esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Description of the Validation Set  
Segmenting and interpreting clusters provide a method to distinguish between mown and unknown parcels. However, it is essential to recognize that the machine's perception might not always align with actual field reality. To externally validate the findings of our study, we utilized grazing calendars collected by our partners at the \"Centre des Technologies Agronomiques\" (CTA, Stree, Belgium) and the \"Fourrages Mieux\" ASBL (Bastogne, Belgium), covering the same timeframe as the one used for CSH predictions. The validation set comprised 55 distinct parcels with confirmed mowing events, situated in the Walloon region of Belgium. Given that certain parcels were affiliated with the same farms, Figure 3 provides an overview of their spatial distribution. For most parcels, we achieved the precision of predicting the mowing date within one or two days. In the case of three parcels, our knowledge was limited to the occurrence of mowing events during the summer period. In total, this dataset encompasses 55 distinct parcels, and Table 3 provides a detailed description of this validation dataset. From this validation set, the rate of correctly classified mown parcels was estimated for each UAC and type of dataset.
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
## 1 Introduction
Other more comprehensive WSN applications include LoRa's implementation in the detection of forest fires to raise awareness of the need for forest preservation [23; 24]. Geo-location tracking uses LoRa to track moving objects in the observation area [25]. In the health field, LoRa is used as a fall detection method to monitor old or vulnerable citizens who may be at risk of falling accidentally [26]. Besides, LoRa can help the doctor and the patient's family monitor and track a patient with a mental disorder [27]. Furthermore, LoRa can also be used to monitor blood pressure, glucose, and temperature in rural areas [28]; LoRa is also used as a communication platform in smart city applications [29]. The motivation of this research is to help the forest guard to monitor and supervise the environment of the trees in the forest remotely, so as to reduce the burden on the forest guard in conducting patrols in  
Figure 1: The Standard of Wireless Communication Protocol. the forest. The research aimed to design and implement an IoT application based on WSN technology that can detect illegal logging in forests. The system utilized several sensors and LoRa in each sensor node point. The proposed system also defined the network infrastructure that appropriates with LoRa architecture. The main contribution of this research is to tackle and reduce illegal logging in the forest. So, the proposed system can retain the degradation forest and help forest preservation. This proposed system is focused on detecting, tracking and identifying illegal logging. This paper is presented in several sections. The next sections outline the research methodology and the material, design, and implementation used to compile the research. The third section describes the testing and the discussion of the results testing. At the end of the research, the conclusion is drawn and stated in the last section of this research as the fourth section.
###### Abstract  
We propose in this paper the use of artificial intelligence, especially deep learning algorithms, for the isotopic ratio determination for hydrogen-deuterium mixtures. Our approach is based on the Balmer-\(\alpha\) line emitted by hydrogen and deuterium, but unlike the standard method, it does not consist of fitting the H\(\alpha\)/D\(\alpha\) line spectra. Instead, only some basic spectroscopic features such as the line peak-to-dip wavelength separation, peak-to-peak and dip-to-peak intensity ratios of the Zeeman-Doppler-broadened H\(\alpha\)/D\(\alpha\) line spectra are used by the regression algorithm for training. We demonstrate the proof-of-principle of our approach by applying deep learning from the open-access machine-learning platform TensorFlow to H\(\alpha\)/D\(\alpha\) line profiles, which we have synthetized with pre-determined parameters such as neutral temperatures, the magnetic field strength and the H/(H+D) isotopic ratio. The used regression algorithm allowed us to retrieve with a good accuracy the isotopic ratios used for the synthetized line profiles.  
plasma spectroscopy; isotopic ratio; machine learning; artificial intelligence; deep-learning; fusion plasmas; tokamaks; hydrogen isotopes; Zeeman-Doppler line profiles +
Footnote †: journal: 2022, 12, 9891. [[https://doi.org/10.3390/app12199891](https://doi.org/10.3390/app12199891)]([https://doi.org/10.3390/app12199891](https://doi.org/10.3390/app12199891))
## 2 Materials and Methods
### Assessment of DVB Metabolic Lability  
Before running the incubation of DVB with active HLMs, the metabolic stability of DVB was assessed using WhichP450 in silico software that was developed by Optibrium Ltd. in Cambridge, Massachusetts, USA. The efficacy of performing in vitro incubations was validated using the outcomes acquired via the StarDrop software module. The results underwent further examination using a CSL approach, resulting in significant conclusions about the metabolic stability of DVB. The inclusion of the CSL feature was crucial in determining the DVB metabolic stability before initiating the in vitro HLMs incubation. The target of this step was to validate the importance of implementing the UHPLC-MS/MS approach for estimating the DVB metabolic stability. By incorporating the SMILES notation into the metabolic protocol, it became possible to evaluate the metabolic stability of DVB. Scientists collected data on the reactivity of individual atoms and consequently utilized this information to estimate the CSL value. Therefore, this specific value yielded valuable information about the metabolic instability of DVB. The CSL was computed using Equation (1).  
\[\text{CSL}=\text{k}_{\text{total}}/(\text{k}_{\text{total}}+\text{k}_{\text{w}}) \tag{1}\]  
where \(\text{k}_{\text{w}}\) is the rate constant of water formation.
# Generative Adversarial CT Volume Extrapolation for Robust Small-to-Large Field of View Registration
## 4 Discussion
### Overall Discussion  
We found that our method reduced the median landmark detection error by a very large margin, thus leading to a superior feature-based registration. A better detection yields a better initialization for the intensity-based registration, rendering the alignment of extrapolated images comparable to the alignment of ground-truth images. Due to the enhanced context available in synthetic images, the overlapping error denoted by the median surface distance improved from 5.66 mm to 0.57 mm (\(p\)-value = 4.18 \(\times\) 10\({}^{-6}\)).  
We note that the paper represents only a proof of concept that recent advances in generative networks can improve the robustness of computer aided interventions significantly using augmented data.  
The major challenge of image out-painting remains the determination of the expansion size and direction. To expand the field of view of an image, Ref. [23] propose an extra input for the network, which is a vector representing the padding to be applied in each direction (top, left, bottom and right), hence defining an extrapolation grid. In [22], a binary mask is used to remove 32 pixels from each side of an image, thus solving a symmetrical extrapolation problem. However, our self-supervised learning approach does not require such prior information since it wouldn't be available in real world applications, but it is limited to always quadrupling the thickness of the input. Therefore, to address other use-cases, where the thickness of the intraoperative images is out of distribution with respect to the training data (see Figure 3), the model must be retrained accordingly.  
Another important aspect of our proposed method is the need for an extra-registration step to determine the origin of extrapolated volumes. We are aware of the existence of other (maybe more elegant) approaches, but we have chosen this simple strategy to preserve the non-rigid properties offered by the fully convolutional networks, as well as to maintain that the entire workflow is self explainable.
## 4 Engineering Application
### Monitoring and Measurement  
According to the basic requirements in the construction specifications of the Shikui Road Station, the construction methods and surrounding rock characteristics of Shikui Road Station were monitored, including changes in the stress and settlement of the arch cover during the construction period. To obtain a better inversion effect, it was necessary to select appropriate monitoring information for inversion. Therefore, an automatic monitoring section was set at the arch cover of the station, and an earth pressure box as well as a single-point displacement meter was used to monitor the stress and displacement changes in the arch cover in real time.  
The earth pressure box (TY-1, TY-2, and TY-3) and single-point displacement meter (DW-1, DW-2, and DW-3) were arranged as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Surrounding Rock** & **Thickness/m** & **E/GPa** & \(\mu\) & \(\gamma\)**/kN\(\cdot\)m3** & \(c\)**/MPa** & \(\varphi^{c}\) \\ \hline Plain filling soil & 3.6 & 0.008 & 0.4 & 17 & 0.01 & 15 \\ Strongly weathered quartzite & 2 & 0.05 & 0.35 & 23 & 0.08 & 30 \\ Medium weathered quartzite & 68.6 & 1.3–6 & 0.3–0.35 & 26.5 & 0.2–0.7 & 27–39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Basic mechanical parameters of surrounding rock.  
Figure 5: Numerical calculation model: (**a**) overall model; (**b**) model of the station.  
Figure 6: Automated sensor placement location.  
The actual monitoring data and the calculation results were compared and analyzed. Figures 7 and 8 show the actual monitoring data of the earth pressure box and the single-point displacement meter.
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 5 Giardia intestinalis
### Diseases Caused by G. intestinalis  
The pathogenic mechanism of _Giardia_ spp. includes disruption of the mucus layer and the gastrointestinal epithelium [138; 140]. Symptoms include diarrhea, abdominal bloating, and cramps. If left untreated, the infection can become chronic and asymptomatic over time. Chronicization is associated with malabsorption, which causes growth retardation, weight loss, iron deficiency, anemia, and sometimes cognitive deficit in children [26; 29; 140]. The GEMS reported that _G. intestinalis_ was not associated with severe diarrhea in children and protected from acute diarrhea [141] but increased the risk of persistent diarrhea [26]. In the GEMS study, _G. intestinalis_ spp. were more frequently detected among children older than two years and were associated with growth deficit [17]. _G. intestinalis_ was implicated in stunting at 2 years of age and presented two peaks of morbidity at ages 1-9 years and 45-49 years [29].  
Chronic _G. intestinalis_ infections are associated with food allergies, irritable bowel syndrome (IBS), chronic fatigue, and arthritis [26; 29; 30; 31]. Two cohort studies showed that beyond _Clostridium difficile_, _Helicobacter pylori_, _Norovirus_, and _Candidia_ being implicated in the initiation and exacerbation of the IBS syndrome, _G. intestinalis_ is significantly associated with IBS incidence and increased, though not significantly, risk of IBS [31]. Recently reported cases of giardiasis regarded a 59-year-old man with severe liver diseases, prolonged watery diarrhea, and rectum bleeding that resolved spontaneously [142] and a 67-year-old patient with pancreatic cancer. The association of _G. intestinalis_ infection with pancreatic or gallbladder cancer was also reported previously [143].  
Giardiasis is treated with nitroheterocyclic antiparasitic drugs including the 5-nitro-imidazoles secmidazole and tinidazole whose activity depends on the reduction by ferro-doxin or flavodoxin in organisms lacking mitochondria like _G. duodenalis_[140; 144]. The emergence of resistance mediated by epigenetic or posttranslational modifications led to the evaluation of alternative substances for the treatment of giardiasis, including other nitroheterocyclic substances, benzimidazoles, nitazoxanide, quinacrine anti-malarian drug, aminoglycososides hygromycin and paromomycin, ciprofloxacin, bacitracin, anti-viral protease inhibitors, anti-rheumatic and anti-tumoral substances, molecule combinations, and hybrid molecules. Oneprazole, which inhibits the glycolytic enzyme triosephosphate isomerase of _G. intestinalis_ leading the organism to death, had effects in vitro [144].
## 2 Materials and Methods  
### Overview of Convolutional Neural Network  
A convolutional neural network (CNN) is a multilayer supervised feedforward neural network designed to learn abstract features of the input by alternately stacking convolutional kernels and pooling operations. CNNs are characterized by weight sharing and sparse connectivity and generally consist of a convolutional layer, a pooling layer, and a fully connected layer. The convolutional layer consists of multiple convolutional units that perform different operations by adjusting parameters through a backpropagation algorithm [24]. The convolutional layer is used to extract the feature map using a convolutional kernel. The convolution kernel can be considered as the matrix. For example, a convolution kernel is set up as a 3 \(\times\) 3 matrix and the size of the image to be convolved is \(5\times 5\). The convolution kernel will perform a convolution operation on the first area of the image to be convolved, and the result of this convolution calculation will be used as a point on the output feature map, as shown in Figure 1. Then, the convolution kernel will slide over the input image and perform the next convolution operation to output the next point on the feature map. The above operations are repeated until the complete feature map is output.  
The pooling layer can further filter the image's object features, eliminate unimportant data information, reduce the number of parameters, identify important feature information, simplify the computation procedure, and accelerate the training speed. The fully connected layer is responsible for further analyzing the data extracted by the convolutional and pooling layers and sorting them by regression functions. The advantage of using CNN for feature extraction of images is that the user does not have to care at all about which specific features are present. This greatly solves the previous problem of certain features being difficult to recognize with the human eye. Therefore, CNNs are widely used in material defect, bridge damage, and plant disease identification missions.  
Figure 1: Schematic diagram of convolution process.
# Joint Timekeeping of Navigation Satellite Constellation with Inter-Satellite Links
## 3 ISLT Timescale Algorithm
### Optimal Weights  
According to the bivariate state equation, the frequency deviation \(\hat{y}_{i,Kalman}\) and drift \(\hat{z}_{i,Kalman}\), as estimated by the Kalman filter, are only influenced by RWFM and RRFM, respectively. Therefore, the reconstituted time deviation \(\hat{x}_{i\epsilon}(t)\) is not influenced by WFM. The weights employed in [14] were based on the approximation that \(x_{i}(t)-\hat{x}_{i\epsilon}(t)\) only includes WFM, and they were adjusted according to the intensity of WFM. In this research, the optimal weights were adjusted to minimize the increment of the synthetic timescale.  
The basic ISLT timescale equation can be translated by using the reconstituted time deviation as predicted values into:  
\[x_{\epsilon}(t)=\sum_{i=1}^{n}w_{i}(t)\!\left[x_{i}(t)-x_{i\epsilon}(t-\tau)- \tau\cdot\hat{y}_{i,Kalman}(t-\tau)-\frac{1}{2}\cdot\tau^{2}\cdot\hat{z}_{i, Kalman}(t-\tau)\right] \tag{32}\]  
Based on the definition of relative time deviation, Equation (32) is rewritten as:  
\[x_{\epsilon}(t)=x_{\epsilon}(t-\tau)+\sum_{i=1}^{n}w_{i}(t)\!\left[x_{i}(t)-x _{i}(t-\tau)-\tau\cdot\hat{y}_{i,Kalman}(t-\tau)-\tau^{2}/2\cdot\hat{z}_{i,Kalman }(t-\tau)\right] \tag{33}\]  
Define the increment of a timescale as:  
\[\Delta_{\tau}x(t)=x(t)-x(t-\tau) \tag{34}\]  
Combined with the second and third relationships in Equation (20), the increment form of the basic timescale equation is obtained:  
\[\Delta_{\tau}x_{\epsilon}(t)=\sum_{i=1}^{n}w_{i}(t)\!\left\{\tau\!\left[y_{i} (t-\tau)-\hat{y}_{i,Kalman}(t-\tau)\right]+\tau^{2}/2\!\left[z_{i}(t-\tau)- \hat{z}_{i,Kalman}(t-\tau)\right]+\gamma_{i,x}(t)\right\} \tag{35}\]  
The weight vector is:  
\[W=\left[\begin{array}{cccc}w_{1}(t)&w_{2}(t)&\ldots&w_{n}(t)\end{array}\right] \tag{36}\]A column vector is written as:  
\[M=\left[\begin{array}{c}\tau\cdot\left[y_{1}(t-\tau)-\hat{y}_{1,Kalman}(t-\tau) \right]+\tau^{2}/2\left[z_{1}(t-\tau)-\hat{z}_{1,Kalman}(t-\tau)\right]+\gamma_{ 1,x}(t)\\ \tau\cdot\left[y_{2}(t-\tau)-\hat{y}_{2,Kalman}(t-\tau)\right]+\tau^{2}/2\left[ z_{2}(t-\tau)-\hat{z}_{2,Kalman}(t-\tau)\right]+\gamma_{2,x}(t)\\ \vdots\\ \tau\cdot\left[y_{n}(t-\tau)-\hat{y}_{n,Kalman}(t-\tau)\right]+\tau^{2}/2\left[ z_{n}(t-\tau)-\hat{z}_{n,Kalman}(t-\tau)\right]+\gamma_{n,x}(t)\end{array}\right] \tag{37}\]  
Equation (35) is reformed as:  
\[\Delta_{\tau}x_{e}(t)=WM \tag{38}\]  
The variance of the timescale increment is accordingly:  
\[Var[\Delta_{\tau}x_{e}(t)]=WFW^{\mathrm{T}} \tag{39}\]  
\(F\) is
# FCN-Based 3D Reconstruction with Multi-Source Photometric Stereo
## 3 Results
### Error Metrics  
As shown in Table 1, we used five indices to quantitatively evaluate several methods involved in this experiment which are widely used in the error analysis and accuracy analysis of deep estimation based on deep learning [14; 18; 27]:  
Figure 4: Examples of the collected images and their corresponding ground truth (GT).  
1. root mean squared error(rms) \[\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left|d_{i}-d_{i}^{*}\right|^{2}}\,\] (3)
2. average relative error(rel) \[\frac{1}{N}\sum_{i=1}^{N}\frac{\left|d_{i}-d_{i}^{*}\right|}{d_{i}^{*}},\] (4)
3. threshold accuracy(\(\delta\)) \[\delta=\frac{1}{N}\sum_{i}\eta_{i},\] \[\eta_{i}=\left\{\begin{array}{ll}1&if\ T<t\\ 0&if\ T\geq t\end{array}\right.\,\] (5) \[T=max\left(\frac{d_{i}}{d_{i}^{*}},\frac{d_{i}^{*}}{d_{i}} \right),t\in\left[1.25,1.56,1.95\right]\,\]  
where \(d_{i}^{*}\) and \(d_{i}\) are the GT and predicted depths respectively of each pixel, and according to the different values of t, the results of \(\delta\)(t) are divided into three grades.
## Results
### Atomic Force Microscopy  
AFM was used to measure topology of collagen peptides on four surfaces following microspot patterning; silicon, FOTS-silicon, PDMS, and FOTS-PDMS (Figure 4). Silicon was used instead of glass slides for AFM studies because it is smoother and proved better for resolving peptides. The oxide layer on silicon is similar to the hydroxyl terminated surface of glass and displays the same contact angle as glass when untreated or when functionalized with FOTS (Figure 3); however, wettability is only one factor affecting adsorption. Adsorption of peptides on untreated silicon yields a significant change in RMS roughness with features as high as 2 nm. The FOTS-silicon showed a near monolayer of FOTS on the surface with feature sizes in the order of 2 nm (Figure 4B). When patterning peptides  
Figure 3: Substrate contact angles. Contact angles at the solid-water-air interface for glass, silicon (Si), native PDMS, and FOTS functionalized glass, silicon, and PDMS.  
Figure 2: Microfluidic flow assays with microspot patterning. (**A**) Representative images of platelet accumulation on collagen peptides patterned on glass by microspotting after a 5 min perfusion at wall sheare rates of 300 s\({}^{-1}\) and 1500 s\({}^{-1}\). Scale bar = 50 \(\mu\)m. (**B**) Maximum fluorescence intensity of assays for n = 3.  
on the FOTS-silicon, a three-fold increase in RMS roughness was observed, along with features up to 11 nm high, significantly higher than untreated silicon. Untreated PDMS has a slightly rougher surface than silicon with several nanometer tall features and showed little increase in feature size or roughness following peptide incubation. In contrast, FOTS-PDMS showed features in the order of 20 nm and increased RMS roughness. The observations that collagen peptides adsorb on FOTS-functionalized surfaces is consistent with these surfaces supporting platelet adhesion under flow.  
To recreate conditions where a PDMS microfluidic channel was used to pattern peptides for the flow assays, a PDMS well was used to pattern peptides on silicon and silicon-FOTS (Figure 5). On untreated silicon, there was no difference in topology or roughness between incubation with control (10 mM acetic acid) and the collagen peptide solution conditions. This is in contrast to the microspotting technique where no PDMS was present (Figure 4B). On FOTS-silicon, significant peptide adsorption was observed, although the RMS roughness was less compared to microspotting patterning (1.3 nm vs 2.4 nm). These data suggest that PDMS is interacting with collagen peptides in a way that prevents or slows adsorption to silicon, but this can be partially overcome using FOTS functionalization to hasten adsorption.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 5 Experimental Setup Parameters and Performance Metrics
### Simulation Platform  
The experimental setup parameters are summarized in Table 3. The testbed consists of nine USRPs. One of the USRPs serves as the MC BS that uses a PC for computation, and it connects to the rest of the USRPs via a switch. The rest of the USRPs are connected to RP3. The MC BS provides a backbone route and uses two frequency bands: (a) one channel is used for direct communication with the \(fc\) source node \(fc_{s}\); and (b) one channel is used for direct communication with the \(fc\) destination node \(fc_{d}\). In our testbed, the larger numbers of nodes and D2D links provide three disjoint three-hop routes in the network, which makes it possible to measure cumulative end-to-end delay as shown in Figure 6. Our testbed helps to achieve the main contribution of our paper, which is to investigate a route selection mechanism that enables a source node to select one of these routes: (a) the backbone route that uses direct communication with the MC BS to improve the route performance (i.e., a higher packet delivery ratio and a lower end-to-end delay): and (b) multihop D2D routes that are formed among SCs to offload traffic from the MC. In our testbed, the backbone route has the minimum number of hops (i.e., two hops), while each D2D route has three hops. The D2D routes have a higher number of hops because they are formed among SCs, which have a smaller transmission range. The number of hops for D2D routes are set to three hops so that their packet delivery ratio and cumulative end-to-end delay are comparable to those of the backbone route. As shown in Figure 1, the \(fc\) source node \(fc_{s}\) can also form multihop route to the \(fc\) destination node \(fc_{d}\) via one of the three available routes.