esa-sceva/satcom-chunk-collection huggingface dataset:
# Relationship between [PERSON]'s Apparent Power and Instantaneous Power in Three-Phase Systems
## 6 Conclusions  
A general procedure to obtain Buchholz's apparent power expressions and their components in any power system was established in three-phase, wye-configured, non-sinusoidal, unbalanced power systems. The proposed methodology was based on the formal relationships between the instantaneous and apparent power expressions identified in these systems.  
The proposed method can be applied either to an entire system or part of one. When the methodology was used in a part or subsystem of a power system, the expressions for the apparent power and its components obtained according to our proposed procedure in three-phase systems always led to Buchholz's apparent power expression. When the proposed method was applied to the whole system, novel quantities to analyze power system operations were defined after the application of the proposed procedure, including the neutral-displacement power. This quantity expressed the amount of apparent power in sources and loads caused by the operation of the neutral path (real or virtual) in three-phase, non-sinusoidal, unbalanced systems. The neutral-displacement power was necessary to satisfy the Conservation of Energy Principle throughout the entire system and to explain the origin of the different values of the apparent power of directly connected sources and loads. The proposed neutral-displacement power was not the well-known neutral apparent power, which was defined by the active and reactive power in the neutral conductor, since this novel power included the source and load imbalance and distortion effects. The expression of the neutral-displacement power was consistent in the three-phase, wye-configured systems with no neutral conductor, as this power directly depended on the zero-sequence voltages in sources and loads and the line currents but not on the neutral current.  
The proposed neutral displacement power was measured separately in sources and loads. The neutral-displacement power measured in the load strongly increased with increasing neutral conductor resistance, therefore, the increment of the load neutral-displacement power could be used as a suitable parameter to detect neutral conductor deterioration. However, the source neutral-displacement power was not able to detect neutral conductor malfunctions as its values were usually negligible and constant during neutral conductor deterioration.  
Figure 5: (**a**) Load and source neutral-displacement powers; (**b**) load phase voltages.
# DataStream XES Extension: Embedding IoT Sensor Data into Extensible Event Stream Logs
## 1 Introduction
In order to support the creation of analytical software to perform such analytical tasks, both ex-post and at runtime, the following 5 goals for the design of the DataStream extension are defined:  
* Provide a well-defined set of named XES attributes to describe individual (sensor) events. * Utilize well-established XES concepts such as lists to group the named attributes for simplified analysis. * Establish a set of named XES attributes to store many (sensor) events per process event. * Describe how to store large quantities of (sensor) events, which might occur between the start/end of a process event or a process instance (i.e., establishing a new XES BPAF lifecycle transition). * Establish a set of named XES attributes to connect (sensor) events to groups of process tasks. Subsequently these 5 goals are evaluated in two different real-world scenarios, from the transportation/logistics and manufacturing domains. The extension adds nested attributes to XES, providing a vocabulary for storing IoT data streams with process logs. Thus, the extension can easily be integrated with existing process execution environments or log aggregation mechanisms, as exemplified in Section 4. The DataStream XES extension is intended to lay a foundation for process mining in IoT environments and to promote re-usability and interoperability. The structure of the paper is as follows: In Section 2, we describe the theoretical basis for process mining in IoT and the related literature. Section 3 introduces the proposed DataStream XES extension to specify IoT-enriched event logs. In Section 4, we present application scenarios for IoT-enriched event logs in smart manufacturing and public transportation. Section 5 summarizes the results, lists advantages and limitations, and gives an outlook for future research directions.
## 3 Materials and Methods
### Data Treatment and Multivariate Statistical Analysis  
MSDA was performed using the MetaboAnalyst 5.0 web-based tool [52]. The raw GC-qMS data were pre-processed to remove the VOMs with missing values and then normalized (data transformation by cubic root and data scaling by autoscaling). The data matrix is subjected to a one-way analysis of variance (ANOVA) followed by [PERSON]'s test for post hoc multiple comparisons of means from the six spice varieties data at \(p\)-value \(<0.001\) to identify significant differences. The principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were used to provide insights into the separations among the spices under study, and to detect the VOMs that may represent differences among the sample sets. Importantly, PLS-DA can identify VOMs sets that best discriminate among the different spices analyzed by reducing the size of the data matrix through eliminating redundant variables. The VOMs with variable importance in the projection (VIP) score \(\geq\) 1.45 and differentially expressed in the univariate analysis were potential candidates for characterizing spice varieties. A hierarchical cluster analysis (HCA) was carried out using the 15 most significant VOMs identified in the spice samples obtained by ANOVA. This was generated through [PERSON]'s algorithm and Euclidean distance analysis, aiming to identify clustering patterns for the characterization of the spices analyzed. Significance was established at \(p<0.05\).
## 2 Materials and Methods
### Level of proteolysis  
The level of proteolysis (LP) was determined based on the increase in non-protein nitrogen (NPN) after in vitro digestion. NPN was extracted from fish samples and from intestinal digesta as follows: each fish sample was thoroughly homogenized with a domestic stick blender in ultrapure water (1:7 _w/v_). From these homogenates, pH was measured with pH meter instrument (WTW InoLab, Xylem Inc., Washington, DC, USA). Fish homogenates and intestinal digesta samples were extracted with 60% acetone (1:3 _v/v_) for 10 min at room temperature with vigorous shaking. Extracts were centrifuged (3026\(\times\) g, 20 min, 20 \({}^{\circ}\)C; Centrifuge BR 4i, Joan SA, France) and supernatants were collected. The remaining pellets were extracted with 15 % trichloroacetic acid (TCA; original sample size (g) to TCA ratio 1:2 _w/v_) for 10 min at 5 \({}^{\circ}\)C. Extracts were centrifuged (3026\(\times\) g, 20 min, 20 \({}^{\circ}\)C), and supernatants were collected. Acetone and TCA extracts were combined and filtered with 40 \(\upmu\)m filter paper.  
The nitrogen contents of fish samples (Ntot), fish NPN extracts (NPNfish) and digests NPN extracts (NPNdigesta) were measured with the Kjeldahl method [16]. LP was calculated as follows: (NPNdigesta \(-\) NPNfish)/Ntot \(\times\) 100.
# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 3 Deep Neural Network
### Loss Function  
The loss function of YOLOV4-tiny is composed of the regression loss (\(L_{loc}\)) of the anchor frame, the loss of the prediction category (\(L_{cls}\)), and the confidence loss (\(L_{conf}\)). The overall loss function expression is shown in Equation (2)  
\[L=L_{loc}+L_{cls}+L_{conf} \tag{2}\]  
The loss function is used to calculate the gap between the model's prediction and the actual data. Classic one-stage detection methods, such as YOLO and SSD, have serious category imbalance problems during training. A large number of simple samples and backgrounds generated by the one-stage detection algorithm during training affect the classification accuracy of the model. To enhance the prediction ability of the model for complex samples, a Focal Loss [37] function is used in this paper to regress the confidence of the target. The Focal Loss function can solve the problem of uneven positive and negative samples in the classification process. By reducing the weight of background samples, the model focuses on foreground objects.  
\[L_{fl}=\left\{\begin{array}{ll}-\alpha(1-y)^{\gamma}\log y^{\prime},&y=1\\ -(1-\alpha)y\log(1-y^{\prime}),&y=0\end{array}\right. \tag{3}\]  
where \(\alpha\) is the balance factor, \(\gamma\) is the attenuation parameter, and \(y\) is the predicted label probability.  
The Focal Loss function adds a balance factor \(\alpha\) to balance the imbalance between the positive and negative samples themselves and uses an attenuation parameter \(\gamma\) to control the imbalance between the simple and complex samples.  
When y is very small (samples are difficult to divide, regardless of whether the division is correct), \(\alpha\) approaches 1, and the weight of the sample in the loss function is not affected. When y is large (samples are easy to classify, no matter whether the grading is correct), \(\alpha\) approaches 0, and the weight of samples in the Loss function drops a lot. \(\gamma\) can adjust the reduction degree of the weight of samples that are easy to classify. By appropriately selecting the values of \(\alpha\) and \(\gamma\), Focal Loss effectively enhances the model's predictive accuracy for minority classes. Particularly in object detection tasks, it aids the model in better recognizing small or difficult-to-distinguish targets against complex backgrounds, significantly improving the model's performance when dealing with class-imbalanced data.  
Figure 3: ECA attention module structure [49].
## 3 Results
### Relative Water Content during Drought  
Drought stress was induced in seven-week-old wheat plants. Well-watered wheat plants had a leaf RWC between 84 and 94% (Table 1). Once irrigation was suspended, the pots reached a soil humidity of 30% after five days of water withdrawal, and drought stress was considered at this point. After two days of drought, the WT and 35S plants significantly reduced their RWC (Table 1). In the RD transgenic lines, the leaf RWC did not diminish considerably until day four of drought in line rd-1B, nor until day six in the rd-1C line (Table 1). At the highest degree of drought (day 8), the mean value of RWC was more elevated in the transgenic plants compared to the NT wheat. On the eighth day of drought, the RWC in the WT plants was 63.1%, whereas in the transgenic lines, it was 70.7% in 35S, 67.9% in rd-1B, and 65.6% in rd-1C (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & \\ \hline
**Drought (d)** & **0** & **2** & **4** & **6** & **8** \\ \hline NT & 87.7 \(\pm\) 2.3 \({}^{\mathrm{a}}\) & 77.3 \(\pm\) 2.4 \({}^{\mathrm{b}}\) & 81.9 \(\pm\) 2.3 \({}^{\mathrm{ab}}\) & 70.9 \(\pm\) 3.0 \({}^{\mathrm{bc}}\) & 63.1 \(\pm\) 3.4 \({}^{\mathrm{c}}\) \\ \hline
35S & 92.4 \(\pm\) 2.7 \({}^{\mathrm{a}}\) & 81.9 \(\pm\) 2.7 \({}^{\mathrm{b}}\) & 81.7 \(\pm\) 2.7 \({}^{\mathrm{b}}\) & 77.6 \(\pm\) 3.5 \({}^{\mathrm{bc}}\) & 70.7 \(\pm\) 3.5 \({}^{\mathrm{c}}\) \\ \hline rd-1B & 84.1 \(\pm\) 1.7 \({}^{\mathrm{a}}\) & 86.5 \(\pm\) 1.7 \({}^{\mathrm{a}}\) & 74.2 \(\pm\) 2.1 \({}^{\mathrm{b}}\) & 74.3 \(\pm\) 3.0 \({}^{\mathrm{b}}\) & 67.9 \(\pm\) 2.7 \({}^{\mathrm{b}}\) \\ \hline rd-1C & 84.9 \(\pm\) 1.8 \({}^{\mathrm{a}}\) & 80.6 \(\pm\) 1.7 \({}^{\mathrm{a}}\) & 79.2 \(\pm\) 1.9 \({}^{\mathrm{a}}\) & 66.3 \(\pm\) 3.0 \({}^{\mathrm{b}}\) & 65.6 \(\pm\) 2.7 \({}^{\mathrm{b}}\) \\ \hline \end{tabular} Data show mean \(\pm\) standard error (\(n\) = 5). Letters indicate significant differences compared to non-stressed plants from each line (\(p\)\(\leq\) 0.05).  
\end{table}
Table 1: Relative water content of wheat plants during drought stress treatment.
# A Comparative Analysis of IMUs and Optical Systems in Layup Shot Biomechanics
## 1 Introduction
In contrast, complex motor tasks such as the basketball layup pose unique challenges due to their context-driven nature, where movement execution is heavily influenced by external factors such as defensive pressure and spatial constraints [14]. Context-driven performance in basketball is defined by interactions with the surrounding environment, including opponents, teammates, and situational demands [15]. Defensive conditions are particularly important, as they directly affect the demands and movement variability of the layup [16]. For example, when a defender is present, athletes often modify their approach, jump height, and release angle to optimise success while avoiding a block, introducing variability that may not be observed in undefended trials [17]. Thus, analysis of the layup requires an ecologically valid environment, which IMUs are good for, but they may be less effective at accurately tracking multiplanar motion of the upper and lower body. To have confidence in using IMUs for the analysis of a layup, the ability for IMUs to accurately and consistently measure more complex whole-body tasks needs to be established. Although accuracy of measurement is typically key for biomechanical analyses, there are also questions that can be answered if the data are precise and have sufficient resolution to detect changes between conditions, a question not typically addressed previously by studies assessing IMUs' accuracy. Studying changes in technique with changing environmental constraints is one such area where sufficient resolution and precision can be useful even if absolute accuracy is poor. The purpose of this study was to examine the agreement between an Xsens system and a Vicon system for analysing the basketball layup shot and to determine whether the Xsens system had the ability to detect similar magnitudes of changes in technique under different constraints to Vicon. The present study incorporated both defended and undefended trials to evaluate the performance of IMUs across a spectrum of contextual variability representative of game scenarios. By examining the systems under varying external constraints, the study sought to determine the reliability of IMUs in capturing biomechanical adaptations associated with environmental and task-specific demands. This approach enhances the ecological validity of the findings and contributes to the growing body of research emphasizing the importance of representative task design in sports science [18]. It was hypothesised that the IMU system would demonstrate high accuracy in measuring flexion-extension joint angles during basketball layup shots. Additionally, it was hypothesised that the IMU system would reliably detect technique changes under varying defensive constraints and exhibit similar sensitivity to Vicon in identifying condition-based kinematic adjustments.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 4 Hardware Evaluation for Iterative Decoding of Product Codes
### Hardware Evaluation Platform
#### 4.1.1 The Encoder Architecture  
The encoder architecture of product codes is depicted in Figure 10. In our implementation, multiple parallel encoders composed of nine parallel outer encoders and six parallel LDPC encoders are designed for achieving the trade-off between complexity and throughput. Furthermore, the RAM matrix consisting of 54 independent block RAMs is used to simplify the outer codeword storage.  
During the outer encoding process, \(k_{2}\) columns of information bits are divided into nine groups and input into nine outer encoders respectively. The information bits in the same group are encoded by the same encoder. Due to the parallel design, nine outer codewords can be obtained in parallel and further stored in block RAM matrix. For RAM, the read/write bit width is 1 and the depth is \(n_{1}/6\times k_{2}/9\). The block memory is logically divided into \(n_{1}/6\) rows and \(k_{2}/9\) columns according to the read/write address generating module.  
For convenience and scalability, the LDPC encoding is performed after all the outer codewords are stored in the RAM matrix. The data in the block RAM matrix is read and sent into the six parallel LDPC encoders. Thus, the virtual columns in the same position of six block RAMs store one single outer  
Figure 10: The encoder architecture of product codes.  
Figure 9: The hardware evaluation platform.  
codeword. Specifically, the virtual rows in the same position of nine block RAMs store the information vector for one LDPC codeword. For each LDPC encoder, it encodes \(n_{1}/6\) LDPC codewords sequentially.
# Research on Fault Recognition of Roadheader Based on Multi-Sensor and Multi-Layer Local Projection
## 4 Multi-Sensor and Multi-Layer Fault Recognition Based on LPP
### Second-Layer Low-Dimensional Feature Extraction  
Based on the analysis of the vibration data above, we obtained the low-dimensional mapping space for each sensor. While a single sensor can achieve some level of fault identification, its effectiveness is not particularly ideal. Moreover, the representations vary among sensors, but the HF vector for each fault remains nearly consistent across sensor directions. To address this, we utilize the low-dimensional spaces from six sensors for fusion, creating a multi-sensor fusion mapping.  
\[Mapping=\left[LDM_{1}{}^{T},LDM_{2}{}^{T},LDM_{3}{}^{T},LDM_{4}{}^{T},LDM_{5}{ }^{T},LDM_{6}{}^{T}\right]^{T}\]  
_Mapping_ represents a pseudo-manifold in both spatial and temporal dimensions, constructed from low-dimensional features captured by six sensors across five states at 16 different time points. This feature set forms a spatial-temporal pseudo-manifold. When conducting further dimensionality reduction, it is crucial to preserve significant local spatial features and overarching characteristics across different states. Therefore, locality-preserving projection (LPP) is chosen to reduce dimensions in the fusion mapping process.  
The fusion mappings are reduced in dimension by LPP learning, further purifying the manifold representation between samples. It can realize the depth enhancement processing of the difference of vibration signals in different states. Sensitive features extracted from enhanced shape features of the training samples and test samples are shown in Figures 11 and 12.  
Figure 9: Low-dimensional mapping of training samples by LPP.  
Through the analysis of enhanced epidemic characteristics, the low-dimensional features of five operating states in the two groups of data were effectively distinguished; the same operating state showed good clustering, and the HF vector of the same fault showed good consistency in the two groups of analysis results.  
Figure 10: Low-dimensional mapping of test samples by LPP.
# A Dynamic Grid Index for C\(k\)NN Queries on Large-Scale Road Networks with Moving Objects
## 3 Methodology Overview and Problem Definition
### Problem Definition  
**Definition 1**.: _Road Network. In this paper, we abstractly modeled the real road network as a directed weight graph, \(G=\langle V,E\rangle\), each road was regarded as an edge of \(G\), and the road endpoints were regarded as the corresponding vertices of \(G\), where \(V\) is the set of nodes on the road and \(E\) is the set of edges on the road. A vertex on the road is \(v_{i}=(x_{i},y_{i})\in V\), \(x\) is the abscissa of the vertex, \(y\) is the ordinate of the vertex; an edge on the road is \(e=(u,v,w)\in E\), \(u\) is the road. The starting point of \(e\) is the end point of the road, and \(w\) is the length of the road. The road network used to illustrate the concepts in this paper is shown in Figure 2._  
**Definition 2**.: _kNN Query. The \(NN\) query problem originated from the post office problem proposed by [PERSON] in 1973. The problem can be simply described as: given that set \(P\) in the \(NN\) -dimensional space contains \(n\) data points, find a data point \(p\) in set \(P\) such that the distance between \(p\) and the query point \(q\) is the closest. The \(kNN(k\geq 1)\) query is a generalized form of the \(NN\) query. In this paper, the k-nearest neighbor query is mainly performed on the moving objects in the road network environment, that is, on the road network \(G=\langle V,E\rangle\), there is a group of moving objects  
Figure 1: Methodology Overview.  
Figure 2: Diagram of road network.  
\(O_{r}(\mid O_{r}\mid>k)\); given a query point \(q\) and a positive integer \(k\), find the \(k\) moving objects \(M(q)\) closest to the \(q\) -way network. In addition:  
* \(\mid M(q)\mid=k\);
* \(Mq\subseteq O_{r}\);
* \(\forall o\in O_{r}\), \(o^{\prime}\in O/M(q)\), \(Dist(q,\ o)\leq Dist(q,\ o^{\prime})\)  
**Definition 3**.: _C\(k\)NN Query. In our semantics, moving objects move along the edges of the road network \(G\) and periodically report their positions with time interval \(\Delta t\). The snapshots \(\Delta t\) of all objects are then updated at each time interval. Assuming that the latest snapshot of an object was created at time point \(t_{s}\) and all queries are made at time period \((t_{s},t_{s}+\Delta t)\), the evaluation is first based on this snapshot. Subsequently, the \(k\)NN of each query is periodically updated based on each new snapshot of the object. We call queries following this semantics CKNN queries._
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 2 Materials and Methods
### Gas Exchange System  
Each chamber used an open gas exchange system to measure canopy photosynthesis ([PERSON] _et al._, 2013; [PERSON] _et al._, 2013). The open flow system ensured that humid air (~50% relative humidity) of a constant CO\({}_{2}\) concentration (setpoint \(\pm\)10 umol mol\({}^{-1}\)) fed the chambers at flow rates between 500 and 1100 L min\({}^{-1}\). Air mass flow (MF; mol s\({}^{-1}\)) into the chambers was measured with mass flow meters (Model 730, Sierra Instruments, Monterey, CA, USA). The gas exchange systems were modified to use a dew point hygrometer to measure the water vapor concentration of pre- and post-chamber air from which evapotranspiration was calculated. Two solid-state multiplexers (Model AM-25T, Campbell Scientific, Logan, UT, USA), each referenced to a 100 Ohm platinum resistance thermometer, were used for precision thermocouple measurements. Data acquisition and control were performed with a datalogger (Model CR-10T, Campbell Scientific, Logan, UT, USA).  
Gas exchange fluxes in each chamber were measured continuously and averaged for 2 min every 8 min. Net photosynthesis, P\({}_{\rm net}\), and dark respiration rates were calculated from the difference between pre- and post-chamber CO\({}_{2}\) concentrations (\(\Delta\)CO\({}_{2}\)), multiplied by MF of air into the chambers. \(\Delta\)COwas measured with a differential infrared gas analyzer (Model LI-6251, LI-COR, Lincoln, NE, USA). The temperature, water vapor band broadening, and dilution corrections used for the C fluxes are described in Monje and Bugbee [30]. Chamber evapotranspiration (ET) was determined from the difference in mole fraction of water vapor between pre- and post-chamber air (\(\Delta\)X\({}_{\mathrm{h20}}\)), multiplied by mass flow rate entering the chamber (ET = \(\Delta\)X\({}_{\mathrm{h20}}\)\(\times\) MF). \(\Delta\)X\({}_{\mathrm{h20}}\) was determined from sequential measurements of pre- and post-chamber air dewpoint made with a dewpoint hygrometer (Model Dew-10, General Eastern, Watertown, MA, USA). Air flow was changed to increase \(\Delta\)CO\({}_{\mathrm{2}}\) and \(\Delta\)X\({}_{\mathrm{h20}}\) in the chamber. The flow rate of air entering the chamber was not corrected for the amount of water vapor added by canopy transpiration (a maximum of \(\sim\)15 L/day) because this correction was negligible, which would not be the case in smaller leaf gas exchange systems [32]. Water use efficiency (\(\mathrm{\mu mol~{}mmol^{-1}}\)) was calculated from the ratio of P\({}_{\mathrm{net}}\) to ET.