esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Characterisation and Shrinkage of Thermoactivated Recycled Cement Concrete
## 1 Introduction
[21] investigated the combined effect of the incorporation of recycled aggregates and RC in concrete, having incorporated 5-15% RC of varying fineness (75-300 \(\upmu\)m) treated at different temperatures (400-900 \({}^{\circ}\)C). The authors observed that the incorporation percentage of RC did not significantly influence the compressive and flexural strength, due to the low replacement percentages used in the study. [PERSON] et al. [22] conducted a study on the incorporation of RC treated at 650 \({}^{\circ}\)C in ultra-high performance concrete with a binder composed of lime powder (45.5%), silica future (18.2%) and varying amounts of OPC and RC, for a w/b of about 0.2 and 3% of superplasticizer by weight of binder. The authors found that up to 25% OPC replacement with RC, the flowability decreased less significantly than for higher incorporation percentages, having attributed the workability reduction to the high surface area of the RC and CaO content. The compressive strength generally tended to decrease with the incorporation percentage of RC. The higher internal void volume owed to the reduction of the workability was pointed as the main reason for these findings. However, at 7 days, the authors observed a slight increase of 12.5% RC compared to the reference concrete, having justified the result with the more effective repolymerisation of RC hydration products at this age. The authors concluded that the optimal incorporation percentage of RC was 25%, promoting the hydration through the nucleation effect of the fine particles of RC, while not significantly reducing the workability. The influence of RC incorporation on the volume stability of cement-based materials has barely been studied. [PERSON] et al. [22] investigated the autogenous shrinkage of the above-mentioned high performance concrete, having found that the spontaneous shrinkage rate generally increased with the incorporation percentage of RC. This was attributed to the reduction of internal humidity of concrete due to the high surface area of RC, leading to self-drying and spontaneous contraction of concrete. In sum, there is still insufficient knowledge on the influence of the incorporation of RC in the fresh and hardened behaviour of concrete, which needs to be addressed. Bearing this in mind, this paper aims at characterising the mechanical and shrinkage behaviour of concrete produced with various replacement percentages of OPC with RC, for distinct w/b. To this end, an extensive experimental campaign was carried out, involving the fresh (slump, density) and physical and mechanical (density, compressive strength, tensile splitting strength, ultrasonic pulse velocity, modulus of elasticity) characterisation, as well as the long-term drying shrinkage of concrete produced with partial or total incorporation of recycled cement obtained from waste cement paste or waste concrete.
# Assessing the Techno-Economic Feasibility of Bamboo Residue-Derived Hard Carbon
## 3 Results
### Capital Cost  
Figure 3 presents the total capital investments required for hard carbon production facilities across the ten bamboo-rich provinces, assuming a 10% utilization rate of available bamboo residues. The investment scale is directly proportional to the annual residue availability, which determines the plant processing capacity. Fujian, with the highest bamboo residue (13.8 Mt/year), requires the largest capital investment of approximately CNY 2.05 billion, followed by Guangxi (CNY 1.58 billion) and Guangdong (CNY 0.75 billion). In contrast, provinces such as Anhui, Yunnan, and Sichuan--each with residue availability below 3 Mt/year--require significantly smaller investments, generally under CNY 700 million.  
Despite large variations in absolute capital requirements, the structure of the capital cost remains relatively consistent across provinces. As shown in Figure 3 (right), based on the Fujian case, \"Other direct costs\"--including piping, electrical systems, buildings, and service facilities--constitute the largest share (29%) of total capital. The equipment cost and indirect costs account for 27% and 20%, respectively, followed by equipment installation (11%) and working capital (13%). This reflects the typical cost composition for biomass pyrolysis and graphitization systems with centralized processing infrastructure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Province**} & **Area** & **Avg. Straight** & **Avg. Transport** &
\begin{tabular}{c} **Annual Bamboo** \\ **Residue (Mt)** \\ \end{tabular} \\  & **(10\({}^{3}\) km\({}^{2}\)) [33]** & **Distance (km)** & **Distance (km)** & **[18]** \\ \hline Fujian & 121.4 & 129.7 & 167.4 & 13.8 \\ Guangxi & 236.7 & 181.2 & 233.6 & 9.5 \\ Guangdong & 179.8 & 157.9 & 203.6 & 3.3 \\ Jiangxi & 167.0 & 152.2 & 196.2 & 3.2 \\ Zhejiang & 105.5 & 120.9 & 156.1 & 3.0 \\ Hunan & 211.8 & 171.4 & 221.0 & 3.0 \\ Chongqing & 82.4 & 106.9 & 138.0 & 2.8 \\ Sichuan & 485.0 & 259.3 & 334.1 & 2.7 \\ Yunnan & 394.0 & 233.7 & 301.2 & 2.5 \\ Anhui & 140.1 & 139.4 & 179.8 & 2.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Geographic and transport parameters of ten bamboo-rich provinces.  
Figure 3: Capital cost for ten provinces (**left**); breakdown capital cost for Fujian case (**right**); corresponding numerical results are presented in Table 2.  
To better understand the capital efficiency, we further analyzed the unit capital cost per ton of annual hard carbon capacity. Provinces with smaller scales (e.g., Anhui, Chongqing) tend to have higher per-ton capital costs due to limited economies of scale, whereas larger plants in Fujian and Guangxi benefit from size advantages. These results emphasize the importance of aligning plant scale with regional feedstock availability to optimize capital deployment.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Vertical Structure of Rainfall
#### 5.1.3 Vertical Profiles of Radar Reflectivity  
Studies analyzed patterns of the vertical profile of reflectivity (VPR) and the influencing factors. On the whole, they revealed that, at low altitudes, VPR had a larger negative slope for convective precipitation over land than stratiform precipitation over oceans, while, at high altitudes, the decrease of reflectivity with the height was more slowly for convective precipitation over land than stratiform precipitation over oceans [109; 119]. It was also noticed that the underlying surface and the terrain in different regions had significant impact on the VPR [105; 107; 119].
## 3 Results and Discussion
### Comparative Study on Ground Heat Exchangers
#### 3.1.2 Analysis of Heat Extraction Indexes of GHEs  
Apart from the difference in water temperature in the ground side, the heat extraction per GHE is another key difference between GHEs in SD-GHPs and DBHEs in MD-GHPs. Table 2 lists the field test heat extraction rate (\(Q_{g}\)) per GHE and heat transferred per unit length (\(q_{g}\)) with relevant water temperature. As for the SD-GHPs, the average \(Q_{g}\) per GHE is calculated to be 2.9 kW with a highest value of 4.3 kW and lowest value of 1.6 kW. Thus, the average \(q_{g}\) per GHE is 25.2 W/m with a highest value of 14.3 W/m and lowest value of 37.7 W/m. As for the MD-GHPs, the average \(Q_{g}\) per DBHE is calculated to be 280.5 kW with a highest value of 672 kW and lowest value of 122.7 kW. For the \(q_{g}\), the average value is 118.9 W/m with a highest value of 240 W/m and lowest value of 61.4 W/m. It can be seen that, the heat extraction from DBHEs in MD-GHPs is much higher than that of GHEs in SD-GHPs. Taking the DBHE with a depth of 2500 m for example, the heat extraction could reach 195.2 kW-302.8 kW, which equals to the heat extraction from 68-106 GHEs in SD-GHPs.  
Table 3 compares the space occupation of GHEs (\(A_{G}\)) in each project. Then the \(A_{G}\) is divided by the space heating area (\(A_{H}\)) of the project; the ratio (\(A_{G}\)/\(A_{H}\)) reflects the space occupation of GHEs per space heating area. It can be seen that the ratio in SD-GHPs varies from 0.0178 to 0.0674 with an  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{**Depth (m)**} & **Inlet Water** & **Outlet Water** & \multirow{2}{*}{\(Q_{g}\) per GHE (kW)} & \multirow{2}{*}{\(q_{g}\) (kW)} \\  & & **Temperature (\({}^{\circ}\)C)** & **Temperature (\({}^{\circ}\)C)** & & \\ \hline SG-1 & 100 & 7.7 & 5.5 & 1.6 & 16.3 \\ SG-2 & 120 & 12.1 & 9.1 & 4.0 & 33.7 \\ SG-3 & 120 & 13.7 & 10.2 & 4.3 & 35.5 \\ SG-4 & 120 & 8.3 & 6.7 & 1.7 & 14.3 \\ SG-5 & 120 & 9.6 & 6.2 & 2.1 & 17.8 \\ SG-6 & 110 & 7.9 & 5.8 & 2.5 & 22.5 \\ SG-7 & 110 & 11.0 & 8.2 & 4.1 & 37.7 \\ SG-8 & 100 & 12.2 & 10.0 & 2.3 & 22.6 \\ SG-9 & 120 & 13.3 & 11.8 & 3.2 & 26.9 \\ MG-1 & 2000 & 26.9 & 18.9 & 257.6 & 128.8 \\ MG-2 & 2000 & 29.8 & 19.3 & 151.9 & 76.0 \\ MG-3 & 2000 & 20.0 & 9.7 & 294.4 & 147.2 \\ MG-4 & 2000 & 23.3 & 19.3 & 122.7 & 61.4 \\ MG-5 & 2500 & 29.1 & 18.4 & 247.2 & 98.9 \\ MG-6 & 2500 & 28.9 & 20.0 & 195.2 & 78.1 \\ MG-7 & 2500 & 34.0 & 20.2 & 302.7 & 121.1 \\ MG-8 & 2800 & 41.0 & 23.0 & 672.0 & 240.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Field test results of heat extraction rate (\(Q_{g}\)) and heat transferred per unit length (\(q_{g}\)).  
Figure 6: The water temperature of the heat source during the heating season.  
average value of 0.385. While the ratio in MD-GHPs varies from 0.0005 to 0.0024 with an average value of 0.0012. Consequently, the space occupation of the DBHEs in MD-GHPs is almost 3% of the GHEs in SD-GHPs when applied for the same heating area, greatly reducing the space occupation and making MD-GHPs much more applicable.
# A Study on Data Profiling: Focusing on Attribute Value Quality Index
## Materials and Methods
### Research Model
#### 2.5.2 Data Analysis Methods for Model Development  
The data analysis methodology includes Knowledge Discovery in Database (KDD) and CRISP-DM (CRSS Industrial Standard Process for Data Mining) [25]. In this study, the existing methodology was customized. The big data analysis methodology of Korea Data Agency (KDA) is a hierarchical process model with three phases: Phase, Task, and step. The top level consists of analysis planning, data preparation, data analysis, system implementation, evaluation, and deployment [26]. Analysis planning is a step in understanding the business, identifying the problems of the domain, and determining the scope of the project. Data preparation is the step of preparing the datasets necessary for data analysis to develop models that reflect business requirements. Data analysis is the step of analyzing the data to achieve the goals established in the analysis planning stage using the fixed and unstructured data. System implementation is the step of implementing the results of exploratory data analysis or data analysis model as a system. The evaluation and deployment steps assess whether the objectives of the analysis planning phase are met, and they end the big data analysis project. KDD is a data mining methodology for discovering knowledge in a database. It is composed of data selection, data preprocessing, transformation, data mining, and interpreting/evaluation [27]. CRISP-DM is a hierarchical data mining methodology developed by many companies including Teradata, NCR, SPSS, and Daimler AG, which started in 1996 in the European Union ESPRIT project [28]. CRISP-DM consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. Currently, many companies implement big data analysis methodology. The methodology applied to this study was customized to suit the data profiling of the big data analysis methodology, KDD, and CRISP-DM methodology of KDA. The methodology consisted of data preparation, data refining, modeling, and model evaluation, as shown in Figure 2.  
Figure 1: Data profiling model using a neural network and statistical analysis (DPNS).
# Outage Performance of SWIPT-D2D-Based Hybrid Satellite-Terrestrial Networks
## 2 System Model and SWIPT-D2D Schemes
### TS-D2D Scheme  
In the TS-D2D scheme, which is shown in Figure 2, we assume that \(T\) denotes the duration of the entire transmission process, which is divided into four orthogonal phases.  
Figure 2: Illustration of the TS-D2D-based HSTN model.  
During the first time slot, with a time duration of \((1-\tau)T/3\), \(\tau\in(0,1)\), the satellite \(S\) broadcasts its signal \(\sqrt{P_{s}}x_{s}\) to the terrestrial nodes. Therefore, the received symbols at the terrestrial nodes can be, respectively, given by  
\[y_{si,ls}=\sqrt{P_{s}}h_{si}x_{s}+n_{si}. \tag{11}\]  
During the second time slot, with a time duration of \((1-\tau)T/3\), \(R\) amplifies and forwards \(y_{sr,ls}\) to \(U_{1}\), and the received symbol will be  
\[y_{ru1,ls}=\sqrt{P_{s}}g_{ru1}\frac{y_{sr,ls}}{\sqrt{|y_{sr,ls}|^{2}}}+n_{ru1}. \tag{12}\]  
The SNR of satellite-terrestrial direct link at \(U_{1}\) is given by  
\[\Lambda_{su1,ls}=\eta_{s}|h_{su1}|^{2}. \tag{13}\]  
The SNR of terrestrial relay link at \(U_{1}\) is given by  
\[\Lambda_{su1,ls}=\frac{\Lambda_{sr,ls}\Lambda_{ru1,ls}}{\Lambda_{sr,ls}+ \Lambda_{ru1,ls}+1}, \tag{14}\]  
where \(\Lambda_{sr,ls}=\eta_{s}|h_{sr}|^{2}\), \(\Lambda_{ru1,ls}=\eta_{r}|g_{ru1}|^{2}\).  
During the third time slot, with a time duration of \(\tau T\), \(U_{1}\) harvests all the energy from the RF signal of \(R\). The harvested energy at \(U_{1}\) can be given by  
\[E_{u1,ls}=\omega\tau TP_{r}|g_{ru1}|^{2}. \tag{15}\]  
During the fourth time slot, with a time duration of \((1-\tau)T/3\), \(U_{1}\) decodes and forwards the signal \(x_{u1,ls}\) to \(U_{2}\) with the harvested energy. The transmit power at \(U_{1}\) will be given by  
\[P_{u1,ls}=\frac{3E_{u1,ls}}{(1-\tau)T}=\frac{3\omega\tau P_{r}|g_{ru1}|^{2}}{1 -\tau}. \tag{16}\]  
Thus, the symbol received at \(U_{2}\) is given by  
\[y_{u1u2,ls}=\sqrt{\frac{3\omega\tau P_{r}|g_{ru1}|^{2}}{1-\tau}}g_{u1u2}x_{u1,ls}+n_{u1u2}. \tag{17}\]  
The SNR of the satellite-terrestrial direct link at \(U_{2}\) is given by  
\[\Lambda_{su2,ls}=\eta_{s}|h_{su2}|^{2}. \tag{18}\]  
The SNR of the terrestrial link at \(U_{2}\) is given by  
\[\Lambda_{u1u2,ls}=\frac{3\omega\tau\eta_{r}|g_{ru1}|^{2}|g_{u1u2}|^{2}}{1- \tau}. \tag{19}\]
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 1 Introduction  
Accommodating the heterogeneity of users' requirements is one of the main challenges that both industry and academia are facing in order to make 5G a reality [1]. In fact, next-generation wireless communication systems must be designed to provision different services, each of which with distinct constraints in terms of latency, reliability, and information rate. In particular, 5G is expected to support three different macro-categories of services, namely enhanced Mobile BroadBand (eMBB), massive Machine-Type Communications (mMTC), and Ultra-Reliable and Low-Latency Communications (URLLC) [2; 3; 4].  
eMBB service is meant to provide very-high data-rate communications as compared with current (4G) networks. This can be generally achieved by using codewords that spread over a large number of time-frequency resources, given that latency is not an issue. mMTC supports low-rate bursty communication between a massive number of uncoordinated devices and the network. Finally, URLLC is designed to ensure low-rate ultra-reliable radio access for a few nodes, while guaranteeing very low-latency. As a result, URLLC transmissions need to be localized in time, and hence URLLC packets should be short [5].  
The coexistence among eMBB, mMTC and URLLC traffic types can be ensured by slicing the Radio Access Network (RAN) resources into non-overlapping, or orthogonal, blocks, and by assigningdistinct resources to different services. With the resulting Orthogonal Multiple Access (OMA), the target quality-of-service guarantees can be achieved by designing each service separately [6; 7]. However, when URLLC or mMTC traffic types are characterized by short and bursty transmissions at random time instants, resources allocated statically to these services are likely to be unused for most of time, and thus wasted. A more efficient use of radio resources can be accomplished by Non-Orthogonal Multiple Access (or NOMA), which allows multiple services to share the same physical resources.  
By enabling an opportunistic shared use of the radio resources, NOMA can provide significant benefits in terms of spectrum efficiency, but it also poses the challenge of designing the system so that the heterogeneous requirements of the services are satisfied despite the mutual interference. The objective of this paper is to address this issue by considering a Cloud-RAN (C-RAN) architecture characterized by analog fronthaul links, referred to as Analog Radio-over-X, which is introduced in the next section.
## 2 Proposed Algorithm
### Distortion Correction Based on Affine Mapping  
Due to the image being captured with a camera or mobile phone, the deformation of the switch machine may occur. Therefore, the position of the minimum-area enclosing circle of the screws in the image may deviate from the actual position. We use affine mapping to reduce the distortion and correct the position of the minimum-area enclosing circle.  
For the distortion correction, six key points, which are shown in Figure 6 (A, B, C, D, E, and F), are used to map the distorted image to the corrected image.  
In Figure 6, the coordinates of A, B, C, D, E, and F are (a1,a2), (b1,b2), (c1,c2), (d1,d2), (e1,e2), and (f1,f2), respectively. The distance of AC is:  
\[\text{AC}=\text{L}= \sqrt{\left(\text{a1}-\text{c1}\right)^{2}+\left(\text{a2}-\text{ c2}\right)^{2}}\]  
Figure 5: Minimum-area enclosing circle of the screws. (**a**) Separate second segmentation; (**b**) Minimum-area enclosing circle of the screws in original image.  
Figure 6: Key points for affine mapping.  
As the actual proportion of AC and CD is 3:4, the calculated distance of CD is:  
\[\mathrm{CD}=\mathrm{H}=\mathrm{L}\cdot\frac{4}{3}\]  
The calculated coordinates of B, C, and D are B\({}^{*}\), C\({}^{*}\), and D\({}^{*}\), which are (a1, a2 + H), (a1 + L, a2), and (a1 + L, a2 + H), respectively. Then, the affine mapping function is modeled using:  
\[\begin{bmatrix}x\\ y\\ z\end{bmatrix}=\begin{matrix}a_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ a_{31}&a_{32}&a_{33}\\ \end{bmatrix}\begin{bmatrix}u\\ v\\ 1\end{bmatrix} \tag{1}\]  
Therefore, Equation (1) can be converted to:  
\[x^{\prime}=\frac{x}{z}=\frac{a_{11}u+a_{12}v+a_{13}}{a_{31}u+a_{32}v+a_{33}} \tag{2}\]  
\[y^{\prime}=\frac{y}{z}=\frac{a_{21}u+a_{22}v+a_{23}}{a_{31}u+a_{32}v+a_{33}} \tag{3}\]  
where \((x^{\prime},y^{\prime})\) are the corrected pixels and \((u,v)\) are the original pixels. \(\begin{bmatrix}a_{21}&a_{22}&a_{23}\\ a_{31}&a_{32}&a_{33}\\ \end{bmatrix}\) is the perspective transformation affine matrix. Taking A, B\({}^{*}\), C\({}^{*}\), and D\({}^{*}\) and A, B, C, and D into \((x^{\prime},y^{\prime})\) and \((u,v)\), respectively, the solution of the perspective transformation affine matrix can be obtained. The distortion correction results are shown in Figure 7.  
From Figure 7(a-1,a-2), it can be seen that the switch machine in Figure 7a shows deformation in a three-dimensional direction. However, in Figure 7b, the deformation is corrected extremely. Similarly, in Figure 7(b-1), the deformation of the switch machine is mainly in the horizontal and vertical direction, and the corrected result in Figure 7(b-2) is almost same as the actual one.
# A Survey of Ledger Technology-Based Databases
## 4 Permissioned Blockchain Based Databases
### BigchainDB
#### 4.2.1 Owner-Controlled Assets  
The concept of owner-controlled assets, as with most blockchains, is present in BigchainDB. Thus, a transfer of an asset is possible only by the owner/s. Transfers are not possible even by node operators. Usually, there is only one built in asset in a blockchain technology, such as in Bitcoin or [PERSON] (2016); however, in BigchainDB, as many assets as are required can be created. An asset can store arbitrary data in JSON with only one restriction as opposed to a limit. This restriction is the transaction size, which is adjustable. The owner-controlled asset is divisible but cannot be less than a single unit. Data and metadata are where arbitrary JSON documents can be stored.  
As previously mentioned, stored asset data cannot be changed or deleted; the only possibility to add new information to transactions is to use the metadata field. This is a useful tool in the TRANSFER transaction to add new information to transactions regarding what has been changed, for example, the kilometres driven on a car, the age of the vehicle, or any type of data. There are five types of transaction operations:  
* CREATE
* TRANSFER
* VALIDATOR_ELECTION
* CHAIN_MIGRATION_ELECTION
* VOTEThe final three operations are connected to the consensus mechanism. The CREATE and TRANSFER operations are more important from the data creation and modification point of view.  
**Create:** To register any type of asset, a CREATE transaction can be used with arbitrary data and metadata. A CREATE transaction can be used to register any kind of asset (divisible or indivisible), along with arbitrary metadata. An asset can have any number of owners, including the possibility of none. Later, only owners can use their assets. The number of owners agreed on the validity of a transaction regarding a certain asset is configurable.  
**Transfer:** A user must fulfil the condition in order to transfer an asset. Thus, permission to transfer is only afforded to certain users. In this way, the owner/s of an asset can be changed or metadata can be modified.