esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
## 1 Introduction
[PERSON] extracted a shoulder line by combining the P-N opening of the terrain and the threshold segmentation of a difference image [30]. Compared to the previous two methods, edge detection operators can quickly and effectively extract such changing features, and some operators can even extract weak mutation features to better  
Figure 1: An illustration of shoulder line of the Loess Plateau. reflect details; this provides a method for indistinct shoulder line extraction. However, the shoulder lines extracted with edge detection methods tend to be less closed, and the line segments are fragmented and do not match the actual shoulder lines. In conclusion, the above methods share the same problem in that they require a means to effectively balance continuity and accuracy. Drawing on the advantages of the good applicability of the edge detection method, finding ways to further improve the integrity of shoulder lines is an urgent problem to be solved. The regional growing algorithm, a fundamental feature in image segmentation research, can produce continuous, closed regions with relatively little domain information [31; 32]. The regional growing method has advantages for solving the continuity problem of shoulder lines based on DEM data [33]. However, the resolution of DEM data has a great influence on the extraction results, and it is difficult to obtain high-precision DEM data, especially in large areas. Hence, a shoulder line extraction method that combines the edge detection method and regional growing algorithm is proposed here to comprehensively improve the continuity and accuracy of shoulder lines. Based on high-resolution satellite images, this study uses the advantages of edge detection in highlighting details to complete the extraction of an original shoulder line. At the same time, based on DEM data, the regional growing algorithm is used to realize the determination of P-N terrain, to overcome the discontinuities of the original shoulder line, and to complete the accurate extraction of the shoulder line.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 2 Background and Related Work
### Join Operation and Map-Reduce  
The \"join\" operation on two or more datasets is one of the standard operations in relational DBs and it is part of the SQL standard. The operation combines records from input datasets by some rule (predicate), so the output may contain information in a single record from any or all datasets. It is used in some steps of our algorithm and therefore it is relevant here.  
Figure 3: MRApriori and IMRApriori Block Diagram.  
Standard Map-Reduce does not provide built-in functions to join datasets, so several algorithms were proposed [30; 33; 34]. One of the strategies for join is called repartition join. The idea is that data from all datasets are being distributed across all Mappers. Each Mapper identifies which dataset is responsible for its Split and outputs the input line together with a dataset \"tag\". The key of the map output is the predicate value (in case of equi-join, it would be the value by which the join is being done). Then the Reducer collects records and groups them by the input key. For each input key, it extracts the dataset tag that is associated with each record from all the records of the different datasets and generates all possible join combinations. It is used in our algorithm and is therefore relevant here.
## 8 Introduction to Proprietary and Standards-based Solutions
### Standards-Based Technologies  
#### 8.2.1 LoRa and LoRaWAN  
LoRa is a physical layer technology that works in unlicensed sub-GHz industrial, scientific, and medical (ISM) band and is based on the chirped spread spectrum (CSS) technique [27]. CSS is a wideband linear frequency modulation in which carrier frequency varies for the defined extent of time. LoRa works on pure ALOHA principles and supports different ISM frequencies viz. 868 MHz (Europe), 915 MHz (North America), and 433 MHz (Asia). It is basically single-hop technology, which relays the messages received from LoRa sensor nodes to the central server via gateways. The data transmission rate supported by LoRa varies from 300 bps to 50 kbps, depending on the spreading factor and channel bandwidth settings. LoRa transmissions with different spreading factors are quasi-orthogonal and allow multiple transmissions with different spreading factors (SFs) simultaneously [28]. To support LoRa on the Internet, LoRa alliance has developed LoRaWAN [29], which includes network and upper layer functionalities. LoRaWAN provides three classes of end devices to address the different requirements of a wide range of IoT applications, for example, latency requirements. LoRa is one of the best candidates for long distance and low power transmissions.
## 3 Results and Discussions
### Effect of the Water Content of Molasses on MMBs
#### 3.1.3 Ductility  
The ductility test results shown in Table 3 indicate that the ductility of the binders increased with the reduction in the water content of molasses. No correlation was observed between the molasses' water content and molasses' content of the binders because the increase in the ductility was 16.1% for MMB5, 18.8% for MMB10, 17.1% for MMB15, and 11.6% for MMB20 when the water content of molasses was reduced by about 80%. This could be due to the hydrophilic property of molasses, which affects the stability of molecules and leads to a decrease in the intermolecular attraction and surface tension of the MMBs. This phenomenon could result in the reduction of the MMBs' ability to stretch and become flexible [48].  
The ductility of the binders reduced with the content of molasses, irrespective of its moisture content. MMB5 and MMB10 containing low water content molasses were the only binders that satisfied the specified limit for binders. Most of the results of the specimens from the high water content binders exceeded the specified limits while those of the low water content specimens, except for MMB20, satisfied the limit.  
Therefore, the presence of moisture has a negative effect on the properties of MMBs when molasses is used as a partial replacement for BB in HMA. When the amount of moisture is reduced, the MMBs have shown some improvements in their properties. To improve the rheological properties of the MMBs, it is necessary to reduce the moisture content of molasses. Since some of the properties of the MMBs did not meet the required specifications at low moisture contents. Additional testing such as Fourier transformation infrared (FTIR) and SuperPave rheological tests was conducted to draw definite conclusions.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 2 Basic Principle and Functionality
### LiDAR Equation  
The signal received by a LiDAR system carries information about various properties of the target, including distance, reflectivity, and motion. A theoretical framework for understanding and quantifying these measurements is provided by the LiDAR equation. It relates the received return signal to the characteristics of the transmitted pulse, the propagation medium, and the properties of the target. The general form of the LiDAR equation is [7; 65; 66]:  
\[P(R)=KG(R)\beta(R)T(R) \tag{1}\]  
where  
* \(P(R)\) is the power received from a distance \(R\).
* \(K\) is a constant factor.
* \(G(R)\) describes the geometric spreading (like \(\frac{1}{R^{2}}\) fall-off due to spreading loss).
* \(\beta(R)\) is the backscatter coefficient at a distance \(R\).  
\(T(R)\) propagation medium transmission factor.  
The parameters \(K\) and \(G(R)\) are determined by the LiDAR system configuration, including transmitter power and overall system efficiency. The backscatter coefficient \(\beta(R)\) characterizes the target's ability to scatter light back toward the receiver, while \(T(R)\) accounts for transmission losses due to absorption and scattering along the beam path. These terms can be further specified based on particular LiDAR systems and applications [24; 66].
# Quality Assessment of Dry Soil Mixing Columns in Soft Soil Areas of Eastern China
## 4 Results of Quality Assessments
### Results of SCQD  
Figure 5 shows the SCQD of DSM columns varied with the depth in different soft soil areas. Unfortunately, SCQD data of lacustrine soft soil were lacking. It can be seen that the SCQD was distributed between 30 and 90, and it was scattered irregularly. Meanwhile, as depth increased, the mean value of the SCQD first increased and then decreased. The maximum mean value occurred at a depth of about 5 m, and the maximum mean value in the littoral soft soil area was 75, which was greater than those in other areas. The mean value fluctuated in a small range when the depth was greater than 11 m. For all soft soil areas, most SCQD averages of the DSM columns were less than 60. The standard deviations of SCQD in the alluvial soft soil (Ancient Yellow River) area and alluvial soft soil (Yangtze River) area changed little as depth increased, but the standard deviation of SCQD in the littoral soft soil area and lagoon soft soil area first increased and then decreased. The SCQD in the littoral soft soil area and alluvial soft soil (Yangtze River) area were more spread out than in other areas.  
The test results show that the blow counts of SPT, UCS, and SCQD of the DSM column decreased as depth increased. During DSM installation, the deeper the mixing, the greater the injection pressure required for injecting the binder, and the more difficult it is to stir the binder and soil uniformly, and the mixed slurry will rise to the ground's surface. As a result, as depth increased, the quality of DSM columns decreased.  
Figure 5: Relationship between SCQD and depth: (**a**) littoral soft soil; (**b**) lagoon soft soil; (**c**) alluvial soft soil (Ancient Yellow River); (**d**) alluvial soft soil (Yangtze River).
# A Traffic-Based Method to Predict and Map Urban Air Quality
## 3 Results and Discussion
### PM\({}_{2.5}\) Mapping
Data extracted from Google Maps Traffic application, or other traffic monitoring application program interfaces (APIs), enables us to build a database on the urban traffic, which is used to predict the real-time urban air pollution.
## 4 Variational Bayesian Approximation-Based Adaptive Dual Extended Kalman Filter
First set: \(\hat{X}_{k}^{(0)}=\hat{X}_{k}^{-}\), \(P_{x,k}^{(0)}=P_{x,k}^{-}\), \(\hat{\theta}_{k}^{(0)}=\hat{\theta}_{k}^{-}\), \(P_{\theta,k}^{(0)}=P_{\theta,k}^{-}\) \(\hat{\alpha}_{x,k}=1/2+\hat{\alpha}_{x,k}^{-}\), \(\hat{\beta}_{x,k}^{(0)}=\hat{\beta}_{-k}^{-}\hat{\alpha}_{\theta,k}=1/2+\hat{ \alpha}_{\theta,k}^{-}\), \(\hat{\beta}_{\theta,k}^{(0)}=\hat{\beta}_{\theta,k}^{-}\) For \(n=1:N\), iterate the following \(N\) (\(N\) denotes iterated times) steps: \(\bullet\) Measurement variances: \(\hat{\Sigma}_{x,k}^{(n)}=\hat{\beta}_{x,k}^{(n)}/\hat{\alpha}_{x,k}^{(n)}\), \(\hat{\Sigma}_{\theta,k}^{(n)}=\hat{\beta}_{\theta,k}^{(n)}/\hat{\alpha}_{ \theta,k}^{(n)}\) \(\bullet\) State estimate and its covariance: \(\hat{X}_{k}^{(n+1)}=\hat{X}_{k}^{-}+P_{x,k}^{-}H_{x,k}^{T}(H_{x,k}P_{x,k}^{-}H _{x,k}^{T}+\hat{\Sigma}_{x,k}^{(n)})^{-1}(y_{k}-h(\hat{X}_{k}^{-},I_{k,k}, \hat{\theta}_{k}^{-}))\) \(P_{x,k}^{(n+1)}=P_{x,k}^{-}-P_{x,k}^{-}H_{x,k}^{T}(H_{x,k}P_{x,k}^{-}H_{x,k}^{ T}+\hat{\Sigma}_{x,k}^{(n)})^{-1}H_{x,k}P_{x,k}^{-}\) \(\bullet\) Battery parameters estimate and its covariance: \(\hat{\theta}_{k}^{(n+1)}=\hat{\theta}_{k}^{-}+P_{\theta,k}^{-}H_{\theta,k}^{T} (H_{\theta,k}P_{\theta,k}^{-}H_{\theta,k}^{T}+\hat{\Sigma}_{\theta,k}^{(n)})^ {-1}(y_{k}-h(\hat{X}_{k}^{-},I_{k,k},\hat{\theta}_{k}^{-}))\) \(P_{\theta,k}^{(n+1)}=P_{\theta,k}^{-}-P_{\theta,k}^{-}H_{\theta,k}^{T}(H_{ \theta,k}P_{\theta,k}^{-}H_{\theta,k}^{T}+\hat{\Sigma}_{\theta,k}^{(n)})^{-1}H_ {\theta,k}P_{\theta,k}^{-}\) \(\bullet\) Parameters for the measurement noise variances estimation: \(\hat{\beta}_{x,k}^{(n+1)}=\hat{\beta}_{x,k}^{-}+\frac{1}{2}\big{(}y_{k}-h(\hat{X }_{k}^{(n+1)},I_{k,k},\hat{\theta}_{k}^{(n+1)})\big{)}^{2}+\frac{1}{2}H_{x,k}P _{x,k}^{(n+1)}H_{x,k}^{T}\) \(\hat{\beta}_{\theta,k}^{(n+1)}=\hat{\beta}_{\theta,k}^{-}+\frac{1}{2}\big{(}y_{k}-h( \hat{X}_{k}^{(n+1)},I_{k,k},\hat{\theta}_{k}^{(n+1)})\big{)}^{2}+\frac{1}{2}H_{ \theta,k}P_{\theta,k}^{(n+1)}H_{\theta,k}^{T}\) End for And set \(\hat{\beta}_{x,k}\)=\(\hat{\beta}_{x,k}^{(N)}\), \(\hat{\beta}_{\theta,k}\)=\(\hat{\beta}_{\theta,k}^{(N)}\), \(\hat{X}_{k}=\hat{X}_{k}^{(N)}\), \(P_{x,k}=P_{x,k}^{(N)}\), \(\hat{\theta}_{k}=\hat{\theta}_{k}^{(N)}\), \(P_{\theta,k}=P_{\theta,k}^{(N)}\)
```  
**Algorithm 1** VB-ADEKF. By alternatively using two VB-based extended Kalman filters for online estimation of the battery SOC and model parameters, while compensating for the uncertainties in the model parameters by simultaneous estimation of the measurement noise variances, the adaptability of the proposed algorithm to dynamic changes in battery characteristics is greatly improved. Hence, it is very promising to further increase the SOC estimation accuracy and robustness.
# Transcriptome-Wide Analysis of Stationary Phase Small ncRNAs in _E. coli_
## 4 Materials and Methods
### Cell Growth and Sample Collection  
For sequencing and subsequent validation, 10 uL of bacterial DMSO (dimethyl sulfoxide) stock [60] was used to inoculate 2 mL of MOPS that was then incubated for 24 h at 37 \({}^{\circ}\)C. Two hundred microliters or 1 mL of this pre-culture were used to inoculate 200 mL media (LB and MOPS-Glc, respectively). The cultures were incubated at 37 \({}^{\circ}\)C, and cells were collected at desired time points (exponential phase: OD600 0.4, stationary phase: 20, 28, 40, and 48 h for LB, 2 or 5 days for MOPS) by pelting for 5 min at 20 \({}^{\circ}\)C (for volumes \(\leq\) 200 mL) or for 10 min (for volumes > 200 mL) at 11,000\(\times\)\(g\) at 4 \({}^{\circ}\)C. Cells were washed with 1-2 mL 1\(\times\)PBS (Phosphate-buffered saline, 137 mM NaCl, 2.7 mM KCl, 10 mM Na\({}_{2}\)HPO\({}_{4}\), 1.8 mM KH\({}_{2}\)PO\({}_{4}\)) and pelleted again by centrifugation for 5 min at 8000\(\times\)\(g\) at 4 \({}^{\circ}\)C (Eppendorf 5804R Centrifuge, Hamburg, Germany). The pelleted cells were frozen in liquid nitrogen and stored at \(-\)80 \({}^{\circ}\)C for later RNA extraction.  
For overexpression experiments, MG1655 bacteria carrying plasmids pBBE6k-35 mer, pBBE6k-empty, or nothing, were grown overnight at 37 \({}^{\circ}\)C. Fifty-millimeter cultures were started from overnight cultures and grown to an OD\({}_{600}\) = 0.4. Induction with IPTG was then done. Samples for RNA extraction were collected before induction and 2 h after induction.
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 4 Design Implementation
### Congestion Detection  
To measure congestion in wireless sensor networks, we present a combination of two different congestion indicators to accurately detect congestion at each sensor node. The first indicator is buffer occupancy \(T\), and second indicator is buffer occupancy change rate \(\delta\). The buffer occupancy is a very important indicator of congestion. It is adopted to evaluate the amount of buffer occupied by the packets at a node over its end-to-end transmission. The advantage of buffer occupancy is that congestion at each node can be directly and quickly detected. Buffer occupancy \(T\) can be expressed into three states: normal state, slow state, and urgent state as shown in Figure 5. To distinguish between different buffer states, we use two different thresholds \(T_{1}\) and \(T_{2}\), as shown in Figure 5. Buffer occupancy change rate \(\delta\) is used to detect the network congestion and can be defined as:  
\[\delta_{i}=\frac{T_{i}^{c}-T_{i}^{L}}{T_{max}-T_{i}^{c}},\;\;i\in\{I\;frame,P \;frame\} \tag{1}\]  
Figure 4: Queue schedular.  
in which \(T_{i}^{c}\) and \(T_{i}^{L}\), \(i\in\{1\;Frame,P\;Frame\}\) show the buffer occupancy in current and last round, respectively. From above equation, we can get the buffer occupancy change rate \(\delta\)  
\[\delta=\frac{\sum_{i}w_{i}\times\delta_{i}}{\sum_{i}w_{i}},\;\;i\in\{1\;frame, P\;frame\} \tag{2}\]  
All priority queues have the same buffer size \(T_{max}\). The buffer occupancy change rate \(\delta\) reflects the tendency of buffer occupancy. For a given buffer occupancy, the larger the value of \(\delta\), the higher the probability of queue overflow is. Similarly, a negative \(\delta\) shows that congestion has been alleviated and buffer occupancy is reduced. According to these two congestion indicators, the sensor nodes have a total of three states.
## 5 Evaluation
### Average Transmission Completion Time  
In this section, we count the average transmission completion time achieved by the combinations of each cache allocation mechanism and each cache strategy, including the time spent in the recovery phase. Graphics given in Figure 6 illustrate the variation of average transmission completion time against different cache size.  
As the cache size increases, more chunks are cached by the MTNs, and the loss recovery delay decreases, so the average transmission completion time achieved by various  
Figure 6: Comparison of average transmission completion time. (**a**) Comparison of average transmission completion time under LCE; (**b**) comparison of average transmission completion time under CAPC; (**c**) comparison of average transmission completion time under Prob; (**d**) comparison of average transmission completion time under ProbCache.  
combinations decreases. Under the same cache strategy, the DCAM can achieve a lower average transmission completion time than other cache allocation mechanisms, which shows that the DCAM is more reasonable. In addition, in Figure 5(b), when the cache allocation mechanism adopts DCAM and the cache strategy adopts CAPC, the lowest average transmission completion time can be achieved. It can be seen that this performance comparison result is consistent with the conclusion of the NLRD comparison.
## 3 Results and Discussion
### Characterization of BNC/\(\kappa\)-Car Composites  
#### 3.2.1 Morphology and Fibers' Architecture of BNC/\(\kappa\)-Car Composites  
Bionanocellulose sub-elementary fibrils, which are 3-6 nm in diameter, and are extruded by bacterial cells during cultivation, immediately form microfibrils and crystallize, yielding type I cellulose [36]. The network of fibers is compact and diameters of single fibers are around 40-60 nm (Figure 1a). Medium viscosity has a great impact on the bacterial cellulose structure. According to [37] BNC synthesized in viscous culture media consist of bundles, which either represent type II cellulose or contain a mixture of type I and type II cellulose.  
The structure of composites produced by the first method, was slightly different from the structure of BNC derived from the unmodified SH medium (Figure 1b-d). The spaces between cellulose fibers, visible in the Figure 1, were enlarged when the concentration of \(\kappa\)-Car in the culture medium was increased. Only in case of the BNC\({}_{0.2}\) composite, which was obtained at a relatively low \(\kappa\)-Car concentration, the empty spaces between BNC fibers were not visible. We assume that the medium viscosity as well as adhesion of \(\kappa\)-Car to cellulose ribbons significantly influenced the diameter of fibers in case of BNC \(\kappa\)-Car\({}_{0.5}\) and BNC \(\kappa\)-Car\({}_{0.8}\). The average diameters of strands were 83 nm and 119 nm, respectively (Figure 2c,d). The latter value was 2.4-fold greater compared to the unmodified BNC fibers.  
The second method resulted in formation of highly porous structures of both BNC/\(\kappa\)-Car\({}_{2.0}\) and BNC/\(\kappa\)-Car\({}_{4.0}\) composites (Figure 1e,f). Winding of cellulose ribbons around \(\kappa\)-Car agglomerates gave rise to the very loose 3D network. Furthermore, most of \(\kappa\)-Car was incorporated into the surface layers of membranes that was visible in SEM images. Therefore, despite the increased porosity, the latter composites were relatively rigid, as the compression modulus indicated (Section 3.2.5). The diameters of fibers were reduced to 48 and 35 nm for BNC/\(\kappa\)-Car\({}_{2.0}\) and BNC/\(\kappa\)-Car\({}_{4.0}\), respectively (Figure 2e,f).
# Mechanism to Estimate Effective Spectrum Availability Inside Smart Buildings
## 4 Interference Model Validation
### C2 Interference Condition Model Validation  
As for the previous case, we validated the behavior of the MWM model used by C2 to determine the possible interference \(I_{SS-PS}\) on the reception system of the primary service located on the rooftop of the building. This check was only performed in the small building.  
It should be emphasized that, for the measurement of the interference, we used the receiver system installed in the middle sector of the rooftop and the transmitter system operated from the central position of each floor of the small building.  
A comparison of the measured values (black line) against the values obtained in the simulation with the MWM model (red line) is shown in Figure 10. The dashed red lines show the maximum and minimum limits of the CI = 95% due to shadowing. The figure shows that the behavior of the values of the model was similar and that all the values of the measurements were within the limits of the CIs. Finally, since the correlation between the measured and simulated data was calculated and found to be 0.987, the C2 interference model was fully validated using the MWM model.  
Figure 9: Behavior of the simulated interference levels using the EGLI model, CI = 95%, in the apartments of the middle sector of the building compared with the measurements.
## 5 Key 6G Technologies
### New Spectrum
#### 5.1.2 Terahertz (THz) Technology for 6G Communication Systems  
In spite of the current abundance of spectral redundancy in mmWave, it falls short of meeting the escalating demand for bandwidth expected in the coming decade. We may anticipate that the next-generation radio access network (RAN) will function at higher frequencies, including THz or optical frequency bands. It is believed that these technologies will outperform existing wireless options and have very high bandwidth.  
A significant evolution from 5G involves the introduction of THz technology, addressing the surge in mobile data traffic and low latency services, and the demand for high data rates. Operating in the 0.1-3 THz range, THz exhibits characteristics like high frequency, large bandwidth, and a narrow beam, making it a promising solution for applications such as HD holographic video conferences, vehicle-to-vehicle (V2V) communications, and nanoscale devices [51; 52; 53; 21]. Despite challenges, including hardware development and propagation losses, recent advancements, such as the establishment of the THz Interest Group by IEEE 802.15 and breakthroughs in photonics-aided transparent fiber-THz-fiber transmission systems, signal steady progress toward harnessing THz's transformative potential [54; 55; 21; 56; 51].  
Moreover, as a part of new spectrum exploration, THz is anticipated to revolutionize communication, sensing, imaging, and positioning, presenting challenges that necessitate further research and development [55; 51].  
In conclusion, the pursuit of 6G involves pushing the boundaries of communication systems through innovations like THz technology, with ongoing research and breakthroughs paving the way for transformative changes in human life and social development.