esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of Polyurethanes Containing Cork Waste  
Cork particles, easily identifiable by their characteristic color, were homogeneously distributed within the polyurethane matrix as displayed by optical microscopy (Figure 8b). The highly porous structure of polyurethane, which makes it an insulating material, is recognizable both for the reference sample (Figure 8a) and the sample containing 15 wt% of cork (Figure 8b). However, it was also evident that by increasing the percentage of cork, some of the pores generally filled with air were filled with cork grains.  
As stated for mortars (Section 3.2), the introduction of increasing cork content involved a significant color variation in polyurethane panels. Figure 9 reports visual and macroscopic evidence (Figure 9a) and colorimetry graphical results (Figure 9b).  
The colorimetry results (also reported in Table 4) mainly display a variation of the red  
Figure 8: Optical microscopy of polyurethane samples at 20 magnification with 0 wt% (a) and 15 wt% (b) of cork waste.  
Thermal conductivity measurements were performed primarily to set if the addition of cork within the polyurethane mix could affect the definition of insulating materials. Figure 10 displays similar thermal conductivity values between polyurethanes containing 0 wt%, 5 wt%, 10 wt%. In contrast, an increase of around 33% was observed for the sample with 15 wt% of cork (0.03981 W/mK) compared to that with 10 wt%. In fact, the insulating capacity of the polyurethane related to the high air volume was affected by higher cork content, which substituted air inside the pores. However, since cork is an insulating material itself, this increase in thermal conductivity (0.03981 W/mK) did not affect the definition of polyurethane as a thermally insulating material. Thus, all polyurethane mixes can be considered insulating materials due to thermal conductivity lower than 0.06 W/mK, which is the characteristic thermal conductivity of a standard thermally insulating polyurethane.  
In conclusion, it is possible to add up to 10 wt% of cork and obtain an insulating material with an equal thermal insulating capacity to the reference one. Still, it is also possible to add 15 wt% of cork, obtaining a material that can be defined as an insulator as well.  
Following these promising results, future works will investigate the effect of cork waste addition on durability, temperature-dependent mechanical properties, and flame resistance of mortar and polyurethane samples.  
Figure 10: Correlation between apparent density (bar chart) and thermal conductivity (black line) of polyurethanes containing increasing cork content. The error bars stand for standard deviation error.  
Figure 9: Color variation of polyurethane samples induced by the addition of cork waste: vísual macroscopic evidence of polyurethane containing, from left to right, 0 wt%, 5 wt%, 10 wt%, 15 wt% of cork (**a**) and colorimetry values (**b**).
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 1 Introduction
This is based on the development of a new cellular network control plane in which user's context information is collected and processed to forecast traffic requests, thus enabling a proactive resource allocation. This paper aligns with the objectives of the project in [1] by combining mmWave communications with edge/fog computing to facilitate 5G ecosystem. Along these lines, this paper proposes a novel resource allocation and node placement in mmWave F-RAN for content caching of popular NFs at the edge of the fog nodes. The goal is to allocate the best fog node that allows for the largest content caching, along with reduced delay and cost. The work includes complete modeling and analysis that accounts for the physical and network layers. This includes the network model, user beamforming model for mmWave transmission, request model, content popularity, and cache model. This paper is organized as follows. Prior work on content caching in mmWave F-RAN is presented in Section 2. The network architecture and model are then introduced in Section 3. This is followed by the proposed caching methods in Section 4, along with conclusions in Section 5.
## Nomenclature  
### _C_CHP plant  
\(F_{\rm{i}}\), \(F_{\rm{o}}\)  
\(W_{\rm{i}}\), \(W_{\rm{o}}\)  
\(Q_{\rm{i}}\), \(Q_{\rm{o}}\)  
\(R_{\rm{i}}\), \(R_{\rm{o}}\)  
\(H\)  
Fuel thermal energy input/output (MW)  
Electricity input/output (MW)  
Heat input/output (MW)  
Cooling power input/output (MW)  
Efficiency matrix of CCHP plant  
Figure 16: The detailed optimized dispatch result. (**a**) Deviation of EV charging price. (**b**) EDF of the CCHP plant. (**c**) Input and output of the CCHP plant. (**d**) The dispatch result of P2H/G2P plant.  
_Appl. Sci._**2018**, \(8\), 2273  
Entires in \(\mathbf{H}\) (subscripts denote the type of energy outputs and energy inputs)  
EDF array of CCHP plant  
Efficiency of CHP for electricity and heat production  
Efficiency of CHG  
Coefficient of performance of EP for heat and cooling power production  
The proportion of electricity input for heat production  
Coefficient of performance of WARG  
Dumped Heat of CCHP plant (MW)  
Operation mode indicator  
Output of P2H/G2P plant (MW)  
Conversion efficiencies of G2P and P2H processes  
Lower and upper output limits of P2H/G2P plants in G2P and P2H processes (MW)  
Storage level of P2H/G2P plant (MW-h)  
Lower and upper storage limits (MW-h)  
Hydrogen power demand of other sectors (MW)  
Upper limits of \(H^{\text{oth}}\) (MW)  
Electricity consumption in period \(i\) (MW)  
Electricity price in period \(i\) ($)  
Self-elasticity coefficient in period \(i\)  
Cross-elasticity coefficient between period \(i\) and period \(j\)  
Electricity consumption change (MW), price change ($) in period \(i\)  
Electricity consumption during peak, flat and valley periods (MW)  
Electricity price during peak, flat, and valley periods ($)  
Factors used to describe the linearized relationships between electricity Consumption and price during peak, flat,  
Peak, flat, and valley periods sets  
Base electricity price array and TOU price ($)  
Decision variable vector  
Objective function \(i\)  
Output array of generators (MW)  
Output array of P2H/G2P plants (MW)  
Buses, generators and RES set  
EV charging facilities, P2H/G2P and CCHP plants set  
Outputs of generators (MW)  
Outputs of P2H/G2P plants in P2H/G2P mode (MW)  
Consumption characteristics of generators  
Consumption characteristics of P2H/G2P plants  
Electric load (MW)  
EV charging load (MW)  
Selling price of electricity ($)  
TOU EV charging price ($)  
Fuel price for electricity production ($)  
Fuel price for heat and cooling power production ($)  
Maximal RES outputs (MW)
## 3 Act
### Adaptive Queue Operation in Congestion  
The queue in a network layer works in the First Come First Serve (FCFS) mode [Figure 10-(A)-(1)]. If there is no packet in the sending queue, the packet is served immediately [Figure 10-(A)-(2)]. If some packets are already in the queue, the packet that was inserted last has to wait for the previous packets to be served [Figure 10-(A)-(3)]. If there is no congestion, packets will be served in the order in which they were inserted. In contrast, if packets are congested in the queue, they must wait until the congestion is reduced [Figure 10-(A)-(4)]. Data packets may be delayed for some time or dropped in congestion. However, routing packets should not be delayed or dropped because during congestion, they must be propagated fast so as to reduce the traffic from child nodes. In a conventional congestion control protocol, the back pressure is propagated slowly because routing packets are considerably delayed due to the congested queue. In order to address this problem, the ACT controls the direction for fast propagation of congestion notification. During congestion, if the queue is served in the clockwise direction and a routing packet is inserted at the end of the queue (pointed by the back pointer), the ACT changes the direction to counterclockwise, and thus, the back and front pointers are also changed. Therefore, the routing packet inserted at the last position in the queue becomes the first packet in the queue and is served first. In this case, if the first packet in the queue is a data packet, it cannot be served until a reply packet is received from the parent node. However, a routing is broadcasted to the child nodes and therefore can be served directly even if the parent is congested. As a consequence, a routing packet with congestion notification can be propagated fast to the child nodes.  
Figure 9: Operation of ACT.
## 2 Methodology: Computation of the Altimetry Performance
### Non-Instrumental Error Sources in GNSS-R Altimetry
#### 2.2.1 Electromagnetic Bias  
The evaluation of Equation (6) can be done analytically, but requires many assumptions and approximation in order to be mathematically treatable, and in the end, it requires a numerical integration. In [11], a technique was proposed to evaluate Equation (6) numerically, with a minimum number of approximations. To do that the surface wave height and \(\sigma^{0}\) are required. The sea surface wave height and orientation of each facet are known, as they are the outputs of a temporal sea surface generator. The value of \(\sigma^{0}\) is computed using the Physical Optics under the Kirchhoff Approximation (KA-PO).  
The main results are summarized in Table 2. Note that the EM bias increases with increasing incidence angle, and it can be as high as \(\sim\)\(-\)25 cm, with an azimuthal signature up to 5 cm, peak-to-peak for U\({}_{10}\) = 15 m/s. EM bias must be corrected by means of look-up tables, wind speed and direction auxiliary data, but still if the wind speed has an uncertainty of \(\pm\)2 m/s, the residual EM bias can be as high as \(\pm\)5 cm.
# Lab-on-Chip Culturing System for Fungi--Towards Nanosatellite Missions
## 2 Materials and Methods
### Object of the Study--F. culmorum  
_Fusarium_ is ubiquitous, cosmopolitan and one of the most important genera, which can be found in most regions of the world. _Fusarium_ spp. has been of interest to scientists for many years and is encountered in plants, soil, water and organic matter biofilms.  
In particular, this genus is made up of pathogens that produce thread-like hyphae and penetrate plant surfaces as primary or secondary pathogens [49]. An interesting but also dangerous aspect is the production of toxic secondary metabolites by fungi of the genus _Fusarium_. The presence of mycotoxins in products of plant origin or fodder has a negative impact on our and animal health [50].  
One of the most important fungal _Fusarium_ spp. is _F. culmorum_. This species produces thick-walled and curved macroconidia. Usually, they are 3-5 septate-type and measure 30-50 \(\upmu\)m \(\times\) 5.0-7.5 \(\upmu\)m. Microconidia are absent herein, chlamospores in turn may occur singly, in chains or in clumps. Macroscale colonies of _F. culmorum_ grow rapidly on potato dextrose agar (PDA). The aerial mycelium is dense, whitish, light yellow or red. The underside is greyish-rose, carmine red or burgundy [49].  
A similar method of _F. culmorum_ culture preparation was used in these studies. A fungal colony was cultivated on PDA medium for 14-days at room temperature. Next, utilising a sterile cotton-tipped swab, a mass of spores was transferred from the pure culture surface (without gouging the agar) into a 1.5 mL Eppendorf tube containing 1.0 mL sterile H\({}_{2}\)O. In order to effectively separate the spores from the mycelium, the tube was centrifuged for 2 min at 6000 RPM. Next, only the spores of _F. culmorum_ were transferred to a new 1.5 mL tube. Then, circa 70 \(\upmu\)L of the sample containing the fungi spores was introduced into the lab-on-chip. The lab-on-chip was finally dried for 3 h at 35 \({}^{\circ}\)C to achieve the powdered form of the fungi. (When dealing desiccated material, cultivation research in LOCs can be initialised even with a 3-month delay in relation to nanosatellite integration. Based on this, dosing of the life-supporting media can be performed after the launch to the LEO, when the proper position, inclination and communication with the nanosatellite is achieved).
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 3 Acoustic Methods for Locating Buried Pipelines
### Seismic Wave Methods
#### 3.4.3 Surface Wave (Rayleigh Wave) Method  
A major application of the surface wave method is landmine detection [107]. When the ground is insonified by an acoustic source, the motion of the landmine can be distinguished from that of surrounding soil since the structural resonances possessed by landmines are excited by Rayleigh waves. This results in larger motion in the region directly above the landmines. Accordingly, the location of landmines can be determined by measuring local seismic displacements. A diagram of the mine detection system is shown in Figure 16. Generally, a remote source and non-contact sensor are employed in this case, leading to substantial research on acoustic-to-seismic coupling for landmine detection [108; 109; 110].  
In addition to landmine detection, surface wave has been applied for locating underground obstacles [97; 111], voids [112] and cavities under pavements [113; 114]. In these studies, the experimental set-ups for testing were similar to that displayed in Figure 11, with a source and an array of receivers located on ground surface. An example of the experimental set-ups are displayed in Figure 17 for the detection of underground voids using the surface wave [112].  
Although the sizes and burial depths of some targets mentioned above may be different from those of buried pipelines, progress in this area has been simulated by the desire to apply surface wave method in buried pipe detection. The studies relevant to the surface wave detection method have validated its potential capability for locating buried pipelines, which might be a research direction for future work.  
Figure 16: Mine detection system using surface wave method [107], Copyright IEEE, 2001.  
Figure 17: Experimental set-ups of surface wave method in present of (**a**) a void; (**b**) a metal barrel [112].
## 4 Results  
A statistical analysis of the obtained results was performed, taking into account average values, standard deviations, and histograms. The measurements are presented using graphs and charts, enabling an easy comparison of various conditions and settings. An example UWB measurement graph for the lowest possible voltage in a time interval of 300 s for LoS conditions is presented in Figure 3. The obtained results from various conditions and settings were compared, and the influence of various factors on the accuracy of UWB beacon measurements was analyzed. The values were interpreted, identifying the most significant factors affecting the accuracy of UWB beacon measurements. The study results were summarized, highlighting the key observations and relationships. During the research, the beacons were installed in a room according to the recommendations of their suppliers. For static measurements that did not involve a mobile platform, the distance was obtained with an accuracy of \(\pm\)(0.3 + 0.2 L) mm, where L is the distance measured in meters.
## 3 Material and Method  
### Experimental Setup  
This study was conducted at an IT facility for P.T. XYZ, an oil and gas company in Indonesia. This location was selected for the experiment because of its status as an unmanned facility that ensured uninterrupted data collection and analysis. Monitoring experiments at this unmanned facility were highly appropriate. In this monitoring system, as shown in Figure 1, we opted to utilize voltage and temperature sensors, along with an Arduino microcontroller, to integrate and process data from both sensors. The Ethernet Shield module was used to connect the Arduino to the LAN network. This module enables Arduino to communicate with the server using the HTTP protocol. Here, we implement a Web API, which serves as an endpoint for receiving data from Arduino. The Web API processes requests sent by Arduino, extracts sensor data, and stores them in a database through a LAN network using a 10-m-long cable. It is important to note that the measured battery voltage is related to the flat open circuit voltage (OCV) condition. However, these data do not encompass situations where the battery is charged or used during normal operation.
## 5 Use Cases
### Worldwide Temporal Patterns: Cumulative Data Heatmap  
Drawing on the process sequencing approach [6; 7], the cumulative heatmap aggregates data over multiple years, demonstrating how events are causally connected within policy sequences. This visualization particularly addresses the institutional memory aspects highlighted in our theoretical framework, showing how patterns persist and evolve across time periods.  
The heatmap provides:  
* Global comparisons: enabling cross-regional benchmarking of performance metrics.
* Temporal coherence: supporting Fourier and ARIMA models by identifying consistent periodicities.
* Market stability: highlighting regions with consistent patterns, ideal for risk-averse investments.
## 3 Results
### Topic Analysis
#### 3.2.4 Used Technologies  
In Figure 10, one can see the main technologies (other than machine learning) on which the applications described in the identified publications were based: it includes only those technologies that were mentioned by more than one paper. As can be observed therein, the most often mentioned was web technology, followed by mobile technology (both surpassing the threshold of 20 publications). The only proper name of a technology that was mentioned in more than one paper of the analyzed set was Unity (by 10 publications). The other two technologies that made it to the chart were augmented reality (AR) and virtual reality (VR). Note that the technologies are not disjunct (the same application can be based on web technology, virtual reality, and Unity at the same time).
## 3 Modeling and Error Analysis of the Navigation Process in RINS  
For any carrier moving in a free space, the navigation algorithm needs to resolve at least nine degrees of motional freedom, including position, velocity and attitude of carrier in three directions. With known acceleration and angular increment, the carrier's motion state can be estimated from its previous movement state. The function of an inertial navigation algorithm is to update the motion state of the carrier with minimum calculation errors. Currently, a typical navigation algorithm usually contains two steps: the initial alignment and the motion update process. In the initial alignment process, the initial attitude of the carrier can be determined and is expressed with a transformation matrix between the navigation and body frame. Usually, the initial attitude is estimated through synthesizing inertial measurement results over a period with known earth gravity and earth rotational speed. In the navigation update process, the motion state of a carrier is updated continuously from the previous motion state, with measured angular and acceleration increments during calculation periods. The navigation-resolving process will be presented in detail in this section.
## 2 Prerequisites
### Low-Density Parity-Check (LDPC) Codes  
An LDPC code is defined by a sparse \(N_{c}\times N_{v}\) parity check matrix \(\mathbf{H}\). To analyze the structure of \(\mathbf{H}\), an LDPC code can be visualized using a Tanner graph. A Tanner graph is a bipartite graph consisting of one set for the \(N_{v}\) variable nodes and the other set for the \(N_{c}\) check nodes. This is schematically illustrated in Figure 1. Irregular LDPC codes are typically described using their ensemble characteristics, and therefore, the connections between the two sets are characterized probabilistically by the edge-degree distributions for the variable nodes and the check nodes [20]:  
\[\lambda(z)=\sum_{d=2}^{\lambda_{\max}}\lambda_{d}z^{d-1},\hskip 56.905512 pt \rho(z)=\sum_{d=2}^{\rho_{\max}}\rho_{d}z^{d-1}, \tag{1}\]  
where \(\lambda_{d}\) denotes the fraction of edges connected to variable nodes with degree \(d\) and \(\rho_{d}\) denotes the fraction of edges connected to check nodes with degree \(d\). The LDPC code is called regular if all variable nodes have the same degree \(d_{v}\) and all check nodes have the same degree \(d_{c}\), and otherwise the LDPC code is called irregular. The general derivations in the next sections hold for both node types (i.e., variable nodes and check nodes). Thus, we formally introduce a general edge-degree distribution  
\[\omega(z)=\sum_{d=2}^{\omega_{\max}}\omega_{d}z^{d-1} \tag{2}\]  
to indicate when the derived equations are applicable for any node type.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 4 Threats and Vulnerabilities
### Smart Grid Vulnerabilities
#### 4.2.2 Safety Vulnerabilities  
This type of vulnerability includes hazards, accidents, weaknesses, or potential risks surrounding the smart grid infrastructure that can compromise the safety of individuals, equipment, and/or the environment and cause physical harm, property damage, or adverse effects on public safety [96]. They can be the result of design flaws, equipment malfunctions, or human errors and can lead to electrical shocks, fires, explosions, equipment failure [97], or environmental contamination (i.e., radiation such as Chernobyl 1986 and Fukushima 2011 nuclear disasters) [98; 99] or cyberattack such as the case of Stuxnet and BlackEnergy malware [100; 101]. To address this issue, safety measures can be added, such as continuous system monitoring, safety performance evaluation, regular equipment inspections and maintenance, proper personnel training, emergency response protocols, and resilient risk assessment protocols to achieve electric grid safety and reliability. In fact, the AMI and power management systems of smart grids are susceptible to potential failures that could result in overloading, equipment burnout, and electrical fires [102]. In addition, using complex software algorithms to ensure grid stability raises the possibility of error or malfunction, which could result in dangers such as voltage instability, blackouts, and physical harm to the public and maintenance staff [103].
# A Review of PCI Express Protocol-Based Systems in Response to 5G Application Demand
## 3 Technology Analysis Using PCI Express, CXL, USB, NVMe, and Security
### Compute Express Link  
The Compute Express Link (CXL) is based on PCIe 5.0 and includes three types, as shown in Figure 5: CXL_IO, CXL_Cache, and CXL_MEM. CXL redefines the root complex capability in three types, which can enhance system performance through CXL devices and root complex [22]. The cache behavior defined in CXL enables remote memory component usage and allows cacheable resources to increase performance through communication across the system topology. CXL features a modified high-speed serial bus that extends the high-speed serial bus capability. This is included in the cache and memory method in CXL, and is backward compatible with PCIe, as seen in Figure 5[22].