esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurate diagnostics of the railway track axis are crucial for enabling higher operational speeds of rail vehicles. Consequently, assessment methods are under continuous refinement. The precision of mobile GNSS-based measurements is influenced by external factors such as terrain obstructions and satellite geometry. This study presents an innovative approach that continuously monitors a predefined multi-receiver GNSS configuration in real time to enhance positioning accuracy. Field experiments on operational railway lines demonstrate that the proposed method significantly improves measurement reliability and reduces uncertainty compared to the conventional CQ-based quality assessment. The algorithm underlying the proposed method is formulated through analytical equations, allowing it to be implemented in any programming environment. The method's effectiveness is demonstrated by comparing the expanded uncertainty calculated using GNSS-provided data with that obtained through the proposed approach.  
railway track axis; measurement uncertainty; GNSS measurement mobile technique 2025  
2025 172461  
1 Faculty of Electrical and Control Engineering, Gdansk University of Technology, Narattowicza 11/12, 80-233 Gdansk, Poland; [EMAIL_ADDRESS] (S.J.; [EMAIL_ADDRESS] (A.W.); [EMAIL_ADDRESS] (K.K.); [EMAIL_ADDRESS] (S.J.); [EMAIL_ADDRESS] (M.M.)  
80-233 Gdansk, Poland; [EMAIL_ADDRESS] (W.K.); [EMAIL_ADDRESS] (R.L.); [EMAIL_ADDRESS] (P.S.D.); [EMAIL_ADDRESS] (S.J.); [EMAIL_ADDRESS] (S.G.)
## 5 Material and Methods  
A security system called SD-VANET_Guard has been proposed for the detection of DDoS attacks targeting the controller within the SD-VANET architecture. This system is designed to operate within the Software Defined Networking (SDN) controller of VANET networks to assist in protecting against DDoS attacks. The SD-VANET_Guard security system incorporates a hybrid artificial intelligence model known as 1 DCNN-DT for detecting DDoS attacks. Our detection methodology involves collecting network traffic data using the Ryu controller, processing this data, and classifying the network traffic data. The experimental topology created to collect DDoS attack data and legitimate network traffic data are illustrated in Figure 4.  
Figure 4: SD-VANET_Guard intrusion detection system integration.  
The experimental SD-VANET topology comprises 20 vehicles (vehicle IP range: 10.0.0.1-10.0.0.20) and 6 Access Points (APs) that support WPA2 encryption. The APs are placed along the roadside to provide wireless connectivity to the vehicles located in the data plane. The vehicles connect to the APs using Intelligent Transportation Systems (ITS) connections that simulate the IEEE 802.11p wireless standard, also known as WAVE (Wireless Access in Vehicular Environments) [40]. The Ryu controller establishes a connection with communication devices in the data plane through the APs using the OpenFlow protocol. The simulation is integrated with the SUMO emulator, which generates the movement of vehicles on a predefined road network.  
sFlow has been integrated into the network for real-time monitoring of network traffic. sFlow collects data traffic from the experimental SD-VANET network. When sFlow is initiated, it automatically connects to the sFlow agent defined on the devices in the SD-VANET network and begins collecting data traffic, including IP addresses, sampling rates, and polling intervals.  
The Ryu controller and Mininet-WIFI operate on the same virtual machine, while sFlow runs on a different virtual machine. Mininet-WIFI and Ryu-SDN are installed on Ubuntu 20.04-1 with 4 GB of RAM and a dual-core CPU, whereas sFlow is installed on Ubuntu 20.04-1 with 4 GB of RAM and a single-core CPU.  
The performance of the SD-VANET model was evaluated under conditions similar to those of intelligent transportation systems using the Mininet-WiFi simulator. This simulation demonstrated the system's robustness in handling real-time traffic flows and DDoS attack scenarios. Consistent with recent studies, SD-VANETs have shown efficacy in detecting sophisticated attacks within dynamic environments [41]. Future research will extend the simulation scenarios to further validate the model's scalability and resilience under a broader range of conditions.
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 4 Recent Trends in Remanufacturing
### Additive Manufacturing for Remanufacturing  
Additive manufacturing (AM) is a manufacturing technique whereby material is continuously added onto another to form a product. There have been advancements in this domain with laser additive manufacturing being used, which offers much more precision and better results [124, 125]. Some EoL products are in such bad shape upon collection that normal techniques cannot be used to replace them. This is when AM steps into remanufacturing, as it is one of the most advanced manufacturing techniques and can be used to remanufact complicated parts [126]. There have been a lot of improvements in this manufacturing technique, notably the fact that it is no longer limited and can be used in various domains. The inclusion of this technology in remanufacturing is boosting remanufacturing companies, especially for products which pose complications [127]. For example, in the remanufacturing of car engines, there are certain parts of the interior of the piston that are difficult to replace but they can be repaired with the use of AM. Direct energy deposition (DED) and powder bed fusion (PBF) are the main AM methods for metals that are used in refurbishing, repair and remanufacturing of metal parts [59]. DED- and PBF-based technologies are quickly rising and they are playing a significant role in the circular economy. An algorithm for remanufacturing has been developed by [128] to identify products that need to be repaired or remanufactured by 3D deposition.  
One of the greatest benefits of AM, especially laser AM, is that the gas emission percentage is much lower than other manufacturing techniques like milling, drilling, grinding and others [129]. Also, it can be used in different processes, like repair, refurbishment and maintenance, not just for manufacturing or remanufacturing. [PERSON] et al. [60] have described how an EoL component can be optimized based on AI in support of AM for remanufacturing and repair. This will be very helpful in building a circular economy and it will be a great bonus in remanufacturing.
###### Abstract  
Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.  
fog computing; edge computing; resource management; intelligent network layer; local cluster heterogeneous network; internet of things applications; mobile ad hoc network +
Footnote †: journal: Sensors  
os
## 2 B-Spline Corner Smoothing Method
### Curvature Optimization  
In the machining process of the corners, the minimum transition velocity \(v_{\min-t}\) of corner is the most important factor that limits the machining efficiency, and also affects the acceleration \(A_{n}\) and jerk \(J_{n}\). According to the literature [28; 29], the velocity can be expressed as  
\[v_{\min-t}=\min\left(\sqrt{\frac{A_{n}}{k}},\sqrt[3]{\frac{I_{n}}{k^{2}}}\right) \tag{25}\]  
As can be shown in Equation (25), the velocity \(v_{\min-t}\) is inversely proportional to the curvature. In order to improve the velocity, smaller curvature should be ensured to obtain higher machining efficiency. In addition, when the curvature reaches the extreme value, it also means that the velocity reaches the extreme value. The increase in the number of curvature extremes leads to the redundant deceleration or acceleration at the corner, which can cause violent fluctuation in speed and the vibration of machining. Therefore, to obtain the unique curvature extremum, the curvature of transitional curve should monotonously increase within [0, 0.5] and monotonically decrease within [0.5, 1]. This constraint relationship can be expressed as:  
\[\left\{\begin{array}{l}k_{s}^{\prime}=\frac{dk}{du}\frac{du}{ds}\geq 0\quad u \in[0,0.5]\\ k_{s}^{\prime}=\frac{dk}{du}\frac{du}{ds}\leq 0\quad u\in[0.5,1]\end{array}\right. \tag{26}\]  
According to the above description, a smoother curve can be obtained by optimizing the curvature to enhance the machining efficiency. Considering the constraints mentioned above, the optimization model can be given:  
Objective function:  
\[f=\min(k(0.5)) \tag{27}\]  
Constraint functions:  
\[\left\{\begin{array}{l}L_{t,1}\leq\min(\overline{P_{1,0}C_{0}},\overline{P_{ 1,5}P_{2,3}}/2)\\ L_{t,j}\leq\min(\overline{P_{j,3}P_{j-1,5}}/2,\overline{P_{j,5}P_{j+1,3}}/2)(j=2,\cdots,n-2)\\ L_{t,n-1}\leq\min(\overline{P_{n-1,3}P_{n-2,5}}/2,\overline{P_{n-1,5}C_{n}}) \\ k_{s}^{\prime}=\frac{dk}{du}\frac{du}{ds}\geq 0\ u\in[0,0.5]\\ k_{s}^{\prime}=\frac{dk}{du}\frac{du}{ds}\leq 0\ u\in[0.5,1]\end{array}\right. \tag{28}\]  
Design variables: \(b\)  
To solve the above problems, linear programming method is usually used to find the optimal solution. However, these optimal methods would consume much time. Therefore, this paper proposes a simple searching algorithm, which is given as follows.  
Step 1: Initialize \(b_{m}=0,m=1,\cdots,M\).  
Step 2: Determine the control point by Equation (11) and obtain the curve Equation (3) with parameter \(b\).  
Step 3: Set \(b_{m}=b_{m}+\Delta_{b}\) and \(\Delta_{b}=0.01\).  
Step 4: Calculate the curvature of the transition curve with Equation (27); if Equation (28) is satisfied, the curvature and the corresponding \(b_{m}\) are stored and repeat step 3; if \(m=M\), go to step 5.  
Step 5: Select \(b\) corresponding to the minimum curvature.
## 5 Experiments
### Computational Cost of TFT

Table 3: Comparison of the quality of attribution maps: the original attribution maps (Original), the original attribution maps optimized by the threshold fine-tuning (TFT), and the attribution maps generated with the CNN optimized by the activation fine-tuning (AFT) without any threshold fine-tuning. The numbers in boldfaces indicate performance improvements over the original cases. 10 different threshold values in our experiments, and therefore a serial evaluation of the prediction probabilities requires an equal number of forward passes. To curtail runtime, we have adopted GPU-based batch processing to calculate the prediction probabilities for all masked images at once (lines 9-11 of Algorithm 1). In Table 5, we show the runtime to create an input attribution map with and without our TFT. The measurements are conducted on a load-free Linux machine with an Intel CPU Xeon Silver 4214 CPU, an NVIDIA RTX 2080 Ti GPU, and 128 GB of RAM. The results show that our method takes less than 0.03 seconds across all cases in our experimental environment, which we believe allowable considering the runtime of attribution map creation themselves and the expected quality improvement due to TFT.
###### Abstract  
The current study sought to investigate the changes in surface hardness, roughness, and moisture absorption of the Vertex ThermoSens polymer (Vertex Dental, 3D Systems, The Netherlands) following immersion in artificial saliva for various periods (7, 14, and 28 days). A total of 60 rectangular specimens with dimensions of 20 mm in length, 20 mm in width, and 3 mm in thickness were made. Due to insufficient mold solidification, these specimens were made utilizing the injection molding process. A Mitutoyo Surfest 4 roughness meter (Mitutoyo, Aurora, IL, USA) was used to measure the surface roughness of the test materials. The ThermoSens polymer hardness was assessed using the Shor method and D\(-\)HSpD scale, while absorption was measured with a Surforus analytical balance. Results indicated the highest mean hardness after 28 days (M = 77.6) (Surface 1) and the lowest for the control group (M = 59) (Surface 2). The maximum surface roughness occurred in direction 2.2 pre-immersion (Ra = 2.88 \(\upmu\)m) and 7 days post-removal (Ra = 2.95 \(\upmu\)m). The control group exhibited the lowest absorption (Wsp = 1.524 mg/mm\({}^{3}\)), with the highest mean values over 28 days (Wsp = 1.541 mg/mm\({}^{3}\)). The elevated flask and plaster temperature slowed polymer solidification, resulting in longer macromolecules and improved mechanical properties and surface features.  
thernoplastic polymer; injection-molded; surface hardness; surface roughness; absorption; denture base polymers; solidification +
Footnote †: journal: Applied Sciences
# Acoustical Properties of Fiberglass Blankets Impregnated with Silica Aerogel
## 1 Introduction
[20] investigated the acoustical behavior of synthesized silica aerogels mixed into UPVC blankets of different weight ratios. They found that neat UPVC only had a maximum sound absorption of 17% at a frequency of 1800 Hz; however, when silica aerogel was applied at 0.5, 1.5 and 3 weight %, the maximum sound absorption of UPVC increased to 24, 28 and 43%, respectively, therefore highlighting that acoustical properties were greatly increased upon the addition of silica aerogel. A more extensive review of acoustical properties of aerogels can be found in reference [4]. However, there is a general lack of understanding regarding what leads to the observed acoustical properties of granular aerogels embedded into fibrous mats. A majority of previous works have not attempted to apply any valid theoretical models to predict key acoustical properties of these systems to explain the measured data. There are limited data on the effect of the filling ratio on the acoustical properties of aerogel impregnated fibrous blankets. Additionally, despite some previous efforts (e.g., [6; 9]), there is a limited understanding on the ability of some prediction models to explain the general acoustical behavior of these materials. There was no discussion on the values of the non-acoustical parameters that the authors of references [6; 9] had to use in the prediction models they chose in their works to simulate the measured absorption coefficient data. Our work aims to address this gap via a careful characterization of the acoustical behavior of granular silica aerogels impregnated into fiberglass mats. The acoustical properties of five samples of aerogel blankets with varying concentrations of aerogel powder (at micrometric particle sizes) at filling ratios of 0, 25, 50, 75 and 100% were measured and predicted using a mathematical model. This work helps to better understand the relation between their micro-structure and measured acoustical performance. The structure of this paper is as follows. Section 2 highlights the various techniques used to characterize the chemical and physical material properties of aerogel blankets. Section 3 looks at the experimental acoustical data derived from the analysis of these materials. Section 4 attempts to explain these data with a mathematical model to understand what intrinsic properties of aerogel blankets make them acoustically absorbing.
## 3 Results and Discussion
### Correlation Analysis of Color, FAV and Volatile Compounds  
The correlation thermogram of color, FAV, and eleven volatile compounds of the high-moisture brown rice during storage is shown in Figure 2. L* exhibited a significant positive correlation with a* and undecanal, respectively. a* demonstrated a significant positive correlation with 2-hexyl-1-decanol and 2-pentylfuran, respectively. In addition, 1-octanol had a significant positive correlation with 1-nonanol, nonanal, 2,6,10-trimethyl-14-pentadecanone, undecanal, (E)-2-nonenal, and (E)-2-decneal, respectively. A significant positive correlation was also found between dodecane and tridecane, which had a gasoline odor.  
Noticeably, FAV was negatively correlated with L*, a*, and b*, as well as 1-octanol, 1-nonanol, 2-hexyl-1-decanol, 2,6,10-trimethyl-14-pentadecanone, undecanal, nonanal, (E)-2-nonenal, (E)-2-decneal, dodecane, tridecane, and 2-pentylfuran (Figure 2). The deterioration degree of brown rice quality during storage can be reflected by the change in FAV, shown as an important index for measuring the freshness and aging degree of brown rice [6]. This suggested that the colorimetric values, 1-octanol, 1-nonanol, 2-hexyl-1-decanol, 2,6,10  
Figure 2: Heat map of correlation among color, FAV, and volatile compounds.  
trimethyl-14-pentadecanone, undecanal, nonanal, (E)-2-nonanal, (E)-2-decenal, dodecane, tridecane, and 2-pentylfuran, might indirectly reflect the storage quality of high-moisture japonica brown rice. It has been pointed out that the quality of cashew oil could be reflected by the color value [33]. Similarly, [PERSON] et al. [34] also found that undecanal, nonanal, 2-decenal, and 2-pentylfuran were potential markers for evaluating lipid oxidation in fried soybean oil.
## 2 Materials and Methods
### UV/AtOx Exposure Experimental Setup  
The adopted UV/AtOx test facility simulates the ultraviolet irradiation and the atomic oxygen impact effect in low orbit space environment [16; 19; 20]. It is constituted by a small chamber (see Figure 2) equipped with a rotary vane pump and a Varian V-550 turbo-molecular pump in order to set ultra-high vacuum conditions (minimum pressure achievable: 10\({}^{-5}\) Pa). The aim of the UV/AtOx test is the evaluation of the material degradation in terms of mass loss due to erosion: The samples are weighted before and after the test, using a Mettler-Toledo XP26 DR microbalance (precision: 1 \(\mu\)g). The tests are performed in a clean room conditioned at room temperature for 48 h, at a mean pressure of 2.6 \(\times\) 10\({}^{-1}\) mbar, in order to avoid weight errors due to moisture losses.  
Figure 1: Picture of the flammability test facility.  
The UV generator is a highly stable mercury-xenon lamp produced by Hamamatsu. The system has high intensity UV line spectra with an elliptical reflector (UV cold mirror) having reflectivity higher than 90% in the UV range, and a quartz light guide with UV transmittance. The lamp works in horizontal position in order to have an optical system with low light loss. The spectral emittance field range is 200-600 nm, with maximum emission at 365 nm. The radiation intensity of the lamp system is 410 mW/cm\({}^{2}\) (around 10 Suns) at a distance of 60 mm, with an aperture size of 20 mm.  
The AtOx flux is simulated by generating an oxygen plasma, using an OS-PREY Plasma Source, which is a radio-frequency inductively coupled plasma source produced by Oxford Scientific (SPECS) Instruments. The operating principle is based on the ionization of the oxygen molecules by coupling the energy from a radio-frequency power source (13.56 MHz) to an ionized gas. The source generates a beam constituted by 99% of neutral elements (around 60% of O atoms and 40% of O\({}_{2}\) molecules) and 1% of O\({}^{+}\) ions with energy 5 \(\div\) 25 eV. The sample distance from the quartz emission aperture at a right angle orientation respect to the beam is of 50 mm, as it is the worst colliding condition. The plasma flux is calculated by using a reference sample of Kapton HN, a material of known in-space erosion yield, as suggested by ASTM E2089. The samples are exposed to AtOx at an effective fluence (flux over time) of 1.25 \(\times\) 10\({}^{20}\) atom/cm\({}^{2}\); based on the measured mass loss, the erosion rate is calculated from the sample density, the sample exposed area and the produced fluence.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
#### 4.1.2 Cooja (With Contiki OS)
This highlights the need for scalability enhancements when using Cooja for large-scale IoT deployments. Notable Studies Using Cooja
* Ref. [6]: WSN-Maintain enhances Cooja by optimizing sensor coverage, extending network lifetime via redundant node management. * Ref. [54]: Simulated security attacks and defense mechanisms in IoT networks. * Ref. [70]: Evaluated the performance of IoT protocols under various network densities. * Ref. [84]: Analyzed DDoS attack scenarios and potential mitigation strategies in IoT networks. Performance Metrics
* Energy Consumption: Cooja focuses heavily on energy efficiency, making it a go-to tool for simulating low-power IoT networks. * Latency: The simulator is often evaluated based on latency in real-time applications, although it struggles with scalability when simulating larger networks. * Packet Delivery Ratio (PDR): The PDR is another critical performance metric used in Cooja, especially in resource-constrained IoT environments.