esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Numerical Simulation Test
### Conventional Data Sets  
To simulate a conventional data setup, we assumed that the sources and receivers are placed on the ground surface at equal offsets, and there is one source at each receiver point. For the conventional data, we generated three different source-receiver layouts; the first has a source interval (_ds_) and a receiver interval (_dg_) equal to 1m, the second has _ds_ and _dg_ equal to 2 m, and the third has _ds_ and _dg_ equal to 5 m. In shallow application conventional surveys, the _ds_ and _dg_ are usually 2 to 5 m, however, we added the 1 m interval to compare it with the land-streamer data that will be discussed in the next section.  
The calculated traveltimes are then inverted to generate the traveltime tomograms shown in Figure 8. The total number of iterations is 23, and the minimum root mean squares (RMS) error is 8 ms.  
The three tomograms (Figure 8) failed to show the fourth layer (5000 m/s) due to the short profile length. We believe that a longer profile would detect the fourth layer; however, we did not repeat the simulation with a longer profile since our target was detecting the shallow anomalies at depths less than 20 m. The first and second refractors (red- and blue-lines in Figure 8) are detected using the conventional setup, the velocity values, and the thicknesses of layers 1 to 3 are close to the correct values; however, the refractors are shown on the synthetic velocity models (Figure 7) as horizontal lines, while in the tomogram it appears as irregular refractors. These irregularities in the refractor are caused by the presence of shallow near-surface anomalies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer No.** & **Velocity (m/s)** & **Thickness (m)** \\ \hline
1 & 800 & 20 \\
2 & 1500 & 50 \\
3 & 3200 & 80 \\
4 & 5000 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Velocity and thickness values of the synthetic model.  
Figure 7: The synthetic velocity model used in the simulation test. A, B, C, and D are the four local anomalies added to the 1D homogenous velocity model.  
The three local anomalies (B, C, and D) are shown on the three tomograms (Figure 8a-c), however, their sizes are larger than the actual sizes shown in Figure 7 and Table 2, for example, the thickness of anomaly (D) is 20 m, however, it is shown as 27 m in the tomograms. Due to the increase in the size of the anomaly at the final tomogram, we missed the local anomaly (A) in all three tomograms.  
Although all four local anomalies are disconnected in the true velocity model (Figure 7), they are shown as one layer (connected) in the tomograms with irregular bottom, these irregularities reflect the shape and location of the local anomalies. This might lead to an inaccurate interpretation of the subsurface geology.
## 1 Introduction
However, to the best of our knowledge, there is no work which uses the behavior of the tasks processing to provide a dynamic feedback to a scheduler for an optimal scheduling algorithm. The multi-installment algorithm, studied in [11], proposed a model of scheduling communication and computation, in which communication time and computation time are assumed to be proportional and an application's tasks are allocated in a sequence to a list of computing workers. A multiround algorithm [12] was built based on [11] by adding communication and computation latencies to the model. However, it assumed no memory limit in computing workers. The effect of memory limitation was studied in [13] under an assumption that the time of returning the results of computations from a computing worker is negligible, and that computations are suspended by communications. By contrast, in this work, we develop a novel model of the system and we derive formulas to compute the blocking probability of computing workers following tasks allocations as a feedback mechanism to a dynamic scheduling algorithm for an optimal performance and load balanced distribution of system utilization. Those probabilities help a dynamic scheduler to dynamically modify the load, in terms of the sizes of the tasks that should be allocated to computing workers, to increase the performance of an application. The rest of the paper is structured as follows. Section 2 provides the background and the motivations for this work. Section 3 describes the computing platform model we consider and the assumptions we set in this work. The model and its analysis are described in Section 4. In Section 5, we evaluate the proposed model and discuss the obtained results. Section 6 concludes the work.
## 4 Simulation and Result Analysis
### System Performance Validation
#### 4.3.2 System Power of ICI  
The UWA multi-carrier system is more susceptible to Doppler frequency offset. The OSDM system signal is similar to a traditional OFDM [24]. Therefore, the power of ICI caused by the Doppler spread can be bounded by:  
\[P_{ICI}\leq\frac{\alpha_{1}}{12}{(2\pi f_{d}T_{s})}^{2} \tag{19}\]  
where, \(T_{s}=\frac{1}{\Delta f}=\frac{N}{B}\), \(\alpha_{1}\leq 1\)[25],\(f_{d}=\frac{v}{c}f_{c}\).  
The formula (19) can be expressed as:  
\[P_{ICI}\leq\frac{1}{12}{(2\pi\frac{vf_{c}}{Bc}N)}^{2} \tag{20}\]  
The relationship between \(P_{ICI}\) and relative velocity of transmitter and receiver is shown in Figure 10, low \(P_{ICI}\), is achieved by reducing the subcarrier number N. However, the spectrum flexibility decreases with the subcarrier number decreasing.
## 5 Results
### Computational Analysis  
It was observed that this algorithm is extremely fast and takes only a few seconds on a normal laptop. This means that the preview corresponding to a start point and direction  
Figure 31: Concave—convex surface, indirect method (two start points).  
Figure 32: Concave—convex surface, direct method (viewed from two angles).  
chosen by the user can be generated nearly in real time without causing frustration to the user. We can see from Algorithm 7 that the computational complexity of the algorithm is \(O(n^{2}M)\) where n is the number of nodes and M is the number of triangle faces in the mesh. This is because Algorithms 6 and 7 both iterate over the number of nodes (n) and Algorithm 3 iterates over the number of triangles (M), thus leading to the basic formula of \(O(n^{2}M)\). Thus the algorithm runs in polynomial time. It can be noted that the number of nodes n can be computed approximately in advance by dividing the panel surface area by the surface area covered by each node. Hence, n depends on the camera parameters (field of view, distance from the surface, etc.) and also on the size of the panel.  
While a direct comparison with optimization-based methods is outside the scope of this paper (due to the absence of literature addressing this specific topic of panel inspections), we can gain some insight into the speed efficiency of this method by looking at the performance of the drone-based coverage algorithm in [5] that is doing the coverage path planning over a church represented as a triangle mesh. In [5], the coverage planning algorithm takes 2.48 min to complete for the church model represented by just 200 triangles. In comparison, the method presented in this paper takes only a few seconds to cover the panels represented by over 10,000 triangles.  
Further computational improvements can be made by using connectivity information between triangles (instead of searching the whole mesh every time in Algorithm 3) and also between nodes in the 2D parametric space. This would necessitate using a half-edge data structure for the mesh and additional changes. More computational improvements are possible by modifying Algorithms 6 and 7 to search only an appropriate subset of nodes instead of iterating over all nodes. However, because the current ('inefficient') algorithm still runs in just a few seconds on a normal laptop, the run speed was not considered a significant problem and the focus was more on simplicity rather than the efficiency of the algorithms. This can be explored in future work.  
Since these results are obtained for models of actual panels used in the aerospace industry, we believe that our algorithm can directly be applied in this area for non-destructive testing of actual panels. The digital models of the camera and the surface are faithfully followed in order to get representative results relevant to the industry.
# Protoporphyrin IX Based All-Solid-State Ion-Selective Electrodes for Choline Determination In Vitro
###### Abstract  
Protoporphyrin IX-based all-solid-state choline (Ch) ion-selective electrodes (ISEs) were fabricated and characterized. Poly (3,4-ethylene dioxythiophene) doped with poly (styrene sulfonate) (PEDOT/PSS) functioning as an ion-to-electron transducer was electropolymerized on the gold wire (0.5 mm diameter). The conductive polymer was covered with a Ch selective membrane containing protoporphyrin IX as an ionophore, which exhibited a lower detection limit of 0.49 \(\upmu\)M with the potentiometric method. The Ch sensor performed a wide linear range from 1 \(\upmu\)M to 1 mM, a fast response time of less than 5 s, and a decent selectivity of common inorganic and organic ions in the human body. Characteristics such as pH and temperature stability, life span, reproducibility and repeatability were also investigated to be satisfied. With the background of artificial cerebrospinal fluid, the recovery rate in 10\({}^{-5}\) M of Ch solution was measured by the standard addition method, revealing the potential for biological application.  
choline determination; all-solid-state; protoporphyrin IX; potentiometric; artificial cerebrospinal fluid
# Path Algorithms for Contact Sequence Temporal Graphs +
## 2 Materials and Methods
### TRG Data Structures
#### 2.1.3 TRG_Ours  
Our TRG data structure improves over TRG_Wu by using fewer vertices and edges. Let \(G^{\prime\prime\prime}=(V^{\prime\prime\prime},E^{\prime\prime\prime})\) denote our TRG transformation of \(G=(V,E)\). Let \(V_{out}(u)\) have the same definition as in TRG_Wu. We define \(t_{m}=\max\{t:t\in T_{in}(u)\}\). Let \(V^{\prime}_{out}(u)=V_{out}(u)\cup\{(u,t_{m})\}\) in case \(T_{out}(u)=\mathcal{O}\) or \(t_{m}>\max\{t:t\in T_{out}(u)\}\). \(V^{\prime}_{out}(u)\) is equal to \(V_{out}(u)\) otherwise. We only use the \(V_{out}\) nodes in our transformation. Therefore, we need to add the helper nodes (i.e., \((u,t_{m})\)), or otherwise, there will be the possibility of erroneously eliminating some nodes and edges. The set of nodes in TRG_Ours is constructed as follows\[V^{\prime\prime\prime}=\bigcup_{u\in V}V^{\prime}_{out}(u).\]  
For each edge \((u,v,t,w)\) in \(E\), \(E^{\prime\prime\prime}\) has a directed edge from \((u,t)\in V^{\prime\prime\prime}\) to \((v,t^{\prime})\in V^{\prime\prime\prime}\) such that \(t^{\prime}\) is the smallest time \(\geq t+w\) in \(\{\{t:(v,I)\in V^{\prime}_{out}(v)\}\). The weight of this edge is \(w\). Additionally, for every vertex \(v\), the vertices in \(V^{\prime}_{out}(v)\) are chained in ascending timestamp order. The weight of a chain edge is 0. From this point on, we use the term \"chain neighbors\" of node \((u,t)\) to refer to all nodes in \(V^{\prime}_{out}(v)\). Also, by the term \"'immediate chain neighbor\" of node \((u,t)\), we mean the node that follows \((u,t)\) in the sorted list of \(V_{out}(u)\) nodes. Figure 1e shows TRG_Ours for the example graph of Figure 1a. We have highlighted the \"helper nodes\". If we omit the helper nodes, then the algorithms on the resulting graph will work as if node \(C\in V\) and both its incoming edges and edge \((A,B,3,4)\) have been removed from the original graph \(G=(V,E)\). Again, one may verify that TRG_Ours has the same time-respecting properties as does TRG_Wu. Bounds on \(|V^{\prime\prime\prime}|\) and \(|E^{\prime\prime\prime}|\) are computed below.  
\[|V^{\prime\prime\prime}|\leq\sum_{u\in V}(|V_{out}(u)|+1)\leq|E|+|V| \tag{3}\]  
\[|E^{\prime\prime\prime}|\leq|E|+\sum_{u\in V}|Tout(u)|\leq|E|+|E|=2|E| \tag{4}\]  
It is easy to see that TRG_Ours will never have more vertices or more edges than either TRG_Wu or TRG_Zschoche.
# Dual-IoTID: A Session-Based Dual IoT Device Identification Model
## 2 Related Work
IoTsense collects both incoming and outgoing network traffic and then extracts relevant features using statistical tools, aggregates these features, and uses them as references for device type identification. This approach enables the monitoring of device behavior throughout its entire lifecycle. The results of the study showed recall rates of 93-100% for each device, with an average accuracy of 99%. [PERSON] et al. [19] utilized a large dataset obtained from 28 types of IoT devices (including motion sensors, digital cameras, plugs, smart lights, and healthcare-related monitoring devices) over a six-month period to classify IoT devices. They employed four device network activity attributes: (1) flow volume; (2) flow duration; (3) average flow rate; (4) device sleep time; and four application layer protocol attributes: (1) server port numbers; (2) domain name server queries; (3) Network Time Protocol (NTP) queries; (4) cipher suites. In total, eight key attributes were used for IoT device identification. The classification accuracy for the 28 IoT devices reached 99.88%. However, four devices from the dataset were not utilized, and some elements in the feature set were overly specific, thereby not focusing on device behavior, such as port numbers, DNS queries, and cipher suites. In Reference [20], the authors proposed an identification mechanism called IoT Sentinel, which automatically identifies IoT devices connected to the network based on the traffic generated during device boot-up. The study monitored device behavior by generating specific fingerprints for each device using data captured from IoT devices at Aalto University. IoT Sentinel extracts 23 features from each packet, describing its protocol, IP options, length, destination IP address, and port, and then selects the first 12 packets of each device to construct a feature matrix with 276 values as the device fingerprint. The study employed nine classifiers, among which Random Forest performed the best, achieving 95% accuracy. However, this method requires capturing traffic during the device boot-up phase for device identification, and a drawback is that once the device is operational, it cannot extract features and build models. Additionally, this method cannot identify devices from the same vendor. Meanwhile, [PERSON] et al. [21] proposed a machine-learning-based method for IoT device identification called IoTDevID. The study classified device behavior based on a single packet. In the feature extraction process, redundant features are eliminated using importance voting. The optimal feature subset is determined using a genetic algorithm, reducing the complexity of the model. The accuracy on the Aalto dataset was 83.30%, and in the UNSW dataset, it was 94.30%. IoTDevID achieves high accuracy for IoT devices at the packet level. Despite these advancements, a common issue still exists--the computational burden caused by excessive feature extraction, leading to intricate feature selection and complex features.
# Numerical Calculation and Experimental Study of the Axial Force of Aero Fuel Centrifugal Pumps
## 4 Theoretical Calculation Formula of Axial Force  
### Theoretical Formula Calculation  
The axial force of a centrifugal pump is mainly composed of cover plate force and dynamic reaction force [29]. For the centrifugal pump studied in this paper, because the oil inlet and outlet are radial, the axial force caused by the dynamic reaction can be ignored. For the closed impeller, on the one hand, the front and rear cover plates of the impeller are asymmetrical, that is, the front cover plates are not covered in the suction eyes. On the other hand, the rotation of the front and rear cover plates of the impeller will drive the rotation of the liquid in the front and rear cavities. It is generally believed that the liquid pressure in the side cavity is distributed in a parabolic pattern [20]. Without considering the leakage of the front and rear mouth rings, the hydraulic force on the rear cover plate is subtracted from the hydraulic force on the front cover plate and then the pressure remaining from the suction pressure is subtracted to generate an axial force and the direction is directed toward the inlet of the impeller. According to the theoretical head of the centrifugal pump, the axial surface velocity of the impeller inlet and outlet section is assumed to be equal, and the inlet prerotation is ignored. Then, according to the Bernoulli equation and other relevant theories, the intermediate process is omitted and the axial force (\(T\)) of the impeller cover plate can be deduced, as follows:  
\[T=\pi\rho g(R_{\rm m}^{2}-R_{\rm h}^{2})\Bigg{[}H_{\rm p}-\frac{\omega^{2}}{8 g}(R_{2}^{2}-\frac{R_{\rm m}^{2}+R_{\rm h}^{2}}{2})\Bigg{]} \tag{4}\]  
where \(R_{\rm m}\) and \(R_{\rm h}\) are, respectively, the radius of mouth ring and hub, m; \(H_{\rm p}\) is potential lift, m; \(R_{2}\) is the outlet radius of the impeller, m; and \(\omega\) is the angular velocity of impeller rotation, rad/s.  
Figure 13: Comparison of the simulation value and experiment value of axial force.  
Taking the centrifugal pump speed of 8500 r/min and fuel flow rate of 24 m\({}^{3}\)/h as an example, the axial force of the impeller component calculated by Formula (4) is 2742 N, which has a large error with the 4045 N measured by the test. This is because the formula ignores the pump cavity leakage and other factors and the default impeller front and rear hub diameter is the same and cannot calculate the axial force on the inducer blade, resulting in the use of this formula to calculate the axial force of the fuel centrifugal pump studied in this paper is not accurate.
# Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation
## 2 Related Works
### Weakly/Semi-Supervised Learning in 3D Human Mesh Estimation  
For most human pose estimation methods [36; 37; 43; 47; 48; 49; 50], supervised learning pre-vails; however, securing the 3D mesh ground truth is non-trivial. Weakly or semi-supervised methods are designed to solve the issue of the lack of quality annotation by using the available easier annotations. In the context of the 3D human mesh estimation task, semi-supervised learning uses 3D skeletons that are coarser than 3D meshes, while weakly-supervised learning uses either 2D annotation [36; 51; 52] or pseudo-3D annotation [53; 54; 55; 56]. In [36], 2D keypoint annotations are exploited to estimate SMPL body model parameters from CNNs to recover human 3D meshes following predicted body part segmentation masks. In [51], two anatomically inspired losses are proposed and used with a weakly supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthetic 3D data. The authors of [52] suggest weakly-supervised solutions by adopting multi-view consistency loss in 2.5D pose representations leveraged by independent 2D annotated samples. In [53; 54; 55; 56], multi-view geometry is used to resolve 3D depth ambiguity. We developed our original methods in the self-supervised setting only using the input 2D images; however, to compare with these weakly or semi-supervised approaches, we extended our model to be additionally learned by 2D or 3D skeletons if they are available. In this manner, we compared our method to the state-of-the-art self/weakly/semi-supervised approaches.
## 3 Results and Discussion
### EPR Spectroscopic Assessment of Antioxidant Capacity of Hop Oils  
The antioxidant capacity of the seven commercial hop oils was evaluated using electron paramagnetic resonance (EPR) spectroscopy and compared against three different free radical scavengers, i.e., Trolox (TR), gallic acid (GA), and ascorbic acid (AA), with the results expressed in equivalent antioxidant capacity (EAC) units, i.e., in TREAC, GAEAC, and AAEAC, respectively (Table 3). Graphical illustration of the assumed EPR spectroscopic method, in the form of the series of EPR spectra of DPPH\(\bullet\) recorded for the commercial Ahhhroma hop oil, clearly showed a decrease in the EPR signal intensity with increased sample volume (Figure 2).  
The numerical values of TREAC and AAEAC obtained for the individual hop oil samples were similar, and the numerical values of GAEAC were lower than the remaining ones by ca. 55-60%. This might have resulted from the different molecular structures of TR, GA, and AA, and hence from the different mechanisms of their respective free radical scavenging reactions. Comparing the EAC values of the individual hop oils, analogous trends were observed within each dataset. Thus, independent of the free radical scavenger used, we could point out Ahhhroma, Azacca, and Chinook oils as those with the highest antioxidant capacity. Amarillo oil showed a perceptibly lower antioxidant capacity, and  
Figure 1: Chromatograms recorded with use of the GC-FID system for 5 uL Azacca hop oil; (a) commercial sample and (b) hydrodistilled sample.  
Centennial oil showed a rather negligible antioxidant capacity. The antioxidant effect of the Cascade and Saaz oils was completely lacking. Summing up, the antioxidant capacity of the individual hop oils discussed in this study was quite diverse and yet, in general, the respective numerical values were in the range of 0-45 umol of TR, GA, or AA per 100 mL hop oil sample (Table 3). This meant that, compared to other edible liquids, the antioxidant capacity of the commercial hop oils was lower than that of red wines [47; 48] and comparable with the data obtained from EPR spectroscopy studies of beers [49] and white wines [47; 48; 50]. For example, the numerical values of TREAC reported in reference [49] for 63 commercial beers (pilsner, lager etc.) fell within the range of 100 to 1000 umol TR/100 mL sample, while the average numerical value of TREAC reported in reference [47] for Slovak white wines was equal to 76 \(\pm\) 27 umol TR/100 mL sample, and that for Austrian Burgenland white wines was equal to 90 \(\pm\) 19 umol TR/100 mL sample.
# Design and Optimization of Piezoelectric-Powered Portable UV-LED Water Disinfection System
## 3 Modeling of the System
### UV-LED Disinfection System  
UV disinfection is a well-known technique that neutralizes or kills harmful microorganisms with UV-C band radiation. UV-C beams prevent harmful activations of microorganisms by damaging protein structures such as DNA, RNA, enzymes [31; 32]. Typically, DNA/RNA protein structures have an absorption range of 200-300 nm with a peak around 260 nm [33]. However, some of the microorganisms have DNA repair mechanisms [34]. The DNA repair mechanism can be destroyed by breaking the repairing enzymes with more absorbable UV radiation [35; 36]. For all these, the energy dose (\(D_{\mathrm{UV}}\)) plays a critical role, and it is defined by the Bunsen-Roscoe reciprocity law as follows [37].  
\[D_{\mathrm{UV}}=\mathrm{Radiation\ Intensity}\Big{[}\mathrm{W}/\mathrm{m}^{2} \Big{]}\cdot\mathrm{Exposure\ Time}[\mathrm{s}]. \tag{8}\]  
In the proposed system, two UV-LEDs at two different wavelengths, 255 nm and 280 nm, are employed. The lower is for DNA and RNA disinfection, while the higher is for breaking DNA-RNA repair enzymes. Based on the standards for UV mercury lamp-based (254 nm) UV water disinfection [38], the bounds of the most appropriate dosage can be estimated roughly within a safety margin of 15%. The list of the microorganisms and estimated dosages are well-documented for water disinfection systems in [4]. Then, UV-LED power consumption (\(P_{d}\)) and UV-LED flux power (\(\phi\)) can be estimated accordingly, as shown in Table 3. The ranges could still be improved based on experimentation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Range** \\ \hline Thickness of \(PZT\) ceramic & \(0.001\leq h_{3}\leq 0.003\) m \\ Diameter of \(PZT\) ceramic & \(0.01\leq R_{PZT}\leq 0.02\) m \\ Number of \(PZT\) ceramic & \(0<N\leq 10\) \\ Bottle shake frequency & \(3.5\leq f_{s}\leq 5.5\) Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Piezoelectric Energy Harvester Parameters.