esa-sceva/satcom-chunk-collection huggingface dataset:
# Generic Language for Partial Model Extraction from an IFC Model Based on Selection Set
## 1 Introduction
Industry Foundation Classes (IFC) was developed to support the full range of data exchange [6]. Many studies related to partial model extraction have been carried out, and more details about these studies will be presented in the following section. However, most methods were developed to extract specific model data for specific business tasks. The users hardly extract model data by their intents, and sometimes manually select the required objects. It is necessary to develop an innovative method to extract the required model data based on the users' requirements. In this paper, a generic language was designed to extract partial models from IFC models based on the eXtensible Markup Language (XML) format. By using the proposed language, users can design a configuration file to define extraction requirements, and then the required model data is automatically extracted and formed as a partial model. The proposed method supports diverse definitions of extraction requirements, including object types, attributes, and relations. To make the definitions of extraction requirements more rigorous and standardized, the Selection Set was proposed to represent extraction requirements. Furthermore, mathematical logic and set theory [7] were used to describe rules for partial model extraction, so IFC data with multiple representations could be processed to form valid partial IFC models. The rest of the paper is organized as follows. A review of related work for partial model extraction from two aspects (that is, task-specific and user-defined methods) was first introduced. Second, according to comparative analysis, the concept of the Selection Set was proposed to integrate users' requirements. Third, seven rules in the extraction of syntactically and semantically valid partial IFC models were designed. Subsequently, with the adoption of XML schema, a generic language was developed for partial model extraction from an IFC model based on the Selection Set (PMESS), and then the proposed method was validated through a test case. The final section summarizes the most important conclusions.
# Simple Approximate Formulas for Postbuckling Deflection of Heavy Elastic Columns
## 3 Result
### Power-Law Evolution of the Tip-Tilt Angle  
Figure 4a demonstrates the power-law behavior of \(\theta_{0}\) as a function of \(\alpha\) for various fixed values of \(\beta\). The series of power-law curves are the edges of the cutting plane that appear when the three-dimensional \(\theta_{0}(\alpha,\beta)\) surface spanning within the \(\alpha\)-\(\beta\)-\(\theta_{0}\) space (highlighted by the rainbow-colored portion in Figure 3) is cut along a plane parallel to the \(\alpha\)-axis (and perpendicular to the page). All the curves are well fitted to the following expression:  
\[\theta_{0}=A\left(\alpha-\alpha_{c}\right)^{p_{\alpha}}, \tag{22}\]  
particularly in the \(\theta_{0}\leq 1.0\) range. The threshold \(\alpha_{c}\) decreases monotonically with the increase in \(\beta\), as observed in the contour map in Figure 3. In contrast, the values of the coefficient \(A\) and exponent \(p_{\alpha}\) are found to be insensitive to the change in \(\beta\); they are approximately estimated as \(A\simeq 1.6\) and \(p_{\alpha}\simeq 0.48\), with slight dependence on \(\beta\), as shown in Figure 4c.  
Figure 3: Contour map of the tip-tilt angle \(\theta_{0}\) (unit: radian). Inset: Magnified view of the right-bottom region. See text for the dashed-dotted line.  
Another class of \(\theta_{0}\) power-law behaviors be deduced when \(\alpha\) is fixed and \(\beta\) is tuned. Figure 4b depicts the power-law behavior of \(\theta_{0}\) as a function of \(\beta\) for fixed \(\alpha\). The fitting curves obey the following expression:  
\[\theta_{0}=B\left(\beta-\beta_{c}\right)^{p_{\beta}}, \tag{23}\]  
with \(B\simeq 0.9\) and \(p_{\beta}\simeq 0.48\). Similar to the previous case, both \(B\) and \(p_{\beta}\) are insensitive to the variation in the fixed value of \(\alpha\), as shown in Figure 4d.  
Emphasis should be placed on the fact that the optimal values of \(p_{\alpha}\) and \(p_{\beta}\), both nearly equal to \(0.5\), are robust against the variations of \(\alpha\) and \(\beta\) that characterize the loading conditions. This implies that the tip-tilt angle \(\theta_{0}\) is almost a square root of the increment, if either the concentrated or distributed load is amplified gradually. These nearly square-root behaviors of \(\theta_{0}\) with the load increment can be approximately expressed as  
\[\theta_{0} \propto \sqrt{\alpha-\alpha_{c}}\ \ \text{when}\ \beta\ \text{is fixed},\] \[\theta_{0} \propto \sqrt{\beta-\beta_{c}}\ \ \text{when}\ \alpha\ \text{is fixed}. \tag{24}\]  
Using these approximate formulas, the observed increase in \(\theta_{0}\) with the increase in load can be estimated easily by measuring the initial value of \(\theta_{0}\) before increasing the load. These square root formulas are the second main finding of this study.
_Applied_  
_sciences_  
_Article_  
**Interfacial Pockels Effect of Solvents with a Larger Static Dielectric Constant than Water and an Ionic Liquid on the Surface of a Transparent Oxide Electrode**  
**[PERSON] 1, [PERSON] 2,3,4 and [PERSON] 1,4,*  
1 Department of Physics, Faculty of Science, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan; [EMAIL_ADDRESS]  
2 Advanced Ultrafast Laser Research Center, Department of Engineering Science, The University of Electro-Communications, Chofu, Tokyo 182-8858, Japan; [PERSON]S]  
3 Department of Electrophysics, National Chiao Tung University, 1001 Ta Hsueh Road, Hsinchu 300, Taiwan  
4 Research Center for Water Frontier Science and Technology, Tokyo University of Science, 1-3 Kagurazaka, Shinjuku-ku, Tokyo 162-8601, Japan  
*Correspondence: [EMAIL_ADDRESS]  
**Featured Application: Electro-optic modulators or interfacial sensors involving water, organic solvents, and ionic liquids.**  
**Abstract:** The optical Pockels effect is a change in the refractive index proportional to an applied electric field. As a typical example of the interfacial Pockels effect occurring at interfaces where the spatial inversion symmetry is broken, it is known that water in the electric double layer (EDL) on the transparent oxide electrode surface has a large Pockels coefficient, but the physical factors that determine its size are not clear. Therefore, we experimentally studied the Pockels effect of water and other characteristic liquids--formamide (FA), methylformamide (NMF) (these two have larger static dielectric constants than water), dimethylformamide (DMF), and an ionic liquid that is itself salts (IL, [BMIM] [BF\({}_{4}\)])--and evaluated their Pockels coefficients in the EDL on the transparent electrode surface. The magnitude of the Pockels coefficient was found to be in the order of water, DMF, FA, NMF, and IL, with the magnitude of the static dielectric constant not being an important factor.  
**Keywords:** interfacial Pockels effect; electric double layer; water; formamide; methylformamide; dimethylformamide; ionic liquid; electro-modulation spectroscopy; indium tin oxide; multichannel lock-in amplifier
# An Investigation into Healthcare-Data Patterns
## 1 Introduction
### Motivating Scenario  
Hospital networks are frequently upgraded with new digital technologies, rather than being replaced, due to the cost involved with new developments. The security implications mean that unique custom-written systems, also known as bespoke systems, need to be put in place to safeguard and protect data. However, the reliance on legacy software, and small scale bespoke security solutions, result is an increased vulnerability to cyber-attacks from external sources [4]. For example, London hospitals lost all network connectivity due to several malware infection by the MyTob worm that occurred at the end of 2008 [5]. In 2005, a Chicago hospital lost its entire pharmacy database, and in order to reconstruct medication records for its patients, paper printouts were required to be collected from nurses' stations [6]. And in October 2016 a UK Hospital in Lincolnshire was shut down for four days by a variant of the Globe2 ransomware [7], with all planned operations, outpatient appointmentsand diagnostic procedures cancelled, with patients turned away and 2800 patient appointments cancelled because of the disruption.  
This risk is further exacerbated by the bring-your-own-devices (BYOD) revolution. This is a term referring to the technologies enabling employees to access and utilise internal corporate IT resources, with their personal devices [8]. BYOD policies have numerous benefits including reduced costs and improved productivity, convenience and efficiency of work, as such a significant amount of users require access to patient records to facilitate their occupation. Healthcare infrastructures may also involve temporary employees or visitors from partner organisations. The BYOD approach also means that users from visiting healthcare organisations have access to hospital networks and the Internet. However, this also carries numerous risks including data loss/leakage or theft, application security, network availability, legal liability and regulatory compliance and loss of brand identity, posing various challenges for IT departments who support and secure [8]. BYOD is a large contributor to the era of ubiquitous computing, also known as pervasive computing, where various technologies interact with one another, with potentially sensitive data being transmitted over less secure means of communication, such as wirelessly [9]. With an increased use of technology there is a corresponding risk of increased exposure to cyber security threats. These wireless vulnerabilities pose a particular risk to the healthcare network, in that potentially insecure devices are granted access to hospital infrastructure and confidential data. An attacker can use this to their advantage by hacking a BYOD to gain back-door access onto a hospital network.
# Advanced Servo Control and AI Integration in 3-DoF Platforms for Enhanced Simulation Interactivity
## 2 Control System
### Proposed Servo Control
\begin{split} u_{i}(n)&=(1-\alpha)u_{i-1}(n)+L[(y_{d}(n+1) -y_{i-1}(n+1))-(y_{d}(n)-y_{i-1}(n))+K(y_{d}(n)-y_{i-1}(n))]\\ &=(1-\alpha)u_{i-1}(n)+L[(e_{i-1}(n+1)-e_{i-1}(n))+Ke_{i-1}(n)]\\ &=(1-\alpha)u_{i-1}(n)+L[de_{i-1}(n+1)+Ke_{i-1}(n)]\end{split}  \tag{19}\]  
where \(e_{i-1}\) is the previous error and \(de_{i-1}(n+1)=e_{i-1}(n+1)-e_{i-1}(n)\) is the previous learning's error change. In practical applications, a single controller is used to generate three-axis pulse commands for the driver. To avoid excessive use of internal memory and to enhance software computational efficiency, the approach described in reference [20] is adopted. In Equation (19), the learning mechanism based on the previous error is completely discarded. Instead, only the real-time (current) error is utilized, employing a feedback learning control method. Therefore, in Equation (19), after adopting the real-time error, the input term is selected as the pulse frequency command for the driver. This results in the iterative learning control (ILC) equation for the roll axis among the three axes. \[f_{i}^{Roll}(n)=(1-\alpha)f_{i-1}^{Roll}(n)+L\Big{[}\Big{(}y_{d}^{Roll}(n+1)-y_ {l}^{Roll}(n+1)\Big{)}-\Big{(}y_{d}^{Roll}(n)-y_{l}^{Roll}(n)\Big{)}+K\Big{(}y_ {d}^{Roll}(n)-y_{l}^{Roll}(n)\Big{)}\Big{]} \tag{20}\]  
The equation above can be reformulated as  
\[f_{i}^{Roll}(n)=(1-\alpha)f_{i-1}^{Roll}(n)+Lde_{i}^{Roll}(n+1)+LKe_{i}^{Roll}(n). \tag{21}\]  
The pitch axis (Pitch) and yaw axis (Yaw) are represented as follows:  
\[f_{i}^{Pitch}(n)=(1-\alpha)f_{i-1}^{Pitch}(n)+Lde_{i}^{Pitch}(n+1)+LKe_{i}^{Pitch }(n); \tag{22}\]  
\[f_{i}^{Yaw}(n)=(1-\alpha)f_{i-1}^{Yaw}(n)+Lde_{i}^{Yaw}(n+1)+LKe_{i}^{Yaw}(n) \tag{23}\]  
where \(\alpha\) is a positive value used to correct the divergence problem caused by the initial non-zero learning error. In this paper, the sampling time was set to 1 kHz (0.001 s), and the number of learning iterations \(N\) was 50. The system updates the control input pulse frequencies \(f_{i}^{Roll}\), \(f_{i}^{Pitch}\), and \(f_{i}^{Yaw}\) every 0.05 s. Figure 5 illustrates the control system architecture, which integrates both ILC and an iterative learning adjuster. The ILC calculates the frequency using Equations (21)-(23), while the iterative learning adjuster continuously outputs pulse commands through the digital signal processor's digital output channel. Together, they provide comprehensive pulse command and frequency control. Based on the error between \(y_{d}^{k}\) and \(y_{l}^{k}\), the pulse command is activated or deactivated. Activation signals the motor to run, maintaining constant speed with a fixed frequency or accelerating/decelerating with a varying frequency. Deactivation halts the motor, indicating completion of positioning. Refer to Figure 6 for the detailed flowchart of the process.
## 6 Existing Routing Protocols
### DD (Directed Diffusion)  
Directed diffusion [17, 18] is a data-centric (DC) and application-aware protocol in which data generated by sensor nodes is named by attribute-value pairs. It consists of four elements: [14] interests, data messages, gradients and reinforcements. An interest (a list of attribute value pairs) describes a task. Data messages are named using attribute value pairs. A gradient specifies data rate as well as the direction of event and reinforcement selects a particular path from a number of paths. In the DC protocol data coming from different sources are combined and thus eliminating redundancy, minimizing the number of transmissions, saving network energy and prolonging its lifetime. DC routing searches for a destination from multiple sources. In directed diffusion, a base station diffuses a query towards nodes in the interested region. The query or interest is diffused through the network hop-by-hop. Each sensor receives the interest and sets up a gradient toward the sensor nodes from which it receives the interest. This process continues until gradients are set up from the sources back to the BS. The sensed data are then returned to the BS along that reverse path. The intermediate nodes may aggregate their data depending on the data message (data's name and attribute value pair) thus reducing the communication cost. Since in this case data transmission is not reliable the BS periodically refreshes and resends the interest when it starts to receive data from the source(s). Directed Diffusion protocols are application specific and hence can save energy by selecting optimal paths by caching and processing data in the network. It has some drawbacks [14]. First of all, for data aggregation it needs time synchronization technique that is not very easy to achieve in WSNs. Another problem is associated with the overhead involved in recording information thus increasing the cost of a sensor node. The DD Protocol is described in Figure 6.
# An Algorithm to Retrieve Precipitable Water Vapor from Sentinel-2 Data
## 3 Results  
### Validation of Various PWV Products  
Since 16 GPS receivers were deployed in this experiment on 1 August 2021 and 6 August 2021, only a small range of PWV could be validated. GPS PWV observations have high temporal resolution and high accuracy, and are often used as reference data to evaluate PWV measurements retrieved from satellite instruments [17]. Therefore, in the first step, in addition to validating the S2-L2A PWV, the accuracy of PWV retrieved from ESA-L2A and MOD05 was also verified using GPS measurements, to in turn verify the accuracy of S2-L2A PWV over a wide range using these two PWV products in the second step.
## 4 Showcasing Spectral Analyses on Planetary Soils  
This section showcases some of the spectral analyses that could be performed with the proposed, developed, and tested test rig. In fact, the _Exoland Simulator_, given the presence of the two spectrometers (Ocean Insight SR-2 VIS400-25 (Ocean Optics, Inc., Orlando, FL, USA) 0.38-1.07 \(\upmu\)m and NIRQuest+ 2.2 \(\upmu\)m), will be able to collect reflectance spectra similar to those shown in Figures 10-12. These three figures showcase three of the possible different measurements that could be performed with the test rig in order to illustrate its possibilities and suggestions for applying this instrument.  
Figure 11: Reflectance spectra of natural ammonium-bearing minerals mascagnite (blue), salmon-niac (orange), larderellite (red), struvite (black), and tschernigite (green) at room temperature [29].  
Figure 10: Reflectance spectra of synthetic glass sample S (shoshonite, blue), B (basalt, orange), RB (rholyite, black), and M (hawaiite, yellow) at room temperature [54].  
The reflectance spectra are characterized by absorption bands whose position and related parameters (area, depth, FWHM) are diagnostic of the type of excited interatomic bond and, thus, of the type and abundance of the mineral and/or rock. In addition, the different slopes of the spectra are diagnostic for glass species, as shown in Figure 10. In detail, the spectra reported in Figures 11 and 12 regard natural and synthetic ammonium-bearing minerals, respectively. Ammonium-bearing minerals' spectra are characterized by the presence of spectral features due to overtones and a combination of NH\({}_{4}^{+}\) modes located at \(\simeq\)1.06, \(\simeq\)1.3, \(\simeq\)1.56, \(\simeq\)2.02, and \(\simeq\)2.2 um, which are useful to discriminate these salts by remote sensing data. These spectral features are sensitive to the degrees in symmetry of the NH\({}_{4}^{+}\) group inside the crystal structure [55], the strength of hydrogen bonds [27; 29], and the distance between donor-acceptor atoms in the structure [56]. Concerning the glass samples (Figure 10), they are essentially featureless spectra, but characterized by different slopes, albedos, and spectral ratios. These parameters are sensitive to the iron and composite Silicium-Calcium-iron-Magnesium contents (SCFM parameter) [32].  
In addition, from the spectral analysis, the spectral ranges most affected by different physicochemical properties will be identified.  
These collected data have a wide range of applications in planetology from rocky to clay planetary bodies for interpreting their surface composition. Furthermore, other spectral data collected on thin films, lubricants, and friction interfaces can be advantageously used to study the tribological behavior of surfaces, the wear mechanisms, and the contamination of surfaces.
## 2 Materials and Methods  
### Experimental Setup  
The experimental work was conducted in the hydroponic glasshouse facility of the Precision Agriculture Research Chair, King Saud University (Figure 1), located in the Educational Farm of the College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia (46\({}^{\circ}\)37\({}^{\prime}\)10\({}^{\prime\prime}\) N, 24\({}^{\circ}\)44\({}^{\prime}\)12\({}^{\prime\prime}\) N). Three tomato varieties (Ghandowra-F1, Forester-F1, and Feisty-Red) were grown hydroponically in a glasshouse equipped with a semi-closed hydroponic system and MACQU (Geosmart, Athens, Greece) systems to control the indoor climate and irrigation and carry out treatments. The greenhouse is also equipped with sensors for relative humidity, temperature, and light intensity, the data of which are used to control the climate. The relative humidity of the glasshouse was maintained between 55% and 65%, while the temperature was maintained at 22 \({}^{\circ}\)C and 18 \({}^{\circ}\)C during the day and night, respectively.  
Seedlings of the selected tomato varieties, after the formation of 3-4 leaves, were transplanted into perlite sacks on 4 December 2022, and the experimental work continued until the last harvest of tomato fruits on 28 March 2023. Three salinity levels of the nutrient solution were applied, namely with EC values of 2.5 dS m\({}^{-1}\) (low salinity \"S-1\"), 6.0 dS m\({}^{-1}\) (medium salinity \"S-2\"), and 9.5 dS m\({}^{-1}\) (high salinity \"S-3\"), while the pH of the nutrient solution was maintained between 5.5 and 6.5.  
Figure 1: **(A)** The hydroponic glasshouse, **(B)** the control Unit, **(C)** the steel troughs (plant lines) including perlite sacks and plants as well as the irrigation system.
## 5 Experiments  
### Dataset  
We conduct experiments on two widely used real-world location-based social network (LBSN) datasets [41]: Foursquare-NYC (abbreviated as NYC) and Foursquare-TKY (abbreviated as TKY). These datasets were independently collected from New York City and Tokyo City, respectively, and span a continuous period of 11 months. The user check-in data is obtained from the Foursquare platform and includes detailed temporal and spatial information on user visits to various location predictions.  
To ensure data quality, we apply a series of preprocessing steps. First, we chronologically sort all user interactions and filter out unpopular location predictions that have been visited by fewer than 5 users. Next, we segment each user's complete check-in history into sessions, where each session contains all check-ins that occur within a 24-h window. Sessions with fewer than three check-in records are discarded to ensure meaningful interaction sequences. Additionally, we exclude inactive users whose total number of sessions is fewer than three.  
We use the first 80% of sessions from each user for training, while the remaining 20% are reserved for testing. To prevent information leakage during evaluation, we only include location predictions in the test set that occur after all check-ins in the corresponding user's training data. The detailed statistics of the preprocessed datasets are provided in Table 1.
## 3 Case Study: Lishanyuan Landslide
### Thresholds Setting  
After deploying and debugging the Lishanyuan landslide monitoring system, we set the thresholds for the landslide early warning. After receiving the first data on 15 April, monitors need to artificially pay attention to data changes every day for the next week. After collecting data for one week, the initial monitoring threshold is determined by using the method in Section 2.3.1 for monitoring data. The statistical results are shown in Figure 5 and the nearest integer of the upper whisker is taken as the value of \(S_{ref}\) and \(v_{ref}\). In order to ensure the immediacy of early warning, \(S_{ia}\) and \(S_{ka}\) are set as 1.5 times and 3 times \(S_{ref}\), respectively. The initial warning strategy is the conventional warning in the single-method strategy, and the time window is still 48 h.  
On 13 May, the monitors observed that the data from both GNSS monitoring stations on the landslide showed CDS. The method described in Section 2.2.1 was used to automatically pick up the start and end time of the CDS of each GNSS monitoring station. Then, three different methods were used to calculate the velocity of the CDS (see Table 2), and the average of their results is taken as the final \(v_{m}\). Finally, \(v_{31}\), \(v_{32}\), \(v_{33}\), and \(v_{34}\) of each GNSS monitoring station was updated. Table 3 summarizes the landslide's threshold settings. Because the landslide has previously obtained real alarms, the risk of instability is very high, and once it collapses, it will cause serious damage to the road. The data quality is also good, and the monitoring and warning system has many computing resources, so the strategy has been switched to the conservative strategy at this time to ensure the immediacy of the warning.  
Figure 4: Monitoring data of the Lishanyuan landslide.