esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Scientists, curators, historians and archaeologists are always looking for new techniques for the study of archaeological artefacts, especially if they are non-destructive. With most non-destructive investigations, it is challenging to measure beneath the surface. Among the vast board of techniques used for cultural heritage studies, it is difficult to find one able to give information about the bulk and the compositional variations, along with the depth. In addition, most other techniques have self-absorption issues (i.e., only surface sensitive) and limited sensitivity to low \(Z\) atoms. In recent years, more and more interest has been growing around large-scale facility-based techniques, thanks to the possibility of adding new and different insights to the study of material in a non-destructive way. Among them, muonic X-ray spectroscopy is a very powerful technique for material characterization. By using negative muons, scientists are able to perform elemental characterization and depth profile studies. In this work, we give an overview of the technique and review the latest applications in the field of cultural heritage.  
muonic X-ray; muonic atoms; muon spectroscopy; elemental analysis; depth profiling; archaeometry +
Footnote †: journal: Medical Imaging  
0000-0001-8999-22000/0000-0000-0000-0000-0000-0000-0000-  
information both from the surface and from the core of a sample. In recent years, a new tool for non-destructive surface and bulk analysis is represented by Muonic Atom X-ray Emission Spectroscopy (\(\mu\)-XES) [7]. The basis of \(\mu\)-XES is similar to X-ray fluorescence: it relies on the interaction between a fundamental particle--the negative muon--and a capturing material and the detection of the following X-ray emission. Given that the muon mass is about 200 times bigger than the electron, the emitted X-rays are highly energetic. This allows overcoming problems of self-absorption of common techniques, such as XRF or PIXE, and makes low Z atoms detectable. For example, the K\(\alpha\) transition energy for muonic lithium (Z = 3) is 19 keV [8], which corresponds to the energy of the K\(\alpha\) of rhodium (Z = 45) in conventional X-ray fluorescence. Furthermore, as mentioned above, the muon is bigger than the electron but smaller than the proton--this mass difference corresponds to a less effective bremsstrahlung effect (compared to electrons) and a deeper penetration depth (compared to protons), thus making bulk analysis feasible. Finally, by varying the energy of the incident muon beam, it is possible to vary the penetration depth and select the position of the analysis. For these reasons, muonic atom X-ray spectroscopy represents an invaluable tool for elemental analysis; owing to the high penetration depth and negligible self-absorption effects, it can overcome the problems of the aforementioned techniques and give new insights into the study of an archaeological artefact. In this paper, we give an overview of the technique and review the latest applications in the field of cultural heritage.
## 4 System Architecture
### Back-End Server  
The back-end database and image viewer completes our system. We used a database server at CENS (Center for Embedded Networked Sensing), which stores environmental data and images. As mentioned, the images and environmental data are collected at a Linux server running at James Reserve. The data were pulled from this machine and entered into the database at regular, 15-min intervals. The images and data are finally displayed on one of two flash viewers. One allows easy image browsing, while the other is used to monitor the status of the system in real time.
## 1 Introduction  
In seasonal frozen-soil regions, the freeze-thaw cycle (FTC) is an important factor affecting soil's physical and mechanical properties [1]. As the temperature decreases, water in soil turns into ice, volume expansion increases pores, and the freezing process leads to aggregate separation and the breaking of soil particles. Changes in soil structure during the freezing process do not completely recover as the temperatures rises [2]. Under the effect of repeated FTCs, the soil structure is repeatedly adjusted, so its physical and mechanical properties are changed, such as porosity [3, 4], severity [5], hydrologic properties [6], permeability [7, 8], and strength and deformation [9]. Thus, soil mechanical properties are key considerations in construction projects in cold areas.  
Because soil- structure changes caused by FTCs have a significant influence on soil properties, there are many previous studies on shear-strength changes of soils after FTCs [5, 9, 10, 11]. In one, the expansive soil remained stable after the first cycle, but soil volume increased, and c and \(\varphi\) decreased as the number of FTCs increased [5]. In another, the elastic modulus, failure strength, and c and \(\varphi\) of silt sand varied with the increase of FTCs [9]. One study showed that the shear strength of saline-soil samples decreased with the increase of S, and long-term FTC could break down soil particles and change soil structure [10]. After seven cycles, the volume of Qinghai-Tibet clay increased, c decreased, and \(\varphi\) was minorly affected by the FTC [11].  
Therefore, it is imperative to study soil strength and deformation after FTC. The stress-strain relationship is a comprehensive reflection of soil deformation and strength characteristics. To master the working state of soil in engineering structures, it is necessary to select the appropriate material constitutive model for analysis according to the actual situation [12]. The Duncan-Chang constitutive model has clear concepts and is easy to understand [13; 14], so it is widely used in hydraulic and geotechnical engineering because it can better reflect the nonlinear behavior of soil [15; 16; 17].  
In view of geotechnical engineering problems in western Jilin, this paper took Nong'an saline soil as the research object and analyzed the FTC effect on the soil's strength and deformation. Undergoing 0, 1, 5, 10, 30, 60, and 120 FTCs, remolded samples with salt contents (S) of 0%, 0.5%, 1%, 2%, and 3% were tested with an unconsolidated-undrained triaxial test. The stress-strain relationship and sheer strength of the saline soil affected by FTCs was analyzed. Considering FTC influence, the Duncan-Chang constitutive model was established to simulate the deformation and failure process of remolded saline soil. Empirical formulas of the model parameters were obtained by regression analysis that could provide reference for relevant geotechnical engineering design.
###### Abstract  
The challenging requirements of the fifth generation (5G) cellular networks motivate the need to explore the feasibility of delivering services over new network architectures. Information-Centric Networking (ICN) is an emerging and promising network to satisfy 5G requirements. The name resolution is at the heart of ICN. We focus on satisfying the delay-sensitive requirement of the Name Resolution Service (NRS) in a 5G-ICN integrated network. We aim to design a local NRS that provides a deterministic low latency name resolution service. In this paper, we propose a temporally hierarchical deployment architecture for an Enhanced Name Resolution System (ENRS) to realize deterministic latency. The ENRS quantifiably organizes the nodes into hierarchical and nested domains by latency constraints. We design demand-aware name registration and resolution schemes to achieve constant forwarding hops in order to realize local resolution and forwarding locality. We introduce a tolerable latency-based peer resolver forwarding algorithm to improve the query hit ratio. We present a proactive name binding replicas distribution approach based on temporal-spatial features to reduce the resolution latency and query traffic. The video streaming monitoring service in Smart Home is used as a typical use case to show the continuity of service guaranteed by ENRS. Analysis demonstrates that ENRS can achieve deterministic latency. Evaluation results show that the average query hit ratio of ENRS outperforms the K-NearestNeighbor-Distributed Name Resolution System (KNN-DNRS) and Random Name Resolution System (Random-NRS) with 23.2% and 18.1%, respectively. The query traffic overhead of ENRS is up to 33.3 times smaller than KNN-DNRS. ENRS can process up to 21 GB/s name resolution traffic when the user nodes are in the magnitude order of 10\({}^{6}\).  
5G; ICN; name resolution; deterministic latency; traffic offloading 2019 9 2891 10.3390/apr9142891 10.
## 3 Results and Discussion
### Particles Combustion in the Channel
#### 3.2.2 Two-Stage Particle Combustion Mechanism  
Combustion at an air velocity of 20 m s\({}^{-1}\) and a furnace temperature of 800 \({}^{\circ}\)C is characterized by the presence of a flame of volatile matter near the side and back faces of the particle. The time delay between placing the sample into the furnace and its ignition is significantly shorter than in the case of a similar air velocity and a furnace temperature of 600 \({}^{\circ}\)C. This delay is 3 s compared to 55 s, respectively. During the first 6 s of conversion, the particle intensely loses volatile substances and gets charred (Figure 11). In this case, the surface char layer is virtually not consumed, and the degree of overlapping of the stages is 4%.  
From the sixth second of particle combustion on, simultaneously with devolatilization and an increase in the mass of the surface char layer, the char layer also gets consumed. The relative content of fixed carbon (\(FC/FC_{pyrp}\)) in the charred sample decreases from 98% to 78% from the sixth to the 42 nd second. In this time interval, the degree of stage overlapping ranges from 12 to 24%.  
The maximum amount of char is formed in the particle at the 42 nd second of conversion and is 10% of the sample initial mass. The degree of stage overlapping by this point is 24%. It should be noted that simultaneously with the maximum mass of char, the charred sample contains a wood residue with a relative mass of 5%. By the 48 th second of the process, the sample is already completely composed of the char and the combustion stage of the char residue becomes the main one.  
The temperature of the thermocouple junction increases almost linearly from the sixth to the 42 nd second of the process. Thermocouple readings are significantly affected by the flame of volatiles surrounding the sample holder and heating the thermocouple wires.  
Figure 11: Characteristics of two-stage combustion of wood particles in the channel at T furnace of 800 \({}^{\circ}\)C and air velocity of 20 m s\({}^{-1}\): \(m\)—mass of the charred particle, \(m_{cave}\)—mass of particle wood core, \(m_{chur}\)—mass of the char layer, \(X\)—degree of overlapping of stages; \(i_{ign}\)—average time of particle ignition start, s; \(T_{c}\)—temperature of sample center, \({}^{\circ}\)C.  
Nevertheless, the course of the temperature profile reflects the change from the stage when devolatilization prevails to the stage when combustion of the char residue prevails. From the 42 nd second on, the heating rate of the thermocouple junction increases significantly.  
Combustion of samples at a furnace temperature of 800 \({}^{\circ}\)C and air velocity of 20 m s\({}^{-1}\) can be characterized as a predominantly staged process, with the degree of overlapping of the char residue combustion stage with the devolatilization stage of 6 to 40%, depending on the considered time interval of sample combustion.
# Behavior of Circular CFST Columns Subjected to Different Lateral Impact Energy
## 3 Test Results
### Failure Modes  
Based on varied bulged positions, typical failure processes of circular CFST columns under axial compression tests are summarized. Four kinds of failure modes are classified in the paper, which are shown in Table 5. The relevant developing processes of the four typical failure modes are shown in Figures 6-9.  
A. **The first bulged position emerged at the end of the specimen opposed to the impact point.** Then second bulged position occurred at the impact point. Lastly, the first bulge developed a ringed bulge and the specimen failed. The failure mode is called Mode One, and the failure process is shown in Figure 6. The feature of this failure mode is that impact location is close to the end of the specimen including nine specimens with impact location at 0.25 \(H\) and three specimens with impact location at 0.33 \(H\). For failure Mode One, residual ultimate axial capacity of these specimens is lower than that of other specimens. Loss of initial stiffness of the twelve specimens is greater than that of no damaged specimens. With the increase of axial compressive load, deformation of circular CFST columns is enlarged, leading to the failure of the column.
B. **The first bulged position occurred at the impact point.** The second bulged position emerged at the end surface of the specimen (the surface was opposed to the impact point). The failure mode is called Mode Two, and developing the process of this failure mode is shown in Figure 7. Thirteen circular CFST columns failed in Mode Two. Deformation of the specimens after the impact test was large. When axial compressive load was applied to the specimen, the deformation was enlarged at the impact point. As axial compressive load increased, local pressure increased at the end of specimen end, so the deformation of the specimen end increased. Lastly, the deformation of the specimen end and the deformation of impact point attined the max value at the same time, and then specimen failed.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \(N_{\mathbf{r}}\) & \(N^{\mathbf{P}}\) & \(f_{\mathbf{y}}\) & \(f_{\mathbf{u}}\) & \(f_{\mathbf{cu}}\) & \multirow{2}{*}{\(\rho\)} & \multirow{2}{*}{\(k_{\mathrm{c}}\)} & \(\Delta_{\mathbf{u}}\) & \(\Delta_{\mathbf{y}}\) & \multirow{2}{*}{\(\mu\)} & \multirow{2}{*}{**Failure Modes**} \\  & **(kN)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & & & & & **(mm)** & **(mm)** & **(mm)** \\ \hline C30-L0.50-E10000 & 540.10 & 544 & 264 & 352 & 32.70 & 0.93 & 1.13 & 29.99 & 5.94 & 5.05 & mode 3 \\ C30-L0.50-E12500 & 486.80 & 544
# Harvesting the Aggregate Computing Power of Commodity Computers for Supercomputing Applications
## 4 Experimental Evaluation and Results
### HPDA Cluster  
As part of this study, the evaluation was conducted on the HPDA cluster, running a WordCount program that counts the number of words specified in a given large text file and calculates _pi_. The following are benchmark applications on the HPDA cluster:  
* iterative Jobs,
* interactive Analytics,
* distributed Machine Learning,
* streaming Analytics.
* distributed Graphs Processing.  
Figure 3 depicts the processing time on a single node and multinode clusters.  
The execution time depends on the network communication (I/O), the number of search operations of words, size of the input file. The whole process of MapReduce processes and builds a cost function that explicitly models the relationship between the volume of input data, available system resources (map and reduce slots), and complexity of the reduce function for the target MapReduce job.  
The job execution order is compared with a single compute node for base-line comparison. Single Compute Node, FCFS scheduling performance is used as a benchmark for this experiment and the performance result is seen in Table 9.  
As per [PERSON]'s law, in this case, the speedup in compute time (CT) and total time (TAT) should be twice as of the single processor benchmark. But due to Ethernet latencies it is decreased by 15%. Similarly, we have increased the number of compute nodes in the experiment and computed the performance parameters in each setup. Since workload and other conditions are fixed we can use speedup as a measure of performance of our HPC cluster.  
From the Hadoop cluster setup of 10 nodes, we were able to aggregate a distributed file system of 6.45 terabytes (TB) from 1 master and 9 live workers running. From this cluster setup, we get 24.9 GB of memory which is enough to run medium size data-intensive applications or jobs. On this cluster, the authors run a WordCount application of 1.23 GB on HDFS and observed when the file is replicated to the worker nodes as this is based on the size of the input file.  
Figure 3: HPDA performance Comparison.  
Similarly, the researchers were also able to run the finding _pi_ application on the HPDA cluster and result that the submitted WordCount application used 72 cores and successfully processed in 5 s only as seen in Table 10. We have run the above-mentioned applications i.e., WordCount and _pi_ on a single machine that has 4 cores and 4 GB RAM. Then, the WordCount application successfully processed in 36 s, and _pi_ application processed in 19 s which is been shown in Table 10.  
It has been observed from Table 10, that the execution time depends on the network communication (I/O), the number of search operations of words, size of the input file. The whole process of MapReduce processing and building up a cost function that explicitly models the relationship between the amount of input data, the available system resources (Map and Reduce slots), and the complexity of the Reduce function for the target MapReduce job.
# Experimental Investigation on Effect of Temperature on FDM 3D Printing Polymers: ABS, PETG, and PLA
## 4 Conclusions  
This paper has focused on investigating the response of common FDM 3D-printed polymers (i.e., ABS, PETG, and PLA) to temperature after the parts are fabricated. Each designed sample was heated and cooled to 85, 105, and 125 \({}^{\circ}\)C and room temperature, respectively. The complete motion of each sample was recorded in x and y coordinates and led us to measure the instanton radius of curvature for all samples. It was found that PLA is the sample most sensitive to the temperature stimulus after 3D printing is performed. The curling increased as the heating temperature increased. ABS showed minimum response to  
Figure 8: Final x position and radius of curvature for ABS, PLA, and PETG materials during three cycles of cooling for three different temperatures: 85, 105, and 125 \({}^{\circ}\)C: (**A**,**C**,**E**) are x positions for three materials (ABS, PETG, PLA) for different heating 85, 105, and 125 \({}^{\circ}\)C corresponding to first, second and third cycles, respectively. (**B**,**D**,**F**) are radius of curvature for three materials (ABS, PETG, PLA) for different heating 85, 105, and 125 \({}^{\circ}\)C corresponding to first, second and third cycles, respectively.  
curling. These findings can be useful when deciding which material to choose that will go through heating cycles during application. For example, if the design requires minimum expansion or change in shape, ABS might be a better choice compared to PLA or PETG. However, if one requires having the part form a specific shape that is not possible with 3D printing but doable with 4D printing, PLA seems to be the better choice. It should also be noted that although these trends hold true, the quantitative values are dependent on the design of the 3D-printed parts presented in this paper. This paper shows 4D printing is a promising technology that can have different applications. For example, the self-assembly of 3D-printed parts when exposed to the desired stimulus can drastically reduce fabrication and assembly times. For example, soft robotics can benefit from automated hinging and locking mechanisms, packaging, and assembly processes. Four-dimensional-printed parts can be useful for space applications as well. For example, 3D-printed flat parts can be printed on earth and be transformed into their final shape in space.  
Conceptualization, R.M. and B.E.; methodology, R.M.; software, R.M.; validation, R.M. and B.E.; formal analysis, B.E.; investigation, B.E.; resources, B.E.; data curation, B.E.; writing--original draft preparation, R.M. and B.E.; writing--review and editing, B.E.; visualization, R.M.; supervision, B.E.; project administration; funding acquisition, B.E. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Pennsylvania Department of Community and Economic Development under PA Manufacturing Innovation Program with grant number 1060170-458516.  
Not applicable.  
Data Availability Statement: Data can be available upon request.  
The authors declare no conflict of interest.
# NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing
## 4 Subcarrier Power Allocation Algorithm
### The Description of Power Allocation Problem
The interference power brought by cognitive users to authorized users is shown in Equation (11):  
\[I_{i}(P_{i})=P_{i}\Big{|}h^{SP}\Big{|}^{2}T_{s}\Big{|}_{f_{i}-B_{P}/2}^{f_{i} +B_{P}/2}\bigg{(}\frac{\sin(\pi fT_{S})}{\pi fT_{S}}\bigg{)}^{2}df, \tag{11}\]  
where \(B_{P}\) is the bandwidth of the authorized user, and \(h^{SP}\) is the channel gain coefficient introduced by the cognitive user to the authorized user.
## 2 Experimental Section  
### In-Situ Inspection  
To establish a more reasonable rebar corrosion initiation condition, the carbonation process of concrete and the corrosion status of rebars in existing structures should be known. In addition, the variation in pH values and phase compositions of different cover concrete depths must be determined.  
An in-situ durability inspection was conducted on the former Chongqing Steel Mill in Chongqing, China. The mill is a reinforced concrete frame structure, and its total area is about 34,723 m\({}^{2}\). A random sampling inspection was carried out on the reinforced concrete columns in Plant A for 30 years of service (the isothermal furnace workshop), Plant B for 52 years of service (finishing workshop), and Plant C for 77 years of service (steel rolling workshop).  
The reinforced concrete columns were inspected according to the requirements of the Chinese standard for durability assessment of concrete structures (CECS 220-2007). The inspection of reinforced concrete columns included the following steps:  
1. Visual inspection: The appearance of each column was inspected, and the maximum width of cracked concrete cover was measured.
2. Samples of concrete cover: An electric hammer was used to drill the cover concrete from the corner of the rectangular columns until the rebar is exposed, as shown in Figure 1. Then the concrete cover samples were sealed instantly with a vacuum bag to isolate from the air.  
Cover depths measurement: As Figure 2 shows, a digital Vernier caliper with an accuracy of 0.01 mm was used to measure depth of the cover, while the corrosion status of the rebar was recorded with a high resolution digital camera.
4. Testing the depth of carbonation: Carbonation depth was measured by spraying a 1% solution of phenolphthalein in ethanol to the fresh concrete surface. Each specimen was measured at three points and the average value was calculated as the value of each measurement.
5. Samples of drilled concrete core: Cylinder specimens were drilled out from each column where concrete cover samples were collected.