esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Test Setup  
The three-layered soil system constructed in the cylindrical container is shown in Figure 3. The experimental setup contained three main components, which were (a) the soil column compacted in a cylindrical transparent Plexiglas tube with a diameter of 16 cm, (b) the precipitation simulator, a peristaltic pump to achieve a constant precipitation rate, and (c) the measuring instruments: TDR, tipping bucket, data logger, and computer.  
The top and bottom soils were layered and compacted during the soil column tests to attain the specified bulk density and water content, as illustrated in Table 3. These values were on the drier side of the optimum water content. The materials used in the middle layer, comprising fine sand, medium sand, coarse sand, and fine gravel, were vibrated with a hammer to achieve the targeted density specified in Table 3. The soil column's top and bottom layers were 15 cm in height, while the middle layer was 10 cm in height. Precipitation was applied to the soil surface at a 1 mL/min rate using a Longer Pump model [1] peristaltic pump. Surface runoff was directed through a pipe placed in a hole drilled in the Plexiglas. The pipe was positioned at ground level to prevent ponding. Several holes were drilled along the side walls of the Plexiglas tube to install the instruments in the soil column. Four model [1] (Campbell Scientific) TDRs (Time Domain Reflectometers) were installed at different depths--12 cm, 20 cm, 30 cm, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Type** & \(\mathbf{\theta_{s}}\) & \(\mathbf{\theta_{r}}\) & \(\mathbf{\alpha\) (kPa)}\) & **n** \\ \hline SC (e = 0.48) & 0.32 & 0.1 & 1.4407 & 1.1341 \\ SC (e = 0.40) & 0.29 & 0.1 & 2.3403 & 1.1482 \\ SC (e = 0.30) & 0.23 & 0.1 & 3.4807 & 1.1523 \\ Fine gravel & 0.39 [1] & 0.006 [1] & 0.0606 [1] & 4.0 [1] \\ Coarse sand & 0.4 [1] & 0.006 [1] & 0.0662 [1] & 2.7 [1] \\ Medium sand & 0.4 [1] & 0.02 [1] & 0.06896 [1] & 1.99 [1] \\ Fine sand & 0.41 [1] & 0.023 [1] & 0.11627 [1] & 1.16 [1] \\ \hline \hline \end{tabular} \({}^{\dagger}\)Kesgin [36].  
\end{table}
Table 2: van Genuchten parameters of the soil water characteristic curves.  
Figure 3: Schematic view of the test setup.  
35 cm--to monitor moisture content. Two were placed in the top layer, one in the middle, and one in the bottom. TDR sensors assess the dielectric conductivity of the environment rather than at a specific point, requiring accurate positioning to avoid interactions between sensors. The TDR configuration has been optimized to overcome this problem. For the 1D infiltration tests, infiltration was applied to the soil surface at a 1 mL/min rate using the Longer Pump BQ50-1J peristaltic pump. The soil column was positioned on the tipping bucket (Texas Electronics Inc. Dallas, TX, USA TR-525M), which included a perforated metal plate resembling a grid. This setup allowed water drainage and measured percolated water using tipping buckets (Figure 3).
# Tree Planting Density and Canopy Position Affect 'Cerasuola' and 'Koroneiki' Olive Oil Quality
## 2 Materials and Methods
### Phenolic Compounds  
Identification and quantification of phenolic compounds was performed for each olive oil sample. Phenols were extracted according to IOC procedure (COI/T20/Doc. No 29/Rev.1 2017) and identified according to [PERSON] et al. (2018). Specifically, in a centrifuge tube, 2 g of olive oil were mixed with 5 mL of a solution of methanol and water (80:20 \(v/v\)). The tube was vortexed for 1 min, held in an ultrasonic bath for 15 min, and centrifuged at 5000 rpm for 25 min. The supernatant was filtered with a 0.45 \(\upmu\)m PTFE syringe filter and kept at 4 \({}^{\circ}\)C. Triplicate samples of olive oil were used for each cultivar, planting density, and canopy position. Phenolic compounds were identified by ultra-high performance liquid chromatography, heated electrospray coupled with high resolution mass spectrometry (UHPLC-HESI-MS) analysis using a quadrupole Orbitrap mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). UHPLC analysis was performed using a Dionex Ultimate 3000 System (Dionex Softron GmbH, Germering, Germany) equipped with an autosampler controlled by Chrompeleon 7.2 Software (Thermo Fisher Scientific, Bremen, Germany). A UHPLC column (Phenomenex Luna C18(2) 50 \(\times\) 1 mm, 2.5 \(\upmu\)) was used for separation of the selected compounds at 35 \({}^{\circ}\)C. The mobile phases used were 0.1% formic acid in water (\(v/v\)) (A) and methanol (B). The elution gradient program was: 0-5 min 10% B; 5-50 min linear increase to 99% B, 50-56 min 10% B coming back to the initial conditions until full stabilization. The column temperature was set at 30 \({}^{\circ}\)C and the injection volume at 1 \(\upmu\)L. The flow rate was 50 \(\upmu\)L min\({}^{-1}\). The total UHPLC-HESI-MS method runtime was 60 min. Detection was based on calculated exact mass and on retention time of target compounds, and data were evaluated by Quan/Qual browser Xcalibur 3.0 (Thermo Fisher Scientific, San Jose, CA, USA). Reference phenolic compounds including hydroxytyrosol, tyrosol, oleocanthal, were purchased from Sigma-Aldrich (Steinheim, Germany). Linearity of the MS response was verified with solutions containing all standards at six different concentration levels over the range from 0.250 to 5 ppm. Each point of the calibration curve corresponded to the average of five independent injections.
## 3 An Approach to Generate the MEP Logic Chain from BIMs
### Spatial Topological Analysis within MEP Models
#### 3.1.1 The IFC-Based Parametric Topological Analysis Algorithm  
In the IFC schema, object entities are subtypes of the abstract entity IfcObject. Objects that appear in a spatial or geometric context are described by abstract entities of type IfcProduct. As a subtype of IfcProduct, MEP products are described by the abstract entity IfcElement, which can be connected to other IFC elements. Then the objectified relationship entity IfcRelConnectsElements is used to describe connections between elements. Specific subtypes of IfcElement entities are used to further distinguish the semantic meaning of a MEP product, including specific object entities to describe HVAC elements, plumbing elements, etc. In some well-developed BIMs, the connection information  
Figure 4: BIM-based topological analysis within MEP systems.  
can be extracted directly by IfcDistributionPort, IfcDistributionElement and other tools. As shown in Figure 5, according to IFC schema, MEP components are represented by IfcDistributionElement and its subclasses such as IFCFan and IFCPipeFitting, etc. The connection relationship between these components is represented through IfcDistributionPort. According to the port between the component and connection form, IfcDistributionPort connects to the equipment by IfcRelConnectsPortToElement. The connection relationship between IfcDistributionPort is defined by IfcRelConnectsPorts, and the direction of the upstream and downstream relationship is defined through the two reverse attributes (ConnectedFrom and ConnectedTo) of IfcDistributionPort.  
There are two descriptions of connective information in MEP subsystems, implicit connection and explicit connection. For implicit connection, IfcDistributionPort only relies on the MEP equipment or terminal component. If these two equipment or terminals are connected, they are defined in IfcRelConnectsPorts and then the RealizingElement attribute value of the relationship is set to pipe fittings or segments that connect them while, in the explicit connection, the IfcDistributionPort is attached to not only equipment or terminals, but also pipe fittings or segments. The equipment and terminals must be connected via the IfcRelConnectsPorts with pipe fittings or segments that connect them, ignoring the attribute value of the RealizingElement.  
Furthermore, geometric information can also be used for topological analysis even if connection information is ignored in BIM model. For example, a pipe is parametrically represented in diverse BIM tools, as a \"SweptSolid\", IFC model defines the cross section, the starting position of the extrusion, the direction of the extrusion and its length. The pipe's Cartesian coordinate value will also be illustrated in the local coordinate system. Using this information, two pipes can be assessed as connected if the cross-section parameters of the starting point of one component is approximately equal to the ones at the end point of another component.  
Figure 5: Expression of MEP connection information in IFC.
## 4 State of Art
### Bulk-Optic Current Sensors
It was shown that the use of a Faraday rotator mirror can reduce the angle of rotation of the polarization plane due to the linear birefringence from 20\({}^{\circ}\) to 5\({}^{\circ}\)[36]. The dependence of the Verdet constant with source wavelength is also important, because it changes the sensitivity of the sensor [37]. Also, it was shown that the errors introduced by a small bandwidth optical source are very small and therefore it is reasonable to use the models which assume a monochromatic light source [38]. As in the all-fiber optic sensors, bulk-optic ones also have temperature dependency of the Verdet constant. A solution was measuring the temperature and compensating its effect by introducing a correction factor that depends on the temperature [39]. Many interesting ideas for optical current sensing may be found in literature, but many are not designed for high voltage and/or high current sensing operations. However, specific applications for high current [40] and high voltage [41] can be found. In the case of high currents, the sensors are usually limited to measurements between 0\({}^{\circ}\) and 360\({}^{\circ}\), in order to avoid rotation angle ambiguities. This problem was solved [40] by using a technique which counted the number of times that the phase crossed 0\({}^{\circ}\) and allowed to measure up to 720 kA. Also this type of sensor has been successfully introduced to the market. PowerSense [42] is a company that have a commercial product based on the sensing scheme like the one shown in Figure 2, which permits AC current measurement from 20 to 20,000 A, with maximum errors of 2%.
## 1 Background
This technique provides approximations over time of the NS equations, averaging the results on short periods [55]. DNS numerically solves full Navier-Stokes equations using a very fine mesh to capture all the scales that are present in a given flow. LES computes large-scale motions similarly to DNS but with a larger grid, and sub-grid scale dynamics are solved using an averaging method such as the RANS approach. While all three CFD computational approaches are able to provide accurate predictions for airflow, the RANS approach is the most popular one for the indoor environment. This is because the other two CFD approaches are significantly more computationally expensive without a justified improvement in prediction accuracy for most cases [56]. Several CFD-based models and simulators have been developed for indoor context [57; 58; 59]. [PERSON] and [PERSON] [57] developed a 3D CFD model for close space with airflow in whole building settings. The authors integrated the outcomes of the CFD simulation with building energy simulations to achieve a more accurate time-series analysis of building energy consumption compared to conventional energy simulations [57]. [PERSON] et al. [58] proposed a 3D CFD model to calculate the velocity, temperature, and moisture distribution in an existing empty and loaded cool store. The authors validated their model with data from several experiments and showed that the model was capable of predicting both the air and product temperature with reasonable accuracy [58]. [PERSON] et al. [59] reviewed the application of CFD and other numerical modeling techniques to the prediction of airflow in refrigerated food applications including cool stores, transport equipment, and retail display cabinets. The authors show that given enough computation power, CFD-based models achieve high accuracy in all these tasks.Cravero and [PERSON] showed that a CFD model can provide an accurate description of a breathing individual in a room. This description includes breathing dynamics, air ventilation, and air diffusion [60]. Thus, CFD-based models accurately describe the air movement dynamics across a wide range of environments in general and room types in particular. As such, the CFD model implemented using the RANS method is used in this research.
# Quantum Computing Approaches for Mission Covering Optimization
## 4 Algorithm Implementation  
In this work, we tested three techniques to solve the MCO problem with the two scenarios described in the previous section, quantum annealing and two types of QAOA algorithms.  
* Quantum Annealing (QA) [21]
* Quantum Alternating Operator Ansatz (QAOA) [17]. It implements constraints by means of a Lagrange multiplier embedded into a cost function Hamiltonian.
* QAOAH, which is a version of QAOA with a constrained mixer. It is denoted as QAOAH as it was developed by [PERSON] et al. [20]. It engineers the constraints to remain within a constraint space during the entire solution process.  
All three approaches use similar means for encoding the objective function but differ in the way they implement their constraints and what machines support them.  
Since QA and QAOA are both derived from the same formulation, the implementation details are described together. In QAOAH, constraints are embedded into the Mixing Hamiltonian, which results in a different formulation. The following sub-sections outline the implementation for both scenarios using the three approaches described above.  
Figure 2: Matrix view for Solution. This solution is shown for a problem with three missions, four resources of type \(\mathbf{R}_{1}\) and four resources of type \(\mathbf{R}_{2}\). \(\texttt{REQ}_{M}\) describes how many \(\mathbf{R}_{1}\) resources must be assigned to mission \(m\). Each \(\mathbf{R}_{1}\) resource is paired with exactly one \(\mathbf{R}_{2}\) resource for each mission.
# SCMs: Systematic Conglomerated Models for Audio Cough Signal Classification
## 2 Proposed Method 1: Robust Models with Gabor Dictionary Analysis and Machine Learning
### LASSO Model  
LASSO is quite powerful in selecting variables and constructing regression techniques. A penalty term is added to the model's residual sum of squares in the concept of LASSO [33]. If the penalty term is exceeded, then a greater number of variables will be penalized. A regularization process is added to the penalty term, and so, some of its coefficients are shrunk to zero. As the least correlated variables shrink, the interpretability of the regression model increases. The irrelevant variables can be eliminated, and so, only the explanatory variables are brought on board, thereby eliminating overfitting. During training, the model can fit well, but it cannot predict the testing data accurately. Finally, in between the variance and bias, a suitable tradeoff could be found, and so, the model can be less complex and well balanced. In this linear model, the mathematical formulation is as follows:  
\[G=H\beta+\varepsilon \tag{10}\]  
where the response variable is denoted as \(G\) and the explanatory variable is denoted as \(H\). \(H\) is assessed as an \(H_{n\times k}\) matrix and \(G\) is assessed as \(G_{n\times 1}\) matrix in a vector format. The RSS is minimized by LASSO where the upper bound is specified by \(t\). The formula for LASSO is expressed in the following equation:  
\[\text{min}{\sum_{i=1}^{n}}\left(g_{i}-{\sum_{j}}\,h_{ij}\beta_{j} \right)^{2}\text{ subject to }{\sum_{j=1}^{k}}\big{|}\beta_{j}\big{|}<t \tag{11}\]  
The optimization of it in the vector form is expressed as follows:  
\[\text{min}\left(\frac{|G-H\beta|_{2}}{n}\right)^{2}\text{ subject to }{\sum_{j=1}^{k}}\big{|}\beta_{j}\big{|}<t \tag{12}\]The addition of a penalty term is conducted before the sum of the absolute value of the model parameters. If the penalty term is large, then the model will have more shrinkage. The estimation of the parameters is represented as follows:  
\[\hat{\beta}(\lambda)=\underset{\beta}{\mathrm{argmin}}\left(\frac{|G-H\beta|_{2 }^{2}}{n}+\lambda\mathrm{\sum}_{j=1}^{k}|\beta_{j}|\right) \tag{13}\]  
The \(j^{th}\) coefficient is assumed to be zero, and it will be eliminated from the model. The penalty values can be altered so that the number of selected features can be changed.
## 2 Related Works  
Recently, there were several studies on how to make disaster response services faster and more efficient. Among them, there were papers that studied the agent-based simulation service model considering the interaction between crowds and surrounding objects. Also, there have been studies on situation-aware services and automation system for the safety of personnel in the area by linking sensors and IoT devices in the disaster area.
## 3 Method Implementation
### Feature Extraction and Graph Neural Network
Using this method, GraphSAGE samples and aggregates features of neighboring nodes layer by layer to update the central node's representation. As shown in Figure 3b, GraphSAGE's neighbor sampling method is illustrated. At each layer, neighboring nodes are randomly sampled, with sampled nodes (in orange) used for feature aggregation, while unsampled nodes remain white. This sampling mechanism reduces computational complexity while maintaining efficient aggregation, making GraphSAGE suitable for consumer electronics fault diagnosis tasks. Figure 3c depicts the aggregation and inference steps of GraphSAGE. The model aggregates features from various layers and inputs them into the inference network, where three different aggregators (Aggregator1, Aggregator2, Aggregator3) fuse the information to predict the label of the central node. In the figure, 'MF' represents the multimodal feature vector, and 'Label' denotes the node's feature. By leveraging GraphSAGE's sampling and aggregation, the model uses multimodal feature vectors to infer fault diagnosis results. \[J_{\mathcal{G}}=-log(\sigma(z_{u}^{\top}z_{v}))-Q\cdot\mathbb{E}_{z_{v}\sim p_{ n}}(v)log(\sigma(-z_{u}^{\top}z_{v_{n}})) \tag{3}\]  
Equation (3) represents the loss function of GraphSAGE, where \(z_{u}\) and \(z_{v}\) denote the embedding vectors of two nodes \(u\) and \(v\). These embeddings are node representations computed by GraphSAGE through aggregating neighboring node features. \(z_{v_{n}}\) represents the embedding vector of a negative sample \(v_{n}\), which is a randomly selected other node used for contrastive learning to help the model differentiate between positive and negative samples. \(Q\) denotes the negative sampling multiplier, indicating how many negative samples should be paired with each positive sample. \(\mathbb{E}_{v_{n}\sim p_{n}}(v)\) represents the expectation of the sampling probability distribution \(p_{n}\) for negative sample nodes \(v_{n}\). By defining this loss function, GraphSAGE can efficiently accomplish the inference task.
# Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
## 4 Security Requirements for Autonomous Ships
### Derive Security Requirements
#### 4.2.6 Elicit Security Requirements  
Potential threats of autonomous ships were analyzed to devise various attack scenarios. To formulate security requirements for attack scenarios and security risks, security requirements derivation techniques and other artifacts were used to formulate security requirements in terms of what the autonomous navigation systems should do.  
* R-01. The sensor's function for verifying the identification data should be provided.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & & **Risk** \\ \hline \multirow{6}{*}{High} & \(\bullet\) & Trick sensor’s identification data to cause AI malfunction \\  & \(\bullet\) & Tampering sensor data to cause AI malfunction \\  & \(\bullet\) & Denial-of-service attacks to block transmission of sensor data \\  & \(\bullet\) & Buffer overflow or brute forcing to paralyze AI functions \\  & \(\bullet\) & DoS attack on communication protocols to block internal \\  & \(\bullet\) & network communication \\  & \(\bullet\) & Tampering with the data transmitted over vulnerable protocols \\ \hline \multirow{6}{*}{Medium} & \(\bullet\) & AI malfunction or performance degradation due to contaminated \\  & \(\bullet\) & AI malfunction due to a backdoor attack on training data \\  & \(\bullet\) & AI malfunction due to sensor data disturbance \\  & \(\bullet\) & AI function paralysis due to malware injection into an artificial \\  & & neural network \\ \hline \hline \end{tabular}
\end{table}
Table 4: Risk assessment results.  
* R-02. Sensor data transmission must be protected against tampering.
* R-03. In the event of a denial-of-service attack, minimum functions for the operation of the ship should be provided.
* R-04. Security design must be performed to eliminate defects in the neural network.
* R-05. An encryption mechanism should be used to protect communication data.
* R-06. AI must be trained using reliable training data.
* R-07. Unauthorized network connections should be blocked.
* R-08. Protective measures should be implemented to prevent malware.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
###### Abstract  
The compressive sensing (CS) of mechanical signals is an emerging research topic for remote condition monitoring. The signals generated by machines are mostly periodic due to the rotating nature of its components. Often, these vibrations witness strong interactions among two or multiple rotating sources, leading to modulation phenomena. This paper is specifically concerned with the CS of this particular class of signals using a Bayesian approach. The main contribution of this paper is to consider the particular spectral structure of these signals through two families of hierarchical models. The first one adopts a block-sparse model that jointly estimates the sparse coefficients at identical or symmetrical positions around the carrier frequencies. The second is a spike-and-slab model where the spike component takes into account the symmetrical properties of the support of non-zero-coefficients in the spectrum. The resulting posterior distribution is approximated using a Gibbs sampler. Simulations show that considering the structure in the prior model yields better noise shrinkage and better reconstruction of small side-bands. Application to condition monitoring of a gearbox through CS of vibration signals highlights the good performance of the proposed models in reconstructing the signal, offering an accurate fault detection with relatively high compression rate.  
compression sensing; Bayesian model; modulated signals; structure; hierarchical models; block-sparse; spectrum; gearbox; diagnosis Article On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals [PERSON], [PERSON], A., and A., and [PERSON], M., and A., 2021, 11, 2626, 13 February 2021