esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 1: Collect System Information
#### 3.1.2 Step 1.2: Collect Solar, Critical Load, and Current Microgrid Data  
Next, site-specific information is collected which will help a base energy manager to understand the feasibility of implementing nanogrids on specific critical loads. Data collected include load profile data, solar irradiance data, existing energy generation and storage systems including support infrastructure (e.g., fuel storage tanks, etc.), existing microgrid power infrastructure (e.g., transmission lines, switches, protection equipment, transformers, feeder lines, PCC, etc.), and other related information.  
Data such as site-specific historical solar irradiance information is used in later steps to better size PV generation assets for nanogrids. Load profile data is used to understand the average and peak loads, and how much energy is needed over the course of a potential outage scenario. We recommend that load data be collected over at least the course of one year and indeed these data are often available for many years from existing SCADA systems.
## 2 Background Study
### Slant Path in Rain Cell  
The path length that remains in the rain cell structure suffers rain attenuation. There may be some other meteorological effects on the extended portion of the slant link, but there exists no rain attenuation effect. As a result, the slant path length that falls within the rain cell must be determined. However, since the length exists in the space, it is impossible to determine precisely, rather than considering the estimated length. The rain height estimation helps to estimate the slant path length in the precipitation.
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 3 Factors Responsible for the Spontaneous Heating of Haystacks  
### Bale Size  
The possibility of spontaneous combustion increases with the size of the body material in thermal contact [9]. For larger bales, it will be difficult for the material to dissipate heat generated by the self-heating process. The heat dissipation capacity of hay is linked with surface-core distance, which is applicable to both individual bales and haystacks. Higher density, large sized bales are at greater risk of high core temperatures compared with the small rectangular bales [16] and require reduced threshold moisture concentrations for baling [17] and storage [14]. Large round bales can also exhibit extensive spontaneous heating, such as heating degree days (HDD) > 300 \({}^{\circ}\)C or maximum internal bale temperature, compared to small rectangular hay bales [12]. [PERSON] et al. [18] reported that large round bales of _Dactylis glomerta_ (1.2 \(\times\) 1.5 m\({}^{2}\)) were prone to significant mould attack followed by heating and quality losses, even at lower moisture concentrations. This does not imply that small rectangular bales are not at risk of self-heating, which primarily depends on the heat generated after storage at the centre of the stack. Generally, self-heating occurs when hay bales are stored at >20% moisture content with 5-10% loss in dry matter [19]. Generally, the rule of thumb for maintaining acceptable moisture contents of haystacks in storage is 20% or less. Small rectangular sized bales can be stored safely baled at 18-20% moisture content. But the same hay as large round or rectangular bales should not contain higher than 16-18% moisture.
## 4 Experimental Validations
### Comparisons of Real-Time Multi-GNSS PPP with and without Quality Control
For the GRC and GRE combination, the PDOP value is about 0.70, which is slightly larger than the quad-system constellation since the latter has more visible satellites. Therefore, the quality control can remove all possible outliers one by one, benefiting from the best geometry. Table 3 shows that with the quality control, the GR, GRC, and GRE combinations achieve an accuracy of about 1 cm in the east and north components, while it is about 4 cm in the upward component, which is only about 2 mm worse than the GRCE combined PPP. Their convergence time is less than 10 min in horizontal and vertical components. The number of visible satellites for the GCE combination is almost equal to that of the GR combination. The average satellite numbers are 15 and 16, respectively, while the corresponding PDOP values are 0.81 and 0.76. Therefore, the GR combination can achieve comparable positioning performance with the triple-system combinations. However, the number of visible satellites is only about 12 and the PDOP value is about 0.90 for both GC and GE combination, which leads to relatively worse positioning accuracy and much longer convergence time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multicolumn{3}{c}{**No Control**} & \multicolumn{3}{c}{**Quality Control**} \\ \cline{2-7}  & **E (cm)** & **N (cm)** & **U (cm)** & **E (cm)** & **N (cm)** & **U (cm)** \\ \hline
**GRCE** & 4.66 & 4.87 & 10.17 & 0.79 & 0.86 & 4.24 \\
**GRC** & 4.86 & 5.23 & 11.35 & 0.99 & 0.88 & 4.44 \\
**GRE** & 4.33 & 1.45 & 7.55 & 0.90 & 0.85 & 4.43 \\
**GCE** & 5.09 & 7.22 & 11.50 & 1.14 & 1.77 & 4.96 \\
**GR** & 4.37 & 1.51 & 7.37 & 0.98 & 0.82 & 4.35 \\
**GE** & 4.71 & 1.61 & 7.94 & 1.32 & 1.79 & 4.72 \\
**GC** & 6.68 & 8.78 & 12.80 & 1.81 & 1.82 & 5.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The RMS statistic of the positioning error in east, north, and upward components after convergence. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multicolumn{3}{c}{**No Control**} & \multicolumn{3}{c}{**Quality Control**} \\ \cline{2-5}  & **H (min)** & **V (min)** & **H (min)** & **V (min)** \\ \hline
**GRCE** & 5.5 & 45.5 & 8.0 & 6.5 \\
**GRC** & 31.0 & 49.0 & 9.0 & 9.5 \\
**GRE** & 22.5 & 4.5 & 8.5 & 4.5 \\
**GCE** & 48.5 & 49.0 & 13.0 & 31.5 \\
**GR** & 31.5 & 49.0 & 9.0 & 6.5 \\
**GE** & 45.5 & 49.5 & 14.5 & 31.5 \\
**GC** & 64.0 & 80.5 & 29.5 & 40.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The convergence time statistic in horizontal (H) and vertical (V) components.
### TRPA1 Channel as the Target of Pungent Principles in Plants  
The TRPA1 channel is a protein in the family of transient receptor potential ion channels. Previously called ANKTM1 (ankyrin repeat-containing ion channel 1), TRPA1 is the only member of the TRPA sub-family reported in mammals [16]. TRPA1 is a polymodal sensor involved in the somatosensory perception of several stimuli, among which are cold, pungent compounds, airborne irritants and cannabinoids, as well as finding use in audition, proprioception, neurogenic inflammatory responses and pain perception [17; 18]. TRPA1 is expressed in the dorsal root ganglion (DRG), vagal ganglion (VG), nodose and trigeminal ganglion (TG) neurons [19]. It has been reported that TRPA1 is also expressed in non-neural cells that are also relevant for chemosensation such as skin keratinocytes [20; 21] and airway epithelial cells [22].  
TRPA1 is activated by a large number of chemicals found in many plants, food, cosmetics, and pollutants. Electrophilic, pungent compounds such as unsaturated aldehydes, ketones, isothianates, and thiosulfinates can be found in many plants, and particularly in Brassicaceae. These plants (cabbage, broccoli, cauliflower, mustard, horseradish, radish, wasabi, and many more) are important crops for human and animal diets; many of them, including SO, are also commonly used as traditional remedies. Alylisothiocyanate (mustard oil) and derivatives such as benzyl-, phenylethyl-, isopropyl-, and methyl-isothiocyanates are the main pungent ingredients inducing TRPA1 activation [23].
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 3 The Proposed VQoSRR Framework
### Network Model Architecture
#### 3.1.2 Control Plane  
* **The Topology Manager** keeps track of the network topology graph, requesting and receiving information from the data plane about the connected forwarding devices, new attached elements, or failed links.
* **The Statistics Collector** collects information from OpenFlow switches and periodically polls it so the controller can get an idea of the network's state, such as the availability of resources and whether the network is congested or not.  
Figure 1: The VQoSRR SDN architecture.  
* **The QoS Routing Manager** module is responsible for QoS-based route calculation; it applies the routing algorithms to obtain a path for the flows based on its requirements. Further, it is responsible for flow admission control, determining whether the specific route is available to maintain the QoS guarantee of ongoing traffic, and informing the controller of this information. For storing calculated paths, this module uses a route cache structure. Further, the module keeps track of which resources can be admitted and which cannot by storing certain flags.
* **The QoS Resource Manager's** primary role is to reserve the resources for video flow, classify the traffic, and manage flow classes and queues. In addition, the module task is to set up and install flow rules for new incoming flows or update existing ones in forwarding devices.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 5 Singular Pulses
### The Step Function  
The step function response of a dispersive medium is related to the complex error function. Let the initial pulse's profile be a step function, i.e.,  
\[A(z=0,t)=A_{0}u(-t) \tag{19}\]  
where \(u(t)=\begin{cases}1&t>0\\ 0&t\leq 0\end{cases}\) is the Heaviside step-function, then the pulse at the end of the medium is  
\[A(t,z)=A_{0}\int\limits_{-\infty}^{0}\left(-2\pi i\beta_{2}z\right)^{-1/2} \exp\Biggl{(}-i\frac{(t-t^{\prime})^{2}}{2\beta_{2}z}\Biggr{)}dt^{\prime}= \frac{1}{2}A_{0}erfc\Biggl{(}\frac{t}{\sqrt{-2i\beta_{2}z}}\Biggr{)} \tag{20}\]  
Clearly, if initially  
\[A(z=0,t)=A_{0}u(-t+T) \tag{21}\]  
then  
\[A(t,z)=\frac{1}{2}A_{0}erfc\Biggl{(}\frac{t-T}{\sqrt{-2i\beta_{2}z}}\Biggr{)} \tag{22}\]  
Similarly, using Equation (6), the initial boosted step function pulse  
\[A(t,z=0)=A_{0}\exp(i\omega_{0}t)u(-t+T) \tag{23}\]  
will propagate to read  
\[A(t,z>0)=\frac{1}{2}A_{0}\exp\biggl{(}i\frac{\beta_{2}}{2}\omega_{0}^{2}z+i \omega_{0}t\biggr{)}erfc\Biggl{(}\frac{t-T+\beta_{2}\omega_{0}z}{\sqrt{-2i \beta_{2}z}}\Biggr{)} \tag{24}\]  
This result is known in the literature as Moshinsky's function. In quantum mechanics, this function was used to simulate an electrons' beam, which was blocked by a beam shutter [19; 20]. In the quantum analogy, the wavefunction exhibits interference in time.
## 2 Materials and Methods
### Stage 2: Inputs and Outputs Determination
#### 2.2.1 Order-Picking: Batching Strategy  
In order to determine the number of items picked in a day and the associated costs, we need to find the average picking rate for each joint routing-batching strategy. Assuming a picker speed of respectively 45, 65, and 80 m per minute, the picking rate is the average number of items picked by a picker in an hour. We use the mixed integer linear programming (MILP) model developed by Ref. [24] to first simulate the average picking distance traveled by a picker per batch given the picking cart capacity, and then use Equation (1) to get the picking rate. In Equation (1), \(\hat{p}^{\text{unij}}\) designates the average picking rate at warehouse \(w\) when routing policy \(i\) and technology \(j\) are used. \(S^{w}\), \(K^{w}\), and \(D^{\text{unij}}\) are respectively the picker's speed, the picking cart capacity, and the average distance traveled per batch. \(t_{net}^{\text{unij}}\) represents the time of getting information about the product, searching, picking, and confirming the product picked. Basically, to determine the picking rate, we randomly generated each order and its items following a uniform distribution. Orders and items are respectively assumed to be between 1 and 14, and between 5 and 19. Each cart picked constitutes a batch. The distance travelled is approximately proportional to the number of batches and \(t_{net}^{\text{unij}}\), allowing the picking rate to be applicable to any distance used in Equation (1):  
\[\hat{p}^{\text{unij}}=\frac{60\cdot K^{w}}{\frac{D^{\text{unij}}}{\mathbb{S}^ {w}}+t_{net}^{\text{unij}}}. \tag{1}\]  
The picking rate was determined for each policy set presented in Table 1. As Equation (1) shows, the higher the capacity of the picking cart, the higher is the picking rate. Likewise, the longer the travel distance, the lower the picking rate.  
To minimize the relative error corresponding to the travel distance estimation and thus to the picking rate, we used the following equation:  
\[n(\gamma)=min\left\{i\geq n_{0}:\frac{t_{i-1,1-\frac{\beta}{2}}\sqrt{\frac{S^{ 2}(n_{0})}{i}}}{\left|\mathbb{X}(n_{0})\right|}\leq\gamma^{\prime}\right\}, \tag{2}\]  
where \(\gamma=\frac{\left|\mathbb{X}-\mu\right|}{\left|\mu\right|}\) and \(\gamma^{\prime}=\frac{\gamma}{1-\gamma}\).  
Equation (2) yields the number of replications to make of each simulated travel distance to make sure that the relative error \(\gamma\) does not exceed the confidence level \(\alpha\), since \(\mu\), the travel distance, is unknown. \(\mathbb{X}\) represents the estimated travel distance. \(S^{2}(n_{0})\) is the variance of the first \(n_{0}\) replications. \(i\) replications are made until \(\frac{t_{i-1,1-\frac{\beta}{2}}\sqrt{\frac{S^{2}(n_{0})}{i}}}{\left|\mathbb{X }(n_{0})\right|}\) is less or equal to \(\gamma^{\prime}\). We chose \(\gamma=0.1\) and \(\alpha=0.05\). We generated \(n_{0}=50\) replications.
## 7 Simulation Results with ITU-R P.452-17
The conclusion regarding the frequency-dependent behavior of the model is further substantiated by the discrepancies evident in Table 8.  
Figure 26: Distribution of field strength values, 3.6 GHz, ITU-R P.452-17. Figure 25: Differences between the 6 GHz and 3.6 GHz simulations (ITU-R P.452-17). To illustrate the behavior along a specific path, Figure 28 depicts the field strength profile from TestBS_2 to test point T36. This figure, previously unmentioned, offers valuable insight into how signal attenuation evolves with distance and terrain obstructions. A local advantage for 6 GHz around 20 km confirms the impact of smaller Fresnel zones in nearly line-of-sight scenarios.
## 4 Methods
### Discrete Wavelet Transform (DWT)  
Wavelet transform actually decomposes the data of the time series in both time and frequency domain, even though the data is non-stationary. Wavelet transform has achieved numerous successful applications in engineering fields such as signal processing and image processing. The basic idea of a classic wavelet denoising model is shown below:  
\[s\left(n\right)=f\left(n\right)+\varepsilon\left(t\right), \tag{4}\]  
where \(s\left(n\right)\) is known to be the observed signal (noisy signal), \(f\left(n\right)\) is the real signal and \(\varepsilon\left(t\right)\) remains the Gaussian white noise. The essence of denoising the wavelet is to filter out the \(\varepsilon\left(t\right)\) as much as possible.  
The theoretical basis of the wavelet threshold denoising method based on [PERSON]'s theory assumes that the low-frequency approximation part and high frequency information portion of a signal can be fully reconstructed [30]. Suppose an original sensor reading denoted by \(s(n)\), is given by:  
\[s\left(n\right)=\sum_{k\in z}c_{jk}\varphi_{j,k}\left(n\right)+\sum_{i=1}^{j} \sum_{k=z}d_{i,k}\Psi_{i,k}\left(n\right), \tag{5}\]  
where \(z\) is an integer, \(c_{jk}\) is the approximate coefficient, \(\varphi_{j,k}\) is the scaling function, while \(j\) is the decomposition level, \(\Psi_{i,k}\left(n\right)\) is the wavelet basis function and \(d_{i,k}\) is the detailed coefficient. \(c_{j,k}\) contains information on the low frequency of the original discrete signal \(s(n)\), which is stated as follows  
\[c_{j,k}=\left\langle s\left(n\right),\varphi_{j,k}\left(n\right)\right\rangle, \tag{6}\]  
where \(\left\langle s\left(n\right),\varphi_{j,k}\left(n\right)\right\rangle\) denotes the orthogonal relationship between \(s\left(n\right)\) and \(\varphi_{j,k}\left(n\right)\). The notation \(d_{i,k}\) has the original discrete signal's high frequency information, which is defined as follows:  
\[d_{i,k}=\left\langle s\left(n\right),\Psi_{j,k}\left(n\right),\right\rangle \tag{7}\]  
where \(s\left(n\right)\) and \(\Psi_{j,k}\) are orthogonal to each other.  
The wavelet threshold denoising approach uses its main profile to be the low-frequency part of the signal, while the high-frequency part represents its details. The details of each level has its own noise information after decomposition of the wavelet. Threshold function tune the description coefficients of each level \(d_{i,k}\) and is computed with the approximate coefficients of the last level. The denoising wavelet threshold process is shown in Figure 5.  
In Figure 5, \(s\left(n\right)\) is the original nosy signal, while \(m_{j,k}\) is the wavelet coefficient as a result of wavelet decomposition of the \(s\left(n\right)\), \(m_{j,k}\) is obtained from the combination of the approximate coefficient \(c_{j,k}\) and the detailed coefficient \(d_{i,k}\), \(\varphi_{j,k}\) remains the estimated wavelet coefficient after the denoising threshold, and \(\hat{f}\left(n\right)\) is the estimated \(s\left(n\right)\) derived from the reconstruction of \(v_{j,k}\).
## 3 Proposed Architecture
(a) Upper tank monitoring unit:  
Upper tank monitoring is integrated for sensing the water level and water quality in the upper tank and it is shown in Figure 2. To sense the water level and water quality, it consists of the following component namely: controller unit, pH sensor, water level sensor, flow sensor, 2.4 GHz RF module, Wi-Fi module, and power supply unit. The Controller unit is for processing the sensor data and activate other components integrated into it. The water level sensor is the primary sensor for monitoring the status of the tank in terms of water level. As discussed earlier, if the amount of water in the upper tank is below the threshold level, then it sends the instructions to switch on the water pump of the lower tank. Here 2.4 GHz RF module enables communication with the lower tank related to the trigger of the water pump. pH sensor checks the pH level in the water tank and the flow sensor is for sensing the inlet and outlet of the water. ESP 8266 Wi-Fi module enables the upper tank monitoring unit to communicate the information related to the upper and lower tank to the cloud server. (b) Lower tank monitoring unit:  
Lower tank monitoring is integrated for sensing the water level and water quality in the upper tank and it is shown in Figure 3. It consists of the following components, namely: controller unit, water level sensor, 2.4 GHz RF module, relay, water pump, and power supply unit. The controller unit is for processing the sensor data and activate other components integrated into it. The 2.4 GHz RF module enables to receive the information from the upper tank monitoring unit namely to switch on the water pump for filling the upper tank. The relay is connected to this unit for controlling the water pump. Figure 1: Proposed IoT architecture for water tank monitoring and water quality monitoring.
## 3 Results and Discussion
### Evaluation of Volatile Compounds during the Processing of Smoked Dry-Cured Ham
Their amount was higher in BF muscle. Four ketones (3,5-dimethyl-2(5H)-furanone, 3,4-Dimethyl-2-cyclopent-1-one, 3-Ethyl-2-hydroxy-2-cyclopent-1-one, and 1-cyclohexyl-1-propanone) were found only after the ripening phase. Esters decreased significantly (\(p<0.05\)) from the raw piece (4.88 and 2.35% in BF and SM, respectively) to the drying phase and remained constant after the ripening phase (0.99 and 0.47% in BF and SM, respectively). BF showed higher amounts of esters compared to SM throughout the manufacturing process, which is consistent with other studies [8]. Esters greatly enhance the dry-cured ham flavor due to their low odor thresholds. Fruity notes have esters formed from short-chain acids, while esters with long-chain acids have a slightly fatty odor [51]. Ester, isohexyl hexanoate was found only after the ripening phase in both muscles. Aromatic hydrocarbons have a major impact on the overall aroma due to their lower thresholds. They can originate from lipid oxidation, microbial activity, compounds of plant origin present in the diet, or from the smoking phase of production [8]. Eight (8) aromatic hydrocarbons were detected during the processing of smoked dry-cured ham. Only three aromatic hydrocarbons were detected in the raw ham (benzene, methoxy-phenil-oxime, and 1,3,5-trimethylbenzene). After the drying and smoking phases, there was a statistically significant difference in the content for BF and SM. Following a similar pattern to phenols, aromatic hydrocarbons were more abundant in SM than in BF, which may be attributed to the anatomical location of the muscle. Aliphatic hydrocarbons, due to their high odor threshold, are not considered important for the aroma of dry-cured ham. Only four aliphatic hydrocarbons were identified: 3,3,4-trimethylheptane, hexane, 1-tridecene, and 3-ethyl-2-methyl-1,3-hexadiene and were present in lower amounts throughout the production process. This is in agreement with another study on smoked dry-cured ham [51]. Terpenes originate from the addition of spices, but also from animal feed that also may be a source of the terpene limonene [51]. However, smoked dry-cured ham is produced without spices and only three terpenes (myrcene, limonene, and linalool) were found in small amounts after the ripening phase (0.71 and 1.00% in BF and SM, respectively).