esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
### Implementation and Post-Test Phases  
During the implementation, the development teams choose freely which user stories they would implement. After the implementation, we examined their deliverables and accepted or rejected the increments for a particular user story. We analysed only empirical data (the actual time spent on development tasks) generated for user stories that were both reported to be finished and accepted. The summary is shown in Table 2. The data show that despite the different effort estimates, they delivered a comparable number of user stories in the time given to perform the implementation. The actual time that was spent per task and user story was also similar, despite the different estimates.  
Similarly to user story estimate, the actual effort of the user story is calculated as a sum of all the tasks' actual effort belonging to the user story (see Equation (2)). Please note that all tasks were not necessarily implemented, especially in the case of the experimental group. The unimplemented tasks' actual effort was reported to be 0 h.  
\[\text{Actual effort(US}_{i})=\sum_{\text{US};\text{tasks}}\text{Actual effort(task)} \tag{2}\]  
Another insight on the average actual effort per user story is shown in Figure 8. In the case of user stories 1 to 5, we can see effort from both the experimental and control groups. User stories 6, 7, and 8 show only the average development time in the control, experimental, or no group. The reason is simple: the actual implementation of User Story #6 was only carried out by development teams in the control group; User Story #8 was implemented only in the experimental group; and no-one decided to implement User Story #7. One can see that five User Stories' implementations, carried out by both groups, were problematic in terms of possible comparison, but, in fact, they were not. The empirical data actually boast of 32 user stories' implementations and 101 implemented tasks to perform the accuracy calculations and comparisons, seen in the next Section 6.  
In parallel with judging the delivered system increments, the developers from the experimental group were asked to participate in a post-test questionnaire (see step 7b in Figure 2). We were interested in two answers:  
\begin{table}
\begin{tabular}{l c c} \hline  & **Experimental Group** & **Control Group** \\ \hline Developed user stories & 17 & 15 \\ \hline Developed user stories per development team & 2.83 & 2.14 \\ \hline Actual effort per task & average [h] & 0.67 & 0.61 \\  & st. dev & 0.91 & 0.59 \\ \hline Actual effort per user story & average [h] & 2 & 1.92 \\  & st. dev & 1.7 & 1.26 \\ \hline \end{tabular}
\end{table}
Table 2: Implemented user stories.  
Figure 8: Average user story effort estimates per group in hours.  
* Do you think you would prefer to use AI tools, conventional methods or a combination of both in future projects? (Conventional methods--0%; AI tools--0%; A combination of both--100%)
* Do you think AI tools can provide more accurate task estimates compared to conventional methods? (Yes--10%; No--55%; Do not know--35%)
## 2 Theory
### The Unscented Kalman Filter
When the target was located, its current position was compared with previous positions to estimate its velocity and the most possible position in the next frame, through which the state model of UKF was updated. The combination of UKF and the mean shift algorithm makes a closed loop tracking system which can track fast moving targets in real-time effectively.
## 5 Simulation and Data Analysis  
This section evaluates the performance of previously developed parameter estimators through Monte Carlo simulation studies. Additionally, a real-world data analysis is presented to validate the proposed methodologies.
# Analysis of Flow Characteristics and Effects of Turbulence Models for the Butterfly Valve
###### Abstract  
In the present study, the flow characteristics of butterfly valves with different sizes DN 80 (nominal diameter: 76.2 mm), DN 262 (nominal diameter: 254 mm), DN 400 (nominal diameter: 406 mm) were numerically investigated under different valve opening percentages. Representative two-equation turbulence models of two-equation k-epsilon model of [PERSON] and [PERSON], two-equation k-omega model of [PERSON], and two-equation k-omega SST model of [PERSON] were selected. Flow characteristics of butterfly valves were examined to determine turbulence model effects. It was determined that increasing turbulence effect could cause many discrepancies between turbulence models, especially in areas with large pressure drop and velocity increase. In addition, sensitivity analysis of flow properties was conducted to determine the effect of constants used in each turbulence model. It was observed that the most sensitive flow properties were turbulence dissipation rate (Epsilon) for the k-epsilon model and turbulence specific dissipation rate (Omega) for the k-omega turbulence model.  
butterfly valve; two-equation turbulence model; valve flow coefficient; sensitivity coefficient Article
## 2 Experimental Section/Methods
### Synthesis of Photoacid Generators TPS-SS  
The monomer TPS-SS was synthesized by the electrostatic interaction between sodium _p_-styrenesulfonate and triphenylsulfonium chloride [15]. Sodium _p_-styrenesulfonate (3.093 g, 15 mmol) and triphenylsulfonium chloride (2.988 g, 10 mmol) were dissolved in 50 mL of deionized water. The mixed solution was then transferred to a Schlenk flask and stirred in the dark for 6 h. After the reaction, the mixed solution was extracted with 50 mL of dichloromethane twice. The organic phases were combined and washed with a saturated NaCl solution. The organic phase obtained was then concentrated by rotary evaporation to remove the solvent and was subsequently dried in a vacuum drying oven, resulting in the formation of a white solid, TPS-SS, with a yield of 70%. \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)): \(\delta\) ppm: 7.79 (d, 2H), 7.75-7.55 (m, 15H), 7.24 (d, 2H), 6.61 (q, 1H), 5.65 (d, 1H), 5.16 (d, 1H).  
In addition, the small molecule photoacid triphenylsulfonium _p_-toluenesulfonate (TPS-ST) was synthesized to be used as a control experiment. The synthesis of TPS-ST followed a similar procedure to that of TPS-SS, except that sodium _p_-styrenesulfonate was replaced with sodium _p_-toluenesulfonate. \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)): \(\delta\) ppm: 7.81-7.62 (m, 17H), 7.08 (d, 2H), 2.30 (s, 3H).
# Compressing BERT for Binary Text Classification via Adaptive Truncation before Fine-Tuning
## 2 Related Work
### Neural Model Compression  
As previously mentioned, contemporary neural model compression methods fall into four main categories: quantization, matrix decomposition, knowledge distillation and model pruning [17]. All have been successfully applied to compressing BERT. Among them, the last category is most closely related to our proposed method. Thus, we only recap the key ideas of the first three, and devote more attention to existing pruning methods below.  
_Quantization_ uses a smaller number of unique values to represent model weights, and hence reduces the resources required to store and compute them. _Matrix decomposition_ refers to decomposing large parameter matrices into small ones, and thus reduces resource consumption. _Knowledge distillation_ is transferring knowledge from one or more highly regularized large models (called the teachers) into a much smaller, distilled model (called the student). _Pruning_ accomplishes compression by identifying and dropping unimportant or redundant model weights or units. The lottery ticket hypothesis (LTH), first proposed by [PERSON] and [PERSON] [19], substantiates the existence of sub-networks that can reach test accuracy that is comparable with the original network, and has inspired many later works on pruning. Existing methods can be grouped into two classes, viz., structured and unstructured pruning. The former reduces models by dropping several entire layers or some coherent groups of blocks [10; 34; 35]. In contrast, unstructured pruning is a form of dropout that compresses model by reducing weights independently [19; 20; 21; 36].  
Generally, these existing compression methods, distillation and pruning in particular, require a lot of extra training to transfer knowledge from teachers to student or identify units or weights to be removed. They sometimes even need to conduct fine-tuning followed by iterative compressing. In comparison, our approach, though it looks like a kind of structured pruning as it drops contiguous model units (viz., Transformer layers), differs significantly from existing compression methods in two main aspects: (i) it only needs to fine-tune the (truncated) model once and requires no extra training to determine the truncation position, and (ii) it decides the number of Transformer layers to be retained for each task using a metric evaluated on the corresponding labeled dataset, and hence is adaptive to downstream tasks. Thus, it not only saves resources for inference, but also those for training. It is noteworthy that the method proposed in this article is complementary to quantization, matrix decomposition, distillation, and even some pruning methods, as it can provide a better start point than the full BERT, in terms of its smaller model size and often higher classification accuracy, for these existing compression methods.
## 2 Research on UAV Post-Disaster Path Planning Problem
### Fitness Function
When the coordinates of the UAV are \((x_{i},y_{i},z_{i})\), its expression is:  
\[D=\frac{1}{1+\sqrt{\alpha_{r}/\beta_{r}}\left|\beta_{r}/E_{r}\right|^{1/(4P_{ s})}} \tag{10}\]  
\[E_{r}=\frac{1}{2}(M+Q)v^{2}+(M+Q)gz_{i} \tag{11}\]  
In the equation, \(P_{s}\) is the shielding coefficient of the environment, with a value range of \((0,\,1]\), indicating the degree of exposure of the ground personnel; \(\alpha\) is the required impact energy when the shielding coefficient is equal to 0.5 and the mortality rate reaches 50%; \(\beta\) is the threshold of impact energy required when the shielding coefficient is approximately equal to 0 and causes death; \(E_{r}\) is the kinetic energy generated when the UAV collides with the ground; \(M\) is the weight of the UAV; \(v\) is the operating speed of the UAV; \(Q\) is the maximum carrying capacity of the UAV; \(z_{i}\) is the height at which the UAV experiences a system malfunction; \(g\) is the gravitational acceleration. The fitness function for the path, obtained by integrating the costs of path optimality, obstacle threat, and flight altitude, is given as follows:  
\[\left\{ \begin{array}{l}T(C_{i})=b_{1}T_{1}(C_{i})+b_{2}T_{2}(C_{i})+b_{3}T_ {3}(C_{i})\\ b_{1}+b_{2}+b_{3}=1\end{array} \right. \tag{12}\]  
In the equation, \(b_{1},b_{2},b_{3}\) is a weight coefficient ranging from 0 to 1. The magnitude of each weight represents the importance of the corresponding flight cost. By adjusting the weight coefficients, the path requirements for different application needs can be met. For example, in rescue missions where UAVs need to maintain stable flight at a certain altitude, the weight coefficient \(b_{2}\) for the flight altitude cost can be appropriately increased.
## 3 Legal Challenges Posed by Mega-Constellations to Existing International Space Law Regime
### The Issue of Space Traffic Management
#### 3.3.1 Space Situational Awareness and Space Traffic Management
To reiterate ([PERSON], 2020), constellation operators do not intend to hold, or occupy, these orbits exclusively ([PERSON] et al., 2020). Space law applies strict liability whereby the intention of the space actor or not would constitute appropriation. However, even if one were to argue intention as a prerequisite for appropriation, no such intention can be found in the operators of global constellations, which raises the question of the applicability of Article 2 of the Outer Space Treaty and how orbital slots can be successfully distributed given the nomadic nature of spacecraft in LEO or MEO. This all serves to prove how ungoverned the LEO is compared to the GSO. Moreover, it should be noted that the fact that there is no complaint about something does not make it right or legal.
## 2 Data and Methods
### Identifying the Effect of GNSS-IWV and CAPE on Extreme Rainfall Formation
For the quantile regression, a linear model for the conditional quantile function is defined as follows [47]:  
\[Q_{yi}(\tau|x)=x_{11}^{\prime}\beta_{1}(\tau)+x_{22}^{\prime}\beta_{2}(\tau), \tag{7}\]  
Let \(\hat{\beta}\) be acquired by minimizing the problem:  
\[\hat{V}(\tau)=\min_{(b\in R^{p})}\sum\rho_{\tau}(y_{i}-x_{i}^{\prime}b), \tag{8}\]  
which is an unrestricted quantile regression. Let \(\hat{\beta}\) be acquired by minimizing the constrained problem:  
\[\hat{V}(\tau)=\min_{(b_{1}\in R^{p-4})}\sum\rho_{\tau}(y_{i}-x_{1i}^{\prime}b _{1}), \tag{9}\]  
which is a restricted quantile regression and where \(\rho_{\tau}(u)=u(\tau-I(u<0))\) is the loss function. A goodness-of-fit value is then calculated following [47]:  
\[R^{1}(\tau)=1-\frac{\hat{V}(\tau)}{\hat{V}(\tau)} \tag{10}\]  
\(R\) has a value between 0 and 1 and a value of 1 shows a perfect quantile regression model. Seventh, we analyse the temporal relation between extreme rainfall and both GNSS-IWV and CAPE on the 6 h scale. We select all data with rainfall above the 90 th, 95 th, and 99 th percentiles, respectively, and their corresponding CAPE and GNSS-IWV amounts. We then averaged the 6 h GNSS-IWV and CAPE data within the 72 h (event day plus day before and day after) for each percentile in both stations, and we show the correlation between both variables and extreme _rainfall_.
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 2 Materials and Methods
### Conditioning Factors
#### 2.3.2 Lithology  
Lithology has a very important effect on the occurrence of landslides because different lithologies can produce different influences during landslide occurrence [52]. The lithology of the area primarily consists of sand slate, limestone, breccia, and quaternary sediments. Influenced by the intensity of rock weathering and soil formation, according to the hardness, it can be divided into the following categories: hard rock, harder rock, softer rock, soft rock and extremely soft rock according to engineering geology (Figure 3d).
## 3 Method of Analysis  
### PV Generator Model  
The studied PV generator sizes are 0.1, 1 and 10 MW and their direct land areas are 2500, 27,000 and 300,000 m\({}^{2}\), respectively. These power ratings and land areas represent a typical commercial rooftop power plant and small and medium sized utility scale power plants [13]. Direct land area includes solar panels, walkways between panel rows, roads and building structures. Therefore, the bigger the PV power plant power is, the larger is the required relative area. In addition, it is not always necessary to have such a compact panel layout on ground-mounted systems as on rooftops where the installation area is limited.  
PV generator output power is directly proportional to the irradiance. Geographical distribution of PV panels smoothens the PV generator output power because irradiance changes do not occur simultaneously on all panels. PV generator output can be modelled by low-pass filter method [14].  
Figure 1: Irradiation measurements of one sensor on (**a**) 26 th of April; (**b**) 23 rd of April and (**c**) 12 th of August in 2015.  
Inputs of the model are direct land area of the PV generator and irradiance measurement from a single point. Effective irradiance \(G_{\mathrm{S}}\) of the PV generator can be expressed as  
\[G_{\mathrm{s}}(t)=\frac{G(t)}{\tau s+1}, \tag{1}\]  
where \(G\) is the irradiance on a single point, \(t\) is time and \(s\) is the Laplace transfer function. The low-pass filter time constant \(\tau\) is calculated as follows  
\[\tau=\frac{\sqrt{A}}{2\pi\cdot a}, \tag{2}\]  
where \(A\) is the direct land area of the PV generator and \(a\) is an experimental coefficient. The value of the coefficient \(a\) = 200 Hz \(\times\) m is obtained from a fit to the cutoff frequencies of the power spectra of various PV power plants plotted as a function of the plant area [14; 15]. Temperature dependency of the PV generator is ignored, because irradiance transitions are much faster than temperature changes of the modules due to their thermal mass.  
Filtered standard test condition (STC) irradiance of 1000 W/m\({}^{2}\) is scaled to correspond to the nominal PV power plant power \(P_{\mathrm{nom}}\). Operation of spatial smoothing algorithm is demonstrated in Figure 2 by comparing unfiltered irradiance measurement to effective irradiances of 0.1, 1 and 10 MW PV generators. The bigger the PV generator direct land area is, the slower it reacts to changes in irradiation. Fast irradiance fluctuations lasting only for some seconds, e.g., at 12:01, does not cause big changes in effective irradiance of 10 MW PV generator, whereas the fluctuation can be seen clearly at 0.1 MW PV generator.
## 2 Results and Discussion  
In the routine quality control of EO, tens of samples are analysed every year, and usually, some borderline samples may be found. These EO demand special attention.  
EO adulteration can successfully be testified only when a suitable reference profiling of genuineness obtained with the appropriate analytical methods (i.e., % areas through conventional GC analysis, enantiomeric composition through enantioselective GC analysis and true quantitation) is available either from international organisations (e.g., pharmacopoeias or ISO norms) or built in-house by analysing a consistent number of certified genuine samples.  
This study is fully in line with this strategy and consists of: (1) the creation of the reference profiles of the EO under investigation, including their enantiomeric recognition, via the analysis of different batches of genuine EO (20 samples of each EO), (2) the analysis of EO adulterated via the addition of synthetic volatile compounds or cheap EO and (3) the analysis of adulterated EO via the addition of vegetable oil requiring a true quantitation to detect the dilution.  
This study focuses on some commercially available EO samples that were found to be at the limit of acceptance when compared to the pharmacopoeia monograph--namely, two laverder, one bergamot and two tea tree (TTO) EO.
## 3 Results
### Effects of Fiber Length and Content on Soil Properties Under Two Soil Conditions
#### 3.1.1 Physical Properties
As shown in Figure 1, the addition of coir fiber significantly improved the shear strength parameters of both soil types. With increasing fiber length and content, the overall trend exhibited an initial increase followed by a decline. However, the optimal fiber length and content combinations differed between the two soil types. For cohesion, both Soil-type A and Soil-type B exhibited the highest values at a fiber length of 3 cm, particularly at 0.4% and 0.6% content values. For the internal friction angle, most combinations showed no significant differences (\(p>0.05\)). In Soil-type A, the internal friction angle increased with fiber length before stabilizing, reaching its peak at 3 cm (28.73\({}^{\circ}\)), while in Soil-type B, the highest value was recorded at a 4 cm fiber length (27.03\({}^{\circ}\)). For the peak deviatoric stress, the optimal fiber length and content combinations were more complex and varied with increasing confining pressure. Regarding the reinforcement effect coefficient, Soil-type A achieved the best performance at a 3 cm fiber length and 0.4% content, with values ranging from 1.33 to 1.56. Soil-type B displayed a distinct \"double-peak\" pattern, with the 3 cm + 0.4% combination remaining the most effective (coefficient: 1.17-1.54), followed by 4 cm + 0.4%. For interface fiber quantity, both soil types showed a similar trend--fiber quantity increased with a higher fiber content but decreased with a longer fiber length. The highest interface fiber quantity was observed at a 1 cm fiber length with 0.6% content. For the dispersion coefficient, the most uniform fiber distribution was recorded under a 1 cm fiber length and 0.4% content, where the coefficient was closest to 1, indicating better fiber dispersion within the soil matrix.
###### Abstract  
Over the past several decades, the formulation of novel nanofiber-based drug-delivery systems has been a frequent focus of scientists around the world. Aiming to introduce a novel nanofibrous transdermal drug-delivery system to treat pain, the nanofiber mats of buprenorphine-loaded poly (vinyl pyrrolidone) (Bup/PVP) and buprenorphine-loaded poly(vinyl alcohol)/poly(vinyl pyrrolidone) (Bup/PVP/PVA) were successfully fabricated by the electrospinning process for transdermal drug delivery. Similarly, PVP and PVP/PVA nanofibers were fabricated in the same conditions for comparison. The viscosity and electrical conductivity of all electrospinning solutions were measured, and nanofiber mats were characterized by scanning electron microscopy (SEM), atomic force microscopy (AFM), Fourier transform infrared (FT-IR) spectroscopy and contact angle analysis. The conductivity of PVP and PVP/PVA solutions showed a considerable increase by the addition of buprenorphine due to the polarity of buprenorphine. SEM images showed a smooth, fine and porous nanofibrous structure without any adhesion or knot for all of the samples. The contact angle analysis showed the increased hydrophilicity and wettability of PVP/PVA and Bup/PVP/PVA nanofibers compared to PVP and Bup/PVP nanofibers which can be attributed to the addition of PVA. Attenuated total reflectance (ATR) FT-IR results confirmed that the electrospinning process did not affect the chemical integrity of the drug. For the modification of the drug release rate, the cross-linking of nanofiber mats was carried out using glutaraldehyde. Drug release measurements using high-performance liquid chromatography (HPLC) analysis demonstrated that Bup/PVP/PVA nanofibers exhibited better physical and chemical properties compared to Bup/PVP. Furthermore, the cross-linking of nanofibers led to an increase in drug release time. Thus, the novel buprenorphine-loaded nanofibers can be efficient biomaterial patches for transdermal delivery against pain improving carrier retention and providing a controlled release of the drug.  
nanofibers; buprenorphine; poly (vinyl pyrrolidone) poly (vinyl alcohol); drug delivery 40.1