esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Plasma Produced with Electrical Discharges in Gases for Agriculture and Biomedicine
## 3 Review of Plasma Applications in Agriculture and Medicine
### Plasma Application in Biomedicine
#### 3.2.2 Wound Healing  
The main aspects of LTP plasma wound treatment rely on two types of mechanism: physical, due to the effects of plasma-generated species, and biological, due to the processes of cell-membrane degradation and modification of its DNA. Plasma efficiency in this area is highly dependent on the possibility of inducing tissue regeneration and inactivating microbial cells.  
[PERSON] et al. [184] distinguished the following effects of plasma on wound healing: antimicrobial, self-healing, tissue remodelling, angiogenesis, anti-inflammatory, and growing factors (Figure 6). The scientists also suggested that the main advantages of nitride oxides generated from plasma are the prevention of tissue infection and promoting its healing [185; 186], anti-inflammatory properties, and remodelling and angiogenesis [187]. According to [188; 189], NO, ROS, and H\({}_{2}\)O\({}_{2}\) species contribute to regulating re-epithelialisation and wound contraction. Plasma also causes the reduction in pH at the infected zone [190]. One of the key factors of understanding effective plasma treatment in wound healing is RONS interaction with cell membrane [189; 191]. Plasma medicine in vitro and in vivo applications on the diabetic wound also demonstrates healing stimulation after helium gas plasma treatment [166].  
[PERSON] et al. in [62] developed the interdependency of wound characteristics, and unknown or non-standardised gas plasma therapeutic schemes, which can lead to the optimisation of wound therapy, but absolutely need clinical trials (Figure 7). The authors found that the issue of LTP application on wounds is complex and depends on a large number of factors such as: wound type, physical dimensions or volume, location, and possibility of proper plasma delivery.  
The important factors playing a decisive role in wound treatment are also wound age and chronification, previous history of wound care [192; 193], co-morbidities, immune status [194], wound colonisation profiles, and antimicrobial resistance against drugs [195]. Based on this information, a similar variety of gas plasma therapy parameters (duration, frequency, device type, gas mixture, etc.) must be taken into account to conduct safe and effective wound therapy.  
Figure 6: Processes initiated by exposure of LTP and associated wound healing [184].
# SCGFormer: Semantic Chebyshev Graph Convolution Transformer for 3D Human Pose Estimation
## 5 Discussion  
Through experimental validation, SCGraFormer has demonstrated promising results. In comparison to GraFormer [28], we excluded LAM-GConv and employed SemGConv as a subsequent network layer for Transformer. As indicated by the ablation experiments in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **MPJPE** & **Params** & **FLOPs** \\ \hline GraFormer & 51.8 & 0.96 M & 1.12 G \\ SCGraFormer & 50.4 & 1.24 M & 1.53 G \\ \hline \hline \end{tabular}
\end{table}
Table 6: SGAFormer complexity comparison.  
Figure 11: Visual comparison of some occluded frames between GraFormer [28] and our model in the test set of Human3.6M [30; 31].  
Table 4, the use of a learnable adjacency matrix in LAM-GConv (as shown in Figure 10) leads to larger errors compared to the fixed adjacency matrix used in SemGConv (as shown on the left side of Figure 9). Subsequently, we refined the adjacency matrix of ChebGConv and named this network layer AcChebGConv. The error was further reduced, with a decrease of approximately one and a half points compared to GraFormer.  
However, our work has limitations: the model's performance suffers when capturing rapid and significant movements, as illustrated in Figure 12. For instance, when a person rapidly raises their arm from bottom to top (top side of the figure), our model's prediction places the arm too high compared to the ground truth. In another scenario, when a person transitions from running to a sudden stop (bottom side of the figure), the model exhibits errors at the ends of the arms compared to the ground truth. These actions involve rapid and wide-ranging motions occurring in a short duration. The red circles highlight these noticeable errors. In future research, exploring more refined human body structural constraints to capture critical information about extremities (e.g., hand and foot joints) could be a promising avenue for improvement.  
In terms of application, 3D human pose estimation has broad prospects in the field of action recognition. It can provide detailed joint positions and motion information, providing a strong foundation for motion analysis and recognition. This is of great significance for understanding human actions in different scenarios, such as sports competitions, medical research, and interactive actions in virtual reality applications.
# Experiments on the State Boundary Surface of Aeolian Sand for Road Building in the Tengger Desert
###### Abstract  
As a special road-building material widely distributed in desert areas, critical state soil mechanics is used to study the mechanical properties of sand and make up for the lack of research on its engineering characteristics. A series of drained and undrained triaxial compression tests with a loading rate of 0.12 mm/min medium-density aeolian sands taken from Tengger Desert in the northwest of China was carried out to obtain the three-dimensional state boundary surface. The test results reveal that the strength gained from drained and undrained tests increased, respectively, linearly and non-linearly with the increase of the effective confining pressure. Affected by the variation of pore pressure and shear rate, the undrained strength was higher than the drained strength at low effective confining pressures, and the two types of strengths tend to be consistent when the effective confining pressure becomes greater than 800 kPa. The volumetric changes of the aeolian specimens transition from dilatation to contraction when the effective confining pressures increase. The investigation of the strength, deformation and failure characteristics gives rise to the shape parameters of its state boundary surface, which provides not only a basis for the constitutive modelling of the aeolian sand, but also a reference for roadbed construction and other foundation engineering in desert areas.  
aeolian sand; triaxial test; state boundary surface; strength characteristics; deformation Article
###### Abstract  
The sun drying of agricultural products is a complicated process involving heat transfer, mass transfer, and variable weather conditions. Surface reflection (albedo), a crop's radiative property, plays an essential role in energy balance, and understanding its contribution can improve the thermal analysis. In this study, field experiments were conducted in the Philippines to explore the influence of surface albedo on the sun drying of paddy rice. First, we implemented energy and mass balance equations in a transient model with the surroundings using a graphical programming language in Matlab/Simulink(r). Second, we identified the influence of albedo on the sun drying model by using a sensitivity analysis. Third, we investigated the relationship of paddy rice albedo and the solar zenith angle. Lastly, we integrated the albedo function into the sun drying model. The simulation outputs were validated with field experiments. A better estimation of the measured exit temperature and instantaneous mass were obtained when a variable albedo was applied. This study makes clear that introducing a variable albedo has a positive impact on model improvement. This information is important for application in solar drying technologies, so that the drying process can be better assessed.  
solar energy; thermal analysis; sun drying; paddy rice; albedo; dynamic modeling +
Footnote †: journal: Applied Physics Letters B
## 4 Dynamic Response of the System
### Train Running Safety Indicators
#### 4.2.2 Rate of Wheel Load Reduction  
In addition to the derailment coefficient, people also use the rate of wheel load reduction to determine the train running safety. When the wheels are substantially loaded down, that is, the vertical force on the wheels and rail is very small, the corresponding lateral force on the wheels and rail is often very small, and due to the impact of measurement error, it is difficult to calculate the derailment coefficient, especially when the wheel and rail are detached, which provides the derailment coefficient to assess the train running safety, addressing this serious problem.  
Therefore, the increase in the rate of wheel load reduction can be used to comprehensively and effectively assess the derailment stability of the train's running. The rate of wheel load reduction is defined as the ratio of the wheel weight reduction \(\Delta P\) on the reduced side to the average static wheel weight \(\overline{P}\) of the wheelset. The rate of wheel load reduction standard stipulated by China is as follows [41]:  
\[\left\{\begin{array}{l}\Delta P/\overline{P}\leq 0.65\ (\text{First limit, eligibility criteria})\\ \Delta P/\overline{P}\leq 0.6\ (\text{Second limit, increased safety margin criteria})\end{array}\right. \tag{17}\]
# A Microservices-Based Control Plane for Time-Sensitive Networking
## 3 \(\mu\)TSN-CP Architecture
### Introduction  
The general structure of \(\mu\)TSN-CP is shown in Figure 1. We now describe the typical flow of execution of events. First, whenever an end station (talker) wants to send a data flow to another end station (listener), the talker notifies the Centralized User Configuration (CUC), which collects the characteristics and the latency requirements of the streams, and passes this information to the CNC. Within the CNC, the TSN Controller receives the information about the requested data streams from the CUC via the UNI by means of RESTCONF. The scheduler determines a configuration of the switches (ingress and egress ports, timeslots) that complies with the requirements of the data streams according to the network topology information received from the SDN Controller through RESTCONF. Once the configuration is ready, the SDN Controller distributes the configuration commands to the switches via NETCONF and YANG models. The switches execute the configuration and the data flow sent from the talker is treated accordingly.  
Regarding the microservice architecture, Figure 2 illustrates the microservices created for mapping the control plane (CP) functions in the CNC, as well as the communication tools and interfaces between them. Following the same typical flow of events described earlier, the Jetconf [18] microservice gathers the data stream information from the CUC and sends it to the Preprocessing microservice. This block combines the stream information with the topology information (received from the Topology Discovery function), and sends it to the scheduler (ILP Calculator). The configuration resulting from the scheduler is sent  
Figure 1: Overview of the \(\mu\)TSN-CP prototype.  
to the Southconf microservice, which forwards it to the OpenDaylight SDN Controller. This element is responsible for configuring the TSN switches (via NETCONF) accordingly, so as to satisfy the time constraints of the data flows.  
The following subsections describe the functionalities of each microservice.
## 3 Explanatory Application of the Method
### Monitoring Strategy 1 (Rare Behavior)  
The obtained SOM could then be applied to the whole database with the aim of detecting rare behaviors, according to the following steps:  
1. Among the clusters obtained from the trained SOM, one of those to which few examples belong was chosen. This was assumed as an index of anomalous behavior, and then the patterns falling in this cluster were labeled as \"warning\". Cluster no. 291 having only one occurrence was set as the \"reference cluster\", as an example. Figure 8a plots the time diagram relevant to the point of the training set to which the window falling in the reference cluster 291 (in red) belonged. Such a window was expected to precede an anomalous event.
2. To test the method, we assumed the 280 th week of 305 as the monitoring instant, allowing an investigation of how the series evolved in the successive weeks. Monitoring considered the windows that closed at the monitoring instant. The Monitoring was applied to the geographical points of the whole dataset.
3. The preprocessing applied to the training set (see point iv of Section 3.1) was also applied to the monitoring set, taking care to refer to the same parameters adopted in the training (average, ranges, normalization, principal components, etc.).
4. The monitoring patterns were fed to the SOM, which assigned them to different clusters. The \"warning\" patterns falling into the reference cluster were given attention for a more in-depth study.  
Figure 7: Histogram of occurrences’ incidence in the clusters.  
By applying this monitoring strategy, we found only one warning over 411,581 points. The time diagram relevant to this warning point is plotted in Figure 8b, where the red line indicates the monitoring window (which is assumed to precede the anomalous event). The points which Figure 8a,b refers to are shown in the map of Figure 8c, with this figure having just an illustrative purpose. It can be noted that Strategy 1 was able to put in evidence an anomalous phenomenon (a rather high vertical displacement which developed in a very short time) at its very early stage. It is to be stressed that the method is simply aimed at warning against possible anomalous behaviors at their early stage, while the most accurate study of them should be entrusted to specialistic assessments and in situ inspections.
## 4 SAR Raw Signal Processing
### Omega-k Algorithm for SAR System
#### 4.1.4 2D inverse Fast Fourier Transform  
We used the two-dimensional inverse Fourier transform to go from the two-dimensional frequency domain to the time domain, thus generating a SAR image.
## 2 Subjective Experiment Using Full-Scale Room Models
### Specification of Evaluation Spaces  
Two axisymmetric evaluation spaces and front chamber shown in Figure 1a were set in an area of 3.5 \(\times\) 3.5 m\({}^{2}\) in a dwelling built using a wooden panel system. The evaluation spaces had 12.5-mm-thick wooden floor, and the walls and ceiling were finished with a 12.5-mm-thick plasterboard on the plywood board. The floor finish of the front chamber was a plywood board. Figure 2 presents five absorption patterns, from \"A\" to \"E\". Polyester nonwoven panels with a thickness of 50 mm and density of 32 kg/m\({}^{3}\) were used as absorbers [56].  
Before the subjective experiment, the reverberation time of each pattern and that of the front chamber were measured according to ISO3382-2 [57]. A loudspeaker (YAMAHA MSR100) was located on the floor at position \"S\" as shown in Figure 1b and exposed a time-stretched pulse (TSP) signal. Microphones (ONOSOKKI MI-1235, class 1) were located at five point marked positions with a height of 1.5 m. The absorption coefficients were calculated using the measurement results (Table 1 and Figure 3). The reverberation times gradually decreased as the absorption area increased from pattern \"A\" to \"E\". Pattern \"O\" indicates the result of the front chamber. The higher absorption coefficients at 125 Hz than at 250 Hz in all patterns were caused by the plywood board plane vibration.  
Figure 1: Two evaluation spaces and front chamber (scale unit: millimeter). (**a**) Floor plan of the full-scale model for the subjective experiment; (**b**) loudspeaker and microphone positions for the reverberation time measurements.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Pattern**} & \multicolumn{6}{c}{**Octave Band Frequency (Hz)**} \\ \cline{3-8}  & & **125** & **250** & **500** & **1000** & **2000** & **4000** \\ \hline \multirow{4}{*}{Reverberation Time} & A & 0.29 & 0.66 & 0.91 & 0.83 & 0.75 & 0.65 \\  & B & 0.26 & 0.45 & 0.46 & 0.40 & 0.39 & 0.38 \\ \cline{1-1}  & C & 0.22 & 0.32 & 0.32 & 0.28 & 0.27 & 0.24 \\ \cline{1-1}  & D & 0.14 & 0.21 & 0.25 & 0.17 & 0.15 & 0.16 \\ \cline{1-1}  & E & 0.14 & 0.18 & 0.14 & 0.11 & 0.11 & 0.14 \\ \cline{1-1}  & O & 0.26 & 0.39 & 0.49 & 0.60 & 0.60 & 0.53 \\ \hline \multirow{4}{*}{Absorption Coefficient} & A & 0.16 & 0.07 & 0.05 & 0.06 & 0.06 & 0.07 \\ \cline{1-1}  & B & 0.17 & 0.11 & 0.10 & 0.12 & 0.12 & 0.12 \\ \cline{1-1}  & C & 0.20 & 0.14 & 0.14 & 0.16 & 0.17 & 0.19 \\ \cline{1-1}  & D & 0.29 & 0.21 & 0.18 & 0.25 & 0.28 & 0.27 \\ \cline{1-1}  & E & 0.30 & 0.24 & 0.31 & 0.35 & 0.37 & 0.30 \\ \cline{1-1}  & O & 0.22 & 0.15 & 0.12 & 0.10 & 0.10 & 0.11 \\ \hline \end{tabular}
\end{table}
Table 1: Results of the measurement in the front chamber (O) and five absorption patterns (A–E).  
Figure 2: Five absorption patterns used for the pair comparison experiment. Polyester nonwoven absorbers installed at the colored positions.
## 2 Turbulence Modeling
### Generalized Two-Equation Turbulence Model  
The new GEKO procedure has been recently introduced as a two-equation eddy-viscosity turbulence model capable of providing a single RANS approach that may be customized to a wide range of general flow conditions and applications [11]. The model has been claimed to achieve flexibility by extending the \(k\)-\(\omega\) SST model with six free coefficients that can be prescribed by the user without compromising the fundamental model calibration for both free shear flows and boundary layers. This allows the user to fine-tune the turbulence closure in a safe parameter space, which requires (at least in principle) no advanced skills or great knowledge in turbulence modeling and simulation. Even though not fully described in the literature, this generalized modeling procedure appears very promising, and various works calibrating the GEKO model coefficients for different industrial fluid dynamics applications have been presented, e.g., [18; 19; 20].  
Following the GEKO approach, the transport equations for the two turbulence variables are reformulated as:\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{i})}{ \partial x_{i}} = P_{k}-C_{\mu}\rho k\omega+\frac{\partial}{\partial x_{i}}\bigg{[} \bigg{(}\mu+\frac{\mu_{t}}{\sigma_{k}}\bigg{)}\frac{\partial k}{\partial x_{i}} \bigg{]}\, \tag{3}\] \[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial(\rho\omega u _{i})}{\partial x_{i}} = C_{\omega_{1}}F_{1}\frac{\omega}{k}P_{k}-C_{\omega_{2}}F_{2} \rho\omega^{2}+\] (4) \[+ F_{3}\frac{2}{\sigma_{\omega}}\frac{\rho}{\omega}\frac{\partial k }{\partial x_{i}}\frac{\partial\omega}{\partial x_{i}}+\frac{\partial}{ \partial x_{i}}\bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{\omega}}\bigg{)} \frac{\partial\omega}{\partial x_{i}}\bigg{]}\,\]  
where the additional functions \(F_{1}\), \(F_{2}\), and \(F_{3}\) contain the free model coefficients that were introduced [11]. In the above equations, \(P_{k}=-\tau_{ij}\frac{\partial u_{i}}{\partial x_{j}}\) represents the production of turbulence kinetic energy. Standard values can be used for the constants \(C_{\mu}\), \(C_{\omega_{1}}\), and \(C_{\omega_{2}}\) as well as for the turbulent Prandtl numbers \(\sigma_{k}\) and \(\sigma_{\omega}\)[16].  
The six free parameters of the GEXO model are summarized in Table 1 together with their specific controlling function, recommended range of variation, and default value [21]. Two of these coefficients are explicitly designed for wall-bounded flows, namely \(C_{\rm sep}\) and \(C_{\rm mw}\), while \(C_{\rm mix}\) is however correlated with \(C_{\rm sep}\). The coefficient \(C_{\rm corner}\) takes into account the corner flow correction. In this study, the effect of these four coefficients was systematically examined for testing the accuracy of the model in predicting aeronautical flows of engineering significance. The other two coefficients, controlling the accurate prediction of jet flows (\(C_{\rm jet}\)) and flows with curvature (\(C_{\rm curv}\)), were not considered here while being maintained to the associate default values.
# Vegetation Productivity Losses Linked to Mediterranean Hot and Dry Events
## 2 Materials and Methods
### Remote Sensing Data
#### 2.2.2 Land Surface Temperature  
Land surface temperature was used in this study as a proxy of air temperature and was retrieved from MOD11A2 Collection 6 of the TERRA satellite [36]. The product consists of an 8-day composite, which corresponds to an average of daily daytime and night-time LST measurements over eight days, with 1 km of spatial resolution.  
The determination of LST relies on the computation of surface emissivity, derived from two thermal infrared band channels (MODIS bands 31 and 32) [46]. The split-window algorithm also corrects atmospheric effects [47], using MODIS information about land cover types, clouds, snow, atmospheric water vapour and lower boundary air surface. More details related to the algorithm can be found in [36].  
The latest MODIS LST version shows improvements related to adjustments on emissivity difference in bands 31 and 32 and several refinements of the split-window algorithm to compute LST of bare soil regions [36]. Moreover, [46] showed LST bias lower than 1 \({}^{\circ}\)C in the range of \(-\)10 to 58 \({}^{\circ}\)C. The low error values across this temperature range indicate the high accuracy and robustness of MODIS LST data to determine near-surface air temperature. The LST data were resampled to the resolution of GPP data (500 m) using the nearest neighbour interpolation scheme.
###### Abstract  
This paper models and evaluates three FSA-based (Frame Slotted ALOHA) MAC (Medium Access Control) protocols, namely, FSA-ACK (FSA with ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA). The protocols are modeled using an AMC (Absorbing Markov Chain), which allows to derive analytic expressions for the average packet delay, as well as the energy consumption of both the network coordinator and the end-devices. The results, based on computer simulations, show that the analytic model is accurate and outline the benefits of DFSA. In terms of delay, DFSA provides a reduction of 17% (FSA-FBP) and 32% (FSA-ACK), whereas in terms of energy consumption DFSA provides savings of 23% (FSA-FBP) and 28% (FSA-ACK) for the coordinator and savings of 50% (FSA-FBP) and 24% (FSA-ACK) for end-devices. Finally, the paper provides insights on how to configure each FSA variant depending on the network parameters, i.e., depending on the number of end-devices, to minimize delay and energy expenditure. This is specially interesting for massive data collection in IoT (Internet-of-Things) scenarios, which typically rely on FSA-based protocols and where the operation has to be optimized to support a large number of devices with stringent energy consumption requirements.  
Internet-of-Things; massive communication; data collection; Medium Access Control; Frame Slotted ALOHA; delay; energy consumption +
Footnote †: journal: Applied sciences