esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions  
### Properties of Materials  
The XRF results for the chemical compositions of the CWP and GGBFS are given in Table 6. Compared with the OPC showing SiO\({}_{2}\) (21-43%) and CaO (51-63%) contents [38; 39], the contents of the CWP were measured to be approximately 45% and 19%, respectively, and the relatively higher SiO\({}_{2}\) and lower CaO contents indicated that a significant proportion of powder was likely to originate from fine aggregate rather than cementitious waste during the crushing, grinding, and separation processes for the pretreatment of CW. GGBFS exhibited a larger content of CaO (approximately 37%) and seemed relevant for use as an admixture (adjusting agent).  
The effect of burning temperature on the mineralogical composition of CWP was investigated using XRD, and the results are shown in Figure 1. Usually, the peaks of CH are known to be strong in hardened cement paste as a result of hydration, but those of \(\beta\)-C\({}_{2}\)S (\(\beta\)-belite) are not likely to be detected because CH is consumed by the process of hydration [6; 21]. However, the peaks corresponding to the CH were not observed at the burning temperatures of control (room temperature), 500 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C, which is attributed to its transformation to calcite due to the mediation of CO\({}_{2}\). At burning temperatures over 700 \({}^{\circ}\)C, the calcite peaks were weakened, and those of \(\beta\)-C\({}_{2}\)S appeared due to the decarboxylation of calcite [21; 40]. Consequently, the results suggest that CWP should be burned at temperatures over 700 \({}^{\circ}\)C for use in RC. Furthermore, 700 \({}^{\circ}\)C was determined to be the optimum burning temperature because the intensity of the \(\beta\)-belite peaks did not increase above 700 \({}^{\circ}\)C. Therefore, the burning temperature was set to be 700 \({}^{\circ}\)C for the other experiments. Constituents, such as C\({}_{3}\)S and \(\beta\)-C\({}_{2}\)S, are required for the hydration reaction of cement. C\({}_{3}\)S and \(\beta\)-C\({}_{2}\)S are known to form when Ca and Si contents are high, respectively [41]. \(\beta\)-C\({}_{2}\)S was the focus of this study because the Si content appeared to be comparatively higher owing to the mingling of the fine aggregate constituent, as mentioned above. The effect of burning temperature on the compressive strength of the SRC was investigated, and the results are shown in Figure 2. After 28 days of curing, the results of burning temperatures up to 600 \({}^{\circ}\)C did not meet the disposal standard of compressive strength, but those above 700 \({}^{\circ}\)C satisfied the criterion. These results indicate that the compressive strength of the RC improved because of the CSH produced from the hydration reaction of \(\beta\)-C\({}_{2}\)S.
## 2 Materials and Methods
### Tree Size Classes and Productivity Index  
In order to analyse carbon fluxes for different tree groups, trees were divided into three size classes according to their stem diameter (dbh) (as a surrogate for tree age): small trees with a dbh of 0-30 cm (DC\({}_{\text{s}}\)), medium size trees including trees with a dbh of 30-60 cm (DC\({}_{\text{M}}\)) and large trees (DC\({}_{\text{L}}\)) above 60 cm (see Figure 1).  
In addition, we are using an index to compare productivity (\(A_{GPP}\)) and respiration (\(A_{R}\)) across species and tree size classes. Therefore the sum of \(GPP\) or autotrophic respiration (\(R_{a}\)) over all trees within a species group or within a tree size class is divided by the total basal area (BA) of the corresponding group.  
\[A_{GPP}=\frac{\sum_{i=1}^{N}GPP_{tree}}{\sum_{i=1}^{N}BA_{tree}}\]  
\[A_{R}=\frac{\sum_{i=1}^{N}Ra_{tree}}{\sum_{i=1}^{N}BA_{tree}}\]  
where \(GPP_{tree}\) (Mg C ha\({}^{-1}\) a\({}^{-1}\)) is the productivity per tree, \(BA_{tree}\) (m\({}^{2}\) ha\({}^{-1}\)) is the basal area per tree and \(Ra_{tree}\) (Mg C ha\({}^{-1}\) a\({}^{-1}\)) is the \(R_{a}\) per tree. The calculated index can be seen as the relative carbon use or respiration per basal area (Mg C m\({}^{-2}\) a\({}^{-1}\)). This normalization enables a better comparison of the productivity and respiration across tree size classes and species. By dividing the productivity and the respiration by basal area (as a proxy for leaf area and tree biomass), the index gives information on the carbon allocation efficiency in a mixed deciduous forest across several species and tree-size classes. The higher the index, the more productive a species or a tree size class is per basal area.
## 2 Mobility Models in FANETs
### Time-Dependent Mobility Model
#### 2.3.1 Boundless Simulation Area  
The Boundless Simulation Area's (BSA) mobility concept is built on a relation among previous speed and direction and the current ones [56]. Every period, speed and direction qualities are updated every cycle, resulting in a smooth change of motion. Whenever a mobile node gets to one side of the simulation area, it keeps moving until it reaches  
Figure 11: The trajectory of FANETs using PRS models.  
Figure 10: The trajectory of FANETs using NC models.  
the opposing side, which is performed uniquely compared to other models. Figure 12 depicts UAV movement using BSA, and the point density changes smoothly, as can be seen. Additionally, BSA lets mobile nodes move throughout the simulation area without inhibiting and deleting all simulation evaluation edge effects. In FANETs, this model is not extensively used [48].
# An Efficient Two-Level-Partitioning-Based Double Array and Its Parallelization
## 3 TLP-Based Partitioned DAs
### TLP-Based Partitioned DA  
Based on TLP, we can easily implement TLP-based partitioned DA (TPDA) both in serial and in parallel. For ease of description, we only discuss constructing TPDA in parallel. To do so, this paper employs a popular parallel library, OpenMP, to construct the final TPDA in parallel. Using OpenMP, \(n\) threads are started, each thread is responsible for the DAs corresponding to a _UP_. Based on the levels the DAs created on, two kinds of TPDAs are designed as Figure 7 shows.  
Figure 5: Initialize min-heap.  
Figure 6: Final min-heap and partition query table (PQT).  
1. DAs are created on _UP_(UP-DA), one DA for each _UP_, \(n\) DAs in total;
2. DAs are created on _LP_(LP-DA), one DA for each _LP_, \(m\) DAs in total. A _UP_ corresponds to multiple independent DAs.  
Both UP-DA and LP-DA consist of PQT and DA. PQT is responsible for determining the UPN for a string, while DA is for indexing and retrieving strings. Despite the different number of DAs created for the two structures, for each thread, the loads are still relatively balanced because threads are _UP_ oriented. Generally speaking, LP-DA is expected to have a much better performance for it creates multiple smaller DAs, thus reducing the collisions and the collision-handling costs.  
Taking UP-DA as an example, the algorithm for constructing UP-DA is shown in Algorithm 3.  
```
//Input: \(D\), a string dataset \(n\), number of partitions //Output: PQT, partition query table 1. \(\Lleftarrow\) deviole D into partitions by initial characters 2. PQT \(\leftarrow\) MHG-Merge(L, n)
```  
**Algorithm 3** CreateUPDA
## 1 Introduction
### Contribution  
In ISDB-T, the scattered pilot (SP) and continuous pilot (CP) are specified to help the receiver accomplish frequency and channel estimation. Meanwhile, the auxiliary channel (AC) and the transmission and multiplexing configuration controls (TMCC) play the role of an additional channel that can function as a control signal needed for demodulation, which can be used for synchronization purposes. Since the number of explicit CPs differs depending on the modulation mode adopted in the ISDB-T segment, which is available after TMCC information and perfectly decoded, a direct application of the conventional IFO estimation algorithm to the ISDB-T system can result in some performance loss. To perform a reliable estimation independent of the TMCC decoding process, the conventional IFO estimation schemes have to be modified to take the information-conveying TMCC signal into account, which is common, regardless of the segment type.  
To account for this problem, an efficient IFO estimation method using the information-bearing TMCC signal is proposed in the ISDB-T system. Since the TMCC signal is transmitted in the same format, independent of the modulation mode, the redundancy present in the multiple transmitted TMCC information enables the estimation of the IFO in a blind manner. To assess the feasibility of the proposed detection method, the probability of erroneous estimation is numerically derived, including the effect of the FFO. It is shown from simulation results that the proposed IFO estimator achieves robust estimation with low processing complexity in the presence of the FFO, where the conventional approaches show unsatisfactory performance.  
The remainder of this paper is structured as follows. A signal model used in this paper is described in Section 2. In Section 3, the existing IFO detection methods are briefly addressed when applied to the ISDB-T system. Section 4 presents an efficient IFO estimation method utilizing the TMCC information as a pilot symbol, and its detection accuracy is numerically discussed in terms of the probability of erroneous estimations. In Section 5, simulation results are provided to verify the reliable operation of the proposed IFO estimator, followed by concluding remarks in Section 6.
## 4 Experimental Results and Security Analysis
### The Linear Complexity of the Proposed Chaotic Stream Cipher Based on Improved CML  
In the stream cipher, linear complexity is one important parameter of the pseudo-random number sequence. For a given output sequence, the linear complexity is the minimum order of LFSR that can produce the same sequence. Higher linear complexity can make the output sequence stronger.  
Figure 16 presents the linear complexity of the proposed improved CML, one-way CML and the compound CML-based cipher. In these figures, \(N\) is the notation of the length of input sequences. In our experiment, the maximum \(N\) is \(10^{5}\). These experimental results show that the linear complexity is close to \(N/2\).
## 5 Case Study
### Implementation of Application Pipeline  
To highlight the flexibility of the REIP SDK, we have created an application pipeline that constitutes a multimodal _smart traffic event detector_ for urban bicycle accident data collection. Figure 5 shows the block diagram of this pipeline, where raw video and multi-channel audio uploads are preserved only when a salient event is detected, which in this case is the near miss of a cyclist with a motorised vehicle. To ensure that the maximum amount of useful data is retrieved, both video and audio cues are used to signify a salient event. For video, object detection block using the MobileNet V2 [50] model is fed with video frames at 4 fps from the GStreamer [29]-based camera block, which is also recording the full speed (\(\approx\)15 fps) video feed into files. An event detector block independently monitors the output of the object detections and deletes the corresponding video fragments if a bicycle and motorised vehicle were not captured in the same frame for any frame in the video fragment. In cases where the camera and object detector block struggle to capture an event, such as: under low-light conditions, when an event is off-camera, or when it is occluded, the SPL (Sound Pressure Level) computation block will report a spike in decibel level, caused, for example by a horn blast, impact, or tire screech from emergency braking. This information is also used by the event detection block to decide on which data fragments contain events of interest and should be preserved.
### Scope, Contribution and Hypothesis of the Study  
Although the greatest strength of machine-learning models is to yield system descriptions based on data only, this is another major drawback. Machine-learning models are most often treated as black-box systems. Thus, the rationale behind the obtained predictions remains unclear, and the explainability and interpretability are rather limited [44; 45]. This challenge is also apparent for the models obtained in our preceding work [36]. In that study, we produced machine-learning-based models that predicted the acoustic model parameters of the JCAL model for a generic porous material based on the microscale geometry parameters. Although the obtained models appeared to yield acceptable predictions for the acoustic material parameters from the microscale of the porous materials, an interpretation of which microscale parameters affects which acoustic model parameter, and how and why such interaction is observed, could hardly be drawn. In order to further investigate the credibility of the machine-learning models obtained in the preceding study, and to derive insights into the underlying physics that are coded in the machine-learning models, here, the machine-learning models are subjected to a thorough sensitivity analysis. This approach is common in the field of explainable artificial intelligence (YAI) [46] and, although testing the sensitivity of the prediction rather than the prediction itself is a limitation, the approach is accepted to support the understanding of the inferences of black-box machine-learning models [45; 47; 48].  
As mentioned in the previous section, machine-learning is already quite common in acoustics in general, and specifically in modeling porous materials. However, to the best of the authors' knowledge, no attempts to increase the explainability of machine-leaning models applied to porous materials are available in the literature. Thus, the scope of this contribution is the explainability of machine-learning models for porous materials. Building on the preceding study by the authors [36], the goal of this study is to derive insights into the underlying physics of generic porous materials coded in the machine-learning models. Therefore, the sensitivity of the outputs of the machine-learning models, i.e., the acoustic model parameters, with respect to the inputs, i.e., the microscale geometry of the porous material, is investigated. The hypothesis of this contribution is as follows: the relationships of the acoustic model parameters and the material's microscale can be modeled using machine-learning approaches, and can be investigated by applying statistics-based sensitivity analysis to the resulting machine-learning model.  
The presented study should be viewed as a natural follow-up of our previous paper on designing generic porous materials using machine-learning approaches [36]. Moreover, the presented paper is an extension of a prior conference contribution (German only) [49].
###### Abstract  
All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to \"Make cities inclusive, safe, resilient and sustainable\". In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator _Detrended Rate Matrix_ was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992-2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities.  
SDG; surface urban heat island; Geo Big Data; Google Earth Engine; global monitoring service 201810.1488; doi:10.3390/rs10091488
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 4 Multi-QoS Routing Model  
### Software-Defined Satellite Network  
As an important part of the next generation of 6G mobile communication, LEO satellites communication has been playing an increasingly important The on-board processing and storage capacity of LEO satellites are very limited and they can't meet the need of storing and processing satellite connection relationships.  
In this section, we modeled the routing in the air-space-ground integrated network. The SDSN architecture is as follows:  
As shown in Figure 1, the data plane is composed of LEO satellites and portable satellite terminals (PSTs). The control plane is composed of ground stations, controllers and location servers. The controller generates and sends the routing instruction to the LEO satellites via a SNOF (satellite network OpenFlow) channel. The LEO satellites communicate with each other through ISLs.
# Impacts of Thermal Maturity on the Carbon Isotopes of
## 2 Samples and Experiments
### Experiments
#### 2.2.3. Soxhlet Extraction and Hopane Separation  
Approximately 10 g of residual sample was pulverized into powder smaller than 120 mesh and extracted in a Soxhlet extractor using a mixture of dichloromethane and methanol (V:V = 93:7) for 72 h. The extracts were then used to isolate hopane compounds. The specific experimental methods for hopane separation are described in the study [30]. The separation procedure is as follows: (1) 50 mg soluble extracts were combined with 1 mL dichloromethane and 20 mL petroleum ether in the container. The mixture was left undisturbed for 12 h to precipitate out the bitumen. (2) After filtering out the asphaltene, 20 mL filtrate was obtained and concentrated to 1 mL via nitrogen purging. (3) The chromatographic column (the column length is 180 mm) was prepared. The 1 mL filtrate was transferred into the dry chromatographic column filled with 5 g of 100-200 um neutral alumina to allow for full adsorption. (4) The straight chain alkanes were eluted with a 1.5 mL mixed reagent (V hexane:V petroleum ether = 8:2), followed by hopane collection using 2 mL of the same solvent (1 mL/min flow rate). (5) The chromatography steps (3) and (4) were repeated to enhance hopanes' purity. (6) Enriched hopanes were concentrated to 100 \(\mu\)L under a mild N\({}_{2}\) flow for subsequent analysis using GC-MS and GC-IRMS.
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 3 Algorithm to Detect Vacant Parking Spaces  
### Algorithm Flowchart and Framework  
Figure 3 illustrates the framework of the algorithm to detect vacant parking spaces. The two major stages of the algorithm are parking space detection and space occupancy classification. In the parking space detection stage, corner features of parking space markings are extracted first. The extracted features are filtered by the circular intensity proportion and the maximum vertical intensity profile in the \(x\)-axis. After filtration, a random sampling algorithm is adopted to identify the lane boundary line. Next, the lane boundary line is used as a template to scan and record the intensity proportion of the divider lines; this allows the system to determine the type of parking space. The widths of the parking space and parking space markings are then obtained. These widths, in conjunction with scanning and sampling of the lane boundary lines, are used to identify the divider lines, signaling the completion of parking space detection. In the space occupancy classification stage, region growing and edge detection are applied to extract road and obstacle features within the parking space. Finally, the features are output to an NB classifier to classify whether the parking space is occupied.
targets over short distances using the Channel State Information-Reference Signal (CSI-RS) in 5G. Additionally, a rotating target experimental model employing a stepper motor is constructed to accurately simulate target movement scenarios. To conduct experimental measurements, the CSI-RS in the 5G signal is applied in different experimental scenarios. Then, by comparing the measurement results with the set parameters of the experimental system, the correctness of the method of measuring rotating targets using 5G signals is verified. In a word, this paper provides an approach for small target detection based on 5G passive radar.
# A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors
## 4 Discussion
Existing chips have low power consumption, and although this cannot currently be ascertained for the proposed chip, it is expected to be even lower.
## 2 Materials and Methods
### Radiological Analysis  
For the radiological study, a high-quality micro-CT machine was used (Bruker preclinical Albira CT, Billerica, MA, USA). Micro-computed tomography (\(\mu\)-CT) is used to identify small objects with high-quality spatial resolution. This type of tomography is the gold standard for measuring bone microstructures and bone morphometry [18]. The 360\({}^{\circ}\) images were taken at maximum resolution with a 45 kV radiographic projection and an acquisition time of 30 min for each image. 2D and 3D images with 8.3 voxels/mm were generated with Imaris v.9.5 software (Bitplane, Belfast, UK). The variables analyzed were bone volume (mm\({}^{3}\)), mean density of bone volume (Hounsfield Units, HU), and accumulated density (Hounsfield Units, HU). The Bonej software, which is an ImageJ plug-in ([PERSON], National Institutes of Health, Bethesda, MD, USA), was used for image processing. A segmentation of the area of interest was made on every image, a threshold was applied to eliminate the \"titanium\" density and, later, the measurements of the three variables were made based on 1 mm of surrounding bone around a 2 mm thick space of the tibial cortical bone in transversal cuts (Figure 4).  
For the radiological analysis, the scheme represented in Figure 4B was followed, in which four regions were defined, two adjacent to the implant (1 mm each) and two non-adjacent to the implant (also 1 mm). For each of the variables, the results were calculated as the difference between the values obtained in non-adjacent regions and the adjacent regions. Then, the mean and standard deviation of each of the variables were calculated. This was made to avoid biases and compensate the strong artifact in the implant area.  
Figure 3: Clinical images of implant placement in animal test rabbits.