esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Therapeutic Potential: PPK2 Inhibitors  
Given the important roles of PPK2s in bacterial virulence and infection, these enzymes have been suggested as attractive drug targets. However, relatively few PPK2 inhibitorshave been documented in vitro, and to the best of our knowledge, only one has been validated for PPK2 inhibition activity in vivo. A summary of published PPK2 inhibitors is provided in Table 2. Using a luciferase-based activity assay, [PERSON] et al. screened a 2300-member National Cancer Institute small molecule library against purified maltose binding protein (MBP)-tagged _M. tuberculosis_ PPK2. This screen identified the heteroaromatic compounds NSC 35676, NSC 30205, NSC 345647, and NSC 9037 as inhibitors of PPK2 at low-micromolar concentrations [55]. More recently, a series of (bis)phosphonic acid-derived PPK2 inhibitors have been developed [81]. Initially, three aryl phosphonate inhibitor molecules were co-crystallized with _C. Hutchinson_ PPK2, revealing the value of a (bis)phosphonate-based scaffold for PPK2 inhibition since this moiety was observed to competitively occupy the polyP channel [46]. A follow-up study synthesized 32 (bis)phosphonic acid derivatives, the best of which inhibited _C. Hutchinson_ PPK2 with an IC\({}_{50}\) of approximately 60 \(\upmu\)M [81] (Table 2). The most potent PPK2 inhibitor documented to date is a G-quadruplex DNA aptamer that specifically binds _M. tuberculosis_ PPK2 (IC\({}_{50}\) = 40 nM) [64]. However, none of the aforementioned studies tested their inhibitors in bacteria. We recently discovered that the polyphenonic molecule gallein inhibits _P. aeruginosa_ PPK1 and PPK2s in vitro at low-micromolar doses. Importantly, we showed that gallein also inhibits both PPK1 and PPK2s in the bacterial cell, phenocypying the _ApolyP_ strain (knockout lacking all four _ppk_ genes) to attenuate polyP accumulation, biofilm formation, pyoverdine production, and virulence in _C. elegans_. Gallein also synergized with the antibiotics tetracycline and ciprofloxacin to improve _C. elegans_ survival following _P. aeruginosa_ infection [57; 82]. In a follow-up study, we also demonstrated that gallein inhibits _A. baumannii_ and _K. pneumoniae_ PPK2s in vitro [42] (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
## 2 Aims and Methodology  
The main objective of this research is to experimentally determine the capillary action and permeability of a SLM wick for a loop heat pipe [25; 26; 27] and to compare both results with those of other similar wicks made of conventional manufacturing techniques (sintering, mesh, etc.) in order to evaluate the wick performance.  
Experimentation is presented as an efficient means of obtaining the flow velocity through a porous media, the capillary action curve, which will be obtained by measuring liquid height versus time for several materials and techniques: (i) sintering, (ii) meshing, and (iii) SLM. The experimental method is also the most accurate way of determining the permeability of a medium as porous as an LHP wick with a complex internal structure. In this case, samples with different pore sizes (D) were built through two different technologies, sintering and SLM, and were finally tested.  
The working liquid used throughout all the experiments was methanol, the thermophysical parameters of which are: density \(\rho\) = 786.47 (kg/m\({}^{3}\)), enthalpy of vaporization \(h_{\ell\
u}\) = 1,169.2 (kJ/kg), surface tension \(\sigma_{lv}\) = 22.16 (N/m), and viscosity \(\mu\) = 544.61 (\(\mu\)Pas).
## 2 Data and Methods
### Study Methods  
Five different study sites were selected to evaluate the impacts of precession on surface reflectance from Hyperion and ALI at different latitudes and land cover types to characterize sensor performance (Table 1). It would have been desirable to have more test sites in differing regions of the world, but it was difficult to find time series data, which was needed to show effects of changing precession over time. This was because EO-1 was never conceptualized to be a robust repeat sensor. For this reason, several of the selected sites were PIC sites.  
(1) Park Falls, Wisconsin: The Normalized Difference Vegetation Index (NDVI) [25; 26] obtained from ALI TOA reflectance was compared with that from Landsat NDVI, and an NDVI difference was computed for a mixed forest site in multiple years;  
(2) Howland Forest, Maine: The NDVI obtained from Hyperion surface reflectance was compared to the NDVI obtained with the Moderate Resolution Imaging Spectroradiometer (MODIS) for an experimental mixed forest, in multiple years;  
(3) US Department of Agriculture/Beltsville Agriculture Research Center (USDA/BARC) in Beltsville, Maryland: The temporal change in the Hyperion surface reflectance was evaluated using 1 st derivative analysis, in an agriculture site over multiple years;  
(4) Rail Road Valley Playa (RRVP), Nevada: For a Pseudo-Invariant Calibration Site (PICS), a Hyperion surface reflectance time-series was evaluated for a bright desert target site; and  
(5) The Libya-4 PICS, Libya: Statistical evaluation of the change in surface reflectance obtained in different spectral intervals and over time was evaluated using a dense Hyperion surface reflectance time-series for a bright desert target site.
## 6 Climate Benefits of Improved Beef \(\times\) Diary Systems  
In New Zealand, it has been suggested that a large reduction in GHG emissions could be achieved if surplus calves from the dairy industry were reared for beef production [62]. In this study, it was predicted that dairy beef calves had a 29% lower emission intensity when compared to suckler-beef calves, and that the average emission intensity of beef production could be reduced by up to 22%.  
While double-muscled sired progeny from dairy cattle exhibit similar conformation and meat characteristics to beef breed progeny and have high carcass meat yields, this also coincides with a reduced organ mass [35]. Accordingly, voluntary feed intake is decreased, and feed efficiency is improved. The _MSTN_ c.821(del11) allele may therefore help reduce GHG gas emissions and nitrogen leaching by reducing intake per kilogram of beef produced.  
Climate change benefits and improved livestock production could therefore go hand in hand if double-muscled homozygous _MSTN_ c.821(del11) Angus sires were mated to dairy cows. These sires would not only improve meat production from the crossbred progeny, thus allowing a reduction in beef cow numbers to achieve the same amount of quality meat output, but also potentially improve feed efficiency, reducing the GHG footprint per kilogram of meat produced.  
However, while dairy-beef systems may offer a reduction in GHG emissions, the role of traditional beef grazing systems, and their contribution to food production on land unsuitable for other purposes like dairy farming, must also be acknowledged. Beef can be produced from extensive, wholly unirrigated range-land systems that are not amenable to dairy farming, or cropping, or that may be unsuitable for producing meat from other smaller ruminants like sheep or goats because of the presence of predators. Additionally, the interconnectedness of milk and beef production systems means that changes in one sector can have implications for the other, which might be positive if, for example, dairy returns were high and meat was low, or vice versa.  
Figure 1: The typical Angus sire (**a**), typical New Zealand Dairy cows (**b** = Jersey, **c** = Friesian, **d** = crossbred) resulting progeny (**e**) from DM angus sire with classic Angus phenotype (solid Black, Polled and beef Phenotype.
## 3 Feature selection for HMMs using Heuristic and Genetic Algorithms
### Feature subset evaluation  
To evaluate the utility of a given feature subset an HMM _wrapper_ is going to be used. The utility of the feature subset will be the accuracy of the HMM for classifying some small duration activity sequences. Given a set of training sequences \(O=\{O_{1}\dots O_{N}\}\), \(O_{i}=\{o_{i}1\dots o_{i}k\}\) containing \(m\) different activities, and their corresponding set of hidden states \(A=\{A_{1}\dots A_{N}\}\), \(A_{i}=\{a_{i1}\dots a_{ik}\}\), the estimation of HMM parameters is straightforward. The HMM will have \(m\) hidden states, one per activity. To estimate he initial state probability distribution, the frequency of each state to be the first in each training sequence has to be computed. The state transition probability distribution is going to be computed using the frequency of each state transition along the training sequences. To simplify even more the estimation of parameters, the observation symbol probability distribution is supposed to be a single multivariate gaussian distribution. Other type of distributions, like Gaussian Mixtures or KDE [17] can be used, but the construction of the HMM will be harder, and it is not important for the objective of this work. The parameters of each state emission distribution to be estimated are the mean and covariance matrices.  
Once that the Hidden Markov Model has been estimated, a test sequence \(O=\{o_{1},\dots,o_{K}\}\) can be classified using Viterbi Algorithm. The obtained state sequence \(q^{*}\) is compared to ground truth data, and the accuracy of the model for predicting each hidden state is taken as the subset utility.
## Materials and Methods
### Measurement and Identification of Essential Oil Compositions  
To extract the essential oil, fruit hand-collected at 60, 75, 90, 105, 120, 135, 150 and 165 DAF were air-dried and subjected to hydrodistillation using a Clevenger-type apparatus for essential oil. The volatile distilled oils were dried over anhydrous sodium sulphate (Na\({}_{2}\)SO\({}_{4}\)) and stored at 4 \({}^{\circ}\)C until analysis. The essential oil compositions were analysed using gas chromatography-mass spectrometry (GC-MS) methodology. GC-MS was performed on an Agilent 6890N gas chromatograph (Palo Alto, CA, USA) equipped with an Agilent 5975B Mass Spectrometer using an HP-5 MS fused silica capillary column (30 m \(\times\) 0.25 mm internal diameter, 0.25 \(\upmu\)m film thickness). The temperature program of 50 \({}^{\circ}\)C for 2 min was increased to 120 \({}^{\circ}\)C for 2 min at a rate of 3 \({}^{\circ}\)C/min, then to 250 \({}^{\circ}\)C for 2 min at 15 \({}^{\circ}\)C/min. The carrier gas was N2 at a flow rate of 1 mL/min. The injected volume was 1.0 \(\upmu\)L (1:10 in Et2O) and the injector temperature was 220 \({}^{\circ}\)C. Mass spectrometer conditions were as follows: GC-MS interface temperature, 250 \({}^{\circ}\)C; ion source temperature, 230 \({}^{\circ}\)C; quadrupole temperature, 150 \({}^{\circ}\)C; ionisation mode, EI; and ionisation energy, 70 eV. Compounds were identified by comparing their mass spectra with the mass spectra obtained from an MS database (NIST 08). The MS identifications were confirmed by comparing the GC retention times of the analysed samples with those from pure standards. The identification was confirmed by comparing the retention indices (RI) of the samples with those reported in the literature [40; 41]. The RI was calculated using a series of n-alkanes (C7-C30) under identical operating conditions. The relative amounts of the individual components were calculated using peak area normalisation.
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 2 Materials and Methods
### Station Design  
The real-time river monitoring station comprises an ultrasonic liquid level sensor, a Sim 800 L GSM module, a 12-volt 7 Ah battery, a microprocessor card, an RS 485 converter, and an integrated solar panel (see Figure 2). The manufactured station possesses energy independence through an MPPT solar energy control unit and battery group, eliminating the need for an external power supply. The manufactured station can perform river level measurements within the desired timing period. The measurement data obtained can be transmitted to a central server using cellular communication infrastructure.  
Figure 1: Orencik Bridge and Dogancay HPP I.  
Figure 2: Real-Time River Monitoring Station Components.  
The station's electronic components are housed within a waterproof cabin to ensure protection from environmental conditions. The energy required for operation is supplied by a solar panel integrated into the top of the cabin. Direct current electricity generated by the solar panel is transferred to gel batteries via an MPPT regulator. This setup ensures continuous and uninterrupted operation of components such as the microcontroller, ultrasonic sensor, and a GSM module without external power sources. The solar panel is positioned at a 38-degree angle, optimized for the solar radiation conditions of the Sakarya region, maximizing energy capture throughout the year.  
The energy requirements of the system have been carefully calculated. The ultrasonic sensor consumes 1.2 Wh per hour, while other components, including the microcontroller and a GSM module, consume 4 Wh per hour, resulting in a total system consumption of 5.2 Wh per hour. To ensure uninterrupted operation for one week, the system requires 873.6 Wh of energy (168 h \(\times\) 5.2 Wh). The integrated 12 V, 72 Ah battery is sufficient to meet this demand, allowing the station to function reliably even under low sunlight conditions.  
To securely mount the ultrasonic sensor, a custom-designed cradle was developed to ensure stable placement and protection against external interferences. The cradle design allows for easy removal, when necessary, while ensuring the measurement area remains un-obstructed. Figure 3 illustrates the station's schematic and fabricated designs, highlighting the optimized placement of the sensor and the solar panel orientation.
###### Abstract  
In this paper, we discuss the design and feasibility of a multifunctional laser instrument capable of precision ranging, time transfer, and wideband communications over interplanetary distances throughout our solar system. To simplify the communications discussion, On-Off Keying (OOK) is assumed for the high bandwidth (MHz to GHz) modulation format, and the required laser powers and transmit and receive apertures are determined for each Earth-planet link as a function of data rate. Optimization of the transmitter and receiver antenna gains is reviewed. It is further assumed that the spacecraft is in orbit about the planet of interest in order to justify the need for large data downloads rates and that single-photon sensitive detector arrays can be utilized to aid in the acquisition, detection, and tracking of the opposite terminal. Satellite acquisition and tracking is complicated by the long one-way propagation distances (up to 40 AU), the finite speed of light, the orbital and rotational velocities of the two planets, and, if applicable, the spacecraft orbital speed about the planet. Taking advantage of the highly circular and coplanar planetary orbits within our solar system, point ahead angles are estimated for all combinations of possible Earth-planet positions revealing solar orbital geometries where planetary motion does not introduce a need for point ahead angles. Finally, possible methods for simplifying the initial mutual acquisition of the planetary and Earth terminals and maintaining the coalignment of their respective optical systems is discussed.  
lasers; wideband communications; ranging; time transfer; ook modulation; interplanetary link equation; point ahead angles; mutual terminal acquisition and telescope co-alignment; bit error rates +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 3 Related Technologies
### Rnn  
Recurrent Neural Network (RNN) consists of connected cyclic units and featured by recursion in sequences' evolutionary direction. Compared with CNN, it is better suited to modeling sequential data, such as text. At each time step (for each token position in the text), each loop unit uses shared parameters, and both the input of the current time step and the output of the previous time step can affect the output. Figure 2 shows the structure of RNN. Where \(W\), \(U\), and \(V\) are the training process's weight matrices of learning parameters.  
Although the model structure of RNN is good at dealing with sequence problems, it is possible that gradient disappearance or gradient explosion occur in the training process for long sequences. To effectively handle long-distance dependence, many variants of RNN have emerged, among which the most distinctive representative is Long Short Term Memory Network (LSTM) [36]. The structure diagram of its cyclic unit is shown in Figure 3. Its improvement is that the input gate, output gate, and forgetting gate are added to each cycle unit. The sigmoid function limits the output of the gate between 0 and 1. Such a gating mechanism controls how much the output from the last moment should be forgotten and how much the input from the current moment should be retained and then calculates the output of the current moment. For a given input sequence \([x_{1},x_{2},\ldots,x_{t},\ldots x_{n}]\), LSTM calculation formula for moment \(t\) is shown in the following formula:  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{1}\]  
\[f_{t}=\sigma\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\Big{)} \tag{2}\]  
\[o_{t}=\sigma(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}) \tag{3}\]  
\[\bar{C}_{t}=\tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{4}\]  
\[C_{t}=f_{t}\otimes C_{t-1}+i_{t}\otimes\bar{C}_{t}) \tag{5}\]  
\[h_{t}=O_{t}\otimes\tanh(C_{t}) \tag{6}\]  
\(i_{t},f_{t},o_{t}\) are respectively LSTM network input to the door, forgetting door and output, \(W\) and \(U\) are the weighting matrix, \(b\) is for bias, \(C_{t}\) is cell state, \(h_{t}\) is the network output. In practice, LSTM with forward and reverse directions is usually used to process text, and implicit layer vectors are spliced to obtain text representation with context information.  
Figure 2: Illustration of RNN(Redrawn from [36]).
## 3 Experiments and Results
### Metrics  
Beyond the Dice coefficient, we evaluated the performance of NR-UNet using an assortment of standard overlap metrics: intersection over union (IoU), accuracy (Acc), precision (Pre), recall (Rec), and specificity (Spe). These evaluation measures are detailed in Equation (13), where TP, TN, FP, and FN represent the number of pixels classified as true positive, true negative, false positive, or false negative. The IoU measures the extent of the overlap between the predictions of our model and the ground truth; accuracy evaluates the overall correctness of the model's predictions, with high accuracy in a medical context potentially signifying a model's efficacy in distinguishing between different classes--a crucial factor for disease detection and treatment planning; precision and recall are crucial performance indicators that reflect a model's proficiency in accurately identifying positive cases and evading false negatives, respectively. In the realm of medical image analysis, high precision ensures that positive detections are indeed relevant, thereby mitigating risks associated with false positives. Simultaneously, high recall improves the model's capability to spot all areas of concern, minimizing overlooked detections. Specificity, on theother hand, quantifies the model's capacity to correctly identify negative cases. Clinically, high specificity can curtail false-positive rates, thus preventing patients from undergoing unwarranted further examinations or treatments. Through the implementation of these diverse metrics, our intention is to offer an exhaustive evaluation of the proposed model's performance, substantiating its potential value in clinical scenarios.  
\[Dice =\frac{2 TP}{2 TP+FP+FN} \tag{13}\] \[IoU =\frac{TP}{TP+FP+FN}\] \[Accuracy =\frac{TP+TN}{TP+TN+FP+FN}\] \[Precision =\frac{TP}{TP+FP}\] \[Recall =\frac{TP}{TP+FN}\] \[Specificity =\frac{TN}{TN+FP}\]
## 2 Materials and Methods  
### Research Objects  
Three types of the low-pressure gas-phase injectors were used in the study: AC W01-4 (Figure 1a), Barracuda 115 (Figure 1b), and Matrix HS 211.20 (Figure 1c). The selected injectors were characterized by different designs in relation to each other. The AC W01-4 injector is a plunger injector with transverse gas flow. The Barracuda 115 injector, on the other hand, is also a plunger injector, with the difference that the gas flow occurs along the plunger. The Matrix HS injector has a flow control element in the form of a plate. The injectors shown in Figure 1 are among the very popular in Poland and Europe. They are commonly used in alternative power systems for cars.  
The subject variation in the diameters of the outlet nozzle bore was realized by gradual reaming. For this purpose, calibrated drills with gradations of 0.2 mm were used. Figure 2 shows the outlet nozzles of the injectors (from below: AC W01-4; Barracuda 115; Matrix HS 211.20).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **AC W01-4** & **Barracuda 115** & **Matrix HS 211.20** \\ \hline Type of injector & plunger, cross flow & plunger, longitudinal flow & flap \\ Max. flow at continuous opening & 125 L\({}_{\text{N}}\)/min & 115 L\({}_{\text{N}}\)/min & 95 L\({}_{\text{N}}\)/min \\ Max. performance & 39 kW & 33 kW & 30 kW \\ Coil resistance & 2 \(\Omega\) & 1.9 \(\Omega\) & 2 \(\Omega\) \\ Opening time & 2.3 ms & 1.9 ms & 1.0 ms \\ Closing time & 1.4 ms & 1.2 ms & 1.0 ms \\ Max. working pressure & \(4.5\times 10^{5}\) Pa & \(4.5\times 10^{5}\) Pa & \(3.0\times 10^{5}\) Pa \\ Operating temperature & \((-20\dots+120)+273.15\) K & \((-20\dots+120)+273.15\) K & \((-20\dots+120)+273.15\) K \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic technical data of the tested injectors. Data taken from Refs. [58; 59; 60].  
Figure 1: Tested gas injectors: (**a**) AC W01-4; (**b**) Barracuda 115; (**c**) Matrix HS 211.20.  
In the case of the Matrix injector nozzles, it was not possible to achieve a diameter of 3.1 mm by the drilling method. Therefore, in the last case, the injector was left without a nozzle, finding that the spigot diameter was 0.07 mm larger than the required 3.1 mm.  
Verification of the diameters of randomly selected nozzles was carried out. For this purpose, a FALCON microscope (Figure 3a), based on a 5:1 CCD camera, incremental lines of the X, Y, Z axes-1 \(\upmu\)m, and software with a QC5000 VED edge sensor (Figure 3a) was used. The average value of the deviations was 0.058 mm.