esa-sceva/satcom-chunk-collection huggingface dataset:
### The ASTRI Mini-Array  
The ASTRI (_Astrofisica con Specchi a Tecnologia Replicante Italiana_) Mini-Array [8] is an international project led by the Italian National Institute for Astrophysics (INAF) to build and operate an array of nine 4-m class IACTs at the _Observatorio del Teide_ (Tenerife, Spain). The array is designed to be sensitive to energies in the 1-200 TeV energy range, and to achieve an angular resolution of \(\sim\)3\({}^{\prime}\), and an energy resolution of \(\sim\)10% above about 10 TeV [9].  
The primary scientific objective of the project is to advance gamma-ray astronomy at TeV and multi-TeV energy scales, contributing to a deeper understanding of topics such as the origin of cosmic rays, the extra-galactic background light (EBL), and other fundamental physics questions. They also focus on time-domain and multi-messenger astrophysics at the TeV and multi-TeV energy scales, as well as on the detection and study of gamma-ray bursts and multi-messenger transients in the very high-energy (VHE) domain. Additionally, the ASTRI Mini-Array will conduct stellar intensity interferometry studies. More information on the ASTRI Mini-Array Core Science program can be found in the dedicated paper by the ASTRI Collaboration [10].  
The first telescope of the ASTRI Mini-Array has been completed and is now in its commissioning phase. Within 2025, two more telescopes out of the planned nine will be operative on site. As of today, the prototype telescope ASTRI-Horn [11; 12; 13] is fully operational at the _Osservatorio Astrofisico_ \"M.G. Fracastoro\" on Mount Etna. The prototype is used mostly for calibration and test purposes (together with a good fraction of time dedicated to scientific observation). The Mini-Array telescopes will feature some significant differences with respect to the ASTRI-Horn setup, among which (and of interest for this paper) we mention the implementation of the pixel time tag readout (see Section 2).
## 3 The Circuit-Partitioning Algorithm Based on Partition Connectivity Clustering and Tabu Search
### Refinement Partitioning Algorithm Based on Tabu Search
#### 3.4.3 Tabu List and Tabu Tenure Settings  
Each element in the tabu list contains the following information: vertex \(v\), original subset \(G_{c}\), and tabu tenure \(t\). The format is as follows: { \(v\), \(G_{c}\), _t_}, where \(v\) is the vertex being migrated, \(G_{c}\) is the original subset containing \(v\), and \(t\) is the tabu tenure, indicating that vertex \(v\) cannot be moved back to its original subset \(G_{c}\) within the tabu tenure \(t\).  
\[t(v,G_{c})=\left|V_{\text{neig}}(G_{c})\right|\cdot\alpha+\text{random}(0,2) \tag{8}\]  
Here, \(V_{\text{neig}}(G_{c})\) represents the number of boundary vertices relative to \(G_{c}\), \(\alpha\) is the boundary vertex ratio coefficient, and \(\text{random}(0,2)\) is a random integer in the range \([0,2]\).  
Whenever vertex \(v\) moves from subset \(G_{c}\) to another subset \(G_{m}\), it is forbidden to move \(v\) back to its original subset \(G_{c}\) for the next \(t\) iterations (the tabu period). The length of the tabu period \(t\) is dynamically adjusted based on the number of boundary vertices relative to \(G_{c}\). This dynamic adjustment is effective in avoiding revisiting the same solution, thereby improving the diversity and efficiency of the search process.
# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 2 Materials and Methods
### Modeling the DDI Relation Classification Task  
In order to model the drug-drug interaction classification task and utilize DDI Knowledge Graph representations, a subgraph \(G=(V,E,X,K,R)\) of the complete graph is selected. \(V\) and \(K\) denote the sets of nodes and the classes they belong to, with class instances denoted as \(k\) defining a subset of \(V_{k}\). \(X\) constitutes an accompanying matrix of node characteristics (node feature vector), dimension \(|V|\times d_{BERT}\), where \(d_{BERT}\) denotes the dimension of the BERT-based LM vector representation. Finally, \(R\) refers to the types of edges (properties) that associate the nodes, while \(E\) is the set of edges of the graph expressed in the form of a coordinate list (coordinate list format--COO).  
Each interaction node is associated with a pair of specific drug mentions occurring in a sentence. Figure 3 shows the schema of the subgraph resulting from \(K=\{Interaction(1),\textit{Token}(T),\textit{Sentence}(S)\}\) and \(R=\{\textit{in\_interaction},\textit{contains\_interaction}\}\). Therefore, the target is to classify the Interaction nodes \(V_{I}\), or otherwise to determine the object of each triplet \(<v_{I},\textit{rdftype},c_{v_{I}}>\), where \(c_{v_{I}}\) is a subclass of Interaction, from the three elements (i.e., the two tokens and the sentence) that determine its type.  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline  & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \hline  & & & DNER & RC \\ \hline  & Documents & 714 & 112 & 191 \\  & Sentences & 6976 & 665 & 1299 \\ \hline  & Drug & 9425 & 351 & 1864 \\ Drug Entities & Group & 3399 & 155 & 667 \\  & Brand & 1437 & 59 & 369 \\  & Drug\_n & 504 & 120 & 140 \\ \hline  & Mechanism & 1322 & - & 303 \\  & Effect & 1700 & - & 363 \\ DDIs & Advice & 827 & - & 222 \\  & Int & 188 & - & 96 \\  & Negative & 23,771 & - & 4737 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The DDI-Extraction 2013 corpus statistics.
# Simulating 3D Human Postural Stabilization in Vibration and Dynamic Driving
## 2 Materials and Methods  
### Human Model  
The human active model (version 3.3), as distributed with MADYMO 2022, was adopted using Matlab and Simulink for running simulations and post-processing. The model was developed and validated primarily to simulate high-severity crashes [22; 23; 24; 25] and extended with postural stabilization for low-severity conditions [26; 27; 28; 29]. It represents a mid-size male adult of 75 kg, 1.76 m standing height and 0.92 m erect sitting height. The model includes active controllers to stabilize body segments, with adjustable feedback parameters specified for each body segment. These parameters manipulate the feedback gains of postural controllers. The head orientation can be controlled relative to a global coordinate reference system, resulting in so called \"head-in-space\" control founded on visual and vestibular motion perception or alternatively relative to a local segment, such as the trunk, resulting in \"head-on-trunk\" control associated with muscle spindle feedback. Feedback can also be selected to be relative to another body, such as the vehicle, representing head orientation in vehicle control. The suitability of these alternative control strategies to capture head postural control is described in the Results and Discussion sections.  
Recorded vehicle or seat motion was applied to the seat and floor, which interact with the body through contact with feet, seat cushion and seat back. The model interacted with the seat cushion and floor using multibody contact surfaces, and gravity was simulated.  
to body segment (head, trunk and pelvis) acceleration was determined using a Hanning window with 15 segments (i.e., a window size of 24 s), with 50 percent overlap [31].  
The postural control gains for neck, spine and hips were optimized, fitting the experimental data, whereas arm and knee control parameters were adapted accordingly. Control gains were separately fitted for the slalom and for the fore-aft vibration. For lateral and vertical vibration, we present results using parameters fitted for the fore-aft vibration. Hence, we assume postural control strategies to vary with seat motion amplitude but not with seat motion direction.
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 5 Development of Relay Nodes Using SDR
This information contains four bits to identify the canalization used in the cell, three bits to define the channel PHICH (Physical Hybrid ARQ Indicator Channel) structure which is used to transport recognition information about the HARQ (Hybrid Automatic Repeat Request) mechanism, and seven bits in order to identify the frame number (System Frame Number, SFN) [6]. Figure 4: Relay Node Processing Blocks. ```
Input :\(\hat{\mathbf{Y}},\hat{\mathbf{H}}\) Output :\(\mathbf{S}_{Tx}\) for\(j\gets 0:N_{Frame}\)do for\(i\gets 0:N_{Subframe}\)do if\(i==0\)then \(\text{pbch}=\text{PBCH}_{dem}(\hat{\mathbf{Y}}^{(:)}(j,i),\hat{\mathbf{H}}^{(: )}(j,i))\); \(\text{mib}=\text{MIB}_{dec}(\text{pbch})\), \(\text{Tx}_{pbch}=\text{PBCH}_{mod}(\text{mib})\) & \(\text{Tx}_{mib}=\text{MIB}_{cod}(\text{Tx}_{pbch})\);  end for \(\text{pcfich}=\text{PCFICH}_{dem}(\hat{\mathbf{Y}}^{(:)}(j,i),\hat{\mathbf{H}}^{(: )}(j,i))\); \(\text{cfi}=\text{CFI}_{search}(\text{pcfich})\), \(\text{pdcch}=\text{PDCCH}_{dem}(\text{cfi})\) & \(\text{dci}=\text{DCI}_{search}(\text{pdcch})\); if\(Mod(j,2)==0\) && \(i==5\)then \(\text{pdcsh}=\text{PDSCH}_{dem}(\text{dci})\), \(\text{sib}=\text{SIB}_{dec}(\text{pdcsh})\) & \(\text{Tx}_{sib}=\text{SIB}_{cod}(\text{sib})\); \(\text{Tx}_{pdcsh}=\text{PDSCH}_{mod}(\text{Tx}_{sib})\) & \(\text{Tx}_{pdcch}=\text{PDCCH}_{mod}(\text{Tx}_{pdcsh},\text{Tx}_{dci})\) \(\text{Tx}_{pcfich}=\text{PCFICH}_{mod}(\text{Tx}_{pdcch},\text{Tx}_{cfi})\); else \(\text{pdcsh}=\text{PDSCH}_{dem}(\text{dci})\); \(\text{dlsch}=\text{DLSCH}_{dec}(\text{pdcsh})\) & \(\text{Tx}_{pdcsh}=\text{PDSCH}_{mod}(\text{Tx}_{dlslch})\); \(\text{Tx}_{pdcch}=\text{PDCCH}_{mod}(\text{Tx}_{pdcsh},\text{Tx}_{dci})\) & \(\text{Tx}_{pcfich}=\text{PCFICH}_{mod}(\text{Tx}_{pdcch},\text{Tx}_{cfi})\);  end if if\(j\sim=0\) && \(i==0\)then \(\mathbf{S}_{Tx}^{(:)}(j,i)=\text{PIns}(\text{Tx}_{pcfich},\text{Tx}_{mib})\); else \(\mathbf{S}_{Tx}^{(:)}(j,i)=\text{PIns}(\text{Tx}_{pcfich})\);  end if  end for  end for
```  
**Algorithm 4**De-modulation & Modulation Subframe
## 4 Results and Discussion
### The Optimum Architecture of the ANN Model  
The ANN is built in the Matlab environment using data points \(n=143\), used to develop the MLR model, with the following characteristics: (i) the scaled conjugate gradient back-propagation algorithm, (ii) a root mean square error (\(RMSE\)) of \(10^{-5}\) to constrain the ANN training, and (iii) a maximum of 100 training epochs.  
The ANN model proposed and developed in this work is an optimum version of that developed in [54] for the same purpose. The architecture of the ANN model is here optimized in terms of (i) the number of hidden neurons, \(H_{h}\), and (ii) the hidden neuron activation function, \(G()\). To this end, we follow an exhaustive searching procedure by considering possible values that span the interval [1:70] for the number of hidden neurons and 12 activation functions for the hidden neuron activationfunction. The 12 activation functions considered in this work are the built-in functions in the Matlab environment, such as \"_satlin_\", \"_poslin_\", \"_radbas_\" (interested readers can refer to [60; 61; 62] for more details on the ANN's hidden neuron activation functions).  
The performance of each ANN candidate architecture is evaluated by computing the standard performance metrics (Section 3.4) on the obtained predictions of the validation set of data. Specifically, 100 cross-validations procedure are carried out to robustly quantify the performance metrics: the training and validation sets of data are sampled randomly from the \(n=143\) data points with fractions of 70% and 30%, respectively. The ANN model is trained and its internal parameters are optimally defined. Then, the cross-validation procedure is repeated 100 times leading to 100 values of each performance metric. The ultimate value of each metric is then computed by averaging the 100 available values on the validation set of data.  
The best ANN candidate architecture is found at \(H_{h}=22\) hidden neurons using the \"_poslin_\" neuron activation function. For clarification purposes, Figure 4 shows the influence of the hidden neurons on the four performance metrics when the \"_poslin_\" activation function is used. One can notice that the prediction performance of the ANN model increases as long as the number of hidden neurons increases; e.g., \(MSE\) values decrease along with the number of hidden neurons. However, for large numbers of hidden neurons, i.e., \(H_{h}\geq 22\), it seems that the ANN performance starts to saturate and, thus, increasing the number of hidden neurons might not be necessary to avoid the computational efforts of the ANN operation. In this regard, the optimum ANN architecture is defined by a hidden layer composed by 22 neurons using a \"_poslin_\" activation function for which the average performance metrics values on the validation set of data are \(MSE=6.82\%\), \(Accuracy=98.47\%\), \(R^{2}=85.21\%\), and \(R^{2}_{adjusted}=84.43\%\).
# Automatic Brix Measurement for Watermelon Breeding
## 2 Materials and Methods
### Automatic Watermelon Brix Measurement Method
#### 2.3.3 Digital Tube Recognition Using Image Analysis  
Since the PEN-PRO refractometer has no data interface, it is designed only to be read through a digital tube screen. To achieve automatic measurement, we use an RGB camera to capture the images of the digital tube, where the camera and the refractometer are installed in relatively fixed positions. As shown in Figure 5, the recognition procedure is as follows: (i) converting RGB images into gray as Input; (ii) dividing each digital area in the grayscale image into 7 ROIs according to the positions of the digital tubes; (iii) creating a new image, namely Output, and setting its values with the following equation  
\[\text{Output(x,y)}\ =\ \left\{\begin{array}{l}255\ \text{while Input(x,y)}\ >\ 260\ -\ \text{Mean}\\ 0\end{array}\right. \tag{6}\]  
where x and y are the image indices, and the Mean represents the average gray value of each ROI. By calculating the number of non-zero pixels contained in the ROIs representing the position of the digital tube in the generated image, it can be easily determined whether the digital tube is activated according to some specific patterns.
## 3 Results
### The Relationship between Cloud Physical and Optical Properties and Precipitation
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CC** & **RMSE (mm)** & **BIAS (mm)** \\ \hline IMERG\_O & 0.644 & 3.091 & \(-\)0.013 \\ IMERG\_D & 0.691 & 2.623 & 0.002 \\ CMORPH\_O & 0.615 & 3.081 & 0.050 \\ CMORPH\_D & 0.631 & 2.817 & 0.076 \\ ERA5\_O & 0.542 & 3.852 & 0.885 \\ ERA5\_D & 0.565 & 3.620 & 1.008 \\ \hline \hline \end{tabular} Note: CMORPH_O, IMERG_O and ERA5_O represent the original satellite data, CMORPH_D, IMERG_D and ERA5_D stand for the downscaled satellite data.  
\end{table} 
Table 4: Accuracy estimation before and after downscaling three satellite precipitation data. Figure 5: _Cont_. Figure 5: Spatial distribution of original daily scale precipitation data from different satellites, downscaled data, and calibrated rain gauge data: (**a**) 24 September 2010, (**b**) 11 August 2012, and (**c**) 7 July 2014. O represents the original satellite data; D stands for the downscaled satellite data. C denotes the satellite data after rain gauge calibration. Figure 6: Scatter plots between rain gauge daily observations and (**a**) the downscaled CMORPH, (**b**) the downscaled ERA5, and (**c**) the downscaled IMERG at one selected station location for the period of 2010–2014.
## 4 Results
### Model Generalization and Cross-Domain Validation
#### 4.6.2 Cross-Domain Validation Results  
The Kuala Lumpur commercial complex presented unique challenges due to mixed retail and office use. Initial predictions showed systematic errors during retail peak hours (MAPE 18.4%). After adaptation, accuracy improved significantly (MAPE 9.2%), with particular enhancement in retail-specific temporal patterns.  
The Sydney healthcare facility provided insights into 24-h operations. The adapted model captured shift changes and emergency department variations effectively, improving RMSE from 0.0284 to 0.0172. Staff shift patterns required only 72 h of facility-specific data to achieve stable predictions.  
Melbourne's educational facility demonstrated successful adaptation to semester-based variations. The transfer learning approach captured student movement patterns between classes, reducing prediction errors during transition periods from 23.5% to 8.9%. The model effectively identified and adapted to distinct weekday patterns based on class schedules.
# Analysis of the Dynamic Stability of Tailing Dams:
## 3 Analysis of the Test Results
### Theoretical Analysis of the Law of Pore Pressure Evolution of Tailing Silt
(2) Dynamic stress's effect on critical pore pressure  
As evident from Formula (2), the critical pore pressure, \(\mu_{cr}\), is linearly related to the dynamic stress, \(\sigma_{d}\); the linear proportional coefficient is \(\frac{(\sin\varphi^{\prime}-1)}{2\sin\varphi^{\prime}}\); and the coefficient is always negative. Therefore, the larger the dynamic force provided by the sample, the lower the critical pore pressure, and the weaker the anti-liquefaction ability of tailing silt. (3) Sample density's effect on critical pore pressure  
With an increase in sample density, the angle of internal friction increases, the connection between tailing silt particles becomes stronger, the dynamic strength is bound to increase, the critical pore pressure (\(\mu_{cr}\)) increases, and the liquefaction resistance of tailing silt increases.
# Case Study of Power Plants in the Slovak Republic and Construction of Microgrid and Smart Grid
## 4 Modeling RES in MATLAB/Simulink
### Model of Photovoltaic Station  
This model of photovoltaic station was designed for an output of 100 kW. The PV array has seven modules per string connected in series, which are connected in thirty-five strings in parallel. Wherein the DC link electric potential is 480 V and the output phase-phase alternating voltage from the inverter is 250 V, which is in the end increased to a 22 kV by means of a step-up transformer. Figure 8 shown schematic model of PV station.  
Figure 7: Schematic of the microgrid system.
# Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach
###### Abstract
Figure 3: Sample floor plan image with ground truth. Various furniture class objects available in the image are highlighted. Figure 2: Sample floor plan image from SESYD [3] dataset containing various furniture object classes in different rooms. For example, blue boxes represent sink class, whereas red boxes highlight table class. Although object detection algorithms have been applied before on floor plan images [9; 10; 11], most of them employ Faster R-CNN [12], YOLO [13] or other pattern matching algorithms [14]. Contrarily, we propose a framework that operates Cascaded Mask R-CNN [15] for object detection in floor plan images. We employ both conventional convolution and deformable convolutional networks (DCN) [16] on the backbone network and compare the performance, comparing them with baseline methods. This paper presents an end-to-end approach for object detection in floor plan images. The main contributions of this paper are as follows:  
1. We present an end-to-end trainable framework that works on Cascade Mask R-CNN [15] with conventional and deformable [16] convolutional backbone network to detect various objects in floor plan images. 2. We publish SFPI (Synthetic Floor Plan Images), a novel floor plan dataset comprising 10,000 images. This dataset includes ten different floor plan layouts and 16 different classes of furniture objects. The dataset is available here [[https://cloud.dfli.de/owncloud/index.php/s/mkg5](https://cloud.dfli.de/owncloud/index.php/s/mkg5) HBBntRbNo8X]([https://cloud.dfli.de/owncloud/index.php/s/mkg5](https://cloud.dfli.de/owncloud/index.php/s/mkg5) HBBntRbNo8X) (accessed on 5 October 2021). 3. Our proposed method accomplishes state-of-the-art results on the publicly available SESYD dataset [3] and establishes impressive baseline results for the newly proposed dataset. The rest of the paper is organized as follows. In Section 2, we describe the literature survey in the field of floor plan image object detection. Section 3 discusses the architecture of our proposed model. We discuss the architecture of Cascade Mask R-CNN [15], our backbone network [17], and deformable convolutions [16]. In Section 4, we talk about existing datasets and problems related to these datasets. Then, we analyze the peculiarities of our custom floor plan dataset. In Section 5, we explain our implementation configurations and different experiments. We also evaluate the results of these experiments and compare them with the previous state-of-the-art results. Our conclusions and pointers for future work are explained in Section 6.
# Adsorption of Ammonium, Nitrate, and Phosphate on Hydrochars and Biochars
## 3 Results and Discussion
### BC and HC Characteristics
The observed peaks in the spectra can be attributed to the presence of carboxyl and ester functional groups, as previously reported [34]. The study in [35] reported comparable transmittance values in the range of 4000 to 650 cm\({}^{-1}\) for two samples of HCs. This suggests that there were no significant chemical structural modifications resulting from an increase in the residence period. A correlation was observed between the pyrolysis temperature and the presence of functional groups in the BCs, with a decrease in the number of functional groups as the temperature increased. The obtained spectra exhibited distinct peaks at specific wavenumbers, namely, 2907 cm\({}^{-1}\) for methyl CH stretching vibrations, 1703 cm\({}^{-1}\) for aromatic carbonyl/carboxyl C=O, 1595 cm\({}^{-1}\) for aromatic C=C and C=O, 1028 cm\({}^{-1}\) for aliphatic COC and OH, and 814 cm\({}^{-1}\) for aromatic CH [36]. The observed shifts in the appearance of the bands are consistent with the findings of previous studies [37; 38] and were attributed to the increase in pyrolysis temperature. According to [PERSON] and colleagues [18], the peaks observed in the range of 1000 to 600 cm\({}^{-1}\) are associated with  
Figure 1: The FTIR spectra of (**a**) HC30, (**b**) HC180, (**c**) BC300, (**d**) BC500, (**e**) BC700, and (**f**) AC before and after NH\({}_{4}\)+, NO\({}_{3}\) –, and PO\({}_{4}\)3– adsorption. the undulating vibration of aromatic C-H. With an increase in pyrolysis temperature, a reduction in the polar functional groups (OH and C-O) was observed, as indicated by the smaller peaks in their respective spectra [16]. Upon the adsorption of NH4+, NO3-, and PO4-, both the biochars (BCs) and hydrochars (HCs) exhibited comparable peaks. Nevertheless, the peaks observed in the BC spectra were relatively feeble, whereas those in the HC spectra were relatively robust. Consistent with previous studies [16; 39; 40], our FTIR spectra analysis revealed the absence of a significant band subsequent to phosphate adsorption. Upon analysing the wavelengths of particular peaks depicted in Figure 1, slight deviations were observed, consistent with findings reported previously [41; 42]. The present study demonstrates the complexation phenomenon between ammonium ions and ionised -OH groups, as well as the bonded -OH bands of carboxylic acids. This was evidenced by a discernible shift in the wavenumber of multiple peaks within the range of 3500 to 3200 cm-1. The confirmation of the ion-exchange phenomenon between the protons of aliphatic C-H and the symmetric stretching vibration of CH2 with ammonium ions has been established through shifts observed at 2920 and 2860 cm-1, as previously reported [42].