esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method
### Shapelets Extraction in This Work
\[D_{m}(T_{s},T_{ss})=\min_{i=1,..,r-m+1}\frac{1}{m}\sum_{l=1}^{m}(T_{s_{i+l-1}}- \mu_{T_{s}}+\mu_{T_{ss}}-T_{ss_{l}})^{2} \tag{2}\]  
Here, \(\mu_{T_{s}}=\frac{1}{m}\sum\limits_{l=1}^{m}T_{s_{i+l-1}}\) and \(\mu_{T_{ss}}=\frac{1}{m}\sum\limits_{l=1}^{m}T_{ss_{l}}\). The two average terms are used to shift the active portion of time-series sequence (envelope) and the associated subsequence to the same DC level. The underlying reason behind modifying (1) to get (2) is embedded into an anticipation of device operational characteristics. Since, current data from different devices are experimented to strive for accusation from a single point sensor for NILM, it may generally happen that when a device gets started, already another device is running implying that it has already been drawing current for itself. Thereby, the current of the new device would add to the current from the already running device. Now, if the shapelet from the same type of device is matched with this current, it would hardly produce convincing results. Hence, the DC levels of both the active sequence (envelope) and subsequence are made same at first, while calculating the distance. There is an inherent advantage of conceptualizing and applying (2) rather than (1) to measure the distance between any subsequence and its best matching location in a time-series. (2) provides the flexibility to handle high power devices with equal footing of low power devices. It can be implied that current envelopes from a high power appliance would have higher level of DC value, but since (2) is used in this work making both the subsequence and the time-series being matched at the same DC level, a high  
Figure 1: Normalized current signature of a particular device in COOLL database. Figure 2: Envelope and subsequence of the envelope extracted from the current shown in Figure 1.  
power device will not be of any difference compared to a low power device. Thereby, the analysis works fine for any power appliances. In this work, partial shapelets or partial distance measurements in the current data are not considered. Partial shapelets are assumed as components or segments of any particular shapelet. Based on the operatives of electrical appliances, it can be asserted that after the start (turn on) of any device, the shape of a current envelope of the next few seconds holds the distinguishing feature of that particular device. Then, the question of partial shapelet may arise only if few seconds of current are not available, implying that the device is already turned off. In that case, there is no need to identify the load in real-time application, which is supposed to be turned on for such a short period of time. Another perspective of not analyzing partial segments in a shapelet relates to unusual and momentary changes in the measurement. There may be some sharp rise or fall in current for very short intervals due to the faults or glitches in the power line. It will be unwise to try to disaggregate any load based on those impulsive changes.
## 1 Introduction
Compact optical microscopes have been developed based on the idea that researchers built mobile phone-mounted light microscopes to detect diseases in developing regions where health treatment is unaffordable, due to expensive equipment and training costs, but which are well served with phone networks [19]. The small-size characteristic of LEDs is the key that has contributed to the designs of many compact, portable optical or fluorescence microscopes [20; 21]. The functionality of LED light sources in fluorescence microscopes is multipurpose. For example, they provide a multi-wavelength spectrum, and combine with a digital camera system, and they are suitable for the built-in function of commercial devices [22; 23; 24]. Moreover, LEDs in high-power applications are designed in array form to enhance the illumination of the images [25; 26; 27; 28; 29; 30; 31]. However, both single LED and LED array designs suffer from the critical problem that the light from LEDs has a large divergent angle and a wide emitting surface area, making the etendue of the light source large, so that the light energy is not easily sufficiently concentrated. Hence, a LED lighting system with excellent light energy concentrating skill is necessary for a fluorescence microscope, and can provide brighter illumination for the specimen in lower electrical power. Given that the optical design of LEDs is important, especially for the light energy absorption of fluorescent molecules, we propose a novel LED light source system composed of a \(3\times 3\) LED array to replace the original light source system of the Zeiss fluorescence microscope (Axio Imager 2). The original light source of the Axio Imager fluorescence microscope is a white light halogen lamp with 120-watt electrical power and a circle project pattern with a 27 mm diameter, while the distance between the light source and the specimen of the microscope is 550 mm. The central illuminance value of its halogen lamp projected on the specimen is approximately 10,578 lux. In order to use LEDs to replace the halogen lamp, we have designed a confocal total internal reflection lens array system (CTLAS), which is composed of a total internal reflection (TIR) lens array [32; 33; 34; 35; 36; 37; 38; 39] and a confocal LED array system [40; 41; 42]. This system can efficiently collect light energy and project it onto the specimen. The assembly tolerance of the LED light sources will be discussed in the latter part of this paper.
## 2 Materials and Methods
### Calculation of the Strain \(\varepsilon\) for the Quarter-Bridge Circuit
For the optical measurement, two 2D measurement areas were subsequently constructed in the image recordings of the individual tests, each corresponding to the base areas of the strain gauges. These measurement areas were defined once for each test series and transferred to the assigned test recordings. To enable the correct calculation and evaluation of these measurement surfaces, a coordinate system was previously defined in the ARAMIS Professional software for each of the image series. The respective superstructure was defined as a rigid body, which in turn compensated for its movement during loading by calculation. This guaranteed that only the strain of the artificial bone block was measured. For the sake of comparability, the test configuration was not changed between the 15 individual tests in a test series. Figure 2 shows strain gauge 1 with a view to the frontal plane of the bone block and the corresponding virtual measuring surface of the ARAMIS system directly above under increasing force application. Figure 3 shows strain gauge 2 looking at the horizontal plane of the bone block and the corresponding virtual measuring surface of the ARAMIS system directly above it, also under increasing force application. Figure 1: Experimental setup in long shot (**a**), artificial bone block with stochastic pattern and strain gauges as well as inserted implants and superstructure made of ceramics (zirconia) (**b**), artificial bone block with stochastic pattern and strain gauges as well as inserted implants and superstructure made of npm (**c**), artificial bone block with stochastic pattern and strain gauges as well as inserted implants and superstructure made of PEEK (**d**). Figure 2: Experimental setup showing the frontal surface with superstructure as well as the optical measuring surface above strain gauge 1 at increasing mastocatory forces from left to right. Using the ARAMIS Professional software, the changes in length of the strain gauges and the virtual measurement surfaces were transferred to Excel as a function of the applied force. These data formed the basis for the statistical analysis, which was performed using the program R ver. 3.6.3 (R Core Team (2019), R Foundation for Statistical Computing, Vienna, Austria). Here, the equivalence of the two measurement methods was analyzed using equivalency tests (two one-tailed t-tests: TOST) with a tolerance of 100 \(\upmu\)m/m (50 \(\upmu\)m/m). In addition, intraclass correlation coefficients (ICC) were calculated and used to determine the concordance between the two measurement methods. These results are presented in the form of Bland-Altman plots. To achieve a differentiated observation, measured values at force applications of 50, 100, 150, and 200 N were statistically evaluated for both strain gauges.
## 2 Non-Blind LP-DSS Watermarking Scheme
### Remaining Issues  
The non-blind LP-DSS method has two remaining issues: blind-detectability and confidentiality.  
Blind-detectability: A high possibility of detecting the embedded data without using the original or reference signal must be satisfied. In voice communication channels such as VoIP and PSTN, the watermarked signal and the host signal cannot be sent simultaneously. Therefore, the non-blind LP-DSS method cannot be used for VoIP and PSTN. For the LP-DSS method to achieve blind detection, the LP residue used for embedding must be robust and accurate. Even if the LP residue is obtained from the watermarked signal again by LP analysis, the accuracy depends on the embedding-strength level. If the embedding-strength level is \(-20\) dB, \(y(n)\) is nearly equal to \(x(n)\), and the accurate LP residue can be obtained from \(y(n)\). Needless to say, it is difficult to detect the security code robustly and accurately when the embedding strength is low. LP analysis is not robust to noise, so the LP residue cannot be obtained accurately when the embedding strength is very high. In addition, this method is processed on a frame-by-frame basis, which means the security code cannot be detected correctly if the position of the embedded security code is different from the position of the detected security code. To solve this problem, we need to synchronize the frames accurately and blindly to detect the security code. Therefore, it is necessary to inform the detection side of the synchronized position.  
Confidentiality: Secure and undetectable concealment of embedded data must be satisfied. The non-blind LP-DSS method uses LP residue instead of the PN signal used in the DSS method, which means it can satisfy the confidentiality of the watermarking algorithm under non-public conditions. However, security codes are easily detected in public conditions because LP residues can possibly be obtained from the watermarked signals. The DSS method uses a key to create the PN signal to enhance confidentiality in public environments, but this is limited to non-blind environments. Therefore, the non-blind LP-DSS method is less confidential under public conditions than the DSS method.  
We investigated ways of addressing the blind-detectability and confidentiality to further develop the LP-DSS scheme.
## 3 Prediction of Compressor Faults Using Machine Learning Is a Supervised Problem  
The following procedure for handling the data sets will assist in forecasting future anomalies in the compressor machine and in identifying the target variable and the steps to take to accomplish these aims. The following figure (Figure 3) provides a general overview of the prediction process.  
Regarding the data collection, in this study a set of data collected by sensors linked to the compressor machine recorded hourly voltage, pressure, flow, humidity, temperature, etc. The data understanding phase also includes activities to familiarize oneself with the data, identify data quality issues, gain preliminary insights into the data, or identify interesting subsets to develop hypotheses for hidden information. The data understanding phase starts with initial data collection. The following steps are considered for data processing and understanding [12].  
* Data Normalization: Normalization is the process of organizing data in a database. In effect, it is a passive transformation that examines input strings and creates normalized versions of those strings.
* Data Cleaning: Data cleaning refers to the process of identifying incorrect, incomplete, inaccurate, irrelevant, or missing portions of data and then modifying, replacing, or deleting them as necessary. Data cleaning is considered a fundamental part of basic data science. In machine learning, if the data are irrelevant or error-prone, it leads to the construction of an incorrect model.  
Figure 3: Description of the existing prediction process.  
Missing Values: Dealing with missing values is crucial because if they are left unresolved, the analysis and machine learning models will be impacted. Checking to see if the dataset has any missing values is crucial. If so, one of three actions must be taken to solve the issue:  
1. Keep the missing values;
2. Fill in the missing values;
3. Delete the missing values.
## 2 Sensing Principle
Hereby, in practical application, the temperature distribution curve along the entire optical fiber can be obtained by just measuring out the electrical levels of \(\Phi_{AS}(T)\), \(\Phi_{S}(T)\), \(\Phi_{AS}(T_{0})\) and \(\Phi_{S}(T_{0})\) after the photoelectric conversion, as shown in Equation (7):  
\[\frac{\Phi_{AS}(T)/\Phi_{S}(T)}{\Phi_{AS}(T_{0})/\Phi_{S}(T_{0})}=\exp\left[- \frac{h\Delta v}{k}\left(\frac{1}{T}-\frac{1}{T_{0}}\right)\right] \tag{7}\]  
The relative sensitivity (\(S_{R}\)) of this demodulation method can be calculated out by differentiating the temperature \(T\) of Equation (6), as exhibited in Equation (8):  
\[S_{R}=\frac{h\Delta v}{kT^{2}}\cdot\frac{\exp(h\Delta v/kT_{0})-1}{\left[\exp (h\Delta v/kT)-1\right]^{2}}\cdot\exp(h\Delta v/kT) \tag{8}\]  
In the range of 0 \({}^{\circ}\)C to 120 \({}^{\circ}\)C, the average temperature sensitivity \(S_{R}\) = 1.065%/\({}^{\circ}\)C.  
The working process of the optical fiber temperature sensor is shown in Figure 2. The pulsed laser enters the fiber through one end of the integrated wavelength division multiplexer (involving a 1 \(\times\) 2 bidirectional coupler (BDC) and an optical fiber wavelength division multiplexer (OWDM)). Then its back scattering will be divided into Stokes and anti-Stokes Raman light by the integrated wavelength division multiplexer. After the photoelectric conversion in avalanche photodiode (APD) and high-speed analog-to-digital conversion, the processed signal will be delivered to a computer for temperature demodulation and data storage to achieve online distributed temperature measurement [30].
## 1 Introduction  
Undergraduate programs face the challenge of keeping up with globalization and internationalization processes to stay up-to-date and respond to the demands of international quality standards [1]. In this context, UNESCO [2] has declared that higher education programs need to support their training processes with competency-based curricula, as competencies prepare students to live under variable circumstances while building knowledge and easily adapting to changes in the professional and personal environment [3].  
Thus, competency-based curricula should answer society's present-day requirements to provide competent and competitive professionals to each particular field of knowledge while leading it toward progress by proposing potential solutions to the arising challenges. Consequently, competency-based curricula focus on integral performances while developing learning strategies such as self-regulation, autonomy, leadership, teamwork, problem-solving, and critical thinking, among others [4]. Moreover, they enhance lifelong learning (LL), a competency itself, through a dynamic combination of knowledge, skills, attitudes, and values [5].  
Therefore, a competency-based curriculum should be constantly revised and updated according to the transformation of each profession. This is what acknowledged accreditation boards worldwide seek to certify [6]. In the case of the Architecture, Engineering, Construction, and Operation (AECO) industry, the Fourth Industrial Revolution or Industry 4.0 [7] has brought some changes to the field, such as introducing new technologies, workflows, and methodologies [8]. Building Information Modelling (BIM) is a process that involves the use of shared digital representations of built assets to facilitate design, construction, and operations processes to form a reliable base for decision-making [9].  
BIM relies on tools, technologies, and graphic and non-graphic information for digital representation generation, management, and operation [10]. BIM has been adopted by the AECO industry since mid-2000 as a collaborative process for project management throughout projects' lifecycle to overcome low construction efficiency [11]. In the last 10 years, BIM technologies have continued to develop, organizing the interdependencies between stakeholders in the design, bidding, construction, operation, and maintenance processes. Furthermore, since BIM benefits have come to governments' attention, this methodology has also become necessary in public projects [12].  
In this context, the study develops a methodology for embedding BIM in an undergraduate civil engineering program within the framework of a competency-based curriculum. This research implies two (2) main products: the curricular design and its implementation. The implementation was measured and assessed through the number of freshmen, employability rate, and the number of international agreements.
## Abbreviations  
Alkali activated Fly ash-slag (AFS); Activator to Binder ratio (A/B); Alkali activated materials (AAM); Bagasse Ash (BA); Carbon dioxide (CO\({}_{2}\)); Calcium Silicate Hydrate (CSH); Fly Ash (FA); Fly Ash-Silica tune (FAS); Fly Ash-Silica tune-furnace slag (FASS); Geopolymer concrete (GPC); Geopolymer mortar (GPM); Global warming potential (GWP); Ground granulated blast furnace slag (GGBFS); Micro-Encapsulated phase change materials (MPCM); Nano Silica (NS); Ordinary Portland Cement (OPC); Palm Oil fuel ash (POFA); Potassium Hydrooxide (KOH); Potassium Silicate (K\({}_{2}\)SiO\({}_{3}\)); Rice husk ash (RHA/RA); Silica Funge (SF); Self compacting geopolymer concrete (SGC); Sodium Silicate (Na\({}_{2}\)SiO\({}_{3}\)); Sodium Hydroxide (NaOH); Wheat straw ash (WSA); X-ray fluorescence (XRF).
# A Novel Computational Imaging Algorithm for Electrical Capacitance Tomography
## 3 Solving for Capacitance Correlation Coefficients  
For two random variables \(X=\{x_{1},x_{2},\cdots x_{m}\}\) and \(Y=\{y_{1},y_{2},\cdots y_{m}\}\), the correlation coefficient between them is as follows [11]:  
\[\gamma(X,Y)=\frac{\sum_{i}{(x_{i}-\overline{x_{i}})(y_{i}-\overline{y_{i}})}}{ \sqrt{\sum_{i}(x_{i}-\overline{x_{i}})^{2}}\sqrt{\sum_{i}(y_{i}-\overline{y_{i }})^{2}}} \tag{1}\]  
where \(\overline{x_{i}}\) and \(\overline{y_{i}}\) are the mean values of \(X\) and \(Y\), respectively. \(\gamma(X,Y)\) reflects the correlation degree of the two vectors (\(X\) and \(Y\)), and its value is between \(-1\) and \(1\).  
According to the research conclusions of relevant scholars for decades, it has been a consensus in the industry that the image reconstruction model of the ECT can be expressed using the following mathematical formula when considering capacitive noise [12]:  
\[C+\alpha=SG \tag{2}\]  
where \(C\) is the capacitance vector, \(S\) is sensitivity matrix, \(G\) is the permittivity vector, and \(\alpha\) is noise capacitance vector. The image reconstruction process of the ECT is essentially a  
Figure 1: Imaging procedure of general extreme learning machine.  
Figure 2: Imaging procedure of regularized extreme learning machine.  
process of solving Equation (2). However, Equation (2) is only a basic model, and it is very difficult to solve it directly. Therefore, we must find effective numerical methods to solve it.  
When the number of electrodes is \(n\) and the measured area is divided into \(t\) units, the high permittivity medium (\(\epsilon_{2}\)) is placed in the first micro unit and the low permittivity medium (\(\epsilon_{1}\)) is placed in the other units. All capacitances between electrodes are measured and normalized to obtain capacitance vector \(C_{\text{s1}}\). Similarly, the normalized capacitance matrix can be obtained as follows:  
\[C_{s}=\left(C_{\text{s1}},C_{\text{s2}},\cdots,C_{si},\cdots,C_{st}\right) \tag{3}\]  
where \(C_{si}=\left(c_{1},c_{2},\cdots,c_{m}\right)^{T}\), \(C_{si}\in R^{m\times 1}\), \(C_{s}\in R^{N\times t}\), \(m=\frac{n(n-1)}{2}\). \(C_{s}\) is essentially a sensitivity field matrix.  
When there is a medium to be measured in the measured area, the measured capacitance value is \(C_{d}(C_{d}\in R^{m\times 1})\). According to Equation (1), the capacitance correlation coefficient \(\gamma_{i}=\gamma(C_{si},C_{d})\) can be obtained, and the following capacitance correlation coefficient vector can be obtained in the same way.  
\[\gamma=\left(\gamma_{1},\gamma_{2},\cdots,\gamma_{t}\right) \tag{4}\]  
where \(\gamma\in R^{1\times t}\) is essentially a dielectric constant matrix and can be directly used for imaging. Reference [13] has confirmed the advantages of this method. In this paper, we take this further and use it as the input of the RELM model to enhance the imaging quality.
## 4 Results and Discussion  
### Analysis of Total Heavy Metals  
The results of soil heavy metal content and pH in the study area are shown in Table 5. The soil in the study area was acidic as a whole and with reference to the background value of the soil environment in Guangxi [18] with the value of pH < 5.5 in the screening value of \"Soil Environmental Quality Risk Control Standard for Soil Contamination of Agricultural Land\" (GB 15618-2018) [26], the average concentrations of Cr, Ni, Cu, Zn, As, Cd, Pb, and Hg in the study area's soil exceeded the background value of soil in Guangxi, and the average concentrations of Cr, As, Cd and Hg exceeded the risk screening value (pH < 5.5). The coefficient of variation (CV) can reflect the dispersion of heavy metal contents and the influence of anthropogenic activities on the heavy metal contents, and the larger the value of its coefficient, the more uneven the spatial distribution of the content of the element and the greater the degree of interference by human activities, namely CV < 10% as weak variation, 10% \(\leq\) CV \(\leq\) 30% as moderate variation, and CV > 30% as strong variation [27; 28]. The CV of the As, Cd and Hg elements in the soils of the study area reached 63.56%, 49.82% and 32.67%, respectively, which are strong variations, indicating that the As, Cd and Hg elements in the soils of the study area are highly influenced by anthropogenic activities. The remaining five heavy metal elements were moderately variable, indicating their uneven spatial distribution.
## 4 Analysis and Evaluation of the PPP-B2b and Broadcast Ephemeris Performance
### Analysis of the Characteristics of the PPP-B2b Correction Time Series  
Taking a three-day dataset as an example, the PPP-B2b orbit and clock correction time series for the GPS (top) and BDS-3 (bottom) are shown in Figure 2. The GPS broadcast ephemeris (GPS LANV broadcast ephemeris) orbit errors and their PPP-B2b corrections in the along direction are the largest among the three axial directions of the centroid orbit coordinate system. Similarly, the BDS-3 medium Earth orbit (MEO) satellite orbit errors and their PPP-B2b corrections in the along direction are the largest among the three axial directions of the centroid orbit coordinate system. In addition, the broadcast ephemeris orbit errors and their PPP-B2b corrections are larger for the BDS-3 Inclined GeoSynchronous Orbit (IGSO) satellites than for the BDS-3 MEO satellites. A comparison of the different navigation systems reveals that the broadcast ephemeris orbit errors and their PPP-B2b corrections are smaller for the BDS-3 MEO satellites than for the GPS satellites. It is concluded from the above summary and Figure 2 that larger orbit errors and clock offset errors correspond to larger corrections and that the broadcast ephemeris errors positively correlate with the PPP-B2b corrections.  
Figure 2: PPP-B2b correction time series.