esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data Sources  
### Overview of the Study Area  
The study area is situated in the forested region of the Qinba Mountain range in China (referred to as the Qinba Mountain area), encompassing six provinces: Shaanxi, Henan, Hubei, Chongqing, Sichuan, and Gansu (as shown in Figure 1). The geographic coordinates range from 102\({}^{\circ}\)28\({}^{\prime}\)E to 113\({}^{\circ}\)39\({}^{\prime}\)E and 30\({}^{\circ}\)43\({}^{\prime}\)N to 35\({}^{\circ}\)24\({}^{\prime}\)N, with an east-west extent of approximately 1000 km, a north-south width of about 300 km, and a total area of 225,000 km\({}^{2}\). According to the results of China's ninth national forest resource inventory [26], the country's total forest area is approximately 220 million hm\({}^{2}\), with a total vegetation carbon storage of about 918.6 million t. The forest area within the Qinba Mountain region covers 20.8856 million hm\({}^{2}\). Based on the area proportion, the total vegetation carbon storage in the Qinba Mountain area is estimated at approximately 87.2 million t. This substantial carbon storage plays a critical role in supporting China's \"dual carbon\" goals and positions the region as a key player in the global carbon cycle.  
The dominant vegetation types in the Qinba Mountain area include coniferous forests (902,100 hm\({}^{2}\)), broadleaf forests (2,021,100 hm\({}^{2}\)), and conifer-broadleaf mixed forests (3,015,600 hm\({}^{2}\)) [27]. The region's terrain is characterized by a west-to-east elevation gradient, with altitudes decreasing from the Qinling-Daba Mountains toward the surrounding areas and featuring numerous hilly basins and significant topographic variation. Climatically, the eastern part of the region is humid, while the western part is arid, resulting in a pronounced east-west climatic differentiation [28].  
This study focuses on the forest area surrounding the Huoditang flux station as a typical case for detailed analysis. Located on the southern slope of the Qinling Mountains in China, the Huoditang forest area spans between 33\({}^{\circ}\)18\({}^{\prime}\)-33\({}^{\circ}\)28\({}^{\prime}\)N and 108\({}^{\circ}\)21\({}^{\prime}\)-108\({}^{\circ}\)39\({}^{\prime}\)E, with an elevation range from 800 to 2500 m. The region experiences a subtropical mountain climate, with an average annual temperature between 8 \({}^{\circ}\)C and 10 \({}^{\circ}\)C. Annual precipitation ranges from 900 to 1200 mm, with the majority falling during the months of July, August, and September, accounting for approximately half of the total annual rainfall. These conditions create an ideal environment for vegetation growth during the summer [29] and enhance the area's representativeness [30].  
Figure 1: Forest area of the Qinba Mountain region.
## 3 Results and Discussion  
After process development in a controlled lab environment, field trials on an actual brownfield were carried out. There were two fundamental shortcomings of the brownfield \"N77 petroleum refinery Droessing\":  
1. In the groundwater, mineral oil was found to be free-floating. In each of the five novel wells, plus the upstream, already-existing well, a black, oily phase could be pumped from the groundwater. It was approx. 0.5 L during each start of the pump, and the oil phase needed 1-2 days to be present again in a well that had been pumped off. This very high contamination load made quantification of the flushing experiments difficult.
2. The site, to a large extent, is made up of clay, which exhibits a very low water permeability. In fact, the soil was very dry, and it was discovered that both in the saturated and unsaturated zones, easily degradable hydrocarbons were still present, even after 80 years. That observation stresses the difficulty of natural attenuation.  
Still, the results show a novel process combination for the remediation of aged brownfields that has a good potential. Below, the main results are discussed.  
Figure 8: The inclined bed filter (IBF) in the workshop (**left**) and in the field (**right**). In the right picture, two IBCs with mineral hydrocarbons pumped from the ground and separated by the IBF with non-wovens can be seen in the font.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Residual Oil Content after the Processes** & **Groundwater** & **Emulsion** \\ \hline Classic oil separator (OS) & 0.8 mg/L & 0.8 mg/L \\ \hline OS + non-wovens on inclined bed filter (IBF) & 0.3 mg/L & 0.7 mg/L \\ \hline Non-wovens (IBF) & 0.4 mg/L & 0.6 mg/L \\ \hline \hline \end{tabular}
\end{table}
Table 8: Oil removal from water using non-wovens.
###### Abstract  
Background: The prolonged use of smartphones may lead to cervical posture deformities and other associated issues. Among these conditions, cervical extension type is one of the most commonly observed, characterized by increased cervical lordosis, forward head posture, and thoracic kybosis. These biomechanical changes may lead to neck pain, a restricted range of motion (ROM), and heightened cervical muscle tone. The purpose of this study was to evaluate the impact of combining cervical stabilization exercises with either scapular stabilization or thoracic exercises on the mechanical properties of cervical muscles, the pressure pain threshold (PPT), and the ROM in individuals with cervical extension type. Methods: This study included 32 subjects with cervical extension type who were randomly divided into two groups: 16 subjects were placed in either the cervical and scapular stabilization exercises group (CSG) or the cervical stabilization with thoracic exercises group (CTG). After four weeks of exercise intervention, the following assessments were conducted: measurement of muscle tone, stiffness, and elasticity of the sternocleidomastoid (SCM) and upper trapezius (UT) muscles using Myoton PRO; evaluation of the PPT of the SCM and UT muscles using a pressure pain threshold meter; and assessment of cervical ROM (extension, flexion, and rotation) using motion analysis equipment. Results: Both groups showed significant differences in muscle tone, stiffness, elasticity, PPT, and cervical ROM (\(p\) < 0.05). The PPT of the UT muscle was significantly improved in the CSG compared to the CTG (\(p\) < 0.05). Conclusions: There were significant improvements in muscle tone, stiffness, elasticity, pain, and cervical ROM after exercise intervention in both groups of subjects with cervical extension. Cervical stabilization exercises, along with scapular stabilization, have better effects on improving UT muscle pain.  
capural extension type; cervical stabilization; muscle tone; muscle elasticity; muscle stiffness +
Footnote †: journal: Applied Sciences  
Article
# Modified Smith Input-Shaper Crane-Controller for Position Control and Sway Reduction
###### Abstract  
In this paper, the control structure for a crane system is proposed. It is designed to achieve fast cargo transfer with minimum cargo sway. The proposed control structure reduces the cargo sway generated by the position controller, which accelerates and decelerates cargo to transfer it with minimum time from the start to the desired location. A comparison between results achieved by simulation and experiments in the laboratory is given. Each segment of the proposed control structure is analyzed, and reasons for their use in this control structure are explained. The laboratory model's parameters are identified to parameterize the position controller and sway-reduction control structure. This control structure uses only the cargo's position feedback because the main reason for cargo sway, for which a sway reduction is needed, is crane movement, which is controlled by the position controller. Other control structures use two types of feedback, while this proposed control structure uses only one. Because of this, it is also economical.  
crane; control structure; sway reduction; input shaper; position control; cargo 2025
## 2 Electro-Thermal Modeling and Analysis of Composite Materials Under Lightning Strike
### Mathematical Models
#### 2.1.3 Boundary Condition Equation  
The thermal exchange behavior between the surface of composite materials and their surrounding environment is described by the electro-thermal coupling boundary condition equation, involving parameters such as the convective heat transfer coefficient, radiation conditions, and ambient temperature. The top and side surfaces of laminates release a large amount of resistive heat abruptly due to the conduction of lightning current, resulting in significant temperature differences with the surrounding environment. Consequently, heat transfer to the surroundings is predominantly facilitated by thermal radiation, employing a third-type boundary condition as follows:  
\[q_{r}=F_{B}\Big{(}\theta_{B}-\theta^{Z}\Big{)}^{4}-F_{B}\Big{(}\theta_{A}- \theta^{Z}\Big{)}^{4} \tag{7}\]  
Due to the absorption or dissipation of lightning currents, primarily by the upper layers of the structure, the temperature variation at the bottom surface is minimal, and the temperature gradient with the surrounding environment approaches zero. This is primarily because lightning strikes typically involve high peak currents with short durations, causing the current and heat to be dissipated mainly in the surface layers. Additionally, the anisotropic thermal conductivity of composite materials further limits heat transfer to the bottom layers. Therefore, the thermal exchange effect at this interface can be negligibly small, warranting the adoption of second-type boundary conditions, which can be described as follows:  
\[q_{c}=k_{g}(\theta_{B}-\theta_{A}) \tag{8}\]  
where \(\theta_{A}\) represents the surface temperature of the metallic shielding layer, \(\theta_{B}\) denotes the ambient temperature, \(\theta_{C}\) is the absolute zero value of the temperature scale, \(q_{r}\) is the surface heat flux density, \(F_{B}\) is the Stefan-Boltzmann radiation constant, and \(k_{g}\) is the contact thermal conductivity.
# A Cross-Modal Hash Retrieval Method with Fused Triples
## 5 Experiment
### Experimental Programme  
We experimented with the proposed scheme on three publicly available authoritative retrieval datasets, and the experiments validated the practicality and effectiveness of the method in this paper. The three datasets selected are the IAPR-TC12 [30] data set, the MIRFLICKR-25K [31] data set, and the NUS-WIDE [32] data set.  
The IAPR TC-12 [30] data set, consisting of 20,000 images, each with textual information corresponding to it, was annotated using 255 labels. Through data processing, 17,652 images from 168 semantic tags were ultimately retained.  
The MIRFLICKR-25K [31] data set consists of 25,000 images, each with textual information corresponding to it, with all samples annotated by one or more of the 24 semantic tags. Through data processing, 20,485 images from 18 semantic tags were finally retained.  
The NUS-WIDE [32] data set consists of 269,648 images, each with corresponding textual information, and all samples were annotated with one or more of the 81 semantic labels. In this paper, experiments were conducted using 195,834 sample pairs of the 21 most common concepts, which is a vast data set compared to the two large datasets above.  
For the IAPR TC-12 and MIRFLICKR-25K datasets, 3000 image text pairs were randomly selected as the test set, 10,000 image text pairs were selected from the rest of the data as the training set, and the remaining image text pairs were used as the cross-modal retrieval set. For the NUS-WIDE dataset, 10,000 picture text pairs were chosen at random as the test set, 30,000 image text pairs as the training set, and the remaining image text pairings were utilized as the cross-modal retrieval set.  
To validate the performance of cross-modal hash retrieval methods incorporating triples in cross-modal retrieval tasks, we use Hamming sorting and hash lookup, widely used in retrieval, as evaluation criteria. A widely used metric in Hamming ranking is mean average precision (mAP) [33], where a higher mAP value indicates a higher average precision of the retrieved results. Precision-recall curve (PR curve) [34] is a commonly used metric to evaluate the precision of hash lookups, with recall as the independent variable and precision as the dependent variable, indicating the trend of retrieval precision with recall, and, generally, the more prominent the recall, the lower the corresponding precision.
## 2 Problem
### Occupancy Grid and Quantised Communication  
Based on a spacial set given by \([-\tilde{x},\tilde{x}]\times[-\tilde{y},\tilde{y}]\), we construct the occupancy grid by partitioning the spacial set into equidistant cells: \(\mathcal{G}:=[0:a_{\max}-1]\times[0:b_{\max}-1]\subset\mathbb{N}_{0}^{2}\) with cell width (= height) \(c\). The partitioning is defined by \(\tilde{x}\) and \(\tilde{y}\), both multipliers of \(c\) with  
\[a_{\max}=\frac{2\tilde{x}}{c}\qquad\text{and}\qquad b_{\max}=\frac{2\tilde{y}} {c}\]  
where \(\tilde{x}\) and \(\tilde{y}\) are both multiples of \(c\). Furthermore, we assign an index \((a,b)\in\mathcal{G}\) to each cell; see Figure 1 for details.  
For the quantisation of communication among the robots, we define \(q\colon Z\to\mathcal{G}\):  
\[q\left(z_{p}\right)=\left(a_{p},b_{p}\right):=\left(\left\lfloor\frac{x_{p}+ \tilde{x}}{c}\right\rfloor,\left\lfloor\frac{y_{p}+\tilde{y}}{c}\right\rfloor \right), \tag{5}\]to map a state \(z_{p}\) onto the grid \(\mathcal{G}\). Now, this enables robot \(p\) to broadcast the occupied cells instead of a continuous predicted state trajectory as a sequence of _occupancy tuples_\(\mathcal{I}_{p}\left(n\right)\in(\mathbb{N}_{0}\times\mathcal{G})^{N+1}\) at time \(n\) with  
\[\mathcal{I}_{p}(n):=\left(q\left(z_{p}^{u}\left(n+k,z_{p}^{0}\right)\right) \right)_{k=0}^{N}.\]  
All received occupancy grid tuples \(\mathcal{I}_{p}(n)\) from the other robots \(q\) with \(q\in[1:P]\setminus\{p\}\) are assembled to  
\[i_{p}\left(n\right):=\left(\mathcal{I}_{1}\left(n\right),\ldots,\mathcal{I}_{ p-1}\left(n\right),\mathcal{I}_{p+1}\left(n\right),\ldots,\mathcal{I}_{P}\left(n \right)\right) \tag{6}\]  
and denoted as the _occupancy grid_. To convert a quantised state back to obtain the spacial coordinates, we require the backward mapping \(h\colon\mathcal{G}\to\mathbb{R}^{2}\) by  
\[h\left(a,b\right)=\left(\left(a+0.5\right)c-\bar{x},\left(b+0.5\right)c-\bar {y}\right)^{\top} \tag{7}\]  
which maps a cell index \(\left(a,b\right)\in\mathcal{G}\) to the spatial coordinates of the cell centre ignoring in both cases the time index \(n\). This is then used in the following subsection to construct the coupling constraints, which are later used in the OCP of each robot.
## 3 Methodology
### Multi-Channel Traffic and Weather Scene Image
#### 3.1.1 Data Description  
This study uses Weather Advective Flow (WAF) data together with air traffic data, which are further categorized into dynamic flight data and static airspace information. WAF data represent convective cloud information captured by weather radar and are used to predict and monitor extreme weather events (e.g., torrential rains, thunderstorms, and tornadoes) [25]. Each WAF dataset consists of 1250 \(\times\) 1250 data points, with each point indicating convective weather intensity over a 200 m \(\times\) 200 m area. Static airspace data includes fixed aeronautical information, such as latitude and longitude, which define the structure of the airspace. Dynamic flight data, collected via satellite communication or ADS-B, provides real-time updates on aircraft position, altitude, speed, navigation, and flight status. In the proposed MTWSI-based deep learning method, the airspace is first rasterized using the static data. Then, the resulting grid is filled with dynamic flight data to form detailed traffic and weather scenarios. These images are then used for feature extraction to predict airport delay levels.
## 4 Results
### SLO/SLA and the Obtained User-Engagement Score Correlation
This strong association between user-engagement score and reviews/subscribers count exists because they are used as the main arguments for the equation. We consider correlation of other dataset features with the obtained user-engagement score, so that more insights can be received. To study the dataset covariance and obtain the most critical features for dimensionality reduction, we performed Principal Component Analysis (PCA). We used the singular value decomposition method, which examines the covariances between individuals in the dataset. This statistical method allowed us to simplify the correlation observation and define features that have the highest value for web-projects user-engagement and popularity. Figure 4 shows that the first dimension (PC1), with eigenvalue and variance out of all principal components equals 35%. We observe that such features as the number of subscribers, number of reviews, weights, user-engagement, boolean value for free/paid entity, and content activeness time (how long a course is available on the platform) are included in this component. Figure 3: Pearson’s association coefficients and plots for the user-engagement score, number of subscribers and reviews to the Udemy Business and Finance courses. The PCA biplot (Figure 5) proved again that user-engagement, the number of reviews, and the number of subscribers are positively correlated. We also found that the higher the price, the greater the number of published lectures, and the longer the course duration, which is obvious. Moreover, the time since a course is available correlates with the user-engagement score and corresponding to its values. We assume that the longer a web-project content is available, the larger the chance to obtain high user-engagement and many subscribers/reviews. A negative correlation between the course payment option (free/paid) and the number of subscribers, which has significance for the user-engagement score, is examined. Correspondingly, if a web-project content is free of charge, it has a higher probability to obtain high user-engagement and attention than the paid ones. Figure 4: PCA squared coordinates correlation plot. Figure 5: PCA biplot of business and finance course generated numerical features. The above analyses can help to obtain new knowledge about the data and determine types of web-project content that have high user-engagement and are attractive for customers. The main features that correlate with the user-engagement score are defined. The PCA results can be applied for the development of predictive machine learning models to solve various tasks in the fields of e-learning and e-commerce.
## 5 Case Studies
### Case 3: Wind Speed of 7.0 m/s, Wind Penetration of 20%, Disturbance of 100 MW  
To investigate the scenario with low wind speed, the wind speed is set to be 7.0 m/s. Figure 13 displays the simulation results for Case 3. The frequency nadir of the no frequency  
Figure 12: Comparison results for Case 1 and Case 2: (**a**) maximum ROCOF; (**b**) frequency nadir; (**c**) Settling frequency.  
response is 58.983 Hz, which is similar to Case 1 due to the same wind penetration and disturbance (see Figures 8a and 13a). As displayed in Figure 13a, the maximum ROCOF and the frequency nadir of the conventional frequency response strategy are \(-0.770\) Hz/s and 59.260 Hz, respectively. Compared with the conventional frequency response scheme, the frequency nadir and the maximum ROCOF increase to 59.333 Hz and \(-0.761\) Hz/s, respectively, using the proposed enhanced frequency support scheme. This is mainly because of the proposed power reference.  
As shown in Figure 13a, the setting frequencies for no frequency response, conventional frequency response, and proposed enhanced frequency support scheme are 59.594 Hz, 59.607 Hz, and 59.604 Hz, respectively. The reason for this phenomenon is  
Figure 13: Results for Case 3: (**a**) Frequency; (**b**) Active power; (**c**) Rotor speed.  
that output power is more in the proposed enhanced frequency support and conventional frequency response schemes after supporting the frequency (see Figure 13b).  
The simulation results of Case 3 clearly demonstrate that the proposed enhanced frequency support scheme can also heighten the frequency nadir and the settling frequency and reduce the maximum ROCOF with more efficiency under low wind speed.
## 7 Future Work  
There are multiple research directions we could pursue in the future. First and foremost, there is scope to do additional research using various offloading technologies like Intel's Data Plane Development Kit (DPDK), various FPGA-based approaches, and SR-IOV. We could also combine this research with scaling across faster network standards by leveraging Ethernet standards faster than 10 Gbit/s, such as 25, 40, 50, 100, 200, and 400 Gigabit Ethernet, and comparing the results. An essential part of research like this should also be integrating the necessary software stack for performance evaluation, which poses a significant challenge. This could lead to developing a model describing how this methodology scales with bandwidth.  
Our methodology can also be improved by using random evaluation instead of predefined evaluations entirely controlled by the code. This could be done by integrating a software network traffic generator to monitor and measure what happens when different streams and data types are sent over the network. Considering that this methodology was meant to be used in HPC and cloud environments, where the default design principle is to use the same hosts that build the clusters, the general use case was different from the development direction taken.  
We also see a promising research direction in extending our code to evaluate as many hosts as necessary. We could use the following approach to evaluate each host against each other (evaluating hosts in mesh formation) and then try to create a model or an algorithm that would work with scaling the environment to an arbitrary number of hosts. This approach would take a lot of time and research, but it could offer substantial opportunities for large-scale environments if a model or an algorithm existed. This approach should use the cluster design principle of using the hosts with the same capabilities when considering long-term QoS for any large HPC or cloud-scale environment. When designing a data center, the default cluster design principle is to use the same hosts with the same capabilities to have the same building blocks within a QoS domain.  
Another research direction that might offer additional benefits would be incorporating the automated performance evaluation methodology with a Kubernetes autoscaling approach based on slots [60]. This could mean a new research direction regarding network profiling for autoscaling decisions, which could add significant value to running high-performance applications with optimized networking.  
Additionally, an AI (Artificial Intelligence) engine could be integrated into the methodology to learn about the environment where these evaluations could be done. The engine could then tune the network and Kubernetes CNI parameters to improve performance in any given scenario. This would be a step in the \"general use case\" direction that could be very usable for many Kubernetes environments. Again, this methodology was developed for HPC-based environments so that scientists can do performance evaluations before building out their environments without having to study Ansible or BASH shell scripting for months.