esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method and Materials
### Measurement of the Water Use Efficiency for Soil Conservation  
In its broadest sense, the water use efficiency is the net return for a unit of water used [11], and in previous research the crop water use efficiency is expressed as the amount of grain yield (e.g., kilograms of grain) obtained per unit of water consumption (e.g., cubic meters of water) [19,40]. Besides, depending on the type of water sources considered, crop water use efficiency is generally expressed as grain yield per unit water evapotranspiration or grain yield per unit total water input (irrigation plus rainfall) [40]. For example, there are three major definitions of water use efficiency that are widely used, _i.e._, (1) Gross primary production (GPP) based water use efficiency: GPP/ET; (2) Net primary productivity (NPP) based water use efficiency: NPP/ET; (3) Net ecosystem carbon production (NEP) based water use efficiency: NEP/ET [20]. All these definitions only reflect the water use efficiency for primary production, but all involve ET, which is the most active process in the hydrological cycle and is also a major component of energy and water balance in agriculture ecosystems [41,42], and therefore the water consumption is also represented with ET in this study. In addition, since this study primarily focuses on the soil conservation service, which is one of the most important ecosystem services provided in the lower Heihe River Basin, the water use efficiency for the soil conservation is calculated as the soil conservation amount divided by ET as follows.  
\[WUE\text{-}SC=SC\,/\,ET \tag{5}\]  
where \(WUE\text{-}SC\) is the water use efficiency for soil conservation (Unit: t mm\({}^{-1}\)), \(SC\) is the soil conservation amount (Unit: t), and \(ET\) is the evapotranspiration (Unit: mm), which is assumed to be the water used by vegetation to provide the ecosystem services such as soil conservation. In this study, the average \(WUE\text{-}SC\) over a region was calculated as the area-weighted average value of all grid cells. It is noteworthy that the \(WUE\text{-}SC\) of water bodies was also calculated in the same way as other land cover types in this study.
# Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer's Disease
## 4 Evaluation
### Experimental Results  
The classification performance was assessed using the following four common specificity indicators: accuracy (ACC), sensitivity (SEN), specificity (SPE), and precision (PRE). True positive, true negative, false positive, and false negative are labeled TP, TN, FP, and FN. Then, ACC, SEN, SPE, and PRE can be expressed as  
\[\text{ACC}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+\text{ FN}}\]  
\[\text{SEN}=\frac{\text{TP}}{\text{TP}+\text{FN}}\]  
\[\text{SPE}=\frac{\text{TN}}{\text{TN}+\text{FP}}\]  
\[\text{PRE}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
In order to demonstrate the superiority of this method, we compared various studies of different types in this field on ADNI public datasets, as shown in Table 2. Experimental results show that our method has excellent performance. At the same time, we compared studies based on Transformers. These studies also performed well, benefiting from the ability of Transformers to capture distant information. Compared with the research in [21], which also combines transfer learning technology, the performance of the RST architecture is significantly improved. It can be seen that our proposed RST architecture can effectively classify AD and NC.  
Figure 5: **(a–c)** The variation in ACC with epoch for different batch sizes with learning rates of \(1\times 10^{-5}\), \(5\times 10^{-5}\), and \(1\times 10^{-6}\), respectively. **(d)** Comparison of the two curves with the optimal variation in ACC.
## 4 Experiments
### Baseline Methods  
The proposed STGNN model is compared with two types of methods: non-graph methods and graph-based methods. For the non-graph methods, the chosen baseline models are (1) LSTM [51]: Long Short-Term Memory network, a special RNN model. (2) Reformer [31]: An effective transformer replaces dot-product attention with one that uses locality-sensitive hashing for long sequence modeling. (3) Autoformer [47]: A novel decomposition architecture uses an auto-correlation mechanism based on the series periodicity to conduct the dependencies discovery and representation aggregation at the sub-series level. (4) TimesNet [52]: A temporal 2D variation model extends the analysis of temporal variations into 2D space by transforming the 1D time series into a set of 2D tensors based on multiple periods.  
As for the graph-based methods, the chosen baseline models are (5) GCN [53]: A graph convolution network is used to capture spatial dependence. (6) STG-NODE [54]: A spatial-temporal graph network based on a neural-controlled differential equation extends the concept of a neural-controlled differential equation and designs two neural-controlled differential equations for temporal processing and spatial processing. (7) T-GCN [25]: A temporal graph convolution network combines graph convolution and gated recurrent units to capture spatial dependence and temporal dependence. (8) ASTGCN [41]: a spatial-temporal convolutional network based on an attention mechanism uses temporal and spatial attention to capture spatial-temporal dependencies. (9) ASTGNN [55]: a spatial-temporal graph network is based on attention mechanisms, graph convolution networks, learning dynamics, and the heterogeneity of spatial-temporal graph data.
## 3 An Update on Genetic Resistance to Soilborne Fungal Pathogens of Small-Grain Cereals  
Adapted small-grain cereal cultivars lack effective resistance or tolerance against _Fusarium calmorum_ and _Rhizoctonia solani_, and genetic resistance in wild relatives has been scarce. An interesting locus in wheat was identified from mapping the recombinant inbred line population of the landrace IWA8608077 (resistant) crossed to Louise [25]. A major QTL for _Pratylenchus_ resistance on Chr 5A also was associated with partial resistance to _F. culmorum_ and _R. solani_ AG-8. Partial resistance was evident as reduced discoloration of internode and crown tissue for _F. culmorum_, and reduced foliar stunting for _R. solani_ AG-8, compared to controls. The _P. neglectus_ and _R. solani_ loci co-mapped to the same genetic interval (cM), whereas recombination was observed between that locus, the _P. thornei_ locus, and _F. culmorum_ locus. These and additional findings suggested that three different genes or gene regions from IWA8608077 were contributing to the resistances on Chr 5A. A QTL on Chr 5A of spring wheat accessions was involved in resistance against _F. culmorum_ and _F. gramitearum_, pathogens of the Fusarium head blight complex [60].  
Genetic resistance to _R. solani_ AG-8 of wheat and barley has been reported [61; 62], but not integrated into breeding programs due to lack of molecular markers or field performance. Recent screening and mapping of populations derived from synthetic or synthetic-derived CIMMYT wheat accessions resulted in five new sources of resistance or partial resistance; mapping of the trait was carried out for two sources [63; 64]. For both sources, reduced disease severity was deemed to be due to tolerance, rather than resistance, although soil population densities of the pathogen were not quantified. Two evaluations for shoot-stunting were carried out in field plots where pathogen buildup in the soil was favored. The field screens were interspersed with a greenhouse root rot assay. In 190 recombinant inbred lines from a cross of synthetic-derived SCPB-3104 and susceptible PNW spring wheat cultivar Louise, three tolerance QTL were identified using genotyping-by-sequencing and composite interval mapping [63]. Markers associated with the QTL mapped to Chr 1 BL, 2 AS, and 2 DL. A similar study of a cross between SYN-172 and Louise yielded three consistent QTL on Chr 1 AL, 7 DS, and 7 DL, with 7 DL derived from Louise [64]. Transgressive segregation was observed for both the SPCB-3104 and SYN-172 mapping populations.
# An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks
## 4 Performance Evaluation  
We evaluate our Origin-based Continuous Object Tracking (OCOT) through computational simulation with existing studies: TTDD [21] and LBDD [22]. The purpose of simulations is verification that OCOT is suitable for continuous object tracking supporting sink mobility in terms of energy-efficiency and network lifetime rather than existing studies.
## 4 Investigating Parameters' Impact on Beacon Rate Adaptation
### Parameters Used and Their Significance
In the presence of a substantial number of neighboring nodes, it is advisable to decrease the beacon frequency to alleviate the load on the channel. The count of neighbors is determined by the number of adjacent vehicles within the transmission range of vehicle \(x\). The signal-to-interference-noise ratio (SINR) is computed by utilizing beacons obtained from neighboring vehicles. It is utilized to estimate the link quality [53; 58]. A large value of the SINR could mean a neighboring vehicle is closer to the sending vehicle, requiring beacon frequency to be reduced. The SINR is computed using the following equation:  
\[SINR=\frac{S}{I+N} \tag{5}\]  
In this context, \(S\) denotes the received signal power, \(I\) represents the cumulative power of interference signals, and \(N\) indicates the noise power [59]. Subsequently, the span SINR is calculated to obtain the SINR factor, given by:  
\[SINRFactor=\frac{SINR_{x}-SINR_{min}}{SINR_{max}-SINR_{min}} \tag{6}\]  
where \(SINR_{x}\) is the SINR computed by vehicle \(x\). \(SINR_{max}=max[SINR_{i}]\) and \(SINR_{min}=min[SINR_{i}]\), where \(i=\{1,..,n\}\).
## 5 Analysis of the Dynamic Response Law of Non-Homogeneous Slopes
### Dynamic Response Analysis of Non-Homogeneous Slopes
#### 5.2.3 Comparison of the Amplification Effect of Non-Homogeneous Slopes and Nodal Inclined Slopes  
Figure 19 shows the patterns of M\({}_{\rm PGA}\) variation with elevation at different monitoring points on the slope surface for layered slopes corresponding to joint dip angles of 0\({}^{\circ}\), 15\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 75\({}^{\circ}\), and 90\({}^{\circ}\). From Figure 20, it can be observed that the maximum M\({}_{\rm PGA}\) value is observed in the slope with a 60\({}^{\circ}\) joint dip angle, at 1.78, while the minimum value is observed in the slope with a 0\({}^{\circ}\) joint dip angle, at 1.35, resulting in a maximum-to-minimum ratio of 1.32.  
Figure 19: M\({}_{\rm PGA}\) variation pattern of joint slope surface.  
Figure 18: PGA variation patterns of different shape parameters in multi lithology models. (**a**) Slope surface; (**b**) slope interior.  
Figure 20 shows that the models include random distributions of the elastic modulus, cohesion, and friction angle, with shape parameters of 1, 3, 5, 7, and 9, representing different scenarios of increasing dispersion of these three parameters. The graph depicts the variation of \(\mathrm{M_{PGA}}\) at various monitoring points on the slope surface with respect to elevation under the action of the blasting vibration. From Figure 20, it is observed that the maximum \(\mathrm{M_{PGA}}\) value is observed in model \(\beta=1\) at 1.39, while the minimum value is observed in model \(\beta=9\) at 1.08, resulting in a maximum-to-minimum ratio of 1.29.  
Comparing the amplification coefficients of joint dip angle slopes and heterogeneous slopes, under the same physical mechanical parameters of the rock mass, the amplification coefficient of the joint dip angle slopes is generally greater than that of the heterogeneous slopes. This indicates that structural distribution has a stronger influence on slope amplification effects than rock mass heterogeneity, especially at relative heights of 0.2\(\sim\)0.5, where the amplification effect of the joint structures weakens the attenuation of the blasting vibration waves with an increasing distance from the blast center. Comparing the ratio of amplification coefficients for different dip angles with the ratio of amplification coefficients for different random distribution parameters, the two are relatively close. This suggests that studying the relative amplification level of different joint dip angle structural distributions can reference the relative amplification level of rock mass heterogeneity distributions. This is also a novel application of heterogeneous random distribution methods.
## 2 Special Issue Papers
Also, they report on the experimental assessment of this core building block for interconnecting network elements as well as computing and storage resources, which verifies the advantages of using SOAs to realize the WDM cross-connect switch in terms of transparency, switching speed, photonic integrated amplification for lossless operation, and gain equalization. [PERSON] in Ref. [26] reviews the current status of fast reconfigurable medium-scale Indium Phosphide (InP) integrated photonic switch matrices based on the use of SOA gates. The focus is on broadband and cross-connecting monolithic implementations that grant multi-input/output port and channel connectivity based on a packet-compliant SOA multi-stage switching matrix. The opportunities for increasing connectivity, enabling nanosecond-order reconfigurability, and introducing distributed optical power monitoring at the physical layer are highlighted. Furthermore, complementary architectures based on resonant switching elements developed on the same material platform for power efficient switching are considered. Lastly, performance projections related to the physical layer are presented, and strategies for improvements in view of opening a route towards large-scale power efficient fast reprogrammable photonic integrated switching circuits are discussed. The leveraging of SOAs as nonlinear elements in the context of all-optical functionalities is treated by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] in Ref. [27]. In particular a wavelength converter is developed, which is comprised of an SOA that takes advantage of four wave mixing (FWM) and a fast-switching sampled grating distributed Bragg reflector (SG-DBR) tunable laser as one of the pump sources. By studying phase noise issues in FWM as well as vector theory in SOAs in conjunction with a detailed characterization of the SGDBR laser, rapid and reconfigurable wavelength conversion is experimentally demonstrated on bursty/packet data of advanced modulation formats towards dynamic, adaptive, and bandwidth-efficient next generation transparent optical networks. Finally, the research activity on Optical Code Division Multiple Access (OCDMA) is represented by [PERSON] and [PERSON] in Ref. [28]. This paper explores the use of SOAs in the transmitter side of such a system based on multi-wavelength picoseconds code carriers for mitigation of temporal distortion of an OCDMA auto-correlation affected by the temperature induced dispersion changes in a fiber optic transmission link. To this aim, it is shown both experimentally and by simulations, that a distorted OCDMA auto-correlation due to the temperature-induced fiber dispersion can be corrected by manipulating the chirp of code carriers when traversing a biased SOA prior to entering the transmission link.
# Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
## 3 Results and Discussion  
Vegetation indices derived from remote-sensing images are an efficient tool for highlighting the spectral differences due to changes in leaf pigments and internal leaf structure, which are indicators of plant health [57]. The NDVI can detect changes in vegetation chlorophyll content, internal leaf structure, and water content that relate to stress [31]. The leaf chlorophyll content is sensitive to hydrocarbon pollution, and organic molecules from crude oil transported into the plant vascular system and intercellular spaces can lead to cell and tissue damage [58]. Accordingly, higher NDVI values would be expected for healthy vegetation with high chlorophyll content, whereas low values would be anticipated for vegetation whose health has been impacted by oil spills. The changes in vegetation health in oil-polluted sites may depend on the volume of a given oil spill because it is assumed that a larger volume of oil spills may have a greater impact on the surrounding vegetation [30]. In this study, we combined satellite base-derived NDVI with statistical analysis to determine the impact of the oil spill volume and the time after an oil spill using linear regression. We also determine the difference between the changes in the NDVI values of the spill site and their corresponding non-spill location using a paired \(t\)-test.
## 5 Evaluation
### Multi-Case Evaluation Methodology
The multi-case evaluation methodology includes a study protocol that can be followed to evaluate the usability of a DSML, taking into account the above execution and quality dimensions. The study protocol consists of three major phases: Preparation, Execution, and Analysis [88]. In the _Preparation_ phase, research questions are defined, case studies are specified, and the evaluator teams participating in the evaluation studies are determined. The _Execution_ phase covers the design and implementation of the software systems required for the use cases with and without using the DSML in question. Evaluator teams perform analysis, design/modeling, implementation, and testing tasks for each case study during the Execution phase of the methodology. Following the system development, all participants answer questions to measure the features of the DSML according to the FQAD characteristics, in addition to a set of open-ended questions. Finally, in the _Analysis_ phase, all data collected from the execution phase are used to prepare quantitative and qualitative assessment reports, tables and charts.
# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 4 Performance Evaluation
### Comparative Evaluation of UDP under Increased Localization Inaccuracy  
The previous results refer to the case where the estimation of user locations in the region of interest is perfect and there are no localization errors. Moreover, the authors in [28] state that 95% of localization errors in LTE systems are less than 20 m. Figure 9 illustrates the impact of the localization error of 5 m, 10 m and 20 m to the average received signal strength of the UEs located in the area of interest for the UDP and KDP methods, respectively. It is noteworthy that these localization errors can be well-approximated via a zero-mean normal distribution with standard deviation \(\sigma\) of 0 m, 5 m, and 10 m [23]. In addition, we evaluate the performance of the proposed UDP method against the KDP in case of an error bigger than 20 m, raising the standard deviation equal to 20, where such a state can be considered as an extreme case. The results show that there is approximately 1 dBm difference regarding the received signal strength between the UDP best scenario (no error) and the worst scenario (\(\sigma\) = 20 m). It is noticed that even with a significant error in user location estimation, UDP development still retains its advantage over the KDP method. This is due to the fact that the algorithm is not sensitive to extreme outliers, which most likely occur with the addition of an error greater than 20 m.
## 3 Results and Discussion
### RTFOT Results  
Table 12 gives RTFOT mass loss values and S/N values calculated according to the \"smaller is better\" performance statistic. The optimum levels for minimizing the RTFOT mass loss percentage were found as A4, B2, C1, and D1. The ratio of the Cecabase RT BIO10 additive had a notable impact on the mass loss. As the additive rate increased, the mass loss decreased. The mixing temperature, time, and speed had a significantly lesser effect than the additive ratio. RTFOT mass loss is an essential indicator of the amount of  
oxidative aging and hardening the bitumen undergoes [72]. The additive increased the pavement performance and improved durability and flexibility by making virgin bitumen more resistant to aging.
###### Abstract  
Cognitive radio networks have emerged to exploit optimally the scarcely-available radio spectrum resources to enable evolving 5G wireless communication systems. These networks tend to cater to the ever-increasing demands of higher data rates, lower latencies and ubiquitous coverage. By using the buffer-aided cooperative relaying, a cognitive radio network can enhance both the spectral efficiency and the range of the network; although, this could incur additional end-to-end delays. To mitigate this possible limitation of the buffer-aided relaying in the underlay cognitive network, a virtual duplex multi-hop scheme, referred as buffer-aided multi-hop relaying, is proposed, which improves throughput and reduces end-to-end delays while keeping the outage probability to a minimum as well. This scheme simultaneously takes into account the inter-relay interference and the interference to the primary network. The proposed scheme is modeled as a Markov chain, and Monte Carlo simulations under various scenarios are conducted to evaluate several key performance metrics such as throughput, outage probability, and average packet delay. The results show that the proposed scheme outperforms many non-buffer-aided relaying schemes in terms of outage performance. When compared with other buffer-aided relaying schemes such as max-max, max-link, and buffer-aided relay selection with reduced packet delay, the proposed scheme demonstrated better interference mitigation without compromising the delay performance as well.  
c pacs: 895
## 2 Non-Equilibrium Thermodynamic-Based Model Framework  
### Thermodynamic State Variables  
From the point of view of material structure, geotechnical materials are significantly different to ordinary fluids or solids, such as crystals and metals [28]. In addition to macro and micro space levels, geotechnical materials also have a granular level, also called the mesoscopic level. The interaction at this level is the contact force between particles. There is also irregular motion at the mesoscopic level, such as particle sliding, collision, and rolling, which is also called the granular fluctuation motion. Condensed-matter physicists use the concept of granular entropy \(S_{g}\) or granular temperature \(T_{g}\) to describe the fluctuation motion at the mesoscopic level by analogy with the irregular motion of molecules at the micro level [29]. Additionally, solids, including geotechnical materials, have a special feature of spatial translational symmetry breaking [30], which will cause the material to add a state variable in relation to deformation, i.e., elastic strain \(\varepsilon^{e}_{ij}\). It is worth noting that the total strain \(\varepsilon_{ij}\) is not directly used as the state variable, because it may contain plastic strain \(\varepsilon^{p}_{ij}\) and cannot correspond to the thermodynamic state.  
Thus, \(\left\{\rho,m,S,\varepsilon^{e}_{ij},S_{g}\right\}\) can be taken as thermodynamic state variables for geotechnical materials, where \(\rho\), \(m\), and \(S\) are density, momentum, and entropy, respectively, which are the common state variables for ordinary fluids and solids. Since unsaturated geotechnical materials are a mixture of solid (S), liquid (L), and gas (G), its thermodynamic state variables can be written as bulk density \(\rho^{a}=n^{a}\rho_{a}\), momentum \(m^{a}_{i}=\rho^{a}v^{e}_{i}\), total entropy \(S=n^{S}S_{S}+n^{l}S_{L}+n^{G}S_{G}\), granular entropy \(S_{g}\), and elastic strain \(\epsilon_{ij}^{e}\), where \(\rho_{a}\), \(n^{a}\), and \(v_{i}^{a}\) are intrinsic density, porosity, and velocity, respectively, and \(a=[S,L,G]\). Additionally, its corresponding conjugate variables are \(\left\{\mu_{ca},v_{i}^{a},T,\pi_{ij},T_{g}\right\}\), where \(\mu_{ca}\) is the chemical potential, \(T\) is the temperature, \(T_{g}\) is the granular temperature, and \(\pi_{ij}\) is the interaction force between particles, respectively [31].