esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Implementation Using the Ring Language  
The architecture of the proposed visual programming language (shown in Figure 4) is divided into three main layers: Use Cases, PWCT Environment, and Ring Language. Each layer contains specific components that contribute to the overall functionality of the system. The bottommost layer, Ring Language, includes Development Tools, Libraries, Compiler, and Virtual Machine (VM). Development Tools provide various tools that assist in the development process, such as the package manager and Ring2 EXE. Libraries consist of pre-written code libraries that developers can use to add functionality to their programs, saving time and effort by providing reusable code for common tasks. The compiler processes Ring textual source code and generates bytecode for the Ring Virtual Machine if the program is correct. If there are issues, it produces compile-time errors. The Virtual Machine (VM) provides a runtime environment for the programs written in Ring, executing the compiled code and managing the program's execution [23; 30].  
The middle layer, PWCT Environment, consists of Visual Programming, the Ring to PWCT converter, and the File System (Visual Source). Visual Programming allows developers to create programs using visual elements rather than traditional text-based code, providing a more user-friendly development experience. This is achieved through tools such as the Steps Tree editor, time machine, components browser, interaction pages (data-entry forms), and form designer. The Ring to PWCT converter assists in importing the textual source code written in Ring and visualizing the structure and flow of the program by offering graphical representations of the program's logic. This enables us to continue program development using visual programming instead of relying on the textual source code. The File System (Visual Source) manages the visual source files within the PWCT environment, organizing and storing information about the visual elements used in the development process. PWCT can generate Ring source code from these visual source files, enabling the execution of these programs using the Ring compiler and virtual machine.  
The topmost layer, Use Cases, includes the various applications and 2D games that can be developed using PWCT based on the Ring programming language, ranging from simple utilities to complex software solutions. Overall, the architecture of the proposed system is designed to provide a comprehensive and user-friendly environment for visual programming based on the Ring programming language, making it accessible to both novice and experienced Ring developers.
## 5 Discussion
### Implications for Shale Oil Exploration  
As discussed above, the sand-silt intervals have relatively higher contents of brittle minerals such as quartz and feldspar and relatively larger detrital grain sizes compared to the organic-rich mudstones (Figures 3-5, Table 1) [7; 25; 29; 53; 54]. This makes the sand-silt intervals more brittle than the organic-rich layers. Moreover, the sand-silt intervals have relatively larger pore sizes and higher porosity (Figures 9 and 10) [25; 29; 53; 54], providing a larger capacity for hydrocarbon storage and migration. In the sand-silt intervals, inorganic pores are more common, with some being filled with porous MSB. In addition, there are more interconnected pores with lager size and microfractures adjacent to the sand-silt intervals [24; 25; 53; 54; 52]. Therefore, in addition to having larger pore sizes and higher porosity, the sand-silt intervals also have better connectivity of the pore networks. The content of clay minerals and TOC in the sand-silt intervals is lower than those in mudstones. Hydrocarbons generated from kerogen in organic-rich mudstones migrate to the sand-silt intervals. Due to physical and chemical compositional fractionation by adsorption or migration, the hydrocarbons in the sand-silt intervals have lower sorption affinities and are characterized by lighter oil with smaller size and mass, lower content of asphaltenes,higher content of saturates, weaker polarity, and lower viscosity [4; 6; 10; 35; 77; 78; 79; 80; 81]. As a result, the sand-silt intervals have higher OSI and better oil mobility. Therefore, sections with densely developed organic-lean sand-silt intervals are sweet spots for shale oil and gas, usually contributing to high oil production potential.  
Consequently, the sand-silt intervals exhibit higher OSI values and enhanced oil mobility. Thus, within the Yanchang shale, zones or areas where organic-lean sand-silt intervals are densely developed are identified as sweet spots for shale oil and gas accumulation, consistently exhibiting higher oil production potential.
## 2 Materials and Methods
### Methods
#### 2.2.2 Data Processing  
The procedure described below was applied to the database of EPCs (Figure 3). The database initially contained 68,062 energy certificate records. The first step was to unify the format of the records according to the attributes: year of construction, building regulations, typology of the record and postcode. Subsequently, the records in the database were filtered according to their typology, selecting and separating only those corresponding to 'Dwellings in Multi-family residential Buildings' (DMB) and 'Single-family dwellings' (SFD). Afterwards, the cadastral references, which uniquely identify each property by means of 14-20 digits, were verified to ensure that they were correctly registered. Those data that did not meet this requirement were eliminated.  
As a last step, registers with missing data, duplicate cadastral references, and those located outside Seville city were excluded. An analysis of the variables was carried out to identify and eliminate data with atypical and verifiable values in the cadastral database, such as year of construction and area of construction, in order to have more representative values in the sample. This step has resulted in a 32.5% reduction of the initial data, i.e., a total of 45,908 records have been used in this study (of which 40,676 (88.6%) are DMB and 5232 (11.4%) are SFD).
# Improved PR Control Strategy for an LCL Three-Phase Grid-Connected Inverter Based on Active Damping
## 1 Introduction
Theoretically, the PR controller has infinite gain at the specific frequency, but the PR controller has poor adaptability to grid frequency fluctuations, so quasi-proportional resonant controllers are widely used [31; 32]. The QPR controller has strong adaptability to grid frequency fluctuations, but the gain at the fundamental frequency is limited, so it can only approximately realize error-free tracking [33; 34]. In addition, there are hysteresis control, repetitive control, and \(H_{\infty}\) control, but their control performance is not significantly better than that of PI, PR, and QPR controllers [28]. For three-phase LCL grid-connected inverters, few studies consider the steady-state error of grid-connected current and the power grid frequency fluctuation at the same time, and relevant control technologies need further research. This paper studies the controller of the three-phase LCL grid-connected inverter in the \(\alpha\)-\(\beta\) coordinate system. A current closed-loop control strategy based on an improved QPIR controller is proposed while considering the steady-state error of grid-connected current, power decoupling, and grid frequency fluctuations. Theoretical analysis and case simulation show that the QPIR controller can track the grid-connected current without static error and has good output waveform quality, which proves the correctness and effectiveness of the controller. This paper consists of the following parts: In Section 2, the mathematical model of the LCL three-phase grid-connected inverter is established, and the advantages of independent power control in the \(\alpha\)-\(\beta\) coordinate system are pointed out. In Section 3, the control method of active damping is given, and compared with the advantages and disadvantages of a traditional PR controller and QPR controller, an improved QPIR controller strategy is proposed. In Section 4, the simulation results and some discussion under different conditions are given through an LCL three-phase grid-connected inverter simulation model. The conclusion is given in Section 5.
# IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare--A Review
## 5 Machine Learning
### Supervised Learning  
In supervised machine learning, developers provide the algorithms with a prepared set of data as a training source. The task of the algorithms is only to recognize the pattern: Why does this information belong in category A and not in category B? Supervised learning is used for such algorithms that are intended to categorize natural data (photos, handwriting, language, etc.). In addition, so-called regression problems are a typical field of application for supervised learning. The algorithms should be able to make predictions, for example about the health of consumers, based on certain habits [32].  
Let us assume that one would like to train algorithms to distinguish cancerous tumors from non-cancerous ones. The developers would then prepare a very large data set for this. This would contain scans that all already have a tag, i.e., belong to a category. You could imagine three different groups: cancerous, non-cancerous and other. It is important that the data collection also shows the greatest possible variance. Simply put, if you only have scans of non-cancerous tumors in your training set, the algorithm will assume that all tumors are cancerous. The data set should therefore map the actual range of variations as well as possible.  
During training, the algorithm first receives the content (unsorted), decides independently and then compares it with the output specified by the developers. The system checks its own result against the correct one and draws conclusions from this that affect the next assessments during the training. The training continues until the machine and its assessments have come close enough to the correct results.  
Figure 4: General classification of machine learning algorithms.  
Figure 5: Machine learning algorithms used in healthcare sector [3].  
Which teaching method one should choose depends heavily on the later tasks of the algorithms. For categorization and regression problems, supervised learning is preferable to the other methods. In general, you can use monitored learning to train algorithms so that they are perfectly prepared for the area of application. Since you have complete control over the training material, you only need enough input and time to set the algorithms correctly. The emphasis here is clearly on input: the compilation must be large-scale. Since each element has to be provided with a label in supervised learning, this entails considerable effort for developers and scientists. The effort is relatively high, but it is also relatively easy to understand what is going on. While in unsupervised learning a lot remains unclear because the algorithms work for themselves without any real instructions, in supervised learning what the machine does is precisely defined. However, that can also be a disadvantage: the learned algorithms then also work within the restrictions that have been imposed on them. One cannot expect creative solutions.
###### Abstract  
While there are several ways to identify customer behaviors, few extract this value from information already in a database, much less extract relevant characteristics. This paper presents the development of a prototype using the recency, frequency, and monetary attributes for customer segmentation of a retail database. For this purpose, the standard K-means, K-medoids, and MiniBatch K-means were evaluated. The standard K-means clustering algorithm was more appropriate for data clustering than other algorithms as it remained stable until solutions with six clusters. The evaluation of the clusters' quality was obtained through the internal validation indexes Silhouette, Calinski Harabasz, and Davies Bouldin. When consensus was not obtained, three external validation indexes were applied: global stability, stability per cluster, and segment-level stability across solutions. Six customer segments were obtained, identified by their unique behavior: lost customers, disinterested customers, recent customers, less recent customers, loyal customers, and best customers. Their behavior was evidenced and analyzed, indicating trends and preferences. The proposed method combining recency, frequency, monetary value (RFM), K-means clustering, internal indices, and external indices achieved return rates of 17.50%, indicating acceptable selectivity of the customers.  
retailing; customer behavior; clustering; segmentation; external validation indices
## 2 Monitoring Protocols and Methods
### Defining the Monitoring Area
The number of monitoring points results from a compromise between the operator's point of view, oriented towards a best fit between costs and benefits to ensure storage integrity, and the scientific point of view that might have required a larger number of monitoring locations to establish a more statistically reliable database. A third aspect to be considered was public acceptance of the Lacq-Rousse pilot at the time the project was launched, as some opposition existed to the project [31]. The site operator insisted that surveillance should occur at well-defined locations, the access to which is managed through specific agreements with each landowner. No other monitoring points could be created. Consequently, the 35 sites pre-selected in spring 2008 were first surveyed in September 2008 to define the exact location of measurement points, and then surveyed during subsequent data acquisitions. The monitoring points cover six different geological units. Most measurements were made in the Jurancon hills 'puddingstone' (7 points; m2a-1 on Figure 1), Pontian to Tortonian clays (9 points; m3-2b) and alluvial deposits (10 points; Fz). Other measurements were located over Eocene limestone and sandstone (5 points; e4), Pliocene clays (3 points; p) and colluvial deposits (2 points; E). These 35 locations were monitored as part of an evolutionary scheme:  
* Six baseline measurement campaigns, from September 2008 to December 2009, were done quarterly as required by the MMV plan, including the September 2008 campaign that was initially designed to select the monitoring locations and thus is only partially included in the database used for defining baseline values. * Measurements during the CO\({}_{2}\) injection period (January 2010 to March 2013) were set by the site operator at two sessions per year, one at the end of fall and the other at the end of winter. Seven campaigns, referred to as \"low season\" campaigns, were carried out during this phase. In order to complete the dataset, other data acquisitions--not required by French law--maintained the quarterly frequency of data acquisitions. Funding for these two additional campaigns was obtained from a related research project, leading to four additional \"high season\" campaigns in spring and summer. * Finally, post-injection monitoring (March 2013 to December 2015) was restricted to \"low season\" data acquisitions performed three times as required by French law. Site measurements thus took place from September 2008 to December 2015, resulting in a dataset of 20 different campaigns available for the duration of the entire project.
## 3 Analysis and Discussion  
### Characteristics of Surface Micro-Morphological Changes  
Figure 3 shows micro-morphological changes of rock specimens before and after reaction under different pH conditions. Macroscopic observations revealed distinct dissolution patterns: specimens from static dissolution experiments exhibited minimal surface alteration, primarily manifesting as slight color lightening, whereas dynamic dissolution specimens showed pronounced morphological changes, particularly in the HY10 specimen with the largest fracture aperture, where protruded particles, intensive etching pits, and dissolution striations developed along fracture margins.  
Microscopic analysis further corroborated enhanced dissolution under hydrodynamic conditions. Pre-experiment specimens displayed intact mineral grains with well-defined polyhedral crystal boundaries and tightly packed micro-lamination structures. Post-static dissolution, the HY03 specimen exhibited the most severe micro-scale degradation among static groups, featuring fragmented crystal integrity and dense etching pits. In contrast, the dynamically treated HY09 specimen, despite only 15 days of reaction, demonstrated catastrophic surface erosion: complete obliteration of primary grain boundaries, extensive leaching of soluble components leaving insoluble skeletal frameworks, and pervasive micro-fracture networks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Upper Surface** & **Lower Surface** & **Side of the** & **Initial C(HF)** & **Initial C(H\({}_{2}\)SO\({}_{4}\))** \\
**Pressure of the** & **Pressure of the** & **Model** & **Model** & **[mol/m\({}^{3}\)]** & **[mol/m\({}^{3}\)]** \\ \hline
0.1 & 0 & No-flow & & \\  & & boundary & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Initial and boundary conditions.  
Figure 3: Micro-morphological changes of rock specimens before and after reaction under different pH conditions (HY01-HY03 in static experiments, HY07-HY09 in dynamic experiments). These SEM images captured at 1.00 k magnification.  
EDS analysis (as summarized in Table 8) revealed a systematic increase in surface oxygen content across all specimens, with static dissolution experiments demonstrating a more pronounced enhancement compared to dynamic dissolution counterparts.
# Thermal Mapping of Power Semiconductors in H-Bridge Circuit +
## 3 Loss Dissipation and Junction Temperature of Power Devices
### Junction Temperature Estimation  
The thermal stress of the power devices is usually determined by the thermal parameters of the power module itself (from junction to case), the Thermal Interface Material (TIM), and the cooling method. A typical thermal model of the power module is graphically depicted in Figure 4. It can be  
Figure 3: Schematic waveforms of the loss calculation under the steady-state operation. (Note: \(T_{f}\) denotes the fundamental period, and \(T_{sw}\) denotes the switching period.).  
seen that the power switches share the common cooling system, since these power devices are located in the same housing of the IGBT module.  
Generally, the thermal time constant of the cooling system is from dozens of seconds to hundreds of seconds, while the thermal time constant of the power device is hundreds of milliseconds. On the other hand, the maximum fundamental period of the converter current is around several seconds, which implies that the thermal cycling caused by the cooling can almost be neglected [21; 22]. As a result, for the steady-state analysis, the thermal model of the cooling method will only affect the mean junction temperature, but has no effect on the junction temperature fluctuation.  
Consequently, the mean junction temperature \(T_{jm}\) can be calculated as,  
\[T_{jm\_T/D}=P_{T/D}\cdot(\sum_{i=1}^{4}R_{thJ\_C\_T/D(i)}+R_{thCH\_T/D})+T_{H} \tag{10}\]  
Within a fundamental period, the junction temperature fluctuation is caused by half-cycle conduction and half-cycle blocking of the power semiconductor. Assuming the periodic pulse power loss, the junction temperature fluctuation \(dT_{j}\) can be calculated as [19],  
\[dT_{j\_T/D}=2P_{T/D}\cdot\sum_{i=1}^{4}R_{thJ\_C\_T/D(i)}\cdot\frac{\left(1-e^ {-\frac{t_{0i}}{t_{thJ\_C\_T/D(i)}}}\right)^{2}}{1-e^{-\frac{t_{0i}}{t_{thJ\_C \_T/D(i)}}}} \tag{11}\]  
In Equation (10), \(R_{thJ\_C}\), \(R_{thCH}\) denote the thermal resistance from the junction to case of the power module, and the TIM, respectively. Subscripts \(T\) and \(D\) denote the IGBT and the freewheeling diode, where subscripts \(i\) denotes a four-layer Foster structure for the power module. \(P\) is the power loss of each power semiconductor, and \(T_{H}\) is the heatsink temperature. In Equation (11), \(t_{p}\) denotes the fundamental period of the current, \(t_{on}\) denotes a half of fundamental period, and \(\tau\) denotes the thermal time constant of each Foster layer.
# A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks
## 4 Experimental Evaluation  
In this section we evaluate the performance of the proposed channel assignment technique called CB-AIIM. To evaluate the proposed channel assignment technique, we have made use of the model used in Section 2 using Python as the programming language. The specific parameters used for the model are \(P_{t}=30\) mW, \(G_{t}=G_{r}=0\) dB, \(L=40\) dB, \(SIR_{min}=10\) dB and \(SIR_{max}=40\) dB. The specific  
Figure 3: Example of the differences in interferences considered in AIIM and CB-AIIM. (**a**) AIIM. (**b**) CB-AIIM.  
layouts with which the channel assignment technique have been tested (described in Section 4.1) have been modeled using graphs that are stored as GML (Graph Modeling Language) files and that has been built using the networkx library in Python.
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 2 Materials and Methods
### Data and Model
#### 2.2.3 Streamflow Data  
Pakistan Water and Power Development Authority (WAPDA) provided discharge data for the Indus River at Taunsa Barrage from 1986 to 2014, which were used to identify the maximum daily discharge. The HEC-RAC model, however, requires as input the maximum instantaneous peak flow. As there is a lack of instantaneous peak flow data at the study location, it was estimated using Equation (1), a linear regression model based on 12 pairs of maximum instantaneous and maximum daily discharge data with a Pearson correlation coefficient of 0.94 [32]:  
\[Q_{p}=1.238Q_{M}-305.47 \tag{1}\]  
where \(Q_{p}\) = maximum instantaneous discharge (m\({}^{3}\)-s\({}^{-1}\)) and \(Q_{M}\) = maximum daily discharge (m\({}^{3}\)-s\({}^{-1}\)). Table 1 depicts the resulting maximum instantaneous discharge at Taunsa Barrage.