esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Copper Wire Technology: Graphene/Cu Composites for Superior Conductivity and Strength
## 2 Materials and Methods  
Materials: The Gr/Cu composite powders were sourced from Shanghai SIMBATT Energy Co., Ltd. (Shanghai, China) and Beijing TANRAND Technology Co., Ltd. (Beijing, China), designated as Gr/Cu-Z and Gr/Cu-T, respectively. The graphene content in these powders was 0.01-0.02 wt.% for Gr/Cu-Z and 0.04-0.05 wt.% for Gr/Cu-T. The original copper powder is high-purity copper with a purity of over 99.9% and a particle size ranging from 50.0 to 70.0 \(\upmu\)m.  
Wire Fabrication: Approximately 2.0 kg of the Gr/Cu composite powder was sintered under a hot press at 900 \({}^{\circ}\)C in an argon atmosphere, with a dwell time of 6 h and a pressure of 40 MPa. The system was then allowed to cool to room temperature to form a composite block. This block was subsequently subjected to hot extrusion and then annealed at 800 \({}^{\circ}\)C under an argon-hydrogen mixed atmosphere for 5 h. Multiple drawing processes followed until the wire diameter was reduced to 2.0 mm and the length exceeded 1.5 m. A final annealing step was conducted to further enhance the properties of the wire. It is worth noting that the deviation in wire diameter is within \(\pm\)0.01 mm. For comparison, pure copper wires of the same size were prepared under the same conditions.  
Material Characterization: The structural information of the composite powder was obtained using Bruker D8 X-ray diffraction (XRD) (Bruker, Billerica, MA, USA) with monochromatic Cu K\(\alpha\) radiation (\(\lambda\) = 1.54060 A). Raman spectroscopy was performed using a Horiba LabRAM HR Evolution instrument (HORIBA LabRAM, Evanston, IL, USA) with an excitation wavelength of 532 nm, revealing the characteristic features of graphene in the composite powder. The microstructure and interfacial morphology of the composite powder and wire samples were characterized using scanning electron microscopy (SEM, ZEISS Sigma 300, ZEISS, Oberkochen, Germany) and high-resolution transmission electron microscopy (HRTEM, Thermo Fisher Talos F200X, Thermo Fisher, Waltham, MA, USA). The metallographic properties, grain size, and orientation of the wire samples were analyzed through electron backscatter diffraction (EBSD, Symmetry hikari XP, AMETEK, Berwyn, PA, USA). The electrical conductivity of the wire samples at room temperature (20 \(\pm\) 0.1 \({}^{\circ}\)C) was measured using a QJ36B-4 digital bridge (Shanghai Diyi Instrument, Shanghai, China, measurement range: from 0.1 m\(\Omega\) to 400 k\(\Omega\), accuracy: \(\leq\)0.5%), with sample dimensions of 1.2 m in length and 2.0 mm in diameter. The allowable measurement error for testing the composite wires is \(\pm\)0.40%. The tensile strength of the composite wires was tested using a TSE 304C microcomputer-controlled universal testing machine (WANCE, Shenzhen, China, measurement range: 0.3-30 kN, accuracy: \(\pm\)1%), with an effective sample length of 250.0 mm and a tensile rate of 0.5 mm/min. In this paper, all experimental data are presented with one significant figure for consistency.
## 3 Results and Discussion  
The present study identified the kinetics of nitrification, denitrification, and organic pollutant removal from wastewater generated by wintertime airport maintenance. The experiment was conducted in four temperature variants and at different doses of the de-icing agents (sodium formate and potassium diacetate) characterized by C/N ratios, with the urea dose being constant. The use of biofilter filling promotes the formation of complex biofilms, providing favorable conditions for the development of both aerobic and anaerobic microorganisms [15]. A diverse range of parameters (which ensures the presence of various bacterial groups with different needs) creates favorable conditions for wastewater bio-treatment [17]. This, in the context of the present study, enabled the removal of organic pollutants and nitrogen species from airport de-icing wastewater. The reaction rates for the experimental variants and the goodness of fit for the model (R\({}^{2}\)) are given in Supplementary Table S1. Changes in nitrogen concentration due to nitrification and denitrification performed to the zero-order reaction formula, whereas the organic removal rate followed the first-order reaction formula. A comparison between the reaction rates by temperature and the biofilter's organic load was used to identify the impact of these parameters on the biofilter performance (Figure 2).  
Figure 2: Effect of temperature and C/N on the rates of nitrification (**A**), denitrification (**B**), and organic pollutant removal (**C**).
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
## 3 Improved Parcel Delivery System Design
### GPS Data Collection Stage  
When data are received from the GPS module of the IoT device, data about the current location of the IoT device are stored. If data cannot be received from the GPS module, the following steps are required. First, you should get a nearby access point (AP) with a Wi-Fi module. Then, obtain the current location information from the MAC address and API of AP [25]. If the Wi-Fi AP is not accessible, it receives the current location information from the Bluetooth module attached to the IoT device of the courier smartphone. If you don't get location information, you have to wait for a specific delay and start all over again. This algorithm is shown in the Algorithm 1.  
Figure 2: Wireless connection of Improved Parcel Delivery System Components.  
```
0: NULL
0: Geopositioning Data
1:if GPS.available() then
2:return Get.GPS_data();
3:else
4:if WiFi.available then
5:return Get.WiFi_Data();
6:else
7: Connect_Bluetooth()
8:return Get.Data_From_Bluetooth();
9:endif
10:delay(default_time)
```  
**Algorithm 1** Getting location data en route  
Figure 3: Sequence diagram of Improved Parcel Delivery System: Server Oriented.
# A Fast Simulation Model of Pantograph-Stitched-Catenary Interaction in Long-Distance Travel
## 3 Validation and Discussion
### Validation
#### 3.1.3 Validation of the Calculation Efficiency with a Long Vehicle Travel Distance  
After validating the accuracy of the reduced pantograph-stitched-catenary model, the calculation efficiency of the reduced pantograph-stitched-catenary interaction model with a long vehicle travel distance is further validated with the traditional model. In the traditional model, the pantograph-catenary interaction is developed based on the MSM and achieved in MATLAB 2022a. The pantograph-catenary interaction case used in Section 3.1 is also considered, where the train operation speed is \(V\) = 200 km/h. The long vehicle travel distance is considered, where the train operation time is chosen from 5 s, 10 s and 20 s, and their corresponding catenary lengths are 300, 600 and 1200 m, respectively. In the traditional model, the full length of the catenary needs to be modeled; however, the length of the reduced stitched catenary model is set at 180 m.  
The DOFs and CPU times of the traditional MSM model and the proposed reduced pantograph-stitched-catenary model at various train moving times are shown in Figure 7. While the number of DOFs of the traditional pantograph-stitched-catenary model increases with the train running distance, the number of DOFs of the reduced pantograph-stitched-catenary model is the same. Therefore, the calculation time of the traditional pantograph-catenary model increases from 2452 s to 21,206 s, whereas the calculation time of the reduced catenary model only increases from 1159 s to 3108 s. The relative difference between the present and traditional model calculation times also increases from 439% to 687%. This means that the calculation time of the present model is only 1/7 of that of the traditional MSM, and it can further increase with increasing train operation time. Therefore, the present reduced pantograph-stitched-catenary interaction model is highly computationally efficient in solving the dynamic responses of pantograph-stitched-catenary interactions during long-distance travel. This will greatly benefit the investigation of long-term pantograph-catenary interaction dynamics. For example, when investigating the high-speed pantograph contact strip wear problem, a much longer catenary structure can be considered, and the wear rate of the contact strip under different conditions can be more efficiently and accurately obtained based on the present model. The comparison of the present model, FEM model, and MSM model is shown in Table 3, which further describes the advantages of different models.  
Figure 6: Static shape of the contact wire with respect to different models.
## 4 Experiment Evaluations
### Rmo_SODSDA Efficiency
#### 4.2.2 Evaluation Measures  
To evaluate authorization performance of the RMO_SODSDA, we implemented the experiments and compared query time as the number of roles increased according to the three scenarios.
## 3 Cooperative Perception Information-Sharing  
Vehicle-to-everything is an important guarantee for realizing the cooperative perception of automatic driving in the IoV environment. Information interaction between autonomous vehicles and edge nodes is the basis for realizing cooperative perception. The transmission of cooperative perception messages (CPMs) between vehicles and edge nodes requires a certain bandwidth and has strict delay constraints, which sets higher requirements for network performance. Vehicle mobility and market penetration have a great negative impact on cooperative perception effectiveness and network communication quality. In addition, a reasonable CPM-sharing strategy is also very important, which defines how often vehicles share perception data and which perception data to share. Therefore, reasonable network communication technology and sharing strategies are necessary. Usually, it is necessary to select an appropriate communication network and set a reasonable sharing strategy according to different communication and traffic scenarios.
# On Ad Hoc Communication in Industrial Environments
## 3 Comparing Methods for the Extraction of NEPs
### Behavioral Differences  
The average number of simultaneous encounters \(N_{\emptyset}^{XX}\) and the average encounter duration \(D_{\emptyset}^{XX}\) are two metrics that change their behavior in regard to the applied acquisition method. Direct acquisition by means of a tracing protocol enables the more precise examination of a network. Using this method, both metrics behave as expected. The number of encounters increases linearly with the number of mobile nodes. The duration of the encounters is constant when the communication range and node speed are not changed. Both extensive simulation and the proposed model confirm this intuitively expected behavior.  
The indirect acquisition by means of network traces shows a different behavior. The number of access points is important when observing the behavior with this method. The duration of encounters is lower than expected, and further declines when the density of access points is increased. A higher density of access points is beneficial when observing the number of encounters. The best fit to the direct observation is at the point of full coverage (\(N_{AP}A_{r}=A\)). At higher access point density the number of encounters decreases. In both metrics the decrease is caused by the higher probability for overlapping of the communication ranges of the access points.  
The direct acquisition by means of a tracing protocol is therefore recommended. Even if the number of access points is known, a correction of these metrics is hardly possible due to the complex spatial distribution of nodes and access points in real networks [33].  
Using a tracing protocol for the acquisition of NEPs also has the advantage that encounters can be directional. This means that node \(A\) can send data to node \(B\), while node \(B\) cannot send data to node \(A\). This directionality in encounters can not be extracted when a network trace is the basis of the NEP. However, this is of major importance when evaluating the applicability of certain routing protocols.
# Radial Gradient Seismic Metamaterials with Ultra-Low Frequency and Ultra-Wide Band Gap
## 3 The Radial Gradient Seismic Metamaterial Structure  
### Unit Cell Model  
The radial gradient seismic metamaterial (RGSM) structure designed in this paper is periodically arranged around the building to be protected along the radial direction, forming a seismic metamaterial barrier. The unit cell lattice structure is shown in Figure 1a; \(a\) represents the length of its unit lattice, \(b\) represents the length of the side length of the filler, and the three sides of the cross section of the filler are equal and located in the center of the square structure. Its three-dimensional structure is composed of a radial section rotating around the central axis as shown in Figure 1b; Figure 1c shows the formation of its three-dimensional structure more intuitively. The triangular cross-section radial metamaterial unit cell is composed of an external square soil frame nested with an internal triangular cross-section steel ring and placed on the soil surface. The surface is the same height as the surface of the surrounding soil layer, as shown in Figure 1d,e.  
The yellow area in the figure is soil, and the gray area is steel. According to the survey data of different earthquakes, the corresponding wavelength of seismic waves varies from hundreds of meters to several meters. Therefore, the unit cell structure with a lattice constant of 4 m studied in this chapter is almost one 20 th of the wavelength of seismic waves. The geometric parameters of the unit cell are lattice constant \(a=4\) m and triangle side length \(b=3\) m. In order to effectively control the project cost, the building materials used in this structure are soil and steel. The materials used are assumed to be linear elastic, homogeneous and isotropic. The material parameters of the barrier unit model are soil density \(\rho_{s}=1800\) kg/m\({}^{3}\); [PERSON]'s modulus \(E_{s}=2\times 10^{7}\) Pa; [PERSON]'s ratio \(v_{s}=0.3\). The density of steel \(\rho_{c}=7784\) kg/m\({}^{3}\); [PERSON]'s modulus \(E_{c}=2.07\times 10^{11}\) Pa; [PERSON]'s ratio \(v_{c}=0.3\).
# Application of Soundproofing Materials for Noise Reduction in Dental CAD/CAM Milling Machines
## 4 Determining the Best Soundproofing Materials
### Determining the Best Soundproofing Materials  
The noise reduction level was measured following the application of soundproofing materials (Thinsulator and the triple soundproofing mat (butyl + aluminum + sound-insulating material)) attached to the milling machine, as shown in Figure 10, as they were selected as the most appropriate of the seven types of soundproofing material. As shown in Figure 10A, the noise was measured 50 cm away from the noise source to the left, right, and front directions. The soundproofing material was attached to the milling machine housing for evaluation, as shown in Figure 10B. All experiments were independently repeated three times. The triple soundproofing mat (butyl + aluminum + noise insulating material) is well known to be effective in noise insulation, dust proofing, and dust elimination [22]. Thinsulator, a representative sound-absorbing material, was also combined to enhance target noise reduction [23]. Figure 11 shows the results of attaching and applying the selected or combined (to enhance the noise reduction efficiency) soundproofing materials directly to the milling. The combination of Thinsulator and the triple-soundproofing mat showed the greatest noise reduction effect. When only the triple-soundproofing mat was used, the noise reduction was found to be greatest around 560 Hz of the target frequency. On the other hand, when the combination of Thinsulator and the triple-soundproofing mat was used for reducing the overall noise, the noise was reduced across all frequencies rather than only in a certain frequency range, as shown in the graph.  
As shown in Table 2, the results of applying Thinsulator and the triple-soundproofing mat indicate that the target frequency noise level was reduced by 9.0 dB on average, while the total noise level was reduced by 8.3 dB. Based on the results shown in Figure 11 and Table 2, it is considered that the use of a triple-soundproofing mat and Thinsulator (sound-absorbing material) would be the most effective for soundproofing.  
Figure 9: Types of soundproofing materials. (**A**) artboard; (**B**) rubber sound insulation; (**C**) sound-absorbing sponge; (**D**) aluminum; (**E**) sponge; (**F**) Thinsulator; (**G**) triple soundproofing mat (butyl + aluminum + sound-insulating material).  
Figure 11: Application of passive noise control. (**A**) without soundproofing material; (**B**) with the triple-soundproofing mat; (**C**) with the Thinsulator and the triple-soundproofing mat.  
Figure 10: Measuring noise reduction after the application of soundproofing material. (**A**) measurement method; (**B**) the application of soundproofing material to the milling machine housing.
# A Comparative Analysis of Wi-Fi Offloading and Cooperation in Small-Cell Network
## 4 Comparison between Cooperation and Wi-Fi Offloading in Small-Cell Network  
In this section, we present the problem formulation of cooperation and Wi-Fi offloading strategies separately and propose solutions to solve the formulated problems. Cooperation and Wi-Fi offloading are sophisticated algorithms of the two-tiered network to further enhance the data rate experienced by UEs in the network. We consider these two data rate enhancement strategies as these can be added to the existing network through firmware upgrades only, unlike other technologies such as massive MIMO that require major hardware upgrades. In cooperation, a user becomes associated with multiple SCs if the achievable data rate by a single SC is not satisfied. However, in the case of Wi-Fi offloading, the UEs satisfy their partial data rate through the Wi-Fi network. Next, we investigate the joint problem of RB and power allocation for cooperation and Wi-Fi offloading strategies.
# Non-Destructive Testing Applications for Steel Bridges
## 4 New and Developing Methods
### Vision-Based Inspection Methods  
The application of vision-based methods for structural monitoring goes back decades. Some of the early uses of this technique was employed for the static and dynamic monitoring of bridges [75, 76, 77]. Many vision-based monitoring methods have been introduced to measure structural displacement, monitor vibration response and the stress/strain, and detect cracks and other anomalies with further processing [78, 79, 80]. [PERSON] et al. (2018) performed a comprehensive review of vision-based structural health monitoring systems [81]. Their review has pointed out the growing use of vision-based methods for structural health monitoring for buildings and bridges [82, 83, 84, 85]. They also proposed a simple vision-based method based on two major abilities: non-contacting and multi-point simultaneous sensing.  
A vision-based measurement system typically includes an image acquisition device (digital camera, image grabber, and lens), image processing software, and a computer. In this technique, the image processing software plays an important role that is later integrated with the specific computational algorithm for obtaining the mechanical parameters in structural monitoring. Figure 30 indicates the methodology for this technique, in which the images consist of some predefined targets captured by the digital camera. By employing the digital image processing and pattern-matching algorithm, the targets are tracked, so that the displacement at the target positions on the structure is obtained. Accordingly, this method has great potential in detecting cracks and flaws in steel elements [86]. Vision-based methods are appropriate for measuring structural displacement, monitoring vibration response and stress/strain, and detecting cracks and other anomalies. These methods are not applicable to internal defects.  
Figure 29: Laser vibrometer targeting cables (**a**); sample force comparison (**b**) [72].
# An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks
### Vertical Clustering  
Vertical clustering segregates UAVs with similar nature or behavior into logical groups across different network cells in order to improve network scalability and cluster stability. While ultra-densification and large transmission range increase network connectivity among UAVs in a cluster, network connectivity among UAVs are affected by high heterogeneity and dynamicity. Traditionally, a cluster is comprised of _cluster head_ (CH), _cluster member_ (CM), and _cluster gateway_ (CG) as explained in Table 3. The CH, which serves as the cluster leader, manages and handles cluster-level operations (e.g., routing), and performs intra- and inter-cluster communications. The CM, which is associated with a CH, performs intra-cluster communication. The CG, which is associated with a CH, interacts with neighboring clusters in inter-cluster communication. In vertical clustering, _vertical cluster gateway_ (VCG) is introduced to enable interaction among UAVs in different clusters across different network planes, which is conveniently known as inter-plane communication. In Figure 1, a cluster \(C_{1,1}\) is formed across different network planes (i.e., macro-plane and pico-plane). In this paper, DCs use long-lifetime (e.g., transmission range) and short-lifetime (e.g., geographical location) data to perform vertical clustering, which is a local task to form inter-plane clusters, in order to enhance cluster stability and network scalability. Meanwhile, existing clustering schemes improve cluster stability, which helps in enhancing load balancing, social awareness, fairness, and QoS over a single network plane in 5G networks [2].  
Vertical routing enables UAVs to collaborate and coordinate among themselves to establish routes across different network planes, which helps to offload traffic from macrocell to SCs, including picocell and femtocell. In this paper, the CC uses long-lifetime (i.e., resid  
Figure 1: Our framework consists of DQN-based vertical routing over a clustered FANET in a 5G network. Cluster \(C_{1,1}\) is in the macro-plane, clusters \(C_{2,1}\) and \(C_{2,2}\) are in the pico-plane, and cluster \(C_{3,1}\) is in the femto-plane. Gray-shaded area represents a cluster boundary (i.e., the transmission range of a CH) across different network planes. \(CG_{1,1,1}\) and \(CG_{3,2,1}\) are not connected with CGs of other clusters. Explanation of the function of the network entities is presented in Table 3.  
ual energy) and short-lifetime (i.e., mobility) data received from DCs to perform vertical routing by selecting next-hop UAVs with lower mobility and higher residual energy in order to increase the network lifetime.
## 4 Discussion  
The proposed method is specifically designed for bright and stable land surface, such as desert and the Gobi. In addition, the introduction of MODIS imagery greatly lowers the spatial resolution of the retrieved AOD and the applicability of the new method is subsequently restricted to those regions with relatively stable atmospheres; consequently, it cannot be applied to areas with highly varied atmospheres, such as urban areas and their surroundings.  
The BRDF characterization fitting is the key step of the proposed method and the fitting accuracy of the Staylor-Suttles model is strongly recommended based on the four testing sites; however, the double verification of BRDF models is strongly suggested for better accuracy while applying the new method at other sites.  
In the near future, the method will be applied to MODIS data at global scale and the retrieved AOD will be compared with that from the deep blue method.