esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on the In-Plane Seismic Performance of a New Type of Masonry Wall System
## 4 Conclusions  
The current paper presented the experimental tests of two types of new concrete block walls as well as a typical block wall and investigated their in-plane behavior, loading bearing capacity, energy dissipation capacity and reuse ratio. The following major findings were obtained:  
* The in-plane seismic performances of Specimens PS and DS were much higher than that of Specim CB. In particular, the deformability of the proposed system has improved remarkably due to the interlocking mechanism between the main blocks and the key blocks.
* The calculated lateral loads based on the simple rocking mechanism agreed well with the experimental results.
* The energy dissipation capacities of the proposed systems were considerably superior to that of Specim CB. Furthermore, the remarkable deterioration of the ratios of the proposed systems were not found until the final loading.
* The proposed seismic block systems can reuse more than 70% after the final loading. This result implies that the proposed concrete block wall systems are economical and eco-friendly.  
The current paper focused only on the in-plane experimental behavior. If the proposed system is established as one of the masonry systems in the future, it will improve the seismic performance, convenience of construction and reliability of construction, and thus the earthquake damage of masonry buildings will be reduced. Although the gap of 1.5 mm between the main block and the key block did not adversely affect the in-plane seismic performance, its size needs to be comprehensively examined, including the results of out-of-plane shaking table experiments. For these purposes, the experimental data should be investigated from numerical perspectives, and the out-of-plane experimental and numerical behaviors should be carried out in future studies.  
All authors have contributed to the development of the research and in the elaboration of this article. Particularly, [PERSON] contributed to the methodology and the experimental research, and [PERSON]-[PERSON] contributed the methodology and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This research was supported by a grant (2020-MOIS31-012) of the Fundamental Technology Development program for Extreme Disaster Response, funded by Ministry of Interior and Safety (MOIS, Korea).  
The authors declare no competing interests.  
All datasets generated in this study are available from the corresponding author upon reasonable request.  
Figure 19: Monotonic loading tests vs. calculation results.
## 2 Optimization Model
### Torque  
1. Hold section \[M_{i+1}=M_{i}+\mu q\sin\alpha(L_{i+1}-L_{i})D_{t}/2\] (16) where \(D_{t}\) is the diameter of the pipe string, mm.
2. Build-up section The expressions of the axial force and torque for the microelement segment ignoring sliding friction are \[T_{i+1}=T_{i}+qR(\cos\beta_{i}-\cos\beta_{i+1})\] (17) \[M_{i+1}=M_{i}+\mu|q\cos\beta_{i+1}-T_{i+1}/R|D_{t}/2000R(\beta_{i+1}-\beta_{i})\] (18)
3. Drop-off section The expressions of the axial force and torque for the microelement segment ignoring sliding friction are \[T_{i+1}=T_{i}+qR(\sin\alpha_{i+1}-\sin\alpha_{i})\] (19) \[M_{i+1}=M_{i}+\mu|q\sin\alpha_{i+1}+T_{i+1}/R|D_{t}/2000R(\alpha_{i+1}-\alpha _{i})\] (20)  
In order to reduce the drilling difficulty, torque should be minimized. The minimum objective function of torque for rotary drilling before the target can be expressed as  
\[\text{min}M=\text{min}\left[M_{0}+\mu qD_{t}/2\underset{i=1}{\overset{j}{\sum }}(L_{i}\sin\alpha_{i})+\underset{i=1}{\overset{n}{\sum}}M_{i}\right] \tag{21}\]
## 2 Materials and Methods
### Biochar Adsorption Mechanisms Study
#### 2.3.1 \(\mathrm{NH_{4}}^{+}\) Maximum Adsorption Capacity  
The maximum adsorption capacity (qmax) was detected under laboratory batch adsorption experiments at room temperature using NH4 Cl aqueous solutions at different concentrations, adapting the procedure described in [33]. A quantity of 0.5 g of the dried biochar was added into five Polyethylene (PE) vessels containing 40 mL of solution with 0, 5, 10, 50, or 100 mg/L of N-NH4\({}^{+}\), respectively. Each solution was characterized in terms of initial N-NH4\({}^{+}\) concentration. A biochar suspension in distilled water without NH4 Cl was used as a blank run. The vessels containing the suspensions were closed to avoid evaporation and shaken at 400 rpm in a magnetic stirrer for 16 h. Then, the biochar was separated from the aqueous solution by vacuum filtration with MF-Millipore membrane filters, 3 um pore size. Finally, the filtered solutions were analysed for determining TAN concentration using the spectrophotometric procedure of the indophenol blue method [34]. Adsorbed nitrogen was determined as the difference between the nitrogen concentration of the aqueous solutions before and after the adsorption tests.
## 4 The Global SAC and Grid Systems
### The Tetrahedron Version of the Global Grid System  
#### 4.1.1 The Tetrahedron Version of SACs  
For the first example, we show the development of the tetrahedron version of the SAC system. To acquire an elegant form of the transformation, we place the vertices of the tetrahedron at  
\[A=[1\quad 1\quad 1]/\sqrt{3};\quad B=[-1\quad 1-1]/\sqrt{3};\quad C=[-1-1 \quad 1]/\sqrt{3};\quad D=[1-1-1]/\sqrt{3}.\]  
The initial global grid consists of four equal-area spherical equilateral triangles, \(T_{i},i=1,2,3,4\) (shown in Figure 7), and the area is \(\pi\). Now, we use the method in Section 3.2 to establish the coordinates on each cell. The tetrahedron version of the SACs is an ordered tetrad of real numbers \((\lambda_{i}),i=1,2,3,4\), and we specify that  
\[if\ \mathbf{p}\in T_{i},\quad\lambda_{5-i}=0.\]  
where \(\xi(u,v,w)\) and \(\tau(u,v,w)\) are third-order determinants:  
\[\tau(u,v,w)=\begin{vmatrix}\sqrt{3}\sin\frac{u}{2}&-\cos\frac{u}{2}&-\cos\frac{u }{2}\\ -\cos\frac{v}{2}&\sqrt{3}\sin\frac{v}{2}&-\cos\frac{v}{2}\\ -\cos\frac{w}{2}&-\cos\frac{w}{2}&\sqrt{3}\sin\frac{w}{2}\end{vmatrix},\quad \xi(u,v,w)=\begin{vmatrix}\sin\frac{u}{2}&\cos\frac{u}{2}&\cos\frac{u}{2}\\ \sin\frac{v}{2}&-\sqrt{3}\sin\frac{v}{2}&\cos\frac{v}{2}\\ \sin\frac{w}{2}&\cos\frac{w}{2}&-\sqrt{3}\sin\frac{w}{2}\end{vmatrix}.\]
# Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions
## 2 Description of the Problem  
The vehicle routing problem discussed in this paper is characterized as follows: a logistics center operates with an unlimited fleet of vehicles, delivering services to numerous customers with predetermined locations and demands; customer demands must be satisfied within specified time windows. The objective is to minimize total costs while adhering to vehicle load, time, and speed constraints. To facilitate this analysis, the following assumptions are made:1. All distribution activities originate from a distribution center with a known location and return there upon completion.
2. The type, age, and level of maintenance of the distribution vehicles are not different.
3. The demand from customers is predetermined and remains within the vehicle's maximum loading capacity.
4. Deliveries must be finalized within the time frames designated by the customers.
5. The weight of the goods carried must comply with the vehicle's carrying capacity.
6. Each customer is served by only one vehicle.
7. Vehicle speed remains constant and is not influenced by factors like traffic congestion or environmental conditions.  
For ease of description, the symbols involved in the model are explained, with details provided in the Abbreviations.
## 5 Methodology
### Performance Evaluation Framework
Customizable Tuned Profiles: Tuned profiles are built into the framework to enable fine-tuned configurations for network testing, including TCP/UDP bandwidth and latency tests for various packet sizes and frame configurations. Two tuned profiles, named kernel_optimizations and nic_optimizations, were made available with the code included in this paper. Kernel_optimizations tuned profile contains changed kernel settings (deployed as a custom-tuned profile) for:  
* [noitemsep,topsep=0 pt]
* Core read buffer size;
* Core write buffer size;
* TCP read buffer size;
* TCP write buffer size;
* TCP window scaling;  
Figure 2: An example Grafana dashboard with CNI performance overhead when using HPC compute tuned profile, compared to the evaluation of the physical network using the same tuned profile. * TCP timestamps;
* TCP selective acknowledgment;
* TCP fast open;
* TCP FIN timeout;
* TCP SYN backlog;
* TCP tw buckets. Documentation describing the meaning of these settings is available online [58]. Also, for the nic_optimizations tuned profile, the following changes were made to runtime parameters:  
* Enable GRO (Generic Receive Offload);
* Enable Large GRO;
* Disable TSO (TCP Segmentation Offload);
* Set ring buffer sizes. These settings substantially influence bandwidth results, bringing substantial improvements. A paper outlining this methodology's results clearly shows the importance of automated evaluations like these for HPC and other scenarios [57]. The significance of these kernel and runtime parameters is also clearly visible in the published bandwidth results, offering a significant increase in performance for different CNI plugins. The automated approach in this methodology also solves the most prominent problem with CNI plugins: it takes care of their installation and de-installation, allowing for easy reproducibility of performance evaluations.
# Multimodal Few-Shot Learning for Gait Recognition
## 4 Discussion  
To verify that the system forms a discriminative cluster for each subject, we present the t-SNE [47] plots of the embedding vectors of the unit steps in the _unknown known_ and the _unknown unknown_ test dataset in Figure 10. Considering that the networks were trained with subjects' unit steps in the _training_ set only, these plots show that the proposed system learns the general characteristics of unknown subjects' gait patterns satisfactorily.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & \(\gamma\) & \(\
u\) & **Sensing** & \(\tau\) & **TPR** & **TNR** & **ACC** \\ \hline \multirow{4}{*}{Ensemble} & \multirow{4}{*}{1.9} & \multirow{4}{*}{0.06} & Multi & \(-0.1\) & 0.9342 & 0.9375 & **0.9360** \\  & & & Pressure & \(-0.09\) & 0.8889 & 0.8845 & 0.8876 \\  & & & Acceleration & \(-0.08\) & 0.8871 & 0.9087 & 0.8985 \\  & & & Rotation & \(-0.1\) & 0.8965 & 0.8895 & 0.8930 \\ \hline \multirow{4}{*}{CNN} & \multirow{4}{*}{1.8} & \multirow{4}{*}{0.06} & Multi & \(-0.1\) & 0.9274 & 0.9250 & **0.9263** \\  & & & Pressure & \(-0.08\) & 0.8803 & 0.8802 & 0.8808 \\  & & & Acceleration & \(-0.09\) & 0.8892 & 0.8788 & 0.8840 \\  & & & Rotation & \(-0.08\) & 0.8705 & 0.8919 & 0.8816 \\ \hline \multirow{4}{*}{RNN} & \multirow{4}{*}{2.2} & \multirow{4}{*}{0.08} & Multi & \(-0.1\) & 0.8745 & 0.8759 & **0.8757** \\  & & & Pressure & \(-0.07\) & 0.7752 & 0.7760 & 0.7757 \\ \cline{1-1}  & & & Acceleration & \(-0.1\) & 0.8173 & 0.8283 & 0.8224 \\ \cline{1-1}  & & & Rotation & \(-0.1\) & 0.8015 & 0.8221 & 0.8129 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of the network models using multi-modal and uni-modal sensing. The blue color denotes the best accuracy of each network model.  
Figure 9: Performance comparison between sensing modalities.  
To enable us to quantitatively analyze our results, we devised a distance function between two unit steps using their embedding vectors. This distance function is defined by  
\[d(\mathbf{s},\mathbf{s}^{\prime})=||f(\mathbf{s})-f(\mathbf{s}^{\prime})||_{2}=|| \mathbf{v}-\mathbf{v}^{\prime}||_{2}. \tag{4}\]  
The distributions of the distances between homogeneous and heterogeneous unit steps, respectively, are plotted in Figure 11. The blue line shows the distribution of the distances between homogeneous unit steps, which are two unit steps of identical subjects, and the orange line shows the distribution of distances between heterogeneous unit steps, which are two unit steps of different subjects. A clear distinction between the two distribution curves would signify the recognition accuracy of the system to be outstanding. Unfortunately, the two curves overlap to a certain extent, indicating that potential recognition errors may occur.
# Bioelectrical Signals for the Diagnosis and Therapy of Functional Gastrointestinal Disorders
## 3 Stomach  
Functional gastric disorders, such as gastroparesis, functional dyspepsia and chronic unexplained nausea and vomiting, are linked with impaired motility activities and patterns in the stomach [13]. Endoscopy is a common method for assessment and diagnosis of gastric conditions. However, its use is primarily related to structural evaluation [52]. Once structural abnormalities are excluded, assessment of gastric emptying can be performed using scintigraphy, breath tests, or wireless motility capsules [53].  
Gastric emptying scintigraphy involves eating a meal that contains a small amount of radioactive material and monitoring the rate at which this food leaves the stomach over a 2-4 h period [54]. The wireless capsule test involves swallowing a non-digestible pill which can detect an abrupt rise in pH when it leaves the acidic environment of the stomach [55]. Combinations of pH and temperature signatures in different parts of the gastrointestinal tract help to determine the time course of the capsule along its route to egestion. The gastric emptying breath test involves eating a meal containing [13C]-Spirulina platensis that is absorbed after leaving the stomach and then eventually excreted by the lungs as 13 CO2. As wireless capsule and breath tests involve no radiation, they may be favored, however, they are only able to provide indirect measures of gastric emptying [56]. Moreover, the efficacy and precision of all methods are highly dependent on the disorder and hence designing a methodology tailored for each FGID is essential to obtain reliable results [53]. Magnetic resonance imaging (MRI) is an emerging technique for quantifying gastric volume and emptying by visualizing the stomach and its contents. Studies have shown that gastric volume assessments were reproducible and consistent with intragastric balloons, and emptying scores obtained from MRI were correlated with those of gamma scintigraphy [57]. Even though monitoring gastric emptying by exploiting such techniquesgives insights about gastric dysmotility, the observations do not explain the underlying factors of such impairments.
## 3 Results
### Early Earthquake Magnitude Estimation as a Classification Task
Ref. [52] proposed a machine learning magnitude classification framework (MCFrame), a rapid earthquake magnitude classification method based on ML. Using single-station data, it classifies seismic events as high magnitude (\(M\geq 5.5\)) or low magnitude (\(M<5.5\)). The model combines CNN layers, RNNs, and an AM to extract features from P-wave signals during the first seconds after an earthquake. The model was trained with different P-wave time windows, and the results showed that a 5 s window provided the best performance. Different classifiers were analyzed, and a probability threshold of 0.5 was established to determine whether an event is \(M\geq 5.5\) or lower. To eliminate the effect of epicentral distance, it was fixed at 10 km during training. It was trained and tested using 129,513 Japanese K-NET seismic network records, corresponding to 2794 seismic events with magnitudes ranging from \(M3\) to \(M8\). Results showed an accuracy of over 99% for low-magnitude events and more than 90% for high-magnitude events. MCFrame was validated with 496 independent seismic events, achieving 87% accuracy for high-magnitude events. MCFrame outperforms traditional baseline models such as standalone CNN and LSTM networks. These conventional methods focus on regression-based magnitude estimation but suffer from limitations in accuracy and generalization, particularly near classification boundaries (e.g., \(M5.5\)). In [54], a 3D-CNN-RNN model was proposed to classify earthquake magnitudes as a multi-label problem, allowing a seismic event to belong to multiple categories simultaneously. The model extracts temporal and spatial features from seismic signals using log-mel spectrograms and combining 3D convolutional layers with gated recurrent units (GRUs). Unlike traditional multi-class approaches, this method better captures the continuous nature of earthquake magnitudes, reducing misclassification at class boundaries. Earthquake magnitudes were grouped into five classes (0 to 8) and an additional category for non-earthquake events. Key results showed significant improvements in multi-labelclassification compared to multi-class approaches, achieving an accuracy of up to 97% for small earthquakes (0-1) and 99% for non-earthquake events. However, performance declined for lower magnitudes due to their weaker signal characteristics and spectral overlap with noise, highlighting a challenge in detecting small events. Regarding performance, the model achieved an F1 score of 0.91 for earthquakes of magnitude 3-4, although performance was lower for smaller magnitudes. Tests also demonstrated improved classification of earthquakes across multiple simultaneous magnitudes, which is crucial for precise and early disaster response. This suggests that integrating additional data preprocessing techniques or hybrid architectures could further enhance classification reliability, particularly for low-magnitude events.
# Performance Analysis of Quantum Key Distribution Technology for Power Business
## 3 Performance Characteristics of Power QKD System  
As the transmission of strong light signals, quantum light signals are affected by media properties such as fiber loss and fiber dispersion. At the same time, due to the diversity and complexity of the operating environment of the power network, the optical signals with stronger quantum light signals are more susceptible to environmental factors. In this section, we analyze the key factors that affect the performance of a simulated power communication QKD network.
# Collaborative Facilitation and Collaborative Inhibition in Virtual Environments
###### Abstract  
Worldwide, organizations and small and medium-sized enterprises have already disruptively changed in many ways their physiological inner mechanisms, because of information and communication technologies (ICT) revolution. Nevertheless, the still ongoing COVID-19 worldwide emergency definitely promoted a wide adoption of teleworking modalities for many people around the world, making it more relevant than before to understand the real impact of virtual environments (VEs) on teamwork dynamics. From a psychological point of view, a critical question about teleworking modalities is how the social and cognitive dynamics of collaborative facilitation and collaborative inhibition would affect teamwork within VEs. This study analyzed the impact of a virtual environment (VE) on the recall of individuals and members of nominal and collaborative groups. The research assessed costs and benefits for collaborative retrieval by testing the effect of experimental conditions, stimulus materials, group size, experimental conditions order, anxiety state, personality traits, gender group composition and social interactions. A total of 144 participants were engaged in a virtual Deese-Roediger-McDermott (DRM) classical paradigm, which involved remembering word lists across two successive sessions, in one of four protocols: \(I\)-individual/nominal, \(II\)-nominal/individual, \(III\)-nominal/collaborative, \(IV\)-collaborative/nominal. Results suggested, in general, a reduced collaborative inhibition effect in the collaborative condition than the nominal and individual condition. A combined effect between experimental condition and difficulty of the task appears to explain the presence of collaborative inhibition or facilitation. Nominal groups appeared to enhance the collaborative groups' performance when virtual nominal groups come before collaborative groups. Variables such as personality traits, gender and social interactions may have a contribution to collaborative retrieval. In conclusion, this study indicated how VEs could maintain those peculiar social dynamics characterizing the participants' engagement in a task, both working together and individually, and could affect their intrinsic motivation as well as performances. These results could be exploited in order to design brand new and evidenced-based practices, to improve teleworking procedures and workers well-being, as well as teleworking teamwork effectiveness.  
virtual dynamics; collaborative inhibition; collaborative facilitation; DRM paradigm; virtual teamwork [2020] 12, 118; doi:10.3390/612070118
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 3 Measurement Setups
### Offshore Ground-Based Comparative Measurement Campaign
#### 3.1.2 Ultrasonic Anemometer Measurements  
The 3D ultrasonic anemometer used for the comparison with the LiDAR data is a Gill R3-50 mounted at the meteorological mast FINO1 at the height of \(h\) = 41.5 m on a 6.5 m long boom orientated at 308\({}^{\circ}\). Vertical wind speed, horizontal wind speed, wind direction and air temperature data have been recorded with a sampling frequency of \(f_{\text{USA}}=20\) Hz. The original wind direction measurements have been corrected on the basis of the approach of [PERSON] et al. (2016) by using staring LiDAR measurement to determine misalignments. The correction of [PERSON] et al. includes the previous correction of the mast influence performed by [PERSON] et al. (2017). Figure 8 shows the frequency of the wind speed and wind direction distribution within the time period. The temporal change of the wind speed and wind direction can be seen in Figure 9. Horizontal lines within Figure 9 indicate a possible wake shading of the named turbines for that particular wind direction. Due to simplicity, these wind directions have been calculated on the basis of geometric correlations, and we neglect wake expansion and meandering effects.  
Figure 7: Visualisation of the line-of-sight velocity turbulence intensity in dependency of the wind direction measured by the ultrasonic anemometer from 21.12.2013 15:35 h (UTC) till 19.01.2014 7:55 h (UTC). Gaps in the plot visualise unavailability of anemometer data. Individual 10 min mean values are shown in light blue whereas the binned averaged is marked in dark blue. Black vertical dashed lines indicate the wind direction of possible wake shading of the anemometer on FINO1 based on geometrical correlations. The red line shows the perpendicular wind direction to the azimuthal orientation of the laser beam.