esa-sceva/satcom-chunk-collection huggingface dataset:
# Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning
## 2 Proposed Methodology
### Residual Signal Generation  
Regarding Figure 1, the digital twin was used to model and estimate the signals. First, the normal condition signal was modeled using the proposed MS technique. Next, the estimator was designed and tuned for the normal state signal using the HRKF method. After that, the normal and abnormal state (unknown) signals were estimated using the proposed digital twin (MS-HRKF) scheme. The accuracy, robustness, and reliability of the digital twin are quite significant for signal classification. Before the signal classification using the machine learning algorithm, the residual signal, which is defined by the difference between the original RAW signal of the bearing and the estimated signal using the digital twin, is computed using the following formulation.  
\[R_{MS-HRKF}(k)=Z(k)-Z_{MS-HRKF}(k) \tag{38}\]  
where \(R_{MS-HRKF}(k)\) is the residual signal of unknown signals using the proposed digital twin (MS-HRKF) technique. In the next part, the machine learning technique is used for FPRCI of the bearing.
## 2 Mathematical Model
### Generalized Model Description
#### 2.2.3 Scheduling Constraints: Job Step Start-End Formulation  
In this case we use step binaries \(S^{*}_{jt}\) and \(E^{*}_{jt}\) with a following rule:  
* if job \(j\) starts at period \(t\), then \(\forall t_{1}<t\ S^{*}_{jt_{1}}=0\) and \(\forall t_{2}\geq t\ S^{*}_{jt_{2}}=1\);
* if job \(j\) ends at period \(t\), then \(\forall t_{1}\leq t\ E^{*}_{jt}=0\) and \(\forall t_{2}>t\ E^{*}_{jt}=1\).  
This approach is defined as step formulation because for each job the plot with decision variables looks like a non decreasing step function. Firstly, we require proper values for all binary step variables. The job may be ended only if it was started in the same period or before, and the values of start and end step variables \(S^{*}_{jt}\) and \(E^{*}_{jt}\) must be non-decreasing for each job \(j\in J\):  
\[S^{*}_{jt}\geq E^{*}_{jt},\ \forall j\in J,\ \forall t\in T; \tag{14}\]  
\[S^{*}_{jt}\leq S^{*}_{j,t+1},\ \forall j\in J,\ \forall t\in T; \tag{15}\]  
\[E^{*}_{jt}\leq E^{*}_{j,t+1},\ \forall j\in J,\ \forall t\in T. \tag{16}\]  
Secondly, we set up the correspondence between binaries and decision variables \(d_{jt}\in[0,d]\). As in previous cases, we imply that \(d_{jt}=0\) if the job was not started before period \(t\) or it was finished in some period before \(t\):  
\[d_{jt}\leq d\ (S^{*}_{jt}-E^{*}_{jt}),\ \forall j\in J,\ \forall t\in T. \tag{17}\]  
If a job is implemented in three or more periods, inside all periods between the first one and the last one job duration is the same as period length:  
\[d_{jt}\geq d\ (S^{*}_{jt}+S^{*}_{j,t-1}-1-E^{*}_{jt}-E^{*}_{j,t+1}),\ \forall j\in J,\ \forall t\in T. \tag{18}\]  
It is also necessary to configure precedence constraints. Successor and predecessor both might be implemented in one period only if it is the last period of predecessor and the first period of successor:  
\[S^{*}_{jt}\leq E^{*}_{jt,t+1},\ \forall t\in T,\ \forall(j_{1},j_{2})\in P; \tag{19}\]  
\[d_{jt}+d_{jt}\leq d,\ \forall t\in T,\ \forall(j_{1},j_{2})\in P. \tag{20}\]
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 4 Results and Discussion
### Cluster Member Lifetime (CML) Analysis  
Table 8 and Figure 5b clearly illustrate that an increase in cluster head lifetime (CHL) results in corresponding enhancements in cluster member lifetime (CML), thereby ensuring uninterrupted network connectivity.
## 4 Discussion
One study used SOS to take an input pulse with amplitude of 500 kV and pulse duration at the full-width half-maximum (FWHM) of 7 ns to a gyromagnetic NLTL to output 740 kV with a pulse duration \(\sim\)2 ns into a 40 \(\Omega\) load [33]. A subsequent study used two gyromagnetic NLTLs as magnetic compressions lines to take a similar input pulse and output it at 1.1 MV with a pulse duration of 0.65 ns and an increase in peak power from 6 to 30 GW [34]. Thus, future system construction could take our system where an NLTL can act simultaneously as a PFL and HPM source and explore designs using semiconductor switches. However, it is important to note that these systems can be quite large [32; 35]. Thus, applying semiconductor switching necessitates close attention to system weight since stacks of semiconductors will most likely be needed for power handling. Depending upon application, replacing the spark gap with semiconductor switching could realize a fully solid-state, high power system with our configuration providing tunability based on composite properties. Moreover, the ability to tune the impedance, by eliminating the need to match the impedance of a given PFL, provides additional flexibility in system output that does not exist in standard devices. This may enable a user to modify NLTL electrical performance by altering the composite blend without needing to adjust the physical dimensions of the NLTL. This would allow an operator to simply change out an NLTL to achieve the desired waveform without making any other changes to the system. Future work will assess different materials, loadings, and impedances to assess how much flexibility this approach provides. The reduction of system size is a crucial step towards achieving portable HPM systems. The reported topology offers significant weight and size reduction, making it appealing to applications where systems need to be robust and highly mobile. Such systems necessitate careful consideration of system size, length, and weight. System parameter space depends heavily on application and mission needs. Modern gyromagnetic NLTLs with coaxial geometry have ranged in length from \(\sim\)10-100 cm [36; 37; 38]. The NLTLs presented here measure 20 cm in length. Other systems have also required pulse forming networks and biasing coils for proper function. While benchtop gyromagnetic NLTLs have been realized recently, output powers are less than those shown here [37]. Admittedly, removing the biasing field reduces system tunability since the output can only controlled by the parameters of the input pulse; however, it may provide alternative tunability by permitting the rapid changeout of NLTLs with different composite loadings to achieve desired output waveforms. This necessitates a comprehensive knowledge of the composite behavior and careful consideration of NLTL length and impedance.
## 3 Problem Statement and Results
### Scenario Description and Formulation
Although it is possible to formulate different functionals according to the specific target we want to optimize, we will focus on just the previously mentioned main objectives: minimization of the variance of the lateral distance (as we will see, by minimizing the lateral variance of the distances we simultaneously maximize lateral distance), and minimization of the final lateral speed. As we said, this can be justified due to the fact that the lower the variance of the distances of the lateral gaps (between two vehicles, two obstacles or vehicle to obstacle), the higher the lateral distance to other elements on the road will be. On the other hand, minimizing the final lateral speed will turn into a null lateral inertia when approaching the end of the path as we will see later. Even though we restrict ourselves to these parameters, alternative functionals can be constructed. The four proposed functionals are described next:  
1. **Final lateral distance maximization and final lateral speed minimization**. In this case we want to minimize the final variance of the lateral distances left by the vehicle after \(t_{f}\), while minimizing the lateral speed at the end of the trajectory. The equation corresponding to this functional takes the form: \[J_{D1}=x_{1}^{2}\left(t_{f}\right)+\left(W-x_{1}(t_{f})\right)^{2}+v_{1}\left( t_{f}\right)\] (4)
2. **Final lateral distance maximization**. In this case we skip the minimization of the lateral speed at the end of the trajectory. We only perform here the maximization of the final lateral distance. \[J_{D2}=x_{1}^{2}\left(t_{f}\right)+\left(W-x_{1}(t_{f})\right)^{2}\] (5)
3. **Instantaneous lateral distance maximization and final lateral speed minimization**. This functional aims at maximizing the instantaneous lateral distance while minimizing the lateral speed at the end of the trajectory. \[J_{D3}=\int_{0}^{t_{f}}\left[x_{1}^{2}\left(t\right)+\left(W-x_{1}\left(t \right)\right)^{2}\right]dt+v_{1}\left(t_{f}\right)\] (6)
4. **Instantaneous lateral distance maximization**. In this case, we skip the minimization of the lateral speed at the end of the trajectory, but we maximize the instantaneous lateral distance (during the maneuver). \[J_{D4}=\int_{0}^{t_{f}}\left[x_{1}^{2}\left(t\right)+\left(W-x_{1}\left(t \right)\right)^{2}\right]dt\] (7)For the optimization of the aforementioned functional, we will rely on the _Gradient Projection Algorithm_, a class of _Gradient Descent_ procedure that includes functionals that can only be optimized inside a region determined by the constraints of the problem [11]. This method requires to discretize the trajectory into \(N\) steps. Apart from the evaluation of trajectories, in the next subsections we will explore how the discretization factor \(N\) influences on the determination of trajectories and the resolution we need according to the specific functional in order to reach a reasonable trade-off between curve error and computational load.
# Changes in Quality Features of Pork Burgers Prepared with Chokeberry Pomace During Storage
## 3 Results and Discussion
### Microbiological Quality of Pork Burgers During Storage  
The black chokeberry pomace used in the burgers exhibited good microbiological quality. In the pomace samples, only aerobic mesophilic microorganisms and bacteria from the _Enterobacteriaceae_ family were detected, both at a level of \(1\times 10^{2}\) cfu/g. No lactic acid bacteria, yeasts, or molds were found in the pomace samples. The microbial quality results for pork burgers are presented in Table 5.  
Aerobic mesophilic microorganisms were detected in all burger treatments, with counts ranging from \(1.1\times 10^{1}\) cfu/g in the B3.5 product on day 1 to \(4.2\times 10^{3}\) cfu/g in the same product on day 14 (Table 5). \begin{table}
\begin{tabular}{c c c c c} \hline
**Storage Time [Days]** & **BC*** & **B2*** & **B3.5*** & **BS*** \\ \hline
1 & 20.83 \({}^{\text{a}}\)\(\pm\) 3.40 & 21.01 \({}^{\text{ab}}\)\(\pm\) 1.80 & 21.03 \({}^{\text{ab}}\)\(\pm\) 3.77 & 24.43 \({}^{\text{abcde}}\)\(\pm\) 2.52 \\
7 & 21.55 \({}^{\text{abc}}\)\(\pm\) 21.55 & 21.67 \({}^{\text{abe}}\)\(\pm\) 1.64 & 23.28 \({}^{\text{abd}}\)\(\pm\) 2.04 & 25.88 \({}^{\text{de}}\)\(\pm\) 3.41 \\
14 & 24.17 \({}^{\text{abcd}}\)\(\pm\) 2.04 & 24.64 \({}^{\text{bcdc}}\)\(\pm\) 2.48 & 24.82 \({}^{\text{cde}}\)\(\pm\) 2.46 & 27.99 \({}^{\text{e}}\)\(\pm\) 2.88 \\ \hline \(p\)-value & Treatment: \(p=0.0000\) & Storage time: \(p=0.0000\) & Treatment \(\times\) Storage time: \\  & & & & \(p=0.9568\) \\ \hline \end{tabular}  
* Treatments: BC—pork burgers without chokeberry pomace; B2—pork burgers with 2.0% chokeberry pomace; B3.5 pork burgers with 3.5% chokeberry pomace; B5—pork burgers with 5.0% chokeberry pomace; a——mean values marked with different letters are significantly different at \(p<0.05\).  
\end{table} 
Table 4: Changes in shear force value [N] (\(n=36\)) in pork burgers with added black chokeberry pomace during storage (mean value \(\pm\) standard deviation). Only storage time significantly affected (\(p\) < 0.05) the total number of mesophilic microorganisms in pork burgers; product treatment had no significant effect (\(p\) > 0.05), nor was there an interaction between treatment and storage time (\(p\) > 0.05). These findings suggest that aerobic mesophilic microorganisms introduced into the B2-B5 products via chokeberry pomace did not significantly (\(p\) > 0.05) increase burger contamination compared to the control product (BC). Conversely, the addition of chokeberry pomace did not significantly inhibit the growth of aerobic mesophilic microorganisms. The microbiological quality of the burgers was likely influenced by heat treatment, which reduced the initial microbial load in the meat product.
## 6 Results and Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vehicle Speed** & **Good-Put Time** & **Data Packet** & **Latency (ms)** & **Avg. Throughput** \\
**(km/h)** & **(s)** & **Size (Bytes)** & **Latency (ms)** & **(Mbps)** \\ \hline
30 & 42.437 & 1202 & 0.12 & 1.519 \\
40 & 40.056 & 1202 & 0.20 & 1.361 \\
50 & 32.561 & 1202 & 0.31 & 1.225 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test measurement results for Vehicle-to-Vehicle communication (V2V).
## 4 Results
### Vegetation Indices
Monthly RVI results over the case study area are shown in Figure 6-left, while Figure 6-right shows the results of the NDVI (whereas a-l in both figures refers to months starting from May 2019). Higher RVI values that might correspond to vegetated areas are highlighted with a red colour in Figure 6-left, while the dark-green colour in Figure 6-right shows vegetation at the optical products. Examining Figure 6, it is evident that optical Sentinel-2 images (NDVI) can depict the phenological changes of the vegetation very well over the area of interest throughout the year. Indeed, during the summer period (June-August 2019), the NDVI is decreased compared to other periods. In contrast, the interpretation of the RVI is still problematic (Figure 6-left). However, some increase in vegetation (red colour) is evident during the months Dec. 2019 to Feb. 2020 (Figure 6-left, h-j). This increase of vegetation is also visible to optical products as well (Figure 6-right, h-j), indicating that a pattern over vegetated areas can be extracted from both Sentinel-1 and -2 sensors. Figure 5: Scatterplot of NDVI and RVI values over 1000 random points in the case study area.
# Smart Meter Gateways: Options for a BSI-Compliant Integration of Energy Management Systems
## 6 Assessment of EMS Integration Options  
The exact requirements for executing energy management are case specific, although many energy management use cases require rather frequent access to measurements. Take for example a local battery storage system that has been installed to buffer surplus photovoltaic energy. In this case, a period of one or a few minutes may be acceptable, although more frequent measurements are desirable to improve the matching of the battery's charging/discharging power with the photovoltaic generation and the local electricity consumption. On the other hand, an EMS controlling heating, ventilation and air conditioning may operate on timely more coarse-grained meter measurements as the respective decisions are more static due to a lower reaction speed and minimum runtimes requirements of related devices. Additionally, a local EMS may or may not interact with a remote EMS (such as a regional EMS providing DSM incentives that are, e.g., reflecting grid conditions).
# Sulfuric Acid Resistance of CNT-Cementitious Composites
###### Abstract  
This study analyzed changes in the durability characteristics of cement mortar incorporating carbon nanotube (CNT) and the electrical properties subjected to deterioration induced by sulfate attack. Powder types of multi-walled or single-walled CNTs were used and added to the composites with 1.0% and 2.0% mass fraction, and the specimens were immersed in 5% and 10% sulfuric acid solutions to investigate the durability of CNTentitious composites. Although mechanical performance decreased due to relatively large pores (370-80 um) caused by CNTs, specimens incorporating CNTs exhibited enhanced resistance to sulfuric acid as CNTs, which offered strong resistance to acid corrosion, and prevented contact between the cement hydrate and the sulfuric acid solution. Therefore, it is expected that self-sensing performance was exhibited because there were no significant differences in the electrical properties of cement mortar subjected to the deterioration by sulfate attack.  
cementitious composites; carbon nanotube (CNT); multi-walled CNT; single walled CNT; self-sensing; durability; sulfate attack Article
## 4 Discussion
The discrepancy was more prominent in our study than in these previous studies (between 0.41 \(\pm\) 0.24 and 0.48 \(\pm\) 0.23 mm at T0 and between 0.31 \(\pm\) 0.21 and 0.35 \(\pm\) 0.19 mm at T1). However, the following should be considered: (1) In other studies, digital impression obtained using an industry-grade scanning device was used as a reference [13,22]. Again, an issue regarding the accuracy of the impression concerning the actual tissue situation may be posed. Thus, the use of the term \"trueness\" here may leave room for further speculation. Moreover, this term in this study was utilized only to manifest the difference between the two modalities, not to attest to the superiority or inferiority of the modalities. (2) Bleeding from the extraction socket (at T1) may reflect light from the scanning device and obscure the boundaries of the extraction socket. These cause inaccuracy in some areas [11]. Despite the issues mentioned, the values measured in our study are in line with those reported in a systematic review [23], demonstrating 3.79 \(\pm\) 0.23 mm of horizontal reduction in the ridge width. However, the study also showed that ridge resorption could continue for over three months after tooth extraction. Thus, some changes in the values from this study may be expected if more extended observation periods are established. We investigate several factors that could influence the tissue change over time and discrepancies according to the time points. Among the factors (except the impression modalities and time points), the jaw (maxilla or mandible) significantly affected the tissue changes and discrepancies, which is in line with previous findings [1]. Such an effect might be related to bone morphology and tissue thickness. Areas with a flat ridge morphology, such as the maxillary palatal area and mandibular buccal shelf, have low pressure, and where the ridge morphology is sharp, the pressure is high. The thicker the tissue, the lesser influence it has on bone morphology, and the lesser the discrepancy in the maxilla with thick tissue [24, 25, 26]. Our study has some limitations. First, the sample size was small. Second, the effects of intraoral scanning devices were not investigated. Third, the baseline was set immediately after tooth extraction. Inconsistencies in some tissue parts at this stage might have affected the study results. Based on the results of this preliminary study, further research is needed in a larger sample.
## 2 Materials and Methods
### Implementing Digital Signal Processing Techniques for CSI Data Analysis
#### 2.5.3 Linear Interpolation  
Given the variable and sometimes unstable sampling rate of CSI data from the ESP32, typically around 70 Hz, we employ Linear Interpolation to standardize the sequence dimensions. This technique allows us to resample CSI data at a consistent rate, set at 60 Hz for this study. An example of this interpolation is presented in Figure 8, demonstrating how data initially logged at an unstable rate of 70-80 Hz is normalized to a stable 60 Hz.  
To generate CSI data at a 60 Hz rate, we employ a straightforward mathematical weighting equation to interpolate between two closest timestamped data points from the original dataset. This approach, outlined in Equation (11), allows us to adjust the frequency of the entire CSI dataset to a consistent 60 Hz.  
Figure 7: Example of applying Gaussian filtering when \(\gamma=5\). The Gaussian filtering enhances the smoothness of the CSI data.  
\[CSI_{now}=CSI_{before} \tag{11}\] \[+\left(\frac{ts_{now}-ts_{before}}{ts_{after}-ts_{before}}\times(CSI_{ after}-CSI_{before})\right),\]  
where \(ts_{now}\), \(ts_{before}\), \(ts_{after}\) are the interested timestamp, timestamp of the closest CSI occured before the interested timestamp and timestamp of the closest CSI occurred after the interested timestamp respectively. Furthermore, \(CSI_{now}\), \(CSI_{before}\), \(CSI_{after}\) are CSI logged at \(ts_{now}\), CSI logged at \(ts_{before}\) and CSI logged at \(ts_{after}\) respectively.
# GNSS Timing Performance Assessment and Results Analysis
## 2 The Key Indicators and Assessment Method of GNSS Timing Performance
### Assessment Method of GNSS Time Transfer Accuracy
#### 2.2.3 Data Processing Strategy of GNSS Time Transfer Accuracy Assessment  
The real-time data of the time offset UTC(NTSC)-UTC\({}_{\text{GNSS}}\) are seriously contaminated by various sources error or noise including the satellite clock correction error in the space segment, ionosphere delay correction error and tropospheric delay correction error in the transmission segment, and receiver measurement noises [28]. Therefore, it is necessary to correct errors in the pseudo-range measurement. Then, data smoothing processing is applied in order to obtain accuracy offset UTC(NTSC)-UTC\({}_{\text{GNSS}}\) with 1 day time interval. Furthermore, the time transfer error sequence UTC\({}_{\text{GNSS}}\)-UTC\({}_{\text{true}}\) is calculated according to Equations (2)-(5). Finally, we calculate the statistics of offset UTC\({}_{\text{GNSS}}\)-UTC\({}_{\text{true}}\), including the 95 th percentile level (95%), the root-mean-square error (RMS), average (AVG), standard deviation (STD), and maximum (MAX). The calculation formula of RMS is as follows:  
\[\text{RMS}=\sqrt{\frac{\sum\limits_{i=1}^{\text{N}}{{{(\Delta\text{UTC})}^{2}} }}{\text{N}}}, \tag{6}\]  
\[\text{STD}=\sqrt{\frac{\sum\limits_{i=1}^{\text{N}}{{{(\Delta\text{UTC}- \overline{\Delta\text{UTC})}^{2}}}}}{\text{N}}}, \tag{7}\]  
where N is the number of samples; i = 1, 2., N, \(\Delta\text{UTC}\) is time transfer error sequence, and \(\overline{\Delta\text{UTC}}\) is the mean of the time transfer error sequence.