esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Setup
### Data Sources  
Our experiments utilized historical market data obtained through the yfinance library, integrating real-time market data. The dataset comprises two distinct categories of financial instruments:  
* Traditional Equity Securities:
* Oracle Corporation (ORCL): Representative of established technology sector;
* Taiwan Semiconductor Manufacturing Company (TSM): Major semiconductor industry constituent;
* JPMorgan Chase & Co (JPM): Leading financial services institution.
* Cryptocurrency Assets:
* Bitcoin/USD (BTC/USD): Primary cryptocurrency benchmark;
* Ethereum/USD (ETH/USD): Leading smart contract platform;
* Solana/USD (SOL/USD): Emerging blockchain protocol.  
The analysis utilizes both daily and hourly intervals to capture fractal formations and detailed wave structure evolution. The historical data span approximately four years (2020-2024) for the daily interval and approximately 4 months for the hourly interval (June-September 2024). This extended temporal scope ensures sufficient data density for pattern validation while capturing the various market phases.  
We used OpenAI's GPT-4o-mini model [20], with default settings for temperature parameter, as the core language model for the agent system. It enables natural language processing capabilities across the multi-agent architecture. This choice was based on evaluation of contemporary language models, including Mistral, Claude, and other GPT variants. While all tested models demonstrated competent natural language processing capabilities, GPT-4o-mini emerged as the optimal choice due to two primary factors: its context length management and favorable cost-efficiency ratio.  
Figure 14: Logic inside Backtester agent.
## 1 Introduction  
Half-metallic ferromagnets [1; 2; 3; 4] are potential candidates for spintronic applications owing to their completely spin-polarized band structures [5; 6; 7; 8]. Of all known materials exhibiting half-metallicity, Heusler alloys have attracted considerable interest because of their high spin magnetic moments and high Curie temperatures [9; 10; 11]. Original and stoichiometric full-Heusler alloys have X\({}_{2}\)YZ in their chemical formulas, where X and Y denote transition metal elements and Z is a primary group element. These Heuslers usually crystalize with a cubic L2\({}_{1}\) structure (space group Fm-3m, No. 255) and individually contain four interpenetrating fcc sublattices in their ideal forms. Several theoretical and experimental studies confirmed a series of Heusler compounds with half-metallicity [12; 13; 14; 15]. Recently, quaternary Heusler alloys have increasingly attracted interest [16; 17; 18; 19; 20; 21] owing to their half-metallicity and spin gapless band structures. These compounds show a distinct structural symmetry (space group F-43m, No. 216) resulting from the partial replacement of X in X\({}_{2}\)YZ by another element X'. This crystal structure can be defined as a LiMgPdSn prototype. Most of these compounds, such as in Co\({}_{2}\)Mn\({}_{1-x}\)Fe\({}_{x}\)Si, were designed for adjusting the Fermi energy to the middle of the gap [22]. Equivalent stoichiometric quaternary Heusler alloys have been extensively researched. CoFeMnZ compounds(Z=Al, Ga, Si, or Ge) with 1: 1: 1: 1 stoichiometries show half-metallicity [23]. [PERSON] et al. performed a theoretical study on 60 LiMgPdSn-type quaternary Heusler alloys and found that most of the alloys exhibit half-metallicity [24]. Ideal properties, such as stable half-metallicity, large spin magnetic moment, and high Curie temperature, can be observed in the ordered bulk phase. Unfortunately, temperature, impurity, and other external factors can disrupt the completely spin-polarized band structures and further degrade the half-metallicity of ideal compounds [25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. For example, in the traditional full Heusler compound Co\({}_{2}\)MnSi, a remarkable gap with 0.4 eV in minority band at 985 K may disappear, and only a spin polarization of 61% can be observed at a barrier interface consisting of a single electrode made of a Co\({}_{2}\)MnSi film [35; 36; 37; 38].  
A dramatically large spin-down gap of 0.93 eV was detected in a quaternary Heusler TiZrCoIn [39]. The Ti of this compound was inclined to sit at A(0, 0, 0), Zr at B(0.25, 0.25, 0.25), Co at C(0.5, 0.5, 0.5), and In at Z(0.75, 0.75, 0.75) in the Wyckoff position coordinate. This compound might be an excellent candidate for spin-injectors because of its extraordinary wide band gap. Its performance for spintronic applications can be enhanced by modulating its magnetic and electronic properties on the basis of valence-electron count. In the present work, we searched for a mixed compound in the series TiZrCoIn\({}_{1-x}\)Ge\({}_{x}\) where the half-metallic behavior is stable against the variation of impurity.
# 3 MSF: A Multi-Modal Adaptation of the 6 TiSCH Minimal Scheduling Function for the Industrial IoT
## 4 The Multi-Modal Minimal Scheduling Function
### Timeslot Timings and Supercell Length
Unit-length superslot timings are disseminated conventionally, i.e., with the TSCH Timeslot IE [2] (Section 7.4.4.4) in EBs. Similarly, we designed a new sub-IE for the IETF IE, which we termed the \"Timing\" sub-IE, to carry the three coefficients used to derive \(TxOffset\), \(TxAckDelay\), \(MaxTx\), and \(MaxAck\) from their unit-duration counterparts. More specifically, as shown in Figure 7, the Timing sub-IE contains a variable-length FactorList, where each entry follows a specific format. That is, each entry maps a supercell length greater than 1 to three 8-bit fixed-point scaling factors. The two factors for \(TxOffset\) and \(TxAckDelay\) have six spaces behind the binary point, whereas the scaling factor for In Section 4.1, we mentioned how the supercell length for a simultaneous TSCH mode equates to the longest supercell length we would ordinarily use for any non-simultaneous TSCH mode corresponding to the communication modes to which it (i.e., the simultaneous TSCH mode) points. As always, its length dictates the timing intervals to use in a supercell. As such, for simultaneous transmission, the same timings apply to all underlying communication modes, which makes synchronization relatively straightforward. That is, assuming transmission is truly simultaneous at the link layer, we need only derive the drift from the first ACK (with ACK-based synchronization) or the first packet (with frame-based synchronization) received.
# Quasi-Wireless Capacitive Power Transfer for Wire-Free Robotic Joints +
## 2 Materials and Methods
### Experimental Setup  
A 1-degree of freedom prototype was fabricated and is shown in Figure 4. The prototype consisted of a motor with an encoder, a QWR, a frame, and a concentric base plate. The frame was 3-D (Drawn using Autodesk Inventor Professoinal 2021 Build:421 and printed using Original Prusa i3 MK3S) printed using Polylactic acid (PLA) filament, which was then coated with conductive paint (841 AR Super Shield Nickel Conductive Paint). Permanent magnets were attached to the bottom of the frame such that the the prototype would magnetically connect to the base plate. The base plate comprised an acrylic sheet that housed two electrodes: a steel \"anode\" plate and a grounded steel \"cathode\"  
Figure 2: Thermal imagery of the QWR with a parallel load along the transmission line. This has the concentration of current at the tap location rather than the bottom of the coil.  
plate. RF power was supplied to the center section of the base plate via a coaxial cable. Nylon M3 screws were used to secure the electrodes to the acrylic plate. Due to the high impedance operation of the QWR, a ferrite toroidal transform was used in the matching branch Figure 4a, while also providing isolation for the load. The matching transformer was composed of 20 gauge magnet wire and a Fair-Rite type 67 core. The toroid had an outer diameter of 35 mm, inner diameter of 22.5 mm, and a thickness of 13 mm Figure 5. The overall circuit is shown in Figure 4a, where the QWR was constructed of 176 turns of 22 gauge magnet wire with a total physical length of 135 mm and an outer diameter of 47.5 mm. Taps were placed every 15 turns along the entirety of the QWR.  
Figure 4: (**a**) A schematic of the circuit, (**b**) The robot with 1 degree of freedom functioning.  
Figure 3: The theory compared to the experimental current distribution of a tapped coil. Equation (6) was used to find the theoretical current with the values calculated per turn.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 5 Performance Evaluation
### Simulation Results
#### 5.3.3 Delays Experienced by Different Packet Types vs. Data Generation Rate  
This experiment investigates the effects of varying data generation rates on the average delays experienced by different data packet types. The _average packet delivery delay_ constantly increases for all the studied systems with the increasing data generation rates, as illustrated in Figure 6. As a result of event-influenced prioritization, Critical Events (CE) experienced the least amount of delay compared to Relevant Events (RE) and Normal Events (NE), as expected theoretically. It is also revealed from the graphs of the Figure 6 that, on an average, the delays of the RE and NE packets are almost 1.5 times and 3 times higher, respectively, compared to the CE packets.  
Figure 5: Impacts of varying number of User Equipment (UEs).  
Such an excellent performance improvement is achieved due to the fact that, with the increasing data generation rates, the PAUM system efficiently categorizes different events and allocates radio resources in a prioritized manner and mitigating interference, whereas the generation of additional data packets cause resource allocation overhead to both the _iRRM_ and _bRRM_ systems since they do not differentiate among the data packets; rather, they try to allocate resources to all packets simultaneously, causing interference and congestion. Thus, the data delivery performances of the latter systems are degraded.
# A Study on the Calibrated Confidence of Text Classification Using a Variational Bayes
## 3 Proposed Method
Figure 11b is the decoder of the VB-based generative model. To calibrate the confidence, the proposed method concatenates the generated and training data and then uses it as an input for the classifier. We propose three research hypotheses for the validity of the proposed methodology. The research hypothesis of the proposed method is as follows:  
**Hypothesis 1**. : _The quantitative indicators of text have different distributions depending on the document's purpose._  
The model for classifying patents uses quantitative indicators, such as claims, and the number of inventors as predictors. Then, the predictors of the classification model should be able to explain events such as technology litigation, valuation, and transfer. Hypothesis 1 expresses that quantitative indicators will have different distributions depending on the target label. If the distribution of indicators is similar, they will be unsuitable predictors for classifying data [65, 66]. Therefore, we assume that the indicators used in the proposed methodology reflect the data characteristics. **Hypothesis 2**. : _In the latent space obtained using a generative model, each document according to a label will have a disentangled representation._  
The proposed method calibrates the confidence by learning the data generated through VB. When classifying technology transfer, data that reflect the features of the transferred patent should be generated. Therefore, following Hypothesis 2, the latent space of a patent according to technology transfer in the generative model should be composed of disentangled variables. It is important to secure a disentangled latent space for the generative model; if the data characteristics are generated in entangled space, such as noise, it will have a negative effect on improving the performance of the predictive model [67]. Therefore, we need to statistically test whether the latent space obtained from the generative model is disentangled depending on the data characteristics. **Hypothesis 3**. : _The proposed method improves the calibrated confidence of document classification._  
Finally, we need to calibrate the confidence of the document classification. This study extracts various predictors and builds a VB-based generative model. Next, generative models generate data in the disentangled space. That is, Hypothesis 3 is the basis for judging whether the proposed method helps calibrate the confidence of the model. We expect that the confidence of a classification model that has undergone this process will be calibrated. To this end, this paper will not only intuitively present the results of the method proposed through experiments through various graphs, but it will also secure the validity of the study through statistical tests. Therefore, our research hypothesis focuses on calibrating the confidence of document classification and improving the validity of the classification model and the efficiency of the generative model. All hypotheses are statistically tested with the experiments in Section 4.
## 3 Experimental Setup
### Experiment Procedure  
First, the thermographic images acquired by the cylinder measurements and the two airfoil test cases are pre-processed by separating the objects' surface from the background in each image. For the measurement on the cylinder object, all images in the image series are rectified, and the areas in the two-dimensional image plane (640 px \(\times\) 39 px) are allocated to the angular values \(\theta=0^{\circ}\) to \(\theta=180^{\circ}\) between the stagnation point and the opposite point on the cylinder object. The images of the two airfoil measurements are additionally cropped between the leading edge and the end of the turbulent flow regime, as the focus of this work is the distinguishability between these flow regimes, and surface modifications downstream create thermal artefacts that are not addressed in this work. The images have a dimensionality of 250 px \(\times\) 152 px. In order to quantify the flow transition location normalized to the chord, the chord length \(c\) is calculated prior the cropping of the images.  
The images of all three experiments are afterwards evaluated by the PCA, and the PC images are calculated. To compare the PCA with classical image processing methods for creating flow visualizations, two additional methods are chosen. Firstly, the temporal mean value of the image series is considered as it is particularly successful in reducing white measurement noise and, therefore, increasing the CNR between different flow regimes. Secondly, the temporal standard deviation of the image series is considered because of its capability to reduce reflections and systematic gradients. The resulting flow visualizations of the PCA-based and the two classical image processing methods are compared with regard to the distinguishability between the laminar and turbulent flow regime quantified by the CNR, see Section 2.2. Additionally, all three flow visualizations are evaluated and compared concerning the systematic and random error of the flow transition localization conducted with both the Gaussian and Gaussian error fit methods. By assessing the different advantages of either image processing and flow localization method, the question how the PCA can be used as a complementary method to classical evaluations is studied.
# Experimental Study on the Strength and Durability for Slag Cement Mortar with Bentonite
## 2 Materials and Proportioning Design
### Bentonite Material  
The material of bentonite used in the study was purchased from the local industry. The properties of the bentonite complied with API-13A (American Petroleum Institute) [26]. The basic properties of the bentonite used in this study are listed in Table 2. It can be noted from Table 2 that the particle size of bentonite is very fine, such that 96% by weight of the bentonite material passes a screen of 75 \(\upmu\)m. The specific density of the bentonite is almost the same as that of cement. Therefore, the advantage of physical property similarity is useful for the partial substitution of the cement material by bentonite.
## 2 Results
### Transcriptome Analysis of Active Ingredients
#### 2.6.2 Flavonoid Biosynthesis  
Flavonoids are abundant compounds in medicinal plants, and their biosynthetic pathway is relatively well characterized. The flavonoid metabolic pathway utilizes L-phenylalanine as a substrate and generates dihydrokaemferol through the action of phenylalanine ammonia lyase (PAL), _trans_ cinnate 4-hydroxylase (C4H), 4-comarate CoA ligase (4 CL), chalcone synthase (CHS), chalcone isomerase (CHI), and fl  
Figure 8: The phylogenetic relationship of class I terpene synthase in _A. paniculata_ with class I terpene synthase in other species. The evolutionary tree was constructed by using MEGA X 10.2.1 software, and the sequence alignment was performed by using muscle comparison, and the evolutionary tree was the Neighbor-Joining method.  
naringenin 3-dioxygenase (F3H). Dihydrokaemferol is then modified by enzymes such as flavonol synthase (FLS), flavonoid 3\({}^{\prime}\),5\({}^{\prime}\)-hydroxylase (F3\({}^{\prime}\)5\({}^{\prime}\)H), bifunctional dihydroflavonol 4-reductase/flavanone 4-reductase (DFR), anthocyanin synthase (ANS), and anthocyanin-din reductase (ANR) to yield myricetin, (-)-Epigallocatechin, and other flavonoids [38; 39] (Figure 9A).  
Although numerous flavonoids have been isolated from _A. paniculata_, flavonoid biosynthesis in this species remains unreported. Here, we mined the _A. paniculata_ transcriptome and identified 60 candidate genes involved in the flavonoid pathway, including 23 DEGs (Figure 9B; Table S12). Most genes were highly expressed in the root, and least expressed in the leaf, which is consistent with previous research showing that the content of flavonoids is high in roots [40]. Among these genes, PAL (Cluster-13593.8899), C4H (Cluster-13593.9354 and Cluster-13593.9354), 4 CL (Cluster-13593.7173, Cluster-13593.5821, Cluster-13593.19817, Cluster-13593.10743, Cluster-13858.0, and Cluster-13593.22909), CHS (Cluster-13593.18560), CHI (Cluster-13593.21724 and Cluster-13593.13031), F3H (Cluster-13593.17726), and ANR (Cluster-13593.6264) were significantly upregulated in the root, suggesting that these enzymes may be critical in the biosynthesis of flavonoids.
# Mechanical Strength Study of a Cranial Implant Using Computational Tools
## 2 Materials and Methods  
### PEEK Viscoplasticity  
To define the material model, a work presented by [PERSON] et al. [18] was taken into account and is explained in this section. This material model offers a more complete and suitable solution to define the material, including damage, dynamic behavior, and interesting thermomechanical phenomena reported for PEEK. The material model in this work adopts the Johnson-Cook (JC) model. The approach is justified by the observed strain rate and temperature sensitivity of PEEK, which indicates a viscoplastic behavior. This model is frequently used in finite element codes, including explicit analysis in Abaqus, and the strength model can be described as  
\[\overline{\sigma}\left(\vec{\varepsilon}^{p},\vec{\varepsilon}^{p},T\right)= \left[A+B\cdot(\vec{\varepsilon}^{p})\right]\left[1+C\cdot ln\frac{\dot{ \vec{\varepsilon}}^{p}}{\dot{\vec{\varepsilon}}_{n}}\right]\left[1-\Theta^{m }\right], \tag{1}\]  
where \(A\) is the yield strength at a quasi-static strain rate, \(B\) and \(n\) represent the influence of the strain hardening, \(m\) is the temperature sensitivity, \(T_{0}\) is the initial temperature, and \(T_{m}\) is the melting temperature, while the first term is the strain hardening \(\vec{\varepsilon}^{p}\), the second one is the strain rate sensitivity \(\dot{\vec{\varepsilon}}^{p}\), and the third one is related to thermal softening \(\Theta\), where  
\[\Theta=\frac{T-T_{0}}{T-T_{m}}. \tag{2}\]The equation  
\[\Delta T\left(\vec{x}^{p},\vec{\xi}^{p},T_{0}\right)=\frac{\beta}{\rho\cdot C_{p}} \int_{\varepsilon}^{\vec{x}^{p}}\overline{\sigma}\left(\vec{x}^{p},\vec{\xi}^{p },T\right)d\vec{x}^{p} \tag{3}\]  
allows us to obtain the temperature increment in adiabatic conditions, with \(\Delta T\) being the temperature increment, \(T_{0}\) the room temperature, \(\beta\) the Quinney-Taylor heat fraction coefficient, \(\rho\) the material density, and \(C_{p}\) the specific heat at constant pressure. In this work, the author assumed the Quinney-Taylor heat fraction coefficient \(\beta\) to be constant and equal to 0.9.  
As reported by [PERSON] et al. [18], all these parameters were determined by analyzing the results of the various compressive tests at different strain rates and temperatures following methodologies reported for ductile metals and polymers [19]. Parameters \(A\), \(B\), and \(n\) were determined by tests at room temperature and parameter \(C\) was identified by Equation (4) with different strain rates and null plastic strain (\(\overline{\sigma}=\overline{\sigma}_{y}\)) at room temperature. Thus, the temperature sensitivity parameter m was determined by Equation (5) for a range of temperatures ranging from the initial temperature to the melting temperature.  
\[C=\frac{\overline{\sigma}_{y}-A}{A\cdot ln\frac{\vec{x}^{p}}{T_{0}}} \tag{4}\]  
\[m=\frac{1-\frac{\overline{\sigma}}{\left(A+B\cdot\left(\vec{x}^{p}\right)^{n }\right)}}{ln\frac{T-T_{0}}{T-T_{m}}} \tag{5}\]  
Table 1 contains the constants used to define the material behavior, in other words, the JC parameters.
###### Abstract  
Sound event localization and detection (SELD) is a joint task that unifies sound event detection (SED) and direction-of-arrival estimation (DOAE). The task has become such a popular topic that it was introduced into the challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) Task3 in 2019. In this paper, we propose a method based on dual cross-modal attention (DCMA) and parameter sharing to simultaneously detect and localize sound events. In particular, the DCMA-based decoder commonly used for multiple predictions efficiently learns the associations between SED and DOAE features by exchanging SED and DOAE information in the process of attention, in addition to the encoder with parameter sharing. Furthermore, acoustic features that have not been usually used in the SELD task are additionally adopted to improve the performance, and data augmentation techniques of the mixup to simulate polyphonic events and channel rotation for spatial augmentation are conducted for this task. Experimental results demonstrate that our efficient model using one common decoder block based on the DCMA to predict multiple events in the track-wise output format is effective for the SELD task with up to three overlapping events.  
sound event localization and detection; dual cross-modal attention; parameter sharing; transformer; spectrogram +
Footnote †: journal: Applied sciences