(-\)1) fatigue test was performed on simply supported specimens. In the displacement-controlled mechanism, the variation in the control signal during the test due to the stiffness variation of the specimen provides a unique insight into identifying the nucleation and propagation phase. The fatigue performance of the wrought 304 and additively manufactured 304L stainless steel was compared applying a control signal monitoring (CSM) method. The test results and analyses validate the design of the specimen and the effective implementation of the test bench in fatigue testing of additively manufactured materials.  
adaptive control; fatigue testing; simply supported bending; mini specimen; additive manufacturing; 304L stainless steel Article
# On the Effect of a Rate-Dependent Work of Adhesion in the Detachment of a Dimpled Surface
## 2 Griffith Equilibrium Solution for a Dimple
### Effect of a Rate-Dependent Work of Adhesion  
We have introduced above the [PERSON] et al. [17] LEFM elastic model for the detachment a dimpled half-space from a flat substrate. Nevertheless, soft matter has viscoelastic behavior; hence, we here extend the [PERSON] et al. [17] model to account for a rate-dependent work of adhesion. According to Equation (1), the effective work of adhesion is a power-law function of the peeling velocity \(v_{p}=dc/dt\) (\(t\) is the time), i.e., the velocity at the crack tip, which can be written as  
\[v_{p}=\frac{dc}{dt}=\pm\dot{\sigma_{A}}\frac{dc}{d\sigma_{A}} \tag{9}\]  
Figure 2: Remote stress \(\widehat{\sigma}_{A}\) versus the crack radius \(\widehat{c}\) in the [PERSON] et al. [17] elastic model for \(\alpha_{d}=[0.1,0.5,0.9]\). For the curve \(\alpha_{d}=0.5\), point “C” determines the equilibrium position \((\widehat{\sigma}_{A}=0)\), point “B” the “weak” adhesive state, while point A is the point where the jump to full contact happens.  
where \(\dot{\sigma}_{A}=d\sigma_{A}/dt\) [Pa/s] is the externally imposed unloading rate and the \"\(+\)\" (\"\(-\)\") sign holds when the crack advances (\(dc/dt>0\)) or retracts (\(dc/dt<0\)). Hence, using (1), (6) and (9) and imposing the [PERSON] equilibrium concept, one obtains  
\[\left\{\begin{array}{ll}w_{0}\Big{[}1+\left(-\frac{\dot{\sigma}_{A}}{V_{0}} \frac{dc}{d\sigma_{A}}\right)^{n}\Big{]}=\frac{2c}{\pi\mathbb{E}^{*}}\bigg{(} \sigma_{A}+\frac{E^{*}\delta_{0}}{2b}\Big{)}^{2},&\xi\leq 1\\ w_{0}\Big{[}1+\left(\frac{\dot{\sigma}_{A}}{V_{0}}\frac{dc}{d\sigma_{A}} \right)^{n}\Big{]}=\frac{2c}{\pi\mathbb{E}^{*}}\Bigg{[}\sigma_{A}+\frac{E^{* }\delta_{0}}{2b}\left(1-\sqrt{1-\left(\frac{b}{c}\right)^{2}}\right)\Bigg{]}^ {2},&\xi>1\end{array}\right. \tag{10}\]  
which in dimensionless notation reads  
\[\left\{\begin{array}{ll}\frac{d\widetilde{c}}{d\sigma_{A}}=-\frac{1}{\dot{ \sigma}_{A}}\bigg{[}\frac{\widehat{c}}{a_{d}^{2}}\big{(}\widehat{\sigma}_{A} +1\big{)}^{2}-1\bigg{]}^{1/n},&\widehat{c}\leq 1\\ \frac{d\widetilde{c}}{d\sigma_{A}}=\frac{1}{\widehat{\sigma}_{A}}\bigg{\{} \frac{\widehat{c}}{a_{d}^{2}}\Big{[}\widehat{\sigma}_{A}+\left(1-\sqrt{1- \frac{1}{c^{2}}}\right)\Big{]}^{2}-1\bigg{\}}^{1/n},&\widehat{c}>1\end{array}\right. \tag{11}\]  
where the dimensionless unloading rate \(\dot{\widehat{\sigma}}_{A}=\frac{\dot{\widehat{\sigma}}_{A}}{TV_{0}/b}\) has been introduced. The ordinary differential equations in (11) are of first order and can be easily solved numerically using as a starting point a solution of the elastic problem in Equation (7). Clearly, physically admissible starting points are those on the stable branch AB in Figure 2; hence, as we shall see later, a rate-dependent work of adhesion will not affect the \"strong\" adhesive state, but only the \"weak\" one. In the next section we shall investigate the effects of the various parameters \(\left\{n,\hat{\sigma}_{A},\alpha_{d}\right\}\) on the detachment stress in partial contact (\"weak\" state).