esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As the supply air inlets of SV are horizontally arranged on the same wall, multiple parallel jets interact with each other during delivery to the occupied zone, which directly affects indoor air distribution. Based on numerical simulations, [PERSON] et al. [26] found that jet spacing has an important impact on jet interaction, which decreases with an increase in jet spacing. The merge point between parallel jets varies significantly with the change in jet spacing. [PERSON] et al. [27] conducted an experimental study on twin jet flow generated by two identical parallel axisymmetric nozzles. The results showed that the turbulence energy increased when the Reynolds number increased. A high Reynolds number indicates a strong mutual attraction between the twin jets. [PERSON] et al. [28] conducted a theoretical analysis and experimental studies and found a similarity criterion between two and three parallel jets. In addition, researchers have found that the characteristics of twin parallel jets are influenced by the ratio of nozzle axis spacing \(d\) to nozzle width \(w\) (nozzle spacing ratio, \(d/w\)). [PERSON] and [PERSON] [29] demonstrated the periodic interaction between twin jets owing to the existence of a Carmen vortex at \(d/w=1\). [PERSON] et al. [30] found that the peak value of jet velocity decreased with an increase in the nozzle spacing ratio at 0.6 \(\leq d/w\leq\) 1.4. [PERSON] et al. [31] studied rectangular nozzles and found that the nozzle spacing ratio and merging point exhibited consistent trends. For SV, multiple identical air inlets are often arranged in parallel on the side wall of the room; interaction between the jets due to the influence of the jet spacing cannot be neglected. Recent studies have focused mainly on the effects of the supply air temperature, supply air angle, and air terminal type. Research on the influence of parallel jet spacing on SV performance is lacking. Air inlet spacing of SV directly affects the air jet interaction and air distribution, affecting thermal comfort and inhaled air quality. If the parallel jet spacing is too small, multiple air jets enter the occupied zone after merging with insufficient airflow diffusion. A large parallel jet spacing results in an occupied zone that cannot be fully covered by the supply air. Thus, this study innovatively investigates the effect of air inlet spacing on the flow characteristics and ventilation performance of SV using multiple parallel jets. Experiments and computational fluid dynamics (CFD) simulations were conducted. The effective draft temperature for SV (EDTS), air diffusion performance index (ADPI), the local mean age of air (LMAA), and energy utilization coefficient (EUC) were used to comprehensively evaluate the ventilation performance. The six theoretical axial velocity attenuation formulas were adopted, and the predicted results were compared with the experimental results. The optimal air inlet spacing was obtained using the TOPSIS multi-objective optimization method. The research results enrich the understanding of airflow characteristics and optimization of SV design.
# Latitude-Adaptive Integer Bit Allocation for Quantization of Omnidirectional Images
## 4 Theoretical Analysis
**Theorem 1**: _When the assumption of Lemma 1 holds, the expected WMSE distortion in one image is_  
\[D=2K^{2}G2^{-2\tilde{b}}. \tag{12}\]  
_The bit allocated to one block at latitude \(k\) is_  
\[b_{k}=\frac{B}{2K^{2}}+\frac{L^{2}}{2}\log_{2}\frac{g_{k}}{G}, \tag{13}\]  
_where \(\tilde{b}=\frac{B}{MN}\), \(G=\left(\prod_{k=1}^{K}g_{k}\right)^{\frac{1}{K}}\)._  
Proof. : The geometric mean inequality states that for any positive numbers \(a_{k},k=1,2,\ldots,K\), it satisfies that  
\[\frac{1}{K}\sum_{k=1}^{K}a_{k}\geq\left(\prod_{i=k}^{K}a_{k}\right)^{\frac{1}{ k}}, \tag{14}\]  
with equality if the \(a_{k}\) are all equal. Apply this inequality to (11), considering \(\sum_{k=1}^{K}b_{k}\leq\frac{B}{2K}\), and then we get  
\[ \begin{array}{l}\frac{1}{K}\sum_{k=1}^{K}g_{k}2^{-2\frac{b_{k}}{L^{2}}}\geq \left(\prod_{k=1}^{K}g_{k}2^{-2\frac{b_{k}}{L^{2}}}\right)^{\frac{1}{K}}\\ =\left(\prod_{k=1}^{K}g_{k}\right)^{\frac{1}{K}}2^{-\frac{2}{K}\sum_{k=1}^{K} \frac{b_{k}}{L^{2}}}\\ =G2^{-2\sum_{k=1}^{K}\frac{b_{k}}{ML}}\\ \geq G2^{-2\frac{B}{2 KML}}\\ =G2^{-2\tilde{b}}.\end{array}  \tag{15}\]  
Equality holds if, for each \(k\),  
\[g_{k}2^{-2\frac{b_{k}}{L^{2}}}=G2^{-2\frac{B}{2K}}, \tag{16}\]  
so that \(b_{k}=\frac{B}{2K^{2}}+\frac{L^{2}}{2}\log_{2}\frac{g_{k}}{G}\), which is (13). Considering there are \(2K\) blocks in a tile, the expected overall distortion in one image is (12). We establish Theorem 1 by leveraging the WMSE distortion of the block without NNI constraints, and obtain the bits to be allocated to latitude \(k\). Once \(b_{k}\) is determined, the computation of \(b_{k,l}\) according to (8) is straightforward. Building upon Theorem 1, we derive Corollary 1. **Corollary 1**: _When the minimum expected WMSE distortion in Theorem 1 is achieved, quantizers of different tiles produce identical distortion, which is \(D_{\text{tile}}=2 KG2^{-2\tilde{b}}\)._  
The proposed Corollary 1 can be used to present the theoretical underpinnings of our greedy algorithm, which indicates that we consistently assign bits to the tile incurring the highest distortion.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
###### Abstract  
Automated food safety inspection systems rely heavily on the visual detection of contamination, spoilage, and foreign objects in food products. Current approaches typically require extensive labeled training data for each specific hazard type, limiting generalizability to novel or rare safety issues. We propose a zero-shot detection framework for visual food safety hazards that enables the identification of previously unseen contamination types without requiring explicit training examples. Our approach adapts and extends the Knowledge-Enhanced Feature Synthesizer (KEFS) methodology to the food safety domain by constructing a specialized knowledge graph that encodes visual safety attributes and their correlations with food categories. We introduce a Food Safety Knowledge Graph (FSKG) that models the relationships between 26 food categories and 48 visual safety attributes (e.g., discoloration, mold patterns, foreign material characteristics) extracted from food safety databases and expert knowledge. Using this graph as the prior knowledge, our system synthesizes discriminative visual features for unseen hazard classes through a multi-source graph fusion module and region feature diffusion model. Experiments on our newly constructed Food Safety Visual Hazards (FSVH) dataset demonstrate that our approach achieves 63.7% mAP in zero-shot hazard detection, outperforming state-of-the-art general zero-shot detection methods by 6.9%. Furthermore, our framework demonstrates robust generalization to fine-grained novel hazard categories while maintaining high detection performance (59.8% harmonic mean) in generalized zero-shot scenarios where both seen and unseen hazards may occur simultaneously. This work represents a significant advancement toward automated, generalizable food safety inspection systems capable of adapting to emerging visual hazards without a costly retraining process.  
zero-shot detection; food safety; knowledge graphs; feature synthesis +
Footnote †: journal: Applied Sciences  
1 The Department of Food Science and Human Nutrition, University of Illinois Urbana-Champaign, Champaign, IL 61801, USA; [PERSON]ESS]  
2 Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ 07030, USA; [EMAIL_ADDRESS]  
3 School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA  
4 Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA
## 3 Problem Description
Since each request takes approximately 15 min, the time taken for collecting a time-dependent matrix could be more than 165 min if a travel time of each trip is collected on an hourly basis between 9 a.m. and 7 p.m. as in our case. In our courier business problems, daily delivery requests from potential customers are available in the morning, and a good decision for the vehicle routes must be made within 30 min. Taking into our consideration the conditions outlined above, we face scenarios with the following constraints:  
* The _vehicle-zone map_ (i.e., city coverage area) which maps locations to vehicle zones is available. * Each location has service demands in which a driver must deliver commodities. * Each location requires a service duration of 4 min during which the assigned driver stops to make a delivery. * There is no road restriction, and vehicles can make a delivery at any location. * Travel routes or distances must be road distances. * Travel duration must consider time-dependent traffic information. * Drivers have limited working hours where drivers should return to the depot. * Deliveries can be made at any time of the day. * Vehicle times should be balanced. * Results of the final plan should match that of the original vehicle-zone map as much as possible.
## 3 Materials and Methods
### Magnetometry  
The drone was equipped with a GEM-Systems GSMP-35U potassium vapour magnetometer, which has a sensitivity of 0.0002 nT/1 Hz. This system also has the capability to simultaneously record the magnetic field and to incorporate a real-time single-frequency (L1) GPS receiver.  
This equipment consists of multiple components, including a sensor connected by cable to a controller, a data logger, a 5 V battery power supply and a GPS receiver, with a  
Figure 3: Flight plan made for magnetometry.  
total weight not exceeding 2 kg. To ensure the stability and safety of the system, careful adjustments were made to locate and balance the data logger and batteries inside the drone's cargo compartment. In addition, the GPS antenna was placed on top of the drone to ensure continuous signal reception.  
Regarding the accuracy of the magnetic sensor, it is important to note that interference from vegetation, topography, soil moisture and rock features is negligible. However, it is crucial to keep in mind that topography plays a decisive and essential role in the generation of the three-dimensional (3D) model.  
Due to the presence of magnetic interference generated by the electromagnetic motors inside the drone platform, significant measures were implemented to counteract this effect [18; 19]. The magnetic sensor was placed at a distance of 3 m from the base of the drone and connected to the main system via a cable. This strategic arrangement allowed the influence of the magnetic field produced by the motors to be minimised, preventing it from affecting the measurements made by the GSMP-35U magnetometer [20].  
To prevent pendulum motion of the sensor, which could cause variations in roll, pitch and yaw axes, additional 25 m segments were added at each end of the flight lines during 180-degree turns. This approach allowed greater leeway and reduced the likelihood of sudden or unexpected movements affecting the measurements.  
The installation of the magnetic sensor was carried out without rotation restrictions on any of the axes, which provided greater freedom of movement during operations. Specific measures were implemented in the project design to ensure the accuracy of the measurements and minimise any possible interference.  
In addition, the flight speed was reduced, which contributed to a more stable and controlled operation. The lower speed facilitated better control of the drone, which in turn helped to minimise unwanted sensor oscillations.  
Simultaneously, a fixed magnetometer was installed in a nearby location free from sources of magnetic interference. Its purpose was to calculate the correction for the diurnal effect, which occurs due to the temporal variation of the magnetic field throughout the day (Figure 2B).  
The fixed magnetometer was set up with a total field recording interval of 1 s.
# Experimental Study of Primary Atomization Characteristics of Sonic Air-Assist Atomizers
## 3 Results  
### Flow Dynamics  
#### 3.1.1 Gas Flow Study  
The primary breakup mechanism for the two kinds of atomizers tested was conjectured to be different owing to the distinct jet characteristics. In the converging atomizer, an underexpansion flow pattern is shown in Figure 4a, which results in the Prandtl-Meyer expansion waves [17] with the Mach disc visible (Figure 4). The irregular pressure distribution pattern may try to deflect the liquid sheet in and out of the centerline, thus delaying the sheet contraction (owing to the surface tension effect) even at low liquid flow rates. Besides, owing to the shear force between the air and liquid sheet, it might form instability waves on the inner side of the annular sheet, which gradually performs sheet thinning, through which half of the waves are torn off (like a planar sheet) when wave amplitude reaches a critical threshold, forming ligaments, which further disintegrate to form large globules/droplets depending on the aerodynamic interaction between the high-speed air-jet and ligaments.  
On the other hand, the converging-diverging (CD) nozzle undergoes mild overexxpansion (as seen in Figure 4b) for the higher airflow rates employed, thus forming the oblique shock waves (diamond shock cells) pattern up to certain downstream near the nozzle exit with a high interface strength of the jet boundary. This jet boundary interface strength might play two roles: firstly, forming sufficiently high amplitude unstable waves  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Quantity** & **Range** \\ \hline Airflow rate 1 & 5–35 \\ Water flow rate 1 & 100–350 \\ Air-to-liquid ratio (ALR) & 0.014–0.35 \\ Liquid Weber number (Wel) & 17.2–844.9 \\ Air Reynolds number (Reg) & 33,000–229,000 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: The range of operating conditions and representative dimensionless numbers.  
(Kelvin-Helmholtz instability) on the sheet surface. Secondly, the irregular pressure distribution due to the alternate compression and expansion of the air-jet may drive the sheet into sudden acceleration and sudden retardation, which corresponds to the alternate sheet deflection towards and away from the centerline of the jet; thus, irregularly shaped liquid parcels might tear off from the sheet. The length of the sonic jet region (Figure 1) can also affect the primary breakup, which eventually affects the secondary atomization [16].  
The maximum operational Mach number (M) values based on the total pressure-Mach number (\(P_{t}\) - \(M\)) relation employed [18] are given by Equation (1).  
\[\frac{Pt}{P}=\left(1+\frac{\gamma-1}{2}M^{2}\right)^{\frac{\gamma}{\gamma-1}} \tag{1}\]  
where \(P\) (=1.0 bar (g)) is ambient pressure and \(\gamma\) (=1.4) is the ratio of specific heats.
# A Novel High Gain Monopole Antenna Array for 60 GHz Millimeter-Wave Communications
###### Abstract  
This paper presented the design and implementation of a 60 GHz single element monopole antenna as well as a two-element array made of two 60 GHz monopole antennas. The proposed antenna array was used for 5G applications with radiation characteristics that conformed to the requirements of wireless communication systems. The proposed single element was designed and optimized to work at 60 GHz with a bandwidth of 6.6 GHz (57.2-63.8 GHz) and a maximum gain of 11.6 dB. The design was optimized by double T-shaped structures that were added in the rectangular slots, as well as two external stubs in order to achieve a highly directed radiation pattern. Moreover, ring and circular slots were made in the partial ground plane at an optimized distance as a defected ground structure (DGS) to improve the impedance bandwidth in the desired band. The two-element array was fed by a feed network, thus improving both the impedance bandwidth and gain. The single element and array were fabricated, and the measured and simulated results mimicked each other in both return loss and antenna gain.  
monopole antenna; 5G applications; DGS; antenna array +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Radiocarbon of LV-WSOC in PM  
In \({}^{14}\)C analysis it is customary to correct \({}^{14}\)C isotope ratios using the measured \(\delta^{13}\)C values as the reference values [38]. This is to define the starting point of \({}^{14}\)C aging. The correction assumes that the deviation from \(-25\%\) is evidence of bias in \({}^{14}\)C aging. However, this assumption is not valid in some cases [39], and in our case as well, since our purpose for using pMC is not about aging but understanding the mixing state of modern and dead carbons. It is also since one of the expected end members was an annually growing C4 plant species, which has \(\delta^{13}\)C values significantly heavier than \(-25\%\), as discussed in the previous subsection. Corrected and uncorrected pMCs were presented in Table 4, together with the \(\delta^{13}\)C measured by AMS. The difference between corrected and uncorrected pMCs ranged from 0.5 to 1.7%. Such differences may occasionally be significant in \({}^{14}\)C aging but have a minor impact on source apportionment.  
The results of the radiocarbon analysis of LV-WSOC in PM from noyaki showed pMC higher than 99 and 98% with and without \(\delta^{13}\)C corrections, respectively (Table 4). The observations confirmed no substantial \({}^{14}\)C isotope fractionation during the biomass burning of habitat plants. It was often assumed that the radiocarbon composition emitted from biomass burning would reflect that of burnt material. In this study we confirmed this, showing that noyaki did not significantly discriminate the radiocarbon of LV-WSOC and was a useful tool in source apportionment of atmospheric particulate LV-WSOC.
# Hybrid Beamforming for Reduction of Inter-Beam Interference in Millimeter-Wave Cellular Systems
## 2 Hybrid Beamforming Techniques for a Multicell Multiuser Environment
### Type-2 Precoder  
In the Type-1 precoder, we cannot find a pseudo inverse matrix for the effective network channel. To solve the problem, we define an effective combined network channel as a square matrix combining all effective network channels \(\widetilde{\mathbf{H}}^{b_{n}}\) in neighboring cells. The information on the effective network channel is shared by a backhaul link, as shown in Figure 2b.
# A Systematic Review of Insole Sensor Technology: Recent Studies and Future Directions
## 4 Insole Sensor Technology
### Sensor Placement  
Before sensor placement, a sensitivity analysis is essential to evaluate the performance of gait event detection and determine whether it depends on the IMU's location on the foot [15]. Sensor misplacement can be related to two types of errors. The first type involves the sensor being placed on the correct segment but with an arbitrary orientation relative to the underlying bone. The second type, which is the focus of this study, occurs when the sensor is placed on the incorrect segment, such as switching between the left and right limbs or between the lower and upper limbs.  
Although a verification procedure could mitigate this error, it imposes an additional burden on the user, who may lack technical expertise. Furthermore, it extends the installation time, which should be minimized, particularly for patient measurements. Human error is also a concern, especially in multi-sensor applications where each sensor must be attached to a specific body segment. This error becomes even more significant in scenarios where patients or their caregivers are responsible for sensor installation [22].  
Gyroscope orientation remains constant relative to the body segment, and gyroscopes are unaffected by gravity and less prone to noise. In contrast, accelerometers are noisier, influenced by gravity, and sensitive to both position and orientation [33].  
Unlike IMU, sensor placement is not a significant issue for insole pressure sensors. While IMU can be placed anywhere on the body, insole pressure sensors are typically located within the subject's shoe. The conventional method for placing FSR in an insole involves positioning them at specific pressure points, such as the heel, toe, and first and fifth metastasis. These insole pressure sensors require the correct foot size to ensure proper alignment with the pressure points [33].  
Some products in the wearable sensor market have fixed the IMU position relative to the insole pressure sensor, thereby reducing errors caused by variations in IMU placement between and within segments and across different datasets and subjects [33].  
A conventional approach involves positioning the sensors at predefined locations. This method includes three steps: First, the plantar surface is segmented into several regions based on foot structure or function. Second, specific regions are selected according to the characteristics of the target pathology. Third, within these chosen regions, the positions most associated with disease progression, such as peak stress points or symmetrical positions on the medial and lateral aspects of the foot, are identified [5].
## 2 Materials and Methods
### Blood Property and Simulation Condition Settings
A flow rate boundary condition was applied at the inlet, with the flow waveform shown in Figure 8a. A pressure boundary condition was applied at the outlet, with the aortic outlet pressure pulsation waveforms set to three different conditions: hypertension (160/90 mmHg), normal pressure (120/70 mmHg), and heart failure pressure (100/50 mmHg), as shown in Figure 8b. The inlet and outlet settings at both ends of the vessel were fixed, while the vessel walls were allowed to move within the physiological range to simulate the elastic deformation of the vessel. For the AD model, the entire AD vessel wall is modeled as a hyperelastic, anisotropic HGO (Holzapfel-Gasser-Ogden), hyperelastic material. The momentum conservation equation for the solid domain in the Lagrangian coordinate system is given by:  
\[\rho_{s}\frac{\partial^{2}u_{s}}{\partial t^{2}}-\
abla\cdot\sigma_{s}=0 \tag{15}\]  
\(\rho_{s}\) is the density of the vessel wall, 1100 kg/m\({}^{3}\), \(u_{s}\) is the displacement of the vessel wall, and \(\sigma_{s}\) is the Cauchy stress tensor. The strain energy function of the AD is given in Equation (1). Boundary conditions were set at the vessel inlet and outlet as well as on the vessel wall. The ends of the vessel at the inlet and outlet were fixed, allowing only radial movement  
\[\mu=\mu_{\infty}+(\mu_{0}-\mu_{\infty})\Big{[}1+(\dot{\gamma}\lambda)^{a} \Big{]}^{(n-1)/a} \tag{8}\]0 (**a**) Three-cycle inlet flow waveform. (**b**) Three-cycle outlet pressure waveform. \begin{table}
\begin{tabular}{c c c c} \hline
**Relaxation Time (s)** & **Power-Law Index** & **Yasuda Index** & **Zero-Shear Viscosity (mPAs)** & **Infinite-Shear Viscosity (mPAs)** \\ \hline
0.1 & 0.392 & 0.664 & 22 & 2.2 \\ \hline \end{tabular}
\end{table} 
Table 3: Non-Newtonian blood parameters. of the vessel wall. The surrounding tissue has a damping effect on the deformation of the aortic wall, and Rayleigh damping was incorporated into the numerical simulation, since the damping matrix is a combination of the mass matrix and the stiffness matrix. It can be expressed as:  
\[[C]\ =\ \alpha[M]\ +\ \beta[K] \tag{16}\]  
In the equation, \([M]\) and \([K]\) represent the mass matrix and the stiffness matrix, respectively. The values of parameters \(\alpha\) and \(\beta\) are 5650 and 0.1 respectively. The contact surface between the blood and the vessel wall is the inner surface of the aorta. All data exchanges between the two-way FSI are completed through this interface. The interface satisfies the coupling boundary conditions, such as displacement compatibility and traction balance. It can be expressed as:  
\[u_{f}=u_{s} \tag{17}\]  
\[\tau_{f}\cdot n=\tau_{s}\cdot n \tag{18}\]  
In the equation, \(u_{f}\) and \(u_{s}\) represent the displacements of the blood and the arterial wall, respectively, and \(n\) is the normal direction of the coupling interface.