esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
## 2 Materials and Methods
### Performance Evaluation  
To evaluate and compare the performance of classification algorithms, several experiments were carried out using the metrics: Accuracy, Sensitivity, Specificity, Precision, F-score, and Area Under the Curve (AUC). The first five metrics are defined by (2) to (6), where _TP_, _FP_, _TN_ and _FN_ represent the number of cases of True Positive, False Positive, True Negative and False Negative, respectively.  
\[Accuracy=\frac{TP+FN}{TP+TN+FP+FN} \tag{2}\]  
\[Sensitivity=\frac{TP}{TP+FN} \tag{3}\]  
\[Specificity=\frac{TN}{FP+TN} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[F-score=\frac{2*TP}{2\times TP+FP+FN} \tag{6}\]  
A Receiver Operating Characteristic (ROC) curve was used to capture the trade-off between the true positive rate (sensitivity) and false positive rate (1-specificity) at thresholds over a continuous range.  
The AUC summarizes the information obtained using the ROC curve and, instead of depending on a specific operational point, it is an effective and combined measure of sensitivity and specificity that describes the inherent validity of diagnostic tests [23], with an AUC of 1.0 representing a perfect test, while an AUC of 0.5 defines a random test. The higher this metric (closer to 1), the better the discriminatory power of the model.  
In binary classification, accuracy, sensitivity, specificity, precision, and F-score are calculated using the expressions (2) to (6). In the case of multi-class classification, those metrics can be calculated for each class in a one-vs.-all method and determine how often each class makes accurate predictions.  
As in the used dataset, the classes are not balanced [15], that is, the percentage of samples in each one of the classes is not similar; it is important to pay attention to the impact of class imbalance on the performance metrics of the classification. With this in mind, the metrics that should be taken into greater consideration are F-score and AUC.
## 2 Materials and Methods
### Antimicrobial Activity  
The extracted compounds were evaluated for their in vitro antibacterial activity against Gram-positive bacteria, including _Enterococcus faecalis_ and _Staphylococcus aureus_ (ATCC 25923), as well as Gram-negative bacteria: _Escherichia coli_ (ATCC 25925), _Pseudomonas aeruginosa_ (ATCC 27853), and _Klebsiella pneumoniae_ (ATCC 27736). The antibacterial testing followed the standard broth microdilution protocol, in alignment with the Clinical and Laboratory Standards Institute (CLSI) guidelines [22; 23], to determine the minimum inhibitory concentration (MIC) of each compound. The assays were conducted using U-bottomed, sterile 96-well microdilution plates (Falcon 3077, Becton Dickinson Labware, New York, USA) in cation-adjusted Mueller-Hinton broth (containing Ca\({}^{2+}\) and Mg\({}^{2+}\); Becton Dickinson and Co., Cockeysville, MD, USA). Serial two-fold dilutions were performed to prepare compound concentrations ranging from 512 to 0.25 \(\upmu\)g/mL. The bacterial inoculum was standardized using the direct colony suspension method, with the final concentration adjusted to 5 \(\times\) 10\({}^{5}\) CFU/mL, as described in CLSI document M7-A7. Following inoculation with the test compounds, the plates were incubated at 35 \(\pm\) 2 \({}^{\circ}\)C for 16 to 20 h in ambient air. The determination of MIC was carried out using a BioSan Microplate Photometer, HiPo MPP-96. This instrument enabled precise optical density measurements to be made to quantify bacterial growth inhibition. After incubation, the absorbance was measured at 600 nm to assess bacterial proliferation in each well. The MIC was identified as the lowest concentration of the test compound that resulted in a significant reduction in absorbance, indicating no visible bacterial growth compared to the untreated control wells. All measurements were performed in duplicate to ensure accuracy and reproducibility of the results. Additionally, control experiments using known antibiotic standards were included to validate the experimental setup and the observed antibacterial effects of the test compounds.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 2 The Impact Factor of Solar Power Forecasting  
Several factors can influence the effectiveness of solar irradiance forecasting, including forecasting horizon, weather classification, performance evaluation metrics, and input feature optimization. The related factors are further elaborated in the following subsections.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 3 Sparse Codes  
### Preliminary: Limitations in Conventional Backscatter  
In the previous section, we considered the dense network scenarios with \(U\) multiple sources as well as \(N\) multiple tags. When there are multiple tags backscattering data to the reader, the most common and simplest method of data uploading is using TDMA [32]. In TDMA where \(K\) time slots are allocated to \(N\) tags, the number of time slots required for data uploading is \(K\geq N\) and the duty cycle of each tag is \(D\leq\frac{1}{K}\). Unfortunately, there are fundamental limitations in TDMA, which prohibit application of AmBC for low-power Internet of things (IoT) network with massive number of sources and tags.  
* **Short-Range Communication:** TDMA-based AmBC (TD-AmBC) system has a short communication range because the ambient backscatter signals received at the reader are significantly attenuated by both the forward channels and backward channels. By poor propagation properties in the composite forward-backward channels, or dyadic backscatter channels [6; 7], backscatter signals are too weak to be decoded at the reader when the distance among tags and the reader increases. Furthermore, simple repetition coding by increasing the number of time slots \(K\) (equivalently, symbol period) [3; 5] in the TD-AmBC is not sufficient enough to combat channel fading in practice. Besides, in the TD-AmBC, only one tag is activated in a time slot for backscatter communication and the rest of \(N-1\) tags remain idle (or harvesting energy). By adopting orthogonal multiple access (OMA) among tags in the TD-AmBC, the duty cycle is limited to \(D\leq 1/N\). If a massive number of tags \(N\gg 1\) are connected to the reader, such as in low-power wide area network (LP-WAN), the duty cycle \(D\) goes to zero and the data rate of individual tag diminishes accordingly. Hence, by the channel and connectivity issues, the TD-AmBC is limited to short-range applications, typically less than few meters [1; 2; 3; 5].
* **Low-Rate Communication:** In the TD-AmBC, the data rates of tags decrease when the channel conditions are bad, or there are multiple concurrent backscatter signals from tags to the reader as we discussed above. In addition, due to hardware limitation of the tags with small form-factor, tags' data is modulated in low-order modulation schemes, typically ranging from \(M=2\) (e.g.,on-off keying (OOK) [1; 2; 5] to \(M=16\) (e.g., 16-PSK [3], 16-QAM [20]). However, to implement high-order backscatter modulators, we should add a series of load impedances and corresponding RF switches to the tags' circuit, which inevitably increases installation costs and form-factor of the tags. As such, the TD-AmBC is limited to low-rate applications in practice.  
To tackle these limitations, we first introduce the distinct characteristics of sparse codes in the following subsection.
# Simulation Modelling of Silicon Gated Field Emitter Based Electronic Circuits
## 1 Introduction  
As one of the primary vital elements in electronics, vacuum electron devices have a crucial history in several areas of applications, such as communications, radars, medical, aerospace, and electronic warfare [1; 2; 3; 4]. However, with the development of modern-day electronics, there is a requirement for a new generation of micro- and nanoscale vacuum electron device development, which can be easily integrated, has good reliability, and has substantial efficiency [5]. Currently, nano- and microscale vacuum channel devices are being fabricated to realize new generations of vacuum transistors (VT) with high-frequency response [6]. VT devices have proven to be insusceptible to high temperatures (400 \({}^{\circ}\)C) [7] and ionizing radiation (1 krad proton and 100 krad g radiation) [8; 9]. A planar nanoscale vacuum channel transistor (NVCT) with a dimension that can be compared with a modern-day field effect transistor was demonstrated recently [10]. In these structures, electron transport takes place using quantum tunneling [11], where drift-diffusion is the primary mode of electron transportation in traditional semiconductors [12]. Recent work also suggested that these devices can be operated at a voltage as low as 15 V [13]. Also, scattering or collision loss is negligible thanks to the short vacuum channel length, which is a few hundred nm [12]. These virtues make these VTs a perfect candidate for harsh environment electronics [9]. Additionally, in the optimization of materials for electrodes such as metal and/or graphene or other low-dimensional materials, VTs are an important candidate for on-chip vacuum electron devices that can be easily integrated into circuits [14]. The reported work [3; 15] shows the research interests for VTs [16].  
To realize functional circuits, it is required to develop a simulation model to inspect VTs in terms of functional circuits such as oscillators [17] and high-speed logic gates [18]. In this study, the circuit modeling software LTspice XVII v17.0 was used to create an electronic model of VTs [9; 18]. We also explore the possibilities of executing active circuits like oscillators and logic circuits such as NOR and NAND gates using multiple VTs. We directly employ the device characteristics from the experimental data to the device simulation module. The results presented in this work incorporate simulation models with an emphasis on functional circuit development, thus providing a crucial foundation for the subsequent development of on-chip nano- or microscale vacuum electron devices.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 6 Conclusions  
In this paper, we propose a novel self-supervised learning scheme with equivariance for point clouds. Our method exhibits robust performance and demonstrates a high degree of sensitivity in effectively learning rotation information. At the same time, it also shows that our network has equivariance through the visualization of features. Our method has good results in rotation-invariant tasks such as object classification, part segmentation, and few-shot learning. In addition, we also verify that our network has a good ability to learn equivariant features in rotation-equivariant reconstruction and the pose change estimation tasks of point clouds, and is robust in rotation-invariant tasks such as classification and segmentation. At the same time, as a self-supervised learning scheme, our network can be fine-tuned to adapt to different downstream tasks after pre-training, which can better adapt it to more tasks. Our network can output some rotation-invariant label information and rotation-equivariant pose change information. The output of diversified information is more practical. For example, it can help visual equipment better identify object information in different poses, and it can also use pose change information to help robots better grasp scattered objects in 3D space.  
Conceptualization, K.S. and M.X.; methodology, K.S. and M.X.; software, K.S.; validation, K.S. and M.X.; formal analysis, K.S.; investigation, M.X.; resources, J.Z.; data curation, K.S.; writing--original draft preparation, K.S.; writing--review and editing, K.S., M.X., and J.Z.; visualization, K.S.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China under grant no. 62471266, the Natural Science Foundation of Zhejiang Province under grant no. LZ22F020001, and the 2025 Key Technological Innovation Program of Ningbo City under grant no. 2023Z224.  
Data Availability Statement: Data are contained within the article.
## 3 Photon and Phonon Tunneling through a Vacuum Gap
### Phonon Coupling at Sub-Nanometer Gaps  
As the interfacial gap decreases, the near-field heat transfer is replaced by the tunneling of acoustic phonons through sub-nanometer gaps. Phonon tunneling can be explained by the modulation of interfacial dispersion forces due to surface vibrations. Thus, lattice vibrations can be transmitted through a very thin vacuum gap between contacting bodies. It is noteworthy that phonon and photon tunneling can be described by a single formalism, which demonstrates the complete picture of the transition from heat transfer by evanescent thermal radiation to phonon heat conduction [14]. Low-frequency acoustic phonons tunnel through the vacuum gap due to interaction with damped electric fields. Thus, an additional heat transfer channel is formed. The phonon tunneling provides a significant increase in heat transfer compared to photon tunneling at sub-nanometer gaps. Thus, there is a transition from near-field thermal radiation to phonon heat conduction, and acoustic phonons become the dominant heat carriers at \(d<1\) nm [14]. This effect can lead to an increase in the interfacial thermal conductance to a value associated with phonon tunneling \(G_{ph}\), which is about \(10^{7}\)-\(10^{8}\) W/m\({}^{2}\)K at \(d<1\) nm and \(T>100\) K [14].
# Incremental Canonical Correlation Analysis
## 4 Experiments
### Results
#### 4.2.3 Quantitative Comparison  
To quantify the visual tracking performance of ICCA, seven trackers were evaluated and compared by success rate and precision. It can be seen from Figure 3 that ICCA has almost the best comprehensive performance, and has a high success rate on different attributes, such as background speckle illumination, scale change, rapid movement, and small rotation of objects, but it achieves slightly worse performance than other trackers on out-of-plane rotation, occlusion, and deformation.  
In particular, ICCA merely depends on the subspace learning, which as usual is not very robust. ICCA mainly accelerates CCA in an incremental way, in which the solution to CCA is a closed-form rather than stochastic approximation. In addition, ICCA primarily focuses on the efficiency and applicability in real scenarios.  
Figure 4 shows the precision comparison of seven trackers over 18 video sequences. ICCA is consistently superior to other trackers for high accuracy on most attributes. Since ICCA just accelerates CCA without improving the robustness of target appearance, the trackers based on ICCA are still sensitive to the occlusion, scale size, and deformation. Thus, on such attributes, the performance based on ICCA is slightly lower than other trackers. We cannot guarantee our tracker to work on all the videos because we only improve the efficiency of CCA rather than its model capacity.  
In short, according to the comparison of success rate and accuracy, the ICCA tracker is better than other previous trackers and exhibits better performance on many video sequences.  
Figure 2: Tracking results of several trackers on eight videos. The bounding boxes of several trackers on three representative frames of (**a**) crossing, (**b**) dog1, (**c**) jogging-2, (**d**) jumping, (**e**) mhyang, (**f**) mountainBike, (**g**) singer1, and (**h**) walking, respectively.  
Figure 4: Precision rate of seven trackers on different attributes.  
Figure 3: Success rate of seven trackers on different attributes.
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 2 Literature Review
### Review of Related Studies  
Since the results of our work showed that the Self-Attention Long Short-Term Memory (SA-LSTM) model with a Butterworth low-pass filter provides the best performance among all the experiments, this subsection details several research studies that used the LSTM technique and viewed the GPS transactions as time-series data.  
First, [PERSON] et al. [13] proposed an LSTM neural network with an attention mechanism for travel time prediction and carried out an experiment from a dataset provided by Highways England. In the raw dataset, the highway had several road segments, and the length of each segment in the dataset was about 18 km. The dataset contained the road segments' names, dates, times, travel times, and distances. Then, the authors of this work sampled the data in intervals of 15 min. The LSTM was tuned to have seven units by which to predict the travel time of the next interval. The experiment found that the MAE of the proposed model was 5.788 s, which was better than the LSTM without attention by about 3.19%.  
Second, [PERSON] et al. [20] conducted travel time prediction experiments for Taiwan's provincial highway number 61 with deep learning techniques. The dataset was processed into travel time between two gates of the highway with timestamps. The authors of this work adopted several techniques for long-term prediction, such as LSTM, bidirectional LSTM (BiLSTM), Facebook time-series forecasting model (FBProphet), and an extended ARIMA named the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX). In the experiment results, the LSTM, BiLSTM, and SARIMAX showed similar performances. However, in the overall experiment, the LSTM had the lowest Mean Relative Error (MRE), which was a mean absolute error per km.  
Lastly, [PERSON] et al. [14] proposed a convolutional LSTM model (ConvLSTM) with attention to predicting the travel time of buses. The dataset was a general transit feed specification (GTFS) from Google Transit APIs. The data contained trips, stop sequences, arrival times, and departure times. The authors of this work processed the GTFS data into running times between two stops and waiting times at each stop. The ConvLSTM model predicted both running times and waiting times. The hyperparameter of the model included a learning rate of 0.001, 20 epochs, and a batch size of 16. In addition, the authors allowed the developers to determine an appropriate number of LSTM units according to the datasets and experiments. For the results of the running time prediction, the MAE of the ConvLSTM with attention was 36.328 s, which was about 3.21% higher than that of the ConvLSTM without attention and about 22.95% higher than that of the LSTM without attention.
# Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data
## 7 Conclusions and Future Work
### Benchmark Results  
The results in Section 6 strongly suggest that graph database technology and, specifically TigerGraph, provide the faster response times when querying data sets under spatio-temporal constraints. One notable observation is how inconsistent JanusGraph performed, which may be due to JanusGraph's caching implementation. JanusGraph maintains multiple levels of caching both on the transaction level and database level. This excludes the storage backend's caching--in this case Cassandra. The cache has an expiration time and, since these experiments were run serially but chosen randomly, jobs in many cases faced expired caches. Another potential reason for the inconsistent gradient between the means in each result may be due to the fact that the dataset adds unpredictable levels of complexity--in terms of how connected the data is--at the end of an import for a given percentage. The horizontal scaling for the graph databases, however, suggests that the impact is minimal. Nevertheless, as the queries became more and more complicated, the graph databases maintained a horizontal scale whereas PostgreSQL grew linearly in these cases. When the queries were not complicated, as was the case in Section 6.5 or the dataset was small (cf. the medical response dataset), PostgreSQL responded nearly as fast as TigerGraph and even outperformed JanusGraph. The spatio-temporal constraints seem to play a minor role in influencing the mean response times compared to the influence of the size of the dataset and complexity of the queries.
# Multi-Point Time-Synchronized Waveform Recording for the Analysis of Wide-Area Harmonic Propagation
## 4 Results
### Multiple Harmonic Sources under Sinusoidal Power Supply
While currents through Substation G are slightly higher, Figure 9 suggests that both PV inverters and EV chargers can be deemed to be responsible for the fraction of emissions at 1250 Hz. A notable observation is related to 1250 Hz current component of Triphase visible in Figure 10. Since no injection was set at this frequency, we can conclude that this component resulted from the absorption of harmonics by the filter at the output stage of this device. Finally, in Figure 7, we observe that propagated voltage distortion at LV side of Substation G settles to lower values when compared to the LV side of Substation A. As it was discussed before, the fundamental reason for this is lesser system impedance governed by short underground cable connection. An exception here is voltage harmonic at 1250 Hz recorded on the LV side of Substation G.
# Time Evolution of CO2 Diffusivity of Carbonated Concrete
## 3 Results and Discussion
### Comparison with Previous Research  
The rate of gas penetration depends largely on the saturation of concrete. The rate of gas penetration decreased if concrete is more than 50% RH [17]. The diffusivity was constant if concrete is exposed to condition with less than 60% RH. That is, the diffusivity decreased significantly when RH exceeded 60% [16]. The result was similar to the experiment of [PERSON] et al. [29]. They studied the CO\({}_{2}\) diffusivity of porous media, not concrete, and the diffusivity greatly decreased as RH exceeded around 55%. The apparent and the effective CO\({}_{2}\) diffusivity of concrete cured for 28 days was calculated to be in the range of \(3.5\times 10^{-3}\) to \(6.1\times 10^{-3}\) cm\({}^{2}\)/s, \(2.1\times 10^{-4}\) to \(8.3\times 10^{-4}\) cm\({}^{2}\)/s, respectively. In particular, the effective diffusivity of carbonated concrete matched with the limit suggested by CEB 1990 Model Code [30].  
In this study, the diffusivity of concrete was estimated from the individual diffusivities of cement and aggregate. The effect of Interfacial Transition Zone (ITZ) at aggregate surface on gas diffusion was not considered. Although the effect of ITZ on the durability of concrete is well known, research on the effect of the CO\({}_{2}\) diffusivity is rare. In the future, it is necessary to study the CO\({}_{2}\) diffusivity of concrete with three phases, cement paste, aggregate, and ITZ and the influence of carbonation on the porosity of ITZ.