esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Sample Test: Linen  
After studying the spatial distribution properties and the capability to resolve objects of our TPI system, we used this experimental apparatus on a sample test: a natural linen pattern. This is a quite regular pattern with horizontal and vertical linen fibers, separated by air holes (see Figure 9f). Each vertical fiber width is \(\sim\)0.40 \(\pm\) 0.05 mm (\(\sim\)20 fibers/cm) and each horizontal fiber \(\sim\)0.15 \(\pm\) 0.05 mm (\(\sim\)17 fibers/cm). Holes sizes vary from \(\sim\)50 \(\upmu\)m to \(\sim\)600 \(\upmu\)m.  
The image corresponding to 0.2 THz is a uniform distribution of intensity pixel values. The low contrast and spatial resolution result in no distinguishable linen structure, due to its strong sub-diffraction nature. At 0.7 THz, a horizontal dark line starts to become visible. It is associated to the presence of a not resolved linen fiber (or more). Moving to high frequencies and increasing the spatial resolution, more linen fibers are identifiable, as it is fibers (black zones) are visible, while the white zones are the hole between linen fibers. Figure 9f shows the digital image and can be compared to the 1.3 THz image, that has the best resolution, among linen _spectral_ images. The two vertical linen fibers are well resolved because spaced by big holes (>150 \(\upmu\)m), while the three horizontal pairs are not resolved and only three dark regions are visible.  
In order to obtain image de-blurring, we performed image deconvolution. With respect to Equation (2), we chose Figure 10a (1.3 THz linen image) as the object \(o(x,y)\) being the most resolved linen image and 10b (200 \(\upmu\)m pinhole image at 1.3 THz) as \(PSF(x,y)\). By deconvolving them with ImageJ deconvolution tool, we obtained Figure 10c.  
As seen in the de-blurred Figure 10c, the dark regions, corresponding to the linen fibers, become better identifiable, thus an image quality enhancement is visible, resulting in a sharper image. In Figure 10d,e we report the intensity profile and its derivative, for both the original and deconvolved linen images. The red boxes in Figure 10a,c show the pixel lines selected for intensity profile and derivative plots. In Figure 10e \(D_{1}\) and \(D_{2}\) denote the differences between derivative maximum and minimum for original and deconvolved images, respectively. By computing the ratio \(R=D_{2}/D_{1}\) an estimation of the quality improvement can be given. The deconvoluted image shows a 1.6-fold quality improvement.  
Figure 9: Six images of the same linen sample. (**a**–**e**) Five _spectral_ images (12 px \(\times\) 30 px, 1.2 mm \(\times\) 3 mm) at 0.2, 0.7, 0.8, 1.0, 1.3 THz. They have different gain \(g\) values, see Equation (10). The scale bar is 0.3 mm and the spectral resolution is 0.1 THz. 100% and 0% transmittance are referred to white and black gray levels, respectively. (**f**) Digital image.
## 4 A Typical Application: Wuhan Huoshenshan Hospital
### Evaluating the Design Scheme of the Air Outlets and Fresh-Air Intakes  
According to the simulation results of the conceptual designs, the air outlet heights for the hospital were designed as 8 m and 10 m. The fresh-air intakes were mainly distributed at the height of 2 m, while a few of the intakes were arranged at the heights of 3.9 m and 7.0 m due to construction constraints. The air outlets were located at the end of the ward building, while the fresh-air intakes were mainly distributed in the middle of the ward building. Based on the SketchUp model constructed by the design agency, the FDS model shown in Figure 17 was constructed through the modeling method introduced in Section 2.1.3. The numbers of air outlets and monitoring points near fresh-air intakes were 52 and 141, respectively. In the FDS model, the pollutant release rate for each air outlet was determined based on the HVAC design results of the Wuhan Huoshenshan Hospital, and then the mass fraction of harmful air for each air outlet was calculated through Equation (1).  
As the hospital is approximately 360 m \(\times\) 320 m in size with a height of 12 m for the tallest building, a computational domain of 680 m \(\times\) 640 m \(\times\) 80 m was constructed, which is in agreement with the AIJ [38] best practice guideline. In terms of the grid scheme, the grid size of the domain under the height of 25 m was set to 0.5 m \(\times\) 0.5 m \(\times\) 0.5 m. The grid size of the domain between the heights of 25 m and 50 m was set to 1.0 m \(\times\) 1.0 m \(\times\) 1.0 m, while that of the remaining domain was set to 2.0 m \(\times\) 2.0 m \(\times\) 2.0 m. The total number of cells is \(9.955\times 10^{7}\). According to the FDS user guide [33], the \"PERIODIC\" boundary condition was adopted for the inlet, outlet, and lateral boundaries, while the \"GROUND\" and \"OPEN\" boundary conditions were used for the ground and top boundary, respectively.  
Figure 16: Maximum concentrations of harmful air near fresh-air intakes with different heights.  
Figure 17: FDS model of the Wuhan Huoshenshan Hospital.
## Materials and Methods
### Data
#### 2.2.2 Remote-Sensing Data  
At the site scale, the MODIS products of the _LST_, enhanced vegetation index (_EVI_), and land surface water index (_LSWI_) at each site-year (2003-2013) were downloaded from the University of Oklahoma Data Center ([[http://www.eomf.ou.edu/visualization/manual/](http://www.eomf.ou.edu/visualization/manual/)]([http://www.eomf.ou.edu/visualization/manual/](http://www.eomf.ou.edu/visualization/manual/))), based on the pixels where the flux towers located, to match up with the flux data for model parametrization.  
At the spatial scale, the 8-day (best observation in 8 days) MODIS land surface reflectance datasets (MOD09A1, Level 3, Collection 5) during 2001-2010 were downloaded from NASA Land Processes Distributed Active Archive Center ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/))) to generate the spatial _EVI_ and _LSWI_ data using Equations (1) and (2), as model drivers in regional simulation.  
\[EVI=G\times(\rho_{nir}-\rho_{red})/(\rho_{nir}+(C_{1}\times\rho_{red}-C_{2} \times\rho_{blue})+L), \tag{1}\]  
\[LSWI=(\rho_{nir}-\rho_{swir})\ /\ (\rho_{nir}+\rho_{swir}), \tag{2}\]  
where \(\rho_{nir}\), \(\rho_{red}\), \(\rho_{blue}\), and \(\rho_{swir}\) represent the reflectance of near infrared bands (NIR, 841-875 nm), red bands (620-670 nm), blue bands (459-478 nm), and short wave infrared bands (SWIR, 1628-1652 nm), respectively. The coefficients \(G\), \(C_{1}\), \(C_{2}\) and \(L\) used in the _EVI_ algorithm are 2.5, 6, 7.5 and 1, respectively [37].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
The MOD11A2 and MYD11A2 products (Level 3, Collection 5), which are 8-day average values of cloud free observations, were also downloaded from the LP DAAC data pool to obtain the spatial daytime \(LST\) and nighttime \(LST\) data, respectively. The spatial resolution of \(EVI\) and \(LSWI\) is 500 m, and that of \(LST\) is 1 km. For the regional application in China's grasslands, the extracted \(EVI\) and \(LSWI\) were resampled to a 1-km resolution. The temporal resolution of these products is 8-day, which ensures a better data quality as selected on the basis of the absence of clouds or aerosol, etc., and is confirmed by extensive \(RE\) modeling research to be able to robustly capture the seasonal variation of \(RE\)[24; 27; 28]. Additionally, to further reduce the effect of cloud and to capture the seasonality of \(EVI\), the original time series were smoothed using the double logistic curve fit in the TIMESAT software (Lund University, Lund, Sweden) [38].
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## 1 Introduction
### What is a TMA?
The SR can be either minimized or, as explained in the forthcoming sections, profitably exploited.
## 3 Results
### Case 3 (20 February 2015)  
On 20 February 2015, it was a geomagnetically quiet day (minimum Dst \(\sim-22\) nT; daily SSN: 64). During this day, the FSF ionogram signatures were displayed over Fairford, Dourbes, and Roquetes around 23:30 UT, as shown in Figure 4a. At Fairford and Dourbes, a southwestward oblique trace was identified, while no such trace was noted over Roquetes. No significant ionospheric activities were noted in the GPS d-TEC and ROTI maps (Figure 4b,c) except over Roquetes. An MSTID signature over Roquetes was identified in the GPS d-TEC map (Figure 4b), coinciding with the occurrence of SF activity.  
To investigate the influence of SF on the topside ionosphere, the latitude Ne and corresponding dNe from Swarm B were analyzed around 23:29 UT across these three locations, as presented in Figure 4e,f. A pronounced northward gradient in the latitude Ne profile was observed over Fairford and Dourbes, as shown in Figure 4e, indicating the coexistence of FSF signatures and associated irregularities in the topside ionosphere. This relation was further explored through Swarm B dNe, displayed in Figure 4f. A fluctuation with an amplitude of approximately dNe \(\sim 1\times 10^{4}\) elec/cm\({}^{3}\) was observed  
Figure 4: (**a**) observation of SF events over Fairford, Dourbes, and Roquetes at 23:30 UT; (**b**) MSTID signatures in GPS d-TEC map (pink markers indicate locations of Fairford, Dourbes, and Roquetes); (**c**) ROTI fluctuations observed over Fairford, Dourbes, and Roquetes (pink markers); (**d**) Swarm B track over Fairford, Dourbes, and Roquetes; and (**e**) latitude Ne and (**f**) dNe profiles of Swarm B over Fairford, Dourbes, and Roquetes latitude.  
over Fairford and Dourbes, while a lower fluctuation of dNe \(\sim\) 0.5 \(\times\) 10\({}^{4}\) elec/cm\({}^{3}\) was noted over Roquetes.  
From this case study, it appears that higher midlatitude stations (latitudes \(>\) 45\({}^{\circ}\) N), such as Fairford and Dourbes, are more vulnerable to equatorward-propagating irregularities. This increased susceptibility leads to more frequent and pronounced SF events, as well as associated topside ionospheric disturbances [18]. In contrast, lower midlatitude stations (latitudes \(<\) 45\({}^{\circ}\) N), such as Roquetes, are less impacted by these irregularities, resulting in reduced occurrence of SF and weaker topside fluctuations.
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 3 Methods  
The GEE cloud computing platform offers unprecedented opportunities for the rapid processing of large-scale remote sensing image data [40]. The GEE platform provides timely updates of Sentinel-1 and Sentinel-2 data on a global scale and preprocesses the data in advance. With simple coding, users can quickly access the required high-quality imagery, significantly reducing the time required for data preprocessing in large-scale studies. Moreover, the computational power offered by GEE enables efficient processing of imagery and data for the entire Shanwei City area, facilitating complex multi-source data processing tasks without the need for local high-performance computing infrastructure. Leveraging GEE's cloud computing capabilities and vast data resources, this study facilitates the efficient mapping of cropland and extraction of non-agriculturalization information. The first step in our approach involves using time-series SAR data, combined with optical imagery, for cropland mapping. This allows us to capture dynamic changes over time and accurately delineate cropland areas. Next, we collected open-source land-use/land cover (LULC) data and identified temporally and spatially stable cropland sample points to establish a cropland sample library. Following this, optical and SAR images were preprocessed, and feature sets were extracted. A Random Forest (RF) model was then trained to predict the distribution of cropland in Shanwei. Within the predicted cropland areas, unsupervised change detection (CVA) was performed by combining optical imagery and SAR data. The OTSU thresholding algorithm was applied to obtain a reference threshold for extracting typical cropland non-agriculturalization information, while the Enhanced Vegetation Index (EVI) was introduced to remove pseudo-changes. Finally, the spatial distribution and temporal change patterns of cropland change patches were analyzed. The overall technical workflow is shown in Figure 2.  
Figure 2: Flowchart of the method.
## 1 Introduction
### Motivations  
Deep neural network (DNN) has been recognized as a feasible way to overcome the drawbacks of handcrafted methods in enhancing feature discrimination. However, when the sample number in an image dataset is limited, for instance, there are only 1125 and 1907 leaf images in the Swedish and Flavia datasets, training a deep convolution network from scratch is not a preferred choice, because the overfitting issue will occur definitely. Fortunately, transfer learning provides a solution to avoid the overfitting problem; however, the retraining or fine-tuning (FT) process also requires a large computational burden and sophisticated transfer experience and tricks.  
Moreover, the past four years have witnessed the prosperity of attention mechanism [21] in computer vision, which learns an attention map, that is, a weight matrix or tensor, to rearrange the feature map, aiming to improve the model's capability. The squeeze-and-excitation network learns global channel attention weight to obtain more informative features [22], which has become a plug-and-play module. We can observe and summarize that rearranging the features in a feature map via attention weight may be a plausible way to enhance discrimination ability.  
In addition, different features can be learned by different CNNs, and with the deepening of the network layer, the level of learned features is gradually growing: from low-level to mid-level to high-level. Generally speaking, there always exist complementary elementsbetween different levels of features; therefore, fusing them could improve the representation power of leaf image features.  
Motivated by the analysis above, in this paper, we propose a novel method to learn features for plant leaf images, where the feature maps from multiple pretrained CNNs and multiple layers are adopted directly without the need of parameter fine-tuning; the spatial weighting and channel weighting are used to recalibrate the features without any parameter training. The feature distribution in two-dimensional space for 600 leaves from 9 species of the same genus Acer is shown in Figure 1. Although they belong to the same genus and have similar appearance, the features learned via our method for leaves from the same species are gathered closely, and the learned features for leaves from diverse species are separated clearly. This can reveal that our learned leaf features have strong discriminative power.
# A Systematic Analysis of Narrowband IoT Quality of Service
## 4 Results
### PHY Measurements: Coverage Extension  
In this evaluation, the selection of different extended coverage levels (ECLs) by the NB-IoT network was examined. This allowed understanding how NB-IoT used signal repetitions to adjust to changing signal levels. Various coverage situations were simulated using the attenuator. For each 5 dB attenuation step, 30 packets were transmitted in the uplink and downlink direction, and the selected ECL was recorded for each packet.  
Figure 7 illustrates the ECL assigned to the UE by the eNodeB depending on the DL-RSRP signal level. In general, higher ECL were selected for lower RSRP levels. There was an overlap between the individual ECL regions, which indicated a hysteresis in the ECL selection algorithm of the MNO. This technique avoided constant switching between different ECL levels for minor variations in the signal level. The network used ECL 0 for RSRP signal levels above approximately \(-115\;\mathrm{dBm}\), ECL1 between \(-126\;\mathrm{dBm}\) and \(-97\;\mathrm{dBm}\), and ECL2 below \(-125\;\mathrm{dBm}\) RSRP. This allocation indicated that ECL2 was selected for detecting signals at or below the effective noise floor \(P_{N,eff}=P_{N}+NF\).
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 6 Implementation
### Testbed #3: Environmental Monitoring in Oliveyard  
On this occasion, the testbed was built in an oliveyard of the _Falset-Marca Cooperative_[50] from the town of _Falset_ (Catalonia), represented in Figure 13a. As in Testbed #2, the first goal was to determine the maximum range coverage of the ZolertiaTM RE-Mote nodes in an area where trees may affect the line-of-sight propagation. For this purpose, a GW was placed on the roof of a cabin over 2 m high, as it can be seen in Figure 13b, and an STA was moved further away until it lost the connection with the network. This finally happened at approximately 258 m in east direction and 160 m in west direction from the GW's position (see Figure 14).  
Then, 7 ZolertiaTM RE-Mote running the HARE protocol stack were deployed on the same oliveyard according to the placement from Figure 15. One of them acted as a GW and was placed  
Figure 14: Maximum range coverage achieved at Testbed #3.  
Figure 13: Detail of landscape and devices employed in Testbed #3.  
again on the roof of the cabin to cover the maximum range, while the rest acted as STAs collecting data from environmental sensors and were placed hanging down from olive tree branches (see Figure 13c). X-MAC was the selected RDC sublayer and the whole system worked under a multi-hop topology limited to five children per STA.  
The test lasted four hours, the GW being configured to initially start the beaconing system with a _network association primary beacon_ and then ask STAs for new data every 10 min (\(T_{p}=10\) min). To send their packets, STAs had five available transmission windows (\(w=5\)). Under these conditions, the network self-organized in two rings and achieved a packet delivery ratio (PDR) of 99.26%.
## 4 Current Approaches to Open Challenges of F Estimations from Proximal to Airborne Scale  
### Atmospheric Influences  
One of the major challenges in measuring F is accounting for atmospheric influences. Oxygen's absorption band-depth and spectral shape are affected by, for instance, changes in the surface pressure and temperature conditions [116] and the atmospheric scattering effects produced in the presence of aerosol and molecules [26]. At a distance of a few meters between the target and sensor, the effects of oxygen absorption must always be compensated in the strong O\({}_{2}\)-A band [117]. For larger target-sensor distances (e.g., when measurements are taken from UAVs or airborne and satellite platforms), not only oxygen absorption but also aerosol scattering plays a key role in both visible and near-infrared measurements [26] and must be, therefore, properly corrected.  
At the canopy level, in order to disentangle F from the reflected solar irradiance, it is necessary to measure (1) the downwelling incident solar irradiance (\(\overline{E}\)) and (2) the upwelling radiance from the surface (\(\overline{L}\)), i.e., the reflected solar irradiance plus the contribution of the emitted F. Hereafter, the overlined symbol refers to magnitudes at the canopy level (see Figure 6). At a larger scale, on systems mounted on towers (e.g., [118; 119]), the downwelling and the upwelling radiances are acquired at distances determined by the altitude of the sensor above the canopy (Figure 6). Similarly, when using UAV platforms, both downwelling and upwelling radiant fluxes are also measured at a distance determined by the altitude of the platform above the canopy, as the irradiance sensor is often mounted on the UAV. Conversely, when moving toward higher vertical scales, such as when using airborne platforms, only the upwelling radiance from the canopy is typically measured. Consequently, the use of an atmospheric RTM is required to simulate and estimate the actual incident solar irradiance at the canopy level. The following subsections discuss how atmospheric effects disturb the acquired radiance in the oxygen regions (O\({}_{2}\)-B and O\({}_{2}\)-A) for different vertical scales. In addition, we provide a short review of the current techniques typically applied to compensate for atmospheric effects when aiming to estimate F using passive remote sensing techniques.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### OSI Reference Layers  
Layered network design provides seamless communication among different network elements. LoRaWAN layers can be mapped to OSI Reference for better understanding underlying technology as presented in Figure 3. Generic LoRaWAN network deployments consist of multiple end-nodes, one or  
Figure 2: LoRaWAN architecture.  
more gateways and at least one network server to run a complete network. LoRaWAN networks are distinguished with classical TCP/IP communication with GWs and end-nodes. Network servers are plain application services that operate over Transport Layer however, all MAC layer functionalities of the whole network are controlled by the Network Server.  
LoRa is a Physical layer technology that operates on L1, and the main function is to transmit Application layer data to the medium. LoRa is explained in detail in Section 2.3. OSI reference L2 Data Link Layer matches with the LoRaWAN protocol (Section 2.4), which defines secure medium access and end-node management strategies. Usually, end-nodes do one simple specific task to minimize energy consumption thus, end-nodes perform L1, L2, and L7 functionalities in OSI reference. GWs are the links between the end-node and the network server. Network Server is the main factor that controls the medium.