esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Directions
In particular, future works could take into account remote sensing and reanalysis data as input for ML models aimed at streamflow predictions. For instance, [PERSON] et al. [75] employed Evapotranspiration (ET) data to limit the uncertainty of ET and enhance streamflow predictions, showing that incorporating additional variables could enhance the outcome of the forecasts. Furthermore, [PERSON] et al. [76] also reported that the usage of ET data obtained through remote sensing could improve streamflow forecasting. Furthermore, in the present research, time-lagged streamflow and daily average rainfall data were leveraged as input features. However, for future works, it might be interesting to include the surface runoff as an additional input feature. To achieve the latter point, the underground flow component could be eliminated using numerical filters, as done in previous research works which employed daily precipitation and runoff with a 2 day lag to forecast the streamflow in mountainous regions [77, 78, 79]. The latter method could potentially enhance the prediction of peak streamflows in the basin. Finally, despite the obtained satisfactory results in predicting the streamflow using rainfall data derived from remote sensing, the employed data were not calibrated with respect to surface data. Lackof calibration could lead to estimation errors caused by the global scale of the employed products. Therefore, calibrating data could enhance the effectiveness of the used products.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 3 Results and Discussion
### Computational Performance: GPU Acceleration vs CPU
From a practical deployment perspective, reducing training time from nearly an hour to approximately 27-41 min means that models can be retrained daily or multiple times per day, allowing the system to:  
* Adapt to sudden changes in water consumption patterns;
* Update forecasts based on real-time sensor data;
* Perform continuous hyperparameter tuning to optimize performance. Had GPUs not been used, training each model would have taken close to an hour, making frequent updates challenging. In contrast, GPU acceleration allows for efficient retraining, ensuring that the forecasting system remains accurate as new data is incorporated.
# A Novel Hybrid Approach for a Content-Based Image Retrieval Using Feature Fusion
###### Abstract
Although multiple methods of CBIR exist, the semantic gap issue is one of the big problems between the high-level semantics that humans observed and the low-level pixels of images that machines captured [18]. DL algorithms are used in multiple diverse fields, such as in object detection present in the surveillance video [19], leaf disease detection [20], video summarization [21], and detection of glaucoma in fundus imaging [22], as well as in CBIR. Nowadays, CBIR is the most commonly adopted system for retrieving images based on feature extraction. The feature extraction process is the most prominent step in the CBIR method [23]. Therefore, in this paper, we extract the feature with the help of two different DL models and fuse them to get the most prominent features. The following are the key contributions:  
* We proposed a novel system to capture the top five most identical images among 10,972 images, focusing on their visual features;
* The algorithm synthesizes two deep learning models: VGG16 and Resnet50, and KNN machine learning. We have extracted the features of images using Resnet50 and VGG16 and fused them to get better results and the most prominent features;
* To increase the performance of the proposed CBIR method, images are pre-processed, i.e., all the images are initially transformed from RGB to BGR for feature extraction, and then each color channel is zero-centered to the ImageNet dataset without scaling;
* We built a web interface to show the result or performance of the proposed CBIR system;
* We used the images from three different datasets: the coral-images dataset, the natural images dataset, and the fruits 360 dataset [24; 25; 26]. The images of these three datasets are used to retrieve similar images out of 28 different classes. The remaining sections are as follows. Section 2 describes the related work. Section 3 concentrates on the CBIR system's methodology and implementation. Section 4 shows the results of our proposed CBIR system. Section 5 compares the proposed CBIR system with the existing ones. Finally, Section 6 concludes the work.
## 10 What Modifications Does DnaA Undergo and What Are Their Roles?  
It has been proposed that a hyperstructure might be assembled if enzymes (such as protein kinases and acetyltransferases) and their NAP substrates were to associate with one another in a positive feedback loop in which, for example, the modification of an NAP by its cognate enzyme increases the probability of colocation of both the NAPs and the enzyme [69]. In line with this, the acetylation of a lysine residue (K178) prevents DnaA from binding to ATP and inhibits initiation, whilst the acetylation of another lysine residue (K243) also inhibits initiation but does not affect the ATP/ADP binding affinity of DnaA or the ability of DnaA to bind to the _dnaA_ promoter region and to DARS1 [70].  
DnaA binds cAMP with a Kd of a similar order to that with which it binds to ATP; indeed, the affinity of DnaA for cAMP is such that most of the cell's DnaA should be bound to cAMP when the latter is present at the physiological concentration of 1 \(\upmu\)M [49]. cAMP bound to DnaA is chased by ATP but not by ADP (note that there is only one cAMP binding site on the protein [49]). In vitro, cAMP stimulates DnaA binding to _oriC_ and to DnaA sites elsewhere in the chromosome [49]; in vivo, the addition of cAMP to a _cya_ mutant (which encodes the adenylate cyclase that catalyses the production of cAMP) increased the level of DnaA [71]. Significantly, despite DnaA's stability in vivo, it has recently been shown to be degraded in vivo in ATP depletion conditions [72], and one possibility is that cAMP helps to both protect DnaA from degradation and regenerate DnaA-ATP from DnaA-ADP (by causing the release of the bound ADP). This raises the question of whether the state of the environment as reflected in a cAMP signal is transduced by the level of DnaA and by the DnaA-ATP: DnaA-ADP ratio into the expression of DnaA-regulated genes and cell cycle timing.  
In the case of _Caulobacter crescentus_, the phosphorylation status of CtrA is central to cell cycle progress [73]. The many possible post-translational modifications to DnaA and to other proteins in the initiation and replication hyperstructures, therefore, include phosphorylation, and several other modifications, such as succinylation, methylation, propranolol, malonylation, deamidation of asparagines, and glycosylation (for references see [69]). Another post-translational modification--and one that is largely ignored--is the covalent addition of poly-(R)-3-hydroxybutyrate (PHB) to proteins [74]; one proposed function of such addition to NAPs would be to regulate their interaction with nucleic acids [75]. An important question is, therefore, whether DnaA undergoes modifications like the addition of PHB and, if so, does such modification help the type of hyperstructure into which DnaA assembles?
## 2 AnFIS-PID
### Implementation  
Currently, standard ANFIS controllers are implemented in two main architectures, parallel and serial, where parallel computation is fast but consumes a lot of resources, while serial requires a lot of execution time [28]. In this paper, the ANFIS-PID controller is implemented using a combination of parallel and serial approaches. Based on the sampling frequency of the temperature acquisition chip, a timeline for a single output of the controller is created, and according to the time forward, the layers are executed serially in sequence with each other, while the contents of the same layer are executed in parallel. At the same time, an FPGA with high-speed parallel computing capability is used as the carrier for the ANFIS-PID controller implementation.
## 3 Results and Discussion
### Fragility and CMR Analysis
For the far-field seismic scenario, the CMR equals 11.67, whereas, for near-field excitations, the CMR value drops to 6.67, which is 50% less safe than the far-field events. However, for repetitive earthquakes, the CMR score is 4.67 which is more significant to obtain more vulnerable detections on the structures. As a result, the collapse rate of the tower under far-field ground motion excitation is lower than for the near-field and repetitive ground motion cases, since the CMR of the far-field ground motion shows the highest value. Thus, the use of CMR as a seismic measure can be useful for the evaluation of the tower during seismic events. The outcomes from the IDA and fragility analysis are consistent with the CMR. Furthermore, the analyses demonstrate that the transmission steel tower under near-field ground motion excitation is more likely to collapse than a tower under far-field ground motion excitation during a single event, and more likely to damage under multiple seismic events, showing that the tower can withstand the desired seismic performance or MCE of 0.30 g in keeping with the Lebanese seismic norm. Figure 15 shows the fragility curve related to CMR values and their intensity measures and Table 8 shows the CMR values obtained from the fragility curves. Finally, the results show that the frame elements of the transmission steel tower would suffer significant damages under near-field and repetitive ground motion than far-field ground motion, despite the fact that the damage probability at far-field ground motion is greater than near-field ground motion before achieving 1.0 g. As a result, the tower damages caused by near-field ground motion as a single event and repetitive ground motions as multiple events are extremely devastating, as the damage probabilities for the three performance levels are much higher than the far-field ground motion.
## 4 Numerical Results with Fixed Time-Step Sizes
### Experiment 1 with Small Value of Parameter \(m\)  
In this experiment, the following parameters are used:  
\[m=2.4\;,\;\;\alpha=3.1\;,\;\;c_{2}=5.96\times 10^{-13}\;,\;\;N=1000\;,\;x_{0}=0. 055\;,\;\;\Delta x=3\times 10^{-4},\;t^{0}=0.5,\;t^{\mathrm{fin}}=1.5 \tag{19}\]  
The CFL limit is increasing from \(h_{\mathrm{CFL}}^{EE}\left(t^{0}\right)=2.4\times 10^{-7}\) to \(h_{\mathrm{CFL}}^{EE}\left(t^{\mathrm{fin}}\right)=9.0\times 10^{-7}\) and the stiffness ratio is decreasing from \(4.1\times 10^{6}\) to \(2.6\times 10^{6}\). The errors as a function of the time-step size are presented in a log-log diagram in Figure 5. The errors cannot decrease below the residual error (from space discretization), which is visible in the bottom left of the figure, because we employ a fixed space step size and only reduce the time-step size. The RK4 method is unstable for large and medium time-step sizes, and therefore it is visible only at the bottom left side of the figure. Two groups of the stable methods are clearly distinguishable: the slowly and the quickly converging algorithms. This latter one consists of the OOEH, RH, SH, ASH, DE, and LH methods, and the remaining 11 converge more slowly. In our paper [48], we explained the reasons for this behavior based on the truncation errors. Note that a slowly converging algorithm does not mean that it is useless, because it can possess other useful properties, such as the previously mentioned minimum-maximum principle.  
In Figure 6, one can see the analytical \(u\) function at the initial and final time, as well as the numerical solution at the final time in the case of two methods, namely the CCL and the LH methods, for those time-step sizes when they start to produce a solution which may be acceptable in some engineering applications. Moreover, in Figure 7, we also plotted the time development of the maximum errors of all but the RK4 schemes for a fixed time-step size \(h=2\times 10^{-4}\).
## 3 Results and Discussion  
### Preliminary Experiments on Glycerol Removal and Freeze-Drying  
The NEB polymerase Bst 3.0 contains a non-negligible glycerol content of 50 \(v/v\%\) in its storage buffer. Despite the low volume of polymerase used (final glycerol concentration in the LAMP reaction mixture 2 \(v/v\%\)), the hygroscopic properties of glycerol can lead to incomplete drying or a reduced shelf life of the enzyme when lyophilizing the reaction mixtures [19]. Therefore, a comparison was first made between the removal of glycerol from the polymerase storage buffer by dialysis or by spin filtration with subsequent lyophilization. In addition, GFP was used and conducted according to the same procedure regarding lyophilization. The procedure is described in the Supplementary Material (Figure S1, Supporting Information).  
Both spin filtering the polymerase and dialyzing led to satisfactory results, but the results with spin filtering were much more reproducible. This is possibly due to the longer duration of dialysis (18 h) compared to spin filtering (maximum 2 h), as the activity of the polymerase can be attacked despite reduced temperatures (6 \({}^{\circ}\)C in both cases). The probability of this increases with the duration of the process.  
The reduction in the stabilizer concentration (trehalose) from 0.3 M to 0.15 M led to a significant loss of activity. No activity was detected without the addition of trehalose. Therefore, all further experiments were carried out with the spin-filtered Bst 3.0 or the GFP and 0.3 M trehalose in the reaction mixture.  
The effect of different stabilizers on different proteins and the optimal concentration differs from protein to protein with 0.3 M being the proposed minimal concentration for effective stabilization [33]. Using the example of maltose and catalase, [PERSON] et al. were able to show that it is not the absolute concentration of the stabilizer but the ratio of stabilizer to protein that is decisive. The minimum concentration must be sufficient to cover the protein surface with a monomolecular layer [34].
## 2 Materials and Methods
### Groundwater Depth and Soil Moisture and Salt Measurement  
Within the present study site, a well located at a distance of 30 m away from the EC tower and 100 m from one tributary was drilled to about 6 m depth; while an automatic pressure transducer (HOBO-U20, Onset Computer Corporation, Bourne, MA, USA) was installed in order to monitor the changes in the submerged pressure (\(P_{\text{sub}}\), kPa) at 0.5 h intervals. The recorded atmospheric pressure (\(P_{\text{a}}\), kPa) from the EC tower was subtracted from \(P_{\text{sub}}\) in order to obtain the pressure that was exerted only by the water column above the sensors. Water head data were then converted to the groundwater depth (GWD, m), using the measured distance between sensors and the ground surface at the observation well. The ground water depth (GWD) refers to the distance from the underground water to the surface, and the values can be positive according to other researchers [38]. The larger the value is, the deeper the groundwater is.  
In 2015 and 2016, the soil moisture/salts/temperature sensors (SMEC 300, Spectrum Technologies, Inc., Aurora, IL, USA) were installed at 10, 30, 50 and 80 cm depths in six positions (i.e., in two directions, north and west, and three distances of 50, 150 cm and 250 cm from the selected trees) in order to obtain the soil volumetric moisture content (\(\theta\), %) and the electrical conductivity (EC, mS cm\({}^{-1}\)). These were used to reflect the soil moisture and salt regime in the stand, respectively. The mean values of the different directions and distances were used to analyze the temporal variations in \(\theta\) and EC of the stand.
## 4 Thermal Model of Power Semiconductors
Thus, the junction temperature (\(T_{j}\)) should always be kept below the maximum allowable value. In addition, the temperature swing (\(\Delta T_{j}\)), maximum (\(T_{jmax}\)), and minimum (\(T_{jmin}\)) temperatures are also important factors for failure [48]. Mechanical failures occur when the power semiconductor has experienced a specified number of thermal cycles, which will be discussed more in detail at a later moment. Therefore, the thermal model should also be able to determine the temperature fluctuations precisely. There are different approaches for estimating the junction temperatures such as the finite elements method (FEM) and thermal equivalent circuits [49]. Resistance-Capacitance (RC) thermal model is the most popular way of estimating temperature due to its simplicity and considerable accuracy. This flexible model was first introduced in 1959 [50], and today it can be used for 1-D, 2-D, and 3-D modeling. The RC thermal model is usually defined by two types: Foster and Cauer. Although the Foster model does not provide a physical concept, it is easy and possible to transform this model into the same-order Cauer model by solving mathematical equations [51]. In this study, a Cauer ladder network is considered for the junction-to-case section. In order to implement an IGBT/diode into this thermal model, it is required to represent the heat conduction path from the upper layer (junction) to the lowest layer (case); then, it is needed to continue this heat flux path from the case to the ambient through a heatsink. Due to the similarity of the electro-thermal model, power losses would be considered as a continuous current source in the RC thermal model. This process is depicted in Figure 7.  
It is evident that there are different layers with various materials and physical dimensions from junction to case, and one RC-pair is assigned to each layer based on the layer's thickness, thermal conductivity, effective area, specific heat, and density. Moreover, physical modeling in the simulation environment can be used to display the heat transfer between the case and the ambient, so that the case-to-heatsink and heatsink-to-ambient thermal transfers are conduction and convective, respectively. More detailed explanations of the implemented physical model are provided in [47]. Due to the dependence of the power dissipations on the temperature, computations must be made as a closed recursive loop, as shown in Figure 8.  
Figure 7: Cross\(-\)sectional representation of IGBT power module.
## 2 Literature Review
### User Preferences and Logistics Path Planning  
There is a significant gap between the courier services available for last-mile delivery and consumers' preferences for these services [24]. The effort required for social interaction deters consumers from opting for manned delivery methods, making unattended alternatives--such as home delivery and self-pickup--even more appealing [25]. To enhance user accessibility, it is necessary to develop corresponding positioning strategies during the delivery process, integrating user preferences with the selection of self-service lockers. User behavior preferences are closely related to spatial-temporal trajectories, which contain a wealth of information about user travel preferences, including spatial characteristics, temporal sequences, and other external descriptive attributes [26]. Thus, trajectory data is extensively used across various domains, including the identification of user hotspots, the recommendation of optimal routes, and the suggestion of points of interest [27; 28; 29]. The integration of user behavior patterns into logistics planning is not new, but their application to PLC selection is relatively underexplored. Studies have shown that individuals exhibit regular and predictable spatiotemporal behavior, which can be harnessed to improve the efficiency of parcel locker usage [2]. This study integrates behavioral geography and operations research, conceptualizing users' spatiotemporal trajectories as a \"dynamic demand field,\" thereby innovatively expanding the application boundaries of KDE in the logistics domain. Specifically:  
Contribution from Geography: Through activity hotspot clustering, the spatial anchor points and path dependency characteristics of user behavior can be revealed, forming a methodological complement to the trajectory recommendation algorithm proposed by [PERSON] et al. (2021) [26].  
Contribution from Operations Research: A multi-objective optimization model is constructed to simultaneously balance distance, time, and parcel attributes, overcoming the limitations of traditional single-objective models (e.g., [PERSON] et al., 2015) [20].  
This interdisciplinary framework establishes a new paradigm for \"human-centric logistics,\" bridging the theoretical gap between behavioral analysis and facility optimization.
## 2 Selenium from Soil to Food Bowl  
### Selenium in Soil and Surface Waters  
Selenium is a common element found in nature, in the Earth's atmosphere, lithosphere, biosphere and hydrosphere [17]. Se is present in the soil in inorganic forms, such as selenide (Se\({}^{2-}\)), elemental selenium (Se\({}^{(0)}\)), selenate (SeO\({}_{4}\)\({}^{2-}\)) and selenite (SeO\({}_{3}\)\({}^{2-}\)) and in organic forms (selenocysteine \"Se-Cys\" and selenomethionine \"Se-Met\") [18]. The availability of selenium for plant absorption is highly dependent on the chemical and physical conditions of the soil. The concentration of Se in the soil is very different from one country to another and even from one region to another within the same country [19]. Together with atmospheric input, bedrocks are the main source of Se in soil, with varying levels depending on their nature, sedimentary rocks (e.g., shale, limestone and sandstone) being the richest in Se [20]. Thus, the concentration of Se in soils is strongly dependent on the concentrations of the bedrock [21].  
In water, Se is present in trace amounts, mainly in the form of selenate and selenite, where Se is present in +VI and +IV oxidation state, respectively [22]. Se is more abundant in groundwater than in seawater, due to the elution of Se from source rocks and over-fertilization of soils with mixtures rich in Se compounds [21,23].  
Another source of Se in the soil is due to natural and anthropogenic input from the atmosphere [24]. The combustion of coal, crude oil and the use of agro-technical processes mainly in fertilization contribute enormously to the content of Se in the soil [25]. It has been reported that Se levels are important in people living near processing plants, for example, sulfuric acid production or cooking [26,27]. Volcanic gases are also a major source of Se to the atmosphere, together with Se methylation and volatilization by ocean algal bloom [24]. Biomethylation of Se by microorganisms and the decomposition of Se-rich organic matter are additional factors that contribute to the enrichment of the atmosphere with Se [28]. In these cases, volatile selenium compounds such as dimethylselenium (DMeSe), hydrogen selenide (H\({}_{2}\)Se) and selenium oxide (SeO\({}_{2}\)) are produced [29].
## 2 State-of-the-Art in Light Dimming and Lights-Off Visible Light Communications
### Related Work  
The data transmission in VLC is done through Intensity Modulation (IM), i.e., the variation of the intensity of light in the time domain, and the data reception is done through Direct Detection (DD). The capability of an LED in terms of bandwidth is no more than a few megahertz, so one research area is related to modulation formats that can overcome this limitation. Based on this requirement, the modulation techniques mainly used in VLC can be classified as Single Carrier Modulation (SCM), Color Shift Keying (CSK) modulation, and Multi-Carrier Modulation (MCM) [24] (Figure 3). Various dimming techniques can be implemented based on these modulations.  
Figure 2: Light dimming in VLC: (**a**) The frame structure for the method of inserting compensation symbols; (**b**) Example of controlling the brightness in OOK modulation by adding compensation symbols; (**c**) Example of controlling the brightness in OOK modulation by pulse width variation; (**d**) Example of varying the brightness by controlling the amplitude of ones and/or zeros; (**e**) Example of varying the brightness in the out-of-band frequency.