esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Co-Flow Criterion for Printing Process  
As illustrated in the proposed approach, an assembled coaxial nozzle was designed to enable the co-flow extrusion, whereas water was used as the sacrificial layer, and the UV curable adhesive was used as the outer fluid for shell formation. Because the two liquids were immiscible with each other,  
Figure 4: UV rheological characterization results: (**a**) viscosity as a function of UV exposure time (365 nm, 1 W/cm\({}^{2}\)); and (**b**) moduli as a function of UV exposure time (365 nm, 1 W/cm\({}^{2}\)). The gelation point is defined as the cross over point of the storage modulus G’ and the loss modulus G”.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**UV Intensity (W/cm\({}^{2}\))** & **0.5** & **1.0** & **1.5** & **2.0** & **2.5** & **3.0** & **5.0** \\ \hline Gelation Time (s) & 10.0 & 6.9 & 6.4 & 7.6 & 7.2 & 6.3 & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Gelation time under different UV intensity (n = 1).  
the core liquid could present a continuous jetting or dripping (pinching off to droplets) behavior while flowing together [36; 37; 38]. Many relevant parameters influence the co-flow regimes such as viscosity, fluid density, flow rate, channel dimension, and the interfacial tension. Under the specific coaxial nozzle design and two selected fluids, flow rates were the significant controllable parameters which determine the co-flow regimes in the system.  
Different co-flow behavior regimes were observed with varying inner and outer flow rates \(Q_{i}\) and \(Q_{o}\). The inner flow (water) was marked with black ink to distinguish it from the transparent adhesive. Figure 6a represents four kinds of typical co-flow behavior regime including dripping, outer flow, undulant jetting, and thread jetting. As both \(Q_{i}\) and \(Q_{o}\) were small, inner flow (water) would pinch off to droplets, result in dripping. When increasing \(Q_{o}\) under small \(Q_{i}\), the co-flow behavior would be changed to outer flow, in which the inner flow could not be extruded out due to the high external pressure. When increasing \(Q_{i}\) under small \(Q_{o}\), the co-flow behavior regime would be transferred to undulant jetting with the inner fluids of flow instability. When increasing \(Q_{i}\) and \(Q_{o}\) simultaneously, the inner flow (water) would be well constrained to thread-like flowing status by the external pressure, which is described as thread jetting. Co-flow behavior regimes were parametric investigated, which covers the flow rate range of 0.07-5.6 mL/min for both \(Q_{i}\) and \(Q_{o}\). Figure 6b concludes the co-flow behavior region and thread jetting were distinguished with a blue shadow. To fabricate hollow connectors with a continuous and uniform channel, the thread-jetting regime was chosen for the co-flow criterion for the hollow connectors direct printing process.
## 6 Results and Discussion
### Comparison with the State of the Art Approaches
Only these four datasets are included as examples to avoid visual clutter and maintain clarity in the figure. Including all datasets would have made the diagram overly complex and difficult to interpret. However, the algorithm applies the same processing steps to the entire feature set of each dataset. As seen in the convergence curves, the proposed algorithm achieves the best results for the N1 and N5 datasets and produces results close to the best for the N7 and N10 datasets. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multicolumn{1}{l}{**ID**} & \multicolumn{2}{c}{**bWSO-log**} & \multicolumn{1}{l}{**bAAA**} & \multicolumn{1}{l}{**bBAT**} & \multicolumn{1}{l}{**bFA**} & \multicolumn{1}{l}{**bGWO**} & \multicolumn{1}{l}{**bMFO**} & \multicolumn{1}{l}{**bMVO**} & \multicolumn{1}{l}{**bPSO**} & \multicolumn{1}{l}{**bWOA**} \\ \hline \multirow{4}{*}{N11} & \(\uparrow\)mean & 0.936 & 0.932 & 0.918 & 0.934 & **0.937** & 0.930 & 0.934 & 0.925 & 0.920 \\  & \(\pm\)std & 0.024 & 0.020 & 0.023 & 0.023 & **0.019** & 0.020 & 0.020 & 0.021 & 0.028 \\  & \(\uparrow\)best & **0.991** & 0.974 & 0.956 & 0.982 & 0.982 & 0.974 & 0.974 & 0.965 & 0.965 \\  & \(\uparrow\)worst & 0.860 & 0.886 & 0.860 & **0.904** & 0.895 & 0.886 & 0.895 & 0.886 & 0.868 \\ \hline \multirow{4}{*}{N12} & \(\uparrow\)mean & **0.855** & 0.840 & 0.838 & 0.838 & 0.853 & 0.843 & 0.853 & 0.838 & 0.854 \\  & \(\pm\)std & **0.036** & 0.044 & 0.049 & 0.048 & 0.038 & 0.053 & 0.046 & 0.048 & 0.049 \\  & \(\uparrow\)best & 0.930 & 0.944 & 0.901 & 0.944 & 0.915 & 0.901 & 0.930 & 0.958 & **0.972** \\  & \(\uparrow\)worst & **0.789** & 0.761 & 0.746 & 0.732 & 0.775 & 0.676 & 0.746 & 0.761 & 0.732 \\ \hline \multirow{4}{*}{N13} & \(\uparrow\)mean & 0.933 & 0.947 & 0.722 & 0.941 & **0.954** & 0.951 & 0.941 & 0.896 & 0.939 \\  & \(\pm\)std & 0.047 & 0.029 & 0.064 & 0.038 & 0.028 & **0.023** & 0.030 & 0.076 & 0.036 \\  & \(\uparrow\)best & 0.986 & **1.000** & 0.824 & 0.986 & **1.000** & **1.000** & **1.000** & 0.986 & **1.000** \\  & \(\uparrow\)worst & 0.811 & 0.865 & 0.581 & 0.824 & 0.878 & **0.892** & 0.865 & 0.757 & 0.838 \\ \hline \multirow{4}{*}{N14} & \(\uparrow\)mean & 0.933 & 0.923 & 0.907 & 0.897 & 0.930 & 0.927 & 0.940 & 0.927 & **0.963** \\  & \(\pm\)std & 0.096 & 0.097 & 0.120 & 0.150 & 0.115 & 0.091 & 0.097 & 0.126 & **0.061** \\  & \(\uparrow\)best & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** & **1.000** \\  & \(\uparrow\)worst & 0.700 & 0.600 & 0.600 & 0.400 & 0.500 & 0.700 & 0.700 & 0.400 & **0.800** \\ \hline \multirow{4}{*}{N15} & \(\uparrow\)mean & 0.830 & **0.844** & 0.799 & 0.838 & 0.834 & 0.840 & 0.835 & 0.816 & 0.835 \\  & \(\pm\)std & 0.020 & 0.022 & 0.040 & **0.018** & 0.032 & 0.029 & 0.027 & 0.029 & 0.028 \\  & \(\uparrow\)best & 0.858 & 0.882 & 0.877 & 0.867 & **0.891** & 0.882 & 0.882 & 0.863 & 0.886 \\  & \(\uparrow\)worst & 0.777 & **0.806** & 0.701 & 0.796 & 0.768 & 0.758 & 0.768 & 0.749 & 0.777 \\ \hline \multirow{4}{*}{N16} & \(\
# TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data
## 3 Preliminaries
### Problem Definition
: _Given a geo-multimedia data query \(q=(q.loc,q.m)\) and a geo-multimedia data object \(o\), then, the similarity between \(q\) and \(o\) is defined as the weighted sum of spatial similarity and cross-modal semantic similarity, which can be expressed as:_  
\[F_{d}=\lambda\cdot f_{s}(q,o)+(1-\lambda)\cdot f_{c}(q,o), \tag{2}\]  
_where \(f_{s}(q,o)\) and \(f_{c}(q,o)\) are spatial similarity and cross-modal semantic similarity. Besides, parameter \(\lambda\in[0,1]\) is the weight factor used to balance spatial similarity and cross-modal semantic similarity. Finally, geo-multimedia data similarity score refers to the weighted score of similarity between query object and dataset object in spatial and semantic aspects, which can better meet the query processing in practical application scenarios._  
**Definition 4** (Spatial multimedia \(k\) nearest neighbor query). : _Given a Spatial Multimedia Query \(q=(q.loc,q.m)\) and a space object set \(O\), make \(\forall o\in R\wedge\forall o^{\prime}\in(O-R)\) and \(F_{d}(q,o)\geq F_{d}(q,o^{\prime})\) to find a subset \(R\) having \(k\) spatial objects of \(O\) as a result of the query._  
_Spatial text object query spatial image object \(q_{t2i}=(q.loc,q.m^{T})\) and return a result set \(R_{t2i}\) with \(k\) spatial image objects, which can be described as:_  
\[R_{t2i}=\{o|\forall o\in R\wedge\forall o^{\prime}\in(O-R_{t2i}),F_{d}(q_{t2i},o)>F_{d}(q_{t2i},o^{\prime})\}.\]  
_Spatial image object query spatial text object \(q_{t2i}=(q.loc,q.m^{I})\) and return a result set \(R_{t2i}\) with \(k\) spatial text objects, which can be described as:_  
\[R_{t2i}=\{o|\forall o\in R\wedge\forall o^{\prime}\in(O-R_{t2i}),F_{d}(q_{t2i},o)>F_{d}(q_{t2i},o^{\prime})\}.\]As shown in Table 2, it gives an example of a space text object query space image object GMkNN query, as a user visited a city, because the itinerary arrangement can only selectively visit a certain scenic spot. At this point, the user can enter a text to describe the site of interest, search engine can return to the user's interest and close to the site of the image, video and other content. Users can visually browse the image or video of the scenic spot to decide whether to visit the scenic spot. In addition, there are six scenic spots, \(o_{1},o_{2},o_{3},o_{4},o_{5},o_{6}\), in the city. For a given spatial text, query \(q_{2i}=(q.loc,q.m^{T})\), and set \(\alpha\) as 0.5. Compute the spatial similarity and cross-modal semantic similarity of query object and space object, respectively, \(o_{1},o_{2},o_{3},o_{4},o_{5},o_{6}\), and the geo-multimedia data similarity score between query object \(q_{2i}\) and space objects \(o_{1},o_{2},o_{3},o_{4},o_{5},o_{6}\); therefore, as \(k=1\), the query \(q_{t2i}\) result of GMkNN is \(\{o_{2}\}\), which returns the object \(o_{2}\) of image content to the user.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 1 Introduction  
Physics-informed deep learning (PIDL) [1], also named \"theory-guided data science\" [2], \"model-informed machine learning\" [3], or \"physics-informed machine learning\" [4], has gained increasing traction in various scientific and engineering fields. Its underlying rationale is to leverage the pros of both physics-based and data-driven approaches while compensating the cons of each. A physics-based approach refers to using scientific hypotheses of what underlying physics govern observations, e.g., first principles. Normally, scientists or engineers first come up with a prior assumption of how a quantity of interest is computed from other physics quantities. Then, laboratory or field experiments are designed to collect data that are used to calibrate the involved parameters. In contrast, a data-driven approach does not bear any prior knowledge of how things work and how different quantities are correlated. Instead, it relies on machine learning (ML) techniques, such as deep learning (DL), to learn and infer patterns from data. The former is data-efficient and interpretable but may not be generalizable to unobserved data, while the latter is generalizable at the cost of relying on huge amounts of training samples and may be incapable of offering deductive insights. Thus, the PIDL paradigm opens up a promising research direction that leverages the strengths of both physics-based and data-driven approaches. Figure 1 summarizes the amounts of data (in x-axis) and scientific theory (in y-axis) used for each paradigm. Model-based approaches heavily rely on scientific theory discovered from the domain knowledge and use little data for system discovery, while machine learning approaches mostly rely on data for mechanism discovery, In contrast, PIDL employs a small amount of data (i.e., \"small data\" [4]) for pattern discovery while leveraging a certain amount of scientific knowledge to impose physically consistent constraints. The \"sweetspot\" of PIDLlies in the small data and partial knowledge regime. In other words, PIDL achieves the best performance in accuracy and robustness with small data and partial domain knowledge. In this paper, we will validate the advantage of PIDL using transportation applications, and present a series of experiments using the same real-world dataset against conventional physics-based models.
# A Review of Femtosecond Laser-Induced Emission Techniques for Combustion and Flow Field Diagnostics
## 5 Femtosecond Lasers Guided High Voltage Electric Discharges
An example of curved discharges guided by timed sequences of three laser filaments in the dome and zigzag formations is shown in Figure 10, and a scheme for the guidance of natural lightning based on the application of multiple chirped femtosecond laser pulses was proposed. Our group developed an fs laser-induced filament guiding discharges tagging velocimetry [188], named filamentary anemometry using femtosecond laser-extended electric discharge (FALED). FALED technique uses the filament to ignite a pulsed electric discharge between two electrodes. The laser-guided thin filamentary discharge plasma column was blown up perpendicularly by an air jet placed beneath in-between the two electrodes. The conductivity of the plasma channel was observed to sustain much longer so that a sequence of discharge filaments was generated as the plasma channel was blown up by the jet flow. The sequential bright thin discharge filaments can be photographed using a household camera to calculate the flow velocity distribution of the jet flow, as shown in Figure 11. The velocity measured by FALED agrees well with that measured by FLEET [188], and FALED has a better signal to noise ratio and a thinner tagged line. Compared with FLEET and FLICC, FALED largely extends the application scenario and does not suffer from quenching. Furthermore, it should be emphasized that the discharge current is supposed to be extremely low in order to reduce intrusiveness. Figure 11: Filamentary anemometry using femtosecond laser-extended electric discharge (FALED) photo taken by the single lens reflex (SLR) camera of four sequential discharges with a separation of 40 \(\upmu\)s and a gas flow speed of 35 m/s. Figure 10: Examples of curved discharges guided by timed sequences of three laser filaments in the dome and zigzag formations. (**A**), (**C**) Photographs of fluorescence by the filament plasmas, averaged over 100 laser shots. (**B**), (**D**) Single-shot images of the corresponding guided discharges [187]. (Reproduced from [PERSON], Multi-pulse scheme for laser-guided electrical breakdown of air. _Appl. Phys. Lett._ 2017, 111, 161102, with the permission of AIP Publishing). In summary, fs laser guiding discharges can have a lot of applications, such as laser-triggered switching, guiding natural lightning, and velocity measurements. There are many exciting phenomena that are worthy of examination. Shown in Figure 12 are spatially resolved plasma spectra of fs laser-induced filament guiding discharges. The plasma spectra indicate that the species in the discharged filament are totally different when the discharge current is changed by varying current limiting resistors. This phenomenon may be used to develop a method for species measurement with the potential to achieve one-dimensional measurement. The accurate controlling of discharges in both time and space also provides a possible way to study the spatiotemporal evolution processes of discharges plasma.
## 3 Results and Discussion
### Experimentations over IHEPC, AEP Dataset and Comparison with other Models  
In this section, we evaluate the performance comparison of the proposed model with existing models for short-term load prediction (one hour ahead) over the IHEPC and AEP datasets. For the IHEPC dataset, the proposed model achieved 0.42, 0.18, and 0.29 RMSE, MSE, and MAE, respectively. The performance prediction over the test data is displayed in Figure 2a. A comparison of the proposed model over IHEPC dataset for short-term load prediction with other baseline models is shown in Figure 3. For more detail the performance of the proposed model is compared with [1; 17; 33; 34; 35; 60; 61] in the short-term horizon. In [60] the authors used deep learning methodology for residential load prediction and obtained 0.79 RMSE and 0.59 MAE, whereas [33] used a CNN-LSTM hybrid network for short-term residential load prediction and achieved 0.59, 0.35, and 0.33 RMSE, MSE, and MAE, respectively. 0.47, 0.19 and 0.31 RMSE, MSE and MAE was reported in [17] whereas [34] reports 0.56, 0.31 and 0.34 values for these metrics. In [61] the authors achieved 0.38 MSE and 0.39 MAE, whereas [1] reported 0.66 RMSE. Another strategy presented in [35] attained 0.47, 0.22, and 0.33 RMSE, MSE, and MAE, respectively. Among these results, the proposed model achieved the lowest error rate for short-term electric load prediction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S.no** & **Data Features** & **Units** \\ \hline
1 & Date & dd/mm/yyyy \\
2 & Time & hh: mm: ss \\
3 & GAP & Kw \\
4 & GRP & Kw \\
5 & Voltage & V \\
6 & Global intensity & Amp \\
7 & Submeetring-1 & W \\
8 & Submeetring-2 & W \\
9 & Submeetring-3 & W \\ \hline \hline \end{tabular}
\end{table}
Table 2: Individual household electricity consumption prediction (IHEPC) dataset variables description and units.  
and MAE, respectively. Compared to these models, the proposed model performed better in reducing the RMSE and MAE error rate, while only the results of [PERSON] et al. [35] in terms of MSE is better than the proposed model in the short-term horizon.
# Evaluating the Impact of Optical Interconnects on a Multi-Chip Machine-Learning Architecture
## 2 Background and Motivation
### Impact of Off-Chip Interconnect on Performance
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Frequency & 606 MHz & Local eDRAM latency & 3 cycles \\ \hline \# of tiles & 16 & Central eDRAM latency & 10 cycles \\ \hline \# of 16-bit multipliers & 288/tile & SRAM buffer capacity & 16 KB/tile \\ \hline \# of 16-bit adders & 288/tile & Local eDRAM capacity & 2 MB/tile \\ \hline Peak performance & 2.09 TeraOPS & Central eDRAM capacity & 4 MB \\ \hline Peak power w/o PHY & 7.96 W & Peak per-PHY power & \\  & & - Electrical 25.6 Gbps & 2.00 W \\  & & - Optical 100 Gbps & 3.50 W \\  & & - Optical 400 Gbps & 6.00 W \\ \hline Accel.-to-accel. bandwidth & (bidirectional) & Accel.-to-accel. latency & \\ - Electrical 25.6 Gbps & 6.4 GB/s & - Electrical 25.6 Gbps & 80 ns \\ - Optical 100 Gbps & 25 GB/s & - Optical 100 Gbps & 80 ns \\ - Optical 400 Gbps & 100 GB/s & - Optical 400 Gbps & 160 ns \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the DaDianNao accelerator chip. Figure 2: The ratios of computation and communication time in total training time of various CNNs. A number in parentheses represents the number of accelerator chips required.
## 1 Introduction  
Bovine milk contains approximately 3.5% protein, three-fourths of which is casein; the remainder consists of whey proteins, especially \(\beta\)-lactoglobulin and \(\alpha\)-lactalbumin [1]. When milk is made into cheese, the casein is coagulated and the whey is drained and saved for other uses. Frequently the whey is dried and filtered to obtain whey protein concentrate (WPC, 29%-89% protein) and whey protein isolate (at least 90% protein), which are sold as dietary supplements and food ingredients [2]. Aged cheese contains a multitude of flavor compounds from lipolysis, lipid oxidation reactions, and carbohydrate catabolism, with proteolysis providing mostly basic tastes and background flavors. Milk is a relatively bland food that is discarded when it starts to go sour, and therefore is not consumed once bacteria generate an undesirable amount of lactic acid. WPC contains little fat and is also bland.  
The flavor profiles of milk, cheese, and whey protein concentrate/isolate are important factors in consumer acceptance. The compounds responsible for flavor in food are usually identified and quantitated by extracting them from the sample matrix and passing them through a gas chromatograph with a mass spectrometer detector (GC-MS). Flavor compounds in dairy products are often isolated by solid phase microextraction (SPME) [3], developed by [PERSON] and coworkers in 1990 as a method of preparing samples for chromatographic analysis without using solvents [4,5]. In SPME, volatile compounds are adsorbed on a stationary phase that is coated on a fused silica fiber. The fiber is inserted into the headspace above the sample, which has been allowed to equilibrate at 40-60 \(\,\)C for 15-30 min, and is exposed for 5-30 min. Liquid samples are frequently added to a concentrated NaCl solution to decrease solubility of the volatile compounds and force them into the headspace. The compounds adsorbed on the fiber are then thermally desorbed for 5 min in the GC injector port and then sent through the column and detector. The combination of SPME and GC-MS has been used to examine the flavor of milk [6], cheese [7], and whey solution [8].  
SPME is an equilibrium extraction and desorption process, so precise control of sampling conditions is important [9]. Recoveries may be enhanced by raising the temperature and time, but compounds may be displaced from the absorption sites by doing this [9] and compounds that were not originally present in the sample may be formed. Therefore, the procedures employed must be applicable to the type of sample to prevent spurious results. This paper will show that SPME procedures for analyzing flavor compounds in milk, WPC, and Queso Fresco (QF), a non-melting cheese variety, must be varied to determine optimal conditions and meaningful results.
# Learning-Assisted Rain Attenuation Prediction Models
## 2 Preliminaries
### Algorithms Used in LARA Models  
Several types of learning-aided algorithms are used to estimate rain attenuation via the LARA models. Mostly, the algorithms used here are a modified version of the back-propagation algorithm and a basic structure of an artificial neural network. Table 1 presents all the learning techniques used for attenuation prediction to date as per the best of our knowledge in major scientific publication database records accessed till July 2020.
## 6 Results and Discussion
### Cluster Analysis of the Prediction Errors
Despite having fewer observations, there is a greater share of high MAPE prediction errors in peak hours, the early afternoon, and late at night, which is also in line with what was presented in Figure 4.  
To verify the cases in which the models overestimate or underestimate the observed fares, Table 8 presents the over-prediction and under-prediction by cluster and model. The models show more over-prediction than under-prediction in all clusters, especially in clusters #1 (Low error) and #3 (High error). Cluster #2 (Medium error) presents similar percentages of over-prediction and under-prediction and is the only one where the Linear Regression model differs more from the tree-based models.
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 6 Conclusions  
In this manuscript, a planar and compact quad-element MIMO antenna suitable for UWB applications is presented. The novel semi-elliptical radiating elements are realized on low-cost FR-4 substrate. Various design modifications in the patch itself and ground plane ensure the wideband impedance matching. Isolation level of at least 20 dB is achieved for the entire band owing to the two distinct isolation mechanisms of parasitic resonators and ground plane with reactive stub loading. Simulated and measured results of the proposed design are in good agreement. The diversity performance criteria indicate suitability of the proposed design for UWB-MIMO applications. The proposed antenna system is an excellent candidate for WPAN applications for consumer electronics.  
F.A. performed antenna designing and software simulation under the supervision of R.S. [PERSON], [PERSON], and [PERSON] managed the writing of the paper. M.F.S. provided fabrication, testing and measurement facilities.  
This research received no external funding.  
The authors declare no conflicts of interest.