esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 4 Methodology
### Anomaly Detection

Table 1: Comparison of the multiple anomaly detection methods  
Figure 6: Choosing the anomaly detection method: (**a**) Distribution of the relative distances to the diagonal of the STM, and (**b**) CoMs of the characteristic matrices with labeled anomaly measures results. The tensor-based anomaly detection method is presented in Algorithm 1. The algorithm begins with the empty lists initialization, namely, list of tensors \(\mathcal{T}\) and the list of anomalous characteristic matrices \(\mathbf{M}\). Every tensor \(\mathcal{T}^{(i)}\) is constructed by flattening every STM recorded inside the spatial cell \(g^{(i)}\in G\), and constructing a frontal slice \(\mathbf{T}_{::t}\) explained in detail in Section 4.2. Then, on every tensor, the Non-negative Tensor Decomposition (NTD) is applied to compute three factor matrices. \(\mathbf{A}_{i}\) represents extracted traffic patterns, where every row \(a_{:i}\) represent flattened extracted characteristic matrix, \(\mathbf{B}_{i}\) spatial factor matrix, and \(\mathbf{C}_{i}\) temporal factor matrix. Then, every \(a_{:i}\) is reshaped into two dimensional STM \(\mathbf{X}_{ch}^{(i)}\) and CoM coordinates are computed using Equations (3)-(6). The final step is the anomaly detection, which is estimated by using two distances \(d_{CoM}\) and \(d_{A}\), where \(d_{CoM}\) represents the distance from the CoM to the diagonal of the STM, and \(d_{A}\) threshold distance for the anomaly detection visually shown in Figure 6. The anomaly is detected if the \(d_{CoM}\) is larger or equal than the \(d_{A}\) and placed into list \(\mathbf{M}\). ```
1:Initialize empty list of tensors \(\mathcal{T}\)
2:Initialize empty list of anomalous characteristic matrices \(\mathbf{M}\)
3:for each spatial cell \(g^{(i)}\) in \(G\)do
4: Construct a new tensor \(\mathcal{T}^{(i)}\in R^{m\times n\times t}\) using STMs for cell \(g^{(i)}\)
5: Add new tensor \(\mathcal{T}^{(i)}\) to list \(\mathcal{T}\)
6:endfor
7:for each tensor \(\mathcal{T}^{(i)}\) in list \(\mathcal{T}\)do
8: Apply Non-negative Tensor Decomposition on \(\mathcal{T}^{(i)}\) and store the result in matrices \(\mathbf{A}_{i}\), \(\mathbf{B}_{i}\) and \(\mathbf{C}_{i}\)
9:for each flattened characteristic matrix \(a_{:i}\) in \(\mathbf{A}_{i}\)do
10: Reshape matrix \(a_{:i}\) to \(20\times 20\) matrix and set it as \(X_{ch}^{(i)}\)
11: Compute CoM coordinates \(c_{x}^{(i)}\) and \(c_{y}^{(i)}\) from \(X_{ch}^{(i)}\)
12: Compute distance \(d_{CoM}\) between CoM coordinates and the diagonal of \(X_{ch}^{(i)}\)
13:if\(d_{CoM}\geq d_{A}\)then
14: Add \(X_{ch}^{(i)}\) to list \(\mathbf{M}\)
15:endif
16:endfor
```  
**Algorithm 1** Tensor-based anomaly detection pseudo code
## 2 Results and Discussion
### Switch Regions of CDC42 Are Not the Main Binding Sites for the GRDs
#### 2.2.2 IQGAP C794/C795 Binding Is Impaired by Switch Region and GRD Mutations  
Next, we measured the K\({}_{\text{d}}\) values for the interaction of IQGAP1 C794 or IQGAP2 C795, containing the GRD, RGCT and CT domains, with mGDP-bound and mGppNHp-bound CDC42 variants. Data shown in Figure 2D (Supplementary Figure S4) indicate that the substitution of two amino acids in the switch regions was not sufficient to largely impair the CDC42-C794 interaction. However, mGppNHp-bound, but not mGDP-bound CDC42 8 xSW exhibited a drastic reduction (86-fold) in its binding affinity for C794. For mGDP-bound CDC42, introduction of SW mutations only slightly decreased the affinity of C794. The IQGAP2 C795 binding to the CDC42 switch regions was not impaired by neither 2x nor 8x mutants of CDC42 in mGppNHp-bound state. Interestingly, IQGAP2 C795 showed a slightly decreased binding to the mGDP-bound CDC42 2 xSW mutant but no binding to the 8 xSW mutant, a much different result than obtained for GRD2 binding alone. The data from real-time stopped-flow fluorescence spectrometry (Figure 2E and Figure S5) showed both IQGAPs associated with similar k\({}_{\text{obs}}\) values, as observed in Figure 2D.  
The next question addressed was to what extent CDC42 binding of IQGAP1 FL was affected by the switch region mutations. Therefore, endogenous IQGAP1 was pulled down from HEK293 lysates using GDP-bound and GppNHp-bound GST-CDC42 WT, 2 xSW and 8 xSW. As shown in Figure 2F, IQGAP1 binding to CDC42 did not change with two amino acid substitution of the switch regions but was disrupted with the eight mutations. These experiments support our kinetic and equilibrium measurements and clearly indicate that the switch regions are significant for the IQGAP1 interaction with both GDP-bound and GppNHp-bound CDC42.  
Taken together, the presented data suggest a slightly different binding behavior of IQGAP1 and IQGAP2 variants for CDC42. Our results do not support the interacting mode between IQGAP and CDC42 based on the crystal structure [12] and the central role of the GRD in it [54] since the introduction of SW mutations of CDC42 clearly affected C795/C794 binding but only little the GRD binding. We, in contrary, propose that the interactions sites on IQGAP for complex formation with CDC42 GTP are clearly within the RGCT and might be different between IQGAP1 and IQGAP2.
## 2 Methodology
### Travellines for Ideal and Error Cases
#### 2.4.2 Position's Error Removal  
The travel time difference due to position error (\(\Delta Te_{xn}\)) is the travel time of the \(n\)th receiver calculated using Equation (10):  
\[\Delta Te_{xn}=\frac{e_{xn}\sin\theta_{c}}{V_{1}}, \tag{10}\]  
To calculate the first arrival travel time of the \(n\)th receiver for the position removal error case (\(T_{xn}\)), \(\Delta Te_{xn}\) must be removed from the perturbed case traveltime (\(T_{n}\)) as follows:  
\[T_{xn}=T_{n}-\Delta Te_{xn} \tag{11}\]
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 1 Introduction
### Modeling and Optimization--Tools and Techniques
For example, [PERSON] and [PERSON] [38] compared NSGA-II and PSO for the multi-objective optimization of a hybrid renewable energy system, finding that NSGA-II produced a more diverse set of solutions and a broader Pareto front, while PSO achieved faster convergence but with less diversity. In addition to these established techniques, newer nature-inspired algorithms such as Grey Wolf Optimization (GWO) [41] and the Moth-Flame Optimization (MFO) algorithm have been increasingly explored for energy system planning [42]. GWO, which mimics the social hierarchy and hunting strategies of grey wolves, has demonstrated strong performance in complex energy optimization tasks, offering a good balance between exploration and exploitation and showing robustness in avoiding local optima. Similarly, MFO, inspired by the navigation patterns of moths in nature, has been applied to power transmission and distribution problems, often in hybrid approaches with PSO to enhance search capabilities. A highly relevant study by [PERSON] et al. [43] addresses the optimization of the Short-Term HydroThermal Scheduling (STHTS) problem. The primary objective of the STHTS problem is to minimize the overall cost of power generation from a water reservoir while simultaneously meeting load demand within a specified time frame. The study presents a comprehensive comparative analysis of multiple algorithms, including genetic algorithms, the chaotic-chimp sine cosine algorithm (C-CHOA-SC), PSO, gravitational search algorithm (GSA), among others. The comparison includes more than 20 different algorithms. The results indicate that, overall, the performance differences among the various algorithms are not substantial, with maximum average performance deviations between algorithms of just over 1%. Nevertheless, the algorithm proposed by the authors (C-CHOA-SC) effectively solved the STHTS problem, achieving notable reductions in both fuel costs and emissions.
# Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning
## 3 Fault Feature-Extraction Method
### Fuzzy Entropy  
The value of the fuzzy entropy [31] is used to represent the signal complexity, and it uses the mean algorithm and the affiliation-function method to make the similarity measure between vectors fuzzy. The fuzzy entropy is similar to the theoretical properties of sample entropy and approximate entropy, and its fuzzy-entropy value is more stable than when the parameters are changed.  
With a set of sampling points of \(N\) of sequences \(\{u(i):1\leq i\leq N\}\), the fuzzy entropy is solved as follows.  
1. The sequence is formed as \(m\) dimensional vector, as shown in Equation (8): \[X(i)=\{u(i),\ u(i+1),\ \cdots,\ u(i+m-1)\}-u_{0}(i)\] (8) where \(i=(1,\ 2,\ \cdots,\ N)\), and \(u_{0}(i)\) is the mean of vector \(\{u(i),u(i+1),\cdots,\ u(i+m-1)\}\).
2. Calculate the maximum amount \(d_{ij}\) of the distance difference between the vectors \(X_{i}\) and \(X_{j}\), as shown in Equation (9). \[d_{ij}=max\big{\{}\big{|}X_{i}-X_{j}\big{|}\big{\}}\] (9)
3. Calculate the similarity \(D_{ij}\), which is defined by the exponential function \(u\), as shown in Equation (10): \[D_{ij}=u\big{(}d_{ij},\ n,\ r\big{)}=exp\big{[}-\big{(}d_{ij}/r\big{)}^{n} \big{]}\] (10) where \(u\) is the fuzzy-affiliation function of \(X_{i}\) and \(X_{j}\), and \(n\) and \(r\) are the gradient and the width of its boundary, respectively.
4. Define \(\varphi^{m}(n,r)\); the result is shown in Equation (11): \[\varphi^{m}(n,r)=\frac{1}{N-m
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 2 Methods  
This research was carried out at PT Kereta Api Indonesia (Persero), also known as PT KAI (Persero), which is Indonesia's largest railway operator. Figure 1 shows the research methodology used in this paper. First, an electric current sensor was developed to monitor the RPM equipment, which is the most applicable sensor in RPM monitoring implementations [11] and delivers a high level of precision in describing the electro-mechanical conditions [13]. Second, various measurement information was acquired for each type of RPM equipment condition using current-data acquisition (DAQ). The three conditions that were monitored were normal, warning, and failure. The obtained time-series current data were extracted into some relevant time-domain features. Third, the features were filtered to become training and testing datasets for machine learning modeling. This study employed six types of ML algorithms: Logistic Regression (LR), k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). These are supervised learning ML algorithms widely used in predictive maintenance applications, especially in fault diagnosis [5; 20]. The confusion matrix approach was utilized to evaluate the classification results of the six ML models [21]. The results were compared to determine which model achieved the best accuracy in predicting RPM equipment conditions.
## 3 The Proposed Approach
### Maintenance Optimization  
The optimization problem with the proposed model is aimed at finding optimal values for the following variables: the number of maintenance actions, the times to perform these maintenance activities, and their severities that minimize the overall maintenance cost. This second phase of optimization was performed using the genetic algorithm, following the steps illustrated in Figure 2.  
Figure 2: Illustration of the proposed optimization process.
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 4 Computational Experiments  
To design and analyse the RID models, simulation techniques have been applied to evaluate the distribution models' effectiveness as the simulation techniques provide greater precision [66]. For the computational experiment, information is synthesised based on the case study and related parameters presented in different disaster case studies [14; 67; 68; 69]. Disaster regions and supply point locations have been created analogous to the Chi-Chi earthquake in Taiwan [14]. The disaster region's data such as population and travel routes are considered as it is in the case of the Chi-Chi earthquake. At first, a static distribution model is applied and analysed to justify why the dynamic RID model is required for effective relief item distribution, hence, different ways of implementation of the dynamic distribution model are considered based on information availability.
# Probing Globular Protein Self-Assembling Dynamics by Heterodyne Transient Grating Experiments
## 2 Experimental Methods
### Transient Grating Experiments  
The HD-TG experiment is a time-resolved optical technique, based on the third-order non-linear optical response. The experimental details have been described in previous works [28; 33; 37; 40] and here we summarize only the main experimental aspects. Two infrared (1064 nm) laser pulses, with a temporal duration of 20 ps, interfere and produce into the sample temperature and density spatially periodic variation that generates a refractive index grating. The angle between the two pump pulses defines the wave-vector \(q\), which characterizes the spatial modulation of the sample optical properties. Its modulus is \(q=\left(\frac{4\pi}{\lambda_{ex}}\right)sin(\vartheta_{ex})\) where \(\lambda_{ex}\) and \(\vartheta_{ex}\) are the wavelength and the incidence angle of the exciting pulses, respectively. A third continuous-wave laser beam, at 532 nm, is used to probe the modulation of the material optical properties, induced by the pump beams. The time-resolved intensity of the diffracted probe provide information on collective relaxation phenomena in a wide temporal range, from nanoseconds to milliseconds, in a single experiment. To measure the natural damping of the induced acoustic oscillations properly, the pump was focused with a cylindrical shape to have on the sample an excitation grating extended in the \(q\)-direction, while the probe beam was focused to a circular spot in order to have a probe area with smaller dimensions in \(q\)-direction. The pump beams produced two acoustic pressure waves (i.e., two longitudinal bulk ultrasonic waves), which counter-propagated in the \(q\)-direction, their superposition created a stationary wave. The probe beam was scattered by the standing wave, which produced the oscillations in the acoustic signal. The induced standing wave had a lifetime related to the pump spatial extension in the \(q\)-direction (geometrical damping time). The natural damping time of the acoustic wave was properly extracted if it was shorter, compared to the geometrical one. Since in our samples the natural damping time was quite long (tens and hundreds of ns), a very precise alignment of the pump and the probe beams was required in order to measure the natural damping. Therefore, the alignment was done through a camera, which permitted micrometer control on the centering and on the spatial superposition of the beams.
# The Impact of Grafted Larvae and Collection Day on Royal Jelly's Production and Quality
###### Abstract  
Royal jelly (RJ), a secretion from nurse bees, is a key factor in honeybee caste differentiation and a high-value product in apitherapy. Despite its economic and biological importance, factors affecting its yield and composition remain insufficient. This study investigated the impact of grafted larval age and sex and the collection day of RJ on its yield and physicochemical characteristics. Three independent experiments were conducted using strong _Apis mellifera_ L. colonies. Larvae of different ages (first, second, and third) were grafted, and RJ was harvested 24, 48, and 72 h post grafting. Additionally, worker and drone larvae were used to assess the effect of larval sex. RJ was analyzed for moisture, protein, sugar, and 10-hydroxy-2-decenoic acid (10-had) content. Results showed that RJ yield significantly increased with collection day, with the third day being optimal. Protein content declined over time, while moisture content rose, although sugar levels and 10-HDA remained stable. Second-day larvae yielded the highest RJ volume without affecting composition. Larval sex did not significantly influence either RJ yield or composition. The results of this study may provide valuable insights into the quality determinants of royal jelly, enabling beekeepers to optimize production for both enhanced royal jelly yield and the rearing of higher-quality queen bees.  
Royal jelly production; age of grafted larvae; sex of grafted larvae; collection day; physicochemical characteristics; 10-HDA Article  
Laboratory of Agriculture-Seriatric, Aristotle University of Thessaloniki, Aristotle Farm, 57001 Thermi, Greece; [EMAIL_ADDRESS] (V.L.); [EMAIL_ADDRESS] (M.A.R.); [EMAIL_ADDRESS] (F.F)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS] (D.K.); [EMAIL_ADDRESS] (C.T.); Tel: +30-2310991752 (D.K. & C.T.)
# Cationic Lipid Derived from a Basic Amino Acid: Design and Synthesis
## 3 Results
### TEM Analysis Reveals Size and Morphology for Transfection  
The total lipid/pDNA ratio of 1:0.4 demonstrated an optimal electrostatic interaction, which is ideal for transfection, as it strikes a balance that is neither too strong nor too weak, ensuring efficient delivery of the genetic material, which was used for the subsequent assays. To characterize the liposomes, those that were stable were selected for future transfection, including those containing cholesterol in molar ratios of 1:1 and 2:1, as well as those containing DOPE in molar ratios of 1:1 and 1:2. The liposomes appeared spherical and avoid with a heterogeneous size ranging from 26.4 to 126 nm in diameter in all mixtures (Figure 8). Similarly, lipoplexes were analyzed by TEM, revealing complexes of various sizes ranging from 110 to 420 nm that formed associations with tubular-shaped (CholCadLys-cholesterol 2:1) (Figure 8D) junctions (CholCadLys-cholesterol 1:1) (Figure 8B) in all lipid mixtures (Figure 8).  
Figure 7: Electropherograms of lipoplexes formed with CholCadLys and helper lipids at different \(w/w\) ratios (1 \(\upmu\)g total lipids/different amounts of pDNA). **(A)** CholCadLys–DOPE 2:1; **(B)** CholCadLys–CholCad 1.2; **(C)** CholCadLys–CholCad 2:1; **(D)** CholCadLys–CholCad-DOPE 1:1;2; **(E)** CholCadLys–CholCad–DOPE 2:1:1. 1. pDNA control. 2. Ratio of 1:0.66 \(\upmu\)g DNA. 3. Ratio of 1:0.40 \(\upmu\)g DNA. 4. Ratio of 1:0.28 \(\upmu\)g DNA. 5. Ratio of 1:0.22 \(\upmu\)g DNA. Ratio of 1:0.18 \(\upmu\)g DNA.  
Comparative Analysis of Size and Concentration of Liposomes and Lipoplexes for Transfection Applications  
The liposomes were observed to be spherical with a heterogeneous size ranging from 102 to 136.6 nm in diameter, and the approximate particle count was 2 \(\times\) 10\({}^{8}\) particles/mL (Table 1). Similarly, lipoplexes with a total lipid/pDNA ratio of 1:0.4 were analyzed, revealing associations among them in multiple forms, with a heterogeneous size ranging from 92.3 to 142 nm in diameter, and the approximate particle count was 2 \(\times\) 10\({}^{8}\) particles/mL. The quantity of liposomes per ml is crucial for transfection (Table 1).  
Figure 8: TEM micrographs of liposomes and lipoplexes (total lipid/pDNA ratio of 1:0.4). (**A**) Liposomes formed with CholCadLys–cholesterol at a molar ratio of 1:1. (**B**) Lipoplexes formed with CholCadLys–cholesterol at a molar ratio of 1:1. (**C**) Liposomes formed with CholCadLys–cholesterol at a molar ratio of 2:1. (**D**) Lipoplexes formed with CholCadLys–cholesterol at a molar ratio of 2:1. (**E**) Liposomes formed with CholCadLys–DOPE at a molar ratio of 1:1. (**F**) Lipoplexes formed with CholCadLys–DOPE at a molar ratio of 1:1. (**G**) Liposomes formed with CholCadLys–DOPE at a molar ratio of 1:2. (**H**) Lipoplexes formed with CholCadLys–DOPE at a molar ratio of 1:2.
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 4 Societal Benefit
### Water  
It is beyond the limitation of this paper to enumerate the importance of water to human life, and the approaches to monitor water in various forms. Generally, water related applications fall into the following categories: health, flood, water resources and management [122]. We have addressed health category in Section 4.5, here we list briefly other categories.  
With increasing populations and water usage coupled with climate variability and change, water issues are being reported by numerous groups as the most critical environmental problems facing us in the 21 st century [123]. At the turn of this century, over 2 billion people on the Earth lacked adequate drinking water or sanitation; projected to 2025, population growth is expected to impact water availability more significantly than the impacts of greenhouse warming on water systems [122]. To address this water resource and management issue, a group of satellite-borne sensors keep monitoring evaporation [124], atmospheric river [125], clouds and precipitation [126], surface water extent, elevations, water level, groundwater [127], soil moisture [128], _etc._ These Earth observations provide information for informed decision making and for monitoring conditions and progress at multiple spatial and temporal scales. Hence, most physical variables relating to these processes are identified as ECVs, and archived as CDRs. For example, other than directly monitoring global precipitation [22], the PERSIANN-CDR can contribute to applications such as hydrological changes and flood increase [129], fresh water resources over water-stressed islands [130], and more.  
Climate variability and changes, together with land use changes, have altered naturally occurring flood hydrographs. The patterns and extents of floods directly impact the economies, lives, and health of communities within flooded areas. As a consequence, floods and associated landslides caused more than 55% of the worldwide significant natural disasters during 2002-2011; they killed over 65,000 people, affected over 1.1 billion people and cost an estimated $280 billion (US Dollars in 2011), according to World Disaster Report [131]. The contribution of satellite remote sensing to this issue is two-fold: (1) directly mapping inundation area using optical or Synthetic Aperture Radar imagery; and (2) using remotely observation and derived CDRs of precipitation, land cover, vegetation, topography, _etc._ as inputs to hydrologic models for flood monitoring and forecasting [132].  
Due to the vital importance of water, the institutions, such as National Science Foundation, Department of Defense, NOAA and NASA have their own water related projects, e.g., NASA Applied Sciences Program [133] has supported such projects to meet the societal requirements.