esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Principal Component and Interaction Network Analysis of Different Indexes
A further analysis of the interaction of the soil chemical indexes, key aroma compounds and odor characteristics showed (Figure 4) that the soil pH and CEC were positively correlated with geraniol, \(\alpha\)-ionone, \(\beta\)-ionone, linalool, methyl salicate, floral, wood characteristics and burnt characteristics; the soil OM was positively correlated with geraniol, geranylacetone and floral characteristics; there was a significant positive correlation between the soil TN and 1-Octen-3-ol; the AN content was significantly and positively correlated with geraniol, linalool and floral characteristics; the AP content was positively correlated with geraniol, geranylacetone, linalool and floral characteristics; and there was a significant positive correlation between the soil AK content and geraniol, \(\alpha\)-ionone, linalool and floral characteristics; however, there was no significant correlation between the soil TP and TK contents, the key compounds and the odor characteristics. A further analysis found that the soil chemical indexes except for TP and TK were positively correlated with geraniol and floral characteristics. It was reported that when the soil pH was 4.5-5.5, it was suitable for planting tea trees, and when the soil pH was 5.0-5.5, it was the most suitable for tea tree planting [1; 2]. In this study, it was found that P3 and P4 were beneficial to the formation of six odor characteristics of tea leaves, where P3 was dominated by fruity, green and fatty characteristics and their main contributing compounds were \(\beta\)-cyclodextrin, D-limonene and 1-octen-3-ol, while P4 was dominated by floral, woody and burnt characteristics, with the main contributing compounds being geranylacetone, geraniol, linalool, \(\alpha\)-ionone, methyl salicate and \(\beta\)-ionone. Secondly, it was found that except for TP, the soil chemical indexes had the greatest influence on the odor characteristics and key compounds of the P4 samples. It was evident that with the increase in the soil pH, the soil chemical indexes changed significantly, which was conducive to the synthesis of key compounds in the tea leaves with odor characteristics such as floral, woody and burnt. It can be seen that after the pH of the tea rhizosphere soil changed, the chemical indexes (CEC, OM, AN, AP and AK) in the soil changed immediately, and the chemical indexes affected the synthesis of compounds and the formation of odor characteristics in the tea leaves. This effect is manifested by the fact that an increase in soil pH favors the synthesis of compounds with floral characteristics, especially geraniol, in tea leaves, which is conducive to the enhancement of the floral characteristics of tea.
## 3 Problem Formulation
### Objective Function  
The VM requests (\(R\)_eq\(Q\)_) are accepted at the beginning of each reservation cycle. The VM request in \(R\)_eq\(Q\)_ with (\(F_{i}\), \(C_{i}\), \(\tau_{i}\)) remains constant. \(\tau_{i}\) is the total number of reservation cycles reserved by the VM for the resource (\(F_{i}\times C_{i}\)). \(Utl_{j}(t)\) is the current utilization of server \(j\). The power consumption of _jth_ physical machine with utilization \(l\) at time \(t\) is represented as \(P_{j,l}(t)\) and derived as [42]  
\[P_{j,l}(t)=\frac{(Utl_{j}(t))-(U_{j,l})}{(U_{j,l+1})-(U_{j,l})}\times\left( \left(P_{j,l+1}\right)-\left(P_{j,l}\right)\right)+\left(P_{j,l}\right) \tag{4}\]  
where, \(U_{j,l}<Utl_{j}(t)<U_{j,l+1}\), \(0\leq l<k+1\).  
The energy consumption of _jth_ PM within a reservation cycle (_rc_) can be calculated as  
\[\int_{0}^{rc}\sum_{l=0}^{k+1}P_{j,l}(t)dt \tag{5}\]  
The total energy consumption of all the PMs within a reservation interval [0, _rc_] can be calculated as  
\[\sum_{j=1}^{M}\int_{0}^{rc}\sum_{l=0}^{k+1}P_{j,l}(t)dt \tag{6}\]  
The energy consumption (_E_) of all the physical machines in the data center within an interval [0, _T_] can be partitioned as number of reservation cycles (_nrc_) segments and it is obtained by  
\[E(D)=\sum_{l=1}^{M}\sum_{rc=1}^{nrc}\sum_{l=0}^{k+1}\sum_{i=1}^{N}R_{j,i}(t_{ rc})\times P_{j,l}(t_{rc})\times(t_{rc}-t_{rc-1}) \tag{7}\]  
The resource allocation problem in datacenters is formulated as  
\[G(x)=\left\{\begin{array}{c}G_{energy}(x)=\min E(D)\\ s.t(9)-(13)\end{array}\right\} \tag{8}\]  
The objective function \(G(x)\) is subject to the following constraints:  
The total number of VM's allocated to a machine should not exceed the servers computing (_CPU_) and memory capacity (_mem_).  
\[\sum_{i=1}^{N}R_{j,i}^{cpu}\leq PM_{i}^{cpu.max} \tag{9}\]  
\[\sum_{i=1}^{N}R_{j,i}^{mem}\leq PM_{j}^{mem.max} \tag{10}\]  
The relation \(R\) between VM and PM is many-to one, i.e., \(R\subseteq\mathrm{N}\times\mathrm{M}\) if  
\[\forall\;ieN\&\forall\;j,keM:(i,j)\in R\;\wedge(i,k)\in R\Rightarrow j=k \tag{11}\]  
The total energy (_eng_) consumed by all the VM's should not exceed the available brown energy (_B_) at the datacenter  
\[\sum_{i=1}^{N}R_{j,i}^{eng}\leq Total\;Available\;B \tag{12}\]  
The total brown energy consumed should not exceed the cloud providers' agreed upon grid electricity consumption (_G_)  
\[Total\;available\;B\leq Total\;assigned\;G. \tag{13}\]
## 4 Implementation Details
### Timing Controller  
The timing controller coordinates the sequential operations of each block within the system. Illustrated in Figure 10a, the state machine of the timing controller comprises four distinct states: \(ST_{rst}\) (resetting the \(V_{c}\) node), \(ST_{charge}\) (charging the \(V_{c}\) node), \(ST_{quantize}\) (quantizing the voltage on the \(V_{c}\) node and comparing it to the threshold voltage), and \(ST_{discharge}\) (discharging the \(V_{c}\) node). Each state is associated with a finished signal, denoted as \(F_{n}\), where \(n\) represents one of the four states. Additionally, there is a \(Skip\) signal that determines whether the system is in the discharging phase. After the state machine enters the \(ST_{charge}\) state twice, the \(Skip\) signal is raised, restricting the state machine's transitions to alternate between the \(ST_{quantize}\) and \(ST_{discharge}\) states.  
Upon initialization of the time-based sensor, \(ST_{rst}\) is set high, generating a reset pulse, \(T_{rst}\), which resets the voltage on the \(V_{c}\) node, as demonstrated in Figure 10b. Subsequently, the falling edge of the pulse triggers a DFF, raising the finished signal \(F_{rst}\) to a high state. Once activated, this signal lowers \(ST_{rst}\) and initiates the \(ST_{charge}\) state. For clarity, only two states logic are shown in Figure 10b, yet the remaining states are implemented in a similar manner.
## 1 Introduction
In addition, GPS, which is widely used for vehicle positioning, measures a vehicle's current position through a satellite using a function for transmitting navigation signals; consequently, it cannot measure the vehicle's position in indoor and other GPS-shaded areas [3]. Therefore, INS-GPS cannot produce precise position measurements in areas where signal reception is difficult. Thus, while studies are continuously being conducted on sensor-measurement fusion technologies, many issues must still be addressed [10; 11; 12; 13; 14; 15]. To improve positioning in GPS-shaded areas, many researchers have begun focusing on the visible light communication (VLC) technique using light emitting diode (LED) lighting [10; 11; 12; 13; 14; 15]. VLC requires multiple LED networks, in which a unique ID is assigned to each LED transmitter, and positions are calculated via trilateration, whereby the distances from a receiver to the three closest transmitters are measured. Various measurement methods for LED-based VLC are available, including proximity [16], finger printing [17], received signal strength [18], angle of arrival [19], time of arrival, time difference of arrival, phase difference of arrival, and image-based positioning [20]. Although the proximity has a simple and inexpensive process, it yields low accuracy [21]. In contrast, the finger printing, received signal strength, arrival data (i.e., angle of arrival, time of arrival, time and phase difference of arrival), and image-based techniques are well-known for achieving high accuracy in LED-based indoor positioning. However, the equipment size and the complexity of hardware hinder their practical application [22; 23]. In our previous study, a fuzzy-logic-based positioning system was developed using the chromaticity and frequency components of LED light [24], and it could successfully detect the position of a mobile robot in the developed simulator. In this present study, the fuzzy positioning system was implemented for two autonomous mobile robot navigation systems, and the performance of the fuzzy positioning system in each navigation system were compared. The first navigation system comprises fuzzy-logic-based behavior rules that determine the motion of the mobile robot while it moves toward a target and avoids obstacles simultaneously. The second navigation system is based on the potential field method wherein the robot finds a path such that the attractive force from the target is maximized and the repulsive forces from obstacles are minimized [25; 26]. The mobile robot operating based on the two proposed navigation algorithms utilizes the chromaticity and frequency component ratio of LED light obtained via the optimal route to avoid and escape obstacles and reach targets without having any pre-installed localized information.
## 5 Simulation Results
### Performance Evaluation
For AlexNet, there was no observable performance variation between using 1 GB and 2 GB of memory until four requests were processed. This implies that 1 GB of memory is sufficient for up to four parallel requests. After five requests, it was confirmed that performance improved with the use of 2 GB of memory. This phenomenon can be attributed to the positive correlation between the expansion of available memory resources in the UAV swarm and the consequent increase in the number of requests processed in parallel. For YOLO and VGG16, it was confirmed that using 2 GB of memory performs better than 1 GB. The increased memory capacity allows a single UAV to process more CNN layers. This can reduce the number of intermediate data transmissions between UAVs and consequently lowers overall transmission latency. The end-to-end latency in a scenario with inoperable UAVs is shown in Figure 6. The simulation was conducted using AlexNet with 15 UAVs. Simulations were conducted with 15 UAVs, 1 GB of memory, and a computational capacity of 11.2 GFLOPS using the AlexNet model. The end-to-end latency for each model is illustrated in Figure 6 under three conditions: all UAVs fully operational, one inoperable UAV, and two inoperable UAVs. It was confirmed that the proposed model exhibits few increase in the end-to-end latency than the comparison of the three baseline methods. Therefore, the proposed model can maintain mission continuity by reallocating layers to the remaining UAVs using the available resources. In contrast, the comparison models lack these resilient mechanisms and are more adversely affected by inoperable UAVs. The proposed DRL-RLD model demonstrates superior performance compared to the baseline methods when UAVs becomes unexpectedly inoperable. Figure 4: The performance of the proposed model compared to three baselines: (**a**) AlexNet; (**b**) YOLO; (**c**) VGG16. The proposed DRL-RLD system outperformed existing methods by optimizing CNN layer allocation to reduce the end-to-end latency. As the number of UAVs in swarm increases, the converged reward approaches its maximum value. Additionally, the increase in the end-to-end latency can be diminished by dynamically reallocating layers when arbitrary UAVs become inoperative. Overall, the system operated efficiently across various UAV swarm configurations and resource constraints.
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 5 Improvement in Train Position Accuracy Based on Yaw Angular Velocity
### Position Correction Method Using Yaw Rate  
We applied the method proposed by [PERSON] et al. to estimate the curve start/end points [35]. [PERSON] et al. calculated and corrected the train position using vehicle speed measured using a tachometer generator, which has high accuracy on vehicle speed. However, the proposed method has not been evaluated using vehicle speed measured by a GNSS. In this study, the correction target was the train position information obtained by map matching using a GNSS.  
The curvature of the track can be estimated from the operating speed and yaw angular velocity measured using the onboard sensing device. The curve start/endpoints were estimated from the estimated curvatures and compared with the track information for track management to obtain and correct the error between the distance for track management and the distance obtained via map matching. Figure 11 shows the position correction method.  
Figure 11: Position correction method.  
Figure 10: Map-matching-based train position identification.  
The curvature \(\kappa\) was calculated from the yaw angular velocity \(\omega\) [rad/s] obtained from the onboard sensing device and GNSS velocity \(v_{GNSS}\) [m/s] using the following equation:  
\[\kappa=\frac{\omega}{v_{GNSS}}. \tag{1}\]  
Subsequently, a linear approximation was performed using the value of the central section of the transition-curve region of the calculated curvature. We set the point at which the extension of the obtained approximated line intersected with the line where the curvature \(\kappa\) was \(0\) [1/m] as the curve start/endpoint. The track data used when the railway operator managed the track were used as the position data for the curve to be compared.  
The magnification \(A\) to be corrected is calculated as  
\[A=\frac{x_{1}}{x_{2}}, \tag{2}\]  
where \(x_{1}\) [m] is the distance between the beginning and end of the curve obtained in the previous section, and \(x_{2}\) [m] is the position of the end of the curve in the track data.  
The distance information was dilated and corrected by multiplying the calculated magnification A by the distance obtained via map matching.
# Comparison of Selected Characteristics of SARS-CoV-2, SARS-CoV, and HCoV-NL63
### Morphology, Structure, and Replication of SARS-CoV-2
#### 1.3.3 Replication
It consists of three transmembrane domains. The M protein has an N-terminal ectodomain and a C-terminal end domain. It gives the virion an overall curved shape. The CoV envelope (E) protein is a small, transmembrane E protein and it is 8-12 kD in size and is thought to function in SARS-CoV-2 through involvement in several aspects of the virus' life cycle, such as assembly, budding, envelope formation, and pathogenesis. It contains an N-terminal ectodomain and a C-terminal endodomain. The E protein is thought to be a pentameric structure when the protein is contained in micelles. Here, the C-terminal end of the protein forms an extramembranous alpha-helix. The N protein is part of the nucleocapsid; it contains an N-terminal domain and the C-domain has the ability to bind to RNA. The N protein is phosphorylated, which increases its affinity for RNA; together, they form a complex structure [27]. The N protein aids in the re-creation of the viral envelope by the M protein. It also mediates the binding of RNA replicase and reverse transcriptase [29]. In cells infected with SARS-CoV-2, granular regions containing viral RNA and proteins have been observed. These areas have not been observed in other CoVs strains. They are thought to be viral translation centers. Translated structural proteins translocate into endoplasmic reticulum (ER) membranes and transit through the ER-to-Golgi intermediate compartment, where interaction with N-encapsulated, newly produced genomic RNA results in budding into the lumen of secretory vesicular compartments. Virions are secreted from the infected cell by exocytosis. [30; 31] (Figure 3). Figure 3: The coronavirus virion and life cycle. The SARS-CoV-2 virion consists of structural proteins, spike—S, envelope—E, membrane—M, nucleocapsid—N, and specific binding of the S protein to ACE2 receptor and the cofactor cell surface serine protease TMPRSS2. Following entry, the release genomic RNA subject it to the immediate translation of two large open reading frames, ORF1a and ORF1b. The resulting polyproteins pp1a and pp1 ab are co-translationally and post-translationally processed into the individual non-structural proteins (nsps) that form the viral replication and transcription complex. For viral genomic RNA replication and transcription of subgenomic mRNAs (sg mRNAs) comprise the characteristic set of coronavirus mRNAs. Translated structural proteins translocate into endoplasmic reticulum (ER) membranes and transit through the ER-to-Golgi intermediate compartment, where interaction with N-encapsulated, newly produced genomic RNA results in budding into the lumen of secretory vesicular compartments. Virions are secreted from the infected cell by exocytosis.
## 2 Materials and Methods
### Dataset
#### 2.2.4 Loss Functions  
In object detection algorithms, the loss function consists of the sum of target localization, target classification, and target confidence loss functions. Most object detection algorithms use IoU to determine the overlap between the predicted box and the target box in the dataset. The original framework uses GIoU, as shown in the Figure 6 as below:  
The specific formula is as follows:  
\[\text{GIoU}=\text{IoU}-\frac{\text{O}-(\text{G}\cup\text{P})}{\text{O}} \tag{11}\]  
Figure 5: BiFormer structure.  
Figure 6: The positional relationship between the predicted rectangle P and the ground truth rectangle G.  
where G and P represent the ground truth box and the predicted box, O is the minimum bounding rectangle of P and G, and L is the center rectangle formed by connecting the center points of P and G. This method utilizes the WIoU [45] loss function instead of the GIoU loss function.  
Since the experimental data are annotated with clinical images, the most challenging aspect of ureteral orifice recognition during clinical surgery lies in the variation of ureteral orifice morphology at different angles and the unclear separation from the surrounding bladder wall. Despite efforts to collect samples, there is evidently a disparity in the overall contribution among different samples. Thus, assigning different loss function weights to samples of varying detection difficulty is a preferable solution.  
WIoU consists of two parts, one of which is the distance attention part, and the specific formula is as follows:  
\[L_{WIoU}=D_{attention}\times L_{IoU} \tag{12}\]  
\[D_{attention}=exp\left(\frac{{{W_{L}}^{2}+{H_{L}}^{2}}}{{{\left({{W_{O}}^{2}+ {H_{O}}^{2}}\right)}^{*}}}\right) \tag{13}\]  
\[L_{IoU}=1-IoU \tag{14}\]  
In order to prevent the minimum bounding rectangle from hindering convergence, the operation of detaching it from the computation graph was adopted, represented by * in the formula. At the same time, inspired by the Focal loss, a monotonic focusing mechanism for cross-entropy was designed, and the final loss function is as follows:  
\[L_{WIoU}=rL_{WIoU} \tag{15}\]  
\[r=\frac{\beta}{\delta\alpha^{\beta-\delta}} \tag{16}\]  
\[\beta=\frac{L_{IoU}^{*}}{L_{IoU}} \tag{17}\]  
where \(\overline{L_{IoU}}\) is an added normalization factor to prevent the gradient from decreasing with IoU, which is a running mean with momentum m. \(\alpha\) is analogous to the coefficient in Focal loss, and when \(\delta=\beta\), \(r\) is 1, causing the loss function to degrade to its initial state.
## 5 Open Loop Processing Architecture
In principle, the PRN code could be removed by a wipe-off operation, as in a pure open loop receiver, but this would require sub chip-level time synchronization and fine assistance [31; 32]. This condition is difficult to be satisfied only exploiting proper A-GNSS information and good knowledge of the position of the receiver. Therefore, a different strategy for code removal has to be identified, based on a blind self-estimation of the code. Since thermal noise can be modeled as a zero mean Gaussian random variable, an accumulation process can increase the SNR (Signal-to-Noise Ratio) of the signal up to the point in which the code chips emerge from the noise floor. The digital signal after carrier and data wipe-off, \(z[n]\), is divided in chunks of length \(T_{code}\), where \(T_{code}\) is the spreading code periodicity, as, for example, 1 ms for the GPS C/A  
Figure 4: Block diagram of the assisted open loop architecture used to perform the analysis. signal. Supposing a period is composed of \(N\) samples, \(L\) periods are coherently summed together, obtaining the signal of length \(N\)  
\[w[n]=\sum_{k=0}^{L-1}z\left[n+kN\right]\ \ \text{for}\ \ \ 1\leq n\leq N \tag{4}\]  
as depicted in Figure 5.  
The scope of this coherent accumulation is the estimation of the GNSS spreading code, and this technique has been widely used in the past for the blind identification of the codes transmitted by new GNSS satellites [33] or to detect anomalies and evil waveforms [34; 35]. The estimated binary GNSS code can be extracted by means of the sign operator:  
\[\hat{c}\left[n\right]=\text{sign}\{w[n]\}\ \ \text{for}\ \ \ 1\leq n\leq N, \tag{5}\]  
The spreading code is then wiped-off from the GNSS signal, by multiplying the binary sequence \(\hat{c}\) times the signal at output of the accumulation, \(W[n]\), thus obtaining a complex signal \(y[n]\) affected only by a residual noise component and by scintillation:  
\[y[n]=w[n]\cdot\hat{c}[n]. \tag{6}\]  
The digital signal \(y[n]\) is eventually fed to the _scintillation metric_ block. The code retrieval process is depicted in Figure 6: few chips of the the signal after accumulation, \(w[n]\), are depicted in blue, while the estimated binary spreading code \(\hat{c}[n]\) is depicted in red. On the reconstructed code \(w[n]\), some residual distortion can be noted due to any other un-modeled effect that cannot be averaged to zero by the accumulation process, and, as well, due to some residual noise contribution. \[r[n]=\sum_{i=1}^{N_{\text{sat}}}\left\{\sqrt{2P_{RX}\left[n\right]}d_{i}\left(nT_ {s}-\tau_{i}\right)c_{i}\left(nT_{s}-\tau_{i}\right)\cos\left(2\pi f_{i}nT_{s}+ \varphi_{i}\right)\right\}+\eta[n] \tag{1}\]0 Accumulation of \(L\) chunks of \(N\) samples. \[r[n]=\sum_{i=1}^{N_{\text{sat}}}\left\{\sqrt{2P_{RX}\left[n\right]}d_{i}\left(nT_ {s}-\tau_{i}\right)c_{i}\left(nT_{s}-\tau_{i}\right)\cos\left(2\pi f_{i}nT_{s}+ \varphi_{i}\right)\right\}+\eta[n] \tag{1}\]1 Reconstructed code with transition samples vs. theoretical code.