esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Compressive Strength
#### 3.2.3 Influence of AAS Content  
Alkaline activator solution content is a vital parameter of GPC since it starts the dissolution of aluminosilicate in source raw materials. A sufficient quantity of aluminosilicate materials needs to be available in the matrix to confirm that adequate Si, Al, and Ca ions are present to take part in the dissolution process. Therefore, it is essential to ensure that the proportion of AAS content and binder materials must be adequate to provide an effective dissolution process. In the previous section, it is observed that the workability of mixes with reduced alkaline content (35%) was much lower than the mixes with higher alkaline activator content (i.e., 40%). Hence, an optimal alkaline activator content must be determined that should provide workable mix with acceptable compressive strength. The comparison of the compressive strength of GPC mixes with different AAS content (35% and 40%) is shown in Figure 5.  
It can be observed that the compressive strength of group C mixes (35% AAS content) was higher than the corresponding mixes of Group A (40% AAS content). Therefore, it can be inferred that the compressive strength of FS-GPC is increased by decreasing the amount of alkaline activator content from 40 to 35%. The 28 days strength of Group C mixes, C1-S10 (26 MPa), C2-S20 (46 MPa), C3-S30 (61 MPa), and C4-S50 (68 MPa) was increased by 30%, 15%, 13%, and 11% respectively compared to A1-S10 (20 MPa), A2-S20 (40 MPa), A3-S30 (54 MPa), and A4-S50 (62 MPa) of Group A mixes. The influence of alkaline activator content is observed to be more pronounced at lower level of slag content. Hence, as the slag content is increased, the difference between compressive strengths becomes smaller. It was also observed that the influence of alkaline activator content was more noticeable at the early age strength of GPC up to 14 days, with little or no effect on 90 days strength. The difference between 14 days compressive strength of group A and C mixes was 44% (A1-S10 vs. C1-S10), 31% (A2-S20 vs. C2-S20), 20% (A3-S30 vs. C3-S30) and 18% (A4-S50 vs. C4-S50) respectively. However, this difference tends to decrease after the age of 14 days and becomes negligible for both groups at 90 days. This can be attributed to the decreased consistency of the mix due to reduced alkaline activator content [34; 41]. At early age,the speedy alkaline activation process accelerates the dissolution process of FA and SG, enhancing reaction products formation, thereby increasing the rate of development of compressive strength at the early age.
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
## 5 Conclusions  
In this paper, we proposed a network device identification method based on packet temporal features. This method first extracts the basic behavioral information of the packet, then uses the feature selection method for dimensionality reduction, designs a TCN model with an Inception structure to extract the temporal features of each feature of the packet, and uses the multi-head self-attention mechanism for the fusion of each feature to generate a feature fingerprint for network device identification. This approach added the temporal relationship between the data and increased the discriminative nature of the device fingerprints compared to identification using network packet behavioral information alone. The experimental results on the UNSW dataset showed that our method achieved better accuracy. We analyzed the recognition effect of a single type of device, and the results showed that the classification of devices produced by the same manufacturer or of different models of the same type of device was poor. This should remain a focus of subsequent research.  
Conceptualization, L.H.; methodology, B.Z. and G.W.; investigation, G.W.; formal analysis, B.Z.; writing--original draft preparation, B.Z.; writing--review and editing, L.H. and B.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
Author [PERSON] was employed by the company Zhejiang Guo Fu Environmental Technology Co. Ltd. The remaining authors declare that the re-search was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 3 Proposal Architecture
### On-Board Unit  
The OBU device installed in the vehicles possess a System on Module (SoM) that includes a Cortex A9 quad core processor at 1 GHz with 1 GB DDR3 of RAM memory on a 64 bit architecture. Also, the OBU possesses a touch-screen and an 802.11 b radio which supports ad-hoc communication with a transmission capacity of 12 dBm and an omnidirectional antenna with a 5 dBi gain. The OBU performs the LORA-CBF communication protocol and a monitoring algorithm to propose alternate routes.
## 3 Experimental Results
### Comparative Experiments  
#### 3.4.1 Data Analysis  
To compare the performance of the proposed algorithm LDKA-NET with the baseline model and various state-of-the-art (SOTA) algorithms, this experiment trained each different type of SOTA algorithm using the same training set. The algorithms included the two-stage detection network Faster R-CNN [16], the single-stage detection network SSD [17], YOLOv5 [18], the anchor-free single-stage network FCOS [19], and the end-to-end detection network DETR [20,21]. The number of training iterations was uniformly set to 200, and the same test set and hyperparameters were used for validation. The results are shown in Table 2.  
The experimental data demonstrate the differences in performance metrics among various object detection algorithms. The baseline model, which uses CSPDarkNet as its backbone, performs exceptionally well in terms of precision and mean average precision ([EMAIL_ADDRESS]), achieving 98.66% and 96.23%, respectively. However, its recall rate is somewhat lacking at only 91.87%. In comparison, while YOLOv5-L [22,23] also employs a similar backbone, it falls slightly short in precision and [EMAIL_ADDRESS], with values of 93.61% and 95.90%, respectively.  
Faster R-CNN and FCOS use ResNet50 as their backbone network. However, Faster R-CNN, due to its two-stage feature extraction mechanism, results in a larger computational load and relatively lower precision, achieving only 63.79%. In contrast, FCOS [24] implements object detection through per-pixel prediction, avoiding all computations and hyperparameters associated with anchor boxes, demonstrating excellent detection performance with an [EMAIL_ADDRESS] of 96.19%. Nevertheless, it still lags behind the baseline model and the proposed algorithm, indicating significant room for improvement.  
The SSD model, which employs VGG as its backbone, shows good performance in both precision and recall but has a relatively low [EMAIL_ADDRESS] of only 94.02%. This indicates that while the SSD model has certain advantages, its performance is constrained when handling complex detection tasks. Additionally, the DETR [25] network generally performs poorly across all metrics, with an [EMAIL_ADDRESS] of only 71.11%, indicating substantial room for improvement.  
The proposed algorithm utilizes WFVC Net as its backbone and demonstrates high performance in precision, recall, and [EMAIL_ADDRESS], achieving 99.42%, 95.13%, and 99.01%, respectively. Compared to other algorithms, the proposed algorithm also has relatively fewer parameters and lower computational demands, showcasing its ability to maintain a lightweight model while still achieving outstanding detection results. This indicates strong applicability and efficiency in practical applications.
## 4 Discussion
### Effects of Self-Selected Music on Fatigue  
In this study, listening to self-selected music during a warm-up had no significant effect on fatigue, HR, and RPE after the Wingate test. When exercise leads to a deepening of central fatigue, the inhibitory progresses in the cerebral cortex are more dominant and the excitatory progresses are at a disadvantage [45; 47]. The \(\theta/\beta\) and \((\theta+\alpha)/(\alpha+\beta)\) levels are commonly used to determine fatigue, and increases in \(\theta/\beta\) and \((\theta+\alpha)/(\alpha+\beta)\) imply an inhibition of arousal levels and the production of central fatigue [30; 31; 48]. EEG data showed that listening to self-selected music during a warm-up significantly downregulated the \(\theta/\beta\) levels in the F3 and O1 regions, and \((\theta+\alpha)/(\alpha+\beta)\) levels in the F3 region compared to no music intervention. However, since C3 and C4 are the most representative regions in the sensorimotor area of the cerebral cortex [31], reduction in \(\theta/\beta\) and \((\theta+\alpha)/(\alpha+\beta)\) levels in the F3 and O1 regions did not result in a reduction in exercise-induced fatigue, indicating that listening to self-selected music during a warm-up did not reverse exercise-induced fatigue, which was consistent with the results of [PERSON] et al. [49], showing that listening to music during a warm-up had a beneficial effect on short-term supramaximal performances, while FI, HR, and RPE during the Wingate test were not affected by music. In addition, [PERSON] et al. [50] found no significant difference in HR between the music intervention and no music intervention, suggesting the limited effect of pre-exercise music intervention on HR.  
The effect of music on fatigue may be related to the intensity of the exercise performed after the music intervention. [PERSON] et al. [12] reported that during moderate-intensity exercise, self-selected music intervention resulted in a reduced peripheral, central, and overall RPE compared to no music intervention. However, when the intensity of exercise rises to the extreme load, the effect of music falls far short of counteracting the fatigue associated with high-intensity exercise, which may be caused by the combined effect of mechanical and chemical stimulation received by the receptors of muscles, heart, and lungs during exercise [51]. Furthermore, although FI is directly related to the power values obtained in the Wingate test, it is more dependent on the ratio of minimum power to peak power [10]. Therefore, the enhancing effect of music on anaerobic performance may be evident at the beginning of the exercise and diminish as exercise progresses [8; 52], making the effect of pre-exercise music interventions limited in improving fatigue and RPE. To sum up, listening to self-selected music during a warm-up cannot simultaneously improve anaerobic performance and reduce exercise fatigue.
## Theoretical Basis
It follows from expression (2) that both variants of the UAV, having different probabilities \(p_{ei}\) and \(p_{gi}\ (i=1,2)\), under the condition \(p_{e1}p_{g2}=p_{e2}p_{g2}\), have the same values of criterion (2), i.e., they are equivalent. This decision has an error, which is confirmed by the following simple example. Let the first variant be characterized by the probabilities \(p_{e1}=0.5,\ p_{g1}=0.9\), and the second be characterized by the probabilities \(p_{e2}=0.9,\ p_{g2}=0.5\). The global criterion for these options has the same value, namely \(0.45\). \[W_{1}\big{(}p_{e1},p_{g1}\big{)}=W_{2}\big{(}p_{e2},p_{g2}\big{)}=0.45. \tag{3}\]  
However, it is difficult to imagine that, according to (3), the second option, for which every second UAV is inoperable, can be chosen as the best of the two options. It follows from this that preference should be given to the first option, i.e., the equality of the values of the main criterion does not guarantee equivalence. However, from a purely mathematical point of view, the considered options are equivalent, because in the end they lead to the same value of the criteria \(W(p_{e},p_{g})\), whereby the physical essence of which is the probability of solving the target problem. To eliminate this disadvantage of multiplicative convolution of partial criteria, restrictions are usually introduced on their lower bound of values in the form of inequalities \(p_{i}\geq p_{0i}\). However, the presence of restrictions in the form of inequalities can lead to the loss of a feasible solution and does not remove the internal inconsistency of the criterion in the form of (1), which is not able to clearly separate different options [18; 19]. In view of this, an urgent task is such a modification of criterion (1), which allows for the elimination of the above disadvantages whilst retaining its structure, i.e., multiplicative convolution. One of the possible solutions is to use correspondence functions \(s_{i}(p_{i})\) instead of the partial criterion \(p_{i}\), which would reflect the significance of the achieved value of the partial criterion. In this case, by the correspondence function, we mean a real one-place function, (one-place function) \(s:\left[0,\ 1\right]\rightarrow\left[0,\ 1\right]\), which satisfies the following conditions: the function \(s(p)\) must:  
* Have a clear physical meaning;
* Be continuous and not have jumps and breaks;* Be non-decreasing and take values at the ends of the domains \(s(0)=0\) and \(s(1)=1\).
## 3 Results
### Characterization of Copolymers
In order to determine the thermal stability of 4 VP copolymers in the air atmosphere the TG analysis was carried out. Figure 6 presents the TG, DTG, and DSC curves. The initial decomposition temperature, defined as the 2% mass loss of the sample (\(T_{2\%}\)), is 301 \({}^{\circ}\)C and 316 \({}^{\circ}\)C for TRIM and 14 DMB crosslinked copolymer, respectively (Table 2). The thermal decomposition of both copolymers runs in two well-separated stages, which are seen on DTG curves. The first stage is observed in the temperature range of 301 \({}^{\circ}\)C-360 \({}^{\circ}\)C (with the maximum at \(T_{max1}\)) with about 66% and 71% mass loss (\(\Delta\)\(m_{1}\)) for 14 DMB and TRIM crosslinked copolymer, respectively. The second one spreads from 361 \({}^{\circ}\)C with a maximum of about 460 \({}^{\circ}\)C (\(T_{max2}\)) and mass loss of about 30% (\(\Delta\)\(m_{2}\)). From DSC curves, it is visible that the decomposition processes are exothermic. Based on the courses of TG and DTG curves and also the values of \(T_{2\%}\) and \(T_{50\%}\) (the temperature of 50% mass loss), it is seen that the 4 VP copolymer crosslinked with 14 DMB shows higher thermal stability than that crosslinked with TRIM. Previous detailed studies on the thermal decomposition of poly(4 VP-co-TRIM) microspheres revealed that their decomposition starts with a depolymerization reaction, during which pyridine is evolved [37]. It is evidenced that pyridine is the catalyst for the hydrolysis of ester bonds. In the case of TRIM crosslinked copolymer, the number of ester bonds is higher than in 14 DMB copolymer. Therefore, a higher first mass loss is observed for poly(4 VP-co-TRIM). The residual mass (\(R_{m}\)) for both copolymers is lower than 5%. Finally, it can be stated that both studied 4 VP copolymers possess high thermal stability, and they can be used as sorbents operating in the room as well as at elevated temperatures.
## 7 Remaining Useful Life Estimate
If, on the other hand, the system experiences cumulative degradation, an exponential degradation model is used, in which the physical process of degradation is modeled by an exponential stochastic process. For civil engineering applications, the best models for RUL estimation are degradation models (i.e., models in which the failure threshold is known, Figure 14). Other types of models (i.e., similarity models and survival models, Figure 14) were initially discarded because structural and infrastructure works are typically unique, thus it is difficult to have the required data for the operating conditions, including degradation data. These models require as input a single health indicator, which, herein, is derived from the raw data obtained from the AI model, i.e., the estimated corrosion thickness, as indicated in the previous section. The degradation models used herein is the exponential model, which implements a stochastic model with an exponential degradation over time [41; 42; 43; 44; 45; 46; 47; 48]:  
Figure 14: Methods of estimating RUL. \[S(t)\ =\ \phi+\theta(t)e^{\beta(t)t+\epsilon(t)-\frac{\sigma^{2}}{2}} \tag{2}\]  
where:  
\(\phi\) is the intercept of the mathematical model at the origin  
\(\theta(t)\) is a random variable modeled with Lognormal distribution  
\(\beta(t)\) is a random variable modeled with Gaussian distribution  
\(\epsilon(t)\) is the model noise implemented as a random variable with normal distribution  
\(\sigma^{2}\) is the variance of the noise
# Lab-on-Chip Culturing System for Fungi--Towards Nanosatellite Missions
## 3 Results and Discussion
### Fungi Cultivation in Space  
_Concept_  
As the results of fungi cultivation in both terrestrial and simulated microgravity conditions were successful, a concept of microfluidic laboratory platform, being a payload  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the fungi culture development on-chip in comparison with a reference Petri dish culture. The tests were performed at least 5 times and similar, appropriate culturing results were obtained. Scale bar—100 \(\upmu\)m.  
solution for the biological nanosatellite mission, has been proposed (Figure 10). The same, all-glass LOCs are the \"heart\" of the platform, which material and manufacturing techniques fulfil the low outgassing criteria required for the space tools. Apart from the culturing microflow system proposed and tested in this work, a miniaturized CMOS camera equipped with motorized lens and white LED lightning is used herein to acquire the image samples. As the optimal temperature for the fungi growth is 27 \({}^{\circ}\)C, flexible PCB heaters maintain the defined value, based on on-chip sensors that work in a closed feedback loop. Thermal aluminum cover (thermos) protects the lab-on-chip platform from the harsh space environment. Moreover, sensors of humidity, temperature, pressure, and radiation are embedded within to constantly monitor the thermos atmosphere. All the experimental data is managed on-board and transmitted via the CAN bus to the nanosatellite module and ultimately, to the ground station. Overall structure of the nanosatellite do not exceed 2U.
## 4 Case Study  
The presented model is validated using the IEEE 118-bus system. Topology and parameters of the IEEE 118-bus system are found in [34]. With the aid of MATPOWER [35], a measurement set is obtained, which consists of 712 measurement leading to a global redundancy level (GRL) 3.029. A Gaussian noise with zero mean and known variance is added to the measurement set. In addition, a measurement dataset corresponding to eight consecutive days each of which one contains 21,600 samples based on a common daily load profile that contains temporal information of a power system's changing state is generated and fed to machine learning models for measurements prediction and measurements'weight. It is worth noting that different noise levels were used for generating multiple datasets. Real line power flows, reactive line power flows, bus power injections, and voltage magnitudes are included in each measurement set. The implementation and evaluation of the machine learning algorithm was executed using python libraries such as NumPy [36], SciPy [37], Matplotlib [38] and Scikit-learn [39]. The first 50% of the samples in each day are used to train multiple linear regression models.  
In the following, two different parameter FDI attack scenarios are presented. In each scenario, attack detection is flagged if the objective function \(J(x)\) is above threshold value \(C=\chi^{2}_{p,dof}\). Identification is performed by building a descending list of \(CME^{N}\) (based on their absolute values) [19]. In the correction step, the presented model in Section 2.1 is solved.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 2 Materials and Methods
### Verification Techniques
#### 2.3.3 Estimating Total Correlated Error  
Single- and double-differencing are common techniques to calibrate remote sensing instruments, especially radiometers [39; 40] and radars [41]. Both are useful for quantifying the correlated error structures in CYGNSS observations.  
For each track of data, there are two single-differenced datasets,  
\[SD_{obs}=\sigma_{1}^{o}-\sigma_{5}^{o} \tag{8a}\] \[SD_{mod}=\sigma_{mod,1}^{o}-\sigma_{mod,5}^{o} \tag{8b}\]  
where \(SD_{obs}\) is the single-differenced data from the two matched-up observations from FM1 and FM5, and \(SD_{mod}\) is the single-differenced data of the model-generated NBRCS for the specific coordinates of FM1 and FM5. The double-difference is computed by differencing these two quantities:  
\[DD=SD_{obs}-SD_{mod} \tag{9}\]  
Both the SDs and DD are useful for our analysis. The \(SD_{obs}\) term represents the difference in \(\sigma^{o}\) measured by two different CYGNSS satellites. Despite the strict matchup conditions, these assets are still measuring different fields of view at different times. These differences may result in some residual correlated structure. In addition, any correlated structure in \(SD_{obs}\) may not reveal structure imposed from fundamental properties of the earth system. Therefore, \(SD_{mod}\) allows us to identify the correlated structure of any systematic differences in observation target. An example of the utility of these differencing techniques is shown in Figure 2.  
Since we are primarily interested in correlated error, and not absolute error, our primary investigative tool is the autocorrelation function. The correlated error for each track can be computed by autocorrelating the DD using the standard formulation:  
\[\rho(\tau)=\frac{1}{(N-\tau)}\frac{1}{\sigma_{xi}}\frac{1}{\sigma_{xi+\tau}} \sum_{i=1}^{N-\tau}{(X_{i}-\bar{X})(X_{i+\tau}-\bar{X})} \tag{10}\]  
where \(\tau\) is the lag, \(N\) is the total number of lags observed, \(X_{i}\) is the value of timeseries \(X\) at time \(i\), \(\bar{X}\) is the mean of timeseries \(X\), and the standard deviation is formulated as normal, \(\sigma_{i}=\sqrt{\frac{\sum_{i=1}^{N-\tau}{(X_{i}-\bar{X})^{2}}}{N-\tau}}\).  
Because the error correlation behavior can vary from track-to-track, we also construct a bulk autocorrelation which approximates the autocorrelation behavior for all tracks sampled. For this we consolidate \(M\) tracks, the bulk autocorrelation is:  
\[\mathcal{R}(\tau)=\frac{1}{M(N-\tau)}\frac{1}{\sigma_{y_{i}}}\frac{1}{\sigma_{y _{i+\tau}}}\sum_{i=1}^{N-\tau}{(Y_{i}-\bar{Y})(Y_{i+\tau}-\bar{Y})} \tag{11}\]  
where \(Y_{i}=\sum_{j=1}^{M}{X_{i,j}}\), \(j\) is the index for track number, and the standard deviation and mean are calculated as before, but with the consolidated track series \(Y\).