esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Forward and Inversion Theory
#### 2.2.1 Finite Element Method (FEM) Theory
Step 3: Substituting Equation (4) into Equation (3) and performing integration over each tetrahedral element and boundary element, the following are obtained:  
\[\int_{e}\left[\frac{1}{2}\sigma(\
abla u)^{2}-I\delta(A)u\right]d\Omega=\frac {1}{2}\mathbf{u}_{e}^{\mathsf{T}}\mathbf{k}_{1e}\mathbf{u}_{e}-\mathbf{u}_{e }^{\mathsf{T}}\mathbf{s}, \tag{5}\]  
\[\frac{1}{2}\int_{\Gamma_{e}}C\sigma u^{2}d\Gamma=\frac{1}{2}\mathbf{u}_{e}^{ \mathsf{T}}\mathbf{k}_{2e}\mathbf{u}_{e}, \tag{6}\]  
where \(\mathbf{u}_{e}\) is the column vector of the node potentials for the tetrahedral element; \(\mathbf{k}_{1e}\) and \(\mathbf{k}_{2e}\) are the element's volume integral matrix and boundary integral matrix, respectively [43],  
Figure 4: Schematic diagram of 3D model mesh discretization: (**a**) geoelectric model, (**b**) hexahedral element and numbering, (**c**) tetrahedral element and numbering. where \(\mathbf{k}_{1e}=\left(k_{1 ij}\right)=\left(k_{1 ji}\right)\) and \(\mathbf{k}_{2e}=\left(k_{2 ij}\right)=\left(k_{2 ji}\right)\); and \(\mathbf{s}\) is the column vector related to the source. Step 4: Summing the integration results from all the tetrahedral elements and boundary elements, and then expanding them into matrices and arrays composed of all nodes, the following is obtained:  
\[F(\mathbf{u})=\frac{1}{2}\mathbf{u}^{\mathrm{T}}\mathbf{\sum}\left(\mathbf{ \overline{k}}_{1e}+\mathbf{\overline{k}}_{2e}\right)\mathbf{u}-\mathbf{u}^{ \mathrm{T}}\mathbf{s}=\frac{1}{2}\mathbf{u}^{\mathrm{T}}\mathbf{k}\mathbf{u}- \mathbf{u}^{\mathrm{T}}\mathbf{s}, \tag{7}\]  
where \(\mathbf{u}\) is the column vector of the potential of all nodes; \(\mathbf{\overline{k}}_{1e}\) and \(\mathbf{\overline{k}}_{2e}\) are the expanded matrices of \(\mathbf{k}_{1e}\) and \(\mathbf{k}_{2e}\), respectively; and \(\mathbf{s}\) is the column vector related to the source. By setting the variation of Equation (7) to zero, a system of linear equations can be obtained:  
\[\mathbf{k}\mathbf{u}=\mathbf{s}, \tag{8}\]  
Solving this system of linear equations yields the potential distribution in the subsurface half-space.
# A Web-Based Application for Smart City Data Analysis and Visualization
## 4 Experimental Protocol: System Implementation
### Dataset Management and Description
#### 4.1.1 Air Quality Metrics  
Air pollution is recorded via multiple parameters to evaluate its spatial and temporal behavior:  
* NO2 (Nitrogen dioxide), a major traffic-related pollutant emitted primarily by combustion engines. Tracking NO2 permits quantifying the vehicular impact on urban air quality.
* O3 (Ozone), a secondary pollutant formed through photochemical reactions. Monitoring ozone concentrations is vital in assessing smog formation and respiratory risk, particularly during warm seasons.
* PM2.5 (particulate matter) comprises fine airborne particles less than 2.5 microns in diameter, often linked to adverse health effects. Their measurement helps identify pollution peaks and assess long-term exposure patterns.  
Figure 3: Overview of the sensors used in the Basel Smart Road project [68]: from left to right, an air quality sensor, a noise sensor, a traffic monitoring camera, and an electric vehicle charging station.
# Domestic Use Simulation and Secondary Shelf Life Assessment of Industrial _Pesto alla genovese_
## 2 Materials and Methods
### Simulation of Domestic Use and Sampling Plan  
The SSL assessment was performed using a deterministic approach, as suggested by [PERSON] and [PERSON] [12], which consists in storing the opened food product under the expected environmental conditions (i.e., at home in the case of shelf-stable pesto) and the worst-case scenario. The pesto samples were divided into five lots, each referred to a different domestic environment (coded from A to E). Pesto jars were stored at ambient temperature until use, according to protocols described hereafter. Among the possible way to simulate the worst-case found in literature, repeating the container opening and closure during the household storage was the selected approach for this study [12]. Two levels of use (referred to as \"scenarios\", based on the number of openings and duration of each opening) were tested in the five home environments mentioned above, to simulate a real utilization by the consumer (Table 2), while the analytical determinations were carried out at the laboratories of the Department of Life Sciences of the University of Modena and Reggio Emilia. This is an innovative feature that distinguishes this study from most of the research in the field of SSL [13; 14; 15].  
Scenario 1 (S1) consisted of a single opening for each jar, corresponding to the beginning of SSL, hence referred to as time zero (t\({}_{0}\)). Two tablespoons of pesto were removed from each jar, which remained uncapped for 2 min, then the jars were closed and stored in the domestic refrigerator, where they were kept for 20 days.  
Scenario 2 (S2) consisted of three openings of each jar at 2-days intervals. At each opening, two tablespoons of pesto were removed from each jar, which remained uncapped for 3 min, then the jars were closed and kept at ambient temperature for 30 min before placing in the domestic refrigerator, where they were stored for 20 days.  
Dataloggers (mini-TH, XS Instruments, Carpi, Italy) were used to monitor conditions of the five domestic refrigerators where samples were stored, by recording temperature and humidity at 1-h intervals up to 168 h (one week).  
The opening procedure described above for each scenario was performed simultaneously on four pesto jars in each home environment. Following the opening protocols, pesto jars were stored in domestic refrigerators and delivered to the laboratory on day 5, 11, 16, and 20 after the first opening. On the scheduled day, microbiological, sensory, and chemical-physical analyses were performed on the pesto samples from the five domestic environments.
## 3 Results and Discussion
### Noncellular In Vitro Antioxidant Activity  
The ability of food compounds to reduce DPPH radicals is often used as an indicator of their antioxidant potential. The results presented in Table 1 show that all BWSPHs demonstrated DPPH radical scavenging activity with half-maximal inhibitory concentration (IC\({}_{50}\)) values ranging from 2.10 \(\pm\) 0.12 to 2.47 \(\pm\) 0.04 mg/mL, indicating all BWSPHs possess electron-donating abilities. The DPPH radical scavenging data obtained for BWSPHs (Table 1) are comparable with previous studies investigating the DPPH radical scavenging activity of protein hydrolyses from various fish species such as Klunzinger's mullet (IC\({}_{50}\) = 2.08 \(\pm\) 0.13 mg/mL), shortfin scad (IC\({}_{50}\) = 1.89 mg/mL) and yellow-fin tuna waste (IC\({}_{50}\) = 1.89 mg/mL) [34; 35; 36]. However, although BWSPHs possessed varying physicochemical properties [26], no significant differences in DPPH scavenging activity were observed (_p_ > 0.05). Published studies have reported a variability in DPPH scavenging activity of BWPH depending on protease employed for hydrolysis [19; 20]. BWPH produced with subtilisin, trypsin or a subtilisin-trypsin combination with varying DH (4-12%) inhibitedDPPH by 50% at concentrations ranging from 1.36-2.46 mg protein/mL [19]. Preparation of BWPH with Flavourzyme(r) 500L resulted in higher DPPH scavenging activity compared with BWPH generated with Protamex(r) or Savinase(r) 16 L (_p_ < 0.001) possibly due to the higher proportion of di- and tripeptides and free amino acids (mainly Leu, Phe + Tyr, and Glu) [20]. Amino acid residues Trp, Phe, Tyr, Cys, and His can reportedly contribute to antioxidant activity [37], however, no relationship was observed between the amino acid composition and the antioxidant activity of BWSPHs in this study. The ORAC and FRAP activity of the BWSPHs and their simulated GI digests are presented in Table 2. Prior to SGID, ORAC values of 330.79 \(\pm\) 9.76 to 393.32 \(\pm\) 3.23 \(\upmu\)mol TE/g dw were obtained for BWSPHs with BW-SPH-C and BW-SPH-D mediating the lowest and highest activity, respectively. A significant increase (_p_ < 0.05) in ORAC activity was observed with samples BW-SPH-A-GI, -B-GI, -D-GI, and -F-GI following SGID (Table 2). As previously stated, the DH data, molecular mass distribution, and RP-UPLC profiles indicate that all BWSPHs were further hydrolysed during SGID, which would indicate that gut enzymes liberated peptides with superior ORAC activity from precursor peptides. Interestingly, the ORAC activity exhibited by BW-SPH-C-GI, the hydrolysate which was hydrolysed by the greatest extent during SGID (from 27.82 \(\pm\) 1.11 pre-SGID to 55.37 \(\pm\) 1.83 post-SGID [26], was similar to that prior to in vitro digestion. It is possible that bioactive peptides were not further hydrolysed by SGID or that peptides were further hydrolysed with the loss and gain of individual bioactivities, resulting in no overall change to the total bioactivity.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## 3 Results and Discussion
### The Yaw Angle Differences and the Duration of Eclipse  
#### 3.2.1 GPS IIR and IIF Satellites  
When the satellites are in eclipse, the actual yaw angle or the modeled yaw angle will depart from the nominal one. First, the differences between the modeled and the nominal yaw angle of the GPS IIR satellites are analyzed. Only the differences during noon maneuver are shown in Figure 2 because of the similar results of the shadow crossing (midnight maneuver). With the decrease of the sun angle, as shown in Figure 2a-c, the differences become larger and the duration of the noon maneuver becomes shorter. The maximum differences and the duration are also listed in Table 5. When the mean sun angle is \(-0.08^{\circ}\) in Figure 2c, the difference can be up to around \(131.87^{\circ}\) and the duration can be up to around \(14\) min.  
For the GPS IIF satellites, as shown in Table 2, the threshold value of the sun angle for the noon maneuver is nearly twice than that of the IIR satellites. Therefore, the duration of the noon maneuver for GPS IIF satellites are longer. The duration and the difference of G06 for different sun angles, from \(-0.38^{\circ}\) to \(3.97^{\circ}\), are shown in Figure 3. Similar to IIR satellites, the differences become larger and the duration of the noon maneuver becomes shorter when the sun angle goes smaller. It is important to note that there is an anomalous IIF noon maneuver for small negative sun angle (\(-0.38^{\circ}\) for G06 on 15 July 2017) because of the negative yaw bias. At this time, the opposite noon turn direction makes the angle differences up to \(360^{\circ}\) and the duration is up to approximately \(30\) min.  
When the IIF satellites enter into the shadow crossing regime, the yaw rate (about \(0.06^{\circ}/\mathrm{s}\)) will be computed to ensure that at the shadow exit the nominal yaw angle is reached. Due to the large sun angle limit (\(13.5^{\circ}\)) for the shadow crossing, the duration is relatively long. According to the average orbital angular velocities of GPS (\(0.00836^{\circ}/\mathrm{s}\)), the maximum duration is about \(54\) min. However, the yaw angle differences are usually smaller than \(20^{\circ}\) when the sun angle is bigger than \(5^{\circ}\). For example, the maximum angle differences in Figure 4a-c are \(21.58^{\circ}\), \(43.51^{\circ}\) and \(74.51^{\circ}\), respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Beta** & **Max Differences** & **Duration** \\ \hline \(1.63^{\circ}\) & \(13.51^{\circ}\) & \(7.5\) min \\ \(0.77^{\circ}\) & \(55.75^{\circ}\) & \(11\) min \\ \(-0.08^{\circ}\) & \(131.87^{\circ}\) & \(14\) min \\ \hline \hline \end{tabular}
\end{table}
Table 5: The maximum differences and duration for different sun angles (G21).  
Figure 2: The yaw angle of G21 (IIR) during noon maneuver for different sun angles: (**a**) beta = \(1.63^{\circ}\); (**b**) beta = \(0.77^{\circ}\); and (**c**) beta = \(-0.08^{\circ}\). The nominal yaw angle is shown in green, while the modeled one is in red.
# Lightweight Stacked Hourglass Network for Human Pose Estimation
## 4 Experiments and Results
### Lightweight and Bottleneck Structure  
To evaluate the network weight-reduction performance, we used depthwise separable convolution and looked at the effect of the bottleneck structure. Table 2 shows the experimental result obtained by constructing a single-stack hourglass network with each residual block in Figure 5; the number of parameters in the table represents the total number of parameters in a single hourglass network.  
In general, in a problem involving localization, such as human pose estimation, performance reduction occurs when using residual blocks in a bottleneck structure that uses \(1\times 1\) convolution to reduce the size of the network [18]. However, using \(1\times 1\) convolution was inevitable in this study because the network was made lighter by using depthwise separable convolution. Therefore, in order to confirm the effect of the bottleneck structure using \(1\times 1\) convolution in this experiment, the \(1\times 1\) convolutions of the first and last layers of the original residual block (Figure 5a) used \(3\times 3\) kernels. We experimented with a residual block (Figure 5c) that increased the kernel size to \(3\times 3\) and applied the depthwise separable convolution. Although the accuracy and parameters increased slightly, our result confirmed that the \(1\times 1\) convolution had no significant effect on our experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Residual Block** & **\# Params** &
\begin{tabular}{c} **[EMAIL_ADDRESS]** \\ **(Mean)** \\ \end{tabular} \\ \hline Bottleneck (Original) (Figure 5a) & 3.6M & 86.21 \\ Bottleneck (Original) + & 1.4M & 85.41 \\ Depthwise Separable (Figure 5b) & 1.6M & 85.67 \\ Bottleneck (\(3\times 3\)) + & 2.0M & 86.12 \\ Depthwise Separable (Figure 5c) & & \\ Multidilated Light Residual Block (Ours) & & \\ (Figure 5d) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results for MPII validation sets in a single-stack hourglass network, depending on the type of residual block. (# Params is total number of parameters in a single-stack hourglass network.).  
Figure 6: (**a**) Loss during training and (**b**) [EMAIL_ADDRESS] on the MPII validation set.  
Through this experiment, we confirmed that the multidilated light residual blocks proposed in this paper showed improved performance in terms of achieving a more lightweight network through a 56% reduction in the number of parameters. In addition, it was confirmed that the [EMAIL_ADDRESS] performance was reduced by approximately 0.09, despite the large reduction in the number of parameters, as compared to the original residual block (Figure 5a). This slight reduction in accuracy was overcome by using the additional skip connection structure described in Section 3.1.
# Green Sweet Pepper Fruit and Peduncle Detection Using Mask R-CNN in Greenhouses
## 2 Materials and Methods
### Evaluation Metrics  
In machine learning, a confusion matrix [38] classifies the object detection output (bounding boxes, masks, among others) as one of four possibilities [39]: true positive (TP), false positive (FP), true negative (TN), or false negative (FN).  
Three analytical criteria are derived from these four possibilities, as defined by (1)-(3): accuracy, precision, and recall. A high degree of proportionality between precision and recall usually means a valid prediction algorithm.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{1}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
As defined by Equation (4), the F1-score combines precision and recall in a single metric taking its harmonic mean, which we can use as an indicator of general precision [40]. The F1-score can also be used to show the stability of the model. The higher the value, the greater the stability of the model.  
\[\text{F1-score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{4}\]  
The mean value of the average precision of all classes is the \(mAP\), which is a comprehensive metric based on precision and recall. The \(mAP\) is calculated as shown in Equation (5):  
\[mAP=\frac{1}{n}{\sum_{i=1}^{n}{\int_{0}^{1}{P_{i}(R_{i})}dR_{i}}} \tag{5}\]  
where \(P\) and \(R\) represent precision and recall, respectively; \(n\) represents the number of total classes, and \(i\) represents the current class.  
The precision rate (2), recall rate (3), F1-score (4), and mean average precision (\(mAP\)), as defined by Equation (5), were used to evaluate the model performance of the trained Mask R-CNN model. These metrics are calculated by comparing the ground truth of the annotated segmentation mask against the segmentation mask predicted by the model, as opposed to the more common use of bounding boxes.  
Figure 5: Mask R-CNN architecture for sweet pepper detector training.
## 4 Discussion  
The presented work describes the development of a smart garment for monitoring the environmental conditions and vital signs of users who work in workplaces with health risk sources. The aim is to develop a monitoring system applicable to every working scenario for quickly detecting dangerous situations that threaten workers' safety. In fact, the developed smart jacket constitutes the source nodes of the monitoring system, gathering local and accurate information related to the worker's status. In particular, the garment integrates a wide range of sensors for detecting biophysical data (HR, SpO\({}_{2}\), body temperature, step count, falls, etc.) and workplace conditions, such as the presence of dangerous gas species and the oxygen concentration. For this purpose, two low-power conditioning boards for electrochemical gas sensors are used to detect the air concentrations of CO, CH\({}_{2}\)O, O\({}_{2}\), and H\({}_{2}\)S. The acquired data are forwarded to the IBM Cloud through a custom mobile application, which gathers data from the smart jacket and acts as a gateway, providing Internet access to the wearable device. In particular, the collected data are arranged in JSON packets and sent to the IBM Watson IoT service through the MQTT protocol. The data are sent to the company's security managers by a suitable dashboard, stored by the IBM Cloudant database, and analyzed by the IBM Cloud Functions service to detect anomalies and send notifications when dangerous situations are found. Furthermore, a multisource harvesting system was integrated into the garment, recovering energy from light, heat, and limb movements and collecting it in a storage device (LiPo battery). The scavenged charge is used to feed the sensing section, making the smart garment energy-autonomous in common operating scenarios, as demonstrated in Section 3.3.
# Delamination Effect on the Buckling Behaviour of Carbon-Epoxy Composite Typical Aeronautical Panels
## 2 Theoretical Background
### Eigenvalue Buckling Analysis  
Linear buckling is the most common and easiest type of numerical analysis to perform in the initial design stages of a structure. Indeed, thin structures, such as aircraft structures, when subjected to compressive loads may exhibit global buckling before service load.  
Therefore, it is essential to investigate the structural stability of aircraft components from the early design phases.  
When buckling occurs, structures experience a large variation in displacement without load changes (beyond minor load perturbations). Eigenvalue or linear buckling analyses calculate the theoretical buckling resistance of an ideal linear elastic structure. Actually, most real structures do not reach the theoretical elastic buckling resistance due to imperfections and non-linear behaviour. Indeed, linear buckling generally produces non-conservative results because it does not take these effects into account.  
Even though it is unconservative, linear buckling has the benefit of being computationally inexpensive compared to non-linear buckling solutions. To obtain the buckling load multiplier \(\lambda_{i}\), the eigenvalue problem in Equation (1) can be solved:  
\[([\text{K}]+\lambda_{i}[\text{S}])\{\psi_{i}\}=0 \tag{1}\]  
where [K] is the stiffness matrix, [S] is the stress stiffness matrix, \(\lambda_{i}\) is the i-th eigenvalue (used to multiply the loads that generate [S]) and \(\{\psi_{i}\}\) is the i-th eigenvector of displacements. Different eigenvalue and eigenvector extraction procedures are available. In this paper, the subspace method was considered [25].
# DynamiChain: Development of Medical Blockchain Ecosystem Based on Dynamic Consent System
## 4 Implementation  
### DynamiChain Network Based on Hyperledger Fabric  
Because of the sensitivity of healthcare data, DynamiChain was implemented using Hyperledger Fabric Blockchain technology (version 2.2.0), one of the permissioned and private blockchains that enhanced privacy [35]. It was established as a network that allows data utilizers to operate peer nodes. A hyperledger chaincode-based smart contract was implemented to store data in the blockchain and to distribute it to peer nodes. The original medical data from the data provider are implemented in the off-chain of the blockchain system, and the hash values of the medical data are stored on-chain, where dynamic consent system rules are applied to control the data provider's medical data.  
Three organizations, such as data providers, data utilizers, and hospital, form a consortium in DynamiChain (Figure 4). Mainly, data providers can set their dynamic consent rules, and we updated the chaincode according to the rules. Hospitals can store medical data transmission records and manage overall health examination data transmission. Data utilizers can compare health examination data hashes and read health examination data, respectively. The consortium is joined by all three organizations through one channel (Channel1). Additional participation on the consortium can be established through the configuration block stored in the ordering service, including peer, channel, client, network policy, and channel policy, after establishing the ordering service node, Orderer1, under consultation between organizations. Channel1 is created through Orderer1, and Channel1 provides the ability to share data only between organizations that share interests among consortiums. Organization1-3 will install their own peer in their data center and then participate in Channel1. Every peer has the role of communing/reader/anchor peer, and only the Peer3 has the role of endorsing peer additionally. The peers participating in Channel1 can store Distributed Ledger1 used on Channel1 in their local repository and share data. Chaincode1 has a smart contract function for the purpose of the Channel1 participants and is installed on every peer. Every participant on Channel1 must use our newly developed Dapp to participate in the network and send a transaction to the peers where the Chaincode1 is installed. Dapp commonly consists of a UI front end, REST API [36] backend, and Fabric Software Development Kit [37] (SDK, based on node.js).  
Figure 4: DynamiChain Network based on Hyperledger Fabric.