esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Methods
### RANS/AD Method  
To simulate the flow passes wind turbine rotor, the general equations are modified by adding the aerodynamic loadings of the rotor as external volume force terms in the momentum equations of the NS equations  
\[\frac{\partial u_{i}}{\partial x_{i}}=0\ \ \ (i=1\,\ 2\ ) \tag{1}\]  
\[\frac{\partial u_{i}}{\partial t}+u_{j}\frac{\partial u_{i}}{\partial x_{j}}=- \frac{1}{\rho}\frac{\partial P}{\partial x_{i}}+\frac{\partial}{\partial x_{j} }(\
u\frac{\partial u_{i}}{\partial x_{j}}-\overline{u^{\prime}_{i}u^{\prime }_{j}})+f_{ext} \tag{2}\]  
\[-\rho\overline{u^{\prime}_{i}u^{\prime}_{j}}=u_{t}\left(\frac{\partial u_{i}}{ \partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}\right)-\frac{2}{3}\rho k \delta_{ij} \tag{3}\]  
where \(x_{i}\) is the displacement in the inertial coordinate system and \(u_{i}\) is the velocity components. \(t\), \(\rho\), and \(P\) represent time, air density and pressure respectively. \(\
u\) is the kinematic viscosity term, \(f_{ext}\) is the added source term. With the assumption based on the axisymmetric property of the HAWT, 2D-RANS/AD method is utilized such that the computational effort can be greatly decreased.  
The rotor is assumed as permeable disc such that the mass equation remains unchanged. To avoid numerical instability the volume force in Equation (2) should be smeared along the flow direction using 1D Gaussian function. For the elements which are away from the disc with a normal distance of \(d\), the smeared force \(f^{\prime}\) is computed by convolution computation  
\[f{t}=f_{AD}\otimes\eta^{1D} \tag{4}\]  
\[\eta^{1D}(d)=1/\left(\varepsilon\sqrt{\pi}\right)\exp\left[-\left(d/ \varepsilon\right)^{2}\right] \tag{5}\]  
Figure 1: Schematic diagram of relations between the developed solvers.  
where the free parameter \(\varepsilon=3\Delta z\) is three times larger than the axial size of reference grid \(\Delta z\). What is more, the body forces of rotor can be computed using blade element theory with the aerodynamic data of airfoil and the simulated information of flowfield.  
The present 2D-AD method solves the ideal axisymmetric flowfield. The axial and tangential induced velocities are naturally combined with the NS equations, such that the inflow angle and the relative inflow velocity are determined from  
\[\phi=\tan^{-1}\left(\frac{V_{0}-W_{z}}{\Omega r+W_{\theta}}\right),\ V_{rel}^{2} =(V_{0}-W_{z})^{2}+(\Omega r+W_{\theta})^{2} \tag{6}\]  
The local AOA is easily computed by \(\alpha=\phi-\gamma\), where \(\gamma\) is the real twist angle (added by pitch) at local blade element. The lift and drag forces per unit span are computed using Equation (7) with the aerodynamic data of airfoil.  
\[L=\frac{1}{2}\rho V_{rel}^{2}cBC_{l},\ D=\frac{1}{2}\rho V_{rel}^{2}cBC_{d} \tag{7}\]  
The blade forces are updated after each iteration using newly computed velocity field, then the external volume force terms of the NS equations apply renewed blade forces in the next iteration.
# Resonant Laser Ionization and Fine-Structure Study of Silver in an Ablation Plume
## 4 Experimental Conditions and Results
### Isotopic Identification  
Figure 4 shows a TOF signal of silver as collected on the MCP. As can be seen, the two isotopes \({}^{107}\)Ag and \({}^{109}\)Ag were detected separately with a satisfying time resolution. In the configuration of the ion-optics described in Section 3.2, \({}^{107}\)Ag and \({}^{109}\)Ag arrived at the MCP at 2.672 us and 2.698 us, respectively.  
Figure 3: A schematic representation of the ion-optics inside the TOF-MS. The four electrodes indicated are: a repeller plate (Rep), an extraction grid (Ext), an electrostatic lens and a zero-voltage grid. The grey triangle (not to scale) represents an upward-expanding conical plume of silver and the purple cross-mark indicates the excitation lasers incidence orthogonal to the vertical axis of the plume. The grey dot represents a laser-ionized silver ion which is guided to the MCP with the ion-optics system.  
Doppler-Suppressed Scheme Test: 4d\({}^{10}\)5s \({}^{2}\)5\({}_{1/2}\)\(\rightarrow\) 4d\({}^{10}\)5p \({}^{2}\)2\({}^{o}_{3/2}\)\(\rightarrow\) 4d\({}^{10}\)6d \({}^{2}\)D\({}_{3/2}\)  
The key to avoiding Doppler broadening is to avoid different velocity components in the given environment. The approach used to avoid this broadening was to probe only a tiny volume inside the plume, largely containing only single velocity components.  
Both the excitation lasers were directed into the ablation plume in the plane orthogonal to the vertical central axis of the plume. This implies that the Doppler effect was confined with just the horizontal components of the atomic velocities. If the excitation lasers are injected non-collinearly to each other, this will reduce the volume of the interaction region by a large factor compared to collinear injection. Smaller diameters of the laser beams provide a smaller interaction volume and are essential for selecting a group of atoms with almost zero horizontal velocity close to the central axis of the plume. Higher angular separation of the excitation lasers ensures this selection even further.  
The diameters of the laser beams used to attain the desired interaction volume were 0.6-0.8 mm and 1.0-1.2 mm for the first and second step transition lasers, respectively. These laser beams were maintained at an angular separation of \(\sim\)8.5 degrees and, in this configuration, a very small intersection volume, \(\sim\)0.5 mm\({}^{3}\), was achieved inside the ablation plume. This precise selection of the interaction volume enabled us to produce a nearly Doppler-suppressed scan of the transition lines in the ablation plume.  
Figures 5 and 6 show clear resonance of the first step and the second step at 328.163 nm and 421.402 nm, respectively. The resonance profiles reflect very narrow linewidths of \(\sim\)1.2 pm (3.3 GHz) and \(\sim\)1.0 pm (1.7 GHz). The respective laser profiles, with corresponding linewidths, were reproduced (see Table 2), showing practically no effect of Doppler broadening.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 1 Introduction
### Monoamine Neurotransmitters
#### 1.2.1 Dopamine  
Dopamine is one of the neurotransmitters of great clinical importance for motor functions and motivational behaviors. Its dysfunction is involved in many psychiatric disorders, including drug addiction, schizophrenia, Parkinson's and Huntington's disease. The dopaminergic neurons are localized in the substantia nigra pars compacta and in the ventral tegmental aera [52].  
Dopamine seems to also be involved in drug addiction. For instance, it is known that cocaine inhibits the dopamine transporter [53]. However, cocaine can also block the norepinephrine and serotonin transporters, which can also reuptake dopamine.  
Schizophrenic behavior includes a disturbance of cognition and perception with symptoms including delusions, hallucination, antisocial behavior and lack of motivation [54]. The cognitive symptoms may be caused by a hypodopaminergic activity in the mesocortical pathway, while the psychotic symptoms may be caused by a hyperdopaminergic activity in the mesolimbic pathway [55]. The role of dopamine in schizophrenia is strongly supported by the fact that dopamine antagonists are efficient to treat patients, and positive symptoms are related to dopamine dysregulation [54].
# Evaluating Complex Entity Knowledge Propagation for Knowledge Editing in LLMs
## 2 Related Works
### Evaluating KE Methods in LLMs  
KE methods are used to update and edit factual knowledge in pre-trained LLMs. To verify the updated knowledge for consistency, correctness, and accuracy, evaluation of the editing methods is conducted using different evaluation metrics. It is also important to evaluate irrelevant knowledge to identify if it is corrupted or not [16; 24; 26; 27]. KE evaluation is becoming popular, and various benchmarks have been introduced recently to evaluate and verify the performance of KE methods such as [10; 33] who used zero-shot relation extraction (zsRE), which is a question-answering dataset that uses specific queries to relations and paraphrases generated by humans to evaluate the robustness of the editing models. Other benchmarks include CounterFact [16], which contains a more complex settings where the LLM assigns a lower probability to edited facts and these edited facts are counterfactuals.  
The zsRE and CounterFact both evaluate editing models based on some vital aspects, i.e., (i) efficacy, which ensures if the model produces the target object after the edit; (ii) specificity, which ensures that irrelevant objects are unaffected after the edit; (iii) robustness, which ensures if the model generates a correct response for the input paraphrases; (iv) quality measurements for the generated content based on consistency and fluency, ensuring the similarity and repetitiveness of the generated text with the subject in the query.  
Another study found that traditional editing methods exhibit inconsistencies when paraphrasing questions are used in new contexts and that adding entity definitions can facilitate the propagation of the injected external knowledge [34]. Existing KE methods might face challenges of low specificity [35]. MEMI-CSKPROBE is a dataset introduced by [36] for common sense knowledge editing in LLMs. This dataset includes semantic generalization of common sense edits. MQUAKE is another benchmark introduced by [12]; it is a multi-hop question-answering-based benchmark for evaluating model multi-hop reasoning capabilities after editing. KLoB (Knowledge Locating Benchmark), a novel benchmark for evaluating the locating methods in language models, delineates three essential criteria that a reliable knowledge locating method should satisfy and then evaluates the efficacy of the locating methods by examining these criteria [37]. RIPPLEEDITS proposes six different evaluation metrics and focuses on a comprehensive evaluation of edits rather than focusing on only a single editing effect [24]. However, they restrict this evaluation to three hops. In this work, we take this a step further and consider a much more comprehensive representation and edit evaluation for a given subject.
## 3 Numerical Simulation
### Computational Domain  
The terrain data were obtained from the latest geographic information of Google Map by Point-Taking (2020) software. The terrain grid was generated by interpolation with Surfer (2020) software, and the computing grid was set twice as dense as the interpolation grid. The computational domain dimension was about 150 km \(\times\) 120 km \(\times\) 2.57 km, as shown in Figure 5. M1 to M5 denote the locations of the five main stations. Their specific site names and wind speed parameters are shown in Table 2.  
Figure 4: The overall procedure to determine the reference wind pressure.  
Figure 5: Computational domain of the CFD model (m).  
A grid sensitivity analysis was performed, and three grid schemes are shown in Table 4. The grid sensitivity analysis shows that Scheme 2 could produce an accurate simulation with reasonable computational cost. The grid details for Scheme 2 are as follows. In the horizontal direction, the grid size was 100 \(\times\) 100 m. In the vertical direction, the grid dense was gradually reduced with the increase in height. The grid size near the ground is 2 m, used for a height of 20 m; then the grid size becomes 5 m for a height of 50 m; the top grid size was 100 m with 25 layers for a height of 2500 m. The total dimension in the altitude direction was 2570 m. The total number of grids was 8.1 \(\times\) 10\({}^{7}\). The y+ value at the surface was in the range of 30-180, which satisfies the requirement of RANS (Reynolds averaged Navier-Stokes) simulation. The generated grids are shown in Figure 6.
###### Abstract  
Aggression in patients with dementia poses significant caregiving and clinical issues. In this work, fusion approaches--Early Fusion and Late Fusion--were compared to classify aggression using audio and visual signals. Early Fusion integrates the extracted features of the two modalities into one dataset before classification, while Late Fusion integrates the prediction probabilities of standalone audio and visual classifiers with a meta-classifier. Both models were tested using a Random Forest classifier with five-fold cross-validation, and the performance was compared on accuracy, precision, recall, F1-score, ROC-AUC, and inference time. The results showcase that Late Fusion is superior to Early Fusion in terms of accuracy (0.876 vs. 0.828), recall (0.914 vs. 0.818), F1-score (0.867 vs. 0.835), and ROC-AUC score (0.970 vs. 0.922), proving more suitable for high-sensitivity use cases like healthcare and security. However, Early Fusion exhibited higher precision (0.852 vs. 0.824), indicating that in cases when false positives are a requirement, Early Fusion is preferable. Paired \(t\)-tests were applied for statistical comparison and indicate that precision alone is significantly different, with the advantage of Early Fusion. Late Fusion also performs slightly less in inference time, which makes it suitable for use in real-time systems. These findings provide significant information on multimodal fusion strategies and their applicability in the detection of aggressive behavior, which can contribute to the development of efficient monitoring systems for dementia care.  
machine learning; multimodal analysis; late fusion; early fusion; comparative analysis; meta-classifier; dementia; aggressive outbursts +
Footnote †: journal: Applied sciences
## 3 Design of Hardware and Software for the Data Monitoring System
### Slave Module to Wireless Module Communication  
The Slave module algorithm is designed to collect data from all sensors connected while the Master module does not request communication. That way, the state of its sensors is always updated. This method of keeping all data updated is useful for future applications as the Slave module can control in real-time the state of the wired network of sensors. Thus, the slave can start a communication with the Master module through the Wireless to activate, for example, an alarm.  
When the Wireless module to which it is connected indicates that a data transmission has been requested _PIN_R_ = 1, so an interrupt inside the microcontroller occurs and the Wireless module starts the data transmission using the I\({}^{2}\)C protocol. The Slave module asks for the requested channel sending an _O_SRCh_ command (Figure 9) to the Wireless slave module, then, it loads into its I\({}^{2}\)C exit buffer the number of the channel requested by the Master Module and indicates that the message is available to be read. Therefore, the Slave module sends a _RFrom_ command to read the message with the requested channel.  
Figure 9: Flowchart of wireless orders from Slave module.  
After this, the Slave module, using the _O_SSDt_ command (Figure 9), sends the sensors stored data and the Wireless stores this data inside the indicated _DP_. When the Slave module has finished, it sends an _O_STDt_ command with the number of _LDP_ in order to indicate that the Wireless module can start the transmission with the emitter of the request. If the transmission between Wireless modules has been accepted _PIN_R_ = 0, indicating that the transmission has been performed correctly.  
The communications protocols between one Master and one Slave are shown in Figure 10 in simplified as an example; note that a wireless network could have more Slave modules.
## 4 Results and Discussions
### Discussions
In this situation, omission of some true cloud-free pixels could be possible. However, as discussed above, such small omission would not significantly impact the quality of the reconstructed cloud-free time-series images. Among the three types of cloud contaminated pixels (cloudy, cloud shadow, and haze pixels), the accuracy of cloudy and cloud shadow pixel identification was higher than that of haze pixel identification. The main reason was that a cloudy and cloud shadow pixel usually had a large reflectance difference compared to a cloud-free pixel while the value difference of a haze and a haze-free pixel was relatively small. A haze pixel may also contain some of the surface reflectance underneath. Hence, due to the smaller difference, bias from the synthetic image prediction may contribute to over or under identification of haze pixels. Because of this, the method for haze pixel identification needs to be further polished.
## 2 Test Overview and Arrangement
### Measurement Scheme  
Trial piles were set up at two locations in the construction site's northeast corner to assess soil disturbance during pile construction. Tests included earth pressure, soil displacement, excess pore water pressure, vibration, and noise, as shown in Figure 3.  
For the driven pile, one earth pressure hole was at 2.1 m from the pile axis, and two inclinometer points were at 3.3 and 4.2 m, respectively. A pore pressure hole was at 2.1 m. For the PGP pile, two earth pressure holes were at 2.1 and 3.3 m, respectively. Two inclinometer points were at 3.3 and 4.2 m, respectively, and three pore pressure holes were at 0.9, 2.1, and 3.3 m, respectively. Earth pressure cells were at dep  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Soil Layer** & **Thickness (m)** & \(w\) **(\%)** & \(\gamma\) **(kN/m3)** & \(e\)** & \(w_{\rm L}\) **(\%)** & \(w_{\rm P}\) **(\%)** & \(Es\) **(MPa)** & \(c\) **(kPa)** & \(\varphi\) (\({}^{\circ}\)) \\ \hline
1-1 back-filling & 1.7 & - & - & - & - & - & - & - & - \\
1-2 clayy silt & 2.3 & 28.8 & 18.9 & 0.809 & 33 & 24.3 & 6.3 & 12.7 & 17.8 \\
1-3 muddy silty clay & 2.1 & 40.2 & 17.7 & 1.124 & 36.9 & 21.5 & 3.29 & 11.3 & 10.2 \\
3-1 clayy silt & 2.9 & 27.8 & 19 & 0.783 & 33.1 & 24.1 & 9.1 & 12.6 & 20.5 \\
3-2 clayy silt & 7.3 & 28.6 & 18.9 & 0.809 & 33.3 & 24.3 & 8.9 & 12.6 & 20 \\
3-3 sandy silt & 3.2 & 26.8 & 19.1 & 0.761 & 32 & 23.4 & 11.4 & 12.4 & 20.9 \\
4-1 muddy silty clay & 8.7 & 39.6 & 17.7 & 1.107 & 36.5 & 21.5 & 3.32 & 11.7 & 10.4 \\
4-1 silty clay & 8.6 & 34.8 & 18.2 & 0.987 & 38.5 & 22.1 & 3.98 & 26 & 12.7 \\
4-3 silty clay & 6.1 & 34.9 & 18.2 & 0.978 & 38.2 & 22.8 & 3.9 & 25.4 & 14.3 \\
6-1 clayy silt & 0.8 & 25.7 & 19.3 & 0.734 & 31.1 & 22.6 & 8.3 & 12.3 & 21.5 \\
6-2 fine sand & - & 19.4 & - & - & - & - & 26 & - & - \\ \hline \end{tabular}
\end{table}
Table 1: Soil layer and its basic parameters.  
Figure 2: Measurement points.  
respectively; inclinometer reaches a depth of 50 m; pore pressure gauges were at depths of 12, 22, and 32 m, respectively.  
Vibration accelerometers were placed on the pile driver, nearby building walls (\(r=20\) m), and a pipeline concrete column (\(r=50\) m) to measure driver vibrations. For the planting pile machine, accelerometers were on drilling and planting machines, building walls (\(r=20\) m), container workshop walls (\(r=30\) m), construction fence (\(r=30\) m), and pipeline column (\(r=40\) m). Noise levels were measured during construction at 1, 5, 10, and 20 m from the pile driver and planting machine.
# MBPPE: A Modular Batch Processing Platform for Electroencephalography
## 2 Materials and Methods
### Preprocessing
#### 2.3.3 Resampling  
To ensure high temporal resolution of EEG signals, researchers typically opt for a sampling rate of 1 kHz or higher during the data acquisition phase. However, in subsequent analysis, such a high sampling rate imposes significant demands on storage and memory. For studies less sensitive to temporal resolution, downsampling strategies can effectively conserve computational resources. Moreover, resampling techniques are extensively employed to address inconsistencies in sampling rates among different datasets, thereby preventing potential impacts on the predictive performance of machine learning algorithms [19].  
In this study, we implemented a resampling method for a polyphase filter using the resample_poly method from the SciPy library. During execution, EEG data is upsampled by an up factor, followed by the application of a zero-phase low-pass FIR filter to reduce additional phase distortion and maintain the key features of the original signal. The data are then downsampled by a down factor. The final sampling rate is the \(\frac{up}{down}\) multiple of the original sampling rate. The sampling algorithm used in this process adheres to the description provided by [PERSON] [20].  
By default, we take the user-input sampling rate as the upsample factor and the original sampling rate as the downsample factor. The calculation method of the final sampling rate is shown in Equation (1) where \(o\) represents the original sampling rate, \(up\) represents the upsample factor, \(down\) represents the downsample factor, and \(n\) represents the target sampling rate.  
\[\begin{split}\text{resample\_poly}(o,\,\text{up},\,\text{down} )&=\frac{up}{\text{down}}\cdot o\\ &=\frac{n}{o}\cdot o\\ &=n\end{split} \tag{1}\]  
This method enhances practical applications by permitting users to input the target sampling rate directly to accomplish resampling. Compared to resampling using the fast Fourier transform (FFT), this approach circumvents time-frequency transformation, offering a boost in computational efficiency.
# Real-Time Semantic Image Segmentation with Deep Learning for Autonomous Driving: A Survey
## 2 Approaches for Inference Time Reduction
### Convolution Factorization--Depthwise Separable Convolutions  
It is known that for most deep-learning models the convolutional layers are vital structural components. Therefore, transforming the convolutions that are performed in the layers of the network into more computationally efficient actions is an excellent way to improve the model's performance in terms of speed. A popular design choice for improving convolutions is the use of depthwise separable convolutions, which is a type of factorized/decomposed convolutions [2]. Standard convolution performs channel-wise and spatial-wise computation in a single step. On the contrary, depthwise separable convolution breaks the computation process into two steps. In the first step, a single convolutional filter per each input channel is applied (depthwise convolution), while in the second step, a linear combination of the output of the depthwise convolution is considered by means of a pointwise convolution [3]. These two different procedures are shown in Figure 1. It is important to compare the computational burden of these two tasks. More specifically, for \(N\) filters of size \(D\times D\), the ratio of computational complexity between depthwise separable convolutions and standard convolutions equals to \(Ratio\ =\ 1/N\ +\ 1/D^{2}\). For example, given 100 filters of size \(256\times 256\), the ratio of complexity is 0.01, which means that a series of depthwise separable convolution layers execute 100 times less multiplications than a corresponding block of standard convolutional layers.  
This approach was first used in Xception [4], where the designers replaced Inception module [5] with depthwise separable convolutions. Likewise, MobileNets [6] comprise depthwise separable convolutions to improve efficiency. Overall, the efficiency by design offered by this approach is a strong argument for being used in any new efficiency-oriented network implementations.