esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd-Generation Partnership Project  
5G  Fifth Generation  
5 GC 5G Core  
B5G  Beyond 5G  
BBM  Break-Before-Make  
BS  Base station  
CHO  Conditional Handover  
CIO  Cell Individual Offset  
CSI  Channel State Information  
D2D  Device-to-Device  
DoA  Directional of Arrival  
DRL  Deep Reinforcement Learning  
eMBB  Enhanced Mobile Broadband  
EPC  Evolved Packet Core  
FD  Full Duplex  
FHO  Frequent Handover  
FLC  Fuzzy-Logic Controller  
HCP  Handover Control Parameter  
HD  High-Definition  
HetNet  Heterogeneous Network  
H-HO  Hard Handover  
HHO  Horizontal Handover  
HO  Handover  
HPPV  Handover Ping-Pong Probability  
HODP  Handover Decision Parameter  
HOET  Handover Execution Time  
HOF  Handover Failure Rate  
HOIT  Handover Interruption Time  
HOM  Handover Margin  
HOPP  Handover Ping-Pong  
HOSR  Handover Success Rate  
ICI  Inter-Cell Interference  
ICIC  Inter-Cell Interference Coordination  
IoT  Internet of Things  
ITU  International Telecommunication Union  
KF  Kalman Filter  
KNN  K-Nearest NeighborsHOPP  
Handover Ping-Pong  
HOSR  
Handover Success Rate  
ICI  
Inter-Cell Interference  
ICIC  
Inter-Cell Interference Coordination  
IoT  
Internet of Things  
ITU  
International Telecommunication Union  
KF  
Kalman Filter  
KNN  
K-Nearest Neighbors  
KPI  
Key Performance Indicator  
LB  
Load Balancing  
LBO  
Load-Balancing Optimization  
LTE  
Long-Term Evolution  
MBB  
Make-Before-Break  
MBS  
Macro Base station  
MC  
Macro Cell  
ML  
Machine Learning  
M-MIMO  
Massive Multiple-input-Multiple-output  
mMTC  
Massive Machine-Type Communication  
mm-Wave  
Millimeter Wave  
MR  
Measurement Report  
MRO  
Mobility Robustness Optimization  
NFV  
Network Function Virtualization  
NG  
Next-Generation  
NN  
Neural Network  
NR  
New Radio  
NSA  
Non-Standalone  
OP  
Outage Probability  
QoE  
Quality of Experience  
QoS  
Quality of Service  
RAT  
Radio Access Technology  
RL  
Reinforcement Learning  
RLF  
Radio Link Failure  
RLRF  
Radio Link Failure Rate  
RSRP  
Reference Signal Received Power  
RSRQ  
Reference Signal Received Quality  
RSS  
Received Signal Strength  
SRS  
Sstandalone  
SC  
Small Cell  
SDN  
Software-Defined Networking  
SE  
Spectral Efficiency  
S-gNB  
Source gNB  
S-HO  
Soft Handover  
SNR  
Signal-to-Noise Ratio  
T-EHO  
Too-Early Handover  
T-gNB  
Target gNB  
Time-to-Trigger  
TTT  
TLHO  
Too-Late Handover  
UAV  
Unmanned Aerial Vehicle  
UD-HetNet  
Ultra-Dense HetNet  
UDSC  
Ultra-Dense Small Cell  
UE  
User Equipment  
UHO  
Unnecessary Handover  
URLLC  
Ultra-Reliable Low-Latency Communication  
V2V  
Vehicle-to-Vehicle  
V2X  
hicle-to-Everything  
VHO  
Vertical HO  
WCS  
Wireless Communication Systems
## 3 Compression Algorithm Design
### Rationale of the Proposed Algorithm  
LEC algorithm compresses data in its entropy encoder with the use of a fixed table leading to a very fast execution performance. This table is an extension of the table used in the JPEG algorithm to reach the size necessary for the resolution of the ADC in use. Additionally, it is based on the fact that the closer the absolute of a value is to zero more frequently it is observed in the differential signal.  
However, it has been noticed that this frequency distribution may be valid for a file or a stream of data, however it is not always accurate considering fractions of the file or the data stream. Based on this observation ALEC algorithm uses a small amount of fixed Huffman Codes tables that can be alternatively used to produce smaller code for a packet of data. Furthermore, the specific table is not optimal for the specific data under test at each particular experiment.  
Therefore, in the proposed scheme (as depicted in Figure 1) a novel approach is introduced, where the Huffman Codes lookup tables used are continuously adjusted according to the characteristics of the specific data used. Also the degree that the tables are adjusted is also configurable, offering fine tuning capabilities and enhancing the added value of the respective novel approach.
## 4 Methodology
### Candidate Point Tracking Based on Image Semantic Information  
After the relative pose between the keyframe and the subsequent frame is obtained, the candidate points in the keyframe are tracked in the subsequent frame. Epipolar search and optimization of photometric error are adopted to find the best matching position in the subsequent frame for a candidate point in the keyframe, and inverse depth of this point is updated.  
Due to the occlusion and repetitive areas, there will be mismatches, outlier and occlusion detection is required. Two traditional rules are applied to handle with it. Firstly, points for which the minimum of is not sufficiently distinct are discarded when doing the epipolar search. Secondly, point observations for which the photometric error \(E_{pj}\) calculated by Equation (2) exceeds a threshold are removed.  
In addition to the above two traditional rules, we take the semantic information of dynamic objects in the image into account. Candidate points in the keyframe for which the best matching position is in dynamic regions of the subsequent frame are considered as outlier.
## 3 Results and Discussion
### Intestinal Toxicity of NCs  
The intestinal toxicity of developed HME-processed NCs was assessed in rats after oral administration. The jejunum was stained with H&E reagent to test the epithelium toxicity (Figure 7). It was reported that zinc can modify the tight junctions and change the barrier roles in the epithelial monolayers of the intestine [26]. Additionally, in relatively high Zn concentrations, it may show cytotoxicity to intestinal cells by apoptosis and other mechanisms [27]. In this study, the administration dose for an acute toxicity test was determined considering the maintenance of a nano-size of NC dispersion. As shown in Figure 7, there was no significant difference in the morphology of epithelium between ZnSO4 and ZnSO4/SP NCs. It is expected that acute toxicity is absent in the intestinal epithelium after single dosing. Amphiphilic SP can decrease the surface tension during the fabrication of NCs, and it may help to reduce the hydrodynamic size of NCs. However, the toxicity of SP should also be considered for the development of ZnSO4/SP NCs. According to the manufacturer's data (BASF SE, Ludwigshafen, Germany), the oral LD50 value of SP was reported to be greater than 5 g/kg [14]. Considering the LD50 value, SP can be used safely at the current administration dose. Both the dispersibility and safety of ZnSO4/SP NCs indicate successful in vivo application.  
Figure 6: Hydrodynamic size of ZnSO4/SP NCs in DW, a pH 1.2 medium, and a pH 6.8 buffer after incubating for 6 h. Each point indicates the mean \(\pm\) standard deviation (SD) (\(n\) = 3).  
Figure 7: H&E staining results of ZnSO4 and ZnSO4/SP NC-treated groups. The length of the scale bar in the image is 100 \(\upmu\)m.
## 5 Thermal Characterization
### Dynamic Self-Heating and Thermal Impedance Modeling in InP DHBTs
#### 5.1.3 Thermal Impedance (\(Z_{\rm TH}\)) Extraction and Model Validation  
Low-frequency 5-parameters have been measured on six device geometries. Figure 10 illustrates the results comparing the magnitudes and phases of the measured low-frequency \(Y\)-parameters (symbols), \(Y_{12}\) and \(Y_{22}\), and the scalable thermal model simulation (solid lines) for different device geometries. The results are shown for a bias condition of \(V_{BE}=0.85\) V and \(V_{CE}=1.2\) V, chosen specifically around peak \(f_{T}\) of the transistors, where self-heating becomes dominant. Both \(Y\)-parameters show good agreement between the measurement and the simulation results. Furthermore, good model scalability has been observed across all geometries.  
Figure 11 presents the magnitudes of the thermal impedance, \(Z_{TH}\), extracted using (6), comparing the measurements (solid symbols) and the scalable thermal impedance model simulation (solid lines), depicting good model accuracy and scalability. Figure 11a shows the magnitude of the \(Z_{TH}\) for emitter length scaling for the fixed emitter width of (a) 0.7 \(\upmu\)m and (b) 0.5 \(\upmu\)m. In both cases, the model describes the behavior of the dynamic self-heating (below \(f_{TH}\)) quite well.  
Figure 10: Comparison of the magnitudes (**a**) and phases (**b**) of the measured low-frequency \(Y\)-parameters (solid symbols), \(Y_{12}\) and \(Y_{22}\), and the scalable thermal model simulation (solid lines) for different geometries.  
The thermal resistances and capacitances for the three regions of the intrinsic device are shown in Table 1, extracted from compact model simulations, for six geometries with different emitter lengths and two chosen emitter widths of 0.5 and 0.7 \(\upmu\)m. The total thermal resistance is calculated taking into account both intrinsic devices and the contribution from the emitter metal layers, \(R_{THM}\). \(R_{TH}\) is extracted from the measurement using the intersection technique [14]. The extraction results are shown in Table 2 for all geometries, depicting a very good estimation of the thermal resistances by the developed model.
## 1 Introduction  
Aerodynamic roughness (AR) plays a significant role in characterizing the interface between the earth's surface and the atmosphere. Developing an effective remote sensing-based AR estimation method will promote research on the temporal and spatial process of evapotranspiration, hydrology, land surface processes, and sand transport [1; 2]. Most of the current research on remote sensing-based AR estimations is focused on farmland, forest, and desert regions. Researchers have used the normalized difference vegetation index (NDVI), the airborne Light Detection and Ranging (LIDAR), multi-angle observations, radar backscattering coefficients, and a morphology method to estimate the AR over these land surfaces [3; 4; 5; 6; 7; 8; 9; 10; 11; 12].  
For grassland, recent work has primarily concentrated on ground-based experimental data analyses. [PERSON] (1999) provided the referenced AR value for different land covers, including prairie grasslands [13]. [PERSON] et al. (2002) analyzed in-situ turbulent data over the grassy marshland surface of the Tibetan Plateau [14]. [PERSON] et al. (2003) investigated the AR at three sites--including Anduo PBL, Naqu, and MS3478 PAM, which are covered with sparse and short grasses--in the monsoon season based on wind and temperature profiles [15]. However, thematic research on remote sensing-based AR estimations over grassland is seriously lacking, which restricts associated research on the interaction between grasslands and the atmosphere at a regional level. Although [PERSON] et al. (2012) used airborne LIDAR data to estimate the AR in a mixed grassland prairie by the empirical relationship between wind profile-derived AR and airborne LIDAR-derived vegetation height [16], acquiring airborne LIDAR terrain coverage is an apparent limitation for large-scale applications.  
This paper analyzed the relationship between the field AR over grasslands which was determined based on the profile wind data measured at the A'rou station, which is in the upper stream of the Heihe River Basin (HRB), and the temporal Moderate-resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) bi-directional reflectance distribution function (BRDF) \(R\) index, MODIS NDVI, a combination of the BRDF and NDVI indices, and the average wind speed during the vegetation growing period of 2013-2014. The proposed combination index (CI) in this paper provides a new scenario for remote sensing-based AR estimations over grasslands during growing season.
## 3 Parametric Study  
This section will present a parametric study of the DGS slot width (T), height (u), and the distance (r) of the slot from one side of the ground. The dimensions of the slot affect the bandwidth of the antenna. The proposed parameters (r, T, and u) are shown in Table 1. The slot is etched into the back side of the microstrip line because the microstrip line is used for matching, and its matching bandwidth will be affected by variations in the dimensions of the DGS slot. The distance of the slot from the left side of the ground is denoted by 'r'; its parametric study results are shown in Figure 3b. This shows that when 'r' increases or decreases from its proposed value, the bandwidth of the antenna decreases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Values (mm)** & **Parameters** & **Values (mm)** & **Parameters** & **Values (mm)** \\ \hline a & 7 & L & 35 & z & 15.2 \\ b & 2.4 & S & 35 & W & 9.10 \\ c & 1.6 & n & 3.5 & j & 4 \\ d & 5.2 & p & 8.5 & k & 1 \\ e & 13.4 & q & 18 & x & 7 \\ f & 5 & r & 9 & m & 2.5 \\ i & 5 & u & 1.5 & T & 2.5 \\ O & 5 & v & 11.7 & & \\ g & 2.8 & y & 2.8 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of the Proposed Antenna.  
Figure 3: (a) S\({}_{11}\) (dB) results comparison of Antenna 1 without DGS and Antenna 2 with DGS, (b) Variation of S11 (dB) with different values of DGS structure width (T), height (u), and its placement from one side of the ground (r).  
The second parameter is the width of the slot, which is denoted by 'T', as shown in Figure 3b. The figure shows that if the width of the slot decreases, the bandwidth will decrease, while when the width of the slot increases, the bandwidth will be also affected.  
The third parameter is the height of the slot, denoted by 'u', as shown in Figure 3b). The figure shows that if the proposed height (u) decreases or increases, then the resulting bandwidth of the antenna will decrease. When one parameter was changed in the parametric study, the others were kept constant and equal to the proposed values given in Table 1.
## 4 Discussion  
### Changing Trends of NDVI and LULC  
The spatiotemporal characteristics of vegetation coverage in the coastal regions of the Guangdong Province reflected by the NDVI showed that although there were fluctuations in areas and regions with different levels of vegetation coverage over time, the high coverage and above categories still occupied the majority of the region (Figure 3).  
Figure 8: The contribution rate index of vegetation coverage response to LULC changes. **(a)** The statistics of the contribution rate index of the LULC transfer to NDVI changes **(b)** Net contribution rate index of certain LULC types to other types (contribution rate index of total minus invariant region), where the upper and lower ends of the column represent the range of the contribution rate index, and the orange points represent the mean contribution rate index.  
The vegetation coverage changes were mainly in the plain regions, especially in urban agglomerations, which may reflect the impact of human activities on vegetation coverage (Figure 4). As it is a transitional region from the south subtropical zone to the northropic zone [61; 62], the natural environment here is relatively favorable, with widespread distribution of evergreen broad-leaved forests [75; 76; 77] resulting in high vegetation coverage. The reason for the improvement of vegetation coverage in the Guangdong Province, in addition to climate factors, may also be related to the greater emphasis on protecting the ecological environment.  
The spatiotemporal characteristics of the LULC in the coastal areas of the Guangdong Province indicate that plain areas are conducive to urban construction. Here, manifests more in the conversion of cultivated land to built-up land, and the area of construction land is gradually expanding (Figure 5). The Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA) is a core development region of the Guangdong Province, and one of the main urban agglomerations in China. The LULC in this region also strongly demonstrates an increase in built-up land [6]. In addition, over time, different types of LULC transfer to each other more frequently. At the same time, it can also be observed that ecological environment protection has received attention, manifesting in the gradual slowing down of the dynamic increase inbuilt-up land changes, as well as the gradual slowing down of the dynamic decrease in cultivated land and the increase in grasslands and water bodies after 2010 (Table 2).
# An Intelligent Real-Time Object Detection System on Drones
## 4 Results and Discussion
### Comparison with Other Approaches  
To evaluate the performance of our design, we compare our work with other approaches, as illustrated in Table 5. We randomly select 500 images from the public COCO dataset [12] for performance evaluation. It is evident that our work produces object detection with extremely low power at 3.525 W, which is significantly lower than the power of 23 W from work [11]. This is because we implement neural networks without SoC, which eliminates the operating system and, thus, dramatically reduces power consumption.  
In addition, the runtime latency of our work is 130 ms, and it is much smaller than the runtime of work [9]. It is also comparable to the latency of work [10]. However, the FPGA of work [10] runs at 150 MHz, while ours runs at 100 MHz, which means that our design outperforms the work of [10] at the same operating frequency.  
The object detection speed of our design is only 8 FPS, which is a disadvantage compared to other approaches. This is because our design is limited by the I/O property of the FPGA board, which is an inherent limitation, as indicated by the roofline model. We can improve the speed by using an FPGA board with a high I/O bandwidth in future designs.
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 3 Related Work  
Our work relates to the effort of increasing the robustness of WSN conducted by The Leapfrog Collaboration (LFC) [16; 17]. This work was recently extended in [18] which proposes PAREO where packet retransmission, replication, and overhearing is evaluated in combination. However, to the best of our knowledge, no related work exists in terms of a reverse mechanism or replication delay. Although, in [19] the authors visit the idea of a reverse packet mechanism in Ethernet, with the reversing of packet elimination to free bandwidth upstream. However, the proposal is not further discussed or investigated.  
Packet replication and elimination techniques in WSN increases energy consumption significantly. Such a rise in energy consumption is expected as the number of packets traversing the network at minimum doubles. This is shown in [7], where the authors implement disjoint paths and transmit a replicated packet at each path. In short, the source duplicates the packet and the sink drops the last arriving packet. The authors show that packet replication decreased packets lost with almost 90%, however, the energy consumption in their scenario increased with 86.3%.  
Leapfrog Collaboration (LFC) utilizes the same principle as [7] by dropping the last arriving packet. However, LFC implements replication and dropping at each hop. By taking advantage of promiscuous overhearing, LFC utilizes a Leapfrog Beacon which enables all nodes to make the same cell available for receiving (RX). Opening the same cell allows all nodes except the node transmitting to listen for data, and LFC uses the overhearing function to double the probability that the packet will traverse over the DODAG network. As a result, delay decreases significantly as the packet propagates the favorable path. If no nodes are within sensing range, normal retransmission schemes are used.  
Figure 1: Illustration of a IPv6 over the TSCH mode of IEEE 802.15.4e (6 TiSCH) track from 3 to 0.  
LFC achieves minimum end-to-end reliability of 99.1% for all simulations, with the lowest packet delivery ratio (PDR) link quality at 70% [17]. However, with a drawback as the energy consumption increases. LFC reduces the average delay by 41% compared to schemes with four retransmission attempts [17].
# Low-Latency QC-LDPC Encoder Design for 5G NR
## 2 5G NR QC-LDPC Encoding
### Characteristics of 5G QC-LDPC Codes  
The matrix \(H\) is uniquely defined by the matrix \(H_{BG}\), extended permutation matrix \(P\) (also PCM), and lifting size \(Z\). The matrix \(P\) passes through a matrix dispersion, and the elements in \(P\) are replaced by \(Z\times Z\) cyclic unit matrix or zero matrix, resulting in a complete parity check matrix \(H\). Sets of LDPC lifting size \(Z\) and its corresponding shift value table are described in the NR standard specification TS 38.212 [1].  
Figure 3 shows the parameters and region division of matrix \(H\) under BG1 and BG2. Region [_A B_] is kernel matrix and [_C D I_] and the all zero matrix in the upper right corner are extended matrix. The kernel matrix can be used to encode information bits at a high bit rate. There are four kinds of \(B\) matrix corresponding to the parity bits. The \(B\) matrix adopts a dual-diagonal special structure to avoid complicated operations involved in encoding, such as matrix inversion. When the target code rate is higher than that of the kernel matrix, punching is performed on the parity bits. If the target code rate is lower than that of the kernel matrix, the parity bits with low rate are obtained using the single parity relationship of the extended matrix. Because there are many non-zero elements in the first two columns of the \(H\) matrix, in order to improve the decoding performance,  
Figure 1: 5G PDSCH information transmission process.  
the information bits corresponding to the first two columns will be punched, Figure 4 shows the matrix structure of 5G LDPC codes and the corresponding punching and shortening operations. 5G QC-LDPC can support any length of code by filling bits at the end of the message and combining with multiple lifting sizes. Through punching operation, it also can support incremental redundancy hybrid automatic repeated request (IR-HARQ) and various code rates.  
Figure 3: Region division and parameters of base matrix.  
Figure 2: 5G QC-LDPC encoding process.
# Application of Lignin for Slope Bioengineering: Effect on Soil Improvement and Plant Growth
## 1 Introduction  
Slope erosion has long been an environmental and geotechnical concern in many countries, resulting in economic loss. Different engineering solutions, mostly involving cement and geosynthetics, have been successfully used in practice to tackle this issue. However, recent research has shown that alternative environmentally friendly products like biopolymers can also be employed for soil stabilization [1, 2, 3, 4, 5, 6, 7, 8]. For instance, lignin, a complex and heterogeneous biopolymer that is a component of plant cell walls, has lately drawn the attention of the scientific community due to its availability and relatively low cost. Lignin is a by-product of sugar cane and paper mill production, which is considered industrial waste. Several studies [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29] have been performed on soil treated with lignin, predominantly focusing on soil strength improvement in the laboratory environment, with limited research on its long-term field performance. In addition, although a few studies have been performed on biopolymers [30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51], only limited research on the effect of lignin on seed germination and growth has been conducted to date. Vegetation can decrease soil erosion and stabilize slopes by increasing the strength of soil mass by vegetation roots and reducing water runoff on the slope surface.  
This study aims to improve our understanding of the use of lignin in current engineering and environmental practices. The objectives of this study were to establish (a) how lignin solutions could affect the strength of different soils, (b) what method of lignin application could produce superior outcomes and (c) how lignin could affect germinationand growth of vegetation. The results of this study will enhance understanding of soil improvement with lignin and assist engineers and practitioners with selecting alternative environmentally friendly solutions to erosion issues.