esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Reference [18] develops a Monte-Carlo approach to find the best configuration to optimize bundle adjustment among the GCPs network. However, such approaches require deploying a large number of GCPs all over the study area, which is very time-consuming, both on the field during the survey and during the processing [1; 9]. In practice, corridor mapping is a today a matter of concern for transportation [19; 20], inspection of pipelines or power lines [21; 22], and coastlines or river corridors monitoring [15; 23]. Various strategies are proposed to improve accuracy with a minimal GCP network [24] or without GCP, for instance: (i) equipping the UAV with precise position and attitude sensors and a pre-calibrated camera [25], or (ii) using Kinematic GPS Precise Point Positioning (PPP) [26] under certain conditions (long flights, favourable satellite constellation), or (iii) using point-and-scale measurements of kinematic ground control points [20]. Nevertheless, these solutions without GCP involve computing lever arm offsets and boresight corrections, and ensuring synchronisation between the different sensors. Furthermore, as mentioned in reference [24], this demand on precision and thus on high quality of inertial navigation system and/or GNSS sensors can be incompatible with the limited UAV payload capability and radically increase the price of the system. In coastal environments, linear landforms are also usual (Figure 1), but their survey can present some peculiarities, such as being time-limited because of tides or tourist attendance. Moreover, it can be impossible to install and measure GCPs in some parts of the study area because of the spatial extent, inaccessibility (topography, rising tide, vegetation, private properties) or because of GNSS satellite masking (cliffs, vegetation cover, buildings, etc.). These constraints can also limit the spatial distribution of tie points detected during image matching. This article aims to provide practical suggestions to limit \"bowl effect\" on the resulting DEM in linear context with sub-optimal distribution of GCPs. The field experiment conceived for this study does not seek to be realistic or to optimize the DEM quality. The purpose is to assess in which extent the acquisition conditions can impact the topographic modelling performed with SfMphotogrammetric software. Different flight plans are tested to identify the most relevant flight scenario for each camera model to limit geometric distortions in the reconstructed surface. As DEM outputs may be significantly different depending on the selected software package [27], the quality of the reconstruction is examined using two software solutions based on SfM algorithms: Agisoft PhotoScan(r) Pro v1.2.3 and the open-source IGN(r) MicMac(r), using different camera distortion models.
# Development of a New Evaporation Exposure Model: Chemical Product Evaporation Model (CPEM)
###### Abstract  
The evaporation of chemicals in applied chemical products such as cleaning products and paint has been evaluated using the evaporation mode of the ConsExpo model. However, it remains controversial whether the ConsExpo model can be used for non-applied chemical products such as air fresheners, because the ConsExpo model assumes that the mass of the non-applied chemical products does not change during the time of use. If most of the materials in the product are volatile, the product mass can decrease. To explain the effect of a change in product mass, the 'Chemical Product Evaporation Model (CPEM)' was developed. This study demonstrated that the product mass decreases linearly when the surface area of the product is invariant, theoretically and experimentally. It was found that the ConsExpo evaporation model can be applied to products in which the other materials do not evaporate, and the CPEM can be applied to products in which the other materials are volatile. If the target substance in a product evaporates completely before the exhaustion time, the average concentration of a target substance in the air can be estimated simply from its initial concentration in the product and the product mass reduction rate. Otherwise, we recommend using the CPEM.  
inhalation exposure; evaporation; Chemical Product Evaporation Model (CPEM); ConsExpo model; mass-balance model Article
# WaQuPs: A ROS-Integrated Ensemble Learning Model for Precise Water Quality Prediction
## 3 Theoretical Frameworks  
### Random Forest  
Random Forest, our primary ensemble model, stands out as a versatile technique that leverages decision trees to construct an ensemble of trees. Each individual tree in the Random Forest approach independently classifies the data, and the final prediction results derive from a weighted combination of the outputs from these individual trees. This approach is well-recognized for its efficacy in mitigating overfitting, handling high-dimensional data, and facilitating feature selection. In terms of prediction, the beauty of Random Forest lies in its use of subsets of the dataset for individual trees. This unique approach allows the 'out-of-bag' samples, which were not used in the training of specific trees, to serve as an internal test set to evaluate the performance of the model. To effectively configure Random Forest, we need to set a few essential parameters, such as the maximum depth of the trees, the number of predictor variables considered at each node, and the total number of trees in the ensemble [20].  
Let us consider an n-dimensional random vector, \(A=(A_{1},A_{2},\ldots,A_{n})^{T}\), representing the predictor variables, and a random variable, b, signifying the output. Random Forest (RF) is geared towards identifying a prediction function, \(f(a)\), that can accurately forecast b. This prediction function is determined by minimizing a defined loss function, \(L(A,\,B)\), with the objective of minimizing the expected value of the loss.  
\[E[AB(L(B,f(A)))], \tag{1}\]  
Usually, in a classification problem, like facies or fracture classification, the loss can be binary, taking values of either 0 or 1. When we denote the set of potential values for B as B, the objective is to minimize \(L(B,f(A))\) for a 0-1 loss.  
\[f(A)=argmaxP(B=b|A=a),where\quad b\in B \tag{2}\]  
In classification problems, \(f(a)\) often represents the most frequently predicted class, determined through a voting scheme  
\[f(a)=argmax\Sigma I(b=h(a)) \tag{3}\]  
where \(h(a)\) represents the base learners.
## 5 Data Processing
### Synchronization of GNSS and Camera Modules  
With a GPS sampling rate of 1 Hz, a position is calculated every second. Nevertheless, the frequency of image acquisition is 0.4 Hz, which introduces a desynchronization between GNSS and camera system. To determine the position of the camera at exposure, a parabolic interpolation was carried out taking into account the GNSS-derived position accuracy and velocity. Figure 4 depicts the desynchronization between the two systems; circles in blue represent GNSS-derived positions and pyramids represent camera frames.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Flight** & **s1-n50** & **s2-n50** & **s2-n3070** & **s2-o50** \\ \hline Nb of images & 395 & 315 & 200 & 323 \\ Height (m) & 50 & 50 & 30,70 & 50 \\ Orientation & nadir & nadir & nadir & oblique \\ Nb of strips & 3 & 3 & 2 & 3 \\ \hline \multirow{2}{*}{Overlap (\%)} & forward & \multicolumn{3}{c}{80} \\ \cline{2-5}  & side & \multicolumn{3}{c}{70} \\ \hline \multirow{2}{*}{GCP accuracy (mm)} & horizontal & \multicolumn{3}{c}{1.3} \\ \cline{2-5}  & vertical & \multicolumn{3}{c}{1} \\ \hline Camera focal length (mm) & \multicolumn{3}{c}{35} \\ \hline GSD (mm) & 10 & 10 & 6, 14 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details on the conducted flights.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline Positioning Mode & Kinematic & \multicolumn{1}{l}{Toposphere Correction} & Saastamoinen \\ Frequencies & L1 & Satellite Ephemeris & Broadcast \\ Filter type & Combined & Navigation System & GPS \\ Elevation Mask & 15\({}^{\circ}\) & Integer Ambiguity Resolution & Fix and Hold \\ Ionosphere Correction & Broadcast & Min Ratio to Fix Ambiguity & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RTKLib parameterization of GNSS trajectory processing.  
Figure 4: An illustration of the desynchronization between GNSS (blue circles) and camera (pyramid) modules.
## 5 Simulation Results
### System Parameter Setting  
Without loss of generality, we use the following parameter settings in the experiments. Assume that the distance between the mobile device and the AP follows the uniform distribution with \(d_{i}(t)\geq 2\) m. The number of CPU cycles required for \(MD_{i}\) to process one bit is set as \(C_{i}\in[1000,1200]\) cycles/bit, \(\forall i\in K\). For \(\forall i\in K\), the maximum CPU cycle frequency of \(MD_{i}\) is \(f_{i}^{max}=1\) GHz. The maximum computing capacity of the MEC server is set as \(Q_{max}=2\times 10^{10}\) cycles. The energy efficiency coefficient at \(MD_{i}\) is set as \(\zeta_{i}=10^{-28}\), \(\forall i\in K\). The total system bandwidth \(B=3\) MHz, and the receiver noise power at the AP is set as \(\sigma^{2}=10^{-9}\). The uplink and downlink channel power gains are modeled as \(h_{i,u}(t)=g_{i,d}(t)=10^{-3}d_{i}^{-a}\phi_{i}^{2}(t)\), \(\forall i\in K\), \(\forall t\in\mathcal{T}\). Where \(\phi_{i}^{2}(t)\) represents short-term fading which is an exponentially distributed random variable, and \(a=2\) represents the path-loss exponent. The length \(T\) of each time block is set to 3 s, and the maximum transmit power of the AP is set as \(P_{d}(t)=200\) W, \(\forall t\in\mathcal{T}\). The transmission power \(p_{i,o}(t)\) is the random value in the range of [0.4, 0.6] W, \(\forall i\in K\), \(\forall t\in\mathcal{T}\). The energy conversion efficiency for \(MD_{i}\) is set as \(\eta_{i}\in[0.8,0.9]\), \(\forall i\in K\) The constant variable \(\varepsilon\) is set to \(10^{-8}\), and the equipment state is \(s_{i}(t)\in\{0,1\}\), \(\forall i\in K\), \(\forall t\in\mathcal{T}\).  
In the simulation, the system parameters are set as follows. The simulation experiment has been repeated 500 times and then the average value of the simulation results is calculated but we change the above settings according to the experimental situation and randomly select other unfixed parameters to guarantee the authenticity of the experiment.
## 2 Existing Concepts for Reinforcement Integration  
### Overview  
It is already foreseeable today that additive manufacturing of concrete using extrusion-based deposition methods has groundbreaking potential and can fundamentally change the way we build. However, in many cases, the non-reinforced concrete members produced with previous additive manufacturing strategies are not sufficient to reliably resist the loading actions real structures are exposed to. Consequently, methods will be required that enable the integration of steel reinforcement into the printing process and thus the additive production of reinforced concrete in the future. So far, it is largely unclear how steel reinforcement can be integrated into the 3D concrete printing process. Today's solutions for reinforcement integration are mainly of conceptual nature, and only few research studies have been carried out, so far [15].  
Figure 1 gives an overview of the most important concepts. In detail these are:  
(a) Contour Crafting with post-installed reinforcement: Additive production of the outer contour of the member with unreinforced concrete. The 3D-printed contour serves as a lost formwork. In a second step, reinforcement is conventionally (manually) post-installed and the contour is subsequently filled with concrete (e.g., [4], Win Sun [16], Apis Cor [17]).  
(b) Special case of (1), where in addition to the contour of the member, the interior part of the member is also printed with unreinforced concrete. Here, channels are left out in the plain concrete for post-installation of reinforcement bars, external reinforcement installation or prestressing with tendons [18; 19].  
(c) Encasing of (manually) preinstalled steel reinforcement with extruded concrete from two or more nozzles without external formwork (e.g., HuaShang Tengda Ltd., Beijing, China) [20].  
(d) Spraying of (manually) preinstalled steel reinforcement with shotcrete without external formwork (so-called Shotcrete 3D printing (SC3 DP) of the Technical University of Braunschweig) [21; 22].  
(e) Multi-arm printing of steel reinforcement and concrete: Reinforcing steel and concrete are produced in parallel by independent printing processes for concrete extrusion or shape welding (TU Dresden) [11; 23].  
(f) Mesh Mold approach: Robot-supported production of complex-shaped spatial steel meshes into which concrete is subsequently casted. Besides their function as reinforcement, the steel meshes also serve as lost formwork (ETH Zurich) [24].  
(g) Online reinforcement integration in the concrete filament: In this process, formable (flexible) reinforcement elements (textile reinforcement, cables, etc.) are fed to the extruded concrete filament in the print head so that the resulting component is reinforced parallel to the extrusion direction [25];  
(h) Layer-by-layer installation of reinforcing elements that can be plugged or screwed into each other between successive concrete extrusion layers. Installation of reinforcement elements is either done manually or by robot [26].
## 4 Proposed MiniatureVQNet Model
### Network Architecture and Training  
The proposed MiniatureVQNet architecture comprises fully connected and bidirectional GRU neural network layers with rectified linear unit (ReLu) activation. The network has four fully connected layers with ReLu activation, followed by two bidirectional gated recurrent unit (GRU) layers. The last bidirectional GRU layer feeds to a fully connected layer connected to the output layer. The number of neurons in the first four fully connected layers is given by 32-32-32-8. The number of neurons in the GRU is 8, and the last two fully connected layers is 8-1. The model was trained using stochastic gradient descent with an exponential decay learning rate schedule at an initial learning rate of 0.01, and the decay steps were set to 1000 at the decay rate of 0.9. The training dataset batch size was 64. The Figure 1 below shows the proposed network architecture diagram.  
Figure 1: Proposed model network architecture.  
Post-training DNN model optimization improves the model performance in a constrained resource environment. Quantization is a post-training model optimization technique that reduces the precision of weights and activation functions. As a result, model quantization results in smaller model sizes and faster computation in low-powered devices. The effect of post-training model quantization is a reduced model accuracy. However, the benefit of model quantization outweighs the accuracy loss, as [PERSON] [34] demonstrated that networks could be condensed to use 8-bit dynamic fixed points for network weights and activations with a less than 1% degradation of classification accuracy. In this study, we examined two post-training quantization methods: full-integer and float16 quantization. Full-integer quantization converts all weights and activation outputs into the nearest 8-bit fixed-point numbers. Float16 quantization converts the weights and activation to float16.
# Measuring and Modeling the Polarized Upwelling Radiance Distribution in Clear and Coastal Waters
## 3 Results
### Overall View of the Upwelling Polarization Signal
\begin{table}
\begin{tabular}{c c c c c} \hline
**Experiment** & \(\mathbf{N(\lambda\text{ nm})}\) & \(\mathbf{N(\lambda\text{ nm})}\) & \(\mathbf{N(\lambda\text{ nm})}\) & \(\mathbf{N(\lambda\text{ nm})}\) \\ \hline Hawaii & 27 (436 nm) & 31 (486 nm) & 28 (526 nm) & 24 (548 nm) \\ Ligurian Sea & 9 (442 nm) & 9 (488 nm) & 13 (520 nm) & 14 (550 nm) \\ New York Bight & 7 (442 nm) & 8 (488 nm) & 9 (520 nm) & 7 (550 nm) \\ \hline \end{tabular}
\end{table} 
Table 1: Number, \(N\), of reduced images used for model–data comparisons in each of the three datasets in each of four spectral bands. Note, the PolRADS and DPOL cameras used different filters with slightly different band centers; the center of each 10 nm-wide filter is listed in parenthesis. see that in this more turbid case, the single scattering was a poor approximation of all parameters, with the exception of \(\chi\). Figure 2: Comparison of the Stokes vector components \(I,Q/I,U/I,DOLP,\) and \(\chi\) for clear water. Shown are the calculation for single scattering (left column), RT model (center column), and data (right column). The data were taken on 2 December 2005, at 20:46 UTC off of Oahu, Hawaii. The conditions were: SZA = 48\({}^{\circ}\), 442 nm, \(Chl\) = 0.1 mg/m\({}^{3}\), \(c_{t}\) = 0.1 m\({}^{-1}\) (calculated from \(Chl\) as described in text), \(\omega_{0}\) = 0.8, and clear skies. The intensity for the model was adjusted to match the data at nadir. Each image is a fisheye projection. Nadir is in the center of the circle, nadir angle is linearly proportional to radius from center. The angle of the Snell’s circle (48\({}^{\circ}\) nadir angle) is shown in as the white circle. The principal plane (plane containing the anti-solar point and the nadir direction) is a vertical line through the center of the image. The anti-solar direction is towards the bottom of the image, and the sun direction is towards the top of the image.
# Pathogenic Roles of RNA-Binding Proteins in Sarcomas
## 2 Regulations of RNA Metabolism by RBPs
### Stability  
RNA stability is associated with its nucleotide sequence, modification, 5\({}^{\prime}\) m\({}^{7}\)G cap, and 3\({}^{\prime}\) poly (A) tail [39]. These determinants for RNA stability regulate the mRNA decay and translation initiation [40]. More specifically, mRNA degradation is mainly regulated by the following two mechanisms: (1) one mechanism starts with deadenylation of the 3\({}^{\prime}\) poly (A) tail, which is followed by 5\({}^{\prime}\) cap removal and 5\({}^{\prime}\)-to-3\({}^{\prime}\) decay; (2) the other mechanism begins after hydrolysis of the 3\({}^{\prime}\) poly (A) tail and 3\({}^{\prime}\)-to-5\({}^{\prime}\) decay [40].  
Several RBPs such as the mRNA decapping enzyme scavenger (DCPS), CUGBP Elav-like family member 2 (CELF2), insulin-like growth factor 2 mRNA protein (IGF2 BP) family, HuR, QKI-5, RBMS3, and TARBP2 play important roles in cancer biology [6]. For example, [PERSON] et al. performed a genome-wide CRISPR/Cas9 screen in murine AML models with the oncogenic fusions CALM/AF10 and MLL/AF9 and identified DCPS as a promising target for AML [41; 42]. The 5\({}^{\prime}\) end of eukaryotic mRNA is characterized by a distinctive \"cap\", which consists of an N7 methylated guanine (m\({}^{7}\)GpppN). The 5\({}^{\prime}\) cap is important for promoting splicing of the first intron, exporting mRNA to the cytoplasm, and allowing translation of the mRNA. Removal of the cap (\"decapping\") results in silencing of mRNA expression. The decapping enzyme DCPS is characterized as a pyrophosphatase that hydrolyzes the 5\({}^{\prime}\) m\({}^{7}\)Gppp and m\({}^{7}\)Gpp cap structure generated following 3\({}^{\prime}\)-to-5\({}^{\prime}\) and 5\({}^{\prime}\)-to-3\({}^{\prime}\) decay. Messenger RNAs containing an AU-rich element (ARE) in the 3\({}^{\prime}\) UTR are rapidly degraded in the cytoplasm. ARE-mediated decay is initiated by deadenylation, which is followed by the 3\({}^{\prime}\)-to-5\({}^{\prime}\) decay through a complex of exonucleases (termed exosome). In this process, DCPS hydrolyzes the remaining cap [43]. On the other hand, the 5\({}^{\prime}\)-to-3\({}^{\prime}\) decay pathway is initiated by cleavage of the 5\({}^{\prime}\) cap structure to release the m\({}^{7}\)Gpp and monophosphorylated RNA, followed by the DCPS-mediated hydrolysis of m\({}^{7}\)Gpp to release m\({}^{7}\)Gpp [44]. Therefore, DCPS plays an essential role in the final step of removing the residual cap in both directions of RNA decay. Intriguingly, DCPS was shown to be dispensable for normal hematopoiesis, which was supported by the observation that clinical parameters including blood cell counts in persons with germline biallelic loss-of-function mutations in DCPS were not significantly affected. In summary, DCPS is potentially an AML-selective vulnerability for which development of a targeted therapy is expected.
## A Wireless Sensor Network-Based Approach with Decision Support for Monitoring Lake Water Quality  
**[PERSON] 1,2, [PERSON] 1,*, [PERSON] 1 and [PERSON] 1**  
1 Department of Mechanical Engineering, East China University of Science and Technology, Shanghai 200237, China; E-Mails: [EMAIL_ADDRESS] (X.H.); [EMAIL_ADDRESS] (S.C.); [EMAIL_ADDRESS] (X.Z.)  
2 Institute of Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-138-186-953-30.  
Academic Editor: [PERSON]  
_Received: 9 September 2015 / Accepted: 11 November 2015 / Published: 19 November 2015_  
**Abstract:** Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, the WSN node has to be prepared to deal with varying situations. This paper presents a WSN self-configuration approach for lake water quality monitoring. The approach is based on the integration of a semantic framework, where a reasoner can make decisions on the configuration of WSN services. We present a WSN ontology and the relevant water quality monitoring context information, which considers its suitability in a pervasive computing environment. We also propose a rule-based reasoning engine that is used to conduct decision support through reasoning techniques and context-awareness. To evaluate the approach, we conduct usability experiments and performance benchmarks.  
**Keywords:** WSN; decision; monitor; lake water
# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
## 2 System Design and Methodology
### System Setup  
The system platform is demonstrated in Figure 16, where an Arduino control board, a stepper motor drive board, a linear-slider driving system, a solenoid valve, a water storage container and a stereo camera are all installed on the three-axis motion platform. This platform is an aluminum extruded structure of 61.6 cm (width) \(\times\) 61.6 cm (length) \(\times\) 40.2 cm (height); the accessory carrier is an aluminum extruded structure of 21.0 cm (width) \(\times\) 40.2 cm (length) \(\times\) 62.5 cm (height). Furthermore, the watering nozzle is at a height of 22.9 cm from the ground.  
For the image processing, the system employs the binocular cameras, being horizontally downward and parallel to the ground, fixed at a height of 63 cm from the ground. For the object detection and the obtainment of depth maps with YOLOv3, the left lens of the stereo camera is used as the main image acquisition device, so the center of the left lens is placed at the horizontally symmetric axis of the three-axis motion platform. Figure 17 defines positive directions indicated by yellow arrows labeled as X(+), Y(+) and Z(+), respectively towards positive directions of X-, Y-, and Z-axes of camera coordinates. As drawn in Figure 18, the origin of the three-axis motion platform is denoted as (0, 0, 0), and yellow arrows labeled with X(+), Y(+) and Z(+), representing the positive directions of X-, Y-, and Z-axes, respectively. Furthermore, the allowable travel lengths of the platform in the X-, Y-, and Z-directions are 40 cm, 40 cm and 16 cm, respectively.  
Figure 16: The platform of the automatic watering system.  
Figure 17: The coordinate system of the binocular camera.