esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
We address vehicular sensing applications, where vehicles cooperate to sense and collect environmental data that can be requested, for example, by public administrations or private data centers [7]. A general vision on architectures and technologies for V2X can be found in [8].  
Narrowing, indeed, the field of research to VSNs, some principles and challenges were provided in [9; 10], describing typical topology and architecture and exploring the main requirements at the different levels of the protocol pillar. In [11], a survey of the most significant approaches for wireless and mobility modeling in vehicular networks' simulation is given, listing the main channel models that have been employed in the literature for IEEE 802.11p and related standards. In [12], an overview on the communication and networking aspects of vehicular ad hoc networks is presented, but the paper dates 2008 when the only standard for these kinds of networks was IEEE 802.11p. A brief report on vehicular traffic flow models and some radio channel basics required for realistic assessments of systems and protocols are also discussed. A survey on mobility models in VSNs is proposed in [13], also providing methodologies for the cooperation between mobility models and network simulators.  
An extended survey, but related to vehicular social networks, has been provided in [14], where social characteristics and human behavior are considered for their impact on vehicular networks and the impact of social-based protocols is taken into account to identify socially-similar nodes sharing common interests and roads. In [15], VSNs operating in a smart city are discussed, followed by an overview of a range of associated technologies. The authors also highlight the importance of an accurate topological structure and propose an information source selection model to improve the attainable network capacity. In [16], an analysis of the strengths and uses of dedicated short-range communications (DSRC) and cellular vehicular networks is presented, exploring strategies, beyond government mandates, to encourage vehicular network deployment.  
Several works related to VSNs focus also on data traffic mapping and data analytics. In [17], for example, VSNs are considered for traffic monitoring in urban areas: probe vehicles, such as taxis, buses and floating cars in the city of Shangai are used as mobile sensors of the urban traffic and send the reports to a traffic-monitoring center for traffic estimation. The authors of this work propose a method to fully exploit the acquired data and also estimate the traffic condition of the unsampled roads.  
Differently from the cited references, in this paper, we aim at the underlying motivation to VSNs also from an economic perspective; then, we focus on actual and future wireless access technologies, also presenting the roadmap for potential mandatory rules on vehicular connectivity at international level and we conclude with a performance comparison in realistic scenarios of the two main candidates technologies for short-range VSNs: IEEE 802.11p and LTE-V2V.
## 3 Results and Discussion
### Statistical Analysis  
The results are reported as the means of four replicates. The data obtained were subjected to one-way analysis of variance (ANOVA) for assessing the significance of quantitative changes in the variables as a result of biochar treatments. The statistical analysis was done by the Statistical Package for Social Science (IBM SPSS Statistics 23.0, Chicago, IL, USA)  
According to the statistical analysis (Table 3) of the effect of pH on the adsorption capacity, there is a significant difference (\(p\) < 0.05) between the means of the adsorption capacities of X-PR by the two materials BS-HVL and BC-HVL; on the other hand, there is no significant difference (\(p\) = 0.211) between the means of the adsorption capacities of MGC by the two studied materials (BS-HVL and BC-HVL). Furthermore, the statistical analysis of the effect of the dose shows that there is no significant difference (\(p\) > 0.05) between the means of the adsorption capacities of X-PR and MGC by the two materials BS-HVL and BC-HVL. On the one hand, the statistical analysis of the effect of the initial concentration shows that the test is insignificant at the 5% level, with no significant difference between the means of the adsorption capacities of X-PR and MGC by the two materials BS-HVL and BC-HVL. On the other hand, the statistical analysis of the effect of contact time shows that the test is highly significant at the 1% value; there is a significant difference between the mean adsorption capacities of X-PR and MGC by the two materials BS-HVL and BC-HVL. Moreover, the statistical analysis of the effect of temperature shows a highly significant test at the 5% threshold, indicating that there is a significant difference between the means of adsorption capacities of X-PR and MGC by the two materials BS-HVL and BC-HVL.
## 3 Conventional and Proposed Control Schemes of the Battery-SC HESS
As \(k\) increases (\(R_{dSC1}\) decreases), one closed loop real root moves towards the infinity and the other, dominant, towards the vertical axis. For a high enough \(k\), one can neglect the non-dominant root. Figure 4:4 Simplified small-signal model for the analysis of the proposed SC control scheme. As mentioned before, small values for \(R_{dSC1}\), from 2% to 10% of \(R_{dB}\), should lead to a large enough burst of current to assist with dynamic DC bus voltage regulation what should also prevent the battery from supplying fast changing currents. Once the value of \(R_{dSC1}\) is selected, one can compute \(\tau_{F}\) based on the desired duration of the high burst of power (\(T_{s}\)). For a system (\(G_{T}\)) with a dominant real root, one can say that \(T_{s}=3\tau\), where \(\tau\) is the time constant of the dominant root. For a system with a negligible root, \(\zeta\gg 1\) and  
Figure 4:5  
Then, for a specific desired time constant \(\tau\), one can solve (12) with \(\omega_{n}\) from (10) and \(\zeta\) from (11) to find \(\tau_{F}\). A closed form equation for \(\tau_{F}\) is not straightforward. Alternatively, one can obtain the value of \(\tau_{F}\) from a plot. Figure 8 shows the value of \(1/\tau\) as a function of \(\tau_{F}\) for different values of \(R_{dSC1}/R_{dB}\) and for a system with \(C_{DC3}=1500\)\(\mu\)F. As an example of usage of this approach, for \(R_{dSC1}=0.05R_{dB}\), a HPF with \(\tau_{F}=3.7\) ms gives the settling time \(T_{s}=0.22\) s. For \(R_{dSC1}=0.1R_{dB}\), and the same HPF (\(\tau_{F}=3.7\) ms), the settling time becomes \(T_{s}=0.12\) s. Alternatively, the original settling time (0.22 s) can be achieved with \(R_{dSC1}=0.1R_{dB}\) and a HPF with a time constant of \(\tau_{F}=6.9\) ms. It should be noted that as one decreases \(R_{dSC1}\), to have a higher burst of current and a smoother DC bus voltage variation following a sudden power unbalance in the DC nanogrid, a smaller time constant (\(\tau_{F}\)) of HPF is needed for a given settling time. In addition, Figure 8 also shows the impact of \(\tau_{F}\) on the damping ratio \(\zeta\) and the natural frequency \(\omega_{n}\). Figure 4:6 Root locus for varying the value of \(R_{dSC1}\) with \(\tau_{F}=3.18\) ms.
# Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation
## 2 Materials and Methods
### Statistical Analysis  
Descriptive statistics, the Kolmogorov-Smirnov and Levene's test were used to assess the normality and homogeneity. A one-way analysis of variance (ANOVA) for repeated measures were tested to identify differences between age group, playing position and training day. A factorial ANOVA (factor 1: age group, factor 2: training day, factor 3: weekly microcycle, factor 4: playing position) was used to analyze changes in the external training load, sRPE, RPE and TQR during training sessions. When a significant difference occurred, Bonferroni post-hoc tests were used to identify localized effects. Games-Howell post-hoc tests were applied if variances were not homogeneous. The effect size index (eta square: \(\eta^{2}\)) was computed and interpreted as: (i) without effect if \(0<\eta^{2}\leq 0.04\); (ii) minimum if \(0.04<\eta^{2}\leq 0.25\); (iii) moderate if \(0.25<\eta^{2}\leq 0.64\); and (iv) strong if \(\eta^{2}>0.64\)[49]. The independent t-test was performed to analyze the differences between weekly microccycles. Standardized effect sizes (ES) were calculated with [PERSON]'s \(d\), classified as: without effect if \(d<0.2\), moderate effect if \(0.2>d\geq 0.5\) and strong effect if \(d>0.5\)[50; 51]. Statistical significance was set at \(p<0.05\). Data are presented as the mean \(\pm\) one standard deviation (SD). Mean differences (\(\Delta\)) were presented in absolute values and percentage (%). All statistical analyses were conducted using SPSS for Windows Version 22.0 (SPSS Inc., Chicago, IL, USA).
## 7 Summary, Conclusions, and Discussion
### Possible Limitations from the Assumptions  
The actual description of how the 2D sensor array should be built is out of our scope. Each sensor, in charge of sampling the fingerprints for a sub-tile, could be constructed using an antenna or a receiver with a built-in antenna. Thus, it may be composed of an antenna array or a receiver array.  
We did not consider the actual antenna design aspects related to the construction of the 2D sensor array. In this context, we worked with the numeric modules equivalent to the received signal strength, ignoring the effects of the constructive/destructive phases of radio-waves, the optimal antenna spacing, the effect on the antenna spacing and SNR, and variable AoAs of the radio-waves with respect to the 2D sensor array's position.  
It was observed that for the methods 3-MultFingIn-PosOut and 4-MultFingIn-ClassOut, strictly for the given assumptions and system considered, the smaller the sub-tile size (and hence the smaller the 2D sensor array) the smaller the error distance (or, in other words, the better the performance in terms of minimizing the positioning error; see Sections 6.4 and 6.5). These results are valid for the given assumptions and the system considered. However, it is counterintuitive to us that a smaller 2D sensor array leads to a better performance. It should be noted that we ignored some practical considerations present in a real system, as mentioned above, which may counteract the performance gain experienced when reducing the 2D sensor array's size in a real system.  
The side lengths of the 2D sensor array (tiles) at which the best results were found using pattern-matching methods 3-MultFingIn-PosOut and 4-MultFingIn-ClassOut are summarized in Table 16.  
We observe in Table 16 that for the best results obtained with pattern-matching methods 3-MultFingIn-PosOut and 4-MultFingIn-ClassOut, when the sub-tile and tile sizes decrease, the number of sub-tiles in the tiles (2D sensor array) increases. So, one conjecture is the following. Since we do not have a constraint in terms of the actual antenna design aspects related to the construction of the 2D sensor array, the best solution aims to increase the number of fingerprint contributions from the side information while reducing the tile size. This could be the result of the system aiming to decrease the inter-tile distance to increase the positioning resolution while increasing the amount of side information.
###### Abstract  
Inaccurate state-of-health (SoH) estimation of battery can lead to over-discharge as the actual depth of discharge will be deeper, or a more-than-necessary number of charges as the calculated SoC will be underestimated, depending on whether the inaccuracy in the maximum stored charge is over or under estimated. Both can lead to increased degradation of a battery. Inaccurate SoH can also lead to the continuous use of battery below 80% actual SoH that could lead to catastrophic failures. Therefore, an accurate and rapid on-line SoH estimation method for lithium ion batteries, under different operating conditions such as varying ambient temperatures and discharge rates, is important. This work develops a method for this purpose, and the method combines the electrochemistry-based electrical model and semi-empirical capacity fading model on a discharge curve of a lithium-ion battery for the estimation of its maximum stored charge capacity, and thus its state of health. The method developed produces a close form that relates SoH with the number of charge-discharge cycles as well as operating temperatures and currents, and its inverse application allows us to estimate the remaining useful life of lithium ion batteries (LiB) for a given SoH threshold level. The estimation time is less than 5 s as the combined model is a closed-form model, and hence it is suitable for real time and on-line applications.  
state of health; remaining useful life; electrochemistry based electrical model; semi-empirical capacity fading model; useful life distribution; quality and reliability assurance
## 3 System Description
### Smart PV Modules
#### 3.1.2 Communication System  
Two different wireless communications can be identified in the solar plant (see Figure 4):  
1. Communication within the _Monitoring Plant Network: Smart Modules_ send information to a _Central Node_ on their tower; and
2. Communication between these _Central Nodes_ and the communication interface in the _Monitoring Center_.  
In order to establish the communication between _Smart Modules_ and the Central Node, each PV panel includes an XBee 802.15.4 low-power module from Digi [31] and an 8-bit PIC microcontroller from Microchip [32]. This node will also include all the circuitry in charge of sensing the magnitudes of interest. In the prototype, a current sensor from Allegro (ACS711 [33]) suitable to measure currents up to 12.5 A is included.  
The Central Node (Figure 4 again) also consists of an XBee 802.15.4 low-power module and a PIC microcontroller, but it includes as well an XBee-PRO 802.15.4 extended-range module so that the communication with the _Monitoring Center_ can be implemented.  
Figure 4: General structure of the wireless monitoring system.  
Finally, an additional XBee-PRO 802.15.4 extended-range module and a PIC microcontroller will be required for the communication interface in the _Monitoring Center_. A device making it possible to connect this interface to a PC (such as a MAX232 [34] or FTDI FT245 [35], for instance) is also necessary.
# COVID-19 Detection from Chest X-ray Images Based on Deep Learning Techniques
## 4 Experimental Result Analysis
### Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline
**Layer (Type)** & **Output Shape** & **Param** \\ \hline model\_1 (Model) & (None, 100, 100, 384) & 11,008 \\ \hline conv2d\_4 (Conv2D) & (None, 98, 98, 64) & 221,248 \\ \hline activation\_1 (Activation) & (None, 98, 98, 64) & 0 \\ \hline max\_pooling2d\_1 (MaxPooling2) & (None, 49, 49, 64) & 0 \\ \hline conv2d\_5 (Conv2D) & (None, 47, 47, 32) & 18,464 \\ \hline activation\_2 (Activation) & (None, 47, 47, 32) & 0 \\ \hline max\_pooling2d\_2 (MaxPooling2) & (None, 23, 23, 32) & 0 \\ \hline flatten\_1 (Flatten) & (None, 16928) & 0 \\ \hline dropout\_1 (Dropout) & (None, 16928) & 0 \\ \hline dense\_1 (Dense) & (None, 128) & 2,166,912 \\ \hline dropout\_2 (Dropout) & (None, 128) & 0 \\ \hline dense\_2 (Dense) & (None, 64) & 8256 \\ \hline dropout\_3 (Dropout) & (None, 64) & 0 \\ \hline dense\_3 (Dense) & (None, 2) & 130 \\ \hline \end{tabular}
\end{table} 
Table 1: Detector model summary for “sequential_1”. In Figure 8, we present the performance metrics results of our proposed CNN model. This figure includes key metrics such as sensitivity, specificity, accuracy, and the F1 score, which are essential for a comprehensive evaluation of the diagnostic tool. These metrics offer insights into the model's ability to correctly identify COVID-19 cases and its ability to accurately classify non-COVID-19 cases. In contrast to using pre-trained models, which often demand significant computational resources due to their deep and complex architectures, we opted to design a customized Convolutional Neural Network (CNN) for our COVID-19 detection system. This customized CNN is intentionally lighter in structure, resulting in faster processing times and reduced computational demands. During testing, our proposed model demonstrated promising results, outperforming computationally expensive alternatives. Detailed discussions and comparative analyses are provided in the following section. We recommend our customized model for its efficient performance and streamlined architecture.
## 2 AETA System  
### AETA Devices and Data Acquisition  
AETA, the multi-component earthquake monitoring and prediction system, was developed by the Key Laboratory of Integrated Microsystems of Peking University Shenzhen Graduate School for earthquake prediction. This system consists of two sensors, a terminal, a cloud platform, and a data analysis system. The two sensors are an electromagnetic sensor and a geoacoustic sensor, both of which are used to collect electromagnetic and geoacoustic signals, respectively. Then they transmit data to the data-processing terminal through cables, undergo sampling, compression, and filtering processes, and finally upload the data to the cloud server through the network for subsequent data analysis, the capture of earthquake precursor anomaly signals and research of seismic-related activities [30].  
The electromagnetic sensor mainly monitors the electromagnetic signal band containing a very low frequency (VLF) and an ultra-low frequency (ULF), with a frequency range of 0.1 Hz to 10 kHz, an amplitude range of 0.1 to 1000 nT, a sensitivity of >20 mV/[EMAIL_ADDRESS] Hz to 10 kHz, a resolution of 18 bits, and a sampling rate of 500 Hz at low frequency and 30 kHz at full frequency [31]. The frequency range of the geoacoustic signals monitored by the geoacoustic sensor is 0.1 Hz to 50 kHz, with a resolution of 18 bits, the sensitivity of 3 LSB/pa@ 0.1 Hz to 50 kHz, and the sampling rate of 500 Hz at low frequencies and 150 kHz at full frequencies [32].  
The AETA sensor system is low-cost and convenient for large-scale, high-density deployment. Up to now, nearly 300 sets of AETA devices have been deployed in some of the most seismically active regions in China, such as Sichuan, Yunnan, and the surrounding provinces. The AETA system has accumulated over 50 TB of data over 5 years of observation. The rich observation data has laid a good foundation for the establishment and comparative analysis of earthquake-prediction models.
###### Abstract  
Sun-induced chlorophyll fluorescence (SIF) is a radiation flux emitted by chlorophyll molecules in the red (RSIF) and far red region (FRSIF), and is considered as a potential indicator of the functional state of photosynthesis in remote sensing applications. Recently, ground studies and space observations have demonstrated a strong empirical linear relationship between FRSIF and carbon uptake through photosynthesis (GPP, gross primary production). In this study, we investigated the potential of RSIF and FRSIF to represent the functional status of photosynthesis at canopy level on a wheat crop. RSIF and FRSIF were continuously measured in the O\({}_{2}\)-B (SIF687) and O\({}_{2}\)-A bands (SIF760) at a high frequency rate from a nadir view at a height of 21 m, simultaneously with carbon uptake using eddy covariance (EC) techniques. The relative fluorescence yield (Fyield) and the photochemical yield were acquired at leaf level using active fluorescence measurements. SIF was normalized with photosynthetically active radiation (PAR) to derive apparent spectral fluorescence yields (ASFY687, ASFY760). At the diurnal scale, we found limited variations of ASFY687 and ASFY760 during sunny days. We also did not find any link between Fyield and light use efficiency (LUE) derived from EC, which would prevent SIF from indicating LUE changes. The coefficient of determination (\(r^{2}\)) of the linear regression between SIF and GPP is found to be highly variable, depending on the emission wavelength, the time scale of observation, sky conditions, and the phenological stage. Despite its photosystem II (PSII) origin, SIF687 correlates less than SIF760 with GPP in any cases. The strongest SIF-GPP relationship was found for SIF760 during canopy growth. When canopy is in a steady state, SIF687 and SIF760 are almost as effective as PAR in predicting GPP. Our results imply some constraints in the use of simple linear relationships to infer GPP from SIF, as they are expected to be better predictive with far red SIF for canopies with a high dynamic range of green biomass and a low LUE variation range.  
sun-induced fluorescence (SIF); red SIF; far red SIF; gross primary production (GPP); diurnal cycle; photosynthesis efficiency; fluorescence quantum yield; photochemical yield; absorbed photosynthetically active radiation (APAR); wheat; oxygen absorption band +
Footnote †: journal: Remote Sensing
# A Survey on Bias in Deep NLP
## 4 Discussion
### Association Tests
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Year** & **Ref.** & **Sterotypes(s)** & **Model** & **Data** & **Lang.** & **Evaluation** & **Mitigation** & **Stage** & **Task** \\ \hline
2018 [37] & Gender & GloVe [27] & OntokNotes 5.0, **WinBlas.** & English & Prediction Accuracy & Data Augmentation (Gender & After & Conference \\  & Occupation Data (BLS), BdL & & & Swapergingly, Vector Space & & Resolution \\ \hline
2018 [33] & Gender & GloVe [27], **GN-GiveVe** & 2017 English Wikipedia & English & Prediction Accuracy, & Attribute Protection, Vector & Train (1), & Conference \\  & **Hard-Give** & **Hard-Give** & dump, **SemBlas (3)** & & Analogies (3) & Space Manipulation (1), & After (2) & resolution \\ \hline
2019 [67] & Gender, Ethnicity, & Google Perspective API & WikiDotex, Wiki Madlibs, Twitter, WordNet & English & Classification Accuracy, & Data correction, Data & Before & Hate Speech \\  & **Oritention** & & Twitter, WordNet & & likelihood & Augmentation, Attribute & & Detection \\ \hline
2019 [68] & Gender & fastText, Bwl, DBNN with Custom Dataset & Common Crawul, & English & Prediction Accuracy & Attribute protection (Removing Cande and NE) & Before & Hiring \\ \hline
2019 [69] & Account Age, user features & Graph Embeddings [70] & WikiData & English & Accuracy & Attribute Protection (Remove & Train & Vandalsim \\  & **Oritention** & & & user information) & & Detection & Detection \\ \hline
2019 [38] & Ethnicity, Gender, Religion & Word2 Vec & Reddit L2 corpus & English & PCA, WEAT, MAC, Clustering & Vector Space Manipulation & After & POS tagging, POS chungling, NER \\ \hline
2019 [63] & Gender & ELMo, GloVe & One Billion Word Benchmark, WinBlas, OntokNotes 5.0 & English & PCA, Prediction Accuracy & Data Augmentation (1), & Train (1), & Conference \\  & **Oritention** & & & authorics & authorics & After (2) & Resolution \\ \hline
2019 [52] & Gender & CROW & English Gigoword, Wikipedia, Google Analogy, SimLe-999 & English & Analogies, WEAT, Sentiment Classification, Clustering & Hard-Debiasing, CDA, **CDS** & Train & - \\ \hline
2020 [71] & Ethnicity & CPT-2 & Science fiction story corpus, Pletto, ROC stories, toxic and Sentiment datasets & English & Classification Accuracy & Loss function modification & Fine & Normative text \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Previous work on bias detection and treatment in NLP: data manipulation.