esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis  
To address the performance bottlenecks of GIS platforms under high concurrency, this study proposes an adaptive task traffic-shaping algorithm for resource-constrained environments. To validate the effectiveness, accuracy, and superiority of the proposed method, this paper compares the proposed method with the classic token bucket algorithm proposed by [PERSON] (hereafter referred to as the classic token bucket algorithm). The experiment is divided into three parts: (1) the optimal token fill rate probing for the classic token bucket algorithm under multiple types of tasks; (2) a comparison of the average processing delay between the classic token bucket algorithm (with optimal token fill rate) and the adaptive task traffic-shaping algorithm under different task scenarios; and (3) a comparison of the average throughput between the two methods under different task scenarios.  
The selection of average latency and throughput as performance metrics is critical for evaluating QoS in GIS platforms, where real-time spatio-temporal data processing demands seamless user interactions (e.g., map rendering, query responses) and efficient resource allocation. Latency directly impacts user experience in GIS applications, such as time-sensitive disaster management or location-based services, while throughput reflects the system's capacity to handle concurrent tasks like bulk data downloads or complex spatial analytics. This paper designed and conducted multiple comparative experiments. Through systematic performance analysis of task processing latency and throughput via comparisons with the classical token bucket algorithm, the proposed method demonstrates its advantages in real-world spatio-temporal big data center environments. These results provide a more efficient solution for handling high-concurrency tasks in GIS platforms.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 3 Architecture of the Network Emulation Platform  
### Architecture  
As shown in Figure 1, the proposed platform can be divided into four planes, namely the logical plane, control plane, data plane, and measurement plane. A particular network scenario is set at the logical plane. Corresponding settings are stored in the database. The control plane reads these setting and procedure commands to drive emulation in the data plane. The measurement plane serves to monitor network performances of simulation scenarios.
# Effects of Propanol on the Performance and Emissions of a Dual-Fuel Industrial Diesel Engine
## 3 Results and Discussion
### Emission Characteristics
#### 3.3.4 Carbon Dioxide  
Apart from methane, carbon dioxide is one of the gaseous compounds that dominate the greenhouse effect, increasing the average temperature on Earth, among other things. Carbon dioxide is a product of the complete combustion of fuel, which fully oxidizes CO and is produced in both the spark-ignition engine and the diesel engine cylinder. Figure 10 shows the changes in CO\({}_{2}\) emissions of a dual-fuel engine powered by diesel and propanol, compared to the reference fuel engine. It is evident that increasing the amount of propanol, up to 65%, co-combusted with diesel fuel in a compression-ignition engine results in a reduction of CO\({}_{2}\) emissions. For the DP65, compared to the D100, there was a decrease of 1.6%, or about 20%. The reason for the reduced carbon dioxide emissions is primarily a reduction in the amount of elemental carbon supplied with the fuel to the engine cylinder.  
Soot is a carbon compound with adsorbed products of incomplete combustion of hydrocarbon fuel. The mixing-controlled combustion phase (diffusion phase), which is dominant in a diesel engine, plays a major role in the soot formation process. The soot content in the cylinder changes during combustion and expansion until the exhaust valve opens. In the diffusion combustion phase, throughout the period of heat release, the concentration of soot increases. After the heat release is complete, with the availability of oxygen, the soot particles are burnt off until the exhaust valve is opened. The burnout of soot is favored by intensive swirling of the charge in the cylinder, caused, among others, by high engine speed.  
Figure 11 shows the changes in the soot emissions of the test engine, in which diesel fuel was co-combusted with propanol, in relation to the emissions of the engine combusting only diesel. It is evident that even a small addition of alcohol caused a significant reduction in the soot concentration in the exhaust of a compression-ignition engine. For DP15, compared to D100, a decrease in the concentration of soot was achieved by 854 mg/m\({}^{3}\), i.e., by 76%. The highest, almost complete, reduction of soot was recorded for DP65, and it was 1114 mg/m\({}^{3}\), i.e., 99%. The reason for the decrease in the soot concentration in the exhaust gas of a dual-fuel engine powered by diesel and propanol was the increasing oxygen content in the fuel, which limited the formation of oxidant-poor areas. The decrease in soot concentration in the exhaust gas is also caused by the decrease in the amount of elemental carbon supplied to the engine cylinder with the increase in the alcohol fuel content.  
Figure 10: Emission of CO and CO\({}_{2}\) for dual-fuel engine fueled by diesel and propanol.
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 3 Results
### Applications of LoRaWAN
#### 3.1.3 Mobility  
Mobility is another area that benefits from LoRaWAN technology. Applications range from motion-sensor data communication to driving assistance using the movement detection of animals and people.  
In [26], a project was developed in which it was planned to monitor garbage trucks in real time. To monitor them, four devices were used: two commercial and two assembled ones.  
In [27], a LoRaWAN-based tracking system was implemented for the vehicle fleet of the municipality of Kozani, Greece. An experimental study was carried out with the objective of comparing the LoRaWAN network developed using two GPS trackers: an industrial solution and a customer solution. For this, a comparison is made between two types of sensors, RAK7200 and TTGO-TBeam, in terms of the number of packets and signal strength. The result shows the benefits of using external antennas; the most effective was the RAK7200 sensor with a 3 dBi external antenna.  
In [28], a service based on the use of parking sensors buried in the asphalt is presented to be able to detect if there is a vehicle at a certain distance above the sensor. The sensors were placed in two different geographical areas. In the first zone, 45 sensors were placed at a range of 250 and 385 m, while in the second, 24 sensors were placed at a distance between 25 and 60 m. The first scenario showed higher packet losses due to the distance from the gateways and the different obstacles presented in the way.  
In [29], a road-condition monitoring system was proposed. For this, mobile sensors connected to a LoRaWAN network were used. The accelerometer and GPS of a smartphone were also used to measure vibrations in order to know the road conditions. The results allowed obtaining the classification of potholes, speed breaks, and damaged or patched streets. In [30], a solution for detecting and recognizing animal or people movement on the roadside to prevent accidents was proposed. This solution was focused on autonomous vehicles. LoRaWAN mesh technology was used to provide the data transmission of the remote networks and the main station, and the data were analyzed in a microcloud infrastructure.
# Carbon Dioxide Adsorption on Carbon Nanofibers with Different Porous Structures
## 3 Results and Discussion
### Co\({}_{2}\) Adsorption Equilibrium  
The adsorption equilibrium isotherms of CO\({}_{2}\) on all samples at different temperatures (25, 40, and 55 \({}^{\circ}\)C) are shown in Figure 7. With increasing pressure and a decreasing temperature, the CO\({}_{2}\) adsorbed amounts increased. This demonstrated that CO\({}_{2}\) adsorption at 25 to 55 \({}^{\circ}\)C on the samples was exothermic. At 25 \({}^{\circ}\)C and 1 atm, the adsorption amounts of CO\({}_{2}\) on PANf, PANh15, PANh12, and PANp were 2.95, 3.04, 3.16, and 2.86 mmol/g, respectively, which were superior to that of commercial activated carbon fibers with a fiber size of approximately 6.8 \(\upmu\)m (1.92 mmol/g) [41]. The CO\({}_{2}\) uptake at 25 \({}^{\circ}\)C and 0.15 atm (a typical untreated flue gas composition) followed the order PANf (0.97 mmol/g) \(>\) PANh12 (0.94 mmol/g) \(>\) PANp (0.88 mmol/g) \(>\) PANh15 (0.86 mmol/g). These data are also shown in Table 1. Table 3 summarizes the CO\({}_{2}\) adsorption amounts in this work with various related adsorbents in the literature. Compared with our previous studies, the carbonization temperature in this work was higher in order to completely remove the PMMA. It is believed that the carbonization temperature was too high to get a high CO\({}_{2}\) uptake.  
The Q\({}_{\text{st}}\) values changing with the CO\({}_{2}\) loading (0.2 to 1.2 mmol/g) are shown in Figure 8, where the Q\({}_{\text{st}}\) values log-linearly decreased with the CO\({}_{2}\) loading. This phenomenon implies that the active sites on the surface of ACNFs were energetically heterogeneous for CO\({}_{2}\) adsorption [42]. In addition, the enhanced micropore confinement or the interactions between CO\({}_{2}\) molecules and the nitrogen-containing functional groups [43] could be responsible for the higher Q\({}_{\text{st}}\) values at a lower CO\({}_{2}\) loading. According to the Q\({}_{\text{st}}\) values, almost lower than 40 kJ/mole, it was indicative of a typical physical adsorption for the CO\({}_{2}\) adsorption on these adsorbents.  
In addition, in investigating the important properties of adsorbents for the CO\({}_{2}\) adsorption capacity, it was observed that the CO\({}_{2}\) uptake at 0.15 atm had high correlation coefficients with chemisorbed NO\({}_{2}\) and the ratio of V\({}_{<\,0.7\,\text{mm}}\) to V\({}_{t}\) using the Pearson  
Figure 7: Adsorption isotherms of CO\({}_{2}\) of the ACNFs: (**a**) PANf; (**b**) PANh15; (**c**) PANh12; (**d**) PANp.  
correlation analysis. Moreover, the CO\({}_{2}\) uptake at 1 atm was highly related to pyridine-N oxides and the ratio of V\({}_{\text{mi}}\) to V\({}_{\text{t}}\).
## 7 Security Analysis
### Attack Analysis
#### 7.2.4 Tracking Attack  
An outsider attacker can track the part along the supply chain even if they do not belong to the organization based on the responses of the SE. In this proposed solution, the SE performs an ECDSA signature given an external hash. The ECDSA algorithm always gives back a different and unpredictable signature based on a random secret number \(K\) internally created by the signer and unique to each message. Therefore, even if an attacker with access to the SE always gives a constant hash to it, the attacker will always retrieve an unpredictable response and will be unable to track the SE.
## 4 Discussion  
Recently, the demand for Wheatstone bridge readout ICs has grown for various sensor applications. Because the input resistance change of a Wheatstone bridge sensor is generally small, the change of the converted voltage signal also has a small value. As a result, low noise IAs that can accurately amplify small input voltage changes is an active research area. Chopping and autozeroing are mainly implemented for low noise IAs, and there are various types of IAs, such as three-operational amplifier IAs, CCIAs, and CFIAs. In this paper, the three-operational amplifier IA structure is implemented to easily acquire high input impedance and adjustable gain; however, CCIAs and CFIAs also have certain advantages, so further research is required regarding these. In addition, the fabricated chopper-stabilized multipath operational amplifier must consume a significant amount of current to provide the low noise characteristics. Consequently, the power consumption of Wheatstone bridge readout ICs has increased significantly, and measures to reduce it are needed. The following are potential ways to reduce power consumption: The first method is to lessen the power consumption of the operational amplifier itself. This can be done by altering the size and bias voltage of the devices in the amplifier. However, because the size of the device is directly related to the noise component, it must be adjusted appropriately to avoid a significant increase in input referred noise. The second method is to diminish the power dissipation in LPF, buffer, etc. in addition to the operational amplifier. For instance, if the resistors of the LPF are replaced with a switched capacitor architecture, the cutoff frequency may not be obvious, but it has the advantage that power consumption is considerably decreased. Additionally, eliminating the buffer implemented for monitoring can cut power consumption. These methods can reduce the power consumption of the proposed Wheatstone bridge readout IC, but further discussion of them will be required in the future.
## 4 Discussion of the Results Analysis: First Findings on Trends and Best Practices
### Main Findings for the Grey Literature Sample
#### 4.2.3 Trends in Heritage Practice
Hence, the scientific contribution should consider the already-established common practices as fertile ground to then propose innovative action for identified activities, such as the use of satellite imagery for the monitoring of specific phenomena. The process for such action might be longer than the usual timing required and employed in scientific literature, while at the same time, these should serve as a reference for good practices to be conducted over a significant period of time (years or decades) and usually regard a set of technologies used to provide the desired application and/or service. In conclusion, it should be no surprise that the number of Grey literature documents is lower and that it usually considers, as comprehensively as possible, the full list of technologies and of concrete benefits of their applications. In our view, an important bridging activity would be the continuous promotion of the technological solutions that are developed together with the final user, and hence out of the \"service-provider-client\" mode but rather in the service-provider-informed-customer environment. Major benefits could be achieved if this kind of exchange took place at the local level and hence in a local language where possible. In such a practice, the users (and even site managers) could actively take part in the development of the applications and services that meet the purposes of their specific site and possibly in their own language, in closer coherence with the terminology of their local and national legislation.
## 5 Multi-Objective Optimization of Seat Dynamic Comfort Parameters
### Multi-Objective Optimization Using the NSGA-II Algorithm
The calculations revealed that the maximum transmissibility was reduced by 13.6% and the resonance frequency decreased by 7.6% compared to the pre-optimized configurations. The optimized vibration transmissibility of the seat is significantly reduced, the resonance frequency is shifted outside the human-sensitive frequency range, and the dynamic comfort of the seat is significantly improved. These results highlight the enhanced vibration isolation performance achieved through the multi-objective optimization framework. These optimized parameters can also be applied to industrial production. The stiffness and damping parameters of the optimized seat cushion can guide manufacturers in selecting more suitable seat cushion materials, thereby enhancing the comfort of drivers. The optimized stiffness of the air spring and the damping coefficient of the shock absorber can guide seat manufacturers in designing the volume and height of the air spring airbag, as well as the type of damper. However, when manufacturing seats, the selected seat cushion material should ideally possess the optimal stiffness and damping characteristics obtained through the above optimization. This type of material may be relatively expensive. Considering the manufacturing costs for enterprises, seats using such materials may be difficult to produce on a large scale. Additionally, due to the limited seat suspension space, the height of the airbag designed based on the optimized air spring stiffness may exceed the suspension space, potentially affecting the movement of the seat suspension.
## 2 Materials and Methods  
### Materials  
The commercial whole rice flour (RF; origin Greece) (moisture 11.2%, proteins 7.1%, fats 2.8%, fibers 4.6%) and quinoa flour (QF; origin Peru) (moisture 9.2%, proteins 14.0%, fats 6.1%, fibers 7.0%) distributed by Solaris Plant S.R.L. (Bucharest, Romania) and the soy protein isolate (SPI; Supro(r) XT 221D IP, distributed by KUK Romania, Voluntari, Romania) (moisture 4.8%, proteins 87.1%) were used in the study. Enzymes of various origins were selected for preparing the protein hydrolyses: bromelin of vegetal origin (Carl Roth, Karlsruhe, Germany), Neutrase 5.0 BG of microbial origin (Novo Nordisk, Bagsvaerd, Denmark) and trypsin of animal origin (Merck, Darmstadt, Germany).  
1,1-diphenyl-2-picrylhydrazyl (DPPH), 2,2-azino-bis(3-ethylbenzothazoline-6-sulfonic acid) diammonium salt (ABTS), 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox) and sodium dodecyl sulphate (SDS) were purchased from Sigma-Aldrich Chemie GmbH (Taufkirchen, Germany).
## 1 Introduction
Figure 1: Comparison of the proposed method with the state-of-the-art trackers kernelized correlation filter (KCF) [21], discriminative scale space tracker (DSST) [22], fragment-based tracker (FRAG) [15], locality sensitive histograms tracker (LSHT) [37], multiple instance learning (MIL) [6], structured output tracking with kernels (STRUCK) [5] and tracking-learning-detection (TLD) [9]. The sequences include Tiger2, Freeman3 and Shaking from OTB-100. The composition of this paper is as follows: Section 2 describes the technical background of tracking with related works. Section 3 presents the proposed method with the object translation estimation using the partial block scheme, the object scaling estimation using scale space and the adaptive update model. After summarizing experimental results in Section 4, Section 5 concludes the paper.
# Method for Handling Massive IoT Traffic in 5G Networks
## 6 Analytical Model
This implies that the chance of getting more than \(x\) IoT data packets inside a multiplexed large packet is \(a_{x}\), which is given in the form of \(P[X>x]\) for \(1\leq x<r\) as:  
\[\begin{split} P[X>x]&=1-\left(\sum_{i=0}^{x-1}\frac{ e^{-\lambda T_{max}}}{l!}(\lambda T_{max})^{i}\right)\\ &\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad This is illustrated with an example. Consider that \(a_{1}=0.6\), which implies that 60% of the multiplexed large packets should be having a size greater than one small packet, which indicates that \(P[X>1]=0.6\). Now the likelihood of \(X>0\) with \(x\) having value of 0 must always be 1, i.e., \(P[X>0]=1\). This is due to the fact that multiplexing requires a minimum of one packet in the multiplexing buffer, which means that the value of \(X\) must be above 0 if multiplexing has to occur. Now by setting \(x=0\) in Equation (3), \(P[X>0]\) turns out to be 1. In this fashion, the chance of \(X\) greater than or equal to \(r\), i.e., \(P[X\geq r]\) is always 0.  
Since, \(X\) is a RV of discrete values, the likelihood \(P[X=x]\) is established as:  
\[P[X=x]=P[X>x-1]-P[X>x] \tag{4}\]  
This gives \(P[X=x]\) as:  
\[P[X=x]=1-\sum_{i=0}^{x-2}\frac{e^{-\lambda T_{max}}}{i!}(\lambda T_{max})^{i}- \left(1-\sum_{i=0}^{x-1}\frac{e^{-\lambda T_{max}}}{i!}(\lambda T_{max})^{i}\right) \tag{5}\]  
\[\Rightarrow P[X=x]=-\sum_{i=0}^{x-2}\frac{e^{-\lambda T_{max}}}{i!}(\lambda T_ {max})^{i}+\sum_{i=0}^{x-1}\frac{e^{-\lambda T_{max}}}{i!}(\lambda T_{max})^{i} \tag{6}\]  
\[\Longrightarrow P[X=x]=-\sum_{i=0}^{x-2}\frac{e^{-\lambda T_{max}}}{i!}( \lambda T_{max})^{i}+\sum_{i=0}^{x-2}\frac{e^{-\lambda T_{max}}}{i!}(\lambda T _{max})^{i}+\frac{e^{-\lambda T_{max}}}{(x-1)!}(\lambda T_{max})^{x-1} \tag{7}\]  
\[\Longrightarrow P[X=x]=\frac{e^{-\lambda T_{max}}}{(x-1)!}(\lambda T_{max})^{ x-1} \tag{8}\]
# Soil Classification from Piezoone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory
## 2 Soil Classification Method for CPT and PCPT
### Soil Classification Using Fuzzy Theory
#### 2.2.1 [PERSON]'s Study  
[PERSON] [6] suggested a soil classification method using fuzzy theory. He developed fuzzy membership functions for input variables \(q_{t}\), \(F_{r}(=f_{s}/q_{t})\), and \(B_{q}\) according to [PERSON] et al. [2]. However, soil types were only classified into \"clay\", \"silt\", and \"sand\". The maximum grade for membership functions was limited to 0.8 when considering uncertainty in soil classification. Figure 2 shows the membership functions of three soil sets in terms of \(q_{t}\), \(B_{q}\), and \(F_{r}\) respectively. The reader is referred to [PERSON] [6] for the detailed expression of fuzzy membership functions.  
Three soil fuzzy sets for clay, silt, and sand were named CL, SI, and SA, as defined in Equation (4). \(\mu_{CL}\), \(\mu_{SI}\), and \(\mu_{SA}\) represent the summation of membership function values from each chart for clay, silt, and sand, respectively.  
\[\begin{array}{l}\text{Clayey soil}:\ \text{CL}=\sum\mu_{CL}(a_{s})/a_{i}(i=1,2,3), \\ \text{Silty soil}:\ \text{SI}=\sum\mu_{SI}(a_{i})/a_{i}(i=1,2,3),\\ \text{Sandy soil}:\ \text{SA}=\sum\mu_{SA}(a_{i})/a_{i}(i=1,2,3),\end{array} \tag{4}\]  
where \(a_{1}=q_{t}\), \(a_{2}=F_{r}\), and \(a_{3}=B_{q}\).