esa-sceva/satcom-chunk-collection huggingface dataset:
# Pharmacological and Cosmeceutical Potential of Seaweed Beach-Casts of Macaronesia
## 3 Results and Discussion
### Antibacterial and Antifungal Activities  
Antibiotic resistance, increasingly recognized as a growing global health issue, hinders the treatment of certain types of infections and can be a cause of death. Antibiotic drugs are losing effectiveness at an alarming rate, mainly due to their misuse in humans and animals [37]. In order to slow this trend, antibiotics should be used only when needed, and the discovery of new antimicrobial drugs is urgently demanded to solve such resistance [38]. Furthermore, it is known that Gram-positive bacteria are more sensitive to antibiotic compounds because these lack the extra outer layer of lipopolysaccharidesaccharides and proteins in their cell membrane complex structure. Contrarily, this structure is present in Gram-negative bacteria, which often prevents transport and inclusion of antibiotic drugs, providing higher resistance and protection of Gram-negative bacteria against antibiotic molecules [39].  
This study attempted to add a contribution to the quest of new antibiotic products by evaluating the seaweed beach-casts extracts and fractions following a microdilution assay for determining cell survival, conducted employing four species of bacteria (_B. subtilis_, _E. coli_, _S. aureus_, _S. enterica_). However, no activity was recorded for any of the beach-cast extracts against the four bacterium strains assayed at tested concentration of 100 ug/mL.  
Antifungal activity was also evaluated following a variation of the microdilution cell inhibition method determining cell survival and/or growth inhibition of two yeasts, i.e., _S. cerevisiae_ and _C. albicans_, but no antifungal activity was recorded for any of the beach-cast samples at the tested concentration of 100 ug/mL.  
Finally, all samples were also evaluated against a battery of eight major phytopathogenic fungi (Aspergillus fumigatus, Botrytis cinerea, Fusarium solani, Geotrichium candidum, Screrotinia sclerotiorum, Rhizcotnia solani, Trichoderma viride, Verticillium dahliae) performing a growth inhibition assay on solid agar growth medium aseptically loaded with 100 ug/mL of each individual alga extracts contained in small Petri dishes; however, no inhibition results were obtained.
## 3 Problem Formulation  
Let \(b_{ij}\) be a binary variable that denotes the absence or presence of an association between the \(i\)th device and \(j\)th BS and let \(u_{ij}\) be the utility resulting from this association. The BS assignment problem can be posed as the search for those \(b_{ij}\) that maximize the global network utility subject to several network constraints. This can be expressed as an optimization problem of the form:  
\[\max_{b_{ij}} \sum_{j=1}^{N}\sum_{i=1}^{M}u_{ij}b_{ij}\] (4a) s.t. \[\sum_{i=1}^{M}\alpha_{ij}b_{ij}\leq 1,\;j=1,\ldots,N \tag{4b}\] \[\sum_{i=1}^{M}\beta_{ij}b_{ij}\leq 1,\;j=1,\ldots,N\] (4c) \[\sum_{j=1}^{N}b_{ij}=1,\;i=1,\ldots,M\] (4d) \[b_{ij}\in\{0,1\} \tag{4e}\]  
The constraints (4b) and (4c) ensure that the number of associated devices does not exceed the total radio and backhaul resources at BSs, respectively. The restriction (4d) means that each device can only be associated with one BS. Finally, recall that (4e) suggests that the association variable \(b_{ij}\) must be binary. The problem (4a)-(4e) is a combinatorial optimization problem due to the binary variable \(b_{ij}\), so that solving the problem with exact algorithms may be difficult, even for a small number of \(N\) and \(M\)[33, 34]. Therefore, we reformulate the original BS assignment problem in order to make the problem more tractable. In particular, we aim to reduce the number of constraints and model the association process as a message passing based on pricing values of BSs. To this end, we make use of the dual decomposition theory that has been commonly used to design distributed algorithms in communication networks.
## 4 Results and Discussion  
### Anglesite  
Anglesite is a lead sulfate (PbSO4) which takes its name from the Island of Anglesey in the Irish Sea (Wales, UK), where it was first identified in 1783. Considerable crystals come from Sardinia (Italy), Scotland, Australia, and Mexico. The pure mineral is colourless, but it can take on shades of yellow to green or blue, although the latter are very rare.  
Anglesite in the Masticarro Mine is an oxidation product of galena [1; 2; 3; 4]. In terms of its crystal structure, anglesite is comprised of SO4 tetrahedra with Pb2+ in pseudo-eight- to twelve-fold co-ordination with the oxygen ions [25]. In the study area, it usually has an orthorhombic euhedral habit, at times elongated, and is colourless with a vitreous or adamantine luster (Figure 2). In some areas, it has streaks along the main axis of elongation (Figure 2). Colourless crystals of anglesite measure up to 2 mm in length (Figure 2) and they occasionally show autoepitaxial growth parallel to the c axis (Figure 2). Under SEM, all anglesite crystals appear to be euhedral and were often found nestled in cavities lined with galena (Figure 3).  
Figure 2: Optical image of well-developed colourless anglesite crystals from the Masticarro Barite Mine (Catanzaro, Southern Italy).  
The semi-quantitative EDS/SEM chemical analysis revealed a PbO content of 71.8 wt% and a SO\({}_{3}\) content of 22.6 wt%, in agreement with the existing literature data [26,27]. Moreover, copper impurities (about 5 wt%) were detected on the crystal surfaces (Figure 3).  
The Raman spectrum of anglesite (Figure 4) reveals the typical bands at 1156, 1060, 978, 641, 609, 451, 439, 135, 100, and 60 cm\({}^{-1}\) (Table 1). The relatively huge unit cell gives ascent to a somewhat complex Raman vibrational spectrum (Figure 4). In particular, we have the \(v_{1}\) (symmetric stretch of the (SO\({}_{4}\))\({}^{-2}\) tetrahedra) mode at 978 cm\({}^{-1}\), the \(v_{2}\) (tetrahedral symmetric bend) mode with two components at 439 and 451 cm\({}^{-1}\), the \(v_{3}\) (tetrahedral antisymmetric stretch) mode with two components at 1156 and 1160 cm\({}^{-1}\), and the \(v_{4}\) (tetrahedral antisymmetric bend) mode at 641 cm\({}^{-1}\) and 609 cm\({}^{-1}\)[28, 29, 30, 31, RRUFF ID R050408].  
Figure 3: (**a,b**) SEM image with magnification of anglesite from the Mastricarro Barite Mine (Catanzaro, Southern Italy) and corresponding EDS spot analysis (**c**) acquired in the point indicated by red filled square.
## 7 Simulation Results and Comparison
### Simulation Results  
The CCC (current chopping control) method is common in SRM control and was investigated in [27]. The simulation results for the proposed TSF1 (\(r\) = 4) are compared with the CCC and the conventional TSFs at a torque reference \(T_{ref}\) = 1.5 Nm and a rotor speed of 1000 rpm (Figure 11a-d). Since the rotor speed 1000 rpm of all TSFs is higher than the maximum rotor speed \(\omega_{max}\), the phase current cannot track its reference. The torque ripple factors T.R% for the CCC, the linear, sinusoidal, and the proposed TSFs are 116.7%, 28.7%, 22.0%, 23.5%, respectively. The rms currents are 3.5 A for the linear and sinusoidal TSFs, and 3 A for the TSF1 (\(r\) = 4).  
The simulation results for the CCC, the linear, the sinusoidal, and the proposed TSFs at a torque reference of \(T_{ref}\) = 1.5 Nm and a speed of 3000 rpm are shown in Figure 12a-d. As the rotor speeds up, the phase current deviates from its reference significantly during the current rising region. And the phase current exhibits a tail phenomenon at the same time during the current declining region. Therefore, the output torque cannot track its reference accurately and the torque ripple increases obviously. The torque ripple factors \(T.R\)% are 182.3%, 61.3%, 60.8%, and 36.6%, respectively. For the CCC, the linear, the sinusoidal, and the proposed TSF1 (\(r\) = 4), the peak phase currents are 8.8 A, 9.4 A, 9.5 A, and 5.5 A, and the rms phase currents are about 5.4 A, 5.4 A, 5.3 A and 3.8 A, respectively. It is evident that the CCC method results in a comparatively smaller peak phase current compared with the conventional TSFs. However, the torque ripple is not acceptable, because it is three times higher than that of the proposed TSF1 (\(r\) = 4). The linear and sinusoidal TSFs almost have the same performance with regard to the torque ripple and the peak phase current, which is approximately twice as high as those for the proposed TSF1 (\(r\) = 4). The proposed TSF1 (\(r\) = 4) results in the lowest torque ripple, which is 1/2 of the torque ripple of the linear TSF. In addition, the required phase current is lower than for the CCC. The control performance is clearly improved by using the proposed TSF1 (\(r\) = 4).  
Figure 10: Block diagram of the torque control using the TSF method.  
Figure 11: Simulation results (\(T_{n\!f}\) = 1.5 Nm, 1000 rpm): (**a**) current chopping control (CCC); (**b**) Linear TSF; (**c**) Sinusoidal TSF; (**d**) TSF (\(r\) = 4).  
Figure 12: Simulation results (\(T_{n\!f}\) = 1.5 Nm, 3000 rpm): (**a**) CCC; (**b**) Linear TSF; (**c**) Sinusoidal TSF; (**d**) TSF1 (\(r\) = 4).
## 4 Experiments
### Experimental Design  
The primary objective of the experiments conducted in this study is to evaluate the effectiveness of multimodal feature fusion for urban infrastructure analysis using scene graphs and image data. By integrating scene graph embeddings with image features, we aim to improve the classification accuracy of economic activities in urban areas.  
In this study, we applied five-fold cross-validation to evaluate the performance of the CNN model. This method divides the entire dataset into five subsets, with each subset being used once as a validation set while the remaining four subsets are used for training. This process is repeated five times, and the average performance across all folds is reported. This approach ensures that the model is tested on all parts of the dataset and provides a more reliable estimate of the model's generalization ability.  
To compare the performance of the CNN model with other methods, we used several performance metrics, including accuracy, F1 score, precision, and recall. These metrics were compared across all models to assess the effectiveness of each in handling multimodal data. In particular, the CNN model was compared with other baseline methods such as traditional machine learning models and pre-existing deep learning architectures, with the goal of demonstrating its superior capability in capturing spatial and contextual relationships within the dataset.  
The dataset was split into three subsets: training, validation, and testing. The training set, which accounts for 70% of the total dataset, consists of 31,500 images, providing the model with a substantial amount of data for learning. The validation set, representing 15% of the dataset, includes 6750 images and is utilized for hyperparameter tuning and model  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Number of Images** & **Number of Scene Graphs** \\ \hline Commercial Areas & 10,000 & 8000 \\ Residential Areas & 15,000 & 12,000 \\ Industrial Areas & 8000 & 6000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset distribution across urban categories.  
selection. Finally, the testing set, also comprising 15% of the dataset with 6750 images, is reserved for evaluating the model's generalization performance on unseen data.
## 3 OFDM Methodology  
It is used for high data rate transmission over the communication system, where it also solves the problem of multipath and delay time for wireless communication. OFDM uses the multi-carrier to enhance the bandwidth efficiency and data rate [49]. In this work, we use OFDM under the different methods of implementation in FFT, DWT and FrFT. An OFDM transmitter and receiver can be described using the Inverse Discrete Fourier Transform (IDFT) and Discrete Fourier Transform (DFT) blocks [50] and also the IFFT and FFT algorithms are then used to dramatically reduce the system's complexity [51; 52]. The transmitted signal data from the user represented as N point of IFFT:  
\[x_{i}=\frac{1}{N}\sum_{n=0}^{N-1}X_{n}e^{\frac{\beta m\beta}{N}}, \tag{1}\]  
and FFT can be represent as:  
\[X_{n}=\frac{1}{N}\sum_{i=0}^{N-1}x_{i}e^{\frac{-\beta m\beta}{N}}. \tag{2}\]
# Some Critical Insights into the Synthesis and Applications of Hydrophobic Solid Catalysts
## 2 Synthesis of Hydrophobic Catalysts  
In this section, the hydrophobization strategies for the synthesis or modification of zeolites, silicas and polymeric catalyst will be discussed.  
There are mainly two ways to make a catalyst surface hydrophobic: A total synthesis of a hydrophobic catalyst is possible by carefully tuning the precursor or the reaction conditions to achieve the proper amount of polar functionality on the surface. On the other hand, post-synthesis functionalization of an already prepared catalyst with mainly organic or organosilane molecules (Figure 3) is another way to increase the hydrophobicity (Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Solid Materials**} & \multicolumn{2}{c}{**Hydrophobization Method**} \\ \cline{2-3}  & **During Synthesis** & **Post-Synthesis** \\ \hline \multirow{2}{*}{**Zeolites**} & Modulation of Si/Al ratio & Silane functionalization \\  & Microwave crystallization [15] & Organic Functionalization \\ \multirow{2}{*}{**Silicas**} & Sol–gel process and supercritical drying of silica acogels [16] & Silane functionalization \\  & & Organic Functionalization \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strategies for the synthesis of hydrophobic catalysts.  
Figure 2: Simple scheme that illustrates preferential adsorption and desorption.
# Representing and Inferring Massive Network Traffic Condition: A Case Study in Nashville, Tennessee
## 4 Discussion
Future work could be carried out to include different types of road networks to analyze their traffic speed patterns and investigate how clustering will impact the CUR decomposition performance on different types of road networks. As has been discussed in [PERSON]'s work [10], clustering by different road categories improved the inferring performance for the entire network when the CR was between 2 and 10. Our future work could focus on analyzing the speed patterns on different road types and clustering based on speed profile and road type together. Due to the data availability issues, this study only focused on a case study in Nashville, TN, and all detectors are located on urban interstates. To comprehensively evaluate the practical applicability, additional research could be carried out to investigate the generalizability of the proposed framework to other cities or regions and for other time periods. It is essential to extend this research to different locations and time frames to assess its spatial and temporal transferability. In this study, external factors, such as adverse weather, special events and scheduled road work, were not included in the framework, which might impact the representing and inferring performance when there happened to be many non-recurring incidents during the training and testing period. One area for future work involves exploring the impact of external factors in greater depth to gain a more comprehensive understanding of its applications. Another interesting avenue for future research is to explore the possibility of incorporating other traffic data, such as vehicle count and vehicular types, into the methodology for a more comprehensive representation of the traffic condition at specific detectors, thus resulting in a better representation of the entire network. This involves methods like column-based decomposition for multidimensional tensors, proposed by [PERSON] et al. [12] in 2019.
## 6 Simulation Results and Discussion  
The vehicles and UAV are trained using a random 10% slice of _V2X-Sim_ dataset, split into 60% training data and 40% testing data, to generate sensor data and learn local models [70]. The length and width of the road segment are 300 m by 20 m and the vehicle speed varies between 10 and 100 km/h. The interference experienced by each vehicle is proportional to the relative velocity between the vehicle and the UAV. The threshold for the maximum UAV altitude is (\(\mathcal{H}\))+1 km, the noise power is set to \(-\)10 dBm, and the BER tolerance is \(10^{-4}\). The size of the vehicle data and UAV data for each task is distributed randomly between 1-10 MB and 1-3 MB. Note, the batch size of the local models equals that of the gross data offloading. The transmission and reception powers of the UAV are set to 120 mW and 60 mW, respectively, and the channel bandwidth is set to 20 MHz. We implement the proposed model using _Python_ and utilize the _Amazon EC-2_ instance to process the datasets. The simulation experiments are executed 50 times for 100-2000 iterations, and the average parameter values are calculated. Table 4 lists the main parameters used in the simulations. Next, we discuss the variation in UAV power (\(P_{i}(t)\)) with respect to channel uncertainty caused by the number of vehicles (\(V\)), UAV trajectory (\(\mathbf{q}_{(x,y)}\)), and UAV height (\(\mathcal{H}\)).  
Figure 5: UAV trajectory and vehicle coverage depending on UAV transmit power (\(P_{i}(t)\)) and altitude (\(\mathcal{H}\)). The shaded triangular region (\(P_{i}(t)\)) indicates the coverage range of the UAV when the UAV is at a specific altitude (\(\mathcal{H}\)).
# Presenting a Human Pupil Orbit Model (HPOM) for Eye-Gaze Tracking
###### Abstract  
Eye tracking technology has been continuously researched for application in various fields. In the past, studies have been conducted to interpret eye movements in 3D space in order to solve the problem of not being able to find the centre of rotation of the eye. In this paper, we propose a novel pre-processing method for eye-gaze tracking by monitoring the front of the face with a camera. Our method works regardless of the distance between the eye and the camera. The proposed method includes an analysis technique that simplifies conventional three-dimensional space analysis to two dimensions. The contribution this work presents is a method to simplify gaze direction detection. The errors in our model's estimations appear to be under 1 pixel. In addition, our approach has an execution time of less than 1 s, enabling an adaptive model that responds to user movements in real time. The proposed method was able to overcome various problems that methods in existing studies still suffer from, including accurately finding the rotational centre of the user's eye-ball. Moreover, even when a user's pupil can only be monitored from a distance, our approach still makes it possible to produce accurate estimations.  
Human Pupil Orbit Model; ocular motor model; ocular rotational centre; eye-tracking; gaze direction angle +
Footnote †: journal: Medical Biotechnology, Dongguk University Biomedical Campus, 32, Dongguk-ro, Ilsan dong-gu, Goyang-si 10326, Korea 1
## 6 Data Acquisition, Description, and Processing  
Land-use maps and satellite images were acquired from the Google Earth Engine for four different timestamps: 1990, 2000, 2010, and 2020. Landsat 7 images, with a spatial resolution of 30 m \(\times\) 30 m, were obtained with consideration about the cloud cover limit of less than 10%. These Landsat images were used because their spatial and temporal resolution is appropriate for the data and period of the study. Five land-use classes were set for the classification, based on historical information on the study area. These include:  
* Settlement class, which compromises both rural and urban areas;
* Water bodies, including streams, lakes, rivers, plantations, and reservoirs;
* Vegetation class, made up of various kinds of plantations, crops, harvested land, and forests;
* Bare land, constituting stony waste, sheetrock, sand, gullies, ravines, and abandoned mining pits;
* The mining area was also examined for features such as abandoned tailing dams and waste dump sites.
## 2 Results and Discussion
### Structural Fluctuation and Flexibility
worth noting that the MSAs of the GTP- and GDP-bound WT, A59E, and K117R variants of HRAS exhibit similar fluctuating ranges and display stable fluctuations (Figure S4A,C). The MSAs of the GTP-bound WT, A59E, and K117R HRAS are concentrated around 8142, 8597, and 8276 A\({}^{2}\), respectively, indicating that A59E and K117R mutations increase the exposure of HRAS to solvents compared to the GTP-bound WT HRAS (Figure S4B). The MSAs of the GDP-bound A59E and K117R variants show a 310 A\({}^{2}\) increase compared to the GDP-bound WT HRAS (Figure S4D), suggesting that these two mutations, A59E and K117R, also enhance the exposure of HRAS to solvents relative to the GDP-bound WT HRAS. In summary, the mutations A59E and K117R significantly impact the structural stability of GTP and GDP within the binding pocket of HRAS, thus affecting their interaction with HRAS. Furthermore, A59E and K117R greatly influence the structural flexibility of the SW1, SW2, and L2 loop regions, which disrupt the binding of HRAS to effectors and the allosteric regulation of HRAS activity. Additionally, A59E and K117R increase the surface areas of the GTP-bound active form of HRAS and the GDP-bound inactive form that are exposed to solvents, affecting the HRAS activity. Previous studies have also shown the influence of these two mutations on the switch domains of HRAS, providing strong support for our findings [27; 33].
## 3 Results and Discussion
### Data Analysis
#### 3.3.2 Velocity Calculation  
The average dune velocity is \(V_{1}=313\) m/s, calculated from the direct-wave velocities in the five shot records (Table 1). Head-wave velocities \(V_{2}\) are calculated from the slopes of the best-fit lines to the first arrival traveltimes of the up-dip and down-dip directions (Figure 5). The critical angle and true head-wave velocity are calculated by Equations (5) and (6) to be \(\theta_{c}=9.2^{\circ}\) and \(V_{2}=1949\) m/s, respectively (Table 2).  
Figure 4: Two different recorded shot gathers (raw data): (**a**) shot gather 1 (down-dip); (**b**) shot gather 3 (up-dip), with 12 traces each.