esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The nutrient releasing characteristics of a blended biochar pellet comprising a mixture of biochar and pig manure compost ratio (4.6) uploaded with nitrogen (N), phosphorus (P) and potassium (K) nutrient solutions were investigated with the application of a modified Hyperbola model during a 77-day precipitation period. The experiment consisted of five treatments, i.e., the control, as 100% pig manure compost pellet (PMCP), a urea solution made at room temperature (TN), a urea solution heated to 60 \({}^{\circ}\)C (HTN), N, P and K solutions made at room temperature (TNPK), and N, P and K solutions heated to 60 \({}^{\circ}\)C (HTNPK). The cumulative ammonium nitrogen (NH\({}_{4}\)-N) in the blended biochar pellets was slow released over the 77 days of precipitation period, but nitrite nitrogen (NO\({}_{3}\)-N) was rapidly released, i.e., within 15 days of precipitation (Phase I), close behind on a slower release rate within the final precipitation (Phase II). Accumulated phosphate phosphorus (PO\({}_{4}\)-P) concentrations were not much different, and slowly released until the final precipitation period, while the highest accumulated K amount was 2493.8 mg L\({}^{-1}\) in the TNPK at 8 days, which then remained at a stage state of K. Accumulated silicon dioxide (SiO\({}_{2}\)) concentrations abruptly increased until 20 days of precipitation, regardless of treatments. For the application of the releasing model for nutrient releasing characteristics, the estimations of accumulated NH\({}_{4}\)-N, NO\({}_{3}\)-N, PO\({}_{4}\)-P, K and SiO\({}_{2}\) in all the treatments were significantly (\(p<0.01\)) fitted with a modified Hyperbola model. These findings indicate that blended biochar pellets can be used as a slow release fertilizer for agricultural practices.  
blended biochar pellet; modified Hyperbola; nutrient release; slow release fertilizer
# Ammonia Gas Sensing Characteristic of P3 HT-rGO-MWCNT Composite Films
[22] demonstrated that the NH\({}_{3}\) wireless sensors which operate at room temperature with a response time of 7.5 s and a recovery time of 20 s in an NH\({}_{3}\) concentration ranging from 5 to 100 ppm can be made from Ag-rGO composite films. Moreover, the electrical properties of multi-wall carbon nano tubes (MWCNTs) are a critical function of their atomic structure--any mechanical strain or chemical doping can cause large changes in electrical conductivity [23]. Therefore, to overcome the limitations of semiconductor gas sensors, MWCNT-based gas sensor has been fabricated for high gas adsorption capacity and small size, i.e., the advantages of MWCNTs. MWCNTs are expected to be an economically viable material for use in gas detection. For these reasons, so far, MWCNTs have been explored to sense gases in a number of different configurations [24]. Sensors based on PEDOT: PSS can detect both ammonia gas [25] and humidity are recently reported by [PERSON] et al. [26] where PEDOT: PSS+rGO+AgNWs-based sensors have good sensitivity and are sensitive to relative humidity (RH). For practical applications, a sensor should be able to detect gas with high sensitivity and selectivity. The OSCs study, which uses poly(3-hexylthiophene) (P3 HT) as a photoactive layer [27], shows that the P3 HT film exposed to air with a relative humidity greater than 60% gives good quality as that in a dried nitrogen glove box. This suggests that a combination of rGO, P3 HT, and MWCNTs can be utilized in gas sensor fabrication to monitor atmospheric NH\({}_{3}\). Therefore, this work focused on the preparation of the P3 HT-rGO-MWCNT composite film that sensors ammonia in the range of 10-90 ppm.
## 2 Theoretical Background
### Extended Reality  
Recently, extended reality (XR) systems have been increasingly used to address various fields, such as training, education, security, etc. [14]. XR has been used to encompass all those technologies that generate some kind of user immersion, such as virtual, augmented and mixed reality (VR, AR, MR) technologies and conceptual approaches to spatial interfaces studied by researchers in engineering, computer science and human-computerinteraction (HCI) over several decades [15]. On one hand, VR offers a fully immersive experience, while AR promotes interaction between the user, digital content and the real world, thus displaying virtual images while remaining transparent [16]. Instead, MR refers to a seamlessly blended connection between computer-generated content and the real/physical environment as a realistic form of AR [17].
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## 3 New Compact Dual Source CRB for Delay and Doppler Estimation with Band-Limited Signals
### Decoupled Fisher Information Matrix Terms  
The two last diagonal blocks of the FIM (9) were derived for the single source CSM case in [17,18]. For the sake of completeness, the main results are recalled in the sequel:  
\[\mathbf{F}_{\mathbf{\theta}_{i}|\mathbf{\epsilon}}(\mathbf{\epsilon})=\frac{2F_{s}}{ \sigma_{n}^{2}}\text{Re}\left\{\mathbf{Q}_{i}\mathbf{W}\mathbf{Q}_{i}^{H} \right\}\, \tag{10}\]  
where \(\mathbf{Q}_{i},\ i\in\{1,2\}\) is defined in (A9) and \(\mathbf{W}\) is defined as:  
\[\mathbf{W}=\left[\begin{array}{ccc}w_{1}&w_{2}^{*}&w_{3}^{*}\\ w_{2}&W_{2,2}&w_{4}^{*}\\ w_{3}&w_{4}&W_{3,3}\end{array}\right]\, \tag{11}\]  
with the easy-to-use formulation w.r.t. the baseband signal samples:\[w_{1}=\frac{1}{F_{s}}\mathbf{s}^{H}\mathbf{s},\ w_{2}=\frac{1}{F_{s} ^{2}}\mathbf{s}^{H}\mathbf{D}\mathbf{s},\ w_{3}=\mathbf{s}^{H}\mathbf{V}^{ \Delta,1}(0)\mathbf{s},\ w_{4}=\frac{1}{F_{s}}\mathbf{s}^{H}\mathbf{D}\mathbf{ V}^{\Delta,1}(0)\mathbf{s},\] \[W_{2,2}=\frac{1}{F_{s}^{3}}\mathbf{s}^{H}\mathbf{D}^{2}\mathbf{s},\ W_{3,3}=F_{s}\mathbf{s}^{H}\mathbf{V}^{\Delta,2}(0)\mathbf{s}, \tag{12}\]  
where \(\mathbf{s}\), the baseband sample vector, is defined in (A36), \(\mathbf{D}\) in (A39), \(\mathbf{V}^{\Delta,1}\) in (A41) and \(\mathbf{V}^{\Delta,2}\) in (A42). The inversion of (10) proposed in [17; 18] leads to a closed-form CRB expression for the delay, Doppler, phase and amplitude estimation, where a noteworthy feature is that the CRBs are expressed w.r.t. the baseband signal samples. Such CRBs were studied for different GNSS signals in [35; 44].
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 7 Conclusions  
The possibility of using D2D underlay cellular technology for VANETs could improve the utilization of cellular spectrum and reduce the energy consumption of V2V (Vehicle-to-Vehicle) communications. Although the reuse gain comes at the expense of increasing the interference level,  
\begin{table}
\begin{tabular}{c c c c} \hline
**CUE’s Message** & **Underlay V2V** & **Our Approach V2V** & **Overlay V2V** \\
**Size (Bytes)** & **Energy Efficiency (Bytes)** & **Energy Efficiency (Bytes)** & **Energy Efficiency (Bytes)** \\ \hline
100 & 4.4618 & 6.7902 & 5.3890 \\
200 & 3.9224 & 5.7585 & 5.0450 \\
300 & 3.8978 & 5.4545 & 4.9905 \\
400 & 3.7932 & 5.0135 & 4.8062 \\
500 & 3.66  
it is possible to be neatly controlled by the network designing an RRM to mitigate the interferences and ensure the different QoS requirements of ITS applications.  
In this paper, firstly, we briefly analyze various concepts presented hitherto in the literature related to efficient sharing of the available radio resources between common cellular and V2X communications. Secondly, this paper presents the design of an underlay RRM methodology capable of improving cellular spectral efficiency while having minimal impact on cellular communications. Finally, we provide system-simulations results, which evidence the improvement of spectral efficiency provided by the presented methodology. As future work, in order to improve the energy efficiency results obtained, we plan to design a more accurate power transmission assignment, which will reduce the interference generated.  
This work has been made thanks to the grant offered by the Basque Government, under the predoctoral formation program (ref: PRE_2016\(2\)0163).  
All the authors of this article have contributed significantly to the research work and the writing of the paper. [PERSON] and the corresponding author have contributed in the principal design of the work, [PERSON] and [PERSON] have design and implement the metaheuristic algorithm. The corresponding author have developed the implementation of the rest of the methodology and carried out the simulations and the analysis of the results. The drafting of the article have been done by the corresponding author and, at least, one section of the paper has been written by each author.  
The authors declare no conflict of interest.
## 3 Results  
### Inherent Safety Analysis  
#### 3.1.1 Chemical Inherent Safety Index  
The results for the chemical safety index (\(I_{CI}\)) are presented in Figure 2. In the crude palm oil extraction process, no reaction took place, thus the chemical reactivity subindex was null. The subindices of dangerous chemical substance \(\left(I_{FL}+I_{EX}+I_{TOX}\right)_{max}\) and chemical interaction \(\left(I_{INT,max}\right)\) were calculated with information from the safety data sheet of the components. The subindex of dangerous chemical substance was estimated at 1 according to the fatty acids of palm oil are only combustible (flash point > 55 \({}^{\circ}\)C). The most dangerous chemical interaction is represented by the formation of nonflammable vapors, hence a score of 1 was assigned. Furthermore, the substances present in the process are not corrosive; therefore, no special material is required for the construction of the equipment and a score equal to 0 is assigned for the corrosion subindex. The inherent chemical safety index was estimated at 2.
companies [2]. Wind gust forecasts in the time scales from a day to weeks ahead are useful in preparedness planning and surveillance in different fields of operation such as aviation [3,4], shipping [5] and land transport [6], but also in the energy sector [7,8]. In boreal regions, storms may cause long-lasting and wide-ranging power cuts as trees fall over power lines. For wind energy, information on wind extremes is needed in the planning phase to ensure the strength of the turbine structure [7]. When operating wind turbines, the time scales of interest vary from minutes to multiple days ahead [8]. At shorter time scales, up to minutes ahead, wind information can be used to recognize patterns of oncoming wind speed and/or direction changes, which may lead to extreme load and cause enhanced fatigue or even break turbine components [9,10,11].  
To measure wind gusts, a high temporal resolution of the measurements is needed. This means that the whole measurement chain including the anemometer, data acquisition, processing, recording and reporting has to support this. In the dawn of modern anemometry, this was a clear challenge, whereas today, anemometer systems can provide the required high resolution. In the following subsection, the history of anemometry from the perspective of wind gust measurements is briefly reviewed.
# Scale-Sensitive Attention for Multi-Scale Maritime Vessel Detection Using EO/IR Cameras
## 1 Introduction
The Proposal of a Novel CNN-Attention Hybrid Model: This study proposes a ML-MHA structure that combines SSCA and SA within the YOLOv8 model, with the goal of achieving high accuracy while maintaining real-time performance. The model effectively extracts object features of various sizes by leveraging CNN in the Backbone and Neck of YOLOv8 to capture local features at different scales. SSCA is employed for detecting small objects, while SA is used for detecting large objects, maximizing multi-scale object detection performance. Especially, the novel approach of applying key local pattern information, extracted during the convolution process, to SA enables more effective detection of objects of varying sizes compared to the Vision Transformer (ViT) [2] SA model, which divides input data into patches to learn global context. This structure is expected to demonstrate superior performance in precise object detection across a variety of environments. 2. Color Palette for Precise Detection with IR Cameras: To overcome the limited information provided by the single-channel images of IR cameras, this study applies thermal color palettes to significantly enhance object detection performance. This method proves to be highly effective, particularly in challenging environments such as nighttime or adverse weather conditions. It is expected that this approach will greatly improve detection performance in cases where feature identification is difficult with single-channel data, or where structural features are hard to distinguish, even with multi-channel data.
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 4 Results
### Analysis of Single Evaluation Indicator
#### 4.2.3 Results of Correlation Indicator  
The calculation result of the correlation evaluation between sensors is shown in Figure 6. It can be seen that there were high correlations between certain sensors, so the gas response data acquired by the initial sensor array may contain high information redundancy. For example, the correlation between sensors S6 and S7 was equal to 0.98. In order to reduce information redundancy and shorten calculation time, it may be considered to keep only one of the two sensors. Figure 7 shows the average value of the correlation coefficients between each sensor and the other 8 sensors. It can be observed from the calculation results that the three sensors with the smallest average correlation coefficients were S2 (0.69), S1 (0.71), and S6 (0.81), and the three sensors with the largest correlation coefficients were S8 (0.90), S9 (0.89), and S5 (0.89). A small correlation coefficient indicates that the sensor has less information redundancy with other sensors. From the perspective of improving the optimization performance of the sensor array, the sensors with small correlation coefficients were reserved. It should be noted that the pairwise correlation is more relevant than looking at the average correlations over all sensors. The average correlations were calculated just for observing the differences between sensors under the correlation evaluation indicator. Therefore, the comprehensive evaluation model proposed in this study is based on the pairwise sensor correlation.  
Figure 4: Average sensitivities of different sensors to the experimental gas mixtures.  
Figure 5: Selectivity of the initial sensor array to the experimental gases.  
Figure 6: Correlation coefficients between sensors in the initial sensor array.
## 1 Introduction
### Physical Modeling  
Waveguide synthesis has been used since the second half of the 1980's to model a wide range of musical instruments [24; 25; 26; 27]. The main advantages of this technique are its simplicity and efficiency while still sounding adequately real. It allows for the accurate modeling of a wide range of instruments (string and wind instruments, tonal percussion, etc.) just with a single \"filtered delay loop.\" This technique was used in many commercial synthesizers in the 1990s such as the Yamaha VL1.  
While any instrument part implementing a quasi harmonic series (e.g., a linear string, tube, etc.) can be modeled with a single digital waveguide, other parts must be modeled using other techniques such as modal synthesis.  
Modal synthesis [28] consists of implementing each mode of a linear system as an exponentially decaying sine wave. Each mode can then be configured with its frequency, gain, and resonance duration (T60). Since each harmonic is implemented with an independent sine wave generator, this technique is a lot more computationally expensive than waveguide modeling. The parameters of a modal synthesizer (essentially a list of frequencies, gains, and T60s) can be calculated from the impulse response of a physical object [29] or by using the Finite Element Method (FEM) on a volumetric mesh [30]. This technique strengthens the link between physical elements and their virtual counterparts as it allows for the design of an instrument part on a computer using CAD software, and turn it into a physical model that could also be materialized using digital fabrication. This concept is further developed in Section 5.2.  
Other methods such as _finite-difference schemes_[31] can be used to implement physical models of musical instruments and provide more flexibility and accuracy in some cases. However, most of them are computationally more expensive than waveguide models and modal synthesis. An overview of these techniques is provided in [27]. Since this paper is targeting the use of physical models on mobile platforms with a limited computational power, we're focusing on CPU-efficient techniques.
## 4 Conclusions  
This work investigated the influence of graphene on the carbonation depth of concrete under the simulated changing climate conditions. A set of experimental explorations were performed. Graphene concrete adopts the optimal dosage of 0.05%wt. GC0 and GC0.05 specimens were prepared for accelerated carbonation experiments. The main conclusions were as follows.  
1. When the concentration of carbon dioxide changed from 6% to 10% (the temperature was 25 \({}^{\circ}\)C and the humidity was 65%), the changes in the carbonation depth and microstructure of concrete with different corrosion degrees were revealed. The results show that the addition of graphene can delay the carbonation reaction of concrete.
2. When the temperature changed from 25 to 45 \({}^{\circ}\)C (the humidity was 65% and the CO2 concentration was 6%), the test results show that in different temperature ranges, GNP  
Figure 10: Relationship between concrete carbonation depth, time, and humidity.  
has different effects on concrete. At 25-33 \({}^{\circ}\)C, the incorporation of GNP improves the carbonation resistance of concrete; however, above 33 \({}^{\circ}\)C, GNP concrete does not delay the carbonation of concrete. This means that graphene concrete may increase the carbonation rate of concrete in high-temperature areas. Additionally, the relationship between different grades graphene concrete with temperature with respect to the other parameters can be investigated in future work.
3. When the humidity changed from 50% to 85% (the temperature was 25 \({}^{\circ}\)C and the concentration of CO\({}_{2}\) was 6%), the carbonation depth of graphene concrete was 15% lower than that of ordinary concrete.
4. Under the condition of simulating climate change, it is considered that the influencing factors of carbonation of concrete are CO\({}_{2}\) concentration, humidity, and temperature in order of magnitude.  
This paper only discusses the influence of graphene on the carbonation depth of concrete under the condition of climate change, but in the actual service environment, concrete works under combined effects of some factors, such as freeze-thaw cycles and carbonation, acid rain, and carbonation, etc., which needs further research.  
[PERSON] wrote the manuscript, [PERSON] and [PERSON] designed the theoretical framework, [PERSON], [PERSON] and [PERSON] performed the experiments. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shandong Provincial Natural Science Foundation (Grant No. ZR2019 MEE090 and ZR2020 ME239).  
Not applicable.  
We thank the Analysis and Testing Center of Harbin University of Technology (Weihai) for assistance with the SEM analysis.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Methods  
#### 2.2.1 High Resolution Surveying: LiDAR-Derived Digital Terrain Model (DTM)  
The survey used data from three measurement campaigns in July 2010, July 2013 and August 2013. High-precision TLS surveys were performed using a Leica Scan Station C10 terrestrial laser scanner. It is a scanner with a range up to 300 m emitting green laser pulses (532 nm) at 50,000 points per second [pt/s] [33]. During the 2010 and 2013 campaigns, various referencing techniques were used [34]. In 2010, a scan of the test area was performed in a local reference system. The analyzed section of the bottom was scanned from eleven sites that were registered by nine ground control points (GCPs) with an accuracy of \(\pm\) 0.009 m. After the integration of the entire model by Leica's Cyclone 8.1 software (Leica Geosystems AG, Heerbrugg, Switzerland), it was georeferenced basing it on position five of the proportionally distributed GCPs in the scanned area (measured by a Leica 500 dGPS with an accuracy of \(\pm\)0.02 m). In 2013, a network of eleven GCPs was first established to determine georeferencing. The instrumentation was based on the use of dual-frequency GNSS TOPCON receivers, namely TopCon Hiper II, which were operated by employing the base-rover survey method. An application of the real-time kinematic Global Navigation Satellite System (rtkGNSS) (GPS+GLONASS; both a Global Positioning System (GPS) and its Russian equivalent named Globalnaya Navigacionaya Sputnikovaya Sistema (GLONASS)) allowed us to make measurements with an accuracy of \(\pm\)0.02 m. The GCPs network was then used to locate both the scanner and the reference points. In 2010, the majority of the scans were made in high resolution mode (0.05/100 m), while for some part of July and the ensuing month of August in 2013, all measurements were obtained in medium resolution (0.1 m/100 m).
###### Abstract  
Assessing the effects of future land use and land cover change (LULC) on ecological processes and functions is crucial for improving regional sustainability in arid and semiarid areas. Taking the Agro-Pastoral Transitional Zone of Northern China (APTZNC) as an example, four IPCC Special Report on Emissions Scenarios scenarios (Scenario of economic emphasis on a regional scale (A1B), Scenario of economic emphasis on a global scale (A2), Scenario of environmental protection on a regional scale (B1), Scenario of environmental protection on a global scale (B2)) were adopted in the study to analyze the influence of the future land use and land cover change on the net primary production (NPP), soil organic matter (SOM), soil total nitrogen (TN), and soil erosion (ERO) using the model of Terrestrial Ecosystem Simulator-Land use/land cover model (TES-LUC) linking ecological processes and land-use change dynamics. The results were analyzed from the perspectives of LULC components, LULC conversions, and landscape patterns under the four scenarios. The main results include the following: (1) Environmentally oriented scenarios (A1B and B1) experienced the conservation of forest and grassland; economically oriented scenarios (A2 and B2) were characterized by significant loss of natural land covers and expansion of agricultural and urban land uses. (2) The NPP and soil nutrients are the highest while the ERO is the lowest in the woodland; the trend in cultivated land is opposite to that in woodland; the grassland ecosystem function is relatively stable and could make an important contribution to effectively mitigate global climate change. (3) The general trend in NPP, SOM, and TN under the four scenarios is B1 \(>\) A1B \(>\) baseline (2010) \(>\) B2 \(>\) A2, and that in ERO is A2 \(>\) B2 \(>\) baseline (2010) \(>\) A1B \(>\) B1. (4) Trade-offs between ecosystem functions and the ecological effects of LULC can be evaluated and formulated into decision-making.  
Keywords:land use and land cover change; IPCC SRES scenarios; net primary productivity; soil nutrient; terrestrial ecosystem simulator; land use/land cover model +
Footnote †: journal: Applied Sciences