esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, through the experiment examining nitrogen migration and transformation in the groundwater fluctuating zone, we analyzed the nitrogen migration and transformation process. A numerical model of nitrogen migration and transformation in the groundwater level fluctuating zone was established with the help of the HYDRUS-1D model. The paper obtained the following main conclusions:  
* Groundwater level fluctuations can significantly affect the nitrogen transport and transformation patterns in soil-groundwater. The nitrate nitrogen concentration increased and the ammonium nitrogen mass concentration decreased when the water level decreased. Moreover, the nitrate nitrogen mass concentration decreased and the ammonium nitrogen mass concentration increased when the water level increased; the nitrite nitrogen did not change significantly.
* In this study, indoor soil column experiments were combined with the Hydrus-1D software simulation prediction to simulate the theoretical values of the tri-nitrogen transformation process in indoor soil columns. Although simple, the software does not take into account the existence of non-homogeneous changes in the soil column, is not accurate enough, there are certain limitations, and the influence of biological processes on the transformation of pollutants has not been taken into account. Furthermore, there is still a certain gap with the actual results.
* As an important factor of the hydrological mechanism, the water level plays a vital role in the groundwater system, and the effect of the level and intensity of fluctuation of the groundwater level on the migration and transformation of nitrogen cannot be ignored. The change in water level will affect the water content in the soil, the state of water movement, the physical and chemical properties of soil and the state of the microorganisms, which in turn, will affect the migration and transformation of nitrogen in the soil.  
Conceptualization, Y.L. and J.Q.; methodology, Y.L., L.W. and X.Z.; software, L.W. and X.Z.; validation, Y.L., L.W. and J.Q.; writing--original draft preparation, Y.L. and L.W.; writing--review and editing, Y.L. and J.Q.; visualization, G.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Doctoral Research Fund of North China University of Water Resources and Electric Power (40651) and the Key Project of Science and Technology Research of Henan Education Department (14A170006), and by the Key R&D and Promotion Projects in Henan Province (202102310012).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Numerical Models, Solution Methods and Boundary Conditions
#### 2.2.2 Turbulence Model
The realizable k-\(\varepsilon\) model can predict the moderate intensity of the cyclonic flow and is more accurate for the calculating rotational flow, separation flow, and other cases [1]. Therefore, the turbulence model is chosen as the realizable k-\(\varepsilon\) model. Since the realizable k-\(\varepsilon\) model has a high degree of agreement with the cavitation model in this paper, and can accurately and completely simulate the fluid flow process inside the rotating hydrodynamic cavitation generator, the realizable k-\(\varepsilon\) model is selected in this paper. The control equations are as follows. \[\frac{\partial}{\partial T}(\varrho k)+\frac{\partial}{\partial x_{i}}(\rho ku _{i})=\frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{T}}{\sigma_{ k}}\right)\frac{\partial k}{\partial x_{j}}\right]+G_{k}+G_{b}-\rho \varepsilon-Y_{M}+S_{k} \tag{7}\]  
Figure 2:0Figure 2:1  
Figure 2:2  
Figure 2:3  
where \(G_{k}\) is the turbulent kinetic energy generated by the laminar velocity gradient, \(G_{b}\) is the turbulent kinetic energy generated by buoyancy, \(Y_{M}\) is the fluctuation in compressible turbulence generated by transition diffusion, \(C_{1\varepsilon}\), \(C_{2}\), and \(C_{3\varepsilon}\) are constants of 1.44, 1.92, and 0.09, respectively, \(\sigma_{k}\) and \(\sigma_{\varepsilon}\) are 1 and 1.2, respectively, \(S_{k}\) and \(S_{\varepsilon}\) are the user-defined turbulence kinetic energy terms, and \(\mu_{T}\) is the vortex viscosity.
## Materials
### Methods  
Intramuscular injection with a combination of 0.4 mL of ketamine and 0.3 mL of xylazine was used as general anesthesia. For preparation of PRF, 10 mL blood sample from the ear vein of each rabbit were procured and centrifuged for 12 min at 400\(\times\)_g_. After centrifugation of blood, three distinct layers were formed. The PRF was collected from the middle layer. The surgical area was first shaved, and then povidone-iodine antiseptic was used for disinfection. An incision was made in the periosteum. The tibia bone was exposed by sharp subperiosteal dissection. Two bone holes in the tibia were formed monocortically with a trephine bur. a distance of 5 mm was kept between the two holes. About 2 mm of peri-implant defect were created on each side. a dental implant, 8 mm in length and 3 mm in diameter (Dentium(r), Seoul, Korea), was installed in these defects (Figure 1). The groups were randomly assigned. All implants were anchored to the opposing cortex. The peri-implant gap in experimental group 1 was filled with tooth powder and PRF. In experimental group 2 tooth powder alone was used in the defect. The peri-implant defect around the implant in experimental group 3 was filled with PRF alone, while that of the control group was left empty. The implantation site was uniformly allocated and randomized. a 3-0 silk suture was used for closing periosteum and skin. Postoperatively, gentamicin 1 mg/kg was delivered intramuscularly 3 times a day for 3 days. The animals were sacrificed after 8 weeks. The specimens were removed using a round-shaped bur, and then examined grossly for inflammation. After microscopic computed tomography (\(\mu\)-CT) examination, the specimens were fixed in 10% buffered formalin solution for histomorphometric evaluation.  
Figure 1: **(A)** Incision is made for tibial exposition. **(B)** Exposed tibial bone. **(C)** Trephine bur is used for preparation of defects. **(D)** Implants placed in the defects.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology  
This study proposes a deep learning framework for estimating forest carbon stock using Sentinel-2 multispectral imagery and an enhanced residual network. The overall workflow is illustrated in Figure 2 and consists of three main components:  
(1) Input Preparation: 32 \(\times\) 32 multispectral image patches are extracted around each field plot from preprocessed Sentinel-2 composites. Each patch includes 10 spectral bands and aligns spatially with the corresponding ground measurements.  
(2) Model Architecture: An enhanced ResNet is constructed by integrating multiscale residual blocks and channel attention modules. This design improves the network's ability to model both spatial heterogeneity and spectral relevance for carbon estimation.  
(3) Model Training and Evaluation: The model is trained in a supervised regression setting using ground-based carbon stock values. Performance is assessed using multiple evaluation metrics and compared against conventional machine learning and deep learning baselines.  
Figure 2: Overview of the proposed deep learning framework for forest carbon stock estimation.
## 4 Experimental Evaluation
### Join Query Performance  
To evaluate the join query performance of the proposed learned semantic (LearS) index structure, we performed 15 SPARQL join queries that expressed the above seven join query types. Figure 4 illustrates the join query performance for various join query types. Experiment results show that the query performance of LearnS was always superior to those of other methods. This is mainly due to the learned indexing structure, which prunes a lot of unnecessary data scans during the join query. But we can see that Q4 approach performance was exceptionally slightly worse than Quad. The Darq and Midas approaches performed considerably worse than the Quad, Dams, Two-step, and LearnS approaches because of considerably larger intermediate results and more time for I/O and CPU. Although Darq and Dams are distributed approaches, Dams adopts the distributed semi-join query to minimize join query numbers without inter-communicating with endpoints, so it greatly reduces the communication cost. This characteristic has also been verified in the LinkedGeoData dataset. The Quad approach showed slightly worse performance than the Dams, Two-step, and LearnS approaches because Quad needs to load six B+-trees to support all access query patterns and decompress intermediate results in a short time. However, the Two-step approach considers only an R*-tree with \(k\)-d trees without storing all possible access combinations. The Two-step approach performed worse than LearnS because it is based on the hash function that does not describe exact coordinates in the multidimensional space; it contains a number of false hits not fulfilling the query condition. However, the LearnS approach turns heterogeneous hash codes into homogeneous sets of the adjacent embedding space. It further reduces the intermediate results using the clustering techniques described in Section 3.2.  
Figure 4: _Cont._Figure 5 shows the join query performance on the LinkedGeoData dataset. In this figure, join query types only focus on the most common star, complex, chain, and cycle types. This figure shows that experimental results for LinkedGeoData are almost identical to the experimental results for LUBM. Under the LUBM dataset, the join query performance of the LearnS, Dams, and Two-step approaches achieved better results than those of the other methods. Similarly, under the LinkedGeoData dataset, the query performance of LearnS always beat the performance of other methods. However, the performance gap between LearnS and Two-step was particularly prominent in the chain type.
# Highly Linear 2.6 GHz Band InGaP/GaAs HBT Power Amplifier IC Using a Dynamic Predistorter
###### Abstract  
This paper presents a highly linear two-stage InGaP/GaAs power amplifier integrated circuit (PAIC) using a dynamic predistorter for 5G small-cell applications. The proposed predistorter, based on a diode-connected transistor, utilizes a supply voltage to accurately control the linearization characteristics by adjusting its dc current. It is connected in parallel with an inter-stage of the two-stage PAIC through a series configuration of a resistor and an inductor, and features a shunt capacitor at the base of the transistor. These passive components have been optimized to enhance the linearization performance by managing the RF signal's coupling to the diode. Using these optimized components, the AM\(-\)AM and AM\(-\)PM nonlinearities arising from the nonlinear resistance and capacitance in the diode can be effectively used to significantly flatten the AM\(-\)AM and AM\(-\)PM characteristics of the PAIC. The proposed predistorter was applied to the 2.6 GHz two-stage InGaP/GaAs HBT PAIC. The IC was tested using a 5 \(\times\) 5 mm\({}^{2}\) module package based on a four-layer laminate. The load network was implemented off-chip on the laminate. By employing a continuous-wave (CW) signal, the AM\(-\)AM and AM\(-\)PM characteristics at 2.55-2.65 GHz were improved by approximately 0.05 dB and 3\({}^{\circ}\), respectively. When utilizing the new radio (NR) signal, based on OFDM cyclic prefix (CP) with a signal bandwidth of 100 MHz and a peak-to-average power ratio (PAPR) of 9.7 dB, the power-added efficiency (PAE) reached at least 11.8%, and the average output power was no less than 24 dBm, achieving an adjacent channel leakage power ratio (ACLR) of \(-\)40.0 dBc.  
predistorter; InGaP/GaAs HBT; 5G small cell +
Footnote †: journal: Accepted: 2 June 2025  
[[https://creativecommons.org/](https://creativecommons.org/)]([https://creativecommons.org/](https://creativecommons.org/))
# Network Traffic Type-Based Quality of Experience (QoE) Assessment for Universal Services
## 4 Results  
### Data Collection  
To verify our proposed method, we select 11 different types of traffic service: on-demand video, on-demand audio, live video, live audio, web browsing, instant message, file sharing, online game, location, meeting video, and voice call. Here, experiments are conducted with the ground truth data captured from internet with the \".pcap\" file format. The data is collected from a university campus, for each type of network traffic, 9-11 different corresponding APPs are used. Details of the collected network flow data has been presented in Table 3, where \"APP Number\" denotes the number of different APPs used for a service type and \"File Number\" denotes the number of the collected \".pcap\" files.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 5 Discussion
### Nonfunctional Metrics  
The main goals of V2V and V2I communications are to improve safety, reduce road traffic, and reduce accidents. So, the main task of a routing protocol is to improve the opportunity to send a message by the best route, delivering data in a faster mode. Nonetheless, an important issue in vehicular communication is if the shared information is correct. In general, all of the traditional aspects of security-privacy, confidentiality, integrity, nonrepudiation, and others, must be applied to VANET communication to prevent compromising traffic safety and causing material damage or even loss of life [74; 75]. So, security aspects could be considered nonfunctional metrics. In [76], the authors proposed a communication model to provide vehicle-location privacy. In case that V2I communication is being established, driver privacy must be guaranteed. Trust management is crucial in VANET routing because interchanging false information could cause much damage. Reliable routing and communication are very important, which are analyzed in [77]. Some of these nonfunctional metrics could be introduced in the routing protocols for VANETs in order to guarantee data reliability, as mentioned below.  
* Anonymity/Privacy: the objective is to conceal who communicates to whom to a neighbor who observes the interchange of data of the anonymous communication channel.
* Trust: it is important to incorporate mutual trust between vehicles, mainly in the routing process, because one or more malicious neighbors may attempt to disrupt route discovery or data transmission in the network.  
When V2I communication is established, information about the vehicle (location, route, etc.) is shared. So, some proposals to maintain a level of privacy were presented, such as [76] that proposed the Pass and Run protocol that first passes information through the VANET (considered in this case was a delay tolerant network (DTN)) before reaching an RSU to maintain vehicle-location privacy. In the DTN, metrics such as distance and direction are taken into account from vehicle source to RSU. In [78], a protocol was presented based on Crowd [79], an approach where each vehicle probabilistically decides to directly send a message to a common receiver or to forward it to a peer, who is asked to repeat the process. The objective is to offer privacy in V2V communication. The proposal was evaluated jointly with AODV and GPSR as VANET routing protocols. This potentially opens a new set of routing protocols where there is a tradeoff between speed and secure forwarding.
# Power System Reliability Assessment Considering Coal-Fired Unit Peaking Characteristics
## 7 Conclusions  
In this study, we proposed an improved PPS method that integrates coal-fired unit degradation dynamics and wind power fluctuations within a multi-state reliability framework. Unlike traditional methods, our approach dynamically updates state transition probabilities based on operating conditions, ramping constraints, unit degradation, and load demand, leading to a more accurate and computationally efficient reliability assessment. The UGF framework was employed to optimize computational efficiency and overcome state-space explosion issues.  
The simulation results of the IEEE-RTS79 test system demonstrate that the proposed method more accurately reflects real-world operational conditions. For conventional generating units, system reliability is improved when the ramp rate is set between 2 and 3% and the load-following depth is maintained at 55-65%. However, if the ramp rate is increased from 1% to 5%, the LOLP increases by 48%, confirming that forcibly enhancing a unit's load-following capability leads to a higher failure rate, thereby exposing the entire system to greater risk.  
While this study focuses on wind power integration, the method can be extended to solar power and hybrid renewable systems. Future research will explore economic factors, electricity market pricing, and further real-world validations. This approach provides a more practical and efficient tool for reliability assessment in modern power systems.  
Conceptualization, P.W. and X.T.; methodology, P.W., K.S. and X.T.; software, Z.W.; validation, P.W., K.S. and X.T.; formal analysis, Z.W. and Y.H.; investigation, P.W.; resources, Y.H.; data curation, K.S.; writing--original draft preparation, K.S.; writing--review and editing, P.W. and L.S.; visualization, Y.H; supervision, L.S.; project administration, P.W.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
Figure 9: Comparison of system generation costs under different peak shaving schemes.  
**Funding:** This research was funded by the Grant of The State Grid Economic and Technological Research Institute Co., Ltd., grant number ZZKJ-2024-23, grant title: Research on the Impact of Flexible Adjustment Operation of Coal Power Units on System Reliability Under the Dual Carbon Goals.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. (The data are not publicly available due to privacy or ethical restrictions.)  
**Conflicts of Interest:** Author [PERSON], [PERSON], [PERSON] and [PERSON] was employed by the company \"State Grid Economic and Technological Research Institute Co. Ltd\". The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# DGBL-YOLOv8s: An Enhanced Object Detection Model for Unmanned Aerial Vehicle Imagery
## 6 Conclusions  
This study proposes an improved object detection model, DGBL-YOLOv8s, designed to address challenges in drone-captured scenarios, such as weak feature extraction, poor information fusion, and low detection accuracy caused by small target scales, large quantities, and long distances. Firstly, the YOLOv8 backbone network is augmented by incorporating the Dilated Residual Module (DWR) into the C2f module, enhancing its feature extraction and contextual information capture capabilities. Secondly, the neck structure employs a Global-to-Local Spatial Attention Bidirectional Feature Pyramid Network (GLSA-BiFPN), which improves multi-scale feature fusion by effectively combining local spatial details with global spatial semantics. Additionally, the detection head incorporates the concepts of shared convolution and group normalization, along with the introduction of a small  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Baseline** & **GFLOPs** \\ \hline I & YOLOv8s & 28.5 \\ II & DWR & 28.1 \\ III & GLSA-BiFPN & 26.7 \\ IV & LSCDH & 45.7 \\ V & DGBL-YOLOv8s & 53.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computational complexity of the improved model.  
target detection head, which promotes detection accuracy while shortening computational complexity and the number of parameters. The experimental results indicate that the DGBL-YOLOv8s model achieves an 8.5% increase in mean average precision (mAP) over the baseline model, alongside a 35.1% reduction in parameters (from 11.1 M to 7.2 M) and a 33.9% decrease in model size (from 21.5 MB to 14.2 MB). These improvements confirm the efficacy and advantages of the proposed enhancements for drone-based object detection. Future efforts will concentrate on further refining the model architecture to lower computational demands and investigating its applicability in more intricate scenarios.  
Conceptualization, C.W.; Methodology, C.W.; Software, C.W.; Validation, C.W.; Investigation, H.Y.; Resources, H.Y.; Writing--original draft, C.W.; Writing--review and editing, C.W.; Visualization, C.W.; Supervision, H.Y.; Project administration, H.Y.; Funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All of the data are contained within the article. To request the data and code, please send an email to the first or corresponding author.  
The authors declare no conflicts of interest.
## 3 Materials and Methods  
### Glacier Area Changes  
The outlines of the Echaurren Norte Glacier were derived from satellite images and aerial photographs from different sources and quality (Table 1). The oldest aerial photographs available are from the 1955 HYCON photogrammetric survey, a flight campaign carried out as part of an agreement between Chile and the Inter-American Geodetic survey of the United States. In 1961, an agreement between the Organization of American States (OAS/OEA) and the Chilean government led to a photogrammetric survey carried out by the Aero Service Company. Analogue aerial photographs were obtained and subsequently administrated by Servicio Aerofotogrametrico (SAF) of the Chilean Air Force (FACH). Another aerial photograph survey was obtained in 1997 (GEOTEC) by SAF. These aerial photographs were scanned using a photogrammetric scanner at high resolution (1200 DPI). The photos were oriented using fiducial marks, camera calibration data and ground control points (GCPs) derived from a LiDAR dataset from DGA of 2009 and 2015 [40]. The RMSE for the photogrammetric triangulations are 1.2 m for the 1955 photographs, and 0.9 m for 1997.  
In addition, we used optical satellite data from SPOT 6 imagery acquired in January 2015 (RGB-Pansharpened) [41] and several Landsat TM images from between 1989 and 2000. Glacier extent classifications for the multispectral imagery were done by applying a simple band combination approach known as band ratio segmentation using Near Infrared (NIR-TM4) and Shortwave Infrared (SWIR-TM5) bands [42; 43]. All datasets were projected to the Universal Transverse Mercator (UTM) 19S zone and WGS-84 Datum. Changes in the glacier extent were measured by manual digitizing from this multitemporal datasets. We considered an error of 5% of the area for each Landsat satellite image [44]. For aerial photographs and the SPOT image, we considered an error of 1-pixel size uncertainty for the glacier delineation [45]. The consolidated dataset was further refined in conjunction with several photographs acquired during field trips, specifically for glacier area interpretation in 2015 (Figure S1).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Date** & **Survey** & **Spatial Resolution** & **Source** \\ \hline
24 February 1955 & HYCON aerial photos & 1 m & IGM \\
1961 & OEA aerial photos & 1 m & IGM/SAF \\
02 March 1989 & Landsat TM satellite image & 30 m & USGS \\
05 March 1990 & Landsat TM satellite image & 30 m & USGS \\
29 March 1993 & Landsat TM satellite image & 30 m & USGS \\ March 1997 & GEOTEC aerial photos & 1 m & SAF \\
20 February 2000 * & Landsat TM satellite image & 30 m & USGS \\
28 April 2009 & DGA LiDAR orthophoto & 0.2 m & DGA \\
30 January 2015 & SPOT satellite image & 1.5 m & DGA \\ \hline \end{tabular}
* Glacier area corresponds to the SRTM acquisition date.  
\end{table}
Table 1: Overview of the data sets used for glacier area change mapping. Glacier areas were estimated from the orthorectified and georeferenced images.