esa-sceva/satcom-chunk-collection huggingface dataset:
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion
### Congestion Control Under Dynamic Bandwidth
#### 5.2.2 Network Throughput  
We analyze both the overall throughput of the network and the data transmission rate on the consumer side. The simulation scenario is the same as Section 5.2.1. We compare the performance of CCLF against PCON, and the results are shown in Figures 16 and 17.  
Using the CCLF method, Figure 16 shows that, initially, Flow 4 with the highest priority and Flow 3 with the second-highest priority occupy Link 1. After 5.08 s, all Data packets of Flow 4 are transmitted, allowing packets of Flow 3 buffered in the CS of Router 2 to fully utilize the available bandwidth of Link 1. By 10.18 s, Flow 3 transmission completes, and then Flow 2 and Flow 1, with equal priority, share the available bandwidth equally for data forwarding. Flow 2 completes transmission at 15.06 s, and then Flow 1 occupies the entire bandwidth. Notably, the overall network throughput under the CCLF method remains consistently at a high level.  
By contrast, when using the PCON method, all packets are forwarded based on their receiving order, and bandwidth is allocated in a balanced manner. Figure 17 shows that the FCT of critical flows, such as Flow 3 and Flow 4, exceeds 21 s each. Thus, the PCON method fails to meet the QoS requirements of urgent flows.
## 4 Results
### Second Validation Cycle
#### 4.2.1 Optimization Cases  
The optimization cases depend on the angle of attack \(\theta\) assumed by the wings for different system spans. Therefore, defining the case requires an analysis of the OF simulation results for each span presented in Table 1. Since there is symmetry, only the results for one wing were considered.  
The distribution of the angles of attack along the wing, for the narrow span, is presented in Figure 20a for two values of \(u_{\mathrm{stw}}\). As can be seen, with \(u_{\mathrm{stw}}=0.5\) m/s, the angles of attack were concentrated between \(0^{\circ}\) and \(2^{\circ}\). That trend strengthened if the speed was increased to 0.75 m/s. Therefore, for a narrow span, \(C_{D,\mathrm{OF},\mathrm{\mu}}\) should be optimized for \(\theta\leq 2^{\circ}\).  
Figure 19: Dependence of the combined towline tension on the \(u_{\mathrm{stw}}\). Superscripts 16, 480, and 630 indicate the span in meters. The colored areas are the 15% increase in the mean load. GPGP data selected from August 2021 to August 2022. The error bar estimation is explained in Section 2.1.  
The distribution of the angles of attack for the nominal span is presented in Figure 20 for two values of \(u_{\text{stw}}\). In this case, for both values of \(u_{\text{stw}}\), about 50% of the angles were between \(20^{\circ}\) and \(30^{\circ}\) and the rest were between \(30^{\circ}\) and \(85^{\circ}\). According to the CFD results and AS calculation, no major shielding effect was expected for \(\theta>24^{\circ}\). Therefore, the shielding effect was limited for the nominal span, meaning that \(C_{D,\text{OF},\mu}\) can be optimized for angles between \(30^{\circ}\) and \(90^{\circ}\) in order to cover 50% of the angles and give more importance to the higher drag forces close to \(90^{\circ}\).  
The distribution of the angles of attack for the wide span are presented in Figure 20 for two values of \(u_{\text{stw}}\). As can be deduced, for both \(u_{\text{stw}}\) values, about 50% of the angles were between \(30^{\circ}\) and \(45^{\circ}\), whereas the other 50% were between \(45^{\circ}\) and \(90^{\circ}\). From the CFD simulations and AS calculation, no major shielding effect was expected for \(\theta>24^{\circ}\). Therefore, the shielding effect was very limited for a wide span. Therefore, \(C_{D,\text{OF},\mu}\) can be optimized for \(\theta\in[45:90]^{\circ}\) in order to cover 50% of the angles and give more importance to the higher drag forces close to \(90^{\circ}\).  
The optimization cases are summarized in Table 3:
## 1 Introduction  
Thanks to its characteristics, thermography is a very well-suited technique to operate in extremely diverse contexts, such as building, plant engineering, human medicine, and, likewise, precision animal husbandy [1]. Within animal husbandry, its applications are innumerable, and they encompass silage control, under health, oestrus detection, stress assessment, and early diagnosis of certain diseases, to name a few [2].  
Swine husbandry has good prospects because of its high feed conversion efficiency, quick body weight gain, high fecundity, and prolificacy and economic aspects of a swine breeding depends on maintaining good herd health. The animals' exposure to stressful conditions has been increased together with the intensity of pig production and could be harmful to their welfare and productivity [3].  
In the field of pig farming, thermography is highly suitable to operate by remotely sensing the skin temperature, being able to measure temperatures without coming into contact with or interacting with the individual, thus not altering their behavior, and in a completely noninvasive manner insofar as it does not require any restraint.  
Such a range of applications makes infrared thermography a widely usable technique for obtaining some important information about the thermal status of animals, through which the management of production stages and operations on individuals can be faster and more effective [4]. The proper use of the thermal imaging camera is not uncomplicated, and thermographic techniques at the farm should be carried out with care. Indeed, the thermal imaging cameras don't directly measure the surface temperature like the contact probes, but they capture the electromagnetic radiation emitted from any object at a temperature above absolute zero (\(-273.15\) degC). This radiation, which is not harmful to living organisms, and not detectable by the human eye, is closely connected to the object temperature through specific physical formulas, called Planck, Wien, and Stefan-Boltzmann laws, that are part of the camera software [5]. It follows that the IRT camera is a device capable of measuring the surface temperature of a body by detecting the infrared radiation emitted by it.  
The two-dimensional thermal map of the captured body is then presented as a _false-color image_ (Figure 1), in which each pixel represents the surface temperature of the object at that point. The colors chosen are based on a chromatic scale, which can be adjusted using various dedicated software on the market and chosen based on requirements. Multiple confounding factors, both environmental and animal-specific, can affect this process, as will be examined in Section 3.
# A Brief Review of Multipath TCP for Vehicular Networks
## 4 Cross Layer MPTCP, MPTCP with SDN, and Energy Efficient MPTCP
### MPTCP with SDN  
[PERSON] et al. [87] proposed the first work about combining MPTCP and OpenFlow. OpenFlow is an enabler of SDN that can conduct traffic engineering within the network, such as identifying the network topology. SDN technology can also be used to select the best path for the first subflow [88].  
Equal-cost multipath protocol (ECMP) [89] can divide a data stream and send across different paths. However, it only forwards the flows through the shortest path and hence it may forward the subflows through the same path. In [90], [PERSON] et al. proposed a method, called multiflow, by combining MPTCP and OpenFlow together. Unlike ECMP, the main purpose of this method is splitting data stream over different paths of a connection in shared bottlenecks. Multiflow is able to achieve fine-grained control of subflows by dividing MPTCP subflows for OpenFlow networks. However, the throughput of the MPTCP connection increases only when enough disjointed paths exist.  
Resource pooling principle is also discussed in the MPTCP design. In [91], a SDN-based MPTCP, namely, called S-MPTCP, is proposed for the efficient utilization of bandwidth and fair resource allocation by utilizing global network information.  
Many studies are only devoted to improving the transmission performance by MPTCP, while neglecting the price cost. [PERSON] et al. [92] proposed SOS-MPTCP in a software defined wireless network (SDWN), which aims to balance the performance and cost properly.
# Functionalization and Modification of Bitumen by Silica Nanoparticles
###### Abstract  
A study on the effect of silica nanoparticles (SNPs) dispersion in bitumen is herein reported. First, the size of the nanoparticles was finely tuned by controlling the experimental conditions during their synthesis, obtaining spherical SNPs with diameter ranging from 95 up to 900 nm. Subsequently, SNPs were embedded with peripheral amine groups by using APTES (3-aminopropyltriethoxysilane) as functionalized agent (NH2@SNP), and ultimately long alkyl chains were grafted by reacting the free amine with an alkylated aldehyde (C1N@SNP). All SNPs (ca. 1 wt%.) were dispersed in bitumen to probe their effect on the rheological properties of bitumen. No significant change in the thermorheological properties of bitumen was observed upon varying the size of the SNPs. Slight improvement was observed when using NH2@SNPs, while the best results were obtained by using C1A@SNPs, showing the crucial role that hydrophobic substituents play in bitumen binders which leads to significant improvements.  
bitumen; silica nanoparticles; thermorheology; inorganic-organic composite Article
## 5 Experimental Results and Discussion
### Image Matching and Pattern Recognition
For 100 camera frames for each pattern image (600 frames in total), the recognition accuracy, i.e., the ratio between the number of successful pattern recognitions in the camera frames over the total number of frames, is 100% for all combinations (AGAST-BRISK-3, AGAST-BRISK-2, FAST-ORB-2000 and FAST-ORB-500) for camera frames where the  
Figure 7: Clustered bar charts illustrating the total computational time required by the four basic steps of the image matching process for each experiment performed via ModAR. resolution of the pattern in the frame is 0.9 to 4 times the resolution of the pattern image, regardless of any in-plane rotation. The recognition accuracy for resolution of the pattern in the frame from 0.4 to 0.9 times the resolution of the pattern image drops to 93% for FAST-ORB-2000 and 88% for FAST-ORB-500. The recognition accuracy for the combination AGAST-BRSIK-3 and AGAST-BRISK-2 for resolution of the pattern in the frame from 0.4 to 0.9 times the resolution of the pattern image is better (97% and 95% respectively); however, the loss in speed is really significant and these combinations are discarded for being used in ModAR. The FAST-ORB-2000 parameterization achieved more stable results, compared to FAST-ORB-500, as proved by the aforementioned recognition accuracy. Despite the slightly better performance in terms of computational time and CPU usage of the latter, the FAST-ORB-2000 yields the overall best performance in terms of stability of pattern recognition and good performance in time. Hence, this is the parameterization finally used by ModAR (and thus adopted by the screenshots from ModAR illustrating the pattern recognition and AR augmentation processes in Figure 4). An example of the minimum pattern scale (maximum distance from the pattern object) for which successful pattern recognition is achieved using the two different detectors--descriptors (FAST-ORB-2000 and AGAST-BRISK-3) is illustrated in Figure 8, where it is observed that BRISK permits pattern recognition in bigger changes of scale than ORB. However, FAST-ORB-2000 yields successful results is an acceptable range of scale, as shown in Figure 9, where pattern recognition performed by ModAR in camera frames under different scales and rotations as well as different illumination conditions and occlusion is shown. Figure 8: Screenshots from the ModAR prototype illustrating the minimum pattern scale for which successful pattern recognition is achieved for FAST-ORB-2000 (**left**) and AGAST-BRISK-3 (**right**).
## 4 Experiments
### Experimental Results
For example, in case an application can tolerate a loss of data at the extent of 10%, then even if the performance is 90% successful, it can be considered sufficient. The design of experiments can provide with such information about the limits of a system and the extent at which certain performances can be reached. We have applied all the steps of the interoperability testing methodology to a simple AMI use case, where the interaction between a data concentrator and a smart meter is evaluated. Initially, it was verified that the devices can operate together correctly as a system and information can be exchanged effectively. Subsequently, two parameters of the system were deliberately varied, namely the sampling rate with which information is acquired and the number of smart meters that communicate with the data concentrator. The design of experiments was applied, and the regions in the space of parameters where the system maintains a certain performance were identified. The design of experiments determines not only the parameters for which interoperability is lost (it is considered that interoperability is lost if there is less than 100% success in the experiments) but also gives a hint on the performance that can be reached defining if this could be acceptable or not when realising an application. Phasor Measurement Units (PMUs) sample voltage and current, at a given location in the electricity distribution, many times a second. They are mainly used in use cases where the real-time control of the state of the power system is the goal. The focus of this work is on SMs and DCs; PMUs and any relevant communication architectures have not been studied here. Hence, no conclusions regarding their interoperability with any other potential EUT can take place. Experimental results are given as an example of this simple use case which prove the importance of the system parameters in the overall performance. It is shown that the rate with which the smart meters are interrogated plays an important role as well as the number of smart meters that are connected to the system. The infrastructure available may not represent a real robust AMI system, but it gives an idea of the issues that can come up when smart meter data is requested with a higher rate. The latter fact is a tendency in modern systems since several smart grid applications (i.e., demand response programs) require smart meter readings even in real-time. The paper highlights the importance of the methodology, and the experimental results complement the theoretical part to stress the usefulness of the design of experiments and the methodology as a whole. Indeed, the methodology of performing tests under stress conditions can provide valuable conclusions with respect to the extent at which a system can be used and the applications that it can support. Therefore, it is important that smart grid systems are tested thoroughly to detect potential interoperability issues and to define whether or not altering vital parameters can result in a compromise on the system's performance.
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 5 Results and Evaluation
### Analysis of Experimental Results
#### 5.2.4 Influence of a Different Position  
To verify that the different positions of the personnel had a particular impact on the experimental results, we selected three positions with different distances from the device, as shown in Figure 13: close to the device, in the middle position, and far away from the device. The three positions were separated by 1.5 m and were verified in the walls of four materials: glass, wooden door, gypsum, and concrete. The errors in different personnel positions are shown in Figure 13. Figure 13a shows the error ratio of different wall materials when approaching the equipment. The error ratio of glass walls was lower than that of wooden doors, followed by gypsum walls, and the most significant error was concrete material. When using the same material wall to conduct experiments, it can be seen that the error ratio of the position close to the equipment was smaller than that of the intermediate position and the position far away from the equipment. The final results showed that when the human body was close to the device, the error ratio was the smallest, the recognition accuracy rate was the highest, and the experimental effect was better. When the human body was far away from the device, the error ratio was the largest, the recognition accuracy rate was the lowest, and the experimental effect was worse. To obtain better results, the location of the personnel is crucial and needs to be as close to the equipment as possible.
# The Removal of Crystal Violet from Textile Wastewater Using Palm Kernel Shell-Derived Biochar
## 3 Results and Discussion
### Effect of BC-PKS Dosage  
The removal of CV by BC-PKS was studied using six different BC-PKS dosages (0.1\(-\)1.0 g) with 30 mL of CV with an initial concentration of 400 mg/L; the results are plotted in Figure 4. The percentage removal of CV increases steadily as the BC-PKS dosage increases because of the increase in the number of adsorption sites [97]. As the BC-PKS dosage increases from 0.1 to 1.0 g, the percentage removal increases from 50.8% to 86.4%. Therefore, 30.9 g/L of BC-PKS dosage is required to remove 80% of the CV. Conversely, an increased BC-PKS dose reduced the amount of CV adsorption to unit mass of BC-PKS from 60.4 mg/g to 10.4 mg/g due to the split in the flux or the concentration gradient between the solute concentration in solution and the solute concentration at the BC-PKS surface [79; 98].  
Figure 3: Effect of solution pH (2–10) on CV adsorption to BC-PKS (initial concentration: 400 mg/L; volume: 30 mL; adsorbent dosage: 0.5 g; reaction time: 24 h; temperature: 25 \({}^{\circ}\)C).
## 2 Data
### Radiosonde, CrIS, ATMS, and Reanalysis Data  
Similar to [15], this study also compared the STAR retrieval temperature and water vapor profiles with those collocated Vaisala radiosonde (RAOB) RS41 and RS92. Equipped with advanced temperature and moisture sensors, the RS41 soundings provide slightly better accurate temperature measurements in the lower stratosphere (the precision is \(\sim\)0.1 K) and moisture measurements in the troposphere when compared to those of RS92 (see [15]). In addition, we also applied the STAR-derived temperature and water vapor profiles to simulate the CrIS and ATMS TBs and compared them to the collocated CrIS and ATMS measurements. CrIS is a Fourier transform spectrometer [43] that can provide precise atmospheric temperature and moisture observations. With a spectral resolution of 0.625 cm\({}^{-1}\), CrIS provides detailed atmospheric thermal information covering long-wave infrared (LWIR, 650-1095 cm\({}^{-1}\)), mid-wave infrared (MWIR, 1210-1750 cm\({}^{-1}\)), and short-wave infrared (SWIR, 2155-2550 cm\({}^{-1}\)). Flying on both the SNPP and NOAA-20 satellites, CrIS has 2211 spectral channels. Onboard the NPP and NOAA-20, the ATMS has 22 channels covering from 23 GHz through 183 GHz. A detailed instrument description of ATMS can be found at [[https://www.ospo.noaa.gov/Products/atmosphere/mirs/sat_atms.html](https://www.ospo.noaa.gov/Products/atmosphere/mirs/sat_atms.html)]([https://www.ospo.noaa.gov/Products/atmosphere/mirs/sat_atms.html](https://www.ospo.noaa.gov/Products/atmosphere/mirs/sat_atms.html)) (accessed on 26 September 2022). We also used the European Center for Medium-range Weather Forecast (ECMWF) reanalysis version 5 (ERA-5) data to validate our retrievals. Note that we only compared our results using COSMIC-2 data in October 2019. Since COSMIC-2 data were not assimilated into ERA-5 before March 2022, our 1D-Var retrievals are considered independent with ERA-5 outputs.
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 2 The Equivalent Layers of Coal Seam Gas Emission
### Prediction Algorithm for Gas Emission from Equivalent Layers  
Equivalent layer gas emissions can be categorized into two main types: mining and excavation. The predictive valuation encompasses not just the gas emissions originating from the coal seam but also all additional emissions stemming from both human and natural factors. The total equivalent layer gas emission is equivalent to the sum of the basic amount and the influence amount. The influence amount, specifically, pertains to factors that transcend the boundaries of the equivalent layer. During excavation, the additional gas emissions are quantified through an impact coefficient in the calculation.  
In the equation, the influence amount signifies the extra emissions arising from excavation, which are represented by an influence coefficient supplementary to the equivalent layer (refer to Table 1 or Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
The gas emission (\(Q_{p}\), in units of m\({}^{3}\)/min) can be determined using Equation (1):  
\[Q_{p}=k_{f}\left(Q_{j}+Q_{z}\right)*\prod k_{ij} \tag{1}\]  
In the context provided, \(k_{f}\) represents the surplus coefficient, \(Q_{j}\) (measured in m\({}^{3}\)/min) denotes the basic quantity of an equivalent layer, \(Q_{z}\) (also measured in m\({}^{3}\)/min) signifies the incremental gas volume with respect to depth, and \(\prod k_{ij}\) is the cumulative effect of influencing factor coefficients.  
\[Q_{z}=q_{t}*\ (H-h)/100 \tag{2}\]  
where \(q_{t}\) is the gas gradient of an equivalent layer, m\({}^{3}\)/(min\(\cdot\)100m), \(H\) denotes excavation depth, m, \(h\) denotes Initial depth.
## 6 Numerical Examples
### Data for Stress-Softening Effect at Unloading  
We first consider the data from [27]. These data (Figure 8) are concerned with a SMP resin sample for the monotone loading case till the strain 23% and then for two unloading cases starting at two strains of 11% and 23%, separately.  
The initial elastic curve is given by Equation (36) with  
\[h_{0}=0.015,\ \tau_{0}=10.30\ \text{MPa},\ E_{0}=686.67\ \text{MPa}.\]  
The shape function for the monotone loading curve is given by Equation (37) with the parameter values below:  
\[\tau_{0}=10.30\ \text{MPa},\ E=680\ \text{MPa},\ \ \xi_{0}=0.001852\ \text{MPa}^{-1},\]  
\[\xi_{1}=0.067568\ \text{MPa}^{-1},\ s=12.5\ \text{MPa},\ m=0.5,\ a_{0}=0.3.\]  
The two unloading curves are fitted by the shape function Equation (34) with  
\[h_{p}=0.0475,\ u=0.154,\ E=5.782\ \text{MPa},\ \gamma=-53.179,\]for the first unloading curve, and  
\[h_{p}=0.162,\ u=0.167,\ E=12.361\ \text{MPa},\ \gamma=-13.218,\]  
for the second unloading curve. Results are shown in Figure 2 by plotting the curves of the axial true stress against the axial Hencky strain. Good agreement with the given data is achieved.