esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 2 Related Work
### Routing Protocol Optimization  
The authors of [15] showed that cluster-based routing protocols can render the sensor network more energy efficient. The CH filters and aggregates the data sent by sensor nodes in same cluster, which can optimize the total amount of the sent data and reduce the distance of the communication. LEACH is a classic routing protocol based on the clustering structure [18; 19]. Each cluster and CH are all elected dynamically in each round. However, the election of the CHs does not consider the remaining energy of the nodes. Sensor nodes with less residual energy may be selected, which will influence the performance of the network. Leach-centralized (Leach-c) is a centralized version of LEACH [20]. In Leach-c, each node sends its own location information and energy information to the BS, and the BS calculates the network average residual energy. During the cluster head election, only the node whose residual energy is greater than the network residual energy can become the cluster head. Therefore, in terms of packet transfer rate and energy consumption, Leach-c produces better results than LEACH. In [21], an improved LEACH protocol is proposed and clustering is carried out based on the principle of Voronoi graph. The sensor nodes in the same Voronoi graph become a cluster, and the ant colony algorithm is added to the protocol to optimize the multi-hop routing protocol. The proposed method does improve the energy efficiency of the node, but it also increases the complexity of the algorithm. Considering the remaining energy of the node, DEEC proposed in [22] modified the probability of CHs to avoid the early death of the sensor node. In [14], when it comes to the selection of the CH, to make the load of the network more even, the distribution of the energy is considered. At the same time, the optimal number of CHs in the WSN is demonstrated. The distance between sensor nodes and the sink is also an important factor influencing the network performance, which is, however, not considered. In this paper, we take the distance from sensor nodes to the sink and the energy distribution of the network into account. The I-DEEC routing protocol is also designed to further prolong the lifetime of the WSN.
# A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments
## 2 Methodology and Data
### TOA Radiance Simulations  
Our approach to simulating TOA radiance for TEMPO, GEMS, and SENTINEL-4 is to first simulate radiance at scales smaller than the satellite resolution, and then aggregate the sub-pixels to the satellite resolution using the independent pixel approximation. This approach allows us to simulate realistic sub-grid scale variability of surface and cloud reflectance. The higher resolution radiance dataset also allows us to test different approaches to combining simultaneous measurements from the relatively coarse resolution air quality satellites with higher resolution weather satellites, such as GOES-R. The nominal sub-pixel resolution in our simulations is 1 km, corresponding to, for example, 8 sub-pixels for every one 2 km by 4 km TEMPO pixel.  
To achieve this high resolution radiance simulation we use high resolution surface reflectance datasets (when available), and statistically downscale the G5 NR simulated moisture fields following the approach in [PERSON] et al. (2019), which derives cloud liquid water path, cloud ice water path, and relative humidity for each sub-pixel. Aerosol dry mass mixing ratios in the sub-pixels are derived  
Figure 8: Lambertian equivalent reflectance mapped to the TEMPO grid from a 0.25\({}^{\circ}\) monthly climatology based on TOMS observations.  
from interpolating the G5 NR model outputs to the coarse TEMPO, GEMS, and SENTINEL-4 grids, which have comparable resolutions. Thus, because we lack a reliable method to downscale the aerosol concentration further, the aerosol dry mass mixing ratios within the coarse satellite pixel is constant. However, the downscaled 1 km relative humidity data is used to determine the aerosol hygroscopic growth, allowing for sub-grid scale variability in aerosol optical properties.
## 4 Relay Based QoS-Aware Routing Protocol for I2O WBAN
### Proposed Protocols
#### 4.4.1 Initialization Phase  
All in-body sensor nodes and relays are assigned a unique ID. The data transmission time division multiple access (TDMA) schedules for sensors and relays are based on those IDs. The first round starts once the network initialization and configuration have finished. The coordinator checks the energy status of all in-body sensor nodes.
## 2 Related Works
### Point Cloud Representation
#### 2.2.5 Advantages and Disadvantages of Selected Cartographic Projections  
The advantage of using the point cloud in the form of a spherical image is the processing speed for its generation, without the necessity of performing additional interpolation of new pixel values as the process is performed directly on the raw point cloud. However, the disadvantages of this method should be also considered. They include deformations of a generated image, such as the effect of \"distortion\" and large deformations, which occur for large values of angles, i.e., in the upper and lower parts of the raster. These geometric failures considerably influence the number and the distribution of tie points detected by algorithms applied in digital image processing [12; 36; 37].  
The main advantages of point cloud representation in the form of a \"virtual image\" include the possibility of applying algorithms that are commonly used for matching images and of eliminating the influence of \"distortion\", which occurs in spherical images. The basic disadvantage of this method is caused by the selection of an appropriate constant value for the \"virtual camera\". Any change in this parameter directly influences the resolution of the resulting image and makes it necessary to interpolate new pixel values. Additional problems occur when the reference plane is defined, since it requires additional transformations of the XYZ coordinates acquired by the terrestrial laser scanner [11; 17].  
The advantage of representing the point cloud in the form of an orthoimage is the elimination of the geometric deformations occurring in spherical images. Recording the data in an arranged way simplifies the generation of the DSM in the grid form. Moreover, this method of DSM recording does not require interpolation of new pixel values. The main disadvantage of the method is related to difficulties in detection and interpolation of planes used to generate the DSM. Detailed descriptions and comparisons of different algorithms for detection of planes are presented in publications on computer vision and photogrammetry [43; 44; 45]  
The advantage of using cartographic projections to represent the point cloud is the possibility of considering deformations in the process of raster data generation. The Mercator projection allows for projection of the upper fragments of scans with smaller geometric deformations.
## 1 Introduction
Explicitly alternative land use scenarios have been adopted to understand the impacts of land conversion on biodiversity [50; 51], hydrology [52; 53], carbon fluxes [54], and climate change [55; 56]. Facing the broad range of future uncertainties, IPCC SRES scenarios are helpful for land planners and stakeholders to visualize possible land use futures, explore likely effects of LULC on ecosystems, improve planning, and optimize management practices [57; 58; 59; 60]. The limitation of SRES land change scenarios are that they are unable to supply current demands, since high-resolution land use change products are inaccessible for the local scale. However, [PERSON] et al. [49] produced the relative new land use maps at a 1-km resolution under the four SRES scenarios. They provided the spatial detail necessary to identify the spatial heterogeneous response of land use, which can capture the complex land use patterns and depict human activities more accurately in the APTZNC. The APTZNC, located in the arid and semiarid regions, is characterized by rapid economic development and a fragile ecological environment, such as water shortage and soil erosion [61; 62]. Its structure, function, and carbon sink-source shift are vulnerable to environmental changes. Land degradation, such as a decrease in NPP, SOM, and TN, has occurred in the past several decades due to harsh natural conditions, overgrazing, and intensive cultivation. The coexistence of grassland, forest, and cropland makes the zone an ideal location to study how land use conversion alters the ecosystem functions [63]. Thus, the four ecosystem functions, NPP, SOM, TN, and ERO, served as ideal candidates for indicating ecosystem dynamics in our study. In the APTZNC, sustainable utilization of natural resources can help predict the future state of the natural resources through assessment of effects of LULC on different ecological systems and also aid with appropriate land-use policies and strategies. The objective of this study was to assess and understand the impacts of LULC on key ecological functions in the APTZNC that face an uncertain future, by coupling the land use change model and ecosystem process model. Therefore, the following questions motivated our work: (1) What are the spatial patterns of LULC under different scenarios? (2) What are the major consequences of land-use changes on ecosystem functions in the future? (3) What are the implications of mitigation and adaptation strategies to climate change?
## 2 System Model and Assumptions
### Decoding Conflict Model and Multimode Underwater Acoustic Communication  
The conventional model for network interference collisions is derived from terrestrial wireless networks and does not consider the characteristics of multipath propagation in underwater acoustic channels. As a result, it may provide an inaccurate description of interference conflicts. This paper presents a conflict decoding model designed to align with the propagation characteristics of underwater acoustic channels to achieve precise modeling of conflicts within underwater networks. The decoding conflict model is based on the bit error rate of receiving node \(i\), which determines whether receiving node \(j\) can successfully receive data packets. If the bit error rate of receiving node \(i\) is below a certain threshold, then node \(j\) can successfully receive data packets from node \(j\); otherwise, node \(j\) cannot receive data successfully. It can be expressed by the following formula:  
\[\begin{array}{l}b(w_{i},w_{j},d_{ij},SINR)\geq ber_{th}\\ SINR=\frac{P_{i}}{P_{i}+Noise}\end{array} \tag{8}\]  
where \(SINR\) represents the received signal-to-interference plus noise ratio at receiving node \(i\), \(P_{s}\) is the received signal power, \(P_{l}\) is the interference signal power, \(Noise\) is the noise power, \(w_{i}\) and \(w_{j}\) denote the positions of nodes \(i\) and \(j\), and \(d_{ij}\) stands for the Doppler between transmitting and receiving nodes. Thus, \(b(w_{i},w_{j},d_{ij},SINR)\) is the bit error rate value obtained under the current condition, and \(ber_{th}\) is the bit error rate requirement of the application layer.  
The following formula describes the decoding conflict model through the received signal-to-interference plus noise ratio:  
\[SINR_{ij}\geq SNR_{th}\big{(}w_{i},w_{j},d_{ij},ber_{th},f\big{)} \tag{9}\]  
where \(f\) is the processing algorithm of the current communication mode, and \(SNR_{th}(w_{i},w_{j},d_{ij},ber_{th},f_{c})\) is the minimum received signal-to-interference plus noise ratio to achieve conflict-free transmission under the constraints of the current sending and receiving conditions and the transmission requirements of the application layer.  
It is assumed that the network nodes all support three communication modes, namely, PSK, MFSK, and PRSS, and the communication rate is \(v_{PSK}>v_{MFSK}>v_{PRSS}\).
## 2 Vehicle Detection Framework Using Selective Multi-Stage Features
### Selective Multi-Stage Feature Fusion  
Visualization steps 1-13 show how the feature map visualizations learned for each layer in the CNN model can be examined. We can identify distinct feature map properties in each layer. However, it is not appropriate to use all these feature maps learned in the CNN model as the vehicle detection feature set. We require an efficient feature set to improve vehicle detection performance while reducing feature dimensionality. Therefore, we propose to fuse selective multi-stage features based on the feature map visualizations, as shown in Section 2.4.  
The selective multi-stage feature fusion process was as follows: First, the feature maps were examined using the visualization technique and a few features were selectively extracted from each layer. We then concatenated the selected features into a 1D vector. The criterion for selecting features in each layer was to include the characteristic vehicle image information or to select a feature map with a large activation among the 60 learned feature maps. The characteristic information is an important factor in vehicle detection, because this information is the most effective cue that the detector uses to distinguish whether the target object is a vehicle or non-vehicle. The HOG and LBP features used for conventional vehicle detection frameworks cannot be considered to include the characteristic vehicle image information, because the existing features only use the edge gradient or brightness information. Thus, the proposed fusion of the selective multi-stage feature method differs significantly. Figure 6 shows that information such as the vehicle's head lamp is the characteristic vehicle image information. Only some of the feature maps among the 60 in Maxpool_1 layer intensively learned the vehicle head lamp information.  
We selected the feature maps with a large activation because the corresponding feature was the best learned in each layer. Therefore, the proposed selection and fusing of the maximum value feature maps is effective in enhancing vehicle detection performance. Figure 7 shows the maximum value feature maps for each layer. The white pixels show that significant activation for these feature maps is considerably larger than that for the other feature maps in each layer.  
Thus, considering the feature dimensions to be fused and the characteristic vehicle image information, we extracted features from the Maxpool_1, fourth convolution, and fifth convolution layers, and combined them into a 1D vector to create the feature set for the proposed vehicle detection framework. As several features could be extracted from each layer, we compared performances by fusing features in various combinations, where the best performing feature set was chosen.  
Figure 6: Characteristic vehicle image information from the Maxpool_1 layer: (**a**) 24 th, (**b**) 42 th, and (**c**) 45 th feature maps.
## 3 Results and Discussion
### Characterization of Hydration Products
#### 3.3.1 XRD Results  
Based on the XRD patterns of the 28-day hydration products of the cement samples shown in Figure 9, the evolution of cement reactivity is consistent across all cases, and comparable hydration products are observed. The diffraction peak of calcium hydroxide(Ca(OH)2) is clearly evident. Silicate is the primary product of calcium silicate hydration, with hydrated calcium silicate forming concurrently. Its presence further confirms that the reaction rate of the cement samples correlates with the reduced intensity of the corresponding clinker peak. This further substantiates the reliability of the data presented in Table 6. Although C-S-H gel is the predominant product after hydration, it does not produce a distinct diffraction peak in the XRD spectrum due to its amorphous nature or its presence in the form of semi-crystalline needles. After 28 days of curing, the hydration of C4 AF remains incomplete. In the XRD diffraction patterns of all samples, a mi  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Setting Time (min)**} & \multicolumn{1}{c}{**Consistency**} & \multicolumn{1}{c}{**Fineness**} & \multicolumn{1}{c}{**Soundness**} \\  & **Initial** & **Final** & **(\%)** & **(\%\%)** & **(\%mm)** \\ \hline H\({}_{0}\) & 197 & 305 & 23.8 & 14.0 & 1.2 \\ H\({}_{\text{DG1}}\) & 193 & 303 & 23.8 & 14.2 & 1.3 \\ H\({}_{\text{DG2}}\) & 189 & 302 & 23.9 & 14.2 & 1.4 \\ H\({}_{\text{YMX1}}\) & 189 & 301 & 24.1 & 14.1 & 1.0 \\ H\({}_{\text{YMX2}}\) & 184 & 299 & 24.5 & 14.2 & 1.2 \\ H\({}_{\text{HMI}}\) & 193 & 303 & 23.8 & 14.3 & 1.1 \\ H\({}_{\text{IM2}}\) & 191 & 303 & 23.8 & 14.5 & 1.1 \\ H\({}_{\text{SY1}}\) & 195 & 302 & 23.8 & 13.8 & 1.2 \\ H\({}_{\text{SY2}}\) & 191 & 301 & 23.9 & 14.0 & 1.2 \\ Reference & \(\geq\)45 & \(\leq\)390 & - & \(\geq\)5.0 & \(\leq\)5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Physical properties of nine cement samples.  
nor peak corresponding to the unreacted C\({}_{4}\)AF is observed at approximately 44.5\({}^{\circ}\). Unlike H\({}_{0}\), the other four samples exhibited distinct Na\({}_{2}\)SO\({}_{4}\) diffraction peaks at approximately 32\({}^{\circ}\), attributed to the higher alkali content in the ash of biomass alternative fuels compared to coal ash. The presence of alkali promotes sulfate formation, further corroborating the conclusion drawn in Section 3.2.4.
# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
## 1 Introduction
### Factors That Raise Marine Traffic Engineering Awareness
#### 1.2.3 Impact of Maritime Accidents  
A maritime accident would cause irreparable damage to the ecosystem of the Aegean Sea and socio-economic losses to both Greece and Turkey. In the past, within the Aegean Sea, several accidents have occurred, causing irreparable damage to the environment. The sinking of the tanker \"Irene Serenade\" resulted in a crude oil spill of 120,000 tons near the touristic ad historic Greek city of Pylos. The sinking of the cruise ship \"Sea Diamond\", which happened in the Greek island of Santorini in 2007, was a marine accident that cost two lives, about 7.2 million Euros to the Greek state and caused major ecological disaster in the Aegean Sea after spilling 250,000 liters of diesel [6]. Nowadays, 11 years later, the ship continues to contaminate the area of the continuous leakage of oil and a series of toxic pollutants such as asbestos and mercury, as the hull of the ship is decomposing. Studies have shown that these contaminants can pass into the human food chain, since local fishes caught for consumption.  
Figure 1: A 24h shipping trajectory compilation in the Aegean Sea. Source: Archipelagos®, Samos, Greece.
## 6 Recent Advancements in Analytical Methods
### Clean-up  
After the preliminary extraction, the crude extract typically requires further clean-up. In this regard, SPE has been frequently used for the removal of residual or co-extracted materials from the compound of interest, namely BPA. However, the performance of this method depends on the careful selection of a suitable sorbent material for the BPA analytes as well as selection of appropriate elution solvents. Isolation of BPA from homogenized samples of animal origin based on two consecutive SPE steps has been described by [PERSON] and [PERSON] [6; 7; 89]. These authors reported using a moderate/highly specific adsorption surfaced (>1000 m\({}^{2}\)/g) polystyrene-divinylbenzene polymer (PS-DVB) conditioned with methanol and water for the first stage, and a molecularly imprinted polymer (MIP) stationary phase conditioned with methanol, formic acid, acetonitrile, and water for the second SPE. While PS-DVB is able to retain hydrophobic compounds similar to reverse-phase chromatography, MIP stationary phases are based on a specific functionality to selectively separate estrogenic compounds due to phenolic interactions, thus making the retention of BPA more specific [94]. [PERSON] et al. compared silica and amino-propyl SPE cartridges for the purification of crude meat samples and found recoveries of more than 91% for each cartridge [84]. However, samples cleaned with silica cartridges contained more fat after SPE. Other sorbents, such as an amino sorbent (Strata NH2 cartridge) conditioned with methanol/acetone and hexane were used by [PERSON] and [PERSON] [15; 20]. [PERSON] et al. used a Florisil column conditioned with acetone/heptane (5:95, \(v/v\)) to remove fat from the extracts [83]. Recently, [PERSON] et al. reported using a reversed-phase polymeric (Strata-X) SPE cartridge for the analysis of bisphenol S (BPS) including five other bisphenols from several food items including meat [76]. The mean recovery of the proposed SPE method ranged from 81 to 108% for all types of bisphenol present in food items. This SPE cartridge offers multiple retention characteristics including hydrophobic, \(\pi\)-\(\pi\)-interactions as well as hydrogen bonding. Moreover, as a result of its multimode interactions and its polymer base, it retains polar analytes more tightly than traditional C18 sorbents resulting in high recoveries and clean eluents [95].
## 6 Conclusions  
Modifying the HIPERLAN/2 system to include and utilize a higher modulation scheme increases the data rate at the cost of increasing the required SNR that can achieve the same BER. In addition, introducing the MIMO system and transmit code diversity (Alamouti code) improves system performance, such as the data rate and capacity. The paper also suggested the use of full-duplex mode,  
Figure 17: Modified HIPERLAN/2 performance, 32 QAM, when the transmitted power is varying (a) nTx = 2, nRx = 1, and (b) nTx = 2, nRx = 2.  
where the transmit and the receive antennas are closely located. From the simulated results, it can be noticed that less SNR is required as the modulation scheme complexity is reduced, because the probability of recovering the received symbols will be increase as the modulation scheme complexity is decreased. Moreover, the results showed that the performance of the suggested system is heavily depended on the amount of the transmitted power, received power, and feedback accuracy. As the transmitted power increases, the system performance will be decreased when the other parameters are constant, and at the same time, when the transmitted power of the first user is 50 dBm, the receiver was not able to recover the transmitted signal from the other user because the amount of the transmitted power of the first user blocks the received signal. On the other hand, the increase of the received power helps in reducing the BER and improving the system performance when the other parameters are constant. Finally, the feedback accuracy is directly proportional to the system performance.  
Conceptualization, M.A.Y., I.A.S., O.I.A. and A.M.A.; methodology, A.M.A., M.A.Y. and O.I.A.; software, A.M.A., R.A.A.-A. and M.A.Y.; validation, R.A.A.-A., N.O.P., I.A.S. and Y.I.A.A.-Y.; formal analysis, R.A.A.-A., N.O.P., Y.I.A.A.-Y. and I.A.S.; investigation, M.A.Y., I.A.S., O.I.A., N.O.P. and Y.A.-Y.; resources, A.M.A., M.A.Y., R.A.A.-A., O.I.A. and Y.I.A.A.-Y.; data curation, M.A.Y., A.M.A. and R.A.A.-A.; writing--original draft preparation, A.M.A., M.A.Y. and R.A.A.-A.; writing--review and editing, O.I.A., I.A.S., A.M.A., M.A.Y., N.O.P. and R.A.A.-A.; visualization, M.A.Y., A.M.A., I.A.S. and O.I.A.  
This project has partially received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424.  
Authors wish to express their thanks for the support provided by the innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424.  
The authors declare no conflict of interest.