esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Tests with the Laboratory Specimens
#### 3.2.1 Accelerated Carbonation  
The carbonated (grey-colored) area and the area where no carbonation has taken place at the measuring time (pink-colored area) are presented in Figure 10. The visual analysis already shows that the carbonated area in WR2 is significantly larger than in all other specimens. The percentage of the carbonated area in all SAP-containing mixtures is placed between WR1 and WR2 (68%, 75%, 55%, 61%, and 66% for WR1, WR2, WS1, WS2, and WSC, respectively).  
The depth of the carbonation front was measured in at least three different points of each of the four surfaces of the specimen. The average values for each mixture were taken considering all measurements in the four surfaces of the three specimens evaluated per batch. The results are presented in Figure 11. The SAP-containing mixtures performed similarly or slightly better than WR1, in contrast, WR2 presented the deepest carbonation front. Again, the smaller carbonation depth found for the mixtures with SAP1 and SAP2 could be related to a densification of the matrix caused by internal curing, and it is more pronounced for SAP1 than SAP2.  
Figure 11: Average carbonation depth. The error bars represent the standard error of the average.  
Figure 10: The carbonation front is indicated with the colorimetric method. The pink-colored region represents the area not affected by carbonation.  
Despite the fact that the inclusion of SAPs with additional water might increase the macro porosity of the mixture in comparison to a reference where no SAPs were added [34; 35], this might not be the only parameter influencing the progress of the carbonation front in the mixtures. The smaller carbonated area found in the specimens with SAPs can be an indication that the SAPs are actually causing densification of the cementitious matrix near the SAP void due to the effects of internal curing and further hydration [36]. This effect is more pronounced in SAP1 and can be linked to the more efficient water release and internal curing observed in this SAP in comparison to SAP2 in other studies [37]. Another explanation for the less severe carbonation in the SAP specimens could be that a higher internal humidity of the specimens (caused by the SAPs) would delay the carbonation. However, before entering the carbonation chamber, all specimens were pre-conditioned under a controlled atmosphere of 20 \(\pm\) 2 \({}^{\circ}\)C and 60 \(\pm\) 5% RH for 14 days. This curing condition enables the occurrence of drying, which is more intense for the SAP-containing mixtures, as already observed during the drying shrinkage tests under the same environmental conditions (as described in [19]), ensuring that by the time the specimens enter into the carbonation chamber, their internal humidity level is comparable (regardless of the presence of SAPs). Thus, the hypothesis of matrix densification and improved internal curing seems more plausible.
## 4 Results  
### Forest Types Map  
We classified the Landsat ETM+ images of 2008 and 2012 into three different forest types including broadleaf forest, needle leaf forest, and mixed forest types (Figure 3). Using the landcover map for 2012 as an example, by building the confusion matrix to compare the computer-based classification with manually selected 256 validation samples of different topography and forest types, we found that the producer's accuracy for three forest categories were better than 80% while a little lower for the non-forest category (77.8%). The overall accuracy for the whole study area was 84.0% with kappa coefficient of 0.76.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Needle Leaf Forest** & **Broadleaf Forest** & **Mixed Forests** \\ \hline
**Wood CAC** & 0.301 & 0.462 & 0.382 \\
**Leaf CAC** & 0.213 & 0.223 & 0.208 \\
**Coarse root CAC** & 0.148 & 0.119 & 0.154 \\
**Fine root CAC** & 0.348 & 0.196 & 0.257 \\
**Wood TR** & 0.028 & 0.029 & 0.028 \\
**Leaf TR** & 1.000 & 1.000 & 1.000 \\
**Coarse root TR** & 0.027 & 0.045 & 0.027 \\
**Fine root TR** & 0.595 & 0.595 & 0.595 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Carbon allocation coefficients (CAC) and turnover rates (TR) used in the process-based BEPS model.
# ACapMed: Automatic Captioning for Medical Imaging
## 2 Background
This model is further improved to deal with medical images and to focus on important parts of the image (parts where the most significant features are obtained) using attention mechanisms. For instance, the authors of [PERSON] et al. [1] proposed detecting lesion areas, extracting automatic visual features from them, diagnosing pathological information, and reporting the findings from medical images using an encoder-decoder architecture. Similarly, [PERSON] et al. [14] employed a deep CNN-based multi-label classifier as the encoder and a hierarchical RNN as the decoder. First, the model detects abnormalities in medical images, which are then used to generate long medical annotations based on an attention mechanism. Likewise, [PERSON] et al. [15] exploited the show-attend-tell model [16] (extension of the show-and-tell model, using attention mechanisms) and changed the decoder with a GRU network for medical image captioning. In contrast to encoder-decoder architectures, visual and semantic features were fused and exploited to generate captions in merged models. In general, merged models employ CNN for visual feature extraction and an RNN for textual features. Then, textual and visual features are merged to assign a new caption to a given medical image. Obviously, they constitute a variant of encoder-decoder architecture with the fusion of textual information. For instance, [PERSON] et al. [17] employed a joint representation of image-text pairs calculated using a variational autoencoder model for medical report generation. They used a topic model theory to model semantic topics of images that were exploited, in addition to deep fuzzy logic rules designed based on diagnosis logic, to summarize and interpret abnormalities in medical images. Likewise, [PERSON] et al. [18] proposed using encoding and decoding architectures for the generation of effective captions where SSA-based hyperparameter optimizers in both parts were used to attain effective results. Some works combined techniques of the previously enumerated categories to accurately generate descriptions, which were more relevant and focused on. For instance, [PERSON] et al. [19] proposed a topic-guided attention mechanism to generate descriptions for abnormal visual observations by giving evidence on locations of the abnormalities. Moreover,Li et al. [2020] proposed abnormality graph learning, allowing them to detect abnormalities and use them to retrieve text templates, which were further adapted, enriched, and corrected using a paraphraser. In [PERSON] et al. [6], the authors proposed a hybrid retrieval-generation reinforced agent that decided whether to retrieve a template sentence or generate a new one for each constructed topic related to medical images. Similarly, in [PERSON] et al. [21], the authors proposed a model that incorporated a generation-based approach and a retrieval-based method. It employed visual features and retrieved similar textual features to generate the final captions.
## 2 Definition of POD Controller  
The main objective of the POD is to damp power oscillations. A POD controller, when implemented in FACTS devices, WFs, or PV plants, provides damping capabilities similar to those of synchronous generators with PSSs. The POD controller can be installed in the PPC to send a unified damping signal to all generators or each generator individually. In this article, the POD controller is configured within the PPC of the PV plants.  
The POD-P controller operates as a supplementary control loop, adding an extra reference signal (dP_POD) to the PPC active power control, as shown in Figure 1.  
As mentioned in Section 1, this article considers only the active-power POD-P, as it has been shown to be more versatile than POD-Q for damping multiple oscillations [46].  
The POD controller consists of three main parts [16]: a stabilizer gain Kpod, a washout filter with the time constant Tw, and two-phase compensation blocks with the time constants Tb1, Ta1, Tb2, and Ta2. Moreover, a limiter is applied to the output. The POD processes oscillation signals from the power system, such as active power, voltage, or frequency. This study uses the frequency at the PV plant terminal as the input signal. The output of the  
Figure 1: POD-P controller block diagram.  
POD-P controller (dP_POD) provides the command to damp oscillations by modifying the generator controller's original active power signal [16].
## 4 Computational and Mathematical Aspects
### Anisotropic Finite Element Macro-Model  
The basic concepts of the finite element method are well documented and are not repeated in this paper. Only the essential features are presented. In this paper, an anisotropic (orthotropic) finite element model is proposed for the macro-modeling of masonry structures. Specifically, a four-node isoparametric rectangular finite element model with eight degrees of freedom (DOF) is used (Figure 6).  
The major assumption when modeling masonry behavior under plane stress is that the material is homogeneous and anisotropic. Specifically, the material shows a different modulus of elasticity in the \(x\) direction (\(E_{x}\)) (direction parallel to the bed joints of brick masonry) and the \(y\) direction (\(E_{y}\)) (perpendicular to the bed joints). In the case of plane stress, the elasticity matrix is defined by  
\[D=\left[\begin{array}{cc}\frac{E_{x}}{1-v_{xy}v_{yx}}&\frac{E_{x}v_{yx}}{1-v _{xy}v_{yx}}&0\\ \frac{E_{y}v_{xy}}{1-v_{xy}v_{yx}}&\frac{E_{y}}{1-v_{xy}v_{yx}}&0\\ 0&0&G_{xy}\end{array}\right] \tag{8}\]  
where \(v_{xy}\) and \(v_{yx}\) are the [PERSON]'s ratios in the xy and yx plane, respectively, and \(G_{xy}\) is the shear modulus in the xy plane. It is worth noticing that, in the case of plane stress in an anisotropic material, the following equation holds  
\[E_{x}v_{yx}=E_{y}v_{xy} \tag{9}\]
###### Abstract  
The strength of unsaturated soil is defined by the soil water retention behavior and soil suction acting inside the soil matrix. In order to obtain the suction and moisture profile in the vadose zone, specific measuring techniques are needed. Time domain reflectometry (TDR) conventionally measures moisture at individual points only. Therefore, spatial time domain reflectometry (spatial TDR) was developed for characterizing the moisture content profile along the unsaturated soil strata. This paper introduces an experimental set-up used for measuring dynamic moisture profiles with high spatial and temporal resolution. The moisture measurement method is based on inverse modeling the telegraph equation with a capacitance model of soil/sensor environment using an optimization technique. With the addition of point-wise soil suction measurement using tensiometers, the soil water retention curve (SWRC) can be derived in the transient flow condition instead of the static or steady-state condition usually applied for conventional testing methodologies. The experiment was successfully set up and conducted with thorough validations to demonstrate the functionalities in terms of detecting dynamic moisture profiles, dynamic soil suction, and outflow seepage flux under transient flow condition. Furthermore, some TDR measurements are presented with a discussion referring to the inverse analysis of TDR traces for extracting the dielectric properties of soil. The detected static SWRC is finally compared to the static SWRC measured by the conventional method. The preliminary outcomes underpin the success of applying the spatial TDR technique and also demonstrate several advantages of this platform for investigating the unsaturated soil seepage issue under transient flow conditions.  
spatial TDR; moisture content; soil suction; SWRC; transient flow +
Footnote †: journal: Applied Sciences
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 3 Key Technologies of IoSB
### Interactive Technologies  
The interaction between shared bicycles and users involves not only the user's account security, but also the interests of the service provider. Therefore, some specialized interactive technologiesshould be applied in IoSB, which can make the process as easy and efficient as possible while ensuring security. Some technologies used for mobile payment may work, such as QR code or barcode in Alipay [66], Near Field Communication (NFC) [67], and Bluetooth. On the other hand, acoustic communication (just like sound pay in Alipay) could be chosen to be a bicycle unlock way too [68].  
QR code is a type of matrix bar code or two-dimensional code that can store data information and designed to be read by smart phones [69]. In IoSB, people can use a smart phone to scan the QR code on the shared bicycle and then transmit a unlock request to cloud sever. However, QR code also have some security flaws, as mentioned in the previous chapter on the discussion of bicycle security. Unlike QR code, NFC technology can provide a secure form of authentication that is being used in smart cards and mobile authentication [67; 70]. For example, in September 2017, OFO launched a bicycle with NFC module, and compared with the QR code unlock scheme, NFC has advantages in user experience, data transmission and security. Besides, Bluetooth also known as the IEEE 802.15.1 [71], can be used for establishing Personal Area Networks (PAN) communication and unlocking the shared bicycle securely in IoSB. However, for the shared bicycle unlocking, the process of Bluetooth connecting is cumbersome and waste a lot of time as a result.
## 2 System Architecture
### Environmental Control, Data Storage Access and Observation System Hardware Configuration  
The primary function of the environmental control assurance system is to realize the remote image acquisition of the observation room on the ground, the temperature and humidity control in the cabin, the environmental heating, and the power management of the ground equipment. The hardware structure of the system is shown in Figure 2.  
Figure 1: Block diagram of the entire system.  
The primary sensors include two all-sky imagers: imaging lens observation band 630.0 nm (all-sky monochromatic imaging, Keo Scientific Calgary, Calgary, AB, Canada) imager iKon-M (i Kon-M 934 Series, Oxford Instruments, Abingdon, UK) and imaging lens observation band 557.7 nm (all-sky monochromatic imaging, Keo Scientific Calgary, Calgary, AB, Canada) imager iKon-M (i Kon-M 934 Series, Oxford Instruments, Abingdon, UK), magnetic through-door magnetometer (LEMI-025, LC ISR, Naukova St., Lviv, UKR), inductive magnetometer (LEMI-120, LC ISR, Naukova St., Lviv, UKR), and TEC ionospheric scintillation Instrument (CJW-1H, CETC Xin Xiang, He Nan, China). The file storage system is a NAS file storage (TS869L, QNAP, Xintai 5 th Rd, Xizhi City, Taipei, China), a network switch (TL-SG3226, TP-LINK, Guangming New District, Shenzhen, Guangdong, China) GPS timing instrument (DINTS-8, Neutron, Tong Zhou District, Beijing, China). The related equipment types of the sensing observation system and file storage system are shown in Table 1.
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
## 4 Receiver Architecture
Such a device, which is available as a system-on-chip (SoC), includes the quadrature demodulators and provides the I/Q samples to a digital modem, which, in turn, implements all the decoding and error correction functions of the selected protocol stack (DVB-S2). The modem is implemented as a custom solution via a state-of-the-art field programmable gate array (FPGA). Finally, it is worth noting that the SDR derives a receiver signal strength indicator (RSSI) signal that is acquired by the receiver central processing unit (CPU) and that is used to implement an automatic gain control (AGC) loop by adjusting the IF amplifier gain.
## 2 Materials and Methods  
### Geometry and Simulations  
We explored a geometry based on non-stoichiometric silicon nitride (SiN) in combination with silicon dioxide (SiO\({}_{2}\)) (Figure 1). This choice is motivated by the need to have both a refractive index larger than that of SiO\({}_{2}\) (2.35 vs. 1.46) [48; 51], resulting in smaller waveguide dimensions, and a large energy bandgap (close to 5 eV), resulting in lower material losses [44] and the absence of a two-photon absorption (TPA) in the NIR/MIR. We performed two kinds of simulations for the WGs:  
1. Optical WG behavior from 500 nm to 4000 nm, in order to optimize waveguiding at 1.5 \(\upmu\)m and 2.5-3.1 \(\upmu\)m (crucial for environmental monitoring and quantum processes [52; 53]).
2. Mechanical stress in the multilayer structure. The implications of these simulations concerned possible local second-order non-linearities of such devices. For this study, a superficial charge was applied at layer interfaces, according to the number of layers. The Young's modulus, Poisson coefficient, and density were fixed to values from the relevant literature [54]. The results are given in the next paragraphs.  
Waveguides were fabricated by FBK in order to carry out an initial evaluation of their properties. We investigated the losses experienced along the multilayer WG, measuring the transmitted power value at one WG end.
## 3 Results And Discussion
### Workflow II Results  
The second feature setup, named workflow II, highlights the SVM's capability in better subdividing dataspace when dimensionality reduction is performed, beating its previously acquired results in workflow I and its competitors in workflow II.  
The DT offers the least stability with the largest standard deviation, however, with a nominal improvement in the recall value with respect to the previous setups.  
Feature-reduction-training-based classifiers led to differences only in precision, stating superior scores of SVM with respect to the DT (\(p=0.008\)). Instead, [PERSON] tests on recall and f1 score give \(p=0.504\) and \(p=0.074\), respectively.
# Historically Based Room Acoustic Analysis and Auralization of a Church in the 1470s
## 3 Digital and Historical Reconstruction
### Model Creation
#### 3.1.2 The Nuns' Gallery  
The construction of an elevated platform for the runs in the middle of the church is prescribed in the building instructions of the abbey [45]. The only visible remnant of this platform today is a niche near the ceiling vaults that was once its entrance (Figure 4). Research has several suggestions on the specific form and placement of the nuns' gallery [36,38], but there is so far no consensus on its specific location, size, or configuration.  
The suggested configuration of the gallery in this project is based on historical documents and physical traces in the church room, and shown in Figure 4. It is surrounded by high panels, and its size was estimated to \(10\times 18\,\mathrm{m}^{2}\). This is large enough to accommodate the congregation of nuns and the furnishings required for religious purposes [45,46]. In addition, this size allows for the display of a series of paintings along the interior western side of the gallery, which are recorded in [47]. The high panels surrounding the gallery ensure that the nuns could not be seen by anyone else in the church, although see-through lattices allow them visual access to key altars, as required by religious documents [48].  
The location of the gallery was determined by using information from a geophysical investigation of the church floor. This investigation revealed anomalies which were interpreted as foundations of support pillars for the gallery. The final model of the gallery, shown in Figure 4, satisfies requirements regarding function and size, is structurally sound and matches the physical traces well.  
Figure 3: 3D model of three reconstructed chapels (left foreground, middle, right background). In the far left corner is the side of an altar with textile curtains, and a platform on its front covered by a carpet. View from the northeastern side of the nuns’ gallery (compare with Figure 4) towards southwest.  
This interpretation of the gallery is located close to the vaulted ceiling and is separated from the rest of the naive by its floor and panel walls. As such, it likely acts as an acoustical subspace for the congregation of nuns, as is often found in the choir [7; 31; 32]. As the congregation of nuns gathered there, it is also plausible that the space was characterized by increased absorption by the presence of nuns and their clothing, leading to decreased reverberation and increased clarity. In addition, the gallery's central position close to the vaulted ceiling may have had a significant effect on the transmission of sounds from the gallery.
# A Layered Method Based on Depth of Focus for Rapid Generation of Computer-Generated Holograms
## 4 Texture Mapping
Since the CCD used only senses light intensity and not color, grayscale results shown in Figure 13 were obtained. Figure 11: Numerical reconstruction of a hologram generated by the proposed method. (**a**) The numerical reconstruction focusing on the tiger’s hind legs. (**b**) The numerical reconstruction focusing on the tiger’s forelimbs. (**c,d**) and (**e,f**) are enlarged portions of the box in (**a**) and (**b**), respectively. Figure 12: Schematic diagram of the optical reconstruction experiment. As can be seen, the optical reconstruction results also contain texture. In addition, the optical reconstruction image can focus on different places when reconstructed at different distances. Therefore, texture mapping can work effectively in the proposed method. Fast generation of polygon-based texture holograms is a major challenge. Currently, one of the latest polygon-based texture hologram generation methods is an analytic-based method [45]. This method is more computationally efficient than the previous ones, but it is still heavily affected by the number of triangles. In this paper, we approximate the diffraction calculation of tilted triangles by a hierarchical projection based on the depth of focus, which also facilitates the mapping of the texture. Although the computation is fast enough to use traditional texture mapping methods, this is an approximation method, so the computation results are not as accurate as those based on the analytic method. It is more suitable for scenes that only need to satisfy human eye observation and do not require high imaging accuracy.