esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Collaborative Monitoring Algorithms for Situational Assessment
### Evaluation Indicators for Traffic Trend Prediction  
In this paper, the road traffic trend prediction results are mainly analyzed using _Accuracy_, _Precision_, _Recall_ and _F\({}_{1}\) score_ as evaluation indicators. In the following equation, the data samples are classified as true positive (true positive, TP), true negative (true negative, TN), false positive (false positive, FP) and false negative (false negative, FN) according to the combination of their true results and the prediction results of the model. Accuracy refers to the ratio of the number of samples predicted by the model to the total number of samples, as shown in Equation (34).  
\[\text{Accuracy }=\frac{TP+TN}{TP+TN+FP+FN} \tag{34}\]  
_Precision_ refers to the ratio of the number of samples that the model predicts to be true and the number of samples that are actually true to the number of samples predicted by the model to be true, as shown in Equation (35).  
\[\text{Precision }=\frac{TP}{TP+FP} \tag{35}\]  
_Recall_ is the ratio of the number of samples in which both the predicted and actual results of the model are true to the actual number of true samples, as shown in Equation (36).  
\[\text{Recall }=\frac{TP}{TP+FN} \tag{36}\]  
Generally speaking, _Precision_ and _Recall_ are mutually exclusive. A high precision rate becomes a lower recall rate, and a high recall rate becomes a lower precision rate. Therefore, this paper uses a metric named _F_-measure that considers both _Precision_ and _Recall_. _Precision_ and _Recall_ are the two components of _F_-measure [42]. The _F_-measure value increases proportionally with the increase in _Precision_ and _Recall_, as shown in Equation (37).  
\[\text{\emph{F-measure}}=\frac{\left(1+\beta^{2}\right)\times\text{\emph{ Precision}}\times\text{\emph{Recall}}}{\text{\emph{Precision}}+\text{\emph{Recall}}} \tag{37}\]  
\(\beta\) is a coefficient of relative importance that is used to adjust _Precision_ to _Recall_. This study sets the \(\beta\) to 1, which represents equal consideration of importance in _Precision_ and_Recall_. This performance measure can also be referred to as the \(F_{1}\)_score_[43]. The \(F_{1}\)_score_ value is the summed average of the _Precision_ and \(Recall\).  
\[F_{1}\;score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{38}\]
## 4 Experimental Framework
### Datasets  
There are a total of 13 real-world datasets employed in the experiments. The summary of their characteristics is shown in Table 1.  
The first to the tenth datasets were gathered from biological experiments on the budding yeast Saccharomyces cerevisiae [7]. Each dataset consists of 2465 yeast genes, and an associated phylogenetic profile vector with a length of 24 is used to characterize each gene. In a biological experiment, the level of gene expression is usually uneven at each discrete time point, so the labels correspond to the time point.  
Datasets JAFFE [64] and BU_3 DFE [65] are two widely used facial expression image datasets. A total of 213 gray-scale expression images in the JAFFE dataset while BU_3 DFE contains 2500 facial expression images. The images in JAFFE have been rated by 60 people based on the six primary emotion labels with a 5-level scale, i.e., fear, disgust, happiness, anger, sadness, surprise, and the images in BU_3 DFE have been ranked by 23 people using the same scale as used in JAFFE. Each dataset is composed by a 243-dimensional feature vector extracted by the Local Binary Patterns method (LBP) [66]. The score for each emotion is considered the degree of description, and the description degrees (normalized gene expression level) of the six emotions make a label distribution for a particular facial expression image.  
The Natural Scene dataset is compiled from 2000 natural scene images that have been inconsistently classified by ten human rankers. A 294-dimensional feature vector extracted in [2] represents each image and is linked with a multi-label selected from nine possible labels, i.e., sky, mountain, water, sun, cloud, snow, building, desert and plant. Then rankers are then required to sort the labels in descending order of relevance. Predictably, the ratings for the same image from different rankers are highly inconsistent. So, a nonlinear programming process [67] is applied to achieve the label distribution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Datasets** & **Examples (\(m\))** & **Features (\(q\))** & **Labels (\(c\))** \\ \hline
1 & Yeast\_alpha & 2465 & 24 & 18 \\
2 & Yeast\_cdc & 2465 & 24 & 15 \\
3 & Yeast\_cold & 2465 & 24 & 4 \\
4 & Yeast\_diau & 2465 & 24 & 7 \\
5 & Yeast\_dtt & 2465 & 24 & 4 \\
6 & Yeast\_elu & 2465 & 24 & 14 \\
7 & Yeast\_heat & 2465 & 24 & 6 \\
8 & Yeast\_spo & 2465 & 24 & 6 \\
9 & Yeast\_spo5 & 2465 & 24 & 3 \\
10 & Yeast\_spoem & 2465 & 24 & 2 \\
11 & SJAFFE & 213 & 243 & 6 \\
12 & SBU\_3 DFE & 2500 & 243 & 6 \\
13 & Natural\_Scene & 2000 & 294 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets used in experiments.
## 3 Methodology
### Logging Application Developments  
In order to log the measurements inside the Android device, an app is used to interact with the Android API. Several apps exist today to log GNSS or sensor data, as reviewed in [11]. The most common one is GNSS logger [35], maintained by Google. It can log GNSS data in multiple formats (e.g., CSV, RINEX, NMEA). Logging other motion sensors is also possible, such as accelerometers, gyroscopes and magnetometers. However, several limitations of the existing applications were found during the first stages of our research. Firstly, only a partial version of the GNSSLogger source code is available on GitHub (old versions of the app), which prevents easy forking to the current version. Secondly, the logging is limited to GNSS and motion sensors logging only (no barometer data), with no possibility of controlling the sampling frequency. Thirdly, GNSSLogger does not exist for smartwatches, and, to the best of the authors' knowledge, there is currently no app available for logging GNSS data from smartwatches.  
Consequently, we decided to develop our own Android app called \"Mimir\", a name based on the God of [PERSON] in Nordic mythology (see [[https://www.britannica.com/topic/Mimir](https://www.britannica.com/topic/Mimir)]([https://www.britannica.com/topic/Mimir](https://www.britannica.com/topic/Mimir)) (accessed on 21 November 2023)). Mimir was developed with the goal of enabling the logging of any sensors present in an Android device. The code is released as open-source, under the Apache 2.0 license and is available on Github [36]. As mentioned earlier, while the Android smartwatch implements different flavors of the WearOS, they are based on a common Android API. Thus, the development of smartwatch apps is similar to smartphones, except for the UI design of the app. It allows the development of a common Android library for sensor logging, implemented in two different apps, for smartphones and smartwatches, respectively. Currently, the app allows logging of GNSS, motion and environment sensors, as defined in the Android developer documentation. The raw data are logged in a Comma Separated Value (CSV) format, similar to GNSSLogger logging format. For compatibility with the former processing software, all measurements related to GNSS have kept the same columns as in GNSSLogger. Other sensors are logged using our own defined format (see Appendix A). Furthermore, the app also allows the logging of health data on the smartwatch version. However, the recording of health data has been tested with the Google Pixel Watch only and is not recorded in the dataset provided for ethical reasons. Future developments planned are discussed in Section 7.  
Note that similarly to GNSSLogger, no processing of the sensor is performed in the app. It is only used for sensor data gathering, logging the measurements exactly as provided by the Android API. Any post-processing is left to the computer processing software.
## 7 Nanodiagnostics and Imaging
### A Bioluminescence Based Assay for the Simple and Rapid Diagnosis of Urinary Tract Infection  
[PERSON] \({}^{1,2,4}\), [PERSON] \({}^{4}\), [PERSON] \({}^{1,2}\), [PERSON] \({}^{4}\), [PERSON] \({}^{4}\), [PERSON] \({}^{2,3}\), [PERSON] \({}^{4}\) and [PERSON] \({}^{4}\)  
\({}^{1}\) FEU-Nicanor Reyes Medical Foundation, Institute of Medicine, Quezon City 1118, Philippines  
\({}^{2}\) University of Santo Tomas, The Graduate School, Espana Manila 1015, Philippines  
\({}^{3}\) University of Santo Tomas, College of Science Espana Manila 1015, Philippines  
\({}^{4}\) University of Miami Miller School of Medicine, Department of Biochemistry and Molecular Biology, Miami FL 33136, USA  
A novel bioluminescence assay (Tube Bioluminescence Extinction Technology Urine, TuBETUr) was developed and evaluated for the detection of UTI using viable cells of _Photobacterium mandapamensis_ USTCMS 1132. Our assay involved analysis of the concentration of uro-pathogens, cut-off time to detect the significant microbial cell density and design of a portable paper strip assay for the detection of UTI and monitoring antibiotic resistance. A standardized bioluminescence bioassay was developed using artificial urine containing known serial dilutions of each of the four common UTI pathogens. Standardized viable cells of bioluminescent bacteria were used as reagent in detecting \(\geq\)10\({}^{5}\) cfu/ml microbial load in patient's urine using TuBETUr. In the portable paper-based flow strip assay, the bioluminescent organism _Photobacterium mandapamensis_ USTCMS 1132 will be dried and placed on a paper-platforms to detect urinary tract infections. Antimicrobial resistant bacteria will be identified using isothermal amplification technology. ANOVA showed no significant difference among the bioluminescence extinction periods of 14.8 \(\pm\) 4.10 minutes and was utilized in this intact cell Tube Bioluminescence Extinction Technology Urine (TuBETUr). For the 30 positive UTI samples, which yielded \(\geq\)10\({}^{5}\) cfu/ml cultures, a total of 29 urine specimens were correctly detected by TuBETUr as UTI positive based on the 15 min or less cut-off time. The 29 UTI positive urine specimens gave \(<\)10 s to a high of 7 min interpreted by TuBETUr as UTI positive. In fact, the periods of 7 min and \(<\)10 s are both indicative of culture counts \(>\)10\({}^{5}\) log\({}_{10}\) cell density/ml of urine. In the paper-based flow strip assay dry luminescent bacteria served as a biosensor. The TuBETUr can rapidly diagnose significant bacteriuria compared to the standard and complicated methods of diagnosing urinary tract infections. The development of the portable paper-based flow strip assay can change the way of clinical management, treatment, and monitoring UTI.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 3 Queueing Models
### Additional Features  
Up to this point, we considered queueing models of which the buffers have infinite capacity, and arriving messages simultaneously have an infinite lifetime. This affects both the ratio of successfully served messages and the response time. However, it may be unrealistic to model IoT interactions with such characteristics. As an example, in the case of the long disconnection period of an IoT sensor (e.g., 30 min), the produced messages may well exceed the buffer capacity of the IoT sensor, and some of the oldest messages (data) may simultaneously become obsolete. We now describe the features that can consider such constraints and apply them to the already defined queueing models.
# IMU: A Content Replacement Policy for CCN, Based on Immutance Content Selection
## 4 Performance Evaluation
### Path Stretch (Hop Count)
With the higher request rate, the least recently used content has been evicted from the privilege partition and that content pushes it to the unprivileged partition. When unpopular content is pushed into the unprivileged partition, the content stays in the cache space for a long time. Further, these techniques are not focused on the maturity of the content. IMU outperformed FIFO, LRU, LFU, and LFRU in terms of path stretch by 11.33%, 6.16%, 5.77%, and 3.82%, respectively. Table 11 illustrates the improvement of IMU in terms of path stretch using different content placement strategies with content eviction policies. We have observed IMU perform better in low to high cache space. In addition, IMU is better in high popularity. When the cache space is full, IMU selects immature content and evicts it from the cache. Therefore, popular content and content that may be popular remain in the cache. However, the consumer's request for specific content is fulfilled from the nearest node. Figure 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\alpha\) & **Cache Size** & **Placement** & **FIFO (\%)** & **LRU (\%)** & **LFU (\%)** & **LFRU (\%)** \\ \hline
0.6 & &
\begin{tabular}{c} LCE \\ CL4M \\ \end{tabular} & 8.76 & 3.97 & 2.68 & 1.26 \\
0.6 & &
\begin{tabular}{c} CL4M \\ \end{tabular} & 6.17 & 2.80 & 2.20 & 1.42 \\
0.6 & &
\begin{tabular}{c} LCD \\ opt-Cache \\ \end{tabular} & 3.84 & 1.65 & 1.30 & 0.94 \\ \hline
0.8 & &
\begin{tabular}{c} LCE \\ \end{tabular} & 10.32 & 4.88 & 4.30 & 2.44 \\
0.8 & &
\begin{tabular}{c} CL4M \\ \end{tabular} & 8.56 & 5.40 & 5.34 & 3.14 \\
0.8 & &
\begin{tabular}{c} LCD \\ opt-Cache \\ \end{tabular} & 7.42 & 4.96 & 4.20 & 3.16 \\
0.8 & &
\begin{tabular}{c} opt-Cache \\ \end{tabular} & 10.14 & 6.10 & 5.56 & 3.18 \\ \hline
1.0 & &
\begin{tabular}{c} LCE \\ CL4M \\ \end{tabular} & 21.51 & 14.47 & 14.54 & 10.00 \\
1.0 & &
\begin{tabular}{c} CL4M \\ \end{tabular} & 20.35 & 9.04 & 9.27 & 6.25 \\
1.0 & &
\begin{tabular}{c} LCD \\ opt-Cache \\ \end{tabular} & 10.73 & 8.49 & 8.69 & 7.13 \\
1.0 & &
\begin{tabular}{c} opt-Cache \\ \end{tabular} & 15.30 & 9.06 & 8.17 & 4.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: IMU path stretch percentage improvement. Figure 4: Latency with different cache sizes and \(\alpha\), using different placement policies. (**a**) Latency with LCE. (**b**) Latency with CL4M. (**c**) Latency with ProbCache. (**d**) Latency with LCD. (**e**) Latency with opt-Cache.
## 3 Results and Discussion
### Optimization of UAE of the Antioxidant Bioactive Compounds
\begin{split}& TPC=14.8945+(0.0945833\cdot Ratio)-(0.163824\cdot Amplitude )+(1.05176\cdot Time-0.0212917\cdot\left(Ratio\right)^{2}\\ &+(0.0102042\cdot Ratio\cdot Amplitude)+(0.049625\cdot Ratio\cdot Time )+0.00526481\cdot\left( Amplitude\right)^{2}+\\ &(0.0137333\cdot Amplitude\cdot Time)-0.0790741\cdot\left(Time \right)\ ^{2}\\ & FRAP=17.2539+(1.56819\cdot Ratio)+(0.439769\cdot Amplitude)+(1.6 0485\cdot Time)-0.0409677\cdot\left(Ratio\right)^{2}+\\ &(0.00626667\cdot Ratio\cdot Amplitude)+(0.0869333\cdot Ratio \cdot Time)-0.00734259\cdot\left(amplitude\right)^{2}+(0.0371111\cdot\\ Amplitude\cdot Time)-0.145904\cdot\left(Time\right)^{2}\\ &\text{WP}\\ & TPC=52.3174-(0.4568\cdot Ratio)-(0.266311\cdot Amplitude)-(1.83954 \cdot Time)-0.00506735\cdot\left(Ratio\right)^{2}\\ &+(0.00907594\cdot Ratio\cdot Amplitude)+(0.0217452\cdot Ratio \cdot Time)+0.000199704\cdot\\ &\left(Amplitude\right)^{2}+(0.0317853\cdot Amplitude \cdot Time)+0.0143494\cdot\left(Time\right)^{2}\\ & FRAP=56.3087+(2.41467\cdot Ratio)-(0.799285\cdot Amplitude)-(2.249 23\cdot Time)-0.0542652\cdot\left(Ratio\right)^{2}-\\ &(0.0061721\cdot Ratio\cdot Amplitude)+(0.0780597\cdot Ratio \cdot Time)+0.0251357\cdot\left(amplitude\right)^{2}+(\ 0.0539508\cdot\\ Amplitude\cdot Time-0.0361915)\cdot\left(Time\right)^{2}\\ \end{split}  \tag{5}\]  
The optimal theoretical values in terms of TPC and FRAP were obtained using a combination of a ratio of 1:37, an amplitude of 40% and a time of 22 min on the RP (43.57 mg GAE/g dw and 102.57 mg TE/g dw, respectively) and a ratio of 1:45, an amplitude of 40% and a time of 25 min on the WP (46.74 mg GAE/g dw and 115.58 mg TE/g dw, respectively). To verify the validity of the model, these conditions were experimentally tested, obtaining a TPC and FRAP of 32.70 \(\pm\) 0.68 mg GAE/g dw and 84.16 \(\pm\) 1.53 mg TE/g dw for the RP and 37.23 \(\pm\) 4.29 mg GAE/g dw and 95.27 \(\pm\) 2.87 mg TE/g dw for the WP, respectively. The R\({}^{2}\) values observed for the TPC (0.73 for the RP and 0.84 for the WP) and the FRAP (0.77 for the RP and 0.84 for the WP) indicate that the model has a discrete predictive ability. However, there is room for improvement to optimize the model's accuracy even further. Ideal R\({}^{2}\) values should be at least above 0.75, and values above 0.90 represent a well-fitted model [41; 42; 43]; however, numerous previous studies have shown that models with an R\({}^{2}\) value of less than 0.90 can still explain the experimental variability in the response variables satisfactorily [44; 45; 46; 47]. Although the R\({}^{2}\) and Adj-R\({}^{2}\) values suggest that the accuracy of the model can be improved, the results on the F-ratio and the lack of fit are not significant
## 5 The Potential of Computer Vision Research in UAV Datasets
### Leveraging UAV Datasets for Computer Vision Applications
#### 5.1.3 Traffic Surveillance and Vehicle Re-Identification  
In traffic surveillance, AU-AIR prioritizes real-time performance and offers annotations for a variety of object categories, including cars, buses, and pedestrians. It bridges the gap between computer vision and robotics by offering multi-modal sensor data for advanced research in data fusion applications. VRAI is the largest UAV-based vehicle re-identification dataset, containing over 137,613 images of 13,022 vehicles with annotations for unique IDs, color, vehicle type, attributes, and discriminative parts. It supports vehicle ReID tasks with diverse scenarios and advanced algorithms. VERI-Wild, which contains over 400,000 photos of 40,000 vehicles taken by 174 CCTV cameras in various urban settings, is essential for research on vehicle re-identification. It uses techniques like FDA-Net to improve ReID accuracy by addressing variations in backgrounds, illumination, occlusion, and viewpoints.
# An 8 kb RRAM-Based Nonvolatile SRAM with Pre-Decoding and Fast Storage/Restoration Time
## 2 RRAM-Based nvSRAM Structure and Memory Cell  
### nvSRAM Structure  
The system structure of nvSRAM is shown in Figure 1. It is mainly composed of a memory cell array, decoding circuit, input and output driving circuit, power control circuit, data storage and restoration circuit, and system control circuit. The system selects a memory cell in the array through the control circuit and decoding circuit. The write driving circuit makes the memory cell enter write mode. When the data are input through I/O, the data are quickly written into the memory cell. At the same time, the written data can be read out through the read driving circuit. In actual operation, a power threshold is set by software. When the power supply voltage is lower than this threshold, the circuit outputs an enable signal. After about 10 ns, the data-storage circuit starts. So, when the system has a voltage fault, the data in the memory cell are stored through the data storage circuit. When the power supply of the system recovers, the data are restored to the memory cell through the data restoration circuit.  
Figure 1: nvSRAM system structure.
## 4 Anisotropy Analysis  
### Evaluation of Anisotropic Mechanical Properties  
The strength variations of 3 DPC under different loading directions reflect the degree of mechanical anisotropy, quantified using the anisotropy coefficient (\(I_{\mathrm{a}}\)) [44]. For compressive strength, \(I_{\mathrm{a}}\) is calculated via Equations (1) and (2), while flexural strength anisotropy is  
Figure 7: Flexural cracks along the interface in the X direction: (**a**) X1; (**b**) X2.  
evaluated using Equations (3) and (4). Splitting tensile strength anisotropy is not discussed, as it involves only two loading directions (C and T).  
\[I_{\text{direction}}=\sqrt{\left(f_{\text{X}}-f_{\text{direction}}\right)^{2}+ \left(f_{\text{Y}}-f_{\text{direction}}\right)^{2}+\left(f_{\text{Z}}-f_{\text{ direction}}\right)^{2}}/f_{\text{direction}} \tag{1}\]  
\[I_{\text{a}}=\left(I_{\text{X}}+I_{\text{Y}}+I_{\text{Z}}\right)/3 \tag{2}\]  
\[I_{\text{direction}}=\frac{\sqrt{\left(f_{\text{X1}}-f_{\text{direction}} \right)^{2}+\left(f_{\text{X2}}-f_{\text{direction}}\right)^{2}+\left(f_{\text {Y}}-f_{\text{direction}}\right)^{2}+\left(f_{\text{Z}}-f_{\text{direction}} \right)^{2}}}{f_{\text{direction}}} \tag{3}\]  
\[I_{a}=\left(I_{\text{X1}}+I_{\text{X2}}+I_{\text{Y}}+I_{\text{Z}}\right)/4 \tag{4}\]  
Here, \(I_{\text{direction}}\) represents strength in a specific direction, and \(f_{\text{X}},f_{\text{Y}},f_{\text{Z}}\) (or \(f_{\text{X1}},f_{\text{X2}},f_{\text{Y}},f_{\text{Z}}\)) denote compressive/flexural strengths along respective axes.  
The anisotropy coefficient \(I_{\text{a}}\) quantitatively reflects the directional strength disparity in 3 DPC, where a higher \(I_{\text{a}}\) indicates greater anisotropy. As demonstrated in Figure 8, increasing the flowability from 153 mm to 191 mm substantially reduced anisotropy in both compressive and flexural strengths. Specifically, the compressive strength \(I_{\text{a}}\) decreased from 0.42 to 0.14 (66.7% reduction), while the flexural strength \(I_{\text{a}}\) declined from 3.22 to 1.07 (66.8% for reduction). This improvement suggests that enhanced flowability optimizes the material's rheological properties and pore microstructure, effectively mitigating interfacial defects induced during printing and consequently improving structural uniformity and stability.  
Notably, the flexural strength exhibited significantly higher anisotropy than compressive strength (3.5-10 times greater \(I_{\text{a}}\) values) at the same flowability. This difference arises because flexural loading generates tensile stresses concentrated at the interlayer and intralayer interfaces (X1/X2 directions), where insufficient pore filling and weak interfacial bonding readily initiate cracks, leading to substantial strength reduction and pronounced directional disparities. In contrast, compressive strength benefits from multiaxial stress constraints, resulting in lower sensitivity to interfacial defects.
# Supporting Heterogenous Traffic on Top of Point-to-Multipoint Light-Trees +
## 4 Illustrative Results  
In this section, we first compare via simulation the optical performance of OCS and DSCM. Then, to assess the viability of OCS, quantitative analyses are performed for both OCS and DSCM considering the dynamic traffic conditions for two distinct scenarios in which the traffic handled by the light tree is (i) P2P and (ii) P2P + P2 MP.
## 4 Results
### Daily Time Variation of Dielectric Constant
Two correlation coefficients show positive correlation, and they are comparable with each other, similar to RMSE and ubRMSE, as seen in Table 1. The results indicate that the correlation coefficient results also support the compatibility of the computation approach to compute the dielectric constant using the GNSS reflectometry instrument. On the other hand, it will also arise in future analysis when considering the regional characteristics. Therefore, it can be concluded that the developed method for calculating the dielectric constant using CYGNSS data produces a consistent dielectric constant that corresponds to satellite-based references, especially when the specular point occurs on flat and grass field areas. Future work will determine the dielectric constant that is measured at locations other than core validation sites, and distinct land classifications will be explored. Investigations of other locations or vegetation will also be instructive in finding out the characteristics of the dielectric constant or soil moisture according to topography, as per [50].